


"""simple docstring"""


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCamelCase						:				List[Any]             =      version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=False ,			)      ->		List[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  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ = False	)      ->		List[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(UpperCAmelCase__	)

		# 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(
		    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__":
				UpperCamelCase						:				Optional[int]             =      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")

				UpperCamelCase						:				Dict             =      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"""


from statistics import mean, stdev







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 3	)      ->		list:







		"""simple docstring"""





		A					       = min(UpperCamelCase__	)
		A					       = max(UpperCamelCase__	)
		# normalize data
		return [round((x - x_min) / (x_max - x_min) ,			UpperCamelCase__	) for x in data]







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 3	)      ->		list:







		"""simple docstring"""





		A					       = mean(UpperCamelCase__	)
		A					       = stdev(UpperCamelCase__	)
		# standardize data
		return [round((x - mu) / (sigma) ,			UpperCamelCase__	) for x in data]





