




"""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	:	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	:	int   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	List[str]=None   ,							_UpperCAmelCase	:	Dict=1   ,							_UpperCAmelCase	:	Union[str, Any]="binary"   ,							_UpperCAmelCase	:	List[str]=None		):
       _A        =       fa_score(
           _UpperCAmelCase   ,							_UpperCAmelCase   ,							labels=_UpperCAmelCase   ,							pos_label=_UpperCAmelCase   ,							average=_UpperCAmelCase   ,							sample_weight=_UpperCAmelCase		)
       return {"f1": float(_UpperCAmelCase		) if score.size == 1 else score}







"""simple docstring"""

import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
       import transformers






def 						_snake_case					( _snake_case							: int							)   -> Any:






      '''simple docstring'''


      random.seed(_snake_case							)
      np.random.seed(_snake_case							)
      torch.manual_seed(_snake_case							)
      torch.cuda.manual_seed_all(_snake_case							)
      # ^^ safe to call this function even if cuda is not available







class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	Tuple   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]   ,							_UpperCAmelCase	:	float = 0.9999   ,							_UpperCAmelCase	:	float = 0.0   ,							_UpperCAmelCase	:	int = 0   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	Union[float, int] = 1.0   ,							_UpperCAmelCase	:	Union[float, int] = 2 / 3   ,							_UpperCAmelCase	:	Optional[Any] = None   ,							_UpperCAmelCase	:	Dict[str, Any] = None   ,							**_UpperCAmelCase	:	Optional[int]   ,							):

       if isinstance(_UpperCAmelCase   ,							torch.nn.Module		):
             _A        =       (
                 'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
                 'Please pass the parameters of the module instead.'
             )
             deprecate(
                 'passing a `torch.nn.Module` to `ExponentialMovingAverage`'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase   ,							)
             _A        =       parameters.parameters()

             # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
             _A        =       True

       if kwargs.get('max_value'   ,							_UpperCAmelCase		) is not None:
             _A        =       'The `max_value` argument is deprecated. Please use `decay` instead.'
             deprecate('max_value'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
             _A        =       kwargs['max_value']

       if kwargs.get('min_value'   ,							_UpperCAmelCase		) is not None:
             _A        =       'The `min_value` argument is deprecated. Please use `min_decay` instead.'
             deprecate('min_value'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
             _A        =       kwargs['min_value']

       _A        =       list(_UpperCAmelCase		)
       _A        =       [p.clone().detach() for p in parameters]

       if kwargs.get('device'   ,							_UpperCAmelCase		) is not None:
             _A        =       'The `device` argument is deprecated. Please use `to` instead.'
             deprecate('device'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
             self.to(device=kwargs['device']		)

       _A        =       None

       _A        =       decay
       _A        =       min_decay
       _A        =       update_after_step
       _A        =       use_ema_warmup
       _A        =       inv_gamma
       _A        =       power
       _A        =       0
       _A        =       None  # set in `step()`

       _A        =       model_cls
       _A        =       model_config

 @classmethod
 def       lowerCAmelCase_							(			cls	:	Any   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[int]		):
       _A       ,   _A        =       model_cls.load_config(_UpperCAmelCase   ,							return_unused_kwargs=_UpperCAmelCase		)
       _A        =       model_cls.from_pretrained(_UpperCAmelCase		)

       _A        =       cls(model.parameters()   ,							model_cls=_UpperCAmelCase   ,							model_config=model.config		)

       ema_model.load_state_dict(_UpperCAmelCase		)
       return ema_model

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Tuple		):
       if self.model_cls is None:
             raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.'		)

       if self.model_config is None:
             raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.'		)

       _A        =       self.model_cls.from_config(self.model_config		)
       _A        =       self.state_dict()
       state_dict.pop('shadow_params'   ,							_UpperCAmelCase		)

       model.register_to_config(**_UpperCAmelCase		)
       self.copy_to(model.parameters()		)
       model.save_pretrained(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	int		):
       _A        =       max(0   ,							optimization_step - self.update_after_step - 1		)

       if step <= 0:
             return 0.0

       if self.use_ema_warmup:
             _A        =       1 - (1 + step / self.inv_gamma) ** -self.power
       else:
             _A        =       (1 + step) / (10 + step)

