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__	:  Tuple 		=True
  			from torch.cuda.amp import autocast
  
  
  lowerCAmelCase__	:  Tuple 		=logging.getLogger(__name__)
  
  @dataclass
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				str       						=						field(
  						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
  						UpperCamelCase__    :				Optional[str]       						=						field(
  						    default=UpperCamelCase_					,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,					)
  						UpperCamelCase__    :				Optional[bool]       						=						field(
  						    default=UpperCamelCase_					,					metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
  						UpperCamelCase__    :				Optional[bool]       						=						field(
  						    default=UpperCamelCase_					,					metadata={'''help''': '''Whether to log verbose messages or not.'''}					,					)
  
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__     )       ->				int:
  							logging.basicConfig(
  							    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     handlers=[logging.StreamHandler(sys.stdout     )]							,     )
  							__SCREAMING_SNAKE_CASE								=      logging.WARNING
  							if model_args.verbose_logging:
  														__SCREAMING_SNAKE_CASE								=      logging.DEBUG
  							elif trainer_utils.is_main_process(training_args.local_rank     ):
  														__SCREAMING_SNAKE_CASE								=      logging.INFO
  							logger.setLevel(a__     )
  
  @dataclass
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				str       						=						field(
  						    default=UpperCamelCase_					,					metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
  						UpperCamelCase__    :				Optional[str]       						=						field(
  						    default=UpperCamelCase_					,					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=UpperCamelCase_					,					metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}					,					)
  						UpperCamelCase__    :				Optional[float]       						=						field(
  						    default=UpperCamelCase_					,					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=UpperCamelCase_					,					metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
  						UpperCamelCase__    :				Optional[int]       						=						field(
  						    default=UpperCamelCase_					,					metadata={'''help''': '''The number of processes to use for the preprocessing.'''}					,					)
  
  
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				bool       						=						False
  						UpperCamelCase__    :				Optional[str]       						=						None
  						UpperCamelCase__    :				Optional[str]       						=						"|"
  						UpperCamelCase__    :				Optional[Dict[str, str]]       						=						field(default_factory=UpperCamelCase_ )
  						UpperCamelCase__    :				Optional[Set[str]]       						=						field(default_factory=UpperCamelCase_ )
  						UpperCamelCase__    :				Optional[Callable[[str], str]]       						=						None
  
  
  
  
  
  
  						@classmethod
  						def  _A			(					cls   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if name == "librispeech":
  																				return cls()
  													if name == "timit":
  																				return cls(
  																				    do_lower_case=_A   ,							translation_table=str.maketrans({'-': ' '}	)   ,							)
  													if name == "buckwalter":
  																				__SCREAMING_SNAKE_CASE								=      {
  																				    '-': ' ',  # sometimes used to represent pauses
  																				    '^': 'v',  # fixing "tha" in arabic_speech_corpus dataset
  																				}
  																				return cls(
  																				    vocab_file=pathlib.Path(_A	).parent.joinpath('vocab/buckwalter.json'	)   ,							word_delimiter_token='/'   ,							translation_table=str.maketrans(_A	)   ,							words_to_remove={'sil'}   ,							untransliterator=arabic.buckwalter.untransliterate   ,							)
  													raise ValueError(f"""Unsupported orthography: '{name}'."""	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if len(self.translation_table	) > 0:
  																				__SCREAMING_SNAKE_CASE								=      text.translate(self.translation_table	)
  													if len(self.words_to_remove	) == 0:
  																				__SCREAMING_SNAKE_CASE								=      ' '.join(text.split()	)  # clean up whitespaces
  													else:
  																				__SCREAMING_SNAKE_CASE								=      ' '.join(w for w in text.split() if w not in self.words_to_remove	)  # and clean up whilespaces
  													return text
  
  
  
  
  
  
  						def  _A			(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      WavaVecaFeatureExtractor.from_pretrained(
  													    model_args.model_name_or_path   ,							cache_dir=model_args.cache_dir	)
  													if self.vocab_file:
  																				__SCREAMING_SNAKE_CASE								=      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:
  																				__SCREAMING_SNAKE_CASE								=      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(_A   ,							_A	)
  
  
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						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   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      [{'input_values': feature['input_values']} for feature in features]
  													__SCREAMING_SNAKE_CASE								=      [{'input_ids': feature['labels']} for feature in features]
  
  													__SCREAMING_SNAKE_CASE								=      self.processor.pad(
  													    _A   ,							padding=self.padding   ,							max_length=self.max_length   ,							pad_to_multiple_of=self.pad_to_multiple_of   ,							return_tensors='pt'   ,							)
  													__SCREAMING_SNAKE_CASE								=      self.processor.pad(
  													    labels=_A   ,							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
  													__SCREAMING_SNAKE_CASE								=      labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1	)   ,							-100	)
  
