
"""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    (_UpperCamelCase	,  _UpperCamelCase		):
	warnings.warn(_UpperCamelCase	,  _UpperCamelCase		)
	requires_backends(_UpperCamelCase	,  'sklearn'		)
	return (preds == labels).mean()
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	warnings.warn(_UpperCamelCase	,  _UpperCamelCase		)
	requires_backends(_UpperCamelCase	,  'sklearn'		)
	__lowerCAmelCase      :						Tuple						      =				simple_accuracy(_UpperCamelCase	,  _UpperCamelCase		)
	__lowerCAmelCase      :						Union[str, Any]						      =				fa_score(y_true=_UpperCamelCase	,  y_pred=_UpperCamelCase		)
	return {
	    "acc": acc,
	    "f1": fa,
	    "acc_and_f1": (acc + fa) / 2,
	}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
	warnings.warn(_UpperCamelCase	,  _UpperCamelCase		)
	requires_backends(_UpperCamelCase	,  'sklearn'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				pearsonr(_UpperCamelCase	,  _UpperCamelCase		)[0]
	__lowerCAmelCase      :						Optional[int]						      =				spearmanr(_UpperCamelCase	,  _UpperCamelCase		)[0]
	return {
	    "pearson": pearson_corr,
	    "spearmanr": spearman_corr,
	    "corr": (pearson_corr + spearman_corr) / 2,
	}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	warnings.warn(_UpperCamelCase	,  _UpperCamelCase		)
	requires_backends(_UpperCamelCase	,  'sklearn'		)
	assert len(_UpperCamelCase		) == len(_UpperCamelCase		), F"Predictions and labels have mismatched lengths {len(_UpperCamelCase		)} and {len(_UpperCamelCase		)}"
	if task_name == "cola":
		return {"mcc": matthews_corrcoef(_UpperCamelCase	,  _UpperCamelCase		)}
	elif task_name == "sst-2":
		return {"acc": simple_accuracy(_UpperCamelCase	,  _UpperCamelCase		)}
	elif task_name == "mrpc":
		return acc_and_fa(_UpperCamelCase	,  _UpperCamelCase		)
	elif task_name == "sts-b":
		return pearson_and_spearman(_UpperCamelCase	,  _UpperCamelCase		)
	elif task_name == "qqp":
		return acc_and_fa(_UpperCamelCase	,  _UpperCamelCase		)
	elif task_name == "mnli":
		return {"mnli/acc": simple_accuracy(_UpperCamelCase	,  _UpperCamelCase		)}
	elif task_name == "mnli-mm":
		return {"mnli-mm/acc": simple_accuracy(_UpperCamelCase	,  _UpperCamelCase		)}
	elif task_name == "qnli":
		return {"acc": simple_accuracy(_UpperCamelCase	,  _UpperCamelCase		)}
	elif task_name == "rte":
		return {"acc": simple_accuracy(_UpperCamelCase	,  _UpperCamelCase		)}
	elif task_name == "wnli":
		return {"acc": simple_accuracy(_UpperCamelCase	,  _UpperCamelCase		)}
	elif task_name == "hans":
		return {"acc": simple_accuracy(_UpperCamelCase	,  _UpperCamelCase		)}
	else:
		raise KeyError(_UpperCamelCase		)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	warnings.warn(_UpperCamelCase	,  _UpperCamelCase		)
	requires_backends(_UpperCamelCase	,  'sklearn'		)
	if len(_UpperCamelCase		) != len(_UpperCamelCase		):
		raise ValueError(F"Predictions and labels have mismatched lengths {len(_UpperCamelCase		)} and {len(_UpperCamelCase		)}"		)
	if task_name == "xnli":
		return {"acc": simple_accuracy(_UpperCamelCase	,  _UpperCamelCase		)}
	else:
		raise KeyError(_UpperCamelCase		)

"""simple docstring"""







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate




@dataclass(frozen=_lowerCamelCase)
class 			A__	(     _lowerCamelCase):
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		A_						:	str           =     field(default='text-classification'	,					metadata={'include_in_asdict_even_if_is_default': True})
		A_						:	ClassVar[Features]           =     Features({'text': Value('string')})
		A_						:	ClassVar[Features]           =     Features({'labels': ClassLabel})
		A_						:	str           =     "text"
		A_						:	str           =     "labels"

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			if self.label_column not in features:
				raise ValueError(f"Column {self.label_column} is not present in features."			)
			if not isinstance(features[self.label_column]	,		_SCREAMING_SNAKE_CASE			):
				raise ValueError(f"Column {self.label_column} is not a ClassLabel."			)
			__lowerCAmelCase      :						Any						      =				copy.deepcopy(self			)
			__lowerCAmelCase      :						Dict						      =				self.label_schema.copy()
			__lowerCAmelCase      :						List[Any]						      =				features[self.label_column]
			__lowerCAmelCase      :						Dict						      =				label_schema
			return task_template







		@property
		def 							__lowerCamelCase							(				self			):
			return {
			    self.text_column: "text",
			    self.label_column: "labels",
			}