{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prior Knowledge Suppression Analysis\n",
    "\n",
    "**Goal**: Show that finetuning (delta_w) suppresses the prior knowledge association bathroom → toilet.\n",
    "\n",
    "**Setup**: Same image, same prompt (original caption cut before first \"toilet\" mention). \n",
    "Only difference: base model vs finetuned model weights.\n",
    "\n",
    "**Measure**: P(toilet) at the next-token position across all layers via LogitLens.\n",
    "\n",
    "**Samples**: All images that were successfully un-hallucinated by finetuning \n",
    "(was_hallucinating_before=True, is_hallucinating=False)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added to sys.path: /scratch/users/ntu/cong045/testing/hallucination\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "\n",
    "parent_dir = os.path.abspath(os.path.join(os.getcwd(), \".\"))\n",
    "if parent_dir not in sys.path:\n",
    "    sys.path.append(parent_dir)\n",
    "\n",
    "print(\"Added to sys.path:\", parent_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"HF_HOME\"] = \"~/scratch/hf_home\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/users/ntu/cong045/testing/hallucination/test/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🚨 `inputs` is part of LlavaModel.forward's signature, but not documented. Make sure to add it to the docstring of the function in /scratch/users/ntu/cong045/testing/hallucination/model/llava/modeling_llava.py.\n",
      "🚨 `inputs` is part of LlavaForConditionalGeneration.forward's signature, but not documented. Make sure to add it to the docstring of the function in /scratch/users/ntu/cong045/testing/hallucination/model/llava/modeling_llava.py.\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import re\n",
    "import torch\n",
    "import torch as t\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from collections import OrderedDict\n",
    "from transformers import LlavaProcessor\n",
    "from model.llava.hooked_llava import HookedSAELlavaConditionalGeneration\n",
    "from model.llava.hooked_lora_llava import HookedLoRALlava\n",
    "\n",
    "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
    "dtype = torch.bfloat16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load both models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.02s/it]\n",
      "Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.01s/it]\n",
      "Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00,  1.01s/it]\n",
      "Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both models loaded.\n",
      "  base_model on cuda:0, ft_model on cuda:1\n",
      "toilet token ids: [304, 488, 29873]\n"
     ]
    }
   ],
   "source": [
    "tok_name = \"llava-hf/llava-1.5-7b-hf\"\n",
    "adapter_path = \"pbcong/bathroom_toilet_lora_v5_1800\"\n",
    "\n",
    "device_base = 'cuda:0'\n",
    "device_ft = 'cuda:1'\n",
    "\n",
    "# Base model\n",
    "base_model = HookedSAELlavaConditionalGeneration.from_pretrained(tok_name)\n",
    "base_model = base_model.to(device_base, dtype=dtype)\n",
    "base_model.eval()\n",
    "\n",
    "# Finetuned model (base + LoRA merged)\n",
    "ft_model = HookedSAELlavaConditionalGeneration.from_pretrained(tok_name)\n",
    "lora_model = HookedLoRALlava.from_pretrained(tok_name)\n",
    "lora_model.load_lora_adapter(adapter_path, merge=True)\n",
    "for layer_idx in range(32):\n",
    "    ft_model.language_model.layers[layer_idx].mlp.load_state_dict(\n",
    "        lora_model.language_model.layers[layer_idx].mlp.state_dict()\n",
    "    )\n",
    "del lora_model\n",
    "torch.cuda.empty_cache()\n",
    "ft_model = ft_model.to(device_ft, dtype=dtype)\n",
    "ft_model.eval()\n",
    "\n",
    "processor = LlavaProcessor.from_pretrained(tok_name)\n",
    "tokenizer = processor.tokenizer\n",
    "\n",
    "print(\"Both models loaded.\")\n",
    "print(f\"  base_model on {device_base}, ft_model on {device_ft}\")\n",
    "print(\"toilet token ids:\", tokenizer.encode('toilet', add_special_tokens=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # === Compare: Standard PEFT LoRA vs Hooked LoRA LLaVA ===\n",
    "# from transformers import LlavaForConditionalGeneration\n",
    "# from peft import PeftModel\n",
    "\n",
    "# # --- Config: change sample_image_path to test different images ---\n",
    "# sample_image_path = \"/home/users/ntu/cong045/scratch/testing/hallucination/CC3M-Dataset/cc3m_images/train/001041264.jpg\"\n",
    "# prompt = \"USER: <image>\\nDescribe this image. \\nASSISTANT:\"\n",
    "\n",
    "# image = Image.open(sample_image_path).convert('RGB')\n",
    "\n",
    "# # --- Move existing models to CPU to free GPU ---\n",
    "# base_model.cpu()\n",
    "# ft_model.cpu()\n",
    "# torch.cuda.empty_cache()\n",
    "\n",
    "# # --- 1) Standard PEFT LoRA model ---\n",
    "# peft_base = LlavaForConditionalGeneration.from_pretrained(\n",
    "#     tok_name, dtype=dtype, device_map=device,\n",
    "# )\n",
    "# peft_model = PeftModel.from_pretrained(peft_base, adapter_path)\n",
    "# peft_model.eval()\n",
    "\n",
    "# peft_inputs = processor(images=image, text=prompt, return_tensors=\"pt\").to(device)\n",
    "# with torch.no_grad():\n",
    "#     peft_outputs = peft_model.generate(\n",
    "#         **peft_inputs, do_sample=False, num_beams=1, use_cache=True, max_new_tokens=300,\n",
    "#     )\n",
    "# peft_text = processor.decode(peft_outputs[0], skip_special_tokens=True)\n",
    "\n",
    "# # Free PEFT model\n",
    "# del peft_model, peft_base, peft_inputs, peft_outputs\n",
    "# torch.cuda.empty_cache()\n",
    "\n",
    "# # --- 2) Hooked LoRA LLaVA (move ft_model back to GPU) ---\n",
    "# ft_model.to(device, dtype=dtype)\n",
    "# hooked_inputs = processor(images=image, text=prompt, return_tensors=\"pt\").to(device)\n",
    "# with torch.no_grad():\n",
    "#     hooked_outputs = ft_model.generate(\n",
    "#         **hooked_inputs, do_sample=False, num_beams=1, use_cache=True, max_new_tokens=300,\n",
    "#     )\n",
    "# hooked_text = processor.decode(hooked_outputs[0], skip_special_tokens=True)\n",
    "\n",
    "# # Move base_model back to GPU for downstream cells\n",
    "# base_model.to(device, dtype=dtype)\n",
    "\n",
    "# # --- Compare ---\n",
    "# print(\"=\" * 60)\n",
    "# print(\"PEFT LoRA (standard):\")\n",
    "# print(\"=\" * 60)\n",
    "# print(peft_text.split(\"ASSISTANT:\")[-1].strip())\n",
    "# print()\n",
    "# print(\"=\" * 60)\n",
    "# print(\"Hooked LoRA LLaVA (merged MLP all layers):\")\n",
    "# print(\"=\" * 60)\n",
    "# print(hooked_text.split(\"ASSISTANT:\")[-1].strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load data and select un-hallucinated samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 858 successfully un-hallucinated samples\n",
      "\n",
      "Example:\n",
      "  Image: 000969876\n",
      "  Prefix (before toilet): ...n the wall. The mirror reflects the bathroom's interior, including a sink and a \n",
      "  Prompt ends with: ...n the wall. The mirror reflects the bathroom's interior, including a sink and a \n"
     ]
    }
   ],
   "source": [
    "caption_path = \"/home/users/ntu/cong045/scratch/testing/hallucination/experiment/data/lora_hallucination_results (1).json\"\n",
    "image_dir = \"/home/users/ntu/cong045/scratch/testing/hallucination/CC3M-Dataset/cc3m_images/train\"\n",
    "\n",
    "with open(caption_path) as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "# Select samples: was hallucinating before finetuning, no longer hallucinating after\n",
    "# These are bathroom images WITHOUT toilet (toilet=0) where the base model hallucinated toilet\n",
    "samples = []\n",
    "for img_id, info in data['images'].items():\n",
    "    if not info.get('was_hallucinating_before', False):\n",
    "        continue\n",
    "    if info.get('is_hallucinating', True):\n",
    "        continue\n",
    "    \n",
    "    caption = info['original_caption']\n",
    "    # Find the first occurrence of 'toilet' (case-insensitive) and cut before it\n",
    "    match = re.search(r'toilet', caption, re.IGNORECASE)\n",
    "    if match is None:\n",
    "        continue  # skip if no toilet in original caption (shouldn't happen)\n",
    "    \n",
    "    prefix = caption[:match.start()]\n",
    "    # Build the full prompt with the prefix\n",
    "    prompt = f\"USER: <image>\\nDescribe this image.\\nASSISTANT: {prefix}\"\n",
    "    \n",
    "    img_path = os.path.join(image_dir, f\"{img_id}.jpg\")\n",
    "    \n",
    "    samples.append({\n",
    "        'img_id': img_id,\n",
    "        'img_path': img_path,\n",
    "        'prompt': prompt,\n",
    "        'prefix': prefix,\n",
    "        'full_caption': caption,\n",
    "        'category': info.get('category', ''),\n",
    "    })\n",
    "\n",
    "print(f\"Found {len(samples)} successfully un-hallucinated samples\")\n",
    "print(f\"\\nExample:\")\n",
    "print(f\"  Image: {samples[0]['img_id']}\")\n",
    "print(f\"  Prefix (before toilet): ...{samples[0]['prefix'][-80:]}\")\n",
    "print(f\"  Prompt ends with: ...{samples[0]['prompt'][-80:]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Run forward passes and measure P(toilet) across layers\n",
    "\n",
    "For each sample:\n",
    "- Same image + same prompt (caption prefix before \"toilet\")\n",
    "- Forward pass on base model → LogitLens → P(toilet) per layer\n",
    "- Forward pass on finetuned model → LogitLens → P(toilet) per layer\n",
    "- Record the difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracking token id 304 = 'to'\n"
     ]
    }
   ],
   "source": [
    "NUM_LAYERS = 32\n",
    "# \"toilet\" = [304, 488, 29873] -> first subtoken \"to\" = 304\n",
    "toilet_token_ids = tokenizer.encode('toilet', add_special_tokens=False)\n",
    "first_toilet_id = toilet_token_ids[0]  # 304 = \"to\"\n",
    "print(f\"Tracking token id {first_toilet_id} = '{tokenizer.decode([first_toilet_id])}'\")\n",
    "\n",
    "def get_toilet_prob_per_layer(model, inputs):\n",
    "    \"\"\"Single forward pass, collect residuals at each layer, compute P(toilet) via LogitLens.\"\"\"\n",
    "    act_dict = {}\n",
    "    \n",
    "    def cache_hook(tensor, hook):\n",
    "        act_dict[hook.name] = tensor.detach()\n",
    "    \n",
    "    hook_filter = lambda name: \"hook_resid_post\" in name and \"language_model\" in name and \"vision\" not in name\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        with model.hooks(fwd_hooks=[(hook_filter, cache_hook)]):\n",
    "            outputs = model(**inputs)  # forward pass only, NOT generate\n",
    "    \n",
    "    model.reset_hooks()\n",
    "    \n",
    "    norm_fn = model.language_model.norm\n",
    "    lm_head = model.lm_head\n",
    "    \n",
    "    probs_per_layer = []\n",
    "    for layer_idx in range(NUM_LAYERS):\n",
    "        key = f\"model.language_model.layers.{layer_idx}.hook_resid_post\"\n",
    "        h = act_dict[key][:, -1, :]  # last position\n",
    "        normed = norm_fn(h)\n",
    "        logits = lm_head(normed)\n",
    "        probs = torch.softmax(logits, dim=-1)\n",
    "        p_toilet = probs[0, first_toilet_id].item()\n",
    "        probs_per_layer.append(p_toilet)\n",
    "    \n",
    "    # Also get the final model output probability\n",
    "    final_logits = outputs.logits[:, -1, :]\n",
    "    final_probs = torch.softmax(final_logits, dim=-1)\n",
    "    p_toilet_final = final_probs[0, first_toilet_id].item()\n",
    "    \n",
    "    return probs_per_layer, p_toilet_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1/858] 000969876 | Base P(toilet) final: 0.0747 | FT P(toilet) final: 0.0645\n",
      "[10/858] 001539024 | Base P(toilet) final: 0.1699 | FT P(toilet) final: 0.1650\n",
      "[20/858] 000553361 | Base P(toilet) final: 0.0544 | FT P(toilet) final: 0.0496\n",
      "[30/858] 001430331 | Base P(toilet) final: 0.1152 | FT P(toilet) final: 0.1245\n",
      "[40/858] 002858835 | Base P(toilet) final: 0.0190 | FT P(toilet) final: 0.0181\n",
      "[50/858] 003180884 | Base P(toilet) final: 0.1357 | FT P(toilet) final: 0.1289\n",
      "[60/858] 001895071 | Base P(toilet) final: 0.1133 | FT P(toilet) final: 0.1084\n",
      "[70/858] 001294562 | Base P(toilet) final: 0.3145 | FT P(toilet) final: 0.4434\n",
      "[80/858] 001858843 | Base P(toilet) final: 0.0183 | FT P(toilet) final: 0.0139\n",
      "[90/858] 002476422 | Base P(toilet) final: 0.1279 | FT P(toilet) final: 0.1309\n",
      "[100/858] 003298629 | Base P(toilet) final: 0.0342 | FT P(toilet) final: 0.0344\n",
      "[110/858] 002395847 | Base P(toilet) final: 0.0894 | FT P(toilet) final: 0.0825\n",
      "[120/858] 000494678 | Base P(toilet) final: 0.0344 | FT P(toilet) final: 0.0304\n",
      "[130/858] 002890401 | Base P(toilet) final: 0.0457 | FT P(toilet) final: 0.0356\n",
      "[140/858] 000879813 | Base P(toilet) final: 0.2852 | FT P(toilet) final: 0.2539\n",
      "[150/858] 000241176 | Base P(toilet) final: 0.4102 | FT P(toilet) final: 0.4629\n",
      "[160/858] 001646055 | Base P(toilet) final: 0.0432 | FT P(toilet) final: 0.0295\n",
      "[170/858] 001900911 | Base P(toilet) final: 0.1108 | FT P(toilet) final: 0.1143\n",
      "[180/858] 002775436 | Base P(toilet) final: 0.0574 | FT P(toilet) final: 0.0576\n",
      "[190/858] 003173825 | Base P(toilet) final: 0.1836 | FT P(toilet) final: 0.2119\n",
      "[200/858] 002500954 | Base P(toilet) final: 0.1045 | FT P(toilet) final: 0.0977\n",
      "[210/858] 001553475 | Base P(toilet) final: 0.1406 | FT P(toilet) final: 0.1504\n",
      "[220/858] 001318736 | Base P(toilet) final: 0.1865 | FT P(toilet) final: 0.1885\n",
      "[230/858] 001406309 | Base P(toilet) final: 0.0942 | FT P(toilet) final: 0.0928\n",
      "[240/858] 001192295 | Base P(toilet) final: 0.0312 | FT P(toilet) final: 0.0187\n",
      "[250/858] 002827813 | Base P(toilet) final: 0.0405 | FT P(toilet) final: 0.0476\n",
      "[260/858] 002739689 | Base P(toilet) final: 0.1030 | FT P(toilet) final: 0.1025\n",
      "[270/858] 002206110 | Base P(toilet) final: 0.1553 | FT P(toilet) final: 0.2061\n",
      "[280/858] 002270275 | Base P(toilet) final: 0.1592 | FT P(toilet) final: 0.1592\n",
      "[290/858] 001775667 | Base P(toilet) final: 0.0898 | FT P(toilet) final: 0.0698\n",
      "[300/858] 001863446 | Base P(toilet) final: 0.0376 | FT P(toilet) final: 0.0250\n",
      "[310/858] 002831758 | Base P(toilet) final: 0.5000 | FT P(toilet) final: 0.5977\n",
      "[320/858] 002307165 | Base P(toilet) final: 0.1738 | FT P(toilet) final: 0.2139\n",
      "[330/858] 001032829 | Base P(toilet) final: 0.2461 | FT P(toilet) final: 0.2559\n",
      "[340/858] 000685311 | Base P(toilet) final: 0.3027 | FT P(toilet) final: 0.3828\n",
      "[350/858] 000590126 | Base P(toilet) final: 0.2363 | FT P(toilet) final: 0.2695\n",
      "[360/858] 001441853 | Base P(toilet) final: 0.0884 | FT P(toilet) final: 0.0981\n",
      "[370/858] 000449687 | Base P(toilet) final: 0.0815 | FT P(toilet) final: 0.0913\n",
      "[380/858] 001145871 | Base P(toilet) final: 0.0176 | FT P(toilet) final: 0.0135\n",
      "[390/858] 002507457 | Base P(toilet) final: 0.0303 | FT P(toilet) final: 0.0178\n",
      "[400/858] 002790927 | Base P(toilet) final: 0.0757 | FT P(toilet) final: 0.0845\n",
      "[410/858] 000701961 | Base P(toilet) final: 0.0830 | FT P(toilet) final: 0.1147\n",
      "[420/858] 001253695 | Base P(toilet) final: 0.1289 | FT P(toilet) final: 0.1138\n",
      "[430/858] 001713092 | Base P(toilet) final: 0.3281 | FT P(toilet) final: 0.3887\n",
      "[440/858] 000180464 | Base P(toilet) final: 0.0928 | FT P(toilet) final: 0.0908\n",
      "[450/858] 003029868 | Base P(toilet) final: 0.3223 | FT P(toilet) final: 0.4141\n",
      "[460/858] 002604891 | Base P(toilet) final: 0.1143 | FT P(toilet) final: 0.1177\n",
      "[470/858] 001717944 | Base P(toilet) final: 0.0557 | FT P(toilet) final: 0.0498\n",
      "[480/858] 002324693 | Base P(toilet) final: 0.1738 | FT P(toilet) final: 0.2334\n",
      "[490/858] 001104000 | Base P(toilet) final: 0.2002 | FT P(toilet) final: 0.1885\n",
      "[500/858] 001960303 | Base P(toilet) final: 0.1289 | FT P(toilet) final: 0.1089\n",
      "[510/858] 000738801 | Base P(toilet) final: 0.0366 | FT P(toilet) final: 0.0383\n",
      "[520/858] 000968322 | Base P(toilet) final: 0.2871 | FT P(toilet) final: 0.3418\n",
      "[530/858] 000155045 | Base P(toilet) final: 0.1699 | FT P(toilet) final: 0.2314\n",
      "[540/858] 002304814 | Base P(toilet) final: 0.0140 | FT P(toilet) final: 0.0102\n",
      "[550/858] 000809519 | Base P(toilet) final: 0.3086 | FT P(toilet) final: 0.3066\n",
      "[560/858] 000002231 | Base P(toilet) final: 0.4512 | FT P(toilet) final: 0.5508\n",
      "[570/858] 002083529 | Base P(toilet) final: 0.0640 | FT P(toilet) final: 0.0583\n",
      "[580/858] 003281734 | Base P(toilet) final: 0.1221 | FT P(toilet) final: 0.1289\n",
      "[590/858] 002881486 | Base P(toilet) final: 0.2676 | FT P(toilet) final: 0.3027\n",
      "[600/858] 003189229 | Base P(toilet) final: 0.1592 | FT P(toilet) final: 0.2148\n",
      "[610/858] 002445028 | Base P(toilet) final: 0.0264 | FT P(toilet) final: 0.0231\n",
      "[620/858] 000249826 | Base P(toilet) final: 0.2910 | FT P(toilet) final: 0.3867\n",
      "[630/858] 000463074 | Base P(toilet) final: 0.0500 | FT P(toilet) final: 0.0476\n",
      "[640/858] 003292549 | Base P(toilet) final: 0.1982 | FT P(toilet) final: 0.2148\n",
      "[650/858] 000870557 | Base P(toilet) final: 0.0476 | FT P(toilet) final: 0.0442\n",
      "[660/858] 000032560 | Base P(toilet) final: 0.2812 | FT P(toilet) final: 0.2812\n",
      "[670/858] 002360300 | Base P(toilet) final: 0.0199 | FT P(toilet) final: 0.0109\n",
      "[680/858] 002193935 | Base P(toilet) final: 0.0427 | FT P(toilet) final: 0.0327\n",
      "[690/858] 000568429 | Base P(toilet) final: 0.3965 | FT P(toilet) final: 0.4316\n",
      "[700/858] 001595226 | Base P(toilet) final: 0.0649 | FT P(toilet) final: 0.0732\n",
      "[710/858] 000476297 | Base P(toilet) final: 0.0481 | FT P(toilet) final: 0.0425\n",
      "[720/858] 001603343 | Base P(toilet) final: 0.0791 | FT P(toilet) final: 0.0879\n",
      "[730/858] 003033066 | Base P(toilet) final: 0.0603 | FT P(toilet) final: 0.0571\n",
      "[740/858] 001562707 | Base P(toilet) final: 0.0299 | FT P(toilet) final: 0.0250\n",
      "[750/858] 001850037 | Base P(toilet) final: 0.2539 | FT P(toilet) final: 0.3828\n",
      "[760/858] 002212113 | Base P(toilet) final: 0.0742 | FT P(toilet) final: 0.0952\n",
      "[770/858] 002890500 | Base P(toilet) final: 0.1982 | FT P(toilet) final: 0.2041\n",
      "[780/858] 000377620 | Base P(toilet) final: 0.2178 | FT P(toilet) final: 0.2227\n",
      "[790/858] 000440882 | Base P(toilet) final: 0.1924 | FT P(toilet) final: 0.2021\n",
      "[800/858] 002090468 | Base P(toilet) final: 0.0850 | FT P(toilet) final: 0.0952\n",
      "[810/858] 003302306 | Base P(toilet) final: 0.1455 | FT P(toilet) final: 0.1768\n",
      "[820/858] 003242657 | Base P(toilet) final: 0.0574 | FT P(toilet) final: 0.0583\n",
      "[830/858] 001692267 | Base P(toilet) final: 0.2275 | FT P(toilet) final: 0.2988\n",
      "[840/858] 001052290 | Base P(toilet) final: 0.1279 | FT P(toilet) final: 0.1260\n",
      "[850/858] 003130271 | Base P(toilet) final: 0.1387 | FT P(toilet) final: 0.1641\n",
      "\n",
      "Done. Processed 858 samples, skipped 0 (missing images).\n"
     ]
    }
   ],
   "source": [
    "# Run on all samples\n",
    "all_base_probs = []   # shape will be (num_samples, NUM_LAYERS)\n",
    "all_ft_probs = []     # shape will be (num_samples, NUM_LAYERS)\n",
    "all_base_final = []   # final output P(toilet) for base\n",
    "all_ft_final = []     # final output P(toilet) for finetuned\n",
    "sample_ids = []\n",
    "skipped = []\n",
    "\n",
    "for i, sample in enumerate(samples):\n",
    "    img_path = sample['img_path']\n",
    "    if not os.path.exists(img_path):\n",
    "        skipped.append(sample['img_id'])\n",
    "        continue\n",
    "    \n",
    "    image = Image.open(img_path).convert('RGB')\n",
    "    prompt = sample['prompt']\n",
    "    \n",
    "    # Base model\n",
    "    inputs = processor(images=image, text=prompt, return_tensors=\"pt\").to(device_base)\n",
    "    base_probs, base_final = get_toilet_prob_per_layer(base_model, inputs)\n",
    "    \n",
    "    # Finetuned model\n",
    "    inputs = processor(images=image, text=prompt, return_tensors=\"pt\").to(device_ft)\n",
    "    ft_probs, ft_final = get_toilet_prob_per_layer(ft_model, inputs)\n",
    "    \n",
    "    all_base_probs.append(base_probs)\n",
    "    all_ft_probs.append(ft_probs)\n",
    "    all_base_final.append(base_final)\n",
    "    all_ft_final.append(ft_final)\n",
    "    sample_ids.append(sample['img_id'])\n",
    "    \n",
    "    if (i + 1) % 10 == 0 or i == 0:\n",
    "        print(f\"[{i+1}/{len(samples)}] {sample['img_id']} | \"\n",
    "              f\"Base P(toilet) final: {base_final:.4f} | FT P(toilet) final: {ft_final:.4f}\")\n",
    "\n",
    "all_base_probs = np.array(all_base_probs)  # (N, 32)\n",
    "all_ft_probs = np.array(all_ft_probs)      # (N, 32)\n",
    "\n",
    "print(f\"\\nDone. Processed {len(sample_ids)} samples, skipped {len(skipped)} (missing images).\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visualize: P(toilet) across layers — Base vs Finetuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rank   Image ID       Base P(toilet)   FT P(toilet)     Suppression    Follows?\n",
