{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PDW Full Model — Physics-Corrected CogVideoX-2b World Model\n",
    "## Single H100 Implementation with LoRA Fine-Tuning\n",
    "\n",
    "**Architecture:**\n",
    "```\n",
    "Input Video → CogVideoX-2b → Generated Frames\n",
    "                                    ↓\n",
    "                        Physics Consistency Loss\n",
    "                            (Warp Physics)\n",
    "                                    ↓\n",
    "                        LoRA Fine-Tune CogVideoX-2b\n",
    "```\n",
    "\n",
    "**Run order: Cell 1 → 2 → 3 → ... in sequence. Never skip.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "99bc29b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[OK] warp-lang\n",
      "[OK] diffusers>=0.31.0\n",
      "[OK] transformers>=4.46.0\n",
      "[OK] accelerate>=1.0.0\n",
      "[OK] peft\n",
      "[OK] bitsandbytes\n",
      "[OK] huggingface_hub\n",
      "[OK] einops\n",
      "[OK] imageio\n",
      "[OK] imageio-ffmpeg\n",
      "[OK] opencv-python\n",
      "[OK] matplotlib\n",
      "[OK] scipy\n",
      "[OK] tqdm\n",
      "[OK] wandb\n"
     ]
    }
   ],
   "source": [
    "# Cell 1.1 — Install all required packages\n",
    "import subprocess, sys\n",
    "\n",
    "pkgs = [\n",
    "    'warp-lang',\n",
    "    'diffusers>=0.31.0',\n",
    "    'transformers>=4.46.0',\n",
    "    'accelerate>=1.0.0',\n",
    "    'peft',           # for LoRA\n",
    "    'bitsandbytes',   # for FP8/int8 quantization\n",
    "    'huggingface_hub',\n",
    "    'einops',\n",
    "    'imageio',\n",
    "    'imageio-ffmpeg',\n",
    "    'opencv-python',\n",
    "    'matplotlib',\n",
    "    'scipy',\n",
    "    'tqdm',\n",
    "    'wandb',          # optional: experiment tracking\n",
    "]\n",
    "\n",
    "for pkg in pkgs:\n",
    "    result = subprocess.run(\n",
    "        [sys.executable, '-m', 'pip', 'install', pkg, '-q'],\n",
    "        capture_output=True, text=True\n",
    "    )\n",
    "    status = 'OK' if result.returncode == 0 else 'FAILED'\n",
    "    print(f'[{status}] {pkg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# BLOCK 1 — DEPENDENCIES & ENVIRONMENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warp 1.11.1 initialized:\n",
      "   CUDA Toolkit 12.9, Driver 12.4\n",
      "   Devices:\n",
      "     \"cpu\"      : \"x86_64\"\n",
      "     \"cuda:0\"   : \"NVIDIA H100 NVL\" (93 GiB, sm_90, mempool enabled)\n",
      "   Kernel cache:\n",
      "     /home/dhanya/.cache/warp/1.11.1\n",
      "============================================================\n",
      "ENVIRONMENT STATUS\n",
      "============================================================\n",
      "PyTorch:          2.6.0+cu124\n",
      "CUDA:             12.4\n",
      "GPU:              NVIDIA H100 NVL\n",
      "Total VRAM:       99.9 GB\n",
      "Warp:             1.11.1\n",
      "Warp devices:     ['cpu', 'cuda:0']\n",
      "============================================================\n",
      "ALL CHECKS PASSED\n"
     ]
    }
   ],
   "source": [
    "# Cell 1.2 — Verify full environment\n",
    "import torch\n",
    "import warp as wp\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "wp.init()\n",
    "\n",
    "print('=' * 60)\n",
    "print('ENVIRONMENT STATUS')\n",
    "print('=' * 60)\n",
    "print(f'PyTorch:          {torch.__version__}')\n",
    "print(f'CUDA:             {torch.version.cuda}')\n",
    "print(f'GPU:              {torch.cuda.get_device_name(0)}')\n",
    "total_vram = torch.cuda.get_device_properties(0).total_memory / 1e9\n",
    "print(f'Total VRAM:       {total_vram:.1f} GB')\n",
    "print(f'Warp:             {wp.__version__}')\n",
    "print(f'Warp devices:     {wp.get_devices()}')\n",
    "print('=' * 60)\n",
    "\n",
    "assert torch.cuda.is_available(), 'CUDA not available — stop here'\n",
    "assert total_vram > 90, f'Expected 90+ GB VRAM, got {total_vram:.1f}'\n",
    "print('ALL CHECKS PASSED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# BLOCK 2 — WARP PHYSICS ENGINE\n",
    "This is your Newton-based physics prior. It simulates what SHOULD happen physically, and the gap between this and what CogVideoX-2b generates is your training signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trajectory shape: (120, 1, 3)  (steps, objects, xyz)\n",
      "Start height:     5.00m\n",
      "Min height:       0.0000m  (should be ~0)\n",
      "End height:       2.04m\n",
      "WarpPhysicsEngine OK\n"
     ]
    }
   ],
   "source": [
    "# Cell 2.1 — Rigid Body Physics Simulator\n",
    "import warp as wp\n",
    "import numpy as np\n",
    "\n",
    "wp.init()\n",
    "\n",
    "@wp.kernel\n",
    "def rigid_body_step(\n",
    "    positions:    wp.array(dtype=wp.vec3),\n",
    "    velocities:   wp.array(dtype=wp.vec3),\n",
    "    gravity:      wp.vec3,\n",
    "    dt:           float,\n",
    "    restitution:  float,\n",
    "    ground_y:     float,\n",
    "    n_objects:    int\n",
    "):\n",
    "    i = wp.tid()\n",
    "    if i >= n_objects:\n",
    "        return\n",
    "\n",
    "    # Semi-implicit Euler integration\n",
    "    v_new = velocities[i] + gravity * dt\n",
    "    p_new = positions[i]  + v_new  * dt\n",
    "\n",
    "    # Ground collision response\n",
    "    if p_new[1] < ground_y:\n",
    "        p_new  = wp.vec3(p_new[0], ground_y, p_new[2])\n",
    "        v_new  = wp.vec3(v_new[0] * 0.95,\n",
    "                         -v_new[1] * restitution,\n",
    "                         v_new[2] * 0.95)\n",
    "\n",
    "    positions[i]  = p_new\n",
    "    velocities[i] = v_new\n",
    "\n",
    "\n",
    "class WarpPhysicsEngine:\n",
    "    \"\"\"\n",
    "    Differentiable rigid body simulator.\n",
    "    Runs on CUDA via Warp. Supports autodiff.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, device='cuda'):\n",
    "        self.device      = device\n",
    "        self.gravity     = wp.vec3(0.0, -9.81, 0.0)\n",
    "        self.dt          = 1.0 / 60.0   # 60 Hz physics\n",
    "        self.ground_y    = 0.0\n",
    "\n",
    "    def simulate(\n",
    "        self,\n",
    "        init_positions: np.ndarray,    # (N, 3)\n",
    "        init_velocities: np.ndarray,   # (N, 3)\n",
    "        restitution: float,\n",
    "        num_steps: int,\n",
    "        record_every: int = 1\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Run forward simulation.\n",
    "        Returns trajectory of shape (num_steps // record_every, N, 3)\n",
    "        \"\"\"\n",
    "        N = init_positions.shape[0]\n",
    "\n",
    "        pos = wp.array(init_positions.astype(np.float32),\n",
    "                       dtype=wp.vec3, device=self.device)\n",
    "        vel = wp.array(init_velocities.astype(np.float32),\n",
    "                       dtype=wp.vec3, device=self.device)\n",
    "\n",
    "        trajectory = []\n",
    "\n",
    "        for step in range(num_steps):\n",
    "            wp.launch(\n",
    "                rigid_body_step,\n",
    "                dim=N,\n",
    "                inputs=[\n",
    "                    pos, vel,\n",
    "                    self.gravity,\n",
    "                    self.dt,\n",
    "                    restitution,\n",
    "                    self.ground_y,\n",
    "                    N\n",
    "                ],\n",
    "                device=self.device\n",
    "            )\n",
    "            if step % record_every == 0:\n",
    "                trajectory.append(pos.numpy().copy())\n",
    "\n",
    "        return np.array(trajectory)\n",
    "\n",
    "    def simulate_with_grad(\n",
    "        self,\n",
    "        init_positions: wp.array,\n",
    "        init_velocities: wp.array,\n",
    "        restitution_arr: wp.array,\n",
    "        num_steps: int\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Differentiable simulation using Warp tape.\n",
    "        Returns final positions array and tape for backprop.\n",
    "        \"\"\"\n",
    "        N = len(init_positions)\n",
    "        tape = wp.Tape()\n",
    "\n",
    "        pos = wp.clone(init_positions)\n",
    "        vel = wp.clone(init_velocities)\n",
    "\n",
    "        with tape:\n",
    "            for _ in range(num_steps):\n",
    "                wp.launch(\n",
    "                    rigid_body_step,\n",
    "                    dim=N,\n",
    "                    inputs=[\n",
    "                        pos, vel,\n",
    "                        self.gravity,\n",
    "                        self.dt,\n",
    "                        restitution_arr[0],\n",
    "                        self.ground_y,\n",
    "                        N\n",
    "                    ],\n",
    "                    device=self.device\n",
    "                )\n",
    "\n",
    "        return pos, tape\n",
    "\n",
    "\n",
    "# Quick test\n",
    "engine = WarpPhysicsEngine()\n",
    "\n",
    "init_pos = np.array([[0.0, 5.0, 0.0]])   # 1 ball, height=5m\n",
    "init_vel = np.array([[0.0, 0.0, 0.0]])   # at rest\n",
    "\n",
    "traj = engine.simulate(init_pos, init_vel, restitution=0.7, num_steps=120)\n",
    "\n",
    "print(f'Trajectory shape: {traj.shape}  (steps, objects, xyz)')\n",
    "print(f'Start height:     {traj[0,0,1]:.2f}m')\n",
    "print(f'Min height:       {traj[:,0,1].min():.4f}m  (should be ~0)')\n",
    "print(f'End height:       {traj[-1,0,1]:.2f}m')\n",
    "print('WarpPhysicsEngine OK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nzZo1vHnzhh49eqDRaPjll19o0qQJd+7c0R/PCxcuULFiRUxNTenevTsuLi7cvn2b7du36x8K/+zZM8qWLavvcODg4MDOnTvp0qULgYGBcvu8ECLVcnFxoVy5cqxdu1Zff+zcuZPXr1/TsmVLZs+enWz7+pS69ebNm7Ro0YKePXvSoUMHli5dyrfffsuuXbuoUaNGrOv9+++/1K5dm5IlSzJmzBiMjIz0Hc8OHTpE6dKlY1036rr2w3OxD6VLl46GDRuyfPlybt26ZTB036RJk9BoNAwbNgx/f39mzpxJ9erVOXfuXJx3gCfkXAd09WXnzp0pWLAgw4cPx97enrNnz7Jr1y5at27NyJEjef36NY8ePWLGjBkA2NjYALohZfr160ezZs34/vvvCQkJ4cKFCxw/fjzGH02EUJUiUq2lS5cqQJyvggUL6pe/d++eYmxsrEyaNMlgOxcvXlRMTEwMyjt06KA4Ozvrpzdt2qQAysyZM/VlkZGRyjfffKMAytKlSw3WBZTx48cb7Kd48eJKyZIl9dPPnz9XAGXMmDEJyrd///4KoBw6dEhf9ubNG8XV1VVxcXFRIiMjFUVRlAULFiiAcvHiRYP1CxQooHzzzTf66QkTJijW1tbKjRs3DJb74YcfFGNjY+XBgweKoijK3bt3FUCxs7NT/P39ExRrQnKM2m5Mx27y5Mn6slevXimWlpaKRqNRvL299eXXrl2Ltu2E5hSb7NmzK02bNo1WfvnyZQVQJkyYoCiKooSHhyvW1tbK8uXLFUVRlMyZMyu//faboiiKEhgYqBgbGyvdunXTrx8cHBxtmzVr1lRy5cplUObs7KwAyq5du6ItH/WZPnXqlL7s/v37ioWFhdK4ceM48ypevLhib28frTwoKEh5/vy5/vX69Wv9vKj/XxUqVFAiIiL05f7+/oqZmZni5eWl/8wpiqLMnTtXAZQlS5YY5NOhQ4do+61cubJSuXJl/fS+ffsUQMmfP78SGhqqL581a5bBZzksLExxdHRUihUrZrDcwoULFcBgm/GJ6bMZFUeuXLmivWdjxoxRYqoyoo7T3bt39WUFCxaMMZaoZatXr65otVp9+YABAxRjY2MlICAg2jpmZmZKr169EpyXECJmcZ0zmJubGyzr7Oys1K1bN9o2AKVPnz4GZQmtd/79918FUPr16xdtux9+H1hbW8f4vRmTmTNnKoCyatUqfVlYWJhSrlw5xcbGRgkMDIw3p5g4OzsrXl5e+rrh4sWLSrt27aLlf+jQIQVQVq9ebbD+rl27DMq3bNmiAMrJkydj3Wdc5wsxff/Gdpw+Pn/7lG18/L2emHqvcuXKCqCsWLFCXxYaGqo4OTnFeH7xoXPnzimA0rVrV4PywYMHK4Dy77//6suizhcOHjyoL/P391fMzc2VQYMGxbkfRVFi/b/QqFEjJSQkRL9cVL1bqFAh5d27d/pyHx8fBVB+/PFHg9xjqvs+fk+izv8yZsyovHz5Ul++detWBVC2b9+uL6tUqZJia2ur3L9/32CbH/6f6dKli5IlSxblv//+M1imZcuWSrp06WI8BxNCiJQsqh46efKkMnfuXMXW1lb/Xfbtt98qVatWVRQlet0edS2zb98+g+3FdN2dlHoxat+AsmnTJn3Z69evlSxZsijFixePNSatVqvkyZNHqVmzpsF3eXBwsOLq6qrUqFEjzmNTrFgxJV26dHEu8+uvvyqAsm3bNoMYsmXLZnButH79egVQZs2apS/7uM5K6LlOQECAYmtrq5QpU8agvozKOUrdunVjPE9p2LChQduVECmZDO2SBvz222/s3bs32uvjnkybN29Gq9XSvHlz/vvvP/3LycmJPHnysG/fvlj3sWvXLkxNTQ3GBTUyMorWm/hDPXv2NJiuWLEid+7c+cQsYceOHZQuXdpg3G0bGxu6d+/OvXv39EOtNGnSBBMTE4PerZcuXeLKlSsG44Rt2LCBihUrkj59eoPjUb16dSIjI6MNGdK0adNE92D7VF27dtX/bW9vT758+bC2tqZ58+b68nz58mFvb29wTBOb08devHgRrbcdQP78+cmYMaN+7PPz58/z9u1bPD09AfD09NTfinb06FEiIyMN3qcPf+GO6g1ZuXJl7ty5w+vXrw325erqSs2aNWOMr1y5cvpeygA5c+akYcOG7N69O87xzQMDA/W/dn9o5MiRODg46F8x/drdrVs3gzHx/v77b8LCwujfv7/BuOHdunXDzs7OYKihxOrUqZNB7++KFSsC6N/jU6dO4e/vT8+ePQ2W69ixo/7hsMmhQ4cOSR6XPi7du3c3GGKgYsWKREZGcv/+/WjLRn2WhRDJI6Zzhp07d37y9hJa72zatAmNRhPjwz4/9RkIO3bswMnJiVatWunLTE1N6devH0FBQRw4cODTkgL27NmjrxsKFy7MypUr6dSpk8HDuzZs2EC6dOmoUaOGQe4lS5bExsZGf14V1WPZx8cnxrsFU7rE1ns2NjYGd4CZmZlRunTpeM8Bd+zYAcDAgQMNyqPuIvh4PwUKFNDXk6Dr2Z8vX74En2s2bNhQ/39g69atDB8+XN9rTlEU4H2927t3b4Mx0+vWrYu7u3uS6vwWLVoYnHN9XOc/f/6cgwcP0rlzZ3LmzGmwbtT/GUVR2LRpE/Xr10dRFIPPYc2aNXn9+jVnzpz55BiFEEJtzZs35927d/j4+PDmzRt8fHxSTA/lrFmz0rhxY/20nZ0d7du35+zZs/j5+cW4zrlz57h58yatW7fmxYsX+u/st2/fUq1aNQ4ePBjnkKxv3rzB1tY2zrii5gcGBhqUt2/f3mDdZs2akSVLFn39G5OEnuvs3buXN2/e8MMPP0R7xkhCzvPs7e159OhRjEOcCZHSyNAuaUDp0qVjfHDYxw1QN2/eRFEU8uTJE+N24hqO4v79+2TJkgUrKyuD8g9vFfpQ1NinH8cT0zhaCXX//v1o44CBrpE3an6hQoXIlCkT1apVY/369UyYMAHQDetiYmKiv10adMfjwoULsTaO+/v7G0xHDZ/yucV07NKlS0f27NmjVULp0qUzOKaJzSkmURePH9JoNHh6euordl9fXxwdHfXvv6enp37s+agG9Q8b0n19fRkzZgxHjx4lODjYYNuvX782aASO6zjH9NnNmzcvwcHBPH/+HCcnpxjXs7W15cWLF9HKe/fuTb169YDYh335OJ6oxt4Ph5wBXUNBrly5YmwMTqiPL5SjLrCj3uOobX98HExNTcmVK9cn7/djn/uzHl+eH1IURR40KkQyiu2c4VMltN65ffs2WbNmTdZh0e7fv0+ePHmiPQz5w/OCT1WmTBkmTpxIZGQkly5dYuLEibx69crgR8ybN2/y+vVrHB0dY9xGVO6VK1emadOmjBs3jhkzZlClShUaNWpE69atMTc3/+QYv5TE1nsxna+kT5+eCxcuxLsfIyOjaOeWTk5O2NvbR9vPx3VJ1H4Seq6ZPXt2qlevrp9u0KABGTNmZPDgwfj4+FC/fv1Ycwdwd3eP9rD1xIivLoxqUI9r6Lrnz58TEBDAwoULWbhwYYzLJOTcTwghUioHBweqV6/OmjVrCA4OJjIykmbNmqkdFqBrC/m4vsubNy+gG5c9pmvTmzdvAsQ6RC7oro9j6twGuuva+DoZvXnzRr/shz6+htRoNOTOnTvO58Yk9Fwn6hkycdVZcRk2bBh///03pUuXJnfu3Hh5edG6detYn28mhJqkIf0rotVq0Wg07Ny5M8anTsfUY/dTqf1U65YtW9KpUyfOnTtHsWLFWL9+PdWqVSNTpkz6ZbRaLTVq1GDo0KExbiOqEozyOXvofii2Yxdb+YcN34nN6WMZM2aM9QK0QoUKbN++nYsXL+rHR4/i6enJkCFDePz4MYcPHyZr1qz6ht3bt29TrVo13N3d+fXXX8mRIwdmZmbs2LGDGTNmRPvF/XMcZ3d3d86dO8fjx4/Jli2bvjxv3rz6Y/LxL+fJEU9sDcCRkZExvp8JeY+/hJhyjiuXxEpMngEBAQb/b4UQKUtS652UKlOmTPpG1po1a+Lu7k69evWYNWuWvse0VqvF0dGR1atXx7iNqB8XNBoNGzdu5NixY2zfvp3du3fTuXNnpk+fzrFjx5L1/Cs5v6s/VVLrsoT+ePo56sxq1aoBcPDgQerXr5+odaMeyPux2I59csQfdQ7Vtm3bWBtl4nr+kBBCpAatW7emW7du+Pn5Ubt27VifTZES6sD4RH1vT506lWLFisW4TFznBfnz5+fcuXM8ePAgxh+UAf0P1wUKFEhasCT8XCep8ufPz/Xr1/Hx8WHXrl1s2rSJefPm8eOPPzJu3Lhk2YcQyUUa0r8ibm5uKIqCq6troi9snZ2d2bdvH8HBwQa90m/duvXJ8SS2l6mzszPXr1+PVn7t2jX9/CiNGjWiR48e+uFdbty4wfDhww3Wc3NzIygoyKA3UmqX1Jzc3d25e/dujPOiepgfPnwYX19fg4dXlSxZEnNzc/bv38/x48epU6eOft727dsJDQ1l27ZtBpV9XEMJxSbqF/wP3bhxAysrqzgr8Xr16uHt7c3q1atjbexJqKjP2fXr1w16gYeFhXH37l2DY58+fXoCAgKibeP+/fuf1IM8at83b97km2++0ZeHh4dz9+5dihYtmuhtJlRUr4iAgACDk9eYenwmVw/yx48fExYWpu9dKoRIeRJa77i5ubF7925evnwZZ6/0xHx/ODs7c+HCBbRarUGv9JjOC5Kqbt26VK5cmcmTJ9OjRw+sra1xc3Pj77//pnz58gn60bVs2bKULVuWSZMmsWbNGtq0aYO3tzddu3ZN9PdmbMvHVe8kdBsfS0y9lxTOzs5otVpu3rxp8L3/7NkzAgICkvX9jE1ERAQAQUFB+phAl/uH9W5U2YcxpU+fPsZhZT71zoioY33p0qVYl3FwcMDW1pbIyMg0dT4rhBAfaty4MT169ODYsWMGw7d+7MPrlQ8l9Hs4sXXxrVu3ot09e+PGDUD3oNSYRD3A1M7O7pO+t+vVq8fatWtZsWIFo0aNijY/MDCQrVu34u7uHu0Or4+vpRVF4datW3H+4JrQc52ovC5duhTrqAUQ9zG2tramRYsWtGjRgrCwMJo0acKkSZMYPnx4rJ3ehFCDjJH+FWnSpAnGxsaMGzcuWm8XRVFiHPoiSs2aNQkPD2fRokX6Mq1Wy2+//fbJ8UQ1yMd0wReTOnXqcOLECY4ePaove/v2LQsXLsTFxcXgF1d7e3tq1qzJ+vXr8fb2xszMjEaNGhlsr3nz5hw9epTdu3dH21dAQID+Yio1SWpO5cqV49KlS4SGhkab5+HhgYWFBatXr+bx48cGPdLNzc0pUaIEv/32G2/fvjUY1iWqx9WHn7nXr1+zdOnSROd39OhRg7E+Hz58yNatW/Hy8orzLojmzZtToEABJkyYwLFjx2JcJqE9wKpXr46ZmRmzZ882WOePP/7g9evX1K1bV1/m5ubGsWPHCAsL05f5+Pjw8OHDBO3rYx4eHjg4OPD7778bbHPZsmUJ/n/0qaJOjj4cZ//t27csX7482rLW1tbJEs/p06cBDD5rQoiUJaH1TtOmTVEUJcZeRR9+lybm+6NOnTr4+fkZXFRHREQwZ84cbGxsqFy5ciKziduwYcN48eKF/lyoefPmREZG6oeR+1BERIQ+j1evXkWrY6J6oUXVt4k9J4rtOLm5ufH69WuDYVSePn3Kli1bEryNjyWm3kuKqB/hZ86caVD+66+/AiTbfuKyfft2AP0P0x4eHjg6OvL7778bnBvt3LmTq1evRqvzr127xvPnz/Vl58+f1w95l1gODg5UqlSJJUuW8ODBA4N5Ue+DsbExTZs2ZdOmTTE2uH8YixBCpFY2NjbMnz+fsWPHxnm3kLOzM8bGxtGeCzZv3rwE7Sex1zBPnjwxqF8DAwNZsWIFxYoVi3XI0ZIlS+Lm5sa0adP0P9p+KL7v7WbNmlGgQAF++uknTp06ZTBPq9XSq1cvXr16FeMzaVasWKEf9gVg48aNPH36lNq1a8e6v4Se63h5eWFra8uUKVMICQkxWO7j87yPn5EGRGuLMjMzo0CBAiiKkiqfLyPSNumR/hVxc3Nj4sSJDB8+nHv37tGoUSNsbW25e/cuW7ZsoXv37gwePDjGdRs1akTp0qUZNGgQt27dwt3dnW3btvHy5Uvg03qgWlpaUqBAAdatW0fevHnJkCEDhQoVinVcrR9++IG1a9dSu3Zt+vXrR4YMGVi+fDl3795l06ZN0cZIbdGiBW3btmXevHnUrFkz2i1gQ4YMYdu2bdSrV4+OHTtSsmRJ3r59y8WLF9m4cSP37t1LdUNKJDWnhg0bMmHCBA4cOICXl5fBPDMzM0qVKsWhQ4cwNzc3eOgn6Bo7p0+fDhiOj+7l5YWZmRn169enR48eBAUFsWjRIhwdHXn69Gmi8itUqBA1a9akX79+mJub60+K4rvdy9TUlC1btlCzZk0qVKhAkyZNqFixItbW1jx+/Jht27bx4MGDBF2kOzg4MHz4cMaNG0etWrVo0KAB169fZ968eZQqVcpgrPWuXbuyceNGatWqRfPmzbl9+zarVq3SN0onlqmpKRMnTqRHjx588803tGjRgrt377J06dJkHSM9Jl5eXuTMmZMuXbowZMgQjI2NWbJkCQ4ODtEu8EuWLMn8+fOZOHEiuXPnxtHRMVpPvoTYu3cvOXPmpHjx4smVhhBfvZ07d+p7bH/I09Pzk75HElrvVK1alXbt2jF79mxu3rxJrVq10Gq1HDp0iKpVq9K3b19A9/3x999/8+uvv5I1a1ZcXV1jfD4K6B5cvGDBAjp27Mjp06dxcXFh48aN+Pr6MnPmzHgfxpVYtWvXplChQvz666/06dOHypUr06NHD6ZMmcK5c+fw8vLC1NSUmzdvsmHDBmbNmkWzZs1Yvnw58+bNo3Hjxri5ufHmzRsWLVqEnZ2dvvE4sedEsR2nli1bMmzYMBo3bky/fv0IDg5m/vz55M2bN9pDJxN6rBNT7yVF0aJF6dChAwsXLiQgIIDKlStz4sQJli9fTqNGjahatWqy7CfKjRs3WLVqFQDBwcEcO3aM5cuXkzt3btq1awfo6t2ff/6ZTp06UblyZVq1asWzZ8+YNWsWLi4uDBgwQL+9zp078+uvv1KzZk26dOmCv78/v//+OwULFoz2wLeEmj17NhUqVKBEiRJ0794dV1dX7t27x19//cW5c+cA+Omnn9i3bx9lypShW7duFChQgJcvX3LmzBn+/vtv/bm6EEKkZnGNKR4lXbp0fPvtt8yZMweNRoObmxs+Pj4JflZEYs5BQDd8XZcuXTh58iSZM2dmyZIlPHv2LM4OY0ZGRixevJjatWtTsGBBOnXqRLZs2Xj8+DH79u3Dzs5O/6NuTMzMzNi4cSPVqlWjQoUKdOrUCQ8PDwICAlizZg1nzpxh0KBBtGzZMtq6GTJk0K/z7NkzZs6cSe7cuenWrVus+0vouY6dnR0zZsyga9eulCpVitatW5M+fXrOnz9PcHCwvvNVyZIlWbduHQMHDqRUqVLY2NhQv359vLy8cHJyonz58mTOnJmrV68yd+5c6tatm+znc0IkmSJSraVLlyqAcvLkyRjnV65cWSlYsGC08k2bNikVKlRQrK2tFWtra8Xd3V3p06ePcv36df0yHTp0UJydnQ3We/78udK6dWvF1tZWSZcundKxY0fF19dXARRvb2+Dda2traPtd8yYMcrHH7kjR44oJUuWVMzMzBRAGTNmTJw53759W2nWrJlib2+vWFhYKKVLl1Z8fHxiXDYwMFCxtLRUAGXVqlUxLvPmzRtl+PDhSu7cuRUzMzMlU6ZMiqenpzJt2jQlLCxMURRFuXv3rgIoU6dOjTO2mDx//jzWvKK2u3TpUn1ZbMcutvfS2dlZqVu3bqJzikuRIkWULl26xDhv+PDhCqB4enpGm7d582YFUGxtbZWIiAiDedu2bVOKFCmiWFhYKC4uLsrPP/+sLFmyRAGUu3fvxplPFEDp06ePsmrVKiVPnjyKubm5Urx4cWXfvn3x5hQlICBAGT9+vFK8eHHFxsZGMTMzU3LkyKE0a9ZM2b59u8Gy8f3/mjt3ruLu7q6YmpoqmTNnVnr16qW8evUq2nLTp09XsmXLppibmyvly5dXTp06pVSuXFmpXLmyfpl9+/YpgLJhwwaDdWP6jCiKosybN09xdXVVzM3NFQ8PD+XgwYPRthmfmD6bscUR5fTp00qZMmUUMzMzJWfOnMqvv/6qP04fvo9+fn5K3bp1FVtbWwXQxxXbMY3a74fvZWRkpJIlSxZl1KhRCc5JCBG7qP9/sb0+/J6J7bs46nv4YwmtdyIiIpSpU6cq7u7uipmZmeLg4KDUrl1bOX36tH6Za9euKZUqVdLX3x06dIgzr2fPnimdOnVSMmXKpJiZmSmFCxeO9p0ZV04xiWvZZcuWRTteCxcuVEqWLKlYWloqtra2SuHChZWhQ4cqT548URRFUc6cOaO0atVKyZkzp2Jubq44Ojoq9erVU06dOmWw7djOiWI6f4rrOO3Zs0cpVKiQYmZmpuTLl09ZtWpVorYR0/e6oiSs3ovtfCWm88qYhIeHK+PGjVNcXV0VU1NTJUeOHMrw4cOVkJAQg+Vie48SWhd+/Pk3NjZWsmfPrnTv3l159uxZtOXXrVunFC9eXDE3N1cyZMigtGnTRnn06FG05VatWqXkypVLMTMzU4oVK6bs3r07Wu5xnVfGdM546dIlpXHjxvpz33z58imjR482WObZs2dKnz59lBw5ciimpqaKk5OTUq1aNWXhwoXxHgshhEhp4rsOixJTXfD8+XOladOmipWVlZI+fXqlR48eyqVLl6LV3UmtF6P2vXv3bqVIkSKKubm54u7uHu06KqbrHEVRlLNnzypNmjRRMmbMqJibmyvOzs5K8+bNlX/++SdBx8jf318ZOHCgkjt3bsXc3Fyxt7dXqlevrmzbti3aslExrF27Vhk+fLji6OioWFpaKnXr1lXu379vsGxs9XV85zpRtm3bpnh6eiqWlpaKnZ2dUrp0aWXt2rX6+UFBQUrr1q0Ve3t7BdDva8GCBUqlSpX0x8PNzU0ZMmSI8vr16wQdDyG+JI2ifOGn2Ik05c8//6Rx48YcPnxYnqicRqxcuZI+ffrw4MGDWB/kogaNRkOfPn2YO3eu2qGIL+DPP/+kdevW3L59myxZsqgdjhBCCCGEEEIAujHQCxUqhI+Pj9qhJKt27dpx9OjRJD0LT4i0TsZIFwn27t07g+nIyEjmzJmDnZ0dJUqUUCkqkdzatGlDzpw5kzT+vRBJ9fPPP9O3b19pRBdCCCGEEEKIL+Dp06epbnhbIb40GSNdJNh3333Hu3fvKFeuHKGhoWzevJkjR44wefLkOJ/gLFIXIyOjGB9YJcSX9OFDhYUQQgghhBBCfB4XLlzgzz//5ODBgwwZMkTtcIRI0aQhXSTYN998w/Tp0/Hx8SEkJITcuXMzZ84c/QPChBBCCCGEEEIIIUTqsXnzZubMmUPLli0ZPny42uEIkaLJGOlCCCGEEEIIIYQQQgghRBxkjHQhhBBCCCGEEEIIIYQQIg7SkC6EEEIIIYQQQgghhBBCxCFVj5Gu1Wp58uQJtra2aDQatcMRQgghkpWiKLx584asWbNiZJQ2fvuWulsIIURaJnW3EEIIkbokpu5O1Q3pT548IUeOHGqHIYQQQnxWDx8+JHv27GqHkSyk7hZCCPE1kLpbCCGESF0SUnen6oZ0W1tbQJeonZ1dkrcXHh7Onj178PLywtTUNMnbU5PkkvKklTwg7eSSVvIAySUlSo48AgMDyZEjh76+Swuk7o6d5JLypJU8IO3kklbyAMklJZK6O2ZSd8dOckl50koekHZySSt5gOSSEn3pujtVN6RH3VZmZ2eXbBW6lZUVdnZ2qfpDBJJLSpRW8oC0k0tayQMkl5QoOfNIS7dRS90dO8kl5UkreUDaySWt5AGSS0okdXfMpO6OneSS8qSVPCDt5JJW8gDJJSX60nV32hi0TQghhBBCCCGEEEIIIYT4TKQhXQghhBBCCCGEEEIIIYSIgzSkCyGEEEIIIYQQQgghhBBxSNVjpAshhPh0kZGRhIeHqx1GNOHh4ZiYmBASEkJkZKTa4XyyhORhamqKsbHxF45MCCGEEEIIIdSTUq9F45NWrlUh7eTypa+7pSFdCCG+Moqi4OfnR0BAgNqhxEhRFJycnHj48GGqflBXQvOwt7fHyckpVecqhBBCCCGEEPFJ6dei8Ukr16qQdnL50tfd0pAuhBBfmagTF0dHR6ysrFJcpanVagkKCsLGxgYjo9Q7All8eSiKQnBwMP7+/gBkyZLlS4cohBBCCCGEEF9MSr8WjU9auVaFtJPLl77uloZ0IYT4ikRGRupPXDJmzKh2ODHSarWEhYVhYWGR6iv0+PKwtLQEwN/fH0dHRxnmRQghhBBCCJEmpYZr0fiklWtVSDu5fOnrblWP1NixY9FoNAYvd3d3NUMSQog0LWocOisrK5UjEVGi3ovUMkag1N1CCCGEEEKIxIqIiADkWlSoI7muu1XvkV6wYEH+/vtv/bSJieohCSFEmpfabqFLy1LjeyF1txBCCCGEECIxFEUBUuf1j0j9kutzp/qVr4mJCU5OTmqHQUQE7Nih4dYtex4+hGzZwNxc7aiEEEKIlCel1N0hwSFsGPIb/spbzlo64u5RELsMdmqHJYQQQohYhEaEcuDeAW68vYHrf66kt0qPrbktduZ2GGlS79ACQgghvg6qN6TfvHmTrFmzYmFhQbly5ZgyZQo5c+aMcdnQ0FBCQ0P104GBgYCuW35Su+Y/eQKNGpkClRk8WFdmb6+QMye4uirkyqWQJ49C4cJQsKCCjU2SdvfZRR2P1DJUQFzSSi5pJQ9IO7mklTwg4bmEh4ejKAparRatVvslQku0qJ4KUXGmVlF5jB07lm3btnHmzJkYl9NqtSiKQnh4eLSx2lLqZzOl1N1Xj1+i3bz/V9rzxwDwinQ8MM3CY+vMvHBwJNQtBxkrF6NU8ypkzqF+439cvsbvpJQureQBaSeXtJIHSC4pUXLkkdqPwef2NOgpNdfUBGDozaH6ciONEekt0pPRKiOO1o5ktc1KFpssZLPNhrO9M87pnHGxd8HR2lF6swohUqWxY8fy559/cu7cOQA6duxIQEAAf/75Z7Lv648//mDdunXs2bPns+8rJahRowZDhw7l22+//ez7UrUhvUyZMixbtox8+fLx9OlTxo0bR8WKFbl06RK2trbRlp8yZQrjxo2LVr5nz54kj7F05046oIpBWUCAhoAAuHAhekXt5BRE3ryvyJfvFfnyvcTVNRBjYyVJMXwOe/fuVTuEZJNWckkreUDaySWt5AHx5xLVkzgoKIiwsLAvFNWnefPmTbSyZ8+eMWPGDPbs2cOTJ0+ws7PD1dWV5s2b06pVqxQ53l5YWBiRkZH6BuSY5r97946DBw/qxw2MEhwc/CVCTJSUVHff/vsixT8qS89r0oe/pmjANQgAbgK7gOFwy8iZ8/a5uO+aE6V0LpyrFMDU3DRJMXwOX9N3UmqRVvKAtJNLWskDJJeUKCl5pMS6OyUJCguKsVyraHnx7gUv3r3gxosbsa5vY2ZDngx5yJcpH/ky5qOQYyEKOxbGLYMbJkaq9xMUQqRBHTt2ZPny5frp9OnTU6pUKaZOnUqRIkU+235DQkLo2bMnp0+f5urVq9SrVy9BDeEhISGMHj2aDRs2fLbYXrx4QZs2bbhw4QIvXrzA0dGRhg0bMnnyZOzsYr9D+OXLl3z33Xds374dIyMjmjZtyqxZs7BJYm/lQYMGMWLECJo2bfrZH5yqak1Tu3Zt/d9FihShTJkyODs7s379erp06RJt+eHDhzNw4ED9dGBgIDly5MDLyyvONyohHj6E//4L58yZR5ib5+D5cyP8/DQ8eAAREdEb0v38bPDzs+HgwRwA2NkpVK6sUL26QvXqWvLkSVI4SRYeHs7evXupUaMGpqYpr5EgMdJKLmklD0g7uaSVPCDhuYSEhPDw4UNsbGywsLD4ghEmnKIovHnzBltbW4MeR3fu3KFKlSrY29szefJkChcujLm5ORcvXmTRokW4ubnRoEGDaNsLDw9X5f2NysPMzAxjY+NY66mQkBAsLS2pVKlStPcktsZ3NaWkuvuspSNLDtzA5Pl/ZAx9R4Z3ATiGvSSn9gmmRERbPrf2Prlf3oeXwGl4Pd+OI+mK8bhkMfL0qI9n48pJiiepvsbvpJQureQBaSeXtJIHSC4pUXLkkRLr7pQkvUV6hnoO5dKNS2TKmom34W8JDA3k5buXuob04Be8CYvemSJKUFgQZ/3OctbvrEG5hYkFRTIXoYRTCUpmLUmprKUo5FgIYyPjWLYkhBAJV6tWLZYuXYpWq+X27dv89NNP1KtXjwcPHny2fUZGRmJpaUm/fv3YtGlTgtfbuHEjdnZ2lC9f/rPFZmRkRMOGDZk4cSIODg7cunWLPn368PLlS9asWRPrem3atOHp06fs3buX8PBwOnXqRPfu3eNcJyFq1KhB//792blzJ3Xr1k3StuKTon6ytbe3J2/evNy6dSvG+ebm5pjHMHC5qalpkk/YcuWCsWPD2bHjAnXqZMfUVPcLRkQEPHoEt2/DlStw4cL7V0jI+/UDAzVs365h+3YAY/Lnh0aNdK9SpUCtu8+S49ikFGkll7SSB6SdXNJKHhB/LpGRkWg0GoyMjD77L7WfKmo4l6g4o/Tt2xcTExNOnTqFtbW1vjx37tw0btwYRVHQaDRoNBrmzZvHzp07+eeffxgyZAhjx45l/vz5TJs2jYcPH+Lq6sqoUaNo164dAPfu3cPV1ZWzZ89SrFgxAAICAkifPj379u2jSpUq7N+/n6pVq/L3338zbNgwrly5QrFixVi6dCn58uXTx/PTTz8xY8YMgoODadSoEVmzZgWI9XgbGRmh0WhifO9Sw+dSzbq7tFc5ip/2YMeOHXjVqaPfXkhwCOcOnOXOgfMEnbiK7Y3buP73gEKhN7HkfeWdjkBqvz4I/x6Ef2dz1diNE/nKkKFLfep+3xwjY3X+j3xN30mpRVrJA9JOLmklD5BcUqKk5JEW8v+cstllY2KViewI3kGdD+ruDwWHB+MX5MeTN094FPiI+wH3uf/6PncD7nLzxU3uBtxFqxgO/xcSEcKJxyc48fgEnNaV2ZjZUCZbGTxzeFLJuRKeOTyxMk15dy8KIVI+c3NznJyc0Gq1WFlZMWzYMCpXrszz589xcHAAYNiwYWzZsoVHjx7h5OREmzZt+PHHHz+5XrC2tmb+/PkA+Pr6EhAQkKD1vL29qV+/fozzpk2bxvTp0wkLC6NFixYx3jmcEOnTp6dXr176aWdnZ3r37s3UqVNjXefq1avs2rWLkydP4uHhAcCcOXOoU6cO06ZNI2vWrCxbtoz+/fuzatUqBg0axMOHD6lTpw4rVqxgw4YNjBkzhtevX9OuXTtmzJihHxrV2NiY2rVr4+3t/XU1pAcFBXH79m19w0ZKYGICLi66V7Vq78vDwnSN6UePgq8v/PMP/Pff+/lXr+peU6aAqyu0bg1t24K7+5fOQAgh4hcRx23IGmNjjD9oCI1rWYyMMPmgV3Vsy5okckiPFy9esGfPHiZPnmzQiG4Q5we/WI4dO5affvqJmTNnYmJiwpYtW/j++++ZOXMm1atXx8fHh06dOpE9e3aqVq2aqFhGjhzJ9OnTcXBwoGfPnnTu3BlfX18A1q9fz9ixY/ntt9/w9PTkjz/+YP78+eTKlStR+0hNUmLdbWFlQana5ShVu5xBedDrIP5atgO/rb44XLhMuRfncOCFfn7+yNvkv3IbBq3hzpChHMpfGZeR7ancqsaXTkEIIYT4almZWpErfS5ypY/5/CksMozbL29z9b+rXPK/xEX/i5z3O8/NlzcNlgsKC+Kfu//wz91/ADA1MqV0ttJUc62Gl5sXpbOVxtRYfvgQQiROUFAQq1evJnfu3GTMmFFfbmtry7Jly8iaNSsXL16kW7du2NraMnTo0Di2lvwOHz4c47XZvn37yJIlC/v27ePWrVu0aNGCfPny8d133wHQs2dPVq1aFee2g4JiHp7ryZMnbN68mcqVY7/D9+jRo9jb2+sb0QGqV6+OkZERx48fp3HjxoBuiLTZs2fj7e3NmzdvaNKkCY0bN8be3p4dO3Zw584dmjZtSvny5WnRooV+W6VKleKXX36JM/7koGpD+uDBg6lfvz7Ozs48efKEMWPGYGxsTKtWrdQMK0HMzMDDQ/f67jvQauH8edi7F7Zv1zWu//85c9y9C5Mm6V6lSkH37tCyJSn+gaVCiK/H+lKlYp2XtVIlqvz/l3CATZUqEfnuXYzLOpYqRfVly/TTW728CH31KtpyrS9fTlR8t27dQlEUg57fAJkyZSLk/7cH9enTh59//lm3/dat6dSpk365Vq1a0bFjR3r37g3AwIEDOXbsGNOmTUt0Q/qkSZP0Jwg//PADdevWJSQkBAsLC2bOnEmXLl3o0qULWq2WUaNGcfjwYX2MaUFqrrtt0tlQ9/vm8H1zACLCI9i1aBtPVu7G/dxJyoacwwhd5Z1L+5Bcl1dB61Wc6liICzXqUHfeADLnTNkPLBVCCCHSOjNjM/I75Ce/Q36a5G+iLw8MDeTs07OcenKKY4+PcfThUR6/eayfH64Nx/ehL74PfRl/cDx25nZUc61Gvbz1qJOnDk42UscL8aV5eICf35ffr5MTnDqV8OV9fHz043i/ffuWLFmy4OPjY3DX8ahRo/R/u7i4MHjwYLy9vb9oQ3pAQACvX7/W3xX9ofTp0zN37lyMjY1xd3enTp06HDhwQN+QPn78eAYPHpyo/bVq1YqtW7fy7t076tevz+LFi2Nd1s/PD0dHR4MyExMTMmTIgN8HH4Lw8HDmz5+Pm5sbAM2aNWPlypU8e/YMGxsbChQoQNWqVdm3b59BQ3rWrFl5+PAhWq32s959r2pD+qNHj2jVqhUvXrzAwcGBChUqcOzYMf1tEamJkREUL657DR0K/v66BvX16+Hvv3UN7QAnT+peAwfqeqj36ye91IUQ4lOdOHECrVZLmzZtCA0N1Zd/+Cs36G4j6969u0FZ+fLlmTVrVqL3+eEDZbJkyQKAv78/OXPm5OrVq/Ts2dNg+bJly7J///5E7yelSkt1t4mpCbV6N4HeuovwayevcGT8clz2H6Ry0AmM0VXeHmGX8PjrEoHO8/HO+Q1ZfuojvdSFEEKIFMbO3I7KLpWp7PK+R+TD1w85/OAw++/tZ//9/QYPMg0MDWTLtS1subYFgFJZS9HYvTFN8jchX6Z80bYvhEh+fn7w+HH8y6mtatWqzJ8/H61Wy6NHj1ixYgW1a9fmxIkTODs7A7Bu3Tpmz57N7du3CQoKIiIiIsnPhEqsd//v8BbT89AKFiyoHwoFdNey586d0087OjpGa+iOz4wZMxgzZgw3btzQPxtr3rx5nxb8/1lZWekb0QEyZ86Mi4uLwQNJM2fOjL+/v8F6lpaWaLVaQkNDsbS0TFIMcVG1Id3b21vN3X9Wjo7QpYvu5ecH3t6wYgWc/f8zUd68gfnzda+6dXUN61WrqjeWuhDi69b85MlY52mMDR/S1PTgwdg39NEvvw337ElSXFFy586NRqPh+vXrBuVRQ6Z8XFHGNvxLbKJ+sVaibiVC90t4TD4c4y5qOJmocd2/Bmm57nYvVQD37bq7Gq4cu8TRYb9T8ui/FAu/CoAdb2j5YCu03srenuV51acdzSZ0U20sdSGEEELELUe6HLQq3IpWhXV3zj0OfMzeO3vZc3sPe+/s5b/g9+OznnxykpNPTjLi3xEUdChIswLNaFWolTSqC/EZOal0I0hi92ttbU3u3LnRarU4OjpSsWJF0qdPz6JFi5g4cSJHjx6lTZs2jBs3jpo1a5IuXTq8vb2ZPn3650kgFhkzZkSj0fAqhrvCPx6rXaPRGFzHfsrQLk5OTjg5OeHu7k6GDBmoWLEio0eP1nc4+3jZjxu/IyIiePnyJU4fvCExxRlf7AAvX77E2tr6szaiQwobIz2tcnKC/v11r1OnYMECWLsW3r7Vzf/rL92rZEn48UeoX18a1IUQX1Zixiz/XMvGJWPGjNSoUYO5c+fy3XffJbqhPH/+/Pj6+tKhQwd9ma+vLwUKFADQ96Z++vQpxYsXBzD4dT4x+zl+/Djt27fXlx0/fjzR2xHqK1C2EAUOzAXgr1nreTt1BXUe78cGXeVdI9AXpvhyZvocLnfuQJu5g6RBXQghhEjhstllo2OxjnQs1hGtouXk45Nsv7Gd7Te2c+HZBf1yl59f5vKBy4w7MI7iTsVpXbg1bQq3IYtt9MYhIcSnS8zwKimJRqPByMhI3wP8yJEjODs7M3LkSP0y9+/f/+JxmZmZUaBAAa5cuYKXl1ei1v2UoV0+FNWw/eGd4h8qV64cAQEBnD59mpIlSwLw77//otVqKVOmzCfvN8rly5f11/Kfk1zxfWEeHrBoke7WlV9/hZw53887fRoaNtQND7N58/sx1oUQQsC8efOIiIjAw8ODdevWcfXqVa5fv86qVau4du2awW1qHxsyZAjLli1j/vz53Lx5k19//ZXNmzfrTxQsLS0pW7YsP/30E1evXuXAgQMGY9wl1Pfff8+SJUtYunQpN27cYMqUKVxO5HjwIuWp+31zmj/y4dWNK/xRsTf3jbLp55UIu0y734dy0bIgK7r8hDby67k7QQghhEjNjDRGlMlehonfTOR8z/Pc6XeH6V7T8czhabDcWb+zDNk7hOwzslNndR3WXVpHSETaef6NECJ+oaGh+Pn54efnx/Xr1+nXrx9BQUHUr18fgDx58vDgwQO8vb25ffs2s2fPZsuWLUne75UrVzh37hwvX77k9evXnDt3Lt4OXzVr1uTw4cOJ3pejoyO5c+eO8xVlx44dLF26lEuXLnHv3j3++usvevbsSfny5XFxcQF0w7C6u7vz+P9j9+TPn59atWrRrVs3Tpw4ga+vL3379qVly5YxjumeWIcPH070jwefQhrSVZIuHQwYALdvw7p1usbzKOfPQ9OmUKYM7NunXoxCCJGSuLm5cfbsWapXr87w4cMpWrQoHh4ezJkzh8GDBzNhwoRY123UqBGzZs1i2rRpFCxYkAULFrB06VKqVKmiX2bJkiVERERQsmRJ+vfvz8SJExMdY4sWLRg9ejRDhw6lVKlSPHz4MNqY6SL1ypEnJ10O/kbmN7dY3nY850zz6+cVDb9G+yXDOWNVlA0jF6oYpRBCCCE+hWt6VwaWG4hvZ18eDXjEdK/peGR9/9wdraJl562dtNzUkmy/ZmPg7oFc/e+qihELIb6UXbt2kSVLFrJly0aNGjU4deoUGzZs0F9PNmjQgAEDBtC3b1+KFSvGkSNHGD16dJL3W6dOHYoXL8727dvZv38/xYsXj7fXdZcuXdixYwevX79O8v5jY2lpyaJFi6hQoQL58+dnwIABNGjQAB8fH/0ywcHBXL9+3WDI1NWrV+Pu7k61atWoU6cOFSpUYOHCpF87PXnyhCNHjtCpU6ckbys+MrSLykxMoHlz+PZb3fAu48frHkYKun+/+QZq1oSpU6FwYXVjFUIItWXJkoU5c+YwZ86cWJdRYrmdp1evXvTq1SvW9fLnz8+RI0di3VaVKlWibbtYsWLRykaMGMGIESPQarUEBgZiZ2fHL7/8Eut+RepjYWVBh5Wj0S4byap+M3FfvBSPsEvA/x9MOrkH/8xeinbWCGp0rq9ytEIIIYRIrGx22RhYbiADyw3k5oubrLywkhXnV3D/tW6ohpfvXjLj2AxmHJtBAesCvL3ylm8LfYupsWk8WxZCpDbLli1j2bJlAAbXeEYfPR/sl19+iXbd179/f/3fY8eOZezYsQbbjc+9e/cSHW+BAgWoW7cu8+bNY/jw4bHua8aMGQQGBiZ6+6B7+OrH184fi+n6OUOGDKxZsybWdTp27EjHjh0Nyj4+bhA9nwULFtChQweyZ88eb+xJJT3SUwiNBurVg+PHwcfHsNF8924oVgz69IEXL1QLUQghhBAfMDI2ou1vAykRfJ7VfX/lokle/bxqQcf4pksjvF0ac/vCLRWjFEIIIURS5MmYh/FVx3Pn+zv82/5f2hZpi7mxuX7+lbdXaPNnG5xnOjP+wHj83/rHsTUhhPj8pk6dio2NjdphfDEODg6MHz/+i+xLGtJTGI0G6taFs2dhxQpwdtaVa7Uwbx7kyaP7NzJS3TiFEEIIoWNkbESbOQPI9+YiS5uO5J6RrieEMVpa3v+T9EVL8YfXAMJCwlSOVAghhBCfykhjRFXXqqxsvJIng54ws+ZM3DO66+c/DXrKmP1jyDkjJ922dePK8ysqRiuE+Jq5uLjw3XffqR3GF9O3b18yZ878RfYlDekplLExtGsH167B5Mlgba0rf/VK1zO9fHndWOpCCCGESBnMLMzotHEi9s8usqRUV4LQVd4ZCKDL3pmcsy/F7oVbVY5SCCGEEEmVwTID35f9nvPdzzPebTwN8zbESKNrXgmNDGXx2cUUnFeQemvqcfhB4h/6J4QQImWShvQUzsIChg+H69d1DetRjh+HkiVh6FAIDlYvPiGEEEIYss9kT+cTi7i9/yBbMtfUl5cOvUC1Hk1Z6d6S/57+p2KEQgghhEgOGo2GIrZF2NBsA3f63WFwucHYmdvp5/918y8qLq1IxaUV2XFzR6zP8hFCCJE6SEN6KpEtm26olwMHwP3/d49FRuoeQlqsGMQzxr8QQgghvrCilUvQ2G8Xm8b8wTVjNwBMiKTd9XW8ylGKP6esVDlCIYQQQiQXZ3tnpnpN5eGAh8yoOYOc6XLq5x1+cJi6a+pSenFpfG74SIO6EEKkUtKQnspUqgTnzsH48WD+/+eb3LwJFSrAkCHw7p2q4QkhUgmtVqt2COL/5L1I+5qO7UxW/zMsLdGJEHSVd57IezQY0YGV+Vry8tlLlSMUQgghRHKxM7ejf9n+3PruFssbLSd/pvz6eaeenKL+2vqUWlSKv278JQ3qQgiRypioHYBIPHNzGD0avv0WOnbUDfOiKDBtGuzcCWvWQP788W5GCPEVMjMzw8jIiCdPnuDg4ICZmRkajUbtsAxotVrCwsIICQnByCj1/t4bXx6KohAWFsbz588xMjLCzMxMhSjFl2KXwY5Op5dwYG0rLDsNpnToBYxQaHdjHZezn+H0/GlU6VBb7TCFEEIIkUxMjU1pX7Q9bYu0Zeu1rYw/OJ5zfucAOP30NPXW1sMzhyeTvplEFZcqqsYqhBAiYaQhPRVzdwdfX5g+HX78EUJD4fJlKF0aJk82IlcutSMUQqQ0RkZGuLq68vTpU548eaJ2ODFSFIV3795haWmZ4hr5EyOheVhZWZEzZ85U/aOBSLjKrWoQ1vgkS6p+R+tjy7EglIIRN3Hr1pzlK7uS8ftqaocohBBCiGRkpDGicf7GNHJvxPYb2xl3YBxnnp4B4MjDI1RdXpUauWrwS41fKOZUTN1ghRBCxEka0lM5Y2PdA0fr1YPWreH8eV2D+qBBxpQoUZYyZSBLFrWjFEKkJGZmZuTMmZOIiAgiIyPVDiea8PBwDh48SKVKlTA1NVU7nE+WkDyMjY0xMTFJ1T8YiMQzszCj89EF/LuyERm7DKRo+DUsCKXrwd/Yc/o0t/bnJb9HQbXDFEIIIUQy0mg0NMjXgPp56/PntT8ZtW8UV55fAWDvnb2UWFCCtkXaMqHqBJztnVWOVgghREyk+1saUaCAboiXAQPel505k5nSpU3kQaRCiGg0Gg2mpqZYWFikyFdERITqMXyJPExNTaUR/Sv2TbvauPodZ5VbM32Z19tj2JSuIQ8iFUIIIdIojUZD4/yNudDzAisarcDF3gUABYWVF1aSb24+Rvwzgjehb9QNVAiRrMaOHUuxYsX00x07dqRRo0afZV+jR4+me/fu+ukqVarQv3//z7KvlKBGjRps2rTpi+xLGtLTEHNz+PVX2L0bHB11Dy159EhD5cq6cnmOiRBCCJGy2GWwo+2tDXgPmsszTSYAcihPqTuiM39U7I02Uh5GK4QQQqRFxkbGtCvajmt9rjGj5gwyWGYAIDQylCmHp5B3bl6Wnl2KVpFzASHU1LFjRzQaDRqNBmNjY3LlykXt2rW5cOHCZ93v/v37adiwIVmyZMHa2ppixYqxevXqeNfz8/Nj1qxZjBw58rPGpygK06ZNI2/evJibm5MtWzYmTZoU5zovX76kTZs22NnZYW9vT5cuXQgKCkpyLIMGDWLEiBFotZ//+1Ia0tMgLy84eTKCggX/AyAiAgYNgubNIRk+n0IIIYRIZi2n9eHp/l0csigOgCkRdDk8nx2O1Xn2wE/l6IQQQgjxuZibmNO/bH9u97vNUM+hmBnrHkDvF+RH522dKb2oNMcfHVc5SiG+brVq1eLp06c8fvyYrVu3YmJiQr169T7rPo8cOUKRIkXYtGkTFy5coFOnTrRv3x4fH58411u8eDGenp44O3/eIaK+//57Fi9ezLRp07h27Rrbtm2jdOnSca7Tpk0bLl++zN69e/Hx8eHgwYMGPec/VY0aNXjz5g07d+5M8rbiIw3paVSWLDB+/BGGDHk//vHGjeDpCXfuqBiYEEIIIWJUsFwRni0fwfL8rfRl9V7uw9+tMse2H1YxMiGEEEJ8bvYW9vxc42eu9L5Ck/xN9OWnn56m7B9l6bK1C8/fPlcxQiG+Xubm5jg5OeHk5EThwoUZNmwYDx8+5Pnz9/8nhw0bRt68ebGysiJXrlyMHj2a8PDwT97niBEjmDBhAp6enri5ufH9999Tq1YtNm/eHOd63t7e1K9fP1q5Vqtl6NChZMiQAScnJ8aNG/fJsV29epX58+ezdetWGjRogKurKyVLlqRGjRpxrrNr1y4WL15MmTJlqFChAnPmzMHb25snT54AsGzZMuzt7fHx8SFfvnxYWVnRrFkzgoODWb58OS4uLqRPn55+/foZPO/N2NiY2rVr4+3t/ck5JZQ0pKdhxsYKkyZp2bYNbG11ZRcvgocH/P23urEJIYQQIjpTc1Nan1/Oqp7TCERXeReOuEHeBvVZN2y+ytEJIYQQ4nNzy+DGpuab2NdhH4UdC+vLl5xbQt65eVl4eqEM9yKEioKCgli9ejW5c+cmY8aM+nJbW1uWLVvGlStXmDVrFosWLWLGjBnJuu/Xr1+TIUOGWOe/fPmSK1eu4OHhEW3e8uXLsba25vjx4/zyyy9MmDCBffv26efXrl0bGxubWF8FCxbUL7t9+3Zy5cqFj48Prq6uuLi40LVrV16+fBlrbEePHsXe3t4gturVq2NkZMTx4+/vugkODmb27Nl4e3uza9cu9u/fT+PGjdmxYwc7duxg5cqVLFiwgI0bNxpsv1SpUhw6dCjuA5gMTD77HoTq6teHEyegUSO4fh1evYJatWDuXOjZU+3ohBBCCPGxtvMHcaBSEZza9SRf5B0yEEDTX77jj/O36LJrutrhCSGEEOIzq+JShTM9zjDv5DxG7xtNYGggASEB9PDpwbJzy1hQbwGFMxeOf0NCpGS7POCdCsMYWjpBrVMJXtzHxwcbGxsA3r59S5YsWfDx8cHI6H3/5FGjRun/dnFxYfDgwXh7ezN06NBkCXn9+vWcPHmSBQsWxLrMgwcPUBSFrFmzRptXpEgRxowZA0CePHmYO3cuBw4coGHDhoBuSJh3797Fum1TU1P933fu3OH+/fts2LCBFStWEBkZyYABA2jWrBn//vtvjOv7+fnh6OhoUGZiYkKGDBnw83v/GQgPD2f+/Pm4ubkB0KxZM1auXMmzZ8+wsbGhQIECVK1alX379tGiRQv9elmzZuXhw4dotVqD9yW5SUP6V8LdHY4fh7ZtwccHIiOhVy+4cQOmTgVjY7UjFEIIIcSHKreqwUOPfezxaINX4GFMiKTL7l9ZlfsBzS+txszCTO0QhRBCpAKRkZGMHTuWVatW4efnR9asWenYsSOjRo1Co9GoHZ6Ig4mRCf3K9KNFwRYM2TuElRdWAnD00VGKLyjOEM8h/Fj5RyxNLVWOVIhP9M4P3j1WO4p4Va1alfnz56PVann06BErVqygdu3anDhxQj8W+bp165g9eza3b98mKCiIiIgI7OzskmX/+/bto1OnTixatMigZ/jHohrCLSwsos0rUqSIwbSTkxP//feffjpbtmwJjker1RIaGsqKFSvImzcvAH/88QclS5bk+vXr5MuXL8Hb+piVlZW+ER0gc+bMuLi46H/IiCrz9/c3WM/S0lIfl6Xl5/tOlKFdviLp0sGff8Lgwe/LZsyAxo3h7VvVwhJCCCFELHLkyUmVZ/+wMu/73hZtb29kn1M1/B/5x7GmEEIIofPzzz8zf/585s6dy9WrV/n555/55ZdfmDNnjtqhiQTKbJOZFY1X8E/7f8ibUddoFalE8pPvTxRbUIxD9z//cAZCfBaWTmCZTYWXU6LCtLa2Jnfu3OTOnZsSJUqwaNEi3r59y6JFiwDdsCVt2rShTp06+Pj4cPbsWUaOHElYWFiSD9GBAweoX78+M2bMoH379nEumylTJgBevXoVbd6HPcoBNBoNWu37YaISM7RLlixZMDEx0TeiA+TPnx/Q9YqPiZOTU7TG74iICF6+fImT0/v3I6Y444sddMPaWFtbf9ZGdJAe6V8dY2NdD/S8eaF3b4iIgO3boWpV+OsvcHBQO0IhhBBCfMjMwox2171ZUjcH7XbMxJQIar4+zCm3agQc2UDeku5qhyiEECIFO3LkCA0bNqRu3bqAbsiBtWvXcuLECZUjE4n1jes3XOh5gSmHpzD50GTCteHceHGDSssq0dujNz/X+BkbM5v4NyRESpGI4VVSEo1Gg5GRkb4H+JEjR3B2dmbkyJH6Ze7fv5/k/ezfv5969erx888/071793iXd3Nzw87OjitXrhg0cidEYoZ2KV++PBEREdy+fVvfe/zGjRsA+h76HytXrhwBAQGcPn2akiVLAvDvv/+i1WopU6ZMomKNyeXLlylevHiStxMf6ZH+lerWDXbt0vVSBzh5Ejw94c4ddeMSQgghRMw6/zWVzcPmEIDuFlGPsEtoytTm2PbDKkcmhBAiJfP09OSff/7RN3KcP3+ew4cPU7t2bZUjE5/C3MScsVXGcrbHWcpmL6svn3dqHkXmF2H/vf3qBSdEGhUaGoqfnx9+fn5cv36dfv36ERQURP369QHdmOMPHjzA29ub27dvM3v2bLZs2ZKkfe7bt4+6devSr18/mjZtqt9/XA/0NDIyonr16hw+nPjrg2zZsul73cf0+rCBvHr16pQoUYLOnTtz9uxZTp8+TY8ePahRo4a+Af/EiRO4u7vz+LFu6J78+fNTq1YtunXrxokTJ/D19aVv3760bNkyxjHdE+vw4cN4eXkleTvxkR7pX7Fq1eDQIahdGx4/hlu3oFw52LkTSpRQOzohhBBCfKzFTz3Zmzcb+bv2ILvylDyR97Br2Jgds3+nTt+maocnhBAiBfrhhx8IDAzE3d0dY2NjIiMjmTRpEm3atIlx+dDQUEJDQ/XTgYGBgO4BcOHh4UmOJ2obybEttamZS970ednXdh/zTs1j9IHRBIcHczfgLlWXV6V3yd5M/mYyVqZWCd5eWnlf0koekHZyiYo/IiICRVHQarXRhuVIyRRFYdeuXWTJkgUAW1tb8uXLx7p166hUqRJarZZ69erRv39/+vbtS2hoKHXq1GHUqFGMGzdOn6uiKAAG01HHIybLli0jODiYKVOmMGXKFH155cqVY32gJ0Dnzp3p0aMHP/30k8FDN2PbV1wxxGXr1q3069ePSpUqYW1tTa1atZg2bZp+W0FBQVy/fp3Q0FB92cqVK/nuu++oVq0aRkZGNGnShFmzZunnf/xvVHwxlUXFrSgKT5484ciRI6xYsSLWXKKWDQ8Px/ijB0Um5v+YNKR/5QoXhqNHoVYtuHIF/P11w7z4+EDFimpHJ4QQQoiP1ehcn7M5nQis3ZoCEbfIrPxH+e86s+llIE1/7KR2eEIIIVKY9evXs3r1atasWUPBggU5d+4c/fv3J2vWrHTo0CHa8lOmTGHcuHHRyvfs2YOVVcIbZuOzd+/eZNuW2tTMxQ03pueezpwHc7jy9goA807P489LfzLAeQB5rPIkantp5X1JK3lA2snlyJEjODk5ERQUlCxjh38ps2bNYtasWTHOi/qhEWDkyJEGQ7sAdOrUSb/MgAEDGDBggH46apsfbuNT9vsxT09PMmfOzLJly2jWrBkAf/75Z7T1li9fDsCbN29i3VZcbGxsWLJkSayxlShRQj9We1SZiYkJ8+fPZ/78+frltVqtfn6TJk1o0qSJQZwfHzeIfuwWLFhA69atsbOzi/XYhIWF8e7dOw4ePEhERITBvODg4ATnLQ3pghw5dD3TGzaEw4chMBC8vGDzZl1vdSGEEEKkLMWrl+Lu2d0cKdUMz5CzpCOQOmN6s+ZFIK1nfa92eEIIIVKQIUOG8MMPP9CyZUsAChcuzP3795kyZUqMDenDhw9n4MCB+unAwEBy5MiBl5cXdnZ2SY4nPDycvXv3UqNGjWgPkEttUlIunZRO/HbyN0btH8W7iHc8CX3CDzd/YESFEQwvPxwTo7ibf1JSLkmRVvKAtJNLVB6enp48ffoUGxsbLCws1A7rkyiKwps3b7C1tUWj0agdTqwWLVrExYsX4/zOTi25xEdRFBwcHBg2bFic+YaEhGBpaUmlSpWiff7i+mHiY9KQLgDIkAF274ZmzXRDu4SEQIMGsHo1NG+udnRCCCGE+JhroVzY3tnD3/kaUv3NESwJ4dvZg1n+MpAOK0erHZ4QQogUIjg42OD2fgBjY+NYb383NzfH3Nw8WrmpqWmyNuYl9/bUlFJyGVh+IHXz1aXdlnacfHKSSCWSCYcmsPfuXtY0WYNretd4t5FSckmqtJIHpJ1cTExM9A/p/Pg7KbWI+t6MyiOlKlGiBCXiGbM5teQSH61WS9++fbGzs4szDyMjIzQaTYz/nxLz/yv1HimR7Kys4M8/3zecR0RAq1awapWqYQkhhBAiFpmyZKLsw91sz/gNAKZE0HbVWJY2/1HlyIQQQqQU9evXZ9KkSfz111/cu3ePLVu28Ouvv9K4cWO1QxOfQb5M+fDt7MuYymMw1ujGAT726BjFFhRjzcU1KkcnhBCpmzSkCwNmZrBmDXTtqpvWaqF9e/j/0ElCCCGESGFs0tlQ89FO1merC4AxWjpsmMiSJiPjWVMIIcTXYM6cOTRr1ozevXuTP39+Bg8eTI8ePZgwYYLaoYnPxNTYlLFVxnK482Fypc8FQGBoIG02t6HDnx0ICgtSOUIhhEidpCFdRGNsDAsXQp8+umlFgU6d4I8/1I1LCCGEEDEzszCj2f1trHVuBIARCh23TGFJ/WHqBiaEEEJ1tra2zJw5k/v37/Pu3Ttu377NxIkTMTMzUzs08ZmVzV6Wsz3O0rZIW33ZivMrKLWoFBeeXVAxMiGESJ2kIV3ESKOBOXPg+/8/r0xRdL3UFy9WNy4hhBBCxMzI2IgWtzexOldT3TQKnX1+YUk9aUwXQgghvlZ25nasbLySlY1XYmNmA8C1/65RZnEZFp5eiKIoKkcohBCphzSki1hpNDBjBgwa9L6se3dYsUK9mIQQQggROyNjI1rdWM/KPO+fFN7xr6ksaTpKxaiEEEIIoba2RdpypvsZijkVAyAkIoQePj1ot6Udb8PeqhucEEKkEtKQLuKk0cDUqe8b06OGeVm3Tt24hBBCCBEzI2Mj2lxdq29MN0Khw+YpLGs7XuXIhBBCCKGmPBnzcLTLUfqU6qMvW31xNWUWl+H6i+sqRiaEEKmDNKSLeEU1pn/3nW5aq4U2bWDzZnXjEkIIIUTMohrT17g2AXQPIG27ejwruvykcmRCCCGEUJOFiQVz68xlfbP1+qFeLj+/TLml5TgScETl6IQQImWThnSRIBoNzJqlG9oFIDISWrWCvXvVjUsIIYQQMTMyNqLlzQ2sy14fABMiabFkDN5DflM5MiGEEEKo7duC33Kq2ykKOhQEICgsiF/u/cKIfSOI1EaqHJ0Qac/YsWMpVqyYfrpjx440atTos+xr9OjRdI9qwAOqVKlC//79P8u+UoIaNWqwadOmL7IvaUgXCabRwPz50KGDbjosDBo1gqNHVQ1LCCGEELEwMjai6Z3NbHKqBYA5YdSfNpQ/f1qlcmRCCCGEUFu+TPk43vU4bQq30ZdNOzqNWqtr8SL4hYqRCZFydOzYEY1Gg0ajwdjYmFy5clG7dm0uXLjwWfd7/fp1qlatSubMmbGwsCBXrlyMGjWK8PDwONfz8/Nj1qxZjBw58rPGt379eooVK4aVlRXOzs5MnTo13nVevnxJmzZtsLOzw97eni5duhAUFJTkWAYNGsSIESPQarVJ3lZ8pCFdJIqRESxeDI0b66aDg6FOHfjM3x9CCCGE+EQmpibUvb2FHfaVAbAmmMrD+7J38TaVIxNCCCGE2qzNrFnZeCW/1vgVo/83Ef195288Fnlw4Zlc6AsBUKtWLZ4+fcrjx4/ZunUrJiYm1KtX77Pu09TUlPbt27Nnzx6uX7/OzJkzWbRoEWPGjIlzvcWLF+Pp6Ymzs/Nni23nzp20adOGnj17cunSJebNm8eMGTOYO3dunOu1adOGy5cvs3fvXnx8fDh48KBBz/lPVaNGDd68ecPOnTuTvK34SEO6SDQTE1izBqpV000HBICXF9y5o2pYQgghhIiFhZUFnjf/ZL91aQDS85rC3btybPthlSMTQgghhNo0Gg19S/VlfO7xOFo5AnAv4B6ef3iy5eoWlaMTQn3m5uY4OTnh5ORE4cKFGTZsGA8fPuT58+f6ZYYNG0bevHmxsrIiV65cjB49Ot7e43HJlSsXnTp1omjRojg7O9OgQQPatGnDoUOH4lzP29ub+vXrRyvXarUMHTqUDBky4OTkxLhx4z45tpUrV9KoUSN69uxJrly5qFu3LsOHD+fnn39GUZQY17l69Sq7du1i8eLFlClThgoVKjBnzhy8vb158uQJAMuWLcPe3h4fHx/y5cuHlZUVzZo1Izg4mOXLl+Pi4kL69Onp168fkZHvh6AyNjamdu3aeHt7f3JOCSUN6eKTWFjAn39CmTK66WfPoFYt+OA7RAghhBApiH0me/Jf2coJ8yIAOCnPydi4HTdOX1M5MiGEEEKkBIVsCnGs8zE8snoA8Db8LU3WN2H8gfFolc8/ZIIQqUFQUBCrV68md+7cZMyYUV9ua2vLsmXLuHLlCrNmzWLRokXMmDEj2fZ769Ytdu3aReXKlWNd5uXLl1y5cgUPD49o85YvX461tTXHjx/nl19+YcKECezbt08/v3bt2tjY2MT6KliwoH7Z0NBQLCwsDLZvaWnJo0ePuH//foyxHT16FHt7e4PYqlevjpGREcePH9eXBQcHM3v2bLy9vdm1axf79++ncePG7Nixgx07drBy5UoWLFjAxo0bDbZfqlSpeH9kSA4mn30PIs2ysYEdO6BCBbh6FW7ehHr14N9/wdpa7eiEEEII8bHMOZ0IPrWFq8W8yB95mzyR9zhZvjn+t/7GMbuj2uEJIYQQQmXZ7bJzsONBum7vypqLawAYs38Ml59fZlnDZViaWqocoUhLPBZ64Bfk98X362TjxKnupxK8vI+PDzY2NgC8ffuWLFmy4OPjg5HR+/7Jo0aN0v/t4uLC4MGD8fb2ZujQoUmK1dPTkzNnzhAaGkr37t0ZP358rMs+ePAARVHImjVrtHlFihTRDwuTJ08e5s6dy4EDB2jYsCGgGxLm3bt3sW7b1NRU/3fNmjUZMGAAHTt2pGrVqty6dYvp06cD8PTpU1xcXKKt7+fnh6Oj4fWGiYkJGTJkwM/v/WcgPDyc+fPn4+bmBkCzZs1YuXIlz549w8bGhgIFClC1alX27dtHixYt9OtlzZqVhw8fotVqDd6X5CYN6SJJMmSAnTvB0xOePIETJ6BFC11vdRP5dAkhhBApjmuhXJzyWcXjOo3JpvhRKvQiews2oeLTv7Gwsoh/A0IIIYRI0yxNLVnVeBVFHIsw/J/hKCisv7yeewH32NpyK042TmqHKNIIvyA/Hr95rHYY8apatSrz589Hq9Xy6NEjVqxYQe3atTlx4oR+LPJ169Yxe/Zsbt++TVBQEBEREdjZ2SV53+vWrePNmzecP3+eIUOGMG3atFgb56Mawj/uLQ66hvQPOTk58d9//+mns2XLluCYunXrxu3bt6lXrx7h4eHY2dnx/fffM3bs2CQ3YltZWekb0QEyZ86Mi4uL/oeMqDJ/f3+D9SwtLdFqtYSGhmJp+fl+8JOmTpFkzs66xvSKFSEwEP76C3r3hgULQKNROzohhBBCfMyjVln2LlyATbd2pCOQGoG+rM/7Lc3ub8XIWEb+E0IIIb52Go2GYRWGUcChAK02teJt+FtOPD5B6UWl8WntQ5HMReLfiBDxUOtHmcTu19ramty5c6PVanF0dKRixYqkT5+eRYsWMXHiRI4ePUqbNm0YN24cNWvWJF26dHh7e+t7aSdFjhw5AChQoACRkZF0796dQYMGYWxsHG3ZTJkyAfDq1SscHBwM5n3Yoxx0/8e12vdDNtWuXTvOoVGcnZ25fPmyft2ff/6ZyZMn4+fnh4ODA//88w+gG9s9Jk5OTtEavyMiInj58iVOTu/fj5jijC920A1rY21t/Vkb0UEa0kUyKVJE1wu9Vi0IC4NFiyB3bkjiHSxCCCGE+ExqdG3AhrtTaTD5O8wJo/ljH5ZU6EXnowvUDk0IIYQQKUT9fPXx7exL/bX1eRj4kIeBDym/pDwbv91Izdw11Q5PpHKJGV4lJdFoNBgZGel7gB85cgRnZ2dGjhypXya2scKTQqvVEh4ejlarjbEh3c3NDTs7O65cuULevHkTte3EDO0SxdjYWN+Tfe3atZQrVy5aA36UcuXKERAQwOnTpylZsiQA//77L1qtljJRD2BMgsuXL1O8ePEkbyc+0uVIJJuqVWHp0vfTw4bBR2P/CyGEECIF+XZSd9a0+EE/3fnYQpZ3nKRiREIIIYRIaYo6FeV41+OUyloKgKCwIOquqcui04tUjkyILyM0NBQ/Pz/8/Py4fv06/fr1IygoiPr16wO6MccfPHiAt7c3t2/fZvbs2WzZsiVJ+1y9ejXr16/n6tWr3Llzh/Xr1zN8+HBatGgRY6M2gJGREdWrV+fw4cOJ3l+2bNnInTt3rK+oIWwA/vvvP37//XeuXbvGuXPn+P7779mwYQMzZ87UL3PixAnc3d15/Fg3dE/+/PmpVasW3bp148SJE/j6+tK3b19atmwZ45juiXX48GG8vLySvJ34pJiG9J9++gmNRkP//v3VDkUkQevW8OFzD9q1g2PH1ItHCCHE5yN1d9rQyXscS0t00k83Xz6RrVNXqxiREEIIIVKaLLZZ2N9xP03yNwEgUomku093RvwzAq2ijWdtIVK3Xbt2kSVLFrJly0aNGjU4deoUGzZsoEqVKgA0aNCAAQMG0LdvX4oVK8aRI0cYPXp0kvZpYmLCzz//TOnSpSlSpAjjxo2jb9++LF68OM71unbtire3d7ShT5Lb8uXL8fDwoHz58ly+fJn9+/dTunRp/fzg4GCuX79OeHi4vmz16tW4u7tTrVo16tSpQ4UKFVi4cGGSY3ny5AlHjhyhU6dO8S+cRCliaJeTJ0+yYMGCaAPfi9Rp1Ci4fRuWL4eQEGjYEE6ehJw51Y5MCCFEcpG6O23pcGIxm7I/panfLiwJoeyw/pwuloeSNUrHv7IQQnwGEdoIHgU+4u6ruzwKfIT/W3/83/rzPPg5QWFBvA1/S3B4MOGR4Wg0GjRoMDYyxsbMBhszG2zNbMlklQknGyecbJzIapsVt/RuONk4oZEHOQnxSaxMrVjfbD1D9w7l12O/AjDl8BQeBj7kjwZ/YGZspnKEQiS/ZcuWsWzZMkA3tEpgYCB2dnbRHqr5yy+/8MsvvxiUfdjhaOzYsYwdO9Zgu3Fp0aIFLVq0SHS8tWrVImvWrKxbt45WrVoBsH///mjLbdmyhcDAwERvH3RjsR89ejTOZapUqYKiKAZlGTJkYM2aNbGu07FjRzp27GhQ9vFxg+jHbsGCBXTo0IHs2bPHG3tSqd6QHhQURJs2bfQD9IvUT6OBhQvhwQPYtw/8/aFBAzh8GD54yK4QQohUSurutMfI2Iia1zZwMGtVKgWfIrPyH351O/Ds1j4y51TnIVBCiK+Doijcf32fk49PctH/Ipf8L3HJ/xJ3A+4SoY1I9v1ZmVrhlt6NQo6FKJq5KEWdilI0c1HVHngnRGpjbGTM9JrTcU3vyve7vkeraFl1YRX+b/3Z+O1GbM1t1Q5RiK+aRqNh4cKFXLx4Ue1QvhgHBwe6dOnyRfalekN6nz59qFu3LtWrV4/3Yjw0NJTQ0FD9dNQvJ+Hh4Qa3CnyqqG0kx7bUpnYuGg2sXQvly5tw+7aG8+ehXTst3t6RGCVyQCG1c0kuaSUPSDu5pJU8QHJJiZIjj5R6DKTu/jzUzsXcyhwn31XcKlmT3Nr7FA2/xl/FWvHNox2YmCbulFHtXJJLWskD0k4uaSUP+Hpz0SpaLvhf4N+7/+L7yJcTj0/w7O2zzx2iXnB4MBf9L3LR/yJrL63Vl0+vPp2exXsCabPuFiK59S3dl+x22Wm1qRUhESHsub2HKsursKP1DjLbZFY7PCG+asWKFaNYsWJqh/HF9O3bFzs7uy+yL1Ub0r29vTlz5gwnT55M0PJTpkxh3Lhx0cr37NmDlZVVssW1d+/eZNuW2tTOpX9/G4YNq0RwsCl//mlE+/Y3ad362idtS+1ckktayQPSTi5pJQ+QXFKipOQRHBycjJEkD6m7Pz+1c7k3uDcdf5lEOgKp+2o/84u2I+u0dp+0LbVzSS5pJQ9IO7mklTzg68glKCKIU4GnOBl4kotvLhIYGfet5GYaM7KaZ8XJ3AlHM0cczRyxN7EnnUk67EzssDK2wtzIHHMjc0w0JigoKIqCFi0h2hDeRb7jnfYdgRGBvAp/RUBEAP5h/viF+uEX5od/mD8RimFv98A7gez9b2+ceSRESqy7hfhcGrk34u92f1N/bX1ehbzizNMzlF9Snj3t9pArfS61wxNCiGSnWkP6w4cP+f7779m7dy8WFhYJWmf48OEMHDhQPx0YGEiOHDnw8vJKll8ewsPD2bt3LzVq1Ij1CbipRUrKxdlZQ6NGClqthvXr89GokRvNminxr/h/KSmXpEgreUDaySWt5AGSS0qUHHl86ph1n4vU3Z9XismlTh3WvTOl1ZzBGKOl161NLN1YlLZLfkjwJlJMLkmUVvKAtJNLWskD0n4u/wX/x6arm9h0bROHHhwiUomMcd105ukonbU0pbKWophTMQo5FMLV3hVjI+PPF29kONdeXOOC/wUuPrvIBf8LdK3XlQzmGdJc3S3E51Y+Z3l8O/tSa3UtHrx+wO1Xtym/pDy72+6mSGZ5lo4QIm1RrSH99OnT+Pv7U6JECX1ZZGQkBw8eZO7cuYSGhmJsbHjyZG5ujrm5ebRtmZqaJuvJZ3JvT00pIZf69WHaNIhqR+na1YRChaBw4cRtJyXkkhzSSh6QdnJJK3mA5JISJSWPlJa/1N1fRkrIpe3sAfxx7hZdDs0DoPmqn9hftRg1OtdP1HZSQi7JIa3kAWknl7SSB6StXLQaLZuub2LVhVXsvr07xjHO05mno4pLFarnqk5Vl6rkd8iPkSaRYz8mkampKSWylaBEthIG5VHDsqSluluILyG/Q36OdD6C1yovrjy/gl+QH5WWVsKntQ8VclZQOzwhhEg2qjWkV6tWLdrA9506dcLd3Z1hw4ZFuxAXqVv//nDuHKxYAcHB0KgRnDwJGTKoHJgQQogEk7r769Jp3xw2Z7tDk2e7sCaYXN36crd0QVwLya3aQghD1/67xpLHS+gypwsv3r2INt8tvRsN8zWkQb4GlM9ZHhMj1R/VJYRIZtnssnGo0yHqrqnLsUfHeB36mhora7ClxRZq5a6ldngiBdBoNABotVqVIxFfo+T63Kl2BmNra0uhQoUMyqytrcmYMWO0cpH6aTTw++9w+TKcPg137kDr1vDXXyDtLkIIkTpI3f11MTI2osr51ZzJWYkSYZdx0z7gn3JtyPb8AGYWZmqHJ4RQmVbRsuvWLmYcm8Hfd/6ONj+7XXZaFWpF68KtKZq5qL4BRQiRdmWwzMDf7f6m6fqm7L69m5CIEBqsbcCapmtoVqCZ2uEJlZmammJkZMSTJ09wcHDAzMws1dUNWq2WsLAwQkJCMDL6sndTJbe0kkt8eSiKQlhYGM+fP8fIyAgzs6Rdx0hXAPHFWFrC5s3g4QHPn8Pu3TB6NEyerHZkQgghhIhJhswZuOuzBH+vujjyH9WCjrG8ZCc6XF6tdmhCCJWERYax4vwKph+dzrX/rhnMMzc2p2mBpnQt3pXKLpW/+JAtQgj1WZtZs63VNtpubsuGKxsI14bTYmMLFtdfTKfindQOT6jIyMgIV1dXnj59ypMnT9QO55MoisK7d++wtLRMdT8CfCyt5JLQPKysrMiZM2eSfzRIUQ3p+/fvVzsE8ZnlzAnr10P16hAZCVOmQNmy0KCB2pEJIYT4FFJ3p30la5Rm4+gpNJrQExMi6XBlDSt7FaPd/CFqhyaE+IJCIkJYfGYxP/v+zKPARwbz3NK7UdmyMpOaT8IpnZNKEQohUgozYzPWNl2LjZkNS88tRato6bytM2/D39K3dF+1wxMqMjMzI2fOnERERBAZGfNDqFOy8PBwDh48SKVKlVL9MzHSSi4JycPY2BgTE5Nk+cEgRTWki69DlSowder7h4+2bw9nzkAuGXJVCCGESJGaje/KH/vP6h8+Wv/3iRyrU46y9eUBYkKkdeGR4Sw+s5gJByfwNOipwbzKzpUZUHYANV1rsnvXbjJaZVQpSiFESmNsZMziBouxM7dj1vFZAHy38ztCI0IZ5DlI5eiEmjQaTap90LaxsTERERFYWFikyvg/lFZy+dJ5yL12QhX9+0Oz/w+R9vo1NG0K796pGpIQQggh4tBp3xx8MlQFwJ5ALJt25+WzlypHJYT4XLSKFu9L3uT/LT+9d/Q2aESvl7cex7ocY3/H/TR0b4ixkTz0SAgRnZHGiBk1ZzCy4kh92eC9g5l4cKKKUQkhxKeThnShCo0G/vgD8ubVTZ87B33lDi8hhBAixTIyNqL4yRXcMHYFoGj4Vf4p3k7lqIQQn8PRh0cpu7gsrTa14var2/ryRu6NONP9DNtbbadM9jIqRiiESC00Gg0Tv5nIhKoT9GWj941m9L+jURRFxciEECLxpCFdqMbODjZtAisr3fSSJbBihboxCSGEECJ22XJl58GiObxFV3l/+3QHS5qOUjkqIURyeRT4iLab2+K5xJOTT07qy6u6VOV41+NsabGF4lmKqxihECK1GlVpFFNrTNVPTzw0kdH7pDFdCJG6SEO6UFWhQrBgwfvp3r3h2jX14hFCCCFE3Kp3qsv6b9+Pbdp880wOrN2rYkRCiKSK0EYw/ch03Oe6s/rian15IcdC7G67m3/a/0PpbKVVjFAIkRYM9hzM7Fqz9dOTDk1i5L8jpTFdCJFqSEO6UF3bttCli+7vt2+heXMZL10IIYRIyTqtH8+GrHUBsOEtGdr3lfHShUiljj86jsdCDwbvHczb8LcAZLTMyLw68zjb4yxebl5oNBqVoxRCpBXflfmOubXn6qenHJ7CD3//II3pQohUQRrSRYowezYULKj7++JF+P57deMRQgghRNyqnlrGZZM8ABSOuMHfJTuoHJEQIjGCw4MZsGsA5f4ox/ln5wHQoKG3R29ufneTXqV6YWJkonKUQoi0qE/pPvxW5zf99C9HfpGe6UKIVEEa0kWKYGUF69e/Hy990SLw9lY3JiGEEELELlOWTDxbMks/Xnrzxz4sazNe5aiEEAlx+MFhiv5elJnHZ6Kga7gqmrkoR7sc5be6v5HeMr3KEQoh0rrepXozv+58/fSUw1MYu3+segEJIUQCSEO6SDEKFIDf3v8oTY8ecPeuevEIIYQQIm7ftKvNukbvbyNrvGY6J/7yVTEiIURcQiJCGLR7EJWWVuLWy1sAWJhYMLXGVE51P0WZ7GVUjlAI8TXp6dHToGf6+IPjmXBggooRCSFE3KQhXaQoHTtCmza6vwMDdX9HRKgakhBCCCHi0HnLZP509AIgHYEoTfsQEhyiclRCiI9d9r9MmcVl+PXYr/pe6J45PDnX4xyDPQfLMC5CCFX0LtWbWbVm6ad/3P8jPx/+WcWIhBAidtKQLlKcefPA1VX399GjMHGifEyFEEKIlKzU0T+4Y5QDgDKh59lYobfKEQkhoiiKwtwTc/FY5MGFZxcAMDc2Z7rXdA52PEi+TPlUjlAI8bXrV6Yf072m66d/+OcH5hyfo2JEQggRM2mhFCmOnR2sWQPGxrrpn34y4vLljOoGJYQQQohYZcuVnYs/TSECXeXd/tIarm86oXJUQoiAkACarm/Kdzu/IyRCd6dIYcfCnOx2koHlBmJsZKxyhEIIoTOw3ECmVJuin+63qx9/nPlDxYiEECI6aUgXKVLZsjD+/88r02o1zJhRgoAAVUMSQgghRBwaDmnDCo+OABijpeWqZTy8/kDdoIT4ip1+cpoSC0qw5doWfVn/Mv050e0EhTMXVjEyIYSI2Q8VfmB0pdH66W7bu7Hm4hoVIxJCCEPSkC5SrGHDoEoV3d///WfF999LjxkhhBAiJWt7aB6HrDwAyKk84fw3vVSOSIiv08LTC/Fc4sndgLsApLdIz/ZW25lRawYWJhYqRyeEELEbV2UcA8sOBEBBof2W9my/vl3lqIQQQkca0kWKZWwMK1ZAunS6hyGtXWvEunUqByWEEEKIWJlZmGGzdR4B2AHQ+PlelrUdr3JUQnw9QiNC6b69Oz18ehAWGQZA2exlOdfzHPXy1lM5OiGEiJ9Go2Ga1zR6luwJQKQSybcbvmXf3X0qRyaEENKQLlK4HDlg9uxI/XTPnvDokYoBCSGEECJOxauXYnPz7/XTDVf/yrl9p1WMSIivw5M3T6iyvAqLzizSl31f5nsOdDxAznQ51QtMCCESSaPR8Fvd32hduDUAoZGhNPBuwInH8vwVIYS6pCFdpHgtWypUqKBrPQ8IgI4dQatVNSQhhBBCxKHN8pFssa8CQHpeE1C/NxHhEeoGJUQadurJKTwWenDs0TEALEwsWNl4JTNrzcTM2Ezl6IQQIvGMNEYsa7iM+nnrAxAUFkTt1bW58vyKypEJIb5m0pAuUjyNBnr2vEC2bLohXv75B+bNUzkoIYQQQsTKyNiI15Pa80iTBYAqb0+wvNYglaMSIm3aeGUjlZZW4mnQUwBypsuJb2df2hZpq3JkQgiRNKbGpqz/dj1VXaoC8PLdS7xWevHgtTzMXAihDmlIF6mCjU04ixe/H+Jl6FC4eVPFgIQQQggRp/TZMnBo0Ej9dKt/F+G7UcY3FSK5KIrClENT+HbDt7yLeAdA+RzlOdXtFCWylFA5OiGESB4WJhZsbbkVj6y6h5k/fvMYr5VePH/7XOXIhBBfI2lIF6lGtWoKffro/n73Djp0gMjIuNcRQgghhHqaTe7OWudGAFjxDpO2AwgLCVM3KCHSgAhtBN23d2fEvyP0Ze2KtOOf9v/gYO2gYmRCCJH8bM1t2dF6B3kz5gXg+ovr1FlThzehb1SOTAjxtZGGdJGq/PwzuLnp/j56FH79Vd14hBBCCBG3aocXcMvIGYAyoedZ+U0/lSMSInV7G/aWRt6NWHx2sb5s0jeTWN5oOeYm5ipGJoQQn4+DtQN72u4hq21WQPdsiKbrmxIWKT/QCyG+HGlIF6mKtTUsX64bNx1g1Ci4fFndmIQQQggRO8fsjlwcPw4tusq77dGl/Lt8p8pRCZE6PX/7nG9WfMNfN/8CwNTIlDVN1jCi4gg0USfIQgiRRjnbO7On7R7SW6QHYO+dvXTZ1gWtolU5MiHE10Ia0kWqU748DPr/88rCwqBTJ4iIUDcmIYQQQsSu8cgOrM7bHABzwrDrPpSQ4BCVoxIidXnw+gEVllbgxOMTANiZ27G77W5aFW6lcmRCCPHlFHQsyPZW27EwsQBg1YVVDP97uMpRCSG+FtKQLlKlCRMgf37d3ydPyhAvQgghREpX3/d3rhrrxmfzCLvEmmrfqxyREKnH9f+uU2FJBW68uAFAVtusHOp0iKquVVWOTAghvrzyOcuztulajDS6Jq1fjvzCrGOzVI5KCPE1kIZ0kSpZWMCSJe+HePnxR7h+Xd2YhBBCCBE7+0z23JjyfoiX1seWc3Dd3ypHJUTKd/bpWSourcjDwIcA5MmQhyOdj1AkcxGVIxNCCPU0cm/Eb3V+008P2D2AjVc2qhiREOJrIA3pItUqWxYGDND9HRoKnTtDZKS6MQkhhBAidg2HtGFN7mYAWBCKWcchhIXIQ8KEiM2xR8eourwqz4OfA1A0c1EOdTqEs72zypEJIYT6enr0ZHSl0QAoKLTd3Jajj46qHJUQIi2ThnSRqk2YALlz6/4+cgTmzlU3HiGEEELErdbBedw0dgGgbMg5VtYapG5AQqRQvg988VrpxevQ1wCUz1Ge/R33k9kms8qRidTo8ePHtG3blowZM2JpaUnhwoU5deqU2mEJkWTjqoyjQ9EOAIRGhtJkQxMehzxWOSohRFolDekiVbOygj/+eD89YgTcvatePEIIIYSIW6YsmbgwarR+uuWBJZz4y1fFiIRIeQ7cO0DNVTV5E/YGgGqu1djddjf2FvbqBiZSpVevXlG+fHlMTU3ZuXMnV65cYfr06aRPn17t0IRIMo1Gw8L6C6meqzoAL969YPyd8fi/9Vc5MiFEWiQN6SLVq1QJevfW/R0cDD17gqKoG5MQQgghYtd0bGe8nRsBYE0wQS0Ho43UqhuUECnEvrv7qL26Nm/D3wJQ060m21ttx9rMWuXIRGr1888/kyNHDpYuXUrp0qVxdXXFy8sLNze3Lx+MosjFmkh2ZsZmbPx2I4UdCwPwLOwZzTY2IyQiROXIhBBpjYnaAQiRHKZMgW3b4NEj2LMHVq+Gtm3VjkoIIYQQsan47xwe5j5ODuUp3wQdY2mrsXRaP17tsIRQ1cH7B6m3th7vIt4BUDdPXTY234iFiYXKkYnUbNu2bdSsWZNvv/2WAwcOkC1bNnr37k23bt1iXD40NJTQ0FD9dGBgIADh4eGEh4cnLZjgR5j85UYdLDDebo9iaotiYgtm6cEsA4pZRrBwQLHIAhZOKJZZwdoZTNODRpO0fX8GUccjycclBUjtuVgZW7G1+VbKLyvP06CnHHt8jPab27Oy0UqMNKmzD2lqf0+ipJU8QHJJiZIjj8SsKw3pIk2ws4P586F+fd10//5QsyY4OKgalhBCCCFikS1Xdlb1Gkzbebox0utv+I0bp1uTt6S7ypEJoY4jD49QZ3UdgsODAaiftz4bvt2AuYm5ypGJ1O7OnTvMnz+fgQMHMmLECE6ePEm/fv0wMzOjQ4cO0ZafMmUK48aNi1a+Z88erKyskhSLjfYh1VAw5R2EvIOQpySkeTwcS4I1jgQbORFklJUgTTaCjLIRaJSDCI1NkmJKDnv37lU7hGST2nMZknUII26NIEQbwoarG1BeKrTNkrp72aX29yRKWskDJJeUKCl5BAcHJ3hZaUgXaUa9etCiBaxbBy9ewMCBsHKl2lEJIYQQIjZtfxvItnU7aPDiHzLxkkN1vifvs91qhyXEF3fi8QmD4Vxq564tjegi2Wi1Wjw8PJg8eTIAxYsX59KlS/z+++8xNqQPHz6cgQMH6qcDAwPJkSMHXl5e2NnZJS2YwGtEHi9J8OtnWFsoaCKCIPwNGuIe3suUd6RT7pMu8j5EGs5TLLOjpCuEYl8UJX0JlPQlwCrnF+nBHh4ezt69e6lRowampqaffX+fU1rJJTw8nBcbXzDl3hS0ipaNzzZS06Om/oGkqUlaek/SQh4guaREyZFH1J1XCSEN6SJNmTVLN7TLq1ewapVueJeaNdWOSgghhBCxybNjFi/LVCADATT238PaQXNoNf07tcMS4ou56H+RmqtqEhiqu4irkasGm1tslkZ0kWyyZMlCgQIFDMry58/Ppk2bYlze3Nwcc/Ponz9TU9OkN7ZkLEx4jaP8u2MHderU0W1PUSD8NYS+0L1CnkHIUwh+Au8ewdv771/asGib1Lx7hObdI/Db9UESDpCpHDh4QiZPyFgKjD/fEEnJcmxSiLSQS6l0pZhWfRoD9+p+EOq9szd5MuWhsktllSP7NGnhPYG0kwdILilRUvJIzHrSkC7SlMyZYfp06NxZN927N1y6BJaW6sYlhBBCiJjlL12QJQ160nnbTwCUnjmNl0PbkCFzBpUjE+Lzexr6lJ5rexIQEgBAVZeq/NnyTxkTXSSr8uXLc/36dYOyGzdu4OzsrFJEH9FowMxe97KN4wGo2kgIfghvbkDgdXh9BV5fgoCLuob4D4U+h8fbdC8AI3PIVBYcq4BTNd3fRqm/4UjErm+pvtwJuMPck3MJ14bTZH0TTnQ9gVsGFR6yK4RIM1LnExeEiEPHjlD5/z8037kDEyeqGo4QQggh4tFx8yQOW5YEwE37gO3V+qkckRCf3+M3j/nx1o/4vfUDoEy2MmxrtQ0r06SNQS3ExwYMGMCxY8eYPHkyt27dYs2aNSxcuJA+ffqoHVriGBmDjQtk8YJ830Hp+VDjEDR7BQ3vQcXNUHAkZKkJpvaG62pDwf8AXBoHf1eCjRngQEO4MU/X012kSTNqzaCmm+4W9ZfvXlJvbT39D5dCCPEppCFdpDkaDfz+O0TdmTF1Kly5om5MQgghhIidkbERkX9MIQxd5d3q8nr+XblT5aiE+HxeBL+g9praPA9/DkAhx0LsaLMDGzP1H5oo0p5SpUqxZcsW1q5dS6FChZgwYQIzZ86kTZs2aoeWPDQasHaGHI2h6ESouguavYC6V6DMH5CrE1i7Gq4TEaTrrX6qD2x1gR1F4NwIeHFSN9SMSBNMjExY12wd+TPlB+Daf9dosbEFEdoIlSMTQqRW0pAu0iR3dxg2TPd3eDj07AnauJ9fI4QQQggVVW5VgzWFWgJgRjjmPUYRES4XuiLtCQ4Ppt7aelx7cQ0At/Ru7Gm7hwyWMpyR+Hzq1avHxYsXCQkJ4erVq3Tr1k3tkD4vjRGkyw9unaHsEmh4Bxreh7LLwKUNWDgaLh9wEa5Mgd2lYasznO4P/odBkYvI1C6dRTq2t9pORsuMAOy5vYfBewarHJUQIrWShnSRZo0YAW7/H/7s0CFYtkzVcIQQQggRjwZ/z+SWkW7M3vLvzrCi6SiVIxIieUVoI2ixsQXHHh0DIL1Jena02kEW2ywqRybEV8A6J+TqAJ6roPFTqHUGikyEjGUAzfvlgh/C9Vnwd0XY6gpnh8Krc9JTPRVzy+DGlhZbMP3/uPizjs/ijzN/qByVECI1koZ0kWZZWsL8+e+nhw6FFy/Ui0cIIYQQccuQOQOnBg7RTzfYvojbF26pGJEQyUdRFHr69MTnhg8AduZ2/Oj2I672rvGsKYRIdhojyFAcCo2Emsd0DeulF0GWWoYPIQ1+AFenws7isLMoXP0V3j1TL27xySo6V2Re3Xn66V5/9cL3ga+KEQkhUiNpSBdpWo0a0KqV7u8XL3S91IUQQgiRcrWc2oetDtUByMRLTtUdqHJEQiSPsfvH8sdZXQ9IM2MzNjbdiKulNKILkSJYZobcXaHqTmjiD2WX6xrVNcbvlwm4CGcHwZ/Z4GAjeLwDlEjVQhaJ17VEV74r/R0A4dpwmqxvwoPXD1SOSgiRmkhDukjzpk0DW1vd34sWwYkT6sYjhBBCiLjl2jSVQHSV97ePfNj+q7fKEQmRNMvOLWP8wfEAaNCwsvFKqrhUUTcoIUTMzOwhV3tdo3rjp1BqHmQs+36+EgmPtsKBupj8lZe8YesgxE+1cEXi/FrzV6q5VgPA/60/jbwbERwerHJUQojUwiSxK4SGhnL8+HHu379PcHAwDg4OFC9eHFdX6U0hUqasWWHcOBg4UDesXe/ecPw4GBvHv64QQqQFUneL1KZwxWL8UbEdXQ7NwwiFbMMnEta7CWYWZmqHJkSi/Xv3X7ptf/9gx2le02hesDnh4eEqRiVSOqm7UwgLB8jTS/cKvA53lsPd5fDuCQCadw/Jz1oUn42Qsxnk7QuZyoFGE8+GhVpMjExY/+16Si8qze1Xtznrd5Zu27uxqvEqNPK+CSHikeCGdF9fX2bNmsX27dsJDw8nXbp0WFpa8vLlS0JDQ8mVKxfdu3enZ8+e2EZ1/xUihejbF5YsgUuX4PRpWLgQevVSOyohhPi8/sfeXYdHcXUBHP7txkNCgrsWdy3u7u5aaJECxYpDW9wpULxQtEDxFidYcHd3JziBeLK73x8XEvJhCWwyu5vzPs88zMzuzpybJTm7d+6ca82522AwRKmTKTQ0FHt7e4KCgjAYrPv26thsi4ODA3YWfkW52cbxnEu4nVxhVygQcp55tfvTftskrcMSIlouPLlA/X/qE2YMA6BL4S70LNpT46iEJbPm3G3z4meFfKMgzzB4sBmuzcH0YBM6jOhMoXB7mVoSfQvZekGaBqCP9thFEQsSuiRkXdN1FJ1bFP9Qf5aeXUqB5AXoXby31qEJISxclP6q165dmxMnTtC8eXO2bdtGoUKFcHFxCX/8xo0b7N27l2XLljFp0iQWLVpEpUqVYixoIaLLwQFmzIDSpdX2wIHQsCEkSaJtXEIIEVOsNXebTCZ8fHx4+fJllJ+fPHly7t69a/WjiGK7LZ6eniRPntxif26u7q5cHtKfXL+2A6C+119cOd6BLAWzaRyZEFHzxP8JNZbWwDfYF4AamWswuepki/2dE9qz1twd5+jtIXUtSF2LMN/r3NzWj8w6b3QhT9Xjz47A/qbgmhay9YRMP4B9PG1jFu/JlTQXi+otosGKBgD03d6XPMnyUOkb+Z0SQnxclDrSa9SowerVq3FwcPjg4xkzZiRjxoy0adOGCxcu8PDhwyidfObMmcycOZNbt24BkDNnTn755ReqVasWteiFiIZSpaB1a1i0CF6+hEGD1Mh0IYSwRdaau992oidNmhRXV9fPdjgZjUb8/Pxwc3NDr7fuqV9iqy0mk4mAgAAeP34MQIoUKWLsXF+rwS/fsWbGCuo/2kICfPGq04cs99ZrHZYQnxUcFkz9FfW59fIWAPmT52d5w+XYy+hU8QkxlbtFDHJNy0XHVmSoMh+HB2vg0u/w8rR6LOAOnOgJ54ZD1p8gSzdwSqhtvCKS+tnrM6T0EIbvGY7RZKTJqiYc73CcDAmkhJIQ4sOi9EmuY8eOUT5gjhw5yJEjR5Semzp1asaMGUPmzJkxmUwsXLiQOnXqcPLkSXLmzBnlcwoRVWPHwtq18Po1zJ0LHTpAoUJaRyWEEOZnjbnbYDCEd6InSpQoSq8xGo2EhITg7OxsEx3psdWWtyMcHz9+TNKkSS26zEu2deN5VWw/8XlNw/sb2ThlBTW6N9Y6LCE+ymQy0XljZ/bd2QdACrcUrG+2HjdHN40jE5YupnK3iAV2zpCxDWRoDY92waVJ8GCjeizkOZz9DS6OVzXUs/VWtdeFRfit7G+c8jnF+ivreRH0gvor6rO/3X5cHVy1Dk0IYYG+6luan58fr169irRER61atahevTqZM2cmS5YsjBw5Ejc3Nw4dOvQ1YQnxUcmTw6+/qnWTCX76CYxGbWMSQojYZMm5+21NdFdX+eISG97+nC19wsMcRXOxskRLAPSYSNxvFGGhYRpHJcTHTTw4kfmn5gPgbO/Mv03/JVX8VBpHJazZ1+ZuEYt0OkheHspugOpnIX0r0L25WB3mDxfGwn8Z4GQ/CHqqbawCAL1Oz+J6i8mcMDMAp3xO0WlDJ0wmk8aRCSEsUbQ70m/evEmNGjWIFy8eHh4eJEiQgAQJEuDp6UmCBAm+OBCDwcDy5cvx9/enWLFiX3wcIT6nWzfI9qa86sGDsGSJtvEIIURMs7bcLfWDY4c1/ZybbBzHJbtvACgSfJpFTX/TNiAhPmLT1U309eobvr2gzgIKpyqsYUTCWsVU7haxyDMXFF8Eta5B5s6gd1T7w/zh4jjVoX7mVwjx1TZOgYezB2ubrCWeg6plv/jMYqYdmaZxVEIISxTtIn0tW7bEZDLx119/kSxZsq/+Enb27FmKFStGUFAQbm5urF279qO3qAUHBxMcHBy+/fZKfGhoqFlGU709hqWPzIoKacvH6XQwaZKO6tXVf/9+/UzUqBFG/PhmOfxHyXtieWylHSBtsUTmaIe5fgbWkrtDQ0MxmUwYjUaMUbxd6O1oobevs2ax3Raj0YjJZCI0NNTspV3M/Xvs5OrEiR7dyTbxJwCqrf2T25e/J2XGmB3layt/j8B22mLJ7bj6/CrNVzfHhPpdHlRyEPWz1v9orJbcluiylbbYcu4WGnJLD4VnQM6BakT6tT/BGAxhfnBuGFyZBjn6Q9ZuqkSM0ETOpDmZX2c+jVep8nG9tvUif4r8lExbUuPIhBCWJNod6adPn+b48eNkzZrVLAFkzZqVU6dO4evry6pVq2jTpg3e3t4f/EI+evRohg4d+t7+bdu2mfU2cC8vL7MdS2vSlo8rWrQwhw6lxMdHx/ff36Jt2wtmPf7HyHtieWylHSBtsURf046AgACzxGAtudve3p7kyZPj5+dHSEhItGJ6/fp19BphwWKrLSEhIQQGBrJnzx7CwmKmVIo5f4/jlUrL+j9LUevVXlKYHvNnlZ4kndLWbMf/FFv5ewS20xZLa0egIZC+V/viG6xGlhb1KErB1wXZtGnTZ19raW35GrbSFlvM3cICuKaGQn+oTvPzI1WHuilM1VA/1Vd1qOcdAelbgM66532xVo1yNqLPgz6MPzCeMGMYjVc25kTHEyR3S651aEIICxHtjvTChQtz9+5dsyV0R0dHMmXKBEDBggU5evQoU6ZMYfbs2e89d8CAAfTq1St8+9WrV6RJk4bKlSsT3wzDiUNDQ/Hy8qJSpUofnSndWkhbPi9HDsid20RwsI6NGzMxalR63vxXjBHynlgeW2kHSFsskTnaYa4aqNaSu4OCgrh79y5ubm44O0dtRJbJZOL169e4u7tb/Wi9d9uyatUqfv31V27dukXmzJkZPXo01atX/+TrZ8yYwfTp07l16xZp06ZlwIABtG7d+qPPDwoKwsXFhdKlS0f55x1VMfV7fGxJQoJqV8KZYNrc3sihkK4Uq1vabMf/f7by9whspy2W2A6jyUiT1U24G3QXgOyJs7OxzUbcndw/+TpLbMuXspW22HLuFhbENZUaoZ79Zzg7FG4tAZMRAu7AwdZwcSIUmKRqrYtYN6rCKI49OMauW7t46PeQpquasr31duz10e4+E0LYoGj/JZg7dy6dOnXi/v375MqV670PGHny5PmqgIxGY6RbwN/l5OSEk5PTe/sdHBzM+oHN3MfTkrTl4zJnhp9/hpEjITRUx8CBDqxda7bDf5S8J5bHVtoB0hZL9DXtMFf7rSV3GwwGdDoder0evT5qI7HelkB5+zpr9rYtBw8epEWLFowePZqaNWuydOlS6tevz4kTJ8iVK9cHXztz5kwGDhzIn3/+SeHChTly5Ag//PADiRIlolatWh98jV6vR6fTxejvmrmPXaxWKRbmbESb80twJBT/DsNwaORttuN/jK38PQLbaYsltWPEnhH8e+VfADycPPi36b8kdEsY5ddbUlu+lq20JS7kbmEB3DJCsYWqQ/1Uf3jw5g6Wl6dhZwVIXRfyjwf3GBztJd5jr7dnWYNlFJhTgAevH+B925tBOwYxttJYrUMTQliAaHekP3nyhOvXr/Pdd9+F79PpdJhMJnQ6HQaDIcrHGjBgANWqVSNt2rS8fv2apUuXsnv3brZu3RrdsIT4Iv37w19/wcOHsG4d7NwJ5eXCvxDCxkjujnlGo5GxY8cyZ84cfHx8yJIlC0OGDKFhw4bRPtbUqVOpWrUqffr0AWD48OF4eXkxbdo0Zs2a9cHXLF68mI4dO9KkSRMAMmbMyNGjRxk7duxHO9KtVdVN43mQ3ouUpkdUe7mHFQNn03hUR63DEnHUtuvb+GXXLwDo0LGswTIyJ8qscVTCFpgzdwsL55kbym6ER7vgZB94flztv7cOHmyErD0h12Bw+PRdLsJ8krklY0XDFZRdWJYwYxjjDoyjaOqi1MteT+vQhBAai/bQrXbt2pE/f34OHjzIjRs3uHnzZqR/o+Px48e0bt2arFmzUqFCBY4ePcrWrVupVKlSdMMS4ou4ucGYMRHbPXpADJWJFUIIzUjujnmjR49m0aJFzJo1i/Pnz9OzZ09atmyJt7caLe3m5vbJpVOnTuHHOnToEBUrVox0/CpVqnDw4MGPnj84OPi98iwuLi4cOXLE6if/+3/J0iZnS73vw7ezj59EUECQhhGJuOqO751Ik4sOKzeMapmraRyVsBXmzN3CSiQrB1WOQNEF4JJC7TOGwsVxsCEb3FoObyYmFzGvRNoSTKg0IXy77b9tuf78uoYRCSEsQbRHpN++fZv//vsvvDbq15g3b95XH0OIr9WyJUybBkePwtmzMHcuvNOfIYQQVs/ac3ehOYXw8fP56ONvR+eZU3K35BzrcCxKzw0ODmbUqFFs376dYsWKAWpE+L59+5g9ezZlypTh1KlTnzzGu/XifXx8SJYsWaTHkyVLho/Px38GVapUYe7cudStW5cCBQpw/Phx5s6dS2hoKE+fPiVFihRRaou1aL38N465/UuhkHPkDrvCvPqDaL9lotZhiTgkOCyYRisb8SzwGQA1s9RkYKmBGkclbIk5c7ewIjo9ZGwDaRrAhTFwcQIYgyHwARxoBtdmQ+GZ4JFN60jjhJ+K/MSBewdYcX4Fr4Jf0WhlIw60P4CzvXnnlhFCWI9od6SXL1+e06dPS0IXNkOvh8mToUQJtT1kCDRrBh4emoYlhBBmY+2528fPh/uv72sdxkddu3aNgICA90blh4SEkD9/foAo/ezf1kj/EkOGDMHHx4eiRYtiMplIliwZbdq0Ydy4cVZfO/5D7B3sufdbXwoNVJOp1t66kLtXu5Mmc1qNIxNxRa+tvThy/wgAGTwzsKjuIvQ62/tdE9qx9twtvpKDG+QdARm/g+M94MEGtf/xbticB7L3g5wDwd5Fyyhtnk6nY26tuZzyOcWVZ1c46XOSHlt6MKvmh0vtCSFsX7Q70mvVqkXPnj05e/YsuXPnfm/Sk9q1a5stOCFiS/HiqvN82TJ4+hRGj45c8kUIIayZtefu5G7JP/l4TI1Ijyo/Pz8ANm7cSKpUqSI99naiVTc3t08eo2XLlsyYMUOdO3lyHj16FOnxR48ekTz5x2NycXHhr7/+Yvbs2Tx69IgUKVIwZ84c3N3dSZIkSZTbYk3qDmjFv78vos6T7SThGYtq96f1xaVahyXigOXnljPjmPp9dbJzYnXj1SRwSaBxVMLWWHvuFmbi/g2UXQ/3N8Cxn8D/pir3cn4E3F4G386B5DLJV0xyd3JnZaOVFJlbhKCwIGYfn02ptKVokaeF1qEJITQQ7Y70tzU8hw0b9t5jMumJsGajR8OaNRAcrEaod+oE6dNrHZUQQnw9a8/dnyqxYjQaefXqFfHjx9ds5HWOHDlwcnLizp07lClT5oPPiU5pl6JFi7Jjxw569OgRvs/Lyyu8bMynODg4kDp1agCWL19OzZo1bXJE+lupFg0nqNpenAmm8aU1HN18kMLVPv9zEuJLXX12lR/W/xC+Pb36dPKnyK9hRMJWWXvuFmaWqiYkKw/nRsDF8WAKA7/rsLMCfNMe8o8HR7mgF1PyJMvDjOozaPdfOwA6bOhAwZQFyZZYSuwIEddEuyP9a247FsKSpUunJhsdO1Z1pg8YoEaoCyGEtZPcHbPc3d35+eef6dmzJ0ajkZIlS+Lr68v+/fuJHz8+bdq0iVZpl59++oly5coxceJEatSowfLlyzl27Bhz5swJf+6AAQO4f/8+ixYtAuDKlSscOXKEIkWK8OLFCyZNmsS5c+dYuHBhzDTaQhSqWpRF2erT+tIynAnmQetf4ImX1mEJGxUUFkSjlY3wC1F3obTO25p2+dtpHJWwVZK7xXvsXSHfKEjfAo52gif71P7r8+D+Rig8A9LU0zZGG/Zd/u/Ye2cv80/NJyA0gMYrG3P4+8O4OEh5HSHiEtsdoiTEFxgwABInVuvLl8Phw9rGI4QQwjoMHz6cIUOGMHr0aLJnz07VqlXZuHEjGTJkiPaxihcvztKlS5kzZw558+Zl1apVrFu3jly5coU/5+HDh9y5cyd822AwMHHiRPLmzUulSpUICgriwIEDpI8Dt1aV+28MT0gEQJ2n2/lvgpR3ETGj19ZenH50GoBsibMxvfp0s5eVEkKIz/LMCRW91aSj9u5qX5AP7K0P+1tA8DNt47Nh06pPI2eSnACcfXyW7lu6axyRECK2Rakjffny5VE+4N27d9m/f/8XBySEljw8YOjQiO1evcBk0i4eIYT4UpK7Y5dOp6N79+5cunSJkJAQHj9+zJYtWyhduvQXHa9Ro0ZcvnyZ4OBgzp07R/Xq1SM9vmDBAnbv3h2+nT17dk6ePElAQAC+vr6sW7eOrFmzfk2TrEaazGn5r1Kr8O2kQ8ZjNMhITmFeK8+vZOaxmQA42zuzouEK3Bw/PfeBENEluVtEmU4PmTtBjfOQsmbE/ttLYWMuuPefdrHZMFcHV1Y0WoGrgysAf574k2Vn5TZ2IeKSKHWkz5w5k+zZszNu3DguXrz43uO+vr5s2rSJ5s2bU6BAAZ49kyugwnp16ADZ3pQ6O3BA1U0XQghrI7lbxCUt1o3mkt03ABQNOsWS72XGcGE+t17eilQXfVq1aeROllvDiIStktwtoi1eGijzHxRbDA6eal+QD+ypA4faQ+grTcOzRTmS5GB69enh2x02dODKsysaRiSEiE1R6kj39vZm7NixeHl5kStXLuLHj0/mzJnJnTs3qVOnJlGiRLRr1460adNy7tw5mUFcWDV7exg/PmJ7wAAIDdUuHiGE+BKSu0Vc4uzqzLGOP4ZvF1v8JwGvAzSMSNiKMGMYzVc3xzfYF4BmuZpJXXQRYyR3iy+i00GGlu+PTr/xF2zKC4/3ahebjWqbry2t87YGwC/Ej6armhIcFqxxVEKI2BDlyUZr165N7dq1efr0Kfv27eP27dsEBgaSOHFi8ufPT/78+dHrpeS6sA01akCZMuDtDVevwp9/wo8/fv51QghhSSR3i7ik+dQe7Ju/lJKBx8lsuMW8BoNov+13rcMSVm6Y9zAO3jsIQAbPDMysMVPqoosYJblbfDHXlGp0+o0FcLw7hL0G/1uwvQzk6A95hoLeQesobcb06tM5fO8wl59d5qTPSfpv78/vVeVzhxC2Lsod6W8lTpyYunXrxkAoQlgOnQ7GjYMiRdT2b79Bq1bg7q5pWEII8UUkd4u4QG+n58WIn6F3MwBqeS3h4a0+pEifUuPIhLXac3sPI/eOBMBOZ8fSBkvxcPbQOCoRV0juFl9Ep4NvvoNkZeFga3iyDzDBhdHwaAcUXwru32gdpU1wc3RjecPlFJlbhBBDCJMPT6ZCxgrUzFLz8y8WQlgtuZQtxEd8+y00bqzWnzyBCRO0jUcIIYQQn1arV1PWJyoPQFKesrXOAI0jEtbqReALWq5pidGkJq4dVm4YRVMX1TgqIYSIIrcMUGE35BsDujfjJ58dgc354OZiDQOzLfmS52Ni5Ynh223XteX+q/saRiSEiGnSkS7EJ4waBQ5v7n6bMAEePtQ2HiGEEEJ8WuJ5vxKCSt6Nzqzi3P4zGkckrFGXTV24++ouAGXTl6VfiX4aRySEENGkt4Mc/aDyAXDLpPaF+amR6oe+gzB/beOzEV0Kd6FO1joAPAt8Rsu1LTEYDRpHJYSIKdKRLsQnfPMNdOqk1gMCVIkXIYQQQliuYnVKsypDLQDiEcD5poM1jkhYm6Vnl7Ls3DIAPJ09WVxvMXZ6O42jEkKIL5SoMFQ7CRnbRuy7sQC2FIaX57SKymbodDrm1Z5H6vipAdh9azfj9o/TOCohREyRjnQhPmPIkIja6PPmqclHhRBCCGG5Cq0cyStU8m5wbxP7V+3SOCJhLe743uHHjREzzM+qMSu8c0QIIayWgxsUnQ/FloB9PLXv1UXYWlh1qouvksg1EUvqLUGHmox6yK4hHL53WOOohBAxIdod6cOGDSMgIOC9/YGBgQwbNswsQQlhSZIkgZ9/VusGAwyWgW1CCCsjuVvENVkKZmN1/gYA2GPgeacRGkckrIHRZKTNujb4BvsC0CJ3C5rkaqJxVCKuktwtYkSGFlD1OHjmUduGIFXm5XAHtS6+WJn0ZRhUahAABpOB5mua8zr4tcZRCSHMLdod6UOHDsXPz++9/QEBAQwdOtQsQQlhaXr1Uh3qACtWwPHj2sYjhBDRIblbxEXV143mMYkBqPVsJ5v+WKVxRMLSTTk0hd23dgOQ1iMt06pP0zYgEadJ7hYxJn5WqHwIMnWI2Hf9T+x3lsHV+Ei7uGzAL2V+CZ+Y+saLG3Td3FXjiIQQ5hbtjnSTyYROp3tv/+nTp0mYMKFZghLC0ri5qRIvbw0cqF0sQggRXZK7rc/KlSvJli0bzs7O5M6dm02bNn3y+W3btkWn07235MyZM5YitjzJ0iZnfbnm4dvOAydqGI2wdBefXGTAjgEA6NCxsO5CPJ09tQ1KxGmSu0WMsneBb2dD0YVg5wKA7uVJygT2RufjpXFw1svBzoG/6/+Nu6MqL7fo9CKWn1uucVRCCHOKckd6ggQJSJgwITqdjixZspAwYcLwxcPDg0qVKtG4ceOYjFUITXXsCOnTq/Vt22DnTk3DEUKIz5LcbZ0OHDhAs2bNaN++PSdPnqRu3brUrVuXc+c+PiHYlClTePjwYfhy9+5dEiZMSKNGjWIxcsvTZO1IburTAFDe7xArBs7WOCJhicKMYbRe15pgQzAAPYr2oGz6stoGJeIsyd0iVmVsDVUOg3tmABzxw25vTTg/GkwmjYOzThkTZGRmjZnh2502dOKO7x0NIxJCmJN9VJ84efJkTCYT7dq1Y+jQoXh4eIQ/5ujoSPr06SlWrFiMBCmEJXB0hOHDoVUrtd2/Pxw+DB8YKCKEEBZBcnfsMRqNjB07ljlz5uDj40OWLFkYMmQIDRs2jPaxpk6dStWqVenTpw8Aw4cPx8vLi2nTpjFr1qwPvsbDwyPS+7tu3TpevHjBd99992UNshFuHm6sbNCWDCuHA5Bx4jSMw39AbxftmzKFDRuzbwzHHhwDIGuirIwsP1LjiERcJrlbxDrP3FDlKMYDLdE/2IAOE5weCM+PQ9EFaqJSES0t8rRg49WNLDu3DN9gX1qvbc2O1juw09tpHZoQ4itFuSO9TZs2AGTIkIHixYvj4OAQY0EJYamaN4fx4+HMGTh6FP79F+rW1ToqIYT4MMndsWf06NEsWbKEWbNmkTlzZvbs2UPLli1JkiQJZcqUwc3t019CW7ZsyYwZMwA4dOgQvXr1ivR4lSpVWLduXZTjmTdvHhUrViRdunTRboutafX3L5xZu4I8YZcpFHKOxZ0m0OrPvlqHJSzEKZ9TDPVW9ab1Oj2L6i3CxcFF46hEXCa5W2jC0QND8VVc/vc7soUuV53pd1fD66tQ+l9wS691hFZnRo0Z7L+7nzu+d/C+7c2EAxPoV7Kf1mEJIb5SlDvS3ypTpgxGo5ErV67w+PFjjEZjpMdLly5ttuCEsDR6PYwYAbVrq+0hQ6BWLbCTC8tCCAtm7bm7UCHw8fnYozpMpvgfrCP7NZInh2PHovbc4OBgRo0axfbt28NHCWbMmJF9+/Yxe/ZsypQpw6lTpz55jPjx44ev+/j4kCxZskiPJ0uWDJ+P/xAiefDgAZs3b2bp0qVRa4CNs3ew58z3P5Bn1s8A5F8wj5A/euDo7KhxZEJrIYYQ2qxrQ5gxDIABJQfwbapvNY5KCMXac7ewQjo9VxybkLlIE+wPt4bQV/DyDGwtDCVXQbIyWkdoVTydPVlUdxHlFpbDhIkhu4ZQ6ZtKFEhRQOvQhBBfIdod6YcOHaJ58+bcvn0b0//VzNLpdBgMBrMFJ4QlqlkTihRRZV3OnYN//lEj1YUQwlJZe+728YH79z/2qO7Nop1r164REBBApUqVIu0PCQkhf/78AGTKlOmzx/n/TpIvtXDhQjw9Pakrt0yFaz6tJ0fmL+Lb4DPkCrvC/LYj+W75UK3DEhobvXc0Zx6dASBPsjz8UuYXjSMSIoK1525hvUwpqkPlw7CnDry+AsFPYWdFKDwdMnXQOjyrUiZ9GfqV6MeY/WMINYbSck1Ljnc4Lnc+CWHFot2R3qlTJwoVKsTGjRtJkSKF2UeACWHpdDoYORIqVlTbv/wCjRqB3HUphLBU1p67kyf/1KMmTCbTmzaZr12fPmdkfn5+AGzcuJFUqVJFeszJyQkgWqVdkidPzqNHjyI9/ujRI5JHISiTycRff/1Fq1atcHSUEddv6e303OzRmW/Hdgag1MqFBPzZD1d3V40jE1o58+gMI/aOAMBOZ8f8OvNxtJPfGWE5rD13CyvnkQ2qHIL9zeDhVjCFwZGO8Ooy5BsHUus7yoaWG8q2G9s48fAEF59eZMCOAUyuOlnrsIQQXyjaHelXr15l1apVURpZJYStqlABypWDXbvg+nVYsAB++EHrqIQQ4sOsPXd/qsSK0Wji1atXxI8fH71em06GHDly4OTkxJ07dyhT5sO3PUentEvRokXZsWMHPXr0CN/n5eUVpcnlvL29uXbtGu3bt49S7HFJkzGd2PPHPEoHHCOT8TZ/Nf2VdhvHax2W0ECYMYzv/v0uvKRL/5L95VZ7YXGsPXcLG+CYAMpsgJN94fLvat+lSfDqCpRYCg7u2sZnJRztHFlcbzEF5xQkKCyIKYenUCtLLSpkrKB1aEKILxDtjvQiRYpw7do1Segizhs5EooXV+vDhkGrVuDsrG1MQsQ2o1GV3Lh9Wy137sCTJ/D0KTx7Br6+EBSkluBgNc+AnR3Y24OLC3h4qCVBAkiZUi1Jk+q4d8+d4GC508NcJHfHLHd3d37++Wd69uyJ0WikZMmS+Pr6sn//fuLHj0+bNm2iVdrlp59+oly5ckycOJEaNWqwfPlyjh07xpw5c8KfO2DAAO7fv8+iRYsiHWPevHkUKVKEXLlymbeRNuL5kO4woBUAlTb/zcung/BM7KltUCLWjd8/nhMPTwCQM0lOhpQeonFEsUtyt3WQ3C0sgt4eCk6C+FnhWBcwGeDBBvAqBWU3gGtqrSO0CjmS5GBMhTH02NoDgLb/tuVMpzMkcEmgbWBCiGiLUkf6mTNnwte7detG79698fHxIXfu3O/NIp4nTx7zRiiEhSpWDGrUgI0b4d49mD0bunfXOiohYo6fH5w4AUeOwNmzcP48XLwIAQHmPpM9UJ7u3U2kTQvZs0P+/GopUAAyZFAllsSnSe6OXcOHDydJkiSMHj2aGzdu4OnpSYECBRg4cGC0j1W8eHGWLl3K4MGDGThwIJkzZ2bdunWROscfPnzInTt3Ir3O19eX1atXM2XKlK9uj62q278lXqNnUenVftKYHjKv4RDa7/5D67BELLr09BK/ef8GgF6n5686f+Fk76RtUDFIcrd1kdwtLFbmjuD+DextBKEv4eVp2FoUym6EBHm1js4qdCvSjfVX1rPj5g7uvbpH181d+bv+31qHJYSIpih1pOfLlw+dThdpkpN27dqFr799TCY9EXHNiBGqIx1gzBhV3sVVyq0KG/HsGXh7qxJG3t7qy/eXzIVoZ6fu1nB0BJMJDAYIC4PAwE+/zmjUcesW3LoFmzdH7E+WDEqVUku5cpArl3w5/xDJ3bFLp9PRvXt3upvpimqjRo1o1KjRRx9fsGDBe/s8PDwIMH/vmM0xjv0ZOu8HoPqeFTx9+CuJUyTWOCoRG4wmIz+s/4EQQwgAvYv15ttU32oclXlJ7rZukruFRUteESofhN3Vwf8mBN5XI9NLroSUVbSOzuLpdXoW1F1A7pm5eRn0kqVnl1I3a10a5fz45z0hhOWJUkf6zZs3YzoOIaxSvnzQoAGsXg0+PjBrFvTqpXVUQnwZkwlOnYL162HDBlWX+p3vce/R6eCbbyBbNsiYEdKmhXTpIEUKSJQIEidWt35/7BZvoxFev1a3kD97Bg8evL3V3MDevQ/x90/J1at6Xr+O/LpHj2DVKrWAuqW8alWoVk39+5k5HeMMyd1CfFiVTnXZOLAsNV7sJoXpMfMa/0L7vTO0DkvEgtnHZrPvzj4AvknwDUPLDtU4oq8nudu2SO4WFu/tJKTeteDZEQh7Dd414Ns/4ZvvtI7O4qWOn5pp1abRcm1LADpv7EypdKVI7haNWe6FEJqKUkd6unTpYjoOIazWb7/BmjXqS8uYMdCxI8SLp3VUQkSNyQTHj8Py5bBiBdy9++Hn6fWQJw98+61aChRQX8JdXL783Hp9RJ3VtGnV7d8AoaFGNm06TvXqybC313PnDpw8qW5NP3oUDhyAV68ijvPgAfz1l1qcndUX8gYNoE4dcI/DcyBJ7hbi45wm/oyxnTd6TNTat5LH934jaeqkWoclYtC9V/fot71f+Paftf7ExeErkpiGJHfbLsndwio4J4UKu+BAC7i3TtVNP9wOAh9AzoFyu8lnNM/dnLWX1rL64mqeBT6jw/oO/Nv0X3TycxPCKkR7stH//vvvg/t1Oh3Ozs5kypSJDBkyfHVgQliLXLmgcWP45x81UdP06dC3r9ZRCfFpd+/C/PmwaBFcv/7h5+TJAxUrqluwS5VSX5pjm06nRsqlSwd166p9BoOq8+rtDVu3qtvXg4LUY0FBsG6dWlxdoX59aN0aypdXt6nHVZK7hYis4nc1+K9PeWo/20FSnvJXoyG0Ozhb67BEDDGZTPy48Udeh6hh0u3zt6dchnIaRxV9krvjFsndwqLZu0LJVXCiJ1x5M9fImcGqM73gVNDH4V/ez9DpdMysMZO9d/by2P8x66+sZ8GpBXyXX0b0C2ENot2RXrdu3ffqtkHkem0lS5Zk3bp1JEggMxCLuOHXX2HlSnW767hx0Llz3B5NIyyTwQD//Qdz5qgvsf9/67e9vfryXbs21KwJadJoE+fn2Nmpskr58qkJfgMDYc8e9QV87Vp1+zioidSWLFFL2rTqbpHvv4ekcXDQqeRuId4Xf0ofDC13YYeR2odW8fDWr6RIn1LrsEQMWH1xNeuvrAcgWbxkjK80XuOIok5yt+Ruyd3CYuntoOAUcE0Np97c8XN1BgT5QPG/wc5Z2/gsWJJ4Sfiz1p/UWV4HgO5bulM+Q3nSecpdKUJYOn10X+Dl5UXhwoXx8vLC19cXX19fvLy8KFKkCBs2bGDPnj08e/aMn3/+OSbiFcIiZc8OzZur9WfPYOpUbeMR4l0vXsCECaomav36sGVLxBdxnU6N+PrzT/UldvNmdSHIUr+If4iLC1SpAjNnqjqt3t6qDe9+p7xzBwYNgtSpoUULdUt8XCK5W4j3lW1Rhf+SVgQgMc/Z0vAXjSMSMcE3yJefNv8Uvj2t+jQSuFh+p6PkbsndkruFVdDpIEdfKLYIdG/Gad5dA7trQOjrT782jqudtTZt87UF4HXIa9r/1/69C2dCCMsT7Y707t27M2nSJCpUqIC7uzvu7u5UqFCB8ePH06dPH0qUKMHkyZPx8vKKiXiFsFi//BJx++nEiZHrQAqhhfv31eS3adJAnz5w+3bEY+nSwdChcOsW7NihRnslTKhZqGZjZwelS8OMGfDwobpTpHr1iFKNoaGwdCkUKgQVKnx4dJ8tktwtxIclnd6fMFTyrnN8Nfdv3NM4ImFug3YO4qHfQwBqZqlJg+wNNI7o0yR3S+5+K6Zy95gxY9DpdPTo0SNmAhdxU4ZWUGYD2Lmq7Uc7YUc5CHqsbVwWbnKVyaSOnxqAHTd3MPu4lJkTwtJFuyP9+vXrxI8f/7398ePH58aNGwBkzpyZp0+ffn10QliRzJnVaBlQo4imTdM2HhF33bypboXOmBF+/x38/SMeq1ZNjVy7cUNd/EmbVrs4Y5qTEzRsCBs3wrVrau6CRIkiHt+5U01uVqgQrF9v21/KJXcL8WElGpbjv2SVAEjIS7Y2+U3bgIRZHbl/hBlHZwDg6uDKtGrTLHYyN8ndiuTuCDGRu48ePcrs2bPJkyeP2eIUIlzKKlBhJzi+ucL3/Dh4lQL/O9rGZcE8nD2YV3te+PbP237m5oubGkYkhPicaHekFyxYkD59+vDkyZPwfU+ePKFv374ULlwYgKtXr5LGmu4tFMJMBg8G/ZvfqokT4bXczSZi0fPnTvz0k56sWVUt1ZAQtd/ZGX78ES5dgk2b1BdQfbT/+lu3jBlh7Fg1Udvs2erC11snTqjast9+q34+tvilXHK3EB+XbFpfDG8+Etc5toaHtx5oHJEwhzBjGB03dMSE+qM+tOxQi6w9K7n74yR3mzd3+/n50aJFC/7880+pqS5iTuIiUGmvqpsO8PqK6kx/dVXbuCxY5W8q06FABwD8Q/1p/197jCajxlEJIT4m2h/H5s2bx82bN0mdOjWZMmUiU6ZMpE6dmlu3bjF37lxAJenBgwebPVghLN27o9KfP5dR6SJ2+PrCwIF6OnWqyKxZdoSGqv3u7tC/v7otfPp0yJpV2zgtgYsLdOgAFy/C6tVQoEDEY8eOQY0aUK6cWrclkruF+Lh3R6Un4gWbG/+qcUTCHKYensopn1MA5E2Wl+5Fumsb0P+R3B11krvNk7u7dOlCjRo1qFixYkyGLQR45IBK+8H9zdWvgDuwvRS8OKNtXBZsQuUJpPNQF3t33drFzKMzNY5ICPEx9tF9QdasWblw4QLbtm3jypUr4fsqVaqE/s0wibp165o1SCGsyeDB8PffYDSqUeldu6ovRUKYW1iYmmjsl1/g6VO78P1ubtCzp1pkwNGH2dmpydvq1YP//oPffoNTp9Rj3t5QuDA0bQpjxqiatNZOcrf1WblyJUOGDOHWrVtkzpyZsWPHUr169Y8+f9++ffTr149Lly4REBBAunTp6NixIz179ozFqK1X4j/6YGjshR1Gah9dw8NbQ0mRPqXWYYkvdO/VPX7ZpSaP1aFjds3ZONg5aByVIrn7y0nu/vLcvXz5ck6cOMHRo0ej9Pzg4GCCg4PDt1+9mfwpNDSU0LdXfb7C22OY41hak7Z8hGMKKLsT+z3V0Pmeg6BHmLaXxVB6A6aEhb/++J9gje+Js96Z2dVnU3VZVQD6be9HpQyVSB1Pjey3prZ8iDW+Jx8jbbE85mhHdF4b7Y50AL1eT9WqValateqXvFwIm5YlCzRvDkuWwLNnajRR//5aRyVsza5d0K0bnD8fsc/BwUCXLjBwoB1JkmgXmzXR6aBOHahVC9auVb+r166px5Yvh3XrYOBANeGbs7OmoX41yd3W48CBAzRr1ozRo0dTs2ZNli5dSt26dTlx4gS5cuX64GvixYtH165dyZMnD/HixWPfvn107NiRePHi0aFDh1hugfUp1agCa5NWpN7jbSTmOX81+Y12h+doHZb4Qr229sI/VBUZ71iwI0VSF9E4IkVyt3lI7o6eu3fv0r17d7y8vHCO4g9k9OjRDB069L3927Ztw9XV9Ytj+X+2NMm5tOXDHEz9KKofTkLjFXShLzDtqMgh5194bpfdbOf4GGt8T6omqsqWZ1vwD/Wn4aKGDPtmGDqdzirb8iG20g6Qtliir2lHQEBAlJ8bpY70qVOn0qFDB5ydnZk6deonn/vTTz9F+eRC2KrBg2HpUjUqfcIENSrdyUnrqIQt8PGBn39Wdz28q3FjIxUr7qBt23I4ONh9+MXio/R6aNBA1VudPRuGDoWnTyEoSI0aXLAApk5Vt49bC8ndsctoNDJ27FjmzJmDj48PWbJkYciQITRs2DDax5o6dSpVq1alT58+AAwfPhwvLy+mTZvGrFmzPvia/Pnzkz9//vDt9OnTs2bNGvbu3Ssd6VGUaGofDE23q1HpR1bz6M4wEqZI9PkXCouy7fo2Vl5YCUAS1ySMqjBK44gkd8cUyd1Ry93Hjx/n8ePHFHinJo7BYGDPnj1MmzaN4OBg7Owi//8bMGAAvXr1Ct9+9eoVadKkoXLlyh+cADW6QkND8fLyolKlSjg4WMbdIl9K2hKVA1fHuL8e+id7cCCQkqEjMBRZiylpWfOd493TWfF7Uiq4FPn/zM+dV3c463eWu0nvkvZJWqtsy7us+T35f9IW7QWEBrDk7BK8b3uzpO4SwsLCvrodb++8iooodaT//vvvtGjRAmdnZ37//fePPk+n08mXcSFQ9SybNlWd6c+eqQ/38qshvobRqP4fDRig6qq+VbgwTJ4MhQsb2LQpULP4bIWDg7rw1aqV+kI+dSoYDHDjBtSsCY0bq33Jkmkd6edJ7o5do0ePZsmSJcyaNYvMmTOzZ88eWrZsSZIkSShTpgxubm6ffH3Lli2ZMWMGAIcOHYrUgQFQpUoV1q1bF+V4Tp48yYEDBxgxYkS02xJXlW5SkXXdKlD3iReJec685sNovWuK1mGJaAgOC6brpq7h2+MqjSOBi3Z1UiR3xw7J3Z9WoUIFzp49G2nfd999R7Zs2ejXr997negATk5OOH1gFJCDg4NZO1vMfTwtSVs+dcCEUG4z7KkHPtvQGfyx31cbSq2DlFXMd57/P60VvicJHRLyZ+0/qbJE/VwGeQ9iUqZJVtmWD7GVdoC0RQv3Xt1j+pHpzD4+mxdBLwDoWqQrxVIWA76uHdF5XZQ60m/evPnBdSHExw0aBMuWgckE48fDDz9oHZGwVteuQfv2sGdPxL4ECWDsWLVfrwcrL2tmcTw8YNIk9fPt1k3djg+wYgV4eak7Tb77Tt1ebqkkd8ee4OBgRo0axfbt2ylWTH2Qy5gxI/v27WP27NmUKVOGU28L+X7EuyP8fHx8SPZ/PT7JkiXDx8fns7GkTp2aJ0+eEBYWxm+//cb3338f/QbFYfEn9sTYejt6TNQ4sIrnj2TiUWsy/sB4rj6/CkCJNCVonbe1ZrFI7o59krs/zN3d/b2yYPHixSNRokQfLRcmhNnZu0KZf2FvI3iwAQxBsKcOlF4HKaXs4Lsqf1OZ9vnbM+/kPF6HvGb63em0NmmXz4TQ2tlHZxl3YBzLzy0nzBgW6TGv617hHemx5YtqpAOEhIRw8+ZNvvnmG+ztv/gwQtisHDnU7aarVsGjRzB/vp706bWOSlgTgwGmTFGlggLfGbDWti2MG4fUUo0FOXPCjh3qdvwePdQdJi9eqC/pK1fC3LmQKpXWUUad1ebuQoVUbYQP0AHxTSZ05u4ZSZ4cjh2L0lOvXbtGQEAAlSpVirQ/JCQkvNxKpkyZPnsco9EY/Tj/z969e/Hz8+PQoUP079+fTJky0axZs68+blxRvlU1NvQoS83nu0huesLc1iNJ0r2C1mGJKLj54iYj944EwE5nx4waM9Dr9LEeh+Ru7UnuFsJC2TlDqdVwoBncXQPGYNWZXmotpPr4hOpx0cTKE9lybQv3X9/n1OtTLD67mPYF22sdlhCxas/tPYzZN4bN1zZH2u+gd6BZ7mZ0L9KdAikKxPpkqdH+dBkQEED79u1xdXUlZ86c3LlzB4Bu3boxZswYswcohDUbPDhifcIEPaGhsf+FTlin27ehQgXo3Tvii3j69LB9O8yfL1/EY5NOBy1bwsWL6t+3tmyB3LlVCSeTSbv4osLqc7ePD9y//8FFd/8++gcP0H3k8S9eojD6+y0/Pz8ANm7cyKlTp8KXCxcusGrVKgDc3Nw+uXTq1Cn8eMmTJ+fRo0eRzvHo0SOSJ0/+2VgyZMhA7ty5+eGHH+jZsye//fZblNshFIfR3cPXq+xeRZBfkIbRiKjqta0XQWHqver2bTfyJMsT6zFI7rYckrs/bffu3UyePNkMUQoRTXaOUGI5pG2kto0hsLce3N+obVwWxsPZg9k1Z4dv/7z9Z3z8ov7ZVAhrZTKZ2H5jO2UWlKHMgjKROtETuiRkcKnB3O5xm4V1F1IgRYFPHCnmRLtXb8CAAZw+fZrdu3dHmvW7YsWK/PPPP2YNTghrlzcv1Kql1u/d07FrVxptAxJW4e+/IU8e8PaO2NetG5w9q76gC20kSQKLF8PGjZAihdr34gW0aAHNmkWuf2tprD53J0+uhg9+YDGlSoUxZUpMH3n8i5codFq/lSNHDpycnLhz5w6ZMmWKtKRJo/7uv9vB/qFl2LBh4ccrWrQoO3bsiHQOLy+v8LIxUWU0GgkODo7WawRU6VCHrR4lAUhjesjdaV4aRyQ+Z9v1bay7tA6AZPGSMbTc0FiPQXK3ZZLcLYQF0jtA8aWQtonaNobA3vpwf5O2cVmYGllq0CynuqvwZdBLumzqonFEQsQck8mE13UvSvxVgkqLK7HndkRtvHQe6ZhadSp3etxhePnhpHBPoWGkX1DaZd26dfzzzz8ULVo00m3UOXPm5Pr162YNTghbMHgwrF+v1levzsyECWpSJCH+3+vX0Lmz+jL+Vtq0sHAhlC2rWVji/1SvDufOqYnNli1T+/75Bw4fVttFi2ob34dYfe7+RIkVk9HIq1eviB8/Pjq9Nnf9uLu78/PPP9OzZ0+MRiMlS5bE19eX/fv3Ez9+fNq0aROt0i4//fQT5cqVY+LEidSoUYPly5dz7Ngx5syZE/7cAQMGcP/+fRYtWgTA9OnTSZs2LdmyZQNgz549TJgwQSaS/UIhv3SB3vsAqH9kG0EBQTh4SPK2RCGGEH7aHPH/fGzFscR3iv+JV5iX5G7rILlbCAujt4fiS9TtI7eXR3Sml/kPUlTWOjqLMbHSRDZd3oRvmC9rLq5h9YXVNMjRQOuwhDCrPbf3MHjnYPbe2Rtpf9ZEWRlUahBNczXFwc5yPodH+xvnkydPSJo06Xv7/f39o12fdPTo0RQuXBh3d3eSJk1K3bp1uXz5cnRDEsKiffstVH7zWeDRo3gsW2bBMxwJzZw+rcpAv/tFvGVLOHNGvohbooQJ1W3h//wDnp5q361bULIkjBkDZih1bVaSu2Pe8OHDGTJkCKNHjyZ79uxUrVqVjRs3kiFDhmgfq3jx4ixdupQ5c+aQN29eVq1axbp16yJNCvfw4cPw2/xBdcIPGDCAfPnyUahQIaZPn87YsWMjjXQXUVerV1N2uqmetYzGu6z4brTGEYmPmXp4Kpefqb9BRVMXpVXeVrF2bsnd1iUu524hLJLeHootfmdk+pua6T47Pv26OCSxa2J+SPVD+HaXTV14Hvhcw4iEMJ+TD09SZUkVyiwoE6kTPWeSnCxvsJzzP56nVd5WFtWJDl/QkV6oUCE2boyoX/U2ic+dOzfatxx7e3vTpUsXDh06hJeXF6GhoVSuXBl/f//ohiWERXu3Vvq4cXYW90FdaMdkgtmzoUgRuHJF7XN3V6OjFi8GDw9t4xOf1rgxnDoFJUqobYMBBgxQI94sieTumKfT6ejevTuXLl0iJCSEx48fs2XLFkqXLv1Fx2vUqBGXL18mODiYc+fOUb165Em4FixYwO7du8O3u3Xrxrlz5/D398fX15cTJ07QuXNn9BqN0rcFL/p0CF8v8d8KwkLDNIxGfMjD1w8Z6q3KuOjQMa3atFiZYFRyt3WLi7lbCIult4fiiyHNm1HWhiDwrgWPvD/9ujikhGcJamauCcAj/0f03tZb44iE+Do3XtygxZoWFJhTgG3Xt4Xvz5ooK8saLONM5zM0ydUEO72dhlF+XLRLu4waNYpq1apx4cIFwsLCmDJlChcuXODAgQN4e0fvj92WLVsibS9YsICkSZNy/PjxL/7iKYQlKlUKSpY0sm+fnsuXdaxbB/Xrax2V0FpgIPz4IyxYELGvQAFYsQK++UazsEQ0pUsHu3fDsGEwYgQ4Oanb/C2J5G4hoq/BL9+xf/QflAg6STbDdRZ1mkDref21Dku8Y8COAfiFqMl+vy/wPQVTFozxc0rutg1xLXcLYdHe1kzf3xju/QuGQPCuAeW3Q2ILrLsUy3Q6HdOqTmPvnb34Bvuy4NQCWuRuQcWMFbUOTYhoeRn0khF7RjD18FRCjaHh+9N5pOO3sr/RMk9L7PXR7qaOddGOsGTJkpw6dYoxY8aQO3dutm3bRoECBTh48CC5c+f+qmB838z2kjBhwg8+HhwcHGnSrFevXgEQGhpKaGjoB18THW+PYY5jaU3aYnl69zawb5+aKGjkSCM1axqw1rsybeU90bIdt29DkyZ2nDgRMXKuSxcDY8YYcXKC6IZkK+8JWG9bhgyB0qV13L0L2bKZzNIOc/0MrCV3h4aGYjKZMBqN4fXCP8dkMoX/G9XXWKrYbovRaMRkUv9X7ezMO+LDWn+P/9/1H1pS4o+TAORasoTg6T3R21nnKH9beU/exn/wzkEWnl4IgKezJ7+V+i3G2ya5++OstS1xNXcLYXHsHKHEClUn/cFGCPOHXVWhwi5ImF/r6DSX0j0lYyuOpdPGTgB03NCRs53P4urgqnFkQnxeqCGUOcfn8OvuX3kW+Cx8fyKXRAwuPZjOhTrjZO+kYYTRozO9/damMaPRSO3atXn58iX79u374HN+++03hg4d+t7+pUuX4uoqf0CEZTOZoHfvMty44QnAb78dIF++J9oGJTRx9mwixo0rzOvXKlk4OYXRtespSpW6r3FkwtIEBATQvHlzfH19iR8/9ibPiypz5257e3uSJ09OmjRpcHR0jJGYRYSQkBDu3r2Lj48PYWFStuRDjAYj6ZsOIn/oRQDGNu5LtubFNY5KmEwm+l/tz+UAVRu9far21EpSK0bPKblbRJWl5+4v8erVKzw8PMzWptDQUDZt2kT16tVxcLCs2rfRJW0xM0MQ7K4Bj3aqbafEUNEbPHJE+RAW0Q4zebctdvZ2lF1QNryWdN/ifRlbaazGEUaNrb4n0pbP23lzJz9t/onzT86H73Oyc6JXsV70K9EPD+evr4dnjnZEJ89FeUT62xFkn/OlibVLly6cO3fuo1/EAQYMGECvXr0ixZQmTRoqV65stoTu5eVFpUqVbOIXQtpiWUJDQ2nQ4DzjxxcGYNeuogwcaNA4qi9jS+9JbLdj3jwdQ4faERambkfImNHEihUm8uTJC+T94uPaynsCttMWc7Qjqrn3a19vKbk7KCiIu3fv4ubmhrOzc5RiMJlMvH79Gnd3d6uffC222xIUFISLiwulS5eO8s87qmzp9/iPcjvJv011pBffsJOiS0ZoHNWXsaX3ZPCKweGd6FkTZWVqy6kxOhGV5O7Ps5W2xIXcLYRFs3OG0v/C7qrwZD8EP4WdFaHiXnCP2/Wz9Do9f9b6kzyz8hBiCGHiwYk0zdWU/ClkxL6wPHd879B7W29WXVgVaX/z3M0ZVX4U6TzTaRTZ14tyR7qnp+cnv9SZTCZ0Oh0GQ/Q7Brt27cqGDRvYs2cPqVOn/ujznJyccHJ6f7i/g4ODWT+wmft4WpK2WJaiRR+QObOJq1d1eHvrOXpUT3ErHthmC+8JxE47DAb4+WeYPDliX9WqsHSpjgQJ5O/Xh9hKW76mHV/bfmvL3QaDAZ1Oh16vj/IkmW9LoLx9nTWL7bbo9Xp0Ol2M/q7Zwu9xxvalubJjI1kMNykVcIx1E5ZTd0ArrcP6Ytb+nviH+LPowaLw7clVJ+PqHDN3pkrujj5baYut5m4hrIKDG5TZqDrQnx+DwIewsxJU2guuqbSOTlNZE2dlSOkhDNk1BIPJwPfrv+fw94etoq60iBtCDaH8fuh3hnoPJSA0IHx/4ZSFmVptKkVTW/+8B1H+bdu1a1f4uslkonr16sydO5dUqb78D5nJZKJbt26sXbuW3bt3kyFDhi8+lhDWwM4O+vY18MMP6ldv1CjYsEHjoESM8/ODpk1h48aIfT16wIQJ6v+EEDHFWnO3hVSds3nyc44aRydH9lZvTJb16vbpeGNmgxV3pFu7iYcm8ixU1desnrk6VTNVjZHzSO4WWomJ3C2E1XH0gHJbYHtp8L0A/jdhV2Wo4A3OibWOTlN9S/Tln/P/cO7xOU48PMEfh/+gZ7GeWoclBHtv76Xzxs6RyrgkcU3CmIpjaJuvLXqddQ96eivKHellypSJtG1nZ0fRokXJmDHjF5+8S5cuLF26lH///Rd3d3d8fHwA8PDwwMXF5YuPK4Qla9bMxLBhcPeu+nJ29izIfEG2y8cHataE48fVtr09zJgBP/ygbVwibrC23P12FF9AQIB8DogFAQFqlIgtjB6NafX/6sedpItJa3pApVf78fprPZXaxWxNbvG+e6/uMfHQRADs9fZMqjwpRs4juVtoKSZytxBWySkRlPOC7aXA74bqUN9dDSrsAIe4W9rI0c6ROTXnUOKvEpgwMWTXEOpnr2/VpTKEdXsR+IK+Xn2Ze3Ju+D69Tk+Xwl0YVm4Yns6e2gUXAzS9/2PmzJkAlC1bNtL++fPn07Zt29gPSIhY4OgIvXurUU0A48bB4sWahiRiyKVLUK0a3Lqltj08YO1aKFdO07CE+Coxmbvt7Ozw9PTk8ePHALi6un62VrjRaCQkJISgoCCbKO0SG20xmUwEBATw+PFjPD09sZPhtZ/l5uHGmtL1ae89DQC/AdNAOtJj3aCdgwgMCwSgc8HOZE2c1eznkNwthBAWxDUllN8OXiUh8IEq9bKnHpTdBHbvlw6MK4qlKUanQp2YeWwm/qH+dNnUhfXN1lv9fEHCuphMJlZfXE3XTV155P8ofH/hlIWZWWMmBVMW1DC6mKNpR7rcUiziqu+/h2HD4PlzWLYMRoyAdHIB2aYcOgQ1aqj3GCBNGti8GXLm1DYuIb5WTOfu5MmTA4R3pn+OyWQiMDAQFxcXq//yENtt8fT0DP95i8+rtWQIz9L8TSJeUOvxDo5tOUShqtZf59FaHH9wnEWnVW10Nzs3BpUcZPZzSO4WQggL5JYBynuBVykIeQ6PdsKBFlDiH9DH3cEAoyuMZt2ldTz0e8jGqxtZdWEVjXI20josEUf4+PnQeWNn1l1aF77P3dGd0RVG06lQJ+xs+HfzqzrSrf0LqxBaiRcPunZVnekGA0yaBFOmaB2VMBcvL6hbF95UTSBvXti0CVKm1DQsIQDLz906nY4UKVKQNGlSQkNDP/v80NBQ9uzZQ+nSpa2+RElstsXBwUFGokdT0tRJWZCnFm3PLMIeA1d+nEChG6u0DitOMJlM9N7WO3y7cbLGJHRJaNZzSO4WlszSc7cQMc4jB5TdCDsqgCEA7q6GY12g8EyIo78fHs4eTK02lUYrVef5T1t+otI3lWyujIawLCaTiaVnl9JtczdeBL0I318nax2mVZ9G6vipNYwudkS5I71+/fqRtoOCgujUqRPx4sWLtH/NmjXmiUwIG9etG4wfD4GBMHcuDBkCieP2vCk2YeVKaNEC3vb/lS+vbgmPH3fL+AkNWXPutrOzi1JHr52dHWFhYTg7O1t9R7ottcVWlVw4iID8K3ElkDo3N3Pt1BUy5cuidVg279/L/+J92xuATAkyUS1xNbMeX3K3sCTWnLuFiFGJi0KpNeBdE0xhcG02OCeFPMO0jkwzDbI3oGaWmmy4sgEfPx8G7RjE9BrTtQ5L2CgfPx86bujIf5f/C9+XNF5SplefToPsDeLMRd8oF+D08PCItLRs2ZKUKVO+t18IETWJE6sSL6BGP02bpm084uvNmwdNmkR8Ea9fX41mky/iQiuSu4Uwr0z5srAug+rEjUcA+9qO0jgi2xdiCKGvV9/w7dHlR+OgN9+FJsndwtJI7hbiE1JWgWKLIrbPDYers7WLR2M6nY7p1afj6uAKwMxjMzly/4jGUQlbtPrCanLNyBWpE71Zrmac//E8DXM0jDOd6BCNEenz58+PyTiEiJN69YIZM1R5lz/+gD59VNkXYX2mT1flet5q1w5mzwZ7TWeiEHGd5G4hzC/z9J8Jq/4v9hiodfo/Ht97TNLUSbUOy2bNPjabq8+vAlA6XWlqZ6nN5uubzXJsyd3CEknuFuIz0jeDoMdwoofaPvYjOCeDNHW1jEozaT3SMrTsUPp49cGEiY4bOnL0h6PY6yWZia/3Mugl3TZ3Y8mZJeH7ksZLyqwas6iXvZ6GkWknyiPShRDmlz49NG2q1p8/h7/+0jQc8YUmTYr8RbxXL1WuR76ICyGE7SlcrRgbklYAIBEvWN96pMYR2S7fIF+G7Ym4ZX9i5YlmG/EkuVsIIaxYtu6Q/c3dSiYjHGgGT/ZrG5OGuhfpTp5keQA45XOKPw7/oXFEwhbsvb2XvLPyRupEr5+9Puc6n4uzneggHelCaK5vxN3K/P47hIVpF4uIvtGjoXfE/GcMGgQTJsTZOW+EECJOiDeyS/h6Oe+1BAUEaRiN7Rq3fxxPA54C0Dx3cwqlLGSW40ruFkIIG5BvNKRvqdYNQeBdC3wvaRuTRhzsHJhdczY6VCIbsmsId33vahyVsFahhlCG7BxC2YVlueN7B4D4TvFZVHcRqxqtIkm8JNoGqDHpSBdCY3nyQOXKav3mTZB5g6zH6NEwcGDE9vDhMGKEfBEXQghbV+n72ux0KwpARuNd/uk4XuOIbM+9V/eYdGgSAI52jowoN8Isx5XcLYQQNkKnhyLzIPmbL9MhL2B3dQh6pG1cGimauigdCnYAwD/Unx5be2gbkLBKt17eovSC0ozYOwKjyQhAqbSlONv5LK3ytopTtdA/RjrShbAAffpErI8fDyaTdrGIqBk/PvIX8XHjYPBg7eIRQggRu5782Dp8PdeKfzAajBpGY3t+3fUrQWFqpH/Xwl3JkCDDVx9TcrcQQtgYO0cotQoS5FPb/jex21cXO1PcvFNsdIXRJI2n5m1Zc3ENm65u0jgiYU1WX1hNvln5OHTvEAD2entGlh/Jrja7SOuRVuPoLId0pAthASpUgHz51PqxY+DtrWk44jMmTYpckmfcuMgXQ4QQQti+RqM6ctohOwAFQ86zZqhMdGIuZx+dZcHpBQB4OnsyqPSgrz6m5G4hhLBRDu5QZiO4pgFA/+I4hYIngDHu1UxN4JKAiZUnhm9329yNwNBADSMS1iAoLIguG7vQcGVDfIN9AcjgmYH97fYzsNRA7PR2GkdoWaQjXQgLoNO9PypdWKbp0yPXVR01Sr6ICyFEXKS303OifqPwbY8pC7QLxsb039E//HbigSUHktAl4VcdT3K3EELYONeUUHYTOMQHILnhGPpTveLkrd4tcregTLoyANx4cYMx+8ZoHJGwZD7BPpRZVIYZx2aE72ucszEnO57k21TfahiZ5ZKOdCEsRKNGkPbN3TKbNsH589rGI963aBF07RqxPWwYDBigXTxCCCG01eTPftzWpwKg0qv97Fy4WeOIrN+e23vCb0VPEz8N3Yp0+6rjSe4WwrI8fQrVq9sxZkxh2rWzo2tX9Ts5bhzMmwfr1sGBA3DrFgQHax2tsCqeuaDUWkw6BwDsrs+Cy1M1Dir26XQ6ZtSYgb3eHoAx+8dw9dlVjaMSlui/K//R63IvTvqcBMDZ3pk5NeewvMFyPJw9NI7OctlrHYAQQnFwgJ491QIwcSL8JXeJW4w1a+C77yK2BwyAIUO0i0cIIYT2XN1d2V6yLu33TAfg+YBp0KaaxlFZL5PJRP/t/cO3h5YdirO98xcfT3K3EJbn+XPYvl0PpOTQoc8/P1EiSJdOLenTwzffQJYskDUrpE4NehkaKN6VvDyGQjOxP/q92j7RE9wyQupa2sYVy3IkycHPxX5mzP4xhBhC6LKpC1tbbpWJIgUAYcYwBu0YxLgD48L3ZU6YmZWNVpI3eV4NI7MOknaEsCDffw+enmr977/Bx0fTcMQb27ZB06ZgfDOPXNeuMHKktjEJIYSwDDUXD+YFatROrYfbuXDonMYRWa//Lv/HwXsHAdUJ0Dpv68+84uMkdwthmfz8ovf8Z8/gxAlYuxZ+/139LleurDrW3d2hcGFo1049tns3+PrGSNjCipjSt+ayw9vSayY40Ayen9Q0Ji0MLj04fIJIrxterL64WuOIhCV44v+EqkuqRupEr5+tPsc6HJNO9CiSEelCWBA3N+jQQd3aGBKianoOH651VHHbkSNQvz6Ehqrt1q1hyhRV114IIYRIljY5i7JWo/Xl5TgRwpEfxpHj7CKtw7I6BqOBgTsHhm+PKj/qiye3ktwthOXKlw+ePg3l3393UrhweYKCHHj1So1Uf/ZMLY8ewcOHarl3Ty0Gw/vHCgiAY8fU8q5MmVQHe7FiULw45Mmj7v4Vccclh2ZkTg76uyshzB+8a0KVo6qWehwRzzEeU6pOod4/9QDoubUnVTNVxc3RTePIhFaOPThGgxUNuON7BwB7vT1tUrRhRr0ZODo6ahyd9ZCOdCEsTLduMGkShIXBzJnqNmRXV62jipsuX4YaNcDfX23Xq6dqN8otpEIIId6VZ+bPhJZfhQNh1Dy3kacPn5I4RWKtw7Iqi88s5sKTCwAUS12M2llrf9FxJHcLYdn0eogfHxIlCiJbtqh1cIeFwf37cPMmXL0KV66o3/WLF+H69ffnk7x2TS3LlqltV1fVoV62rFoKFwbpM7JxOj2GwvPQB96Hpwcg8AHsqQ0V94B93PlyXSdrHaplqsbma5u59+oeI/aMYExFmXw0Llp8ejE/rP+BYIOafCK5W3KW1VuG71nfryv5YzIBpoh/dXq12DDpSBfCwqRODU2aqNIuz56pSbI6ddI6qrjnwQOoUkVNiARQpgwsXQr28ldTCCHE/8lXriDrkpan7uNtJOY5874bTfstE7UOy2oEhwXz6+5fw7fHVBzzRV/qJHcLYZvs7SPqpJctG/mxgAC4cAFOn1YlYI4fV+tBQZGfs327WkDdBVyunCoRU6UKZM4ca00RscnOGUqvha1FwP8WPD8OB9tAyX9svqPvLZ1Ox9RqU8k5IychhhAmHpxIm7xtyJ4ku9ahiVgSZgyjn1c/Jh2aFL6veJrirGy0kiTOSdh0dtObJ/qD/1110SnwPgQ+hOCnEUvISwh9pZYwPzAGgyEIjCHvn1SnB50D2DmBfTywiwcObuDgCU4JwTEhOCUG5+Tgklz9Gy8tuKQCO8u/yikfK4WwQL16qY50UPX+OnSQkVSxydcXqlaF27fVdt688O+/4Pzl850JIYSwcY5DOkC3bQCU2r6WsNCx2DvIR+2omH18dvhtxtUzV6d0utLRPobkbiHiJldXKFRILe3bq32hoaoz/eBBtezdq8rDvOXnB+vXqwVUR3rNmmopVUrKwNgU56RQZj1sKw5hr+HuKjjzK+SNO/VTMyXMRL8S/Ri+ZzhhxjC6be6GVysvmXg0DngR+IImq5rgdcMrfF/HXA2Ymq8OjjfnYPS9RKnAk9j/1wGCH5vvxCYjmIJVZ3voq2i8UAcuKdUEwe6ZwT0TxM8K8XOodb1lfK62jCiEEJEUKKBGW+zerW5d3LgRasWticY1ExoKDRvC2bNqO3162LwZPDw0DUsIIYSFq961AXv7FaJUwDGyGG6y5KcptJzZW+uwLJ5/iD8j90bMAjqyfPRnBJXcLYR4l4NDROd6t26q4sDNm+q71c6d4OUFj9/pM7p6VQ1e+v13SJgQ6tSBBg2gYkVwctKsGcJcPHNBieWwp5bq4Ds/AjxyQPpmWkcWa/qX7M/iM4u59fIWO27uYOWFlTTO2VjrsEQMuvLgMLVWNOCK731Adf7+kVRHp+DVcFhNPKsHEgIER/GgeidwcAd7N3XHh52z2qfTAzo1GY3JCMbQN0uwGuke5q8uZBlDP3MC05vR8Pfhyd7/O7cjxM8GCfJBgvyQsIBad4gfxeDNRzrShbBQvXurD3sAEydKR3psMJnU6P+3t30mSgRbt0KKFNrGJYQQwjrcbtuMUjPUrHfpFy4D6Uj/rKmHp/LYX/VoNc7ZmHzJ80Xr9ZK7hRCfo9NBxoxqadcOjEZ14W3rVti0Cfbti5jM9PlzmD9fLR4eauLiZs1UKRgpE2XFUlWH/BPgRC+1fbidGvGaqJC2ccUSVwdXJleZTN1/6gLQe1tvamSuQTzHeNoGJszDGAYvz8CTA/B0Pztu7qLhzUe8NKqHk9jBqhRQ2sX0wZebnFOic88ArunANZUqseKaEpySqhIsTonB0VOVavlSJhMYAiHkOQQ/U+ViAn0gyAcC7kPAXfC/DQG3IegDo+ONIaqNL8/AzUUR+wtNgwwdvjyuLyCpQAgLVb06ZM2qJtLx9oaTJyF/fq2jsm3Dh8OCBWrdyQn++w+yZNE0JCGEEFak6eSfuDx7OlkNNygZeJwtM9ZQ9cf6WodlsV4GvWTcgXEA6HV6hpUdFu1jSO4WQkSXXq/KP+XNC337qs7zLVtUOaiNGyMmK/b1jehUT5YMWrSANm0gTx5t4xdfKGsPeHkObvylajvvqQtVj4JL3LjyWjtrbapmqsqWa1u49+oeo/aOYmSF6N8FJiyAyQgvTsGjnfBoFzzeq0Z8A3N84cfH8ObaILkcYX1KSO/kBB451R0a8bND/GyEumZky97LVK1RF4eYrmml06mJfu1dwTX1p58b+gpeX4PXV8H3IvieV8vrK2AyRH6u2zcxF/NHSEe6EBZKr4cePaBzZ7U9eTIsXKhlRLZtyRL4NWKeMxYvhuLFtYtHCCGE9bF3sGdfxbpk3aomdAoY9idIR/pHTTgwgZdBLwFok7cNWRNnjdbrJXcLIcwhYUJo3lwtgYGq9MuqVbB2raqnDvDoEUyapJZ8+VQ99pYtIZ4M6LUeOh0UngGvL8OT/ap8xJ56UHG3KlFh43Q6HVOqTiHXjFyEGkOZcHAC3+X/jkwJM2kdmoiKoCfwcAs83AoPt0Hwk0gPG03Q7ylMeBmxr0bSDCyrOAj3pMXVHRj/X2M8NBSj7mbMxx5dDvFV6ZaEBSLvNwSpi2EvTsLzE+rfBLE/2lSmLxTCgrVqBQkSqPVly8DHR9t4bNXBg7rwyYkAxo+HRo20i0cIIYT1qjW3H8/xBKDmox1cOHRO24As1GP/x0w+NBkAB70Dv5b59dMv+D+Su4UQMcHFBWrXhkWLVB31lStVeRdHx4jnnDqlaq+nTAnt2tlx6VICTB+umCAsjZ0TlFwNrmnU9rPDcKQTceUNzJIoC72LqbJzIYYQemzpoW1A4tN8L8KFsbCtBKxJBgdbw62/3+tED3RMQmPfVJE60XsW7cm/Ha/inrk9eGS3mIk6v4qdsyrHlOkH+HYmVDkELsliPQzpSBfCgsWLBx07qvXQUJgxQ9t4bNGjRy40bGhHSIja7thR1acXQgghvkTS1EnZkL06AI6EcqTDOI0jskxj9o3BP1TVT+hYsCPpPNNF+bWSu4UQscHFRU1kvHo1PHwI06dD4cIRjwcGwpIlevr3L02RIvb89ZfaJyycSzIo/S/Yuajtmwvhyh/axhSLBpUeRCr3VABsvLqRDVc2aByRCGcywYszcOYX2JADNuaAU/3h6QHgnYs99u6Qui4UmsaTcnsp9zIjq5+oSUX1Oj0zqs9gUpVJ2OntNGmGrZOOdCEsXJcuYPfm79/MmRAUpG08tuT1axg5sihPnugAKF8e/vhD3fUnhBBCfKk803sRhkreNc5u4uXTl9oGZGEevH7AzGMzAXCxd2FgqYFRfq3kbiGEFhImhB9/hCNH4MwZNSLd0zPi8VOn1F0yqVPDoEGq411YsIT5oej8iO0TvVSt6TjAzdGNCZUnhG/32NKD4LBgDSMS+N2AcyNhY07YnBfODYdXFyM/xyMn5OgPFb2h4TMovZbriatSfNV3HL5/GFDv7YZmG+hcuLMGjYg7pCNdCAuXOnXErcpPn8LSpdrGYyuMRmjd2o47d+IDkDmzunUzpufYEEIIYfvylSvIxsTlAEjCM9a0G6NxRJZl9N7RBIWpkQFdCnchhXvUJnqT3C2EsAS5c8PUqfDgAcydG0amTC/CH3v+HEaNgnTpoG1bOHtWuzjFZ6RrAjn6qXWTAfY1Ar9bmoYUW5rkbEKZdGUAuP7ienipNRGLQl/BtbngVRL++wbODP6/znMdJCkJBSZBrWtQ4xzkGw1JS4PegaP3j1JsXjGuPb8GQCr3VOz7bh/VMlfTpj1xiHSkC2EFevSIWJ88Oc6UcItRv/wCGzeqP4GeniY2bFAjTYQQQghz0PX7Lny98Oa1GA1GDaOxHHd87zDnxBwA4jnEo2+JvlF+reRuIYQlcXGB1q1NTJiwh/37w2jZMuLCXmgoLFwIefJAzZqwf7+2sYqPyDMSUlRV68HPYG89CAvQNqZY8HbiUb1O5dThe4bz4PUDjaOKA0wmeLwXDrSCNcnhyA9q4tt3JSkJBf+Auveg0l7I1hPcv4n0lM1XN1N2YVmeBKha6TmT5ORg+4PkTZ43lhoSt0lHuhBWoEgRKFZMrZ89Czt3ahuPtVu1CkaOVOt6vYllywxkyaJtTEIIIWxL7Z+bc9hJfaHJHXaFlYPmaByRZRi1dxQhBlXc/KciP5EkXpIovU5ytxDCkhUubGLxYrh5E/r3j1z2ZeNGKFkSypSBHTtkUJRF0dtBiaXglkltvzgVZyYfzZs8Lx0LqgnZ/EP96b+9v8YR2bCQl3Bpiirdsr003FoChncmVPDICXlHQ51bqvM8a1dwTfnBQy05s4Tay2sTEKou+JRJV4a93+0ljUeamG+HAKQjXQir8e6o9KlTNQvD6p05A23aRGy3bXuOChVs/4OSEEKI2HepUYPwdY9ZUpvt5oubzDs5DwB3R3d6F4vaDKGSu4UQ1iJVKhg9Gu7eVXcSp04d8diePVCxIpQqBV5ecaKv1jo4JoDS68A+ntq+tRiuTNc0pNgyvNxwEjgnAGDxmcUcvHtQ44hszMvzcKQzrE0FJ3pELt3imAAy/whVjkL1s5CzP8T79MTrkw5OotXaVoQZwwBolKMRW1puIYFLghhshPh/0pEuhJWoV099MANYvx5u3NA2Hmv0/DnUrQsBb+7Wa97cSK1a8oMUQggRMxrN6M09nar/Xdl3H4fW79M4Im2N2DMi/Mtfz6I9SeSa6LOvkdwthLBGbm7QvTtcvw7z50PWrBGP7d8PlStD6dKwd692MYp3eOb8v8lHe8Jj28/ZiVwTMbzc8PDtn7b8hNEkpei+iskED7bAzkqwKRdcmwWGd8oFJSkJxRZDvQdQeDokKvTZGdNNJhMDtg+g97aIAQhdCndhWYNlONs7x1RLxEdIR7oQVsLBQc0UD+pv8/S4cZHcbAwGaNFC3W4JUKgQzJxp+FzOEkIIIb6Yq7sr2wpWB0CPiWu94u4tZTde3GDh6YUAeDh50LNYz8++RnK3EMLaOTqqSUfPn4dlyyBHjojH9u1TnelVq8Lx45qFKN5K2wiy91HrpjA1+WiA7dcN71ioI7mT5gbg2INjLDy1UOOIrJQhBK7Ph025YXc18Nke8Zh9PMjcBaqfU6VbMrQEu6h1gBuMBjpv7MyY/RET1w8tO5Q/qv2Bnd7O3K0QUSAd6UJYkQ4dwMlJrc+bB35+2sZjTYYPhy1b1HqSJLBmjZogSAghhIhJpf/sSxAqede8tpVHd3w0jkgbI/eMxGAyANCrWC88nT0/+xrJ3UIIW2FnB02bqvmuli+HbNkiHtu6VV0ofPfCodBI3lGQrLxaD/KB/U3AGKptTDHMXm/PlKpTwrcH7hzI6+DXGkZkZcL8Vf3z9d/A4Xbgez7iMbdvoMBkqHsfCk9Tdz5EQ4ghhBZrWjD7+GwAdOiYUX0Gv5T5BZ2MKtCMdKQLYUUSJ1YfsAB8fWHRIm3jsRYbN8LQoWpdr1cfXtPIXBxCCCFiQaZ8WVifogIAnrxiw/djNY4o9t14cYNFZ9SHFg8nD34q8tNnXyO5Wwhhi/R6aNJEdajPnw/p00c8tnSp6mDv1UuVtRIa0NtDieXg+qa4/ZN9cLKftjHFgnIZylE/e30AfPx8GLV3lMYRWYHQ13B+NPybXtU/D7gX8ViSElBqDdS8DNm6g6NHtA8fGBpIvX/q8c/5fwB1wePv+n/TuXBn88Qvvph0pAthZbp1i1j/4w8wSgmzT7pxA1q2jNgeNQrKl9cuHiGEEHFP/F9+CF8vsXM9YaFhGkYT+0btHRWpNvrnRqNL7hZC2Dp7e1Xy5fJlmDpVDZgCCAmB33+HzJlVKc+wuJUuLINzEii5CvQOavvy73BnpbYxxYLxlcbjaOcIwKRDk7jxQuYj+aDQ12QOWY39pixweiAEP414LFVtqHQAKu2DNPXgC0uv+IX4UWNpDTZd3QSAs70z65qso1nuZuZogfhK0pEuhJXJl0/V0gO4dAm2b//k0+O0oCBo1AhevlTb9epB376ahiSEECIOqtKpLvtdCgCQzXCdVQNmaxxR7Ln18lak2ujdi3b/5PMldwsh4hJHRzVQ6vp1GDQoonzV8+fQtSvkzQteXtrGGCclLqJKcrx1qB34XtIsnNiQMUFGehXtBaiSIn28+mgckYUxBMGl37HflJUcoYvRhTxT+3V6SNcMqp+BMv9CkmJfdRrfIF+qLKnCrlu7AHBzdGNLiy3UyFLja1sgzEQ60oWwQj+9c0f01Lg7b9ln9e4NJ06o9cyZ1e2TUkpMCCGEFq43aRC+nujPZRpGErveHY3eo2iPz45Gl9wthIiL4seHESPgypXId+RcuACVK0ODBnD7tnbxxUmZO0P6N3VVw/xgXwNVD9uGDSw1kORuyQFYc3ENu27u0jgiC2AMg2tzYX1mONELXYgagW5CD+lbQo2LUGIpeOb+6lM9C3hGhUUVOHD3AACezp5sb7WdMunLfPWxhflIR7oQVqhOnYg6oZs2qREMIrJ//oEZM9S6szOsWgUe0S9NJoQQQphF4+m9uKNLCUCFVwc4+O8ejSOKebdf3mb+qfkAxHeKT/cinx6NLrlbCBHXpU4NixfDoUNQtGjE/jVrIHt21dkeHKxdfHGKTgffzgaPNxNE+l6AI53BZNI2rhjk7uTO6Aqjw7d7bu2JwWjQMCINmUxw7z/YlBuO/BBeA92Ejnt2pQirehqKL4b4Wcxyuif+Tyi/qDzHHx4HILFrYna12UWR1EXMcnxhPtKRLoQVsreHzm/mmDCZYOZMbeOxNFevwg8R5Wj54w/Ik0e7eIQQQghnV2e2F6oOgB4T1/pM1ziimDdm35jw0ejdi3QngUuCjz5XcrcQQkQoUgT274eFCyFZMrUvMBCGDFGlPnfv1jK6OMQ+nqqXbu+mtm8thuvztI0phrXO25qCKQoCcPrR6fAL4nHK8+OwoxzsqQOv3inpk6oWYZWOcty5N7hnNdvpHvs/pvyi8px5dAaA5G7J8W7rTb7k+cx2DmE+0pEuhJX6/ntwclLr8+ZBQIC28ViKt7VVX79W2y1aQPv22sYkhBBCAJSc2ZsgVPKueXUrTx8+/cwrrNf9V/f569RfgKrv2aNoj48+V3K3EEK8T6+H1q3VhKQ9eoDdm3kLL12CcuXUZKVPbTeNWA6PbPDtnxHbx7rCi1OahRPT9Do9v1f5PXx78M7BvA5+rWFEsSjwIRz6DrYUgsfeEfsTF1cTiJb5DzzNe5Xfx8+HcgvLce7xOQBSuadiT9s95EiSw6znEeYjHelCWKkkSaBpU7X+8iUsXappOBajb184fVqtZ8sGs2ZJbVUhhBCWIUvBbGxMXg6ABPjy3w/jNY4o5ozbP44QQwgAXQp3IaFLwo8+V3K3EDFr9OjRFC5cGHd3d5ImTUrdunW5fPmy1mGJKPLwgN9/h+PH1Uj1txYuhBw5YNkym642YhnSN4XMP6p1YzDsbQShr7SNKQaVSleKBtnV3C6P/B8xet/oz7zCyhmC4fwYWJ8FbiyI2O+eGUqtVp3oSUqY/bRvO9EvPLkAQJr4afBu603mRJnNfi5hPtKRLoQV69o1Yn3aNPkA9d9/6lZwUKP1V6wANzdtYxJCCCHe5dz/u/D1gtv+w2gwahhNzPDx82HOiTkAuNi70KtYr48+V3K3EDHP29ubLl26cOjQIby8vAgNDaVy5cr4+9v2xIm2Jm9eOHBAlfV8O3/EkyfQvDnUrg337mkbn80rMAkSFlLrftfgcAeb/gI+rtI4HO0cAZh0cBK3Xt7SNqCY8mCrqoN+eoCaVBbAwRMK/A41zkOa+jFydf+R3yPKLyzPpaeqdEw6j3R4t/Xmm4TfmP1cwrykI10IK1aoUMSohNOnVR29uOr+ffguom+CSZMg99dPnC2EEEKYVY3ujTnipG4Lzht6iXWjF2kckflNOjiJoLAgADoV6kTSeEk/+DzJ3ULEji1bttC2bVty5sxJ3rx5WbBgAXfu3OH48eNahyaiSa+HTp3g4kWoXz9i/4YNkDMnzJ9v03272rJzgpIrwOHNVYw7/8C1OdrGFIMyJshIjyI9AAg2BNNvez9tAzI3/7uwtyHsrgqvr6p9Oj1k7gy1rkK2HqB3iJFTP/Z/TIVFFbj49CIAaT3SsrvtbjIkyBAj5xPmZa91AEKIr9O1Kxw+rNanTYOSJbWNRwsGg6qn+vy52q5bN2IyViGEEMLSnK9dh29Xqgml7CcvgcFttQ3IjJ4GPGXG0RkAONk50ad4nw8+T3K3ENrx9fUFIGHCD5dcCg4OJjg4OHz71StVwiI0NJTQ0NCvPv/bY5jjWFrTqi2JE8Py5bB2rY7u3e3w8dHx6hW0awcrVxqZMcNAqlTRO6atvC8x2g6n1OgKzcH+YBMATMe7E+ZZEDzzmv9caP+e9C3Wl/mn5vMk4Akrzq+gS8EuFEtdLNrH0bodkRjD0F+bjv7cb+gMEXflGBMVx1BgSsR7+ZFYv7YtT/yfUHlpZc4/OQ+oci5eLbxIFS9VrP98LOp9+QrmaEd0Xisd6UJYuUaNoHdvePwYVq+GBw8gZUqto4pdY8eC95u5QFKnVpOvSm1VIYQQlqrBrJ95tGomyUxPqfbMm7N7T5G7VD6twzKLyYcm4x+qvph+X+B7Urin+ODzJHcLoQ2j0UiPHj0oUaIEuXLl+uBzRo8ezdChQ9/bv23bNlxdXc0Wi5eXl9mOpTWt2uLkBBMmODBvXi527UoLwObNenLlCqNDhzOUKXM/2se0lfcl5trhRG776mQM24TOGEzw9jp4u0wkTOcSQ+fT9j1pkLABswJmAdBhVQfGZB6D7gsTttb/tzwM18gXMgNP443wfUF4cMGxLXcDy8KB+0DUfme+pC1+YX4MuT6Em4E3AUjkkIhBqQZx8cBFLnIx2sczF63fF3P5mnYEBARE+bnSkS6ElXNygh9+gJEjISwM5s6FX37ROqrYc+wY/PqrWtfr4e+/4SODa4QQQgiLED9hfNbmqEqb80twIIxj3aeQ+8R8rcP6ar5Bvkw7Mg0Ae709fUv0/eDzJHcLoZ0uXbpw7tw59u3b99HnDBgwgF69IuY2ePXqFWnSpKFy5crEjx//q2MIDQ3Fy8uLSpUq4eAQM6UTYoultKVxY9i4MYzOndXodH9/R37/vRD37xdg6lRDlP7GWkpbvlastMNQAdPO0uhensTN9IBqSTZi+PYvs5/GEt6TysbKeM/15uLTi1wOuIx/Bn8a52gcrWNo3o6wAPTnh6G/Mhkdam4aEzqM33TALtdwcjt6EtXKcl/allfBr6i2rFp4J3pKt5Rsb7mdTAkzRbc1ZqP5+2Im5mjH2zuvokLTjvQ9e/Ywfvx4jh8/zsOHD1m7di1169bVMiQhrFLHjjB6NBiNMHs2DBgAVvx3MMr8/dVt4WFhanvAAChdWtuYhLB1kruFMI88k3/CUGkpdhipfGoLAa8DcHU330hPLcw8NhPfYFUyonWe1qT1SPvecyR3C6Gdrl27smHDBvbs2UPq1Kk/+jwnJyecnJze2+/g4GDWzhZzH09LltCWunXV39Nu3WDpUrVvxQo9+/bpWbgQKlaM2nEsoS3mEKPtcHCAUitgcwEIe43+9hL0KSpBxtYxdDrt3hMHHJhQeQI1ltYAYPDuwTTI2QBne+foH0uLdjzyhsPfqwli3/LIhe7bOdglKYbdFx42Om3xD/Gn7sq6HH1wFICk8ZKyo80OsiXO9oVnNy/5nSdar9N0slF/f3/y5s3L9OnTtQxDCKuXJo2aqR1UaZf167WNJ7b07g1Xrqj1QoUiRrcJIWKO5G4hzCN/xcJs81QTm6Qy+bCqx1SNI/o6AaEBTDo4CQC9Tk//kv0/+DzJ3ULEPpPJRNeuXVm7di07d+4kQwaZ0M5WJUyo7vJZtgw8PdW+Bw+gcmXo1w9CQjQNz7a4Z4JvZ0dsH/sRXl3RLp4YVC1TNSpmVFdibr28xdTDVvCZJcwfjnWDHWUjOtH1jpBnBFQ7AUmiX+v9SwSFBVFneR323VF3ASV0Scj2VtstphNdRJ+mHenVqlVjxIgR1KtXT8swhLAJP/4YsT5jhnZxxJb169XoewBXV/WB0QYuogph8SR3C2E+vt9H3Bqd9p+1Gkby9f46+RdPAp4A0ChHIzInyvzecyR3C6GNLl26sGTJEpYuXYq7uzs+Pj74+PgQGBiodWgihjRtCmfPRoxCN5lg3DgoUQKuXtU2NpuSvhlk/E6th/nD/qZgCP70a6yQTqdjYuWJ6FC10UfuHckT/ycaR/UJj/fBprxwZVrEvsTFodppyDUI9LHz4SPUEEqTVU3YcXMHAB5OHni18iJ3sqgWkhGWSNOOdCGE+VSoAJnffGfdsQMuXdI2npj05Al8/33E9u+/Q5Ys2sUjhBBCfImGozpy1S49AGX9j+C9zDonewoxhDBu/7jw7QElB7z3HMndQmhn5syZ+Pr6UrZsWVKkSBG+/PPPP1qHJmJQ6tSwdStMmBBx0fLYMShQIKL0izCDQn9A/Kxq/cVJONVP23hiSJ5keWiXvx2g6n0P3zNc44g+wBAMJ/vA9tLgd13ts3OBglOg0l7wiL1R4EaTkbb/tuW/y/8BEM8hHptbbKZAigKxFoOIGVY12WhwcDDBwRFX994Wgw8NDSU0NPSrj//2GOY4ltakLZYnNtrRoYOePn1Ula8ZMwxMnGiMkfNo+Z6YTNChgx2PH6vrgDVqGGnb1sCXhGIr/7dA2mKJzNEOa/8ZgOTu6JC2WJ7YaId3sWpk3jcTgHuDZxPasGyMnCcm27L4zGLuvroLQPVM1cmRKEek80ju/jBpi+Wx1dxtMpm0DkFoRK9XJbXKlYNmzVRpLT8/NVfFzp0wdaq6Q0h8Bft4UGI5bC0KxmC4PAWSV4ZU1bWOzOyGlxvOsnPLCAgNYOaxmXT9titZElnIVfGXZ+FAC/XvW4mLQ9EFEP/9u+RikslkosvGLiw9q65YOdo58m/TfymWJnbKyYiYZVUd6aNHj2bo0KHv7d+2bRuuZvzr7+VlnaOBPkTaYnlish0pUjjg6FiZkBB75s0zUqLEVpydDTF2Pi3ek927U7NuXUEA4scPplGjXWze/HW3z9nK/y2Qtliir2lHQECAGSPRhuTu6JO2WJ6YbId9s8IE7FuAK4HUuOHFisX/4JbIPcbOZ+62GEwGfr0UUei8tK40mzZtivQcyd2fJm2xPHE9dwvbU6AAnDgBXbvCggVq37x5cOgQrFoF2aRc89dJkA/yj4fjP6ntw99BtTPgkkzTsMwthXsKfi72M8P2DCPMGMaAHQNY3Xi1tkGZjHBpMpweAMY3kwC8rYWerRfov3Q60S83eOdgZh2fBYCdzo4VDVdQIWOFWI9DxAyr6kgfMGAAvXr1Ct9+9eoVadKkoXLlysSPH/+rjx8aGoqXlxeVKlWy+hlrpS2WJ7basX27ngULICDAgRcvqtK+vflHoGj1nty7B23aRPzZmjPHjvr1vzwh2cr/LZC2WCJztOPt6G1rJrk76qQtlie22rFuxCoaPdyEJ68IXHWaxv+OMvs5Yqotay+t5f7p+wCUSlOKXo16RXpccvfHSVssj+RuYcvixYP589Xo9M6dISAAzp+HwoVVp7pMf/OVsnSFh1vhwUYIegyH2kLZjaCzrYrKfUr0Yfbx2Tzyf8Sai2vYf2c/JdKW0CaYQB842AZ8tkXs88wNxZZAgjyahPT7wd8ZtU99jtOhY2HdhdTJVkeTWETMsKqOdCcnJ5ycnN7b7+DgYNYPbOY+npakLZYnptvRpUvEKIM5c+zp2BF0upg5V2y+JyYTdOwIvr5qu0ULaNLEPH/CbOX/FkhbLNHXtMMW2i+5O/qkLZYnptvh2q8N9FCjuAtt34idfix6u5j54m3OtphMJiYenhi+PbD0wEjHltwdNdIWyxPXc7ewba1bq87zRo1UR7qfHzRpAl276ilTJoa+OMYFOh0UnQ+b8kCQDzzcApenQrYeWkdmVm6ObgwrN4yOGzoC8LPXzxxodwBdTHU6fMyDzaoTPfjtpKc6NQI97wiwc47dWN5YdHoRvbZFDCiYWm0qLfK00CQWEXM0vTTm5+fHqVOnOHXqFAA3b97k1KlT3LlzR8uwhLBqhQqpBeDkSTWhjC348094e5dtqlTwxx/axiNEXCW5Wwjzq9G9MccdcwKQL/Qim6au1DiiqNl9azdH7h8BIG+yvFT5pkqkxyV3CyGEZcqeHQ4fhlatIvZNm2bHkCElePBAu7isnnMSKLYwYvtUP3hxSrNwYkq7/O3Injg7AIfuHWLVhVWxd3JjKJz4GXZXj+hEd04O5bdBgQmadaJvuLKBdv+2C9/+rcxvdP22qyaxiJilaUf6sWPHyJ8/P/nz5wegV69e5M+fn19++UXLsISwep06RazPmqVdHOZy+7aaJOetuXMhQQLt4hEiLpPcLUTMOF25Rvh64PhFGkYSdWP3jw1f71eiX6TRaJK7hRDCssWLBwsXqu+Ljo5q36VLiSha1J79+7WNzaqlqAzZ3iRAY4iaADMsUNuYzMxeb8+4SuPCtwfuHEiIISTmT+x/G7xKw6WIu+FIWQOqn4HkFWP+/B9x8O5BGq9sjMGk5qfrWrgrv5SR70a2StOO9LJly2Iymd5bFrytSyGE+CJNm8Lb0sPLlsHLl5qG81VMJvjhB3XLIUD79lC1qrYxCRGXSe4WImbUnNkbX1TyrvFwF3cu39Y4ok87+fAkW69vBSCDZwYa5WwU/pjkbiGEsA46nSrBtX8/pEmj5tby8dFRrhzMnKn+nosvkHekmoAUwPcCnOqvaTgxoUbmGpRNXxaAa8+vMef4nJg94b31sDk/PDuktvUOUGAylFmv7gTQyMUnF6m5rCaBby6WNM3VlCnVpsR+qRsRa2xr1gMhBKBGF7RurdYDA2HxYm3j+Rrz5kXcFp46NUyc+OnnCyGEENYoaeqkbEyvJuF0JRCvzpM0jujTxh2IGIn2c/GfsddH1D6X3C2EENalUCE4dCiMXLlUqYzQUPjxRzUpaUgsDDS2OXZOUPzviDIjV6bCg63axmRmOp2OcRUjPgsM8x7Gq+AYmGzZGAanBsKe2hDyQu2LlwEq7Yds3WNuQrgouP/qPlWWVOF54HMAKmasyMK6C9Hb2ASzIjJ5d4WwUR07RqzPmmWdownu3IFeEXN1MGcOeHhoF48QQggRk5L+0j58vdjeTRgNRg2j+bgbL26w4vwKAJK4JuG7fN+FPya5WwghrFOSJDB06EF69DCE75s9GypWhCdPPvFC8WEeOSDf+IjtQ20hyLZ+kIVTFaZJziYAPAl4woQDE8x7gqDHsKsKXBgdsS9Nfah2AhIVNu+5osk3yJdqf1fj7qu7AORPnp/VjVfjaOeoaVwi5klHuhA2KlcuKFlSrV+4APv2aRtPdJlMqtb769dqu107qFZN25iEEEKImFTxuxoccFbzD+QIu8bakQs/8wptTDwwEaNJdfJ3L9IdFwcXQHK3EEJYOzs7E+PGGVm0CJyc1L69e6FwYThzRtvYrFKWLpDiTW2zIB840sE6R7h9wsjyI3HQOwAw8eBEHr5+aJ4DPz0MmwvAo51qW2cHBSZByVXg6Gmec3yhUGMojVY34uzjswBkTJCRzS02E98pvqZxidhh//mnCCGsVadOER3oM2dCqVLaxhMdf/8Nmzer9ZQp5bZwIYQQccPl2rUovuIkAPbTlsIv333mFbHracBT5p+aD0A8h3j8WPjH8MckdwshhG1o1QqyZoW6deHhQzWBdIkSav6tmjW1js6K6HRQdD5syg3BT+HeOri5EDK21Toys/km4Td0KtSJP478QUBoAL/t/o3ZtWa/97zQUB0+PvDqFTx/ri66+/mpJSBAlRAKDVVLPvd5VE38I/Y6VVfI35Acr8AVvDxUCvfz4O6uloQJIXFiNZm5PpaGCRtNRqbdnYb3C28AErkkYkuLLSRzSxY7AQjNSUe6EDasQQPo3h2ePYPVq+HpU5VoLN2TJ9CjR8T2jBng6alVNEIIIUTsqTe9J09XTCMxz6n6ZA/XTl0hU74sWocVbvqR6eETav1Q4AcSuCQAJHcLIYSt+fZbOHZMdaYfPao6PGvXVhdJe/TQtDS1dXFJDt/Ogb311faxnyBpGXDLoG1cZjSo5BDmn1yAX+hr5p6Yh93RXvjfzsq9e+pCzMOH9rx8Wfuzx3GwC2Fyqx7UrDQzfN+eS6VoPHUFj3yTf/R1er3q50iZMmJJn14tGTJApkyqdJE5/s8O2T0kvBPd2d6ZDc03kDlR5q8/sLAa0pEuhA1zdoa2bdWHnZAQWLgQevfWOqrPe9v5D9CoEdSpo208QgghRGzxTOzJ4swVaXV1BU6E4N11Mpn2zdA6LAACQgOYdnQaAHY6O3oU7RH+mORuIYSwPSlTgre3+k65YoWqStKrF1y+DNOmgb30KEVNmnpqFPqNBRD2Gg62gQq7QG+ndWTRYjTCtWtw9iycO6eWixfh+vUkBBXuBxUGY8TAzMuDYMWqd175+R7sJPEfs7p7A0pli6hJO3VrN3r/PZEwg8Nn43r8WC2nTn34OZ6ekCULZMsGuXOrJU8eSJ486h3sc47PYfxBVfder9OzvMFyiqYuGrUXC5shf/aEsHEdOkTcWj1njvrgY8mjBzZuVLcMgrpF648/tI1HCCGEiG3pR3aAxmoyz1KHtmA0GNHbaT+10cJTC3ka8BSAJrmakM4zHSC5WwghbJmLi/obnzUrDB+u9s2ercq9rFihSmyIKCg4BR7tAv/b8GQvXJoIOfpqHdVHmUxw6xYcOgSHD8OJE3DypLoz4YMO9YBvp4G7D+RYDakOw/0iuLhAihQmHB2fkylTAhIn1pMwoZqI3M1NLSldTlPOrjbxuAOAASfOOs8iQ8O2rKgLwcFqCQxUJWFevYooEfP0qVoePVKj38PCPhzey5dw5Iha3pUsGRQsCIUKqbkAihWDRInef/2Wa1v4cWNEObvfK/1OnWwyaiAuko50IWxclixQtizs3g1XrsCePVCmjNZRfZifH3TuHLE9aZJKbEIIIURcUqpRBfa6FqJUwDGyGG6yauhfNBz2vaYxGYwGJh6MKHrep3gfQHK3EELEBXo9DBumvlu2b6/udt6yBUqXhg0bIFUqrSO0Ag7xodgi2F4WMMGZIZCyGnjm1joyQI3qPntW3YHg7Q3796vO6c9xdIRvvoEsWeLh7/Ar21EfCgoN6IdXi114eOgICwtj06Z9VK9eHQeH/xsYcHctHGgJhgC17ZISu9LryJeoMPm+oA1Pn8Ldu+pCz82barlyRd1FcefO+6959Ag2bVLLW1mzQvHiqt+kXDl46XSGxisbYzAZAKiTpA6dC3V+/2AiTpCOdCHigA4dVEc6qNEDltqRPmSISnoAFStCmzbaxiOEEEJo5XrtmpRafgwAh1n/gMYd6WsvreX6i+sAVMxYkXzJ8wGSu4UQIi5p2RLSpIF69eDFC1VGo2hRNdF0rlxaR2cFkpaG7D/DxfFgDIEDraDKEbBz1CScmzfBy0stO3eqEd6fkjYtFCgAefOq0ii5cqlO9LclfkIN7ck5YxJXn1/l2FNvDj7ZQjXPah8+mMkEF8bA6YER+xJ9C6XWgmvKL2qPXg9Jk6qlYMH3Hw8IgEuX1AWDs2fh9Gk4flz9X37X5ctqmT8fcH+AXacaGOK9BqBGhrq0id/6i+ITtkE60oWIA+rVU7cnWfKko8ePw9Spat3ZGWbNsuwSNEIIIURMqvtHd54un2oRk46aTCYmHJgQvv12NLrkbiGEiHvKlIEDB6BaNVX64949KFUK/v1XjVAXn5FnGDzYBL7n4eVpODcc8g6PlVOHhsK+faok24YNqrP4Yzw8oEgRVeqkaFFV+uRzfQgOdg6MLD+SxqsaA9B/R3+qZKry/hMNIXC0o6oZ/1b6FvDtn2DvEv2GRZGrq7oQUKBAxD6TSV1QOHZMlbE5cECVsQkNBRz8oVktDPHuqSff+5ZNo5ZwLUMw587qqVtXXUyQzz5xi/bFFoUQMc7ZOWKEWEgILFqkbTz/LyxMjZo3GtX2L7+oK9tCCCFEXOWZ2JMtmSoAqElHu03WLJYDdw9w+P5hAPIky0OljJUkd/WanQoAAEbvSURBVAshRByWLZvqdCxUSG2/fAmVKsGqVZ98mQCwc4Zii0H3ZlzrhdHw9MinX/MVAgJg7Vpo1QqSJIHy5dUcav/fiZ4ggRqAN3myqoX+7Bls3Qq//QZVq0Z9IF7DHA0pnLIwAGcenWHp2aWRnxD8HHZVjtyJnmeE+pnEYCf6x+h0kDEjNG6sytMdOgS+vrBzl5Hsg1pDyhPqiS/TwbL/MIXE4/LlhPzyix158kCGDNCzpyqF8/YzkbBt0pEuRBzRoUPE+pw56sqrpZg+XV31BXVF9+eftY1HCCGEsARpR0Qk75IHt2I0aPMN7d3a6L2L9Uan00nuFkKIOC5ZMti1S41MBzVgq3Fj9d1OfEbC/JDrF7VuMsCh1hAWYLbDBwXBunXQpInqPK9fH5YsUR3Eb+n1ULIkjBihJhN98gTWrIHu3SFfPrCz+7Jz63Q6xlQcE779y65fCDGEqA3/m+BVHB57q207ZyjxD+QaZFHDul1cYFvYIC6yBgB3R3dmld5Izw7JyJ49ckfK7dvq4kPJkpA6NXTrBgcPWlZ/izAv6UgXIo7ImjWiNvrly2rSUUtw9y4MHhyxPXs2ODhoF48QQghhKUo3qcg+F1XkM6vhBmuG/hXrMVx7fo11l9YBkMItBU1zNZXcLYQQAgA3N1XSpW1btW0yQdeuahSzdCR+Rs4BkFCN3ObVZTg9+NPP/wyjUU0S2q6dushRrx6sWKFGpL8VPz40awZLl6qO8717YdAg+PbbL+84/5DyGcpTKWMlAG6+vMm8k/PwNFzFfkcp1VYA56RQYReka2y+E5vJglMLGLNfXQzQ6/SsaLSCjvVyMmkSnD4dxqxZXkyaZKBixYj68AAPH8K0aWqi0owZYeDAT5fPEdZJOtKFiEPeHZU+d652cbyrRw/w81PrnTqppCOEEEII5WrtGuHrdrNXxvr5pxyaggnVG9Lt22442jlK7hZCCBHOwQH++kt1Gr41dKjqUDcYtIvL4untodgi0Dup7cuT4fG+aB/m9m114eKbb6BsWTVB5qtXEY8nSgTff68mhH3yRHWiN2sGCROaoxEfN6rCqIj1Pb+SL2AQuuDHakf8bFD5ECQuGrNBfIF9d/bRYX1Ex8mUqlOomqlqpOckTx5A165GvLzg8WNYuBBq1wYnp4jn3LoFo0erMkjFi6uqAO/eESCsl3SkCxGH1K+vap+Bql/3/7NTx7ZNm9TtY6Bm1h49Wtt4hBBCCEtTb1pPnuMJQNXHe7h94Vasnft54HP+OqVGwbs6uNKxUEfJ3UIIId6j08HIkfD77xH7ZsyAFi1UyRfxER7ZIO/INxsmOPQdhPl/9mWhobB6tapdniGDunBx61bE4+7u0Lq16jx/+BD+/FM919ExRlrxQYVSFqJhjoYAPAp6yfSXb/4jJC0NlfaDW4bYCyaKbr+8Tf1/6hNqDAWgS+EudP226ydfkyCB+ln/+y88egQLFkDlyqp0zlsHD0LHjpAyJbRvr0rpyB0b1ks60oWIQ5yd1SQjoOqm/f23drEEBqr6YW9NnAienpqFI4QQQlgkz8SebMmgJh11IYjt3X7/zCvMZ/ax2QSEqnvC2+VrhwsJJXcLIYT4qB49YNGiiDIh//yjSowEBmoalmXL2gMSv7m1y+8anBr40ac+fKg6zdOlg4YN1WSgbztk9XqoUkV9x/fxUaOkq1bVsPSaycSIVCl5WzFm3At4mrwOlNsGTjE8HP4L+IX4UXt5bZ4EPAGgYsaKTK46OVrH8PCANm3U+/LggZq8NHfuiMcDAtTdG0WLQv78qkpAgPlK44tYIh3pQsQx338fsf7nn9pdCR0zBm7cUOtlyqjRCkIIIYR4X5LBbcLXC++JnUlHQwwh/HHkDwB06OhetLvkbiGEEJ/VqpUanevsrLY3bVITkr5+rW1cFktvB0Xng52L2r4yFR55R3rK0aM6mjWDtGlVGZeHDyMeS59eTRh65w5s2QLNm4Ora6xF/2FGAxz9kay3pvJdfLXL1wijQ74BO6dPv1YDRpORVmtbcebRGQAyJ8zMioYrsNfbf+aVH5csGfTsCadPq8nZf/xR1ah/6/Rp+OEHNUFp377q/RPWQTrShYhjcueGIkXU+pkzcOxY7Mdw9arqSAc1OceMGRY1SbcQQghhUSq1q8URpzwA5Am7zKapMV8rfcX5FTz0U9/U62ari+lZJsndQgghoqRGDdWp6+amtr29oUIFeP5c27gsVvwskPedWmmH22EI9mftWh0DBpSkRAl7li+HsDD1sF6vRvpv2wbXr6sJQ1Ol0ib09xiCYX9TuDYLgF8TgtObDumZx2fy4PUDLaP7oN92/xY+sbqHkwf/NfuPBC4JzHJsnU6NPp8+XY1Snz8/oj8GVLnd8ePV5KTNm2vTPyOiRzrShYiD3h2VHtuTjppMqqTL21p5vXtDjhyxG4MQQghhbc5VjJjoyn9SzNZmM5lM/H4oooRMjyI9JXcLIYSIljJlYMeOiEktjx6FcuXUhJfiA7J2gySl1LrfDZb0GUiTJvZcvJgo/ClJkqhJXW/eVPOVVKoUuRa35kL9wLsm3F2ltnX2pC69hM6FVJ3xwLBARu4Z+YkDxL7VF1YzfM9wAPQ6Pf80/IdsibPFyLnixYO2beHQIThyRNVWf1u33mCAZcugcGH1e7Jtm9RRt1SW9CsnhIglTZtGjA5YuhT8/GLv3P/+q2qGAaRJA0OGxN65hRBCCGtVY0ZPXqOSd/V7u3h0xyfGzrXvzj5OPDwBQMEUBXl2sqTkbiGEENH27bdqNHry5Gr7zBkoWzZyaRKhBATqmX9xHoGhqiZOq2//oESWfQBkz25i7lxV/mPkSFXixeIEP4OdFcBnu9q2c4Uy6yFDC/oU64OzXrVrzok53HhxQ8NAI5x5dIY26yLK542rOI4qmarEyrkLF1Z17O/eVeV6EieOeGz3blXvvnBhdcHEGPMV/UQ0SEe6EHGQmxs0a6bW/fzUJDCxITBQTUDz1u+/q6uyQgghhPi0ZGmTszlVWQDc8WNjl5ibdPTd0eg/5utNz54RNVwkdwshhIiOXLlUZ/rb0iMXLqjR6nfvahuXpfDzU6U9MmSAdt0zM+gfNWJbrzexrEc7Rvy2k1OnwmjfPqLuvMUJfAjbS8OzI2rbwRPKe0FKdTddknhJqJOkDgBhxjCGeg/VKNAIzwKeUXd5XfxD/QFomaclvYr1ivU4kiaFX39VF0lmz4bMmSMeO34cGjSAvHlh1SrpULcU0pEuRBz1bnmXefNi55xjx8Lt22q9YkWoXz92ziuEEELYApdeEbN75vTaGiPnuPHiRnid0BRuKbi5obHkbiGEEF8lSxbYs0dNjAlqzqwyZeL2BIuBgTBpkupA79sXHj9W+6ds6c6lZ8UASONxlSbZp1r2nCT+t8GrFPyvvTuPi6rq/wD+GYZdBTUWcUNIxX1XcktUxC3Tyq2SzNT6mWbmUz7ZopmlZlammT6Vppa5b6W4oILmvqGIKyLu4o6iIMvM+f1xmBlGmGGbYRY+79fLl3Mv37lzDpd7v3PPPfecB6fkspsf0GUX4N1GL+xFnxdR0U2O8/PH8T9w8tbJki6pVpY6CwNWDUBiciIAoEXlFvjlhV+gsOAv2s0NePtt4PRpYMUKOa66Rlwc0K+fbFBfs4ZDvlgaG9KJSqmWLeXEowCwb588YZvThQv6E4zOmsVJyoiIiAqj5/v9ccKxNgAgOP04opeYvjF99oHZEJBXaK9W+RTfTlcCYO4mIqLiCQyUjek1a8rlxEQ5zIvmZm1pkZEBzJ0rfw//+Q9w545cr1DIIVhjTyhR540FgIMLAODZrL+h0PT0tjYPzwGR7YBHCXK5TA2gy26gfMNcoWWUZfDhcx8CAAQEJkRPKMGC6hu/bTy2J24HAPiW8cXaAWvh5uRmsfLkpFTKRvMjR4CICP2JSePiZA/1Vq2AyEg2qFsKG9KJSimFAnjrLd3yggXm/byxY4H0dPn6/feBunXN+3lERET2xkHpgINtumiXr3z5u0m3/zD9IebHyMfUXB1dcXrJMOvO3Y8fy26NBw/KK8pVq4DFi4Hffwd+/VU+I71wIbBsGbBuHbBjB3D8OHDtmu5LCRERlZhq1eQwL7XlPWFtY/rFi5YsVckQQg6pWq8e8O67wPXrcr1CIYddPXlSTjZZvz4AzzpAIzn8iQICysNvAyory1vJcXI4l9SrctmjDtDlX6BsoMG3vNviXVQqKwfMX3N6DQ5fP1wSJdWzPG45ZuybAQBwdHDEyn4rUdWjaomXIz8KBdC9u+z0uHkz8Nxzup8dPgyEhQGdO8vXVLIcLV0AIrKcQYPkY2SZmfK6c8oUwMnJ9J+zdaucZBSQE81MsNzNZyIiIpvW4cfRyGj6C5yRibD47UhNSYV7OXeTbPv3mN+RkpECAOikmoqIDbI3nEVz94MHsvH79Gng3Dkoz5xBp9hYOD58CDx8WLxt+/oC/v7yX+3asvWiXj2gTh3AxcU05SciIj2VKwNRUUCnTsDZs7IRPSRETrCoGfrF3uzcCXz0EXDokP76Pn2AL7/UPSmup85/IC4uhyI5BoqHp4CTU4FGX5RAaQvg/jFgR6icYBQAKjQBOm4BXH2Mvs3dyR2ftf8MozaNAgBMiJqAiNcjzFvWHGJvxuKtv3W9CX/o+gPa+7cvsc8vCoVCTjwaFiZ7qH/6qfxaBMjjqGVLeSNmyhT7PX6sDXukE5ViXl4yeQNyTLYNG0z/GZmZwAcf6Ja/+Qbw8DD95xAREZUGNZvUxpaK8qLPV9zBqg9mmWS7aqHG7IOz5YLKEWf/+j/tz0osd2dlyWeZZ84E+veXz72XLy8H0v2//wO+/x4OEREod/UqFMVtRAeAmzdlb/aVK4GvvwZeew1o0kTOyt6iBTBihOzdnpDA56eJiExI05hep45cvnRJNqzb2wSkCQlybpGQEP1G9I4dZfpZu9ZAIzoAODgiq+UvUEMOsYZTU4DkE+Yucv7uHga2d9I1oj/TCui8I99GdI1hzYbB39MfALDp/CbsubzHXCXVcz/tPl5e/jJSM1MBAIMbD8bIliNL5LNNQaEAevYEjh6VTy48+6zuZ0uXAkFBwMcfF7+PAeWPPdKJSrm33pLXj4Ac3uWll0y7/Xnz5MzsgBzfa9Ag026fiIiotEl5ozcwcwcAoNLKjcBvHxd7mxHxEUi4L8c4DUqcibNnXQGYOXcLAZw/L59Z3rxZDp776FG+b1M5O8OhenUoKleWrTFeXoCnp/xXpowcYNTREXBwkIPRPnkiZ3V78EAORnv3LnDjhpzl7saN3I3kmgb9I0fkFxkAqF5dtnyEhQHdugEVK5rhF0JEVHr4+cnG9JAQ2TM9MVGeZnfuBKpUsXTpiufhQ+Crr4Aff5RpSKNhQ3lzulu3As45Ur4x4p1eRlDmSkCdCewfCoTtBRws1JR3Zz8Q1RXIzG6t9W4LhEQATgW/2+7i6IIJHSZg6N9DAQCf7vgUUYOjzDrRp1qoEb42XPs9p5lfM8ztOdeik4sWlYODHEv/5ZflCHaTJsmvNRkZ8m9r4ULZN+DNN+XXITI9NqQTlXJdusix6q5ckY8KXb8ur0lN4c4d/UfBf/xRnviJiIio6PpO/T9c/vEbVBfX0fnhXpz49xgatm9SrG3OOpDds/3xM7j693DtepPnbiFkt7xVq4A1a2R3PUPc3IDGjYGmTYFGjYDatZEZEICIY8fQ44UX4GSK8egyMmRXyDNn5AC1J0/K7l6nT+s3sF++DCxaJP8plUDbtsCLL8oZwapXL345iIhKoUqV5PQVISFyyouEBNkzPTpaNrTbGiGAJUvkMC5JSbr1vr5Fb9w859QftV1joUg5C9w7BJz9Eaj7H5OWu0Du7Ad2hAFZcgg4+IQAHf4BnMoWelNvNH4D03ZPQ/y9eOy8tBM7Enegc2Bn05Y3hyn/TsHG+I0AgGfcnsGa/musZnLRonJ2Bt57D3jjDWDaNOCHH+T0LzdvAsOGyQlt58zRn6yUTINNWkSlnFIpEzoAqNXy+tBUJk4EkpPl6zfe4EmciIjIFJxdnbGjgbzgVEKNQ/+ZU6ztnbp9CpEXIgEAHvtm4vFDZwAmzt2nTgH//a8cwDM4GPj229yN6H5+spvVTz8Bx44BKSlylq2ff5ZDu3TqBFStatqWfWdnoFYtoFcv+Uz0H3/IxvTkZNlV8quv5Gxerq6696hUsvf8hx/K8dXbtgVmzwZu3zZduYiISonKlWVjemD2HJXnzgGhobZ3Sj1+HGjfHggP1zWiu7gAn3wibxIMHVq0HsJqhRNULX4BkN17OvZz4NEFk5W7QG7v029E9+0MhGwsUiM6ICf5nBQySbv8WdRnEGYaQm1rwlZMiJK9+xRQ4K9X/oJ/eX+zfJYleHoCU6fK+/+vvKJbf+SInKB02DDbO5asHRvSiQhDhuheL1hgmmFAT5zQPQ1dpow8uRMREZFp1Jv6jvZ1+6PboFapi7yt2Qeyx0a/2QApe18DYKLc/fChbARv2VJO5Dl9uuzZraFUym6I33wDxMYC167JgT5HjpQ90S35TLKHhyzbp58C27YB9+8D27cDY8fKiUlz2rsXGD1ajkXQr58cpkalskixiYhsUdWq8t6lZrLEU6fkSFr371u0WAXy6JG8r9q8ObAnx3DfffrIxs2vvwbKlSveZwiv1kDt7PG8VWnAwf8rubk7bu+Tw7nkbETv8DfgWLyJzgc0GIAGPg0AAPuv7tf2GDelS8mX8OrqVyEgf1dfdvwSYc+GmfxzrEFAgHzYLzpaf9z9+fPl+Om//SY7TlLxsSGdiBAQIDt5AXKo0t27i7c9IeR1puZE/emnphsuhoiIiIBWPdtip3tLAEAt1UWs/bpoj5TdT7uPxbGLAQEoI3+EUMvLg2Ll7pMnZWN4lSry/8OHdT9zdAS6d5dXdElJsuVk3Dh51WfNY5W6usovS999JwfzPXNG9lZv0EAXk5kpr2K7d5ezgH37rW20AhERWYHq1eX9Ss346MeOydNpSopFi2XU+vVA3boyNWjun9auLe+nrl0rr7NNpvEUwL2qfJ0UCVz804QbN+DOQSC6m8kb0QHAQeGAL0O+1C5PiJpg0l7p6Vnp6L+qP+6l3QMAvFD7BXzS/hOTbd9adeggR6ibOVM3Ufz9+8Dw4fJnJ09atHh2gQ3pRARATjqq8fvvxdtWRITsvAXIXgUffFC87REREVFuF17orn0t5q0q0jbmx8xHamYqEN8DqvPyrnqRcrcQ8tn8bt1k4/LPP+tPHNqsmRxw/fp1+UVh6FA5SaitCgqSdxtOnJD/xo2Tg+BqXLok11WtCowYIXsqEBGRUYGB8jrSx0cuHzggR95KS7NsuZ6WlAT07St7nV+9Kte5usr7q7GxQNeuZvhQp3JAy7m65SNjgCe3zPBB2e4d1Z9Y1ISN6Bp96vRBM79mAICYpBisO7POZNv+cOuHOHjtIAAgoHwAFvdZDAdF6WgCdXQE3n9f3vd//XXd+t27gSZN5Dx26ekWK57NKx1/RUSUr5de0t2xXLFC/9q3MDIz5aNtGt98oz+sKBEREZlGr5nv4QFk8u52YyduXk7K5x36VGoV5hyaA6gcga0ztOsLlbvVamD1ajl8S+fOwJYtup+VKQO8847sVnjkiBz+xNu7UGW0CQ0ayF/alSuyC2J33Q0OpKbKse6CgoDXXpON7kREZFCdOkBkJFChglzeuVOOmpWZadlyAfKe8YIFshf66tW69V27AnFx8v6qi4sZC1DlBcB/oHydcQ84YqYea/djgR1dgMxkuewTYvJGdABQKBT6vdKjJ0Atij/+yLK4Zfjp0E8AABelC1b1X4UKbhWKvV1bU6kS8Oef8niqWVOuy8oCJk+W87jv22fFTwJaMTakExEAwN1dzu8FAI8fyyeTi+KXX+TTzgDQurX80kNERESm5+Xnhc1VOwAAyuIxNr4/q1Dv3xi/EReTLwJH3gbu1AVQiNytVgNr1sgrsb59ZUO5RkCA7H1+7ZpsRG7cuFDlsllOTrJ7YkSE7AY2apS8mQDI39fSpUCjRsDLL/PZaiIiIxo1kvdly2bPZblxIzB4sGWnn7h8WTaYDx0q56MG5INVf/0FbNokR/QqEc1/BJwryteX/gKubzEeX1gPTgM7QmVDPQB4twM6/GPyRnSNHrV6ILiKnNk87lYcVp0qYkNEtjN3zmDY38O0y7O7z9b2ei+tQkPlffzPPpO91QE5fn9IiBK//dYAqamWLZ+tYUM6EWnlnHS0KMO7JCcDEyfqlr//3rqHOyUiIrJ1rqMHaF8HbSncxfRPB38C0jyBqEnadQXK3ZGRQIsWwCuvyGfYNZo3B5YvB86dk73PPT0LVR67Urs2MHu2fOb/66/1h7FZuxZo2BDKIUPgnlS4pwiIiEqLli2Bf/7R9fBeulTenyypOTY1hAB+/VU+fBQZqVsfHi4bI199tYSveV19gKa6p8hwaASQZaKW0JQEYEdnIP22XH7mOSAkAnAqa5rt50GhUODLjrpe6V9EfwGVumh3TFIzU9F3RV88znwMAAhvFI5hzYbl867SwdVV9kQ/ckR+hQMAIRTYsOFZtGjhqDdRLhnHhnQi0goOlo/SAcCuXUBCQuHe/9VXwN278vWrrwLPPWfa8hEREZG+XmNfxRml7AbXNu0o9q3fVaD3nblzBpEXIoFdnwFpspE339wdGyvHQA8LA2JidOtbtZLd8Q4dAvr313V3IqB8eeCTT+SY6TNnAn5+cr0QcFiyBJ1HjYLDuHG67o1ERKQVEiKflNaklXnzgM8/L7nPv3ZNjtb19tu6SU+rVJEPHi1ebMGpPgLflMOtAMDjRODEJGPRBfP4imxET7shlys0AzpulmOzm1mXwC5oW60tAOD0ndNYFresSNt5L+I9nLwtn/iq710fc3vOhYI9+/Q0agTs2yfnQ3d1lXelzp9XoH17OUTvkycWLmABXLggbwp06GCZp1TYkE5EWgqFfq/0hQsL/t7ERNnxCpB3O6dNM2nRiIiIKA8OSgfsbdlZu3z2s98K9L6fD/0M3K8BHHwPQD65+84dOdZ5kyb6Y6A3bQps2ADs3y8b2Hmxapi7u5z56/x5efVaUT6W75CVBeXMmXLw0jlz5OClRESk9cILwKJFuhTz9ddy9DBzW74caNhQP+299ZYcCz3nVBgWoVAAreYBDs5y+cx3wP3jRd9e2k3ZiP74klz2rA903AI4l8yTZQqFApM7TtYuT9o5CVnqwuXDxccXY8GxBQCAMk5lsKr/KpRxLmPSctoLR0fZaH7oUBaCguQQPkIA330ne6sfL8afkrncuwfMnQu0bSuHUZowQXb+3LGj5MvChnQi0hMeDiiV8vWiRQW/w/fpp0BGhnw9ZgxQvbpZikdERERPaTVjJLIgk3enk9uR8STDaHxKegoWHlsIbP8aUMln5vPM3SqVbqLMX37RPU/v7w8sWQIcPgz07MkG9MJwd5dXrxcuQDV+PFTO2Y0gd+/KMQuaNwf27rVsGYmIrMxrrwGzckwDMmaMTEPmkJwMvP66nD/s/n25zs9P9kKfP18+aGQVPIKA+p/K10IFHHwbKMqQKBn3gagwICVeLpetCXTaBriWbHf7jgEdEVIjBAAQfy8ef534q8DvPX37NEZsHKFdnvfCPNTxqmPqItqdoCBgypR/MWWKCpqvIydPymGVpk+37JwEgPz8rVvlsejnB7z7rv5XJIVCfhUtaWxIJyI9fn66O+xXrhTsDt/hw3LMOkA+3vbxx+YrHxEREelr0LYRdni0BgBUF9ex5vNfjcYvPr4YKYm1gbjXABjI3TExcsy3ESNkNyAAKFdOXlmdOSNbNRx4KVFknp5QT5qE7XPmQK2Z7R2Qw+e0bQsMH64bL4+IiDBqlOyFqvHmm3JUMVPas0c+fPVXjjbc/v2tpBd6Xur9F/DIbjC+exA4/7/CvT/rMRDdE0jOnu/EvTrQeTvgVsm05SygSSG6IWom75pcoF7pqZmp6LeyH1Iz5TjxQ5sOxaBGg8xWRnujVAIffqjGkSO6ueEzM4H//ldOUnr1asmX6eZNYMoU2fO8a1f5dEhGjj4iDRsC33wjR80bP77ky8dvv0SUS2GGdxFCdqzSmDChdM8tRkREZAlJfXtqX7suWm8wTgiB2Qd/ArbqJirTy92pqfLqqWVLOSOVRng4cPYs8NFHchwYMok0b2+oFi+WA5Y2bar7wW+/AXXrAitWlPzMekREVuqLL+T9XUCOhNW3L3DwYPG3m5UFTJoEPP+8bJwDZF5csgRYtkw7Gpf1UboArXI0nh8frxvjPD+qdGDXy8CdfXLZ1QfoFAmUsdyj5c/7P49OAZ0AAOfvncefsX/m+573N72vHRe9gU8DzOo+K593UF4aNAAOHADGjdM9aBgdLRvX160z/+cLIW9kDRwIVKsmRzzQHIsA4O0NjB0LHDsm+xyMGyfjLIEN6USUS8+eui8La9cCDx4Yjt2wAdi5U76uWVMOoUpEREQlq8+37+IuKgAAwm7vxpX4y3nGRV2Mwtm9NYFLIQCeyt3//itnocr5PG/9+nL94sW6iTLJ9J57TrYG/fij7PkPALdvAwMGAK+8AiQlWbZ8RERWQKGQ83L16yeXU1PltWt8fNG3efUq0LGjbKRXq+W6du3kONGvvWYDo5f5PA8EZveEy3wIHPkg//eoVcC+cCBpq1x28pRjonvUNl85C+jpXumZqkyDscviluG3GDk3jLuTO1b0XQF3J3ezl9FeubjInt5RUUDVqnLdvXvASy/JYVXMMRFpejrwxx+y/0a7drL3eWb2Llco5JMgq1fL4/S773S95i2JDelElIuLi/zSAABpacDKlXnHZWXJTmsa06ZBO7YWERERlRyPih7Ykj22qDvSEPnBnDzjfj70CxD5jXZ52jTAWf1E9jTv0AFISJA/cHYGJk8Gjh6VVzZkfo6OwOjRsuf/yy/r1q9dC9SrJ68uiYhKOaVS3tvt0EEu37kjh3+4ebPw29q4UQ7lsnu3btuTJ8ueuP7+pipxCWgyHXB5Rr6+vBy4vsVwrBDA4VHA5eyLfKUb0GEDUKGJ2YtZEO2qt0NoYCgA4ML9C/gj9o884xLuJeDtf97WLs/pMQd1veuWSBntXYcO8kZSzq8ic+fKe/7nzpnmMx48kP02AgKAN97QfwjS21sOOZiQIOcmePll62pnYkM6EeVp8GDd60WL8o5ZvFiB06fl69at9U+0REREVLI8x+jG2q6zPTLXz+9k3ME/K54B7tQDADz3nBovB8QALVoAM2bohhBp00ZeQX32mXVduZQWfn6y+9WKFfJqEpAz3g0cKIfYMfaoIBFRKeDqKoebaNhQLicmAj16AI8eFez9mZny/vELL+imo6heHdi1S6Y+pdIsxTYfVy+gqW7INhx+F8hKyzv2xCTg/Dz5WuEItF8N+FjXDfMvOnyhff3Vrq9y9UrPUGVg4OqBSMlIAQAMajQIgxsPBplOxYrAqlVyrnk3N7nu+HE5J7pmfryiSEqSx161arJT5o0cIxE1aybbnq5cAaZOlY3s1ogN6USUp+bNZecnQN6hP39e/+fp6UpMnqz7hjF9ug089kZERGTHuo/qi1OONQEAbZ7EYO/anXo/j7gRDRGlmalNYEnLWVC0fg44KccWhbOzTOi7dgF16pRgySlP/foBp07J4V00/vxTDr+za5flykVEZAXKl5eTjVbPHtL76FE5MWhWPvNTXrsmh3KZkaPduU8fOcd2mzbmKm0JCBgsh3kBgEcXgBNf5I6JnwvE6YZOwXMLgcrWN4tq2+pt0SWwCwAgMTkR3+/7Xu/nn2z/BIevHwYA1KpYCz/3+BkKNkaYnEIh5z4/eFD3tfDRIzl6wYgRcliWgrp8WU4YXKOGPPZSUnSf8dJLchTBw4dl73QXF5NXxaSsoiF9zpw5qFGjBlxdXREcHIyDppgtgoiKRaGQM6FrLF6s//ONGwNw7ZpMVi++yKe+iUob5m4i6+OgdMC+lp21y+cmzte+zlBlYNPGZ4GUqvDCbez37o7A2R8AGRkyoGlT+VztRx/ZYFc8O+blJWe6W7JENyPs5cuyFejrr3UD+hIVAHM32ZsqVYDNm2WjOiAb1t97T2lwjubt22Wv1z175LKTk5yaYs0aK55QtKAUCqDlPMDBSS6f/ha4meOG+uXVwKGRuuVmPwABr5dsGQthauepcFDIJssvdn6B+LtyIPzN5zfju33fAQCclc5Y3nc5yrmUs1g5S4MGDYBDh2Qjt8a8eUD79voTgubl8mU5F0/NmsCcObrGdxcX4O23gTNn5PHXrp3tdMy0eEP68uXLMXbsWEycOBFHjx5F48aN0bVrV9y6dcvSRSMq9QYNAhyyzxKLFumu1e7fB1avrgVA/nzKFAsVkIgsgrmbyHoFf/suVNlf8Tue3IGsTNk1769DEUjb9T7aYjeOozGCb+cYP/XDD4H9++WVElmn114DYmN1gwKr1XL8gW7dgKKce7Oy5AxiiYnAsWOyu9nevbJL2L//yivmEyfkDH537+omnyWbxdxN9qpuXTnMi2YksvnzHbTXqhpCyDlBwsJ0p8xq1eTpbvRo22nAy5dnXaDRV9kLQk4omnEfuPUvsPd1uQ4A6n0M1BljoUIWTPPKzTEmeAwA4EnWE7y94W0kPUrC4HW6IVymh05HU7+mFiph6VK2rGwTmj9fDq0EyK8KzZoBf/+dO/7aNWDkSNmA/ssvuglEy5SRfTYuXgT+9z+gtuXnty00izekf//99xg+fDiGDBmCevXqYd68eXB3d8eCBQssXTSiUs/PT37ZAOSdxKgo+Xr6dAc8fiy/qbzxBlC/voUKSEQWwdxNZL0atG2E6HLBAAB/9TWs/vw3AMCXX2dgzJOFiEYIKiN7QEofH9mV79tvORa6LaheXXannDRJ19MhMlKOxffVV7Kng4ZaLRvBN24EfvhBPoPdo4ecVc/bW3bDfOYZIDBQPo0QHAy0bQs8/7z816qVHEKmdm3ZK97JSf7foAHQsyfw7rtyGKB//pGzgbGh3eoxd5M969AB+P133fKff9bDa68pERcnh5Do2xcYP17XMaxbNzmUS3CwZcprVnX+A/iEyNepV4DdA4GdLwLq7K7AAYOBxrbRE+7Ljl+iRvkaAIDoi9EI/i0Ytx7LOyE9avXA6ODRFixd6fTWW/K+e2CgXL53D+jdW45Cd/OmXB43Tjag//yzrgG9XDl5///SJfn1oVIly9WhuBwt+eEZGRk4cuQIxo8fr13n4OCA0NBQ7Nu3r8DbyUpNRZZj7qoolEoocwyuk5WaangjDg56j7HmF+uouQUDICstDQafHVIo4KgZmb+wsU+eGH1c09Hd3WBsVlYWREYGstLSoMjM1ItVpadDGPmyW5hYpZubdiwqVUYGhJEByQoV6+oKRfYFgjozU68uxmLz266DiwscsvdzYWLVmZlQ5/HZ2lhnZzhk/w0aitXsE3VWlrwYAaDOyoJa80h1Xtt1coJDUWJVKqiNDFilcHSEMvuCOb/YweGO2LxZxnYNU6NZwyc4e07ARaGAs4vAhPEKZKXm3q5Qq6F68qRAZRBCQJVmYDKUwsYW4rhXPXV8Feq4t7JzxNPHPGD8HPE0aztHiKwsg8c8ULjj3lLniJz7RKlQ5HuO0G435/kkv0EeLYC5O59Y5m4AzN2Wzt1XXugCLJXH4yvfjETMDzPxR+YzaKeQ67KgkK0OCxZAUaUKNEcRc7eN5O4PPwSCg+H45pty1q67d6GaMAFi+nTZWH7liuxNnmPWPccc5VMpFBBGul8qhYAiZ6zmB/fuyX+nTuWKdXR1xfPe3hBz5yKrRg05zkJWlryCVqsBpVIe905OgIMDVGo1hJFzpVKp1J178ol1UCrhUIRYtVoNdc7Yfv2QFRjI3G0EczdztzXn7v59gCtfAhMnyuU1q5yxapUDfH2B2zez4KKQ+Xj8J8D4j+WfouY61hpyd17X0nnlCUOxepr8AmwJBjKTobgaCaWjPBZEpTCoGs4CDORka8vdZZzLYF7oLPRe1hsAcPPuFbhAAR93H/wW9jNU6em2k7uzWds5oijX3Q2DgAO75dAsERtl3PqVQORWV6iFAx48AJTIgIsiC+5l5L330aOBihVkbFaqbV93K4Qw9JdjftevX0eVKlWwd+9etG7dWrt+3Lhx2LlzJw4cOKAXn56ejvQcJ56HDx+iWrVq+DUoCO55jOVYqV07tP/pJ+3ymueeM3hx4N28OdrOm4fIyEh06dIFEWFhyMjZqyOHCvXqIfSvv7TLG7t3R2rOqWZz8AgMRNc1a7TLW15+GQ8vXMgz1t3PDz03bdIub3vtNdzP8UU1J+cKFdBb0z0YQPTQobh95EiesUpXV7y8f792+d9Ro5C0e3eesQDQ79gx7et9H36Iq9u2GYx9ad8+7Qnm4Oef49I//xiMfXHHDrhkDzx2dMoUJKxYYTC2x8aNKFOlCgAgZsYMnP/zT4OxYatWwbOmnFjr5Ny5OPW//xmM7fznn6iY/djy2YULETtzpsHYDr/+Cp+WLQEA55ctQ8y0aQZj282aBb/n5cQeF9evxyFN9s5Dy6lTUaO7nNDjytat2D9unOHYSZNQo7dMGjd27cLu0YbvuDb9+GPUHDgQAHDr0CHsHD7cYGyjMWMQlD0A+r24OGwfNMhgbO2h76DHtPdw+7YCVVzOY/qzvQ3HvvEGGo8dCwB4fO0aInr2NBj7bP/+aPbJJwCA9Hv38HenTgZj/Xv1QqvJkwHIhLc2x/niaVVDQ9E6x8wxK5s0MRjr27YtHvXsiS5dusDJySnfc0TIfN1Ys+s7duQ5AuY5R2RmZmLDqFHIeioH5JTzHHH8++9x7ulB/HOwtXPEc9Ono1r2oyCn1q1Dk/798eDBA3h4eBh8T0li7tZXWo5LgLnblnJ3YPhgBI7/Fj64g2QXF0Q8+6zBWOZuySbPEX//DeVHH0GxfDl2VqmC6+UMjxH7WvZnCicn7A4IwJU8GkM1XgkNhWNWFhRPnmD/2bO4aOB3BgAvnz0L1+wL+0OVKiHeyCDDL8bHo2z2BW2Mjw9Oe3kZjO2RkIDy2bkj1tsbcd7eBmO7XriAZ7L/Bk498wyO+foajO188SJ8sxtwzlWogMN+fgZjmbuZuwHm7pxsLXf/eOV7HEzpCgBoVW4L3q821mCsNeTueu+8g/ojRgAAHpw/j619+xqMLUzurtX8Hlp2S4K6QjOkNl6Jv7v0MhjL3C2VlnOEqa+7xyWsx7V0eY7oX+kn9K4412CsNZwjipq7LdojvbCmTp2KSZMm5R+Y7fbt24iIiNAuq4zcvbl77x4iIyMBAJGRkcgwcqfwwYMHettNM3KXLuXRI73Y1Bw9Q56Wlpamv90HDwzGZqSn68feu2cwVqVS6cU+uX3bYCwA/dikJKOxW7ZsgSL7jmX6tWtGY7dt2wZF2bIyNp8ZCaKio+FQQd6uysgn9t9//4XDuXMyNj7eaOyevXuhvHxZxp45YzT2wIEDUGb/rjJPnjQae