
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE							:str							 =				version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_		,   a_=False		,   )		->	Optional[Any]:







				"""simple docstring"""



				output_path.parent.mkdir(parents=a_		,   exist_ok=a_						)
				# 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(
								    a_		,   a_		,   f=output_path.as_posix()		,   input_names=a_		,   output_names=a_		,   dynamic_axes=a_		,   do_constant_folding=a_		,   use_external_data_format=a_		,   enable_onnx_checker=a_		,   opset_version=a_		,   )
				else:
								export(
								    a_		,   a_		,   f=output_path.as_posix()		,   input_names=a_		,   output_names=a_		,   dynamic_axes=a_		,   do_constant_folding=a_		,   opset_version=a_		,   )


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_ = False						)		->	Any:







				"""simple docstring"""



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

				# VAE DECODER
				__A   			= AutoencoderKL.from_pretrained(model_path + "/vae"						)
				__A   			= vae_decoder.config.latent_channels
				# forward only through the decoder part
				__A   			= vae_decoder.decode
				onnx_export(
				    a_		,   model_args=(
				        torch.randn(1		,   a_		,   2_5		,   2_5						).to(device=a_		,   dtype=a_						),
				        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=a_		,   )
				del vae_decoder


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							: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')

					SCREAMING_SNAKE_CASE							:int							 =				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			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) <= 1:
								return [tuple(a_						)]

				__A   			= []

				def generate(a_		,   a_						):
								if k == 1:
												res.append(tuple(arr[:]						)						)
												return

								generate(k - 1		,   a_						)

								for i in range(k - 1						):
												if k % 2 == 0:  # k is even
																__A			,							__A   			= arr[k - 1], arr[i]
												else:  # k is odd
																__A			,							__A   			= arr[k - 1], arr[0]
												generate(k - 1		,   a_						)

				generate(len(a_						)		,   a_						)
				return res


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:int							 =				input('Enter numbers separated by a comma:\n').strip()
					SCREAMING_SNAKE_CASE							:Dict							 =				[int(item) for item in user_input.split(',')]
					print(heaps(arr))
