





import random



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=     num - 1
      SCREAMING_SNAKE_CASE	=     0

      while s % 2 == 0:
            SCREAMING_SNAKE_CASE	=     s // 2
            t += 1

      for _ in range(5			):
            SCREAMING_SNAKE_CASE	=     random.randrange(2				,		num - 1			)
            SCREAMING_SNAKE_CASE	=     pow(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
            if v != 1:
                  SCREAMING_SNAKE_CASE	=     0
                  while v != (num - 1):
                        if i == t - 1:
                              return False
                        else:
                              SCREAMING_SNAKE_CASE	=     i + 1
                              SCREAMING_SNAKE_CASE	=     (v**2) % num
      return True



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     bool:


      '''simple docstring'''


      if num < 2:
            return False

      SCREAMING_SNAKE_CASE	=     [
          2,
          3,
          5,
          7,
          11,
          13,
          17,
          19,
          23,
          29,
          31,
          37,
          41,
          43,
          47,
          53,
          59,
          61,
          67,
          71,
          73,
          79,
          83,
          89,
          97,
          1_01,
          1_03,
          1_07,
          1_09,
          1_13,
          1_27,
          1_31,
          1_37,
          1_39,
          1_49,
          1_51,
          1_57,
          1_63,
          1_67,
          1_73,
          1_79,
          1_81,
          1_91,
          1_93,
          1_97,
          1_99,
          2_11,
          2_23,
          2_27,
          2_29,
          2_33,
          2_39,
          2_41,
          2_51,
          2_57,
          2_63,
          2_69,
          2_71,
          2_77,
          2_81,
          2_83,
          2_93,
          3_07,
          3_11,
          3_13,
          3_17,
          3_31,
          3_37,
          3_47,
          3_49,
          3_53,
          3_59,
          3_67,
          3_73,
          3_79,
          3_83,
          3_89,
          3_97,
          4_01,
          4_09,
          4_19,
          4_21,
          4_31,
          4_33,
          4_39,
          4_43,
          4_49,
          4_57,
          4_61,
          4_63,
          4_67,
          4_79,
          4_87,
          4_91,
          4_99,
          5_03,
          5_09,
          5_21,
          5_23,
          5_41,
          5_47,
          5_57,
          5_63,
          5_69,
          5_71,
          5_77,
          5_87,
          5_93,
          5_99,
          6_01,
          6_07,
          6_13,
          6_17,
          6_19,
          6_31,
          6_41,
          6_43,
          6_47,
          6_53,
          6_59,
          6_61,
          6_73,
          6_77,
          6_83,
          6_91,
          7_01,
          7_09,
          7_19,
          7_27,
          7_33,
          7_39,
          7_43,
          7_51,
          7_57,
          7_61,
          7_69,
          7_73,
          7_87,
          7_97,
          8_09,
          8_11,
          8_21,
          8_23,
          8_27,
          8_29,
          8_39,
          8_53,
          8_57,
          8_59,
          8_63,
          8_77,
          8_81,
          8_83,
          8_87,
          9_07,
          9_11,
          9_19,
          9_29,
          9_37,
          9_41,
          9_47,
          9_53,
          9_67,
          9_71,
          9_77,
          9_83,
          9_91,
          9_97,
      ]

      if num in low_primes:
            return True

      for prime in low_primes:
            if (num % prime) == 0:
                  return False

      return rabin_miller(_SCREAMING_SNAKE_CASE			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE = 10_24			)				->     int:


      '''simple docstring'''


      while True:
            SCREAMING_SNAKE_CASE	=     random.randrange(2 ** (keysize - 1)				,		2 ** (keysize)			)
            if is_prime_low_num(_SCREAMING_SNAKE_CASE			):
                  return num


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = generate_large_prime()
      print(("""Prime number:""", num))
      print(("""is_prime_low_num:""", is_prime_low_num(num)))









import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = 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.""",
      )
      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

      SCREAMING_SNAKE_CASE_		     = 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)


