


def 							UpperCamelCase_  (				__snake_case			:						int	,			__snake_case			:						int	,			__snake_case			:						int	,			__snake_case			:						int     )->      int:




						'''simple docstring'''
						return len_board * len_board_column * row + column
def 							UpperCamelCase_  (				__snake_case			:						list[list[str]]	,			__snake_case			:						str	,			__snake_case			:						int	,			__snake_case			:						int	,			__snake_case			:						int	,			__snake_case			:						set[int]	,			)->      bool:




						'''simple docstring'''

						if board[row][column] != word[word_index]:
												return False

						if word_index == len(__snake_case     ) - 1:
												return True

						__magic_name__ 		=			[(0, 1), (0, -1), (-1, 0), (1, 0)]
						__magic_name__ 		=			len(__snake_case     )
						__magic_name__ 		=			len(board[0]     )
						for direction in traverts_directions:
												__magic_name__ 		=			row + direction[0]
												__magic_name__ 		=			column + direction[1]
												if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
																		continue

												__magic_name__ 		=			get_point_key(__snake_case	,			__snake_case	,			__snake_case	,			__snake_case     )
												if key in visited_points_set:
																		continue

												visited_points_set.add(__snake_case     )
												if exits_word(__snake_case	,			__snake_case	,			__snake_case	,			__snake_case	,			word_index + 1	,			__snake_case     ):
																		return True

												visited_points_set.remove(__snake_case     )

						return False
def 							UpperCamelCase_  (				__snake_case			:						list[list[str]]	,			__snake_case			:						str     )->      bool:




						'''simple docstring'''
						__magic_name__ 		=			(
						    'The board should be a non empty matrix of single chars strings.'
						)

						__magic_name__ 		=			len(__snake_case     )
						if not isinstance(__snake_case	,			__snake_case     ) or len(__snake_case     ) == 0:
												raise ValueError(__snake_case     )

						for row in board:
												if not isinstance(__snake_case	,			__snake_case     ) or len(__snake_case     ) == 0:
																		raise ValueError(__snake_case     )

												for item in row:
																		if not isinstance(__snake_case	,			__snake_case     ) or len(__snake_case     ) != 1:
																								raise ValueError(__snake_case     )

    # Validate word
						if not isinstance(__snake_case	,			__snake_case     ) or len(__snake_case     ) == 0:
												raise ValueError(
												    'The word parameter should be a string of length greater than 0.'     )

						__magic_name__ 		=			len(board[0]     )
						for i in range(__snake_case     ):
												for j in range(__snake_case     ):
																		if exits_word(
																		    __snake_case	,			__snake_case	,			__snake_case	,			__snake_case	,			0	,			{get_point_key(__snake_case	,			__snake_case	,			__snake_case	,			__snake_case     )}     ):
																								return True

						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:					Tuple 				= "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:					Dict 				= "AutoTokenizer"
					def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			**_lowerCAmelCase					:	List[str]     )       ->				Optional[int]:



											'''simple docstring'''





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

											__magic_name__ 		=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You have to specify an image_processor.'     )
											if tokenizer is None:
																	raise ValueError('You have to specify a tokenizer.'     )

											super().__init__(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			self.image_processor
					def __call__(	self					:	str  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	int=None  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	int     )       ->				int:



											'''simple docstring'''





											if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																	__magic_name__ 		=			self.tokenizer(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if images is not None:
																	__magic_name__ 		=			self.image_processor(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if text is not None and images is not None:
																	__magic_name__ 		=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**_lowerCAmelCase     )  ,			tensor_type=_lowerCAmelCase     )
					def   a (	self					:	Optional[int]  ,			*_lowerCAmelCase					:	Optional[int]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]  ,			*_lowerCAmelCase					:	str  ,			**_lowerCAmelCase					:	Tuple     )       ->				int:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					@property
					def   a (	self					:	List[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer.model_input_names
											__magic_name__ 		=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
					@property
					def   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





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







					@property
					def   a (	self					:	Dict     )       ->				Tuple:



											'''simple docstring'''





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

