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'''):
  					UpperCAmelCase__      =							True
  					from torch.cuda.amp import autocast
  
  
  UpperCAmelCase__      =							logging.getLogger(__name__)
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							str							= field(
  			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      )
  			A	:							Optional[str]							= field(
  			    default=a_			,     metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}			,     )
  			A	:							Optional[bool]							= field(
  			    default=a_			,     metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}      )
  			A	:							Optional[bool]							= field(
  			    default=a_			,     metadata={'''help''': '''Whether to log verbose messages or not.'''}			,     )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   ModelArguments,_SCREAMING_SNAKE_CASE				:   TrainingArguments   ):
  
  
  
  
  
  
  			"""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(_SCREAMING_SNAKE_CASE   )
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							str							= field(
  			    default=a_			,     metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}      )
  			A	:							Optional[str]							= field(
  			    default=a_			,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}      )
  			A	:							Optional[str]							= field(
  			    default='''train'''			,     metadata={
  			        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
  			    }			,     )
  			A	:							Optional[str]							= field(
  			    default='''validation'''			,     metadata={
  			        '''help''': (
  			            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
  			        )
  			    }			,     )
  			A	:							Optional[str]							= field(
  			    default='''text'''			,     metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''}			,     )
  			A	:							Optional[str]							= field(
  			    default='''file'''			,     metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}			,     )
  			A	:							Optional[bool]							= field(
  			    default=a_			,     metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''}			,     )
  			A	:							Optional[float]							= field(
  			    default=a_			,     metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''}			,     )
  			A	:							Optional[str]							= field(
  			    default='''librispeech'''			,     metadata={
  			        '''help''': (
  			            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
  			            ''' \'buckwalter\'.'''
  			        )
  			    }			,     )
  			A	:							bool							= field(
  			    default=a_			,     metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}      )
  			A	:							Optional[int]							= field(
  			    default=a_			,     metadata={'''help''': '''The number of processes to use for the preprocessing.'''}			,     )
  
  
  @dataclass
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							bool							= False
  			A	:							Optional[str]							= None
  			A	:							Optional[str]							= "|"
  			A	:							Optional[Dict[str, str]]							= field(default_factory=a_      )
  			A	:							Optional[Set[str]]							= field(default_factory=a_      )
  			A	:							Optional[Callable[[str], str]]							= None
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : str ,						lowerCAmelCase_       : str  )			->		List[Any]:
  						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 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : str  )			->		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 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : ModelArguments  )			->		WavaVecaProcessor:
  						__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 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							WavaVecaProcessor
  			A	:							Union[bool, str]							= True
  			A	:							Optional[int]							= None
  			A	:							Optional[int]							= None
  			A	:							Optional[int]							= None
  			A	:							Optional[int]							= None
  			def __call__(     self       : str ,						lowerCAmelCase_       : List[Dict[str, Union[List[int], torch.Tensor]]]  )			->		Dict[str, 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 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : nn.Module ,						lowerCAmelCase_       : Dict[str, Union[torch.Tensor, Any]]  )			->		torch.Tensor:
  						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 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
  
  			__A,  __A,  __A=  parser.parse_args_into_dataclasses()
  			configure_logger(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE   )
  
  			__A=  Orthography.from_name(data_args.orthography.lower()   )
  			__A=  orthography.create_processor(_SCREAMING_SNAKE_CASE   )
  			__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(_SCREAMING_SNAKE_CASE   ) == 1   )
  			__A=  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,)
  			__A=  []
  
  			def prepare_example(_SCREAMING_SNAKE_CASE				:   Optional[int]   ):  # TODO(elgeish) make use of multiprocessing?
  						__A,  __A=  librosa.load(example[data_args.speech_file_column],sr=_SCREAMING_SNAKE_CASE   )
  						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('',_SCREAMING_SNAKE_CASE   )
  						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(_SCREAMING_SNAKE_CASE,remove_columns=[data_args.speech_file_column]   )
  			__A=  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				:   Union[str, Any]   ):
  									return example["duration_in_seconds"] <= data_args.max_duration_in_seconds
  
