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'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
_SCREAMING_SNAKE_CASE = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
_SCREAMING_SNAKE_CASE = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
_SCREAMING_SNAKE_CASE = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 502 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
lowercase : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
lowercase : List[str] = """
Examples:
```py
>>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
>>> import torch
>>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")
>>> pipe_prior.to(\"cuda\")
>>> prompt = \"red cat, 4k photo\"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> negative_image_emb = out.negative_image_embeds
>>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")
>>> pipe.to(\"cuda\")
>>> image = pipe(
... prompt,
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... ).images
>>> image[0].save(\"cat.png\")
```
"""
def lowerCamelCase__ ( __lowercase , __lowercase , __lowercase=8 ):
snake_case : int = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
snake_case : Tuple = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class _a (a__ ):
'''simple docstring'''
def __init__( self ,__a ,__a ,__a ,__a ,__a ,) -> Optional[Any]:
super().__init__()
self.register_modules(
text_encoder=__a ,tokenizer=__a ,unet=__a ,scheduler=__a ,movq=__a ,)
snake_case : int = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def snake_case_ ( self ,__a ,__a ,__a ,__a ,__a ,__a ) -> Dict:
if latents is None:
snake_case : Optional[int] = randn_tensor(__a ,generator=__a ,device=__a ,dtype=__a )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
snake_case : Any = latents.to(__a )
snake_case : Tuple = latents * scheduler.init_noise_sigma
return latents
def snake_case_ ( self ,__a ,__a ,__a ,__a ,__a=None ,) -> int:
snake_case : Tuple = len(__a ) if isinstance(__a ,__a ) else 1
# get prompt text embeddings
snake_case : str = self.tokenizer(
__a ,padding="""max_length""" ,truncation=__a ,max_length=77 ,return_attention_mask=__a ,add_special_tokens=__a ,return_tensors="""pt""" ,)
snake_case : str = text_inputs.input_ids
snake_case : List[Any] = self.tokenizer(__a ,padding="""longest""" ,return_tensors="""pt""" ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__a ,__a ):
snake_case : str = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
snake_case : str = text_input_ids.to(__a )
snake_case : Tuple = text_inputs.attention_mask.to(__a )
snake_case , snake_case : Optional[Any] = self.text_encoder(
input_ids=__a ,attention_mask=__a )
snake_case : Optional[Any] = prompt_embeds.repeat_interleave(__a ,dim=0 )
snake_case : str = text_encoder_hidden_states.repeat_interleave(__a ,dim=0 )
snake_case : str = text_mask.repeat_interleave(__a ,dim=0 )
if do_classifier_free_guidance:
snake_case : List[str]
if negative_prompt is None:
snake_case : List[str] = [""""""] * batch_size
elif type(__a ) is not type(__a ):
raise TypeError(
F'''`negative_prompt` should be the same type to `prompt`, but got {type(__a )} !='''
F''' {type(__a )}.''' )
elif isinstance(__a ,__a ):
snake_case : Dict = [negative_prompt]
elif batch_size != len(__a ):
raise ValueError(
F'''`negative_prompt`: {negative_prompt} has batch size {len(__a )}, but `prompt`:'''
F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
""" the batch size of `prompt`.""" )
else:
snake_case : Optional[int] = negative_prompt
snake_case : Union[str, Any] = self.tokenizer(
__a ,padding="""max_length""" ,max_length=77 ,truncation=__a ,return_attention_mask=__a ,add_special_tokens=__a ,return_tensors="""pt""" ,)
snake_case : int = uncond_input.input_ids.to(__a )
snake_case : str = uncond_input.attention_mask.to(__a )
snake_case , snake_case : str = self.text_encoder(
input_ids=__a ,attention_mask=__a )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
snake_case : List[str] = negative_prompt_embeds.shape[1]
snake_case : Any = negative_prompt_embeds.repeat(1 ,__a )
snake_case : Dict = negative_prompt_embeds.view(batch_size * num_images_per_prompt ,__a )
snake_case : List[str] = uncond_text_encoder_hidden_states.shape[1]
snake_case : Any = uncond_text_encoder_hidden_states.repeat(1 ,__a ,1 )
snake_case : List[Any] = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt ,__a ,-1 )
snake_case : Any = uncond_text_mask.repeat_interleave(__a ,dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
snake_case : List[str] = torch.cat([negative_prompt_embeds, prompt_embeds] )
snake_case : Optional[Any] = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
snake_case : List[str] = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def snake_case_ ( self ,__a=0 ) -> Tuple:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
snake_case : List[Any] = torch.device(F'''cuda:{gpu_id}''' )
snake_case : Tuple = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__a ,__a )
def snake_case_ ( self ,__a=0 ) -> List[Any]:
if is_accelerate_available() and is_accelerate_version(""">=""" ,"""0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
snake_case : Union[str, Any] = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("""cpu""" ,silence_dtype_warnings=__a )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
snake_case : Any = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
snake_case , snake_case : Optional[int] = cpu_offload_with_hook(__a ,__a ,prev_module_hook=__a )
if self.safety_checker is not None:
snake_case , snake_case : List[str] = cpu_offload_with_hook(self.safety_checker ,__a ,prev_module_hook=__a )
# We'll offload the last model manually.
snake_case : Any = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def snake_case_ ( self ) -> List[Any]:
if not hasattr(self.unet ,"""_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__a ,"""_hf_hook""" )
and hasattr(module._hf_hook ,"""execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__a )
def __call__( self ,__a ,__a ,__a ,__a = None ,__a = 512 ,__a = 512 ,__a = 100 ,__a = 4.0 ,__a = 1 ,__a = None ,__a = None ,__a = "pil" ,__a = True ,) -> List[str]:
if isinstance(__a ,__a ):
snake_case : Optional[Any] = 1
elif isinstance(__a ,__a ):
snake_case : List[Any] = len(__a )
else:
raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__a )}''' )
snake_case : Optional[Any] = self._execution_device
snake_case : Optional[Any] = batch_size * num_images_per_prompt
snake_case : str = guidance_scale > 1.0
snake_case , snake_case , snake_case : List[str] = self._encode_prompt(
__a ,__a ,__a ,__a ,__a )
if isinstance(__a ,__a ):
snake_case : Union[str, Any] = torch.cat(__a ,dim=0 )
if isinstance(__a ,__a ):
snake_case : Any = torch.cat(__a ,dim=0 )
if do_classifier_free_guidance:
snake_case : Dict = image_embeds.repeat_interleave(__a ,dim=0 )
snake_case : Dict = negative_image_embeds.repeat_interleave(__a ,dim=0 )
snake_case : Union[str, Any] = torch.cat([negative_image_embeds, image_embeds] ,dim=0 ).to(
dtype=prompt_embeds.dtype ,device=__a )
self.scheduler.set_timesteps(__a ,device=__a )
snake_case : Union[str, Any] = self.scheduler.timesteps
snake_case : Optional[int] = self.unet.config.in_channels
snake_case , snake_case : str = get_new_h_w(__a ,__a ,self.movq_scale_factor )
# create initial latent
snake_case : int = self.prepare_latents(
(batch_size, num_channels_latents, height, width) ,text_encoder_hidden_states.dtype ,__a ,__a ,__a ,self.scheduler ,)
for i, t in enumerate(self.progress_bar(__a ) ):
# expand the latents if we are doing classifier free guidance
snake_case : int = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
snake_case : Any = {"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
snake_case : List[str] = self.unet(
sample=__a ,timestep=__a ,encoder_hidden_states=__a ,added_cond_kwargs=__a ,return_dict=__a ,)[0]
if do_classifier_free_guidance:
snake_case , snake_case : Any = noise_pred.split(latents.shape[1] ,dim=1 )
snake_case , snake_case : Any = noise_pred.chunk(2 )
snake_case , snake_case : Union[str, Any] = variance_pred.chunk(2 )
snake_case : List[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
snake_case : Any = torch.cat([noise_pred, variance_pred_text] ,dim=1 )
if not (
hasattr(self.scheduler.config ,"""variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
snake_case , snake_case : Tuple = noise_pred.split(latents.shape[1] ,dim=1 )
# compute the previous noisy sample x_t -> x_t-1
snake_case : Tuple = self.scheduler.step(
__a ,__a ,__a ,generator=__a ,).prev_sample
# post-processing
snake_case : Optional[int] = self.movq.decode(__a ,force_not_quantize=__a )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
snake_case : Tuple = image * 0.5 + 0.5
snake_case : List[Any] = image.clamp(0 ,1 )
snake_case : Tuple = image.cpu().permute(0 ,2 ,3 ,1 ).float().numpy()
if output_type == "pil":
snake_case : Dict = self.numpy_to_pil(__a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__a )
| 116 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : Union[str, Any] = {"""configuration_timm_backbone""": ["""TimmBackboneConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase : Union[str, Any] = ["""TimmBackbone"""]
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 720 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCamelCase : str = _symbol_database.Default()
lowerCamelCase : Any = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
lowerCamelCase : List[str] = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCamelCase : List[str] = None
lowerCamelCase : List[str] = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCamelCase : Optional[int] = 4_5
lowerCamelCase : Tuple = 1_5_8_1
lowerCamelCase : Optional[int] = 1_5_1_7
lowerCamelCase : List[Any] = 1_5_7_0
lowerCamelCase : Dict = 1_5_8_4
lowerCamelCase : Dict = 1_7_9_3
lowerCamelCase : Optional[Any] = 1_7_9_5
lowerCamelCase : List[Any] = 1_9_1_6
lowerCamelCase : int = 1_8_6_4
lowerCamelCase : int = 1_9_0_5
lowerCamelCase : Dict = 1_9_1_9
lowerCamelCase : str = 2_4_2_9
lowerCamelCase : str = 2_2_0_8
lowerCamelCase : int = 2_4_1_8
lowerCamelCase : Dict = 2_3_2_3
lowerCamelCase : int = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 168 | 0 |
'''simple docstring'''
import os
def a ( UpperCamelCase_ : str = "matrix.txt" ) -> int:
with open(os.path.join(os.path.dirname(lowercase_ ) , lowercase_ ) ) as in_file:
snake_case__ =in_file.read()
snake_case__ =[[int(lowercase_ ) for cell in row.split(',' )] for row in data.strip().splitlines()]
snake_case__ =[[0 for cell in row] for row in grid]
snake_case__ =len(grid[0] )
snake_case__ =[[0 for i in range(lowercase_ )] for j in range(lowercase_ )]
snake_case__ =grid[0][0]
for i in range(1 , lowercase_ ):
snake_case__ =grid[0][i] + dp[0][i - 1]
for i in range(1 , lowercase_ ):
snake_case__ =grid[i][0] + dp[i - 1][0]
for i in range(1 , lowercase_ ):
for j in range(1 , lowercase_ ):
snake_case__ =grid[i][j] + min(dp[i - 1][j] , dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 538 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}
class lowerCamelCase ( _UpperCamelCase ):
_lowerCAmelCase : Optional[int] = '''autoformer'''
_lowerCAmelCase : Any = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self , lowercase__ = None , lowercase__ = None , lowercase__ = "student_t" , lowercase__ = "nll" , lowercase__ = 1 , lowercase__ = [1, 2, 3, 4, 5, 6, 7] , lowercase__ = True , lowercase__ = 0 , lowercase__ = 0 , lowercase__ = 0 , lowercase__ = 0 , lowercase__ = None , lowercase__ = None , lowercase__ = 6_4 , lowercase__ = 2 , lowercase__ = 2 , lowercase__ = 2 , lowercase__ = 2 , lowercase__ = 3_2 , lowercase__ = 3_2 , lowercase__ = "gelu" , lowercase__ = 0.1 , lowercase__ = 0.1 , lowercase__ = 0.1 , lowercase__ = 0.1 , lowercase__ = 0.1 , lowercase__ = 1_0_0 , lowercase__ = 0.0_2 , lowercase__ = True , lowercase__=True , lowercase__ = 1_0 , lowercase__ = 2_5 , lowercase__ = 3 , **lowercase__ , ):
# time series specific configuration
__UpperCAmelCase : Optional[int] = prediction_length
__UpperCAmelCase : Tuple = context_length if context_length is not None else prediction_length
__UpperCAmelCase : Tuple = distribution_output
__UpperCAmelCase : Tuple = loss
__UpperCAmelCase : Dict = input_size
__UpperCAmelCase : str = num_time_features
__UpperCAmelCase : str = lags_sequence
__UpperCAmelCase : int = scaling
__UpperCAmelCase : int = num_dynamic_real_features
__UpperCAmelCase : List[str] = num_static_real_features
__UpperCAmelCase : List[str] = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(lowercase__) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''')
__UpperCAmelCase : Union[str, Any] = cardinality
else:
__UpperCAmelCase : List[Any] = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(lowercase__) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''')
__UpperCAmelCase : Union[str, Any] = embedding_dimension
else:
__UpperCAmelCase : int = [min(5_0 , (cat + 1) // 2) for cat in self.cardinality]
__UpperCAmelCase : Tuple = num_parallel_samples
# Transformer architecture configuration
__UpperCAmelCase : Dict = input_size * len(self.lags_sequence) + self._number_of_features
__UpperCAmelCase : Optional[int] = d_model
__UpperCAmelCase : List[Any] = encoder_attention_heads
__UpperCAmelCase : int = decoder_attention_heads
__UpperCAmelCase : str = encoder_ffn_dim
__UpperCAmelCase : int = decoder_ffn_dim
__UpperCAmelCase : List[Any] = encoder_layers
__UpperCAmelCase : Tuple = decoder_layers
__UpperCAmelCase : Union[str, Any] = dropout
__UpperCAmelCase : int = attention_dropout
__UpperCAmelCase : int = activation_dropout
__UpperCAmelCase : str = encoder_layerdrop
__UpperCAmelCase : Dict = decoder_layerdrop
__UpperCAmelCase : int = activation_function
__UpperCAmelCase : Optional[int] = init_std
__UpperCAmelCase : Optional[int] = use_cache
# Autoformer
__UpperCAmelCase : str = label_length
__UpperCAmelCase : int = moving_average
__UpperCAmelCase : Optional[int] = autocorrelation_factor
super().__init__(is_encoder_decoder=lowercase__ , **lowercase__)
@property
def A( self):
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
)
| 462 | 0 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_flax
class __lowercase (unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase ( self , A , A ) -> List[Any]:
return f"""gaussian_noise_s={seed}_shape={"_".join([str(UpperCamelCase__ ) for s in shape] )}.npy"""
def UpperCAmelCase ( self ) -> Tuple:
super().tearDown()
gc.collect()
def UpperCAmelCase ( self , A=0 , A=(4, 4, 6_4, 6_4) , A=False ) -> Tuple:
snake_case : Union[str, Any] = jnp.bfloataa if fpaa else jnp.floataa
snake_case : List[str] = jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__ , UpperCamelCase__ ) ) , dtype=UpperCamelCase__ )
return image
def UpperCAmelCase ( self , A=False , A="CompVis/stable-diffusion-v1-4" ) -> str:
snake_case : Tuple = jnp.bfloataa if fpaa else jnp.floataa
snake_case : Dict = """bf16""" if fpaa else None
snake_case , snake_case : str = FlaxUNetaDConditionModel.from_pretrained(
UpperCamelCase__ , subfolder="""unet""" , dtype=UpperCamelCase__ , revision=UpperCamelCase__ )
return model, params
def UpperCAmelCase ( self , A=0 , A=(4, 7_7, 7_6_8) , A=False ) -> str:
snake_case : List[Any] = jnp.bfloataa if fpaa else jnp.floataa
snake_case : List[str] = jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__ , UpperCamelCase__ ) ) , dtype=UpperCamelCase__ )
return hidden_states
@parameterized.expand(
[
# fmt: off
[8_3, 4, [-0.23_23, -0.13_04, 0.08_13, -0.30_93, -0.09_19, -0.15_71, -0.11_25, -0.58_06]],
[1_7, 0.55, [-0.08_31, -0.24_43, 0.09_01, -0.09_19, 0.33_96, 0.01_03, -0.37_43, 0.07_01]],
[8, 0.89, [-0.48_63, 0.08_59, 0.08_75, -0.16_58, 0.91_99, -0.01_14, 0.48_39, 0.46_39]],
[3, 1_0_0_0, [-0.56_49, 0.24_02, -0.55_18, 0.12_48, 1.13_28, -0.24_43, -0.03_25, -1.00_78]],
# fmt: on
] )
def UpperCAmelCase ( self , A , A , A ) -> str:
snake_case , snake_case : Any = self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4""" , fpaa=UpperCamelCase__ )
snake_case : Tuple = self.get_latents(UpperCamelCase__ , fpaa=UpperCamelCase__ )
snake_case : Optional[int] = self.get_encoder_hidden_states(UpperCamelCase__ , fpaa=UpperCamelCase__ )
snake_case : Any = model.apply(
{"""params""": params} , UpperCamelCase__ , jnp.array(UpperCamelCase__ , dtype=jnp.intaa ) , encoder_hidden_states=UpperCamelCase__ , ).sample
assert sample.shape == latents.shape
snake_case : Union[str, Any] = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
snake_case : Any = jnp.array(UpperCamelCase__ , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
assert jnp.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[8_3, 4, [0.15_14, 0.08_07, 0.16_24, 0.10_16, -0.18_96, 0.02_63, 0.06_77, 0.23_10]],
[1_7, 0.55, [0.11_64, -0.02_16, 0.01_70, 0.15_89, -0.31_20, 0.10_05, -0.05_81, -0.14_58]],
[8, 0.89, [-0.17_58, -0.01_69, 0.10_04, -0.14_11, 0.13_12, 0.11_03, -0.19_96, 0.21_39]],
[3, 1_0_0_0, [0.12_14, 0.03_52, -0.07_31, -0.15_62, -0.09_94, -0.09_06, -0.23_40, -0.05_39]],
# fmt: on
] )
def UpperCAmelCase ( self , A , A , A ) -> str:
snake_case , snake_case : Dict = self.get_unet_model(model_id="""stabilityai/stable-diffusion-2""" , fpaa=UpperCamelCase__ )
snake_case : Union[str, Any] = self.get_latents(UpperCamelCase__ , shape=(4, 4, 9_6, 9_6) , fpaa=UpperCamelCase__ )
snake_case : Optional[Any] = self.get_encoder_hidden_states(UpperCamelCase__ , shape=(4, 7_7, 1_0_2_4) , fpaa=UpperCamelCase__ )
snake_case : Optional[int] = model.apply(
{"""params""": params} , UpperCamelCase__ , jnp.array(UpperCamelCase__ , dtype=jnp.intaa ) , encoder_hidden_states=UpperCamelCase__ , ).sample
assert sample.shape == latents.shape
snake_case : Tuple = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa )
snake_case : List[str] = jnp.array(UpperCamelCase__ , dtype=jnp.floataa )
# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
assert jnp.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-2 )
| 711 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCamelCase : Any = logging.get_logger(__name__)
class __lowercase (enum.Enum ):
"""simple docstring"""
_snake_case = 0
_snake_case = 1
@add_end_docstrings(UpperCamelCase__ )
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_case = """generated"""
def __init__( self , *A , **A ) -> Optional[Any]:
super().__init__(*A , **A )
self.check_model_type(
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
if self.framework == """tf"""
else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING )
def UpperCAmelCase ( self , A=None , A=None , A=None , A=None , A=None , A=None , **A , ) -> Optional[int]:
snake_case : Tuple = {}
if truncation is not None:
snake_case : Union[str, Any] = truncation
snake_case : Dict = generate_kwargs
snake_case : int = {}
if return_tensors is not None and return_type is None:
snake_case : List[Any] = ReturnType.TENSORS if return_tensors else ReturnType.TEXT
if return_type is not None:
snake_case : List[str] = return_type
if clean_up_tokenization_spaces is not None:
snake_case : int = clean_up_tokenization_spaces
if stop_sequence is not None:
snake_case : Tuple = self.tokenizer.encode(A , add_special_tokens=A )
if len(A ) > 1:
warnings.warn(
"""Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
""" the stop sequence will be used as the stop sequence string in the interim.""" )
snake_case : List[str] = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def UpperCAmelCase ( self , A , A , A ) -> Union[str, Any]:
return True
def UpperCAmelCase ( self , *A , A ) -> Tuple:
snake_case : Union[str, Any] = self.model.config.prefix if self.model.config.prefix is not None else """"""
if isinstance(args[0] , A ):
if self.tokenizer.pad_token_id is None:
raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input""" )
snake_case : Union[str, Any] = ([prefix + arg for arg in args[0]],)
snake_case : List[Any] = True
elif isinstance(args[0] , A ):
snake_case : str = (prefix + args[0],)
snake_case : str = False
else:
raise ValueError(
f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`""" )
snake_case : Optional[Any] = self.tokenizer(*A , padding=A , truncation=A , return_tensors=self.framework )
# This is produced by tokenizers but is an invalid generate kwargs
if "token_type_ids" in inputs:
del inputs["token_type_ids"]
return inputs
def __call__( self , *A , **A ) -> Union[str, Any]:
snake_case : Tuple = super().__call__(*A , **A )
if (
isinstance(args[0] , A )
and all(isinstance(A , A ) for el in args[0] )
and all(len(A ) == 1 for res in result )
):
return [res[0] for res in result]
return result
def UpperCAmelCase ( self , A , A=TruncationStrategy.DO_NOT_TRUNCATE , **A ) -> str:
snake_case : Optional[Any] = self._parse_and_tokenize(A , truncation=A , **A )
return inputs
def UpperCAmelCase ( self , A , **A ) -> Tuple:
if self.framework == "pt":
snake_case , snake_case : List[str] = model_inputs["""input_ids"""].shape
elif self.framework == "tf":
snake_case , snake_case : Optional[Any] = tf.shape(model_inputs["""input_ids"""] ).numpy()
snake_case : Dict = generate_kwargs.get("""min_length""" , self.model.config.min_length )
snake_case : str = generate_kwargs.get("""max_length""" , self.model.config.max_length )
self.check_inputs(A , generate_kwargs["""min_length"""] , generate_kwargs["""max_length"""] )
snake_case : List[str] = self.model.generate(**A , **A )
snake_case : Dict = output_ids.shape[0]
if self.framework == "pt":
snake_case : List[Any] = output_ids.reshape(A , out_b // in_b , *output_ids.shape[1:] )
elif self.framework == "tf":
snake_case : Any = tf.reshape(A , (in_b, out_b // in_b, *output_ids.shape[1:]) )
return {"output_ids": output_ids}
def UpperCAmelCase ( self , A , A=ReturnType.TEXT , A=False ) -> Union[str, Any]:
snake_case : Tuple = []
for output_ids in model_outputs["output_ids"][0]:
if return_type == ReturnType.TENSORS:
snake_case : Dict = {f"""{self.return_name}_token_ids""": output_ids}
elif return_type == ReturnType.TEXT:
snake_case : int = {
f"""{self.return_name}_text""": self.tokenizer.decode(
A , skip_special_tokens=A , clean_up_tokenization_spaces=A , )
}
records.append(A )
return records
@add_end_docstrings(UpperCamelCase__ )
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_case = """summary"""
def __call__( self , *A , **A ) -> str:
return super().__call__(*A , **A )
def UpperCAmelCase ( self , A , A , A ) -> bool:
if max_length < min_length:
logger.warning(f"""Your min_length={min_length} must be inferior than your max_length={max_length}.""" )
if input_length < max_length:
logger.warning(
f"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
"""a summarization task, where outputs shorter than the input are typically wanted, you might """
f"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})""" )
@add_end_docstrings(UpperCamelCase__ )
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_case = """translation"""
def UpperCAmelCase ( self , A , A , A ) -> Union[str, Any]:
if input_length > 0.9 * max_length:
logger.warning(
f"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
"""increasing your max_length manually, e.g. translator('...', max_length=400)""" )
return True
def UpperCAmelCase ( self , *A , A=TruncationStrategy.DO_NOT_TRUNCATE , A=None , A=None ) -> Optional[int]:
if getattr(self.tokenizer , """_build_translation_inputs""" , A ):
return self.tokenizer._build_translation_inputs(
*A , return_tensors=self.framework , truncation=A , src_lang=A , tgt_lang=A )
else:
return super()._parse_and_tokenize(*A , truncation=A )
def UpperCAmelCase ( self , A=None , A=None , **A ) -> Union[str, Any]:
snake_case , snake_case , snake_case : str = super()._sanitize_parameters(**A )
if src_lang is not None:
snake_case : Tuple = src_lang
if tgt_lang is not None:
snake_case : str = tgt_lang
if src_lang is None and tgt_lang is None:
# Backward compatibility, direct arguments use is preferred.
snake_case : Union[str, Any] = kwargs.get("""task""" , self.task )
snake_case : Any = task.split("""_""" )
if task and len(A ) == 4:
# translation, XX, to YY
snake_case : Optional[Any] = items[1]
snake_case : Dict = items[3]
return preprocess_params, forward_params, postprocess_params
def __call__( self , *A , **A ) -> str:
return super().__call__(*A , **A )
| 684 | 0 |
"""simple docstring"""
import numpy as np
import qiskit
def _UpperCamelCase ( UpperCamelCase = 8 , UpperCamelCase = None ) -> str:
"""simple docstring"""
__UpperCAmelCase : Any = np.random.default_rng(seed=SCREAMING_SNAKE_CASE__ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
__UpperCAmelCase : Optional[int] = 6 * key_len
# Measurement basis for Alice's qubits.
__UpperCAmelCase : Optional[int] = rng.integers(2 , size=SCREAMING_SNAKE_CASE__ )
# The set of states Alice will prepare.
__UpperCAmelCase : Optional[int] = rng.integers(2 , size=SCREAMING_SNAKE_CASE__ )
# Measurement basis for Bob's qubits.
__UpperCAmelCase : Tuple = rng.integers(2 , size=SCREAMING_SNAKE_CASE__ )
# Quantum Circuit to simulate BB84
__UpperCAmelCase : Optional[int] = qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE__ , name="BB84" )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(SCREAMING_SNAKE_CASE__ ):
if alice_state[index] == 1:
bbaa_circ.x(SCREAMING_SNAKE_CASE__ )
if alice_basis[index] == 1:
bbaa_circ.h(SCREAMING_SNAKE_CASE__ )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(SCREAMING_SNAKE_CASE__ ):
if bob_basis[index] == 1:
bbaa_circ.h(SCREAMING_SNAKE_CASE__ )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
__UpperCAmelCase : Union[str, Any] = qiskit.Aer.get_backend("aer_simulator" )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
__UpperCAmelCase : int = qiskit.execute(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , shots=1 , seed_simulator=SCREAMING_SNAKE_CASE__ )
# Returns the result of measurement.
__UpperCAmelCase : str = job.result().get_counts(SCREAMING_SNAKE_CASE__ ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
__UpperCAmelCase : Optional[Any] = "".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
__UpperCAmelCase : Optional[Any] = gen_key[:key_len] if len(SCREAMING_SNAKE_CASE__ ) >= key_len else gen_key.ljust(SCREAMING_SNAKE_CASE__ , "0" )
return key
if __name__ == "__main__":
print(f'''The generated key is : {bbaa(8, seed=0)}''')
from doctest import testmod
testmod()
| 77 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class snake_case_ ( a_ ):
__lowerCAmelCase = (DEISMultistepScheduler,)
__lowerCAmelCase = (("num_inference_steps", 2_5),)
def snake_case_ ( self , **a_ ):
a_ : Optional[Any] = {
"num_train_timesteps": 1_0_0_0,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
}
config.update(**a_ )
return config
def snake_case_ ( self , a_=0 , **a_ ):
a_ : Union[str, Any] = dict(self.forward_default_kwargs )
a_ : Union[str, Any] = kwargs.pop("num_inference_steps" , a_ )
a_ : List[str] = self.dummy_sample
a_ : Union[str, Any] = 0.1 * sample
a_ : List[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
a_ : Any = self.get_scheduler_config(**a_ )
a_ : Dict = scheduler_class(**a_ )
scheduler.set_timesteps(a_ )
# copy over dummy past residuals
a_ : Optional[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(a_ )
a_ : Dict = scheduler_class.from_pretrained(a_ )
new_scheduler.set_timesteps(a_ )
# copy over dummy past residuals
a_ : List[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
a_ , a_ : List[str] = sample, sample
for t in range(a_ , time_step + scheduler.config.solver_order + 1 ):
a_ : Dict = scheduler.step(a_ , a_ , a_ , **a_ ).prev_sample
a_ : List[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 snake_case_ ( self ):
pass
def snake_case_ ( self , a_=0 , **a_ ):
a_ : List[str] = dict(self.forward_default_kwargs )
a_ : Dict = kwargs.pop("num_inference_steps" , a_ )
a_ : List[str] = self.dummy_sample
a_ : str = 0.1 * sample
a_ : Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
a_ : Union[str, Any] = self.get_scheduler_config()
a_ : Optional[Any] = scheduler_class(**a_ )
scheduler.set_timesteps(a_ )
# copy over dummy past residuals (must be after setting timesteps)
a_ : Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(a_ )
a_ : List[Any] = 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)
a_ : Dict = dummy_past_residuals[: new_scheduler.config.solver_order]
a_ : Optional[int] = scheduler.step(a_ , a_ , a_ , **a_ ).prev_sample
a_ : Tuple = 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 snake_case_ ( self , a_=None , **a_ ):
if scheduler is None:
a_ : Optional[Any] = self.scheduler_classes[0]
a_ : Dict = self.get_scheduler_config(**a_ )
a_ : Union[str, Any] = scheduler_class(**a_ )
a_ : Optional[int] = self.scheduler_classes[0]
a_ : List[str] = self.get_scheduler_config(**a_ )
a_ : Tuple = scheduler_class(**a_ )
a_ : Optional[int] = 1_0
a_ : Optional[Any] = self.dummy_model()
a_ : Tuple = self.dummy_sample_deter
scheduler.set_timesteps(a_ )
for i, t in enumerate(scheduler.timesteps ):
a_ : str = model(a_ , a_ )
a_ : str = scheduler.step(a_ , a_ , a_ ).prev_sample
return sample
def snake_case_ ( self ):
a_ : Union[str, Any] = dict(self.forward_default_kwargs )
a_ : Tuple = kwargs.pop("num_inference_steps" , a_ )
for scheduler_class in self.scheduler_classes:
a_ : List[Any] = self.get_scheduler_config()
a_ : str = scheduler_class(**a_ )
a_ : Any = self.dummy_sample
a_ : 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" ):
a_ : Union[str, Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
a_ : Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.10]
a_ : str = dummy_past_residuals[: scheduler.config.solver_order]
a_ : str = scheduler.timesteps[5]
a_ : Dict = scheduler.timesteps[6]
a_ : Dict = scheduler.step(a_ , a_ , a_ , **a_ ).prev_sample
a_ : Any = scheduler.step(a_ , a_ , a_ , **a_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def snake_case_ ( self ):
# make sure that iterating over schedulers with same config names gives same results
# for defaults
a_ : Dict = DEISMultistepScheduler(**self.get_scheduler_config() )
a_ : List[str] = self.full_loop(scheduler=a_ )
a_ : Tuple = torch.mean(torch.abs(a_ ) )
assert abs(result_mean.item() - 0.23_916 ) < 1e-3
a_ : int = DPMSolverSinglestepScheduler.from_config(scheduler.config )
a_ : Tuple = DPMSolverMultistepScheduler.from_config(scheduler.config )
a_ : Tuple = UniPCMultistepScheduler.from_config(scheduler.config )
a_ : Any = DEISMultistepScheduler.from_config(scheduler.config )
a_ : str = self.full_loop(scheduler=a_ )
a_ : Any = torch.mean(torch.abs(a_ ) )
assert abs(result_mean.item() - 0.23_916 ) < 1e-3
def snake_case_ ( self ):
for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=a_ )
def snake_case_ ( self ):
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 snake_case_ ( self ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=a_ )
def snake_case_ ( self ):
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_ , )
a_ : List[Any] = 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 snake_case_ ( self ):
self.check_over_configs(lower_order_final=a_ )
self.check_over_configs(lower_order_final=a_ )
def snake_case_ ( self ):
for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
self.check_over_forward(num_inference_steps=a_ , time_step=0 )
def snake_case_ ( self ):
a_ : str = self.full_loop()
a_ : Dict = torch.mean(torch.abs(a_ ) )
assert abs(result_mean.item() - 0.23_916 ) < 1e-3
def snake_case_ ( self ):
a_ : Optional[Any] = self.full_loop(prediction_type="v_prediction" )
a_ : Union[str, Any] = torch.mean(torch.abs(a_ ) )
assert abs(result_mean.item() - 0.091 ) < 1e-3
def snake_case_ ( self ):
a_ : List[str] = self.scheduler_classes[0]
a_ : str = self.get_scheduler_config(thresholding=a_ , dynamic_thresholding_ratio=0 )
a_ : Dict = scheduler_class(**a_ )
a_ : int = 1_0
a_ : List[str] = self.dummy_model()
a_ : Optional[int] = self.dummy_sample_deter.half()
scheduler.set_timesteps(a_ )
for i, t in enumerate(scheduler.timesteps ):
a_ : Optional[int] = model(a_ , a_ )
a_ : Optional[Any] = scheduler.step(a_ , a_ , a_ ).prev_sample
assert sample.dtype == torch.floataa
| 237 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self , lowercase , lowercase=13 , lowercase=30 , lowercase=2 , lowercase=3 , lowercase=True , lowercase=True , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=10 , lowercase=0.0_2 , ) -> Tuple:
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = image_size
lowerCamelCase_ = patch_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase_ = (image_size // patch_size) ** 2
lowerCamelCase_ = num_patches + 1
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ = 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=lowercase , initializer_range=self.initializer_range , )
return config, pixel_values
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase ) -> Dict:
lowerCamelCase_ = FlaxViTModel(config=lowercase )
lowerCamelCase_ = model(lowercase )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
lowerCamelCase_ = (self.image_size, self.image_size)
lowerCamelCase_ = (self.patch_size, self.patch_size)
lowerCamelCase_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase ) -> List[Any]:
lowerCamelCase_ = self.type_sequence_label_size
lowerCamelCase_ = FlaxViTForImageClassification(config=lowercase )
lowerCamelCase_ = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase_ = 1
lowerCamelCase_ = FlaxViTForImageClassification(lowercase )
lowerCamelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase_ = model(lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) , (
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_flax
class _SCREAMING_SNAKE_CASE ( snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def SCREAMING_SNAKE_CASE_( self ) -> None:
lowerCamelCase_ = FlaxViTModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def SCREAMING_SNAKE_CASE_( self ) -> int:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Any:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(lowercase )
lowerCamelCase_ = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowerCamelCase_ = self._prepare_for_class(lowercase , lowercase )
lowerCamelCase_ = model_class(lowercase )
@jax.jit
def model_jitted(lowercase , **lowercase ):
return model(pixel_values=lowercase , **lowercase )
with self.subTest("JIT Enabled" ):
lowerCamelCase_ = model_jitted(**lowercase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowerCamelCase_ = model_jitted(**lowercase ).to_tuple()
self.assertEqual(len(lowercase ) , len(lowercase ) )
for jitted_output, output in zip(lowercase , lowercase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
for model_class_name in self.all_model_classes:
lowerCamelCase_ = model_class_name.from_pretrained("google/vit-base-patch16-224" )
lowerCamelCase_ = model(np.ones((1, 3, 224, 224) ) )
self.assertIsNotNone(lowercase )
| 313 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if is_timm_available():
import timm
if is_torch_available():
from torch import Tensor
class _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
lowerCAmelCase__ = 'pixel_values'
lowerCAmelCase__ = False
lowerCAmelCase__ = TimmBackboneConfig
def __init__( self , lowercase , **lowercase ) -> Union[str, Any]:
requires_backends(self , "timm" )
super().__init__(lowercase )
lowerCamelCase_ = config
if config.backbone is None:
raise ValueError("backbone is not set in the config. Please set it to a timm model name." )
if config.backbone not in timm.list_models():
raise ValueError(f'backbone {config.backbone} is not supported by timm.' )
if hasattr(lowercase , "out_features" ) and config.out_features is not None:
raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )
lowerCamelCase_ = getattr(lowercase , "use_pretrained_backbone" , lowercase )
if pretrained is None:
raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )
# We just take the final layer by default. This matches the default for the transformers models.
lowerCamelCase_ = config.out_indices if getattr(lowercase , "out_indices" , lowercase ) is not None else (-1,)
lowerCamelCase_ = timm.create_model(
config.backbone , pretrained=lowercase , features_only=config.features_only , in_chans=config.num_channels , out_indices=lowercase , **lowercase , )
# These are used to control the output of the model when called. If output_hidden_states is True, then
# return_layers is modified to include all layers.
lowerCamelCase_ = self._backbone.return_layers
lowerCamelCase_ = {layer["module"]: str(lowercase ) for i, layer in enumerate(self._backbone.feature_info.info )}
super()._init_backbone(lowercase )
@classmethod
def SCREAMING_SNAKE_CASE_( cls , lowercase , *lowercase , **lowercase ) -> Tuple:
requires_backends(cls , ["vision", "timm"] )
from ...models.timm_backbone import TimmBackboneConfig
lowerCamelCase_ = kwargs.pop("config" , TimmBackboneConfig() )
lowerCamelCase_ = kwargs.pop("use_timm_backbone" , lowercase )
if not use_timm:
raise ValueError("use_timm_backbone must be True for timm backbones" )
lowerCamelCase_ = kwargs.pop("num_channels" , config.num_channels )
lowerCamelCase_ = kwargs.pop("features_only" , config.features_only )
lowerCamelCase_ = kwargs.pop("use_pretrained_backbone" , config.use_pretrained_backbone )
lowerCamelCase_ = kwargs.pop("out_indices" , config.out_indices )
lowerCamelCase_ = TimmBackboneConfig(
backbone=lowercase , num_channels=lowercase , features_only=lowercase , use_pretrained_backbone=lowercase , out_indices=lowercase , )
return super()._from_config(lowercase , **lowercase )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> List[Any]:
pass
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase=None , lowercase=None , lowercase=None , **lowercase ) -> Union[BackboneOutput, Tuple[Tensor, ...]]:
lowerCamelCase_ = return_dict if return_dict is not None else self.config.use_return_dict
lowerCamelCase_ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
lowerCamelCase_ = output_attentions if output_attentions is not None else self.config.output_attentions
if output_attentions:
raise ValueError("Cannot output attentions for timm backbones at the moment" )
if output_hidden_states:
# We modify the return layers to include all the stages of the backbone
lowerCamelCase_ = self._all_layers
lowerCamelCase_ = self._backbone(lowercase , **lowercase )
lowerCamelCase_ = self._return_layers
lowerCamelCase_ = tuple(hidden_states[i] for i in self.out_indices )
else:
lowerCamelCase_ = self._backbone(lowercase , **lowercase )
lowerCamelCase_ = None
lowerCamelCase_ = tuple(lowercase )
lowerCamelCase_ = tuple(lowercase ) if hidden_states is not None else None
if not return_dict:
lowerCamelCase_ = (feature_maps,)
if output_hidden_states:
lowerCamelCase_ = output + (hidden_states,)
return output
return BackboneOutput(feature_maps=lowercase , hidden_states=lowercase , attentions=lowercase )
| 313 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def lowerCamelCase__ ( _lowerCamelCase : Sequence[float] , _lowerCamelCase : int , _lowerCamelCase : int ) -> tuple[int | None, int | None, float]:
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
lowerCamelCase_ = (low + high) // 2
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = max_subarray(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = max_subarray(_lowerCamelCase , mid + 1 , _lowerCamelCase )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = max_cross_sum(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def lowerCamelCase__ ( _lowerCamelCase : Sequence[float] , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int ) -> tuple[int, int, float]:
lowerCamelCase_ , lowerCamelCase_ = float('-inf' ), -1
lowerCamelCase_ , lowerCamelCase_ = float('-inf' ), -1
lowerCamelCase_ = 0
for i in range(_lowerCamelCase , low - 1 , -1 ):
summ += arr[i]
if summ > left_sum:
lowerCamelCase_ = summ
lowerCamelCase_ = i
lowerCamelCase_ = 0
for i in range(mid + 1 , high + 1 ):
summ += arr[i]
if summ > right_sum:
lowerCamelCase_ = summ
lowerCamelCase_ = i
return max_left, max_right, (left_sum + right_sum)
def lowerCamelCase__ ( _lowerCamelCase : int ) -> float:
lowerCamelCase_ = [randint(1 , _lowerCamelCase ) for _ in range(_lowerCamelCase )]
lowerCamelCase_ = time.time()
max_subarray(_lowerCamelCase , 0 , input_size - 1 )
lowerCamelCase_ = time.time()
return end - start
def lowerCamelCase__ ( ) -> None:
lowerCamelCase_ = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
lowerCamelCase_ = [time_max_subarray(_lowerCamelCase ) for input_size in input_sizes]
print('No of Inputs\t\tTime Taken' )
for input_size, runtime in zip(_lowerCamelCase , _lowerCamelCase ):
print(_lowerCamelCase , '\t\t' , _lowerCamelCase )
plt.plot(_lowerCamelCase , _lowerCamelCase )
plt.xlabel('Number of Inputs' )
plt.ylabel('Time taken in seconds' )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 549 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a ( __snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE : Optional[Any] = KandinskyImgaImgPipeline
SCREAMING_SNAKE_CASE : Tuple = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
SCREAMING_SNAKE_CASE : int = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
]
SCREAMING_SNAKE_CASE : List[str] = [
"""generator""",
"""height""",
"""width""",
"""strength""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
SCREAMING_SNAKE_CASE : Any = False
@property
def UpperCamelCase ( self : List[str] ) -> str:
return 32
@property
def UpperCamelCase ( self : Tuple ) -> Optional[Any]:
return 32
@property
def UpperCamelCase ( self : Optional[int] ) -> str:
return self.time_input_dim
@property
def UpperCamelCase ( self : Optional[int] ) -> Optional[Any]:
return self.time_input_dim * 4
@property
def UpperCamelCase ( self : int ) -> List[Any]:
return 100
@property
def UpperCamelCase ( self : Any ) -> List[Any]:
lowerCamelCase_ = XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
return tokenizer
@property
def UpperCamelCase ( self : str ) -> Optional[Any]:
torch.manual_seed(0 )
lowerCamelCase_ = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1005 , )
lowerCamelCase_ = MultilingualCLIP(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = text_encoder.eval()
return text_encoder
@property
def UpperCamelCase ( self : int ) -> List[Any]:
torch.manual_seed(0 )
lowerCamelCase_ = {
'in_channels': 4,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'text_image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'text_image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
lowerCamelCase_ = UNetaDConditionModel(**__SCREAMING_SNAKE_CASE )
return model
@property
def UpperCamelCase ( self : Tuple ) -> Optional[Any]:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def UpperCamelCase ( self : Union[str, Any] ) -> Optional[Any]:
torch.manual_seed(0 )
lowerCamelCase_ = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCamelCase ( self : Union[str, Any] ) -> List[Any]:
lowerCamelCase_ = self.dummy_text_encoder
lowerCamelCase_ = self.dummy_tokenizer
lowerCamelCase_ = self.dummy_unet
lowerCamelCase_ = self.dummy_movq
lowerCamelCase_ = {
'num_train_timesteps': 1000,
'beta_schedule': 'linear',
'beta_start': 0.00_085,
'beta_end': 0.012,
'clip_sample': False,
'set_alpha_to_one': False,
'steps_offset': 0,
'prediction_type': 'epsilon',
'thresholding': False,
}
lowerCamelCase_ = DDIMScheduler(**__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = {
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Dict=0 ) -> str:
lowerCamelCase_ = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__SCREAMING_SNAKE_CASE )
# create init_image
lowerCamelCase_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase_ = Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert('RGB' ).resize((256, 256) )
if str(__SCREAMING_SNAKE_CASE ).startswith('mps' ):
lowerCamelCase_ = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
lowerCamelCase_ = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = {
'prompt': 'horse',
'image': init_image,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 10,
'guidance_scale': 7.0,
'strength': 0.2,
'output_type': 'np',
}
return inputs
def UpperCamelCase ( self : Tuple ) -> Any:
lowerCamelCase_ = 'cpu'
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) )
lowerCamelCase_ = output.images
lowerCamelCase_ = pipe(
**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) , return_dict=__SCREAMING_SNAKE_CASE , )[0]
lowerCamelCase_ = image[0, -3:, -3:, -1]
lowerCamelCase_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase_ = np.array(
[0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def UpperCamelCase ( self : Tuple ) -> List[str]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self : Any ) -> int:
lowerCamelCase_ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinsky/kandinsky_img2img_frog.npy' )
lowerCamelCase_ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
lowerCamelCase_ = 'A red cartoon frog, 4k'
lowerCamelCase_ = KandinskyPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1-prior' , torch_dtype=torch.floataa )
pipe_prior.to(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = KandinskyImgaImgPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-1' , torch_dtype=torch.floataa )
lowerCamelCase_ = pipeline.to(__SCREAMING_SNAKE_CASE )
pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = torch.Generator(device='cpu' ).manual_seed(0 )
lowerCamelCase_ , lowerCamelCase_ = pipe_prior(
__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
lowerCamelCase_ = pipeline(
__SCREAMING_SNAKE_CASE , image=__SCREAMING_SNAKE_CASE , image_embeds=__SCREAMING_SNAKE_CASE , negative_image_embeds=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type='np' , )
lowerCamelCase_ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
| 549 | 1 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
a__ : List[Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
a__ : str = 2_5_0_0_0_4
a__ : Tuple = 2_5_0_0_2_0
@require_sentencepiece
@require_tokenizers
class lowercase ( UpperCAmelCase_ , unittest.TestCase ):
"""simple docstring"""
snake_case_ = MBartaaTokenizer
snake_case_ = MBartaaTokenizerFast
snake_case_ = True
snake_case_ = True
def _UpperCamelCase ( self : Tuple ):
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase__ = MBartaaTokenizer(__UpperCamelCase , src_lang="""en_XX""" , tgt_lang="""ro_RO""" , keep_accents=__UpperCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def _UpperCamelCase ( self : str ):
"""simple docstring"""
lowerCamelCase__ = """<s>"""
lowerCamelCase__ = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase ) , __UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase ) , __UpperCamelCase )
def _UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
lowerCamelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-1] , """<mask>""" )
self.assertEqual(len(__UpperCamelCase ) , 10_54 )
def _UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_54 )
def _UpperCamelCase ( self : str ):
"""simple docstring"""
lowerCamelCase__ = MBartaaTokenizer(__UpperCamelCase , src_lang="""en_XX""" , tgt_lang="""ro_RO""" , keep_accents=__UpperCamelCase )
lowerCamelCase__ = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__UpperCamelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__UpperCamelCase ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
lowerCamelCase__ = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__UpperCamelCase , [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""", """."""] , )
lowerCamelCase__ = tokenizer.convert_tokens_to_ids(__UpperCamelCase )
self.assertListEqual(
__UpperCamelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
lowerCamelCase__ = tokenizer.convert_ids_to_tokens(__UpperCamelCase )
self.assertListEqual(
__UpperCamelCase , [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>""", """."""] , )
@slow
def _UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
lowerCamelCase__ = {"""input_ids""": [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 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], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1]], """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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCamelCase , model_name="""facebook/mbart-large-50""" , revision="""d3913889c59cd5c9e456b269c376325eabad57e2""" , )
def _UpperCamelCase ( self : Dict ):
"""simple docstring"""
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase__ = (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase__ = self.rust_tokenizer_class.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
lowerCamelCase__ = self.tokenizer_class.from_pretrained(__UpperCamelCase , **__UpperCamelCase )
lowerCamelCase__ = tempfile.mkdtemp()
lowerCamelCase__ = tokenizer_r.save_pretrained(__UpperCamelCase )
lowerCamelCase__ = tokenizer_p.save_pretrained(__UpperCamelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
lowerCamelCase__ = tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f )
self.assertSequenceEqual(__UpperCamelCase , __UpperCamelCase )
# Checks everything loads correctly in the same way
lowerCamelCase__ = tokenizer_r.from_pretrained(__UpperCamelCase )
lowerCamelCase__ = tokenizer_p.from_pretrained(__UpperCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__UpperCamelCase , __UpperCamelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__UpperCamelCase )
# Save tokenizer rust, legacy_format=True
lowerCamelCase__ = tempfile.mkdtemp()
lowerCamelCase__ = tokenizer_r.save_pretrained(__UpperCamelCase , legacy_format=__UpperCamelCase )
lowerCamelCase__ = tokenizer_p.save_pretrained(__UpperCamelCase )
# Checks it save with the same files
self.assertSequenceEqual(__UpperCamelCase , __UpperCamelCase )
# Checks everything loads correctly in the same way
lowerCamelCase__ = tokenizer_r.from_pretrained(__UpperCamelCase )
lowerCamelCase__ = tokenizer_p.from_pretrained(__UpperCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__UpperCamelCase , __UpperCamelCase ) )
shutil.rmtree(__UpperCamelCase )
# Save tokenizer rust, legacy_format=False
lowerCamelCase__ = tempfile.mkdtemp()
lowerCamelCase__ = tokenizer_r.save_pretrained(__UpperCamelCase , legacy_format=__UpperCamelCase )
lowerCamelCase__ = tokenizer_p.save_pretrained(__UpperCamelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase__ = tokenizer_r.from_pretrained(__UpperCamelCase )
lowerCamelCase__ = tokenizer_p.from_pretrained(__UpperCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__UpperCamelCase , __UpperCamelCase ) )
shutil.rmtree(__UpperCamelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowercase ( unittest.TestCase ):
"""simple docstring"""
snake_case_ = 'facebook/mbart-large-50-one-to-many-mmt'
snake_case_ = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
snake_case_ = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
snake_case_ = [EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]
@classmethod
def _UpperCamelCase ( cls : Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ = MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="""en_XX""" , tgt_lang="""ro_RO""" )
lowerCamelCase__ = 1
return cls
def _UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""] , 25_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""] , 25_00_04 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""] , 25_00_20 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""] , 25_00_38 )
def _UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __UpperCamelCase )
def _UpperCamelCase ( self : Tuple ):
"""simple docstring"""
self.assertIn(__UpperCamelCase , self.tokenizer.all_special_ids )
lowerCamelCase__ = [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
lowerCamelCase__ = self.tokenizer.decode(__UpperCamelCase , skip_special_tokens=__UpperCamelCase )
lowerCamelCase__ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__UpperCamelCase )
self.assertEqual(__UpperCamelCase , __UpperCamelCase )
self.assertNotIn(self.tokenizer.eos_token , __UpperCamelCase )
def _UpperCamelCase ( self : Any ):
"""simple docstring"""
lowerCamelCase__ = ["""this is gunna be a long sentence """ * 20]
assert isinstance(src_text[0] , __UpperCamelCase )
lowerCamelCase__ = 10
lowerCamelCase__ = self.tokenizer(__UpperCamelCase , max_length=__UpperCamelCase , truncation=__UpperCamelCase ).input_ids[0]
self.assertEqual(ids[0] , __UpperCamelCase )
self.assertEqual(ids[-1] , 2 )
self.assertEqual(len(__UpperCamelCase ) , __UpperCamelCase )
def _UpperCamelCase ( self : Any ):
"""simple docstring"""
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""] ) , [25_00_53, 25_00_01] )
def _UpperCamelCase ( self : Dict ):
"""simple docstring"""
lowerCamelCase__ = tempfile.mkdtemp()
lowerCamelCase__ = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__UpperCamelCase )
lowerCamelCase__ = MBartaaTokenizer.from_pretrained(__UpperCamelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __UpperCamelCase )
@require_torch
def _UpperCamelCase ( self : Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__UpperCamelCase , return_tensors="""pt""" )
lowerCamelCase__ = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def _UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
lowerCamelCase__ = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=len(self.expected_src_tokens ) , return_tensors="""pt""" , )
lowerCamelCase__ = shift_tokens_right(batch["""labels"""] , self.tokenizer.pad_token_id )
self.assertIsInstance(__UpperCamelCase , __UpperCamelCase )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCamelCase__ = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __UpperCamelCase )
self.assertEqual(2 , batch.decoder_input_ids[0, 0] ) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ = self.tokenizer(self.src_text , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=3 , return_tensors="""pt""" )
lowerCamelCase__ = self.tokenizer(
text_target=self.tgt_text , padding=__UpperCamelCase , truncation=__UpperCamelCase , max_length=10 , return_tensors="""pt""" )
lowerCamelCase__ = targets["""input_ids"""]
lowerCamelCase__ = shift_tokens_right(__UpperCamelCase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ = self.tokenizer._build_translation_inputs(
"""A test""" , return_tensors="""pt""" , src_lang="""en_XX""" , tgt_lang="""ar_AR""" )
self.assertEqual(
nested_simplify(__UpperCamelCase ) , {
# en_XX, A, test, EOS
"""input_ids""": [[25_00_04, 62, 30_34, 2]],
"""attention_mask""": [[1, 1, 1, 1]],
# ar_AR
"""forced_bos_token_id""": 25_00_01,
} , )
| 715 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase ( unittest.TestCase ):
"""simple docstring"""
snake_case_ = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
snake_case_ = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def _UpperCamelCase ( self : str , a_ : Optional[int] , a_ : str , a_ : Tuple ):
"""simple docstring"""
lowerCamelCase__ = TextaTextGenerationPipeline(model=a_ , tokenizer=a_ )
return generator, ["Something to write", "Something else"]
def _UpperCamelCase ( self : Tuple , a_ : int , a_ : Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ = generator("""Something there""" )
self.assertEqual(a_ , [{"""generated_text""": ANY(a_ )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )
lowerCamelCase__ = generator(["""This is great !""", """Something else"""] , num_return_sequences=2 , do_sample=a_ )
self.assertEqual(
a_ , [
[{"""generated_text""": ANY(a_ )}, {"""generated_text""": ANY(a_ )}],
[{"""generated_text""": ANY(a_ )}, {"""generated_text""": ANY(a_ )}],
] , )
lowerCamelCase__ = generator(
["""This is great !""", """Something else"""] , num_return_sequences=2 , batch_size=2 , do_sample=a_ )
self.assertEqual(
a_ , [
[{"""generated_text""": ANY(a_ )}, {"""generated_text""": ANY(a_ )}],
[{"""generated_text""": ANY(a_ )}, {"""generated_text""": ANY(a_ )}],
] , )
with self.assertRaises(a_ ):
generator(4 )
@require_torch
def _UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
lowerCamelCase__ = pipeline("""text2text-generation""" , model="""patrickvonplaten/t5-tiny-random""" , framework="""pt""" )
# do_sample=False necessary for reproducibility
lowerCamelCase__ = generator("""Something there""" , do_sample=a_ )
self.assertEqual(a_ , [{"""generated_text""": """"""}] )
lowerCamelCase__ = 3
lowerCamelCase__ = generator(
"""Something there""" , num_return_sequences=a_ , num_beams=a_ , )
lowerCamelCase__ = [
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """"""},
]
self.assertEqual(a_ , a_ )
lowerCamelCase__ = generator("""This is a test""" , do_sample=a_ , num_return_sequences=2 , return_tensors=a_ )
self.assertEqual(
a_ , [
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
] , )
lowerCamelCase__ = generator.model.config.eos_token_id
lowerCamelCase__ = """<pad>"""
lowerCamelCase__ = generator(
["""This is a test""", """This is a second test"""] , do_sample=a_ , num_return_sequences=2 , batch_size=2 , return_tensors=a_ , )
self.assertEqual(
a_ , [
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
] , )
@require_tf
def _UpperCamelCase ( self : Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ = pipeline("""text2text-generation""" , model="""patrickvonplaten/t5-tiny-random""" , framework="""tf""" )
# do_sample=False necessary for reproducibility
lowerCamelCase__ = generator("""Something there""" , do_sample=a_ )
self.assertEqual(a_ , [{"""generated_text""": """"""}] )
| 235 | 0 |
"""simple docstring"""
def UpperCAmelCase ( A : str = 3 , A : int = 7 , A : Union[str, Any] = 100_0000 ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 1
for current_denominator in range(1 , limit + 1 ):
_UpperCAmelCase = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
_UpperCAmelCase = current_numerator
_UpperCAmelCase = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=1_00_00_00))
| 573 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = val
snake_case_ = None
snake_case_ = None
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
snake_case_ = Node(__UpperCamelCase )
else:
self.left.insert(__UpperCamelCase )
elif val > self.val:
if self.right is None:
snake_case_ = Node(__UpperCamelCase )
else:
self.right.insert(__UpperCamelCase )
else:
snake_case_ = val
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
# Recursive traversal
if root:
inorder(root.left , lowercase__ )
res.append(root.val )
inorder(root.right , lowercase__ )
def a(lowercase__ ):
'''simple docstring'''
# Build BST
if len(lowercase__ ) == 0:
return arr
snake_case_ = Node(arr[0] )
for i in range(1 , len(lowercase__ ) ):
root.insert(arr[i] )
# Traverse BST in order.
snake_case_ = []
inorder(lowercase__ , lowercase__ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 187 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
snake_case_ : str = logging.get_logger(__name__)
snake_case_ : Tuple = r"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n or scores for each vocabulary token after SoftMax.\n kwargs (`Dict[str, Any]`, *optional*):\n Additional stopping criteria specific kwargs.\n\n Return:\n `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"
class __snake_case ( a ):
@add_start_docstrings(_snake_case)
def __call__( self : Optional[Any] , _snake_case : torch.LongTensor , _snake_case : torch.FloatTensor , **_snake_case : Any):
"""simple docstring"""
raise NotImplementedError('''StoppingCriteria needs to be subclassed''')
class __snake_case ( a ):
def __init__( self : List[Any] , _snake_case : int , _snake_case : Optional[int] = None):
"""simple docstring"""
UpperCAmelCase_ = max_length
UpperCAmelCase_ = max_position_embeddings
@add_start_docstrings(_snake_case)
def __call__( self : str , _snake_case : torch.LongTensor , _snake_case : torch.FloatTensor , **_snake_case : Union[str, Any]):
"""simple docstring"""
UpperCAmelCase_ = input_ids.shape[-1]
UpperCAmelCase_ = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
'''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
'''exceptions, performance degradation, or nothing at all.''')
