code: 
  
  
  
  import logging
  import pathlib
  import re
  import sys
  from dataclasses import dataclass, field
  from typing import Any, Callable, Dict, List, Optional, Set, Union
  
  import datasets
  import librosa
  import numpy as np
  import torch
  from lang_trans import arabic
  from packaging import version
  from torch import nn
  
  from transformers import (
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaForCTC,
      WavaVecaProcessor,
      is_apex_available,
      trainer_utils,
  )
  
  
  if is_apex_available():
   from apex import amp
  
  if version.parse(version.parse(torch.__version__).base_version) >= version.parse('''1.6'''):
   __A       					=		True
   from torch.cuda.amp import autocast
  
  
  __A       					=		logging.getLogger(__name__)
  
  
  
  
  
  
  @dataclass
  class 	lowercase		:
  
  
  
  
  
  
   """simple docstring"""
  
  
   a__   :      str	     =							field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"})
   a__   :      Optional[str]	     =							field(
       default=__lowerCamelCase				,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,					)
   a__   :      Optional[bool]	     =							field(
       default=__lowerCamelCase				,					metadata={"help": "Whether to freeze the feature extractor layers of the model."})
   a__   :      Optional[bool]	     =							field(
       default=__lowerCamelCase				,					metadata={"help": "Whether to log verbose messages or not."}				,					)
  
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[int]    ,lowerCAmelCase_     :      Dict       )   ->				Tuple:
  
  
  
  
  
  
        '''simple docstring'''
  
  
  
        logging.basicConfig(
            format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""    ,datefmt="""%m/%d/%Y %H:%M:%S"""    ,handlers=[logging.StreamHandler(sys.stdout       )]    ,)
        UpperCAmelCase_=     logging.WARNING
        if model_args.verbose_logging:
              UpperCAmelCase_=     logging.DEBUG
        elif trainer_utils.is_main_process(training_args.local_rank       ):
              UpperCAmelCase_=     logging.INFO
        logger.setLevel(lowerCAmelCase_       )
  
  
  
  
  
  
  @dataclass
  class 	lowercase		:
  
  
  
  
  
  
   """simple docstring"""
  
  
   a__   :      str	     =							field(
       default=__lowerCamelCase				,					metadata={"help": "The name of the dataset to use (via the datasets library)."})
   a__   :      Optional[str]	     =							field(
       default=__lowerCamelCase				,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
   a__   :      Optional[str]	     =							field(
       default="train"				,					metadata={
           "help": "The name of the training data set split to use (via the datasets library). Defaults to \'train\'"
       }				,					)
   a__   :      Optional[str]	     =							field(
       default="validation"				,					metadata={
           "help": (
               "The name of the validation data set split to use (via the datasets library). Defaults to \'validation\'"
           )
       }				,					)
   a__   :      Optional[str]	     =							field(
       default="text"				,					metadata={"help": "Column in the dataset that contains label (target text). Defaults to \'text\'"}				,					)
   a__   :      Optional[str]	     =							field(
       default="file"				,					metadata={"help": "Column in the dataset that contains speech file path. Defaults to \'file\'"}				,					)
   a__   :      Optional[bool]	     =							field(
       default=__lowerCamelCase				,					metadata={"help": "Resample loaded audio to target feature extractor\'s sampling rate or not."}				,					)
   a__   :      Optional[float]	     =							field(
       default=__lowerCamelCase				,					metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."}				,					)
   a__   :      Optional[str]	     =							field(
       default="librispeech"				,					metadata={
           "help": (
               "Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or"
               " \'buckwalter\'."
           )
       }				,					)
   a__   :      bool	     =							field(
       default=__lowerCamelCase				,					metadata={"help": "Overwrite the cached preprocessed datasets or not."})
   a__   :      Optional[int]	     =							field(
       default=__lowerCamelCase				,					metadata={"help": "The number of processes to use for the preprocessing."}				,					)
  
  
  
  
  
  
  @dataclass
  class 	lowercase		:
  
  
  
  
  
