





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


snake_case_				=			version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def lowerCamelCase__    (							snake_case_	:  List[Any]       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  str       ,      snake_case_	:  Tuple       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  List[str]       ,      snake_case_	:  str=False       ,      )      ->				Optional[Any]:
   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 lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  int       ,      snake_case_	:  Union[str, Any] = False       )      ->				int:
   __snake_case							       =   torch.floataa if fpaa else torch.floataa
   if fpaa and torch.cuda.is_available():
      __snake_case							       =   "cuda"
   elif fpaa and not torch.cuda.is_available():
      raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''       )
   else:
      __snake_case							       =   "cpu"
   __snake_case							       =   Path(__lowerCamelCase       )

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

    snake_case_				=			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"""





from collections import namedtuple

_a		       =							namedtuple('from_to', 'from_ to')

_a		       =							{
    'cubicmeter': from_to(1, 1),
    'litre': from_to(0.001, 1_000),
    'kilolitre': from_to(1, 1),
    'gallon': from_to(0.0_0454, 264.172),
    'cubicyard': from_to(0.7_6455, 1.3_0795),
    'cubicfoot': from_to(0.028, 35.3147),
    'cup': from_to(0.0_0023_6588, 4226.75),
}







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   if from_type not in METRIC_CONVERSION:
      raise ValueError(
          f"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
          + ", ".join(__lowerCamelCase  )  )
   if to_type not in METRIC_CONVERSION:
      raise ValueError(
          f"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
          + ", ".join(__lowerCamelCase  )  )
   return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
    import doctest

    doctest.testmod()


