



"""simple docstring"""






def 			_lowercase				(  )						->   list[list[int]]:
			return [list(range(1_000 - i					,-1_000 - i					,-1	)	) for i in range(1_000	)]


__snake_case							:   Optional[int]      =    generate_large_matrix()
__snake_case							:   Union[str, Any]      =    (
    [[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 			_lowercase				(  __snake_case	)						->   None:
			assert all(row == sorted(__snake_case					,reverse=__snake_case	) for row in grid	)
			assert all(list(__snake_case	) == sorted(__snake_case					,reverse=__snake_case	) for col in zip(*__snake_case	)	)


def 			_lowercase				(  __snake_case	)						->   int:
			__lowerCAmelCase      :  int												=       0
			__lowerCAmelCase      :  List[Any]												=       len(__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:
						__lowerCAmelCase      :  Any												=       (left + right) // 2
						__lowerCAmelCase      :  Union[str, Any]												=       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:
									__lowerCAmelCase      :  List[Any]												=       mid + 1
						else:
									__lowerCAmelCase      :  Tuple												=       mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
			return len(__snake_case	)


def 			_lowercase				(  __snake_case	)						->   int:
			__lowerCAmelCase      :  List[Any]												=       0
			__lowerCAmelCase      :  Union[str, Any]												=       len(grid[0]	)

			for i in range(len(__snake_case	)	):
						__lowerCAmelCase      :  Optional[int]												=       find_negative_index(grid[i][:bound]	)
						total += bound
			return (len(__snake_case	) * len(grid[0]	)) - total


def 			_lowercase				(  __snake_case	)						->   int:
			return len([number for row in grid for number in row if number < 0]	)


def 			_lowercase				(  __snake_case	)						->   int:
			__lowerCAmelCase      :  List[Any]												=       0
			for row in grid:
						for i, number in enumerate(__snake_case	):
									if number < 0:
												total += len(__snake_case	) - i
												break
			return total


def 			_lowercase				(  )						->   None:
			from timeit import timeit

			print("Running benchmarks"	)
			__lowerCAmelCase      :  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
			):
						__lowerCAmelCase      :  Optional[int]												=       timeit(F"""{func}(grid=grid)"""					,setup=__snake_case					,number=500	)
						print(F"""{func}() took {time:0.4f} seconds"""	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__snake_case							:   Optional[int]      =    logging.get_logger(__name__)



class 			A__   (  __SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	'maskformer-swin'

	SCREAMING_SNAKE_CASE          =	{
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		int=224       ,			_SCREAMING_SNAKE_CASE:		Tuple=4       ,			_SCREAMING_SNAKE_CASE:		int=3       ,			_SCREAMING_SNAKE_CASE:		List[Any]=96       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=[2, 2, 6, 2]       ,			_SCREAMING_SNAKE_CASE:		Any=[3, 6, 12, 24]       ,			_SCREAMING_SNAKE_CASE:		List[str]=7       ,			_SCREAMING_SNAKE_CASE:		List[str]=4.0       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=True       ,			_SCREAMING_SNAKE_CASE:		Tuple=0.0       ,			_SCREAMING_SNAKE_CASE:		Any=0.0       ,			_SCREAMING_SNAKE_CASE:		Any=0.1       ,			_SCREAMING_SNAKE_CASE:		str="gelu"       ,			_SCREAMING_SNAKE_CASE:		Tuple=False       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=0.02       ,			_SCREAMING_SNAKE_CASE:		str=1e-5       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=None       ,			_SCREAMING_SNAKE_CASE:		str=None       ,			**_SCREAMING_SNAKE_CASE:		Union[str, Any]       ,			)				->       List[str]:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  int												=       image_size
				__lowerCAmelCase      :  Any												=       patch_size
				__lowerCAmelCase      :  Tuple												=       num_channels
				__lowerCAmelCase      :  Any												=       embed_dim
				__lowerCAmelCase      :  Any												=       depths
				__lowerCAmelCase      :  Dict												=       len(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       num_heads
				__lowerCAmelCase      :  Tuple												=       window_size
				__lowerCAmelCase      :  Dict												=       mlp_ratio
				__lowerCAmelCase      :  Any												=       qkv_bias
				__lowerCAmelCase      :  Union[str, Any]												=       hidden_dropout_prob
				__lowerCAmelCase      :  int												=       attention_probs_dropout_prob
				__lowerCAmelCase      :  Tuple												=       drop_path_rate
				__lowerCAmelCase      :  int												=       hidden_act
				__lowerCAmelCase      :  Optional[int]												=       use_absolute_embeddings
				__lowerCAmelCase      :  List[str]												=       layer_norm_eps
				__lowerCAmelCase      :  Any												=       initializer_range
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				__lowerCAmelCase      :  Optional[Any]												=       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE) - 1))
				__lowerCAmelCase      :  Any												=       ["stem"] + [F"""stage{idx}""" for idx in range(1       ,			len(_SCREAMING_SNAKE_CASE) + 1)]
				__lowerCAmelCase						,	__lowerCAmelCase      :  List[str]												=       get_aligned_output_features_output_indices(
				    out_features=_SCREAMING_SNAKE_CASE       ,			out_indices=_SCREAMING_SNAKE_CASE       ,			stage_names=self.stage_names)