  
   """simple docstring"""
  
  
   a__   :      bool	     =							False
   a__   :      Optional[str]	     =							None
   a__   :      Optional[str]	     =							"|"
   a__   :      Optional[Dict[str, str]]	     =							field(default_factory=__lowerCamelCase)
   a__   :      Optional[Set[str]]	     =							field(default_factory=__lowerCamelCase)
   a__   :      Optional[Callable[[str], str]]	     =							None
  
  
  
  
   @classmethod
   def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		str  )    ->				int:
  
  
  
  
  
  
         if name == "librispeech":
               return cls()
         if name == "timit":
               return cls(
                   do_lower_case=__UpperCAmelCase		,						translation_table=str.maketrans({"""-""": """ """}  )		,						)
         if name == "buckwalter":
               UpperCAmelCase_=     {
                   """-""": """ """,  # sometimes used to represent pauses
                   """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
               }
               return cls(
                   vocab_file=pathlib.Path(__UpperCAmelCase  ).parent.joinpath("""vocab/buckwalter.json"""  )		,						word_delimiter_token="""/"""		,						translation_table=str.maketrans(__UpperCAmelCase  )		,						words_to_remove={"""sil"""}		,						untransliterator=arabic.buckwalter.untransliterate		,						)
         raise ValueError(F"""Unsupported orthography: '{name}'."""  )
  
  
  
  
   def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		str  )    ->				str:
  
  
  
  
  
  
         if len(self.translation_table  ) > 0:
               UpperCAmelCase_=     text.translate(self.translation_table  )
         if len(self.words_to_remove  ) == 0:
               UpperCAmelCase_=     """ """.join(text.split()  )  # clean up whitespaces
         else:
               UpperCAmelCase_=     """ """.join(w for w in text.split() if w not in self.words_to_remove  )  # and clean up whilespaces
         return text
  
  
  
  
   def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		ModelArguments  )    ->				WavaVecaProcessor:
         UpperCAmelCase_=     WavaVecaFeatureExtractor.from_pretrained(
             model_args.model_name_or_path		,						cache_dir=model_args.cache_dir  )
         if self.vocab_file:
               UpperCAmelCase_=     WavaVecaCTCTokenizer(
                   self.vocab_file		,						cache_dir=model_args.cache_dir		,						do_lower_case=self.do_lower_case		,						word_delimiter_token=self.word_delimiter_token		,						)
         else:
               UpperCAmelCase_=     WavaVecaCTCTokenizer.from_pretrained(
                   model_args.model_name_or_path		,						cache_dir=model_args.cache_dir		,						do_lower_case=self.do_lower_case		,						word_delimiter_token=self.word_delimiter_token		,						)
         return WavaVecaProcessor(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  
  
  @dataclass
  class 	lowercase		:
  
  
  
  
  
  
   """simple docstring"""
  
  
   a__   :      WavaVecaProcessor
   a__   :      Union[bool, str]	     =							True
   a__   :      Optional[int]	     =							None
   a__   :      Optional[int]	     =							None
   a__   :      Optional[int]	     =							None
   a__   :      Optional[int]	     =							None
  
  
  
  
   def __call__(			self			:		Any		,						__UpperCAmelCase			:		List[Dict[str, Union[List[int], torch.Tensor]]]  )    ->				Dict[str, torch.Tensor]:
         UpperCAmelCase_=     [{"""input_values""": feature["""input_values"""]} for feature in features]
         UpperCAmelCase_=     [{"""input_ids""": feature["""labels"""]} for feature in features]
  
         UpperCAmelCase_=     self.processor.pad(
             __UpperCAmelCase		,						padding=self.padding		,						max_length=self.max_length		,						pad_to_multiple_of=self.pad_to_multiple_of		,						return_tensors="""pt"""		,						)
         UpperCAmelCase_=     self.processor.pad(
             labels=__UpperCAmelCase		,						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
         UpperCAmelCase_=     labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1  )		,						-100  )
  
         UpperCAmelCase_=     labels
  
         return batch
  
  
  
