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'''):
  	lowerCAmelCase					 =	True
  	from torch.cuda.amp import autocast
  
  
  lowerCAmelCase					 =	logging.getLogger(__name__)
  
  
  
  
  
  @dataclass
  class    A					:
  							UpperCamelCase_     :							str =field(
  							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
  							UpperCamelCase_     :							Optional[str] =field(
  							    default=A_      ,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}      ,       )
  							UpperCamelCase_     :							Optional[bool] =field(
  							    default=A_      ,       metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}				)
  							UpperCamelCase_     :							Optional[bool] =field(
  							    default=A_      ,       metadata={'''help''': '''Whether to log verbose messages or not.'''}      ,       )
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			List[Any]:
  
  		'''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			)]     ,				)
  		__lowercase=    logging.WARNING
  		if model_args.verbose_logging:
  				__lowercase=    logging.DEBUG
  		elif trainer_utils.is_main_process(training_args.local_rank			):
  				__lowercase=    logging.INFO
  		logger.setLevel(lowercase__			)
  
  
  
  
  
  @dataclass
  class    A					:
  							UpperCamelCase_     :							str =field(
  							    default=A_      ,       metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}				)
  							UpperCamelCase_     :							Optional[str] =field(
  							    default=A_      ,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
  							UpperCamelCase_     :							Optional[str] =field(
  							    default='''train'''      ,       metadata={
  							        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
  							    }      ,       )
  							UpperCamelCase_     :							Optional[str] =field(
  							    default='''validation'''      ,       metadata={
  							        '''help''': (
  							            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
  							        )
  							    }      ,       )
  							UpperCamelCase_     :							Optional[str] =field(
  							    default='''text'''      ,       metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}      ,       )
  							UpperCamelCase_     :							Optional[str] =field(
  							    default='''file'''      ,       metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}      ,       )
  							UpperCamelCase_     :							Optional[bool] =field(
  							    default=A_      ,       metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}      ,       )
  							UpperCamelCase_     :							Optional[float] =field(
  							    default=A_      ,       metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}      ,       )
  							UpperCamelCase_     :							Optional[str] =field(
  							    default='''librispeech'''      ,       metadata={
  							        '''help''': (
  							            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
  							            ''' \'buckwalter\'.'''
  							        )
  							    }      ,       )
  							UpperCamelCase_     :							bool =field(
  							    default=A_      ,       metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}				)
  							UpperCamelCase_     :							Optional[int] =field(
  							    default=A_      ,       metadata={'''help''': '''The number of processes to use for the preprocessing.'''}      ,       )
  
  
  
  
  
  @dataclass
  class    A					:
  							UpperCamelCase_     :							bool =False
  							UpperCamelCase_     :							Optional[str] =None
  							UpperCamelCase_     :							Optional[str] ="|"
  							UpperCamelCase_     :							Optional[Dict[str, str]] =field(default_factory=A_				)
  							UpperCamelCase_     :							Optional[Set[str]] =field(default_factory=A_				)
  							UpperCamelCase_     :							Optional[Callable[[str], str]] =None
  
  
  							@classmethod
  							def     _A (cls   ,       lowerCAmelCase      ):
  									if name == "librispeech":
  											return cls()
  									if name == "timit":
  											return cls(
  											    do_lower_case=lowerCAmelCase   ,       translation_table=str.maketrans({'-': ' '}      )   ,       )
  									if name == "buckwalter":
  											__lowercase=    {
  											    '-': ' ',  # sometimes used to represent pauses
  											    '^': 'v',  # fixing "tha" in arabic_speech_corpus dataset
  											}
  											return cls(
  											    vocab_file=pathlib.Path(lowerCAmelCase      ).parent.joinpath('vocab/buckwalter.json'      )   ,       word_delimiter_token='/'   ,       translation_table=str.maketrans(lowerCAmelCase      )   ,       words_to_remove={'sil'}   ,       untransliterator=arabic.buckwalter.untransliterate   ,       )
  									raise ValueError(f'Unsupported orthography: \'{name}\'.'      )
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
  									if len(self.translation_table      ) > 0:
  											__lowercase=    text.translate(self.translation_table      )
  									if len(self.words_to_remove      ) == 0:
  											__lowercase=    ' '.join(text.split()      )  # clean up whitespaces
  									else:
  											__lowercase=    ' '.join(w for w in text.split() if w not in self.words_to_remove      )  # and clean up whilespaces
  									return text
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									__lowercase=    WavaVecaFeatureExtractor.from_pretrained(
  									    model_args.model_name_or_path   ,       cache_dir=model_args.cache_dir      )
  									if self.vocab_file:
  											__lowercase=    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:
  											__lowercase=    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(lowerCAmelCase   ,       lowerCAmelCase      )
  
  
  
  
  
  @dataclass
  class    A					:
  							UpperCamelCase_     :							WavaVecaProcessor
  							UpperCamelCase_     :							Union[bool, str] =True
  							UpperCamelCase_     :							Optional[int] =None
  							UpperCamelCase_     :							Optional[int] =None
  							UpperCamelCase_     :							Optional[int] =None
  							UpperCamelCase_     :							Optional[int] =None
  
  
  							def __call__(self   ,       lowerCAmelCase      ):
  									# split inputs and labels since they have to be of different lenghts and need
  									# different padding methods
  									__lowercase=    [{'input_values': feature['input_values']} for feature in features]
  									__lowercase=    [{'input_ids': feature['labels']} for feature in features]
  