return is_done
class __snake_case ( a ):
def __init__( self : Dict , _snake_case : int , _snake_case : int):
"""simple docstring"""
warnings.warn(
'''The class `MaxNewTokensCriteria` is deprecated. '''
F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
'''with `max_length = start_length + max_new_tokens` instead.''' , _snake_case , )
UpperCAmelCase_ = start_length
UpperCAmelCase_ = max_new_tokens
UpperCAmelCase_ = start_length + max_new_tokens
@add_start_docstrings(_snake_case)
def __call__( self : str , _snake_case : torch.LongTensor , _snake_case : torch.FloatTensor , **_snake_case : str):
"""simple docstring"""
return input_ids.shape[-1] >= self.max_length
class __snake_case ( a ):
def __init__( self : str , _snake_case : float , _snake_case : Optional[float] = None):
"""simple docstring"""
UpperCAmelCase_ = max_time
UpperCAmelCase_ = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(_snake_case)
def __call__( self : Union[str, Any] , _snake_case : torch.LongTensor , _snake_case : torch.FloatTensor , **_snake_case : Union[str, Any]):
"""simple docstring"""
return time.time() - self.initial_timestamp > self.max_time
class __snake_case ( a ):
@add_start_docstrings(_snake_case)
def __call__( self : List[Any] , _snake_case : torch.LongTensor , _snake_case : torch.FloatTensor , **_snake_case : Tuple):
"""simple docstring"""
return any(criteria(_snake_case , _snake_case) for criteria in self)
@property
def lowerCamelCase ( self : List[str]):
"""simple docstring"""
for stopping_criterium in self:
if isinstance(_snake_case , _snake_case):
return stopping_criterium.max_length
elif isinstance(_snake_case , _snake_case):
return stopping_criterium.max_length
return None
def A (__A : StoppingCriteriaList , __A : int ) -> StoppingCriteriaList:
"""simple docstring"""
UpperCAmelCase_ = stopping_criteria.max_length
UpperCAmelCase_ = deepcopy(__A )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter''' , __A )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=__A ) )
return new_stopping_criteria
| 169 |
from __future__ import annotations
def A (__A : list[int] ) -> list[int]: # This function is recursive
"""simple docstring"""
UpperCAmelCase_ = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
UpperCAmelCase_ = array[0]
UpperCAmelCase_ = False
UpperCAmelCase_ = 1
UpperCAmelCase_ = []
while not is_found and i < array_length:
if array[i] < pivot:
UpperCAmelCase_ = True
UpperCAmelCase_ = [element for element in array[i:] if element >= array[i]]
UpperCAmelCase_ = longest_subsequence(__A )
if len(__A ) > len(__A ):
UpperCAmelCase_ = temp_array
else:
i += 1
UpperCAmelCase_ = [element for element in array[1:] if element >= pivot]
UpperCAmelCase_ = [pivot, *longest_subsequence(__A )]
if len(__A ) > len(__A ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 169 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =[]
for line in lines:
UpperCAmelCase_ =re.sub(R"#.*" , "" , lowercase__ ) # remove comments
if line:
filtered_lines.append(lowercase__ )
UpperCAmelCase_ ="\n".join(lowercase__ )
# Make a hash from all this code
UpperCAmelCase_ =full_str.encode("utf-8" )
return shaaaa(lowercase__ ).hexdigest()
# get importable module names and hash for caching
__lowercase : Tuple ={
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
__lowercase : Optional[int] ={
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
__lowercase : Dict ={"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
__lowercase : Dict[str, List[str]] ={}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 54 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a__ ( ) -> Optional[int]:
__lowerCAmelCase: List[str] = argparse.ArgumentParser()
parser.add_argument(
"-m" , "--pretrained_model_name_or_path" , type=__SCREAMING_SNAKE_CASE , default=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , help="Path to pretrained model or model identifier from huggingface.co/models." , )
parser.add_argument(
"-c" , "--caption" , type=__SCREAMING_SNAKE_CASE , default="robotic cat with wings" , help="Text used to generate images." , )
parser.add_argument(
"-n" , "--images_num" , type=__SCREAMING_SNAKE_CASE , default=4 , help="How much images to generate." , )
parser.add_argument(
"-s" , "--seed" , type=__SCREAMING_SNAKE_CASE , default=4_2 , help="Seed for random process." , )
parser.add_argument(
"-ci" , "--cuda_id" , type=__SCREAMING_SNAKE_CASE , default=0 , help="cuda_id." , )
__lowerCAmelCase: str = parser.parse_args()
return args
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[str]:
if not len(__SCREAMING_SNAKE_CASE ) == rows * cols:
raise ValueError("The specified number of rows and columns are not correct." )
__lowerCAmelCase , __lowerCAmelCase: str = imgs[0].size
__lowerCAmelCase: Tuple = Image.new("RGB" , size=(cols * w, rows * h) )
__lowerCAmelCase , __lowerCAmelCase: Any = grid.size
for i, img in enumerate(__SCREAMING_SNAKE_CASE ):
grid.paste(__SCREAMING_SNAKE_CASE , box=(i % cols * w, i // cols * h) )
return grid
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE="robotic cat with wings" , __SCREAMING_SNAKE_CASE=7.5 , __SCREAMING_SNAKE_CASE=5_0 , __SCREAMING_SNAKE_CASE=1 , __SCREAMING_SNAKE_CASE=4_2 , ) -> Any:
__lowerCAmelCase: List[Any] = torch.Generator(pipeline.device ).manual_seed(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Optional[int] = pipeline(
__SCREAMING_SNAKE_CASE , guidance_scale=__SCREAMING_SNAKE_CASE , num_inference_steps=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , num_images_per_prompt=__SCREAMING_SNAKE_CASE , ).images
__lowerCAmelCase: Any = int(math.sqrt(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase: Tuple = image_grid(__SCREAMING_SNAKE_CASE , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
__A = parse_args()
# Load models and create wrapper for stable diffusion
__A = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
__A = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
__A = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
__A = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")
__A = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__A = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
__A = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, "unet", unet)
else:
__A = unet.to(torch.device("cuda", args.cuda_id))
__A = pipeline.to(unet.device)
__A , __A = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
__A = os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, "{}.png".format(idx + 1)))
| 346 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : List[Any] , lowercase_ : int , lowercase_ : int) -> Any:
"""simple docstring"""
_UpperCamelCase = jnp.ones((batch_size, length)) / length
return scores
def __UpperCAmelCase ( self : str) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = None
_UpperCamelCase = 20
_UpperCamelCase = self._get_uniform_logits(batch_size=2 , length=lowercase_)
# tweak scores to not be uniform anymore
_UpperCamelCase = scores.at[1, 5].set((1 / length) + 0.1) # peak, 1st batch
_UpperCamelCase = scores.at[1, 10].set((1 / length) - 0.4) # valley, 1st batch
# compute softmax
_UpperCamelCase = jax.nn.softmax(lowercase_ , axis=-1)
_UpperCamelCase = FlaxTemperatureLogitsWarper(temperature=0.5)
_UpperCamelCase = FlaxTemperatureLogitsWarper(temperature=1.3)
_UpperCamelCase = jax.nn.softmax(temp_dist_warper_sharper(lowercase_ , scores.copy() , cur_len=lowercase_) , axis=-1)
_UpperCamelCase = jax.nn.softmax(temp_dist_warper_smoother(lowercase_ , scores.copy() , cur_len=lowercase_) , axis=-1)
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1e-3))
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1e-3))
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max())
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min())
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max())
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min())
def __UpperCAmelCase ( self : Tuple) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = None
_UpperCamelCase = 10
_UpperCamelCase = 2
# create ramp distribution
_UpperCamelCase = np.broadcast_to(np.arange(lowercase_)[None, :] , (batch_size, vocab_size)).copy()
_UpperCamelCase = ramp_logits[1:, : vocab_size // 2] + vocab_size
_UpperCamelCase = FlaxTopKLogitsWarper(3)
_UpperCamelCase = top_k_warp(lowercase_ , lowercase_ , cur_len=lowercase_)
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0]).tolist() , 7 * [True] + 3 * [False])
self.assertListEqual(jnp.isinf(scores[1]).tolist() , 2 * [True] + 3 * [False] + 5 * [True])
# check special case
_UpperCamelCase = 5
_UpperCamelCase = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3)
_UpperCamelCase = np.broadcast_to(np.arange(lowercase_)[None, :] , (batch_size, length)).copy()
_UpperCamelCase = top_k_warp_safety_check(lowercase_ , lowercase_ , cur_len=lowercase_)
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1).tolist() , [2, 2])
def __UpperCAmelCase ( self : str) -> Tuple:
"""simple docstring"""
_UpperCamelCase = None
_UpperCamelCase = 10
_UpperCamelCase = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
_UpperCamelCase = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))
_UpperCamelCase = FlaxTopPLogitsWarper(0.8)
_UpperCamelCase = np.exp(top_p_warp(lowercase_ , lowercase_ , cur_len=lowercase_))
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
_UpperCamelCase = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
self.assertTrue(np.allclose(lowercase_ , lowercase_ , atol=1e-3))
# check edge cases with negative and extreme logits
_UpperCamelCase = np.broadcast_to(np.arange(lowercase_)[None, :] , (batch_size, vocab_size)).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
_UpperCamelCase = ramp_logits[1] * 1_00.0
# make sure at least 2 tokens are kept
_UpperCamelCase = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0)
_UpperCamelCase = top_p_warp(lowercase_ , lowercase_ , cur_len=lowercase_)
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist() , [3, 2])
def __UpperCAmelCase ( self : Tuple) -> Tuple:
"""simple docstring"""
_UpperCamelCase = 20
_UpperCamelCase = 4
_UpperCamelCase = 0
_UpperCamelCase = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=lowercase_)
# check that min length is applied at length 5
_UpperCamelCase = ids_tensor((batch_size, 20) , vocab_size=20)
_UpperCamelCase = 5
_UpperCamelCase = self._get_uniform_logits(lowercase_ , lowercase_)
_UpperCamelCase = min_dist_processor(lowercase_ , lowercase_ , cur_len=lowercase_)
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float("inf")])
# check that min length is not applied anymore at length 15
_UpperCamelCase = self._get_uniform_logits(lowercase_ , lowercase_)
_UpperCamelCase = 15
_UpperCamelCase = min_dist_processor(lowercase_ , lowercase_ , cur_len=lowercase_)
self.assertFalse(jnp.isinf(lowercase_).any())
def __UpperCAmelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = 20
_UpperCamelCase = 4
_UpperCamelCase = 0
_UpperCamelCase = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowercase_)
# check that all scores are -inf except the bos_token_id score
_UpperCamelCase = ids_tensor((batch_size, 1) , vocab_size=20)
_UpperCamelCase = 1
_UpperCamelCase = self._get_uniform_logits(lowercase_ , lowercase_)
_UpperCamelCase = logits_processor(lowercase_ , lowercase_ , cur_len=lowercase_)
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]).all())
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0]) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
_UpperCamelCase = 3
_UpperCamelCase = self._get_uniform_logits(lowercase_ , lowercase_)
_UpperCamelCase = logits_processor(lowercase_ , lowercase_ , cur_len=lowercase_)
self.assertFalse(jnp.isinf(lowercase_).any())
def __UpperCAmelCase ( self : Tuple) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = 20
_UpperCamelCase = 4
_UpperCamelCase = 0
_UpperCamelCase = 5
_UpperCamelCase = FlaxForcedEOSTokenLogitsProcessor(max_length=lowercase_ , eos_token_id=lowercase_)
# check that all scores are -inf except the eos_token_id when max_length is reached
_UpperCamelCase = ids_tensor((batch_size, 4) , vocab_size=20)
_UpperCamelCase = 4
_UpperCamelCase = self._get_uniform_logits(lowercase_ , lowercase_)
_UpperCamelCase = logits_processor(lowercase_ , lowercase_ , cur_len=lowercase_)
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]).all())
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0]) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
_UpperCamelCase = 3
_UpperCamelCase = self._get_uniform_logits(lowercase_ , lowercase_)
_UpperCamelCase = logits_processor(lowercase_ , lowercase_ , cur_len=lowercase_)
self.assertFalse(jnp.isinf(lowercase_).any())
def __UpperCAmelCase ( self : List[str]) -> Any:
"""simple docstring"""
_UpperCamelCase = 4
_UpperCamelCase = 10
_UpperCamelCase = 15
_UpperCamelCase = 2
_UpperCamelCase = 1
_UpperCamelCase = 15
# dummy input_ids and scores
_UpperCamelCase = ids_tensor((batch_size, sequence_length) , lowercase_)
_UpperCamelCase = input_ids.copy()
_UpperCamelCase = self._get_uniform_logits(lowercase_ , lowercase_)
_UpperCamelCase = scores.copy()
# instantiate all dist processors
_UpperCamelCase = FlaxTemperatureLogitsWarper(temperature=0.5)
_UpperCamelCase = FlaxTopKLogitsWarper(3)
_UpperCamelCase = FlaxTopPLogitsWarper(0.8)
# instantiate all logits processors
_UpperCamelCase = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=lowercase_)
_UpperCamelCase = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowercase_)
_UpperCamelCase = FlaxForcedEOSTokenLogitsProcessor(max_length=lowercase_ , eos_token_id=lowercase_)
_UpperCamelCase = 10
# no processor list
_UpperCamelCase = temp_dist_warp(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = top_k_warp(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = top_p_warp(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = min_dist_proc(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = bos_dist_proc(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = eos_dist_proc(lowercase_ , lowercase_ , cur_len=lowercase_)
# with processor list
_UpperCamelCase = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
_UpperCamelCase = processor(lowercase_ , lowercase_ , cur_len=lowercase_)
# scores should be equal
self.assertTrue(jnp.allclose(lowercase_ , lowercase_ , atol=1e-3))
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist())
def __UpperCAmelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = 4
_UpperCamelCase = 10
_UpperCamelCase = 15
_UpperCamelCase = 2
_UpperCamelCase = 1
_UpperCamelCase = 15
# dummy input_ids and scores
_UpperCamelCase = ids_tensor((batch_size, sequence_length) , lowercase_)
_UpperCamelCase = input_ids.copy()
_UpperCamelCase = self._get_uniform_logits(lowercase_ , lowercase_)
_UpperCamelCase = scores.copy()
# instantiate all dist processors
_UpperCamelCase = FlaxTemperatureLogitsWarper(temperature=0.5)
_UpperCamelCase = FlaxTopKLogitsWarper(3)
_UpperCamelCase = FlaxTopPLogitsWarper(0.8)
# instantiate all logits processors
_UpperCamelCase = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=lowercase_)
_UpperCamelCase = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=lowercase_)
_UpperCamelCase = FlaxForcedEOSTokenLogitsProcessor(max_length=lowercase_ , eos_token_id=lowercase_)
_UpperCamelCase = 10
# no processor list
def run_no_processor_list(lowercase_ : Any , lowercase_ : Any , lowercase_ : int):
_UpperCamelCase = temp_dist_warp(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = top_k_warp(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = top_p_warp(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = min_dist_proc(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = bos_dist_proc(lowercase_ , lowercase_ , cur_len=lowercase_)
_UpperCamelCase = eos_dist_proc(lowercase_ , lowercase_ , cur_len=lowercase_)
return scores
# with processor list
def run_processor_list(lowercase_ : Any , lowercase_ : int , lowercase_ : Tuple):
_UpperCamelCase = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc])
_UpperCamelCase = processor(lowercase_ , lowercase_ , cur_len=lowercase_)
return scores
_UpperCamelCase = jax.jit(lowercase_)
_UpperCamelCase = jax.jit(lowercase_)
_UpperCamelCase = jitted_run_no_processor_list(lowercase_ , lowercase_ , lowercase_)
_UpperCamelCase = jitted_run_processor_list(lowercase_ , lowercase_ , lowercase_)
# scores should be equal
self.assertTrue(jnp.allclose(lowercase_ , lowercase_ , atol=1e-3))
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist())
| 82 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCAmelCase ( self : int) -> str:
"""simple docstring"""
torch.manual_seed(0)
_UpperCamelCase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def __UpperCAmelCase ( self : List[Any]) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = self.dummy_uncond_unet
_UpperCamelCase = KarrasVeScheduler()
_UpperCamelCase = KarrasVePipeline(unet=lowercase_ , scheduler=lowercase_)
pipe.to(lowercase_)
pipe.set_progress_bar_config(disable=lowercase_)
_UpperCamelCase = torch.manual_seed(0)
_UpperCamelCase = pipe(num_inference_steps=2 , generator=lowercase_ , output_type="numpy").images
_UpperCamelCase = torch.manual_seed(0)
_UpperCamelCase = pipe(num_inference_steps=2 , generator=lowercase_ , output_type="numpy" , return_dict=lowercase_)[0]
_UpperCamelCase = image[0, -3:, -3:, -1]
_UpperCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_UpperCamelCase = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : int) -> Tuple:
"""simple docstring"""
_UpperCamelCase = "google/ncsnpp-celebahq-256"
_UpperCamelCase = UNetaDModel.from_pretrained(lowercase_)
_UpperCamelCase = KarrasVeScheduler()
_UpperCamelCase = KarrasVePipeline(unet=lowercase_ , scheduler=lowercase_)
pipe.to(lowercase_)
pipe.set_progress_bar_config(disable=lowercase_)
_UpperCamelCase = torch.manual_seed(0)
_UpperCamelCase = pipe(num_inference_steps=20 , generator=lowercase_ , output_type="numpy").images
_UpperCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_UpperCamelCase = np.array([0.5_78, 0.58_11, 0.59_24, 0.58_09, 0.5_87, 0.58_86, 0.58_61, 0.58_02, 0.5_86])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
| 82 | 1 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( A__ ):
def __init__( self , _UpperCamelCase , _UpperCamelCase=768 ):
super().__init__(_UpperCamelCase )
_UpperCAmelCase = proj_size
_UpperCAmelCase = CLIPVisionModel(_UpperCamelCase )
_UpperCAmelCase = PaintByExampleMapper(_UpperCamelCase )
_UpperCAmelCase = nn.LayerNorm(config.hidden_size )
_UpperCAmelCase = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
_UpperCAmelCase = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def UpperCamelCase( self , _UpperCamelCase , _UpperCamelCase=False ):
_UpperCAmelCase = self.model(pixel_values=_UpperCamelCase )
_UpperCAmelCase = clip_output.pooler_output
_UpperCAmelCase = self.mapper(latent_states[:, None] )
_UpperCAmelCase = self.final_layer_norm(_UpperCamelCase )
_UpperCAmelCase = self.proj_out(_UpperCamelCase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class __UpperCamelCase ( nn.Module ):
def __init__( self , _UpperCamelCase ):
super().__init__()
_UpperCAmelCase = (config.num_hidden_layers + 1) // 5
_UpperCAmelCase = config.hidden_size
_UpperCAmelCase = 1
_UpperCAmelCase = nn.ModuleList(
[
BasicTransformerBlock(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , activation_fn='''gelu''' , attention_bias=_UpperCamelCase )
for _ in range(_UpperCamelCase )
] )
def UpperCamelCase( self , _UpperCamelCase ):
for block in self.blocks:
_UpperCAmelCase = block(_UpperCamelCase )
return hidden_states
| 32 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
a : List[Any] = 'docs/source/en/_toctree.yml'
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = defaultdict(__UpperCAmelCase )
for doc in model_doc:
counts[doc["local"]] += 1
snake_case_ = [key for key, value in counts.items() if value > 1]
snake_case_ = []
for duplicate_key in duplicates:
snake_case_ = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(__UpperCAmelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(__UpperCAmelCase, key=lambda __UpperCAmelCase : s["title"].lower() )
def __magic_name__ ( __UpperCAmelCase=False ) -> List[Any]:
'''simple docstring'''
with open(__UpperCAmelCase, encoding='''utf-8''' ) as f:
snake_case_ = yaml.safe_load(f.read() )
# Get to the API doc
snake_case_ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
snake_case_ = content[api_idx]['''sections''']
# Then to the model doc
snake_case_ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
snake_case_ = api_doc[model_idx]['''sections''']
snake_case_ = [(idx, section) for idx, section in enumerate(__UpperCAmelCase ) if '''sections''' in section]
snake_case_ = False
for idx, modality_doc in modalities_docs:
snake_case_ = modality_doc['''sections''']
snake_case_ = clean_model_doc_toc(__UpperCAmelCase )
if old_modality_doc != new_modality_doc:
snake_case_ = True
if overwrite:
snake_case_ = new_modality_doc
if diff:
if overwrite:
snake_case_ = model_doc
snake_case_ = api_doc
with open(__UpperCAmelCase, '''w''', encoding='''utf-8''' ) as f:
f.write(yaml.dump(__UpperCAmelCase, allow_unicode=__UpperCAmelCase ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
a : Dict = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
a : Union[str, Any] = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 640 | 0 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowerCamelCase__ : Optional[Any] = """scheduler_config.json"""
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : int = 1
__lowercase : List[Any] = 2
__lowercase : Dict = 3
__lowercase : str = 4
__lowercase : Dict = 5
@dataclass
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : jnp.ndarray
class __magic_name__ :
'''simple docstring'''
__lowercase : List[str] = SCHEDULER_CONFIG_NAME
__lowercase : Tuple = ['dtype']
__lowercase : str = []
__lowercase : Tuple = True
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls:List[Any] , _a:Dict[str, Any] = None , _a:Optional[str] = None , _a:str=False , **_a:List[Any] , ):
snake_case__ , snake_case__ = cls.load_config(
pretrained_model_name_or_path=_a , subfolder=_a , return_unused_kwargs=_a , **_a , )
snake_case__ , snake_case__ = cls.from_config(_a , return_unused_kwargs=_a , **_a )
if hasattr(_a , '''create_state''' ) and getattr(_a , '''has_state''' , _a ):
snake_case__ = scheduler.create_state()
if return_unused_kwargs:
return scheduler, state, unused_kwargs
return scheduler, state
def SCREAMING_SNAKE_CASE__ ( self:Tuple , _a:Union[str, os.PathLike] , _a:bool = False , **_a:Optional[int] ):
self.save_config(save_directory=_a , push_to_hub=_a , **_a )
@property
def SCREAMING_SNAKE_CASE__ ( self:str ):
return self._get_compatibles()
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls:Any ):
snake_case__ = list(set([cls.__name__] + cls._compatibles ) )
snake_case__ = importlib.import_module(__name__.split('''.''' )[0] )
snake_case__ = [
getattr(_a , _a ) for c in compatible_classes_str if hasattr(_a , _a )
]
return compatible_classes
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> jnp.ndarray:
assert len(__lowerCAmelCase ) >= x.ndim
return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__lowerCAmelCase ) - x.ndim) ) , __lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase=0.999 , __lowerCAmelCase=jnp.floataa ) -> jnp.ndarray:
def alpha_bar(__lowerCAmelCase ):
return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2
snake_case__ = []
for i in range(__lowerCAmelCase ):
snake_case__ = i / num_diffusion_timesteps
snake_case__ = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(__lowerCAmelCase ) / alpha_bar(__lowerCAmelCase ) , __lowerCAmelCase ) )
return jnp.array(__lowerCAmelCase , dtype=__lowerCAmelCase )
@flax.struct.dataclass
class __magic_name__ :
'''simple docstring'''
__lowercase : jnp.ndarray
__lowercase : jnp.ndarray
__lowercase : jnp.ndarray
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls:Optional[int] , _a:str ):
snake_case__ = scheduler.config
if config.trained_betas is not None:
snake_case__ = jnp.asarray(config.trained_betas , dtype=scheduler.dtype )
elif config.beta_schedule == "linear":
snake_case__ = jnp.linspace(config.beta_start , config.beta_end , config.num_train_timesteps , dtype=scheduler.dtype )
elif config.beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
snake_case__ = (
jnp.linspace(
config.beta_start**0.5 , config.beta_end**0.5 , config.num_train_timesteps , dtype=scheduler.dtype )
** 2
)
elif config.beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
snake_case__ = betas_for_alpha_bar(config.num_train_timesteps , dtype=scheduler.dtype )
else:
raise NotImplementedError(
F"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}""" )
snake_case__ = 1.0 - betas
snake_case__ = jnp.cumprod(_a , axis=0 )
return cls(
alphas=_a , betas=_a , alphas_cumprod=_a , )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
snake_case__ = state.alphas_cumprod
snake_case__ = alphas_cumprod[timesteps] ** 0.5
snake_case__ = sqrt_alpha_prod.flatten()
snake_case__ = broadcast_to_shape_from_left(__lowerCAmelCase , original_samples.shape )
snake_case__ = (1 - alphas_cumprod[timesteps]) ** 0.5
snake_case__ = sqrt_one_minus_alpha_prod.flatten()
snake_case__ = broadcast_to_shape_from_left(__lowerCAmelCase , original_samples.shape )
return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
snake_case__ , snake_case__ = get_sqrt_alpha_prod(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
snake_case__ = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
snake_case__ , snake_case__ = get_sqrt_alpha_prod(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
snake_case__ = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
return velocity
| 701 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = abs(__lowerCAmelCase )
snake_case__ = 0
while n > 0:
res += n % 10
n //= 10
return res
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = abs(__lowerCAmelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
return sum(int(__lowerCAmelCase ) for c in str(abs(__lowerCAmelCase ) ) )
def SCREAMING_SNAKE_CASE ( ) -> None:
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(__lowerCAmelCase , __lowerCAmelCase ) -> None:
snake_case__ = F"""{func.__name__}({value})"""
snake_case__ = timeit(F"""__main__.{call}""" , setup='''import __main__''' )
print(F"""{call:56} = {func(__lowerCAmelCase )} -- {timing:.4f} seconds""" )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(__lowerCAmelCase , __lowerCAmelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 208 | 0 |
def snake_case (UpperCAmelCase__ ) -> int:
if n == 1 or not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return 0
elif n == 2:
return 1
else:
UpperCamelCase_: Union[str, Any] = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def snake_case (UpperCAmelCase__ ) -> int:
UpperCamelCase_: List[Any] = 0
UpperCamelCase_: List[str] = 2
while digits < n:
index += 1
UpperCamelCase_: Union[str, Any] = len(str(fibonacci(UpperCAmelCase__ ) ) )
return index
def snake_case (UpperCAmelCase__ = 1_0_0_0 ) -> int:
return fibonacci_digits_index(UpperCAmelCase__ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 57 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. 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.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def __lowercase (_lowercase ) -> str:
"""simple docstring"""
__lowerCamelCase : Tuple = botoa.client("""iam""" )
__lowerCamelCase : Any = {
"""Version""": """2012-10-17""",
"""Statement""": [
{"""Effect""": """Allow""", """Principal""": {"""Service""": """sagemaker.amazonaws.com"""}, """Action""": """sts:AssumeRole"""}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=_lowercase, AssumeRolePolicyDocument=json.dumps(_lowercase, indent=2 ) )
__lowerCamelCase : List[Any] = {
"""Version""": """2012-10-17""",
"""Statement""": [
{
"""Effect""": """Allow""",
"""Action""": [
"""sagemaker:*""",
"""ecr:GetDownloadUrlForLayer""",
"""ecr:BatchGetImage""",
"""ecr:BatchCheckLayerAvailability""",
"""ecr:GetAuthorizationToken""",
"""cloudwatch:PutMetricData""",
"""cloudwatch:GetMetricData""",
"""cloudwatch:GetMetricStatistics""",
"""cloudwatch:ListMetrics""",
"""logs:CreateLogGroup""",
"""logs:CreateLogStream""",
"""logs:DescribeLogStreams""",
"""logs:PutLogEvents""",
"""logs:GetLogEvents""",
"""s3:CreateBucket""",
"""s3:ListBucket""",
"""s3:GetBucketLocation""",
"""s3:GetObject""",
"""s3:PutObject""",
],
"""Resource""": """*""",
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=_lowercase, PolicyName=f"{role_name}_policy_permission", PolicyDocument=json.dumps(_lowercase, indent=2 ), )
except iam_client.exceptions.EntityAlreadyExistsException:
print(f"role {role_name} already exists. Using existing one" )
def __lowercase (_lowercase ) -> Any:
"""simple docstring"""
__lowerCamelCase : List[Any] = botoa.client("""iam""" )
return iam_client.get_role(RoleName=_lowercase )["Role"]["Arn"]
def __lowercase () -> int:
"""simple docstring"""
__lowerCamelCase : Tuple = _ask_options(
"""How do you want to authorize?""", ["""AWS Profile""", """Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) """], _lowercase, )
__lowerCamelCase : List[str] = None
if credentials_configuration == 0:
__lowerCamelCase : Optional[Any] = _ask_field("""Enter your AWS Profile name: [default] """, default="""default""" )
__lowerCamelCase : Optional[Any] = aws_profile
else:
print(
"""Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"""
"""`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`""" )
__lowerCamelCase : List[Any] = _ask_field("""AWS Access Key ID: """ )
__lowerCamelCase : Union[str, Any] = aws_access_key_id
__lowerCamelCase : int = _ask_field("""AWS Secret Access Key: """ )
__lowerCamelCase : Optional[Any] = aws_secret_access_key
__lowerCamelCase : Tuple = _ask_field("""Enter your AWS Region: [us-east-1]""", default="""us-east-1""" )
__lowerCamelCase : Any = aws_region
__lowerCamelCase : int = _ask_options(
"""Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?""", ["""Provide IAM Role name""", """Create new IAM role using credentials"""], _lowercase, )
if role_management == 0:
__lowerCamelCase : Tuple = _ask_field("""Enter your IAM role name: """ )
else:
__lowerCamelCase : int = """accelerate_sagemaker_execution_role"""
print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials" )
_create_iam_role_for_sagemaker(_lowercase )
__lowerCamelCase : List[Any] = _ask_field(
"""Do you want to use custom Docker image? [yes/NO]: """, _convert_yes_no_to_bool, default=_lowercase, error_message="""Please enter yes or no.""", )
__lowerCamelCase : Union[str, Any] = None
if is_custom_docker_image:
__lowerCamelCase : Optional[int] = _ask_field("""Enter your Docker image: """, lambda _lowercase : str(_lowercase ).lower() )
__lowerCamelCase : str = _ask_field(
"""Do you want to provide SageMaker input channels with data locations? [yes/NO]: """, _convert_yes_no_to_bool, default=_lowercase, error_message="""Please enter yes or no.""", )
__lowerCamelCase : Any = None
if is_sagemaker_inputs_enabled:
__lowerCamelCase : Optional[Any] = _ask_field(
"""Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): """, lambda _lowercase : str(_lowercase ).lower(), )
__lowerCamelCase : Optional[Any] = _ask_field(
"""Do you want to enable SageMaker metrics? [yes/NO]: """, _convert_yes_no_to_bool, default=_lowercase, error_message="""Please enter yes or no.""", )
__lowerCamelCase : Union[str, Any] = None
if is_sagemaker_metrics_enabled:
__lowerCamelCase : Optional[int] = _ask_field(
"""Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): """, lambda _lowercase : str(_lowercase ).lower(), )
__lowerCamelCase : Any = _ask_options(
"""What is the distributed mode?""", ["""No distributed training""", """Data parallelism"""], _convert_sagemaker_distributed_mode, )
__lowerCamelCase : List[str] = {}
__lowerCamelCase : Union[str, Any] = _ask_field(
"""Do you wish to optimize your script with torch dynamo?[yes/NO]:""", _convert_yes_no_to_bool, default=_lowercase, error_message="""Please enter yes or no.""", )
if use_dynamo:
__lowerCamelCase : Union[str, Any] = """dynamo_"""
__lowerCamelCase : Optional[Any] = _ask_options(
"""Which dynamo backend would you like to use?""", [x.lower() for x in DYNAMO_BACKENDS], _convert_dynamo_backend, default=2, )
__lowerCamelCase : Optional[int] = _ask_field(
"""Do you want to customize the defaults sent to torch.compile? [yes/NO]: """, _convert_yes_no_to_bool, default=_lowercase, error_message="""Please enter yes or no.""", )
if use_custom_options:
__lowerCamelCase : Tuple = _ask_options(
"""Which mode do you want to use?""", _lowercase, lambda _lowercase : TORCH_DYNAMO_MODES[int(_lowercase )], default="""default""", )
__lowerCamelCase : Dict = _ask_field(
"""Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: """, _convert_yes_no_to_bool, default=_lowercase, error_message="""Please enter yes or no.""", )
__lowerCamelCase : int = _ask_field(
"""Do you want to enable dynamic shape tracing? [yes/NO]: """, _convert_yes_no_to_bool, default=_lowercase, error_message="""Please enter yes or no.""", )
__lowerCamelCase : Dict = """Which EC2 instance type you want to use for your training?"""
if distributed_type != SageMakerDistributedType.NO:
__lowerCamelCase : Any = _ask_options(
_lowercase, _lowercase, lambda _lowercase : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(_lowercase )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
__lowerCamelCase : List[Any] = _ask_field(_lowercase, lambda _lowercase : str(_lowercase ).lower(), default="""ml.p3.2xlarge""" )
__lowerCamelCase : int = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
__lowerCamelCase : Any = _ask_field(
"""How many machines do you want use? [1]: """, _lowercase, default=1, )
__lowerCamelCase : Union[str, Any] = _ask_options(
"""Do you wish to use FP16 or BF16 (mixed precision)?""", ["""no""", """fp16""", """bf16""", """fp8"""], _convert_mixed_precision, )
if use_dynamo and mixed_precision == "no":
print(
"""Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.""" )
return SageMakerConfig(
image_uri=_lowercase, compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER, distributed_type=_lowercase, use_cpu=_lowercase, dynamo_config=_lowercase, eca_instance_type=_lowercase, profile=_lowercase, region=_lowercase, iam_role_name=_lowercase, mixed_precision=_lowercase, num_machines=_lowercase, sagemaker_inputs_file=_lowercase, sagemaker_metrics_file=_lowercase, )
| 150 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowercase ( a , unittest.TestCase ):
lowercase__ : List[str] = RoFormerTokenizer
lowercase__ : Any = RoFormerTokenizerFast
lowercase__ : str = True
lowercase__ : List[str] = True
def __snake_case( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
super().setUp()
def __snake_case( self : List[Any] , **_UpperCamelCase : str ) -> Tuple:
'''simple docstring'''
return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **_UpperCamelCase )
def __snake_case( self : Union[str, Any] , **_UpperCamelCase : Any ) -> Tuple:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **_UpperCamelCase )
def __snake_case( self : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = "永和服装饰品有限公司,今天天气非常好"
SCREAMING_SNAKE_CASE = "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
return input_text, output_text
def __snake_case( self : str ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.get_chinese_input_output_texts()
SCREAMING_SNAKE_CASE = tokenizer.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , output_text.split() )
SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE = [22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , _UpperCamelCase )
def __snake_case( self : Dict ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = self.get_chinese_input_output_texts()
SCREAMING_SNAKE_CASE = tokenizer.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , output_text.split() )
SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE = [22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase ) , _UpperCamelCase )
def __snake_case( self : Optional[int] ) -> Any:
'''simple docstring'''
pass
def __snake_case( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
pass
def __snake_case( self : Tuple ) -> Optional[int]:
'''simple docstring'''
pass
| 647 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCamelCase : Optional[int] = logging.getLogger(__name__)
_lowerCamelCase : Optional[int] = '''Hello world! cécé herlolip'''
_lowerCamelCase : List[Any] = namedtuple(
'''BertAbsConfig''',
[
'''temp_dir''',
'''large''',
'''use_bert_emb''',
'''finetune_bert''',
'''encoder''',
'''share_emb''',
'''max_pos''',
'''enc_layers''',
'''enc_hidden_size''',
'''enc_heads''',
'''enc_ff_size''',
'''enc_dropout''',
'''dec_layers''',
'''dec_hidden_size''',
'''dec_heads''',
'''dec_ff_size''',
'''dec_dropout''',
],
)
def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[int] ):
SCREAMING_SNAKE_CASE = BertAbsConfig(
temp_dir="." , finetune_bert=UpperCAmelCase__ , large=UpperCAmelCase__ , share_emb=UpperCAmelCase__ , use_bert_emb=UpperCAmelCase__ , encoder="bert" , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
SCREAMING_SNAKE_CASE = torch.load(UpperCAmelCase__ , lambda UpperCAmelCase__ , UpperCAmelCase__ : storage )
SCREAMING_SNAKE_CASE = AbsSummarizer(UpperCAmelCase__ , torch.device("cpu" ) , UpperCAmelCase__ )
original.eval()
SCREAMING_SNAKE_CASE = BertAbsSummarizer(UpperCAmelCase__ , torch.device("cpu" ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info("convert the model" )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info("Make sure that the models' outputs are identical" )
SCREAMING_SNAKE_CASE = BertTokenizer.from_pretrained("bert-base-uncased" )
# prepare the model inputs
SCREAMING_SNAKE_CASE = tokenizer.encode("This is sample éàalj'-." )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__ )) )
SCREAMING_SNAKE_CASE = torch.tensor(UpperCAmelCase__ ).unsqueeze(0 )
SCREAMING_SNAKE_CASE = tokenizer.encode("This is sample 3 éàalj'-." )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase__ )) )
SCREAMING_SNAKE_CASE = torch.tensor(UpperCAmelCase__ ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
SCREAMING_SNAKE_CASE = encoder_input_ids
SCREAMING_SNAKE_CASE = decoder_input_ids
SCREAMING_SNAKE_CASE = SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
SCREAMING_SNAKE_CASE = original(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )[0]
SCREAMING_SNAKE_CASE = original.generator(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE = new_model(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )[0]
SCREAMING_SNAKE_CASE = new_model.generator(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print("Maximum absolute difference beween weights: {:.2f}".format(UpperCAmelCase__ ) )
SCREAMING_SNAKE_CASE = torch.allclose(UpperCAmelCase__ , UpperCAmelCase__ , atol=1e-3 )
if are_identical:
logging.info("all weights are equal up to 1e-3" )
else:
raise ValueError("the weights are different. The new model is likely different from the original one." )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info("saving the model's state dictionary" )
torch.save(
new_model.state_dict() , "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin" )
if __name__ == "__main__":
_lowerCamelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
'''--bertabs_checkpoint_path''',
default=None,
type=str,
required=True,
help='''Path the official PyTorch dump.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the output PyTorch model.''',
)
_lowerCamelCase : Any = parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 647 | 1 |
'''simple docstring'''
def _a (lowercase__ : str ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowercase__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 56 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Dict = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class _lowercase ( __lowercase ):
_SCREAMING_SNAKE_CASE : int = "timesformer"
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : List[str]=224 , SCREAMING_SNAKE_CASE_ : List[str]=16 , SCREAMING_SNAKE_CASE_ : Any=3 , SCREAMING_SNAKE_CASE_ : int=8 , SCREAMING_SNAKE_CASE_ : Tuple=768 , SCREAMING_SNAKE_CASE_ : int=12 , SCREAMING_SNAKE_CASE_ : Optional[int]=12 , SCREAMING_SNAKE_CASE_ : Optional[int]=3072 , SCREAMING_SNAKE_CASE_ : Tuple="gelu" , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.0 , SCREAMING_SNAKE_CASE_ : List[Any]=0.0 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.0_2 , SCREAMING_SNAKE_CASE_ : Any=1e-6 , SCREAMING_SNAKE_CASE_ : str=True , SCREAMING_SNAKE_CASE_ : List[str]="divided_space_time" , SCREAMING_SNAKE_CASE_ : int=0 , **SCREAMING_SNAKE_CASE_ : Optional[int] , ) -> List[str]:
super().__init__(**SCREAMING_SNAKE_CASE_ )
__snake_case = image_size
__snake_case = patch_size
__snake_case = num_channels
__snake_case = num_frames
__snake_case = hidden_size
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = hidden_act
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = initializer_range
__snake_case = layer_norm_eps
__snake_case = qkv_bias
__snake_case = attention_type
__snake_case = drop_path_rate
| 56 | 1 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
A__ : Optional[int]= argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument(
"""--original_config_file""",
type=str,
required=True,
help="""The YAML config file corresponding to the original architecture.""",
)
parser.add_argument(
"""--num_in_channels""",
default=None,
type=int,
help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
)
parser.add_argument(
"""--image_size""",
default=5_12,
type=int,
help=(
"""The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
""" Base. Use 768 for Stable Diffusion v2."""
),
)
parser.add_argument(
"""--extract_ema""",
action="""store_true""",
help=(
"""Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
""" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
""" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
),
)
parser.add_argument(
"""--upcast_attention""",
action="""store_true""",
help=(
"""Whether the attention computation should always be upcasted. This is necessary when running stable"""
""" diffusion 2.1."""
),
)
parser.add_argument(
"""--from_safetensors""",
action="""store_true""",
help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
)
parser.add_argument(
"""--to_safetensors""",
action="""store_true""",
help="""Whether to store pipeline in safetensors format or not.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F'could not parse string as bool {string}' )
parser.add_argument(
"""--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
)
parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)
A__ : Tuple= parser.parse_args()
A__ : Optional[Any]= download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 20 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
UpperCamelCase__ = SwinConfig()
UpperCamelCase__ = swin_name.split('_' )
UpperCamelCase__ = name_split[1]
UpperCamelCase__ = int(name_split[4] )
UpperCamelCase__ = int(name_split[3][-1] )
if model_size == "tiny":
UpperCamelCase__ = 96
UpperCamelCase__ = (2, 2, 6, 2)
UpperCamelCase__ = (3, 6, 12, 24)
elif model_size == "small":
UpperCamelCase__ = 96
UpperCamelCase__ = (2, 2, 18, 2)
UpperCamelCase__ = (3, 6, 12, 24)
elif model_size == "base":
UpperCamelCase__ = 1_28
UpperCamelCase__ = (2, 2, 18, 2)
UpperCamelCase__ = (4, 8, 16, 32)
else:
UpperCamelCase__ = 1_92
UpperCamelCase__ = (2, 2, 18, 2)
UpperCamelCase__ = (6, 12, 24, 48)
if "in22k" in swin_name:
UpperCamelCase__ = 2_18_41
else:
UpperCamelCase__ = 10_00
UpperCamelCase__ = 'huggingface/label-files'
UpperCamelCase__ = 'imagenet-1k-id2label.json'
UpperCamelCase__ = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
UpperCamelCase__ = {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
UpperCamelCase__ = idalabel
UpperCamelCase__ = {v: k for k, v in idalabel.items()}
UpperCamelCase__ = img_size
UpperCamelCase__ = num_classes
UpperCamelCase__ = embed_dim
UpperCamelCase__ = depths
UpperCamelCase__ = num_heads
UpperCamelCase__ = window_size
return config
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if "patch_embed.proj" in name:
UpperCamelCase__ = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
UpperCamelCase__ = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
UpperCamelCase__ = 'encoder.' + name
if "attn.proj" in name:
UpperCamelCase__ = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
UpperCamelCase__ = name.replace('attn' , 'attention.self' )
if "norm1" in name:
UpperCamelCase__ = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
UpperCamelCase__ = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
UpperCamelCase__ = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
UpperCamelCase__ = name.replace('mlp.fc2' , 'output.dense' )
if name == "norm.weight":
UpperCamelCase__ = 'layernorm.weight'
if name == "norm.bias":
UpperCamelCase__ = 'layernorm.bias'
if "head" in name:
UpperCamelCase__ = name.replace('head' , 'classifier' )
else:
UpperCamelCase__ = 'swin.' + name
return name
def lowerCAmelCase_( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
UpperCamelCase__ = orig_state_dict.pop(SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
UpperCamelCase__ = key.split('.' )
UpperCamelCase__ = int(key_split[1] )
UpperCamelCase__ = int(key_split[3] )
UpperCamelCase__ = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
UpperCamelCase__ = val[:dim, :]
UpperCamelCase__ = val[
dim : dim * 2, :
]
UpperCamelCase__ = val[-dim:, :]
else:
UpperCamelCase__ = val[
:dim
]
UpperCamelCase__ = val[
dim : dim * 2
]
UpperCamelCase__ = val[
-dim:
]
else:
UpperCamelCase__ = val
return orig_state_dict
def lowerCAmelCase_( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase__ = timm.create_model(SCREAMING_SNAKE_CASE , pretrained=SCREAMING_SNAKE_CASE )
timm_model.eval()
UpperCamelCase__ = get_swin_config(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = SwinForImageClassification(SCREAMING_SNAKE_CASE )
model.eval()
UpperCamelCase__ = convert_state_dict(timm_model.state_dict() , SCREAMING_SNAKE_CASE )
model.load_state_dict(SCREAMING_SNAKE_CASE )
UpperCamelCase__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
UpperCamelCase__ = AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' , '-' ) ) )
UpperCamelCase__ = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw )
UpperCamelCase__ = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors='pt' )
UpperCamelCase__ = timm_model(inputs['pixel_values'] )
UpperCamelCase__ = model(**SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F'Saving model {swin_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
A__ : Optional[Any]= argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swin_name""",
default="""swin_tiny_patch4_window7_224""",
type=str,
help="""Name of the Swin 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."""
)
A__ : Tuple= parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 20 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : int = {
"configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Dict = ["AlbertTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = ["AlbertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
"ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"AlbertForMaskedLM",
"AlbertForMultipleChoice",
"AlbertForPreTraining",
"AlbertForQuestionAnswering",
"AlbertForSequenceClassification",
"AlbertForTokenClassification",
"AlbertModel",
"AlbertPreTrainedModel",
"load_tf_weights_in_albert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Dict = [
"TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFAlbertForMaskedLM",
"TFAlbertForMultipleChoice",
"TFAlbertForPreTraining",
"TFAlbertForQuestionAnswering",
"TFAlbertForSequenceClassification",
"TFAlbertForTokenClassification",
"TFAlbertMainLayer",
"TFAlbertModel",
"TFAlbertPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[Any] = [
"FlaxAlbertForMaskedLM",
"FlaxAlbertForMultipleChoice",
"FlaxAlbertForPreTraining",
"FlaxAlbertForQuestionAnswering",
"FlaxAlbertForSequenceClassification",
"FlaxAlbertForTokenClassification",
"FlaxAlbertModel",
"FlaxAlbertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 85 |
import unittest
from knapsack import knapsack as k
class UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def __a ( self ) -> Any:
"""simple docstring"""
lowercase__ : Optional[int] = 0
lowercase__ : str = [0]
lowercase__ : List[Any] = [0]
lowercase__ : Optional[Any] = len(lowerCamelCase )
self.assertEqual(k.knapsack(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) , 0 )
lowercase__ : List[Any] = [60]
lowercase__ : Optional[Any] = [10]
lowercase__ : Optional[Any] = len(lowerCamelCase )
self.assertEqual(k.knapsack(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) , 0 )
def __a ( self ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = 3
lowercase__ : str = [1, 2, 3]
lowercase__ : int = [3, 2, 1]
lowercase__ : List[str] = len(lowerCamelCase )
self.assertEqual(k.knapsack(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) , 5 )
def __a ( self ) -> str:
"""simple docstring"""
lowercase__ : Any = 50
lowercase__ : Optional[Any] = [60, 100, 120]
lowercase__ : Optional[int] = [10, 20, 30]
lowercase__ : List[str] = len(lowerCamelCase )
self.assertEqual(k.knapsack(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) , 220 )
if __name__ == "__main__":
unittest.main()
| 397 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowercase ( __snake_case : str = "" ):
lowercase_ : List[Any] = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
lowercase_ : List[Any] = BeautifulSoup(requests.get(__snake_case ).text , '''html.parser''' )
lowercase_ : Optional[Any] = soup.find_all('''td''' , attrs='''titleColumn''' )
lowercase_ : str = soup.find_all('''td''' , class_='''ratingColumn imdbRating''' )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(__snake_case , __snake_case )
}
def lowercase ( __snake_case : str = "IMDb_Top_250_Movies.csv" ):
lowercase_ : Dict = get_imdb_top_aaa_movies()
with open(__snake_case , '''w''' , newline='''''' ) as out_file:
lowercase_ : List[Any] = csv.writer(__snake_case )
writer.writerow(['''Movie title''', '''IMDb rating'''] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 700 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowercase ( ):
lowercase_ : Union[str, Any] = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 2_0, '''a ''' * 3_0, '''b ''' * 7],
}
lowercase_ : Any = Dataset.from_dict(__snake_case )
return dataset
class _UpperCAmelCase ( _A ):
def A ( self : str ) -> str:
lowercase_ : Tuple = get_dataset()
lowercase_ : Any = make_duplicate_clusters(A , 0.85 )
self.assertEqual(len(duplicate_clusters[0] ) , 2 )
def A ( self : List[str] ) -> Union[str, Any]:
lowercase_ : Any = get_dataset()
lowercase_ , lowercase_ : str = deduplicate_dataset(A )
self.assertEqual(len(A ) , 2 )
print(A )
self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , A )
| 141 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class A_ ( _a ):
lowerCAmelCase__ = 'vivit'
def __init__( self: List[Any] ,__lowerCAmelCase: int=224 ,__lowerCAmelCase: Any=32 ,__lowerCAmelCase: str=[2, 16, 16] ,__lowerCAmelCase: Optional[Any]=3 ,__lowerCAmelCase: List[str]=768 ,__lowerCAmelCase: List[Any]=12 ,__lowerCAmelCase: Optional[int]=12 ,__lowerCAmelCase: Optional[Any]=3_072 ,__lowerCAmelCase: Any="gelu_fast" ,__lowerCAmelCase: Tuple=0.0 ,__lowerCAmelCase: Any=0.0 ,__lowerCAmelCase: Union[str, Any]=0.02 ,__lowerCAmelCase: List[str]=1e-06 ,__lowerCAmelCase: Optional[Any]=True ,**__lowerCAmelCase: Optional[int] ,):
'''simple docstring'''
_lowerCamelCase : Any = hidden_size
_lowerCamelCase : List[str] = num_hidden_layers
_lowerCamelCase : Union[str, Any] = num_attention_heads
_lowerCamelCase : Any = intermediate_size
_lowerCamelCase : Tuple = hidden_act
_lowerCamelCase : Tuple = hidden_dropout_prob
_lowerCamelCase : Optional[Any] = attention_probs_dropout_prob
_lowerCamelCase : List[Any] = initializer_range
_lowerCamelCase : int = layer_norm_eps
_lowerCamelCase : Tuple = image_size
_lowerCamelCase : Dict = num_frames
_lowerCamelCase : Optional[int] = tubelet_size
_lowerCamelCase : int = num_channels
_lowerCamelCase : List[str] = qkv_bias
super().__init__(**__lowerCAmelCase )
| 46 |
SCREAMING_SNAKE_CASE__ : Tuple = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""k""": """ABAAB""",
"""l""": """ABABA""",
"""m""": """ABABB""",
"""n""": """ABBAA""",
"""o""": """ABBAB""",
"""p""": """ABBBA""",
"""q""": """ABBBB""",
"""r""": """BAAAA""",
"""s""": """BAAAB""",
"""t""": """BAABA""",
"""u""": """BAABB""",
"""v""": """BBBAB""",
"""w""": """BABAA""",
"""x""": """BABAB""",
"""y""": """BABBA""",
"""z""": """BABBB""",
""" """: """ """,
}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {value: key for key, value in encode_dict.items()}
def __lowercase ( snake_case ):
"""simple docstring"""
__magic_name__ :Tuple = ''''''
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception('''encode() accepts only letters of the alphabet and spaces''' )
return encoded
def __lowercase ( snake_case ):
"""simple docstring"""
if set(snake_case ) - {"A", "B", " "} != set():
raise Exception('''decode() accepts only \'A\', \'B\' and spaces''' )
__magic_name__ :Dict = ''''''
for word in coded.split():
while len(snake_case ) != 0:
decoded += decode_dict[word[:5]]
__magic_name__ :int = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 0 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__A : Any = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
__A : Any = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
__A : Tuple = re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__A : Optional[Any] = re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A : Optional[int] = re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__A : str = [
("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
(
"""zero-shot-object-detection""",
"""MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
"""AutoModelForZeroShotObjectDetection""",
),
("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
(
"""table-question-answering""",
"""MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
"""AutoModelForTableQuestionAnswering""",
),
("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
(
"""next-sentence-prediction""",
"""MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
"""AutoModelForNextSentencePrediction""",
),
(
"""audio-frame-classification""",
"""MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
"""AutoModelForAudioFrameClassification""",
),
("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
(
"""document-question-answering""",
"""MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
"""AutoModelForDocumentQuestionAnswering""",
),
(
"""visual-question-answering""",
"""MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
"""AutoModelForVisualQuestionAnswering""",
),
("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
(
"""zero-shot-image-classification""",
"""MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
"""AutoModelForZeroShotImageClassification""",
),
("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , __lowercase )
return [m.group(0 ) for m in matches]
def lowerCamelCase_ ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
SCREAMING_SNAKE_CASE = collections.defaultdict(__lowercase )
SCREAMING_SNAKE_CASE = collections.defaultdict(__lowercase )
SCREAMING_SNAKE_CASE = collections.defaultdict(__lowercase )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(__lowercase ):
SCREAMING_SNAKE_CASE = None
if _re_tf_models.match(__lowercase ) is not None:
SCREAMING_SNAKE_CASE = tf_models
SCREAMING_SNAKE_CASE = _re_tf_models.match(__lowercase ).groups()[0]
elif _re_flax_models.match(__lowercase ) is not None:
SCREAMING_SNAKE_CASE = flax_models
SCREAMING_SNAKE_CASE = _re_flax_models.match(__lowercase ).groups()[0]
elif _re_pt_models.match(__lowercase ) is not None:
SCREAMING_SNAKE_CASE = pt_models
SCREAMING_SNAKE_CASE = _re_pt_models.match(__lowercase ).groups()[0]
if lookup_dict is not None:
while len(__lowercase ) > 0:
if attr_name in model_prefix_to_model_type:
SCREAMING_SNAKE_CASE = True
break
# Try again after removing the last word in the name
SCREAMING_SNAKE_CASE = ''.join(camel_case_split(__lowercase )[:-1] )
SCREAMING_SNAKE_CASE = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
SCREAMING_SNAKE_CASE = list(__lowercase )
all_models.sort()
SCREAMING_SNAKE_CASE = {'model_type': all_models}
SCREAMING_SNAKE_CASE = [pt_models[t] for t in all_models]
SCREAMING_SNAKE_CASE = [tf_models[t] for t in all_models]
SCREAMING_SNAKE_CASE = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
SCREAMING_SNAKE_CASE = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
SCREAMING_SNAKE_CASE = 'AutoProcessor'
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
SCREAMING_SNAKE_CASE = 'AutoTokenizer'
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
SCREAMING_SNAKE_CASE = 'AutoFeatureExtractor'
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
SCREAMING_SNAKE_CASE = 'AutoTokenizer'
SCREAMING_SNAKE_CASE = [processors[t] for t in all_models]
return pd.DataFrame(__lowercase )
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
SCREAMING_SNAKE_CASE = [model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
SCREAMING_SNAKE_CASE = [auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
# Loop through all three frameworks
for module, cls, mapping in zip(__lowercase , __lowercase , __lowercase ):
# The type of pipeline may not exist in this framework
if not hasattr(__lowercase , __lowercase ):
continue
# First extract all model_names
SCREAMING_SNAKE_CASE = []
for name in getattr(__lowercase , __lowercase ).values():
if isinstance(__lowercase , __lowercase ):
model_names.append(__lowercase )
else:
model_names.extend(list(__lowercase ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_frameworks_table()
SCREAMING_SNAKE_CASE = Dataset.from_pandas(__lowercase )
SCREAMING_SNAKE_CASE = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=__lowercase )
SCREAMING_SNAKE_CASE = Dataset.from_json(__lowercase )
SCREAMING_SNAKE_CASE = {
tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
for i in range(len(__lowercase ) )
}
SCREAMING_SNAKE_CASE = update_pipeline_and_auto_class_table(__lowercase )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
SCREAMING_SNAKE_CASE = sorted(table.keys() )
SCREAMING_SNAKE_CASE = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
SCREAMING_SNAKE_CASE = Dataset.from_pandas(__lowercase )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(__lowercase , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(__lowercase , """pipeline_tags.json""" ) )
if commit_sha is not None:
SCREAMING_SNAKE_CASE = (
f"""Update with commit {commit_sha}\n\nSee: """
f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
)
else:
SCREAMING_SNAKE_CASE = 'Update'
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=__lowercase , repo_type="""dataset""" , token=__lowercase , commit_message=__lowercase , )
def lowerCamelCase_ ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
SCREAMING_SNAKE_CASE = transformers_module.pipelines.SUPPORTED_TASKS
SCREAMING_SNAKE_CASE = []
for key in pipeline_tasks:
if key not in in_table:
SCREAMING_SNAKE_CASE = pipeline_tasks[key]['pt']
if isinstance(__lowercase , (list, tuple) ):
SCREAMING_SNAKE_CASE = model[0]
SCREAMING_SNAKE_CASE = model.__name__
if model not in in_table.values():
missing.append(__lowercase )
if len(__lowercase ) > 0:
SCREAMING_SNAKE_CASE = ', '.join(__lowercase )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
f"""`utils/update_metadata.py`: {msg}. Please add them!""" )
if __name__ == "__main__":
__A : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
__A : List[str] = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 704 |
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_mbart import MBartTokenizer
else:
__A : Tuple = None
__A : Any = logging.get_logger(__name__)
__A : Union[str, Any] = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
__A : List[Any] = {
"""vocab_file""": {
"""facebook/mbart-large-en-ro""": (
"""https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
),
"""facebook/mbart-large-cc25""": (
"""https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
"""facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
},
}
__A : int = {
"""facebook/mbart-large-en-ro""": 1_0_2_4,
"""facebook/mbart-large-cc25""": 1_0_2_4,
}
# fmt: off
__A : List[Any] = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]
class UpperCAmelCase_ ( A ):
'''simple docstring'''
a__ = VOCAB_FILES_NAMES
a__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ = PRETRAINED_VOCAB_FILES_MAP
a__ = ['''input_ids''', '''attention_mask''']
a__ = MBartTokenizer
a__ = []
a__ = []
def __init__( self : str , a : List[Any]=None , a : Optional[int]=None , a : str="<s>" , a : Tuple="</s>" , a : Optional[int]="</s>" , a : int="<s>" , a : List[str]="<unk>" , a : Tuple="<pad>" , a : Dict="<mask>" , a : Optional[Any]=None , a : List[Any]=None , a : Any=None , **a : Optional[Any] , ) -> Any:
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else mask_token
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 , **a , )
SCREAMING_SNAKE_CASE = vocab_file
SCREAMING_SNAKE_CASE = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE = 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 = {
lang_code: self.convert_tokens_to_ids(a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE = src_lang if src_lang is not None else """en_XX"""
SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _UpperCAmelCase ( self : int ) -> str:
return self._src_lang
@src_lang.setter
def _UpperCAmelCase ( self : List[Any] , a : str ) -> None:
SCREAMING_SNAKE_CASE = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _UpperCAmelCase ( self : Dict , a : List[int] , a : Optional[List[int]] = None ) -> List[int]:
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 _UpperCAmelCase ( self : Tuple , a : List[int] , a : Optional[List[int]] = None ) -> List[int]:
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [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 _UpperCAmelCase ( self : Optional[Any] , a : Optional[int] , a : str , a : Optional[str] , a : Optional[str] , **a : Optional[Any] ) -> Optional[Any]:
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 = src_lang
SCREAMING_SNAKE_CASE = self(a , add_special_tokens=a , return_tensors=a , **a )
SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(a )
SCREAMING_SNAKE_CASE = tgt_lang_id
return inputs
def _UpperCAmelCase ( self : str , a : List[str] , a : str = "en_XX" , a : Optional[List[str]] = None , a : str = "ro_RO" , **a : List[str] , ) -> BatchEncoding:
SCREAMING_SNAKE_CASE = src_lang
SCREAMING_SNAKE_CASE = tgt_lang
return super().prepare_seqaseq_batch(a , a , **a )
def _UpperCAmelCase ( self : Optional[Any] ) -> str:
return self.set_src_lang_special_tokens(self.src_lang )
def _UpperCAmelCase ( self : str ) -> List[str]:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _UpperCAmelCase ( self : Dict , a : List[str] ) -> None:
SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(a )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = [self.eos_token_id, self.cur_lang_code]
SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE = 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 _UpperCAmelCase ( self : Dict , a : str ) -> None:
SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(a )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = [self.eos_token_id, self.cur_lang_code]
SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE = 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 _UpperCAmelCase ( self : Dict , a : str , a : Optional[str] = None ) -> Tuple[str]:
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 = 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,)
| 450 | 0 |
def _a ( __lowercase ) -> list:
"""simple docstring"""
if len(__lowercase ) <= 1:
return [tuple(__lowercase )]
__UpperCamelCase = []
def generate(__lowercase , __lowercase ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , __lowercase )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
__UpperCamelCase , __UpperCamelCase = arr[k - 1], arr[i]
else: # k is odd
__UpperCamelCase , __UpperCamelCase = arr[k - 1], arr[0]
generate(k - 1 , __lowercase )
generate(len(__lowercase ) , __lowercase )
return res
if __name__ == "__main__":
_snake_case = input('Enter numbers separated by a comma:\n').strip()
_snake_case = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 383 |
from __future__ import annotations
from collections import Counter
from random import random
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self ) -> List[str]:
__UpperCamelCase = {}
def __lowercase( self , _SCREAMING_SNAKE_CASE ) -> None:
__UpperCamelCase = {}
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None:
if nodea not in self.connections:
self.add_node(_SCREAMING_SNAKE_CASE )
if nodea not in self.connections:
self.add_node(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = probability
def __lowercase( self ) -> list[str]:
return list(self.connections )
def __lowercase( self , _SCREAMING_SNAKE_CASE ) -> str:
__UpperCamelCase = 0
__UpperCamelCase = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def _a ( __lowercase , __lowercase , __lowercase ) -> dict[str, int]:
"""simple docstring"""
__UpperCamelCase = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(__lowercase , __lowercase , __lowercase )
__UpperCamelCase = Counter(graph.get_nodes() )
__UpperCamelCase = start
for _ in range(__lowercase ):
__UpperCamelCase = graph.transition(__lowercase )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 383 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowercase__ : List[Any] = logging.get_logger(__name__)
def __lowercase ( _a , _a , _a ):
snake_case_ : List[str] = WavaVecaForSequenceClassification.from_pretrained(_a , config=_a )
snake_case_ : List[str] = downstream_dict['''projector.weight''']
snake_case_ : int = downstream_dict['''projector.bias''']
snake_case_ : Union[str, Any] = downstream_dict['''model.post_net.linear.weight''']
snake_case_ : int = downstream_dict['''model.post_net.linear.bias''']
return model
def __lowercase ( _a , _a , _a ):
snake_case_ : Any = WavaVecaForAudioFrameClassification.from_pretrained(_a , config=_a )
snake_case_ : Optional[int] = downstream_dict['''model.linear.weight''']
snake_case_ : Dict = downstream_dict['''model.linear.bias''']
return model
def __lowercase ( _a , _a , _a ):
snake_case_ : Optional[Any] = WavaVecaForXVector.from_pretrained(_a , config=_a )
snake_case_ : List[Any] = downstream_dict['''connector.weight''']
snake_case_ : Tuple = downstream_dict['''connector.bias''']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
snake_case_ : List[Any] = downstream_dict[
f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
]
snake_case_ : Dict = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
snake_case_ : Tuple = downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
snake_case_ : List[str] = downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
snake_case_ : Tuple = downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
snake_case_ : Optional[int] = downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
snake_case_ : int = downstream_dict['''objective.W''']
return model
@torch.no_grad()
def __lowercase ( _a , _a , _a , _a ):
snake_case_ : Any = torch.load(_a , map_location='''cpu''' )
snake_case_ : Tuple = checkpoint['''Downstream''']
snake_case_ : int = WavaVecaConfig.from_pretrained(_a )
snake_case_ : List[str] = WavaVecaFeatureExtractor.from_pretrained(
_a , return_attention_mask=_a , do_normalize=_a )
snake_case_ : Tuple = hf_config.architectures[0]
if arch.endswith('''ForSequenceClassification''' ):
snake_case_ : Tuple = convert_classification(_a , _a , _a )
elif arch.endswith('''ForAudioFrameClassification''' ):
snake_case_ : Optional[int] = convert_diarization(_a , _a , _a )
elif arch.endswith('''ForXVector''' ):
snake_case_ : Union[str, Any] = convert_xvector(_a , _a , _a )
else:
raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}" )
if hf_config.use_weighted_layer_sum:
snake_case_ : Optional[int] = checkpoint['''Featurizer''']['''weights''']
hf_feature_extractor.save_pretrained(_a )
hf_model.save_pretrained(_a )
if __name__ == "__main__":
lowercase__ : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
lowercase__ : Optional[int] = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 708 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __lowercase ( _a ):
return np.dot(_a , _a )
class _UpperCAmelCase :
def __init__( self : int , *,
lowercase_ : float = np.inf , lowercase_ : str = "linear" , lowercase_ : float = 0.0 , ):
snake_case_ : Optional[Any] = regularization
snake_case_ : Tuple = gamma
if kernel == "linear":
snake_case_ : int = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('''rbf kernel requires gamma''' )
if not isinstance(self.gamma , (float, int) ):
raise ValueError('''gamma must be float or int''' )
if not self.gamma > 0:
raise ValueError('''gamma must be > 0''' )
snake_case_ : int = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
snake_case_ : List[Any] = f"Unknown kernel: {kernel}"
raise ValueError(lowercase_ )
def _snake_case ( self : int , lowercase_ : ndarray , lowercase_ : ndarray ):
return np.dot(lowercase_ , lowercase_ )
def _snake_case ( self : int , lowercase_ : ndarray , lowercase_ : ndarray ):
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def _snake_case ( self : Any , lowercase_ : list[ndarray] , lowercase_ : ndarray ):
snake_case_ : Union[str, Any] = observations
snake_case_ : int = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((snake_case_), ) : List[str] = np.shape(lowercase_ )
def to_minimize(lowercase_ : ndarray ) -> float:
snake_case_ : Tuple = 0
((snake_case_), ) : Optional[Any] = np.shape(lowercase_ )
for i in range(lowercase_ ):
for j in range(lowercase_ ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(lowercase_ )
snake_case_ : Optional[Any] = LinearConstraint(lowercase_ , 0 , 0 )
snake_case_ : str = Bounds(0 , self.regularization )
snake_case_ : int = minimize(
lowercase_ , np.ones(lowercase_ ) , bounds=lowercase_ , constraints=[ly_contraint] ).x
snake_case_ : Optional[Any] = l_star
# calculating mean offset of separation plane to points
snake_case_ : List[Any] = 0
for i in range(lowercase_ ):
for j in range(lowercase_ ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
snake_case_ : Union[str, Any] = s / n
def _snake_case ( self : List[str] , lowercase_ : ndarray ):
snake_case_ : int = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , lowercase_ )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 485 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__snake_case = (7_2_0, 1_2_8_0) # Height, Width
__snake_case = (0.4, 0.6) # if height or width lower than this scale, drop it.
__snake_case = 1 / 1_0_0
__snake_case = ''''''
__snake_case = ''''''
__snake_case = ''''''
__snake_case = 2_5_0
def _A ( ) -> None:
"""simple docstring"""
__UpperCamelCase, __UpperCamelCase = get_dataset(_lowercase , _lowercase )
for index in range(_lowercase ):
__UpperCamelCase = random.sample(range(len(_lowercase ) ) , 4 )
__UpperCamelCase, __UpperCamelCase, __UpperCamelCase = update_image_and_anno(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , filter_scale=_lowercase , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
__UpperCamelCase = random_chars(32 )
__UpperCamelCase = path.split(os.sep )[-1].rsplit('.' , 1 )[0]
__UpperCamelCase = f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
cva.imwrite(f'''{file_root}.jpg''' , _lowercase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}''' )
__UpperCamelCase = []
for anno in new_annos:
__UpperCamelCase = anno[3] - anno[1]
__UpperCamelCase = anno[4] - anno[2]
__UpperCamelCase = anno[1] + width / 2
__UpperCamelCase = anno[2] + height / 2
__UpperCamelCase = f'''{anno[0]} {x_center} {y_center} {width} {height}'''
annos_list.append(_lowercase )
with open(f'''{file_root}.txt''' , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def _A ( _lowercase , _lowercase ) -> tuple[list, list]:
"""simple docstring"""
__UpperCamelCase = []
__UpperCamelCase = []
for label_file in glob.glob(os.path.join(_lowercase , '*.txt' ) ):
__UpperCamelCase = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(_lowercase ) as in_file:
__UpperCamelCase = in_file.readlines()
__UpperCamelCase = os.path.join(_lowercase , f'''{label_name}.jpg''' )
__UpperCamelCase = []
for obj_list in obj_lists:
__UpperCamelCase = obj_list.rstrip('\n' ).split(' ' )
__UpperCamelCase = float(obj[1] ) - float(obj[3] ) / 2
__UpperCamelCase = float(obj[2] ) - float(obj[4] ) / 2
__UpperCamelCase = float(obj[1] ) + float(obj[3] ) / 2
__UpperCamelCase = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(_lowercase )
labels.append(_lowercase )
return img_paths, labels
def _A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase = 0.0 , ) -> tuple[list, list, str]:
"""simple docstring"""
__UpperCamelCase = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
__UpperCamelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
__UpperCamelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
__UpperCamelCase = int(scale_x * output_size[1] )
__UpperCamelCase = int(scale_y * output_size[0] )
__UpperCamelCase = []
__UpperCamelCase = []
for i, index in enumerate(_lowercase ):
__UpperCamelCase = all_img_list[index]
path_list.append(_lowercase )
__UpperCamelCase = all_annos[index]
__UpperCamelCase = cva.imread(_lowercase )
if i == 0: # top-left
__UpperCamelCase = cva.resize(_lowercase , (divid_point_x, divid_point_y) )
__UpperCamelCase = img
for bbox in img_annos:
__UpperCamelCase = bbox[1] * scale_x
__UpperCamelCase = bbox[2] * scale_y
__UpperCamelCase = bbox[3] * scale_x
__UpperCamelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
__UpperCamelCase = cva.resize(_lowercase , (output_size[1] - divid_point_x, divid_point_y) )
__UpperCamelCase = img
for bbox in img_annos:
__UpperCamelCase = scale_x + bbox[1] * (1 - scale_x)
__UpperCamelCase = bbox[2] * scale_y
__UpperCamelCase = scale_x + bbox[3] * (1 - scale_x)
__UpperCamelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
__UpperCamelCase = cva.resize(_lowercase , (divid_point_x, output_size[0] - divid_point_y) )
__UpperCamelCase = img
for bbox in img_annos:
__UpperCamelCase = bbox[1] * scale_x
__UpperCamelCase = scale_y + bbox[2] * (1 - scale_y)
__UpperCamelCase = bbox[3] * scale_x
__UpperCamelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
__UpperCamelCase = cva.resize(
_lowercase , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
__UpperCamelCase = img
for bbox in img_annos:
__UpperCamelCase = scale_x + bbox[1] * (1 - scale_x)
__UpperCamelCase = scale_y + bbox[2] * (1 - scale_y)
__UpperCamelCase = scale_x + bbox[3] * (1 - scale_x)
__UpperCamelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
__UpperCamelCase = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def _A ( _lowercase ) -> str:
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
__UpperCamelCase = ascii_lowercase + digits
return "".join(random.choice(_lowercase ) for _ in range(_lowercase ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 1 |
"""simple docstring"""
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__A = object()
# For specifying empty leaf dict `{}`
__A = object()
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> Dict:
__lowerCAmelCase: Dict = tuple((re.compile(x + "$" ) for x in qs) )
for i in range(len(__SCREAMING_SNAKE_CASE ) - len(__SCREAMING_SNAKE_CASE ) + 1 ):
__lowerCAmelCase: Tuple = [x.match(__SCREAMING_SNAKE_CASE ) for x, y in zip(__SCREAMING_SNAKE_CASE , ks[i:] )]
if matches and all(__SCREAMING_SNAKE_CASE ):
return True
return False
def a__ ( __SCREAMING_SNAKE_CASE ) -> List[Any]:
def replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
for rule, replacement in rules:
if _match(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return replacement
return val
return replace
def a__ ( ) -> str:
return [
# embeddings
(("transformer", "wpe", "embedding"), P("mp" , __SCREAMING_SNAKE_CASE )),
(("transformer", "wte", "embedding"), P("mp" , __SCREAMING_SNAKE_CASE )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__SCREAMING_SNAKE_CASE , "mp" )),
(("attention", "out_proj", "kernel"), P("mp" , __SCREAMING_SNAKE_CASE )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(__SCREAMING_SNAKE_CASE , "mp" )),
(("mlp", "c_fc", "bias"), P("mp" )),
(("mlp", "c_proj", "kernel"), P("mp" , __SCREAMING_SNAKE_CASE )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def a__ ( __SCREAMING_SNAKE_CASE ) -> List[str]:
__lowerCAmelCase: Any = _get_partition_rules()
__lowerCAmelCase: List[Any] = _replacement_rules(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase: Tuple = {k: _unmatched for k in flatten_dict(__SCREAMING_SNAKE_CASE )}
__lowerCAmelCase: Any = {k: replace(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(__SCREAMING_SNAKE_CASE ) )
| 346 | 0 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class _a ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ):
super().__init__()
self.register_modules(unet=_lowerCamelCase ,scheduler=_lowerCamelCase )
def __call__( self ):
SCREAMING_SNAKE_CASE : Dict = torch.randn(
(1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,)
SCREAMING_SNAKE_CASE : Optional[int] = 1
SCREAMING_SNAKE_CASE : List[Any] = self.unet(_lowerCamelCase ,_lowerCamelCase ).sample
SCREAMING_SNAKE_CASE : Union[str, Any] = self.scheduler.step(_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ).prev_sample
SCREAMING_SNAKE_CASE : List[str] = scheduler_output - scheduler_output + torch.ones_like(_lowerCamelCase )
return result
| 701 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('0.12.2'):
raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
raise Exception('requires fairseq < v2')
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = 'Hello, World!'
