




"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int							,				A__:    int							,				A__:    list[list[int]]     )  ->     int:

			def update_area_of_max_square(A__:    int							,				A__:    int     ) -> int:
						# BASE CASE
						if row >= rows or col >= cols:
									return 0

						__lowerCamelCase					:	List[str]	       =				update_area_of_max_square(A__							,				col + 1     )
						__lowerCamelCase					:	Union[str, Any]	       =				update_area_of_max_square(row + 1							,				col + 1     )
						__lowerCamelCase					:	Union[str, Any]	       =				update_area_of_max_square(row + 1							,				A__     )

						if mat[row][col]:
									__lowerCamelCase					:	Union[str, Any]	       =				1 + min([right, diagonal, down]     )
									__lowerCamelCase					:	Tuple	       =				max(largest_square_area[0]							,				A__     )
									return sub_problem_sol
						else:
									return 0

			__lowerCamelCase					:	List[Any]	       =				[0]
			update_area_of_max_square(0							,				0     )
			return largest_square_area[0]





def 						UpperCAmelCase				(			A__:    int							,				A__:    int							,				A__:    list[list[int]]     )  ->     int:

			def update_area_of_max_square_using_dp_array(
			    A__:    int							,				A__:    int							,				A__:    list[list[int]]     ) -> int:
						if row >= rows or col >= cols:
									return 0
						if dp_array[row][col] != -1:
									return dp_array[row][col]

						__lowerCamelCase					:	List[str]	       =				update_area_of_max_square_using_dp_array(A__							,				col + 1							,				A__     )
						__lowerCamelCase					:	Optional[Any]	       =				update_area_of_max_square_using_dp_array(row + 1							,				col + 1							,				A__     )
						__lowerCamelCase					:	List[Any]	       =				update_area_of_max_square_using_dp_array(row + 1							,				A__							,				A__     )

						if mat[row][col]:
									__lowerCamelCase					:	Optional[Any]	       =				1 + min([right, diagonal, down]     )
									__lowerCamelCase					:	List[str]	       =				max(largest_square_area[0]							,				A__     )
									__lowerCamelCase					:	List[str]	       =				sub_problem_sol
									return sub_problem_sol
						else:
									return 0

			__lowerCamelCase					:	Optional[int]	       =				[0]
			__lowerCamelCase					:	int	       =				[[-1] * cols for _ in range(A__     )]
			update_area_of_max_square_using_dp_array(0							,				0							,				A__     )

			return largest_square_area[0]





def 						UpperCAmelCase				(			A__:    int							,				A__:    int							,				A__:    list[list[int]]     )  ->     int:
			__lowerCamelCase					:	List[Any]	       =				[[0] * (cols + 1) for _ in range(rows + 1     )]
			__lowerCamelCase					:	Optional[Any]	       =				0
			for row in range(rows - 1							,				-1							,				-1     ):
						for col in range(cols - 1							,				-1							,				-1     ):
									__lowerCamelCase					:	Optional[int]	       =				dp_array[row][col + 1]
									__lowerCamelCase					:	Optional[int]	       =				dp_array[row + 1][col + 1]
									__lowerCamelCase					:	Optional[int]	       =				dp_array[row + 1][col]

									if mat[row][col] == 1:
												__lowerCamelCase					:	List[Any]	       =				1 + min(A__							,				A__							,				A__     )
												__lowerCamelCase					:	Tuple	       =				max(dp_array[row][col]							,				A__     )
									else:
												__lowerCamelCase					:	Tuple	       =				0

			return largest_square_area





def 						UpperCAmelCase				(			A__:    int							,				A__:    int							,				A__:    list[list[int]]     )  ->     int:
			__lowerCamelCase					:	Dict	       =				[0] * (cols + 1)
			__lowerCamelCase					:	int	       =				[0] * (cols + 1)
			__lowerCamelCase					:	Optional[Any]	       =				0
			for row in range(rows - 1							,				-1							,				-1     ):
						for col in range(cols - 1							,				-1							,				-1     ):
									__lowerCamelCase					:	List[Any]	       =				current_row[col + 1]
									__lowerCamelCase					:	Union[str, Any]	       =				next_row[col + 1]
									__lowerCamelCase					:	Tuple	       =				next_row[col]

									if mat[row][col] == 1:
												__lowerCamelCase					:	List[Any]	       =				1 + min(A__							,				A__							,				A__     )
												__lowerCamelCase					:	Dict	       =				max(current_row[col]							,				A__     )
									else:
												__lowerCamelCase					:	Tuple	       =				0
						__lowerCamelCase					:	str	       =				current_row

			return largest_square_area


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))









"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						List[str]     		= 'Speech2TextFeatureExtractor'
					__a						:						Optional[int]     		= 'Speech2TextTokenizer'
					def __init__(    self						,	__a						,	__a     ):
								super().__init__(__a						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False
					def __call__(    self						,	*__a						,	**__a     ):
								# For backward compatibility
								if self._in_target_context_manager:
											return self.current_processor(*__a						,	**__a     )

								if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'     )
											__lowerCamelCase					:	str	       =				kwargs.pop('raw_speech'     )
								else:
											__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('audio'						,	__a     )
								__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('sampling_rate'						,	__a     )
								__lowerCamelCase					:	Optional[int]	       =				kwargs.pop('text'						,	__a     )
								if len(__a     ) > 0:
											__lowerCamelCase					:	Optional[int]	       =				args[0]
											__lowerCamelCase					:	str	       =				args[1:]

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

								if audio is not None:
											__lowerCamelCase					:	Dict	       =				self.feature_extractor(__a						,	*__a						,	sampling_rate=__a						,	**__a     )
								if text is not None:
											__lowerCamelCase					:	Optional[int]	       =				self.tokenizer(__a						,	**__a     )

								if text is None:
											return inputs
								elif audio is None:
											return encodings
								else:
											__lowerCamelCase					:	Any	       =				encodings['input_ids']
											return inputs
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.batch_decode(*__a						,	**__a     )
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.decode(*__a						,	**__a     )





					@contextmanager
					def     snake_case_				(    self     ):
								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 audio inputs, or in a separate call.'     )
								__lowerCamelCase					:	int	       =				True
								__lowerCamelCase					:	Union[str, Any]	       =				self.tokenizer
								yield
								__lowerCamelCase					:	Tuple	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False



