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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)))
| 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() = }')
| 12 | 0 |
"""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 )
| 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()
| 12 | 0 |
"""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__)
| 715 |
"""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,)
| 12 | 0 |
"""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()
| 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()
####################################################################################################
| 12 | 0 |
"""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()
| 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.''' )
| 12 | 0 |
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)
| 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 )
| 12 | 0 |
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
| 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 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 ) , )
| 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 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()
| 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()
| 12 | 0 |
"""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}
| 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"""
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,
)
| 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 )
| 12 | 0 |
"""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 , [] )
| 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() = }')
| 12 | 0 |
"""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
| 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 ) )
| 12 | 0 |
"""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()
| 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 )
| 12 | 0 |
"""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()
####################################################################################################
| 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 ) )
| 12 | 0 |
"""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
| 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()
| 12 | 0 |
"""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.")
| 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__)
| 12 | 0 |
"""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}\''
)
| 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 )
| 12 | 0 |
"""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)
| 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
| 12 | 0 |
"""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 )
| 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()
| 12 | 0 |
"""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}')
| 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,)
| 12 | 0 |
"""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()
| 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}')
| 12 | 0 |
"""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()}')
| 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() = }')
| 12 | 0 |
"""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()
| 12 | 0 |
"""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() = }')
| 715 |
"""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,)
| 12 | 0 |
"""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()
####################################################################################################
| 12 | 0 |
"""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,)
| 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.''' )
| 12 | 0 |
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
| 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 )
| 12 | 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()
| 12 | 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) = }')
| 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"""
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
)
| 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 )
| 12 | 0 |
"""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 )
}
| 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() = }')
| 12 | 0 |
"""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 ) )
| 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 ) )
| 12 | 0 |
"""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()
| 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 )
| 12 | 0 |
"""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)
| 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 ) )
| 12 | 0 |
"""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 )
| 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()
| 12 | 0 |
"""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()
| 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__)
| 12 | 0 |
"""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 )
| 12 | 0 |
"""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
| 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
| 12 | 0 |
"""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 )
| 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()
| 12 | 0 |
"""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()
| 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,)
| 12 | 0 |
"""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()
| 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}')
| 12 | 0 |
"""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 )
| 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() = }')
| 12 | 0 |
"""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()
| 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()
| 12 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> bool:
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 715 |
"""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,)
| 12 | 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()
| 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()
####################################################################################################
| 12 | 0 |
"""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.''' )
| 12 | 0 |
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 )
| 12 | 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 ✅")
| 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"""
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() = }')
| 12 | 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()
| 12 | 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
| 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"""
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()
| 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 )
| 12 | 0 |
"""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 )
| 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() = }')
| 12 | 0 |
"""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 )
| 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 ) )
| 12 | 0 |
"""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
)
| 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 )
| 12 | 0 |
"""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
| 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 ) )
| 12 | 0 |
"""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.''' )
| 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()
| 12 | 0 |
"""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 )
| 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__)
| 12 | 0 |
"""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
| 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 )
| 12 | 0 |
"""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
| 12 | 0 |
"""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()
| 12 | 0 |
"""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,)
| 12 | 0 |
"""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}')
| 12 | 0 |
"""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() = }')
| 12 | 0 |
"""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)
| 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()
| 12 | 0 |
"""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() = }')
| 715 |
"""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,)
| 12 | 0 |
"""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
| 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()
####################################################################################################
| 12 | 0 |
"""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__)
| 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.''' )
| 12 | 0 |
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__)
| 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 )
| 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
| 12 | 0 |
"""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}''' )
| 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"""
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
| 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()
| 12 | 0 |
"""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 )
| 12 | 0 |
"""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() = }')
| 12 | 0 |
"""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 ) )
| 12 | 0 |
"""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 )
| 12 | 0 |
"""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 ) )
| 12 | 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()
| 12 | 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__)
| 12 | 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 )
| 12 | 0 |
"""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)
| 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
| 12 | 0 |
"""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()
| 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()
| 12 | 0 |
"""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)
| 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,)
| 12 | 0 |
"""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 )
| 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}')
| 12 | 0 |
"""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
| 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() = }')
| 12 | 0 |
"""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 )
| 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()
| 12 | 0 |
"""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),
] )
| 715 |
"""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,)
| 12 | 0 |
"""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()
| 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()
####################################################################################################
| 12 | 0 |
"""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()
| 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.''' )
| 12 | 0 |
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 , )
| 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 )
| 12 | 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()
| 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"""
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")
| 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"""
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
| 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()
| 12 | 0 |
"""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__)
| 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"""
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__)
| 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 )
| 12 | 0 |
"""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() = }')
| 12 | 0 |
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