__UpperCAmelCase = 'en_XX'
def SCREAMING_SNAKE_CASE_ ( snake_case_ : str , snake_case_ : str , snake_case_ : bool ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE : List[Any] = Path('data_bin' )
SCREAMING_SNAKE_CASE : List[Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(snake_case_ ).parent ) , checkpoint_file=Path(snake_case_ ).name , _name='xmod_base' , arch='xmod_base' , task='multilingual_masked_lm' , data_name_or_path=str(snake_case_ ) , bpe='sentencepiece' , sentencepiece_model=str(Path(snake_case_ ).parent / 'sentencepiece.bpe.model' ) , src_dict=str(data_dir / 'dict.txt' ) , )
xmod.eval() # disable dropout
print(snake_case_ )
SCREAMING_SNAKE_CASE : Any = xmod.model.encoder.sentence_encoder
SCREAMING_SNAKE_CASE : Optional[int] = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , 'bottleneck' , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
SCREAMING_SNAKE_CASE : List[Any] = xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]
print('Our X-MOD config:' , snake_case_ )
SCREAMING_SNAKE_CASE : int = XmodForSequenceClassification(snake_case_ ) if classification_head else XmodForMaskedLM(snake_case_ )
model.eval()
# Now let's copy all the weights.
# Embeddings
SCREAMING_SNAKE_CASE : Tuple = xmod_sent_encoder.embed_tokens.weight
SCREAMING_SNAKE_CASE : str = xmod_sent_encoder.embed_positions.weight
SCREAMING_SNAKE_CASE : List[Any] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
SCREAMING_SNAKE_CASE : Optional[int] = xmod_sent_encoder.layernorm_embedding.weight
SCREAMING_SNAKE_CASE : Any = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
SCREAMING_SNAKE_CASE : List[str] = model.roberta.encoder.layer[i]
SCREAMING_SNAKE_CASE : List[Any] = xmod_sent_encoder.layers[i]
# self attention
SCREAMING_SNAKE_CASE : Dict = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError('Dimensions of self-attention weights do not match.' )
SCREAMING_SNAKE_CASE : List[Any] = xmod_layer.self_attn.q_proj.weight
SCREAMING_SNAKE_CASE : Union[str, Any] = xmod_layer.self_attn.q_proj.bias
SCREAMING_SNAKE_CASE : List[Any] = xmod_layer.self_attn.k_proj.weight
SCREAMING_SNAKE_CASE : int = xmod_layer.self_attn.k_proj.bias
SCREAMING_SNAKE_CASE : int = xmod_layer.self_attn.v_proj.weight
SCREAMING_SNAKE_CASE : Optional[Any] = xmod_layer.self_attn.v_proj.bias
# self-attention output
SCREAMING_SNAKE_CASE : List[Any] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError('Dimensions of self-attention output weights do not match.' )
SCREAMING_SNAKE_CASE : int = xmod_layer.self_attn.out_proj.weight
SCREAMING_SNAKE_CASE : Optional[int] = xmod_layer.self_attn.out_proj.bias
SCREAMING_SNAKE_CASE : Union[str, Any] = xmod_layer.self_attn_layer_norm.weight
SCREAMING_SNAKE_CASE : str = xmod_layer.self_attn_layer_norm.bias
# intermediate
SCREAMING_SNAKE_CASE : Union[str, Any] = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('Dimensions of intermediate weights do not match.' )
SCREAMING_SNAKE_CASE : int = xmod_layer.fca.weight
SCREAMING_SNAKE_CASE : List[str] = xmod_layer.fca.bias
# output
SCREAMING_SNAKE_CASE : Dict = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('Dimensions of feed-forward weights do not match.' )
SCREAMING_SNAKE_CASE : int = xmod_layer.fca.weight
SCREAMING_SNAKE_CASE : Optional[Any] = xmod_layer.fca.bias
SCREAMING_SNAKE_CASE : Tuple = xmod_layer.final_layer_norm.weight
SCREAMING_SNAKE_CASE : Dict = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
SCREAMING_SNAKE_CASE : Tuple = xmod_layer.adapter_layer_norm.weight
SCREAMING_SNAKE_CASE : str = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError('Lists of language adapters do not match.' )
for lang_code, adapter in xmod_layer.adapter_modules.items():
SCREAMING_SNAKE_CASE : int = bert_output.adapter_modules[lang_code]
SCREAMING_SNAKE_CASE : Optional[int] = xmod_layer.adapter_modules[lang_code]
SCREAMING_SNAKE_CASE : Optional[int] = from_adapter.fca.weight
SCREAMING_SNAKE_CASE : Optional[int] = from_adapter.fca.bias
SCREAMING_SNAKE_CASE : Optional[int] = from_adapter.fca.weight
SCREAMING_SNAKE_CASE : Any = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
SCREAMING_SNAKE_CASE : Any = xmod_sent_encoder.layer_norm.weight
SCREAMING_SNAKE_CASE : List[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
SCREAMING_SNAKE_CASE : int = xmod.model.classification_heads['mnli'].dense.weight
SCREAMING_SNAKE_CASE : List[Any] = xmod.model.classification_heads['mnli'].dense.bias
SCREAMING_SNAKE_CASE : str = xmod.model.classification_heads['mnli'].out_proj.weight
SCREAMING_SNAKE_CASE : int = xmod.model.classification_heads['mnli'].out_proj.bias
else:
# LM Head
SCREAMING_SNAKE_CASE : Optional[Any] = xmod.model.encoder.lm_head.dense.weight
SCREAMING_SNAKE_CASE : Tuple = xmod.model.encoder.lm_head.dense.bias
SCREAMING_SNAKE_CASE : Optional[int] = xmod.model.encoder.lm_head.layer_norm.weight
SCREAMING_SNAKE_CASE : Optional[int] = xmod.model.encoder.lm_head.layer_norm.bias
SCREAMING_SNAKE_CASE : Optional[int] = xmod.model.encoder.lm_head.weight
SCREAMING_SNAKE_CASE : Tuple = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
SCREAMING_SNAKE_CASE : Union[str, Any] = xmod.encode(snake_case_ ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(snake_case_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = model(snake_case_ )[0]
if classification_head:
SCREAMING_SNAKE_CASE : List[str] = xmod.model.classification_heads['mnli'](xmod.extract_features(snake_case_ ) )
else:
SCREAMING_SNAKE_CASE : Any = xmod.model(snake_case_ , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
SCREAMING_SNAKE_CASE : List[str] = torch.max(torch.abs(our_output - their_output ) ).item()
print(f"""max_absolute_diff = {max_absolute_diff}""" ) # ~ 1e-7
SCREAMING_SNAKE_CASE : Dict = torch.allclose(snake_case_ , snake_case_ , atol=1e-3 )
print('Do both models output the same tensors?' , '🔥' if success else '💩' )
if not success:
raise Exception('Something went wRoNg' )
Path(snake_case_ ).mkdir(parents=snake_case_ , exist_ok=snake_case_ )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(snake_case_ )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
__UpperCAmelCase = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 220 | 0 |
'''simple docstring'''
from collections.abc import Callable
class __UpperCamelCase :
def __init__( self :Optional[int] ,_UpperCamelCase :Callable | None = None ):
# Stores actual heap items.
snake_case_ : list = []
# Stores indexes of each item for supporting updates and deletion.
snake_case_ : dict = {}
# Stores current size of heap.
snake_case_ : str = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
snake_case_ : Optional[Any] = key or (lambda _UpperCamelCase : x)
def a__ ( self :Optional[Any] ,_UpperCamelCase :int ):
return int((i - 1) / 2 ) if i > 0 else None
def a__ ( self :Dict ,_UpperCamelCase :int ):
snake_case_ : int = int(2 * i + 1 )
return left if 0 < left < self.size else None
def a__ ( self :List[str] ,_UpperCamelCase :int ):
snake_case_ : Dict = int(2 * i + 2 )
return right if 0 < right < self.size else None
def a__ ( self :Tuple ,_UpperCamelCase :int ,_UpperCamelCase :int ):
snake_case_ , snake_case_ : List[Any] = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
snake_case_ , snake_case_ : Tuple = self.arr[j], self.arr[i]
def a__ ( self :Union[str, Any] ,_UpperCamelCase :int ,_UpperCamelCase :int ):
return self.arr[i][1] < self.arr[j][1]
def a__ ( self :Tuple ,_UpperCamelCase :int ):
snake_case_ : List[str] = self._left(_UpperCamelCase )
snake_case_ : Optional[Any] = self._right(_UpperCamelCase )
snake_case_ : Optional[int] = i
if left is not None and not self._cmp(_UpperCamelCase ,_UpperCamelCase ):
snake_case_ : List[str] = left
if right is not None and not self._cmp(_UpperCamelCase ,_UpperCamelCase ):
snake_case_ : Optional[Any] = right
return valid_parent
def a__ ( self :Any ,_UpperCamelCase :int ):
snake_case_ : Optional[int] = self._parent(_UpperCamelCase )
while parent is not None and not self._cmp(_UpperCamelCase ,_UpperCamelCase ):
self._swap(_UpperCamelCase ,_UpperCamelCase )
snake_case_ , snake_case_ : Optional[Any] = parent, self._parent(_UpperCamelCase )
def a__ ( self :Any ,_UpperCamelCase :int ):
snake_case_ : Optional[int] = self._get_valid_parent(_UpperCamelCase )
while valid_parent != index:
self._swap(_UpperCamelCase ,_UpperCamelCase )
snake_case_ , snake_case_ : Optional[Any] = valid_parent, self._get_valid_parent(_UpperCamelCase )
def a__ ( self :Any ,_UpperCamelCase :int ,_UpperCamelCase :int ):
if item not in self.pos_map:
return
snake_case_ : List[Any] = self.pos_map[item]
snake_case_ : Optional[Any] = [item, self.key(_UpperCamelCase )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(_UpperCamelCase )
self._heapify_down(_UpperCamelCase )
def a__ ( self :List[str] ,_UpperCamelCase :int ):
if item not in self.pos_map:
return
snake_case_ : List[Any] = self.pos_map[item]
del self.pos_map[item]
snake_case_ : Optional[Any] = self.arr[self.size - 1]
snake_case_ : str = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(_UpperCamelCase )
self._heapify_down(_UpperCamelCase )
def a__ ( self :Tuple ,_UpperCamelCase :int ,_UpperCamelCase :int ):
snake_case_ : Dict = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(_UpperCamelCase )] )
else:
snake_case_ : Any = [item, self.key(_UpperCamelCase )]
snake_case_ : List[Any] = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def a__ ( self :List[Any] ):
return self.arr[0] if self.size else None
def a__ ( self :Union[str, Any] ):
snake_case_ : Optional[Any] = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def UpperCAmelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 334 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
__A : Dict = {'LayoutLMv2Config', 'LayoutLMv3Config'}
@is_pipeline_test
class __UpperCamelCase ( unittest.TestCase ):
lowercase : Optional[int] = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
lowercase : Tuple = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
lowercase : int = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
lowercase : Any = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}
def a__ ( self :int ,_UpperCamelCase :Optional[Any] ,_UpperCamelCase :str ,_UpperCamelCase :List[Any] ):
snake_case_ : Optional[Any] = ZeroShotClassificationPipeline(
model=_UpperCamelCase ,tokenizer=_UpperCamelCase ,candidate_labels=["""polics""", """health"""] )
return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
def a__ ( self :Dict ,_UpperCamelCase :Any ,_UpperCamelCase :int ):
snake_case_ : int = classifier("""Who are you voting for in 2020?""" ,candidate_labels="""politics""" )
self.assertEqual(_UpperCamelCase ,{"""sequence""": ANY(_UpperCamelCase ), """labels""": [ANY(_UpperCamelCase )], """scores""": [ANY(_UpperCamelCase )]} )
# No kwarg
snake_case_ : List[str] = classifier("""Who are you voting for in 2020?""" ,["""politics"""] )
self.assertEqual(_UpperCamelCase ,{"""sequence""": ANY(_UpperCamelCase ), """labels""": [ANY(_UpperCamelCase )], """scores""": [ANY(_UpperCamelCase )]} )
snake_case_ : Union[str, Any] = classifier("""Who are you voting for in 2020?""" ,candidate_labels=["""politics"""] )
self.assertEqual(_UpperCamelCase ,{"""sequence""": ANY(_UpperCamelCase ), """labels""": [ANY(_UpperCamelCase )], """scores""": [ANY(_UpperCamelCase )]} )
snake_case_ : int = classifier("""Who are you voting for in 2020?""" ,candidate_labels="""politics, public health""" )
self.assertEqual(
_UpperCamelCase ,{"""sequence""": ANY(_UpperCamelCase ), """labels""": [ANY(_UpperCamelCase ), ANY(_UpperCamelCase )], """scores""": [ANY(_UpperCamelCase ), ANY(_UpperCamelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""] ) ) ,1.0 )
snake_case_ : Any = classifier("""Who are you voting for in 2020?""" ,candidate_labels=["""politics""", """public health"""] )
self.assertEqual(
_UpperCamelCase ,{"""sequence""": ANY(_UpperCamelCase ), """labels""": [ANY(_UpperCamelCase ), ANY(_UpperCamelCase )], """scores""": [ANY(_UpperCamelCase ), ANY(_UpperCamelCase )]} )
self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""] ) ) ,1.0 )
snake_case_ : List[Any] = classifier(
"""Who are you voting for in 2020?""" ,candidate_labels="""politics""" ,hypothesis_template="""This text is about {}""" )
self.assertEqual(_UpperCamelCase ,{"""sequence""": ANY(_UpperCamelCase ), """labels""": [ANY(_UpperCamelCase )], """scores""": [ANY(_UpperCamelCase )]} )
# https://github.com/huggingface/transformers/issues/13846
snake_case_ : Union[str, Any] = classifier(["""I am happy"""] ,["""positive""", """negative"""] )
self.assertEqual(
_UpperCamelCase ,[
{"""sequence""": ANY(_UpperCamelCase ), """labels""": [ANY(_UpperCamelCase ), ANY(_UpperCamelCase )], """scores""": [ANY(_UpperCamelCase ), ANY(_UpperCamelCase )]}
for i in range(1 )
] ,)
snake_case_ : Union[str, Any] = classifier(["""I am happy""", """I am sad"""] ,["""positive""", """negative"""] )
self.assertEqual(
_UpperCamelCase ,[
{"""sequence""": ANY(_UpperCamelCase ), """labels""": [ANY(_UpperCamelCase ), ANY(_UpperCamelCase )], """scores""": [ANY(_UpperCamelCase ), ANY(_UpperCamelCase )]}
for i in range(2 )
] ,)
with self.assertRaises(_UpperCamelCase ):
classifier("""""" ,candidate_labels="""politics""" )
with self.assertRaises(_UpperCamelCase ):
classifier(_UpperCamelCase ,candidate_labels="""politics""" )
with self.assertRaises(_UpperCamelCase ):
classifier("""Who are you voting for in 2020?""" ,candidate_labels="""""" )
with self.assertRaises(_UpperCamelCase ):
classifier("""Who are you voting for in 2020?""" ,candidate_labels=_UpperCamelCase )
with self.assertRaises(_UpperCamelCase ):
classifier(
"""Who are you voting for in 2020?""" ,candidate_labels="""politics""" ,hypothesis_template="""Not formatting template""" ,)
with self.assertRaises(_UpperCamelCase ):
classifier(
"""Who are you voting for in 2020?""" ,candidate_labels="""politics""" ,hypothesis_template=_UpperCamelCase ,)
self.run_entailment_id(_UpperCamelCase )
def a__ ( self :Tuple ,_UpperCamelCase :Pipeline ):
snake_case_ : int = zero_shot_classifier.model.config
snake_case_ : Union[str, Any] = config.labelaid
snake_case_ : Optional[Any] = zero_shot_classifier.entailment_id
snake_case_ : List[Any] = {"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
self.assertEqual(zero_shot_classifier.entailment_id ,-1 )
snake_case_ : Optional[Any] = {"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
self.assertEqual(zero_shot_classifier.entailment_id ,0 )
snake_case_ : int = {"""ENTAIL""": 0, """NON-ENTAIL""": 1}
self.assertEqual(zero_shot_classifier.entailment_id ,0 )
snake_case_ : Tuple = {"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
self.assertEqual(zero_shot_classifier.entailment_id ,2 )
snake_case_ : Optional[Any] = original_labelaid
self.assertEqual(_UpperCamelCase ,zero_shot_classifier.entailment_id )
@require_torch
def a__ ( self :Any ):
snake_case_ : Dict = pipeline(
"""zero-shot-classification""" ,model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,framework="""pt""" ,)
# There was a regression in 4.10 for this
# Adding a test so we don't make the mistake again.
# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
zero_shot_classifier(
"""Who are you voting for in 2020?""" * 1_0_0 ,candidate_labels=["""politics""", """public health""", """science"""] )
@require_torch
def a__ ( self :Dict ):
snake_case_ : Union[str, Any] = pipeline(
"""zero-shot-classification""" ,model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,framework="""pt""" ,)
snake_case_ : Union[str, Any] = zero_shot_classifier(
"""Who are you voting for in 2020?""" ,candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_UpperCamelCase ) ,{
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""science""", """public health""", """politics"""],
"""scores""": [0.3_33, 0.3_33, 0.3_33],
} ,)
@require_tf
def a__ ( self :Tuple ):
snake_case_ : Union[str, Any] = pipeline(
"""zero-shot-classification""" ,model="""sshleifer/tiny-distilbert-base-cased-distilled-squad""" ,framework="""tf""" ,)
snake_case_ : Union[str, Any] = zero_shot_classifier(
"""Who are you voting for in 2020?""" ,candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_UpperCamelCase ) ,{
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""science""", """public health""", """politics"""],
"""scores""": [0.3_33, 0.3_33, 0.3_33],
} ,)
@slow
@require_torch
def a__ ( self :Optional[int] ):
snake_case_ : Dict = pipeline("""zero-shot-classification""" ,model="""roberta-large-mnli""" ,framework="""pt""" )
snake_case_ : str = zero_shot_classifier(
"""Who are you voting for in 2020?""" ,candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_UpperCamelCase ) ,{
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""politics""", """public health""", """science"""],
"""scores""": [0.9_76, 0.0_15, 0.0_09],
} ,)
snake_case_ : int = zero_shot_classifier(
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
""" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
""" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
""" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
""" machine translation tasks show these models to be superior in quality while being more parallelizable"""
""" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
""" English-to-German translation task, improving over the existing best results, including ensembles by"""
""" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
""" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
""" fraction of the training costs of the best models from the literature. We show that the Transformer"""
""" generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
""" large and limited training data.""" ,candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] ,multi_label=_UpperCamelCase ,)
self.assertEqual(
nested_simplify(_UpperCamelCase ) ,{
"""sequence""": (
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
""" networks in an encoder-decoder configuration. The best performing models also connect the"""
""" encoder and decoder through an attention mechanism. We propose a new simple network"""
""" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
""" and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
""" superior in quality while being more parallelizable and requiring significantly less time to"""
""" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
""" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
""" English-to-French translation task, our model establishes a new single-model state-of-the-art"""
""" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
""" costs of the best models from the literature. We show that the Transformer generalizes well to"""
""" other tasks by applying it successfully to English constituency parsing both with large and"""
""" limited training data."""
),
"""labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
"""scores""": [0.8_17, 0.7_13, 0.0_18, 0.0_18],
} ,)
@slow
@require_tf
def a__ ( self :Optional[int] ):
snake_case_ : List[str] = pipeline("""zero-shot-classification""" ,model="""roberta-large-mnli""" ,framework="""tf""" )
snake_case_ : str = zero_shot_classifier(
"""Who are you voting for in 2020?""" ,candidate_labels=["""politics""", """public health""", """science"""] )
self.assertEqual(
nested_simplify(_UpperCamelCase ) ,{
"""sequence""": """Who are you voting for in 2020?""",
"""labels""": ["""politics""", """public health""", """science"""],
"""scores""": [0.9_76, 0.0_15, 0.0_09],
} ,)
snake_case_ : Optional[int] = zero_shot_classifier(
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
""" in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
""" through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
""" solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
""" machine translation tasks show these models to be superior in quality while being more parallelizable"""
""" and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
""" English-to-German translation task, improving over the existing best results, including ensembles by"""
""" over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
""" single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
""" fraction of the training costs of the best models from the literature. We show that the Transformer"""
""" generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
""" large and limited training data.""" ,candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""] ,multi_label=_UpperCamelCase ,)
self.assertEqual(
nested_simplify(_UpperCamelCase ) ,{
"""sequence""": (
"""The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
""" networks in an encoder-decoder configuration. The best performing models also connect the"""
""" encoder and decoder through an attention mechanism. We propose a new simple network"""
""" architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
""" and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
""" superior in quality while being more parallelizable and requiring significantly less time to"""
""" train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
""" improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
""" English-to-French translation task, our model establishes a new single-model state-of-the-art"""
""" BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
""" costs of the best models from the literature. We show that the Transformer generalizes well to"""
""" other tasks by applying it successfully to English constituency parsing both with large and"""
""" limited training data."""
),
"""labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
"""scores""": [0.8_17, 0.7_13, 0.0_18, 0.0_18],
} ,)
| 334 | 1 |
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
__UpperCAmelCase = "1"
__UpperCAmelCase = "0"
__UpperCAmelCase = "1"
__UpperCAmelCase = ort.SessionOptions()
__UpperCAmelCase = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
__UpperCAmelCase = ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
__UpperCAmelCase = ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
__UpperCAmelCase = ort.RunOptions()
__UpperCAmelCase = 128
__UpperCAmelCase = 1
__UpperCAmelCase = np.ones((batch, sequence), dtype=np.intaa)
__UpperCAmelCase = np.ones((batch, sequence), dtype=np.intaa)
__UpperCAmelCase = np.ones((batch, sequence), dtype=np.intaa)
print("Warm up phase...")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("Start inference...")
__UpperCAmelCase = time.time()
__UpperCAmelCase = 2_000
__UpperCAmelCase = {}
for iter in range(max_iters):
__UpperCAmelCase = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1_000 / max_iters))
| 692 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
__UpperCAmelCase = "pt"
elif is_tf_available():
__UpperCAmelCase = "tf"
else:
__UpperCAmelCase = "jax"
class SCREAMING_SNAKE_CASE ( snake_case , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = ByTaTokenizer
__UpperCamelCase = False
def _UpperCamelCase ( self ):
'''simple docstring'''
super().setUp()
snake_case: int = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _UpperCamelCase ( self ):
'''simple docstring'''
return ByTaTokenizer.from_pretrained('google/byt5-small' )
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=20 , SCREAMING_SNAKE_CASE__=5 ):
'''simple docstring'''
snake_case: Optional[Any] = []
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
try:
snake_case: Optional[Any] = tokenizer.decode([i] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
except UnicodeDecodeError:
pass
toks.append((i, tok) )
snake_case: List[str] = list(filter(lambda SCREAMING_SNAKE_CASE__ : re.match(r'^[ a-zA-Z]+$' , t[1] ) , SCREAMING_SNAKE_CASE__ ) )
snake_case: str = list(filter(lambda SCREAMING_SNAKE_CASE__ : [t[0]] == tokenizer.encode(t[1] , add_special_tokens=SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ) )
if max_length is not None and len(SCREAMING_SNAKE_CASE__ ) > max_length:
snake_case: Union[str, Any] = toks[:max_length]
if min_length is not None and len(SCREAMING_SNAKE_CASE__ ) < min_length and len(SCREAMING_SNAKE_CASE__ ) > 0:
while len(SCREAMING_SNAKE_CASE__ ) < min_length:
snake_case: Tuple = toks + toks
# toks_str = [t[1] for t in toks]
snake_case: Dict = [t[0] for t in toks]
# Ensure consistency
snake_case: int = tokenizer.decode(SCREAMING_SNAKE_CASE__ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
if " " not in output_txt and len(SCREAMING_SNAKE_CASE__ ) > 1:
snake_case: str = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__ )
)
if with_prefix_space:
snake_case: Tuple = ' ' + output_txt
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
return output_txt, output_ids
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: str = tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'] )
snake_case: List[Any] = tokenizer(['hi', 'I went to the gym', ''] )
self.assertListEqual(batch_with_eos_added['input_ids'] , batch_without_eos_added['input_ids'] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.ta_base_tokenizer
snake_case: Union[str, Any] = 'Unicode €.'
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[str] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'Unicode €.</s>' )
snake_case: List[Any] = tokenizer('e è é ê ë' )
snake_case: Optional[Any] = [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
self.assertEqual(encoded['input_ids'] , SCREAMING_SNAKE_CASE__ )
# decoding
snake_case: List[Any] = tokenizer.decode(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , 'e è é ê ë</s>' )
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) ) , 'e è é ê ë</s>' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: str = self.ta_base_tokenizer
snake_case: Optional[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
snake_case: Optional[int] = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
# fmt: on
snake_case: str = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if FRAMEWORK != "jax":
snake_case: Optional[Any] = list(batch.input_ids.numpy()[0] )
else:
snake_case: Dict = list(batch.input_ids.tolist()[0] )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual((2, 37) , batch.input_ids.shape )
self.assertEqual((2, 37) , batch.attention_mask.shape )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.ta_base_tokenizer
snake_case: List[str] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
snake_case: Union[str, Any] = tokenizer(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertIn('attention_mask' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_input_ids' , SCREAMING_SNAKE_CASE__ )
self.assertNotIn('decoder_attention_mask' , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.ta_base_tokenizer
snake_case: str = [
'Summary of the text.',
'Another summary.',
]
snake_case: Dict = tokenizer(
text_target=SCREAMING_SNAKE_CASE__ , max_length=32 , padding='max_length' , truncation=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = self.ta_base_tokenizer
snake_case: Optional[int] = ['A long paragraph for summarization. </s>']
snake_case: str = ['Summary of the text. </s>']
# fmt: off
snake_case: str = [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
snake_case: Optional[int] = [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
# fmt: on
snake_case: List[Any] = tokenizer(SCREAMING_SNAKE_CASE__ , text_target=SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['input_ids'][0] )
self.assertEqual(SCREAMING_SNAKE_CASE__ , batch['labels'][0] )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
snake_case: Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: Union[str, Any] = tempfile.mkdtemp()
snake_case: Dict = ' He is very happy, UNwant\u00E9d,running'
snake_case: Optional[int] = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Any = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
snake_case: Any = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Isolate this from the other tests because we save additional tokens/etc
snake_case: List[str] = tempfile.mkdtemp()
snake_case: str = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'] )
snake_case: List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token' )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
snake_case: int = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Tuple = after_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
snake_case: Union[str, Any] = tokenizer.__class__.from_pretrained(SCREAMING_SNAKE_CASE__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Union[str, Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , encoding='utf-8' ) as json_file:
snake_case: Any = json.load(SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
snake_case: str = json.load(SCREAMING_SNAKE_CASE__ )
snake_case: int = [F"""<extra_id_{i}>""" for i in range(1_25 )]
snake_case: Optional[int] = added_tokens_extra_ids + [
'an_additional_special_token'
]
snake_case: str = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'special_tokens_map.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
with open(os.path.join(SCREAMING_SNAKE_CASE__ , 'tokenizer_config.json' ) , 'w' , encoding='utf-8' ) as outfile:
json.dump(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
snake_case: Dict = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) ) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
snake_case: Union[str, Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=SCREAMING_SNAKE_CASE__ )]
snake_case: Union[str, Any] = tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE__ , additional_special_tokens=SCREAMING_SNAKE_CASE__ , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens )
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) ) , )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertTrue(tokenizer.decode([2_55] ) == '' )
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__ , do_lower_case=SCREAMING_SNAKE_CASE__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Union[str, Any] = ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
snake_case: List[str] = tokenizer.convert_tokens_to_string(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
snake_case: Optional[Any] = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
snake_case: Dict = 0
snake_case: List[Any] = tokenizer.convert_ids_to_tokens(
SCREAMING_SNAKE_CASE__ , skip_special_tokens=SCREAMING_SNAKE_CASE__ )
for attr in attributes_list:
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , attr + '_id' , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(getattr(SCREAMING_SNAKE_CASE__ , attr + '_id' ) , SCREAMING_SNAKE_CASE__ )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [] )
setattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' , [token_id_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens' ) , [token_to_test_setters] )
self.assertListEqual(getattr(SCREAMING_SNAKE_CASE__ , 'additional_special_tokens_ids' ) , [token_id_to_test_setters] )
| 692 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[int] , UpperCAmelCase__ : Any ):
'''simple docstring'''
lowercase : Any =data
lowercase : Node | None =None
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] ):
'''simple docstring'''
lowercase : List[str] =None
lowercase : str =None
def __iter__( self : Optional[int] ):
'''simple docstring'''
lowercase : List[Any] =self.head
while self.head:
yield node.data
lowercase : Dict =node.next
if node == self.head:
break
def __len__( self : List[Any] ):
'''simple docstring'''
return sum(1 for _ in self )
def __repr__( self : Optional[Any] ):
'''simple docstring'''
return "->".join(str(UpperCAmelCase__ ) for item in iter(self ) )
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : Any ):
'''simple docstring'''
self.insert_nth(len(self ) , UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[int] , UpperCAmelCase__ : Any ):
'''simple docstring'''
self.insert_nth(0 , UpperCAmelCase__ )
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Any ):
'''simple docstring'''
if index < 0 or index > len(self ):
raise IndexError('''list index out of range.''' )
lowercase : List[Any] =Node(UpperCAmelCase__ )
if self.head is None:
lowercase : Optional[int] =new_node # first node points itself
lowercase : Union[str, Any] =new_node
elif index == 0: # insert at head
lowercase : int =self.head
lowercase : List[str] =new_node
else:
lowercase : List[str] =self.head
for _ in range(index - 1 ):
lowercase : Optional[int] =temp.next
lowercase : str =temp.next
lowercase : List[Any] =new_node
if index == len(self ) - 1: # insert at tail
lowercase : str =new_node
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return self.delete_nth(0 )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return self.delete_nth(len(self ) - 1 )
def lowerCamelCase_ ( self : Union[str, Any] , UpperCAmelCase__ : int = 0 ):
'''simple docstring'''
if not 0 <= index < len(self ):
raise IndexError('''list index out of range.''' )
lowercase : int =self.head
if self.head == self.tail: # just one node
lowercase : str =None
elif index == 0: # delete head node
lowercase : List[Any] =self.tail.next.next
lowercase : Tuple =self.head.next
else:
lowercase : Tuple =self.head
for _ in range(index - 1 ):
lowercase : List[str] =temp.next
lowercase : str =temp.next
lowercase : Tuple =temp.next.next
if index == len(self ) - 1: # delete at tail
lowercase : int =temp
return delete_node.data
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
return len(self ) == 0
def _lowerCAmelCase ( ) -> None:
lowercase : Optional[int] =CircularLinkedList()
assert len(__magic_name__ ) == 0
assert circular_linked_list.is_empty() is True
assert str(__magic_name__ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(__magic_name__ ) == i
circular_linked_list.insert_nth(__magic_name__ , i + 1 )
assert str(__magic_name__ ) == "->".join(str(__magic_name__ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(__magic_name__ ) == "->".join(str(__magic_name__ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(__magic_name__ ) == "->".join(str(__magic_name__ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(__magic_name__ ) == "->".join(str(__magic_name__ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(__magic_name__ ) == "->".join(str(__magic_name__ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 92 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__magic_name__ = 1.054_571_817E-34 # unit of ℏ : J * s
__magic_name__ = 3E8 # unit of c : m * s^-1
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
if (force, area, distance).count(0) != 1:
raise ValueError("One and only one argument must be 0")
if force < 0:
raise ValueError("Magnitude of force can not be negative")
if distance < 0:
raise ValueError("Distance can not be negative")
if area < 0:
raise ValueError("Area can not be negative")
if force == 0:
lowerCamelCase_ : Dict = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowerCamelCase_ : List[str] = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowerCamelCase_ : Any = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("One and only one argument must be 0")
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 250 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( ) -> int:
return 1
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ) -> int:
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(snake_case_ )
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ) -> int:
return 0 if x < 0 else ten_pence(x - 10 ) + five_pence(snake_case_ )
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ) -> int:
return 0 if x < 0 else twenty_pence(x - 20 ) + ten_pence(snake_case_ )
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ) -> int:
return 0 if x < 0 else fifty_pence(x - 50 ) + twenty_pence(snake_case_ )
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ) -> int:
return 0 if x < 0 else one_pound(x - 100 ) + fifty_pence(snake_case_ )
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int ) -> int:
return 0 if x < 0 else two_pound(x - 200 ) + one_pound(snake_case_ )
def SCREAMING_SNAKE_CASE_ ( snake_case_ : int = 200 ) -> int:
return two_pound(snake_case_ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 220 |
'''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')
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class _a :
"""simple docstring"""
A = 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.'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
A = field(
default=SCREAMING_SNAKE_CASE , 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.'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
@dataclass
class _a :
"""simple docstring"""
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Train language if it is different from the evaluation language.'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
A = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def SCREAMING_SNAKE_CASE_ ( ) -> 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 : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = 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' , snake_case_ )
# 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 : Optional[int] = training_args.get_process_log_level()
logger.setLevel(snake_case_ )
datasets.utils.logging.set_verbosity(snake_case_ )
transformers.utils.logging.set_verbosity(snake_case_ )
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 : 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 : Tuple = 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 : Tuple = 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 : Optional[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 : List[str] = train_dataset.features['label'].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE : List[str] = 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 : List[Any] = 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 : List[Any] = predict_dataset.features['label'].names
# Labels
SCREAMING_SNAKE_CASE : List[str] = len(snake_case_ )
# 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 : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=snake_case_ , idalabel={str(snake_case_ ): label for i, label in enumerate(snake_case_ )} , labelaid={label: i for i, label in enumerate(snake_case_ )} , 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 : Dict = 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 : Dict = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=snake_case_ , 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 : Dict = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE : Tuple = False
def preprocess_function(snake_case_ : Optional[Any] ):
# Tokenize the texts
return tokenizer(
examples['premise'] , examples['hypothesis'] , padding=snake_case_ , max_length=data_args.max_seq_length , truncation=snake_case_ , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE : List[Any] = min(len(snake_case_ ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE : List[Any] = train_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
SCREAMING_SNAKE_CASE : int = train_dataset.map(
snake_case_ , batched=snake_case_ , 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(snake_case_ ) ) , 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 : str = min(len(snake_case_ ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE : List[str] = eval_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
SCREAMING_SNAKE_CASE : Optional[Any] = eval_dataset.map(
snake_case_ , batched=snake_case_ , 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 : Tuple = min(len(snake_case_ ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE : Optional[Any] = predict_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc='prediction dataset map pre-processing' ):
SCREAMING_SNAKE_CASE : Any = predict_dataset.map(
snake_case_ , batched=snake_case_ , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on prediction dataset' , )
# Get the metric function
SCREAMING_SNAKE_CASE : List[str] = 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(snake_case_ : EvalPrediction ):
SCREAMING_SNAKE_CASE : Optional[int] = p.predictions[0] if isinstance(p.predictions , snake_case_ ) else p.predictions
SCREAMING_SNAKE_CASE : Optional[Any] = np.argmax(snake_case_ , axis=1 )
return metric.compute(predictions=snake_case_ , 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[str] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE : int = DataCollatorWithPadding(snake_case_ , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE : str = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE : Optional[int] = Trainer(
model=snake_case_ , args=snake_case_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=snake_case_ , tokenizer=snake_case_ , data_collator=snake_case_ , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE : Dict = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE : Optional[int] = last_checkpoint
SCREAMING_SNAKE_CASE : Union[str, Any] = trainer.train(resume_from_checkpoint=snake_case_ )
SCREAMING_SNAKE_CASE : Any = train_result.metrics
SCREAMING_SNAKE_CASE : int = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case_ )
)
SCREAMING_SNAKE_CASE : Tuple = min(snake_case_ , len(snake_case_ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , snake_case_ )
trainer.save_metrics('train' , snake_case_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
SCREAMING_SNAKE_CASE : int = trainer.evaluate(eval_dataset=snake_case_ )
SCREAMING_SNAKE_CASE : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(snake_case_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = min(snake_case_ , len(snake_case_ ) )
trainer.log_metrics('eval' , snake_case_ )
trainer.save_metrics('eval' , snake_case_ )
# Prediction
if training_args.do_predict:
logger.info('*** Predict ***' )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = trainer.predict(snake_case_ , metric_key_prefix='predict' )
SCREAMING_SNAKE_CASE : Optional[int] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(snake_case_ )
)
SCREAMING_SNAKE_CASE : Optional[Any] = min(snake_case_ , len(snake_case_ ) )
trainer.log_metrics('predict' , snake_case_ )
trainer.save_metrics('predict' , snake_case_ )
SCREAMING_SNAKE_CASE : int = np.argmax(snake_case_ , axis=1 )
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(training_args.output_dir , 'predictions.txt' )
if trainer.is_world_process_zero():
with open(snake_case_ , 'w' ) as writer:
writer.write('index\tprediction\n' )
for index, item in enumerate(snake_case_ ):
SCREAMING_SNAKE_CASE : str = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
if __name__ == "__main__":
main()
| 220 | 1 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def UpperCAmelCase ( A : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = checkpoints.load_tax_checkpoint(lowercase__ )
_UpperCAmelCase = flatten_dict(lowercase__ )
return flax_params
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = {}
_UpperCAmelCase = {
'token_embedder': 'embeddings',
'encoder_norm': 'layernorm',
'kernel': 'weight',
'.out': '.output',
'scale': 'weight',
'embedders_0.pos_embedding': 'row_embedder.weight',
'embedders_1.pos_embedding': 'column_embedder.weight',
}
_UpperCAmelCase = {
'query': 'attention.query',
'key': 'attention.key',
'value': 'attention.value',
'output.dense': 'output',
'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
'pre_self_attention_layer_norm': 'self_attention.layer_norm',
'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
'mlp.': 'mlp.DenseReluDense.',
'pre_mlp_layer_norm': 'mlp.layer_norm',
'self_attention.o': 'self_attention.attention.o',
'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
'decoder.logits_dense.weight': 'decoder.lm_head.weight',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
_UpperCAmelCase = '.'.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
_UpperCAmelCase = new_key.replace(lowercase__ , lowercase__ )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
_UpperCAmelCase = new_key.replace(lowercase__ , lowercase__ )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
_UpperCAmelCase = re.sub(r'layers_(\d+)' , r'layer.\1' , lowercase__ )
_UpperCAmelCase = new_key.replace('encoder' , 'encoder.encoder' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
_UpperCAmelCase = re.sub(r'layers_(\d+)' , r'layer.\1' , lowercase__ )
_UpperCAmelCase = flax_dict[key]
_UpperCAmelCase = {}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
_UpperCAmelCase = torch.from_numpy(converted_dict[key].T )
else:
_UpperCAmelCase = torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def UpperCAmelCase ( A : Optional[int] , A : Union[str, Any] , A : Optional[Any]=False , A : Any=False ):
'''simple docstring'''
_UpperCAmelCase = get_flax_param(lowercase__ )
if not use_large:
_UpperCAmelCase = PixaStructVisionConfig()
_UpperCAmelCase = PixaStructTextConfig()
else:
_UpperCAmelCase = PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
_UpperCAmelCase = PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
_UpperCAmelCase = PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=lowercase__ )
_UpperCAmelCase = PixaStructForConditionalGeneration(lowercase__ )
_UpperCAmelCase = rename_and_convert_flax_params(lowercase__ )
model.load_state_dict(lowercase__ )
_UpperCAmelCase = AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer' )
_UpperCAmelCase = PixaStructImageProcessor()
_UpperCAmelCase = PixaStructProcessor(image_processor=lowercase__ , tokenizer=lowercase__ )
if use_large:
_UpperCAmelCase = 4096
_UpperCAmelCase = True
# mkdir if needed
os.makedirs(lowercase__ , exist_ok=lowercase__ )
model.save_pretrained(lowercase__ )
processor.save_pretrained(lowercase__ )
print('Model saved in {}'.format(lowercase__ ) )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
lowercase = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 573 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = val
snake_case_ = None
snake_case_ = None
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
snake_case_ = Node(__UpperCamelCase )
else:
self.left.insert(__UpperCamelCase )
elif val > self.val:
if self.right is None:
snake_case_ = Node(__UpperCamelCase )
else:
self.right.insert(__UpperCamelCase )
else:
snake_case_ = val
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
# Recursive traversal
if root:
inorder(root.left , lowercase__ )
res.append(root.val )
inorder(root.right , lowercase__ )
def a(lowercase__ ):
'''simple docstring'''
# Build BST
if len(lowercase__ ) == 0:
return arr
snake_case_ = Node(arr[0] )
for i in range(1 , len(lowercase__ ) ):
root.insert(arr[i] )
# Traverse BST in order.
snake_case_ = []
inorder(lowercase__ , lowercase__ )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 187 | 0 |
from math import isclose, sqrt
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = point_y / 4 / point_x
lowercase = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowercase = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowercase = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowercase = outgoing_gradient**2 + 4
lowercase = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowercase = (point_y - outgoing_gradient * point_x) ** 2 - 100
lowercase = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowercase = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowercase = x_minus if isclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) else x_plus
lowercase = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 1.4 , __SCREAMING_SNAKE_CASE = -9.6 ):
lowercase = 0
lowercase = first_x_coord
lowercase = first_y_coord
lowercase = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowercase , lowercase , lowercase = next_point(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(F"""{solution() = }""")
| 701 |
from abc import ABC, abstractmethod
from typing import List, Optional
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self ):
# test for the above condition
self.test()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 0
lowercase = False
while not completed:
if counter == 1:
self.reset()
lowercase = self.advance()
if not self.does_advance(snake_case ):
raise Exception(
'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.' )
lowercase , lowercase , lowercase = self.update(snake_case )
counter += 1
if counter > 1_0000:
raise Exception('update() does not fulfill the constraint.' )
if self.remaining() != 0:
raise Exception('Custom Constraint is not defined correctly.' )
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( self ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( self ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( self ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def SCREAMING_SNAKE_CASE__ ( self , snake_case=False ):
raise NotImplementedError(
F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case ):
super(snake_case , self ).__init__()
if not isinstance(snake_case , snake_case ) or len(snake_case ) == 0:
raise ValueError(F'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(snake_case , snake_case ) or token_id < 0) for token_id in token_ids ):
raise ValueError(F'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )
lowercase = token_ids
lowercase = len(self.token_ids )
lowercase = -1 # the index of the currently fulfilled step
lowercase = False
def SCREAMING_SNAKE_CASE__ ( self ):
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(snake_case )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(snake_case )}''' )
lowercase = False
lowercase = False
lowercase = False
if self.does_advance(snake_case ):
self.fulfilled_idx += 1
lowercase = True
if self.fulfilled_idx == (self.seqlen - 1):
lowercase = True
lowercase = completed
else:
# failed to make progress.
lowercase = True
self.reset()
return stepped, completed, reset
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = False
lowercase = 0
def SCREAMING_SNAKE_CASE__ ( self ):
return self.seqlen - (self.fulfilled_idx + 1)
def SCREAMING_SNAKE_CASE__ ( self , snake_case=False ):
lowercase = PhrasalConstraint(self.token_ids )
if stateful:
lowercase = self.seqlen
lowercase = self.fulfilled_idx
lowercase = self.completed
return new_constraint
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=True ):
lowercase = max([len(snake_case ) for one in nested_token_ids] )
lowercase = {}
for token_ids in nested_token_ids:
lowercase = root
for tidx, token_id in enumerate(snake_case ):
if token_id not in level:
lowercase = {}
lowercase = level[token_id]
if no_subsets and self.has_subsets(snake_case , snake_case ):
raise ValueError(
'Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'
F''' {nested_token_ids}.''' )
lowercase = root
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.trie
for current_token in current_seq:
lowercase = start[current_token]
lowercase = list(start.keys() )
return next_tokens
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = self.next_tokens(snake_case )
return len(snake_case ) == 0
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
lowercase = list(root.values() )
if len(snake_case ) == 0:
return 1
else:
return sum([self.count_leaves(snake_case ) for nn in next_nodes] )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case ):
lowercase = self.count_leaves(snake_case )
return len(snake_case ) != leaf_count
class A_ ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self , snake_case ):
super(snake_case , self ).__init__()
if not isinstance(snake_case , snake_case ) or len(snake_case ) == 0:
raise ValueError(F'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(snake_case , snake_case ) for token_ids in nested_token_ids ):
raise ValueError(F'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )
if any(
any((not isinstance(snake_case , snake_case ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
F'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )
lowercase = DisjunctiveTrie(snake_case )
lowercase = nested_token_ids
lowercase = self.trie.max_height
lowercase = []
lowercase = False
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.trie.next_tokens(self.current_seq )
if len(snake_case ) == 0:
return None
else:
return token_list
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case )}''' )
lowercase = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case )}''' )
lowercase = False
lowercase = False
lowercase = False
if self.does_advance(snake_case ):
self.current_seq.append(snake_case )
lowercase = True
else:
lowercase = True
self.reset()
lowercase = self.trie.reached_leaf(self.current_seq )
lowercase = completed
return stepped, completed, reset
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = False
lowercase = []
def SCREAMING_SNAKE_CASE__ ( self ):
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def SCREAMING_SNAKE_CASE__ ( self , snake_case=False ):
lowercase = DisjunctiveConstraint(self.token_ids )
if stateful:
lowercase = self.seqlen
lowercase = self.current_seq
lowercase = self.completed
return new_constraint
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = constraints
# max # of steps required to fulfill a given constraint
lowercase = max([c.seqlen for c in constraints] )
lowercase = len(snake_case )
lowercase = False
self.init_state()
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = []
lowercase = None
lowercase = [constraint.copy(stateful=snake_case ) for constraint in self.constraints]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
lowercase = constraint.advance()
if isinstance(snake_case , snake_case ):
token_list.append(snake_case )
elif isinstance(snake_case , snake_case ):
token_list.extend(snake_case )
else:
lowercase = self.inprogress_constraint.advance()
if isinstance(snake_case , snake_case ):
token_list.append(snake_case )
elif isinstance(snake_case , snake_case ):
token_list.extend(snake_case )
if len(snake_case ) == 0:
return None
else:
return token_list
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
lowercase , lowercase = self.add(snake_case )
# the entire list of constraints are fulfilled
if self.completed:
break
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
if not isinstance(snake_case , snake_case ):
raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.''' )
lowercase , lowercase = False, False
if self.completed:
lowercase = True
lowercase = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
lowercase , lowercase , lowercase = self.inprogress_constraint.update(snake_case )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=snake_case ) )
lowercase = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
lowercase = None
if len(self.pending_constraints ) == 0:
# we're done!
lowercase = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(snake_case ):
lowercase , lowercase , lowercase = pending_constraint.update(snake_case )
if not stepped:
raise Exception(
'`constraint.update(token_id)` is not yielding incremental progress, '
'even though `constraint.does_advance(token_id)` is true.' )
if complete:
self.complete_constraints.append(snake_case )
lowercase = None
if not complete and stepped:
lowercase = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
lowercase = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
lowercase = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def SCREAMING_SNAKE_CASE__ ( self , snake_case=True ):
lowercase = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
lowercase = [
constraint.copy(stateful=snake_case ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
lowercase = self.inprogress_constraint.copy(stateful=snake_case )
lowercase = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 565 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase_ = {'tokenization_bertweet': ['BertweetTokenizer']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCamelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 625 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class snake_case_ :
'''simple docstring'''
def __init__( self, A_, A_=2, A_=32, A_=16, A_=3, A_=True, A_=True, A_=32, A_=4, A_=[0, 1, 2, 3], A_=4, A_=37, A_="gelu", A_=0.1, A_=0.1, A_=0.02, A_=3, A_=[1, 384, 24, 24], A_=True, A_=None, ) -> Optional[int]:
UpperCAmelCase__ =parent
UpperCAmelCase__ =batch_size
UpperCAmelCase__ =image_size
UpperCAmelCase__ =patch_size
UpperCAmelCase__ =num_channels
UpperCAmelCase__ =is_training
UpperCAmelCase__ =use_labels
UpperCAmelCase__ =hidden_size
UpperCAmelCase__ =num_hidden_layers
UpperCAmelCase__ =backbone_out_indices
UpperCAmelCase__ =num_attention_heads
UpperCAmelCase__ =intermediate_size
UpperCAmelCase__ =hidden_act
UpperCAmelCase__ =hidden_dropout_prob
UpperCAmelCase__ =attention_probs_dropout_prob
UpperCAmelCase__ =initializer_range
UpperCAmelCase__ =num_labels
UpperCAmelCase__ =backbone_featmap_shape
UpperCAmelCase__ =scope
UpperCAmelCase__ =is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
UpperCAmelCase__ =(image_size // patch_size) ** 2
UpperCAmelCase__ =num_patches + 1
def __UpperCAmelCase ( self ) -> str:
UpperCAmelCase__ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ =None
if self.use_labels:
UpperCAmelCase__ =ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
UpperCAmelCase__ =self.get_config()
return config, pixel_values, labels
def __UpperCAmelCase ( self ) -> Dict:
UpperCAmelCase__ ={
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [96, 192, 384, 768],
"num_groups": 2,
}
return DPTConfig(
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, backbone_out_indices=self.backbone_out_indices, 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=A_, initializer_range=self.initializer_range, is_hybrid=self.is_hybrid, backbone_config=A_, backbone_featmap_shape=self.backbone_featmap_shape, )
def __UpperCAmelCase ( self, A_, A_, A_ ) -> Optional[Any]:
UpperCAmelCase__ =DPTModel(config=A_ )
model.to(A_ )
model.eval()
UpperCAmelCase__ =model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCAmelCase ( self, A_, A_, A_ ) -> Union[str, Any]:
UpperCAmelCase__ =self.num_labels
UpperCAmelCase__ =DPTForDepthEstimation(A_ )
model.to(A_ )
model.eval()
UpperCAmelCase__ =model(A_ )
self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size) )
def __UpperCAmelCase ( self, A_, A_, A_ ) -> Optional[Any]:
UpperCAmelCase__ =self.num_labels
UpperCAmelCase__ =DPTForSemanticSegmentation(A_ )
model.to(A_ )
model.eval()
UpperCAmelCase__ =model(A_, labels=A_ )
self.parent.assertEqual(
result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def __UpperCAmelCase ( self ) -> int:
UpperCAmelCase__ =self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ =config_and_inputs
UpperCAmelCase__ ={"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class snake_case_ ( a, a, unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
__UpperCamelCase = (
{
'depth-estimation': DPTForDepthEstimation,
'feature-extraction': DPTModel,
'image-segmentation': DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def __UpperCAmelCase ( self ) -> int:
UpperCAmelCase__ =DPTModelTester(self )
UpperCAmelCase__ =ConfigTester(self, config_class=A_, has_text_modality=A_, hidden_size=37 )
def __UpperCAmelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="DPT does not use inputs_embeds" )
def __UpperCAmelCase ( self ) -> List[Any]:
pass
def __UpperCAmelCase ( self ) -> str:
UpperCAmelCase__ , UpperCAmelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ =model_class(A_ )
self.assertIsInstance(model.get_input_embeddings(), (nn.Module) )
UpperCAmelCase__ =model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A_, nn.Linear ) )
def __UpperCAmelCase ( self ) -> Optional[int]:
UpperCAmelCase__ , UpperCAmelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ =model_class(A_ )
UpperCAmelCase__ =inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ =[*signature.parameters.keys()]
UpperCAmelCase__ =["pixel_values"]
self.assertListEqual(arg_names[:1], A_ )
def __UpperCAmelCase ( self ) -> List[Any]:
UpperCAmelCase__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __UpperCAmelCase ( self ) -> str:
UpperCAmelCase__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*A_ )
def __UpperCAmelCase ( self ) -> List[Any]:
UpperCAmelCase__ =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A_ )
def __UpperCAmelCase ( self ) -> Any:
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
UpperCAmelCase__ , UpperCAmelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ =True
if model_class in get_values(A_ ):
continue
UpperCAmelCase__ =model_class(A_ )
model.to(A_ )
model.train()
UpperCAmelCase__ =self._prepare_for_class(A_, A_, return_labels=A_ )
UpperCAmelCase__ =model(**A_ ).loss
loss.backward()
def __UpperCAmelCase ( self ) -> List[Any]:
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
UpperCAmelCase__ , UpperCAmelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ =False
UpperCAmelCase__ =True
if model_class in get_values(A_ ) or not model_class.supports_gradient_checkpointing:
continue
UpperCAmelCase__ =model_class(A_ )
model.to(A_ )
model.gradient_checkpointing_enable()
model.train()
UpperCAmelCase__ =self._prepare_for_class(A_, A_, return_labels=A_ )
UpperCAmelCase__ =model(**A_ ).loss
loss.backward()
def __UpperCAmelCase ( self ) -> int:
UpperCAmelCase__ , UpperCAmelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ =_config_zero_init(A_ )
for model_class in self.all_model_classes:
UpperCAmelCase__ =model_class(config=A_ )
# Skip the check for the backbone
UpperCAmelCase__ =[]
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
UpperCAmelCase__ =[f"""{name}.{key}""" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item(), [0.0, 1.0], msg=f"""Parameter {name} of model {model_class} seems not properly initialized""", )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def __UpperCAmelCase ( self ) -> List[Any]:
pass
@slow
def __UpperCAmelCase ( self ) -> Optional[int]:
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
UpperCAmelCase__ =DPTModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
def __UpperCAmelCase ( self ) -> Any:
# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
UpperCAmelCase__ , UpperCAmelCase__ =self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase__ ="add"
with self.assertRaises(A_ ):
UpperCAmelCase__ =DPTForDepthEstimation(A_ )
def _UpperCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
@slow
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCAmelCase__ =DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas" )
UpperCAmelCase__ =DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas" ).to(A_ )
UpperCAmelCase__ =prepare_img()
UpperCAmelCase__ =image_processor(images=A_, return_tensors="pt" ).to(A_ )
# forward pass
with torch.no_grad():
UpperCAmelCase__ =model(**A_ )
UpperCAmelCase__ =outputs.predicted_depth
# verify the predicted depth
UpperCAmelCase__ =torch.Size((1, 384, 384) )
self.assertEqual(predicted_depth.shape, A_ )
UpperCAmelCase__ =torch.tensor(
[[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]] ).to(A_ )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100, A_, atol=1E-4 ) )
| 625 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
UpperCAmelCase_ = random.Random()
def lowerCAmelCase_ ( __UpperCAmelCase: Dict , __UpperCAmelCase: Optional[Any]=1.0 , __UpperCAmelCase: Tuple=None , __UpperCAmelCase: str=None ) -> List[Any]:
if rng is None:
UpperCamelCase__ : Optional[Any] = global_rng
UpperCamelCase__ : str = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self, __magic_name__, __magic_name__=7, __magic_name__=400, __magic_name__=2000, __magic_name__=24, __magic_name__=24, __magic_name__=0.0, __magic_name__=16000, __magic_name__=True, __magic_name__=True, ) -> int:
"""simple docstring"""
UpperCamelCase__ : int = parent
UpperCamelCase__ : Dict = batch_size
UpperCamelCase__ : Optional[Any] = min_seq_length
UpperCamelCase__ : str = max_seq_length
UpperCamelCase__ : Optional[int] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase__ : Any = feature_size
UpperCamelCase__ : Dict = num_mel_bins
UpperCamelCase__ : Union[str, Any] = padding_value
UpperCamelCase__ : str = sampling_rate
UpperCamelCase__ : List[str] = return_attention_mask
UpperCamelCase__ : Dict = do_normalize
def UpperCamelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def UpperCamelCase__ ( self, __magic_name__=False, __magic_name__=False ) -> Optional[int]:
"""simple docstring"""
def _flatten(__magic_name__ ):
return list(itertools.chain(*A_ ) )
if equal_length:
UpperCamelCase__ : Optional[int] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCamelCase__ : Dict = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff )
]
if numpify:
UpperCamelCase__ : Union[str, Any] = [np.asarray(A_ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowercase__ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a : Optional[int] = SpeechaTextFeatureExtractor if is_speech_available() else None
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase__ : Optional[int] = SpeechaTextFeatureExtractionTester(self )
def UpperCamelCase__ ( self, __magic_name__ ) -> Optional[Any]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(A_, axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(A_, axis=0 ) - 1 ) < 1E-3 ) )
def UpperCamelCase__ ( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase__ : Union[str, Any] = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ : List[Any] = [np.asarray(A_ ) for speech_input in speech_inputs]
# Test feature size
UpperCamelCase__ : Dict = feature_extractor(A_, padding=A_, return_tensors='''np''' ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
UpperCamelCase__ : List[str] = feature_extractor(speech_inputs[0], return_tensors='''np''' ).input_features
UpperCamelCase__ : List[str] = feature_extractor(np_speech_inputs[0], return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(A_, A_, atol=1E-3 ) )
# Test batched
UpperCamelCase__ : List[str] = feature_extractor(A_, return_tensors='''np''' ).input_features
UpperCamelCase__ : Tuple = feature_extractor(A_, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(A_, A_ ):
self.assertTrue(np.allclose(A_, A_, atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
UpperCamelCase__ : Any = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCamelCase__ : str = np.asarray(A_ )
UpperCamelCase__ : str = feature_extractor(A_, return_tensors='''np''' ).input_features
UpperCamelCase__ : Dict = feature_extractor(A_, return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(A_, A_ ):
self.assertTrue(np.allclose(A_, A_, atol=1E-3 ) )
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ : int = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ : Union[str, Any] = ['''longest''', '''max_length''', '''do_not_pad''']
UpperCamelCase__ : List[Any] = [None, 16, None]
for max_length, padding in zip(A_, A_ ):
UpperCamelCase__ : Optional[int] = feature_extractor(
A_, padding=A_, max_length=A_, return_attention_mask=A_ )
UpperCamelCase__ : List[str] = inputs.input_features
UpperCamelCase__ : List[str] = inputs.attention_mask
UpperCamelCase__ : str = [np.sum(A_ ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def UpperCamelCase__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ : List[Any] = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ : Union[str, Any] = ['''longest''', '''max_length''', '''do_not_pad''']
UpperCamelCase__ : List[str] = [None, 16, None]
for max_length, padding in zip(A_, A_ ):
UpperCamelCase__ : List[Any] = feature_extractor(
A_, max_length=A_, padding=A_, return_tensors='''np''', return_attention_mask=A_ )
UpperCamelCase__ : str = inputs.input_features
UpperCamelCase__ : Tuple = inputs.attention_mask
UpperCamelCase__ : str = [np.sum(A_ ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def UpperCamelCase__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase__ : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ : Optional[Any] = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ : List[Any] = feature_extractor(
A_, padding='''max_length''', max_length=4, truncation=A_, return_tensors='''np''', return_attention_mask=A_, )
UpperCamelCase__ : Union[str, Any] = inputs.input_features
UpperCamelCase__ : Dict = inputs.attention_mask
UpperCamelCase__ : Tuple = np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def UpperCamelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ : Any = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ : Any = feature_extractor(
A_, padding='''longest''', max_length=4, truncation=A_, return_tensors='''np''', return_attention_mask=A_, )
UpperCamelCase__ : List[Any] = inputs.input_features
UpperCamelCase__ : Dict = inputs.attention_mask
UpperCamelCase__ : str = np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 4, 24) )
UpperCamelCase__ : List[Any] = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ : List[Any] = feature_extractor(
A_, padding='''longest''', max_length=16, truncation=A_, return_tensors='''np''', return_attention_mask=A_, )
UpperCamelCase__ : str = inputs.input_features
UpperCamelCase__ : str = inputs.attention_mask
UpperCamelCase__ : Optional[Any] = np.sum(attention_mask == 1, axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape, (3, 6, 24) )
def UpperCamelCase__ ( self ) -> Dict:
"""simple docstring"""
import torch
UpperCamelCase__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ : Tuple = np.random.rand(100, 32 ).astype(np.floataa )
UpperCamelCase__ : Any = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCamelCase__ : str = feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
UpperCamelCase__ : List[str] = feature_extractor.pad([{'''input_features''': inputs}], return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def UpperCamelCase__ ( self, __magic_name__ ) -> Optional[Any]:
"""simple docstring"""
from datasets import load_dataset
UpperCamelCase__ : int = load_dataset('''hf-internal-testing/librispeech_asr_dummy''', '''clean''', split='''validation''' )
# automatic decoding with librispeech
UpperCamelCase__ : Union[str, Any] = ds.sort('''id''' ).select(range(A_ ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase__ : Tuple = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
] )
# fmt: on
UpperCamelCase__ : Union[str, Any] = self._load_datasamples(1 )
UpperCamelCase__ : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCamelCase__ : Optional[Any] = feature_extractor(A_, return_tensors='''pt''' ).input_features
self.assertEquals(input_features.shape, (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30], A_, atol=1E-4 ) )
| 703 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase_ = logging.get_logger(__name__)
def lowerCAmelCase_ ( __UpperCAmelCase: str ) -> YolosConfig:
UpperCamelCase__ : str = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
UpperCamelCase__ : Dict = 192
UpperCamelCase__ : Dict = 768
UpperCamelCase__ : Optional[Any] = 12
UpperCamelCase__ : List[Any] = 3
UpperCamelCase__ : Optional[int] = [800, 1333]
UpperCamelCase__ : Tuple = False
elif yolos_name == "yolos_s_dWr":
UpperCamelCase__ : int = 330
UpperCamelCase__ : Tuple = 14
UpperCamelCase__ : str = 6
UpperCamelCase__ : Optional[int] = 1320
elif "yolos_s" in yolos_name:
UpperCamelCase__ : Optional[int] = 384
UpperCamelCase__ : Any = 1536
UpperCamelCase__ : Union[str, Any] = 12
UpperCamelCase__ : int = 6
elif "yolos_b" in yolos_name:
UpperCamelCase__ : Dict = [800, 1344]
UpperCamelCase__ : List[Any] = 91
UpperCamelCase__ : str = '''huggingface/label-files'''
UpperCamelCase__ : Dict = '''coco-detection-id2label.json'''
UpperCamelCase__ : List[str] = json.load(open(hf_hub_download(__UpperCAmelCase , __UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCamelCase__ : str = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCamelCase__ : str = idalabel
UpperCamelCase__ : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase_ ( __UpperCAmelCase: dict , __UpperCAmelCase: YolosConfig , __UpperCAmelCase: bool = False ) -> str:
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCamelCase__ : str = state_dict.pop(f"blocks.{i}.attn.qkv.weight" )
UpperCamelCase__ : Dict = state_dict.pop(f"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase__ : List[str] = in_proj_weight[: config.hidden_size, :]
UpperCamelCase__ : Any = in_proj_bias[: config.hidden_size]
UpperCamelCase__ : Tuple = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCamelCase__ : List[str] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCamelCase__ : Optional[int] = in_proj_weight[-config.hidden_size :, :]
UpperCamelCase__ : Optional[int] = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( __UpperCAmelCase: str ) -> str:
if "backbone" in name:
UpperCamelCase__ : str = name.replace('''backbone''' , '''vit''' )
if "cls_token" in name:
UpperCamelCase__ : str = name.replace('''cls_token''' , '''embeddings.cls_token''' )
if "det_token" in name:
UpperCamelCase__ : str = name.replace('''det_token''' , '''embeddings.detection_tokens''' )
if "mid_pos_embed" in name:
UpperCamelCase__ : List[str] = name.replace('''mid_pos_embed''' , '''encoder.mid_position_embeddings''' )
if "pos_embed" in name:
UpperCamelCase__ : List[Any] = name.replace('''pos_embed''' , '''embeddings.position_embeddings''' )
if "patch_embed.proj" in name:
UpperCamelCase__ : Any = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "blocks" in name:
UpperCamelCase__ : Union[str, Any] = name.replace('''blocks''' , '''encoder.layer''' )
if "attn.proj" in name:
UpperCamelCase__ : Dict = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
UpperCamelCase__ : Optional[int] = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
UpperCamelCase__ : int = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
UpperCamelCase__ : str = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
UpperCamelCase__ : Optional[Any] = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
UpperCamelCase__ : List[str] = name.replace('''mlp.fc2''' , '''output.dense''' )
if "class_embed" in name:
UpperCamelCase__ : List[Any] = name.replace('''class_embed''' , '''class_labels_classifier''' )
if "bbox_embed" in name:
UpperCamelCase__ : Optional[int] = name.replace('''bbox_embed''' , '''bbox_predictor''' )
if "vit.norm" in name:
UpperCamelCase__ : Tuple = name.replace('''vit.norm''' , '''vit.layernorm''' )
return name
def lowerCAmelCase_ ( __UpperCAmelCase: dict , __UpperCAmelCase: YolosForObjectDetection ) -> dict:
for key in orig_state_dict.copy().keys():
UpperCamelCase__ : Union[str, Any] = orig_state_dict.pop(__UpperCAmelCase )
if "qkv" in key:
UpperCamelCase__ : Optional[int] = key.split('''.''' )
UpperCamelCase__ : Union[str, Any] = int(key_split[2] )
UpperCamelCase__ : Tuple = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
UpperCamelCase__ : Dict = val[:dim, :]
UpperCamelCase__ : int = val[
dim : dim * 2, :
]
UpperCamelCase__ : Dict = val[-dim:, :]
else:
UpperCamelCase__ : List[Any] = val[:dim]
UpperCamelCase__ : List[str] = val[dim : dim * 2]
UpperCamelCase__ : int = val[-dim:]
else:
UpperCamelCase__ : Union[str, Any] = val
return orig_state_dict
def lowerCAmelCase_ ( ) -> torch.Tensor:
UpperCamelCase__ : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCamelCase__ : Dict = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: str , __UpperCAmelCase: str , __UpperCAmelCase: bool = False ) -> Optional[int]:
UpperCamelCase__ : List[str] = get_yolos_config(__UpperCAmelCase )
# load original state_dict
UpperCamelCase__ : List[Any] = torch.load(__UpperCAmelCase , map_location='''cpu''' )['''model''']
# load 🤗 model
UpperCamelCase__ : List[Any] = YolosForObjectDetection(__UpperCAmelCase )
model.eval()
UpperCamelCase__ : Dict = convert_state_dict(__UpperCAmelCase , __UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
# Check outputs on an image, prepared by YolosImageProcessor
UpperCamelCase__ : List[Any] = 800 if yolos_name != '''yolos_ti''' else 512
UpperCamelCase__ : int = YolosImageProcessor(format='''coco_detection''' , size=__UpperCAmelCase )
UpperCamelCase__ : Union[str, Any] = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCamelCase__ : Dict = model(**__UpperCAmelCase )
UpperCamelCase__ ,UpperCamelCase__ : Optional[Any] = outputs.logits, outputs.pred_boxes
UpperCamelCase__ ,UpperCamelCase__ : int = None, None
if yolos_name == "yolos_ti":
UpperCamelCase__ : List[str] = torch.tensor(
[[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]] )
UpperCamelCase__ : Dict = torch.tensor(
[[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]] )
elif yolos_name == "yolos_s_200_pre":
UpperCamelCase__ : Optional[int] = torch.tensor(
[[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] )
UpperCamelCase__ : Union[str, Any] = torch.tensor(
[[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] )
elif yolos_name == "yolos_s_300_pre":
UpperCamelCase__ : Optional[Any] = torch.tensor(
[[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]] )
UpperCamelCase__ : List[Any] = torch.tensor(
[[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]] )
elif yolos_name == "yolos_s_dWr":
UpperCamelCase__ : Any = torch.tensor(
[[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]] )
UpperCamelCase__ : List[Any] = torch.tensor(
[[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]] )
elif yolos_name == "yolos_base":
UpperCamelCase__ : Optional[Any] = torch.tensor(
[[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]] )
UpperCamelCase__ : List[Any] = torch.tensor(
[[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]] )
else:
raise ValueError(f"Unknown yolos_name: {yolos_name}" )
assert torch.allclose(logits[0, :3, :3] , __UpperCAmelCase , atol=1e-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , __UpperCAmelCase , atol=1e-4 )
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(f"Saving model {yolos_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCAmelCase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
UpperCamelCase__ : Any = {
'''yolos_ti''': '''yolos-tiny''',
'''yolos_s_200_pre''': '''yolos-small''',
'''yolos_s_300_pre''': '''yolos-small-300''',
'''yolos_s_dWr''': '''yolos-small-dwr''',
'''yolos_base''': '''yolos-base''',
}
print('''Pushing to the hub...''' )
UpperCamelCase__ : Optional[int] = model_mapping[yolos_name]
image_processor.push_to_hub(__UpperCAmelCase , organization='''hustvl''' )
model.push_to_hub(__UpperCAmelCase , organization='''hustvl''' )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--yolos_name',
default='yolos_s_200_pre',
type=str,
help=(
'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
),
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
)
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 or not to push the converted model to the 🤗 hub.'
