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'):
  				_lowerCamelCase				:	List[Any]				    =       True
  				from torch.cuda.amp import autocast
  
  
  _lowerCamelCase				:	Optional[Any]				    =       logging.getLogger(__name__)
  
  
  
  @dataclass
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    field(
  		    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
  		__lowerCAmelCase 				=    field(
  		    default=A__		,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,					)
  		__lowerCAmelCase 				=    field(
  		    default=A__		,					metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}						)
  		__lowerCAmelCase 				=    field(
  		    default=A__		,					metadata={'''help''': '''Whether to log verbose messages or not.'''}		,					)
  
  
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase    )      ->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    )]       ,			)
  	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(UpperCAmelCase    )
  
  
  
  @dataclass
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    field(
  		    default=A__		,					metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}						)
  		__lowerCAmelCase 				=    field(
  		    default=A__		,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
  		__lowerCAmelCase 				=    field(
  		    default='''train'''		,					metadata={
  		        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
  		    }		,					)
  		__lowerCAmelCase 				=    field(
  		    default='''validation'''		,					metadata={
  		        '''help''': (
  		            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
  		        )
  		    }		,					)
  		__lowerCAmelCase 				=    field(
  		    default='''text'''		,					metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}		,					)
  		__lowerCAmelCase 				=    field(
  		    default='''file'''		,					metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}		,					)
  		__lowerCAmelCase 				=    field(
  		    default=A__		,					metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}		,					)
  		__lowerCAmelCase 				=    field(
  		    default=A__		,					metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}		,					)
  		__lowerCAmelCase 				=    field(
  		    default='''librispeech'''		,					metadata={
  		        '''help''': (
  		            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
  		            ''' \'buckwalter\'.'''
  		        )
  		    }		,					)
  		__lowerCAmelCase 				=    field(
  		    default=A__		,					metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}						)
  		__lowerCAmelCase 				=    field(
  		    default=A__		,					metadata={'''help''': '''The number of processes to use for the preprocessing.'''}		,					)
  
  
  
  
  
  
  @dataclass
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    False
  		__lowerCAmelCase 				=    None
  		__lowerCAmelCase 				=    "|"
  		__lowerCAmelCase 				=    field(default_factory=A__						)
  		__lowerCAmelCase 				=    field(default_factory=A__						)
  		__lowerCAmelCase 				=    None
  
  
  
  
  
  
  
  		@classmethod
  		def        A     (cls						:      Optional[int]      ,      _lowerCAmelCase						:      str      ):
  			if name == "librispeech":
  				return cls()
  			if name == "timit":
  				return cls(
  				    do_lower_case=_lowerCAmelCase      ,      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(_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						:      List[str]      ,      _lowerCAmelCase						:      str      ):
  			# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
  			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        A     (self						:      str      ,      _lowerCAmelCase						:      ModelArguments      ):
  			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(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  
  
  
  
  
  @dataclass
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    42
  		__lowerCAmelCase 				=    True
  		__lowerCAmelCase 				=    None
  		__lowerCAmelCase 				=    None
  		__lowerCAmelCase 				=    None
  		__lowerCAmelCase 				=    None
  
  
  
  
  
  
  
  		def __call__(self						:      Any      ,      _lowerCAmelCase						:      List[Dict[str, Union[List[int], torch.Tensor]]]      ):
  			# split inputs and labels since they have to be of different lenghts and need
  			# different padding methods
  			A							  =      [{"""input_values""": feature["""input_values"""]} for feature in features]
  			A							  =      [{"""input_ids""": feature["""labels"""]} for feature in features]
  
  			A							  =      self.processor.pad(
  			    _lowerCAmelCase      ,      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=_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
  			A							  =      labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1      )      ,      -100      )
  
  			A							  =      labels
  
  			return batch
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      nn.Module      ,      _lowerCAmelCase						:      Dict[str, Union[torch.Tensor, Any]]      ):
  			model.train()
  			A							  =      self._prepare_inputs(_lowerCAmelCase      )
  
  			if self.use_amp:
  				with autocast():
  					A							  =      self.compute_loss(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			else:
  				A							  =      self.compute_loss(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  			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(_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    __a (     )      ->List[str]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
  
  	A  ,    A  ,    A							  =      parser.parse_args_into_dataclasses()
  	configure_logger(UpperCAmelCase       ,			UpperCAmelCase    )
  
