




from __future__ import annotations

from fractions import Fraction




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						int				)  ->							bool:
    return (
        num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
    )




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int				)  ->							list[str]:
    UpperCAmelCase_										=					[]
    UpperCAmelCase_										=					11
    UpperCAmelCase_										=					int('''1''' + '''0''' * digit_len				)
    for num in range(__UpperCamelCase	,					__UpperCamelCase				):
        while den <= 99:
            if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                if is_digit_cancelling(__UpperCamelCase	,					__UpperCamelCase				):
                    solutions.append(f'{num}/{den}'				)
            den += 1
        num += 1
        UpperCAmelCase_										=					10
    return solutions




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int = 2				)  ->							int:
    UpperCAmelCase_										=					1.0
    for fraction in fraction_list(__UpperCamelCase				):
        UpperCAmelCase_										=					Fraction(__UpperCamelCase				)
        result *= frac.denominator / frac.numerator
    return int(__UpperCamelCase				)


if __name__ == "__main__":
 print(solution())











import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




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


_lowerCamelCase      			=						CamembertTokenizer.from_pretrained('camembert-base')
_lowerCamelCase      			=						CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_lowerCamelCase      			=						'Le camembert est <mask> :)'


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





