"""simple docstring"""





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


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


def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	tuple						,   _lowerCamelCase:	Path						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	List[Any]=False						,   ) ->      Union[str, Any]:



							'''simple docstring'''

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


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	str						,   _lowerCamelCase:	str						,   _lowerCamelCase:	int						,   _lowerCamelCase:	bool = False      ) ->      Tuple:



							'''simple docstring'''

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

							# VAE DECODER
							__lowerCamelCase					:		List[Any]							   =     AutoencoderKL.from_pretrained(model_path + "/vae"      )
							__lowerCamelCase					:		str							   =     vae_decoder.config.latent_channels
							# forward only through the decoder part
							__lowerCamelCase					:		Dict							   =     vae_decoder.decode
							onnx_export(
							    UpperCAmelCase__						,   model_args=(
							        torch.randn(1						,   UpperCAmelCase__						,   25						,   25      ).to(device=UpperCAmelCase__						,   dtype=UpperCAmelCase__      ),
							        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=UpperCAmelCase__						,   )
							del vae_decoder


if __name__ == "__main__":
				__A  =				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''')

				__A  =				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"""





import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class  _snake_case     :
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			False
						snake_case__			  =			True
						snake_case__			  =			None
						snake_case__			  =			1
						snake_case__			  =			None
						snake_case__			  =			False
						snake_case__			  =			None
						snake_case__			  =			None






						def       lowerCamelCase__     (       self      :			Any    ):
													return self.__class__(**{k: copy.deepcopy(UpperCAmelCase    ) for k, v in self.__dict__.items()}    )