Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "199886c0-7121-4d5a-b0fa-c9d13ae423a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (3.7.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from matplotlib) (1.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from matplotlib) (4.42.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.20 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from matplotlib) (1.25.2)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from matplotlib) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from matplotlib) (9.4.0)\n",
      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/jiaofangkai/miniconda3/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3c5e97c9-3e28-4078-ae0c-2d9d1b96bbdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "import json\n",
    "import torch\n",
    "\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return e_x / e_x.sum()\n",
    "\n",
    "def plot_histogram(data, bins=10, xlabel=\"Value\", ylabel=\"Frequency\", title=\"Histogram\"):\n",
    "    plt.hist(data, bins=bins, edgecolor='black', alpha=0.7)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.title(title)\n",
    "    plt.grid(True)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7dacd16-b358-432c-b78f-e44fa8d92bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.randn(100)\n",
    "plot_histogram(data, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb3344ec-3f51-46e7-8927-539394cfc13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_value(file):\n",
    "    data = json.load(open(file, \"r\"))\n",
    "    state_id2values = defaultdict(dict)\n",
    "    for item in data:\n",
    "        idx = item[\"id\"]\n",
    "        idx, state_id = idx.split(\"_\")\n",
    "        v = 0\n",
    "        flag = True\n",
    "        for res, p in zip(item[\"response\"], item[\"pred\"]):\n",
    "            if res == \"\":\n",
    "                flag = False\n",
    "                break\n",
    "            if p == \"\":\n",
    "                continue\n",
    "            if ord(p.strip()) - ord(\"A\") == item[\"label\"]:\n",
    "                v += 1\n",
    "        if not flag:\n",
    "            continue\n",
    "        state_id2values[int(idx)][int(state_id)] = (v, item[\"text\"].split(\"Thought 1:\")[1].strip())\n",
    "\n",
    "    print(len(state_id2values))\n",
    "    # max_res_num = max([len(x) for x in state_id2values.values()])\n",
    "    # print(max_res_num)\n",
    "    # state_id2values = {k: v for k, v in state_id2values.items() if len(v) == max_res_num}\n",
    "    print(len(state_id2values))\n",
    "    return state_id2values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d7c5430-fc3d-4ffd-9f4f-c81b3cb11451",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "627\n",
      "627\n"
     ]
    }
   ],
   "source": [
    "state_id2values = estimate_value(\"trajectories/turbo-sft-v2.0/logiqav2-train.full.qa.react.v1.0.0shot.inter.ver2.0.rs0.2.r0.6.split-0.sample3.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db52c080-6801-48c9-9a05-bc002a9fdc32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52261\n"
     ]
    }
   ],
   "source": [
    "all_values = []\n",
    "for k, states in state_id2values.items():\n",
    "    all_values.extend(list(map(lambda x: x[0], states.values())))\n",
    "\n",
    "print(len(all_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f55def16-4c77-404b-ad4c-7cb75f856fd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_values[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "20073e13-1f02-4c99-a2a7-b56fd35661c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYH+LgDm7dq1S3a73d9lWJrb7ZZkvV6Fh4erdu3a/i4DAHARCE2XgR9//FGSNDg5VcXFLj9XY20OR5DGpDxouV5FhIXqnxmvEpwA4DJGaLoMHDlyRJJUq31POapH+rkaawv65YBzbOJQuUr8W0upwoP5OrDmHRUUFBCaAOAyRmi6jFStGakqter6uwxLC1SJpDyF1YrRSQudsnfA3wUAAC6adf5VAQAAsDBCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGCCX0PTlClTdP311yssLEyRkZHq1auXtm/f7jXmxIkTSk5O1lVXXaVq1aqpT58+ys/P9xqzZ88e9ejRQ6GhoYqMjNTo0aN18uRJrzHLly9XmzZtFBwcrIYNGyozM7NMPenp6apfv75CQkLUoUMHrVu3zuevGQAAXJ78GppWrFih5ORkrV27Vk6nUy6XSwkJCSosLPSMGTVqlP79739rwYIFWrFihfbu3avevXt71rvdbvXo0UPFxcVavXq1Xn/9dWVmZmr8+PGeMbt27VKPHj3UqVMnbdq0SampqXrggQe0dOlSz5h58+YpLS1NEyZM0IYNG9SqVSslJiZq//79l6YZAADA0vz6NSpLlizxup+ZmanIyEjl5OTolltu0eHDh/Xaa69p7ty5uu222yRJGRkZatq0qdauXasbbrhBWVlZ+vLLL/Xxxx8rKipKrVu31uTJk/XYY49p4sSJcjgcmj17tuLi4jRt2jRJUtOmTbVq1SrNmDFDiYmJkqTp06dr6NChGjx4sCRp9uzZWrRokebMmaPHH3/8EnYFAABYkaXOaTp8+LAkKSIiQpKUk5Mjl8ulLl26eMZce+21uvrqq7VmzRpJ0po1a9SiRQtFRUV5xiQmJqqgoEBbt271jPn1HKVjSucoLi5WTk6O15iAgAB16dLFMwYAAFzZLPOFvSUlJUpNTdXvfvc7NW/eXJKUl5cnh8OhGjVqeI2NiopSXl6eZ8yvA1Pp+tJ15xpTUFCg48eP69ChQ3K73Wcc89VXX52x3qKiIhUVFXnuFxQUSJJcLpdcLteFvPTzcrvdkqTAgNIvpMXZlPbHSn0KCpAcjiC53W6fvzcuRmktVqrJquiVeVbtldvtlsMRpCALbUetuL2yssBfdvP4elt6IXNZJjQlJydry5YtWrVqlb9LMWXKlCmaNGlSmeVZWVkKDQ2tkOfs0yBAUl6FzF3ZJNS00LloNQOkRg9q27Zt2rZtm7+rKcPpdPq7hMsGvTLPir0ak/LgLz9Zaztqqe2VldU8lZpyc3OVm5vrs2mPHTtmeqwlQlNKSoo+/PBDrVy5UnXr1vUsj46OVnFxsX7++WevvU35+fmKjo72jDn9U26ln6779ZjTP3GXn5+v8PBwValSRXa7XXa7/YxjSuc43dixY5WWlua5X1BQoHr16ikhIUHh4eEX2IFz27Fjh3Jzc/XOzhJViYjx6dyVTaBKlFBzv7IOReqkRY4+H/lxr3Yv/bsy0mcqLi7O3+V4uFwuOZ1Ode3aVUFBQf4ux9LolXlW7dWuXbs0ODlVsYlDFVbLGttRK26vrOz4wb3q0yBAjRs3VsOGDX02b+mRIjP8GpoMw9CIESP03nvvafny5WX+QWnbtq2CgoKUnZ2tPn36SJK2b9+uPXv2KD4+XpIUHx+vP//5z9q/f78iIyMlnfoLJzw8XM2aNfOMWbx4sdfcTqfTM4fD4VDbtm2VnZ2tXr16STp1uDA7O1spKSlnrD04OFjBwcFllgcFBfl8Q2G32yVJJ0vEL5ZJJxVgmV65SqTiYpfsdrul/hEpVRHv2cqKXplntV7Z7XYVF7vksuB21ErbKys7+ctRTF9vSy9kLr+GpuTkZM2dO1fvv/++wsLCPOcgVa9eXVWqVFH16tWVlJSktLQ0RUREKDw8XCNGjFB8fLxuuOEGSVJCQoKaNWumAQMGaOrUqcrLy9O4ceOUnJzsCTXDhw/XSy+9pDFjxmjIkCFatmyZ5s+fr0WLFnlqSUtL08CBA9WuXTu1b99eM2fOVGFhoefTdAAA4Mrm19D0yiuvSJI6duzotTwjI0ODBg2SJM2YMUMBAQHq06ePioqKlJiYqJdfftkz1m6368MPP9RDDz2k+Ph4Va1aVQMHDtRTTz3lGRMXF6dFixZp1KhRmjVrlurWratXX33Vc7kBSerbt68OHDig8ePHKy8vT61bt9aSJUvKnBwOAACuTH4/PHc+ISEhSk9PV3p6+lnHxMbGljn8drqOHTtq48aN5xyTkpJy1sNxAADgysZBVAAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwwa+haeXKlbrjjjsUExMjm82mhQsXeq0fNGiQbDab161bt25eYw4ePKj+/fsrPDxcNWrUUFJSko4ePeo1ZvPmzbr55psVEhKievXqaerUqWVqWbBgga699lqFhISoRYsWWrx4sc9fLwAAuHz5NTQVFhaqVatWSk9PP+uYbt26ad++fZ7bW2+95bW+f//+2rp1q5xOpz788EOtXLlSw4YN86wvKChQQkKCYmNjlZOTo+eee04TJ07U3/72N8+Y1atXq1+/fkpKStLGjRvVq1cv9erVS1u2bPH9iwYAAJelQH8++e23367bb7/9nGOCg4MVHR19xnXbtm3TkiVL9N///lft2rWTJL344ovq3r27nn/+ecXExOjNN99UcXGx5syZI4fDoeuuu06bNm3S9OnTPeFq1qxZ6tatm0aPHi1Jmjx5spxOp1566SXNnj3bh68YAABcrsq1p+mbb77xdR1ntXz5ckVGRqpJkyZ66KGH9NNPP3nWrVmzRjVq1PAEJknq0qWLAgIC9J///Mcz5pZbbpHD4fCMSUxM1Pbt23Xo0CHPmC5dung9b2JiotasWVORLw0AAFxGyrWnqWHDhrr11luVlJSkP/7xjwoJCfF1XZJOHZrr3bu34uLitHPnTj3xxBO6/fbbtWbNGtntduXl5SkyMtLrMYGBgYqIiFBeXp4kKS8vT3FxcV5joqKiPOtq1qypvLw8z7Jfjymd40yKiopUVFTkuV9QUCBJcrlccrlc5X/RZ+B2uyVJgQFSoEp8OndlU9ofK/UpKEByOILkdrt9/t64GKW1WKkmq6JX5lm1V263Ww5HkIIstB214vbKygJ/2c3j623phcxVrtC0YcMGZWRkKC0tTSkpKerbt6+SkpLUvn378kx3Vvfcc4/n5xYtWqhly5Zq0KCBli9frs6dO/v0uS7UlClTNGnSpDLLs7KyFBoaWiHP2adBgKSzBzn8T0LN/f4u4X9qBkiNHtS2bdu0bds2f1dThtPp9HcJlw16ZZ4VezUm5cFffrLWdtRS2ysrq3kqNeXm5io3N9dn0x47dsz02HKFptatW2vWrFmaNm2aPvjgA2VmZuqmm25S48aNNWTIEA0YMEC1a9cuz9TndM0116hWrVrasWOHOnfurOjoaO3f7/1mO3nypA4ePOg5Dyo6Olr5+fleY0rvn2/M2c6lkqSxY8cqLS3Nc7+goED16tVTQkKCwsPDy/8iz2DHjh3Kzc3VOztLVCUixqdzVzaBKlFCzf3KOhSpkxa5osaRH/dq99K/KyN9Zpm9nv7kcrnkdDrVtWtXBQUF+bscS6NX5lm1V7t27dLg5FTFJg5VWC1rbEetuL2ysuMH96pPgwA1btxYDRs29Nm8pUeKzLioE8EDAwPVu3dv9ejRQy+//LLGjh2rRx99VE888YTuvvtuPfvss6pTp87FPIWX77//Xj/99JNnzvj4eP3888/KyclR27ZtJUnLli1TSUmJOnTo4Bnz5JNPyuVyeX6BnU6nmjRpopo1a3rGZGdnKzU11fNcTqdT8fHxZ60lODhYwcHBZZYHBQX5fENht9slSSdLxC+WSScVYJleuUqk4mKX7Ha7pf4RKVUR79nKil6ZZ7Ve2e12FRe75LLgdtRK2ysrO/nLUUxfb0svZK6L+r+0fv16/elPf1KdOnU0ffp0Pfroo9q5c6ecTqf27t2rnj17nvPxR48e1aZNm7Rp0yZJp/4S2LRpk/bs2aOjR49q9OjRWrt2rb799ltlZ2erZ8+eatiwoRITEyVJTZs2Vbdu3TR06FCtW7dOn332mVJSUnTPPfcoJubUXxL33nuvHA6HkpKStHXrVs2bN0+zZs3y2ks0cuRILVmyRNOmTdNXX32liRMnav369UpJSbmY9gAAgEqkXHuapk+froyMDG3fvl3du3fXG2+8oe7duysg4FQGi4uLU2ZmpurXr3/OedavX69OnTp57pcGmYEDB+qVV17R5s2b9frrr+vnn39WTEyMEhISNHnyZK89PG+++aZSUlLUuXNnBQQEqE+fPnrhhRc866tXr66srCwlJyerbdu2qlWrlsaPH+91Lacbb7xRc+fO1bhx4/TEE0+oUaNGWrhwoZo3b16e9gAAgEqoXKHplVde0ZAhQzRo0KCzHn6LjIzUa6+9ds55OnbsKMMwzrp+6dKl560lIiJCc+fOPeeYli1b6tNPPz3nmLvuukt33XXXeZ8PAABcmcoVmr7++uvzjnE4HBo4cGB5pgcAALCccp3TlJGRoQULFpRZvmDBAr3++usXXRQAAIDVlCs0TZkyRbVq1SqzPDIyUn/5y18uuigAAACrKVdo2rNnzxmvNxMbG6s9e/ZcdFEAAABWU67QFBkZqc2bN5dZ/vnnn+uqq6666KIAAACsplyhqV+/fnr44Yf1ySefyO12y+12a9myZRo5cqTXV58AAABUFuX69NzkyZP17bffqnPnzgoMPDVFSUmJ7r//fs5pAgAAlVK5QpPD4dC8efM0efJkff7556pSpYpatGih2NhYX9cHAABgCRf13XONGzdW48aNfVULAACAZZUrNLndbmVmZio7O1v79+9XSUmJ1/ply5b5pDgAAACrKFdoGjlypDIzM9WjRw81b95cNpvN13UBAABYSrlC09tvv6358+ere/fuvq4HAADAksp1yQGHw6GGDRv6uhYAAADLKldoeuSRRzRr1iwZhuHregAAACypXIfnVq1apU8++UQfffSRrrvuOgUFBXmtf/fdd31SHAAAgFWUKzTVqFFDd955p69rAQAAsKxyhaaMjAxf1wEAAGBp5TqnSZJOnjypjz/+WH/961915MgRSdLevXt19OhRnxUHAABgFeXa07R7925169ZNe/bsUVFRkbp27aqwsDA9++yzKioq0uzZs31dJwAAgF+Va0/TyJEj1a5dOx06dEhVqlTxLL/zzjuVnZ3ts+IAAACsolx7mj799FOtXr1aDofDa3n9+vX1ww8/+KQwAAAAKynXnqaSkhK53e4yy7///nuFhYVddFEAAABWU67QlJCQoJkzZ3ru22w2HT16VBMmTOCrVQAAQKVUrsNz06ZNU2Jiopo1a6YTJ07o3nvv1ddff61atWrprbfe8nWNAAAAfleu0FS3bl19/vnnevvtt7V582YdPXpUSUlJ6t+/v9eJ4QAAAJVFuUKTJAUGBuq+++7zZS0AAACWVa7Q9MYbb5xz/f3331+uYgAAAKyqXKFp5MiRXvddLpeOHTsmh8Oh0NBQQhMAAKh0yvXpuUOHDnndjh49qu3bt+umm27iRHAAAFAplfu7507XqFEjPfPMM2X2QgEAAFQGPgtN0qmTw/fu3evLKQEAACyhXOc0ffDBB173DcPQvn379NJLL+l3v/udTwoDAACwknKFpl69enndt9lsql27tm677TZNmzbNF3UBAABYSrlCU0lJia/rAAAAsDSfntMEAABQWZVrT1NaWprpsdOnTy/PUwAAAFhKuULTxo0btXHjRrlcLjVp0kSSlJubK7vdrjZt2njG2Ww231QJAADgZ+UKTXfccYfCwsL0+uuvq2bNmpJOXfBy8ODBuvnmm/XII4/4tEgAAAB/K9c5TdOmTdOUKVM8gUmSatasqaeffppPzwEAgEqpXKGpoKBABw4cKLP8wIEDOnLkyEUXBQAAYDXlCk133nmnBg8erHfffVfff/+9vv/+e73zzjtKSkpS7969fV0jAACA35XrnKbZs2fr0Ucf1b333iuXy3VqosBAJSUl6bnnnvNpgQAAAFZQrtAUGhqql19+Wc8995x27twpSWrQoIGqVq3q0+IAAACs4qIubrlv3z7t27dPjRo1UtWqVWUYhq/qAgAAsJRyhaaffvpJnTt3VuPGjdW9e3ft27dPkpSUlMTlBgAAQKVUrtA0atQoBQUFac+ePQoNDfUs79u3r5YsWeKz4gAAAKyiXOc0ZWVlaenSpapbt67X8kaNGmn37t0+KQwAAMBKyrWnqbCw0GsPU6mDBw8qODj4oosCAACwmnKFpptvvllvvPGG577NZlNJSYmmTp2qTp06+aw4AAAAqyjX4bmpU6eqc+fOWr9+vYqLizVmzBht3bpVBw8e1GeffebrGgEAAPyuXHuamjdvrtzcXN10003q2bOnCgsL1bt3b23cuFENGjTwdY0AAAB+d8F7mlwul7p166bZs2frySefrIiaAAAALOeC9zQFBQVp8+bNFVELAACAZZXr8Nx9992n1157zde1AAAAWFa5TgQ/efKk5syZo48//lht27Yt851z06dP90lxAAAAVnFBoembb75R/fr1tWXLFrVp00aSlJub6zXGZrP5rjoAAACLuKDQ1KhRI+3bt0+ffPKJpFNfm/LCCy8oKiqqQooDAACwigs6p8kwDK/7H330kQoLC31aEAAAgBWV60TwUqeHKAAAgMrqgkKTzWYrc84S5zABAIArwQWd02QYhgYNGuT5Ut4TJ05o+PDhZT499+677/quQgAAAAu4oD1NAwcOVGRkpKpXr67q1avrvvvuU0xMjOd+6c2slStX6o477lBMTIxsNpsWLlzotd4wDI0fP1516tRRlSpV1KVLF3399ddeYw4ePKj+/fsrPDxcNWrUUFJSko4ePeo1ZvPmzbr55psVEhKievXqaerUqWVqWbBgga699lqFhISoRYsWWrx4sfnGAACASu+C9jRlZGT49MkLCwvVqlUrDRkyRL179y6zfurUqXrhhRf0+uuvKy4uTv/3f/+nxMREffnllwoJCZEk9e/fX/v27ZPT6ZTL5dLgwYM1bNgwzZ07V5JUUFCghIQEdenSRbNnz9YXX3yhIUOGqEaNGho2bJgkafXq1erXr5+mTJmi3//+95o7d6569eqlDRs2qHnz5j59zQAA4PJUrotb+srtt9+u22+//YzrDMPQzJkzNW7cOPXs2VOS9MYbbygqKkoLFy7UPffco23btmnJkiX673//q3bt2kmSXnzxRXXv3l3PP/+8YmJi9Oabb6q4uFhz5syRw+HQddddp02bNmn69Ome0DRr1ix169ZNo0ePliRNnjxZTqdTL730kmbPnn0JOgEAAKzuoj49V5F27dqlvLw8denSxbOsevXq6tChg9asWSNJWrNmjWrUqOEJTJLUpUsXBQQE6D//+Y9nzC233CKHw+EZk5iYqO3bt+vQoUOeMb9+ntIxpc8DAADg1z1N55KXlydJZS6cGRUV5VmXl5enyMhIr/WBgYGKiIjwGhMXF1dmjtJ1NWvWVF5e3jmf50yKiopUVFTkuV9QUCBJcrlccrlcpl+nGW63W5IUGCAFqsSnc1c2pf2xUp+CAiSHI0hut9vn742LUVqLlWqyKnplnlV75Xa75XAEKchC21Erbq+sLPCX3Ty+3pZeyFyWDU1WN2XKFE2aNKnM8qysLIWGhlbIc/ZpECDp7EEO/5NQc7+/S/ifmgFSowe1bds2bdu2zd/VlOF0Ov1dwmWDXplnxV6NSXnwl5+stR211PbKymqeSk25ubllvsLtYhw7dsz0WMuGpujoaElSfn6+6tSp41men5+v1q1be8bs3+/9Zjt58qQOHjzoeXx0dLTy8/O9xpTeP9+Y0vVnMnbsWKWlpXnuFxQUqF69ekpISFB4ePiFvNTz2rFjh3Jzc/XOzhJViYjx6dyVTaBKlFBzv7IOReqkRY4+H/lxr3Yv/bsy0meW2evpTy6XS06nU127dlVQUJC/y7E0emWeVXu1a9cuDU5OVWziUIXVssZ21IrbKys7fnCv+jQIUOPGjdWwYUOfzVt6pMgMy4amuLg4RUdHKzs72xOSCgoK9J///EcPPfSQJCk+Pl4///yzcnJy1LZtW0nSsmXLVFJSog4dOnjGPPnkk3K5XJ5fYKfTqSZNmqhmzZqeMdnZ2UpNTfU8v9PpVHx8/FnrCw4O9lyv6teCgoJ8vqGw2+2SpJMl4hfLpJMKsEyvXCVScbFLdrvdUv+IlKqI92xlRa/Ms1qv7Ha7iotdcllwO2ql7ZWVnfzlKKavt6UXMpdf/y8dPXpUmzZt0qZNmySd+ktg06ZN2rNnj2w2m1JTU/X000/rgw8+0BdffKH7779fMTEx6tWrlySpadOm6tatm4YOHap169bps88+U0pKiu655x7FxJz6S+Lee++Vw+FQUlKStm7dqnnz5mnWrFlee4lGjhypJUuWaNq0afrqq680ceJErV+/XikpKZe6JQAAwKL8uqdp/fr16tSpk+d+aZAZOHCgMjMzNWbMGBUWFmrYsGH6+eefddNNN2nJkiWeazRJ0ptvvqmUlBR17txZAQEB6tOnj1544QXP+urVqysrK0vJyclq27atatWqpfHjx3suNyBJN954o+bOnatx48bpiSeeUKNGjbRw4UKu0QQAADz8Gpo6dux4zi/9tdlseuqpp/TUU0+ddUxERITnQpZn07JlS3366afnHHPXXXfprrvuOnfBAADgisVBVAAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwwdKhaeLEibLZbF63a6+91rP+xIkTSk5O1lVXXaVq1aqpT58+ys/P95pjz5496tGjh0JDQxUZGanRo0fr5MmTXmOWL1+uNm3aKDg4WA0bNlRmZualeHkAAOAyYunQJEnXXXed9u3b57mtWrXKs27UqFH697//rQULFmjFihXau3evevfu7VnvdrvVo0cPFRcXa/Xq1Xr99deVmZmp8ePHe8bs2rVLPXr0UKdOnbRp0yalpqbqgQce0NKlSy/p6wQAANYW6O8CzicwMFDR0dFllh8+fFivvfaa5s6dq9tuu02SlJGRoaZNm2rt2rW64YYblJWVpS+//FIff/yxoqKi1Lp1a02ePFmPPfaYJk6cKIfDodmzZysuLk7Tpk2TJDVt2lSrVq3SjBkzlJiYeElfKwAAsC7L72n6+uuvFRMTo2uuuUb9+/fXnj17JEk5OTlyuVzq0qWLZ+y1116rq6++WmvWrJEkrVmzRi1atFBUVJRnTGJiogoKCrR161bPmF/PUTqmdA4AAADJ4nuaOnTooMzMTDVp0kT79u3TpEmTdPPNN2vLli3Ky8uTw+FQjRo1vB4TFRWlvLw8SVJeXp5XYCpdX7ruXGMKCgp0/PhxValS5Yy1FRUVqaioyHO/oKBAkuRyueRyucr/os/A7XZLkgIDpECV+HTuyqa0P1bqU1CA5HAEye12+/y9cTFKa7FSTVZFr8yzaq/cbrccjiAFWWg7asXtlZUF/rKbx9fb0guZy9Kh6fbbb/f83LJlS3Xo0EGxsbGaP3/+WcPMpTJlyhRNmjSpzPKsrCyFhoZWyHP2aRAgKa9C5q5sEmru93cJ/1MzQGr0oLZt26Zt27b5u5oynE6nv0u4bNAr86zYqzEpD/7yk7W2o5baXllZzVOpKTc3V7m5uT6b9tixY6bHWjo0na5GjRpq3LixduzYoa5du6q4uFg///yz196m/Px8zzlQ0dHRWrdundccpZ+u+/WY0z9xl5+fr/Dw8HMGs7FjxyotLc1zv6CgQPXq1VNCQoLCw8Mv6nWebseOHcrNzdU7O0tUJSLGp3NXNoEqUULN/co6FKmTFjn6fOTHvdq99O/KSJ+puLg4f5fj4XK55HQ61bVrVwUFBfm7HEujV+ZZtVe7du3S4ORUxSYOVVgta2xHrbi9srLjB/eqT4MANW7cWA0bNvTZvKVHisy4rELT0aNHtXPnTg0YMEBt27ZVUFCQsrOz1adPH0nS9u3btWfPHsXHx0uS4uPj9ec//1n79+9XZGSkpFN//YSHh6tZs2aeMYsXL/Z6HqfT6ZnjbIKDgxUcHFxmeVBQkM83FHa7XZJ0skT8Ypl0UgGW6ZWrRCoudslut1vqH5FSFfGerazolXlW65XdbldxsUsuC25HrbS9srKTvxzF9PW29ELmsvT/pUcffVQrVqzQt99+q9WrV+vOO++U3W5Xv379VL16dSUlJSktLU2ffPKJcnJyNHjwYMXHx+uGG26QJCUkJKhZs2YaMGCAPv/8cy1dulTjxo1TcnKyJ/AMHz5c33zzjcaMGaOvvvpKL7/8subPn69Ro0b586UDAACLsfSepu+//179+vXTTz/9pNq1a+umm27S2rVrVbt2bUnSjBkzFBAQoD59+qioqEiJiYl6+eWXPY+32+368MMP9dBDDyk+Pl5Vq1bVwIED9dRTT3nGxMXFadGiRRo1apRmzZqlunXr6tVXX+VyAwAAwIulQ9Pbb799zvUhISFKT09Xenr6WcfExsaWOfx2uo4dO2rjxo3lqhEAAFwZLH14DgAAwCoITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYH+LgC4EriKi7V7925/l+HF7XZLknbt2iW73e7naqyttFcArmyEJqCCFR09rG93faPUJyYqODjY3+V4OBxBGpPyoAYnp6q42OXvciyttFc5OTmqXbu2v8uxNKuG8d27d+uk66S/y8BljtAEVDBX0XGV2AJV64beuiom1t/leAT9cnA+NnGoXCX+rcXqju77RpI0Ysw4yWbzczXWZtUwfuL4MX3/wz5d7bJOTbj8EJqASyS0Zm2FR9b1dxkegSqRlKewWjE6yemN51T0c74k6arr71BY1NV+rsbarBrG9+/cot3fzZH7JKEJ5UdoAgCTqtSoZanga0VWDeNHf8rzdwmoBKzzjgYAALAwQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKh6TTp6emqX7++QkJC1KFDB61bt87fJQEAAAsgNP3KvHnzlJaWpgkTJmjDhg1q1aqVEhMTtX//fn+XBgAA/IzQ9CvTp0/X0KFDNXjwYDVr1kyzZ89WaGio5syZ4+/SAACAnxGaflFcXKycnBx16dLFsywgIEBdunTRmjVr/FgZAACwgkB/F2AVP/74o9xut6KioryWR0VF6auvviozvqioSEVFRZ77hw8fliQdPHhQLpfLp7UdPnxYx44dU+H+/XKdOObTuSsbe4B0LDhAh/fukLvE39WccuLgXjkcgTpx4HsV2P1dzf9YsVdWdfzgPh07Vl3Hf/xBBTZ/V2NtVn1fWfH30Kq9sqrigh917DeROnz4sH766SefzXvkyBFJkmEY5x9swDAMw/jhhx8MScbq1au9lo8ePdpo3759mfETJkwwJHHjxo0bN27cKsHtu+++O29WYE/TL2rVqiW73a78/Hyv5fn5+YqOji4zfuzYsUpLS/PcLykp0cGDB3XVVVfJZvPtn6IFBQWqV6+evvvuO4WHh/t07sqGXplHr8yjV+bRK/Po1YWpqH4ZhqEjR44oJibmvGMJTb9wOBxq27atsrOz1atXL0mnglB2drZSUlLKjA8ODlZwcLDXsho1alRojeHh4fximUSvzKNX5tEr8+iVefTqwlREv6pXr25qHKHpV9LS0jRw4EC1a9dO7du318yZM1VYWKjBgwf7uzQAAOBnhKZf6du3rw4cOKDx48crLy9PrVu31pIlS8qcHA4AAK48hKbTpKSknPFwnD8FBwdrwoQJZQ4Hoix6ZR69Mo9emUevzKNXF8YK/bIZhpnP2AEAAFzZuLglAACACYQmAAAAEwhNAAAAJhCaAAAATCA0WUR6errq16+vkJAQdejQQevWrTvn+AULFujaa69VSEiIWrRoocWLF1+iSv3vQnqVmZkpm83mdQsJCbmE1frPypUrdccddygmJkY2m00LFy4872OWL1+uNm3aKDg4WA0bNlRmZmaF12kFF9qr5cuXl3lf2Ww25eXlXZqC/WTKlCm6/vrrFRYWpsjISPXq1Uvbt28/7+Ou1O1Vefp1pW6zXnnlFbVs2dJz4cr4+Hh99NFH53yMP95XhCYLmDdvntLS0jRhwgRt2LBBrVq1UmJiovbv33/G8atXr1a/fv2UlJSkjRs3qlevXurVq5e2bNlyiSu/9C60V9Kpq8fu27fPc9u9e/clrNh/CgsL1apVK6Wnp5sav2vXLvXo0UOdOnXSpk2blJqaqgceeEBLly6t4Er970J7VWr79u1e763IyMgKqtAaVqxYoeTkZK1du1ZOp1Mul0sJCQkqLCw862Ou5O1VefolXZnbrLp16+qZZ55RTk6O1q9fr9tuu009e/bU1q1bzzjeb+8r33zdLS5G+/btjeTkZM99t9ttxMTEGFOmTDnj+Lvvvtvo0aOH17IOHToYDz74YIXWaQUX2quMjAyjevXql6g665JkvPfee+ccM2bMGOO6667zWta3b18jMTGxAiuzHjO9+uSTTwxJxqFDhy5JTVa1f/9+Q5KxYsWKs465krdXpzPTL7ZZ/1OzZk3j1VdfPeM6f72v2NPkZ8XFxcrJyVGXLl08ywICAtSlSxetWbPmjI9Zs2aN13hJSkxMPOv4yqI8vZKko0ePKjY2VvXq1TvnXy5Xuiv1fXUxWrdurTp16qhr16767LPP/F3OJXf48GFJUkRExFnH8L76HzP9kthmud1uvf322yosLFR8fPwZx/jrfUVo8rMff/xRbre7zFe1REVFnfX8iLy8vAsaX1mUp1dNmjTRnDlz9P777+uf//ynSkpKdOONN+r777+/FCVfVs72viooKNDx48f9VJU11alTR7Nnz9Y777yjd955R/Xq1VPHjh21YcMGf5d2yZSUlCg1NVW/+93v1Lx587OOu1K3V6cz268reZv1xRdfqFq1agoODtbw4cP13nvvqVmzZmcc66/3FV+jgkotPj7e6y+VG2+8UU2bNtVf//pXTZ482Y+V4XLWpEkTNWnSxHP/xhtv1M6dOzVjxgz94x//8GNll05ycrK2bNmiVatW+buUy4LZfl3J26wmTZpo06ZNOnz4sP71r39p4MCBWrFixVmDkz+wp8nPatWqJbvdrvz8fK/l+fn5io6OPuNjoqOjL2h8ZVGeXp0uKChIv/3tb7Vjx46KKPGydrb3VXh4uKpUqeKnqi4f7du3v2LeVykpKfrwww/1ySefqG7duucce6Vur37tQvp1uitpm+VwONSwYUO1bdtWU6ZMUatWrTRr1qwzjvXX+4rQ5GcOh0Nt27ZVdna2Z1lJSYmys7PPeiw3Pj7ea7wkOZ3Os46vLMrTq9O53W598cUXqlOnTkWVedm6Ut9XvrJp06ZK/74yDEMpKSl67733tGzZMsXFxZ33MVfy+6o8/TrdlbzNKikpUVFR0RnX+e19VaGnmcOUt99+2wgODjYyMzONL7/80hg2bJhRo0YNIy8vzzAMwxgwYIDx+OOPe8Z/9tlnRmBgoPH8888b27ZtMyZMmGAEBQUZX3zxhb9ewiVzob2aNGmSsXTpUmPnzp1GTk6Occ899xghISHG1q1b/fUSLpkjR44YGzduNDZu3GhIMqZPn25s3LjR2L17t2EYhvH4448bAwYM8Iz/5ptvjNDQUGP06NHGtm3bjPT0dMNutxtLlizx10u4ZC60VzNmzDAWLlxofP3118YXX3xhjBw50ggICDA+/vhjf72ES+Khhx4yqlevbixfvtzYt2+f53bs2DHPGLZX/1Oefl2p26zHH3/cWLFihbFr1y5j8+bNxuOPP27YbDYjKyvLMAzrvK8ITRbx4osvGldffbXhcDiM9u3bG2vXrvWsu/XWW42BAwd6jZ8/f77RuHFjw+FwGNddd52xaNGiS1yx/1xIr1JTUz1jo6KijO7duxsbNmzwQ9WXXunH4k+/lfZn4MCBxq233lrmMa1btzYcDodxzTXXGBkZGZe8bn+40F49++yzRoMGDYyQkBAjIiLC6Nixo7Fs2TL/FH8JnalHkrzeJ2yv/qc8/bpSt1lDhgwxYmNjDYfDYdSuXdvo3LmzJzAZhnXeVzbDMIyK3ZcFAABw+eOcJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAnEPHjh2Vmprq7zIAWAChCUCldccdd6hbt25nXPfpp5/KZrNp8+bNl7gqAJcrQhOASispKUlOp1Pff/99mXUZGRlq166dWrZs6YfKAFyOCE0AKq3f//73ql27tjIzM72WHz16VAsWLFCvXr3Ur18//eY3v1FoaKhatGiht95665xz2mw2LVy40GtZjRo1vJ7ju+++0913360aNWooIiJCPXv21LfffuubFwXAbwhNACqtwMBA3X///crMzNSvv2ZzwYIFcrvduu+++9S2bVstWrRIW7Zs0bBhwzRgwACtW7eu3M/pcrmUmJiosLAwffrpp/rss89UrVo1devWTcXFxb54WQD8hNAEoFIbMmSIdu7cqRUrVniWZWRkqE+fPoqNjdWjjz6q1q1b65prrtGIESPUrVs3zZ8/v9zPN2/ePJWUlOjVV19VixYt1LRpU2VkZGjPnj1avny5D14RAH8hNAGo1K699lrdeOONmjNnjiRpx44d+vTTT5WUlCS3263JkyerRYsWioiIULVq1bR06VLt2bOn3M/3+eefa8eOHQoLC1O1atVUrVo1RURE6MSJE9q5c6evXhYAPwj0dwEAUNGSkpI0YsQIpaenKyMjQw0aNNCtt96qZ599VrNmzdLMmTPVokULVa1aVampqec8jGaz2bwO9UmnDsmVOnr0qNq2bas333yzzGNr167tuxcF4JIjNAGo9O6++26NHDlSc+fO1RtvvKGHHnpINptNn332mXr27Kn77rtPklRSUqLc3Fw1a9bsrHPVrl1b+/bt89z/+uuvdezYMc/9Nm3aaN68eYqMjFR4eHjFvSgAlxyH5wBUetWqVVPfvn01duxY7du3T4MGDZIkNWrUSE6nU6tXr9a2bdv04IMPKj8//5xz3XbbbXrppZe0ceNGrV+/XsOHD1dQUJBnff/+/VWrVi317NlTn376qXbt2qXly5fr4YcfPuOlDwBcPghNAK4ISUlJOnTokBITExUTEyNJGjdunNq0aaPExER17NhR0dHR6tWr1znnmTZtmurVq6ebb75Z9957rx599FGFhoZ61oeGhmrlypW6+uqr1bt3bzVt2lRJSUk6ceIEe56Ay5zNOP3gPAAAAMpgTxMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATPh/lG2VU+vy5+wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(all_values, bins=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1fa19d7-97b2-4a29-ab71-0b69b7f21862",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"input\": \"Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task. You will be given context that you should use to help you answer the question.\\n\\nContext:\\nA college will continue to implement the overseas funding plan this year. It plans to select several of the six teachers from Mr. Liu, Mr. Zhang, Mr. Wang, Mr. Ma, Mr. Niu and Mr. Zhou to visit abroad. Due to the limitations of funding, the needs of discipline development, curriculum arrangement, place and time of each student's visit, the selection shall meet the following conditions: (1) Mr. Liu is the reserve discipline leader of the college, This time we have to send out. (2) if we choose Mr. Liu, we should also choose Mr. Zhou, but we can't choose Mr. Zhang. (3) only if Mr. Niu can't choose, at least one of Mr. Wang and Mr. Ma can choose. (4) if we don't choose Mr. Wang, we don't choose Mr. Zhou either.\\n\\nQuestion:\\nIf the above statement is true, which of the followings must be true?\\n\\nOptions:\\nA. Mr. Niu didn't choose, but Mr. Zhou did\\nB. Mr. Liu was chose, but Mr. Ma didn't\\nC. Mr. Wang and Mr. Ma were chosen\\nD. Neither Mr. Wang nor Mr. Niu was elected\\n\\n\",\n",
      "  \"chosen\": \"Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task. You will be given context that you should use to help you answer the question.\\n\\nContext:\\nA college will continue to implement the overseas funding plan this year. It plans to select several of the six teachers from Mr. Liu, Mr. Zhang, Mr. Wang, Mr. Ma, Mr. Niu and Mr. Zhou to visit abroad. Due to the limitations of funding, the needs of discipline development, curriculum arrangement, place and time of each student's visit, the selection shall meet the following conditions: (1) Mr. Liu is the reserve discipline leader of the college, This time we have to send out. (2) if we choose Mr. Liu, we should also choose Mr. Zhou, but we can't choose Mr. Zhang. (3) only if Mr. Niu can't choose, at least one of Mr. Wang and Mr. Ma can choose. (4) if we don't choose Mr. Wang, we don't choose Mr. Zhou either.\\n\\nQuestion:\\nIf the above statement is true, which of the followings must be true?\\n\\nOptions:\\nA. Mr. Niu didn't choose, but Mr. Zhou did\\nB. Mr. Liu was chose, but Mr. Ma didn't\\nC. Mr. Wang and Mr. Ma were chosen\\nD. Neither Mr. Wang nor Mr. Niu was elected\\n\\nWho must be chosen or who must not be chosen according to the context by one-step reasoning?\\nAction 1: Take condition (1).\\nObservation 1: Mr. Liu must be chosen.\\nThought 2: Based on Observation 1, who must be chosen or who must not be chosen one step further?\\nAction 2: Take condition (2).\\nObservation 2: Mr. Zhou must be chosen, but Mr. Zhang must not be chosen.\\nThought 3: Based on Observation 2, who must be chosen one step further? And who must not be chosen?\\nAction 3: Take condition (3).\\nObservation 3: Mr. Niu must be chosen, but Mr. Ma must be chosen.\\nThought 4: Based on Observation 3, who must be chosen one step further? And who must not be chosen?\\nAction 4: Take condition (4).\\nObservation 4: If Mr. Wang is chosen, Mr. Zhou must not be chosen.\\nThought 5: Which observations and conditions are related to Option A?\\nAction 5: Observation 1, Observation 2, Observation 3, and Observation 4.\\nThought 6: Which observations and conditions are related to Option B?\\nAction 6: Observation 1, Observation 2, and Observation 4.\\nThought 7: Which observations and conditions are related to Option C?\\nAction 7: Observation 3 and Observation 4.\\nThought 8: Which observations and conditions are related to Option D?\\nAction 8: Observation 2, Observation 3, and Observation 4.\\nThought 9: Which observations and conditions are related to Option A?\\nAction 9: Observation 1, Observation 2, Observation 3, and Observation 4.\\nThought 10: Which observations and conditions are related to Option B?\\nAction 10: Observation 1, Observation 2, and Observation 4.\\nThought 11: Which observations and conditions are related to Option C?\\nAction 11: Observation 3 and Observation 4.\\nThought 12: Which observations and conditions are related to Option D?\\nAction 12: Observation 2, Observation 3, and Observation 4.\\nThought 13: Based on Observation 1, who must be chosen one step further?\\nAction 13: Mr. Liu must be chosen.\\nThought 14: Based on Observation 2, who must be chosen one step further?\\nAction 14: Mr. Zhou must be chosen.\\nThought 15: Based on Observation 3, who must be chosen one step further?\\nAction 15: Mr. Niu must be chosen.\\nThought 16: Based on Observation 4, who must not be chosen one step further?\\nAction 16: Mr. Wang must not be chosen.\\nThought 17: Which observations and conditions are related to Option A?\\nAction 17: Observation 1, Observation 2, Observation 3, and Observation 4.\\nThought 18: Which observations and conditions are related to Option B?\\nAction 18: Observation 1, Observation 2, and Observation 4.\\nThought 19: Which observations and conditions are related to Option C?\\nAction 19: Observation 3 and Observation 4.\\nThought 20: Which observations and conditions are related to Option D?\\nAction 20: Observation 2, Observation 3, and Observation 4.\\nThought 21: Which answer is true?\\nAction 21: Option A.\\nAction 22: Finish[The answer is A.]</s>\",\n",
      "  \"rejected\": \"Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task. You will be given context that you should use to help you answer the question.\\n\\nContext:\\nA college will continue to implement the overseas funding plan this year. It plans to select several of the six teachers from Mr. Liu, Mr. Zhang, Mr. Wang, Mr. Ma, Mr. Niu and Mr. Zhou to visit abroad. Due to the limitations of funding, the needs of discipline development, curriculum arrangement, place and time of each student's visit, the selection shall meet the following conditions: (1) Mr. Liu is the reserve discipline leader of the college, This time we have to send out. (2) if we choose Mr. Liu, we should also choose Mr. Zhou, but we can't choose Mr. Zhang. (3) only if Mr. Niu can't choose, at least one of Mr. Wang and Mr. Ma can choose. (4) if we don't choose Mr. Wang, we don't choose Mr. Zhou either.\\n\\nQuestion:\\nIf the above statement is true, which of the followings must be true?\\n\\nOptions:\\nA. Mr. Niu didn't choose, but Mr. Zhou did\\nB. Mr. Liu was chose, but Mr. Ma didn't\\nC. Mr. Wang and Mr. Ma were chosen\\nD. Neither Mr. Wang nor Mr. Niu was elected\\n\\nWho must be chosen or who must not be chosen according to the context by one-step reasoning?\\nAction 1: Take condition (1).\\nObservation 1: Mr. Liu must be chosen.\\nThought 2: Based on Observation 1, who must be chosen or who must not be chosen one step further?\\nAction 2: Take condition (2).\\nObservation 2: Mr. Zhou must be chosen, but Mr. Zhang must not be chosen.\\nThought 3: Based on Observation 2, who must be chosen one step further? And who must not be chosen?\\nAction 3: Take condition (3).\\nObservation 3: If Mr. Niu is chosen, at least one of Mr. Wang and Mr. Ma must be chosen. If Mr. Niu is not chosen, Mr. Wang and Mr. Ma must be chosen.\\nThought 4: Based on Observation 3, who must be chosen or who must not be chosen one step further?\\nAction 4: Take condition (4).\\nObservation 4: If Mr. Wang is chosen, Mr. Zhou must not be chosen. If Mr. Zhou is not chosen, Mr. Wang must be chosen.\\nThought 5: Based on Observation 4, who must be chosen or who must not be chosen one step further?\\nAction 5: Take the information that Mr. Liu is the reserve discipline leader of the college.\\nObservation 5: Mr. Liu must be chosen.\\nThought 6: Based on Observation 5, who must be chosen or who must not be chosen one step further?\\nAction 6: Take the information that Mr. Ma can't be chosen.\\nObservation 6: Mr. Ma must not be chosen.\\nThought 7: Based on Observation 6, who must be chosen or who must not be chosen one step further?\\nAction 7: Take the information that Mr. Wang is not chosen.\\nObservation 7: Mr. Wang must not be chosen.\\nThought 8: Based on Observation 7, who must be chosen or who must not be chosen one step further?\\nAction 8: Take the information that Mr. Zhang can't be chosen.\\nObservation 8: Mr. Zhang must not be chosen.\\nThought 9: Based on Observation 8, who must be chosen or who must not be chosen one step further?\\nAction 9: Take the information that Mr. Zhou must be chosen.\\nObservation 9: Mr. Zhou must be chosen.\\nThought 10: Based on Observation 9, who must be chosen or who must not be chosen one step further?\\nAction 10: Take the information that Mr. Liu was chosen.\\nObservation 10: Mr. Liu was chosen.\\nThought 11: Which observations and conditions are related to Option A?\\nAction 11: Observation 1, Observation 2, and Observation 9.\\nThought 12: Which observations and conditions are related to Option B?\\nAction 12: Observation 1, Observation 2, and Observation 9.\\nThought 13: Which observations and conditions are related to Option C?\\nAction 13: Observation 3, Observation 4, and Observation 9.\\nThought 14: Which observations and conditions are related to Option D?\\nAction 14: Observation 3, Observation 4, and Observation 9.\\nAction 15: Finish[The answer is C.]</s>\",\n",