  													__SCREAMING_SNAKE_CASE								=      labels
  
  													return batch
  
  
  
  
  
  
  
  class      UpperCAmelCase_						(     UpperCamelCase_ ):
  
  						'''simple docstring'''
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													model.train()
  													__SCREAMING_SNAKE_CASE								=      self._prepare_inputs(_A	)
  
  													if self.use_amp:
  																				with autocast():
  																											__SCREAMING_SNAKE_CASE								=      self.compute_loss(_A   ,							_A	)
  													else:
  																				__SCREAMING_SNAKE_CASE								=      self.compute_loss(_A   ,							_A	)
  
  													if self.args.n_gpu > 1:
  																				if model.module.config.ctc_loss_reduction == "mean":
  																											__SCREAMING_SNAKE_CASE								=      loss.mean()
  																				elif model.module.config.ctc_loss_reduction == "sum":
  																											__SCREAMING_SNAKE_CASE								=      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:
  																				__SCREAMING_SNAKE_CASE								=      loss / self.args.gradient_accumulation_steps
  
  													if self.use_amp:
  																				self.scaler.scale(_A	).backward()
  													elif self.use_apex:
  																				with amp.scale_loss(_A   ,							self.optimizer	) as scaled_loss:
  																											scaled_loss.backward()
  													elif self.deepspeed:
  																				self.deepspeed.backward(_A	)
  													else:
  																				loss.backward()
  
  													return loss.detach()
  
  
  
  
  
  
  
  def 			__lowercase     (				)       ->				int:
  							# 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.
  
  							__SCREAMING_SNAKE_CASE								=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
  
  							__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      parser.parse_args_into_dataclasses()
  							configure_logger(a__							,     a__     )
  
  							__SCREAMING_SNAKE_CASE								=      Orthography.from_name(data_args.orthography.lower()     )
  							__SCREAMING_SNAKE_CASE								=      orthography.create_processor(a__     )
  							__SCREAMING_SNAKE_CASE								=      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     )							,     )
  
  							__SCREAMING_SNAKE_CASE								=      datasets.load_dataset(
  							    data_args.dataset_name							,     data_args.dataset_config_name							,     split=data_args.train_split_name     )
  							__SCREAMING_SNAKE_CASE								=      datasets.load_dataset(
  							    data_args.dataset_name							,     data_args.dataset_config_name							,     split=data_args.validation_split_name     )
  
  							__SCREAMING_SNAKE_CASE								=      datasets.load_metric('wer'     )
  							__SCREAMING_SNAKE_CASE								=      processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
  							__SCREAMING_SNAKE_CASE								=      ''.join(t for t in processor.tokenizer.get_vocab().keys() if len(a__     ) == 1     )
  							__SCREAMING_SNAKE_CASE								=      re.compile(  # remove characters not in vocabulary
  							    Rf"""[^\s{re.escape(a__     )}]"""							,     flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0							,     )
  							__SCREAMING_SNAKE_CASE								=      []
  
  							def prepare_example(a__     ):  # TODO(elgeish) make use of multiprocessing?
  														__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      librosa.load(example[data_args.speech_file_column]							,     sr=a__     )
  														if data_args.max_duration_in_seconds is not None:
  																					__SCREAMING_SNAKE_CASE								=      len(example['speech']     ) / example['sampling_rate']
  														# Normalize and clean up text; order matters!
  														__SCREAMING_SNAKE_CASE								=      orthography.preprocess_for_training(example[data_args.target_text_column]     )
  														__SCREAMING_SNAKE_CASE								=      vocabulary_text_cleaner.sub(''							,     a__     )
  														if updated_text != example[data_args.target_text_column]:
  																					text_updates.append((example[data_args.target_text_column], updated_text)     )
  																					__SCREAMING_SNAKE_CASE								=      updated_text
  														return example
  
  							__SCREAMING_SNAKE_CASE								=      train_dataset.map(a__							,     remove_columns=[data_args.speech_file_column]     )
  							__SCREAMING_SNAKE_CASE								=      val_dataset.map(a__							,     remove_columns=[data_args.speech_file_column]     )
  
  							if data_args.max_duration_in_seconds is not None:
  
  														def filter_by_max_duration(a__     ):
  																					return example["duration_in_seconds"] <= data_args.max_duration_in_seconds
  
