





"""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       a__					(					_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  ,	):




		"""simple docstring"""


		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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = False    ):




		"""simple docstring"""


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

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





lowerCAmelCase__     			=			'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase__     			=			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase__     			=			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






