





'''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'''
			)










'''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)



