




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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




def 							_UpperCAmelCase  (UpperCamelCase_ : Dict      ,    UpperCamelCase_ : tuple      ,    UpperCamelCase_ : Path      ,    UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : Optional[Any]      ,    UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : Dict=False      ,    ):
				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 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : str      ,    UpperCamelCase_ : int      ,    UpperCamelCase_ : bool = False       ):
				_lowerCAmelCase    :    Tuple										=      torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								_lowerCAmelCase    :    str										=      """cuda"""
				elif fpaa and not torch.cuda.is_available():
								raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""       )
				else:
								_lowerCAmelCase    :    Dict										=      """cpu"""
				_lowerCAmelCase    :    Optional[Any]										=      Path(UpperCamelCase__       )

				# VAE DECODER
				_lowerCAmelCase    :    List[Any]										=      AutoencoderKL.from_pretrained(model_path + """/vae"""       )
				_lowerCAmelCase    :    Optional[int]										=      vae_decoder.config.latent_channels
				# forward only through the decoder part
				_lowerCAmelCase    :    Dict										=      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__":
	_lowerCamelCase      :  List[str]      						=						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")

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







from math import loga




def 							_UpperCAmelCase  (UpperCamelCase_ : int       ):
				'''simple docstring'''
				if a < 0:
								raise ValueError("""Input value must be a positive integer"""       )
				elif isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								raise TypeError("""Input value must be a 'int' type"""       )
				return 0 if (a == 0) else int(loga(a & -a       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

