




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase      :						List[Any]							=  logging.get_logger(__name__)

_lowerCamelCase      :						Optional[Any]							=  {
    '''microsoft/swinv2-tiny-patch4-window8-256''': (
        '''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
    ),
}


class 							UpperCamelCase_       (					a__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''swinv2'''

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


    def __init__(     self :      Any   ,					UpperCAmelCase__ :      List[str]=224   ,					UpperCAmelCase__ :      Dict=4   ,					UpperCAmelCase__ :      Tuple=3   ,					UpperCAmelCase__ :      Union[str, Any]=96   ,					UpperCAmelCase__ :      Any=[2, 2, 6, 2]   ,					UpperCAmelCase__ :      Tuple=[3, 6, 12, 24]   ,					UpperCAmelCase__ :      Optional[int]=7   ,					UpperCAmelCase__ :      Any=4.0   ,					UpperCAmelCase__ :      Dict=True   ,					UpperCAmelCase__ :      Any=0.0   ,					UpperCAmelCase__ :      List[Any]=0.0   ,					UpperCAmelCase__ :      int=0.1   ,					UpperCAmelCase__ :      Optional[Any]="gelu"   ,					UpperCAmelCase__ :      Any=False   ,					UpperCAmelCase__ :      str=0.02   ,					UpperCAmelCase__ :      List[str]=1e-5   ,					UpperCAmelCase__ :      Union[str, Any]=32   ,					**UpperCAmelCase__ :      List[str]   ,					)		->Optional[Any]:





           '''simple docstring'''

           super().__init__(**_UpperCAmelCase)

           A__ 					=       image_size
           A__ 					=       patch_size
           A__ 					=       num_channels
           A__ 					=       embed_dim
           A__ 					=       depths
           A__ 					=       len(_UpperCAmelCase)
           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 Swinv2 work with VisionEncoderDecoderModel
           # this indicates the channel dimension after the last stage of the model
           A__ 					=       int(embed_dim * 2 ** (len(_UpperCAmelCase) - 1))
           A__ 					=       (0, 0, 0, 0)









'''simple docstring'''




from __future__ import annotations






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

     __A		:					float						=			0
     __A		:					list[float]						=			[0] * len(__snake_case     )
     for i in index:
          if weight[i] <= capacity:
               __A		:					Optional[int]						=			1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __A		:					List[Any]						=			capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()