
'''simple docstring'''




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__=5		):




    '''simple docstring'''



    assert masked_input.count('''<mask>'''		) == 1
    A			:  Optional[int]   =		torch.tensor(tokenizer.encode(snake_case__						,							add_special_tokens=snake_case__		)		).unsqueeze(0		)  # Batch size 1
    A			:  Optional[int]   =		model(snake_case__		)[0]  # The last hidden-state is the first element of the output tuple
    A			:  int   =		(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    A			:  Union[str, Any]   =		logits[0, masked_index, :]
    A			:  Dict   =		logits.softmax(dim=0		)
    A,    A			:  Optional[Any]   =		prob.topk(k=snake_case__						,							dim=0		)
    A			:  Tuple   =		''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()		) for i in range(len(snake_case__		)		)]		)
    A			:  Optional[int]   =		tokenizer.mask_token
    A			:  Union[str, Any]   =		[]
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''		)		):
        A			:  Union[str, Any]   =		predicted_token_bpe.replace('''\u2581'''						,							''' '''		)
        if " {0}".format(snake_case__		) in masked_input:
            topk_filled_outputs.append(
                (
                    masked_input.replace(''' {0}'''.format(snake_case__		)						,							snake_case__		),
                    values[index].item(),
                    predicted_token,
                )		)
        else:
            topk_filled_outputs.append(
                (
                    masked_input.replace(snake_case__						,							snake_case__		),
                    values[index].item(),
                    predicted_token,
                )		)
    return topk_filled_outputs


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

lowercase     :							List[str]       						=     'Le camembert est <mask> :)'







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






'''simple docstring'''




lowercase     :							dict[str, float]       						=     {
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.6_02_17_66_34e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.35_58_18,
}







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
        A			:  int   =		(
            F'Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'
            F'Valid values are: {", ".join(snake_case__		)}'
        )
        raise ValueError(snake_case__		)
    return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




