

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


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






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	['torch', 'torchsde']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """torchsde"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)