  									__lowercase=    self.processor.pad(
  									    lowerCAmelCase   ,       padding=self.padding   ,       max_length=self.max_length   ,       pad_to_multiple_of=self.pad_to_multiple_of   ,       return_tensors='pt'   ,       )
  									__lowercase=    self.processor.pad(
  									    labels=lowerCAmelCase   ,       padding=self.padding   ,       max_length=self.max_length_labels   ,       pad_to_multiple_of=self.pad_to_multiple_of_labels   ,       return_tensors='pt'   ,       )
  
  									# replace padding with -100 to ignore loss correctly
  									__lowercase=    labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1      )   ,       -1_0_0      )
  
  									__lowercase=    labels
  
  									return batch
  
  
  
  
  
  class    A					(     A_				):
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									model.train()
  									__lowercase=    self._prepare_inputs(lowerCAmelCase      )
  
  									if self.use_amp:
  											with autocast():
  													__lowercase=    self.compute_loss(lowerCAmelCase   ,       lowerCAmelCase      )
  									else:
  											__lowercase=    self.compute_loss(lowerCAmelCase   ,       lowerCAmelCase      )
  
  									if self.args.n_gpu > 1:
  											if model.module.config.ctc_loss_reduction == "mean":
  													__lowercase=    loss.mean()
  											elif model.module.config.ctc_loss_reduction == "sum":
  													__lowercase=    loss.sum() / (inputs['labels'] >= 0).sum()
  											else:
  													raise ValueError(f'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'      )
  
  									if self.args.gradient_accumulation_steps > 1:
  											__lowercase=    loss / self.args.gradient_accumulation_steps
  
  									if self.use_amp:
  											self.scaler.scale(lowerCAmelCase      ).backward()
  									elif self.use_apex:
  											with amp.scale_loss(lowerCAmelCase   ,       self.optimizer      ) as scaled_loss:
  													scaled_loss.backward()
  									elif self.deepspeed:
  											self.deepspeed.backward(lowerCAmelCase      )
  									else:
  											loss.backward()
  
  									return loss.detach()
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			Dict:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
  
  		__lowercase,							__lowercase,							__lowercase=    parser.parse_args_into_dataclasses()
  		configure_logger(lowercase__     ,				lowercase__			)
  
  		__lowercase=    Orthography.from_name(data_args.orthography.lower()			)
  		__lowercase=    orthography.create_processor(lowercase__			)
  		__lowercase=    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			)     ,				)
  
  		__lowercase=    datasets.load_dataset(
  		    data_args.dataset_name     ,				data_args.dataset_config_name     ,				split=data_args.train_split_name			)
  		__lowercase=    datasets.load_dataset(
  		    data_args.dataset_name     ,				data_args.dataset_config_name     ,				split=data_args.validation_split_name			)
  
  		__lowercase=    datasets.load_metric('wer'			)
  		__lowercase=    processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
  		__lowercase=    ''.join(t for t in processor.tokenizer.get_vocab().keys() if len(lowercase__			) == 1			)
  		__lowercase=    re.compile(  # remove characters not in vocabulary
  		    RF'[^\s{re.escape(lowercase__			)}]'     ,				flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0     ,				)
  		__lowercase=    []
  
  		def prepare_example(lowercase__			):  # TODO(elgeish) make use of multiprocessing?
  				__lowercase,							__lowercase=    librosa.load(example[data_args.speech_file_column]     ,				sr=lowercase__			)
  				if data_args.max_duration_in_seconds is not None:
  						__lowercase=    len(example['speech']			) / example['sampling_rate']
  				# Normalize and clean up text; order matters!
  				__lowercase=    orthography.preprocess_for_training(example[data_args.target_text_column]			)
  				__lowercase=    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)			)
  						__lowercase=    updated_text
  				return example
  
  		__lowercase=    train_dataset.map(lowercase__     ,				remove_columns=[data_args.speech_file_column]			)
  		__lowercase=    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
  
  				__lowercase=    len(lowercase__			)
  				__lowercase=    len(lowercase__			)
  				__lowercase=    train_dataset.filter(lowercase__     ,				remove_columns=['duration_in_seconds']			)
  				__lowercase=    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}"'			)
  		__lowercase=    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}.'
  
  				__lowercase=    processor(
  				    audio=batch['speech']     ,				text=batch[data_args.target_text_column]     ,				sampling_rate=batch['sampling_rate'][0]			)
  				batch.update(lowercase__			)
  				return batch
  
  		__lowercase=    train_dataset.map(
  		    lowercase__     ,				batch_size=training_args.per_device_train_batch_size     ,				batched=lowercase__     ,				num_proc=data_args.preprocessing_num_workers     ,				)
  		__lowercase=    val_dataset.map(
  		    lowercase__     ,				batch_size=training_args.per_device_train_batch_size     ,				batched=lowercase__     ,				num_proc=data_args.preprocessing_num_workers     ,				)
  
  		__lowercase=    DataCollatorCTCWithPadding(processor=lowercase__     ,				padding=lowercase__			)
  
  		def compute_metrics(lowercase__			):
  				__lowercase=    pred.predictions
  				__lowercase=    np.argmax(lowercase__     ,				axis=-1			)
  
  				__lowercase=    processor.tokenizer.pad_token_id
  
  				__lowercase=    processor.batch_decode(lowercase__			)
  				# we do not want to group tokens when computing the metrics
  				__lowercase=    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__			)}"'			)
  
  				__lowercase=    wer_metric.compute(predictions=lowercase__     ,				references=lowercase__			)
  
  				return {"wer": wer}
  
  		if model_args.freeze_feature_extractor:
  				model.freeze_feature_extractor()
  
  		__lowercase=    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: 
  
  
  def  _lowerCamelCase(			lowercase__			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return " ".join([word[::-1] for word in input_str.split()]			)
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  
  