



import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer

def        a						(      _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					List[Any]=5						):







		'''simple docstring'''





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


__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))










import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format

class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       None




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	self.feature_extraction_class(**self.feat_extract_dict )
					__UpperCAmelCase    :     Optional[int]													=	json.loads(feat_extract.to_json_string() )
					for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]       ,       a_ )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     Union[str, Any]													=	os.path.join(a_       ,       '''feat_extract.json''' )
							feat_extract_first.to_json_file(a_ )
							__UpperCAmelCase    :     Any													=	self.feature_extraction_class.from_json_file(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     List[str]													=	feat_extract_first.save_pretrained(a_ )[0]
							check_json_file_has_correct_format(a_ )
							__UpperCAmelCase    :     Optional[Any]													=	self.feature_extraction_class.from_pretrained(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.feature_extraction_class()
					self.assertIsNotNone(a_ )