  														__SCREAMING_SNAKE_CASE								=      len(a__     )
  														__SCREAMING_SNAKE_CASE								=      len(a__     )
  														__SCREAMING_SNAKE_CASE								=      train_dataset.filter(a__							,     remove_columns=['duration_in_seconds']     )
  														__SCREAMING_SNAKE_CASE								=      val_dataset.filter(a__							,     remove_columns=['duration_in_seconds']     )
  														if len(a__     ) > old_train_size:
  																					logger.warning(
  																					    f"""Filtered out {len(a__     ) - old_train_size} train example(s) longer than"""
  																					    f""" {data_args.max_duration_in_seconds} second(s)."""     )
  														if len(a__     ) > old_val_size:
  																					logger.warning(
  																					    f"""Filtered out {len(a__     ) - old_val_size} validation example(s) longer than"""
  																					    f""" {data_args.max_duration_in_seconds} second(s)."""     )
  							logger.info(f"""Split sizes: {len(a__     )} train and {len(a__     )} validation."""     )
  
  							logger.warning(f"""Updated {len(a__     )} 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}\""""     )
  							__SCREAMING_SNAKE_CASE								=      None
  
  							def prepare_dataset(a__     ):
  														# 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}."""
  
  														__SCREAMING_SNAKE_CASE								=      processor(
  														    audio=batch['speech']							,     text=batch[data_args.target_text_column]							,     sampling_rate=batch['sampling_rate'][0]     )
  														batch.update(a__     )
  														return batch
  
  							__SCREAMING_SNAKE_CASE								=      train_dataset.map(
  							    a__							,     batch_size=training_args.per_device_train_batch_size							,     batched=a__							,     num_proc=data_args.preprocessing_num_workers							,     )
  							__SCREAMING_SNAKE_CASE								=      val_dataset.map(
  							    a__							,     batch_size=training_args.per_device_train_batch_size							,     batched=a__							,     num_proc=data_args.preprocessing_num_workers							,     )
  
  							__SCREAMING_SNAKE_CASE								=      DataCollatorCTCWithPadding(processor=a__							,     padding=a__     )
  
  							def compute_metrics(a__     ):
  														__SCREAMING_SNAKE_CASE								=      pred.predictions
  														__SCREAMING_SNAKE_CASE								=      np.argmax(a__							,     axis=-1     )
  
  														__SCREAMING_SNAKE_CASE								=      processor.tokenizer.pad_token_id
  
  														__SCREAMING_SNAKE_CASE								=      processor.batch_decode(a__     )
  														# we do not want to group tokens when computing the metrics
  														__SCREAMING_SNAKE_CASE								=      processor.batch_decode(pred.label_ids							,     group_tokens=a__     )
  														if logger.isEnabledFor(logging.DEBUG     ):
  																					for reference, predicted in zip(a__							,     a__     ):
  																												logger.debug(f"""reference: \"{reference}\""""     )
  																												logger.debug(f"""predicted: \"{predicted}\""""     )
  																												if orthography.untransliterator is not None:
  																																			logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(a__     )}\""""     )
  																																			logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(a__     )}\""""     )
  
  														__SCREAMING_SNAKE_CASE								=      wer_metric.compute(predictions=a__							,     references=a__     )
  
  														return {"wer": wer}
  
  							if model_args.freeze_feature_extractor:
  														model.freeze_feature_extractor()
  
  							__SCREAMING_SNAKE_CASE								=      CTCTrainer(
  							    model=a__							,     data_collator=a__							,     args=a__							,     compute_metrics=a__							,     train_dataset=a__							,     eval_dataset=a__							,     tokenizer=processor.feature_extractor							,     )
  
  							trainer.train()
  
  
  if __name__ == "__main__":
  			main()
  
  
  
  
  
  
  
style_context: 
  
  
  
  def 			__lowercase     (				a__     )       ->				list:
  							__SCREAMING_SNAKE_CASE								=      1.3
  							__SCREAMING_SNAKE_CASE								=      len(a__     )
  							__SCREAMING_SNAKE_CASE								=      False
  
  							while not completed:
  														# Update the gap value for a next comb
  														__SCREAMING_SNAKE_CASE								=      int(gap / shrink_factor     )
  														if gap <= 1:
  																					__SCREAMING_SNAKE_CASE								=      True
  
  														__SCREAMING_SNAKE_CASE								=      0
  														while index + gap < len(a__     ):
  																					if data[index] > data[index + gap]:
  																												# Swap values
  																												__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      data[index + gap], data[index]
  																												__SCREAMING_SNAKE_CASE								=      False
  																					index += 1
  
  							return data
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  			lowerCAmelCase__	:  int 		=input('''Enter numbers separated by a comma:\n''').strip()
  			lowerCAmelCase__	:  Dict 		=[int(item) for item in user_input.split(''',''')]
  			print(comb_sort(unsorted))
  
  
  
  
  
  
  