





'''simple docstring'''




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      List[Any]			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]=5       )		->       Optional[Any]:


     '''simple docstring'''



     assert masked_input.count("<mask>"       ) == 1
     _UpperCAmelCase     							=   torch.tensor(tokenizer.encode(__lowercase			,     add_special_tokens=__lowercase       )       ).unsqueeze(0       )  # Batch size 1
     _UpperCAmelCase     							=   model(__lowercase       )[0]  # The last hidden-state is the first element of the output tuple
     _UpperCAmelCase     							=   (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     _UpperCAmelCase     							=   logits[0, masked_index, :]
     _UpperCAmelCase     							=   logits.softmax(dim=0       )
     _UpperCAmelCase							,  _UpperCAmelCase     							=   prob.topk(k=__lowercase			,     dim=0       )
     _UpperCAmelCase     							=   " ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(__lowercase       )       )]       )
     _UpperCAmelCase     							=   tokenizer.mask_token
     _UpperCAmelCase     							=   []
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
          _UpperCAmelCase     							=   predicted_token_bpe.replace("\u2581"			,     " "       )
          if " {0}".format(__lowercase       ) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(" {0}".format(__lowercase       )			,     __lowercase       ),
                       values[index].item(),
                       predicted_token,
                   )       )
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(__lowercase			,     __lowercase       ),
                       values[index].item(),
                       predicted_token,
                   )       )
     return topk_filled_outputs


__SCREAMING_SNAKE_CASE     :Any       =				CamembertTokenizer.from_pretrained('''camembert-base''')
__SCREAMING_SNAKE_CASE     :Optional[Any]       =				CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

__SCREAMING_SNAKE_CASE     :int       =				'''Le camembert est <mask> :)'''






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











'''simple docstring'''




import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
  from scipy.stats import pearsonr, spearmanr
  from sklearn.metrics import fa_score, matthews_corrcoef


__SCREAMING_SNAKE_CASE     :List[str]       =				(
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      Tuple       )		->       int:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     return (preds == labels).mean()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      str       )		->       Optional[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   simple_accuracy(__lowercase			,     __lowercase       )
     _UpperCAmelCase     							=   fa_score(y_true=__lowercase			,     y_pred=__lowercase       )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[int]			,     __lowercase							:      List[str]       )		->       List[Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     _UpperCAmelCase     							=   pearsonr(__lowercase			,     __lowercase       )[0]
     _UpperCAmelCase     							=   spearmanr(__lowercase			,     __lowercase       )[0]
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
         "corr": (pearson_corr + spearman_corr) / 2,
     }
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      str			,     __lowercase							:      str       )		->       Tuple:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     assert len(__lowercase       ) == len(__lowercase       ), f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'
     if task_name == "cola":
          return {"mcc": matthews_corrcoef(__lowercase			,     __lowercase       )}
     elif task_name == "sst-2":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mrpc":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "sts-b":
          return pearson_and_spearman(__lowercase			,     __lowercase       )
     elif task_name == "qqp":
          return acc_and_fa(__lowercase			,     __lowercase       )
     elif task_name == "mnli":
          return {"mnli/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "mnli-mm":
          return {"mnli-mm/acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "qnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "rte":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "wnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     elif task_name == "hans":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )






def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Dict			,     __lowercase							:      str       )		->       Union[str, Any]:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     if len(__lowercase       ) != len(__lowercase       ):
          raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase       )} and {len(__lowercase       )}'       )
     if task_name == "xnli":
          return {"acc": simple_accuracy(__lowercase			,     __lowercase       )}
     else:
          raise KeyError(__lowercase       )




