





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case=5							)       ->   Union[str, Any]:







	'''simple docstring'''







	assert masked_input.count('''<mask>'''							) == 1
	lowerCamelCase__              =	torch.tensor(tokenizer.encode(a__ ,add_special_tokens=a__							)							).unsqueeze(0							)  # Batch size 1
	lowerCamelCase__              =	model(a__							)[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=a__ ,dim=0							)
	lowerCamelCase__              =	''' '''.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()							) for i in range(len(a__							)							)]							)
	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(a__							) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(''' {0}'''.format(a__							) ,a__							),
			        values[index].item(),
			        predicted_token,
			    )							)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(a__ ,a__							),
			        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))







# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


_a						=   open  # noqa: we just need to have a builtin inside this module to test it properly



