





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






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






import warnings
from functools import wraps
from typing import Callable






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Callable    ):

		@wraps(UpperCamelCase__    )
		def _inner_fn(*UpperCamelCase__:							Dict					,   **UpperCamelCase__:							Any    ):
				warnings.warn(
				    (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')					,   UpperCamelCase__					,   )
				return fn(*UpperCamelCase__					,   **UpperCamelCase__    )

		return _inner_fn