





"""simple docstring"""





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE__					=   version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						tuple							,						SCREAMING_SNAKE_CASE     :						Path							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						Optional[Any]=False							,						):







      '''simple docstring'''




      output_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE							,						exist_ok=SCREAMING_SNAKE_CASE			)
      # 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(
                SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						f=output_path.as_posix()							,						input_names=SCREAMING_SNAKE_CASE							,						output_names=SCREAMING_SNAKE_CASE							,						dynamic_axes=SCREAMING_SNAKE_CASE							,						do_constant_folding=SCREAMING_SNAKE_CASE							,						use_external_data_format=SCREAMING_SNAKE_CASE							,						enable_onnx_checker=SCREAMING_SNAKE_CASE							,						opset_version=SCREAMING_SNAKE_CASE							,						)
      else:
            export(
                SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						f=output_path.as_posix()							,						input_names=SCREAMING_SNAKE_CASE							,						output_names=SCREAMING_SNAKE_CASE							,						dynamic_axes=SCREAMING_SNAKE_CASE							,						do_constant_folding=SCREAMING_SNAKE_CASE							,						opset_version=SCREAMING_SNAKE_CASE							,						)


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						bool = False			):







      '''simple docstring'''




      lowerCAmelCase  						=       torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowerCAmelCase  						=       """cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""			)
      else:
            lowerCAmelCase  						=       """cpu"""
      lowerCAmelCase  						=       Path(SCREAMING_SNAKE_CASE			)

      # VAE DECODER
      lowerCAmelCase  						=       AutoencoderKL.from_pretrained(model_path + """/vae"""			)
      lowerCAmelCase  						=       vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowerCAmelCase  						=       vae_decoder.decode
      onnx_export(
          SCREAMING_SNAKE_CASE							,						model_args=(
              torch.randn(1							,						SCREAMING_SNAKE_CASE							,						25							,						25			).to(device=SCREAMING_SNAKE_CASE							,						dtype=SCREAMING_SNAKE_CASE			),
              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=SCREAMING_SNAKE_CASE							,						)
      del vae_decoder


if __name__ == "__main__":
       SCREAMING_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")

       SCREAMING_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'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    List[str]  =					0
 while num > 0:
  digit_sum += num % 10
  num //= 10
 return digit_sum
def 			__UpperCamelCase       (   UpperCAmelCase = 100     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Optional[int]  =					2

 for i in range(2		,     max_n + 1     ):
  lowercase__				:    List[str]  =					pre_numerator
  lowercase__				:    Optional[Any]  =					2 * i // 3 if i % 3 == 0 else 1
  lowercase__				:    Union[str, Any]  =					cur_numerator
  lowercase__				:    int  =					e_cont * pre_numerator + temp

 return sum_digits(UpperCAmelCase     )


if __name__ == "__main__":
  print(F'{solution() = }')

