




"""simple docstring"""


import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_A													=			{
    """debug""": logging.DEBUG,
    """info""": logging.INFO,
    """warning""": logging.WARNING,
    """error""": logging.ERROR,
    """critical""": logging.CRITICAL,
}

_A													=			logging.WARNING



def        lowercase_      ( )						->				Tuple:
      lowerCAmelCase__			: Dict  					=       os.getenv("""DATASETS_VERBOSITY"""				,		_snake_case      )
      if env_level_str:
            if env_level_str in log_levels:
                  return log_levels[env_level_str]
            else:
                  logging.getLogger().warning(
                      f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
                      f"""has to be one of: { ', '.join(log_levels.keys()      ) }"""      )
      return _default_log_level



def        lowercase_      ( )						->				str:
      return __name__.split("""."""      )[0]



def        lowercase_      ( )						->				logging.Logger:
      return logging.getLogger(_get_library_name()      )



def        lowercase_      ( )						->				None:
      lowerCAmelCase__			: Any  					=       _get_library_root_logger()
      library_root_logger.setLevel(_get_default_logging_level()      )



def        lowercase_      ( )						->				None:
      lowerCAmelCase__			: Optional[int]  					=       _get_library_root_logger()
      library_root_logger.setLevel(logging.NOTSET      )



def        lowercase_      ( __UpperCAmelCase = None      )						->				logging.Logger:





      if name is None:
            lowerCAmelCase__			: Optional[int]  					=       _get_library_name()
      return logging.getLogger(_snake_case      )



def        lowercase_      ( )						->				int:
      return _get_library_root_logger().getEffectiveLevel()



def        lowercase_      ( __UpperCAmelCase      )						->				None:
      _get_library_root_logger().setLevel(_snake_case      )



def        lowercase_      ( )						->				str:
      return set_verbosity(_snake_case      )



def        lowercase_      ( )						->				str:
      return set_verbosity(_snake_case      )



def        lowercase_      ( )						->				Any:
      return set_verbosity(_snake_case      )



def        lowercase_      ( )						->				Tuple:
      return set_verbosity(_snake_case      )



def        lowercase_      ( )						->				None:
      lowerCAmelCase__			: List[str]  					=       False



def        lowercase_      ( )						->				None:
      lowerCAmelCase__			: Any  					=       True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()


class _lowerCamelCase				:






   def __init__(		self   :		List[str]    ,	*UpperCamelCase   :		Optional[Any]    ,	**UpperCamelCase   :		Optional[Any]    ) ->						List[Any]:  # pylint: disable=unused-argument

         """simple docstring"""
         lowerCAmelCase__			: Dict  					=       args[0] if args else None






   def __iter__(		self   :		List[str]    ) ->						Tuple:

         """simple docstring"""
         return iter(self._iterator    )






   def __getattr__(		self   :		Tuple    ,	UpperCamelCase   :		List[str]    ) ->						Dict:

         """simple docstring"""

         def empty_fn(*UpperCamelCase   :		List[Any]    ,	**UpperCamelCase   :		Any    ):  # pylint: disable=unused-argument
               return

         return empty_fn






   def __enter__(		self   :		Optional[Any]    ) ->						List[Any]:

         """simple docstring"""
         return self






   def __exit__(		self   :		List[Any]    ,	UpperCamelCase   :		Union[str, Any]    ,	UpperCamelCase   :		Optional[int]    ,	UpperCamelCase   :		Optional[Any]    ) ->						str:

         """simple docstring"""
         return






_A													=			True


class _lowerCamelCase				:






   def __call__(		self   :		List[Any]    ,	*UpperCamelCase   :		str    ,	UpperCamelCase   :		List[Any]=False    ,	**UpperCamelCase   :		Any    ) ->						Union[str, Any]:

         """simple docstring"""

         if _tqdm_active and not disable:
               return tqdm_lib.tqdm(*a_    ,	**a_    )
         else:
               return EmptyTqdm(*a_    ,	**a_    )






   def        _lowerCAmelCase    (		self   :		List[Any]    ,	*UpperCamelCase   :		Optional[Any]    ,	**UpperCamelCase   :		List[Any]    ) ->						str:

         """simple docstring"""
         lowerCAmelCase__			: int  					=       None
         if _tqdm_active:
               return tqdm_lib.tqdm.set_lock(*a_    ,	**a_    )






   def        _lowerCAmelCase    (		self   :		Tuple    ) ->						str:

         """simple docstring"""

         if _tqdm_active:
               return tqdm_lib.tqdm.get_lock()






_A													=			_tqdm_cls()



def        lowercase_      ( )						->				bool:
      global _tqdm_active
      return bool(_tqdm_active      )



def        lowercase_      ( )						->				Optional[Any]:
      global _tqdm_active
      lowerCAmelCase__			: Optional[Any]  					=       True



def        lowercase_      ( )						->				Tuple:
      global _tqdm_active
      lowerCAmelCase__			: Optional[int]  					=       False









"""simple docstring"""
from sklearn.metrics import recall_score

import datasets


SCREAMING_SNAKE_CASE :    Dict									=				"""
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


SCREAMING_SNAKE_CASE :    Any									=				"""
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while 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 y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If 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 target labels and predictions 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. Note that it 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`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


SCREAMING_SNAKE_CASE :    Tuple									=				"""
@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 			_UpperCAmelCase (							datasets.Metric				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''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.recall_score.html''']				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_=None				,       a_=1				,       a_="binary"				,       a_=None				,       a_="warn"				,       ):





			'''simple docstring'''

			__snake_case	:						Any						=				recall_score(
			    a_				,       a_				,       labels=a_				,       pos_label=a_				,       average=a_				,       sample_weight=a_				,       zero_division=a_				,       )
			return {"recall": float(a_					) if score.size == 1 else score}





