'''simple docstring'''

from sklearn.metrics import accuracy_score

import datasets


__snake_case       :   str  		=    """
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
"""


__snake_case       :   int  		=    """
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}
"""


__snake_case       :   Any  		=    """
@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 							lowercase_    (     datasets.Metric						):







    def   lowerCamelCase_    (	self				)				->       str:





     """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   lowerCamelCase_    (	self       ,     UpperCamelCase__       ,     UpperCamelCase__       ,     UpperCamelCase__=True       ,     UpperCamelCase__=None				)				->       Dict:





     """simple docstring"""

     return {
         "accuracy": float(
             accuracy_score(__A       ,     __A       ,     normalize=__A       ,     sample_weight=__A				)				)
     }
'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case       :   Optional[int]  		=    {
    '''configuration_gptsan_japanese''': ['''GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTSanJapaneseConfig'''],
    '''tokenization_gptsan_japanese''': ['''GPTSanJapaneseTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   List[Any]  		=    [
          '''GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''GPTSanJapaneseForConditionalGeneration''',
          '''GPTSanJapaneseModel''',
          '''GPTSanJapanesePreTrainedModel''',
      ]
      __snake_case       :   Optional[int]  		=    [
          '''GPTSanJapaneseTokenizer''',
      ]


if TYPE_CHECKING:
      from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
      from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gptsan_japanese import (
                GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTSanJapaneseForConditionalGeneration,
                GPTSanJapaneseModel,
                GPTSanJapanesePreTrainedModel,
            )
            from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
      import sys

      __snake_case       :   Tuple  		=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
