


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=5      )->						str:




      """simple docstring"""

      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


UpperCAmelCase    =					CamembertTokenizer.from_pretrained("""camembert-base""")
UpperCAmelCase    =					CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

UpperCAmelCase    =					"""Le camembert est <mask> :)"""




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



import numpy as np

import datasets


UpperCAmelCase    =					"""
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""

UpperCAmelCase    =					"""\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
"""

UpperCAmelCase    =					"""
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {'mahalanobis': array([0.5])}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,		_KWARGS_DESCRIPTION )
class lowerCAmelCase_		(				datasets.Metric ):

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       return datasets.MetricInfo(
           description=_DESCRIPTION	,   citation=_CITATION	,   inputs_description=_KWARGS_DESCRIPTION	,   features=datasets.Features(
               {
                   '''X''': datasets.Sequence(datasets.Value('''float'''	,   id='''sequence'''      )	,   id='''X'''      ),
               }      )	,   )





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       # convert to numpy arrays
       snake_case_ =   np.array(_UpperCAmelCase      )
       snake_case_ =   np.array(_UpperCAmelCase      )

       # Assert that arrays are 2D
       if len(X.shape      ) != 2:
             raise ValueError('''Expected `X` to be a 2D vector'''      )
       if len(reference_distribution.shape      ) != 2:
             raise ValueError('''Expected `reference_distribution` to be a 2D vector'''      )
       if reference_distribution.shape[0] < 2:
             raise ValueError(
                 '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'''      )

       # Get mahalanobis distance for each prediction
       snake_case_ =   X - np.mean(_UpperCAmelCase      )
       snake_case_ =   np.cov(reference_distribution.T      )
       try:
             snake_case_ =   np.linalg.inv(_UpperCAmelCase      )
       except np.linalg.LinAlgError:
             snake_case_ =   np.linalg.pinv(_UpperCAmelCase      )
       snake_case_ =   np.dot(_UpperCAmelCase	,   _UpperCAmelCase      )
       snake_case_ =   np.dot(_UpperCAmelCase	,   X_minus_mu.T      ).diagonal()

       return {"mahalanobis": mahal_dist}