





import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar


class   A			(unittest.TestCase    ):
			'''simple docstring'''




			def    a_       (			self   :    Dict   )	->	Optional[Any]:







					"""simple docstring"""






					A__        =   logging.get_logger()

					# the current default level is logging.WARNING
					A__        =   logging.get_verbosity()

					logging.set_verbosity_error()
					self.assertEqual(logger.getEffectiveLevel()							,	logging.get_verbosity()   )

					logging.set_verbosity_warning()
					self.assertEqual(logger.getEffectiveLevel()							,	logging.get_verbosity()   )

					logging.set_verbosity_info()
					self.assertEqual(logger.getEffectiveLevel()							,	logging.get_verbosity()   )

					logging.set_verbosity_debug()
					self.assertEqual(logger.getEffectiveLevel()							,	logging.get_verbosity()   )

					# restore to the original level
					logging.set_verbosity(__lowerCAmelCase   )






			def    a_       (			self   :    int   )	->	str:







					"""simple docstring"""






					A__        =   logging.get_verbosity()

					A__        =   logging.get_logger("""transformers.models.bart.tokenization_bart"""   )
					A__        =   """Testing 1, 2, 3"""

					# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
					if level_origin <= logging.WARNING:
							with CaptureLogger(__lowerCAmelCase   ) as cl:
									logger.warning(__lowerCAmelCase   )
							self.assertEqual(cl.out							,	msg + """\n"""   )

					# this is setting the level for all of `transformers.*` loggers
					logging.set_verbosity_error()

					# should not be able to log warnings
					with CaptureLogger(__lowerCAmelCase   ) as cl:
							logger.warning(__lowerCAmelCase   )
					self.assertEqual(cl.out							,	""""""   )

					# should be able to log warnings again
					logging.set_verbosity_warning()
					with CaptureLogger(__lowerCAmelCase   ) as cl:
							logger.warning(__lowerCAmelCase   )
					self.assertEqual(cl.out							,	msg + """\n"""   )

					# restore to the original level
					logging.set_verbosity(__lowerCAmelCase   )






			@mockenv(TRANSFORMERS_VERBOSITY="""error"""   )
			def    a_       (			self   :    List[str]   )	->	List[Any]:







					"""simple docstring"""






					transformers.utils.logging._reset_library_root_logger()
					# this action activates the env var
					A__        =   logging.get_logger("""transformers.models.bart.tokenization_bart"""   )

					A__        =   os.getenv("""TRANSFORMERS_VERBOSITY"""							,	__lowerCAmelCase   )
					A__        =   logging.log_levels[env_level_str]

					A__        =   logging.get_verbosity()
					self.assertEqual(
					    __lowerCAmelCase							,	__lowerCAmelCase							,	f'TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'							,	)

					# restore to the original level
					A__        =   """"""
					transformers.utils.logging._reset_library_root_logger()






			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""   )
			def    a_       (			self   :    List[str]   )	->	str:







					"""simple docstring"""






					transformers.utils.logging._reset_library_root_logger()
					A__        =   logging.logging.getLogger()
					with CaptureLogger(__lowerCAmelCase   ) as cl:
							# this action activates the env var
							logging.get_logger("""transformers.models.bart.tokenization_bart"""   )
					self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""							,	cl.out   )

					# no need to restore as nothing was changed







			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					transformers.utils.logging._reset_library_root_logger()

					A__        =   logging.get_logger("""transformers.models.bart.tokenization_bart"""   )
					A__        =   """Testing 1, 2, 3"""

					with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""   ):
							# nothing should be logged as env var disables this method
							with CaptureLogger(__lowerCAmelCase   ) as cl:
									logger.warning_advice(__lowerCAmelCase   )
							self.assertEqual(cl.out							,	""""""   )

					with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""   ):
							# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
							with CaptureLogger(__lowerCAmelCase   ) as cl:
									logger.warning_advice(__lowerCAmelCase   )
							self.assertEqual(cl.out							,	msg + """\n"""   )







def    __lowerCamelCase					(							)		->				List[Any]:






		"""simple docstring"""

		disable_progress_bar()
		assert are_progress_bars_disabled()

		enable_progress_bar()
		assert not are_progress_bars_disabled()










from sklearn.metrics import fa_score

import datasets


A    :					Any =      '''
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)
'''


A    :					List[Any] =      '''
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. ])}
'''


A    :					List[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   A			(datasets.Metric    ):
			'''simple docstring'''




			def    a_       (			self   :    Optional[int]   )	->	List[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.f1_score.html"""]							,	)







			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    Dict=None							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    Any="binary"							,	__lowerCAmelCase   :    Optional[int]=None   )	->	List[Any]:







					"""simple docstring"""






					A__        =   fa_score(
					    __lowerCAmelCase							,	__lowerCAmelCase							,	labels=__lowerCAmelCase							,	pos_label=__lowerCAmelCase							,	average=__lowerCAmelCase							,	sample_weight=__lowerCAmelCase   )
					return {"f1": float(__lowerCAmelCase   ) if score.size == 1 else score}



