code: 
  
  
  
  
  """simple docstring"""
  
  
  
  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"):
        SCREAMING_SNAKE_CASE       = True
        from torch.cuda.amp import autocast
  
  
  SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)
  
  
  
  
  @dataclass
  class 			UpperCAmelCase_						:
      lowercase__		      =   field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}							)
      lowercase__		      =   field(
          default=A_,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''},     )
      lowercase__		      =   field(
          default=A_,     metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}							)
      lowercase__		      =   field(
          default=A_,     metadata={'''help''': '''Whether to log verbose messages or not.'''},     )
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	List[Any]:
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,				datefmt="%m/%d/%Y %H:%M:%S"    ,				handlers=[logging.StreamHandler(sys.stdout		)]    ,				)
   A__ =       logging.WARNING
   if model_args.verbose_logging:
    A__ =       logging.DEBUG
   elif trainer_utils.is_main_process(training_args.local_rank		):
    A__ =       logging.INFO
   logger.setLevel(lowercase_		)
  
  
  
  
  @dataclass
  class 			UpperCAmelCase_						:
      lowercase__		      =   field(
          default=A_,     metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}							)
      lowercase__		      =   field(
          default=A_,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}							)
      lowercase__		      =   field(
          default='''train''',     metadata={
              '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
          },     )
      lowercase__		      =   field(
          default='''validation''',     metadata={
              '''help''': (
                  '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
              )
          },     )
      lowercase__		      =   field(
          default='''text''',     metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''},     )
      lowercase__		      =   field(
          default='''file''',     metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''},     )
      lowercase__		      =   field(
          default=A_,     metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''},     )
      lowercase__		      =   field(
          default=A_,     metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''},     )
      lowercase__		      =   field(
          default='''librispeech''',     metadata={
              '''help''': (
                  '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
                  ''' \'buckwalter\'.'''
              )
          },     )
      lowercase__		      =   field(
          default=A_,     metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}							)
      lowercase__		      =   field(
          default=A_,     metadata={'''help''': '''The number of processes to use for the preprocessing.'''},     )
  
  
  
  
  
  
  @dataclass
  class 			UpperCAmelCase_						:
      lowercase__		      =   False
      lowercase__		      =   None
      lowercase__		      =   "|"
      lowercase__		      =   field(default_factory=A_							)
      lowercase__		      =   field(default_factory=A_							)
      lowercase__		      =   None
  
  
  
  
      @classmethod
      def 							__magic_name__							(					cls		: Optional[int]			,				snake_case_		: str   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       if name == "librispeech":
        return cls()
       if name == "timit":
        return cls(
            do_lower_case=snake_case_			,				translation_table=str.maketrans({"-": " "}   )			,				)
       if name == "buckwalter":
        A__ =       {
            "-": " ",  # sometimes used to represent pauses
            "^": "v",  # fixing "tha" in arabic_speech_corpus dataset
        }
        return cls(
            vocab_file=pathlib.Path(snake_case_   ).parent.joinpath("vocab/buckwalter.json"   )			,				word_delimiter_token="/"			,				translation_table=str.maketrans(snake_case_   )			,				words_to_remove={"sil"}			,				untransliterator=arabic.buckwalter.untransliterate			,				)
       raise ValueError(F"""Unsupported orthography: '{name}'."""   )
  
  
  
  
      def 							__magic_name__							(					self		: Dict			,				snake_case_		: str   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       if len(self.translation_table   ) > 0:
        A__ =       text.translate(self.translation_table   )
       if len(self.words_to_remove   ) == 0:
        A__ =       " ".join(text.split()   )  # clean up whitespaces
       else:
        A__ =       " ".join(w for w in text.split() if w not in self.words_to_remove   )  # and clean up whilespaces
       return text
  
  
  
  
      def 							__magic_name__							(					self		: Optional[Any]			,				snake_case_		: ModelArguments   )   ->							WavaVecaProcessor:
  
  
  
  
       '''simple docstring'''
  
