def METHOD_NAME(img, inpainting_processor, args):
    start_time = perf_counter()
    #--- Generating mask
    if args.auto_mask_random:
        mask = create_random_mask(args.parts, args.max_vertex, args.max_length, args.max_brush_width,
                                inpainting_processor.input_height, inpainting_processor.input_width)
    else:
        # argument comes in RGB mode, but we will use BGR notation below
        top = np.full(img.shape, args.auto_mask_color[::-1], np.uint8)
        mask = cv2.inRange(img, top, top)
        mask = cv2.resize(mask, (inpainting_processor.input_width, inpainting_processor.input_height))
        _, mask = cv2.threshold(mask, 1, 1, cv2.THRESH_BINARY)
        mask = np.expand_dims(mask, 2)
    #--- Resizing image and removing masked areas from it
    img = cv2.resize(img, (inpainting_processor.input_width, inpainting_processor.input_height))
    masked_image = (img * (1 - mask) + 255 * mask).astype(np.uint8)
    #--- Inpaint and show results
    output_image = inpainting_processor.process(masked_image, mask)
    concat_imgs = np.hstack((masked_image, output_image))
    total_latency = (perf_counter() - start_time) * 1e3
    cv2.putText(concat_imgs, 'original', (5, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 100))
    cv2.putText(concat_imgs, 'result', (concat_imgs.shape[1] - 5 - cv2.getTextSize('result', cv2.FONT_HERSHEY_COMPLEX, 0.5, 1)[0][0], 15),
                cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 100))
    cv2.putText(concat_imgs, 'Latency: {:.1f} ms'.format(total_latency), (5, 35), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 0, 200))
    log.info("Metrics report:")
    log.info("\tLatency: {:.1f} ms".format(total_latency))
    return concat_imgs, output_image