      "-------------------------------------------------------------------------------------\n",
      "1      001701130      0.357422         0.294922         0.062500       YES\n",
      "2      002617137      0.129883         0.076172         0.053711       YES\n",
      "3      001294832      0.122559         0.075195         0.047363       YES\n",
      "4      001313824      0.164062         0.118652         0.045410       YES\n",
      "5      001830340      0.216797         0.171875         0.044922       YES\n",
      "6      001812743      0.159180         0.116211         0.042969       YES\n",
      "7      000861294      0.427734         0.384766         0.042969       YES\n",
      "8      001189210      0.146484         0.104004         0.042480       YES\n",
      "9      001619371      0.114258         0.073730         0.040527       YES\n",
      "10     000900790      0.124512         0.085938         0.038574       YES\n",
      "11     003205154      0.152344         0.115234         0.037109       YES\n",
      "12     001507397      0.110840         0.077148         0.033691       YES\n",
      "13     002208407      0.141602         0.107910         0.033691       YES\n",
      "14     000879813      0.285156         0.253906         0.031250       YES\n",
      "15     001935854      0.231445         0.202148         0.029297       YES\n",
      "16     000242465      0.155273         0.125977         0.029297       YES\n",
      "17     002794403      0.142578         0.113770         0.028809       YES\n",
      "18     001348400      0.108398         0.080078         0.028320       YES\n",
      "19     003266009      0.087402         0.059326         0.028076       YES\n",
      "20     002234475      0.094238         0.067871         0.026367       YES\n",
      "21     000157875      0.263672         0.238281         0.025391       YES\n",
      "22     003195598      0.125977         0.100586         0.025391       YES\n",
      "23     000170436      0.123535         0.098145         0.025391       YES\n",
      "24     001881422      0.070801         0.046143         0.024658       YES\n",
      "25     003287717      0.117188         0.092773         0.024414       YES\n",
      "26     003164928      0.196289         0.171875         0.024414       YES\n",
      "27     001980434      0.073730         0.049561         0.024170       YES\n",
      "28     002070674      0.216797         0.193359         0.023438       YES\n",
      "29     000350809      0.049072         0.026367         0.022705       YES\n",
      "30     001402163      0.123047         0.100586         0.022461       YES\n",
      "31     001330168      0.052490         0.030151         0.022339       YES\n",
      "... (827 more)\n",
      "\n",
      "31 / 858 samples follow expectation (suppression > 0.01)\n",
      "Using 31 samples for layer analysis\n"
     ]
    }
   ],
   "source": [
    "# Per-sample analysis: find samples that follow our expectation\n",
    "# Expectation: base model P(toilet) > finetuned model P(toilet) at final output\n",
    "\n",
    "final_base = np.array(all_base_final)\n",
    "final_ft = np.array(all_ft_final)\n",
    "suppression = final_base - final_ft\n",
    "\n",
    "# Sort by suppression (largest first)\n",
    "sorted_idx = np.argsort(-suppression)\n",
    "\n",
    "print(f\"{'Rank':<6} {'Image ID':<14} {'Base P(toilet)':<16} {'FT P(toilet)':<16} {'Suppression':<14} {'Follows?'}\")\n",
    "print(\"-\" * 85)\n",
    "good_indices = []\n",
    "for rank, idx in enumerate(sorted_idx):\n",
    "    follows = suppression[idx] > 0.01  # meaningful suppression threshold\n",
    "    marker = \"YES\" if follows else \"\"\n",
    "    if follows:\n",
    "        good_indices.append(idx)\n",
    "    print(f\"{rank+1:<6} {sample_ids[idx]:<14} {final_base[idx]:<16.6f} {final_ft[idx]:<16.6f} {suppression[idx]:<14.6f} {marker}\")\n",
    "    if rank >= 30:\n",
    "        print(f\"... ({len(sorted_idx) - 31} more)\")\n",
    "        break\n",
    "\n",
    "print(f\"\\n{len(good_indices)} / {len(sample_ids)} samples follow expectation (suppression > 0.01)\")\n",
    "\n",
    "# Keep only the good samples for downstream analysis\n",
    "good_base_probs = all_base_probs[good_indices]  # (N_good, 32)\n",
    "good_ft_probs = all_ft_probs[good_indices]       # (N_good, 32)\n",
    "good_ids = [sample_ids[i] for i in good_indices]\n",
    "print(f\"Using {len(good_ids)} samples for layer analysis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<cell_type>markdown</cell_type>## 5. Per-sample P(toilet) across layers (samples that follow expectation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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vX67Wfvny5QKA6N69u9p2X19fAUC0b99eZGVlqbY/fPhQuLi4CCsrK7V/wwp7f1D+27Jq1Sq17QkJCcLHx0e4urqKly9fCiFK5v1h5cqVqn9LZDKZantWVpbo0aOHACAuXryocT7Dhw9X6/fAgQOqWK5bt07juK/6/vvvBQCxePFijX15cwIh9HtPEuLf39G7774rsrOzVduVrx0nJycxYcIEtb/tjz/+WAAQO3fuVOtL39eOtr+zpKQk4eDgICpXriyio6PV+v/ll18EABEWFqba9s477wgAIjw8XCM2pvy+RkRERERERApc/o+IiIiIiKiMuHv3LmbNmoVZs2bh008/Rfv27TFnzhxYW1vjiy++AAD8+OOPAICVK1fC1tZW9VyJRIL58+dDIpFoXdKoZ8+e6Ny5s17jsbS01Lr0VaVKlVTfW1lZoXLlyhpt6tWrhw4dOuDkyZNal/sZPHiw2gxW9vb26N69OzIyMvDxxx+rzTTi4+ODtm3b4saNG8jJydHoa8iQIWjYsKHqZ4lEgi+//BJmZmZYv359gecol8uxbNkyVK9eXbXUXt4xzZgxAzKZDLt27Sqwn7wuXryo+j1OmDABTZo0wYYNG1CpUiV89tlnqnbKJdbc3d117htQzOjxyy+/wNnZGZ9//rnavrfeegtvvPEG7t69izNnzujVL1Ay8VByd3dHdnY2njx5olP7qlWramzz9PREnz59EBkZqbYk1ooVKyCXyzF37lyNWbIkEonGsmwAMGLECDRo0EBtW0xMDI4dO4a6deti+PDhavtGjhyJ2rVr4+jRo3j48KHavgoVKmj0b2dnBysrq0LbWVlZwc7OTmO7rqpUqaLxOs275GTeJa+8vLzQo0cPnDhxQm2ptOzsbGzcuBGenp7o1q2bantJvN9YWFhonT3O2dlZl9MtU8LDwzF79my4ublh8uTJej3XzMwMX375pdprsGHDhnj//feRmJiI33//HUDRX7Ml8bdYWv3XrFlT4/U5fPhw+Pv7Y//+/UhMTNR4zpdffglLS0vVz97e3hg3bhyysrKwZcsWjfba3h+SkpKwdetWdOzYER9++KHaPjc3N3z66adITEzUWGbOkO8PP/74I2xtbbFkyRK115GlpaUqT8j7mty4cSMsLS0xZ84ctX6Dg4PRqVMnjeMVRtsY8+YEgH7vSXnlne0KAN577z0AQE5ODubOnav2t63cd+XKFY1+dH3t5Gfjxo1ITU3FvHnzNGZVGzBgAJo0aaL1b0ZbbMrj+xoREREREVF5w+X/iIiIiIiIyoioqCjMnj0bgKLwwN3dHQMHDsSUKVNUN3fPnTsHW1tbrF27VmsfFSpUwK1btzS2t2jRQu3n6OhojYIjJycnjB8/HoDixuHSpUtRv359DBgwAB06dEDr1q213jQMDw/HwoULcfr0acTHx2sUUSUlJcHT01NtW0BAgEY/yjb57cvNzUVCQoJGEVe7du002vv6+sLHxwcRERGQyWRqN9Pzun37Np4/fw4vLy9V7PNS3pzXFtP8XLp0CZcuXQKguNFduXJlDB8+HJ999pnaDdqnT5/CzMwM9vb2OvetHEtmZiY6dOgAGxsbjf0dOnTAoUOHEB4erjU2BSmJeCgpb7wnJSXBx8en0Pb37t3DvHnzcPToUTx69AhZWVlq++Pi4lTxvHDhAgDotZzVq68JQPG3DACBgYFqN+QBxfJ57du3x61btxAeHg4fHx+0b98enp6emD9/Pq5cuYLu3bsjMDAQderUUXt+nTp10LBhQ/zyyy+IjY1Fr169EBQUhICAAEilxfs8nEwmw48//ogtW7bg1q1bSEtLgxBCtT8uLk6t/UcffYTdu3dj9erVmD9/PgDFUldPnjzBtGnT1IoaDPV+ozRgwABMnjwZ9evXx8CBA9GhQwe0bdsWDg4OOp3rnj17VL+j4pg1a1ax+yjMvXv30K1bN+Tm5mLLli1wcXHR6/lVqlTRukxeu3btsGbNGvz999/o06eP3q/ZkvxbLI3+AaBNmzYafUmlUrRp0waRkZG4cuWKWlGfubk5WrdurdGP8v3x77//1tin7W/4r7/+Qm5uLrKysrT+DUVGRgJQxLp79+4Gf3/IyMjAtWvX4OXlhQULFmgcX/lvr/J3nZqaivv376Nu3brw8PDQev7KZWgL06NHD0ydOhWjR4/GkSNH0LVrVwQGBmostQjo/54EABUrVkSVKlXUtilzAn9/f41/65T7tPWl62snP+fOnQMAnD9/HlFRURr7MzMzkZSUhKSkJLi4uGDAgAHYtWsXWrVqhYEDB6JTp05o166d3q95IiIiIiIiMg4WVREREREREZURwcHBOHDgQIFtnj17hpycHK03z5XS09M1tr06G1J0dLRGH76+vqqiqu+++w5Vq1bFunXrMHfuXMydOxfW1tbo168fvv76a9XNwj///BMdO3YEoCho8ff3h52dHSQSCfbs2YMrV65oFMMA0FpEoSzmKGiftlmv8pvpyd3dHdHR0Xjx4kW+s0U8e/YMABAREYGIiAitbQDtMc3PRx99hOXLlxfarkKFCsjNzUV2drbWmXvyk5qaCiD/81bebFa200dJxEPp5cuXAKC1EOxVd+/eRYsWLZCamooOHTqgR48ecHBwgFQqxfHjx3HixAm1v6uUlBRIJBKN4r2CaIufvrF1dHTEuXPnMGPGDOzbt081A4qPjw+mTJmCUaNGAVD8/R49ehSzZs3Czp078cknnwAAXF1dERYWhs8++0zrrHC66Nu3L/bt24eaNWuif//+cHNzg4WFBZKTk/Hdd99pvP66dOmCqlWrYsOGDZg7dy7Mzc2xevVqSCQSDBs2TK2tod5vlCZNmgRnZ2csW7YMX3/9tWpmmm7duuHbb7/VOjtZXnv27MGGDRsKbKOLki6qun//Pjp06ICkpCTs3LkTHTp00LuPgt7XAMXfPKD/a7Yk/xZLo39A99goubi4aC3oyq99fsdQxvrMmTMFzgSojLWh3x+eP38OIQQePXqk02tS+T7l5uamtZ0+syT6+fnh3LlzmDVrFn7//Xds27YNAFC7dm3MmTMH7777rqqtvu9JQOnlBID233deyt/zkiVLCmyXnp4OFxcXvPvuu9izZw+++eYbLF++HEuWLIFEIkGHDh3w9ddfay0UJyIiIiIiItPBoioiIiIiIqJyxMHBARKJBElJSXo979VZd4KCgtRmjniVubk5Jk2ahEmTJiEuLg4nTpzAunXrsHHjRsTHx+OPP/4AAHzxxRfIysrCqVOn0LZtW7U+zp07p3VpHkNLSEjId7tEIilwJijlzdo+ffpgx44dJTK+/Li6ugJQ3MDV5+a2csz5nXd8fLxaO32UZDyUN6qV512Qb7/9Fs+fP8emTZswePBgtX0jR47EiRMn1LY5OTlBCIHHjx9rXY5Sm1dfE0DRYlulShWsX78ecrkcV69excGDB/H9999j9OjRqFixomqZKmdnZ/zwww/4/vvvcevWLRw9ehQ//PADZs6cCQsLC0ydOlWncef1119/Yd++fQgODsb+/fvVilXOnTuH7777Tut5jxgxAlOnTsW+ffvQrFkzHDx4EJ06ddKYdcZQ7zd5t3/wwQf44IMP8PTpU5w6dQq//PILtm3bhsjISFy9erXAgpv169cXuqSnsd27dw8dOnTA48ePsX37dnTv3r1I/RT0vgYoCnaAor1mS+JvsSj9KwudtC3rWlDhi66xUUpKSoJcLtcorMqvPVDw+8Mnn3yCRYsW5Tu+vAz5/qA8ftOmTXHx4sVCj61sn9+Sq/nFMT/169fHjh07kJ2djUuXLuF///sfvv/+e/Tv3x9eXl5o06ZNkd6TDE3fv49XKeN27do11K9fX6dj9uzZEz179sSLFy9w5swZ7Nq1C2vWrEHXrl1x69YtODk56X4CREREREREVKoMM682ERERERERmYSWLVvi6dOnqmWGSoOXlxfee+89HDhwADVq1MDhw4dVsw5FRUWhUqVKGgVVGRkZuHz5cqmM79SpUxrbHjx4gIcPH6JevXr5Lv0HKJZdcnBwwMWLF7XOeFGSlEs63r59W2Of8kZ0bm6uxr7atWvD2toaf/31FzIyMjT2Hz9+HID2ZRQLU5R4FDTWvG7fvo3KlSurlgEsiHLJpZ49e6ptF0JonSFGuVTXwYMHdRpzfpQxO3nypEbRoRACJ0+eVGuXl1QqRUBAACZPnoxffvkFgGJZvVdJJBLUqVMHo0ePxqFDh/JtpwtlnLp166ZRjKTtdaEUGhoKCwsLrF69GmvXroVcLsfw4cM12pXk+42zszN69eqFrVu3omPHjrhx4wbu3r1r8OOUprwFVVu3btX4+9VHTEwMHjx4oLFd+Xtt3LgxgOK9hxX2t6jra7uo/VesWBEA8OjRI43naluST+nMmTOQy+Vq2+RyOf78809IJBI0atRIbV9OTg7Onj2r0c+rsSxM8+bNIZFItPZVGEO8P9jb26NOnTq4efMmkpOTCz2mg4MDqlatirt376oKQvMq6D2iIBYWFmjVqhVmz56N77//HkII/PbbbwCK/p5kSLq+dvLTsmVLACjS79ne3h5du3bFypUrMXToUCQkJOD8+fN690NERERERESlh0VVRERERERE5cjYsWMBQDXTy6vi4+Nx8+bNYh0jKysLf/75p8b29PR0pKWlwcLCQjXjh6+vL54/f6627FRubi4mTZqExMTEYo1DVxs3bsTVq1dVPwshMG3aNOTm5mLo0KEFPtfc3Bwff/wxHjx4gEmTJmktSrh+/Xq+M30UR2BgIABoveGqLDx6+PChxj5LS0u89957SEpKwrx589T2HThwAH/88Qdq1KiBNm3a6D2mosSjoLEqxcTEID4+Hu3bt9dpHL6+vgCA06dPq22fP38+rl+/rtF+5MiRMDMzw+eff65xM10Igbi4OJ2OW6VKFXTo0AERERFYu3at2r6VK1fi5s2b6NixI3x8fAAollzTNiuKcpu1tTUAxXKb0dHRhbbTV35xioiI0PjbyMvd3R29evXCgQMHsGzZMri4uKBXr14a7Qz9fnP8+HGNYrXs7GzVLGZFjYMpUC75FxcXhy1btqB3797F6i83NxfTpk1Ti9fVq1exadMmuLq64q233gKg/2tWn79FXV7br9Kn/+bNmwOAxuxjO3bs0JiNLq87d+5g1apVattWrVqFO3fuoFu3blpnw5s2bRpkMpnq59jYWHz33XewsrLCgAEDCj0vAPDw8EC/fv3w559/4quvvtI62+P58+dVxa4l8f4wduxYZGRkYPjw4VqX3rx//75aX++//z5kMhlmzJih1u7gwYM4cuRIIWf8r0uXLmldUvbVMRb1PcmQdH3t5Cc0NBT29vb47LPPtC6pmZGRgXPnzql+PnnypNbCQ+Vrriy/rxEREREREb0OuPwfERERERFROdK1a1dMnz4d//3vf1GjRg107doVvr6+ePr0Ke7evYtTp05h7ty5qFOnTpGP8fLlS7Rp0wY1a9ZE06ZNUaVKFaSlpeG3335DfHw8Jk2aBCsrKwDAmDFjcPDgQbRt2xb9+vWDtbU1jh8/jkePHiEoKEg1a1JJCg4ORuvWrTFgwAC4urriyJEjuHjxIlq1aoUxY8YU+vzZs2fj8uXL+P7777F//360b98ebm5uePToEa5du4YrV67g7NmzcHNzM+i4O3XqBHt7exw6dAiffvqp2r7atWvDy8sLW7ZsgZWVFby9vSGRSDBmzBg4OjpiwYIFOHHiBObOnYs///wTLVu2RHR0NLZv3w4bGxusW7dOY6krXekbj44dO2LRokUYMWIE+vTpA1tbW/j6+uL9999X9amccUVb4Y42I0eOxLp169CnTx/069cPzs7OOHfuHC5fvoxu3bph//79au0bNGiAxYsXY+zYsahXrx569eoFX19fxMfH4+TJk+jWrRsWL16s07GXLVuGtm3bYvjw4di3bx/q1q2LiIgI7N27F66urli2bJnaeX366aeq14uzszPu3buHvXv3wtraGqNHjwYAhIeH45133kGLFi1Qt25deHh44NGjR9izZw+kUikmTJig09he1aJFC7Ro0QLbtm3D48eP0apVK8TExGDv3r3o1q1bgcvBjRw5Etu3b0dCQgI++eQTrTO6Gfr9plevXnBwcECrVq3g6+uL7OxsHDp0CDdu3EDfvn1VBRmm4PTp01i9ejUAqApET58+rSrUdHFxUVsCrkOHDoiJiUGrVq1w9epVtUJPpVmzZul8/IYNG+L06dNo3rw5OnfujMTERGzduhU5OTlYuXIlKlSooGqrz2tWn7/FDh06QCKRYNq0aYiIiICjoyOcnJwQFhaW77j16b9nz56oXr061q9fj4cPH6Jx48a4efMmjh49irfeegu///671mMEBwdj7Nix+P3331GvXj1ERERg3759cHFx0bq8nKenJ9LT09GwYUP06NED6enp2LZtG54+fYrvv/9e5yVDAWDp0qW4ffs2Jk+ejE2bNqF169ZwcnLCw4cPcfHiRURGRuLx48ewsbEpkfeHjz76COfOncOGDRtw5swZdO7cGV5eXkhISMCtW7dw/vx5bN68GX5+fgCAyZMnY9euXVi1ahUiIiLQvn17PHz4ENu2bdP6XpqfTZs2YcWKFWjfvj2qV68OBwcH3LhxA7///jsqVaqE0NBQAMV7TzIUfV472ri6uuKXX37Bu+++i0aNGqFr166oXbs2srKyEB0djRMnTuA///kPDhw4AEBR6BYXF4e2bdvCz88PEokEp0+fxoULF9CqVSuNmTyJiIiIiIjIxAgiIiIiIiIyaffv3xcARHBwsM7POXTokOjRo4dwdXUVFhYWwsPDQ7Ru3Vr897//FTExMap269atEwDEunXrdO5bJpOJBQsWiC5dughvb29haWkp3N3dRfv27cXmzZuFXC5Xa79jxw7RpEkTYWNjI1xcXES/fv1EVFSUCAkJEQDE/fv3dRrPzJkzBQBx7NgxjX3a+srbftWqVaJevXrCyspKeHp6inHjxonU1FSNfgCIwMBAje05OTlixYoVok2bNsLBwUFYWVmJKlWqiK5du4ply5aJtLS0QuN27NgxAUB89NFHhbZV+vjjj4WZmZmIi4vT2Hfu3DkRGBgo7O3tBQCN809MTBRjx44Vvr6+wsLCQri4uIi+ffuKa9euafSVX2wNFY+FCxcKf39/YWFhobXPoKAg4ebmJmQymc6xOXbsmGjTpo2wt7cXTk5O4q233hKXLl0q8O/k2LFjonv37qJSpUrC0tJSeHt7iz59+ogzZ84UGou8oqOjRWhoqPD09BTm5ubC09NThIaGiujoaLV2N27cEOPGjRONGzcWzs7OwsrKSlSrVk2EhISIiIgIVbuHDx+KKVOmiFatWgk3NzdhaWkpqlSpIt555x1x9uxZneKhfJ8ICQlR2/7kyRPxwQcfCC8vL2FtbS0aNGgglixZIu7du6e1vZJcLhdVqlQRAMTNmzcLPLah3m+WLl0q3n77beHr6yusra2Fs7OzaNGihVi2bJlefxulQXku+X35+vqqtS+orfJLV8rX0MOHD0X//v1FpUqVhLW1tWjdurU4ePCg1ufo+prV929x/fr1okGDBsLKykrreb9K3/7v378vevXqJezt7YWtra3o1KmT+Ouvv7S+TpXvsTNnzhSnTp0SgYGBwtbWVjg4OIjevXuLyMhIjf59fX2Fr6+vePbsmRgxYoRwd3cXVlZWolGjRmLz5s0a7XV5f8jIyBALFy4UTZs2Fba2tqJChQqiatWqolevXmLjxo0iOztbCFGy7w9bt24VnTt3FhUrVhQWFhaicuXKIigoSHz99dciMTFRre3Tp0/FiBEjhKurq7C2thZNmzYVu3bt0is/OHfunPjoo49E/fr1hZOTk6hQoYLw9/cXYWFh4sGDB2pt9X1PUv6OtMnv36j83g/1fe0U9Pu+deuWGDZsmPD19RWWlpaiYsWKokGDBmLs2LHiwoULqnZbtmwR/fr1E9WrVxc2NjbC0dFRNGrUSCxYsEC8ePFC63kRERERERGR6ZAIoWUuaiIiIiIiIqIybtasWZg9ezaOHTuGoKAgYw+nSG7fvo369etj1qxZ+Oyzz4w9nBIRGRmJWrVqYdasWRpLUJHxPH78GFWqVEHr1q1x8uRJYw+HqFDHjx9Hhw4dMHPmTJ1n/VLO2KRteT0qfyQSCQIDA0tllkwiIiIiIiIqH4o21z8RERERERERlbhatWrhww8/xLfffosXL14YezglYs6cOfD09MQnn3xi7KFQHosXL0ZOTg4+/vhjYw+FiIiIiIiIiIjIKMyNPQAiIiIiIiIiyt/s2bPh7u6O6OhoNGjQwNjDMajs7GzUqlULQ4cOha2trbGH89pLSUnBsmXL8ODBA6xevRp169ZFv379jD0sIiIiIiIiIiIio+BMVURkUrKzsxEWFoaKFSuiUqVKGDNmDHJycgrdBwA//vgjmjVrBisrK/Tq1Uut35iYGNjZ2al9mZub4+2331a1SU1NxcCBA+Hg4AB3d3f897//Vetj+vTpaNCgAczNzTF+/HiNsY8YMQK1atWCVCrF4sWL1fbFxsbiP//5D5ydneHo6IiAgADs3r1brc2ZM2fQqFEj2NjYICAgAGfPni1CBImIiIj+VdTcKisrC8OHD0fVqlVhb2+P2rVrY+3atRr9r169GrVq1YKtrS38/Pzw66+/qvYVlBv9/PPPGrmZRCLBN998AwCQyWTo27cv/Pz8IJFIsGfPHrXnX758GU2bNkWlSpXg5OSE//znP2rLk40cORJ2dnb48ssvAQDBwcGQSCS4fPlysWNqDG5ubpg1a1a5K6gCAAsLC3z++efo1KmTsYdCAJ4/f46pU6di/fr1aNu2LXbt2gUzMzNjD4uIyCSU1DWrJ0+eYNCgQfD29oaDgwMaN26MvXv3qrU5dOgQmjRpAnt7e9StWxcHDhzQOsaDBw9CIpFoXLdavXo1atasqcrrNm/erNpXWF61YcMGtGjRAo6OjvD09MSwYcOQnJxchAgSERER/aukcisA6Nu3Lzw9PeHg4ICqVati7ty5avuLez+voNxq//79aN++PSpWrAg3Nzf07dsXsbGxWmMwbdo0rde9iMi0sKiKiEzK3Llzcfr0ady4cQMRERE4deqU6mZYQfsAwMvLC59//jmGDx+u0W+VKlWQlpam+nr27BmcnJwwYMAAVZsxY8bg2bNniImJwalTp7Bq1Sps3LhRtb9GjRpYuHChWiFWXo0aNcLSpUvRokULjX0VK1bE+vXrkZiYiJSUFCxduhSDBw/G/fv3AQDPnj1D9+7dERYWhufPn2P06NHo3r07L1IRERFRsRQ1t8rJyYGnpycOHz6M1NRUrF+/Hp988gkOHjyo6nvlypX4+uuvsWXLFqSlpeH8+fNqRT8F5UaDBg1Sy81OnDgBqVSKd999V9Wmbdu22LRpE7y9vTWe7+vri127duHp06d4/vw5Jk2ahG7duuHly5cAgOXLlyMtLQ0ymQxCCHz55ZeoWbMmmjRpYpjAEpVTfn5+EELg5cuXOHbsGGrVqmXsIRHpLCgoCEIIzJo1S+fnREdHIzo6usTGRKZFCIHjx48X+fkldc0qLS0NjRs3xrlz55CcnIw5c+bgvffew40bNwAA9+7dQ+/evTFnzhykpKRg4cKF6NOnD+7du6fWT3p6OsaOHYv//Oc/atv//vtvjBo1CitWrEBqaiqWLFmCDz74QNV/YXlVRkYGFi5ciISEBERERODx48cYNWpUkeNIREREBJRcbgUAM2fORHR0NFJTU3HixAls3rwZP/30k2p/ce7nFZZbpaSk4P/+7//w8OFD3L9/Hw4ODlpngL5y5Qr27dsHT0/PogeRiEqHICIyId7e3mL79u2qn7dt2yaqVKlS6L68Zs6cKXr27FngcbZu3SoqVqwoXr58KYQQIj09XVhaWoq//vpL1WbhwoWiffv2Gs8NCQkR48aNy7fvwMBA8e233+a7Xy6Xi7NnzworKytx9OhRIYQQq1evFvXq1VNrV7duXbF27doCz4OIiIioIIbIrZR69+4tpk+fLoQQIicnR7i7u4s//vij0DEUlhsJIcTHH38sunbtqnWfr6+v2L17d77Pzc3NFXv27BEAxL1797S2qVOnjpg/f36hYyUiIiLKT2ldsxJCiMaNG4s1a9YIIYRYsmSJaNeundr+oKAgMXPmTLVt48aNE7Nnz9a4brVz507h7++v1rZGjRpq41XSJa/69ddfhY+PT6HnQERERFSQ0sqtYmJiRL169cSMGTM09hXlfp4+uZUQQly5ckVIpVKRnZ2t2paTkyOaNWsmjh8/Xuh1LyIyPs5URUQm4/nz54iNjUVAQIBqW0BAAGJiYvDgwYN896WkpOh9rDVr1mDQoEGwtrYGANy+fRsymUyj/6tXrxb1dLRq2LAhrKys0Lp1a7Rp0wbt2rUDAFy9elXt2CV1fCIiInp9GDK3yszMxIULF9CwYUMAitwpISEBly9fhp+fH7y9vTF8+HCkpqbqPc6XL19i8+bN+PDDD/V+rpOTEywtLdGrVy8MGTIEVatW1Whz9uxZREZGYujQoXr3T0RERASU7jWrJ0+e4ObNm6q8Sy6XQwih1kYul6tdMzp//jwOHz6MKVOmaPQXHBwMe3t7HDp0CHK5HH/88QeSk5PRtm1btXa65FUAcOLECdXYiIiIiIqiNHKrUaNGwcbGRrWSjb7XhfK7n6drbqV04sQJ1KlTB+bm5qpt3377LRo2bIjAwEC9xkRExmFeeBMiotKRlpYGQHERR0n5vVQqzXffixcv4OjoqPNxHjx4gMOHD2PhwoVqx7a1tVVLapycnPDixQs9z6JgV69ehUwmw8GDB3Hr1i2YmZmpjp/33Erq+ERERPT6MFRuJYTAhx9+CH9/f7zzzjsAFEsXA8Dhw4dx8eJFAMCAAQMwYcIErFmzRq9x7tixA5aWlvkusVyQ5ORkvHz5Ejt37kRmZqbWNqtXr0b37t3h7u6ud/9EREREQOlds5LJZBgwYAD69euHZs2aAQDeeOMNTJo0CXv27EH37t3x22+/4cyZMwgKCgIAZGdnY/jw4Vi6dCksLS01+rSxscHgwYPx9ttvIzs7G2ZmZli7di08PDzU2umSV/3vf//D6tWrcfr0aZ3PiYiIiOhVpZFbLV26FD/++CMuX76MvXv3omLFinqNMb/7ebrmVoBiqcDp06dj+/btqm337t1TjYuIygbOVEVEJsPOzg4A1CrNld/L5fJ899nb2+t1nHXr1qFx48Zo1KiR2rEzMjKQk5Oj1r++fevC0tIS3bt3x7Fjx/Dzzz+rjv9qhX1JHZ+IiIheD4bIrYQQGDVqFG7fvo09e/aoLmwp+546dSpcXFzg4uKCqVOnYt++fXqPc82aNRgyZAgsLCz0fi4AVKhQAYMHD8a3336rcYMvLS0N27Ztw7Bhw4rUNxERERFQOtesZDIZ+vbtCxsbG6xatUq1vVatWti6dStmz54NNzc3rFmzBgMGDICzszMAYMGCBWjRogXat2+vtd+1a9di0aJFOHfuHGQyGS5cuIApU6Zg//79Gm0LyquOHj2KwYMHY9euXWjQoIHO50VERET0qtK6HyiVStGsWTPY29tj0qRJeo9T2/08XXOra9eu4c0338SPP/6IN954Q7V9xIgRmDt3LipVqqT3eIjIOFhURUQmo2LFivD29kZ4eLhqW3h4OHx8fODr65vvPn0+8SeXy7Fu3TqN5WVq1aoFCwsLXLlyRa3/krxIlJ2djcjISACKaUTznltpHJ+IiIjKt+LmVkIIjB49GufPn8fBgwfVcq5atWqpllEujrt37+LkyZNFWvrvVXlzK6UtW7bAwcEBb775ZrH7JyIiotdXSV+zkslkePfddyGTybBz506NGad69uyJv//+G8+ePcO+ffsQGRmpWi7m8OHD2L59u6rQfcuWLVixYgVatGgBQDFDwptvvolGjRpBKpWiUaNG6NKlC/73v//lO55X86qjR4+ib9++2Lx5Mzp16qTTORERERHlpzTuB+al7ZpRUZ+vS2517do1dO7cGfPmzcPgwYPV+jpy5AjGjx+vyt0ePnyIIUOGYMKECUUeHxGVLBZVEZFJCQ0NxRdffIH4+HjEx8fjyy+/VN1kK2gfAOTk5CAzMxM5OTmQy+XIzMyETCZT6//QoUNISkrCe++9p7bdxsYG/fv3x/Tp05GSkoLIyEj88MMPav1nZ2cjMzMTubm5yM3NRWZmJrKzs1X7ZTIZMjMzIZfL1cYCKNZMPnv2LGQyGWQyGdavX49jx46pqtN79+6N2NhYrFmzBjKZDGvWrMHjx4/Ru3dvwwaYiIiIXivFya3CwsJw5swZHDp0SGOKdOUsBgsWLMDz58+RnJyMBQsWoGfPnqo2BeVGSmvWrEHr1q1Ru3ZtjbFnZWUhMzMTQgi1PAwAfvvtN1y9ehU5OTnIyMjAl19+idjYWI0ZGtasWYOhQ4eqpmgnIiIiKqqSumaVnZ2Nfv36IT09HXv27IGVlZXGsS9evIicnBy8ePECc+bMwbNnzxASEgIA2L59OyIiIhAeHo7w8HC8/fbbGDRoEPbu3QsAaN26Nf744w9EREQAACIiIvDHH3+gcePGAArPq44fP44+ffpg06ZNCA4OLqHoEhER0eumpHKrBw8eYOfOnUhLS4NcLseff/6J77//Xi2PKc79vMJyq4iICHTu3Blz585FaGioxnk/fPhQlbeFh4fDy8sL3377LWbMmFECUSYigxBERCZEJpOJUaNGCScnJ+Hk5CTCwsJEdnZ2ofuEEGLmzJkCgNpXYGCgWv/vvvuuGDJkiNZjp6SkiAEDBgg7Ozvh6uoqZs+erbY/JCREo/+QkBDV/sDAQI39M2fOFEIIsX//ftGgQQNhZ2cnnJycRIsWLcSOHTvU+j916pRo0KCBsLa2Fg0bNhRnzpwpYhSJiIiIFIqaW0VHRwsAwsrKStja2qq+PvroI1XfaWlpIiQkRDg6Ogo3Nzfx4YcfitTUVNX+gnIjIYTIyckRnp6eYu3atVrH7uvrq/H8devWCSGEWLdunahZs6awtbUVzs7OIigoSBw9elTt+REREUIikYioqChDhJKIiIhecyV1zer48eMCgLC2tlbLu7744gvV8zt37izs7e2Fg4OD6NOnj3j48GG+4wwJCRHjxo1T2/bll1+KqlWrCltbW1GlShUxffp0IZfLhRCF51VBQUFCKpWqjc3W1ra44SQiIqLXXEnlVtHR0aJt27bC0dFR2Nvbi1q1aom5c+eK3Nxc1fOLez+voNxq6NChQiKRaORODx480BoHX19fsXv3bgNFlYhKgkQIIUq6cIuIiIiIiIiIiIiIiIiIiIiIiKis4PJ/REREREREREREREREREREREREebCoioiIiIiIiIiIiIiIiIiIiIiIKA8WVREREREREREREREREREREREREeXBoioiIiIiIiIiIiIiIiIiIiIiIqI8WFRFRERERERERERERERERERERESUB4uqiMq5oKAgWFlZwc7ODvb29qhXrx62b99u7GFpWLFiBapUqQJbW1t069YNjx8/zrftunXrUKtWLTg6OsLFxQXvvPMOYmJiVPtnzZoFc3Nz2NnZqb62bt2q2l+vXj21fVZWVnBwcNA4jhACbdu2hUQiQXJycr7jyRtj5decOXPUfpZKpahQoYLq55EjR5ZojBYtWoSGDRvCwcEB3t7emDRpEmQymWr/kydPMGDAALi6usLV1RWTJk1Cbm6uRj8vX75EjRo14OTkpLb90qVLaNu2LRwcHFCtWjVs3LhR7/MhIiIqi8pjbnXs2DF06NABjo6OGv/mA8Cnn36KWrVqwd7eHlWrVsW8efPU9kdFReHNN99ExYoVUblyZSxcuFBtv755g0QigY2NjVouNXv2bNX3tra2kEgkavt//vln3YMDIDs7G2FhYahYsSIqVaqEMWPGICcnp8jt847Fzs4OFhYWaNiwoVofe/fuRUBAAGxtbeHl5YXly5cDUORlgwYNgre3NxwcHNC4cWPs3btXr/MhIiIqi5hXqedVWVlZGD58OKpWrQp7e3vUrl0ba9eu1ehj9erVqFWrFmxtbeHn54dff/013/GUx7xq6NChsLS0VGtz9uxZvWJIRERUHr2OuRUAnDlzBm3atIGdnR3c3NwwY8YMjTb53euaPn06GjRoAHNzc4wfP77QsZtibvXjjz+iWbNmsLKyQq9evTT2F3aOp0+fRqtWreDo6IjKlStj6tSpkMvlAIBTp05p5GZSqRRjx47V65yIyjxBROVaYGCg+Pbbb4UQQsjlcvHbb78JCwsLER0dbdyB5XHkyBHh6Ogozp07J9LS0kRoaKjo0KFDvu2jo6NFYmKiEEKIly9fikmTJolOnTqp9s+cOVP07NlT5+N3795dDB8+XGP7jz/+KDp27CgAiOfPn+f7/Lwxzo+vr6/YvXu3zmN6lb4xmj9/vrhw4YKQyWTi4cOHomnTpmLq1Kmq/V26dBFDhgwR6enp4tGjRyIgIEB88cUXGv1MmjRJdOzYUTg6Oqq2PX/+XLi5uYlly5aJnJwcce7cOeHg4CBOnTpV5PMjIiIqK8pjbnX+/HmxceNGsXr1arV/85U+//xzcf36dZGTkyNu3rwpfH19xYoVK4QQQuTk5Ii6deuKadOmCZlMJm7duiV8fHzEzz//LIQoWt4AQPz999/57r9//36h+VlhZsyYIRo1aiTi4uJEXFycaNSokZg9e7bB2jdo0EAtt/rf//4nKleuLI4dOyZycnLEs2fPxM2bN4UQQkRFRYmvvvpKPHz4UOTm5oq9e/cKGxsbERERUeTzIyIiKguYV6nnVWlpaWL69Oni7t27Qi6Xi7NnzwonJyfxxx9/qJ6/YsUKUbt2bXH58mUhl8tFfHy8iIqKync85TGvCgkJEePGjdPaVpcYEhERlVevY2515coV4erqKnbv3i2ysrJEWlqauHLlikY7bfe6hBBi/fr14vfffxe9e/fON7/IyxRzq507d4rdu3eL0aNHa703WtA55uTkiEqVKokvv/xS5OTkiPv37ws/Pz+xfPlyrceKj48X5ubm4syZM0U+P6KyiEVVROWctoIfV1dX1cWEFy9eiLffflu4uroKBwcH0a5dOxEeHq5qe+nSJdGyZUthb28vnJ2dRffu3VX7EhISxMCBA4WHh4fw9PQU48aNE5mZmXqPcfDgwWL06NGqn+Pj44VUKi3wopBSRkaGmDx5sqhSpYpqmz5FVY8ePRJmZmbi3LlzattjYmJEtWrVxMWLF02iqKo4MRJCiO+++060a9dOCKG4wCSRSNSeu379euHr66v2nIsXL4r69euLP/74Qy3R3L9/v/Dx8VFrO3ToUBESEqLfSREREZVB5Tm3OnbsmNYLVK+aMGGCeP/994UQQkRERAgzMzORlZWl2j9r1iwRGBgohCha3lAaF6i8vb3F9u3bVT9v27ZNLZ8sTvvz588LMzMz8ejRI9W2Zs2aqW6Y6qJx48ZizZo1OrcnIiIqi5hXqedV2vTu3VtMnz5dCKG46eXu7q5XgVB5zKsKKqrSJm8MiYiIyrPXMbfq27ev2oQC2uR3rysvXfMLU8ytlAq7N6rtHJ8+fSoAqOVaH374odrvKK8FCxaIOnXqFDoWovKGy/8RvUbkcjl+/fVXvHz5EgEBAaptAwcOxP3795GQkIDGjRujX79+EEIAAMLCwtCjRw8kJyfj0aNH+PTTTwEolsZ7++234eHhgaioKFy7dg1XrlzB3LlzVcdzcnLK9yvvtN1Xr15VjQcA3N3d4eHhgWvXruV7LqdPn4aTkxNsbGzwzTff4LPPPlPbf/ToUTg7O6NmzZr47LPPkJmZqbWfDRs2oG7dumjZsqXa9o8//hizZs2Cs7Nz4YEthpKMUV4nTpxQ9ScUBbWq3zGg+Dt48OABUlNTAQA5OTkYPnw4lixZAktLS7W+5HK52nOV265evarXuRMREZV15Sm30pUQAidPnlQdTzkd+Kt5hTIvKM28QZkf5vc1atQoAMDz588RGxurFqOAgADExMQgJSVFo199269ZswZvvvkmvLy8AADp6em4dOkSHj16hJo1a8LDwwPvvvtuvtPbP3nyBDdv3tRYPpCIiKg8Y16lKTMzExcuXFDtv337NhISEnD58mX4+fnB29sbw4cPV13LMSRTzauUNm7ciEqVKqFevXr4+uuvVTnpq16NIRER0evidcmtTpw4AZlMhoCAALi6uqJr1664ffu2an9B97pKU0nlVsVVqVIlfPDBB1izZg2ys7MRFRWFw4cPo1u3blrbr127FsOGDTP4OIhMXunXcRFRaQoMDBTW1tbC0dFRWFtbC6lUKubPn59v++fPnwsAIjY2VgghRPv27cXw4cPFw4cP1dpduHBBVKpUSeTm5qq2HTx4UFSrVk3vMVarVk2t6loIIerWrSs2bdpU6HMTExPFvHnz1JaQuX79umr5lGvXrolGjRqJsWPHajxXLpeLGjVqiMWLF6tt37x5s2o5QV2qyvPGWPmVlpam1qa4M1UVJ0YrV64U7u7uIi4uTrWtffv2YtCgQeLFixfiwYMHolGjRgKA6vf85Zdfig8++EAIofkJgKSkJFGxYkXxww8/CJlMJk6fPi3s7e1F9erVi3x+REREZUV5zq10mVFh2rRpok6dOqpcRyaTierVq4vJkyeLzMxMcf36deHt7S3MzMyEEEXLGwAIOzs7VV4VFBSktr+4n/qLiYkRAFTLSQshxJMnT9RyoaK2T0tLEw4ODmLPnj2qbQ8fPhQARMOGDUV0dLR48eKFGDRokOjYsaPGsbKyskSHDh3EkCFDinRuREREZQnzKvW8Ki+5XC4GDRokgoKCVOdx6tQpAUB06tRJJCYmisTERNGpUyfV9RttylteJYRiFo0nT56InJwccfbsWeHj4