


"""simple docstring"""





from sklearn.metrics import accuracy_score

import datasets


__UpperCAmelCase    				=      "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


__UpperCAmelCase    				=      "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


__UpperCAmelCase    				=      "\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  lowercase_  ( datasets.Metric						):







      def 				_lowerCAmelCase       (      self :							List[str]    ):
        return datasets.MetricInfo(
            description=_DESCRIPTION		,  citation=_CITATION		,  inputs_description=_KWARGS_DESCRIPTION		,  features=datasets.Features(
                {
                    "predictions": datasets.Sequence(datasets.Value("int32"    )    ),
                    "references": datasets.Sequence(datasets.Value("int32"    )    ),
                }
                if self.config_name == "multilabel"
                else {
                    "predictions": datasets.Value("int32"    ),
                    "references": datasets.Value("int32"    ),
                }    )		,  reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]		,  )







      def 				_lowerCAmelCase       (      self :							int		,  _lowercase :							List[Any]		,  _lowercase :							Tuple		,  _lowercase :							Tuple=True		,  _lowercase :							List[Any]=None    ):
        return {
            "accuracy": float(
                accuracy_score(_lowercase		,  _lowercase		,  normalize=_lowercase		,  sample_weight=_lowercase    )    )
        }




"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase    				=      {"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SEWDForCTC",
       "SEWDForSequenceClassification",
       "SEWDModel",
       "SEWDPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

   __UpperCAmelCase    				=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
