






from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__lowerCamelCase	:		Any										=   1.054_571_817e-34  # unit of ℏ : J * s

__lowerCamelCase	:		Dict										=   3e8  # unit of c : m * s^-1







def 			A__							(      _a  :					float		,  _a  :					float		,  _a  :					float	):





 '''simple docstring'''







 if (force, area, distance).count(0	) != 1:
  raise ValueError("""One and only one argument must be 0"""	)
 if force < 0:
  raise ValueError("""Magnitude of force can not be negative"""	)
 if distance < 0:
  raise ValueError("""Distance can not be negative"""	)
 if area < 0:
  raise ValueError("""Area can not be negative"""	)
 if force == 0:
  snake_case__  :    Tuple							       =(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
      240 * (distance) ** 4
  )
  return {"force": force}
 elif area == 0:
  snake_case__  :    List[Any]							       =(240 * force * (distance) ** 4) / (
      REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
  )
  return {"area": area}
 elif distance == 0:
  snake_case__  :    Dict							       =(
      (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
  ) ** (1 / 4)
  return {"distance": distance}
 raise ValueError("""One and only one argument must be 0"""	)


# Run doctest
if __name__ == "__main__":
       import doctest

       doctest.testmod()













import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
       __lowerCamelCase	:		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.""",
       )
       __lowerCamelCase	:		Optional[Any]										=   parser.parse_args()

       __lowerCamelCase	:		Optional[int]										=   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)





