


import math as m


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase=0.001				)    ->  Optional[Any]:
						return (f(a + h				) - f(a - h				)) / (2 * h)


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase=0							,     _UpperCAmelCase=100							,     _UpperCAmelCase=0.0001							,     _UpperCAmelCase=1e-6							,     _UpperCAmelCase=False				)    ->  List[Any]:
						_a     =			xa  # set the initial guess
						_a     =			[a]
						_a     =			abs(f(_UpperCAmelCase				)				)
						_a     =			lambda _UpperCAmelCase				: calc_derivative(_UpperCAmelCase							,     _UpperCAmelCase							,     h=_UpperCAmelCase				)  # noqa: E731  Derivative of f(x)
						for _ in range(_UpperCAmelCase				):
												if fa(_UpperCAmelCase				) == 0:
																		raise ValueError('No converging solution found'				)
												_a     =			a - f(_UpperCAmelCase				) / fa(_UpperCAmelCase				)  # Calculate the next estimate
												if logsteps:
																		steps.append(_UpperCAmelCase				)
												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

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






from typing import List, Union

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


lowercase_								=    logging.get_logger(__name__)

lowercase_								=    {
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}




class 					_UpperCamelCase (      lowerCamelCase__	):







							'''simple docstring'''
							_A  				=  "led"
							_A  				=  {
							    "num_attention_heads": "encoder_attention_heads",
							    "hidden_size": "d_model",
							    "attention_probs_dropout_prob": "attention_dropout",
							    "initializer_range": "init_std",
							}







							def __init__(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]=5_0_2_6_5		,				SCREAMING_SNAKE_CASE_  :				str=1_6_3_8_4		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_2		,				SCREAMING_SNAKE_CASE_  :				Any=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=1_6		,				SCREAMING_SNAKE_CASE_  :				str=1_2		,				SCREAMING_SNAKE_CASE_  :				List[Any]=4_0_9_6		,				SCREAMING_SNAKE_CASE_  :				List[str]=1_6		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=0.0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				List[Any]=True		,				SCREAMING_SNAKE_CASE_  :				str=True		,				SCREAMING_SNAKE_CASE_  :				List[str]="gelu"		,				SCREAMING_SNAKE_CASE_  :				List[Any]=1_0_2_4		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.1		,				SCREAMING_SNAKE_CASE_  :				Dict=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]=0.0		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.02		,				SCREAMING_SNAKE_CASE_  :				str=2		,				SCREAMING_SNAKE_CASE_  :				Tuple=0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[int]=1		,				SCREAMING_SNAKE_CASE_  :				str=0		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]=2		,				SCREAMING_SNAKE_CASE_  :				Union[List[int], int] = 5_1_2		,				**SCREAMING_SNAKE_CASE_  :				Dict		,				):
													_a     =			vocab_size
													_a     =			max_encoder_position_embeddings
													_a     =			max_decoder_position_embeddings
													_a     =			d_model
													_a     =			encoder_ffn_dim
													_a     =			encoder_layers
													_a     =			encoder_attention_heads
													_a     =			decoder_ffn_dim
													_a     =			decoder_layers
													_a     =			decoder_attention_heads
													_a     =			dropout
													_a     =			attention_dropout
													_a     =			activation_dropout
													_a     =			activation_function
													_a     =			init_std
													_a     =			encoder_layerdrop
													_a     =			decoder_layerdrop
													_a     =			classifier_dropout
													_a     =			use_cache
													_a     =			encoder_layers
													_a     =			attention_window

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


