
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					:			int			      =		logging.get_logger(__name__)

__lowerCAmelCase					:			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 						__lowerCAmelCase	(	lowerCAmelCase_ , lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			str        =    '''swin'''

      A__	:			int        =    {
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
      }




      def __init__(      self		:       List[Any]				,       _snake_case		:       int=224				,       _snake_case		:       Union[str, Any]=4				,       _snake_case		:       str=3				,       _snake_case		:       List[Any]=96				,       _snake_case		:       Tuple=[2, 2, 6, 2]				,       _snake_case		:       int=[3, 6, 12, 24]				,       _snake_case		:       Dict=7				,       _snake_case		:       Optional[int]=4.0				,       _snake_case		:       Dict=True				,       _snake_case		:       Optional[int]=0.0				,       _snake_case		:       Optional[Any]=0.0				,       _snake_case		:       Optional[int]=0.1				,       _snake_case		:       str="gelu"				,       _snake_case		:       Any=False				,       _snake_case		:       Dict=0.02				,       _snake_case		:       Optional[int]=1E-5				,       _snake_case		:       List[Any]=32				,       _snake_case		:       List[Any]=None				,       _snake_case		:       Tuple=None				,       **_snake_case		:       Tuple				,       ):
          super().__init__(**_snake_case				)

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






class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			List[str]        =    version.parse('''1.11'''      )




      @property
      def 		snake_case_			(      self		:       int				):
          return OrderedDict(
              [
                  ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
              ]				)




      @property
      def 		snake_case_			(      self		:       int				):
          return 1E-4




from math import factorial

__lowerCAmelCase					:			Dict			      =		{str(d): factorial(d) for d in range(10)}


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						) ->						int:
    __lowercase		:			int	    =       7 * factorial(9	) + 1
    return sum(i for i in range(3	,					__lowerCAmelCase	) if sum_of_digit_factorial(__lowerCAmelCase	) == i	)


if __name__ == "__main__":
 print(F'{solution() = }')