       A__ =       WavaVecaFeatureExtractor.from_pretrained(
           model_args.model_name_or_path			,				cache_dir=model_args.cache_dir   )
       if self.vocab_file:
        A__ =       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:
        A__ =       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(snake_case_			,				snake_case_   )
  
  
  
  
  
  
  @dataclass
  class 			UpperCAmelCase_						:
      lowercase__		      =   42
      lowercase__		      =   True
      lowercase__		      =   None
      lowercase__		      =   None
      lowercase__		      =   None
      lowercase__		      =   None
  
  
  
  
      def __call__(					self		: Optional[int]			,				snake_case_		: List[Dict[str, Union[List[int], torch.Tensor]]]   )   ->							Dict[str, torch.Tensor]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       [{"input_values": feature["input_values"]} for feature in features]
       A__ =       [{"input_ids": feature["labels"]} for feature in features]
  
       A__ =       self.processor.pad(
           snake_case_			,				padding=self.padding			,				max_length=self.max_length			,				pad_to_multiple_of=self.pad_to_multiple_of			,				return_tensors="pt"			,				)
       A__ =       self.processor.pad(
           labels=snake_case_			,				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
       A__ =       labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1   )			,				-100   )
  
       A__ =       labels
  
       return batch
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
  
  
  
  
      def 							__magic_name__							(					self		: Tuple			,				snake_case_		: nn.Module			,				snake_case_		: Dict[str, Union[torch.Tensor, Any]]   )   ->							torch.Tensor:
  
  
  
  
       '''simple docstring'''
  
       model.train()
       A__ =       self._prepare_inputs(snake_case_   )
  
       if self.use_amp:
        with autocast():
         A__ =       self.compute_loss(snake_case_			,				snake_case_   )
       else:
        A__ =       self.compute_loss(snake_case_			,				snake_case_   )
  
       if self.args.n_gpu > 1:
        if model.module.config.ctc_loss_reduction == "mean":
         A__ =       loss.mean()
        elif model.module.config.ctc_loss_reduction == "sum":
         A__ =       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:
        A__ =       loss / self.args.gradient_accumulation_steps
  
       if self.use_amp:
        self.scaler.scale(snake_case_   ).backward()
       elif self.use_apex:
        with amp.scale_loss(snake_case_			,				self.optimizer   ) as scaled_loss:
         scaled_loss.backward()
       elif self.deepspeed:
        self.deepspeed.backward(snake_case_   )
       else:
        loss.backward()
  
       return loss.detach()
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							)		->	str:
   # 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.
  
   A__ =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
  
   A__,   A__,   A__ =       parser.parse_args_into_dataclasses()
   configure_logger(lowercase_    ,				lowercase_		)
  
   A__ =       Orthography.from_name(data_args.orthography.lower()		)
   A__ =       orthography.create_processor(lowercase_		)
   A__ =       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		)    ,				)
  
   A__ =       datasets.load_dataset(
       data_args.dataset_name    ,				data_args.dataset_config_name    ,				split=data_args.train_split_name		)
   A__ =       datasets.load_dataset(
       data_args.dataset_name    ,				data_args.dataset_config_name    ,				split=data_args.validation_split_name		)
  
   A__ =       datasets.load_metric("wer"		)
   A__ =       processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
   A__ =       "".join(t for t in processor.tokenizer.get_vocab().keys() if len(lowercase_		) == 1		)
   A__ =       re.compile(  # remove characters not in vocabulary
       Rf"""[^\s{re.escape(lowercase_		)}]"""    ,				flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0    ,				)
   A__ =       []
  
   def prepare_example(lowercase_		):  # TODO(elgeish) make use of multiprocessing?
    A__,   A__ =       librosa.load(example[data_args.speech_file_column]    ,				sr=lowercase_		)
    if data_args.max_duration_in_seconds is not None:
     A__ =       len(example["speech"]		) / example["sampling_rate"]
    # Normalize and clean up text; order matters!
    A__ =       orthography.preprocess_for_training(example[data_args.target_text_column]		)
    A__ =       vocabulary_text_cleaner.sub(""    ,				lowercase_		)
    if updated_text != example[data_args.target_text_column]:
     text_updates.append((example[data_args.target_text_column], updated_text)		)
     A__ =       updated_text
    return example
  
