






from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase = "x" ,		__lowerCAmelCase = 10**-10 ,		__lowerCAmelCase = 1 ,		)->  List[str]:





						"""simple docstring"""



						_UpperCAmelCase									=			symbols(_UpperCAmelCase	)
						_UpperCAmelCase									=			lambdify(_UpperCAmelCase ,		_UpperCAmelCase	)
						_UpperCAmelCase									=			lambdify(_UpperCAmelCase ,		diff(_UpperCAmelCase ,		_UpperCAmelCase	)	)

						_UpperCAmelCase									=			starting_point

						while True:
												if diff_function(_UpperCAmelCase	) != 0:
																		_UpperCAmelCase									=			prev_guess - multiplicity * func(_UpperCAmelCase	) / diff_function(
																		    _UpperCAmelCase	)
												else:
																		raise ZeroDivisionError('Could not find root'	) from None

												# Precision is checked by comparing the difference of consecutive guesses
												if abs(next_guess - prev_guess	) < precision:
																		return next_guess

												_UpperCAmelCase									=			next_guess


# Let's Execute
if __name__ == "__main__":
		# Find root of trigonometric function
		# Find value of pi
		print(F'''The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}''')

		# Find root of polynomial
		# Find fourth Root of 5
		print(F'''The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}''')

		# Find value of e
		print(
		    '''The root of log(y) - 1 = 0 is ''',
		    F'''{newton_raphson('log(y) - 1', 2, variable='y')}''',
		)

		# Exponential Roots
		print(
		    '''The root of exp(x) - 1 = 0 is''',
		    F'''{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}''',
		)

		# Find root of cos(x)
		print(F'''The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}''')





import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case						:Any      		=     logging.getLogger(__name__)

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[Any]											=						'''masked_bert'''



       def __init__(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     int=30_522     ,		__SCREAMING_SNAKE_CASE							:     str=768     ,		__SCREAMING_SNAKE_CASE							:     List[str]=12     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=3_072     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     int=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1E-12     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0     ,		__SCREAMING_SNAKE_CASE							:     List[str]="topK"     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="constant"     ,		__SCREAMING_SNAKE_CASE							:     int=0.0     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]     ,		):


           '''simple docstring'''

           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)

           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				hidden_act
           __a						       =				intermediate_size
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				initializer_range
           __a						       =				layer_norm_eps
           __a						       =				pruning_method
           __a						       =				mask_init
           __a						       =				mask_scale





