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"""simple docstring""" import random def _lowercase ( __lowerCAmelCase ) -> Tuple: SCREAMING_SNAKE_CASE__ : int = num - 1 SCREAMING_SNAKE_CASE__ : List[Any] = 0 while s % 2 == 0: SCREAMING_SNAKE_CASE__ : Optional[int] = s // 2 t += 1 for _ in range(5 ): SCREAMING_SNAKE_CASE__ : Dict = random.randrange(2 , num - 1 ) SCREAMING_SNAKE_CASE__ : Any = pow(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) if v != 1: SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 while v != (num - 1): if i == t - 1: return False else: SCREAMING_SNAKE_CASE__ : int = i + 1 SCREAMING_SNAKE_CASE__ : int = (v**2) % num return True def _lowercase ( __lowerCAmelCase ) -> Optional[int]: if num < 2: return False SCREAMING_SNAKE_CASE__ : int = [ 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997, ] if num in low_primes: return True for prime in low_primes: if (num % prime) == 0: return False return rabin_miller(UpperCAmelCase__ ) def _lowercase ( __lowerCAmelCase = 1024 ) -> Any: while True: SCREAMING_SNAKE_CASE__ : List[str] = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) ) if is_prime_low_num(UpperCAmelCase__ ): return num if __name__ == "__main__": a :Tuple = generate_large_prime() print(("Prime number:", num)) print(("is_prime_low_num:", is_prime_low_num(num)))
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"""simple docstring""" from math import sqrt def _lowercase ( __lowerCAmelCase ) -> bool: if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = 0 SCREAMING_SNAKE_CASE__ : Tuple = 1 while count != nth and number < 3: number += 1 if is_prime(__lowerCAmelCase ): count += 1 while count != nth: number += 2 if is_prime(__lowerCAmelCase ): count += 1 return number if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" import inspect import unittest from transformers import ViTConfig from transformers.testing_utils import ( require_accelerate, require_torch, require_torch_gpu, require_vision, slow, torch_device, ) from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import ViTImageProcessor class __a : '''simple docstring''' def __init__( self , _a , _a=13 , _a=30 , _a=2 , _a=3 , _a=True , _a=True , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=10 , _a=0.02 , _a=None , _a=2 , ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = parent SCREAMING_SNAKE_CASE__ : List[str] = batch_size SCREAMING_SNAKE_CASE__ : Tuple = image_size SCREAMING_SNAKE_CASE__ : List[Any] = patch_size SCREAMING_SNAKE_CASE__ : Optional[int] = num_channels SCREAMING_SNAKE_CASE__ : Dict = is_training SCREAMING_SNAKE_CASE__ : Optional[Any] = use_labels SCREAMING_SNAKE_CASE__ : Dict = hidden_size SCREAMING_SNAKE_CASE__ : int = num_hidden_layers SCREAMING_SNAKE_CASE__ : List[str] = num_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = intermediate_size SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_act SCREAMING_SNAKE_CASE__ : int = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_range SCREAMING_SNAKE_CASE__ : Union[str, Any] = scope SCREAMING_SNAKE_CASE__ : Optional[Any] = encoder_stride # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) SCREAMING_SNAKE_CASE__ : Dict = (image_size // patch_size) ** 2 SCREAMING_SNAKE_CASE__ : str = num_patches + 1 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) SCREAMING_SNAKE_CASE__ : Optional[Any] = None if self.use_labels: SCREAMING_SNAKE_CASE__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size ) SCREAMING_SNAKE_CASE__ : Any = self.get_config() return config, pixel_values, labels def _a ( self ) -> List[str]: """simple docstring""" return ViTConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , ) def _a ( self , _a , _a , _a ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = ViTModel(config=__UpperCamelCase ) model.to(__UpperCamelCase ) model.eval() SCREAMING_SNAKE_CASE__ : Any = model(__UpperCamelCase ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def _a ( self , _a , _a , _a ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ViTForMaskedImageModeling(config=__UpperCamelCase ) model.to(__UpperCamelCase ) model.eval() SCREAMING_SNAKE_CASE__ : Optional[int] = model(__UpperCamelCase ) self.parent.assertEqual( result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) ) # test greyscale images SCREAMING_SNAKE_CASE__ : Optional[Any] = 1 SCREAMING_SNAKE_CASE__ : Dict = ViTForMaskedImageModeling(__UpperCamelCase ) model.to(__UpperCamelCase ) model.eval() SCREAMING_SNAKE_CASE__ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(__UpperCamelCase ) self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) ) def _a ( self , _a , _a , _a ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = self.type_sequence_label_size SCREAMING_SNAKE_CASE__ : List[Any] = ViTForImageClassification(__UpperCamelCase ) model.to(__UpperCamelCase ) model.eval() SCREAMING_SNAKE_CASE__ : Dict = model(__UpperCamelCase , labels=__UpperCamelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) # test greyscale images SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1 SCREAMING_SNAKE_CASE__ : Any = ViTForImageClassification(__UpperCamelCase ) model.to(__UpperCamelCase ) model.eval() SCREAMING_SNAKE_CASE__ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] ) SCREAMING_SNAKE_CASE__ : Optional[int] = model(__UpperCamelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_config_and_inputs() ( ( SCREAMING_SNAKE_CASE__ ) , ( SCREAMING_SNAKE_CASE__ ) , ( SCREAMING_SNAKE_CASE__ ) , ) : str = config_and_inputs SCREAMING_SNAKE_CASE__ : Optional[int] = {"""pixel_values""": pixel_values} return config, inputs_dict @require_torch class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :int = ( ( ViTModel, ViTForImageClassification, ViTForMaskedImageModeling, ) if is_torch_available() else () ) _SCREAMING_SNAKE_CASE :int = ( {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification} if is_torch_available() else {} ) _SCREAMING_SNAKE_CASE :int = True _SCREAMING_SNAKE_CASE :List[str] = False _SCREAMING_SNAKE_CASE :int = False _SCREAMING_SNAKE_CASE :str = False def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = ViTModelTester(self ) SCREAMING_SNAKE_CASE__ : Optional[int] = ConfigTester(self , config_class=__UpperCamelCase , has_text_modality=__UpperCamelCase , hidden_size=37 ) def _a ( self ) -> str: """simple docstring""" self.config_tester.run_common_tests() @unittest.skip(reason="""ViT does not use inputs_embeds""" ) def _a ( self ) -> List[str]: """simple docstring""" pass def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE__ : Union[str, Any] = model_class(__UpperCamelCase ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) SCREAMING_SNAKE_CASE__ : int = model.get_output_embeddings() self.assertTrue(x is None or isinstance(__UpperCamelCase , nn.Linear ) ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE__ : List[str] = model_class(__UpperCamelCase ) SCREAMING_SNAKE_CASE__ : Any = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic SCREAMING_SNAKE_CASE__ : List[Any] = [*signature.parameters.keys()] SCREAMING_SNAKE_CASE__ : Dict = ["""pixel_values"""] self.assertListEqual(arg_names[:1] , __UpperCamelCase ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__UpperCamelCase ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase ) def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase ) @slow def _a ( self ) -> Tuple: """simple docstring""" for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE__ : Dict = ViTModel.from_pretrained(__UpperCamelCase ) self.assertIsNotNone(__UpperCamelCase ) def _lowercase ( ) -> Dict: SCREAMING_SNAKE_CASE__ : List[str] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) return image @require_torch @require_vision class __a (unittest.TestCase): '''simple docstring''' @cached_property def _a ( self ) -> Any: """simple docstring""" return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None @slow def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" ).to(__UpperCamelCase ) SCREAMING_SNAKE_CASE__ : Any = self.default_image_processor SCREAMING_SNAKE_CASE__ : Any = prepare_img() SCREAMING_SNAKE_CASE__ : Any = image_processor(images=__UpperCamelCase , return_tensors="""pt""" ).to(__UpperCamelCase ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE__ : int = model(**__UpperCamelCase ) # verify the logits SCREAMING_SNAKE_CASE__ : Tuple = torch.Size((1, 1_000) ) self.assertEqual(outputs.logits.shape , __UpperCamelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([-0.2_744, 0.8_215, -0.0_836] ).to(__UpperCamelCase ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , __UpperCamelCase , atol=1E-4 ) ) @slow def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = ViTModel.from_pretrained("""facebook/dino-vits8""" ).to(__UpperCamelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = ViTImageProcessor.from_pretrained("""facebook/dino-vits8""" , size=480 ) SCREAMING_SNAKE_CASE__ : List[str] = prepare_img() SCREAMING_SNAKE_CASE__ : Dict = image_processor(images=__UpperCamelCase , return_tensors="""pt""" ) SCREAMING_SNAKE_CASE__ : Dict = inputs.pixel_values.to(__UpperCamelCase ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE__ : List[str] = model(__UpperCamelCase , interpolate_pos_encoding=__UpperCamelCase ) # verify the logits SCREAMING_SNAKE_CASE__ : Dict = torch.Size((1, 3_601, 384) ) self.assertEqual(outputs.last_hidden_state.shape , __UpperCamelCase ) SCREAMING_SNAKE_CASE__ : int = torch.tensor( [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]] ).to(__UpperCamelCase ) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , __UpperCamelCase , atol=1E-4 ) ) @slow @require_accelerate @require_torch_gpu def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = ViTModel.from_pretrained("""facebook/dino-vits8""" , torch_dtype=torch.floataa , device_map="""auto""" ) SCREAMING_SNAKE_CASE__ : int = self.default_image_processor SCREAMING_SNAKE_CASE__ : str = prepare_img() SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(images=__UpperCamelCase , return_tensors="""pt""" ) SCREAMING_SNAKE_CASE__ : Tuple = inputs.pixel_values.to(__UpperCamelCase ) # forward pass to make sure inference works in fp16 with torch.no_grad(): SCREAMING_SNAKE_CASE__ : Tuple = model(__UpperCamelCase )
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"""simple docstring""" class __a : '''simple docstring''' def __init__( self , _a , _a , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = value SCREAMING_SNAKE_CASE__ : List[Any] = weight def __repr__( self ) -> List[Any]: """simple docstring""" return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})''' def _a ( self ) -> Dict: """simple docstring""" return self.value def _a ( self ) -> int: """simple docstring""" return self.name def _a ( self ) -> Optional[Any]: """simple docstring""" return self.weight def _a ( self ) -> Dict: """simple docstring""" return self.value / self.weight def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict: SCREAMING_SNAKE_CASE__ : Any = [] for i in range(len(__lowerCAmelCase ) ): menu.append(Things(name[i] , value[i] , weight[i] ) ) return menu def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0 for i in range(len(__lowerCAmelCase ) ): if (total_cost + items_copy[i].get_weight()) <= max_cost: result.append(items_copy[i] ) total_cost += items_copy[i].get_weight() total_value += items_copy[i].get_value() return (result, total_value) def _lowercase ( ) -> List[str]: pass if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import _LazyModule a :int = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]} if TYPE_CHECKING: from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM else: import sys a :List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from tokenizers import processors from ...tokenization_utils import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_nllb import NllbTokenizer else: a :Optional[int] = None a :Optional[Any] = logging.get_logger(__name__) a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} a :Union[str, Any] = { "vocab_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model" ), }, "tokenizer_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json" ), }, } a :Any = { "facebook/nllb-large-en-ro": 1_024, "facebook/nllb-200-distilled-600M": 1_024, } # fmt: off a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"] class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""] _SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer _SCREAMING_SNAKE_CASE :List[int] = [] _SCREAMING_SNAKE_CASE :List[int] = [] def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour super().__init__( vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy() if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} ) SCREAMING_SNAKE_CASE__ : List[str] = { lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES } SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn""" SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang ) SCREAMING_SNAKE_CASE__ : Dict = tgt_lang self.set_src_lang_special_tokens(self._src_lang ) @property def _a ( self ) -> str: """simple docstring""" return self._src_lang @src_lang.setter def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" if token_ids_a is None: return self.prefix_tokens + token_ids_a + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple: """simple docstring""" if src_lang is None or tgt_lang is None: raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" ) SCREAMING_SNAKE_CASE__ : Dict = src_lang SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id return inputs def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = src_lang SCREAMING_SNAKE_CASE__ : Dict = tgt_lang return super().prepare_seqaseq_batch(_a , _a , **_a ) def _a ( self ) -> Optional[Any]: """simple docstring""" return self.set_src_lang_special_tokens(self.src_lang ) def _a ( self ) -> str: """simple docstring""" return self.set_tgt_lang_special_tokens(self.tgt_lang ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : List[Any] = [] SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' ) return SCREAMING_SNAKE_CASE__ : Dict = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
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"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> List[str]: if not isinstance(__lowerCAmelCase , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Dict = F'''Input value of [number={number}] must be an integer''' raise TypeError(__lowerCAmelCase ) if number < 1: SCREAMING_SNAKE_CASE__ : Optional[int] = F'''Input value of [number={number}] must be > 0''' raise ValueError(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = 1 for i in range(1 , __lowerCAmelCase ): current_number *= 4 * i - 2 current_number //= i + 1 return current_number if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" # Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #################################################################################################### # # Note: If when running this conversion script you're getting an exception: # ModuleNotFoundError: No module named 'megatron.model.enums' # you need to tell python where to find the clone of Megatron-LM, e.g.: # # cd /tmp # git clone https://github.com/NVIDIA/Megatron-LM # PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ... # # if you already have it cloned elsewhere, simply adjust the path to the existing path # # If the training was done using a Megatron-LM fork, e.g., # https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one # in your path, i.e., /path/to/Megatron-DeepSpeed/ # import argparse import os import re import zipfile import torch from transformers import AutoTokenizer, GPTaConfig def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any: # Format the message. if name is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None else: SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}""" SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase ) # Print and recurse (if needed). if isinstance(__lowerCAmelCase , __lowerCAmelCase ): if msg is not None: print(__lowerCAmelCase ) for k in val.keys(): recursive_print(__lowerCAmelCase , val[k] , spaces + 2 ) elif isinstance(__lowerCAmelCase , torch.Tensor ): print(__lowerCAmelCase , """:""" , val.size() ) else: print(__lowerCAmelCase , """:""" , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] # for compatibility with later versions of NVIDIA Megatron-LM. # The inverse operation is performed inside Megatron-LM to read checkpoints: # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 # If param is the weight tensor of the self-attention block, the returned tensor # will have to be transposed one more time to be read by HuggingFace GPT2. SCREAMING_SNAKE_CASE__ : Tuple = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:] SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 ) SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:] SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous() SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase ) return param def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: # The converted output model. SCREAMING_SNAKE_CASE__ : List[str] = {} # old versions did not store training args SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase ) if ds_args is not None: # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint # from pprint import pprint # pprint(vars(ds_args)) SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size # pprint(config) # The number of heads. SCREAMING_SNAKE_CASE__ : List[str] = config.n_head # The hidden_size per head. SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head # Megatron-LM checkpoint version if "checkpoint_version" in input_state_dict.keys(): SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""] else: SCREAMING_SNAKE_CASE__ : Tuple = 0.0 # The model. SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""] # The language model. SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""] # The embeddings. SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""] # The word embeddings. SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""] # Truncate the embedding table to vocab_size rows. SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :] SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings # The position embeddings. SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""] # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size] SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 ) if n_positions != config.n_positions: raise ValueError( F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' ) # Store the position embeddings. SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings # The transformer. SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""] # The regex to extract layer names. SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" ) # The simple map of names for "automated" rules. SCREAMING_SNAKE_CASE__ : Optional[int] = { """attention.dense""": """.attn.c_proj.""", """self_attention.dense""": """.attn.c_proj.""", """mlp.dense_h_to_4h""": """.mlp.c_fc.""", """mlp.dense_4h_to_h""": """.mlp.c_proj.""", } # Extract the layers. for key, val in transformer.items(): # Match the name. SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase ) # Stop if that's not a layer if m is None: break # The index of the layer. SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) ) # The name of the operation. SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 ) # Is it a weight or a bias? SCREAMING_SNAKE_CASE__ : str = m.group(3 ) # The name of the layer. SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}''' # For layernorm(s), simply store the layer norm. if op_name.endswith("""layernorm""" ): SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2""" SCREAMING_SNAKE_CASE__ : List[Any] = val # Transpose the QKV matrix. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "weight": # Insert a tensor of 1x1xDxD bias. SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view( 1 , 1 , __lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = causal_mask # Insert a "dummy" tensor for masked_bias. SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa ) SCREAMING_SNAKE_CASE__ : List[str] = masked_bias SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D. SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous() # Store. SCREAMING_SNAKE_CASE__ : Dict = out_val # Transpose the bias. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Store. No change of shape. SCREAMING_SNAKE_CASE__ : str = out_val # Transpose the weights. elif weight_or_bias == "weight": SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 ) # Copy the bias. elif weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : Dict = val # DEBUG. assert config.n_layer == layer_idx + 1 # The final layernorm. SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""] SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""] # For LM head, transformers' wants the matrix to weight embeddings. SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings # It should be done! return output_state_dict def _lowercase ( ) -> List[Any]: # Create the argument parser. SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser() parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" ) parser.add_argument( """path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , ) parser.add_argument( """--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , ) SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args() # Extract the basename. SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint ) # Load the model. # the .zip is very optional, let's keep it for backward compatibility print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' ) if args.path_to_checkpoint.endswith(""".zip""" ): with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint: with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict: SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" ) else: SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" ) SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase ) # Read the config, or default to the model released by NVIDIA. if args.config_file == "": if ds_args is not None: if ds_args.bias_gelu_fusion: SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast""" elif ds_args.openai_gelu: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new""" else: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu""" else: # in the very early days this used to be "gelu_new" SCREAMING_SNAKE_CASE__ : Any = """gelu_new""" # Spell out all parameters in case the defaults change. SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig( vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , ) else: SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file ) SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""] # Convert. print("""Converting""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) # Print the structure of converted state dict. if args.print_checkpoint_structure: recursive_print(__lowerCAmelCase , __lowerCAmelCase ) # Add tokenizer class info to config # see https://github.com/huggingface/transformers/issues/13906) if ds_args is not None: SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type if tokenizer_type == "GPT2BPETokenizer": SCREAMING_SNAKE_CASE__ : Any = """gpt2""" elif tokenizer_type == "PretrainedFromHF": SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path else: raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__ SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class # Store the config to file. print("""Saving config""" ) config.save_pretrained(__lowerCAmelCase ) # Save tokenizer based on args print(F'''Adding {tokenizer_class} tokenizer files''' ) tokenizer.save_pretrained(__lowerCAmelCase ) # Store the state_dict to file. SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" ) print(F'''Saving checkpoint to "{output_checkpoint_file}"''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) #################################################################################################### if __name__ == "__main__": main() ####################################################################################################
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"""simple docstring""" import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import numpy as np from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.31.0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") a :int = logging.getLogger(__name__) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_28 , metadata={ """help""": ( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=__lowerCAmelCase , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}) _SCREAMING_SNAKE_CASE :bool = field( default=__lowerCAmelCase , metadata={ """help""": ( """Whether to pad all samples to `max_seq_length`. """ """If False, will pad the samples dynamically when batching to the maximum length in the batch.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=__lowerCAmelCase , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of training examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=__lowerCAmelCase , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of evaluation examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=__lowerCAmelCase , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of prediction examples to this """ """value if set.""" ) } , ) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :str = field( default=__lowerCAmelCase , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}) _SCREAMING_SNAKE_CASE :str = field( default=__lowerCAmelCase , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=__lowerCAmelCase , metadata={"""help""": """Train language if it is different from the evaluation language."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=__lowerCAmelCase , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=__lowerCAmelCase , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=__lowerCAmelCase , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , ) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=__lowerCAmelCase , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=__lowerCAmelCase , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , ) _SCREAMING_SNAKE_CASE :str = field( default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=__lowerCAmelCase , metadata={ """help""": ( """Will use the token generated when running `huggingface-cli login` (necessary to use this script """ """with private models).""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=__lowerCAmelCase , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , ) def _lowercase ( ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ : Optional[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) SCREAMING_SNAKE_CASE__ : List[str] = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("""run_xnli""" , __lowerCAmelCase ) # Setup logging logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() SCREAMING_SNAKE_CASE__ : int = training_args.get_process_log_level() logger.setLevel(__lowerCAmelCase ) datasets.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}''' + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' ) logger.info(F'''Training/evaluation parameters {training_args}''' ) # Detecting last checkpoint. SCREAMING_SNAKE_CASE__ : Optional[int] = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: SCREAMING_SNAKE_CASE__ : int = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. ''' """Use --overwrite_output_dir to overcome.""" ) elif last_checkpoint is not None: logger.info( F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change ''' """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" ) # Set seed before initializing model. set_seed(training_args.seed ) # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # Downloading and loading xnli dataset from the hub. if training_args.do_train: if model_args.train_language is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = load_dataset( """xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = load_dataset( """xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Optional[int] = train_dataset.features['''label'''].names if training_args.do_eval: SCREAMING_SNAKE_CASE__ : Tuple = load_dataset( """xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : str = eval_dataset.features['''label'''].names if training_args.do_predict: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features['''label'''].names # Labels SCREAMING_SNAKE_CASE__ : str = len(__lowerCAmelCase ) # Load pretrained model and tokenizer # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. SCREAMING_SNAKE_CASE__ : str = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : int = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Optional[int] = AutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , ) # Preprocessing the datasets # Padding strategy if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : Optional[int] = '''max_length''' else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch SCREAMING_SNAKE_CASE__ : Optional[Any] = False def preprocess_function(__lowerCAmelCase ): # Tokenize the texts return tokenizer( examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , ) if training_args.do_train: if data_args.max_train_samples is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples ) SCREAMING_SNAKE_CASE__ : Optional[Any] = train_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""train dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : Tuple = train_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , ) # Log a few random samples from the training set: for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ): logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' ) if training_args.do_eval: if data_args.max_eval_samples is not None: SCREAMING_SNAKE_CASE__ : Optional[int] = min(len(__lowerCAmelCase ) , data_args.max_eval_samples ) SCREAMING_SNAKE_CASE__ : int = eval_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""validation dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , ) if training_args.do_predict: if data_args.max_predict_samples is not None: SCREAMING_SNAKE_CASE__ : Optional[int] = min(len(__lowerCAmelCase ) , data_args.max_predict_samples ) SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , ) # Get the metric function SCREAMING_SNAKE_CASE__ : Any = evaluate.load("""xnli""" ) # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : List[str] = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 ) return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids ) # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : Optional[Any] = default_data_collator elif training_args.fpaa: SCREAMING_SNAKE_CASE__ : Optional[Any] = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 ) else: SCREAMING_SNAKE_CASE__ : Tuple = None # Initialize our Trainer SCREAMING_SNAKE_CASE__ : Optional[int] = Trainer( model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , ) # Training if training_args.do_train: SCREAMING_SNAKE_CASE__ : str = None if training_args.resume_from_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Tuple = training_args.resume_from_checkpoint elif last_checkpoint is not None: SCREAMING_SNAKE_CASE__ : List[str] = last_checkpoint SCREAMING_SNAKE_CASE__ : Tuple = trainer.train(resume_from_checkpoint=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = train_result.metrics SCREAMING_SNAKE_CASE__ : int = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Optional[int] = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("""train""" , __lowerCAmelCase ) trainer.save_metrics("""train""" , __lowerCAmelCase ) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("""*** Evaluate ***""" ) SCREAMING_SNAKE_CASE__ : List[str] = trainer.evaluate(eval_dataset=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[int] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Any = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""eval""" , __lowerCAmelCase ) trainer.save_metrics("""eval""" , __lowerCAmelCase ) # Prediction if training_args.do_predict: logger.info("""*** Predict ***""" ) SCREAMING_SNAKE_CASE__ : str = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = ( data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : List[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""predict""" , __lowerCAmelCase ) trainer.save_metrics("""predict""" , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" ) if trainer.is_world_process_zero(): with open(__lowerCAmelCase , """w""" ) as writer: writer.write("""index\tprediction\n""" ) for index, item in enumerate(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Union[str, Any] = label_list[item] writer.write(F'''{index}\t{item}\n''' ) if __name__ == "__main__": main()
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"""simple docstring""" import re from pathlib import Path from unittest import TestCase import pytest @pytest.mark.integration class __a (UpperCamelCase_): '''simple docstring''' def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read() SCREAMING_SNAKE_CASE__ : str = regexp.search(_a ) return match def _a ( self , _a ) -> Optional[Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL ) SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read() # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search` SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a ) SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None] return matches[0] if matches else None def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_encoding_on_file_open(str(_a ) ): raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_print_statements(str(_a ) ): raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available, is_torch_available, ) a :Optional[Any] = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :List[Any] = ['PLBartTokenizer'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Tuple = [ 'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST', 'PLBartForCausalLM', 'PLBartForConditionalGeneration', 'PLBartForSequenceClassification', 'PLBartModel', 'PLBartPreTrainedModel', ] if TYPE_CHECKING: from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_plbart import PLBartTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_plbart import ( PLBART_PRETRAINED_MODEL_ARCHIVE_LIST, PLBartForCausalLM, PLBartForConditionalGeneration, PLBartForSequenceClassification, PLBartModel, PLBartPreTrainedModel, ) else: import sys a :str = _LazyModule(__name__, globals()["__file__"], _import_structure)
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"""simple docstring""" import tempfile import unittest from transformers import TaConfig, is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel class __a : '''simple docstring''' def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = parent SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length SCREAMING_SNAKE_CASE__ : str = decoder_seq_length # For common tests SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length SCREAMING_SNAKE_CASE__ : Tuple = is_training SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask SCREAMING_SNAKE_CASE__ : List[str] = use_labels SCREAMING_SNAKE_CASE__ : str = vocab_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Any = num_attention_heads SCREAMING_SNAKE_CASE__ : Any = d_ff SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id SCREAMING_SNAKE_CASE__ : Any = None SCREAMING_SNAKE_CASE__ : str = decoder_layers def _a ( self ) -> Tuple: """simple docstring""" return TaConfig.from_pretrained("""google/umt5-base""" ) def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any: """simple docstring""" if attention_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id ) if decoder_attention_mask is None: SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id ) if head_mask is None: SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a ) if decoder_head_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a ) if cross_attn_head_mask is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones( config.num_decoder_layers , config.num_attention_heads , device=_a ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size ) # we need to clamp the input ids here to avoid having pad token in between # this is because for NllbMoe the position_ids are prepared such that # all pad tokens have pos id = 2 and rest are between 2..seq_length # and the seq_length here is seq_length - num_pad_tokens # but when using past, there is no way of knowing if the past input ids had # pad tokens in them, which results in incorrect seq_lenth and which in turn results in # position_ids being off by num_pad_tokens in past input SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config() SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a ) return config, input_dict def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs() return config, inputs_dict def _a ( self ) -> List[str]: """simple docstring""" return TaConfig( vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self ) -> List[Any]: """simple docstring""" return TaConfig( vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a ) model.to(_a ) model.eval() SCREAMING_SNAKE_CASE__ : Dict = model( input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) ) self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) ) # There should be `num_layers` key value embeddings stored in decoder_past self.parent.assertEqual(len(_a ) , config.num_layers ) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple self.parent.assertEqual(len(decoder_past[0] ) , 4 ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval() # first forward pass SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a ) SCREAMING_SNAKE_CASE__ : str = model(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a ) self.parent.assertTrue(len(_a ) == len(_a ) ) self.parent.assertTrue(len(_a ) == len(_a ) + 1 ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ) # append to next input_ids and SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""] SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""] # select random slice SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item() SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach() SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) ) def _a ( self , _a , _a , ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval() SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""] self.parent.assertFalse(torch.isnan(_a ).any().item() ) @require_torch class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = ( (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else () ) _SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else () _SCREAMING_SNAKE_CASE :List[str] = ( { """conversational""": UMTaForConditionalGeneration, """feature-extraction""": UMTaModel, """summarization""": UMTaForConditionalGeneration, """text2text-generation""": UMTaForConditionalGeneration, """translation""": UMTaForConditionalGeneration, """question-answering""": UMTaForQuestionAnswering, } if is_torch_available() else {} ) _SCREAMING_SNAKE_CASE :Union[str, Any] = True _SCREAMING_SNAKE_CASE :Tuple = False _SCREAMING_SNAKE_CASE :Optional[Any] = False _SCREAMING_SNAKE_CASE :List[Any] = True _SCREAMING_SNAKE_CASE :List[str] = True # The small UMT5 model needs higher percentages for CPU/MP tests _SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9] def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self ) @unittest.skip("""Test has a segmentation fault on torch 1.8.0""" ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a ) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( _a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , ) @unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fpaa_forward(*_a ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""] SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0] SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval() model.to(_a ) SCREAMING_SNAKE_CASE__ : List[str] = { """head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ), """decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), } for attn_name, (name, mask) in zip(_a , head_masking.items() ): SCREAMING_SNAKE_CASE__ : List[str] = {name: mask} # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified if name == "head_mask": SCREAMING_SNAKE_CASE__ : str = torch.ones( config.num_decoder_layers , config.num_heads , device=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate( config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , ) # We check the state of decoder_attentions and cross_attentions just from the last step SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 ) @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" ) def _a ( self ) -> Dict: """simple docstring""" pass @require_torch @require_sentencepiece @require_tokenizers class __a (unittest.TestCase): '''simple docstring''' @slow @unittest.skip( """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a ) SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [ """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""", """No se como puedo <extra_id_0>.""", """This is the reason why we <extra_id_0> them.""", """The <extra_id_0> walks in <extra_id_1>, seats""", """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""", ] SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids # fmt: off SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor( [ [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0], [ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1], ] ) # fmt: on torch.testing.assert_allclose(_a , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) ) SCREAMING_SNAKE_CASE__ : int = [ """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""", """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", ] SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a ) self.assertEqual(_a , _a )
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from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging a :int = logging.get_logger(__name__) a :int = { """facebook/data2vec-vision-base-ft""": ( """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json""" ), } class __a (lowercase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[Any] = '''data2vec-vision''' def __init__( self , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.0 , _a=0.0 , _a=0.02 , _a=1E-1_2 , _a=224 , _a=16 , _a=3 , _a=False , _a=False , _a=False , _a=False , _a=0.1 , _a=0.1 , _a=True , _a=[3, 5, 7, 11] , _a=[1, 2, 3, 6] , _a=True , _a=0.4 , _a=256 , _a=1 , _a=False , _a=255 , **_a , ) -> List[str]: """simple docstring""" super().__init__(**_a ) SCREAMING_SNAKE_CASE__ : int = hidden_size SCREAMING_SNAKE_CASE__ : List[str] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Any = num_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = intermediate_size SCREAMING_SNAKE_CASE__ : int = hidden_act SCREAMING_SNAKE_CASE__ : Tuple = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : List[Any] = initializer_range SCREAMING_SNAKE_CASE__ : List[str] = layer_norm_eps SCREAMING_SNAKE_CASE__ : Any = image_size SCREAMING_SNAKE_CASE__ : Optional[Any] = patch_size SCREAMING_SNAKE_CASE__ : List[Any] = num_channels SCREAMING_SNAKE_CASE__ : List[str] = use_mask_token SCREAMING_SNAKE_CASE__ : Optional[int] = use_absolute_position_embeddings SCREAMING_SNAKE_CASE__ : str = use_relative_position_bias SCREAMING_SNAKE_CASE__ : Any = use_shared_relative_position_bias SCREAMING_SNAKE_CASE__ : Tuple = layer_scale_init_value SCREAMING_SNAKE_CASE__ : Tuple = drop_path_rate SCREAMING_SNAKE_CASE__ : List[str] = use_mean_pooling # decode head attributes (semantic segmentation) SCREAMING_SNAKE_CASE__ : Union[str, Any] = out_indices SCREAMING_SNAKE_CASE__ : Optional[int] = pool_scales # auxiliary head attributes (semantic segmentation) SCREAMING_SNAKE_CASE__ : int = use_auxiliary_head SCREAMING_SNAKE_CASE__ : List[Any] = auxiliary_loss_weight SCREAMING_SNAKE_CASE__ : Tuple = auxiliary_channels SCREAMING_SNAKE_CASE__ : str = auxiliary_num_convs SCREAMING_SNAKE_CASE__ : Tuple = auxiliary_concat_input SCREAMING_SNAKE_CASE__ : List[str] = semantic_loss_ignore_index class __a (lowercase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[int] = version.parse("""1.11""") @property def _a ( self ) -> str: """simple docstring""" return OrderedDict( [ ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}), ] ) @property def _a ( self ) -> Optional[int]: """simple docstring""" return 1E-4
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"""simple docstring""" import multiprocessing from typing import TYPE_CHECKING, Optional, Union from .. import Dataset, Features, config from ..formatting import query_table from ..packaged_modules.sql.sql import Sql from ..utils import logging from .abc import AbstractDatasetInputStream if TYPE_CHECKING: import sqlitea import sqlalchemy class __a (UpperCamelCase_): '''simple docstring''' def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]: """simple docstring""" super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a ) SCREAMING_SNAKE_CASE__ : List[Any] = Sql( cache_dir=_a , features=_a , sql=_a , con=_a , **_a , ) def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = None SCREAMING_SNAKE_CASE__ : Union[str, Any] = None SCREAMING_SNAKE_CASE__ : Dict = None SCREAMING_SNAKE_CASE__ : Optional[int] = None self.builder.download_and_prepare( download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , ) # Build dataset for splits SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset( split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory ) return dataset class __a : '''simple docstring''' def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any: """simple docstring""" if num_proc is not None and num_proc <= 0: raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' ) SCREAMING_SNAKE_CASE__ : int = dataset SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = con SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE SCREAMING_SNAKE_CASE__ : int = num_proc SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs ) return written def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs SCREAMING_SNAKE_CASE__ : Any = query_table( table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , ) SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas() SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a ) return num_rows or len(_a ) def _a ( self , _a , **_a ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = 0 if self.num_proc is None or self.num_proc == 1: for offset in logging.tqdm( range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += self._batch_sql((offset, index, to_sql_kwargs) ) else: SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size with multiprocessing.Pool(self.num_proc ) as pool: for num_rows in logging.tqdm( pool.imap( self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += num_rows return written
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"""simple docstring""" import os import re import shutil import sys import tempfile import unittest import black a :Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, "utils")) import check_copies # noqa: E402 # This is the reference code that will be used in the tests. # If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated. a :Union[str, Any] = " \"\"\"\n Output class for the scheduler\'s step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n" class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = tempfile.mkdtemp() os.makedirs(os.path.join(self.diffusers_dir , """schedulers/""" ) ) SCREAMING_SNAKE_CASE__ : Dict = self.diffusers_dir shutil.copy( os.path.join(_a , """src/diffusers/schedulers/scheduling_ddpm.py""" ) , os.path.join(self.diffusers_dir , """schedulers/scheduling_ddpm.py""" ) , ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = """src/diffusers""" shutil.rmtree(self.diffusers_dir ) def _a ( self , _a , _a , _a , _a=None ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code if overwrite_result is not None: SCREAMING_SNAKE_CASE__ : Tuple = comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result SCREAMING_SNAKE_CASE__ : Dict = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = black.format_str(_a , mode=_a ) SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.diffusers_dir , """new_code.py""" ) with open(_a , """w""" , newline="""\n""" ) as f: f.write(_a ) if overwrite_result is None: self.assertTrue(len(check_copies.is_copy_consistent(_a ) ) == 0 ) else: check_copies.is_copy_consistent(f.name , overwrite=_a ) with open(_a , """r""" ) as f: self.assertTrue(f.read() , _a ) def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" ) self.assertEqual(_a , _a ) def _a ( self ) -> Optional[int]: """simple docstring""" self.check_copy_consistency( """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , REFERENCE_CODE + """\n""" , ) # With no empty line at the end self.check_copy_consistency( """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , _a , ) # Copy consistency with rename self.check_copy_consistency( """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , re.sub("""DDPM""" , """Test""" , _a ) , ) # Copy consistency with a really long name SCREAMING_SNAKE_CASE__ : str = """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason""" self.check_copy_consistency( f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}''' , f'''{long_class_name}SchedulerOutput''' , re.sub("""Bert""" , _a , _a ) , ) # Copy consistency with overwrite self.check_copy_consistency( """# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , _a , overwrite_result=re.sub("""DDPM""" , """Test""" , _a ) , )
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"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> int: if divisor % 5 == 0 or divisor % 2 == 0: return 0 SCREAMING_SNAKE_CASE__ : List[Any] = 1 SCREAMING_SNAKE_CASE__ : int = 1 while repunit: SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor repunit_index += 1 return repunit_index def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = limit - 1 if divisor % 2 == 0: divisor += 1 while least_divisible_repunit(__lowerCAmelCase ) <= limit: divisor += 2 return divisor if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" from manim import * class __a (snake_case__): '''simple docstring''' def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = Rectangle(height=0.5 , width=0.5 ) SCREAMING_SNAKE_CASE__ : Any = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 ) SCREAMING_SNAKE_CASE__ : Any = [mem.copy() for i in range(6 )] SCREAMING_SNAKE_CASE__ : Dict = [mem.copy() for i in range(6 )] SCREAMING_SNAKE_CASE__ : Optional[Any] = VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 ) SCREAMING_SNAKE_CASE__ : Optional[Any] = VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 ) SCREAMING_SNAKE_CASE__ : Optional[int] = VGroup(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 ) SCREAMING_SNAKE_CASE__ : int = Text("""CPU""" , font_size=24 ) SCREAMING_SNAKE_CASE__ : int = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE ) cpu.move_to([-2.5, -0.5, 0] ) self.add(_SCREAMING_SNAKE_CASE ) SCREAMING_SNAKE_CASE__ : Tuple = [mem.copy() for i in range(1 )] SCREAMING_SNAKE_CASE__ : Optional[Any] = VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 ) SCREAMING_SNAKE_CASE__ : Tuple = Text("""GPU""" , font_size=24 ) SCREAMING_SNAKE_CASE__ : int = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE ) gpu.align_to(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) gpu.set_x(gpu.get_x() - 1 ) self.add(_SCREAMING_SNAKE_CASE ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = [mem.copy() for i in range(6 )] SCREAMING_SNAKE_CASE__ : List[Any] = VGroup(*_SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = Text("""Model""" , font_size=24 ) SCREAMING_SNAKE_CASE__ : Any = Group(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).arrange(_SCREAMING_SNAKE_CASE , buff=0.5 , aligned_edge=_SCREAMING_SNAKE_CASE ) model.move_to([3, -1.0, 0] ) self.play( Create(_SCREAMING_SNAKE_CASE , run_time=1 ) , Create(_SCREAMING_SNAKE_CASE , run_time=1 ) , Create(_SCREAMING_SNAKE_CASE , run_time=1 ) , ) SCREAMING_SNAKE_CASE__ : Any = MarkupText( f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''' , font_size=24 , ) SCREAMING_SNAKE_CASE__ : str = Square(side_length=2.2 ) key.move_to([-5, 2, 0] ) SCREAMING_SNAKE_CASE__ : List[Any] = MarkupText( f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , ) key_text.move_to([-5, 2.4, 0] ) step_a.move_to([2, 2, 0] ) self.play(Write(_SCREAMING_SNAKE_CASE , run_time=2.5 ) , Write(_SCREAMING_SNAKE_CASE ) , Write(_SCREAMING_SNAKE_CASE ) ) self.add(_SCREAMING_SNAKE_CASE ) SCREAMING_SNAKE_CASE__ : List[Any] = [] SCREAMING_SNAKE_CASE__ : Any = [] SCREAMING_SNAKE_CASE__ : str = [] for i, rect in enumerate(_SCREAMING_SNAKE_CASE ): SCREAMING_SNAKE_CASE__ : List[Any] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(_SCREAMING_SNAKE_CASE , opacity=0.7 ) cpu_target.move_to(_SCREAMING_SNAKE_CASE ) cpu_target.generate_target() SCREAMING_SNAKE_CASE__ : Optional[Any] = 0.46 / 4 SCREAMING_SNAKE_CASE__ : Optional[Any] = 0.46 / 3 if i == 0: cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=_SCREAMING_SNAKE_CASE ) cpu_target.target.set_x(cpu_target.target.get_x() + 0.1 ) elif i == 3: cpu_target.target.next_to(cpu_targs[0].target , direction=_SCREAMING_SNAKE_CASE , buff=0.0 ) else: cpu_target.target.next_to(cpu_targs[i - 1].target , direction=_SCREAMING_SNAKE_CASE , buff=0.0 ) cpu_targs.append(_SCREAMING_SNAKE_CASE ) first_animations.append(rect.animate(run_time=0.5 ).set_stroke(_SCREAMING_SNAKE_CASE ) ) second_animations.append(MoveToTarget(_SCREAMING_SNAKE_CASE , run_time=1.5 ) ) self.play(*_SCREAMING_SNAKE_CASE ) self.play(*_SCREAMING_SNAKE_CASE ) self.wait()
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"""simple docstring""" import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import numpy as np from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.31.0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") a :Union[str, Any] = logging.getLogger(__name__) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_28 , metadata={ """help""": ( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Whether to pad all samples to `max_seq_length`. """ """If False, will pad the samples dynamically when batching to the maximum length in the batch.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of training examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of evaluation examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of prediction examples to this """ """value if set.""" ) } , ) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}) _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , ) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , ) _SCREAMING_SNAKE_CASE :str = field( default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Will use the token generated when running `huggingface-cli login` (necessary to use this script """ """with private models).""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , ) def _lowercase ( ) -> Union[str, Any]: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("""run_xnli""" , __lowerCAmelCase ) # Setup logging logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level() logger.setLevel(__lowerCAmelCase ) datasets.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}''' + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' ) logger.info(F'''Training/evaluation parameters {training_args}''' ) # Detecting last checkpoint. SCREAMING_SNAKE_CASE__ : Any = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. ''' """Use --overwrite_output_dir to overcome.""" ) elif last_checkpoint is not None: logger.info( F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change ''' """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" ) # Set seed before initializing model. set_seed(training_args.seed ) # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # Downloading and loading xnli dataset from the hub. if training_args.do_train: if model_args.train_language is None: SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset( """xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) else: SCREAMING_SNAKE_CASE__ : str = load_dataset( """xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names if training_args.do_eval: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names if training_args.do_predict: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names # Labels SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase ) # Load pretrained model and tokenizer # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , ) # Preprocessing the datasets # Padding strategy if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : str = """max_length""" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch SCREAMING_SNAKE_CASE__ : Optional[Any] = False def preprocess_function(__lowerCAmelCase ): # Tokenize the texts return tokenizer( examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , ) if training_args.do_train: if data_args.max_train_samples is not None: SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples ) SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""train dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , ) # Log a few random samples from the training set: for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ): logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' ) if training_args.do_eval: if data_args.max_eval_samples is not None: SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""validation dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , ) if training_args.do_predict: if data_args.max_predict_samples is not None: SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , ) # Get the metric function SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" ) # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 ) return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids ) # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator elif training_args.fpaa: SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None # Initialize our Trainer SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer( model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , ) # Training if training_args.do_train: SCREAMING_SNAKE_CASE__ : Dict = None if training_args.resume_from_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint elif last_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics SCREAMING_SNAKE_CASE__ : Optional[int] = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("""train""" , __lowerCAmelCase ) trainer.save_metrics("""train""" , __lowerCAmelCase ) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("""*** Evaluate ***""" ) SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""eval""" , __lowerCAmelCase ) trainer.save_metrics("""eval""" , __lowerCAmelCase ) # Prediction if training_args.do_predict: logger.info("""*** Predict ***""" ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" ) SCREAMING_SNAKE_CASE__ : List[str] = ( data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""predict""" , __lowerCAmelCase ) trainer.save_metrics("""predict""" , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" ) if trainer.is_world_process_zero(): with open(__lowerCAmelCase , """w""" ) as writer: writer.write("""index\tprediction\n""" ) for index, item in enumerate(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item] writer.write(F'''{index}\t{item}\n''' ) if __name__ == "__main__": main()
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"""simple docstring""" from sklearn.metrics import mean_squared_error import datasets a :str = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n" a :Dict = "\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n" a :Union[str, Any] = "\nArgs:\n predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Estimated target values.\n references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n Ground truth (correct) target values.\n sample_weight: array-like of shape (n_samples,), default=None\n Sample weights.\n multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n squared : bool, default=True\n If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n mse : mean squared error.\nExamples:\n\n >>> mse_metric = datasets.load_metric(\"mse\")\n >>> predictions = [2.5, 0.0, 2, 8]\n >>> references = [3, -0.5, 2, 7]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'mse': 0.375}\n >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n >>> print(rmse_result)\n {'mse': 0.6123724356957945}\n\n If you're using multi-dimensional lists, then set the config as follows :\n\n >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n >>> references = [[0, 2], [-1, 2], [8, -5]]\n >>> results = mse_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'mse': 0.7083333333333334}\n >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n {'mse': array([0.41666667, 1. ])}\n" @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION) class __a (datasets.Metric): '''simple docstring''' def _a ( self ) -> Dict: """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[ """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html""" ] , ) def _a ( self ) -> Tuple: """simple docstring""" if self.config_name == "multilist": return { "predictions": datasets.Sequence(datasets.Value("""float""" ) ), "references": datasets.Sequence(datasets.Value("""float""" ) ), } else: return { "predictions": datasets.Value("""float""" ), "references": datasets.Value("""float""" ), } def _a ( self , _a , _a , _a=None , _a="uniform_average" , _a=True ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = mean_squared_error( _a , _a , sample_weight=_a , multioutput=_a , squared=_a ) return {"mse": mse}
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"""simple docstring""" # Note: if you intend to run this script make sure you look under scripts/fsmt/ # to locate the appropriate script to do the work correctly. There is a set of scripts to: # - download and prepare data and run the conversion script # - perform eval to get the best hparam into the config # - generate model_cards - useful if you have multiple models from the same paper import argparse import json import os import re from collections import OrderedDict from os.path import basename, dirname import fairseq import torch from fairseq import hub_utils from fairseq.data.dictionary import Dictionary from transformers import FSMTConfig, FSMTForConditionalGeneration from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE from transformers.utils import WEIGHTS_NAME, logging logging.set_verbosity_warning() a :str = 2 # based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping` # values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults: # # * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users) # * `early_stopping`: `False` consistently scored better # * `length_penalty` varied, so will assign the best one depending on the model a :int = { # fairseq: "wmt19-ru-en": {"length_penalty": 1.1}, "wmt19-en-ru": {"length_penalty": 1.15}, "wmt19-en-de": {"length_penalty": 1.0}, "wmt19-de-en": {"length_penalty": 1.1}, # allenai: "wmt16-en-de-dist-12-1": {"length_penalty": 0.6}, "wmt16-en-de-dist-6-1": {"length_penalty": 0.6}, "wmt16-en-de-12-1": {"length_penalty": 0.8}, "wmt19-de-en-6-6-base": {"length_penalty": 0.6}, "wmt19-de-en-6-6-big": {"length_penalty": 0.6}, } # this remaps the different models to their organization names a :Dict = {} for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]: a :List[Any] = "facebook" for m in [ "wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1", "wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big", ]: a :str = "allenai" def _lowercase ( __lowerCAmelCase ) -> Any: # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up, # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7} SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() ) SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split() # restore the special tokens for k in keep_keys: del da[F'''{k}</w>'''] SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore return da def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]: # prep assert os.path.exists(__lowerCAmelCase ) os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase ) print(F'''Writing results to {pytorch_dump_folder_path}''' ) # handle various types of models SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models() SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""} SCREAMING_SNAKE_CASE__ : Optional[Any] = """.""" # note: since the model dump is old, fairseq has upgraded its model some # time later, and it does a whole lot of rewrites and splits on the saved # weights, therefore we can't use torch.load() directly on the model file. # see: upgrade_state_dict(state_dict) in fairseq_model.py print(F'''using checkpoint {checkpoint_file}''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] ) SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""] SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""] SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase ) # dicts SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" ) print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # detect whether this is a do_lower_case situation, which can be derived by checking whether we # have at least one uppercase letter in the source vocab SCREAMING_SNAKE_CASE__ : Optional[Any] = True for k in src_vocab.keys(): if not k.islower(): SCREAMING_SNAKE_CASE__ : Tuple = False break SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" ) print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # merges_file (bpecodes) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] ) for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code" SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) if os.path.exists(__lowerCAmelCase ): break with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin: SCREAMING_SNAKE_CASE__ : Any = fin.read() SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number print(F'''Generating {merges_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout: fout.write(__lowerCAmelCase ) # model config SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" ) # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe - # may have to modify the tokenizer if a different type is used by a future model assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}''' assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}''' SCREAMING_SNAKE_CASE__ : str = { """architectures""": ["""FSMTForConditionalGeneration"""], """model_type""": """fsmt""", """activation_dropout""": args["""activation_dropout"""], """activation_function""": """relu""", """attention_dropout""": args["""attention_dropout"""], """d_model""": args["""decoder_embed_dim"""], """dropout""": args["""dropout"""], """init_std""": 0.02, """max_position_embeddings""": args["""max_source_positions"""], """num_hidden_layers""": args["""encoder_layers"""], """src_vocab_size""": src_vocab_size, """tgt_vocab_size""": tgt_vocab_size, """langs""": [src_lang, tgt_lang], """encoder_attention_heads""": args["""encoder_attention_heads"""], """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""], """encoder_layerdrop""": args["""encoder_layerdrop"""], """encoder_layers""": args["""encoder_layers"""], """decoder_attention_heads""": args["""decoder_attention_heads"""], """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""], """decoder_layerdrop""": args["""decoder_layerdrop"""], """decoder_layers""": args["""decoder_layers"""], """bos_token_id""": 0, """pad_token_id""": 1, """eos_token_id""": 2, """is_encoder_decoder""": True, """scale_embedding""": not args["""no_scale_embedding"""], """tie_word_embeddings""": args["""share_all_embeddings"""], } # good hparam defaults to start with SCREAMING_SNAKE_CASE__ : Tuple = 5 SCREAMING_SNAKE_CASE__ : str = False if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]: SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""] else: SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0 print(F'''Generating {fsmt_model_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # tokenizer config SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = { """langs""": [src_lang, tgt_lang], """model_max_length""": 1024, """do_lower_case""": do_lower_case, } print(F'''Generating {fsmt_tokenizer_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # model SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0] SCREAMING_SNAKE_CASE__ : int = model.state_dict() # rename keys to start with 'model.' SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() ) # remove unneeded keys SCREAMING_SNAKE_CASE__ : str = [ """model.model""", """model.encoder.version""", """model.decoder.version""", """model.encoder_embed_tokens.weight""", """model.decoder_embed_tokens.weight""", """model.encoder.embed_positions._float_tensor""", """model.decoder.embed_positions._float_tensor""", ] for k in ignore_keys: model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase ) # check that it loads ok model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase ) # save SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) print(F'''Generating {pytorch_weights_dump_path}''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) print("""Conversion is done!""" ) print("""\nLast step is to upload the files to s3""" ) print(F'''cd {data_root}''' ) print(F'''transformers-cli upload {model_dir}''' ) if __name__ == "__main__": a :Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--fsmt_checkpoint_path", default=None, type=str, required=True, help=( "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts," " bpecodes, etc." ), ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) a :List[str] = parser.parse_args() convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
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"""simple docstring""" import argparse import json import os from collections import OrderedDict import torch from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer from transformers.tokenization_utils_base import AddedToken @torch.no_grad() def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]: # Load configuration defined in the metadata file with open(__lowerCAmelCase ) as metadata_file: SCREAMING_SNAKE_CASE__ : Union[str, Any] = json.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = LukeConfig(use_entity_aware_attention=__lowerCAmelCase , **metadata["""model_config"""] ) # Load in the weights from the checkpoint_path SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )["""module"""] # Load the entity vocab file SCREAMING_SNAKE_CASE__ : Dict = load_original_entity_vocab(__lowerCAmelCase ) # add an entry for [MASK2] SCREAMING_SNAKE_CASE__ : Union[str, Any] = max(entity_vocab.values() ) + 1 config.entity_vocab_size += 1 SCREAMING_SNAKE_CASE__ : Dict = XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""] ) # Add special tokens to the token vocabulary for downstream tasks SCREAMING_SNAKE_CASE__ : List[Any] = AddedToken("""<ent>""" , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = AddedToken("""<ent2>""" , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]} ) config.vocab_size += 2 print(F'''Saving tokenizer to {pytorch_dump_folder_path}''' ) tokenizer.save_pretrained(__lowerCAmelCase ) with open(os.path.join(__lowerCAmelCase , """tokenizer_config.json""" ) , """r""" ) as f: SCREAMING_SNAKE_CASE__ : Union[str, Any] = json.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = """MLukeTokenizer""" with open(os.path.join(__lowerCAmelCase , """tokenizer_config.json""" ) , """w""" ) as f: json.dump(__lowerCAmelCase , __lowerCAmelCase ) with open(os.path.join(__lowerCAmelCase , MLukeTokenizer.vocab_files_names["""entity_vocab_file"""] ) , """w""" ) as f: json.dump(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = MLukeTokenizer.from_pretrained(__lowerCAmelCase ) # Initialize the embeddings of the special tokens SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.convert_tokens_to_ids(["""@"""] )[0] SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.convert_tokens_to_ids(["""#"""] )[0] SCREAMING_SNAKE_CASE__ : Any = state_dict["""embeddings.word_embeddings.weight"""] SCREAMING_SNAKE_CASE__ : int = word_emb[ent_init_index].unsqueeze(0 ) SCREAMING_SNAKE_CASE__ : Any = word_emb[enta_init_index].unsqueeze(0 ) SCREAMING_SNAKE_CASE__ : Dict = torch.cat([word_emb, ent_emb, enta_emb] ) # add special tokens for 'entity_predictions.bias' for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]: SCREAMING_SNAKE_CASE__ : List[str] = state_dict[bias_name] SCREAMING_SNAKE_CASE__ : List[str] = decoder_bias[ent_init_index].unsqueeze(0 ) SCREAMING_SNAKE_CASE__ : List[Any] = decoder_bias[enta_init_index].unsqueeze(0 ) SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] ) # Initialize the query layers of the entity-aware self-attention mechanism for layer_index in range(config.num_hidden_layers ): for matrix_name in ["query.weight", "query.bias"]: SCREAMING_SNAKE_CASE__ : Union[str, Any] = F'''encoder.layer.{layer_index}.attention.self.''' SCREAMING_SNAKE_CASE__ : Union[str, Any] = state_dict[prefix + matrix_name] SCREAMING_SNAKE_CASE__ : Tuple = state_dict[prefix + matrix_name] SCREAMING_SNAKE_CASE__ : List[Any] = state_dict[prefix + matrix_name] # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks SCREAMING_SNAKE_CASE__ : List[Any] = state_dict["""entity_embeddings.entity_embeddings.weight"""] SCREAMING_SNAKE_CASE__ : Optional[Any] = entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0 ) SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([entity_emb, entity_mask_emb] ) # add [MASK2] for 'entity_predictions.bias' SCREAMING_SNAKE_CASE__ : Optional[int] = state_dict["""entity_predictions.bias"""] SCREAMING_SNAKE_CASE__ : Optional[int] = entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0 ) SCREAMING_SNAKE_CASE__ : Any = torch.cat([entity_prediction_bias, entity_mask_bias] ) SCREAMING_SNAKE_CASE__ : Optional[int] = LukeForMaskedLM(config=__lowerCAmelCase ).eval() state_dict.pop("""entity_predictions.decoder.weight""" ) state_dict.pop("""lm_head.decoder.weight""" ) state_dict.pop("""lm_head.decoder.bias""" ) SCREAMING_SNAKE_CASE__ : str = OrderedDict() for key, value in state_dict.items(): if not (key.startswith("""lm_head""" ) or key.startswith("""entity_predictions""" )): SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict[key] else: SCREAMING_SNAKE_CASE__ : Dict = state_dict[key] SCREAMING_SNAKE_CASE__ : int = model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase ) if set(__lowerCAmelCase ) != {"luke.embeddings.position_ids"}: raise ValueError(F'''Unexpected unexpected_keys: {unexpected_keys}''' ) if set(__lowerCAmelCase ) != { "lm_head.decoder.weight", "lm_head.decoder.bias", "entity_predictions.decoder.weight", }: raise ValueError(F'''Unexpected missing_keys: {missing_keys}''' ) model.tie_weights() assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all() assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all() # Check outputs SCREAMING_SNAKE_CASE__ : Optional[Any] = MLukeTokenizer.from_pretrained(__lowerCAmelCase , task="""entity_classification""" ) SCREAMING_SNAKE_CASE__ : Tuple = """ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).""" SCREAMING_SNAKE_CASE__ : str = (0, 9) SCREAMING_SNAKE_CASE__ : int = tokenizer(__lowerCAmelCase , entity_spans=[span] , return_tensors="""pt""" ) SCREAMING_SNAKE_CASE__ : Optional[int] = model(**__lowerCAmelCase ) # Verify word hidden states if model_size == "large": raise NotImplementedError else: # base SCREAMING_SNAKE_CASE__ : Tuple = torch.Size((1, 33, 768) ) SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([[0.0_892, 0.0_596, -0.2_819], [0.0_134, 0.1_199, 0.0_573], [-0.0_169, 0.0_927, 0.0_644]] ) if not (outputs.last_hidden_state.shape == expected_shape): raise ValueError( F'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}''' ) if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=1E-4 ): raise ValueError # Verify entity hidden states if model_size == "large": raise NotImplementedError else: # base SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size((1, 1, 768) ) SCREAMING_SNAKE_CASE__ : Any = torch.tensor([[-0.1_482, 0.0_609, 0.0_322]] ) if not (outputs.entity_last_hidden_state.shape == expected_shape): raise ValueError( F'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is''' F''' {expected_shape}''' ) if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , __lowerCAmelCase , atol=1E-4 ): raise ValueError # Verify masked word/entity prediction SCREAMING_SNAKE_CASE__ : Tuple = MLukeTokenizer.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[int] = """Tokyo is the capital of <mask>.""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = (24, 30) SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer(__lowerCAmelCase , entity_spans=[span] , return_tensors="""pt""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = encoding["""input_ids"""][0].tolist() SCREAMING_SNAKE_CASE__ : str = input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>""" ) ) SCREAMING_SNAKE_CASE__ : Dict = outputs.logits[0][mask_position_id].argmax(dim=-1 ) assert "Japan" == tokenizer.decode(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = outputs.entity_logits[0][0].argmax().item() SCREAMING_SNAKE_CASE__ : List[str] = [ entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id ] assert [e for e in multilingual_predicted_entities if e.startswith("""en:""" )][0] == "en:Japan" # Finally, save our PyTorch model and tokenizer print("""Saving PyTorch model to {}""".format(__lowerCAmelCase ) ) model.save_pretrained(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : List[Any] = ["""[MASK]""", """[PAD]""", """[UNK]"""] SCREAMING_SNAKE_CASE__ : List[str] = [json.loads(__lowerCAmelCase ) for line in open(__lowerCAmelCase )] SCREAMING_SNAKE_CASE__ : int = {} for entry in data: SCREAMING_SNAKE_CASE__ : List[Any] = entry["""id"""] for entity_name, language in entry["entities"]: if entity_name in SPECIAL_TOKENS: SCREAMING_SNAKE_CASE__ : Optional[Any] = entity_id break SCREAMING_SNAKE_CASE__ : List[Any] = F'''{language}:{entity_name}''' SCREAMING_SNAKE_CASE__ : Union[str, Any] = entity_id return new_mapping if __name__ == "__main__": a :str = argparse.ArgumentParser() # Required parameters parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.") parser.add_argument( "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration." ) parser.add_argument( "--entity_vocab_path", default=None, type=str, help="Path to an entity_vocab.tsv file, containing the entity vocabulary.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model." ) parser.add_argument( "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted." ) a :int = parser.parse_args() convert_luke_checkpoint( args.checkpoint_path, args.metadata_path, args.entity_vocab_path, args.pytorch_dump_folder_path, args.model_size, )
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,) def _a ( self , **_a ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """num_train_timesteps""": 1_000, """beta_start""": 0.0_001, """beta_end""": 0.02, """beta_schedule""": """linear""", """variance_type""": """fixed_small""", """clip_sample""": True, } config.update(**_a ) return config def _a ( self ) -> str: """simple docstring""" for timesteps in [1, 5, 100, 1_000]: self.check_over_configs(num_train_timesteps=_a ) def _a ( self ) -> str: """simple docstring""" for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=_a , beta_end=_a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_a ) def _a ( self ) -> Any: """simple docstring""" for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=_a ) def _a ( self ) -> Optional[int]: """simple docstring""" for clip_sample in [True, False]: self.check_over_configs(clip_sample=_a ) def _a ( self ) -> int: """simple docstring""" self.check_over_configs(thresholding=_a ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=_a , prediction_type=_a , sample_max_value=_a , ) def _a ( self ) -> str: """simple docstring""" for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=_a ) def _a ( self ) -> str: """simple docstring""" for t in [0, 500, 999]: self.check_over_forward(time_step=_a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Any = len(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model() SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : str = pred_prev_sample SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 258.9_606 ) < 1E-2 assert abs(result_mean.item() - 0.3_372 ) < 1E-3 def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" ) SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Dict = len(_a ) SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model() SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : int = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 202.0_296 ) < 1E-2 assert abs(result_mean.item() - 0.2_631 ) < 1E-3 def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps for i, timestep in enumerate(_a ): if i == len(_a ) - 1: SCREAMING_SNAKE_CASE__ : Optional[Any] = -1 else: SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1] SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item() self.assertEqual(_a , _a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0] with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ): scheduler.set_timesteps(timesteps=_a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0] SCREAMING_SNAKE_CASE__ : List[str] = len(_a ) with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ): scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a ) def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps] with self.assertRaises( _a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ): scheduler.set_timesteps(timesteps=_a )
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"""simple docstring""" from unittest import TestCase from datasets import Sequence, Value from datasets.arrow_dataset import Dataset class __a (UpperCamelCase_): '''simple docstring''' def _a ( self ) -> Union[str, Any]: """simple docstring""" return [ {"col_1": 3, "col_2": "a"}, {"col_1": 2, "col_2": "b"}, {"col_1": 1, "col_2": "c"}, {"col_1": 0, "col_2": "d"}, ] def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = {"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]} return Dataset.from_dict(_a ) def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self._create_example_records() SCREAMING_SNAKE_CASE__ : Tuple = Dataset.from_list(_a ) self.assertListEqual(dset.column_names , ["""col_1""", """col_2"""] ) for i, r in enumerate(_a ): self.assertDictEqual(_a , example_records[i] ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self._create_example_records() SCREAMING_SNAKE_CASE__ : Tuple = Dataset.from_list(_a ) SCREAMING_SNAKE_CASE__ : Any = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} ) self.assertEqual(dset.info , dset_from_dict.info ) def _a ( self ) -> Dict: # checks what happens with missing columns """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = [{"""col_1""": 1}, {"""col_2""": """x"""}] SCREAMING_SNAKE_CASE__ : Optional[int] = Dataset.from_list(_a ) self.assertDictEqual(dset[0] , {"""col_1""": 1} ) self.assertDictEqual(dset[1] , {"""col_1""": None} ) # NB: first record is used for columns def _a ( self ) -> str: # checks if the type can be inferred from the second record """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = [{"""col_1""": []}, {"""col_1""": [1, 2]}] SCREAMING_SNAKE_CASE__ : List[str] = Dataset.from_list(_a ) self.assertEqual(dset.info.features["""col_1"""] , Sequence(Value("""int64""" ) ) ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dataset.from_list([] ) self.assertEqual(len(_a ) , 0 ) self.assertListEqual(dset.column_names , [] )
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"""simple docstring""" import os a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000} def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Any = 0 SCREAMING_SNAKE_CASE__ : Dict = 0 while index < len(__lowerCAmelCase ) - 1: SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]] SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]] if current_value < next_value: total_value -= current_value else: total_value += current_value index += 1 total_value += SYMBOLS[numerals[index]] return total_value def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Optional[int] = """""" SCREAMING_SNAKE_CASE__ : int = num // 1000 numerals += m_count * "M" num %= 1000 SCREAMING_SNAKE_CASE__ : List[str] = num // 100 if c_count == 9: numerals += "CM" c_count -= 9 elif c_count == 4: numerals += "CD" c_count -= 4 if c_count >= 5: numerals += "D" c_count -= 5 numerals += c_count * "C" num %= 100 SCREAMING_SNAKE_CASE__ : List[Any] = num // 10 if x_count == 9: numerals += "XC" x_count -= 9 elif x_count == 4: numerals += "XL" x_count -= 4 if x_count >= 5: numerals += "L" x_count -= 5 numerals += x_count * "X" num %= 10 if num == 9: numerals += "IX" num -= 9 elif num == 4: numerals += "IV" num -= 4 if num >= 5: numerals += "V" num -= 5 numerals += num * "I" return numerals def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int: SCREAMING_SNAKE_CASE__ : int = 0 with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea: SCREAMING_SNAKE_CASE__ : str = filea.readlines() for line in lines: SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip() SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase ) savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase ) return savings if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast from ...utils import logging if TYPE_CHECKING: from ...feature_extraction_utils import FeatureExtractionMixin from ...tokenization_utils_base import PreTrainedTokenizerBase from ...utils import TensorType a :Dict = logging.get_logger(__name__) a :Dict = { "openai/whisper-base": "https://huggingface.co/openai/whisper-base/resolve/main/config.json", } # fmt: off a :Tuple = [ 1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 357, 366, 438, 532, 685, 705, 796, 930, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377, 1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211, 4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786, 11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791, 17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409, 34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361 ] a :Tuple = [ 1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1_350, 1_853, 1_982, 2_460, 2_627, 3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647, 7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793, 14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675, 22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865, 42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362 ] class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = """whisper""" _SCREAMING_SNAKE_CASE :Optional[Any] = ["""past_key_values"""] _SCREAMING_SNAKE_CASE :Union[str, Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""} def __init__( self , _a=51_865 , _a=80 , _a=6 , _a=4 , _a=6 , _a=4 , _a=1_536 , _a=1_536 , _a=0.0 , _a=0.0 , _a=50_257 , _a=True , _a=True , _a="gelu" , _a=256 , _a=0.0 , _a=0.0 , _a=0.0 , _a=0.02 , _a=False , _a=1_500 , _a=448 , _a=50_256 , _a=50_256 , _a=50_256 , _a=None , _a=[220, 50_256] , _a=False , _a=256 , _a=False , _a=0.05 , _a=10 , _a=2 , _a=0.0 , _a=10 , _a=0 , _a=7 , **_a , ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size SCREAMING_SNAKE_CASE__ : List[Any] = num_mel_bins SCREAMING_SNAKE_CASE__ : List[Any] = d_model SCREAMING_SNAKE_CASE__ : int = encoder_layers SCREAMING_SNAKE_CASE__ : Any = encoder_attention_heads SCREAMING_SNAKE_CASE__ : Dict = decoder_layers SCREAMING_SNAKE_CASE__ : List[Any] = decoder_attention_heads SCREAMING_SNAKE_CASE__ : str = decoder_ffn_dim SCREAMING_SNAKE_CASE__ : Tuple = encoder_ffn_dim SCREAMING_SNAKE_CASE__ : Tuple = dropout SCREAMING_SNAKE_CASE__ : int = attention_dropout SCREAMING_SNAKE_CASE__ : List[str] = activation_dropout SCREAMING_SNAKE_CASE__ : List[Any] = activation_function SCREAMING_SNAKE_CASE__ : Optional[Any] = init_std SCREAMING_SNAKE_CASE__ : List[Any] = encoder_layerdrop SCREAMING_SNAKE_CASE__ : Tuple = decoder_layerdrop SCREAMING_SNAKE_CASE__ : List[str] = use_cache SCREAMING_SNAKE_CASE__ : Optional[int] = encoder_layers SCREAMING_SNAKE_CASE__ : Any = scale_embedding # scale factor will be sqrt(d_model) if True SCREAMING_SNAKE_CASE__ : Any = max_source_positions SCREAMING_SNAKE_CASE__ : Optional[int] = max_target_positions # Audio Classification-specific parameters. Feel free to ignore for other classes. SCREAMING_SNAKE_CASE__ : Optional[int] = classifier_proj_size SCREAMING_SNAKE_CASE__ : List[str] = use_weighted_layer_sum # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 SCREAMING_SNAKE_CASE__ : Optional[int] = apply_spec_augment SCREAMING_SNAKE_CASE__ : Optional[Any] = mask_time_prob SCREAMING_SNAKE_CASE__ : Dict = mask_time_length SCREAMING_SNAKE_CASE__ : Any = mask_time_min_masks SCREAMING_SNAKE_CASE__ : Any = mask_feature_prob SCREAMING_SNAKE_CASE__ : Any = mask_feature_length SCREAMING_SNAKE_CASE__ : Dict = mask_feature_min_masks SCREAMING_SNAKE_CASE__ : str = median_filter_width super().__init__( pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , decoder_start_token_id=_a , suppress_tokens=_a , begin_suppress_tokens=_a , **_a , ) class __a (UpperCamelCase_): '''simple docstring''' @property def _a ( self ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = OrderedDict( [ ("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}), ] ) if self.use_past: SCREAMING_SNAKE_CASE__ : Union[str, Any] = {0: """batch"""} else: SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""} if self.use_past: self.fill_with_past_key_values_(_a , direction="""inputs""" ) return common_inputs def _a ( self , _a , _a = -1 , _a = -1 , _a = False , _a = None , _a = 22_050 , _a = 5.0 , _a = 220 , ) -> Mapping[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = OrderedDict() SCREAMING_SNAKE_CASE__ : List[Any] = OnnxConfig.generate_dummy_inputs( self , preprocessor=preprocessor.feature_extractor , batch_size=_a , framework=_a , sampling_rate=_a , time_duration=_a , frequency=_a , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = encoder_inputs["""input_features"""].shape[2] SCREAMING_SNAKE_CASE__ : Union[str, Any] = encoder_sequence_length // 2 if self.use_past else seq_length SCREAMING_SNAKE_CASE__ : Optional[Any] = super().generate_dummy_inputs( preprocessor.tokenizer , _a , _a , _a , _a ) SCREAMING_SNAKE_CASE__ : Any = encoder_inputs.pop("""input_features""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = decoder_inputs.pop("""decoder_input_ids""" ) if "past_key_values" in decoder_inputs: SCREAMING_SNAKE_CASE__ : Optional[Any] = decoder_inputs.pop("""past_key_values""" ) return dummy_inputs @property def _a ( self ) -> float: """simple docstring""" return 1E-3
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"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow if is_tf_available(): import numpy as np import tensorflow as tf from transformers import TFCamembertModel @require_tf @require_sentencepiece @require_tokenizers class __a (unittest.TestCase): '''simple docstring''' @slow def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" ) SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor( [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !" SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""] SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) ) self.assertEqual(output.shape , _a ) # compare the actual values for a slice. SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor( [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , ) # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0') # camembert.eval() # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach() self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
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"""simple docstring""" from __future__ import annotations def _lowercase ( __lowerCAmelCase ) -> float: if not nums: raise ValueError("""List is empty""" ) return sum(__lowerCAmelCase ) / len(__lowerCAmelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices a :List[Any] = logging.get_logger(__name__) a :Optional[int] = { "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json", } class __a (UpperCamelCase_ , UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = """focalnet""" def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]: """simple docstring""" super().__init__(**_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size SCREAMING_SNAKE_CASE__ : str = patch_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes SCREAMING_SNAKE_CASE__ : Optional[int] = depths SCREAMING_SNAKE_CASE__ : Any = focal_levels SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows SCREAMING_SNAKE_CASE__ : Any = hidden_act SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate SCREAMING_SNAKE_CASE__ : str = use_layerscale SCREAMING_SNAKE_CASE__ : int = layerscale_value SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator SCREAMING_SNAKE_CASE__ : str = initializer_range SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps SCREAMING_SNAKE_CASE__ : Any = encoder_stride SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices( out_features=_a , out_indices=_a , stage_names=self.stage_names )
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"""simple docstring""" import argparse import os import re import zipfile import torch from transformers import AutoTokenizer, GPTaConfig def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any: # Format the message. if name is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None else: SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}""" SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase ) # Print and recurse (if needed). if isinstance(__lowerCAmelCase , __lowerCAmelCase ): if msg is not None: print(__lowerCAmelCase ) for k in val.keys(): recursive_print(__lowerCAmelCase , val[k] , spaces + 2 ) elif isinstance(__lowerCAmelCase , torch.Tensor ): print(__lowerCAmelCase , """:""" , val.size() ) else: print(__lowerCAmelCase , """:""" , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] # for compatibility with later versions of NVIDIA Megatron-LM. # The inverse operation is performed inside Megatron-LM to read checkpoints: # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 # If param is the weight tensor of the self-attention block, the returned tensor # will have to be transposed one more time to be read by HuggingFace GPT2. SCREAMING_SNAKE_CASE__ : Tuple = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:] SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 ) SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:] SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous() SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase ) return param def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: # The converted output model. SCREAMING_SNAKE_CASE__ : List[str] = {} # old versions did not store training args SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase ) if ds_args is not None: # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint # from pprint import pprint # pprint(vars(ds_args)) SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size # pprint(config) # The number of heads. SCREAMING_SNAKE_CASE__ : List[str] = config.n_head # The hidden_size per head. SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head # Megatron-LM checkpoint version if "checkpoint_version" in input_state_dict.keys(): SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""] else: SCREAMING_SNAKE_CASE__ : Tuple = 0.0 # The model. SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""] # The language model. SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""] # The embeddings. SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""] # The word embeddings. SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""] # Truncate the embedding table to vocab_size rows. SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :] SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings # The position embeddings. SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""] # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size] SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 ) if n_positions != config.n_positions: raise ValueError( F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' ) # Store the position embeddings. SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings # The transformer. SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""] # The regex to extract layer names. SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" ) # The simple map of names for "automated" rules. SCREAMING_SNAKE_CASE__ : Optional[int] = { """attention.dense""": """.attn.c_proj.""", """self_attention.dense""": """.attn.c_proj.""", """mlp.dense_h_to_4h""": """.mlp.c_fc.""", """mlp.dense_4h_to_h""": """.mlp.c_proj.""", } # Extract the layers. for key, val in transformer.items(): # Match the name. SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase ) # Stop if that's not a layer if m is None: break # The index of the layer. SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) ) # The name of the operation. SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 ) # Is it a weight or a bias? SCREAMING_SNAKE_CASE__ : str = m.group(3 ) # The name of the layer. SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}''' # For layernorm(s), simply store the layer norm. if op_name.endswith("""layernorm""" ): SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2""" SCREAMING_SNAKE_CASE__ : List[Any] = val # Transpose the QKV matrix. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "weight": # Insert a tensor of 1x1xDxD bias. SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view( 1 , 1 , __lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = causal_mask # Insert a "dummy" tensor for masked_bias. SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa ) SCREAMING_SNAKE_CASE__ : List[str] = masked_bias SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D. SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous() # Store. SCREAMING_SNAKE_CASE__ : Dict = out_val # Transpose the bias. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Store. No change of shape. SCREAMING_SNAKE_CASE__ : str = out_val # Transpose the weights. elif weight_or_bias == "weight": SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 ) # Copy the bias. elif weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : Dict = val # DEBUG. assert config.n_layer == layer_idx + 1 # The final layernorm. SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""] SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""] # For LM head, transformers' wants the matrix to weight embeddings. SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings # It should be done! return output_state_dict def _lowercase ( ) -> List[Any]: # Create the argument parser. SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser() parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" ) parser.add_argument( """path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , ) parser.add_argument( """--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , ) SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args() # Extract the basename. SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint ) # Load the model. # the .zip is very optional, let's keep it for backward compatibility print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' ) if args.path_to_checkpoint.endswith(""".zip""" ): with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint: with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict: SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" ) else: SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" ) SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase ) # Read the config, or default to the model released by NVIDIA. if args.config_file == "": if ds_args is not None: if ds_args.bias_gelu_fusion: SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast""" elif ds_args.openai_gelu: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new""" else: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu""" else: # in the very early days this used to be "gelu_new" SCREAMING_SNAKE_CASE__ : Any = """gelu_new""" # Spell out all parameters in case the defaults change. SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig( vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , ) else: SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file ) SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""] # Convert. print("""Converting""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) # Print the structure of converted state dict. if args.print_checkpoint_structure: recursive_print(__lowerCAmelCase , __lowerCAmelCase ) # Add tokenizer class info to config # see https://github.com/huggingface/transformers/issues/13906) if ds_args is not None: SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type if tokenizer_type == "GPT2BPETokenizer": SCREAMING_SNAKE_CASE__ : Any = """gpt2""" elif tokenizer_type == "PretrainedFromHF": SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path else: raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__ SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class # Store the config to file. print("""Saving config""" ) config.save_pretrained(__lowerCAmelCase ) # Save tokenizer based on args print(F'''Adding {tokenizer_class} tokenizer files''' ) tokenizer.save_pretrained(__lowerCAmelCase ) # Store the state_dict to file. SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" ) print(F'''Saving checkpoint to "{output_checkpoint_file}"''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) #################################################################################################### if __name__ == "__main__": main() ####################################################################################################
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"""simple docstring""" import unittest import numpy as np from transformers import RoFormerConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax.numpy as jnp from transformers.models.roformer.modeling_flax_roformer import ( FlaxRoFormerForMaskedLM, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerModel, ) class __a (unittest.TestCase): '''simple docstring''' def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = parent SCREAMING_SNAKE_CASE__ : Tuple = batch_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length SCREAMING_SNAKE_CASE__ : Optional[int] = is_training SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels SCREAMING_SNAKE_CASE__ : int = vocab_size SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads SCREAMING_SNAKE_CASE__ : Dict = intermediate_size SCREAMING_SNAKE_CASE__ : int = hidden_act SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size SCREAMING_SNAKE_CASE__ : int = initializer_range SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = None if self.use_attention_mask: SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] ) SCREAMING_SNAKE_CASE__ : Tuple = None if self.use_token_type_ids: SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , ) return config, input_ids, token_type_ids, attention_mask def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask} return config, inputs_dict @require_flax class __a (UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = True _SCREAMING_SNAKE_CASE :Optional[Any] = ( ( FlaxRoFormerModel, FlaxRoFormerForMaskedLM, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, ) if is_flax_available() else () ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self ) @slow def _a ( self ) -> int: """simple docstring""" for model_class_name in self.all_model_classes: SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a ) SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) ) self.assertIsNotNone(_a ) @require_flax class __a (unittest.TestCase): '''simple docstring''' @slow def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" ) SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] ) SCREAMING_SNAKE_CASE__ : str = model(_a )[0] SCREAMING_SNAKE_CASE__ : List[Any] = 50_000 SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size) self.assertEqual(output.shape , _a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array( [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] ) self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
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"""simple docstring""" import enum import os from hashlib import shaaaa from typing import Optional from .. import config from .logging import get_logger a :str = get_logger(__name__) class __a (enum.Enum): '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = """all_checks""" _SCREAMING_SNAKE_CASE :Union[str, Any] = """basic_checks""" _SCREAMING_SNAKE_CASE :List[str] = """no_checks""" class __a (UpperCamelCase_): '''simple docstring''' class __a (UpperCamelCase_): '''simple docstring''' class __a (UpperCamelCase_): '''simple docstring''' class __a (UpperCamelCase_): '''simple docstring''' def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> Optional[int]: if expected_checksums is None: logger.info("""Unable to verify checksums.""" ) return if len(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) > 0: raise ExpectedMoreDownloadedFiles(str(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) ) if len(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) > 0: raise UnexpectedDownloadedFile(str(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]] SCREAMING_SNAKE_CASE__ : str = """ for """ + verification_name if verification_name is not None else """""" if len(__lowerCAmelCase ) > 0: raise NonMatchingChecksumError( F'''Checksums didn\'t match{for_verification_name}:\n''' F'''{bad_urls}\n''' """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error""" ) logger.info("""All the checksums matched successfully""" + for_verification_name ) class __a (UpperCamelCase_): '''simple docstring''' class __a (UpperCamelCase_): '''simple docstring''' class __a (UpperCamelCase_): '''simple docstring''' class __a (UpperCamelCase_): '''simple docstring''' def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]: if expected_splits is None: logger.info("""Unable to verify splits sizes.""" ) return if len(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) > 0: raise ExpectedMoreSplits(str(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) ) if len(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) > 0: raise UnexpectedSplits(str(set(__lowerCAmelCase ) - set(__lowerCAmelCase ) ) ) SCREAMING_SNAKE_CASE__ : str = [ {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]} for name in expected_splits if expected_splits[name].num_examples != recorded_splits[name].num_examples ] if len(__lowerCAmelCase ) > 0: raise NonMatchingSplitsSizesError(str(__lowerCAmelCase ) ) logger.info("""All the splits matched successfully.""" ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = True ) -> dict: if record_checksum: SCREAMING_SNAKE_CASE__ : Optional[int] = shaaaa() with open(__lowerCAmelCase , """rb""" ) as f: for chunk in iter(lambda: f.read(1 << 20 ) , B"""""" ): m.update(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = m.hexdigest() else: SCREAMING_SNAKE_CASE__ : Any = None return {"num_bytes": os.path.getsize(__lowerCAmelCase ), "checksum": checksum} def _lowercase ( __lowerCAmelCase ) -> List[str]: if dataset_size and config.IN_MEMORY_MAX_SIZE: return dataset_size < config.IN_MEMORY_MAX_SIZE else: return False
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"""simple docstring""" a :List[str] = [ (1_000, "M"), (900, "CM"), (500, "D"), (400, "CD"), (100, "C"), (90, "XC"), (50, "L"), (40, "XL"), (10, "X"), (9, "IX"), (5, "V"), (4, "IV"), (1, "I"), ] def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000} SCREAMING_SNAKE_CASE__ : List[Any] = 0 SCREAMING_SNAKE_CASE__ : List[str] = 0 while place < len(__lowerCAmelCase ): if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]): total += vals[roman[place + 1]] - vals[roman[place]] place += 2 else: total += vals[roman[place]] place += 1 return total def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Any = [] for arabic, roman in ROMAN: ((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase ) result.append(roman * factor ) if number == 0: break return "".join(__lowerCAmelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from math import factorial def _lowercase ( __lowerCAmelCase = 20 ) -> int: SCREAMING_SNAKE_CASE__ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1, # 2, 3,... SCREAMING_SNAKE_CASE__ : Dict = n // 2 return int(factorial(__lowerCAmelCase ) / (factorial(__lowerCAmelCase ) * factorial(n - k )) ) if __name__ == "__main__": import sys if len(sys.argv) == 1: print(solution(20)) else: try: a :List[str] = int(sys.argv[1]) print(solution(n)) except ValueError: print("Invalid entry - please enter a number.")
