
import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      List[Any]=5       )  ->    Dict:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count("""<mask>"""       ) == 1
							lowerCamelCase_							  =					torch.tensor(tokenizer.encode(UpperCAmelCase__			,				add_special_tokens=UpperCAmelCase__       )       ).unsqueeze(0       )  # Batch size 1
							lowerCamelCase_							  =					model(UpperCAmelCase__       )[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=UpperCAmelCase__			,				dim=0       )
							lowerCamelCase_							  =					""" """.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(UpperCAmelCase__       )       )]       )
							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(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


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

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


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







from abc import ABC, abstractmethod
from argparse import ArgumentParser





class       __A(				UpperCAmelCase       ):

					@staticmethod
					@abstractmethod
					def    lowercase__      (	__UpperCamelCase					:     ArgumentParser   ):
												raise NotImplementedError()



					@abstractmethod
					def    lowercase__      (	self					:     Any   ):
												raise NotImplementedError()





