





import copy
import re


class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       Dict							=    '''hp'''
			__lowerCamelCase	:       List[str]							=    {}
			__lowerCamelCase	:       Any							=    None






			@classmethod
			def    a_       (			cls   :    List[str]							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Any   )	->	List[str]:







					"""simple docstring"""






					A__        =   prefix
					A__        =   defaults
					cls.build_naming_info()






			@staticmethod
			def    a_       (			__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    int   )	->	str:







					"""simple docstring"""






					if len(__lowerCAmelCase   ) == 0:
							return ""
					A__        =   None
					if any(char.isdigit() for char in word   ):
							raise Exception(f'Parameters should not contain numbers: \'{word}\' contains a number'   )
					if word in info["short_word"]:
							return info["short_word"][word]
					for prefix_len in range(1							,	len(__lowerCAmelCase   ) + 1   ):
							A__        =   word[:prefix_len]
							if prefix in info["reverse_short_word"]:
									continue
							else:
									A__        =   prefix
									break

					if short_word is None:
							# Paranoid fallback
							def int_to_alphabetic(__lowerCAmelCase   :    List[str]   ):
									A__        =   """"""
									while integer != 0:
											A__        =   chr(ord("""A"""   ) + integer % 10   ) + s
											integer //= 10
									return s

							A__        =   0
							while True:
									A__        =   word + """#""" + int_to_alphabetic(__lowerCAmelCase   )
									if sword in info["reverse_short_word"]:
											continue
									else:
											A__        =   sword
											break

					A__        =   short_word
					A__        =   word
					return short_word






			@staticmethod
			def    a_       (			__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    Optional[Any]   )	->	str:







					"""simple docstring"""






					A__        =   param_name.split("""_"""   )

					A__        =   [TrialShortNamer.shortname_for_word(__lowerCAmelCase							,	__lowerCAmelCase   ) for word in words]

					# We try to create a separatorless short name, but if there is a collision we have to fallback
					# to a separated short name
					A__        =   ["""""", """_"""]

					for separator in separators:
							A__        =   separator.join(__lowerCAmelCase   )
							if shortname not in info["reverse_short_param"]:
									A__        =   shortname
									A__        =   param_name
									return shortname

					return param_name






			@staticmethod
			def    a_       (			__lowerCAmelCase   :    Any							,	__lowerCAmelCase   :    List[Any]   )	->	Optional[Any]:







					"""simple docstring"""






					A__        =   TrialShortNamer.shortname_for_key(__lowerCAmelCase							,	__lowerCAmelCase   )
					A__        =   short_name
					A__        =   param_name






			@classmethod
			def    a_       (			cls   :    Dict   )	->	Tuple:







					"""simple docstring"""






					if cls.NAMING_INFO is not None:
							return

					A__        =   {
					    """short_word""": {},
					    """reverse_short_word""": {},
					    """short_param""": {},
					    """reverse_short_param""": {},
					}

					A__        =   list(cls.DEFAULTS.keys()   )

					for k in field_keys:
							cls.add_new_param_name(__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   info






			@classmethod
			def    a_       (			cls   :    str							,	__lowerCAmelCase   :    Optional[int]   )	->	Any:







					"""simple docstring"""






					cls.build_naming_info()
					assert cls.PREFIX is not None
					A__        =   [copy.copy(cls.PREFIX   )]

					for k, v in params.items():
							if k not in cls.DEFAULTS:
									raise Exception(f'You should provide a default value for the param name {k} with value {v}'   )
							if v == cls.DEFAULTS[k]:
									# The default value is not added to the name
									continue

							A__        =   cls.NAMING_INFO["""short_param"""][k]

							if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   ):
									A__        =   1 if v else 0

							A__        =   """""" if isinstance(__lowerCAmelCase							,	(int, float)   ) else """-"""
							A__        =   f'{key}{sep}{v}'
							name.append(__lowerCAmelCase   )

					return "_".join(__lowerCAmelCase   )







			@classmethod
			def    a_       (			cls   :    List[str]							,	__lowerCAmelCase   :    Optional[Any]   )	->	str:







					"""simple docstring"""






					A__        =   repr[len(cls.PREFIX   ) + 1 :]
					if repr == "":
							A__        =   []
					else:
							A__        =   repr.split("""_"""   )

					A__        =   {}

					for value in values:
							if "-" in value:
									A__      ,			A__        =   value.split("""-"""   )
							else:
									A__        =   re.sub("""[0-9.]"""							,	""""""							,	__lowerCAmelCase   )
									A__        =   float(re.sub("""[^0-9.]"""							,	""""""							,	__lowerCAmelCase   )   )

							A__        =   cls.NAMING_INFO["""reverse_short_param"""][p_k]

							A__        =   p_v

					for k in cls.DEFAULTS:
							if k not in parameters:
									A__        =   cls.DEFAULTS[k]

					return parameters










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}