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) a :Any = { "configuration_roberta_prelayernorm": [ "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaPreLayerNormConfig", "RobertaPreLayerNormOnnxConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Union[str, Any] = [ "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "RobertaPreLayerNormForCausalLM", "RobertaPreLayerNormForMaskedLM", "RobertaPreLayerNormForMultipleChoice", "RobertaPreLayerNormForQuestionAnswering", "RobertaPreLayerNormForSequenceClassification", "RobertaPreLayerNormForTokenClassification", "RobertaPreLayerNormModel", "RobertaPreLayerNormPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Optional[Any] = [ "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRobertaPreLayerNormForCausalLM", "TFRobertaPreLayerNormForMaskedLM", "TFRobertaPreLayerNormForMultipleChoice", "TFRobertaPreLayerNormForQuestionAnswering", "TFRobertaPreLayerNormForSequenceClassification", "TFRobertaPreLayerNormForTokenClassification", "TFRobertaPreLayerNormMainLayer", "TFRobertaPreLayerNormModel", "TFRobertaPreLayerNormPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :List[Any] = [ "FlaxRobertaPreLayerNormForCausalLM", "FlaxRobertaPreLayerNormForMaskedLM", "FlaxRobertaPreLayerNormForMultipleChoice", "FlaxRobertaPreLayerNormForQuestionAnswering", "FlaxRobertaPreLayerNormForSequenceClassification", "FlaxRobertaPreLayerNormForTokenClassification", "FlaxRobertaPreLayerNormModel", "FlaxRobertaPreLayerNormPreTrainedModel", ] if TYPE_CHECKING: from .configuration_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaPreLayerNormConfig, RobertaPreLayerNormOnnxConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaPreLayerNormForCausalLM, RobertaPreLayerNormForMaskedLM, RobertaPreLayerNormForMultipleChoice, RobertaPreLayerNormForQuestionAnswering, RobertaPreLayerNormForSequenceClassification, RobertaPreLayerNormForTokenClassification, RobertaPreLayerNormModel, RobertaPreLayerNormPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_roberta_prelayernorm import ( TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, TFRobertaPreLayerNormForCausalLM, TFRobertaPreLayerNormForMaskedLM, TFRobertaPreLayerNormForMultipleChoice, TFRobertaPreLayerNormForQuestionAnswering, TFRobertaPreLayerNormForSequenceClassification, TFRobertaPreLayerNormForTokenClassification, TFRobertaPreLayerNormMainLayer, TFRobertaPreLayerNormModel, TFRobertaPreLayerNormPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_roberta_prelayernorm import ( FlaxRobertaPreLayerNormForCausalLM, FlaxRobertaPreLayerNormForMaskedLM, FlaxRobertaPreLayerNormForMultipleChoice, FlaxRobertaPreLayerNormForQuestionAnswering, FlaxRobertaPreLayerNormForSequenceClassification, FlaxRobertaPreLayerNormForTokenClassification, FlaxRobertaPreLayerNormModel, FlaxRobertaPreLayerNormPreTrainedModel, ) else: import sys a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" from __future__ import annotations from typing import TypedDict class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :str _SCREAMING_SNAKE_CASE :int def _lowercase ( __lowerCAmelCase ) -> list[str]: if not isinstance(__lowerCAmelCase , __lowerCAmelCase ): raise TypeError("""The parameter s type must be str.""" ) return [s[i:] + s[:i] for i in range(len(__lowerCAmelCase ) )] def _lowercase ( __lowerCAmelCase ) -> BWTTransformDict: if not isinstance(__lowerCAmelCase , __lowerCAmelCase ): raise TypeError("""The parameter s type must be str.""" ) if not s: raise ValueError("""The parameter s must not be empty.""" ) SCREAMING_SNAKE_CASE__ : str = all_rotations(__lowerCAmelCase ) rotations.sort() # sort the list of rotations in alphabetically order # make a string composed of the last char of each rotation SCREAMING_SNAKE_CASE__ : BWTTransformDict = { "bwt_string": "".join([word[-1] for word in rotations] ), "idx_original_string": rotations.index(__lowerCAmelCase ), } return response def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str: if not isinstance(__lowerCAmelCase , __lowerCAmelCase ): raise TypeError("""The parameter bwt_string type must be str.""" ) if not bwt_string: raise ValueError("""The parameter bwt_string must not be empty.""" ) try: SCREAMING_SNAKE_CASE__ : str = int(__lowerCAmelCase ) except ValueError: raise TypeError( """The parameter idx_original_string type must be int or passive""" """ of cast to int.""" ) if idx_original_string < 0: raise ValueError("""The parameter idx_original_string must not be lower than 0.""" ) if idx_original_string >= len(__lowerCAmelCase ): raise ValueError( """The parameter idx_original_string must be lower than""" """ len(bwt_string).""" ) SCREAMING_SNAKE_CASE__ : List[str] = [""""""] * len(__lowerCAmelCase ) for _ in range(len(__lowerCAmelCase ) ): for i in range(len(__lowerCAmelCase ) ): SCREAMING_SNAKE_CASE__ : Any = bwt_string[i] + ordered_rotations[i] ordered_rotations.sort() return ordered_rotations[idx_original_string] if __name__ == "__main__": a :Union[str, Any] = "Provide a string that I will generate its BWT transform: " a :List[str] = input(entry_msg).strip() a :List[str] = bwt_transform(s) print( f'Burrows Wheeler transform for string \'{s}\' results ' f'in \'{result["bwt_string"]}\'' ) a :Dict = reverse_bwt(result["bwt_string"], result["idx_original_string"]) print( f'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' ' f'we get original string \'{original_string}\'' )
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"""simple docstring""" import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import BertTokenizer, BertTokenizerFast from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import AlignProcessor, EfficientNetImageProcessor @require_vision class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp() SCREAMING_SNAKE_CASE__ : Dict = [ """[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest""", ] SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) ) SCREAMING_SNAKE_CASE__ : Optional[Any] = { """do_resize""": True, """size""": 20, """do_center_crop""": True, """crop_size""": 18, """do_normalize""": True, """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073], """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711], } SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a ) with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp: json.dump(_a , _a ) def _a ( self , **_a ) -> List[Any]: """simple docstring""" return BertTokenizer.from_pretrained(self.tmpdirname , **_a ) def _a ( self , **_a ) -> List[Any]: """simple docstring""" return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a ) def _a ( self , **_a ) -> Any: """simple docstring""" return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a ) def _a ( self ) -> List[Any]: """simple docstring""" shutil.rmtree(self.tmpdirname ) def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )] SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs] return image_inputs def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer() SCREAMING_SNAKE_CASE__ : str = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a ) processor_slow.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a ) SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a ) processor_fast.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname ) self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() ) self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() ) self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() ) self.assertIsInstance(processor_slow.tokenizer , _a ) self.assertIsInstance(processor_fast.tokenizer , _a ) self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertIsInstance(processor_slow.image_processor , _a ) self.assertIsInstance(processor_fast.image_processor , _a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() ) processor.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" ) SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 ) SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained( self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 ) self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() ) self.assertIsInstance(processor.tokenizer , _a ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , _a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor() SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" ) for key in input_image_proc.keys(): self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer""" SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a ) SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] , encoded_processor[key] ) def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.get_image_processor() SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer""" SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a ) self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] ) # test if it raises when no input is passed with pytest.raises(_a ): processor() def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a ) self.assertListEqual(_a , _a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer""" SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a ) self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
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"""simple docstring""" import argparse import os import torch from transformers import FlavaImageCodebook, FlavaImageCodebookConfig def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: SCREAMING_SNAKE_CASE__ : str = s.rsplit(__lowerCAmelCase , __lowerCAmelCase ) return new.join(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Dict: # encoder.embeddings are double copied in original FLAVA return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items() ) def _lowercase ( __lowerCAmelCase ) -> Any: SCREAMING_SNAKE_CASE__ : Dict = {} SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""group_1""", """group_2""", """group_3""", """group_4"""] for key, value in state_dict.items(): for group_key in group_keys: if group_key in key: SCREAMING_SNAKE_CASE__ : Optional[int] = key.replace(F'''{group_key}.''' , F'''{group_key}.group.''' ) if "res_path" in key: SCREAMING_SNAKE_CASE__ : Tuple = key.replace("""res_path.""" , """res_path.path.""" ) if key.endswith(""".w""" ): SCREAMING_SNAKE_CASE__ : Any = rreplace(__lowerCAmelCase , """.w""" , """.weight""" , 1 ) if key.endswith(""".b""" ): SCREAMING_SNAKE_CASE__ : List[Any] = rreplace(__lowerCAmelCase , """.b""" , """.bias""" , 1 ) SCREAMING_SNAKE_CASE__ : Any = value.float() return upgrade @torch.no_grad() def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=True ) -> List[str]: from dall_e import Encoder SCREAMING_SNAKE_CASE__ : int = Encoder() if os.path.exists(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Optional[int] = torch.load(__lowerCAmelCase ) else: SCREAMING_SNAKE_CASE__ : int = torch.hub.load_state_dict_from_url(__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : List[str] = ckpt.state_dict() encoder.load_state_dict(__lowerCAmelCase ) if config_path is not None: SCREAMING_SNAKE_CASE__ : str = FlavaImageCodebookConfig.from_pretrained(__lowerCAmelCase ) else: SCREAMING_SNAKE_CASE__ : List[Any] = FlavaImageCodebookConfig() SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlavaImageCodebook(__lowerCAmelCase ).eval() SCREAMING_SNAKE_CASE__ : Any = encoder.state_dict() SCREAMING_SNAKE_CASE__ : int = upgrade_state_dict(__lowerCAmelCase ) hf_model.load_state_dict(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = hf_model.state_dict() SCREAMING_SNAKE_CASE__ : int = count_parameters(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = count_parameters(__lowerCAmelCase ) assert torch.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-3 ) if save_checkpoint: hf_model.save_pretrained(__lowerCAmelCase ) else: return hf_state_dict if __name__ == "__main__": a :Any = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") a :List[Any] = parser.parse_args() convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
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"""simple docstring""" from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxSeqaSeqConfigWithPast from ...utils import logging a :Optional[Any] = logging.get_logger(__name__) a :Union[str, Any] = { "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json", "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json", "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json", "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json", "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json", } class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[Any] = """t5""" _SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""] _SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""} def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size SCREAMING_SNAKE_CASE__ : Tuple = d_model SCREAMING_SNAKE_CASE__ : int = d_kv SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers SCREAMING_SNAKE_CASE__ : int = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry SCREAMING_SNAKE_CASE__ : Tuple = num_heads SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj SCREAMING_SNAKE_CASE__ : str = use_cache SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" ) SCREAMING_SNAKE_CASE__ : Dict = act_info[-1] SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated""" if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2: raise ValueError( f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.''' """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """ """'gated-gelu' or 'relu'""" ) # for backwards compatibility if feed_forward_proj == "gated-gelu": SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new""" super().__init__( pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , ) class __a (UpperCamelCase_): '''simple docstring''' @property def _a ( self ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """input_ids""": {0: """batch""", 1: """encoder_sequence"""}, """attention_mask""": {0: """batch""", 1: """encoder_sequence"""}, } if self.use_past: SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence""" SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""} SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""} else: SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""} SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""} if self.use_past: self.fill_with_past_key_values_(_a , direction="""inputs""" ) return common_inputs @property def _a ( self ) -> int: """simple docstring""" return 13
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"""simple docstring""" import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_squeezebert import SqueezeBertTokenizer a :Dict = logging.get_logger(__name__) a :Tuple = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"} a :Any = { "vocab_file": { "squeezebert/squeezebert-uncased": ( "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt" ), "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt", "squeezebert/squeezebert-mnli-headless": ( "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt" ), }, "tokenizer_file": { "squeezebert/squeezebert-uncased": ( "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json" ), "squeezebert/squeezebert-mnli": ( "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json" ), "squeezebert/squeezebert-mnli-headless": ( "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json" ), }, } a :Tuple = { "squeezebert/squeezebert-uncased": 512, "squeezebert/squeezebert-mnli": 512, "squeezebert/squeezebert-mnli-headless": 512, } a :Dict = { "squeezebert/squeezebert-uncased": {"do_lower_case": True}, "squeezebert/squeezebert-mnli": {"do_lower_case": True}, "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True}, } class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Dict = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :Dict = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_INIT_CONFIGURATION _SCREAMING_SNAKE_CASE :Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :List[Any] = SqueezeBertTokenizer def __init__( self , _a=None , _a=None , _a=True , _a="[UNK]" , _a="[SEP]" , _a="[PAD]" , _a="[CLS]" , _a="[MASK]" , _a=True , _a=None , **_a , ) -> Optional[Any]: """simple docstring""" super().__init__( _a , tokenizer_file=_a , do_lower_case=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , tokenize_chinese_chars=_a , strip_accents=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get("""lowercase""" , _a ) != do_lower_case or normalizer_state.get("""strip_accents""" , _a ) != strip_accents or normalizer_state.get("""handle_chinese_chars""" , _a ) != tokenize_chinese_chars ): SCREAMING_SNAKE_CASE__ : Dict = getattr(_a , normalizer_state.pop("""type""" ) ) SCREAMING_SNAKE_CASE__ : Tuple = do_lower_case SCREAMING_SNAKE_CASE__ : Union[str, Any] = strip_accents SCREAMING_SNAKE_CASE__ : Dict = tokenize_chinese_chars SCREAMING_SNAKE_CASE__ : Optional[int] = normalizer_class(**_a ) SCREAMING_SNAKE_CASE__ : int = do_lower_case def _a ( self , _a , _a=None ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self._tokenizer.model.save(_a , name=_a ) return tuple(_a )
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"""simple docstring""" from __future__ import annotations import time import numpy as np a :Optional[Any] = [8, 5, 9, 7] a :List[Any] = [ [2, 0, 1, 1], [0, 1, 2, 1], [4, 0, 0, 3], [0, 2, 1, 0], [1, 0, 3, 0], ] a :int = [ [3, 2, 1, 4], [0, 2, 5, 2], [5, 1, 0, 5], [1, 5, 3, 0], [3, 0, 3, 3], ] class __a : '''simple docstring''' def __init__( self , _a , _a , _a , ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = claim_vector SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table def _a ( self ) -> list[int]: """simple docstring""" return [ sum(p_item[i] for p_item in self.__allocated_resources_table ) for i in range(len(self.__allocated_resources_table[0] ) ) ] def _a ( self ) -> list[int]: """simple docstring""" return np.array(self.__claim_vector ) - np.array( self.__processes_resource_summation() ) def _a ( self ) -> list[list[int]]: """simple docstring""" return [ list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) ) for i, allocated_resource in enumerate(self.__allocated_resources_table ) ] def _a ( self ) -> dict[int, list[int]]: """simple docstring""" return {self.__need().index(_a ): i for i in self.__need()} def _a ( self , **_a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need() SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources() SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager() for kw, val in kwargs.items(): if kw and val is True: self.__pretty_data() print("""_""" * 50 + """\n""" ) while need_list: SCREAMING_SNAKE_CASE__ : List[str] = False for each_need in need_list: SCREAMING_SNAKE_CASE__ : Dict = True for index, need in enumerate(_a ): if need > available_resources[index]: SCREAMING_SNAKE_CASE__ : Optional[int] = False break if execution: SCREAMING_SNAKE_CASE__ : Any = True # get the original index of the process from ind_ctrl db for original_need_index, need_clone in need_index_manager.items(): if each_need == need_clone: SCREAMING_SNAKE_CASE__ : Tuple = original_need_index print(f'''Process {process_number + 1} is executing.''' ) # remove the process run from stack need_list.remove(_a ) # update available/freed resources stack SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array( alloc_resources_table[process_number] ) print( """Updated available resource stack for processes: """ + """ """.join([str(_a ) for x in available_resources] ) ) break if safe: print("""The process is in a safe state.\n""" ) else: print("""System in unsafe state. Aborting...\n""" ) break def _a ( self ) -> Any: """simple docstring""" print(""" """ * 9 + """Allocated Resource Table""" ) for item in self.__allocated_resources_table: print( f'''P{self.__allocated_resources_table.index(_a ) + 1}''' + """ """.join(f'''{it:>8}''' for it in item ) + """\n""" ) print(""" """ * 9 + """System Resource Table""" ) for item in self.__maximum_claim_table: print( f'''P{self.__maximum_claim_table.index(_a ) + 1}''' + """ """.join(f'''{it:>8}''' for it in item ) + """\n""" ) print( """Current Usage by Active Processes: """ + """ """.join(str(_a ) for x in self.__claim_vector ) ) print( """Initial Available Resources: """ + """ """.join(str(_a ) for x in self.__available_resources() ) ) time.sleep(1 ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool: SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1 SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1 # dp is a 2d matrix where dp[i][j] denotes whether prefix string of # length i of input_string matches with prefix string of length j of # given pattern. # "dp" stands for dynamic programming. SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )] # since string of zero length match pattern of zero length SCREAMING_SNAKE_CASE__ : Dict = 1 # since pattern of zero length will never match with string of non-zero length for i in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 # since string of zero length will match with pattern where there # is at least one * alternatively for j in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0 # now using bottom-up approach to find for all remaining lengths for i in range(1 , __lowerCAmelCase ): for j in range(1 , __lowerCAmelCase ): if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".": SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1] elif pattern[j - 1] == "*": if dp[i][j - 2] == 1: SCREAMING_SNAKE_CASE__ : List[str] = 1 elif pattern[j - 2] in (input_string[i - 1], "."): SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j] else: SCREAMING_SNAKE_CASE__ : Optional[int] = 0 else: SCREAMING_SNAKE_CASE__ : Dict = 0 return bool(dp[-1][-1] ) if __name__ == "__main__": import doctest doctest.testmod() # inputing the strings # input_string = input("input a string :") # pattern = input("input a pattern :") a :Any = "aab" a :Optional[Any] = "c*a*b" # using function to check whether given string matches the given pattern if match_pattern(input_string, pattern): print(f'{input_string} matches the given pattern {pattern}') else: print(f'{input_string} does not match with the given pattern {pattern}')
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_xlnet import XLNetTokenizer else: a :List[Any] = None a :Optional[int] = logging.get_logger(__name__) a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} a :Optional[int] = { "vocab_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model", }, "tokenizer_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json", }, } a :Dict = { "xlnet-base-cased": None, "xlnet-large-cased": None, } a :int = "▁" # Segments (not really needed) a :Dict = 0 a :Optional[int] = 1 a :Tuple = 2 a :List[str] = 3 a :Optional[Any] = 4 class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = """left""" _SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token super().__init__( vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3 SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case SCREAMING_SNAKE_CASE__ : List[str] = remove_space SCREAMING_SNAKE_CASE__ : int = keep_accents SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return SCREAMING_SNAKE_CASE__ : Tuple = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
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"""simple docstring""" import cmath import math def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> complex: SCREAMING_SNAKE_CASE__ : Optional[int] = math.radians(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Any = math.radians(__lowerCAmelCase ) # Convert voltage and current to rectangular form SCREAMING_SNAKE_CASE__ : int = cmath.rect(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = cmath.rect(__lowerCAmelCase , __lowerCAmelCase ) # Calculate apparent power return voltage_rect * current_rect if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool: SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1 SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1 # dp is a 2d matrix where dp[i][j] denotes whether prefix string of # length i of input_string matches with prefix string of length j of # given pattern. # "dp" stands for dynamic programming. SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )] # since string of zero length match pattern of zero length SCREAMING_SNAKE_CASE__ : Dict = 1 # since pattern of zero length will never match with string of non-zero length for i in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 # since string of zero length will match with pattern where there # is at least one * alternatively for j in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0 # now using bottom-up approach to find for all remaining lengths for i in range(1 , __lowerCAmelCase ): for j in range(1 , __lowerCAmelCase ): if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".": SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1] elif pattern[j - 1] == "*": if dp[i][j - 2] == 1: SCREAMING_SNAKE_CASE__ : List[str] = 1 elif pattern[j - 2] in (input_string[i - 1], "."): SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j] else: SCREAMING_SNAKE_CASE__ : Optional[int] = 0 else: SCREAMING_SNAKE_CASE__ : Dict = 0 return bool(dp[-1][-1] ) if __name__ == "__main__": import doctest doctest.testmod() # inputing the strings # input_string = input("input a string :") # pattern = input("input a pattern :") a :Any = "aab" a :Optional[Any] = "c*a*b" # using function to check whether given string matches the given pattern if match_pattern(input_string, pattern): print(f'{input_string} matches the given pattern {pattern}') else: print(f'{input_string} does not match with the given pattern {pattern}')
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"""simple docstring""" from math import factorial a :dict[str, int] = {str(digit): factorial(digit) for digit in range(10)} def _lowercase ( __lowerCAmelCase ) -> int: if not isinstance(__lowerCAmelCase , __lowerCAmelCase ): raise TypeError("""Parameter number must be int""" ) if number < 0: raise ValueError("""Parameter number must be greater than or equal to 0""" ) # Converts number in string to iterate on its digits and adds its factorial. return sum(DIGIT_FACTORIAL[digit] for digit in str(__lowerCAmelCase ) ) def _lowercase ( __lowerCAmelCase = 60 , __lowerCAmelCase = 100_0000 ) -> int: if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or not isinstance(__lowerCAmelCase , __lowerCAmelCase ): raise TypeError("""Parameters chain_length and number_limit must be int""" ) if chain_length <= 0 or number_limit <= 0: raise ValueError( """Parameters chain_length and number_limit must be greater than 0""" ) # the counter for the chains with the exact desired length SCREAMING_SNAKE_CASE__ : str = 0 # the cached sizes of the previous chains SCREAMING_SNAKE_CASE__ : dict[int, int] = {} for start_chain_element in range(1 , __lowerCAmelCase ): # The temporary set will contain the elements of the chain SCREAMING_SNAKE_CASE__ : Optional[Any] = set() SCREAMING_SNAKE_CASE__ : List[str] = 0 # Stop computing the chain when you find a cached size, a repeating item or the # length is greater then the desired one. SCREAMING_SNAKE_CASE__ : str = start_chain_element while ( chain_element not in chain_sets_lengths and chain_element not in chain_set and chain_set_length <= chain_length ): chain_set.add(__lowerCAmelCase ) chain_set_length += 1 SCREAMING_SNAKE_CASE__ : Union[str, Any] = digit_factorial_sum(__lowerCAmelCase ) if chain_element in chain_sets_lengths: chain_set_length += chain_sets_lengths[chain_element] SCREAMING_SNAKE_CASE__ : str = chain_set_length # If chain contains the exact amount of elements increase the counter if chain_set_length == chain_length: chains_counter += 1 return chains_counter if __name__ == "__main__": import doctest doctest.testmod() print(f'{solution()}')
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"""simple docstring""" from math import sqrt def _lowercase ( __lowerCAmelCase ) -> bool: if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = 0 SCREAMING_SNAKE_CASE__ : Tuple = 1 while count != nth and number < 3: number += 1 if is_prime(__lowerCAmelCase ): count += 1 while count != nth: number += 2 if is_prime(__lowerCAmelCase ): count += 1 return number if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" from typing import Optional, Union import torch from torch import nn from ...configuration_utils import ConfigMixin, register_to_config from ...models.modeling_utils import ModelMixin class __a (UpperCamelCase_ , UpperCamelCase_): '''simple docstring''' @register_to_config def __init__( self , _a = 768 , ) -> Optional[Any]: """simple docstring""" super().__init__() SCREAMING_SNAKE_CASE__ : str = nn.Parameter(torch.zeros(1 , _a ) ) SCREAMING_SNAKE_CASE__ : List[str] = nn.Parameter(torch.ones(1 , _a ) ) def _a ( self , _a = None , _a = None , ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = nn.Parameter(self.mean.to(_a ).to(_a ) ) SCREAMING_SNAKE_CASE__ : Optional[Any] = nn.Parameter(self.std.to(_a ).to(_a ) ) return self def _a ( self , _a ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = (embeds - self.mean) * 1.0 / self.std return embeds def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = (embeds * self.std) + self.mean return embeds
714
"""simple docstring""" class __a : '''simple docstring''' def __init__( self , _a , _a , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = value SCREAMING_SNAKE_CASE__ : List[Any] = weight def __repr__( self ) -> List[Any]: """simple docstring""" return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})''' def _a ( self ) -> Dict: """simple docstring""" return self.value def _a ( self ) -> int: """simple docstring""" return self.name def _a ( self ) -> Optional[Any]: """simple docstring""" return self.weight def _a ( self ) -> Dict: """simple docstring""" return self.value / self.weight def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict: SCREAMING_SNAKE_CASE__ : Any = [] for i in range(len(__lowerCAmelCase ) ): menu.append(Things(name[i] , value[i] , weight[i] ) ) return menu def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0 for i in range(len(__lowerCAmelCase ) ): if (total_cost + items_copy[i].get_weight()) <= max_cost: result.append(items_copy[i] ) total_cost += items_copy[i].get_weight() total_value += items_copy[i].get_value() return (result, total_value) def _lowercase ( ) -> List[str]: pass if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from __future__ import annotations def _lowercase ( __lowerCAmelCase ) -> list[int]: SCREAMING_SNAKE_CASE__ : Optional[int] = [True] * limit SCREAMING_SNAKE_CASE__ : int = False SCREAMING_SNAKE_CASE__ : Any = False SCREAMING_SNAKE_CASE__ : int = True for i in range(3 , int(limit**0.5 + 1 ) , 2 ): SCREAMING_SNAKE_CASE__ : Dict = i * 2 while index < limit: SCREAMING_SNAKE_CASE__ : int = False SCREAMING_SNAKE_CASE__ : List[str] = index + i SCREAMING_SNAKE_CASE__ : Any = [2] for i in range(3 , __lowerCAmelCase , 2 ): if is_prime[i]: primes.append(__lowerCAmelCase ) return primes def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int: SCREAMING_SNAKE_CASE__ : List[Any] = prime_sieve(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = 0 SCREAMING_SNAKE_CASE__ : Tuple = 0 for i in range(len(__lowerCAmelCase ) ): for j in range(i + length , len(__lowerCAmelCase ) ): SCREAMING_SNAKE_CASE__ : Tuple = sum(primes[i:j] ) if sol >= ceiling: break if sol in primes: SCREAMING_SNAKE_CASE__ : Any = j - i SCREAMING_SNAKE_CASE__ : Dict = sol return largest if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from tokenizers import processors from ...tokenization_utils import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_nllb import NllbTokenizer else: a :Optional[int] = None a :Optional[Any] = logging.get_logger(__name__) a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} a :Union[str, Any] = { "vocab_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model" ), }, "tokenizer_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json" ), }, } a :Any = { "facebook/nllb-large-en-ro": 1_024, "facebook/nllb-200-distilled-600M": 1_024, } # fmt: off a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"] class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""] _SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer _SCREAMING_SNAKE_CASE :List[int] = [] _SCREAMING_SNAKE_CASE :List[int] = [] def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour super().__init__( vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy() if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} ) SCREAMING_SNAKE_CASE__ : List[str] = { lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES } SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn""" SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang ) SCREAMING_SNAKE_CASE__ : Dict = tgt_lang self.set_src_lang_special_tokens(self._src_lang ) @property def _a ( self ) -> str: """simple docstring""" return self._src_lang @src_lang.setter def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" if token_ids_a is None: return self.prefix_tokens + token_ids_a + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple: """simple docstring""" if src_lang is None or tgt_lang is None: raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" ) SCREAMING_SNAKE_CASE__ : Dict = src_lang SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id return inputs def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = src_lang SCREAMING_SNAKE_CASE__ : Dict = tgt_lang return super().prepare_seqaseq_batch(_a , _a , **_a ) def _a ( self ) -> Optional[Any]: """simple docstring""" return self.set_src_lang_special_tokens(self.src_lang ) def _a ( self ) -> str: """simple docstring""" return self.set_tgt_lang_special_tokens(self.tgt_lang ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : List[Any] = [] SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' ) return SCREAMING_SNAKE_CASE__ : Dict = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
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"""simple docstring""" import tempfile import torch from diffusers import ( DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, UniPCMultistepScheduler, ) from .test_schedulers import SchedulerCommonTest class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = (DEISMultistepScheduler,) _SCREAMING_SNAKE_CASE :List[Any] = (("""num_inference_steps""", 25),) def _a ( self , **_a ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """num_train_timesteps""": 1_000, """beta_start""": 0.0_001, """beta_end""": 0.02, """beta_schedule""": """linear""", """solver_order""": 2, } config.update(**_a ) return config def _a ( self , _a=0 , **_a ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = dict(self.forward_default_kwargs ) SCREAMING_SNAKE_CASE__ : Optional[Any] = kwargs.pop("""num_inference_steps""" , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_sample SCREAMING_SNAKE_CASE__ : str = 0.1 * sample SCREAMING_SNAKE_CASE__ : str = [residual + 0.2, residual + 0.15, residual + 0.10] for scheduler_class in self.scheduler_classes: SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_scheduler_config(**_a ) SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) scheduler.set_timesteps(_a ) # copy over dummy past residuals SCREAMING_SNAKE_CASE__ : Any = dummy_past_residuals[: scheduler.config.solver_order] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_a ) SCREAMING_SNAKE_CASE__ : Tuple = scheduler_class.from_pretrained(_a ) new_scheduler.set_timesteps(_a ) # copy over dummy past residuals SCREAMING_SNAKE_CASE__ : str = dummy_past_residuals[: new_scheduler.config.solver_order] SCREAMING_SNAKE_CASE__ : Any = sample, sample for t in range(_a , time_step + scheduler.config.solver_order + 1 ): SCREAMING_SNAKE_CASE__ : str = scheduler.step(_a , _a , _a , **_a ).prev_sample SCREAMING_SNAKE_CASE__ : Any = new_scheduler.step(_a , _a , _a , **_a ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def _a ( self ) -> Tuple: """simple docstring""" pass def _a ( self , _a=0 , **_a ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = dict(self.forward_default_kwargs ) SCREAMING_SNAKE_CASE__ : int = kwargs.pop("""num_inference_steps""" , _a ) SCREAMING_SNAKE_CASE__ : Any = self.dummy_sample SCREAMING_SNAKE_CASE__ : Any = 0.1 * sample SCREAMING_SNAKE_CASE__ : Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.10] for scheduler_class in self.scheduler_classes: SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a ) scheduler.set_timesteps(_a ) # copy over dummy past residuals (must be after setting timesteps) SCREAMING_SNAKE_CASE__ : Tuple = dummy_past_residuals[: scheduler.config.solver_order] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(_a ) SCREAMING_SNAKE_CASE__ : Tuple = scheduler_class.from_pretrained(_a ) # copy over dummy past residuals new_scheduler.set_timesteps(_a ) # copy over dummy past residual (must be after setting timesteps) SCREAMING_SNAKE_CASE__ : Any = dummy_past_residuals[: new_scheduler.config.solver_order] SCREAMING_SNAKE_CASE__ : List[Any] = scheduler.step(_a , _a , _a , **_a ).prev_sample SCREAMING_SNAKE_CASE__ : str = new_scheduler.step(_a , _a , _a , **_a ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def _a ( self , _a=None , **_a ) -> Union[str, Any]: """simple docstring""" if scheduler is None: SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Any = self.get_scheduler_config(**_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : List[str] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config(**_a ) SCREAMING_SNAKE_CASE__ : str = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : str = 10 SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_model() SCREAMING_SNAKE_CASE__ : Dict = self.dummy_sample_deter scheduler.set_timesteps(_a ) for i, t in enumerate(scheduler.timesteps ): SCREAMING_SNAKE_CASE__ : str = model(_a , _a ) SCREAMING_SNAKE_CASE__ : Tuple = scheduler.step(_a , _a , _a ).prev_sample return sample def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = dict(self.forward_default_kwargs ) SCREAMING_SNAKE_CASE__ : List[str] = kwargs.pop("""num_inference_steps""" , _a ) for scheduler_class in self.scheduler_classes: SCREAMING_SNAKE_CASE__ : List[str] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : str = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_sample SCREAMING_SNAKE_CASE__ : Optional[int] = 0.1 * sample if num_inference_steps is not None and hasattr(_a , """set_timesteps""" ): scheduler.set_timesteps(_a ) elif num_inference_steps is not None and not hasattr(_a , """set_timesteps""" ): SCREAMING_SNAKE_CASE__ : Optional[Any] = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) SCREAMING_SNAKE_CASE__ : Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.10] SCREAMING_SNAKE_CASE__ : List[Any] = dummy_past_residuals[: scheduler.config.solver_order] SCREAMING_SNAKE_CASE__ : Optional[int] = scheduler.timesteps[5] SCREAMING_SNAKE_CASE__ : Any = scheduler.timesteps[6] SCREAMING_SNAKE_CASE__ : Optional[Any] = scheduler.step(_a , _a , _a , **_a ).prev_sample SCREAMING_SNAKE_CASE__ : str = scheduler.step(_a , _a , _a , **_a ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = DEISMultistepScheduler(**self.get_scheduler_config() ) SCREAMING_SNAKE_CASE__ : Tuple = self.full_loop(scheduler=_a ) SCREAMING_SNAKE_CASE__ : List[Any] = torch.mean(torch.abs(_a ) ) assert abs(result_mean.item() - 0.23_916 ) < 1E-3 SCREAMING_SNAKE_CASE__ : Dict = DPMSolverSinglestepScheduler.from_config(scheduler.config ) SCREAMING_SNAKE_CASE__ : Tuple = DPMSolverMultistepScheduler.from_config(scheduler.config ) SCREAMING_SNAKE_CASE__ : Tuple = UniPCMultistepScheduler.from_config(scheduler.config ) SCREAMING_SNAKE_CASE__ : str = DEISMultistepScheduler.from_config(scheduler.config ) SCREAMING_SNAKE_CASE__ : str = self.full_loop(scheduler=_a ) SCREAMING_SNAKE_CASE__ : List[Any] = torch.mean(torch.abs(_a ) ) assert abs(result_mean.item() - 0.23_916 ) < 1E-3 def _a ( self ) -> List[str]: """simple docstring""" for timesteps in [25, 50, 100, 999, 1_000]: self.check_over_configs(num_train_timesteps=_a ) def _a ( self ) -> Dict: """simple docstring""" self.check_over_configs(thresholding=_a ) for order in [1, 2, 3]: for solver_type in ["logrho"]: for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample"]: self.check_over_configs( thresholding=_a , prediction_type=_a , sample_max_value=_a , algorithm_type="""deis""" , solver_order=_a , solver_type=_a , ) def _a ( self ) -> Any: """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=_a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" for algorithm_type in ["deis"]: for solver_type in ["logrho"]: for order in [1, 2, 3]: for prediction_type in ["epsilon", "sample"]: self.check_over_configs( solver_order=_a , solver_type=_a , prediction_type=_a , algorithm_type=_a , ) SCREAMING_SNAKE_CASE__ : Optional[int] = self.full_loop( solver_order=_a , solver_type=_a , prediction_type=_a , algorithm_type=_a , ) assert not torch.isnan(_a ).any(), "Samples have nan numbers" def _a ( self ) -> int: """simple docstring""" self.check_over_configs(lower_order_final=_a ) self.check_over_configs(lower_order_final=_a ) def _a ( self ) -> str: """simple docstring""" for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]: self.check_over_forward(num_inference_steps=_a , time_step=0 ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = self.full_loop() SCREAMING_SNAKE_CASE__ : Dict = torch.mean(torch.abs(_a ) ) assert abs(result_mean.item() - 0.23_916 ) < 1E-3 def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.full_loop(prediction_type="""v_prediction""" ) SCREAMING_SNAKE_CASE__ : List[str] = torch.mean(torch.abs(_a ) ) assert abs(result_mean.item() - 0.091 ) < 1E-3 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config(thresholding=_a , dynamic_thresholding_ratio=0 ) SCREAMING_SNAKE_CASE__ : Optional[Any] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = 10 SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_model() SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter.half() scheduler.set_timesteps(_a ) for i, t in enumerate(scheduler.timesteps ): SCREAMING_SNAKE_CASE__ : List[Any] = model(_a , _a ) SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a ).prev_sample assert sample.dtype == torch.floataa
716
"""simple docstring""" # Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #################################################################################################### # # Note: If when running this conversion script you're getting an exception: # ModuleNotFoundError: No module named 'megatron.model.enums' # you need to tell python where to find the clone of Megatron-LM, e.g.: # # cd /tmp # git clone https://github.com/NVIDIA/Megatron-LM # PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ... # # if you already have it cloned elsewhere, simply adjust the path to the existing path # # If the training was done using a Megatron-LM fork, e.g., # https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one # in your path, i.e., /path/to/Megatron-DeepSpeed/ # import argparse import os import re import zipfile import torch from transformers import AutoTokenizer, GPTaConfig def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any: # Format the message. if name is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None else: SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}""" SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase ) # Print and recurse (if needed). if isinstance(__lowerCAmelCase , __lowerCAmelCase ): if msg is not None: print(__lowerCAmelCase ) for k in val.keys(): recursive_print(__lowerCAmelCase , val[k] , spaces + 2 ) elif isinstance(__lowerCAmelCase , torch.Tensor ): print(__lowerCAmelCase , """:""" , val.size() ) else: print(__lowerCAmelCase , """:""" , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] # for compatibility with later versions of NVIDIA Megatron-LM. # The inverse operation is performed inside Megatron-LM to read checkpoints: # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 # If param is the weight tensor of the self-attention block, the returned tensor # will have to be transposed one more time to be read by HuggingFace GPT2. SCREAMING_SNAKE_CASE__ : Tuple = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:] SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 ) SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:] SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous() SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase ) return param def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: # The converted output model. SCREAMING_SNAKE_CASE__ : List[str] = {} # old versions did not store training args SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase ) if ds_args is not None: # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint # from pprint import pprint # pprint(vars(ds_args)) SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size # pprint(config) # The number of heads. SCREAMING_SNAKE_CASE__ : List[str] = config.n_head # The hidden_size per head. SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head # Megatron-LM checkpoint version if "checkpoint_version" in input_state_dict.keys(): SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""] else: SCREAMING_SNAKE_CASE__ : Tuple = 0.0 # The model. SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""] # The language model. SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""] # The embeddings. SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""] # The word embeddings. SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""] # Truncate the embedding table to vocab_size rows. SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :] SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings # The position embeddings. SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""] # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size] SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 ) if n_positions != config.n_positions: raise ValueError( F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' ) # Store the position embeddings. SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings # The transformer. SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""] # The regex to extract layer names. SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" ) # The simple map of names for "automated" rules. SCREAMING_SNAKE_CASE__ : Optional[int] = { """attention.dense""": """.attn.c_proj.""", """self_attention.dense""": """.attn.c_proj.""", """mlp.dense_h_to_4h""": """.mlp.c_fc.""", """mlp.dense_4h_to_h""": """.mlp.c_proj.""", } # Extract the layers. for key, val in transformer.items(): # Match the name. SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase ) # Stop if that's not a layer if m is None: break # The index of the layer. SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) ) # The name of the operation. SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 ) # Is it a weight or a bias? SCREAMING_SNAKE_CASE__ : str = m.group(3 ) # The name of the layer. SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}''' # For layernorm(s), simply store the layer norm. if op_name.endswith("""layernorm""" ): SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2""" SCREAMING_SNAKE_CASE__ : List[Any] = val # Transpose the QKV matrix. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "weight": # Insert a tensor of 1x1xDxD bias. SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view( 1 , 1 , __lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = causal_mask # Insert a "dummy" tensor for masked_bias. SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa ) SCREAMING_SNAKE_CASE__ : List[str] = masked_bias SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D. SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous() # Store. SCREAMING_SNAKE_CASE__ : Dict = out_val # Transpose the bias. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Store. No change of shape. SCREAMING_SNAKE_CASE__ : str = out_val # Transpose the weights. elif weight_or_bias == "weight": SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 ) # Copy the bias. elif weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : Dict = val # DEBUG. assert config.n_layer == layer_idx + 1 # The final layernorm. SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""] SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""] # For LM head, transformers' wants the matrix to weight embeddings. SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings # It should be done! return output_state_dict def _lowercase ( ) -> List[Any]: # Create the argument parser. SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser() parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" ) parser.add_argument( """path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , ) parser.add_argument( """--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , ) SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args() # Extract the basename. SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint ) # Load the model. # the .zip is very optional, let's keep it for backward compatibility print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' ) if args.path_to_checkpoint.endswith(""".zip""" ): with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint: with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict: SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" ) else: SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" ) SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase ) # Read the config, or default to the model released by NVIDIA. if args.config_file == "": if ds_args is not None: if ds_args.bias_gelu_fusion: SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast""" elif ds_args.openai_gelu: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new""" else: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu""" else: # in the very early days this used to be "gelu_new" SCREAMING_SNAKE_CASE__ : Any = """gelu_new""" # Spell out all parameters in case the defaults change. SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig( vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , ) else: SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file ) SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""] # Convert. print("""Converting""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) # Print the structure of converted state dict. if args.print_checkpoint_structure: recursive_print(__lowerCAmelCase , __lowerCAmelCase ) # Add tokenizer class info to config # see https://github.com/huggingface/transformers/issues/13906) if ds_args is not None: SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type if tokenizer_type == "GPT2BPETokenizer": SCREAMING_SNAKE_CASE__ : Any = """gpt2""" elif tokenizer_type == "PretrainedFromHF": SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path else: raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__ SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class # Store the config to file. print("""Saving config""" ) config.save_pretrained(__lowerCAmelCase ) # Save tokenizer based on args print(F'''Adding {tokenizer_class} tokenizer files''' ) tokenizer.save_pretrained(__lowerCAmelCase ) # Store the state_dict to file. SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" ) print(F'''Saving checkpoint to "{output_checkpoint_file}"''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) #################################################################################################### if __name__ == "__main__": main() ####################################################################################################
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"""simple docstring""" import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging a :Optional[int] = logging.get_logger(__name__) a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model"} a :Union[str, Any] = { "vocab_file": { "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model", } } a :Any = { "camembert-base": 512, } a :Any = "▁" class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Dict = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :Optional[int] = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = ["""input_ids""", """attention_mask"""] def __init__( self , _a , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=["<s>NOTUSED", "</s>NOTUSED"] , _a = None , **_a , ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token SCREAMING_SNAKE_CASE__ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , additional_special_tokens=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , ) SCREAMING_SNAKE_CASE__ : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(_a ) ) SCREAMING_SNAKE_CASE__ : int = vocab_file # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual # sentencepiece vocabulary (this is the case for <s> and </s> SCREAMING_SNAKE_CASE__ : Any = {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3} SCREAMING_SNAKE_CASE__ : List[str] = len(self.fairseq_tokens_to_ids ) SCREAMING_SNAKE_CASE__ : List[str] = len(self.sp_model ) + len(self.fairseq_tokens_to_ids ) SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cls_token_id] SCREAMING_SNAKE_CASE__ : Tuple = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def _a ( self , _a , _a = None , _a = False ) -> List[int]: """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a ) if token_ids_a is None: return [1] + ([0] * len(_a )) + [1] return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1] def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : List[str] = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def _a ( self ) -> Union[str, Any]: """simple docstring""" return len(self.fairseq_tokens_to_ids ) + len(self.sp_model ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def _a ( self , _a ) -> List[str]: """simple docstring""" return self.sp_model.encode(_a , out_type=_a ) def _a ( self , _a ) -> Dict: """simple docstring""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] elif self.sp_model.PieceToId(_a ) == 0: # Convert sentence piece unk token to fairseq unk token index return self.unk_token_id return self.fairseq_offset + self.sp_model.PieceToId(_a ) def _a ( self , _a ) -> Optional[Any]: """simple docstring""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset ) def _a ( self , _a ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = [] SCREAMING_SNAKE_CASE__ : Union[str, Any] = """""" SCREAMING_SNAKE_CASE__ : int = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(_a ) + token SCREAMING_SNAKE_CASE__ : Union[str, Any] = True SCREAMING_SNAKE_CASE__ : str = [] else: current_sub_tokens.append(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = False out_string += self.sp_model.decode(_a ) return out_string.strip() def __getstate__( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.__dict__.copy() SCREAMING_SNAKE_CASE__ : List[Any] = None return state def __setstate__( self , _a ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): SCREAMING_SNAKE_CASE__ : List[str] = {} SCREAMING_SNAKE_CASE__ : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return SCREAMING_SNAKE_CASE__ : List[str] = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , _a ) elif not os.path.isfile(self.vocab_file ): with open(_a , """wb""" ) as fi: SCREAMING_SNAKE_CASE__ : str = self.sp_model.serialized_model_proto() fi.write(_a ) return (out_vocab_file,)
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"""simple docstring""" import re from pathlib import Path from unittest import TestCase import pytest @pytest.mark.integration class __a (UpperCamelCase_): '''simple docstring''' def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read() SCREAMING_SNAKE_CASE__ : str = regexp.search(_a ) return match def _a ( self , _a ) -> Optional[Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL ) SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read() # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search` SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a ) SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None] return matches[0] if matches else None def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_encoding_on_file_open(str(_a ) ): raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_print_statements(str(_a ) ): raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
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a :int = "0.18.2" from .configuration_utils import ConfigMixin from .utils import ( OptionalDependencyNotAvailable, is_flax_available, is_inflect_available, is_invisible_watermark_available, is_k_diffusion_available, is_k_diffusion_version, is_librosa_available, is_note_seq_available, is_onnx_available, is_scipy_available, is_torch_available, is_torchsde_available, is_transformers_available, is_transformers_version, is_unidecode_available, logging, ) try: if not is_onnx_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_onnx_objects import * # noqa F403 else: from .pipelines import OnnxRuntimeModel try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_pt_objects import * # noqa F403 else: from .models import ( AutoencoderKL, ControlNetModel, ModelMixin, PriorTransformer, TaFilmDecoder, TransformeraDModel, UNetaDModel, UNetaDConditionModel, UNetaDModel, UNetaDConditionModel, VQModel, ) from .optimization import ( get_constant_schedule, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, get_cosine_with_hard_restarts_schedule_with_warmup, get_linear_schedule_with_warmup, get_polynomial_decay_schedule_with_warmup, get_scheduler, ) from .pipelines import ( AudioPipelineOutput, ConsistencyModelPipeline, DanceDiffusionPipeline, DDIMPipeline, DDPMPipeline, DiffusionPipeline, DiTPipeline, ImagePipelineOutput, KarrasVePipeline, LDMPipeline, LDMSuperResolutionPipeline, PNDMPipeline, RePaintPipeline, ScoreSdeVePipeline, ) from .schedulers import ( CMStochasticIterativeScheduler, DDIMInverseScheduler, DDIMParallelScheduler, DDIMScheduler, DDPMParallelScheduler, DDPMScheduler, DEISMultistepScheduler, DPMSolverMultistepInverseScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, HeunDiscreteScheduler, IPNDMScheduler, KarrasVeScheduler, KDPMaAncestralDiscreteScheduler, KDPMaDiscreteScheduler, PNDMScheduler, RePaintScheduler, SchedulerMixin, ScoreSdeVeScheduler, UnCLIPScheduler, UniPCMultistepScheduler, VQDiffusionScheduler, ) from .training_utils import EMAModel try: if not (is_torch_available() and is_scipy_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_scipy_objects import * # noqa F403 else: from .schedulers import LMSDiscreteScheduler try: if not (is_torch_available() and is_torchsde_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_torchsde_objects import * # noqa F403 else: from .schedulers import DPMSolverSDEScheduler try: if not (is_torch_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .pipelines import ( AltDiffusionImgaImgPipeline, AltDiffusionPipeline, AudioLDMPipeline, CycleDiffusionPipeline, IFImgaImgPipeline, IFImgaImgSuperResolutionPipeline, IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, IFPipeline, IFSuperResolutionPipeline, ImageTextPipelineOutput, KandinskyImgaImgPipeline, KandinskyInpaintPipeline, KandinskyPipeline, KandinskyPriorPipeline, KandinskyVaaControlnetImgaImgPipeline, KandinskyVaaControlnetPipeline, KandinskyVaaImgaImgPipeline, KandinskyVaaInpaintPipeline, KandinskyVaaPipeline, KandinskyVaaPriorEmbaEmbPipeline, KandinskyVaaPriorPipeline, LDMTextToImagePipeline, PaintByExamplePipeline, SemanticStableDiffusionPipeline, ShapEImgaImgPipeline, ShapEPipeline, StableDiffusionAttendAndExcitePipeline, StableDiffusionControlNetImgaImgPipeline, StableDiffusionControlNetInpaintPipeline, StableDiffusionControlNetPipeline, StableDiffusionDepthaImgPipeline, StableDiffusionDiffEditPipeline, StableDiffusionImageVariationPipeline, StableDiffusionImgaImgPipeline, StableDiffusionInpaintPipeline, StableDiffusionInpaintPipelineLegacy, StableDiffusionInstructPixaPixPipeline, StableDiffusionLatentUpscalePipeline, StableDiffusionLDMaDPipeline, StableDiffusionModelEditingPipeline, StableDiffusionPanoramaPipeline, StableDiffusionParadigmsPipeline, StableDiffusionPipeline, StableDiffusionPipelineSafe, StableDiffusionPixaPixZeroPipeline, StableDiffusionSAGPipeline, StableDiffusionUpscalePipeline, StableUnCLIPImgaImgPipeline, StableUnCLIPPipeline, TextToVideoSDPipeline, TextToVideoZeroPipeline, UnCLIPImageVariationPipeline, UnCLIPPipeline, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder, VersatileDiffusionDualGuidedPipeline, VersatileDiffusionImageVariationPipeline, VersatileDiffusionPipeline, VersatileDiffusionTextToImagePipeline, VideoToVideoSDPipeline, VQDiffusionPipeline, ) try: if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403 else: from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline try: if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403 else: from .pipelines import StableDiffusionKDiffusionPipeline try: if not (is_torch_available() and is_transformers_available() and is_onnx_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403 else: from .pipelines import ( OnnxStableDiffusionImgaImgPipeline, OnnxStableDiffusionInpaintPipeline, OnnxStableDiffusionInpaintPipelineLegacy, OnnxStableDiffusionPipeline, OnnxStableDiffusionUpscalePipeline, StableDiffusionOnnxPipeline, ) try: if not (is_torch_available() and is_librosa_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_librosa_objects import * # noqa F403 else: from .pipelines import AudioDiffusionPipeline, Mel try: if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: from .pipelines import SpectrogramDiffusionPipeline try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_flax_objects import * # noqa F403 else: from .models.controlnet_flax import FlaxControlNetModel from .models.modeling_flax_utils import FlaxModelMixin from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel from .models.vae_flax import FlaxAutoencoderKL from .pipelines import FlaxDiffusionPipeline from .schedulers import ( FlaxDDIMScheduler, FlaxDDPMScheduler, FlaxDPMSolverMultistepScheduler, FlaxKarrasVeScheduler, FlaxLMSDiscreteScheduler, FlaxPNDMScheduler, FlaxSchedulerMixin, FlaxScoreSdeVeScheduler, ) try: if not (is_flax_available() and is_transformers_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_flax_and_transformers_objects import * # noqa F403 else: from .pipelines import ( FlaxStableDiffusionControlNetPipeline, FlaxStableDiffusionImgaImgPipeline, FlaxStableDiffusionInpaintPipeline, FlaxStableDiffusionPipeline, ) try: if not (is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from .utils.dummy_note_seq_objects import * # noqa F403 else: from .pipelines import MidiProcessor
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"""simple docstring""" import tempfile import unittest from transformers import TaConfig, is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel class __a : '''simple docstring''' def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = parent SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length SCREAMING_SNAKE_CASE__ : str = decoder_seq_length # For common tests SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length SCREAMING_SNAKE_CASE__ : Tuple = is_training SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask SCREAMING_SNAKE_CASE__ : List[str] = use_labels SCREAMING_SNAKE_CASE__ : str = vocab_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Any = num_attention_heads SCREAMING_SNAKE_CASE__ : Any = d_ff SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id SCREAMING_SNAKE_CASE__ : Any = None SCREAMING_SNAKE_CASE__ : str = decoder_layers def _a ( self ) -> Tuple: """simple docstring""" return TaConfig.from_pretrained("""google/umt5-base""" ) def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any: """simple docstring""" if attention_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id ) if decoder_attention_mask is None: SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id ) if head_mask is None: SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a ) if decoder_head_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a ) if cross_attn_head_mask is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones( config.num_decoder_layers , config.num_attention_heads , device=_a ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size ) # we need to clamp the input ids here to avoid having pad token in between # this is because for NllbMoe the position_ids are prepared such that # all pad tokens have pos id = 2 and rest are between 2..seq_length # and the seq_length here is seq_length - num_pad_tokens # but when using past, there is no way of knowing if the past input ids had # pad tokens in them, which results in incorrect seq_lenth and which in turn results in # position_ids being off by num_pad_tokens in past input SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config() SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a ) return config, input_dict def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs() return config, inputs_dict def _a ( self ) -> List[str]: """simple docstring""" return TaConfig( vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self ) -> List[Any]: """simple docstring""" return TaConfig( vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a ) model.to(_a ) model.eval() SCREAMING_SNAKE_CASE__ : Dict = model( input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) ) self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) ) # There should be `num_layers` key value embeddings stored in decoder_past self.parent.assertEqual(len(_a ) , config.num_layers ) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple self.parent.assertEqual(len(decoder_past[0] ) , 4 ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval() # first forward pass SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a ) SCREAMING_SNAKE_CASE__ : str = model(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a ) self.parent.assertTrue(len(_a ) == len(_a ) ) self.parent.assertTrue(len(_a ) == len(_a ) + 1 ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ) # append to next input_ids and SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""] SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""] # select random slice SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item() SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach() SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) ) def _a ( self , _a , _a , ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval() SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""] self.parent.assertFalse(torch.isnan(_a ).any().item() ) @require_torch class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = ( (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else () ) _SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else () _SCREAMING_SNAKE_CASE :List[str] = ( { """conversational""": UMTaForConditionalGeneration, """feature-extraction""": UMTaModel, """summarization""": UMTaForConditionalGeneration, """text2text-generation""": UMTaForConditionalGeneration, """translation""": UMTaForConditionalGeneration, """question-answering""": UMTaForQuestionAnswering, } if is_torch_available() else {} ) _SCREAMING_SNAKE_CASE :Union[str, Any] = True _SCREAMING_SNAKE_CASE :Tuple = False _SCREAMING_SNAKE_CASE :Optional[Any] = False _SCREAMING_SNAKE_CASE :List[Any] = True _SCREAMING_SNAKE_CASE :List[str] = True # The small UMT5 model needs higher percentages for CPU/MP tests _SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9] def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self ) @unittest.skip("""Test has a segmentation fault on torch 1.8.0""" ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a ) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( _a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , ) @unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fpaa_forward(*_a ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""] SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0] SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval() model.to(_a ) SCREAMING_SNAKE_CASE__ : List[str] = { """head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ), """decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), } for attn_name, (name, mask) in zip(_a , head_masking.items() ): SCREAMING_SNAKE_CASE__ : List[str] = {name: mask} # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified if name == "head_mask": SCREAMING_SNAKE_CASE__ : str = torch.ones( config.num_decoder_layers , config.num_heads , device=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate( config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , ) # We check the state of decoder_attentions and cross_attentions just from the last step SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 ) @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" ) def _a ( self ) -> Dict: """simple docstring""" pass @require_torch @require_sentencepiece @require_tokenizers class __a (unittest.TestCase): '''simple docstring''' @slow @unittest.skip( """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a ) SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [ """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""", """No se como puedo <extra_id_0>.""", """This is the reason why we <extra_id_0> them.""", """The <extra_id_0> walks in <extra_id_1>, seats""", """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""", ] SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids # fmt: off SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor( [ [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0], [ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1], ] ) # fmt: on torch.testing.assert_allclose(_a , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) ) SCREAMING_SNAKE_CASE__ : int = [ """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""", """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", ] SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a ) self.assertEqual(_a , _a )
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0
from __future__ import annotations from functools import lru_cache from math import ceil a :Optional[int] = 100 a :Optional[int] = set(range(3, NUM_PRIMES, 2)) primes.add(2) a :int for prime in range(3, ceil(NUM_PRIMES**0.5), 2): if prime not in primes: continue primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime))) @lru_cache(maxsize=100 ) def _lowercase ( __lowerCAmelCase ) -> set[int]: if number_to_partition < 0: return set() elif number_to_partition == 0: return {1} SCREAMING_SNAKE_CASE__ : set[int] = set() SCREAMING_SNAKE_CASE__ : int SCREAMING_SNAKE_CASE__ : int for prime in primes: if prime > number_to_partition: continue for sub in partition(number_to_partition - prime ): ret.add(sub * prime ) return ret def _lowercase ( __lowerCAmelCase = 5000 ) -> int | None: for number_to_partition in range(1 , __lowerCAmelCase ): if len(partition(__lowerCAmelCase ) ) > number_unique_partitions: return number_to_partition return None if __name__ == "__main__": print(f'{solution() = }')
719
"""simple docstring""" import multiprocessing from typing import TYPE_CHECKING, Optional, Union from .. import Dataset, Features, config from ..formatting import query_table from ..packaged_modules.sql.sql import Sql from ..utils import logging from .abc import AbstractDatasetInputStream if TYPE_CHECKING: import sqlitea import sqlalchemy class __a (UpperCamelCase_): '''simple docstring''' def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]: """simple docstring""" super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a ) SCREAMING_SNAKE_CASE__ : List[Any] = Sql( cache_dir=_a , features=_a , sql=_a , con=_a , **_a , ) def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = None SCREAMING_SNAKE_CASE__ : Union[str, Any] = None SCREAMING_SNAKE_CASE__ : Dict = None SCREAMING_SNAKE_CASE__ : Optional[int] = None self.builder.download_and_prepare( download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , ) # Build dataset for splits SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset( split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory ) return dataset class __a : '''simple docstring''' def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any: """simple docstring""" if num_proc is not None and num_proc <= 0: raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' ) SCREAMING_SNAKE_CASE__ : int = dataset SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = con SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE SCREAMING_SNAKE_CASE__ : int = num_proc SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs ) return written def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs SCREAMING_SNAKE_CASE__ : Any = query_table( table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , ) SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas() SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a ) return num_rows or len(_a ) def _a ( self , _a , **_a ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = 0 if self.num_proc is None or self.num_proc == 1: for offset in logging.tqdm( range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += self._batch_sql((offset, index, to_sql_kwargs) ) else: SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size with multiprocessing.Pool(self.num_proc ) as pool: for num_rows in logging.tqdm( pool.imap( self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += num_rows return written
12
0
"""simple docstring""" import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import ViTImageProcessor class __a (unittest.TestCase): '''simple docstring''' def __init__( self , _a , _a=13 , _a=3 , _a=224 , _a=30 , _a=400 , _a=True , _a=None , _a=True , _a=[0.5, 0.5, 0.5] , _a=[0.5, 0.5, 0.5] , ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = size if size is not None else {"""height""": 18, """width""": 18} SCREAMING_SNAKE_CASE__ : Union[str, Any] = parent SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size SCREAMING_SNAKE_CASE__ : Optional[Any] = num_channels SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size SCREAMING_SNAKE_CASE__ : Optional[int] = min_resolution SCREAMING_SNAKE_CASE__ : Any = max_resolution SCREAMING_SNAKE_CASE__ : Tuple = do_resize SCREAMING_SNAKE_CASE__ : int = size SCREAMING_SNAKE_CASE__ : Union[str, Any] = do_normalize SCREAMING_SNAKE_CASE__ : Any = image_mean SCREAMING_SNAKE_CASE__ : str = image_std def _a ( self ) -> Optional[int]: """simple docstring""" return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, } @require_torch @require_vision class __a (UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Dict = ViTImageProcessor if is_vision_available() else None def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = EfficientFormerImageProcessorTester(self ) @property def _a ( self ) -> List[str]: """simple docstring""" return self.image_proc_tester.prepare_image_processor_dict() def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(_a , """image_mean""" ) ) self.assertTrue(hasattr(_a , """image_std""" ) ) self.assertTrue(hasattr(_a , """do_normalize""" ) ) self.assertTrue(hasattr(_a , """do_resize""" ) ) self.assertTrue(hasattr(_a , """size""" ) ) def _a ( self ) -> Dict: """simple docstring""" pass def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict ) # create random PIL images SCREAMING_SNAKE_CASE__ : Tuple = prepare_image_inputs(self.image_proc_tester , equal_resolution=_a ) for image in image_inputs: self.assertIsInstance(_a , Image.Image ) # Test not batched input SCREAMING_SNAKE_CASE__ : int = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_proc_tester.num_channels, self.image_proc_tester.size["""height"""], self.image_proc_tester.size["""width"""], ) , ) # Test batched SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(_a , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_proc_tester.batch_size, self.image_proc_tester.num_channels, self.image_proc_tester.size["""height"""], self.image_proc_tester.size["""width"""], ) , ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors SCREAMING_SNAKE_CASE__ : Any = prepare_image_inputs(self.image_proc_tester , equal_resolution=_a , numpify=_a ) for image in image_inputs: self.assertIsInstance(_a , np.ndarray ) # Test not batched input SCREAMING_SNAKE_CASE__ : int = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_proc_tester.num_channels, self.image_proc_tester.size["""height"""], self.image_proc_tester.size["""width"""], ) , ) # Test batched SCREAMING_SNAKE_CASE__ : Optional[Any] = image_processor(_a , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_proc_tester.batch_size, self.image_proc_tester.num_channels, self.image_proc_tester.size["""height"""], self.image_proc_tester.size["""width"""], ) , ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors SCREAMING_SNAKE_CASE__ : Tuple = prepare_image_inputs(self.image_proc_tester , equal_resolution=_a , torchify=_a ) for image in image_inputs: self.assertIsInstance(_a , torch.Tensor ) # Test not batched input SCREAMING_SNAKE_CASE__ : str = image_processor(image_inputs[0] , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_proc_tester.num_channels, self.image_proc_tester.size["""height"""], self.image_proc_tester.size["""width"""], ) , ) # Test batched SCREAMING_SNAKE_CASE__ : List[str] = image_processor(_a , return_tensors="""pt""" ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_proc_tester.batch_size, self.image_proc_tester.num_channels, self.image_proc_tester.size["""height"""], self.image_proc_tester.size["""width"""], ) , )
720