      "  \"index\": 0,\n",
      "  \"chosen_reward\": [\n",
      "    1.2734375\n",
      "  ],\n",
      "  \"rejected_reward\": [\n",
      "    -1.0625\n",
      "  ]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "reward_data = json.load(open(\"trajectories/70b-distil/train_decay0.95.diff2.6.rewards.v1.0/test-checkpoint-400/eval_predictions_rank1.json\"))\n",
    "\n",
    "item = reward_data[0]\n",
    "print(json.dumps(item, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b0464c2-3f5b-42d8-853d-ccc77b2c66d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "chosen_rewards = [item[\"chosen_reward\"][0] for item in reward_data]\n",
    "reject_rewards = [item[\"rejected_reward\"][0] for item in reward_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17b44922-841c-412b-96d6-6a93a2db2734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81681"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chosen_rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2f0412c-2bb7-47b1-904d-513b97ae319f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(chosen_rewards, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39f576d2-80ac-4d99-b13e-20df2621c525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(reject_rewards, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9fe8f02b-30df-4441-9199-c37ce14b530c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = json.load(open(\"trajectories/70b-distil/debug.json\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "089b5083-3cf0-4b8b-8c86-d1cca0f08dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "chosen_rewards = [item[\"chosen_reward\"] for item in data]\n",
    "reject_rewards = [item[\"reject_reward\"] for item in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "86a356d9-2bf6-41ab-9b24-e42f0c88c705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(chosen_rewards, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "da875efd-ba8c-43ed-b65b-63b7e762d87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(reject_rewards, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8f44337-6d38-4618-8fba-eccfa651cb42",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = json.load(open(\"trajectories/70b-distil/debug-2.json\"))\n",
    "chosen_rewards = [item[\"chosen_reward\"] for item in data]\n",
    "reject_rewards = [item[\"reject_reward\"] for item in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ef53483c-21e9-4ed1-9ec0-64e8f3c571d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(chosen_rewards, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5993a5b4-1b0b-4fbe-9912-ec2cec4360dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rs4GuHNBqdZDL5Y/1c+TvQwX2gT0wsIQ+1Gb7Zg1NsbGxGDJkCFq1aoV79+5h+/btOHToEBITE3Hx4kVs374dQ4cORfPmzXH69GnMmTMHffv2RdeuXQEAISEh6Ny5MyZMmIBVq1YhNzcXCxcuRGRkpBhoZsyYgQ0bNmD+/PmYPHkyDh48iJ07d2LPnj1iHTExMQgLC0OvXr3Qu3dvrFu3DkVFReK76YiIiIjMGpry8/MxceJE5OTkoFmzZujatSsSExMxaNAgXLlyBQcOHBADjI+PD0aNGoWFCxeKy8vlcuzevRszZ85EUFAQmjRpgrCwMCxbtkwc4+fnhz179mDOnDlYv349WrZsiU2bNokfNwAAo0ePxo0bN7Bo0SLk5uaie/fu2L9/f5WLw4mIiKjxMmto2rx580Pn+fj44PDhw49ch6+vb5XTb5X169cPaWlpNY6Jioqq8XQcERERNW7WcUEBERERkZkxNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBGYNTR988AG6du0KlUoFlUqFoKAg7Nu3T5xfUlKCyMhING/eHE2bNsWoUaOQl5dntI7s7GyEhobC0dERHh4emDdvHsrKyozGHDp0CD179oSdnR3atm2LhISEKrXExcWhdevWsLe3R2BgIE6cOFEv+0xERETWyayhqWXLlnj77beRmpqKkydPYsCAARg+fDgyMjIAAHPmzMG3336LXbt24fDhw7h+/TpGjhwpLq/X6xEaGgqtVotjx47h448/RkJCAhYtWiSOycrKQmhoKPr374/09HRER0djypQpSExMFMfs2LEDMTExWLx4MU6dOoVu3bpBrVYjPz+/4ZpBREREFs2soWnYsGEYOnQo2rVrh/bt2+Ott95C06ZNcfz4cdy9exebN2/GmjVrMGDAAAQEBCA+Ph7Hjh3D8ePHAQBJSUk4e/Ystm7diu7du2PIkCFYvnw54uLioNVqAQAbN26En58fVq9ejU6dOiEqKgr/+7//i7Vr14p1rFmzBlOnTkV4eDg6d+6MjRs3wtHREVu2bDFLX4iIiMjy2Jq7AAO9Xo9du3ahqKgIQUFBSE1NhU6nQ3BwsDimY8eOaNWqFVJSUtCnTx+kpKTA398fnp6e4hi1Wo2ZM2ciIyMDPXr0QEpKitE6DGOio6MBAFqtFqmpqYiNjRXny2QyBAcHIyUl5aH1lpaWorS0VHxcUFAAANDpdLC1tRXvN1aGfW/MPQDqvw96vR5KpQIKGWCL8nrZxuMy1GX4VyG3gb29nUXXXJlCBiiVCuj1+jr9LPn7UIF9YA8MLKkPtanB7KHpl19+QVBQEEpKStC0aVN8+eWX6Ny5M9LT06FUKuHs7Gw03tPTE7m5uQCA3Nxco8BkmG+YV9OYgoIC/Pnnn7h9+zb0en21Y86dO/fQulesWIGlS5dWmZ6UlARHR0cAgEajkdCBJxt7UKE++zA/avr9e7n1tg1TCHG5f7o7sAUiAlfen2rZNYtcZEC76cjMzERmZmadV8PfhwrsA3tgYAl9KC4uljzW7KGpQ4cOSE9Px927d/H5558jLCwMhw8fNndZjxQbG4uYmBjxcUFBAXx8fBASEgIHBwdoNBoMGjQICoXCjFWaj06na/Q9AOq/D1lZWQiPjIaveiqc3LxNvn5TsEU5QlzykXTbA2WQIefXNBz/dB2em/wGPHzamrs8Se7dvI7LiR8hPm4d/Pz8ar08fx8qsA/sgYEl9cFwpkgKs4cmpVKJtm0rnjgDAgLw008/Yf369Rg9ejS0Wi3u3Llj9GpTXl4evLy8AABeXl5V3uVmeHfdg2Mqv+MuLy8PKpUKDg4OkMvlkMvl1Y4xrKM6dnZ2sLOzqzJdoVCIB8CD9xsr9qBCffVBLpdDq9VBVw6UWfgniJRBhjLIoNMLKCkptYqaDXTlgFarg1wuf6yfI38fKrAP7IGBJfShNtu3uGes8vJylJaWIiAgAAqFAsnJyeK88+fPIzs7G0FBQQCAoKAg/PLLL0bvctNoNFCpVOjcubM45sF1GMYY1qFUKhEQEGA0pry8HMnJyeIYIiIiIrO+0hQbG4shQ4agVatWuHfvHrZv345Dhw4hMTERzZo1Q0REBGJiYuDq6gqVSoVZs2YhKCgIffr0AQCEhISgc+fOmDBhAlatWoXc3FwsXLgQkZGR4qtAM2bMwIYNGzB//nxMnjwZBw8exM6dO7Fnzx6xjpiYGISFhaFXr17o3bs31q1bh6KiIoSHh5ulL0RERGR5zBqa8vPzMXHiROTk5KBZs2bo2rUrEhMTMWjQIADA2rVrIZPJMGrUKJSWlkKtVuP9998Xl5fL5di9ezdmzpyJoKAgNGnSBGFhYVi2bJk4xs/PD3v27MGcOXOwfv16tGzZEps2bYJarRbHjB49Gjdu3MCiRYuQm5uL7t27Y//+/VUuDiciIqLGy6yhafPmzTXOt7e3R1xcHOLi4h46xtfXF3v37q1xPf369UNaWlqNY6KiohAVFVXjGCIiImq8LO6aJiIiIiJLxNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBKYNTStWLECzzzzDJycnODh4YERI0bg/PnzRmP69esHGxsbo9uMGTOMxmRnZyM0NBSOjo7w8PDAvHnzUFZWZjTm0KFD6NmzJ+zs7NC2bVskJCRUqScuLg6tW7eGvb09AgMDceLECZPvMxEREVkns4amw4cPIzIyEsePH4dGo4FOp0NISAiKioqMxk2dOhU5OTnibdWqVeI8vV6P0NBQaLVaHDt2DB9//DESEhKwaNEicUxWVhZCQ0PRv39/pKenIzo6GlOmTEFiYqI4ZseOHYiJicHixYtx6tQpdOvWDWq1Gvn5+fXfCCIiIrJ4tubc+P79+40eJyQkwMPDA6mpqejbt6843dHREV5eXtWuIykpCWfPnsWBAwfg6emJ7t27Y/ny5ViwYAGWLFkCpVKJjRs3ws/PD6tXrwYAdOrUCUePHsXatWuhVqsBAGvWrMHUqVMRHh4OANi4cSP27NmDLVu24LXXXquP3SciIiIrYtbQVNndu3cBAK6urkbTt23bhq1bt8LLywvDhg3DG2+8AUdHRwBASkoK/P394enpKY5Xq9WYOXMmMjIy0KNHD6SkpCA4ONhonWq1GtHR0QAArVaL1NRUxMbGivNlMhmCg4ORkpJSba2lpaUoLS0VHxcUFAAAdDodbG1txfuNlWHfG3MPgPrvg16vh1KpgEIG2KK8XrbxuAx1Gf5VyG1gb29n0TVXppABSqUCer2+Tj9L/j5UYB/YAwNL6kNtarCY0FReXo7o6Gg899xz6NKlizh93Lhx8PX1hbe3N06fPo0FCxbg/Pnz+OKLLwAAubm5RoEJgPg4Nze3xjEFBQX4888/cfv2bej1+mrHnDt3rtp6V6xYgaVLl1aZnpSUJAY6jUZTmxY8kdiDCvXZh/lR0+/fy623bZhCiMv9U92BLRARuPL+VMuuWeQiA9pNR2ZmJjIzM+u8Gv4+VGAf2AMDS+hDcXGx5LEWE5oiIyNx5swZHD161Gj6tGnTxPv+/v5o0aIFBg4ciIsXL6JNmzYNXaYoNjYWMTEx4uOCggL4+PggJCQEDg4O0Gg0GDRoEBQKhdlqNCedTtfoewDUfx+ysrIQHhkNX/VUOLl5m3z9pmCLcoS45CPptgfKIEPOr2k4/uk6PDf5DXj4tDV3eZLcu3kdlxM/QnzcOvj5+dV6ef4+VGAf2AMDS+qD4UyRFBYRmqKiorB7924cOXIELVu2rHFsYGAgAODChQto06YNvLy8qrzLLS8vDwDE66C8vLzEaQ+OUalUcHBwgFwuh1wur3bMw66lsrOzg52dXZXpCoVCPAAevN9YsQcV6qsPcrkcWq0OunKgzMI/QaQMMpRBBp1eQElJqVXUbKArB7RaHeRy+WP9HPn7UIF9YA8MLKEPtdm+WZ+xBEFAVFQUvvzySxw8eFDS/+DS09MBAC1atAAABAUF4ZdffjF6l5tGo4FKpULnzp3FMcnJyUbr0Wg0CAoKAgAolUoEBAQYjSkvL0dycrI4hoiIiBo3s77SFBkZie3bt+Prr7+Gk5OTeA1Ss2bN4ODggIsXL2L79u0YOnQomjdvjtOnT2POnDno27cvunbtCgAICQlB586dMWHCBKxatQq5ublYuHAhIiMjxVeCZsyYgQ0bNmD+/PmYPHkyDh48iJ07d2LPnj1iLTExMQgLC0OvXr3Qu3dvrFu3DkVFReK76YiIiKhxM2to+uCDDwBUfIDlg+Lj4zFp0iQolUocOHBADDA+Pj4YNWoUFi5cKI6Vy+XYvXs3Zs6ciaCgIDRp0gRhYWFYtmyZOMbPzw979uzBnDlzsH79erRs2RKbNm0SP24AAEaPHo0bN25g0aJFyM3NRffu3bF///4qF4cTERFR42TW0CQIQo3zfXx8cPjw4Ueux9fXF3v37q1xTL9+/ZCWllbjmKioKERFRT1ye0RERNT4WMdVmERERERmxtBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCRBnULT77//buo6iIiIiCxanUJT27Zt0b9/f2zduhUlJSWmromIiIjI4tQpNJ06dQpdu3ZFTEwMvLy8MH36dJw4ccLUtRERERFZjDqFpu7du2P9+vW4fv06tmzZgpycHDz//PPo0qUL1qxZgxs3bpi6TiIiIiKzeqwLwW1tbTFy5Ejs2rULK1euxIULF/Dqq6/Cx8cHEydORE5OjqnqJCIiIjKrxwpNJ0+exD/+8Q+0aNECa9aswauvvoqLFy9Co9Hg+vXrGD58uKnqJCIiIjIr27ostGbNGsTHx+P8+fMYOnQoPvnkEwwdOhQyWUUG8/PzQ0JCAlq3bm3KWomIiIjMpk6h6YMPPsDkyZMxadIktGjRotoxHh4e2Lx582MVR0RERGQp6hSafvvtt0eOUSqVCAsLq8vqiYiIiCxOna5pio+Px65du6pM37VrFz7++OPHLoqIiIjI0tQpNK1YsQJubm5Vpnt4eOBf//rXYxdFREREZGnqFJqys7Ph5+dXZbqvry+ys7MfuygiIiIiS1On0OTh4YHTp09Xmf7zzz+jefPmj10UERERkaWpU2gaO3YsXnnlFXz33XfQ6/XQ6/U4ePAgZs+ejTFjxpi6RiIiIiKzq9O755YvX45Lly5h4MCBsLWtWEV5eTkmTpzIa5qIiIjoiVSn0KRUKrFjxw4sX74cP//8MxwcHODv7w9fX19T10dERERkEeoUmgzat2+P9u3bm6oWIiIiIotVp9Ck1+uRkJCA5ORk5Ofno7y83Gj+wYMHTVIcERERkaWoU2iaPXs2EhISEBoaii5dusDGxsbUdRERERFZlDqFps8++ww7d+7E0KFDTV0PEZFF0mm1uHz5cp2W1ev1AICsrCzI5XJTllUjlUoFd3f3Btse0ZOuzheCt23b1tS1EBFZpNLCu7iU9TuiX18COzu7Wi+vVCowP2o6wiOjodXq6qHC6rk6OWJr/CYGJyITqVNomjt3LtavX48NGzbw1BwRPfF0pX+i3MYWbn1Gorl37d8lrLj/iXi+6qnQldc81lSKbuXhRsp/UVBQwNBEZCJ1Ck1Hjx7Fd999h3379uHpp5+GQqEwmv/FF1+YpDgiIkvi6OIOlUfLWi9ni3IAuXBy80ZZ3T5TuE5uNNiWiBqHOoUmZ2dnvPjii6auhYiIiMhi1Sk0xcfHm7oOIiIiIotW59eJy8rKcODAAfznP//BvXv3AADXr19HYWGhyYojIiIishR1Ck2XL1+Gv78/hg8fjsjISNy4UXHmfOXKlXj11Vclr2fFihV45pln4OTkBA8PD4wYMQLnz583GlNSUoLIyEg0b94cTZs2xahRo5CXl2c0Jjs7G6GhoXB0dISHhwfmzZuHsrIyozGHDh1Cz549YWdnh7Zt2yIhIaFKPXFxcWjdujXs7e0RGBiIEydOSN4XIiIierLVKTTNnj0bvXr1wu3bt+Hg4CBOf/HFF5GcnCx5PYcPH0ZkZCSOHz8OjUYDnU6HkJAQFBUViWPmzJmDb7/9Frt27cLhw4dx/fp1jBw5Upyv1+sRGhoKrVaLY8eO4eOPP0ZCQgIWLVokjsnKykJoaCj69++P9PR0REdHY8qUKUhMTBTH7NixAzExMVi8eDFOnTqFbt26Qa1WIz8/vy4tIiIioidMna5p+v7773Hs2DEolUqj6a1bt8a1a9ckr2f//v1GjxMSEuDh4YHU1FT07dsXd+/exebNm7F9+3YMGDAAQMX1VJ06dcLx48fRp08fJCUl4ezZszhw4AA8PT3RvXt3LF++HAsWLMCSJUugVCqxceNG+Pn5YfXq1QCATp064ejRo1i7di3UajUAYM2aNZg6dSrCw8MBABs3bsSePXuwZcsWvPbaa3VpExERET1B6hSaysvLxU+4fdDVq1fh5ORU52Lu3r0LAHB1dQUApKamQqfTITg4WBzTsWNHtGrVCikpKejTpw9SUlLg7+8PT09PcYxarcbMmTORkZGBHj16ICUlxWgdhjHR0dEAAK1Wi9TUVMTGxorzZTIZgoODkZKSUm2tpaWlKC0tFR8XFBQAAHQ6HWxtbcX7jZVh362tBzdv3hSv0TMFw+/JhQsX6uWToK9cuQK5jQwKmeFt7ZbHUJfhX4XcBvb2dhZdc2WPW3PlHjQEhaziQzX1er3F/B5a6/OCKbEHFSypD7WpoU6hKSQkBOvWrcOHH34IALCxsUFhYSEWL15c569WKS8vR3R0NJ577jl06dIFAJCbmwulUglnZ2ejsZ6ensjNzRXHPBiYDPMN82oaU1BQgD///BO3b9+GXq+vdsy5c+eqrXfFihVYunRplelJSUlwdHQEAGg0Gim7/kRjDyr8+uuv9bbu2Dn/uH8vt962YQohLvdPdQe2QETgyvtTLbtmkYlqFnvQEFxkQLvpyMzMRGZmZsNtVwI+L7AHBpbQh+LiYslj6xSaVq9eDbVajc6dO6OkpATjxo3Db7/9Bjc3N3z66ad1WSUiIyNx5swZHD16tE7LN7TY2FjExMSIjwsKCuDj44OQkBA4ODhAo9Fg0KBBVT74s7HQ6XRW14OsrCyER0bDrfdwNHHxMMk6bWXAqDYy/PdiOcrq4UWGG1ln8fPe/8Nzk9+Ah49lfrWRLcoR4pKPpNseKIMMOb+m4fin6yy65soet+bKPWgI925ex+XEjxAftw5+fn4Nss1HscbnBVNjDypYUh8MZ4qkqFNoatmyJX7++Wd89tlnOH36NAoLCxEREYHx48cbXRguVVRUFHbv3o0jR46gZcv//2m7Xl5e0Gq1uHPnjtGrTXl5efDy8hLHVH6Xm+HddQ+OqfyOu7y8PKhUKjg4OEAul0Mul1c7xrCOyuzs7Kr9DiqFQiEeAA/eb6ysqQdyuRxarQ7KZh5wcKv9pz5Xx/BJ0A6u9fNJ0LY3clFSUgpdORr0k6brogwylEEGnV6wmpoNTFWzoQcNQVcOaLU6yOVyi/sdtKbnhfrCHlSwhD7UZvt1Ck0AYGtri5dffrmuiwMABEHArFmz8OWXX+LQoUNV/jcUEBAAhUKB5ORkjBo1CgBw/vx5ZGdnIygoCAAQFBSEt956C/n5+fDwqHh1QKPRQKVSoXPnzuKYvXv3Gq1bo9GI61AqlQgICEBycjJGjBgBoOJ0YXJyMqKioh5rH4mIiOjJUKfQ9Mknn9Q4f+LEiZLWExkZie3bt+Prr7+Gk5OTeA1Ss2bN4ODggGbNmiEiIgIxMTFwdXWFSqXCrFmzEBQUhD59+gCouL6qc+fOmDBhAlatWoXc3FwsXLgQkZGR4itBM2bMwIYNGzB//nxMnjwZBw8exM6dO7Fnzx6xlpiYGISFhaFXr17o3bs31q1bh6KiIvHddERERNS41Sk0zZ492+ixTqdDcXExlEolHB0dJYemDz74AADQr18/o+nx8fGYNGkSAGDt2rWQyWQYNWoUSktLoVar8f7774tj5XI5du/ejZkzZyIoKAhNmjRBWFgYli1bJo7x8/PDnj17MGfOHKxfvx4tW7bEpk2bxI8bAIDRo0fjxo0bWLRoEXJzc9G9e3fs37+/ysXhRERE1DjVKTTdvn27yrTffvsNM2fOxLx58ySvRxCER46xt7dHXFwc4uLiHjrG19e3yum3yvr164e0tLQax0RFRfF0HBEREVXLZFcktmvXDm+//XaVV6GIiIiIngQmfRuHra0trl+/bspVEhEREVmEOp2e++abb4weC4KAnJwcbNiwAc8995xJCiMiIiKyJHUKTYa35RvY2NjA3d0dAwYMEL/fjYiIiOhJUufvniMiIiJqTKzj43iJiIiIzKxOrzQ9+J1rj7JmzZq6bIKIiIjIotQpNKWlpSEtLQ06nQ4dOnQAUPEt7nK5HD179hTH2djYmKZKIiIiIjOrU2gaNmwYnJyc8PHHH8PFxQVAxQdehoeH44UXXsDcuXNNWiQRERGRudXpmqbVq1djxYoVYmACABcXF7z55pt89xwRERE9keoUmgoKCnDjxo0q02/cuIF79+49dlFERERElqZOoenFF19EeHg4vvjiC1y9ehVXr17Ff//7X0RERGDkyJGmrpGIiIjI7Op0TdPGjRvx6quvYty4cdDpdBUrsrVFREQE3nnnHZMWSERERGQJ6hSaHB0d8f777+Odd97BxYsXAQBt2rRBkyZNTFocERERkaV4rA+3zMnJQU5ODtq1a4cmTZpAEART1UVERERkUeoUmv744w8MHDgQ7du3x9ChQ5GTkwMAiIiI4McNEBER0ROpTqFpzpw5UCgUyM7OhqOjozh99OjR2L9/v8mKIyIiIrIUdbqmKSkpCYmJiWjZsqXR9Hbt2uHy5csmKYyIiIjIktTplaaioiKjV5gMbt26BTs7u8cuioiIiMjS1Ck0vfDCC/jkk0/ExzY2NigvL8eqVavQv39/kxVHREREZCnqdHpu1apVGDhwIE6ePAmtVov58+cjIyMDt27dwg8//GDqGomIiIjMrk6vNHXp0gW//vornn/+eQwfPhxFRUUYOXIk0tLS0KZNG1PXSERERGR2tX6lSafTYfDgwdi4cSP++c9/1kdNRERERBan1q80KRQKnD59uj5qISIiIrJYdTo99/LLL2Pz5s2mroWIiIjIYtXpQvCysjJs2bIFBw4cQEBAQJXvnFuzZo1JiiMiIiKyFLUKTb///jtat26NM2fOoGfPngCAX3/91WiMjY2N6aojIiIishC1Ck3t2rVDTk4OvvvuOwAVX5vy73//G56envVSHBEREZGlqNU1TYIgGD3et28fioqKTFoQERERkSWq04XgBpVDFBEREdGTqlahycbGpso1S7yGiYiIiBqDWl3TJAgCJk2aJH4pb0lJCWbMmFHl3XNffPGF6SokIiIisgC1Ck1hYWFGj19++WWTFkNERERkqWoVmuLj4+urDiIiIiKL9lgXghMRERE