



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

      # VAE DECODER
      UpperCAmelCase_    				=							AutoencoderKL.from_pretrained(model_path + "/vae"   )
      UpperCAmelCase_    				=							vae_decoder.config.latent_channels
      # forward only through the decoder part
      UpperCAmelCase_    				=							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__":
  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""")









"""simple docstring"""





lowerCamelCase      =		{
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.355_818,
}
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
            UpperCAmelCase_    				=							(
                f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
                f"""Valid values are: {', '.join(lowerCAmelCase__   )}"""
            )
            raise ValueError(lowerCAmelCase__   )
      return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




