'''simple docstring'''


import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCAmelCase	:       str						       =	version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   tuple    ,					UpperCamelCase__				:   Path    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   Union[str, Any]=False    ,					):


	"""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       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   int    ,					UpperCamelCase__				:   bool = False       ):


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

	# VAE DECODER
	__UpperCAmelCase							=			AutoencoderKL.from_pretrained(model_path + '''/vae'''       )
	__UpperCAmelCase							=			vae_decoder.config.latent_channels
	# forward only through the decoder part
	__UpperCAmelCase							=			vae_decoder.decode
	onnx_export(
	    UpperCamelCase__    ,					model_args=(
	        torch.randn(1    ,					UpperCamelCase__    ,					2_5    ,					2_5       ).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	:       List[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	:       Optional[int]						       =	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'''


import glob
import os
import random
from string import ascii_lowercase, digits

import cva
__lowerCAmelCase	:       Any						       =	""
__lowerCAmelCase	:       int						       =	""
__lowerCAmelCase	:       Union[str, Any]						       =	""
__lowerCAmelCase	:       Any						       =	1  # (0 is vertical, 1 is horizontal)





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase				,			__UpperCAmelCase							=			get_dataset(UpperCamelCase__    ,					UpperCamelCase__       )
	print('''Processing...'''       )
	__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			update_image_and_anno(UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__       )

	for index, image in enumerate(UpperCamelCase__       ):
		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		__UpperCAmelCase							=			random_chars(3_2       )
		__UpperCAmelCase							=			paths[index].split(os.sep       )[-1].rsplit('''.'''    ,					1       )[0]
		__UpperCAmelCase							=			f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
		cva.imwrite(f"""/{file_root}.jpg"""    ,					UpperCamelCase__    ,					[cva.IMWRITE_JPEG_QUALITY, 8_5]       )
		print(f"""Success {index+1}/{len(UpperCamelCase__       )} with {file_name}"""       )
		__UpperCAmelCase							=			[]
		for anno in new_annos[index]:
			__UpperCAmelCase							=			f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
			annos_list.append(UpperCamelCase__       )
		with open(f"""/{file_root}.txt"""    ,					'''w'''       ) as outfile:
			outfile.write('''\n'''.join(line for line in annos_list       )       )





def       lowerCAmelCase					(		UpperCamelCase__				:   str    ,					UpperCamelCase__				:   str       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	for label_file in glob.glob(os.path.join(UpperCamelCase__    ,					'''*.txt'''       )       ):
		__UpperCAmelCase							=			label_file.split(os.sep       )[-1].rsplit('''.'''    ,					1       )[0]
		with open(UpperCamelCase__       ) as in_file:
			__UpperCAmelCase							=			in_file.readlines()
		__UpperCAmelCase							=			os.path.join(UpperCamelCase__    ,					f"""{label_name}.jpg"""       )

		__UpperCAmelCase							=			[]
		for obj_list in obj_lists:
			__UpperCAmelCase							=			obj_list.rstrip('''\n'''       ).split(''' '''       )
			boxes.append(
			    [
			        int(obj[0]       ),
			        float(obj[1]       ),
			        float(obj[2]       ),
			        float(obj[3]       ),
			        float(obj[4]       ),
			    ]       )
		if not boxes:
			continue
		img_paths.append(UpperCamelCase__       )
		labels.append(UpperCamelCase__       )
	return img_paths, labels





def       lowerCAmelCase					(		UpperCamelCase__				:   list    ,					UpperCamelCase__				:   list    ,					UpperCamelCase__				:   int = 1       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	__UpperCAmelCase							=			[]
	for idx in range(len(UpperCamelCase__       )       ):
		__UpperCAmelCase							=			[]
		__UpperCAmelCase							=			img_list[idx]
		path_list.append(UpperCamelCase__       )
		__UpperCAmelCase							=			anno_list[idx]
		__UpperCAmelCase							=			cva.imread(UpperCamelCase__       )
		if flip_type == 1:
			__UpperCAmelCase							=			cva.flip(UpperCamelCase__    ,					UpperCamelCase__       )
			for bbox in img_annos:
				__UpperCAmelCase							=			1 - bbox[1]
				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]       )
		elif flip_type == 0:
			__UpperCAmelCase							=			cva.flip(UpperCamelCase__    ,					UpperCamelCase__       )
			for bbox in img_annos:
				__UpperCAmelCase							=			1 - bbox[2]
				new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]       )
		new_annos_lists.append(UpperCamelCase__       )
		new_imgs_list.append(UpperCamelCase__       )
	return new_imgs_list, new_annos_lists, path_list





def       lowerCAmelCase					(		UpperCamelCase__				:   int = 3_2       ):


	"""simple docstring"""
	assert number_char > 1, "The number of character should greater than 1"
	__UpperCAmelCase							=			ascii_lowercase + digits
	return "".join(random.choice(UpperCamelCase__       ) for _ in range(UpperCamelCase__       )       )


if __name__ == "__main__":
					main()
					print("DONE ✅")
