


"""simple docstring"""

from collections.abc import Iterable
from typing import Generic, TypeVar

UpperCAmelCase__  							=TypeVar("_T")





class    lowerCamelCase__						(		Generic[_T]    ):
      def __init__(		self	:	int      ,       A_	:	Any = None							):






       '''simple docstring'''


       __lowercase									=	list(iterable or []							)
       __lowercase									=	[]
      def __len__(		self	:	Union[str, Any]							):






       '''simple docstring'''


       return len(self._stacka							) + len(self._stacka							)
      def __repr__(		self	:	List[Any]							):






       '''simple docstring'''


       return F'''Queue({tuple(self._stacka[::-1] + self._stacka							)})'''
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       A_	:	str							):






       '''simple docstring'''


       self._stacka.append(__lowerCAmelCase							)






      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self._stacka.pop
       __lowercase									=	self._stacka.append

       if not self._stacka:
        while self._stacka:
         stacka_append(stacka_pop()							)

       if not self._stacka:
        raise IndexError("""Queue is empty"""							)
       return self._stacka.pop()


if __name__ == "__main__":
       from doctest import testmod

       testmod()




"""simple docstring"""

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


UpperCAmelCase__  							=logging.get_logger(__name__)

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





class    lowerCamelCase__						(		_a    ):
      a :     List[Any]											=		"""swinv2"""

      a :     Any											=		{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(		self	:	List[str]      ,       A_	:	Any=2_2_4      ,       A_	:	int=4      ,       A_	:	Optional[int]=3      ,       A_	:	List[Any]=9_6      ,       A_	:	List[Any]=[2, 2, 6, 2]      ,       A_	:	List[str]=[3, 6, 1_2, 2_4]      ,       A_	:	Union[str, Any]=7      ,       A_	:	int=4.0      ,       A_	:	List[str]=True      ,       A_	:	str=0.0      ,       A_	:	Any=0.0      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	int=False      ,       A_	:	List[Any]=0.02      ,       A_	:	Tuple=1e-5      ,       A_	:	Tuple=3_2      ,       **A_	:	int      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

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