



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


lowercase_								=     (
    """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 (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			warnings.warn(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
			requires_backends(SCREAMING_SNAKE_CASE_						,  "sklearn"  )
			return (preds == labels).mean()



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



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



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			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_  )









from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def __lowerCAmelCase (     ):
			lowercase__					  =				HfArgumentParser(SCREAMING_SNAKE_CASE_  )
			lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			lowercase__					  =				TensorFlowBenchmark(args=SCREAMING_SNAKE_CASE_  )
			try:
						lowercase__					  =				parser.parse_args_into_dataclasses()[0]
			except ValueError as e:
						lowercase__					  =				"Arg --no_{0} is no longer used, please use --no-{0} instead."
						lowercase__					  =				" ".join(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[:-1]  )
						lowercase__					  =				""
						lowercase__					  =				eval(str(SCREAMING_SNAKE_CASE_  ).split(" "  )[-1]  )
						lowercase__					  =				[]
						for arg in depreciated_args:
									# arg[2:] removes '--'
									if arg[2:] in TensorFlowBenchmark.deprecated_args:
												# arg[5:] removes '--no_'
												full_error_msg += arg_error_msg.format(arg[5:]  )
									else:
												wrong_args.append(SCREAMING_SNAKE_CASE_  )
						if len(SCREAMING_SNAKE_CASE_  ) > 0:
									lowercase__					  =				full_error_msg + begin_error_msg + str(SCREAMING_SNAKE_CASE_  )
						raise ValueError(SCREAMING_SNAKE_CASE_  )
			benchmark.run()


if __name__ == "__main__":
							main()




