


'''simple docstring'''






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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






def 		_A			(lowerCAmelCase__					:Any			,  lowerCAmelCase__					:tuple			,  lowerCAmelCase__					:Path			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:str			,  lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Dict=False			,  )				->					int:




     '''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						)				->					int:




     '''simple docstring'''



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

     # VAE DECODER
     _a      =							AutoencoderKL.from_pretrained(model_path + '/vae'						)
     _a      =							vae_decoder.config.latent_channels
     # forward only through the decoder part
     _a      =							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__":
 a_   :      Union[str, 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")

 a_   :      str				    =      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 		_A			(lowerCAmelCase__					:list						)				->					float:




     '''simple docstring'''



     _a      =							0
     while len(lowerCAmelCase__						) > 1:
          _a      =							0
          # Consider two files with minimum cost to be merged
          for _ in range(2						):
               _a      =							files.index(min(lowerCAmelCase__						)						)
               temp += files[min_index]
               files.pop(lowerCAmelCase__						)
          files.append(lowerCAmelCase__						)
          optimal_merge_cost += temp
     return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()