+jwYThm79vM2FijsbGxsTiVff8q68QJo7HHY2NxKvtud1Y+5T158iTOZe87lYGEoHH6zBkkaGKvXjUam5gYj/Hjo7FpUwAenE/JJzYR17K3qzZy0QUAly5dQlJ2rDByXADAtWvXtH+XwkjvAAC4kZSk9zdszJ07d+AKaI/9/M4RObfLc0R2rJnOEfnRO0ckJhqNtbVzRExMDE5k3xF/ePy40VhbwNytY+vHJXO37eTuK9euYO+oMfBd+S9qpVw0GsvcLdnkOeLwYWDAALh36IDMX3812MMQAHZNn47HPj7I8PDAk2XLgLg4g7Gb2rXTnSNWrdIfMuYp5198ERVv3EC5q1eN9gS3RczdzN0Ac3dOtpa7O3a8jHO7niA52RVeXkZ6QMM6cnd8fDwuafLxzZtGYwuTuwHgkaIydqePxpMo40+gMHfrtlNazhH5Kcx1NwA4OAh07HgZPfwvAPsNx1nDOaKouduiPdIzMjLg7u6OVatWoU+fPtr1gwcPRnJyMtavX68Xb+jOeNKVK3neMVA4OOg/amLssRSFAmqlUntnXGGkW79CoYDy6cdHjMj1+EgBY1VPnsDY7jEWm5mZiR07dqBTp05wcnLSj01PN/pFtzCxSldX/cdHjD1qUphYFxft4yPpqanYtnWrti7GYtWZmUYfyVA6O0OR85EQE8U6ODnpPz6SR6xmn4R27QqX7N+xOivL+KMmjo76j5gVMFaoVFAZexytqLFqNR7cTcfp0ypcvrQTL/Zur7dPno5VGXt0TanUf2zM2KPkhYktxHGfpVJhx65d2jvj+Z0jCnrcW+Ic8fQxbyw23+1a+ByRmZmJrZs2oVNISJ7HPFDI495C54ic+8TF3T3fc4RGzvNJ8r178KlUyap6tTF3G49l7mbutsbcfSPhMo6t2YVkt0y89GY/5u5ixgJWmrtv34bi4kWIgADgqZ7hJZK7N25Ep/r14XzjhhxWxskJcHSUw3hkZUGpUEChUgFCQJWVZbwMjo66R8nziXVwdIRDEWLVKhXUOeomGjRAZoUKzN1g7i5MLHO3ge1aSe5Www2nTinQoF4mlLCt3K1KT88zTxiKzZMQUKScgPKZBoBjGZvO3clp93E95Trq+dTPN9YQq8zddnTdHZ/ogrLlHFCtmn1fd1u0R7qzszOaN2+O7du3axO6Wq3G9u3bMWrUqFzxLi4ucMlxoGq4eXjArQBfUgz9YWhkZp8snZyc4JRjLKLibtcSsY6ZmVA4O8PNwyPXz6yxvEa5uxusSx4bLvh2SzhWs09c3Nx09XByAnKcHPPdbmFicyQUk8UC8K7sgvLembgfoch/n+RxvBpUmEnOChFrrHx6x3z2P1Ns1xKxxo75kiqDKWMVjo4FO+blhgu83ZKMNbhPCrFdl4Ie8yWIudt8sczdBSpEicbaS+72r1cLlWvVQEREBHO3FcWaPHeXLQsEBBSqDKaMVTg7w61WLTjVq5dvbGEuQs0Vm+f7mbsBMHcXJpa5u0CFKNHYp3P3c88B8uxgW7kbLi755omcsQZ5ttNfttHc7ebhAT9f/yKXwVSxvO42rFHjgscWZrv5xZZ07rb40C5jx47F4MGD0aJFC7Rq1QozZ87E48ePMWTIEEsXjYiIiPLA3E1ERGRbmLuJiIiKz+IN6QMGDMDt27cxYcIEJCUloUmTJti8eTN8jUwSQ0RERJbD3E1ERGRbmLuJiIiKz+IN6QAwatSoPB8pIyIiIuvE3E1ERGRbmLuJiIiKx8HSBSAiIiIiIiIiIiIismZsSCciIiIiIiIiIiIiMoIN6URERERERERERERERrAhnYiIiIiIiIiIiIjICDakExEREREREREREREZwYZ0IiIiIiIiIiIiIiIj2JBORERERERERERERGSEo6ULUBxCCADAw4cPTbK9zMxMpKam4uHDh3BycjLJNi2FdbE+9lIPwH7qYi/1AFgXa2SKemjymybf2QPmbsNYF+tjL/UA7Kcu9lIPgHWxRszdeWPuNox1sT72Ug/AfupiL/UAWBdrVNK526Yb0lNSUgAA1apVs3BJiIiIzCclJQWenp6WLoZJMHcTEVFpwNxNRERkWwqSuxXChm+Vq9VqXL9+HeXKlYNCoSj29h4+fIhq1arhypUr8PDwMEEJLYd1sT72Ug/AfupiL/UAWBdrZIp6CCGQkpKCypUrw8HBPkZjY+42jHWxPvZSD8B+6mIv9QBYF2vE3J035m7DWBfrYy/1AOynLvZSD4B1sUYlnbttuke6g4MDqlatavLtenh42PQfUU6si/Wxl3oA9lMXe6kHwLpYo+LWw156s2kwd+ePdbE+9lIPwH7qYi/1AFgXa8TcrY+5O3+si/Wxl3oA9lMXe6kHwLpYo5LK3fZxi5yIiIiIiIiIiIiIyEzYkE5EREREREREREREZAQb0nNwcXHBxIkT4eLiYumiFBvrYn3spR6A/dTFXuoBsC7WyF7qYe3s6ffMulgfe6kHYD91sZd6AKyLNbKXelg7e/o9sy7Wx17qAdhPXeylHgDrYo1Kuh42PdkoEREREREREREREZG5sUc6EREREREREREREZERbEgnIiIiIiIiIiIiIjKCDelEREREREREREREREbYfUP6nDlzUKNGDbi6uiI4OBgHDx40Gr9y5UrUqVMHrq6uaNiwISIiIvR+LoTAhAkT4OfnBzc3N4SGhiI+Pt6cVQBQuHr8+uuvaN++PSpUqIAKFSogNDQ0V/ybb74JhUKh969bt27mrgaAwtVl4cKFucrp6uqqF2OpfQIUri4hISG56qJQKNCzZ09tjCX2y65du9CrVy9UrlwZCoUC69aty/c90dHRaNasGVxcXFCzZk0sXLgwV0xhjz1TKGxd1qxZgy5dusDb2xseHh5o3bo1tmzZohfzxRdf5NonderUMWMtCl+P6OjoPP+2kpKS9OJsYZ/kdQwoFArUr19fG2OJfTJ16lS0bNkS5cqVg4+PD/r06YOzZ8/m+z5rzSnWjrmbuducmLuZu82BuZu5u7Rj7mbuNifmbuZuc2DuZu4uCrtuSF++fDnGjh2LiRMn4ujRo2jcuDG6du2KW7du5Rm/d+9evPrqqxg6dChiYmLQp08f9OnTB3FxcdqY6dOnY9asWZg3bx4OHDiAMmXKoGvXrnjy5InV1CM6OhqvvvoqoqKisG/fPlSrVg1hYWG4du2aXly3bt1w48YN7b+lS5earQ5FrQsAeHh46JXz0qVLej+3xD4pSl3WrFmjV4+4uDgolUr069dPL66k98vjx4/RuHFjzJkzp0DxiYmJ6NmzJzp27Ihjx45hzJgxGDZsmF4iLMp+NoXC1mXXrl3o0qULIiIicOTIEXTs2BG9evVCTEyMXlz9+vX19snu3bvNUXytwtZD4+zZs3rl9PHx0f7MVvbJjz/+qFeHK1euoGLFirmOk5LeJzt37sTIkSOxf/9+REZGIjMzE2FhYXj8+LHB91hrTrF2zN3M3dZUF+Zu68sTzN3Wt0+Yu5m7mbuZu62pLszd1pcnmLutb58wdxfj/CXsWKtWrcTIkSO1yyqVSlSuXFlMnTo1z/j+/fuLnj176q0LDg4W77zzjhBCCLVaLSpVqiS+/fZb7c+Tk5OFi4uLWLp0qRlqIBW2Hk/LysoS5cqVE4sWLdKuGzx4sOjdu7epi5qvwtbl999/F56enga3Z6l9IkTx98sPP/wgypUrJx49eqRdZ6n9ogFArF271mjMuHHjRP369fXWDRgwQHTt2lW7XNzfjSkUpC55qVevnpg0aZJ2eeLEiaJx48amK1ghFaQeUVFRAoC4f/++wRhb3Sdr164VCoVCXLx4UbvO0vtECCFu3bolAIidO3cajLHWnGLtmLsl5m7zYO7WYe42H+Zu5u7ShrlbYu42D+ZuHeZu82HuZu4uKLvtkZ6RkYEjR44gNDRUu87BwQGhoaHYt29fnu/Zt2+fXjwAdO3aVRufmJiIpKQkvRhPT08EBwcb3GZxFaUeT0tNTUVmZiYqVqyotz46Oho+Pj4ICgrCiBEjcPfuXZOW/WlFrcujR4/g7++PatWqoXfv3jh58qT2Z5bYJ4Bp9sv8+fMxcOBAlClTRm99Se+XwsrvODHF78ZS1Go1UlJSch0r8fHxqFy5MgIDA/H666/j8uXLFiqhcU2aNIGfnx+6dOmCPXv2aNfb8j6ZP38+QkND4e/vr7fe0vvkwYMHAJDrbyUna8wp1o65W4e52/SYu5m7rRFzd8lh7jYP5m4d5m7TY+5m7rZGzN0lxxpzt902pN+5cwcqlQq+vr566319fXONX6SRlJRkNF7zf2G2WVxFqcfT/vvf/6Jy5cp6fzTdunXD4sWLsX37dnzzzTfYuXMnunfvDpVKZdLy51SUugQFBWHBggVYv349/vzzT6jVarRp0wZXr14FYJl9AhR/vxw8eBBxcXEYNmyY3npL7JfCMnScPHz4EGlpaSb5m7WUGTNm4NGjR+jfv792XXBwMBYuXIjNmzdj7ty5SExMRPv27ZGSkmLBkurz8/PDvHnzsHr1aqxevRrVqlVDSEgIjh49CsA05xFLuH79OjZt2pTrOLH0PlGr1RgzZgzatm2LBg0aGIyzxpxi7Zi7dZi7TY+5m7mbudv8mLtLH+ZuHeZu02PuZu5m7jY/5u7CcSz0O8imTJs2DcuWLUN0dLTeZCEDBw7Uvm7YsCEaNWqEZ599FtHR0ejcubMlipqn1q1bo3Xr1trlNm3aoG7duvjf//6HyZMnW7BkxTN//nw0bNgQrVq10ltvK/vFHv3111+YNGkS1q9frzfGWffu3bWvGzVqhODgYPj7+2PFihUYOnSoJYqaS1BQEIKCgrTLbdq0QUJCAn744Qf88ccfFixZ8SxatAjly5dHnz599NZbep+MHDkScXFxZh8fjkov5m7rxNxtfZi7rQ9zN5VWzN3Wibnb+jB3Wx/m7sKx2x7pXl5eUCqVuHnzpt76mzdvolKlSnm+p1KlSkbjNf8XZpvFVZR6aMyYMQPTpk3D1q1b0ahRI6OxgYGB8PLywvnz54tdZkOKUxcNJycnNG3aVFtOS+wToHh1efz4MZYtW1agE09J7JfCMnSceHh4wM3NzST7uaQtW7YMw4YNw4oVK3I9EvS08uXLo3bt2la1T/LSqlUrbRltcZ8IIbBgwQKEh4fD2dnZaGxJ7pNRo0Zhw4YNiIqKQtWqVY3GWmNOsXbM3czdzN3mwdzN3F0SmLutc7+YG3M3czdzt3kwdzN3lwTm7sLvF7ttSHd2dkbz5s2xfft27Tq1Wo3t27fr3WnNqXXr1nrxABAZGamNDwgIQKVKlfRiHj58iAMHDhjcZnEVpR6AnJF28uTJ2Lx5M1q0aJHv51y9ehV3796Fn5+fScqdl6LWJSeVSoUTJ05oy2mJfQIUry4rV65Eeno6Bg0alO/nlMR+Kaz8jhNT7OeStHTpUgwZMgRLly5Fz549841/9OgREhISrGqf5OXYsWPaMtraPgHkbN3nz58v0BffktgnQgiMGjUKa9euxY4dOxAQEJDve6wxp1g75m7mbuZu82DuZu4uCczd1rlfzI25m7mbuds8mLuZu0sCc3cR9kuhpye1IcuWLRMuLi5i4cKF4tSpU+Ltt98W5cuXF0lJSUIIIcLDw8XHH3+sjd+zZ49wdHQUM2bMEKdPnxYTJ04UTk5O4sSJE9qYadOmifLly4v169eL2NhY0bt3bxEQECDS0tKsph7Tpk0Tzs7OYtWqVeLGjRvafykpKUIIIVJSUsSHH34o9u3bJxITE8W2bdtEs2bNRK1atcSTJ0/MVo+i1GXSpEliy5YtIiEhQRw5ckQMHDhQuLq6ipMnT+rVt6T3SVHqotGuXTsxYMCAXOsttV9SUlJETEyMiImJEQDE999/L2JiYsSlS5eEEEJ8/PHHIjw8XBt/4cIF4e7uLj766CNx+vRpMWfOHKFUKsXmzZu1Mfn9bqylLkuWLBGOjo5izpw5esdKcnKyNuY///mPiI6OFomJiWLPnj0iNDRUeHl5iVu3bllNPX744Qexbt06ER8fL06cOCHef/994eDgILZt26aNsZV9ojFo0CARHByc5zYtsU9GjBghPD09RXR0tN7fSmpqqjbGVnKKtWPuZu62prpoMHebD3M3c7e5MHeXHOZu5m5rqosGc7f5MHczd5uLLeRuu25IF0KI2bNni+rVqwtnZ2fRqlUrsX//fu3POnToIAYPHqwXv2LFClG7dm3h7Ows6tevLzZu3Kj3c7VaLT7//HPh6+srXFxcROfOncXZs2etqh7+/v4CQK5/EydOFEIIkZqaKsLCwoS3t7dwcnIS/v7+Yvjw4WY/sItSlzFjxmhjfX19RY8ePcTRo0f1tmepfVLYugghxJkzZwQAsXXr1lzbstR+iYqKyvPvRVP2wYMHiw4dOuR6T5MmTYSzs7MIDAwUv//+e67tGvvdWEtdOnToYDReCCEGDBgg/Pz8hLOzs6hSpYoYMGCAOH/+vFXV45tvvhHPPvuscHV1FRUrVhQhISFix44dubZrC/tECCGSk5OFm5ub+OWXX/LcpiX2SV51AKD3t29LOcXaMXczd1tLXYRg7jY35m7mbnNh7i5ZzN3M3dZSFyGYu82NuZu521xsIXcrsgtKRERERERERERERER5sNsx0omIiIiIiIiIiIiITIEN6URERERERERERERERrAhnYiIiIiIiIiIiIjICDakExEREREREREREREZwYZ0IiIiIiIiIiIiIiIj2JBORERERERERERERGQEG9KJiIiIiIiIiIiIiIxgQzoRERERERERERERkRFsSCcqBd5880306dPHYp8fHh6OKVOmFCh24MCB+O6778xcIiIiIuvG3E1ERGRbmLuJ7J9CCCEsXQgiKjqFQmH05xMnTsQHH3wAIQTKly9fMoXK4fjx4+jUqRMuXbqEsmXL5hsfFxeH559/HomJifD09CyBEhIREZUs5m4iIiLbwtxNRAAb0olsXlJSkvb18uXLMWHCBJw9e1a7rmzZsgVKpOYybNgwODo6Yt68eQV+T8uWLfHmm29i5MiRZiwZERGRZTB3ExER2RbmbiICOLQLkc2rVKmS9p+npycUCoXeurJly+Z6xCwkJATvvfcexowZgwoVKsDX1xe//vorHj9+jCFDhqBcuXKoWbMmNm3apPdZcXFx6N69O8qWLQtfX1+Eh4fjzp07BsumUqmwatUq9OrVS2/9zz//jFq1asHV1RW+vr7o27ev3s979eqFZcuWFf+XQ0REZIWYu4mIiGwLczcRAWxIJyq1Fi1aBC8vLxw8eBDvvfceRowYgX79+qFNmzY4evQowsLCEB4ejtTUVABAcnIyOnXqhKZNm+Lw4cPYvHkzbt68if79+xv8jNjYWDx48AAtWrTQrjt8+DBGjx6NL7/8EmfPnsXmzZvx/PPP672vVatWOHjwINLT081TeSIiIhvE3E1ERGRbmLuJ7Asb0olKqcaNG+Ozzz5DrVq1MH78eLi6usLLywvDhw9HrVq1MGHCBNy9exexsbEAgJ9++glNmzbFlClTUKdOHTRt2hQLFixAVFQUzp07l+dnXLp0CUqlEj4+Ptp1ly9fRpkyZfDCCy/A398fTZs2xejRo/XeV7lyZWRkZOg9PkdERFTaMXcTERHZFuZuIvvChnSiUqpRo0ba10qlEs888wwaNmyoXefr6wsAuHXrFgA5eUlUVJR27LeyZcuiTp06AICEhIQ8PyMtLQ0uLi56E7N06dIF/v7+CAwMRHh4OJYsWaK9+67h5uYGALnWExERlWbM3URERLaFuZvIvrAhnaiUcnJy0ltWKBR66zRJWK1WAwAePXqEXr164dixY3r/4uPjcz0ipuHl5YXU1FRkZGRo15UrVw5Hjx7F0qVL4efnhwkTJqBx48ZITk7Wxty7dw8A4O3tbZK6EhER2QPmbiIiItvC3E1kX9iQTkQF0qxZM5w8eRI1atRAzZo19f6VKVMmz/c0adIEAHDq1Cm99Y6OjggNDcX06dMRGxuLixcvYseOHdqfx8XFoWrVqvDy8jJbfYiIiOwdczcREZFtYe4msm5sSCeiAhk5ciTu3buHV199FYcOHUJCQgK2bNmCIUOGQKVS5fkeb29vNGvWDLt379au27BhA2bNmoVjx47h0qVLWLx4MdRqNYKCgrQx//77L8LCwsxeJyIiInvG3E1ERGRbmLuJrBsb0omoQCpXrow9e/ZApVIhLCwMDRs2xJgxY1C+fHk4OBg+lQwbNgxLlizRLpcvXx5r1qxBp06dULduXcybNw9Lly5F/fr1AQBPnjzBunXrMHz4cLPXiYiIyJ4xdxMREdkW5m4i66YQQghLF4KI7FdaWhqCgoKwfPlytG7dOt/4uXPnYu3atdi6dWsJlI6IiIiextxNRERkW5i7iUoGe6QTkVm5ublh8eLFuHPnToHinZycMHv2bDOXioiIiAxh7iYiIrItzN1EJYM90omIiIiIiIiIiIiIjGCPdCIiIiIiIiIiIiIiI9iQTkRERERERERERERkBBvSiYiIiIiIiIiIiIiMYEM6EREREREREREREZERbEgnIiIiIiIiIiIiIjKCDelEREREREREREREREawIZ2IiIiIiIiIiIiIyAg2pBMRERERERERERERGcGGdCIiIiIiIiIiIiIiI9iQTkRERERERERERERkxP8D45Xr9tDuKhkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plot saved: warp_trajectories.png\n"
     ]
    }
   ],
   "source": [
    "# Cell 2.2 — Visualise Warp Trajectory\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "time_axis = np.arange(len(traj)) / 60.0  # 60 Hz\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
    "\n",
    "axes[0].plot(time_axis, traj[:, 0, 1], 'b-', lw=2)\n",
    "axes[0].axhline(0, color='brown', ls='--', label='Ground')\n",
    "axes[0].set_title('Height over Time (Warp Ground Truth)')\n",
    "axes[0].set_xlabel('Time (s)'); axes[0].set_ylabel('Height (m)')\n",
    "axes[0].legend(); axes[0].grid(True)\n",
    "\n",
    "# Different restitution values\n",
    "for r, color in [(0.9, 'green'), (0.7, 'blue'), (0.3, 'red')]:\n",
    "    t = engine.simulate(init_pos, init_vel, restitution=r, num_steps=120)\n",
    "    axes[1].plot(time_axis, t[:, 0, 1], color=color, label=f'e={r}', lw=2)\n",
    "axes[1].axhline(0, color='brown', ls='--')\n",
    "axes[1].set_title('Effect of Restitution on Bounce')\n",
    "axes[1].set_xlabel('Time (s)'); axes[1].set_ylabel('Height (m)')\n",
    "axes[1].legend(); axes[1].grid(True)\n",
    "\n",
    "# 3 balls at different heights\n",
    "multi_pos = np.array([[0, 3, 0], [2, 6, 0], [4, 9, 0]], dtype=np.float32)\n",
    "multi_vel = np.zeros((3, 3), dtype=np.float32)\n",
    "mt = engine.simulate(multi_pos, multi_vel, restitution=0.7, num_steps=120)\n",
    "for i, color in enumerate(['blue', 'orange', 'green']):\n",
    "    axes[2].plot(time_axis, mt[:, i, 1], color=color,\n",
    "                 label=f'Ball {i+1} (h={multi_pos[i,1]}m)', lw=2)\n",
    "axes[2].axhline(0, color='brown', ls='--')\n",
    "axes[2].set_title('Multiple Objects')\n",
    "axes[2].set_xlabel('Time (s)'); axes[2].set_ylabel('Height (m)')\n",
    "axes[2].legend(); axes[2].grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('/mnt/Data_volume/Dhanya/warp_trajectories.png', dpi=150)\n",
    "plt.show()\n",
    "print('Plot saved: warp_trajectories.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# BLOCK 3 — CogVideoX-2b MODEL LOADING\n",
    "Load CogVideoX-2b in bfloat16. We use LoRA so we never update all 2B weights — only small adapter layers.\n",
    "CogVideoX-2b uses only ~12GB for inference, leaving 74GB free for Warp + LoRA training on your H100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd0307b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting peft>=0.17.0\n",
      "  Downloading peft-0.18.1-py3-none-any.whl (556 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m557.0/557.0 KB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: transformers in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (4.47.1)\n",
      "Requirement already satisfied: accelerate>=0.21.0 in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (1.12.0)\n",
      "Requirement already satisfied: numpy>=1.17 in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (2.2.6)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (26.0)\n",
      "Requirement already satisfied: torch>=1.13.0 in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (2.5.1+cu124)\n",
      "Requirement already satisfied: psutil in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (7.2.2)\n",
      "Requirement already satisfied: pyyaml in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (6.0.3)\n",
      "Requirement already satisfied: tqdm in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (4.67.3)\n",
      "Requirement already satisfied: safetensors in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (0.7.0)\n",
      "Requirement already satisfied: huggingface_hub>=0.25.0 in /home/dhanya/.local/lib/python3.10/site-packages (from peft>=0.17.0) (0.36.2)\n",
      "Requirement already satisfied: requests in /home/dhanya/.local/lib/python3.10/site-packages (from huggingface_hub>=0.25.0->peft>=0.17.0) (2.32.5)\n",
      "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /home/dhanya/.local/lib/python3.10/site-packages (from huggingface_hub>=0.25.0->peft>=0.17.0) (1.2.0)\n",
      "Requirement already satisfied: filelock in /home/dhanya/.local/lib/python3.10/site-packages (from huggingface_hub>=0.25.0->peft>=0.17.0) (3.20.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/dhanya/.local/lib/python3.10/site-packages (from huggingface_hub>=0.25.0->peft>=0.17.0) (4.15.0)\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /home/dhanya/.local/lib/python3.10/site-packages (from huggingface_hub>=0.25.0->peft>=0.17.0) (2025.9.0)\n",
      "Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (10.3.5.147)\n",
      "Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (12.4.5.8)\n",
      "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (12.4.127)\n",
      "Requirement already satisfied: networkx in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (3.4.2)\n",
      "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (2.21.5)\n",
      "Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (11.2.1.3)\n",
      "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (12.4.127)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (12.4.127)\n",
      "Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (12.3.1.170)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (12.4.127)\n",
      "Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (11.6.1.9)\n",
      "Requirement already satisfied: jinja2 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (3.1.6)\n",
      "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (12.4.127)\n",
      "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (9.1.0.70)\n",
      "Requirement already satisfied: triton==3.1.0 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (3.1.0)\n",
      "Requirement already satisfied: sympy==1.13.1 in /home/dhanya/.local/lib/python3.10/site-packages (from torch>=1.13.0->peft>=0.17.0) (1.13.1)\n",
      "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /home/dhanya/.local/lib/python3.10/site-packages (from sympy==1.13.1->torch>=1.13.0->peft>=0.17.0) (1.3.0)\n",
      "Requirement already satisfied: tokenizers<0.22,>=0.21 in /home/dhanya/.local/lib/python3.10/site-packages (from transformers->peft>=0.17.0) (0.21.1)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /home/dhanya/.local/lib/python3.10/site-packages (from transformers->peft>=0.17.0) (2026.1.15)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/dhanya/.local/lib/python3.10/site-packages (from jinja2->torch>=1.13.0->peft>=0.17.0) (3.0.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests->huggingface_hub>=0.25.0->peft>=0.17.0) (2020.6.20)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests->huggingface_hub>=0.25.0->peft>=0.17.0) (3.3)\n",
      "Requirement already satisfied: charset_normalizer<4,>=2 in /home/dhanya/.local/lib/python3.10/site-packages (from requests->huggingface_hub>=0.25.0->peft>=0.17.0) (3.4.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/dhanya/.local/lib/python3.10/site-packages (from requests->huggingface_hub>=0.25.0->peft>=0.17.0) (2.6.3)\n",
      "Installing collected packages: peft\n",
      "  Attempting uninstall: peft\n",
      "    Found existing installation: peft 0.14.0\n",
      "    Uninstalling peft-0.14.0:\n",
      "      Successfully uninstalled peft-0.14.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "unsloth 2026.2.1 requires transformers!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.0,!=4.57.4,!=4.57.5,<=4.57.6,>=4.51.3, but you have transformers 4.47.1 which is incompatible.\n",
      "unsloth 2026.2.1 requires trl!=0.19.0,<=0.24.0,>=0.18.2, but you have trl 0.12.1 which is incompatible.\n",
      "unsloth-zoo 2026.2.1 requires torchao>=0.13.0, but you have torchao 0.7.0 which is incompatible.\n",
      "unsloth-zoo 2026.2.1 requires transformers!=4.52.0,!=4.52.1,!=4.52.2,!=4.52.3,!=4.53.0,!=4.54.0,!=4.55.0,!=4.55.1,!=4.57.4,!=4.57.5,<=4.57.6,>=4.51.3, but you have transformers 4.47.1 which is incompatible.\n",
      "unsloth-zoo 2026.2.1 requires trl!=0.19.0,<=0.24.0,>=0.18.2, but you have trl 0.12.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed peft-0.18.1\n"
     ]
    }
   ],
   "source": [
    "# Cell 3.0 — Fix peft version\n",
    "!pip install --upgrade \"peft>=0.17.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading CogVideoX-2b from /mnt/Data_volume/Dhanya/cogvideo_model...\n",
      "This takes 1-2 minutes on first load...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading weights: 100%|██████████| 219/219 [00:01<00:00, 149.59it/s, Materializing param=shared.weight]\n",
      "Loading pipeline components...: 100%|██████████| 5/5 [00:03<00:00,  1.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CogVideoX-2b loaded successfully\n",
      "VRAM used: 0.6 GB  (expect ~12GB)\n",
      "VRAM free: 0.6 GB  (expect ~87GB spare)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Cell 3.1 — Load CogVideoX-2b pipeline\nimport torch\nfrom diffusers import CogVideoXPipeline\nfrom diffusers.utils import export_to_video\n\nMODEL_PATH = '/mnt/Data_volume/Dhanya/cogvideo_model'\nDEVICE     = 'cuda'\n\n# Check if downloaded\nimport os\nif not os.path.exists(MODEL_PATH) or len(os.listdir(MODEL_PATH)) == 0:\n    print('ERROR: CogVideoX-2b model not found at', MODEL_PATH)\n    print('Run this in terminal first:')\n    print('  huggingface-cli download THUDM/CogVideoX-2b --local-dir', MODEL_PATH, '--local-dir-use-symlinks False')\n    raise FileNotFoundError('CogVideoX-2b model missing')\n\nprint(f'Loading CogVideoX-2b from {MODEL_PATH}...')\nprint('This takes 1-2 minutes on first load...')\n\npipe = CogVideoXPipeline.from_pretrained(\n    MODEL_PATH,\n    torch_dtype=torch.bfloat16,\n)\n# ── CPU offload DISABLED: 99GB VRAM is sufficient ──────────────────────────\n# enable_model_cpu_offload / enable_sequential_cpu_offload attach _hf_hook\n# objects via object.__setattr__, which torch Module.__delattr__ cannot\n# remove (raises AttributeError: _hf_hook). With 99GB VRAM we load\n# everything on-device and avoid the entire hook machinery.\npipe = pipe.to('cuda')\npipe.vae.enable_slicing()              # reduces VAE memory peak\npipe.vae.enable_tiling()               # handles long videos cleanly\n\nprint('CogVideoX-2b loaded successfully')\nprint(f'VRAM used: {torch.cuda.memory_allocated() / 1e9:.1f} GB')\nprint(f'VRAM free: {(torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_reserved()) / 1e9:.1f} GB spare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating: ball_drop...\n",
      "  Prompt length: 131 words\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:55<00:00,  1.12s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Saved: /mnt/Data_volume/Dhanya/baseline_videos/ball_drop_baseline.mp4\n",
      "\n",
      "Generating: cube_slide...\n",
      "  Prompt length: 116 words\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:56<00:00,  1.12s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Saved: /mnt/Data_volume/Dhanya/baseline_videos/cube_slide_baseline.mp4\n",
      "\n",
      "Generating: cloth_drape...\n",
      "  Prompt length: 129 words\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:56<00:00,  1.13s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Saved: /mnt/Data_volume/Dhanya/baseline_videos/cloth_drape_baseline.mp4\n",
      "\n",
      "Generating: collision...\n",
      "  Prompt length: 140 words\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:56<00:00,  1.13s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Saved: /mnt/Data_volume/Dhanya/baseline_videos/collision_baseline.mp4\n",
      "\n",
      "All baseline videos saved.\n",
      "TASK: Watch each video and fill in the failure log in the next cell.\n"
     ]
    }
   ],
   "source": [
    "# Cell 3.2 — Generate baseline test video (BEFORE fine-tuning)\n",
    "# CogVideoX-2b REQUIRES long, detailed, descriptive prompts (100+ tokens)\n",
    "# Short prompts = blurry, wrong, incoherent output\n",
    "# Formula: SUBJECT + MATERIAL + ACTION (step by step) + ENVIRONMENT + CAMERA + LIGHTING + STYLE\n",
    "\n",
    "PROMPTS = [\n",
    "    (\n",
    "        'ball_drop',\n",
    "        \"\"\"A smooth, shiny red rubber ball is filmed from a fixed side-view camera.\n",
    "        The ball begins at the top of the frame, suspended in the air at a height of approximately five meters\n",
    "        above a flat, hard concrete floor. The ball is released and falls straight downward under gravity,\n",
    "        accelerating as it descends. It strikes the concrete floor with a sharp impact, compresses slightly,\n",
    "        then rebounds upward at reduced speed. It reaches a lower peak height, falls again, bounces a second\n",
    "        time with even less energy, and finally rolls to a complete stop on the floor.\n",
    "        The concrete floor is grey and smooth. The background is a plain white wall.\n",
    "        Soft studio lighting illuminates the scene evenly with no shadows.\n",
    "        The motion is slow and clear. Cinematic quality, photorealistic, high detail.\"\"\"\n",
    "    ),\n",
    "    (\n",
    "        'cube_slide',\n",
    "        \"\"\"A solid wooden cube with a light oak finish is placed on the left side of a long flat wooden table.\n",
    "        Viewed from a fixed side-angle camera, the cube is given a single sharp push to the right and begins\n",
    "        sliding across the table surface. As it slides, friction gradually slows it down. The cube decelerates\n",
    "        smoothly and continuously, never speeding up, until it comes to a complete stop near the right edge of\n",
    "        the table. The table surface is polished hardwood with visible grain texture. No objects fall off the\n",
    "        table. The background is a plain beige wall with soft warm lighting from above.\n",
    "        The motion is steady and physically plausible. Realistic, cinematic, high resolution.\"\"\"\n",
    "    ),\n",
    "    (\n",
    "        'cloth_drape',\n",
    "        \"\"\"A square piece of white cotton cloth is held flat and horizontal in the air above a solid\n",
    "        dark grey sphere resting on a flat surface. Filmed from a fixed front-facing camera, the cloth\n",
    "        is released and falls under gravity. As it descends, the centre of the cloth contacts the top\n",
    "        of the sphere first and begins to drape over it. The edges and corners of the cloth fall outward\n",
    "        and downward, wrapping around the sides of the sphere and settling into natural folds and creases\n",
    "        due to gravity. The final resting position shows the cloth fully draped over the sphere with\n",
    "        fabric pooling on the surface around the base.\n",
    "        Soft diffuse lighting, neutral grey background, photorealistic fabric texture, slow motion,\n",
    "        cinematic quality, high detail, physically accurate cloth simulation.\"\"\"\n",
    "    ),\n",
    "    (\n",
    "        'collision',\n",
    "        \"\"\"Two identical solid billiard balls are seen from directly above on a flat green felt surface.\n",
    "        Ball one, coloured solid red, moves from the left side of the frame in a straight horizontal line\n",
    "        toward ball two, which is coloured solid white and is completely stationary at the centre of the frame.\n",
    "        The red ball travels at a steady speed and makes direct head-on contact with the white ball.\n",
    "        At the moment of collision, the red ball transfers nearly all of its momentum to the white ball.\n",
    "        The red ball stops abruptly or moves only slightly forward, while the white ball accelerates\n",
    "        forward along the same line of motion and exits the right side of the frame.\n",
    "        The surface is flat green billiard felt. Overhead lighting, top-down camera, no spin, no curve.\n",
    "        Cinematic quality, physically accurate, high resolution, slow motion.\"\"\"\n",
    "    ),\n",
    "]\n",
    "\n",
    "import os\n",
    "from diffusers.utils import export_to_video\n",
    "\n",
    "os.makedirs('/mnt/Data_volume/Dhanya/baseline_videos', exist_ok=True)\n",
    "\n",
    "for name, prompt in PROMPTS:\n",
    "    print(f'Generating: {name}...')\n",
    "    print(f'  Prompt length: {len(prompt.split())} words')\n",
    "\n",
    "    output = pipe(\n",
    "        prompt=prompt,\n",
    "        num_inference_steps=50,       # keep at 50 for baseline — quality matters here\n",
    "        num_frames=49,\n",
    "        height=480,\n",
    "        width=640,\n",
    "        guidance_scale=8.0,           # raised from 6.0 — forces stronger prompt adherence\n",
    "        generator=torch.Generator(device=\"cuda\").manual_seed(42),  # reproducible\n",
    "    ).frames[0]\n",
    "\n",
    "    path = f'/mnt/Data_volume/Dhanya/baseline_videos/{name}_baseline.mp4'\n",
    "    export_to_video(output, path, fps=8)\n",
    "    print(f'  Saved: {path}')\n",
    "    print()\n",
    "\n",
    "print('All baseline videos saved.')\n",
    "print('TASK: Watch each video and fill in the failure log in the next cell.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=================================================================\n",
      "AUTOMATED PHYSICS FAILURE ANALYSIS\n",
      "=================================================================\n",
      "\n",
      "Analysing: ball_drop\n",
      "  Loaded: 49 frames, 640x480px\n",
      "  Severity:      HIGH\n",
      "  Failure types: ['disappear']\n",
      "  ✗ Object not detected in >70% of frames — likely disappeared or wrong color\n",
      "\n",
      "Analysing: cube_slide\n",
      "  Loaded: 49 frames, 640x480px\n",
      "  Severity:      HIGH\n",
      "  Failure types: ['friction']\n",
      "  ✗ Object barely moved — no sliding motion detected\n",
      "  ✗ Object did not decelerate properly: init_speed=0.0000, final_speed=0.0013\n",
      "\n",
      "Analysing: cloth_drape\n",
      "  Loaded: 49 frames, 640x480px\n",
      "  Severity:      HIGH\n",
      "  Failure types: ['gravity']\n",
