

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a_      =     logging.get_logger(__name__)

a_      =     {
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class _UpperCamelCase				(				__A				,						__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='swin'

			lowerCamelCase__							     ={
			    'num_attention_heads': 'num_heads',
			    'num_hidden_layers': 'num_layers',
			}
			def __init__(      self				:			Union[str, Any]   ,   a				:			Tuple=224   ,   a				:			Optional[Any]=4   ,   a				:			Optional[Any]=3   ,   a				:			Optional[Any]=96   ,   a				:			Union[str, Any]=[2, 2, 6, 2]   ,   a				:			Tuple=[3, 6, 12, 24]   ,   a				:			Union[str, Any]=7   ,   a				:			List[str]=4.0   ,   a				:			Optional[Any]=True   ,   a				:			Union[str, Any]=0.0   ,   a				:			Optional[int]=0.0   ,   a				:			Union[str, Any]=0.1   ,   a				:			List[Any]="gelu"   ,   a				:			Union[str, Any]=False   ,   a				:			Any=0.02   ,   a				:			str=1e-5   ,   a				:			Any=32   ,   a				:			Optional[int]=None   ,   a				:			str=None   ,   **a				:			Dict   ,   )				->							Any:

				"""simple docstring"""

				super().__init__(**a						)

				SCREAMING_SNAKE_CASE :   int					  =							image_size
				SCREAMING_SNAKE_CASE :   Any					  =							patch_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							num_channels
				SCREAMING_SNAKE_CASE :   Any					  =							embed_dim
				SCREAMING_SNAKE_CASE :   List[str]					  =							depths
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							len(a						)
				SCREAMING_SNAKE_CASE :   Dict					  =							num_heads
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							window_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							mlp_ratio
				SCREAMING_SNAKE_CASE :   int					  =							qkv_bias
				SCREAMING_SNAKE_CASE :   List[Any]					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   List[Any]					  =							drop_path_rate
				SCREAMING_SNAKE_CASE :   int					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							use_absolute_embeddings
				SCREAMING_SNAKE_CASE :   Tuple					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   Tuple					  =							encoder_stride
				# 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
				SCREAMING_SNAKE_CASE :   int					  =							int(embed_dim * 2 ** (len(a						) - 1)						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							["stem"] + [F"stage{idx}" for idx in range(1   ,   len(a						) + 1						)]
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Any					  =							get_aligned_output_features_output_indices(
				    out_features=a   ,   out_indices=a   ,   stage_names=self.stage_names						)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     =version.parse('1.11' )
			@property
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				    ]						)






			@property
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							float:

				"""simple docstring"""

				return 1e-4


from __future__ import annotations

def 			lowerCamelCase__			(       _a):
	if len(_a) == 0:
		return []
	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							min(_a), max(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							int(max_value - min_value) + 1
	SCREAMING_SNAKE_CASE :   list[list]					  =							[[] for _ in range(_a)]

	for i in my_list:
		buckets[int(i - min_value)].append(_a)

	return [v for bucket in buckets for v in sorted(_a)]


if __name__ == "__main__":
			from doctest import testmod

			testmod()
			assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
			assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]