



"""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


__snake_case							:   Union[str, 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 			_lowercase				(  __snake_case					,__snake_case	)						->   List[str]:
			warnings.warn(__snake_case					,__snake_case	)
			requires_backends(__snake_case					,"sklearn"	)
			return (preds == labels).mean()


def 			_lowercase				(  __snake_case					,__snake_case	)						->   int:
			warnings.warn(__snake_case					,__snake_case	)
			requires_backends(__snake_case					,"sklearn"	)
			__lowerCAmelCase      :  Union[str, Any]												=       simple_accuracy(__snake_case					,__snake_case	)
			__lowerCAmelCase      :  str												=       fa_score(y_true=__snake_case					,y_pred=__snake_case	)
			return {
			    "acc": acc,
			    "f1": fa,
			    "acc_and_f1": (acc + fa) / 2,
			}


def 			_lowercase				(  __snake_case					,__snake_case	)						->   Union[str, Any]:
			warnings.warn(__snake_case					,__snake_case	)
			requires_backends(__snake_case					,"sklearn"	)
			__lowerCAmelCase      :  Union[str, Any]												=       pearsonr(__snake_case					,__snake_case	)[0]
			__lowerCAmelCase      :  str												=       spearmanr(__snake_case					,__snake_case	)[0]
			return {
			    "pearson": pearson_corr,
			    "spearmanr": spearman_corr,
			    "corr": (pearson_corr + spearman_corr) / 2,
			}


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





def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Union[str, Any]:
			warnings.warn(__snake_case					,__snake_case	)
			requires_backends(__snake_case					,"sklearn"	)
			if len(__snake_case	) != len(__snake_case	):
						raise ValueError(F"""Predictions and labels have mismatched lengths {len(__snake_case	)} and {len(__snake_case	)}"""	)
			if task_name == "xnli":
						return {"acc": simple_accuracy(__snake_case					,__snake_case	)}
			else:
						raise KeyError(__snake_case	)




"""simple docstring"""






import re


def 			_lowercase				(  __snake_case	)						->   str:

			if len(re.findall("[ATCG]"					,__snake_case	)	) != len(__snake_case	):
						raise ValueError("Invalid Strand"	)

			return dna.translate(dna.maketrans("ATCG"					,"TAGC"	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()