
'''simple docstring'''



from typing import Dict

from .base import GenericTensor, Pipeline

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def        UpperCamelCase				(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Optional[int]:
          if tokenize_kwargs is None:
             _SCREAMING_SNAKE_CASE      =				{}

          if truncation is not None:
             if "truncation" in tokenize_kwargs:
                raise ValueError(
                    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""      )
             _SCREAMING_SNAKE_CASE      =				truncation

          _SCREAMING_SNAKE_CASE      =				tokenize_kwargs

          _SCREAMING_SNAKE_CASE      =				{}
          if return_tensors is not None:
             _SCREAMING_SNAKE_CASE      =				return_tensors

          return preprocess_params, {}, postprocess_params






       def        UpperCamelCase				(							self       ,							A__       ,							**A__      )    ->			Dict[str, GenericTensor]:
          _SCREAMING_SNAKE_CASE      =				self.framework
          _SCREAMING_SNAKE_CASE      =				self.tokenizer(A__       ,							return_tensors=A__       ,							**A__      )
          return model_inputs






       def        UpperCamelCase				(							self       ,							A__      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				self.model(**A__      )
          return model_outputs






       def        UpperCamelCase				(							self       ,							A__       ,							A__=False      )    ->			Optional[Any]:
          # [0] is the first available tensor, logits or last_hidden_state.
          if return_tensors:
             return model_outputs[0]
          if self.framework == "pt":
             return model_outputs[0].tolist()
          elif self.framework == "tf":
             return model_outputs[0].numpy().tolist()






       def __call__(							self       ,							*A__       ,							**A__      )    ->			List[str]:
          return super().__call__(*A__       ,							**A__      )








'''simple docstring'''



from sklearn.metrics import matthews_corrcoef

import datasets


UpperCamelCase__      :		List[str]       							=  "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

UpperCamelCase__      :		List[Any]       							=  "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

UpperCamelCase__      :		Any       							=  "\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"

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





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
                  {
                      """predictions""": datasets.Value("""int32"""      ),
                      """references""": datasets.Value("""int32"""      ),
                  }      )       ,							reference_urls=[
                  """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
              ]       ,							)






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__=None      )    ->			List[str]:
          return {
              "matthews_correlation": float(matthews_corrcoef(A__       ,							A__       ,							sample_weight=A__      )      ),
          }






