"""simple docstring"""





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_UpperCamelCase   :						int      =    version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def 				a_     (      _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : tuple   , _lowerCAmelCase  : Path   , _lowerCAmelCase  : str   , _lowerCAmelCase  : Optional[Any]   , _lowerCAmelCase  : int   , _lowerCAmelCase  : str   , _lowerCAmelCase  : Tuple=False   , ):





       '''simple docstring'''

       output_path.parent.mkdir(parents=_lowerCAmelCase   , exist_ok=_lowerCAmelCase     )
       # 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(
                  _lowerCAmelCase   , _lowerCAmelCase   , f=output_path.as_posix()   , input_names=_lowerCAmelCase   , output_names=_lowerCAmelCase   , dynamic_axes=_lowerCAmelCase   , do_constant_folding=_lowerCAmelCase   , use_external_data_format=_lowerCAmelCase   , enable_onnx_checker=_lowerCAmelCase   , opset_version=_lowerCAmelCase   , )
       else:
              export(
                  _lowerCAmelCase   , _lowerCAmelCase   , f=output_path.as_posix()   , input_names=_lowerCAmelCase   , output_names=_lowerCAmelCase   , dynamic_axes=_lowerCAmelCase   , do_constant_folding=_lowerCAmelCase   , opset_version=_lowerCAmelCase   , )





@torch.no_grad()
def 				a_     (      _lowerCAmelCase  : str   , _lowerCAmelCase  : str   , _lowerCAmelCase  : int   , _lowerCAmelCase  : bool = False     ):





       '''simple docstring'''

       lowercase__			:		List[str]       =	torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              lowercase__			:		Optional[int]       =	'cuda'
       elif fpaa and not torch.cuda.is_available():
              raise ValueError('`float16` model export is only supported on GPUs with CUDA'     )
       else:
              lowercase__			:		Union[str, Any]       =	'cpu'
       lowercase__			:		str       =	Path(_lowerCAmelCase     )

       # VAE DECODER
       lowercase__			:		str       =	AutoencoderKL.from_pretrained(model_path + '/vae'     )
       lowercase__			:		Optional[Any]       =	vae_decoder.config.latent_channels
       # forward only through the decoder part
       lowercase__			:		Optional[Any]       =	vae_decoder.decode
       onnx_export(
           _lowerCAmelCase   , model_args=(
               torch.randn(1   , _lowerCAmelCase   , 25   , 25     ).to(device=_lowerCAmelCase   , dtype=_lowerCAmelCase     ),
               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=_lowerCAmelCase   , )
       del vae_decoder


if __name__ == "__main__":
       _UpperCamelCase   :						Any      =    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=14,
           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")

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





'''simple docstring'''


def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):

		if density <= 0:
				raise ValueError("Impossible fluid density"      )
		if bulk_modulus <= 0:
				raise ValueError("Impossible bulk modulus"      )

		return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
						import doctest

						doctest.testmod()