  
  
  
  class 	lowercase		(				__lowerCamelCase):
  
  
  
  
  
  
   """simple docstring"""
  
  
   def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple		,						__UpperCAmelCase			:		nn.Module		,						__UpperCAmelCase			:		Dict[str, Union[torch.Tensor, Any]]  )    ->				torch.Tensor:
         model.train()
         UpperCAmelCase_=     self._prepare_inputs(__UpperCAmelCase  )
  
         if self.use_amp:
               with autocast():
                     UpperCAmelCase_=     self.compute_loss(__UpperCAmelCase		,						__UpperCAmelCase  )
         else:
               UpperCAmelCase_=     self.compute_loss(__UpperCAmelCase		,						__UpperCAmelCase  )
  
         if self.args.n_gpu > 1:
               if model.module.config.ctc_loss_reduction == "mean":
                     UpperCAmelCase_=     loss.mean()
               elif model.module.config.ctc_loss_reduction == "sum":
                     UpperCAmelCase_=     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:
               UpperCAmelCase_=     loss / self.args.gradient_accumulation_steps
  
         if self.use_amp:
               self.scaler.scale(__UpperCAmelCase  ).backward()
         elif self.use_apex:
               with amp.scale_loss(__UpperCAmelCase		,						self.optimizer  ) as scaled_loss:
                     scaled_loss.backward()
         elif self.deepspeed:
               self.deepspeed.backward(__UpperCAmelCase  )
         else:
               loss.backward()
  
         return loss.detach()
  
  
  
  def  __a  (					)   ->				Tuple:
  
  
  
  
  
  
        '''simple docstring'''
  
  
  
        UpperCAmelCase_=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
  
        UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     parser.parse_args_into_dataclasses()
        configure_logger(lowerCAmelCase_    ,lowerCAmelCase_       )
  
        UpperCAmelCase_=     Orthography.from_name(data_args.orthography.lower()       )
        UpperCAmelCase_=     orthography.create_processor(lowerCAmelCase_       )
        UpperCAmelCase_=     WavaVecaForCTC.from_pretrained(
            model_args.model_name_or_path    ,cache_dir=model_args.cache_dir    ,gradient_checkpointing=training_args.gradient_checkpointing    ,vocab_size=len(processor.tokenizer       )    ,)
  
        UpperCAmelCase_=     datasets.load_dataset(
            data_args.dataset_name    ,data_args.dataset_config_name    ,split=data_args.train_split_name       )
        UpperCAmelCase_=     datasets.load_dataset(
            data_args.dataset_name    ,data_args.dataset_config_name    ,split=data_args.validation_split_name       )
  
        UpperCAmelCase_=     datasets.load_metric("""wer"""       )
        UpperCAmelCase_=     processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
        UpperCAmelCase_=     """""".join(t for t in processor.tokenizer.get_vocab().keys() if len(lowerCAmelCase_       ) == 1       )
        UpperCAmelCase_=     re.compile(  # remove characters not in vocabulary
            rF"""[^\s{re.escape(lowerCAmelCase_       )}]"""    ,flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0    ,)
        UpperCAmelCase_=     []
  
        def prepare_example(lowerCAmelCase_     :      List[Any]       ):  # TODO(elgeish) make use of multiprocessing?
              UpperCAmelCase_,     UpperCAmelCase_=     librosa.load(example[data_args.speech_file_column]    ,sr=lowerCAmelCase_       )
              if data_args.max_duration_in_seconds is not None:
                    UpperCAmelCase_=     len(example["""speech"""]       ) / example["""sampling_rate"""]
              # Normalize and clean up text; order matters!
              UpperCAmelCase_=     orthography.preprocess_for_training(example[data_args.target_text_column]       )
              UpperCAmelCase_=     vocabulary_text_cleaner.sub(""""""    ,lowerCAmelCase_       )
              if updated_text != example[data_args.target_text_column]:
                    text_updates.append((example[data_args.target_text_column], updated_text)       )
                    UpperCAmelCase_=     updated_text
              return example
  
