






'''simple docstring'''







from __future__ import annotations
def 						A__			(				__lowerCAmelCase		:			list[int]   ,    __lowerCAmelCase		:			list[int]   ,    __lowerCAmelCase		:			int						):
						lowerCamelCase__         =  list(range(len(__lowerCAmelCase						)						)						)
						lowerCamelCase__         =  [v / w for v, w in zip(__lowerCAmelCase   ,    __lowerCAmelCase						)]
						index.sort(key=lambda __lowerCAmelCase						: ratio[i]   ,    reverse=__lowerCAmelCase						)

						lowerCamelCase__         =  0
						lowerCamelCase__         =  [0] * len(__lowerCAmelCase						)
						for i in index:
												if weight[i] <= capacity:
																		lowerCamelCase__         =  1
																		max_value += value[i]
																		capacity -= weight[i]
												else:
																		lowerCamelCase__         =  capacity / weight[i]
																		max_value += value[i] * capacity / weight[i]
																		break

						return max_value, fractions


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,
														)

