code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import os
  import warnings
  from pathlib import Path
  from typing import Optional, Union
  
  from .dynamic_module_utils import custom_object_save
  from .tokenization_utils_base import PreTrainedTokenizerBase
  from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging
  
  
  a__				: int									=    logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  a__				: List[str]									=    direct_transformers_import(Path(__file__).parent)
  
  
  a__				: Optional[Any]									=    {
      '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
      '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
      '''AutoImageProcessor''': '''ImageProcessingMixin''',
  }
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     ['''feature_extractor''', '''tokenizer''']
  	# Names need to be attr_class for attr in attributes
  	__lowerCAmelCase						=     None
  	__lowerCAmelCase						=     None
  	__lowerCAmelCase						=     None
  
  
  
  
  
  	def __init__(		self			,					*UpperCamelCase_			,					**UpperCamelCase_		)			->		List[Any]:
  		# Sanitize args and kwargs
  		for key in kwargs:
  			if key not in self.attributes:
  				raise TypeError(F'''Unexpected keyword argument {key}.'''		)
  		for arg, attribute_name in zip(UpperCamelCase_			,					self.attributes		):
  			if attribute_name in kwargs:
  				raise TypeError(F'''Got multiple values for argument {attribute_name}.'''		)
  			else:
  				snake_case__  					=      arg
  
  		if len(UpperCamelCase_		) != len(self.attributes		):
  			raise ValueError(
  			    F'''This processor requires {len(self.attributes		)} arguments: {', '.join(self.attributes		)}. Got '''
  			    F'''{len(UpperCamelCase_		)} arguments instead.'''		)
  
  		# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
  		for attribute_name, arg in kwargs.items():
  			snake_case__  					=      getattr(self			,					F'''{attribute_name}_class'''		)
  			# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
  			snake_case__  					=      AUTO_TO_BASE_CLASS_MAPPING.get(UpperCamelCase_			,					UpperCamelCase_		)
  			if isinstance(UpperCamelCase_			,					UpperCamelCase_		):
  				snake_case__  					=      tuple(getattr(UpperCamelCase_			,					UpperCamelCase_		) for n in class_name if n is not None		)
  			else:
  				snake_case__  					=      getattr(UpperCamelCase_			,					UpperCamelCase_		)
  
  			if not isinstance(UpperCamelCase_			,					UpperCamelCase_		):
  				raise ValueError(
  				    F'''Received a {type(UpperCamelCase_		).__name__} for argument {attribute_name}, but a {class_name} was expected.'''		)
  
  			setattr(self			,					UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def __repr__(		self		)			->		Any:
  		snake_case__  					=      [F'''- {name}: {repr(getattr(self			,					UpperCamelCase_		)		)}''' for name in self.attributes]
  		snake_case__  					=      '\n'.join(UpperCamelCase_		)
  		return F'''{self.__class__.__name__}:\n{attributes_repr}'''
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_			,					UpperCamelCase_ = False			,					**UpperCamelCase_		)			->		Tuple:
  		os.makedirs(UpperCamelCase_			,					exist_ok=UpperCamelCase_		)
  
  		if push_to_hub:
  			snake_case__  					=      kwargs.pop('commit_message'			,					UpperCamelCase_		)
  			snake_case__  					=      kwargs.pop('repo_id'			,					save_directory.split(os.path.sep		)[-1]		)
  			snake_case__  					=      self._create_repo(UpperCamelCase_			,					**UpperCamelCase_		)
  			snake_case__  					=      self._get_files_timestamps(UpperCamelCase_		)
  		# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
  		# loaded from the Hub.
  		if self._auto_class is not None:
  			snake_case__  					=      [getattr(self			,					UpperCamelCase_		) for attribute_name in self.attributes]
  			snake_case__  					=      [(a.init_kwargs if isinstance(UpperCamelCase_			,					UpperCamelCase_		) else a) for a in attrs]
  			custom_object_save(self			,					UpperCamelCase_			,					config=UpperCamelCase_		)
  
  		for attribute_name in self.attributes:
  			snake_case__  					=      getattr(self			,					UpperCamelCase_		)
  			# Include the processor class in the attribute config so this processor can then be reloaded with the
  			# `AutoProcessor` API.
  			if hasattr(UpperCamelCase_			,					'_set_processor_class'		):
  				attribute._set_processor_class(self.__class__.__name__		)
  			attribute.save_pretrained(UpperCamelCase_		)
  
  		if self._auto_class is not None:
  			# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
  			for attribute_name in self.attributes:
  				snake_case__  					=      getattr(self			,					UpperCamelCase_		)
  				if isinstance(UpperCamelCase_			,					UpperCamelCase_		):
  					del attribute.init_kwargs["auto_map"]
  
  		if push_to_hub:
  			self._upload_modified_files(
  			    UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_			,					commit_message=UpperCamelCase_			,					token=kwargs.get('use_auth_token'		)			,					)
  