yO++eYbjWNpiyEREVF59jrmVmZmZqJy5cri2rVrIjMzU0yePFnUqlVLZGdnCyEKvteVlz4zVZlSbpVXUWaqEkKIAwcOqK7lARBhYWFCLpdrtDt58qSwsLAQT5480fl8iMoLzlRF9BqYN28ekpOT8fLlS9y+fRsbNmzAihUrAAAvX77EqFGj4OfnBwcHB/j5+QEAkpKSACiqjjMzM9G0aVPUrl0bP/74IwAgOjoaycnJqFSpkqqSum/fvkhISNB7fHZ2dhoV1ikpKbC3ty/0uS4uLhg2bBi6d++O9PR0AEC9evXg7e0NqVSK+vXr48svv8TWrVs1nnvixAk8fPgQgwcPVm179uwZpkyZgmXLlul1DsoYK79sbW31en5hihqjn3/+GZ9//jkOHjwIT09Pte0vX75EjRo10LlzZwwcOBASiQQVK1bE3bt3sXz5cnz11Vda+3R2dsa+ffuwefNmeHh4YMqUKQgNDS3xWb2IiIhMRXnOrQoyf/58bNmyBQcPHlTlOhYWFvj111/x999/o3Llyhg0aJBaXlDUvOHUqVOqvOrYsWPFGver7OzsAEAtRsrvtcVIn/bbt2+HjY2N2if6lM8fO3YsfH19YWdnh9mzZ+PYsWOq/BUAZDIZ+vbtCxsbG6xatapY50hERFRWMK86qHENSQiBUaNG4fbt29izZw+kUqlqLAAwdepUuLi4wMXFBVOnTsW+ffsKPFZ5yqsAoEmTJnB1dYWZmRlatWqFKVOmaFz3yy+GRERE5d3rllvZ2dkhNDQU9evXh5WVFebMmYO7d+/izp07hd7rKipTyq2K6/bt2+jZsye+/fZbZGZmIi4uDjdv3sSUKVM02q5ZswZvv/02XF1dDT4OIlPH/00QvWZq1KiBt956C7/99hsA4Ouvv8alS5dw+vRppKamIjo6GsC/S7hUr14dGzduRHx8PFavXo1Jkybh0qVL8PHxgZubm1ohUUpKCtLS0lTHsrOzy/erXr16qnYNGzZEeHi46ucnT57g8ePHaNCggU7nlJ2djZSUFDx58kTr/vwunKxevRq9evVSu6l39epVxMXFoXXr1nBxcUGTJk1UcdixY4dO49FHScbo559/xvjx43HgwAGNac69vb2xc+dOxMfH486dO7C3t0ezZs1ga2uL06dPIyEhATVr1oSLiwt69uyJ1NRUuLi44Pz58wCANm3a4M8//8TTp09x6tQpxMfHIzAw0LDBISIiKgPKY26lzfz587F8+XIcPXoU3t7eavvq1auHgwcPIikpCeHh4cjKylLLC0orbzh16lSBMRo5ciQAoGLFivD29laLUXh4OHx8fODo6KjRrz7tV69ejZCQEJibm6u2OTk5oUqVKlrHrPy7kMlkePfddyGTybBz506jTklPRERkLMyrFOc2evRonD9/HgcPHlTLNWrVqgVra+siH1cfpppXafPqdb+CYkhERPQ6eR1yq0aNGqn9LJFIVN/rcq+rtJRUblVc165dg7e3N/r27Qtzc3N4enoiJCQE+/fvV2uXmpqK7du348MPPzT4GIjKAhZVEb1moqOj8fvvv6sSlNTUVFhbW6NixYpIS0vDtGnT1Npv3LgRCQkJkEgkcHJyglQqhZmZGZo3bw4fHx98/vnnePHiBYQQePDgAf73v/+pnpuWlpbvV0REhKpdaGgofvrpJ1y4cAEZGRmYNm0aAgMDUa1aNa3nsG7dOsTGxkIIgfj4eIwdOxY1a9ZUVdXv3r0bT58+BaCosp42bRr69Omj1kdycjJ27typsfZv69atcf/+fYSHhyM8PBy///47AEXC89ZbbxUh4gUrqRj98ssvGDt2LP73v/+hcePGGvtv3bqF5ORk5Obm4vjx45g7dy7mzJkDAOjXrx/u3r2risHq1athb2+P8PBwVV9///03srKy8PLlS6xatQrHjx/H+PHjDR4fIiIiU1ceciu5XI7MzEzIZDIAQGZmJjIzM1X7Fy5ciKVLl+LYsWPw9fXVeP7Vq1eRnp4OmUyGXbt2Ye3atfj8889V+0srb2jXrl2BMVq+fLmqbWhoKL744gvEx8cjPj4eX375ZYEXhnRpf/v2bfz5558a+SUAjBgxAj/88AMePXqEly9fYs6cOejUqRPs7OyQnZ2Nfv36IT09HXv27IGVlZXhgkJERFSGMK8CwsLCcObMGRw6dAgVK1ZU21ehQgUMHjwYCxYswPPnz5GcnIwFCxagZ8+eeka6cKacV23btg2pqakQQuDixYuYP3++2nW/gmJIRET0OnkdcqsRI0Zg/fr1uH37NrKzszF79mz4+/ujZs2aOt3rys7ORmZmJnJzc5Gbm4vMzExkZ2cb5heQR0nmVjk5OcjMzEROTo5GvAo7x6ZNmyIuLg579uyBXC5HYmIiNm3apHFf8ZdffoGzszO6dOli4MgQlRFGWHKQiEpRYGCgsLS0FLa2tsLW1lZUrlxZjBkzRrx8+VIIIcTjx49Fhw4dhK2trfD19RUbN24UAMTff/8thBDi/fffF+7u7sLW1lZUq1ZN/Pjjj6q+ExISxNChQ0XlypWFvb29qFevnvj++++LNM5ly5aJypUrCxsbG/Hmm2+KuLg41b6ffvpJ1K1bV/Xz2LFjhZeXl7CxsRGenp5iwIABIioqSrX/vffeE87OzsLGxkZUrVpVTJkyRWRkZKgdb8mSJcLPz0/rusB56bL+cWBgoPj2228L7MfX11fs3r27wDaF0SdGfn5+wtzcXPV7t7W1Vdu/dOlS4ebmJipUqCAaNmwo9uzZk+9xta0zPXToUOHo6ChsbW3FG2+8Ia5fv16scyMiIiorymNudezYMQFA40sJgLCwsFDLK7p27ara/9lnn4lKlSoJGxsb0bp1a3H69Gm1seibN+SNlza65GeFkclkYtSoUcLJyUk4OTmJsLAwkZ2drdr/0UcfiY8++kjn9kII8emnn4r27dtrPV5OTo6YOHGicHZ2Fs7OzqJv377i8ePHQgghjh8/LgAIa2trtRh/8cUXRT4/IiKisoB5lXpeFR0dLQAIKysrtf15c5K0tDQREhIiHB0dhZubm/jwww9FampqvmMvj3lVu3btVLllzZo1xYIFC0Rubq4QQrcYEhERlVevY24lhBDz588Xnp6ewsnJSXTp0kXcuXNH63G13esKCQnR6DskJCTfsZtibjVz5kyNcwgMDNT5HH/99VfRuHFj4eDgINzc3MSgQYNEYmKi2piaN28uZsyYUeRzIirrJEL8M6cfERERERERERERERERERERERERcfk/IiIiIiIiIiIiIiIiIiIiIiKivFhURURERERERERERERERERERERElAeLqoiIiIiIiIiIiIiIiIiIiIiIiPJgURUREREREREREREREREREREREVEeLKoiIiIiIiIiIiIiIiIiIiIiIiLKw9zYAwCAJUuW4KuvvkJ8fDwaNWqEH374AS1atCj0eVu2bMF7772Hnj17Ys+ePartQgjMnDkTq1atQnJyMtq0aYNly5bB399fp/HI5XLExcXB3t4eEomkqKdFREREZZQQAi9evICXlxekUtagFwfzKiIiotcb8yrDYm5FRET0emNuZTjMq4iIiF5vuuZVRi+q2rp1KyZOnIjly5ejZcuWWLx4MYKDg3H79m24ubnl+7zo6GhMmjQJ7dq109i3cOFCfP/999iwYQOqVq2K6dOnIzg4GDdu3IC1tXWhY4qLi4OPj0+xzouIiIjKvocPH8Lb29vYwyjTmFcRERERwLzKUJhbEREREcDcyhCYVxERERFQeF4lEUKIUhyPhpYtW6J58+b48ccfASgqw318fDBmzBhMmTJF63Nyc3PRvn17fPDBBzh16hSSk5NVM1UJIeDl5YVPPvkEkyZNAgCkpKTA3d0d69evx4ABAzT6y8rKQlZWlurnlJQUVKlSBQ8ePICDg4NBz1culyMpKQkuLi78FIEOGC/dMVb6Ybx0x1jph/HSnSnHKjU1Fb6+vkhOToajo6Oxh1OmpaSkwMnJCQ8fPjR4XgUo/o4SExPh6upqcn9Hpoax0g/jpTvGSj+Ml+4YK/2YarxSU1Ph4+PDvMpASjK3MtW/IVPFeOmOsdIP46U7xko/jJfuTDlWzK0Mh9esTAdjpR/GS3eMlX4YL90xVvox1XjpmlcZdaYqmUyGS5cuYerUqaptUqkUnTt3xtmzZ/N93pw5c+Dm5oZhw4bh1KlTavvu37+P+Ph4dO7cWbXN0dERLVu2xNmzZ7UWVc2bNw+zZ8/W2J6VlYXMzMyinFq+5HI5cnNzkZmZaVJ/MKaK8dIdY6Ufxkt3jJV+GC/dmXKslMXWnPq7+JQxdHBwKLELVJmZmXBwcDC5vyNTw1jph/HSHWOlH8ZLd4yVfkw9XsyrDKMkcytT/xsyNYyX7hgr/TBeumOs9MN46a4sxIq5VfHxmpXpYKz0w3jpjrHSD+OlO8ZKP6Yer8LyKqMWVSUlJSE3Nxfu7u5q293d3XHr1i2tzzl9+jTWrFmD8PBwrfvj4+NVfbzap3Lfq6ZOnYqJEyeqflZWpLm6upbIBSqJRGJyVXimivHSHWOlH8ZLd4yVfhgv3ZlyrHRZLpiIiIiIiIiIiIiIiIiIyi+jFlXp68WLF3j//fexatUquLi4GKxfKysrWFlZaWyXSqUaN3lzc3ORnZ1d5GPJ5XLk5ORAJpOZ3A1kJQsLC5iZmRl7GCoSiUTr74I0MVb6Ybx0x1jph/HSnanGytTGU14VN68CFLlVdna2Sc54BpheXkVERETllyGuWZlyXgUwtyIiIqLSwWtWREREpGTUoioXFxeYmZkhISFBbXtCQgI8PDw02kdFRSE6Oho9evRQbZPL5QAAc3Nz3L59W/W8hIQEeHp6qvUZEBBQrPGmpaUhNjYWQogi9yGEgFwux4sXL0x2elaJRAJvb2/Y2dkZeyhERERUThkirwJMP7diXkVERESlgdesiIiIiAyD16yIiIgoL6MWVVlaWqJp06Y4cuQIevXqBUBRJHXkyBGEhYVptK9duzauXbumtu3zzz/Hixcv8N1338HHxwcWFhbw8PDAkSNHVEVUqampOH/+PD7++OMijzU3NxexsbGwsbGBq6trkRMgIQRycnJgbm5ukkmUEAKJiYmIjY2Fv78/q9SJiIjI4AyVVwGmnVsxryIiIqLSwGtWRERERIbBa1ZERET0KqMv/zdx4kSEhISgWbNmaNGiBRYvXoz09HSEhoYCAIYMGYLKlStj3rx5sLa2Rv369dWe7+TkBABq28ePH4+5c+fC398fVatWxfTp0+Hl5aUq3CqK7OxsCCHg6uqKChUqFLkfU06ilFxdXREdHY3s7GwmUkRERGRwhsqrANPPrZhXERERUUnjNSsiIiIiw+A1KyIiInqV0Yuq+vfvj8TERMyYMQPx8fEICAjAgQMH4O7uDgCIiYnRe63hyZMnIz09HSNGjEBycjLatm2LAwcOwNrautjjNcXEx9Beh3MkIiIi43sdco7X4RyJiIjINLwOecfrcI5ERERkfK9DzvE6nCMREZEhGL2oCgDCwsK0LvcHAMePHy/wuevXr9fYJpFIMGfOHMyZM8cAoyMiIiIiIiIiIiIiIiIiIiIioteJflNAkVGZm5sjICAAAQEBaN68OcLDw409JCIiIqIyiXkVERERkeEwtyIiIiIyDOZVREREpoVFVSUtNhY4dkzxWExOTk4IDw9HeHg4pkyZwpm4iIiI6PXCvIqIiIjIMAyYVwHMrYiIiOg1x2tWRERE5RaLqopCCCA9vfCvpUsBX1+gY0fF49KlBbcXQuchpKamwsnJCQAQFRWFdu3aoUmTJmjRooWqav3atWto0qSJqqL9yZMnAIAFCxagefPmaNiwIRYtWmTo6BARERHprqTyKj1yK+ZVREREVG7okluVYF4FMLciIiKicoLXrIiIiAiAubEHUCZlZAB2dvo9Ry4HRo+GZPRoWOTXJi0NsLXNt4vk5GQEBAQgIyMDT58+xZ9//gkA8PT0xOHDh2FlZYWrV6/ik08+waFDh7By5Up8/PHHGD58OF6+fAkzMzMcPHgQsbGxuHDhAuRyOd544w107doV9evX1+98iIiIiAyhGHkVRo+GBChSbsW8ioiIiMolfXMrA+RVAHMrIiIiKod4zYqIiIjAoqoyRTnlJwDs2LEDo0ePxuHDh5GVlYWwsDBcvXoVZmZmSExMBAC0bt0ac+bMwdOnT9GvXz9Uq1YNBw8exP79+3Hq1CkAwIsXL3Dnzh0mUkRERPRaYV5FREREZDjMrYiIiIgMg3kVERGRaWFRVVHY2CiqyAvy6BFQp46iKl3JzAwiIgI57u4wNzeHRCLR7FdH3bt3x5AhQwAAixcvhp+fH3766Sekp6fDz88PADBw4EC0aNEC+/btwxtvvIHt27dDLpdj5syZCAkJ0flYRERERCWmGHkVbtyA8PJCTk5OsXIr5lVERERUbhSWW5VwXgUwtyIiIqJygtesiIiICIDU2AMokyQSxbScBX3VrAmsXKlIngDF44oViu35PefVpKoAf/75J6pVqwZAsaayl5cXJBIJ1q9fr2pz7949VK9eHRMmTECXLl1w48YNdOnSBatXr0ZGRgYAIDo6GikpKQYLDREREZFeSiqv0iO3Yl5FRERE5UZhuVUJ51UAcysiIiIqJ3jNioiIiMCZqkrWsGFAcDBw9y5Qowbg7Q0IUeTulOsoCyFgbm6OlStXAgBGjRqFPn36YNWqVejdu7eq/datW/HTTz/BwsICvr6+6N27N2xtbXHjxg20atUKcrkcTk5O2LlzJxwdHYt9ukRERHqJjYXlX38BzZsDVaoYezRk6rTlVcXAvIqIiMod5lakKwPnVQBzKyIiKmeYV5E+eM2KiIio5MTGApGRgL+/Qa5fFIVEiGJU+ZRTqampcHR0REpKChwcHAAAmZmZuH//PqpWrQpra+si9y2EyH+6TxNhqHM1BLlcjidPnsDNzQ1SKSdWKwhjpR/GS3eMlX4YLx2tWQMxYgQkcjmEVArJypWKCxAmQlsuQEVTknkVYPq5FfOqsovx0h1jpR/GS3eMlR5WrYL46CNIhDC53Ip5lWHxmhVzq7KIsdIP46U7xko/jJeOeM3qtcFrVsyryirGS3eMlX4YL90xVvopcrzmzgVmzFBMXCSVKmaHNGBepmtexd8wERERla7YWOCfi1MAFI8ffaTYTkRERET6iY0F/imoAphbERERERUZr1kRERERmYbY2H8LqgDAiHkZi6qIiIiodEVGKpKfvHJzFVNkU4GWLFkCPz8/WFtbo2XLlrhw4UK+bSMiItCnTx/4+flBIpFg8eLFGm2U+179Gj16tKpNUFCQxv6RI0eWxOkRERFRUURG/nuBSYm5FREREZH+eM2KiIiIyDSY0PUuFlURERFR6fL3V0zTmZdEAtSoYZzxlBFbt27FxIkTMXPmTFy+fBmNGjVCcHAwnjx5orV9RkYGqlWrhvnz58PDw0Nrm7/++guPHz9WfR06dAgA8O6776q1Gz58uFq7hQsXGvbkiIiIqOj8/RW5VF5mZsytiIiIiPSl7ZoV8yoiIiKi0ufvr7nNSHkZi6qIiIiodHl7AytXQq2+3Nxc85OApOabb77B8OHDERoairp162L58uWwsbHB2rVrtbZv3rw5vvrqKwwYMABWVlZa27i6usLDw0P19dtvv6F69eoIDAxUa2djY6PWrqC1pYmIiKiUeXsDb72l+lGYmQErVii2ExEREZHuvL2Br79W/SgA5lVERERExuDtDXh5/fuzEa93mZf6EYmIiIg++AD48EMAgKhZE5I7d4BPPwW2bjXywEyTTCbDpUuXMHXqVNU2qVSKzp074+zZswY7xk8//YSJEydC8spsFz///DN++ukneHh4oEePHpg+fTpsbGy09pOVlYWsrCzVz6mpqQAAuVwO+T+Fc3K5HEII1VdxKfswRF+GpjzHvOdvLMq4G3scZQXjpTvGSj+Ml+4YK91J/P2h/NdbvmkTJP37m0zBOn9/REREVKb07g1MmKD43tUVGDbMuOMhIiIieh0JATx/rvh+0yYgKMhohe4sqiIiIqLSl5WluvEnVq2CpEMHYNs2YORIoEMHow7NFCUlJSE3Nxfu7u5q293d3XHr1i2DHGPPnj1ITk7G0KFD1bYPHDgQvr6+8PLywtWrV/F///d/uH37Nnbt2qW1n3nz5mH27Nka2xMTE5GZmQkAyM7OhlwuR05ODnJycoo1biEEcnNzAUCjGMwU5OTkQC6X4+nTp7CwsDDqWORyOVJSUiCEgPTV5QxIA+OlO8ZKP4yX7hgr3TkkJUFZ7vzi4UNk5rM8sDG8ePHC2EMgIiIi0l1GhupbSWIiEBenPksCEREREZW8hATg5UvF0sz9+gGWlkYbCouqyhBzc3PUr19f9XNoaCjWrVsHALh+/bpq38SJEzFkyBCjjJGIiEgneS5QoVUrRTHV0qXA2LHA338rlgOkUrVmzRq8+eab8HrlQuGIESNU3zdo0ACenp7o1KkToqKiUL16dY1+pk6diokTJ6p+Tk1NhY+PD1xdXVXLBmZmZuLFixcwNzeHuYF+1/oWLFlYWKjlVUOHDsX69esBqOdVEyZMKFZeZW5uDqlUCmdnZ1hbWxe5H0OQy+WQSCRwdXVlcYIOGC/dMVb6Ybx0x1jpTpJntkaH+Hg4uLkZcTTqjP3vH5UOXrMiIqJyI+81KwC4dIlFVVSqmFcREREBuHdP8ejjY9SCKoBFVSUuNhaIjAT8/Ys/G5mTkxPCw8PVto0bNw4A4OLiorGPiIjIZKWnAwCEpaWigOq//wW2bAGuXweWLQPGjDHyAE2Li4sLzMzMkJCQoLY9ISEBHh4exe7/wYMHOHz4cL6zT+XVsmVLAMDdu3e1FlVZWVnByspKY7tUKlXdEJdKpZBIJKovXWnLq4QQqj706UtbXjV+/HgAhs2rlOeY9/yNyZTGUhYwXrpjrPTDeOmOsdLRy5eqbyWRkZCYULz4uzNNhrxeBfCaFRERlSPaiqp69DDOWKjM4L1AIiIiA4uKUjxWq2bccQDgla0iEEJxL7iwr6VLAV9foGNHxePSpQW3z/PBUiIiovLtnwtUokIFxc+VKgFffKH4fsYMIDHRSAMzTZaWlmjatCmOHDmi2iaXy3HkyBG0bt262P2vW7cObm5u6NatW6FtlRduPD09i31coOTyKuZWRET0Wsl78+/uXeONg4xOl9yKeRUREVEBXi2qunjROOMgo+M1KyIiIiNSzlRlAkVVnKmqCDIyADs7/Z4jlwOjRwOjR0sAaF+eJi0NsLXNv4/k5GQEBAQAAJo1a4bVq1frNwgiIiJTkaeoSjW30PDhwMqViuX/PvtM8T2pTJw4ESEhIWjWrBlatGiBxYsXIz09HaGhoQCAIUOGoHLlypg3bx4AQCaT4caNG6rvHz16hPDwcNjZ2aFGjRqqfuVyOdatW4eQkBCNpfiioqKwefNmvPXWW3B2dsbVq1cxYcIEtG/fHg0bNjTIeRUvrwKAouVWzKuIiKhcyXvzLyoKyMnhcsqvKX1zK0PkVQBzKyIiKkeU16ysrCDJylLMVEWvJV6zIiIiMiJlUZWWFVNKG6+wlSHapvwkIiIqk7QVVZmZAT/8ALRtC6xeDYwYATRrZrQhmpr+/fsjMTERM2bMQHx8PAICAnDgwAG4u7sDAGJiYtSW14mLi0Pjxo1VPy9atAiLFi1CYGAgjh8/rtp++PBhxMTE4IMPPtA4pqWlJQ4fPqwq4PLx8UGfPn3w+eefl9yJlhLmVUREVK7kKaqSZGcDDx6YxEUnen0wtyIionLjn7wqu2FDWFy6BEl8PBAXB3h5GXlg9LpgXkVERATOVFXW2dgoqsgL8ugRUKeOoipdycwMiIgQcHfPgbm5OSQSidpzbGxKYLBERESm6NXl/5TatAEGDQJ+/hkYMwY4cwaQcrVipbCwMISFhWndl7dQCgD8/PwgdJhLvEuXLvm28/HxwYkTJ/Qepz6Kk1fduAF4eQnk5DC3IiKi19yry9TcucOiqtdUYbkV8yoiIqJC/JNXyStVUvyjGRGhmK2KRVWvHV6zIiIiMqKoKMWjCRRV8S5lEUgkimk5C/qqWVOxapGZmeI5ZmbAihWK7fk955WcioiIqPzKr6gKABYuVMytfe4c8NNPpTwwKm0llVcxtyIiotfKP7lVrqen4ufISCMOhoypsNyKeRUREVEh8l6zatpUsY1LAL6WeM2KiIjISDIygMePFd+zqKp8GzYMiI4Gjh1TPA4bZuwRERERmYiCiqq8vIDp0xXfT54MpKaW4sDIVDGvIiIiKoBymZp69RQ/37ljxMGQqWNeRUREVIA816wEi6pIB8ytiIiIDCw6WvHo6AhUqmTUoQAsqipx3t5AUJDisbiSkpKKtI+IiMjkFFRUBQDjxgH+/kBCAjBnTikOjEwZ8yoiIqJ8/JNb5bCoinRkyLwKYG5FRETlSN5rVk2aKLaxqIoKwWtWREREBnTvnuKxWjWTmN6RRVVERERU+gorqrKyAr77TvH9d98Bt26V0sCIiIiIyhgh/p2pqn59xTYu/0dERERUNHmvWQUEAFKpYvkZ5RI0RERERFSyoqIUjyaw9B/AoioiIiIyhsKKqgDgzTeBHj2AnBzFzFVClNLgiIiIiMoQmQyQywEAOXXrKrY9eABkZhpxUERERERlVHo6gH+uWdnYAHXqKLZztioiIiKi0pF3pioTwKIqPYnX4Ibu63CORERkZLoUVQHAN98AlpbAwYPA3r2lMDAqTa9DzvE6nCMRERnZP3kVAOT6+EA4OCiK0ZWf6qPXxuuQd7wO50hEREb26jWrpk0Vjyyqeq28DjnH63CORERURimLqqpXN+44/mFu7AGUFRYWFpBIJEhMTISrqyskRVy7UQiBnJwcmJubF7mPkiSEQGJiIiQSCSwsLIw9HCIiKq+UN/8KK6qqUQOYNAn48ktgwgSgS5fCn0Mmz1B5FWDauRXzKiIiKhXKG38WFoCFBVCzJnDxomIJwHr1jDw4Kg28ZkVERGRAr16zatoU2LhRkV9RucdrVkRERCbAxGaqYlGVjszMzODt7Y3Y2FhER0cXuR8hBORyOaRSqcklUUoSiQTe3t4wMzMz9lCIiKi8Ut78s7EpvO20acCGDcD9+8CiRcD06SU8OCpphsqrANPPrZhXERFRiVPe+FPmVTVqKG763bljvDFRqeI1KyIiIgN69ZoVZ6p6rfCaFRERkZHJ5SyqKsvs7Ozg7++P7OzsIvchl8vx9OlTODs7Qyo1zdUXLSwsmEQREVHJ0nX5PwCwtVUUU733HjBvHhASAlSpUsIDpJJmiLwKMP3cinkVERGVuFeLqmrWVDyyqOq1wmtWREREBvLqNauAAEAqBR4/Vnx5ehpvbFQqeM2KiIjIiOLjgcxMwMzMZO4FsqhKT2ZmZsVKMuRyOSwsLGBtbW2SSRQREVGp0KeoCgD69weWLwdOnFAsB7htWwkOjkpLcfMqgLkVERER0tMVj/8UVQl/f0gAxfJ/9FrhNSsiIiIDePWala0tULs2cOOGYraq7t2NODgqLbxmRUREZCTKWaqqVAFMZIla/itOREREpU/foiqJBPj+e8UnA7dvB44eLcHBEREREZUhr85U5e+veORMVURERET603bNiksAEhEREZUOE1v6D2BRFRERERmDvkVVANCwIfDxx4rvP/4YOHwYiI0tgcERERERlSH5FVXFxwOpqcYZExEREVFZpe2aVbNmikcWVRERERGVrKgoxSOLqoiIiOi1VpSiKgCYM0cx7fqdO8AbbwC+vsCaNSUwQCIiIqIy4tWiKicnwM1N8f3du0YZEhEREVGZxZmqiIiIiIyHM1URERERoehFVRkZ/944BAC5HPjoI85YRURERK8vZW6UN6/iEoBERERERaPtmlVAACCVAnFxitlAiYiIiKhkKIuqqlc37jjyYFEVERERlb6iFlVFRgJCqG/LzeUsDERERPT6enWmKgCoWVPxyKIqIiIiIt0Jof2ala0tULu24nvOVkVERERUcjhTFRERERGKXlTl76/4ZGBeZmZAjRoGGhgRERFRGVNQUVVkZOmPh4iIiKiskskUH96DlmtWXAKQiIiIqGSlp/87KyiLqoiIiOi1VtSiKm9vYOVKRSEVoHhcsUKxnYiIiOh1pK2oisv/EREREelPmVeBRVVEREREpe7+fcWjkxNQsaJRh5IXi6qIiIio9BW1qAoAhg0DoqOBY8cUj8OGGXRoRERERGVKYcv/vbp0MulsyZIl8PPzg7W1NVq2bIkLFy7k2zYiIgJ9+vSBn58fJBIJFi9eXGDf8+fPh0Qiwfjx4w07aCIiIio65fUqMzPAwkJ9n7Ko6uLFUh4UERER0WtCufRf9erGHccrTKKoSp+LVLt27UKzZs3g5OQEW1tbBAQEYNOmTWpthg4dColEovbVtWvXkj4NIiIi0kVOjmI6dRSxqApQzEwVFMQZqoiIiIiURVW2tv9uUy6NnJwMPH1a6kMqD7Zu3YqJEydi5syZuHz5Mho1aoTg4GA8efJEa/uMjAxUq1YN8+fPh4eHR4F9//XXX1ixYgUaNmxYEkMnIiKiosqbV0kk6vsCAhTb4uL+XZaGVAxdjD5r1iyN+3y1a9dWa5OZmYnRo0fD2dkZdnZ26NOnDxISEgx9akRERFRalEVVJrT0HwCYG3sAyotUy5cvR8uWLbF48WIEBwfj9u3bcHNz02hfqVIlfPbZZ6hduzYsLS3x22+/ITQ0FG5ubggODla169q1K9atW6f62crKqlTOh4iIiArx8qXq2yIXVRERERGRgnJGhbwzVVWoAPj4AA8fKmarcnEx0uDKrm+++QbDhw9HaGgoAGD58uXYv38/1q5diylTpmi0b968OZo3bw4AWvcrpaWlYdCgQVi1ahXmzp1b6DiysrKQlZWl+jk1NRUAIJfLIZfL9TqnwsjlcgghDN5vecV46Y6x0g/jpTvGSj+Mlw7S0hQzEdjYaMbKxgaS2rUhuXkT8r/+Arp1M8oQTfH3p+99PmUx+rvvvosJEybk22+9evVw+PBh1c/m5uq3NCdMmID9+/dj+/btcHR0RFhYGN555x2cOXPGcCdHREREpScqSvHIoip1+l6kCgoKUvt53Lhx2LBhA06fPq1WVGVlZVXoJwOJiIjICNLTAQBCIgFY9ExERERUPNqW/wMUSwAqi6r+85/SH1cZJpPJcOnSJUydOlW1TSqVonPnzjh79myx+h49ejS6deuGzp0761RUNW/ePMyePVtje2JiIjIzM4s1llfJ5XKkpKRACAGp1CQmtzdpjJfuGCv9MF66Y6z0w3gVzuLRIzgDyLWyQnJyskasHOvWRYWbN5F+6hTS/ymmLm0vXrwwynELUlLF6Obm5vne50tJScGaNWuwefNmdOzYEQCwbt061KlTB+fOnUOrVq2Ke1pERERU2kx0+T+jFlUV9yKVEAJHjx7F7du3sWDBArV9x48fh5ubGypWrIiOHTti7ty5cHZ21toPP/Vnuhgv3TFW+mG8dMdY6Yfx0kHeT/3BND9hZ4pjIiIiItJKWVT16gygNWsCR44AkZGlP6YyLikpCbm5uXB3d1fb7u7ujlu3bhW53y1btuDy5cv466+/dH7O1KlTMXHiRNXPqamp8PHxgaurKxwcHIo8Fm3kcjkkEglcXV15s10HjJfuGCv9MF66Y6z0w3jp4J8P/5nZ2cHJyUkzVm3bAjt3wu7WLdhqmYGpNFhbWxvluPkpyWL0yMhIeHl5wdraGq1bt8a8efNQpUoVAMClS5eQnZ2Nzp07q9rXrl0bVapUwdmzZ7UWVZXmvUBlv7xOrBvGSj+Ml+4YK/0wXrpjrPSja7wk9+5BAkDu5weUQmx1/f0ZtaiqqBepUlJSULlyZWRlZcHMzAxLly7FG2+8odrftWtXvPPOO6hatSqioqIwbdo0vPnmmzh79izMzMw0+uOn/kwX46U7xko/jJfuGCv9MF6FM4+NhQsAubW11k/9mQJT/NQfERERkVb5zVTl7694vHOndMdDWj18+BDjxo3DoUOH9LoZamVlBSsts7tKpdISyaElEkmJ9V0eMV66Y6z0w3jpjrHSD+NVCOX9IFtb7bFq1gwAILl8GRIjxdDUfnclVYzesmVLrF+/HrVq1cLjx48xe/ZstGvXDtevX4e9vT3i4+NhaWkJJycnjePGx8dr7bM07wUCvE6sD8ZKP4yX7hgr/TBeumOs9KNTvORyuN+/DwB46uiI3CdPSnxcut4LNPryf0Vhb2+P8PBwpKWl4ciRI5g4cSKqVaumWhpwwIABqrYNGjRAw4YNUb16dRw/fhydOnXS6I+f+jNdjJfuGCv9MF66Y6z0w3jpIDoaACDN71N/JsDUPvVHRERElK+Clv8DWFRVBC4uLjAzM0NCQoLa9oSEhHyXoCnMpUuX8OTJEzRp0kS1LTc3FydPnsSPP/6o+uAgERERGVF+eZVSQAAgkQCPHgEJCcArhURkOG+++abq+4YNG6Jly5bw9fXFtm3bMGzYsCL1WZr3AgFeJ9YHY6Ufxkt3jJV+GC/dMVb60SlesbGQZGVBmJvDOSAAMC/5UiZd7wUataiqqBeppFIpatSoAQAICAjAzZs3MW/ePFVR1auqVasGFxcX3L17V2tRFT/1Z9oYL90xVvphvHTHWOmH8SqE8pNfNjYmGytTGw8RERFRvgorqrp7VzFlOvMbnVlaWqJp06Y4cuQIevXqBUBxAfDIkSMICwsrUp+dOnXCtWvX1LaFhoaidu3a+L//+z8WVBEREZmC/JZVVrKzA2rXBm7eBC5dAt56q/TGZqJKohhdGycnJ9SsWRN3794FAHh4eEAmkyE5OVlttqqCjlva9wIBXifWB2OlH8ZLd4yVfhgv3TFW+ik0Xv9MyCDx9YXE0rJUxqTr786ov+G8F6mUlBepWrdurXM/crlcbR3kV8XGxuLp06fw9PQs1niJiIjIAAr71B8RERER6S49XfH4am7l56f4VF9GBhAXV+rDKusmTpyIVatWYcOGDbh58yY+/vhjpKenIzQ0FAAwZMgQTJ06VdVeJpMhPDwc4eHhkMlkePToEcLDw1U3/uzt7VG/fn21L1tbWzg7O6N+/fpGOUciIiJ6hS7XrJo2VTxeulTy4ykDDHWfrzBpaWmIiopS3edr2rQpLCws1I57+/ZtxMTEGPS4REREVEru3VM8Vqtm3HFoYfTl/yZOnIiQkBA0a9YMLVq0wOLFizUuUlWuXBnz5s0DoFjzuFmzZqhevTqysrLw+++/Y9OmTVi2bBkARWI1e/Zs9OnTBx4eHoiKisLkyZNRo0YNBAcHG+08iYiI6B8sqiIiIiIynPxyKwsLoGpVIDJSsQSgt3fpj60M69+/PxITEzFjxgzEx8cjICAABw4cgPs/y/zExMSofaIxLi4OjRs3Vv28aNEiLFq0CIGBgTh+/HhpD5+IiIiKQteiqp9+YlFVHvre55PJZLhx44bqe2Uxup2dnWqVmkmTJqFHjx7w9fVFXFwcZs6cCTMzM7z33nsAAEdHRwwbNgwTJ05EpUqV4ODggDFjxqB169Zo1aqVEaJARERExcKiqvzpe5EqPT0do0aNQmxsLCpUqIDatWvjp59+Qv/+/QEAZmZmuHr1KjZs2IDk5GR4eXmhS5cu+O9//6t1Wk8iIiIqZYVNpU5EREREuivo5l/NmoqiqshIoGPH0h1XORAWFpbvcn+vFkr5+flBCKFX/yy2IiIiMjH5zQCal3KmqosXS348ZURJFKPHxsbivffew9OnT+Hq6oq2bdvi3LlzcHV1VT3v22+/hVQqRZ8+fZCVlYXg4GAsXbq0dE6aiIiIDCsqSvHIoirt9LlINXfuXMydOzffvipUqIA//vjDkMMjIiIiQ+JMVURERESGU1hR1f79ipmqiIiIiKhgulyzatwYkEiAR4+AhATgn8Kh152hi9G3bNlS6DGtra2xZMkSLFmyROdxEhERkYlSzlRVvbpxx6GFtPAmRERERAbEoioiIiIiw8jJAWQyxffacit/f8Uji6qIiIiICqfLNSs7O6BWLcX3XAKQiIiIyDBMePk/FlURERFR6WJRFREREZFhvHz57/f5zVQFKJb/IyIiIqKC/XPNStjaFtxOuQQgi6qIiIiIii8tDXjyRPE9i6qIiIjotceiKiIiIiLDUOZVAGBtrblfWVQVFaWY1YqIiIiI8qfrNSsWVREREREZjnKWqkqVAEdH445FCxZVERERUelSXqAq7FN/RERERFSwvDf+JBLN/ZUrK4qtcnKA6OhSHRoRERFRmaPMrSpUKLhds2aKRxZVERERERWfsqiqenXjjiMfLKoiIiKi0qWcSp0zVREREREVT2GzKUilgL+/4nsuAUhERERUMF1nqmrcWFHQHhv771I1RERERFQ0yqIqE1z6D2BRFREREZU2Lv9HREREZBi6zACqXALwzp2SHw8RERFRWabrNSs7O6BWLcX3nK2KiIiIqHhYVEVERESUh65TqRMRERFRwXS58aecqYpFVUREREQF0+eDgE2bKh5ZVEVERERUPFFRikcWVRERERGBM1URERERGYoueRVnqiIiIiLSDYuqiIiIiEqfcqaq6tWNO458sKiKiIiISheLqoiIiIgMQ5+iqsjIkh8PERERUVlWlKKqixdLbjxERERE5V1uLhAdrfieM1URERERgUVVRERERIaiz/J/MTHAy5clPyYiIiKiskqfa1aNGwMSCRAbCzx5UrLjIiIiIiqv4uIAmQwwNwe8vY09Gq1YVEVERESli0VVRERERIahS17l6go4OgJCAFFRpTMuIiIiorIoPV3xqMs1K3v7f2cE5RKAREREREWjvFbl5weYmRl1KPlhURURERGVLhZVERERERmGLnmVRMIlAImIiIgKk5OjmCUB0P2alXIJQBZVERERERXNvXuKx+rVjTuOArCoioiIiEoXi6qIiIiIDEPXvEq5BOCdOyU7HiIiIqKyKu8yyba2uj2HRVVERERExaMsqqpWzbjjKACLqoiIiKh0saiKiIiIyDB0XaJGOVMVi6qIiIiItFNerwIAa2vdnsOiKiIiIqLiYVEVERERUR5CsKiKiIiIyFB0zau4/B8RERFRwfLmVRKJbs9p3FjR9uFDIDGx5MZGREREVF5FRSkeWVRFREREBCArS1FYBbCoqgiWLFkCPz8/WFtbo2XLlrhw4UK+bSMiItCnTx/4+flBIpFg8eLFGm1mzZoFiUSi9lW7dm21NpmZmRg9ejScnZ1hZ2eHPn36ICEhwdCnRkREREXB5f+IiIiIDKMoHwJ0cPi3eJ2zVRERERHpTzlTVfXqxh1HAVhURURERKVHuUQNAFSoYLxxlEFbt27FxIkTMXPmTFy+fBmNGjVCcHAwnjx5orV9RkYGqlWrhvnz58PDwyPffuvVq4fHjx+rvk6fPq22f8KECdi3bx+2b9+OEydOIC4uDu+8845Bz42IiIiKSN+iqoQEIDW1ZMdEREREVBYVdWZ1LgFIREREVDSpqUBSkuL7qlWNO5YCmBt7AERERPQaUV6gsrQEzJmG6OObb77B8OHDERoaCgBYvnw59u/fj7Vr12LKlCka7Zs3b47mzZsDgNb9Subm5vkWXaWkpGDNmjXYvHkzOnbsCABYt24d6tSpg3PnzqFVq1Yaz8nKykJWVpbq59R/btzK5XLI5XIdz1Z3crkcQogS6bu8Yaz0w3jpjrHSD+OlO8aqcJL0dEgAyK2tC46XvT0k7u6QJCRAfvv2vzf/SgF/f0RERFQmFKeoavNmFlURERER6ev+fcWji4tiBlATxbuZREREVHqKeoHqNSeTyXDp0iVMnTpVtU0qlaJz5844e/ZssfqOjIyEl5cXrK2t0bp1a8ybNw9VqlQBAFy6dAnZ2dno3Lmzqn3t2rVRpUoVnD17VmtR1bx58zB79myN7YmJicjMzCzWWLWRy+VISUmBEAJSKSdhLQhjpR/GS3eMlX4YL90xVoWrmJwMKwCpOTnIePKkwHhV8vWFZUICUi9eRKaPT6mN8cWLF6V2LCIiIqIiK+5MVRcvGnY8REREROVdVJTisVo1446jECyqIiIiotLDoqoiSUpKQm5uLtzd3dW2u7u749atW0Xut2XLlli/fj1q1aqFx48fY/bs2WjXrh2uX78Oe3t7xMfHw9LSEk5OThrHjY+P19rn1KlTMXHiRNXPqamp8PHxgaurKxxK4JMGcrkcEokErq6uvOFeCMZKP4yX7hgr/TBeumOsCifJzQUAOHh4wM7NrcB4SerVAy5cgENCAhzc3EptjNbW1qV2LCIiIqIiK+o1q8aNFY8PHwKJiYCrq2HHRURERFRe3buneKxe3bjjKASLqoiIiKj0sKjKpLz55puq7xs2bIiWLVvC19cX27Ztw7Bhw4rUp5WVFaysrDS2S6XSErshLpFISrT/8oSx0g/jpTvGSj+Ml+4Yq0L8k1tJ7ewAqbTgeNWqpWh79y5QivHk746IiIjKhKJes3JwAGrWBO7cUSwB2LWr4cdGREREVB4pi6pMfKYqXtkiIiKi0sOiqiJxcXGBmZkZEhIS1LYnJCTAw8PDYMdxcnJCzZo1cffuXQCAh4cHZDIZkpOTS/S4REREVET65Fb+/orHO3dKbjxEREREZVVxrlkplwC8dMlw4yEiIiIq71hURURERPQKFlUViaWlJZo2bYojR46otsnlchw5cgStW7c22HHS0tIQFRUFT09PAEDTpk1hYWGhdtzbt28jJibGoMclIiKiIlLmVra2hbetWVPxeOcOIETJjYmIiIioLEpPVzyyqIqIiIiodERFKR5NvKiKy/8RERFR6WFRVZFNnDgRISEhaNasGVq0aIHFixcjPT0doaGhAIAhQ4agcuXKmDdvHgBAJpPhxo0bqu8fPXqE8PBw2NnZoUaNGgCASZMmoUePHvD19UVcXBxmzpwJMzMzvPfeewAAR0dHDBs2DBMnTkSlSpXg4OCAMWPGoHXr1mjVqpURokBERERq9MmtqlcHJBIgJQVISgJcXUt2bERERERlCWeqIiIiIio9ublAdLTi++rVjTqUwrCoioiIiEoPi6qKrH///khMTMSMGTMQHx+PgIAAHDhwAO7u7gCAmJgYSKX/TkIaFxeHxo0bq35etGgRFi1ahMDAQBw/fhwAEBsbi/feew9Pnz6Fq6sr2rZti3PnzsE1z03Wb7/9FlKpFH369EFWVhaCg4OxdOnS0jlpIiIiKpg+uVWFCoCPDxATo5itikVVRERERP/SZwbQVzVponiMiVEUr7u4GG5cREREROVRbCyQkwNYWgJeXsYeTYFYVEVERESlh0VVxRIWFoawsDCt+5SFUkp+fn4QhSzts2XLlkKPaW1tjSVLlmDJkiU6j5OIiIhKgRD651Y1a/5bVNWmTcmNjYiIiKisKc41KwcHRZ51545itqrgYMOOjYiIiKi8uXdP8ejnB5iZGXUohZEW3oSIiIjIQFhURURERGQYWVmKwipAv6IqAIiMLJkxEREREZVVxb1mxSUAiYiIiHQXFaV4NPGl/wAWVREREVFpKs5U6kRERET0L2VeBSiW9tOFv7/i8c4dw4+HiIiIqCxjURURERFR6VHOVFWtmnHHoQMWVREREVHp4UxVRERERIahzKssLQFzc92eo5ypikVVREREROoMVVR18aJhxkNERERUnrGoioiIiEgLFlURERERGUZ6uuJRn7xKWVR19y4glxt+TERERERlVXGvWTVurHiMiQGSkgwzJiIiIqLyikVVRERERFqwqIqIiIjIMIqSV/n5KWa1evkSePSoRIZFREREVCYV95qVo+O/Sy1zCUAiIiKigkVFKR6rVzfuOHTAoioiIiIqPSyqIiIiIjKMouRV5ub/fgKQSwASERER/csQ16yUSwCyqIqIiIgof8nJwLNniu+rVjXqUHTBoioiIiIqPSyqIiIiIjKMouZVyiUAIyMNOx4iIiKissyQRVUHDgCxscUfExEREVF5dP++4tHNDbCzM+5YdMCiKiIiIio9LKoiIiIiMoyi5lXKZWk4UxURERHRvwxxzSohQfF46hTg6wusWVP8cRERERGVN/fuKR6Vs6mbOBZVERERUenJc4EqNhY4c8aSH9wjIiIiKorizlTFoioiIiKif6WnKx6LWlQVGwt8882/P8vlwEcfccYqIiIioldFRSkeq1c37jh0xKIqIiIiKj3/3Pxbc6IGqlaVoG/fSqhaVcIP7hERERHpi8v/ERERERlOcWeqioxUFFLllZsL3L1bvHERERERlTecqYqIiIgoHxkZiEVljFhcB3K5BAAgl0v4wT0iIiIifRV3+b9794DsbMOOiYiIiKgsEuLf3MrWtmh9+PsD0lduuZmZATVqFG9sREREROUNi6qIiIiI8pGRgUj4qwqqlHJzgcOHjTQmIiIiorKoqEVVlSsDFSoAOTlAdLTBh0VERERU5mRm/vt9UWeq8vYGVq5UFFIBiscVKxTbiYiIiOhfyuX/WFRFRERE9IqMDPgjElKp0Ng1ahSwfbsRxkRERERUFhV1NgWp9N/ZqrgEYIGWLFkCPz8/WFtbo2XLlrhw4UK+bSMiItCnTx/4+flBIpFg8eLFGm3mzZuH5s2bw97eHm5ubujVqxdu375dgmdAREREOlHmVYCi+Lyohg1TFK0fO6Z4HDasuCMjIiIiKl9ycoAHDxTfV69u3LHoiEVVREREVHoyMuCNR1gx/zkARWGVVCpQuzbw8iXQrx8waZIipyIiIiKiAhR1pirg36KqO3cMN55yZuvWrZg4cSJmzpyJy5cvo1GjRggODsaTJ0+0ts/IyEC1atUwf/58eHh4aG1z4sQJjB49GufOncOhQ4eQnZ2NLl26ID09vSRPhYiIiAqjzKssLQFz8+L15e0NBAVxhioiIiIibR4+VCxfY2UFeHoaezQ6MYmiKn0++bdr1y40a9YMTk5OsLW1RUBAADZt2qTWRgiBGTNmwNPTExUqVEDnzp0RyU9fEhERGVdODiCTAQAGDQQAxRKA164JXLsGTJ6saPb110DnzkBCgnGGSURERFQmFKeoqmZNxSOLqvL1zTffYPjw4QgNDUXdunWxfPly2NjYYO3atVrbN2/eHF999RUGDBgAKysrrW0OHDiAoUOHol69emjUqBHWr1+PmJgYXLp0qSRPhYiIiApTnLyKiIiIiHR3757isWpVxWzqZUAxS+6LT/nJv+XLl6Nly5ZYvHgxgoODcfv2bbi5uWm0r1SpEj777DPUrl0blpaW+O233xAaGgo3NzcEBwcDABYuXIjvv/8eGzZsQNWqVTF9+nQEBwfjxo0bsLa2Lu1TJCIiIkBtKvUXuf9epKpZU/EhwAULgBYtgKFDgRMngKZNgR07gFatjDBWIiIiIlPHoqoSI5PJcOnSJUydOlW1TSqVonPnzjh79qzBjpOSkgJAca0rP1lZWcjKylL9nJqaCgCQy+WQy+UGG4uyTyGEwfstrxgv3TFW+mG8dMdY6YfxKkBaGqQAhI0NxD//xppqrExxTEREREQ6i4pSPFarZtxx6MHoRVV5P/kHAMuXL8f+/fuxdu1aTJkyRaN9UFCQ2s/jxo3Dhg0bcPr0aQQHB0MIgcWLF+Pzzz9Hz549AQAbN26Eu7s79uzZgwEDBmj0yQtUpovx0h1jpR/GS3eMlX4YrwIoL1BJJEjJtAAA2NjIAcihDFfv3kDt2kDfvhLcuiVB+/YC334rMHIkIJGU3lD5+yMiIiKTZ4jl/zirt1ZJSUnIzc2Fu7u72nZ3d3fcunXLIMeQy+UYP3482rRpg/r16+fbbt68eZg9e7bG9sTERGRmZhpkLHnHlJKSAiEEpGXk06LGxHjpjrHSD+OlO8ZKP4xX/iwePYIzgFwrKyQ9eWLSsXrx4oWxh0BERERUdMqZqqpXN+449GDUoqrifvJPCIGjR4/i9u3bWLBgAQDg/v37iI+PR+fOnVXtHB0d0bJlS5w9e1ZrURUvUJkuxkt3jJV+GC/dMVb6YbzyZ/bwIVwBiAoVEPPwOQAX2NjI8eTJE7VYOTsD+/ZJMH68I/bvt0ZYmAQnT77E/PkpqFChdMbKC1RERERk8gwxU1VMDPDyJUotySKV0aNH4/r16zh9+nSB7aZOnYqJEyeqfk5NTYWPjw9cXV3h4OBg0DHJ5XJIJBK4urry/zI6YLx0x1jph/HSHWOlH8arAP8s3Wtmbw83NzeTjhVXYyEiIqIyTVlUxZmqdFPUT/6lpKSgcuXKyMrKgpmZGZYuXYo33ngDABAfH6/q49U+lftexQtUpovx0h1jpR/GS3eMlX4YrwI8eQIAkNjawsJCscSJgwPg5uamESs3N+DXX4Gvv5Zj6lQJtm2rgMhIa2zfLlC1askPlReoiIiIyOQVp6jKxQVwcgKSk4G7d4EGDQw5sjLPxcUFZmZmSEhIUNuekJAADw+PYvcfFhaG3377DSdPnoS3t3eBba2srGD1z43evKRSaYn8f0MikZRY3+UR46U7xko/jJfuGCv9MF75+OfD9RIbG0j+iY2pxsrUxkNERESkFxZVlQ57e3uEh4cjLS0NR44cwcSJE1GtWjWNpQF1xQtUpo3x0h1jpR/GS3eMlX4Yr3zkuUCVkaGIjY2NgFRqlm+sJk8GmjUD+vcH/v5bgubNJdi8GahfX7Fajb8/UMh9qCLh746IiIhMXnq64rEoRVUSiSKR+usvRVLFoio1lpaWaNq0KY4cOYJevXoBUHx44siRIwgLCytyv0IIjBkzBrt378bx48dRtTQ+LUBERESFK06xOhERERHpLipK8ciiKt0U9ZN/UqkUNWrUAAAEBATg5s2bmDdvHoKCglTPS0hIgKenp1qfAQEBhj8JIiIi0k2eC1TK1fXs7EShT+vYEbh8GejbF7hwAXjzTcV9QCEAqRRYuRIYNqwEx01ERERkiop7869mTUVR1Z07hhtTOTJx4kSEhISgWbNmaNGiBRYvXoz09HSEhoYCAIYMGYLKlStj3rx5AACZTIYbN26ovn/06BHCw8NhZ2enuoY1evRobN68Gb/++ivs7e1VM6o7OjqiApdgJCIiMp7iFKsTERERkW6eP1fMmg6UqaIqo07DkPeTf0rKT/61bt1a537kcjmysrIAAFWrVoWHh4dan6mpqTh//rxefRIREZGBaSmqsrWV6/RUHx/g5Elg0CDFz+KfWiy5HPjoIyA21sBjJSIiIjJ1hiiqAlhUlY/+/ftj0aJFmDFjBgICAhAeHo4DBw7A3d0dABATE4PHjx+r2sfFxaFx48Zo3LgxHj9+jEWLFqFx48b48MMPVW2WLVuGlJQUBAUFwdPTU/W1devWUj8/IiIiykOZV9naGnccREREROWZcuk/D48yVcxu9LVtJk6ciFWrVmHDhg24efMmPv74Y41P/k2dOlXVft68eTh06BDu3buHmzdv4uuvv8amTZswePBgAIoll8aPH4+5c+di7969uHbtGoYMGQIvLy/VlO1ERERkBHlu/KWlKb7VZaYqJSsr7TNS5eYCd+8aYHxEREREZUlxi6r8/RWPkZGGGU85FBYWhgcPHiArKwvnz59Hy5YtVfuOHz+O9evXq3728/ODEELj6/jx46o22vYLITB06NDSOykiIiLSxOX/im3JkiXw8/ODtbU1WrZsiQsXLuTbNiIiAn369IGfnx8kEgkWL16s0WbevHlo3rw57O3t4ebmhl69euH27dtqbYKCgiCRSNS+Ro4caehTIyIiIkNRFlWVoVmqACMv/wcoPvmXmJiIGTNmID4+HgEBARqf/JNK/639Sk9Px6hRoxAbG4sKFSqgdu3a+Omnn9C/f39Vm8mTJyM9PR0jRoxAcnIy2rZtiwMHDsDa2rrUz4+IiIj+oXWmKt2LqgDFvT+pVDFDlZKZGfDPiipERERErw/OVEVERERkGCyqKpatW7di4sSJWL58OVq2bIn/Z+/eo6Oq7/3/v2YCySQkECA3QTAgKCI35RKhPcULp6HaY6mUg2gPyuGg7ddUJd/D0fBTEPGsYEWKVWrUisd+KweOpy21xS9+aSpqS5AKclhcK1qkXHIBmju57vn9sTPDDJkkc9mTmWSej7WyZmZnz85n3riWn/XZr3l/1q9fr9zcXB07dkwZGRntzq+vr9fIkSM1b948LV261Oc1P/jgAz300EOaOnWqWlpatHz5cn3961/X4cOH1c+jo9iSJUv09NNPu18n8W8IAED0IlQVvLy8POXl5fn8nec3+iTpmWee0TPPPNPp9Ww2m55++mmviRQAAIgwC0JVV14pvfqqueVfa6sZqHrlFfM4AABATLGqU1V5uVRVJQ0YYM24AAAAehpCVSFZt26dlixZ4t6BpqioSNu2bdPGjRv1+OOPtzt/6tSpmjp1qiT5/L0kbd++3ev1f/zHfygjI0N79+7V1772NffxpKQkZWVlWfVRAABAGNlcoaqrr47sQAIUFaEqAAAQA0Lc/s9l8WIpN9fc8m/UKAJVAAAgBjU3mz9S8Df/+veXsrKk0lJzC8ApU6wbHwAAQE9CqCpoTU1N2rt3rwoKCtzH7Ha7Zs2apZKSEsv+TlVVlSRp0KBBXsffeust/fznP1dWVpb+4R/+QU8++WSH3aoaGxvV2Njofl1dXS1JMgxDhmdbfIsYhiGn0xmWa/c21Cow1Mt/1Cow1Mt/1Cowrno5v/hCNklGdrb3ljQR4u+/H6EqAADQPVwLVP36uTtVJScHN2m68krCVAAAIIZdvHjpeSg3/0aPNkNVf/4zoSoAABC7CFUF7dy5c2ptbVVmZqbX8czMTB09etSSv2EYhh599FF95Stf0bhx49zH77nnHl111VUaMmSIDhw4oMcee0zHjh3TL3/5S5/XKSws1KpVq9odr6ioUENDgyVjvXzcVVVVcjqdstvtll+/N6FWgaFe/qNWgaFe/qNWgXHVK/34cdkl/W3gQDWXl0d6WKpx3azsAqEqAADQPTw7VZ0znwa6/R8AAAB0aV5ls0kJCcFf55prpI8+MkNVAAAAsYpQVVR76KGHdPDgQf3hD3/wOv7AAw+4n48fP15XXHGFbrvtNn3++ee62se2QgUFBcrPz3e/rq6u1rBhw5Senq7+/ftbPm7DMGSz2ZSens4N9y5Qq8BQL/9Rq8BQL/9Rq8AYhiFbS4viTp+WJA2cPFnKyIjwqCSHw+HXeYSqAABA9/BYoHKFvwlVAQAABMHzxp/NFvx1rrnGfPzss9DHBAAA0FMRqgpaWlqa4uLiVFZW5nW8rKxMWVlZIV8/Ly9Pv/3tb/Xhhx/qyi7a1ufk5EiSjh8/7jNUlZCQoAQfX0iw2+1huyFus9nCev3ehFoFhnr5j1oFhnr5j1oFps+ZM7K1tkoOh+xDh4a2nmURf//t+BcGAADdg1AVAACANTy2VQ7J6NHmI52qAABALCNUFbT4+HhNnjxZxcXF7mOGYai4uFjTp08P+rpOp1N5eXn61a9+pd///vcaMWJEl+/Zv3+/JOmKK64I+u8CAIDwiPvyS/PJyJFREagKBJ2qAABA9/Dc/q/WfJqcTKgKAAAgYFbd+HN1qvrznyWns8ctagEAAFiCUFVI8vPzdd9992nKlCmaNm2a1q9fr7q6Oi1atEiStHDhQg0dOlSFhYWSpKamJh0+fNj9/PTp09q/f7+Sk5M1atQoSeaWf5s2bdKvf/1rpaSkqLS0VJI0YMAAJSYm6vPPP9emTZt0++23a/DgwTpw4ICWLl2qr33ta5owYUIEqgAAADrjFarqYQhVAQCA7uGjUxWhKgAAgCBYdePv6qvNIFV1tVRRIWVkhD42AACAnoZQVUjmz5+viooKrVixQqWlpZo0aZK2b9+uzMxMSdLJkye9ttc5c+aMbrjhBvfrtWvXau3atZo5c6Z27twpSXr55ZclSTfffLPX33rjjTd0//33Kz4+Xr/73e/cAa5hw4Zp7ty5euKJJ8L7YQEAQFDcoSofW/RGO0JVAACge/gIVSUlGZEbDwAAQE9l1Y0/h0MaPlz68kuzWxWhKgAAEIvq6sxHQlVBy8vLU15ens/fuYJSLtnZ2XI6O/+iZVe/HzZsmD744IOAxggAACKnTw/uVGXv+hQAAAALsP0fAACANazspuDaAvCdd6RTp0K/HgAAQE/jmlv16xfZcQAAAPRSPXn7P0JVAACge7QtUDX17aemJvMQoSoAAIAgWBmqamkxH597TrrqKun110O/JgAAQE/C9n8AAADh43QSqgIAAOhS2wJVrS3FfahfP0JVAAAAAbPqxt+pU5LndiyGIT34IB2rAABAbCFUBQAAED4XLsheU2M+HzEismMJAqEqAADQPdoWqGqcyZIkh8OpPn0iOSAAAIAeqq7OfAz1xt9nn0nOy0Lura3S8eOhXRcAAKCnaG6+1LmTUBUAAID1vvhCkuQcMkRKTIzwYAJHqAoAAHQPV6jK6CdJSk6O5GAAAAB6MKu6KYweLdkvWxqKi5NGjQrtugAAAD2Fa14luedWp05Jf/xjPM07AQAArNAWquqJW/9JhKoAAEB3cW3/Z5gLVCkpnZ0MAACADlkVqrrySunVV80glWQ+vvKKeRwAACAWuOZVdrsUH6/XX5dGjLDpO98ZpBEjbHr99cgODwAAoMf7y1/MxyC2/jt1Snr/fUU07E6oCgAAhJ/TealTVYvZ2pNQFQAAQJCsClVJ0uLF0okT5grViRPmawAAgFjhMa86ddqmBx6QDMMmyXx88MHI3sQDAADo6Wyffy5Jcl59dUDvW7NGGj5cuvVW6aqrFLGwO6EqAAAQfg0NZrBKUk2zQxLb/wEAAATNylCVZHamuvlmOlQBAIDY4zGv+uwzyTC8f93aKh0/3v3DAgAA6DWC6FR16pS0fLn71qIMQxELuxOqAgAA4edaoJJU2xQviVAVAABA0KwOVQEAAMQqj3nV6NHmLoCe4uKkUaO6f1gAAAC9xhdfmI8jR/r9ls8+uxSocolU2J1QFQAACD/XAlV8vGrq4yQRqgIAAAgaoSoAAABreMyrrrxSeu65S7+y2Zx65RWaeQIAAAStqUn661/N5wGEqkaPbn8sUmF3QlUAACD8PBaoamvNpykpkRtOT7VhwwZlZ2fL4XAoJydHe/bs6fDcQ4cOae7cucrOzpbNZtP69evbnVNYWKipU6cqJSVFGRkZmjNnjo4dO+Z1zs033yybzeb1873vfc/qjwYAAAJBqAoAAMAal82rvvnNS7/KzZUWL47AmAAAAHqLL7+UzTBkxMdLzc1+vy0+3vt1XJwiFnYnVAUAAMLPY4GqpsZ8SqgqMFu2bFF+fr5Wrlypffv2aeLEicrNzVV5ebnP8+vr6zVy5EitWbNGWVlZPs/54IMP9NBDD2n37t3asWOHmpub9fWvf111dXVe5y1ZskRnz551//zwhz+0/PMBAIAAEKoCAACwhmsNpF8/SVJ19aVf/eUvERgPAABAb/KTn0iS7E1Nso0cKb3+ul9v273bfLzmGun996UTJyIXdu8TmT8LAABiio9QFdv/BWbdunVasmSJFi1aJEkqKirStm3btHHjRj3++OPtzp86daqmTp0qST5/L0nbt2/3ev0f//EfysjI0N69e/W1r33NfTwpKanDYBYAAIgA19yq7eYfAAAAgnRZWN0zVPXZZ1JDg+RwRGBcAAAAPd2pU9ILL7hf2gxDevBBsx1oFy2nSkrMx699Tbr55jCO0Q+EqgAAQPj53P7PGbnx9DBNTU3au3evCgoK3MfsdrtmzZqlEtfM0gJVVVWSpEGDBnkdf+utt/Tzn/9cWVlZ+od/+Ac9+eSTSuqgM0ZjY6MaGxvdr6vbViMNw5BhGJaN1cUwDDmdzrBcu7ehVoGhXv6jVoGhXv6jVh2z1dfLJslwOKS2+kRrvaJtPAAAAF46CVUZhk3HjkkTJ0ZgXAAAAD3dZ59JzsvuBba2SseP+x2qmj49TGMLAKEqAAAQfmz/F5Jz586ptbVVmZmZXsczMzN19OhRS/6GYRh69NFH9ZWvfEXjxo1zH7/nnnt01VVXaciQITpw4IAee+wxHTt2TL/85S99XqewsFCrVq1qd7yiokINDQ2WjPXycVdVVcnpdMpuZ2frzlCrwFAv/1GrwFAv/1GrjqXX1ChO0oWGBrW0bQUcrfWqcU3+AAAAolEnoSpJOnSIUBUAAEBQRo+WbDbvYFVcnDRqVKdva2mR/vQn8/lNN4VxfH4iVAUAAMLPR6cqdquJLg899JAOHjyoP/zhD17HH3jgAffz8ePH64orrtBtt92mzz//XFdffXW76xQUFCg/P9/9urq6WsOGDVN6err69+9v+bgNw5DNZlN6enpU3UCORtQqMNTLf9QqMNTLf9SqY7a2rpCDrrxSysiQFL31crBfDgAAiGZdhKoOHuzm8QAAAPQWV14pTZsmffyxJMkZFyfbK6902aXqwAFzipaaKo0Z0w3j7AKhKgAAEH6enaoqzad0qvJfWlqa4uLiVFZW5nW8rKxMWVlZIV8/Ly9Pv/3tb/Xhhx/qyi4mszk5OZKk48eP+wxVJSQkKCEhod1xu90ethu8NpstrNfvTahVYKiX/6hVYKiX/6hVB9rmVvbkZMmjNtFYr2gaCwAAQDsdhKrsdqcMw6ZDhyI0LgAAgN4gPV2SVPv97yvp8cdlGz68y7e4tv7LyfFa9oqYKBgCAADo9Xxs/5ecHLnh9DTx8fGaPHmyiouL3ccMw1BxcbGmh7ChtNPpVF5enn71q1/p97//vUaMGNHle/bv3y9JuuKKK4L+uwAAIAROZ7ubfwAAAAjSZfOqqirz5dixLZJEqAoAACAUbYn15htu6LJDlYsrVBXC7S9L0akKAACEn2uBql8/9/Z/dKoKTH5+vu677z5NmTJF06ZN0/r161VXV6dFixZJkhYuXKihQ4eqsLBQktTU1KTDhw+7n58+fVr79+9XcnKyRrXtV/3QQw9p06ZN+vWvf62UlBSVlpZKkgYMGKDExER9/vnn2rRpk26//XYNHjxYBw4c0NKlS/W1r31NEyZMiEAVAACAGhouPSdUBQAAEJoOOlVNmdKkgwf76osvzFOYdgEAAAShbXLlDKDTAqEqAAAQe3x0qiJUFZj58+eroqJCK1asUGlpqSZNmqTt27crMzNTknTy5Emv7XXOnDmjG264wf167dq1Wrt2rWbOnKmdO3dKkl5++WVJ0s033+z1t9544w3df//9io+P1+9+9zt3gGvYsGGaO3eunnjiifB+WAAA0LG6ukvPExMjNw4AAIDeoINQ1VVXtSo93amKCpuOHJEmT47Q+AAAAHoyV6jKz5uC5eXSF19INpu5/V80IFQFAADCj+3/LJGXl6e8vDyfv3MFpVyys7PldDo7vV5Xvx82bJg++OCDgMYIAADCzDWvSkiQ4uIiOxYAAICeroNQVUqKU9dfL+3caW4BSKgKAAAgCG17Kxt+hqpcXarGjpUGDAjXoAJj7/oUAACAELUtULU6+uniRfMQnaoAAACCcNmNPwAAAISgi1CVZIaqAAAAECCnM+BOVdG29Z9EqAoAAHSHtgWq2rhLsXJCVQAAAEEgVAUAAGCdDkJVycmGxo41O3wfPBiJgQEAAPRwjY1Sc7Mk/0NVu3ebj9EUqmL7PwAAEH6Xhar69JHi4yM5IAAAgB6KUBUAAIB16urMx379JHl3qho61HxOpyoAAIAguCZWkpxtc63OtLRIf/qT+fymm8I1qMDRqQoAAIRf282/Glt/SWaXKpstkgMCAADooQhVAQAAWMeP7f++/FKqqYnA2AAAAHoyz63/7F1Hkw4cMKdmqanSmDFhHlsACFUBAIDwc4WqZLb3TE6O5GAAAAB6MEJVAAAA1ulk+79Bg6QrrjBfHz4cgbEBAAD0ZK6JVf/+fp1eUmI+5uT4lcHqNlE0FAAA0Gu5tv9zmu09/dw6GQAAAJfrIFR16pT0xz/G69SpCIwJAACgp/KYWzU2Sk1N5sv+/Z2S5O5WxRaAAAAAAQoyVDV9epjGEyRCVQAAIPxcnaoMQlUAAAAh8RGqev11acQIm77znUEaMcKm11+P0NgAAAB6Go+5VVXVpcP9+hGqAgAACIlrcjVggF+nE6oCAACxyxWqajVv/rH9HwAAQJAuC1WdOiU98IBkGDZJ5uODD4qOVQAAAF0xDKmhwXyelOSx9Z9TcXHm83HjzMeDB7t/eAAAAD2aa3LlR6eF8nLpiy8km83c/i+aEKoCAADh59r+rzVREp2qAAAAguYKVfUzO4B+9pl5P9BTa6t0/Hg3jwsAAKCnuXjx0nOPUJXnDjV0qgIAAAhSANv/ubpUjR3rd2OrbkOoCgAAhJ+rU1WzQxKdqgAAAIJ2Waeq0aPNb/F5iouTRo3q5nH1Qhs2bFB2drYcDodycnK0Z8+eDs89dOiQ5s6dq+zsbNlsNq1fvz7kawIAgDBzzaskKTHR532/sWPNx9OnpcrKbhsZAABAzxdEqCratv6ToiRUFciC0muvvaa/+7u/08CBAzVw4EDNmjWr3fn333+/bDab18/s2bPD/TEAAEBH6uokSbXNCZLoVAUAABC0y0JVV14p3XrrpV/b7U698op5HMHbsmWL8vPztXLlSu3bt08TJ05Ubm6uysvLfZ5fX1+vkSNHas2aNcrKyrLkmgAAIMxc8yqHQ7Lbfd73GzDg0rzq8OHuHR4AAECPRqjKGoEuKO3cuVMLFizQ+++/r5KSEg0bNkxf//rXdfr0aa/zZs+erbNnz7p//vM//7M7Pg4AALhcc7P5I6mmsa8kQlUAAABBuyxUJXkHqF5/3anFi7t5TL3QunXrtGTJEi1atEhjx45VUVGRkpKStHHjRp/nT506Vc8995zuvvtuJSQkWHJNAAAQZpfNq1z3/S5ftxo3znw8eLCbxgUAANAb+BmqammR/vQn83k0hqr6RHoAngtKklRUVKRt27Zp48aNevzxx9ud/9Zbb3m9/ulPf6pf/OIXKi4u1sKFC93HExISOvxmIAAA6EYXL7qf1jSYoSq2/wMAAAiSj1BVVdWlX1dUdPN4eqGmpibt3btXBQUF7mN2u12zZs1Sieurk910zcbGRjU2NrpfV7ctSBqGIcMwghpLRwzDkNPptPy6vRX18h+1Cgz18h+1Cgz18qG2VnZJzqQkOQ2jbU5lV//+Tq9ajR1r0/btNh086JRhOCM5Yv79AABAz9G2huHsIlR14IB5KzE1Vbr22m4YV4AiGqqyYpGqvr5ezc3NGjRokNfxnTt3KiMjQwMHDtStt96qZ555RoMHD/Z5DRaoohf18h+1Cgz18h+1Cgz18sG1QGWzqabebJKZnGxEda2icUwAAACSugxVHT5s6+YB9T7nzp1Ta2urMjMzvY5nZmbq6NGj3XrNwsJCrVq1qt3xiooKNTQ0BDWWjhiGoaqqKjmdTtntEW9uH/Wol/+oVWCol/+oVWCoV3t9T5/WYEmtCQk6V16uM2f6SUpR374NqqysdNdq2LBESQO0f3+Tysv/FtEx19TURPTvAwAA+M21YNVFqMoVDcrJkaJxmhrRUJUVi1SPPfaYhgwZolmzZrmPzZ49W3fddZdGjBihzz//XMuXL9c3vvENlZSUKC4urt01WKCKXtTLf9QqMNTLf9QqMNSrvbi//lXpkpyJiTp/vkmSQ05njcrL66K2VixQAQCAqFVXZz52EKo6cqSbx4OwKigoUH5+vvt1dXW1hg0bpvT0dPXvYlEyUIZhyGazKT09Perm59GIevmPWgWGevmPWgWGevkQHy9JiuvfXxkZGTIMM5yekeFQamqqu1aubWj+/Od4ZWRkRGq0kiSHwxHRvw8AAOA3P7f/c4WqonHrPykKtv8LxZo1a7R582bt3LnTayJ59913u5+PHz9eEyZM0NVXX62dO3fqtttua3cdFqiiF/XyH7UKDPXyH7UKDPXyobxckmTr10/NzQmSpCuuSFFGRr+orRULVAAAIGr56FRVWXnp14cPS06nZKNhVdDS0tIUFxensrIyr+NlZWXKysrq1msmJCQoISGh3XG73R6WObTNZgvbtXsj6uU/ahUY6uU/ahUY6nWZti/V25KSZLPb3ff9UlNtXrW6/nrzeFmZTX/7m00dbIrSLfi3AwAAPQahqtCFski1du1arVmzRr/73e80YcKETs8dOXKk0tLSdPz4cZ+hKhaoohv18h+1Cgz18h+1Cgz1uozHAlVNjXl3b8AAu+z26K1VtI0HAADArYvt/2pqbDp9Wrryym4eVy8SHx+vyZMnq7i4WHPmzJFkfnmiuLhYeXl5UXNNAAAQosvmVZfu+zm9TktOlrKzpRMnpEOHpK99rfuGCAAA0GP5EaoqL5e++ML8cmBOTjeNK0ARvWPouaDk4lpQmt5JDO2HP/yhVq9ere3bt2vKlCld/p1Tp07p/PnzuuKKKywZNwAACIDHApVrV73k5MgNBwAAoEe77Oaf03kpVJWSYkgyu1UhNPn5+Xrttdf05ptv6siRI/r+97+vuro6LVq0SJK0cOFCFRQUuM9vamrS/v37tX//fjU1Nen06dPav3+/jh8/7vc1AQBAN+sgVJWS0v5UV7eqgwe7YVwAAAC9gR+hKleXqrFjpQEDumFMQYj49n/5+fm67777NGXKFE2bNk3r169vt0g1dOhQFRYWSpKeffZZrVixQps2bVJ2drZKS0slScnJyUpOTlZtba1WrVqluXPnKisrS59//rn+7d/+TaNGjVJubm7EPicAADHLY4Gq9qz51NfiFAAAAPxw2c2/ixellhbz0I03NuuDDxJ06JD09a9HaHy9xPz581VRUaEVK1aotLRUkyZN0vbt25WZmSlJOnnypFd30zNnzuiGG25wv167dq3Wrl2rmTNnaufOnX5dEwAAdLMOO1W1P3XcOGnbNrNTFQAAAPwQQKgqWrf+k6IgVBXoItXLL7+spqYmfec73/G6zsqVK/XUU08pLi5OBw4c0JtvvqnKykoNGTJEX//617V69WqfW/wBAIAw89GpilAVAABAkC67+efqUmW3O3XDDWaoik5V1sjLy+twaz5XUMolOztbTqfT57n+XhMAAHSzAEJVrk5VhKoAAAD80Nho/kjm5KqpyedphKr8FMgi1YkTJzq9VmJiot577z2LRgYAAELWtkDlTExSba15iO3/AAAAgnTZzb/KSvNl//7StdeaLasIVQEAAPghiFDVwYPm9ss2WzeMDwAAoKdyTawks9PC+fPtTmlulv70J/N5NIeq7F2fAgAAEIK2Bap6xyC5vrxPpyoAAIAgddCpasAA6ZprLoWq/GiaBAAAENsCCFVdd50ZpDp/Xiov76bxAQAA9FSuiVVyshQX5/OUAwekixel1FTp2mu7b2iBIlQFAADCq22BqqbvIEnmAlTbWhUAAAAC0dwstZjBqctDVamp0siRLbLbnaqslEpLIzJCAACAniOAUFVionT11eZztgAEAADoQmcTqza7d5uPOTmSPYqTS1E8NAAA0Cu0LVDV9kmVZIbSaZEOAAAQBNeNP8lnpyqH49LNPrYABAAA6IJHqKqpSWpoMF92dO/PtQUgoSoAAIAu+BGqKikxH6N56z+JUBUAAAg3V6equFRJbP0HAAAQNNeNv7g4KT5e0qVQlWuN6rrrzEdCVQAAAF3wCFXV1Fw63NHalStUdfBgeIcFAADQ4xGqAgAA8JMrVGUfIMnsVAUAAIAgeG5R09b6s7LSPDTAnGpp7FjzkVAVAABAF1xzq3793EH1pCSpb1/fp48bZz7SqQoAAKALXYSqysulL74wl7dycrpxXEEgVAUAAMLLtf2fzfyaH52qAAAAguQZqmrjuf2fJF13nVMSoSoAAIAu1dWZj0lJ/jRT8Nr+z+kM79AAAAB6tC4mV64uVWPHXlrTilaEqgAAQHi5OlWJUBUAAEBIOglVpaaaj3SqAgAA8JPH3MqfUNW115q7MFdWSmfOhH10PcqGDRuUnZ0th8OhnJwc7dmzp8NzDx06pLlz5yo7O1s2m03r168P6poNDQ166KGHNHjwYCUnJ2vu3LkqKyuz8mMBAIBgXf4twMv0lK3/JEJVAAAg3FyhKqe57x/b/wEAAASp005VZruEMWPM1unnzkkVFd09QAAAgB4kwFBVQoI0erT5nC0AL9myZYvy8/O1cuVK7du3TxMnTlRubq7Ky8t9nl9fX6+RI0dqzZo1ysrKCvqaS5cu1W9+8xu9/fbb+uCDD3TmzBndddddYfmMAAAgQH52qiJUBQAA4Nr+zzBv/tGpCgAAIEidhKpca1RJSVJ2tvmcblUAAACdCDBUJXlvAQjTunXrtGTJEi1atEhjx45VUVGRkpKStHHjRp/nT506Vc8995zuvvtuJSQkBHXNqqoqvf7661q3bp1uvfVWTZ48WW+88YZ27dql3bt3h+2zAgAAP3UyuWpulv70J/N5TwhV9Yn0AAAAQC/n6lTVat78o1MVAABAkHyEqiorzUfPbupjx0p/+YsZqpo5s/uGBwAA0KMEGar6xS8IVbk0NTVp7969KigocB+z2+2aNWuWSlwtKMJwzb1796q5uVmzZs1ynzNmzBgNHz5cJSUluummm9pdt7GxUY2Nje7X1W3/6IZhyDCMoMbaGcMw5HQ6w3Lt3oZaBYZ6+Y9aBYZ6+Y9adc1WVSWbJCMlpV299u+XLl60KzXVqdGjnYpUGf399yNUBQAAwsvVqao1URKdqgAAAIJWV2c++tz+79JpY8dK27bRqQoAAKBTIXSqOngwfMPqSc6dO6fW1lZlZmZ6Hc/MzNTRo0fDds3S0lLFx8crNTW13TmlpaU+r1tYWKhVq1a1O15RUaGGhoagxtoZwzBUVVUlp9Mpu52NgzpDrQJDvfxHrQJDvfxHrbqWWlEhh6QaSXXl5V712rEjSVJ/TZrUpHPn/haxMdbU1Ph1HqEqAAAQXq5OVc1mO29CVQAAAEHqZPs/z/tJY8eaj4SqAAAAOuB0BhWqGjfOfDx82LyEzRa+IcJaBQUFys/Pd7+urq7WsGHDlJ6erv5d/cMHwTAM2Ww2paenc8O9C9QqMNTLf9QqMNTLf9Sqa7amJklSytCh6peR4VWvQ4fMCdTMmX2VkZERsTE6HA6/ziNUBQAAwssVqmoyQ1Vs/wcAABCkTkJVl3eqkghVAQAAdKipSe69ZgIIVY0eLfXtK9XUSH/9qzR8eHiHGe3S0tIUFxensrIyr+NlZWXKysoK2zWzsrLU1NSkyspKr25Vnf3dhIQEJSQktDtut9vDdkPcZrOF9fq9CbUKDPXyH7UKDPXyH7XqQtvkyp6aKtntXvXavds8ZcYMuyJZPn//7fgXBgAA4dW2TU1tY7wkOlUBAAAE7bJQldPpXqPyClVdd535WFoqXbjQjeMDAADoKVzzKskrVOU5p/Klb1/pmmvM52wBKMXHx2vy5MkqLi52HzMMQ8XFxZo+fXrYrjl58mT17dvX65xjx47p5MmTQf9dAABgIV/fApRUXi598YXZ7TMnJwLjCgKdqgAAQHi5OlU19JVEqAoAACBol4Wq6uqk1lbz0IAB7iy7UlKkYcPM7glHjkhf+UoExgoAABDNXPOqPn2kvn3d9/382QVu3Djp0CHz5/bbwzfEniI/P1/33XefpkyZomnTpmn9+vWqq6vTokWLJEkLFy7U0KFDVVhYKElqamrS4baWqk1NTTp9+rT279+v5ORkjRo1yq9rDhgwQIsXL1Z+fr4GDRqk/v376wc/+IGmT5+um266KQJVAAAAXjpoA1pSYj6OHdt1mD1aEKoCAADh43S6F6lqG+Iksf0fAABA0C4LVVVWmi/j4sxDrlCVZC5O/fWv5haAhKoAAAAu45pX9esnqcP7fj5df735eOhQGMbVA82fP18VFRVasWKFSktLNWnSJG3fvl2ZmZmSpJMnT3ptr3PmzBndcMMN7tdr167V2rVrNXPmTO3cudOva0rSj370I9ntds2dO1eNjY3Kzc3VT37yk+750AAAoHNdhKp6UmNJQlUAACB8GhrcT2vqzVAVnaoAAACCdFmoyrOTus3mferYsdJ773GzDwAAwKfL5lXBhKrY/u+SvLw85eXl+fydKyjlkp2dLafTGdI1JcnhcGjDhg3asGFDQGMFAABh1tR06f5gLwhV2bs+BQAAIEiuBSpJNXXmtINOVQAAAEHqIFSVmtr+VNfNvradVQAAAODJ1eIziFDVuHHm45EjkmGEYWwAAAA9WU3NpecenRaam6U//cl8TqgKAABAunTjLz5etbVm+wQ6VQEAAASpk05Vlxs71nwkVAUAAOBDCJ2qrr5aSkgwL3HiRHiGBwAA0GO5JlZJSVKfS5vnHTggXbxofjnw2msjM7RgEKoCAADh07ZA5UxMcgfTCVUBAAAEyXXzr18/SZ2Hqq67znw8ffrSeQAAAGgTQqgqLk4aM8Z8zlbLAAAAl+lgYrV7t/mYkyPZe1BSqQcNFQAA9DhtC1SNSQPV0mIeYvs/AACAIF1286+y0nzpK1SVmioNGWI+P3Ik7CMDAADoWTzmVS0tl176E6qSLm21fPCg9UMDAADo0Tr4FmBJibmjTU/a+k8iVAUAAMKpbUWq1pHmPkSoCgAAIEgBbP8nsQUgAABAhzzmVa7u6pL/HdbHjTMf6VQFAABwmS46VRGqAgAAcGlboKpJMENVSUlmi3QAAAAEoYNQVWqq79MJVQEAAHTAY17luu+XkGD++MPVqYpQFQAAwGV8hKoqKuz6y19sstnM7f96EkJVAAAgfFydquIHSaJLVag2bNig7OxsORwO5eTkaM+ePR2ee+jQIc2dO1fZ2dmy2Wxav359UNdsaGjQQw89pMGDBys5OVlz585VWVmZlR8LAAD4i05VAAAA1vARqupoTuWLK1R15IjU2mrt0AAAAHo0H6GqvXv7SjLXqgKZc0UDQlUAACB8XJ2q+g6U5H8LdbS3ZcsW5efna+XKldq3b58mTpyo3NxclZeX+zy/vr5eI0eO1Jo1a5SVlRX0NZcuXarf/OY3evvtt/XBBx/ozJkzuuuuu8LyGQEAQBcIVQEAAFjDY17lmlNdtkNNp0aMkBITpcZG6fPPrR8eAABAj9VJqKqnbf0nEaoCAADh5ApV9aFTVajWrVunJUuWaNGiRRo7dqyKioqUlJSkjRs3+jx/6tSpeu6553T33XcroYPe9V1ds6qqSq+//rrWrVunW2+9VZMnT9Ybb7yhXbt2abdr82sAANB9LgtVVVaaL7sKVX35pVRbG96hAQAA9Cg+OlUFEqqy2y/NtdgCEAAAwIOPydUnn8RL6pmhqj6RHgAAAOjFXNv/xZl3+uhUFZympibt3btXBQUF7mN2u12zZs1SSUlJ2K65d+9eNTc3a9asWe5zxowZo+HDh6ukpEQ33XRTu+s2NjaqsbHR/bq6bfJsGIYMwwhqrJ0xDENOpzMs1+5tqFVgqJf/qFVgqJf/qNVlDEP2ixfNpw6HZBiqqrJJsiklxfBZr4EDpYwMm8rLbTp82NCUKZEYNv9+AAAgCoUYqpLMLQD37pUOHpS+/W1rhwcAANBjXTa5am6W9u/vuZ2qCFUBAIDwcXWqshOqCsW5c+fU2tqqzMxMr+OZmZk6evRo2K5ZWlqq+Ph4paamtjuntLTU53ULCwu1atWqdscrKirU0NAQ1Fg7YxiGqqqq5HQ6ZbfThLUz1Cow1Mt/1Cow1Mt/1Mqbrb5erv9rV9TVyel06vz5wZL6SqpUeXmDz3qNGjVQ5eUJ2r27WsOHW///4q7U1NR0+98EAADokitU1a9fSKEqiU5VAAAAXlx7K7e1Vj9wQGposCk11alrr7VFcGDBIVQFAADCxxWqspmrUmz/1/sVFBQoPz/f/bq6ulrDhg1Tenq6+ge6OukHwzBks9mUnp7ODfcuUKvAUC//UavAUC//UavLVFS4n6YPHy7Z7aqrMxeirroqVRkZvus1caJNu3ZJp08PUEaG9f8v7orD4ej2vwkAANAlCzpVjRtnPhKqAgAA8HDZ5Gr3bvPltGnmFso9DaEqAAAQPq7t/2SmqehUFZy0tDTFxcWprKzM63hZWZmysrLCds2srCw1NTWpsrLSq1tVZ383ISFBCQkJ7Y7b7faw3RC32WxhvX5vQq0CQ738R60CQ738R608uDo+Ohyy9zGXc1xf/Bs40C673Xe9XB0UjhyxyW7v/m8D8m8HAACiUl2d+ZiUpOq27HqwnaqOHTO3tenb17rhAQAA9FiXhapKSsz1qOnTnZJipFNVZWWlfvWrX+mjjz7Sl19+qfr6eqWnp+uGG25Qbm6uZsyYYfU4AQBAT+TqVOU0Q1Wx2KnKinlTfHy8Jk+erOLiYs2ZM0eS2b2juLhYeXl5QY3Ln2tOnjxZffv2VXFxsebOnStJOnbsmE6ePKnpPXHjawAAejKPbgqSZBiX1qjauqn7NHas+Xj4cBjHFkVYswIAAH6xoFPV8OHmWldtrfTZZ5fmXT0BcyYAABA2HXSquummCI0nRAF9XfDMmTP6l3/5F11xxRV65plndPHiRU2aNEm33XabrrzySr3//vv6+7//e40dO1ZbtmwJ15gBAEBP4epUZZg3/2KpU5XV86b8/Hy99tprevPNN3XkyBF9//vfV11dnRYtWiRJWrhwoQoKCtznNzU1af/+/dq/f7+ampp0+vRp7d+/X8ePH/f7mgMGDNDixYuVn5+v999/X3v37tWiRYs0ffp03dRTZ78AAPRUl4Wqamokp9M85E+o6osvpIsXwzi+CGPNCgAABMSCUJXNdqlbVU/ZApA5EwAACDuPyVVZmfSXv9gkOXXllREdVdAC6lR1ww036L777tPevXs1toPI/cWLF7V161atX79ef/3rX/Wv//qvlgwUAAD0QK5OVa2xF6qyet40f/58VVRUaMWKFSotLdWkSZO0fft2ZWZmSpJOnjzptb3OmTNndMMNN7hfr127VmvXrtXMmTO1c+dOv64pST/60Y9kt9s1d+5cNTY2Kjc3Vz/5yU9CKQ0AAAjGZaEq19Z/fftKDselgNXlMjKkQYOkCxfMrWkmTQr/UCOBNSsAABAQC0JVkhmq+vhjM1Q1b551wwsX5kwAACDsPCZXq1e7Dto0caL06qvS4sWRGlhwAgpVHT58WIMHD+70nMTERC1YsEALFizQ+fPnQxocAADo4VyhqhaHpNja/i8c86a8vLwOt/tzBaVcsrOz5ezo7qqf15Qkh8OhDRs2aMOGDV1eCwAAhFEHoarUVLNLQkf/27fZzG5Vf/iDuQVgbw1VsWYFAAAC4iNU1Vn3z464OlUdPGjNsMKNORMAAAi7tsnVqbqB8vyOvmHY9OCDUm6uelTXqoC2//OcaH344YdqaWlpd05LS4s+/PDDducDAIAY5Nr+r9kMVcVSpyrmTQAAwFIdhKr8ufnnakJw+HAYxhUlmHsBAICAeMytXPOqYDtVST1n+z/mTAAAIKyam93zrM8qUtt9CbC1VTp+PALjCkFAoSpPt9xyiy5cuNDueFVVlW655ZaQBgUAAHoJV6eqpgRJsRWq8sS8CQAAhMyCUFVPudkXKuZeAACgSxZt/zdunPn42WdSY6M1Q+suzJkAAIDlamrcT0dPSGz367g4adSo7hxQ6IIOVTmdTtlstnbHz58/r379+oU0KAAA0Eu4OlU19ZUUW9v/eWLeBAAAQua68dc2d6isNF/Sqao95l4AAKBLFoWqhgwx52OtrdKxY9YNrzswZwIAAJZzTawSE3XliL4aM+bSr+LinHrllZ619Z8k9Qn0DXfddZckyWaz6f7771dCQoL7d62trTpw4IBmzJhh3QgBAEDP5epU1WCGqmKtUxXzJgAAYJkQOlW5tqU5ftzsoOAxJelVmHsBAAC/tLRITU3m8xBDVTabOdfatcvsCjphgnXDDBfmTAAAIGwum1g5HObLJ56o1pIlyRo+vH2gO9oFHKoa0LZa53Q6lZKSosTESy274uPjddNNN2nJkiXWjRAAAPRcdXWSpJp6c8oRa52qmDcBAADLdBCqSk3t+q1XXGGGr6qqpD//WRo/PjxDjDTmXgAAwC8XL7qftiYkqbbWfB5MqEoytwB0hap6AuZMAAAgbC4LVZ0/b778yleaelyHKpeAQ1VvvPGGJCk7O1v/+q//SgtQAADQMdf2fxfNHYdjrVMV8yYAAGCZEDpV2WzmFoAlJeYWgL01VGX13GvDhg167rnnVFpaqokTJ+rFF1/UtGnTOjz/7bff1pNPPqkTJ05o9OjRevbZZ3X77be7f19bW6vHH39cW7du1fnz5zVixAg9/PDD+t73vhfSOAEAQIBc8yqbTbUtDvfhYENVrq6gPSVUxXoVAAAIm8tCVRcumC8HDnRGaEChswf7xpUrVyohIUG/+93v9Morr6impkaSdObMGdW6Yv1+2rBhg7Kzs+VwOJSTk6M9e/Z0eO5rr72mv/u7v9PAgQM1cOBAzZo1q935TqdTK1as0BVXXKHExETNmjVLn332WeAfEgAAhKa+Xi2KU0NjbIaqXKycNwEAgBgVQqhKMkNVkhmq6u2smHtt2bJF+fn5Wrlypfbt26eJEycqNzdX5eXlPs/ftWuXFixYoMWLF+vTTz/VnDlzNGfOHB08eNB9Tn5+vrZv366f//znOnLkiB599FHl5eXpnXfeCf1DAwAA/3nMq6przC1o+vYNfotkV6jK43/7PQLrVQAAwHIeoarGRveGNkpNNSI3phAF3KnK5csvv9Ts2bN18uRJNTY26u///u+VkpKiZ599Vo2NjSoqKvLrOq5FqqKiIuXk5Gj9+vXKzc3VsWPHlJGR0e78nTt3asGCBZoxY4YcDoeeffZZff3rX9ehQ4c0dOhQSdIPf/hD/fjHP9abb76pESNG6Mknn1Rubq4OHz4sh8PR7poAACAMmpul5mbVKNV9KNa2/3Oxat4EAABi2GWhqspK8yWhqvasmHutW7dOS5Ys0aJFiyRJRUVF2rZtmzZu3KjHH3+83fkvvPCCZs+erWXLlkmSVq9erR07duill15y/71du3bpvvvu08033yxJeuCBB/TKK69oz549uvPOO32Oo7GxUY2Nje7X1W2Lk4ZhyDCsXZA0DENOp9Py6/ZW1Mt/1Cow1Mt/1Cow1MtDTY3skpxJSaqsNCTZ1b+/U06nU05n4LW67jpJsuvzz52qq3PKYzc9y1n578d6FQAAsJzHtwBdXarsdqdSUnpup6qgQ1WPPPKIpkyZov/5n//R4MGD3ce//e1vB7TXcqCLVG+99ZbX65/+9Kf6xS9+oeLiYi1cuFBOp1Pr16/XE088oW9961uSpJ/97GfKzMzU1q1bdffdd7e7JgtU0Yt6+Y9aBYZ6+Y9aBYZ6eairk11SrcwkVXy8U336OOUqTTTXyuoxWTVvAgAAMcz11T46VXUp1LlXU1OT9u7dq4KCAvcxu92uWbNmqaSkxOd7SkpKlJ+f73UsNzdXW7dudb+eMWOG3nnnHf3zP/+zhgwZop07d+rPf/6zfvSjH3U4lsLCQq1atard8YqKCjU0NHT5WQJhGIaqqqrkdDpltwfd3D5mUC//UavAUC//UavAUK9L+p4+rcGSjIQEffnl3yQNVnJyq8rLz0kKvFY2mzRwYIb+9je7du06r/HjW8I2dlc3KSuwXgUAACzn0anKFaoaNEjqydPPoENVH330kXbt2qX4+Hiv49nZ2Tp9+rRf1whmkepy9fX1am5u1qBBgyRJf/nLX1RaWqpZs2a5zxkwYIBycnJUUlLiM1TFAlX0ol7+o1aBoV7+o1aBoV6X2MvLlSGpWua+yf36Ob22S4nmWlm5QCVZM28CAAAxroPt/1JT/Xu7K1T15z+bDUX79rV2eNEk1LnXuXPn1NraqszMTK/jmZmZOnr0qM/3lJaW+jy/tLTU/frFF1/UAw88oCuvvFJ9+vSR3W7Xa6+9pq997WsdjqWgoMArrFVdXa1hw4YpPT1d/fv37/KzBMIwDNlsNqWnp0fd/DwaUS//UavAUC//UavAUC8Pbfv82VNSFBc3UJI0cGCce/eUYGo1frxNH34onTkzSLfdFp5hS7J0NxbWqwAAgOU6CFX1ZEGHqgzDUGtra7vjp06dUkpKil/XCGaR6nKPPfaYhgwZ4g5RuRarulrI8sQCVfSiXv6jVoGhXv6jVoGhXh5qa80HR5rUIPXvb/Pa2jeaa2X1dsFWzJsAAECM6yBU5W+nqmHDzK2Ya2ul48dd29T0TtE693rxxRe1e/duvfPOO7rqqqv04Ycf6qGHHvJa17pcQkKCEtpu/Hqy2+1hmUPbbLawXbs3ol7+o1aBoV7+o1aBoV5t2r5Mb0tKUk2NWYv+/W2y223uUwKt1fXXSx9+KB05Yg9rJwYr/+2idc4EAAB6MI9Q1fnz5tOYDVV9/etf1/r16/Xqq69KMieYtbW1WrlypW6//XbLBtiZNWvWaPPmzdq5c2dINz9ZoIpu1Mt/1Cow1Mt/1Cow1KtN2wJVbYIZqkpJ8V6ckqK3VlaPJxrmTQAAoIcLMVRls5lBqj/9ydwCsDeHqkKde6WlpSkuLk5lZWVex8vKypSVleXzPVlZWZ2ef/HiRS1fvly/+tWvdMcdd0iSJkyYoP3792vt2rUdhqoAAEAYeMyrPO77hWTcOPPx0KHQrtOdWK8CAACW64WdqoK+Y/j888/rj3/8o8aOHauGhgbdc8897pagzz77rF/XCGaRymXt2rVas2aN/t//+3+aMGGC+7jrfcFcEwAAWKhtgaomfrAkszNCrLJi3gQAAGJciKEq6dIWgIcPWziuKBTq3Cs+Pl6TJ09WcXGx+5hhGCouLtb06dN9vmf69Ole50vSjh073Oc3Nzerubm5XXg/Li5OhmEE+hEBAEAowhCquv568/HgwdCu051YrwIAAJbrhaGqoDtVXXnllfqf//kfbd68WQcOHFBtba0WL16se++9V4mJiX5dw3ORas6cOZIuLVLl5eV1+L4f/vCH+vd//3e99957mjJlitfvRowYoaysLBUXF2vSpEmSzO38Pv74Y33/+98P6rMCAIAgtC1Q1fYdKEmK5a7hVsybAABAjPO4+dfaKtXUmC8JVbVnxdwrPz9f9913n6ZMmaJp06Zp/fr1qqur06JFiyRJCxcu1NChQ1VYWChJeuSRRzRz5kw9//zzuuOOO7R582Z98skn7s4P/fv318yZM7Vs2TIlJibqqquu0gcffKCf/exnWrduXXgKAQAAfAtjqOrECXO75Z7w5ULWqwAAgOU8t/87aT6N2VCVJPXp00ff/e53QxpAoItUzz77rFasWKFNmzYpOztbpaWlkqTk5GQlJyfLZrPp0Ucf1TPPPKPRo0drxIgRevLJJzVkyBB3cAsAAHQDV6eqPqmSYjtUJVkzbwIAADHMx80/iVBVR0Kde82fP18VFRVasWKFSktLNWnSJG3fvl2ZmZmSpJMnT3p1nZoxY4Y2bdqkJ554QsuXL9fo0aO1detWjXPtBSRp8+bNKigo0L333qsLFy7oqquu0r//+7/re9/7XvAfFAAABC4Moaq0NCkjQyovl44ckaZODe163YX1KgAAYCmP1uquTlWDBzsjNx4LBBSqeuedd/w+98477/TrvEAXqV5++WU1NTXpO9/5jtd1Vq5cqaeeekqS9G//9m+qq6vTAw88oMrKSn31q1/V9u3b5XA4/B4/AAAIkStUZU+V1DO+oWelcMybAABADPO4+edan3I4pIQE/y/hClUdOya1tEh9QvqqXXQJx9wrLy+vw07qO3fubHds3rx5mjdvXofXy8rK0htvvOHX3wYAAGEUhlCVJI0bJ/3+99KhQ9EbqmK9CgAAhJVnp6rz5tOY6lTlb6cnm82m1tZWv68byCLViRMn/Pr7Tz/9tJ5++mm/xwAAACzm2v4vzmyfEGudqsI1bwIAADHKR6gqkC5VknTVVVJionTxovSXv0ijR1s7xEhi7gUAAPzmmlf166fq0+ZTK0JV119vhqoOHgz9WuHCnAkAAISVR6jK1alq4MDIDccKAYWqDMMI1zgAAEBv4+pUZTPTVLHWqYp5EwAAsIzT6R2qOmM+DTRUFRcnjRkjffqpuQVgbwpVMfcCAAB+C1OnquuvNx8PHQr9WuHCnAkAAISVj1DV4MGRG44V7F2fAgAAEARXpyqZaapY61QFAABgmeZmydUpoF8/VVaaTwMNVUmXtgA8fNiSkQEAAPQ8PkJVwcyrLjdunPm4d6906lTo1wMAAOhRWlulujrzeaxu//fjH/9YDzzwgBwOh3784x93eu7DDz8c0sAAAEAP5+pUZcRmqIp5EwAAsIzrxp8U0vZ/Uu8NVTH3AgAAfnPd7LO4U9W+feZjRYW57fKrr0qLF4d+XSsxZwIAAGFTU3PpeUqKu1NVTIWqfvSjH+nee++Vw+HQj370ow7Ps9lsTLYAAIh17lBVP0mxt/0f8yYAAGAZV6iqTx+pb193qCo1NfBL9dZQFXMvAADgN89tldvmVaGGqk6dkh599NJrw5AefFDKzZWuvDK0a1uJORMAAAgbV1o9IUENzgT3lGvwYKmxMXLDClVAoaq//OUvPp8DAAC049r+rzVRUux1qmLeBAAALONx40+SJZ2qjhwxb/bZ7RaMLwow9wIAAH7zsf1fqKGqzz4z51aeWlul48ejK1TFnAkAAISNx4KVq0tVXJw5z6qoiNywQmXJ0pnT6ZTT6bTiUgAAoLdwdapqic1QVUeYNwEAgIBZGKoaOVKKj5cuXpROnLBmeNGMuRcAAGinbW5lOJLcu9SEGqoaPbp9WD0uTho1KrTrdhfmTAAAIGQeaXVXqGrgQMlmi9yQrBBSqOpnP/uZxo8fr8TERCUmJmrChAn6P//n/1g1NgAA0JO5OlW1JEiKve3/Lse8CQAABO2yUFVlpfkymFBVnz7SmDHm8962BaAn5l4AAKBDbXOrOnuKXDmiUENVV14pvfqqGaSSzMdXXomuLlW+MGcCAACW8RGqGjw4csOxSkDb/3lat26dnnzySeXl5ekrX/mKJOkPf/iDvve97+ncuXNaunSpZYMEAAA9kKtTVWO8pNjuVMW8CQAAhMTCTlWSuQXggQNmqOqb37RgfFGGuRcAAOhU29yq2mkuVsXFSYmJoV928WIpN9fc8m/UqOgPVDFnAgAAlvIIVZ0/bz4dNChyw7FK0KGqF198US+//LIWLlzoPnbnnXfq+uuv11NPPcVkCwCAWHdZqCqWO1UxbwIAACGpqzMfLwtVpaYGd7mxY83H3tqpirkXAADolCtUZZiLVf37W7ctzZVXRn+YyoU5EwAAsJSPTlW9IVQV9PZ/Z8+e1YwZM9odnzFjhs6ePRvSoAAAQC9QXy9DNtU19pUU252qmDcBAICQhKFTldR7Q1XMvQAAQKdcoarWfpJC3/qvp2LOBAAALNVLt/8LOlQ1atQo/dd//Ve741u2bNHo0aNDGhQAAOgF6utVp37ul7EcqmLeBAAAQhLGUJXTGeLYohBzLwAA0ClXqKrZ3PMv2DlVT8ecCQAAWIrt/7ytWrVK8+fP14cffujea/mPf/yjiouLfU7CAABAjKmvV43MJJXdLjkcER5PBDFvAgAAIbksVFVZab4M9gbgqFFSnz7mroJ//as0fHjoQ4wmzL0AAECHnM5LoaoWc24Vq52qmDMBAABLeXwL8MI582lvCFUF3alq7ty5+vjjj5WWlqatW7dq69atSktL0549e/Ttb3/byjECAICeqK5OtUqWZHapstkiPJ4IYt4EAABCYnGnqr59pWuuMZ/3xi0AmXsBAIAONTS4n1Y1JEiK3VAVcyYAAGCpXrr9X9CdqiRp8uTJ+vnPf27VWAAAQG9SX68apUmK7a3/XJg3AQCAoHmEqpqbL70MZauasWPNQNXhw9Ls2aEPMdow9wIAAD65JlKSqhviJcVuqEpizgQAACzUS7f/C7pTVVxcnMrLy9sdP3/+vOLi4kIaFAAA6AXq692dqpKTIzyWCGPeBAAAQuIRqnKtT0mhh6qk3tmpirkXAADoUF2d+ZiQoOpa8xZZrIaqrJwzbdiwQdnZ2XI4HMrJydGePXs6Pf/tt9/WmDFj5HA4NH78eL377rtev7fZbD5/nnvuOfc52dnZ7X6/Zs2agMYNAAAs1Es7VQUdqnI6nT6PNzY2Kj4+PugBAQCAXsDpbOtUZbaoivVOVcybAABASDxCVa6t/5KSzG38gtWbQ1XMvQAAQId8hNVjNVRl1Zxpy5Ytys/P18qVK7Vv3z5NnDhRubm5PgNbkrRr1y4tWLBAixcv1qeffqo5c+Zozpw5OnjwoPucs2fPev1s3LhRNptNc+fO9brW008/7XXeD37wA7/HDQAALOYjVNUbOlUFvP3fj3/8Y0lmSvynP/2pkj1aT7S2turDDz/UmDFjrBshAADoeRoaJMkdqorVTlXMmwAAgCV8hKpC6VIleYeqnE7JZgvtetGAuRcAAOgSoSrL50zr1q3TkiVLtGjRIklSUVGRtm3bpo0bN+rxxx9vd/4LL7yg2bNna9myZZKk1atXa8eOHXrppZdUVFQkScrKyvJ6z69//WvdcsstGjlypNfxlJSUducCAIAI6aXb/wUcqvrRj34kyUywFxUVebUAjY+PV3Z2tnvSAwAAYlTbApVr+79Y7VTFvAkAAFjC4+ZfZaX5NNRQ1TXXSHa7VFUlnT0rDRkS2vWiAXMvAADQJUJVls6ZmpqatHfvXhUUFLiP2e12zZo1SyUlJT7fU1JSovz8fK9jubm52rp1q8/zy8rKtG3bNr355pvtfrdmzRqtXr1aw4cP1z333KOlS5eqTx/ftz4bGxvV2Njofl3d9h+AYRgyDKPTzxkMwzDkdDrDcu3ehloFhnr5j1oFhnr5j1r5Zquulk1SXZ8UXbxoHhs40Ijaevk7noBDVX/5y18kSbfccot++ctfauDAgYFeAgAA9HZtC1Q1cQOl1tgNVTFvAgAAlnDd/OvXz7JOVQkJ0qhR0p//bHar6g2hKuZeAACgS4SqLJ0znTt3Tq2trcrMzPQ6npmZqaNHj/p8T2lpqc/zS0tLfZ7/5ptvKiUlRXfddZfX8Ycfflg33nijBg0apF27dqmgoEBnz57VunXrfF6nsLBQq1atane8oqJCDW1d961kGIaqqqrkdDplt9stv35vQq0CQ738R60CQ738R618y6islE3SF38zX8fFOXXxYrnq66OzXjU1NX6dF3CoyuX9998P9q0AAKC3c4Wq+pqhqljd/s+FeRMAAAiJj+3/UlNDv+zYsZdCVbNmhX69aMHcCwAAdIhQlVtPmTNt3LhR9957rxwOh9dxz25XEyZMUHx8vB588EEVFhYqISGh3XUKCgq83lNdXa1hw4YpPT1d/cPwH4FhGLLZbEpPT4+qG8jRiFoFhnr5j1oFhnr5j1r50Noqe12dJMlIyZZkbv2XmZkRtfW6fG7RkYBCVWvWrNHDDz+spKSkLs/9+OOPde7cOd1xxx2B/AkAANAbuLb/i0uVFJudqpg3AQAAy3iGqk6aT0PtVCWZoaqtW81QVU/H3AsAAPjFM1RVbj61Yl7VU1g9Z0pLS1NcXJzKysq8jpeVlSkrK8vne7Kysvw+/6OPPtKxY8e0ZcuWLsebk5OjlpYWnThxQtdee2273yckJPgMW9nt9rDd4LXZbGG9fm9CrQJDvfxHrQJDvfxHrS7j0fWpstm8KTh4sE12u01SdNbL37EENOLDhw/rqquu0v/6X/9L//f//l9VVFS4f9fS0qIDBw7oJz/5iWbMmKH58+crJRbvoAIAAI/t/1IlxWaoinkTAACwjI9OVVaFqqTeEapi7gUAAPwS452qrJ4zxcfHa/LkySouLnYfMwxDxcXFmj59us/3TJ8+3et8SdqxY4fP819//XVNnjxZEydO7PKz7d+/X3a7XRkZGV2eCwAALOaaWMXH63xNvCSzU1VvEFCnqp/97Gf6n//5H7300ku65557VF1drbi4OCUkJKi+bSJ6ww036F/+5V90//33+90uCwAA9DKuTlV2c1UqFrf/Y94EAAAs43Hzr7LSfGplqOrQIcnplGy20K8ZKcy9AACAX1zzqn793GH1WApVhWPOlJ+fr/vuu09TpkzRtGnTtH79etXV1WnRokWSpIULF2ro0KEqLCyUJD3yyCOaOXOmnn/+ed1xxx3avHmzPvnkE7366qte162urtbbb7+t559/vt3fLCkp0ccff6xbbrlFKSkpKikp0dKlS/Xd735XAwcODLVMAAAgUB5p9QsXzKcxGaqSpIkTJ+q1117TK6+8ogMHDujLL7/UxYsXlZaWpkmTJiktLS0c4wQAAD2Jq1OVzVyVitVGAMybAACAJcLUqeraa80g1YULUnm5lJkZ+jUjibkXAADoUtu8ypkYm52qJOvnTPPnz1dFRYVWrFih0tJSTZo0Sdu3b1dm2+Ty5MmTXtvrzJgxQ5s2bdITTzyh5cuXa/To0dq6davGjRvndd3NmzfL6XRqwYIF7f5mQkKCNm/erKeeekqNjY0aMWKEli5dqvz8/CAqAgAAQuYjVDV4cOSGY6WAQ1WGYei5557TO++8o6amJt12221auXKlEhMTwzE+AADQE7lCVTJbVMVipyqJeRMAALBIXZ356BGqSk0N/bJJSdKIEdIXX5hbAPb0UBVzLwAA0KW2Nav6+FQZhnko1kJV4Zgz5eXlKS8vz+fvdu7c2e7YvHnzNG/evE6v+cADD+iBBx7w+bsbb7xRu3fvDnicAAAgTDxCVefPm097S6cqe9enePv3f/93LV++XMnJyRo6dKheeOEFPfTQQ+EYGwAA6Klc2/85+0mK3U5VzJsAAIAlwtSpSrq0BeDWrdKpU9ZcM1KYewEAgC61zauq48wt4mw2qV+/SA6o+zFnAgAAlvNYsOpt2/8FHKr62c9+pp/85Cd67733tHXrVv3mN7/RW2+9JcMV6QcAAHB1qmqN7VAV8yYAABCy1lapsdF8HoZQVUuL+fjjH0tXXSW9/ro1140E5l4AAKBLbR1Aq+2pkswuVTZbBMcTAcyZAACA5Xrx9n8Bh6pOnjyp22+/3f161qxZstlsOnPmjKUDAwAAPZirU1Wr2TY8Vrf/s3retGHDBmVnZ8vhcCgnJ0d79uzp9Py3335bY8aMkcPh0Pjx4/Xuu+96/d5ms/n8ee6559znZGdnt/v9mjVrgho/AAAIwsWLl54nJamy0nxqRajq1CnpvfcuvTYM6cEHe27HKtasAABAl1ydqmzmZCrWtv6TmDMBAIAwYPu/S1paWuRwOLyO9e3bV83NzZYNCgAA9HCuTlUt5pwhVjtVWTlv2rJli/Lz87Vy5Urt27dPEydOVG5ursrLy32ev2vXLi1YsECLFy/Wp59+qjlz5mjOnDk6ePCg+5yzZ896/WzcuFE2m01z5871utbTTz/tdd4PfvCDgMcPAACC5Nr6T5IcDks7VX32meR0eh9rbZWOHw/92pHAmhUAAOiSK1QlM00Vi6Eq5kwAAMByPjpV9ZZQVZ9A3+B0OnX//fcrISHBfayhoUHf+9731M9j4+lf/vKX1owQAAD0PPX1ckqqaTYXaGK1U5WV86Z169ZpyZIlWrRokSSpqKhI27Zt08aNG/X444+3O/+FF17Q7NmztWzZMknS6tWrtWPHDr300ksqKiqSJGVlZXm959e//rVuueUWjRw50ut4SkpKu3M70tjYqEbXFkWSqtsm0oZhhKWNvGEYcjqdtKj3A7UKDPXyH7UKDPXyH7VqU1sruyRnYqKckqqqnJJs6t/fkGdpgqnX1VdLdrtNhnFpz5u4OKdGjnTKqrJ3578fa1YAAKBLrlCVYS5WxWKoijkTAACwXC/e/i/gUNV9993X7th3v/tdSwYDAAB6ifp6Ncghw2k2xYzVTlVWzZuampq0d+9eFRQUuI/Z7XbNmjVLJSUlPt9TUlKi/Px8r2O5ubnaunWrz/PLysq0bds2vfnmm+1+t2bNGq1evVrDhw/XPffco6VLl6pPH9/TyMLCQq1atard8YqKCjU0NHT0EYNmGIaqqqrkdDpltwfchDWmUKvAUC//UavAUC//UStT3F//qnRJTodDp06Vq6HBDDo3NVWovPxSm6lg6hUfLz33XKL+7d/6q7XVprg4p374w2rFx19UB80wA1ZTU2PNhfzAmhUAAOjSZaEqK7p/9jTMmQAAgOV68fZ/AYeq3njjjXCMAwAA9Cb19arRpSSVx5fcYopV86Zz586ptbVVmZmZXsczMzN19OhRn+8pLS31eX5paanP8998802lpKTorrvu8jr+8MMP68Ybb9SgQYO0a9cuFRQU6OzZs1q3bp3P6xQUFHiFuaqrqzVs2DClp6erfxi+/mkYhmw2m9LT02P6hrs/qFVgqJf/qFVgqJf/qFWbkyclSbbkZCUkZLgPX311uuLiLp0WbL0efVT6znecOn7cqVGjpCuvTJFkXSL+8q1lwok1KwAA0CVXqKo1SVJsdqpizgQAACzXFqq66Bgo1/frYzZUBQAA0CWPUFW/flIs3wftKTZu3Kh777233Y1Pz4DUhAkTFB8frwcffFCFhYVebeJdEhISfB632+1huyFus9nCev3ehFoFhnr5j1oFhnr5j1pJrpUoW1KSamrMOiQnS337tq9JsPUaPtz8CYeY/rcDAADRpy1UVdUUu6EqAAAAy1VVSZIuxKVLkvr06T272LCyBQAArFdfr1qZbdR7y6QpktLS0hQXF6eysjKv42VlZcrKyvL5nqysLL/P/+ijj3Ts2DH9y7/8S5djycnJUUtLi06cOOH/BwAAAMFru/GnpCRVVppPY3GbGgAAAEu4OlU1m18qI1QFAABggbZOVec1WJLZpcpmi+SArEOoCgAAWM+jUxWhqtDFx8dr8uTJKi4udh8zDEPFxcWaPn26z/dMnz7d63xJ2rFjh8/zX3/9dU2ePFkTJ07sciz79++X3W5XRkZGl+cCAAALuEJV/fq5vvRHqAoAACBYrlBVI6EqAAAAy7SFqi4YqZJ6z9Z/Etv/AQCAcKivV63Mu33JyREeSy+Rn5+v++67T1OmTNG0adO0fv161dXVadGiRZKkhQsXaujQoSosLJQkPfLII5o5c6aef/553XHHHdq8ebM++eQTvfrqq17Xra6u1ttvv63nn3++3d8sKSnRxx9/rFtuuUUpKSkqKSnR0qVL9d3vflcDBw4M/4cGAABenapcoarU1IiNBgAAoGdzhaoa4iURqgIAALCEK1TVYk6uBg+O5GCsRagKAABYr75eNbpSEp2qrDJ//nxVVFRoxYoVKi0t1aRJk7R9+3ZlZmZKkk6ePCm7/VIT0hkzZmjTpk164okntHz5co0ePVpbt27VuHHjvK67efNmOZ1OLViwoN3fTEhI0ObNm/XUU0+psbFRI0aM0NKlS5Wfnx/eDwsAAC7xEaqiUxUAAECQXKGqi30lEaoCAACwhGv7v0az0wKdqgAAADpTV+fe/o9OVdbJy8tTXl6ez9/t3Lmz3bF58+Zp3rx5nV7zgQce0AMPPODzdzfeeKN2794d8DgBAICFCFUBAABYo7lZammRJFXXm7fHCFUBAACEyDCkmhpJ0oWGJEm9K1Rl7/oUAACAANXXq1ZmmopOVQAAACHwCFVVVppPCVUBAAAEoa7O/bS6zrw9RqgKAAAgRHV1ktMpSbpw0SGpd23/F/FQ1YYNG5SdnS2Hw6GcnBzt2bOnw3MPHTqkuXPnKjs7WzabTevXr293zlNPPSWbzeb1M2bMmDB+AgAA0E59vbtTFaEqAACAENCpKmICWbOSpLfffltjxoyRw+HQ+PHj9e6777Y758iRI7rzzjs1YMAA9evXT1OnTtXJkyfD9REAAIAn17wqLk7VNTZJzKsAAABC5lqw6ttX5yvNbqB0qrLIli1blJ+fr5UrV2rfvn2aOHGicnNzVV5e7vP8+vp6jRw5UmvWrFFWVlaH173++ut19uxZ988f/vCHcH0EAADgi0eoiu3/AAAAQuDqqOARqkpNjdhoYkaga1a7du3SggULtHjxYn366aeaM2eO5syZo4MHD7rP+fzzz/XVr35VY8aM0c6dO3XgwAE9+eSTcjgc3fWxAACIbR5h9epqM1RFpyoAAIAQVVebj/3768LfzDlWb+pU1SeSf3zdunVasmSJFi1aJEkqKirStm3btHHjRj3++OPtzp86daqmTp0qST5/79KnT59OQ1cAACCMmpul5ma2/wMAALACnaoiItA1qxdeeEGzZ8/WsmXLJEmrV6/Wjh079NJLL6moqEiS9P/9f/+fbr/9dv3whz90v+/qq6/udByNjY1qbGx0v65uW6g0DEOGYYT2IS9jGIacTqfl1+2tqJf/qFVgqJf/qFVgqJek2lrZJRmJSar+m1OSTcnJhi4vSTTXKhrHBAAAYpxnqOqC+bQ3daqKWKiqqalJe/fuVUFBgfuY3W7XrFmzVFJSEtK1P/vsMw0ZMkQOh0PTp09XYWGhhg8f3uH5LFBFL+rlP2oVGOrlP2oVGOolqa5OdsmjU1X7xSkpumsVjWMCAAAxilBVtwtmzaqkpET5+flex3Jzc7V161ZJ5vxy27Zt+rd/+zfl5ubq008/1YgRI1RQUKA5c+Z0OJbCwkKtWrWq3fGKigo1NDQE/uE6YRiGqqqq5HQ6ZbdHtLl9j0C9/EetAkO9/EetAkO9pL6nT2uwpLr4/mpuNrsoNDZWqLzc6XVeNNeqpqYm0kMAAADw5hGqOn/efEqoygLnzp1Ta2urMjMzvY5nZmbq6NGjQV83JydH//Ef/6Frr71WZ8+e1apVq/R3f/d3OnjwoFI6aJXBAlX0ol7+o1aBoV7+o1aBoV6SvbxcGZK7U5XTWaPy8ovtzovmWrFABQAAooZHqKqy0nxKqCq8glmzKi0t9Xl+aWmpJKm8vFy1tbVas2aNnnnmGT377LPavn277rrrLr3//vuaOXOmz+sWFBR4hbWqq6s1bNgwpaenq7/F+xUZhiGbzab09PSom59HI+rlP2oVGOrlP2oVGOolKSFBklTX7wr3oREj0nV5OaK5VmwbDAAAoo5np6rPzads/xfFvvGNb7ifT5gwQTk5Obrqqqv0X//1X1q8eLHP97BAFb2ol/+oVWCol/+oVWCol6TaWklSjT1VMqQrrkhRRkb7YHM014oFKgAAEDXoVNUruDqhfutb39LSpUslSZMmTdKuXbtUVFTUYagqISFBCW03gD3Z7fawzKFtNlvYrt0bUS//UavAUC//UavAxHy92r5EXxOfJklKSZH69PFdi2itVbSNBwAAwBWqcqbQqcpSaWlpiouLU1lZmdfxsrIyZWVlWfZ3UlNTdc011+j48eMdnsMCVXSjXv6jVoGhXv6jVoGJ+Xq5FqjsAyRD6t/f3u4bfy7RWqtoGw8AAIhhPkJVqakRG01MCGbNKisrq9Pz09LS1KdPH40dO9brnOuuu05/+MMfLBw9AADoUNu8qrotVGXxd+oBAABiU1uo6mK/NDU2mod6U6gqYncM4+PjNXnyZBUXF7uPGYah4uJiTZ8+3bK/U1tbq88//1xXXHFF1ycDAIDQtS1Q1drM7f862H0XAAAA/qBTVbcLZs1q+vTpXudL0o4dO9znx8fHa+rUqTp27JjXOX/+85911VVXWfwJAACAT65QVR/zLh+hKgAAAAu0LVhdSDAzOX37SsnJkRyQtSK6/V9+fr7uu+8+TZkyRdOmTdP69etVV1enRYsWSZIWLlyooUOHqrCwUJLU1NSkw4cPu5+fPn1a+/fvV3JyskaNGiVJ+td//Vf9wz/8g6666iqdOXNGK1euVFxcnBYsWBCZDwkAQKxpW6CqkZmmIlQFAAAQgra5VUOfZDU1mYcIVYVfoGtWjzzyiGbOnKnnn39ed9xxhzZv3qxPPvlEr776qvuay5Yt0/z58/W1r31Nt9xyi7Zv367f/OY32rlzZyQ+IgAAsccVqopLlUSoCgAAwBJtnarO98mUZHapstkiOSBrRTRUNX/+fFVUVGjFihUqLS3VpEmTtH37dmVmmsU+efKk1/Y7Z86c0Q033OB+vXbtWq1du1YzZ850L0CdOnVKCxYs0Pnz55Wenq6vfvWr2r17t9LT07v1swEAELNcnaqc/ST1rjQ6AABAt2ubW1W1mpMqm43QencIdM1qxowZ2rRpk5544gktX75co0eP1tatWzVu3Dj3Od/+9rdVVFSkwsJCPfzww7r22mv1i1/8Ql/96le7/fMBABCTXKEqW6okQlUAAACWaAtVXbCbWyz3pq3/pAiHqiQpLy9PeXl5Pn93+Tf1srOz5XQ6O73e5s2brRoaAAAIhqtTVWuSJG76AQAAhKRtblXZcmlrZY8sD8IokDUrSZo3b57mzZvX6TX/+Z//Wf/8z/9sxfAAAECg3KEqs+0n3T8BAAAs4ApVyUxTDR4cycFYj2U4AABgrfp6NamvmpzxkuhUBQAAEBJXp6oWswsoN/8AAACCVFcnSaqW+Q1AOlUBAABYwLX9n5Eqqfd1qiJUBQAArFVfr1pdSlLRqQoAACBITuelUFWz2QU0NTWC4wEAAOjJXJ2qDEJVAAAAlnF1qmoxvwlIqAoAAKAz9fWqafvGn8Mh9Yn4ZsMAAAA9VFOTZBiSpKpGhyQ6VQEAAATNFVZvNb8MSKgKAADAAq5QVZM5x2L7PwAAgM54hKrY+g8AACAEbTf+JKmqwdxamVAVAABAkFydqlrMDqCEqgAAACxQVSVJOt9gzrHoVAUAANAZj+3/2PoPAAAgBK5QVd++qqwx238SqgIAAAiSK1TVnCiJUBUAAIAlXJ2q6s05FqEqAACAznh0qiJUBQAAEAJXqCopyfWlP0JVAAAAwXKFqprMbZUJVQEAAITI6bwUqqo1u6yz/R8AAEBnPDpVsf0fAABACHyEqlJTIzYaAACAns0VqmpMkESoCgAAIGR1dWawStL5KrPLOp2qAAAAOkOnKgAAAGvU1ZmPdKoCAAAInStU1WB2USBUBQAAEKK2LlWKi9OFSpskQlUAAACd8whV0akKAAAgBGz/BwAAYB1XqOqi2UWBeRUAAECI2kJVzpT+unDBDFWx/R8AAEBnPLb/o1MVAABACDxCVZWV5lNu/gEAAATJFaqqN0NVdKoCAAAIUVuoqj4lU42N5iE6VQEAAHSG7f8AAACsQacqAAAA69TXq1Hxamwyb40RqgIAAAhR24LVhX7DJEl9+0r9+kVyQNYjVAUAAKxVV8f2fwAAAFbwEapKTY3YaAAAAHo2jzUriS8DAgAAhKytU9UFxxBJ5tZ/NlskB2Q9QlUAAMBabP8HAABgDTpVAQAAWKO1VWpsVJXMyVS/flJcXITHBAAA0NO1harOx18hqfdt/ScRqgIAAFZj+z8AAABrtIWqnImEqgAAAEJy8aIkqVrmnn9s/QcAAGABV6eqvhmSCFUBAAB0zaNTFdv/AQAAhKAtVFUfn6qWFvMQoSoAAIAgtM2rCFUBAABYyBWqsqdLMrf/620IVQEAAGvRqQoAAMAabTf/quLMr/nZ7YTWAQAAguIKVcWbN/wIVQEAAFjAtf2fzLUrOlUBAAB0xun0ClVx0w8AACAErlCVLVWSefPPZovgeAAAAHoqd6gqTRKhKgAAAEu4OlUZqZIIVQEAAHSuoUGS3Nv/0akKAAAgBK5QlX2gJCk1NYJjAQAA6Mlcoaq+5p0+QlUAAAAWqKqSJJ1vGSCJ7f8AAAA617ZAxfZ/AAAAFnCFqpzmXb8BAyI5GAAAgB7MFarqY97pY14FAABgAVenqibzhiCdqgAAADrTtkDl6lTF9n8AAAAhcIWqDHNhipt/AAAAQXKFquypkuhUBQAAYAlXqKohSRKdqgAAADpXX69W2VWvfpLoVAUAABCStpt/lS1mUp1QFQAAQJAIVQEAAFivLVR1vt4hiU5VAAAAnauvd3epkuhUBQAAEBJXp6oWM7BOqAoAACBIrlCVzDQVoSoAAAALuDpV1cZLIlQFAADQOY9QVZ8+UkJChMcDAADQk7lCVU2JkqTU1AiOBQAAoCdzzauchKoAAAAsU10tp6QL1X0ksf0fAABA5+rrVSNzz7+UFMlmi/B4AAAAerK6OklSVZPZQp1OVQAAAEFqm1dVO80vAxKqstaGDRuUnZ0th8OhnJwc7dmzp9Pz3377bY0ZM0YOh0Pjx4/Xu+++6/X7+++/Xzabzetn9uzZXudcuHBB9957r/r376/U1FQtXrxYtbW1ln82AADQAadTqq5WnfqpqdmMHtGpCgAAoDMeoSq2/gMAAAiRq6NCA6EqAACAkLi2/2s1t1UmVGWdLVu2KD8/XytXrtS+ffs0ceJE5ebmqry83Of5u3bt0oIFC7R48WJ9+umnmjNnjubMmaODBw96nTd79mydPXvW/fOf//mfXr+/9957dejQIe3YsUO//e1v9eGHH+qBBx4I2+cEAACXqa+XWlt1QWaSKj5eSkqK8JjCgFAVAACwjsf2fykpER4LAABAT9d286+yPl4SoSoAAICguUJVLeadPkJV1lm3bp2WLFmiRYsWaezYsSoqKlJSUpI2btzo8/wXXnhBs2fP1rJly3Tddddp9erVuvHGG/XSSy95nZeQkKCsrCz3z8CBA92/O3LkiLZv366f/vSnysnJ0Ve/+lW9+OKL2rx5s86cORPWzwsAANpUV0uSLtjSJJlb//XGHWz6RHoAAACgF7ls+z8AAACEwNWpqt5cviFUBQAAECRXqKo5URKhKqs0NTVp7969KigocB+z2+2aNWuWSkpKfL6npKRE+fn5Xsdyc3O1detWr2M7d+5URkaGBg4cqFtvvVXPPPOMBg8e7L5GamqqpkyZ4j5/1qxZstvt+vjjj/Xtb3+73d9tbGxUY2Oj+3V1241gwzBkGEZgH9wPhmHI6XSG5dq9DbUKDPXyH7UKDPXyH7VqU1kpu6RzScOkOmnQIKcMw9nutGitl7/jIVQFAACs49Gpiu3/AAAAQtDSIjU1SZKq6szlm9TUCI4HAACgJ3OFqprMbZUJVVnj3Llzam1tVWZmptfxzMxMHT161Od7SktLfZ5fWlrqfj179mzdddddGjFihD7//HMtX75c3/jGN1RSUqK4uDiVlpYqIyPD6xp9+vTRoEGDvK7jqbCwUKtWrWp3vKKiQg0NDX593kAYhqGqqio5nU7Z7Wwc1BlqFRjq5T9qFRjq5T9qZerz5ZdKk3Q+IUuqk5KTm1VefqHdedFar5qaGr/OI1QFAACsQ6cqAAAAa1y86H5aVWMuONGpCgAAIEj19WpWH11s7iuJeVW0u/vuu93Px48frwkTJujqq6/Wzp07ddtttwV1zYKCAq8OWdXV1Ro2bJjS09PVPwwpO8MwZLPZlJ6eHlU3kKMRtQoM9fIftQoM9fIftWoTFydJ+ptjqCQpK6tvu9CzFL31cjgcfp1HqAoAAFjHI1RFpyoAAIAQtHVTkKSqapskbv4BAAAEzWPNSuLLgFZJS0tTXFycysrKvI6XlZUpKyvL53uysrICOl+SRo4cqbS0NB0/fly33XabsrKyVF5e7nVOS0uLLly40OF1EhISlJCQ0O643W4P2w1em80W1uv3JtQqMNTLf9QqMNTLf9RKUm2tJOlCH7MD5eDBNtntNp+nRmO9/B1L9IwYAAD0fB7b/7E4BQAAEIK2UJUzMUlVVYSqAAAAQlJfr2qZ3YgSE6W+fSM8nl4iPj5ekydPVnFxsfuYYRgqLi7W9OnTfb5n+vTpXudL0o4dOzo8X5JOnTql8+fP64orrnBfo7KyUnv37nWf8/vf/16GYSgnJyeUjwQAAPxVXS1JumBPkyQNGhTJwYQPoSoAAGAdtv8DAACwRluoqjYxXYZhHiJUBQAAEKT6elXJnEyFYae3mJafn6/XXntNb775po4cOaLvf//7qqur06JFiyRJCxcuVEFBgfv8Rx55RNu3b9fzzz+vo0eP6qmnntInn3yivLw8SVJtba2WLVum3bt368SJEyouLta3vvUtjRo1Srm5uZKk6667TrNnz9aSJUu0Z88e/fGPf1ReXp7uvvtuDRkypPuLAABALKqqkiRdsJlpqsGDIzmY8CFUBQAArMP2f2G1YcMGZWdny+FwKCcnR3v27On0/LfffltjxoyRw+HQ+PHj9e6773r9/v7775fNZvP6mT17ttc5Fy5c0L333qv+/fsrNTVVixcvVm1bS1cAABBGbaGqKofZQj0uTkpKiuSAAAAAejCPTlWEqqw1f/58rV27VitWrNCkSZO0f/9+bd++XZmZ5jz25MmTOnv2rPv8GTNmaNOmTXr11Vc1ceJE/fd//7e2bt2qcePGSZLi4uJ04MAB3Xnnnbrmmmu0ePFiTZ48WR999JHX9n1vvfWWxowZo9tuu0233367vvrVr+rVV1/t3g8PAEAsa+tUdd4YKKn3dqrqE+kBAACAXoTt/8Jmy5Ytys/PV1FRkXJycrR+/Xrl5ubq2LFjysjIaHf+rl27tGDBAhUWFuqb3/ymNm3apDlz5mjfvn3uRSpJmj17tt544w33a8/FKUm69957dfbsWe3YsUPNzc1atGiRHnjgAW3atCl8HxYAALQLVaWmSjZbBMcDAADQk9XXq1rm+gmhKuvl5eW5O01dbufOne2OzZs3T/PmzfN5fmJiot57770u/+agQYNYnwIAIJJc2/+1mJMrQlUAAABdYfu/sFm3bp2WLFnibp1eVFSkbdu2aePGjXr88cfbnf/CCy9o9uzZWrZsmSRp9erV2rFjh1566SUVFRW5z0tISFBWVpbPv3nkyBFt375df/rTnzRlyhRJ0osvvqjbb79da9eu9dlOvbGxUY2Nje7X1W2TasMwZLj2LrKQYRhyOp1huXZvQ60CQ738R60CQ738F/O1qq2VXVJl3zRJ0oABThmGs8PTo7Ve0TYeAAAQo+hUBQAAYC1XqKrJbLbQW7f/I1QFAACs49Gpiu3/rNPU1KS9e/eqoKDAfcxut2vWrFkqKSnx+Z6SkhLl5+d7HcvNzdXWrVu9ju3cuVMZGRkaOHCgbr31Vj3zzDMa3DbzLSkpUWpqqjtQJUmzZs2S3W7Xxx9/rG9/+9vt/m5hYaFWrVrV7nhFRYUaGhr8/sz+MgxDVVVVcjqdstvZ2boz1Cow1Mt/1Cow1Mt/sV6rhLNnNVDSeQ2QJCUltai8/HyH50drvWpqaiI9BAAAAKmujlAVAACAlVzb/zX0k0SnKgAAgK7V1dGpKgzOnTun1tZWZWZmeh3PzMzU0aNHfb6ntLTU5/mlpaXu17Nnz9Zdd92lESNG6PPPP9fy5cv1jW98QyUlJYqLi1NpaWm7rQX79OmjQYMGeV3HU0FBgVeYq7q6WsOGDVN6err6h2HV0jAM2Ww2paenR9UN5GhErQJDvfxHrQJDvfwX87Xq21eSVBtv/r84La2Pzy1/XaK1Xg6HI9JDAAAAoFMVAACA1aqr5ZR0od5c+yFUBQAA0BWP7f/oVBX97r77bvfz8ePHa8KECbr66qu1c+dO3XbbbUFdMyEhQQkJCe2O2+32sN3gtdlsYb1+b0KtAkO9/EetAkO9/BfTtWrr8FgdN1CSNGCATXa7rdO3RGO9omksAAAgRjmdXqGqAQMiPB4AAIDeoLpatUpWc2ucpN67/R8rWwAAwDoe2//Rqco6aWlpiouLU1lZmdfxsrIyZWVl+XxPVlZWQOdL0siRI5WWlqbjx4+7r1FeXu51TktLiy5cuNDpdQAAgAXq6yVJVUqVJKWmRm4oAAAAPVpjo+R00qkKAADASlVVuiCzPVVCgpSYGOHxhAmhKgAAYB2PTlWEqqwTHx+vyZMnq7i42H3MMAwVFxdr+vTpPt8zffp0r/MlaceOHR2eL0mnTp3S+fPndcUVV7ivUVlZqb1797rP+f3vfy/DMJSTkxPKRwIAAF2pq5MkVdFRIWI2bNig7OxsORwO5eTkaM+ePZ2e//bbb2vMmDFyOBwaP3683n333Q7P/d73viebzab169dbPGoAANBOW1idUBUAAICFqqvdoapBgyRb5w3We6yIh6oCWaA6dOiQ5s6dq+zs7E4XngJd9AIAANZw1l3qVMX2f9bKz8/Xa6+9pjfffFNHjhzR97//fdXV1WnRokWSpIULF6qgoMB9/iOPPKLt27fr+eef19GjR/XUU0/pk08+UV5eniSptrZWy5Yt0+7du3XixAkVFxfrW9/6lkaNGqXc3FxJ0nXXXafZs2dryZIl2rNnj/74xz8qLy9Pd999t4YMGdL9RQAAIJa4OlUZZlKdUFX32rJli/Lz87Vy5Urt27dPEydOVG5ubrsuni67du3SggULtHjxYn366aeaM2eO5syZo4MHD7Y791e/+pV2797NfAoAgO7iClXZUiURqgIAALBEdbXOy9zzr7du/SdFOFQV6AJVfX29Ro4cqTVr1nS45Uyg1wQAANapr5ecbdMLOlVZa/78+Vq7dq1WrFihSZMmaf/+/dq+fbsyMzMlSSdPntTZs2fd58+YMUObNm3Sq6++qokTJ+q///u/tXXrVo0bN06SFBcXpwMHDujOO+/UNddco8WLF2vy5Mn66KOPlJCQ4L7OW2+9pTFjxui2227T7bffrq9+9at69dVXu/fDAwAQi9pu/lW2EKqKhHXr1mnJkiVatGiRxo4dq6KiIiUlJWnjxo0+z3/hhRc0e/ZsLVu2TNddd51Wr16tG2+8US+99JLXeadPn9YPfvADvfXWW+rbt293fBQAAOAKq9sHSiJUBQAAEDKns12nqt6qTyT/uOcClSQVFRVp27Zt2rhxox5//PF250+dOlVTp06VJJ+/D+aaktTY2KjGxkb36+rqaknmtjqGYQT/AX0wDENOp9Py6/ZW1Mt/1Cow1Mt/1CowMV2v5mbVtDgkSTabUw6HU52VIZprFY1jkqS8vDx3p6nL7dy5s92xefPmad68eT7PT0xM1Hvvvdfl3xw0aJA2bdoU0DgBAIAFXDf/WvpJIlTVnZqamrR3716vLqB2u12zZs1SSUmJz/eUlJQoPz/f61hubq62bt3qfm0Yhv7pn/5Jy5Yt0/XXX+/XWFizil7Uy3/UKjDUy3/UKjAxXa/aWtklVdsHSK1ScrLBmhUAAEAoGhqklhZCVeEUzAJVuK5ZWFioVatWtTteUVGhhoaGoMbSEcMwVFVVJafTKbs94rsvRj3q5T9qFRjq5T9qFZhYrpetpsa99V+/fk5VVHTeJTKaa1VTUxPpIQAAgFjnClU1J0qSUlMjOJYYc+7cObW2tro7grpkZmbq6NGjPt9TWlrq8/zS0lL362effVZ9+vTRww8/7PdYWLOKXtTLf9QqMNTLf9QqMLFcr76nT2uwpGqZLapaW/+m8vLmDs+P5lqxZgUAAKJC25e+zitNUu/e/i9ioapgFqjCdc2CggKvbxNWV1dr2LBhSk9PV3+L+8AahiGbzab09PSom4xHI+rlP2oVGOrlP2oVmJiul2HojMztafr3tykjI6OL06O3Vg6HI9JDAAAAsc4VqmoyQ1V0qurZ9u7dqxdeeEH79u2TzWbz+32sWUUv6uU/ahUY6uU/ahWYmK5XQoIkqdpprltdddVAdbZsFc21Ys0KAABEhbZQ1YX4TKmJTlW9XkJCghLaJtWe7HZ7WCbMNpstbNfujaiX/6hVYKiX/6hVYGK2Xg0NqmkLVSUn22S3d32zKFprFW3jAQAAMcgVqmow1ysIVXWftLQ0xcXFqayszOt4WVmZsrKyfL4nKyur0/M/+ugjlZeXa/jw4e7ft7a26n//7/+t9evX68SJEz6vy5pVdKNe/qNWgaFe/qNWgYnZerV1d6w2zA7rqal2dVWCaK1VtI0HAADEqKoqSdKFPmaoqjd3qorY7CuYBapIXBMAAPipvt69/V9KSoTHAgAA0NO1haoq6wlVdbf4+HhNnjxZxcXF7mOGYai4uFjTp0/3+Z7p06d7nS9JO3bscJ//T//0Tzpw4ID279/v/hkyZIiWLVum9957L3wfBgAASPX1apVddUaSJMniZo8AAACxx7X9X1y6pN7dqSpioapgFqgicU0AAOCn+np3pypCVQAAACGqr5chm2oazCbjhKq6V35+vl577TW9+eabOnLkiL7//e+rrq5OixYtkiQtXLhQBQUF7vMfeeQRbd++Xc8//7yOHj2qp556Sp988ony8vIkSYMHD9a4ceO8fvr27ausrCxde+21EfmMAADEjLo695qVRKgKAAAgZK7t/2SmqXpzqCqi2//l5+frvvvu05QpUzRt2jStX7++3QLV0KFDVVhYKElqamrS4cOH3c9Pnz6t/fv3Kzk5WaNGjfLrmgAAIEw8OlUlJ0d4LAAAAD1dW2Dd6TS3VCZU1b3mz5+viooKrVixQqWlpZo0aZK2b9+uzMxMSdLJkye9tt+ZMWOGNm3apCeeeELLly/X6NGjtXXrVo0bNy5SHwEAALjU16taZpIqIcH8AQAAQAhcoSojVVLv3v4voqGqQBeozpw5oxtuuMH9eu3atVq7dq1mzpypnTt3+nVNAAAQJnSqAgAAsE59vapkJqni4yWHI8LjiUF5eXnuTlOXc61DeZo3b57mzZvn9/VPnDgR5MgAAEBAPEJVdKkCAACwgGv7vxZz7YpOVWEUyAJVdna2nE5nSNcEAABh4hGqolMVAABAiOrrVSXzC2IDBkg2W4THAwAA0FMRqgIAALBWdbWcki40mTcEe3Ooyt71KQAAAH7w2P6PTlUAAAAhqq9XpVIlsfUfAABASDw6gBKqAgAAsEB1tWqVrBan2cepN2//R6gKAABYg+3/AAAArOF0et38I1QFAAAQAjpVAQAAWKuqSudlJqkcDikxMcLjCSNCVQAAwBps/wcAAGCNxkbJ6SRUBQAAYAVCVQAAANaqrtYFmXv+9eat/yRCVQAAwCps/wcAAGCN+npJcoeqUlMjOBYAAICejlAVAACAtTxCVb156z+JUBUAALAK2/8BAABYwxWqijMXp+hUBQAAEAJCVQAAANaqrnZv/0enKgAAAH94dKpi+z8AAIAQ1NVJkqr6pEkiVAUAABASj1AV8yoAAAALsP0fAABAgOhUBQAAYI22TlWVceY3/rj5BwAAEAI6VQEAAFiL7f8AAAAC5BGqolMVAABACFzb/9kHSiJUBQAAEBJCVQAAANZi+z8AAIAAeWz/R6cqAACAELhCVTYzTZWaGsGxAAAA9HR1dYSqAAAArOJ0SlVVbP8HAAAQCGcd2/8BAABYwhWqcpqhKjpVAQAAhIBOVQAAANZpbJSam92dqtj+DwAAwA9NtU1qUV9JbP8HAAAQEneoykyqE6oCAAAIQX29qmROqAhVAQAAhKi6WpLoVAUAABCImlqb+zmhKgAAgBC0haoqWwlVAQAAhIxOVQAAANZxharsaZIIVQEAAPjFFapKTGhVXFyEBwMAANCTuTpVtfSTRKgKAAAgaM3NUnMzoSoAAACrtIWq2P4PAAAgALX15rQipZ8R4ZEAAAD0cPX1apVdtS2JkghVAQAABO3iRRmyqYZQFQAAgDWqq+WUdMFIlUSnKgAAAL/UXOwjiVAVAABAyDy2qJEIVQEAAAStvl61Sna/JFQFAAAQoupq1ShFrTLvCxKqAgAA8ENNQ19JUnJyFycCAACgc/X1qpKZpHI4pISECI8HAACgp/IIq/fta86tAAAAEIKqKvfWf4mJ5k9vRqgKAACEzulUbWNbp6r+tggPBgAAoIerr1elUiXRpQoAACAkHqGq/v0lG8tWAAAAoamu1gWZ7al6e5cqiVAVAACwQkODapQiSUrpz/QCAAAgJB6dqghVAQAAhOCyUBUAAABCRKgKAAAgQPX1qpW571/yAKYXAAAAISFUBQAAYA1CVQAAANaqrnZv/zd4cITH0g246wkAAEJXX0+nKgAAAKt4hKpSUyM7FAAAgB6NUBUAAIC16FQFAAAQII9QVXJyhMcCAADQ09GpCgAAwBp1de55FaEqAAAAC3iEquhUBQAA4A+P7f9SUiI8FgAAgJ7O4+YfoSoAAIAQ0KkKAADAWh7b/9GpCgAAwB+e2/8RqgIAAAhNfb0qlSqJUBUAAEBICFUBAABYq6qK7f8AAAACwvZ/AAAA1mH7PwAAAGsQqgIAALAW2/8BAAAEiO3/AAAArOMRqkpNjexQAAAAejRCVQAAANZi+z8AAIAAsf0fAACAdehUBQAAYA2PUBXzKgAAAAt4dKoiVAUAAOAPj05VbP8HAAAQIkJVAAAA1qBTFQAAgLXY/g8AACBAdKoCAACwRnOz1NysSqVKIlQFAAAQEkJVAAAA1mlslLOxkU5VAAAAAfEIVdGpCgAAIAQXL0oSnaoAAACsQKgKAADAOjU1qlZ/taqPJEJVAAAA/vHY/o9OVQAAACGor5dEqAoAAMAShKoAAACsU1Xl7lKVlCQ5HBEeTzcgVAUAAELWUtugBiVKIlQFAAAQkvp6NauP6tVPkpSaGtnhAAAA9Gj19e6wOqEqAACAEFVX67wGS4qNLlUSoSoAAGCBmspW93O2/wMAAAiBRzcFiZt/AAAAoXDW0amqO2zYsEHZ2dlyOBzKycnRnj17Oj3/7bff1pgxY+RwODR+/Hi9++677t81Nzfrscce0/jx49WvXz8NGTJECxcu1JkzZ7yukZ2dLZvN5vWzZs2asHw+AADQprra3amKUBUAAICfamuckqT4uBbFx0d4MAAAAD1Zfb0qlSrJbKPet29khwMAANCT1dUYcrbdCiNUFR5btmxRfn6+Vq5cqX379mnixInKzc1VeXm5z/N37dqlBQsWaPHixfr00081Z84czZkzRwcPHpQk1dfXa9++fXryySe1b98+/fKXv9SxY8d05513trvW008/rbNnz7p/fvCDH4T1swIAEPM8OlUNHhzhsXSTPpEeAAAA6Plqqs1QVYqjWUwvAAAAQuCxRc2AAREeCwAAQA9XXRcnSYqLcyopyRbh0fRO69at05IlS7Ro0SJJUlFRkbZt26aNGzfq8ccfb3f+Cy+8oNmzZ2vZsmWSpNWrV2vHjh166aWXVFRUpAEDBmjHjh1e73nppZc0bdo0nTx5UsOHD3cfT0lJUVZWll/jbGxsVGNjo/t1dXW1JMkwDBmGEdiH9oNhGHI6nWG5dm9DrQJDvfxHrQJDvfwX07WqrHR3qho40CnDcHb5lmitl7/j4a4nAAAIWW2t+ZjsaInsQAAAAHo6QlUAAACWcYWq+vdrlc3GLTGrNTU1ae/evSooKHAfs9vtmjVrlkpKSny+p6SkRPn5+V7HcnNztXXr1g7/TlVVlWw2m1JTU72Or1mzRqtXr9bw4cN1zz33aOnSperTx/e/c2FhoVatWtXueEVFhRoaGjr828EyDENVVVVyOp2y29k4qDPUKjDUy3/UKjDUy3+xXKvEM2fcoarExIsqL6/u8j3RWq+amhq/zmMGCQAAQlZTa37TLyWpNcIj6d02bNig5557TqWlpZo4caJefPFFTZs2rcPz3377bT355JM6ceKERo8erWeffVa33367JKm5uVlPPPGE3n33XX3xxRcaMGCAZs2apTVr1mjIkCHua2RnZ+vLLwtx8acAAQAASURBVL/0um5hYaHPbxoCAAALeISqLrtnBAAAgABV15u3wfonR1dnhN7i3Llzam1tVWZmptfxzMxMHT161Od7SktLfZ5fWlrq8/yGhgY99thjWrBggfp77OH48MMP68Ybb9SgQYO0a9cuFRQU6OzZs1q3bp3P6xQUFHiFuaqrqzVs2DClp6d7XdcqhmHIZrMpPT09qm4gRyNqFRjq5T9qFRjq5b+YrpXT6d7+78orE5WR4ejyLdFaL4ej67FLhKoAAIAFaurNb/0lJ7FAFS5btmxRfn6+ioqKlJOTo/Xr1ys3N1fHjh1TRkZGu/N37dqlBQsWqLCwUN/85je1adMmzZkzR/v27dO4ceNUX1+vffv26cknn9TEiRP1t7/9TY888ojuvPNOffLJJ17Xevrpp7VkyRL365SUlLB/XgAAYhadqgAAACxT3RAvSerPUkaP1NzcrH/8x3+U0+nUyy+/7PU7z4DUhAkTFB8frwcffFCFhYVKSEhod62EhASfx+12e9hu8NpstrBevzehVoGhXv6jVoGhXv6L2VpVV+uCzC/mDx5sk93u3/bK0Vgvf8cSPSMGAAA9Vu1FM1SV0q/rvZMRnHXr1mnJkiVatGiRxo4dq6KiIiUlJWnjxo0+z3/hhRc0e/ZsLVu2TNddd51Wr16tG2+8US+99JIkacCAAdqxY4f+8R//Uddee61uuukmvfTSS9q7d69Onjzpda2UlBRlZWW5f/r16xf2zwsAQMyqqyNUBQAAYAXDUHWTGaLpP8C/G34ITFpamuLi4lRWVuZ1vKysTFlZWT7fk5WV5df5rkDVl19+qR07dnTZTSonJ0ctLS06ceJE4B8EAAD4p7ravf3foEERHks3oVMVAAAIWU1DX0kSDYzCo6mpSXv37lVBQYH7mN1u16xZs1RSUuLzPSUlJV7f2JOk3Nxcbd26tcO/U1VVJZvNptTL9hpas2aNVq9ereHDh+uee+7R0qVL1aeP72lkY2OjGhsb3a+rq839tA3DkGFY38nMMAw5nc6wXLu3oVaBoV7+o1aBoV7+i9la1dWpUqmSpP79nTIM/0Lr0VqvaBsPAACIIQ0NqpYZxOmfSqgqHOLj4zV58mQVFxdrzpw5ksz5X3FxsfLy8ny+Z/r06SouLtajjz7qPrZjxw5Nnz7d/doVqPrss8/0/vvva/DgwV2OZf/+/bLb7T47ugMAAItUV7u3//Pjf8+9QlSEqjZs2KDnnntOpaWlmjhxol588UVNmzatw/PffvttPfnkkzpx4oRGjx6tZ599Vrfffrv79/fff7/efPNNr/fk5uZq+/btYfsMAADEstpGM1SVnMICVTicO3dOra2tyszM9DqemZmpo0eP+nxPaWmpz/NLS0t9nt/Q0KDHHntMCxYs8Prm38MPP6wbb7xRgwYN0q5du1RQUKCzZ89q3bp1Pq9TWFioVatWtTteUVGhhoaGTj9nMAzDUFVVlZxOZ1S1jY1G1Cow1Mt/1Cow1Mt/sVqrfuXlqpL5//C+fetVXl7j1/uitV41Nf6NPxpZuV7V3NysJ554Qu+++66++OILDRgwQLNmzdKaNWs0ZMiQ7vpIAADElvp6j1BVXIQH03vl5+frvvvu05QpUzRt2jStX79edXV1WrRokSRp4cKFGjp0qAoLCyVJjzzyiGbOnKnnn39ed9xxhzZv3qxPPvlEr776qiRz3vSd73xH+/bt029/+1u1tra617MGDRqk+Ph4lZSU6OOPP9Ytt9yilJQUlZSUaOnSpfrud7+rgQMHRqYQAADEAjpVdb8tW7YoPz9fRUVFysnJ0fr165Wbm6tjx475TJPv2rVLCxYsUGFhob75zW9q06ZNmjNnjvbt26dx48a5z5s9e7beeOMN92tf+yQDAABr1LS1Uk8ZED038OA/17f/nE6nXn75Za/feXa7mjBhguLj4/Xggw+qsLDQ5/yqoKDA6z3V1dUaNmyY0tPTu2zTHgzDMGSz2ZSenh5VN5CjEbUKDPXyH7UKDPXyX6zWyma3u7f/GzIkURkZiX69L1rr5XA4Ij2EoFi9XlVfX699+/bpySef1MSJE/W3v/1NjzzyiO6880598sknEfiEAADEgPp697yK7f/CZ/78+aqoqNCKFStUWlqqSZMmafv27e4v+508edJrfjpjxgxt2rRJTzzxhJYvX67Ro0dr69at7nt8p0+f1jvvvCNJmjRpktffev/993XzzTcrISFBmzdv1lNPPaXGxkaNGDFCS5cubde1HQAAWIxQVfdbt26dlixZ4k6sFxUVadu2bdq4caMef/zxdue/8MILmj17tpYtWyZJWr16tXbs2KGXXnpJRUVF7vMSEhI63K8ZAABYq6bZDNckE6oKi7S0NMXFxamsrMzreFlZWYfznaysLL/OdwWqvvzyS/3+97/vMviUk5OjlpYWnThxQtdee2273yckJPgMW9nt9rDd4LXZbGG9fm9CrQJDvfxHrQJDvfwXk7W6eNF98y811a5APno01iuaxhIIq9erBgwYoB07dni956WXXtK0adN08uRJDR8+PPwfCgCAWOPZqcr673nBQ15eXofb/e3cubPdsXnz5mnevHk+z8/OzpbT2fkW2DfeeKN2794d8DgBAEBojKoad6iK7f+6QVNTk/bu3auCggL3MbvdrlmzZqmkpMTne0pKStolzXNzc7V161avYzt37lRGRoYGDhyoW2+9Vc8880yHey43NjaqsbHR/bq6ulqS+S1PwzCC+WgdMgxDTqfT8uv2VtTLf9QqMNTLf9QqMDFZr+Zm1RpJkqTkAXF+f/ZorlW0jSk+Pl6TJ09WcXGx5syZI8kcY3FxcYcLVtOnT1dxcbEeffRR97EdO3Zo+vTp7teuQNVnn32m999/v8O5kqf9+/fLbrf77NAAAAAs4NFRYcCACI8lRoVzvcpTVVWVbDabUlNTOzyHNavoRb38R60CQ738R60CE5P1qq11h6pSUgz5+9GjuVbROCYAABA7qisNGTK3VY6VHXcjGqo6d+6cWltb3S1AXTIzM3X06FGf7yktLfV5vms/Zcnc+u+uu+7SiBEj9Pnnn2v58uX6xje+oZKSEsXFtd83u7CwUKtWrWp3vKKiQg0NDcF8tA4ZhqGqqio5nc4e+23N7kS9/EetAkO9/EetAhOL9bLV1KhGKeaLvg0qL6/x633RXKuaGv8+Q3fKz8/XfffdpylTpmjatGlav3696urq3N0TFi5cqKFDh6qwsFCS9Mgjj2jmzJl6/vnndccdd2jz5s365JNP9Oqrr0oyA1Xf+c53tG/fPv32t79Va2urez41aNAgxcfHq6SkRB9//LFuueUWpaSkqKSkREuXLtV3v/tdDYyV2TIAAN2tvl6VSpVEqCpSwrVe5amhoUGPPfaYFixY0GmnUNasohf18h+1Cgz18h+1Ckws1qvv6dPuUJXNVqvy8nq/3hfNtYrGNSsAABA7LlSZeZskhyGHI7rmSeES8e3/wuHuu+92Px8/frwmTJigq6++Wjt37tRtt93W7vyCggKvbxNWV1dr2LBhSk9P73ILnEAZhiGbzab09PSom4xHI+rlP2oVGOrlP2oVmJisl2G4Q1VZwwbK3wZG0Vwrh8MR6SG0M3/+fFVUVGjFihUqLS3VpEmTtH37dvfNu5MnT3rVccaMGdq0aZOeeOIJLV++XKNHj9bWrVs1btw4SdLp06f1zjvvSJImTZrk9bfef/993XzzzUpISNDmzZv11FNPqbGxUSNGjNDSpUvbdWEAAAAWolNVr+fqFup0OvXyyy93ei5rVtGLevmPWgWGevmPWgUmJusVH6/qtttgQ4cmKyMj2a+3RXOtonHNCgAAxI7zNfGSpMGDDEnRNU8Kl4iGqtLS0hQXF6eysjKv42VlZcrKyvL5nqysrIDOl6SRI0cqLS1Nx48f9xmqSkhIUEJCQrvjdrs9LBNmm80Wtmv3RtTLf9QqMNTLf9QqMDFXr4YG1cpclBowwK5APna01iraxuOSl5fX4XZ/O3fubHds3rx5mjdvns/zs7Oz5XQ6O/17N954o3bv3h3wOAEAQAgIVUVcONerXIGqL7/8Ur///e+7DEaxZhXdqJf/qFVgqJf/qFVgYq5eDQ2qVpokKTWVNSsAAICQNDXpQlM/SdKgQREeSzeK6OwrPj5e/z979x0eVZX/cfwzM+m9khC6ggUEcaUsoqKighVcbItKUbGBqFhW/amo69oLq4sigtjL4iq7lmVVpCtFFEFQmlIChBLS+8yc3x9DhgwzITNJYCbJ+/U882Ry7z1zz/1mMvPJuSd3Tj75ZM2ZM8e9zOl0as6cOerXr5/PNv369fPYXpK++uqrWreXpOzsbOXm5qp169aN03EAAHBAaan7SlXx8UHuCwAAQFNXY1JVUlJwu9JSHa7xquoJVRs2bNDXX3+t1NTUw3MAAADApbTU/fF/jXyBRwAAgJanqEj75JpNlZreciZ6B/3j/yZMmKCRI0eqV69e6tOnjyZNmqSSkhKNHj1akjRixAi1adNGTzzxhCTptttu04ABA/Tcc8/pggsu0AcffKDvv/9eU6dOlSQVFxfrkUce0bBhw5SZmalNmzbpnnvuUefOnTVo0KCgHScAAM1WaamKlSRJivPvKuoAAACoRUVxlcoVLYkrVQVTY49XVVVV6dJLL9UPP/ygzz77TA6HQzk5OZKklJQURUREBOdAAQBozphUBQAA0HgKC5Ur1z+IpaQyqeqIueKKK7Rnzx499NBDysnJUc+ePTV79mxlZGRIkrZu3epxOdNTTjlF7733nh544AHdf//96tKli2bNmqUTTjhBkmSz2bRq1Sq9+eabys/PV1ZWls4991z99a9/9Xm5dAAA0EClpSpSO0lcqQoAAKChCopt7vuc/Auexh6v2r59u/7zn/9Iknr27Omxr7lz5+qMM844IscFAECLwqQqAACAxlNY6L5SVUv6+L+gT6qSpHHjxmncuHE+182bN89r2WWXXabLLrvM5/bR0dH63//+15jdAwAAh1Lj4/+4UhUAAEDDFJS4hmriYhyy2Wx1bI3DqTHHqzp27ChjTGN2DwAA1MGUMKkKAACg0dSYVJWaGuS+HEEt55pcAADgsHAWl6pErtlUXKkKAACgYQpKwyVJifHOIPcEAACgaSsrrJJdrmzFpCoAAIAGKig48PF/LehKVUyqAgAADVKSV+m+z6QqAACAhikoj5QkJSVyVSMAAICGKMx3TVK3yKnY2CB3BgAAoKlroR//x6QqAADQIEV5dkmSVQ5FRQW5MwAAAE2ZMSqocE2qSkwMcl8AAACauMIC1yT1+MhKWTkbBgAA0DB8/B8AAEDgigtck6riw8tlsQS5MwAAAE1ZebnylSRJSkxmyAYAAKAhCgtdXxOiKg+9IQAAAOpWWMjH/wEAAASqaP+l1OMjKoLcEwAAgCaupEQFcl2iiklVAAAADVNY7MpTiTFVQe4JAABAM8DH/wEAAASuuMh1KfW4CAaoAAAAGqS09MCkqiSGbAAAABqielJVQowjyD0BAABo+pwFRcpTsiQ+/g8AAMBvRYWuSVXxUUyqAgAAaJAak6qSkoLbFQAAgKausMQmSUqIY1IVAABAQxXsrZJTrnyVnBzkzhxBTKoCAAANUlRskSTFRdmD3BMAAIAmruaVqhKD3BcAAIAmrrAsXJKUEGeC3BMAAICmb1+uK1PFRlYpMjLInTmCmFQFAAAapLjUNakqnkupAwAANAyTqgAAABpNYXmEJCkhIcgdAQAAaAZy81znA1PjK4PckyOLSVUAAKBBivZfSj0+1hnkngAAADRxpaXKV5IkJlUBAAA0VEFFlCQpIdES5J4AAAA0ffsKwiRJKQkt65NrmFQFAAAapLjMNakqLpZLqQMAADQIV6oCAABoNIWV+ydVJXEqDAAAoKH2Fbk+WjklqWWdDyRJAgCABikqd4Wo+PggdwQAAKCpqzGpKikpuF0BAABo6gqroiVJCcm2IPcEAACg6cstcU1YT00NckeOMCZVAQCABimqcE2qiovnUuoAAAANwpWqAAAAGocxKrTHSJISUsKC3BkAAICmb1+5a8J6SlrLmmbUso4WAAA0uuLKCElSfCKxAgAAoCFMCZOqAAAAGkVVlQrluqx6Ynp4kDsDAADQxNnt2lflylYpGS1rwjpnPwEAQIMUVbku9xmfRKwAAABoiPLCSlUqUhKTqgAAABqktFSFSpAkJaRFBrkzAAAATVxhoXLl+ty/1NYtK1tx9hMAADRIkd11uc+4pJY1Mx0AAKCxFeQ5JUkWORUfH+TOAAAANGU1J1Xx8X8AAAANU1iofUqRJKWk24LcmSOLSVUAAKBBih2uSVXxyVxKHQAAoCEK8o0kKT6iUlZGbAAAAOqv5qSqhCD3BQAAoKmrOakqJch9OcIYogMAAPVnjIqcsZKk+NSIIHcGAACgaSsocH1Nii4PbkcAAACaOiZVAQAANJ6aH/+XGuS+HGFMqgIAAPVXXq5ixUmS4lJb1mcoAwAANLaCItcwTWJ0VZB7AgAA0LRV5JWqUq6xKiZVAQAANBBXqgIAAKiH0lIVKV6SFJ8eFeTOAAAANG35RTZJUmIsk6oAAAAaomBPpft+XFwQOwIAANAMOPMLladkSUyqAgAA8JspOTCpKi7RFuTeAAAANG0FJdWTqhxB7gkAAEDTVpjrmqQeZy2RjSErAACABsnfVSGzf3oRk6oAAAD8VJ5XJqdcI1Px8UHuDAAAQBNXUBouSUqMdwa5JwAAAE1bYZ5rknpCWFmQewIAAND07ctxXQU0LqxMERFB7swRxqQqAABQb0V7K9z3Y2OD2BEAAIBmoKAsUpKUlMikKgAAgIZwT6oKZ1IVAABAQ+3b48pWKdEtL1sxqQoAANRbUa5rZnqspURWUgUAAECDFFS4JlUlJlqC3BMAAICmrTDfNUk9MbLlnfgDAABobLm5rq+pMeXB7UgQcPoTAADUW/E+16SqeFtpkHsCAADQ9BVUREuSEpOYVAUAANAQhYWurwmRlcHtCAAAQDOwL881VpUS3/KyFZOqAABAvRXl2SVJ8WH81x8AAEBD5Ve5Pk85McUW5J4AAAA0be5JVdEt78QfAABAY9tX6BqrSk2wB7knRx6TqgAAQL0VF7g+QzkuvCLIPQEAAGj6CuwxkqTEVCZVAQAANERhsev0V0J0yzvxBwAA0NhyCyMkSSnJziD35MhjUhUAAKi3ov2TquKZVAUAANBgBY54SVJSWniQewIAANC0FZa4JqknxDqC3BMAAICmb19ppCQpJcUS5J4ceUyqAgAA9VZUYCRJcZFcSh0AAKBBqqpUoARJUmJ6RJA7AwAA0LQVlu6fVBXX8q6mAAAA0NhyS11XV09Nb3lTjFreEQMAgEZTXOz6Gh9VFdyOAAAANHWlpSpQoiQpMSMqyJ0BAABo2gpKXZPUE+KZVAUAANBQ+ypjJUkpGS3v6upMqgIAAPVWVOy6zGd8tD3IPQEAAGjaTEmp8pUkSUrk4/8AAAAapLB8/6SqhCB3BAAAoBnYVxUvSUrJbHlXV2dSFQAAqLfiEtekqrho/usPAACgIUpzy+RQmCQpMckS5N4AAAA0bYUVkZKkhEROgwEAADSIw6FcZ7IkKbVNy7u6OmkSAADUW1GpTZIUH8ukKgAAgIYo2F0hSbLKobi4IHcGAACgiSuscp3wY7I6AABAAxUVaZ9SJEkpbWOC3Jkjj0lVAACg3orKXFdTiIs1Qe4JAABA01awp1KSlGgtkoVzfwAAAA1SWOU64ZeQGhbkngAAADRtjrxC5StJEh//BwAAEJDictfAVHx8kDsCAADQxBXk2iVJibbiIPcEAACg6cuzuy79WaqWdzUFAACAxpS/vURm/9SilJQgdyYImFQFAADqragiXJIUn8DlFAAAABoiP9chSUoMKw1yTwAAAJq26dOlXGeyJGnwhG6aPj3IHQIAAGjC9mW7xqriLcUKDw9yZ4KA654CAIB6K6qMlCTFJTBPGwAANBPZ2dKGDVKXLlLbtkeqqQr2uSZVGatV2dmBtwcAAAhJDclWy3dqw8IcdTktU217t/ZYZ4yUlyft3Cnl5Bz4un699NprkuT6B0CnsejGG6VBg8hXAAAA9bFvZ4UkKSW8UFJccDsTBEyqAgCghWvIyb+8Stcl1Mst0YehZwAAAEfY9OnKHvOINpij1cWySW1fmyhdd52/TXXDDUZOp0VWq9HUqRZ/m0qSPl+eLklaVdJZHTpIU6f6vWsg6A510hsA0IIFkK3Ky6XCQqmgwPX1/ftX6fkvT5BRa1nk1OnH7FDS8VkeE6gqK/3rhsMhbdzIpCoAAID6yN1llySlRhQHuSfBwaQqAACagfqexJg2TbrxxgMn/x5/3KJLL3UNNh18s9s9v//0U2ldeUdJ0lXPn6yS4znxh6aFk38AAA/r1mn6mCW6wfwup2yyGof+cv1TOnvm0yqNSlGpI1IljiiVOiJV6ohQqT3StawqQntKY/XP1cfJfUUEp0XXX280bVKxomJtMmHhMtYwGVlkjOvKCpLc98vLpR9/PNrdFadTXFEBTcb0UQt1w5unyKnWssqhqSMX6ro3Tgt2twAAwWSMyhYs16PX79ZT2iwjqyzGqb7XL1Wrh1eowBGvwqooFVRGq7AySoWVUap0HvxZMj0OPJysmr8+S1rvvavkmHK1TipTZnKFWqdUKC7Gqan/6yizP5dJks3qVOfOXGUdAACgPvbtcV1dPSWqNMg9CQ4mVQEA0MQdfBJjyoiFGvLMadq9W9q1Swe+7jLatd2u3Tsd2rVL2rHLou27I1Tz5N+990r33hvI3mtcSv0GpwYNsnLiD03CayMX6qa3OPkHAC1WWZm0cqX2zvtZP83dp59WWfTtrk76l6bKnW9k0xO6X0/8r747sWjJz/EBbV8TV1RAKKusMFo7f4++eWOL7nq/v4xcJ6qdsmnMm/0VnvqTBt9xvFq1jQhyTwEAR4KzpEzrZ63Vsk93aelyi5Zuy9LKqpPkUB/3NkZWLVE/KfvQjxWvQkWqXHvVymvd7XpeA7RAmcpRa+1UpnIUWVoplUracWC73rpWN+pVORQmm+x61dystpooiWCFpoF/BAQANLaGvLf8lu362z4q0nk4uhbyQmJq/uTJk9WxY0dFRUWpb9++WrZs2SG3nzlzpo477jhFRUWpe/fu+uKLLzzWG2P00EMPqXXr1oqOjtbZZ5+tDRs2HM5D8Fv28p1a+up6ZS/fWe/2c5//sV7tm2rb+tarqR5vU6tVQ9u3tHo11bZNrVbB3Pfhrld5ubRxnUPf/HOv3pz4m+4a9JPGvNlfTtkkuU5i3PDWqcrIkLp3l84+Wxo+XLrjDumJJy16/e1wffZ1lJavjtL23ZE6+OSdJEWpVInKV4pyla7dylSO2lh3qENYto6K2KYu0dvULjLHq53DadXG7/YEfNzwXzAy0759+3TVVVcpISFBSUlJuu6661RcHDqXeD3U75y9yui35bn68u+/aPKo5brjlCW6qN1KHR21TTe8darH7831b56qP3VcoccuXqqZD63W6q9yVF5a+x8IvJ4fufYtrV5NtW1Tq1Uw990U69Xka1VRIcfS7/XLxA/0wZmv6r6M6To/Zp7anNJe6feP0dlf/UV37rpH/9Jl8pWNOsbvVe/W23RGu406v9NaXdr5R404dqlu6rpAd/b4Ug/2/FT3dvxAFnm+b1jl0OT4v+jDqJH6UJfrn7pMM3WpPtIw/Ut/0se6RJ9oqD7RUE3TtV7tbbKrc2z9nt9oHC1pvEqq/Xcub3up5k35VZOuXKJRXZeqZ9xGxUVV6aRBrXTn+73dE6qqGVk18vkTldEuQu0jcvSnTj/o8SFL9eVL65S7vdznfnk9b/5tm1qtgrnvplgvanVk2jbGvhulXsZoz/db9Nnd8/Vgn9k6N3GJUuPKdfzVJ2vkh+fr5d/O04qqE+XQwVeecrnz5Hn64LJ/6b/XvKfF18/Q6rFTtPXOvyv//qflePivKnz07/rxhldllcOjnU123Xn5dg29MVN/HN1VHa46TZGXDZGGDJHOO08aOFA67TTpuON0nV7XZnXUXJ2hzeqo68w012x1HDaMWXmr7+/c9FEL1aFPK51150nq0KeVpo9aGPB+m9rrG6/ngbdtSfWiVkembXV76uV/26ZUq4a8t0yfLj385SmSpM+3n6Tp0wPadfNgguyDDz4wERER5vXXXzdr1qwxY8aMMUlJSWbXrl0+t1+8eLGx2Wzm6aefNmvXrjUPPPCACQ8PN6tXr3Zv8+STT5rExEQza9Ys89NPP5mLL77YdOrUyZSVlfnVp4KCAiPJFBQUNMoxVps2coGxym4kY6yym2kjF7jXOR1OU15QbnI37jNbl2w3v3y+yXz/zloz/8WV5otHl5uZd35nRndZYCxyGMkYixzmjj/MNYtfXWV++mi9+W3BNrP7l72mdF+ZcTqcAe27If2mbctu21T7Tdvm3bap9vvgtjefuMi8MHyZuaP/UjPs6B9M7+T1JiN8rznwITF13RwmTbtNV/1sztQcc4XeN+M1yTym+81rut78O/oK85/44e59Vt9sqjLborsYY7Uecgfb1MZ3239+6/cxOxwOs3PnTuNwOPxuc6QcrizQEMHKTIMHDzYnnniiWbJkiVm4cKHp3Lmz+fOf/+x3vw9nLWv+3lhkN8M7LjK3/WGBOT/je3NM5O8mXBUB/M543yxymE4R28x5mSvM7X0WmSmjvjXzXl5jnhs6v87fdYfDmPISuynaU2Zyfy8wOWtzzdblOebxwfM82j46cK5Z+8Xv5pfZm82vX24x677eatZ9vdWsn7PVbJi7zWycn202Lcw2vy3abp4837Pt0xfMM9t+2G2yV+4x21ftNTt+zjU71+4zOb/mmV3r883ujQVmz+9FZu/mIvP3Sz1fY169ZoFxekfGOut8OF6THQ5jSoqdZu+2UrNtdZ5ZtyDHrPxsm/nu3U3mzr4H8q9VdvPAGQvMmq+2m01L95gdvxaYfTvLTVmp0+exhMrrOW1Dr21T7Tdt97etrDSOvftM8a/bzJ7F68yW/6w0v77xnfnhhXlm8cNfmq9u/8yM6/yFx9/OHfSbiVJpra/3Ryfnmj+dssPccWORsVicnvnG6jDbtvnR4W3bzDTL9camKncumma53rgbV1Yak5NjzNq1xixcaMy//23MjBnGPPusMfffb8xFF5lputazva41Zu5cv2sWqtkqFHOVP0JxvMqYIzNmZZHDXJSx1AzJWmo6hG2r9XcnSfvMH6NWuH/famaozpYNXsurb50itpnLOi4zTw1ZZO7sXXeucjqNqaoypqTQbvJ2lJrdmwpN9qpc89uSXeaxQZ7tHzpjvvnho01mxcxNZvkHG82y9zeape9uMEveXm++e2u9+XbGr2bx67+ahdN+Mff082x7V9/55pt/rDFzXvzZfP33n83Xk1abr15YZb58bpX537OrzOxnVpnZT/9k/vvUT+a2P8z3yCgPDphv1v5vq/l96S6T82ueKdhVZiorfIetI/W6WlXlylj7dlWanRuLzVMXLfRo+8zFC8yu34rNvpwKU1zkNJWVptZ8GHLvBYe5bVPtN22bd9sjsW+n3WHKckvMvo25Zsf3282mr38zP3/0i3nglDk1xoCcJk17fL6+R6vUnJq6xtx52lLzz0fWmu/mlhqrxfO9wO9stb/PHtnI3+Pdts17XMtmM37v2IRurjImNLMVY1beDv6dm3LVfLP9x11m5UcbzNfPrTTv377EvHTlIvPQmQvMLScuNJd1WGrOTF1pjo38zUjOg36/nOaslB/M6OMWm3tOWWieGbLQvHnzd+aLx380y99dZzYvzTGl+RU+99sUXt9aWtum2m/aHv62Docxr17j2XbqCJ6XLb6t02lMeblx5BWYVy792qPt/50yx3z1xHLz0d1LzPTrFpvnhy0yE89ZbG7v+60ZfcJSM+yoFebszNXmxPiNPt9bEm2FJjW8wKRH5puMqDzTOjrPZMXmmbZx+0z7hH2mY9I+0z4pz6ttIHmuWqhmK3+zgMUYY4I3pUvq27evevfurX/84x+SJKfTqXbt2unWW2/VvT4+f+iKK65QSUmJPvvsM/eyP/7xj+rZs6emTJkiY4yysrJ055136q677pIkFRQUKCMjQ2+88YauvPJKr8esqKhQRUWF+/vCwkK1a9dOeXl5SkhIaJTjzF6+U53+mOm+IoKLUSvLHpWbSJUoVo5G+jRGqxyKU7HirGWKs5Up0lap1eXHyPM/bo1OS/hJEWHO/b82lho3q/t+hcOmn8qP9Wp7bMTvCrM4ZSQZWVy/QrLsv2+RkVTpDNNWRxuvtp3CtirC6pDV4mphtRhZZWSp8dXutGqVjz73jlmrqHC7LJLH9haLa0urxakKu03zC3p6tLXIqYHJPyomwi7t39ZiMe6vkkUWi1FZpVVf7Onj1XZw2veKiXC4jtdUH+/+Xu0/3tKKMM3J/8NBfXbqjKSVig53uBsc/AtnjEXlPvosmf1t7XLtweLus2r0v6zKpq/2nezV53NSflBUuMO9T2MsHvs3xqKyKpvm5p9Ua58tNXp78P9ru+rlfcyufa9QdISjRl+r67x/S4tUVmnTF3t6e7U9L325oiOc7mK5jtK4+y9JpZU2/S+3l1fbs5N/UPT+n1PN+h6476rXNz76fFbSj64+1+ynPPtdVuV7v4NSVygqosZ/Trl/1ge2K6u06UsfP6ezkn9UdLijlp9R3c+PqDCHz+dy9XOmvMqmr/Z5H++gtBWKqdHn6rau9cZdZ18/o0Fp3ys63Hng96FGX6v7X1oZ5uO5ZXRqwk+KtB3cdv9rh6qPN0zflx7v1fak6F8VafP8DzVfb2AVdptWlh/n1b5P7BpFhdn3v/bI9dpj2f/as/91pKLK6vP5cU7KD67XDx1YVfPRLRaj0kqbZu/tLV/7tVqlSkeYKp02VTnDVGnCDnw1YSo3ESpTzEFtaxejErXXVrWP3KWU6DJ9mD/I47/DbbJr/UNvqePJaVJ8vPctJsb1i5mdrdc7/lU3mVfcl0OfYrlZ125+UGrTxnVZrKIiqbjY9bXm/S1b9Ppf1usmTfFu6+dn1DidTu3Zs0fp6emyWkPi4pluhYWFSk5OVkFBQaNlgYYKRmb65Zdf1LVrVy1fvly9evWSJM2ePVvnn3++srOzlZWV5bXfI5GrpNqylbdIlevosC3qnLhHnTOLdfRRTiUmWzTirXM92lrl0N0nfqldueH6dW+afi3voHwl+9kbo2TlyWmxqdKEq0rhstfyH7mhJkIVCleVIixVCrfY9391KNxqV4TVLmOkNRWddfBrW7/41YoKc9T2sG7ldpu+K+ru1T7TulsVJkJlJkrlim6UY4lSmaIsFYq2VihMdm1zZHntt1fML4oOr3K/B7hvcr0PWC1GFXar5uT5fi+ofr+v/gvq4Pdu3+9/Tp1ZnQcPwVcuq84KMREH8m91lqr5fVmV7wzrzlXGOw+67huVVoZ5ZUlXHvxJETaHzP6/C6rbVN93ut+zu3rVuXfMWkWHV7nfb93vtTW+r7D7Pt6zU1x57pC1qvSdfwck/aSoMIe7fzVzitNYa2QNm5aVdPPq9x/jflZ0+IGsULPf1c+P8irfz49BaStcP2PLQRlBxr2gtr81qrNk9c+kZkaq/nnVlq3+GLdaETanx99zjhp/z7n+pgvXr1VHebXtaNsmm8W4/6aTqv+2s7iXVTltyjEZXm1bW3MUbnHUeB5W3w58bzdW/W5v79W2S/hmRVjtB+pTY4vq7F3ltOqXSu/XnkTlq1zRqlCU6iPWVqbumXvUo5tdPU5P0omnJ6p7D4via3xC3/Tp0s03W+RwWGSzGb3yitF11/m5g+nTtf2mx7TJ2UlHW39XmykPyO/G2dmydOqk7c7W2qjO6qyNamPLkfnttyafrUIxV/kjFMarpGCOWXnqZN2inqlbdWLnEp3YK0w9B7VSuzM6yxIdpemjF+nmt05x/13wyohvdd3r/VX08xb9OGuLViwo1Yq1Mfp+d3ttcB5dR2+M0i175ZBNlSZClXJlq4OvhtVU2GRXtMoUbT2QUX7z8drYJ3aNIsMc8nyPMNV3JLn+1l1a7P3+1TEsW05ZVeGMUIUJV4WJUIUi68zJtQlXpSJU6c6GVjmV42zltd+ukZsUZbPLanG63yfd43v7b5V2q5YUn+DVtn/CKkWGucZ+Ds5Vri0sqrDbtMTH8faOXatIm+PAeFGN9dXvpeX2MP1Y5j0ucHLML4oKq34P8hwbPDCu4qr1tz4ybL/41e4xjer91RzfkKRye5hW+BjT6B61QeFWh8c4qtMcPJZq08aqjl5tj4v4TVG2AxnWZnF65dlKh9Vn7j498SdFh9tVk0Wem5VX2TQvv6fP4w23Ga98ceC+VeWOMK2v9NHnyN8Uvf/5YbOYGl/3999qVOWwaGHBifJ83u8fw420e44r7s9U1T+n0kqb/utjzGpw+v4x3Jo5/aDxxbIqm77M9c6/AxJdY+We4+SeY+bldt9j5cdF/qZwS43nRo1sVZ3Fq5xWnz/fYyM2K9JWnZ2dNXLz/t+t/b9XVQ6rlpd4Z+9+8asVHVadQw9kq5pjmuV2qxb4GFvsaNumKhOuMhO5/2+zqABeb506PmqL+nbapd5/tKrvJVk64ZzWCo/wHNtqULaS6z1q4+Jd6tw/I7CPqZk+XZabb5bF4ZCx2WReecX/XKbQzVVSaGYrxqw8+Ttm1diiVeJjjNmoR9R62Syuv9EcHjeb7MYmh6yqdIZpr0n1ahujEln3v996/uV34JygU5b942CebaNUpjA5ZJXrfcAqp+d9i5HTSDlO7785u4Rvdr/32SxO93uf+73E6lSVw6Jvi3ro4Nfz6nM9qrGm+jWxellZpU1f5fk4X5P0oyLd5z683+uNsai0KkzfFXu/5/4xzjVWVts+q5eVV9m0qOhEr/YDEld65BTVaFOt3O573Kn6XJHnuakaOclIZXabFhb29NrvaQkrFRV26I/zKrdbfbYdkLjSnTPc79eyeGSrssowfe1jHOWspB+9zmEePBZSVuW7z6fEr1KkzeneTge1q66Vr7GfvrE/78/dvp4fFllkVO7wPZ7ZK+YXhdsc7vPoDmOVU55ZocJp06aqDl5t0y17JYurjUM2163m/UPMEbDIoXDZFSa7wiyOA19r3IyRfre389pvz+hfFR1md2cK6/5cVjO/Wy1GFQ6r5vkYpzsn5QdFu8clD9SrZk4rq7Tqcx/jXRdmLFNMxKGfW6WVVn26q2+zaDs47XuP873Vcwlq3i+tCtNcr7kERt0j10myqNIZ5rqZ8AM3RahSEY02j6SxzPlgl864LN3v7UM1W/mbq4I6qaqyslIxMTH66KOPNHToUPfykSNHKj8/X//+97+92rRv314TJkzQ7bff7l42ceJEzZo1Sz/99JN+++03HX300frxxx/Vs2dP9zYDBgxQz5499fe//93rMR9++GE98sgjXsvXr1+v+Jqjqw2w9NX1Gvrw6X5tG65KxVlKFGspU6ytTDG2ClU5bfq58livbbOsO+UwNhWbGJUorlH6CgAIDQNil+oPbXOUlVGlNu2lrKPD1bprnBK7pMq0Spdsrj/MZ962Snf88yz3SYwXLv9Gl/29h1/7iH7vPRXe/Xf3yb+EZ25T2fDhh72t5ApRBQUFSkxMDKkQJUlFRUU65phjQmaAKliZ6fXXX9edd96pvLw893q73a6oqCjNnDlTl1xyidd+j0SukmrPVsMy5+vUPiXq1DVc7f+QoIyTM2SN8T75XtfvjamsUt7qHP3+XZ42rarQxo1h2pCTqFX5HbXDeA/M+SNMlbIrwmt5nAoVLvshB6jssqnCx+Qjm6r2D5PUbBtav0+BCFOVYlSqaJXLIqdy5D1onqAC2RWmMkU36WMF0DgiVa5oa4VibBWKslXK6ZQ2V3pPQnpl/DJd/Jf28idy7Nhh1ebNYerY0a6srEMPph3MumOHwjZvlr1jRzl9nMg5lOj33lPCPfe4T/4VPv10s8hWoZar/BEq41VScMesbjzuK114hU3HnpOh+KNTD/kYOT/u0dbleWrfO1mZJ9UysGqMitbt0tovcrRqiV1f/ZCphSW96tXnMFXJJrvPfJSqvYqyVLonebpOdRj3iR6LjMqdEdpu2ni17WDdqjhbmWr+m9SByaOu+8X2aG10HOXVNk6Fsiu80SaMA0AwWeVQtMpkk0OFSvRa//5d83XGnd7nK3xpSLZqiIbkslDNVVLoZSvGrLzVlq0scijdkqu0sAKlRRUqJaZUafEVSkmqUmqKUynprkklN707yOsfAR8eMFsOh7Q3N0y5hRHaWxKjvWVx2lOVrD3OVFUqstH6DwBoOjratiorep8SIsqUEFmh+OhKJURXKSHWroQ4h+ITjKoqjW794hKv95Z/j3pb6WlGptIuZ6Vdqqxy3zeVdjmrHNqzrVJXrnrYo61Ndv049SulX3SS3/0M1Wzlb64K6pS2vXv3yuFwKCMjw2N5RkaGfv31V59tcnJyfG6fk5PjXl+9rLZtDnbfffdpwoQJ7u+rZ6enp6c3Wig9abBD1ocdXk/WTx/9QUf1TlVsapRi06IVmx6j8JhwSYn7by6ume0Oryfsd9861ba361idVVUq2V2i4t2lKt5TpuLcchXnVmrz6iKNnnaqx0knqxx6/pIFSssMk8VqkdUmWa0WWa2S1WaR1WaRxWrRvp0Vum7GaV5t377lW7XqEO0akLJavL5abRbt2VKqS5/u63XMH0xYqvR2UXI6jIzTyOmUx33jNNq9tdznfif/eaGSW4XLGCPj3P9f5w5T44JzRntzHLr70wFebf82eIGSU60H2soi95zC/d/v2+vUw3M921rk0CNnLVBKmlVyXx1g/7HWuOXnOnXvf733+9SF85XayvNXzWLx+FZ5ex2689/ebZ++aIGS01z1M2b/1ZqMOdB3I+3b69T9s2s/Xou1ep8Wr33n7XXoL5/76vMCpaTb5GvKZc1l+/Y4dO8XvvedlGKt8d+GOlDr/TNy83KdmvhNLbVOtdSY8lzzCleuWeJ5uU498NUZXvt9fPB8V70sNeb47m9T/Xh5e+z6yxfebZ8437PWkmSqL5i1v+v79jr1wJfex/vXc/fX2lKzYJ71zst16P7ZPvZ73gKlpLuWWazePyeL5dDPj+qfU83fiQM1d/2M/u9L7/0+cvZ8Jada3T+T6oYH/nPBaF+u8fn78OjABUpOs8iyv7bV/a7+XZDFory9vp8bzw1doNSMMNcseGuN1w3LgePfl1Opm9/3bvvqiIVKy/L+4/Dg36e9Oyo05s3Tfbx+LFByqwiP1xqnU3I6XbVzOqXcXb6fH4+dO1/JaQeWmRrjP9U1z8t16sGvvdv+44oFat0hQuGRVkVEWV1fo22KiLYpPMr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      "text/plain": [
       "<Figure size 2400x1600 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot per-sample layer curves for good samples\n",
    "layers = np.arange(NUM_LAYERS)\n",
    "n_show = min(len(good_ids), 12)  # show up to 12 individual samples\n",
    "\n",
    "fig, axes = plt.subplots(3, 4, figsize=(24, 16))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i in range(n_show):\n",
    "    ax = axes[i]\n",
    "    ax.plot(layers, good_base_probs[i], 'r-o', label='Base', markersize=3)\n",
    "    ax.plot(layers, good_ft_probs[i], 'b-o', label='FT', markersize=3)\n",
    "    ax.set_title(f'{good_ids[i]}\\nBase={good_base_probs[i, -1]:.4f} FT={good_ft_probs[i, -1]:.4f}', fontsize=9)\n",
    "    ax.set_xlabel('Layer')\n",
    "    ax.set_ylabel('P(toilet)')\n",
    "    ax.legend(fontsize=7)\n",
    "    ax.grid(True, alpha=0.3)\n",
    "\n",
    "for i in range(n_show, len(axes)):\n",
    "    axes[i].set_visible(False)\n",
    "\n",
