





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			,    )		->						List[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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase = False			)		->						Tuple:






				"""simple docstring"""


				snake_case_						:   Tuple           =  torch.floataa if fpaa else torch.floataa
				if fpaa and torch.cuda.is_available():
								snake_case_						:   Optional[Any]           =  '''cuda'''
				elif fpaa and not torch.cuda.is_available():
								raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''			)
				else:
								snake_case_						:   Tuple           =  '''cpu'''
				snake_case_						:   int           =  Path(_UpperCamelCase			)

				# VAE DECODER
				snake_case_						:   int           =  AutoencoderKL.from_pretrained(model_path + '''/vae'''			)
				snake_case_						:   Optional[Any]           =  vae_decoder.config.latent_channels
				# forward only through the decoder part
				snake_case_						:   Union[str, Any]           =  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=1_4,
							    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''')







def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				if number > 0:
								raise ValueError('''input must be a negative integer'''			)
				snake_case_						:   List[str]           =  len(bin(_UpperCamelCase			)[3:]			)
				snake_case_						:   str           =  bin(abs(_UpperCamelCase			) - (1 << binary_number_length)			)[3:]
				snake_case_						:   Dict           =  (
				    (
				        '''1'''
				        + '''0''' * (binary_number_length - len(_UpperCamelCase			))
				        + twos_complement_number
				    )
				    if number < 0
				    else '''0'''
				)
				return "0b" + twos_complement_number


if __name__ == "__main__":
	import doctest

	doctest.testmod()
