

import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


lowerCAmelCase							:  Tuple            =						version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")







def       A_				(   _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       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase = False   ):
						SCREAMING_SNAKE_CASE_:			str  		=	torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												SCREAMING_SNAKE_CASE_:			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:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"cpu"
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Path(_UpperCAmelCase   )

						# VAE DECODER
						SCREAMING_SNAKE_CASE_:			int  		=	AutoencoderKL.from_pretrained(model_path + "/vae"   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	vae_decoder.config.latent_channels
						# forward only through the decoder part
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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							:  Optional[Any]            =						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							:  List[Any]            =						parser.parse_args()
						print(args.output_path)
						convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
						print("""SD: Done: ONNX""")









def       A_				(   _UpperCAmelCase = 10**9   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	1
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	2

						SCREAMING_SNAKE_CASE_:			int  		=	0
						SCREAMING_SNAKE_CASE_:			Dict  		=	0
						SCREAMING_SNAKE_CASE_:			List[str]  		=	0
						while perimeter <= max_perimeter:
												perimeters_sum += perimeter

												prev_value += 2 * value
												value += prev_value

												SCREAMING_SNAKE_CASE_:			Any  		=	2 * value + 2 if i % 2 == 0 else 2 * value - 2
												i += 1

						return perimeters_sum


if __name__ == "__main__":
						print(f'''{solution() = }''')






