
'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=5				)     ->							Dict:
					'''simple docstring'''

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


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

__lowercase							:   Optional[int]					=     'Le camembert est <mask> :)'






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




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowercase							:   int					=     {}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:   Any					=     ['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					__lowercase							:   Union[str, Any]					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


