






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def 	lowerCAmelCase_		(lowercase__						:       List[str]     ,					lowercase__						:       Tuple     ,					lowercase__						:       Dict     ,					lowercase__						:       List[Any]=5       )		->    Tuple:


				'''simple docstring'''




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


_UpperCAmelCase			:		Tuple   				=    CamembertTokenizer.from_pretrained("camembert-base")
_UpperCAmelCase			:		Dict   				=    CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_UpperCAmelCase			:		Union[str, Any]   				=    "Le camembert est <mask> :)"




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













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


_UpperCAmelCase			:		Optional[Any]   				=    {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "ErnieForCausalLM",
			    "ErnieForMaskedLM",
			    "ErnieForMultipleChoice",
			    "ErnieForNextSentencePrediction",
			    "ErnieForPreTraining",
			    "ErnieForQuestionAnswering",
			    "ErnieForSequenceClassification",
			    "ErnieForTokenClassification",
			    "ErnieModel",
			    "ErniePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ernie import (
						    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ErnieForCausalLM,
						    ErnieForMaskedLM,
						    ErnieForMultipleChoice,
						    ErnieForNextSentencePrediction,
						    ErnieForPreTraining,
						    ErnieForQuestionAnswering,
						    ErnieForSequenceClassification,
						    ErnieForTokenClassification,
						    ErnieModel,
						    ErniePreTrainedModel,
						)

else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





