code: 
  
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForQuestionAnswering,
      AutoTokenizer,
      DataCollatorWithPadding,
      HfArgumentParser,
      SquadDataset,
      Trainer,
      TrainingArguments,
  )
  from transformers import SquadDataTrainingArguments as DataTrainingArguments
  from transformers.trainer_utils import is_main_process
  
  
  A_		:			Union[str, Any]										=   logging.getLogger(__name__)
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       field(
          metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
      a__     							=       field(default=_a ,     metadata={"help": "Set this flag to use fast tokenization."}						)
      # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
      # or just modify its tokenizer_config.json.
      a__     							=       field(
          default=_a ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,     )
  
  
  
  
  
  
  def       __a       (   )      ->     int:
  
  
  
  
  
  
      '''simple docstring'''
      # 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.
  
      __UpperCAmelCase									=			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.
          __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
      else:
          __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			parser.parse_args_into_dataclasses()
  
      if (
          os.path.exists(training_args.output_dir      )
          and os.listdir(training_args.output_dir      )
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          raise ValueError(
              f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
              ''' --overwrite_output_dir to overcome.'''      )
  
      # Setup logging
      logging.basicConfig(
          format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,   datefmt='''%m/%d/%Y %H:%M:%S'''       ,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,   )
      logger.warning(
          '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''       ,   training_args.local_rank       ,   training_args.device       ,   training_args.n_gpu       ,   bool(training_args.local_rank != -1      )       ,   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()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
      logger.info('''Training/evaluation parameters %s'''       ,   SCREAMING_SNAKE_CASE      )
  
      # Prepare Question-Answering task
      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
  
      __UpperCAmelCase									=			AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,   cache_dir=model_args.cache_dir       ,   )
      __UpperCAmelCase									=			AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,   cache_dir=model_args.cache_dir       ,   use_fast=SCREAMING_SNAKE_CASE       ,   )
      __UpperCAmelCase									=			AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path       ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )       ,   config=SCREAMING_SNAKE_CASE       ,   cache_dir=model_args.cache_dir       ,   )
  
      # Get datasets
      __UpperCAmelCase									=			hasattr(model.config       ,   '''lang2id'''      )
      __UpperCAmelCase									=			(
          SquadDataset(
              SCREAMING_SNAKE_CASE       ,   tokenizer=SCREAMING_SNAKE_CASE       ,   is_language_sensitive=SCREAMING_SNAKE_CASE       ,   cache_dir=model_args.cache_dir      )
          if training_args.do_train
          else None
      )
      __UpperCAmelCase									=			(
          SquadDataset(
              SCREAMING_SNAKE_CASE       ,   tokenizer=SCREAMING_SNAKE_CASE       ,   mode='''dev'''       ,   is_language_sensitive=SCREAMING_SNAKE_CASE       ,   cache_dir=model_args.cache_dir       ,   )
          if training_args.do_eval
          else None
      )
  
      # Data collator
      __UpperCAmelCase									=			DataCollatorWithPadding(SCREAMING_SNAKE_CASE       ,   pad_to_multiple_of=8      ) if training_args.fpaa else None
  
      # Initialize our Trainer
      __UpperCAmelCase									=			Trainer(
          model=SCREAMING_SNAKE_CASE       ,   args=SCREAMING_SNAKE_CASE       ,   train_dataset=SCREAMING_SNAKE_CASE       ,   eval_dataset=SCREAMING_SNAKE_CASE       ,   data_collator=SCREAMING_SNAKE_CASE       ,   )
  
      # Training
      if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_master():
              tokenizer.save_pretrained(training_args.output_dir      )
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Optional[int]:
  
  
  
  
  
  
      '''simple docstring'''
      # For xla_spawn (TPUs)
      main()
  
  
  if __name__ == "__main__":
         main()
  
  
  
  
  
style_context: 
  
  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_		:			Optional[int]										=   True
         from torch.cuda.amp import autocast
  
  
  A_		:			List[Any]										=   logging.getLogger(__name__)
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       field(
          metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,     )
      a__     							=       field(
          default=_a ,     metadata={"help": "Whether to freeze the feature extractor layers of the model."}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "Whether to log verbose messages or not."} ,     )
  
  
  
  
  
  
  def       __a       (   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )      ->     str:
  
  
  
  
  
