
import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Optional[Any]		,  snake_case__:   int		,  snake_case__:   Dict=5   ):


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


__a         =   CamembertTokenizer.from_pretrained('camembert-base')
__a         =   CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

__a         =   'Le camembert est <mask> :)'


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






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a         =   logging.get_logger(__name__)
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      'encoder-decoder'
	a     :Any       	=      True
	def __init__(			self    :     List[Any]			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	Optional[int]:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					assert (
					    "encoder" in kwargs and "decoder" in kwargs
					), "Config has to be initialized with encoder and decoder config"
					lowercase_	    =   kwargs.pop('''encoder'''			)
					lowercase_	    =   encoder_config.pop('''model_type'''			)
					lowercase_	    =   kwargs.pop('''decoder'''			)
					lowercase_	    =   decoder_config.pop('''model_type'''			)

					from ..auto.configuration_auto import AutoConfig

					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   AutoConfig.for_model(SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   True
	@classmethod
	def 		_lowercase    (			cls    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				SCREAMING_SNAKE_CASE_    :     PretrainedConfig			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	PretrainedConfig:
					logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''			)
					lowercase_	    =   True
					lowercase_	    =   True

					return cls(encoder=encoder_config.to_dict()			,				decoder=decoder_config.to_dict()			,				**SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					lowercase_	    =   copy.deepcopy(self.__dict__			)
					lowercase_	    =   self.encoder.to_dict()
					lowercase_	    =   self.decoder.to_dict()
					lowercase_	    =   self.__class__.model_type
					return output




