
"""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


lowerCAmelCase__							=				(
    '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 ( __a,							__a    ):

		'''simple docstring'''





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



def        _lowerCamelCase ( __a,							__a    ):

		'''simple docstring'''





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



def        _lowerCamelCase ( __a,							__a    ):

		'''simple docstring'''





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



def        _lowerCamelCase ( __a,							__a,							__a    ):

		'''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        _lowerCamelCase ( __a,							__a,							__a    ):

		'''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"""




def        _lowerCamelCase ( __a    ):

		if not isinstance(__a,							__a    ):
				SCREAMING_SNAKE_CASE_						 = F'Input value of [number={number}] must be an integer'
				raise TypeError(__a    )

		if number < 1:
				SCREAMING_SNAKE_CASE_						 = F'Input value of [number={number}] must be > 0'
				raise ValueError(__a    )

		SCREAMING_SNAKE_CASE_						 = 1

		for i in range(1,							__a    ):
				current_number *= 4 * i - 2
				current_number //= i + 1

		return current_number


if __name__ == "__main__":
						import doctest

						doctest.testmod()