'''simple docstring'''
from sklearn.metrics import fa_score

import datasets


a=  '''
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=  '''
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=  '''
@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 			__lowercase		(						datasets.Metric						):







     """simple docstring"""






     def lowerCAmelCase     (		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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=None     ,			_lowerCamelCase=1     ,			_lowerCamelCase="binary"     ,			_lowerCamelCase=None    ):
         __UpperCamelCase	:	int		    =  fa_score(
             _lowerCamelCase     ,			_lowerCamelCase     ,			labels=_lowerCamelCase     ,			pos_label=_lowerCamelCase     ,			average=_lowerCamelCase     ,			sample_weight=_lowerCamelCase    )
         return {"f1": float(_lowerCamelCase    ) if score.size == 1 else score}




'''simple docstring'''
from ...processing_utils import ProcessorMixin






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5FeatureExtractor'''
     SCREAMING_SNAKE_CASE__		   =						'''SpeechT5Tokenizer'''







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         super().__init__(_lowerCamelCase     ,			_lowerCamelCase    )







     def __call__(		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('audio'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Tuple		    =  kwargs.pop('text'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('text_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('audio_target'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Any		    =  kwargs.pop('sampling_rate'     ,			_lowerCamelCase    )

         if audio is not None and text is not None:
             raise ValueError(
                 'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'    )
         if audio_target is not None and text_target is not None:
             raise ValueError(
                 'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'    )
         if audio is None and audio_target is None and text is None and text_target is None:
             raise ValueError(
                 'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'    )

         if audio is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
         elif text is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if audio_target is not None:
             __UpperCamelCase	:	Dict		    =  self.feature_extractor(audio_target=_lowerCamelCase     ,			*_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_values']
         elif text_target is not None:
             __UpperCamelCase	:	Any		    =  self.tokenizer(_lowerCamelCase     ,			**_lowerCamelCase    )
             __UpperCamelCase	:	Optional[int]		    =  targets['input_ids']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	List[Any]		    =  labels

             __UpperCamelCase	:	Any		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	int		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  kwargs.pop('input_values'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('input_ids'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  kwargs.pop('labels'     ,			_lowerCamelCase    )

         if input_values is not None and input_ids is not None:
             raise ValueError('Cannot process both `input_values` and `input_ids` inputs.'    )
         if input_values is None and input_ids is None and labels is None:
             raise ValueError(
                 'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'    )

         if input_values is not None:
             __UpperCamelCase	:	Optional[int]		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
         elif input_ids is not None:
             __UpperCamelCase	:	int		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
         else:
             __UpperCamelCase	:	str		    =  None

         if labels is not None:
             if "input_ids" in labels or (isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) and "input_ids" in labels[0]):
                 __UpperCamelCase	:	Dict		    =  self.tokenizer.pad(_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	str		    =  targets['input_ids']
             else:
                 __UpperCamelCase	:	int		    =  self.feature_extractor.feature_size
                 __UpperCamelCase	:	Any		    =  self.feature_extractor.num_mel_bins
                 __UpperCamelCase	:	Dict		    =  self.feature_extractor.pad(_lowerCamelCase     ,			*_lowerCamelCase     ,			**_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  feature_size_hack
                 __UpperCamelCase	:	Tuple		    =  targets['input_values']
         else:
             __UpperCamelCase	:	List[str]		    =  None

         if inputs is None:
             return targets

         if targets is not None:
             __UpperCamelCase	:	Optional[int]		    =  labels

             __UpperCamelCase	:	Union[str, Any]		    =  targets.get('attention_mask'    )
             if decoder_attention_mask is not None:
                 __UpperCamelCase	:	Optional[Any]		    =  decoder_attention_mask

         return inputs







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.batch_decode(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         return self.tokenizer.decode(*_lowerCamelCase     ,			**_lowerCamelCase    )



