





'''simple docstring'''


from __future__ import annotations

import math

a__			:					int       						=						'2020.9.26'
a__			:					Optional[Any]       						=						'xcodz-dot, cclaus, dhruvmanila'
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float )							->     tuple[float, float]:

       """simple docstring"""


       if not all(isinstance(SCREAMING_SNAKE_CASE_			,	(float, int) ) for val in locals().values() ):
              UpperCAmelCase  =						f"Input values must either be float or int: {list(locals().values() )}"
              raise TypeError(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						((x * distance) / (z + distance)) * scale
       UpperCAmelCase  =						((y * distance) / (z + distance)) * scale
       return projected_x, projected_y
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			float			,	SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			float )							->     tuple[float, float, float]:

       """simple docstring"""


       if not isinstance(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ):
              raise TypeError('''Axis must be a str''' )
       UpperCAmelCase  =						locals()
       del input_variables["axis"]
       if not all(isinstance(SCREAMING_SNAKE_CASE_			,	(float, int) ) for val in input_variables.values() ):
              UpperCAmelCase  =						(
                  '''Input values except axis must either be float or int: '''
                  f"{list(input_variables.values() )}"
              )
              raise TypeError(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						(angle % 360) / 450 * 180 / math.pi
       if axis == "z":
              UpperCAmelCase  =						x * math.cos(SCREAMING_SNAKE_CASE_ ) - y * math.sin(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						y * math.cos(SCREAMING_SNAKE_CASE_ ) + x * math.sin(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						z
       elif axis == "x":
              UpperCAmelCase  =						y * math.cos(SCREAMING_SNAKE_CASE_ ) - z * math.sin(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						z * math.cos(SCREAMING_SNAKE_CASE_ ) + y * math.sin(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						x
       elif axis == "y":
              UpperCAmelCase  =						x * math.cos(SCREAMING_SNAKE_CASE_ ) - z * math.sin(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						z * math.cos(SCREAMING_SNAKE_CASE_ ) + x * math.sin(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						y
       else:
              raise ValueError('''not a valid axis, choose one of \'x\', \'y\', \'z\'''' )

       return new_x, new_y, new_z


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
    print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")






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


snake_case							:       List[str]										=	(
    '''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 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						Optional[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      return (preds == labels).mean()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:Dict									=			simple_accuracy(UpperCAmelCase_					,    UpperCAmelCase_		)
      a		:Any									=			fa_score(y_true=UpperCAmelCase_					,    y_pred=UpperCAmelCase_		)
      return {
          "acc": acc,
          "f1": fa,
          "acc_and_f1": (acc + fa) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Dict					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





      warnings.warn(UpperCAmelCase_					,    UpperCAmelCase_		)
      requires_backends(UpperCAmelCase_					,    '''sklearn'''		)
      a		:str									=			pearsonr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      a		:Union[str, Any]									=			spearmanr(UpperCAmelCase_					,    UpperCAmelCase_		)[0]
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
          "corr": (pearson_corr + spearman_corr) / 2,
      }
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						Dict		):




      """simple docstring"""





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






def 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						List[Any]		):




      """simple docstring"""





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