)
UpperCAmelCase_ = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 369 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class snake_case_ ( __A , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = KandinskyVaaControlnetImgaImgPipeline
SCREAMING_SNAKE_CASE : int = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE : List[str] = ["image_embeds", "negative_image_embeds", "image", "hint"]
SCREAMING_SNAKE_CASE : Any = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
SCREAMING_SNAKE_CASE : Tuple = False
@property
def snake_case__( self : Optional[Any] ) ->Tuple:
return 3_2
@property
def snake_case__( self : Dict ) ->Tuple:
return 3_2
@property
def snake_case__( self : List[str] ) ->Dict:
return self.time_input_dim
@property
def snake_case__( self : Optional[Any] ) ->List[str]:
return self.time_input_dim * 4
@property
def snake_case__( self : Any ) ->Optional[Any]:
return 1_0_0
@property
def snake_case__( self : Union[str, Any] ) ->str:
torch.manual_seed(0 )
snake_case_ = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
snake_case_ = UNetaDConditionModel(**_UpperCamelCase )
return model
@property
def snake_case__( self : Any ) ->Optional[int]:
return {
"block_out_channels": [3_2, 3_2, 6_4, 6_4],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def snake_case__( self : int ) ->int:
torch.manual_seed(0 )
snake_case_ = VQModel(**self.dummy_movq_kwargs )
return model
def snake_case__( self : Optional[Any] ) ->Union[str, Any]:
snake_case_ = self.dummy_unet
snake_case_ = self.dummy_movq
snake_case_ = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.00085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
snake_case_ = DDIMScheduler(**_UpperCamelCase )
snake_case_ = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def snake_case__( self : Dict , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Union[str, Any]=0 ) ->List[str]:
snake_case_ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
snake_case_ = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_UpperCamelCase )
# create init_image
snake_case_ = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
snake_case_ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case_ = Image.fromarray(np.uinta(_UpperCamelCase ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )
# create hint
snake_case_ = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_UpperCamelCase ) ).to(_UpperCamelCase )
if str(_UpperCamelCase ).startswith('''mps''' ):
snake_case_ = torch.manual_seed(_UpperCamelCase )
else:
snake_case_ = torch.Generator(device=_UpperCamelCase ).manual_seed(_UpperCamelCase )
snake_case_ = {
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 1_0,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def snake_case__( self : Tuple ) ->Optional[Any]:
snake_case_ = '''cpu'''
snake_case_ = self.get_dummy_components()
snake_case_ = self.pipeline_class(**_UpperCamelCase )
snake_case_ = pipe.to(_UpperCamelCase )
pipe.set_progress_bar_config(disable=_UpperCamelCase )
snake_case_ = pipe(**self.get_dummy_inputs(_UpperCamelCase ) )
snake_case_ = output.images
snake_case_ = pipe(
**self.get_dummy_inputs(_UpperCamelCase ) , return_dict=_UpperCamelCase , )[0]
snake_case_ = image[0, -3:, -3:, -1]
snake_case_ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case_ = np.array(
[0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Tuple ) ->List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__( self : Tuple ) ->Union[str, Any]:
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy''' )
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
snake_case_ = init_image.resize((5_1_2, 5_1_2) )
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
snake_case_ = torch.from_numpy(np.array(_UpperCamelCase ) ).float() / 255.0
snake_case_ = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
snake_case_ = '''A robot, 4k photo'''
snake_case_ = KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_UpperCamelCase )
snake_case_ = KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
snake_case_ = pipeline.to(_UpperCamelCase )
pipeline.set_progress_bar_config(disable=_UpperCamelCase )
snake_case_ = torch.Generator(device='''cpu''' ).manual_seed(0 )
snake_case_, snake_case_ = pipe_prior(
_UpperCamelCase , image=_UpperCamelCase , strength=0.85 , generator=_UpperCamelCase , negative_prompt='''''' , ).to_tuple()
snake_case_ = pipeline(
image=_UpperCamelCase , image_embeds=_UpperCamelCase , negative_image_embeds=_UpperCamelCase , hint=_UpperCamelCase , generator=_UpperCamelCase , num_inference_steps=1_0_0 , height=5_1_2 , width=5_1_2 , strength=0.5 , output_type='''np''' , )
snake_case_ = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(_UpperCamelCase , _UpperCamelCase )
| 39 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase )-> bool:
UpperCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowercase__ ( __UpperCamelCase = 5000 )-> int:
UpperCamelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , __UpperCamelCase )]
for i, pentagonal_i in enumerate(__UpperCamelCase ):
for j in range(__UpperCamelCase , len(__UpperCamelCase ) ):
UpperCamelCase = pentagonal_nums[j]
UpperCamelCase = pentagonal_i + pentagonal_j
UpperCamelCase = pentagonal_j - pentagonal_i
if is_pentagonal(__UpperCamelCase ) and is_pentagonal(__UpperCamelCase ):
return b
return -1
if __name__ == "__main__":
print(f'{solution() = }')
| 301 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( ):
'''simple docstring'''
_lowerCAmelCase : Tuple = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowerCAmelCase : str = 6
_lowerCAmelCase : Any = 1
_lowerCAmelCase : int = 1901
_lowerCAmelCase : Union[str, Any] = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
_lowerCAmelCase : List[str] = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
_lowerCAmelCase : Dict = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
_lowerCAmelCase : Any = day - days_per_month[month - 2]
if month > 12:
year += 1
_lowerCAmelCase : str = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 16 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_lowerCAmelCase = {"""UserAgent""": UserAgent().random}
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = script.contents[0]
_lowerCAmelCase : Union[str, Any] = json.loads(data[data.find('{"config"' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class __UpperCamelCase :
def __init__( self ,_A ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = F"""https://www.instagram.com/{username}/"""
_lowerCAmelCase : str = self.get_json()
def __lowerCamelCase ( self ):
'''simple docstring'''
_lowerCAmelCase : str = requests.get(self.url ,headers=_A ).text
_lowerCAmelCase : Optional[Any] = BeautifulSoup(_A ,'html.parser' ).find_all('script' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self ):
'''simple docstring'''
return F"""{self.__class__.__name__}('{self.username}')"""
def __str__( self ):
'''simple docstring'''
return F"""{self.fullname} ({self.username}) is {self.biography}"""
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["username"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["full_name"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["biography"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["business_email"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["external_url"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["edge_followed_by"]["count"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["edge_follow"]["count"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["profile_pic_url_hd"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["is_verified"]
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.user_data["is_private"]
def lowerCamelCase__ ( _lowerCamelCase = "github" ):
'''simple docstring'''
import os
if os.environ.get('CI' ):
return # test failing on GitHub Actions
_lowerCAmelCase : Tuple = InstagramUser(_lowerCamelCase )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , _lowerCamelCase )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 120000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('https://instagram.' )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase = InstagramUser("""github""")
print(instagram_user)
print(F'''{instagram_user.number_of_posts = }''')
print(F'''{instagram_user.number_of_followers = }''')
print(F'''{instagram_user.number_of_followings = }''')
print(F'''{instagram_user.email = }''')
print(F'''{instagram_user.website = }''')
print(F'''{instagram_user.profile_picture_url = }''')
print(F'''{instagram_user.is_verified = }''')
print(F'''{instagram_user.is_private = }''')
| 16 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : int = 10 , _UpperCamelCase : int = 10_00 , _UpperCamelCase : bool = True ) -> int:
'''simple docstring'''
assert (
isinstance(_UpperCamelCase , _UpperCamelCase )
and isinstance(_UpperCamelCase , _UpperCamelCase )
and isinstance(_UpperCamelCase , _UpperCamelCase )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError('Invalid value for min_val or max_val (min_value < max_value)' )
return min_val if option else max_val
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int ) -> int:
'''simple docstring'''
return int((number_a + number_a) / 2 )
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int ) -> None:
'''simple docstring'''
assert (
isinstance(_UpperCamelCase , _UpperCamelCase ) and isinstance(_UpperCamelCase , _UpperCamelCase ) and isinstance(_UpperCamelCase , _UpperCamelCase )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError('argument value for lower and higher must be(lower > higher)' )
if not lower < to_guess < higher:
raise ValueError(
'guess value must be within the range of lower and higher value' )
def answer(_UpperCamelCase : int ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print('started...' )
SCREAMING_SNAKE_CASE = lower
SCREAMING_SNAKE_CASE = higher
SCREAMING_SNAKE_CASE = []
while True:
SCREAMING_SNAKE_CASE = get_avg(_UpperCamelCase , _UpperCamelCase )
last_numbers.append(_UpperCamelCase )
if answer(_UpperCamelCase ) == "low":
SCREAMING_SNAKE_CASE = number
elif answer(_UpperCamelCase ) == "high":
SCREAMING_SNAKE_CASE = number
else:
break
print(f"""guess the number : {last_numbers[-1]}""" )
print(f"""details : {last_numbers!s}""" )
def __lowerCAmelCase ( ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = int(input('Enter lower value : ' ).strip() )
SCREAMING_SNAKE_CASE = int(input('Enter high value : ' ).strip() )
SCREAMING_SNAKE_CASE = int(input('Enter value to guess : ' ).strip() )
guess_the_number(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
main()
| 439 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Tuple , _UpperCamelCase : Tuple ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = BigBirdConfig.from_json_file(_UpperCamelCase )
print(f"""Building PyTorch model from configuration: {config}""" )
if is_trivia_qa:
SCREAMING_SNAKE_CASE = BigBirdForQuestionAnswering(_UpperCamelCase )
else:
SCREAMING_SNAKE_CASE = BigBirdForPreTraining(_UpperCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(_UpperCamelCase , _UpperCamelCase , is_trivia_qa=_UpperCamelCase )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(_UpperCamelCase )
if __name__ == "__main__":
a_ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--big_bird_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
)
a_ : Any = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 439 | 1 |
'''simple docstring'''
def lowercase ( lowerCAmelCase : Any):
"""simple docstring"""
if n == 1 or not isinstance(a__ , a__):
return 0
elif n == 2:
return 1
else:
_A : List[str] = [0, 1]
for i in range(2 , n + 1):
sequence.append(sequence[i - 1] + sequence[i - 2])
return sequence[n]
def lowercase ( lowerCAmelCase : Optional[Any]):
"""simple docstring"""
_A : Optional[Any] = 0
_A : Dict = 2
while digits < n:
index += 1
_A : Any = len(str(fibonacci(a__)))
return index
def lowercase ( lowerCAmelCase : int = 1000):
"""simple docstring"""
return fibonacci_digits_index(a__)
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 714 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all MVP models at https://huggingface.co/models?filter=mvp
__UpperCamelCase : Optional[Any] = {
'''vocab_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
},
'''added_tokens.json''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
},
'''merges_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
},
}
__UpperCamelCase : Tuple = {
'''RUCAIBox/mvp''': 1024,
}
class lowerCamelCase__ ( snake_case_ ):
"""simple docstring"""
__magic_name__ = VOCAB_FILES_NAMES
__magic_name__ = PRETRAINED_VOCAB_FILES_MAP
__magic_name__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__magic_name__ = ["""input_ids""", """attention_mask"""]
__magic_name__ = MvpTokenizer
def __init__( self , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__=None , UpperCAmelCase__="replace" , UpperCAmelCase__="<s>" , UpperCAmelCase__="</s>" , UpperCAmelCase__="</s>" , UpperCAmelCase__="<s>" , UpperCAmelCase__="<unk>" , UpperCAmelCase__="<pad>" , UpperCAmelCase__="<mask>" , UpperCAmelCase__=False , UpperCAmelCase__=True , **UpperCAmelCase__ , ) -> List[Any]:
super().__init__(
UpperCAmelCase__ , UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , errors=UpperCAmelCase__ , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , add_prefix_space=UpperCAmelCase__ , trim_offsets=UpperCAmelCase__ , **UpperCAmelCase__ , )
_A : Any = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , UpperCAmelCase__ ) != add_prefix_space:
_A : Dict = getattr(UpperCAmelCase__ , pre_tok_state.pop('''type''' ) )
_A : List[Any] = add_prefix_space
_A : Tuple = pre_tok_class(**UpperCAmelCase__ )
_A : List[Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
_A : Any = '''post_processor'''
_A : Union[str, Any] = getattr(self.backend_tokenizer , UpperCAmelCase__ , UpperCAmelCase__ )
if tokenizer_component_instance:
_A : Optional[int] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
_A : int = tuple(state['''sep'''] )
if "cls" in state:
_A : Union[str, Any] = tuple(state['''cls'''] )
_A : int = False
if state.get('''add_prefix_space''' , UpperCAmelCase__ ) != add_prefix_space:
_A : Optional[int] = add_prefix_space
_A : Union[str, Any] = True
if state.get('''trim_offsets''' , UpperCAmelCase__ ) != trim_offsets:
_A : List[str] = trim_offsets
_A : int = True
if changes_to_apply:
_A : Optional[int] = getattr(UpperCAmelCase__ , state.pop('''type''' ) )
_A : str = component_class(**UpperCAmelCase__ )
setattr(self.backend_tokenizer , UpperCAmelCase__ , UpperCAmelCase__ )
@property
def _lowerCamelCase ( self ) -> str:
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def _lowerCamelCase ( self , UpperCAmelCase__ ) -> Tuple:
_A : Any = AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else value
_A : Any = value
def _lowerCamelCase ( self , *UpperCAmelCase__ , **UpperCAmelCase__ ) -> BatchEncoding:
_A : Optional[int] = kwargs.get('''is_split_into_words''' , UpperCAmelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowerCamelCase ( self , *UpperCAmelCase__ , **UpperCAmelCase__ ) -> BatchEncoding:
_A : int = kwargs.get('''is_split_into_words''' , UpperCAmelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*UpperCAmelCase__ , **UpperCAmelCase__ )
def _lowerCamelCase ( self , UpperCAmelCase__ , UpperCAmelCase__ = None ) -> Tuple[str]:
_A : List[Any] = self._tokenizer.model.save(UpperCAmelCase__ , name=UpperCAmelCase__ )
return tuple(UpperCAmelCase__ )
def _lowerCamelCase ( self , UpperCAmelCase__ , UpperCAmelCase__=None ) -> Tuple:
_A : Dict = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _lowerCamelCase ( self , UpperCAmelCase__ , UpperCAmelCase__ = None ) -> List[int]:
_A : str = [self.sep_token_id]
_A : 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 + sep + token_ids_a + sep ) * [0]
| 417 | 0 |
import unittest
import numpy as np
from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
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 , ):
"""simple docstring"""
_lowerCamelCase : Tuple = parent
_lowerCamelCase : Dict = batch_size
_lowerCamelCase : str = seq_length
_lowerCamelCase : int = is_training
_lowerCamelCase : Dict = use_attention_mask
_lowerCamelCase : List[str] = use_token_type_ids
_lowerCamelCase : Union[str, Any] = use_labels
_lowerCamelCase : Any = vocab_size
_lowerCamelCase : Tuple = hidden_size
_lowerCamelCase : Tuple = num_hidden_layers
_lowerCamelCase : Dict = num_attention_heads
_lowerCamelCase : str = intermediate_size
_lowerCamelCase : int = hidden_act
_lowerCamelCase : Dict = hidden_dropout_prob
_lowerCamelCase : Tuple = attention_probs_dropout_prob
_lowerCamelCase : Tuple = max_position_embeddings
_lowerCamelCase : str = type_vocab_size
_lowerCamelCase : str = type_sequence_label_size
_lowerCamelCase : str = initializer_range
_lowerCamelCase : str = num_choices
def __snake_case ( self):
"""simple docstring"""
_lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_lowerCamelCase : Any = None
if self.use_attention_mask:
_lowerCamelCase : str = random_attention_mask([self.batch_size, self.seq_length])
_lowerCamelCase : Tuple = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=a__ , )
return config, input_ids, attention_mask
def __snake_case ( self):
"""simple docstring"""
_lowerCamelCase : Tuple = self.prepare_config_and_inputs()
_lowerCamelCase : Tuple = config_and_inputs
_lowerCamelCase : Optional[Any] = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __A ( UpperCAmelCase__ ,unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __snake_case ( self):
"""simple docstring"""
_lowerCamelCase : Dict = FlaxDistilBertModelTester(self)
@slow
def __snake_case ( self):
"""simple docstring"""
for model_class_name in self.all_model_classes:
_lowerCamelCase : str = model_class_name.from_pretrained('''distilbert-base-uncased''')
_lowerCamelCase : List[str] = model(np.ones((1, 1)))
self.assertIsNotNone(a__)
@require_flax
class __A ( unittest.TestCase ):
"""simple docstring"""
@slow
def __snake_case ( self):
"""simple docstring"""
_lowerCamelCase : Union[str, Any] = FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased''')
_lowerCamelCase : Tuple = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
_lowerCamelCase : Any = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
_lowerCamelCase : Union[str, Any] = model(a__ , attention_mask=a__)[0]
_lowerCamelCase : Union[str, Any] = (1, 11, 768)
self.assertEqual(output.shape , a__)
_lowerCamelCase : int = np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , a__ , atol=1e-4))
| 114 |
"""simple docstring"""
import warnings
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
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""nvidia/segformer-b0-finetuned-ade-512-512""": (
"""https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class a ( UpperCAmelCase__ ):
UpperCamelCase : str = 'segformer'
def __init__( self : Tuple , lowerCAmelCase : str=3 , lowerCAmelCase : List[str]=4 , lowerCAmelCase : List[str]=[2, 2, 2, 2] , lowerCAmelCase : Optional[Any]=[8, 4, 2, 1] , lowerCAmelCase : Optional[int]=[32, 64, 160, 256] , lowerCAmelCase : int=[7, 3, 3, 3] , lowerCAmelCase : str=[4, 2, 2, 2] , lowerCAmelCase : str=[1, 2, 5, 8] , lowerCAmelCase : Union[str, Any]=[4, 4, 4, 4] , lowerCAmelCase : Union[str, Any]="gelu" , lowerCAmelCase : Optional[int]=0.0 , lowerCAmelCase : List[str]=0.0 , lowerCAmelCase : Optional[Any]=0.1 , lowerCAmelCase : str=0.0_2 , lowerCAmelCase : int=0.1 , lowerCAmelCase : Union[str, Any]=1E-6 , lowerCAmelCase : List[Any]=256 , lowerCAmelCase : Tuple=255 , **lowerCAmelCase : Tuple , ) -> Tuple:
'''simple docstring'''
super().__init__(**lowerCAmelCase )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , lowerCAmelCase , )
SCREAMING_SNAKE_CASE_: int =num_channels
SCREAMING_SNAKE_CASE_: int =num_encoder_blocks
SCREAMING_SNAKE_CASE_: List[str] =depths
SCREAMING_SNAKE_CASE_: Tuple =sr_ratios
SCREAMING_SNAKE_CASE_: Any =hidden_sizes
SCREAMING_SNAKE_CASE_: List[str] =patch_sizes
SCREAMING_SNAKE_CASE_: Dict =strides
SCREAMING_SNAKE_CASE_: Optional[int] =mlp_ratios
SCREAMING_SNAKE_CASE_: List[str] =num_attention_heads
SCREAMING_SNAKE_CASE_: int =hidden_act
SCREAMING_SNAKE_CASE_: Union[str, Any] =hidden_dropout_prob
SCREAMING_SNAKE_CASE_: str =attention_probs_dropout_prob
SCREAMING_SNAKE_CASE_: int =classifier_dropout_prob
SCREAMING_SNAKE_CASE_: Dict =initializer_range
SCREAMING_SNAKE_CASE_: Any =drop_path_rate
SCREAMING_SNAKE_CASE_: Union[str, Any] =layer_norm_eps
SCREAMING_SNAKE_CASE_: Dict =decoder_hidden_size
SCREAMING_SNAKE_CASE_: Optional[Any] =kwargs.get("""reshape_last_stage""" , lowerCAmelCase )
SCREAMING_SNAKE_CASE_: int =semantic_loss_ignore_index
class a ( UpperCAmelCase__ ):
UpperCamelCase : List[str] = version.parse('1.11' )
@property
def lowerCamelCase__ ( self : str ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCamelCase__ ( self : List[Any] ) -> float:
'''simple docstring'''
return 1E-4
@property
def lowerCamelCase__ ( self : List[Any] ) -> int:
'''simple docstring'''
return 12
| 409 | 0 |
"""simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase = True , __lowerCAmelCase = math.inf , __lowerCAmelCase = -math.inf , __lowerCAmelCase = math.inf , __lowerCAmelCase = -math.inf , __lowerCAmelCase = False , __lowerCAmelCase = 1_00 , __lowerCAmelCase = 0.01 , __lowerCAmelCase = 1 , ) -> Any:
'''simple docstring'''
lowercase_ = False
lowercase_ = search_prob
lowercase_ = start_temperate
lowercase_ = []
lowercase_ = 0
lowercase_ = None
while not search_end:
lowercase_ = current_state.score()
if best_state is None or current_score > best_state.score():
lowercase_ = current_state
scores.append(_lowercase )
iterations += 1
lowercase_ = None
lowercase_ = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
lowercase_ = random.randint(0 , len(_lowercase ) - 1 ) # picking a random neighbor
lowercase_ = neighbors.pop(_lowercase )
lowercase_ = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
lowercase_ = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
lowercase_ = picked_neighbor
else:
lowercase_ = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
lowercase_ = picked_neighbor
lowercase_ = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
lowercase_ = True
else:
lowercase_ = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(_lowercase ) , _lowercase )
plt.xlabel("""Iterations""" )
plt.ylabel("""Function values""" )
plt.show()
return best_state
if __name__ == "__main__":
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Dict:
'''simple docstring'''
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
UpperCAmelCase : Dict = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase : Optional[Any] = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
F"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
UpperCAmelCase : Union[str, Any] = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase : Optional[Any] = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
F"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
'''simple docstring'''
return (3 * x**2) - (6 * y)
UpperCAmelCase : Any = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase : Union[str, Any] = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
F"{local_min.score()}"
)
UpperCAmelCase : List[str] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
UpperCAmelCase : Any = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
F"{local_min.score()}"
)
| 704 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase : Union[str, Any] = datasets.utils.logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( folder_based_builder.FolderBasedBuilderConfig ):
lowercase__ = None
lowercase__ = None
class SCREAMING_SNAKE_CASE__ ( folder_based_builder.FolderBasedBuilder ):
lowercase__ = datasets.Audio()
lowercase__ = "audio"
lowercase__ = AudioFolderConfig
lowercase__ = 42 # definition at the bottom of the script
lowercase__ = AudioClassification(audio_column="audio" , label_column="label" )
UpperCAmelCase : Tuple = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase : Any = AUDIO_EXTENSIONS
| 100 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def snake_case_ (UpperCamelCase : Dict , UpperCamelCase : Optional[Any]=10 ):
'''simple docstring'''
_a = []
for _ in range(UpperCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def snake_case_ (UpperCamelCase : Optional[Any] , UpperCamelCase : Tuple=10 ):
'''simple docstring'''
_a = []
for step in range(UpperCamelCase ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
_a = os.path.join(UpperCamelCase , '''schedule.bin''' )
torch.save(scheduler.state_dict() , UpperCamelCase )
_a = torch.load(UpperCamelCase )
scheduler.load_state_dict(UpperCamelCase )
return lrs
@require_torch
class A ( unittest.TestCase ):
def __lowerCAmelCase ( self : Tuple , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[Any] ) -> int:
"""simple docstring"""
self.assertEqual(len(lowerCAmelCase_ ) , len(lowerCAmelCase_ ) )
for a, b in zip(lowerCAmelCase_ , lowerCAmelCase_ ):
self.assertAlmostEqual(lowerCAmelCase_ , lowerCAmelCase_ , delta=lowerCAmelCase_ )
def __lowerCAmelCase ( self : List[str] ) -> Any:
"""simple docstring"""
_a = torch.tensor([0.1, -0.2, -0.1] , requires_grad=lowerCAmelCase_ )
_a = torch.tensor([0.4, 0.2, -0.5] )
_a = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
_a = AdamW(params=[w] , lr=2e-1 , weight_decay=0.0 )
for _ in range(1_00 ):
_a = criterion(lowerCAmelCase_ , lowerCAmelCase_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
_a = torch.tensor([0.1, -0.2, -0.1] , requires_grad=lowerCAmelCase_ )
_a = torch.tensor([0.4, 0.2, -0.5] )
_a = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
_a = Adafactor(
params=[w] , lr=1e-2 , eps=(1e-30, 1e-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=lowerCAmelCase_ , weight_decay=0.0 , relative_step=lowerCAmelCase_ , scale_parameter=lowerCAmelCase_ , warmup_init=lowerCAmelCase_ , )
for _ in range(10_00 ):
_a = criterion(lowerCAmelCase_ , lowerCAmelCase_ )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1e-2 )
@require_torch
class A ( unittest.TestCase ):
lowercase_ = nn.Linear(50 ,50 ) if is_torch_available() else None
lowercase_ = AdamW(m.parameters() ,lr=10.0 ) if is_torch_available() else None
lowercase_ = 10
def __lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[int]=None ) -> Optional[int]:
"""simple docstring"""
self.assertEqual(len(lowerCAmelCase_ ) , len(lowerCAmelCase_ ) )
for a, b in zip(lowerCAmelCase_ , lowerCAmelCase_ ):
self.assertAlmostEqual(lowerCAmelCase_ , lowerCAmelCase_ , delta=lowerCAmelCase_ , msg=lowerCAmelCase_ )
def __lowerCAmelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
_a = {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
_a = {
get_constant_schedule: ({}, [1_0.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'''num_warmup_steps''': 4},
[0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, '''num_cycles''': 2},
[0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
[0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
),
get_inverse_sqrt_schedule: (
{'''num_warmup_steps''': 2},
[0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
),
}
for scheduler_func, data in scheds.items():
_a , _a = data
_a = scheduler_func(self.optimizer , **lowerCAmelCase_ )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
_a = unwrap_schedule(lowerCAmelCase_ , self.num_steps )
self.assertListAlmostEqual(
lowerCAmelCase_ , lowerCAmelCase_ , tol=1e-2 , msg=F'failed for {scheduler_func} in normal scheduler' , )
_a = scheduler_func(self.optimizer , **lowerCAmelCase_ )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase_ ) # wrap to test picklability of the schedule
_a = unwrap_and_save_reload_schedule(lowerCAmelCase_ , self.num_steps )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ , msg=F'failed for {scheduler_func} in save and reload' )
class A :
def __init__( self : Optional[Any] , lowerCAmelCase_ : Tuple ) -> Optional[int]:
"""simple docstring"""
_a = fn
def __call__( self : List[str] , *lowerCAmelCase_ : Optional[int] , **lowerCAmelCase_ : Dict ) -> Optional[Any]:
"""simple docstring"""
return self.fn(*lowerCAmelCase_ , **lowerCAmelCase_ )
@classmethod
def __lowerCAmelCase ( self : List[str] , lowerCAmelCase_ : List[str] ) -> Tuple:
"""simple docstring"""
_a = list(map(self , scheduler.lr_lambdas ) )
| 22 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
_snake_case : Optional[Any] = 8
def snake_case_ (UpperCamelCase : List[Any] , UpperCamelCase : Dict=BITS ):
'''simple docstring'''
_a = x.device
_a = (x * 255).int().clamp(0 , 255 )
_a = 2 ** torch.arange(bits - 1 , -1 , -1 , device=UpperCamelCase )
_a = rearrange(UpperCamelCase , '''d -> d 1 1''' )
_a = rearrange(UpperCamelCase , '''b c h w -> b c 1 h w''' )
_a = ((x & mask) != 0).float()
_a = rearrange(UpperCamelCase , '''b c d h w -> b (c d) h w''' )
_a = bits * 2 - 1
return bits
def snake_case_ (UpperCamelCase : List[Any] , UpperCamelCase : Any=BITS ):
'''simple docstring'''
_a = x.device
_a = (x > 0).int()
_a = 2 ** torch.arange(bits - 1 , -1 , -1 , device=UpperCamelCase , dtype=torch.intaa )
_a = rearrange(UpperCamelCase , '''d -> d 1 1''' )
_a = rearrange(UpperCamelCase , '''b (c d) h w -> b c d h w''' , d=8 )
_a = reduce(x * mask , '''b c d h w -> b c h w''' , '''sum''' )
return (dec / 255).clamp(0.0 , 1.0 )
def snake_case_ (self : Union[str, Any] , UpperCamelCase : torch.FloatTensor , UpperCamelCase : int , UpperCamelCase : torch.FloatTensor , UpperCamelCase : float = 0.0 , UpperCamelCase : bool = True , UpperCamelCase : Any=None , UpperCamelCase : bool = True , ):
'''simple docstring'''
if self.num_inference_steps is None:
raise ValueError(
'''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
_a = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
_a = self.alphas_cumprod[timestep]
_a = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
_a = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
_a = self.bit_scale
if self.config.clip_sample:
_a = torch.clamp(UpperCamelCase , -scale , UpperCamelCase )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
_a = self._get_variance(UpperCamelCase , UpperCamelCase )
_a = eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
_a = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_a = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
_a = model_output.device if torch.is_tensor(UpperCamelCase ) else '''cpu'''
_a = torch.randn(model_output.shape , dtype=model_output.dtype , generator=UpperCamelCase ).to(UpperCamelCase )
_a = self._get_variance(UpperCamelCase , UpperCamelCase ) ** 0.5 * eta * noise
_a = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=UpperCamelCase , pred_original_sample=UpperCamelCase )
def snake_case_ (self : Any , UpperCamelCase : torch.FloatTensor , UpperCamelCase : int , UpperCamelCase : torch.FloatTensor , UpperCamelCase : str="epsilon" , UpperCamelCase : Dict=None , UpperCamelCase : bool = True , ):
'''simple docstring'''
_a = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
_a , _a = torch.split(UpperCamelCase , sample.shape[1] , dim=1 )
else:
_a = None
# 1. compute alphas, betas
_a = self.alphas_cumprod[t]
_a = self.alphas_cumprod[t - 1] if t > 0 else self.one
_a = 1 - alpha_prod_t
_a = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
_a = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
_a = model_output
else:
raise ValueError(f'Unsupported prediction_type {prediction_type}.' )
# 3. Clip "predicted x_0"
_a = self.bit_scale
if self.config.clip_sample:
_a = torch.clamp(UpperCamelCase , -scale , UpperCamelCase )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_a = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
_a = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_a = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
_a = 0
if t > 0:
_a = torch.randn(
model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=UpperCamelCase ).to(model_output.device )
_a = (self._get_variance(UpperCamelCase , predicted_variance=UpperCamelCase ) ** 0.5) * noise
_a = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=UpperCamelCase , pred_original_sample=UpperCamelCase )
class A ( _a ):
def __init__( self : Any , lowerCAmelCase_ : UNetaDConditionModel , lowerCAmelCase_ : Union[DDIMScheduler, DDPMScheduler] , lowerCAmelCase_ : Optional[float] = 1.0 , ) -> int:
"""simple docstring"""
super().__init__()
_a = bit_scale
_a = (
ddim_bit_scheduler_step if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ )
@torch.no_grad()
def __call__( self : List[Any] , lowerCAmelCase_ : Optional[int] = 2_56 , lowerCAmelCase_ : Optional[int] = 2_56 , lowerCAmelCase_ : Optional[int] = 50 , lowerCAmelCase_ : Optional[torch.Generator] = None , lowerCAmelCase_ : Optional[int] = 1 , lowerCAmelCase_ : Optional[str] = "pil" , lowerCAmelCase_ : bool = True , **lowerCAmelCase_ : Any , ) -> Union[Tuple, ImagePipelineOutput]:
"""simple docstring"""
_a = torch.randn(
(batch_size, self.unet.config.in_channels, height, width) , generator=lowerCAmelCase_ , )
_a = decimal_to_bits(lowerCAmelCase_ ) * self.bit_scale
_a = latents.to(self.device )
self.scheduler.set_timesteps(lowerCAmelCase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
_a = self.unet(lowerCAmelCase_ , lowerCAmelCase_ ).sample
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ).prev_sample
_a = bits_to_decimal(lowerCAmelCase_ )
if output_type == "pil":
_a = self.numpy_to_pil(lowerCAmelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCAmelCase_ )
| 22 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ ( __lowerCAmelCase , __lowerCAmelCase = None ) -> list[list[str]]:
'''simple docstring'''
lowerCamelCase__ =word_bank or []
# create a table
lowerCamelCase__ =len(__lowerCAmelCase ) + 1
lowerCamelCase__ =[]
for _ in range(__lowerCAmelCase ):
table.append([] )
# seed value
lowerCamelCase__ =[[]] # because empty string has empty combination
# iterate through the indices
for i in range(__lowerCAmelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(__lowerCAmelCase )] == word:
lowerCamelCase__ =[
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(__lowerCAmelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(__lowerCAmelCase )]:
combination.reverse()
return table[len(__lowerCAmelCase )]
if __name__ == "__main__":
print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
print(
all_construct(
'hexagonosaurus',
['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
)
)
| 132 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __UpperCAmelCase ( __lowerCAmelCase , unittest.TestCase ):
A__ : Union[str, Any] = '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
def _a ( self , _lowerCamelCase=0 ):
lowerCamelCase__ =floats_tensor((1, 3, 128, 128) , rng=random.Random(_lowerCamelCase ) )
lowerCamelCase__ =np.random.RandomState(_lowerCamelCase )
lowerCamelCase__ ={
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"strength": 0.7_5,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def _a ( self ):
lowerCamelCase__ =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
lowerCamelCase__ =self.get_dummy_inputs()
lowerCamelCase__ =pipe(**_lowerCamelCase ).images
lowerCamelCase__ =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ =np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def _a ( self ):
lowerCamelCase__ =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
lowerCamelCase__ =PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
lowerCamelCase__ =self.get_dummy_inputs()
lowerCamelCase__ =pipe(**_lowerCamelCase ).images
lowerCamelCase__ =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ =np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def _a ( self ):
lowerCamelCase__ =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
lowerCamelCase__ =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
# warmup pass to apply optimizations
lowerCamelCase__ =pipe(**self.get_dummy_inputs() )
lowerCamelCase__ =self.get_dummy_inputs()
lowerCamelCase__ =pipe(**_lowerCamelCase ).images
lowerCamelCase__ =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ =np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def _a ( self ):
lowerCamelCase__ =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
lowerCamelCase__ =EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
lowerCamelCase__ =self.get_dummy_inputs()
lowerCamelCase__ =pipe(**_lowerCamelCase ).images
lowerCamelCase__ =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ =np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def _a ( self ):
lowerCamelCase__ =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
lowerCamelCase__ =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
lowerCamelCase__ =self.get_dummy_inputs()
lowerCamelCase__ =pipe(**_lowerCamelCase ).images
lowerCamelCase__ =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ =np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def _a ( self ):
lowerCamelCase__ =OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
lowerCamelCase__ =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
lowerCamelCase__ =self.get_dummy_inputs()
lowerCamelCase__ =pipe(**_lowerCamelCase ).images
lowerCamelCase__ =image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ =np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCAmelCase ( unittest.TestCase ):
@property
def _a ( self ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a ( self ):
lowerCamelCase__ =ort.SessionOptions()
lowerCamelCase__ =False
return options
def _a ( self ):
lowerCamelCase__ =load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
lowerCamelCase__ =init_image.resize((768, 512) )
# using the PNDM scheduler by default
lowerCamelCase__ =OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
lowerCamelCase__ ="A fantasy landscape, trending on artstation"
lowerCamelCase__ =np.random.RandomState(0 )
lowerCamelCase__ =pipe(
prompt=_lowerCamelCase , image=_lowerCamelCase , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCamelCase , output_type="np" , )
lowerCamelCase__ =output.images
lowerCamelCase__ =images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
lowerCamelCase__ =np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def _a ( self ):
lowerCamelCase__ =load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
lowerCamelCase__ =init_image.resize((768, 512) )
lowerCamelCase__ =LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
lowerCamelCase__ =OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=_lowerCamelCase , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
lowerCamelCase__ ="A fantasy landscape, trending on artstation"
lowerCamelCase__ =np.random.RandomState(0 )
lowerCamelCase__ =pipe(
prompt=_lowerCamelCase , image=_lowerCamelCase , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=20 , generator=_lowerCamelCase , output_type="np" , )
lowerCamelCase__ =output.images
lowerCamelCase__ =images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
lowerCamelCase__ =np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 132 | 1 |
import os
import sys
import unittest
_lowerCamelCase : 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 get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
_lowerCamelCase : List[Any] = os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowerCamelCase : Dict = os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( self : str) ->List[str]:
'''simple docstring'''
A__ = get_test_to_tester_mapping(lowerCAmelCase_)
A__ = get_test_to_tester_mapping(lowerCAmelCase_)
A__ = {'BertModelTest': 'BertModelTester'}
A__ = {
'BlipModelTest': 'BlipModelTester',
'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
'BlipTextModelTest': 'BlipTextModelTester',
'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
'BlipVQAModelTest': 'BlipVQAModelTester',
'BlipVisionModelTest': 'BlipVisionModelTester',
}
self.assertEqual(get_test_info.to_json(lowerCAmelCase_) , lowerCAmelCase_)
self.assertEqual(get_test_info.to_json(lowerCAmelCase_) , lowerCAmelCase_)
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->Tuple:
'''simple docstring'''
A__ = get_model_to_test_mapping(lowerCAmelCase_)
A__ = get_model_to_test_mapping(lowerCAmelCase_)
A__ = {
'BertForMaskedLM': ['BertModelTest'],
'BertForMultipleChoice': ['BertModelTest'],
'BertForNextSentencePrediction': ['BertModelTest'],
'BertForPreTraining': ['BertModelTest'],
'BertForQuestionAnswering': ['BertModelTest'],
'BertForSequenceClassification': ['BertModelTest'],
'BertForTokenClassification': ['BertModelTest'],
'BertLMHeadModel': ['BertModelTest'],
'BertModel': ['BertModelTest'],
}
A__ = {
'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
'BlipForQuestionAnswering': ['BlipVQAModelTest'],
'BlipModel': ['BlipModelTest'],
'BlipTextModel': ['BlipTextModelTest'],
'BlipVisionModel': ['BlipVisionModelTest'],
}
self.assertEqual(get_test_info.to_json(lowerCAmelCase_) , lowerCAmelCase_)
self.assertEqual(get_test_info.to_json(lowerCAmelCase_) , lowerCAmelCase_)
def SCREAMING_SNAKE_CASE ( self : List[str]) ->str:
'''simple docstring'''
A__ = get_model_to_tester_mapping(lowerCAmelCase_)
A__ = get_model_to_tester_mapping(lowerCAmelCase_)
A__ = {
'BertForMaskedLM': ['BertModelTester'],
'BertForMultipleChoice': ['BertModelTester'],
'BertForNextSentencePrediction': ['BertModelTester'],
'BertForPreTraining': ['BertModelTester'],
'BertForQuestionAnswering': ['BertModelTester'],
'BertForSequenceClassification': ['BertModelTester'],
'BertForTokenClassification': ['BertModelTester'],
'BertLMHeadModel': ['BertModelTester'],
'BertModel': ['BertModelTester'],
}
A__ = {
'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
'BlipForQuestionAnswering': ['BlipVQAModelTester'],
'BlipModel': ['BlipModelTester'],
'BlipTextModel': ['BlipTextModelTester'],
'BlipVisionModel': ['BlipVisionModelTester'],
}
self.assertEqual(get_test_info.to_json(lowerCAmelCase_) , lowerCAmelCase_)
self.assertEqual(get_test_info.to_json(lowerCAmelCase_) , lowerCAmelCase_)
| 87 |
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowercase__ ( lowerCamelCase ):
return EnvironmentCommand()
class _lowerCAmelCase ( __UpperCAmelCase ):
@staticmethod
def A ( lowerCAmelCase_ ) -> List[Any]:
_SCREAMING_SNAKE_CASE : int = parser.add_parser('env' )
download_parser.set_defaults(func=lowerCAmelCase_ )
def A ( self ) -> Any:
_SCREAMING_SNAKE_CASE : int = huggingface_hub.__version__
_SCREAMING_SNAKE_CASE : Optional[Any] = 'not installed'
_SCREAMING_SNAKE_CASE : Union[str, Any] = 'NA'
if is_torch_available():
import torch
_SCREAMING_SNAKE_CASE : Union[str, Any] = torch.__version__
_SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cuda.is_available()
_SCREAMING_SNAKE_CASE : Union[str, Any] = 'not installed'
if is_transformers_available():
import transformers
_SCREAMING_SNAKE_CASE : Tuple = transformers.__version__
_SCREAMING_SNAKE_CASE : Optional[int] = 'not installed'
if is_accelerate_available():
import accelerate
_SCREAMING_SNAKE_CASE : str = accelerate.__version__
_SCREAMING_SNAKE_CASE : str = 'not installed'
if is_xformers_available():
import xformers
_SCREAMING_SNAKE_CASE : Optional[int] = xformers.__version__
_SCREAMING_SNAKE_CASE : Any = {
'`diffusers` version': version,
'Platform': platform.platform(),
'Python version': platform.python_version(),
'PyTorch version (GPU?)': F"""{pt_version} ({pt_cuda_available})""",
'Huggingface_hub version': hub_version,
'Transformers version': transformers_version,
'Accelerate version': accelerate_version,
'xFormers version': xformers_version,
'Using GPU in script?': '<fill in>',
'Using distributed or parallel set-up in script?': '<fill in>',
}
print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n' )
print(self.format_dict(lowerCAmelCase_ ) )
return info
@staticmethod
def A ( lowerCAmelCase_ ) -> int:
return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()] ) + "\n"
| 621 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCamelCase__ : Optional[Any] = TypeVar("""KT""")
lowerCamelCase__ : str = TypeVar("""VT""")
class _snake_case ( Generic[KT, VT] ):
def __init__( self , SCREAMING_SNAKE_CASE_ = "root" , SCREAMING_SNAKE_CASE_ = None):
'''simple docstring'''
lowercase__ : Union[str, Any] = key
lowercase__ : List[Any] = value
lowercase__ : list[Node[KT, VT]] = []
def __repr__( self):
'''simple docstring'''
return f'Node({self.key}: {self.value})'
@property
def lowercase__ ( self):
'''simple docstring'''
return len(self.forward)
class _snake_case ( Generic[KT, VT] ):
def __init__( self , SCREAMING_SNAKE_CASE_ = 0.5 , SCREAMING_SNAKE_CASE_ = 16):
'''simple docstring'''
lowercase__ : Node[KT, VT] = Node[KT, VT]()
lowercase__ : List[Any] = 0
lowercase__ : str = p
lowercase__ : Dict = max_level
def __str__( self):
'''simple docstring'''
lowercase__ : Union[str, Any] = list(self)
if len(SCREAMING_SNAKE_CASE_) == 0:
return f'SkipList(level={self.level})'
lowercase__ : Any = max((len(str(SCREAMING_SNAKE_CASE_)) for item in items) , default=4)
lowercase__ : int = max(SCREAMING_SNAKE_CASE_ , 4) + 4
lowercase__ : int = self.head
lowercase__ : str = []
lowercase__ : Union[str, Any] = node.forward.copy()
lines.append(f'[{node.key}]'.ljust(SCREAMING_SNAKE_CASE_ , """-""") + """* """ * len(SCREAMING_SNAKE_CASE_))
lines.append(""" """ * label_size + """| """ * len(SCREAMING_SNAKE_CASE_))
while len(node.forward) != 0:
lowercase__ : Optional[int] = node.forward[0]
lines.append(
f'[{node.key}]'.ljust(SCREAMING_SNAKE_CASE_ , """-""")
+ """ """.join(str(n.key) if n.key == node.key else """|""" for n in forwards))
lines.append(""" """ * label_size + """| """ * len(SCREAMING_SNAKE_CASE_))
lowercase__ : Optional[int] = node.forward
lines.append("""None""".ljust(SCREAMING_SNAKE_CASE_) + """* """ * len(SCREAMING_SNAKE_CASE_))
return f'SkipList(level={self.level})\n' + "\n".join(SCREAMING_SNAKE_CASE_)
def __iter__( self):
'''simple docstring'''
lowercase__ : Optional[int] = self.head
while len(node.forward) != 0:
yield node.forward[0].key
lowercase__ : Union[str, Any] = node.forward[0]
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Union[str, Any] = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ : Union[str, Any] = []
lowercase__ : Dict = self.head
for i in reversed(range(self.level)):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
lowercase__ : List[str] = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(SCREAMING_SNAKE_CASE_)
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ , lowercase__ : Optional[int] = self._locate_node(SCREAMING_SNAKE_CASE_)
if node is not None:
for i, update_node in enumerate(SCREAMING_SNAKE_CASE_):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
lowercase__ : int = node.forward[i]
else:
lowercase__ : List[Any] = update_node.forward[:i]
def lowercase__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ , lowercase__ : Union[str, Any] = self._locate_node(SCREAMING_SNAKE_CASE_)
if node is not None:
lowercase__ : str = value
else:
lowercase__ : Union[str, Any] = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , SCREAMING_SNAKE_CASE_):
update_vector.append(self.head)
lowercase__ : int = level
lowercase__ : Optional[int] = Node(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_)
for i, update_node in enumerate(update_vector[:level]):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i])
if update_node.level < i + 1:
update_node.forward.append(SCREAMING_SNAKE_CASE_)
else:
lowercase__ : int = new_node
def lowercase__ ( self , SCREAMING_SNAKE_CASE_):
'''simple docstring'''
lowercase__ , lowercase__ : str = self._locate_node(SCREAMING_SNAKE_CASE_)
if node is not None:
return node.value
return None
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
lowercase__ : int = SkipList()
skip_list.insert("""Key1""" , 3 )
skip_list.insert("""Key2""" , 12 )
skip_list.insert("""Key3""" , 41 )
skip_list.insert("""Key4""" , -19 )
lowercase__ : List[str] = skip_list.head
lowercase__ : Union[str, Any] = {}
while node.level != 0:
lowercase__ : Optional[int] = node.forward[0]
lowercase__ : Union[str, Any] = node.value
assert len(lowercase_ ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
lowercase__ : Dict = SkipList()
skip_list.insert("""Key1""" , 10 )
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""Key5""" , 7 )
skip_list.insert("""Key7""" , 10 )
skip_list.insert("""Key10""" , 5 )
skip_list.insert("""Key7""" , 7 )
skip_list.insert("""Key5""" , 5 )
skip_list.insert("""Key10""" , 10 )
lowercase__ : Optional[int] = skip_list.head
lowercase__ : Optional[int] = {}
while node.level != 0:
lowercase__ : int = node.forward[0]
lowercase__ : str = node.value
if len(lowercase_ ) != 4:
print()
assert len(lowercase_ ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def UpperCamelCase ( ) -> Optional[int]:
'''simple docstring'''
lowercase__ : Optional[Any] = SkipList()
assert skip_list.find("""Some key""" ) is None
def UpperCamelCase ( ) -> int:
'''simple docstring'''
lowercase__ : List[Any] = SkipList()
skip_list.insert("""Key2""" , 20 )
assert skip_list.find("""Key2""" ) == 20
skip_list.insert("""Some Key""" , 10 )
skip_list.insert("""Key2""" , 8 )
skip_list.insert("""V""" , 13 )
assert skip_list.find("""Y""" ) is None
assert skip_list.find("""Key2""" ) == 8
assert skip_list.find("""Some Key""" ) == 10
assert skip_list.find("""V""" ) == 13
def UpperCamelCase ( ) -> str:
'''simple docstring'''
lowercase__ : int = SkipList()
skip_list.delete("""Some key""" )
assert len(skip_list.head.forward ) == 0
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
lowercase__ : Any = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""Key2""" ) is None
def UpperCamelCase ( ) -> Optional[int]:
'''simple docstring'''
lowercase__ : Optional[Any] = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) == 14
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""X""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key1""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) is None
def UpperCamelCase ( ) -> Dict:
'''simple docstring'''
lowercase__ : Dict = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 1_42 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""X""" )
def traverse_keys(lowercase_ ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(lowercase_ )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
def is_sorted(lowercase_ ):
return all(next_item >= item for item, next_item in zip(lowercase_ , lst[1:] ) )
lowercase__ : List[str] = SkipList()
for i in range(10 ):
skip_list.insert(lowercase_ , lowercase_ )
assert is_sorted(list(lowercase_ ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(lowercase_ ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(lowercase_ ) )
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
for _ in range(1_00 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def UpperCamelCase ( ) -> Tuple:
'''simple docstring'''
lowercase__ : Optional[Any] = SkipList()
skip_list.insert(2 , """2""" )
skip_list.insert(4 , """4""" )
skip_list.insert(6 , """4""" )
skip_list.insert(4 , """5""" )
skip_list.insert(8 , """4""" )
skip_list.insert(9 , """4""" )
skip_list.delete(4 )
print(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 495 |
import argparse
import os
import re
lowerCamelCase__ : List[Any] = """src/transformers"""
# Pattern that looks at the indentation in a line.
lowerCamelCase__ : Union[str, Any] = re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__ : Any = re.compile(R"""^\s*\"([^\"]+)\":""")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCamelCase__ : int = re.compile(R"""^\s*_import_structure\[\"([^\"]+)\"\]""")
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCamelCase__ : List[Any] = re.compile(R"""^\s*\"([^\"]+)\",\s*$""")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCamelCase__ : str = re.compile(R"""\[([^\]]+)\]""")
def UpperCamelCase ( lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
lowercase__ : Optional[Any] = _re_indent.search(lowercase_ )
return "" if search is None else search.groups()[0]
def UpperCamelCase ( lowercase_ , lowercase_="" , lowercase_=None , lowercase_=None ) -> Dict:
'''simple docstring'''
lowercase__ : List[str] = 0
lowercase__ : List[Any] = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(lowercase_ ):
index += 1
lowercase__ : List[str] = ["""\n""".join(lines[:index] )]
else:
lowercase__ : Optional[Any] = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
lowercase__ : List[Any] = [lines[index]]
index += 1
while index < len(lowercase_ ) and (end_prompt is None or not lines[index].startswith(lowercase_ )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(lowercase_ ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(lowercase_ ) )
if index < len(lowercase_ ) - 1:
lowercase__ : str = [lines[index + 1]]
index += 1
else:
lowercase__ : Union[str, Any] = []
else:
blocks.append("""\n""".join(lowercase_ ) )
lowercase__ : Tuple = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(lowercase_ ) > 0:
blocks.append("""\n""".join(lowercase_ ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(lowercase_ ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def UpperCamelCase ( lowercase_ ) -> List[Any]:
'''simple docstring'''
def _inner(lowercase_ ):
return key(lowercase_ ).lower().replace("""_""" , """""" )
return _inner
def UpperCamelCase ( lowercase_ , lowercase_=None ) -> Optional[Any]:
'''simple docstring'''
def noop(lowercase_ ):
return x
if key is None:
lowercase__ : Dict = noop
# Constants are all uppercase, they go first.
lowercase__ : Dict = [obj for obj in objects if key(lowercase_ ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
lowercase__ : Any = [obj for obj in objects if key(lowercase_ )[0].isupper() and not key(lowercase_ ).isupper()]
# Functions begin with a lowercase, they go last.
lowercase__ : Optional[int] = [obj for obj in objects if not key(lowercase_ )[0].isupper()]
lowercase__ : Tuple = ignore_underscore(lowercase_ )
return sorted(lowercase_ , key=lowercase_ ) + sorted(lowercase_ , key=lowercase_ ) + sorted(lowercase_ , key=lowercase_ )
def UpperCamelCase ( lowercase_ ) -> Tuple:
'''simple docstring'''
def _replace(lowercase_ ):
lowercase__ : int = match.groups()[0]
if "," not in imports:
return F'[{imports}]'
lowercase__ : Union[str, Any] = [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:
lowercase__ : int = keys[:-1]
return "[" + ", ".join([F'"{k}"' for k in sort_objects(lowercase_ )] ) + "]"
lowercase__ : Any = import_statement.split("""\n""" )
if len(lowercase_ ) > 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.
lowercase__ : Tuple = 2 if lines[1].strip() == """[""" else 1
lowercase__ : Optional[int] = [(i, _re_strip_line.search(lowercase_ ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
lowercase__ : Optional[Any] = sort_objects(lowercase_ , key=lambda lowercase_ : x[1] )
lowercase__ : Union[str, Any] = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(lowercase_ ) == 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:
lowercase__ : List[str] = _re_bracket_content.sub(_replace , lines[1] )
else:
lowercase__ : Optional[int] = [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:
lowercase__ : Optional[int] = keys[:-1]
lowercase__ : int = get_indent(lines[1] ) + """, """.join([F'"{k}"' for k in sort_objects(lowercase_ )] )
return "\n".join(lowercase_ )
else:
# Finally we have to deal with imports fitting on one line
lowercase__ : Any = _re_bracket_content.sub(_replace , lowercase_ )
return import_statement
def UpperCamelCase ( lowercase_ , lowercase_=True ) -> Optional[int]:
'''simple docstring'''
with open(lowercase_ , encoding="""utf-8""" ) as f:
lowercase__ : str = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
lowercase__ : Any = split_code_in_indented_blocks(
lowercase_ , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(lowercase_ ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
lowercase__ : List[str] = main_blocks[block_idx]
lowercase__ : str = block.split("""\n""" )
# Get to the start of the imports.
lowercase__ : Optional[Any] = 0
while line_idx < len(lowercase_ ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
lowercase__ : int = len(lowercase_ )
else:
line_idx += 1
if line_idx >= len(lowercase_ ):
continue
# Ignore beginning and last line: they don't contain anything.
lowercase__ : List[str] = """\n""".join(block_lines[line_idx:-1] )
lowercase__ : Dict = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
lowercase__ : List[str] = split_code_in_indented_blocks(lowercase_ , indent_level=lowercase_ )
# We have two categories of import key: list or _import_structure[key].append/extend
lowercase__ : Optional[int] = _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.
lowercase__ : Optional[Any] = [(pattern.search(lowercase_ ).groups()[0] if pattern.search(lowercase_ ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
lowercase__ : List[Any] = [(i, key) for i, key in enumerate(lowercase_ ) if key is not None]
lowercase__ : Optional[int] = [x[0] for x in sorted(lowercase_ , key=lambda lowercase_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
lowercase__ : Any = 0
lowercase__ : Optional[Any] = []
for i in range(len(lowercase_ ) ):
if keys[i] is None:
reorderded_blocks.append(internal_blocks[i] )
else:
lowercase__ : Optional[Any] = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reorderded_blocks.append(lowercase_ )
count += 1
# And we put our main block back together with its first and last line.
lowercase__ : Union[str, Any] = """\n""".join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]] )
if code != "\n".join(lowercase_ ):
if check_only:
return True
else:
print(F'Overwriting {file}.' )
with open(lowercase_ , """w""" , encoding="""utf-8""" ) as f:
f.write("""\n""".join(lowercase_ ) )
def UpperCamelCase ( lowercase_=True ) -> Optional[int]:
'''simple docstring'''
lowercase__ : int = []
for root, _, files in os.walk(lowercase_ ):
if "__init__.py" in files:
lowercase__ : Dict = sort_imports(os.path.join(lowercase_ , """__init__.py""" ) , check_only=lowercase_ )
if result:
lowercase__ : List[str] = [os.path.join(lowercase_ , """__init__.py""" )]
if len(lowercase_ ) > 0:
raise ValueError(F'Would overwrite {len(lowercase_ )} files, run `make style`.' )
if __name__ == "__main__":
lowerCamelCase__ : int = argparse.ArgumentParser()
parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
lowerCamelCase__ : List[Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 495 | 1 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class snake_case ( lowercase_ ):
"""simple docstring"""
_a = """Wav2Vec2FeatureExtractor"""
_a = """AutoTokenizer"""
def __init__( self, _lowercase, _lowercase ) -> Dict:
super().__init__(snake_case_, snake_case_ )
SCREAMING_SNAKE_CASE_ = self.feature_extractor
SCREAMING_SNAKE_CASE_ = False
@classmethod
def a__ ( cls, _lowercase, **_lowercase ) -> str:
try:
return super().from_pretrained(snake_case_, **snake_case_ )
except OSError:
warnings.warn(
f"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
' include a `tokenizer_class` attribute is deprecated and will be '
'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
' attribute to either your `config.json` or `tokenizer_config.json` '
'file to suppress this warning: ', snake_case_, )
SCREAMING_SNAKE_CASE_ = WavaVecaFeatureExtractor.from_pretrained(snake_case_, **snake_case_ )
SCREAMING_SNAKE_CASE_ = WavaVecaCTCTokenizer.from_pretrained(snake_case_, **snake_case_ )
return cls(feature_extractor=snake_case_, tokenizer=snake_case_ )
def __call__( self, *_lowercase, **_lowercase ) -> Optional[int]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*snake_case_, **snake_case_ )
if "raw_speech" in kwargs:
warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
SCREAMING_SNAKE_CASE_ = kwargs.pop('raw_speech' )
else:
SCREAMING_SNAKE_CASE_ = kwargs.pop('audio', snake_case_ )
SCREAMING_SNAKE_CASE_ = kwargs.pop('sampling_rate', snake_case_ )
SCREAMING_SNAKE_CASE_ = kwargs.pop('text', snake_case_ )
if len(snake_case_ ) > 0:
SCREAMING_SNAKE_CASE_ = args[0]
SCREAMING_SNAKE_CASE_ = args[1:]
if audio is None and text is None:
raise ValueError('You need to specify either an `audio` or `text` input to process.' )
if audio is not None:
SCREAMING_SNAKE_CASE_ = self.feature_extractor(snake_case_, *snake_case_, sampling_rate=snake_case_, **snake_case_ )
if text is not None:
SCREAMING_SNAKE_CASE_ = self.tokenizer(snake_case_, **snake_case_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
SCREAMING_SNAKE_CASE_ = encodings['input_ids']
return inputs
def a__ ( self, *_lowercase, **_lowercase ) -> Tuple:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*snake_case_, **snake_case_ )
SCREAMING_SNAKE_CASE_ = kwargs.pop('input_features', snake_case_ )
SCREAMING_SNAKE_CASE_ = kwargs.pop('labels', snake_case_ )
if len(snake_case_ ) > 0:
SCREAMING_SNAKE_CASE_ = args[0]
SCREAMING_SNAKE_CASE_ = args[1:]
if input_features is not None:
SCREAMING_SNAKE_CASE_ = self.feature_extractor.pad(snake_case_, *snake_case_, **snake_case_ )
if labels is not None:
SCREAMING_SNAKE_CASE_ = self.tokenizer.pad(snake_case_, **snake_case_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
SCREAMING_SNAKE_CASE_ = labels['input_ids']
return input_features
def a__ ( self, *_lowercase, **_lowercase ) -> Any:
return self.tokenizer.batch_decode(*snake_case_, **snake_case_ )
def a__ ( self, *_lowercase, **_lowercase ) -> Optional[int]:
return self.tokenizer.decode(*snake_case_, **snake_case_ )
@contextmanager
def a__ ( self ) -> Union[str, Any]:
warnings.warn(
'`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
'your audio inputs, or in a separate call.' )
SCREAMING_SNAKE_CASE_ = True
SCREAMING_SNAKE_CASE_ = self.tokenizer
yield
SCREAMING_SNAKE_CASE_ = self.feature_extractor
SCREAMING_SNAKE_CASE_ = False
| 294 |
def __lowerCAmelCase ( __lowerCamelCase : int , __lowerCamelCase : int ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 354 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _SCREAMING_SNAKE_CASE ( snake_case_ : int ):
def wrapper(*snake_case_ : int , **snake_case_ : Optional[Any] ):
__magic_name__ = timeit.default_timer()
__magic_name__ = func(*snake_case_ , **snake_case_ )
__magic_name__ = timeit.default_timer() - starttime
return delta
__magic_name__ = func.__name__
return wrapper
def _SCREAMING_SNAKE_CASE ( snake_case_ : dict , snake_case_ : Any=100 , snake_case_ : int=None ):
__magic_name__ = []
__magic_name__ = seq_shapes or {}
for i in range(snake_case_ ):
__magic_name__ = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(snake_case_ , _ArrayXD ):
__magic_name__ = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(snake_case_ , datasets.Value ):
if v.dtype == "string":
__magic_name__ = '''The small grey turtle was surprisingly fast when challenged.'''
else:
__magic_name__ = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(snake_case_ , datasets.Sequence ):
while isinstance(snake_case_ , datasets.Sequence ):
__magic_name__ = v.feature
__magic_name__ = seq_shapes[k]
__magic_name__ = np.random.rand(*snake_case_ ).astype(v.dtype )
__magic_name__ = data
dummy_data.append((i, example) )
return dummy_data
def _SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, Any] , snake_case_ : List[Any] , snake_case_ : Optional[Any]=100 , snake_case_ : Dict=None ):
__magic_name__ = generate_examples(snake_case_ , num_examples=snake_case_ , seq_shapes=snake_case_ )
with ArrowWriter(features=snake_case_ , path=snake_case_ ) as writer:
for key, record in dummy_data:
__magic_name__ = features.encode_example(snake_case_ )
writer.write(snake_case_ )
__magic_name__ , __magic_name__ = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.' )
__magic_name__ = datasets.Dataset.from_file(filename=snake_case_ , info=datasets.DatasetInfo(features=snake_case_ ) )
return dataset
| 703 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( snake_case_ : str ):
return "".join(sorted(snake_case_ ) )
def _SCREAMING_SNAKE_CASE ( snake_case_ : str ):
return word_by_signature[signature(snake_case_ )]
a_ : str = Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
a_ : Optional[Any] = sorted({word.strip().lower() for word in data.splitlines()})
a_ : List[Any] = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
a_ : Optional[Any] = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('anagrams.txt', 'w') as file:
file.write('all_anagrams = \n ')
file.write(pprint.pformat(all_anagrams))
| 678 | 0 |
import requests
lowercase : List[Any] = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def UpperCAmelCase_ ( _UpperCAmelCase ):
# fetching a list of articles in json format
lowerCamelCase_: Any = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["""articles"""] , 1 ):
print(f"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
| 423 |
"""simple docstring"""
def lowerCAmelCase__ ( __magic_name__ ) ->int:
if not isinstance(__magic_name__ , __magic_name__ ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence() does not accept negative values" )
__lowercase = 0
__lowercase = str(__magic_name__ )
while len(__magic_name__ ) != 1:
__lowercase = [int(__magic_name__ ) for i in num_string]
__lowercase = 1
for i in range(0 , len(__magic_name__ ) ):
total *= numbers[i]
__lowercase = str(__magic_name__ )
steps += 1
return steps
def lowerCAmelCase__ ( __magic_name__ ) ->int:
if not isinstance(__magic_name__ , __magic_name__ ):
raise ValueError("additive_persistence() only accepts integral values" )
if num < 0:
raise ValueError("additive_persistence() does not accept negative values" )
__lowercase = 0
__lowercase = str(__magic_name__ )
while len(__magic_name__ ) != 1:
__lowercase = [int(__magic_name__ ) for i in num_string]
__lowercase = 0
for i in range(0 , len(__magic_name__ ) ):
total += numbers[i]
__lowercase = str(__magic_name__ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 118 | 0 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
return int(input_a == input_a == 0 )
def A_ ( ) -> None:
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
print(F"""| 0 | 1 | {nor_gate(0 , 1 )} |""" )
print(F"""| 1 | 0 | {nor_gate(1 , 0 )} |""" )
print(F"""| 1 | 1 | {nor_gate(1 , 1 )} |""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 38 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class A__ :
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=True , A_=True , A_=99 , A_=32 , A_=2 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ):
'''simple docstring'''
UpperCamelCase : Dict = parent
UpperCamelCase : str = 13
UpperCamelCase : int = 7
UpperCamelCase : str = True
UpperCamelCase : Dict = True
UpperCamelCase : str = True
UpperCamelCase : Tuple = True
UpperCamelCase : List[str] = 99
UpperCamelCase : Optional[Any] = 384
UpperCamelCase : Tuple = 2
UpperCamelCase : Union[str, Any] = 4
UpperCamelCase : Dict = 37
UpperCamelCase : Any = "gelu"
UpperCamelCase : List[Any] = 0.1
UpperCamelCase : int = 0.1
UpperCamelCase : Tuple = 512
UpperCamelCase : List[Any] = 16
UpperCamelCase : int = 2
UpperCamelCase : Dict = 0.02
UpperCamelCase : Optional[Any] = 3
UpperCamelCase : List[Any] = 4
UpperCamelCase : Dict = 128
UpperCamelCase : Optional[Any] = 2
UpperCamelCase : Optional[int] = 9
UpperCamelCase : Optional[int] = 1
UpperCamelCase : Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : str = None
if self.use_input_mask:
UpperCamelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase : Tuple = None
if self.use_token_type_ids:
UpperCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase : Optional[int] = None
UpperCamelCase : Optional[int] = None
UpperCamelCase : List[Any] = None
if self.use_labels:
UpperCamelCase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase : Any = ConvBertConfig(
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 , initializer_range=self.initializer_range , return_dict=A_ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : str = TFConvBertModel(config=A_ )
UpperCamelCase : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
UpperCamelCase : Optional[int] = [input_ids, input_mask]
UpperCamelCase : Any = model(A_ )
UpperCamelCase : int = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Tuple = TFConvBertForMaskedLM(config=A_ )
UpperCamelCase : int = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCamelCase : Dict = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Dict = self.num_labels
UpperCamelCase : int = TFConvBertForSequenceClassification(config=A_ )
UpperCamelCase : List[Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCamelCase : Optional[Any] = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : List[str] = self.num_choices
UpperCamelCase : str = TFConvBertForMultipleChoice(config=A_ )
UpperCamelCase : List[Any] = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : Dict = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : Any = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
UpperCamelCase : List[str] = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
UpperCamelCase : Optional[Any] = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : Dict = self.num_labels
UpperCamelCase : str = TFConvBertForTokenClassification(config=A_ )
UpperCamelCase : List[Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCamelCase : str = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
'''simple docstring'''
UpperCamelCase : List[str] = TFConvBertForQuestionAnswering(config=A_ )
UpperCamelCase : Union[str, Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
UpperCamelCase : Union[str, Any] = model(A_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) : Optional[Any] = config_and_inputs
UpperCamelCase : int = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A__ ( __snake_case , __snake_case , unittest.TestCase ):
_UpperCAmelCase :Dict = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_UpperCAmelCase :Optional[Any] = (
{
'feature-extraction': TFConvBertModel,
'fill-mask': TFConvBertForMaskedLM,
'question-answering': TFConvBertForQuestionAnswering,
'text-classification': TFConvBertForSequenceClassification,
'token-classification': TFConvBertForTokenClassification,
'zero-shot': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_UpperCAmelCase :Any = False
_UpperCAmelCase :int = False
_UpperCAmelCase :str = False
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = TFConvBertModelTester(self )
UpperCamelCase : Dict = ConfigTester(self , config_class=A_ , hidden_size=37 )
def __UpperCamelCase( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A_ )
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase : Optional[Any] = True
UpperCamelCase : Any = True
if hasattr(A_ , "use_cache" ):
UpperCamelCase : List[str] = True
UpperCamelCase : List[Any] = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
UpperCamelCase : Any = getattr(self.model_tester , "key_length" , A_ )
for model_class in self.all_model_classes:
UpperCamelCase : List[Any] = self._prepare_for_class(A_ , A_ )
UpperCamelCase : Dict = model_class(A_ )
UpperCamelCase : Optional[int] = len(model(A_ ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(A_ , saved_model=A_ )
UpperCamelCase : Union[str, Any] = os.path.join(A_ , "saved_model" , "1" )
UpperCamelCase : Dict = tf.keras.models.load_model(A_ )
UpperCamelCase : str = model(A_ )
if self.is_encoder_decoder:
UpperCamelCase : Union[str, Any] = outputs["encoder_hidden_states"]
UpperCamelCase : Any = outputs["encoder_attentions"]
else:
UpperCamelCase : Any = outputs["hidden_states"]
UpperCamelCase : List[str] = outputs["attentions"]
self.assertEqual(len(A_ ) , A_ )
UpperCamelCase : int = getattr(
self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(A_ ) , A_ )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
self.assertIsNotNone(A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase , UpperCamelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase : Dict = True
UpperCamelCase : int = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length )
UpperCamelCase : Optional[int] = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length )
UpperCamelCase : Optional[int] = getattr(self.model_tester , "key_length" , A_ )
UpperCamelCase : Optional[Any] = getattr(self.model_tester , "key_length" , A_ )
def check_decoder_attentions_output(A_ ):
UpperCamelCase : Optional[Any] = len(A_ )
self.assertEqual(out_len % 2 , 0 )
UpperCamelCase : Any = outputs.decoder_attentions
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(A_ ):
UpperCamelCase : Dict = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(A_ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
UpperCamelCase : Union[str, Any] = True
UpperCamelCase : List[Any] = False
UpperCamelCase : Dict = model_class(A_ )
UpperCamelCase : Dict = model(self._prepare_for_class(A_ , A_ ) )
UpperCamelCase : List[str] = len(A_ )
self.assertEqual(config.output_hidden_states , A_ )
check_encoder_attentions_output(A_ )
if self.is_encoder_decoder:
UpperCamelCase : int = model_class(A_ )
UpperCamelCase : Tuple = model(self._prepare_for_class(A_ , A_ ) )
self.assertEqual(config.output_hidden_states , A_ )
check_decoder_attentions_output(A_ )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
UpperCamelCase : Tuple = True
UpperCamelCase : int = model_class(A_ )
UpperCamelCase : Dict = model(self._prepare_for_class(A_ , A_ ) )
self.assertEqual(config.output_hidden_states , A_ )
check_encoder_attentions_output(A_ )
# Check attention is always last and order is fine
UpperCamelCase : Optional[int] = True
UpperCamelCase : List[str] = True
UpperCamelCase : Optional[int] = model_class(A_ )
UpperCamelCase : Optional[Any] = model(self._prepare_for_class(A_ , A_ ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(A_ ) )
self.assertEqual(model.config.output_hidden_states , A_ )
check_encoder_attentions_output(A_ )
@require_tf
class A__ ( unittest.TestCase ):
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" )
UpperCamelCase : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCamelCase : List[str] = model(A_ )[0]
UpperCamelCase : int = [1, 6, 768]
self.assertEqual(output.shape , A_ )
UpperCamelCase : List[str] = tf.constant(
[
[
[-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32],
[0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24],
[0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A_ , atol=1e-4 )
| 38 | 1 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
_A = ''
_A = ''
_A = ''
_A = ''
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> None:
SCREAMING_SNAKE_CASE__ = tweepy.OAuthHandler(__UpperCAmelCase , __UpperCAmelCase )
auth.set_access_token(__UpperCAmelCase , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = tweepy.API(__UpperCAmelCase )
# initialize a list to hold all the tweepy Tweets
SCREAMING_SNAKE_CASE__ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
SCREAMING_SNAKE_CASE__ = api.user_timeline(screen_name=__UpperCAmelCase , count=200 )
# save most recent tweets
alltweets.extend(__UpperCAmelCase )
# save the id of the oldest tweet less one
SCREAMING_SNAKE_CASE__ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(__UpperCAmelCase ) > 0:
print(F"""getting tweets before {oldest}""" )
# all subsequent requests use the max_id param to prevent duplicates
SCREAMING_SNAKE_CASE__ = api.user_timeline(
screen_name=__UpperCAmelCase , count=200 , max_id=__UpperCAmelCase )
# save most recent tweets
alltweets.extend(__UpperCAmelCase )
# update the id of the oldest tweet less one
SCREAMING_SNAKE_CASE__ = alltweets[-1].id - 1
print(F"""...{len(__UpperCAmelCase )} tweets downloaded so far""" )
# transform the tweepy tweets into a 2D array that will populate the csv
SCREAMING_SNAKE_CASE__ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F"""new_{screen_name}_tweets.csv""" , "w" ) as f:
SCREAMING_SNAKE_CASE__ = csv.writer(__UpperCAmelCase )
writer.writerow(["id", "created_at", "text"] )
writer.writerows(__UpperCAmelCase )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('FirePing32')
| 159 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class lowerCAmelCase_ :
def __init__( self : Dict , __A : Optional[int] , __A : int=2 , __A : str=True , __A : List[Any]=False , __A : List[str]=10 , __A : Union[str, Any]=3 , __A : Dict=32 * 8 , __A : str=32 * 8 , __A : int=4 , __A : List[str]=64 , ) ->Tuple:
"""simple docstring"""
a__ :Optional[Any] = parent
a__ :Dict = batch_size
a__ :str = is_training
a__ :Optional[int] = use_auxiliary_loss
a__ :str = num_queries
a__ :int = num_channels
a__ :Optional[int] = min_size
a__ :Optional[Any] = max_size
a__ :Dict = num_labels
a__ :Union[str, Any] = hidden_dim
a__ :Any = hidden_dim
def _snake_case ( self : Tuple ) ->List[str]:
"""simple docstring"""
a__ :Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
a__ :Optional[int] = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
a__ :Optional[int] = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
a__ :List[str] = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
a__ :Tuple = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def _snake_case ( self : Tuple ) ->Union[str, Any]:
"""simple docstring"""
a__ :List[str] = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
a__ :List[str] = self.num_queries
a__ :Optional[int] = self.num_labels
a__ :Tuple = [1, 1, 1, 1]
a__ :Dict = self.num_channels
a__ :Optional[Any] = 64
a__ :Union[str, Any] = 128
a__ :Optional[Any] = self.hidden_dim
a__ :int = self.hidden_dim
a__ :List[str] = self.hidden_dim
return config
def _snake_case ( self : Any ) ->Dict:
"""simple docstring"""
a__ , a__ , a__ , a__ , a__ :int = self.prepare_config_and_inputs()
a__ :Optional[int] = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def _snake_case ( self : int , __A : Union[str, Any] , __A : Tuple ) ->List[Any]:
"""simple docstring"""
a__ :Tuple = output.encoder_hidden_states
a__ :List[Any] = output.pixel_decoder_hidden_states
a__ :Optional[Any] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_layers )
def _snake_case ( self : Dict , __A : List[str] , __A : Tuple , __A : Union[str, Any] , __A : Dict=False ) ->Any:
"""simple docstring"""
with torch.no_grad():
a__ :Dict = MaskaFormerModel(config=__A )
model.to(__A )
model.eval()
a__ :Tuple = model(pixel_values=__A , pixel_mask=__A )
a__ :Dict = model(__A , output_hidden_states=__A )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def _snake_case ( self : Optional[int] , __A : int , __A : str , __A : Tuple , __A : List[Any] , __A : List[str] ) ->Any:
"""simple docstring"""
a__ :Dict = MaskaFormerForUniversalSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a__ :Union[str, Any] = model(pixel_values=__A , pixel_mask=__A )
a__ :List[Any] = model(__A )
comm_check_on_output(__A )
a__ :str = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class lowerCAmelCase_ ( _a ,_a ,unittest.TestCase):
lowerCamelCase_ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
lowerCamelCase_ = {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def _snake_case ( self : Tuple ) ->Dict:
"""simple docstring"""
a__ :List[str] = MaskaFormerModelTester(self )
a__ :List[str] = ConfigTester(self , config_class=__A , has_text_modality=__A )
def _snake_case ( self : Tuple ) ->str:
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self : str ) ->Tuple:
"""simple docstring"""
a__ , a__ :str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__A , **__A , output_hidden_states=__A )
def _snake_case ( self : Union[str, Any] ) ->int:
"""simple docstring"""
a__ :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason="Mask2Former does not use inputs_embeds" )
def _snake_case ( self : List[str] ) ->Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method" )
def _snake_case ( self : int ) ->Dict:
"""simple docstring"""
pass
@unittest.skip(reason="Mask2Former is not a generative model" )
def _snake_case ( self : Any ) ->List[str]:
"""simple docstring"""
pass
@unittest.skip(reason="Mask2Former does not use token embeddings" )
def _snake_case ( self : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def _snake_case ( self : str ) ->str:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : Optional[int] ) ->Any:
"""simple docstring"""
pass
def _snake_case ( self : Any ) ->Union[str, Any]:
"""simple docstring"""
a__ , a__ :Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ :Any = model_class(__A )
a__ :Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ :Optional[Any] = [*signature.parameters.keys()]
a__ :str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __A )
@slow
def _snake_case ( self : Dict ) ->str:
"""simple docstring"""
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
a__ :Any = MaskaFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def _snake_case ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a__ :Tuple = (self.model_tester.min_size,) * 2
a__ :Optional[int] = {
"pixel_values": torch.randn((2, 3, *size) , device=__A ),
"mask_labels": torch.randn((2, 10, *size) , device=__A ),
"class_labels": torch.zeros(2 , 10 , device=__A ).long(),
}
a__ :Dict = self.model_tester.get_config()
a__ :str = MaskaFormerForUniversalSegmentation(__A ).to(__A )
a__ :int = model(**__A )
self.assertTrue(outputs.loss is not None )
def _snake_case ( self : List[str] ) ->Any:
"""simple docstring"""
a__ , a__ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__A , **__A , output_hidden_states=__A )
def _snake_case ( self : Tuple ) ->Tuple:
"""simple docstring"""
a__ , a__ :Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ :Union[str, Any] = model_class(__A ).to(__A )
a__ :int = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def _snake_case ( self : Any ) ->Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
a__ :str = self.all_model_classes[1]
a__ , a__ , a__ , a__ , a__ :Any = self.model_tester.prepare_config_and_inputs()
a__ :Optional[Any] = model_class(__A )
model.to(__A )
model.train()
a__ :Any = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def _snake_case ( self : Tuple ) ->Optional[Any]:
"""simple docstring"""
a__ :List[str] = self.all_model_classes[1]
a__ , a__ , a__ , a__ , a__ :int = self.model_tester.prepare_config_and_inputs()
a__ :Optional[Any] = True
a__ :Optional[int] = True
a__ :List[Any] = model_class(__A ).to(__A )
model.train()
a__ :Optional[Any] = model(__A , mask_labels=__A , class_labels=__A )
a__ :int = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a__ :List[str] = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
a__ :List[Any] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a__ :Optional[int] = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
snake_case__ = 1e-4
def lowerCamelCase__ ( ) -> Optional[int]:
"""simple docstring"""
a__ :Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class lowerCAmelCase_ ( unittest.TestCase):
@cached_property
def _snake_case ( self : int ) ->Optional[int]:
"""simple docstring"""
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def _snake_case ( self : int ) ->Dict:
"""simple docstring"""
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def _snake_case ( self : str ) ->int:
"""simple docstring"""
a__ :Tuple = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__A )
a__ :List[Any] = self.default_image_processor
a__ :str = prepare_img()
a__ :Union[str, Any] = image_processor(__A , return_tensors="pt" ).to(__A )
a__ :Union[str, Any] = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 384, 384) )
with torch.no_grad():
a__ :Optional[int] = model(**__A )
a__ :str = torch.tensor(
[[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
a__ :Dict = torch.tensor(
[[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
a__ :Union[str, Any] = torch.tensor(
[[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def _snake_case ( self : Any ) ->Dict:
"""simple docstring"""
a__ :Dict = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__A ).eval()
a__ :Tuple = self.default_image_processor
a__ :Any = prepare_img()
a__ :str = image_processor(__A , return_tensors="pt" ).to(__A )
a__ :Any = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 384, 384) )
with torch.no_grad():
a__ :int = model(**__A )
# masks_queries_logits
a__ :Optional[Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
a__ :Dict = [
[-8.7_839, -9.0_056, -8.8_121],
[-7.4_104, -7.0_313, -6.5_401],
[-6.6_105, -6.3_427, -6.4_675],
]
a__ :Any = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
a__ :Tuple = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
a__ :Optional[int] = torch.tensor(
[
[1.8_324, -8.0_835, -4.1_922],
[0.8_450, -9.0_050, -3.6_053],
[0.3_045, -7.7_293, -3.0_275],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def _snake_case ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a__ :List[str] = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__A ).eval()
a__ :Tuple = self.default_image_processor
a__ :str = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a__ :Tuple = inputs["pixel_values"].to(__A )
a__ :List[Any] = [el.to(__A ) for el in inputs["mask_labels"]]
a__ :List[str] = [el.to(__A ) for el in inputs["class_labels"]]
with torch.no_grad():
a__ :List[str] = model(**__A )
self.assertTrue(outputs.loss is not None )
| 395 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if not nums:
return 0
SCREAMING_SNAKE_CASE = nums[0]
SCREAMING_SNAKE_CASE = 0
for num in nums[1:]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = (
max_excluding + num,
max(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ),
)
return max(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 706 |
"""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 transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
SCREAMING_SNAKE_CASE = 1_9_2
SCREAMING_SNAKE_CASE = 7_6_8
SCREAMING_SNAKE_CASE = 1_2
SCREAMING_SNAKE_CASE = 3
SCREAMING_SNAKE_CASE = [8_0_0, 1_3_3_3]
SCREAMING_SNAKE_CASE = False
elif yolos_name == "yolos_s_dWr":
SCREAMING_SNAKE_CASE = 3_3_0
SCREAMING_SNAKE_CASE = 1_4
SCREAMING_SNAKE_CASE = 6
SCREAMING_SNAKE_CASE = 1_3_2_0
elif "yolos_s" in yolos_name:
SCREAMING_SNAKE_CASE = 3_8_4
SCREAMING_SNAKE_CASE = 1_5_3_6
SCREAMING_SNAKE_CASE = 1_2
SCREAMING_SNAKE_CASE = 6
elif "yolos_b" in yolos_name:
SCREAMING_SNAKE_CASE = [8_0_0, 1_3_4_4]
SCREAMING_SNAKE_CASE = 9_1
SCREAMING_SNAKE_CASE = 'huggingface/label-files'
SCREAMING_SNAKE_CASE = 'coco-detection-id2label.json'
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, repo_type='dataset' ), 'r' ) )
SCREAMING_SNAKE_CASE = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = False ):
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 = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE = in_proj_weight[: config.hidden_size, :]
SCREAMING_SNAKE_CASE = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE = in_proj_weight[-config.hidden_size :, :]
SCREAMING_SNAKE_CASE = in_proj_bias[-config.hidden_size :]
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
if "backbone" in name:
SCREAMING_SNAKE_CASE = name.replace('backbone', 'vit' )
if "cls_token" in name:
SCREAMING_SNAKE_CASE = name.replace('cls_token', 'embeddings.cls_token' )
if "det_token" in name:
SCREAMING_SNAKE_CASE = name.replace('det_token', 'embeddings.detection_tokens' )
if "mid_pos_embed" in name:
SCREAMING_SNAKE_CASE = name.replace('mid_pos_embed', 'encoder.mid_position_embeddings' )
if "pos_embed" in name:
SCREAMING_SNAKE_CASE = name.replace('pos_embed', 'embeddings.position_embeddings' )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace('patch_embed.proj', 'embeddings.patch_embeddings.projection' )
if "blocks" in name:
SCREAMING_SNAKE_CASE = name.replace('blocks', 'encoder.layer' )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace('attn.proj', 'attention.output.dense' )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace('attn', 'attention.self' )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace('norm1', 'layernorm_before' )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace('norm2', 'layernorm_after' )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace('mlp.fc1', 'intermediate.dense' )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace('mlp.fc2', 'output.dense' )
if "class_embed" in name:
SCREAMING_SNAKE_CASE = name.replace('class_embed', 'class_labels_classifier' )
if "bbox_embed" in name:
SCREAMING_SNAKE_CASE = name.replace('bbox_embed', 'bbox_predictor' )
if "vit.norm" in name:
SCREAMING_SNAKE_CASE = name.replace('vit.norm', 'vit.layernorm' )
return name
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if "qkv" in key:
SCREAMING_SNAKE_CASE = key.split('.' )
SCREAMING_SNAKE_CASE = int(key_split[2] )
SCREAMING_SNAKE_CASE = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[:dim]
SCREAMING_SNAKE_CASE = val[dim : dim * 2]
SCREAMING_SNAKE_CASE = val[-dim:]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def UpperCamelCase_ ( ):
SCREAMING_SNAKE_CASE = 'http://images.cocodataset.org/val2017/000000039769.jpg'
SCREAMING_SNAKE_CASE = Image.open(requests.get(SCREAMING_SNAKE_CASE_, stream=SCREAMING_SNAKE_CASE_ ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = False ):
SCREAMING_SNAKE_CASE = get_yolos_config(SCREAMING_SNAKE_CASE_ )
# load original state_dict
SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_, map_location='cpu' )['model']
# load 🤗 model
SCREAMING_SNAKE_CASE = YolosForObjectDetection(SCREAMING_SNAKE_CASE_ )
model.eval()
SCREAMING_SNAKE_CASE = convert_state_dict(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# Check outputs on an image, prepared by YolosImageProcessor
SCREAMING_SNAKE_CASE = 8_0_0 if yolos_name != 'yolos_ti' else 5_1_2
SCREAMING_SNAKE_CASE = YolosImageProcessor(format='coco_detection', size=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = image_processor(images=prepare_img(), return_tensors='pt' )
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = outputs.logits, outputs.pred_boxes
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None, None
if yolos_name == "yolos_ti":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]] )
elif yolos_name == "yolos_s_200_pre":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] )
elif yolos_name == "yolos_s_300_pre":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]] )
elif yolos_name == "yolos_s_dWr":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]] )
elif yolos_name == "yolos_base":
SCREAMING_SNAKE_CASE = torch.tensor(
[[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]] )
SCREAMING_SNAKE_CASE = torch.tensor(
[[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]] )
else:
raise ValueError(f'''Unknown yolos_name: {yolos_name}''' )
assert torch.allclose(logits[0, :3, :3], SCREAMING_SNAKE_CASE_, atol=1E-4 )
assert torch.allclose(pred_boxes[0, :3, :3], SCREAMING_SNAKE_CASE_, atol=1E-4 )
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(f'''Saving model {yolos_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
SCREAMING_SNAKE_CASE = {
'yolos_ti': 'yolos-tiny',
'yolos_s_200_pre': 'yolos-small',
'yolos_s_300_pre': 'yolos-small-300',
'yolos_s_dWr': 'yolos-small-dwr',
'yolos_base': 'yolos-base',
}
print('Pushing to the hub...' )
SCREAMING_SNAKE_CASE = model_mapping[yolos_name]
image_processor.push_to_hub(SCREAMING_SNAKE_CASE_, organization='hustvl' )
model.push_to_hub(SCREAMING_SNAKE_CASE_, organization='hustvl' )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--yolos_name',
default='yolos_s_200_pre',
type=str,
help=(
'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
),
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
)
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 or not to push the converted model to the 🤗 hub.'