  
      '''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(SCREAMING_SNAKE_CASE      )
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       field(
          default=_a ,     metadata={"help": "The name of the dataset to use (via the datasets library)."}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
      a__     							=       field(
          default="train" ,     metadata={
              "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
          } ,     )
      a__     							=       field(
          default="validation" ,     metadata={
              "help": (
                  "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
              )
          } ,     )
      a__     							=       field(
          default="text" ,     metadata={"help": "Column in the dataset that contains label (target text). Defaults to 'text'"} ,     )
      a__     							=       field(
          default="file" ,     metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} ,     )
      a__     							=       field(
          default=_a ,     metadata={"help": "Resample loaded audio to target feature extractor's sampling rate or not."} ,     )
      a__     							=       field(
          default=_a ,     metadata={"help": "Filters out examples longer than specified. Defaults to no filtering."} ,     )
      a__     							=       field(
          default="librispeech" ,     metadata={
              "help": (
                  "Orthography used for normalization and tokenization: 'librispeech' (default), 'timit', or"
                  " 'buckwalter'."
              )
          } ,     )
      a__     							=       field(
          default=_a ,     metadata={"help": "Overwrite the cached preprocessed datasets or not."}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "The number of processes to use for the preprocessing."} ,     )
  
  
  
  
  
  
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       False
      a__     							=       None
      a__     							=       "|"
      a__     							=       field(default_factory=_a						)
      a__     							=       field(default_factory=_a						)
      a__     							=       None
  
  
      @classmethod
      def      lowerCAmelCase_						(cls     ,      lowercase__  )     ->      Union[str, Any]:
          if name == "librispeech":
              return cls()
          if name == "timit":
              return cls(
                  do_lower_case=lowercase__     ,      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(lowercase__  ).parent.joinpath('''vocab/buckwalter.json'''  )     ,      word_delimiter_token='''/'''     ,      translation_table=str.maketrans(lowercase__  )     ,      words_to_remove={'''sil'''}     ,      untransliterator=arabic.buckwalter.untransliterate     ,      )
          raise ValueError(F'''Unsupported orthography: \'{name}\'.'''  )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      str:
          # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
          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      lowerCAmelCase_						(self     ,      lowercase__  )     ->      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(lowercase__     ,      lowercase__  )
  
  
  
  
  
  
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       42
      a__     							=       True
      a__     							=       None
      a__     							=       None
      a__     							=       None
      a__     							=       None
  
  
      def __call__(self     ,      lowercase__  )     ->      Dict[str, torch.Tensor]:
          # split inputs and labels since they have to be of different lenghts and need
          # different padding methods
          __UpperCAmelCase									=			[{'''input_values''': feature['''input_values''']} for feature in features]
          __UpperCAmelCase									=			[{'''input_ids''': feature['''labels''']} for feature in features]
  
          __UpperCAmelCase									=			self.processor.pad(
              lowercase__     ,      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=lowercase__     ,      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 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      torch.Tensor:
          model.train()
          __UpperCAmelCase									=			self._prepare_inputs(lowercase__  )
  
          if self.use_amp:
              with autocast():
                  __UpperCAmelCase									=			self.compute_loss(lowercase__     ,      lowercase__  )
          else:
              __UpperCAmelCase									=			self.compute_loss(lowercase__     ,      lowercase__  )
  
          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(lowercase__  ).backward()
          elif self.use_apex:
              with amp.scale_loss(lowercase__     ,      self.optimizer  ) as scaled_loss:
                  scaled_loss.backward()
          elif self.deepspeed:
              self.deepspeed.backward(lowercase__  )
          else:
              loss.backward()
  
          return loss.detach()
  
  
  
  
  
  
  def       __a       (   )      ->     int:
  
  
  
  
  
  
      '''simple docstring'''
      # 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.
  
      __UpperCAmelCase									=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
  
      __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			parser.parse_args_into_dataclasses()
      configure_logger(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      )
  
      __UpperCAmelCase									=			Orthography.from_name(data_args.orthography.lower()      )
      __UpperCAmelCase									=			orthography.create_processor(SCREAMING_SNAKE_CASE      )
      __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(SCREAMING_SNAKE_CASE      ) == 1      )
      __UpperCAmelCase									=			re.compile(  # remove characters not in vocabulary
          rf'''[^\s{re.escape(SCREAMING_SNAKE_CASE      )}]'''       ,   flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0       ,   )
      __UpperCAmelCase									=			[]
  
      def prepare_example(SCREAMING_SNAKE_CASE      ):  # TODO(elgeish) make use of multiprocessing?
          __UpperCAmelCase					,     __UpperCAmelCase									=			librosa.load(example[data_args.speech_file_column]       ,   sr=SCREAMING_SNAKE_CASE      )
          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(''''''       ,   SCREAMING_SNAKE_CASE      )
          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(SCREAMING_SNAKE_CASE       ,   remove_columns=[data_args.speech_file_column]      )
      __UpperCAmelCase									=			val_dataset.map(SCREAMING_SNAKE_CASE       ,   remove_columns=[data_args.speech_file_column]      )
  
      if data_args.max_duration_in_seconds is not None:
  
          def filter_by_max_duration(SCREAMING_SNAKE_CASE      ):
              return example["duration_in_seconds"] <= data_args.max_duration_in_seconds
  
