


"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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

						__lowerCAmelCase				    =			update_area_of_max_square(_UpperCamelCase    , col + 1      )
						__lowerCAmelCase				    =			update_area_of_max_square(row + 1    , col + 1      )
						__lowerCAmelCase				    =			update_area_of_max_square(row + 1    , _UpperCamelCase      )

						if mat[row][col]:
									__lowerCAmelCase				    =			1 + min([right, diagonal, down]      )
									__lowerCAmelCase				    =			max(largest_square_area[0]    , _UpperCamelCase      )
									return sub_problem_sol
						else:
									return 0

			__lowerCAmelCase				    =			[0]
			update_area_of_max_square(0    , 0      )
			return largest_square_area[0]


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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

						__lowerCAmelCase				    =			update_area_of_max_square_using_dp_array(_UpperCamelCase    , col + 1    , _UpperCamelCase      )
						__lowerCAmelCase				    =			update_area_of_max_square_using_dp_array(row + 1    , col + 1    , _UpperCamelCase      )
						__lowerCAmelCase				    =			update_area_of_max_square_using_dp_array(row + 1    , _UpperCamelCase    , _UpperCamelCase      )

						if mat[row][col]:
									__lowerCAmelCase				    =			1 + min([right, diagonal, down]      )
									__lowerCAmelCase				    =			max(largest_square_area[0]    , _UpperCamelCase      )
									__lowerCAmelCase				    =			sub_problem_sol
									return sub_problem_sol
						else:
									return 0

			__lowerCAmelCase				    =			[0]
			__lowerCAmelCase				    =			[[-1] * cols for _ in range(_UpperCamelCase      )]
			update_area_of_max_square_using_dp_array(0    , 0    , _UpperCamelCase      )

			return largest_square_area[0]


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[[0] * (cols + 1) for _ in range(rows + 1      )]
			__lowerCAmelCase				    =			0
			for row in range(rows - 1    , -1    , -1      ):
						for col in range(cols - 1    , -1    , -1      ):
									__lowerCAmelCase				    =			dp_array[row][col + 1]
									__lowerCAmelCase				    =			dp_array[row + 1][col + 1]
									__lowerCAmelCase				    =			dp_array[row + 1][col]

									if mat[row][col] == 1:
												__lowerCAmelCase				    =			1 + min(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )
												__lowerCAmelCase				    =			max(dp_array[row][col]    , _UpperCamelCase      )
									else:
												__lowerCAmelCase				    =			0

			return largest_square_area


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[0] * (cols + 1)
			__lowerCAmelCase				    =			[0] * (cols + 1)
			__lowerCAmelCase				    =			0
			for row in range(rows - 1    , -1    , -1      ):
						for col in range(cols - 1    , -1    , -1      ):
									__lowerCAmelCase				    =			current_row[col + 1]
									__lowerCAmelCase				    =			next_row[col + 1]
									__lowerCAmelCase				    =			next_row[col]

									if mat[row][col] == 1:
												__lowerCAmelCase				    =			1 + min(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )
												__lowerCAmelCase				    =			max(current_row[col]    , _UpperCamelCase      )
									else:
												__lowerCAmelCase				    =			0
						__lowerCAmelCase				    =			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]]))








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







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'ClapFeatureExtractor'
 SCREAMING_SNAKE_CASE__      =       ('RobertaTokenizer', 'RobertaTokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  **_lowerCamelCase    ):
       a						:Dict 							=       kwargs.pop('''sampling_rate'''					,  _lowerCamelCase    )

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

       if text is not None:
             a						:Optional[int] 							=       self.tokenizer(_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if audios is not None:
             a						:Tuple 							=       self.feature_extractor(
                 _lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and audios is not None:
             a						:Union[str, Any] 							=       audio_features.input_features
             return encoding
       elif text is not None:
             return encoding
       else:
             return BatchEncoding(data=dict(**_lowerCamelCase    )					,  tensor_type=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.tokenizer.model_input_names
       a						:str 							=       self.feature_extractor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )





