"""VIP."""

import json
import re

import cv2
from tqdm import trange
import vip


def make_prompt(description, top_n=3):
  return f"""
INSTRUCTIONS:
You are tasked to locate an object, region, or point in space in the given annotated image according to a description.
The image is annoated with numbered circles.
Choose the top {top_n} circles that have the most overlap with and/or is closest to what the description is describing in the image.
You are a five-time world champion in this game. 
Give a one sentence analysis of why you chose those points.
Provide your answer at the end in a valid JSON of this format:

{{"points": []}}

DESCRIPTION: {description}
IMAGE:
""".strip()


def extract_json(response, key):
  json_part = re.search(r"\{.*\}", response, re.DOTALL)
  parsed_json = {}
  if json_part:
    json_data = json_part.group()
    # Parse the JSON data
    parsed_json = json.loads(json_data)
  else:
    print("No JSON data found ******\n", response)
  return parsed_json[key]


def vip_perform_selection(prompter, vlm, im, desc, arm_coord, samples, top_n):
  """Perform one selection pass given samples."""
  image_circles_np = prompter.add_arrow_overlay_plt(
      image=im, samples=samples, arm_xy=arm_coord, log_image=False
  )

  _, encoded_image_circles = cv2.imencode(".png", image_circles_np)

  prompt_seq = [make_prompt(desc, top_n=top_n), encoded_image_circles]
  response = vlm.query(prompt_seq)

  arrow_ids = extract_json(response, "points")
  return arrow_ids, image_circles_np


def vip_runner(
    vlm,
    im,
    desc,
    style,
    action_spec,
    n_samples_init=25,
    n_samples_opt=10,
    n_iters=3,
    recursion_level=0,
):
  """VIP."""

  prompter = vip.VisualIterativePrompter(
      style, action_spec, vip.SupportedEmbodiments.META_NAVIGATION
  )

  output_ims = []
  arm_coord = (int(im.shape[1] / 2), int(im.shape[0] / 2))

  if recursion_level == 0:
    center_mean = action_spec["loc"]
    center_std = action_spec["scale"]
    selected_samples = []
    for itr in trange(n_iters):
      if itr == 0:
        style["num_samples"] = n_samples_init
      else:
        style["num_samples"] = n_samples_opt
      samples = prompter.sample_actions(im, arm_coord, center_mean, center_std)
      arrow_ids, image_circles_np = vip_perform_selection(
          prompter, vlm, im, desc, arm_coord, samples, top_n=3
      )

      # plot sampled circles as red
      selected_samples = []
      for selected_id in arrow_ids:
        sample = samples[selected_id]
        sample.coord.color = (255, 0, 0)
        selected_samples.append(sample)
      image_circles_marked_np = prompter.add_arrow_overlay_plt(
          image_circles_np, selected_samples, arm_coord
      )
      output_ims.append(image_circles_marked_np)

      # if at last iteration, pick one answer out of the selected ones
      if itr == n_iters - 1:
        arrow_ids, _ = vip_perform_selection(
            prompter, vlm, im, desc, arm_coord, selected_samples, top_n=1
        )

        selected_samples = []
        for selected_id in arrow_ids:
          sample = samples[selected_id]
          sample.coord.color = (255, 0, 0)
          selected_samples.append(sample)
        image_circles_marked_np = prompter.add_arrow_overlay_plt(
            im, selected_samples, arm_coord
        )
        output_ims.append(image_circles_marked_np)
      center_mean, center_std = prompter.fit(arrow_ids, samples)

    if output_ims:
      return (
          output_ims,
          prompter.action_to_coord(center_mean, im, arm_coord).xy,
          selected_samples,
      )
  else:
    new_samples = []
    for i in range(3):
      out_ims, _, cur_samples = vip_runner(
          vlm=vlm,
          im=im,
          desc=desc,
          style=style,
          action_spec=action_spec,
          n_samples_init=n_samples_init,
          n_samples_opt=n_samples_opt,
          n_iters=n_iters,
          recursion_level=recursion_level - 1,
      )
      output_ims += out_ims
      new_samples += cur_samples
    # adjust sample label to avoid duplications
    for sample_id in range(len(new_samples)):
      new_samples[sample_id].label = str(sample_id)
    arrow_ids, _ = vip_perform_selection(
        prompter, vlm, im, desc, arm_coord, new_samples, top_n=1
    )

    selected_samples = []
    for selected_id in arrow_ids:
      sample = new_samples[selected_id]
      sample.coord.color = (255, 0, 0)
      selected_samples.append(sample)
    image_circles_marked_np = prompter.add_arrow_overlay_plt(
        im, selected_samples, arm_coord
    )
    output_ims.append(image_circles_marked_np)
    center_mean, _ = prompter.fit(arrow_ids, new_samples)

    if output_ims:
      return (
          output_ims,
          prompter.action_to_coord(center_mean, im, arm_coord).xy,
          selected_samples,
      )
  return [], "Unable to understand query"