
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCamelCase__    :		Optional[Any]          =   version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_=False , )		->	Tuple:


  '''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 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_ = False   )		->	Any:


  '''simple docstring'''






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

  # VAE DECODER
  lowercase__  :   Union[str, Any]    		=      AutoencoderKL.from_pretrained(model_path + """/vae"""   )
  lowercase__  :   List[Any]    		=      vae_decoder.config.latent_channels
  # forward only through the decoder part
  lowercase__  :   Tuple    		=      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__    :		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=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""")








def 		UpperCamelCase	(      lowercase_ = 10_00   )		->	int:


  '''simple docstring'''






  return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1   )   )


if __name__ == "__main__":
     print(solution())






