from __future__ import annotations

import math



def        lowerCamelCase_       (    _lowercase							)				->			bool:

		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(__UpperCAmelCase							) + 1							)   ,      6							):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True


UpperCamelCase     					=      [num for num in range(3, 100_001, 2) if not is_prime(num)]



def        lowerCamelCase_       (    _lowercase							)				->			list[int]:

		if not isinstance(__UpperCAmelCase   ,      __UpperCAmelCase							):
				raise ValueError("n must be an integer"							)
		if n <= 0:
				raise ValueError("n must be >= 0"							)

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

		return []



def        lowerCamelCase_       (    )				->			int:
		return compute_nums(1							)[0]


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











'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
							a    :							List[str]										=    argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)
							# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
							parser.add_argument(
							    '--original_config_file',
							    default=None,
							    type=str,
							    help='The YAML config file corresponding to the original architecture.',
							)
							parser.add_argument(
							    '--num_in_channels',
							    default=None,
							    type=int,
							    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
							)
							parser.add_argument(
							    '--scheduler_type',
							    default='pndm',
							    type=str,
							    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
							)
							parser.add_argument(
							    '--pipeline_type',
							    default=None,
							    type=str,
							    help=(
							        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
							        '. If `None` pipeline will be automatically inferred.'
							    ),
							)
							parser.add_argument(
							    '--image_size',
							    default=None,
							    type=int,
							    help=(
							        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
							        ' Base. Use 768 for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--prediction_type',
							    default=None,
							    type=str,
							    help=(
							        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
							        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--extract_ema',
							    action='store_true',
							    help=(
							        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
							        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
							        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
							    ),
							)
							parser.add_argument(
							    '--upcast_attention',
							    action='store_true',
							    help=(
							        'Whether the attention computation should always be upcasted. This is necessary when running stable'
							        ' diffusion 2.1.'
							    ),
							)
							parser.add_argument(
							    '--from_safetensors',
							    action='store_true',
							    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
							)
							parser.add_argument(
							    '--to_safetensors',
							    action='store_true',
							    help='Whether to store pipeline in safetensors format or not.',
							)
							parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
							parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
							parser.add_argument(
							    '--stable_unclip',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
							)
							parser.add_argument(
							    '--stable_unclip_prior',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
							)
							parser.add_argument(
							    '--clip_stats_path',
							    type=str,
							    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
							    required=False,
							)
							parser.add_argument(
							    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
							)
							parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
							parser.add_argument(
							    '--vae_path',
							    type=str,
							    default=None,
							    required=False,
							    help='Set to a path, hub id to an already converted vae to not convert it again.',
							)
							a    :							str										=    parser.parse_args()

							a    :							List[Any]										=    download_from_original_stable_diffusion_ckpt(
							    checkpoint_path=args.checkpoint_path,
							    original_config_file=args.original_config_file,
							    image_size=args.image_size,
							    prediction_type=args.prediction_type,
							    model_type=args.pipeline_type,
							    extract_ema=args.extract_ema,
							    scheduler_type=args.scheduler_type,
							    num_in_channels=args.num_in_channels,
							    upcast_attention=args.upcast_attention,
							    from_safetensors=args.from_safetensors,
							    device=args.device,
							    stable_unclip=args.stable_unclip,
							    stable_unclip_prior=args.stable_unclip_prior,
							    clip_stats_path=args.clip_stats_path,
							    controlnet=args.controlnet,
							    vae_path=args.vae_path,
							)

							if args.half:
														pipe.to(torch_dtype=torch.floataa)

							if args.controlnet:
														# only save the controlnet model
														pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
							else:
														pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

