


"""simple docstring"""


import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


A     :   Tuple			    =			version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=False    , ):

			'''simple docstring'''




			output_path.parent.mkdir(parents=_UpperCamelCase    , exist_ok=_UpperCamelCase      )
			# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
			# so we check the torch version for backwards compatibility
			if is_torch_less_than_1_11:
						export(
						    _UpperCamelCase    , _UpperCamelCase    , f=output_path.as_posix()    , input_names=_UpperCamelCase    , output_names=_UpperCamelCase    , dynamic_axes=_UpperCamelCase    , do_constant_folding=_UpperCamelCase    , use_external_data_format=_UpperCamelCase    , enable_onnx_checker=_UpperCamelCase    , opset_version=_UpperCamelCase    , )
			else:
						export(
						    _UpperCamelCase    , _UpperCamelCase    , f=output_path.as_posix()    , input_names=_UpperCamelCase    , output_names=_UpperCamelCase    , dynamic_axes=_UpperCamelCase    , do_constant_folding=_UpperCamelCase    , opset_version=_UpperCamelCase    , )


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase = False      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			torch.floataa if fpaa else torch.floataa
			if fpaa and torch.cuda.is_available():
						__lowerCAmelCase				    =			"cuda"
			elif fpaa and not torch.cuda.is_available():
						raise ValueError("`float16` model export is only supported on GPUs with CUDA"      )
			else:
						__lowerCAmelCase				    =			"cpu"
			__lowerCAmelCase				    =			Path(_UpperCamelCase      )

			# VAE DECODER
			__lowerCAmelCase				    =			AutoencoderKL.from_pretrained(model_path + "/vae"      )
			__lowerCAmelCase				    =			vae_decoder.config.latent_channels
			# forward only through the decoder part
			__lowerCAmelCase				    =			vae_decoder.decode
			onnx_export(
			    _UpperCamelCase    , model_args=(
			        torch.randn(1    , _UpperCamelCase    , 25    , 25      ).to(device=_UpperCamelCase    , dtype=_UpperCamelCase      ),
			        False,
			    )    , output_path=output_path / "vae_decoder" / "model.onnx"    , ordered_input_names=["latent_sample", "return_dict"]    , output_names=["sample"]    , dynamic_axes={
			        "latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
			    }    , opset=_UpperCamelCase    , )
			del vae_decoder


if __name__ == "__main__":
	A     :   Optional[int]			    =			argparse.ArgumentParser()

	parser.add_argument(
	    "--model_path",
	    type=str,
	    required=True,
	    help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
	)

	parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
	parser.add_argument(
	    "--opset",
	    default=1_4,
	    type=int,
	    help="The version of the ONNX operator set to use.",
	)
	parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

	A     :   Optional[int]			    =			parser.parse_args()
	print(args.output_path)
	convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
	print("SD: Done: ONNX")










import baseaa







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       bytes:





				"""simple docstring"""


				return baseaa.aaaencode(string.encode('utf-8'      )      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      bytes      )     ->       str:





				"""simple docstring"""


				return baseaa.aaadecode(UpperCamelCase__      ).decode('utf-8'      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()


