




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCamelCase       :	Union[str, Any]									=     version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]			,							lowercase			: Any			,							lowercase			: int			,							lowercase			: Any			,							lowercase			: str			,							lowercase			: Union[str, Any]			,							lowercase			: List[str]			,							lowercase			: Any=False			,							):


      '''simple docstring'''



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

@torch.no_grad()
def     _SCREAMING_SNAKE_CASE    (							lowercase			: Optional[int]			,							lowercase			: Optional[Any]			,							lowercase			: Tuple			,							lowercase			: int = 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(lowerCAmelCase__  )

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


if __name__ == "__main__":
 lowerCamelCase       :	Any									=     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")

 lowerCamelCase       :	List[Any]									=     parser.parse_args()
 print(args.output_path)
 convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
 print("SD: Done: ONNX")









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



