

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		:   Dict										=       (
    """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__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							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__      ):







							'''simple docstring'''






							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__      )







from PIL import Image





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    (259 * (level + 255)) / (255 * (259 - level))

							def contrast(UpperCamelCase__      ) -> int:
														return int(128 + factor * (c - 128)      )

							return img.point(UpperCamelCase__      )


if __name__ == "__main__":
					# Load image
					with Image.open("""image_data/lena.jpg""") as img:
										# Change contrast to 170
										_UpperCAmelCase		:   Tuple										=       change_contrast(img, 170)
										cont_img.save("""image_data/lena_high_contrast.png""", format="""png""")




