




'''simple docstring'''



from sklearn.metrics import fa_score

import datasets


lowerCamelCase_			=						'\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'


lowerCamelCase_			=						'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    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.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    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\'`.\n\n        - \'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.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'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.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'f1\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'f1\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> 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])\n        >>> print(round(results[\'f1\'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n        >>> print(round(results[\'f1\'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'f1\': array([0.8, 0. , 0. ])}\n'


lowerCamelCase_			=						'\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,  _KWARGS_DESCRIPTION       )
class 		lowercase_   (			datasets.Metric       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Dict   ):

      """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 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict=None  ,				__lowerCamelCase :     List[Any]=1  ,				__lowerCamelCase :     str="binary"  ,				__lowerCamelCase :     Optional[int]=None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				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'''



import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowerCamelCase_			=						logging.get_logger(__name__)







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''linear'''
   lowerCamelCase_      =						'''cosine'''
   lowerCamelCase_      =						'''cosine_with_restarts'''
   lowerCamelCase_      =						'''polynomial'''
   lowerCamelCase_      =						'''constant'''
   lowerCamelCase_      =						'''constant_with_warmup'''
   lowerCamelCase_      =						'''piecewise_constant'''


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int = -1  )			->			Optional[int]:
   return LambdaLR(__A	,					lambda __A  : 1	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int = -1  )			->			Dict:

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1.0	,					__A  )  )
      return 1.0

   return LambdaLR(__A	,					__A	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			str	,					__A    :			int = -1  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				{}
   _SCREAMING_SNAKE_CASE	=				step_rules.split(","  )
   for rule_str in rule_list[:-1]:
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				rule_str.split(":"  )
      _SCREAMING_SNAKE_CASE	=				int(__A  )
      _SCREAMING_SNAKE_CASE	=				float(__A  )
      _SCREAMING_SNAKE_CASE	=				value
   _SCREAMING_SNAKE_CASE	=				float(rule_list[-1]  )

   def create_rules_function(__A    :			Tuple	,					__A    :			List[Any]  ):
      def rule_func(__A    :			int  ) -> float:
         _SCREAMING_SNAKE_CASE	=				sorted(rules_dict.keys()  )
         for i, sorted_step in enumerate(__A  ):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   _SCREAMING_SNAKE_CASE	=				create_rules_function(__A	,					__A  )

   return LambdaLR(__A	,					__A	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Union[str, Any]=-1  )			->			str:

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      return max(
          0.0	,					float(num_training_steps - current_step  ) / float(max(1	,					num_training_steps - num_warmup_steps  )  )  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			float = 0.5	,					__A    :			int = -1  )			->			Any:

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _SCREAMING_SNAKE_CASE	=				float(current_step - num_warmup_steps  ) / float(max(1	,					num_training_steps - num_warmup_steps  )  )
      return max(0.0	,					0.5 * (1.0 + math.cos(math.pi * float(__A  ) * 2.0 * progress  ))  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			int = 1	,					__A    :			int = -1  )			->			str:

   def lr_lambda(__A    :			Optional[int]  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _SCREAMING_SNAKE_CASE	=				float(current_step - num_warmup_steps  ) / float(max(1	,					num_training_steps - num_warmup_steps  )  )
      if progress >= 1.0:
         return 0.0
      return max(0.0	,					0.5 * (1.0 + math.cos(math.pi * ((float(__A  ) * progress) % 1.0)  ))  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Any=1e-7	,					__A    :			Optional[Any]=1.0	,					__A    :			List[Any]=-1  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				optimizer.defaults["lr"]
   if not (lr_init > lr_end):
      raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""  )

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         _SCREAMING_SNAKE_CASE	=				lr_init - lr_end
         _SCREAMING_SNAKE_CASE	=				num_training_steps - num_warmup_steps
         _SCREAMING_SNAKE_CASE	=				1 - (current_step - num_warmup_steps) / decay_steps
         _SCREAMING_SNAKE_CASE	=				lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(__A	,					__A	,					__A  )


lowerCamelCase_			=						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, SchedulerType]	,					__A    :			Optimizer	,					__A    :			Optional[str] = None	,					__A    :			Optional[int] = None	,					__A    :			Optional[int] = None	,					__A    :			int = 1	,					__A    :			float = 1.0	,					__A    :			int = -1	,					)			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				SchedulerType(__A  )
   _SCREAMING_SNAKE_CASE	=				TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(__A	,					last_epoch=__A  )

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(__A	,					step_rules=__A	,					last_epoch=__A  )

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""  )

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(__A	,					num_warmup_steps=__A	,					last_epoch=__A  )

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""  )

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          __A	,					num_warmup_steps=__A	,					num_training_steps=__A	,					num_cycles=__A	,					last_epoch=__A	,					)

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          __A	,					num_warmup_steps=__A	,					num_training_steps=__A	,					power=__A	,					last_epoch=__A	,					)

   return schedule_func(
       __A	,					num_warmup_steps=__A	,					num_training_steps=__A	,					last_epoch=__A  )

