
"""simple docstring"""





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


_a									=				logging.get_logger(__name__)

_a									=				{
    '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 A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						"""nystromformer"""







  def __init__(						self							,  lowercase_=3_0000							,  lowercase_=768							,  lowercase_=12							,  lowercase_=12							,  lowercase_=3072							,  lowercase_="gelu_new"							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=510							,  lowercase_=2							,  lowercase_=64							,  lowercase_=64							,  lowercase_=65							,  lowercase_=False							,  lowercase_=0.02							,  lowercase_=1E-5							,  lowercase_=1							,  lowercase_=0							,  lowercase_=2							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       vocab_size
     UpperCAmelCase_   :   List[Any]     	=       max_position_embeddings
     UpperCAmelCase_   :   Tuple     	=       hidden_size
     UpperCAmelCase_   :   List[str]     	=       num_hidden_layers
     UpperCAmelCase_   :   Any     	=       num_attention_heads
     UpperCAmelCase_   :   Union[str, Any]     	=       intermediate_size
     UpperCAmelCase_   :   Tuple     	=       hidden_act
     UpperCAmelCase_   :   Tuple     	=       hidden_dropout_prob
     UpperCAmelCase_   :   Union[str, Any]     	=       attention_probs_dropout_prob
     UpperCAmelCase_   :   List[str]     	=       initializer_range
     UpperCAmelCase_   :   Optional[int]     	=       type_vocab_size
     UpperCAmelCase_   :   int     	=       segment_means_seq_len
     UpperCAmelCase_   :   Dict     	=       num_landmarks
     UpperCAmelCase_   :   Any     	=       conv_kernel_size
     UpperCAmelCase_   :   List[str]     	=       inv_coeff_init_option
     UpperCAmelCase_   :   int     	=       layer_norm_eps
     super().__init__(pad_token_id=lowercase_							,  bos_token_id=lowercase_							,  eos_token_id=lowercase_							,  **lowercase_    )




"""simple docstring"""






import math as m






def 		_snake_case			(      lowercase__						:    Any ,	lowercase__						:    Any ,	lowercase__						:    Optional[int]=0.001						)    ->							Tuple:


			'''simple docstring'''





			return (f(a + h						) - f(a - h						)) / (2 * h)






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    List[str]=0 ,	lowercase__						:    Union[str, Any]=1_0_0 ,	lowercase__						:    Any=0.0001 ,	lowercase__						:    List[str]=1E-6 ,	lowercase__						:    str=False						)    ->							List[str]:


			'''simple docstring'''





			lowerCAmelCase_       :Dict     	=    xa  # set the initial guess
			lowerCAmelCase_       :int     	=    [a]
			lowerCAmelCase_       :int     	=    abs(f(lowercase__						)						)
			lowerCAmelCase_       :Optional[int]     	=    lambda lowercase__						: calc_derivative(lowercase__ ,	lowercase__ ,	h=lowercase__						)  # noqa: E731  Derivative of f(x)
			for _ in range(lowercase__						):
						if fa(lowercase__						) == 0:
									raise ValueError("""No converging solution found"""						)
						lowerCAmelCase_       :Tuple     	=    a - f(lowercase__						) / fa(lowercase__						)  # Calculate the next estimate
						if logsteps:
									steps.append(lowercase__						)
						if error < maxerror:
									break
			else:
						raise ValueError("""Iteration limit reached, no converging solution found"""						)
			if logsteps:
						# If logstep is true, then log intermediate steps
						return a, error, steps
			return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				__UpperCAmelCase 					=    lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				__UpperCAmelCase					,					__UpperCAmelCase					,					__UpperCAmelCase 					=    newton_raphson(
				    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel('step')
				plt.ylabel('error')
				plt.show()
				print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")