"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> int: if divisor % 5 == 0 or divisor % 2 == 0: return 0 SCREAMING_SNAKE_CASE__ : List[Any] = 1 SCREAMING_SNAKE_CASE__ : int = 1 while repunit: SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor repunit_index += 1 return repunit_index def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = limit - 1 if divisor % 2 == 0: divisor += 1 while least_divisible_repunit(__lowerCAmelCase ) <= limit: divisor += 2 return divisor if __name__ == "__main__": print(f'{solution() = }')
12
0
"""simple docstring""" from typing import TYPE_CHECKING from ...utils import _LazyModule a :Optional[Any] = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]} if TYPE_CHECKING: from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM else: import sys a :Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
721
"""simple docstring""" import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import numpy as np from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.31.0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") a :Union[str, Any] = logging.getLogger(__name__) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_28 , metadata={ """help""": ( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Whether to pad all samples to `max_seq_length`. """ """If False, will pad the samples dynamically when batching to the maximum length in the batch.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of training examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of evaluation examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of prediction examples to this """ """value if set.""" ) } , ) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}) _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , ) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , ) _SCREAMING_SNAKE_CASE :str = field( default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Will use the token generated when running `huggingface-cli login` (necessary to use this script """ """with private models).""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , ) def _lowercase ( ) -> Union[str, Any]: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("""run_xnli""" , __lowerCAmelCase ) # Setup logging logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level() logger.setLevel(__lowerCAmelCase ) datasets.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}''' + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' ) logger.info(F'''Training/evaluation parameters {training_args}''' ) # Detecting last checkpoint. SCREAMING_SNAKE_CASE__ : Any = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. ''' """Use --overwrite_output_dir to overcome.""" ) elif last_checkpoint is not None: logger.info( F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change ''' """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" ) # Set seed before initializing model. set_seed(training_args.seed ) # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # Downloading and loading xnli dataset from the hub. if training_args.do_train: if model_args.train_language is None: SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset( """xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) else: SCREAMING_SNAKE_CASE__ : str = load_dataset( """xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names if training_args.do_eval: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names if training_args.do_predict: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names # Labels SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase ) # Load pretrained model and tokenizer # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , ) # Preprocessing the datasets # Padding strategy if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : str = """max_length""" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch SCREAMING_SNAKE_CASE__ : Optional[Any] = False def preprocess_function(__lowerCAmelCase ): # Tokenize the texts return tokenizer( examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , ) if training_args.do_train: if data_args.max_train_samples is not None: SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples ) SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""train dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , ) # Log a few random samples from the training set: for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ): logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' ) if training_args.do_eval: if data_args.max_eval_samples is not None: SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""validation dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , ) if training_args.do_predict: if data_args.max_predict_samples is not None: SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , ) # Get the metric function SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" ) # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 ) return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids ) # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator elif training_args.fpaa: SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None # Initialize our Trainer SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer( model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , ) # Training if training_args.do_train: SCREAMING_SNAKE_CASE__ : Dict = None if training_args.resume_from_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint elif last_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics SCREAMING_SNAKE_CASE__ : Optional[int] = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("""train""" , __lowerCAmelCase ) trainer.save_metrics("""train""" , __lowerCAmelCase ) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("""*** Evaluate ***""" ) SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""eval""" , __lowerCAmelCase ) trainer.save_metrics("""eval""" , __lowerCAmelCase ) # Prediction if training_args.do_predict: logger.info("""*** Predict ***""" ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" ) SCREAMING_SNAKE_CASE__ : List[str] = ( data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""predict""" , __lowerCAmelCase ) trainer.save_metrics("""predict""" , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" ) if trainer.is_world_process_zero(): with open(__lowerCAmelCase , """w""" ) as writer: writer.write("""index\tprediction\n""" ) for index, item in enumerate(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item] writer.write(F'''{index}\t{item}\n''' ) if __name__ == "__main__": main()
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0
"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> list: for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ): SCREAMING_SNAKE_CASE__ : str = False for j in range(__lowerCAmelCase , 0 , -1 ): if unsorted[j] < unsorted[j - 1]: SCREAMING_SNAKE_CASE__ : str = unsorted[j - 1], unsorted[j] SCREAMING_SNAKE_CASE__ : Dict = True for j in range(__lowerCAmelCase ): if unsorted[j] > unsorted[j + 1]: SCREAMING_SNAKE_CASE__ : Tuple = unsorted[j + 1], unsorted[j] SCREAMING_SNAKE_CASE__ : Any = True if not swapped: break return unsorted if __name__ == "__main__": import doctest doctest.testmod() a :List[Any] = input("Enter numbers separated by a comma:\n").strip() a :Optional[int] = [int(item) for item in user_input.split(",")] print(f'{cocktail_shaker_sort(unsorted) = }')
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"""simple docstring""" # Note: if you intend to run this script make sure you look under scripts/fsmt/ # to locate the appropriate script to do the work correctly. There is a set of scripts to: # - download and prepare data and run the conversion script # - perform eval to get the best hparam into the config # - generate model_cards - useful if you have multiple models from the same paper import argparse import json import os import re from collections import OrderedDict from os.path import basename, dirname import fairseq import torch from fairseq import hub_utils from fairseq.data.dictionary import Dictionary from transformers import FSMTConfig, FSMTForConditionalGeneration from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE from transformers.utils import WEIGHTS_NAME, logging logging.set_verbosity_warning() a :str = 2 # based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping` # values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults: # # * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users) # * `early_stopping`: `False` consistently scored better # * `length_penalty` varied, so will assign the best one depending on the model a :int = { # fairseq: "wmt19-ru-en": {"length_penalty": 1.1}, "wmt19-en-ru": {"length_penalty": 1.15}, "wmt19-en-de": {"length_penalty": 1.0}, "wmt19-de-en": {"length_penalty": 1.1}, # allenai: "wmt16-en-de-dist-12-1": {"length_penalty": 0.6}, "wmt16-en-de-dist-6-1": {"length_penalty": 0.6}, "wmt16-en-de-12-1": {"length_penalty": 0.8}, "wmt19-de-en-6-6-base": {"length_penalty": 0.6}, "wmt19-de-en-6-6-big": {"length_penalty": 0.6}, } # this remaps the different models to their organization names a :Dict = {} for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]: a :List[Any] = "facebook" for m in [ "wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1", "wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big", ]: a :str = "allenai" def _lowercase ( __lowerCAmelCase ) -> Any: # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up, # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7} SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() ) SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split() # restore the special tokens for k in keep_keys: del da[F'''{k}</w>'''] SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore return da def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]: # prep assert os.path.exists(__lowerCAmelCase ) os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase ) print(F'''Writing results to {pytorch_dump_folder_path}''' ) # handle various types of models SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models() SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""} SCREAMING_SNAKE_CASE__ : Optional[Any] = """.""" # note: since the model dump is old, fairseq has upgraded its model some # time later, and it does a whole lot of rewrites and splits on the saved # weights, therefore we can't use torch.load() directly on the model file. # see: upgrade_state_dict(state_dict) in fairseq_model.py print(F'''using checkpoint {checkpoint_file}''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] ) SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""] SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""] SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase ) # dicts SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" ) print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # detect whether this is a do_lower_case situation, which can be derived by checking whether we # have at least one uppercase letter in the source vocab SCREAMING_SNAKE_CASE__ : Optional[Any] = True for k in src_vocab.keys(): if not k.islower(): SCREAMING_SNAKE_CASE__ : Tuple = False break SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" ) print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # merges_file (bpecodes) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] ) for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code" SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) if os.path.exists(__lowerCAmelCase ): break with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin: SCREAMING_SNAKE_CASE__ : Any = fin.read() SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number print(F'''Generating {merges_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout: fout.write(__lowerCAmelCase ) # model config SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" ) # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe - # may have to modify the tokenizer if a different type is used by a future model assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}''' assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}''' SCREAMING_SNAKE_CASE__ : str = { """architectures""": ["""FSMTForConditionalGeneration"""], """model_type""": """fsmt""", """activation_dropout""": args["""activation_dropout"""], """activation_function""": """relu""", """attention_dropout""": args["""attention_dropout"""], """d_model""": args["""decoder_embed_dim"""], """dropout""": args["""dropout"""], """init_std""": 0.02, """max_position_embeddings""": args["""max_source_positions"""], """num_hidden_layers""": args["""encoder_layers"""], """src_vocab_size""": src_vocab_size, """tgt_vocab_size""": tgt_vocab_size, """langs""": [src_lang, tgt_lang], """encoder_attention_heads""": args["""encoder_attention_heads"""], """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""], """encoder_layerdrop""": args["""encoder_layerdrop"""], """encoder_layers""": args["""encoder_layers"""], """decoder_attention_heads""": args["""decoder_attention_heads"""], """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""], """decoder_layerdrop""": args["""decoder_layerdrop"""], """decoder_layers""": args["""decoder_layers"""], """bos_token_id""": 0, """pad_token_id""": 1, """eos_token_id""": 2, """is_encoder_decoder""": True, """scale_embedding""": not args["""no_scale_embedding"""], """tie_word_embeddings""": args["""share_all_embeddings"""], } # good hparam defaults to start with SCREAMING_SNAKE_CASE__ : Tuple = 5 SCREAMING_SNAKE_CASE__ : str = False if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]: SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""] else: SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0 print(F'''Generating {fsmt_model_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # tokenizer config SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = { """langs""": [src_lang, tgt_lang], """model_max_length""": 1024, """do_lower_case""": do_lower_case, } print(F'''Generating {fsmt_tokenizer_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # model SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0] SCREAMING_SNAKE_CASE__ : int = model.state_dict() # rename keys to start with 'model.' SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() ) # remove unneeded keys SCREAMING_SNAKE_CASE__ : str = [ """model.model""", """model.encoder.version""", """model.decoder.version""", """model.encoder_embed_tokens.weight""", """model.decoder_embed_tokens.weight""", """model.encoder.embed_positions._float_tensor""", """model.decoder.embed_positions._float_tensor""", ] for k in ignore_keys: model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase ) # check that it loads ok model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase ) # save SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) print(F'''Generating {pytorch_weights_dump_path}''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) print("""Conversion is done!""" ) print("""\nLast step is to upload the files to s3""" ) print(F'''cd {data_root}''' ) print(F'''transformers-cli upload {model_dir}''' ) if __name__ == "__main__": a :Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--fsmt_checkpoint_path", default=None, type=str, required=True, help=( "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts," " bpecodes, etc." ), ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) a :List[str] = parser.parse_args() convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
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"""simple docstring""" from typing import List, Optional from ...configuration_utils import PretrainedConfig from ...utils import logging a :Dict = logging.get_logger(__name__) a :str = { "huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json", } class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[Any] = """autoformer""" _SCREAMING_SNAKE_CASE :Tuple = { """hidden_size""": """d_model""", """num_attention_heads""": """encoder_attention_heads""", """num_hidden_layers""": """encoder_layers""", } def __init__( self , _a = None , _a = None , _a = "student_t" , _a = "nll" , _a = 1 , _a = [1, 2, 3, 4, 5, 6, 7] , _a = True , _a = 0 , _a = 0 , _a = 0 , _a = 0 , _a = None , _a = None , _a = 64 , _a = 2 , _a = 2 , _a = 2 , _a = 2 , _a = 32 , _a = 32 , _a = "gelu" , _a = 0.1 , _a = 0.1 , _a = 0.1 , _a = 0.1 , _a = 0.1 , _a = 100 , _a = 0.02 , _a = True , _a=True , _a = 10 , _a = 25 , _a = 3 , **_a , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = prediction_length SCREAMING_SNAKE_CASE__ : List[Any] = context_length if context_length is not None else prediction_length SCREAMING_SNAKE_CASE__ : List[str] = distribution_output SCREAMING_SNAKE_CASE__ : str = loss SCREAMING_SNAKE_CASE__ : str = input_size SCREAMING_SNAKE_CASE__ : str = num_time_features SCREAMING_SNAKE_CASE__ : Optional[Any] = lags_sequence SCREAMING_SNAKE_CASE__ : Union[str, Any] = scaling SCREAMING_SNAKE_CASE__ : Optional[int] = num_dynamic_real_features SCREAMING_SNAKE_CASE__ : int = num_static_real_features SCREAMING_SNAKE_CASE__ : Optional[Any] = num_static_categorical_features if cardinality is not None and num_static_categorical_features > 0: if len(_a ) != num_static_categorical_features: raise ValueError( """The cardinality should be a list of the same length as `num_static_categorical_features`""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = cardinality else: SCREAMING_SNAKE_CASE__ : Dict = [0] if embedding_dimension is not None and num_static_categorical_features > 0: if len(_a ) != num_static_categorical_features: raise ValueError( """The embedding dimension should be a list of the same length as `num_static_categorical_features`""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = embedding_dimension else: SCREAMING_SNAKE_CASE__ : Tuple = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality] SCREAMING_SNAKE_CASE__ : List[str] = num_parallel_samples # Transformer architecture configuration SCREAMING_SNAKE_CASE__ : Optional[int] = input_size * len(self.lags_sequence ) + self._number_of_features SCREAMING_SNAKE_CASE__ : List[Any] = d_model SCREAMING_SNAKE_CASE__ : Optional[Any] = encoder_attention_heads SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = encoder_ffn_dim SCREAMING_SNAKE_CASE__ : List[Any] = decoder_ffn_dim SCREAMING_SNAKE_CASE__ : Union[str, Any] = encoder_layers SCREAMING_SNAKE_CASE__ : Union[str, Any] = decoder_layers SCREAMING_SNAKE_CASE__ : str = dropout SCREAMING_SNAKE_CASE__ : List[str] = attention_dropout SCREAMING_SNAKE_CASE__ : Optional[int] = activation_dropout SCREAMING_SNAKE_CASE__ : Union[str, Any] = encoder_layerdrop SCREAMING_SNAKE_CASE__ : List[str] = decoder_layerdrop SCREAMING_SNAKE_CASE__ : Optional[Any] = activation_function SCREAMING_SNAKE_CASE__ : Dict = init_std SCREAMING_SNAKE_CASE__ : List[str] = use_cache # Autoformer SCREAMING_SNAKE_CASE__ : Dict = label_length SCREAMING_SNAKE_CASE__ : Tuple = moving_average SCREAMING_SNAKE_CASE__ : Any = autocorrelation_factor super().__init__(is_encoder_decoder=_a , **_a ) @property def _a ( self ) -> int: """simple docstring""" return ( sum(self.embedding_dimension ) + self.num_dynamic_real_features + self.num_time_features + self.num_static_real_features + self.input_size * 2 # the log1p(abs(loc)) and log(scale) features )
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,) def _a ( self , **_a ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """num_train_timesteps""": 1_000, """beta_start""": 0.0_001, """beta_end""": 0.02, """beta_schedule""": """linear""", """variance_type""": """fixed_small""", """clip_sample""": True, } config.update(**_a ) return config def _a ( self ) -> str: """simple docstring""" for timesteps in [1, 5, 100, 1_000]: self.check_over_configs(num_train_timesteps=_a ) def _a ( self ) -> str: """simple docstring""" for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=_a , beta_end=_a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_a ) def _a ( self ) -> Any: """simple docstring""" for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=_a ) def _a ( self ) -> Optional[int]: """simple docstring""" for clip_sample in [True, False]: self.check_over_configs(clip_sample=_a ) def _a ( self ) -> int: """simple docstring""" self.check_over_configs(thresholding=_a ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=_a , prediction_type=_a , sample_max_value=_a , ) def _a ( self ) -> str: """simple docstring""" for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=_a ) def _a ( self ) -> str: """simple docstring""" for t in [0, 500, 999]: self.check_over_forward(time_step=_a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Any = len(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model() SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : str = pred_prev_sample SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 258.9_606 ) < 1E-2 assert abs(result_mean.item() - 0.3_372 ) < 1E-3 def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" ) SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Dict = len(_a ) SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model() SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : int = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 202.0_296 ) < 1E-2 assert abs(result_mean.item() - 0.2_631 ) < 1E-3 def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps for i, timestep in enumerate(_a ): if i == len(_a ) - 1: SCREAMING_SNAKE_CASE__ : Optional[Any] = -1 else: SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1] SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item() self.assertEqual(_a , _a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0] with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ): scheduler.set_timesteps(timesteps=_a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0] SCREAMING_SNAKE_CASE__ : List[str] = len(_a ) with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ): scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a ) def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps] with self.assertRaises( _a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ): scheduler.set_timesteps(timesteps=_a )
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"""simple docstring""" from typing import Dict, List from nltk.translate import gleu_score import datasets from datasets import MetricInfo a :Any = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n and Jeffrey Dean},\n year={2016},\n eprint={1609.08144},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n" a :int = "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n" a :List[str] = "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n predictions (list of str): list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references (list of list of str): list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n 'google_bleu': google_bleu score\n\nExamples:\n Example 1:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.44\n\n Example 2:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.61\n\n Example 3:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.53\n\n Example 4:\n >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n ... 'heed', 'the', 'cat', 'commands']\n >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n ... 'of', 'the', 'cat']\n\n >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n ... 'interested', 'in', 'world', 'history']\n >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n ... 'because', 'he', 'read', 'the', 'book']\n\n >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n >>> hypotheses = [hyp1, hyp2]\n >>> google_bleu = datasets.load_metric(\"google_bleu\")\n >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n >>> print(round(results[\"google_bleu\"], 2))\n 0.4\n" @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION) class __a (datasets.Metric): '''simple docstring''' def _a ( self ) -> MetricInfo: """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ), """references""": datasets.Sequence( datasets.Sequence(datasets.Value("""string""" , id="""token""" ) , id="""sequence""" ) , id="""references""" ), } ) , ) def _a ( self , _a , _a , _a = 1 , _a = 4 , ) -> Dict[str, float]: """simple docstring""" return { "google_bleu": gleu_score.corpus_gleu( list_of_references=_a , hypotheses=_a , min_len=_a , max_len=_a ) }
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"""simple docstring""" import os a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000} def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Any = 0 SCREAMING_SNAKE_CASE__ : Dict = 0 while index < len(__lowerCAmelCase ) - 1: SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]] SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]] if current_value < next_value: total_value -= current_value else: total_value += current_value index += 1 total_value += SYMBOLS[numerals[index]] return total_value def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Optional[int] = """""" SCREAMING_SNAKE_CASE__ : int = num // 1000 numerals += m_count * "M" num %= 1000 SCREAMING_SNAKE_CASE__ : List[str] = num // 100 if c_count == 9: numerals += "CM" c_count -= 9 elif c_count == 4: numerals += "CD" c_count -= 4 if c_count >= 5: numerals += "D" c_count -= 5 numerals += c_count * "C" num %= 100 SCREAMING_SNAKE_CASE__ : List[Any] = num // 10 if x_count == 9: numerals += "XC" x_count -= 9 elif x_count == 4: numerals += "XL" x_count -= 4 if x_count >= 5: numerals += "L" x_count -= 5 numerals += x_count * "X" num %= 10 if num == 9: numerals += "IX" num -= 9 elif num == 4: numerals += "IV" num -= 4 if num >= 5: numerals += "V" num -= 5 numerals += num * "I" return numerals def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int: SCREAMING_SNAKE_CASE__ : int = 0 with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea: SCREAMING_SNAKE_CASE__ : str = filea.readlines() for line in lines: SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip() SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase ) savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase ) return savings if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" from __future__ import annotations import inspect import unittest import numpy as np from transformers import ResNetConfig from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import cached_property, is_tf_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFResNetForImageClassification, TFResNetModel from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class __a : '''simple docstring''' def __init__( self , _a , _a=3 , _a=32 , _a=3 , _a=10 , _a=[10, 20, 30, 40] , _a=[1, 1, 2, 1] , _a=True , _a=True , _a="relu" , _a=3 , _a=None , ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = parent SCREAMING_SNAKE_CASE__ : Dict = batch_size SCREAMING_SNAKE_CASE__ : List[Any] = image_size SCREAMING_SNAKE_CASE__ : Tuple = num_channels SCREAMING_SNAKE_CASE__ : Dict = embeddings_size SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes SCREAMING_SNAKE_CASE__ : Any = depths SCREAMING_SNAKE_CASE__ : Union[str, Any] = is_training SCREAMING_SNAKE_CASE__ : List[Any] = use_labels SCREAMING_SNAKE_CASE__ : List[Any] = hidden_act SCREAMING_SNAKE_CASE__ : Tuple = num_labels SCREAMING_SNAKE_CASE__ : List[str] = scope SCREAMING_SNAKE_CASE__ : Optional[Any] = len(_a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) SCREAMING_SNAKE_CASE__ : str = None if self.use_labels: SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.num_labels ) SCREAMING_SNAKE_CASE__ : str = self.get_config() return config, pixel_values, labels def _a ( self ) -> List[Any]: """simple docstring""" return ResNetConfig( num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , ) def _a ( self , _a , _a , _a ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = TFResNetModel(config=_a ) SCREAMING_SNAKE_CASE__ : int = model(_a ) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , ) def _a ( self , _a , _a , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.num_labels SCREAMING_SNAKE_CASE__ : Optional[int] = TFResNetForImageClassification(_a ) SCREAMING_SNAKE_CASE__ : List[str] = model(_a , labels=_a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : Optional[Any] = config_and_inputs SCREAMING_SNAKE_CASE__ : Dict = {"""pixel_values""": pixel_values} return config, inputs_dict @require_tf class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[str] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else () _SCREAMING_SNAKE_CASE :Optional[Any] = ( {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification} if is_tf_available() else {} ) _SCREAMING_SNAKE_CASE :Tuple = False _SCREAMING_SNAKE_CASE :Union[str, Any] = False _SCREAMING_SNAKE_CASE :Tuple = False _SCREAMING_SNAKE_CASE :Optional[Any] = False _SCREAMING_SNAKE_CASE :Dict = False def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = TFResNetModelTester(self ) SCREAMING_SNAKE_CASE__ : Dict = ConfigTester(self , config_class=_a , has_text_modality=_a ) def _a ( self ) -> List[str]: """simple docstring""" self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def _a ( self ) -> Optional[Any]: """simple docstring""" return @unittest.skip(reason="""ResNet does not use inputs_embeds""" ) def _a ( self ) -> Optional[Any]: """simple docstring""" pass @unittest.skip(reason="""ResNet does not support input and output embeddings""" ) def _a ( self ) -> Union[str, Any]: """simple docstring""" pass def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE__ : Union[str, Any] = model_class(_a ) SCREAMING_SNAKE_CASE__ : Dict = inspect.signature(model.call ) # signature.parameters is an OrderedDict => so arg_names order is deterministic SCREAMING_SNAKE_CASE__ : int = [*signature.parameters.keys()] SCREAMING_SNAKE_CASE__ : Any = ["""pixel_values"""] self.assertListEqual(arg_names[:1] , _a ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*_a ) def _a ( self ) -> Optional[int]: """simple docstring""" def check_hidden_states_output(_a , _a , _a ): SCREAMING_SNAKE_CASE__ : Dict = model_class(_a ) SCREAMING_SNAKE_CASE__ : Dict = model(**self._prepare_for_class(_a , _a ) ) SCREAMING_SNAKE_CASE__ : Optional[int] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.num_stages self.assertEqual(len(_a ) , expected_num_stages + 1 ) # ResNet's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , ) SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common() SCREAMING_SNAKE_CASE__ : Optional[int] = ["""basic""", """bottleneck"""] for model_class in self.all_model_classes: for layer_type in layers_type: SCREAMING_SNAKE_CASE__ : Dict = layer_type SCREAMING_SNAKE_CASE__ : Union[str, Any] = True check_hidden_states_output(_a , _a , _a ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] SCREAMING_SNAKE_CASE__ : Optional[Any] = True check_hidden_states_output(_a , _a , _a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*_a ) @slow def _a ( self ) -> List[str]: """simple docstring""" for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE__ : Optional[Any] = TFResNetModel.from_pretrained(_a ) self.assertIsNotNone(_a ) def _lowercase ( ) -> List[Any]: '''simple docstring''' SCREAMING_SNAKE_CASE__ : int = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) return image @require_tf @require_vision class __a (unittest.TestCase): '''simple docstring''' @cached_property def _a ( self ) -> List[Any]: """simple docstring""" return ( AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None ) @slow def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.default_image_processor SCREAMING_SNAKE_CASE__ : Optional[int] = prepare_img() SCREAMING_SNAKE_CASE__ : str = image_processor(images=_a , return_tensors="""tf""" ) # forward pass SCREAMING_SNAKE_CASE__ : str = model(**_a ) # verify the logits SCREAMING_SNAKE_CASE__ : List[Any] = tf.TensorShape((1, 1_000) ) self.assertEqual(outputs.logits.shape , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = tf.constant([-11.1_069, -9.7_877, -8.3_777] ) self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _a , atol=1E-4 ) )
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"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow if is_tf_available(): import numpy as np import tensorflow as tf from transformers import TFCamembertModel @require_tf @require_sentencepiece @require_tokenizers class __a (unittest.TestCase): '''simple docstring''' @slow def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" ) SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor( [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !" SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""] SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) ) self.assertEqual(output.shape , _a ) # compare the actual values for a slice. SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor( [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , ) # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0') # camembert.eval() # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach() self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
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"""simple docstring""" def _lowercase ( ) -> list[list[int]]: return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )] a :str = generate_large_matrix() a :List[str] = ( [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]], [[3, 2], [1, 0]], [[7, 7, 6]], [[7, 7, 6], [-1, -2, -3]], grid, ) def _lowercase ( __lowerCAmelCase ) -> None: assert all(row == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for row in grid ) assert all(list(__lowerCAmelCase ) == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for col in zip(*__lowerCAmelCase ) ) def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Any = 0 SCREAMING_SNAKE_CASE__ : List[str] = len(__lowerCAmelCase ) - 1 # Edge cases such as no values or all numbers are negative. if not array or array[0] < 0: return 0 while right + 1 > left: SCREAMING_SNAKE_CASE__ : Optional[Any] = (left + right) // 2 SCREAMING_SNAKE_CASE__ : int = array[mid] # Num must be negative and the index must be greater than or equal to 0. if num < 0 and array[mid - 1] >= 0: return mid if num >= 0: SCREAMING_SNAKE_CASE__ : int = mid + 1 else: SCREAMING_SNAKE_CASE__ : Optional[int] = mid - 1 # No negative numbers so return the last index of the array + 1 which is the length. return len(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : List[Any] = 0 SCREAMING_SNAKE_CASE__ : Tuple = len(grid[0] ) for i in range(len(__lowerCAmelCase ) ): SCREAMING_SNAKE_CASE__ : Dict = find_negative_index(grid[i][:bound] ) total += bound return (len(__lowerCAmelCase ) * len(grid[0] )) - total def _lowercase ( __lowerCAmelCase ) -> int: return len([number for row in grid for number in row if number < 0] ) def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : str = 0 for row in grid: for i, number in enumerate(__lowerCAmelCase ): if number < 0: total += len(__lowerCAmelCase ) - i break return total def _lowercase ( ) -> None: from timeit import timeit print("""Running benchmarks""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = ( """from __main__ import count_negatives_binary_search, """ """count_negatives_brute_force, count_negatives_brute_force_with_break, grid""" ) for func in ( "count_negatives_binary_search", # took 0.7727 seconds "count_negatives_brute_force_with_break", # took 4.6505 seconds "count_negatives_brute_force", # took 12.8160 seconds ): SCREAMING_SNAKE_CASE__ : Dict = timeit(F'''{func}(grid=grid)''' , setup=__lowerCAmelCase , number=500 ) print(F'''{func}() took {time:0.4f} seconds''' ) if __name__ == "__main__": import doctest doctest.testmod() benchmark()
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices a :List[Any] = logging.get_logger(__name__) a :Optional[int] = { "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json", } class __a (UpperCamelCase_ , UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = """focalnet""" def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]: """simple docstring""" super().__init__(**_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size SCREAMING_SNAKE_CASE__ : str = patch_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes SCREAMING_SNAKE_CASE__ : Optional[int] = depths SCREAMING_SNAKE_CASE__ : Any = focal_levels SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows SCREAMING_SNAKE_CASE__ : Any = hidden_act SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate SCREAMING_SNAKE_CASE__ : str = use_layerscale SCREAMING_SNAKE_CASE__ : int = layerscale_value SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator SCREAMING_SNAKE_CASE__ : str = initializer_range SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps SCREAMING_SNAKE_CASE__ : Any = encoder_stride SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices( out_features=_a , out_indices=_a , stage_names=self.stage_names )
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"""simple docstring""" import argparse import json import os import re import shutil import torch from transformers import BioGptConfig, BioGptForCausalLM from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE from transformers.utils import WEIGHTS_NAME, logging logging.set_verbosity_warning() a :Optional[int] = 2 class __a : '''simple docstring''' def __init__( self , *, # begin keyword-only arguments _a="<s>" , _a="<pad>" , _a="</s>" , _a="<unk>" , _a=None , ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = bos, unk, pad, eos SCREAMING_SNAKE_CASE__ : Optional[int] = [] SCREAMING_SNAKE_CASE__ : List[str] = [] SCREAMING_SNAKE_CASE__ : str = {} SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.add_symbol(_a ) SCREAMING_SNAKE_CASE__ : Tuple = self.add_symbol(_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = self.add_symbol(_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = self.add_symbol(_a ) if extra_special_symbols: for s in extra_special_symbols: self.add_symbol(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = len(self.symbols ) def __eq__( self , _a ) -> Union[str, Any]: """simple docstring""" return self.indices == other.indices def __getitem__( self , _a ) -> List[str]: """simple docstring""" if idx < len(self.symbols ): return self.symbols[idx] return self.unk_word def __len__( self ) -> str: """simple docstring""" return len(self.symbols ) def __contains__( self , _a ) -> Optional[Any]: """simple docstring""" return sym in self.indices @classmethod def _a ( cls , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = cls() d.add_from_file(_a ) return d def _a ( self , _a , _a=1 , _a=False ) -> Optional[Any]: """simple docstring""" if word in self.indices and not overwrite: SCREAMING_SNAKE_CASE__ : str = self.indices[word] SCREAMING_SNAKE_CASE__ : Any = self.count[idx] + n return idx else: SCREAMING_SNAKE_CASE__ : List[str] = len(self.symbols ) SCREAMING_SNAKE_CASE__ : List[str] = idx self.symbols.append(_a ) self.count.append(_a ) return idx def _a ( self , _a ) -> Tuple: """simple docstring""" return 0 def _a ( self , _a ) -> Tuple: """simple docstring""" if isinstance(_a , _a ): try: with open(_a , """r""" , encoding="""utf-8""" ) as fd: self.add_from_file(_a ) except FileNotFoundError as fnfe: raise fnfe except UnicodeError: raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_a ) ) return SCREAMING_SNAKE_CASE__ : Any = f.readlines() SCREAMING_SNAKE_CASE__ : Dict = self._load_meta(_a ) for line in lines[indices_start_line:]: try: SCREAMING_SNAKE_CASE__ : List[Any] = line.rstrip().rsplit(""" """ , 1 ) if field == "#fairseq:overwrite": SCREAMING_SNAKE_CASE__ : List[Any] = True SCREAMING_SNAKE_CASE__ : Any = line.rsplit(""" """ , 1 ) else: SCREAMING_SNAKE_CASE__ : Tuple = False SCREAMING_SNAKE_CASE__ : str = int(_a ) SCREAMING_SNAKE_CASE__ : List[str] = line if word in self and not overwrite: raise RuntimeError( """Duplicate word found when loading Dictionary: '{}'. """ """Duplicate words can overwrite earlier ones by adding the """ """#fairseq:overwrite flag at the end of the corresponding row """ """in the dictionary file. If using the Camembert model, please """ """download an updated copy of the model file.""".format(_a ) ) self.add_symbol(_a , n=_a , overwrite=_a ) except ValueError: raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'""" ) def _lowercase ( __lowerCAmelCase ) -> Dict: # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up, # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7} SCREAMING_SNAKE_CASE__ : int = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = """<s> <pad> </s> <unk>""".split() # restore the special tokens for k in keep_keys: del da[F'''{k}</w>'''] SCREAMING_SNAKE_CASE__ : str = d[k] # restore return da def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]: # prep if not os.path.exists(__lowerCAmelCase ): raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!''' ) os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase ) print(F'''Writing results to {pytorch_dump_folder_path}''' ) # handle various types of models SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , """checkpoint.pt""" ) if not os.path.isfile(__lowerCAmelCase ): raise ValueError(F'''path to the file {checkpoint_file} does not exist!''' ) SCREAMING_SNAKE_CASE__ : Tuple = torch.load(__lowerCAmelCase , map_location="""cpu""" ) SCREAMING_SNAKE_CASE__ : Dict = chkpt["""cfg"""]["""model"""] # dicts SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """dict.txt""" ) if not os.path.isfile(__lowerCAmelCase ): raise ValueError(F'''path to the file {dict_file} does not exist!''' ) SCREAMING_SNAKE_CASE__ : int = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = rewrite_dict_keys(src_dict.indices ) SCREAMING_SNAKE_CASE__ : Tuple = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""vocab_file"""] ) print(F'''Generating {src_vocab_file} of {src_vocab_size} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # merges_file (bpecodes) SCREAMING_SNAKE_CASE__ : List[Any] = os.path.join(__lowerCAmelCase , """bpecodes""" ) if not os.path.isfile(__lowerCAmelCase ): raise ValueError(F'''path to the file {bpecodes_file} does not exist!''' ) SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] ) shutil.copyfile(__lowerCAmelCase , __lowerCAmelCase ) # model config SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """config.json""" ) SCREAMING_SNAKE_CASE__ : List[Any] = { """activation_dropout""": args["""activation_dropout"""], """architectures""": ["""BioGptForCausalLM"""], """attention_probs_dropout_prob""": args["""attention_dropout"""], """bos_token_id""": 0, """eos_token_id""": 2, """hidden_act""": args["""activation_fn"""], """hidden_dropout_prob""": args["""dropout"""], """hidden_size""": args["""decoder_embed_dim"""], """initializer_range""": 0.02, """intermediate_size""": args["""decoder_ffn_embed_dim"""], """layer_norm_eps""": 1E-12, """layerdrop""": args["""decoder_layerdrop"""], """max_position_embeddings""": args["""max_target_positions"""], """model_type""": """biogpt""", """num_attention_heads""": args["""decoder_attention_heads"""], """num_hidden_layers""": args["""decoder_layers"""], """pad_token_id""": 1, """scale_embedding""": not args["""no_scale_embedding"""], """tie_word_embeddings""": args["""share_decoder_input_output_embed"""], """vocab_size""": src_vocab_size, } # good hparam defaults to start with print(F'''Generating {biogpt_model_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # tokenizer config SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = { """bos_token""": """<s>""", """eos_token""": """</s>""", """model_max_length""": 1024, """pad_token""": """<pad>""", """special_tokens_map_file""": None, """tokenizer_class""": """BioGptTokenizer""", """unk_token""": """<unk>""", } print(F'''Generating {biogpt_tokenizer_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # model SCREAMING_SNAKE_CASE__ : Union[str, Any] = chkpt["""model"""] # remove unneeded keys SCREAMING_SNAKE_CASE__ : Tuple = [ """decoder.version""", ] for k in ignore_keys: model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[int] = list(model_state_dict.keys() ) for layer_name in layer_names: if layer_name.endswith("""output_projection.weight""" ): SCREAMING_SNAKE_CASE__ : Tuple = model_state_dict.pop(__lowerCAmelCase ) else: SCREAMING_SNAKE_CASE__ : List[str] = model_state_dict.pop(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = BioGptConfig.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = BioGptForCausalLM(__lowerCAmelCase ) # check that it loads ok model_new.load_state_dict(__lowerCAmelCase ) # save SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) print(F'''Generating {pytorch_weights_dump_path}''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) print("""Conversion is done!""" ) if __name__ == "__main__": a :Union[str, Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--biogpt_checkpoint_path", default=None, type=str, required=True, help=( "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts," " bpecodes, etc." ), ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) a :Dict = parser.parse_args() convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
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"""simple docstring""" import unittest import numpy as np from transformers import RoFormerConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax.numpy as jnp from transformers.models.roformer.modeling_flax_roformer import ( FlaxRoFormerForMaskedLM, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerModel, ) class __a (unittest.TestCase): '''simple docstring''' def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = parent SCREAMING_SNAKE_CASE__ : Tuple = batch_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length SCREAMING_SNAKE_CASE__ : Optional[int] = is_training SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels SCREAMING_SNAKE_CASE__ : int = vocab_size SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads SCREAMING_SNAKE_CASE__ : Dict = intermediate_size SCREAMING_SNAKE_CASE__ : int = hidden_act SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size SCREAMING_SNAKE_CASE__ : int = initializer_range SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = None if self.use_attention_mask: SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] ) SCREAMING_SNAKE_CASE__ : Tuple = None if self.use_token_type_ids: SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , ) return config, input_ids, token_type_ids, attention_mask def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask} return config, inputs_dict @require_flax class __a (UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = True _SCREAMING_SNAKE_CASE :Optional[Any] = ( ( FlaxRoFormerModel, FlaxRoFormerForMaskedLM, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, ) if is_flax_available() else () ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self ) @slow def _a ( self ) -> int: """simple docstring""" for model_class_name in self.all_model_classes: SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a ) SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) ) self.assertIsNotNone(_a ) @require_flax class __a (unittest.TestCase): '''simple docstring''' @slow def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" ) SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] ) SCREAMING_SNAKE_CASE__ : str = model(_a )[0] SCREAMING_SNAKE_CASE__ : List[Any] = 50_000 SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size) self.assertEqual(output.shape , _a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array( [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] ) self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
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"""simple docstring""" import argparse import logging import sys from unittest.mock import patch import run_glue_deebert from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow logging.basicConfig(level=logging.DEBUG) a :List[str] = logging.getLogger() def _lowercase ( ) -> int: SCREAMING_SNAKE_CASE__ : List[str] = argparse.ArgumentParser() parser.add_argument("""-f""" ) SCREAMING_SNAKE_CASE__ : str = parser.parse_args() return args.f class __a (UpperCamelCase_): '''simple docstring''' def _a ( self ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = logging.StreamHandler(sys.stdout ) logger.addHandler(_a ) def _a ( self , _a ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = get_gpu_count() if n_gpu > 1: pass # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560 # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py" # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split() # cmd = [sys.executable] + distributed_args + args # execute_subprocess_async(cmd, env=self.get_env()) # XXX: test the results - need to save them first into .json file else: args.insert(0 , """run_glue_deebert.py""" ) with patch.object(_a , """argv""" , _a ): SCREAMING_SNAKE_CASE__ : Dict = run_glue_deebert.main() for value in result.values(): self.assertGreaterEqual(_a , 0.666 ) @slow @require_torch_non_multi_gpu def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = """ --model_type roberta --model_name_or_path roberta-base --task_name MRPC --do_train --do_eval --do_lower_case --data_dir ./tests/fixtures/tests_samples/MRPC/ --max_seq_length 128 --per_gpu_eval_batch_size=1 --per_gpu_train_batch_size=8 --learning_rate 2e-4 --num_train_epochs 3 --overwrite_output_dir --seed 42 --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --plot_data_dir ./examples/deebert/results/ --save_steps 0 --overwrite_cache --eval_after_first_stage """.split() self.run_and_check(_a ) SCREAMING_SNAKE_CASE__ : int = """ --model_type roberta --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --task_name MRPC --do_eval --do_lower_case --data_dir ./tests/fixtures/tests_samples/MRPC/ --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --plot_data_dir ./examples/deebert/results/ --max_seq_length 128 --eval_each_highway --eval_highway --overwrite_cache --per_gpu_eval_batch_size=1 """.split() self.run_and_check(_a ) SCREAMING_SNAKE_CASE__ : List[str] = """ --model_type roberta --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --task_name MRPC --do_eval --do_lower_case --data_dir ./tests/fixtures/tests_samples/MRPC/ --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage --plot_data_dir ./examples/deebert/results/ --max_seq_length 128 --early_exit_entropy 0.1 --eval_highway --overwrite_cache --per_gpu_eval_batch_size=1 """.split() self.run_and_check(_a )
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"""simple docstring""" a :List[str] = [ (1_000, "M"), (900, "CM"), (500, "D"), (400, "CD"), (100, "C"), (90, "XC"), (50, "L"), (40, "XL"), (10, "X"), (9, "IX"), (5, "V"), (4, "IV"), (1, "I"), ] def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000} SCREAMING_SNAKE_CASE__ : List[Any] = 0 SCREAMING_SNAKE_CASE__ : List[str] = 0 while place < len(__lowerCAmelCase ): if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]): total += vals[roman[place + 1]] - vals[roman[place]] place += 2 else: total += vals[roman[place]] place += 1 return total def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Any = [] for arabic, roman in ROMAN: ((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase ) result.append(roman * factor ) if number == 0: break return "".join(__lowerCAmelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> float: if not nums: # Makes sure that the list is not empty raise ValueError("""List is empty""" ) SCREAMING_SNAKE_CASE__ : Optional[int] = sum(__lowerCAmelCase ) / len(__lowerCAmelCase ) # Calculate the average return sum(abs(x - average ) for x in nums ) / len(__lowerCAmelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) a :Any = { "configuration_roberta_prelayernorm": [ "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaPreLayerNormConfig", "RobertaPreLayerNormOnnxConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Union[str, Any] = [ "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "RobertaPreLayerNormForCausalLM", "RobertaPreLayerNormForMaskedLM", "RobertaPreLayerNormForMultipleChoice", "RobertaPreLayerNormForQuestionAnswering", "RobertaPreLayerNormForSequenceClassification", "RobertaPreLayerNormForTokenClassification", "RobertaPreLayerNormModel", "RobertaPreLayerNormPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Optional[Any] = [ "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRobertaPreLayerNormForCausalLM", "TFRobertaPreLayerNormForMaskedLM", "TFRobertaPreLayerNormForMultipleChoice", "TFRobertaPreLayerNormForQuestionAnswering", "TFRobertaPreLayerNormForSequenceClassification", "TFRobertaPreLayerNormForTokenClassification", "TFRobertaPreLayerNormMainLayer", "TFRobertaPreLayerNormModel", "TFRobertaPreLayerNormPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :List[Any] = [ "FlaxRobertaPreLayerNormForCausalLM", "FlaxRobertaPreLayerNormForMaskedLM", "FlaxRobertaPreLayerNormForMultipleChoice", "FlaxRobertaPreLayerNormForQuestionAnswering", "FlaxRobertaPreLayerNormForSequenceClassification", "FlaxRobertaPreLayerNormForTokenClassification", "FlaxRobertaPreLayerNormModel", "FlaxRobertaPreLayerNormPreTrainedModel", ] if TYPE_CHECKING: from .configuration_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaPreLayerNormConfig, RobertaPreLayerNormOnnxConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaPreLayerNormForCausalLM, RobertaPreLayerNormForMaskedLM, RobertaPreLayerNormForMultipleChoice, RobertaPreLayerNormForQuestionAnswering, RobertaPreLayerNormForSequenceClassification, RobertaPreLayerNormForTokenClassification, RobertaPreLayerNormModel, RobertaPreLayerNormPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_roberta_prelayernorm import ( TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, TFRobertaPreLayerNormForCausalLM, TFRobertaPreLayerNormForMaskedLM, TFRobertaPreLayerNormForMultipleChoice, TFRobertaPreLayerNormForQuestionAnswering, TFRobertaPreLayerNormForSequenceClassification, TFRobertaPreLayerNormForTokenClassification, TFRobertaPreLayerNormMainLayer, TFRobertaPreLayerNormModel, TFRobertaPreLayerNormPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_roberta_prelayernorm import ( FlaxRobertaPreLayerNormForCausalLM, FlaxRobertaPreLayerNormForMaskedLM, FlaxRobertaPreLayerNormForMultipleChoice, FlaxRobertaPreLayerNormForQuestionAnswering, FlaxRobertaPreLayerNormForSequenceClassification, FlaxRobertaPreLayerNormForTokenClassification, FlaxRobertaPreLayerNormModel, FlaxRobertaPreLayerNormPreTrainedModel, ) else: import sys a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" import argparse import requests import torch from PIL import Image from torchvision.transforms import Compose, Normalize, Resize, ToTensor from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor def _lowercase ( __lowerCAmelCase ) -> List[str]: SCREAMING_SNAKE_CASE__ : Union[str, Any] = SwinaSRConfig() if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Tuple = 4 elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Dict = 4 SCREAMING_SNAKE_CASE__ : Optional[int] = 48 SCREAMING_SNAKE_CASE__ : str = """pixelshuffle_aux""" elif "Swin2SR_Lightweight_X2_64" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Dict = [6, 6, 6, 6] SCREAMING_SNAKE_CASE__ : List[str] = 60 SCREAMING_SNAKE_CASE__ : Optional[int] = [6, 6, 6, 6] SCREAMING_SNAKE_CASE__ : int = """pixelshuffledirect""" elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Dict = 4 SCREAMING_SNAKE_CASE__ : Tuple = """nearest+conv""" elif "Swin2SR_Jpeg_dynamic" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Any = 1 SCREAMING_SNAKE_CASE__ : str = 1 SCREAMING_SNAKE_CASE__ : Optional[Any] = 126 SCREAMING_SNAKE_CASE__ : Dict = 7 SCREAMING_SNAKE_CASE__ : List[str] = 255.0 SCREAMING_SNAKE_CASE__ : List[Any] = """""" return config def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int: if "patch_embed.proj" in name and "layers" not in name: SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" ) if "patch_embed.norm" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""patch_embed.norm""" , """embeddings.patch_embeddings.layernorm""" ) if "layers" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""layers""" , """encoder.stages""" ) if "residual_group.blocks" in name: SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""residual_group.blocks""" , """layers""" ) if "attn.proj" in name: SCREAMING_SNAKE_CASE__ : int = name.replace("""attn.proj""" , """attention.output.dense""" ) if "attn" in name: SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""attn""" , """attention.self""" ) if "norm1" in name: SCREAMING_SNAKE_CASE__ : Dict = name.replace("""norm1""" , """layernorm_before""" ) if "norm2" in name: SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""norm2""" , """layernorm_after""" ) if "mlp.fc1" in name: SCREAMING_SNAKE_CASE__ : Dict = name.replace("""mlp.fc1""" , """intermediate.dense""" ) if "mlp.fc2" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""mlp.fc2""" , """output.dense""" ) if "q_bias" in name: SCREAMING_SNAKE_CASE__ : str = name.replace("""q_bias""" , """query.bias""" ) if "k_bias" in name: SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""k_bias""" , """key.bias""" ) if "v_bias" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""v_bias""" , """value.bias""" ) if "cpb_mlp" in name: SCREAMING_SNAKE_CASE__ : Any = name.replace("""cpb_mlp""" , """continuous_position_bias_mlp""" ) if "patch_embed.proj" in name: SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""patch_embed.proj""" , """patch_embed.projection""" ) if name == "norm.weight": SCREAMING_SNAKE_CASE__ : Union[str, Any] = """layernorm.weight""" if name == "norm.bias": SCREAMING_SNAKE_CASE__ : str = """layernorm.bias""" if "conv_first" in name: SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""conv_first""" , """first_convolution""" ) if ( "upsample" in name or "conv_before_upsample" in name or "conv_bicubic" in name or "conv_up" in name or "conv_hr" in name or "conv_last" in name or "aux" in name ): # heads if "conv_last" in name: SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""conv_last""" , """final_convolution""" ) if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]: if "conv_before_upsample.0" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""conv_before_upsample.0""" , """conv_before_upsample""" ) if "upsample.0" in name: SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""upsample.0""" , """upsample.convolution_0""" ) if "upsample.2" in name: SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""upsample.2""" , """upsample.convolution_1""" ) SCREAMING_SNAKE_CASE__ : List[str] = """upsample.""" + name elif config.upsampler == "pixelshuffledirect": SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""upsample.0.weight""" , """upsample.conv.weight""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""upsample.0.bias""" , """upsample.conv.bias""" ) else: pass else: SCREAMING_SNAKE_CASE__ : Tuple = """swin2sr.""" + name return name def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: for key in orig_state_dict.copy().keys(): SCREAMING_SNAKE_CASE__ : Union[str, Any] = orig_state_dict.pop(__lowerCAmelCase ) if "qkv" in key: SCREAMING_SNAKE_CASE__ : str = key.split(""".""" ) SCREAMING_SNAKE_CASE__ : Dict = int(key_split[1] ) SCREAMING_SNAKE_CASE__ : Tuple = int(key_split[4] ) SCREAMING_SNAKE_CASE__ : Any = config.embed_dim if "weight" in key: SCREAMING_SNAKE_CASE__ : Any = val[:dim, :] SCREAMING_SNAKE_CASE__ : Optional[int] = val[dim : dim * 2, :] SCREAMING_SNAKE_CASE__ : List[str] = val[-dim:, :] else: SCREAMING_SNAKE_CASE__ : List[Any] = val[:dim] SCREAMING_SNAKE_CASE__ : Any = val[dim : dim * 2] SCREAMING_SNAKE_CASE__ : Dict = val[-dim:] pass else: SCREAMING_SNAKE_CASE__ : List[str] = val return orig_state_dict def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]: SCREAMING_SNAKE_CASE__ : List[Any] = get_config(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = SwinaSRForImageSuperResolution(__lowerCAmelCase ) model.eval() SCREAMING_SNAKE_CASE__ : Dict = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location="""cpu""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_state_dict(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[int] = model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase ) if len(__lowerCAmelCase ) > 0: raise ValueError("""Missing keys when converting: {}""".format(__lowerCAmelCase ) ) for key in unexpected_keys: if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key): raise ValueError(F'''Unexpected key {key} in state_dict''' ) # verify values SCREAMING_SNAKE_CASE__ : Any = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true""" SCREAMING_SNAKE_CASE__ : Optional[int] = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw ).convert("""RGB""" ) SCREAMING_SNAKE_CASE__ : Any = SwinaSRImageProcessor() # pixel_values = processor(image, return_tensors="pt").pixel_values SCREAMING_SNAKE_CASE__ : str = 126 if """Jpeg""" in checkpoint_url else 256 SCREAMING_SNAKE_CASE__ : Tuple = Compose( [ Resize((image_size, image_size) ), ToTensor(), Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ), ] ) SCREAMING_SNAKE_CASE__ : Optional[Any] = transforms(__lowerCAmelCase ).unsqueeze(0 ) if config.num_channels == 1: SCREAMING_SNAKE_CASE__ : Optional[Any] = pixel_values[:, 0, :, :].unsqueeze(1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(__lowerCAmelCase ) # assert values if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Any = torch.Size([1, 3, 512, 512] ) SCREAMING_SNAKE_CASE__ : Any = torch.tensor( [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]] ) elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Dict = torch.Size([1, 3, 1024, 1024] ) SCREAMING_SNAKE_CASE__ : Any = torch.tensor( [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]] ) elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url: # TODO values didn't match exactly here SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 3, 1024, 1024] ) SCREAMING_SNAKE_CASE__ : Tuple = torch.tensor( [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]] ) elif "Swin2SR_Lightweight_X2_64" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 3, 512, 512] ) SCREAMING_SNAKE_CASE__ : Dict = torch.tensor( [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]] ) elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url: SCREAMING_SNAKE_CASE__ : List[Any] = torch.Size([1, 3, 1024, 1024] ) SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor( [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]] ) assert ( outputs.reconstruction.shape == expected_shape ), F'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}''' assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , __lowerCAmelCase , atol=1E-3 ) print("""Looks ok!""" ) SCREAMING_SNAKE_CASE__ : List[Any] = { """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": ( """swin2SR-classical-sr-x2-64""" ), """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": ( """swin2SR-classical-sr-x4-64""" ), """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": ( """swin2SR-compressed-sr-x4-48""" ), """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": ( """swin2SR-lightweight-x2-64""" ), """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": ( """swin2SR-realworld-sr-x4-64-bsrgan-psnr""" ), } SCREAMING_SNAKE_CASE__ : Union[str, Any] = url_to_name[checkpoint_url] if pytorch_dump_folder_path is not None: print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' ) model.save_pretrained(__lowerCAmelCase ) print(F'''Saving image processor to {pytorch_dump_folder_path}''' ) processor.save_pretrained(__lowerCAmelCase ) if push_to_hub: model.push_to_hub(F'''caidas/{model_name}''' ) processor.push_to_hub(F'''caidas/{model_name}''' ) if __name__ == "__main__": a :int = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth", type=str, help="URL of the original Swin2SR checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.") a :List[Any] = parser.parse_args() convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
708
"""simple docstring""" import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import BertTokenizer, BertTokenizerFast from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import AlignProcessor, EfficientNetImageProcessor @require_vision class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp() SCREAMING_SNAKE_CASE__ : Dict = [ """[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest""", ] SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) ) SCREAMING_SNAKE_CASE__ : Optional[Any] = { """do_resize""": True, """size""": 20, """do_center_crop""": True, """crop_size""": 18, """do_normalize""": True, """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073], """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711], } SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a ) with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp: json.dump(_a , _a ) def _a ( self , **_a ) -> List[Any]: """simple docstring""" return BertTokenizer.from_pretrained(self.tmpdirname , **_a ) def _a ( self , **_a ) -> List[Any]: """simple docstring""" return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a ) def _a ( self , **_a ) -> Any: """simple docstring""" return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a ) def _a ( self ) -> List[Any]: """simple docstring""" shutil.rmtree(self.tmpdirname ) def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )] SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs] return image_inputs def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer() SCREAMING_SNAKE_CASE__ : str = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a ) processor_slow.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a ) SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a ) processor_fast.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname ) self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() ) self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() ) self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() ) self.assertIsInstance(processor_slow.tokenizer , _a ) self.assertIsInstance(processor_fast.tokenizer , _a ) self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertIsInstance(processor_slow.image_processor , _a ) self.assertIsInstance(processor_fast.image_processor , _a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() ) processor.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" ) SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 ) SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained( self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 ) self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() ) self.assertIsInstance(processor.tokenizer , _a ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , _a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor() SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" ) for key in input_image_proc.keys(): self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer""" SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a ) SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] , encoded_processor[key] ) def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.get_image_processor() SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer""" SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a ) self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] ) # test if it raises when no input is passed with pytest.raises(_a ): processor() def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a ) self.assertListEqual(_a , _a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer""" SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a ) self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
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"""simple docstring""" import json import os import re import unittest from transformers import CodeGenTokenizer, CodeGenTokenizerFast from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class __a (UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[str] = CodeGenTokenizer _SCREAMING_SNAKE_CASE :Union[str, Any] = CodeGenTokenizerFast _SCREAMING_SNAKE_CASE :Tuple = True _SCREAMING_SNAKE_CASE :Optional[int] = {"""add_prefix_space""": True} _SCREAMING_SNAKE_CASE :int = False def _a ( self ) -> Dict: """simple docstring""" super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt SCREAMING_SNAKE_CASE__ : Union[str, Any] = [ """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """\u0120""", """\u0120l""", """\u0120n""", """\u0120lo""", """\u0120low""", """er""", """\u0120lowest""", """\u0120newer""", """\u0120wider""", """<unk>""", """<|endoftext|>""", ] SCREAMING_SNAKE_CASE__ : Any = dict(zip(_a , range(len(_a ) ) ) ) SCREAMING_SNAKE_CASE__ : Optional[int] = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""] SCREAMING_SNAKE_CASE__ : Any = {"""unk_token""": """<unk>"""} SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) SCREAMING_SNAKE_CASE__ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: fp.write(json.dumps(_a ) + """\n""" ) with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp: fp.write("""\n""".join(_a ) ) def _a ( self , **_a ) -> Optional[int]: """simple docstring""" kwargs.update(self.special_tokens_map ) return CodeGenTokenizer.from_pretrained(self.tmpdirname , **_a ) def _a ( self , **_a ) -> Union[str, Any]: """simple docstring""" kwargs.update(self.special_tokens_map ) return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **_a ) def _a ( self , _a ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = """lower newer""" SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer""" return input_text, output_text def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map ) SCREAMING_SNAKE_CASE__ : Tuple = """lower newer""" SCREAMING_SNAKE_CASE__ : List[Any] = ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""] SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.tokenize(_a , add_prefix_space=_a ) self.assertListEqual(_a , _a ) SCREAMING_SNAKE_CASE__ : Tuple = tokens + [tokenizer.unk_token] SCREAMING_SNAKE_CASE__ : Optional[int] = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , _a ) def _a ( self ) -> Optional[Any]: """simple docstring""" if not self.test_rust_tokenizer: return SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer(add_prefix_space=_a ) SCREAMING_SNAKE_CASE__ : str = """lower newer""" # Testing tokenization SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.tokenize(_a , add_prefix_space=_a ) SCREAMING_SNAKE_CASE__ : int = rust_tokenizer.tokenize(_a ) self.assertListEqual(_a , _a ) # Testing conversion to ids without special tokens SCREAMING_SNAKE_CASE__ : int = tokenizer.encode(_a , add_special_tokens=_a , add_prefix_space=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = rust_tokenizer.encode(_a , add_special_tokens=_a ) self.assertListEqual(_a , _a ) # Testing conversion to ids with special tokens SCREAMING_SNAKE_CASE__ : Tuple = self.get_rust_tokenizer(add_prefix_space=_a ) SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.encode(_a , add_prefix_space=_a ) SCREAMING_SNAKE_CASE__ : str = rust_tokenizer.encode(_a ) self.assertListEqual(_a , _a ) # Testing the unknown token SCREAMING_SNAKE_CASE__ : List[str] = tokens + [rust_tokenizer.unk_token] SCREAMING_SNAKE_CASE__ : Tuple = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a ) , _a ) def _a ( self , *_a , **_a ) -> Union[str, Any]: """simple docstring""" pass def _a ( self , _a=15 ) -> Union[str, Any]: """simple docstring""" for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): SCREAMING_SNAKE_CASE__ : Any = self.rust_tokenizer_class.from_pretrained(_a , **_a ) # Simple input SCREAMING_SNAKE_CASE__ : int = """This is a simple input""" SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""This is a simple input 1""", """This is a simple input 2"""] SCREAMING_SNAKE_CASE__ : Optional[Any] = ("""This is a simple input""", """This is a pair""") SCREAMING_SNAKE_CASE__ : int = [ ("""This is a simple input 1""", """This is a simple input 2"""), ("""This is a simple pair 1""", """This is a simple pair 2"""), ] # Simple input tests self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" ) # Simple input self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" ) # Simple input self.assertRaises( _a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , ) # Pair input self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" ) # Pair input self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" ) # Pair input self.assertRaises( _a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token="""<pad>""" ) # Simple input SCREAMING_SNAKE_CASE__ : Dict = """This is a simple input""" SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""This is a simple input looooooooong""", """This is a simple input"""] SCREAMING_SNAKE_CASE__ : List[str] = ("""This is a simple input""", """This is a pair""") SCREAMING_SNAKE_CASE__ : List[str] = [ ("""This is a simple input loooooong""", """This is a simple input"""), ("""This is a simple pair loooooong""", """This is a simple pair"""), ] SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.pad_token_id SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer(_a , padding="""max_length""" , max_length=30 , return_tensors="""np""" ) SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer(_a , padding=_a , truncate=_a , return_tensors="""np""" ) SCREAMING_SNAKE_CASE__ : List[str] = tokenizer(*_a , padding="""max_length""" , max_length=60 , return_tensors="""np""" ) SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , padding=_a , truncate=_a , return_tensors="""np""" ) # s # test single string max_length padding self.assertEqual(out_s["""input_ids"""].shape[-1] , 30 ) self.assertTrue(pad_token_id in out_s["""input_ids"""] ) self.assertTrue(0 in out_s["""attention_mask"""] ) # s2 # test automatic padding self.assertEqual(out_sa["""input_ids"""].shape[-1] , 33 ) # long slice doesn't have padding self.assertFalse(pad_token_id in out_sa["""input_ids"""][0] ) self.assertFalse(0 in out_sa["""attention_mask"""][0] ) # short slice does have padding self.assertTrue(pad_token_id in out_sa["""input_ids"""][1] ) self.assertTrue(0 in out_sa["""attention_mask"""][1] ) # p # test single pair max_length padding self.assertEqual(out_p["""input_ids"""].shape[-1] , 60 ) self.assertTrue(pad_token_id in out_p["""input_ids"""] ) self.assertTrue(0 in out_p["""attention_mask"""] ) # p2 # test automatic padding pair self.assertEqual(out_pa["""input_ids"""].shape[-1] , 52 ) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_pa["""input_ids"""][0] ) self.assertFalse(0 in out_pa["""attention_mask"""][0] ) # short slice pair does have padding self.assertTrue(pad_token_id in out_pa["""input_ids"""][1] ) self.assertTrue(0 in out_pa["""attention_mask"""][1] ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = """$$$""" SCREAMING_SNAKE_CASE__ : Dict = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=_a , add_bos_token=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = """This is a simple input""" SCREAMING_SNAKE_CASE__ : Optional[int] = ["""This is a simple input 1""", """This is a simple input 2"""] SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer.bos_token_id SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer(_a ) SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a ) self.assertEqual(out_s.input_ids[0] , _a ) self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.decode(out_s.input_ids ) SCREAMING_SNAKE_CASE__ : Dict = tokenizer.batch_decode(out_sa.input_ids ) self.assertEqual(decode_s.split()[0] , _a ) self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) ) @slow def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono""" ) SCREAMING_SNAKE_CASE__ : Tuple = """\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#""" SCREAMING_SNAKE_CASE__ : Tuple = """\nif len_a > len_b: result = a\nelse: result = b""" SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.encode(_a ) SCREAMING_SNAKE_CASE__ : Dict = ["""^#""", re.escape("""<|endoftext|>""" ), """^'''""", """^\"\"\"""", """\n\n\n"""] SCREAMING_SNAKE_CASE__ : int = tokenizer.decode(_a , truncate_before_pattern=_a ) self.assertEqual(_a , _a ) def _a ( self ) -> List[Any]: """simple docstring""" pass
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"""simple docstring""" from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxSeqaSeqConfigWithPast from ...utils import logging a :Optional[Any] = logging.get_logger(__name__) a :Union[str, Any] = { "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json", "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json", "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json", "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json", "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json", } class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[Any] = """t5""" _SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""] _SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""} def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size SCREAMING_SNAKE_CASE__ : Tuple = d_model SCREAMING_SNAKE_CASE__ : int = d_kv SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers SCREAMING_SNAKE_CASE__ : int = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry SCREAMING_SNAKE_CASE__ : Tuple = num_heads SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj SCREAMING_SNAKE_CASE__ : str = use_cache SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" ) SCREAMING_SNAKE_CASE__ : Dict = act_info[-1] SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated""" if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2: raise ValueError( f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.''' """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """ """'gated-gelu' or 'relu'""" ) # for backwards compatibility if feed_forward_proj == "gated-gelu": SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new""" super().__init__( pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , ) class __a (UpperCamelCase_): '''simple docstring''' @property def _a ( self ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """input_ids""": {0: """batch""", 1: """encoder_sequence"""}, """attention_mask""": {0: """batch""", 1: """encoder_sequence"""}, } if self.use_past: SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence""" SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""} SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""} else: SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""} SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""} if self.use_past: self.fill_with_past_key_values_(_a , direction="""inputs""" ) return common_inputs @property def _a ( self ) -> int: """simple docstring""" return 13
