

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


SCREAMING_SNAKE_CASE		:Any						=     (
    """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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Tuple:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Tuple:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE		:List[Any]						=     {"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
 from .tokenization_herbert import HerbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_herbert_fast import HerbertTokenizerFast

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
