


import math as m




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		Tuple=0.001  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Tuple=0  ,			__lowerCamelCase:		Tuple=100  ,			__lowerCamelCase:		int=0.0001  ,			__lowerCamelCase:		List[str]=1E-6  ,			__lowerCamelCase:		Union[str, Any]=False  ):







	'''simple docstring'''



	lowercase_			 = xa  # set the initial guess
	lowercase_			 = [a]
	lowercase_			 = abs(f(__lowerCamelCase  )  )
	lowercase_			 = lambda __lowerCamelCase  : 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"  )
		lowercase_			 = 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__       			=				lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				newton_raphson(
							    f, xa=1_0, maxiter=1_0_0_0, 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 typing import Callable, Optional, Union

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


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				"prophetnet"
							lowerCAmelCase__          =				["past_key_values"]
							lowerCAmelCase__          =				{
							    "num_attention_heads": "num_encoder_attention_heads",
							}




							def __init__(  self						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = "gelu"						,       UpperCAmelCase = 30522						,       UpperCAmelCase = 1024						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 512						,       UpperCAmelCase = 0.02						,       UpperCAmelCase = True						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 2						,       UpperCAmelCase = 32						,       UpperCAmelCase = 128						,       UpperCAmelCase = False						,       UpperCAmelCase = 0.0						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 1						,       UpperCAmelCase = 2						,       **UpperCAmelCase						,       )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = vocab_size
								lowercase_			 = hidden_size
								lowercase_			 = encoder_ffn_dim
								lowercase_			 = num_encoder_layers
								lowercase_			 = num_encoder_attention_heads
								lowercase_			 = decoder_ffn_dim
								lowercase_			 = num_decoder_layers
								lowercase_			 = num_decoder_attention_heads
								lowercase_			 = max_position_embeddings
								lowercase_			 = init_std  # Normal(0, this parameter)
								lowercase_			 = activation_function

								# parameters for prophetnet
								lowercase_			 = ngram
								lowercase_			 = num_buckets
								lowercase_			 = relative_max_distance
								lowercase_			 = disable_ngram_loss
								lowercase_			 = eps

								# 3 Types of Dropout
								lowercase_			 = attention_dropout
								lowercase_			 = activation_dropout
								lowercase_			 = dropout

								lowercase_			 = use_cache

								super().__init__(
								    pad_token_id=UpperCAmelCase						,       bos_token_id=UpperCAmelCase						,       eos_token_id=UpperCAmelCase						,       is_encoder_decoder=UpperCAmelCase						,       add_cross_attention=UpperCAmelCase						,       decoder_start_token_id=UpperCAmelCase						,       **UpperCAmelCase						,       )




							@property
							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								raise NotImplementedError(
								    "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
								    " `num_decoder_layers`."  )