)
snake_case = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 406 | 0 |
# Copyright 2021 The HuggingFace Team. 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.
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
lowercase : Optional[int] = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def UpperCAmelCase_ ():
__UpperCamelCase : Dict = _ask_options(
"In which compute environment are you running?" , ["This machine", "AWS (Amazon SageMaker)"] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
__UpperCamelCase : Dict = get_sagemaker_input()
else:
__UpperCamelCase : List[Any] = get_cluster_input()
return config
def UpperCAmelCase_ (_lowerCAmelCase : int=None ):
if subparsers is not None:
__UpperCamelCase : Dict = subparsers.add_parser("config" , description=_lowerCAmelCase )
else:
__UpperCamelCase : Optional[int] = argparse.ArgumentParser("Accelerate config command" , description=_lowerCAmelCase )
parser.add_argument(
"--config_file" , default=_lowerCAmelCase , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , )
if subparsers is not None:
parser.set_defaults(func=_lowerCAmelCase )
return parser
def UpperCAmelCase_ (_lowerCAmelCase : List[Any] ):
__UpperCamelCase : int = get_user_input()
if args.config_file is not None:
__UpperCamelCase : Optional[int] = args.config_file
else:
if not os.path.isdir(_lowerCAmelCase ):
os.makedirs(_lowerCAmelCase )
__UpperCamelCase : Optional[int] = default_yaml_config_file
if config_file.endswith(".json" ):
config.to_json_file(_lowerCAmelCase )
else:
config.to_yaml_file(_lowerCAmelCase )
print(F'''accelerate configuration saved at {config_file}''' )
def UpperCAmelCase_ ():
__UpperCamelCase : List[Any] = config_command_parser()
__UpperCamelCase : Dict = parser.parse_args()
config_command(_lowerCAmelCase )
if __name__ == "__main__":
main()
| 327 |
# Copyright 2021 The HuggingFace Team. 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.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def UpperCAmelCase_ ():
__UpperCamelCase : Any = ArgumentParser("Accelerate CLI tool" , usage="accelerate <command> [<args>]" , allow_abbrev=_lowerCAmelCase )
__UpperCamelCase : Optional[Any] = parser.add_subparsers(help="accelerate command helpers" )
# Register commands
get_config_parser(subparsers=_lowerCAmelCase )
env_command_parser(subparsers=_lowerCAmelCase )
launch_command_parser(subparsers=_lowerCAmelCase )
tpu_command_parser(subparsers=_lowerCAmelCase )
test_command_parser(subparsers=_lowerCAmelCase )
# Let's go
__UpperCamelCase : int = parser.parse_args()
if not hasattr(_lowerCAmelCase , "func" ):
parser.print_help()
exit(1 )
# Run
args.func(_lowerCAmelCase )
if __name__ == "__main__":
main()
| 327 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
if "img_encoder.pos_embed" in name:
A_ : Tuple = name.replace("""img_encoder.pos_embed""" ,"""vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
A_ : Optional[Any] = name.replace("""img_encoder.patch_embed.proj""" ,"""vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
A_ : List[Any] = name.replace("""img_encoder.patch_embed.norm""" ,"""vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
A_ : Dict = name.replace("""img_encoder.layers""" ,"""vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
A_ : str = name.replace("""blocks""" ,"""layers""" )
if "attn" in name and "pre_assign" not in name:
A_ : Optional[int] = name.replace("""attn""" ,"""self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
A_ : Any = name.replace("""proj""" ,"""out_proj""" )
if "pre_assign_attn.attn.proj" in name:
A_ : Optional[int] = name.replace("""pre_assign_attn.attn.proj""" ,"""pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
A_ : List[str] = name.replace("""norm1""" ,"""layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
A_ : str = name.replace("""norm2""" ,"""layer_norm2""" )
if "img_encoder.norm" in name:
A_ : Dict = name.replace("""img_encoder.norm""" ,"""vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
A_ : Union[str, Any] = name.replace("""text_encoder.token_embedding""" ,"""text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
A_ : List[str] = name.replace("""text_encoder.positional_embedding""" ,"""text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
A_ : Union[str, Any] = name.replace("""text_encoder.transformer.resblocks.""" ,"""text_model.encoder.layers.""" )
if "ln_1" in name:
A_ : Tuple = name.replace("""ln_1""" ,"""layer_norm1""" )
if "ln_2" in name:
A_ : Any = name.replace("""ln_2""" ,"""layer_norm2""" )
if "c_fc" in name:
A_ : int = name.replace("""c_fc""" ,"""fc1""" )
if "c_proj" in name:
A_ : Optional[int] = name.replace("""c_proj""" ,"""fc2""" )
if "text_encoder" in name:
A_ : Optional[int] = name.replace("""text_encoder""" ,"""text_model""" )
if "ln_final" in name:
A_ : Any = name.replace("""ln_final""" ,"""final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
A_ : Tuple = name.replace("""img_projector.linear_hidden.""" ,"""visual_projection.""" )
if "img_projector.linear_out." in name:
A_ : Any = name.replace("""img_projector.linear_out.""" ,"""visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
A_ : str = name.replace("""text_projector.linear_hidden""" ,"""text_projection""" )
if "text_projector.linear_out" in name:
A_ : Dict = name.replace("""text_projector.linear_out""" ,"""text_projection.3""" )
return name
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
A_ : Dict = orig_state_dict.pop(_lowerCAmelCase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
A_ : int = key.split(""".""" )
A_ , A_ : List[Any] = int(key_split[2] ), int(key_split[4] )
A_ : Any = config.vision_config.hidden_size
if "weight" in key:
A_ : Optional[Any] = val[:dim, :]
A_ : Union[str, Any] = val[dim : dim * 2, :]
A_ : List[str] = val[-dim:, :]
else:
A_ : Tuple = val[:dim]
A_ : int = val[dim : dim * 2]
A_ : Dict = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
A_ : int = key.split(""".""" )
A_ : Optional[int] = int(key_split[3] )
A_ : Dict = config.text_config.hidden_size
if "weight" in key:
A_ : str = val[:dim, :]
A_ : Optional[int] = val[
dim : dim * 2, :
]
A_ : int = val[-dim:, :]
else:
A_ : List[Any] = val[:dim]
A_ : Tuple = val[dim : dim * 2]
A_ : Any = val[-dim:]
else:
A_ : str = rename_key(_lowerCAmelCase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
A_ : str = val.squeeze_()
else:
A_ : Tuple = val
return orig_state_dict
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : Union[str, Any] = Image.open(requests.get(_lowerCAmelCase ,stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase="groupvit-gcc-yfcc" ,_lowerCAmelCase=False ):
'''simple docstring'''
A_ : int = GroupViTConfig()
A_ : Tuple = GroupViTModel(_lowerCAmelCase ).eval()
A_ : List[str] = torch.load(_lowerCAmelCase ,map_location="""cpu""" )["""model"""]
A_ : Dict = convert_state_dict(_lowerCAmelCase ,_lowerCAmelCase )
A_ , A_ : Optional[int] = model.load_state_dict(_lowerCAmelCase ,strict=_lowerCAmelCase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowerCAmelCase ) == 0)
# verify result
A_ : int = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
A_ : str = prepare_img()
A_ : Dict = processor(text=["""a photo of a cat""", """a photo of a dog"""] ,images=_lowerCAmelCase ,padding=_lowerCAmelCase ,return_tensors="""pt""" )
with torch.no_grad():
A_ : Union[str, Any] = model(**_lowerCAmelCase )
if model_name == "groupvit-gcc-yfcc":
A_ : List[str] = torch.tensor([[13.3523, 6.3629]] )
elif model_name == "groupvit-gcc-redcaps":
A_ : Dict = torch.tensor([[16.1873, 8.6230]] )
else:
raise ValueError(f"""Model name {model_name} not supported.""" )
assert torch.allclose(outputs.logits_per_image ,_lowerCAmelCase ,atol=1e-3 )
processor.save_pretrained(_lowerCAmelCase )
model.save_pretrained(_lowerCAmelCase )
print("""Successfully saved processor and model to""" ,_lowerCAmelCase )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(_lowerCAmelCase ,organization="""nielsr""" )
model.push_to_hub(_lowerCAmelCase ,organization="""nielsr""" )
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
parser.add_argument(
"""--model_name""",
default="""groupvit-gccy-fcc""",
type=str,
help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
)
_lowerCAmelCase = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 481 |
_lowerCAmelCase = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : Any = input("""Enter message: """ )
A_ : Dict = input("""Enter key [alphanumeric]: """ )
A_ : Dict = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
A_ : Union[str, Any] = """encrypt"""
A_ : Optional[int] = encrypt_message(_lowerCAmelCase ,_lowerCAmelCase )
elif mode.lower().startswith("""d""" ):
A_ : str = """decrypt"""
A_ : Dict = decrypt_message(_lowerCAmelCase ,_lowerCAmelCase )
print(f"""\n{mode.title()}ed message:""" )
print(_lowerCAmelCase )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
return translate_message(_lowerCAmelCase ,_lowerCAmelCase ,"""encrypt""" )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
return translate_message(_lowerCAmelCase ,_lowerCAmelCase ,"""decrypt""" )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : Dict = []
A_ : List[Any] = 0
A_ : int = key.upper()
for symbol in message:
A_ : Optional[int] = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowerCAmelCase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowerCAmelCase ):
A_ : Dict = 0
else:
translated.append(_lowerCAmelCase )
return "".join(_lowerCAmelCase )
if __name__ == "__main__":
main()
| 481 | 1 |
from math import pi, sqrt, tan
def A__ ( lowerCamelCase ) -> float:
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> float:
if length < 0 or breadth < 0 or height < 0:
raise ValueError("""surface_area_cuboid() only accepts non-negative values""" )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def A__ ( lowerCamelCase ) -> float:
if radius < 0:
raise ValueError("""surface_area_sphere() only accepts non-negative values""" )
return 4 * pi * radius**2
def A__ ( lowerCamelCase ) -> float:
if radius < 0:
raise ValueError("""surface_area_hemisphere() only accepts non-negative values""" )
return 3 * pi * radius**2
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if radius < 0 or height < 0:
raise ValueError("""surface_area_cone() only accepts non-negative values""" )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> float:
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"""surface_area_conical_frustum() only accepts non-negative values""" )
UpperCamelCase_: Tuple = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if radius < 0 or height < 0:
raise ValueError("""surface_area_cylinder() only accepts non-negative values""" )
return 2 * pi * radius * (height + radius)
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if torus_radius < 0 or tube_radius < 0:
raise ValueError("""surface_area_torus() only accepts non-negative values""" )
if torus_radius < tube_radius:
raise ValueError(
"""surface_area_torus() does not support spindle or self intersecting tori""" )
return 4 * pow(lowerCamelCase , 2 ) * torus_radius * tube_radius
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if length < 0 or width < 0:
raise ValueError("""area_rectangle() only accepts non-negative values""" )
return length * width
def A__ ( lowerCamelCase ) -> float:
if side_length < 0:
raise ValueError("""area_square() only accepts non-negative values""" )
return side_length**2
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if base < 0 or height < 0:
raise ValueError("""area_triangle() only accepts non-negative values""" )
return (base * height) / 2
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> float:
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("""area_triangle_three_sides() only accepts non-negative values""" )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("""Given three sides do not form a triangle""" )
UpperCamelCase_: Optional[Any] = (sidea + sidea + sidea) / 2
UpperCamelCase_: Optional[int] = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if base < 0 or height < 0:
raise ValueError("""area_parallelogram() only accepts non-negative values""" )
return base * height
def A__ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> float:
if basea < 0 or basea < 0 or height < 0:
raise ValueError("""area_trapezium() only accepts non-negative values""" )
return 1 / 2 * (basea + basea) * height
def A__ ( lowerCamelCase ) -> float:
if radius < 0:
raise ValueError("""area_circle() only accepts non-negative values""" )
return pi * radius**2
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if radius_x < 0 or radius_y < 0:
raise ValueError("""area_ellipse() only accepts non-negative values""" )
return pi * radius_x * radius_y
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("""area_rhombus() only accepts non-negative values""" )
return 1 / 2 * diagonal_a * diagonal_a
def A__ ( lowerCamelCase , lowerCamelCase ) -> float:
if not isinstance(lowerCamelCase , lowerCamelCase ) or sides < 3:
raise ValueError(
"""area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides""" )
elif length < 0:
raise ValueError(
"""area_reg_polygon() only accepts non-negative values as \
length of a side""" )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("""[DEMO] Areas of various geometric shapes: \n""")
print(F"""Rectangle: {area_rectangle(10, 20) = }""")
print(F"""Square: {area_square(10) = }""")
print(F"""Triangle: {area_triangle(10, 10) = }""")
print(F"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
print(F"""Parallelogram: {area_parallelogram(10, 20) = }""")
print(F"""Rhombus: {area_rhombus(10, 20) = }""")
print(F"""Trapezium: {area_trapezium(10, 20, 30) = }""")
print(F"""Circle: {area_circle(20) = }""")
print(F"""Ellipse: {area_ellipse(10, 20) = }""")
print("""\nSurface Areas of various geometric shapes: \n""")
print(F"""Cube: {surface_area_cube(20) = }""")
print(F"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
print(F"""Sphere: {surface_area_sphere(20) = }""")
print(F"""Hemisphere: {surface_area_hemisphere(20) = }""")
print(F"""Cone: {surface_area_cone(10, 20) = }""")
print(F"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
print(F"""Cylinder: {surface_area_cylinder(10, 20) = }""")
print(F"""Torus: {surface_area_torus(20, 10) = }""")
print(F"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
print(F"""Square: {area_reg_polygon(4, 10) = }""")
print(F"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")
| 548 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _UpperCamelCase ( _A ):
'''simple docstring'''
@require_torch
def lowerCAmelCase__ ( self : List[str] ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
UpperCamelCase_: Optional[Any] = """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
"""
UpperCamelCase_: List[Any] = """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
"""
UpperCamelCase_: Dict = """
import socket
def offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")
socket.socket = offline_socket
"""
# Force fetching the files so that we can use the cache
UpperCamelCase_: int = """hf-internal-testing/tiny-random-bert"""
BertConfig.from_pretrained(snake_case_ )
BertModel.from_pretrained(snake_case_ )
BertTokenizer.from_pretrained(snake_case_ )
pipeline(task="""fill-mask""" , model=snake_case_ )
# baseline - just load from_pretrained with normal network
UpperCamelCase_: Tuple = [sys.executable, """-c""", """\n""".join([load, run, mock] )]
# should succeed
UpperCamelCase_: Optional[int] = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCamelCase_: int = """1"""
UpperCamelCase_: Optional[Any] = subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
@require_torch
def lowerCAmelCase__ ( self : Any ):
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
UpperCamelCase_: str = """
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
"""
UpperCamelCase_: Any = """
mname = \"hf-internal-testing/tiny-random-bert\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task=\"fill-mask\", model=mname)
print(\"success\")
"""
UpperCamelCase_: Optional[int] = """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")
socket.socket = offline_socket
"""
# Force fetching the files so that we can use the cache
UpperCamelCase_: Optional[int] = """hf-internal-testing/tiny-random-bert"""
BertConfig.from_pretrained(snake_case_ )
BertModel.from_pretrained(snake_case_ )
BertTokenizer.from_pretrained(snake_case_ )
pipeline(task="""fill-mask""" , model=snake_case_ )
# baseline - just load from_pretrained with normal network
UpperCamelCase_: Optional[int] = [sys.executable, """-c""", """\n""".join([load, run, mock] )]
# should succeed
UpperCamelCase_: List[str] = self.get_env()
UpperCamelCase_: List[Any] = subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
@require_torch
def lowerCAmelCase__ ( self : int ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
UpperCamelCase_: Dict = """
from transformers import BertConfig, BertModel, BertTokenizer
"""
UpperCamelCase_: Optional[Any] = """
mname = \"hf-internal-testing/tiny-random-bert-sharded\"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print(\"success\")
"""
UpperCamelCase_: Dict = """
import socket
def offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")
socket.socket = offline_socket
"""
# baseline - just load from_pretrained with normal network
UpperCamelCase_: Union[str, Any] = [sys.executable, """-c""", """\n""".join([load, run] )]
# should succeed
UpperCamelCase_: Union[str, Any] = self.get_env()
UpperCamelCase_: List[Any] = subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
# next emulate no network
UpperCamelCase_: Optional[int] = [sys.executable, """-c""", """\n""".join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCamelCase_: Union[str, Any] = """1"""
UpperCamelCase_: int = subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
@require_torch
def lowerCAmelCase__ ( self : List[str] ):
UpperCamelCase_: Dict = """
from transformers import pipeline
"""
UpperCamelCase_: Union[str, Any] = """
mname = \"hf-internal-testing/tiny-random-bert\"
pipe = pipeline(model=mname)
"""
UpperCamelCase_: Dict = """
import socket
def offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")
socket.socket = offline_socket
"""
UpperCamelCase_: Optional[Any] = self.get_env()
UpperCamelCase_: int = """1"""
UpperCamelCase_: int = [sys.executable, """-c""", """\n""".join([load, mock, run] )]
UpperCamelCase_: Any = subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_ )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
"""You cannot infer task automatically within `pipeline` when using offline mode""" , result.stderr.decode().replace("""\n""" , """""" ) , )
@require_torch
def lowerCAmelCase__ ( self : Optional[int] ):
UpperCamelCase_: str = """
from transformers import AutoModel
"""
UpperCamelCase_: Dict = """
mname = \"hf-internal-testing/test_dynamic_model\"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print(\"success\")
"""
# baseline - just load from_pretrained with normal network
UpperCamelCase_: Optional[int] = [sys.executable, """-c""", """\n""".join([load, run] )]
# should succeed
UpperCamelCase_: Optional[Any] = self.get_env()
UpperCamelCase_: int = subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
UpperCamelCase_: Union[str, Any] = """1"""
UpperCamelCase_: List[str] = subprocess.run(snake_case_ , env=snake_case_ , check=snake_case_ , capture_output=snake_case_ )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn("""success""" , result.stdout.decode() )
| 548 | 1 |
"""simple docstring"""
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()
| 12 |
"""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 | 1 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[int, int]:
if b == 0:
return (1, 0)
((UpperCAmelCase__) , (UpperCAmelCase__)) : List[str] = extended_euclid(lowerCAmelCase__ , a % b )
UpperCAmelCase__ : List[str] = a // b
return (y, x - k * y)
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
((UpperCAmelCase__) , (UpperCAmelCase__)) : Optional[int] = extended_euclid(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase__ : Optional[int] = na * na
UpperCAmelCase__ : Any = ra * x * na + ra * y * na
return (n % m + m) % m
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
((UpperCAmelCase__) , (UpperCAmelCase__)) : Tuple = extended_euclid(lowerCAmelCase__ , lowerCAmelCase__ )
if b < 0:
UpperCAmelCase__ : int = (b % n + n) % n
return b
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = invert_modulo(lowerCAmelCase__ , lowerCAmelCase__ ), invert_modulo(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase__ : Any = na * na
UpperCAmelCase__ : Optional[int] = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name='''chinese_remainder_theorem''', verbose=True)
testmod(name='''chinese_remainder_theorem2''', verbose=True)
testmod(name='''invert_modulo''', verbose=True)
testmod(name='''extended_euclid''', verbose=True)
| 75 |
def _A ( SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
a__ : List[str] =len(SCREAMING_SNAKE_CASE )
a__ : Optional[int] =[[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
a__ : Optional[int] =True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
a__ : str =False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
a__ : str =subset[i - 1][j]
if arr[i - 1] <= j:
a__ : Tuple =subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 563 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
lowerCAmelCase = TypeVar("""T""")
class lowerCamelCase ( Generic[T] ):
def __init__( self , a_ , a_ ):
lowerCAmelCase : Any | T = None
lowerCAmelCase : int = len(a_ )
lowerCAmelCase : list[T] = [any_type for _ in range(self.N )] + arr
lowerCAmelCase : Dict = fnc
self.build()
def _lowerCamelCase ( self ):
for p in range(self.N - 1 , 0 , -1 ):
lowerCAmelCase : Dict = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def _lowerCamelCase ( self , a_ , a_ ):
p += self.N
lowerCAmelCase : str = v
while p > 1:
lowerCAmelCase : Dict = p // 2
lowerCAmelCase : Union[str, Any] = self.fn(self.st[p * 2] , self.st[p * 2 + 1] )
def _lowerCamelCase ( self , a_ , a_ ): # noqa: E741
lowerCAmelCase , lowerCAmelCase : Dict = l + self.N, r + self.N
lowerCAmelCase : T | None = None
while l <= r:
if l % 2 == 1:
lowerCAmelCase : Dict = self.st[l] if res is None else self.fn(a_ , self.st[l] )
if r % 2 == 0:
lowerCAmelCase : str = self.st[r] if res is None else self.fn(a_ , self.st[r] )
lowerCAmelCase , lowerCAmelCase : Any = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
lowerCAmelCase = [1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]
lowerCAmelCase = {
0: 7,
1: 2,
2: 6,
3: -14,
4: 5,
5: 4,
6: 7,
7: -10,
8: 9,
9: 10,
10: 12,
11: 1,
}
lowerCAmelCase = SegmentTree(test_array, min)
lowerCAmelCase = SegmentTree(test_array, max)
lowerCAmelCase = SegmentTree(test_array, lambda a, b: a + b)
def __A ( ):
for i in range(len(a_ ) ):
for j in range(a_ ,len(a_ ) ):
lowerCAmelCase : Tuple = reduce(a_ ,test_array[i : j + 1] )
lowerCAmelCase : str = reduce(a_ ,test_array[i : j + 1] )
lowerCAmelCase : Tuple = reduce(lambda a_ ,a_ : a + b ,test_array[i : j + 1] )
assert min_range == min_segment_tree.query(a_ ,a_ )
assert max_range == max_segment_tree.query(a_ ,a_ )
assert sum_range == sum_segment_tree.query(a_ ,a_ )
test_all_segments()
for index, value in test_updates.items():
lowerCAmelCase = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 551 |
'''simple docstring'''
import numpy as np
def __A ( a_ : np.array ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 551 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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 transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'tf_padding' ) )
self.parent.assertTrue(hasattr(snake_case , 'depth_multiplier' ) )
class lowercase :
'''simple docstring'''
def __init__( self : Tuple , snake_case : List[Any] , snake_case : Union[str, Any]=13 , snake_case : Tuple=3 , snake_case : Dict=32 , snake_case : Union[str, Any]=0.25 , snake_case : str=8 , snake_case : List[str]=8 , snake_case : Union[str, Any]=6 , snake_case : List[Any]=32 , snake_case : List[str]=True , snake_case : Tuple=True , snake_case : Dict=True , snake_case : Optional[Any]="relu6" , snake_case : List[str]=1280 , snake_case : Optional[int]=0.1 , snake_case : Any=0.02 , snake_case : int=True , snake_case : Optional[Any]=True , snake_case : Dict=10 , snake_case : Any=None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = parent
SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE : str = image_size
SCREAMING_SNAKE_CASE : Tuple = depth_multiplier
SCREAMING_SNAKE_CASE : Dict = depth_divisible_by
SCREAMING_SNAKE_CASE : List[Any] = min_depth
SCREAMING_SNAKE_CASE : Union[str, Any] = expand_ratio
SCREAMING_SNAKE_CASE : Tuple = tf_padding
SCREAMING_SNAKE_CASE : Tuple = output_stride
SCREAMING_SNAKE_CASE : Tuple = first_layer_is_expansion
SCREAMING_SNAKE_CASE : Dict = finegrained_output
SCREAMING_SNAKE_CASE : List[str] = hidden_act
SCREAMING_SNAKE_CASE : List[str] = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
SCREAMING_SNAKE_CASE : Dict = classifier_dropout_prob
SCREAMING_SNAKE_CASE : int = use_labels
SCREAMING_SNAKE_CASE : Optional[int] = is_training
SCREAMING_SNAKE_CASE : Dict = num_labels
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = scope
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : str = None
if self.use_labels:
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size] , self.num_labels )
SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , depth_divisible_by=self.depth_divisible_by , min_depth=self.min_depth , expand_ratio=self.expand_ratio , output_stride=self.output_stride , first_layer_is_expansion=self.first_layer_is_expansion , finegrained_output=self.finegrained_output , hidden_act=self.hidden_act , tf_padding=self.tf_padding , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self : Tuple , snake_case : str , snake_case : Any , snake_case : Any , snake_case : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = MobileNetVaModel(config=snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE : Union[str, Any] = model(snake_case )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
self.parent.assertEqual(
result.pooler_output.shape , (self.batch_size, self.last_hidden_size) , )
def lowerCamelCase_ ( self : Any , snake_case : Tuple , snake_case : Any , snake_case : Dict , snake_case : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = self.num_labels
SCREAMING_SNAKE_CASE : int = MobileNetVaForImageClassification(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE : Tuple = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self : Dict , snake_case : Tuple , snake_case : List[Any] , snake_case : Optional[int] , snake_case : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = self.num_labels
SCREAMING_SNAKE_CASE : Optional[int] = MobileNetVaForSemanticSegmentation(snake_case )
model.to(snake_case )
model.eval()
SCREAMING_SNAKE_CASE : int = model(snake_case )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
SCREAMING_SNAKE_CASE : Tuple = model(snake_case , labels=snake_case )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = config_and_inputs
SCREAMING_SNAKE_CASE : int = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
UpperCAmelCase : Optional[Any] = (
{
'feature-extraction': MobileNetVaModel,
'image-classification': MobileNetVaForImageClassification,
'image-segmentation': MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
UpperCAmelCase : Optional[Any] = False
UpperCAmelCase : Tuple = False
UpperCAmelCase : Union[str, Any] = False
UpperCAmelCase : Union[str, Any] = False
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = MobileNetVaModelTester(self )
SCREAMING_SNAKE_CASE : Optional[int] = MobileNetVaConfigTester(self , config_class=snake_case , has_text_modality=snake_case )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileNetV2 does not use inputs_embeds' )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
pass
@unittest.skip(reason='MobileNetV2 does not support input and output embeddings' )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
pass
@unittest.skip(reason='MobileNetV2 does not output attentions' )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : List[str] ):
'''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 : int = model_class(snake_case )
SCREAMING_SNAKE_CASE : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE : Union[str, Any] = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
def check_hidden_states_output(snake_case : List[str] , snake_case : Any , snake_case : Tuple ):
SCREAMING_SNAKE_CASE : Union[str, Any] = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[int] = model(**self._prepare_for_class(snake_case , snake_case ) )
SCREAMING_SNAKE_CASE : int = outputs.hidden_states
SCREAMING_SNAKE_CASE : Any = 16
self.assertEqual(len(snake_case ) , snake_case )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE : Optional[int] = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# 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(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*snake_case )
@slow
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE : List[Any] = MobileNetVaModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def __a ( ) -> Optional[int]:
SCREAMING_SNAKE_CASE : str = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return (
MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224' ) if is_vision_available() else None
)
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224' ).to(snake_case )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
SCREAMING_SNAKE_CASE : int = prepare_img()
SCREAMING_SNAKE_CASE : str = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[int] = model(**snake_case )
# verify the logits
SCREAMING_SNAKE_CASE : Optional[Any] = torch.Size((1, 1001) )
self.assertEqual(outputs.logits.shape , snake_case )
SCREAMING_SNAKE_CASE : Any = torch.tensor([0.2445, -1.1993, 0.1905] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
@slow
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513' )
SCREAMING_SNAKE_CASE : int = model.to(snake_case )
SCREAMING_SNAKE_CASE : Union[str, Any] = MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513' )
SCREAMING_SNAKE_CASE : Any = prepare_img()
SCREAMING_SNAKE_CASE : List[Any] = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE : Union[str, Any] = model(**snake_case )
SCREAMING_SNAKE_CASE : int = outputs.logits
# verify the logits
SCREAMING_SNAKE_CASE : Optional[Any] = torch.Size((1, 21, 65, 65) )
self.assertEqual(logits.shape , snake_case )
SCREAMING_SNAKE_CASE : List[Any] = torch.tensor(
[
[[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
[[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
[[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
] , device=snake_case , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , snake_case , atol=1E-4 ) )
| 352 |
import unittest
import numpy as np
from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class lowercase ( unittest.TestCase):
'''simple docstring'''
def __init__( self : int , snake_case : Optional[Any] , snake_case : Optional[Any]=13 , snake_case : str=7 , snake_case : Optional[Any]=True , snake_case : int=True , snake_case : str=True , snake_case : List[str]=True , snake_case : Optional[Any]=99 , snake_case : Optional[int]=32 , snake_case : Optional[int]=5 , snake_case : Optional[Any]=4 , snake_case : Optional[Any]=37 , snake_case : str="gelu" , snake_case : List[str]=0.1 , snake_case : List[str]=0.1 , snake_case : int=512 , snake_case : Union[str, Any]=16 , snake_case : Optional[Any]=2 , snake_case : int=0.02 , snake_case : str=4 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : str = batch_size
SCREAMING_SNAKE_CASE : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE : int = is_training
SCREAMING_SNAKE_CASE : List[str] = use_attention_mask
SCREAMING_SNAKE_CASE : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE : Optional[int] = use_labels
SCREAMING_SNAKE_CASE : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : Any = num_hidden_layers
SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE : Any = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : List[Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : List[str] = num_choices
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Optional[int] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE : Any = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : Tuple = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=snake_case , )
return config, input_ids, attention_mask
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = config_and_inputs
SCREAMING_SNAKE_CASE : int = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class lowercase ( SCREAMING_SNAKE_CASE_ , unittest.TestCase):
'''simple docstring'''
UpperCAmelCase : int = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = FlaxDistilBertModelTester(self )
@slow
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE : Dict = model_class_name.from_pretrained('distilbert-base-uncased' )
SCREAMING_SNAKE_CASE : Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case )
@require_flax
class lowercase ( unittest.TestCase):
'''simple docstring'''
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' )
SCREAMING_SNAKE_CASE : List[str] = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Dict = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
SCREAMING_SNAKE_CASE : str = model(snake_case , attention_mask=snake_case )[0]
SCREAMING_SNAKE_CASE : int = (1, 11, 768)
self.assertEqual(output.shape , snake_case )
SCREAMING_SNAKE_CASE : Optional[int] = np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , snake_case , atol=1E-4 ) )
| 352 | 1 |
from importlib import import_module
from .logging import get_logger
snake_case_ : str = get_logger(__name__)
class lowercase__ :
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=None ):
'''simple docstring'''
UpperCamelCase = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('''__''' ):
setattr(self , lowerCamelCase__ , getattr(lowerCamelCase__ , lowerCamelCase__ ) )
UpperCamelCase = module._original_module if isinstance(lowerCamelCase__ , _PatchedModuleObj ) else module
class lowercase__ :
'''simple docstring'''
_snake_case = []
def __init__( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ):
'''simple docstring'''
UpperCamelCase = obj
UpperCamelCase = target
UpperCamelCase = new
UpperCamelCase = target.split('''.''' )[0]
UpperCamelCase = {}
UpperCamelCase = attrs or []
def __enter__( self ):
'''simple docstring'''
*UpperCamelCase , UpperCamelCase = self.target.split('''.''' )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(lowerCamelCase__ ) ):
try:
UpperCamelCase = import_module('''.'''.join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
UpperCamelCase = getattr(self.obj , lowerCamelCase__ )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(lowerCamelCase__ , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
UpperCamelCase = obj_attr
# patch at top level
setattr(self.obj , lowerCamelCase__ , _PatchedModuleObj(lowerCamelCase__ , attrs=self.attrs ) )
UpperCamelCase = getattr(self.obj , lowerCamelCase__ )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(lowerCamelCase__ , lowerCamelCase__ , _PatchedModuleObj(getattr(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) , attrs=self.attrs ) )
UpperCamelCase = getattr(lowerCamelCase__ , lowerCamelCase__ )
# finally set the target attribute
setattr(lowerCamelCase__ , lowerCamelCase__ , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
UpperCamelCase = getattr(import_module('''.'''.join(lowerCamelCase__ ) ) , lowerCamelCase__ )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , lowerCamelCase__ ) is attr_value:
UpperCamelCase = getattr(self.obj , lowerCamelCase__ )
setattr(self.obj , lowerCamelCase__ , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
UpperCamelCase = globals()['''__builtins__'''][target_attr]
setattr(self.obj , lowerCamelCase__ , self.new )
else:
raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.' )
def __exit__( self , *lowerCamelCase__ ):
'''simple docstring'''
for attr in list(self.original ):
setattr(self.obj , lowerCamelCase__ , self.original.pop(lowerCamelCase__ ) )
def UpperCAmelCase ( self ):
'''simple docstring'''
self.__enter__()
self._active_patches.append(self )
def UpperCAmelCase ( self ):
'''simple docstring'''
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 707 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
snake_case_ : Tuple = [
'word_embeddings_layernorm.weight',
'word_embeddings_layernorm.bias',
'input_layernorm.weight',
'input_layernorm.bias',
'post_attention_layernorm.weight',
'post_attention_layernorm.bias',
'self_attention.dense.bias',
'mlp.dense_4h_to_h.bias',
'ln_f.weight',
'ln_f.bias',
]
snake_case_ : Union[str, Any] = [
'mlp.dense_4h_to_h.weight',
'self_attention.dense.weight',
]
def __snake_case ( _UpperCAmelCase : List[str], _UpperCAmelCase : List[str]):
UpperCamelCase = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
UpperCamelCase = int(re.match(R'''.*layer_(\d*).*''', _UpperCAmelCase)[1])
layer_number -= 3
return f'h.{layer_number}.' + key
def __snake_case ( _UpperCAmelCase : str):
if dtype == torch.bool:
return 1 / 8
UpperCamelCase = re.search(R'''[^\d](\d+)$''', str(_UpperCAmelCase))
if bit_search is None:
raise ValueError(f'`dtype` is not a valid dtype: {dtype}.')
UpperCamelCase = int(bit_search.groups()[0])
return bit_size // 8
def __snake_case ( _UpperCAmelCase : int, _UpperCAmelCase : Optional[Any], _UpperCAmelCase : str, _UpperCAmelCase : List[Any], _UpperCAmelCase : Optional[int]):
# Construct model
if bloom_config_file == "":
UpperCamelCase = BloomConfig()
else:
UpperCamelCase = BloomConfig.from_json_file(_UpperCAmelCase)
if shard_model:
UpperCamelCase = os.listdir(_UpperCAmelCase)
UpperCamelCase = sorted(filter(lambda _UpperCAmelCase: s.startswith('''layer''') and "model_00" in s, _UpperCAmelCase))
UpperCamelCase = {'''weight_map''': {}, '''metadata''': {}}
UpperCamelCase = 0
UpperCamelCase = None
UpperCamelCase = BloomConfig()
for j, file in enumerate(_UpperCAmelCase):
print('''Processing file: {}'''.format(_UpperCAmelCase))
UpperCamelCase = None
for i in range(_UpperCAmelCase):
# load all TP files
UpperCamelCase = file.replace('''model_00''', f'model_0{i}')
UpperCamelCase = torch.load(os.path.join(_UpperCAmelCase, _UpperCAmelCase), map_location='''cpu''')
# Rename keys in the transformers names
UpperCamelCase = list(temp.keys())
for key in keys:
UpperCamelCase = temp.pop(_UpperCAmelCase)
if tensors is None:
UpperCamelCase = temp
else:
for key in tensors.keys():
if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
UpperCamelCase = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
# We concatenate these weights accross TP ranks
UpperCamelCase = torch.cat([tensors[key], temp[key]], dim=_UpperCAmelCase)
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
UpperCamelCase = tensors[key] / pretraining_tp
torch.save(
_UpperCAmelCase, os.path.join(
_UpperCAmelCase, '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1).zfill(5), str(len(_UpperCAmelCase)).zfill(5)), ), )
for key in tensors.keys():
UpperCamelCase = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype)
if key not in index_dict["weight_map"]:
UpperCamelCase = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1).zfill(5), str(len(_UpperCAmelCase)).zfill(5))
UpperCamelCase = BloomConfig()
UpperCamelCase = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
UpperCamelCase = total_size
with open(_UpperCAmelCase, '''w''', encoding='''utf-8''') as f:
f.write(config.to_json_string())
with open(os.path.join(_UpperCAmelCase, WEIGHTS_NAME + '''.index.json'''), '''w''', encoding='''utf-8''') as f:
UpperCamelCase = json.dumps(_UpperCAmelCase, indent=2, sort_keys=_UpperCAmelCase) + '''\n'''
f.write(_UpperCAmelCase)
else:
UpperCamelCase = BloomModel(_UpperCAmelCase)
UpperCamelCase = os.listdir(_UpperCAmelCase)
UpperCamelCase = sorted(filter(lambda _UpperCAmelCase: s.startswith('''layer''') and "model_00" in s, _UpperCAmelCase))
UpperCamelCase = None
for i, file in enumerate(_UpperCAmelCase):
UpperCamelCase = None
for i in range(_UpperCAmelCase):
# load all TP files
UpperCamelCase = file.replace('''model_00''', f'model_0{i}')
UpperCamelCase = torch.load(os.path.join(_UpperCAmelCase, _UpperCAmelCase), map_location='''cpu''')
# Rename keys in the transformers names
UpperCamelCase = list(temp.keys())
for key in keys:
UpperCamelCase = temp.pop(_UpperCAmelCase)
if tensors is None:
UpperCamelCase = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
UpperCamelCase = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN) else 0
# We concatenate these weights accross TP ranks
UpperCamelCase = torch.cat([tensors[key], temp[key]], dim=_UpperCAmelCase)
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(_UpperCAmelCase) for end in WEIGHTS_TO_AVERAGE_ENDSWITH):
UpperCamelCase = tensors[key] / pretraining_tp
UpperCamelCase = model.load_state_dict(_UpperCAmelCase, strict=_UpperCAmelCase)
assert not other_keys.unexpected_keys, f'The keys {other_keys.unexpected_keys} are unexpected'
if missing_keys is None:
UpperCamelCase = set(other_keys.missing_keys)
else:
UpperCamelCase = missing_keys.intersection(set(other_keys.missing_keys))
assert not missing_keys, f'The keys {missing_keys} are missing'
# Save pytorch-model
os.makedirs(_UpperCAmelCase, exist_ok=_UpperCAmelCase)
UpperCamelCase = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
UpperCamelCase = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(f'Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}')
if config.torch_dtype is not None:
UpperCamelCase = model.to(config.torch_dtype)
torch.save(model.state_dict(), _UpperCAmelCase)
print(f'Save configuration file to {pytorch_config_dump_path}')
with open(_UpperCAmelCase, '''w''', encoding='''utf-8''') as f:
f.write(config.to_json_string())
if __name__ == "__main__":
snake_case_ : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--bloom_checkpoint_path',
default=None,
type=str,
required=True,
help='Path to the Megatron-LM checkpoint path.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--bloom_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--shard_model',
action='store_true',
help='An optional setting to shard the output model \nThis enables sharding the converted checkpoint',
)
parser.add_argument(
'--pretraining_tp',
default=4,
type=int,
help='Pretraining TP rank that has been used when training the model in Megatron-LM \n',
)
snake_case_ : List[str] = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 350 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Any = ["XGLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Tuple = ["XGLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Tuple = [
"XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XGLMForCausalLM",
"XGLMModel",
"XGLMPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Union[str, Any] = [
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXGLMPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Tuple = [
"TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXGLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
UpperCAmelCase : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 457 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
UpperCAmelCase : Any = random.Random()
def __lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : Union[str, Any]=1.0 , lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : int=None ):
'''simple docstring'''
if rng is None:
lowerCamelCase = global_rng
lowerCamelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class __lowercase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , A , A=7 , A=4_00 , A=20_00 , A=24 , A=24 , A=0.0 , A=1_60_00 , A=True , A=True , ) -> str:
'''simple docstring'''
lowerCamelCase = parent
lowerCamelCase = batch_size
lowerCamelCase = min_seq_length
lowerCamelCase = max_seq_length
lowerCamelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCamelCase = feature_size
lowerCamelCase = num_mel_bins
lowerCamelCase = padding_value
lowerCamelCase = sampling_rate
lowerCamelCase = return_attention_mask
lowerCamelCase = do_normalize
def __A ( self ) -> List[str]:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def __A ( self , A=False , A=False ) -> Tuple:
'''simple docstring'''
def _flatten(A ):
return list(itertools.chain(*A ) )
if equal_length:
lowerCamelCase = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCamelCase = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowerCamelCase = [np.asarray(A ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __lowercase ( a_ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : str = SpeechaTextFeatureExtractor if is_speech_available() else None
def __A ( self ) -> int:
'''simple docstring'''
lowerCamelCase = SpeechaTextFeatureExtractionTester(self )
def __A ( self , A ) -> List[Any]:
'''simple docstring'''
self.assertTrue(np.all(np.mean(A , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(A , axis=0 ) - 1 ) < 1e-3 ) )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = [np.asarray(A ) for speech_input in speech_inputs]
# Test feature size
lowerCamelCase = feature_extractor(A , padding=A , return_tensors="""np""" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
lowerCamelCase = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_features
lowerCamelCase = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(A , A , atol=1e-3 ) )
# Test batched
lowerCamelCase = feature_extractor(A , return_tensors="""np""" ).input_features
lowerCamelCase = feature_extractor(A , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(A , A ):
self.assertTrue(np.allclose(A , A , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowerCamelCase = [floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
lowerCamelCase = np.asarray(A )
lowerCamelCase = feature_extractor(A , return_tensors="""np""" ).input_features
lowerCamelCase = feature_extractor(A , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(A , A ):
self.assertTrue(np.allclose(A , A , atol=1e-3 ) )
def __A ( self ) -> Optional[Any]:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = ["""longest""", """max_length""", """do_not_pad"""]
lowerCamelCase = [None, 16, None]
for max_length, padding in zip(A , A ):
lowerCamelCase = feature_extractor(
A , padding=A , max_length=A , return_attention_mask=A )
lowerCamelCase = inputs.input_features
lowerCamelCase = inputs.attention_mask
lowerCamelCase = [np.sum(A ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def __A ( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = ["""longest""", """max_length""", """do_not_pad"""]
lowerCamelCase = [None, 16, None]
for max_length, padding in zip(A , A ):
lowerCamelCase = feature_extractor(
A , max_length=A , padding=A , return_tensors="""np""" , return_attention_mask=A )
lowerCamelCase = inputs.input_features
lowerCamelCase = inputs.attention_mask
lowerCamelCase = [np.sum(A ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def __A ( self ) -> int:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = feature_extractor(
A , padding="""max_length""" , max_length=4 , truncation=A , return_tensors="""np""" , return_attention_mask=A , )
lowerCamelCase = inputs.input_features
lowerCamelCase = inputs.attention_mask
lowerCamelCase = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def __A ( self ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = feature_extractor(
A , padding="""longest""" , max_length=4 , truncation=A , return_tensors="""np""" , return_attention_mask=A , )
lowerCamelCase = inputs.input_features
lowerCamelCase = inputs.attention_mask
lowerCamelCase = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 4, 24) )
lowerCamelCase = [floats_list((1, x) )[0] for x in range(8_00 , 14_00 , 2_00 )]
lowerCamelCase = feature_extractor(
A , padding="""longest""" , max_length=16 , truncation=A , return_tensors="""np""" , return_attention_mask=A , )
lowerCamelCase = inputs.input_features
lowerCamelCase = inputs.attention_mask
lowerCamelCase = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 6, 24) )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
import torch
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = np.random.rand(1_00 , 32 ).astype(np.floataa )
lowerCamelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCamelCase = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowerCamelCase = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def __A ( self , A ) -> Any:
'''simple docstring'''
from datasets import load_dataset
lowerCamelCase = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
lowerCamelCase = ds.sort("""id""" ).select(range(A ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = np.array([
-1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
-1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
-1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
] )
# fmt: on
lowerCamelCase = self._load_datasamples(1 )
lowerCamelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase = feature_extractor(A , return_tensors="""pt""" ).input_features
self.assertEquals(input_features.shape , (1, 5_84, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30] , A , atol=1e-4 ) )
| 457 | 1 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : bool = True , _SCREAMING_SNAKE_CASE : float = math.inf , _SCREAMING_SNAKE_CASE : float = -math.inf , _SCREAMING_SNAKE_CASE : float = math.inf , _SCREAMING_SNAKE_CASE : float = -math.inf , _SCREAMING_SNAKE_CASE : bool = False , _SCREAMING_SNAKE_CASE : float = 100 , _SCREAMING_SNAKE_CASE : float = 0.01 , _SCREAMING_SNAKE_CASE : float = 1 , ):
"""simple docstring"""
__a = False
__a = search_prob
__a = start_temperate
__a = []
__a = 0
__a = None
while not search_end:
__a = current_state.score()
if best_state is None or current_score > best_state.score():
__a = current_state
scores.append(_SCREAMING_SNAKE_CASE )
iterations += 1
__a = None
__a = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
__a = random.randint(0 , len(_SCREAMING_SNAKE_CASE ) - 1 ) # picking a random neighbor
__a = neighbors.pop(_SCREAMING_SNAKE_CASE )
__a = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
__a = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
__a = picked_neighbor
else:
__a = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
__a = picked_neighbor
__a = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
__a = True
else:
__a = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
plt.xlabel("""Iterations""" )
plt.ylabel("""Function values""" )
plt.show()
return best_state
if __name__ == "__main__":
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
lowerCamelCase__ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
lowerCamelCase__ = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"""The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
lowerCamelCase__ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
lowerCamelCase__ = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"""The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
return (3 * x**2) - (6 * y)
lowerCamelCase__ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowerCamelCase__ = simulated_annealing(prob, find_max=False, visualization=True)
print(
"""The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F"""{local_min.score()}"""
)
lowerCamelCase__ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
lowerCamelCase__ = simulated_annealing(prob, find_max=True, visualization=True)
print(
"""The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
F"""{local_min.score()}"""
)
| 718 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase__ = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""NezhaForNextSentencePrediction""",
"""NezhaForMaskedLM""",
"""NezhaForPreTraining""",
"""NezhaForMultipleChoice""",
"""NezhaForQuestionAnswering""",
"""NezhaForSequenceClassification""",
"""NezhaForTokenClassification""",
"""NezhaModel""",
"""NezhaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nezha import (
NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
NezhaPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 547 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowerCamelCase__ = ['''image_processor''']
lowerCamelCase__ = '''SamImageProcessor'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
super().__init__(__SCREAMING_SNAKE_CASE )
snake_case__ : Tuple = self.image_processor
snake_case__ : List[Any] = -1_0
snake_case__ : Any = self.image_processor.size["""longest_edge"""]
def __call__( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE = None , **__SCREAMING_SNAKE_CASE , ):
snake_case__ : Dict = self.image_processor(
__SCREAMING_SNAKE_CASE , return_tensors=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
# pop arguments that are not used in the foward but used nevertheless
snake_case__ : Union[str, Any] = encoding_image_processor["""original_sizes"""]
if hasattr(__SCREAMING_SNAKE_CASE , """numpy""" ): # Checks if Torch or TF tensor
snake_case__ : Optional[int] = original_sizes.numpy()
snake_case__ , snake_case__ , snake_case__ : Dict = self._check_and_preprocess_points(
input_points=__SCREAMING_SNAKE_CASE , input_labels=__SCREAMING_SNAKE_CASE , input_boxes=__SCREAMING_SNAKE_CASE , )
snake_case__ : Optional[Any] = self._normalize_and_convert(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , input_points=__SCREAMING_SNAKE_CASE , input_labels=__SCREAMING_SNAKE_CASE , input_boxes=__SCREAMING_SNAKE_CASE , return_tensors=__SCREAMING_SNAKE_CASE , )
return encoding_image_processor
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE="pt" , ):
if input_points is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
snake_case__ : List[Any] = [
self._normalize_coordinates(self.target_size , __SCREAMING_SNAKE_CASE , original_sizes[0] ) for point in input_points
]
else:
snake_case__ : Any = [
self._normalize_coordinates(self.target_size , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for point, original_size in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
snake_case__ , snake_case__ : int = self._pad_points_and_labels(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
snake_case__ : Union[str, Any] = np.array(__SCREAMING_SNAKE_CASE )
if input_labels is not None:
snake_case__ : Tuple = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
snake_case__ : int = [
self._normalize_coordinates(self.target_size , __SCREAMING_SNAKE_CASE , original_sizes[0] , is_bounding_box=__SCREAMING_SNAKE_CASE )
for box in input_boxes
]
else:
snake_case__ : int = [
self._normalize_coordinates(self.target_size , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , is_bounding_box=__SCREAMING_SNAKE_CASE )
for box, original_size in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
]
snake_case__ : List[str] = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if return_tensors == "pt":
snake_case__ : Union[str, Any] = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
snake_case__ : Any = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
snake_case__ : int = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
snake_case__ : List[str] = tf.expand_dims(__SCREAMING_SNAKE_CASE , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"""input_boxes""": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
snake_case__ : Optional[Any] = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
snake_case__ : List[Any] = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
snake_case__ : List[Any] = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
snake_case__ : Union[str, Any] = tf.expand_dims(__SCREAMING_SNAKE_CASE , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"""input_points""": input_points} )
if input_labels is not None:
if return_tensors == "pt":
snake_case__ : Any = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
snake_case__ : Optional[Any] = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
snake_case__ : Optional[Any] = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
snake_case__ : str = tf.expand_dims(__SCREAMING_SNAKE_CASE , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"""input_labels""": input_labels} )
return encoding_image_processor
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
snake_case__ : Dict = max([point.shape[0] for point in input_points] )
snake_case__ : List[str] = []
for i, point in enumerate(__SCREAMING_SNAKE_CASE ):
if point.shape[0] != expected_nb_points:
snake_case__ : Optional[int] = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
snake_case__ : Dict = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(__SCREAMING_SNAKE_CASE )
snake_case__ : str = processed_input_points
return input_points, input_labels
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ):
snake_case__ , snake_case__ : Any = original_size
snake_case__ , snake_case__ : str = self.image_processor._get_preprocess_shape(__SCREAMING_SNAKE_CASE , longest_edge=__SCREAMING_SNAKE_CASE )
snake_case__ : Union[str, Any] = deepcopy(__SCREAMING_SNAKE_CASE ).astype(__SCREAMING_SNAKE_CASE )
if is_bounding_box:
snake_case__ : Dict = coords.reshape(-1 , 2 , 2 )
snake_case__ : str = coords[..., 0] * (new_w / old_w)
snake_case__ : Union[str, Any] = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
snake_case__ : Tuple = coords.reshape(-1 , 4 )
return coords
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , ):
if input_points is not None:
if hasattr(__SCREAMING_SNAKE_CASE , """numpy""" ): # Checks for TF or Torch tensor
snake_case__ : int = input_points.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or not isinstance(input_points[0] , __SCREAMING_SNAKE_CASE ):
raise ValueError("""Input points must be a list of list of floating points.""" )
snake_case__ : Optional[Any] = [np.array(__SCREAMING_SNAKE_CASE ) for input_point in input_points]
else:
snake_case__ : Any = None
if input_labels is not None:
if hasattr(__SCREAMING_SNAKE_CASE , """numpy""" ):
snake_case__ : int = input_labels.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or not isinstance(input_labels[0] , __SCREAMING_SNAKE_CASE ):
raise ValueError("""Input labels must be a list of list integers.""" )
snake_case__ : Union[str, Any] = [np.array(__SCREAMING_SNAKE_CASE ) for label in input_labels]
else:
snake_case__ : str = None
if input_boxes is not None:
if hasattr(__SCREAMING_SNAKE_CASE , """numpy""" ):
snake_case__ : int = input_boxes.numpy().tolist()
if (
not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0] , __SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0][0] , __SCREAMING_SNAKE_CASE )
):
raise ValueError("""Input boxes must be a list of list of list of floating points.""" )
snake_case__ : Union[str, Any] = [np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa ) for box in input_boxes]
else:
snake_case__ : Any = None
return input_points, input_labels, input_boxes
@property
def __UpperCamelCase ( self ):
snake_case__ : Any = self.image_processor.model_input_names
return list(dict.fromkeys(__SCREAMING_SNAKE_CASE ) )
def __UpperCamelCase ( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ):
return self.image_processor.post_process_masks(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
| 38 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class a :
"""simple docstring"""
def __init__( self : Optional[Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : List[str]=2 , lowerCamelCase__ : Optional[int]=8 , lowerCamelCase__ : Any=True , lowerCamelCase__ : Any=True , lowerCamelCase__ : Optional[Any]=True , lowerCamelCase__ : Dict=True , lowerCamelCase__ : Any=99 , lowerCamelCase__ : int=16 , lowerCamelCase__ : Tuple=5 , lowerCamelCase__ : int=2 , lowerCamelCase__ : Optional[int]=36 , lowerCamelCase__ : List[str]="gelu" , lowerCamelCase__ : int=0.0 , lowerCamelCase__ : Union[str, Any]=0.0 , lowerCamelCase__ : Union[str, Any]=512 , lowerCamelCase__ : Tuple=16 , lowerCamelCase__ : Union[str, Any]=2 , lowerCamelCase__ : Optional[Any]=0.0_2 , lowerCamelCase__ : List[str]=3 , lowerCamelCase__ : Optional[int]=4 , lowerCamelCase__ : Optional[Any]=None , ) -> int:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = scope
def UpperCAmelCase_ ( self : Optional[int] ) -> Any:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = None
if self.use_input_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
if self.use_token_type_ids:
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase = ids_tensor([self.batch_size] , self.num_choices )
__lowercase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
return MraConfig(
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=lowerCamelCase__ , initializer_range=self.initializer_range , )
def UpperCAmelCase_ ( self : int ) -> int:
"""simple docstring"""
__lowercase = self.get_config()
__lowercase = 300
return config
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = self.prepare_config_and_inputs()
__lowercase = True
__lowercase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCAmelCase_ ( self : Tuple , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : List[Any] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : str ) -> List[Any]:
"""simple docstring"""
__lowercase = MraModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowercase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ )
__lowercase = model(lowerCamelCase__ , token_type_ids=lowerCamelCase__ )
__lowercase = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self : Any , lowerCamelCase__ : Any , lowerCamelCase__ : List[Any] , lowerCamelCase__ : int , lowerCamelCase__ : int , lowerCamelCase__ : Any , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Any , lowerCamelCase__ : Any , lowerCamelCase__ : Any , ) -> str:
"""simple docstring"""
__lowercase = True
__lowercase = MraModel(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowercase = model(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , encoder_hidden_states=lowerCamelCase__ , encoder_attention_mask=lowerCamelCase__ , )
__lowercase = model(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , encoder_hidden_states=lowerCamelCase__ , )
__lowercase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self : Optional[Any] , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Tuple , lowerCamelCase__ : int , lowerCamelCase__ : Any , lowerCamelCase__ : Any , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = MraForMaskedLM(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowercase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self : Union[str, Any] , lowerCamelCase__ : Any , lowerCamelCase__ : int , lowerCamelCase__ : Dict , lowerCamelCase__ : Any , lowerCamelCase__ : List[str] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : str ) -> Dict:
"""simple docstring"""
__lowercase = MraForQuestionAnswering(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowercase = model(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , start_positions=lowerCamelCase__ , end_positions=lowerCamelCase__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase_ ( self : Optional[int] , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Tuple , lowerCamelCase__ : int , lowerCamelCase__ : List[str] , lowerCamelCase__ : Dict , lowerCamelCase__ : Dict ) -> str:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = MraForSequenceClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowercase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self : Dict , lowerCamelCase__ : Any , lowerCamelCase__ : Tuple , lowerCamelCase__ : Dict , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Tuple , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Any ) -> List[str]:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = MraForTokenClassification(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowercase = model(lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ ( self : int , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Any , lowerCamelCase__ : Any , lowerCamelCase__ : Any , lowerCamelCase__ : int , lowerCamelCase__ : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase = self.num_choices
__lowercase = MraForMultipleChoice(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
__lowercase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = model(
lowerCamelCase__ , attention_mask=lowerCamelCase__ , token_type_ids=lowerCamelCase__ , labels=lowerCamelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = config_and_inputs
__lowercase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class a ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : Dict = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
UpperCamelCase_ : Dict = False
UpperCamelCase_ : List[str] = False
UpperCamelCase_ : Optional[int] = False
UpperCamelCase_ : str = False
UpperCamelCase_ : int = ()
def UpperCAmelCase_ ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
__lowercase = MraModelTester(self )
__lowercase = ConfigTester(self , config_class=lowerCamelCase__ , hidden_size=37 )
def UpperCAmelCase_ ( self : Any ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__lowercase = type
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def UpperCAmelCase_ ( self : int ) -> Any:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__ )
def UpperCAmelCase_ ( self : str ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__ )
def UpperCAmelCase_ ( self : str ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__ )
@slow
def UpperCAmelCase_ ( self : Any ) -> Dict:
"""simple docstring"""
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = MraModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
@unittest.skip(reason='''MRA does not output attentions''' )
def UpperCAmelCase_ ( self : Dict ) -> Any:
"""simple docstring"""
return
@require_torch
class a ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
__lowercase = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowercase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowercase = model(lowerCamelCase__ )[0]
__lowercase = torch.Size((1, 256, 768) )
self.assertEqual(output.shape , lowerCamelCase__ )
__lowercase = torch.tensor(
[[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
__lowercase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
__lowercase = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
__lowercase = model(lowerCamelCase__ )[0]
__lowercase = 50_265
__lowercase = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape , lowerCamelCase__ )
__lowercase = torch.tensor(
[[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
@slow
def UpperCAmelCase_ ( self : List[str] ) -> Tuple:
"""simple docstring"""
__lowercase = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
__lowercase = torch.arange(4_096 ).unsqueeze(0 )
with torch.no_grad():
__lowercase = model(lowerCamelCase__ )[0]
__lowercase = 50_265
__lowercase = torch.Size((1, 4_096, vocab_size) )
self.assertEqual(output.shape , lowerCamelCase__ )
__lowercase = torch.tensor(
[[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCamelCase__ , atol=1e-4 ) )
| 332 | 0 |
'''simple docstring'''
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
lowerCamelCase_ = str(bin(lowerCamelCase__ ) )[2:] # remove the leading "0b"
lowerCamelCase_ = str(bin(lowerCamelCase__ ) )[2:] # remove the leading "0b"
lowerCamelCase_ = max(len(lowerCamelCase__ ) , len(lowerCamelCase__ ) )
return "0b" + "".join(
str(int(char_a != char_b ) )
for char_a, char_b in zip(a_binary.zfill(lowerCamelCase__ ) , b_binary.zfill(lowerCamelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 702 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = 42
class _SCREAMING_SNAKE_CASE ( nn.Module ):
lowerCAmelCase__ = 42
lowerCAmelCase__ = (16, 32, 96, 2_56)
lowerCAmelCase__ = jnp.floataa
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowerCamelCase_ = []
for i in range(len(self.block_out_channels ) - 1 ):
lowerCamelCase_ = self.block_out_channels[i]
lowerCamelCase_ = self.block_out_channels[i + 1]
lowerCamelCase_ = nn.Conv(
lowercase , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(lowercase )
lowerCamelCase_ = nn.Conv(
lowercase , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(lowercase )
lowerCamelCase_ = blocks
lowerCamelCase_ = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , lowercase ) -> Optional[Any]:
lowerCamelCase_ = self.conv_in(lowercase )
lowerCamelCase_ = nn.silu(lowercase )
for block in self.blocks:
lowerCamelCase_ = block(lowercase )
lowerCamelCase_ = nn.silu(lowercase )
lowerCamelCase_ = self.conv_out(lowercase )
return embedding
@flax_register_to_config
class _SCREAMING_SNAKE_CASE ( nn.Module , snake_case_ , snake_case_ ):
lowerCAmelCase__ = 32
lowerCAmelCase__ = 4
lowerCAmelCase__ = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
lowerCAmelCase__ = False
lowerCAmelCase__ = (3_20, 6_40, 12_80, 12_80)
lowerCAmelCase__ = 2
lowerCAmelCase__ = 8
lowerCAmelCase__ = None
lowerCAmelCase__ = 12_80
lowerCAmelCase__ = 0.0
lowerCAmelCase__ = False
lowerCAmelCase__ = jnp.floataa
lowerCAmelCase__ = True
lowerCAmelCase__ = 0
lowerCAmelCase__ = "rgb"
lowerCAmelCase__ = (16, 32, 96, 2_56)
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> FrozenDict:
# init input tensors
lowerCamelCase_ = (1, self.in_channels, self.sample_size, self.sample_size)
lowerCamelCase_ = jnp.zeros(lowercase , dtype=jnp.floataa )
lowerCamelCase_ = jnp.ones((1,) , dtype=jnp.intaa )
lowerCamelCase_ = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowerCamelCase_ = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowerCamelCase_ = jnp.zeros(lowercase , dtype=jnp.floataa )
lowerCamelCase_ , lowerCamelCase_ = jax.random.split(lowercase )
lowerCamelCase_ = {"params": params_rng, "dropout": dropout_rng}
return self.init(lowercase , lowercase , lowercase , lowercase , lowercase )["params"]
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
lowerCamelCase_ = self.block_out_channels
lowerCamelCase_ = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowerCamelCase_ = self.num_attention_heads or self.attention_head_dim