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"""simple docstring""" import warnings from typing import List, Optional, Tuple, Union import numpy as np import PIL import torch from ...models import UNetaDModel from ...schedulers import RePaintScheduler from ...utils import PIL_INTERPOLATION, logging, randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput a :Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name def _lowercase ( __lowerCAmelCase ) -> Any: warnings.warn( """The preprocess method is deprecated and will be removed in a future version. Please""" """ use VaeImageProcessor.preprocess instead""" , __lowerCAmelCase , ) if isinstance(__lowerCAmelCase , torch.Tensor ): return image elif isinstance(__lowerCAmelCase , PIL.Image.Image ): SCREAMING_SNAKE_CASE__ : Optional[Any] = [image] if isinstance(image[0] , PIL.Image.Image ): SCREAMING_SNAKE_CASE__ : List[Any] = image[0].size SCREAMING_SNAKE_CASE__ : Dict = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 SCREAMING_SNAKE_CASE__ : int = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image] SCREAMING_SNAKE_CASE__ : Dict = np.concatenate(__lowerCAmelCase , axis=0 ) SCREAMING_SNAKE_CASE__ : Tuple = np.array(__lowerCAmelCase ).astype(np.floataa ) / 255.0 SCREAMING_SNAKE_CASE__ : List[Any] = image.transpose(0 , 3 , 1 , 2 ) SCREAMING_SNAKE_CASE__ : Dict = 2.0 * image - 1.0 SCREAMING_SNAKE_CASE__ : Dict = torch.from_numpy(__lowerCAmelCase ) elif isinstance(image[0] , torch.Tensor ): SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat(__lowerCAmelCase , dim=0 ) return image def _lowercase ( __lowerCAmelCase ) -> Optional[int]: if isinstance(__lowerCAmelCase , torch.Tensor ): return mask elif isinstance(__lowerCAmelCase , PIL.Image.Image ): SCREAMING_SNAKE_CASE__ : Optional[Any] = [mask] if isinstance(mask[0] , PIL.Image.Image ): SCREAMING_SNAKE_CASE__ : Union[str, Any] = mask[0].size SCREAMING_SNAKE_CASE__ : List[Any] = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 SCREAMING_SNAKE_CASE__ : Dict = [np.array(m.convert("""L""" ).resize((w, h) , resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask] SCREAMING_SNAKE_CASE__ : int = np.concatenate(__lowerCAmelCase , axis=0 ) SCREAMING_SNAKE_CASE__ : List[Any] = mask.astype(np.floataa ) / 255.0 SCREAMING_SNAKE_CASE__ : Any = 0 SCREAMING_SNAKE_CASE__ : Dict = 1 SCREAMING_SNAKE_CASE__ : List[str] = torch.from_numpy(__lowerCAmelCase ) elif isinstance(mask[0] , torch.Tensor ): SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.cat(__lowerCAmelCase , dim=0 ) return mask class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :UNetaDModel _SCREAMING_SNAKE_CASE :RePaintScheduler def __init__( self , _a , _a ) -> Any: """simple docstring""" super().__init__() self.register_modules(unet=_a , scheduler=_a ) @torch.no_grad() def __call__( self , _a , _a , _a = 250 , _a = 0.0 , _a = 10 , _a = 10 , _a = None , _a = "pil" , _a = True , ) -> Union[ImagePipelineOutput, Tuple]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = image SCREAMING_SNAKE_CASE__ : List[Any] = _preprocess_image(_a ) SCREAMING_SNAKE_CASE__ : int = original_image.to(device=self.device , dtype=self.unet.dtype ) SCREAMING_SNAKE_CASE__ : Optional[int] = _preprocess_mask(_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = mask_image.to(device=self.device , dtype=self.unet.dtype ) SCREAMING_SNAKE_CASE__ : Optional[int] = original_image.shape[0] # sample gaussian noise to begin the loop if isinstance(_a , _a ) and len(_a ) != batch_size: raise ValueError( f'''You have passed a list of generators of length {len(_a )}, but requested an effective batch''' f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' ) SCREAMING_SNAKE_CASE__ : int = original_image.shape SCREAMING_SNAKE_CASE__ : List[str] = randn_tensor(_a , generator=_a , device=self.device , dtype=self.unet.dtype ) # set step values self.scheduler.set_timesteps(_a , _a , _a , self.device ) SCREAMING_SNAKE_CASE__ : Dict = eta SCREAMING_SNAKE_CASE__ : Dict = self.scheduler.timesteps[0] + 1 SCREAMING_SNAKE_CASE__ : Union[str, Any] = generator[0] if isinstance(_a , _a ) else generator for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ): if t < t_last: # predict the noise residual SCREAMING_SNAKE_CASE__ : Tuple = self.unet(_a , _a ).sample # compute previous image: x_t -> x_t-1 SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler.step(_a , _a , _a , _a , _a , _a ).prev_sample else: # compute the reverse: x_t-1 -> x_t SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler.undo_step(_a , _a , _a ) SCREAMING_SNAKE_CASE__ : int = t SCREAMING_SNAKE_CASE__ : int = (image / 2 + 0.5).clamp(0 , 1 ) SCREAMING_SNAKE_CASE__ : Tuple = image.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": SCREAMING_SNAKE_CASE__ : Tuple = self.numpy_to_pil(_a ) if not return_dict: return (image,) return ImagePipelineOutput(images=_a )
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"""simple docstring""" from __future__ import annotations import time import numpy as np a :Optional[Any] = [8, 5, 9, 7] a :List[Any] = [ [2, 0, 1, 1], [0, 1, 2, 1], [4, 0, 0, 3], [0, 2, 1, 0], [1, 0, 3, 0], ] a :int = [ [3, 2, 1, 4], [0, 2, 5, 2], [5, 1, 0, 5], [1, 5, 3, 0], [3, 0, 3, 3], ] class __a : '''simple docstring''' def __init__( self , _a , _a , _a , ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = claim_vector SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table def _a ( self ) -> list[int]: """simple docstring""" return [ sum(p_item[i] for p_item in self.__allocated_resources_table ) for i in range(len(self.__allocated_resources_table[0] ) ) ] def _a ( self ) -> list[int]: """simple docstring""" return np.array(self.__claim_vector ) - np.array( self.__processes_resource_summation() ) def _a ( self ) -> list[list[int]]: """simple docstring""" return [ list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) ) for i, allocated_resource in enumerate(self.__allocated_resources_table ) ] def _a ( self ) -> dict[int, list[int]]: """simple docstring""" return {self.__need().index(_a ): i for i in self.__need()} def _a ( self , **_a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need() SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources() SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager() for kw, val in kwargs.items(): if kw and val is True: self.__pretty_data() print("""_""" * 50 + """\n""" ) while need_list: SCREAMING_SNAKE_CASE__ : List[str] = False for each_need in need_list: SCREAMING_SNAKE_CASE__ : Dict = True for index, need in enumerate(_a ): if need > available_resources[index]: SCREAMING_SNAKE_CASE__ : Optional[int] = False break if execution: SCREAMING_SNAKE_CASE__ : Any = True # get the original index of the process from ind_ctrl db for original_need_index, need_clone in need_index_manager.items(): if each_need == need_clone: SCREAMING_SNAKE_CASE__ : Tuple = original_need_index print(f'''Process {process_number + 1} is executing.''' ) # remove the process run from stack need_list.remove(_a ) # update available/freed resources stack SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array( alloc_resources_table[process_number] ) print( """Updated available resource stack for processes: """ + """ """.join([str(_a ) for x in available_resources] ) ) break if safe: print("""The process is in a safe state.\n""" ) else: print("""System in unsafe state. Aborting...\n""" ) break def _a ( self ) -> Any: """simple docstring""" print(""" """ * 9 + """Allocated Resource Table""" ) for item in self.__allocated_resources_table: print( f'''P{self.__allocated_resources_table.index(_a ) + 1}''' + """ """.join(f'''{it:>8}''' for it in item ) + """\n""" ) print(""" """ * 9 + """System Resource Table""" ) for item in self.__maximum_claim_table: print( f'''P{self.__maximum_claim_table.index(_a ) + 1}''' + """ """.join(f'''{it:>8}''' for it in item ) + """\n""" ) print( """Current Usage by Active Processes: """ + """ """.join(str(_a ) for x in self.__claim_vector ) ) print( """Initial Available Resources: """ + """ """.join(str(_a ) for x in self.__available_resources() ) ) time.sleep(1 ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import numpy as np from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.31.0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") a :Union[str, Any] = logging.getLogger(__name__) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_28 , metadata={ """help""": ( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Whether to pad all samples to `max_seq_length`. """ """If False, will pad the samples dynamically when batching to the maximum length in the batch.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of training examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of evaluation examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of prediction examples to this """ """value if set.""" ) } , ) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}) _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , ) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , ) _SCREAMING_SNAKE_CASE :str = field( default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Will use the token generated when running `huggingface-cli login` (necessary to use this script """ """with private models).""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , ) def _lowercase ( ) -> Union[str, Any]: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("""run_xnli""" , __lowerCAmelCase ) # Setup logging logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level() logger.setLevel(__lowerCAmelCase ) datasets.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}''' + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' ) logger.info(F'''Training/evaluation parameters {training_args}''' ) # Detecting last checkpoint. SCREAMING_SNAKE_CASE__ : Any = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. ''' """Use --overwrite_output_dir to overcome.""" ) elif last_checkpoint is not None: logger.info( F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change ''' """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" ) # Set seed before initializing model. set_seed(training_args.seed ) # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # Downloading and loading xnli dataset from the hub. if training_args.do_train: if model_args.train_language is None: SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset( """xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) else: SCREAMING_SNAKE_CASE__ : str = load_dataset( """xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names if training_args.do_eval: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names if training_args.do_predict: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names # Labels SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase ) # Load pretrained model and tokenizer # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , ) # Preprocessing the datasets # Padding strategy if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : str = """max_length""" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch SCREAMING_SNAKE_CASE__ : Optional[Any] = False def preprocess_function(__lowerCAmelCase ): # Tokenize the texts return tokenizer( examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , ) if training_args.do_train: if data_args.max_train_samples is not None: SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples ) SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""train dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , ) # Log a few random samples from the training set: for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ): logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' ) if training_args.do_eval: if data_args.max_eval_samples is not None: SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""validation dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , ) if training_args.do_predict: if data_args.max_predict_samples is not None: SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , ) # Get the metric function SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" ) # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 ) return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids ) # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator elif training_args.fpaa: SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None # Initialize our Trainer SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer( model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , ) # Training if training_args.do_train: SCREAMING_SNAKE_CASE__ : Dict = None if training_args.resume_from_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint elif last_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics SCREAMING_SNAKE_CASE__ : Optional[int] = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("""train""" , __lowerCAmelCase ) trainer.save_metrics("""train""" , __lowerCAmelCase ) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("""*** Evaluate ***""" ) SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""eval""" , __lowerCAmelCase ) trainer.save_metrics("""eval""" , __lowerCAmelCase ) # Prediction if training_args.do_predict: logger.info("""*** Predict ***""" ) SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" ) SCREAMING_SNAKE_CASE__ : List[str] = ( data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""predict""" , __lowerCAmelCase ) trainer.save_metrics("""predict""" , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" ) if trainer.is_world_process_zero(): with open(__lowerCAmelCase , """w""" ) as writer: writer.write("""index\tprediction\n""" ) for index, item in enumerate(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item] writer.write(F'''{index}\t{item}\n''' ) if __name__ == "__main__": main()
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_xlnet import XLNetTokenizer else: a :List[Any] = None a :Optional[int] = logging.get_logger(__name__) a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} a :Optional[int] = { "vocab_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model", }, "tokenizer_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json", }, } a :Dict = { "xlnet-base-cased": None, "xlnet-large-cased": None, } a :int = "▁" # Segments (not really needed) a :Dict = 0 a :Optional[int] = 1 a :Tuple = 2 a :List[str] = 3 a :Optional[Any] = 4 class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = """left""" _SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token super().__init__( vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3 SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case SCREAMING_SNAKE_CASE__ : List[str] = remove_space SCREAMING_SNAKE_CASE__ : int = keep_accents SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return SCREAMING_SNAKE_CASE__ : Tuple = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
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"""simple docstring""" import gc import random import unittest import torch from diffusers import ( IFImgaImgPipeline, IFImgaImgSuperResolutionPipeline, IFInpaintingPipeline, IFInpaintingSuperResolutionPipeline, IFPipeline, IFSuperResolutionPipeline, ) from diffusers.models.attention_processor import AttnAddedKVProcessor from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference from . import IFPipelineTesterMixin @skip_mps class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = IFPipeline _SCREAMING_SNAKE_CASE :str = TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""} _SCREAMING_SNAKE_CASE :Optional[int] = TEXT_TO_IMAGE_BATCH_PARAMS _SCREAMING_SNAKE_CASE :Any = PipelineTesterMixin.required_optional_params - {"""latents"""} def _a ( self ) -> Union[str, Any]: """simple docstring""" return self._get_dummy_components() def _a ( self , _a , _a=0 ) -> int: """simple docstring""" if str(_a ).startswith("""mps""" ): SCREAMING_SNAKE_CASE__ : List[Any] = torch.manual_seed(_a ) else: SCREAMING_SNAKE_CASE__ : List[Any] = torch.Generator(device=_a ).manual_seed(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = { """prompt""": """A painting of a squirrel eating a burger""", """generator""": generator, """num_inference_steps""": 2, """output_type""": """numpy""", } return inputs def _a ( self ) -> str: """simple docstring""" self._test_save_load_optional_components() @unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" ) def _a ( self ) -> List[Any]: """simple docstring""" super().test_save_load_floataa(expected_max_diff=1E-1 ) def _a ( self ) -> Any: """simple docstring""" self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 ) def _a ( self ) -> Optional[int]: """simple docstring""" self._test_save_load_local() def _a ( self ) -> Optional[int]: """simple docstring""" self._test_inference_batch_single_identical( expected_max_diff=1E-2 , ) @unittest.skipIf( torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , ) def _a ( self ) -> List[str]: """simple docstring""" self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 ) @slow @require_torch_gpu class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> int: """simple docstring""" super().tearDown() gc.collect() torch.cuda.empty_cache() def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa ) SCREAMING_SNAKE_CASE__ : str = IFSuperResolutionPipeline.from_pretrained( """DeepFloyd/IF-II-L-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa , text_encoder=_a , tokenizer=_a ) # pre compute text embeddings and remove T5 to save memory pipe_a.text_encoder.to("""cuda""" ) SCREAMING_SNAKE_CASE__ : Dict = pipe_a.encode_prompt("""anime turtle""" , device="""cuda""" ) del pipe_a.tokenizer del pipe_a.text_encoder gc.collect() SCREAMING_SNAKE_CASE__ : List[str] = None SCREAMING_SNAKE_CASE__ : List[Any] = None pipe_a.enable_model_cpu_offload() pipe_a.enable_model_cpu_offload() pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() ) pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() ) self._test_if(_a , _a , _a , _a ) pipe_a.remove_all_hooks() pipe_a.remove_all_hooks() # img2img SCREAMING_SNAKE_CASE__ : List[str] = IFImgaImgPipeline(**pipe_a.components ) SCREAMING_SNAKE_CASE__ : int = IFImgaImgSuperResolutionPipeline(**pipe_a.components ) pipe_a.enable_model_cpu_offload() pipe_a.enable_model_cpu_offload() pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() ) pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() ) self._test_if_imgaimg(_a , _a , _a , _a ) pipe_a.remove_all_hooks() pipe_a.remove_all_hooks() # inpainting SCREAMING_SNAKE_CASE__ : Dict = IFInpaintingPipeline(**pipe_a.components ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = IFInpaintingSuperResolutionPipeline(**pipe_a.components ) pipe_a.enable_model_cpu_offload() pipe_a.enable_model_cpu_offload() pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() ) pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() ) self._test_if_inpainting(_a , _a , _a , _a ) def _a ( self , _a , _a , _a , _a ) -> str: """simple docstring""" _start_torch_memory_measurement() SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Optional[int] = pipe_a( prompt_embeds=_a , negative_prompt_embeds=_a , num_inference_steps=2 , generator=_a , output_type="""np""" , ) SCREAMING_SNAKE_CASE__ : Optional[int] = output.images[0] assert image.shape == (64, 64, 3) SCREAMING_SNAKE_CASE__ : List[str] = torch.cuda.max_memory_allocated() assert mem_bytes < 13 * 10**9 SCREAMING_SNAKE_CASE__ : Tuple = load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" ) assert_mean_pixel_difference(_a , _a ) # pipeline 2 _start_torch_memory_measurement() SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Tuple = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = pipe_a( prompt_embeds=_a , negative_prompt_embeds=_a , image=_a , generator=_a , num_inference_steps=2 , output_type="""np""" , ) SCREAMING_SNAKE_CASE__ : List[str] = output.images[0] assert image.shape == (256, 256, 3) SCREAMING_SNAKE_CASE__ : List[str] = torch.cuda.max_memory_allocated() assert mem_bytes < 4 * 10**9 SCREAMING_SNAKE_CASE__ : Dict = load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""" ) assert_mean_pixel_difference(_a , _a ) def _a ( self , _a , _a , _a , _a ) -> Optional[Any]: """simple docstring""" _start_torch_memory_measurement() SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.Generator(device="""cpu""" ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Dict = pipe_a( prompt_embeds=_a , negative_prompt_embeds=_a , image=_a , num_inference_steps=2 , generator=_a , output_type="""np""" , ) SCREAMING_SNAKE_CASE__ : List[str] = output.images[0] assert image.shape == (64, 64, 3) SCREAMING_SNAKE_CASE__ : Tuple = torch.cuda.max_memory_allocated() assert mem_bytes < 10 * 10**9 SCREAMING_SNAKE_CASE__ : Union[str, Any] = load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" ) assert_mean_pixel_difference(_a , _a ) # pipeline 2 _start_torch_memory_measurement() SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Generator(device="""cpu""" ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : List[str] = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_a ) SCREAMING_SNAKE_CASE__ : str = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_a ) SCREAMING_SNAKE_CASE__ : List[str] = pipe_a( prompt_embeds=_a , negative_prompt_embeds=_a , image=_a , original_image=_a , generator=_a , num_inference_steps=2 , output_type="""np""" , ) SCREAMING_SNAKE_CASE__ : Any = output.images[0] assert image.shape == (256, 256, 3) SCREAMING_SNAKE_CASE__ : int = torch.cuda.max_memory_allocated() assert mem_bytes < 4 * 10**9 SCREAMING_SNAKE_CASE__ : int = load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy""" ) assert_mean_pixel_difference(_a , _a ) def _a ( self , _a , _a , _a , _a ) -> Union[str, Any]: """simple docstring""" _start_torch_memory_measurement() SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(_a ) SCREAMING_SNAKE_CASE__ : Any = torch.Generator(device="""cpu""" ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Dict = pipe_a( prompt_embeds=_a , negative_prompt_embeds=_a , image=_a , mask_image=_a , num_inference_steps=2 , generator=_a , output_type="""np""" , ) SCREAMING_SNAKE_CASE__ : List[Any] = output.images[0] assert image.shape == (64, 64, 3) SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cuda.max_memory_allocated() assert mem_bytes < 10 * 10**9 SCREAMING_SNAKE_CASE__ : Optional[Any] = load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" ) assert_mean_pixel_difference(_a , _a ) # pipeline 2 _start_torch_memory_measurement() SCREAMING_SNAKE_CASE__ : int = torch.Generator(device="""cpu""" ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : int = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = pipe_a( prompt_embeds=_a , negative_prompt_embeds=_a , image=_a , mask_image=_a , original_image=_a , generator=_a , num_inference_steps=2 , output_type="""np""" , ) SCREAMING_SNAKE_CASE__ : Any = output.images[0] assert image.shape == (256, 256, 3) SCREAMING_SNAKE_CASE__ : List[str] = torch.cuda.max_memory_allocated() assert mem_bytes < 4 * 10**9 SCREAMING_SNAKE_CASE__ : Dict = load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy""" ) assert_mean_pixel_difference(_a , _a ) def _lowercase ( ) -> Optional[int]: torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats()
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"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool: SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1 SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1 # dp is a 2d matrix where dp[i][j] denotes whether prefix string of # length i of input_string matches with prefix string of length j of # given pattern. # "dp" stands for dynamic programming. SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )] # since string of zero length match pattern of zero length SCREAMING_SNAKE_CASE__ : Dict = 1 # since pattern of zero length will never match with string of non-zero length for i in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 # since string of zero length will match with pattern where there # is at least one * alternatively for j in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0 # now using bottom-up approach to find for all remaining lengths for i in range(1 , __lowerCAmelCase ): for j in range(1 , __lowerCAmelCase ): if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".": SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1] elif pattern[j - 1] == "*": if dp[i][j - 2] == 1: SCREAMING_SNAKE_CASE__ : List[str] = 1 elif pattern[j - 2] in (input_string[i - 1], "."): SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j] else: SCREAMING_SNAKE_CASE__ : Optional[int] = 0 else: SCREAMING_SNAKE_CASE__ : Dict = 0 return bool(dp[-1][-1] ) if __name__ == "__main__": import doctest doctest.testmod() # inputing the strings # input_string = input("input a string :") # pattern = input("input a pattern :") a :Any = "aab" a :Optional[Any] = "c*a*b" # using function to check whether given string matches the given pattern if match_pattern(input_string, pattern): print(f'{input_string} matches the given pattern {pattern}') else: print(f'{input_string} does not match with the given pattern {pattern}')
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"""simple docstring""" import os import sys import unittest a :str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, "utils")) import check_dummies # noqa: E402 from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402 # Align TRANSFORMERS_PATH in check_dummies with the current path a :int = os.path.join(git_repo_path, "src", "diffusers") class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = find_backend(""" if not is_torch_available():""" ) self.assertEqual(_a , """torch""" ) # backend_with_underscore = find_backend(" if not is_tensorflow_text_available():") # self.assertEqual(backend_with_underscore, "tensorflow_text") SCREAMING_SNAKE_CASE__ : Union[str, Any] = find_backend(""" if not (is_torch_available() and is_transformers_available()):""" ) self.assertEqual(_a , """torch_and_transformers""" ) # double_backend_with_underscore = find_backend( # " if not (is_sentencepiece_available() and is_tensorflow_text_available()):" # ) # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text") SCREAMING_SNAKE_CASE__ : Dict = find_backend( """ if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""" ) self.assertEqual(_a , """torch_and_transformers_and_onnx""" ) def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = read_init() # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects self.assertIn("""torch""" , _a ) self.assertIn("""torch_and_transformers""" , _a ) self.assertIn("""flax_and_transformers""" , _a ) self.assertIn("""torch_and_transformers_and_onnx""" , _a ) # Likewise, we can't assert on the exact content of a key self.assertIn("""UNet2DModel""" , objects["""torch"""] ) self.assertIn("""FlaxUNet2DConditionModel""" , objects["""flax"""] ) self.assertIn("""StableDiffusionPipeline""" , objects["""torch_and_transformers"""] ) self.assertIn("""FlaxStableDiffusionPipeline""" , objects["""flax_and_transformers"""] ) self.assertIn("""LMSDiscreteScheduler""" , objects["""torch_and_scipy"""] ) self.assertIn("""OnnxStableDiffusionPipeline""" , objects["""torch_and_transformers_and_onnx"""] ) def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = create_dummy_object("""CONSTANT""" , """'torch'""" ) self.assertEqual(_a , """\nCONSTANT = None\n""" ) SCREAMING_SNAKE_CASE__ : str = create_dummy_object("""function""" , """'torch'""" ) self.assertEqual( _a , """\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" ) SCREAMING_SNAKE_CASE__ : Any = """ class FakeClass(metaclass=DummyObject): _backends = 'torch' def __init__(self, *args, **kwargs): requires_backends(self, 'torch') @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, 'torch') @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, 'torch') """ SCREAMING_SNAKE_CASE__ : Dict = create_dummy_object("""FakeClass""" , """'torch'""" ) self.assertEqual(_a , _a ) def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = """# This file is autogenerated by the command `make fix-copies`, do not edit. from ..utils import DummyObject, requires_backends CONSTANT = None def function(*args, **kwargs): requires_backends(function, [\"torch\"]) class FakeClass(metaclass=DummyObject): _backends = [\"torch\"] def __init__(self, *args, **kwargs): requires_backends(self, [\"torch\"]) @classmethod def from_config(cls, *args, **kwargs): requires_backends(cls, [\"torch\"]) @classmethod def from_pretrained(cls, *args, **kwargs): requires_backends(cls, [\"torch\"]) """ SCREAMING_SNAKE_CASE__ : Any = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} ) self.assertEqual(dummy_files["""torch"""] , _a )
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"""simple docstring""" from math import sqrt def _lowercase ( __lowerCAmelCase ) -> bool: if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = 0 SCREAMING_SNAKE_CASE__ : Tuple = 1 while count != nth and number < 3: number += 1 if is_prime(__lowerCAmelCase ): count += 1 while count != nth: number += 2 if is_prime(__lowerCAmelCase ): count += 1 return number if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" import argparse import os import evaluate import torch from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed from accelerate import Accelerator, DistributedType ######################################################################## # This is a fully working simple example to use Accelerate # and perform gradient accumulation # # This example trains a Bert base model on GLUE MRPC # in any of the following settings (with the same script): # - single CPU or single GPU # - multi GPUS (using PyTorch distributed mode) # - (multi) TPUs # - fp16 (mixed-precision) or fp32 (normal precision) # # To run it in each of these various modes, follow the instructions # in the readme for examples: # https://github.com/huggingface/accelerate/tree/main/examples # ######################################################################## a :int = 16 a :List[str] = 32 def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = 16 ) -> Dict: SCREAMING_SNAKE_CASE__ : Any = AutoTokenizer.from_pretrained("""bert-base-cased""" ) SCREAMING_SNAKE_CASE__ : List[Any] = load_dataset("""glue""" , """mrpc""" ) def tokenize_function(__lowerCAmelCase ): # max_length=None => use the model max length (it's actually the default) SCREAMING_SNAKE_CASE__ : int = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=__lowerCAmelCase , max_length=__lowerCAmelCase ) return outputs # Apply the method we just defined to all the examples in all the splits of the dataset # starting with the main process first: with accelerator.main_process_first(): SCREAMING_SNAKE_CASE__ : Dict = datasets.map( __lowerCAmelCase , batched=__lowerCAmelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , ) # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library SCREAMING_SNAKE_CASE__ : str = tokenized_datasets.rename_column("""label""" , """labels""" ) def collate_fn(__lowerCAmelCase ): # On TPU it's best to pad everything to the same length or training will be very slow. SCREAMING_SNAKE_CASE__ : Tuple = 128 if accelerator.distributed_type == DistributedType.TPU else None # When using mixed precision we want round multiples of 8/16 if accelerator.mixed_precision == "fp8": SCREAMING_SNAKE_CASE__ : int = 16 elif accelerator.mixed_precision != "no": SCREAMING_SNAKE_CASE__ : Optional[int] = 8 else: SCREAMING_SNAKE_CASE__ : Optional[int] = None return tokenizer.pad( __lowerCAmelCase , padding="""longest""" , max_length=__lowerCAmelCase , pad_to_multiple_of=__lowerCAmelCase , return_tensors="""pt""" , ) # Instantiate dataloaders. SCREAMING_SNAKE_CASE__ : Union[str, Any] = DataLoader( tokenized_datasets["""train"""] , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = DataLoader( tokenized_datasets["""validation"""] , shuffle=__lowerCAmelCase , collate_fn=__lowerCAmelCase , batch_size=__lowerCAmelCase ) return train_dataloader, eval_dataloader # For testing only if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1": from accelerate.test_utils.training import mocked_dataloaders a :Union[str, Any] = mocked_dataloaders # noqa: F811 def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[str]: # For testing only if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , __lowerCAmelCase ) == "1": SCREAMING_SNAKE_CASE__ : str = 2 # New Code # SCREAMING_SNAKE_CASE__ : Optional[Any] = int(args.gradient_accumulation_steps ) # Initialize accelerator SCREAMING_SNAKE_CASE__ : List[str] = Accelerator( cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=__lowerCAmelCase ) if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1: raise NotImplementedError( """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`""" ) # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs SCREAMING_SNAKE_CASE__ : int = config["""lr"""] SCREAMING_SNAKE_CASE__ : Union[str, Any] = int(config["""num_epochs"""] ) SCREAMING_SNAKE_CASE__ : int = int(config["""seed"""] ) SCREAMING_SNAKE_CASE__ : List[str] = int(config["""batch_size"""] ) SCREAMING_SNAKE_CASE__ : Optional[int] = evaluate.load("""glue""" , """mrpc""" ) set_seed(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = get_dataloaders(__lowerCAmelCase , __lowerCAmelCase ) # Instantiate the model (we build the model here so that the seed also control new weights initialization) SCREAMING_SNAKE_CASE__ : int = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=__lowerCAmelCase ) # We could avoid this line since the accelerator is set with `device_placement=True` (default value). # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that). SCREAMING_SNAKE_CASE__ : Optional[Any] = model.to(accelerator.device ) # Instantiate optimizer SCREAMING_SNAKE_CASE__ : List[str] = AdamW(params=model.parameters() , lr=__lowerCAmelCase ) # Instantiate scheduler SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_linear_schedule_with_warmup( optimizer=__lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(__lowerCAmelCase ) * num_epochs) , ) # Prepare everything # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the # prepare method. SCREAMING_SNAKE_CASE__ : List[Any] = accelerator.prepare( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) # Now we train the model for epoch in range(__lowerCAmelCase ): model.train() for step, batch in enumerate(__lowerCAmelCase ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) # New code # # We use the new `accumulate` context manager to perform gradient accumulation # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests. with accelerator.accumulate(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Optional[int] = model(**__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = output.loss accelerator.backward(__lowerCAmelCase ) optimizer.step() lr_scheduler.step() optimizer.zero_grad() model.eval() for step, batch in enumerate(__lowerCAmelCase ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) with torch.no_grad(): SCREAMING_SNAKE_CASE__ : Any = model(**__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = outputs.logits.argmax(dim=-1 ) SCREAMING_SNAKE_CASE__ : Tuple = accelerator.gather_for_metrics((predictions, batch["""labels"""]) ) metric.add_batch( predictions=__lowerCAmelCase , references=__lowerCAmelCase , ) SCREAMING_SNAKE_CASE__ : List[str] = metric.compute() # Use accelerator.print to print only on the main process. accelerator.print(F'''epoch {epoch}:''' , __lowerCAmelCase ) def _lowercase ( ) -> Tuple: SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser(description="""Simple example of training script.""" ) parser.add_argument( """--mixed_precision""" , type=__lowerCAmelCase , default=__lowerCAmelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose""" """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.""" """and an Nvidia Ampere GPU.""" , ) # New Code # parser.add_argument( """--gradient_accumulation_steps""" , type=__lowerCAmelCase , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , ) parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" ) SCREAMING_SNAKE_CASE__ : List[Any] = parser.parse_args() SCREAMING_SNAKE_CASE__ : List[str] = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16} training_function(__lowerCAmelCase , __lowerCAmelCase ) if __name__ == "__main__": main()
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"""simple docstring""" class __a : '''simple docstring''' def __init__( self , _a , _a , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = value SCREAMING_SNAKE_CASE__ : List[Any] = weight def __repr__( self ) -> List[Any]: """simple docstring""" return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})''' def _a ( self ) -> Dict: """simple docstring""" return self.value def _a ( self ) -> int: """simple docstring""" return self.name def _a ( self ) -> Optional[Any]: """simple docstring""" return self.weight def _a ( self ) -> Dict: """simple docstring""" return self.value / self.weight def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict: SCREAMING_SNAKE_CASE__ : Any = [] for i in range(len(__lowerCAmelCase ) ): menu.append(Things(name[i] , value[i] , weight[i] ) ) return menu def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0 for i in range(len(__lowerCAmelCase ) ): if (total_cost + items_copy[i].get_weight()) <= max_cost: result.append(items_copy[i] ) total_cost += items_copy[i].get_weight() total_value += items_copy[i].get_value() return (result, total_value) def _lowercase ( ) -> List[str]: pass if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from __future__ import annotations def _lowercase ( __lowerCAmelCase ) -> bool: return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from tokenizers import processors from ...tokenization_utils import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_nllb import NllbTokenizer else: a :Optional[int] = None a :Optional[Any] = logging.get_logger(__name__) a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} a :Union[str, Any] = { "vocab_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model" ), }, "tokenizer_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json" ), }, } a :Any = { "facebook/nllb-large-en-ro": 1_024, "facebook/nllb-200-distilled-600M": 1_024, } # fmt: off a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"] class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""] _SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer _SCREAMING_SNAKE_CASE :List[int] = [] _SCREAMING_SNAKE_CASE :List[int] = [] def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour super().__init__( vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy() if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} ) SCREAMING_SNAKE_CASE__ : List[str] = { lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES } SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn""" SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang ) SCREAMING_SNAKE_CASE__ : Dict = tgt_lang self.set_src_lang_special_tokens(self._src_lang ) @property def _a ( self ) -> str: """simple docstring""" return self._src_lang @src_lang.setter def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" if token_ids_a is None: return self.prefix_tokens + token_ids_a + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple: """simple docstring""" if src_lang is None or tgt_lang is None: raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" ) SCREAMING_SNAKE_CASE__ : Dict = src_lang SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id return inputs def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = src_lang SCREAMING_SNAKE_CASE__ : Dict = tgt_lang return super().prepare_seqaseq_batch(_a , _a , **_a ) def _a ( self ) -> Optional[Any]: """simple docstring""" return self.set_src_lang_special_tokens(self.src_lang ) def _a ( self ) -> str: """simple docstring""" return self.set_tgt_lang_special_tokens(self.tgt_lang ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : List[Any] = [] SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' ) return SCREAMING_SNAKE_CASE__ : Dict = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
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0
"""simple docstring""" import argparse import logging import os import sys import numpy as np import onnxruntime import torch from bart_onnx.generation_onnx import BARTBeamSearchGenerator from bart_onnx.reduce_onnx_size import remove_dup_initializers import transformers from transformers import BartForConditionalGeneration, BartTokenizer logging.basicConfig( format="%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=os.environ.get("LOGLEVEL", "INFO").upper(), stream=sys.stdout, ) a :Dict = logging.getLogger(__name__) a :int = {"facebook/bart-base": BartForConditionalGeneration} a :List[str] = {"facebook/bart-base": BartTokenizer} def _lowercase ( ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ : Optional[int] = argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph.""" ) parser.add_argument( """--validation_file""" , type=__lowerCAmelCase , default=__lowerCAmelCase , help="""A csv or a json file containing the validation data.""" ) parser.add_argument( """--max_length""" , type=__lowerCAmelCase , default=5 , help="""The maximum total input sequence length after tokenization.""" , ) parser.add_argument( """--num_beams""" , type=__lowerCAmelCase , default=__lowerCAmelCase , help=( """Number of beams to use for evaluation. This argument will be """ """passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.""" ) , ) parser.add_argument( """--model_name_or_path""" , type=__lowerCAmelCase , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=__lowerCAmelCase , ) parser.add_argument( """--config_name""" , type=__lowerCAmelCase , default=__lowerCAmelCase , help="""Pretrained config name or path if not the same as model_name""" , ) parser.add_argument( """--device""" , type=__lowerCAmelCase , default="""cpu""" , help="""Device where the model will be run""" , ) parser.add_argument("""--output_file_path""" , type=__lowerCAmelCase , default=__lowerCAmelCase , help="""Where to store the final ONNX file.""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = parser.parse_args() return args def _lowercase ( __lowerCAmelCase , __lowerCAmelCase="cpu" ) -> Optional[Any]: SCREAMING_SNAKE_CASE__ : List[Any] = model_dict[model_name].from_pretrained(__lowerCAmelCase ).to(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase ) if model_name in ["facebook/bart-base"]: SCREAMING_SNAKE_CASE__ : Optional[Any] = 0 SCREAMING_SNAKE_CASE__ : Optional[Any] = None SCREAMING_SNAKE_CASE__ : Optional[int] = 0 return huggingface_model, tokenizer def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]: model.eval() SCREAMING_SNAKE_CASE__ : Dict = None SCREAMING_SNAKE_CASE__ : str = torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase ) ) with torch.no_grad(): SCREAMING_SNAKE_CASE__ : Optional[int] = """My friends are cool but they eat too many carbs.""" SCREAMING_SNAKE_CASE__ : List[str] = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1024 , return_tensors="""pt""" ).to(model.device ) SCREAMING_SNAKE_CASE__ : int = model.generate( inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , num_beams=__lowerCAmelCase , max_length=__lowerCAmelCase , early_stopping=__lowerCAmelCase , decoder_start_token_id=model.config.decoder_start_token_id , ) torch.onnx.export( __lowerCAmelCase , ( inputs["""input_ids"""], inputs["""attention_mask"""], num_beams, max_length, model.config.decoder_start_token_id, ) , __lowerCAmelCase , opset_version=14 , input_names=["""input_ids""", """attention_mask""", """num_beams""", """max_length""", """decoder_start_token_id"""] , output_names=["""output_ids"""] , dynamic_axes={ """input_ids""": {0: """batch""", 1: """seq"""}, """output_ids""": {0: """batch""", 1: """seq_out"""}, } , example_outputs=__lowerCAmelCase , ) logger.info("""Model exported to {}""".format(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Optional[int] = remove_dup_initializers(os.path.abspath(__lowerCAmelCase ) ) logger.info("""Deduplicated and optimized model written to {}""".format(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Dict = onnxruntime.InferenceSession(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = ort_sess.run( __lowerCAmelCase , { """input_ids""": inputs["""input_ids"""].cpu().numpy(), """attention_mask""": inputs["""attention_mask"""].cpu().numpy(), """num_beams""": np.array(__lowerCAmelCase ), """max_length""": np.array(__lowerCAmelCase ), """decoder_start_token_id""": np.array(model.config.decoder_start_token_id ), } , ) np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1E-3 , atol=1E-3 ) logger.info("""Model outputs from torch and ONNX Runtime are similar.""" ) logger.info("""Success.""" ) def _lowercase ( ) -> Tuple: SCREAMING_SNAKE_CASE__ : Union[str, Any] = parse_args() SCREAMING_SNAKE_CASE__ : List[Any] = 5 SCREAMING_SNAKE_CASE__ : Dict = 4 # Make one log on every process with the configuration for debugging. logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , ) logger.setLevel(logging.INFO ) transformers.utils.logging.set_verbosity_error() SCREAMING_SNAKE_CASE__ : Tuple = torch.device(args.device ) SCREAMING_SNAKE_CASE__ : List[str] = load_model_tokenizer(args.model_name_or_path , __lowerCAmelCase ) if model.config.decoder_start_token_id is None: raise ValueError("""Make sure that `config.decoder_start_token_id` is correctly defined""" ) model.to(__lowerCAmelCase ) if args.max_length: SCREAMING_SNAKE_CASE__ : Dict = args.max_length if args.num_beams: SCREAMING_SNAKE_CASE__ : Union[str, Any] = args.num_beams if args.output_file_path: SCREAMING_SNAKE_CASE__ : List[Any] = args.output_file_path else: SCREAMING_SNAKE_CASE__ : Optional[Any] = """BART.onnx""" logger.info("""Exporting model to ONNX""" ) export_and_validate_model(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) if __name__ == "__main__": main()
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"""simple docstring""" # Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #################################################################################################### # # Note: If when running this conversion script you're getting an exception: # ModuleNotFoundError: No module named 'megatron.model.enums' # you need to tell python where to find the clone of Megatron-LM, e.g.: # # cd /tmp # git clone https://github.com/NVIDIA/Megatron-LM # PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ... # # if you already have it cloned elsewhere, simply adjust the path to the existing path # # If the training was done using a Megatron-LM fork, e.g., # https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one # in your path, i.e., /path/to/Megatron-DeepSpeed/ # import argparse import os import re import zipfile import torch from transformers import AutoTokenizer, GPTaConfig def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any: # Format the message. if name is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None else: SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}""" SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase ) # Print and recurse (if needed). if isinstance(__lowerCAmelCase , __lowerCAmelCase ): if msg is not None: print(__lowerCAmelCase ) for k in val.keys(): recursive_print(__lowerCAmelCase , val[k] , spaces + 2 ) elif isinstance(__lowerCAmelCase , torch.Tensor ): print(__lowerCAmelCase , """:""" , val.size() ) else: print(__lowerCAmelCase , """:""" , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] # for compatibility with later versions of NVIDIA Megatron-LM. # The inverse operation is performed inside Megatron-LM to read checkpoints: # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 # If param is the weight tensor of the self-attention block, the returned tensor # will have to be transposed one more time to be read by HuggingFace GPT2. SCREAMING_SNAKE_CASE__ : Tuple = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:] SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 ) SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:] SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous() SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase ) return param def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: # The converted output model. SCREAMING_SNAKE_CASE__ : List[str] = {} # old versions did not store training args SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase ) if ds_args is not None: # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint # from pprint import pprint # pprint(vars(ds_args)) SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size # pprint(config) # The number of heads. SCREAMING_SNAKE_CASE__ : List[str] = config.n_head # The hidden_size per head. SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head # Megatron-LM checkpoint version if "checkpoint_version" in input_state_dict.keys(): SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""] else: SCREAMING_SNAKE_CASE__ : Tuple = 0.0 # The model. SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""] # The language model. SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""] # The embeddings. SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""] # The word embeddings. SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""] # Truncate the embedding table to vocab_size rows. SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :] SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings # The position embeddings. SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""] # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size] SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 ) if n_positions != config.n_positions: raise ValueError( F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' ) # Store the position embeddings. SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings # The transformer. SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""] # The regex to extract layer names. SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" ) # The simple map of names for "automated" rules. SCREAMING_SNAKE_CASE__ : Optional[int] = { """attention.dense""": """.attn.c_proj.""", """self_attention.dense""": """.attn.c_proj.""", """mlp.dense_h_to_4h""": """.mlp.c_fc.""", """mlp.dense_4h_to_h""": """.mlp.c_proj.""", } # Extract the layers. for key, val in transformer.items(): # Match the name. SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase ) # Stop if that's not a layer if m is None: break # The index of the layer. SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) ) # The name of the operation. SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 ) # Is it a weight or a bias? SCREAMING_SNAKE_CASE__ : str = m.group(3 ) # The name of the layer. SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}''' # For layernorm(s), simply store the layer norm. if op_name.endswith("""layernorm""" ): SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2""" SCREAMING_SNAKE_CASE__ : List[Any] = val # Transpose the QKV matrix. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "weight": # Insert a tensor of 1x1xDxD bias. SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view( 1 , 1 , __lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = causal_mask # Insert a "dummy" tensor for masked_bias. SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa ) SCREAMING_SNAKE_CASE__ : List[str] = masked_bias SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D. SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous() # Store. SCREAMING_SNAKE_CASE__ : Dict = out_val # Transpose the bias. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Store. No change of shape. SCREAMING_SNAKE_CASE__ : str = out_val # Transpose the weights. elif weight_or_bias == "weight": SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 ) # Copy the bias. elif weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : Dict = val # DEBUG. assert config.n_layer == layer_idx + 1 # The final layernorm. SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""] SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""] # For LM head, transformers' wants the matrix to weight embeddings. SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings # It should be done! return output_state_dict def _lowercase ( ) -> List[Any]: # Create the argument parser. SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser() parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" ) parser.add_argument( """path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , ) parser.add_argument( """--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , ) SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args() # Extract the basename. SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint ) # Load the model. # the .zip is very optional, let's keep it for backward compatibility print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' ) if args.path_to_checkpoint.endswith(""".zip""" ): with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint: with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict: SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" ) else: SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" ) SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase ) # Read the config, or default to the model released by NVIDIA. if args.config_file == "": if ds_args is not None: if ds_args.bias_gelu_fusion: SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast""" elif ds_args.openai_gelu: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new""" else: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu""" else: # in the very early days this used to be "gelu_new" SCREAMING_SNAKE_CASE__ : Any = """gelu_new""" # Spell out all parameters in case the defaults change. SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig( vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , ) else: SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file ) SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""] # Convert. print("""Converting""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) # Print the structure of converted state dict. if args.print_checkpoint_structure: recursive_print(__lowerCAmelCase , __lowerCAmelCase ) # Add tokenizer class info to config # see https://github.com/huggingface/transformers/issues/13906) if ds_args is not None: SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type if tokenizer_type == "GPT2BPETokenizer": SCREAMING_SNAKE_CASE__ : Any = """gpt2""" elif tokenizer_type == "PretrainedFromHF": SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path else: raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__ SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class # Store the config to file. print("""Saving config""" ) config.save_pretrained(__lowerCAmelCase ) # Save tokenizer based on args print(F'''Adding {tokenizer_class} tokenizer files''' ) tokenizer.save_pretrained(__lowerCAmelCase ) # Store the state_dict to file. SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" ) print(F'''Saving checkpoint to "{output_checkpoint_file}"''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) #################################################################################################### if __name__ == "__main__": main() ####################################################################################################
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"""simple docstring""" import argparse import os import re a :Union[str, Any] = "src/diffusers" # Pattern that looks at the indentation in a line. a :int = re.compile(r"^(\s*)\S") # Pattern that matches `"key":" and puts `key` in group 0. a :Tuple = re.compile(r"^\s*\"([^\"]+)\":") # Pattern that matches `_import_structure["key"]` and puts `key` in group 0. a :Union[str, Any] = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]") # Pattern that matches `"key",` and puts `key` in group 0. a :Any = re.compile(r"^\s*\"([^\"]+)\",\s*$") # Pattern that matches any `[stuff]` and puts `stuff` in group 0. a :Any = re.compile(r"\[([^\]]+)\]") def _lowercase ( __lowerCAmelCase ) -> Optional[int]: SCREAMING_SNAKE_CASE__ : Union[str, Any] = _re_indent.search(__lowerCAmelCase ) return "" if search is None else search.groups()[0] def _lowercase ( __lowerCAmelCase , __lowerCAmelCase="" , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> str: SCREAMING_SNAKE_CASE__ : int = 0 SCREAMING_SNAKE_CASE__ : Optional[Any] = code.split("""\n""" ) if start_prompt is not None: while not lines[index].startswith(__lowerCAmelCase ): index += 1 SCREAMING_SNAKE_CASE__ : Tuple = ["""\n""".join(lines[:index] )] else: SCREAMING_SNAKE_CASE__ : Dict = [] # We split into blocks until we get to the `end_prompt` (or the end of the block). SCREAMING_SNAKE_CASE__ : Union[str, Any] = [lines[index]] index += 1 while index < len(__lowerCAmelCase ) and (end_prompt is None or not lines[index].startswith(__lowerCAmelCase )): if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level: if len(__lowerCAmelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ): current_block.append(lines[index] ) blocks.append("""\n""".join(__lowerCAmelCase ) ) if index < len(__lowerCAmelCase ) - 1: SCREAMING_SNAKE_CASE__ : Optional[Any] = [lines[index + 1]] index += 1 else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = [] else: blocks.append("""\n""".join(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Any = [lines[index]] else: current_block.append(lines[index] ) index += 1 # Adds current block if it's nonempty. if len(__lowerCAmelCase ) > 0: blocks.append("""\n""".join(__lowerCAmelCase ) ) # Add final block after end_prompt if provided. if end_prompt is not None and index < len(__lowerCAmelCase ): blocks.append("""\n""".join(lines[index:] ) ) return blocks def _lowercase ( __lowerCAmelCase ) -> List[str]: def _inner(__lowerCAmelCase ): return key(__lowerCAmelCase ).lower().replace("""_""" , """""" ) return _inner def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None ) -> List[str]: # If no key is provided, we use a noop. def noop(__lowerCAmelCase ): return x if key is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = noop # Constants are all uppercase, they go first. SCREAMING_SNAKE_CASE__ : Optional[int] = [obj for obj in objects if key(__lowerCAmelCase ).isupper()] # Classes are not all uppercase but start with a capital, they go second. SCREAMING_SNAKE_CASE__ : Any = [obj for obj in objects if key(__lowerCAmelCase )[0].isupper() and not key(__lowerCAmelCase ).isupper()] # Functions begin with a lowercase, they go last. SCREAMING_SNAKE_CASE__ : List[str] = [obj for obj in objects if not key(__lowerCAmelCase )[0].isupper()] SCREAMING_SNAKE_CASE__ : Dict = ignore_underscore(__lowerCAmelCase ) return sorted(__lowerCAmelCase , key=__lowerCAmelCase ) + sorted(__lowerCAmelCase , key=__lowerCAmelCase ) + sorted(__lowerCAmelCase , key=__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> List[Any]: # This inner function sort imports between [ ]. def _replace(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : int = match.groups()[0] if "," not in imports: return F'''[{imports}]''' SCREAMING_SNAKE_CASE__ : Optional[int] = [part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: SCREAMING_SNAKE_CASE__ : Any = keys[:-1] return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(__lowerCAmelCase )] ) + "]" SCREAMING_SNAKE_CASE__ : Optional[Any] = import_statement.split("""\n""" ) if len(__lowerCAmelCase ) > 3: # Here we have to sort internal imports that are on several lines (one per name): # key: [ # "object1", # "object2", # ... # ] # We may have to ignore one or two lines on each side. SCREAMING_SNAKE_CASE__ : List[str] = 2 if lines[1].strip() == """[""" else 1 SCREAMING_SNAKE_CASE__ : int = [(i, _re_strip_line.search(__lowerCAmelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )] SCREAMING_SNAKE_CASE__ : Any = sort_objects(__lowerCAmelCase , key=lambda __lowerCAmelCase : x[1] ) SCREAMING_SNAKE_CASE__ : Tuple = [lines[x[0] + idx] for x in sorted_indices] return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] ) elif len(__lowerCAmelCase ) == 3: # Here we have to sort internal imports that are on one separate line: # key: [ # "object1", "object2", ... # ] if _re_bracket_content.search(lines[1] ) is not None: SCREAMING_SNAKE_CASE__ : int = _re_bracket_content.sub(_replace , lines[1] ) else: SCREAMING_SNAKE_CASE__ : List[str] = [part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )] # We will have a final empty element if the line finished with a comma. if len(keys[-1] ) == 0: SCREAMING_SNAKE_CASE__ : int = keys[:-1] SCREAMING_SNAKE_CASE__ : Any = get_indent(lines[1] ) + """, """.join([F'''"{k}"''' for k in sort_objects(__lowerCAmelCase )] ) return "\n".join(__lowerCAmelCase ) else: # Finally we have to deal with imports fitting on one line SCREAMING_SNAKE_CASE__ : List[Any] = _re_bracket_content.sub(_replace , __lowerCAmelCase ) return import_statement def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=True ) -> int: with open(__lowerCAmelCase , """r""" ) as f: SCREAMING_SNAKE_CASE__ : Optional[Any] = f.read() if "_import_structure" not in code: return # Blocks of indent level 0 SCREAMING_SNAKE_CASE__ : Optional[Any] = split_code_in_indented_blocks( __lowerCAmelCase , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" ) # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt). for block_idx in range(1 , len(__lowerCAmelCase ) - 1 ): # Check if the block contains some `_import_structure`s thingy to sort. SCREAMING_SNAKE_CASE__ : Dict = main_blocks[block_idx] SCREAMING_SNAKE_CASE__ : Tuple = block.split("""\n""" ) # Get to the start of the imports. SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 while line_idx < len(__lowerCAmelCase ) and "_import_structure" not in block_lines[line_idx]: # Skip dummy import blocks if "import dummy" in block_lines[line_idx]: SCREAMING_SNAKE_CASE__ : List[str] = len(__lowerCAmelCase ) else: line_idx += 1 if line_idx >= len(__lowerCAmelCase ): continue # Ignore beginning and last line: they don't contain anything. SCREAMING_SNAKE_CASE__ : str = """\n""".join(block_lines[line_idx:-1] ) SCREAMING_SNAKE_CASE__ : int = get_indent(block_lines[1] ) # Slit the internal block into blocks of indent level 1. SCREAMING_SNAKE_CASE__ : Optional[Any] = split_code_in_indented_blocks(__lowerCAmelCase , indent_level=__lowerCAmelCase ) # We have two categories of import key: list or _import_structure[key].append/extend SCREAMING_SNAKE_CASE__ : Any = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key # Grab the keys, but there is a trap: some lines are empty or just comments. SCREAMING_SNAKE_CASE__ : Optional[Any] = [(pattern.search(__lowerCAmelCase ).groups()[0] if pattern.search(__lowerCAmelCase ) is not None else None) for b in internal_blocks] # We only sort the lines with a key. SCREAMING_SNAKE_CASE__ : Any = [(i, key) for i, key in enumerate(__lowerCAmelCase ) if key is not None] SCREAMING_SNAKE_CASE__ : Dict = [x[0] for x in sorted(__lowerCAmelCase , key=lambda __lowerCAmelCase : x[1] )] # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest. SCREAMING_SNAKE_CASE__ : Optional[int] = 0 SCREAMING_SNAKE_CASE__ : Dict = [] for i in range(len(__lowerCAmelCase ) ): if keys[i] is None: reordered_blocks.append(internal_blocks[i] ) else: SCREAMING_SNAKE_CASE__ : Dict = sort_objects_in_import(internal_blocks[sorted_indices[count]] ) reordered_blocks.append(__lowerCAmelCase ) count += 1 # And we put our main block back together with its first and last line. SCREAMING_SNAKE_CASE__ : Optional[int] = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] ) if code != "\n".join(__lowerCAmelCase ): if check_only: return True else: print(F'''Overwriting {file}.''' ) with open(__lowerCAmelCase , """w""" ) as f: f.write("""\n""".join(__lowerCAmelCase ) ) def _lowercase ( __lowerCAmelCase=True ) -> Dict: SCREAMING_SNAKE_CASE__ : List[Any] = [] for root, _, files in os.walk(__lowerCAmelCase ): if "__init__.py" in files: SCREAMING_SNAKE_CASE__ : List[Any] = sort_imports(os.path.join(__lowerCAmelCase , """__init__.py""" ) , check_only=__lowerCAmelCase ) if result: SCREAMING_SNAKE_CASE__ : str = [os.path.join(__lowerCAmelCase , """__init__.py""" )] if len(__lowerCAmelCase ) > 0: raise ValueError(F'''Would overwrite {len(__lowerCAmelCase )} files, run `make style`.''' ) if __name__ == "__main__": a :Any = argparse.ArgumentParser() parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.") a :Dict = parser.parse_args() sort_imports_in_all_inits(check_only=args.check_only)
717
"""simple docstring""" import re from pathlib import Path from unittest import TestCase import pytest @pytest.mark.integration class __a (UpperCamelCase_): '''simple docstring''' def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read() SCREAMING_SNAKE_CASE__ : str = regexp.search(_a ) return match def _a ( self , _a ) -> Optional[Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL ) SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read() # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search` SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a ) SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None] return matches[0] if matches else None def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_encoding_on_file_open(str(_a ) ): raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_print_statements(str(_a ) ): raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
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from __future__ import annotations import unittest from transformers import AutoTokenizer, MBartConfig, is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel @require_tf class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = MBartConfig _SCREAMING_SNAKE_CASE :int = {} _SCREAMING_SNAKE_CASE :int = """gelu""" def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=False , _a=99 , _a=32 , _a=2 , _a=4 , _a=37 , _a=0.1 , _a=0.1 , _a=20 , _a=2 , _a=1 , _a=0 , ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = parent SCREAMING_SNAKE_CASE__ : Tuple = batch_size SCREAMING_SNAKE_CASE__ : Any = seq_length SCREAMING_SNAKE_CASE__ : int = is_training SCREAMING_SNAKE_CASE__ : Optional[Any] = use_labels SCREAMING_SNAKE_CASE__ : List[str] = vocab_size SCREAMING_SNAKE_CASE__ : Tuple = hidden_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads SCREAMING_SNAKE_CASE__ : Optional[Any] = intermediate_size SCREAMING_SNAKE_CASE__ : str = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : int = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : str = max_position_embeddings SCREAMING_SNAKE_CASE__ : Union[str, Any] = eos_token_id SCREAMING_SNAKE_CASE__ : int = pad_token_id SCREAMING_SNAKE_CASE__ : List[Any] = bos_token_id def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : List[Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 ) SCREAMING_SNAKE_CASE__ : str = tf.concat([input_ids, eos_tensor] , axis=1 ) SCREAMING_SNAKE_CASE__ : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Any = self.config_cls( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , ) SCREAMING_SNAKE_CASE__ : int = prepare_mbart_inputs_dict(_a , _a , _a ) return config, inputs_dict def _a ( self , _a , _a ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = TFMBartModel(config=_a ).get_decoder() SCREAMING_SNAKE_CASE__ : Tuple = inputs_dict["""input_ids"""] SCREAMING_SNAKE_CASE__ : Any = input_ids[:1, :] SCREAMING_SNAKE_CASE__ : int = inputs_dict["""attention_mask"""][:1, :] SCREAMING_SNAKE_CASE__ : Optional[int] = inputs_dict["""head_mask"""] SCREAMING_SNAKE_CASE__ : Any = 1 # first forward pass SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , attention_mask=_a , head_mask=_a , use_cache=_a ) SCREAMING_SNAKE_CASE__ : int = outputs.to_tuple() SCREAMING_SNAKE_CASE__ : str = past_key_values[1] def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> Any: if attention_mask is None: SCREAMING_SNAKE_CASE__ : Optional[Any] = tf.cast(tf.math.not_equal(__lowerCAmelCase , config.pad_token_id ) , tf.inta ) if decoder_attention_mask is None: SCREAMING_SNAKE_CASE__ : str = tf.concat( [ tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ), ] , axis=-1 , ) if head_mask is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) ) if decoder_head_mask is None: SCREAMING_SNAKE_CASE__ : Tuple = tf.ones((config.decoder_layers, config.decoder_attention_heads) ) if cross_attn_head_mask is None: SCREAMING_SNAKE_CASE__ : Any = tf.ones((config.decoder_layers, config.decoder_attention_heads) ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } @require_tf class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else () _SCREAMING_SNAKE_CASE :List[Any] = (TFMBartForConditionalGeneration,) if is_tf_available() else () _SCREAMING_SNAKE_CASE :Optional[int] = ( { """conversational""": TFMBartForConditionalGeneration, """feature-extraction""": TFMBartModel, """summarization""": TFMBartForConditionalGeneration, """text2text-generation""": TFMBartForConditionalGeneration, """translation""": TFMBartForConditionalGeneration, } if is_tf_available() else {} ) _SCREAMING_SNAKE_CASE :str = True _SCREAMING_SNAKE_CASE :Optional[Any] = False _SCREAMING_SNAKE_CASE :Optional[Any] = False def _a ( self , _a , _a , _a , _a , _a ) -> Dict: """simple docstring""" if pipeline_test_casse_name != "FeatureExtractionPipelineTests": # Exception encountered when calling layer '...' return True return False def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFMBartModelTester(self ) SCREAMING_SNAKE_CASE__ : Dict = ConfigTester(self , config_class=_a ) def _a ( self ) -> str: """simple docstring""" self.config_tester.run_common_tests() def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*_a ) @require_sentencepiece @require_tokenizers @require_tf class __a (unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :int = [ """ UN Chief Says There Is No Military Solution in Syria""", ] _SCREAMING_SNAKE_CASE :str = [ """Şeful ONU declară că nu există o soluţie militară în Siria""", ] _SCREAMING_SNAKE_CASE :Optional[int] = """facebook/mbart-large-en-ro""" @cached_property def _a ( self ) -> str: """simple docstring""" return AutoTokenizer.from_pretrained(self.model_name ) @cached_property def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name ) return model def _a ( self , **_a ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.translate_src_text(**_a ) self.assertListEqual(self.expected_text , _a ) def _a ( self , **_a ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.tokenizer(self.src_text , **_a , return_tensors="""tf""" ) SCREAMING_SNAKE_CASE__ : Optional[int] = self.model.generate( model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 ) SCREAMING_SNAKE_CASE__ : List[Any] = self.tokenizer.batch_decode(_a , skip_special_tokens=_a ) return generated_words @slow def _a ( self ) -> Union[str, Any]: """simple docstring""" self._assert_generated_batch_equal_expected()
718
"""simple docstring""" import tempfile import unittest from transformers import TaConfig, is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel class __a : '''simple docstring''' def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = parent SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length SCREAMING_SNAKE_CASE__ : str = decoder_seq_length # For common tests SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length SCREAMING_SNAKE_CASE__ : Tuple = is_training SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask SCREAMING_SNAKE_CASE__ : List[str] = use_labels SCREAMING_SNAKE_CASE__ : str = vocab_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Any = num_attention_heads SCREAMING_SNAKE_CASE__ : Any = d_ff SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id SCREAMING_SNAKE_CASE__ : Any = None SCREAMING_SNAKE_CASE__ : str = decoder_layers def _a ( self ) -> Tuple: """simple docstring""" return TaConfig.from_pretrained("""google/umt5-base""" ) def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any: """simple docstring""" if attention_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id ) if decoder_attention_mask is None: SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id ) if head_mask is None: SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a ) if decoder_head_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a ) if cross_attn_head_mask is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones( config.num_decoder_layers , config.num_attention_heads , device=_a ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size ) # we need to clamp the input ids here to avoid having pad token in between # this is because for NllbMoe the position_ids are prepared such that # all pad tokens have pos id = 2 and rest are between 2..seq_length # and the seq_length here is seq_length - num_pad_tokens # but when using past, there is no way of knowing if the past input ids had # pad tokens in them, which results in incorrect seq_lenth and which in turn results in # position_ids being off by num_pad_tokens in past input SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config() SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a ) return config, input_dict def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs() return config, inputs_dict def _a ( self ) -> List[str]: """simple docstring""" return TaConfig( vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self ) -> List[Any]: """simple docstring""" return TaConfig( vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a ) model.to(_a ) model.eval() SCREAMING_SNAKE_CASE__ : Dict = model( input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) ) self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) ) # There should be `num_layers` key value embeddings stored in decoder_past self.parent.assertEqual(len(_a ) , config.num_layers ) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple self.parent.assertEqual(len(decoder_past[0] ) , 4 ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval() # first forward pass SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a ) SCREAMING_SNAKE_CASE__ : str = model(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a ) self.parent.assertTrue(len(_a ) == len(_a ) ) self.parent.assertTrue(len(_a ) == len(_a ) + 1 ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ) # append to next input_ids and SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""] SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""] # select random slice SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item() SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach() SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) ) def _a ( self , _a , _a , ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval() SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""] self.parent.assertFalse(torch.isnan(_a ).any().item() ) @require_torch class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = ( (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else () ) _SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else () _SCREAMING_SNAKE_CASE :List[str] = ( { """conversational""": UMTaForConditionalGeneration, """feature-extraction""": UMTaModel, """summarization""": UMTaForConditionalGeneration, """text2text-generation""": UMTaForConditionalGeneration, """translation""": UMTaForConditionalGeneration, """question-answering""": UMTaForQuestionAnswering, } if is_torch_available() else {} ) _SCREAMING_SNAKE_CASE :Union[str, Any] = True _SCREAMING_SNAKE_CASE :Tuple = False _SCREAMING_SNAKE_CASE :Optional[Any] = False _SCREAMING_SNAKE_CASE :List[Any] = True _SCREAMING_SNAKE_CASE :List[str] = True # The small UMT5 model needs higher percentages for CPU/MP tests _SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9] def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self ) @unittest.skip("""Test has a segmentation fault on torch 1.8.0""" ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a ) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( _a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , ) @unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fpaa_forward(*_a ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""] SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0] SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval() model.to(_a ) SCREAMING_SNAKE_CASE__ : List[str] = { """head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ), """decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), } for attn_name, (name, mask) in zip(_a , head_masking.items() ): SCREAMING_SNAKE_CASE__ : List[str] = {name: mask} # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified if name == "head_mask": SCREAMING_SNAKE_CASE__ : str = torch.ones( config.num_decoder_layers , config.num_heads , device=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate( config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , ) # We check the state of decoder_attentions and cross_attentions just from the last step SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 ) @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" ) def _a ( self ) -> Dict: """simple docstring""" pass @require_torch @require_sentencepiece @require_tokenizers class __a (unittest.TestCase): '''simple docstring''' @slow @unittest.skip( """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a ) SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [ """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""", """No se como puedo <extra_id_0>.""", """This is the reason why we <extra_id_0> them.""", """The <extra_id_0> walks in <extra_id_1>, seats""", """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""", ] SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids # fmt: off SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor( [ [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0], [ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1], ] ) # fmt: on torch.testing.assert_allclose(_a , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) ) SCREAMING_SNAKE_CASE__ : int = [ """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""", """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", ] SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a ) self.assertEqual(_a , _a )
