


'''simple docstring'''






import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
  from scipy.stats import pearsonr, spearmanr
  from sklearn.metrics import fa_score, matthews_corrcoef


__A   :    Optional[int]											= (
    "This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
    "library. You can have a look at this example script for pointers: "
    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  warnings.warn(A__				,       A__   )
  requires_backends(A__				,       """sklearn"""   )
  return (preds == labels).mean()







def 							UpperCamelCase_     (       A__   :  List[Any]				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  warnings.warn(A__				,       A__   )
  requires_backends(A__				,       """sklearn"""   )
  lowerCAmelCase_     : str     =  simple_accuracy(A__				,       A__   )
  lowerCAmelCase_     : Union[str, Any]     =  fa_score(y_true=A__				,       y_pred=A__   )
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[str]   ):
  '''simple docstring'''





  warnings.warn(A__				,       A__   )
  requires_backends(A__				,       """sklearn"""   )
  lowerCAmelCase_     : Any     =  pearsonr(A__				,       A__   )[0]
  lowerCAmelCase_     : Any     =  spearmanr(A__				,       A__   )[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }







def 							UpperCamelCase_     (       A__   :  Optional[int]				,       A__   :  Union[str, Any]				,       A__   :  List[Any]   ):
  '''simple docstring'''





  warnings.warn(A__				,       A__   )
  requires_backends(A__				,       """sklearn"""   )
  assert len(A__   ) == len(A__   ), f'Predictions and labels have mismatched lengths {len(A__   )} and {len(A__   )}'
  if task_name == "cola":
    return {"mcc": matthews_corrcoef(A__				,       A__   )}
  elif task_name == "sst-2":
    return {"acc": simple_accuracy(A__				,       A__   )}
  elif task_name == "mrpc":
    return acc_and_fa(A__				,       A__   )
  elif task_name == "sts-b":
    return pearson_and_spearman(A__				,       A__   )
  elif task_name == "qqp":
    return acc_and_fa(A__				,       A__   )
  elif task_name == "mnli":
    return {"mnli/acc": simple_accuracy(A__				,       A__   )}
  elif task_name == "mnli-mm":
    return {"mnli-mm/acc": simple_accuracy(A__				,       A__   )}
  elif task_name == "qnli":
    return {"acc": simple_accuracy(A__				,       A__   )}
  elif task_name == "rte":
    return {"acc": simple_accuracy(A__				,       A__   )}
  elif task_name == "wnli":
    return {"acc": simple_accuracy(A__				,       A__   )}
  elif task_name == "hans":
    return {"acc": simple_accuracy(A__				,       A__   )}
  else:
    raise KeyError(A__   )







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  List[str]				,       A__   :  Dict   ):
  '''simple docstring'''





  warnings.warn(A__				,       A__   )
  requires_backends(A__				,       """sklearn"""   )
  if len(A__   ) != len(A__   ):
    raise ValueError(f'Predictions and labels have mismatched lengths {len(A__   )} and {len(A__   )}'   )
  if task_name == "xnli":
    return {"acc": simple_accuracy(A__				,       A__   )}
  else:
    raise KeyError(A__   )







'''simple docstring'''






import math







def 							UpperCamelCase_     (       A__   :  float				,       A__   :  float   ):
  '''simple docstring'''





  if initial_intensity < 0:
    raise ValueError("""The value of intensity cannot be negative"""   )
    # handling of negative values of initial intensity
  if angle < 0 or angle > 3_60:
    raise ValueError("""In Malus Law, the angle is in the range 0-360 degrees"""   )
    # handling of values out of allowed range
  return initial_intensity * (math.cos(math.radians(A__   )   ) ** 2)


if __name__ == "__main__":
  import doctest

  doctest.testmod(name="malus_law")



