




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

import cva
__UpperCamelCase   :			List[str]          = ""
__UpperCamelCase   :			Tuple          = ""
__UpperCamelCase   :			Dict          = ""
__UpperCamelCase   :			List[str]          = 1  # (0 is vertical, 1 is horizontal)





def 						__A						(      ) ->			None:
	a							,					a									=				get_dataset(__lowerCamelCase				,     __lowerCamelCase       )
	print("""Processing..."""       )
	a							,					a							,					a									=				update_image_and_anno(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       )

	for index, image in enumerate(__lowerCamelCase       ):
		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		a									=				random_chars(32       )
		a									=				paths[index].split(os.sep       )[-1].rsplit("""."""				,     1       )[0]
		a									=				f'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
		cva.imwrite(f'/{file_root}.jpg'				,     __lowerCamelCase				,     [cva.IMWRITE_JPEG_QUALITY, 85]       )
		print(f'Success {index+1}/{len(__lowerCamelCase       )} with {file_name}'       )
		a									=				[]
		for anno in new_annos[index]:
			a									=				f'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
			annos_list.append(__lowerCamelCase       )
		with open(f'/{file_root}.txt'				,     """w"""       ) as outfile:
			outfile.write("""\n""".join(line for line in annos_list       )       )





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			tuple[list, list]:
	a									=				[]
	a									=				[]
	for label_file in glob.glob(os.path.join(__lowerCamelCase				,     """*.txt"""       )       ):
		a									=				label_file.split(os.sep       )[-1].rsplit("""."""				,     1       )[0]
		with open(__lowerCamelCase       ) as in_file:
			a									=				in_file.readlines()
		a									=				os.path.join(__lowerCamelCase				,     f'{label_name}.jpg'       )

		a									=				[]
		for obj_list in obj_lists:
			a									=				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(__lowerCamelCase       )
		labels.append(__lowerCamelCase       )
	return img_paths, labels





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase = 1       ) ->			tuple[list, list, list]:
	a									=				[]
	a									=				[]
	a									=				[]
	for idx in range(len(__lowerCamelCase       )       ):
		a									=				[]
		a									=				img_list[idx]
		path_list.append(__lowerCamelCase       )
		a									=				anno_list[idx]
		a									=				cva.imread(__lowerCamelCase       )
		if flip_type == 1:
			a									=				cva.flip(__lowerCamelCase				,     __lowerCamelCase       )
			for bbox in img_annos:
				a									=				1 - bbox[1]
				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]       )
		elif flip_type == 0:
			a									=				cva.flip(__lowerCamelCase				,     __lowerCamelCase       )
			for bbox in img_annos:
				a									=				1 - bbox[2]
				new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]       )
		new_annos_lists.append(__lowerCamelCase       )
		new_imgs_list.append(__lowerCamelCase       )
	return new_imgs_list, new_annos_lists, path_list





def 						__A						(      __lowerCamelCase = 32       ) ->			str:
	assert number_char > 1, "The number of character should greater than 1"
	a									=				ascii_lowercase + digits
	return "".join(random.choice(__lowerCamelCase       ) for _ in range(__lowerCamelCase       )       )


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







import operator as op


__UpperCamelCase   :			Optional[Any]          = "scaler.pt"
__UpperCamelCase   :			Optional[Any]          = "pytorch_model"
__UpperCamelCase   :			str          = "random_states"
__UpperCamelCase   :			Optional[int]          = "optimizer"
__UpperCamelCase   :			Optional[int]          = "scheduler"
__UpperCamelCase   :			str          = "pytorch_model.bin"
__UpperCamelCase   :			List[str]          = "pytorch_model.bin.index.json"
__UpperCamelCase   :			List[str]          = "model.safetensors"
__UpperCamelCase   :			Optional[int]          = "model.safetensors.index.json"
__UpperCamelCase   :			List[str]          = "1.10.2"
__UpperCamelCase   :			Dict          = "py38"
__UpperCamelCase   :			List[str]          = "4.17.0"
__UpperCamelCase   :			Any          = ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"]
__UpperCamelCase   :			Any          = ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"]
__UpperCamelCase   :			int          = ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
__UpperCamelCase   :			Dict          = ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
__UpperCamelCase   :			str          = ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
__UpperCamelCase   :			List[Any]          = "2.0.1"
__UpperCamelCase   :			int          = ["pdsh", "standard", "openmpi", "mvapich"]
__UpperCamelCase   :			List[str]          = ["default", "reduce-overhead", "max-autotune"]

__UpperCamelCase   :			List[Any]          = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
__UpperCamelCase   :			List[Any]          = [
    "nnodes",
    "nproc_per_node",
    "rdzv_backend",
    "rdzv_endpoint",
    "rdzv_id",
    "rdzv_conf",
    "standalone",
    "max_restarts",
    "monitor_interval",
    "start_method",
    "role",
    "module",
    "m",
    "no_python",
    "run_path",
    "log_dir",
    "r",
    "redirects",
    "t",
    "tee",
    "node_rank",
    "master_addr",
    "master_port",
]

__UpperCamelCase   :			List[str]          = ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
__UpperCamelCase   :			int          = ["DEEPSPEED", "MULTI_XPU", "FSDP"]