      "  ✗ Very little motion detected — cloth may not have fallen at all\n",
      "  ✗ Cloth did not settle: motion in second half (0.40) similar to first half (0.46)\n",
      "  ✗ Low white coverage in final frame — cloth may not be visible or draped\n",
      "\n",
      "Analysing: collision\n",
      "  Loaded: 49 frames, 640x480px\n",
      "  Severity:      LOW\n",
      "  Failure types: ['none']\n",
      "  ✗ None — momentum transfer appears correct\n",
      "  ✓ No major physics violations detected\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=================================================================\n",
      "RESULTS SAVED\n",
      "  baseline_failure_log.json         — structured failure log\n",
      "  physics_failure_analysis.png      — visual summary chart\n",
      "\n",
      "These become Section 2 (Motivation) of your paper.\n",
      "=================================================================\n"
     ]
    }
   ],
   "source": [
    "# Cell 3.3 — Automated Physics Failure Analysis\n",
    "# Watches the baseline videos automatically and measures physics violations.\n",
    "# No manual filling required — this generates your paper's motivation section.\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "import json\n",
    "import os\n",
    "from scipy.signal import find_peaks\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "# HELPER: Load video frames from .mp4\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "def load_video_frames(path: str) -> np.ndarray:\n",
    "    \"\"\"Returns (T, H, W, 3) uint8 numpy array from mp4.\"\"\"\n",
    "    cap = cv2.VideoCapture(path)\n",
    "    frames = []\n",
    "    while True:\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "        frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n",
    "    cap.release()\n",
    "    if not frames:\n",
    "        raise FileNotFoundError(f'Could not load: {path}')\n",
    "    return np.array(frames)\n",
    "\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "# HELPER: Track a coloured object across frames\n",
    "# Returns (T, 2) array of normalised (x, y) positions, or None per frame\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "def track_object(frames: np.ndarray, color: str = 'red') -> np.ndarray:\n",
    "    T, H, W = frames.shape[:3]\n",
    "    positions = np.full((T, 2), np.nan)\n",
    "\n",
    "    for t, frame in enumerate(frames):\n",
    "        hsv = cv2.cvtColor(frame, cv2.COLOR_RGB2HSV)\n",
    "\n",
    "        if color == 'red':\n",
    "            m1 = cv2.inRange(hsv, (0,   100, 70), (12,  255, 255))\n",
    "            m2 = cv2.inRange(hsv, (168, 100, 70), (180, 255, 255))\n",
    "            mask = cv2.bitwise_or(m1, m2)\n",
    "        elif color == 'white':\n",
    "            mask = cv2.inRange(hsv, (0, 0, 200), (180, 30, 255))\n",
    "        elif color == 'any_moving':\n",
    "            gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n",
    "            _, mask = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)\n",
    "        else:\n",
    "            mask = cv2.inRange(hsv, (0, 0, 180), (180, 60, 255))\n",
    "\n",
    "        # Morphological clean-up\n",
    "        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))\n",
    "        mask   = cv2.morphologyEx(mask, cv2.MORPH_OPEN,  kernel)\n",
    "        mask   = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)\n",
    "\n",
    "        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "        if contours:\n",
    "            largest = max(contours, key=cv2.contourArea)\n",
    "            if cv2.contourArea(largest) > 50:   # ignore tiny blobs\n",
    "                M = cv2.moments(largest)\n",
    "                if M['m00'] > 0:\n",
    "                    positions[t, 0] = M['m10'] / M['m00'] / W\n",
    "                    positions[t, 1] = M['m01'] / M['m00'] / H\n",
    "\n",
    "    return positions\n",
    "\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "# HELPER: Warp reference trajectory (normalised pixel coords)\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "def get_warp_reference_traj(drop_height: float, restitution: float,\n",
    "                             num_frames: int) -> np.ndarray:\n",
    "    import warp as wp\n",
    "    wp.init()\n",
    "\n",
    "    @wp.kernel\n",
    "    def step(pos: wp.array(dtype=wp.vec3),\n",
    "             vel: wp.array(dtype=wp.vec3),\n",
    "             dt: float):\n",
    "        v_new = wp.vec3(vel[0][0], vel[0][1] - 9.81 * dt, vel[0][2])\n",
    "        p_new = pos[0] + v_new * dt\n",
    "        if p_new[1] < 0.0:\n",
    "            p_new = wp.vec3(p_new[0], 0.0,          p_new[2])\n",
    "            v_new = wp.vec3(v_new[0], -v_new[1] * restitution, v_new[2])\n",
    "        pos[0] = p_new\n",
    "        vel[0] = v_new\n",
    "\n",
    "    pos = wp.array([wp.vec3(0.5, drop_height, 0.0)], dtype=wp.vec3, device='cuda')\n",
    "    vel = wp.array([wp.vec3(0.0, 0.0, 0.0)],          dtype=wp.vec3, device='cuda')\n",
    "    dt  = 1.0 / (num_frames * 2)   # sub-step for accuracy\n",
    "\n",
    "    traj = []\n",
    "    for _ in range(num_frames):\n",
    "        for _ in range(2):\n",
    "            wp.launch(step, dim=1, inputs=[pos, vel, dt], device='cuda')\n",
    "        p = pos.numpy()[0]\n",
    "        # Normalise: y=0 (ground) → pixel_y=0.9, y=drop_height → pixel_y=0.05\n",
    "        norm_y = 0.9 - (p[1] / drop_height) * 0.85\n",
    "        traj.append([0.5, float(np.clip(norm_y, 0.0, 1.0))])\n",
    "\n",
    "    return np.array(traj)\n",
    "\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "# ANALYSIS FUNCTIONS — one per scenario\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "\n",
    "def analyse_ball_drop(frames: np.ndarray) -> dict:\n",
    "    \"\"\"\n",
    "    Measures:\n",
    "    - Object detected in video (yes/no)\n",
    "    - Trajectory shape (should be parabolic, falling then bouncing)\n",
    "    - Gravity consistency (y should increase over time early on)\n",
    "    - Bounce count\n",
    "    - Trajectory deviation from Warp reference\n",
    "    \"\"\"\n",
    "    T, H = frames.shape[0], frames.shape[1]\n",
    "    pos  = track_object(frames, color='red')\n",
    "\n",
    "    detected_ratio = np.sum(~np.isnan(pos[:, 0])) / T\n",
    "    failures       = []\n",
    "    metrics        = {}\n",
    "\n",
    "    if detected_ratio < 0.3:\n",
    "        failures.append('Object not detected in >70% of frames — likely disappeared or wrong color')\n",
    "        metrics['detected_ratio'] = float(detected_ratio)\n",
    "        return {\n",
    "            'metrics': metrics,\n",
    "            'failures': failures,\n",
    "            'failure_types': ['disappear'],\n",
    "            'severity': 'high'\n",
    "        }\n",
    "\n",
    "    metrics['detected_ratio'] = float(detected_ratio)\n",
    "\n",
    "    # Fill NaN gaps with interpolation for analysis\n",
    "    valid    = ~np.isnan(pos[:, 1])\n",
    "    t_valid  = np.where(valid)[0]\n",
    "    if len(t_valid) > 3:\n",
    "        y_interp = np.interp(np.arange(T), t_valid, pos[valid, 1])\n",
    "    else:\n",
    "        y_interp = pos[:, 1]\n",
    "\n",
    "    # Check: ball should fall (y increases in pixel coords) in first third\n",
    "    first_third = y_interp[:T//3]\n",
    "    first_third_valid = first_third[~np.isnan(first_third)]\n",
    "    if len(first_third_valid) > 2:\n",
    "        fall_slope = np.polyfit(np.arange(len(first_third_valid)), first_third_valid, 1)[0]\n",
    "        metrics['fall_slope'] = float(fall_slope)\n",
    "        if fall_slope < 0.001:\n",
    "            failures.append(f'Gravity violation: ball not falling in first third (slope={fall_slope:.4f}, expected >0)')\n",
    "\n",
    "    # Check: bounce detection (local minima in y = low pixel = object near top = apex of bounce)\n",
    "    # In pixel coords: high y = near bottom (ground), low y = near top (air)\n",
    "    y_smooth = np.convolve(y_interp, np.ones(3)/3, mode='same')\n",
    "    # Ground contacts = local maxima in pixel y (object near bottom of frame)\n",
    "    peaks, _ = find_peaks(y_smooth, height=0.5, distance=4)\n",
    "    metrics['bounce_count_detected'] = int(len(peaks))\n",
    "    if len(peaks) == 0:\n",
    "        failures.append('No bounces detected — object may float or move horizontally only')\n",
    "    elif len(peaks) > 5:\n",
    "        failures.append(f'Too many bounces ({len(peaks)}) — physically implausible')\n",
    "\n",
    "    # Warp reference comparison\n",
    "    try:\n",
    "        warp_ref = get_warp_reference_traj(drop_height=5.0, restitution=0.7, num_frames=T)\n",
    "        deviation = np.nanmean(np.abs(y_interp - warp_ref[:, 1]))\n",
    "        metrics['trajectory_deviation_from_physics'] = float(deviation)\n",
    "        if deviation > 0.15:\n",
    "            failures.append(f'Trajectory deviates {deviation:.3f} from Warp physics reference (threshold 0.15)')\n",
    "    except Exception as e:\n",
    "        metrics['warp_comparison'] = f'Could not compute: {str(e)}'\n",
    "\n",
    "    # Horizontal drift (ball should fall mostly vertically)\n",
    "    x_valid = pos[~np.isnan(pos[:, 0]), 0]\n",
    "    if len(x_valid) > 3:\n",
    "        x_range = float(x_valid.max() - x_valid.min())\n",
    "        metrics['horizontal_drift'] = x_range\n",
    "        if x_range > 0.3:\n",
    "            failures.append(f'Excessive horizontal drift: {x_range:.3f} (>0.3 of frame width)')\n",
    "\n",
    "    severity = 'low' if len(failures) == 0 else ('high' if len(failures) >= 2 else 'medium')\n",
    "    failure_types = []\n",
    "    for f in failures:\n",
    "        if 'gravity' in f.lower() or 'slope' in f.lower():    failure_types.append('gravity')\n",
    "        if 'bounce' in f.lower():                              failure_types.append('collision')\n",
    "        if 'disappear' in f.lower() or 'detected' in f.lower(): failure_types.append('disappear')\n",
    "        if 'drift' in f.lower() or 'horizontal' in f.lower(): failure_types.append('trajectory')\n",
    "        if 'deviation' in f.lower():                           failure_types.append('physics_deviation')\n",
    "\n",
    "    return {\n",
    "        'metrics':       metrics,\n",
    "        'failures':      failures if failures else ['None detected — physics appears correct'],\n",
    "        'failure_types': list(set(failure_types)) if failure_types else ['none'],\n",
    "        'severity':      severity,\n",
    "    }\n",
    "\n",
    "\n",
    "def analyse_cube_slide(frames: np.ndarray) -> dict:\n",
    "    \"\"\"\n",
    "    Cube should decelerate. Checks:\n",
    "    - Object moves at all\n",
    "    - Speed decreases monotonically (no acceleration after push)\n",
    "    - Object stops before leaving frame\n",
    "    \"\"\"\n",
    "    T    = frames.shape[0]\n",
    "    pos  = track_object(frames, color='any_moving')\n",
    "    failures = []\n",
    "    metrics  = {}\n",
    "\n",
    "    detected_ratio = np.sum(~np.isnan(pos[:, 0])) / T\n",
    "    metrics['detected_ratio'] = float(detected_ratio)\n",
    "\n",
    "    if detected_ratio < 0.3:\n",
    "        failures.append('Object not detected — may not have generated correctly')\n",
    "        return {'metrics': metrics, 'failures': failures,\n",
    "                'failure_types': ['disappear'], 'severity': 'high'}\n",
    "\n",
    "    # Horizontal velocity profile\n",
    "    x_valid_idx = np.where(~np.isnan(pos[:, 0]))[0]\n",
    "    if len(x_valid_idx) > 4:\n",
    "        x_vals = pos[x_valid_idx, 0]\n",
    "        speeds = np.abs(np.diff(x_vals))\n",
    "        metrics['mean_speed']  = float(np.mean(speeds))\n",
    "        metrics['final_speed'] = float(np.mean(speeds[-3:])) if len(speeds) >= 3 else 0.0\n",
    "        metrics['init_speed']  = float(np.mean(speeds[:3]))\n",
    "\n",
    "        if metrics['mean_speed'] < 0.002:\n",
    "            failures.append('Object barely moved — no sliding motion detected')\n",
    "\n",
    "        if metrics['final_speed'] > metrics['init_speed'] * 0.8:\n",
    "            failures.append(\n",
    "                f'Object did not decelerate properly: '\n",
    "                f'init_speed={metrics[\"init_speed\"]:.4f}, '\n",
    "                f'final_speed={metrics[\"final_speed\"]:.4f}'\n",
    "            )\n",
    "\n",
    "        # Check if object leaves frame\n",
    "        if x_vals[-1] > 0.95 or x_vals[-1] < 0.05:\n",
    "            failures.append('Object slid off the frame — did not stop due to friction')\n",
    "\n",
    "    severity = 'low' if not failures else ('high' if len(failures) >= 2 else 'medium')\n",
    "    return {\n",
    "        'metrics': metrics,\n",
    "        'failures': failures if failures else ['None — cube deceleration appears correct'],\n",
    "        'failure_types': ['friction' if 'decelerate' in str(failures) else 'none'],\n",
    "        'severity': severity,\n",
    "    }\n",
    "\n",
    "\n",
    "def analyse_cloth_drape(frames: np.ndarray) -> dict:\n",
    "    \"\"\"\n",
    "    Cloth should fall and settle. Checks:\n",
    "    - Motion present in first half of video (falling)\n",
    "    - Motion stops in second half (settled)\n",
    "    - Coverage of sphere region (cloth should drape over sphere)\n",
    "    \"\"\"\n",
    "    T, H, W = frames.shape[:3]\n",
    "    failures = []\n",
    "    metrics  = {}\n",
    "\n",
    "    # Measure frame-to-frame motion using optical flow approximation\n",
    "    motion_scores = []\n",
    "    for t in range(1, T):\n",
    "        prev = cv2.cvtColor(frames[t-1], cv2.COLOR_RGB2GRAY).astype(float)\n",
    "        curr = cv2.cvtColor(frames[t],   cv2.COLOR_RGB2GRAY).astype(float)\n",
    "        diff = np.mean(np.abs(curr - prev))\n",
    "        motion_scores.append(diff)\n",
    "\n",
    "    motion_scores = np.array(motion_scores)\n",
    "    metrics['mean_motion']       = float(np.mean(motion_scores))\n",
    "    metrics['first_half_motion'] = float(np.mean(motion_scores[:T//2]))\n",
    "    metrics['second_half_motion']= float(np.mean(motion_scores[T//2:]))\n",
    "\n",
    "    if metrics['mean_motion'] < 0.5:\n",
    "        failures.append('Very little motion detected — cloth may not have fallen at all')\n",
    "\n",
    "    # Cloth should move MORE in first half (falling) and LESS in second (settled)\n",
    "    if metrics['second_half_motion'] > metrics['first_half_motion'] * 0.8:\n",
    "        failures.append(\n",
    "            f'Cloth did not settle: motion in second half '\n",
    "            f'({metrics[\"second_half_motion\"]:.2f}) '\n",
    "            f'similar to first half ({metrics[\"first_half_motion\"]:.2f})'\n",
    "        )\n",
    "\n",
    "    # Check bottom of frame for cloth coverage (draping creates coverage near centre-bottom)\n",
    "    final_frame   = frames[-1]\n",
    "    centre_region = final_frame[H//3:2*H//3, W//4:3*W//4]\n",
    "    white_ratio   = np.mean(centre_region > 200)\n",
    "    metrics['final_white_coverage'] = float(white_ratio)\n",
    "    if white_ratio < 0.1:\n",
    "        failures.append('Low white coverage in final frame — cloth may not be visible or draped')\n",
    "\n",
    "    severity = 'low' if not failures else ('high' if len(failures) >= 2 else 'medium')\n",
    "    return {\n",
    "        'metrics': metrics,\n",
    "        'failures': failures if failures else ['None — cloth draping appears physically reasonable'],\n",
    "        'failure_types': ['gravity' if 'motion' in str(failures) else 'none'],\n",
    "        'severity': severity,\n",
    "    }\n",
    "\n",
    "\n",
    "def analyse_collision(frames: np.ndarray) -> dict:\n",
    "    \"\"\"\n",
    "    Ball 1 (red) should stop after collision.\n",
    "    Ball 2 (white) should move after collision.\n",
    "    Checks momentum transfer.\n",
    "    \"\"\"\n",
    "    T     = frames.shape[0]\n",
    "    pos_r = track_object(frames, color='red')\n",
    "    pos_w = track_object(frames, color='white')\n",
    "    failures = []\n",
    "    metrics  = {}\n",
    "\n",
    "    detected_r = np.sum(~np.isnan(pos_r[:, 0])) / T\n",
    "    detected_w = np.sum(~np.isnan(pos_w[:, 0])) / T\n",
    "    metrics['red_detected_ratio']   = float(detected_r)\n",
    "    metrics['white_detected_ratio'] = float(detected_w)\n",
    "\n",
    "    if detected_r < 0.2 and detected_w < 0.2:\n",
    "        failures.append('Neither ball detected — scene did not generate correctly')\n",
    "        return {'metrics': metrics, 'failures': failures,\n",
    "                'failure_types': ['disappear'], 'severity': 'high'}\n",
    "\n",
    "    # Red ball: should be moving in first half, slow/stopped in second half\n",
    "    if detected_r > 0.3:\n",
    "        r_valid = np.where(~np.isnan(pos_r[:, 0]))[0]\n",
    "        if len(r_valid) > 4:\n",
    "            r_x = pos_r[r_valid, 0]\n",
    "            r_speed_early = float(np.mean(np.abs(np.diff(r_x[:len(r_x)//2]))))\n",
    "            r_speed_late  = float(np.mean(np.abs(np.diff(r_x[len(r_x)//2:]))))\n",
    "            metrics['red_speed_early'] = r_speed_early\n",
    "            metrics['red_speed_late']  = r_speed_late\n",
    "            if r_speed_late > r_speed_early * 0.5:\n",
    "                failures.append(\n",
    "                    f'Red ball did not stop after collision: '\n",
    "                    f'late speed ({r_speed_late:.4f}) not much less than early ({r_speed_early:.4f})'\n",
    "                )\n",
    "\n",
    "    # White ball: should be stationary in first half, moving in second half\n",
    "    if detected_w > 0.3:\n",
    "        w_valid = np.where(~np.isnan(pos_w[:, 0]))[0]\n",
    "        if len(w_valid) > 4:\n",
    "            w_x = pos_w[w_valid, 0]\n",
    "            w_speed_early = float(np.mean(np.abs(np.diff(w_x[:len(w_x)//2]))))\n",
    "            w_speed_late  = float(np.mean(np.abs(np.diff(w_x[len(w_x)//2:]))))\n",
    "            metrics['white_speed_early'] = w_speed_early\n",
    "            metrics['white_speed_late']  = w_speed_late\n",
    "            if w_speed_late < w_speed_early:\n",
    "                failures.append(\n",
    "                    f'White ball did not accelerate after collision: '\n",
    "                    f'late speed ({w_speed_late:.4f}) <= early ({w_speed_early:.4f})'\n",
    "                )\n",
    "\n",
    "    severity = 'low' if not failures else ('high' if len(failures) >= 2 else 'medium')\n",
    "    return {\n",
    "        'metrics': metrics,\n",
    "        'failures': failures if failures else ['None — momentum transfer appears correct'],\n",
    "        'failure_types': ['momentum' if failures else 'none'],\n",
    "        'severity': severity,\n",
    "    }\n",
    "\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "# RUN ALL ANALYSES\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "\n",
    "VIDEO_DIR   = '/mnt/Data_volume/Dhanya/baseline_videos'\n",
    "SCENARIOS   = [\n",
    "    ('ball_drop',   'ball_drop_baseline.mp4',  'red',   analyse_ball_drop,   'Ball falls at 9.8 m/s², bounces with partial restitution, decelerates'),\n",
    "    ('cube_slide',  'cube_slide_baseline.mp4', 'any',   analyse_cube_slide,  'Cube decelerates due to friction, stops smoothly'),\n",
    "    ('cloth_drape', 'cloth_drape_baseline.mp4','white', analyse_cloth_drape, 'Cloth falls and drapes over sphere, settles under gravity'),\n",
    "    ('collision',   'collision_baseline.mp4',  'multi', analyse_collision,   'Momentum conserved: ball 1 stops, ball 2 moves at same speed'),\n",
    "]\n",
    "\n",
    "failure_log = {}\n",
    "analysis_results = {}\n",
    "\n",
    "print('='*65)\n",
    "print('AUTOMATED PHYSICS FAILURE ANALYSIS')\n",
    "print('='*65)\n",
    "\n",
    "for name, filename, color, analyse_fn, expected in SCENARIOS:\n",
    "    path = os.path.join(VIDEO_DIR, filename)\n",
    "    print(f'\\nAnalysing: {name}')\n",
    "\n",
    "    if not os.path.exists(path):\n",
    "        print(f'  WARNING: {path} not found — skipping')\n",
    "        failure_log[name] = {\n",
    "            'expected':     expected,\n",
    "            'observed':     'Video not found — run Cell 3.2 first',\n",
    "            'failure_type': 'missing',\n",
    "            'severity':     'unknown',\n",
    "            'metrics':      {}\n",
    "        }\n",
    "        continue\n",
    "\n",
    "    try:\n",
    "        frames = load_video_frames(path)\n",
    "        print(f'  Loaded: {frames.shape[0]} frames, {frames.shape[2]}x{frames.shape[1]}px')\n",
    "        result = analyse_fn(frames)\n",
    "\n",
    "        failure_log[name] = {\n",
    "            'expected':     expected,\n",
    "            'observed':     ' | '.join(result['failures']),\n",
    "            'failure_type': ', '.join(result['failure_types']),\n",
    "            'severity':     result['severity'],\n",
    "            'metrics':      result['metrics'],\n",
    "        }\n",
    "        analysis_results[name] = {'frames': frames, 'result': result}\n",
    "\n",
    "        print(f'  Severity:      {result[\"severity\"].upper()}')\n",
    "        print(f'  Failure types: {result[\"failure_types\"]}')\n",
    "        for f in result['failures']:\n",
    "            print(f'  ✗ {f}')\n",
    "        if result['severity'] == 'low':\n",
    "            print(f'  ✓ No major physics violations detected')\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f'  ERROR during analysis: {e}')\n",
    "        failure_log[name] = {\n",
    "            'expected': expected, 'observed': f'Analysis error: {str(e)}',\n",
    "            'failure_type': 'error', 'severity': 'unknown', 'metrics': {}\n",
    "        }\n",
    "\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "# SAVE + VISUALISE\n",
    "# ─────────────────────────────────────────────────────────────────────────────\n",
    "\n",
    "with open('/mnt/Data_volume/Dhanya/baseline_failure_log.json', 'w') as f:\n",
    "    json.dump(failure_log, f, indent=2)\n",
    "\n",
    "# Summary chart\n",
    "fig, axes = plt.subplots(1, 4, figsize=(20, 4))\n",
    "colors_map = {'high': '#e74c3c', 'medium': '#f39c12', 'low': '#2ecc71', 'unknown': '#95a5a6'}\n",
    "\n",
    "for ax, (name, filename, _, _, _) in zip(axes, SCENARIOS):\n",
    "    if name not in analysis_results:\n",
    "        ax.text(0.5, 0.5, 'Not\\nAvailable', ha='center', va='center', transform=ax.transAxes)\n",
    "        ax.set_title(name)\n",
    "        continue\n",
    "\n",
    "    frames = analysis_results[name]['frames']\n",
    "    result = analysis_results[name]['result']\n",
    "    sev    = result['severity']\n",
    "\n",
    "    # Show frame 10 as thumbnail with severity border\n",
    "    ax.imshow(frames[min(10, len(frames)-1)])\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_edgecolor(colors_map.get(sev, 'grey'))\n",
    "        spine.set_linewidth(6)\n",
    "\n",
    "    short_failures = '\\n'.join(\n",
    "        [f[:40] + '...' if len(f) > 40 else f for f in result['failures'][:2]]\n",
    "    )\n",
    "    ax.set_title(f'{name}\\nSeverity: {sev.upper()}\\n{short_failures}',\n",
    "                 fontsize=7.5, color=colors_map.get(sev, 'black'))\n",
    "    ax.axis('off')\n",
    "\n",
    "plt.suptitle('Baseline CogVideoX-2b Physics Failure Analysis\\n(Border colour: RED=high, ORANGE=medium, GREEN=low)',\n",
    "             fontsize=11, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.savefig('/mnt/Data_volume/Dhanya/physics_failure_analysis.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "print()\n",
    "print('='*65)\n",
    "print('RESULTS SAVED')\n",
    "print('  baseline_failure_log.json         — structured failure log')\n",
    "print('  physics_failure_analysis.png      — visual summary chart')\n",
    "print()\n",
    "print('These become Section 2 (Motivation) of your paper.')\n",
    "print('='*65)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# BLOCK 4 — PHYSICS CONSISTENCY LOSS\n",
    "This is the core innovation. We extract object trajectories from Cosmos output frames, run the same scenario in Warp, and compute how wrong CogVideoX-2b was physically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimpleObjectTracker defined\n",
      "Tracker ready\n"
     ]
    }
   ],
   "source": [
    "# Cell 4.1 — Object Tracker (extracts trajectory from video frames)\n",
    "import cv2\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "class SimpleObjectTracker:\n",
    "    \"\"\"\n",
    "    Extracts the approximate trajectory of the brightest/largest\n",
    "    moving object in a video sequence.\n",
    "    For the ball-drop scenario: tracks the red ball.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, target_color='red'):\n",
    "        self.target_color = target_color\n",
    "\n",
    "    def extract_trajectory(self, frames: list) -> np.ndarray:\n",
    "        \"\"\"\n",
    "        frames: list of numpy arrays (H, W, 3) in RGB, values 0-255\n",
    "        returns: (T, 2) array of (x, y) pixel positions normalised to [0,1]\n",
    "        \"\"\"\n",
    "        positions = []\n",
    "        H, W = frames[0].shape[:2]\n",
    "\n",
    "        for frame in frames:\n",
    "            # Convert to HSV for colour detection\n",
    "            hsv = cv2.cvtColor(frame, cv2.COLOR_RGB2HSV)\n",
    "\n",
    "            if self.target_color == 'red':\n",
    "                # Red wraps around in HSV\n",
    "                mask1 = cv2.inRange(hsv, (0,  120, 70), (10,  255, 255))\n",
    "                mask2 = cv2.inRange(hsv, (170, 120, 70), (180, 255, 255))\n",
    "                mask  = cv2.bitwise_or(mask1, mask2)\n",
    "            else:\n",
    "                # Generic: find largest moving blob\n",
    "                gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n",
    "                _, mask = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)\n",
    "\n",
    "            # Find centroid\n",
    "            contours, _ = cv2.findContours(\n",
    "                mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE\n",
    "            )\n",
    "\n",
    "            if contours:\n",
    "                largest = max(contours, key=cv2.contourArea)\n",
    "                M = cv2.moments(largest)\n",
    "                if M['m00'] > 0:\n",
    "                    cx = M['m10'] / M['m00'] / W   # normalise\n",
    "                    cy = M['m01'] / M['m00'] / H   # normalise\n",
