

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


A_	:			Tuple  	=      (
    '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 (lowercase_:			Optional[Any]   ,				lowercase_:			Tuple )					->				List[str]:
	warnings.warn(lowercase_   ,				lowercase_ )
	requires_backends(lowercase_   ,				"""sklearn""" )
	return (preds == labels).mean()

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

def 						UpperCamelCase (lowercase_:			Tuple   ,				lowercase_:			Union[str, Any] )					->				Tuple:
	warnings.warn(lowercase_   ,				lowercase_ )
	requires_backends(lowercase_   ,				"""sklearn""" )
	A__							:   Union[str, Any]					       =		pearsonr(lowercase_   ,				lowercase_ )[0]
	A__							:   Optional[int]					       =		spearmanr(lowercase_   ,				lowercase_ )[0]
	return {
	    "pearson": pearson_corr,
	    "spearmanr": spearman_corr,
	    "corr": (pearson_corr + spearman_corr) / 2,
	}

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


def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			int   ,				lowercase_:			List[str] )					->				Optional[int]:
	warnings.warn(lowercase_   ,				lowercase_ )
	requires_backends(lowercase_   ,				"""sklearn""" )
	if len(lowercase_ ) != len(lowercase_ ):
		raise ValueError(f"""Predictions and labels have mismatched lengths {len(lowercase_ )} and {len(lowercase_ )}""" )
	if task_name == "xnli":
		return {"acc": simple_accuracy(lowercase_   ,				lowercase_ )}
	else:
		raise KeyError(lowercase_ )






from typing import Any

def 						UpperCamelCase (lowercase_:			list )					->				list[Any]:
	if not input_list:
		return []
	A__							:   Any					       =		[input_list.count(lowercase_ ) for value in input_list]
	A__							:   List[Any]					       =		max(lowercase_ )  # Gets the maximum count in the input list.
	# Gets values of modes
	return sorted({input_list[i] for i, value in enumerate(lowercase_ ) if value == y} )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



