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


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

A     :  List[str]					     =       {
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''swinv2'''

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







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

        SCREAMING_SNAKE_CASE_          =  image_size
        SCREAMING_SNAKE_CASE_          =  patch_size
        SCREAMING_SNAKE_CASE_          =  num_channels
        SCREAMING_SNAKE_CASE_          =  embed_dim
        SCREAMING_SNAKE_CASE_          =  depths
        SCREAMING_SNAKE_CASE_          =  len(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  num_heads
        SCREAMING_SNAKE_CASE_          =  window_size
        SCREAMING_SNAKE_CASE_          =  mlp_ratio
        SCREAMING_SNAKE_CASE_          =  qkv_bias
        SCREAMING_SNAKE_CASE_          =  hidden_dropout_prob
        SCREAMING_SNAKE_CASE_          =  attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_          =  drop_path_rate
        SCREAMING_SNAKE_CASE_          =  hidden_act
        SCREAMING_SNAKE_CASE_          =  use_absolute_embeddings
        SCREAMING_SNAKE_CASE_          =  layer_norm_eps
        SCREAMING_SNAKE_CASE_          =  initializer_range
        SCREAMING_SNAKE_CASE_          =  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
        SCREAMING_SNAKE_CASE_          =  int(embed_dim * 2 ** (len(__magic_name__   ) - 1)   )
        SCREAMING_SNAKE_CASE_          =  (0, 0, 0, 0)



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = None				):
      if version.parse(hfh.__version__				).release < version.parse("0.11.0"				).release:
            # old versions of hfh don't url-encode the file path
            SCREAMING_SNAKE_CASE_          =  quote(__UpperCamelCase				)
      return hfh.hf_hub_url(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"     ,      revision=__UpperCamelCase				)


