


"""simple docstring"""



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		(		lowercase						:						Dict		,							lowercase						:						tuple		,							lowercase						:						Path		,							lowercase						:						Union[str, Any]		,							lowercase						:						Optional[int]		,							lowercase						:						int		,							lowercase						:						Optional[Any]		,							lowercase						:						Any=False		,							)			->		List[str]:


    """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 						__lowerCAmelCase		(		lowercase						:						str		,							lowercase						:						str		,							lowercase						:						int		,							lowercase						:						bool = False				)			->		Optional[int]:


    """simple docstring"""





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

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

from cmath import sqrt







def 						__lowerCAmelCase		(		lowercase						:						int		,							lowercase						:						int		,							lowercase						:						int				)			->		tuple[complex, complex]:


    """simple docstring"""





    if a == 0:
        raise ValueError("Coefficient 'a' must not be zero."				)
    snake_case					:   Dict         =					b * b - 4 * a * c

    snake_case					:   Tuple         =					(-b + sqrt(lowercase				)) / (2 * a)
    snake_case					:   Optional[int]         =					(-b - sqrt(lowercase				)) / (2 * a)

    return (
        root_a.real if not root_a.imag else root_a,
        root_a.real if not root_a.imag else root_a,
    )







def 						__lowerCAmelCase		(		)			->		List[Any]:


    """simple docstring"""





    snake_case      ,snake_case					:   Optional[Any]         =					quadratic_roots(a=5		,							b=6		,							c=1				)
    print(F'The solutions are: {solutiona} and {solutiona}'				)


if __name__ == "__main__":
  main()





