
"""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


lowercase_         =       logging.get_logger(__name__)

lowercase_         =       {
    "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_						(lowerCamelCase_	,			lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  Optional[Any]					 =     """swin"""

 UpperCamelCase_    :  Optional[int]					 =     {
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }




 def __init__(			self							:			str  , a_							:			List[Any]=2_24  , a_							:			Tuple=4  , a_							:			Dict=3  , a_							:			str=96  , a_							:			List[str]=[2, 2, 6, 2]  , a_							:			Tuple=[3, 6, 12, 24]  , a_							:			List[Any]=7  , a_							:			Tuple=4.0  , a_							:			Union[str, Any]=True  , a_							:			int=0.0  , a_							:			Optional[int]=0.0  , a_							:			int=0.1  , a_							:			List[str]="gelu"  , a_							:			Dict=False  , a_							:			Union[str, Any]=0.02  , a_							:			List[str]=1E-5  , a_							:			List[Any]=32  , a_							:			Optional[Any]=None  , a_							:			Dict=None  , **a_							:			Any  , )->       Optional[int]:



      """simple docstring"""

      super().__init__(**a_			)

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



class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 UpperCamelCase_    :  Tuple					 =     version.parse("""1.11"""    )




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



      """simple docstring"""

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






 @property
 def 	a   (			self							:			str			)->       float:



      """simple docstring"""

      return 1E-4



"""simple docstring"""







def       A_      (       lowercase = 1    ,			lowercase = 1000   )  ->				int:


     """simple docstring"""







     UpperCAmelCase_    :     Tuple            =	1
     UpperCAmelCase_    :     List[str]            =	0

     for divide_by_number in range(lowercase    ,			digit + 1   ):
          UpperCAmelCase_    :     list[int]            =	[]
          UpperCAmelCase_    :     Optional[int]            =	numerator
          for _ in range(1    ,			digit + 1   ):
               if now_divide in has_been_divided:
                    if longest_list_length < len(lowercase   ):
                         UpperCAmelCase_    :     Dict            =	len(lowercase   )
                         UpperCAmelCase_    :     Optional[int]            =	divide_by_number
               else:
                    has_been_divided.append(lowercase   )
                    UpperCAmelCase_    :     List[str]            =	now_divide * 10 % divide_by_number

     return the_digit


# Tests
if __name__ == "__main__":
       import doctest

       doctest.testmod()

