





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


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

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


class 						lowerCamelCase       (__lowerCamelCase      ):







		"""simple docstring"""




		UpperCAmelCase_      						=   "swinv2"

		UpperCAmelCase_      						=   {
		    "num_attention_heads": "num_heads",
		    "num_hidden_layers": "num_layers",
		}






		def __init__( self     :					Any,			_UpperCAmelCase     :					int=2_2_4,			_UpperCAmelCase     :					Optional[int]=4,			_UpperCAmelCase     :					Any=3,			_UpperCAmelCase     :					str=9_6,			_UpperCAmelCase     :					Optional[Any]=[2, 2, 6, 2],			_UpperCAmelCase     :					Tuple=[3, 6, 1_2, 2_4],			_UpperCAmelCase     :					Any=7,			_UpperCAmelCase     :					Dict=4.0,			_UpperCAmelCase     :					Any=True,			_UpperCAmelCase     :					int=0.0,			_UpperCAmelCase     :					Optional[int]=0.0,			_UpperCAmelCase     :					Any=0.1,			_UpperCAmelCase     :					Union[str, Any]="gelu",			_UpperCAmelCase     :					Dict=False,			_UpperCAmelCase     :					str=0.02,			_UpperCAmelCase     :					List[Any]=1E-5,			_UpperCAmelCase     :					List[Any]=3_2,			**_UpperCAmelCase     :					Dict,			)				->			Optional[int]:





									"""simple docstring"""
									super().__init__(**_UpperCAmelCase						)

									SCREAMING_SNAKE_CASE__       :      Dict     =						image_size
									SCREAMING_SNAKE_CASE__       :      Dict     =						patch_size
									SCREAMING_SNAKE_CASE__       :      Dict     =						num_channels
									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						embed_dim
									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						depths
									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						len(_UpperCAmelCase						)
									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_heads
									SCREAMING_SNAKE_CASE__       :      Any     =						window_size
									SCREAMING_SNAKE_CASE__       :      str     =						mlp_ratio
									SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						qkv_bias
									SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_dropout_prob
									SCREAMING_SNAKE_CASE__       :      Any     =						attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__       :      int     =						drop_path_rate
									SCREAMING_SNAKE_CASE__       :      List[Any]     =						hidden_act
									SCREAMING_SNAKE_CASE__       :      Any     =						use_absolute_embeddings
									SCREAMING_SNAKE_CASE__       :      Dict     =						layer_norm_eps
									SCREAMING_SNAKE_CASE__       :      Tuple     =						initializer_range
									SCREAMING_SNAKE_CASE__       :      List[str]     =						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__       :      Any     =						int(embed_dim * 2 ** (len(_UpperCAmelCase						) - 1)						)
									SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						(0, 0, 0, 0)













def _a (  SCREAMING_SNAKE_CASE__      :  int       )	->    str:

							'''simple docstring'''






							if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														raise TypeError("'float' object cannot be interpreted as an integer"       )
							if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														raise TypeError("'str' object cannot be interpreted as an integer"       )

							if num == 0:
														return "0b0"

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						False

							if num < 0:
														SCREAMING_SNAKE_CASE__       :      Any     =						True
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						-num

							SCREAMING_SNAKE_CASE__       :      list[int]     =						[]
							while num > 0:
														binary.insert(0			,				num % 2       )
														num >>= 1

							if negative:
														return "-0b" + "".join(str(SCREAMING_SNAKE_CASE__       ) for e in binary       )

							return "0b" + "".join(str(SCREAMING_SNAKE_CASE__       ) for e in binary       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






