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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=False,		)->       str:



			"""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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase = False       )->       Dict:



			"""simple docstring"""







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

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			while a != 0:
						lowercase		,lowercase							=		b % a, a
			return b






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			if gcd(UpperCAmelCase,		UpperCAmelCase       ) != 1:
						lowercase							=		f'mod inverse of {a!r} and {m!r} does not exist'
						raise ValueError(UpperCAmelCase       )
			lowercase		,lowercase		,lowercase							=		1, 0, a
			lowercase		,lowercase		,lowercase							=		0, 1, m
			while va != 0:
						lowercase							=		ua // va
						lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase							=		(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
			return ua % m





