


"""simple docstring"""






import math as m




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__=0.0_0_1				)     ->     int:
   return (f(a + h				) - f(a - h				)) / (2 * h)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__=0       ,   UpperCAmelCase__=100       ,   UpperCAmelCase__=0.0_0_0_1       ,   UpperCAmelCase__=1e-6       ,   UpperCAmelCase__=False				)     ->     int:
   a   :	int						=    xa  # set the initial guess
   a   :	Optional[Any]						=    [a]
   a   :	List[str]						=    abs(f(lowerCAmelCase_				)				)
   a   :	List[str]						=    lambda UpperCAmelCase__				: calc_derivative(lowerCAmelCase_       ,   lowerCAmelCase_       ,   h=lowerCAmelCase_				)  # noqa: E731  Derivative of f(x)
   for _ in range(lowerCAmelCase_				):
      if fa(lowerCAmelCase_				) == 0:
         raise ValueError('No converging solution found'				)
      a   :	str						=    a - f(lowerCAmelCase_				) / fa(lowerCAmelCase_				)  # Calculate the next estimate
      if logsteps:
         steps.append(lowerCAmelCase_				)
      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

 SCREAMING_SNAKE_CASE__				:  List[str]						= lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
 SCREAMING_SNAKE_CASE__			,					SCREAMING_SNAKE_CASE__			,					SCREAMING_SNAKE_CASE__				:  Optional[int]						= newton_raphson(
     f, xa=10, maxiter=1000, 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				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   {
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    List[str]			      = 'opt'
   UpperCamelCase_					:    List[str]			      = ['past_key_values']
   def __init__(     self				,  lowerCAmelCase_=5_02_72				,  lowerCAmelCase_=7_68				,  lowerCAmelCase_=12				,  lowerCAmelCase_=30_72				,  lowerCAmelCase_=20_48				,  lowerCAmelCase_=True				,  lowerCAmelCase_=False				,  lowerCAmelCase_=None				,  lowerCAmelCase_=0.1				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=12				,  lowerCAmelCase_="relu"				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=0.02				,  lowerCAmelCase_=True				,  lowerCAmelCase_=1				,  lowerCAmelCase_=2				,  lowerCAmelCase_=2				,  lowerCAmelCase_=True				,  lowerCAmelCase_=True				,  **lowerCAmelCase_				,  )						->Any:
        super().__init__(
            pad_token_id=lowerCAmelCase_				,  bos_token_id=lowerCAmelCase_				,  eos_token_id=lowerCAmelCase_				,  **lowerCAmelCase_				,  )
        a	:     Dict						      =    vocab_size
        a	:     int						      =    max_position_embeddings
        a	:     List[Any]						      =    num_attention_heads
        a	:     Union[str, Any]						      =    word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        a	:     Any						      =    ffn_dim
        a	:     Optional[int]						      =    hidden_size
        a	:     Optional[Any]						      =    num_hidden_layers
        a	:     Optional[int]						      =    dropout
        a	:     Optional[Any]						      =    attention_dropout
        a	:     Union[str, Any]						      =    activation_function
        a	:     Any						      =    init_std
        a	:     List[Any]						      =    layerdrop
        a	:     str						      =    use_cache
        a	:     Any						      =    do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        a	:     List[Any]						      =    enable_bias
        a	:     List[str]						      =    layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        a	:     Dict						      =    _remove_final_layer_norm