






'''simple docstring'''







from __future__ import annotations
def 						A__			(				__lowerCAmelCase		:			float   ,    __lowerCAmelCase		:			float   ,    __lowerCAmelCase		:			float   ,    ):
						if (stress, tangential_force, area).count(0						) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""						)
						elif stress < 0:
												raise ValueError("""Stress cannot be negative"""						)
						elif tangential_force < 0:
												raise ValueError("""Tangential Force cannot be negative"""						)
						elif area < 0:
												raise ValueError("""Area cannot be negative"""						)
						elif stress == 0:
												return (
												    "stress",
												    tangential_force / area,
												)
						elif tangential_force == 0:
												return (
												    "tangential_force",
												    stress * area,
												)
						else:
												return (
												    "area",
												    tangential_force / stress,
												)


if __name__ == "__main__":
							import doctest

							doctest.testmod()









'''simple docstring'''







import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase    :      Optional[Any]  			=		['small', 'medium', 'large']

UpperCamelCase    :      Dict  			=		'lm_head.decoder.weight'
UpperCamelCase    :      int  			=		'lm_head.weight'
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase						)
						lowerCamelCase__         =  d.pop(__lowerCAmelCase						)
						os.makedirs(__lowerCAmelCase   ,    exist_ok=__lowerCAmelCase						)
						torch.save(__lowerCAmelCase   ,    os.path.join(__lowerCAmelCase   ,    __lowerCAmelCase						)						)


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

