





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
      snake_case 				=       argparse.ArgumentParser()

      parser.add_argument(
          """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
      )
      parser.add_argument(
          """--original_config_file""",
          type=str,
          required=True,
          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(
          """--image_size""",
          default=5_1_2,
          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(
          """--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.)""")

      def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]     )      ->						Dict:
        if string == "True":
          return True
        elif string == "False":
          return False
        else:
          raise ValueError(F"""could not parse string as bool {string}"""     )

      parser.add_argument(
          """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
      )

      parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

      snake_case 				=       parser.parse_args()

      snake_case 				=       download_controlnet_from_original_ckpt(
          checkpoint_path=args.checkpoint_path,
          original_config_file=args.original_config_file,
          image_size=args.image_size,
          extract_ema=args.extract_ema,
          num_in_channels=args.num_in_channels,
          upcast_attention=args.upcast_attention,
          from_safetensors=args.from_safetensors,
          device=args.device,
          use_linear_projection=args.use_linear_projection,
          cross_attention_dim=args.cross_attention_dim,
      )







      controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






snake_case 				=       8.3_144_598

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :float			,							snake_case__  :float     )      ->						float:
  if temperature < 0:
    raise Exception('Temperature cannot be less than 0 K'     )
  if molar_mass <= 0:
    raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'     )
  else:
    return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # example
      snake_case 				=       3_0_0
      snake_case 				=       2_8
      snake_case 				=       rms_speed_of_molecule(temperature, molar_mass)
      print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")