  	A							  =      Orthography.from_name(data_args.orthography.lower()    )
  	A							  =      orthography.create_processor(UpperCAmelCase    )
  	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(UpperCAmelCase    ) == 1    )
  	A							  =      re.compile(  # remove characters not in vocabulary
  	    Rf"""[^\s{re.escape(UpperCAmelCase    )}]"""       ,			flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0       ,			)
  	A							  =      []
  
  	def prepare_example(UpperCAmelCase    ):  # TODO(elgeish) make use of multiprocessing?
  		A  ,    A							  =      librosa.load(example[data_args.speech_file_column]       ,			sr=UpperCAmelCase    )
  		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(""""""       ,			UpperCAmelCase    )
  		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(UpperCAmelCase       ,			remove_columns=[data_args.speech_file_column]    )
  	A							  =      val_dataset.map(UpperCAmelCase       ,			remove_columns=[data_args.speech_file_column]    )
  
  	if data_args.max_duration_in_seconds is not None:
  
  		def filter_by_max_duration(UpperCAmelCase    ):
  			return example["duration_in_seconds"] <= data_args.max_duration_in_seconds
  
  		A							  =      len(UpperCAmelCase    )
  		A							  =      len(UpperCAmelCase    )
  		A							  =      train_dataset.filter(UpperCAmelCase       ,			remove_columns=["""duration_in_seconds"""]    )
  		A							  =      val_dataset.filter(UpperCAmelCase       ,			remove_columns=["""duration_in_seconds"""]    )
  		if len(UpperCAmelCase    ) > old_train_size:
  			logger.warning(
  			    f"""Filtered out {len(UpperCAmelCase    ) - old_train_size} train example(s) longer than"""
  			    f""" {data_args.max_duration_in_seconds} second(s)."""    )
  		if len(UpperCAmelCase    ) > old_val_size:
  			logger.warning(
  			    f"""Filtered out {len(UpperCAmelCase    ) - old_val_size} validation example(s) longer than"""
  			    f""" {data_args.max_duration_in_seconds} second(s)."""    )
  	logger.info(f"""Split sizes: {len(UpperCAmelCase    )} train and {len(UpperCAmelCase    )} validation."""    )
  
  	logger.warning(f"""Updated {len(UpperCAmelCase    )} 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(UpperCAmelCase    ):
  		# 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(UpperCAmelCase    )
  		return batch
  
  	A							  =      train_dataset.map(
  	    UpperCAmelCase       ,			batch_size=training_args.per_device_train_batch_size       ,			batched=UpperCAmelCase       ,			num_proc=data_args.preprocessing_num_workers       ,			)
  	A							  =      val_dataset.map(
  	    UpperCAmelCase       ,			batch_size=training_args.per_device_train_batch_size       ,			batched=UpperCAmelCase       ,			num_proc=data_args.preprocessing_num_workers       ,			)
  
  	A							  =      DataCollatorCTCWithPadding(processor=UpperCAmelCase       ,			padding=UpperCAmelCase    )
  
  	def compute_metrics(UpperCAmelCase    ):
  		A							  =      pred.predictions
  		A							  =      np.argmax(UpperCAmelCase       ,			axis=-1    )
  
  		A							  =      processor.tokenizer.pad_token_id
  
  		A							  =      processor.batch_decode(UpperCAmelCase    )
  		# we do not want to group tokens when computing the metrics
  		A							  =      processor.batch_decode(pred.label_ids       ,			group_tokens=UpperCAmelCase    )
  		if logger.isEnabledFor(logging.DEBUG    ):
  			for reference, predicted in zip(UpperCAmelCase       ,			UpperCAmelCase    ):
  				logger.debug(f"""reference: \"{reference}\""""    )
  				logger.debug(f"""predicted: \"{predicted}\""""    )
  				if orthography.untransliterator is not None:
  					logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(UpperCAmelCase    )}\""""    )
  					logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(UpperCAmelCase    )}\""""    )
  
  		A							  =      wer_metric.compute(predictions=UpperCAmelCase       ,			references=UpperCAmelCase    )
  
  		return {"wer": wer}
  
  	if model_args.freeze_feature_extractor:
  		model.freeze_feature_extractor()
  
  	A							  =      CTCTrainer(
  	    model=UpperCAmelCase       ,			data_collator=UpperCAmelCase       ,			args=UpperCAmelCase       ,			compute_metrics=UpperCAmelCase       ,			train_dataset=UpperCAmelCase       ,			eval_dataset=UpperCAmelCase       ,			tokenizer=processor.feature_extractor       ,			)
  
  	trainer.train()
  
  
  if __name__ == "__main__":
  				main()
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from __future__ import annotations
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Any      ,      _lowerCAmelCase						:      int      ):
  			A							  =      value
  			A							  =      None
  			A							  =      None
  
  
  
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    42
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ):
  			A							  =      0
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      int      ):
  			if node is None:
  				return
  
  			if path_sum == self.target:
  				self.paths += 1
  
  			if node.left:
  				self.depth_first_search(node.left      ,      path_sum + node.left.value      )
  			if node.right:
  				self.depth_first_search(node.right      ,      path_sum + node.right.value      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      int | None = None      ):
  			if node is None:
  				return 0
  			if target is not None:
  				A							  =      target
  
  			self.depth_first_search(_lowerCAmelCase      ,      node.value      )
  			self.path_sum(node.left      )
  			self.path_sum(node.right      )
  
  			return self.paths
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  