






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__magic_name__:					List[str]							=     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=512,
		    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    UpperCamelCase						(	_A			):






								"""simple docstring"""







								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)

		__magic_name__:					Any							=     parser.parse_args()

		__magic_name__:					Any							=     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)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__:					List[Any]							=     {
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[str]							=     ["ConditionalDetrFeatureExtractor"]
		__magic_name__:					int							=     ["ConditionalDetrImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					List[Any]							=     [
		    "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "ConditionalDetrForObjectDetection",
		    "ConditionalDetrForSegmentation",
		    "ConditionalDetrModel",
		    "ConditionalDetrPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ConditionalDetrConfig,
		    ConditionalDetrOnnxConfig,
		)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
				from .image_processing_conditional_detr import ConditionalDetrImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_conditional_detr import (
				    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConditionalDetrForObjectDetection,
				    ConditionalDetrForSegmentation,
				    ConditionalDetrModel,
				    ConditionalDetrPreTrainedModel,
				)

else:
		import sys

		__magic_name__:					List[str]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