    "                    positions.append([cx, cy])\n",
    "                    continue\n",
    "\n",
    "            # If not found, repeat last known position\n",
    "            if positions:\n",
    "                positions.append(positions[-1])\n",
    "            else:\n",
    "                positions.append([0.5, 0.1])  # default top-centre\n",
    "\n",
    "        return np.array(positions, dtype=np.float32)\n",
    "\n",
    "\n",
    "print('SimpleObjectTracker defined')\n",
    "tracker = SimpleObjectTracker(target_color='red')\n",
    "print('Tracker ready')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PhysicsConsistencyLoss defined and ready\n"
     ]
    }
   ],
   "source": [
    "# Cell 4.2 — Physics Consistency Loss Function (EVALUATION ONLY)\n",
    "# ─────────────────────────────────────────────────────────────────\n",
    "# IMPORTANT: This class is used ONLY for evaluation metrics (Block 7).\n",
    "# It is NOT used in the training loop.\n",
    "# It intentionally uses numpy/non-differentiable ops to measure\n",
    "# how wrong the generated video is vs Warp ground truth.\n",
    "# The training loop uses the diffusion MSE loss instead (Block 6).\n",
    "# ─────────────────────────────────────────────────────────────────\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "\n",
    "class PhysicsConsistencyLoss(nn.Module):\n",
    "    \"\"\"\n",
    "    Core loss function of the PDW project.\n",
    "\n",
    "    Given:\n",
    "      - generated_frames: video output from CogVideoX-2b\n",
    "      - physics_params: estimated mass, restitution, friction\n",
    "\n",
    "    Computes:\n",
    "      - Tracked trajectory from generated video\n",
    "      - Ground-truth trajectory from Warp simulation\n",
    "      - L2 loss between the two trajectories\n",
    "\n",
    "    This loss penalises Cosmos for generating\n",
    "    physically implausible motion.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, engine: WarpPhysicsEngine, device='cuda'):\n",
    "        super().__init__()\n",
    "        self.engine  = engine\n",
    "        self.tracker = SimpleObjectTracker(target_color='red')\n",
    "        self.device  = device\n",
    "\n",
    "    def frames_to_numpy(self, frames_tensor: torch.Tensor) -> list:\n",
    "        \"\"\"\n",
    "        Convert tensor frames to list of numpy arrays.\n",
    "        Handles both (T, H, W, 3) and (T, C, H, W) formats.\n",
    "        Output: list of (H, W, 3) uint8 arrays\n",
    "        \"\"\"\n",
    "        ft = frames_tensor.cpu().float()\n",
    "        if ft.ndim == 4 and ft.shape[1] in (1, 3):  # (T, C, H, W) → (T, H, W, C)\n",
    "            ft = ft.permute(0, 2, 3, 1)\n",
    "        if ft.shape[-1] == 1:  # grayscale → RGB\n",
    "            ft = ft.repeat(1, 1, 1, 3)\n",
    "        frames_np = (ft.numpy() * 255).clip(0, 255).astype(np.uint8)\n",
    "        return [frames_np[i] for i in range(len(frames_np))]\n",
    "\n",
    "    def get_warp_reference(\n",
    "        self,\n",
    "        drop_height: float,\n",
    "        restitution: float,\n",
    "        num_steps: int,\n",
    "        video_height: int,\n",
    "        video_width:  int\n",
    "    ) -> np.ndarray:\n",
    "        \"\"\"\n",
    "        Simulate the expected trajectory in Warp.\n",
    "        Projects 3D trajectory to 2D normalised pixel coordinates.\n",
    "        \"\"\"\n",
    "        init_pos = np.array([[0.5, drop_height, 0.0]])   # centred\n",
    "        init_vel = np.zeros((1, 3), dtype=np.float32)\n",
    "\n",
    "        traj_3d = self.engine.simulate(\n",
    "            init_pos, init_vel,\n",
    "            restitution=restitution,\n",
    "            num_steps=num_steps\n",
    "        )  # (T, 1, 3)\n",
    "\n",
    "        # Project: x stays at 0.5 (centred), y maps from [0, drop_height] to [1, 0]\n",
    "        traj_2d = np.zeros((num_steps, 2), dtype=np.float32)\n",
    "        traj_2d[:, 0] = 0.5                                      # x centred\n",
    "        traj_2d[:, 1] = 1.0 - traj_3d[:, 0, 1] / drop_height   # y inverted\n",
    "        traj_2d[:, 1] = np.clip(traj_2d[:, 1], 0.0, 1.0)\n",
    "\n",
    "        return traj_2d\n",
    "\n",
    "    def forward(\n",
    "        self,\n",
    "        generated_frames,       # (T, H, W, 3) or (T, C, H, W) tensor [0,1]\n",
    "        drop_height:  float = 5.0,\n",
    "        restitution:  float = 0.7,\n",
    "    ) -> torch.Tensor:\n",
    "\n",
    "        # Handle both (T, C, H, W) and (T, H, W, C) formats\n",
    "        if generated_frames.ndim == 4 and generated_frames.shape[1] in (1, 3):\n",
    "            T, C, H, W = generated_frames.shape\n",
    "        else:\n",
    "            T, H, W = generated_frames.shape[0], generated_frames.shape[1], generated_frames.shape[2]\n",
    "\n",
    "        # Step 1: track object in generated video\n",
    "        frames_np      = self.frames_to_numpy(generated_frames)\n",
    "        tracked_traj   = self.tracker.extract_trajectory(frames_np)  # (T, 2)\n",
    "\n",
    "        # Step 2: get physics ground-truth from Warp\n",
    "        warp_traj      = self.get_warp_reference(\n",
    "            drop_height, restitution, T, H, W\n",
    "        )  # (T, 2)\n",
    "\n",
    "        # Step 3: compute L2 loss\n",
    "        tracked_tensor = torch.tensor(tracked_traj, device=self.device)\n",
    "        warp_tensor    = torch.tensor(warp_traj,    device=self.device)\n",
    "\n",
    "        # Weight later frames less (occlusion more common)\n",
    "        weights = torch.linspace(1.0, 0.5, T, device=self.device).unsqueeze(1)\n",
    "        loss    = (weights * (tracked_tensor - warp_tensor) ** 2).mean()\n",
    "\n",
    "        return loss, tracked_traj, warp_traj\n",
    "\n",
    "\n",
    "# Initialise\n",
    "physics_engine = WarpPhysicsEngine()\n",
    "physics_loss   = PhysicsConsistencyLoss(physics_engine)\n",
    "print('PhysicsConsistencyLoss defined and ready')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Physics Consistency Loss: 0.015622\n",
      "(Higher = more physically wrong)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Physics loss is working correctly\n"
     ]
    }
   ],
   "source": [
    "# Cell 4.3 — Test the Physics Loss on a synthetic example\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "# Simulate what a 'bad' CogVideoX-2b output might look like\n",
    "# We mock 49 frames where a ball drifts sideways (physics violation)\n",
    "T, H, W = 49, 480, 640\n",
    "mock_frames = torch.zeros(T, H, W, 3)\n",
    "\n",
    "for t in range(T):\n",
    "    # Draw a red circle that moves in a straight line (wrong — ignores gravity)\n",
    "    cx = int(W * 0.3 + t * 3)           # drifts sideways\n",
    "    cy = int(H * 0.2 + t * 2)           # falls too slowly\n",
    "    y_idx = max(0, min(H-1, cy))\n",
    "    x_idx = max(0, min(W-1, cx))\n",
    "    r = 15\n",
    "    y_coords = torch.arange(H).unsqueeze(1).float()\n",
    "    x_coords = torch.arange(W).unsqueeze(0).float()\n",
    "    mask = ((y_coords - y_idx)**2 + (x_coords - x_idx)**2) < r**2\n",
    "    mock_frames[t, :, :, 0][mask] = 1.0  # red channel\n",
    "\n",
    "# Compute physics loss\n",
    "loss_val, tracked, warp_ref = physics_loss(mock_frames)\n",
    "\n",
    "print(f'Physics Consistency Loss: {loss_val.item():.6f}')\n",
    "print(f'(Higher = more physically wrong)')\n",
    "\n",
    "# Visualise\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "t_axis = np.arange(T)\n",
    "\n",
    "ax.plot(t_axis, tracked[:, 1], 'r-o', markersize=3, label='Cosmos output (wrong)', lw=2)\n",
    "ax.plot(t_axis, warp_ref[:, 1], 'b-', label='Warp physics (correct)', lw=2)\n",
    "ax.set_title(f'Physics Consistency: Cosmos vs Warp Ground Truth\\nLoss = {loss_val.item():.4f}',\n",
    "             fontsize=13)\n",
    "ax.set_xlabel('Frame'); ax.set_ylabel('Normalised Y position')\n",
    "ax.legend(); ax.grid(True)\n",
    "ax.invert_yaxis()  # screen coordinates: 0 = top\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('/mnt/Data_volume/Dhanya/physics_loss_test.png', dpi=150)\n",
    "plt.show()\n",
    "print('Physics loss is working correctly')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# BLOCK 5 — SYNTHETIC DATA GENERATION + LORA FINE-TUNING\n",
    "\n",
    "## Why the original approach failed\n",
    "The original training loop tried to:\n",
    "1. Generate video with CogVideoX (diffusion pipeline)\n",
    "2. Track the ball in the output\n",
    "3. Backprop physics loss → LoRA weights\n",
    "\n",
    "This cannot work because diffusion pipelines are NOT differentiable at inference.\n",
    "The generated frames have no `grad_fn` — there is no gradient path.\n",
    "\n",
    "## The correct approach (how all diffusion LoRA fine-tuning actually works)\n",
    "1. **Generate physics-correct synthetic training videos using Warp** (simple renderer)\n",
    "2. **Encode them as latents** using CogVideoX's own VAE\n",
    "3. **Add noise + predict noise** with the transformer (standard diffusion loss)\n",
    "4. **Backprop MSE(predicted_noise, actual_noise)** — gradients flow correctly\n",
    "\n",
    "The physics signal comes FROM the training data (Warp renders it),\n",
    "not from backpropping through the inference loop.\n",
    "This is exactly how DreamBooth, LoRA fine-tuning, and all diffusion training works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LoRA applied to CogVideoX-2b transformer\n",
      "Trainable params: 7,372,800  (7.4M)\n",
      "Total params:     1,701,156,672      (1.70B)\n",
      "Trainable ratio:  0.433%\n",
      "VRAM:             0.6 GB\n",
      "\n",
      "VAE, text encoder, scheduler ready for diffusion training loop.\n"
     ]
    }
   ],
   "source": [
    "# Cell 5.1 — Apply LoRA to CogVideoX-2b transformer\n",
    "from peft import LoraConfig, get_peft_model\n",
    "import torch\n",
    "\n",
    "# Pull out the submodules we need separately\n",
    "transformer = pipe.transformer    # main denoising transformer\n",
    "vae         = pipe.vae            # encodes/decodes video frames\n",
    "scheduler   = pipe.scheduler      # noise schedule (DDIM/DPM)\n",
    "tokenizer   = pipe.tokenizer\n",
    "text_encoder= pipe.text_encoder\n",
    "\n",
    "# Freeze everything first\n",
    "for model_part in [transformer, vae, text_encoder]:\n",
    "    for param in model_part.parameters():\n",
    "        param.requires_grad = False\n",
    "\n",
    "# LoRA config — only train small adapter layers in transformer\n",
    "lora_config = LoraConfig(\n",
    "    r=16,\n",
    "    lora_alpha=32,\n",
    "    target_modules=['to_q', 'to_k', 'to_v', 'to_out.0'],\n",
    "    lora_dropout=0.05,\n",
    "    bias='none',\n",
    ")\n",
    "\n",
    "lora_transformer = get_peft_model(transformer, lora_config)\n",
    "\n",
    "trainable = sum(p.numel() for p in lora_transformer.parameters() if p.requires_grad)\n",
    "total     = sum(p.numel() for p in lora_transformer.parameters())\n",
    "\n",
    "print('LoRA applied to CogVideoX-2b transformer')\n",
    "print(f'Trainable params: {trainable:,}  ({trainable/1e6:.1f}M)')\n",
    "print(f'Total params:     {total:,}      ({total/1e9:.2f}B)')\n",
    "print(f'Trainable ratio:  {100*trainable/total:.3f}%')\n",
    "print(f'VRAM:             {torch.cuda.memory_allocated()/1e9:.1f} GB')\n",
    "print()\n",
    "print('VAE, text encoder, scheduler ready for diffusion training loop.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing physics renderer...\n",
      "Rendered frames shape: (49, 256, 256, 3)\n",
      "Pixel range: [50, 255]\n"
     ]
    },
    {
     "data": {
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ZU+XKlQ1JxoYNGxxtZ86cMaxWq/F///d/jrYhQ4YYFovF2LVrl6Pt/PnzRlhYmMuYmcmo++qfjGs1o7bmzZsb6enp2Z6brVu3GpKMefPmuTy/9u3bO/2umjZtalgsFuOpp55ytKWnpxsVK1Z0uiZyej0tW7bMkGTs3Lnzms85MxUrVjS6devm0r5v3z5DkjF+/HjDMAwjLS3N8Pf3N+bOnWsYhmGUKVPGeO+99wzDMIz4+HjD09PTePLJJx37Z3aO2rdvb1SrVs2pLeP3vXr1apf+Gb+TH3/80dF25MgRw9fX17j//vuv+bwy5vCxY8caZ8+eNU6dOmVs3LjRaNSokSHJWLJkiaPv+PHjDX9/f+PAgQNOY4wYMcLw9PQ0jh496jRmyZIljZiYGEe/5cuXG5KMFStWONpuu+02o1y5ck7z07fffut0jRlG7uaMrM5VbusPCgoyzpw5k+l5W7hwoSHJ2L59e6bbAQDA9WN5DQAACkibNm0UHh6uSpUqqXfv3goICNCyZctUoUIFp34DBgxw+hh/ixYtZLPZdOTIEUmX18J96KGH9NVXXykhIcHRLzo6Ws2aNVPVqlUlSSEhIZKk5cuXO330+FrOnj2rDRs2qH///oqIiHDadmVNISEh2r59u/7555+cnwBd/oIuSQoMDMxR/1WrVqlx48ZO66cGBARowIABOnz4sOPj0hn69euX5bqfV96lK12+wzU4OFht27bVuXPnHD8NGzZUQECA1q1bJ+ny3bcJCQkaMWKEyxrEmS0PkhMWi0Vr1qzR66+/rtDQUH366ad65plnVLlyZfXq1ctpTee+ffsqNTVVS5cudbQtXrxY6enpTmu4ZnjqqaecHrdo0UJ///13rurLboycnrvc2LVrlw4dOqRhw4Y5rt0MeT3PV7v6GpDkdL1cvHhR586dU7NmzWQYhnbt2pXtmG3atHH6MrJ69eopKCgoR+e8Tp06jmUQJCk8PFy1atVy2nf16tVq2rSpbrvtNkdbWFiYY3mQnPr888/13XffOX6io6Odtj/55JMunz648tykpaXp/PnzuummmxQSEpLpUjqPP/640++qSZMmMgxDjz/+uKPN09NTt99+e56up4zrYuXKlZkuL3Qt58+fd7rrN8PNN9+skiVLOtZq/uWXX3Tx4kXHF082a9bM8emQrVu3ymazOc1HV56juLg4nTt3TlFRUfr7778VFxfndKyqVauqffv2mdbXtGlTNWzY0PE4IiJC9913n9asWeOytE1mxowZo/DwcJUtW1YtWrTQ77//rsmTJ6t79+6OPkuWLFGLFi0UGhrqdJ7btGkjm83mspxHr169nM5ZxrWa8bs7efKkdu/erX79+jm+nFGS2rZtqzp16jiNlds5I7Nzldv6u3Xr5lhO6GoZz+vcuXNZn1QAAHBdWF4DAIAC8t5776lmzZry8vJSmTJlVKtWrUy/5O7qsDfjf46vXPeyb9++euutt7Rs2TL17dtXf/zxh3766Se9//77jj69evXSxx9/rCeeeEIjRozQ3XffrQceeEDdu3fP9LjSv2FCdh/nfvvtt9WvXz9VqlRJDRs2VKdOndS3b19Vq1ZNkpSYmKjExERHf09PT4WHhzs+xnxlWH4tR44cUZMmTVzab775Zsf2K2vNCNyv5uXl5fJx74MHDyouLk6lS5fOdJ8zZ85I+ncZlLx8xP1arFarRo4cqZEjR+rkyZNav369pk+frs8++0ze3t5asGCBJKl27dpq1KiRoqOjHeFddHS07rjjDt10001OY/r6+rqELKGhoZmuUZ2VnIyR03OXG/l1njNkdg1I0tGjR/Xqq6/qq6++cjlPV4eGmbn69Srl/JznZN8jR444LaGS4erffXZatmx5zS8SzOy1k7EG++zZs3XixAmndZgzOzdXP5+MILJSpUou7Xm5nqKiotStWzeNHTtWU6dOVatWrdS1a1c9+OCDOVpuxLhqHWnp8hsazZo104YNG2S327V582aVLl3acX6bNWumd999V5Ic4fOVofPmzZs1ZswYbd26VUlJSU5jx8XFOYWxWc1PklSjRg2Xtpo1ayopKUlnz55V2bJlr/ncBgwYoB49eiglJUX/+9//9J///MclrD548KB+/fXXLIPYq1+32f1dlPFGaGa116pVy+mNidzOGZmdq9zWf63znXEtmPWGFgAAcEXoDABAAWncuLFuv/32bPtltdbxlYFJnTp11LBhQy1YsEB9+/bVggUL5OPjo549ezr6+Pn5acOGDVq3bp2+/vprrV69WosXL9Zdd92lb7/9NkdrKmelZ8+eatGihZYtW6Zvv/1W77zzjt566y198cUX6tixoyZNmqSxY8c6+leuXFmHDx9W7dq1JUl79uxR165d83z8rGR1l7PVanUJ2u12u0qXLu1yx2eGrIKN/FCuXDn17t1b3bp1U2RkpD777DPNmTPHsdZz37599eyzz+r48eNKTU3Vtm3bHEHYla7nd5qbMQrTucupzK4Bm82mtm3bKiYmRi+99JJq164tf39/nThxQo8++miOPiGQk9drfuxrtsxeO0OGDNHs2bM1bNgwNW3aVMHBwbJYLOrdu3em5yar55NZ+5XPMafXk8Vi0dKlS7Vt2zatWLFCa9asUf/+/TV58mRt27btmuuolyxZMss3Apo3b64VK1Zoz549jvWcMzRr1kwvvPCCTpw4oU2bNql8+fKON9f++usv3X333apdu7amTJmiSpUqycfHR6tWrdLUqVNdzlFW85MZatSooTZt2kiSunTpIk9PT40YMUKtW7d2/L1jt9vVtm1bvfjii5mOUbNmTafHZl6fuZ0zMjtXua3/Wuc741q41hsxAADg+hA6AwBQRPXt21fPP/+8Tp48qYULF6pz584uHx/38PDQ3XffrbvvvltTpkzRhAkTNHLkSK1bt84RUFwpI0zZu3dvtscvV66cBg0apEGDBunMmTNq0KCB3njjDXXs2FF9+/bN9CPozZs3dywn8corr2QbcFauXFl//PGHS/v+/fsd2/OqevXqWrt2re68885rhhMZSyfs3bv3mneXmnHHnLe3t+rVq6eDBw/q3Llzjrsbe/fureeff16ffvqpkpOT5e3trV69euXpGGbUmdNzl5vjXXmeM7s288OePXt04MABzZ071+lL7b777rsCOX5OVK5cWX/++adLe2ZtZlu6dKn69eunyZMnO9pSUlKcln8xQ26uJ0m64447dMcdd+iNN97QwoUL9dBDD2nRokV64oknstyndu3aOnToUKbbMuaqTZs2afPmzRo2bJhjW8OGDWW1WvXDDz9o+/bt6tSpk2PbihUrlJqaqq+++srpruC8LC9z8OBBl7YDBw6oRIkSeXoTZ+TIkfroo480atQorV69WtLl85yYmGja6ytj/s2s9qvn7dz+jjNjZv2HDh2Sh4eHS1ANAADMw5rOAAAUUX369JHFYtGzzz6rv//+22V935iYGJd9MtaFTU1NzXTM8PBwtWzZUv/973919OhRp20Zd7fZbDaXj9aXLl1a5cuXd4xbrVo1tWnTxvFz5513SpJKlCihl156Sb///rteeumlTO+YW7BggXbs2CFJ6tSpk3bs2KGtW7c6tl+8eFEffvihqlSp4rJuaG707NlTNptN48ePd9mWnp7uCNbatWunwMBATZw4USkpKU79rqzf398/R8sxSJdDmqvPryTFxsZq69atCg0NdQqaSpUqpY4dO2rBggWKjo5Whw4d8nyHXokSJRzHyqucnjvp8nnJybEaNGigqlWratq0aS798+vO34w3Pa4c3zAMTZ8+PV+Olxft27fX1q1btXv3bkdbTExMlneMmsnT09Pl3M+YMSNHawznRk6vpwsXLrjUk92clqFp06bau3dvpv1uv/12+fr6Kjo6WidOnHC609lqtapBgwZ67733dPHiRac30zK7fuLi4jR79uxrP+FMbN261Wk5imPHjmn58uVq165dnj7BEBISooEDB2rNmjWOa6dnz57aunWr1qxZ49I/NjZW6enpuTpGuXLldNttt2nu3LlOc993333nst5+buaMrJhZ/08//aTIyEin5U8AAIC5uNMZAIAiKjw8XB06dNCSJUsUEhKizp07O20fN26cNmzYoM6dO6ty5co6c+aMZs6cqYoVKzoFJ1f7z3/+o+bNm6tBgwYaMGCAqlatqsOHD+vrr7/W7t27lZCQoIoVK6p79+669dZbFRAQoLVr12rnzp1Od0Rm5YUXXtC+ffs0efJkrVu3Tt27d1fZsmV16tQpffnll9qxY4e2bNkiSRoxYoQ+/fRTdezYUUOHDlVYWJjmzp2rQ4cO6fPPP89ybeqciIqK0sCBAzVx4kTt3r1b7dq1k7e3tw4ePKglS5Zo+vTp6t69u4KCgjR16lQ98cQTatSokR588EGFhobql19+UVJSkubOnSvp8h2Rixcv1vPPP69GjRopICBA99xzT6bH/uWXX/Tggw+qY8eOatGihcLCwnTixAnNnTtX//zzj6ZNm+YSNPXt29fxpWCZBTc55efnpzp16mjx4sWqWbOmwsLCVLdu3VytpZzTcyddPi+zZs3S66+/rptuukmlS5fWXXfd5TKmh4eHZs2apXvuuUe33XabHnvsMZUrV0779+/Xvn37Mg2arlft2rVVvXp1DR8+XCdOnFBQUJA+//zzXK2Bnd9efPFFLViwQG3bttWQIUPk7++vjz/+WBEREYqJicnXNWm7dOmi+fPnKzg4WHXq1NHWrVu1du1alSxZ0tTj5PR6mjt3rmbOnKn7779f1atXV0JCgj766CMFBQU53YGcmfvuu0/jx4/X+vXr1a5dO6dtPj4+atSokTZu3Cir1er0hX7S5SU2Mua2K+fOdu3aycfHR/fcc48GDhyoxMREffTRRypdurROnjyZq3NQt25dtW/fXkOHDpXVatXMmTMlyWmZotx69tlnNW3aNL355ptatGiRXnjhBX311Vfq0qWLHn30UTVs2FAXL17Unj17tHTpUh0+fDjXb2ZNnDhRnTt3VvPmzdW/f3/FxMRoxowZioyMdFrXPzdzRlbMqj8tLU3r16/XoEGDcvVcAQBA7hA6AwBQhPXt21crV65Uz549Xb5I695779Xhw4f13//+V+fOnVOpUqUUFRWlsWPHXvPurltvvVXbtm3T6NGjNWvWLKWkpKhy5cqO9aJLlCihQYMG6dtvv9UXX3whu92um266STNnztTTTz+dbc0eHh6aN2+e7rvvPn344YeaNGmS4uPjHXdZv/32244vTitTpoy2bNmil156STNmzFBKSorq1aunFStWuITsefH++++rYcOG+uCDD/TKK6/Iy8tLVapU0cMPP+y4O1uSHn/8cZUuXVpvvvmmxo8fL29vb9WuXVvPPfeco8+gQYO0e/duzZ49W1OnTlXlypWzDJ1btmyp8ePH65tvvtGUKVN09uxZBQYGqn79+nrrrbfUrVs3l33uuecehYaGym636957772u5/3xxx9ryJAheu6553Tp0iWNGTMm11/gl9Nz9+qrr+rIkSN6++23lZCQoKioqExDZ+nyXb3r1q3T2LFjNXnyZNntdlWvXl1PPvnkdT3frHh7e2vFihUaOnSoJk6cKF9fX91///0aPHiwbr311nw5Zm5VqlRJ69at09ChQzVhwgSFh4frmWeekb+/v4YOHSpfX998O/b06dPl6emp6OhopaSk6M4779TatWvVvn1704+Vk+spKipKO3bs0KJFi3T69GkFBwercePGio6OvuaXxkmX3/yoV6+ePvvsM5fQWbocJm/cuNGxnMaV7rzzTk2ePFmBgYFO10WtWrW0dOlSjRo1SsOHD1fZsmX19NNPKzw8XP3798/V84+KilLTpk01duxYHT16VHXq1NGcOXNUr169XI1zpfLly+vBBx/U/Pnz9ddff6l69epav369JkyYoCVLlmjevHkKCgpSzZo1s/17ISsZb3yOGjVKL7/8sqpXr67Zs2dr+fLl+uGHH5z65nTOyEqJEiVMqf/7779XTEyM+vXrl+vnCwAAcs5iuOObSgAAgCmWL1+url27asOGDWrRooW7y0E+Sk9PV/ny5XXPPffok08+cXc5cLNhw4bpgw8+UGJioilfIHkjmD9/vp555hkdPXpUISEh7i7HwWKx6Jlnnsn0y0Fhvq5du8pisWjZsmXuLgUAgGKNNZ0BACjCPvroI1WrVu2ay2WgePjyyy919uxZpy+8w40hOTnZ6fH58+c1f/58NW/enMA5Fx566CFFRETovffec3cpcJPff/9dK1euvK4ligAAQM6wvAYAAEXQokWL9Ouvv+rrr7/W9OnT83VdV7jX9u3b9euvv2r8+PGqX7++oqKi3F0SCljTpk3VqlUr3XzzzTp9+rQ++eQTxcfHa/To0e4urUjx8PDQ3r173V0G3Ojmm2/O9RcmAgCAvCF0BgCgCOrTp48CAgL0+OOP82VIxdysWbO0YMEC3XbbbZozZ467y4EbdOrUSUuXLtWHH34oi8WiBg0a6JNPPlHLli3dXRoAAACQKdZ0BgAAAAAAAACYhjWdAQAAAAAAAACmIXQGAAAAAAAAAJiG0BkAAAAAAAAAYBpCZwAAAAAAAACAaQidAQAAAAAAAACmIXQGAAAAAAAAAJiG0BkAAAAAAAAAYBpCZwAAAAAAAACAaQidAQAAAAAAAACmIXQuYHPmzJHFYsn0Z8SIEe4uzzSpqal66aWXVL58efn5+alJkyb67rvv3F0WgCvcCPNRYmKixowZow4dOigsLEwWi0Vz5szJsv/vv/+uDh06KCAgQGFhYXrkkUd09uzZgisYgMONMEft3LlTgwcPVmRkpPz9/RUREaGePXvqwIEDmfZnjgIKjxthjtq3b5969OihatWqqUSJEipVqpRatmypFStWuPR99NFHMz0XtWvXdkPlAG6EOepqb7zxhiwWi+rWreuyzW636/3339dtt92mgIAAlSlTRh07dtSWLVvcUOmNw8vdBdyoxo0bp6pVqzq1ZfbCKKoeffRRLV26VMOGDVONGjU0Z84cderUSevWrVPz5s3dXR6AKxTn+ejcuXMaN26cIiIidOutt+qHH37Isu/x48fVsmVLBQcHa8KECUpMTNSkSZO0Z88e7dixQz4+PgVXOACH4jxHvfXWW9q8ebN69OihevXq6dSpU3r33XfVoEEDbdu2zel5MkcBhVNxnqOOHDmihIQE9evXT+XLl1dSUpI+//xz3Xvvvfrggw80YMAAp/5Wq1Uff/yxU1twcHBBlgzgKsV5jrrS8ePHNWHCBPn7+2e6/YUXXtCUKVP08MMPa9CgQYqNjdUHH3ygqKgobd68WY0bNy7gim8MhM5u0rFjR91+++056puSkiIfHx95eBSNG9N37NihRYsW6Z133tHw4cMlSX379lXdunX14osv8k4SUMgU5/moXLlyOnnypMqWLasff/xRjRo1yrLvhAkTdPHiRf3000+KiIiQJDVu3Fht27bVnDlzXP7HCkDBKM5z1PPPP6+FCxc6Bca9evXSLbfcojfffFMLFixwtDNHAYVTcZ6jOnXqpE6dOjm1DR48WA0bNtSUKVNc5h0vLy89/PDDBVkigGwU5znqSsOHD9cdd9whm82mc+fOOW1LT0/XrFmz1L17d82fP9/RnvFJjujoaELnfFL0rqRi7ocffpDFYtGiRYs0atQoVahQQSVKlFB8fLxiYmI0fPhw3XLLLQoICFBQUJA6duyoX375JdMxPvvsM40dO1YVKlRQYGCgunfvrri4OKWmpmrYsGEqXbq0AgIC9Nhjjyk1NdWllgULFqhhw4by8/NTWFiYevfurWPHjmX7HJYuXSpPT0+nf4T4+vrq8ccf19atW3M0BgD3Kw7zkdVqVdmyZXP0fD///HN16dLFEeZIUps2bVSzZk199tlnORoDQMEpDnNUs2bNXO5QrlGjhiIjI/X77787tTNHAUVLcZijMuPp6alKlSopNjY20+02m03x8fF5GhtAwSlOc9SGDRu0dOlSTZs2LdPtaWlpSk5OVpkyZZzaS5cuLQ8PD/n5+eX4WMgd7nR2k7i4OJd3X0qVKuX48/jx4+Xj46Phw4crNTVVPj4++u233/Tll1+qR48eqlq1qk6fPu34OMBvv/2m8uXLO403ceJE+fn5acSIEfrzzz81Y8YMeXt7y8PDQxcuXNBrr72mbdu2ac6cOapatapeffVVx75vvPGGRo8erZ49e+qJJ57Q2bNnNWPGDLVs2VK7du1SSEhIls9t165dqlmzpoKCgpzaM9452r17typVqpTXUwfAZMV5PsqpEydO6MyZM5neBdC4cWOtWrXquo8BIG9utDnKMAydPn1akZGRjjbmKKDwuhHmqIsXLyo5OVlxcXH66quv9M0336hXr14u/ZKSkhQUFKSkpCSFhoaqT58+euuttxQQEJDLswrALMV9jrLZbBoyZIieeOIJ3XLLLZn2yfiesTlz5qhp06Zq0aKFYmNjNX78eIWGhvJpsfxkoEDNnj3bkJTpj2EYxrp16wxJRrVq1YykpCSnfVNSUgybzebUdujQIcNqtRrjxo1ztGWMUbduXePSpUuO9j59+hgWi8Xo2LGj0xhNmzY1Kleu7Hh8+PBhw9PT03jjjTec+u3Zs8fw8vJyab9aZGSkcdddd7m079u3z5BkvP/++9fcH0DBuBHmoyvt3LnTkGTMnj07y23z5s1z2fbCCy8YkoyUlJQcHwvA9bvR5qgM8+fPNyQZn3zyiaONOQoofG6kOWrgwIGO5+bh4WF0797diImJceozYsQI46WXXjIWL15sfPrpp0a/fv0MScadd95ppKWl5eg4AMxzo8xR7777rhEcHGycOXPGMAzDiIqKMiIjI136HTx40GjQoIHTeahWrZqxf//+bI+BvONOZzd57733VLNmzSy39+vXz+UWf6vV6vizzWZTbGysAgICVKtWLf38888uY/Tt21fe3t6Ox02aNNGnn36q/v37O/Vr0qSJ/vOf/yg9PV1eXl764osvZLfb1bNnT6d3xMqWLasaNWpo3bp1euWVV7KsPTk52anWDL6+vo7tAAqP4jwf5VTGvJTd3JXZdgD560aao/bv369nnnlGTZs2Vb9+/RztzFFA4XUjzFHDhg1T9+7d9c8//+izzz6TzWbTpUuXnPpMnDjR6XHv3r1Vs2ZNjRw5UkuXLlXv3r2zPQ4A8xXnOer8+fN69dVXNXr0aIWHh2d9EiQFBgYqMjJSTZs21d13361Tp07pzTffVNeuXbVx40anu79hHkJnN2ncuPE1F3O/+ttFJclut2v69OmaOXOmDh06JJvN5thWsmRJl/5Xrvkn/fvNwVcvbREcHCy73a64uDiVLFlSBw8elGEYqlGjRqa1XTmZZMbPzy/TdXpSUlIc2wEUHsV5PsqpjHmJuQsofG6UOerUqVPq3LmzgoODHd+PkYE5Cii8boQ5qnbt2qpdu7aky+FSu3btdM8992j79u2yWCxZ7vfcc89p9OjRWrt2LaEz4CbFeY4aNWqUwsLCNGTIkGv2S09PV5s2bdSqVSvNmDHD0d6mTRtFRkbqnXfe0VtvvXXNMZA3hM6FVGb/4zBhwgSNHj1a/fv31/jx4xUWFiYPDw8NGzZMdrvdpf+V/7OSk3bDMCRdnmAsFou++eabTPtmtyZXuXLldOLECZf2kydPSpLL+j8ACreiPB/lVLly5ST9O09d6eTJkwoLC+MOQqCQKg5zVFxcnDp27KjY2Fht3LjR5d9KzFFA0VUc5qirde/eXQMHDtSBAwdUq1atLPv5+fmpZMmSiomJydNxAOS/ojpHHTx4UB9++KGmTZumf/75x9GekpKitLQ0HT58WEFBQQoLC9OGDRu0d+9eTZkyxWmMGjVq6Oabb9bmzZuzPA6uD6FzEbJ06VK1bt1an3zyiVN7bGysqR8FqF69ugzDUNWqVa/5MYys3HbbbVq3bp3i4+Odvkxw+/b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      "text/plain": [
       "<Figure size 1500x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Renderer working. Frames saved.\n"
     ]
    }
   ],
   "source": [
    "# Cell 5.2 — Synthetic Video Renderer (Warp → frames)\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "def render_physics_video(\n",
    "    trajectory: np.ndarray,   # (T, N, 3) from WarpPhysicsEngine\n",
    "    height: int = 256,\n",
    "    width:  int = 256,\n",
    "    ball_radius: int = 12,\n",
    "    ball_color:  tuple = (220, 60, 60),   # RGB red\n",
    "    bg_color:    tuple = (200, 200, 200), # light grey\n",
    "    floor_color: tuple = (120, 80, 50),   # brown\n",
    ") -> np.ndarray:\n",
    "    \"\"\"\n",
    "    Renders a sequence of physics frames as a simple synthetic video.\n",
    "    Input:  trajectory (T, N, 3) — 3D world coords from Warp\n",
    "    Output: (T, H, W, 3) uint8 numpy array\n",
    "    \"\"\"\n",
    "    T = len(trajectory)\n",
    "    frames = np.zeros((T, height, width, 3), dtype=np.uint8)\n",
    "\n",
    "    # World bounds — map world Y [0, max_height] to pixel Y [height-30, 20]\n",
    "    max_height = trajectory[:, 0, 1].max() * 1.15\n",
    "    floor_px   = height - 25\n",
    "\n",
    "    for t in range(T):\n",
    "        frame = np.full((height, width, 3), bg_color, dtype=np.uint8)\n",
    "\n",
    "        # Draw floor\n",
    "        cv2.rectangle(frame, (0, floor_px), (width, height), floor_color, -1)\n",
    "\n",
    "        # Draw each object (ball)\n",
    "        for obj_idx in range(trajectory.shape[1]):\n",
    "            world_x = trajectory[t, obj_idx, 0]\n",
    "            world_y = trajectory[t, obj_idx, 1]\n",
    "\n",
    "            # Project to pixel space\n",
    "            # World X [-1,1] → pixel X [pad, width-pad]\n",
    "            pad   = 30\n",
    "            px    = int(pad + (world_x + 0.5) * (width - 2*pad))\n",
    "            # World Y [0, max_height] → pixel Y [floor_px, 20] (inverted)\n",
    "            py    = int(floor_px - (world_y / max_height) * (floor_px - 20))\n",
    "            px    = max(ball_radius, min(width  - ball_radius, px))\n",
    "            py    = max(ball_radius, min(floor_px, py))\n",
    "\n",
    "            # Shadow\n",
    "            shadow_y = floor_px - 3\n",
    "            shadow_r = max(3, ball_radius - int(3 * world_y / max(max_height, 0.1)))\n",
    "            cv2.ellipse(frame, (px, shadow_y), (shadow_r, max(2, shadow_r//3)),\n",
    "                        0, 0, 360, (150,150,150), -1)\n",
    "\n",
    "            # Ball with simple shading\n",
    "            cv2.circle(frame, (px, py), ball_radius, ball_color, -1)\n",
    "            highlight = (min(255, ball_color[0]+80),\n",
    "                         min(255, ball_color[1]+80),\n",
    "                         min(255, ball_color[2]+80))\n",
    "            cv2.circle(frame, (px - ball_radius//3, py - ball_radius//3),\n",
    "                       ball_radius//3, highlight, -1)\n",
    "\n",
    "        frames[t] = frame\n",
    "\n",
    "    return frames   # (T, H, W, 3) uint8\n",
    "\n",
    "\n",
    "# Test the renderer\n",
    "print('Testing physics renderer...')\n",
    "engine_test = WarpPhysicsEngine()\n",
    "init_p = np.array([[0.0, 4.0, 0.0]])\n",
    "init_v = np.zeros((1,3), dtype=np.float32)\n",
    "traj_test = engine_test.simulate(init_p, init_v, restitution=0.7, num_steps=49)\n",
    "\n",
    "test_frames = render_physics_video(traj_test, height=256, width=256)\n",
    "print(f'Rendered frames shape: {test_frames.shape}')\n",
    "print(f'Pixel range: [{test_frames.min()}, {test_frames.max()}]')\n",
    "\n",
    "# Show first frame\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axes = plt.subplots(1, 5, figsize=(15,3))\n",
    "for i, idx in enumerate([0, 10, 20, 35, 48]):\n",
    "    axes[i].imshow(test_frames[idx])\n",
    "    axes[i].set_title(f'Frame {idx}')\n",
    "    axes[i].axis('off')\n",
    "plt.suptitle('Physics-Correct Synthetic Training Frames (Warp Renderer)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('/mnt/Data_volume/Dhanya/synthetic_frames_test.png', dpi=150)\n",
    "plt.show()\n",
    "print('Renderer working. Frames saved.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimiser:  AdamW on 240 LoRA tensors\n",
      "Total steps: 1  (1 epochs × 1 steps)\n"
     ]
    }
   ],
   "source": [
    "# Cell 5.3 — Optimiser + Scheduler setup\n",
    "from torch.optim import AdamW\n",
    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
    "\n",
    "lora_params = [p for p in lora_transformer.parameters() if p.requires_grad]\n",
    "\n",
    "optimiser = AdamW(\n",
    "    lora_params,\n",
    "    lr=1e-4,\n",
    "    weight_decay=0.01,\n",
    "    betas=(0.9, 0.999)\n",
    ")\n",
    "\n",
    "NUM_EPOCHS      = 1\n",
    "STEPS_PER_EPOCH = 1\n",
    "TOTAL_STEPS     = NUM_EPOCHS * STEPS_PER_EPOCH\n",
    "\n",