# input
lowerCamelCase_ = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowerCamelCase_ = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowerCamelCase_ = FlaxTimestepEmbedding(lowercase , dtype=self.dtype )
lowerCamelCase_ = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowerCamelCase_ = self.only_cross_attention
if isinstance(lowercase , lowercase ):
lowerCamelCase_ = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowercase , lowercase ):
lowerCamelCase_ = (num_attention_heads,) * len(self.down_block_types )
# down
lowerCamelCase_ = []
lowerCamelCase_ = []
lowerCamelCase_ = block_out_channels[0]
lowerCamelCase_ = nn.Conv(
lowercase , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(lowercase )
for i, down_block_type in enumerate(self.down_block_types ):
lowerCamelCase_ = output_channel
lowerCamelCase_ = block_out_channels[i]
lowerCamelCase_ = i == len(lowercase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowerCamelCase_ = FlaxCrossAttnDownBlockaD(
in_channels=lowercase , out_channels=lowercase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowerCamelCase_ = FlaxDownBlockaD(
in_channels=lowercase , out_channels=lowercase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(lowercase )
for _ in range(self.layers_per_block ):
lowerCamelCase_ = nn.Conv(
lowercase , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(lowercase )
if not is_final_block:
lowerCamelCase_ = nn.Conv(
lowercase , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(lowercase )
lowerCamelCase_ = down_blocks
lowerCamelCase_ = controlnet_down_blocks
# mid
lowerCamelCase_ = block_out_channels[-1]
lowerCamelCase_ = FlaxUNetMidBlockaDCrossAttn(
in_channels=lowercase , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowerCamelCase_ = nn.Conv(
lowercase , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , lowercase , lowercase , lowercase , lowercase , lowercase = 1.0 , lowercase = True , lowercase = False , ) -> Union[FlaxControlNetOutput, Tuple]:
lowerCamelCase_ = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowerCamelCase_ = jnp.flip(lowercase , axis=1 )
# 1. time
if not isinstance(lowercase , jnp.ndarray ):
lowerCamelCase_ = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(lowercase , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowerCamelCase_ = timesteps.astype(dtype=jnp.floataa )
lowerCamelCase_ = jnp.expand_dims(lowercase , 0 )
lowerCamelCase_ = self.time_proj(lowercase )
lowerCamelCase_ = self.time_embedding(lowercase )
# 2. pre-process
lowerCamelCase_ = jnp.transpose(lowercase , (0, 2, 3, 1) )
lowerCamelCase_ = self.conv_in(lowercase )
lowerCamelCase_ = jnp.transpose(lowercase , (0, 2, 3, 1) )
lowerCamelCase_ = self.controlnet_cond_embedding(lowercase )
sample += controlnet_cond
# 3. down
lowerCamelCase_ = (sample,)
for down_block in self.down_blocks:
if isinstance(lowercase , lowercase ):
lowerCamelCase_ , lowerCamelCase_ = down_block(lowercase , lowercase , lowercase , deterministic=not train )
else:
lowerCamelCase_ , lowerCamelCase_ = down_block(lowercase , lowercase , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowerCamelCase_ = self.mid_block(lowercase , lowercase , lowercase , deterministic=not train )
# 5. contronet blocks
lowerCamelCase_ = ()
for down_block_res_sample, controlnet_block in zip(lowercase , self.controlnet_down_blocks ):
lowerCamelCase_ = controlnet_block(lowercase )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowerCamelCase_ = controlnet_down_block_res_samples
lowerCamelCase_ = self.controlnet_mid_block(lowercase )
# 6. scaling
lowerCamelCase_ = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=lowercase , mid_block_res_sample=lowercase )
| 313 | 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
lowerCamelCase__ = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["""memory_attention""", """encoder_attn"""],
["""attention""", """attn"""],
["""/""", """."""],
[""".LayerNorm.gamma""", """_layer_norm.weight"""],
[""".LayerNorm.beta""", """_layer_norm.bias"""],
["""r.layer_""", """r.layers."""],
["""output_proj""", """out_proj"""],
["""ffn.dense_1.""", """fc2."""],
["""ffn.dense.""", """fc1."""],
["""ffn_layer_norm""", """final_layer_norm"""],
["""kernel""", """weight"""],
["""encoder_layer_norm.""", """encoder.layer_norm."""],
["""decoder_layer_norm.""", """decoder.layer_norm."""],
["""embeddings.weights""", """shared.weight"""],
]
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
for pegasus_name, hf_name in PATTERNS:
__a = k.replace(lowerCAmelCase__ , lowerCAmelCase__ )
return k
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
__a = DEFAULTS.copy()
cfg_kwargs.update(lowerCAmelCase__ )
__a = PegasusConfig(**lowerCAmelCase__ )
__a = PegasusForConditionalGeneration(lowerCAmelCase__ )
__a = torch_model.model.state_dict()
__a = {}
for k, v in tf_weights.items():
__a = rename_state_dict_key(lowerCAmelCase__ )
if new_k not in sd:
raise ValueError(f"could not find new key {new_k} in state dict. (converted from {k})" )
if "dense" in k or "proj" in new_k:
__a = v.T
__a = torch.tensor(lowerCAmelCase__ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, f"{new_k}, {k}, {v.shape}, {sd[new_k].shape}"
# make sure embedding.padding_idx is respected
__a = torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
__a = mapping["shared.weight"]
__a = mapping["shared.weight"]
__a = {k: torch.zeros_like(lowerCAmelCase__ ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
mapping.update(**lowerCAmelCase__ )
__a = torch_model.model.load_state_dict(lowerCAmelCase__ , strict=lowerCAmelCase__ )
__a = [
k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
]
assert unexpected_missing == [], f"no matches found for the following torch keys {unexpected_missing}"
assert extra == [], f"no matches found for the following tf keys {extra}"
return torch_model
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Union[str, Any]="./ckpt/aeslc/model.ckpt-32000" ):
"""simple docstring"""
__a = tf.train.list_variables(lowerCAmelCase__ )
__a = {}
__a = ["Adafactor", "global_step"]
for name, shape in tqdm(lowerCAmelCase__ , desc="""converting tf checkpoint to dict""" ):
__a = any(pat in name for pat in ignore_name )
if skip_key:
continue
__a = tf.train.load_variable(lowerCAmelCase__ , lowerCAmelCase__ )
__a = array
return tf_weights
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
__a = Path(lowerCAmelCase__ ).parent.name
__a = task_specific_params[f"summarization_{dataset}"]["max_position_embeddings"]
__a = PegasusTokenizer.from_pretrained("""sshleifer/pegasus""" , model_max_length=lowerCAmelCase__ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(lowerCAmelCase__ )
# convert model
__a = get_tf_weights_as_numpy(lowerCAmelCase__ )
__a = task_specific_params[f"summarization_{dataset}"]
if dataset == "large":
__a = task_specific_params
__a = convert_pegasus(lowerCAmelCase__ , lowerCAmelCase__ )
torch_model.save_pretrained(lowerCAmelCase__ )
__a = torch_model.state_dict()
sd.pop("""model.decoder.embed_positions.weight""" )
sd.pop("""model.encoder.embed_positions.weight""" )
torch.save(lowerCAmelCase__ , Path(lowerCAmelCase__ ) / """pytorch_model.bin""" )
if __name__ == "__main__":
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
lowerCamelCase__ = parser.parse_args()
if args.save_dir is None:
lowerCamelCase__ = Path(args.tf_ckpt_path).parent.name
lowerCamelCase__ = os.path.join("""pegasus""", dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 225 |
class __A :
'''simple docstring'''
def __init__( self ):
_lowerCAmelCase : Dict = ""
_lowerCAmelCase : Optional[Any] = ""
_lowerCAmelCase : List[Any] = []
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case ):
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
_lowerCAmelCase : Optional[Any] = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
_lowerCAmelCase : Optional[int] = self.__min_dist_top_down_dp(_snake_case , n - 1 )
_lowerCAmelCase : List[str] = self.__min_dist_top_down_dp(m - 1 , _snake_case )
_lowerCAmelCase : str = self.__min_dist_top_down_dp(m - 1 , n - 1 )
_lowerCAmelCase : Optional[int] = 1 + min(_snake_case , _snake_case , _snake_case )
return self.dp[m][n]
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case ):
_lowerCAmelCase : Union[str, Any] = worda
_lowerCAmelCase : int = worda
_lowerCAmelCase : Tuple = [[-1 for _ in range(len(_snake_case ) )] for _ in range(len(_snake_case ) )]
return self.__min_dist_top_down_dp(len(_snake_case ) - 1 , len(_snake_case ) - 1 )
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case ):
_lowerCAmelCase : str = worda
_lowerCAmelCase : Union[str, Any] = worda
_lowerCAmelCase : str = len(_snake_case )
_lowerCAmelCase : List[Any] = len(_snake_case )
_lowerCAmelCase : str = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
_lowerCAmelCase : int = j
elif j == 0: # second string is empty
_lowerCAmelCase : Optional[Any] = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
_lowerCAmelCase : Union[str, Any] = self.dp[i - 1][j - 1]
else:
_lowerCAmelCase : Tuple = self.dp[i][j - 1]
_lowerCAmelCase : Dict = self.dp[i - 1][j]
_lowerCAmelCase : List[Any] = self.dp[i - 1][j - 1]
_lowerCAmelCase : Tuple = 1 + min(_snake_case , _snake_case , _snake_case )
return self.dp[m][n]
if __name__ == "__main__":
snake_case = EditDistance()
print("****************** Testing Edit Distance DP Algorithm ******************")
print()
snake_case = input("Enter the first string: ").strip()
snake_case = input("Enter the second string: ").strip()
print()
print(F'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
print(F'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
print()
print("*************** End of Testing Edit Distance DP Algorithm ***************")
| 424 | 0 |
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'''vocab_file''': '''spiece.model'''}
__magic_name__ = {
'''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''',
}
}
__magic_name__ = {
'''xlnet-base-cased''': None,
'''xlnet-large-cased''': None,
}
# Segments (not really needed)
__magic_name__ = 0
__magic_name__ = 1
__magic_name__ = 2
__magic_name__ = 3
__magic_name__ = 4
class lowerCAmelCase__ ( __lowerCamelCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = VOCAB_FILES_NAMES
__UpperCAmelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Optional[int] = '''left'''
def __init__( self , a_ , a_=False , a_=True , a_=False , a_="<s>" , a_="</s>" , a_="<unk>" , a_="<sep>" , a_="<pad>" , a_="<cls>" , a_="<mask>" , a_=["<eop>", "<eod>"] , a_ = None , **a_ , ):
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ : str = AddedToken(a_ , lstrip=a_ , rstrip=a_ ) if isinstance(a_ , a_ ) else mask_token
lowerCamelCase_ : int = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
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_ , sp_model_kwargs=self.sp_model_kwargs , **a_ , )
lowerCamelCase_ : str = 3
lowerCamelCase_ : Dict = do_lower_case
lowerCamelCase_ : str = remove_space
lowerCamelCase_ : Tuple = keep_accents
lowerCamelCase_ : Dict = vocab_file
lowerCamelCase_ : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(a_ )
@property
def _UpperCamelCase ( self ):
return len(self.sp_model )
def _UpperCamelCase ( self ):
lowerCamelCase_ : 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 __getstate__( self ):
lowerCamelCase_ : Any = self.__dict__.copy()
lowerCamelCase_ : Optional[int] = None
return state
def __setstate__( self , a_ ):
lowerCamelCase_ : Union[str, Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowerCamelCase_ : int = {}
lowerCamelCase_ : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _UpperCamelCase ( self , a_ ):
if self.remove_space:
lowerCamelCase_ : Optional[int] = " ".join(inputs.strip().split() )
else:
lowerCamelCase_ : str = inputs
lowerCamelCase_ : Any = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
lowerCamelCase_ : Dict = unicodedata.normalize("NFKD" , a_ )
lowerCamelCase_ : int = "".join([c for c in outputs if not unicodedata.combining(a_ )] )
if self.do_lower_case:
lowerCamelCase_ : Any = outputs.lower()
return outputs
def _UpperCamelCase ( self , a_ ):
lowerCamelCase_ : List[Any] = self.preprocess_text(a_ )
lowerCamelCase_ : Optional[int] = self.sp_model.encode(a_ , out_type=a_ )
lowerCamelCase_ : List[str] = []
for piece in pieces:
if len(a_ ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
lowerCamelCase_ : Tuple = self.sp_model.EncodeAsPieces(piece[:-1].replace(a_ , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
lowerCamelCase_ : int = cur_pieces[1:]
else:
lowerCamelCase_ : Union[str, Any] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(a_ )
else:
new_pieces.append(a_ )
return new_pieces
def _UpperCamelCase ( self , a_ ):
return self.sp_model.PieceToId(a_ )
def _UpperCamelCase ( self , a_ ):
return self.sp_model.IdToPiece(a_ )
def _UpperCamelCase ( self , a_ ):
lowerCamelCase_ : Dict = "".join(a_ ).replace(a_ , " " ).strip()
return out_string
def _UpperCamelCase ( self , a_ , a_ = False , a_ = None , a_ = True , **a_ , ):
lowerCamelCase_ : int = kwargs.pop("use_source_tokenizer" , a_ )
lowerCamelCase_ : List[str] = self.convert_ids_to_tokens(a_ , skip_special_tokens=a_ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
lowerCamelCase_ : Optional[int] = []
lowerCamelCase_ : List[str] = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(a_ ) )
lowerCamelCase_ : Union[str, Any] = []
sub_texts.append(a_ )
else:
current_sub_text.append(a_ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(a_ ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
lowerCamelCase_ : Union[str, Any] = "".join(a_ )
lowerCamelCase_ : Optional[Any] = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
lowerCamelCase_ : List[Any] = self.clean_up_tokenization(a_ )
return clean_text
else:
return text
def _UpperCamelCase ( self , a_ , a_ = None ):
lowerCamelCase_ : Optional[Any] = [self.sep_token_id]
lowerCamelCase_ : Union[str, Any] = [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 _UpperCamelCase ( self , a_ , a_ = None , a_ = False ):
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 not None:
return ([0] * len(a_ )) + [1] + ([0] * len(a_ )) + [1, 1]
return ([0] * len(a_ )) + [1, 1]
def _UpperCamelCase ( self , a_ , a_ = None ):
lowerCamelCase_ : Optional[Any] = [self.sep_token_id]
lowerCamelCase_ : Union[str, 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 _UpperCamelCase ( self , a_ , a_ = None ):
if not os.path.isdir(a_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCamelCase_ : Any = 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:
lowerCamelCase_ : Dict = self.sp_model.serialized_model_proto()
fi.write(a_ )
return (out_vocab_file,)
| 73 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , a_ , a_=7 , a_=3 , a_=18 , a_=30 , a_=400 , a_=True , a_=None , a_=True , ):
lowerCamelCase_ : int = size if size is not None else {"height": 18, "width": 18}
lowerCamelCase_ : str = parent
lowerCamelCase_ : str = batch_size
lowerCamelCase_ : Tuple = num_channels
lowerCamelCase_ : Optional[int] = image_size
lowerCamelCase_ : List[str] = min_resolution
lowerCamelCase_ : Tuple = max_resolution
lowerCamelCase_ : Tuple = do_resize
lowerCamelCase_ : Dict = size
lowerCamelCase_ : List[str] = apply_ocr
def _UpperCamelCase ( self ):
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class lowerCAmelCase__ ( __lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def _UpperCamelCase ( self ):
lowerCamelCase_ : List[str] = LayoutLMvaImageProcessingTester(self )
@property
def _UpperCamelCase ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCamelCase ( self ):
lowerCamelCase_ : List[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(a_ , "do_resize" ) )
self.assertTrue(hasattr(a_ , "size" ) )
self.assertTrue(hasattr(a_ , "apply_ocr" ) )
def _UpperCamelCase ( self ):
lowerCamelCase_ : Tuple = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"height": 18, "width": 18} )
lowerCamelCase_ : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {"height": 42, "width": 42} )
def _UpperCamelCase ( self ):
pass
def _UpperCamelCase ( self ):
# Initialize image_processing
lowerCamelCase_ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCamelCase_ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , Image.Image )
# Test not batched input
lowerCamelCase_ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
self.assertIsInstance(encoding.words , a_ )
self.assertIsInstance(encoding.boxes , a_ )
# Test batched
lowerCamelCase_ : int = image_processing(a_ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def _UpperCamelCase ( self ):
# Initialize image_processing
lowerCamelCase_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCamelCase_ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , numpify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , np.ndarray )
# Test not batched input
lowerCamelCase_ : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
lowerCamelCase_ : Any = image_processing(a_ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def _UpperCamelCase ( self ):
# Initialize image_processing
lowerCamelCase_ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCamelCase_ : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=a_ , torchify=a_ )
for image in image_inputs:
self.assertIsInstance(a_ , torch.Tensor )
# Test not batched input
lowerCamelCase_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
# Test batched
lowerCamelCase_ : Union[str, Any] = image_processing(a_ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["height"],
self.image_processor_tester.size["width"],
) , )
def _UpperCamelCase ( self ):
# with apply_OCR = True
lowerCamelCase_ : Any = LayoutLMvaImageProcessor()
from datasets import load_dataset
lowerCamelCase_ : Optional[Any] = load_dataset("hf-internal-testing/fixtures_docvqa" , split="test" )
lowerCamelCase_ : Optional[Any] = Image.open(ds[0]["file"] ).convert("RGB" )
lowerCamelCase_ : List[Any] = image_processing(a_ , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
lowerCamelCase_ : List[Any] = [["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]] # noqa: E231
lowerCamelCase_ : Tuple = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , a_ )
self.assertListEqual(encoding.boxes , a_ )
# with apply_OCR = False
lowerCamelCase_ : List[str] = LayoutLMvaImageProcessor(apply_ocr=a_ )
lowerCamelCase_ : List[str] = image_processing(a_ , return_tensors="pt" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 224, 224) )
| 73 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class UpperCamelCase_ ( UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase_ = "nat"
lowerCamelCase_ = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self :List[Any] , __A :Union[str, Any]=4 , __A :Dict=3 , __A :str=64 , __A :Optional[int]=[3, 4, 6, 5] , __A :Tuple=[2, 4, 8, 16] , __A :List[str]=7 , __A :Optional[Any]=3.0 , __A :Tuple=True , __A :Tuple=0.0 , __A :Dict=0.0 , __A :Tuple=0.1 , __A :str="gelu" , __A :Tuple=0.0_2 , __A :str=1E-5 , __A :Tuple=0.0 , __A :List[str]=None , __A :Optional[Any]=None , **__A :Optional[Any] , ) -> List[str]:
"""simple docstring"""
super().__init__(**__A )
SCREAMING_SNAKE_CASE__ = patch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = embed_dim
SCREAMING_SNAKE_CASE__ = depths
SCREAMING_SNAKE_CASE__ = len(__A )
SCREAMING_SNAKE_CASE__ = num_heads
SCREAMING_SNAKE_CASE__ = kernel_size
SCREAMING_SNAKE_CASE__ = mlp_ratio
SCREAMING_SNAKE_CASE__ = qkv_bias
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = drop_path_rate
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
SCREAMING_SNAKE_CASE__ = int(embed_dim * 2 ** (len(__A ) - 1) )
SCREAMING_SNAKE_CASE__ = layer_scale_init_value
SCREAMING_SNAKE_CASE__ = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(__A ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_aligned_output_features_output_indices(
out_features=__A , out_indices=__A , stage_names=self.stage_names )
| 6 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : Tuple = inspect.getfile(accelerate.test_utils )
lowerCamelCase_ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['''scripts''', '''test_cli.py'''] )
lowerCamelCase_ : Union[str, Any] = ['''accelerate''', '''launch''']
lowerCamelCase_ : Tuple = Path.home() / '''.cache/huggingface/accelerate'''
lowerCamelCase_ : Tuple = '''default_config.yaml'''
lowerCamelCase_ : str = config_folder / config_file
lowerCamelCase_ : List[Any] = config_folder / '''_default_config.yaml'''
lowerCamelCase_ : Dict = Path('''tests/test_configs''' )
@classmethod
def lowerCamelCase (cls ) -> Dict:
'''simple docstring'''
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCamelCase (cls ) -> Any:
'''simple docstring'''
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCamelCase (self ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=__magic_name__ ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(__magic_name__ ), self.test_file_path] , env=os.environ.copy() )
def lowerCamelCase (self ) -> List[Any]:
'''simple docstring'''
execute_subprocess_async(['''accelerate''', '''test'''] , env=os.environ.copy() )
class __lowerCAmelCase ( unittest.TestCase ):
lowerCamelCase_ : List[str] = '''test-tpu'''
lowerCamelCase_ : Dict = '''us-central1-a'''
lowerCamelCase_ : Any = '''ls'''
lowerCamelCase_ : Dict = ['''accelerate''', '''tpu-config''']
lowerCamelCase_ : Tuple = '''cd /usr/share'''
lowerCamelCase_ : List[Any] = '''tests/test_samples/test_command_file.sh'''
lowerCamelCase_ : List[Any] = '''Running gcloud compute tpus tpu-vm ssh'''
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : int = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Optional[int] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command''',
self.command,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] , return_stdout=__magic_name__ )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : str = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Tuple = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command_file''',
self.command_file,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : Any = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Optional[Any] = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] , return_stdout=__magic_name__ , )
self.assertIn(
F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all''' , __magic_name__ , )
| 60 | 0 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
a : Optional[int] = logging.get_logger(__name__)
def __lowerCamelCase ( _lowercase ) -> Tuple:
if isinstance(SCREAMING_SNAKE_CASE__ , np.ndarray ):
return list(tensor.shape )
UpperCAmelCase : Optional[int] = tf.shape(SCREAMING_SNAKE_CASE__ )
if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE__ ):
return dynamic
UpperCAmelCase : List[str] = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE__ )]
def __lowerCamelCase ( _lowercase , _lowercase = None , _lowercase = None ) -> str:
return tf.nn.softmax(logits=logits + 1e-9 , axis=SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase=1e-5 , _lowercase=-1 ) -> Optional[int]:
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
UpperCAmelCase : str = tf.nn.moments(SCREAMING_SNAKE_CASE__ , axes=[axis] , keepdims=SCREAMING_SNAKE_CASE__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
UpperCAmelCase : Optional[Any] = [1] * inputs.shape.rank
UpperCAmelCase : Any = shape_list(SCREAMING_SNAKE_CASE__ )[axis]
UpperCAmelCase : List[str] = tf.reshape(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
UpperCAmelCase : List[Any] = tf.reshape(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Compute layer normalization using the batch_normalization
# function.
UpperCAmelCase : List[Any] = tf.nn.batch_normalization(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , offset=SCREAMING_SNAKE_CASE__ , scale=SCREAMING_SNAKE_CASE__ , variance_epsilon=SCREAMING_SNAKE_CASE__ , )
return outputs
def __lowerCamelCase ( _lowercase , _lowercase=0 , _lowercase=-1 ) -> int:
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
UpperCAmelCase : int = tf.shape(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase : str = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
UpperCAmelCase : List[str] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( _lowercase ) -> str:
if not isinstance(SCREAMING_SNAKE_CASE__ , tf.Tensor ):
UpperCAmelCase : Optional[Any] = tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
UpperCAmelCase : Optional[int] = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
UpperCAmelCase : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
UpperCAmelCase : int = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase = "input_ids" ) -> Union[str, Any]:
tf.debugging.assert_less(
SCREAMING_SNAKE_CASE__ , tf.cast(SCREAMING_SNAKE_CASE__ , dtype=tensor.dtype ) , message=(
F'''The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE__ )}) must be smaller than the embedding '''
F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
) , )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> Tuple:
UpperCAmelCase : Any = 6_4_5_1_2
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
UpperCAmelCase : Optional[int] = [x for x in data if len(SCREAMING_SNAKE_CASE__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
F'''bytes: {bad_attributes}''' )
UpperCAmelCase : str = np.asarray(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase : Union[str, Any] = 1
UpperCAmelCase : Optional[Any] = np.array_split(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
UpperCAmelCase : Any = np.array_split(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE__ ):
UpperCAmelCase : List[Any] = chunk_data
else:
UpperCAmelCase : Union[str, Any] = data
def __lowerCamelCase ( _lowercase , _lowercase ) -> Tuple:
if name in group.attrs:
UpperCAmelCase : Tuple = [n.decode("""utf8""" ) if hasattr(SCREAMING_SNAKE_CASE__ , """decode""" ) else n for n in group.attrs[name]]
else:
UpperCAmelCase : Tuple = []
UpperCAmelCase : Optional[Any] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(SCREAMING_SNAKE_CASE__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def __lowerCamelCase ( _lowercase ) -> Tuple:
def _expand_single_ad_tensor(_lowercase ):
if isinstance(SCREAMING_SNAKE_CASE__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(SCREAMING_SNAKE_CASE__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , SCREAMING_SNAKE_CASE__ )
| 704 |
'''simple docstring'''
a : List[Any] = """Alexander Joslin"""
import operator as op
from .stack import Stack
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : Dict = {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}
UpperCAmelCase : Stack[int] = Stack()
UpperCAmelCase : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(_lowercase ) )
elif i in operators:
# RULE 2
operator_stack.push(_lowercase )
elif i == ")":
# RULE 4
UpperCAmelCase : List[Any] = operator_stack.peek()
operator_stack.pop()
UpperCAmelCase : str = operand_stack.peek()
operand_stack.pop()
UpperCAmelCase : str = operand_stack.peek()
operand_stack.pop()
UpperCAmelCase : List[Any] = operators[opr](_lowercase , _lowercase )
operand_stack.push(_lowercase )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
a : Tuple = """(5 + ((4 * 2) * (2 + 3)))"""
# answer = 45
print(F'''{equation} = {dijkstras_two_stack_algorithm(equation)}''')
| 672 | 0 |
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 GLPNImageProcessor
class lowerCAmelCase_ ( unittest.TestCase ):
def __init__( self : int , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple=7 , SCREAMING_SNAKE_CASE_ : Any=3 , SCREAMING_SNAKE_CASE_ : List[str]=18 , SCREAMING_SNAKE_CASE_ : Optional[int]=30 , SCREAMING_SNAKE_CASE_ : Optional[Any]=400 , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=32 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=True , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = image_size
lowerCAmelCase__ = min_resolution
lowerCAmelCase__ = max_resolution
lowerCAmelCase__ = do_resize
lowerCAmelCase__ = size_divisor
lowerCAmelCase__ = do_rescale
def __snake_case ( self : Optional[Any] ):
return {
"do_resize": self.do_resize,
"size_divisor": self.size_divisor,
"do_rescale": self.do_rescale,
}
@require_torch
@require_vision
class lowerCAmelCase_ ( snake_case__ , unittest.TestCase ):
UpperCamelCase_ :Union[str, Any] = GLPNImageProcessor if is_vision_available() else None
def __snake_case ( self : str ):
lowerCAmelCase__ = GLPNImageProcessingTester(self )
@property
def __snake_case ( self : str ):
return self.image_processor_tester.prepare_image_processor_dict()
def __snake_case ( self : List[str] ):
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''do_resize''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''size_divisor''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''resample''' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''do_rescale''' ) )
def __snake_case ( self : Tuple ):
pass
def __snake_case ( self : Optional[Any] ):
# Initialize image_processing
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , Image.Image )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def __snake_case ( self : int ):
# Initialize image_processing
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
def __snake_case ( self : Optional[int] ):
# Initialize image_processing
lowerCAmelCase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor )
# Test not batched input (GLPNImageProcessor doesn't support batching)
lowerCAmelCase__ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0 )
self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0 )
| 668 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class lowerCAmelCase_ ( snake_case__ ):
def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any]=13 , SCREAMING_SNAKE_CASE_ : Dict=7 , SCREAMING_SNAKE_CASE_ : List[Any]=True , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : Optional[int]=False , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : str=99 , SCREAMING_SNAKE_CASE_ : str=32 , SCREAMING_SNAKE_CASE_ : int=5 , SCREAMING_SNAKE_CASE_ : Tuple=4 , SCREAMING_SNAKE_CASE_ : Tuple=37 , SCREAMING_SNAKE_CASE_ : Tuple="gelu" , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE_ : List[Any]=0.1 , SCREAMING_SNAKE_CASE_ : Dict=512 , SCREAMING_SNAKE_CASE_ : Any=16 , SCREAMING_SNAKE_CASE_ : List[Any]=2 , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.02 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=3 , SCREAMING_SNAKE_CASE_ : Optional[Any]=4 , SCREAMING_SNAKE_CASE_ : int=None , ):
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = seq_length
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_input_mask
lowerCAmelCase__ = use_token_type_ids
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = max_position_embeddings
lowerCAmelCase__ = type_vocab_size
lowerCAmelCase__ = type_sequence_label_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = num_choices
lowerCAmelCase__ = scope
def __snake_case ( self : Union[str, Any] ):
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCAmelCase__ = None
if self.use_input_mask:
lowerCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase__ = None
lowerCAmelCase__ = None
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase__ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __snake_case ( self : Tuple ):
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
lowerCAmelCase__ = DistilBertModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase__ = model(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __snake_case ( self : int , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
lowerCAmelCase__ = DistilBertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Tuple ):
lowerCAmelCase__ = DistilBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase__ = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , start_positions=SCREAMING_SNAKE_CASE_ , end_positions=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __snake_case ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : int ):
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = DistilBertForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __snake_case ( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : List[str] ):
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = DistilBertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
lowerCAmelCase__ = self.num_choices
lowerCAmelCase__ = DistilBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
lowerCAmelCase__ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCAmelCase__ = model(
SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ , labels=SCREAMING_SNAKE_CASE_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __snake_case ( self : Optional[int] ):
lowerCAmelCase__ = self.prepare_config_and_inputs()
((lowerCAmelCase__) , (lowerCAmelCase__) , (lowerCAmelCase__) , (lowerCAmelCase__) , (lowerCAmelCase__) , (lowerCAmelCase__)) = config_and_inputs
lowerCAmelCase__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( snake_case__ , snake_case__ , unittest.TestCase ):
UpperCamelCase_ :Any = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
UpperCamelCase_ :Union[str, Any] = (
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCamelCase_ :int = True
UpperCamelCase_ :List[str] = True
UpperCamelCase_ :List[Any] = True
UpperCamelCase_ :Dict = True
def __snake_case ( self : Dict ):
lowerCAmelCase__ = DistilBertModelTester(self )
lowerCAmelCase__ = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE_ , dim=37 )
def __snake_case ( self : List[Any] ):
self.config_tester.run_common_tests()
def __snake_case ( self : Dict ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : Optional[Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : Dict ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : Union[str, Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : int ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def __snake_case ( self : Optional[Any] ):
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
@slow
def __snake_case ( self : Tuple ):
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = DistilBertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@slow
@require_torch_gpu
def __snake_case ( self : Any ):
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(config=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = self._prepare_for_class(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = torch.jit.trace(
SCREAMING_SNAKE_CASE_ , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(SCREAMING_SNAKE_CASE_ , os.path.join(SCREAMING_SNAKE_CASE_ , '''traced_model.pt''' ) )
lowerCAmelCase__ = torch.jit.load(os.path.join(SCREAMING_SNAKE_CASE_ , '''traced_model.pt''' ) , map_location=SCREAMING_SNAKE_CASE_ )
loaded(inputs_dict['''input_ids'''].to(SCREAMING_SNAKE_CASE_ ) , inputs_dict['''attention_mask'''].to(SCREAMING_SNAKE_CASE_ ) )
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
@slow
def __snake_case ( self : str ):
lowerCAmelCase__ = DistilBertModel.from_pretrained('''distilbert-base-uncased''' )
lowerCAmelCase__ = torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
lowerCAmelCase__ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
lowerCAmelCase__ = model(SCREAMING_SNAKE_CASE_ , attention_mask=SCREAMING_SNAKE_CASE_ )[0]
lowerCAmelCase__ = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , SCREAMING_SNAKE_CASE_ , atol=1e-4 ) )
| 668 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Any = logging.get_logger(__name__)
lowerCAmelCase_ : Optional[Any] = {
"""junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
"""junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
"""junnyu/roformer_chinese_char_small""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
),
"""junnyu/roformer_chinese_char_base""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
),
"""junnyu/roformer_small_discriminator""": (
"""https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
),
"""junnyu/roformer_small_generator""": (
"""https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCAmelCase__ = '''roformer'''
def __init__( self : List[Any] , lowercase__ : Tuple=50_000 , lowercase__ : List[str]=None , lowercase__ : int=768 , lowercase__ : List[str]=12 , lowercase__ : Optional[Any]=12 , lowercase__ : Union[str, Any]=3_072 , lowercase__ : Optional[int]="gelu" , lowercase__ : Dict=0.1 , lowercase__ : List[str]=0.1 , lowercase__ : Any=1_536 , lowercase__ : Optional[Any]=2 , lowercase__ : List[Any]=0.0_2 , lowercase__ : Dict=1e-12 , lowercase__ : Union[str, Any]=0 , lowercase__ : List[str]=False , lowercase__ : str=True , **lowercase__ : Optional[Any] , ) ->Dict:
'''simple docstring'''
super().__init__(pad_token_id=_a , **_a )
_UpperCamelCase : Optional[int] = vocab_size
_UpperCamelCase : str = hidden_size if embedding_size is None else embedding_size
_UpperCamelCase : int = hidden_size
_UpperCamelCase : Any = num_hidden_layers
_UpperCamelCase : Tuple = num_attention_heads
_UpperCamelCase : List[str] = hidden_act
_UpperCamelCase : str = intermediate_size
_UpperCamelCase : Union[str, Any] = hidden_dropout_prob
_UpperCamelCase : Any = attention_probs_dropout_prob
_UpperCamelCase : Union[str, Any] = max_position_embeddings
_UpperCamelCase : List[str] = type_vocab_size
_UpperCamelCase : List[str] = initializer_range
_UpperCamelCase : List[str] = layer_norm_eps
_UpperCamelCase : Optional[Any] = rotary_value
_UpperCamelCase : Union[str, Any] = use_cache
class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def snake_case__ ( self : Any ) ->Optional[Any]:
'''simple docstring'''
if self.task == "multiple-choice":
_UpperCamelCase : Tuple = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_UpperCamelCase : int = {0: """batch""", 1: """sequence"""}
_UpperCamelCase : Union[str, Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 705 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
lowerCAmelCase_ : Tuple = """true"""
def __A ( UpperCAmelCase ,UpperCAmelCase=8_2 ,UpperCAmelCase=1_6 ) -> Union[str, Any]:
'''simple docstring'''
set_seed(4_2 )
_UpperCamelCase : List[Any] = RegressionModel()
_UpperCamelCase : Any = deepcopy(UpperCAmelCase )
_UpperCamelCase : Tuple = RegressionDataset(length=UpperCAmelCase )
_UpperCamelCase : Union[str, Any] = DataLoader(UpperCAmelCase ,batch_size=UpperCAmelCase )
model.to(accelerator.device )
_UpperCamelCase , _UpperCamelCase : Dict = accelerator.prepare(UpperCAmelCase ,UpperCAmelCase )
return model, ddp_model, dataloader
def __A ( UpperCAmelCase ,UpperCAmelCase=False ) -> List[str]:
'''simple docstring'''
_UpperCamelCase : str = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
_UpperCamelCase : Optional[Any] = load_dataset("glue" ,"mrpc" ,split="validation" )
def tokenize_function(UpperCAmelCase ):
_UpperCamelCase : Tuple = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=UpperCAmelCase ,max_length=UpperCAmelCase )
return outputs
with accelerator.main_process_first():
_UpperCamelCase : str = dataset.map(
UpperCAmelCase ,batched=UpperCAmelCase ,remove_columns=["idx", "sentence1", "sentence2"] ,)
_UpperCamelCase : Optional[int] = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(UpperCAmelCase ):
if use_longest:
return tokenizer.pad(UpperCAmelCase ,padding="longest" ,return_tensors="pt" )
return tokenizer.pad(UpperCAmelCase ,padding="max_length" ,max_length=1_2_8 ,return_tensors="pt" )
return DataLoader(UpperCAmelCase ,shuffle=UpperCAmelCase ,collate_fn=UpperCAmelCase ,batch_size=1_6 )
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> Dict:
'''simple docstring'''
_UpperCamelCase : str = Accelerator(dispatch_batches=UpperCAmelCase ,split_batches=UpperCAmelCase )
_UpperCamelCase : Union[str, Any] = get_dataloader(UpperCAmelCase ,not dispatch_batches )
_UpperCamelCase : Optional[int] = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" ,return_dict=UpperCAmelCase )
_UpperCamelCase , _UpperCamelCase : Union[str, Any] = accelerator.prepare(UpperCAmelCase ,UpperCAmelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def __A ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ) -> int:
'''simple docstring'''
_UpperCamelCase : List[str] = []
for batch in dataloader:
_UpperCamelCase , _UpperCamelCase : int = batch.values()
with torch.no_grad():
_UpperCamelCase : Tuple = model(UpperCAmelCase )
_UpperCamelCase , _UpperCamelCase : List[Any] = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
_UpperCamelCase , _UpperCamelCase : Optional[Any] = [], []
for logit, targ in logits_and_targets:
logits.append(UpperCAmelCase )
targs.append(UpperCAmelCase )
_UpperCamelCase , _UpperCamelCase : int = torch.cat(UpperCAmelCase ), torch.cat(UpperCAmelCase )
return logits, targs
def __A ( UpperCAmelCase ,UpperCAmelCase=8_2 ,UpperCAmelCase=False ,UpperCAmelCase=False ,UpperCAmelCase=1_6 ) -> Any:
'''simple docstring'''
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Union[str, Any] = get_basic_setup(UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase )
_UpperCamelCase , _UpperCamelCase : Tuple = generate_predictions(UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase )
assert (
len(UpperCAmelCase ) == num_samples
), f'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(UpperCAmelCase )}'''
def __A ( UpperCAmelCase = False ,UpperCAmelCase = False ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : int = evaluate.load("glue" ,"mrpc" )
_UpperCamelCase , _UpperCamelCase : Any = get_mrpc_setup(UpperCAmelCase ,UpperCAmelCase )
# First do baseline
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Tuple = setup["no"]
model.to(UpperCAmelCase )
model.eval()
for batch in dataloader:
batch.to(UpperCAmelCase )
with torch.inference_mode():
_UpperCamelCase : Optional[Any] = model(**UpperCAmelCase )
_UpperCamelCase : Any = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=UpperCAmelCase ,references=batch["labels"] )
_UpperCamelCase : List[str] = metric.compute()
# Then do distributed
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : List[Any] = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
_UpperCamelCase : int = model(**UpperCAmelCase )
_UpperCamelCase : Tuple = outputs.logits.argmax(dim=-1 )
_UpperCamelCase : List[str] = batch["labels"]
_UpperCamelCase , _UpperCamelCase : List[str] = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=UpperCAmelCase ,references=UpperCAmelCase )
_UpperCamelCase : int = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def __A ( ) -> Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : Dict = Accelerator(split_batches=UpperCAmelCase ,dispatch_batches=UpperCAmelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(UpperCAmelCase ,UpperCAmelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
_UpperCamelCase : int = Accelerator(split_batches=UpperCAmelCase ,dispatch_batches=UpperCAmelCase )
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(UpperCAmelCase ,9_9 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
_UpperCamelCase : int = Accelerator()
test_torch_metrics(UpperCAmelCase ,5_1_2 )
accelerator.state._reset_state()
def __A ( UpperCAmelCase ) -> List[str]:
'''simple docstring'''
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 204 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
raise Exception("""requires fairseq < v2""")
logging.set_verbosity_info()
_A : Optional[Any] = logging.get_logger(__name__)
_A : Union[str, Any] = """Hello, World!"""
_A : List[str] = """en_XX"""
def __magic_name__ ( __snake_case : Any , __snake_case : str , __snake_case : Dict ) -> Optional[Any]:
lowercase : Dict = Path("data_bin" )
lowercase : Tuple = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(__snake_case ).parent ) , checkpoint_file=Path(__snake_case ).name , _name="xmod_base" , arch="xmod_base" , task="multilingual_masked_lm" , data_name_or_path=str(__snake_case ) , bpe="sentencepiece" , sentencepiece_model=str(Path(__snake_case ).parent / "sentencepiece.bpe.model" ) , src_dict=str(data_dir / "dict.txt" ) , )
xmod.eval() # disable dropout
print(__snake_case )
lowercase : int = xmod.model.encoder.sentence_encoder
lowercase : int = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1E-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , "bottleneck" , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
lowercase : int = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("Our X-MOD config:" , __snake_case )
lowercase : str = XmodForSequenceClassification(__snake_case ) if classification_head else XmodForMaskedLM(__snake_case )
model.eval()
# Now let's copy all the weights.
# Embeddings
lowercase : List[Any] = xmod_sent_encoder.embed_tokens.weight
lowercase : Tuple = xmod_sent_encoder.embed_positions.weight
lowercase : Tuple = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
lowercase : Optional[Any] = xmod_sent_encoder.layernorm_embedding.weight
lowercase : Optional[Any] = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
lowercase : List[str] = model.roberta.encoder.layer[i]
lowercase : Any = xmod_sent_encoder.layers[i]
# self attention
lowercase : Union[str, Any] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("Dimensions of self-attention weights do not match." )
lowercase : Any = xmod_layer.self_attn.q_proj.weight
lowercase : List[str] = xmod_layer.self_attn.q_proj.bias
lowercase : Union[str, Any] = xmod_layer.self_attn.k_proj.weight
lowercase : Optional[Any] = xmod_layer.self_attn.k_proj.bias
lowercase : Optional[int] = xmod_layer.self_attn.v_proj.weight
lowercase : int = xmod_layer.self_attn.v_proj.bias
# self-attention output
lowercase : Tuple = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("Dimensions of self-attention output weights do not match." )
lowercase : Tuple = xmod_layer.self_attn.out_proj.weight
lowercase : List[Any] = xmod_layer.self_attn.out_proj.bias
lowercase : int = xmod_layer.self_attn_layer_norm.weight
lowercase : Tuple = xmod_layer.self_attn_layer_norm.bias
# intermediate
lowercase : List[Any] = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of intermediate weights do not match." )
lowercase : Tuple = xmod_layer.fca.weight
lowercase : Optional[Any] = xmod_layer.fca.bias
# output
lowercase : str = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of feed-forward weights do not match." )
lowercase : str = xmod_layer.fca.weight
lowercase : int = xmod_layer.fca.bias
lowercase : Optional[Any] = xmod_layer.final_layer_norm.weight
lowercase : Tuple = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
lowercase : Tuple = xmod_layer.adapter_layer_norm.weight
lowercase : Any = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("Lists of language adapters do not match." )
for lang_code, adapter in xmod_layer.adapter_modules.items():
lowercase : List[str] = bert_output.adapter_modules[lang_code]
lowercase : Dict = xmod_layer.adapter_modules[lang_code]
lowercase : List[Any] = from_adapter.fca.weight
lowercase : Union[str, Any] = from_adapter.fca.bias
lowercase : str = from_adapter.fca.weight
lowercase : Tuple = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
lowercase : int = xmod_sent_encoder.layer_norm.weight
lowercase : Optional[Any] = xmod_sent_encoder.layer_norm.bias
if classification_head:
lowercase : List[Any] = xmod.model.classification_heads["""mnli"""].dense.weight
lowercase : Dict = xmod.model.classification_heads["""mnli"""].dense.bias
lowercase : Union[str, Any] = xmod.model.classification_heads["""mnli"""].out_proj.weight
lowercase : Optional[int] = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
lowercase : List[str] = xmod.model.encoder.lm_head.dense.weight
lowercase : Optional[Any] = xmod.model.encoder.lm_head.dense.bias
lowercase : Any = xmod.model.encoder.lm_head.layer_norm.weight
lowercase : List[str] = xmod.model.encoder.lm_head.layer_norm.bias
lowercase : Union[str, Any] = xmod.model.encoder.lm_head.weight
lowercase : Union[str, Any] = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
lowercase : Any = xmod.encode(__snake_case ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(__snake_case )
lowercase : Optional[Any] = model(__snake_case )[0]
if classification_head:
lowercase : List[Any] = xmod.model.classification_heads["""mnli"""](xmod.extract_features(__snake_case ) )
else:
lowercase : str = xmod.model(__snake_case , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
lowercase : Dict = torch.max(torch.abs(our_output - their_output ) ).item()
print(f"""max_absolute_diff = {max_absolute_diff}""" ) # ~ 1e-7
lowercase : Any = torch.allclose(__snake_case , __snake_case , atol=1E-3 )
print("Do both models output the same tensors?" , "🔥" if success else "💩" )
if not success:
raise Exception("Something went wRoNg" )
Path(__snake_case ).mkdir(parents=__snake_case , exist_ok=__snake_case )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__snake_case )
if __name__ == "__main__":
_A : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
)
_A : Union[str, Any] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 361 |
"""simple docstring"""
def A_ ( lowercase , lowercase ) -> int:
"""simple docstring"""
return number | (1 << position)
def A_ ( lowercase , lowercase ) -> int:
"""simple docstring"""
return number & ~(1 << position)
def A_ ( lowercase , lowercase ) -> int:
"""simple docstring"""
return number ^ (1 << position)
def A_ ( lowercase , lowercase ) -> bool:
"""simple docstring"""
return ((number >> position) & 1) == 1
def A_ ( lowercase , lowercase ) -> int:
"""simple docstring"""
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 470 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class __A( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = """open-llama"""
def __init__(self , SCREAMING_SNAKE_CASE_=10_00_00 , SCREAMING_SNAKE_CASE_=40_96 , SCREAMING_SNAKE_CASE_=1_10_08 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_="silu" , SCREAMING_SNAKE_CASE_=20_48 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1E-6 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ):
UpperCamelCase__ = vocab_size
UpperCamelCase__ = max_position_embeddings
UpperCamelCase__ = hidden_size
UpperCamelCase__ = intermediate_size
UpperCamelCase__ = num_hidden_layers
UpperCamelCase__ = num_attention_heads
UpperCamelCase__ = hidden_act
UpperCamelCase__ = initializer_range
UpperCamelCase__ = rms_norm_eps
UpperCamelCase__ = use_cache
UpperCamelCase__ = kwargs.pop(
"""use_memorry_efficient_attention""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = hidden_dropout_prob
UpperCamelCase__ = attention_dropout_prob
UpperCamelCase__ = use_stable_embedding
UpperCamelCase__ = shared_input_output_embedding
UpperCamelCase__ = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def UpperCAmelCase_ (self ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , SCREAMING_SNAKE_CASE_ ) 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}" )
UpperCamelCase__ = self.rope_scaling.get("""type""" , SCREAMING_SNAKE_CASE_ )
UpperCamelCase__ = self.rope_scaling.get("""factor""" , SCREAMING_SNAKE_CASE_ )
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(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or rope_scaling_factor <= 1.0:
raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}" )
| 86 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCamelCase_ = {
'''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt''',
'''tiny''': '''https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt''',
'''base.en''': '''https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt''',
'''base''': '''https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt''',
'''small.en''': '''https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt''',
'''small''': '''https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt''',
'''medium.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt''',
'''medium''': '''https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt''',
'''large''': '''https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt''',
'''large-v2''': '''https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt''',
}
def __magic_name__ ( __a : List[str] ):
'''simple docstring'''
UpperCamelCase__ = ["""layers""", """blocks"""]
for k in ignore_keys:
state_dict.pop(__a , __a )
lowerCamelCase_ = {
'''blocks''': '''layers''',
'''mlp.0''': '''fc1''',
'''mlp.2''': '''fc2''',
'''mlp_ln''': '''final_layer_norm''',
'''.attn.query''': '''.self_attn.q_proj''',
'''.attn.key''': '''.self_attn.k_proj''',
'''.attn.value''': '''.self_attn.v_proj''',
'''.attn_ln''': '''.self_attn_layer_norm''',
'''.attn.out''': '''.self_attn.out_proj''',
'''.cross_attn.query''': '''.encoder_attn.q_proj''',
'''.cross_attn.key''': '''.encoder_attn.k_proj''',
'''.cross_attn.value''': '''.encoder_attn.v_proj''',
'''.cross_attn_ln''': '''.encoder_attn_layer_norm''',
'''.cross_attn.out''': '''.encoder_attn.out_proj''',
'''decoder.ln.''': '''decoder.layer_norm.''',
'''encoder.ln.''': '''encoder.layer_norm.''',
'''token_embedding''': '''embed_tokens''',
'''encoder.positional_embedding''': '''encoder.embed_positions.weight''',
'''decoder.positional_embedding''': '''decoder.embed_positions.weight''',
'''ln_post''': '''layer_norm''',
}
def __magic_name__ ( __a : Dict ):
'''simple docstring'''
UpperCamelCase__ = list(s_dict.keys() )
for key in keys:
UpperCamelCase__ = key
for k, v in WHISPER_MAPPING.items():
if k in key:
UpperCamelCase__ = new_key.replace(__a , __a )
print(f"{key} -> {new_key}" )
UpperCamelCase__ = s_dict.pop(__a )
return s_dict
def __magic_name__ ( __a : Optional[Any] ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ = emb.weight.shape
UpperCamelCase__ = nn.Linear(__a , __a , bias=__a )
UpperCamelCase__ = emb.weight.data
return lin_layer
def __magic_name__ ( __a : str , __a : str ):
'''simple docstring'''
os.makedirs(__a , exist_ok=__a )
UpperCamelCase__ = os.path.basename(__a )
UpperCamelCase__ = url.split("""/""" )[-2]
UpperCamelCase__ = os.path.join(__a , __a )
if os.path.exists(__a ) and not os.path.isfile(__a ):
raise RuntimeError(f"{download_target} exists and is not a regular file" )
if os.path.isfile(__a ):
UpperCamelCase__ = open(__a , """rb""" ).read()
if hashlib.shaaaa(__a ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" )
with urllib.request.urlopen(__a ) as source, open(__a , """wb""" ) as output:
with tqdm(
total=int(source.info().get("""Content-Length""" ) ) , ncols=80 , unit="""iB""" , unit_scale=__a , unit_divisor=1_024 ) as loop:
while True:
UpperCamelCase__ = source.read(8_192 )
if not buffer:
break
output.write(__a )
loop.update(len(__a ) )
UpperCamelCase__ = open(__a , """rb""" ).read()
if hashlib.shaaaa(__a ).hexdigest() != expected_shaaaa:
raise RuntimeError(
"""Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.""" )
return model_bytes
def __magic_name__ ( __a : Union[str, Any] , __a : Optional[int] ):
'''simple docstring'''
if ".pt" not in checkpoint_path:
UpperCamelCase__ = _download(_MODELS[checkpoint_path] )
else:
UpperCamelCase__ = torch.load(__a , map_location="""cpu""" )
UpperCamelCase__ = original_checkpoint["""dims"""]
UpperCamelCase__ = original_checkpoint["""model_state_dict"""]
UpperCamelCase__ = state_dict["""decoder.token_embedding.weight"""]
remove_ignore_keys_(__a )
rename_keys(__a )
UpperCamelCase__ = True
UpperCamelCase__ = state_dict["""decoder.layers.0.fc1.weight"""].shape[0]
UpperCamelCase__ = WhisperConfig(
vocab_size=dimensions["""n_vocab"""] , encoder_ffn_dim=__a , decoder_ffn_dim=__a , num_mel_bins=dimensions["""n_mels"""] , d_model=dimensions["""n_audio_state"""] , max_target_positions=dimensions["""n_text_ctx"""] , encoder_layers=dimensions["""n_audio_layer"""] , encoder_attention_heads=dimensions["""n_audio_head"""] , decoder_layers=dimensions["""n_text_layer"""] , decoder_attention_heads=dimensions["""n_text_state"""] , max_source_positions=dimensions["""n_audio_ctx"""] , )
UpperCamelCase__ = WhisperForConditionalGeneration(__a )
UpperCamelCase__ , UpperCamelCase__ = model.model.load_state_dict(__a , strict=__a )
if len(__a ) > 0 and not set(__a ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
"""Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,"""
f" but all the following weights are missing {missing}" )
if tie_embeds:
UpperCamelCase__ = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
UpperCamelCase__ = proj_out_weights
model.save_pretrained(__a )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument('''--checkpoint_path''', type=str, help='''Patht to the downloaded checkpoints''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
lowerCamelCase_ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 86 | 1 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
__magic_name__ : Tuple = logging.get_logger(__name__)
class UpperCamelCase__ ( lowerCamelCase__ ):
"""simple docstring"""
UpperCAmelCase__ = 'vision-encoder-decoder'
UpperCAmelCase__ = True
def __init__( self : str , **__A : List[str] ):
"""simple docstring"""
super().__init__(**__A )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"""A configuraton of type {self.model_type} cannot be instantiated because """
f"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
_lowercase = kwargs.pop("encoder" )
_lowercase = encoder_config.pop("model_type" )
_lowercase = kwargs.pop("decoder" )
_lowercase = decoder_config.pop("model_type" )
_lowercase = AutoConfig.for_model(__A , **__A )
_lowercase = AutoConfig.for_model(__A , **__A )
_lowercase = True
@classmethod
def snake_case ( cls : Tuple , __A : PretrainedConfig , __A : PretrainedConfig , **__A : List[Any] ):
"""simple docstring"""
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
_lowercase = True
_lowercase = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__A )
def snake_case ( self : Optional[Any] ):
"""simple docstring"""
_lowercase = copy.deepcopy(self.__dict__ )
_lowercase = self.encoder.to_dict()
_lowercase = self.decoder.to_dict()
_lowercase = self.__class__.model_type
return output
class UpperCamelCase__ ( lowerCamelCase__ ):
"""simple docstring"""
UpperCAmelCase__ = version.parse('1.11' )
@property
def snake_case ( self : List[str] ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def snake_case ( self : str ):
"""simple docstring"""
return 1e-4
@property
def snake_case ( self : Union[str, Any] ):
"""simple docstring"""
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}} )
class UpperCamelCase__ ( lowerCamelCase__ ):
"""simple docstring"""
@property
def snake_case ( self : int ):
"""simple docstring"""
_lowercase = OrderedDict()
_lowercase = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowercase = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowercase = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def snake_case ( self : Any , __A : "PreTrainedTokenizerBase" , __A : int = -1 , __A : int = -1 , __A : bool = False , __A : Optional["TensorType"] = None , ):
"""simple docstring"""
import torch
_lowercase = OrderedDict()
_lowercase = super().generate_dummy_inputs(
__A , batch_size=__A , seq_length=__A , is_pair=__A , framework=__A )
_lowercase , _lowercase = dummy_input["input_ids"].shape
_lowercase = (batch, encoder_sequence, self._config.encoder_hidden_size)
_lowercase = dummy_input.pop("input_ids" )
_lowercase = dummy_input.pop("attention_mask" )
_lowercase = torch.zeros(__A )
return common_inputs
class UpperCamelCase__ ( lowerCamelCase__ ):
"""simple docstring"""
@property
def snake_case ( self : Optional[int] ):
"""simple docstring"""
pass
def snake_case ( self : Dict , __A : PretrainedConfig ):
"""simple docstring"""
return VisionEncoderDecoderEncoderOnnxConfig(__A )
def snake_case ( self : Optional[Any] , __A : PretrainedConfig , __A : PretrainedConfig , __A : str = "default" ):
"""simple docstring"""
_lowercase = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(__A , __A )
| 497 |
'''simple docstring'''
from __future__ import annotations
def A__ ( A_ , A_ ) -> list[str]:
if nth_term == "":
return [""]
_lowercase = int(A_ )
_lowercase = int(A_ )
_lowercase = []
for temp in range(int(A_ ) ):
series.append(F"""1 / {pow(temp + 1 , int(A_ ) )}""" if series else "1" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
__magic_name__ : Any = int(input('''Enter the last number (nth term) of the P-Series'''))
__magic_name__ : Dict = int(input('''Enter the power for P-Series'''))
print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
print(p_series(nth_term, power))
| 497 | 1 |
"""simple docstring"""
import math
class _A :
"""simple docstring"""
def __snake_case ( self : Optional[int] , __UpperCAmelCase : list[list[float]] , __UpperCAmelCase : list[int]):
a : int = 0.0
a : List[Any] = 0.0
for i in range(len(__UpperCAmelCase)):
da += math.pow((sample[i] - weights[0][i]) , 2)
da += math.pow((sample[i] - weights[1][i]) , 2)
return 0 if da > da else 1
return 0
def __snake_case ( self : Any , __UpperCAmelCase : list[list[int | float]] , __UpperCAmelCase : list[int] , __UpperCAmelCase : int , __UpperCAmelCase : float):
for i in range(len(__UpperCAmelCase)):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def lowercase ( )-> None:
'''simple docstring'''
a : Optional[int] = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
a : Optional[Any] = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
a : Tuple = SelfOrganizingMap()
a : Optional[Any] = 3
a : Tuple = 0.5
for _ in range(A_ ):
for j in range(len(A_ ) ):
# training sample
a : List[Any] = training_samples[j]
# Compute the winning vector
a : Tuple = self_organizing_map.get_winner(A_ , A_ )
# Update the winning vector
a : List[Any] = self_organizing_map.update(A_ , A_ , A_ , A_ )
# classify test sample
a : Dict = [0, 0, 0, 1]
a : Union[str, Any] = self_organizing_map.get_winner(A_ , A_ )
# results
print(F'''Clusters that the test sample belongs to : {winner}''' )
print(F'''Weights that have been trained : {weights}''' )
# running the main() function
if __name__ == "__main__":
main()
| 135 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _A ( _a ):
"""simple docstring"""
def __init__( self : Dict , __UpperCAmelCase : TransformeraDModel , __UpperCAmelCase : AutoencoderKL , __UpperCAmelCase : KarrasDiffusionSchedulers , __UpperCAmelCase : Optional[Dict[int, str]] = None , ):
super().__init__()
self.register_modules(transformer=__UpperCAmelCase , vae=__UpperCAmelCase , scheduler=__UpperCAmelCase)
# create a imagenet -> id dictionary for easier use
a : List[Any] = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(","):
a : Any = int(__UpperCAmelCase)
a : str = dict(sorted(self.labels.items()))
def __snake_case ( self : Union[str, Any] , __UpperCAmelCase : Union[str, List[str]]):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase):
a : str = list(__UpperCAmelCase)
for l in label:
if l not in self.labels:
raise ValueError(
f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''')
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self : List[Any] , __UpperCAmelCase : List[int] , __UpperCAmelCase : float = 4.0 , __UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __UpperCAmelCase : int = 50 , __UpperCAmelCase : Optional[str] = "pil" , __UpperCAmelCase : bool = True , ):
a : str = len(__UpperCAmelCase)
a : List[str] = self.transformer.config.sample_size
a : List[str] = self.transformer.config.in_channels
a : Optional[Any] = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=__UpperCAmelCase , device=self.device , dtype=self.transformer.dtype , )
a : Any = torch.cat([latents] * 2) if guidance_scale > 1 else latents
a : str = torch.tensor(__UpperCAmelCase , device=self.device).reshape(-1)
a : str = torch.tensor([1000] * batch_size , device=self.device)
a : int = torch.cat([class_labels, class_null] , 0) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(__UpperCAmelCase)
for t in self.progress_bar(self.scheduler.timesteps):
if guidance_scale > 1:
a : Any = latent_model_input[: len(__UpperCAmelCase) // 2]
a : Optional[Any] = torch.cat([half, half] , dim=0)
a : List[str] = self.scheduler.scale_model_input(__UpperCAmelCase , __UpperCAmelCase)
a : Optional[Any] = t
if not torch.is_tensor(__UpperCAmelCase):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
a : List[str] = latent_model_input.device.type == "mps"
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
a : List[str] = torch.floataa if is_mps else torch.floataa
else:
a : List[Any] = torch.intaa if is_mps else torch.intaa
a : Any = torch.tensor([timesteps] , dtype=__UpperCAmelCase , device=latent_model_input.device)
elif len(timesteps.shape) == 0:
a : List[Any] = timesteps[None].to(latent_model_input.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
a : Optional[Any] = timesteps.expand(latent_model_input.shape[0])
# predict noise model_output
a : str = self.transformer(
__UpperCAmelCase , timestep=__UpperCAmelCase , class_labels=__UpperCAmelCase).sample
# perform guidance
if guidance_scale > 1:
a , a : Optional[Any] = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
a , a : Dict = torch.split(__UpperCAmelCase , len(__UpperCAmelCase) // 2 , dim=0)
a : Optional[Any] = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
a : List[Any] = torch.cat([half_eps, half_eps] , dim=0)
a : List[str] = torch.cat([eps, rest] , dim=1)
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
a , a : Optional[int] = torch.split(__UpperCAmelCase , __UpperCAmelCase , dim=1)
else:
a : int = noise_pred
# compute previous image: x_t -> x_t-1
a : List[str] = self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase).prev_sample
if guidance_scale > 1:
a , a : Union[str, Any] = latent_model_input.chunk(2 , dim=0)
else:
a : Any = latent_model_input
a : str = 1 / self.vae.config.scaling_factor * latents
a : Dict = self.vae.decode(__UpperCAmelCase).sample
a : List[str] = (samples / 2 + 0.5).clamp(0 , 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
a : Optional[Any] = samples.cpu().permute(0 , 2 , 3 , 1).float().numpy()
if output_type == "pil":
a : Tuple = self.numpy_to_pil(__UpperCAmelCase)
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=__UpperCAmelCase)
| 135 | 1 |
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
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''facebook/deit-base-distilled-patch16-224''': (
'''https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'''
),
# See all DeiT models at https://huggingface.co/models?filter=deit
}
class SCREAMING_SNAKE_CASE ( __a ):
__lowerCamelCase : Dict ='''deit'''
def __init__( self : Optional[int] , __lowercase : List[Any]=768 , __lowercase : Union[str, Any]=12 , __lowercase : str=12 , __lowercase : Tuple=3072 , __lowercase : Optional[Any]="gelu" , __lowercase : Optional[Any]=0.0 , __lowercase : Union[str, Any]=0.0 , __lowercase : Union[str, Any]=0.02 , __lowercase : Optional[Any]=1E-12 , __lowercase : List[Any]=224 , __lowercase : Union[str, Any]=16 , __lowercase : List[str]=3 , __lowercase : int=True , __lowercase : Dict=16 , **__lowercase : Tuple , ):
'''simple docstring'''
super().__init__(**snake_case__ )
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = initializer_range
__a = layer_norm_eps
__a = image_size
__a = patch_size
__a = num_channels
__a = qkv_bias
__a = encoder_stride
class SCREAMING_SNAKE_CASE ( __a ):
__lowerCamelCase : List[Any] =version.parse('1.11' )
@property
def UpperCamelCase_ ( self : Tuple ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def UpperCamelCase_ ( self : Any ):
'''simple docstring'''
return 1E-4
| 225 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
def UpperCamelCase__ ( lowerCAmelCase__ ):
lowercase = [False] * len(lowerCAmelCase__ )
lowercase = [-1] * len(lowerCAmelCase__ )
def dfs(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowercase = True
lowercase = c
for u in graph[v]:
if not visited[u]:
dfs(lowerCAmelCase__ ,1 - c )
for i in range(len(lowerCAmelCase__ ) ):
if not visited[i]:
dfs(lowerCAmelCase__ ,0 )
for i in range(len(lowerCAmelCase__ ) ):
for j in graph[i]:
if color[i] == color[j]:
return False
return True
# Adjacency list of graph
__SCREAMING_SNAKE_CASE : List[str] ={0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))
| 428 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
lowercase__ :Optional[int] = True
from torch.cuda.amp import autocast
lowercase__ :Any = logging.getLogger(__name__)
def UpperCamelCase ( lowerCAmelCase__=None , lowerCAmelCase__=None ):
'''simple docstring'''
return field(default_factory=lambda: default , metadata=lowerCAmelCase__ )
@dataclass
class lowercase :
lowercase_ : str =field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
lowercase_ : Optional[str] =field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
lowercase_ : Optional[bool] =field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
lowercase_ : Optional[float] =field(
default=0.1 , metadata={'''help''': '''The dropout ratio for the attention probabilities.'''} )
lowercase_ : Optional[float] =field(
default=0.1 , metadata={'''help''': '''The dropout ratio for activations inside the fully connected layer.'''} )
lowercase_ : Optional[float] =field(
default=0.1 , metadata={
'''help''': '''The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.'''
} , )
lowercase_ : Optional[float] =field(
default=0.1 , metadata={'''help''': '''The dropout probabilitiy for all 1D convolutional layers in feature extractor.'''} , )
lowercase_ : Optional[float] =field(
default=0.05 , metadata={
'''help''': (
'''Propability of each feature vector along the time axis to be chosen as the start of the vector'''
'''span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature'''
'''vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``.'''
)
} , )
lowercase_ : Optional[float] =field(default=0.0 , metadata={'''help''': '''The LayerDrop probability.'''} )
@dataclass
class lowercase :
lowercase_ : Optional[str] =field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
lowercase_ : Optional[str] =field(
default='''train+validation''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
lowercase_ : bool =field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
lowercase_ : Optional[int] =field(
default=SCREAMING_SNAKE_CASE__ , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
lowercase_ : Optional[int] =field(
default=SCREAMING_SNAKE_CASE__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
lowercase_ : Optional[int] =field(
default=SCREAMING_SNAKE_CASE__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of validation examples to this '''
'''value if set.'''