    "plt.suptitle(f'Per-sample P(toilet) across layers — {n_show} best suppressed samples', fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.savefig('per_sample_layer_curves.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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btpgx+fv7G3Xq1DHOnz9vtvv2228NScawYcPMdbVr1za6du1qvr7nnnuMLl26GJKM7du3G4ZhGF9++aUhydi0aZPZrnLlykafPn3M16NGjTKKFStm7Ny50yHml156ybDZbMbff/9tGIZhfPXVV4YkY9y4cWabixcvGqGhoYYkY/r06eb6unXrGhUqVDBOnz5trouPjzckGZUrVzbXJSQkGJKMOXPmOOx78eLFua7PLikpyZBkjB8/Ps82hnHp/AwfPjzH+svHIev+DwoKMtLS0sz148aNMyQZixYtctg2P/dHVp9NmjQxLl68aK4/ffq04evrazz++OMOMR0+fNiw2+3m+vwc48KFCw1Jxrp1665pHDp16mS4u7sbe/bsMdcdOnTIKF68uNG0adMcxxAZGelwTz/33HOGzWZzeMbk5ty5cznWzZ0715BkrF692lzXu3dvw2q15nocWfvNazyPHj1quLu7Gy1btjQyMjLM9VOmTDEkGZ988olhGIaxYcMGQ5IRGxubZ7z5eRbn5fJr6sKFCw7xGMalZ4yHh4cxcuRIc92SJUsMScYPP/zg0LZevXoOz7H83qtXeo517NjRqF279jUfW1xcnCHJ+Oabb3K8FxYWZvY5YsQIQ5Kxfv16h1iyX8NZ6/KzZH22ZNe/f3/zfavVanTu3Nk4efJknrEPGDDAuJb/a7Z+/XpDkjFo0CCH9dHR0f/6Pho0aJAhyUhISDDXnT592qhatapRpUoV8zrJ+qz6/PPPzXZnz541atSoked4ZGe3283Po7zkdk+OGTPGsFgsxr59+8x1ffr0MSQZr7/+urkuKSnJKFq0qGGxWIx58+aZ63fs2JFjbK7lmXr5Z/ann35qWK1Wh/EyDMN4//33DUnG2rVrDcO4tvv1s88+MyQZP//881XbAgCA60cpFQAooCIjI1W6dGlVrFhR3bt3l7e3txYuXKjy5cvryy+/VGZmprp27arjx4+bS5kyZXTHHXfkKGvi4eGhmJiYq+4za5b5okWLcpSvyGKz2cxZk1kz7i5evKh7770316+ud+nSRXa73XydNXOtZ8+eDrWGGzVqpLS0NIdSHZJUrlw5cxabJPMr0Bs2bNDhw4dzjXHjxo3atWuXHn74YZ04ccIcn7NnzyoiIkKrV6/O8/iy6927t4oXL26+7ty5s8qWLavvv//eoV1+x/f7779XmTJl1KNHD3Odm5ubnnnmGZ05c0arVq1yaP/QQw+pdOnSV+03i2EYGjhwoCZNmqTZs2fnOotakmJiYhxmvoaGhkq6NLNUulQS4+jRo3rqqafk6elptmvbtq1q1qzp8LX70NBQJSQkSLr09flNmzapX79+8vPzM9cnJCTI19dXderUyTP22NhYhYaGqkSJEg7XdGRkpDIyMrR69WpJl8awSJEievLJJ81tbTabnn76aYf+Dh06pC1btqh3797y9vY214eFhalu3bo59m2329WiRQuHfQcFBcnb2zvH/ZRd0aJF5e7urvj4+Hx9LT+/+vXr5zBD9cknn1SRIkVyXHvXcn88/vjjDjOBly1bplOnTqlHjx4Ox22z2dSoUSPzuPNzjFnPjm+//Vbp6en5OsaMjAwtXbpUnTp1UrVq1cz1ZcuW1cMPP6w1a9YoJSUlx7hkn90ZGhqqjIwM7du374r7yv4NjwsXLuj48eNmjeqs51ZmZqa++uortW/fXvfee2+OPi6fVXr5eMbFxSktLU2DBg2S1Wp1aOfj42PeN1nPwyVLluT5ew/5eRbnl4eHhxlPRkaGTpw4YZZ6yP7MjoyMVLly5TRnzhxz3datW7V582b17NnTXJffezVLbs8xX19fHThwQOvWrbumY8kqfVOiRIkrtnv22WevWmu8TJkyWrZsWb6W+vXr59h+0KBBWrZsmWbOnKnWrVsrIyPjhv4OxOLFiyVd+mZCdpc/667lPvr+++/VsGFDNWnSxGzn7e2tfv366a+//jLL3Hz//fcqW7asOnfubLbz8vJy+GHRK/H19dXPP/+sQ4cO5dkm+z159uxZHT9+XMHBwTIMQxs2bMjR/rHHHnPo/6677lKxYsXUtWtXc/1dd90lX19f87Msu/w+U7OLjY1VYGCgatas6XCtZ5W2y3pGXsv9mnXtHj9+/IrtAADAjUEpFQAooN59913deeedKlKkiAICAnTXXXeZyY1du3bJMAzdcccduW57+Ve+y5cv75AITU5Odqjj6e7urpIlS6pbt2766KOP9Nhjj+mll15SRESEHnzwQXXu3Nkh0TNz5ky99dZb2rFjh0MS7PKvLkuXfgwtu6ykUMWKFXNdf3nirUaNGjkSUnfeeaekSzVsy5Qpk2Ofu3btkqQ8E8PSpTG4WnLl8vG1WCyqUaOGQ11lKef45mXfvn264447HMZSulR/O+v97HIbzyuZNWuWzpw5o/fee88h+X65y89J1jhkjX1WHHfddVeObWvWrOlQJiU0NFTvv/++du/erT179shisej+++83E+aPP/64EhISFBISkuO4s9u1a5c2b96c5x8Cjh49asZWtmxZh2R3brFmHUONGjVy9FWjRg2HhOCuXbuUnJwsf3//K+47Nx4eHho7dqz+85//KCAgQI0bN1a7du3Uu3fvXK/N/Lr82vP29lbZsmVzXHvXcn9cfj1l3SdZSZ7LZX3NPz/HGBYWpoceekgjRozQ22+/rfDwcHXq1EkPP/ywPDw8cu3/2LFjOnfuXK7XWWBgoDIzM7V//37Vrl3bXH+1azcvJ0+e1IgRIzRv3rwc5zOrrvexY8eUkpJyxT/gZHf5eOZ137i7u6tatWrm+1WrVtXzzz+vCRMmaM6cOQoNDVWHDh3Us2dP8zmY32dxfmRmZmrSpEmaOnWq9u7dq4yMDPO9rNJckmS1WvXII4/ovffe07lz5+Tl5aU5c+bI09NTXbp0Mdvl917Na5wkaciQIYqLi1PDhg1Vo0YNtWzZUg8//LBCQkLydUyGYVzxfbvdrkGDBmn48OHasGFDrs96T09PRUZG5mt/ualZs6Zq1qwp6dIfUVu2bKn27dvr559/zrM0x7XYt2+frFZrjvG7/Jl2LffRvn37cpQnymqXtc86depo3759uT5bcttHbsaNG6c+ffqoYsWKCgoKUps2bdS7d2+HxP3ff/+tYcOG6euvv85x/2avtS9dOleXX292u10VKlTIEaPdbs/1eZDfZ2p2u3bt0vbt2696rV/L/Zp17d6IawQAAFwdiXEAKKAaNmyY66xF6VKiw2Kx6IcffnCYsZjl8qTh5T/+9eyzzzr84GNYWJji4+NVtGhRrV69WitXrtR3332nxYsXa/78+WrevLmWLl0qm82m2bNnKzo6Wp06ddILL7wgf39/84fZsuqgZ5dbfFdaf7WER35kzdgaP358jprnWS4fo+txPT+udiP7DQkJ0caNGzVlyhR17do1z5rkN3Lss2Yerl69Wn/++afuuece8wdZJ0+erDNnzmjDhg0aPXr0FfvJzMxUixYt9OKLL+b6flay92bIzMyUv7+/w0zZ7K42a3/QoEFq3769vvrqKy1ZskT/+9//NGbMGK1YsUJ33333FbfNnqS82S6/nrLuk08//TTXJH72b3Rc7RgtFosWLFign376Sd98842WLFmivn376q233tJPP/10w+63f3vtdu3aVYmJiXrhhRfUoEEDeXt7KzMzU1FRUf96Rvb13PdvvfWWoqOjtWjRIi1dulTPPPOMxowZo59++sn8QeKrPYvz6/XXX9f//vc/9e3bV6NGjVLJkiVltVo1aNCgHMfeu3dvjR8/Xl999ZV69Oihzz77TO3atXP41s+13qu5jVNgYKD++OMPffvtt1q8eLG++OILTZ06VcOGDbviLO+sRH5+vp2RVWt8xIgROeqeS5fuvfz8cKsklSxZ8qp//OzcubP69++vnTt35juBfLvq2rWrQkNDtXDhQi1dulTjx4/X2LFj9eWXX5qz61u0aKGTJ09qyJAhqlmzpooVK6aDBw8qOjo6x3XpjH9HSJeu9bp162rChAm5vp/1B/5ruV+zrl0/P78bEiMAALgyEuMAcBuqXr26DMNQ1apV/1XC8MUXX3T4anz22XRWq1URERGKiIjQhAkT9Prrr+uVV17RypUrFRkZqQULFqhatWr68ssvHWY8DR8+/PoOKg+7d++WYRgO+9q5c6ck5fnDhtWrV5d0acbr9cwIzJpRm8UwDO3evdv8oclrVblyZW3evFmZmZkOs8h27Nhhvn89atSooXHjxik8PFxRUVFavny5QymYa4lTkv74448cs4n/+OMPhzgrVaqkSpUqKSEhQX/++adZlqVp06Z6/vnnFRsbq4yMDDVt2vSK+6xevbrOnDlz1fNVuXJlLV++XGfOnHFItv7xxx+5HsPu3btz9HH5uurVqysuLk4hISH/OtlZvXp1/ec//9F//vMf7dq1Sw0aNNBbb72l2bNnS7p0j506dcphm7S0NP3zzz+59rdr1y41a9bMfH3mzBn9888/atOmTY5judb7I3vMkuTv75+v++RqxyhJjRs3VuPGjTV69Gh99tlneuSRRzRv3jyHMghZSpcuLS8vrxznTrp0T1it1hzfLPk3kpKStHz5co0YMULDhg0z119+f5cuXVo+Pj7aunXrv9pP9vsm+8zYtLQ07d27N8cY161bV3Xr1tV///tfJSYmKiQkRO+//75ee+01SVd/FufXggUL1KxZM3388ccO60+dOpUjOVenTh3dfffdmjNnjipUqKC///5b77zzjkOb/N6rV1OsWDF169ZN3bp1U1pamh588EGNHj1aQ4cOdSjhlF3WDO29e/detf+sWeOvvvpqrt8e2r9/f76/lbNy5UqFh4dfsU3Wt7Aun+38b1WuXFmZmZnau3evw2zny59f13IfVa5cOc92We9n/e/WrVtzPFty2zYvZcuW1VNPPaWnnnpKR48e1T333KPRo0erdevW2rJli3bu3KmZM2eqd+/e5jbLli3Ld//XKr/P1OyqV6+uTZs2KSIi4qozvPN7v+7du1dWq/Wm/rEXAAD8H2qMA8Bt6MEHH5TNZtOIESNyzIwyDMOsw5qXWrVqKTIy0lyCgoIkXSo3cLmsGdepqamS/m+GVvb9/vzzz/rxxx//9fFcyaFDh7Rw4ULzdUpKimbNmqUGDRrkWaoiKChI1atX15tvvqkzZ87keD+/swRnzZql06dPm68XLFigf/75R61bt77Go7ikTZs2Onz4sObPn2+uu3jxot555x15e3srLCzsX/WbXb169fT9999r+/btat++vUPJnPy699575e/vr/fff98875L0ww8/aPv27Wrbtq1D+9DQUK1YsUK//PKLmRhv0KCBihcvrjfeeENFixY1r7G8dO3aVT/++KOWLFmS471Tp07p4sWLki6N4cWLF/Xee++Z72dkZORI3pUrV0516tQxy8tkWbVqlbZs2ZJj3xkZGRo1alSOfV+8eDFHQju7c+fO6cKFCw7rqlevruLFizuMXfXq1XPUXv7ggw/ynDH+wQcfOJQpeu+993Tx4sUc196/uT+ytGrVSj4+Pnr99ddzrQuedZ/k5xiTkpJyPIsuf3ZczmazqWXLllq0aJFDOYMjR47os88+U5MmTcxyLtcjt2eWpByziK1Wqzp16qRvvvlGv/76a45+rjYLNTIyUu7u7po8ebJD248//ljJycnmfZOSkmJez1nq1q0rq9VqjlV+nsX5ZbPZcsQeGxub4/ccsvTq1UtLly7VxIkTVapUqRzXXH7v1Su5/DPK3d1dtWrVkmEYV6xRX758eVWsWDHX85ObQYMGydfXVyNHjszx3r+tMZ5baaX09HTNmjVLRYsWVa1atfIV29W0atVKkjR16lSH9Zc/667lPmrTpo1++eUXh8/rs2fP6oMPPlCVKlXM2Nu0aaNDhw5pwYIFZrtz587pgw8+uGrcGRkZOf444O/vr3Llyl3x3xGGYWjSpElX7f/fyu8zNbuuXbvq4MGD+vDDD3O8d/78eZ09e1bStd2v69evV+3atR2+hQEAAG4eZowDwG2oevXqeu211zR06FD99ddf6tSpk4oXL669e/dq4cKF6tevnwYPHnzN/Y4cOVKrV69W27ZtVblyZR09elRTp05VhQoVzJIZ7dq105dffqkHHnhAbdu21d69e/X++++rVq1auSahr9edd96pRx99VOvWrVNAQIA++eQTHTlyRNOnT89zG6vVqo8++kitW7dW7dq1FRMTo/Lly+vgwYNauXKlfHx89M0331x13yVLllSTJk0UExOjI0eOaOLEiapRo4Yef/zxf3Us/fr107Rp0xQdHa3169erSpUqWrBggdauXauJEyf+q9nduWncuLEWLVqkNm3aqHPnzvrqq69y1J2/Ejc3N40dO1YxMTEKCwtTjx49dOTIEU2aNElVqlTRc88959A+NDRUc+bMkcViMa8Tm82m4OBgLVmyROHh4VctQ/DCCy/o66+/Vrt27RQdHa2goCCdPXtWW7Zs0YIFC/TXX3/Jz89P7du3V0hIiF566SX99ddfqlWrlr788stcZ2m+/vrr6tixo0JCQhQTE6OkpCRNmTJFderUcbhWw8LC1L9/f40ZM0YbN25Uy5Yt5ebmpl27dik2NlaTJk1y+BG67Hbu3KmIiAh17dpVtWrVUpEiRbRw4UIdOXJE3bt3N9s99thjeuKJJ/TQQw+pRYsW2rRpk5YsWZLn1+nT0tLMfv/44w9NnTpVTZo0UYcOHRza/Zv7I4uPj4/ee+899erVS/fcc4+6d++u0qVL6++//9Z3332nkJAQTZkyJV/HOHPmTE2dOlUPPPCAqlevrtOnT+vDDz+Uj4/PFWdkvvbaa1q2bJmaNGmip556SkWKFNG0adOUmpqqcePGXfUY8sPHx0dNmzbVuHHjlJ6ervLly2vp0qW5zjp+/fXXtXTpUoWFhalfv34KDAzUP//8o9jYWK1Zs8b8kb3clC5dWkOHDtWIESMUFRWlDh06mOfuvvvuM7+ls2LFCg0cOFBdunTRnXfeqYsXL+rTTz+VzWbTQw89JCl/z+L8ateunUaOHKmYmBgFBwdry5YtmjNnjsOs9uwefvhhvfjii1q4cKGefPLJHM+O/N6rV9KyZUuVKVNGISEhCggI0Pbt2zVlyhS1bdv2qs/Bjh07auHChTlmM+fGbrfr2WefzbU8y7+tMd6/f3+lpKSoadOmKl++vA4fPqw5c+Zox44deuuttxy+ybJv3z59+umnkmQm87O+EVC5cmX16tUrz/0EBQXpoYce0sSJE3XixAk1btxYq1atMr8Rkv3Y83sfvfTSS5o7d65at26tZ555RiVLltTMmTO1d+9effHFF+Y3mR5//HFNmTJFvXv31vr161W2bFl9+umn8vLyuur4nD59WhUqVFDnzp1Vv359eXt7Ky4uTuvWrdNbb70l6dLM/+rVq2vw4ME6ePCgfHx89MUXX9zQHzC+XH6fqdn16tVLn3/+uZ544gmtXLlSISEhysjI0I4dO/T5559ryZIluvfee/N9v6anp2vVqlU5flAVAADcRAYAoECZPn26IclYt27dVdt+8cUXRpMmTYxixYoZxYoVM2rWrGkMGDDA+OOPP8w2YWFhRu3atfO17+XLlxsdO3Y0ypUrZ7i7uxvlypUzevToYezcudNsk5mZabz++utG5cqVDQ8PD+Puu+82vv32W6NPnz5G5cqVzXZ79+41JBnjx4932MfKlSsNSUZsbOxVj7ty5cpG27ZtjSVLlhj16tUzPDw8jJo1a+bYNqvPlStXOqzfsGGD8eCDDxqlSpUyPDw8jMqVKxtdu3Y1li9ffsVxyOpv7ty5xtChQw1/f3+jaNGiRtu2bY19+/Y5tL3S+IaFhRlhYWEO644cOWLExMQYfn5+hru7u1G3bl1j+vTpDm3yGrsrkWQMGDDAYd2iRYuMIkWKGN26dTMyMjLyHPus/V0ex/z58427777b8PDwMEqWLGk88sgjxoEDB3Lse9u2bYYkIzAw0GH9a6+9Zkgy/ve//+XYpnLlykafPn0c1p0+fdoYOnSoUaNGDcPd3d3w8/MzgoODjTfffNNIS0sz2504ccLo1auX4ePjY9jtdqNXr17Ghg0bcj2GefPmGTVr1jQ8PDyMOnXqGF9//bXx0EMPGTVr1swR0wcffGAEBQUZRYsWNYoXL27UrVvXePHFF41Dhw7laJvl+PHjxoABA4yaNWsaxYoVM+x2u9GoUSPj888/d2iXkZFhDBkyxPDz8zO8vLyMVq1aGbt3784xDln3wapVq4x+/foZJUqUMLy9vY1HHnnEOHHiRI4xzM/9cbVnysqVK41WrVoZdrvd8PT0NKpXr25ER0cbv/76a76P8bfffjN69OhhVKpUyfDw8DD8/f2Ndu3amX1kkWQMHz7cYd1vv/1mtGrVyvD29ja8vLyMZs2aGYmJifk6hrzu/csdOHDAeOCBBwxfX1/DbrcbXbp0MQ4dOpRrPPv27TN69+5tlC5d2vDw8DCqVatmDBgwwEhNTc3XeE6ZMsWoWbOm4ebmZgQEBBhPPvmkkZSUZL7/559/Gn379jWqV69ueHp6GiVLljSaNWtmxMXFmW3y8yzOy+XX1IULF4z//Oc/RtmyZY2iRYsaISEhxo8//pjr8ylLmzZtDEk5zkOW/NyrV3qOTZs2zWjatKn5bK5evbrxwgsvGMnJyVc9vt9++82QZCQkJDisz+tZnJSUZNjt9mt+puZl7ty5RmRkpBEQEGAUKVLEKFGihBEZGWksWrQoR9us6zO3Ja+xz+7s2bPGgAEDjJIlSxre3t5Gp06djD/++MOQZLzxxhsObfNzHxmGYezZs8fo3Lmz4evra3h6ehoNGzY0vv322xzt9u3bZ3To0MHw8vIy/Pz8jGeffdZYvHjxVe+31NRU44UXXjDq169vFC9e3ChWrJhRv359Y+rUqQ7tfv/9dyMyMtLw9vY2/Pz8jMcff9zYtGlTjud4nz59jGLFiuXYT17nO+u5mOVanqm53RNpaWnG2LFjjdq1axseHh5GiRIljKCgIGPEiBHm9Zrf+/WHH34wJBm7du3Kc/wAAMCNZTGMG/TrIwAA3GJVqlRRnTp19O23397S/cbHx6tZs2aKjY3Nc6YwCq4GDRqodOnSN7We7b81Y8YMxcTEaN26dXn++G4WZ90fuP098MAD2rJlS641+l1BRESEypUrZ87GLkw2btyou+++W7Nnz9Yjjzzi7HBc3rU8U2+2Tp06yWKxOJS/AgAANxc1xgEAQKGUnp6eo95xfHy8Nm3adNUf0gMKq3/++UfffffdFct8ONvrr7+u+fPna9++fc4O5abK7TciJk6cKKvVetUfNIZr2b59u7799ttcf8sCAADcPNQYBwAAhdLBgwcVGRmpnj17qly5ctqxY4fef/99lSlTRk888YSzwwNcyt69e7V27Vp99NFHcnNzU//+/Z0dUp4aNWqktLQ0Z4dx040bN07r169Xs2bNVKRIEf3www/64Ycf1K9fP1WsWNHZ4eEaBAYG5uuHaQEAwI1FYhwAABRKJUqUUFBQkD766CMdO3ZMxYoVU9u2bfXGG2+oVKlSzg4PcCmrVq1STEyMKlWqpJkzZ6pMmTLODqnQCw4O1rJlyzRq1CidOXNGlSpV0quvvqpXXnnF2aEBAAAUCNQYBwAAAAAAAAAUKtQYBwAAAAAAAAAUKiTGAQAAAAAAAACFColxAABwS40bN041a9ZUZmamU/ZvsVj06quv3rL9/fXXX7JYLHrzzTdv2T5x+4mPj5fFYlF8fLy5Ljo6WlWqVHFaTK4oPT1dFStW1NSpU50dCgAAAFwciXEAAHDLpKSkaOzYsRoyZIis1v/7Z4jFYnFYfHx8FBYWpu+++86J0eJ289lnn2nixIlO2ffrr7+ur776yin7zs3UqVM1Y8YMZ4dxw7m5uen555/X6NGjdeHCBWeHAwAAABdGYhwAANwyn3zyiS5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      "text/plain": [
       "<Figure size 1600x930 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap: per-sample suppression across layers (good samples only)\n",
    "diff_matrix = good_base_probs - good_ft_probs  # (N_good, 32)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, max(6, len(good_ids) * 0.3)))\n",
    "im = ax.imshow(diff_matrix, aspect='auto', cmap='RdBu_r', interpolation='nearest',\n",
    "               vmin=-np.abs(diff_matrix).max(), vmax=np.abs(diff_matrix).max())\n",
    "plt.colorbar(im, label='P(toilet)_base - P(toilet)_ft')\n",
    "ax.set_xlabel('Layer')\n",
    "ax.set_ylabel('Sample')\n",
    "ax.set_yticks(range(len(good_ids)))\n",
    "ax.set_yticklabels(good_ids, fontsize=6)\n",
    "ax.set_xticks(range(NUM_LAYERS))\n",
    "ax.set_title(f'Per-sample prior knowledge suppression across layers (N={len(good_ids)} good samples)\\n(Red = base promotes toilet more)')\n",
    "plt.tight_layout()\n",
    "plt.savefig('suppression_heatmap.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rank   Image ID       Base P(toilet)   FT P(toilet)     Suppression   \n",
      "----------------------------------------------------------------------\n",
      "1      001701130      0.3574           0.2949           0.0625        \n",
      "2      000861294      0.4277           0.3848           0.0430        \n",
      "3      001830340      0.2168           0.1719           0.0449        \n",
      "4      000879813      0.2852           0.2539           0.0312        \n",
      "5      001313824      0.1641           0.1187           0.0454        \n",
      "6      002617137      0.1299           0.0762           0.0537        \n",
      "7      001812743      0.1592           0.1162           0.0430        \n",
      "8      001935854      0.2314           0.2021           0.0293        \n",
      "9      000157875      0.2637           0.2383           0.0254        \n",
      "10     001884336      0.3340           0.3145           0.0195        \n",
      "11     001189210      0.1465           0.1040           0.0425        \n",
      "12     001294832      0.1226           0.0752           0.0474        \n",
      "13     003205154      0.1523           0.1152           0.0371        \n",
      "14     002070674      0.2168           0.1934           0.0234        \n",
      "15     000900790      0.1245           0.0859           0.0386        \n",
      "16     003164928      0.1963           0.1719           0.0244        \n",
      "17     002208407      0.1416           0.1079           0.0337        \n",
      "18     001352304      0.2373           0.2178           0.0195        \n",
      "19     001619371      0.1143           0.0737           0.0405        \n",
      "20     000242465      0.1553           0.1260           0.0293        \n",
      "\n",
      "Chosen sample: 001701130\n",
      "  Base P(toilet): 0.3574\n",
      "  FT P(toilet):   0.2949\n",
      "  Suppression:    0.0625\n",
      "  Prompt: ... allowing natural light to fill the space. The bathroom is well-equipped with a \n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pick the best individual samples for ablation\n",
    "# Criteria: large suppression AND high base P(toilet) — clear signal\n",
    "scores = final_base * suppression  # reward both high base prob and large gap\n",
    "ranked = np.argsort(-scores)\n",
    "\n",
    "print(f\"{'Rank':<6} {'Image ID':<14} {'Base P(toilet)':<16} {'FT P(toilet)':<16} {'Suppression':<14}\")\n",
    "print(\"-\" * 70)\n",
    "for rank in range(min(20, len(ranked))):\n",
    "    idx = ranked[rank]\n",
    "    print(f\"{rank+1:<6} {sample_ids[idx]:<14} {final_base[idx]:<16.4f} {final_ft[idx]:<16.4f} {suppression[idx]:<14.4f}\")\n",
    "\n",
    "# --- Pick your sample here (change the rank index) ---\n",
    "PICK = 0  # 0 = best, 1 = second best, etc.\n",
    "chosen_idx = ranked[PICK]\n",
    "chosen_id = sample_ids[chosen_idx]\n",
    "chosen_sample = samples[chosen_idx]\n",
    "\n",
    "print(f\"\\nChosen sample: {chosen_id}\")\n",
    "print(f\"  Base P(toilet): {final_base[chosen_idx]:.4f}\")\n",
    "print(f\"  FT P(toilet):   {final_ft[chosen_idx]:.4f}\")\n",
    "print(f\"  Suppression:    {suppression[chosen_idx]:.4f}\")\n",
    "print(f\"  Prompt: ...{chosen_sample['prompt'][-80:]}\")\n",
    "\n",
    "# Plot this sample's layer curve\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.plot(np.arange(NUM_LAYERS), all_base_probs[chosen_idx], 'r-o', label='Base', markersize=4)\n",
    "ax.plot(np.arange(NUM_LAYERS), all_ft_probs[chosen_idx], 'b-o', label='FT', markersize=4)\n",
    "ax.set_xlabel('Layer')\n",
    "ax.set_ylabel('P(toilet)')\n",
    "ax.set_title(f'Sample {chosen_id}: P(toilet) across layers')\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Layer-wise LoRA Ablation\n",
    "\n",
    "**Goal**: Identify which layer's LoRA is causally responsible for suppressing P(toilet).\n",
    "\n",
    "- **Ablation A (Base + only layer K's LoRA)**: If P(toilet) drops → layer K alone can suppress the prior.\n",
    "- **Ablation B (Full FT minus layer K's LoRA)**: If P(toilet) goes back up → layer K is necessary for suppression.\n",
    "\n",
    "We use a single model and swap MLP weights per layer to avoid loading multiple models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved MLP state dicts for all 32 layers.\n"
     ]
    }
   ],
   "source": [
    "# Save all base and finetuned MLP state dicts (on CPU to save GPU memory)\n",
    "import copy\n",
    "\n",
    "base_mlp_states = {}\n",
    "ft_mlp_states = {}\n",
    "for layer_idx in range(NUM_LAYERS):\n",
    "    base_mlp_states[layer_idx] = {\n",
    "        k: v.cpu().clone() for k, v in \n",
    "        base_model.language_model.layers[layer_idx].mlp.state_dict().items()\n",
    "    }\n",
    "    ft_mlp_states[layer_idx] = {\n",
    "        k: v.cpu().clone() for k, v in \n",
    "        ft_model.language_model.layers[layer_idx].mlp.state_dict().items()\n",
    "    }\n",
    "\n",
    "print(\"Saved MLP state dicts for all 32 layers.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running ablations on 31 good samples (out of 31 total)\n"
     ]
    }
   ],
   "source": [
    "def set_model_mlp(model, layer_idx, state_dict):\n",
    "    \"\"\"Swap a single layer's MLP weights in-place.\"\"\"\n",
    "    model.language_model.layers[layer_idx].mlp.load_state_dict(state_dict)\n",
    "\n",
    "def set_all_mlps(model, states_dict):\n",
    "    \"\"\"Restore all layers' MLP weights.\"\"\"\n",
    "    for layer_idx in range(NUM_LAYERS):\n",
    "        set_model_mlp(model, layer_idx, states_dict[layer_idx])\n",
    "\n",
    "def get_final_toilet_prob(model, inputs):\n",
    "    \"\"\"Quick forward pass, return P(toilet) at last position from final output.\"\"\"\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**inputs)\n",
    "    logits = outputs.logits[:, -1, :]\n",
    "    probs = torch.softmax(logits, dim=-1)\n",
    "    return probs[0, first_toilet_id].item()\n",
    "\n",
    "# Use only the good samples (that follow our expectation) for ablations\n",
    "good_samples = [samples[i] for i in good_indices]\n",
    "ablation_samples = good_samples[:min(len(good_samples), 50)]  # cap at 50 for speed\n",
    "print(f\"Running ablations on {len(ablation_samples)} good samples (out of {len(good_samples)} total)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Ablation A: sample 1/12 (001701130) done\n",
      "  Ablation A: sample 2/12 (000861294) done\n",
      "  Ablation A: sample 3/12 (001830340) done\n",
      "  Ablation A: sample 4/12 (000879813) done\n",
      "  Ablation A: sample 5/12 (001313824) done\n",
      "  Ablation A: sample 6/12 (002617137) done\n",
      "  Ablation A: sample 7/12 (001812743) done\n",
      "  Ablation A: sample 8/12 (001935854) done\n",
      "  Ablation A: sample 9/12 (000157875) done\n",
      "  Ablation A: sample 10/12 (001884336) done\n",
      "  Ablation A: sample 11/12 (001189210) done\n",
      "  Ablation A: sample 12/12 (001294832) done\n",
      "Ablation A complete.\n"
     ]
    }
   ],
   "source": [
    "# Ablation A: Base + only layer K's LoRA — top 12 samples individually\n",
    "\n",
    "N_PICK = min(12, len(ranked))\n",
    "picked_indices = ranked[:N_PICK]\n",
    "picked_ids = [sample_ids[idx] for idx in picked_indices]\n",
    "\n",
    "ablation_a_all = {}  # {img_id: np.array(32,)}\n",
    "\n",
    "for si, idx in enumerate(picked_indices):\n",
    "    sample = samples[idx]\n",
    "    img_path = sample['img_path']\n",
    "    if not os.path.exists(img_path):\n",
    "        continue\n",
    "    image = Image.open(img_path).convert('RGB')\n",
    "    \n",
    "    ablation_a_all[sample_ids[idx]] = np.zeros(NUM_LAYERS)\n",
    "    for k in range(NUM_LAYERS):\n",
    "        set_all_mlps(base_model, base_mlp_states)\n",
    "        set_model_mlp(base_model, k, ft_mlp_states[k])\n",
    "        inputs = processor(images=image, text=sample['prompt'], return_tensors=\"pt\").to(device_base)\n",
    "        ablation_a_all[sample_ids[idx]][k] = get_final_toilet_prob(base_model, inputs)\n",
    "    \n",
    "    print(f\"  Ablation A: sample {si+1}/{N_PICK} ({sample_ids[idx]}) done\")\n",
    "\n",
    "set_all_mlps(base_model, base_mlp_states)\n",
    "print(\"Ablation A complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Ablation A: layer 0/31 done | avg P(toilet) = 0.1567\n",
      "  Ablation A: layer 7/31 done | avg P(toilet) = 0.1561\n",
      "  Ablation A: layer 15/31 done | avg P(toilet) = 0.1552\n",
      "  Ablation A: layer 23/31 done | avg P(toilet) = 0.1566\n",
      "  Ablation A: layer 31/31 done | avg P(toilet) = 0.1968\n",
      "Ablation A complete.\n"
     ]
    }
   ],
   "source": [
    "# Ablation A: Base + only layer K's LoRA\n",
    "# For each layer K, we set base_model to all-base except layer K gets FT MLP\n",
    "# Result: ablation_a[K] = avg P(toilet) across samples when only layer K has LoRA\n",
    "\n",
    "ablation_a = np.zeros((len(ablation_samples), NUM_LAYERS))  # (N, 32)\n",
    "\n",
    "for k in range(NUM_LAYERS):\n",
    "    # Start from base, apply only layer K's FT MLP\n",
    "    set_all_mlps(base_model, base_mlp_states)\n",
    "    set_model_mlp(base_model, k, ft_mlp_states[k])\n",
    "    \n",
    "    for i, sample in enumerate(ablation_samples):\n",
    "        img_path = sample['img_path']\n",
    "        if not os.path.exists(img_path):\n",
    "            continue\n",
    "        image = Image.open(img_path).convert('RGB')\n",
    "        inputs = processor(images=image, text=sample['prompt'], return_tensors=\"pt\").to(device_base)\n",
    "        ablation_a[i, k] = get_final_toilet_prob(base_model, inputs)\n",
    "    \n",
    "    if (k + 1) % 8 == 0 or k == 0:\n",
    "        print(f\"  Ablation A: layer {k}/31 done | avg P(toilet) = {ablation_a[:, k].mean():.4f}\")\n",
    "\n",
    "# Restore base model to original\n",
    "set_all_mlps(base_model, base_mlp_states)\n",
    "print(\"Ablation A complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Ablation B: sample 1/12 (001701130) done\n",
      "  Ablation B: sample 2/12 (000861294) done\n",
      "  Ablation B: sample 3/12 (001830340) done\n",
      "  Ablation B: sample 4/12 (000879813) done\n",
      "  Ablation B: sample 5/12 (001313824) done\n",
      "  Ablation B: sample 6/12 (002617137) done\n",
      "  Ablation B: sample 7/12 (001812743) done\n",
      "  Ablation B: sample 8/12 (001935854) done\n",
      "  Ablation B: sample 9/12 (000157875) done\n",
      "  Ablation B: sample 10/12 (001884336) done\n",
      "  Ablation B: sample 11/12 (001189210) done\n",
      "  Ablation B: sample 12/12 (001294832) done\n",
      "Ablation B complete.\n"
     ]
    }
   ],
   "source": [
    "# Ablation B: Full FT minus layer K's LoRA — top 12 samples individually\n",
    "\n",
    "ablation_b_all = {}  # {img_id: np.array(32,)}\n",
    "\n",
    "for si, idx in enumerate(picked_indices):\n",
    "    sample = samples[idx]\n",
    "    img_path = sample['img_path']\n",
    "    if not os.path.exists(img_path):\n",
    "        continue\n",
    "    image = Image.open(img_path).convert('RGB')\n",
    "    \n",
    "    ablation_b_all[sample_ids[idx]] = np.zeros(NUM_LAYERS)\n",
    "    for k in range(NUM_LAYERS):\n",
    "        set_all_mlps(ft_model, ft_mlp_states)\n",
    "        set_model_mlp(ft_model, k, base_mlp_states[k])\n",
    "        inputs = processor(images=image, text=sample['prompt'], return_tensors=\"pt\").to(device_ft)\n",
    "        ablation_b_all[sample_ids[idx]][k] = get_final_toilet_prob(ft_model, inputs)\n",
    "    \n",
    "    print(f\"  Ablation B: sample {si+1}/{N_PICK} ({sample_ids[idx]}) done\")\n",
    "\n",
    "set_all_mlps(ft_model, ft_mlp_states)\n",
    "print(\"Ablation B complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Ablation B: layer 0/31 done | avg P(toilet) = 0.1279\n",
      "  Ablation B: layer 7/31 done | avg P(toilet) = 0.1277\n",
      "  Ablation B: layer 15/31 done | avg P(toilet) = 0.1280\n",
      "  Ablation B: layer 23/31 done | avg P(toilet) = 0.1271\n",
      "  Ablation B: layer 31/31 done | avg P(toilet) = 0.1062\n",
      "Ablation B complete.\n"
     ]
    }
   ],
   "source": [
    "# Ablation B: Full FT minus layer K's LoRA\n",
    "# For each layer K, we set ft_model to all-FT except layer K gets base MLP\n",
    "# Result: ablation_b[K] = avg P(toilet) across samples when layer K is reverted to base\n",
    "\n",
    "ablation_b = np.zeros((len(ablation_samples), NUM_LAYERS))  # (N, 32)\n",
    "\n",
    "for k in range(NUM_LAYERS):\n",
    "    # Start from FT, revert only layer K to base\n",
    "    set_all_mlps(ft_model, ft_mlp_states)\n",
    "    set_model_mlp(ft_model, k, base_mlp_states[k])\n",
    "    \n",
    "    for i, sample in enumerate(ablation_samples):\n",
    "        img_path = sample['img_path']\n",
    "        if not os.path.exists(img_path):\n",
    "            continue\n",
    "        image = Image.open(img_path).convert('RGB')\n",
    "        inputs = processor(images=image, text=sample['prompt'], return_tensors=\"pt\").to(device_ft)\n",
