






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


__magic_name__:					List[Any]							=     logging.get_logger(__name__)

__magic_name__:					Any							=     {
    "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__ (							__snake_case						,  __snake_case				):
	lowercase__						:					List[Any]											=   "swin"

	lowercase__						:					List[Any]											=   {
	    "num_attention_heads": "num_heads",
	    "num_hidden_layers": "num_layers",
	}
	def __init__(    self      ,       lowerCAmelCase__=2_24      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=96      ,       lowerCAmelCase__=[2, 2, 6, 2]      ,       lowerCAmelCase__=[3, 6, 12, 24]      ,       lowerCAmelCase__=7      ,       lowerCAmelCase__=4.0      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.0      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=1e-5      ,       lowerCAmelCase__=32      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       **lowerCAmelCase__      ,       )							->				int:
							super().__init__(**lowerCamelCase_			)

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





class 	snake_case__ (							__snake_case				):
	lowercase__						:					int											=   version.parse('''1.11'''				)
	@property
	def       __magic_name__    (    self			)							->				Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]			)







	@property
	def       __magic_name__    (    self			)							->				float:
							return 1e-4








def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if not isinstance(_A,   _A			) or number < 0:
												raise ValueError("""Input must be a non-negative integer"""			)

						__magic_name__      :       str       						= 0
						while number:
												# This way we arrive at next set bit (next 1) instead of looping
												# through each bit and checking for 1s hence the
												# loop won't run 32 times it will only run the number of `1` times
												number &= number - 1
												count += 1
						return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()
