


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


_snake_case					:		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_					( __lowerCamelCase							,     __lowerCamelCase     ):
							warnings.warn(__lowerCamelCase							,     __lowerCamelCase     )
							requires_backends(__lowerCamelCase							,     "sklearn"     )
							return (preds == labels).mean()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							warnings.warn(__lowerCamelCase							,     __lowerCamelCase     )
							requires_backends(__lowerCamelCase							,     "sklearn"     )
							__snake_case		:      Any										=       simple_accuracy(__lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      Tuple										=       fa_score(y_true=__lowerCamelCase							,     y_pred=__lowerCamelCase     )
							return {
							    "acc": acc,
							    "f1": fa,
							    "acc_and_f1": (acc + fa) / 2,
							}







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							warnings.warn(__lowerCamelCase							,     __lowerCamelCase     )
							requires_backends(__lowerCamelCase							,     "sklearn"     )
							__snake_case		:      Union[str, Any]										=       pearsonr(__lowerCamelCase							,     __lowerCamelCase     )[0]
							__snake_case		:      Tuple										=       spearmanr(__lowerCamelCase							,     __lowerCamelCase     )[0]
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							    "corr": (pearson_corr + spearman_corr) / 2,
							}







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







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





import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_snake_case					:		Union[str, Any]      =    ["small", "medium", "large"]

_snake_case					:		List[Any]      =    "lm_head.decoder.weight"
_snake_case					:		Optional[Any]      =    "lm_head.weight"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Tuple										=       torch.load(__lowerCamelCase     )
							__snake_case		:      Dict										=       d.pop(__lowerCamelCase     )
							os.makedirs(__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							torch.save(__lowerCamelCase							,     os.path.join(__lowerCamelCase							,     __lowerCamelCase     )     )


if __name__ == "__main__":
			_snake_case					:		Dict      =    argparse.ArgumentParser()
			parser.add_argument("--dialogpt_path", default=".", type=str)
			_snake_case					:		Any      =    parser.parse_args()
			for MODEL in DIALOGPT_MODELS:
						_snake_case					:		Dict      =    os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
						_snake_case					:		List[str]      =    f'''./DialoGPT-{MODEL}'''
						convert_dialogpt_checkpoint(
						    checkpoint_path,
						    pytorch_dump_folder_path,
						)

