
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
    lowerCamelCase      =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_      (			UpperCamelCase__		):
          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)

    lowerCamelCase      =parser.parse_args()

    lowerCamelCase      =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)




lowerCamelCase      ={
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}


