
'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    '''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 			_snake_case		(     SCREAMING_SNAKE_CASE__						,						SCREAMING_SNAKE_CASE__							):
			lowerCAmelCase_					:				Union[str, Any]							=   'swin'

			lowerCAmelCase_					:				Optional[Any]							=   {
			    'num_attention_heads': 'num_heads',
			    'num_hidden_layers': 'num_layers',
			}




			def __init__( self    ,							a__=224    ,							a__=4    ,							a__=3    ,							a__=96    ,							a__=[2, 2, 6, 2]    ,							a__=[3, 6, 12, 24]    ,							a__=7    ,							a__=4.0    ,							a__=True    ,							a__=0.0    ,							a__=0.0    ,							a__=0.1    ,							a__="gelu"    ,							a__=False    ,							a__=0.0_2    ,							a__=1e-5    ,							a__=32    ,							a__=None    ,							a__=None    ,							**a__    ,							)    ->    Dict:

										'''simple docstring'''




										super().__init__(**_SCREAMING_SNAKE_CASE							)

										snake_case_      =				image_size
										snake_case_      =				patch_size
										snake_case_      =				num_channels
										snake_case_      =				embed_dim
										snake_case_      =				depths
										snake_case_      =				len(_SCREAMING_SNAKE_CASE							)
										snake_case_      =				num_heads
										snake_case_      =				window_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_      =				use_absolute_embeddings
										snake_case_      =				layer_norm_eps
										snake_case_      =				initializer_range
										snake_case_      =				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
										snake_case_      =				int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE							) - 1)							)
										snake_case_      =				["stem"] + [F'stage{idx}' for idx in range(1    ,							len(_SCREAMING_SNAKE_CASE							) + 1							)]
										snake_case_      =				get_aligned_output_features_output_indices(
										    out_features=_SCREAMING_SNAKE_CASE    ,							out_indices=_SCREAMING_SNAKE_CASE    ,							stage_names=self.stage_names							)




class 			_snake_case		(     SCREAMING_SNAKE_CASE__							):
			lowerCAmelCase_					:				List[Any]							=   version.parse("1.11"							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    Mapping[str, Mapping[int, str]]:

										'''simple docstring'''




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




			@property
			def        lowerCAmelCase__    ( self							)    ->    float:

										'''simple docstring'''




										return 1e-4










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	list[int]							,    snake_case							:	int					):

							'''simple docstring'''






							snake_case_      =				len(snake_case					)
							snake_case_      =				[[False] * (required_sum + 1) for _ in range(arr_len + 1					)]

							# for each arr value, a sum of zero(0) can be formed by not taking any element
							# hence True/1
							for i in range(arr_len + 1					):
														snake_case_      =				True

							# sum is not zero and set is empty then false
							for i in range(1							,    required_sum + 1					):
														snake_case_      =				False

							for i in range(1							,    arr_len + 1					):
														for j in range(1							,    required_sum + 1					):
																					if arr[i - 1] > j:
																												snake_case_      =				subset[i - 1][j]
																					if arr[i - 1] <= j:
																												snake_case_      =				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

							return subset[arr_len][required_sum]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