1FgxNRERERBKYNTQdOXIEw4YNg7e3N2xsbPDVV18ZzZ80aZL4jj3DbfDgwUZjbt26hfHjx0OlUsHZ2RkREREoLCw0GnP69Gm88MILsLe3h4+PD1atWlWlll27dqFjx46wt7eHv78/9u7da/L9JSIiIutl1tBUVFSEbt26IS4u7qFjBg8ejJycHPH26aefGs0fP348MjIyoNFosHv3bhw5cgTTpk0T5xcUFCAkJAS+vr5ITU3FO++8gyVLluDDDz8Uxxw7dgxjx45FREQE0tLSMGLECIwYMQJnzpwx/U4TERGRVarTF/aaypAhQzBkyJAax9jZ2cHLy6vaeZmZmdi/fz9++ukn9OrVCwDw3nvvYejQoXj33Xfh7e2Nbdu2QavVYsuWLVAqlXj66aeRnp6ONWvWiOFq/fr1GDx4MObNmwcAWL58OTQaDTZs2ICNGzeacI+JiIjIWpk1NElx6NAheHh4wMXFBQMGDMCbb76J5s2bAwBSUlLg7OwsBiYACA4Ohkwmw48//ogXX3wRKSkp6Nu3L5RKpThGrVZj5cqVuH37NlxcXJCSkoKYmBij7arV6iqnCx9UWlqK0tJS8XFBQQEAQKfTwdbWVrzfWBn23Zp6oNfroVQqoJABtig3yToN6zHV+ipTyG1gb29n0ppNrXIPrKHmyh635vo+DqqjkAFKpQJ6vd5ifg+t8XnB1NiDCpbUh9rUYNGhafDgwRg5ciT8/Pxw8eJFvP766xgyZAhSUlIgl8uRm5sLDw8Po2VsbW3h6uqK3NxcAEBubi78/PyMxhi+YDg3NxcuLi7Izc2t8qXDnp6e4jqqs2LFCixdurTK9KSkJDg6OgIANBpN7Xf6CWNtPZgfNf3+vYf/7OsixCXfpOsTBbZARODK+w9MW7OpiT2woppFJqq53o6D6rjIgHbTkZmZiczMzIbbrgTW9rxQH9iDCpbQh+LiYsljLTo0jRkzRrzv7++Prl27ok2bNjh06BAGDhxoxsqA2NhYo1enCgoK4OPjg5CQEDg4OECj0WDQoEFQKBRmrNJ8dDqd1fUgKysL4ZHR8FVPhZObt0nWaYtyhLjkI+m2B8rq4RLCnF/TcPzTdXhu8hvw8Glr8vWbQuUeWEPNlT1uzfV9HFTn3s3ruJz4EeLj1lX5j6O5WOPzgqmxBxUsqQ+GM0VSWHRoquypp56Cm5sbLly4gIEDB8LLywv5+cb/cysrK8OtW7fE66C8vLyQl5dnNMbw+FFjHnYtFVBxrZXh62QepFAoxAPgwfuNlTX1QC6XQ6vVQVcOk/9hK4OsXv5Y6vQCSkpK66VmUzP0wJpqNjBVzfV1HFRHVw5otTrI5XKL+x20pueF+sIeVLCEPtRm+9bxjHXf1atX8ccff6BFixYAgKCgINy5cwepqanimIMHD6K8vByBgYHimCNHjhids9RoNOjQoQNcXFzEMcnJyUbb0mg0CAoKqu9dIiIiIith1tBUWFiI9PR0pKenA6g4PZKeno7s7GwUFhZi3rx5OH78OC5duoTk5GQMHz4cbdu2hVqtBgB06tQJgwcPxtSpU3HixAn88MMPiIqKwpgxY+DtXXF6Zdy4cVAqlYiIiEBGRgZ27NiB9evXG51amz17Nvbv34/Vq1fj3LlzWLJkCU6ePImoqKgG7wkRERFZJrOGppMnT6JHjx7o0aMHACAmJgY9evTAokWLIJfLcfr0afztb39D+/btERERgYCAAHz//fdGp8W2bduGjh07YuDAgRg6dCief/55o89gatasGZKSkpCVlYWAgADMnTsXixYtMvosp2effRbbt2/Hhx9+iG7duuHzzz/HV199hS5dujRcM4iIiMiimfWapn79+kEQhIfOT0xMfOQ6XF1dsX379hrHdO3aFd9//32NY1566SW89NJLj9weERERNU5WdU0TERERkbkwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBGYNTUeOHMGwYcPg7e0NGxsbfPXVV0bzBUHAokWL0KJFCzg4OCA4OBi//fab0Zhbt25h/PjxUKlUcHZ2RkREBAoLC43GnD59Gi+88ALs7e3h4+ODVatWVall165d6NixI+zt7eHv74+9e/eafH+JiIjIepk1NBUVFaFbt26Ii4urdv6qVavw73//Gxs3bsSPP/6IJk2aQK1Wo6SkRBwzfvx4ZGRkQKPRYPfu3Thy5AimTZsmzi8oKEBISAh8fX2RmpqKd955B0uWLMGHH34ojjl27BjGjh2LiIgIpKWlYcSIERgxYgTOnDlTfztPREREVsXWnBsfMmQIhgwZUu08QRCwbt06LFy4EMOHDwcAfPLJJ/D09MRXX32FMWPGIDMzE/v378dPP/2EXr16AQDee+89DB06FO+++y68vb2xbds2aLVabNmyBUqlEk8//TTS09OxZs0aMVytX78egwcPxrx58wAAy5cvh0ajwYYNG7Bx48YG6AQRERFZOrOGpppkZWUhNzcXwcHB4rRmzZohMDAQKSkpGDNmDFJSUuDs7CwGJgAIDg6GTCbDjz/+iBdffBEpKSno27cvlEqlOEatVmPlypW4ffs2XFxckJKSgpiYGKPtq9XqKqcLH1RaWorS0lLxcUFBAQBAp9PB1tZWvN9YGfbdmnqg1+uhVCqgkAG2KDfJOg3rMdX6KlPIbWBvb2fSmk2tcg+soebKHrfm+j4OqqOQAUqlAnq93mJ+D63xecHU2IMKltSH2tRgsaEpNzcXAODp6Wk03dPTU5yXm5sLDw8Po/m2trZwdXU1GuPn51dlHYZ5Li4uyM3NrXE71VmxYgWWLl1aZXpSUhIcHR0BABqN5pH7+aSzth7Mj5p+/97Df/Z1EeKSb9L1iQJbICJw5f0Hpq3Z1MQeWFHNIhPVXG/HQXVcZEC76cjMzERmZmbDbVcCa3teqA/sQQVL6ENxcbHksRYbmixdbGys0atTBQUF8PHxQUhICBwcHKDRaDBo0CAoFAozVmk+Op3O6nqQlZWF8Mho+KqnwsnN2yTrtEU5QlzykXTbA2X1cAlhzq9pOP7pOjw3+Q14+LQ1+fpNoXIPrKHmyh635vo+Dqpz7+Z1XE78CPFx66r8x9FcrPF5wdTYgwqW1AfDmSIpLDY0eXl5AQDy8vLQokULcXpeXh66d+8ujsnPN/6fW1lZGW7duiUu7+Xlhby8PKMxhsePGmOYXx07OzvY2dlVma5QKMQD4MH7jZU19UAul0Or1UFXDpP/YSuDrF7+WOr0AkpKSuulZlMz9MCaajYwVc31dRxUR1cOaLU6yOVyi/sdtKbnhfrCHlSwhD7UZvsW+4zl5+cHLy8vJCcni9MKCgrw448/IigoCAAQFBSEO3fuIDU1VRxz8OBBlJeXIzAwUBxz5MgRo3OWGo0GHTp0gIuLizjmwe0Yxhi2Q0RERGTW0FRYWIj09HSkp6cDqDg9kp6ejuzsbNjY2CA6OhpvvvkmvvnmG/zyyy+YOHEivL29MWLECABAp06dMHjwYEydOhUnTpzADz/8gKioKIwZMwbe3hWnV8aNGwelUomIiAhkZGRgx44dWL9+vdGptdmzZ2P//v1YvXo1zp07hyVLluDkyZOIiopq6JYQERGRhTLr6bmTJ0+if//+4mNDkAkLC0NCQgLmz5+PoqIiTJs2DXfu3MHzzz+P/fv3w97eXlxm27ZtiIqKwsCBAyGTyTBq1Cj8+9//Fuc3a9YMSUlJiIyMREBAANzc3LBo0SKjz3J69tlnsX37dixcuBCvv/462rVrh6+++gpdunRpgC4QERGRNTBraOrXrx8EQXjofBsbGyxbtgzLli176BhXV1ds3769xu107doV33//fY1jXnrpJbz00ks1F0xERESNlsVe00RERERkSRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIiksCs3z1HRET1R6fV4vLly+YuQ6TX6wEAWVlZkMvl1Y5RqVRwd3dvyLKIJGNoIiJ6ApUW3sWlrN8R/foS2NnZmbscAIBSqcD8qOkIj4yGVqurdoyrkyO2xm9icCKLxNBERPQE0pX+iXIbW7j1GYnm3r7mLgcAoLh/QYiveip05VXnF93Kw42U/6KgoIChiSwSQxMR0RPM0cUdKo+W5i4DAGCLcgC5cHLzRtlDLqm90bAlEdUKLwQnIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpLAokPTkiVLYGNjY3Tr2LGjOL+kpASRkZFo3rw5mjZtilGjRiEvL89oHdnZ2QgNDYWjoyM8PDwwb948lJWVGY05dOgQevbsCTs7O7Rt2xYJCQkNsXtERERkRSw6NAHA008/jZycHPF29OhRcd6cOXPw7bffYteuXTh8+DCuX7+OkSNHivP1ej1CQ0Oh1Wpx7NgxfPzxx0hISMCiRYvEMVlZWQgNDUX//v2Rnp6O6OhoTJkyBYmJiQ26n0RERGTZbM1dwKPY2trCy8uryvS7d+9i8+bN2L59OwYMGAAAiI+PR6dOnXD8+HH06dMHSUlJOHv2LA4cOABPT090794dy5cvx4IFC7BkyRIolUps3LgRfn5+WL16NQCgU6dOOHr0KNauXQu1Wt2g+0pERESWy+JD02+//QZvb2/Y29sjKCgIK1asQKtWrZCamgqdTofg4GBxbMeOHdGqVSukpKSgT58+SElJgb+/Pzw9PcUxarUaM2fOREZGBnr06IGUlBSjdRjGREdH11hXaWkpSktLxccFBQUAAJ1OB1tbW/F+Y2XYd2vqgV6vh1KpgEIG2KLcJOs0rMdU66tMIbeBvb2dSWs2tco9sIaaK3vcmuv7OKiOJfb5UX1QyAClUgG9Xm9Vzx21YY3PjfXBkvpQmxpsBEEQ6rGWx7Jv3z4UFhaiQ4cOyMnJwdKlS3Ht2jWcOXMG3377LcLDw42CCwD07t0b/fv3x8qVKzFt2jRcvnzZ6FRbcXExmjRpgr1792LIkCFo3749wsPDERsbK47Zu3cvQkNDUVxcDAcHh2prW7JkCZYuXVpl+vbt2+Ho6GiiDhAREVF9Ki4uxrhx43D37l2oVKoax1r0K01DhgwR73ft2hWBgYHw9fXFzp07HxpmGkpsbCxiYmLExwUFBfDx8UFISAgcHByg0WgwaNAgKBQKM1ZpPjqdzup6kJWVhfDIaPiqp8LJzdsk67RFOUJc8pF02wNl9XAJYc6vaTj+6To8N/kNePi0Nfn6TaFyD6yh5soet+b6Pg6qY4l9flQf7t28jsuJHyE+bh38/PzMUGH9s8bnxvpgSX0wnCmSwqJDU2XOzs5o3749Lly4gEGDBkGr1eLOnTtwdnYWx+Tl5YnXQHl5eeHEiRNG6zC8u+7BMZXfcZeXlweVSlVjMLOzs4OdnV2V6QqFQjwAHrzfWFlTD+RyObRaHXTlMPkftjLI6uWPpU4voKSktF5qNjVDD6ypZgNT1Vxfx0F1LLnPD+uDrhzQanWQy+VW87xRV9b03FifLKEPtdm+Zf0mPUJhYSEuXryIFi1aICAgAAqFAsnJyeL88+fPIzs7G0FBQQCAoKAg/PLLL8jPzxfHaDQaqFQqdO7cWRzz4DoMYwzrICIiIgIsPDS9+uqrOHz4MC5duoRjx47hxRdfhFwux9ixY9GsWTNEREQgJiYG3333HVJTUxEeHo6goCD06dMHABASEoLOnTtjwoQJ+Pnnn5GYmIiFCxciMjJSfJVoxowZ+P333zF//nycO3cO77//Pnbu3Ik5c+aYc9eJiIjIwlj06bmrV69i7Nix+OOPP+Du7o7nn38ex48fh7u7OwBg7dq1kMlkGDVqFEpLS6FWq/H++++Ly8vlcuzevRszZ85EUFAQmjRpgrCwMCxbtkwc4+fnhz179mDOnDlYv349WrZsiU2bNvHjBoiIiMiIRYemzz77rMb59vb2iIuLQ1xc3EPH+Pr6Yu/evTWup1+/fkhLS6tTjURERNQ4WPTpOSIiIiJLwdBEREREJAFDExEREZEEDE1EREREElj0heBk/bKysiCXy81dhiSXL19Gma7M3GUQEZGFYmiienHz5k0AQHhkNLRa838hoxQlfxbj6rUctLKAL5AkIiLLw9BE9eLevXsAALfew6Fs5mHmaqTJv3gGl69sgb6MoYmIiKpiaKJ61cTFAw5uLc1dhiSFf+SauwQiIrJgvBCciIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAJbcxdARERkoNNqcfnyZXOXUSsqlQru7u7mLoMaAEMTERFZhNLCu7iU9TuiX18COzs7c5cjmauTI7bGb2JwagQYmoiIyCLoSv9EuY0t3PqMRHNvX3OXI0nRrTzcSPkvCgoKGJoaAYYmIiKyKI4u7lB5tDR3GZLdMHcB1GB4ITgRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDRVEhcXh9atW8Pe3h6BgYE4ceKEuUsiIiIiC8DvnnvAjh07EBMTg40bNyIwMBDr1q2DWq3G+fPn4eHhYdbabty4gYKCArPWUBtXrlwxdwlEREQmxdD0gDVr1mDq1KkIDw8HAGzcuBF79uzBli1b8Nprr5mtrhs3buDl8Cm4da/YbDXUlqAvwxvzoqHV6eBg7mKIiOqRTqvF5cuXJY3V6/UAgKysLMjl8vos65FUKhXc3d3NWoO1YWi6T6vVIjU1FbGxseI0mUyG4OBgpKSkmLEyoKCgALfuFcM9aBSauHqatRap7lw+CwDQl+nMXAkRUf0pLbyLS1m/I/r1JbCzs3vkeKVSgflR0xEeGQ2t1rzPj02Vcqx8axmaN2/e4Nuua3g0d9BjaLrv5s2b0Ov18PQ0DiWenp44d+5clfGlpaUoLS0VH9+9excAcOvWLdjb26O4uBh//PEHFArFY9d29+5dyGQAykoArZW82qTXori4GH/evIYCG3MXI03JretQKm1RcuMqCkz0H0C5DCi2k+Hu9QvQl5tmnQ+qj5pNrXIPrKHmyh635vo+DqpjiX1+VB8sseZHuZdzEbZ29mjavg9Uro/+Y66Q26C4uBiuXQdApxcaoMLq3buRi4zvv8KM6PlQ2D3+36naUtoq8I/JExARGQ1tLf5z7dzEEe+tecekQe/evXsAAEGQ8PMQSBAEQbh27ZoAQDh27JjR9Hnz5gm9e/euMn7x4sUCAN5444033njj7Qm4Xbly5ZFZga803efm5ga5XI68vDyj6Xl5efDy8qoyPjY2FjExMeLj8vJy3Lp1C82bN8e9e/fg4+ODK1euQKVS1XvtlqigoKDR9wBgHwD2AGAPDNgH9sDAkvogCALu3bsHb2/vR45laLpPqVQiICAAycnJGDFiBICKIJScnIyoqKgq4+3s7Kqcv3Z2dgYA2NhUnI9SqVRmPxjMjT2owD6wBwB7YMA+sAcGltKHZs2aSRrH0PSAmJgYhIWFoVevXujduzfWrVuHoqIi8d10RERE1HgxND1g9OjRuHHjBhYtWoTc3Fx0794d+/fvr3JxOBERETU+DE2VREVFVXs6rjbs7OywePFiSW8/fVKxBxXYB/YAYA8M2Af2wMBa+2AjCFLeY0dERETUuPG754iIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRia6uCtt97Cs88+C0dHR/EDLSvLzs5GaGgoHB0d4eHhgXnz5qGsrKzG9d66dQvjx4+HSqWCs7MzIiIiUFhYWA97YHqHDh2CjY1Ntbeffvrpocv169evyvgZM2Y0YOWm17p16yr79Pbbb9e4TElJCSIjI9G8eXM0bdoUo0aNqvLp9Nbi0qVLiIiIgJ+fHxwcHNCmTRssXrwYWq22xuWs/ViIi4tD69atYW9vj8DAQJw4caLG8bt27ULHjh1hb28Pf39/7N27t4EqrR8rVqzAM888AycnJ3h4eGDEiBE4f/58jcskJCRU+Znb29s3UMWmt2TJkir707FjxxqXedKOA6D650AbGxtERkZWO96ajgOGpjrQarV46aWXMHPmzGrn6/V6hIaGQqvV4tixY/j444+RkJCARYsW1bje8ePHIyMjAxqNBrt378aRI0cwbdq0+tgFk3v22WeRk5NjdJsyZQr8/PzQq1evGpedOnWq0XKrVq1qoKrrz7Jly4z2adasWTWOnzNnDr799lvs2rULhw8fxvXr1zFy5MgGqta0zp07h/LycvznP/9BRkYG1q5di40bN+L1119/5LLWeizs2LEDMTExWLx4MU6dOoVu3bpBrVYjPz+/2vHHjh3D2LFjERERgbS0NIwYMQIjRozAmTNnGrhy0zl8+DAiIyNx/PhxaDQa6HQ6hISEoKioqMblVCqV0c/88uXLDVRx/Xj66aeN9ufo0aMPHfskHgcA8NNPPxn1QKPRAABeeumlhy5jNceBab7utnGKj48XmjVrVmX63r17BZlMJuTm5orTPvjgA0GlUgmlpaXVruvs2bMCAOGnn34Sp+3bt0+wsbERrl27ZvLa65tWqxXc3d2FZcuW1Tjur3/9qzB79uyGKaqB+Pr6CmvXrpU8/s6dO4JCoRB27dolTsvMzBQACCkpKfVQYcNbtWqV4OfnV+MYaz4WevfuLURGRoqP9Xq94O3tLaxYsaLa8X//+9+F0NBQo2mBgYHC9OnT67XOhpSfny8AEA4fPvzQMQ97DrVWixcvFrp16yZ5fGM4DgRBEGbPni20adNGKC8vr3a+NR0HfKWpHqSkpMDf39/ok8TVajUKCgqQkZHx0GWcnZ2NXpUJDg6GTCbDjz/+WO81m9o333yDP/74Q9JX0Gzbtg1ubm7o0qULYmNjUVxc3AAV1q+3334bzZs3R48ePfDOO+/UeGo2NTUVOp0OwcHB4rSOHTuiVatWSElJaYhy693du3fh6ur6yHHWeCxotVqkpqYa/fxkMhmCg4Mf+vNLSUkxGg9UPEc8KT9voOJnDuCRP/fCwkL4+vrCx8cHw4cPf+hzpLX47bff4O3tjaeeegrjx49Hdnb2Q8c2huNAq9Vi69atmDx5svi9rNWxluOAnwheD3Jzc6t89YrhcW5u7kOX8fDwMJpma2sLV1fXhy5jyTZv3gy1Wo2WLVvWOG7cuHHw9fWFt7c3Tp8+jQULFuD8+fP44osvGqhS03vllVfQs2dPuLq64tixY4iNjUVOTg7WrFlT7fjc3Fwolcoq18d5enpa5c++sgsXLuC9997Du+++W+M4az0Wbt68Cb1eX+3v/Llz56pd5mHPEU/Czxuo+LLz6OhoPPfcc+jSpctDx3Xo0AFbtmxB165dcffuXbz77rt49tlnkZGR8cjnDksUGBiIhIQEdOjQATk5OVi6dCleeOEFnDlzBk5OTlXGP+nHAQB89dVXuHPnDiZNmvTQMVZ1HJj7pS5LsWDBAgFAjbfMzEyjZR72kuLUqVOFkJAQo2lFRUUCAGHv3r3Vbv+tt94S2rdvX2W6u7u78P7779d9xx5TXfpy5coVQSaTCZ9//nmtt5ecnCwAEC5cuGCqXTCJuvTBYPPmzYKtra1QUlJS7fxt27YJSqWyyvRnnnlGmD9/vkn343HUpQdXr14V2rRpI0RERNR6e5Z6LFR27do1AYBw7Ngxo+nz5s0TevfuXe0yCoVC2L59u9G0uLg4wcPDo97qbEgzZswQfH19hStXrtRqOa1WK7Rp00ZYuHBhPVXWsG7fvi2oVCph06ZN1c5/0o8DQRCEkJAQ4X/+539qtYwlHwd8pem+uXPn1piEAeCpp56StC4vL68q75wxvBPKy8vroctUvmi0rKwMt27deugyDaEufYmPj0fz5s3xt7/9rdbbCwwMBFDx6kSbNm1qvXx9eZzjIzAwEGVlZbh06RI6dOhQZb6Xlxe0Wi3u3Llj9GpTXl6eWX/2ldW2B9evX0f//v3x7LPP4sMPP6z19iz1WKjMzc0Ncrm8yrsda/r5eXl51Wq8NYmKihLfyFLbVwkUCgV69OiBCxcu1FN1DcvZ2Rnt27d/6P48yccBAFy+fBkHDhyo9avFlnwcMDTd5+7uDnd3d5OsKygoCG+99Rby8/PFU24ajQYqlQqdO3d+6DJ37txBamoqAgICAAAHDx5EeXm5+MfDHGrbF0EQEB8fj4kTJ0KhUNR6e+np6QCAFi1a1HrZ+vQ4x0d6ejpkMlmV068GAQEBUCgUSE5OxqhRowAA58+fR3Z2NoKCgupcs6nVpgfXrl1D//79ERAQgPj4eMhktb980lKPhcqUSiUCAgKQnJyMESNGAKg4PZWcnPzQL/8OCgpCcnIyoqOjxWkajcaift61JQgCZs2ahS+//BKHDh2Cn59frdeh1+vxyy+/YOjQofVQYcMrLCzExYsXMWHChGrnP4nHwYPi4+Ph4eGB0NDQWi1n0ceBuV/qskaXL18W0tLShKVLlwpNmzYV0tLShLS0NOHevXuCIAhCWVmZ0KVLFyEkJERIT08X9u/fL7i7uwuxsbHiOn788UehQ4cOwtWrV8VpgwcPFnr06CH8+OOPwtGjR4V27doJY8eObfD9exwHDhx46Kmqq1evCh06dBB+/PFHQRAE4cKFC8KyZcuEkydPCllZWcLXX38tPPXUU0Lfvn0bumyTOXbsmLB27VohPT1duHjxorB161bB3d1dmDhxojimch8EoeJ0RqtWrYSDBw8KJ0+eFIKCgoSgoCBz7MJju3r1qtC2bVth4MCBwtWrV4WcnBzx9uCYJ+lY+OyzzwQ7OzshISFBOHv2rDBt2jTB2dlZfAfthAkThNdee00c/8MPPwi2trbCu+++K2RmZgqLFy8WFAqF8Msvv5hrFx7bzJkzhWbNmgmHDh0y+pkXFxeLYyr3YenSpUJiYqJw8eJFITU1VRgzZoxgb28vZGRkmGMXHtvcuXOFQ4cOCVlZWcIPP/wgBAcHC25ubkJ+fr4gCI3jODDQ6/VCq1athAULFlSZZ83HAUNTHYSFhVV7Pcd3330njrl06ZIwZMgQwcHBQXBzcxPmzp0r6HQ6cf53330nABCysrLEaX/88YcwduxYoWnTpoJKpRLCw8PFIGYtxo4dKzz77LPVzsvKyjLqU3Z2ttC3b1/B1dVVsLOzE9q2bSvMmzdPuHv3bgNWbFqpqalCYGCg0KxZM8He3l7o1KmT8K9//cvoeqbKfRAEQfjzzz+Ff/zjH4KLi4vg6OgovPjii0Yhw5rEx8c/9JongyfxWHjvvfeEVq1aCUqlUujdu7dw/Phxcd5f//pXISwszGj8zp07hfbt2wtKpVJ4+umnhT179jRwxab1sJ95fHy8OKZyH6Kjo8WeeXp6CkOHDhVOnTrV8MWbyOjRo4UWLVoISqVS+Mtf/iKMHj3a6Jq8xnAcGCQmJgoAhPPnz1eZZ83HgY0gCEIDvrBFREREZJX4OU1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRUQ369etn9DUXRNR4MTQR0RNr2LBhGDx4cLXzvv/+e9jY2OD06dMNXBURWSuGJiJ6YkVERECj0eDq1atV5sXHx6NXr17o2rWrGSojImvE0ERET6z/+Z//gbu7OxISEoymFxYWYteuXRgxYgTGjh2Lv/zlL3B0dIS/vz8+/fTTGtdpY2ODr776ymias7Oz0TauXLmCv//973B2doarqyuGDx+OS5cumWaniMhsGJqI6Illa2uLiRMnIiEhAQ9+zeauXbug1+vx8ssvIyAgAHv27MGZM2cwbdo0TJgwASdOnKjzNnU6HdRqNZycnPD999/jhx9+QNOmTTF48GBotVpT7BYRmQlDExE90SZPnoyLFy/i8OHD4rT4+HiMGjUKvr6+ePXVV9G9e3c89dRTmDVrFgYPHoydO3fWeXs7duxAeXk5Nm3aBH9/f3Tq1Anx8fHIzs7GoUOHTLBHRGQuDE1E9ETr2LEjnn32WWzZsgUAcOHCBXz//feIiIiAXq/H8uXL4e/vD1dXVzRt2hSJiYnIzs6u8/Z+/vlnXLhwAU5OTmjatCmaNm0KV1dXlJSU4OLFi6baLSIyA1tzF0BEVN8iIiIwa9YsxMXFIT4+Hm3atMFf//pXrFy5EuvXr8e6devg7++PJk2aIDo6usbTaDY2Nkan+oCKU3IGhYWFCAgIwLZt26os6+7ubrqdIqIGx9BERE+8v//975g9eza2b9+OTz75BDNnzoSNjQ1++OEHDB8+HC+//DIAoLy8HL/++is6d+780HW5u7sjJydHfPzbb7+huLhYfNyzZ0/s2LEDHh4eUKlU9bdTRNTgeHqOiJ54TZs2xejRoxEbG4ucnBxMmjQJANCuXTtoNBocO3YMmZmZmD59OvLy8mpc14ABA7BhwwakpaXh5MmTmDFjBhQKhTh//PjxcHNzw/Dhw/H9998jKysLhw4dwiuvvFLtRx8QkfVgaCKiRiEiIgK3b9+GWq2Gt7c3AGDhwoXo2bMn1Go1+vXrBy8vL4wYMaLG9axevRo+Pj544YUXMG7cOLz66qtwdHQU5zs6OuLIkSNo1aoVRo4ciU6dOiEiIgIlJSV85YnIytkIlU/OExEREVEVfKWJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKS4P8BavyZIsquJnQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(reject_rewards, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4312e5bd-6d22-43d0-a53f-a19528c7afe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = json.load(open(\"trajectories/70b-distil/full-rm-debug.json\"))\n",
    "chosen_rewards = [item[\"chosen_reward\"] for item in data]\n",
    "reject_rewards = [item[\"reject_reward\"] for item in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c89b5c9e-c708-418c-bf89-2b3ff00ce0a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(chosen_rewards, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9e60d7d6-33a9-4f96-bd9a-2db228dd1695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(reject_rewards, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ab75d65a-7f6c-4553-b00b-68f21c0e2d83",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = json.load(open(\"trajectories/70b-distil/full-rm-debug-2.json\"))\n",