        UpperCAmelCase_=     train_dataset.map(lowerCAmelCase_    ,remove_columns=[data_args.speech_file_column]       )
        UpperCAmelCase_=     val_dataset.map(lowerCAmelCase_    ,remove_columns=[data_args.speech_file_column]       )
  
        if data_args.max_duration_in_seconds is not None:
  
              def filter_by_max_duration(lowerCAmelCase_     :      Any       ):
                    return example["duration_in_seconds"] <= data_args.max_duration_in_seconds
  
              UpperCAmelCase_=     len(lowerCAmelCase_       )
              UpperCAmelCase_=     len(lowerCAmelCase_       )
              UpperCAmelCase_=     train_dataset.filter(lowerCAmelCase_    ,remove_columns=["""duration_in_seconds"""]       )
              UpperCAmelCase_=     val_dataset.filter(lowerCAmelCase_    ,remove_columns=["""duration_in_seconds"""]       )
              if len(lowerCAmelCase_       ) > old_train_size:
                    logger.warning(
                        F"""Filtered out {len(lowerCAmelCase_       ) - old_train_size} train example(s) longer than"""
                        F""" {data_args.max_duration_in_seconds} second(s)."""       )
              if len(lowerCAmelCase_       ) > old_val_size:
                    logger.warning(
                        F"""Filtered out {len(lowerCAmelCase_       ) - old_val_size} validation example(s) longer than"""
                        F""" {data_args.max_duration_in_seconds} second(s)."""       )
        logger.info(F"""Split sizes: {len(lowerCAmelCase_       )} train and {len(lowerCAmelCase_       )} validation."""       )
  
        logger.warning(F"""Updated {len(lowerCAmelCase_       )} transcript(s) using '{data_args.orthography}' orthography rules."""       )
        if logger.isEnabledFor(logging.DEBUG       ):
              for original_text, updated_text in text_updates:
                    logger.debug(F"""Updated text: \"{original_text}\" -> \"{updated_text}\""""       )
        UpperCAmelCase_=     None
  
        def prepare_dataset(lowerCAmelCase_     :      str       ):
              # 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}."""
  
              UpperCAmelCase_=     processor(
                  audio=batch["""speech"""]    ,text=batch[data_args.target_text_column]    ,sampling_rate=batch["""sampling_rate"""][0]       )
              batch.update(lowerCAmelCase_       )
              return batch
  
        UpperCAmelCase_=     train_dataset.map(
            lowerCAmelCase_    ,batch_size=training_args.per_device_train_batch_size    ,batched=lowerCAmelCase_    ,num_proc=data_args.preprocessing_num_workers    ,)
        UpperCAmelCase_=     val_dataset.map(
            lowerCAmelCase_    ,batch_size=training_args.per_device_train_batch_size    ,batched=lowerCAmelCase_    ,num_proc=data_args.preprocessing_num_workers    ,)
  
        UpperCAmelCase_=     DataCollatorCTCWithPadding(processor=lowerCAmelCase_    ,padding=lowerCAmelCase_       )
  
        def compute_metrics(lowerCAmelCase_     :      str       ):
              UpperCAmelCase_=     pred.predictions
              UpperCAmelCase_=     np.argmax(lowerCAmelCase_    ,axis=-1       )
  
              UpperCAmelCase_=     processor.tokenizer.pad_token_id
  
              UpperCAmelCase_=     processor.batch_decode(lowerCAmelCase_       )
              # we do not want to group tokens when computing the metrics
              UpperCAmelCase_=     processor.batch_decode(pred.label_ids    ,group_tokens=lowerCAmelCase_       )
              if logger.isEnabledFor(logging.DEBUG       ):
                    for reference, predicted in zip(lowerCAmelCase_    ,lowerCAmelCase_       ):
                          logger.debug(F"""reference: \"{reference}\""""       )
                          logger.debug(F"""predicted: \"{predicted}\""""       )
                          if orthography.untransliterator is not None:
                                logger.debug(F"""reference (untransliterated): \"{orthography.untransliterator(lowerCAmelCase_       )}\""""       )
                                logger.debug(F"""predicted (untransliterated): \"{orthography.untransliterator(lowerCAmelCase_       )}\""""       )
  