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0
import glob import os import random from string import ascii_lowercase, digits import cva a :Optional[int] = "" a :Dict = "" a :Optional[int] = "" a :Tuple = 1 # (0 is vertical, 1 is horizontal) def _lowercase ( ) -> None: SCREAMING_SNAKE_CASE__ : int = get_dataset(__lowerCAmelCase , __lowerCAmelCase ) print("""Processing...""" ) SCREAMING_SNAKE_CASE__ : Any = update_image_and_anno(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) for index, image in enumerate(__lowerCAmelCase ): # Get random string code: '7b7ad245cdff75241935e4dd860f3bad' SCREAMING_SNAKE_CASE__ : Tuple = random_chars(32 ) SCREAMING_SNAKE_CASE__ : int = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0] SCREAMING_SNAKE_CASE__ : int = F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}''' cva.imwrite(F'''/{file_root}.jpg''' , __lowerCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] ) print(F'''Success {index+1}/{len(__lowerCAmelCase )} with {file_name}''' ) SCREAMING_SNAKE_CASE__ : Optional[int] = [] for anno in new_annos[index]: SCREAMING_SNAKE_CASE__ : List[Any] = F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}''' annos_list.append(__lowerCAmelCase ) with open(F'''/{file_root}.txt''' , """w""" ) as outfile: outfile.write("""\n""".join(line for line in annos_list ) ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> tuple[list, list]: SCREAMING_SNAKE_CASE__ : Optional[int] = [] SCREAMING_SNAKE_CASE__ : int = [] for label_file in glob.glob(os.path.join(__lowerCAmelCase , """*.txt""" ) ): SCREAMING_SNAKE_CASE__ : Any = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0] with open(__lowerCAmelCase ) as in_file: SCREAMING_SNAKE_CASE__ : Tuple = in_file.readlines() SCREAMING_SNAKE_CASE__ : str = os.path.join(__lowerCAmelCase , F'''{label_name}.jpg''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = [] for obj_list in obj_lists: SCREAMING_SNAKE_CASE__ : Any = obj_list.rstrip("""\n""" ).split(""" """ ) boxes.append( [ int(obj[0] ), float(obj[1] ), float(obj[2] ), float(obj[3] ), float(obj[4] ), ] ) if not boxes: continue img_paths.append(__lowerCAmelCase ) labels.append(__lowerCAmelCase ) return img_paths, labels def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1 ) -> tuple[list, list, list]: SCREAMING_SNAKE_CASE__ : List[Any] = [] SCREAMING_SNAKE_CASE__ : Union[str, Any] = [] SCREAMING_SNAKE_CASE__ : Tuple = [] for idx in range(len(__lowerCAmelCase ) ): SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ : int = img_list[idx] path_list.append(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Any = anno_list[idx] SCREAMING_SNAKE_CASE__ : Optional[int] = cva.imread(__lowerCAmelCase ) if flip_type == 1: SCREAMING_SNAKE_CASE__ : Optional[int] = cva.flip(__lowerCAmelCase , __lowerCAmelCase ) for bbox in img_annos: SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1 - bbox[1] new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] ) elif flip_type == 0: SCREAMING_SNAKE_CASE__ : Union[str, Any] = cva.flip(__lowerCAmelCase , __lowerCAmelCase ) for bbox in img_annos: SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1 - bbox[2] new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] ) new_annos_lists.append(__lowerCAmelCase ) new_imgs_list.append(__lowerCAmelCase ) return new_imgs_list, new_annos_lists, path_list def _lowercase ( __lowerCAmelCase = 32 ) -> str: assert number_char > 1, "The number of character should greater than 1" SCREAMING_SNAKE_CASE__ : List[str] = ascii_lowercase + digits return "".join(random.choice(__lowerCAmelCase ) for _ in range(__lowerCAmelCase ) ) if __name__ == "__main__": main() print("DONE ✅")
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"""simple docstring""" import multiprocessing from typing import TYPE_CHECKING, Optional, Union from .. import Dataset, Features, config from ..formatting import query_table from ..packaged_modules.sql.sql import Sql from ..utils import logging from .abc import AbstractDatasetInputStream if TYPE_CHECKING: import sqlitea import sqlalchemy class __a (UpperCamelCase_): '''simple docstring''' def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]: """simple docstring""" super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a ) SCREAMING_SNAKE_CASE__ : List[Any] = Sql( cache_dir=_a , features=_a , sql=_a , con=_a , **_a , ) def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = None SCREAMING_SNAKE_CASE__ : Union[str, Any] = None SCREAMING_SNAKE_CASE__ : Dict = None SCREAMING_SNAKE_CASE__ : Optional[int] = None self.builder.download_and_prepare( download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , ) # Build dataset for splits SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset( split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory ) return dataset class __a : '''simple docstring''' def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any: """simple docstring""" if num_proc is not None and num_proc <= 0: raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' ) SCREAMING_SNAKE_CASE__ : int = dataset SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = con SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE SCREAMING_SNAKE_CASE__ : int = num_proc SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs ) return written def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs SCREAMING_SNAKE_CASE__ : Any = query_table( table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , ) SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas() SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a ) return num_rows or len(_a ) def _a ( self , _a , **_a ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = 0 if self.num_proc is None or self.num_proc == 1: for offset in logging.tqdm( range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += self._batch_sql((offset, index, to_sql_kwargs) ) else: SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size with multiprocessing.Pool(self.num_proc ) as pool: for num_rows in logging.tqdm( pool.imap( self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += num_rows return written
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available a :int = {"tokenization_herbert": ["HerbertTokenizer"]} try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Tuple = ["HerbertTokenizerFast"] if TYPE_CHECKING: from .tokenization_herbert import HerbertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_herbert_fast import HerbertTokenizerFast else: import sys a :str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
720
"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> int: if divisor % 5 == 0 or divisor % 2 == 0: return 0 SCREAMING_SNAKE_CASE__ : List[Any] = 1 SCREAMING_SNAKE_CASE__ : int = 1 while repunit: SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor repunit_index += 1 return repunit_index def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = limit - 1 if divisor % 2 == 0: divisor += 1 while least_divisible_repunit(__lowerCAmelCase ) <= limit: divisor += 2 return divisor if __name__ == "__main__": print(f'{solution() = }')
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0
"""simple docstring""" from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging a :Union[str, Any] = logging.get_logger(__name__) a :Dict = { "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json", "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json", # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1 } class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Dict = """mobilenet_v1""" def __init__( self , _a=3 , _a=224 , _a=1.0 , _a=8 , _a="relu6" , _a=True , _a=0.999 , _a=0.02 , _a=0.001 , **_a , ) -> Dict: """simple docstring""" super().__init__(**_a ) if depth_multiplier <= 0: raise ValueError("""depth_multiplier must be greater than zero.""" ) SCREAMING_SNAKE_CASE__ : List[Any] = num_channels SCREAMING_SNAKE_CASE__ : List[Any] = image_size SCREAMING_SNAKE_CASE__ : Tuple = depth_multiplier SCREAMING_SNAKE_CASE__ : Optional[int] = min_depth SCREAMING_SNAKE_CASE__ : Tuple = hidden_act SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf_padding SCREAMING_SNAKE_CASE__ : int = classifier_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_range SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_eps class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = version.parse("""1.11""") @property def _a ( self ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" return OrderedDict([("""pixel_values""", {0: """batch"""})] ) @property def _a ( self ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" if self.task == "image-classification": return OrderedDict([("""logits""", {0: """batch"""})] ) else: return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] ) @property def _a ( self ) -> float: """simple docstring""" return 1E-4
721
"""simple docstring""" import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import numpy as np from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.31.0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") a :Union[str, Any] = logging.getLogger(__name__) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_28 , metadata={ """help""": ( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Whether to pad all samples to `max_seq_length`. """ """If False, will pad the samples dynamically when batching to the maximum length in the batch.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of training examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of evaluation examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of prediction examples to this """ """value if set.""" ) } , ) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}) _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , ) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , ) _SCREAMING_SNAKE_CASE :str = field( default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Will use the token generated when running `huggingface-cli login` (necessary to use this script """ """with private models).""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , ) def _lowercase ( ) -> Union[str, Any]: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("""run_xnli""" , __lowerCAmelCase ) # Setup logging logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level() logger.setLevel(__lowerCAmelCase ) datasets.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}''' + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' ) logger.info(F'''Training/evaluation parameters {training_args}''' ) # Detecting last checkpoint. SCREAMING_SNAKE_CASE__ : Any = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. ''' """Use --overwrite_output_dir to overcome.""" ) elif last_checkpoint is not None: logger.info( F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change ''' """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" ) # Set seed before initializing model. set_seed(training_args.seed ) # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # Downloading and loading xnli dataset from the hub. if training_args.do_train: if model_args.train_language is None: SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset( """xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) else: SCREAMING_SNAKE_CASE__ : str = load_dataset( """xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names if training_args.do_eval: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names if training_args.do_predict: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names # Labels SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase ) # Load pretrained model and tokenizer # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , ) # Preprocessing the datasets # Padding strategy if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : str = """max_length""" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch SCREAMING_SNAKE_CASE__ : Optional[Any] = False def preprocess_function(__lowerCAmelCase ): # Tokenize the texts return tokenizer( examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , ) if training_args.do_train: if data_args.max_train_samples is not None: SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples ) SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""train dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , ) # Log a few random samples from the training set: for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ): logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' ) if training_args.do_eval: if data_args.max_eval_samples is not None: SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""validation dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , ) if training_args.do_predict: if data_args.max_predict_samples is not None: SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , ) # Get the metric function SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" ) # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 ) return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids ) # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator elif training_args.fpaa: SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None # Initialize our Trainer SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer( model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , ) # Training if training_args.do_train: SCREAMING_SNAKE_CASE__ : Dict = None if training_args.resume_from_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint elif last_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics SCREAMING_SNAKE_CASE__ : Optional[int] = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("""train""" , __lowerCAmelCase ) trainer.save_metrics("""train""" , __lowerCAmelCase ) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("""*** Evaluate ***""" ) SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""eval""" , __lowerCAmelCase ) trainer.save_metrics("""eval""" , __lowerCAmelCase ) # Prediction if training_args.do_predict: logger.info("""*** Predict ***""" ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" ) SCREAMING_SNAKE_CASE__ : List[str] = ( data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""predict""" , __lowerCAmelCase ) trainer.save_metrics("""predict""" , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" ) if trainer.is_world_process_zero(): with open(__lowerCAmelCase , """w""" ) as writer: writer.write("""index\tprediction\n""" ) for index, item in enumerate(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item] writer.write(F'''{index}\t{item}\n''' ) if __name__ == "__main__": main()
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0
"""simple docstring""" from typing import Dict from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available from transformers.testing_utils import ( TestCasePlus, execute_subprocess_async, get_torch_dist_unique_port, require_torch_multi_gpu, require_torch_neuroncore, ) from transformers.training_args import ParallelMode from transformers.utils import logging a :List[str] = logging.get_logger(__name__) if is_torch_available(): import torch from torch import nn from torch.utils.data import Dataset from transformers import Trainer class __a (UpperCamelCase_): '''simple docstring''' def __init__( self , _a = 101 ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = length def __len__( self ) -> Tuple: """simple docstring""" return self.length def __getitem__( self , _a ) -> int: """simple docstring""" return i class __a : '''simple docstring''' def __call__( self , _a ) -> Optional[Any]: """simple docstring""" return {"input_ids": torch.tensor(_a ), "labels": torch.tensor(_a )} class __a (nn.Module): '''simple docstring''' def __init__( self ) -> Optional[int]: """simple docstring""" super().__init__() # Add some (unused) params otherwise DDP will complain. SCREAMING_SNAKE_CASE__ : int = nn.Linear(120 , 80 ) def _a ( self , _a , _a=None ) -> Any: """simple docstring""" if labels is not None: return torch.tensor(0.0 , device=input_ids.device ), input_ids else: return input_ids class __a (UpperCamelCase_): '''simple docstring''' @require_torch_neuroncore def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = f'''--nproc_per_node=2 --master_port={get_torch_dist_unique_port()} {self.test_file_dir}/test_trainer_distributed.py '''.split() SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_auto_remove_tmp_dir() SCREAMING_SNAKE_CASE__ : Dict = f'''--output_dir {output_dir}'''.split() SCREAMING_SNAKE_CASE__ : str = ["""torchrun"""] + distributed_args + args execute_subprocess_async(_a , env=self.get_env() ) # successful return here == success - any errors would have caused an error in the sub-call class __a (UpperCamelCase_): '''simple docstring''' @require_torch_multi_gpu def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = f'''--nproc_per_node={torch.cuda.device_count()} --master_port={get_torch_dist_unique_port()} {self.test_file_dir}/test_trainer_distributed.py '''.split() SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_auto_remove_tmp_dir() SCREAMING_SNAKE_CASE__ : Optional[int] = f'''--output_dir {output_dir}'''.split() SCREAMING_SNAKE_CASE__ : int = ["""torchrun"""] + distributed_args + args execute_subprocess_async(_a , env=self.get_env() ) # successful return here == success - any errors would have caused an error in the sub-call if __name__ == "__main__": # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs: # # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py a :Tuple = HfArgumentParser((TrainingArguments,)) a :List[Any] = parser.parse_args_into_dataclasses()[0] logger.warning( f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, ' f'distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}' ) # Essentially, what we want to verify in the distributed case is that we get all samples back, # in the right order. (this is crucial for prediction for instance) for dataset_length in [101, 40, 7]: a :Union[str, Any] = DummyDataset(dataset_length) def _lowercase ( __lowerCAmelCase ) -> Dict: SCREAMING_SNAKE_CASE__ : Tuple = list(range(len(__lowerCAmelCase ) ) ) SCREAMING_SNAKE_CASE__ : Tuple = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential if not success and training_args.local_rank == 0: logger.warning( """Predictions and/or labels do not match expected results:\n - predictions: """ F'''{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}''' ) return {"success": success} a :Tuple = Trainer( model=DummyModel(), args=training_args, data_collator=DummyDataCollator(), eval_dataset=dataset, compute_metrics=compute_metrics, ) a :Optional[Any] = trainer.evaluate() logger.info(metrics) if metrics["eval_success"] is not True: logger.error(metrics) exit(1) a :Union[str, Any] = trainer.predict(dataset) logger.info(p.metrics) if p.metrics["test_success"] is not True: logger.error(p.metrics) exit(1) a :Tuple = 2 a :str = trainer.evaluate() logger.info(metrics) if metrics["eval_success"] is not True: logger.error(metrics) exit(1) a :Union[str, Any] = trainer.predict(dataset) logger.info(p.metrics) if p.metrics["test_success"] is not True: logger.error(p.metrics) exit(1) a :List[str] = None
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"""simple docstring""" # Note: if you intend to run this script make sure you look under scripts/fsmt/ # to locate the appropriate script to do the work correctly. There is a set of scripts to: # - download and prepare data and run the conversion script # - perform eval to get the best hparam into the config # - generate model_cards - useful if you have multiple models from the same paper import argparse import json import os import re from collections import OrderedDict from os.path import basename, dirname import fairseq import torch from fairseq import hub_utils from fairseq.data.dictionary import Dictionary from transformers import FSMTConfig, FSMTForConditionalGeneration from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE from transformers.utils import WEIGHTS_NAME, logging logging.set_verbosity_warning() a :str = 2 # based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping` # values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults: # # * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users) # * `early_stopping`: `False` consistently scored better # * `length_penalty` varied, so will assign the best one depending on the model a :int = { # fairseq: "wmt19-ru-en": {"length_penalty": 1.1}, "wmt19-en-ru": {"length_penalty": 1.15}, "wmt19-en-de": {"length_penalty": 1.0}, "wmt19-de-en": {"length_penalty": 1.1}, # allenai: "wmt16-en-de-dist-12-1": {"length_penalty": 0.6}, "wmt16-en-de-dist-6-1": {"length_penalty": 0.6}, "wmt16-en-de-12-1": {"length_penalty": 0.8}, "wmt19-de-en-6-6-base": {"length_penalty": 0.6}, "wmt19-de-en-6-6-big": {"length_penalty": 0.6}, } # this remaps the different models to their organization names a :Dict = {} for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]: a :List[Any] = "facebook" for m in [ "wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1", "wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big", ]: a :str = "allenai" def _lowercase ( __lowerCAmelCase ) -> Any: # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up, # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7} SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() ) SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split() # restore the special tokens for k in keep_keys: del da[F'''{k}</w>'''] SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore return da def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]: # prep assert os.path.exists(__lowerCAmelCase ) os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase ) print(F'''Writing results to {pytorch_dump_folder_path}''' ) # handle various types of models SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models() SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""} SCREAMING_SNAKE_CASE__ : Optional[Any] = """.""" # note: since the model dump is old, fairseq has upgraded its model some # time later, and it does a whole lot of rewrites and splits on the saved # weights, therefore we can't use torch.load() directly on the model file. # see: upgrade_state_dict(state_dict) in fairseq_model.py print(F'''using checkpoint {checkpoint_file}''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] ) SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""] SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""] SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase ) # dicts SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" ) print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # detect whether this is a do_lower_case situation, which can be derived by checking whether we # have at least one uppercase letter in the source vocab SCREAMING_SNAKE_CASE__ : Optional[Any] = True for k in src_vocab.keys(): if not k.islower(): SCREAMING_SNAKE_CASE__ : Tuple = False break SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" ) print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # merges_file (bpecodes) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] ) for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code" SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) if os.path.exists(__lowerCAmelCase ): break with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin: SCREAMING_SNAKE_CASE__ : Any = fin.read() SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number print(F'''Generating {merges_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout: fout.write(__lowerCAmelCase ) # model config SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" ) # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe - # may have to modify the tokenizer if a different type is used by a future model assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}''' assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}''' SCREAMING_SNAKE_CASE__ : str = { """architectures""": ["""FSMTForConditionalGeneration"""], """model_type""": """fsmt""", """activation_dropout""": args["""activation_dropout"""], """activation_function""": """relu""", """attention_dropout""": args["""attention_dropout"""], """d_model""": args["""decoder_embed_dim"""], """dropout""": args["""dropout"""], """init_std""": 0.02, """max_position_embeddings""": args["""max_source_positions"""], """num_hidden_layers""": args["""encoder_layers"""], """src_vocab_size""": src_vocab_size, """tgt_vocab_size""": tgt_vocab_size, """langs""": [src_lang, tgt_lang], """encoder_attention_heads""": args["""encoder_attention_heads"""], """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""], """encoder_layerdrop""": args["""encoder_layerdrop"""], """encoder_layers""": args["""encoder_layers"""], """decoder_attention_heads""": args["""decoder_attention_heads"""], """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""], """decoder_layerdrop""": args["""decoder_layerdrop"""], """decoder_layers""": args["""decoder_layers"""], """bos_token_id""": 0, """pad_token_id""": 1, """eos_token_id""": 2, """is_encoder_decoder""": True, """scale_embedding""": not args["""no_scale_embedding"""], """tie_word_embeddings""": args["""share_all_embeddings"""], } # good hparam defaults to start with SCREAMING_SNAKE_CASE__ : Tuple = 5 SCREAMING_SNAKE_CASE__ : str = False if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]: SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""] else: SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0 print(F'''Generating {fsmt_model_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # tokenizer config SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = { """langs""": [src_lang, tgt_lang], """model_max_length""": 1024, """do_lower_case""": do_lower_case, } print(F'''Generating {fsmt_tokenizer_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # model SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0] SCREAMING_SNAKE_CASE__ : int = model.state_dict() # rename keys to start with 'model.' SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() ) # remove unneeded keys SCREAMING_SNAKE_CASE__ : str = [ """model.model""", """model.encoder.version""", """model.decoder.version""", """model.encoder_embed_tokens.weight""", """model.decoder_embed_tokens.weight""", """model.encoder.embed_positions._float_tensor""", """model.decoder.embed_positions._float_tensor""", ] for k in ignore_keys: model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase ) # check that it loads ok model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase ) # save SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) print(F'''Generating {pytorch_weights_dump_path}''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) print("""Conversion is done!""" ) print("""\nLast step is to upload the files to s3""" ) print(F'''cd {data_root}''' ) print(F'''transformers-cli upload {model_dir}''' ) if __name__ == "__main__": a :Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--fsmt_checkpoint_path", default=None, type=str, required=True, help=( "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts," " bpecodes, etc." ), ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) a :List[str] = parser.parse_args() convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
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"""simple docstring""" import numpy as np def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 1E-12 , __lowerCAmelCase = 100 , ) -> tuple[float, np.ndarray]: assert np.shape(__lowerCAmelCase )[0] == np.shape(__lowerCAmelCase )[1] # Ensure proper dimensionality. assert np.shape(__lowerCAmelCase )[0] == np.shape(__lowerCAmelCase )[0] # Ensure inputs are either both complex or both real assert np.iscomplexobj(__lowerCAmelCase ) == np.iscomplexobj(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = np.iscomplexobj(__lowerCAmelCase ) if is_complex: # Ensure complex input_matrix is Hermitian assert np.array_equal(__lowerCAmelCase , input_matrix.conj().T ) # Set convergence to False. Will define convergence when we exceed max_iterations # or when we have small changes from one iteration to next. SCREAMING_SNAKE_CASE__ : Any = False SCREAMING_SNAKE_CASE__ : Optional[int] = 0 SCREAMING_SNAKE_CASE__ : List[str] = 0 SCREAMING_SNAKE_CASE__ : int = 1E12 while not convergence: # Multiple matrix by the vector. SCREAMING_SNAKE_CASE__ : str = np.dot(__lowerCAmelCase , __lowerCAmelCase ) # Normalize the resulting output vector. SCREAMING_SNAKE_CASE__ : Tuple = w / np.linalg.norm(__lowerCAmelCase ) # Find rayleigh quotient # (faster than usual b/c we know vector is normalized already) SCREAMING_SNAKE_CASE__ : List[Any] = vector.conj().T if is_complex else vector.T SCREAMING_SNAKE_CASE__ : Optional[int] = np.dot(__lowerCAmelCase , np.dot(__lowerCAmelCase , __lowerCAmelCase ) ) # Check convergence. SCREAMING_SNAKE_CASE__ : Optional[Any] = np.abs(lambda_ - lambda_previous ) / lambda_ iterations += 1 if error <= error_tol or iterations >= max_iterations: SCREAMING_SNAKE_CASE__ : Tuple = True SCREAMING_SNAKE_CASE__ : List[str] = lambda_ if is_complex: SCREAMING_SNAKE_CASE__ : Optional[Any] = np.real(lambda_ ) return lambda_, vector def _lowercase ( ) -> None: SCREAMING_SNAKE_CASE__ : Tuple = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] ) SCREAMING_SNAKE_CASE__ : Any = np.array([41, 4, 20] ) SCREAMING_SNAKE_CASE__ : Dict = real_input_matrix.astype(np.complexaaa ) SCREAMING_SNAKE_CASE__ : Optional[int] = np.triu(1j * complex_input_matrix , 1 ) complex_input_matrix += imag_matrix complex_input_matrix += -1 * imag_matrix.T SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([41, 4, 20] ).astype(np.complexaaa ) for problem_type in ["real", "complex"]: if problem_type == "real": SCREAMING_SNAKE_CASE__ : Optional[Any] = real_input_matrix SCREAMING_SNAKE_CASE__ : List[str] = real_vector elif problem_type == "complex": SCREAMING_SNAKE_CASE__ : int = complex_input_matrix SCREAMING_SNAKE_CASE__ : Optional[Any] = complex_vector # Our implementation. SCREAMING_SNAKE_CASE__ : Dict = power_iteration(__lowerCAmelCase , __lowerCAmelCase ) # Numpy implementation. # Get eigenvalues and eigenvectors using built-in numpy # eigh (eigh used for symmetric or hermetian matrices). SCREAMING_SNAKE_CASE__ : List[Any] = np.linalg.eigh(__lowerCAmelCase ) # Last eigenvalue is the maximum one. SCREAMING_SNAKE_CASE__ : Optional[int] = eigen_values[-1] # Last column in this matrix is eigenvector corresponding to largest eigenvalue. SCREAMING_SNAKE_CASE__ : Dict = eigen_vectors[:, -1] # Check our implementation and numpy gives close answers. assert np.abs(eigen_value - eigen_value_max ) <= 1E-6 # Take absolute values element wise of each eigenvector. # as they are only unique to a minus sign. assert np.linalg.norm(np.abs(__lowerCAmelCase ) - np.abs(__lowerCAmelCase ) ) <= 1E-6 if __name__ == "__main__": import doctest doctest.testmod() test_power_iteration()
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,) def _a ( self , **_a ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """num_train_timesteps""": 1_000, """beta_start""": 0.0_001, """beta_end""": 0.02, """beta_schedule""": """linear""", """variance_type""": """fixed_small""", """clip_sample""": True, } config.update(**_a ) return config def _a ( self ) -> str: """simple docstring""" for timesteps in [1, 5, 100, 1_000]: self.check_over_configs(num_train_timesteps=_a ) def _a ( self ) -> str: """simple docstring""" for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=_a , beta_end=_a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_a ) def _a ( self ) -> Any: """simple docstring""" for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=_a ) def _a ( self ) -> Optional[int]: """simple docstring""" for clip_sample in [True, False]: self.check_over_configs(clip_sample=_a ) def _a ( self ) -> int: """simple docstring""" self.check_over_configs(thresholding=_a ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=_a , prediction_type=_a , sample_max_value=_a , ) def _a ( self ) -> str: """simple docstring""" for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=_a ) def _a ( self ) -> str: """simple docstring""" for t in [0, 500, 999]: self.check_over_forward(time_step=_a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Any = len(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model() SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : str = pred_prev_sample SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 258.9_606 ) < 1E-2 assert abs(result_mean.item() - 0.3_372 ) < 1E-3 def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" ) SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Dict = len(_a ) SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model() SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : int = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 202.0_296 ) < 1E-2 assert abs(result_mean.item() - 0.2_631 ) < 1E-3 def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps for i, timestep in enumerate(_a ): if i == len(_a ) - 1: SCREAMING_SNAKE_CASE__ : Optional[Any] = -1 else: SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1] SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item() self.assertEqual(_a , _a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0] with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ): scheduler.set_timesteps(timesteps=_a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0] SCREAMING_SNAKE_CASE__ : List[str] = len(_a ) with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ): scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a ) def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps] with self.assertRaises( _a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ): scheduler.set_timesteps(timesteps=_a )
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"""simple docstring""" import unittest from transformers import is_flax_available from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow if is_flax_available(): import optax from flax.training.common_utils import onehot from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration from transformers.models.ta.modeling_flax_ta import shift_tokens_right @require_torch @require_sentencepiece @require_tokenizers @require_flax class __a (unittest.TestCase): '''simple docstring''' @slow def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" ) SCREAMING_SNAKE_CASE__ : Optional[int] = AutoTokenizer.from_pretrained("""google/mt5-small""" ) SCREAMING_SNAKE_CASE__ : Tuple = tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids SCREAMING_SNAKE_CASE__ : Dict = tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids SCREAMING_SNAKE_CASE__ : str = shift_tokens_right(_a , model.config.pad_token_id , model.config.decoder_start_token_id ) SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a , decoder_input_ids=_a ).logits SCREAMING_SNAKE_CASE__ : Union[str, Any] = optax.softmax_cross_entropy(_a , onehot(_a , logits.shape[-1] ) ).mean() SCREAMING_SNAKE_CASE__ : List[str] = -(labels.shape[-1] * loss.item()) SCREAMING_SNAKE_CASE__ : str = -84.9_127 self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
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"""simple docstring""" import os a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000} def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Any = 0 SCREAMING_SNAKE_CASE__ : Dict = 0 while index < len(__lowerCAmelCase ) - 1: SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]] SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]] if current_value < next_value: total_value -= current_value else: total_value += current_value index += 1 total_value += SYMBOLS[numerals[index]] return total_value def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Optional[int] = """""" SCREAMING_SNAKE_CASE__ : int = num // 1000 numerals += m_count * "M" num %= 1000 SCREAMING_SNAKE_CASE__ : List[str] = num // 100 if c_count == 9: numerals += "CM" c_count -= 9 elif c_count == 4: numerals += "CD" c_count -= 4 if c_count >= 5: numerals += "D" c_count -= 5 numerals += c_count * "C" num %= 100 SCREAMING_SNAKE_CASE__ : List[Any] = num // 10 if x_count == 9: numerals += "XC" x_count -= 9 elif x_count == 4: numerals += "XL" x_count -= 4 if x_count >= 5: numerals += "L" x_count -= 5 numerals += x_count * "X" num %= 10 if num == 9: numerals += "IX" num -= 9 elif num == 4: numerals += "IV" num -= 4 if num >= 5: numerals += "V" num -= 5 numerals += num * "I" return numerals def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int: SCREAMING_SNAKE_CASE__ : int = 0 with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea: SCREAMING_SNAKE_CASE__ : str = filea.readlines() for line in lines: SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip() SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase ) savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase ) return savings if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" from typing import Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images from ...utils import TensorType, logging a :List[Any] = logging.get_logger(__name__) class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[str] = ["""pixel_values"""] def __init__( self , _a = True , _a = 1 / 255 , _a = True , _a = 8 , **_a , ) -> None: """simple docstring""" super().__init__(**_a ) SCREAMING_SNAKE_CASE__ : str = do_rescale SCREAMING_SNAKE_CASE__ : List[Any] = rescale_factor SCREAMING_SNAKE_CASE__ : Tuple = do_pad SCREAMING_SNAKE_CASE__ : Optional[int] = pad_size def _a ( self , _a , _a , _a = None , **_a ) -> np.ndarray: """simple docstring""" return rescale(_a , scale=_a , data_format=_a , **_a ) def _a ( self , _a , _a , _a = None ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = get_image_size(_a ) SCREAMING_SNAKE_CASE__ : int = (old_height // size + 1) * size - old_height SCREAMING_SNAKE_CASE__ : Any = (old_width // size + 1) * size - old_width return pad(_a , ((0, pad_height), (0, pad_width)) , mode="""symmetric""" , data_format=_a ) def _a ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale SCREAMING_SNAKE_CASE__ : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor SCREAMING_SNAKE_CASE__ : List[str] = do_pad if do_pad is not None else self.do_pad SCREAMING_SNAKE_CASE__ : Dict = pad_size if pad_size is not None else self.pad_size SCREAMING_SNAKE_CASE__ : List[Any] = make_list_of_images(_a ) if not valid_images(_a ): raise ValueError( """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """ """torch.Tensor, tf.Tensor or jax.ndarray.""" ) if do_rescale and rescale_factor is None: raise ValueError("""Rescale factor must be specified if do_rescale is True.""" ) # All transformations expect numpy arrays. SCREAMING_SNAKE_CASE__ : Dict = [to_numpy_array(_a ) for image in images] if do_rescale: SCREAMING_SNAKE_CASE__ : Optional[int] = [self.rescale(image=_a , scale=_a ) for image in images] if do_pad: SCREAMING_SNAKE_CASE__ : Dict = [self.pad(_a , size=_a ) for image in images] SCREAMING_SNAKE_CASE__ : Any = [to_channel_dimension_format(_a , _a ) for image in images] SCREAMING_SNAKE_CASE__ : int = {"""pixel_values""": images} return BatchFeature(data=_a , tensor_type=_a )
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"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow if is_tf_available(): import numpy as np import tensorflow as tf from transformers import TFCamembertModel @require_tf @require_sentencepiece @require_tokenizers class __a (unittest.TestCase): '''simple docstring''' @slow def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" ) SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor( [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !" SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""] SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) ) self.assertEqual(output.shape , _a ) # compare the actual values for a slice. SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor( [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , ) # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0') # camembert.eval() # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach() self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
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"""simple docstring""" import argparse import json from pathlib import Path import requests import torch from huggingface_hub import cached_download, hf_hub_url from PIL import Image from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor from transformers.utils import logging logging.set_verbosity_info() a :str = logging.get_logger(__name__) def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ : Dict = DPTConfig(embedding_type="""hybrid""" ) if "large" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1024 SCREAMING_SNAKE_CASE__ : Union[str, Any] = 4096 SCREAMING_SNAKE_CASE__ : str = 24 SCREAMING_SNAKE_CASE__ : Any = 16 SCREAMING_SNAKE_CASE__ : str = [5, 11, 17, 23] SCREAMING_SNAKE_CASE__ : Optional[int] = [256, 512, 1024, 1024] SCREAMING_SNAKE_CASE__ : int = (1, 384, 384) if "nyu" or "midas" in checkpoint_url: SCREAMING_SNAKE_CASE__ : Any = 768 SCREAMING_SNAKE_CASE__ : List[str] = [1, 1, 1, 0.5] SCREAMING_SNAKE_CASE__ : Optional[int] = [256, 512, 768, 768] SCREAMING_SNAKE_CASE__ : List[str] = 150 SCREAMING_SNAKE_CASE__ : Any = 16 SCREAMING_SNAKE_CASE__ : Optional[int] = (1, 384, 384) SCREAMING_SNAKE_CASE__ : Optional[Any] = False SCREAMING_SNAKE_CASE__ : Union[str, Any] = """project""" if "ade" in checkpoint_url: SCREAMING_SNAKE_CASE__ : str = True SCREAMING_SNAKE_CASE__ : List[Any] = 768 SCREAMING_SNAKE_CASE__ : int = [1, 1, 1, 0.5] SCREAMING_SNAKE_CASE__ : List[Any] = 150 SCREAMING_SNAKE_CASE__ : List[str] = 16 SCREAMING_SNAKE_CASE__ : Optional[Any] = """huggingface/label-files""" SCREAMING_SNAKE_CASE__ : List[Any] = """ade20k-id2label.json""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) ) , """r""" ) ) SCREAMING_SNAKE_CASE__ : str = {int(__lowerCAmelCase ): v for k, v in idalabel.items()} SCREAMING_SNAKE_CASE__ : Tuple = idalabel SCREAMING_SNAKE_CASE__ : Optional[int] = {v: k for k, v in idalabel.items()} SCREAMING_SNAKE_CASE__ : Tuple = [1, 150, 480, 480] return config, expected_shape def _lowercase ( __lowerCAmelCase ) -> List[str]: SCREAMING_SNAKE_CASE__ : List[Any] = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""] for k in ignore_keys: state_dict.pop(__lowerCAmelCase , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Any: if ( "pretrained.model" in name and "cls_token" not in name and "pos_embed" not in name and "patch_embed" not in name ): SCREAMING_SNAKE_CASE__ : Dict = name.replace("""pretrained.model""" , """dpt.encoder""" ) if "pretrained.model" in name: SCREAMING_SNAKE_CASE__ : int = name.replace("""pretrained.model""" , """dpt.embeddings""" ) if "patch_embed" in name: SCREAMING_SNAKE_CASE__ : Dict = name.replace("""patch_embed""" , """""" ) if "pos_embed" in name: SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""pos_embed""" , """position_embeddings""" ) if "attn.proj" in name: SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""attn.proj""" , """attention.output.dense""" ) if "proj" in name and "project" not in name: SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""proj""" , """projection""" ) if "blocks" in name: SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""blocks""" , """layer""" ) if "mlp.fc1" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" ) if "mlp.fc2" in name: SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""mlp.fc2""" , """output.dense""" ) if "norm1" in name and "backbone" not in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""norm1""" , """layernorm_before""" ) if "norm2" in name and "backbone" not in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""norm2""" , """layernorm_after""" ) if "scratch.output_conv" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""scratch.output_conv""" , """head""" ) if "scratch" in name: SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""scratch""" , """neck""" ) if "layer1_rn" in name: SCREAMING_SNAKE_CASE__ : int = name.replace("""layer1_rn""" , """convs.0""" ) if "layer2_rn" in name: SCREAMING_SNAKE_CASE__ : int = name.replace("""layer2_rn""" , """convs.1""" ) if "layer3_rn" in name: SCREAMING_SNAKE_CASE__ : Any = name.replace("""layer3_rn""" , """convs.2""" ) if "layer4_rn" in name: SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""layer4_rn""" , """convs.3""" ) if "refinenet" in name: SCREAMING_SNAKE_CASE__ : Tuple = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] ) # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3 SCREAMING_SNAKE_CASE__ : List[Any] = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' ) if "out_conv" in name: SCREAMING_SNAKE_CASE__ : int = name.replace("""out_conv""" , """projection""" ) if "resConfUnit1" in name: SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""resConfUnit1""" , """residual_layer1""" ) if "resConfUnit2" in name: SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""resConfUnit2""" , """residual_layer2""" ) if "conv1" in name: SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""conv1""" , """convolution1""" ) if "conv2" in name: SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""conv2""" , """convolution2""" ) # readout blocks if "pretrained.act_postprocess1.0.project.0" in name: SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" ) if "pretrained.act_postprocess2.0.project.0" in name: SCREAMING_SNAKE_CASE__ : Dict = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" ) if "pretrained.act_postprocess3.0.project.0" in name: SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" ) if "pretrained.act_postprocess4.0.project.0" in name: SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" ) # resize blocks if "pretrained.act_postprocess1.3" in name: SCREAMING_SNAKE_CASE__ : str = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" ) if "pretrained.act_postprocess1.4" in name: SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" ) if "pretrained.act_postprocess2.3" in name: SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" ) if "pretrained.act_postprocess2.4" in name: SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" ) if "pretrained.act_postprocess3.3" in name: SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" ) if "pretrained.act_postprocess4.3" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" ) if "pretrained.act_postprocess4.4" in name: SCREAMING_SNAKE_CASE__ : str = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" ) if "pretrained" in name: SCREAMING_SNAKE_CASE__ : Any = name.replace("""pretrained""" , """dpt""" ) if "bn" in name: SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""bn""" , """batch_norm""" ) if "head" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""head""" , """head.head""" ) if "encoder.norm" in name: SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""encoder.norm""" , """layernorm""" ) if "auxlayer" in name: SCREAMING_SNAKE_CASE__ : Dict = name.replace("""auxlayer""" , """auxiliary_head.head""" ) if "backbone" in name: SCREAMING_SNAKE_CASE__ : int = name.replace("""backbone""" , """backbone.bit.encoder""" ) if ".." in name: SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""..""" , """.""" ) if "stem.conv" in name: SCREAMING_SNAKE_CASE__ : str = name.replace("""stem.conv""" , """bit.embedder.convolution""" ) if "blocks" in name: SCREAMING_SNAKE_CASE__ : int = name.replace("""blocks""" , """layers""" ) if "convolution" in name and "backbone" in name: SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""convolution""" , """conv""" ) if "layer" in name and "backbone" in name: SCREAMING_SNAKE_CASE__ : int = name.replace("""layer""" , """layers""" ) if "backbone.bit.encoder.bit" in name: SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""backbone.bit.encoder.bit""" , """backbone.bit""" ) if "embedder.conv" in name: SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""embedder.conv""" , """embedder.convolution""" ) if "backbone.bit.encoder.stem.norm" in name: SCREAMING_SNAKE_CASE__ : Dict = name.replace("""backbone.bit.encoder.stem.norm""" , """backbone.bit.embedder.norm""" ) return name def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: for i in range(config.num_hidden_layers ): # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' ) SCREAMING_SNAKE_CASE__ : Dict = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' ) # next, add query, keys and values (in that order) to the state dict SCREAMING_SNAKE_CASE__ : str = in_proj_weight[: config.hidden_size, :] SCREAMING_SNAKE_CASE__ : int = in_proj_bias[: config.hidden_size] SCREAMING_SNAKE_CASE__ : Union[str, Any] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] SCREAMING_SNAKE_CASE__ : List[str] = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] SCREAMING_SNAKE_CASE__ : Optional[Any] = in_proj_weight[ -config.hidden_size :, : ] SCREAMING_SNAKE_CASE__ : Any = in_proj_bias[-config.hidden_size :] def _lowercase ( ) -> Tuple: SCREAMING_SNAKE_CASE__ : Any = """http://images.cocodataset.org/val2017/000000039769.jpg""" SCREAMING_SNAKE_CASE__ : str = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw ) return im @torch.no_grad() def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : str = get_dpt_config(__lowerCAmelCase ) # load original state_dict from URL # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu") SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" ) # remove certain keys remove_ignore_keys_(__lowerCAmelCase ) # rename keys for key in state_dict.copy().keys(): SCREAMING_SNAKE_CASE__ : Optional[int] = state_dict.pop(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = val # read in qkv matrices read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase ) # load HuggingFace model SCREAMING_SNAKE_CASE__ : str = DPTForSemanticSegmentation(__lowerCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase ) model.load_state_dict(__lowerCAmelCase ) model.eval() # Check outputs on an image SCREAMING_SNAKE_CASE__ : List[Any] = 480 if """ade""" in checkpoint_url else 384 SCREAMING_SNAKE_CASE__ : str = DPTImageProcessor(size=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[int] = prepare_img() SCREAMING_SNAKE_CASE__ : Dict = image_processor(__lowerCAmelCase , return_tensors="""pt""" ) # forward pass SCREAMING_SNAKE_CASE__ : Optional[Any] = model(**__lowerCAmelCase ).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase ).predicted_depth if show_prediction: SCREAMING_SNAKE_CASE__ : List[Any] = ( torch.nn.functional.interpolate( outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="""bicubic""" , align_corners=__lowerCAmelCase , ) .squeeze() .cpu() .numpy() ) Image.fromarray((prediction / prediction.max()) * 255 ).show() if pytorch_dump_folder_path is not None: Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase ) print(F'''Saving model to {pytorch_dump_folder_path}''' ) model.save_pretrained(__lowerCAmelCase ) print(F'''Saving image processor to {pytorch_dump_folder_path}''' ) image_processor.save_pretrained(__lowerCAmelCase ) if push_to_hub: model.push_to_hub("""ybelkada/dpt-hybrid-midas""" ) image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" ) if __name__ == "__main__": a :str = argparse.ArgumentParser() # Required parameters parser.add_argument( "--checkpoint_url", default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt", type=str, help="URL of the original DPT checkpoint you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=False, help="Path to the output PyTorch model directory.", ) parser.add_argument( "--push_to_hub", action="store_true", ) parser.add_argument( "--model_name", default="dpt-large", type=str, help="Name of the model, in case you're pushing to the hub.", ) parser.add_argument( "--show_prediction", action="store_true", ) a :int = parser.parse_args() convert_dpt_checkpoint( args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction )
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices a :List[Any] = logging.get_logger(__name__) a :Optional[int] = { "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json", } class __a (UpperCamelCase_ , UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = """focalnet""" def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]: """simple docstring""" super().__init__(**_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size SCREAMING_SNAKE_CASE__ : str = patch_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes SCREAMING_SNAKE_CASE__ : Optional[int] = depths SCREAMING_SNAKE_CASE__ : Any = focal_levels SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows SCREAMING_SNAKE_CASE__ : Any = hidden_act SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate SCREAMING_SNAKE_CASE__ : str = use_layerscale SCREAMING_SNAKE_CASE__ : int = layerscale_value SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator SCREAMING_SNAKE_CASE__ : str = initializer_range SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps SCREAMING_SNAKE_CASE__ : Any = encoder_stride SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices( out_features=_a , out_indices=_a , stage_names=self.stage_names )
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"""simple docstring""" import doctest import sys import warnings from os.path import abspath, dirname, join import _pytest from transformers.testing_utils import HfDoctestModule, HfDocTestParser # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. a :Optional[Any] = abspath(join(dirname(__file__), "src")) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action="ignore", category=FutureWarning) def _lowercase ( __lowerCAmelCase ) -> str: config.addinivalue_line( """markers""" , """is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested""" ) config.addinivalue_line( """markers""" , """is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested""" ) config.addinivalue_line("""markers""" , """is_pipeline_test: mark test to run only when pipelines are tested""" ) config.addinivalue_line("""markers""" , """is_staging_test: mark test to run only in the staging environment""" ) config.addinivalue_line("""markers""" , """accelerate_tests: mark test that require accelerate""" ) config.addinivalue_line("""markers""" , """tool_tests: mark the tool tests that are run on their specific schedule""" ) def _lowercase ( __lowerCAmelCase ) -> List[Any]: from transformers.testing_utils import pytest_addoption_shared pytest_addoption_shared(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Tuple: from transformers.testing_utils import pytest_terminal_summary_main SCREAMING_SNAKE_CASE__ : Any = terminalreporter.config.getoption("""--make-reports""" ) if make_reports: pytest_terminal_summary_main(__lowerCAmelCase , id=__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int: # If no tests are collected, pytest exists with code 5, which makes the CI fail. if exitstatus == 5: SCREAMING_SNAKE_CASE__ : Any = 0 # Doctest custom flag to ignore output. a :Tuple = doctest.register_optionflag("IGNORE_RESULT") a :Optional[int] = doctest.OutputChecker class __a (UpperCamelCase_): '''simple docstring''' def _a ( self , _a , _a , _a ) -> Optional[Any]: """simple docstring""" if IGNORE_RESULT & optionflags: return True return OutputChecker.check_output(self , _a , _a , _a ) a :List[Any] = CustomOutputChecker a :List[Any] = HfDoctestModule a :Tuple = HfDocTestParser
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"""simple docstring""" import unittest import numpy as np from transformers import RoFormerConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax.numpy as jnp from transformers.models.roformer.modeling_flax_roformer import ( FlaxRoFormerForMaskedLM, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerModel, ) class __a (unittest.TestCase): '''simple docstring''' def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = parent SCREAMING_SNAKE_CASE__ : Tuple = batch_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length SCREAMING_SNAKE_CASE__ : Optional[int] = is_training SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels SCREAMING_SNAKE_CASE__ : int = vocab_size SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads SCREAMING_SNAKE_CASE__ : Dict = intermediate_size SCREAMING_SNAKE_CASE__ : int = hidden_act SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size SCREAMING_SNAKE_CASE__ : int = initializer_range SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = None if self.use_attention_mask: SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] ) SCREAMING_SNAKE_CASE__ : Tuple = None if self.use_token_type_ids: SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , ) return config, input_ids, token_type_ids, attention_mask def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask} return config, inputs_dict @require_flax class __a (UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = True _SCREAMING_SNAKE_CASE :Optional[Any] = ( ( FlaxRoFormerModel, FlaxRoFormerForMaskedLM, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, ) if is_flax_available() else () ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self ) @slow def _a ( self ) -> int: """simple docstring""" for model_class_name in self.all_model_classes: SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a ) SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) ) self.assertIsNotNone(_a ) @require_flax class __a (unittest.TestCase): '''simple docstring''' @slow def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" ) SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] ) SCREAMING_SNAKE_CASE__ : str = model(_a )[0] SCREAMING_SNAKE_CASE__ : List[Any] = 50_000 SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size) self.assertEqual(output.shape , _a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array( [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] ) self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
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"""simple docstring""" import re from pathlib import Path from unittest import TestCase import pytest @pytest.mark.integration class __a (UpperCamelCase_): '''simple docstring''' def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read() SCREAMING_SNAKE_CASE__ : str = regexp.search(_a ) return match def _a ( self , _a ) -> Optional[Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL ) SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read() # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search` SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a ) SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None] return matches[0] if matches else None def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_encoding_on_file_open(str(_a ) ): raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_print_statements(str(_a ) ): raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
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"""simple docstring""" a :List[str] = [ (1_000, "M"), (900, "CM"), (500, "D"), (400, "CD"), (100, "C"), (90, "XC"), (50, "L"), (40, "XL"), (10, "X"), (9, "IX"), (5, "V"), (4, "IV"), (1, "I"), ] def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000} SCREAMING_SNAKE_CASE__ : List[Any] = 0 SCREAMING_SNAKE_CASE__ : List[str] = 0 while place < len(__lowerCAmelCase ): if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]): total += vals[roman[place + 1]] - vals[roman[place]] place += 2 else: total += vals[roman[place]] place += 1 return total def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Any = [] for arabic, roman in ROMAN: ((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase ) result.append(roman * factor ) if number == 0: break return "".join(__lowerCAmelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" import gc import random import unittest import numpy as np import torch from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline from diffusers.pipelines.shap_e import ShapERenderer from diffusers.utils import floats_tensor, load_image, load_numpy, slow from diffusers.utils.testing_utils import require_torch_gpu, torch_device from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference class __a (UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = ShapEImgaImgPipeline _SCREAMING_SNAKE_CASE :Tuple = ["""image"""] _SCREAMING_SNAKE_CASE :Any = ["""image"""] _SCREAMING_SNAKE_CASE :int = [ """num_images_per_prompt""", """num_inference_steps""", """generator""", """latents""", """guidance_scale""", """frame_size""", """output_type""", """return_dict""", ] _SCREAMING_SNAKE_CASE :Tuple = False @property def _a ( self ) -> Dict: """simple docstring""" return 32 @property def _a ( self ) -> Tuple: """simple docstring""" return 32 @property def _a ( self ) -> Any: """simple docstring""" return self.time_input_dim * 4 @property def _a ( self ) -> Any: """simple docstring""" return 8 @property def _a ( self ) -> int: """simple docstring""" torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Optional[int] = CLIPVisionConfig( hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , ) SCREAMING_SNAKE_CASE__ : Optional[int] = CLIPVisionModel(_a ) return model @property def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = CLIPImageProcessor( crop_size=224 , do_center_crop=_a , do_normalize=_a , do_resize=_a , image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073] , image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711] , resample=3 , size=224 , ) return image_processor @property def _a ( self ) -> List[str]: """simple docstring""" torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : List[str] = { """num_attention_heads""": 2, """attention_head_dim""": 16, """embedding_dim""": self.time_input_dim, """num_embeddings""": 32, """embedding_proj_dim""": self.text_embedder_hidden_size, """time_embed_dim""": self.time_embed_dim, """num_layers""": 1, """clip_embed_dim""": self.time_input_dim * 2, """additional_embeddings""": 0, """time_embed_act_fn""": """gelu""", """norm_in_type""": """layer""", """embedding_proj_norm_type""": """layer""", """encoder_hid_proj_type""": None, """added_emb_type""": None, } SCREAMING_SNAKE_CASE__ : Optional[Any] = PriorTransformer(**_a ) return model @property def _a ( self ) -> Dict: """simple docstring""" torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Tuple = { """param_shapes""": ( (self.renderer_dim, 93), (self.renderer_dim, 8), (self.renderer_dim, 8), (self.renderer_dim, 8), ), """d_latent""": self.time_input_dim, """d_hidden""": self.renderer_dim, """n_output""": 12, """background""": ( 0.1, 0.1, 0.1, ), } SCREAMING_SNAKE_CASE__ : List[str] = ShapERenderer(**_a ) return model def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = self.dummy_prior SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_image_encoder SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_image_processor SCREAMING_SNAKE_CASE__ : Dict = self.dummy_renderer SCREAMING_SNAKE_CASE__ : Optional[int] = HeunDiscreteScheduler( beta_schedule="""exp""" , num_train_timesteps=1_024 , prediction_type="""sample""" , use_karras_sigmas=_a , clip_sample=_a , clip_sample_range=1.0 , ) SCREAMING_SNAKE_CASE__ : List[str] = { """prior""": prior, """image_encoder""": image_encoder, """image_processor""": image_processor, """renderer""": renderer, """scheduler""": scheduler, } return components def _a ( self , _a , _a=0 ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(_a ) ).to(_a ) if str(_a ).startswith("""mps""" ): SCREAMING_SNAKE_CASE__ : Optional[int] = torch.manual_seed(_a ) else: SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device=_a ).manual_seed(_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = { """image""": input_image, """generator""": generator, """num_inference_steps""": 1, """frame_size""": 32, """output_type""": """np""", } return inputs def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = """cpu""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dummy_components() SCREAMING_SNAKE_CASE__ : int = self.pipeline_class(**_a ) SCREAMING_SNAKE_CASE__ : Dict = pipe.to(_a ) pipe.set_progress_bar_config(disable=_a ) SCREAMING_SNAKE_CASE__ : List[str] = pipe(**self.get_dummy_inputs(_a ) ) SCREAMING_SNAKE_CASE__ : Optional[int] = output.images[0] SCREAMING_SNAKE_CASE__ : List[Any] = image[0, -3:, -3:, -1] assert image.shape == (20, 32, 32, 3) SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array( [ 0.00_039_216, 0.00_039_216, 0.00_039_216, 0.00_039_216, 0.00_039_216, 0.00_039_216, 0.00_039_216, 0.00_039_216, 0.00_039_216, ] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def _a ( self ) -> Optional[int]: """simple docstring""" self._test_inference_batch_consistent(batch_sizes=[1, 2] ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = torch_device == """cpu""" SCREAMING_SNAKE_CASE__ : Dict = True self._test_inference_batch_single_identical( batch_size=2 , test_max_difference=_a , relax_max_difference=_a , ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.get_dummy_components() SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.pipeline_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe.to(_a ) pipe.set_progress_bar_config(disable=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = 1 SCREAMING_SNAKE_CASE__ : Optional[Any] = 2 SCREAMING_SNAKE_CASE__ : Any = self.get_dummy_inputs(_a ) for key in inputs.keys(): if key in self.batch_params: SCREAMING_SNAKE_CASE__ : Dict = batch_size * [inputs[key]] SCREAMING_SNAKE_CASE__ : Optional[int] = pipe(**_a , num_images_per_prompt=_a )[0] assert images.shape[0] == batch_size * num_images_per_prompt @slow @require_torch_gpu class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> Optional[int]: """simple docstring""" super().tearDown() gc.collect() torch.cuda.empty_cache() def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = load_image( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png""" ) SCREAMING_SNAKE_CASE__ : Dict = load_numpy( """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/test_shap_e_img2img_out.npy""" ) SCREAMING_SNAKE_CASE__ : Optional[int] = ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img""" ) SCREAMING_SNAKE_CASE__ : Any = pipe.to(_a ) pipe.set_progress_bar_config(disable=_a ) SCREAMING_SNAKE_CASE__ : Any = torch.Generator(device=_a ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Dict = pipe( _a , generator=_a , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0] assert images.shape == (20, 64, 64, 3) assert_mean_pixel_difference(_a , _a )
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) a :Any = { "configuration_roberta_prelayernorm": [ "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaPreLayerNormConfig", "RobertaPreLayerNormOnnxConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Union[str, Any] = [ "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "RobertaPreLayerNormForCausalLM", "RobertaPreLayerNormForMaskedLM", "RobertaPreLayerNormForMultipleChoice", "RobertaPreLayerNormForQuestionAnswering", "RobertaPreLayerNormForSequenceClassification", "RobertaPreLayerNormForTokenClassification", "RobertaPreLayerNormModel", "RobertaPreLayerNormPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Optional[Any] = [ "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRobertaPreLayerNormForCausalLM", "TFRobertaPreLayerNormForMaskedLM", "TFRobertaPreLayerNormForMultipleChoice", "TFRobertaPreLayerNormForQuestionAnswering", "TFRobertaPreLayerNormForSequenceClassification", "TFRobertaPreLayerNormForTokenClassification", "TFRobertaPreLayerNormMainLayer", "TFRobertaPreLayerNormModel", "TFRobertaPreLayerNormPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :List[Any] = [ "FlaxRobertaPreLayerNormForCausalLM", "FlaxRobertaPreLayerNormForMaskedLM", "FlaxRobertaPreLayerNormForMultipleChoice", "FlaxRobertaPreLayerNormForQuestionAnswering", "FlaxRobertaPreLayerNormForSequenceClassification", "FlaxRobertaPreLayerNormForTokenClassification", "FlaxRobertaPreLayerNormModel", "FlaxRobertaPreLayerNormPreTrainedModel", ] if TYPE_CHECKING: from .configuration_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaPreLayerNormConfig, RobertaPreLayerNormOnnxConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaPreLayerNormForCausalLM, RobertaPreLayerNormForMaskedLM, RobertaPreLayerNormForMultipleChoice, RobertaPreLayerNormForQuestionAnswering, RobertaPreLayerNormForSequenceClassification, RobertaPreLayerNormForTokenClassification, RobertaPreLayerNormModel, RobertaPreLayerNormPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_roberta_prelayernorm import ( TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, TFRobertaPreLayerNormForCausalLM, TFRobertaPreLayerNormForMaskedLM, TFRobertaPreLayerNormForMultipleChoice, TFRobertaPreLayerNormForQuestionAnswering, TFRobertaPreLayerNormForSequenceClassification, TFRobertaPreLayerNormForTokenClassification, TFRobertaPreLayerNormMainLayer, TFRobertaPreLayerNormModel, TFRobertaPreLayerNormPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_roberta_prelayernorm import ( FlaxRobertaPreLayerNormForCausalLM, FlaxRobertaPreLayerNormForMaskedLM, FlaxRobertaPreLayerNormForMultipleChoice, FlaxRobertaPreLayerNormForQuestionAnswering, FlaxRobertaPreLayerNormForSequenceClassification, FlaxRobertaPreLayerNormForTokenClassification, FlaxRobertaPreLayerNormModel, FlaxRobertaPreLayerNormPreTrainedModel, ) else: import sys a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" import gc import random import tempfile import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline from diffusers.utils import floats_tensor, nightly, torch_device from diffusers.utils.testing_utils import require_torch_gpu class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> List[Any]: """simple docstring""" super().tearDown() gc.collect() torch.cuda.empty_cache() @property def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = 1 SCREAMING_SNAKE_CASE__ : Dict = 3 SCREAMING_SNAKE_CASE__ : Optional[int] = (32, 32) SCREAMING_SNAKE_CASE__ : Any = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(_a ) return image @property def _a ( self ) -> List[str]: """simple docstring""" torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Tuple = UNetaDConditionModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , ) return model @property def _a ( self ) -> List[str]: """simple docstring""" torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Optional[int] = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , ) return model @property def _a ( self ) -> Any: """simple docstring""" torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Tuple = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , ) return CLIPTextModel(_a ) @property def _a ( self ) -> Optional[int]: """simple docstring""" def extract(*_a , **_a ): class __a : '''simple docstring''' def __init__( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = torch.ones([0] ) def _a ( self , _a ) -> List[str]: """simple docstring""" self.pixel_values.to(_a ) return self return Out() return extract def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = """cpu""" # ensure determinism for the device-dependent torch.Generator SCREAMING_SNAKE_CASE__ : Optional[Any] = self.dummy_cond_unet SCREAMING_SNAKE_CASE__ : List[Any] = DDIMScheduler( beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_a , set_alpha_to_one=_a , ) SCREAMING_SNAKE_CASE__ : str = self.dummy_vae SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_text_encoder SCREAMING_SNAKE_CASE__ : List[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" ) # make sure here that pndm scheduler skips prk SCREAMING_SNAKE_CASE__ : Dict = StableDiffusionPipeline( unet=_a , scheduler=_a , vae=_a , text_encoder=_a , tokenizer=_a , safety_checker=_a , feature_extractor=self.dummy_extractor , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = sd_pipe.to(_a ) sd_pipe.set_progress_bar_config(disable=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = """A painting of a squirrel eating a burger""" SCREAMING_SNAKE_CASE__ : List[str] = torch.Generator(device=_a ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : int = sd_pipe([prompt] , generator=_a , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = output.images SCREAMING_SNAKE_CASE__ : Optional[int] = torch.Generator(device=_a ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Any = sd_pipe( [prompt] , generator=_a , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=_a , )[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = image[0, -3:, -3:, -1] SCREAMING_SNAKE_CASE__ : Tuple = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) SCREAMING_SNAKE_CASE__ : List[Any] = np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = """cpu""" # ensure determinism for the device-dependent torch.Generator SCREAMING_SNAKE_CASE__ : Dict = self.dummy_cond_unet SCREAMING_SNAKE_CASE__ : int = PNDMScheduler(skip_prk_steps=_a ) SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_vae SCREAMING_SNAKE_CASE__ : Tuple = self.dummy_text_encoder SCREAMING_SNAKE_CASE__ : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" ) # make sure here that pndm scheduler skips prk SCREAMING_SNAKE_CASE__ : List[str] = StableDiffusionPipeline( unet=_a , scheduler=_a , vae=_a , text_encoder=_a , tokenizer=_a , safety_checker=_a , feature_extractor=self.dummy_extractor , ) SCREAMING_SNAKE_CASE__ : Optional[int] = sd_pipe.to(_a ) sd_pipe.set_progress_bar_config(disable=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = """A painting of a squirrel eating a burger""" SCREAMING_SNAKE_CASE__ : str = torch.Generator(device=_a ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = sd_pipe([prompt] , generator=_a , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" ) SCREAMING_SNAKE_CASE__ : int = output.images SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Generator(device=_a ).manual_seed(0 ) SCREAMING_SNAKE_CASE__ : List[str] = sd_pipe( [prompt] , generator=_a , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=_a , )[0] SCREAMING_SNAKE_CASE__ : List[Any] = image[0, -3:, -3:, -1] SCREAMING_SNAKE_CASE__ : List[Any] = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) SCREAMING_SNAKE_CASE__ : Dict = np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = StableDiffusionPipeline.from_pretrained( """hf-internal-testing/tiny-stable-diffusion-lms-pipe""" , safety_checker=_a ) assert isinstance(_a , _a ) assert isinstance(pipe.scheduler , _a ) assert pipe.safety_checker is None SCREAMING_SNAKE_CASE__ : Dict = pipe("""example prompt""" , num_inference_steps=2 ).images[0] assert image is not None # check that there's no error when saving a pipeline with one of the models being None with tempfile.TemporaryDirectory() as tmpdirname: pipe.save_pretrained(_a ) SCREAMING_SNAKE_CASE__ : Dict = StableDiffusionPipeline.from_pretrained(_a ) # sanity check that the pipeline still works assert pipe.safety_checker is None SCREAMING_SNAKE_CASE__ : Dict = pipe("""example prompt""" , num_inference_steps=2 ).images[0] assert image is not None @unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = self.dummy_cond_unet SCREAMING_SNAKE_CASE__ : Union[str, Any] = PNDMScheduler(skip_prk_steps=_a ) SCREAMING_SNAKE_CASE__ : Tuple = self.dummy_vae SCREAMING_SNAKE_CASE__ : Any = self.dummy_text_encoder SCREAMING_SNAKE_CASE__ : Any = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" ) # put models in fp16 SCREAMING_SNAKE_CASE__ : Optional[int] = unet.half() SCREAMING_SNAKE_CASE__ : Tuple = vae.half() SCREAMING_SNAKE_CASE__ : Dict = bert.half() # make sure here that pndm scheduler skips prk SCREAMING_SNAKE_CASE__ : List[str] = StableDiffusionPipeline( unet=_a , scheduler=_a , vae=_a , text_encoder=_a , tokenizer=_a , safety_checker=_a , feature_extractor=self.dummy_extractor , ) SCREAMING_SNAKE_CASE__ : List[Any] = sd_pipe.to(_a ) sd_pipe.set_progress_bar_config(disable=_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = """A painting of a squirrel eating a burger""" SCREAMING_SNAKE_CASE__ : Optional[Any] = sd_pipe([prompt] , num_inference_steps=2 , output_type="""np""" ).images assert image.shape == (1, 64, 64, 3) @nightly @require_torch_gpu class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> Dict: """simple docstring""" super().tearDown() gc.collect() torch.cuda.empty_cache() def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config ) SCREAMING_SNAKE_CASE__ : Optional[Any] = sd_pipe.to(_a ) sd_pipe.set_progress_bar_config(disable=_a ) SCREAMING_SNAKE_CASE__ : List[Any] = ( """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle""" """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with""" """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and""" """ children from bahnhof zoo, detailed """ ) SCREAMING_SNAKE_CASE__ : Any = 4_003_660_346 SCREAMING_SNAKE_CASE__ : str = 7 # without safety guidance (sld_guidance_scale = 0) SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(_a ) SCREAMING_SNAKE_CASE__ : int = sd_pipe( [prompt] , generator=_a , guidance_scale=_a , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , ) SCREAMING_SNAKE_CASE__ : Tuple = output.images SCREAMING_SNAKE_CASE__ : str = image[0, -3:, -3:, -1] SCREAMING_SNAKE_CASE__ : Tuple = [0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176] assert image.shape == (1, 512, 512, 3) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 # without safety guidance (strong configuration) SCREAMING_SNAKE_CASE__ : int = torch.manual_seed(_a ) SCREAMING_SNAKE_CASE__ : Tuple = sd_pipe( [prompt] , generator=_a , guidance_scale=_a , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2_000 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = output.images SCREAMING_SNAKE_CASE__ : Union[str, Any] = image[0, -3:, -3:, -1] SCREAMING_SNAKE_CASE__ : int = [0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719] assert image.shape == (1, 512, 512, 3) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=_a ) SCREAMING_SNAKE_CASE__ : Any = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config ) SCREAMING_SNAKE_CASE__ : int = sd_pipe.to(_a ) sd_pipe.set_progress_bar_config(disable=_a ) SCREAMING_SNAKE_CASE__ : List[str] = """padme amidala taking a bath artwork, safe for work, no nudity""" SCREAMING_SNAKE_CASE__ : Tuple = 2_734_971_755 SCREAMING_SNAKE_CASE__ : int = 7 SCREAMING_SNAKE_CASE__ : Dict = torch.manual_seed(_a ) SCREAMING_SNAKE_CASE__ : str = sd_pipe( [prompt] , generator=_a , guidance_scale=_a , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , ) SCREAMING_SNAKE_CASE__ : str = output.images SCREAMING_SNAKE_CASE__ : Tuple = image[0, -3:, -3:, -1] SCREAMING_SNAKE_CASE__ : Dict = [0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297] assert image.shape == (1, 512, 512, 3) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = sd_pipe( [prompt] , generator=_a , guidance_scale=_a , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2_000 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = output.images SCREAMING_SNAKE_CASE__ : Tuple = image[0, -3:, -3:, -1] SCREAMING_SNAKE_CASE__ : Dict = [0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443] assert image.shape == (1, 512, 512, 3) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ) SCREAMING_SNAKE_CASE__ : int = sd_pipe.to(_a ) sd_pipe.set_progress_bar_config(disable=_a ) SCREAMING_SNAKE_CASE__ : Any = ( """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.""" """ leyendecker""" ) SCREAMING_SNAKE_CASE__ : Optional[int] = 1_044_355_234 SCREAMING_SNAKE_CASE__ : List[str] = 12 SCREAMING_SNAKE_CASE__ : Any = torch.manual_seed(_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = sd_pipe( [prompt] , generator=_a , guidance_scale=_a , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , ) SCREAMING_SNAKE_CASE__ : List[str] = output.images SCREAMING_SNAKE_CASE__ : Dict = image[0, -3:, -3:, -1] SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] ) assert image.shape == (1, 512, 512, 3) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7 SCREAMING_SNAKE_CASE__ : Any = torch.manual_seed(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = sd_pipe( [prompt] , generator=_a , guidance_scale=_a , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2_000 , sld_warmup_steps=7 , sld_threshold=0.025 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , ) SCREAMING_SNAKE_CASE__ : Dict = output.images SCREAMING_SNAKE_CASE__ : Union[str, Any] = image[0, -3:, -3:, -1] SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561] ) assert image.shape == (1, 512, 512, 3) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
