'''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


UpperCamelCase__  :						Dict					=      (
    '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       :					Dict	,      _A       :					int     ):
   '''simple docstring'''
   warnings.warn(_A	,      _A     )
   requires_backends(_A	,      '''sklearn'''     )
   return (preds == labels).mean()







def __UpperCamelCase( _A       :					Any	,      _A       :					Optional[int]     ):
   '''simple docstring'''
   warnings.warn(_A	,      _A     )
   requires_backends(_A	,      '''sklearn'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	simple_accuracy(_A	,      _A     )
   UpperCAmelCase__				:					Dict 					=	fa_score(y_true=_A	,      y_pred=_A     )
   return {
       "acc": acc,
       "f1": fa,
       "acc_and_f1": (acc + fa) / 2,
   }







def __UpperCamelCase( _A       :					str	,      _A       :					int     ):
   '''simple docstring'''
   warnings.warn(_A	,      _A     )
   requires_backends(_A	,      '''sklearn'''     )
   UpperCAmelCase__				:					int 					=	pearsonr(_A	,      _A     )[0]
   UpperCAmelCase__				:					Any 					=	spearmanr(_A	,      _A     )[0]
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
       "corr": (pearson_corr + spearman_corr) / 2,
   }







def __UpperCamelCase( _A       :					List[str]	,      _A       :					Optional[int]	,      _A       :					int     ):
   '''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       :					Dict	,      _A       :					List[Any]	,      _A       :					Union[str, Any]     ):
   '''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 warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)