              UpperCAmelCase_=     wer_metric.compute(predictions=lowerCAmelCase_    ,references=lowerCAmelCase_       )
  
              return {"wer": wer}
  
        if model_args.freeze_feature_extractor:
              model.freeze_feature_extractor()
  
        UpperCAmelCase_=     CTCTrainer(
            model=lowerCAmelCase_    ,data_collator=lowerCAmelCase_    ,args=lowerCAmelCase_    ,compute_metrics=lowerCAmelCase_    ,train_dataset=lowerCAmelCase_    ,eval_dataset=lowerCAmelCase_    ,tokenizer=processor.feature_extractor    ,)
  
        trainer.train()
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
style_context: 
  
  
  
  import unittest
  
  from transformers import YosoConfig, 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
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  	import torch
  
  	from transformers import (
  	    YosoForMaskedLM,
  	    YosoForMultipleChoice,
  	    YosoForQuestionAnswering,
  	    YosoForSequenceClassification,
  	    YosoForTokenClassification,
  	    YosoModel,
  	)
  	from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Optional[int]=13		,						__UpperCAmelCase			:		Optional[Any]=7		,						__UpperCAmelCase			:		Union[str, Any]=True		,						__UpperCAmelCase			:		Dict=True		,						__UpperCAmelCase			:		int=True		,						__UpperCAmelCase			:		Optional[int]=True		,						__UpperCAmelCase			:		Optional[Any]=99		,						__UpperCAmelCase			:		List[Any]=32		,						__UpperCAmelCase			:		int=5		,						__UpperCAmelCase			:		List[str]=4		,						__UpperCAmelCase			:		Union[str, Any]=37		,						__UpperCAmelCase			:		Any="gelu"		,						__UpperCAmelCase			:		Any=0.1		,						__UpperCAmelCase			:		List[Any]=0.1		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Tuple=16		,						__UpperCAmelCase			:		Any=2		,						__UpperCAmelCase			:		Tuple=0.02		,						__UpperCAmelCase			:		Any=3		,						__UpperCAmelCase			:		Tuple=4		,						__UpperCAmelCase			:		int=None		,						)    ->				str:
  							UpperCAmelCase_=     parent
  							UpperCAmelCase_=     batch_size
  							UpperCAmelCase_=     seq_length
  							UpperCAmelCase_=     is_training
  							UpperCAmelCase_=     use_input_mask
  							UpperCAmelCase_=     use_token_type_ids
  							UpperCAmelCase_=     use_labels
  							UpperCAmelCase_=     vocab_size
  							UpperCAmelCase_=     hidden_size
  							UpperCAmelCase_=     num_hidden_layers
  							UpperCAmelCase_=     num_attention_heads
  							UpperCAmelCase_=     intermediate_size
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     hidden_dropout_prob
  							UpperCAmelCase_=     attention_probs_dropout_prob
  							UpperCAmelCase_=     max_position_embeddings
  							UpperCAmelCase_=     type_vocab_size
  							UpperCAmelCase_=     type_sequence_label_size
  							UpperCAmelCase_=     initializer_range
  							UpperCAmelCase_=     num_labels
  							UpperCAmelCase_=     num_choices
  							UpperCAmelCase_=     scope
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				str:
  							UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size  )
  
  							UpperCAmelCase_=     None
  							if self.use_input_mask:
  													UpperCAmelCase_=     random_attention_mask([self.batch_size, self.seq_length]  )
  
  							UpperCAmelCase_=     None
  							if self.use_token_type_ids:
  													UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.type_vocab_size  )
  
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     None
  							UpperCAmelCase_=     None
  							if self.use_labels:
  													UpperCAmelCase_=     ids_tensor([self.batch_size]		,						self.type_sequence_label_size  )
  													UpperCAmelCase_=     ids_tensor([self.batch_size, self.seq_length]		,						self.num_labels  )
  													UpperCAmelCase_=     ids_tensor([self.batch_size]		,						self.num_choices  )
  
