





"""simple docstring"""




def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Any         =   len(_lowercase       )
						_UpperCamelCase      : Tuple         =   len(matrix[0]       )
						_UpperCamelCase      : str         =   min(_lowercase	,						_lowercase       )

						for row in range(_lowercase       ):
												# Check if diagonal element is not zero
												if matrix[row][row] != 0:
																		# Eliminate all the elements below the diagonal
																		for col in range(row + 1	,						_lowercase       ):
																								_UpperCamelCase      : Optional[int]         =   matrix[col][row] / matrix[row][row]
																								for i in range(_lowercase	,						_lowercase       ):
																														matrix[col][i] -= multiplier * matrix[row][i]
												else:
																		# Find a non-zero diagonal element to swap rows
																		_UpperCamelCase      : Dict         =   True
																		for i in range(row + 1	,						_lowercase       ):
																								if matrix[i][row] != 0:
																														_UpperCamelCase      : Dict         =   matrix[i], matrix[row]
																														_UpperCamelCase      : int         =   False
																														break
																		if reduce:
																								rank -= 1
																								for i in range(_lowercase       ):
																														_UpperCamelCase      : Union[str, Any]         =   matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
																		row -= 1

						return rank


if __name__ == "__main__":
						import doctest

						doctest.testmod()










"""simple docstring"""




from ...processing_utils import ProcessorMixin




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''image_processor''', '''feature_extractor''']
				UpperCamelCase								=   '''TvltImageProcessor'''
				UpperCamelCase								=   '''TvltFeatureExtractor'''






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



										super().__init__(image_processor=lowerCAmelCase__,			feature_extractor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   image_processor
										_UpperCamelCase      : int         =   feature_extractor






				def __call__(			self		:   List[str],			lowerCAmelCase__		:   Optional[int]=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Dict=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Optional[int]=False,			lowerCAmelCase__		:   str=False,			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   Optional[int],			)     ->				Dict:




										'''simple docstring'''



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

										_UpperCamelCase      : Optional[int]         =   None
										if images is not None:
																_UpperCamelCase      : Optional[int]         =   self.image_processor(lowerCAmelCase__,			mask_pixel=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if images_mixed is not None:
																_UpperCamelCase      : str         =   self.image_processor(lowerCAmelCase__,			is_mixed=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if audio is not None:
																_UpperCamelCase      : Union[str, Any]         =   self.feature_extractor(
																    lowerCAmelCase__,			*lowerCAmelCase__,			sampling_rate=lowerCAmelCase__,			mask_audio=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : str         =   {}
										if audio is not None:
																output_dict.update(lowerCAmelCase__							)
										if images is not None:
																output_dict.update(lowerCAmelCase__							)
										if images_mixed_dict is not None:
																output_dict.update(lowerCAmelCase__							)
										return output_dict






				@property
				def     snake_case     (			self		:   List[str]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.image_processor.model_input_names
										_UpperCamelCase      : List[Any]         =   self.feature_extractor.model_input_names
										return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names							)							)