    "chosen_rewards = [item[\"chosen_reward\"] for item in data]\n",
    "reject_rewards = [item[\"reject_reward\"] for item in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "47a14dbf-1040-4503-8895-f2658703bf28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_histogram(chosen_rewards, bins=20)\n",
    "plot_histogram(reject_rewards, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "629732d6-3965-40c8-8448-d4ac5d09871e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = json.load(open(\"trajectories/eval_predictions_rank1.json\"))\n",
    "cnt = 0\n",
    "rewards = []\n",
    "for item in data:\n",
    "    if \"Finish\" in item[\"response\"].split(\"\\n\")[-1]:\n",
    "        cnt += 1\n",
    "        rewards.append(item[\"reward\"][0])\n",
    "\n",
    "plot_histogram(rewards, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06788558-69d1-4e56-9013-8ebbcf80fb87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13499\n"
     ]
    }
   ],
   "source": [
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "24369da5-1029-4279-adc6-c8bdf505adc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13473\n"
     ]
    }
   ],
   "source": [
    "data = json.load(open(\"trajectories/eval_predictions_rank2.json\"))\n",
    "cnt = 0\n",
    "rewards = []\n",
    "for item in data:\n",
    "    if \"Finish[\" in item[\"response\"].split(\"\\n\")[-1]:\n",
    "        cnt += 1\n",
    "        rewards.append(item[\"reward\"][0])\n",
    "\n",
    "plot_histogram(rewards, bins=10)\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5b30abb-6ef5-4259-9ee9-bdd422f0fd0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Who must be chosen or who must not be chosen according to the context by one-step reasoning?\n",
      "Action 1: Take condition (1).\n",
      "Observation 1: Mr. Liu must be chosen.\n",
      "Thought 2: Based on Observation 1, who must be chosen or who must not be chosen one step further?\n",
      "Action 2: Take condition (2).\n",
      "Observation 2: Mr. Zhou must be chosen, but Mr. Zhang must not be chosen.\n",
      "Thought 3: Based on Observation 2, who must be chosen one step further?\n",
      "Action 3: Take condition (3).\n",
      "Observation 3: Mr. Niu must be chosen, but Mr. Wang must be chosen.\n",
      "Thought 4: Based on Observation 3, who must be chosen one step further?\n",
      "Action 4: Take condition (4).\n",
      "Observation 4: Mr. Zhou must not be chosen, and Mr. Wang must not be chosen.\n",
      "Thought 5: Based on Observation 4, who must be chosen one step further?\n",
      "Action 5: No further conditions can be taken to derive new observations.\n",
      "Thought 6: We can evaluate each option to check if there is any contradiction with the observations.\n",
      "Action 6: Take option A.\n",
      "Observation 6: Mr. Niu didn't choose, but Mr. Zhou did.\n",
      "Thought 7: Which observations and conditions are related to Option A?\n",
      "Action 7: Observation 3 and condition (4).\n"
     ]
    }
   ],
   "source": [
    "print(data[0][\"response\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62d58b33-9493-4a0a-b4c8-bd8db6803066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15422\n"
     ]
    }
   ],
   "source": [
    "data = json.load(open(\"trajectories/eval_predictions_rank3.json\"))\n",
    "cnt = 0\n",
    "rewards = []\n",
    "for item in data:\n",
    "    if \"Finish[\" in item[\"response\"].split(\"\\n\")[-1]:\n",
    "        cnt += 1\n",
    "        rewards.append(item[\"reward\"])\n",
    "\n",
    "plot_histogram(rewards, bins=10)\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0d8a706d-8e89-460e-a527-f388f1dbed84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15422\n"
     ]
    }
   ],
   "source": [
    "data = json.load(open(\"trajectories/eval_predictions_rank4.json\"))\n",
    "cnt = 0\n",
    "rewards = []\n",
    "for item in data:\n",
    "    if \"Finish[\" in item[\"response\"].split(\"\\n\")[-1]:\n",
    "        cnt += 1\n",
    "        rewards.append(item[\"reward\"])\n",
    "\n",
    "plot_histogram(rewards, bins=10)\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9521559c-d9f5-4639-a98d-9175c6510c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15422\n"
     ]
    }
   ],
   "source": [
    "data = json.load(open(\"trajectories/eval_predictions_rank5.json\"))\n",
    "cnt = 0\n",
    "rewards = []\n",
    "for item in data:\n",
    "    if \"Finish[\" in item[\"response\"].split(\"\\n\")[-1]:\n",
    "        cnt += 1\n",
    "        rewards.append(item[\"reward\"])\n",
    "\n",
    "plot_histogram(rewards, bins=10)\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d846c87e-045f-40c6-9987-30e583288670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15422\n"
     ]
    }
   ],
   "source": [
    "data = json.load(open(\"trajectories/eval_predictions_rank5.json\"))\n",
    "cnt = 0\n",
    "rewards = []\n",
    "for item in data:\n",
    "    if \"Finish[\" in item[\"response\"].split(\"\\n\")[-1]:\n",
    "        cnt += 1\n",
    "        ending_logits = torch.tensor(item[\"ending_logits\"])\n",
    "        probs = torch.softmax(ending_logits, dim=-1)\n",
    "        probs = probs[:, 3]\n",
    "        rewards.append(probs.prod().item())\n",
    "\n",
    "plot_histogram(rewards, bins=10)\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a76b7b5-5933-4f63-a8ec-0c00985e5dd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15422\n"
     ]
    }
   ],
   "source": [
    "cnt = 0\n",
    "rewards = []\n",
    "for item in data:\n",
    "    if \"Finish[\" in item[\"response\"].split(\"\\n\")[-1]:\n",
    "        cnt += 1\n",
    "        rewards.append(item[\"reward\"])\n",
    "\n",
    "plot_histogram(rewards, bins=100)\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c174c08d-43c5-42d8-8a7a-e7650a6fec6a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input': \"Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task. You will be given context that you should use to help you answer the question.\\n\\nContext:\\nA college will continue to implement the overseas funding plan this year. It plans to select several of the six teachers from Mr. Liu, Mr. Zhang, Mr. Wang, Mr. Ma, Mr. Niu and Mr. Zhou to visit abroad. Due to the limitations of funding, the needs of discipline development, curriculum arrangement, place and time of each student's visit, the selection shall meet the following conditions: (1) Mr. Liu is the reserve discipline leader of the college, This time we have to send out. (2) if we choose Mr. Liu, we should also choose Mr. Zhou, but we can't choose Mr. Zhang. (3) only if Mr. Niu can't choose, at least one of Mr. Wang and Mr. Ma can choose. (4) if we don't choose Mr. Wang, we don't choose Mr. Zhou either.\\n\\nQuestion:\\nIf the above statement is true, which of the followings must be true?\\n\\nOptions:\\nA. Mr. Niu didn't choose, but Mr. Zhou did\\nB. Mr. Liu was chose, but Mr. Ma didn't\\nC. Mr. Wang and Mr. Ma were chosen\\nD. Neither Mr. Wang nor Mr. Niu was elected\\n\\nThought 1: Who must be chosen or who must not be chosen according to the context by one-step reasoning?\\nAction 1: Take condition (1).\\nObservation 1: Mr. Liu must be chosen.\\nThought 2: Based on Observation 1, who must be chosen or who must not be chosen one step further?\\nAction 2: Take condition (2).\\nObservation 2: Mr. Zhou must be chosen, but Mr. Zhang must not be chosen.\\nThought 3: Based on Observation 2, who must be chosen one step further? And who must not be chosen?\\nAction 3: Take condition (3).\\nObservation 3: Mr. Wang must be chosen, but Mr. Ma must not be chosen.\\nThought 4: Based on Observation 3, who must be chosen one step further? And who must not be chosen?\\nAction 4: Take condition (4).\\nObservation 4: Mr. Zhou must not be chosen.\\nThought 5: Based on Observation 4, who must be chosen one step further? And who must not be chosen?\\nAction 5: No further conditions can be taken to derive new observations.\\nThought 6: We can evaluate each option to check if there is any contradiction with the observations.\\nAction 6: Take option A.\\nObservation 5: Mr. Niu didn't choose, but Mr. Zhou did.\\nThought 7: Which observations and conditions are related to Option A?\\nAction 7: Observation 1 and Observation 2.\\nObservation 7: We can confirm that Mr. Liu was chosen and Mr. Zhou was chosen. So Option A is true.\\nAction 8: Take option B.\\nObservation 8: Mr. Liu was chosen, but Mr. Ma didn't.\\nThought 9: Which observations and conditions are related to Option B?\\nAction 9: Observation 3.\\nObservation 9: Mr. Wang must be chosen, but Mr. Ma must not be chosen. This contradicts Observation 8. So Option B is not true.\\nAction 10: Take option C.\\nObservation 10: Mr. Wang and Mr. Ma were chosen.\\nThought 11: Which observations and conditions are related to Option C?\\nAction 11: Observation 3.\\nObservation 11: Mr. Wang must be chosen, but Mr. Ma must not be chosen. This contradicts Observation 10. So Option C is not true.\\nAction 12: Take option D.\\nObservation 12: Neither Mr. Wang nor Mr. Niu was elected.\\nThought 13: Which observations and conditions are related to Option D?\\nAction 13: Observation 1 and Observation 2.\\nObservation 13: Mr. Liu was chosen and Mr. Zhou was chosen. So Option D is true.\\nAction 14: Finish[The answer is D.]\",\n",
       " 'index': 0,\n",
       " 'response': \"Who must be chosen or who must not be chosen according to the context by one-step reasoning?\\nAction 1: Take condition (1).\\nObservation 1: Mr. Liu must be chosen.\\nThought 2: Based on Observation 1, who must be chosen or who must not be chosen one step further?\\nAction 2: Take condition (2).\\nObservation 2: Mr. Zhou must be chosen, but Mr. Zhang must not be chosen.\\nThought 3: Based on Observation 2, who must be chosen one step further? And who must not be chosen?\\nAction 3: Take condition (3).\\nObservation 3: Mr. Wang must be chosen, but Mr. Ma must not be chosen.\\nThought 4: Based on Observation 3, who must be chosen one step further? And who must not be chosen?\\nAction 4: Take condition (4).\\nObservation 4: Mr. Zhou must not be chosen.\\nThought 5: Based on Observation 4, who must be chosen one step further? And who must not be chosen?\\nAction 5: No further conditions can be taken to derive new observations.\\nThought 6: We can evaluate each option to check if there is any contradiction with the observations.\\nAction 6: Take option A.\\nObservation 5: Mr. Niu didn't choose, but Mr. Zhou did.\\nThought 7: Which observations and conditions are related to Option A?\\nAction 7: Observation 1 and Observation 2.\\nObservation 7: We can confirm that Mr. Liu was chosen and Mr. Zhou was chosen. So Option A is true.\\nAction 8: Take option B.\\nObservation 8: Mr. Liu was chosen, but Mr. Ma didn't.\\nThought 9: Which observations and conditions are related to Option B?\\nAction 9: Observation 3.\\nObservation 9: Mr. Wang must be chosen, but Mr. Ma must not be chosen. This contradicts Observation 8. So Option B is not true.\\nAction 10: Take option C.\\nObservation 10: Mr. Wang and Mr. Ma were chosen.\\nThought 11: Which observations and conditions are related to Option C?\\nAction 11: Observation 3.\\nObservation 11: Mr. Wang must be chosen, but Mr. Ma must not be chosen. This contradicts Observation 10. So Option C is not true.\\nAction 12: Take option D.\\nObservation 12: Neither Mr. Wang nor Mr. Niu was elected.\\nThought 13: Which observations and conditions are related to Option D?\\nAction 13: Observation 1 and Observation 2.\\nObservation 13: Mr. Liu was chosen and Mr. Zhou was chosen. So Option D is true.\\nAction 14: Finish[The answer is D.]\",\n",
       " 'ending_logits': [[1.0859375, 1.7734375, 1.4921875, -1.171875],\n",
       "  [1.9765625, 1.1484375, 0.5625, 0.091796875],\n",
       "  [2.84375, 1.1484375, 0.57421875, -0.828125],\n",
       "  [0.6953125, 0.98046875, 0.259765625, -0.41796875],\n",
       "  [0.984375, 0.458984375, 0.76953125, 0.302734375],\n",
       "  [1.515625, 0.95703125, 0.73046875, -0.62109375],\n",
       "  [0.78515625, 1.3671875, 0.63671875, -0.76171875],\n",
       "  [1.1640625, 0.984375, 0.1708984375, -0.6484375],\n",
       "  [2.828125, 1.03125, 0.13671875, -1.53125],\n",
       "  [1.8515625, 1.5234375, 0.1396484375, -1.3984375],\n",
       "  [1.765625, 0.9140625, -0.1201171875, -1.046875],\n",
       "  [2.5, 1.0625, -0.16015625, -2.0],\n",
       "  [2.046875, 1.6875, 0.07373046875, -1.7734375],\n",
       "  [1.4765625, 2.515625, 0.0751953125, -1.9453125],\n",
       "  [1.6484375, 1.4140625, -0.55859375, -1.796875],\n",
       "  [1.6796875, 0.56640625, -0.427734375, -0.298828125],\n",
       "  [1.65625, 1.078125, -0.033203125, -1.1015625],\n",
       "  [1.109375, 0.89453125, 1.1015625, -0.92578125],\n",
       "  [1.8515625, 0.71875, 0.0220947265625, -1.640625],\n",
       "  [1.828125, -0.169921875, -0.59375, -0.54296875],\n",
       "  [1.5703125, 0.1484375, -0.17578125, -0.2099609375],\n",
       "  [1.1953125, 0.53515625, -0.400390625, -0.91796875],\n",
       "  [1.78125, 0.7109375, -0.296875, -1.0234375],\n",
       "  [1.75, 0.67578125, 0.408203125, -1.171875],\n",
       "  [1.984375, 0.21484375, -0.88671875, -1.0234375],\n",
       "  [2.28125, -0.08154296875, -1.3046875, -1.5234375],\n",
       "  [2.0625, 0.2158203125, -1.0703125, -2.234375],\n",
       "  [2.65625, 0.63671875, -0.671875, -1.515625],\n",
       "  [1.9921875, 0.2431640625, -0.64453125, -1.6328125],\n",