  							UpperCAmelCase_=     self.get_config()
  
  							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
  							return YosoConfig(
  							    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=__UpperCAmelCase		,						initializer_range=self.initializer_range		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     self.get_config()
  							UpperCAmelCase_=     300
  							return config
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				str:
  							(
  							    (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     
  							)=     self.prepare_config_and_inputs()
  
  							UpperCAmelCase_=     True
  							UpperCAmelCase_=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
  							UpperCAmelCase_=     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 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Any  )    ->				List[Any]:
  							UpperCAmelCase_=     YosoModel(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )
  							UpperCAmelCase_=     model(__UpperCAmelCase  )
  							self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Dict		,						)    ->				List[str]:
  							UpperCAmelCase_=     True
  							UpperCAmelCase_=     YosoModel(__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(
  							    __UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						encoder_hidden_states=__UpperCAmelCase		,						encoder_attention_mask=__UpperCAmelCase		,						)
  							UpperCAmelCase_=     model(
  							    __UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						encoder_hidden_states=__UpperCAmelCase		,						)
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase  )
  							self.parent.assertEqual(result.last_hidden_state.shape		,						(self.batch_size, self.seq_length, self.hidden_size)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		str  )    ->				List[Any]:
  							UpperCAmelCase_=     YosoForMaskedLM(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						labels=__UpperCAmelCase  )
  							self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.vocab_size)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Optional[int]  )    ->				List[Any]:
  							UpperCAmelCase_=     YosoForQuestionAnswering(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(
  							    __UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						start_positions=__UpperCAmelCase		,						end_positions=__UpperCAmelCase		,						)
  							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 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[str]  )    ->				str:
  							UpperCAmelCase_=     self.num_labels
  							UpperCAmelCase_=     YosoForSequenceClassification(__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						labels=__UpperCAmelCase  )
  							self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]		,						__UpperCAmelCase			:		Tuple  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     self.num_labels
  							UpperCAmelCase_=     YosoForTokenClassification(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     model(__UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						labels=__UpperCAmelCase  )
  							self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.num_labels)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Any		,						__UpperCAmelCase			:		Optional[int]  )    ->				Any:
  							UpperCAmelCase_=     self.num_choices
  							UpperCAmelCase_=     YosoForMultipleChoice(config=__UpperCAmelCase  )
  							model.to(__UpperCAmelCase  )
  							model.eval()
  							UpperCAmelCase_=     input_ids.unsqueeze(1  ).expand(-1		,						self.num_choices		,						-1  ).contiguous()
  							UpperCAmelCase_=     token_type_ids.unsqueeze(1  ).expand(-1		,						self.num_choices		,						-1  ).contiguous()
  							UpperCAmelCase_=     input_mask.unsqueeze(1  ).expand(-1		,						self.num_choices		,						-1  ).contiguous()
  							UpperCAmelCase_=     model(
  							    __UpperCAmelCase		,						attention_mask=__UpperCAmelCase		,						token_type_ids=__UpperCAmelCase		,						labels=__UpperCAmelCase		,						)
  							self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_choices)  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Any:
  							UpperCAmelCase_=     self.prepare_config_and_inputs()
  							(
  							    (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     (
  							    UpperCAmelCase_
  							),     
  							)=     config_and_inputs
  							UpperCAmelCase_=     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
  							return config, inputs_dict
  
  
  
  
  
