






'''simple docstring'''







import math as m


def 			__UpperCamelCase				(			lowercase_					:     Any      ,					lowercase_					:     str      ,					lowercase_					:     Dict=0.001      ):
       """simple docstring"""




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


def 			__UpperCamelCase				(			lowercase_					:     Union[str, Any]      ,					lowercase_					:     Dict=0      ,					lowercase_					:     Optional[int]=100      ,					lowercase_					:     Union[str, Any]=0.0001      ,					lowercase_					:     Tuple=1E-6      ,					lowercase_					:     List[str]=False      ):
       """simple docstring"""




       a_	    = xa  # set the initial guess
       a_	    = [a]
       a_	    = abs(f(lowercase_      )      )
       a_	    = 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'      )
              a_	    = 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

  __lowerCAmelCase					   =       lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
  __lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase					   =       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}}}""")












'''simple docstring'''







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


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    "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      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  _a     :   Union[str, Any]			 =    '''nystromformer'''







  def __init__( self      ,  UpperCamelCase__=30_000      ,  UpperCamelCase__=768      ,  UpperCamelCase__=12      ,  UpperCamelCase__=12      ,  UpperCamelCase__=3_072      ,  UpperCamelCase__="gelu_new"      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=0.1      ,  UpperCamelCase__=510      ,  UpperCamelCase__=2      ,  UpperCamelCase__=64      ,  UpperCamelCase__=64      ,  UpperCamelCase__=65      ,  UpperCamelCase__=False      ,  UpperCamelCase__=0.02      ,  UpperCamelCase__=1e-5      ,  UpperCamelCase__=1      ,  UpperCamelCase__=0      ,  UpperCamelCase__=2      ,  **UpperCamelCase__      ,  ):






         """simple docstring"""


         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=UpperCamelCase__      ,  bos_token_id=UpperCamelCase__      ,  eos_token_id=UpperCamelCase__      ,  **UpperCamelCase__  )




