






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase=5 ):

					'''simple docstring'''







					assert masked_input.count('''<mask>''' ) == 1
					_A								= torch.tensor(tokenizer.encode(_lowercase					,			add_special_tokens=_lowercase ) ).unsqueeze(0 )  # Batch size 1
					_A								= model(_lowercase )[0]  # The last hidden-state is the first element of the output tuple
					_A								= (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
					_A								= logits[0, masked_index, :]
					_A								= logits.softmax(dim=0 )
					_A								= prob.topk(k=_lowercase					,			dim=0 )
					_A								= ' '.join(
					    [tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(_lowercase ) )] )
					_A								= tokenizer.mask_token
					_A								= []
					for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' ''' ) ):
										_A								= 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


__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))











__A      =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__A      =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= []

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					order.append(_lowercase )

					return order




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= [vert]

					for neighbour in reversed_graph[vert]:
										if not visited[neighbour]:
															component += find_components(_lowercase					,			_lowercase					,			_lowercase )

					return component




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= len(_lowercase ) * [False]
					_A								= {vert: [] for vert in range(len(_lowercase ) )}

					for vert, neighbours in graph.items():
										for neighbour in neighbours:
															reversed_graph[neighbour].append(_lowercase )

					_A								= []
					for i, was_visited in enumerate(_lowercase ):
										if not was_visited:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					_A								= []
					_A								= len(_lowercase ) * [False]

					for i in range(len(_lowercase ) ):
										_A								= order[len(_lowercase ) - i - 1]
										if not visited[vert]:
															_A								= find_components(_lowercase					,			_lowercase					,			_lowercase )
															components_list.append(_lowercase )

					return components_list



