




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


lowercase__				:     List[str] 		=						(
    "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"							)
	snake_case_		    =     simple_accuracy(_A     ,	_A							)
	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"							)
	snake_case_		    =     pearsonr(_A     ,	_A							)[0]
	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							)






import functools


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	if not isinstance(_A     ,	_A							) or not all(isinstance(_A     ,	_A							) for day in days							):
		raise ValueError("The parameter days should be a list of integers"							)

	if len(_A							) != 3 or not all(isinstance(_A     ,	_A							) for cost in costs							):
		raise ValueError("The parameter costs should be a list of three integers"							)

	if len(_A							) == 0:
		return 0

	if min(_A							) <= 0:
		raise ValueError("All days elements should be greater than 0"							)

	if max(_A							) >= 366:
		raise ValueError("All days elements should be less than 366"							)

	snake_case_		    =     set(_A							)

	@functools.cache
	def dynamic_programming(_A							) -> int:
		if index > 365:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1							)

		return min(
		    costs[0] + dynamic_programming(index + 1							)     ,	costs[1] + dynamic_programming(index + 7							)     ,	costs[2] + dynamic_programming(index + 30							)     ,	)

	return dynamic_programming(1							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
