






'''simple docstring'''







from __future__ import annotations

import math
def 						A__			(				__lowerCAmelCase		:			int						):

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5   ,    int(math.sqrt(__lowerCAmelCase						) + 1						)   ,    6						):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True


UpperCamelCase    :      str  			=		[num for num in range(3, 10_00_01, 2) if not is_prime(num)]
def 						A__			(				__lowerCAmelCase		:			int						):
						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												raise ValueError("""n must be an integer"""						)
						if n <= 0:
												raise ValueError("""n must be >= 0"""						)

						lowerCamelCase__         =  []
						for num in range(len(__lowerCAmelCase						)						):
												lowerCamelCase__         =  0
												while 2 * i * i <= odd_composites[num]:
																		lowerCamelCase__         =  odd_composites[num] - 2 * i * i
																		if is_prime(__lowerCAmelCase						):
																								break
																		i += 1
												else:
																		list_nums.append(odd_composites[num]						)
																		if len(__lowerCAmelCase						) == n:
																								return list_nums

						return []
def 						A__			(				):
						return compute_nums(1						)[0]


if __name__ == "__main__":
							print(F'{solution() = }')









'''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,
														)