  						__A=  len(_SCREAMING_SNAKE_CASE   )
  						__A=  len(_SCREAMING_SNAKE_CASE   )
  						__A=  train_dataset.filter(_SCREAMING_SNAKE_CASE,remove_columns=['duration_in_seconds']   )
  						__A=  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}\""""   )
  			__A=  None
  
  			def prepare_dataset(_SCREAMING_SNAKE_CASE				:   str   ):
  						# 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(_SCREAMING_SNAKE_CASE   )
  						return batch
  
  			__A=  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,)
  			__A=  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,)
  
  			__A=  DataCollatorCTCWithPadding(processor=_SCREAMING_SNAKE_CASE,padding=_SCREAMING_SNAKE_CASE   )
  
  			def compute_metrics(_SCREAMING_SNAKE_CASE				:   Optional[int]   ):
  						__A=  pred.predictions
  						__A=  np.argmax(_SCREAMING_SNAKE_CASE,axis=-1   )
  
  						__A=  processor.tokenizer.pad_token_id
  
  						__A=  processor.batch_decode(_SCREAMING_SNAKE_CASE   )
  						# we do not want to group tokens when computing the metrics
  						__A=  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   )}\""""   )
  
  						__A=  wer_metric.compute(predictions=_SCREAMING_SNAKE_CASE,references=_SCREAMING_SNAKE_CASE   )
  
  						return {"wer": wer}
  
  			if model_args.freeze_feature_extractor:
  						model.freeze_feature_extractor()
  
  			__A=  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()
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import numpy as np
  from cva import destroyAllWindows, imread, imshow, waitKey
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Dict ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : int  )			->		Optional[int]:
  						__A=  0
  						# max greyscale value for #FFFFFF
  						__A=  int(self.get_greyscale(255 ,						255 ,						255  )  )
  
  						if not self.min_threshold < threshold < self.max_threshold:
  									__A=  F"""Factor value should be from 0 to {self.max_threshold}"""
  									raise ValueError(lowerCAmelCase_  )
  
  						__A=  input_img
  						__A=  threshold
  						__A,  __A=  self.input_img.shape[1], self.input_img.shape[0]
  
  						# error table size (+4 columns and +1 row) greater than input image because of
  						# lack of if statements
  						__A=  [
  						    [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
  						]
  						__A=  np.ones((self.width, self.height, 3) ,						np.uinta  ) * 255
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int  )			->		float:
  						return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red
  
  			def 				lowerCAmelCase    (     self       : str  )			->		None:
  						for y in range(self.height  ):
  									for x in range(self.width  ):
  												__A=  int(self.get_greyscale(*self.input_img[y][x]  )  )
  												if self.threshold > greyscale + self.error_table[y][x]:
  															__A=  (0, 0, 0)
  															__A=  greyscale + self.error_table[x][y]
  												else:
  															__A=  (255, 255, 255)
  															__A=  greyscale + self.error_table[x][y] - 255
  												self.error_table[y][x + 1] += int(8 / 32 * current_error  )
  												self.error_table[y][x + 2] += int(4 / 32 * current_error  )
  												self.error_table[y + 1][x] += int(8 / 32 * current_error  )
  												self.error_table[y + 1][x + 1] += int(4 / 32 * current_error  )
  												self.error_table[y + 1][x + 2] += int(2 / 32 * current_error  )
  												self.error_table[y + 1][x - 1] += int(4 / 32 * current_error  )
  												self.error_table[y + 1][x - 2] += int(2 / 32 * current_error  )
  
  
  if __name__ == "__main__":
  					# create Burke's instances with original images in greyscale
  					UpperCAmelCase__      =							[
  					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
  					    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
  					]
  
  					for burkes in burkes_instances:
  										burkes.process()
  
  					for burkes in burkes_instances:
  										imshow(
  										    F"""Original image with dithering threshold: {burkes.threshold}""",
  										    burkes.output_img,
  										)
  
  					waitKey(0)
  					destroyAllWindows()
  
  
  
  