





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 = 50_00_00_00       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						set()
							SCREAMING_SNAKE_CASE__       :      Dict     =						int((limit - 24) ** (1 / 2)       )

							SCREAMING_SNAKE_CASE__       :      Any     =						set(range(3			,				prime_square_limit + 1			,				2       )       )
							primes.add(2       )
							for p in range(3			,				prime_square_limit + 1			,				2       ):
														if p not in primes:
																					continue
														primes.difference_update(set(range(p * p			,				prime_square_limit + 1			,				SCREAMING_SNAKE_CASE__       )       )       )

							for primea in primes:
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						primea * primea
														for primea in primes:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						primea * primea * primea
																					if square + cube >= limit - 16:
																												break
																					for primea in primes:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						primea * primea * primea * primea
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						square + cube + tetr
																												if total >= limit:
																																			break
																												ret.add(SCREAMING_SNAKE_CASE__       )

							return len(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
		print(f"{solution() = }")






