




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


__lowerCAmelCase						:							int			     =						logging.get_logger(__name__)

__lowerCAmelCase						:							Any			     =						{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class        snake_case__				(__snake_case  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   """nystromformer"""





				def __init__( self							:	List[Any]   , __lowerCamelCase							:	List[str]=3_00_00   , __lowerCamelCase							:	Optional[int]=7_68   , __lowerCamelCase							:	Dict=12   , __lowerCamelCase							:	List[str]=12   , __lowerCamelCase							:	Any=30_72   , __lowerCamelCase							:	Optional[int]="gelu_new"   , __lowerCamelCase							:	Optional[int]=0.1   , __lowerCamelCase							:	Tuple=0.1   , __lowerCamelCase							:	str=5_10   , __lowerCamelCase							:	Optional[int]=2   , __lowerCamelCase							:	List[str]=64   , __lowerCamelCase							:	Tuple=64   , __lowerCamelCase							:	int=65   , __lowerCamelCase							:	Dict=False   , __lowerCamelCase							:	Union[str, Any]=0.02   , __lowerCamelCase							:	List[str]=1e-5   , __lowerCamelCase							:	str=1   , __lowerCamelCase							:	Union[str, Any]=0   , __lowerCamelCase							:	Optional[int]=2   , **__lowerCamelCase							:	List[Any]   , )   ->	Union[str, Any]:
							a       =     vocab_size
							a       =     max_position_embeddings
							a       =     hidden_size
							a       =     num_hidden_layers
							a       =     num_attention_heads
							a       =     intermediate_size
							a       =     hidden_act
							a       =     hidden_dropout_prob
							a       =     attention_probs_dropout_prob
							a       =     initializer_range
							a       =     type_vocab_size
							a       =     segment_means_seq_len
							a       =     num_landmarks
							a       =     conv_kernel_size
							a       =     inv_coeff_init_option
							a       =     layer_norm_eps
							super().__init__(pad_token_id=a_   , bos_token_id=a_   , eos_token_id=a_   , **a_				)










"""simple docstring"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1000






def 						lowercase				(	_snake_case		: float	)	->float:
	"""simple docstring"""





	return x * x






def 						lowercase				(	_snake_case		: Union[str, Any]	,  _snake_case		: float	,  _snake_case		: float	,  _snake_case		: int = 4	)	->float:
	"""simple docstring"""





	assert callable(
	    _snake_case	), f"""the function(object) passed should be callable your input : {function}"""
	assert isinstance(_snake_case	,  (float, int)	), f"""a should be float or integer your input : {a}"""
	assert isinstance(function(_snake_case	)	,  (float, int)	), (
	    "the function should return integer or float return type of your function, "
	    f"""{type(_snake_case	)}"""
	)
	assert isinstance(_snake_case	,  (float, int)	), f"""b should be float or integer your input : {b}"""
	assert (
	    isinstance(_snake_case	,  _snake_case	) and precision > 0
	), f"""precision should be positive integer your input : {precision}"""

	# just applying the formula of simpson for approximate integration written in
	# mentioned article in first comment of this file and above this function

	__snake_case      :		Tuple         =							(b - a) / N_STEPS
	__snake_case      :		Union[str, Any]         =							function(_snake_case	) + function(_snake_case	)

	for i in range(1	,  _snake_case	):
		__snake_case      :		List[str]         =							a + h * i
		result += function(_snake_case	) * (4 if i % 2 else 2)

	result *= h / 3
	return round(_snake_case	,  _snake_case	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()





