



"""simple docstring"""







import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowercase__		:    List[str]				   =     version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Any   ,       lowerCAmelCase__				:    Tuple   ,       lowerCAmelCase__				:    Optional[Any]=False   ,       )						->       Any:

  """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_   (   lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Dict = False						)						->       Union[str, Any]:

  """simple docstring"""




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

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

       lowercase__		:    Union[str, 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     UpperCamelCase_   (   lowerCAmelCase__				:    list[int]   ,       lowerCAmelCase__				:    list[int]						)						->       None:

  """simple docstring"""




  lowerCAmelCase_       :    List[Any]								=			len(lowerCAmelCase__						)
  print('The following activities are selected:'						)

  # The first activity is always selected
  lowerCAmelCase_       :    str								=			0
  print(lowerCAmelCase__   ,       end=','						)

  # Consider rest of the activities
  for j in range(lowerCAmelCase__						):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(lowerCAmelCase__   ,       end=','						)
      lowerCAmelCase_       :    Tuple								=			j


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     lowercase__		:    List[str]				   =     [1, 3, 0, 5, 8, 5]
     lowercase__		:    Dict				   =     [2, 4, 6, 7, 9, 9]
     print_max_activities(start, finish)





