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

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







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):

	if density <= 0:
		raise ValueError('Impossible fluid density'	)
	if bulk_modulus <= 0:
		raise ValueError('Impossible bulk modulus'	)

	return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
							import doctest

							doctest.testmod()