





'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      str						)	->	str:
				return "".join(chr(ord(lowercase						) - 32						) if "a" <= char <= "z" else char for char in word						)


if __name__ == "__main__":
					from doctest import testmod

					testmod()



'''simple docstring'''






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCAmelCase__											=	version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__=False     ,				):






 """simple docstring"""



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


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__     ,				A__ = False      ):






 """simple docstring"""



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

 # VAE DECODER
 __lowercase							  = AutoencoderKL.from_pretrained(model_path + '''/vae'''      )
 __lowercase							  = vae_decoder.config.latent_channels
 # forward only through the decoder part
 __lowercase							  = vae_decoder.decode
 onnx_export(
     A__     ,				model_args=(
         torch.randn(1     ,				A__     ,				25     ,				25      ).to(device=A__     ,				dtype=A__      ),
         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=A__     ,				)
 del vae_decoder


if __name__ == "__main__":
 lowerCAmelCase__											=	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''')

 lowerCAmelCase__											=	parser.parse_args()
 print(args.output_path)
 convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
 print('''SD: Done: ONNX''')