          __UpperCAmelCase									=			len(SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			len(SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			train_dataset.filter(SCREAMING_SNAKE_CASE       ,   remove_columns=['''duration_in_seconds''']      )
          __UpperCAmelCase									=			val_dataset.filter(SCREAMING_SNAKE_CASE       ,   remove_columns=['''duration_in_seconds''']      )
          if len(SCREAMING_SNAKE_CASE      ) > old_train_size:
              logger.warning(
                  f'''Filtered out {len(SCREAMING_SNAKE_CASE      ) - old_train_size} train example(s) longer than'''
                  f''' {data_args.max_duration_in_seconds} second(s).'''      )
          if len(SCREAMING_SNAKE_CASE      ) > old_val_size:
              logger.warning(
                  f'''Filtered out {len(SCREAMING_SNAKE_CASE      ) - old_val_size} validation example(s) longer than'''
                  f''' {data_args.max_duration_in_seconds} second(s).'''      )
      logger.info(f'''Split sizes: {len(SCREAMING_SNAKE_CASE      )} train and {len(SCREAMING_SNAKE_CASE      )} validation.'''      )
  
      logger.warning(f'''Updated {len(SCREAMING_SNAKE_CASE      )} 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(SCREAMING_SNAKE_CASE      ):
          # 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(SCREAMING_SNAKE_CASE      )
          return batch
  
      __UpperCAmelCase									=			train_dataset.map(
          SCREAMING_SNAKE_CASE       ,   batch_size=training_args.per_device_train_batch_size       ,   batched=SCREAMING_SNAKE_CASE       ,   num_proc=data_args.preprocessing_num_workers       ,   )
      __UpperCAmelCase									=			val_dataset.map(
          SCREAMING_SNAKE_CASE       ,   batch_size=training_args.per_device_train_batch_size       ,   batched=SCREAMING_SNAKE_CASE       ,   num_proc=data_args.preprocessing_num_workers       ,   )
  
      __UpperCAmelCase									=			DataCollatorCTCWithPadding(processor=SCREAMING_SNAKE_CASE       ,   padding=SCREAMING_SNAKE_CASE      )
  
      def compute_metrics(SCREAMING_SNAKE_CASE      ):
          __UpperCAmelCase									=			pred.predictions
          __UpperCAmelCase									=			np.argmax(SCREAMING_SNAKE_CASE       ,   axis=-1      )
  
          __UpperCAmelCase									=			processor.tokenizer.pad_token_id
  
          __UpperCAmelCase									=			processor.batch_decode(SCREAMING_SNAKE_CASE      )
          # we do not want to group tokens when computing the metrics
          __UpperCAmelCase									=			processor.batch_decode(pred.label_ids       ,   group_tokens=SCREAMING_SNAKE_CASE      )
          if logger.isEnabledFor(logging.DEBUG      ):
              for reference, predicted in zip(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE      ):
                  logger.debug(f'''reference: "{reference}"'''      )
                  logger.debug(f'''predicted: "{predicted}"'''      )
                  if orthography.untransliterator is not None:
                      logger.debug(f'''reference (untransliterated): "{orthography.untransliterator(SCREAMING_SNAKE_CASE      )}"'''      )
                      logger.debug(f'''predicted (untransliterated): "{orthography.untransliterator(SCREAMING_SNAKE_CASE      )}"'''      )
  
          __UpperCAmelCase									=			wer_metric.compute(predictions=SCREAMING_SNAKE_CASE       ,   references=SCREAMING_SNAKE_CASE      )
  
          return {"wer": wer}
  
      if model_args.freeze_feature_extractor:
          model.freeze_feature_extractor()
  
      __UpperCAmelCase									=			CTCTrainer(
          model=SCREAMING_SNAKE_CASE       ,   data_collator=SCREAMING_SNAKE_CASE       ,   args=SCREAMING_SNAKE_CASE       ,   compute_metrics=SCREAMING_SNAKE_CASE       ,   train_dataset=SCREAMING_SNAKE_CASE       ,   eval_dataset=SCREAMING_SNAKE_CASE       ,   tokenizer=processor.feature_extractor       ,   )
  
      trainer.train()
  
  
  if __name__ == "__main__":
         main()
  
  
  
  
  