





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     _UpperCamelCase     (a__   :Dict   ,			a__   :List[Any]       ):






							"""simple docstring"""





							warnings.warn(a__   ,			a__       )
							requires_backends(a__   ,			"""sklearn"""       )
							return (preds == labels).mean()





def     _UpperCamelCase     (a__   :Tuple   ,			a__   :List[str]       ):






							"""simple docstring"""





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





def     _UpperCamelCase     (a__   :int   ,			a__   :str       ):






							"""simple docstring"""





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





def     _UpperCamelCase     (a__   :Dict   ,			a__   :int   ,			a__   :Any       ):






							"""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     _UpperCamelCase     (a__   :Optional[int]   ,			a__   :str   ,			a__   :Optional[int]       ):






							"""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__       )











from argparse import ArgumentParser

from .env import EnvironmentCommand





def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			ArgumentParser("""Diffusers CLI tool"""   ,			usage="""diffusers-cli <command> [<args>]"""       )
							UpperCamelCase__				     =			parser.add_subparsers(help="""diffusers-cli command helpers"""       )

							# Register commands
							EnvironmentCommand.register_subcommand(a__       )

							# Let's go
							UpperCamelCase__				     =			parser.parse_args()

							if not hasattr(a__   ,			"""func"""       ):
														parser.print_help()
														exit(1       )

							# Run
							UpperCamelCase__				     =			args.func(a__       )
							service.run()


if __name__ == "__main__":
					main()




