

import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A=5       ):





     '''simple docstring'''




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


_SCREAMING_SNAKE_CASE  :						str         =    CamembertTokenizer.from_pretrained('''camembert-base''')
_SCREAMING_SNAKE_CASE  :						Any         =    CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

_SCREAMING_SNAKE_CASE  :						List[str]         =    '''Le camembert est <mask> :)'''







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





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


_SCREAMING_SNAKE_CASE  :						str         =    Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_SCREAMING_SNAKE_CASE  :						str         =    sorted({word.strip().lower() for word in data.splitlines()})

_SCREAMING_SNAKE_CASE  :						List[Any]         =    collections.defaultdict(list)
for word in word_list:
       word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

       with open('''anagrams.txt''', '''w''') as file:
              file.write('''all_anagrams = \n ''')
              file.write(pprint.pformat(all_anagrams))


