


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


__snake_case   							=						logging.get_logger(__name__)

__snake_case   							=						{
    '''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 	__lowerCamelCase			(_a						):
       _lowercase											=     """fnet"""







       def __init__(			self: Optional[Any],A_: str=3_2000,A_: Optional[Any]=768,A_: str=12,A_: List[str]=3072,A_: Union[str, Any]="gelu_new",A_: Optional[int]=0.1,A_: List[str]=512,A_: Optional[Any]=4,A_: Optional[int]=0.0_2,A_: Optional[Any]=1E-12,A_: int=False,A_: Any=512,A_: Optional[Any]=3,A_: List[Any]=1,A_: Tuple=2,**A_: Any,):






            '''simple docstring'''




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

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






def 		_A		(		_lowercase = 1_00 )					-> int:


     """simple docstring"""


     __UpperCamelCase =			0
     __UpperCamelCase =			0
     for i in range(1					,  n + 1 ):
          sum_of_squares += i**2
          sum_of_ints += i
     return sum_of_ints**2 - sum_of_squares


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


