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 						lowerCamelCase			:
  	UpperCamelCase_     :				str								=  field(
  	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
  	UpperCamelCase_     :				Optional[str]								=  field(
  	    default=__lowerCamelCase   ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,		)
  	UpperCamelCase_     :				Optional[bool]								=  field(
  	    default=__lowerCamelCase   ,		metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}				)
  	UpperCamelCase_     :				Optional[bool]								=  field(
  	    default=__lowerCamelCase   ,		metadata={'help': 'Whether to log verbose messages or not.'}   ,		)
  
  
  
  def       a			(   a		,	a  )    ->Tuple:
  
  
  
  
  			'''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  )]		,	)
  			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 						lowerCamelCase			:
  	UpperCamelCase_     :				str								=  field(
  	    default=__lowerCamelCase   ,		metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
  	UpperCamelCase_     :				Optional[str]								=  field(
  	    default=__lowerCamelCase   ,		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=__lowerCamelCase   ,		metadata={'help': 'Resample loaded audio to target feature extractor\'s sampling rate or not.'}   ,		)
  	UpperCamelCase_     :				Optional[float]								=  field(
  	    default=__lowerCamelCase   ,		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=__lowerCamelCase   ,		metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}				)
  	UpperCamelCase_     :				Optional[int]								=  field(
  	    default=__lowerCamelCase   ,		metadata={'help': 'The number of processes to use for the preprocessing.'}   ,		)
  
  
  
  
  
  @dataclass
  class 						lowerCamelCase			:
  	UpperCamelCase_     :				bool								=  False
  	UpperCamelCase_     :				Optional[str]								=  None
  	UpperCamelCase_     :				Optional[str]								=  "|"
  	UpperCamelCase_     :				Optional[Dict[str, str]]								=  field(default_factory=__lowerCamelCase				)
  	UpperCamelCase_     :				Optional[Set[str]]								=  field(default_factory=__lowerCamelCase				)
  	UpperCamelCase_     :				Optional[Callable[[str], str]]								=  None
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:Tuple      ,    lowercase			:str )		->			int:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if name == "librispeech":
  							return cls()
  				if name == "timit":
  							return cls(
  							    do_lower_case=lowercase      ,    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(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    snake_case__    (    self			:str      ,    lowercase			:str )		->			str:
  
  
  
  
  
  
  
  				"""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    snake_case__    (    self			:Any      ,    lowercase			:ModelArguments )		->			WavaVecaProcessor:
  
  
  
  
  
  
  
  				"""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(lowercase      ,    lowercase )
  
  
  
  
  
  @dataclass
  class 						lowerCamelCase			:
  	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			:Any      ,    lowercase			:List[Dict[str, Union[List[int], torch.Tensor]]] )		->			Dict[str, torch.Tensor]:
  
  
  
  
  
  
  
  				"""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(
  				    lowercase      ,    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=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
  				SCREAMING_SNAKE_CASE    	=   labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 )      ,    -1_0_0 )
  
  				SCREAMING_SNAKE_CASE    	=   labels
  
  				return batch
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Tuple      ,    lowercase			:nn.Module      ,    lowercase			:Dict[str, Union[torch.Tensor, Any]] )		->			torch.Tensor:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				model.train()
  				SCREAMING_SNAKE_CASE    	=   self._prepare_inputs(lowercase )
  
  				if self.use_amp:
  							with autocast():
  										SCREAMING_SNAKE_CASE    	=   self.compute_loss(lowercase      ,    lowercase )
  				else:
  							SCREAMING_SNAKE_CASE    	=   self.compute_loss(lowercase      ,    lowercase )
  
  				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(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			(   )    ->Tuple:
  
  
  
  
  			'''simple docstring'''
  
  
  			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: 
  from __future__ import annotations
  
  
  def       a			(   a		,	a  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', '''''', ''''''
  			SCREAMING_SNAKE_CASE    	=   []
  
  			for character in message_part:
  						tmp.append(character_to_number[character]  )
  
  			for each in tmp:
  						one += each[0]
  						two += each[1]
  						three += each[2]
  
  			return one + two + three
  
  
  def       a			(   a		,	a  )    ->tuple[str, str, str]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''
  			SCREAMING_SNAKE_CASE    	=   []
  
  			for character in message_part:
  						this_part += character_to_number[character]
  
  			for digit in this_part:
  						tmp += digit
  						if len(a  ) == len(a  ):
  									result.append(a  )
  									SCREAMING_SNAKE_CASE    	=   ''''''
  
  			return result[0], result[1], result[2]
  
  
  def       a			(   a		,	a  )    ->tuple[str, str, dict[str, str], dict[str, str]]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   alphabet.replace(''' '''		,	''''''  ).upper()
  			SCREAMING_SNAKE_CASE    	=   message.replace(''' '''		,	''''''  ).upper()
  
  			# Check length and characters
  			if len(a  ) != 27:
  						raise KeyError('''Length of alphabet has to be 27.'''  )
  			for each in message:
  						if each not in alphabet:
  									raise ValueError('''Each message character has to be included in alphabet!'''  )
  
      # Generate dictionares
  			SCREAMING_SNAKE_CASE    	=   (
  			    '''111''',
  			    '''112''',
  			    '''113''',
  			    '''121''',
  			    '''122''',
  			    '''123''',
  			    '''131''',
  			    '''132''',
  			    '''133''',
  			    '''211''',
  			    '''212''',
  			    '''213''',
  			    '''221''',
  			    '''222''',
  			    '''223''',
  			    '''231''',
  			    '''232''',
  			    '''233''',
  			    '''311''',
  			    '''312''',
  			    '''313''',
  			    '''321''',
  			    '''322''',
  			    '''323''',
  			    '''331''',
  			    '''332''',
  			    '''333''',
  			)
  			SCREAMING_SNAKE_CASE    	=   {}
  			SCREAMING_SNAKE_CASE    	=   {}
  			for letter, number in zip(a		,	a  ):
  						SCREAMING_SNAKE_CASE    	=   number
  						SCREAMING_SNAKE_CASE    	=   letter
  
  			return message, alphabet, character_to_number, number_to_character
  
  
  def       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
  			    a		,	a  )
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''
  
  			for i in range(0		,	len(a  ) + 1		,	a  ):
  						encrypted_numeric += __encrypt_part(
  						    message[i : i + period]		,	a  )
  
  			for i in range(0		,	len(a  )		,	3  ):
  						encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
  			return encrypted
  
  
  def       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
  			    a		,	a  )
  			SCREAMING_SNAKE_CASE    	=   []
  			SCREAMING_SNAKE_CASE    	=   ''''''
  
  			for i in range(0		,	len(a  ) + 1		,	a  ):
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __decrypt_part(message[i : i + period]		,	a  )
  
  						for j in range(0		,	len(a  )  ):
  									decrypted_numeric.append(a[j] + b[j] + c[j]  )
  
  			for each in decrypted_numeric:
  						decrypted += number_to_character[each]
  
  			return decrypted
  
  
  if __name__ == "__main__":
  							__lowerCAmelCase				      =					'DEFEND THE EAST WALL OF THE CASTLE.'
  							__lowerCAmelCase				      =					encrypt_message(msg, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
  							__lowerCAmelCase				      =					decrypt_message(encrypted, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
  							print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')