


from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)







class  snake_case_     (_UpperCAmelCase   ):
					def __init__(   self     :Tuple    ,__snake_case     :Distribution    ,__snake_case     :Tuple=None    ,__snake_case     :List[str]=None    ,__snake_case     :int=0   )      ->					str:
												a__    =  1.0 if scale is None else scale
												a__    =  0.0 if loc is None else loc

												super().__init__(lowercase_    ,[AffineTransform(loc=self.loc    ,scale=self.scale    ,event_dim=lowercase_   )]   )
					@property
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Union[str, Any]:
												return self.base_dist.mean * self.scale + self.loc
					@property
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					List[Any]:
												return self.base_dist.variance * self.scale**2



					@property
					def 				lowerCamelCase__(   self     :Dict   )      ->					int:
												return self.variance.sqrt()







class  snake_case_     (nn.Module   ):
					def __init__(   self     :Union[str, Any]    ,__snake_case     :int    ,__snake_case     :Dict[str, int]    ,__snake_case     :Callable[..., Tuple[torch.Tensor]]    ,**__snake_case     :List[Any]   )      ->					str:
												super().__init__(**lowercase_   )
												a__    =  args_dim
												a__    =  nn.ModuleList([nn.Linear(lowercase_    ,lowercase_   ) for dim in args_dim.values()]   )
												a__    =  domain_map



					def 				lowerCamelCase__(   self     :int    ,__snake_case     :torch.Tensor   )      ->					Optional[int]:
												a__    =  [proj(lowercase_   ) for proj in self.proj]

												return self.domain_map(*lowercase_   )







class  snake_case_     (nn.Module   ):
					def __init__(   self     :Optional[Any]    ,__snake_case     :List[Any]   )      ->					Optional[int]:
												super().__init__()
												a__    =  function



					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :Tuple    ,*__snake_case     :Optional[int]   )      ->					Optional[int]:
												return self.function(lowercase_    ,*lowercase_   )







class  snake_case_     :
					UpperCAmelCase__      :					List[Any]					   =    4_2
					UpperCAmelCase__      :					List[Any]					   =    4_2
					UpperCAmelCase__      :					int					   =    4_2
					def __init__(   self     :Dict    ,__snake_case     :int = 1   )      ->					str:
												a__    =  dim
												a__    =  {k: dim * self.args_dim[k] for k in self.args_dim}
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Optional[Any]   )      ->					Union[str, Any]:
												if self.dim == 1:
																			return self.distribution_class(*lowercase_   )
												else:
																			return Independent(self.distribution_class(*lowercase_   )    ,1   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :str    ,__snake_case     :Optional[torch.Tensor] = None    ,__snake_case     :Optional[torch.Tensor] = None    ,)      ->					Any:
												a__    =  self._base_distribution(lowercase_   )
												if loc is None and scale is None:
																			return distr
												else:
																			return AffineTransformed(lowercase_    ,loc=lowercase_    ,scale=lowercase_    ,event_dim=self.event_dim   )
					@property
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Tuple:
												return () if self.dim == 1 else (self.dim,)
					@property
					def 				lowerCamelCase__(   self     :Dict   )      ->					Any:
												return len(self.event_shape   )
					@property
					def 				lowerCamelCase__(   self     :List[str]   )      ->					List[Any]:
												return 0.0
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :int   )      ->					Union[str, Any]:
												return ParameterProjection(
												    in_features=lowercase_    ,args_dim=self.args_dim    ,domain_map=LambdaLayer(self.domain_map   )    ,)
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,*__snake_case     :torch.Tensor   )      ->					str:
												raise NotImplementedError()



					@staticmethod
					def 				lowerCamelCase__(   __snake_case     :torch.Tensor   )      ->					List[Any]:
												return (x + torch.sqrt(torch.square(lowercase_   ) + 4.0   )) / 2.0







class  snake_case_     (_UpperCAmelCase   ):
					UpperCAmelCase__      :					Union[str, Any]					   =    {'''df''': 1, '''loc''': 1, '''scale''': 1}
					UpperCAmelCase__      :					Optional[int]					   =    StudentT
					@classmethod
					def 				lowerCamelCase__(   cls     :List[Any]    ,__snake_case     :torch.Tensor    ,__snake_case     :torch.Tensor    ,__snake_case     :torch.Tensor   )      ->					str:
												a__    =  cls.squareplus(lowercase_   ).clamp_min(torch.finfo(scale.dtype   ).eps   )
												a__    =  2.0 + cls.squareplus(lowercase_   )
												return df.squeeze(-1   ), loc.squeeze(-1   ), scale.squeeze(-1   )







class  snake_case_     (_UpperCAmelCase   ):
					UpperCAmelCase__      :					List[str]					   =    {'''loc''': 1, '''scale''': 1}
					UpperCAmelCase__      :					Dict					   =    Normal
					@classmethod
					def 				lowerCamelCase__(   cls     :int    ,__snake_case     :torch.Tensor    ,__snake_case     :torch.Tensor   )      ->					List[Any]:
												a__    =  cls.squareplus(lowercase_   ).clamp_min(torch.finfo(scale.dtype   ).eps   )
												return loc.squeeze(-1   ), scale.squeeze(-1   )







class  snake_case_     (_UpperCAmelCase   ):
					UpperCAmelCase__      :					Union[str, Any]					   =    {'''total_count''': 1, '''logits''': 1}
					UpperCAmelCase__      :					int					   =    NegativeBinomial
					@classmethod
					def 				lowerCamelCase__(   cls     :Optional[int]    ,__snake_case     :torch.Tensor    ,__snake_case     :torch.Tensor   )      ->					int:
												a__    =  cls.squareplus(lowercase_   )
												return total_count.squeeze(-1   ), logits.squeeze(-1   )
					def 				lowerCamelCase__(   self     :int    ,__snake_case     :Tuple   )      ->					str:
												a__    =  distr_args
												if self.dim == 1:
																			return self.distribution_class(total_count=lowercase_    ,logits=lowercase_   )
												else:
																			return Independent(self.distribution_class(total_count=lowercase_    ,logits=lowercase_   )    ,1   )



					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Optional[int]    ,__snake_case     :Optional[torch.Tensor] = None    ,__snake_case     :Optional[torch.Tensor] = None   )      ->					Tuple:
												a__    =  distr_args

												if scale is not None:
																			# See scaling property of Gamma.
																			logits += scale.log()

												return self._base_distribution((total_count, logits)   )





from sklearn.metrics import fa_score

import datasets


snake_case							: Optional[int]      =   '''
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)
'''


snake_case							: 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. ])}
'''


snake_case							: Union[str, 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  snake_case_     (datasets.Metric   ):
					def 				lowerCamelCase__(   self     :Any   )      ->					Any:
												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     :Dict    ,__snake_case     :str    ,__snake_case     :str    ,__snake_case     :Dict=None    ,__snake_case     :str=1    ,__snake_case     :Optional[int]="binary"    ,__snake_case     :Union[str, Any]=None   )      ->					Tuple:
												a__    =  fa_score(
												    __snake_case    ,__snake_case    ,labels=__snake_case    ,pos_label=__snake_case    ,average=__snake_case    ,sample_weight=__snake_case   )
												return {"f1": float(__snake_case   ) if score.size == 1 else score}