       _A        =       min(_UpperCAmelCase   ,							self.decay		)
       # make sure decay is not smaller than min_decay
       _A        =       max(_UpperCAmelCase   ,							self.min_decay		)
       return cur_decay_value

 @torch.no_grad()
 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       if isinstance(_UpperCAmelCase   ,							torch.nn.Module		):
             _A        =       (
                 'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
                 'Please pass the parameters of the module instead.'
             )
             deprecate(
                 'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase   ,							)
             _A        =       parameters.parameters()

       _A        =       list(_UpperCAmelCase		)

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       _A        =       self.get_decay(self.optimization_step		)
       _A        =       decay
       _A        =       1 - decay

       _A        =       contextlib.nullcontext
       if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
             import deepspeed

       for s_param, param in zip(self.shadow_params   ,							_UpperCAmelCase		):
             if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   _A        =       deepspeed.zero.GatheredParameters(_UpperCAmelCase   ,							modifier_rank=_UpperCAmelCase		)

             with context_manager():
                   if param.requires_grad:
                         s_param.sub_(one_minus_decay * (s_param - param)		)
                   else:
                         s_param.copy_(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       _A        =       list(_UpperCAmelCase		)
       for s_param, param in zip(self.shadow_params   ,							_UpperCAmelCase		):
             param.data.copy_(s_param.to(param.device		).data		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	Dict=None		):
       _A        =       [
           p.to(device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		) if p.is_floating_point() else p.to(device=_UpperCAmelCase		)
           for p in self.shadow_params
       ]

 def       lowerCAmelCase_							(			self	:	Dict		):
       return {
           "decay": self.decay,
           "min_decay": self.min_decay,
           "optimization_step": self.optimization_step,
           "update_after_step": self.update_after_step,
           "use_ema_warmup": self.use_ema_warmup,
           "inv_gamma": self.inv_gamma,
           "power": self.power,
           "shadow_params": self.shadow_params,
       }

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       _A        =       [param.detach().cpu().clone() for param in parameters]

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       if self.temp_stored_params is None:
             raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`'		)
       for c_param, param in zip(self.temp_stored_params   ,							_UpperCAmelCase		):
             param.data.copy_(c_param.data		)

       # Better memory-wise.
       _A        =       None



 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	dict		):
       _A        =       copy.deepcopy(_UpperCAmelCase		)

       _A        =       state_dict.get('decay'   ,							self.decay		)
       if self.decay < 0.0 or self.decay > 1.0:
             raise ValueError('Decay must be between 0 and 1'		)

       _A        =       state_dict.get('min_decay'   ,							self.min_decay		)
       if not isinstance(self.min_decay   ,							_UpperCAmelCase		):
             raise ValueError('Invalid min_decay'		)

       _A        =       state_dict.get('optimization_step'   ,							self.optimization_step		)
       if not isinstance(self.optimization_step   ,							_UpperCAmelCase		):
             raise ValueError('Invalid optimization_step'		)

       _A        =       state_dict.get('update_after_step'   ,							self.update_after_step		)
       if not isinstance(self.update_after_step   ,							_UpperCAmelCase		):
             raise ValueError('Invalid update_after_step'		)

       _A        =       state_dict.get('use_ema_warmup'   ,							self.use_ema_warmup		)
       if not isinstance(self.use_ema_warmup   ,							_UpperCAmelCase		):
             raise ValueError('Invalid use_ema_warmup'		)

       _A        =       state_dict.get('inv_gamma'   ,							self.inv_gamma		)
       if not isinstance(self.inv_gamma   ,							(float, int)		):
             raise ValueError('Invalid inv_gamma'		)

       _A        =       state_dict.get('power'   ,							self.power		)
       if not isinstance(self.power   ,							(float, int)		):
             raise ValueError('Invalid power'		)

       _A        =       state_dict.get('shadow_params'   ,							_UpperCAmelCase		)
       if shadow_params is not None:
             _A        =       shadow_params
             if not isinstance(self.shadow_params   ,							_UpperCAmelCase		):
                   raise ValueError('shadow_params must be a list'		)
             if not all(isinstance(_UpperCAmelCase   ,							torch.Tensor		) for p in self.shadow_params		):
                   raise ValueError('shadow_params must all be Tensors'		)

