




import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


__A						:							str							=  logging.get_logger(__name__)

__A						:							str							=  {
    """tensor(bool)""": np.bool_,
    """tensor(int8)""": np.inta,
    """tensor(uint8)""": np.uinta,
    """tensor(int16)""": np.intaa,
    """tensor(uint16)""": np.uintaa,
    """tensor(int32)""": np.intaa,
    """tensor(uint32)""": np.uintaa,
    """tensor(int64)""": np.intaa,
    """tensor(uint64)""": np.uintaa,
    """tensor(float16)""": np.floataa,
    """tensor(float)""": np.floataa,
    """tensor(double)""": np.floataa,
}




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	List[str]       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					List[str]:
										logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""			)
										SCREAMING_SNAKE_CASE           =					model
										SCREAMING_SNAKE_CASE           =					kwargs.get("""model_save_dir"""       ,     a			)
										SCREAMING_SNAKE_CASE           =					kwargs.get("""latest_model_name"""       ,     a			)





							def __call__(					self       :	List[str]       ,     **a       :	Any			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					{k: np.array(a			) for k, v in kwargs.items()}
										return self.model.run(a       ,     a			)





							@staticmethod
							def    _UpperCAmelCase     (					a       :	Union[str, Path]       ,     a       :	Any=None       ,     a       :	Optional[int]=None			)			->					Optional[Any]:
										if provider is None:
													logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""			)
													SCREAMING_SNAKE_CASE           =					"""CPUExecutionProvider"""

										return ort.InferenceSession(a       ,     providers=[provider]       ,     sess_options=a			)





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	Union[str, Path]       ,     a       :	Optional[str] = None       ,     **a       :	str			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					file_name if file_name is not None else ONNX_WEIGHTS_NAME

										SCREAMING_SNAKE_CASE           =					self.model_save_dir.joinpath(self.latest_model_name			)
										SCREAMING_SNAKE_CASE           =					Path(a			).joinpath(a			)
										try:
													shutil.copyfile(a       ,     a			)
										except shutil.SameFileError:
													pass

										# copy external weights (for models >2GB)
										SCREAMING_SNAKE_CASE           =					self.model_save_dir.joinpath(a			)
										if src_path.exists():
													SCREAMING_SNAKE_CASE           =					Path(a			).joinpath(a			)
													try:
																shutil.copyfile(a       ,     a			)
													except shutil.SameFileError:
																pass





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	Union[str, os.PathLike]       ,     **a       :	Tuple       ,     )			->					str:
										if os.path.isfile(a			):
													logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""			)
													return

										os.makedirs(a       ,     exist_ok=a			)

										# saving model weights/files
										self._save_pretrained(a       ,     **a			)





							@classmethod
							def    _UpperCAmelCase     (					cls       :	List[Any]       ,     a       :	Union[str, Path]       ,     a       :	Optional[Union[bool, str, None]] = None       ,     a       :	Optional[Union[str, None]] = None       ,     a       :	bool = False       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     a       :	Optional["ort.SessionOptions"] = None       ,     **a       :	Union[str, Any]       ,     )			->					List[str]:
										SCREAMING_SNAKE_CASE           =					file_name if file_name is not None else ONNX_WEIGHTS_NAME
										# load model from local directory
										if os.path.isdir(a			):
													SCREAMING_SNAKE_CASE           =					OnnxRuntimeModel.load_model(
													    os.path.join(a       ,     a			)       ,     provider=a       ,     sess_options=a			)
													SCREAMING_SNAKE_CASE           =					Path(a			)
										# load model from hub
										else:
													# download model
													SCREAMING_SNAKE_CASE           =					hf_hub_download(
													    repo_id=a       ,     filename=a       ,     use_auth_token=a       ,     revision=a       ,     cache_dir=a       ,     force_download=a       ,     )
													SCREAMING_SNAKE_CASE           =					Path(a			).parent
													SCREAMING_SNAKE_CASE           =					Path(a			).name
													SCREAMING_SNAKE_CASE           =					OnnxRuntimeModel.load_model(a       ,     provider=a       ,     sess_options=a			)
										return cls(model=a       ,     **a			)







							@classmethod
							def    _UpperCAmelCase     (					cls       :	List[Any]       ,     a       :	Union[str, Path]       ,     a       :	bool = True       ,     a       :	Optional[str] = None       ,     a       :	Optional[str] = None       ,     **a       :	Union[str, Any]       ,     )			->					Any:
										SCREAMING_SNAKE_CASE           =					None
										if len(str(a			).split("""@"""			)			) == 2:
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					model_id.split("""@"""			)

										return cls._from_pretrained(
										    model_id=a       ,     revision=a       ,     cache_dir=a       ,     force_download=a       ,     use_auth_token=a       ,     **a       ,     )






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__A						:							Tuple							=  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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
					'''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)

		__A						:							Union[str, Any]							=  parser.parse_args()

		__A						:							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)






