



"""simple docstring"""

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def __lowercase			(							_a		,			_a		,			_a		,			_a=5    ):
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count('''<mask>'''    ) == 1
    snake_case_ :     Union[str, Any]				=	torch.tensor(tokenizer.encode(_a		,			add_special_tokens=_a    )    ).unsqueeze(0    )  # Batch size 1
    snake_case_ :     str				=	model(_a    )[0]  # The last hidden-state is the first element of the output tuple
    snake_case_ :     Optional[int]				=	(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    snake_case_ :     Optional[Any]				=	logits[0, masked_index, :]
    snake_case_ :     List[Any]				=	logits.softmax(dim=0    )
    snake_case_,	snake_case_ :     Optional[Any]				=	prob.topk(k=_a		,			dim=0    )
    snake_case_ :     List[str]				=	''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()    ) for i in range(len(_a    )    )]    )
    snake_case_ :     List[Any]				=	tokenizer.mask_token
    snake_case_ :     List[Any]				=	[]
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''    )    ):
        snake_case_ :     Optional[Any]				=	predicted_token_bpe.replace('''\u2581'''		,			''' '''    )
        if " {0}".format(_a    ) in masked_input:
            topk_filled_outputs.append(
                (
                    masked_input.replace(''' {0}'''.format(_a    )		,			_a    ),
                    values[index].item(),
                    predicted_token,
                )    )
        else:
            topk_filled_outputs.append(
                (
                    masked_input.replace(_a		,			_a    ),
                    values[index].item(),
                    predicted_token,
                )    )
    return topk_filled_outputs


lowercase__    :  int							    =	CamembertTokenizer.from_pretrained('''camembert-base''')
lowercase__    :  Optional[Any]							    =	CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

lowercase__    :  Tuple							    =	'''Le camembert est <mask> :)'''






print(fill_mask(masked_input, model, tokenizer, topk=3))










"""simple docstring"""

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




def __lowercase			(							_a		,			_a		,			_a = "x"		,			_a = 10**-10		,			_a = 1		,			):
    snake_case_ :     Any				=	symbols(_a    )
    snake_case_ :     int				=	lambdify(_a		,			_a    )
    snake_case_ :     Optional[Any]				=	lambdify(_a		,			diff(_a		,			_a    )    )

    snake_case_ :     Optional[Any]				=	starting_point

    while True:
        if diff_function(_a    ) != 0:
            snake_case_ :     Optional[int]				=	prev_guess - multiplicity * func(_a    ) / diff_function(
                _a    )
        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

        snake_case_ :     int				=	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=0.005)}',
     )

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





