'''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[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:			int	       =       parser.parse_args()

  __a:			Tuple	       =       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'''

import numpy as np
def       __UpperCamelCase	(       UpperCAmelCase					):
 return 1 / (1 + np.exp(-vector					))
def       __UpperCamelCase	(       UpperCAmelCase					):
 return vector * sigmoid(UpperCAmelCase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