    "scheduler_lr = CosineAnnealingLR(optimiser, T_max=TOTAL_STEPS, eta_min=1e-6)\n",
    "\n",
    "print(f'Optimiser:  AdamW on {len(lora_params)} LoRA tensors')\n",
    "print(f'Total steps: {TOTAL_STEPS}  ({NUM_EPOCHS} epochs × {STEPS_PER_EPOCH} steps)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# BLOCK 6 — TRAINING LOOP (CORRECT DIFFUSION OBJECTIVE)\n",
    "\n",
    "Training flow per step:\n",
    "```\n",
    "Warp simulation → render frames → VAE encode → add noise\n",
    "                                                    ↓\n",
    "                                         transformer predicts noise\n",
    "                                                    ↓\n",
    "                                         MSE(pred_noise, real_noise)\n",
    "                                                    ↓\n",
    "                                         backprop → LoRA weights updated\n",
    "```\n",
    "Gradients flow correctly because we are training on the DENOISING task,\n",
    "which is exactly what the model was designed for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-generating physics-correct training videos...\n",
      "  ball_drop_low: frames=(49, 256, 256, 3)\n",
      "  ball_drop_high: frames=(49, 256, 256, 3)\n",
      "  ball_elastic: frames=(49, 256, 256, 3)\n",
      "  ball_heavy: frames=(49, 256, 256, 3)\n",
      "\n",
      "All 4 training videos ready.\n",
      "These are ground-truth physics frames from Warp.\n",
      "The model will learn to generate videos that look like these.\n"
     ]
    }
   ],
   "source": [
    "# Cell 6.1 — Training Scenarios Definition\n",
    "import numpy as np\n",
    "\n",
    "TRAINING_SCENARIOS = [\n",
    "    {\n",
    "        'name':        'ball_drop_low',\n",
    "        'prompt':      'A red rubber ball falls from 3 meters onto a flat gray floor and bounces once. Realistic physics simulation.',\n",
    "        'drop_height': 3.0,\n",
    "        'restitution': 0.7,\n",
    "        'init_pos':    np.array([[0.0, 3.0, 0.0]]),\n",
    "    },\n",
    "    {\n",
    "        'name':        'ball_drop_high',\n",
    "        'prompt':      'A red rubber ball falls from 7 meters onto a flat gray floor and bounces twice. Realistic physics simulation.',\n",
    "        'drop_height': 7.0,\n",
    "        'restitution': 0.7,\n",
    "        'init_pos':    np.array([[0.0, 7.0, 0.0]]),\n",
    "    },\n",
    "    {\n",
    "        'name':        'ball_elastic',\n",
    "        'prompt':      'A red superball falls from 5 meters and bounces multiple times with high energy. Realistic physics.',\n",
    "        'drop_height': 5.0,\n",
    "        'restitution': 0.9,\n",
    "        'init_pos':    np.array([[0.0, 5.0, 0.0]]),\n",
    "    },\n",
    "    {\n",
    "        'name':        'ball_heavy',\n",
    "        'prompt':      'A red heavy ball falls from 5 meters onto concrete with very little bounce. Realistic physics.',\n",
    "        'drop_height': 5.0,\n",
    "        'restitution': 0.2,\n",
    "        'init_pos':    np.array([[0.0, 5.0, 0.0]]),\n",
    "    },\n",
    "]\n",
    "\n",
    "# Pre-generate ALL synthetic training videos now (fast, CPU)\n",
    "print('Pre-generating physics-correct training videos...')\n",
    "physics_engine_train = WarpPhysicsEngine()\n",
    "\n",
    "for s in TRAINING_SCENARIOS:\n",
    "    traj = physics_engine_train.simulate(\n",
    "        s['init_pos'],\n",
    "        np.zeros((1,3), dtype=np.float32),\n",
    "        restitution=s['restitution'],\n",
    "        num_steps=49\n",
    "    )\n",
    "    s['frames_np'] = render_physics_video(traj, height=256, width=256)\n",
    "    print(f\"  {s['name']}: frames={s['frames_np'].shape}\")\n",
    "\n",
    "print()\n",
    "print(f'All {len(TRAINING_SCENARIOS)} training videos ready.')\n",
    "print('These are ground-truth physics frames from Warp.')\n",
    "print('The model will learn to generate videos that look like these.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "_hf_hook",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[22], line 20\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;66;03m# ── FIX 1: Remove cpu_offload hooks — they break training ────────────────────\u001b[39;00m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# enable_sequential_cpu_offload adds hooks that interfere with grad computation\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# We have 99GB VRAM — we don't need cpu offload at all\u001b[39;00m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01maccelerate\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhooks\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m remove_hook_from_module\n\u001b[0;32m---> 20\u001b[0m \u001b[43mremove_hook_from_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlora_transformer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrecurse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m remove_hook_from_module(vae,             recurse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m     22\u001b[0m remove_hook_from_module(text_encoder,    recurse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m/mnt/Data_volume/Dhanya/pdw_env/lib/python3.10/site-packages/accelerate/hooks.py:203\u001b[0m, in \u001b[0;36mremove_hook_from_module\u001b[0;34m(module, recurse)\u001b[0m\n\u001b[1;32m    201\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_hf_hook\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    202\u001b[0m     module\u001b[38;5;241m.\u001b[39m_hf_hook\u001b[38;5;241m.\u001b[39mdetach_hook(module)\n\u001b[0;32m--> 203\u001b[0m     \u001b[38;5;28;43mdelattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m_hf_hook\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_old_forward\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    206\u001b[0m     \u001b[38;5;66;03m# Overriding a GraphModuleImpl forward freezes the forward call and later modifications on the graph will fail.\u001b[39;00m\n\u001b[1;32m    207\u001b[0m     \u001b[38;5;66;03m# Reference: https://pytorch.slack.com/archives/C3PDTEV8E/p1705929610405409\u001b[39;00m\n\u001b[1;32m    208\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGraphModuleImpl\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mtype\u001b[39m(module)):\n",
      "File \u001b[0;32m/mnt/Data_volume/Dhanya/pdw_env/lib/python3.10/site-packages/torch/nn/modules/module.py:2040\u001b[0m, in \u001b[0;36mModule.__delattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2038\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_modules[name]\n\u001b[1;32m   2039\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2040\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__delattr__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mAttributeError\u001b[0m: _hf_hook"
     ]
    }
   ],
   "source": [
    "# Cell 6.2 — Full Training Loop (FIXED)\n# Fixes applied:\n#   1. Removed cpu_offload hooks that break training forward pass\n#   2. Fixed latent shape — VAE outputs (B,16,T',H',W'), must stay that way\n#   3. Added image_rotary_emb required by CogVideoXTransformer\n#   4. Correct noisy_latent reshape for CogVideoX patch embedding\n\nimport torch\nimport numpy as np\nimport json, os\nfrom diffusers.utils import export_to_video\n\nos.makedirs('/mnt/Data_volume/Dhanya/checkpoints',     exist_ok=True)\nos.makedirs('/mnt/Data_volume/Dhanya/training_videos', exist_ok=True)\n\n# ── FIX 1: Remove cpu_offload hooks — they break training ────────────────────\n# enable_sequential_cpu_offload adds hooks that interfere with grad computation\n# We have 99GB VRAM — we don't need cpu offload at all\ndef safe_remove_hooks(module, recurse=True):\n    \"\"\"\n    Safely remove accelerate CPU-offload hooks.\n    accelerate uses object.__setattr__ to attach _hf_hook, bypassing\n    nn.Module's registry. torch's __delattr__ therefore cannot find it\n    and raises AttributeError. We bypass this by writing directly to\n    module.__dict__.\n    \"\"\"\n    if recurse:\n        for child in module.children():\n            safe_remove_hooks(child, recurse=True)\n    if hasattr(module, '_hf_hook'):\n        try:\n            module._hf_hook.detach_hook(module)\n        except Exception:\n            pass\n        # Use object.__delattr__ to bypass nn.Module's __delattr__\n        try:\n            object.__delattr__(module, '_hf_hook')\n        except AttributeError:\n            module.__dict__.pop('_hf_hook', None)\n    if hasattr(module, '_old_forward'):\n        try:\n            module.forward = module._old_forward\n            object.__delattr__(module, '_old_forward')\n        except Exception:\n            module.__dict__.pop('_old_forward', None)\n\nsafe_remove_hooks(lora_transformer, recurse=True)\nsafe_remove_hooks(vae,             recurse=True)\nsafe_remove_hooks(text_encoder,    recurse=True)\n\n# Move everything to GPU explicitly\nlora_transformer = lora_transformer.to('cuda')\nvae              = vae.to('cuda')\ntext_encoder     = text_encoder.to('cuda')\n\nprint(f'VRAM after moving to GPU: {torch.cuda.memory_allocated()/1e9:.1f} GB')\n\n# ── FIX 2: Get correct rotary embeddings for CogVideoX ───────────────────────\ndef get_rotary_emb(pipe, latent_shape):\n    \"\"\"\n    Compute 3-D RoPE for CogVideoX-2b (text-to-video).\n    latent_shape: (B, T, C, H, W)  — after permute from VAE output\n\n    CRITICAL NOTES:\n      * Do NOT use grid_type='slice' — that mode halves both spatial dims\n        (designed for CogVideoX-I2V, not T2V), producing T*(H//2)*(W//2)=3328\n        tokens while the transformer expects T*H*W = 13*32*32 = 13312.\n      * Do NOT divide H/W by patch_size here — the transformer applies\n        CogVideoXPatchEmbed internally and expects the RoPE to cover the\n        full latent spatial grid (32x32), not the post-patch grid (16x16).\n      * grid_size = (H, W) = (32, 32)  →  T*H*W = 13*32*32 = 13312 ✓\n    \"\"\"\n    _, T, C, H, W = latent_shape   # (B, T, C, H, W) after permute\n    try:\n        from diffusers.models.embeddings import get_3d_rotary_pos_embed\n        head_dim = (\n            pipe.transformer.config.attention_head_dim\n            if hasattr(pipe.transformer.config, 'attention_head_dim')\n            else 64\n        )\n        # grid_size = full latent spatial dims (no patch_size division)\n        # temporal_size = latent T (13 for 49-frame CogVideoX)\n        # grid_type default = 'linspace' → covers full grid exactly\n        try:\n            rope_emb = get_3d_rotary_pos_embed(\n                embed_dim=head_dim,\n                crops_coords=None,\n                grid_size=(H, W),   # (32, 32) → 13*32*32 = 13312 tokens ✓\n                temporal_size=T,    # 13\n                device='cuda',\n            )\n        except TypeError:\n            # Some diffusers versions don't accept `device` kwarg\n            rope_emb = get_3d_rotary_pos_embed(\n                embed_dim=head_dim,\n                crops_coords=None,\n                grid_size=(H, W),\n                temporal_size=T,\n            )\n            if isinstance(rope_emb, (tuple, list)):\n                rope_emb = tuple(r.to('cuda') for r in rope_emb)\n            else:\n                rope_emb = rope_emb.to('cuda')\n        return rope_emb\n    except Exception:\n        return None   # transformer handles None gracefully\n\n# ── DIAGNOSTIC: Check actual VAE output shape ────────────────────────────────\nprint('Running VAE shape diagnostic...')\ntest_frames = TRAINING_SCENARIOS[0]['frames_np']\ntest_tensor = torch.from_numpy(test_frames).float() / 127.5 - 1.0\ntest_tensor = test_tensor.permute(3, 0, 1, 2).unsqueeze(0)  # (1,3,49,256,256)\ntest_tensor = test_tensor.to('cuda', dtype=torch.bfloat16)\n\nwith torch.no_grad():\n    test_latent = vae.encode(test_tensor).latent_dist.sample()\n\nprint(f'  Input frames shape:  {test_tensor.shape}')\nprint(f'  VAE latent shape:    {test_latent.shape}')\nprint(f'  Expected:            (1, 16, 13, 32, 32)')\n\nB, C, T_lat, H_lat, W_lat = test_latent.shape\nassert C == 16, f'Expected 16 latent channels, got {C}'\nprint(f'  Channel check:       PASSED (C={C})')\ndel test_tensor, test_latent\ntorch.cuda.empty_cache()\n\n# ── TRAINING LOOP ─────────────────────────────────────────────────────────────\ntraining_log = []\nglobal_step  = 0\n\nlora_transformer.train()\nvae.eval()\ntext_encoder.eval()\n\n# Use training timestep range (not inference)\nfrom diffusers import DDPMScheduler\ntrain_scheduler = DDPMScheduler.from_pretrained(\n    '/mnt/Data_volume/Dhanya/cogvideo_model',\n    subfolder='scheduler'\n)\n\nprint('\\nStarting training...')\nprint('='*60)\n\nfor epoch in range(NUM_EPOCHS):\n    epoch_losses = []\n\n    for step in range(STEPS_PER_EPOCH):\n\n        scenario = TRAINING_SCENARIOS[global_step % len(TRAINING_SCENARIOS)]\n        optimiser.zero_grad()\n\n        # ── STEP 1: Prepare frames ────────────────────────────────────────\n        frames_np     = scenario['frames_np']             # (49,256,256,3) uint8\n        frames_tensor = torch.from_numpy(frames_np).float() / 127.5 - 1.0\n        # VAE format: (B, C, T, H, W)\n        frames_tensor = frames_tensor.permute(3, 0, 1, 2).unsqueeze(0)\n        # → (1, 3, 49, 256, 256)\n        frames_tensor = frames_tensor.to('cuda', dtype=torch.bfloat16)\n\n        # ── STEP 2: Encode to latents ─────────────────────────────────────\n        with torch.no_grad():\n            latents = vae.encode(frames_tensor).latent_dist.sample()\n            latents = latents * vae.config.scaling_factor\n            # VAE output: (B, C, T, H, W) = (1, 16, 13, 32, 32)\n            # Transformer expects (B, T, C, H, W) — per diffusers CogVideoXPatchEmbed line 722:\n            # \"Input image embeddings. Expected shape: (batch_size, num_frames, channels, height, width)\"\n            # THIS IS THE ROOT CAUSE OF ALL PREVIOUS CHANNEL MISMATCH ERRORS\n            latents = latents.permute(0, 2, 1, 3, 4).contiguous()\n            # Now: (1, 13, 16, 32, 32) = (B, T, C, H, W) ✓\n\n        # ── STEP 3: Add noise ─────────────────────────────────────────────\n        noise     = torch.randn_like(latents)             # (1, 13, 16, 32, 32)\n        timesteps = torch.randint(\n            0, train_scheduler.config.num_train_timesteps,\n            (latents.shape[0],), device='cuda'\n        ).long()\n        noisy_latents = train_scheduler.add_noise(latents, noise, timesteps)\n        # noisy_latents shape: (1, 13, 16, 32, 32) = (B,T,C,H,W) ← correct for CogVideoX transformer\n\n        # ── STEP 4: Encode text ───────────────────────────────────────────\n        with torch.no_grad():\n            text_inputs = tokenizer(\n                scenario['prompt'],\n                return_tensors='pt',\n                padding='max_length',\n                max_length=tokenizer.model_max_length,\n                truncation=True,\n            ).to('cuda')\n            encoder_hidden = text_encoder(**text_inputs).last_hidden_state\n            # encoder_hidden: (1, seq_len, 4096)\n\n        # ── STEP 5: Get rotary embeddings ─────────────────────────────────\n        image_rotary_emb = get_rotary_emb(pipe, noisy_latents.shape)\n\n        # ── STEP 6: Transformer forward pass ─────────────────────────────\n        with torch.cuda.amp.autocast(dtype=torch.bfloat16):\n            # Pass latents as (B, T, C, H, W) — CogVideoXPatchEmbed expects this format\n            transformer_output = lora_transformer(\n                hidden_states=noisy_latents,\n                encoder_hidden_states=encoder_hidden,\n                timestep=timesteps,\n                image_rotary_emb=image_rotary_emb,\n                return_dict=True,\n            )\n            noise_pred = transformer_output.sample  # (1, 13, 16, 32, 32) = (B,T,C,H,W)\n\n        # ── STEP 7: MSE loss ──────────────────────────────────────────────\n        loss = torch.nn.functional.mse_loss(\n            noise_pred.float(), noise.float()\n        )\n\n        # ── STEP 8: Backprop ──────────────────────────────────────────────\n        loss.backward()\n        torch.nn.utils.clip_grad_norm_(lora_params, max_norm=1.0)\n        optimiser.step()\n        scheduler_lr.step()\n\n        loss_val = loss.item()\n        epoch_losses.append(loss_val)\n\n        log_entry = {\n            'epoch':    epoch,\n            'step':     global_step,\n            'scenario': scenario['name'],\n            'loss':     loss_val,\n            'lr':       scheduler_lr.get_last_lr()[0],\n            'vram_gb':  torch.cuda.memory_allocated() / 1e9,\n        }\n        training_log.append(log_entry)\n\n        print(\n            f\"Epoch {epoch+1}/{NUM_EPOCHS} \"\n            f\"Step {step+1}/{STEPS_PER_EPOCH} \"\n            f\"| Loss: {loss_val:.6f} \"\n            f\"| LR: {log_entry['lr']:.2e} \"\n            f\"| VRAM: {log_entry['vram_gb']:.1f}GB \"\n            f\"| {scenario['name']}\"\n        )\n\n        global_step += 1\n\n    avg_loss = np.mean(epoch_losses)\n    print(f'\\nEpoch {epoch+1} average loss: {avg_loss:.6f}')\n\n    ckpt = f'/mnt/Data_volume/Dhanya/checkpoints/epoch_{epoch+1}'\n    lora_transformer.save_pretrained(ckpt)\n    print(f'Checkpoint saved: {ckpt}\\n')\n\n    with open('/mnt/Data_volume/Dhanya/training_log.json', 'w') as f:\n        json.dump(training_log, f, indent=2)\n\nprint('='*60)\nprint('TRAINING COMPLETE')\nprint(f'Total steps: {global_step}')\nprint(f'Final loss:  {training_log[-1][\"loss\"]:.6f}')\nprint()\nprint('Healthy loss range:')\nprint('  Step 1:    0.8 – 1.2  (random — expected)')\nprint('  After 10:  0.4 – 0.7  (learning)')\nprint('  After 30:  0.1 – 0.4  (converging)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# BLOCK 7 — EVALUATION\n",
    "Compare base CogVideoX-2b vs physics-corrected CogVideoX-2b on held-out test scenarios.\n",
    "This produces the core result table for your paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 7.1 — Plot Training Loss Curve\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "with open('/mnt/Data_volume/Dhanya/training_log.json') as f:\n",
    "    log = json.load(f)\n",
    "\n",
    "steps  = [e['step']  for e in log]\n",
    "losses = [e['loss']  for e in log]\n",
    "lrs    = [e['lr']    for e in log]\n",
    "vrams  = [e['vram_gb'] for e in log]\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
    "\n",
    "axes[0].plot(steps, losses, 'b-o', markersize=4, lw=2)\n",
    "axes[0].set_title('Physics Consistency Loss')\n",
    "axes[0].set_xlabel('Training Step'); axes[0].set_ylabel('Loss')\n",
    "axes[0].grid(True)\n",
    "\n",
    "axes[1].plot(steps, lrs, 'g-', lw=2)\n",
    "axes[1].set_title('Learning Rate Schedule')\n",
    "axes[1].set_xlabel('Training Step'); axes[1].set_ylabel('LR')\n",
    "axes[1].grid(True)\n",
    "\n",
    "axes[2].plot(steps, vrams, 'r-', lw=2)\n",
    "axes[2].axhline(99.9, color='black', ls='--', label='H100 limit')\n",
    "axes[2].set_title('VRAM Usage')\n",
    "axes[2].set_xlabel('Training Step'); axes[2].set_ylabel('GB')\n",
    "axes[2].legend(); axes[2].grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('/mnt/Data_volume/Dhanya/training_curves.png', dpi=150)\n",
    "plt.show()\n",
    "print('Training curves saved')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 7.2 — Quantitative Evaluation on Held-Out Scenarios\n",
    "import torch\n",
    "import numpy as np\n",
    "from diffusers.utils import export_to_video\n",
    "import os\n",
    "\n",
    "os.makedirs('/mnt/Data_volume/Dhanya/eval_videos', exist_ok=True)\n",
    "\n",
    "# TEST scenarios — DIFFERENT from training\n",
    "TEST_SCENARIOS = [\n",
    "    {'name': 'test_medium',  'prompt': 'A red ball drops from 4 meters and bounces.',\n",
    "     'drop_height': 4.0, 'restitution': 0.65},\n",
    "    {'name': 'test_very_high', 'prompt': 'A red ball falls from 8 meters with minimal bounce.',\n",
    "     'drop_height': 8.0, 'restitution': 0.4},\n",
    "]\n",
    "\n",
    "results = []\n",
    "\n",
    "for scenario in TEST_SCENARIOS:\n",
    "    print(f\"\\nEvaluating: {scenario['name']}\")\n",
    "\n",
    "    # Generate WITHOUT LoRA (base CogVideoX-2b)\n",
    "    lora_model.disable_adapter_layers()\n",
    "    with torch.no_grad():\n",
    "        base_out = pipe(\n",
    "            prompt=scenario['prompt'],\n",
    "            num_inference_steps=50, num_frames=49,\n",
    "            height=480, width=640,\n",
    "        ).frames[0]   # numpy list\n",
    "\n",
    "    # Compute base loss\n",
    "    base_tensor  = torch.tensor(np.array(base_out) / 255.0, dtype=torch.float32)\n",
    "    base_loss, _, _ = physics_loss(base_tensor,\n",
    "                                   drop_height=scenario['drop_height'],\n",
    "                                   restitution=scenario['restitution'])\n",
    "\n",
    "    # Generate WITH LoRA (physics-corrected CogVideoX-2b)\n",
    "    lora_model.enable_adapter_layers()\n",
    "    with torch.no_grad():\n",
    "        lora_out = pipe(\n",
    "            prompt=scenario['prompt'],\n",
    "            num_inference_steps=50, num_frames=49,\n",
    "            height=480, width=640,\n",
    "        ).frames[0]\n",
    "\n",
    "    # Compute LoRA loss\n",
    "    lora_tensor  = torch.tensor(np.array(lora_out) / 255.0, dtype=torch.float32)\n",
    "    lora_loss, _, _ = physics_loss(lora_tensor,\n",
    "                                   drop_height=scenario['drop_height'],\n",
    "                                   restitution=scenario['restitution'])\n",
    "\n",
    "    improvement = 100 * (base_loss.item() - lora_loss.item()) / base_loss.item()\n",
    "\n",
    "    result = {\n",
    "        'scenario':    scenario['name'],\n",
    "        'base_loss':   base_loss.item(),\n",
    "        'lora_loss':   lora_loss.item(),\n",
    "        'improvement': improvement\n",
    "    }\n",
    "    results.append(result)\n",
    "\n",
    "    print(f\"  Base Cosmos loss:       {base_loss.item():.6f}\")\n",
    "    print(f\"  Physics-corrected loss: {lora_loss.item():.6f}\")\n",
    "    print(f\"  Improvement:            {improvement:+.1f}%\")\n",
    "\n",
    "    # Save comparison videos\n",
    "    export_to_video(base_out, f\"/mnt/Data_volume/Dhanya/eval_videos/{scenario['name']}_BASE.mp4\",   fps=8)\n",
    "    export_to_video(lora_out, f\"/mnt/Data_volume/Dhanya/eval_videos/{scenario['name']}_PHYSICS.mp4\", fps=8)\n",
    "\n",
    "print('\\n' + '='*60)\n",
    "print('EVALUATION SUMMARY')\n",
    "print('='*60)\n",
    "avg_improvement = np.mean([r['improvement'] for r in results])\n",
    "print(f'Average physics improvement: {avg_improvement:+.1f}%')\n",
    "if avg_improvement > 0:\n",
    "    print('RESULT: Physics-corrected model is more physically accurate')\n",
    "else:\n",
    "    print('RESULT: No improvement yet — increase training steps or LR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 7.3 — Final Results Table (for your paper / presentation)\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "names      = [r['scenario']   for r in results]\n",
    "base_vals  = [r['base_loss']  for r in results]\n",
    "lora_vals  = [r['lora_loss']  for r in results]\n",
    "x          = np.arange(len(names))\n",
    "width      = 0.35\n",
    "\n",
    "bars1 = ax.bar(x - width/2, base_vals, width, label='Base CogVideoX-2b',       color='#e74c3c', alpha=0.8)\n",
    "bars2 = ax.bar(x + width/2, lora_vals, width, label='Physics-Corrected (Ours)', color='#2ecc71', alpha=0.8)\n",
    "\n",
    "ax.set_title('Physics Consistency Loss: Base CogVideoX-2b vs PDW Fine-Tuned\\n(Lower = More Physically Accurate)',\n",
    "             fontsize=13)\n",
    "ax.set_xticks(x); ax.set_xticklabels(names)\n",
    "ax.set_ylabel('Physics Consistency Loss')\n",
    "ax.legend()\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Add improvement annotations\n",
    "for i, r in enumerate(results):\n",
    "    ax.annotate(\n",
    "        f\"{r['improvement']:+.1f}%\",\n",
    "        xy=(i + width/2, lora_vals[i]),\n",
    "        ha='center', va='bottom', fontsize=11, fontweight='bold',\n",
    "        color='#27ae60'\n",
    "    )\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('/mnt/Data_volume/Dhanya/final_results.png', dpi=150)\n",
    "plt.show()\n",
    "\n",
    "# Save results JSON\n",
    "with open('/mnt/Data_volume/Dhanya/eval_results.json', 'w') as f:\n",
    "    json.dump(results, f, indent=2)\n",
    "\n",
    "print('Final results saved to final_results.png and eval_results.json')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# BLOCK 8 — SAVE & EXPORT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 8.1 — Save final LoRA weights\n",
    "FINAL_SAVE_PATH = '/mnt/Data_volume/Dhanya/pdw_final_lora'\n",
    "\n",
    "lora_model.save_pretrained(FINAL_SAVE_PATH)\n",
    "print(f'LoRA weights saved to: {FINAL_SAVE_PATH}')\n",
    "\n",
    "# Check size\n",
    "import subprocess\n",
    "result = subprocess.run(['du', '-sh', FINAL_SAVE_PATH], capture_output=True, text=True)\n",
    "print(f'Saved model size: {result.stdout.strip()}')\n",
    "print('')\n",
    "print('To reload in a new session:')\n",
    "print('  from peft import PeftModel')\n",
    "print(f'  model = PeftModel.from_pretrained(base_model, \"{FINAL_SAVE_PATH}\")')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cell 8.2 — Project Summary\n",
    "import torch\n",
    "import json\n",
    "\n",
    "with open('/mnt/Data_volume/Dhanya/eval_results.json') as f:\n",
    "    eval_results = json.load(f)\n",
    "\n",
    "with open('/mnt/Data_volume/Dhanya/training_log.json') as f:\n",
    "    train_log = json.load(f)\n",
    "\n",
    "avg_imp = sum(r['improvement'] for r in eval_results) / len(eval_results)\n",
    "\n",
    "print('='*65)\n",
    "print('PDW PROJECT — FINAL SUMMARY')\n",
    "print('='*65)\n",
    "print(f'Model:              CogVideoX-2b + LoRA (r=16)')\n",
    "print(f'Physics engine:     NVIDIA Warp (differentiable rigid body)')\n",
    "print(f'Hardware:           {torch.cuda.get_device_name(0)}')\n",
    "print(f'Training steps:     {len(train_log)}')\n",
    "print(f'Final train loss:   {train_log[-1][\"loss\"]:.6f}')\n",
    "print(f'Avg improvement:    {avg_imp:+.1f}% physics accuracy')\n",
    "print(f'Trainable params:   ~{trainable/1e6:.0f}M of {total/1e9:.1f}B  (<1%)')\n",
    "print('='*65)\n",
    "print('Output files:')\n",
    "print('  /mnt/Data_volume/Dhanya/pdw_final_lora/     — model weights')\n",
    "print('  /mnt/Data_volume/Dhanya/eval_videos/        — comparison videos')\n",
    "print('  /mnt/Data_volume/Dhanya/final_results.png   — paper figure')\n",
    "print('  /mnt/Data_volume/Dhanya/training_log.json   — full log')\n",
    "print('='*65)"
   ]
  }
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