






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


UpperCamelCase								=		(
    "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    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Any:
							warnings.warn(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							requires_backends(SCREAMING_SNAKE_CASE     ,     'sklearn'				)
							return (preds == labels).mean()






def    __magic_name__    (	SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)     ->    Optional[Any]:
							warnings.warn(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							requires_backends(SCREAMING_SNAKE_CASE     ,     'sklearn'				)
							_lowercase							:   Dict 							=   simple_accuracy(SCREAMING_SNAKE_CASE     ,     SCREAMING_SNAKE_CASE				)
							_lowercase							:   Any 							=   fa_score(y_true=SCREAMING_SNAKE_CASE     ,     y_pred=SCREAMING_SNAKE_CASE				)
							return {
							    "acc": acc,
							    "f1": fa,
							    "acc_and_f1": (acc + fa) / 2,
							}






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






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






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






"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


A__ :       List[Any]				= logging.get_logger(__name__)


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					*A_			,					**A_							)  ->						None:





         """simple docstring"""




         warnings.warn(
             '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
             ''' Please use SegformerImageProcessor instead.'''			,					A_			,					)
         super().__init__(*A_			,					**A_							)
