


import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')



def     __lowerCAmelCase						(							snake_case	:    Tuple )       ->							Any:
   __lowerCamelCase:				str     =	test_file.split(os.path.sep )
   if components[0:2] != ["tests", "models"]:
      raise ValueError(
          """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
          f'{test_file} instead.' )
   __lowerCamelCase:				Optional[Any]     =	components[-1]
   if not test_fn.endswith("""py""" ):
      raise ValueError(f'`test_file` should be a python file. Got {test_fn} instead.' )
   if not test_fn.startswith("""test_modeling_""" ):
      raise ValueError(
          f'`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.' )

   __lowerCamelCase:				Any     =	components[:-1] + [test_fn.replace(""".py"""						,      """""" )]
   __lowerCamelCase:				str     =	""".""".join(snake_case )

   return test_module_path



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Tuple:
   __lowerCamelCase:				str     =	get_module_path(snake_case )
   __lowerCamelCase:				str     =	importlib.import_module(snake_case )

   return test_module



def     __lowerCAmelCase						(							snake_case	:    str )       ->							List[str]:
   __lowerCamelCase:				Optional[int]     =	[]
   __lowerCamelCase:				Tuple     =	get_test_module(snake_case )
   for attr in dir(snake_case ):
      if attr.endswith("""ModelTester""" ):
         tester_classes.append(getattr(snake_case						,      snake_case ) )

    # sort with class names
   return sorted(snake_case						,      key=lambda snake_case : x.__name__ )



def     __lowerCAmelCase						(							snake_case	:    int )       ->							Tuple:
   __lowerCamelCase:				List[str]     =	[]
   __lowerCamelCase:				List[str]     =	get_test_module(snake_case )
   for attr in dir(snake_case ):
      __lowerCamelCase:				Optional[int]     =	getattr(snake_case						,      snake_case )
      # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
      # `all_model_classes` is not empty (which also excludes other special classes).
      __lowerCamelCase:				Optional[int]     =	getattr(snake_case						,      """all_model_classes"""						,      [] )
      if len(snake_case ) > 0:
         test_classes.append(snake_case )

    # sort with class names
   return sorted(snake_case						,      key=lambda snake_case : x.__name__ )



def     __lowerCAmelCase						(							snake_case	:    List[Any] )       ->							Dict:
   __lowerCamelCase:				List[str]     =	get_test_classes(snake_case )
   __lowerCamelCase:				Optional[int]     =	set()
   for test_class in test_classes:
      model_classes.update(test_class.all_model_classes )

   # sort with class names
   return sorted(snake_case						,      key=lambda snake_case : x.__name__ )



def     __lowerCAmelCase						(							snake_case	:    Optional[int] )       ->							List[Any]:
   __lowerCamelCase:				Optional[int]     =	test_class()
   if hasattr(snake_case						,      """setUp""" ):
      test.setUp()

   __lowerCamelCase:				Optional[int]     =	None
   if hasattr(snake_case						,      """model_tester""" ):
      # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
      if test.model_tester is not None:
         __lowerCamelCase:				List[Any]     =	test.model_tester.__class__

   return model_tester



def     __lowerCAmelCase						(							snake_case	:    Optional[Any]						,      snake_case	:    List[Any] )       ->							str:
   __lowerCamelCase:				int     =	get_test_classes(snake_case )

   __lowerCamelCase:				List[str]     =	[]
   for test_class in test_classes:
      if model_class in test_class.all_model_classes:
         target_test_classes.append(snake_case )

    # sort with class names
   return sorted(snake_case						,      key=lambda snake_case : x.__name__ )



def     __lowerCAmelCase						(							snake_case	:    int						,      snake_case	:    Dict )       ->							Optional[int]:
   __lowerCamelCase:				Dict     =	get_test_classes_for_model(snake_case						,      snake_case )

   __lowerCamelCase:				List[Any]     =	[]
   for test_class in test_classes:
      __lowerCamelCase:				str     =	get_model_tester_from_test_class(snake_case )
      if tester_class is not None:
         tester_classes.append(snake_case )

    # sort with class names
   return sorted(snake_case						,      key=lambda snake_case : x.__name__ )



def     __lowerCAmelCase						(							snake_case	:    List[Any] )       ->							Dict:
   __lowerCamelCase:				Optional[int]     =	get_test_classes(snake_case )
   __lowerCamelCase:				str     =	{test_class: get_model_tester_from_test_class(snake_case ) for test_class in test_classes}
   return test_tester_mapping



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Optional[int]:
   __lowerCamelCase:				Tuple     =	get_model_classes(snake_case )
   __lowerCamelCase:				str     =	{
       model_class: get_test_classes_for_model(snake_case						,      snake_case ) for model_class in model_classes
   }
   return model_test_mapping



def     __lowerCAmelCase						(							snake_case	:    Optional[int] )       ->							Any:
   __lowerCamelCase:				List[str]     =	get_model_classes(snake_case )
   __lowerCamelCase:				int     =	{
       model_class: get_tester_classes_for_model(snake_case						,      snake_case ) for model_class in model_classes
   }
   return model_to_tester_mapping







def     __lowerCAmelCase						(							snake_case	:    Tuple )       ->							Tuple:
   if isinstance(snake_case						,      snake_case ):
      return o
   elif isinstance(snake_case						,      snake_case ):
      return o.__name__
   elif isinstance(snake_case						,      (list, tuple) ):
      return [to_json(snake_case ) for x in o]
   elif isinstance(snake_case						,      snake_case ):
      return {to_json(snake_case ): to_json(snake_case ) for k, v in o.items()}
   else:
      return o






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		:				Dict									=       '''
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		:				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        a					(			datasets.Metric   ):





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, 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  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				str=None    ,	SCREAMING_SNAKE_CASE_					:				str=1    ,	SCREAMING_SNAKE_CASE_					:				List[str]="binary"    ,	SCREAMING_SNAKE_CASE_					:				List[str]=None							):
        __lowerCamelCase:				str     =	fa_score(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_    ,	pos_label=SCREAMING_SNAKE_CASE_    ,	average=SCREAMING_SNAKE_CASE_    ,	sample_weight=SCREAMING_SNAKE_CASE_							)
        return {"f1": float(SCREAMING_SNAKE_CASE_							) if score.size == 1 else score}


