{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import dataclasses\n",
    "import mediapy\n",
    "from huggingface_hub import PyTorchModelHubMixin\n",
    "from huggingface_hub import ModelCard\n",
    "from gpudrive.networks.late_fusion import NeuralNet\n",
    "from gpudrive.env.config import EnvConfig\n",
    "from gpudrive.env.env_torch import GPUDriveTorchEnv\n",
    "from gpudrive.env.dataset import SceneDataLoader\n",
    "from gpudrive.utils.config import load_config \n",
    "import sys\n",
    "import imageio\n",
    "import numpy as np\n",
    "import os\n",
    "from gpudrive.utils.multi_policy_rollout import multi_policy_rollout\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_policy_masks(env, num_sim_agents=2, num_worlds=10):\n",
    "    policy_mask = torch.zeros_like(env.cont_agent_mask, dtype=torch.int)\n",
    "    agent_indices = env.cont_agent_mask.nonzero(as_tuple=True)\n",
    "\n",
    "    for i, (world_idx, agent_idx) in enumerate(zip(*agent_indices)):\n",
    "        policy_mask[world_idx, agent_idx] = (i % num_sim_agents) + 1\n",
    "\n",
    "    policy_masks = {f'pi_{int(policy.item())}': torch.zeros_like(env.cont_agent_mask, dtype=torch.bool,device=device) \n",
    "                    for policy in policy_mask.unique() if policy.item() != 0}\n",
    "\n",
    "    for p in range(1, num_sim_agents + 1):\n",
    "        policy_masks[f'pi_{p}'] = (policy_mask == p).reshape(num_worlds, -1)\n",
    "\n",
    "    return policy_masks\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Configs model has been trained with\n",
    "config = load_config(\"../../examples/experimental/config/reliable_agents_params\")\n",
    "max_agents = config.max_controlled_agents\n",
    "NUM_ENVS = 2\n",
    "device = \"cpu\" # cpu just because we're in a notebook\n",
    "NUM_SIM_AGENTS = 2\n",
    "FPS = 5\n",
    "\n",
    "sim_agent1 = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S10_000_02_27\")\n",
    "sim_agent2 = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S1000_02_27\")\n",
    "\n",
    "# Some other info\n",
    "card = ModelCard.load(\"daphne-cornelisse/policy_S10_000_02_27\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "sim_agent1 = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S10_000_02_27\")\n",
    "sim_agent2 = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S1000_02_27\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_loader = SceneDataLoader(\n",
    "    root='../../data/processed/examples',\n",
    "    batch_size=NUM_ENVS,\n",
    "    dataset_size=100,\n",
    "    sample_with_replacement=False,\n",
    ")\n",
    "\n",
    "# Set params\n",
    "env_config = dataclasses.replace(\n",
    "    EnvConfig(),\n",
    "    ego_state=config.ego_state,\n",
    "    road_map_obs=config.road_map_obs,\n",
    "    partner_obs=config.partner_obs,\n",
    "    reward_type=config.reward_type,\n",
    "    norm_obs=config.norm_obs,\n",
    "    dynamics_model=config.dynamics_model,\n",
    "    collision_behavior=config.collision_behavior,\n",
    "    dist_to_goal_threshold=config.dist_to_goal_threshold,\n",
    "    polyline_reduction_threshold=config.polyline_reduction_threshold,\n",
    "    remove_non_vehicles=config.remove_non_vehicles,\n",
    "    lidar_obs=config.lidar_obs,\n",
    "    disable_classic_obs=config.lidar_obs,\n",
    "    obs_radius=config.obs_radius,\n",
    "    steer_actions = torch.round(\n",
    "        torch.linspace(-torch.pi, torch.pi, config.action_space_steer_disc), decimals=3  \n",
    "    ),\n",
    "    accel_actions = torch.round(\n",
    "        torch.linspace(-4.0, 4.0, config.action_space_accel_disc), decimals=3\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "env = GPUDriveTorchEnv(\n",
    "    config=env_config,\n",
    "    data_loader=train_loader,\n",
    "    max_cont_agents=max_agents,\n",
    "    device=device,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "next_obs = env.reset()\n",
    "\n",
    "\n",
    "control_mask = env.cont_agent_mask\n",
    "\n",
    "policy_mask = create_policy_masks(env, 2,NUM_ENVS)\n",
    "\n",
    "policies_set = {'pi_1': (sim_agent1,policy_mask['pi_1']),\n",
    "                'pi_2': (sim_agent2, policy_mask['pi_2'])\n",
    "                } \n",
    "        \n",
    "\n",
    "\n",
    "metrics,frames=multi_policy_rollout(\n",
    "env,\n",
    "policies_set, \n",
    "device,\n",
    "deterministic=False,\n",
    "render_sim_state = True,\n",
    "render_every_n_steps= 5\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "env.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "mediapy.show_videos(frames, fps=15, width=500, height=500, columns=2, codec='gif')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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