


'''simple docstring'''
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__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__=False    ,   )       ->      Optional[int]:
    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__			(   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__ = False		)       ->      Union[str, Any]:
    UpperCAmelCase__     :						Optional[Any]						=							torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        UpperCAmelCase__     :						Optional[int]						=							'''cuda'''
    elif fpaa and not torch.cuda.is_available():
        raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''		)
    else:
        UpperCAmelCase__     :						Tuple						=							'''cpu'''
    UpperCAmelCase__     :						str						=							Path(UpperCAmelCase_		)

    # VAE DECODER
    UpperCAmelCase__     :						str						=							AutoencoderKL.from_pretrained(model_path + '''/vae'''		)
    UpperCAmelCase__     :						str						=							vae_decoder.config.latent_channels
    # forward only through the decoder part
    UpperCAmelCase__     :						Dict						=							vae_decoder.decode
    onnx_export(
        UpperCAmelCase_    ,   model_args=(
            torch.randn(1    ,   UpperCAmelCase_    ,   25    ,   25		).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__":
  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=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''')

  UpperCamelCase__          = 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 cmath
import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->complex:
	snake_case__	       =   math.radians(UpperCAmelCase_							)
	snake_case__	       =   math.radians(UpperCAmelCase_							)

	# Convert voltage and current to rectangular form
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
			import doctest

			doctest.testmod()



