

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  A						(							_UpperCAmelCase    ,							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'swin'

    lowerCamelCase			=							{
        'num_attention_heads': 'num_heads',
        'num_hidden_layers': 'num_layers',
    }


    def __init__(      self :       Union[str, Any],lowercase_ :       Union[str, Any]=2_2_4,lowercase_ :       List[str]=4,lowercase_ :       int=3,lowercase_ :       int=9_6,lowercase_ :       Optional[Any]=[2, 2, 6, 2],lowercase_ :       Optional[Any]=[3, 6, 1_2, 2_4],lowercase_ :       List[Any]=7,lowercase_ :       List[Any]=4.0,lowercase_ :       List[str]=True,lowercase_ :       Union[str, Any]=0.0,lowercase_ :       Dict=0.0,lowercase_ :       str=0.1,lowercase_ :       List[Any]="gelu",lowercase_ :       Any=False,lowercase_ :       Optional[Any]=0.02,lowercase_ :       List[str]=1E-5,lowercase_ :       Any=3_2,lowercase_ :       Tuple=None,lowercase_ :       Tuple=None,**lowercase_ :       List[Any],)->    Dict:





        '''simple docstring'''







        super().__init__(**lowercase_		)

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




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							version.parse('1.11' )


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





        '''simple docstring'''







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


    @property
    def 						snake_case__     (      self :       Optional[Any]		)->    float:





        '''simple docstring'''







        return 1E-4


"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE = 1000000   )	->int:
  a__:     int										=	limit + 1
  a__:     Optional[int]										=	[0] * limit
  for first_term in range(1		,     _SCREAMING_SNAKE_CASE   ):
    for n in range(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      a__:     List[Any]										=	first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:     Any										=	sum(1 for x in frequency[1:limit] if x == 10   )

  return count


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


