
'''simple docstring'''
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCAmelCase					=			version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE=False				,   ):
							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    __SCREAMING_SNAKE_CASE (    _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE = False ):
							_snake_case  =    torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														_snake_case  =    'cuda'
							elif fpaa and not torch.cuda.is_available():
														raise ValueError("""`float16` model export is only supported on GPUs with CUDA""" )
							else:
														_snake_case  =    'cpu'
							_snake_case  =    Path(_SCREAMING_SNAKE_CASE )

							# VAE DECODER
							_snake_case  =    AutoencoderKL.from_pretrained(model_path + """/vae""" )
							_snake_case  =    vae_decoder.config.latent_channels
							# forward only through the decoder part
							_snake_case  =    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__":
				__lowerCAmelCase					=			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					=			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"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A											=						{}


try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						["""MLukeTokenizer"""]

if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mluke import MLukeTokenizer


else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
