
'''simple docstring'''





from sklearn.metrics import accuracy_score

import datasets


lowercase__			=	"""
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
"""


lowercase__			=	"""
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}
"""


lowercase__			=	"""
@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    A__(self					)       ->   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    A__(self ,  a__ ,  a__ ,  a__=True ,  a__=None					)       ->   int:
         return {
             "accuracy": float(
                 accuracy_score(a__ ,  a__ ,  normalize=a__ ,  sample_weight=a__					)					)
         }







'''simple docstring'''





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__			=	{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase__			=	[
           '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ConvNextV2ForImageClassification''',
           '''ConvNextV2Model''',
           '''ConvNextV2PreTrainedModel''',
           '''ConvNextV2Backbone''',
       ]


if TYPE_CHECKING:
       from .configuration_convnextva import (
           CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
           ConvNextVaConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convnextva import (
                  CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvNextVaBackbone,
                  ConvNextVaForImageClassification,
                  ConvNextVaModel,
                  ConvNextVaPreTrainedModel,
              )

else:
       import sys

       lowercase__			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)

