




"""simple docstring"""
import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


a_				:    int										=     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')





def 						UpperCAmelCase				(			A__:    List[str]							,				A__:    tuple							,				A__:    Path							,				A__:    Union[str, Any]							,				A__:    Dict							,				A__:    Tuple							,				A__:    str							,				A__:    Union[str, Any]=False							,				)  ->     Union[str, Any]:
			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__:    str							,				A__:    str							,				A__:    int							,				A__:    bool = False     )  ->     Tuple:
			__lowerCamelCase					:	Optional[int]	       =				torch.floataa if fpaa else torch.floataa
			if fpaa and torch.cuda.is_available():
						__lowerCamelCase					:	Dict	       =				'cuda'
			elif fpaa and not torch.cuda.is_available():
						raise ValueError('`float16` model export is only supported on GPUs with CUDA'     )
			else:
						__lowerCamelCase					:	List[Any]	       =				'cpu'
			__lowerCamelCase					:	Optional[Any]	       =				Path(A__     )

			# VAE DECODER
			__lowerCamelCase					:	int	       =				AutoencoderKL.from_pretrained(model_path + '/vae'     )
			__lowerCamelCase					:	Any	       =				vae_decoder.config.latent_channels
			# forward only through the decoder part
			__lowerCamelCase					:	Optional[int]	       =				vae_decoder.decode
			onnx_export(
			    A__							,				model_args=(
			        torch.randn(1							,				A__							,				25							,				25     ).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__":
					a_				:    str										=     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_				:    List[str]										=     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 cva import destroyAllWindows, imread, imshow, waitKey





def 						UpperCAmelCase				(			A__:    Tuple     )  ->     Union[str, Any]:
			# getting number of pixels in the image
			__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				img.shape[0], img.shape[1]

			# converting each pixel's color to its negative
			for i in range(A__     ):
						for j in range(A__     ):
									__lowerCamelCase					:	Optional[Any]	       =				[255, 255, 255] - img[i][j]

			return img


if __name__ == "__main__":
					# read original image
					a_				:    Dict										=     imread('''image_data/lena.jpg''', 1)

					# convert to its negative
					a_				:    str										=     convert_to_negative(img)

					# show result image
					imshow('''negative of original image''', img)
					waitKey(0)
					destroyAllWindows()



