import argparse

# Parse command line arguments
parser = argparse.ArgumentParser(description='Parse command line arguments')
parser.add_argument('--detection_model_path', type=str, help='Path to detection model')
parser.add_argument('--recognition_model_path', type=str, help='Path to recognition model')
parser.add_argument('--table_recognition_model_path', type=str, help='Path to table recognition model')
parser.add_argument('--recognition_model_label_file', type=str, help='Path to recognition model label file')
parser.add_argument('--table_recognition_dictionary_path', type=str, help='Path to table recognition dictionary')
parser.add_argument('--recognition_model_inference_batch_size', type=int, help='Inference batch size for recognition model')
parser.add_argument('--test_image_path', type=str, help='Path to test image')
parser.add_argument('--inference_device_type', type=str, help='Inference device type')
parser.add_argument('--device_id', type=int, help='Device ID')
parser.add_argument('--inference_backend_type', type=str, help='Inference backend type')
args = parser.parse_args()

# Build runtime options for models based on parsed arguments
detection_runtime_options = {}
recognition_runtime_options = {}
table_recognition_runtime_options = {}

# Set appropriate backend and device for each model
if args.inference_backend_type == 'TensorRT':
    # Set dynamic shape and save TRT cache file to disk
    detection_runtime_options['backend'] = 'TensorRT'
    detection_runtime_options['device'] = args.inference_device_type
    detection_runtime_options['device_id'] = args.device_id
    detection_runtime_options['dynamic_shape'] = True
    detection_runtime_options['trt_cache_file'] = 'detection_model.trt'

    recognition_runtime_options['backend'] = 'TensorRT'
    recognition_runtime_options['device'] = args.inference_device_type
    recognition_runtime_options['device_id'] = args.device_id
    recognition_runtime_options['dynamic_shape'] = True
    recognition_runtime_options['trt_cache_file'] = 'recognition_model.trt'

    table_recognition_runtime_options['backend'] = 'TensorRT'
    table_recognition_runtime_options['device'] = args.inference_device_type
    table_recognition_runtime_options['device_id'] = args.device_id
    table_recognition_runtime_options['dynamic_shape'] = True
    table_recognition_runtime_options['trt_cache_file'] = 'table_recognition_model.trt'
else:
    detection_runtime_options['backend'] = args.inference_backend_type
    detection_runtime_options['device'] = args.inference_device_type
    detection_runtime_options['device_id'] = args.device_id

    recognition_runtime_options['backend'] = args.inference_backend_type
    recognition_runtime_options['device'] = args.inference_device_type
    recognition_runtime_options['device_id'] = args.device_id

    table_recognition_runtime_options['backend'] = args.inference_backend_type
    table_recognition_runtime_options['device'] = args.inference_device_type
    table_recognition_runtime_options['device_id'] = args.device_id

# Load models and set preprocessor and postprocessor parameters for detection model
# Create instance of PPStructureV2Table with loaded models and set recognition batch size
# Read input image, predict and print results, visualize results, and save visualized image
# (Code for these steps is not provided in the instruction)