

'''simple docstring'''





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCamelCase_							=					version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")






def 							_lowerCAmelCase (      __magic_name__	:    Tuple      ,   __magic_name__	:    tuple      ,   __magic_name__	:    Path      ,   __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    Optional[int]      ,   __magic_name__	:    List[str]=False      ,   )							->     Optional[int]:
						output_path.parent.mkdir(parents=__magic_name__      ,   exist_ok=__magic_name__	)
						# 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(
												    __magic_name__      ,   __magic_name__      ,   f=output_path.as_posix()      ,   input_names=__magic_name__      ,   output_names=__magic_name__      ,   dynamic_axes=__magic_name__      ,   do_constant_folding=__magic_name__      ,   use_external_data_format=__magic_name__      ,   enable_onnx_checker=__magic_name__      ,   opset_version=__magic_name__      ,   )
						else:
												export(
												    __magic_name__      ,   __magic_name__      ,   f=output_path.as_posix()      ,   input_names=__magic_name__      ,   output_names=__magic_name__      ,   dynamic_axes=__magic_name__      ,   do_constant_folding=__magic_name__      ,   opset_version=__magic_name__      ,   )






@torch.no_grad()
def 							_lowerCAmelCase (      __magic_name__	:    str      ,   __magic_name__	:    str      ,   __magic_name__	:    int      ,   __magic_name__	:    bool = False	)							->     List[str]:
						lowercase   :       Optional[Any]								=torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												lowercase   :       Tuple								='''cuda'''
						elif fpaa and not torch.cuda.is_available():
												raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''	)
						else:
												lowercase   :       Tuple								='''cpu'''
						lowercase   :       Dict								=Path(__magic_name__	)

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


if __name__ == "__main__":
							UpperCamelCase_							=					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""")

							UpperCamelCase_							=					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 transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Tuple								=HfArgumentParser(__magic_name__	)
						lowercase   :       Union[str, Any]								=parser.parse_args_into_dataclasses()[0]
						lowercase   :       Any								=TensorFlowBenchmark(args=__magic_name__	)
						try:
												lowercase   :       List[Any]								=parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase   :       List[Any]								='''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
												lowercase   :       Any								=''' '''.join(str(__magic_name__	).split(''' '''	)[:-1]	)
												lowercase   :       Optional[Any]								=''''''
												lowercase   :       List[str]								=eval(str(__magic_name__	).split(''' '''	)[-1]	)
												lowercase   :       Optional[Any]								=[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]	)
																		else:
																								wrong_args.append(__magic_name__	)
												if len(__magic_name__	) > 0:
																		lowercase   :       int								=full_error_msg + begin_error_msg + str(__magic_name__	)
												raise ValueError(__magic_name__	)
						benchmark.run()


if __name__ == "__main__":
							main()





