
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any])      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     List[Any]         =      0
							__UpperCamelCase			:     str         =      len(_lowerCamelCase)

							for i in range(n - 1):
														for j in range(i + 1		,						_lowerCamelCase):
																					if arr[i] > arr[j]:
																												num_inversions += 1

							return num_inversions




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict)      ->					Dict:






							'''simple docstring'''





							if len(_lowerCamelCase) <= 1:
														return arr, 0
							__UpperCamelCase			:     Union[str, Any]         =      len(_lowerCamelCase) // 2
							__UpperCamelCase			:     str         =      arr[0:mid]
							__UpperCamelCase			:     int         =      arr[mid:]

							__UpperCamelCase			:     int         =      count_inversions_recursive(_lowerCamelCase)
							__UpperCamelCase			:     Dict         =      count_inversions_recursive(_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      _count_cross_inversions(_lowerCamelCase		,						_lowerCamelCase)

							__UpperCamelCase			:     Union[str, Any]         =      inversion_p + inversions_q + cross_inversions
							return c, num_inversions




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    List[Any])      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      []
							__UpperCamelCase			:     Optional[int]         =      0
							while i < len(_lowerCamelCase) and j < len(_lowerCamelCase):
														if p[i] > q[j]:
																					# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
																					# These are all inversions. The claim emerges from the
																					# property that P is sorted.
																					num_inversion += len(_lowerCamelCase) - i
																					r.append(q[j])
																					j += 1
														else:
																					r.append(p[i])
																					i += 1

							if i < len(_lowerCamelCase):
														r.extend(p[i:])
							else:
														r.extend(q[j:])

							return r, num_inversion




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      [10, 2, 1, 5, 5, 2, 11]

							# this arr has 8 inversions:
							# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

							__UpperCamelCase			:     List[str]         =      count_inversions_bf(_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      count_inversions_recursive(_lowerCamelCase)

							assert num_inversions_bf == num_inversions_recursive == 8

							print("number of inversions = "		,						_lowerCamelCase)

							# testing an array with zero inversion (a sorted arr_1)

							arr_a.sort()
							__UpperCamelCase			:     Any         =      count_inversions_bf(_lowerCamelCase)
							__UpperCamelCase			:     List[Any]         =      count_inversions_recursive(_lowerCamelCase)

							assert num_inversions_bf == num_inversions_recursive == 0
							print("number of inversions = "		,						_lowerCamelCase)

							# an empty list should also have zero inversions
							__UpperCamelCase			:     str         =      []
							__UpperCamelCase			:     int         =      count_inversions_bf(_lowerCamelCase)
							__UpperCamelCase			:     Optional[int]         =      count_inversions_recursive(_lowerCamelCase)

							assert num_inversions_bf == num_inversions_recursive == 0
							print("number of inversions = "		,						_lowerCamelCase)


if __name__ == "__main__":
		main()

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


lowercase						:      Optional[Any]									= logging.get_logger(__name__)

lowercase						:      List[str]									= {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class 				lowerCamelCase__							(       __lowercase  ,		__lowercase):




	'''simple docstring'''




	_A			=  'nat'

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







	def __init__(      self	:Optional[Any]  ,     a	:Any=4  ,     a	:Any=3  ,     a	:int=6_4  ,     a	:Dict=[3, 4, 6, 5]  ,     a	:Dict=[2, 4, 8, 1_6]  ,     a	:Optional[Any]=7  ,     a	:Any=3.0  ,     a	:Optional[int]=True  ,     a	:int=0.0  ,     a	:Union[str, Any]=0.0  ,     a	:List[Any]=0.1  ,     a	:str="gelu"  ,     a	:Union[str, Any]=0.02  ,     a	:Tuple=1E-5  ,     a	:str=0.0  ,     a	:Optional[int]=None  ,     a	:Dict=None  ,     **a	:Optional[Any]  ,     )	->				int:
								super().__init__(**a		)

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