




'''simple docstring'''





from sklearn.metrics import fa_score

import datasets


lowerCAmelCase_					    = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


lowerCAmelCase_					    = """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'f1': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['f1'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
        >>> print(round(results['f1'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'f1': array([0.8, 0. , 0. ])}
"""


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  _lowerCAmelCase     (  datasets.Metric	):

      def 	__magic_name__(   self		):
        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.f1_score.html''']      ,				)







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=1      ,				__UpperCAmelCase="binary"      ,				__UpperCAmelCase=None		):
        lowerCAmelCase__				:  Optional[int]		       =     fa_score(
            __UpperCAmelCase      ,				__UpperCAmelCase      ,				labels=__UpperCAmelCase      ,				pos_label=__UpperCAmelCase      ,				average=__UpperCAmelCase      ,				sample_weight=__UpperCAmelCase		)
        return {"f1": float(__UpperCAmelCase		) if score.size == 1 else score}







from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch






@require_torch
@require_tf
class  _lowerCAmelCase     (  _lowercase	):

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     SMALL_MODEL_IDENTIFIER
        lowerCAmelCase__				:  List[str]		       =     '''pt'''
        lowerCAmelCase__				:  int		       =     '''tf'''

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     AutoModel.from_pretrained(self.test_model		)
        model_pt.save_pretrained(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     TFAutoModel.from_pretrained(self.test_model      ,				from_pt=__UpperCAmelCase		)
        model_tf.save_pretrained(__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     '''mock_framework'''

        # Framework provided - return whatever the user provides
        lowerCAmelCase__				:  Tuple		       =     FeaturesManager.determine_framework(self.test_model      ,				__UpperCAmelCase		)
        self.assertEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

        # Local checkpoint and framework provided - return provided framework
        # PyTorch checkpoint
        with TemporaryDirectory() as local_pt_ckpt:
          self._setup_pt_ckpt(__UpperCAmelCase		)
          lowerCAmelCase__				:  int		       =     FeaturesManager.determine_framework(__UpperCAmelCase      ,				__UpperCAmelCase		)
          self.assertEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

        # TensorFlow checkpoint
        with TemporaryDirectory() as local_tf_ckpt:
          self._setup_tf_ckpt(__UpperCAmelCase		)
          lowerCAmelCase__				:  List[str]		       =     FeaturesManager.determine_framework(__UpperCAmelCase      ,				__UpperCAmelCase		)
          self.assertEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        # PyTorch checkpoint
        with TemporaryDirectory() as local_pt_ckpt:
          self._setup_pt_ckpt(__UpperCAmelCase		)
          lowerCAmelCase__				:  Tuple		       =     FeaturesManager.determine_framework(__UpperCAmelCase		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_pt		)

        # TensorFlow checkpoint
        with TemporaryDirectory() as local_tf_ckpt:
          self._setup_tf_ckpt(__UpperCAmelCase		)
          lowerCAmelCase__				:  int		       =     FeaturesManager.determine_framework(__UpperCAmelCase		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_tf		)

        # Invalid local checkpoint
        with TemporaryDirectory() as local_invalid_ckpt:
          with self.assertRaises(__UpperCAmelCase		):
            lowerCAmelCase__				:  Tuple		       =     FeaturesManager.determine_framework(__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     MagicMock(return_value=__UpperCAmelCase		)
        with patch('''transformers.onnx.features.is_tf_available'''      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  int		       =     FeaturesManager.determine_framework(self.test_model		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_pt		)

        # PyTorch not in environment -> use TensorFlow
        lowerCAmelCase__				:  Union[str, Any]		       =     MagicMock(return_value=__UpperCAmelCase		)
        with patch('''transformers.onnx.features.is_torch_available'''      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Optional[int]		       =     FeaturesManager.determine_framework(self.test_model		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_tf		)

        # Both in environment -> use PyTorch
        lowerCAmelCase__				:  Union[str, Any]		       =     MagicMock(return_value=__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     MagicMock(return_value=__UpperCAmelCase		)
        with patch('''transformers.onnx.features.is_tf_available'''      ,				__UpperCAmelCase		), patch(
            '''transformers.onnx.features.is_torch_available'''      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[Any]		       =     FeaturesManager.determine_framework(self.test_model		)
          self.assertEqual(__UpperCAmelCase      ,				self.framework_pt		)

        # Both not in environment -> raise error
        lowerCAmelCase__				:  Optional[int]		       =     MagicMock(return_value=__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     MagicMock(return_value=__UpperCAmelCase		)
        with patch('''transformers.onnx.features.is_tf_available'''      ,				__UpperCAmelCase		), patch(
            '''transformers.onnx.features.is_torch_available'''      ,				__UpperCAmelCase		):
          with self.assertRaises(__UpperCAmelCase		):
            lowerCAmelCase__				:  Tuple		       =     FeaturesManager.determine_framework(self.test_model		)

