
"""simple docstring"""


import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowercase							   =				['''small''', '''medium''', '''large''']

_lowercase							   =				'''lm_head.decoder.weight'''
_lowercase							   =				'''lm_head.weight'''






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     str     ):
	A						   =   torch.load(snake_case__     )
	A						   =   d.pop(snake_case__     )
	os.makedirs(snake_case__	,      exist_ok=snake_case__     )
	torch.save(snake_case__	,      os.path.join(snake_case__	,      snake_case__     )     )


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

"""simple docstring"""


def       _snake_case				(		snake_case__   :     list	,      snake_case__   :     list	,      snake_case__   :     int     ):
	A						   =   len(snake_case__     )
	A						   =   [[0] * n for i in range(snake_case__     )]
	for i in range(snake_case__     ):
		A						   =   y_points[i]

	for i in range(2	,      snake_case__     ):
		for j in range(snake_case__	,      snake_case__     ):
			A						   =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
		import doctest

		doctest.testmod()