       "  [2.21875, 0.138671875, -0.81640625, -0.9453125],\n",
       "  [1.9296875, -0.1513671875, -0.921875, -1.2109375],\n",
       "  [1.9296875, -0.09814453125, -1.078125, -1.71875],\n",
       "  [2.59375, 0.07958984375, -0.82421875, -1.46875],\n",
       "  [2.53125, 0.55859375, -0.7578125, -1.9140625],\n",
       "  [2.875, -0.26953125, -1.6015625, -1.6875],\n",
       "  [3.34375, 0.2236328125, -1.28125, -0.3828125],\n",
       "  [3.03125, 0.1572265625, -0.431640625, -0.703125],\n",
       "  [2.84375, 0.365234375, -0.57421875, -0.5546875],\n",
       "  [2.859375, 0.6171875, -0.94140625, -1.4609375],\n",
       "  [3.03125, 0.275390625, -1.0078125, -1.5703125],\n",
       "  [2.71875, 0.462890625, -0.921875, -1.1953125],\n",
       "  [2.625, 0.396484375, -1.171875, -1.5390625],\n",
       "  [2.78125, 0.10986328125, -1.1171875, -1.734375],\n",
       "  [2.765625, 0.4765625, -1.2734375, -1.640625],\n",
       "  [3.0, 0.375, -1.203125, -1.15625],\n",
       "  [3.09375, -0.053466796875, -1.1015625, -1.421875],\n",
       "  [2.609375, 0.2490234375, -1.078125, -1.2890625],\n",
       "  [2.890625, 0.224609375, -1.015625, -1.109375],\n",
       "  [3.046875, 0.275390625, -1.09375, -0.5859375],\n",
       "  [3.109375, 0.2421875, -1.3125, -0.6171875],\n",
       "  [3.3125, 0.09619140625, -1.3125, -0.8203125],\n",
       "  [3.046875, 0.306640625, -1.2734375, -1.46875],\n",
       "  [3.390625, 0.498046875, -1.2265625, -1.7421875],\n",
       "  [3.0, 0.578125, -0.84765625, -1.359375],\n",
       "  [3.140625, 0.2470703125, -1.3125, -1.015625],\n",
       "  [3.375, -0.171875, -1.6484375, -1.0390625],\n",
       "  [3.078125, -0.0673828125, -1.8203125, -2.0625],\n",
       "  [3.9375, 0.267578125, -1.90625, -1.7578125],\n",
       "  [2.859375, -0.04443359375, -1.3671875, -1.5859375],\n",
       "  [3.046875, -0.08544921875, -1.6640625, -0.78515625],\n",
       "  [3.421875, -0.6875, -2.03125, -1.3671875],\n",
       "  [2.96875, -0.00775146484375, -1.84375, -1.8359375],\n",
       "  [4.09375, 0.298828125, -1.8671875, -1.3359375],\n",
       "  [3.234375, -0.11865234375, -1.5703125, -1.265625],\n",
       "  [3.40625, -0.1640625, -1.7890625, -0.46484375],\n",
       "  [3.3125, -0.8828125, -1.59375, -0.193359375],\n",
       "  [3.109375, -0.3046875, -1.6640625, -0.953125],\n",
       "  [3.703125, -0.458984375, -1.671875, -1.140625],\n",
       "  [3.21875, -0.287109375, -1.1640625, -1.1015625],\n",
       "  [3.59375, -1.109375, -1.8125, 0.68359375],\n",
       "  [4.4375, -1.859375, -2.640625, 2.078125]],\n",
       " 'reward': 0.0}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f07d1b7e-a4a1-49a0-90f9-288ddb5e3548",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input': \"Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task. You will be given context that you should use to help you answer the question.\\n\\nContext:\\nA college will continue to implement the overseas funding plan this year. It plans to select several of the six teachers from Mr. Liu, Mr. Zhang, Mr. Wang, Mr. Ma, Mr. Niu and Mr. Zhou to visit abroad. Due to the limitations of funding, the needs of discipline development, curriculum arrangement, place and time of each student's visit, the selection shall meet the following conditions: (1) Mr. Liu is the reserve discipline leader of the college, This time we have to send out. (2) if we choose Mr. Liu, we should also choose Mr. Zhou, but we can't choose Mr. Zhang. (3) only if Mr. Niu can't choose, at least one of Mr. Wang and Mr. Ma can choose. (4) if we don't choose Mr. Wang, we don't choose Mr. Zhou either.\\n\\nQuestion:\\nIf the above statement is true, which of the followings must be true?\\n\\nOptions:\\nA. Mr. Niu didn't choose, but Mr. Zhou did\\nB. Mr. Liu was chose, but Mr. Ma didn't\\nC. Mr. Wang and Mr. Ma were chosen\\nD. Neither Mr. Wang nor Mr. Niu was elected\\n\\nThought 1: Who must be chosen or who must not be chosen according to the context by one-step reasoning?\\nAction 1: Take condition (1).\\nObservation 1: Mr. Liu must be chosen.\\nThought 2: Based on Observation 1, who must be chosen or who must not be chosen one step further?\\nAction 2: Take condition (2).\\nObservation 2: Mr. Zhou must be chosen, but Mr. Zhang must not be chosen.\\nThought 3: Based on Observation 2, who must be chosen or who must not be chosen one step further?\\nAction 3: Take condition (3).\\nObservation 3: If Mr. Niu was not chosen, Mr. Wang must be chosen, but Mr. Ma must not be chosen.\\nThought 4: Based on Observation 3, who must be chosen or who must not be chosen one step further?\\nAction 4: Take condition (4).\\nObservation 4: If Mr. Wang was not chosen, Mr. Zhou must not be chosen.\\nThought 5: Which observations and conditions are related to Option A?\\nAction 5: Observation 1 and Observation 4.\\nThought 6: Which observations and conditions are related to Option B?\\nAction 6: Observation 1 and Observation 2.\\nThought 7: Which observations and conditions are related to Option C?\\nAction 7: Observation 3 and Observation 4.\\nThought 8: Which observations and conditions are related to Option D?\\nAction 8: Observation 1 and Observation 3.\\nThought 9: Which option is true?\\nAction 9: Compare all options with observations and conditions.\\nObservation 9: Option A and Option C are true.\\nThought 10: Which option is more true?\\nAction 10: Evaluate the truth of each option.\\nObservation 10: Option C is more true.\\nAction 11: Finish[The answer is C.]\",\n",
       " 'index': 0,\n",
       " 'response': 'Who must be chosen or who must not be chosen according to the context by one-step reasoning?\\nAction 1: Take condition (1).\\nObservation 1: Mr. Liu must be chosen.\\nThought 2: Based on Observation 1, who must be chosen or who must not be chosen one step further?\\nAction 2: Take condition (2).\\nObservation 2: Mr. Zhou must be chosen, but Mr. Zhang must not be chosen.\\nThought 3: Based on Observation 2, who must be chosen or who must not be chosen one step further?\\nAction 3: Take condition (3).\\nObservation 3: If Mr. Niu was not chosen, Mr. Wang must be chosen, but Mr. Ma must not be chosen.\\nThought 4: Based on Observation 3, who must be chosen or who must not be chosen one step further?\\nAction 4: Take condition (4).\\nObservation 4: If Mr. Wang was not chosen, Mr. Zhou must not be chosen.\\nThought 5: Which observations and conditions are related to Option A?\\nAction 5: Observation 1 and Observation 4.\\nThought 6: Which observations and conditions are related to Option B?\\nAction 6: Observation 1 and Observation 2.\\nThought 7: Which observations and conditions are related to Option C?\\nAction 7: Observation 3 and Observation 4.\\nThought 8: Which observations and conditions are related to Option D?\\nAction 8: Observation 1 and Observation 3.\\nThought 9: Which option is true?\\nAction 9: Compare all options with observations and conditions.\\nObservation 9: Option A and Option C are true.\\nThought 10: Which option is more true?\\nAction 10: Evaluate the truth of each option.\\nObservation 10: Option C is more true.\\nAction 11: Finish[The answer is C.]',\n",
       " 'ending_logits': [[1.078125, 1.765625, 1.453125, -1.1640625],\n",
       "  [1.9609375, 1.15625, 0.6015625, 0.099609375],\n",
       "  [2.890625, 1.15625, 0.578125, -0.8828125],\n",
       "  [0.73828125, 1.0078125, 0.2412109375, -0.419921875],\n",
       "  [1.0, 0.451171875, 0.75390625, 0.283203125],\n",
       "  [1.5234375, 0.9765625, 0.7421875, -0.63671875],\n",
       "  [1.2890625, 1.671875, -0.0732421875, -2.0625],\n",
       "  [1.359375, 1.03125, -0.0286865234375, -0.9140625],\n",
       "  [3.03125, 1.15625, -0.0439453125, -1.6484375],\n",
       "  [2.046875, 1.703125, -0.1875, -1.5859375],\n",
       "  [1.96875, 1.09375, -0.39453125, -1.296875],\n",
       "  [2.015625, 1.0703125, 0.2431640625, -1.2421875],\n",
       "  [1.0703125, 1.1171875, 1.0, -0.58984375],\n",
       "  [1.9140625, 1.125, 0.07373046875, -1.21875],\n",
       "  [1.9296875, 1.0390625, 0.2890625, -0.031494140625],\n",
       "  [1.0546875, 1.1875, 0.10009765625, -1.015625],\n",
       "  [1.734375, 0.53515625, -0.138671875, -0.279296875],\n",
       "  [1.6171875, 0.86328125, -0.158203125, -0.9453125],\n",
       "  [1.5078125, 0.453125, -0.10107421875, -0.02294921875],\n",
       "  [1.6484375, 0.84375, 0.036376953125, -0.90234375],\n",
       "  [2.671875, 0.5859375, -0.59765625, -0.828125],\n",
       "  [2.546875, 0.482421875, -1.1796875, -1.125],\n",
       "  [3.59375, 0.64453125, -1.1875, -2.328125],\n",
       "  [3.484375, -0.0252685546875, -1.59375, -1.09375],\n",
       "  [2.765625, 0.59765625, -1.140625, -1.125],\n",
       "  [3.140625, -0.671875, -2.046875, -1.2890625],\n",
       "  [3.203125, 0.08349609375, -1.53125, -0.51171875],\n",
       "  [2.828125, 0.05810546875, -0.361328125, -0.953125],\n",
       "  [2.6875, 0.291015625, -0.5625, -0.9765625],\n",
       "  [2.8125, 0.5078125, -1.203125, -1.8203125],\n",
       "  [2.875, 0.12890625, -1.1484375, -1.9140625],\n",
       "  [2.5625, 0.25, -0.8359375, -1.25],\n",
       "  [3.1875, 0.5625, -1.3984375, -1.8515625],\n",
       "  [3.03125, 0.083984375, -1.03125, -1.8125],\n",
       "  [2.640625, 0.359375, -1.078125, -1.7109375],\n",
       "  [3.234375, 0.37109375, -1.4609375, -1.3828125],\n",
       "  [3.265625, -0.016845703125, -1.53125, -1.78125],\n",
       "  [2.890625, 0.02587890625, -1.171875, -1.0078125],\n",
       "  [3.359375, -0.1123046875, -1.5078125, -1.3984375],\n",
       "  [3.171875, 0.09716796875, -1.0703125, -1.4375],\n",
       "  [3.765625, 0.71484375, -1.6484375, -1.484375],\n",
       "  [3.125, 0.19140625, -1.3984375, -1.4921875],\n",
       "  [3.4375, -0.4296875, -1.953125, -1.21875],\n",
       "  [3.1875, -0.12060546875, -1.3359375, -0.54296875],\n",
       "  [3.421875, -0.08935546875, -1.6875, -1.21875],\n",
       "  [3.375, -0.2158203125, -1.3984375, -0.45703125],\n",
       "  [3.890625, -0.60546875, -1.8046875, -0.55078125],\n",
       "  [3.375, -0.25, -1.4765625, -0.396484375],\n",
       "  [3.609375, -0.26171875, -1.9296875, -0.482421875],\n",
       "  [4.1875, -0.48046875, -2.328125, -1.2265625],\n",
       "  [3.875, -0.04833984375, -1.765625, -1.3515625],\n",
       "  [3.609375, -1.0078125, -1.9609375, 0.9609375],\n",
       "  [4.71875, -2.28125, -2.984375, 2.046875]],\n",
       " 'reward': 0.0}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ca2cbdae-0048-4a9d-b1ea-75f6ecc52415",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input': 'Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task. You will be given context that you should use to help you answer the question.\\n\\nContext:\\nThe straw effect means that the juice around the tube can be inhaled by inhaling the fruit juice in the cup with a straw. Implanting some kind of straw in a certain area can gather resources and cause obvious regional effect.\\n\\nQuestion:\\nWhich of the following options does not belong to the straw effect?\\n\\nOptions:\\nA. The common sense of petroleum prospecting is that the standard distance between the two mining areas is 100 meters. At present, China has explored eight oil fields in the East China Sea, including the new Bajiaoting oil and gas field\\nB. The function of the city has shifted to the south, and property prices in Zhongcun, Nanhai Pingzhou, Sanshan and Shunde near the new railway station have all been pushed up\\nC. Xijiao Town integrates the resources of the industrial park in the town, and with the strong foundation of its textile industry, it has established the textile characteristics as the main direction of attack, and guided the textile enterprises to transform from extensive to centralized and carry out cluster development\\nD. This year, let the prosperity of the modern service industry be matched with convenient transportation, and the combination of the two will become a prosperous area where capital flows yearn for one after another\\n\\nThought 1: What is the common point of the straw effect?\\nAction 1: Take the condition of the straw effect.\\nObservation 1: The straw effect means that the juice around the tube can be inhaled by inhaling the fruit juice in the cup with a straw. Implanting some kind of straw in a certain area can gather resources and cause obvious regional effect.\\nThought 2: What are the resources gathered in the options?\\nAction 2: Take option A.\\nObservation 2: The distance between the two mining areas is 100 meters.\\nThought 3: Is this related to the straw effect?\\nAction 3: No. This is not related to the straw effect.\\nAction 4: Take option B.\\nObservation 4: The function of the city has shifted to the south, and property prices in Zhongcun, Nanhai Pingzhou, Sanshan and Shunde near the new railway station have all been pushed up.\\nThought 5: Is this related to the straw effect?\\nAction 5: Yes. The railway station is a kind of straw that gathers resources and causes obvious regional effect.\\nAction 6: Take option C.\\nObservation 6: Xijiao Town integrates the resources of the industrial park in the town, and with the strong foundation of its textile industry, it has established the textile characteristics as the main direction of attack, and guided the textile enterprises to transform from extensive to centralized and carry out cluster development.\\nThought 7: Is this related to the straw effect?\\nAction 7: Yes. Xijiao Town is a kind of straw that gathers resources and causes obvious regional effect.\\nAction 8: Take option D.\\nObservation 8: This year, let the prosperity of the modern service industry be matched with convenient transportation, and the combination of the two will become a prosperous area where capital flows yearn for one after another.\\nThought 9: Is this related to the straw effect?\\nAction 9: Yes. The modern service industry and convenient transportation are kinds of straws that gather resources and cause obvious regional effect.\\nAction 10: Finish[The answer is A.]',\n",
