
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


_lowerCamelCase :       List[Any]     					=    logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_lowerCamelCase :       str     					=    direct_transformers_import(Path(__file__).parent)


_lowerCamelCase :       Any     					=    {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Optional[int]					     =      ["feature_extractor", "tokenizer"]
	# Names need to be attr_class for attr in attributes
	_UpperCAmelCase :	Any					     =      None
	_UpperCAmelCase :	str					     =      None
	_UpperCAmelCase :	Union[str, Any]					     =      None



	def __init__(						self			:					List[Any]    ,						*lowercase			:					Optional[int]    ,						**lowercase			:					List[str]			):


				'''simple docstring'''


				for key in kwargs:
							if key not in self.attributes:
										raise TypeError(f'''Unexpected keyword argument {key}.'''			)
				for arg, attribute_name in zip(lowercase    ,						self.attributes			):
							if attribute_name in kwargs:
										raise TypeError(f'''Got multiple values for argument {attribute_name}.'''			)
							else:
										_snake_case						=    arg

				if len(lowercase			) != len(self.attributes			):
							raise ValueError(
							    f'''This processor requires {len(self.attributes			)} arguments: {', '.join(self.attributes			)}. Got '''
							    f'''{len(lowercase			)} 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(lowercase    ,						lowercase			)
							if isinstance(lowercase    ,						lowercase			):
										_snake_case						=    tuple(getattr(lowercase    ,						lowercase			) for n in class_name if n is not None			)
							else:
										_snake_case						=    getattr(lowercase    ,						lowercase			)

							if not isinstance(lowercase    ,						lowercase			):
										raise ValueError(
										    f'''Received a {type(lowercase			).__name__} for argument {attribute_name}, but a {class_name} was expected.'''			)

							setattr(self    ,						lowercase    ,						lowercase			)



	def __repr__(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    [f'''- {name}: {repr(getattr(self    ,						lowercase			)			)}''' for name in self.attributes]
				_snake_case						=    '\n'.join(lowercase			)
				return f'''{self.__class__.__name__}:\n{attributes_repr}'''



	def 				A							(						self			:					Any    ,						lowercase			:					Tuple    ,						lowercase			:					bool = False    ,						**lowercase			:					str			):


				'''simple docstring'''


				os.makedirs(lowercase    ,						exist_ok=lowercase			)

				if push_to_hub:
							_snake_case						=    kwargs.pop('commit_message'    ,						lowercase			)
							_snake_case						=    kwargs.pop('repo_id'    ,						save_directory.split(os.path.sep			)[-1]			)
							_snake_case						=    self._create_repo(lowercase    ,						**lowercase			)
							_snake_case						=    self._get_files_timestamps(lowercase			)
				# 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    ,						lowercase			) for attribute_name in self.attributes]
							_snake_case						=    [(a.init_kwargs if isinstance(lowercase    ,						lowercase			) else a) for a in attrs]
							custom_object_save(self    ,						lowercase    ,						config=lowercase			)

				for attribute_name in self.attributes:
							_snake_case						=    getattr(self    ,						lowercase			)
							# Include the processor class in the attribute config so this processor can then be reloaded with the
							# `AutoProcessor` API.
							if hasattr(lowercase    ,						'_set_processor_class'			):
										attribute._set_processor_class(self.__class__.__name__			)
							attribute.save_pretrained(lowercase			)

				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    ,						lowercase			)
										if isinstance(lowercase    ,						lowercase			):
													del attribute.init_kwargs["auto_map"]

				if push_to_hub:
							self._upload_modified_files(
							    lowercase    ,						lowercase    ,						lowercase    ,						commit_message=lowercase    ,						token=kwargs.get('use_auth_token'			)    ,						)



	@classmethod
	def 				A							(						cls			:					Union[str, Any]    ,						lowercase			:					Union[str, os.PathLike]    ,						lowercase			:					Optional[Union[str, os.PathLike]] = None    ,						lowercase			:					bool = False    ,						lowercase			:					bool = False    ,						lowercase			:					Optional[Union[str, bool]] = None    ,						lowercase			:					str = "main"    ,						**lowercase			:					List[Any]    ,						):


				'''simple docstring'''


				_snake_case						=    cache_dir
				_snake_case						=    force_download
				_snake_case						=    local_files_only
				_snake_case						=    revision

				_snake_case						=    kwargs.pop('use_auth_token'    ,						lowercase			)
				if use_auth_token is not None:
							warnings.warn(
							    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'    ,						lowercase			)
							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(lowercase    ,						**lowercase			)
				return cls(*lowercase			)



	@classmethod
	def 				A							(						cls			:					str    ,						lowercase			:					Any="AutoProcessor"			):


				'''simple docstring'''


				if not isinstance(lowercase    ,						lowercase			):
							_snake_case						=    auto_class.__name__

				import transformers.models.auto as auto_module

				if not hasattr(lowercase    ,						lowercase			):
							raise ValueError(f'''{auto_class} is not a valid auto class.'''			)

				_snake_case						=    auto_class



	@classmethod
	def 				A							(						cls			:					int    ,						lowercase			:					Tuple    ,						**lowercase			:					Any			):


				'''simple docstring'''


				_snake_case						=    []
				for attribute_name in cls.attributes:
							_snake_case						=    getattr(cls    ,						f'''{attribute_name}_class'''			)
							if isinstance(lowercase    ,						lowercase			):
										_snake_case						=    tuple(getattr(lowercase    ,						lowercase			) if n is not None else None for n in class_name			)
										_snake_case						=    kwargs.get('use_fast'    ,						lowercase			)
										if use_fast and classes[1] is not None:
													_snake_case						=    classes[1]
										else:
													_snake_case						=    classes[0]
							else:
										_snake_case						=    getattr(lowercase    ,						lowercase			)

							args.append(attribute_class.from_pretrained(lowercase    ,						**lowercase			)			)
				return args





	@property
	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				_snake_case						=    getattr(self    ,						self.attributes[0]			)
				return getattr(lowercase    ,						'model_input_names'    ,						lowercase			)




_lowerCamelCase :       str     					=    copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
			_lowerCamelCase :       int     					=    ProcessorMixin.push_to_hub.__doc__.format(
			    object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
			)

_lowerCamelCase :       Union[str, Any]     					=    {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

_lowerCamelCase :       Optional[Any]     					=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowerCamelCase :       Dict     					=    [0 for x in range(len(graph))]
_lowerCamelCase :       Dict     					=    []






def  a_	( __lowercase     :			Optional[int] ,      __lowercase     :			Dict				)				->						Optional[int]:
			_snake_case						=    1
			while stack:
						_snake_case						=    stack.pop()
						print(__lowercase ,      clothes[current_clothing]				)
						order += 1






def  a_	( __lowercase     :			Union[str, Any] ,      __lowercase     :			Dict ,      __lowercase     :			Union[str, Any]				)				->						List[Any]:
			_snake_case						=    1
			for v in graph[u]:
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)

			stack.append(__lowercase				)






def  a_	( __lowercase     :			Dict ,      __lowercase     :			int				)				->						List[str]:
			for v in range(len(__lowercase				)				):
						if not visited[v]:
									depth_first_search(__lowercase ,      __lowercase ,      __lowercase				)


if __name__ == "__main__":
			topological_sort(graph, visited)
			print(stack)
			print_stack(stack, clothes)