





"""simple docstring"""




def 	a_    (							):
						return [list(range(1000 - i	,						-1000 - i	,						-1       )       ) for i in range(1000       )]


UpperCamelCase_									=generate_large_matrix()
UpperCamelCase_									=(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)





def 	a_    (							_lowercase       ):
						assert all(row == sorted(SCREAMING_SNAKE_CASE_	,						reverse=SCREAMING_SNAKE_CASE_       ) for row in grid       )
						assert all(list(SCREAMING_SNAKE_CASE_       ) == sorted(SCREAMING_SNAKE_CASE_	,						reverse=SCREAMING_SNAKE_CASE_       ) for col in zip(*SCREAMING_SNAKE_CASE_       )       )





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Optional[int]         =   0
						_UpperCamelCase      : List[Any]         =   len(SCREAMING_SNAKE_CASE_       ) - 1

						# Edge cases such as no values or all numbers are negative.
						if not array or array[0] < 0:
												return 0

						while right + 1 > left:
												_UpperCamelCase      : List[Any]         =   (left + right) // 2
												_UpperCamelCase      : str         =   array[mid]

												# Num must be negative and the index must be greater than or equal to 0.
												if num < 0 and array[mid - 1] >= 0:
																		return mid

												if num >= 0:
																		_UpperCamelCase      : int         =   mid + 1
												else:
																		_UpperCamelCase      : Any         =   mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
						return len(SCREAMING_SNAKE_CASE_       )





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   0
						_UpperCamelCase      : int         =   len(grid[0]       )

						for i in range(len(SCREAMING_SNAKE_CASE_       )       ):
												_UpperCamelCase      : Tuple         =   find_negative_index(grid[i][:bound]       )
												total += bound
						return (len(SCREAMING_SNAKE_CASE_       ) * len(grid[0]       )) - total





def 	a_    (							_lowercase       ):
						return len([number for row in grid for number in row if number < 0]       )





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : str         =   0
						for row in grid:
												for i, number in enumerate(SCREAMING_SNAKE_CASE_       ):
																		if number < 0:
																								total += len(SCREAMING_SNAKE_CASE_       ) - i
																								break
						return total





def 	a_    (							):
						from timeit import timeit

						print('''Running benchmarks'''       )
						_UpperCamelCase      : Optional[int]         =   (
						    '''from __main__ import count_negatives_binary_search, '''
						    '''count_negatives_brute_force, count_negatives_brute_force_with_break, grid'''
						)
						for func in (
						    "count_negatives_binary_search",  # took 0.7727 seconds
						    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
						    "count_negatives_brute_force",  # took 12.8160 seconds
						):
												_UpperCamelCase      : str         =   timeit(F"""{func}(grid=grid)"""	,						setup=SCREAMING_SNAKE_CASE_	,						number=500       )
												print(F"""{func}() took {time:0.4f} seconds"""       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()










"""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							)							)



