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


A     :  str					     =       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					(__A					):
		"""simple docstring"""







		lowerCamelCase__     =						'''swinv2'''

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







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


