code: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  A_		:			Optional[int]										=   {
      'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         A_		:			List[str]										=   [
             'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
             'RwkvForCausalLM',
             'RwkvModel',
             'RwkvPreTrainedModel',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_rwkv import (
                    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
                    RwkvForCausalLM,
                    RwkvModel,
                    RwkvPreTrainedModel,
                )
  else:
         import sys
  
         A_		:			Tuple										=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
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()
  
  
  
  
  