




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


SCREAMING_SNAKE_CASE:     List[str]									= logging.get_logger(__name__)

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



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="swinv2"

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



						def __init__(			self	:   Dict					,							snake_case__	:   Union[str, Any]=2_24					,							snake_case__	:   str=4					,							snake_case__	:   List[Any]=3					,							snake_case__	:   str=96					,							snake_case__	:   Dict=[2, 2, 6, 2]					,							snake_case__	:   Tuple=[3, 6, 12, 24]					,							snake_case__	:   str=7					,							snake_case__	:   Union[str, Any]=4.0					,							snake_case__	:   str=True					,							snake_case__	:   Optional[Any]=0.0					,							snake_case__	:   Tuple=0.0					,							snake_case__	:   int=0.1					,							snake_case__	:   Tuple="gelu"					,							snake_case__	:   Optional[int]=False					,							snake_case__	:   str=0.02					,							snake_case__	:   int=1e-5					,							snake_case__	:   Tuple=32					,							**snake_case__	:   int					,							):

								"""simple docstring"""

								super().__init__(**snake_case__  )

								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(snake_case__  )
								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(snake_case__  ) - 1)  )
								SCREAMING_SNAKE_CASE_					      =							(0, 0, 0, 0)





from math import isqrt


def 					_a							(					lowerCAmelCase )->	bool:
		return all(number % divisor != 0 for divisor in range(2    ,						isqrt(lowerCAmelCase ) + 1 ) )


def 					_a							(					lowerCAmelCase = 10**6 )->	int:
		SCREAMING_SNAKE_CASE_					      =							0
		SCREAMING_SNAKE_CASE_					      =							1
		SCREAMING_SNAKE_CASE_					      =							7
		while prime_candidate < max_prime:
				primes_count += is_prime(lowerCAmelCase )

				cube_index += 1
				prime_candidate += 6 * cube_index

		return primes_count


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