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"""simple docstring""" import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import BertTokenizer, BertTokenizerFast from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import AlignProcessor, EfficientNetImageProcessor @require_vision class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp() SCREAMING_SNAKE_CASE__ : Dict = [ """[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest""", ] SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) ) SCREAMING_SNAKE_CASE__ : Optional[Any] = { """do_resize""": True, """size""": 20, """do_center_crop""": True, """crop_size""": 18, """do_normalize""": True, """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073], """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711], } SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a ) with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp: json.dump(_a , _a ) def _a ( self , **_a ) -> List[Any]: """simple docstring""" return BertTokenizer.from_pretrained(self.tmpdirname , **_a ) def _a ( self , **_a ) -> List[Any]: """simple docstring""" return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a ) def _a ( self , **_a ) -> Any: """simple docstring""" return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a ) def _a ( self ) -> List[Any]: """simple docstring""" shutil.rmtree(self.tmpdirname ) def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )] SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs] return image_inputs def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer() SCREAMING_SNAKE_CASE__ : str = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a ) processor_slow.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a ) SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a ) processor_fast.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname ) self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() ) self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() ) self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() ) self.assertIsInstance(processor_slow.tokenizer , _a ) self.assertIsInstance(processor_fast.tokenizer , _a ) self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertIsInstance(processor_slow.image_processor , _a ) self.assertIsInstance(processor_fast.image_processor , _a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() ) processor.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" ) SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 ) SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained( self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 ) self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() ) self.assertIsInstance(processor.tokenizer , _a ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , _a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor() SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" ) for key in input_image_proc.keys(): self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer""" SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a ) SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] , encoded_processor[key] ) def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.get_image_processor() SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer""" SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a ) self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] ) # test if it raises when no input is passed with pytest.raises(_a ): processor() def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a ) self.assertListEqual(_a , _a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer""" SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a ) self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
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"""simple docstring""" from __future__ import annotations # This is the precision for this function which can be altered. # It is recommended for users to keep this number greater than or equal to 10. a :Union[str, Any] = 10 def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int: for i in range(__lowerCAmelCase , __lowerCAmelCase ): if array[i] == target: return i return -1 def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Optional[int] = 0 SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase ) while left <= right: if right - left < precision: return lin_search(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = (left + right) // 3 + 1 SCREAMING_SNAKE_CASE__ : int = 2 * (left + right) // 3 + 1 if array[one_third] == target: return one_third elif array[two_third] == target: return two_third elif target < array[one_third]: SCREAMING_SNAKE_CASE__ : List[Any] = one_third - 1 elif array[two_third] < target: SCREAMING_SNAKE_CASE__ : str = two_third + 1 else: SCREAMING_SNAKE_CASE__ : List[Any] = one_third + 1 SCREAMING_SNAKE_CASE__ : Optional[int] = two_third - 1 else: return -1 def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int: if left < right: if right - left < precision: return lin_search(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Any = (left + right) // 3 + 1 SCREAMING_SNAKE_CASE__ : Optional[int] = 2 * (left + right) // 3 + 1 if array[one_third] == target: return one_third elif array[two_third] == target: return two_third elif target < array[one_third]: return rec_ternary_search(__lowerCAmelCase , one_third - 1 , __lowerCAmelCase , __lowerCAmelCase ) elif array[two_third] < target: return rec_ternary_search(two_third + 1 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) else: return rec_ternary_search(one_third + 1 , two_third - 1 , __lowerCAmelCase , __lowerCAmelCase ) else: return -1 if __name__ == "__main__": import doctest doctest.testmod() a :Union[str, Any] = input("Enter numbers separated by comma:\n").strip() a :Tuple = [int(item.strip()) for item in user_input.split(",")] assert collection == sorted(collection), f"List must be ordered.\n{collection}." a :Union[str, Any] = int(input("Enter the number to be found in the list:\n").strip()) a :Dict = ite_ternary_search(collection, target) a :str = rec_ternary_search(0, len(collection) - 1, collection, target) if resulta != -1: print(f'Iterative search: {target} found at positions: {resulta}') print(f'Recursive search: {target} found at positions: {resulta}') else: print("Not found")
709
"""simple docstring""" from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxSeqaSeqConfigWithPast from ...utils import logging a :Optional[Any] = logging.get_logger(__name__) a :Union[str, Any] = { "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json", "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json", "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json", "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json", "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json", } class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[Any] = """t5""" _SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""] _SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""} def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size SCREAMING_SNAKE_CASE__ : Tuple = d_model SCREAMING_SNAKE_CASE__ : int = d_kv SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers SCREAMING_SNAKE_CASE__ : int = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry SCREAMING_SNAKE_CASE__ : Tuple = num_heads SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj SCREAMING_SNAKE_CASE__ : str = use_cache SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" ) SCREAMING_SNAKE_CASE__ : Dict = act_info[-1] SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated""" if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2: raise ValueError( f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.''' """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """ """'gated-gelu' or 'relu'""" ) # for backwards compatibility if feed_forward_proj == "gated-gelu": SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new""" super().__init__( pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , ) class __a (UpperCamelCase_): '''simple docstring''' @property def _a ( self ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """input_ids""": {0: """batch""", 1: """encoder_sequence"""}, """attention_mask""": {0: """batch""", 1: """encoder_sequence"""}, } if self.use_past: SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence""" SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""} SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""} else: SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""} SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""} if self.use_past: self.fill_with_past_key_values_(_a , direction="""inputs""" ) return common_inputs @property def _a ( self ) -> int: """simple docstring""" return 13
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_xlnet import XLNetTokenizer else: a :List[Any] = None a :Optional[int] = logging.get_logger(__name__) a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} a :Optional[int] = { "vocab_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model", }, "tokenizer_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json", }, } a :Dict = { "xlnet-base-cased": None, "xlnet-large-cased": None, } a :int = "▁" # Segments (not really needed) a :Dict = 0 a :Optional[int] = 1 a :Tuple = 2 a :List[str] = 3 a :Optional[Any] = 4 class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = """left""" _SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token super().__init__( vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3 SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case SCREAMING_SNAKE_CASE__ : List[str] = remove_space SCREAMING_SNAKE_CASE__ : int = keep_accents SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return SCREAMING_SNAKE_CASE__ : Tuple = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
710
"""simple docstring""" from __future__ import annotations import time import numpy as np a :Optional[Any] = [8, 5, 9, 7] a :List[Any] = [ [2, 0, 1, 1], [0, 1, 2, 1], [4, 0, 0, 3], [0, 2, 1, 0], [1, 0, 3, 0], ] a :int = [ [3, 2, 1, 4], [0, 2, 5, 2], [5, 1, 0, 5], [1, 5, 3, 0], [3, 0, 3, 3], ] class __a : '''simple docstring''' def __init__( self , _a , _a , _a , ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = claim_vector SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table def _a ( self ) -> list[int]: """simple docstring""" return [ sum(p_item[i] for p_item in self.__allocated_resources_table ) for i in range(len(self.__allocated_resources_table[0] ) ) ] def _a ( self ) -> list[int]: """simple docstring""" return np.array(self.__claim_vector ) - np.array( self.__processes_resource_summation() ) def _a ( self ) -> list[list[int]]: """simple docstring""" return [ list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) ) for i, allocated_resource in enumerate(self.__allocated_resources_table ) ] def _a ( self ) -> dict[int, list[int]]: """simple docstring""" return {self.__need().index(_a ): i for i in self.__need()} def _a ( self , **_a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need() SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources() SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager() for kw, val in kwargs.items(): if kw and val is True: self.__pretty_data() print("""_""" * 50 + """\n""" ) while need_list: SCREAMING_SNAKE_CASE__ : List[str] = False for each_need in need_list: SCREAMING_SNAKE_CASE__ : Dict = True for index, need in enumerate(_a ): if need > available_resources[index]: SCREAMING_SNAKE_CASE__ : Optional[int] = False break if execution: SCREAMING_SNAKE_CASE__ : Any = True # get the original index of the process from ind_ctrl db for original_need_index, need_clone in need_index_manager.items(): if each_need == need_clone: SCREAMING_SNAKE_CASE__ : Tuple = original_need_index print(f'''Process {process_number + 1} is executing.''' ) # remove the process run from stack need_list.remove(_a ) # update available/freed resources stack SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array( alloc_resources_table[process_number] ) print( """Updated available resource stack for processes: """ + """ """.join([str(_a ) for x in available_resources] ) ) break if safe: print("""The process is in a safe state.\n""" ) else: print("""System in unsafe state. Aborting...\n""" ) break def _a ( self ) -> Any: """simple docstring""" print(""" """ * 9 + """Allocated Resource Table""" ) for item in self.__allocated_resources_table: print( f'''P{self.__allocated_resources_table.index(_a ) + 1}''' + """ """.join(f'''{it:>8}''' for it in item ) + """\n""" ) print(""" """ * 9 + """System Resource Table""" ) for item in self.__maximum_claim_table: print( f'''P{self.__maximum_claim_table.index(_a ) + 1}''' + """ """.join(f'''{it:>8}''' for it in item ) + """\n""" ) print( """Current Usage by Active Processes: """ + """ """.join(str(_a ) for x in self.__claim_vector ) ) print( """Initial Available Resources: """ + """ """.join(str(_a ) for x in self.__available_resources() ) ) time.sleep(1 ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" import torch from diffusers import EulerDiscreteScheduler from diffusers.utils import torch_device from .test_schedulers import SchedulerCommonTest class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[Any] = (EulerDiscreteScheduler,) _SCREAMING_SNAKE_CASE :Tuple = 10 def _a ( self , **_a ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = { """num_train_timesteps""": 1_100, """beta_start""": 0.0_001, """beta_end""": 0.02, """beta_schedule""": """linear""", } config.update(**_a ) return config def _a ( self ) -> Optional[int]: """simple docstring""" for timesteps in [10, 50, 100, 1_000]: self.check_over_configs(num_train_timesteps=_a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02] ): self.check_over_configs(beta_start=_a , beta_end=_a ) def _a ( self ) -> List[Any]: """simple docstring""" for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=_a ) def _a ( self ) -> Optional[Any]: """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=_a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : str = scheduler_class(**_a ) scheduler.set_timesteps(self.num_inference_steps ) SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : int = self.dummy_model() SCREAMING_SNAKE_CASE__ : Dict = self.dummy_sample_deter * scheduler.init_noise_sigma SCREAMING_SNAKE_CASE__ : Optional[Any] = sample.to(_a ) for i, t in enumerate(scheduler.timesteps ): SCREAMING_SNAKE_CASE__ : List[str] = scheduler.scale_model_input(_a , _a ) SCREAMING_SNAKE_CASE__ : int = model(_a , _a ) SCREAMING_SNAKE_CASE__ : Dict = scheduler.step(_a , _a , _a , generator=_a ) SCREAMING_SNAKE_CASE__ : str = output.prev_sample SCREAMING_SNAKE_CASE__ : List[str] = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 10.0_807 ) < 1E-2 assert abs(result_mean.item() - 0.0_131 ) < 1E-3 def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_scheduler_config(prediction_type="""v_prediction""" ) SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a ) scheduler.set_timesteps(self.num_inference_steps ) SCREAMING_SNAKE_CASE__ : int = torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model() SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma SCREAMING_SNAKE_CASE__ : Tuple = sample.to(_a ) for i, t in enumerate(scheduler.timesteps ): SCREAMING_SNAKE_CASE__ : Tuple = scheduler.scale_model_input(_a , _a ) SCREAMING_SNAKE_CASE__ : int = model(_a , _a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = scheduler.step(_a , _a , _a , generator=_a ) SCREAMING_SNAKE_CASE__ : List[Any] = output.prev_sample SCREAMING_SNAKE_CASE__ : Optional[int] = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 0.0_002 ) < 1E-2 assert abs(result_mean.item() - 2.2_6_7_6E-0_6 ) < 1E-3 def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Tuple = scheduler_class(**_a ) scheduler.set_timesteps(self.num_inference_steps , device=_a ) SCREAMING_SNAKE_CASE__ : Dict = torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Dict = self.dummy_model() SCREAMING_SNAKE_CASE__ : int = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu() SCREAMING_SNAKE_CASE__ : Dict = sample.to(_a ) for t in scheduler.timesteps: SCREAMING_SNAKE_CASE__ : str = scheduler.scale_model_input(_a , _a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a ) SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = output.prev_sample SCREAMING_SNAKE_CASE__ : Dict = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Tuple = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 10.0_807 ) < 1E-2 assert abs(result_mean.item() - 0.0_131 ) < 1E-3 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[Any] = scheduler_class(**_a , use_karras_sigmas=_a ) scheduler.set_timesteps(self.num_inference_steps , device=_a ) SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 ) SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_model() SCREAMING_SNAKE_CASE__ : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu() SCREAMING_SNAKE_CASE__ : Optional[int] = sample.to(_a ) for t in scheduler.timesteps: SCREAMING_SNAKE_CASE__ : List[Any] = scheduler.scale_model_input(_a , _a ) SCREAMING_SNAKE_CASE__ : int = model(_a , _a ) SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ) SCREAMING_SNAKE_CASE__ : List[str] = output.prev_sample SCREAMING_SNAKE_CASE__ : Optional[int] = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Dict = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 124.52_299_499_511_719 ) < 1E-2 assert abs(result_mean.item() - 0.16_213_932_633_399_963 ) < 1E-3
711
"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_xlnet import XLNetTokenizer else: a :List[Any] = None a :Optional[int] = logging.get_logger(__name__) a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} a :Optional[int] = { "vocab_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model", }, "tokenizer_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json", }, } a :Dict = { "xlnet-base-cased": None, "xlnet-large-cased": None, } a :int = "▁" # Segments (not really needed) a :Dict = 0 a :Optional[int] = 1 a :Tuple = 2 a :List[str] = 3 a :Optional[Any] = 4 class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = """left""" _SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token super().__init__( vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3 SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case SCREAMING_SNAKE_CASE__ : List[str] = remove_space SCREAMING_SNAKE_CASE__ : int = keep_accents SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return SCREAMING_SNAKE_CASE__ : Tuple = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
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"""simple docstring""" from ...utils import ( OptionalDependencyNotAvailable, is_flax_available, is_torch_available, is_transformers_available, ) try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .multicontrolnet import MultiControlNetModel from .pipeline_controlnet import StableDiffusionControlNetPipeline from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline if is_transformers_available() and is_flax_available(): from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
712
"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool: SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1 SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1 # dp is a 2d matrix where dp[i][j] denotes whether prefix string of # length i of input_string matches with prefix string of length j of # given pattern. # "dp" stands for dynamic programming. SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )] # since string of zero length match pattern of zero length SCREAMING_SNAKE_CASE__ : Dict = 1 # since pattern of zero length will never match with string of non-zero length for i in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 # since string of zero length will match with pattern where there # is at least one * alternatively for j in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0 # now using bottom-up approach to find for all remaining lengths for i in range(1 , __lowerCAmelCase ): for j in range(1 , __lowerCAmelCase ): if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".": SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1] elif pattern[j - 1] == "*": if dp[i][j - 2] == 1: SCREAMING_SNAKE_CASE__ : List[str] = 1 elif pattern[j - 2] in (input_string[i - 1], "."): SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j] else: SCREAMING_SNAKE_CASE__ : Optional[int] = 0 else: SCREAMING_SNAKE_CASE__ : Dict = 0 return bool(dp[-1][-1] ) if __name__ == "__main__": import doctest doctest.testmod() # inputing the strings # input_string = input("input a string :") # pattern = input("input a pattern :") a :Any = "aab" a :Optional[Any] = "c*a*b" # using function to check whether given string matches the given pattern if match_pattern(input_string, pattern): print(f'{input_string} matches the given pattern {pattern}') else: print(f'{input_string} does not match with the given pattern {pattern}')
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"""simple docstring""" from dataclasses import dataclass, field from typing import Optional @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot""" , metadata={"""help""": """Model name or path of model to be trained."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""./""" , metadata={"""help""": """Save dir where model repo is cloned and models updates are saved to."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot-clean-train""" , metadata={"""help""": """Name or path of training dataset."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot-clean-valid""" , metadata={"""help""": """Name or path of validation dataset."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=2 , metadata={"""help""": """Batch size for training."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=2 , metadata={"""help""": """Batch size for evaluation."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field(default=0.1 , metadata={"""help""": """Value of weight decay."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_00_00 , metadata={"""help""": """Size of buffer used to shuffle streaming dataset."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field(default=2E-4 , metadata={"""help""": """Learning rate fo training."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field(default="""cosine""" , metadata={"""help""": """Learning rate."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=7_50 , metadata={"""help""": """Number of warmup steps in the learning rate schedule."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=16 , metadata={"""help""": """Number of gradient accumulation steps."""}) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=UpperCamelCase_ , metadata={"""help""": """Use gradient checkpointing to reduce memory footprint."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=5_00_00 , metadata={"""help""": """Maximum number of training steps."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=-1 , metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=10_24 , metadata={"""help""": """Sequence lengths used for training."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=1 , metadata={"""help""": """Training seed."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=10_24 , metadata={"""help""": """Interval to save checkpoints. Measured as number of forward passes not training steps."""} , ) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """States path if the training should continue from a checkpoint folder."""}) _SCREAMING_SNAKE_CASE :Optional[bool] = field(default=UpperCamelCase_ , metadata={"""help""": """If True the data is pretokenized."""}) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot""" , metadata={"""help""": """Model name or path of model to be evaluated."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot-clean-valid""" , metadata={"""help""": """Name or path of validation dataset."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=2 , metadata={"""help""": """Batch size used for evaluation."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=-1 , metadata={"""help""": """Maximum number of evaluation steps. If -1 the full dataset is evaluated."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=10_24 , metadata={"""help""": """Length of sequences to be evaluated."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=1 , metadata={"""help""": """Random seed used for evaluation."""}) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot""" , metadata={"""help""": """Model name or path of model to be evaluated."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=UpperCamelCase_ , metadata={"""help""": """Number of workers used for code evaluation."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={"""help""": """The number of human-eval tasks to run. If not included all tasks are evaluated."""} , ) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=UpperCamelCase_ , metadata={"""help""": """Sample from the language model's output distribution."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field(default=0.2 , metadata={"""help""": """Sampling temperature used for generation."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=2_56 , metadata={"""help""": """Maximum number of newly generated tokens."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=0 , metadata={"""help""": """Top-k parameter used for generation."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field(default=0.95 , metadata={"""help""": """Top-p parameter used for nucleus sampling."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=10 , metadata={"""help""": """Number of generations to run in parallel."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=2_00 , metadata={"""help""": """Number of completions to generate for each sample."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=1 , metadata={"""help""": """Random seed used for evaluation."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""eval_results.json""" , metadata={"""help""": """Random seed used for evaluation."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""0""" , metadata={"""help""": """Allow `code_eval` to execute Python code on machine"""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=-1 , metadata={ """help""": ( """Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive""" """ number corresponds to which GPU device id to run on.""" ) } , ) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": """The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.""" } , ) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""transformersbook/codeparrot""" , metadata={"""help""": """Folder or name of dataset to process."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot-clean""" , metadata={"""help""": """Folder to save processed processed dataset."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=10_00_00 , metadata={"""help""": """Number of files to save per JSON output file."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field(default="""content""" , metadata={"""help""": """Column containing text data to process."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field( default=10_00 , metadata={"""help""": """Maximum line length in file, otherwise file is filtered."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field( default=1_00 , metadata={"""help""": """Maximum mean line length in file, otherwise file is filtered."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field( default=0.25 , metadata={"""help""": """Maximum fraction of non-alphanumeric characters, otherwise file is filtered."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field( default=1.5 , metadata={"""help""": """Minimum character token ratio for the file, otherwise file is filtered."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field( default=0.7 , metadata={"""help""": """Probability for filtering config, test and uncommon files."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot""" , metadata={"""help""": """Name or path to the tokenizer."""} , ) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=UpperCamelCase_ , metadata={"""help""": """If True, near-duplicate samples are removed."""}) _SCREAMING_SNAKE_CASE :Optional[float] = field( default=0.85 , metadata={"""help""": """Jaccard threshold for near-duplicate samples."""}) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""gpt2""" , metadata={"""help""": """Base tokenizer to build new tokenizer from."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""transformersbook/codeparrot-train""" , metadata={"""help""": """Dataset to train tokenizer on."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field(default="""content""" , metadata={"""help""": """Column containing text data to process."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=20_00_00 , metadata={"""help""": """Number of examples to train tokenizer on."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=3_27_68 , metadata={"""help""": """Number of examples to train the tokenizer on."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field(default="""codeparrot""" , metadata={"""help""": """Name of new tokenizer."""}) _SCREAMING_SNAKE_CASE :Optional[bool] = field(default=UpperCamelCase_ , metadata={"""help""": """Push saved tokenizer to the hub."""}) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot""" , metadata={"""help""": """Name or path to the tokenizer."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot-clean-train""" , metadata={"""help""": """Name or path to the dataset to pretokenize."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""tokenized-codeparrot-train""" , metadata={"""help""": """Repo name of the pretokenized data."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=UpperCamelCase_ , metadata={"""help""": """Number of workers used for code evaluation."""}) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""gpt2-large""" , metadata={"""help""": """Configuration to use for model initialization."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""codeparrot/codeparrot""" , metadata={"""help""": """Tokenizer attached to model."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field(default="""codeparrot""" , metadata={"""help""": """Name of the created model."""}) _SCREAMING_SNAKE_CASE :Optional[bool] = field(default=UpperCamelCase_ , metadata={"""help""": """Push saved tokenizer to the hub."""})
713
"""simple docstring""" from math import sqrt def _lowercase ( __lowerCAmelCase ) -> bool: if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = 0 SCREAMING_SNAKE_CASE__ : Tuple = 1 while count != nth and number < 3: number += 1 if is_prime(__lowerCAmelCase ): count += 1 while count != nth: number += 2 if is_prime(__lowerCAmelCase ): count += 1 return number if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" import argparse import json from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from timm import create_model from timm.data import resolve_data_config from timm.data.transforms_factory import create_transform from transformers import BitConfig, BitForImageClassification, BitImageProcessor from transformers.image_utils import PILImageResampling from transformers.utils import logging logging.set_verbosity_info() a :Dict = logging.get_logger(__name__) def _lowercase ( __lowerCAmelCase ) -> List[str]: SCREAMING_SNAKE_CASE__ : Union[str, Any] = """huggingface/label-files""" SCREAMING_SNAKE_CASE__ : Tuple = """imagenet-1k-id2label.json""" SCREAMING_SNAKE_CASE__ : Tuple = json.load(open(hf_hub_download(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) , """r""" ) ) SCREAMING_SNAKE_CASE__ : str = {int(__lowerCAmelCase ): v for k, v in idalabel.items()} SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in idalabel.items()} SCREAMING_SNAKE_CASE__ : Optional[Any] = """std_conv""" if """bit""" in model_name else False # note that when using BiT as backbone for ViT-hybrid checkpoints, # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same", # config.conv_layer = "std_conv_same" SCREAMING_SNAKE_CASE__ : Dict = BitConfig( conv_layer=__lowerCAmelCase , num_labels=1000 , idalabel=__lowerCAmelCase , labelaid=__lowerCAmelCase , ) return config def _lowercase ( __lowerCAmelCase ) -> Any: if "stem.conv" in name: SCREAMING_SNAKE_CASE__ : str = name.replace("""stem.conv""" , """bit.embedder.convolution""" ) if "blocks" in name: SCREAMING_SNAKE_CASE__ : str = name.replace("""blocks""" , """layers""" ) if "head.fc" in name: SCREAMING_SNAKE_CASE__ : int = name.replace("""head.fc""" , """classifier.1""" ) if name.startswith("""norm""" ): SCREAMING_SNAKE_CASE__ : str = """bit.""" + name if "bit" not in name and "classifier" not in name: SCREAMING_SNAKE_CASE__ : Optional[int] = """bit.encoder.""" + name return name def _lowercase ( ) -> List[str]: SCREAMING_SNAKE_CASE__ : Union[str, Any] = """http://images.cocodataset.org/val2017/000000039769.jpg""" SCREAMING_SNAKE_CASE__ : Tuple = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw ) return im @torch.no_grad() def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False ) -> Optional[int]: SCREAMING_SNAKE_CASE__ : List[Any] = get_config(__lowerCAmelCase ) # load original model from timm SCREAMING_SNAKE_CASE__ : str = create_model(__lowerCAmelCase , pretrained=__lowerCAmelCase ) timm_model.eval() # load state_dict of original model SCREAMING_SNAKE_CASE__ : int = timm_model.state_dict() for key in state_dict.copy().keys(): SCREAMING_SNAKE_CASE__ : int = state_dict.pop(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = val.squeeze() if """head""" in key else val # load HuggingFace model SCREAMING_SNAKE_CASE__ : Any = BitForImageClassification(__lowerCAmelCase ) model.eval() model.load_state_dict(__lowerCAmelCase ) # create image processor SCREAMING_SNAKE_CASE__ : Dict = create_transform(**resolve_data_config({} , model=__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = transform.transforms SCREAMING_SNAKE_CASE__ : Union[str, Any] = { """bilinear""": PILImageResampling.BILINEAR, """bicubic""": PILImageResampling.BICUBIC, """nearest""": PILImageResampling.NEAREST, } SCREAMING_SNAKE_CASE__ : Tuple = BitImageProcessor( do_resize=__lowerCAmelCase , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=__lowerCAmelCase , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=__lowerCAmelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = prepare_img() SCREAMING_SNAKE_CASE__ : Optional[int] = transform(__lowerCAmelCase ).unsqueeze(0 ) SCREAMING_SNAKE_CASE__ : Any = processor(__lowerCAmelCase , return_tensors="""pt""" ).pixel_values # verify pixel values assert torch.allclose(__lowerCAmelCase , __lowerCAmelCase ) # verify logits with torch.no_grad(): SCREAMING_SNAKE_CASE__ : Optional[int] = model(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = outputs.logits print("""Logits:""" , logits[0, :3] ) print("""Predicted class:""" , model.config.idalabel[logits.argmax(-1 ).item()] ) SCREAMING_SNAKE_CASE__ : Optional[int] = timm_model(__lowerCAmelCase ) assert timm_logits.shape == outputs.logits.shape assert torch.allclose(__lowerCAmelCase , outputs.logits , atol=1E-3 ) print("""Looks ok!""" ) if pytorch_dump_folder_path is not None: Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase ) print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''' ) model.save_pretrained(__lowerCAmelCase ) processor.save_pretrained(__lowerCAmelCase ) if push_to_hub: print(F'''Pushing model {model_name} and processor to the hub''' ) model.push_to_hub(F'''ybelkada/{model_name}''' ) processor.push_to_hub(F'''ybelkada/{model_name}''' ) if __name__ == "__main__": a :str = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="resnetv2_50x1_bitm", type=str, help="Name of the BiT timm model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether to push the model to the hub.", ) a :List[Any] = parser.parse_args() convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
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"""simple docstring""" class __a : '''simple docstring''' def __init__( self , _a , _a , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = value SCREAMING_SNAKE_CASE__ : List[Any] = weight def __repr__( self ) -> List[Any]: """simple docstring""" return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})''' def _a ( self ) -> Dict: """simple docstring""" return self.value def _a ( self ) -> int: """simple docstring""" return self.name def _a ( self ) -> Optional[Any]: """simple docstring""" return self.weight def _a ( self ) -> Dict: """simple docstring""" return self.value / self.weight def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict: SCREAMING_SNAKE_CASE__ : Any = [] for i in range(len(__lowerCAmelCase ) ): menu.append(Things(name[i] , value[i] , weight[i] ) ) return menu def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0 for i in range(len(__lowerCAmelCase ) ): if (total_cost + items_copy[i].get_weight()) <= max_cost: result.append(items_copy[i] ) total_cost += items_copy[i].get_weight() total_value += items_copy[i].get_value() return (result, total_value) def _lowercase ( ) -> List[str]: pass if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from collections import defaultdict from math import ceil, sqrt def _lowercase ( __lowerCAmelCase = 100_0000 , __lowerCAmelCase = 10 ) -> int: SCREAMING_SNAKE_CASE__ : defaultdict = defaultdict(__lowerCAmelCase ) for outer_width in range(3 , (t_limit // 4) + 2 ): if outer_width * outer_width > t_limit: SCREAMING_SNAKE_CASE__ : List[Any] = max( ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1 hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2 for hole_width in range(__lowerCAmelCase , outer_width - 1 , 2 ): count[outer_width * outer_width - hole_width * hole_width] += 1 return sum(1 for n in count.values() if 1 <= n <= 10 ) if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from tokenizers import processors from ...tokenization_utils import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_nllb import NllbTokenizer else: a :Optional[int] = None a :Optional[Any] = logging.get_logger(__name__) a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} a :Union[str, Any] = { "vocab_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model" ), }, "tokenizer_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json" ), }, } a :Any = { "facebook/nllb-large-en-ro": 1_024, "facebook/nllb-200-distilled-600M": 1_024, } # fmt: off a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"] class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""] _SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer _SCREAMING_SNAKE_CASE :List[int] = [] _SCREAMING_SNAKE_CASE :List[int] = [] def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour super().__init__( vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy() if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} ) SCREAMING_SNAKE_CASE__ : List[str] = { lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES } SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn""" SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang ) SCREAMING_SNAKE_CASE__ : Dict = tgt_lang self.set_src_lang_special_tokens(self._src_lang ) @property def _a ( self ) -> str: """simple docstring""" return self._src_lang @src_lang.setter def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" if token_ids_a is None: return self.prefix_tokens + token_ids_a + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple: """simple docstring""" if src_lang is None or tgt_lang is None: raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" ) SCREAMING_SNAKE_CASE__ : Dict = src_lang SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id return inputs def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = src_lang SCREAMING_SNAKE_CASE__ : Dict = tgt_lang return super().prepare_seqaseq_batch(_a , _a , **_a ) def _a ( self ) -> Optional[Any]: """simple docstring""" return self.set_src_lang_special_tokens(self.src_lang ) def _a ( self ) -> str: """simple docstring""" return self.set_tgt_lang_special_tokens(self.tgt_lang ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : List[Any] = [] SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' ) return SCREAMING_SNAKE_CASE__ : Dict = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
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"""simple docstring""" from pathlib import Path import json import tempfile from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES a :Dict = "tiny-wmt19-en-ru" # Build # borrowed from a test a :Tuple = [ "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "w</w>", "r</w>", "t</w>", "lo", "low", "er</w>", "low</w>", "lowest</w>", "newer</w>", "wider</w>", "<unk>", ] a :List[str] = dict(zip(vocab, range(len(vocab)))) a :List[str] = ["l o 123", "lo w 1456", "e r</w> 1789", ""] with tempfile.TemporaryDirectory() as tmpdirname: a :Optional[int] = Path(tmpdirname) a :Dict = build_dir / VOCAB_FILES_NAMES["src_vocab_file"] a :int = build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"] a :List[Any] = build_dir / VOCAB_FILES_NAMES["merges_file"] with open(src_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens)) with open(tgt_vocab_file, "w") as fp: fp.write(json.dumps(vocab_tokens)) with open(merges_file, "w") as fp: fp.write("\n".join(merges)) a :Optional[Any] = FSMTTokenizer( langs=["en", "ru"], src_vocab_size=len(vocab), tgt_vocab_size=len(vocab), src_vocab_file=src_vocab_file, tgt_vocab_file=tgt_vocab_file, merges_file=merges_file, ) a :Dict = FSMTConfig( langs=["ru", "en"], src_vocab_size=1_000, tgt_vocab_size=1_000, d_model=4, encoder_layers=1, decoder_layers=1, encoder_ffn_dim=4, decoder_ffn_dim=4, encoder_attention_heads=1, decoder_attention_heads=1, ) a :Any = FSMTForConditionalGeneration(config) print(f'num of params {tiny_model.num_parameters()}') # Test a :str = tokenizer(["Making tiny model"], return_tensors="pt") a :Union[str, Any] = tiny_model(**batch) print("test output:", len(outputs.logits[0])) # Save tiny_model.half() # makes it smaller tiny_model.save_pretrained(mname_tiny) tokenizer.save_pretrained(mname_tiny) print(f'Generated {mname_tiny}') # Upload # transformers-cli upload tiny-wmt19-en-ru
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"""simple docstring""" # Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #################################################################################################### # # Note: If when running this conversion script you're getting an exception: # ModuleNotFoundError: No module named 'megatron.model.enums' # you need to tell python where to find the clone of Megatron-LM, e.g.: # # cd /tmp # git clone https://github.com/NVIDIA/Megatron-LM # PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ... # # if you already have it cloned elsewhere, simply adjust the path to the existing path # # If the training was done using a Megatron-LM fork, e.g., # https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one # in your path, i.e., /path/to/Megatron-DeepSpeed/ # import argparse import os import re import zipfile import torch from transformers import AutoTokenizer, GPTaConfig def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any: # Format the message. if name is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None else: SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}""" SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase ) # Print and recurse (if needed). if isinstance(__lowerCAmelCase , __lowerCAmelCase ): if msg is not None: print(__lowerCAmelCase ) for k in val.keys(): recursive_print(__lowerCAmelCase , val[k] , spaces + 2 ) elif isinstance(__lowerCAmelCase , torch.Tensor ): print(__lowerCAmelCase , """:""" , val.size() ) else: print(__lowerCAmelCase , """:""" , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] # for compatibility with later versions of NVIDIA Megatron-LM. # The inverse operation is performed inside Megatron-LM to read checkpoints: # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 # If param is the weight tensor of the self-attention block, the returned tensor # will have to be transposed one more time to be read by HuggingFace GPT2. SCREAMING_SNAKE_CASE__ : Tuple = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:] SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 ) SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:] SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous() SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase ) return param def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: # The converted output model. SCREAMING_SNAKE_CASE__ : List[str] = {} # old versions did not store training args SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase ) if ds_args is not None: # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint # from pprint import pprint # pprint(vars(ds_args)) SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size # pprint(config) # The number of heads. SCREAMING_SNAKE_CASE__ : List[str] = config.n_head # The hidden_size per head. SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head # Megatron-LM checkpoint version if "checkpoint_version" in input_state_dict.keys(): SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""] else: SCREAMING_SNAKE_CASE__ : Tuple = 0.0 # The model. SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""] # The language model. SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""] # The embeddings. SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""] # The word embeddings. SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""] # Truncate the embedding table to vocab_size rows. SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :] SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings # The position embeddings. SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""] # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size] SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 ) if n_positions != config.n_positions: raise ValueError( F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' ) # Store the position embeddings. SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings # The transformer. SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""] # The regex to extract layer names. SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" ) # The simple map of names for "automated" rules. SCREAMING_SNAKE_CASE__ : Optional[int] = { """attention.dense""": """.attn.c_proj.""", """self_attention.dense""": """.attn.c_proj.""", """mlp.dense_h_to_4h""": """.mlp.c_fc.""", """mlp.dense_4h_to_h""": """.mlp.c_proj.""", } # Extract the layers. for key, val in transformer.items(): # Match the name. SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase ) # Stop if that's not a layer if m is None: break # The index of the layer. SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) ) # The name of the operation. SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 ) # Is it a weight or a bias? SCREAMING_SNAKE_CASE__ : str = m.group(3 ) # The name of the layer. SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}''' # For layernorm(s), simply store the layer norm. if op_name.endswith("""layernorm""" ): SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2""" SCREAMING_SNAKE_CASE__ : List[Any] = val # Transpose the QKV matrix. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "weight": # Insert a tensor of 1x1xDxD bias. SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view( 1 , 1 , __lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = causal_mask # Insert a "dummy" tensor for masked_bias. SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa ) SCREAMING_SNAKE_CASE__ : List[str] = masked_bias SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D. SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous() # Store. SCREAMING_SNAKE_CASE__ : Dict = out_val # Transpose the bias. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Store. No change of shape. SCREAMING_SNAKE_CASE__ : str = out_val # Transpose the weights. elif weight_or_bias == "weight": SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 ) # Copy the bias. elif weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : Dict = val # DEBUG. assert config.n_layer == layer_idx + 1 # The final layernorm. SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""] SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""] # For LM head, transformers' wants the matrix to weight embeddings. SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings # It should be done! return output_state_dict def _lowercase ( ) -> List[Any]: # Create the argument parser. SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser() parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" ) parser.add_argument( """path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , ) parser.add_argument( """--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , ) SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args() # Extract the basename. SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint ) # Load the model. # the .zip is very optional, let's keep it for backward compatibility print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' ) if args.path_to_checkpoint.endswith(""".zip""" ): with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint: with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict: SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" ) else: SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" ) SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase ) # Read the config, or default to the model released by NVIDIA. if args.config_file == "": if ds_args is not None: if ds_args.bias_gelu_fusion: SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast""" elif ds_args.openai_gelu: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new""" else: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu""" else: # in the very early days this used to be "gelu_new" SCREAMING_SNAKE_CASE__ : Any = """gelu_new""" # Spell out all parameters in case the defaults change. SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig( vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , ) else: SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file ) SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""] # Convert. print("""Converting""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) # Print the structure of converted state dict. if args.print_checkpoint_structure: recursive_print(__lowerCAmelCase , __lowerCAmelCase ) # Add tokenizer class info to config # see https://github.com/huggingface/transformers/issues/13906) if ds_args is not None: SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type if tokenizer_type == "GPT2BPETokenizer": SCREAMING_SNAKE_CASE__ : Any = """gpt2""" elif tokenizer_type == "PretrainedFromHF": SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path else: raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__ SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class # Store the config to file. print("""Saving config""" ) config.save_pretrained(__lowerCAmelCase ) # Save tokenizer based on args print(F'''Adding {tokenizer_class} tokenizer files''' ) tokenizer.save_pretrained(__lowerCAmelCase ) # Store the state_dict to file. SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" ) print(F'''Saving checkpoint to "{output_checkpoint_file}"''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) #################################################################################################### if __name__ == "__main__": main() ####################################################################################################
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) a :Any = { "configuration_roberta_prelayernorm": [ "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaPreLayerNormConfig", "RobertaPreLayerNormOnnxConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Union[str, Any] = [ "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "RobertaPreLayerNormForCausalLM", "RobertaPreLayerNormForMaskedLM", "RobertaPreLayerNormForMultipleChoice", "RobertaPreLayerNormForQuestionAnswering", "RobertaPreLayerNormForSequenceClassification", "RobertaPreLayerNormForTokenClassification", "RobertaPreLayerNormModel", "RobertaPreLayerNormPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Optional[Any] = [ "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRobertaPreLayerNormForCausalLM", "TFRobertaPreLayerNormForMaskedLM", "TFRobertaPreLayerNormForMultipleChoice", "TFRobertaPreLayerNormForQuestionAnswering", "TFRobertaPreLayerNormForSequenceClassification", "TFRobertaPreLayerNormForTokenClassification", "TFRobertaPreLayerNormMainLayer", "TFRobertaPreLayerNormModel", "TFRobertaPreLayerNormPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :List[Any] = [ "FlaxRobertaPreLayerNormForCausalLM", "FlaxRobertaPreLayerNormForMaskedLM", "FlaxRobertaPreLayerNormForMultipleChoice", "FlaxRobertaPreLayerNormForQuestionAnswering", "FlaxRobertaPreLayerNormForSequenceClassification", "FlaxRobertaPreLayerNormForTokenClassification", "FlaxRobertaPreLayerNormModel", "FlaxRobertaPreLayerNormPreTrainedModel", ] if TYPE_CHECKING: from .configuration_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaPreLayerNormConfig, RobertaPreLayerNormOnnxConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaPreLayerNormForCausalLM, RobertaPreLayerNormForMaskedLM, RobertaPreLayerNormForMultipleChoice, RobertaPreLayerNormForQuestionAnswering, RobertaPreLayerNormForSequenceClassification, RobertaPreLayerNormForTokenClassification, RobertaPreLayerNormModel, RobertaPreLayerNormPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_roberta_prelayernorm import ( TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, TFRobertaPreLayerNormForCausalLM, TFRobertaPreLayerNormForMaskedLM, TFRobertaPreLayerNormForMultipleChoice, TFRobertaPreLayerNormForQuestionAnswering, TFRobertaPreLayerNormForSequenceClassification, TFRobertaPreLayerNormForTokenClassification, TFRobertaPreLayerNormMainLayer, TFRobertaPreLayerNormModel, TFRobertaPreLayerNormPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_roberta_prelayernorm import ( FlaxRobertaPreLayerNormForCausalLM, FlaxRobertaPreLayerNormForMaskedLM, FlaxRobertaPreLayerNormForMultipleChoice, FlaxRobertaPreLayerNormForQuestionAnswering, FlaxRobertaPreLayerNormForSequenceClassification, FlaxRobertaPreLayerNormForTokenClassification, FlaxRobertaPreLayerNormModel, FlaxRobertaPreLayerNormPreTrainedModel, ) else: import sys a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" import re from pathlib import Path from unittest import TestCase import pytest @pytest.mark.integration class __a (UpperCamelCase_): '''simple docstring''' def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read() SCREAMING_SNAKE_CASE__ : str = regexp.search(_a ) return match def _a ( self , _a ) -> Optional[Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL ) SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read() # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search` SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a ) SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None] return matches[0] if matches else None def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_encoding_on_file_open(str(_a ) ): raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_print_statements(str(_a ) ): raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
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from typing import TYPE_CHECKING # rely on isort to merge the imports from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available a :str = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Tuple = [ "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST", "FocalNetForImageClassification", "FocalNetForMaskedImageModeling", "FocalNetBackbone", "FocalNetModel", "FocalNetPreTrainedModel", ] if TYPE_CHECKING: from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_focalnet import ( FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST, FocalNetBackbone, FocalNetForImageClassification, FocalNetForMaskedImageModeling, FocalNetModel, FocalNetPreTrainedModel, ) else: import sys a :Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" import tempfile import unittest from transformers import TaConfig, is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel class __a : '''simple docstring''' def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = parent SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length SCREAMING_SNAKE_CASE__ : str = decoder_seq_length # For common tests SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length SCREAMING_SNAKE_CASE__ : Tuple = is_training SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask SCREAMING_SNAKE_CASE__ : List[str] = use_labels SCREAMING_SNAKE_CASE__ : str = vocab_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Any = num_attention_heads SCREAMING_SNAKE_CASE__ : Any = d_ff SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id SCREAMING_SNAKE_CASE__ : Any = None SCREAMING_SNAKE_CASE__ : str = decoder_layers def _a ( self ) -> Tuple: """simple docstring""" return TaConfig.from_pretrained("""google/umt5-base""" ) def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any: """simple docstring""" if attention_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id ) if decoder_attention_mask is None: SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id ) if head_mask is None: SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a ) if decoder_head_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a ) if cross_attn_head_mask is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones( config.num_decoder_layers , config.num_attention_heads , device=_a ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size ) # we need to clamp the input ids here to avoid having pad token in between # this is because for NllbMoe the position_ids are prepared such that # all pad tokens have pos id = 2 and rest are between 2..seq_length # and the seq_length here is seq_length - num_pad_tokens # but when using past, there is no way of knowing if the past input ids had # pad tokens in them, which results in incorrect seq_lenth and which in turn results in # position_ids being off by num_pad_tokens in past input SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config() SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a ) return config, input_dict def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs() return config, inputs_dict def _a ( self ) -> List[str]: """simple docstring""" return TaConfig( vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self ) -> List[Any]: """simple docstring""" return TaConfig( vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a ) model.to(_a ) model.eval() SCREAMING_SNAKE_CASE__ : Dict = model( input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) ) self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) ) # There should be `num_layers` key value embeddings stored in decoder_past self.parent.assertEqual(len(_a ) , config.num_layers ) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple self.parent.assertEqual(len(decoder_past[0] ) , 4 ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval() # first forward pass SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a ) SCREAMING_SNAKE_CASE__ : str = model(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a ) self.parent.assertTrue(len(_a ) == len(_a ) ) self.parent.assertTrue(len(_a ) == len(_a ) + 1 ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ) # append to next input_ids and SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""] SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""] # select random slice SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item() SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach() SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) ) def _a ( self , _a , _a , ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval() SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""] self.parent.assertFalse(torch.isnan(_a ).any().item() ) @require_torch class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = ( (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else () ) _SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else () _SCREAMING_SNAKE_CASE :List[str] = ( { """conversational""": UMTaForConditionalGeneration, """feature-extraction""": UMTaModel, """summarization""": UMTaForConditionalGeneration, """text2text-generation""": UMTaForConditionalGeneration, """translation""": UMTaForConditionalGeneration, """question-answering""": UMTaForQuestionAnswering, } if is_torch_available() else {} ) _SCREAMING_SNAKE_CASE :Union[str, Any] = True _SCREAMING_SNAKE_CASE :Tuple = False _SCREAMING_SNAKE_CASE :Optional[Any] = False _SCREAMING_SNAKE_CASE :List[Any] = True _SCREAMING_SNAKE_CASE :List[str] = True # The small UMT5 model needs higher percentages for CPU/MP tests _SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9] def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self ) @unittest.skip("""Test has a segmentation fault on torch 1.8.0""" ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a ) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( _a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , ) @unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fpaa_forward(*_a ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""] SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0] SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval() model.to(_a ) SCREAMING_SNAKE_CASE__ : List[str] = { """head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ), """decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), } for attn_name, (name, mask) in zip(_a , head_masking.items() ): SCREAMING_SNAKE_CASE__ : List[str] = {name: mask} # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified if name == "head_mask": SCREAMING_SNAKE_CASE__ : str = torch.ones( config.num_decoder_layers , config.num_heads , device=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate( config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , ) # We check the state of decoder_attentions and cross_attentions just from the last step SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 ) @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" ) def _a ( self ) -> Dict: """simple docstring""" pass @require_torch @require_sentencepiece @require_tokenizers class __a (unittest.TestCase): '''simple docstring''' @slow @unittest.skip( """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a ) SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [ """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""", """No se como puedo <extra_id_0>.""", """This is the reason why we <extra_id_0> them.""", """The <extra_id_0> walks in <extra_id_1>, seats""", """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""", ] SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids # fmt: off SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor( [ [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0], [ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1], ] ) # fmt: on torch.testing.assert_allclose(_a , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) ) SCREAMING_SNAKE_CASE__ : int = [ """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""", """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", ] SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a ) self.assertEqual(_a , _a )
12
0
import logging import os import sys from dataclasses import dataclass, field from typing import Optional from seqaseq_trainer import SeqaSeqTrainer from seqaseq_training_args import SeqaSeqTrainingArguments import transformers from transformers import ( AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer, HfArgumentParser, MBartTokenizer, MBartTokenizerFast, set_seed, ) from transformers.trainer_utils import EvaluationStrategy, is_main_process from transformers.training_args import ParallelMode from utils import ( SeqaSeqDataCollator, SeqaSeqDataset, assert_all_frozen, build_compute_metrics_fn, check_output_dir, freeze_embeds, freeze_params, lmap, save_json, use_task_specific_params, write_txt_file, ) a :Any = logging.getLogger(__name__) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :str = field( metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , ) _SCREAMING_SNAKE_CASE :bool = field(default=UpperCamelCase_ , metadata={"""help""": """Whether tp freeze the encoder."""}) _SCREAMING_SNAKE_CASE :bool = field(default=UpperCamelCase_ , metadata={"""help""": """Whether to freeze the embeddings."""}) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :str = field( metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default="""summarization""" , metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""} , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=10_24 , metadata={ """help""": ( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_28 , metadata={ """help""": ( """The maximum total sequence length for target text after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_42 , metadata={ """help""": ( """The maximum total sequence length for validation target text after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded. """ """This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """ """during ``evaluate`` and ``predict``.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_42 , metadata={ """help""": ( """The maximum total sequence length for test target text after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=-1 , metadata={"""help""": """# training examples. -1 means use all."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=-1 , metadata={"""help""": """# validation examples. -1 means use all."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=-1 , metadata={"""help""": """# test examples. -1 means use all."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field(default=UpperCamelCase_ , metadata={"""help""": """Source language id for translation."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field(default=UpperCamelCase_ , metadata={"""help""": """Target language id for translation."""}) _SCREAMING_SNAKE_CASE :Optional[int] = field(default=UpperCamelCase_ , metadata={"""help""": """# num_beams to use for evaluation."""}) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""} , ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict: logger.info(F'''***** {split} metrics *****''' ) for key in sorted(metrics.keys() ): logger.info(F''' {key} = {metrics[key]}''' ) save_json(__lowerCAmelCase , os.path.join(__lowerCAmelCase , F'''{split}_results.json''' ) ) def _lowercase ( ) -> Dict: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. SCREAMING_SNAKE_CASE__ : Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) ) if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. SCREAMING_SNAKE_CASE__ : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) ) else: SCREAMING_SNAKE_CASE__ : str = parser.parse_args_into_dataclasses() check_output_dir(__lowerCAmelCase ) # Setup logging logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , ) logger.warning( """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank ): transformers.utils.logging.set_verbosity_info() logger.info("""Training/evaluation parameters %s""" , __lowerCAmelCase ) # Set seed set_seed(training_args.seed ) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , ) SCREAMING_SNAKE_CASE__ : Dict = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""") for p in extra_model_params: if getattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ): assert hasattr(__lowerCAmelCase , __lowerCAmelCase ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute''' setattr(__lowerCAmelCase , __lowerCAmelCase , getattr(__lowerCAmelCase , __lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : int = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , ) SCREAMING_SNAKE_CASE__ : int = AutoModelForSeqaSeqLM.from_pretrained( model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , ) # use task specific params use_task_specific_params(__lowerCAmelCase , data_args.task ) # set num_beams for evaluation if data_args.eval_beams is None: SCREAMING_SNAKE_CASE__ : Optional[Any] = model.config.num_beams # set decoder_start_token_id for MBart if model.config.decoder_start_token_id is None and isinstance(__lowerCAmelCase , (MBartTokenizer, MBartTokenizerFast) ): assert ( data_args.tgt_lang is not None and data_args.src_lang is not None ), "mBart requires --tgt_lang and --src_lang" if isinstance(__lowerCAmelCase , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.lang_code_to_id[data_args.tgt_lang] else: SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.convert_tokens_to_ids(data_args.tgt_lang ) if model_args.freeze_embeds: freeze_embeds(__lowerCAmelCase ) if model_args.freeze_encoder: freeze_params(model.get_encoder() ) assert_all_frozen(model.get_encoder() ) SCREAMING_SNAKE_CASE__ : List[str] = SeqaSeqDataset # Get datasets SCREAMING_SNAKE_CASE__ : Optional[Any] = ( dataset_class( __lowerCAmelCase , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , ) if training_args.do_train else None ) SCREAMING_SNAKE_CASE__ : int = ( dataset_class( __lowerCAmelCase , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , ) if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO else None ) SCREAMING_SNAKE_CASE__ : Any = ( dataset_class( __lowerCAmelCase , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , ) if training_args.do_predict else None ) # Initialize our Trainer SCREAMING_SNAKE_CASE__ : Tuple = ( build_compute_metrics_fn(data_args.task , __lowerCAmelCase ) if training_args.predict_with_generate else None ) SCREAMING_SNAKE_CASE__ : Dict = SeqaSeqTrainer( model=__lowerCAmelCase , args=__lowerCAmelCase , data_args=__lowerCAmelCase , train_dataset=__lowerCAmelCase , eval_dataset=__lowerCAmelCase , data_collator=SeqaSeqDataCollator( __lowerCAmelCase , __lowerCAmelCase , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , ) SCREAMING_SNAKE_CASE__ : Optional[int] = {} # Training if training_args.do_train: logger.info("""*** Train ***""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = trainer.train( model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None ) SCREAMING_SNAKE_CASE__ : Any = train_result.metrics SCREAMING_SNAKE_CASE__ : List[Any] = data_args.n_train trainer.save_model() # this also saves the tokenizer if trainer.is_world_process_zero(): handle_metrics("""train""" , __lowerCAmelCase , training_args.output_dir ) all_metrics.update(__lowerCAmelCase ) # Need to save the state, since Trainer.save_model saves only the tokenizer with the model trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) ) # For convenience, we also re-save the tokenizer to the same directory, # so that you can share your model easily on huggingface.co/models =) tokenizer.save_pretrained(training_args.output_dir ) # Evaluation if training_args.do_eval: logger.info("""*** Evaluate ***""" ) SCREAMING_SNAKE_CASE__ : List[Any] = trainer.evaluate(metric_key_prefix="""val""" ) SCREAMING_SNAKE_CASE__ : List[str] = data_args.n_val SCREAMING_SNAKE_CASE__ : Dict = round(metrics["""val_loss"""] , 4 ) if trainer.is_world_process_zero(): handle_metrics("""val""" , __lowerCAmelCase , training_args.output_dir ) all_metrics.update(__lowerCAmelCase ) if training_args.do_predict: logger.info("""*** Predict ***""" ) SCREAMING_SNAKE_CASE__ : int = trainer.predict(test_dataset=__lowerCAmelCase , metric_key_prefix="""test""" ) SCREAMING_SNAKE_CASE__ : List[Any] = test_output.metrics SCREAMING_SNAKE_CASE__ : int = data_args.n_test if trainer.is_world_process_zero(): SCREAMING_SNAKE_CASE__ : Optional[Any] = round(metrics["""test_loss"""] , 4 ) handle_metrics("""test""" , __lowerCAmelCase , training_args.output_dir ) all_metrics.update(__lowerCAmelCase ) if training_args.predict_with_generate: SCREAMING_SNAKE_CASE__ : int = tokenizer.batch_decode( test_output.predictions , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = lmap(str.strip , __lowerCAmelCase ) write_txt_file(__lowerCAmelCase , os.path.join(training_args.output_dir , """test_generations.txt""" ) ) if trainer.is_world_process_zero(): save_json(__lowerCAmelCase , os.path.join(training_args.output_dir , """all_results.json""" ) ) return all_metrics def _lowercase ( __lowerCAmelCase ) -> List[Any]: # For xla_spawn (TPUs) main() if __name__ == "__main__": main()
719
