

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=5       ):
						# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
						assert masked_input.count('''<mask>'''       ) == 1
						snake_case_   =	torch.tensor(tokenizer.encode(SCREAMING_SNAKE_CASE__  ,		add_special_tokens=SCREAMING_SNAKE_CASE__       )       ).unsqueeze(0       )  # Batch size 1
						snake_case_   =	model(SCREAMING_SNAKE_CASE__       )[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=SCREAMING_SNAKE_CASE__  ,		dim=0       )
						snake_case_   =	''' '''.join(
						    [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(SCREAMING_SNAKE_CASE__       )       )]       )
						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(SCREAMING_SNAKE_CASE__       ) in masked_input:
																		topk_filled_outputs.append(
																		    (
																		        masked_input.replace(''' {0}'''.format(SCREAMING_SNAKE_CASE__       )  ,		SCREAMING_SNAKE_CASE__       ),
																		        values[index].item(),
																		        predicted_token,
																		    )       )
												else:
																		topk_filled_outputs.append(
																		    (
																		        masked_input.replace(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ),
																		        values[index].item(),
																		        predicted_token,
																		    )       )
						return topk_filled_outputs


lowerCAmelCase_       	=	CamembertTokenizer.from_pretrained('''camembert-base''')
lowerCAmelCase_       	=	CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

lowerCAmelCase_       	=	'''Le camembert est <mask> :)'''
print(fill_mask(masked_input, model, tokenizer, topk=3))


def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   str      =       set()

     # Replace all the whitespace in our sentence
     lowercase__    :   Tuple      =       input_str.replace(' '	,     ''			)
     for alpha in input_str:
          if "a" <= alpha.lower() <= "z":
               frequency.add(alpha.lower()			)
     return len(SCREAMING_SNAKE_CASE_			) == 26

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     lowercase__    :   Dict      =       [False] * 26
     for char in input_str:
          if char.islower():
               lowercase__    :   List[Any]      =       True
          elif char.isupper():
               lowercase__    :   Optional[Any]      =       True
     return all(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str = "The quick brown fox jumps over the lazy dog"	,     ):



     '''simple docstring'''





     return len({char for char in input_str.lower() if char.isalpha()}			) == 26

def 	snake_case__			(    ):



     '''simple docstring'''





     from timeit import timeit

     lowercase__    :   Union[str, Any]      =       'from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest'
     print(timeit('is_pangram()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_faster()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     print(timeit('is_pangram_fastest()'	,     setup=SCREAMING_SNAKE_CASE_			)			)
     # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
     # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       benchmark()

