



import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand




def     UpperCAmelCase__ (							__snake_case							)   -> Optional[Any]:
  return EnvironmentCommand()



class 		_snake_case      (			lowerCamelCase				):





 """simple docstring"""



 @staticmethod
 def lowercase_		(						a					)	->							Dict:




   """simple docstring"""







   _A           =    parser.add_parser('''env'''	,    help='''Print relevant system environment info.'''					)
   download_parser.set_defaults(func=a					)
 def lowercase_		(						self					)	->							Optional[int]:




   """simple docstring"""







   _A           =    {
       '''`datasets` version''': version,
       '''Platform''': platform.platform(),
       '''Python version''': platform.python_version(),
       '''Huggingface_hub version''': huggingface_hub.__version__,
       '''PyArrow version''': pyarrow.__version__,
       '''Pandas version''': pandas.__version__,
   }

   print('''\nCopy-and-paste the text below in your GitHub issue.\n'''					)
   print(self.format_dict(a					)					)

   return info



 @staticmethod
 def lowercase_		(						a					)	->							Optional[int]:




   """simple docstring"""







   return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"




from sklearn.metrics import accuracy_score

import datasets


__lowerCamelCase  				=					"""
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


__lowerCamelCase  				=					"""
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> 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])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


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



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





 """simple docstring"""



 def lowercase_		(						self					)	->							Optional[Any]:




   """simple docstring"""







   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 lowercase_		(						self	,    a	,    a	,    a=True	,    a=None					)	->							Dict:




   """simple docstring"""







   return {
       "accuracy": float(
           accuracy_score(a	,    a	,    normalize=a	,    sample_weight=a					)					)
   }