
'''simple docstring'''
from __future__ import annotations






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							if (direction == 1 and array[indexa] > array[indexa]) or (
							    direction == 0 and array[indexa] < array[indexa]
							):
														_snake_case       =  array[indexa], array[indexa]






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							if length > 1:
														_snake_case       =  int(length / 2				)
														for i in range(UpperCamelCase__							, low + middle				):
																					comp_and_swap(UpperCamelCase__							, UpperCamelCase__							, i + middle							, UpperCamelCase__				)
														bitonic_merge(UpperCamelCase__							, UpperCamelCase__							, UpperCamelCase__							, UpperCamelCase__				)
														bitonic_merge(UpperCamelCase__							, low + middle							, UpperCamelCase__							, UpperCamelCase__				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							if length > 1:
														_snake_case       =  int(length / 2				)
														bitonic_sort(UpperCamelCase__							, UpperCamelCase__							, UpperCamelCase__							, 1				)
														bitonic_sort(UpperCamelCase__							, low + middle							, UpperCamelCase__							, 0				)
														bitonic_merge(UpperCamelCase__							, UpperCamelCase__							, UpperCamelCase__							, UpperCamelCase__				)


if __name__ == "__main__":
				__lowerCAmelCase												=	input('Enter numbers separated by a comma:\n').strip()
				__lowerCAmelCase												=	[int(item.strip()) for item in user_input.split(',')]

				bitonic_sort(unsorted, 0, len(unsorted), 1)
				print('\nSorted array in ascending order is: ', end='')
				print(*unsorted, sep=', ')

				bitonic_merge(unsorted, 0, len(unsorted), 0)
				print('Sorted array in descending order is: ', end='')
				print(*unsorted, sep=', ')




'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase												=	logging.get_logger(__name__)

__lowerCAmelCase												=	{
    '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   (   __snake_case					,							__snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"nat"

							lowerCAmelCase_ 						=						{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}

							def __init__(self  ,  UpperCAmelCase=4  ,  UpperCAmelCase=3  ,  UpperCAmelCase=64  ,  UpperCAmelCase=[3, 4, 6, 5]  ,  UpperCAmelCase=[2, 4, 8, 16]  ,  UpperCAmelCase=7  ,  UpperCAmelCase=3.0  ,  UpperCAmelCase=True  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-5  ,  UpperCAmelCase=0.0  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  **UpperCAmelCase  ,  )					->       str:
														super().__init__(**UpperCAmelCase  )

														_snake_case       =  patch_size
														_snake_case       =  num_channels
														_snake_case       =  embed_dim
														_snake_case       =  depths
														_snake_case       =  len(UpperCAmelCase  )
														_snake_case       =  num_heads
														_snake_case       =  kernel_size
														_snake_case       =  mlp_ratio
														_snake_case       =  qkv_bias
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  drop_path_rate
														_snake_case       =  hidden_act
														_snake_case       =  layer_norm_eps
														_snake_case       =  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
														_snake_case       =  int(embed_dim * 2 ** (len(UpperCAmelCase  ) - 1)  )
														_snake_case       =  layer_scale_init_value
														_snake_case       =  ["""stem"""] + [f"""stage{idx}""" for idx in range(1  ,  len(UpperCAmelCase  ) + 1  )]
														_snake_case,							_snake_case       =  get_aligned_output_features_output_indices(
														    out_features=UpperCAmelCase  ,  out_indices=UpperCAmelCase  ,  stage_names=self.stage_names  )