





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def 						lowerCamelCase__     (     UpperCamelCase__			:       Union[str, Any]       ,    UpperCamelCase__			:       List[Any]       ,    UpperCamelCase__			:       str       ,    UpperCamelCase__			:       List[str]=5						)			-> Dict:



					'''simple docstring'''




					assert masked_input.count('<mask>'						) == 1
					_snake_case									=				torch.tensor(tokenizer.encode(UpperCamelCase__       ,    add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
					_snake_case									=				model(UpperCamelCase__						)[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=UpperCamelCase__       ,    dim=0						)
					_snake_case									=				' '.join(
					    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
					_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(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


UpperCAmelCase_      							=     CamembertTokenizer.from_pretrained("""camembert-base""")
UpperCAmelCase_      							=     CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

UpperCAmelCase_      							=     """Le camembert est <mask> :)"""




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










import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class 			UpperCamelCase_		(  unittest.TestCase					):




					def 	lowerCAmelCase			(		self  )		->							List[Any]:
										debug_launcher(test_script.main  )






					def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
										debug_launcher(test_ops.main  )



