





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCamelCase       :     List[Any]   =     version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=False							,   )						-> int:
 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 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase = False     )						-> Dict:
 UpperCamelCase	:					Optional[Any]       	=   torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  UpperCamelCase	:					str       	=   "cuda"
 elif fpaa and not torch.cuda.is_available():
  raise ValueError("`float16` model export is only supported on GPUs with CUDA"     )
 else:
  UpperCamelCase	:					Optional[Any]       	=   "cpu"
 UpperCamelCase	:					Dict       	=   Path(_lowerCAmelCase     )

 # VAE DECODER
 UpperCamelCase	:					Dict       	=   AutoencoderKL.from_pretrained(model_path + "/vae"     )
 UpperCamelCase	:					Optional[Any]       	=   vae_decoder.config.latent_channels
 # forward only through the decoder part
 UpperCamelCase	:					Optional[int]       	=   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       :     Union[str, 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       :     Any   =     parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print("""SD: Done: ONNX""")







import functools



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Optional[int]       	=   len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   len(_lowerCAmelCase     )

 @functools.cache
 def min_distance(_lowerCAmelCase							,   _lowerCAmelCase     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase	:					Union[str, Any]       	=   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1							,   _lowerCAmelCase     )							,   1 + min_distance(_lowerCAmelCase							,   indexa + 1     )							,   diff + min_distance(indexa + 1							,   indexa + 1     )							,   )

 return min_distance(0							,   0     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