  
  @require_torch
  class 	lowercase		(				snake_case__				,					snake_case__				,					unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							(
  	    (
  	        YosoModel,
  	        YosoForMaskedLM,
  	        YosoForMultipleChoice,
  	        YosoForQuestionAnswering,
  	        YosoForSequenceClassification,
  	        YosoForTokenClassification,
  	    )
  	    if is_torch_available()
  	    else ()
  	)
  	a__   :      Optional[int]	     =							False
  	a__   :      Optional[Any]	     =							False
  	a__   :      Dict	     =							False
  
  	a__   :      int	     =							()
  	a__   :      int	     =							(
  	    {
  	        "feature-extraction": YosoModel,
  	        "fill-mask": YosoForMaskedLM,
  	        "question-answering": YosoForQuestionAnswering,
  	        "text-classification": YosoForSequenceClassification,
  	        "token-classification": YosoForTokenClassification,
  	        "zero-shot": YosoForSequenceClassification,
  	    }
  	    if is_torch_available()
  	    else {}
  	)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Optional[int]:
  							UpperCAmelCase_=     YosoModelTester(self  )
  							UpperCAmelCase_=     ConfigTester(self		,						config_class=__UpperCAmelCase		,						hidden_size=37  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Union[str, Any]:
  							self.config_tester.run_common_tests()
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				Dict:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Any:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							for type in ["absolute", "relative_key", "relative_key_query"]:
  													UpperCAmelCase_=     type
  													self.model_tester.create_and_check_model(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				Any:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Optional[int]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Dict:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				int:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Optional[Any]:
  							UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]  )    ->				Optional[Any]:
  							for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  													UpperCAmelCase_=     YosoModel.from_pretrained(__UpperCAmelCase  )
  													self.assertIsNotNone(__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Tuple:
  							return
  
  
  
  
  
  
  @require_torch
  class 	lowercase		(				unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Optional[int]:
  							UpperCAmelCase_=     YosoModel.from_pretrained("""uw-madison/yoso-4096"""  )
  							UpperCAmelCase_=     torch.tensor([[0, 1, 2, 3, 4, 5]]  )
  
  							with torch.no_grad():
  													UpperCAmelCase_=     model(__UpperCAmelCase  )[0]
  
  							UpperCAmelCase_=     torch.Size((1, 6, 768)  )
  							self.assertEqual(output.shape		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     torch.tensor(
  							    [[[-0.0_611, 0.1_242, 0.0_840], [0.0_280, -0.0_048, 0.1_125], [0.0_106, 0.0_226, 0.0_751]]]  )
  
  							self.assertTrue(torch.allclose(output[:, :3, :3]		,						__UpperCAmelCase		,						atol=1E-4  )  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				List[Any]:
  							UpperCAmelCase_=     YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096"""  )
  							UpperCAmelCase_=     torch.tensor([[0, 1, 2, 3, 4, 5]]  )
  
  							with torch.no_grad():
  													UpperCAmelCase_=     model(__UpperCAmelCase  )[0]
  
  							UpperCAmelCase_=     50_265
  
  							UpperCAmelCase_=     torch.Size((1, 6, vocab_size)  )
  							self.assertEqual(output.shape		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     torch.tensor(
  							    [[[-2.1_313, -3.7_285, -2.2_407], [-2.7_047, -3.3_314, -2.6_408], [0.0_629, -2.5_166, -0.3_356]]]  )
  
  							self.assertTrue(torch.allclose(output[:, :3, :3]		,						__UpperCAmelCase		,						atol=1E-4  )  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Tuple:
  							UpperCAmelCase_=     YosoForMaskedLM.from_pretrained("""uw-madison/yoso-4096"""  )
  							UpperCAmelCase_=     torch.arange(4_096  ).unsqueeze(0  )
  
  							with torch.no_grad():
  													UpperCAmelCase_=     model(__UpperCAmelCase  )[0]
  
  							UpperCAmelCase_=     50_265
  
  							UpperCAmelCase_=     torch.Size((1, 4_096, vocab_size)  )
  							self.assertEqual(output.shape		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     torch.tensor(
  							    [[[-2.3_914, -4.3_742, -5.0_956], [-4.0_988, -4.2_384, -7.0_406], [-3.1_427, -3.7_192, -6.6_800]]]  )
  
  							self.assertTrue(torch.allclose(output[:, :3, :3]		,						__UpperCAmelCase		,						atol=1E-4  )  )
  
  
  
  
  