



"""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:     Optional[Any]										=	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:     Any										=	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)





"""simple docstring"""
from __future__ import annotations

from collections.abc import Generator

def    _snake_case		(		):
 UpperCAmelCase			:      dict[int, int]			=			{}
 UpperCAmelCase			:      str			=			2
 while True:
  UpperCAmelCase			:      List[str]			=			factor_map.pop(UpperCamelCase    ,	UpperCamelCase		)
  if factor:
   UpperCAmelCase			:      Dict			=			factor + prime
   while x in factor_map:
    x += factor
   UpperCAmelCase			:      Tuple			=			factor
  else:
   UpperCAmelCase			:      Optional[int]			=			prime
   yield prime
  prime += 1

def    _snake_case		(		UpperCamelCase		:	float = 1e1_0		):
 UpperCAmelCase			:      Tuple			=			sieve()

 UpperCAmelCase			:      Optional[Any]			=			1
 while True:
  UpperCAmelCase			:      List[str]			=			next(UpperCamelCase		)
  if (2 * prime * n) > limit:
   return n
  # Ignore the next prime as the reminder will be 2.
  next(UpperCamelCase		)
  n += 2


if __name__ == "__main__":
      print(solution())
