






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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







def 			A__							(      _a  :					List[str]		,  _a  :					tuple		,  _a  :					Path		,  _a  :					List[Any]		,  _a  :					Optional[Any]		,  _a  :					List[str]		,  _a  :					int		,  _a  :					List[Any]=False		,  ):





 '''simple docstring'''







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







@torch.no_grad()
def 			A__							(      _a  :					str		,  _a  :					str		,  _a  :					int		,  _a  :					bool = False	):





 '''simple docstring'''







 snake_case__  :    str							       =torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  snake_case__  :    Any							       ="""cuda"""
 elif fpaa and not torch.cuda.is_available():
  raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
 else:
  snake_case__  :    List[str]							       ="""cpu"""
 snake_case__  :    List[Any]							       =Path(_a	)

 # VAE DECODER
 snake_case__  :    int							       =AutoencoderKL.from_pretrained(model_path + """/vae"""	)
 snake_case__  :    Tuple							       =vae_decoder.config.latent_channels
 # forward only through the decoder part
 snake_case__  :    Optional[int]							       =vae_decoder.decode
 onnx_export(
     _a		,  model_args=(
         torch.randn(1		,  _a		,  25		,  25	).to(device=_a		,  dtype=_a	),
         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=_a		,  )
 del vae_decoder


if __name__ == "__main__":
       __lowerCamelCase	:		Optional[int]										=   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	:		Optional[Any]										=   parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")













__lowerCamelCase	:		dict[str, float]										=   {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.60_93_44,
    "knot": 1.8_52,
}

__lowerCamelCase	:		dict[str, float]										=   {
    "km/h": 1.0,
    "m/s": 0.2_77_77_77_78,
    "mph": 0.6_21_37_11_92,
    "knot": 0.5_39_95_68_03,
}







def 			A__							(      _a  :					float		,  _a  :					str		,  _a  :					str	):





 '''simple docstring'''







 if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
  snake_case__  :    Tuple							       =(
      f"Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"
      f"Valid values are: {', '.join(_a	)}"
  )
  raise ValueError(_a	)
 return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]		,  3	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





