

"""simple docstring"""


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_   :      int         =  """AutoTokenizer"""

				def __init__(	self							:				Dict     ,  UpperCamelCase__							:				Optional[Any]=None     ,  UpperCamelCase__							:				List[Any]=None     ,  **UpperCamelCase__							:				int)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			None
						if "feature_extractor" in kwargs:
								warnings.warn(
								    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
								    " instead."     ,  UpperCamelCase__     ,  )
								__lowerCAmelCase:       str          =			kwargs.pop("feature_extractor")

						__lowerCAmelCase:       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__(UpperCamelCase__     ,  UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			self.image_processor
						__lowerCAmelCase:       Dict          =			False

				def __call__(	self							:				Tuple     ,  *UpperCamelCase__							:				Optional[Any]     ,  **UpperCamelCase__							:				Any)->   str:



						'''simple docstring'''


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

						__lowerCAmelCase:       List[Any]          =			kwargs.pop("images"     ,  UpperCamelCase__)
						__lowerCAmelCase:       str          =			kwargs.pop("text"     ,  UpperCamelCase__)
						if len(UpperCamelCase__) > 0:
								__lowerCAmelCase:       Optional[Any]          =			args[0]
								__lowerCAmelCase:       Tuple          =			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:
								__lowerCAmelCase:       Tuple          =			self.image_processor(UpperCamelCase__     ,  *UpperCamelCase__     ,  **UpperCamelCase__)
						if text is not None:
								__lowerCAmelCase:       int          =			self.tokenizer(UpperCamelCase__     ,  **UpperCamelCase__)

						if text is None:
								return inputs
						elif images is None:
								return encodings
						else:
								__lowerCAmelCase:       List[Any]          =			encodings["input_ids"]
								return inputs

				def 							lowercase_     (	self							:				Optional[Any]     ,  *UpperCamelCase__							:				Union[str, Any]     ,  **UpperCamelCase__							:				int)->   Any:



						'''simple docstring'''


						return self.tokenizer.batch_decode(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Dict     ,  *UpperCamelCase__							:				List[Any]     ,  **UpperCamelCase__							:				Optional[Any])->   Optional[int]:



						'''simple docstring'''


						return self.tokenizer.decode(*UpperCamelCase__     ,  **UpperCamelCase__)

				@contextmanager
				def 							lowercase_     (	self							:				Optional[int])->   List[Any]:



						'''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.")
						__lowerCAmelCase:       List[str]          =			True
						__lowerCAmelCase:       int          =			self.tokenizer
						yield
						__lowerCAmelCase:       Union[str, Any]          =			self.image_processor
						__lowerCAmelCase:       int          =			False

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Optional[Any]     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				Optional[Any]=None)->   List[Any]:



						'''simple docstring'''


						if added_vocab is None:
								__lowerCAmelCase:       Tuple          =			self.tokenizer.get_added_vocab()

						__lowerCAmelCase:       int          =			{}

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

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

						if len(UpperCamelCase__):
								return [output] if is_inner_value else output
						else:
								return [] if is_inner_value else {"text_sequence": tokens}

				@property
				def 							lowercase_     (	self							:				List[str])->   Optional[int]:



						'''simple docstring'''


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




				@property
				def 							lowercase_     (	self							:				Tuple)->   int:



						'''simple docstring'''


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





"""simple docstring"""


from __future__ import annotations






def     a__    (    __SCREAMING_SNAKE_CASE )	->	bool:
		__lowerCAmelCase:       Tuple          =			str(__SCREAMING_SNAKE_CASE )
		return len(__SCREAMING_SNAKE_CASE ) == 9 and set(__SCREAMING_SNAKE_CASE ) == set("123456789" )






def     a__    (    )	->	int | None:
		for base_num in range(9_9_9_9					, 4_9_9_9					, -1 ):
				__lowerCAmelCase:       Tuple          =			1_0_0_0_0_2 * base_num
				if is_9_pandigital(__SCREAMING_SNAKE_CASE ):
						return candidate

		for base_num in range(3_3_3					, 9_9					, -1 ):
				__lowerCAmelCase:       int          =			1_0_0_2_0_0_3 * base_num
				if is_9_pandigital(__SCREAMING_SNAKE_CASE ):
						return candidate

		return None


if __name__ == "__main__":
			print(F'''{solution() = }''')


