





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]       ,			UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   Any       ,			UpperCamelCase__   :   Union[str, Any]=5					)      ->    Optional[Any]:

						"""simple docstring"""





						assert masked_input.count('''<mask>'''					) == 1
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.tensor(tokenizer.encode(UpperCamelCase__       ,			add_special_tokens=UpperCamelCase__					)					).unsqueeze(0					)  # Batch size 1
						__SCREAMING_SNAKE_CASE:				Any           =   model(UpperCamelCase__					)[0]  # The last hidden-state is the first element of the output tuple
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
						__SCREAMING_SNAKE_CASE:				str           =   logits[0, masked_index, :]
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   logits.softmax(dim=0					)
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   prob.topk(k=UpperCamelCase__       ,			dim=0					)
						__SCREAMING_SNAKE_CASE:				Any           =   ''' '''.join(
						    [tokenizer.convert_ids_to_tokens(indices[i].item()					) for i in range(len(UpperCamelCase__					)					)]					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   tokenizer.mask_token
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   []
						for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''					)					):
												__SCREAMING_SNAKE_CASE:				Dict           =   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							:       Union[str, Any]					 =	CamembertTokenizer.from_pretrained("""camembert-base""")
lowerCAmelCase							:       Any					 =	CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

lowerCAmelCase							:       Tuple					 =	"""Le camembert est <mask> :)"""




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










def 		lowerCAmelCase  (	UpperCamelCase__   :   list       ,			UpperCamelCase__   :   list       ,			UpperCamelCase__   :   int					)      ->    list:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   len(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   [[0] * n for i in range(UpperCamelCase__					)]
						for i in range(UpperCamelCase__					):
												__SCREAMING_SNAKE_CASE:				str           =   y_points[i]

						for i in range(2       ,			UpperCamelCase__					):
												for j in range(UpperCamelCase__       ,			UpperCamelCase__					):
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   (
																		    (xa - x_points[j - i + 1]) * q[j][i - 1]
																		    - (xa - x_points[j]) * q[j - 1][i - 1]
																		) / (x_points[j] - x_points[j - i + 1])

						return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()