"""simple docstring""" import multiprocessing from typing import TYPE_CHECKING, Optional, Union from .. import Dataset, Features, config from ..formatting import query_table from ..packaged_modules.sql.sql import Sql from ..utils import logging from .abc import AbstractDatasetInputStream if TYPE_CHECKING: import sqlitea import sqlalchemy class __a (UpperCamelCase_): '''simple docstring''' def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]: """simple docstring""" super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a ) SCREAMING_SNAKE_CASE__ : List[Any] = Sql( cache_dir=_a , features=_a , sql=_a , con=_a , **_a , ) def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = None SCREAMING_SNAKE_CASE__ : Union[str, Any] = None SCREAMING_SNAKE_CASE__ : Dict = None SCREAMING_SNAKE_CASE__ : Optional[int] = None self.builder.download_and_prepare( download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , ) # Build dataset for splits SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset( split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory ) return dataset class __a : '''simple docstring''' def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any: """simple docstring""" if num_proc is not None and num_proc <= 0: raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' ) SCREAMING_SNAKE_CASE__ : int = dataset SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = con SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE SCREAMING_SNAKE_CASE__ : int = num_proc SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs ) return written def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs SCREAMING_SNAKE_CASE__ : Any = query_table( table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , ) SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas() SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a ) return num_rows or len(_a ) def _a ( self , _a , **_a ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = 0 if self.num_proc is None or self.num_proc == 1: for offset in logging.tqdm( range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += self._batch_sql((offset, index, to_sql_kwargs) ) else: SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size with multiprocessing.Pool(self.num_proc ) as pool: for num_rows in logging.tqdm( pool.imap( self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += num_rows return written
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging a :Optional[Any] = logging.get_logger(__name__) a :Optional[Any] = { "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json", } class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = """open-llama""" def __init__( self , _a=100_000 , _a=4_096 , _a=11_008 , _a=32 , _a=32 , _a="silu" , _a=2_048 , _a=0.02 , _a=1E-6 , _a=True , _a=0 , _a=1 , _a=2 , _a=False , _a=True , _a=0.1 , _a=0.1 , _a=True , _a=True , _a=None , **_a , ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size SCREAMING_SNAKE_CASE__ : Tuple = max_position_embeddings SCREAMING_SNAKE_CASE__ : str = hidden_size SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size SCREAMING_SNAKE_CASE__ : Tuple = num_hidden_layers SCREAMING_SNAKE_CASE__ : List[Any] = num_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = hidden_act SCREAMING_SNAKE_CASE__ : Any = initializer_range SCREAMING_SNAKE_CASE__ : List[str] = rms_norm_eps SCREAMING_SNAKE_CASE__ : str = use_cache SCREAMING_SNAKE_CASE__ : Union[str, Any] = kwargs.pop( """use_memorry_efficient_attention""" , _a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Dict = attention_dropout_prob SCREAMING_SNAKE_CASE__ : Dict = use_stable_embedding SCREAMING_SNAKE_CASE__ : Union[str, Any] = shared_input_output_embedding SCREAMING_SNAKE_CASE__ : Dict = rope_scaling self._rope_scaling_validation() super().__init__( pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , tie_word_embeddings=_a , **_a , ) def _a ( self ) -> str: """simple docstring""" if self.rope_scaling is None: return if not isinstance(self.rope_scaling , _a ) or len(self.rope_scaling ) != 2: raise ValueError( """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """ f'''got {self.rope_scaling}''' ) SCREAMING_SNAKE_CASE__ : List[Any] = self.rope_scaling.get("""type""" , _a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = self.rope_scaling.get("""factor""" , _a ) if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: raise ValueError( f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''' ) if rope_scaling_factor is None or not isinstance(_a , _a ) or rope_scaling_factor <= 1.0: raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''' )
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"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> int: if divisor % 5 == 0 or divisor % 2 == 0: return 0 SCREAMING_SNAKE_CASE__ : List[Any] = 1 SCREAMING_SNAKE_CASE__ : int = 1 while repunit: SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor repunit_index += 1 return repunit_index def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = limit - 1 if divisor % 2 == 0: divisor += 1 while least_divisible_repunit(__lowerCAmelCase ) <= limit: divisor += 2 return divisor if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" import asyncio import os import shutil import subprocess import sys import tempfile import unittest from distutils.util import strtobool from functools import partial from pathlib import Path from typing import List, Union from unittest import mock import torch from ..state import AcceleratorState, PartialState from ..utils import ( gather, is_bnb_available, is_comet_ml_available, is_datasets_available, is_deepspeed_available, is_mps_available, is_safetensors_available, is_tensorboard_available, is_torch_version, is_tpu_available, is_transformers_available, is_wandb_available, is_xpu_available, ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=False ) -> List[Any]: try: SCREAMING_SNAKE_CASE__ : Dict = os.environ[key] except KeyError: # KEY isn't set, default to `default`. SCREAMING_SNAKE_CASE__ : List[Any] = default else: # KEY is set, convert it to True or False. try: SCREAMING_SNAKE_CASE__ : Tuple = strtobool(__lowerCAmelCase ) except ValueError: # More values are supported, but let's keep the message simple. raise ValueError(F'''If set, {key} must be yes or no.''' ) return _value a :List[str] = parse_flag_from_env("RUN_SLOW", default=False) def _lowercase ( __lowerCAmelCase ) -> Any: return unittest.skip("""Test was skipped""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> str: return unittest.skipUnless(_run_slow_tests , """test is slow""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Optional[int]: return unittest.skipUnless(not torch.cuda.is_available() , """test requires only a CPU""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Optional[int]: return unittest.skipUnless(torch.cuda.is_available() , """test requires a GPU""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> List[Any]: return unittest.skipUnless(is_xpu_available() , """test requires a XPU""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Tuple: return unittest.skipUnless(is_mps_available() , """test requires a `mps` backend support in `torch`""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Optional[int]: return unittest.skipUnless( is_transformers_available() and is_datasets_available() , """test requires the Hugging Face suite""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Any: return unittest.skipUnless(is_bnb_available() , """test requires the bitsandbytes library""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> int: return unittest.skipUnless(is_tpu_available() , """test requires TPU""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> List[Any]: return unittest.skipUnless(torch.cuda.device_count() == 1 , """test requires a GPU""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> int: return unittest.skipUnless(torch.xpu.device_count() == 1 , """test requires a XPU""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> List[Any]: return unittest.skipUnless(torch.cuda.device_count() > 1 , """test requires multiple GPUs""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Any: return unittest.skipUnless(torch.xpu.device_count() > 1 , """test requires multiple XPUs""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> int: return unittest.skipUnless(is_safetensors_available() , """test requires safetensors""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Any: return unittest.skipUnless(is_deepspeed_available() , """test requires DeepSpeed""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Optional[Any]: return unittest.skipUnless(is_torch_version(""">=""" , """1.12.0""" ) , """test requires torch version >= 1.12.0""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Dict: if test_case is None: return partial(__lowerCAmelCase , version=__lowerCAmelCase ) return unittest.skipUnless(is_torch_version(""">=""" , __lowerCAmelCase ) , F'''test requires torch version >= {version}''' )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Dict: return unittest.skipUnless(is_tensorboard_available() , """test requires Tensorboard""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> List[str]: return unittest.skipUnless(is_wandb_available() , """test requires wandb""" )(__lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> List[Any]: return unittest.skipUnless(is_comet_ml_available() , """test requires comet_ml""" )(__lowerCAmelCase ) a :Optional[Any] = ( any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available() ) def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]: return unittest.skipUnless( _atleast_one_tracker_available , """test requires at least one tracker to be available and for `comet_ml` to not be installed""" , )(__lowerCAmelCase ) class __a (unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = True @classmethod def _a ( cls ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = tempfile.mkdtemp() @classmethod def _a ( cls ) -> Optional[int]: """simple docstring""" if os.path.exists(cls.tmpdir ): shutil.rmtree(cls.tmpdir ) def _a ( self ) -> Dict: """simple docstring""" if self.clear_on_setup: for path in Path(self.tmpdir ).glob("""**/*""" ): if path.is_file(): path.unlink() elif path.is_dir(): shutil.rmtree(_a ) class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> Dict: """simple docstring""" super().tearDown() # Reset the state of the AcceleratorState singleton. AcceleratorState._reset_state() PartialState._reset_state() class __a (unittest.TestCase): '''simple docstring''' def _a ( self , _a ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = mocks if isinstance(_a , (tuple, list) ) else [mocks] for m in self.mocks: m.start() self.addCleanup(m.stop ) def _lowercase ( __lowerCAmelCase ) -> List[str]: SCREAMING_SNAKE_CASE__ : Optional[int] = AcceleratorState() SCREAMING_SNAKE_CASE__ : Union[str, Any] = tensor[None].clone().to(state.device ) SCREAMING_SNAKE_CASE__ : Optional[Any] = gather(__lowerCAmelCase ).cpu() SCREAMING_SNAKE_CASE__ : List[Any] = tensor[0].cpu() for i in range(tensors.shape[0] ): if not torch.equal(tensors[i] , __lowerCAmelCase ): return False return True class __a : '''simple docstring''' def __init__( self , _a , _a , _a ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = returncode SCREAMING_SNAKE_CASE__ : List[Any] = stdout SCREAMING_SNAKE_CASE__ : Tuple = stderr async def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str: while True: SCREAMING_SNAKE_CASE__ : Optional[int] = await stream.readline() if line: callback(__lowerCAmelCase ) else: break async def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=False ) -> _RunOutput: if echo: print("""\nRunning: """ , """ """.join(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Any = await asyncio.create_subprocess_exec( cmd[0] , *cmd[1:] , stdin=__lowerCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=__lowerCAmelCase , ) # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait # # If it starts hanging, will need to switch to the following code. The problem is that no data # will be seen until it's done and if it hangs for example there will be no debug info. # out, err = await p.communicate() # return _RunOutput(p.returncode, out, err) SCREAMING_SNAKE_CASE__ : Optional[int] = [] SCREAMING_SNAKE_CASE__ : Union[str, Any] = [] def tee(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase="" ): SCREAMING_SNAKE_CASE__ : int = line.decode("""utf-8""" ).rstrip() sink.append(__lowerCAmelCase ) if not quiet: print(__lowerCAmelCase , __lowerCAmelCase , file=__lowerCAmelCase ) # XXX: the timeout doesn't seem to make any difference here await asyncio.wait( [ asyncio.create_task(_read_stream(p.stdout , lambda __lowerCAmelCase : tee(__lowerCAmelCase , __lowerCAmelCase , sys.stdout , label="""stdout:""" ) ) ), asyncio.create_task(_read_stream(p.stderr , lambda __lowerCAmelCase : tee(__lowerCAmelCase , __lowerCAmelCase , sys.stderr , label="""stderr:""" ) ) ), ] , timeout=__lowerCAmelCase , ) return _RunOutput(await p.wait() , __lowerCAmelCase , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=180 , __lowerCAmelCase=False , __lowerCAmelCase=True ) -> _RunOutput: SCREAMING_SNAKE_CASE__ : Optional[int] = asyncio.get_event_loop() SCREAMING_SNAKE_CASE__ : Union[str, Any] = loop.run_until_complete( _stream_subprocess(__lowerCAmelCase , env=__lowerCAmelCase , stdin=__lowerCAmelCase , timeout=__lowerCAmelCase , quiet=__lowerCAmelCase , echo=__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Any = """ """.join(__lowerCAmelCase ) if result.returncode > 0: SCREAMING_SNAKE_CASE__ : int = """\n""".join(result.stderr ) raise RuntimeError( F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n''' F'''The combined stderr from workers follows:\n{stderr}''' ) return result class __a (UpperCamelCase_): '''simple docstring''' pass def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=False ) -> str: try: SCREAMING_SNAKE_CASE__ : Any = subprocess.check_output(__lowerCAmelCase , stderr=subprocess.STDOUT ) if return_stdout: if hasattr(__lowerCAmelCase , """decode""" ): SCREAMING_SNAKE_CASE__ : Tuple = output.decode("""utf-8""" ) return output except subprocess.CalledProcessError as e: raise SubprocessCallException( F'''Command `{' '.join(__lowerCAmelCase )}` failed with the following error:\n\n{e.output.decode()}''' ) from e
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"""simple docstring""" import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import numpy as np from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.31.0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") a :Union[str, Any] = logging.getLogger(__name__) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_28 , metadata={ """help""": ( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Whether to pad all samples to `max_seq_length`. """ """If False, will pad the samples dynamically when batching to the maximum length in the batch.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of training examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of evaluation examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of prediction examples to this """ """value if set.""" ) } , ) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}) _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , ) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , ) _SCREAMING_SNAKE_CASE :str = field( default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Will use the token generated when running `huggingface-cli login` (necessary to use this script """ """with private models).""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , ) def _lowercase ( ) -> Union[str, Any]: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("""run_xnli""" , __lowerCAmelCase ) # Setup logging logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level() logger.setLevel(__lowerCAmelCase ) datasets.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}''' + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' ) logger.info(F'''Training/evaluation parameters {training_args}''' ) # Detecting last checkpoint. SCREAMING_SNAKE_CASE__ : Any = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. ''' """Use --overwrite_output_dir to overcome.""" ) elif last_checkpoint is not None: logger.info( F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change ''' """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" ) # Set seed before initializing model. set_seed(training_args.seed ) # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # Downloading and loading xnli dataset from the hub. if training_args.do_train: if model_args.train_language is None: SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset( """xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) else: SCREAMING_SNAKE_CASE__ : str = load_dataset( """xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names if training_args.do_eval: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names if training_args.do_predict: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names # Labels SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase ) # Load pretrained model and tokenizer # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , ) # Preprocessing the datasets # Padding strategy if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : str = """max_length""" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch SCREAMING_SNAKE_CASE__ : Optional[Any] = False def preprocess_function(__lowerCAmelCase ): # Tokenize the texts return tokenizer( examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , ) if training_args.do_train: if data_args.max_train_samples is not None: SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples ) SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""train dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , ) # Log a few random samples from the training set: for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ): logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' ) if training_args.do_eval: if data_args.max_eval_samples is not None: SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""validation dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , ) if training_args.do_predict: if data_args.max_predict_samples is not None: SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , ) # Get the metric function SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" ) # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 ) return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids ) # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator elif training_args.fpaa: SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None # Initialize our Trainer SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer( model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , ) # Training if training_args.do_train: SCREAMING_SNAKE_CASE__ : Dict = None if training_args.resume_from_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint elif last_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics SCREAMING_SNAKE_CASE__ : Optional[int] = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("""train""" , __lowerCAmelCase ) trainer.save_metrics("""train""" , __lowerCAmelCase ) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("""*** Evaluate ***""" ) SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""eval""" , __lowerCAmelCase ) trainer.save_metrics("""eval""" , __lowerCAmelCase ) # Prediction if training_args.do_predict: logger.info("""*** Predict ***""" ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" ) SCREAMING_SNAKE_CASE__ : List[str] = ( data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""predict""" , __lowerCAmelCase ) trainer.save_metrics("""predict""" , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" ) if trainer.is_world_process_zero(): with open(__lowerCAmelCase , """w""" ) as writer: writer.write("""index\tprediction\n""" ) for index, item in enumerate(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item] writer.write(F'''{index}\t{item}\n''' ) if __name__ == "__main__": main()
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"""simple docstring""" import unittest import numpy as np import timeout_decorator # noqa from transformers import BlenderbotSmallConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...generation.test_flax_utils import FlaxGenerationTesterMixin from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor if is_flax_available(): import os # The slow tests are often failing with OOM error on GPU # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html a :Tuple = "platform" import jax import jax.numpy as jnp from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import ( FlaxBlenderbotSmallForConditionalGeneration, FlaxBlenderbotSmallModel, shift_tokens_right, ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> Any: if attention_mask is None: SCREAMING_SNAKE_CASE__ : str = np.where(input_ids != config.pad_token_id , 1 , 0 ) if decoder_attention_mask is None: SCREAMING_SNAKE_CASE__ : List[Any] = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 ) if head_mask is None: SCREAMING_SNAKE_CASE__ : Any = np.ones((config.encoder_layers, config.encoder_attention_heads) ) if decoder_head_mask is None: SCREAMING_SNAKE_CASE__ : Tuple = np.ones((config.decoder_layers, config.decoder_attention_heads) ) if cross_attn_head_mask is None: SCREAMING_SNAKE_CASE__ : Any = np.ones((config.decoder_layers, config.decoder_attention_heads) ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": attention_mask, } class __a : '''simple docstring''' def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=False , _a=99 , _a=16 , _a=2 , _a=4 , _a=4 , _a="gelu" , _a=0.1 , _a=0.1 , _a=32 , _a=2 , _a=1 , _a=0 , _a=0.02 , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = parent SCREAMING_SNAKE_CASE__ : List[Any] = batch_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length SCREAMING_SNAKE_CASE__ : Tuple = is_training SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels SCREAMING_SNAKE_CASE__ : Dict = vocab_size SCREAMING_SNAKE_CASE__ : Any = hidden_size SCREAMING_SNAKE_CASE__ : Tuple = num_hidden_layers SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_act SCREAMING_SNAKE_CASE__ : Tuple = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Tuple = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : str = max_position_embeddings SCREAMING_SNAKE_CASE__ : Optional[Any] = eos_token_id SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id SCREAMING_SNAKE_CASE__ : str = bos_token_id SCREAMING_SNAKE_CASE__ : Union[str, Any] = initializer_range def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Any = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 ) SCREAMING_SNAKE_CASE__ : List[Any] = shift_tokens_right(_a , 1 , 2 ) SCREAMING_SNAKE_CASE__ : Tuple = BlenderbotSmallConfig( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=_a , ) SCREAMING_SNAKE_CASE__ : Tuple = prepare_blenderbot_inputs_dict(_a , _a , _a ) return config, inputs_dict def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.prepare_config_and_inputs() return config, inputs_dict def _a ( self , _a , _a , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = 20 SCREAMING_SNAKE_CASE__ : List[str] = model_class_name(_a ) SCREAMING_SNAKE_CASE__ : Any = model.encode(inputs_dict["""input_ids"""] ) SCREAMING_SNAKE_CASE__ : List[Any] = ( inputs_dict["""decoder_input_ids"""], inputs_dict["""decoder_attention_mask"""], ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model.init_cache(decoder_input_ids.shape[0] , _a , _a ) SCREAMING_SNAKE_CASE__ : List[Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" ) SCREAMING_SNAKE_CASE__ : Tuple = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) SCREAMING_SNAKE_CASE__ : List[Any] = model.decode( decoder_input_ids[:, :-1] , _a , decoder_attention_mask=_a , past_key_values=_a , decoder_position_ids=_a , ) SCREAMING_SNAKE_CASE__ : Tuple = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" ) SCREAMING_SNAKE_CASE__ : str = model.decode( decoder_input_ids[:, -1:] , _a , decoder_attention_mask=_a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_a , ) SCREAMING_SNAKE_CASE__ : int = model.decode(_a , _a ) SCREAMING_SNAKE_CASE__ : Any = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) ) self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' ) def _a ( self , _a , _a , _a ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = 20 SCREAMING_SNAKE_CASE__ : List[str] = model_class_name(_a ) SCREAMING_SNAKE_CASE__ : str = model.encode(inputs_dict["""input_ids"""] ) SCREAMING_SNAKE_CASE__ : int = ( inputs_dict["""decoder_input_ids"""], inputs_dict["""decoder_attention_mask"""], ) SCREAMING_SNAKE_CASE__ : List[Any] = jnp.concatenate( [ decoder_attention_mask, jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ), ] , axis=-1 , ) SCREAMING_SNAKE_CASE__ : Any = model.init_cache(decoder_input_ids.shape[0] , _a , _a ) SCREAMING_SNAKE_CASE__ : List[Any] = jnp.broadcast_to( jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , ) SCREAMING_SNAKE_CASE__ : Any = model.decode( decoder_input_ids[:, :-1] , _a , decoder_attention_mask=_a , past_key_values=_a , decoder_position_ids=_a , ) SCREAMING_SNAKE_CASE__ : int = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" ) SCREAMING_SNAKE_CASE__ : Optional[int] = model.decode( decoder_input_ids[:, -1:] , _a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_a , decoder_position_ids=_a , ) SCREAMING_SNAKE_CASE__ : List[str] = model.decode(_a , _a , decoder_attention_mask=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) ) self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' ) @require_flax class __a (unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Dict = 99 def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = np.array( [ [71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 82, 2], [5, 97, 17, 39, 94, 40, 2], [76, 83, 94, 25, 70, 78, 2], [87, 59, 41, 35, 48, 66, 2], [55, 13, 16, 58, 5, 2, 1], # note padding [64, 27, 31, 51, 12, 75, 2], [52, 64, 86, 17, 83, 39, 2], [48, 61, 9, 24, 71, 82, 2], [26, 1, 60, 48, 22, 13, 2], [21, 5, 62, 28, 14, 76, 2], [45, 98, 37, 86, 59, 48, 2], [70, 70, 50, 9, 28, 0, 2], ] , dtype=np.intaa , ) SCREAMING_SNAKE_CASE__ : List[Any] = input_ids.shape[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = BlenderbotSmallConfig( vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , ) return config, input_ids, batch_size def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self._get_config_and_data() SCREAMING_SNAKE_CASE__ : Tuple = FlaxBlenderbotSmallForConditionalGeneration(_a ) SCREAMING_SNAKE_CASE__ : List[str] = lm_model(input_ids=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = (batch_size, input_ids.shape[1], config.vocab_size) self.assertEqual(outputs["""logits"""].shape , _a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = BlenderbotSmallConfig( vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , ) SCREAMING_SNAKE_CASE__ : Optional[int] = FlaxBlenderbotSmallForConditionalGeneration(_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa ) SCREAMING_SNAKE_CASE__ : Any = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa ) SCREAMING_SNAKE_CASE__ : Dict = lm_model(input_ids=_a , decoder_input_ids=_a ) SCREAMING_SNAKE_CASE__ : Any = (*summary.shape, config.vocab_size) self.assertEqual(outputs["""logits"""].shape , _a ) def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa ) SCREAMING_SNAKE_CASE__ : Any = shift_tokens_right(_a , 1 , 2 ) SCREAMING_SNAKE_CASE__ : Dict = np.equal(_a , 1 ).astype(np.floataa ).sum() SCREAMING_SNAKE_CASE__ : Any = np.equal(_a , 1 ).astype(np.floataa ).sum() self.assertEqual(shifted.shape , input_ids.shape ) self.assertEqual(_a , n_pad_before - 1 ) self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() ) @require_flax class __a (UpperCamelCase_ , unittest.TestCase , UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = True _SCREAMING_SNAKE_CASE :Optional[int] = ( ( FlaxBlenderbotSmallModel, FlaxBlenderbotSmallForConditionalGeneration, ) if is_flax_available() else () ) _SCREAMING_SNAKE_CASE :Union[str, Any] = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else () def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = FlaxBlenderbotSmallModelTester(self ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward(_a , _a , _a ) def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs() for model_class in self.all_model_classes: self.model_tester.check_use_cache_forward_with_attn_mask(_a , _a , _a ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__ ): SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(_a , _a ) SCREAMING_SNAKE_CASE__ : str = model_class(_a ) @jax.jit def encode_jitted(_a , _a=None , **_a ): return model.encode(input_ids=_a , attention_mask=_a ) with self.subTest("""JIT Enabled""" ): SCREAMING_SNAKE_CASE__ : str = encode_jitted(**_a ).to_tuple() with self.subTest("""JIT Disabled""" ): with jax.disable_jit(): SCREAMING_SNAKE_CASE__ : Dict = encode_jitted(**_a ).to_tuple() self.assertEqual(len(_a ) , len(_a ) ) for jitted_output, output in zip(_a , _a ): self.assertEqual(jitted_output.shape , output.shape ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__ ): SCREAMING_SNAKE_CASE__ : List[Any] = model_class(_a ) SCREAMING_SNAKE_CASE__ : Dict = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] ) SCREAMING_SNAKE_CASE__ : Any = { """decoder_input_ids""": inputs_dict["""decoder_input_ids"""], """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""], """encoder_outputs""": encoder_outputs, } @jax.jit def decode_jitted(_a , _a , _a ): return model.decode( decoder_input_ids=_a , decoder_attention_mask=_a , encoder_outputs=_a , ) with self.subTest("""JIT Enabled""" ): SCREAMING_SNAKE_CASE__ : List[str] = decode_jitted(**_a ).to_tuple() with self.subTest("""JIT Disabled""" ): with jax.disable_jit(): SCREAMING_SNAKE_CASE__ : Optional[int] = decode_jitted(**_a ).to_tuple() self.assertEqual(len(_a ) , len(_a ) ) for jitted_output, output in zip(_a , _a ): self.assertEqual(jitted_output.shape , output.shape ) @slow def _a ( self ) -> Any: """simple docstring""" for model_class_name in self.all_model_classes: SCREAMING_SNAKE_CASE__ : int = model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" ) # FlaxBlenderbotForSequenceClassification expects eos token in input_ids SCREAMING_SNAKE_CASE__ : Optional[int] = np.ones((1, 1) ) * model.config.eos_token_id SCREAMING_SNAKE_CASE__ : List[Any] = model(_a ) self.assertIsNotNone(_a )
700
"""simple docstring""" # Note: if you intend to run this script make sure you look under scripts/fsmt/ # to locate the appropriate script to do the work correctly. There is a set of scripts to: # - download and prepare data and run the conversion script # - perform eval to get the best hparam into the config # - generate model_cards - useful if you have multiple models from the same paper import argparse import json import os import re from collections import OrderedDict from os.path import basename, dirname import fairseq import torch from fairseq import hub_utils from fairseq.data.dictionary import Dictionary from transformers import FSMTConfig, FSMTForConditionalGeneration from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE from transformers.utils import WEIGHTS_NAME, logging logging.set_verbosity_warning() a :str = 2 # based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping` # values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults: # # * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users) # * `early_stopping`: `False` consistently scored better # * `length_penalty` varied, so will assign the best one depending on the model a :int = { # fairseq: "wmt19-ru-en": {"length_penalty": 1.1}, "wmt19-en-ru": {"length_penalty": 1.15}, "wmt19-en-de": {"length_penalty": 1.0}, "wmt19-de-en": {"length_penalty": 1.1}, # allenai: "wmt16-en-de-dist-12-1": {"length_penalty": 0.6}, "wmt16-en-de-dist-6-1": {"length_penalty": 0.6}, "wmt16-en-de-12-1": {"length_penalty": 0.8}, "wmt19-de-en-6-6-base": {"length_penalty": 0.6}, "wmt19-de-en-6-6-big": {"length_penalty": 0.6}, } # this remaps the different models to their organization names a :Dict = {} for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]: a :List[Any] = "facebook" for m in [ "wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1", "wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big", ]: a :str = "allenai" def _lowercase ( __lowerCAmelCase ) -> Any: # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up, # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7} SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() ) SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split() # restore the special tokens for k in keep_keys: del da[F'''{k}</w>'''] SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore return da def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]: # prep assert os.path.exists(__lowerCAmelCase ) os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase ) print(F'''Writing results to {pytorch_dump_folder_path}''' ) # handle various types of models SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models() SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""} SCREAMING_SNAKE_CASE__ : Optional[Any] = """.""" # note: since the model dump is old, fairseq has upgraded its model some # time later, and it does a whole lot of rewrites and splits on the saved # weights, therefore we can't use torch.load() directly on the model file. # see: upgrade_state_dict(state_dict) in fairseq_model.py print(F'''using checkpoint {checkpoint_file}''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] ) SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""] SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""] SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase ) # dicts SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" ) print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # detect whether this is a do_lower_case situation, which can be derived by checking whether we # have at least one uppercase letter in the source vocab SCREAMING_SNAKE_CASE__ : Optional[Any] = True for k in src_vocab.keys(): if not k.islower(): SCREAMING_SNAKE_CASE__ : Tuple = False break SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" ) print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # merges_file (bpecodes) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] ) for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code" SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) if os.path.exists(__lowerCAmelCase ): break with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin: SCREAMING_SNAKE_CASE__ : Any = fin.read() SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number print(F'''Generating {merges_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout: fout.write(__lowerCAmelCase ) # model config SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" ) # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe - # may have to modify the tokenizer if a different type is used by a future model assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}''' assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}''' SCREAMING_SNAKE_CASE__ : str = { """architectures""": ["""FSMTForConditionalGeneration"""], """model_type""": """fsmt""", """activation_dropout""": args["""activation_dropout"""], """activation_function""": """relu""", """attention_dropout""": args["""attention_dropout"""], """d_model""": args["""decoder_embed_dim"""], """dropout""": args["""dropout"""], """init_std""": 0.02, """max_position_embeddings""": args["""max_source_positions"""], """num_hidden_layers""": args["""encoder_layers"""], """src_vocab_size""": src_vocab_size, """tgt_vocab_size""": tgt_vocab_size, """langs""": [src_lang, tgt_lang], """encoder_attention_heads""": args["""encoder_attention_heads"""], """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""], """encoder_layerdrop""": args["""encoder_layerdrop"""], """encoder_layers""": args["""encoder_layers"""], """decoder_attention_heads""": args["""decoder_attention_heads"""], """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""], """decoder_layerdrop""": args["""decoder_layerdrop"""], """decoder_layers""": args["""decoder_layers"""], """bos_token_id""": 0, """pad_token_id""": 1, """eos_token_id""": 2, """is_encoder_decoder""": True, """scale_embedding""": not args["""no_scale_embedding"""], """tie_word_embeddings""": args["""share_all_embeddings"""], } # good hparam defaults to start with SCREAMING_SNAKE_CASE__ : Tuple = 5 SCREAMING_SNAKE_CASE__ : str = False if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]: SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""] else: SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0 print(F'''Generating {fsmt_model_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # tokenizer config SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = { """langs""": [src_lang, tgt_lang], """model_max_length""": 1024, """do_lower_case""": do_lower_case, } print(F'''Generating {fsmt_tokenizer_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # model SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0] SCREAMING_SNAKE_CASE__ : int = model.state_dict() # rename keys to start with 'model.' SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() ) # remove unneeded keys SCREAMING_SNAKE_CASE__ : str = [ """model.model""", """model.encoder.version""", """model.decoder.version""", """model.encoder_embed_tokens.weight""", """model.decoder_embed_tokens.weight""", """model.encoder.embed_positions._float_tensor""", """model.decoder.embed_positions._float_tensor""", ] for k in ignore_keys: model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase ) # check that it loads ok model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase ) # save SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) print(F'''Generating {pytorch_weights_dump_path}''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) print("""Conversion is done!""" ) print("""\nLast step is to upload the files to s3""" ) print(F'''cd {data_root}''' ) print(F'''transformers-cli upload {model_dir}''' ) if __name__ == "__main__": a :Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--fsmt_checkpoint_path", default=None, type=str, required=True, help=( "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts," " bpecodes, etc." ), ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) a :List[str] = parser.parse_args() convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
12
0
"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int: return abs(__lowerCAmelCase ) if a == 0 else greatest_common_divisor(b % a , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int: while y: # --> when y=0 then loop will terminate and return x as final GCD. SCREAMING_SNAKE_CASE__ : Optional[Any] = y, x % y return abs(__lowerCAmelCase ) def _lowercase ( ) -> List[str]: try: SCREAMING_SNAKE_CASE__ : Optional[Any] = input("""Enter two integers separated by comma (,): """ ).split(""",""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = int(nums[0] ) SCREAMING_SNAKE_CASE__ : Dict = int(nums[1] ) print( F'''greatest_common_divisor({num_a}, {num_a}) = ''' F'''{greatest_common_divisor(__lowerCAmelCase , __lowerCAmelCase )}''' ) print(F'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__lowerCAmelCase , __lowerCAmelCase )}''' ) except (IndexError, UnboundLocalError, ValueError): print("""Wrong input""" ) if __name__ == "__main__": main()
701
"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,) def _a ( self , **_a ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """num_train_timesteps""": 1_000, """beta_start""": 0.0_001, """beta_end""": 0.02, """beta_schedule""": """linear""", """variance_type""": """fixed_small""", """clip_sample""": True, } config.update(**_a ) return config def _a ( self ) -> str: """simple docstring""" for timesteps in [1, 5, 100, 1_000]: self.check_over_configs(num_train_timesteps=_a ) def _a ( self ) -> str: """simple docstring""" for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=_a , beta_end=_a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_a ) def _a ( self ) -> Any: """simple docstring""" for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=_a ) def _a ( self ) -> Optional[int]: """simple docstring""" for clip_sample in [True, False]: self.check_over_configs(clip_sample=_a ) def _a ( self ) -> int: """simple docstring""" self.check_over_configs(thresholding=_a ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=_a , prediction_type=_a , sample_max_value=_a , ) def _a ( self ) -> str: """simple docstring""" for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=_a ) def _a ( self ) -> str: """simple docstring""" for t in [0, 500, 999]: self.check_over_forward(time_step=_a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Any = len(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model() SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : str = pred_prev_sample SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 258.9_606 ) < 1E-2 assert abs(result_mean.item() - 0.3_372 ) < 1E-3 def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" ) SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Dict = len(_a ) SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model() SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : int = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 202.0_296 ) < 1E-2 assert abs(result_mean.item() - 0.2_631 ) < 1E-3 def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps for i, timestep in enumerate(_a ): if i == len(_a ) - 1: SCREAMING_SNAKE_CASE__ : Optional[Any] = -1 else: SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1] SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item() self.assertEqual(_a , _a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0] with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ): scheduler.set_timesteps(timesteps=_a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0] SCREAMING_SNAKE_CASE__ : List[str] = len(_a ) with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ): scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a ) def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps] with self.assertRaises( _a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ): scheduler.set_timesteps(timesteps=_a )
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"""simple docstring""" a :dict[tuple[int, int, int], int] = {} def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int: # if we are absent twice, or late 3 consecutive days, # no further prize strings are possible if late == 3 or absent == 2: return 0 # if we have no days left, and have not failed any other rules, # we have a prize string if days == 0: return 1 # No easy solution, so now we need to do the recursive calculation # First, check if the combination is already in the cache, and # if yes, return the stored value from there since we already # know the number of possible prize strings from this point on SCREAMING_SNAKE_CASE__ : Dict = (days, absent, late) if key in cache: return cache[key] # now we calculate the three possible ways that can unfold from # this point on, depending on our attendance today # 1) if we are late (but not absent), the "absent" counter stays as # it is, but the "late" counter increases by one SCREAMING_SNAKE_CASE__ : Optional[Any] = _calculate(days - 1 , __lowerCAmelCase , late + 1 ) # 2) if we are absent, the "absent" counter increases by 1, and the # "late" counter resets to 0 SCREAMING_SNAKE_CASE__ : int = _calculate(days - 1 , absent + 1 , 0 ) # 3) if we are on time, this resets the "late" counter and keeps the # absent counter SCREAMING_SNAKE_CASE__ : Any = _calculate(days - 1 , __lowerCAmelCase , 0 ) SCREAMING_SNAKE_CASE__ : Optional[int] = state_late + state_absent + state_ontime SCREAMING_SNAKE_CASE__ : Dict = prizestrings return prizestrings def _lowercase ( __lowerCAmelCase = 30 ) -> int: return _calculate(__lowerCAmelCase , absent=0 , late=0 ) if __name__ == "__main__": print(solution())
702
"""simple docstring""" import os a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000} def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Any = 0 SCREAMING_SNAKE_CASE__ : Dict = 0 while index < len(__lowerCAmelCase ) - 1: SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]] SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]] if current_value < next_value: total_value -= current_value else: total_value += current_value index += 1 total_value += SYMBOLS[numerals[index]] return total_value def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Optional[int] = """""" SCREAMING_SNAKE_CASE__ : int = num // 1000 numerals += m_count * "M" num %= 1000 SCREAMING_SNAKE_CASE__ : List[str] = num // 100 if c_count == 9: numerals += "CM" c_count -= 9 elif c_count == 4: numerals += "CD" c_count -= 4 if c_count >= 5: numerals += "D" c_count -= 5 numerals += c_count * "C" num %= 100 SCREAMING_SNAKE_CASE__ : List[Any] = num // 10 if x_count == 9: numerals += "XC" x_count -= 9 elif x_count == 4: numerals += "XL" x_count -= 4 if x_count >= 5: numerals += "L" x_count -= 5 numerals += x_count * "X" num %= 10 if num == 9: numerals += "IX" num -= 9 elif num == 4: numerals += "IV" num -= 4 if num >= 5: numerals += "V" num -= 5 numerals += num * "I" return numerals def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int: SCREAMING_SNAKE_CASE__ : int = 0 with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea: SCREAMING_SNAKE_CASE__ : str = filea.readlines() for line in lines: SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip() SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase ) savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase ) return savings if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" from ...configuration_utils import PretrainedConfig class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[Any] = """bert-generation""" def __init__( self , _a=50_358 , _a=1_024 , _a=24 , _a=16 , _a=4_096 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=0.02 , _a=1E-1_2 , _a=0 , _a=2 , _a=1 , _a="absolute" , _a=True , **_a , ) -> List[str]: """simple docstring""" super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a ) SCREAMING_SNAKE_CASE__ : Dict = vocab_size SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_size SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : int = num_attention_heads SCREAMING_SNAKE_CASE__ : Dict = hidden_act SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : List[Any] = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_position_embeddings SCREAMING_SNAKE_CASE__ : Tuple = initializer_range SCREAMING_SNAKE_CASE__ : Dict = layer_norm_eps SCREAMING_SNAKE_CASE__ : Dict = position_embedding_type SCREAMING_SNAKE_CASE__ : Any = use_cache
703
"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow if is_tf_available(): import numpy as np import tensorflow as tf from transformers import TFCamembertModel @require_tf @require_sentencepiece @require_tokenizers class __a (unittest.TestCase): '''simple docstring''' @slow def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" ) SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor( [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !" SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""] SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) ) self.assertEqual(output.shape , _a ) # compare the actual values for a slice. SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor( [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , ) # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0') # camembert.eval() # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach() self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
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"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> float: return price * (1 + tax_rate) if __name__ == "__main__": print(f'{price_plus_tax(100, 0.25) = }') print(f'{price_plus_tax(125.50, 0.05) = }')
704
"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices a :List[Any] = logging.get_logger(__name__) a :Optional[int] = { "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json", } class __a (UpperCamelCase_ , UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = """focalnet""" def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]: """simple docstring""" super().__init__(**_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size SCREAMING_SNAKE_CASE__ : str = patch_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes SCREAMING_SNAKE_CASE__ : Optional[int] = depths SCREAMING_SNAKE_CASE__ : Any = focal_levels SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows SCREAMING_SNAKE_CASE__ : Any = hidden_act SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate SCREAMING_SNAKE_CASE__ : str = use_layerscale SCREAMING_SNAKE_CASE__ : int = layerscale_value SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator SCREAMING_SNAKE_CASE__ : str = initializer_range SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps SCREAMING_SNAKE_CASE__ : Any = encoder_stride SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices( out_features=_a , out_indices=_a , stage_names=self.stage_names )
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"""simple docstring""" import math def _lowercase ( __lowerCAmelCase ) -> list[int]: SCREAMING_SNAKE_CASE__ : Tuple = [] SCREAMING_SNAKE_CASE__ : List[str] = 2 SCREAMING_SNAKE_CASE__ : Union[str, Any] = int(math.sqrt(__lowerCAmelCase ) ) # Size of every segment SCREAMING_SNAKE_CASE__ : Union[str, Any] = [True] * (end + 1) SCREAMING_SNAKE_CASE__ : Union[str, Any] = [] while start <= end: if temp[start] is True: in_prime.append(__lowerCAmelCase ) for i in range(start * start , end + 1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Optional[Any] = False start += 1 prime += in_prime SCREAMING_SNAKE_CASE__ : Tuple = end + 1 SCREAMING_SNAKE_CASE__ : Any = min(2 * end , __lowerCAmelCase ) while low <= n: SCREAMING_SNAKE_CASE__ : Optional[int] = [True] * (high - low + 1) for each in in_prime: SCREAMING_SNAKE_CASE__ : Union[str, Any] = math.floor(low / each ) * each if t < low: t += each for j in range(__lowerCAmelCase , high + 1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : str = False for j in range(len(__lowerCAmelCase ) ): if temp[j] is True: prime.append(j + low ) SCREAMING_SNAKE_CASE__ : Optional[int] = high + 1 SCREAMING_SNAKE_CASE__ : Union[str, Any] = min(high + end , __lowerCAmelCase ) return prime print(sieve(10**6))
705
"""simple docstring""" import unittest import numpy as np from transformers import RoFormerConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax.numpy as jnp from transformers.models.roformer.modeling_flax_roformer import ( FlaxRoFormerForMaskedLM, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerModel, ) class __a (unittest.TestCase): '''simple docstring''' def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = parent SCREAMING_SNAKE_CASE__ : Tuple = batch_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length SCREAMING_SNAKE_CASE__ : Optional[int] = is_training SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels SCREAMING_SNAKE_CASE__ : int = vocab_size SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads SCREAMING_SNAKE_CASE__ : Dict = intermediate_size SCREAMING_SNAKE_CASE__ : int = hidden_act SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size SCREAMING_SNAKE_CASE__ : int = initializer_range SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = None if self.use_attention_mask: SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] ) SCREAMING_SNAKE_CASE__ : Tuple = None if self.use_token_type_ids: SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , ) return config, input_ids, token_type_ids, attention_mask def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask} return config, inputs_dict @require_flax class __a (UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Any = True _SCREAMING_SNAKE_CASE :Optional[Any] = ( ( FlaxRoFormerModel, FlaxRoFormerForMaskedLM, FlaxRoFormerForSequenceClassification, FlaxRoFormerForTokenClassification, FlaxRoFormerForMultipleChoice, FlaxRoFormerForQuestionAnswering, ) if is_flax_available() else () ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self ) @slow def _a ( self ) -> int: """simple docstring""" for model_class_name in self.all_model_classes: SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a ) SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) ) self.assertIsNotNone(_a ) @require_flax class __a (unittest.TestCase): '''simple docstring''' @slow def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" ) SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] ) SCREAMING_SNAKE_CASE__ : str = model(_a )[0] SCREAMING_SNAKE_CASE__ : List[Any] = 50_000 SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size) self.assertEqual(output.shape , _a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array( [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] ) self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
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0
"""simple docstring""" import os import shutil import tempfile import unittest import numpy as np from transformers import AutoTokenizer, BarkProcessor from transformers.testing_utils import require_torch, slow @require_torch class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = """ylacombe/bark-small""" SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp() SCREAMING_SNAKE_CASE__ : Dict = """en_speaker_1""" SCREAMING_SNAKE_CASE__ : Optional[int] = """This is a test string""" SCREAMING_SNAKE_CASE__ : str = """speaker_embeddings_path.json""" SCREAMING_SNAKE_CASE__ : List[str] = """speaker_embeddings""" def _a ( self , **_a ) -> List[str]: """simple docstring""" return AutoTokenizer.from_pretrained(self.checkpoint , **_a ) def _a ( self ) -> int: """simple docstring""" shutil.rmtree(self.tmpdirname ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Optional[int] = BarkProcessor(tokenizer=_a ) processor.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : Optional[Any] = BarkProcessor.from_pretrained(self.tmpdirname ) self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() ) @slow def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = BarkProcessor.from_pretrained( pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , ) processor.save_pretrained( self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , ) SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" ) SCREAMING_SNAKE_CASE__ : str = BarkProcessor.from_pretrained( self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="""(BOS)""" , eos_token="""(EOS)""" , ) self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = BarkProcessor.from_pretrained( pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = 35 SCREAMING_SNAKE_CASE__ : Any = 2 SCREAMING_SNAKE_CASE__ : Tuple = 8 SCREAMING_SNAKE_CASE__ : Optional[int] = { """semantic_prompt""": np.ones(_a ), """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ), """fine_prompt""": np.ones((nb_codebooks_total, seq_len) ), } # test providing already loaded voice_preset SCREAMING_SNAKE_CASE__ : Tuple = processor(text=self.input_string , voice_preset=_a ) SCREAMING_SNAKE_CASE__ : Any = inputs["""history_prompt"""] for key in voice_preset: self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_a , np.array([] ) ).tolist() ) # test loading voice preset from npz file SCREAMING_SNAKE_CASE__ : str = os.path.join(self.tmpdirname , """file.npz""" ) np.savez(_a , **_a ) SCREAMING_SNAKE_CASE__ : List[Any] = processor(text=self.input_string , voice_preset=_a ) SCREAMING_SNAKE_CASE__ : Tuple = inputs["""history_prompt"""] for key in voice_preset: self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_a , np.array([] ) ).tolist() ) # test loading voice preset from the hub SCREAMING_SNAKE_CASE__ : List[Any] = processor(text=self.input_string , voice_preset=self.voice_preset ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : List[Any] = BarkProcessor(tokenizer=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = processor(text=self.input_string ) SCREAMING_SNAKE_CASE__ : Dict = tokenizer( self.input_string , padding="""max_length""" , max_length=256 , add_special_tokens=_a , return_attention_mask=_a , return_token_type_ids=_a , ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
706
"""simple docstring""" a :List[str] = [ (1_000, "M"), (900, "CM"), (500, "D"), (400, "CD"), (100, "C"), (90, "XC"), (50, "L"), (40, "XL"), (10, "X"), (9, "IX"), (5, "V"), (4, "IV"), (1, "I"), ] def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000} SCREAMING_SNAKE_CASE__ : List[Any] = 0 SCREAMING_SNAKE_CASE__ : List[str] = 0 while place < len(__lowerCAmelCase ): if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]): total += vals[roman[place + 1]] - vals[roman[place]] place += 2 else: total += vals[roman[place]] place += 1 return total def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Any = [] for arabic, roman in ROMAN: ((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase ) result.append(roman * factor ) if number == 0: break return "".join(__lowerCAmelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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0
"""simple docstring""" from __future__ import annotations def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: print(F'''Vertex\tShortest Distance from vertex {src}''' ) for i, d in enumerate(__lowerCAmelCase ): print(F'''{i}\t\t{d}''' ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: for j in range(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Tuple = (graph[j][k] for k in ["""src""", """dst""", """weight"""]) if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]: return True return False def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[float]: SCREAMING_SNAKE_CASE__ : int = [float("""inf""" )] * vertex_count SCREAMING_SNAKE_CASE__ : Tuple = 0.0 for _ in range(vertex_count - 1 ): for j in range(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : int = (graph[j][k] for k in ["""src""", """dst""", """weight"""]) if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]: SCREAMING_SNAKE_CASE__ : int = distance[u] + w SCREAMING_SNAKE_CASE__ : List[str] = check_negative_cycle(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) if negative_cycle_exists: raise Exception("""Negative cycle found""" ) return distance if __name__ == "__main__": import doctest doctest.testmod() a :Dict = int(input("Enter number of vertices: ").strip()) a :Union[str, Any] = int(input("Enter number of edges: ").strip()) a :list[dict[str, int]] = [{} for _ in range(E)] for i in range(E): print("Edge ", i + 1) a :Union[str, Any] = ( int(x) for x in input("Enter source, destination, weight: ").strip().split(" ") ) a :List[str] = {"src": src, "dst": dest, "weight": weight} a :str = int(input("\nEnter shortest path source:").strip()) a :Optional[Any] = bellman_ford(graph, V, E, source) print_distance(shortest_distance, 0)
707
"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) a :Any = { "configuration_roberta_prelayernorm": [ "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaPreLayerNormConfig", "RobertaPreLayerNormOnnxConfig", ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Union[str, Any] = [ "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "RobertaPreLayerNormForCausalLM", "RobertaPreLayerNormForMaskedLM", "RobertaPreLayerNormForMultipleChoice", "RobertaPreLayerNormForQuestionAnswering", "RobertaPreLayerNormForSequenceClassification", "RobertaPreLayerNormForTokenClassification", "RobertaPreLayerNormModel", "RobertaPreLayerNormPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Optional[Any] = [ "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFRobertaPreLayerNormForCausalLM", "TFRobertaPreLayerNormForMaskedLM", "TFRobertaPreLayerNormForMultipleChoice", "TFRobertaPreLayerNormForQuestionAnswering", "TFRobertaPreLayerNormForSequenceClassification", "TFRobertaPreLayerNormForTokenClassification", "TFRobertaPreLayerNormMainLayer", "TFRobertaPreLayerNormModel", "TFRobertaPreLayerNormPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :List[Any] = [ "FlaxRobertaPreLayerNormForCausalLM", "FlaxRobertaPreLayerNormForMaskedLM", "FlaxRobertaPreLayerNormForMultipleChoice", "FlaxRobertaPreLayerNormForQuestionAnswering", "FlaxRobertaPreLayerNormForSequenceClassification", "FlaxRobertaPreLayerNormForTokenClassification", "FlaxRobertaPreLayerNormModel", "FlaxRobertaPreLayerNormPreTrainedModel", ] if TYPE_CHECKING: from .configuration_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaPreLayerNormConfig, RobertaPreLayerNormOnnxConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_roberta_prelayernorm import ( ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, RobertaPreLayerNormForCausalLM, RobertaPreLayerNormForMaskedLM, RobertaPreLayerNormForMultipleChoice, RobertaPreLayerNormForQuestionAnswering, RobertaPreLayerNormForSequenceClassification, RobertaPreLayerNormForTokenClassification, RobertaPreLayerNormModel, RobertaPreLayerNormPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_roberta_prelayernorm import ( TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST, TFRobertaPreLayerNormForCausalLM, TFRobertaPreLayerNormForMaskedLM, TFRobertaPreLayerNormForMultipleChoice, TFRobertaPreLayerNormForQuestionAnswering, TFRobertaPreLayerNormForSequenceClassification, TFRobertaPreLayerNormForTokenClassification, TFRobertaPreLayerNormMainLayer, TFRobertaPreLayerNormModel, TFRobertaPreLayerNormPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_roberta_prelayernorm import ( FlaxRobertaPreLayerNormForCausalLM, FlaxRobertaPreLayerNormForMaskedLM, FlaxRobertaPreLayerNormForMultipleChoice, FlaxRobertaPreLayerNormForQuestionAnswering, FlaxRobertaPreLayerNormForSequenceClassification, FlaxRobertaPreLayerNormForTokenClassification, FlaxRobertaPreLayerNormModel, FlaxRobertaPreLayerNormPreTrainedModel, ) else: import sys a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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0
"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool: return numa ^ numa < 0 if __name__ == "__main__": import doctest doctest.testmod()
708
"""simple docstring""" import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import BertTokenizer, BertTokenizerFast from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import AlignProcessor, EfficientNetImageProcessor @require_vision class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp() SCREAMING_SNAKE_CASE__ : Dict = [ """[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest""", ] SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) ) SCREAMING_SNAKE_CASE__ : Optional[Any] = { """do_resize""": True, """size""": 20, """do_center_crop""": True, """crop_size""": 18, """do_normalize""": True, """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073], """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711], } SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a ) with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp: json.dump(_a , _a ) def _a ( self , **_a ) -> List[Any]: """simple docstring""" return BertTokenizer.from_pretrained(self.tmpdirname , **_a ) def _a ( self , **_a ) -> List[Any]: """simple docstring""" return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a ) def _a ( self , **_a ) -> Any: """simple docstring""" return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a ) def _a ( self ) -> List[Any]: """simple docstring""" shutil.rmtree(self.tmpdirname ) def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )] SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs] return image_inputs def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer() SCREAMING_SNAKE_CASE__ : str = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a ) processor_slow.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a ) SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a ) processor_fast.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname ) self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() ) self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() ) self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() ) self.assertIsInstance(processor_slow.tokenizer , _a ) self.assertIsInstance(processor_fast.tokenizer , _a ) self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertIsInstance(processor_slow.image_processor , _a ) self.assertIsInstance(processor_fast.image_processor , _a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() ) processor.save_pretrained(self.tmpdirname ) SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" ) SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 ) SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained( self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 ) self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() ) self.assertIsInstance(processor.tokenizer , _a ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , _a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor() SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" ) for key in input_image_proc.keys(): self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer""" SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a ) SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] , encoded_processor[key] ) def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.get_image_processor() SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer""" SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a ) self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] ) # test if it raises when no input is passed with pytest.raises(_a ): processor() def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a ) self.assertListEqual(_a , _a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor() SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer""" SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs() SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a ) self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
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"""simple docstring""" import inspect import os import unittest import torch import accelerate from accelerate import Accelerator from accelerate.test_utils import execute_subprocess_async, require_multi_gpu from accelerate.utils import patch_environment class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = inspect.getfile(accelerate.test_utils ) SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_script.py"""] ) SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.sep.join( mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_distributed_data_loop.py"""] ) SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_ops.py"""] ) @require_multi_gpu def _a ( self ) -> Tuple: """simple docstring""" print(f'''Found {torch.cuda.device_count()} devices.''' ) SCREAMING_SNAKE_CASE__ : str = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path] with patch_environment(omp_num_threads=1 ): execute_subprocess_async(_a , env=os.environ.copy() ) @require_multi_gpu def _a ( self ) -> Union[str, Any]: """simple docstring""" print(f'''Found {torch.cuda.device_count()} devices.''' ) SCREAMING_SNAKE_CASE__ : Dict = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path] print(f'''Command: {cmd}''' ) with patch_environment(omp_num_threads=1 ): execute_subprocess_async(_a , env=os.environ.copy() ) @require_multi_gpu def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )] with patch_environment(omp_num_threads=1 ): execute_subprocess_async(_a , env=os.environ.copy() ) @require_multi_gpu def _a ( self ) -> Any: """simple docstring""" print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only''' ) SCREAMING_SNAKE_CASE__ : List[str] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path] with patch_environment(omp_num_threads=1 , cuda_visible_devices="""0,1""" ): execute_subprocess_async(_a , env=os.environ.copy() ) if __name__ == "__main__": a :Tuple = Accelerator() a :Optional[int] = (accelerator.state.process_index + 2, 10) a :Any = torch.randint(0, 10, shape).to(accelerator.device) a :List[Any] = "" a :List[str] = accelerator.pad_across_processes(tensor) if tensora.shape[0] != accelerator.state.num_processes + 1: error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0." if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor): error_msg += "Tensors have different values." if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0): error_msg += "Padding was not done with the right value (0)." a :List[str] = accelerator.pad_across_processes(tensor, pad_first=True) if tensora.shape[0] != accelerator.state.num_processes + 1: error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0." a :Union[str, Any] = accelerator.state.num_processes - accelerator.state.process_index - 1 if not torch.equal(tensora[index:], tensor): error_msg += "Tensors have different values." if not torch.all(tensora[:index] == 0): error_msg += "Padding was not done with the right value (0)." # Raise error at the end to make sure we don't stop at the first failure. if len(error_msg) > 0: raise ValueError(error_msg)
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"""simple docstring""" from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxSeqaSeqConfigWithPast from ...utils import logging a :Optional[Any] = logging.get_logger(__name__) a :Union[str, Any] = { "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json", "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json", "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json", "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json", "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json", } class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[Any] = """t5""" _SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""] _SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""} def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size SCREAMING_SNAKE_CASE__ : Tuple = d_model SCREAMING_SNAKE_CASE__ : int = d_kv SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers SCREAMING_SNAKE_CASE__ : int = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry SCREAMING_SNAKE_CASE__ : Tuple = num_heads SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj SCREAMING_SNAKE_CASE__ : str = use_cache SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" ) SCREAMING_SNAKE_CASE__ : Dict = act_info[-1] SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated""" if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2: raise ValueError( f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.''' """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """ """'gated-gelu' or 'relu'""" ) # for backwards compatibility if feed_forward_proj == "gated-gelu": SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new""" super().__init__( pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , ) class __a (UpperCamelCase_): '''simple docstring''' @property def _a ( self ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """input_ids""": {0: """batch""", 1: """encoder_sequence"""}, """attention_mask""": {0: """batch""", 1: """encoder_sequence"""}, } if self.use_past: SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence""" SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""} SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""} else: SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""} SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""} if self.use_past: self.fill_with_past_key_values_(_a , direction="""inputs""" ) return common_inputs @property def _a ( self ) -> int: """simple docstring""" return 13
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"""simple docstring""" import math import unittest def _lowercase ( __lowerCAmelCase ) -> bool: assert isinstance(__lowerCAmelCase , __lowerCAmelCase ) and ( number >= 0 ), "'number' must been an int and positive" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(math.sqrt(__lowerCAmelCase ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True class __a (unittest.TestCase): '''simple docstring''' def _a ( self ) -> Any: """simple docstring""" self.assertTrue(is_prime(2 ) ) self.assertTrue(is_prime(3 ) ) self.assertTrue(is_prime(5 ) ) self.assertTrue(is_prime(7 ) ) self.assertTrue(is_prime(11 ) ) self.assertTrue(is_prime(13 ) ) self.assertTrue(is_prime(17 ) ) self.assertTrue(is_prime(19 ) ) self.assertTrue(is_prime(23 ) ) self.assertTrue(is_prime(29 ) ) def _a ( self ) -> Union[str, Any]: """simple docstring""" with self.assertRaises(_a ): is_prime(-19 ) self.assertFalse( is_prime(0 ) , """Zero doesn't have any positive factors, primes must have exactly two.""" , ) self.assertFalse( is_prime(1 ) , """One only has 1 positive factor, primes must have exactly two.""" , ) self.assertFalse(is_prime(2 * 2 ) ) self.assertFalse(is_prime(2 * 3 ) ) self.assertFalse(is_prime(3 * 3 ) ) self.assertFalse(is_prime(3 * 5 ) ) self.assertFalse(is_prime(3 * 5 * 7 ) ) if __name__ == "__main__": unittest.main()
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"""simple docstring""" from __future__ import annotations import time import numpy as np a :Optional[Any] = [8, 5, 9, 7] a :List[Any] = [ [2, 0, 1, 1], [0, 1, 2, 1], [4, 0, 0, 3], [0, 2, 1, 0], [1, 0, 3, 0], ] a :int = [ [3, 2, 1, 4], [0, 2, 5, 2], [5, 1, 0, 5], [1, 5, 3, 0], [3, 0, 3, 3], ] class __a : '''simple docstring''' def __init__( self , _a , _a , _a , ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = claim_vector SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table def _a ( self ) -> list[int]: """simple docstring""" return [ sum(p_item[i] for p_item in self.__allocated_resources_table ) for i in range(len(self.__allocated_resources_table[0] ) ) ] def _a ( self ) -> list[int]: """simple docstring""" return np.array(self.__claim_vector ) - np.array( self.__processes_resource_summation() ) def _a ( self ) -> list[list[int]]: """simple docstring""" return [ list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) ) for i, allocated_resource in enumerate(self.__allocated_resources_table ) ] def _a ( self ) -> dict[int, list[int]]: """simple docstring""" return {self.__need().index(_a ): i for i in self.__need()} def _a ( self , **_a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need() SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources() SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager() for kw, val in kwargs.items(): if kw and val is True: self.__pretty_data() print("""_""" * 50 + """\n""" ) while need_list: SCREAMING_SNAKE_CASE__ : List[str] = False for each_need in need_list: SCREAMING_SNAKE_CASE__ : Dict = True for index, need in enumerate(_a ): if need > available_resources[index]: SCREAMING_SNAKE_CASE__ : Optional[int] = False break if execution: SCREAMING_SNAKE_CASE__ : Any = True # get the original index of the process from ind_ctrl db for original_need_index, need_clone in need_index_manager.items(): if each_need == need_clone: SCREAMING_SNAKE_CASE__ : Tuple = original_need_index print(f'''Process {process_number + 1} is executing.''' ) # remove the process run from stack need_list.remove(_a ) # update available/freed resources stack SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array( alloc_resources_table[process_number] ) print( """Updated available resource stack for processes: """ + """ """.join([str(_a ) for x in available_resources] ) ) break if safe: print("""The process is in a safe state.\n""" ) else: print("""System in unsafe state. Aborting...\n""" ) break def _a ( self ) -> Any: """simple docstring""" print(""" """ * 9 + """Allocated Resource Table""" ) for item in self.__allocated_resources_table: print( f'''P{self.__allocated_resources_table.index(_a ) + 1}''' + """ """.join(f'''{it:>8}''' for it in item ) + """\n""" ) print(""" """ * 9 + """System Resource Table""" ) for item in self.__maximum_claim_table: print( f'''P{self.__maximum_claim_table.index(_a ) + 1}''' + """ """.join(f'''{it:>8}''' for it in item ) + """\n""" ) print( """Current Usage by Active Processes: """ + """ """.join(str(_a ) for x in self.__claim_vector ) ) print( """Initial Available Resources: """ + """ """.join(str(_a ) for x in self.__available_resources() ) ) time.sleep(1 ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from __future__ import annotations import time a :Optional[int] = list[tuple[int, int]] a :Any = [ [0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], ] a :Optional[int] = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right class __a : '''simple docstring''' def __init__( self , _a , _a , _a , _a , _a ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = pos_x SCREAMING_SNAKE_CASE__ : Tuple = pos_y SCREAMING_SNAKE_CASE__ : Optional[int] = (pos_y, pos_x) SCREAMING_SNAKE_CASE__ : Optional[int] = goal_x SCREAMING_SNAKE_CASE__ : List[Any] = goal_y SCREAMING_SNAKE_CASE__ : Dict = parent class __a : '''simple docstring''' def __init__( self , _a , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = Node(start[1] , start[0] , goal[1] , goal[0] , _a ) SCREAMING_SNAKE_CASE__ : Tuple = Node(goal[1] , goal[0] , goal[1] , goal[0] , _a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.start] SCREAMING_SNAKE_CASE__ : Union[str, Any] = False def _a ( self ) -> Path | None: """simple docstring""" while self.node_queue: SCREAMING_SNAKE_CASE__ : str = self.node_queue.pop(0 ) if current_node.pos == self.target.pos: SCREAMING_SNAKE_CASE__ : Tuple = True return self.retrace_path(_a ) SCREAMING_SNAKE_CASE__ : Dict = self.get_successors(_a ) for node in successors: self.node_queue.append(_a ) if not self.reached: return [self.start.pos] return None def _a ( self , _a ) -> list[Node]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = [] for action in delta: SCREAMING_SNAKE_CASE__ : Union[str, Any] = parent.pos_x + action[1] SCREAMING_SNAKE_CASE__ : Optional[Any] = parent.pos_y + action[0] if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_a ) - 1): continue if grid[pos_y][pos_x] != 0: continue successors.append( Node(_a , _a , self.target.pos_y , self.target.pos_x , _a ) ) return successors def _a ( self , _a ) -> Path: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = node SCREAMING_SNAKE_CASE__ : int = [] while current_node is not None: path.append((current_node.pos_y, current_node.pos_x) ) SCREAMING_SNAKE_CASE__ : Optional[int] = current_node.parent path.reverse() return path class __a : '''simple docstring''' def __init__( self , _a , _a ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = BreadthFirstSearch(_a , _a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = BreadthFirstSearch(_a , _a ) SCREAMING_SNAKE_CASE__ : List[str] = False def _a ( self ) -> Path | None: """simple docstring""" while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue: SCREAMING_SNAKE_CASE__ : str = self.fwd_bfs.node_queue.pop(0 ) SCREAMING_SNAKE_CASE__ : str = self.bwd_bfs.node_queue.pop(0 ) if current_bwd_node.pos == current_fwd_node.pos: SCREAMING_SNAKE_CASE__ : List[str] = True return self.retrace_bidirectional_path( _a , _a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = current_bwd_node SCREAMING_SNAKE_CASE__ : Tuple = current_fwd_node SCREAMING_SNAKE_CASE__ : List[Any] = { self.fwd_bfs: self.fwd_bfs.get_successors(_a ), self.bwd_bfs: self.bwd_bfs.get_successors(_a ), } for bfs in [self.fwd_bfs, self.bwd_bfs]: for node in successors[bfs]: bfs.node_queue.append(_a ) if not self.reached: return [self.fwd_bfs.start.pos] return None def _a ( self , _a , _a ) -> Path: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = self.fwd_bfs.retrace_path(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = self.bwd_bfs.retrace_path(_a ) bwd_path.pop() bwd_path.reverse() SCREAMING_SNAKE_CASE__ : Optional[int] = fwd_path + bwd_path return path if __name__ == "__main__": # all coordinates are given in format [y,x] import doctest doctest.testmod() a :Union[str, Any] = (0, 0) a :Optional[int] = (len(grid) - 1, len(grid[0]) - 1) for elem in grid: print(elem) a :Dict = time.time() a :Any = BreadthFirstSearch(init, goal) a :str = bfs.search() a :Union[str, Any] = time.time() - start_bfs_time print("Unidirectional BFS computation time : ", bfs_time) a :Union[str, Any] = time.time() a :int = BidirectionalBreadthFirstSearch(init, goal) a :int = bd_bfs.search() a :int = time.time() - start_bd_bfs_time print("Bidirectional BFS computation time : ", bd_bfs_time)
