




"""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__				:      Dict	   = logging.get_logger(__name__)

lowercase__				:      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       a__	( UpperCamelCase__       , UpperCamelCase__				):
     a		:      Any		 =     """swin"""

     a		:      Any		 =     {
         """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=0.0_2  ,		A=1e-5  ,		A=32  ,		A=None  ,		A=None  ,		**A  ,		)  ->   Dict:



          '''simple docstring'''



          super().__init__(**__a			)

          a     =				image_size
          a     =				patch_size
          a     =				num_channels
          a     =				embed_dim
          a     =				depths
          a     =				len(__a			)
          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(__a			) - 1)			)
          a     =				["stem"] + [F'''stage{idx}''' for idx in range(1  ,		len(__a			) + 1			)]
          a  ,				a     =				get_aligned_output_features_output_indices(
              out_features=__a  ,		out_indices=__a  ,		stage_names=self.stage_names			)







class       a__	( UpperCamelCase__				):
     a		:      Tuple		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



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







     @property
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          return 1e-4







from __future__ import annotations

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					list[int]:
     a     =				2
     a     =				[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(__UpperCamelCase)
     if n > 1:
          factors.append(__UpperCamelCase)
     return factors


if __name__ == "__main__":
   import doctest

   doctest.testmod()

