




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCamelCase				:		str     =   version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[int]	,							lowercase	:       Tuple	,							lowercase	:       Tuple	,							lowercase	:       List[Any]	,							lowercase	:       Dict	,							lowercase	:       Optional[int]	,							lowercase	:       Tuple	,							lowercase	:       Any=False	,							):


      '''simple docstring'''



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

@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]	,							lowercase	:       Optional[int]	,							lowercase	:       Tuple	,							lowercase	:       Tuple = 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(lowercase     )

      # 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(
          lowercase	,							model_args=(
              torch.randn(1	,							lowercase	,							25	,							25     ).to(device=lowercase	,							dtype=lowercase     ),
              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=lowercase	,							)
      del vae_decoder


if __name__ == "__main__":
 lowerCamelCase				:		Tuple     =   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				:		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")





def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		if index == number_of_items:
				return 0
		UpperCamelCase		=     0
		UpperCamelCase		=     0
		UpperCamelCase		=     knapsack(lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       index + 1 )
		if weights[index] <= max_weight:
				UpperCamelCase		=     values[index] + knapsack(
				    lowercase    ,       lowercase    ,       lowercase    ,       max_weight - weights[index]    ,       index + 1 )
		return max(lowercase    ,       lowercase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