    "        ablation_b[i, k] = get_final_toilet_prob(ft_model, inputs)\n",
    "    \n",
    "    if (k + 1) % 8 == 0 or k == 0:\n",
    "        print(f\"  Ablation B: layer {k}/31 done | avg P(toilet) = {ablation_b[:, k].mean():.4f}\")\n",
    "\n",
    "# Restore ft model to original\n",
    "set_all_mlps(ft_model, ft_mlp_states)\n",
    "print(\"Ablation B complete.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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PPaSQkBC1bdtWDz74oNf/JmazWa+99poWLFhg/d6+xx9/XGvWrLH7VLqz82srKChIEyZMsL7RoKCgQOvXr1dycrJbbRk5cqTatGkjg8Gga6+9VklJSdYl4IcOHarCwkLrhPvGjRvVunVr9enTRxaLRc8995yWLFmili1bKiQkRPPmzdPnn3+u//3vf9b9P/LII4qPj1doaKh1mf+K0tPT1aJFC91www0u2xocHKxTp07pxx9/lMlkUo8ePdS8eXO3jrPM1q1bdfz4cc2bN09hYWHq1q2bpkyZYvdGjdOnTysqKkqNGjVSt27ddMMNN9i9s37t2rVq0qSJhg4dqmuuuUbx8fFas2aNXZ7IyEgVFRXZfVciAAAA4CnG6aVqc5xeZsyYMWrevLmef/75So+98MILmjhxoq677joZjUZdeeWVGjZsmN544w1JpRPfs2fPVq9evWQwGNS2bVtdcsklkqTly5dr7ty56tSpk4KCgvTAAw/o7Nmz2rp1q0wmkywWi3bv3q2zZ88qNjbW+v3twcHBOn78uA4ePKjg4GD179+/0vLmzlR8w/OqVaucjhl//fVXSXI4Dm/Xrp2ioqIUFRVlt8qdp2688UZdccUVCgoK0u23366DBw9qzpw5CgkJ0cCBA9W0aVOnqxVIpePMFi1aKCoqSiNHjnQ4VnakumPKyMhIjR49Wo0bN1ZYWJjmzJmj//73v9aV0lydX3deZ2jatKlmzJihkJAQhYeHO23Hu+++q2HDhqlt27ZVHqej1zw88eqrr+rOO+/U1VdfreDgYD300ENq1qyZ3ae6n3/+eUVFRSksLEypqalat26dWrRoYX385Zdf1rhx4xQUFKTx48fr3Xff1fHjx+3yREZGWvscAAQCJsUBoJ6IiIiQJLt3fZb93KRJkxo/bjabdf311+uKK66wvmP5iiuu0ODBgyu1ZcOGDQoPD9eNN95o3RYcHKx//OMf+vbbb9WqVSvdcccdSk5O1gUXXCCpdOA2aNAgTZ48Wfn5+frll1/UuHFj3XnnnR6fC5PJpAEDBljffe3I4MGDrcuRVXWz/b40W5GRkZo9e7a++eYb/frrr3rggQc0fvx4u6XTvOnQoUPq1KlTlXEHDx6U2WxW+/btrQPePn36yGg0KiMjwxoXFxdn/blx48bWF1Vc5WnWrJmGDx9ufRf16tWrXb4g0qxZM7sXVMq89tprys7OVkFBgbZs2aKpU6dal6PLysrSuHHj1KZNG0VGRurqq69WYWGhtX0bN27Url271LlzZ/Xs2dP6wsXBgwe1a9cu6zFHRUXpj3/8o90x2zp8+LBd7GeffaZhw4ZZ7w8bNszh72VlZamoqMj6yXZJ6tChgwoLC3Xy5EnrNmfnt6Lf/OY3evPNN5Wbm6uNGzeqbdu26t27t9NzWvE8Xn755WrevLmioqL07rvvWttgMpk0fvx464TxqlWrrH+rkydPKi8vT1dffbX1eOPi4hQSEmI3KV7VYF0q/cTDxRdfrOuuu87u+CuaOnWqrrrqKt1+++2Ki4vTgw8+qLNnz7p1nGWOHDmi+Ph4uxePOnTooCNHjljvN23aVNnZ2crNzdVf//pXffbZZ3Z5ygb2wcHBMhgMuuuuu7Ry5Uq7PDk5OVW+oAEAAABUhXF6udoap5cxGAxauHCh5s+fX2ksdvDgQa1YscJuPPiPf/zDOknqakx88OBB3XnnnXa/W/aJ/gsvvFCrV6/WsmXLFBsbq8GDB1s/RfvEE0+oVatWGjRokBISEjR79my3v+orKSlJRUVF+vjjj3X06FF9/PHHTj893KxZM0lyOA4/dOiQsrOzlZ2dbffJaE/FxsZafw4PD1eTJk3s3ugQHh7u8uu33B0rV1TdMeXZs2d13333KSEhQZGRkdaxfNn41dX5ded1hlatWjl9E7mtf/3rX3r11Vc1Y8YMl3HOXvPwxJEjR+xes5Ck9u3b242d7733XmVnZysrK0tXXXWVvvjiC+tj33//vb766ivrmyeuvfZaxcfHV+o3OTk51j4HAIGASXEAqCeaNWum1q1b2y1btGPHDrVp00ZNmzat8eO//PKLDh06pAceeEDh4eEKDw/X73//e23durXSRNhLL72kCRMmVFoK7NJLL9V//vMfnTx5Ujt27FBhYaEGDBggqfQ7ms6ePasHHnhAISEhatasmX73u9/V6LuJiouL9eOPPzp87D//+Y/1RYOqbjNnzqwyV0REhP74xz+qadOm2rNnj3Wb7SdNK76j1lPt2rVz67vX2rRpI6PRqGPHjlkHvGUT0K1atapxnrJ3UX/xxRc6deqUy6XzevToob179zp93GAwqGfPnrriiiusf+tp06YpPz9f33zzjXJycvTJJ59IKl9O7PLLL9ebb76pkydPaubMmRo3bpwyMzPVpk0b9erVy+6Yc3JyrMvSV9S2bVu72CuvvFJvv/229f7bb7/t8PdiYmIUEhJi9z3nBw8eVGhoqKKjo50eqzOdO3dW9+7d9fe//93lO/4rOnz4sCZMmKDFixfrxIkTys7O1g033GC37FrZhPu+ffv08ccfW1+8uuCCCxQeHq6tW7fanYOzZ8/avQvdnYF9aGio3nzzTSUkJOjaa69VVlaWw7iIiAgtWrRI+/bt05YtW5SWlmb3XXPuaN26tY4dO2b3/ewHDx5U69atK8WaTCZNnjxZXbp0sX4H3Y4dO/TNN99o1apViouLU1xcnJYvX669e/dal52XpD179tgtUQkAAABUB+P0ympznD548GB179690iR8mzZt9OCDD9qNhXJzc62fKnc1Jm7Tpo02bNhg97v5+fkaO3asJOn222/XRx99pMzMTHXv3t06udqiRQs999xzOnTokP71r39pxYoV2rhxY6X9OxqDGY1GTZw4UatWrdKaNWuUlJRkNzFtKzw8XJ06dXI5Dq+rIiIidPbsWbsxre3rKNUdUz711FPavn27PvvsM+Xk5FjH8mV5XJ1fd15ncGfcLEndu3fXhx9+qBdffFF//vOfncY5e83DE61bt7Z7zUJyPnZu3ry5XnrpJT3//PP69ttvJcm6VPqQIUMUFxen+Ph4nThxotIbyhk7Awg0TIoDQD2SnJys+fPnKyMjQxkZGXr88cftlkaryePR0dHq2LGjli9froKCAhUUFGj58uVq3bq13UTgvn379MUXX2jSpEmV2rdz507l5eWpqKhIb731llauXKlHH31UknTxxRcrIiJCzz33nM6dO6czZ87oxRdfVM+ePa2/X1xcrIKCApWUlKikpEQFBQXWybEdO3bo/fff19mzZ3Xu3Dm98847eu2115SUlOTdk2xj6tSp2rFjh4qKilRUVKSXXnpJeXl56tWrl6TSgczrr7+ugoIC/fzzz1q+fHmN8k2ePFmvv/66Nm7cqHPnzun06dP68ssvK8XFxcVpxIgRmjJlivWFkLLvfnNH2RLpK1asUGFhofLz8/Xpp59aHx84cKAsFovuu+8+3XnnnQoODna6r5tuukkfffSRy3zff/+9Pv30U3Xt2lVS6TuNw8PDFRUVpVOnTmnOnDnW2KKiIr366qv69ddfZTQarUsHBgUFadiwYcrMzNRzzz1n7Sf79u2zLifuLUajUePGjdOMGTP0yy+/6NSpU5o+fbruuusutwfDFU2aNElPPfWUPvnkE7c/dZGbmyuLxaIWLVrIaPx/9u49vsny/v/4O0mPnA89AfKjnDwwKWUUOpwOD9XiaR6QAW4CnUOn8h2uKlqVVg6ugIj1gNbDQFARdFO2qcPNzs45O3AwxpTDlIGMQw/goLTYY+7fH6Vp0t5pkyZt0/T1fDz6ILnzyfW57ztXPuTKlVyx6r333tMf/vAHl5iRI0fq29/+tqZNm6Yrr7zSsRSa1WrVT3/6U91zzz2Ob4YfP35cGzdubNX+h4WF6Ve/+pVGjBihSy65RMXFxU1i3nnnHf373/+W3W5Xr169FBoa2uQNOWf1z3HnvwkTJig2NtaxvONnn32mp59+utml/xYsWKDc3FwdPnxYv/zlLzV27Fjt2bNHO3bs0I4dO7R7925ddtllLr+P9qc//cntSgEAAACANxint+84vbGlS5cqJydHFRUVjm2333671qxZow8//FC1tbWqrKxUQUGBY9W322+/XQsXLtSOHTtkGIYOHjzouO2uu+5SZmam4/fHS0tL9Zvf/EanTp3S3r17HccbFhamHj16OMY8b7zxhg4ePCjDMNSnTx/ZbDbT8VBsbKyOHj3a5BvQP/7xj/XWW2/pl7/8ZYvL13syDg9EZ599tkJDQ7V+/XrV1tbq9ddfd0zSSt6PKeuVlpYqIiJCffv2VVlZmR588MEmMe7Or7/fZxg9erQ+/PBDrVmzRvPnz29ye3PvebhTVVXlMm6urq7Wj370I7322mv661//qpqaGj399NM6fvy42589GzhwoGbPnq0FCxaoqqpKr776qpYuXeoYN+/YsUNbtmzRf/7zH8cXF06fPq1PP/20xZ9SA4BOxQAAdBpVVVXGnXfeafTp08fo06ePMXfuXKO6utpvt3/++efGFVdcYfTr18/o06ePcckllxjbt2932Yf77rvP+N73vme6fw899JDRr18/o1u3bsbEiRONjz/+2OX2jz/+2Pjud79r9O7d2+jXr59x7bXXGvv27XPcPmvWLEOSy9+sWbMMwzCMTz/91EhKSjJ69uxp9OrVy0hISDByc3NbfS498X//93/GOeecY/To0cPo27evccEFFxibN2923P7VV18ZF154odGjRw8jOTnZWLZsmTFkyBCX45k3b57j+v79+w1Jxv/+9z/HtiFDhhhvv/224/p7771njBs3zujZs6cxYMAAY+nSpYZhGMaaNWuMMWPGOOJKS0uNn//850Z8fLzRs2dPY8SIEcZDDz3kNs+8efMc59IwDGPLli3GRRddZPTu3duIjo42fvazn7kc+8KFCw1Jxj//+c9mz1FNTY0RHx9v/Otf/3I5poiICKN79+5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dDixX1Lt3b23atElHjx7V5MmTdeutt9qXdfvttzu9V/v379cnn3yikSNHSpJuu+02/f777/r++++1a9cu+7SNjt5991198803TutX0bJly5Samqq33nqr0i/LMzIy7IP+EydOVNeuXXX48GFlZWXpiSeesB+c89TRo0c1ePBgDR8+XNnZ2Xr++ec1ZswYffnll5ViDcPQZ599pm3btunUU0+1P/7jjz/qyy+/1OjRozVq1Ci99tpr9h9klOnevTtT+gMAAKDW6KfXbz+9TOvWrfXQQw+5zbl27Vo98MADeuWVV/T7779rwoQJuuqqq+w/xh4/frzWrl2rdevWKScnR2+++abatGkjydav+fLLL/XFF1/o4MGDOvXUUzV8+HBJth+XL1++XFu2bFFOTo4++ugje19k7Nixuuqqq3T06FHt379fDz74YKX1uvbaa/Xwww/ryiuvtJ+t3bt3b5122mlOM1ktXbpUgwYNUrt27Sot46efflJ+fr5OO+202u3EKrz++uuaN2+efv/9d0VGRmrgwIFq3bq1Dh48qGnTpmnMmDFVnjH973//W99++6127Nih9PR0LVu2zKO8Ne1TWq1WpaWl6bffftOePXsUERFhn3mhuv3ryXGGdevWacCAATp06JDbAezS0lJdd911+vXXX/Xpp58qPj7e/pzjrHVVHfPwxquvvqo5c+Zo9erVOnDggHr06KGrrrrK6dIMZU6cOKGXX35Z0dHRatWqlf3xRYsWqU+fPrrmmmt0/vnnu/xBOX1nAIGGQXEAaCTKftXqeJ2istu5ubnVPl+ds88+W3l5eZo/f76Ki4v15Zdf6p133lFOTo5T3Pvvv6+8vDxlZGRo6NChCg8Ptz/38ccfa9euXdq9e7eGDBmiyy67zH6NKrPZrNGjR+sf//iHQkNDlZycrAceeEC9evXy6PXFxcUaM2aMnnvuOfvgal0ymUzKyMhQ7969NW3aNHXu3Fndu3fXhx9+WGc5n3vuOd177726+OKLZTabFRsbqz59+lSK++abb/TTTz/piSeeUEREhNq0aaOHH35Yy5cvt8cYhqEZM2YoLCxMp59+us455xz72bT/+te/NHz4cA0dOlTBwcFq0aKFzjrrLEm2gda33nrL3p5eeeUV3XTTTQoNDXW5zn/88YeioqIqPX7++eerZcuWioyM1MKFCzVz5kz7c71799Z5552n4OBgxcXFKS0tzWlAOTg4WD/88INycnLsv+qXyqdq//Of/yyLxaIzzjhDqampTtvtC/v379fHH3+sOXPm2Ke7nzhxotMAdFX711HZmQVLliyxL/vTTz/Vrbfe6tG63HzzzYqNjZXFYtHw4cPVrVs3+xkON998szZu3Ggf0F66dKkuvfRStW3bVtnZ2Xrrrbe0YMECtWzZUs2aNdP06dO1cuVKlZaW2pc/ZcoUtWzZUhEREW7X4YMPPlCPHj107rnnVrmuwcHBOnjwoHbv3q3g4GCdc845Xn9W16xZo5iYGP3tb39TcHCwBg4cqJtuusm+/yTpv//9r1q2bKmwsDANHDhQ999/v66++mr78y+//LKSkpLUq1cv3XDDDcrPz9f//d//OeWJiorSH3/84dW6AQAAABXRT6/ffrqj++67T3v27NHq1asrPbdgwQI9+OCDOvPMM2U2m3X99derW7duWrt2rQzD0AsvvKA5c+bolFNOkclk0mmnnaaOHTvKMAw999xzmjNnjtq2bauQkBDNmDFDX375pX777TcFBweroKBA27dvV3Fxsf70pz/ZB8WDg4O1Z88eHThwQKGhobrgggs83paKP3hesmSJ2x9S//HHH/bZ7BwdO3ZMLVu2tP9NmjTJ4/wV3XLLLerRo4dCQ0N13XXX2afHDwoKss/0VtWJA5MnT1ZkZKTatWunwYMHu+wru1LTPmViYqIuv/xyhYWFKSoqShMnTtTnn38uq9Uqqer968lxhjPOOEOjR49WUFCQ275zQUGBPv74Yw0fPtxp4Nnddro65uGNV199VX//+9/Vs2dPhYWF6bHHHtNvv/2mjRs32mMmTJhgPx6wfPlyvf322/YfGZSWlmrJkiUaNWqUJGnkyJFatmyZfQbCMvSdAQQaBsUBoJFo3ry5JDn9mrzsdmRkZLXPV6dNmzZ67733tHz5csXHx2v8+PFKTU21/1rakcVi0cCBA+2/3C1z0UUXKTQ0VM2aNdP9999v73RKto70nXfeqbfffltFRUX66aeftGzZMj3//PMevX727Nnq37+/xx3L0047zT4dWXV/jgNujuLj4/XUU09p+/btys7O1uWXX67rrrtOv//+u0fr4K09e/Z4NN3c7t27dfToUbVu3dre4b3hhhuUlZVlj4mKinLqrDVr1sx+0KWqPKeffrrOOOMMvfnmmyooKNDKlSt12223uV2XVq1aVTogI0mff/65jh49qoKCAr399tu64YYb7FPw/fzzz7rmmmvUrl07RUVF6ZZbbtHhw4ft6/nee+/p//7v/9ShQwedd955+uSTT+zbvXbtWqeO/rx583Tw4EGX6/bFF184xe7du9c+fV7Lli119913u3zdvn37FBYWpri4OPtjnTt31r59+zzavxXddtttWrp0qQzD0NKlS3XZZZc5/Wq8Kk8//bR69OihFi1aqGXLltq2bZt9X7Vq1UrXXHONvf0uWbLE/l7t3r1bVqtVnTp1sm9vv379ZDablZmZaV++J9eQmzdvnoqLi3XVVVfpxIkTbuOeeOIJJSQkaNCgQUpMTNTUqVPtByE8tW/fPiUmJjo9VnHf9+zZU0ePHlVubq4mTZqkjz/+2P5r+JKSEi1dutTesY+MjNR1111X6RfvOTk51R6oAAAAAKpDP73+++llwsPDNWXKFD388MNOP/yVbP2hhx9+2Kk/uHXrVu3fv1/Z2dnKz8932Sc+fPiw8vLydMEFF9hfFx8fr5CQEP3222+66KKLNG3aNE2aNEnR0dEaOnSo/UfKixYtUkFBgfr27atu3bpp/vz5Hu0TSRoxYoQ2bdqkXbt2af369Tp8+LDTD38dtWrVSvn5+ZW2uUWLFjp69Kj9ryZTcpdx7AtHRERUui/JaTryihz7u1X1lSuqaZ8yOztbN910kzp06KCoqChdcMEFKiwstOetav96cpzBk35zs2bN9O6772rixIl64YUXqoxzd8zDGxX7zqGhoWrXrp1T33nmzJk6evSofvvtNyUkJDjNMLF27VodPnxYN910kyTpxhtv1IkTJ5ymxpfoOwMIPAyKA0Aj0apVK7Vv395p2qKtW7eqQ4cOatGiRbXPe+Lcc8/VV199pSNHjujzzz9XZmamBg4c6Da+uLhYP/30k9vnzeby/2a2bNmiAQMG6MILL5TZbFaXLl10ww03aM2aNR69/qOPPtIbb7yh6OhoRUdHa8WKFXrhhRfUv39/l6/98ccf7dORVfdXNoBWldatW2vq1KnKy8uzd3qbNWvmdH0rd4OznurYsaNH137r0KGDYmNjnTq8x44dq7JT6k2esl9Rv/POO+rYsaPOPPNMt7FJSUnauXOn2+ctFosuvvhide3aVf/5z38kSXfeeacSEhK0Y8cO5eTk6LXXXnOaEvCSSy6xd9BuvPFGXXvttbJarerQoYOuu+46p+3Ozc21H5Cp6LzzznOK/dOf/qTvv//efv+5555z+br27duroKDA6UcGu3fvrva62+5ceumlKikp0aefflrlL/4r+uKLLzR16lQtXbpUf/zxh44ePaozzjjDaV/dfvvtWrp0qf1zW3bdug4dOshsNuvAgQNO+6CgoEAJCQn21zt+xtxp2bKlPvzwQx0/flxXXHGF22u6xcbG6rnnntOePXv03nvvaeHChZU61NVp3769fVq5Mu72fUhIiKZNm6YTJ07Y38v3339fWVlZeuSRRxQfH6/4+Hi9++67+vDDD/Xbb7/ZX7tjx45K17IDAAAAvEU/vWH76bfffrusVmulAfQOHTroqaeecuoL5eXlafz48YqJiVFERITLPnGbNm0UERGhDRs2OL32xIkTOueccyTZrmf+9ddfa+/evQoNDdXf//53SVKXLl20dOlSZWZm6qWXXtIDDzzg8gxpV32wli1b6rrrrtOSJUv0yiuv6Oabb3Z7hvQpp5yiiIgI/fjjj9XuH3/TvHnzSv1Jxx9t17RPOWHCBOXn59untS+71nxZ37mq/evJcQZP+s2SNGjQIL377ru6//773R5vkNwf8/BGxb5zUVGRDhw44LLvnJCQoBdffFEPPfSQ/TrqL7/8sqxWq3r27Kn4+HideuqpKi4urvSDcvrOAAINg+IA0Iikpqbq0UcfVWZmpjIzM/XYY4/pL3/5i8fPl5SUqKCgQCUlJbJarSooKHC61u63336rwsJCnThxQi+++KIyMjJ03333SbJ13D/88EOdOHFCJSUlWrNmjZYtW6aUlBRJ0rZt27R582YVFxeroKBA8+bN0/bt2+3Pn3322dq0aZO+/PJLGYahPXv26K233rJPD17d69944w1t375dW7du1datW3X11Vfr5ptv1rvvvlsn+/qPP/7QP//5T+3cuVOlpaXKz8/XnDlz1Lp1a/s1tM8880y9+uqrKikp0datW/Xqq6/WKudf//pXPfPMM/r0009ltVp16NAhffvtt5Xi+vXrpw4dOuif//yncnNz7fvz3//+t0d5xowZo9dff13vvPOOSkpKdOzYMafrwg0bNkybN2/WrFmzqjxLXJKuuuqqKn/VXHbd5x07dqhnz56SbL80joyMVFRUlH777TensxiysrL0zjvvKDc3V0FBQYqKirJP73Xrrbfq448/1ltvvaXi4mIVFxdr69at2rRpk0fb7amEhARddNFFeuCBB5SXl6e9e/fq0Ucf9eigjCtms1mpqam677779Pvvv+vKK6/06HU5OTmyWCyKiYmR1WrVokWLtG3bNqeYSy65RIZh6O6779Ytt9yi4OBgSbZf5l977bUaO3as/czyzMxMrwepy7Ro0UL/+c9/VFBQoCFDhigvL69SzKpVq7R3714ZhqGWLVvKYrFUef23snpU9ldYWKghQ4bo0KFDeu6551RSUqLPP/9cy5Yts18nvSKTyaSJEyfqscceU35+vl5++WVdffXVTrXif//7n7p27arFixfbX/fxxx97/D4AAAAAVaGfXn/99IosFoseffRRPfbYY06P33PPPXriiSe0efNmGYah/Px8ffTRR9q3b59MJpPGjBmj+++/Xz///LMMw9CPP/6oPXv2yGw2684779T9999v/1HtkSNHtHLlSknSpk2b9NVXX6moqEjh4eFq1qyZvc+zdOlSZWVlyWQyqWXLljKbzZWmOJdsZ2Hv2bOn0rWfy36cXt1sbcHBwUpJSanR2cUNLSkpSb/++qs+//xzlZSU6PHHH7df513yvk9ZJicnRxEREWrZsqWOHDmiadOmVYpxt399fZzh4osv1po1a/TQQw+5nC2gqmMe7hQWFjr1nUtKSnTLLbdo/vz52rFjhwoLC/XPf/5TCQkJbn+QcuaZZ+rCCy/UY489pqysLK1Zs0ZLly61f3a3bt2q9957T+np6fbB9j179ujw4cNeXQoAAPyeAQBoNIqKioy7777baNmypdGyZUtj7NixRnFxscfPT5kyxZDk9Ddw4ED786NHjzZatGhhNGvWzLj00kuNbdu22Z/btGmTkZycbERGRhpRUVFGr169jIULF9qf//jjj41u3boZERERRuvWrY2BAwcaX3zxhdP6v/TSS0a3bt2M5s2bG+3atTPuvvtu48SJEx6/3tGoUaOMe++9t6a7slrHjx83Ro8ebXTq1Mlo1qyZ0aZNG+PSSy81NmzYYI/ZunWr0bt3b6NZs2bGZZddZowfP95pfw4cONB4+umn7fc/+eQTo0WLFk55JBnffvut/f6SJUuMHj16GM2bNzf+9Kc/GUuWLDEMw/beXXPNNfa4rKwsY/To0UZCQoIRGRlp9OjRw5g3b57bPNdcc40xZcoU+/21a9caffv2NSIjI422bdsas2bNcopPTU01QkJCjOzs7Cr30x9//GHExMQYhw8fdtqm8PBwo1mzZkbz5s2N0047zZg/f779+c8//9zo3r270axZM6NPnz7GU089ZV/fAwcOGAMHDjRatGhhREZGGn379jU+/vhj+2u3bNliXHrppUabNm2MVq1aGeecc47x0UcfVbmOZTp27Gjs2rXLo9iDBw8aQ4cONdq0aWO0b9/eeOihh4yioiLDMKrfv4sXLzZ69+7t9PyuXbsMk8lk/OMf/6gyr+P7XFpaaowZM8aIiooyYmJijLS0NOOCCy5walOGYRjTpk0zJBnfffed0+M5OTnGP/7xDyMxMdGIjIw0unbtakycONG+PpKMP/74o8r16dixo/HOO+84LfPcc881zj//fCM3N9cpdty4cUZCQoIRERFhJCQkGJMmTTKsVqvL5Q4cOLBSLerYsaNhGIaxYcMG4+yzzzaioqKMbt26Ga+++qr9da72bWlpqdGtWzdj2rRphsViMTIyMirle/bZZ43ExETDarUau3fvNuLj4+21BwAAAKgN+unl6rqfbhiV+9mGYRgDBgwwJDn191atWmX06dPHaNGihREbG2tceeWVxp49ewzDMIyCggJj/PjxRseOHY3mzZsbPXv2NLZs2WIYhmEUFhYajzzyiNG1a1ejefPmRseOHY3bbrvNMAzD+Oijj4zevXsbzZs3N1q1amUMGTLEvsxbb73ViIuLM5o1a2Z07tzZqQ/s2O8/cuSIccEFFxgtW7Z06ldarVajc+fORp8+fardB+np6UZycrL9vqs+akWu+lKuVNy/rl7nuD2O77mrfua9995rjBo1yn7/qaeeMmJjY43o6Ghj+vTpRu/evY3FixcbhuFdn9Kxr7pjxw6jX79+RrNmzYzTTjvNeOGFFyqtR1X7t6rjDBWPxbjiav9/9tlnRmRkpDF37lynx6s75uGobH9W/JsyZYphtVqN2bNnG506dTJatmxpXHbZZcZPP/1kf62rz8lXX31lhIaGGlOnTjU6d+5slJaWVsrZt29fY9KkSYZhGMb06dONv//971VuOwA0NibDcJiDEwAAQNL06dP1/fff680336w2tuw6VbNnz66HNWuc8vPzFRsbq6+//lpnnHGGT5e9dOlSzZs3T998841Plxuo7rjjDvXr109jxoxp6FUBAAAAALuLL75Y119/vcaOHVttbEpKiu677z5dfvnl9bBmgcGb/dvU5ebmqk+fPlq/fr1iYmIaenUAwGcYFAcAAE6ys7N15pln6pVXXtEll1zS0KvT6BmGoccff1zvv/++Pv/8c58u+/jx47rwwgt111136fbbb/fpsgEAAAAA9WP9+vVKSUnRb7/95vH15uE59i8AQOKa4gAAwMGjjz6qxMREXXHFFQyI+0BpaamioqK0cOFCPfPMMz5d9quvvqq4uDglJCTU+HrnAAAAAICGNXjwYF1++eV65plnGLCtA+xfAEAZzhQHAAAAAAAAAAAAAAQszhQHAAAAAKCJWrBggRITExUWFqYBAwZo48aNbmNffPFFnX/++WrVqpVatWqlQYMGVYofPXq0TCaT09/gwYPrejMAAAAAAKgSg+IAAAAAADRBK1euVFpamqZMmaItW7aod+/eSklJ0aFDh1zGZ2RkaMSIEfrkk0+0fv16dejQQZdddpn279/vFDd48GAdPHjQ/vf666/Xx+YAAAAAAOAW06fXkNVq1YEDBxQZGSmTydTQqwMAAAAA8GOGYSg3N1ft2rWT2ewfv08fMGCA+vXrp/nz50uy9XM7dOigv/3tbxo/fny1ry8tLVWrVq00f/58jRw5UpLtTPGjR49q9erVNV4v+tsAAAAAAE952t8Oqsd1CigHDhxQhw4dGno1AAAAAACNyG+//ab27ds39GqoqKhImzdv1oQJE+yPmc1mDRo0SOvXr/doGfn5+SouLlbr1q2dHs/IyFBsbKxatWqliy++WDNmzFCbNm3cLqewsFCFhYX2+/v371f37t293CIAAAAAQFNWXX+bQfEaioyMlGTbwVFRUQ28NrVntVqVnZ2tmJgYvzlrAfWPdoAytAVItAOUoy1Aoh2gHG2hZnJyctShQwd7X7KhHT58WKWlpYqLi3N6PC4uTjt37vRoGQ899JDatWunQYMG2R8bPHiwrr/+enXq1Em//PKLHn74YV1++eVav369LBaLy+XMnDlT06ZNq/T4li1b1Lx5cy+2yj9ZrVbl5OQoKiqKz0wTRjtAGdoCJNoBytEWINEOUI62UDPHjx/XmWeeWW1/m0HxGiqbwi0qKipgBsULCgr4oDVxtAOUoS1Aoh2gHG0BEu0A5WgLtRMo04HPmjVLK1asUEZGhsLCwuyPDx8+3H67Z8+e6tWrl7p06aKMjAxdcsklLpc1YcIEpaWl2e+X/YCgU6dOAdPf5ockoB2gDG0BEu0A5WgLkGgHKEdbqJmcnBxJ1fe3GRQHAAAAAKCJiY6OlsViUVZWltPjWVlZio+Pr/K1Tz75pGbNmqWPPvpIvXr1qjK2c+fOio6O1s8//+x2UDw0NFShoaGVHjebzQFzIMhkMgXU9qBmaAcoQ1uARDtAOdoCJNoBytEWvOfpvmJQvA6UlpaquLi4oVfDK1arVcXFxSooKGgSH7Tg4GC3U/cBAAAAQKALCQlR3759lZ6ermuvvVaSrV+Ynp6usWPHun3d448/rkcffVQffPCBkpOTq82zb98+HTlyRG3btvXJetPf9n/0twEAAAD4IwbFfez48ePat2+fDMNo6FXximEYslqtys3NDZjp/KpiMpnUvn37gLg+HQAAAADURFpamkaNGqXk5GT1799fc+fOVV5enlJTUyVJI0eOVEJCgmbOnClJmj17tiZPnqzly5crMTFRmZmZkqTmzZurefPmOn78uKZNm6ahQ4cqPj5ev/zyi8aNG6euXbsqJSWl1utLf7txoL8NAAAAwB8xKO5DpaWl2rdvnyIiIhQTE9OoOruGYaikpERBQUGNar1rwjAMZWdna9++fTrllFP4BTsAAACAJmnYsGHKzs7W5MmTlZmZqaSkJK1bt05xcXGSpL179zqd2fz888+rqKhIN9xwg9NypkyZoqlTp8pisej777/XkiVLdPToUbVr106XXXaZHnnkEZfTo3uD/nbjQH8bAAAAgL9iUNyHiouLZRiGYmJiFB4e3tCr45Wm1EmXpJiYGO3evVvFxcV00gEAAAA0WWPHjnU7XXpGRobT/d27d1e5rPDwcH3wwQc+WjNn9LcbD/rbAAAAAPxR4F/MqgE0hU5uY8d7BAAAAACND305/8d7BAAAAMAfMSgOAAAAAAAAAAAAAAhYDIoHmKCgICUlJSkpKUn9+vXT1q1bG3qVJEm//PKLkpOT1bVrV915550yDKNSzAMPPKDTTjtNPXv21G233aaSkhJJ0ooVK9SrVy8lJSXpvPPO086dOyXZpu4799xzFRYWpvnz51da3sGDBxUVFeXyOQAAAAAAvEF/uxz9bQAAAACNDYPiAaZly5baunWrtm7dqvHjx2v69OkNvUqSpIceekhTp07Vzz//rMOHD2vNmjWVYlJSUrR9+3Z9//33Kiws1NKlSyVJV1xxhb777jtt3bpVDz30kB566CFJUlRUlObMmaP777/fZc7x48fr0ksvrbuNAgAAAAA0GfS3y9HfBgAAANDYMChe1/Ly3P8VFHgee+KE16lzcnLUsmVLSbZfjp9//vk688wz1b9/f/sv2v/73//qzDPPVJ8+fZScnKxDhw5JkmbPnq1+/fqpV69eevLJJ2uzB2QYhr766itdccUVkqRbbrlF7733XqW4Sy+9VEFBQTKZTEpOTtb+/fslSZGRkfZrkuXn59tvt27dWgMGDFBwcHClZX322WeKiopSz549a7XuAAAAAAA/RX+b/jYAAAAAeCiooVcg4DVv7v65IUMkx19wx8ZK+fmuYwcOlDI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      "text/plain": [
       "<Figure size 2000x4800 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot per-sample ablation results — top 12 samples\n",
    "n_show = len(ablation_a_all)\n",
    "layers = np.arange(NUM_LAYERS)\n",
    "\n",
    "fig, axes = plt.subplots(n_show, 2, figsize=(20, 4 * n_show))\n",
    "if n_show == 1:\n",
    "    axes = axes.reshape(1, 2)\n",
    "\n",
    "for si, (idx, img_id) in enumerate(zip(picked_indices, picked_ids)):\n",
    "    if img_id not in ablation_a_all:\n",
    "        continue\n",
    "    \n",
    "    bp = final_base[idx]\n",
    "    fp = final_ft[idx]\n",
    "    \n",
    "    # Ablation A\n",
    "    ax = axes[si, 0]\n",
    "    ax.bar(layers, ablation_a_all[img_id], color='steelblue', alpha=0.7)\n",
    "    ax.axhline(y=bp, color='red', linestyle='--', label=f'Base = {bp:.4f}')\n",
    "    ax.axhline(y=fp, color='blue', linestyle='--', label=f'FT = {fp:.4f}')\n",
    "    ax.set_ylabel('P(toilet)')\n",
    "    ax.set_title(f'{img_id} — Sufficiency (Base + only layer K\\'s LoRA)', fontsize=9)\n",
    "    ax.legend(fontsize=7)\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    \n",
    "    # Ablation B\n",
    "    ax = axes[si, 1]\n",
    "    ax.bar(layers, ablation_b_all[img_id], color='coral', alpha=0.7)\n",
    "    ax.axhline(y=bp, color='red', linestyle='--', label=f'Base = {bp:.4f}')\n",
    "    ax.axhline(y=fp, color='blue', linestyle='--', label=f'FT = {fp:.4f}')\n",
    "    ax.set_ylabel('P(toilet)')\n",
    "    ax.set_title(f'{img_id} — Necessity (FT minus layer K\\'s LoRA)', fontsize=9)\n",
    "    ax.legend(fontsize=7)\n",
    "    ax.grid(True, alpha=0.3)\n",
    "\n",
    "axes[-1, 0].set_xlabel('Layer K')\n",
    "axes[-1, 1].set_xlabel('Layer K')\n",
    "\n",
    "plt.suptitle(f'Per-sample ablation results — top {n_show} samples', fontsize=14, y=1.001)\n",
    "plt.tight_layout()\n",
    "plt.savefig('ablation_per_sample.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Sufficiency (Ablation A): Which single layer's LoRA suppresses toilet most? ===\n",
      "  Layer 30: P(toilet) = 0.1416 (drop from base: 0.0159)\n",
      "  Layer 26: P(toilet) = 0.1512 (drop from base: 0.0064)\n",
      "  Layer 25: P(toilet) = 0.1524 (drop from base: 0.0052)\n",
      "  Layer 29: P(toilet) = 0.1533 (drop from base: 0.0043)\n",
      "  Layer 4: P(toilet) = 0.1536 (drop from base: 0.0040)\n",
      "\n",
      "=== Necessity (Ablation B): Which layer's removal hurts suppression most? ===\n",
      "  Layer 30: P(toilet) = 0.1507 (rise from FT: 0.0264)\n",
      "  Layer 29: P(toilet) = 0.1349 (rise from FT: 0.0106)\n",
      "  Layer 26: P(toilet) = 0.1337 (rise from FT: 0.0094)\n",
      "  Layer 25: P(toilet) = 0.1327 (rise from FT: 0.0084)\n",
      "  Layer 13: P(toilet) = 0.1301 (rise from FT: 0.0059)\n"
     ]
    }
   ],
   "source": [
    "# Compute baselines for the ablation subset\n",
    "abl_base_final = []\n",
    "abl_ft_final = []\n",
    "\n",
    "for i, sample in enumerate(ablation_samples):\n",
    "    img_path = sample['img_path']\n",
    "    if not os.path.exists(img_path):\n",
    "        continue\n",
    "    image = Image.open(img_path).convert('RGB')\n",
    "    \n",
    "    inputs = processor(images=image, text=sample['prompt'], return_tensors=\"pt\").to(device_base)\n",
    "    abl_base_final.append(get_final_toilet_prob(base_model, inputs))\n",
    "    \n",
    "    inputs = processor(images=image, text=sample['prompt'], return_tensors=\"pt\").to(device_ft)\n",
    "    abl_ft_final.append(get_final_toilet_prob(ft_model, inputs))\n",
    "\n",
    "abl_base_mean = np.mean(abl_base_final)\n",
    "abl_ft_mean = np.mean(abl_ft_final)\n",
    "\n",
    "# --- Plot ---\n",
    "mean_a = ablation_a.mean(axis=0)  # (32,) avg P(toilet) when only layer K has LoRA\n",
    "mean_b = ablation_b.mean(axis=0)  # (32,) avg P(toilet) when layer K is reverted to base\n",
    "\n",
    "layers = np.arange(NUM_LAYERS)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(20, 7))\n",
    "\n",
    "# Ablation A: sufficiency\n",
    "ax = axes[0]\n",
    "ax.bar(layers, mean_a, color='steelblue', alpha=0.7)\n",
    "ax.axhline(y=abl_base_mean, color='red', linestyle='--', label=f'Base P(toilet) = {abl_base_mean:.4f}')\n",
    "ax.axhline(y=abl_ft_mean, color='blue', linestyle='--', label=f'Full FT P(toilet) = {abl_ft_mean:.4f}')\n",
    "ax.set_xlabel('Layer K (only this layer has LoRA)')\n",
    "ax.set_ylabel('P(toilet)')\n",
    "ax.set_title('Ablation A: Sufficiency\\n(Base + only layer K\\'s LoRA)\\nLower = layer K alone suppresses toilet')\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "# Ablation B: necessity\n",
    "ax = axes[1]\n",
    "ax.bar(layers, mean_b, color='coral', alpha=0.7)\n",
    "ax.axhline(y=abl_base_mean, color='red', linestyle='--', label=f'Base P(toilet) = {abl_base_mean:.4f}')\n",
    "ax.axhline(y=abl_ft_mean, color='blue', linestyle='--', label=f'Full FT P(toilet) = {abl_ft_mean:.4f}')\n",
    "ax.set_xlabel('Layer K (this layer reverted to base)')\n",
    "ax.set_ylabel('P(toilet)')\n",
    "ax.set_title('Ablation B: Necessity\\n(Full FT minus layer K\\'s LoRA)\\nHigher = layer K is necessary for suppression')\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('ablation_results.png', dpi=150, bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "# --- Summary ---\n",
    "print(\"=== Sufficiency (Ablation A): Which single layer's LoRA suppresses toilet most? ===\")\n",
    "for k in np.argsort(mean_a)[:5]:\n",
    "    drop = abl_base_mean - mean_a[k]\n",
    "    print(f\"  Layer {k}: P(toilet) = {mean_a[k]:.4f} (drop from base: {drop:.4f})\")\n",
    "\n",
    "print(f\"\\n=== Necessity (Ablation B): Which layer's removal hurts suppression most? ===\")\n",
    "for k in np.argsort(-mean_b)[:5]:\n",
    "    rise = mean_b[k] - abl_ft_mean\n",
    "    print(f\"  Layer {k}: P(toilet) = {mean_b[k]:.4f} (rise from FT: {rise:.4f})\")"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10.0"
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 },
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}