       " 'index': 1,\n",
       " 'response': 'What is the common point of the straw effect?\\nAction 1: Take the condition of the straw effect.\\nObservation 1: The straw effect means that the juice around the tube can be inhaled by inhaling the fruit juice in the cup with a straw. Implanting some kind of straw in a certain area can gather resources and cause obvious regional effect.\\nThought 2: What are the resources gathered in the options?\\nAction 2: Take option A.\\nObservation 2: The distance between the two mining areas is 100 meters.\\nThought 3: Is this related to the straw effect?\\nAction 3: No. This is not related to the straw effect.\\nAction 4: Take option B.\\nObservation 4: The function of the city has shifted to the south, and property prices in Zhongcun, Nanhai Pingzhou, Sanshan and Shunde near the new railway station have all been pushed up.\\nThought 5: Is this related to the straw effect?\\nAction 5: Yes. The railway station is a kind of straw that gathers resources and causes obvious regional effect.\\nAction 6: Take option C.\\nObservation 6: Xijiao Town integrates the resources of the industrial park in the town, and with the strong foundation of its textile industry, it has established the textile characteristics as the main direction of attack, and guided the textile enterprises to transform from extensive to centralized and carry out cluster development.\\nThought 7: Is this related to the straw effect?\\nAction 7: Yes. Xijiao Town is a kind of straw that gathers resources and causes obvious regional effect.\\nAction 8: Take option D.\\nObservation 8: This year, let the prosperity of the modern service industry be matched with convenient transportation, and the combination of the two will become a prosperous area where capital flows yearn for one after another.\\nThought 9: Is this related to the straw effect?\\nAction 9: Yes. The modern service industry and convenient transportation are kinds of straws that gather resources and cause obvious regional effect.\\nAction 10: Finish[The answer is A.]',\n",
       " 'ending_logits': [[-8.0, -2.3125, 1.1953125, 7.75],\n",
       "  [-6.875, -0.34375, -0.1689453125, 5.4375],\n",
       "  [-4.0625, -0.318359375, 1.40625, 5.34375],\n",
       "  [-5.4375, -0.251953125, 1.8828125, 5.4375],\n",
       "  [-4.5625, -1.0, 1.0078125, 5.96875],\n",
       "  [-4.46875, -1.078125, 1.90625, 4.8125],\n",
       "  [-0.10498046875, 0.1484375, 0.33984375, 0.671875],\n",
       "  [-1.15625, 0.388671875, 1.78125, 1.34375],\n",
       "  [-1.984375, -0.4375, 0.7109375, 3.21875],\n",
       "  [-1.90625, -0.1591796875, 1.140625, 2.453125],\n",
       "  [-1.7578125, -1.3515625, 1.0390625, 3.03125],\n",
       "  [-3.046875, -1.375, 0.890625, 3.765625],\n",
       "  [-3.140625, -0.83203125, 0.32421875, 4.9375],\n",
       "  [-2.375, -0.609375, 0.87109375, 4.21875],\n",
       "  [-2.65625, -0.578125, 0.423828125, 4.59375],\n",
       "  [-2.890625, 0.043212890625, 1.6640625, 2.796875],\n",
       "  [-2.203125, -0.86328125, 0.73828125, 3.859375],\n",
       "  [-2.3125, -1.1328125, 0.546875, 3.671875],\n",
       "  [-1.7421875, -0.66796875, 0.2578125, 3.65625],\n",
       "  [-2.703125, -0.2255859375, 1.0, 2.859375],\n",
       "  [-0.58203125, -0.10107421875, 0.46875, 3.234375],\n",
       "  [-0.79296875, -0.06787109375, 0.8515625, 3.109375],\n",
       "  [-1.359375, -0.0296630859375, 0.80078125, 3.21875],\n",
       "  [-1.1328125, -0.26953125, 0.20703125, 3.140625],\n",
       "  [-0.7421875, -0.53515625, 0.1845703125, 3.703125],\n",
       "  [-0.97265625, 0.125, 0.66796875, 2.8125],\n",
       "  [-0.96484375, -0.7421875, -0.1591796875, 3.796875],\n",
       "  [-1.0625, -0.92578125, 0.19140625, 4.0625],\n",
       "  [-1.6953125, -0.84375, 0.314453125, 4.34375],\n",
       "  [-1.6640625, -0.62109375, 0.55859375, 3.671875],\n",
       "  [-1.2890625, -1.03125, -0.283203125, 4.375],\n",
       "  [-1.875, -0.828125, 0.5234375, 3.765625],\n",
       "  [-1.9609375, -1.1953125, 0.126953125, 4.46875],\n",
       "  [-1.71875, -0.8984375, 0.37109375, 4.4375],\n",
       "  [-1.21875, -0.99609375, -0.265625, 4.3125],\n",
       "  [-1.4296875, -0.75390625, 0.24609375, 3.765625],\n",
       "  [-1.640625, -0.99609375, -0.1298828125, 4.3125],\n",
       "  [-1.234375, -0.80078125, 0.287109375, 4.03125],\n",
       "  [-0.96875, -0.8984375, -0.6328125, 4.25],\n",
       "  [-0.80078125, -0.7890625, 0.482421875, 3.828125],\n",
       "  [1.03125, -1.984375, -0.546875, 5.28125]],\n",
       " 'reward': 0.2457028031349182}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ba06ed32-1e75-4385-b6c3-47d91ea2b7e7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input': \"Solve a question answering task by having a Thought, then Finish with your answer. Thought can reason about the current situation. Finish[answer] returns the answer and finishes the task. You will be given context that you should use to help you answer the question.\\n\\nContext:\\nReading is a personal life habit, which is determined by cultural literacy. Without the pursuit of knowledge or the hobby of culture, it is difficult to imagine that people can calm down and read. The habit of reading should be cultivated from an early age. Starting from the beginning, only the love and pursuit of cultural knowledge can make us accustomed to reading. The reading habits and hobbies of Chinese people cannot be solved by a youth reading festival. Therefore, reading is still based on usual habits.\\n\\nQuestion:\\nWhich one of the followings is the topic of the above?\\n\\nOptions:\\nA. Reading habits\\nB. Advocate reading\\nC. Love reading since childhood\\nD. Pursue knowledge\\n\\nThought 1: What is the main idea of the passage?\\nAction 1: Analyze the content of the passage.\\nObservation 1: The passage emphasizes the importance of reading and its relationship with cultural literacy. It also mentions that reading is a personal life habit.\\nThought 2: What is the relationship between reading and cultural literacy?\\nAction 2: Take the first sentence of the passage.\\nObservation 2: Reading is a personal life habit, which is determined by cultural literacy.\\nThought 3: What is the author's attitude towards reading?\\nAction 3: Take the second sentence of the passage.\\nObservation 3: The author believes that reading should be cultivated from an early age.\\nThought 4: What is the purpose of the passage?\\nAction 4: Take the last sentence of the passage.\\nObservation 4: The passage suggests that reading is still based on usual habits.\\nThought 5: Which option best matches the main idea of the passage?\\nAction 5: Compare the options with the main idea.\\nObservation 5: Option A best matches the main idea.\\nAction 6: Finish[The answer is A.]\",\n",
       " 'index': 2,\n",
       " 'response': \"What is the main idea of the passage?\\nAction 1: Analyze the content of the passage.\\nObservation 1: The passage emphasizes the importance of reading and its relationship with cultural literacy. It also mentions that reading is a personal life habit.\\nThought 2: What is the relationship between reading and cultural literacy?\\nAction 2: Take the first sentence of the passage.\\nObservation 2: Reading is a personal life habit, which is determined by cultural literacy.\\nThought 3: What is the author's attitude towards reading?\\nAction 3: Take the second sentence of the passage.\\nObservation 3: The author believes that reading should be cultivated from an early age.\\nThought 4: What is the purpose of the passage?\\nAction 4: Take the last sentence of the passage.\\nObservation 4: The passage suggests that reading is still based on usual habits.\\nThought 5: Which option best matches the main idea of the passage?\\nAction 5: Compare the options with the main idea.\\nObservation 5: Option A best matches the main idea.\\nAction 6: Finish[The answer is A.]\",\n",
       " 'ending_logits': [[0.61328125, 0.546875, -0.578125, 1.125],\n",
       "  [-1.0234375, -0.232421875, -0.32421875, 2.640625],\n",
       "  [0.00762939453125, 0.1884765625, 0.63671875, 2.0625],\n",
       "  [-2.140625, -0.7578125, 0.71484375, 2.671875],\n",
       "  [-0.5078125, -0.25, -1.0078125, 1.7109375],\n",
       "  [-2.65625, -0.75, -0.34765625, 2.8125],\n",
       "  [-2.453125, -0.3828125, 1.328125, 2.0625],\n",
       "  [-1.9296875, -0.5234375, 0.5, 1.9453125],\n",
       "  [0.057373046875, 0.388671875, -0.13671875, 1.1484375],\n",
       "  [-0.81640625, -0.07958984375, 1.234375, 1.359375],\n",
       "  [-1.421875, -0.482421875, 0.54296875, 1.78125],\n",
       "  [-0.859375, -0.40234375, 0.40625, 2.234375],\n",
       "  [-0.68359375, -0.72265625, 0.48046875, 2.921875],\n",
       "  [-0.078125, 0.482421875, 0.1796875, 2.0625],\n",
       "  [0.9375, -0.77734375, -0.96484375, 1.40625],\n",
       "  [1.03125, -0.84375, 0.02392578125, 2.859375],\n",
       "  [0.4296875, -0.2197265625, 0.50390625, 2.296875],\n",
       "  [0.0517578125, -0.1611328125, 0.57421875, 2.234375],\n",
       "  [-0.376953125, -0.29296875, 0.53515625, 2.46875],\n",
       "  [-0.1591796875, -0.056640625, 0.4140625, 2.09375],\n",
       "  [-0.64453125, -0.126953125, 0.5390625, 2.0625],\n",
       "  [-0.333984375, -0.26171875, 0.5234375, 1.59375],\n",
       "  [-0.2392578125, -0.021484375, 0.330078125, 1.453125],\n",
       "  [-0.283203125, -0.04931640625, 0.46484375, 1.5859375],\n",
       "  [0.162109375, -0.56640625, 0.10302734375, 1.7890625],\n",
       "  [0.060302734375, -0.134765625, -0.2294921875, 1.40625],\n",
       "  [-0.1416015625, -0.62109375, 0.28125, 2.28125],\n",
       "  [0.283203125, -0.7421875, -0.1640625, 2.671875],\n",
       "  [0.375, -0.6171875, -0.11083984375, 2.421875],\n",
       "  [0.890625, -1.0390625, -0.365234375, 3.453125],\n",
       "  [1.859375, -2.03125, -1.109375, 4.65625]],\n",
       " 'reward': 0.0031588315032422543}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b9365719-fb68-463c-9c97-c1b24dad9c22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1403\n"
     ]
    }
   ],
   "source": [
    "zero_cnt = 0\n",
    "for item in data:\n",
    "    if item[\"reward\"] == 0:\n",
    "        zero_cnt += 1\n",
    "print(zero_cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7d13fabe-9b93-4df7-8226-f28c8a5f0af3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11597\n"
     ]
    }
   ],
   "source": [
    "zero_cnt = 0\n",
    "for item in data:\n",
    "    if item[\"reward\"] <= 0.05:\n",
    "        zero_cnt += 1\n",
    "print(zero_cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac7ea207-9ce7-4bec-9c34-9cbf61a43052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15422\n"
     ]
    }
   ],
   "source": [
    "data = json.load(open(\"trajectories/eval_predictions_rank3.json\"))\n",
    "\n",
    "def logit2prob(logits):\n",
    "    probs = torch.softmax(logits, dim=-1)\n",
    "    probs = probs[:, 2] + probs[:, 3]\n",
    "    return probs\n",
    "\n",
    "cnt = 0\n",
    "rewards = []\n",
    "for item in data:\n",
    "    if \"Finish[\" in item[\"response\"].split(\"\\n\")[-1]:\n",
    "        cnt += 1\n",
    "        # rewards.append(item[\"reward\"])\n",
    "\n",
    "        ending_logits = torch.tensor(item[\"ending_logits\"])\n",
    "        probs = logit2prob(ending_logits)\n",
    "        acc_probs = 1.0\n",
    "        res = []\n",
    "        for prob in probs:\n",
    "            acc_probs = acc_probs * prob\n",
    "            res.append(acc_probs.item())\n",
    "\n",
    "        rewards.extend(res)\n",
    "        \n",
    "\n",
    "plot_histogram(rewards, bins=10)\n",
    "print(cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7ec0c065-37be-4dcc-a4cc-872faaf35045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = json.load(open(\"trajectories/reward_debug.json\"))\n",
    "\n",
    "correct_rewards = []\n",
    "incorrect_rewards = []\n",
    "for item in data:\n",
    "    responses = item[\"response_reward\"]\n",
    "    for x in responses:\n",
    "        if x[\"is_correct\"]:\n",
    "            correct_rewards.append(x[\"reward\"])\n",
    "        else:\n",
    "            incorrect_rewards.append(x[\"reward\"])\n",
    "\n",
    "plot_histogram(correct_rewards, bins=10, title=\"Correct\")\n",
