






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


lowercase__				:      List[Any]	   = logging.get_logger(__name__)

lowercase__				:      List[str]	   = {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[Any]		 =     '''fnet'''







     def __init__(     self  ,		A=32000  ,		A=768  ,		A=12  ,		A=3072  ,		A="gelu_new"  ,		A=0.1  ,		A=512  ,		A=4  ,		A=0.0_2  ,		A=1e-12  ,		A=False  ,		A=512  ,		A=3  ,		A=1  ,		A=2  ,		**A  ,		)  ->   Dict:



          '''simple docstring'''



          super().__init__(pad_token_id=lowerCAmelCase__  ,		bos_token_id=lowerCAmelCase__  ,		eos_token_id=lowerCAmelCase__  ,		**lowerCAmelCase__			)

          a     =				vocab_size
          a     =				max_position_embeddings
          a     =				hidden_size
          a     =				num_hidden_layers
          a     =				intermediate_size
          a     =				hidden_act
          a     =				hidden_dropout_prob
          a     =				initializer_range
          a     =				type_vocab_size
          a     =				layer_norm_eps
          a     =				use_tpu_fourier_optimizations
          a     =				tpu_short_seq_length







from math import isqrt

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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 10**6)      ->					int:
     a     =				0
     a     =				1
     a     =				7
     while prime_candidate < max_prime:
          primes_count += is_prime(__UpperCamelCase)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


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

