





import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
   import ctypes
   import msvcrt  # noqa




   class 			__UpperCamelCase    (							ctypes.Structure    ):
        # _fields is a specific attr expected by ctypes
        __A	:  int							  =   [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]
def      A__       (    )  ->  Union[str, Any]:






   """simple docstring"""







   if os.name == "nt":
      _UpperCAmelCase           =   CursorInfo()
      _UpperCAmelCase           =   ctypes.windll.kernelaa.GetStdHandle(-11						)
      ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE_				,	ctypes.byref(SCREAMING_SNAKE_CASE_						)						)
      _UpperCAmelCase           =   False
      ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE_				,	ctypes.byref(SCREAMING_SNAKE_CASE_						)						)
   elif os.name == "posix":
      sys.stdout.write('''\033[?25l'''						)
      sys.stdout.flush()
def      A__       (    )  ->  List[Any]:






   """simple docstring"""







   if os.name == "nt":
      _UpperCAmelCase           =   CursorInfo()
      _UpperCAmelCase           =   ctypes.windll.kernelaa.GetStdHandle(-11						)
      ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE_				,	ctypes.byref(SCREAMING_SNAKE_CASE_						)						)
      _UpperCAmelCase           =   True
      ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE_				,	ctypes.byref(SCREAMING_SNAKE_CASE_						)						)
   elif os.name == "posix":
      sys.stdout.write('''\033[?25h'''						)
      sys.stdout.flush()
@contextmanager
def      A__       (    )  ->  str:






   """simple docstring"""







   try:
      hide_cursor()
      yield
   finally:
      show_cursor()







import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__							  =						version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 output_path.parent.mkdir(parents=a      ,				exist_ok=a					)
 # 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(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				use_external_data_format=a      ,				enable_onnx_checker=a      ,				opset_version=a      ,				)
 else:
  export(
      a      ,				a      ,				f=output_path.as_posix()      ,				input_names=a      ,				output_names=a      ,				dynamic_axes=a      ,				do_constant_folding=a      ,				opset_version=a      ,				)
@torch.no_grad()
def 		_a       ( a					:str      ,				a					:str      ,				a					:int      ,				a					:bool = False					)				->     Union[str, Any]:
 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(a					)

 # 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(
     a      ,				model_args=(
         torch.randn(1      ,				a      ,				25      ,				25					).to(device=a      ,				dtype=a					),
         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=a      ,				)
 del vae_decoder


if __name__ == "__main__":
     UpperCAmelCase__							  =						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")

     UpperCAmelCase__							  =						parser.parse_args()
     print(args.output_path)
     convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
     print("SD: Done: ONNX")


