

import math as m


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						int	,				_SCREAMING_SNAKE_CASE   :						Tuple	,				_SCREAMING_SNAKE_CASE   :						Tuple=0.001    ):





    """simple docstring"""


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


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Any	,				_SCREAMING_SNAKE_CASE   :						List[Any]=0	,				_SCREAMING_SNAKE_CASE   :						List[Any]=100	,				_SCREAMING_SNAKE_CASE   :						Tuple=0.0001	,				_SCREAMING_SNAKE_CASE   :						Tuple=1E-6	,				_SCREAMING_SNAKE_CASE   :						Optional[Any]=False    ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  xa  # set the initial guess
    SCREAMING_SNAKE_CASE_													=  [a]
    SCREAMING_SNAKE_CASE_													=  abs(f(_SCREAMING_SNAKE_CASE    )    )
    SCREAMING_SNAKE_CASE_													=  lambda _SCREAMING_SNAKE_CASE    : calc_derivative(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				h=_SCREAMING_SNAKE_CASE    )  # noqa: E731  Derivative of f(x)
    for _ in range(_SCREAMING_SNAKE_CASE    ):
        if fa(_SCREAMING_SNAKE_CASE    ) == 0:
            raise ValueError('No converging solution found'    )
        SCREAMING_SNAKE_CASE_													=  a - f(_SCREAMING_SNAKE_CASE    ) / fa(_SCREAMING_SNAKE_CASE    )  # Calculate the next estimate
        if logsteps:
            steps.append(_SCREAMING_SNAKE_CASE    )
        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__					: Any								=      lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
       UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__					: Optional[int]								=      newton_raphson(
           f, xa=10, maxiter=1_000, 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}}}')





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


UpperCamelCase__					: Optional[int]								=      logging.get_logger(__name__)

UpperCamelCase__					: int								=      {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		List[Any]  	=					'pegasus_x'
  __lowerCAmelCase       :		Optional[int]  	=					['past_key_values']
  __lowerCAmelCase       :		List[str]  	=					{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

  def __init__(   self  ,						_A=96103  ,						_A=16384  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=16  ,						_A=4096  ,						_A=16  ,						_A=0.0  ,						_A=0.0  ,						_A=True  ,						_A=True  ,						_A="gelu"  ,						_A=1024  ,						_A=0.1  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=0  ,						_A=True  ,						_A=0  ,						_A=1  ,						_A=1  ,						_A=32  ,						_A=512  ,						_A=True  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  vocab_size
      SCREAMING_SNAKE_CASE_													=  max_position_embeddings
      SCREAMING_SNAKE_CASE_													=  d_model
      SCREAMING_SNAKE_CASE_													=  encoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  encoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  decoder_ffn_dim
      SCREAMING_SNAKE_CASE_													=  decoder_layers
      SCREAMING_SNAKE_CASE_													=  decoder_attention_heads
      SCREAMING_SNAKE_CASE_													=  dropout
      SCREAMING_SNAKE_CASE_													=  attention_dropout
      SCREAMING_SNAKE_CASE_													=  activation_dropout
      SCREAMING_SNAKE_CASE_													=  activation_function
      SCREAMING_SNAKE_CASE_													=  init_std
      SCREAMING_SNAKE_CASE_													=  encoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  decoder_layerdrop
      SCREAMING_SNAKE_CASE_													=  use_cache
      SCREAMING_SNAKE_CASE_													=  encoder_layers
      SCREAMING_SNAKE_CASE_													=  scale_embedding  # scale factor will be sqrt(d_model) if True

      SCREAMING_SNAKE_CASE_													=  num_global_tokens
      SCREAMING_SNAKE_CASE_													=  block_size
      SCREAMING_SNAKE_CASE_													=  stagger_local_blocks

      super().__init__(
          pad_token_id=_A  ,						eos_token_id=_A  ,						is_encoder_decoder=_A  ,						decoder_start_token_id=_A  ,						forced_eos_token_id=_A  ,						**_A  ,						)

  @property
  def   lowerCAmelCase__       (   self):
      return self.encoder_attention_heads


  @property
  def   lowerCAmelCase__       (   self):
      return self.d_model