   A__ =       train_dataset.map(lowercase_    ,				remove_columns=[data_args.speech_file_column]		)
   A__ =       val_dataset.map(lowercase_    ,				remove_columns=[data_args.speech_file_column]		)
  
   if data_args.max_duration_in_seconds is not None:
  
    def filter_by_max_duration(lowercase_		):
     return example["duration_in_seconds"] <= data_args.max_duration_in_seconds
  
    A__ =       len(lowercase_		)
    A__ =       len(lowercase_		)
    A__ =       train_dataset.filter(lowercase_    ,				remove_columns=["duration_in_seconds"]		)
    A__ =       val_dataset.filter(lowercase_    ,				remove_columns=["duration_in_seconds"]		)
    if len(lowercase_		) > old_train_size:
     logger.warning(
         f"""Filtered out {len(lowercase_		) - old_train_size} train example(s) longer than"""
         f""" {data_args.max_duration_in_seconds} second(s)."""		)
    if len(lowercase_		) > old_val_size:
     logger.warning(
         f"""Filtered out {len(lowercase_		) - old_val_size} validation example(s) longer than"""
         f""" {data_args.max_duration_in_seconds} second(s)."""		)
   logger.info(f"""Split sizes: {len(lowercase_		)} train and {len(lowercase_		)} validation."""		)
  
   logger.warning(f"""Updated {len(lowercase_		)} 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}\""""		)
   A__ =       None
  
   def prepare_dataset(lowercase_		):
    # 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}."""
  
    A__ =       processor(
        audio=batch["speech"]    ,				text=batch[data_args.target_text_column]    ,				sampling_rate=batch["sampling_rate"][0]		)
    batch.update(lowercase_		)
    return batch
  
   A__ =       train_dataset.map(
       lowercase_    ,				batch_size=training_args.per_device_train_batch_size    ,				batched=lowercase_    ,				num_proc=data_args.preprocessing_num_workers    ,				)
   A__ =       val_dataset.map(
       lowercase_    ,				batch_size=training_args.per_device_train_batch_size    ,				batched=lowercase_    ,				num_proc=data_args.preprocessing_num_workers    ,				)
  
   A__ =       DataCollatorCTCWithPadding(processor=lowercase_    ,				padding=lowercase_		)
  
   def compute_metrics(lowercase_		):
    A__ =       pred.predictions
    A__ =       np.argmax(lowercase_    ,				axis=-1		)
  
    A__ =       processor.tokenizer.pad_token_id
  
    A__ =       processor.batch_decode(lowercase_		)
    # we do not want to group tokens when computing the metrics
    A__ =       processor.batch_decode(pred.label_ids    ,				group_tokens=lowercase_		)
    if logger.isEnabledFor(logging.DEBUG		):
     for reference, predicted in zip(lowercase_    ,				lowercase_		):
      logger.debug(f"""reference: \"{reference}\""""		)
      logger.debug(f"""predicted: \"{predicted}\""""		)
      if orthography.untransliterator is not None:
       logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(lowercase_		)}\""""		)
       logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(lowercase_		)}\""""		)
  
    A__ =       wer_metric.compute(predictions=lowercase_    ,				references=lowercase_		)
  
    return {"wer": wer}
  
   if model_args.freeze_feature_extractor:
    model.freeze_feature_extractor()
  
   A__ =       CTCTrainer(
       model=lowercase_    ,				data_collator=lowercase_    ,				args=lowercase_    ,				compute_metrics=lowercase_    ,				train_dataset=lowercase_    ,				eval_dataset=lowercase_    ,				tokenizer=processor.feature_extractor    ,				)
  
   trainer.train()
  
  
  if __name__ == "__main__":
        main()
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE       = {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
        if not is_vision_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DetaForObjectDetection",
            "DetaModel",
            "DetaPreTrainedModel",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
        try:
              if not is_vision_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .image_processing_deta import DetaImageProcessor
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  