





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


__lowerCamelCase :  Any										=  logging.get_logger(__name__)

__lowerCamelCase :  Optional[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       A__     ( __snake_case ,     __snake_case    ):
      _UpperCAmelCase			:List[Any]							=     'swin'

      _UpperCAmelCase			:Optional[int]							=     {
          '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_=0.02 ,							A_=1e-5 ,							A_=32 ,							A_=None ,							A_=None ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(**A_     )

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



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Tuple							=     version.parse('1.11'    )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 1e-4







from math import loga



def 	A_       (  _lowerCAmelCase  )		->				int:
 if a < 0:
  raise ValueError("Input value must be a positive integer"  )
 elif isinstance(_lowerCAmelCase							,     _lowerCAmelCase  ):
  raise TypeError("Input value must be a 'int' type"  )
 return 0 if (a == 0) else int(loga(a & -a  )  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
