


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   							=						(
    '''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 		_A		(		_lowercase					,  _lowercase )					-> str:


     """simple docstring"""


     warnings.warn(_lowercase					,  _lowercase )
     requires_backends(_lowercase					,  'sklearn' )
     return (preds == labels).mean()






def 		_A		(		_lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     warnings.warn(_lowercase					,  _lowercase )
     requires_backends(_lowercase					,  'sklearn' )
     __UpperCamelCase =			simple_accuracy(_lowercase					,  _lowercase )
     __UpperCamelCase =			fa_score(y_true=_lowercase					,  y_pred=_lowercase )
     return {
         "acc": acc,
         "f1": fa,
         "acc_and_f1": (acc + fa) / 2,
     }






def 		_A		(		_lowercase					,  _lowercase )					-> List[str]:


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Dict:


     """simple docstring"""


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






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Union[str, Any]:


     """simple docstring"""


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










import sys

SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		(
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)

def   __lowercase       (			snake_case = N			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   -sys.maxsize - 1
				for i in range(len(snake_case			) - 1_2			):
								__magic_name__		:List[Any]   =   1
								for j in range(1_3			):
												product *= int(n[i + j]			)
								if product > largest_product:
												__magic_name__		:str   =   product
				return largest_product


if __name__ == "__main__":
					print(f"{solution() = }")






