






'''simple docstring'''
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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





def 	_a(					UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  tuple,		UpperCamelCase__			:  Path,		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  Tuple,		UpperCamelCase__			:  int,		UpperCamelCase__			:  Optional[int]=False,		):
      output_path.parent.mkdir(parents=UpperCamelCase__,		exist_ok=UpperCamelCase__							)
      # 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(
                UpperCamelCase__,		UpperCamelCase__,		f=output_path.as_posix(),		input_names=UpperCamelCase__,		output_names=UpperCamelCase__,		dynamic_axes=UpperCamelCase__,		do_constant_folding=UpperCamelCase__,		use_external_data_format=UpperCamelCase__,		enable_onnx_checker=UpperCamelCase__,		opset_version=UpperCamelCase__,		)
      else:
            export(
                UpperCamelCase__,		UpperCamelCase__,		f=output_path.as_posix(),		input_names=UpperCamelCase__,		output_names=UpperCamelCase__,		dynamic_axes=UpperCamelCase__,		do_constant_folding=UpperCamelCase__,		opset_version=UpperCamelCase__,		)





@torch.no_grad()
def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  str,		UpperCamelCase__			:  int,		UpperCamelCase__			:  bool = False							):
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													='''cuda'''
      elif fpaa and not torch.cuda.is_available():
            raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''							)
      else:
            SCREAMING_SNAKE_CASE__       :			Dict													='''cpu'''
      SCREAMING_SNAKE_CASE__       :			int													=Path(UpperCamelCase__							)

      # VAE DECODER
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=AutoencoderKL.from_pretrained(model_path + '''/vae'''							)
      SCREAMING_SNAKE_CASE__       :			Dict													=vae_decoder.config.latent_channels
      # forward only through the decoder part
      SCREAMING_SNAKE_CASE__       :			Dict													=vae_decoder.decode
      onnx_export(
          UpperCamelCase__,		model_args=(
              torch.randn(1,		UpperCamelCase__,		2_5,		2_5							).to(device=UpperCamelCase__,		dtype=UpperCamelCase__							),
              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=UpperCamelCase__,		)
      del vae_decoder


if __name__ == "__main__":
  a_					=	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')

  a_					=	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'''
import math





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      return math.sqrt(UpperCamelCase__							) * math.sqrt(UpperCamelCase__							) == num





def 	_a(					UpperCamelCase__			:  int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[Any]													=0
      SCREAMING_SNAKE_CASE__       :			Any													=n
      while left <= right:
            SCREAMING_SNAKE_CASE__       :			str													=(left + right) // 2
            if mid**2 == n:
                  return True
            elif mid**2 > n:
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=mid - 1
            else:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=mid + 1
      return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()