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_xlnet import XLNetTokenizer else: a :List[Any] = None a :Optional[int] = logging.get_logger(__name__) a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} a :Optional[int] = { "vocab_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model", }, "tokenizer_file": { "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json", "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json", }, } a :Dict = { "xlnet-base-cased": None, "xlnet-large-cased": None, } a :int = "▁" # Segments (not really needed) a :Dict = 0 a :Optional[int] = 1 a :Tuple = 2 a :List[str] = 3 a :Optional[Any] = 4 class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = """left""" _SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token super().__init__( vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3 SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case SCREAMING_SNAKE_CASE__ : List[str] = remove_space SCREAMING_SNAKE_CASE__ : int = keep_accents SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = [2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return SCREAMING_SNAKE_CASE__ : Tuple = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,) def _a ( self , **_a ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """num_train_timesteps""": 1_000, """beta_start""": 0.0_001, """beta_end""": 0.02, """beta_schedule""": """linear""", """variance_type""": """fixed_small""", """clip_sample""": True, } config.update(**_a ) return config def _a ( self ) -> str: """simple docstring""" for timesteps in [1, 5, 100, 1_000]: self.check_over_configs(num_train_timesteps=_a ) def _a ( self ) -> str: """simple docstring""" for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=_a , beta_end=_a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_a ) def _a ( self ) -> Any: """simple docstring""" for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=_a ) def _a ( self ) -> Optional[int]: """simple docstring""" for clip_sample in [True, False]: self.check_over_configs(clip_sample=_a ) def _a ( self ) -> int: """simple docstring""" self.check_over_configs(thresholding=_a ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=_a , prediction_type=_a , sample_max_value=_a , ) def _a ( self ) -> str: """simple docstring""" for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=_a ) def _a ( self ) -> str: """simple docstring""" for t in [0, 500, 999]: self.check_over_forward(time_step=_a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Any = len(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model() SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : str = pred_prev_sample SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 258.9_606 ) < 1E-2 assert abs(result_mean.item() - 0.3_372 ) < 1E-3 def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" ) SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Dict = len(_a ) SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model() SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : int = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 202.0_296 ) < 1E-2 assert abs(result_mean.item() - 0.2_631 ) < 1E-3 def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps for i, timestep in enumerate(_a ): if i == len(_a ) - 1: SCREAMING_SNAKE_CASE__ : Optional[Any] = -1 else: SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1] SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item() self.assertEqual(_a , _a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0] with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ): scheduler.set_timesteps(timesteps=_a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0] SCREAMING_SNAKE_CASE__ : List[str] = len(_a ) with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ): scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a ) def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps] with self.assertRaises( _a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ): scheduler.set_timesteps(timesteps=_a )
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"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool: SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1 SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1 # dp is a 2d matrix where dp[i][j] denotes whether prefix string of # length i of input_string matches with prefix string of length j of # given pattern. # "dp" stands for dynamic programming. SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )] # since string of zero length match pattern of zero length SCREAMING_SNAKE_CASE__ : Dict = 1 # since pattern of zero length will never match with string of non-zero length for i in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 # since string of zero length will match with pattern where there # is at least one * alternatively for j in range(1 , __lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0 # now using bottom-up approach to find for all remaining lengths for i in range(1 , __lowerCAmelCase ): for j in range(1 , __lowerCAmelCase ): if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".": SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1] elif pattern[j - 1] == "*": if dp[i][j - 2] == 1: SCREAMING_SNAKE_CASE__ : List[str] = 1 elif pattern[j - 2] in (input_string[i - 1], "."): SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j] else: SCREAMING_SNAKE_CASE__ : Optional[int] = 0 else: SCREAMING_SNAKE_CASE__ : Dict = 0 return bool(dp[-1][-1] ) if __name__ == "__main__": import doctest doctest.testmod() # inputing the strings # input_string = input("input a string :") # pattern = input("input a pattern :") a :Any = "aab" a :Optional[Any] = "c*a*b" # using function to check whether given string matches the given pattern if match_pattern(input_string, pattern): print(f'{input_string} matches the given pattern {pattern}') else: print(f'{input_string} does not match with the given pattern {pattern}')
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"""simple docstring""" a :Optional[int] = [ "VerificationMode", "Version", "disable_progress_bar", "enable_progress_bar", "is_progress_bar_enabled", "experimental", ] from .info_utils import VerificationMode from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled from .version import Version from .experimental import experimental
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"""simple docstring""" from math import sqrt def _lowercase ( __lowerCAmelCase ) -> bool: if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = 0 SCREAMING_SNAKE_CASE__ : Tuple = 1 while count != nth and number < 3: number += 1 if is_prime(__lowerCAmelCase ): count += 1 while count != nth: number += 2 if is_prime(__lowerCAmelCase ): count += 1 return number if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer from .base import PipelineTool class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :int = """philschmid/bart-large-cnn-samsum""" _SCREAMING_SNAKE_CASE :Dict = ( """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """ """and returns a summary of the text.""" ) _SCREAMING_SNAKE_CASE :Any = """summarizer""" _SCREAMING_SNAKE_CASE :str = AutoTokenizer _SCREAMING_SNAKE_CASE :str = AutoModelForSeqaSeqLM _SCREAMING_SNAKE_CASE :str = ["""text"""] _SCREAMING_SNAKE_CASE :str = ["""text"""] def _a ( self , _a ) -> str: """simple docstring""" return self.pre_processor(_a , return_tensors="""pt""" , truncation=_a ) def _a ( self , _a ) -> Any: """simple docstring""" return self.model.generate(**_a )[0] def _a ( self , _a ) -> int: """simple docstring""" return self.pre_processor.decode(_a , skip_special_tokens=_a , clean_up_tokenization_spaces=_a )
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"""simple docstring""" class __a : '''simple docstring''' def __init__( self , _a , _a , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = value SCREAMING_SNAKE_CASE__ : List[Any] = weight def __repr__( self ) -> List[Any]: """simple docstring""" return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})''' def _a ( self ) -> Dict: """simple docstring""" return self.value def _a ( self ) -> int: """simple docstring""" return self.name def _a ( self ) -> Optional[Any]: """simple docstring""" return self.weight def _a ( self ) -> Dict: """simple docstring""" return self.value / self.weight def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict: SCREAMING_SNAKE_CASE__ : Any = [] for i in range(len(__lowerCAmelCase ) ): menu.append(Things(name[i] , value[i] , weight[i] ) ) return menu def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0 for i in range(len(__lowerCAmelCase ) ): if (total_cost + items_copy[i].get_weight()) <= max_cost: result.append(items_copy[i] ) total_cost += items_copy[i].get_weight() total_value += items_copy[i].get_value() return (result, total_value) def _lowercase ( ) -> List[str]: pass if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging a :str = logging.get_logger(__name__) a :Any = { "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json", "YituTech/conv-bert-medium-small": ( "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json" ), "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json", # See all ConvBERT models at https://huggingface.co/models?filter=convbert } class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[Any] = """convbert""" def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1E-1_2 , _a=1 , _a=0 , _a=2 , _a=768 , _a=2 , _a=9 , _a=1 , _a=None , **_a , ) -> Dict: """simple docstring""" super().__init__( pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size SCREAMING_SNAKE_CASE__ : Dict = hidden_size SCREAMING_SNAKE_CASE__ : Dict = num_hidden_layers SCREAMING_SNAKE_CASE__ : Tuple = num_attention_heads SCREAMING_SNAKE_CASE__ : int = intermediate_size SCREAMING_SNAKE_CASE__ : str = hidden_act SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : int = max_position_embeddings SCREAMING_SNAKE_CASE__ : List[str] = type_vocab_size SCREAMING_SNAKE_CASE__ : str = initializer_range SCREAMING_SNAKE_CASE__ : Tuple = layer_norm_eps SCREAMING_SNAKE_CASE__ : Union[str, Any] = embedding_size SCREAMING_SNAKE_CASE__ : str = head_ratio SCREAMING_SNAKE_CASE__ : Union[str, Any] = conv_kernel_size SCREAMING_SNAKE_CASE__ : int = num_groups SCREAMING_SNAKE_CASE__ : Union[str, Any] = classifier_dropout class __a (UpperCamelCase_): '''simple docstring''' @property def _a ( self ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" if self.task == "multiple-choice": SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""} else: SCREAMING_SNAKE_CASE__ : List[Any] = {0: """batch""", 1: """sequence"""} return OrderedDict( [ ("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis), ] )
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from tokenizers import processors from ...tokenization_utils import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_nllb import NllbTokenizer else: a :Optional[int] = None a :Optional[Any] = logging.get_logger(__name__) a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} a :Union[str, Any] = { "vocab_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model" ), }, "tokenizer_file": { "facebook/nllb-200-distilled-600M": ( "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json" ), }, } a :Any = { "facebook/nllb-large-en-ro": 1_024, "facebook/nllb-200-distilled-600M": 1_024, } # fmt: off a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"] class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES _SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP _SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""] _SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer _SCREAMING_SNAKE_CASE :List[int] = [] _SCREAMING_SNAKE_CASE :List[int] = [] def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour super().__init__( vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , ) SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy() if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} ) SCREAMING_SNAKE_CASE__ : List[str] = { lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES } SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn""" SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang ) SCREAMING_SNAKE_CASE__ : Dict = tgt_lang self.set_src_lang_special_tokens(self._src_lang ) @property def _a ( self ) -> str: """simple docstring""" return self._src_lang @src_lang.setter def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" if token_ids_a is None: return self.prefix_tokens + token_ids_a + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens def _a ( self , _a , _a = None ) -> List[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id] SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple: """simple docstring""" if src_lang is None or tgt_lang is None: raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" ) SCREAMING_SNAKE_CASE__ : Dict = src_lang SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id return inputs def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = src_lang SCREAMING_SNAKE_CASE__ : Dict = tgt_lang return super().prepare_seqaseq_batch(_a , _a , **_a ) def _a ( self ) -> Optional[Any]: """simple docstring""" return self.set_src_lang_special_tokens(self.src_lang ) def _a ( self ) -> str: """simple docstring""" return self.set_tgt_lang_special_tokens(self.tgt_lang ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : str = [] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a ) -> None: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a ) if self.legacy_behaviour: SCREAMING_SNAKE_CASE__ : List[Any] = [] SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code] else: SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code] SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id] SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _a ( self , _a , _a = None ) -> Tuple[str]: """simple docstring""" if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(_a ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' ) return SCREAMING_SNAKE_CASE__ : Dict = os.path.join( _a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ): copyfile(self.vocab_file , _a ) return (out_vocab_file,)
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"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> int: if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or number < 0: raise ValueError("""Input must be a non-negative integer""" ) SCREAMING_SNAKE_CASE__ : Dict = 0 while number: # This way we arrive at next set bit (next 1) instead of looping # through each bit and checking for 1s hence the # loop won't run 32 times it will only run the number of `1` times number &= number - 1 count += 1 return count if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" # Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #################################################################################################### # # Note: If when running this conversion script you're getting an exception: # ModuleNotFoundError: No module named 'megatron.model.enums' # you need to tell python where to find the clone of Megatron-LM, e.g.: # # cd /tmp # git clone https://github.com/NVIDIA/Megatron-LM # PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ... # # if you already have it cloned elsewhere, simply adjust the path to the existing path # # If the training was done using a Megatron-LM fork, e.g., # https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one # in your path, i.e., /path/to/Megatron-DeepSpeed/ # import argparse import os import re import zipfile import torch from transformers import AutoTokenizer, GPTaConfig def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any: # Format the message. if name is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None else: SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}""" SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase ) # Print and recurse (if needed). if isinstance(__lowerCAmelCase , __lowerCAmelCase ): if msg is not None: print(__lowerCAmelCase ) for k in val.keys(): recursive_print(__lowerCAmelCase , val[k] , spaces + 2 ) elif isinstance(__lowerCAmelCase , torch.Tensor ): print(__lowerCAmelCase , """:""" , val.size() ) else: print(__lowerCAmelCase , """:""" , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] # for compatibility with later versions of NVIDIA Megatron-LM. # The inverse operation is performed inside Megatron-LM to read checkpoints: # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 # If param is the weight tensor of the self-attention block, the returned tensor # will have to be transposed one more time to be read by HuggingFace GPT2. SCREAMING_SNAKE_CASE__ : Tuple = param.size() if checkpoint_version == 1.0: # version 1.0 stores [num_heads * hidden_size * num_splits, :] SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:] SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 ) SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous() elif checkpoint_version >= 2.0: # other versions store [num_heads * num_splits * hidden_size, :] SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:] SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous() SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase ) return param def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple: # The converted output model. SCREAMING_SNAKE_CASE__ : List[str] = {} # old versions did not store training args SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase ) if ds_args is not None: # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint # from pprint import pprint # pprint(vars(ds_args)) SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size # pprint(config) # The number of heads. SCREAMING_SNAKE_CASE__ : List[str] = config.n_head # The hidden_size per head. SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head # Megatron-LM checkpoint version if "checkpoint_version" in input_state_dict.keys(): SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""] else: SCREAMING_SNAKE_CASE__ : Tuple = 0.0 # The model. SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""] # The language model. SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""] # The embeddings. SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""] # The word embeddings. SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""] # Truncate the embedding table to vocab_size rows. SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :] SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings # The position embeddings. SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""] # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size] SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 ) if n_positions != config.n_positions: raise ValueError( F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' ) # Store the position embeddings. SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings # The transformer. SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""] # The regex to extract layer names. SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" ) # The simple map of names for "automated" rules. SCREAMING_SNAKE_CASE__ : Optional[int] = { """attention.dense""": """.attn.c_proj.""", """self_attention.dense""": """.attn.c_proj.""", """mlp.dense_h_to_4h""": """.mlp.c_fc.""", """mlp.dense_4h_to_h""": """.mlp.c_proj.""", } # Extract the layers. for key, val in transformer.items(): # Match the name. SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase ) # Stop if that's not a layer if m is None: break # The index of the layer. SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) ) # The name of the operation. SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 ) # Is it a weight or a bias? SCREAMING_SNAKE_CASE__ : str = m.group(3 ) # The name of the layer. SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}''' # For layernorm(s), simply store the layer norm. if op_name.endswith("""layernorm""" ): SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2""" SCREAMING_SNAKE_CASE__ : List[Any] = val # Transpose the QKV matrix. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "weight": # Insert a tensor of 1x1xDxD bias. SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view( 1 , 1 , __lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = causal_mask # Insert a "dummy" tensor for masked_bias. SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa ) SCREAMING_SNAKE_CASE__ : List[str] = masked_bias SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D. SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous() # Store. SCREAMING_SNAKE_CASE__ : Dict = out_val # Transpose the bias. elif ( op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value" ) and weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase ) # Store. No change of shape. SCREAMING_SNAKE_CASE__ : str = out_val # Transpose the weights. elif weight_or_bias == "weight": SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 ) # Copy the bias. elif weight_or_bias == "bias": SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name] SCREAMING_SNAKE_CASE__ : Dict = val # DEBUG. assert config.n_layer == layer_idx + 1 # The final layernorm. SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""] SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""] # For LM head, transformers' wants the matrix to weight embeddings. SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings # It should be done! return output_state_dict def _lowercase ( ) -> List[Any]: # Create the argument parser. SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser() parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" ) parser.add_argument( """path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , ) parser.add_argument( """--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , ) SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args() # Extract the basename. SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint ) # Load the model. # the .zip is very optional, let's keep it for backward compatibility print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' ) if args.path_to_checkpoint.endswith(""".zip""" ): with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint: with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict: SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" ) else: SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" ) SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase ) # Read the config, or default to the model released by NVIDIA. if args.config_file == "": if ds_args is not None: if ds_args.bias_gelu_fusion: SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast""" elif ds_args.openai_gelu: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new""" else: SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu""" else: # in the very early days this used to be "gelu_new" SCREAMING_SNAKE_CASE__ : Any = """gelu_new""" # Spell out all parameters in case the defaults change. SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig( vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , ) else: SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file ) SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""] # Convert. print("""Converting""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) # Print the structure of converted state dict. if args.print_checkpoint_structure: recursive_print(__lowerCAmelCase , __lowerCAmelCase ) # Add tokenizer class info to config # see https://github.com/huggingface/transformers/issues/13906) if ds_args is not None: SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type if tokenizer_type == "GPT2BPETokenizer": SCREAMING_SNAKE_CASE__ : Any = """gpt2""" elif tokenizer_type == "PretrainedFromHF": SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path else: raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__ SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class # Store the config to file. print("""Saving config""" ) config.save_pretrained(__lowerCAmelCase ) # Save tokenizer based on args print(F'''Adding {tokenizer_class} tokenizer files''' ) tokenizer.save_pretrained(__lowerCAmelCase ) # Store the state_dict to file. SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" ) print(F'''Saving checkpoint to "{output_checkpoint_file}"''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) #################################################################################################### if __name__ == "__main__": main() ####################################################################################################
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"""simple docstring""" a :List[str] = [ (1_000, "M"), (900, "CM"), (500, "D"), (400, "CD"), (100, "C"), (90, "XC"), (50, "L"), (40, "XL"), (10, "X"), (9, "IX"), (5, "V"), (4, "IV"), (1, "I"), ] def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000} SCREAMING_SNAKE_CASE__ : List[Any] = 0 SCREAMING_SNAKE_CASE__ : List[str] = 0 while place < len(__lowerCAmelCase ): if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]): total += vals[roman[place + 1]] - vals[roman[place]] place += 2 else: total += vals[roman[place]] place += 1 return total def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Any = [] for arabic, roman in ROMAN: (SCREAMING_SNAKE_CASE__) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase ) result.append(roman * factor ) if number == 0: break return "".join(__lowerCAmelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" import re from pathlib import Path from unittest import TestCase import pytest @pytest.mark.integration class __a (UpperCamelCase_): '''simple docstring''' def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read() SCREAMING_SNAKE_CASE__ : str = regexp.search(_a ) return match def _a ( self , _a ) -> Optional[Any]: """simple docstring""" with open(_a , encoding="""utf-8""" ) as input_file: SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL ) SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read() # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search` SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a ) SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None] return matches[0] if matches else None def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_encoding_on_file_open(str(_a ) ): raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' ) def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" ) SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) ) for dataset in dataset_files: if self._no_print_statements(str(_a ) ): raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
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import unittest from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin a :List[str] = get_tests_dir("fixtures/test_sentencepiece.model") @require_sentencepiece @require_tokenizers class __a (UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :List[Any] = ReformerTokenizer _SCREAMING_SNAKE_CASE :Union[str, Any] = ReformerTokenizerFast _SCREAMING_SNAKE_CASE :Union[str, Any] = True _SCREAMING_SNAKE_CASE :Union[str, Any] = False _SCREAMING_SNAKE_CASE :List[Any] = True def _a ( self ) -> List[str]: """simple docstring""" super().setUp() SCREAMING_SNAKE_CASE__ : List[Any] = ReformerTokenizer(_a , keep_accents=_a ) tokenizer.save_pretrained(self.tmpdirname ) def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = """<s>""" SCREAMING_SNAKE_CASE__ : Optional[int] = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0] , """<unk>""" ) self.assertEqual(vocab_keys[1] , """<s>""" ) self.assertEqual(vocab_keys[-1] , """j""" ) self.assertEqual(len(_a ) , 1_000 ) def _a ( self ) -> Any: """simple docstring""" self.assertEqual(self.get_tokenizer().vocab_size , 1_000 ) def _a ( self ) -> Optional[int]: """simple docstring""" if not self.test_rust_tokenizer: return SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer() SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_rust_tokenizer() SCREAMING_SNAKE_CASE__ : int = """I was born in 92000, and this is falsé.""" SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.tokenize(_a ) SCREAMING_SNAKE_CASE__ : Any = rust_tokenizer.tokenize(_a ) self.assertListEqual(_a , _a ) SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.encode(_a , add_special_tokens=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = rust_tokenizer.encode(_a , add_special_tokens=_a ) self.assertListEqual(_a , _a ) SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer() SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.encode(_a ) SCREAMING_SNAKE_CASE__ : Any = rust_tokenizer.encode(_a ) self.assertListEqual(_a , _a ) def _a ( self , _a=15 ) -> Union[str, Any]: """simple docstring""" for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ): SCREAMING_SNAKE_CASE__ : Dict = self.rust_tokenizer_class.from_pretrained(_a , **_a ) # Simple input SCREAMING_SNAKE_CASE__ : List[Any] = """This is a simple input""" SCREAMING_SNAKE_CASE__ : Optional[Any] = ["""This is a simple input 1""", """This is a simple input 2"""] SCREAMING_SNAKE_CASE__ : int = ("""This is a simple input""", """This is a pair""") SCREAMING_SNAKE_CASE__ : Dict = [ ("""This is a simple input 1""", """This is a simple input 2"""), ("""This is a simple pair 1""", """This is a simple pair 2"""), ] # Simple input tests self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" ) # Simple input self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" ) # Simple input self.assertRaises( _a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , ) # Pair input self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" ) # Pair input self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" ) # Pair input self.assertRaises( _a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , ) def _a ( self ) -> Optional[Any]: """simple docstring""" pass def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = ReformerTokenizer(_a , keep_accents=_a ) SCREAMING_SNAKE_CASE__ : Any = tokenizer.tokenize("""This is a test""" ) self.assertListEqual(_a , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] ) self.assertListEqual( tokenizer.convert_tokens_to_ids(_a ) , [285, 46, 10, 170, 382] , ) SCREAMING_SNAKE_CASE__ : Any = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" ) self.assertListEqual( _a , [ SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """9""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """é""", """.""", ] , ) SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.convert_tokens_to_ids(_a ) self.assertListEqual( _a , [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.convert_ids_to_tokens(_a ) self.assertListEqual( _a , [ SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """<unk>""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """<unk>""", """.""", ] , ) @cached_property def _a ( self ) -> Optional[Any]: """simple docstring""" return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment""" ) @slow def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Any = """Hello World!""" SCREAMING_SNAKE_CASE__ : List[str] = [126, 32, 262, 152, 38, 72, 287] self.assertListEqual(_a , self.big_tokenizer.encode(_a ) ) @slow def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = ( """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will""" """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth""" ) SCREAMING_SNAKE_CASE__ : int = [ 108, 265, 24, 111, 4, 258, 156, 35, 28, 275, 3, 259, 297, 260, 84, 4, 35, 110, 44, 8, 259, 91, 268, 21, 11, 209, 274, 109, 266, 277, 117, 86, 93, 315, 258, 278, 258, 277, 258, 0, 258, 288, 258, 319, 258, 0, 258, 0, 258, 0, 258, 0, 258, 287, 258, 315, 258, 289, 258, 278, 99, 269, 266, 262, 8, 259, 241, 4, 217, 230, 268, 266, 55, 168, 106, 75, 193, 266, 223, 27, 49, 26, 282, 25, 264, 299, 19, 26, 0, 258, 277, 117, 86, 93, 176, 183, 270, 11, 262, 42, 61, 265, ] self.assertListEqual(_a , self.big_tokenizer.encode(_a ) ) @require_torch @slow def _a ( self ) -> Tuple: """simple docstring""" import torch from transformers import ReformerConfig, ReformerModel # Build sequence SCREAMING_SNAKE_CASE__ : Dict = list(self.big_tokenizer.get_vocab().keys() )[:10] SCREAMING_SNAKE_CASE__ : List[Any] = """ """.join(_a ) SCREAMING_SNAKE_CASE__ : str = self.big_tokenizer.encode_plus(_a , return_tensors="""pt""" ) SCREAMING_SNAKE_CASE__ : Tuple = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="""pt""" ) SCREAMING_SNAKE_CASE__ : Optional[int] = ReformerConfig() # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024) SCREAMING_SNAKE_CASE__ : Any = encoded_sequence["""input_ids"""].shape SCREAMING_SNAKE_CASE__ : Any = ReformerModel(_a ) # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320) assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size with torch.no_grad(): model(**_a ) model(**_a ) @slow def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501 # fmt: on # This tokenizer does not know some characters like ")". # That is the reason why we use very simple texts here. # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064 SCREAMING_SNAKE_CASE__ : Tuple = [ """This is a very simple sentence.""", """The quick brown fox jumps over the lazy dog.""", ] self.tokenizer_integration_test_util( expected_encoding=_a , model_name="""google/reformer-crime-and-punishment""" , revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a""" , padding=_a , sequences=_a , )
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"""simple docstring""" import tempfile import unittest from transformers import TaConfig, is_torch_available from transformers.testing_utils import ( require_sentencepiece, require_tokenizers, require_torch, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel class __a : '''simple docstring''' def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = parent SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length SCREAMING_SNAKE_CASE__ : str = decoder_seq_length # For common tests SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length SCREAMING_SNAKE_CASE__ : Tuple = is_training SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask SCREAMING_SNAKE_CASE__ : List[str] = use_labels SCREAMING_SNAKE_CASE__ : str = vocab_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Any = num_attention_heads SCREAMING_SNAKE_CASE__ : Any = d_ff SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id SCREAMING_SNAKE_CASE__ : Any = None SCREAMING_SNAKE_CASE__ : str = decoder_layers def _a ( self ) -> Tuple: """simple docstring""" return TaConfig.from_pretrained("""google/umt5-base""" ) def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any: """simple docstring""" if attention_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id ) if decoder_attention_mask is None: SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id ) if head_mask is None: SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a ) if decoder_head_mask is None: SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a ) if cross_attn_head_mask is None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones( config.num_decoder_layers , config.num_attention_heads , device=_a ) return { "input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, "cross_attn_head_mask": cross_attn_head_mask, } def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size ) # we need to clamp the input ids here to avoid having pad token in between # this is because for NllbMoe the position_ids are prepared such that # all pad tokens have pos id = 2 and rest are between 2..seq_length # and the seq_length here is seq_length - num_pad_tokens # but when using past, there is no way of knowing if the past input ids had # pad tokens in them, which results in incorrect seq_lenth and which in turn results in # position_ids being off by num_pad_tokens in past input SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config() SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a ) return config, input_dict def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs() return config, inputs_dict def _a ( self ) -> List[str]: """simple docstring""" return TaConfig( vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self ) -> List[Any]: """simple docstring""" return TaConfig( vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a ) model.to(_a ) model.eval() SCREAMING_SNAKE_CASE__ : Dict = model( input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) ) self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) ) # There should be `num_layers` key value embeddings stored in decoder_past self.parent.assertEqual(len(_a ) , config.num_layers ) # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple self.parent.assertEqual(len(decoder_past[0] ) , 4 ) def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval() # first forward pass SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a ) SCREAMING_SNAKE_CASE__ : str = model(_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a ) self.parent.assertTrue(len(_a ) == len(_a ) ) self.parent.assertTrue(len(_a ) == len(_a ) + 1 ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size ) # append to next input_ids and SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""] SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""] # select random slice SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item() SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach() SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach() # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) ) def _a ( self , _a , _a , ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval() SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""] self.parent.assertFalse(torch.isnan(_a ).any().item() ) @require_torch class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Union[str, Any] = ( (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else () ) _SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else () _SCREAMING_SNAKE_CASE :List[str] = ( { """conversational""": UMTaForConditionalGeneration, """feature-extraction""": UMTaModel, """summarization""": UMTaForConditionalGeneration, """text2text-generation""": UMTaForConditionalGeneration, """translation""": UMTaForConditionalGeneration, """question-answering""": UMTaForQuestionAnswering, } if is_torch_available() else {} ) _SCREAMING_SNAKE_CASE :Union[str, Any] = True _SCREAMING_SNAKE_CASE :Tuple = False _SCREAMING_SNAKE_CASE :Optional[Any] = False _SCREAMING_SNAKE_CASE :List[Any] = True _SCREAMING_SNAKE_CASE :List[str] = True # The small UMT5 model needs higher percentages for CPU/MP tests _SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9] def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self ) @unittest.skip("""Test has a segmentation fault on torch 1.8.0""" ) def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a ) with tempfile.TemporaryDirectory() as tmpdirname: torch.onnx.export( _a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , ) @unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_fpaa_forward(*_a ) def _a ( self ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""] SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs() SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0] SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval() model.to(_a ) SCREAMING_SNAKE_CASE__ : List[str] = { """head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ), """decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ), } for attn_name, (name, mask) in zip(_a , head_masking.items() ): SCREAMING_SNAKE_CASE__ : List[str] = {name: mask} # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified if name == "head_mask": SCREAMING_SNAKE_CASE__ : str = torch.ones( config.num_decoder_layers , config.num_heads , device=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate( config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , ) # We check the state of decoder_attentions and cross_attentions just from the last step SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1] self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 ) @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" ) def _a ( self ) -> Dict: """simple docstring""" pass @require_torch @require_sentencepiece @require_tokenizers class __a (unittest.TestCase): '''simple docstring''' @slow @unittest.skip( """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a ) SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [ """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""", """No se como puedo <extra_id_0>.""", """This is the reason why we <extra_id_0> them.""", """The <extra_id_0> walks in <extra_id_1>, seats""", """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""", ] SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids # fmt: off SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor( [ [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0], [ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0], [ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1], ] ) # fmt: on torch.testing.assert_allclose(_a , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) ) SCREAMING_SNAKE_CASE__ : int = [ """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""", """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""", ] SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a ) self.assertEqual(_a , _a )
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0
import contextlib import os import sqlitea import pytest from datasets import Dataset, Features, Value from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]: assert isinstance(__lowerCAmelCase , __lowerCAmelCase ) assert dataset.num_rows == 4 assert dataset.num_columns == 3 assert dataset.column_names == ["col_1", "col_2", "col_3"] for feature, expected_dtype in expected_features.items(): assert dataset.features[feature].dtype == expected_dtype @require_sqlalchemy @pytest.mark.parametrize("""keep_in_memory""" , [False, True] ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any: SCREAMING_SNAKE_CASE__ : int = tmp_path / """cache""" SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""} with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase(): SCREAMING_SNAKE_CASE__ : int = SqlDatasetReader( """dataset""" , """sqlite:///""" + sqlite_path , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase ).read() _check_sql_dataset(__lowerCAmelCase , __lowerCAmelCase ) @require_sqlalchemy @pytest.mark.parametrize( """features""" , [ None, {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}, {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""}, {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""}, {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""}, ] , ) def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: SCREAMING_SNAKE_CASE__ : Dict = tmp_path / """cache""" SCREAMING_SNAKE_CASE__ : Tuple = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""} SCREAMING_SNAKE_CASE__ : Optional[Any] = features.copy() if features else default_expected_features SCREAMING_SNAKE_CASE__ : List[str] = ( Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , features=__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read() _check_sql_dataset(__lowerCAmelCase , __lowerCAmelCase ) def _lowercase ( __lowerCAmelCase ) -> Any: with contextlib.closing(sqlitea.connect(__lowerCAmelCase ) ) as con: SCREAMING_SNAKE_CASE__ : Union[str, Any] = con.cursor() cur.execute("""SELECT * FROM dataset""" ) for row in cur: yield row @require_sqlalchemy def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict: SCREAMING_SNAKE_CASE__ : Tuple = tmp_path / """cache""" SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """tmp.sql""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=__lowerCAmelCase ).read() SqlDatasetWriter(__lowerCAmelCase , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=1 ).write() SCREAMING_SNAKE_CASE__ : Tuple = iter_sql_file(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = iter_sql_file(__lowerCAmelCase ) for rowa, rowa in zip(__lowerCAmelCase , __lowerCAmelCase ): assert rowa == rowa @require_sqlalchemy def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any: SCREAMING_SNAKE_CASE__ : Any = tmp_path / """cache""" SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , """tmp.sql""" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=__lowerCAmelCase ).read() SqlDatasetWriter(__lowerCAmelCase , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=2 ).write() SCREAMING_SNAKE_CASE__ : Union[str, Any] = iter_sql_file(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = iter_sql_file(__lowerCAmelCase ) for rowa, rowa in zip(__lowerCAmelCase , __lowerCAmelCase ): assert rowa == rowa @require_sqlalchemy def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: SCREAMING_SNAKE_CASE__ : Union[str, Any] = tmp_path / """cache""" SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , """tmp.sql""" ) SCREAMING_SNAKE_CASE__ : Optional[Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=__lowerCAmelCase ).read() with pytest.raises(__lowerCAmelCase ): SqlDatasetWriter(__lowerCAmelCase , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=0 ).write()
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"""simple docstring""" import multiprocessing from typing import TYPE_CHECKING, Optional, Union from .. import Dataset, Features, config from ..formatting import query_table from ..packaged_modules.sql.sql import Sql from ..utils import logging from .abc import AbstractDatasetInputStream if TYPE_CHECKING: import sqlitea import sqlalchemy class __a (UpperCamelCase_): '''simple docstring''' def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]: """simple docstring""" super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a ) SCREAMING_SNAKE_CASE__ : List[Any] = Sql( cache_dir=_a , features=_a , sql=_a , con=_a , **_a , ) def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = None SCREAMING_SNAKE_CASE__ : Union[str, Any] = None SCREAMING_SNAKE_CASE__ : Dict = None SCREAMING_SNAKE_CASE__ : Optional[int] = None self.builder.download_and_prepare( download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , ) # Build dataset for splits SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset( split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory ) return dataset class __a : '''simple docstring''' def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any: """simple docstring""" if num_proc is not None and num_proc <= 0: raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' ) SCREAMING_SNAKE_CASE__ : int = dataset SCREAMING_SNAKE_CASE__ : Any = name SCREAMING_SNAKE_CASE__ : Optional[Any] = con SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE SCREAMING_SNAKE_CASE__ : int = num_proc SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs def _a ( self ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a ) SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a ) SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs ) return written def _a ( self , _a ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs SCREAMING_SNAKE_CASE__ : Any = query_table( table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , ) SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas() SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a ) return num_rows or len(_a ) def _a ( self , _a , **_a ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[str] = 0 if self.num_proc is None or self.num_proc == 1: for offset in logging.tqdm( range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += self._batch_sql((offset, index, to_sql_kwargs) ) else: SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size with multiprocessing.Pool(self.num_proc ) as pool: for num_rows in logging.tqdm( pool.imap( self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ): written += num_rows return written
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"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> bool: return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") ) def _lowercase ( __lowerCAmelCase ) -> bool: SCREAMING_SNAKE_CASE__ : Optional[int] = credit_card_number SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 SCREAMING_SNAKE_CASE__ : Dict = len(__lowerCAmelCase ) - 2 for i in range(__lowerCAmelCase , -1 , -2 ): # double the value of every second digit SCREAMING_SNAKE_CASE__ : Tuple = int(cc_number[i] ) digit *= 2 # If doubling of a number results in a two digit number # i.e greater than 9(e.g., 6 × 2 = 12), # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6), # to get a single digit number. if digit > 9: digit %= 10 digit += 1 SCREAMING_SNAKE_CASE__ : Optional[Any] = cc_number[:i] + str(__lowerCAmelCase ) + cc_number[i + 1 :] total += digit # Sum up the remaining digits for i in range(len(__lowerCAmelCase ) - 1 , -1 , -2 ): total += int(cc_number[i] ) return total % 10 == 0 def _lowercase ( __lowerCAmelCase ) -> bool: SCREAMING_SNAKE_CASE__ : int = F'''{credit_card_number} is an invalid credit card number because''' if not credit_card_number.isdigit(): print(F'''{error_message} it has nonnumerical characters.''' ) return False if not 13 <= len(__lowerCAmelCase ) <= 16: print(F'''{error_message} of its length.''' ) return False if not validate_initial_digits(__lowerCAmelCase ): print(F'''{error_message} of its first two digits.''' ) return False if not luhn_validation(__lowerCAmelCase ): print(F'''{error_message} it fails the Luhn check.''' ) return False print(F'''{credit_card_number} is a valid credit card number.''' ) return True if __name__ == "__main__": import doctest doctest.testmod() validate_credit_card_number("4111111111111111") validate_credit_card_number("32323")
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"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> int: if divisor % 5 == 0 or divisor % 2 == 0: return 0 SCREAMING_SNAKE_CASE__ : List[Any] = 1 SCREAMING_SNAKE_CASE__ : int = 1 while repunit: SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor repunit_index += 1 return repunit_index def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int: SCREAMING_SNAKE_CASE__ : Dict = limit - 1 if divisor % 2 == 0: divisor += 1 while least_divisible_repunit(__lowerCAmelCase ) <= limit: divisor += 2 return divisor if __name__ == "__main__": print(f'{solution() = }')
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"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: if index == r: for j in range(__lowerCAmelCase ): print(data[j] , end=""" """ ) print(""" """ ) return # When no more elements are there to put in data[] if i >= n: return # current is included, put next at next location SCREAMING_SNAKE_CASE__ : Optional[int] = arr[i] combination_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , index + 1 , __lowerCAmelCase , i + 1 ) # current is excluded, replace it with # next (Note that i+1 is passed, but # index is not changed) combination_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , i + 1 ) # The main function that prints all combinations # of size r in arr[] of size n. This function # mainly uses combinationUtil() def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]: # A temporary array to store all combination one by one SCREAMING_SNAKE_CASE__ : Optional[Any] = [0] * r # Print all combination using temporary array 'data[]' combination_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , 0 , __lowerCAmelCase , 0 ) if __name__ == "__main__": # Driver code to check the function above a :str = [10, 20, 30, 40, 50] print_combination(arr, len(arr), 3) # This code is contributed by Ambuj sahu
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"""simple docstring""" import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import evaluate import numpy as np from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.31.0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") a :Union[str, Any] = logging.getLogger(__name__) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[int] = field( default=1_28 , metadata={ """help""": ( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Whether to pad all samples to `max_seq_length`. """ """If False, will pad the samples dynamically when batching to the maximum length in the batch.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of training examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of evaluation examples to this """ """value if set.""" ) } , ) _SCREAMING_SNAKE_CASE :Optional[int] = field( default=UpperCamelCase_ , metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of prediction examples to this """ """value if set.""" ) } , ) @dataclass class __a : '''simple docstring''' _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}) _SCREAMING_SNAKE_CASE :str = field( default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}) _SCREAMING_SNAKE_CASE :Optional[str] = field( default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , ) _SCREAMING_SNAKE_CASE :Optional[bool] = field( default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , ) _SCREAMING_SNAKE_CASE :str = field( default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={ """help""": ( """Will use the token generated when running `huggingface-cli login` (necessary to use this script """ """with private models).""" ) } , ) _SCREAMING_SNAKE_CASE :bool = field( default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , ) def _lowercase ( ) -> Union[str, Any]: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("""run_xnli""" , __lowerCAmelCase ) # Setup logging logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level() logger.setLevel(__lowerCAmelCase ) datasets.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.set_verbosity(__lowerCAmelCase ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}''' + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' ) logger.info(F'''Training/evaluation parameters {training_args}''' ) # Detecting last checkpoint. SCREAMING_SNAKE_CASE__ : Any = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. ''' """Use --overwrite_output_dir to overcome.""" ) elif last_checkpoint is not None: logger.info( F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change ''' """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" ) # Set seed before initializing model. set_seed(training_args.seed ) # In distributed training, the load_dataset function guarantees that only one local process can concurrently # download the dataset. # Downloading and loading xnli dataset from the hub. if training_args.do_train: if model_args.train_language is None: SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset( """xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) else: SCREAMING_SNAKE_CASE__ : str = load_dataset( """xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names if training_args.do_eval: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names if training_args.do_predict: SCREAMING_SNAKE_CASE__ : int = load_dataset( """xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names # Labels SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase ) # Load pretrained model and tokenizer # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , ) # Preprocessing the datasets # Padding strategy if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : str = """max_length""" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch SCREAMING_SNAKE_CASE__ : Optional[Any] = False def preprocess_function(__lowerCAmelCase ): # Tokenize the texts return tokenizer( examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , ) if training_args.do_train: if data_args.max_train_samples is not None: SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples ) SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""train dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , ) # Log a few random samples from the training set: for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ): logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' ) if training_args.do_eval: if data_args.max_eval_samples is not None: SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""validation dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , ) if training_args.do_predict: if data_args.max_predict_samples is not None: SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples ) SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) ) with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ): SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map( __lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , ) # Get the metric function SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" ) # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 ) return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids ) # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator elif training_args.fpaa: SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 ) else: SCREAMING_SNAKE_CASE__ : Union[str, Any] = None # Initialize our Trainer SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer( model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , ) # Training if training_args.do_train: SCREAMING_SNAKE_CASE__ : Dict = None if training_args.resume_from_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint elif last_checkpoint is not None: SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics SCREAMING_SNAKE_CASE__ : Optional[int] = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("""train""" , __lowerCAmelCase ) trainer.save_metrics("""train""" , __lowerCAmelCase ) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("""*** Evaluate ***""" ) SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""eval""" , __lowerCAmelCase ) trainer.save_metrics("""eval""" , __lowerCAmelCase ) # Prediction if training_args.do_predict: logger.info("""*** Predict ***""" ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" ) SCREAMING_SNAKE_CASE__ : List[str] = ( data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase ) ) SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) ) trainer.log_metrics("""predict""" , __lowerCAmelCase ) trainer.save_metrics("""predict""" , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" ) if trainer.is_world_process_zero(): with open(__lowerCAmelCase , """w""" ) as writer: writer.write("""index\tprediction\n""" ) for index, item in enumerate(__lowerCAmelCase ): SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item] writer.write(F'''{index}\t{item}\n''' ) if __name__ == "__main__": main()
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available a :Union[str, Any] = { "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"], "tokenization_xlm": ["XLMTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :List[str] = [ "XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "XLMForMultipleChoice", "XLMForQuestionAnswering", "XLMForQuestionAnsweringSimple", "XLMForSequenceClassification", "XLMForTokenClassification", "XLMModel", "XLMPreTrainedModel", "XLMWithLMHeadModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :str = [ "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST", "TFXLMForMultipleChoice", "TFXLMForQuestionAnsweringSimple", "TFXLMForSequenceClassification", "TFXLMForTokenClassification", "TFXLMMainLayer", "TFXLMModel", "TFXLMPreTrainedModel", "TFXLMWithLMHeadModel", ] if TYPE_CHECKING: from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig from .tokenization_xlm import XLMTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_xlm import ( XLM_PRETRAINED_MODEL_ARCHIVE_LIST, XLMForMultipleChoice, XLMForQuestionAnswering, XLMForQuestionAnsweringSimple, XLMForSequenceClassification, XLMForTokenClassification, XLMModel, XLMPreTrainedModel, XLMWithLMHeadModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_xlm import ( TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXLMForMultipleChoice, TFXLMForQuestionAnsweringSimple, TFXLMForSequenceClassification, TFXLMForTokenClassification, TFXLMMainLayer, TFXLMModel, TFXLMPreTrainedModel, TFXLMWithLMHeadModel, ) else: import sys a :int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" # Note: if you intend to run this script make sure you look under scripts/fsmt/ # to locate the appropriate script to do the work correctly. There is a set of scripts to: # - download and prepare data and run the conversion script # - perform eval to get the best hparam into the config # - generate model_cards - useful if you have multiple models from the same paper import argparse import json import os import re from collections import OrderedDict from os.path import basename, dirname import fairseq import torch from fairseq import hub_utils from fairseq.data.dictionary import Dictionary from transformers import FSMTConfig, FSMTForConditionalGeneration from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE from transformers.utils import WEIGHTS_NAME, logging logging.set_verbosity_warning() a :str = 2 # based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping` # values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults: # # * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users) # * `early_stopping`: `False` consistently scored better # * `length_penalty` varied, so will assign the best one depending on the model a :int = { # fairseq: "wmt19-ru-en": {"length_penalty": 1.1}, "wmt19-en-ru": {"length_penalty": 1.15}, "wmt19-en-de": {"length_penalty": 1.0}, "wmt19-de-en": {"length_penalty": 1.1}, # allenai: "wmt16-en-de-dist-12-1": {"length_penalty": 0.6}, "wmt16-en-de-dist-6-1": {"length_penalty": 0.6}, "wmt16-en-de-12-1": {"length_penalty": 0.8}, "wmt19-de-en-6-6-base": {"length_penalty": 0.6}, "wmt19-de-en-6-6-big": {"length_penalty": 0.6}, } # this remaps the different models to their organization names a :Dict = {} for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]: a :List[Any] = "facebook" for m in [ "wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1", "wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big", ]: a :str = "allenai" def _lowercase ( __lowerCAmelCase ) -> Any: # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up, # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7} SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() ) SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split() # restore the special tokens for k in keep_keys: del da[F'''{k}</w>'''] SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore return da def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]: # prep assert os.path.exists(__lowerCAmelCase ) os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase ) print(F'''Writing results to {pytorch_dump_folder_path}''' ) # handle various types of models SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models() SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""} SCREAMING_SNAKE_CASE__ : Optional[Any] = """.""" # note: since the model dump is old, fairseq has upgraded its model some # time later, and it does a whole lot of rewrites and splits on the saved # weights, therefore we can't use torch.load() directly on the model file. # see: upgrade_state_dict(state_dict) in fairseq_model.py print(F'''using checkpoint {checkpoint_file}''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] ) SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""] SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""] SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase ) # dicts SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" ) print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # detect whether this is a do_lower_case situation, which can be derived by checking whether we # have at least one uppercase letter in the source vocab SCREAMING_SNAKE_CASE__ : Optional[Any] = True for k in src_vocab.keys(): if not k.islower(): SCREAMING_SNAKE_CASE__ : Tuple = False break SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices ) SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" ) print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # merges_file (bpecodes) SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] ) for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code" SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) if os.path.exists(__lowerCAmelCase ): break with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin: SCREAMING_SNAKE_CASE__ : Any = fin.read() SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number print(F'''Generating {merges_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout: fout.write(__lowerCAmelCase ) # model config SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" ) # validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe - # may have to modify the tokenizer if a different type is used by a future model assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}''' assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}''' SCREAMING_SNAKE_CASE__ : str = { """architectures""": ["""FSMTForConditionalGeneration"""], """model_type""": """fsmt""", """activation_dropout""": args["""activation_dropout"""], """activation_function""": """relu""", """attention_dropout""": args["""attention_dropout"""], """d_model""": args["""decoder_embed_dim"""], """dropout""": args["""dropout"""], """init_std""": 0.02, """max_position_embeddings""": args["""max_source_positions"""], """num_hidden_layers""": args["""encoder_layers"""], """src_vocab_size""": src_vocab_size, """tgt_vocab_size""": tgt_vocab_size, """langs""": [src_lang, tgt_lang], """encoder_attention_heads""": args["""encoder_attention_heads"""], """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""], """encoder_layerdrop""": args["""encoder_layerdrop"""], """encoder_layers""": args["""encoder_layers"""], """decoder_attention_heads""": args["""decoder_attention_heads"""], """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""], """decoder_layerdrop""": args["""decoder_layerdrop"""], """decoder_layers""": args["""decoder_layers"""], """bos_token_id""": 0, """pad_token_id""": 1, """eos_token_id""": 2, """is_encoder_decoder""": True, """scale_embedding""": not args["""no_scale_embedding"""], """tie_word_embeddings""": args["""share_all_embeddings"""], } # good hparam defaults to start with SCREAMING_SNAKE_CASE__ : Tuple = 5 SCREAMING_SNAKE_CASE__ : str = False if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]: SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""] else: SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0 print(F'''Generating {fsmt_model_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # tokenizer config SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Tuple = { """langs""": [src_lang, tgt_lang], """model_max_length""": 1024, """do_lower_case""": do_lower_case, } print(F'''Generating {fsmt_tokenizer_config_file}''' ) with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f: f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) ) # model SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0] SCREAMING_SNAKE_CASE__ : int = model.state_dict() # rename keys to start with 'model.' SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() ) # remove unneeded keys SCREAMING_SNAKE_CASE__ : str = [ """model.model""", """model.encoder.version""", """model.decoder.version""", """model.encoder_embed_tokens.weight""", """model.decoder_embed_tokens.weight""", """model.encoder.embed_positions._float_tensor""", """model.decoder.embed_positions._float_tensor""", ] for k in ignore_keys: model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase ) # check that it loads ok model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase ) # save SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase ) print(F'''Generating {pytorch_weights_dump_path}''' ) torch.save(__lowerCAmelCase , __lowerCAmelCase ) print("""Conversion is done!""" ) print("""\nLast step is to upload the files to s3""" ) print(F'''cd {data_root}''' ) print(F'''transformers-cli upload {model_dir}''' ) if __name__ == "__main__": a :Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--fsmt_checkpoint_path", default=None, type=str, required=True, help=( "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts," " bpecodes, etc." ), ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) a :List[str] = parser.parse_args() convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available a :Optional[int] = { "configuration_mgp_str": ["MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP", "MgpstrConfig"], "processing_mgp_str": ["MgpstrProcessor"], "tokenization_mgp_str": ["MgpstrTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a :Any = [ "MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST", "MgpstrModel", "MgpstrPreTrainedModel", "MgpstrForSceneTextRecognition", ] if TYPE_CHECKING: from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig from .processing_mgp_str import MgpstrProcessor from .tokenization_mgp_str import MgpstrTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mgp_str import ( MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST, MgpstrForSceneTextRecognition, MgpstrModel, MgpstrPreTrainedModel, ) else: import sys a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class __a (UpperCamelCase_): '''simple docstring''' _SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,) def _a ( self , **_a ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE__ : int = { """num_train_timesteps""": 1_000, """beta_start""": 0.0_001, """beta_end""": 0.02, """beta_schedule""": """linear""", """variance_type""": """fixed_small""", """clip_sample""": True, } config.update(**_a ) return config def _a ( self ) -> str: """simple docstring""" for timesteps in [1, 5, 100, 1_000]: self.check_over_configs(num_train_timesteps=_a ) def _a ( self ) -> str: """simple docstring""" for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=_a , beta_end=_a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=_a ) def _a ( self ) -> Any: """simple docstring""" for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=_a ) def _a ( self ) -> Optional[int]: """simple docstring""" for clip_sample in [True, False]: self.check_over_configs(clip_sample=_a ) def _a ( self ) -> int: """simple docstring""" self.check_over_configs(thresholding=_a ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=_a , prediction_type=_a , sample_max_value=_a , ) def _a ( self ) -> str: """simple docstring""" for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=_a ) def _a ( self ) -> str: """simple docstring""" for t in [0, 500, 999]: self.check_over_forward(time_step=_a ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Any = len(_a ) SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model() SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : str = pred_prev_sample SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 258.9_606 ) < 1E-2 assert abs(result_mean.item() - 0.3_372 ) < 1E-3 def _a ( self ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" ) SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Dict = len(_a ) SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model() SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 ) for t in reversed(range(_a ) ): # 1. predict noise residual SCREAMING_SNAKE_CASE__ : int = model(_a , _a ) # 2. predict previous mean of sample x_t-1 SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) ) SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) ) assert abs(result_sum.item() - 202.0_296 ) < 1E-2 assert abs(result_mean.item() - 0.2_631 ) < 1E-3 def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0] scheduler.set_timesteps(timesteps=_a ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps for i, timestep in enumerate(_a ): if i == len(_a ) - 1: SCREAMING_SNAKE_CASE__ : Optional[Any] = -1 else: SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1] SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a ) SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item() self.assertEqual(_a , _a ) def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0] with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ): scheduler.set_timesteps(timesteps=_a ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0] SCREAMING_SNAKE_CASE__ : List[str] = len(_a ) with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ): scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a ) def _a ( self ) -> Tuple: """simple docstring""" SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0] SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config() SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a ) SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps] with self.assertRaises( _a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ): scheduler.set_timesteps(timesteps=_a )
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"""simple docstring""" import sys a :Dict = ( "73167176531330624919225119674426574742355349194934" "96983520312774506326239578318016984801869478851843" "85861560789112949495459501737958331952853208805511" "12540698747158523863050715693290963295227443043557" "66896648950445244523161731856403098711121722383113" "62229893423380308135336276614282806444486645238749" "30358907296290491560440772390713810515859307960866" "70172427121883998797908792274921901699720888093776" "65727333001053367881220235421809751254540594752243" "52584907711670556013604839586446706324415722155397" "53697817977846174064955149290862569321978468622482" "83972241375657056057490261407972968652414535100474" "82166370484403199890008895243450658541227588666881" "16427171479924442928230863465674813919123162824586" "17866458359124566529476545682848912883142607690042" "24219022671055626321111109370544217506941658960408" "07198403850962455444362981230987879927244284909188" "84580156166097919133875499200524063689912560717606" "05886116467109405077541002256983155200055935729725" "71636269561882670428252483600823257530420752963450" ) def _lowercase ( __lowerCAmelCase = N ) -> int: SCREAMING_SNAKE_CASE__ : int = -sys.maxsize - 1 for i in range(len(__lowerCAmelCase ) - 12 ): SCREAMING_SNAKE_CASE__ : str = 1 for j in range(13 ): product *= int(n[i + j] ) if product > largest_product: SCREAMING_SNAKE_CASE__ : Optional[int] = product return largest_product if __name__ == "__main__": print(f'{solution() = }')
702
"""simple docstring""" import os a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000} def _lowercase ( __lowerCAmelCase ) -> int: SCREAMING_SNAKE_CASE__ : Any = 0 SCREAMING_SNAKE_CASE__ : Dict = 0 while index < len(__lowerCAmelCase ) - 1: SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]] SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]] if current_value < next_value: total_value -= current_value else: total_value += current_value index += 1 total_value += SYMBOLS[numerals[index]] return total_value def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : Optional[int] = """""" SCREAMING_SNAKE_CASE__ : int = num // 1000 numerals += m_count * "M" num %= 1000 SCREAMING_SNAKE_CASE__ : List[str] = num // 100 if c_count == 9: numerals += "CM" c_count -= 9 elif c_count == 4: numerals += "CD" c_count -= 4 if c_count >= 5: numerals += "D" c_count -= 5 numerals += c_count * "C" num %= 100 SCREAMING_SNAKE_CASE__ : List[Any] = num // 10 if x_count == 9: numerals += "XC" x_count -= 9 elif x_count == 4: numerals += "XL" x_count -= 4 if x_count >= 5: numerals += "L" x_count -= 5 numerals += x_count * "X" num %= 10 if num == 9: numerals += "IX" num -= 9 elif num == 4: numerals += "IV" num -= 4 if num >= 5: numerals += "V" num -= 5 numerals += num * "I" return numerals def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int: SCREAMING_SNAKE_CASE__ : int = 0 with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea: SCREAMING_SNAKE_CASE__ : str = filea.readlines() for line in lines: SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip() SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase ) SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase ) savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase ) return savings if __name__ == "__main__": print(f'{solution() = }')
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