



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase  				=					{
    """configuration_deformable_detr""": ["""DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeformableDetrConfig"""],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase  				=					["""DeformableDetrFeatureExtractor"""]
    __lowerCamelCase  				=					["""DeformableDetrImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase  				=					[
        """DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DeformableDetrForObjectDetection""",
        """DeformableDetrModel""",
        """DeformableDetrPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
        from .image_processing_deformable_detr import DeformableDetrImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deformable_detr import (
            DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeformableDetrForObjectDetection,
            DeformableDetrModel,
            DeformableDetrPreTrainedModel,
        )

else:
    import sys

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




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					)					)
   }