





"""simple docstring"""






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	Union[str, Any]				     =				["image_processor", "tokenizer"]
				__lowerCamelCase				:	List[Any]				     =				"AutoImageProcessor"
				__lowerCamelCase				:	List[Any]				     =				"AutoTokenizer"
				def __init__(		self    :			int	, a_    :			List[str]=None	, a_    :			Optional[int]=None	, **a_    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	List[Any]		 =			None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead."	, a_	, )
										a__  :	Dict		 =			kwargs.pop("feature_extractor"    )

							a__  :	Optional[Any]		 =			image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."    )
							if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."    )

							super().__init__(a_	, a_    )
							a__  :	Optional[Any]		 =			self.image_processor
							a__  :	Any		 =			False
				def __call__(		self    :			List[str]	, *a_    :			Tuple	, **a_    :			int    )	->	Dict:



							'''simple docstring'''



							if self._in_target_context_manager:
										return self.current_processor(*a_	, **a_    )

							a__  :	Tuple		 =			kwargs.pop("images"	, a_    )
							a__  :	Tuple		 =			kwargs.pop("text"	, a_    )
							if len(a_    ) > 0:
										a__  :	Tuple		 =			args[0]
										a__  :	List[Any]		 =			args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."    )

							if images is not None:
										a__  :	List[Any]		 =			self.image_processor(a_	, *a_	, **a_    )
							if text is not None:
										a__  :	str		 =			self.tokenizer(a_	, **a_    )

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a__  :	int		 =			encodings["input_ids"]
										return inputs
				def       UpperCAmelCase      (		self    :			List[Any]	, *a_    :			List[str]	, **a_    :			Optional[Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							return self.tokenizer.batch_decode(*a_	, **a_    )
				def       UpperCAmelCase      (		self    :			int	, *a_    :			List[str]	, **a_    :			Any    )	->	Tuple:



							'''simple docstring'''



							return self.tokenizer.decode(*a_	, **a_    )
				@contextmanager
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[int]:



							'''simple docstring'''



							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."    )
							a__  :	int		 =			True
							a__  :	Any		 =			self.tokenizer
							yield
							a__  :	Tuple		 =			self.image_processor
							a__  :	Any		 =			False
				def       UpperCAmelCase      (		self    :			Optional[int]	, a_    :			Tuple	, a_    :			List[Any]=False	, a_    :			Any=None    )	->	Union[str, Any]:



							'''simple docstring'''



							if added_vocab is None:
										a__  :	int		 =			self.tokenizer.get_added_vocab()

							a__  :	Dict		 =			{}

							while tokens:
										a__  :	int		 =			re.search(R"<s_(.*?)>"	, a_	, re.IGNORECASE    )
										if start_token is None:
													break
										a__  :	str		 =			start_token.group(1    )
										a__  :	Optional[int]		 =			re.search(RF"</s_{key}>"	, a_	, re.IGNORECASE    )
										a__  :	int		 =			start_token.group()
										if end_token is None:
													a__  :	int		 =			tokens.replace(a_	, ""    )
										else:
													a__  :	Dict		 =			end_token.group()
													a__  :	Dict		 =			re.escape(a_    )
													a__  :	Optional[Any]		 =			re.escape(a_    )
													a__  :	List[str]		 =			re.search(F"{start_token_escaped}(.*?){end_token_escaped}"	, a_	, re.IGNORECASE    )
													if content is not None:
																a__  :	Dict		 =			content.group(1    ).strip()
																if r"<s_" in content and r"</s_" in content:  # non-leaf node
																			a__  :	List[str]		 =			self.tokenajson(a_	, is_inner_value=a_	, added_vocab=a_    )
																			if value:
																						if len(a_    ) == 1:
																									a__  :	Optional[int]		 =			value[0]
																						a__  :	Optional[Any]		 =			value
																else:  # leaf nodes
																			a__  :	List[str]		 =			[]
																			for leaf in content.split(R"<sep/>"    ):
																						a__  :	Dict		 =			leaf.strip()
																						if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																									a__  :	Tuple		 =			leaf[1:-2]  # for categorical special tokens
																						output[key].append(a_    )
																			if len(output[key]    ) == 1:
																						a__  :	int		 =			output[key][0]

													a__  :	Optional[int]		 =			tokens[tokens.find(a_    ) + len(a_    ) :].strip()
													if tokens[:6] == r"<sep/>":  # non-leaf nodes
																return [output] + self.tokenajson(tokens[6:]	, is_inner_value=a_	, added_vocab=a_    )

							if len(a_    ):
										return [output] if is_inner_value else output
							else:
										return [] if is_inner_value else {"text_sequence": tokens}
				@property
				def       UpperCAmelCase      (		self    :			str    )	->	str:



							'''simple docstring'''



							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	, a_	, )
							return self.image_processor_class






				@property
				def       UpperCAmelCase      (		self    :			Any    )	->	Union[str, Any]:



							'''simple docstring'''



							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	, a_	, )
							return self.image_processor






"""simple docstring"""






import functools






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	Any		 =			len(lowerCAmelCase__			)
			a__  :	Optional[int]		 =			len(lowerCAmelCase__			)

			@functools.cache
			def min_distance(lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						a__  :	List[Any]		 =			int(worda[indexa] != worda[indexa]			)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1							,			lowerCAmelCase__			)							,			1 + min_distance(lowerCAmelCase__							,			indexa + 1			)							,			diff + min_distance(indexa + 1							,			indexa + 1			)							,			)

			return min_distance(0							,			0			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()