from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase      		=					'''
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
'''


UpperCAmelCase      		=					'''
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}
'''


UpperCAmelCase      		=					'''
@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  __lowerCAmelCase	(							datasets.Metric    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Any	)			->				Union[str, Any]:
        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        __snake_case       (      self							:		int							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int=True							,      lowerCamelCase							:		Any=None	)			->				Union[str, Any]:
        return {
            "accuracy": float(
                accuracy_score(lowerCamelCase							,      lowerCamelCase							,      normalize=lowerCamelCase							,      sample_weight=lowerCamelCase	)	)
        }





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						__UpperCamelCase = False  ,						__UpperCamelCase = False  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            __UpperCamelCase  ,						split=__UpperCamelCase  ,						features=__UpperCamelCase  ,						cache_dir=__UpperCamelCase  ,						keep_in_memory=__UpperCamelCase  ,						streaming=__UpperCamelCase  ,						num_proc=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							path_or_paths if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else {self.split: path_or_paths}
        UpperCamelCase_											=							Csv(
            cache_dir=__UpperCamelCase  ,						data_files=__UpperCamelCase  ,						features=__UpperCamelCase  ,						**__UpperCamelCase  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        if self.streaming:
               UpperCamelCase_											=							self.builder.as_streaming_dataset(split=self.split  )
        # Build regular (map-style) dataset
        else:
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None
               UpperCamelCase_											=							None

               self.builder.download_and_prepare(
                   download_config=__UpperCamelCase  ,						download_mode=__UpperCamelCase  ,						verification_mode=__UpperCamelCase  ,						base_path=__UpperCamelCase  ,						num_proc=self.num_proc  ,						)
               UpperCamelCase_											=							self.builder.as_dataset(
                   split=self.split  ,						verification_mode=__UpperCamelCase  ,						in_memory=self.keep_in_memory  )
        return dataset



class 		lowercase_							:

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = None  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        if num_proc is not None and num_proc <= 0:
               raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''  )

        UpperCamelCase_											=							dataset
        UpperCamelCase_											=							path_or_buf
        UpperCamelCase_											=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        UpperCamelCase_											=							num_proc
        UpperCamelCase_											=							"""utf-8"""
        UpperCamelCase_											=							to_csv_kwargs

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.to_csv_kwargs.pop("""path_or_buf"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""header"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.to_csv_kwargs.pop("""index"""  ,						__UpperCamelCase  )

        if isinstance(self.path_or_buf  ,						(str, bytes, os.PathLike)  ):
               with open(self.path_or_buf  ,						"""wb+"""  ) as buffer:
                      UpperCamelCase_											=							self._write(file_obj=__UpperCamelCase  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        else:
               UpperCamelCase_											=							self._write(file_obj=self.path_or_buf  ,						header=__UpperCamelCase  ,						index=__UpperCamelCase  ,						**self.to_csv_kwargs  )
        return written

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							args

        UpperCamelCase_											=							query_table(
            table=self.dataset.data  ,						key=slice(__UpperCamelCase  ,						offset + self.batch_size  )  ,						indices=self.dataset._indices  ,						)
        UpperCamelCase_											=							batch.to_pandas().to_csv(
            path_or_buf=__UpperCamelCase  ,						header=header if (offset == 0) else False  ,						index=__UpperCamelCase  ,						**__UpperCamelCase  )
        return csv_str.encode(self.encoding  )

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							0

        if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0  ,						len(self.dataset  )  ,						self.batch_size  )  ,						unit="""ba"""  ,						disable=not logging.is_progress_bar_enabled()  ,						desc="""Creating CSV from Arrow format"""  ,						):
                      UpperCamelCase_											=							self._batch_csv((offset, header, index, to_csv_kwargs)  )
                      written += file_obj.write(__UpperCamelCase  )

        else:
               UpperCamelCase_							,					UpperCamelCase_											=							len(self.dataset  ), self.batch_size
               with multiprocessing.Pool(self.num_proc  ) as pool:
                      for csv_str in logging.tqdm(
                          pool.imap(
                              self._batch_csv  ,						[(offset, header, index, to_csv_kwargs) for offset in range(0  ,						__UpperCamelCase  ,						__UpperCamelCase  )]  ,						)  ,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size  ,						unit="""ba"""  ,						disable=not logging.is_progress_bar_enabled()  ,						desc="""Creating CSV from Arrow format"""  ,						):
                             written += file_obj.write(__UpperCamelCase  )

        return written