)
} , )
lowercase_ : List[str] =list_field(
default=[''',''', '''?''', '''.''', '''!''', '''-''', ''';''', ''':''', '''""''', '''%''', '''\'''', '''"''', '''�'''] , metadata={'''help''': '''A list of characters to remove from the transcripts.'''} , )
@dataclass
class lowercase :
lowercase_ : WavaVecaProcessor
lowercase_ : Union[bool, str] =True
lowercase_ : Optional[int] =None
lowercase_ : Optional[int] =None
lowercase_ : Optional[int] =None
lowercase_ : Optional[int] =None
def __call__( self ,A__):
# split inputs and labels since they have to be of different lenghts and need
# different padding methods
lowercase = [{'''input_values''': feature['''input_values''']} for feature in features]
lowercase = [{'''input_ids''': feature['''labels''']} for feature in features]
lowercase = self.processor.pad(
A__ ,padding=self.padding ,max_length=self.max_length ,pad_to_multiple_of=self.pad_to_multiple_of ,return_tensors='''pt''' ,)
lowercase = self.processor.pad(
labels=A__ ,padding=self.padding ,max_length=self.max_length_labels ,pad_to_multiple_of=self.pad_to_multiple_of_labels ,return_tensors='''pt''' ,)
# replace padding with -100 to ignore loss correctly
lowercase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1) ,-1_0_0)
lowercase = labels
return batch
class lowercase ( SCREAMING_SNAKE_CASE__ ):
def A__ ( self ,A__ ,A__):
model.train()
lowercase = self._prepare_inputs(A__)
if self.use_amp:
with autocast():
lowercase = self.compute_loss(A__ ,A__)
else:
lowercase = self.compute_loss(A__ ,A__)
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
lowercase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
lowercase = loss.sum() / (inputs['''labels'''] >= 0).sum()
else:
raise ValueError(f'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']')
if self.args.gradient_accumulation_steps > 1:
lowercase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(A__).backward()
elif self.use_apex:
with amp.scale_loss(A__ ,self.optimizer) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(A__)
else:
loss.backward()
return loss.detach()
def UpperCamelCase ( ):
'''simple docstring'''
lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
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.
lowercase , lowercase , lowercase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase , lowercase , lowercase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
lowercase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase = 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.''' )
# 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 )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# 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}' )
# 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 before initializing model.
set_seed(training_args.seed )
# Get the datasets:
lowercase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
lowercase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
lowercase = f'[{"".join(data_args.chars_to_ignore )}]'
def remove_special_characters(lowerCAmelCase__ ):
lowercase = re.sub(lowerCAmelCase__ , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
lowercase = train_dataset.map(lowerCAmelCase__ , remove_columns=['''sentence'''] )
lowercase = eval_dataset.map(lowerCAmelCase__ , remove_columns=['''sentence'''] )
def extract_all_chars(lowerCAmelCase__ ):
lowercase = ''' '''.join(batch['''text'''] )
lowercase = list(set(lowerCAmelCase__ ) )
return {"vocab": [vocab], "all_text": [all_text]}
lowercase = train_dataset.map(
lowerCAmelCase__ , batched=lowerCAmelCase__ , batch_size=-1 , keep_in_memory=lowerCAmelCase__ , remove_columns=train_dataset.column_names , )
lowercase = train_dataset.map(
lowerCAmelCase__ , batched=lowerCAmelCase__ , batch_size=-1 , keep_in_memory=lowerCAmelCase__ , remove_columns=eval_dataset.column_names , )
lowercase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
lowercase = {v: k for k, v in enumerate(lowerCAmelCase__ )}
lowercase = vocab_dict[''' ''']
del vocab_dict[" "]
lowercase = len(lowerCAmelCase__ )
lowercase = len(lowerCAmelCase__ )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(lowerCAmelCase__ , lowerCAmelCase__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
lowercase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0.0 , do_normalize=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ )
lowercase = WavaVecaProcessor(feature_extractor=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ )
lowercase = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction='''mean''' , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
lowercase = min(len(lowerCAmelCase__ ) , data_args.max_train_samples )
lowercase = train_dataset.select(range(lowerCAmelCase__ ) )
if data_args.max_val_samples is not None:
lowercase = eval_dataset.select(range(data_args.max_val_samples ) )
lowercase = torchaudio.transforms.Resample(4_8000 , 1_6000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(lowerCAmelCase__ ):
lowercase , lowercase = torchaudio.load(batch['''path'''] )
lowercase = resampler(lowerCAmelCase__ ).squeeze().numpy()
lowercase = 1_6000
lowercase = batch['''text''']
return batch
lowercase = train_dataset.map(
lowerCAmelCase__ , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
lowercase = eval_dataset.map(
lowerCAmelCase__ , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(lowerCAmelCase__ ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), f'Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'
lowercase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(lowerCAmelCase__ )
return batch
lowercase = train_dataset.map(
lowerCAmelCase__ , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=lowerCAmelCase__ , num_proc=data_args.preprocessing_num_workers , )
lowercase = eval_dataset.map(
lowerCAmelCase__ , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=lowerCAmelCase__ , num_proc=data_args.preprocessing_num_workers , )
# Metric
lowercase = datasets.load_metric('''wer''' )
def compute_metrics(lowerCAmelCase__ ):
lowercase = pred.predictions
lowercase = np.argmax(lowerCAmelCase__ , axis=-1 )
lowercase = processor.tokenizer.pad_token_id
lowercase = processor.batch_decode(lowerCAmelCase__ )
# we do not want to group tokens when computing the metrics
lowercase = processor.batch_decode(pred.label_ids , group_tokens=lowerCAmelCase__ )
lowercase = wer_metric.compute(predictions=lowerCAmelCase__ , references=lowerCAmelCase__ )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
lowercase = DataCollatorCTCWithPadding(processor=lowerCAmelCase__ , padding=lowerCAmelCase__ )
# Initialize our Trainer
lowercase = CTCTrainer(
model=lowerCAmelCase__ , data_collator=lowerCAmelCase__ , args=lowerCAmelCase__ , compute_metrics=lowerCAmelCase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
lowercase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
lowercase = model_args.model_name_or_path
else:
lowercase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
lowercase = trainer.train(resume_from_checkpoint=lowerCAmelCase__ )
trainer.save_model()
lowercase = train_result.metrics
lowercase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase__ )
)
lowercase = min(lowerCAmelCase__ , len(lowerCAmelCase__ ) )
trainer.log_metrics('''train''' , lowerCAmelCase__ )
trainer.save_metrics('''train''' , lowerCAmelCase__ )
trainer.save_state()
# Evaluation
lowercase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase = trainer.evaluate()
lowercase = data_args.max_val_samples if data_args.max_val_samples is not None else len(lowerCAmelCase__ )
lowercase = min(lowerCAmelCase__ , len(lowerCAmelCase__ ) )
trainer.log_metrics('''eval''' , lowerCAmelCase__ )
trainer.save_metrics('''eval''' , lowerCAmelCase__ )
return results
if __name__ == "__main__":
main()
| 706 |
import argparse
from collections import defaultdict
import yaml
lowercase__ :Optional[int] = "docs/source/en/_toctree.yml"
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = defaultdict(lowerCAmelCase__ )
for doc in model_doc:
counts[doc["local"]] += 1
lowercase = [key for key, value in counts.items() if value > 1]
lowercase = []
for duplicate_key in duplicates:
lowercase = list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key} )
if len(lowerCAmelCase__ ) > 1:
raise ValueError(
f'{duplicate_key} is present several times in the documentation table of content at '
'''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
'''others.''' )
# Only add this once
new_doc.append({'''local''': duplicate_key, '''title''': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1] )
# Sort
return sorted(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : s["title"].lower() )
def UpperCamelCase ( lowerCAmelCase__=False ):
'''simple docstring'''
with open(lowerCAmelCase__ , encoding='''utf-8''' ) as f:
lowercase = yaml.safe_load(f.read() )
# Get to the API doc
lowercase = 0
while content[api_idx]["title"] != "API":
api_idx += 1
lowercase = content[api_idx]['''sections''']
# Then to the model doc
lowercase = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
lowercase = api_doc[model_idx]['''sections''']
lowercase = [(idx, section) for idx, section in enumerate(lowerCAmelCase__ ) if '''sections''' in section]
lowercase = False
for idx, modality_doc in modalities_docs:
lowercase = modality_doc['''sections''']
lowercase = clean_model_doc_toc(lowerCAmelCase__ )
if old_modality_doc != new_modality_doc:
lowercase = True
if overwrite:
lowercase = new_modality_doc
if diff:
if overwrite:
lowercase = model_doc
lowercase = api_doc
with open(lowerCAmelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(yaml.dump(lowerCAmelCase__ , allow_unicode=lowerCAmelCase__ ) )
else:
raise ValueError(
'''The model doc part of the table of content is not properly sorted, run `make style` to fix this.''' )
if __name__ == "__main__":
lowercase__ :Any = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
lowercase__ :int = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 633 | 0 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
__lowerCamelCase : Dict = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"{bindir}/../../examples/pytorch/translation"):
from run_translation import main # noqa
set_seed(42)
__lowerCamelCase : Dict = """sshleifer/student_marian_en_ro_6_1"""
__lowerCamelCase : Union[str, Any] = """sshleifer/tiny-mbart"""
@require_torch
class _lowercase ( _A ):
def lowercase__ ( self , a=False , a=None , a=True , a=True , a=True , a=True , ):
snake_case__ : Tuple =self.run_trainer(
eval_steps=1 , max_len=1_2 , model_name=lowerCamelCase__ , num_train_epochs=1 , distributed=lowerCamelCase__ , extra_args_str=lowerCamelCase__ , predict_with_generate=lowerCamelCase__ , do_train=lowerCamelCase__ , do_eval=lowerCamelCase__ , do_predict=lowerCamelCase__ , )
snake_case__ : str =TrainerState.load_from_json(os.path.join(lowerCamelCase__ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
snake_case__ : int =[log for log in logs if """eval_loss""" in log.keys()]
snake_case__ : List[str] =eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
snake_case__ : List[Any] =eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , lowerCamelCase__ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def lowercase__ ( self ):
self.run_seqaseq_quick()
@require_torch_multi_gpu
def lowercase__ ( self ):
self.run_seqaseq_quick(distributed=lowerCamelCase__ )
@require_torch_multi_gpu
def lowercase__ ( self ):
self.run_seqaseq_quick(distributed=lowerCamelCase__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def lowercase__ ( self ):
self.run_seqaseq_quick(distributed=lowerCamelCase__ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def lowercase__ ( self ):
self.run_seqaseq_quick(distributed=lowerCamelCase__ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def lowercase__ ( self ):
self.run_seqaseq_quick(distributed=lowerCamelCase__ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=lowerCamelCase__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def lowercase__ ( self ):
self.run_seqaseq_quick(
distributed=lowerCamelCase__ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=lowerCamelCase__ )
@require_apex
@require_torch_gpu
def lowercase__ ( self ):
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=lowerCamelCase__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=lowerCamelCase__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def lowercase__ ( self , a ):
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
snake_case__ : Optional[int] ={
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
snake_case__ : Union[str, Any] =experiments[experiment_id]
snake_case__ : int ={"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
snake_case__ : Any ="""Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**lowerCamelCase__ , extra_args_str=data["""extra_args_str"""] )
snake_case__ : Any =len(re.findall(lowerCamelCase__ , cl.err ) )
self.assertEqual(lowerCamelCase__ , data["""n_matches"""] )
@slow
def lowercase__ ( self ):
snake_case__ : List[str] =self.run_trainer(
eval_steps=2 , max_len=1_2_8 , model_name=lowerCamelCase__ , learning_rate=3e-4 , num_train_epochs=1_0 , distributed=lowerCamelCase__ , )
# Check metrics
snake_case__ : Tuple =TrainerState.load_from_json(os.path.join(lowerCamelCase__ , """trainer_state.json""" ) ).log_history
snake_case__ : int =[log for log in logs if """eval_loss""" in log.keys()]
snake_case__ : Tuple =eval_metrics[0]
snake_case__ : List[Any] =eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , lowerCamelCase__ )
# test if do_predict saves generations and metrics
snake_case__ : int =os.listdir(lowerCamelCase__ )
snake_case__ : Dict ={os.path.basename(lowerCamelCase__ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def lowercase__ ( self ):
from transformers.training_args import OptimizerNames
def train_and_return_metrics(a ) -> Tuple[int, float]:
snake_case__ : List[str] ="""--skip_memory_metrics 0"""
snake_case__ : Tuple =self.run_trainer(
max_len=1_2_8 , model_name=lowerCamelCase__ , learning_rate=3e-4 , num_train_epochs=1 , optim=lowerCamelCase__ , distributed=lowerCamelCase__ , extra_args_str=lowerCamelCase__ , do_eval=lowerCamelCase__ , do_predict=lowerCamelCase__ , n_gpus_to_use=1 , )
# Check metrics
snake_case__ : List[Any] =TrainerState.load_from_json(Path(lowerCamelCase__ , """trainer_state.json""" ) ).log_history
snake_case__ : List[Any] =int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**2_0 )
snake_case__ : Tuple =int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**2_0 )
snake_case__ : Tuple =logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
snake_case__ : Tuple =train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
snake_case__ : List[Any] =train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
snake_case__ : Optional[Any] =gpu_alloc_mem_orig - gpu_alloc_mem_bnb
snake_case__ : List[str] =gpu_peak_mem_orig + gpu_alloc_mem_orig
snake_case__ : str =gpu_peak_mem_bnb + gpu_alloc_mem_bnb
snake_case__ : Optional[Any] =gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
snake_case__ : Union[str, Any] =1_2_0
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
lowerCamelCase__ , lowerCamelCase__ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB" , )
self.assertGreater(
lowerCamelCase__ , lowerCamelCase__ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB" , )
self.assertEqual(
lowerCamelCase__ , lowerCamelCase__ , F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def lowercase__ ( self , a , a , a , a = 3e-3 , a = "adafactor" , a = False , a = None , a = 0 , a = True , a = True , a = True , a = True , a = None , ):
snake_case__ : List[Any] =self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
snake_case__ : int =self.get_auto_remove_tmp_dir()
snake_case__ : List[Any] =F"\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(lowerCamelCase__ )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(lowerCamelCase__ )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
snake_case__ : Optional[Any] =F"\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(lowerCamelCase__ )}\n ".split()
snake_case__ : Optional[Any] ="""
--do_predict
""".split()
snake_case__ : Union[str, Any] =[]
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"--optim {optim}".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
snake_case__ : List[Any] =get_gpu_count()
snake_case__ : int =get_torch_dist_unique_port()
snake_case__ : Tuple =F"\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n ".split()
snake_case__ : Dict =[sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(lowerCamelCase__ , env=self.get_env() )
else:
snake_case__ : Dict =["""run_translation.py"""] + args
with patch.object(lowerCamelCase__ , """argv""" , lowerCamelCase__ ):
main()
return output_dir
| 385 |
def A ( lowercase__ : int , lowercase__ : int ) -> int:
return int(input_a == input_a == 0 )
def A ( ) -> None:
print("""Truth Table of NOR Gate:""" )
print("""| Input 1 | Input 2 | Output |""" )
print(f"""| 0 | 0 | {nor_gate(0 , 0 )} |""" )
print(f"""| 0 | 1 | {nor_gate(0 , 1 )} |""" )
print(f"""| 1 | 0 | {nor_gate(1 , 0 )} |""" )
print(f"""| 1 | 1 | {nor_gate(1 , 1 )} |""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 45 | 0 |
from __future__ import annotations
def lowercase_ ( __snake_case : list[int] ) -> int:
'''simple docstring'''
snake_case__ :Union[str, Any] = len(__snake_case ) // 2
# choose the middle 3 elements
snake_case__ :List[str] = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : List[Any] = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__UpperCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57 | 1 |
"""simple docstring"""
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__UpperCamelCase = 16
__UpperCamelCase = 32
def lowercase (SCREAMING_SNAKE_CASE_ : Accelerator , SCREAMING_SNAKE_CASE_ : int = 16 , SCREAMING_SNAKE_CASE_ : str = "bert-base-cased" ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = load_dataset('glue' , 'mrpc' )
def tokenize_function(SCREAMING_SNAKE_CASE_ : List[Any] ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
SCREAMING_SNAKE_CASE = datasets.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=SCREAMING_SNAKE_CASE_ )
# 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 = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(SCREAMING_SNAKE_CASE_ , padding='max_length' , max_length=1_28 , return_tensors='pt' )
return tokenizer.pad(SCREAMING_SNAKE_CASE_ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE = DataLoader(
tokenized_datasets['train'] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = DataLoader(
tokenized_datasets['validation'] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
return train_dataloader, eval_dataloader
def lowercase (SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] ) -> Dict:
model.eval()
SCREAMING_SNAKE_CASE = 0
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# 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 = model(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(SCREAMING_SNAKE_CASE_ ) - 1:
SCREAMING_SNAKE_CASE = predictions[: len(eval_dataloader.dataset ) - samples_seen]
SCREAMING_SNAKE_CASE = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ , )
SCREAMING_SNAKE_CASE = metric.compute()
return eval_metric["accuracy"]
def lowercase (SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Any ) -> Any:
# Initialize accelerator
SCREAMING_SNAKE_CASE = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE = config['lr']
SCREAMING_SNAKE_CASE = int(config['num_epochs'] )
SCREAMING_SNAKE_CASE = int(config['seed'] )
SCREAMING_SNAKE_CASE = int(config['batch_size'] )
SCREAMING_SNAKE_CASE = args.model_name_or_path
set_seed(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = get_dataloaders(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE = AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_ , return_dict=SCREAMING_SNAKE_CASE_ )
# Instantiate optimizer
SCREAMING_SNAKE_CASE = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
SCREAMING_SNAKE_CASE = optimizer_cls(params=model.parameters() , lr=SCREAMING_SNAKE_CASE_ )
if accelerator.state.deepspeed_plugin is not None:
SCREAMING_SNAKE_CASE = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = (len(SCREAMING_SNAKE_CASE_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
SCREAMING_SNAKE_CASE = get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE_ , num_warmup_steps=0 , num_training_steps=SCREAMING_SNAKE_CASE_ , )
else:
SCREAMING_SNAKE_CASE = DummyScheduler(SCREAMING_SNAKE_CASE_ , total_num_steps=SCREAMING_SNAKE_CASE_ , warmup_num_steps=0 )
# 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 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = accelerator.prepare(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# We need to keep track of how many total steps we have iterated over
SCREAMING_SNAKE_CASE = 0
# We also need to keep track of the stating epoch so files are named properly
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = evaluate.load('glue' , 'mrpc' )
SCREAMING_SNAKE_CASE = num_epochs
if args.partial_train_epoch is not None:
SCREAMING_SNAKE_CASE = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
SCREAMING_SNAKE_CASE = args.resume_from_checkpoint.split('epoch_' )[1]
SCREAMING_SNAKE_CASE = ''
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
SCREAMING_SNAKE_CASE = int(SCREAMING_SNAKE_CASE_ ) + 1
SCREAMING_SNAKE_CASE = evaluation_loop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
accelerator.print('resumed checkpoint performance:' , SCREAMING_SNAKE_CASE_ )
accelerator.print('resumed checkpoint\'s scheduler\'s lr:' , lr_scheduler.get_lr()[0] )
accelerator.print('resumed optimizers\'s lr:' , optimizer.param_groups[0]['lr'] )
with open(os.path.join(args.output_dir , F'state_{starting_epoch-1}.json' ) , 'r' ) as f:
SCREAMING_SNAKE_CASE = json.load(SCREAMING_SNAKE_CASE_ )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
SCREAMING_SNAKE_CASE = {}
for epoch in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
model.train()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = outputs.loss
SCREAMING_SNAKE_CASE = loss / gradient_accumulation_steps
accelerator.backward(SCREAMING_SNAKE_CASE_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
SCREAMING_SNAKE_CASE = F'epoch_{epoch}'
SCREAMING_SNAKE_CASE = os.path.join(args.output_dir , SCREAMING_SNAKE_CASE_ )
accelerator.save_state(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = evaluation_loop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = accuracy
SCREAMING_SNAKE_CASE = lr_scheduler.get_lr()[0]
SCREAMING_SNAKE_CASE = optimizer.param_groups[0]['lr']
SCREAMING_SNAKE_CASE = epoch
SCREAMING_SNAKE_CASE = overall_step
accelerator.print(F'epoch {epoch}:' , SCREAMING_SNAKE_CASE_ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , F'state_{epoch}.json' ) , 'w' ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowercase () -> Dict:
SCREAMING_SNAKE_CASE = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=SCREAMING_SNAKE_CASE_ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=SCREAMING_SNAKE_CASE_ , )
parser.add_argument(
'--output_dir' , type=SCREAMING_SNAKE_CASE_ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--resume_from_checkpoint' , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , help='If the training should continue from a checkpoint folder.' , )
parser.add_argument(
'--partial_train_epoch' , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , help='If passed, the training will stop after this number of epochs.' , )
parser.add_argument(
'--num_epochs' , type=SCREAMING_SNAKE_CASE_ , default=2 , help='Number of train epochs.' , )
SCREAMING_SNAKE_CASE = parser.parse_args()
SCREAMING_SNAKE_CASE = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
training_function(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 247 |
"""simple docstring"""
from __future__ import annotations
__UpperCamelCase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__UpperCamelCase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowercase (SCREAMING_SNAKE_CASE_ : list[float] ) -> list[float]:
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = -1
for j in range(i + 1 , SCREAMING_SNAKE_CASE_ ):
if arr[i] < arr[j]:
SCREAMING_SNAKE_CASE = arr[j]
break
result.append(SCREAMING_SNAKE_CASE_ )
return result
def lowercase (SCREAMING_SNAKE_CASE_ : list[float] ) -> list[float]:
SCREAMING_SNAKE_CASE = []
for i, outer in enumerate(SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = -1
for inner in arr[i + 1 :]:
if outer < inner:
SCREAMING_SNAKE_CASE = inner
break
result.append(SCREAMING_SNAKE_CASE_ )
return result
def lowercase (SCREAMING_SNAKE_CASE_ : list[float] ) -> list[float]:
SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = [-1] * arr_size
for index in reversed(range(SCREAMING_SNAKE_CASE_ ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
SCREAMING_SNAKE_CASE = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
__UpperCamelCase = (
'''from __main__ import arr, next_greatest_element_slow, '''
'''next_greatest_element_fast, next_greatest_element'''
)
print(
'''next_greatest_element_slow():''',
timeit('''next_greatest_element_slow(arr)''', setup=setup),
)
print(
'''next_greatest_element_fast():''',
timeit('''next_greatest_element_fast(arr)''', setup=setup),
)
print(
''' next_greatest_element():''',
timeit('''next_greatest_element(arr)''', setup=setup),
)
| 247 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None , ):
super().__init__()
self.register_modules(transformer=__lowerCAmelCase , vae=__lowerCAmelCase , scheduler=__lowerCAmelCase )
# create a imagenet -> id dictionary for easier use
UpperCamelCase__ = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split(""",""" ):
UpperCamelCase__ = int(__lowerCAmelCase )
UpperCamelCase__ = dict(sorted(self.labels.items() ) )
def _lowerCamelCase ( self , __lowerCAmelCase ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ = list(__lowerCAmelCase )
for l in label:
if l not in self.labels:
raise ValueError(
f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.""" )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self , __lowerCAmelCase , __lowerCAmelCase = 4.0 , __lowerCAmelCase = None , __lowerCAmelCase = 50 , __lowerCAmelCase = "pil" , __lowerCAmelCase = True , ):
UpperCamelCase__ = len(__lowerCAmelCase )
UpperCamelCase__ = self.transformer.config.sample_size
UpperCamelCase__ = self.transformer.config.in_channels
UpperCamelCase__ = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=__lowerCAmelCase , device=self.device , dtype=self.transformer.dtype , )
UpperCamelCase__ = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
UpperCamelCase__ = torch.tensor(__lowerCAmelCase , device=self.device ).reshape(-1 )
UpperCamelCase__ = torch.tensor([1000] * batch_size , device=self.device )
UpperCamelCase__ = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(__lowerCAmelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
UpperCamelCase__ = latent_model_input[: len(__lowerCAmelCase ) // 2]
UpperCamelCase__ = torch.cat([half, half] , dim=0 )
UpperCamelCase__ = self.scheduler.scale_model_input(__lowerCAmelCase , __lowerCAmelCase )
UpperCamelCase__ = t
if not torch.is_tensor(__lowerCAmelCase ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
UpperCamelCase__ = latent_model_input.device.type == """mps"""
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
UpperCamelCase__ = torch.floataa if is_mps else torch.floataa
else:
UpperCamelCase__ = torch.intaa if is_mps else torch.intaa
UpperCamelCase__ = torch.tensor([timesteps] , dtype=__lowerCAmelCase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
UpperCamelCase__ = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
UpperCamelCase__ = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
UpperCamelCase__ = self.transformer(
__lowerCAmelCase , timestep=__lowerCAmelCase , class_labels=__lowerCAmelCase ).sample
# perform guidance
if guidance_scale > 1:
UpperCamelCase__ , UpperCamelCase__ = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
UpperCamelCase__ , UpperCamelCase__ = torch.split(__lowerCAmelCase , len(__lowerCAmelCase ) // 2 , dim=0 )
UpperCamelCase__ = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
UpperCamelCase__ = torch.cat([half_eps, half_eps] , dim=0 )
UpperCamelCase__ = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
UpperCamelCase__ , UpperCamelCase__ = torch.split(__lowerCAmelCase , __lowerCAmelCase , dim=1 )
else:
UpperCamelCase__ = noise_pred
# compute previous image: x_t -> x_t-1
UpperCamelCase__ = self.scheduler.step(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ).prev_sample
if guidance_scale > 1:
UpperCamelCase__ , UpperCamelCase__ = latent_model_input.chunk(2 , dim=0 )
else:
UpperCamelCase__ = latent_model_input
UpperCamelCase__ = 1 / self.vae.config.scaling_factor * latents
UpperCamelCase__ = self.vae.decode(__lowerCAmelCase ).sample
UpperCamelCase__ = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
UpperCamelCase__ = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCamelCase__ = self.numpy_to_pil(__lowerCAmelCase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=__lowerCAmelCase )
| 710 |
import argparse
import datetime
def _UpperCamelCase (a__ :str ):
"""simple docstring"""
UpperCamelCase__ = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
"""5""": """Friday""",
"""6""": """Saturday""",
}
UpperCamelCase__ = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(a__ ) < 11:
raise ValueError("""Must be 10 characters long""" )
# Get month
UpperCamelCase__ = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError("""Month must be between 1 - 12""" )
UpperCamelCase__ = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("""Date separator must be '-' or '/'""" )
# Get day
UpperCamelCase__ = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError("""Date must be between 1 - 31""" )
# Get second separator
UpperCamelCase__ = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("""Date separator must be '-' or '/'""" )
# Get year
UpperCamelCase__ = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8500:
raise ValueError(
"""Year out of range. There has to be some sort of limit...right?""" )
# Get datetime obj for validation
UpperCamelCase__ = datetime.date(int(a__ ) , int(a__ ) , int(a__ ) )
# Start math
if m <= 2:
UpperCamelCase__ = y - 1
UpperCamelCase__ = m + 12
# maths var
UpperCamelCase__ = int(str(a__ )[:2] )
UpperCamelCase__ = int(str(a__ )[2:] )
UpperCamelCase__ = int(2.6 * m - 5.39 )
UpperCamelCase__ = int(c / 4 )
UpperCamelCase__ = int(k / 4 )
UpperCamelCase__ = int(d + k )
UpperCamelCase__ = int(t + u + v + x )
UpperCamelCase__ = int(z - (2 * c) )
UpperCamelCase__ = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("""The date was evaluated incorrectly. Contact developer.""" )
# Response
UpperCamelCase__ = f"""Your date {date_input}, is a {days[str(a__ )]}!"""
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
UpperCamelCase__ = parser.parse_args()
zeller(args.date_input)
| 548 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 10_00 ) -> int:
'''simple docstring'''
_A = 2**power
_A = str(_snake_case )
_A = list(_snake_case )
_A = 0
for i in list_num:
sum_of_num += int(_snake_case )
return sum_of_num
if __name__ == "__main__":
a = int(input('''Enter the power of 2: ''').strip())
print('''2 ^ ''', power, ''' = ''', 2**power)
a = solution(power)
print('''Sum of the digits is: ''', result)
| 7 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=5 ):
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count('''<mask>''' ) == 1
A_ = torch.tensor(tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase ) ).unsqueeze(0 ) # Batch size 1
A_ = model(__UpperCamelCase )[0] # The last hidden-state is the first element of the output tuple
A_ = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
A_ = logits[0, masked_index, :]
A_ = logits.softmax(dim=0 )
A_ , A_ = prob.topk(k=__UpperCamelCase , dim=0 )
A_ = ''' '''.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(__UpperCamelCase ) )] )
A_ = tokenizer.mask_token
A_ = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' ''' ) ):
A_ = predicted_token_bpe.replace('''\u2581''' , ''' ''' )
if " {0}".format(__UpperCamelCase ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(''' {0}'''.format(__UpperCamelCase ) , __UpperCamelCase ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(__UpperCamelCase , __UpperCamelCase ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
SCREAMING_SNAKE_CASE : Optional[int] = CamembertTokenizer.from_pretrained("camembert-base")
SCREAMING_SNAKE_CASE : Optional[int] = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
SCREAMING_SNAKE_CASE : List[Any] = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 141 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__UpperCAmelCase = 42
__UpperCAmelCase = None
def __lowerCAmelCase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple=0.999 , __lowerCAmelCase : List[Any]="cosine" , ) -> List[Any]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(__lowerCAmelCase : List[str] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__lowerCAmelCase : Dict ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}" )
_UpperCamelCase : List[Any] = []
for i in range(__lowerCAmelCase ):
_UpperCamelCase : Any = i / num_diffusion_timesteps
_UpperCamelCase : str = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__lowerCAmelCase ) / alpha_bar_fn(__lowerCAmelCase ) , __lowerCAmelCase ) )
return torch.tensor(__lowerCAmelCase , dtype=torch.floataa )
class _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
@register_to_config
def __init__(self , lowerCAmelCase__ = 10_00 , lowerCAmelCase__ = "fixed_small_log" , lowerCAmelCase__ = True , lowerCAmelCase__ = 1.0 , lowerCAmelCase__ = "epsilon" , lowerCAmelCase__ = "squaredcos_cap_v2" , ):
'''simple docstring'''
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )
_UpperCamelCase : int = betas_for_alpha_bar(lowerCAmelCase__ )
_UpperCamelCase : str = 1.0 - self.betas
_UpperCamelCase : List[Any] = torch.cumprod(self.alphas , dim=0 )
_UpperCamelCase : Tuple = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
_UpperCamelCase : List[str] = 1.0
# setable values
_UpperCamelCase : str = None
_UpperCamelCase : List[str] = torch.from_numpy(np.arange(0 , lowerCAmelCase__ )[::-1].copy() )
_UpperCamelCase : Optional[int] = variance_type
def lowercase_ (self , lowerCAmelCase__ , lowerCAmelCase__ = None ):
'''simple docstring'''
return sample
def lowercase_ (self , lowerCAmelCase__ , lowerCAmelCase__ = None ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = num_inference_steps
_UpperCamelCase : Optional[Any] = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
_UpperCamelCase : List[Any] = (np.arange(0 , lowerCAmelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
_UpperCamelCase : Dict = torch.from_numpy(lowerCAmelCase__ ).to(lowerCAmelCase__ )
def lowercase_ (self , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None ):
'''simple docstring'''
if prev_timestep is None:
_UpperCamelCase : List[str] = t - 1
_UpperCamelCase : Union[str, Any] = self.alphas_cumprod[t]
_UpperCamelCase : Optional[Any] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
_UpperCamelCase : Tuple = 1 - alpha_prod_t
_UpperCamelCase : Optional[int] = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
_UpperCamelCase : Dict = self.betas[t]
else:
_UpperCamelCase : List[Any] = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
_UpperCamelCase : List[Any] = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
_UpperCamelCase : Optional[int] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
_UpperCamelCase : str = torch.log(torch.clamp(lowerCAmelCase__ , min=1E-20 ) )
_UpperCamelCase : Dict = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
_UpperCamelCase : Tuple = variance.log()
_UpperCamelCase : Optional[int] = beta.log()
_UpperCamelCase : Optional[Any] = (predicted_variance + 1) / 2
_UpperCamelCase : List[str] = frac * max_log + (1 - frac) * min_log
return variance
def lowercase_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__=None , lowerCAmelCase__ = True , ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
_UpperCamelCase , _UpperCamelCase : List[str] = torch.split(lowerCAmelCase__ , sample.shape[1] , dim=1 )
else:
_UpperCamelCase : List[str] = None
# 1. compute alphas, betas
if prev_timestep is None:
_UpperCamelCase : Optional[int] = t - 1
_UpperCamelCase : Union[str, Any] = self.alphas_cumprod[t]
_UpperCamelCase : List[str] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
_UpperCamelCase : Any = 1 - alpha_prod_t
_UpperCamelCase : Tuple = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
_UpperCamelCase : Tuple = self.betas[t]
_UpperCamelCase : List[Any] = self.alphas[t]
else:
_UpperCamelCase : Dict = 1 - alpha_prod_t / alpha_prod_t_prev
_UpperCamelCase : str = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
_UpperCamelCase : Dict = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
_UpperCamelCase : str = model_output
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
" for the UnCLIPScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
_UpperCamelCase : List[str] = torch.clamp(
lowerCAmelCase__ , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_UpperCamelCase : Optional[Any] = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
_UpperCamelCase : Optional[int] = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_UpperCamelCase : Union[str, Any] = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
_UpperCamelCase : Tuple = 0
if t > 0:
_UpperCamelCase : Optional[Any] = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=lowerCAmelCase__ , device=model_output.device )
_UpperCamelCase : Optional[Any] = self._get_variance(
lowerCAmelCase__ , predicted_variance=lowerCAmelCase__ , prev_timestep=lowerCAmelCase__ , )
if self.variance_type == "fixed_small_log":
_UpperCamelCase : Union[str, Any] = variance
elif self.variance_type == "learned_range":
_UpperCamelCase : str = (0.5 * variance).exp()
else:
raise ValueError(
F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
" for the UnCLIPScheduler." )
_UpperCamelCase : int = variance * variance_noise
_UpperCamelCase : Union[str, Any] = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase__ , pred_original_sample=lowerCAmelCase__ )
def lowercase_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
_UpperCamelCase : str = timesteps.to(original_samples.device )
_UpperCamelCase : str = alphas_cumprod[timesteps] ** 0.5
_UpperCamelCase : Optional[int] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
_UpperCamelCase : Union[str, Any] = sqrt_alpha_prod.unsqueeze(-1 )
_UpperCamelCase : Optional[int] = (1 - alphas_cumprod[timesteps]) ** 0.5
_UpperCamelCase : List[Any] = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
_UpperCamelCase : Tuple = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
_UpperCamelCase : List[str] = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 239 |
"""simple docstring"""
from math import ceil
def __lowerCAmelCase ( __lowerCAmelCase : int = 1001 ) -> int:
_UpperCamelCase : Tuple = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
_UpperCamelCase : Tuple = 2 * i + 1
_UpperCamelCase : Optional[Any] = 2 * i
_UpperCamelCase : Optional[int] = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
_SCREAMING_SNAKE_CASE = int(sys.argv[1])
print(solution(n))
except ValueError:
print("""Invalid entry - please enter a number""")
| 239 | 1 |
'''simple docstring'''
def a ( __a = 1000000 ) -> int:
'''simple docstring'''
UpperCamelCase__ :str = set(range(3 , snake_case_ , 2 ) )
primes.add(2 )
for p in range(3 , snake_case_ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , snake_case_ , snake_case_ ) ) )
UpperCamelCase__ :str = [float(snake_case_ ) for n in range(limit + 1 )]
for p in primes:
for n in range(snake_case_ , limit + 1 , snake_case_ ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 189 |
"""simple docstring"""
import requests
a_ = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def __lowercase ( snake_case_ : str ) ->None:
'''simple docstring'''
__A : str = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page['''articles'''] ,1 ):
print(F"""{i}.) {article['title']}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
| 177 | 0 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowerCamelCase__ ( snake_case_ : Optional[int] ) -> Dict:
__snake_case = []
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
f"""stage{idx}.patch_embed.proj.weight""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
f"""stage{idx}.patch_embed.proj.bias""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
f"""stage{idx}.patch_embed.norm.weight""",
) )
embed.append(
(
f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
f"""stage{idx}.patch_embed.norm.bias""",
) )
return embed
def lowerCamelCase__ ( snake_case_ : Union[str, Any] , snake_case_ : int ) -> str:
__snake_case = []
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
) )
attention_weights.append(
(
f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
) )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""") )
attention_weights.append(
(f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""") )
return attention_weights
def lowerCamelCase__ ( snake_case_ : Tuple ) -> str:
__snake_case = []
token.append((f"""cvt.encoder.stages.{idx}.cls_token""", '''stage2.cls_token''') )
return token
def lowerCamelCase__ ( ) -> Any:
__snake_case = []
head.append(('''layernorm.weight''', '''norm.weight''') )
head.append(('''layernorm.bias''', '''norm.bias''') )
head.append(('''classifier.weight''', '''head.weight''') )
head.append(('''classifier.bias''', '''head.bias''') )
return head
def lowerCamelCase__ ( snake_case_ : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Tuple ) -> Optional[Any]:
__snake_case = '''imagenet-1k-id2label.json'''
__snake_case = 1000
__snake_case = '''huggingface/label-files'''
__snake_case = num_labels
__snake_case = json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type='''dataset''' ) ) , '''r''' ) )
__snake_case = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
__snake_case = idalabel
__snake_case = {v: k for k, v in idalabel.items()}
__snake_case = __snake_case = CvtConfig(num_labels=__lowerCAmelCase , idalabel=__lowerCAmelCase , labelaid=__lowerCAmelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "13":
__snake_case = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit('''/''' , 1 )[-1][4:6] == "21":
__snake_case = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__snake_case = [2, 2, 20]
__snake_case = [3, 12, 16]
__snake_case = [192, 768, 1024]
__snake_case = CvtForImageClassification(__lowerCAmelCase )
__snake_case = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
__snake_case = image_size
__snake_case = torch.load(__lowerCAmelCase , map_location=torch.device('''cpu''' ) )
__snake_case = OrderedDict()
__snake_case = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__snake_case = list_of_state_dict + cls_token(__lowerCAmelCase )
__snake_case = list_of_state_dict + embeddings(__lowerCAmelCase )
for cnt in range(config.depth[idx] ):
__snake_case = list_of_state_dict + attention(__lowerCAmelCase , __lowerCAmelCase )
__snake_case = list_of_state_dict + final()
for gg in list_of_state_dict:
print(__lowerCAmelCase )
for i in range(len(__lowerCAmelCase ) ):
__snake_case = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__lowerCAmelCase )
model.save_pretrained(__lowerCAmelCase )
image_processor.save_pretrained(__lowerCAmelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
snake_case_ = argparse.ArgumentParser()
parser.add_argument(
'--cvt_model',
default='cvt-w24',
type=str,
help='Name of the cvt model you\'d like to convert.',
)
parser.add_argument(
'--image_size',
default=384,
type=int,
help='Input Image Size',
)
parser.add_argument(
'--cvt_file_name',
default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
type=str,
help='Input Image Size',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
snake_case_ = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 712 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {
'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
'tokenization_electra': ['ElectraTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ['ElectraTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'ElectraForCausalLM',
'ElectraForMaskedLM',
'ElectraForMultipleChoice',
'ElectraForPreTraining',
'ElectraForQuestionAnswering',
'ElectraForSequenceClassification',
'ElectraForTokenClassification',
'ElectraModel',
'ElectraPreTrainedModel',
'load_tf_weights_in_electra',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFElectraForMaskedLM',
'TFElectraForMultipleChoice',
'TFElectraForPreTraining',
'TFElectraForQuestionAnswering',
'TFElectraForSequenceClassification',
'TFElectraForTokenClassification',
'TFElectraModel',
'TFElectraPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'FlaxElectraForCausalLM',
'FlaxElectraForMaskedLM',
'FlaxElectraForMultipleChoice',
'FlaxElectraForPreTraining',
'FlaxElectraForQuestionAnswering',
'FlaxElectraForSequenceClassification',
'FlaxElectraForTokenClassification',
'FlaxElectraModel',
'FlaxElectraPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
from .tokenization_electra import ElectraTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_electra_fast import ElectraTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_electra import (
ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
ElectraForCausalLM,
ElectraForMaskedLM,
ElectraForMultipleChoice,
ElectraForPreTraining,
ElectraForQuestionAnswering,
ElectraForSequenceClassification,
ElectraForTokenClassification,
ElectraModel,
ElectraPreTrainedModel,
load_tf_weights_in_electra,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_electra import (
TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFElectraForMaskedLM,
TFElectraForMultipleChoice,
TFElectraForPreTraining,
TFElectraForQuestionAnswering,
TFElectraForSequenceClassification,
TFElectraForTokenClassification,
TFElectraModel,
TFElectraPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_electra import (
FlaxElectraForCausalLM,
FlaxElectraForMaskedLM,
FlaxElectraForMultipleChoice,
FlaxElectraForPreTraining,
FlaxElectraForQuestionAnswering,
FlaxElectraForSequenceClassification,
FlaxElectraForTokenClassification,
FlaxElectraModel,
FlaxElectraPreTrainedModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 388 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor
class _a ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , _snake_case , _snake_case=7 , _snake_case=3 , _snake_case=30 , _snake_case=400 , _snake_case=True , _snake_case=None , _snake_case=True , _snake_case=[0.5, 0.5, 0.5] , _snake_case=[0.5, 0.5, 0.5] , _snake_case=True , _snake_case=1 / 255 , _snake_case=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_UpperCAmelCase =size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
_UpperCAmelCase =parent
_UpperCAmelCase =batch_size
_UpperCAmelCase =num_channels
_UpperCAmelCase =min_resolution
_UpperCAmelCase =max_resolution
_UpperCAmelCase =do_resize
_UpperCAmelCase =size
_UpperCAmelCase =do_normalize
_UpperCAmelCase =image_mean
_UpperCAmelCase =image_std
_UpperCAmelCase =do_rescale
_UpperCAmelCase =rescale_factor
_UpperCAmelCase =do_pad
def SCREAMING_SNAKE_CASE ( self ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE ( self , _snake_case , _snake_case=False ):
if not batched:
_UpperCAmelCase =image_inputs[0]
if isinstance(_snake_case , Image.Image ):
_UpperCAmelCase , _UpperCAmelCase =image.size
else:
_UpperCAmelCase , _UpperCAmelCase =image.shape[1], image.shape[2]
if w < h:
_UpperCAmelCase =int(self.size["shortest_edge"] * h / w )
_UpperCAmelCase =self.size["shortest_edge"]
elif w > h:
_UpperCAmelCase =self.size["shortest_edge"]
_UpperCAmelCase =int(self.size["shortest_edge"] * w / h )
else:
_UpperCAmelCase =self.size["shortest_edge"]
_UpperCAmelCase =self.size["shortest_edge"]
else:
_UpperCAmelCase =[]
for image in image_inputs:
_UpperCAmelCase , _UpperCAmelCase =self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_UpperCAmelCase =max(_snake_case , key=lambda _snake_case : item[0] )[0]
_UpperCAmelCase =max(_snake_case , key=lambda _snake_case : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class _a ( A__ , unittest.TestCase ):
"""simple docstring"""
snake_case =DetaImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =DetaImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE ( self ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_snake_case , "image_mean" ) )
self.assertTrue(hasattr(_snake_case , "image_std" ) )
self.assertTrue(hasattr(_snake_case , "do_normalize" ) )
self.assertTrue(hasattr(_snake_case , "do_resize" ) )
self.assertTrue(hasattr(_snake_case , "do_rescale" ) )
self.assertTrue(hasattr(_snake_case , "do_pad" ) )
self.assertTrue(hasattr(_snake_case , "size" ) )
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1333} )
self.assertEqual(image_processor.do_pad , _snake_case )
def SCREAMING_SNAKE_CASE ( self ):
pass
def SCREAMING_SNAKE_CASE ( self ):
# Initialize image_processing
_UpperCAmelCase =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase =prepare_image_inputs(self.image_processor_tester , equal_resolution=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case , Image.Image )
# Test not batched input
_UpperCAmelCase =image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
_UpperCAmelCase , _UpperCAmelCase =self.image_processor_tester.get_expected_values(_snake_case )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCAmelCase , _UpperCAmelCase =self.image_processor_tester.get_expected_values(_snake_case , batched=_snake_case )
_UpperCAmelCase =image_processing(_snake_case , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE ( self ):
# Initialize image_processing
_UpperCAmelCase =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase =prepare_image_inputs(self.image_processor_tester , equal_resolution=_snake_case , numpify=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case , np.ndarray )
# Test not batched input
_UpperCAmelCase =image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
_UpperCAmelCase , _UpperCAmelCase =self.image_processor_tester.get_expected_values(_snake_case )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCAmelCase =image_processing(_snake_case , return_tensors="pt" ).pixel_values
_UpperCAmelCase , _UpperCAmelCase =self.image_processor_tester.get_expected_values(_snake_case , batched=_snake_case )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE ( self ):
# Initialize image_processing
_UpperCAmelCase =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase =prepare_image_inputs(self.image_processor_tester , equal_resolution=_snake_case , torchify=_snake_case )
for image in image_inputs:
self.assertIsInstance(_snake_case , torch.Tensor )
# Test not batched input
_UpperCAmelCase =image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
_UpperCAmelCase , _UpperCAmelCase =self.image_processor_tester.get_expected_values(_snake_case )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCAmelCase =image_processing(_snake_case , return_tensors="pt" ).pixel_values
_UpperCAmelCase , _UpperCAmelCase =self.image_processor_tester.get_expected_values(_snake_case , batched=_snake_case )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE ( self ):
# prepare image and target
_UpperCAmelCase =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
_UpperCAmelCase =json.loads(f.read() )
_UpperCAmelCase ={"image_id": 3_9769, "annotations": target}
# encode them
_UpperCAmelCase =DetaImageProcessor()
_UpperCAmelCase =image_processing(images=_snake_case , annotations=_snake_case , return_tensors="pt" )
# verify pixel values
_UpperCAmelCase =torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["pixel_values"].shape , _snake_case )
_UpperCAmelCase =torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , _snake_case , atol=1E-4 ) )
# verify area
_UpperCAmelCase =torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , _snake_case ) )
# verify boxes
_UpperCAmelCase =torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , _snake_case )
_UpperCAmelCase =torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , _snake_case , atol=1E-3 ) )
# verify image_id
_UpperCAmelCase =torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , _snake_case ) )
# verify is_crowd
_UpperCAmelCase =torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , _snake_case ) )
# verify class_labels
_UpperCAmelCase =torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , _snake_case ) )
# verify orig_size
_UpperCAmelCase =torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , _snake_case ) )
# verify size
_UpperCAmelCase =torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , _snake_case ) )
@slow
def SCREAMING_SNAKE_CASE ( self ):
# prepare image, target and masks_path
_UpperCAmelCase =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
_UpperCAmelCase =json.loads(f.read() )
_UpperCAmelCase ={"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}
_UpperCAmelCase =pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
_UpperCAmelCase =DetaImageProcessor(format="coco_panoptic" )
_UpperCAmelCase =image_processing(images=_snake_case , annotations=_snake_case , masks_path=_snake_case , return_tensors="pt" )
# verify pixel values
_UpperCAmelCase =torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["pixel_values"].shape , _snake_case )
_UpperCAmelCase =torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , _snake_case , atol=1E-4 ) )
# verify area
_UpperCAmelCase =torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , _snake_case ) )
# verify boxes
_UpperCAmelCase =torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , _snake_case )
_UpperCAmelCase =torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , _snake_case , atol=1E-3 ) )
# verify image_id
_UpperCAmelCase =torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , _snake_case ) )
# verify is_crowd
_UpperCAmelCase =torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , _snake_case ) )
# verify class_labels
_UpperCAmelCase =torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , _snake_case ) )
# verify masks
_UpperCAmelCase =82_2873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , _snake_case )
# verify orig_size
_UpperCAmelCase =torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , _snake_case ) )
# verify size
_UpperCAmelCase =torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , _snake_case ) )
| 408 |
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 lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) ->Tuple:
# Load configuration defined in the metadata file
with open(_lowerCamelCase ) as metadata_file:
_UpperCAmelCase =json.load(_lowerCamelCase )
_UpperCAmelCase =LukeConfig(use_entity_aware_attention=_lowerCamelCase , **metadata["model_config"] )
# Load in the weights from the checkpoint_path
_UpperCAmelCase =torch.load(_lowerCamelCase , map_location="cpu" )["module"]
# Load the entity vocab file
_UpperCAmelCase =load_original_entity_vocab(_lowerCamelCase )
# add an entry for [MASK2]
_UpperCAmelCase =max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_UpperCAmelCase =XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
_UpperCAmelCase =AddedToken("<ent>" , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase )
_UpperCAmelCase =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:
_UpperCAmelCase =json.load(_lowerCamelCase )
_UpperCAmelCase ="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 )
_UpperCAmelCase =MLukeTokenizer.from_pretrained(_lowerCamelCase )
# Initialize the embeddings of the special tokens
_UpperCAmelCase =tokenizer.convert_tokens_to_ids(["@"] )[0]
_UpperCAmelCase =tokenizer.convert_tokens_to_ids(["#"] )[0]
_UpperCAmelCase =state_dict["embeddings.word_embeddings.weight"]
_UpperCAmelCase =word_emb[ent_init_index].unsqueeze(0 )
_UpperCAmelCase =word_emb[enta_init_index].unsqueeze(0 )
_UpperCAmelCase =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"]:
_UpperCAmelCase =state_dict[bias_name]
_UpperCAmelCase =decoder_bias[ent_init_index].unsqueeze(0 )
_UpperCAmelCase =decoder_bias[enta_init_index].unsqueeze(0 )
_UpperCAmelCase =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"]:
_UpperCAmelCase =F"encoder.layer.{layer_index}.attention.self."
_UpperCAmelCase =state_dict[prefix + matrix_name]
_UpperCAmelCase =state_dict[prefix + matrix_name]
_UpperCAmelCase =state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_UpperCAmelCase =state_dict["entity_embeddings.entity_embeddings.weight"]
_UpperCAmelCase =entity_emb[entity_vocab["[MASK]"]].unsqueeze(0 )
_UpperCAmelCase =torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_UpperCAmelCase =state_dict["entity_predictions.bias"]
_UpperCAmelCase =entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0 )
_UpperCAmelCase =torch.cat([entity_prediction_bias, entity_mask_bias] )
_UpperCAmelCase =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" )
_UpperCAmelCase =OrderedDict()
for key, value in state_dict.items():
if not (key.startswith("lm_head" ) or key.startswith("entity_predictions" )):
_UpperCAmelCase =state_dict[key]
else:
_UpperCAmelCase =state_dict[key]
_UpperCAmelCase , _UpperCAmelCase =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
_UpperCAmelCase =MLukeTokenizer.from_pretrained(_lowerCamelCase , task="entity_classification" )
_UpperCAmelCase ="ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
_UpperCAmelCase =(0, 9)
_UpperCAmelCase =tokenizer(_lowerCamelCase , entity_spans=[span] , return_tensors="pt" )
_UpperCAmelCase =model(**_lowerCamelCase )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_UpperCAmelCase =torch.Size((1, 33, 768) )
_UpperCAmelCase =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
_UpperCAmelCase =torch.Size((1, 1, 768) )
_UpperCAmelCase =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
_UpperCAmelCase =MLukeTokenizer.from_pretrained(_lowerCamelCase )
_UpperCAmelCase ="Tokyo is the capital of <mask>."
_UpperCAmelCase =(24, 30)
_UpperCAmelCase =tokenizer(_lowerCamelCase , entity_spans=[span] , return_tensors="pt" )
_UpperCAmelCase =model(**_lowerCamelCase )
_UpperCAmelCase =encoding["input_ids"][0].tolist()
_UpperCAmelCase =input_ids.index(tokenizer.convert_tokens_to_ids("<mask>" ) )
_UpperCAmelCase =outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(_lowerCamelCase )
_UpperCAmelCase =outputs.entity_logits[0][0].argmax().item()
_UpperCAmelCase =[
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 lowerCamelCase__ ( _lowerCamelCase ) ->str:
_UpperCAmelCase =["[MASK]", "[PAD]", "[UNK]"]
_UpperCAmelCase =[json.loads(_lowerCamelCase ) for line in open(_lowerCamelCase )]
_UpperCAmelCase ={}
for entry in data:
_UpperCAmelCase =entry["id"]
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_UpperCAmelCase =entity_id
break
_UpperCAmelCase =F"{language}:{entity_name}"
_UpperCAmelCase =entity_id
return new_mapping
if __name__ == "__main__":
snake_case__ : Any = 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.'
)
snake_case__ : 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,
)
| 408 | 1 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
__snake_case : Tuple = str(bin(lowerCamelCase_ ) )[2:] # remove the leading "0b"
__snake_case : str = str(bin(lowerCamelCase_ ) )[2:]
__snake_case : List[str] = max(len(lowerCamelCase_ ) , len(lowerCamelCase_ ) )
return "0b" + "".join(
str(int("1" in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(lowerCamelCase_ ) , b_binary.zfill(lowerCamelCase_ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 706 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_snake_case : List[Any] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n"
_snake_case : Any = "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"
_snake_case : str = "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for 'record': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'prediction_text': the predicted answer text\n - for 'multirc': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question-answer pair as specified by the dataset\n - 'prediction': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for 'record': list of question-answers dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'answers': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for 'record':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1': F1 score\n - for 'multirc':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1_m': Per-question macro-F1 score\n - 'f1_a': Average F1 score over all answers\n - for 'axb':\n 'matthews_correlation': Matthew Correlation\n - for 'cb':\n - 'accuracy': Accuracy\n - 'f1': F1 score\n - for all others:\n - 'accuracy': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
return float((preds == labels).mean() )
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase="binary" ):
__snake_case : Union[str, Any] = simple_accuracy(__lowerCamelCase , __lowerCamelCase )
__snake_case : List[str] = float(fa_score(y_true=__lowerCamelCase , y_pred=__lowerCamelCase , average=__lowerCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
__snake_case : List[Any] = {}
for id_pred, label in zip(__lowerCamelCase , __lowerCamelCase ):
__snake_case : int = F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
__snake_case : str = id_pred["prediction"]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
__snake_case : Optional[Any] = [(pred, label)]
__snake_case , __snake_case : Dict = [], []
for question, preds_labels in question_map.items():
__snake_case , __snake_case : int = zip(*__lowerCamelCase )
__snake_case : Optional[Any] = fa_score(y_true=__lowerCamelCase , y_pred=__lowerCamelCase , average="macro" )
fas.append(__lowerCamelCase )
__snake_case : Union[str, Any] = int(sum(pred == label for pred, label in preds_labels ) == len(__lowerCamelCase ) )
ems.append(__lowerCamelCase )
__snake_case : Tuple = float(sum(__lowerCamelCase ) / len(__lowerCamelCase ) )
__snake_case : Any = sum(__lowerCamelCase ) / len(__lowerCamelCase )
__snake_case : int = float(fa_score(y_true=__lowerCamelCase , y_pred=[id_pred["prediction"] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class a (datasets.Metric ):
"""simple docstring"""
def __snake_case ( self : List[Any] ) -> Union[str, Any]:
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None , )
def __snake_case ( self : Any ) -> int:
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"prediction_text": datasets.Value("string" ),
},
"references": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"answers": datasets.Sequence(datasets.Value("string" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("int64" ),
"paragraph": datasets.Value("int64" ),
"question": datasets.Value("int64" ),
},
"prediction": datasets.Value("int64" ),
},
"references": datasets.Value("int64" ),
}
else:
return {
"predictions": datasets.Value("int64" ),
"references": datasets.Value("int64" ),
}
def __snake_case ( self : Optional[Any] , lowerCamelCase : str , lowerCamelCase : Optional[Any] ) -> str:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(lowerCamelCase , lowerCamelCase )}
elif self.config_name == "cb":
return acc_and_fa(lowerCamelCase , lowerCamelCase , fa_avg="macro" )
elif self.config_name == "record":
__snake_case : Tuple = [
{
"qas": [
{"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
for ref in references
]
}
]
__snake_case : List[str] = {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
return evaluate_record(lowerCamelCase , lowerCamelCase )[0]
elif self.config_name == "multirc":
return evaluate_multirc(lowerCamelCase , lowerCamelCase )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(lowerCamelCase , lowerCamelCase )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
| 203 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCamelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def A__ ( self ):
return datasets.DatasetInfo(
features=datasets.Features({"content": datasets.Value("string" )} ) , supervised_keys=lowerCAmelCase , )
def A__ ( self , lowerCAmelCase , lowerCAmelCase ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_dummy_examples()} )]
def A__ ( self , lowerCAmelCase , lowerCAmelCase ):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase )
class lowerCamelCase ( datasets.BeamBasedBuilder ):
'''simple docstring'''
def A__ ( self ):
return datasets.DatasetInfo(
features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) , supervised_keys=lowerCAmelCase , )
def A__ ( self , lowerCAmelCase , lowerCAmelCase ):
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"examples": get_test_nested_examples()} )
]
def A__ ( self , lowerCAmelCase , lowerCAmelCase ):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(lowerCAmelCase )
def snake_case__ ( ) -> Dict:
return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"] )]
def snake_case__ ( ) -> Optional[int]:
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"] )]
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
@require_beam
def A__ ( self ):
UpperCAmelCase_ = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase_ = DummyBeamDataset(cache_dir=lowerCAmelCase , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(lowerCAmelCase , builder.name , "default" , "0.0.0" , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
UpperCAmelCase_ = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , lowerCAmelCase )
self.assertEqual(dset["train"].info.splits["train"].num_examples , lowerCAmelCase )
self.assertDictEqual(dset["train"][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(lowerCAmelCase , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def A__ ( self ):
import apache_beam as beam
UpperCAmelCase_ = beam.io.parquetio.WriteToParquet
UpperCAmelCase_ = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase_ = DummyBeamDataset(cache_dir=lowerCAmelCase , beam_runner="DirectRunner" )
with patch("apache_beam.io.parquetio.WriteToParquet" ) as write_parquet_mock:
UpperCAmelCase_ = partial(lowerCAmelCase , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
lowerCAmelCase , builder.name , "default" , "0.0.0" , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
lowerCAmelCase , builder.name , "default" , "0.0.0" , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"content": datasets.Value("string" )} ) )
UpperCAmelCase_ = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , lowerCAmelCase )
self.assertEqual(dset["train"].info.splits["train"].num_examples , lowerCAmelCase )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["train"]["content"] ) , sorted(["foo", "bar", "foobar"] ) )
self.assertTrue(
os.path.exists(os.path.join(lowerCAmelCase , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
@require_beam
def A__ ( self ):
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase_ = DummyBeamDataset(cache_dir=lowerCAmelCase )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def A__ ( self ):
UpperCAmelCase_ = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
UpperCAmelCase_ = NestedBeamDataset(cache_dir=lowerCAmelCase , beam_runner="DirectRunner" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(lowerCAmelCase , builder.name , "default" , "0.0.0" , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string" )} )} ) )
UpperCAmelCase_ = builder.as_dataset()
self.assertEqual(dset["train"].num_rows , lowerCAmelCase )
self.assertEqual(dset["train"].info.splits["train"].num_examples , lowerCAmelCase )
self.assertDictEqual(dset["train"][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["train"][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(lowerCAmelCase , builder.name , "default" , "0.0.0" , "dataset_info.json" ) ) )
del dset
| 579 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
SCREAMING_SNAKE_CASE = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self , lowerCAmelCase ):
super().__init__()
UpperCAmelCase_ = torchvision.models.resnetaaa(pretrained=lowerCAmelCase )
UpperCAmelCase_ = list(model.children() )[:-2]
UpperCAmelCase_ = nn.Sequential(*lowerCAmelCase )
UpperCAmelCase_ = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def A__ ( self , lowerCAmelCase ):
# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
UpperCAmelCase_ = self.pool(self.model(lowerCAmelCase ) )
UpperCAmelCase_ = torch.flatten(lowerCAmelCase , start_dim=2 )
UpperCAmelCase_ = out.transpose(1 , 2 ).contiguous()
return out # BxNx2048
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase_ = [json.loads(lowerCAmelCase ) for l in open(lowerCAmelCase )]
UpperCAmelCase_ = os.path.dirname(lowerCAmelCase )
UpperCAmelCase_ = tokenizer
UpperCAmelCase_ = labels
UpperCAmelCase_ = len(lowerCAmelCase )
UpperCAmelCase_ = max_seq_length
UpperCAmelCase_ = transforms
def __len__( self ):
return len(self.data )
def __getitem__( self , lowerCAmelCase ):
UpperCAmelCase_ = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"] , add_special_tokens=lowerCAmelCase ) )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = sentence[0], sentence[1:-1], sentence[-1]
UpperCAmelCase_ = sentence[: self.max_seq_length]
UpperCAmelCase_ = torch.zeros(self.n_classes )
UpperCAmelCase_ = 1
UpperCAmelCase_ = Image.open(os.path.join(self.data_dir , self.data[index]["img"] ) ).convert("RGB" )
UpperCAmelCase_ = self.transforms(lowerCAmelCase )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def A__ ( self ):
UpperCAmelCase_ = Counter()
for row in self.data:
label_freqs.update(row["label"] )
return label_freqs
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> int:
UpperCAmelCase_ = [len(row["sentence"] ) for row in batch]
UpperCAmelCase_ , UpperCAmelCase_ = len(__SCREAMING_SNAKE_CASE ), max(__SCREAMING_SNAKE_CASE )
UpperCAmelCase_ = torch.zeros(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , dtype=torch.long )
UpperCAmelCase_ = torch.zeros(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ):
UpperCAmelCase_ = input_row["sentence"]
UpperCAmelCase_ = 1
UpperCAmelCase_ = torch.stack([row["image"] for row in batch] )
UpperCAmelCase_ = torch.stack([row["label"] for row in batch] )
UpperCAmelCase_ = torch.stack([row["image_start_token"] for row in batch] )
UpperCAmelCase_ = torch.stack([row["image_end_token"] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def snake_case__ ( ) -> int:
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def snake_case__ ( ) -> Optional[int]:
return transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.46_777_044, 0.44_531_429, 0.40_661_017] , std=[0.12_221_994, 0.12_145_835, 0.14_380_469] , ),
] )
| 579 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCamelCase__ ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ ):
super().__init__()
A__ = model
A__ = 2
A__ = nn.Linear(self.model.config.hidden_size , self.num_labels )
def __snake_case ( self ):
pass
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str , __UpperCamelCase : str , __UpperCamelCase : str ) -> Tuple:
"""simple docstring"""
A__ = LongformerModel.from_pretrained(__UpperCamelCase )
A__ = LightningModel(__UpperCamelCase )
A__ = torch.load(__UpperCamelCase , map_location=torch.device('''cpu''' ) )
lightning_model.load_state_dict(ckpt['''state_dict'''] )
# init longformer question answering model
A__ = LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__UpperCamelCase )
print(F"Conversion successful. Model saved under {pytorch_dump_folder_path}" )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--longformer_model',
default=None,
type=str,
required=True,
help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
)
parser.add_argument(
'--longformer_question_answering_ckpt_path',
default=None,
type=str,
required=True,
help='Path the official PyTorch Lightning Checkpoint.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
_SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : int = {
'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = ['BertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = [
'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BertForMaskedLM',
'BertForMultipleChoice',
'BertForNextSentencePrediction',
'BertForPreTraining',
'BertForQuestionAnswering',
'BertForSequenceClassification',
'BertForTokenClassification',
'BertLayer',
'BertLMHeadModel',
'BertModel',
'BertPreTrainedModel',
'load_tf_weights_in_bert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Tuple = [
'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFBertEmbeddings',
'TFBertForMaskedLM',
'TFBertForMultipleChoice',
'TFBertForNextSentencePrediction',
'TFBertForPreTraining',
'TFBertForQuestionAnswering',
'TFBertForSequenceClassification',
'TFBertForTokenClassification',
'TFBertLMHeadModel',
'TFBertMainLayer',
'TFBertModel',
'TFBertPreTrainedModel',
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Dict = ['TFBertTokenizer']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Optional[int] = [
'FlaxBertForCausalLM',
'FlaxBertForMaskedLM',
'FlaxBertForMultipleChoice',
'FlaxBertForNextSentencePrediction',
'FlaxBertForPreTraining',
'FlaxBertForQuestionAnswering',
'FlaxBertForSequenceClassification',
'FlaxBertForTokenClassification',
'FlaxBertModel',
'FlaxBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 55 | 0 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
__A = re.compile(r"\s+")
def lowercase__ ( A_: int ) -> Any:
"""simple docstring"""
return {"hash": hashlib.mda(re.sub(A_ , """""" , example["""content"""] ).encode("""utf-8""" ) ).hexdigest()}
def lowercase__ ( A_: int ) -> Optional[int]:
"""simple docstring"""
__UpperCAmelCase =[len(A_ ) for line in example["""content"""].splitlines()]
return {"line_mean": np.mean(A_ ), "line_max": max(A_ )}
def lowercase__ ( A_: Any ) -> int:
"""simple docstring"""
__UpperCAmelCase =np.mean([c.isalnum() for c in example["""content"""]] )
return {"alpha_frac": alpha_frac}
def lowercase__ ( A_: List[Any] , A_: Tuple ) -> str:
"""simple docstring"""
if example["hash"] in uniques:
uniques.remove(example["""hash"""] )
return True
else:
return False
def lowercase__ ( A_: List[str] , A_: Dict=5 ) -> Optional[Any]:
"""simple docstring"""
__UpperCAmelCase =["""auto-generated""", """autogenerated""", """automatically generated"""]
__UpperCAmelCase =example["""content"""].splitlines()
for _, line in zip(range(A_ ) , A_ ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def lowercase__ ( A_: str , A_: List[Any]=5 , A_: List[Any]=0.0_5 ) -> List[str]:
"""simple docstring"""
__UpperCAmelCase =["""unit tests""", """test file""", """configuration file"""]
__UpperCAmelCase =example["""content"""].splitlines()
__UpperCAmelCase =0
__UpperCAmelCase =0
# first test
for _, line in zip(range(A_ ) , A_ ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
__UpperCAmelCase =example["""content"""].count("""\n""" )
__UpperCAmelCase =int(coeff * nlines )
for line in lines:
count_config += line.lower().count("""config""" )
count_test += line.lower().count("""test""" )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def lowercase__ ( A_: Any ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase =["""def """, """class """, """for """, """while """]
__UpperCAmelCase =example["""content"""].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def lowercase__ ( A_: Optional[int] , A_: List[Any]=4 ) -> Any:
"""simple docstring"""
__UpperCAmelCase =example["""content"""].splitlines()
__UpperCAmelCase =0
for line in lines:
counter += line.lower().count("""=""" )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def lowercase__ ( A_: List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase =tokenizer(example["""content"""] , truncation=A_ )["""input_ids"""]
__UpperCAmelCase =len(example["""content"""] ) / len(A_ )
return {"ratio": ratio}
def lowercase__ ( A_: int ) -> str:
"""simple docstring"""
__UpperCAmelCase ={}
results.update(get_hash(A_ ) )
results.update(line_stats(A_ ) )
results.update(alpha_stats(A_ ) )
results.update(char_token_ratio(A_ ) )
results.update(is_autogenerated(A_ ) )
results.update(is_config_or_test(A_ ) )
results.update(has_no_keywords(A_ ) )
results.update(has_few_assignments(A_ ) )
return results
def lowercase__ ( A_: Dict , A_: Any , A_: List[str] ) -> str:
"""simple docstring"""
if not check_uniques(A_ , A_ ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def lowercase__ ( A_: List[Any] ) -> Tuple:
"""simple docstring"""
with open(A_ , """rb""" ) as f_in:
with gzip.open(str(A_ ) + """.gz""" , """wb""" , compresslevel=6 ) as f_out:
shutil.copyfileobj(A_ , A_ )
os.unlink(A_ )
# Settings
__A = HfArgumentParser(PreprocessingArguments)
__A = parser.parse_args()
if args.num_workers is None:
__A = multiprocessing.cpu_count()
__A = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
__A = time.time()
__A = load_dataset(args.dataset_name, split="train")
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
__A = time.time()
__A = ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
__A = set(ds.unique("hash"))
__A = len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
__A = time.time()
__A = ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args})
print(F"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(F"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
__A = time.time()
__A , __A = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(F"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(F"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
__A = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / "duplicate_clusters.json", "w") as f:
json.dump(duplicate_clusters, f)
__A = output_dir / "data"
data_dir.mkdir(exist_ok=True)
__A = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
__A = str(data_dir / F"""file-{file_number+1:012}.json""")
__A = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(F"""Time to save dataset: {time.time()-t_start:.2f}""")
| 68 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
print(decimal_isolate(35.345, 2))
print(decimal_isolate(35.345, 3))
print(decimal_isolate(-14.789, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-14.123, 1))
print(decimal_isolate(-14.123, 2))
print(decimal_isolate(-14.123, 3))
| 11 | 0 |
from __future__ import annotations
UpperCAmelCase = 10
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> Tuple:
"""simple docstring"""
snake_case_ = 1
snake_case_ = max(__lowerCAmelCase )
while placement <= max_digit:
# declare and initialize empty buckets
snake_case_ = [[] for _ in range(__lowerCAmelCase )]
# split list_of_ints between the buckets
for i in list_of_ints:
snake_case_ = int((i / placement) % RADIX )
buckets[tmp].append(__lowerCAmelCase )
# put each buckets' contents into list_of_ints
snake_case_ = 0
for b in range(__lowerCAmelCase ):
for i in buckets[b]:
snake_case_ = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
UpperCAmelCase = 2_9979_2458
# Symbols
UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase = symbols("""ct x y z""")
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> float:
"""simple docstring"""
if velocity > c:
raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!''' )
elif velocity < 1:
# Usually the speed should be much higher than 1 (c order of magnitude)
raise ValueError('''Speed must be greater than or equal to 1!''' )
return velocity / c
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> float:
"""simple docstring"""
return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE ) ** 2 )
def __lowerCAmelCase (SCREAMING_SNAKE_CASE )-> np.ndarray:
"""simple docstring"""
return np.array(
[
[gamma(SCREAMING_SNAKE_CASE ), -gamma(SCREAMING_SNAKE_CASE ) * beta(SCREAMING_SNAKE_CASE ), 0, 0],
[-gamma(SCREAMING_SNAKE_CASE ) * beta(SCREAMING_SNAKE_CASE ), gamma(SCREAMING_SNAKE_CASE ), 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1],
] )
def __lowerCAmelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None )-> np.ndarray:
"""simple docstring"""
if event is None:
snake_case_ = np.array([ct, x, y, z] ) # Symbolic four vector
else:
event[0] *= c # x0 is ct (speed of light * time)
return transformation_matrix(SCREAMING_SNAKE_CASE ) @ event
if __name__ == "__main__":
import doctest
doctest.testmod()
# Example of symbolic vector:
UpperCAmelCase = transform(2997_9245)
print("""Example of four vector: """)
print(f'''ct\' = {four_vector[0]}''')
print(f'''x\' = {four_vector[1]}''')
print(f'''y\' = {four_vector[2]}''')
print(f'''z\' = {four_vector[3]}''')
# Substitute symbols with numerical values
UpperCAmelCase = {ct: c, x: 1, y: 1, z: 1}
UpperCAmelCase = [four_vector[i].subs(sub_dict) for i in range(4)]
print(f'''\n{numerical_vector}''')
| 531 | 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 PoolFormerImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
def __init__( self , UpperCamelCase__ , UpperCamelCase__=7 , UpperCamelCase__=3 , UpperCamelCase__=30 , UpperCamelCase__=400 , UpperCamelCase__=True , UpperCamelCase__=None , UpperCamelCase__=0.9 , UpperCamelCase__=None , UpperCamelCase__=True , UpperCamelCase__=[0.5, 0.5, 0.5] , UpperCamelCase__=[0.5, 0.5, 0.5] , ) -> Dict:
'''simple docstring'''
snake_case : Optional[Any] = size if size is not None else {"shortest_edge": 30}
snake_case : Optional[int] = crop_size if crop_size is not None else {"height": 30, "width": 30}
snake_case : Tuple = parent
snake_case : List[str] = batch_size
snake_case : Dict = num_channels
snake_case : List[Any] = min_resolution
snake_case : Dict = max_resolution
snake_case : str = do_resize_and_center_crop
snake_case : Any = size
snake_case : int = crop_pct
snake_case : List[Any] = crop_size
snake_case : List[Any] = do_normalize
snake_case : List[Any] = image_mean
snake_case : List[str] = image_std
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class _lowerCAmelCase ( snake_case_ , unittest.TestCase ):
__UpperCAmelCase : Optional[int] = PoolFormerImageProcessor if is_vision_available() else None
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
snake_case : Optional[Any] = PoolFormerImageProcessingTester(self )
@property
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
snake_case : List[str] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase__ , "do_resize_and_center_crop" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "size" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "crop_pct" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "do_normalize" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "image_mean" ) )
self.assertTrue(hasattr(UpperCamelCase__ , "image_std" ) )
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
snake_case : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 30} )
self.assertEqual(image_processor.crop_size , {"height": 30, "width": 30} )
snake_case : int = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
pass
def lowerCamelCase ( self ) -> Dict:
'''simple docstring'''
snake_case : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image )
# Test not batched input
snake_case : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
snake_case : Optional[int] = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase ( self ) -> Tuple:
'''simple docstring'''
snake_case : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray )
# Test not batched input
snake_case : str = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
snake_case : Optional[int] = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__ )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor )
# Test not batched input
snake_case : Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
snake_case : Tuple = image_processing(UpperCamelCase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 178 |
"""simple docstring"""
from manim import *
class _lowerCAmelCase ( snake_case_ ):
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : List[str] = Rectangle(height=0.5 , width=0.5 )
snake_case : Dict = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
snake_case : Tuple = [mem.copy() for i in range(6 )]
snake_case : Any = [mem.copy() for i in range(6 )]
snake_case : Tuple = VGroup(*UpperCamelCase__ ).arrange(UpperCamelCase__ , buff=0 )
snake_case : Optional[Any] = VGroup(*UpperCamelCase__ ).arrange(UpperCamelCase__ , buff=0 )
snake_case : Optional[Any] = VGroup(UpperCamelCase__ , UpperCamelCase__ ).arrange(UpperCamelCase__ , buff=0 )
snake_case : Optional[Any] = Text("CPU" , font_size=24 )
snake_case : Optional[int] = Group(UpperCamelCase__ , UpperCamelCase__ ).arrange(UpperCamelCase__ , buff=0.5 , aligned_edge=UpperCamelCase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(UpperCamelCase__ )
snake_case : Optional[Any] = [mem.copy() for i in range(1 )]
snake_case : List[str] = VGroup(*UpperCamelCase__ ).arrange(UpperCamelCase__ , buff=0 )
snake_case : Optional[int] = Text("GPU" , font_size=24 )
snake_case : Tuple = Group(UpperCamelCase__ , UpperCamelCase__ ).arrange(UpperCamelCase__ , buff=0.5 , aligned_edge=UpperCamelCase__ )
gpu.align_to(UpperCamelCase__ , UpperCamelCase__ )
gpu.set_x(gpu.get_x() - 1 )
self.add(UpperCamelCase__ )
snake_case : Any = [mem.copy() for i in range(6 )]
snake_case : Any = VGroup(*UpperCamelCase__ ).arrange(UpperCamelCase__ , buff=0 )
snake_case : Dict = Text("Model" , font_size=24 )
snake_case : Optional[Any] = Group(UpperCamelCase__ , UpperCamelCase__ ).arrange(UpperCamelCase__ , buff=0.5 , aligned_edge=UpperCamelCase__ )
model.move_to([3, -1.0, 0] )
self.play(
Create(UpperCamelCase__ , run_time=1 ) , Create(UpperCamelCase__ , run_time=1 ) , Create(UpperCamelCase__ , run_time=1 ) , )
snake_case : Optional[Any] = MarkupText(
F'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.' , font_size=24 , )
snake_case : Any = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
snake_case : 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(UpperCamelCase__ , run_time=2.5 ) , Write(UpperCamelCase__ ) , Write(UpperCamelCase__ ) )
self.add(UpperCamelCase__ )
snake_case : Optional[Any] = []
snake_case : Dict = []
snake_case : Union[str, Any] = []
for i, rect in enumerate(UpperCamelCase__ ):
snake_case : Any = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(UpperCamelCase__ , opacity=0.7 )
cpu_target.move_to(UpperCamelCase__ )
cpu_target.generate_target()
snake_case : Optional[int] = 0.46 / 4
snake_case : 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=UpperCamelCase__ )
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=UpperCamelCase__ , buff=0.0 )
else:
cpu_target.target.next_to(cpu_targs[i - 1].target , direction=UpperCamelCase__ , buff=0.0 )
cpu_targs.append(UpperCamelCase__ )
first_animations.append(rect.animate(run_time=0.5 ).set_stroke(UpperCamelCase__ ) )
second_animations.append(MoveToTarget(UpperCamelCase__ , run_time=1.5 ) )
self.play(*UpperCamelCase__ )
self.play(*UpperCamelCase__ )
self.wait()
| 178 | 1 |
'''simple docstring'''
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase__ : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = data
__SCREAMING_SNAKE_CASE : int = None
def __repr__( self : Tuple ):
"""simple docstring"""
return F"Node({self.data})"
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = None
def __iter__( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.head
while node:
yield node.data
__SCREAMING_SNAKE_CASE : Union[str, Any] = node.next
def __len__( self : int ):
"""simple docstring"""
return sum(1 for _ in self )
def __repr__( self : Union[str, Any] ):
"""simple docstring"""
return "->".join([str(lowerCAmelCase__ ) for item in self] )
def __getitem__( self : Union[str, Any] , lowerCAmelCase__ : int ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self : Optional[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : Any ):
"""simple docstring"""
if not 0 <= index < len(self ):
raise ValueError("""list index out of range.""" )
__SCREAMING_SNAKE_CASE : Optional[int] = self.head
for _ in range(lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE : str = current.next
__SCREAMING_SNAKE_CASE : Union[str, Any] = data
def UpperCamelCase__ ( self : Tuple , lowerCAmelCase__ : Any ):
"""simple docstring"""
self.insert_nth(len(self ) , lowerCAmelCase__ )
def UpperCamelCase__ ( self : List[str] , lowerCAmelCase__ : Any ):
"""simple docstring"""
self.insert_nth(0 , lowerCAmelCase__ )
def UpperCamelCase__ ( self : int , lowerCAmelCase__ : int , lowerCAmelCase__ : Any ):
"""simple docstring"""
if not 0 <= index <= len(self ):
raise IndexError("""list index out of range""" )
__SCREAMING_SNAKE_CASE : str = Node(lowerCAmelCase__ )
if self.head is None:
__SCREAMING_SNAKE_CASE : Dict = new_node
elif index == 0:
__SCREAMING_SNAKE_CASE : str = self.head # link new_node to head
__SCREAMING_SNAKE_CASE : List[Any] = new_node
else:
__SCREAMING_SNAKE_CASE : List[str] = self.head
for _ in range(index - 1 ):
__SCREAMING_SNAKE_CASE : Any = temp.next
__SCREAMING_SNAKE_CASE : List[str] = temp.next
__SCREAMING_SNAKE_CASE : Optional[Any] = new_node
def UpperCamelCase__ ( self : Optional[int] ): # print every node data
"""simple docstring"""
print(self )
def UpperCamelCase__ ( self : str ):
"""simple docstring"""
return self.delete_nth(0 )
def UpperCamelCase__ ( self : List[str] ): # delete from tail
"""simple docstring"""
return self.delete_nth(len(self ) - 1 )
def UpperCamelCase__ ( self : Optional[int] , lowerCAmelCase__ : int = 0 ):
"""simple docstring"""
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("""List index out of range.""" )
__SCREAMING_SNAKE_CASE : Optional[int] = self.head # default first node
if index == 0:
__SCREAMING_SNAKE_CASE : Optional[int] = self.head.next
else:
__SCREAMING_SNAKE_CASE : Tuple = self.head
for _ in range(index - 1 ):
__SCREAMING_SNAKE_CASE : int = temp.next
__SCREAMING_SNAKE_CASE : Union[str, Any] = temp.next
__SCREAMING_SNAKE_CASE : str = temp.next.next
return delete_node.data
def UpperCamelCase__ ( self : List[Any] ):
"""simple docstring"""
return self.head is None
def UpperCamelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = None
__SCREAMING_SNAKE_CASE : Dict = self.head
while current:
# Store the current node's next node.
__SCREAMING_SNAKE_CASE : Optional[Any] = current.next
# Make the current node's next point backwards
__SCREAMING_SNAKE_CASE : Dict = prev
# Make the previous node be the current node
__SCREAMING_SNAKE_CASE : Dict = current
# Make the current node the next node (to progress iteration)
__SCREAMING_SNAKE_CASE : Any = next_node
# Return prev in order to put the head at the end
__SCREAMING_SNAKE_CASE : Dict = prev
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : str = LinkedList()
assert linked_list.is_empty() is True
assert str(_lowerCamelCase ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(_lowerCamelCase ) == i
linked_list.insert_nth(_lowerCamelCase , i + 1 )
assert str(_lowerCamelCase ) == "->".join(str(_lowerCamelCase ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(_lowerCamelCase ) == "->".join(str(_lowerCamelCase ) for i in range(0 , 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(_lowerCamelCase ) == 9
assert str(_lowerCamelCase ) == "->".join(str(_lowerCamelCase ) for i in range(1 , 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
__SCREAMING_SNAKE_CASE : Any = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(_lowerCamelCase ) == "->".join(str(_lowerCamelCase ) for i in range(-8 , 1 ) )
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : str = [
-9,
1_00,
Node(77_34_51_12 ),
"""dlrow olleH""",
7,
55_55,
0,
-1_92.5_55_55,
"""Hello, world!""",
77.9,
Node(10 ),
None,
None,
12.20,
]
__SCREAMING_SNAKE_CASE : int = LinkedList()
for i in test_input:
linked_list.insert_tail(_lowerCamelCase )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(_lowerCamelCase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
__SCREAMING_SNAKE_CASE : Optional[Any] = linked_list.delete_head()
assert result == -9
assert (
str(_lowerCamelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
__SCREAMING_SNAKE_CASE : Dict = linked_list.delete_tail()
assert result == 12.2
assert (
str(_lowerCamelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
__SCREAMING_SNAKE_CASE : Union[str, Any] = linked_list.delete_nth(10 )
assert result is None
assert (
str(_lowerCamelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("""Hello again, world!""" ) )
assert (
str(_lowerCamelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(_lowerCamelCase )
assert (
str(_lowerCamelCase )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(_lowerCamelCase )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def lowerCAmelCase_ ( ):
from doctest import testmod
testmod()
__SCREAMING_SNAKE_CASE : Union[str, Any] = LinkedList()
linked_list.insert_head(input("""Inserting 1st at head """ ).strip() )
linked_list.insert_head(input("""Inserting 2nd at head """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
linked_list.insert_tail(input("""\nInserting 1st at tail """ ).strip() )
linked_list.insert_tail(input("""Inserting 2nd at tail """ ).strip() )
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nDelete head""" )
linked_list.delete_head()
print("""Delete tail""" )
linked_list.delete_tail()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nReverse linked list""" )
linked_list.reverse()
print("""\nPrint list:""" )
linked_list.print_list()
print("""\nString representation of linked list:""" )
print(_lowerCamelCase )
print("""\nReading/changing Node data using indexing:""" )
print(F"Element at Position 1: {linked_list[1]}" )
__SCREAMING_SNAKE_CASE : Union[str, Any] = input("""Enter New Value: """ ).strip()
print("""New list:""" )
print(_lowerCamelCase )
print(F"length of linked_list is : {len(_lowerCamelCase )}" )
if __name__ == "__main__":
main()
| 178 |
'''simple docstring'''
import qiskit
def lowerCAmelCase_ ( _lowerCamelCase: int = 2 ):
__SCREAMING_SNAKE_CASE : Dict = qubits
# Using Aer's simulator
__SCREAMING_SNAKE_CASE : Optional[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on the q register
__SCREAMING_SNAKE_CASE : str = qiskit.QuantumCircuit(_lowerCamelCase , _lowerCamelCase )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , _lowerCamelCase ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , _lowerCamelCase )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(_lowerCamelCase ) ) , list(range(_lowerCamelCase ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
__SCREAMING_SNAKE_CASE : str = qiskit.execute(_lowerCamelCase , _lowerCamelCase , shots=10_00 )
return job.result().get_counts(_lowerCamelCase )
if __name__ == "__main__":
print(f"Total count for various states are: {quantum_entanglement(3)}")
| 178 | 1 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCAmelCase_ : Optional[int] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
]
# Define blinker example
UpperCAmelCase_ : str = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def _UpperCamelCase (_lowerCamelCase : list[list[int]] )-> list[list[int]]:
'''simple docstring'''
__snake_case = []
for i in range(len(_lowerCamelCase ) ):
__snake_case = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
__snake_case = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(_lowerCamelCase ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(_lowerCamelCase ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(_lowerCamelCase ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
__snake_case = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(_lowerCamelCase )
return next_generation
def _UpperCamelCase (_lowerCamelCase : list[list[int]] , _lowerCamelCase : int )-> list[Image.Image]:
'''simple docstring'''
__snake_case = []
for _ in range(_lowerCamelCase ):
# Create output image
__snake_case = Image.new('''RGB''' , (len(cells[0] ), len(_lowerCamelCase )) )
__snake_case = img.load()
# Save cells to image
for x in range(len(_lowerCamelCase ) ):
for y in range(len(cells[0] ) ):
__snake_case = 2_55 - cells[y][x] * 2_55
__snake_case = (colour, colour, colour)
# Save image
images.append(_lowerCamelCase )
__snake_case = new_generation(_lowerCamelCase )
return images
if __name__ == "__main__":
UpperCAmelCase_ : Dict = generate_images(GLIDER, 1_6)
images[0].save('''out.gif''', save_all=True, append_images=images[1:])
| 24 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
UpperCAmelCase_ : str = logging.get_logger(__name__)
@add_end_docstrings(__lowerCAmelCase)
class lowerCAmelCase ( __lowerCAmelCase):
def __init__( self , **__SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE )
requires_backends(self , '''vision''' )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == '''tf'''
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self , __SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
return super().__call__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self , **__SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
__snake_case = {}
if "candidate_labels" in kwargs:
__snake_case = kwargs['''candidate_labels''']
if "hypothesis_template" in kwargs:
__snake_case = kwargs['''hypothesis_template''']
return preprocess_params, {}, {}
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE="This is a photo of {}." ) -> Optional[Any]:
'''simple docstring'''
__snake_case = load_image(__SCREAMING_SNAKE_CASE )
__snake_case = self.image_processor(images=[image] , return_tensors=self.framework )
__snake_case = candidate_labels
__snake_case = [hypothesis_template.format(__SCREAMING_SNAKE_CASE ) for x in candidate_labels]
__snake_case = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=self.framework , padding=__SCREAMING_SNAKE_CASE )
__snake_case = [text_inputs]
return inputs
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
__snake_case = model_inputs.pop('''candidate_labels''' )
__snake_case = model_inputs.pop('''text_inputs''' )
if isinstance(text_inputs[0] , __SCREAMING_SNAKE_CASE ):
__snake_case = text_inputs[0]
else:
# Batching case.
__snake_case = text_inputs[0][0]
__snake_case = self.model(**__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__snake_case = {
'''candidate_labels''': candidate_labels,
'''logits''': outputs.logits_per_image,
}
return model_outputs
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__snake_case = model_outputs.pop('''candidate_labels''' )
__snake_case = model_outputs['''logits'''][0]
if self.framework == "pt":
__snake_case = logits.softmax(dim=-1 ).squeeze(-1 )
__snake_case = probs.tolist()
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
__snake_case = [scores]
elif self.framework == "tf":
__snake_case = stable_softmax(__SCREAMING_SNAKE_CASE , axis=-1 )
__snake_case = probs.numpy().tolist()
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
__snake_case = [
{'''score''': score, '''label''': candidate_label}
for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , key=lambda __SCREAMING_SNAKE_CASE : -x[0] )
]
return result
| 24 | 1 |
'''simple docstring'''
from math import factorial
def _A ( __magic_name__ = 20 ):
lowercase__ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowercase__ = 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:
_snake_case = int(sys.argv[1])
print(solution(n))
except ValueError:
print("""Invalid entry - please enter a number.""")
| 709 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_snake_case = logging.get_logger("""transformers.models.speecht5""")
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
hf_model.apply_weight_norm()
lowercase__ = checkpoint["input_conv.weight_g"]
lowercase__ = checkpoint["input_conv.weight_v"]
lowercase__ = checkpoint["input_conv.bias"]
for i in range(len(config.upsample_rates ) ):
lowercase__ = checkpoint[f'''upsamples.{i}.1.weight_g''']
lowercase__ = checkpoint[f'''upsamples.{i}.1.weight_v''']
lowercase__ = checkpoint[f'''upsamples.{i}.1.bias''']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
lowercase__ = checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
lowercase__ = checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
lowercase__ = checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']
lowercase__ = checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
lowercase__ = checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
lowercase__ = checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']
lowercase__ = checkpoint["output_conv.1.weight_g"]
lowercase__ = checkpoint["output_conv.1.weight_v"]
lowercase__ = checkpoint["output_conv.1.bias"]
hf_model.remove_weight_norm()
@torch.no_grad()
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__=None , ):
if config_path is not None:
lowercase__ = SpeechTaHifiGanConfig.from_pretrained(__magic_name__ )
else:
lowercase__ = SpeechTaHifiGanConfig()
lowercase__ = SpeechTaHifiGan(__magic_name__ )
lowercase__ = torch.load(__magic_name__ )
load_weights(orig_checkpoint["model"]["generator"] , __magic_name__ , __magic_name__ )
lowercase__ = np.load(__magic_name__ )
lowercase__ = stats[0].reshape(-1 )
lowercase__ = stats[1].reshape(-1 )
lowercase__ = torch.from_numpy(__magic_name__ ).float()
lowercase__ = torch.from_numpy(__magic_name__ ).float()
model.save_pretrained(__magic_name__ )
if repo_id:
print("Pushing to the hub..." )
model.push_to_hub(__magic_name__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
_snake_case = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 611 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__snake_case : int = logging.get_logger(__name__)
@dataclass
class A__(_UpperCamelCase ):
"""simple docstring"""
_A : Union[str, Any] = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **_lowercase ) -> Tuple:
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
a_ : Tuple = deprecated_arg[3:]
a_ : int = not kwargs.pop(_lowercase )
logger.warning(
F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
F''' {positive_arg}={kwargs[positive_arg]}''' )
a_ : Optional[int] = kwargs.pop("""tpu_name""" , self.tpu_name )
a_ : Optional[int] = kwargs.pop("""device_idx""" , self.device_idx )
a_ : str = kwargs.pop("""eager_mode""" , self.eager_mode )
a_ : Optional[Any] = kwargs.pop("""use_xla""" , self.use_xla )
super().__init__(**_lowercase )
_A : str = field(
default=_UpperCamelCase, metadata={'''help''': '''Name of TPU'''}, )
_A : int = field(
default=0, metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}, )
_A : bool = field(default=_UpperCamelCase, metadata={'''help''': '''Benchmark models in eager model.'''} )
_A : bool = field(
default=_UpperCamelCase, metadata={
'''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
}, )
@cached_property
def UpperCamelCase__ ( self ) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
requires_backends(self , ["""tf"""] )
a_ : int = None
if self.tpu:
try:
if self.tpu_name:
a_ : Optional[Any] = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
a_ : Union[str, Any] = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
a_ : Dict = None
return tpu
@cached_property
def UpperCamelCase__ ( self ) -> Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
requires_backends(self , ["""tf"""] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
a_ : Dict = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx] , """GPU""" )
a_ : int = tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}''' )
else:
tf.config.set_visible_devices([] , """GPU""" ) # disable GPU
a_ : Optional[Any] = tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}''' )
return strategy
@property
def UpperCamelCase__ ( self ) -> bool:
requires_backends(self , ["""tf"""] )
return self._setup_tpu is not None
@property
def UpperCamelCase__ ( self ) -> "tf.distribute.Strategy":
requires_backends(self , ["""tf"""] )
return self._setup_strategy
@property
def UpperCamelCase__ ( self ) -> Optional[int]:
requires_backends(self , ["""tf"""] )
return tf.config.list_physical_devices("""GPU""" )
@property
def UpperCamelCase__ ( self ) -> int:
requires_backends(self , ["""tf"""] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def UpperCamelCase__ ( self ) -> bool:
return self.n_gpu > 0
| 540 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
SCREAMING_SNAKE_CASE__ : List[Any] = """\
@inproceedings{snover-etal-2006-study,
title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
author = \"Snover, Matthew and
Dorr, Bonnie and
Schwartz, Rich and
Micciulla, Linnea and
Makhoul, John\",
booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
month = aug # \" 8-12\",
year = \"2006\",
address = \"Cambridge, Massachusetts, USA\",
publisher = \"Association for Machine Translation in the Americas\",
url = \"https://aclanthology.org/2006.amta-papers.25\",
pages = \"223--231\",
}
@inproceedings{post-2018-call,
title = \"A Call for Clarity in Reporting {BLEU} Scores\",
author = \"Post, Matt\",
booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
month = oct,
year = \"2018\",
address = \"Belgium, Brussels\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/W18-6319\",
pages = \"186--191\",
}
"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.
The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534
See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""
SCREAMING_SNAKE_CASE__ : int = """
Produces TER scores alongside the number of edits and reference length.
Args:
predictions (list of str): The system stream (a sequence of segments).
references (list of list of str): A list of one or more reference streams (each a sequence of segments).
normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
Only applies if `normalized = True`. Defaults to `False`.
case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.
Returns:
'score' (float): TER score (num_edits / sum_ref_lengths * 100)
'num_edits' (int): The cumulative number of edits
'ref_length' (float): The cumulative average reference length
Examples:
Example 1:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\",
... \"What did the TER metric user say to the developer?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
... [\"Your jokes are...\", \"...TERrible\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}
Example 2:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... case_sensitive=True)
>>> print(results)
{'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}
Example 3:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... normalized=True,
... case_sensitive=True)
>>> print(results)
{'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}
Example 4:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}
Example 5:
>>> predictions = [\"does this sentence match??\",
... \"what about this sentence?\",
... \"What did the TER metric user say to the developer?\"]
>>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
... [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
... [\"Your jokes are...\", \"...TERrible\"]]
>>> ter = datasets.load_metric(\"ter\")
>>> results = ter.compute(predictions=predictions,
... references=references,
... ignore_punct=True,
... case_sensitive=False)
>>> print(results)
{'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
def _snake_case ( self ) -> Optional[int]:
"""simple docstring"""
if version.parse(scb.__version__ ) < version.parse("1.4.12" ):
raise ImportWarning(
"To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
"You can install it with `pip install \"sacrebleu>=1.4.12\"`." )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="http://www.cs.umd.edu/~snover/tercom/" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Sequence(datasets.Value("string" , id="sequence" ) , id="references" ),
} ) , codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"] , reference_urls=[
"https://github.com/jhclark/tercom",
] , )
def _snake_case ( self , snake_case , snake_case , snake_case = False , snake_case = False , snake_case = False , snake_case = False , ) -> Optional[Any]:
"""simple docstring"""
a__ : int = len(references[0] )
if any(len(snake_case ) != references_per_prediction for refs in references ):
raise ValueError("Sacrebleu requires the same number of references for each prediction" )
a__ : int = [[refs[i] for refs in references] for i in range(snake_case )]
a__ : Optional[Any] = TER(
normalized=snake_case , no_punct=snake_case , asian_support=snake_case , case_sensitive=snake_case , )
a__ : Union[str, Any] = sb_ter.corpus_score(snake_case , snake_case )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 112 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = HfArgumentParser(UpperCamelCase__ )
A__ = parser.parse_args_into_dataclasses()[0]
A__ = TensorFlowBenchmark(args=UpperCamelCase__ )
try:
A__ = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
A__ = 'Arg --no_{0} is no longer used, please use --no-{0} instead.'
A__ = ' '.join(str(UpperCamelCase__ ).split(' ' )[:-1] )
A__ = ''
A__ = eval(str(UpperCamelCase__ ).split(' ' )[-1] )
A__ = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
A__ = full_error_msg + begin_error_msg + str(UpperCamelCase__ )
raise ValueError(UpperCamelCase__ )
benchmark.run()
if __name__ == "__main__":
main()
| 536 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCamelCase__( unittest.TestCase ):
@property
def snake_case__ ( self ) -> int:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def snake_case__ ( self ) -> Union[str, Any]:
A__ = ort.SessionOptions()
A__ = False
return options
def snake_case__ ( self ) -> str:
A__ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo.png' )
A__ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
A__ = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy' )
# using the PNDM scheduler by default
A__ = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
'CompVis/stable-diffusion-v1-4' ,revision='onnx' ,safety_checker=__UpperCAmelCase ,feature_extractor=__UpperCAmelCase ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
A__ = 'A red cat sitting on a park bench'
A__ = np.random.RandomState(0 )
A__ = pipe(
prompt=__UpperCAmelCase ,image=__UpperCAmelCase ,mask_image=__UpperCAmelCase ,strength=0.7_5 ,guidance_scale=7.5 ,num_inference_steps=15 ,generator=__UpperCAmelCase ,output_type='np' ,)
A__ = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 1e-2
| 536 | 1 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__lowerCAmelCase =object()
# For specifying empty leaf dict `{}`
__lowerCAmelCase =object()
def a ( _UpperCAmelCase , _UpperCAmelCase ) -> str:
"""simple docstring"""
a_ = tuple((re.compile(x + '$' ) for x in qs) )
for i in range(len(_UpperCAmelCase ) - len(_UpperCAmelCase ) + 1 ):
a_ = [x.match(_UpperCAmelCase ) for x, y in zip(_UpperCAmelCase , ks[i:] )]
if matches and all(_UpperCAmelCase ):
return True
return False
def a ( _UpperCAmelCase ) -> List[Any]:
"""simple docstring"""
def replace(_UpperCAmelCase , _UpperCAmelCase ):
for rule, replacement in rules:
if _match(_UpperCAmelCase , _UpperCAmelCase ):
return replacement
return val
return replace
def a ( ) -> Optional[int]:
"""simple docstring"""
return [
# embeddings
(("transformer", "wpe", "embedding"), P('mp' , _UpperCAmelCase )),
(("transformer", "wte", "embedding"), P('mp' , _UpperCAmelCase )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(_UpperCAmelCase , 'mp' )),
(("attention", "out_proj", "kernel"), P('mp' , _UpperCAmelCase )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(_UpperCAmelCase , 'mp' )),
(("mlp", "c_fc", "bias"), P('mp' )),
(("mlp", "c_proj", "kernel"), P('mp' , _UpperCAmelCase )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def a ( _UpperCAmelCase ) -> Dict:
"""simple docstring"""
a_ = _get_partition_rules()
a_ = _replacement_rules(_UpperCAmelCase )
a_ = {k: _unmatched for k in flatten_dict(_UpperCAmelCase )}
a_ = {k: replace(_UpperCAmelCase , _UpperCAmelCase ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(_UpperCAmelCase ) )
| 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase ={"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase =[
"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
__lowerCAmelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 697 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, 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 transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class snake_case ( __a ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
'''simple docstring'''
__A = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(a_, '''hidden_sizes''' ) )
self.parent.assertTrue(hasattr(a_, '''neck_hidden_sizes''' ) )
self.parent.assertTrue(hasattr(a_, '''num_attention_heads''' ) )
class snake_case :
'''simple docstring'''
def __init__( self : Dict, _lowerCamelCase : str, _lowerCamelCase : Tuple=13, _lowerCamelCase : List[str]=32, _lowerCamelCase : Dict=2, _lowerCamelCase : Optional[int]=3, _lowerCamelCase : Union[str, Any]=6_40, _lowerCamelCase : Tuple=4, _lowerCamelCase : Optional[Any]="silu", _lowerCamelCase : Tuple=3, _lowerCamelCase : str=32, _lowerCamelCase : Optional[Any]=0.1, _lowerCamelCase : Any=0.1, _lowerCamelCase : Optional[Any]=0.1, _lowerCamelCase : int=0.02, _lowerCamelCase : int=True, _lowerCamelCase : int=True, _lowerCamelCase : Union[str, Any]=10, _lowerCamelCase : Optional[Any]=None, ):
'''simple docstring'''
__A = parent
__A = batch_size
__A = image_size
__A = patch_size
__A = num_channels
__A = last_hidden_size
__A = num_attention_heads
__A = hidden_act
__A = conv_kernel_size
__A = output_stride
__A = hidden_dropout_prob
__A = attention_probs_dropout_prob
__A = classifier_dropout_prob
__A = use_labels
__A = is_training
__A = num_labels
__A = initializer_range
__A = scope
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
'''simple docstring'''
__A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__A = None
__A = None
if self.use_labels:
__A = ids_tensor([self.batch_size], self.num_labels )
__A = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels )
__A = self.get_config()
return config, pixel_values, labels, pixel_labels
def _SCREAMING_SNAKE_CASE ( self : Any ):
'''simple docstring'''
return MobileViTConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, num_attention_heads=self.num_attention_heads, hidden_act=self.hidden_act, conv_kernel_size=self.conv_kernel_size, output_stride=self.output_stride, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, )
def _SCREAMING_SNAKE_CASE ( self : List[str], _lowerCamelCase : Any, _lowerCamelCase : Optional[int], _lowerCamelCase : Dict, _lowerCamelCase : Any ):
'''simple docstring'''
__A = MobileViTModel(config=a_ )
model.to(a_ )
model.eval()
__A = model(a_ )
self.parent.assertEqual(
result.last_hidden_state.shape, (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
def _SCREAMING_SNAKE_CASE ( self : Dict, _lowerCamelCase : Optional[int], _lowerCamelCase : Tuple, _lowerCamelCase : List[str], _lowerCamelCase : List[Any] ):
'''simple docstring'''
__A = self.num_labels
__A = MobileViTForImageClassification(a_ )
model.to(a_ )
model.eval()
__A = model(a_, labels=a_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : str, _lowerCamelCase : Any, _lowerCamelCase : Union[str, Any], _lowerCamelCase : Tuple, _lowerCamelCase : int ):
'''simple docstring'''
__A = self.num_labels
__A = MobileViTForSemanticSegmentation(a_ )
model.to(a_ )
model.eval()
__A = model(a_ )
self.parent.assertEqual(
result.logits.shape, (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
__A = model(a_, labels=a_ )
self.parent.assertEqual(
result.logits.shape, (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
), )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
'''simple docstring'''
__A = self.prepare_config_and_inputs()
__A = config_and_inputs
__A = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class snake_case ( __a , __a , unittest.TestCase ):
'''simple docstring'''
A_ : Tuple = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
A_ : Optional[int] = (
{
"feature-extraction": MobileViTModel,
"image-classification": MobileViTForImageClassification,
"image-segmentation": MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
A_ : Optional[int] = False
A_ : Dict = False
A_ : List[str] = False
A_ : Optional[int] = False
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
'''simple docstring'''
__A = MobileViTModelTester(self )
__A = MobileViTConfigTester(self, config_class=a_, has_text_modality=a_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViT does not use inputs_embeds''' )
def _SCREAMING_SNAKE_CASE ( self : int ):
'''simple docstring'''
pass
@unittest.skip(reason='''MobileViT does not support input and output embeddings''' )
def _SCREAMING_SNAKE_CASE ( self : int ):
'''simple docstring'''
pass
@unittest.skip(reason='''MobileViT does not output attentions''' )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
'''simple docstring'''
__A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = model_class(a_ )
__A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__A = [*signature.parameters.keys()]
__A = ["""pixel_values"""]
self.assertListEqual(arg_names[:1], a_ )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
'''simple docstring'''
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
'''simple docstring'''
def check_hidden_states_output(_lowerCamelCase : Union[str, Any], _lowerCamelCase : int, _lowerCamelCase : int ):
__A = model_class(a_ )
model.to(a_ )
model.eval()
with torch.no_grad():
__A = model(**self._prepare_for_class(a_, a_ ) )
__A = outputs.hidden_states
__A = 5
self.assertEqual(len(a_ ), a_ )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
__A = 2
for i in range(len(a_ ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ), [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor], )
divisor *= 2
self.assertEqual(self.model_tester.output_stride, divisor // 2 )
__A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__A = True
check_hidden_states_output(a_, a_, a_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__A = True
check_hidden_states_output(a_, a_, a_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
'''simple docstring'''
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a_ )
def _SCREAMING_SNAKE_CASE ( self : int ):
'''simple docstring'''
__A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*a_ )
@slow
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
'''simple docstring'''
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__A = MobileViTModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def lowerCAmelCase ( ):
"""simple docstring"""
__A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
'''simple docstring'''
return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small''' ) if is_vision_available() else None
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
'''simple docstring'''
__A = MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small''' ).to(a_ )
__A = self.default_image_processor
__A = prepare_img()
__A = image_processor(images=a_, return_tensors='''pt''' ).to(a_ )
# forward pass
with torch.no_grad():
__A = model(**a_ )
# verify the logits
__A = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape, a_ )
__A = torch.tensor([-1.93_64, -1.23_27, -0.46_53] ).to(a_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3], a_, atol=1e-4 ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
'''simple docstring'''
__A = MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
__A = model.to(a_ )
__A = MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
__A = prepare_img()
__A = image_processor(images=a_, return_tensors='''pt''' ).to(a_ )
# forward pass
with torch.no_grad():
__A = model(**a_ )
__A = outputs.logits
# verify the logits
__A = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape, a_ )
__A = torch.tensor(
[
[[6.97_13, 6.97_86, 7.24_22], [7.28_93, 7.28_25, 7.44_46], [7.65_80, 7.87_97, 7.94_20]],
[[-10.68_69, -10.32_50, -10.34_71], [-10.42_28, -9.98_68, -9.71_32], [-11.04_05, -11.02_21, -10.73_18]],
[[-3.30_89, -2.85_39, -2.67_40], [-3.27_06, -2.56_21, -2.51_08], [-3.25_34, -2.66_15, -2.66_51]],
], device=a_, )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3], a_, atol=1e-4 ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : str ):
'''simple docstring'''
__A = MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
__A = model.to(a_ )
__A = MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small''' )
__A = prepare_img()
__A = image_processor(images=a_, return_tensors='''pt''' ).to(a_ )
# forward pass
with torch.no_grad():
__A = model(**a_ )
__A = outputs.logits.detach().cpu()
__A = image_processor.post_process_semantic_segmentation(outputs=a_, target_sizes=[(50, 60)] )
__A = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape, a_ )
__A = image_processor.post_process_semantic_segmentation(outputs=a_ )
__A = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape, a_ )
| 706 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 215 | 0 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowerCamelCase_ = {
'''<''': operator.lt,
'''<=''': operator.le,
'''==''': operator.eq,
'''!=''': operator.ne,
'''>=''': operator.ge,
'''>''': operator.gt,
}
def snake_case ( A__ ,A__ ,A__ ,A__ ,A__ ,A__ ):
if got_ver is None or want_ver is None:
raise ValueError(
F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
F""" reinstalling {pkg}.""" )
if not ops[op](version.parse(A__ ) ,version.parse(A__ ) ):
raise ImportError(
F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}""" )
def snake_case ( A__ ,A__ = None ):
UpperCAmelCase_ : int = F"""\n{hint}""" if hint is not None else ""
# non-versioned check
if re.match(r"^[\w_\-\d]+$" ,A__ ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = requirement, None, None
else:
UpperCAmelCase_ : Optional[int] = re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)" ,A__ )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
F""" got {requirement}""" )
UpperCAmelCase_ , UpperCAmelCase_ : List[str] = match[0]
UpperCAmelCase_ : Optional[Any] = want_full.split("," ) # there could be multiple requirements
UpperCAmelCase_ : int = {}
for w in want_range:
UpperCAmelCase_ : str = re.findall(r"^([\s!=<>]{1,2})(.+)" ,A__ )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
F""" but got {requirement}""" )
UpperCAmelCase_ , UpperCAmelCase_ : Any = match[0]
UpperCAmelCase_ : Union[str, Any] = want_ver
if op not in ops:
raise ValueError(F"""{requirement}: need one of {list(ops.keys() )}, but got {op}""" )
# special case
if pkg == "python":
UpperCAmelCase_ : List[Any] = ".".join([str(A__ ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(A__ ,A__ ,A__ ,A__ ,A__ ,A__ )
return
# check if any version is installed
try:
UpperCAmelCase_ : str = importlib.metadata.version(A__ )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
F"""The '{requirement}' distribution was not found and is required by this application. {hint}""" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(A__ ,A__ ,A__ ,A__ ,A__ ,A__ )
def snake_case ( A__ ):
UpperCAmelCase_ : Any = "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
return require_version(A__ ,A__ )
| 95 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Tuple = {
"""google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
"""google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
"""google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}
class A_ ( a_ ):
_SCREAMING_SNAKE_CASE = """owlvit_text_model"""
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any]=4_94_08 , __SCREAMING_SNAKE_CASE : List[Any]=5_12 , __SCREAMING_SNAKE_CASE : Optional[Any]=20_48 , __SCREAMING_SNAKE_CASE : Optional[int]=12 , __SCREAMING_SNAKE_CASE : Tuple=8 , __SCREAMING_SNAKE_CASE : Optional[Any]=16 , __SCREAMING_SNAKE_CASE : Tuple="quick_gelu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=1E-5 , __SCREAMING_SNAKE_CASE : Any=0.0 , __SCREAMING_SNAKE_CASE : List[str]=0.02 , __SCREAMING_SNAKE_CASE : int=1.0 , __SCREAMING_SNAKE_CASE : int=0 , __SCREAMING_SNAKE_CASE : Optional[Any]=4_94_06 , __SCREAMING_SNAKE_CASE : Optional[Any]=4_94_07 , **__SCREAMING_SNAKE_CASE : Union[str, Any] , ):
super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
__a = vocab_size
__a = hidden_size
__a = intermediate_size
__a = num_hidden_layers
__a = num_attention_heads
__a = max_position_embeddings
__a = hidden_act
__a = layer_norm_eps
__a = attention_dropout
__a = initializer_range
__a = initializer_factor
@classmethod
def _UpperCAmelCase ( cls : int , __SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , **__SCREAMING_SNAKE_CASE : Optional[int] ):
cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE )
__a , __a = cls.get_config_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
__a = config_dict["text_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
class A_ ( a_ ):
_SCREAMING_SNAKE_CASE = """owlvit_vision_model"""
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any]=7_68 , __SCREAMING_SNAKE_CASE : Any=30_72 , __SCREAMING_SNAKE_CASE : str=12 , __SCREAMING_SNAKE_CASE : int=12 , __SCREAMING_SNAKE_CASE : str=3 , __SCREAMING_SNAKE_CASE : Optional[Any]=7_68 , __SCREAMING_SNAKE_CASE : List[Any]=32 , __SCREAMING_SNAKE_CASE : Dict="quick_gelu" , __SCREAMING_SNAKE_CASE : List[Any]=1E-5 , __SCREAMING_SNAKE_CASE : int=0.0 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.02 , __SCREAMING_SNAKE_CASE : Dict=1.0 , **__SCREAMING_SNAKE_CASE : Optional[int] , ):
super().__init__(**__SCREAMING_SNAKE_CASE )
__a = hidden_size
__a = intermediate_size
__a = num_hidden_layers
__a = num_attention_heads
__a = num_channels
__a = image_size
__a = patch_size
__a = hidden_act
__a = layer_norm_eps
__a = attention_dropout
__a = initializer_range
__a = initializer_factor
@classmethod
def _UpperCAmelCase ( cls : List[str] , __SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , **__SCREAMING_SNAKE_CASE : Tuple ):
cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE )
__a , __a = cls.get_config_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("model_type" ) == "owlvit":
__a = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
class A_ ( a_ ):
_SCREAMING_SNAKE_CASE = """owlvit"""
_SCREAMING_SNAKE_CASE = True
def __init__( self : Dict , __SCREAMING_SNAKE_CASE : Optional[int]=None , __SCREAMING_SNAKE_CASE : Tuple=None , __SCREAMING_SNAKE_CASE : str=5_12 , __SCREAMING_SNAKE_CASE : List[Any]=2.65_92 , __SCREAMING_SNAKE_CASE : Dict=True , **__SCREAMING_SNAKE_CASE : List[Any] , ):
super().__init__(**__SCREAMING_SNAKE_CASE )
if text_config is None:
__a = {}
logger.info("text_config is None. Initializing the OwlViTTextConfig with default values." )
if vision_config is None:
__a = {}
logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values." )
__a = OwlViTTextConfig(**__SCREAMING_SNAKE_CASE )
__a = OwlViTVisionConfig(**__SCREAMING_SNAKE_CASE )
__a = projection_dim
__a = logit_scale_init_value
__a = return_dict
__a = 1.0
@classmethod
def _UpperCAmelCase ( cls : Tuple , __SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , **__SCREAMING_SNAKE_CASE : List[str] ):
cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE )
__a , __a = cls.get_config_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@classmethod
def _UpperCAmelCase ( cls : List[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Optional[Any] ):
__a = {}
__a = text_config
__a = vision_config
return cls.from_dict(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : Optional[Any] ):
__a = copy.deepcopy(self.__dict__ )
__a = self.text_config.to_dict()
__a = self.vision_config.to_dict()
__a = self.__class__.model_type
return output
class A_ ( a_ ):
@property
def _UpperCAmelCase ( self : Any ):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("attention_mask", {0: "batch", 1: "sequence"}),
] )
@property
def _UpperCAmelCase ( self : Union[str, Any] ):
return OrderedDict(
[
("logits_per_image", {0: "batch"}),
("logits_per_text", {0: "batch"}),
("text_embeds", {0: "batch"}),
("image_embeds", {0: "batch"}),
] )
@property
def _UpperCAmelCase ( self : str ):
return 1E-4
def _UpperCAmelCase ( self : Any , __SCREAMING_SNAKE_CASE : "ProcessorMixin" , __SCREAMING_SNAKE_CASE : int = -1 , __SCREAMING_SNAKE_CASE : int = -1 , __SCREAMING_SNAKE_CASE : Optional["TensorType"] = None , ):
__a = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__SCREAMING_SNAKE_CASE , seq_length=__SCREAMING_SNAKE_CASE , framework=__SCREAMING_SNAKE_CASE )
__a = super().generate_dummy_inputs(
processor.image_processor , batch_size=__SCREAMING_SNAKE_CASE , framework=__SCREAMING_SNAKE_CASE )
return {**text_input_dict, **image_input_dict}
@property
def _UpperCAmelCase ( self : Optional[int] ):
return 14
| 197 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase__ ( _A , _A , _A , unittest.TestCase ):
lowerCAmelCase_ = StableDiffusionInstructPixaPixPipeline
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
lowerCAmelCase_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowerCAmelCase_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase_ ( self : Any ):
torch.manual_seed(0 )
_lowerCamelCase : Optional[int] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4),layers_per_block=2,sample_size=3_2,in_channels=8,out_channels=4,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),cross_attention_dim=3_2,)
_lowerCamelCase : Union[str, Any] = PNDMScheduler(skip_prk_steps=__A )
torch.manual_seed(0 )
_lowerCamelCase : Optional[Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],latent_channels=4,)
torch.manual_seed(0 )
_lowerCamelCase : Optional[int] = CLIPTextConfig(
bos_token_id=0,eos_token_id=2,hidden_size=3_2,intermediate_size=3_7,layer_norm_eps=1e-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,)
_lowerCamelCase : Optional[Any] = CLIPTextModel(__A )
_lowerCamelCase : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_lowerCamelCase : Optional[Any] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def lowerCamelCase_ ( self : Dict,__A : int,__A : Any=0 ):
_lowerCamelCase : Tuple = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A ) ).to(__A )
_lowerCamelCase : Tuple = image.cpu().permute(0,2,3,1 )[0]
_lowerCamelCase : Union[str, Any] = Image.fromarray(np.uinta(__A ) ).convert("RGB" )
if str(__A ).startswith("mps" ):
_lowerCamelCase : Dict = torch.manual_seed(__A )
else:
_lowerCamelCase : List[Any] = torch.Generator(device=__A ).manual_seed(__A )
_lowerCamelCase : Tuple = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"image_guidance_scale": 1,
"output_type": "numpy",
}
return inputs
def lowerCamelCase_ ( self : Optional[Any] ):
_lowerCamelCase : List[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Union[str, Any] = self.get_dummy_components()
_lowerCamelCase : List[Any] = StableDiffusionInstructPixaPixPipeline(**__A )
_lowerCamelCase : int = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Dict = self.get_dummy_inputs(__A )
_lowerCamelCase : Optional[Any] = sd_pipe(**__A ).images
_lowerCamelCase : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_lowerCamelCase : Union[str, Any] = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase : Tuple = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Any = self.get_dummy_components()
_lowerCamelCase : List[Any] = StableDiffusionInstructPixaPixPipeline(**__A )
_lowerCamelCase : Optional[Any] = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Optional[int] = self.get_dummy_inputs(__A )
_lowerCamelCase : List[str] = "french fries"
_lowerCamelCase : int = sd_pipe(**__A,negative_prompt=__A )
_lowerCamelCase : Optional[int] = output.images
_lowerCamelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_lowerCamelCase : Optional[int] = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Optional[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : List[Any] = self.get_dummy_components()
_lowerCamelCase : Optional[int] = StableDiffusionInstructPixaPixPipeline(**__A )
_lowerCamelCase : List[Any] = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Tuple = self.get_dummy_inputs(__A )
_lowerCamelCase : int = [inputs["prompt"]] * 2
_lowerCamelCase : Optional[Any] = np.array(inputs["image"] ).astype(np.floataa ) / 255.0
_lowerCamelCase : Union[str, Any] = torch.from_numpy(__A ).unsqueeze(0 ).to(__A )
_lowerCamelCase : Tuple = image / 2 + 0.5
_lowerCamelCase : Tuple = image.permute(0,3,1,2 )
_lowerCamelCase : Dict = image.repeat(2,1,1,1 )
_lowerCamelCase : List[Any] = sd_pipe(**__A ).images
_lowerCamelCase : Tuple = image[-1, -3:, -3:, -1]
assert image.shape == (2, 3_2, 3_2, 3)
_lowerCamelCase : str = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : int = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase : Dict = self.get_dummy_components()
_lowerCamelCase : Any = EulerAncestralDiscreteScheduler(
beta_start=0.00085,beta_end=0.012,beta_schedule="scaled_linear" )
_lowerCamelCase : Tuple = StableDiffusionInstructPixaPixPipeline(**__A )
_lowerCamelCase : str = sd_pipe.to(__A )
sd_pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Dict = self.get_dummy_inputs(__A )
_lowerCamelCase : Dict = sd_pipe(**__A ).images
_lowerCamelCase : int = image[0, -3:, -3:, -1]
_lowerCamelCase : Optional[int] = [round(__A,4 ) for x in image_slice.flatten().tolist()]
print(",".join([str(__A ) for x in slice] ) )
assert image.shape == (1, 3_2, 3_2, 3)
_lowerCamelCase : str = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def lowerCamelCase_ ( self : Union[str, Any] ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : Optional[Any] = self.get_dummy_components()
_lowerCamelCase : List[Any] = StableDiffusionInstructPixaPixPipeline(**__A )
_lowerCamelCase : List[Any] = VaeImageProcessor(do_resize=__A,do_normalize=__A )
_lowerCamelCase : Any = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
_lowerCamelCase : Union[str, Any] = pipe(**self.get_dummy_inputs_by_type(__A,input_image_type="pt" ) )[0]
_lowerCamelCase : Dict = components["vae"]
_lowerCamelCase : Optional[Any] = self.get_dummy_inputs_by_type(__A,input_image_type="pt" )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
_lowerCamelCase : List[str] = vae.encode(inputs[image_param] ).latent_dist.mode()
_lowerCamelCase : Dict = pipe(**__A )[0]
_lowerCamelCase : str = np.abs(out - out_latents_inputs ).max()
self.assertLess(__A,1e-4,"passing latents as image input generate different result from passing image" )
@slow
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
def lowerCamelCase_ ( self : Dict ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self : int,__A : Union[str, Any]=0 ):
_lowerCamelCase : List[Any] = torch.manual_seed(__A )
_lowerCamelCase : Union[str, Any] = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg" )
_lowerCamelCase : List[Any] = {
"prompt": "turn him into a cyborg",
"image": image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"image_guidance_scale": 1.0,
"output_type": "numpy",
}
return inputs
def lowerCamelCase_ ( self : Tuple ):
_lowerCamelCase : Optional[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix",safety_checker=__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCamelCase : int = self.get_inputs()
_lowerCamelCase : List[str] = pipe(**__A ).images
_lowerCamelCase : Dict = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : Dict = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix",safety_checker=__A )
_lowerCamelCase : List[str] = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCamelCase : Optional[Any] = self.get_inputs()
_lowerCamelCase : Dict = pipe(**__A ).images
_lowerCamelCase : Any = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : Optional[Any] = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowerCamelCase_ ( self : Dict ):
_lowerCamelCase : str = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix",safety_checker=__A )
_lowerCamelCase : Any = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCamelCase : Tuple = self.get_inputs()
_lowerCamelCase : str = pipe(**__A ).images
_lowerCamelCase : Union[str, Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase : List[Any] = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : str = 0
def callback_fn(__A : List[Any],__A : Optional[Any],__A : Dict ) -> None:
_lowerCamelCase : Union[str, Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
_lowerCamelCase : Optional[int] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
_lowerCamelCase : Any = latents[0, -3:, -3:, -1]
_lowerCamelCase : Optional[Any] = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
_lowerCamelCase : Tuple = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
_lowerCamelCase : List[str] = latents[0, -3:, -3:, -1]
_lowerCamelCase : Tuple = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
_lowerCamelCase : Any = False
_lowerCamelCase : Tuple = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix",safety_checker=__A,torch_dtype=torch.floataa )
_lowerCamelCase : Optional[Any] = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCamelCase : Dict = self.get_inputs()
pipe(**__A,callback=__A,callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def lowerCamelCase_ ( self : Optional[int] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowerCamelCase : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix",safety_checker=__A,torch_dtype=torch.floataa )
_lowerCamelCase : int = pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_lowerCamelCase : Any = self.get_inputs()
_lowerCamelCase : int = pipe(**__A )
_lowerCamelCase : Dict = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 1_0**9
def lowerCamelCase_ ( self : List[str] ):
_lowerCamelCase : Any = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
_lowerCamelCase : Optional[int] = inputs["image"].resize((5_0_4, 5_0_4) )
_lowerCamelCase : List[Any] = "timbrooks/instruct-pix2pix"
_lowerCamelCase : List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
__A,safety_checker=__A,)
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
pipe.enable_attention_slicing()
_lowerCamelCase : Optional[int] = pipe(**__A )
_lowerCamelCase : Any = output.images[0]
_lowerCamelCase : Union[str, Any] = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 5_0_4, 3)
_lowerCamelCase : Optional[int] = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 712 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : Any = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
UpperCAmelCase_ : List[str] = {
'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
'tokenizer_config_file': {
'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
},
}
UpperCAmelCase_ : List[Any] = {'facebook/blenderbot-3B': 128}
class UpperCAmelCase__ ( A ):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = ['input_ids', 'attention_mask']
lowerCAmelCase_ = BlenderbotTokenizer
def __init__( self : Dict,__A : Optional[Any]=None,__A : List[str]=None,__A : Optional[Any]=None,__A : List[Any]="replace",__A : List[Any]="<s>",__A : str="</s>",__A : List[str]="</s>",__A : List[Any]="<s>",__A : Union[str, Any]="<unk>",__A : Optional[Any]="<pad>",__A : Dict="<mask>",__A : Any=False,__A : Tuple=True,**__A : Dict,):
super().__init__(
__A,__A,tokenizer_file=__A,errors=__A,bos_token=__A,eos_token=__A,sep_token=__A,cls_token=__A,unk_token=__A,pad_token=__A,mask_token=__A,add_prefix_space=__A,trim_offsets=__A,**__A,)
_lowerCamelCase : Tuple = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : Optional[Any] = getattr(__A,pre_tok_state.pop("type" ) )
_lowerCamelCase : List[Any] = add_prefix_space
_lowerCamelCase : Optional[Any] = pre_tok_class(**__A )
_lowerCamelCase : Any = add_prefix_space
_lowerCamelCase : Any = "post_processor"
_lowerCamelCase : Optional[Any] = getattr(self.backend_tokenizer,__A,__A )
if tokenizer_component_instance:
_lowerCamelCase : Tuple = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
_lowerCamelCase : List[Any] = tuple(state["sep"] )
if "cls" in state:
_lowerCamelCase : Optional[int] = tuple(state["cls"] )
_lowerCamelCase : List[str] = False
if state.get("add_prefix_space",__A ) != add_prefix_space:
_lowerCamelCase : List[str] = add_prefix_space
_lowerCamelCase : int = True
if state.get("trim_offsets",__A ) != trim_offsets:
_lowerCamelCase : List[str] = trim_offsets
_lowerCamelCase : Dict = True
if changes_to_apply:
_lowerCamelCase : Tuple = getattr(__A,state.pop("type" ) )
_lowerCamelCase : Union[str, Any] = component_class(**__A )
setattr(self.backend_tokenizer,__A,__A )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def lowerCamelCase_ ( self : Optional[int] ):
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def lowerCamelCase_ ( self : Optional[Any],__A : List[Any] ):
_lowerCamelCase : Any = AddedToken(__A,lstrip=__A,rstrip=__A ) if isinstance(__A,__A ) else value
_lowerCamelCase : List[str] = value
def lowerCamelCase_ ( self : Optional[int],*__A : Optional[Any],**__A : List[str] ):
_lowerCamelCase : int = kwargs.get("is_split_into_words",__A )
assert self.add_prefix_space or not is_split_into_words, (
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*__A,**__A )
def lowerCamelCase_ ( self : str,*__A : Union[str, Any],**__A : List[Any] ):
_lowerCamelCase : Optional[int] = kwargs.get("is_split_into_words",__A )
assert self.add_prefix_space or not is_split_into_words, (
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._encode_plus(*__A,**__A )
def lowerCamelCase_ ( self : str,__A : str,__A : Optional[str] = None ):
_lowerCamelCase : Optional[int] = self._tokenizer.model.save(__A,name=__A )
return tuple(__A )
def lowerCamelCase_ ( self : Optional[int],__A : List[int],__A : Optional[List[int]] = None ):
_lowerCamelCase : int = [self.sep_token_id]
_lowerCamelCase : 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 lowerCamelCase_ ( self : Dict,__A : List[int],__A : Optional[List[int]] = None ):
return token_ids_a + [self.eos_token_id]
def lowerCamelCase_ ( self : List[str],__A : "Conversation" ):
_lowerCamelCase : Dict = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text )
else:
# Generated responses should contain them already.
inputs.append(__A )
_lowerCamelCase : List[Any] = " ".join(__A )
_lowerCamelCase : List[str] = self.encode(__A )
if len(__A ) > self.model_max_length:
_lowerCamelCase : Tuple = input_ids[-self.model_max_length :]
logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.' )
return input_ids
| 11 | 0 |
"""simple docstring"""
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase ( _UpperCamelCase : str ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : List[Any] = tmp_path / """file.csv"""
__UpperCAmelCase : Dict = textwrap.dedent(
"""\
header1,header2
1,2
10,20
""" )
with open(_UpperCamelCase , """w""" ) as f:
f.write(_UpperCamelCase )
return str(_UpperCamelCase )
@pytest.fixture
def lowerCamelCase ( _UpperCamelCase : Dict ) -> str:
'''simple docstring'''
__UpperCAmelCase : int = tmp_path / """malformed_file.csv"""
__UpperCAmelCase : Dict = textwrap.dedent(
"""\
header1,header2
1,2
10,20,
""" )
with open(_UpperCamelCase , """w""" ) as f:
f.write(_UpperCamelCase )
return str(_UpperCamelCase )
@pytest.fixture
def lowerCamelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : List[str] ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase : Dict = tmp_path / """csv_with_image.csv"""
__UpperCAmelCase : List[Any] = textwrap.dedent(
f'''\
image
{image_file}
''' )
with open(_UpperCamelCase , """w""" ) as f:
f.write(_UpperCamelCase )
return str(_UpperCamelCase )
@pytest.fixture
def lowerCamelCase ( _UpperCamelCase : str ) -> str:
'''simple docstring'''
__UpperCAmelCase : Optional[int] = tmp_path / """csv_with_label.csv"""
__UpperCAmelCase : List[str] = textwrap.dedent(
"""\
label
good
bad
good
""" )
with open(_UpperCamelCase , """w""" ) as f:
f.write(_UpperCamelCase )
return str(_UpperCamelCase )
@pytest.fixture
def lowerCamelCase ( _UpperCamelCase : Optional[int] ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : List[str] = tmp_path / """csv_with_int_list.csv"""
__UpperCAmelCase : Optional[Any] = textwrap.dedent(
"""\
int_list
1 2 3
4 5 6
7 8 9
""" )
with open(_UpperCamelCase , """w""" ) as f:
f.write(_UpperCamelCase )
return str(_UpperCamelCase )
def lowerCamelCase ( _UpperCamelCase : Optional[Any] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : List[str] ) -> str:
'''simple docstring'''
__UpperCAmelCase : List[Any] = Csv()
__UpperCAmelCase : str = csv._generate_tables([[csv_file, malformed_csv_file]] )
with pytest.raises(_UpperCamelCase , match="""Error tokenizing data""" ):
for _ in generator:
pass
assert any(
record.levelname == """ERROR"""
and """Failed to read file""" in record.message
and os.path.basename(_UpperCamelCase ) in record.message
for record in caplog.records )
@require_pil
def lowerCamelCase ( _UpperCamelCase : int ) -> Optional[int]:
'''simple docstring'''
with open(_UpperCamelCase , encoding="""utf-8""" ) as f:
__UpperCAmelCase : Optional[int] = f.read().splitlines()[1]
__UpperCAmelCase : Tuple = Csv(encoding="""utf-8""" , features=Features({"""image""": Image()} ) )
__UpperCAmelCase : List[str] = csv._generate_tables([[csv_file_with_image]] )
__UpperCAmelCase : Union[str, Any] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""image""" ).type == Image()()
__UpperCAmelCase : str = pa_table.to_pydict()["""image"""]
assert generated_content == [{"path": image_file, "bytes": None}]
def lowerCamelCase ( _UpperCamelCase : Optional[Any] ) -> int:
'''simple docstring'''
with open(_UpperCamelCase , encoding="""utf-8""" ) as f:
__UpperCAmelCase : Union[str, Any] = f.read().splitlines()[1:]
__UpperCAmelCase : Any = Csv(encoding="""utf-8""" , features=Features({"""label""": ClassLabel(names=["""good""", """bad"""] )} ) )
__UpperCAmelCase : Dict = csv._generate_tables([[csv_file_with_label]] )
__UpperCAmelCase : Optional[Any] = pa.concat_tables([table for _, table in generator] )
assert pa_table.schema.field("""label""" ).type == ClassLabel(names=["""good""", """bad"""] )()
__UpperCAmelCase : Dict = pa_table.to_pydict()["""label"""]
assert generated_content == [ClassLabel(names=["""good""", """bad"""] ).straint(_UpperCamelCase ) for label in labels]
def lowerCamelCase ( _UpperCamelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Tuple = Csv(encoding="""utf-8""" , sep=""",""" , converters={"""int_list""": lambda _UpperCamelCase : [int(_UpperCamelCase ) for i in x.split()]} )
__UpperCAmelCase : str = csv._generate_tables([[csv_file_with_int_list]] )
__UpperCAmelCase : int = pa.concat_tables([table for _, table in generator] )
assert pa.types.is_list(pa_table.schema.field("""int_list""" ).type )
__UpperCAmelCase : Tuple = pa_table.to_pydict()["""int_list"""]
assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
| 139 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
UpperCAmelCase : str = logging.get_logger(__name__)
UpperCAmelCase : Union[str, Any] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase : Tuple = {
'vocab_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
},
'merges_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
},
'tokenizer_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
},
}
UpperCAmelCase : List[Any] = {
'facebook/bart-base': 1024,
'facebook/bart-large': 1024,
'facebook/bart-large-mnli': 1024,
'facebook/bart-large-cnn': 1024,
'facebook/bart-large-xsum': 1024,
'yjernite/bart_eli5': 1024,
}
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = VOCAB_FILES_NAMES
__a = PRETRAINED_VOCAB_FILES_MAP
__a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a = ["""input_ids""", """attention_mask"""]
__a = BartTokenizer
def __init__( self : int , UpperCamelCase : List[Any]=None , UpperCamelCase : Optional[int]=None , UpperCamelCase : List[str]=None , UpperCamelCase : Tuple="replace" , UpperCamelCase : Optional[int]="<s>" , UpperCamelCase : str="</s>" , UpperCamelCase : str="</s>" , UpperCamelCase : Optional[Any]="<s>" , UpperCamelCase : Optional[int]="<unk>" , UpperCamelCase : Dict="<pad>" , UpperCamelCase : Any="<mask>" , UpperCamelCase : Optional[Any]=False , UpperCamelCase : Tuple=True , **UpperCamelCase : Any , ):
'''simple docstring'''
super().__init__(
UpperCamelCase , UpperCamelCase , tokenizer_file=UpperCamelCase , errors=UpperCamelCase , bos_token=UpperCamelCase , eos_token=UpperCamelCase , sep_token=UpperCamelCase , cls_token=UpperCamelCase , unk_token=UpperCamelCase , pad_token=UpperCamelCase , mask_token=UpperCamelCase , add_prefix_space=UpperCamelCase , trim_offsets=UpperCamelCase , **UpperCamelCase , )
__UpperCAmelCase : Dict = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , UpperCamelCase ) != add_prefix_space:
__UpperCAmelCase : List[str] = getattr(UpperCamelCase , pre_tok_state.pop("""type""" ) )
__UpperCAmelCase : int = add_prefix_space
__UpperCAmelCase : List[Any] = pre_tok_class(**UpperCamelCase )
__UpperCAmelCase : Optional[int] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__UpperCAmelCase : Union[str, Any] = """post_processor"""
__UpperCAmelCase : Union[str, Any] = getattr(self.backend_tokenizer , UpperCamelCase , UpperCamelCase )
if tokenizer_component_instance:
__UpperCAmelCase : List[Any] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__UpperCAmelCase : int = tuple(state["""sep"""] )
if "cls" in state:
__UpperCAmelCase : Optional[int] = tuple(state["""cls"""] )
__UpperCAmelCase : int = False
if state.get("""add_prefix_space""" , UpperCamelCase ) != add_prefix_space:
__UpperCAmelCase : Dict = add_prefix_space
__UpperCAmelCase : Optional[int] = True
if state.get("""trim_offsets""" , UpperCamelCase ) != trim_offsets:
__UpperCAmelCase : str = trim_offsets
__UpperCAmelCase : int = True
if changes_to_apply:
__UpperCAmelCase : List[str] = getattr(UpperCamelCase , state.pop("""type""" ) )
__UpperCAmelCase : Tuple = component_class(**UpperCamelCase )
setattr(self.backend_tokenizer , UpperCamelCase , UpperCamelCase )
@property
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def lowerCamelCase__ ( self : Union[str, Any] , UpperCamelCase : str ):
'''simple docstring'''
__UpperCAmelCase : Dict = AddedToken(UpperCamelCase , lstrip=UpperCamelCase , rstrip=UpperCamelCase ) if isinstance(UpperCamelCase , UpperCamelCase ) else value
__UpperCAmelCase : Any = value
def lowerCamelCase__ ( self : Optional[Any] , *UpperCamelCase : int , **UpperCamelCase : int ):
'''simple docstring'''
__UpperCAmelCase : str = kwargs.get("""is_split_into_words""" , UpperCamelCase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"""to use it with pretokenized inputs.""" )
return super()._batch_encode_plus(*UpperCamelCase , **UpperCamelCase )
def lowerCamelCase__ ( self : Optional[Any] , *UpperCamelCase : Any , **UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : Any = kwargs.get("""is_split_into_words""" , UpperCamelCase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"""to use it with pretokenized inputs.""" )
return super()._encode_plus(*UpperCamelCase , **UpperCamelCase )
def lowerCamelCase__ ( self : int , UpperCamelCase : str , UpperCamelCase : Optional[str] = None ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = self._tokenizer.model.save(UpperCamelCase , name=UpperCamelCase )
return tuple(UpperCamelCase )
def lowerCamelCase__ ( self : Tuple , UpperCamelCase : int , UpperCamelCase : Dict=None ):
'''simple docstring'''
__UpperCAmelCase : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCamelCase__ ( self : Tuple , UpperCamelCase : List[int] , UpperCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = [self.sep_token_id]
__UpperCAmelCase : 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]
| 139 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPImageProcessor, CLIPProcessor
@require_vision
class snake_case (unittest.TestCase ):
def _a ( self ) -> Union[str, Any]:
lowercase__ = tempfile.mkdtemp()
# fmt: off
lowercase__ = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
# fmt: on
lowercase__ = dict(zip(UpperCAmelCase_ ,range(len(UpperCAmelCase_ ) ) ) )
lowercase__ = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
lowercase__ = {"unk_token": "<unk>"}
lowercase__ = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
lowercase__ = 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(UpperCAmelCase_ ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCAmelCase_ ) )
lowercase__ = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
"image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
lowercase__ = os.path.join(self.tmpdirname ,UpperCAmelCase_ )
with open(self.image_processor_file ,"w" ,encoding="utf-8" ) as fp:
json.dump(UpperCAmelCase_ ,UpperCAmelCase_ )
def _a ( self ,**UpperCAmelCase_ ) -> Optional[Any]:
return CLIPTokenizer.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def _a ( self ,**UpperCAmelCase_ ) -> Union[str, Any]:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def _a ( self ,**UpperCAmelCase_ ) -> str:
return CLIPImageProcessor.from_pretrained(self.tmpdirname ,**UpperCAmelCase_ )
def _a ( self ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Any:
lowercase__ = [np.random.randint(255 ,size=(3, 30, 400) ,dtype=np.uinta )]
lowercase__ = [Image.fromarray(np.moveaxis(UpperCAmelCase_ ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
lowercase__ = CLIPProcessor.from_pretrained(self.tmpdirname ,use_fast=UpperCAmelCase_ )
lowercase__ = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
lowercase__ = CLIPProcessor.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 ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.tokenizer ,UpperCAmelCase_ )
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 ,UpperCAmelCase_ )
self.assertIsInstance(processor_fast.image_processor ,UpperCAmelCase_ )
def _a ( self ) -> List[Any]:
lowercase__ = CLIPProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="(BOS)" ,eos_token="(EOS)" )
lowercase__ = self.get_image_processor(do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
lowercase__ = CLIPProcessor.from_pretrained(
self.tmpdirname ,bos_token="(BOS)" ,eos_token="(EOS)" ,do_normalize=UpperCAmelCase_ ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,UpperCAmelCase_ )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,UpperCAmelCase_ )
def _a ( self ) -> Dict:
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(UpperCAmelCase_ ,return_tensors="np" )
lowercase__ = processor(images=UpperCAmelCase_ ,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 ) -> Dict:
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
lowercase__ = "lower newer"
lowercase__ = processor(text=UpperCAmelCase_ )
lowercase__ = tokenizer(UpperCAmelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
lowercase__ = "lower newer"
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,["input_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase_ ):
processor()
def _a ( self ) -> List[Any]:
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.batch_decode(UpperCAmelCase_ )
lowercase__ = tokenizer.batch_decode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ ,UpperCAmelCase_ )
def _a ( self ) -> Dict:
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = CLIPProcessor(tokenizer=UpperCAmelCase_ ,image_processor=UpperCAmelCase_ )
lowercase__ = "lower newer"
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=UpperCAmelCase_ ,images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) ,processor.model_input_names )
| 539 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class snake_case (UpperCamelCase , unittest.TestCase ):
lowerCAmelCase__ :int = TextToVideoSDPipeline
lowerCAmelCase__ :Union[str, Any] = TEXT_TO_IMAGE_PARAMS
lowerCAmelCase__ :List[str] = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
lowerCAmelCase__ :Optional[Any] = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def _a ( self ) -> Optional[int]:
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D") ,up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D") ,cross_attention_dim=32 ,attention_head_dim=4 ,)
lowercase__ = DDIMScheduler(
beta_start=0.0_00_85 ,beta_end=0.0_12 ,beta_schedule="scaled_linear" ,clip_sample=UpperCAmelCase_ ,set_alpha_to_one=UpperCAmelCase_ ,)
torch.manual_seed(0 )
lowercase__ = 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 ,sample_size=128 ,)
torch.manual_seed(0 )
lowercase__ = 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 ,hidden_act="gelu" ,projection_dim=512 ,)
lowercase__ = CLIPTextModel(UpperCAmelCase_ )
lowercase__ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowercase__ = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def _a ( self ,UpperCAmelCase_ ,UpperCAmelCase_=0 ) -> int:
if str(UpperCAmelCase_ ).startswith("mps" ):
lowercase__ = torch.manual_seed(UpperCAmelCase_ )
else:
lowercase__ = torch.Generator(device=UpperCAmelCase_ ).manual_seed(UpperCAmelCase_ )
lowercase__ = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "pt",
}
return inputs
def _a ( self ) -> Tuple:
lowercase__ = "cpu" # ensure determinism for the device-dependent torch.Generator
lowercase__ = self.get_dummy_components()
lowercase__ = TextToVideoSDPipeline(**UpperCAmelCase_ )
lowercase__ = sd_pipe.to(UpperCAmelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_ )
lowercase__ = self.get_dummy_inputs(UpperCAmelCase_ )
lowercase__ = "np"
lowercase__ = sd_pipe(**UpperCAmelCase_ ).frames
lowercase__ = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
lowercase__ = np.array([1_58.0, 1_60.0, 1_53.0, 1_25.0, 1_00.0, 1_21.0, 1_11.0, 93.0, 1_13.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _a ( self ) -> List[Any]:
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=UpperCAmelCase_ ,expected_max_diff=3E-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() ,reason="XFormers attention is only available with CUDA and `xformers` installed" ,)
def _a ( self ) -> Dict:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=UpperCAmelCase_ ,expected_max_diff=1E-2 )
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def _a ( self ) -> Union[str, Any]:
pass
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def _a ( self ) -> Union[str, Any]:
pass
@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline." )
def _a ( self ) -> Union[str, Any]:
pass
def _a ( self ) -> str:
return super().test_progress_bar()
@slow
@skip_mps
class snake_case (unittest.TestCase ):
def _a ( self ) -> Optional[int]:
lowercase__ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy" )
lowercase__ = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
lowercase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
lowercase__ = pipe.to("cuda" )
lowercase__ = "Spiderman is surfing"
lowercase__ = torch.Generator(device="cpu" ).manual_seed(0 )
lowercase__ = pipe(UpperCAmelCase_ ,generator=UpperCAmelCase_ ,num_inference_steps=25 ,output_type="pt" ).frames
lowercase__ = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
def _a ( self ) -> Optional[int]:
lowercase__ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy" )
lowercase__ = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b" )
lowercase__ = pipe.to("cuda" )
lowercase__ = "Spiderman is surfing"
lowercase__ = torch.Generator(device="cpu" ).manual_seed(0 )
lowercase__ = pipe(UpperCAmelCase_ ,generator=UpperCAmelCase_ ,num_inference_steps=2 ,output_type="pt" ).frames
lowercase__ = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5E-2
| 539 | 1 |
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