


from math import asin, atan, cos, radians, sin, sqrt, tan

A							:					List[str]     						=     6378137.0
A							:					List[str]     						=     6356752.314245
A							:					Dict     						=     6378137





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	float:


						'''simple docstring'''






						__snake_case				=		(AXIS_A - AXIS_B) / AXIS_A
						__snake_case				=		atan((1 - flattening) * tan(radians(__lowerCAmelCase ) ) )
						__snake_case				=		atan((1 - flattening) * tan(radians(__lowerCAmelCase ) ) )
						__snake_case				=		radians(__lowerCAmelCase )
						__snake_case				=		radians(__lowerCAmelCase )
						# Equation
						__snake_case				=		sin((phi_a - phi_a) / 2 )
						__snake_case				=		sin((lambda_a - lambda_a) / 2 )
						# Square both values
						sin_sq_phi *= sin_sq_phi
						sin_sq_lambda *= sin_sq_lambda
						__snake_case				=		sqrt(sin_sq_phi + (cos(__lowerCAmelCase ) * cos(__lowerCAmelCase ) * sin_sq_lambda) )
						return 2 * RADIUS * asin(__lowerCAmelCase )


if __name__ == "__main__":
	import doctest

	doctest.testmod()









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


A							:					str     						=     logging.get_logger(__name__)

A							:					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     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""fnet"""


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2_0_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Any=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu_new"     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

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

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