  
  
  
  
  	@classmethod
  	def      _snake_case      (		cls			,					UpperCamelCase_			,					UpperCamelCase_ = None			,					UpperCamelCase_ = False			,					UpperCamelCase_ = False			,					UpperCamelCase_ = None			,					UpperCamelCase_ = "main"			,					**UpperCamelCase_			,					)			->		str:
  		snake_case__  					=      cache_dir
  		snake_case__  					=      force_download
  		snake_case__  					=      local_files_only
  		snake_case__  					=      revision
  
  		snake_case__  					=      kwargs.pop('use_auth_token'			,					UpperCamelCase_		)
  		if use_auth_token is not None:
  			warnings.warn(
  			    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'			,					UpperCamelCase_		)
  			if token is not None:
  				raise ValueError(
  				    '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'		)
  			snake_case__  					=      use_auth_token
  
  		if token is not None:
  			# change to `token` in a follow-up PR
  			snake_case__  					=      token
  
  		snake_case__  					=      cls._get_arguments_from_pretrained(UpperCamelCase_			,					**UpperCamelCase_		)
  		return cls(*UpperCamelCase_		)
  
  
  
  
  
  	@classmethod
  	def      _snake_case      (		cls			,					UpperCamelCase_="AutoProcessor"		)			->		str:
  		if not isinstance(UpperCamelCase_			,					UpperCamelCase_		):
  			snake_case__  					=      auto_class.__name__
  
  		import transformers.models.auto as auto_module
  
  		if not hasattr(UpperCamelCase_			,					UpperCamelCase_		):
  			raise ValueError(F'''{auto_class} is not a valid auto class.'''		)
  
  		snake_case__  					=      auto_class
  
  
  
  
  
  	@classmethod
  	def      _snake_case      (		cls			,					UpperCamelCase_			,					**UpperCamelCase_		)			->		Optional[int]:
  		snake_case__  					=      []
  		for attribute_name in cls.attributes:
  			snake_case__  					=      getattr(cls			,					F'''{attribute_name}_class'''		)
  			if isinstance(UpperCamelCase_			,					UpperCamelCase_		):
  				snake_case__  					=      tuple(getattr(UpperCamelCase_			,					UpperCamelCase_		) if n is not None else None for n in class_name		)
  				snake_case__  					=      kwargs.get('use_fast'			,					UpperCamelCase_		)
  				if use_fast and classes[1] is not None:
  					snake_case__  					=      classes[1]
  				else:
  					snake_case__  					=      classes[0]
  			else:
  				snake_case__  					=      getattr(UpperCamelCase_			,					UpperCamelCase_		)
  
  			args.append(attribute_class.from_pretrained(UpperCamelCase_			,					**UpperCamelCase_		)		)
  		return args
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      getattr(self			,					self.attributes[0]		)
  		return getattr(UpperCamelCase_			,					'model_input_names'			,					UpperCamelCase_		)
  
  
  
  
  
  a__				: str									=    copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
  			a__				: Union[str, Any]									=    ProcessorMixin.push_to_hub.__doc__.format(
  			    object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
  			)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  a__				: Optional[int]									=    sys.argv[1]
  a__				: Any									=    sys.argv[2]
  a__				: str									=    int(sys.argv[3])
  
  a__				: str									=    0
  
  a__				: int									=    AutoTokenizer.from_pretrained(model_name_or_path)
  max_len -= tokenizer.num_special_tokens_to_add()
  
  with open(dataset, '''rt''') as f_p:
  			for line in f_p:
  						a__				: Tuple									=    line.rstrip()
  
  						if not line:
  									print(line)
  									a__				: List[str]									=    0
  									continue
  
  						a__				: Optional[int]									=    line.split()[0]
  
  						a__				: Tuple									=    len(tokenizer.tokenize(token))
  
  						# Token contains strange control characters like \x96 or \x95
  						# Just filter out the complete line
  						if current_subwords_len == 0:
  									continue
  
  						if (subword_len_counter + current_subwords_len) > max_len:
  									print('''''')
  									print(line)
  									a__				: List[str]									=    current_subwords_len
  									continue
  
  						subword_len_counter += current_subwords_len
  
  						print(line)
  
  
  
  