    "plot_histogram(incorrect_rewards, bins=10, title=\"Incorrect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2f0beb9c-9b92-478d-afd6-a7f6cf0230eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMMGroWnGjBn6zW9+o9DQUIWFhal///7avXu3U03Xrl3l5+fndBs1apRTzf79+5WYmKiQkBCFhYVp/PjxKi0tdapZt26dOnXqpKCgILVo0ULp6emV+klLS1PTpk0VHBysLl26aMuWLW5/zQAAwDd5NTR9+umnSk5O1ubNm5WRkSG73a6ePXvq7NmzTnUjRozQoUOHHLeZM2c69pWVlSkxMVElJSXatGmTFi5cqPT0dE2ePNlRk5eXp8TERHXr1k05OTkaO3asHn/8ca1evdpRs3TpUqWmpmrKlCn64osv1KFDByUkJOjw4cPVPwgAAFDjBXjzyVetWuV0Pz09XWFhYcrOztbdd9/t2B4SEqKIiIgq11izZo127dqljz/+WOHh4erYsaOmT5+up556SlOnTpXVatW8efMUExOjWbNmSZJat26tjRs36uWXX1ZCQoIkafbs2RoxYoSGDx8uSZo3b55WrFih+fPn6+mnn66Olw8AAHyIV0PTL506dUqS1KBBA6ftixYt0j/+8Q9FRESob9+++vOf/6yQkBBJUlZWltq1a6fw8HBHfUJCgkaPHq2dO3fqlltuUVZWluLj453WTEhI0NixYyVJJSUlys7O1sSJEx37/f39FR8fr6ysrCp7LS4uVnFxseN+YWGhJMlut8tut7s4gcoq1nLnmqgas/YM5uwZzNkzfHXOZWVlsloDFegvBajc2+2YEvDTe2NlZWXV8nvWVA9ue9Zfqby8XGPHjtUdd9yhtm3bOrYPHjxY0dHRioyM1Pbt2/XUU09p9+7d+uCDDyRJ+fn5ToFJkuN+fn7+JWsKCwt17tw5nThxQmVlZVXWfPPNN1X2O2PGDE2bNq3S9jVr1jgCnTtlZGS4fU1UjVl7BnP2DObsGb445wkpT/z0p3yv9mFa/QupKTc3V7m5uW5btqioyHRtjQlNycnJ2rFjhzZu3Oi0feTIkY4/t2vXTo0bN1b37t21d+9eNW/e3NNtOkycOFGpqamO+4WFhYqKilLPnj1ls9nc9jx2u10ZGRnq0aOHAgMD3bYuKmPWnsGcPYM5e4avzjkvL0/Dk8cqOmGEQhtGersdU84dP6iBzf3VsmVLtWjRwm3rVrxTZEaNCE0pKSlavny51q9fryZNmlyytkuXLpKkPXv2qHnz5oqIiKj0KbeCggJJcpwHFRER4dj28xqbzaZatWrJYrHIYrFUWXOxc6mCgoIUFBRUaXtgYGC1/OBU17qojFl7BnP2DObsGb42Z4vFopISu+zlUqmPXH2o9Kd3ES0Wi1tnfSVreXVShmEoJSVFH374odauXauYmJjLPiYnJ0eS1LhxY0lSXFycvvrqK6dPuWVkZMhms6lNmzaOmszMTKd1MjIyFBcXJ0myWq2KjY11qikvL1dmZqajBgAAXNu8eqQpOTlZixcv1v/93/8pNDTUcQ5S3bp1VatWLe3du1eLFy9Wnz59dN1112n79u0aN26c7r77brVv316S1LNnT7Vp00ZDhgzRzJkzlZ+fr0mTJik5OdlxJGjUqFF67bXXNGHCBD322GNau3at3n//fa1YscLRS2pqqoYNG6bOnTvr1ltv1Zw5c3T27FnHp+kAAMC1zauh6Y033pB04QKWP7dgwQI9+uijslqt+vjjjx0BJioqSgMHDtSkSZMctRaLRcuXL9fo0aMVFxen2rVra9iwYXruueccNTExMVqxYoXGjRunuXPnqkmTJnr77bcdlxuQpEGDBunIkSOaPHmy8vPz1bFjR61atarSyeEAAODa5NXQZBjGJfdHRUXp008/vew60dHRWrly5SVrunbtqm3btl2yJiUlRSkpKZd9PgAAcO3xjbO/AAAAvIzQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACV4NTTNmzNBvfvMbhYaGKiwsTP3799fu3budas6fP6/k5GRdd911qlOnjgYOHKiCggKnmv379ysxMVEhISEKCwvT+PHjVVpa6lSzbt06derUSUFBQWrRooXS09Mr9ZOWlqamTZsqODhYXbp00ZYtW9z+mgEAgG/yamj69NNPlZycrM2bNysjI0N2u109e/bU2bNnHTXjxo3Tv//9by1btkyffvqpDh48qAEDBjj2l5WVKTExUSUlJdq0aZMWLlyo9PR0TZ482VGTl5enxMREdevWTTk5ORo7dqwef/xxrV692lGzdOlSpaamasqUKfriiy/UoUMHJSQk6PDhw54ZBgAAqNECvPnkq1atcrqfnp6usLAwZWdn6+6779apU6f0zjvvaPHixbr33nslSQsWLFDr1q21efNm3XbbbVqzZo127dqljz/+WOHh4erYsaOmT5+up556SlOnTpXVatW8efMUExOjWbNmSZJat26tjRs36uWXX1ZCQoIkafbs2RoxYoSGDx8uSZo3b55WrFih+fPn6+mnn/bgVAAAQE3k1dD0S6dOnZIkNWjQQJKUnZ0tu92u+Ph4R02rVq10ww03KCsrS7fddpuysrLUrl07hYeHO2oSEhI0evRo7dy5U7fccouysrKc1qioGTt2rCSppKRE2dnZmjhxomO/v7+/4uPjlZWVVWWvxcXFKi4udtwvLCyUJNntdtnt9l8xBWcVa7lzTVSNWXsGc/YM5uwZvjrnsrIyWa2BCvSXAlTu7XZMCfjpvbGysrJq+T1rqge3PeuvVF5errFjx+qOO+5Q27ZtJUn5+fmyWq2qV6+eU214eLjy8/MdNT8PTBX7K/ZdqqawsFDnzp3TiRMnVFZWVmXNN998U2W/M2bM0LRp0yptX7NmjUJCQky+avMyMjLcviaqxqw9gzl7BnP2DF+c84SUJ376U75X+zCt/oXUlJubq9zcXLctW1RUZLrWpdD03XffqVmzZq489KKSk5O1Y8cObdy40a3rVpeJEycqNTXVcb+wsFBRUVHq2bOnbDab257HbrcrIyNDPXr0UGBgoNvWRWXM2jOYs2cwZ8/w1Tnn5eVpePJYRSeMUGjDSG+3Y8q54wc1sLm/WrZsqRYtWrht3Yp3isxwKTS1aNFC99xzj5KSknTfffcpODjYlWUcUlJStHz5cq1fv15NmjRxbI+IiFBJSYlOnjzpdLSpoKBAERERjppffsqt4tN1P6/55SfuCgoKZLPZVKtWLVksFlksliprKtb4paCgIAUFBVXaHhgYWC0/ONW1Lipj1p7BnD2DOXuGr83ZYrGopMQue7lU6iNXHyr96V1Ei8Xi1llfyVouTeqLL75Q+/btlZqaqoiICD3xxBMufTzfMAylpKToww8/1Nq1axUTE+O0PzY2VoGBgcrMzHRs2717t/bv36+4uDhJUlxcnL766iunT7llZGTIZrOpTZs2jpqfr1FRU7GG1WpVbGysU015ebkyMzMdNQAA4NrmUmjq2LGj5s6dq4MHD2r+/Pk6dOiQ7rzzTrVt21azZ8/WkSNHTK2TnJysf/zjH1q8eLFCQ0OVn5+v/Px8nTt3TpJUt25dJSUlKTU1VZ988omys7M1fPhwxcXF6bbbbpMk9ezZU23atNGQIUP05ZdfavXq1Zo0aZKSk5MdR4JGjRql7777ThMmTNA333yj119/Xe+//77GjRvn6CU1NVVvvfWWFi5cqK+//lqjR4/W2bNnHZ+mAwAA17ZfdUwuICBAAwYM0LJly/Tiiy9qz549evLJJxUVFaWhQ4fq0KFDl3z8G2+8oVOnTqlr165q3Lix47Z06VJHzcsvv6zf/va3GjhwoO6++25FRETogw8+cOy3WCxavny5LBaL4uLi9Mgjj2jo0KF67rnnHDUxMTFasWKFMjIy1KFDB82aNUtvv/2243IDkjRo0CC99NJLmjx5sjp27KicnBytWrWq0snhAADg2vSrPj23detWzZ8/X0uWLFHt2rX15JNPKikpSQcOHNC0adPUr1+/S75tZxjGZZ8jODhYaWlpSktLu2hNdHS0Vq5cecl1unbtqm3btl2yJiUlRSkpKZftCQAAXHtcCk2zZ8/WggULtHv3bvXp00fvvvuu+vTpI3//CweuYmJilJ6erqZNm7qzVwAAAK9xKTS98cYbeuyxx/Too4+qcePGVdaEhYXpnXfe+VXNAQAA1BQuhaZvv/32sjVWq1XDhg1zZXkAAIAax6UTwRcsWKBly5ZV2r5s2TItXLjwVzcFAABQ07gUmmbMmKGGDRtW2h4WFqbnn3/+VzcFAABQ07gUmvbv31/pQpTShU+x7d+//1c3BQAAUNO4FJrCwsK0ffv2Stu//PJLXXfddb+6KQAAgJrGpdD00EMP6Y9//KM++eQTlZWVqaysTGvXrtWYMWP04IMPurtHAAAAr3Pp03PTp0/X999/r+7duysg4MIS5eXlGjp0KOc0AQCAq5JLoclqtWrp0qWaPn26vvzyS9WqVUvt2rVTdHS0u/sDAACoEX7V16i0bNlSLVu2dFcvAAAANZZLoamsrEzp6enKzMzU4cOHVV5e7rR/7dq1bmkOAACgpnApNI0ZM0bp6elKTExU27Zt5efn5+6+AAAAahSXQtOSJUv0/vvvq0+fPu7uBwAAoEZy6ZIDVqtVLVq0cHcvAAAANZZLoelPf/qT5s6dK8Mw3N0PAABAjeTS23MbN27UJ598ov/85z+6+eabFRgY6LT/gw8+cEtzAAAANYVLoalevXr63e9+5+5eAAAAaiyXQtOCBQvc3QcAAECN5tI5TZJUWlqqjz/+WH//+991+vRpSdLBgwd15swZtzUHAABQU7h0pGnfvn3q1auX9u/fr+LiYvXo0UOhoaF68cUXVVxcrHnz5rm7TwAAAK9y6UjTmDFj1LlzZ504cUK1atVybP/d736nzMxMtzUHAABQU7h0pGnDhg3atGmTrFar0/amTZvqxx9/dEtjAAAANYlLR5rKy8tVVlZWafuBAwcUGhr6q5sCAACoaVwKTT179tScOXMc9/38/HTmzBlNmTKFr1YBAABXJZfenps1a5YSEhLUpk0bnT9/XoMHD9a3336rhg0b6r333nN3jwAAAF7nUmhq0qSJvvzySy1ZskTbt2/XmTNnlJSUpIcfftjpxHAAAICrhUuhSZICAgL0yCOPuLMXAACAGsul0PTuu+9ecv/QoUNdagYAAKCmcik0jRkzxum+3W5XUVGRrFarQkJCCE0AAOCq49Kn506cOOF0O3PmjHbv3q0777yTE8EBAMBVyeXvnvulG2+8US+88EKlo1AAAABXA7eFJunCyeEHDx5055IAAAA1gkvnNP3rX/9yum8Yhg4dOqTXXntNd9xxh1saAwAAqElcCk39+/d3uu/n56dGjRrp3nvv1axZs9zRFwAAQI3iUmgqLy93dx8AAAA1mlvPaQIAALhauXSkKTU11XTt7NmzXXkKAACAGsWl0LRt2zZt27ZNdrtdN910kyQpNzdXFotFnTp1ctT5+fm5p0sAAAAvcyk09e3bV6GhoVq4cKHq168v6cIFL4cPH6677rpLf/rTn9zaJAAAgLe5dE7TrFmzNGPGDEdgkqT69evrL3/5C5+eAwAAVyWXQlNhYaGOHDlSafuRI0d0+vTpX90UAABATeNSaPrd736n4cOH64MPPtCBAwd04MAB/e///q+SkpI0YMAAd/cIAADgdS6Fpnnz5ql3794aPHiwoqOjFR0drcGDB6tXr156/fXXTa+zfv169e3bV5GRkfLz89NHH33ktP/RRx+Vn5+f061Xr15ONcePH9fDDz8sm82mevXqKSkpSWfOnHGq2b59u+666y4FBwcrKipKM2fOrNTLsmXL1KpVKwUHB6tdu3ZauXKl+YEAAICrnkuhKSQkRK+//rqOHTvm+CTd8ePH9frrr6t27dqm1zl79qw6dOigtLS0i9b06tVLhw4dctzee+89p/0PP/ywdu7cqYyMDC1fvlzr16/XyJEjHfsLCwvVs2dPRUdHKzs7W3/72980depUvfnmm46aTZs26aGHHlJSUpK2bdum/v37q3///tqxY8cVTAUAAFzNXPr0XIWKIHP33XerVq1aMgzjii4z0Lt3b/Xu3fuSNUFBQYqIiKhy39dff61Vq1bpv//9rzp37ixJevXVV9WnTx+99NJLioyM1KJFi1RSUqL58+fLarXq5ptvVk5OjmbPnu0IV3PnzlWvXr00fvx4SdL06dOVkZGh1157TfPmzTP9egAAwNXLpSNNx44dU/fu3dWyZUv16dNHhw4dkiQlJSW5/XID69atU1hYmG666SaNHj1ax44dc+zLyspSvXr1HIFJkuLj4+Xv76/PP//cUXP33XfLarU6ahISErR7926dOHHCURMfH+/0vAkJCcrKynLrawEAAL7LpSNN48aNU2BgoPbv36/WrVs7tg8aNEipqaluu+xAr169NGDAAMXExGjv3r165pln1Lt3b2VlZclisSg/P19hYWFOjwkICFCDBg2Un58vScrPz1dMTIxTTXh4uGNf/fr1lZ+f79j285qKNapSXFys4uJix/3CwkJJkt1ul91ud/1F/0LFWu5cE1Vj1p7BnD2DOXuGr865rKxMVmugAv2lAPnG98kG/HSYp6ysrFp+z5rqwZUnWLNmjVavXq0mTZo4bb/xxhu1b98+V5as0oMPPuj4c7t27dS+fXs1b95c69atU/fu3d32PK6YMWOGpk2bVmn7mjVrFBIS4vbny8jIcPuaqBqz9gzm7BnM2TN8cc4TUp746U8XP0BQo9S/kJpyc3OVm5vrtmWLiopM17oUms6ePVtlMDh+/LiCgoJcWdKUZs2aqWHDhtqzZ4+6d++uiIgIHT582KmmtLRUx48fd5wHFRERoYKCAqeaivuXq7nYuVSSNHHiRKfv4CssLFRUVJR69uwpm83m+ov8BbvdroyMDPXo0UOBgYFuWxeVMWvPYM6ewZw9w1fnnJeXp+HJYxWdMEKhDSO93Y4p544f1MDm/mrZsqVatGjhtnUr3ikyw6XQdNddd+ndd9/V9OnTJV34jrny8nLNnDlT3bp1c2VJUw4cOKBjx46pcePGkqS4uDidPHlS2dnZio2NlSStXbtW5eXl6tKli6Pm2Wefld1ud/yFzsjI0E033eS4onlcXJwyMzM1duxYx3NlZGQoLi7uor0EBQVVGRADAwOr5QenutZFZczaM5izZzBnz/C1OVssFpWU2GUvl0pdO73Z40p/ehfRYrG4ddZXspZLoWnmzJnq3r27tm7dqpKSEk2YMEE7d+7U8ePH9dlnn5le58yZM9qzZ4/jfl5ennJyctSgQQM1aNBA06ZN08CBAxUREaG9e/dqwoQJatGihRISEiRJrVu3Vq9evTRixAjNmzdPdrtdKSkpevDBBxUZeSE5Dx48WNOmTVNSUpKeeuop7dixQ3PnztXLL7/seN4xY8bonnvu0axZs5SYmKglS5Zo69atTpclAAAA1zaX4mXbtm2Vm5urO++8U/369dPZs2c1YMAAbdu2Tc2bNze9ztatW3XLLbfolltukSSlpqbqlltu0eTJk2WxWLR9+3b9z//8j1q2bKmkpCTFxsZqw4YNTkd4Fi1apFatWql79+7q06eP7rzzTqewU7duXa1Zs0Z5eXmKjY3Vn/70J02ePNnpWk633367Fi9erDfffFMdOnTQP//5T3300Udq27atK+MBAABXoSs+0mS329WrVy/NmzdPzz777K968q5du8owjIvuX7169WXXaNCggRYvXnzJmvbt22vDhg2XrLn//vt1//33X/b5AADAtemKjzQFBgZq+/bt1dELAABAjeXS23OPPPKI3nnnHXf3AgAAUGO5dCJ4aWmp5s+fr48//lixsbGVvm9u9uzZbmkOAACgprii0PTdd9+padOm2rFjhzp16iRJlS4wdSXfPQcAAOArrig03XjjjTp06JA++eQTSRe+NuWVV16p9BUkAAAAV5srOqfpl590+89//qOzZ8+6tSEAAICa6FddBvRSlwsAAAC4mlxRaPLz86t0zhLnMAEAgGvBFZ3TZBiGHn30UccVuc+fP69Ro0ZV+vTcBx984L4OAQAAaoArCk3Dhg1zuv/II4+4tRkAAICa6opC04IFC6qrDwAAgBrtV50IDgAAcK0gNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABM8GpoWr9+vfr27avIyEj5+fnpo48+ctpvGIYmT56sxo0bq1atWoqPj9e3337rVHP8+HE9/PDDstlsqlevnpKSknTmzBmnmu3bt+uuu+5ScHCwoqKiNHPmzEq9LFu2TK1atVJwcLDatWunlStXuv31AgAA3+XV0HT27Fl16NBBaWlpVe6fOXOmXnnlFc2bN0+ff/65ateurYSEBJ0/f95R8/DDD2vnzp3KyMjQ8uXLtX79eo0cOdKxv7CwUD179lR0dLSys7P1t7/9TVOnTtWbb77pqNm0aZMeeughJSUladu2berfv7/69++vHTt2VN+LBwAAPiXAm0/eu3dv9e7du8p9hmFozpw5mjRpkvr16ydJevfddxUeHq6PPvpIDz74oL7++mutWrVK//3vf9W5c2dJ0quvvqo+ffropZdeUmRkpBYtWqSSkhLNnz9fVqtVN998s3JycjR79mxHuJo7d6569eql8ePHS5KmT5+ujIwMvfbaa5o3b54HJgEAAGo6r4amS8nLy1N+fr7i4+Md2+rWrasuXbooKytLDz74oLKyslSvXj1HYJKk+Ph4+fv76/PPP9fvfvc7ZWVl6e6775bVanXUJCQk6MUXX9SJEydUv359ZWVlKTU11en5ExISKr1d+HPFxcUqLi523C8sLJQk2e122e32X/vyHSrWcueaqBqz9gzm7BnM2TN8dc5lZWWyWgMV6C8FqNzb7ZgS8NN7Y2VlZdXye9ZUD257VjfLz8+XJIWHhzttDw8Pd+zLz89XWFiY0/6AgAA1aNDAqSYmJqbSGhX76tevr/z8/Es+T1VmzJihadOmVdq+Zs0ahYSEmHmJVyQjI8Pta6JqzNozmLNnMGfP8MU5T0h54qc/Xfx3XY1S/0Jqys3NVW5urtuWLSoqMl1bY0NTTTdx4kSno1OFhYWKiopSz549ZbPZ3PY8drtdGRkZ6tGjhwIDA922Lipj1p7BnD2DOXuGr845Ly9Pw5PHKjphhEIbRnq7HVPOHT+ogc391bJlS7Vo0cJt61a8U2RGjQ1NERERkqSCggI1btzYsb2goEAdO3Z01Bw+fNjpcaWlpTp+/Ljj8RERESooKHCqqbh/uZqK/VUJCgpSUFBQpe2BgYHV8oNTXeuiMmbtGczZM5izZ/janC0Wi0pK7LKXS6U+cvWh0p/eRbRYLG6d9ZWsVWMnFRMTo4iICGVmZjq2FRYW6vPPP1dcXJwkKS4uTidPnlR2drajZu3atSovL1eXLl0cNevXr3d6zzIjI0M33XST6tev76j5+fNU1FQ8DwAAgFdD05kzZ5STk6OcnBxJFw4X5uTkaP/+/fLz89PYsWP1l7/8Rf/617/01VdfaejQoYqMjFT//v0lSa1bt1avXr00YsQIbdmyRZ999plSUlL04IMPKjLywuHGwYMHy2q1KikpSTt37tTSpUs1d+5cp7fWxowZo1WrVmnWrFn65ptvNHXqVG3dulUpKSmeHgkAAKihvPr23NatW9WtWzfH/YogM2zYMKWnp2vChAk6e/asRo4cqZMnT+rOO+/UqlWrFBwc7HjMokWLlJKSou7du8vf318DBw7UK6+84thft25drVmzRsnJyYqNjVXDhg01efJkp2s53X777Vq8eLEmTZqkZ555RjfeeKM++ugjtW3b1gNTAAAAvsCroalr164yDOOi+/38/PTcc8/pueeeu2hNgwYNtHjx4ks+T/v27bVhw4ZL1tx///26//77L90wAAC4ZtXYc5oAAABqEkITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAkB3m4AAABfl5eXJ4vF4u02TNu3b59K7aXebsPnEJoAAHDR0aNHJUnDk8eqpMTu5W7MO3+uSAd+PKQb7L7Tc01AaAIAwEWnT5+WJDW8tZ+sdcO83I15h/fu0L4f5quslNB0JQhNAAD8SrXrh6lWwybebsO0M8fyvd2CT+JEcAAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGBCjQ5NU6dOlZ+fn9OtVatWjv3nz59XcnKyrrvuOtWpU0cDBw5UQUGB0xr79+9XYmKiQkJCFBYWpvHjx6u01PkqqOvWrVOnTp0UFBSkFi1aKD093RMvDwAA+JAaf52mm2++WR9//LHjfkDA/2t53LhxWrFihZYtW6a6desqJSVFAwYM0GeffSZJKisrU2JioiIiIrRp0yYdOnRIQ4cOVWBgoJ5//nlJFy59n5iYqFGjRmnRokXKzMzU448/rsaNGyshIcGzL/Yqc+TIERUWFnq7DdPKysq83QIAoAar8aEpICBAERERlbafOnVK77zzjhYvXqx7771XkrRgwQK1bt1amzdv1m233aY1a9Zo165d+vjjjxUeHq6OHTtq+vTpeuqppzR16lRZrVbNmzdPMTExmjVrliSpdevW2rhxo15++WVC069w5MgRPTL8cR0/XeTtVkyzWgM1IeUJHT16VI0bN/Z2OwCAGqbGh6Zvv/1WkZGRCg4OVlxcnGbMmKEbbrhB2dnZstvtio+Pd9S2atVKN9xwg7KysnTbbbcpKytL7dq1U3h4uKMmISFBo0eP1s6dO3XLLbcoKyvLaY2KmrFjx16yr+LiYhUXFzvuVxxRsdvtsrvxu3wq1iooKFBRke8EkB9++EGnzpxX5J33qXZ93/hqgZJTRyRJJ0+eVMOGDb3czdWr4u+0O39OUBlz9oyKI9QB/lKAyr3cjXmBFj8FBwcp0If6DvjphKKysrJq+T1rqge3PWs16NKli9LT03XTTTfp0KFDmjZtmu666y7t2LFD+fn5slqtqlevntNjwsPDlZ9/4fLw+fn5ToGpYn/FvkvVFBYW6ty5c6pVq1aVvc2YMUPTpk2rtH3NmjUKCQlx6fVeSnZ2ttvXrG4Tx/3e2y1coQt/D3Jzc5Wbm+vlXq5+GRkZ3m7hmsCcPWNgc39JPvTVJF0aK6nLiz/d8ZG+619ITe7+N/pKDkjU6NDUu3dvx5/bt2+vLl26KDo6Wu+///5Fw4ynTJw4UampqY77hYWFioqKUs+ePWWz2dz2PHa7XRkZGZrz9/my3dLHZ47aHMnbpS9X/n+647E/KyyqhbfbMeXU/q/1UIf6mvnqPMnPz9vtmFa/Tojeev1Vnzk6VvF3ukePHgoMDPR2O1ct5uwZe/bsUW5urv53b7lqNYj0djumHcrdps3vzfGpf6PPHT+ogc391bJlS7Vo4b6er+Tc2xodmn6pXr16atmypfbs2aMePXqopKREJ0+edDraVFBQ4DgHKiIiQlu2bHFao+LTdT+v+eUn7goKCmSz2S4ZzIKCghQUFFRpe2BgYLX8A1Vit8ta13e+EDLgSL7Ony+WvVwqrdkf0nQoOn9ekhTaoZdCw2/wcjfmnD1eoIKs/1VRUZHP/WKsrp8VOGPO1ctisUiSSn3o3zpJspcZPvdvdOlP7yJaLBa3/p2+krV8KjSdOXNGe/fu1ZAhQxQbG6vAwEBlZmZq4MCBkqTdu3dr//79iouLkyTFxcXpr3/9qw4fPqywsAtHaDIyMmSz2dSmTRtHzcqVK52eJyMjw7EGrj216jWULcw3wqkkHfF2AwBwjajRoenJJ59U3759FR0drYMHD2rKlCmyWCx66KGHVLduXSUlJSk1NVUNGjSQzWbTH/7wB8XFxem2226TJPXs2VNt2rTRkCFDNHPmTOXn52vSpElKTk52HCUaNWqUXnvtNU2YMEGPPfaY1q5dq/fff18rVqzw5ksHgGuSr12q5IcffvB2C/CgGh2aDhw4oIceekjHjh1To0aNdOedd2rz5s1q1KiRJOnll1+Wv7+/Bg4cqOLiYiUkJOj11193PN5isWj58uUaPXq04uLiVLt2bQ0bNkzPPfecoyYmJkYrVqzQuHHjNHfuXDVp0kRvv/02lxsAAA/zxUuVGGWl+vP4sSqx2+XdM23hCTU6NC1ZsuSS+4ODg5WWlqa0tLSL1kRHR1d6++2Xunbtqm3btrnUI+Bt9pIS7du3z9ttmMZFRHExhYWFOn66SI3iBqp2g/DLP6AGOLlvlySprJRLO1wLanRoAnBpxWdO6fu87zT2malVfjChJuIioric2g3Cfea8wuKTBZcvwlWD0AT4MHvxOZX7BajhbQN0XWS0t9sxpeTUYUnSrl27fOqCrTabzXFqgC/Jy8tzfMKrptu3b59K7aWXLwS8hNAEXAVC6jfymf8zP1l0SpL09LQZPnU9rAahIfrHgrd9JjgdPXpUkjQ8eaxKSnzjraPz54p04MdDuoGrmKOGIjQB8Ch7yYXrYV33m74+dT2sg5++p6+++krR0b5xRO/777+XJDW8tZ+sdX3joriH9+7Qvh/mc34QaixCEwCv8KXrYfniuWMVn+oKrFPfZ+Z85piPfJ0HrlmEJgC4DF88d4xPdQHuR2gCAJN86dwxPtUFuJ9vfOEMAACAlxGaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0PQLaWlpatq0qYKDg9WlSxdt2bLF2y0BAIAagND0M0uXLlVqaqqmTJmiL774Qh06dFBCQoIOHz7s7dYAAICXEZp+Zvbs2RoxYoSGDx+uNm3aaN68eQoJCdH8+fO93RoAAPAyQtNPSkpKlJ2drfj4eMc2f39/xcfHKysry4udAQCAmiDA2w3UFEePHlVZWZnCw8OdtoeHh+ubb76pVF9cXKzi4mLH/VOnTkmSjh8/Lrvd7ra+7Ha7ioqK5C/p7OF9UkmR29auTuePH5TVGqDzRw6o0OLtbsw5d/yQiorq6tzRH1Xo5+1uzGHOnsGcPYM5e44vzrqk8KiKrg/TqVOndOzYMbete/r0aUmSYRiXLzZgGIZh/Pjjj4YkY9OmTU7bx48fb9x6662V6qdMmWJI4saNGzdu3LhdBbcffvjhslmBI00/adiwoSwWiwoKCpy2FxQUKCIiolL9xIkTlZqa6rhfXl6u48eP67rrrpOfn/v+d6OwsFBRUVH64YcfZLPZ3LYuKmPWnsGcPYM5ewZz9pzqmrVhGDp9+rQiIyMvW0to+onValVsbKwyMzPVv39/SReCUGZmplJSUirVBwUFKSgoyGlbvXr1qq0/m83GD6SHMGvPYM6ewZw9gzl7TnXMum7duqbqCE0/k5qaqmHDhqlz58669dZbNWfOHJ09e1bDhw/3dmsAAMDLCE0/M2jQIB05ckSTJ09Wfn6+OnbsqFWrVlU6ORwAAFx7CE2/kJKSUuXbcd4SFBSkKVOmVHorEO7HrD2DOXsGc/YM5uw5NWHWfoZh5jN2AAAA1zYubgkAAGACoQkAAMAEQhMAAIAJhCYAAAATCE01QFpampo2barg4GB16dJFW7ZsuWT9smXL1KpVKwUHB6tdu3ZauXKlhzr1bVcy57feekt33XWX6tevr/r16ys+Pv6y/13w/1zp3+kKS5YskZ+fn+MCs7i0K53zyZMnlZycrMaNGysoKEgtW7bk3w8TrnTOc+bM0U033aRatWopKipK48aN0/nz5z3UrW9av369+vbtq8jISPn5+emjjz667GPWrVunTp06KSgoSC1atFB6enq198l3z3nZkiVLDKvVasyfP9/YuXOnMWLECKNevXpGQUFBlfWfffaZYbFYjJkzZxq7du0yJk2aZAQGBhpfffWVhzv3LVc658GDBxtpaWnGtm3bjK+//tp49NFHjbp16xoHDhzwcOe+50pnXSEvL8+4/vrrjbvuusvo16+fZ5r1YVc65+LiYqNz585Gnz59jI0bNxp5eXnGunXrjJycHA937luudM6LFi0ygoKCjEWLFhl5eXnG6tWrjcaNGxvjxo3zcOe+ZeXKlcazzz5rfPDBB4Yk48MPP7xk/XfffWeEhIQYqampxq5du4xXX33VsFgsxqpVq6q1T0KTl916661GcnKy435ZWZkRGRlpzJgxo8r6Bx54wEhMTHTa1qVLF+OJJ56o1j593ZXO+ZdKS0uN0NBQY+HChdXV4lXDlVmXlpYat99+u/H2228bw4YNIzSZcKVzfuONN4xmzZoZJSUlnmrxqnClc05OTjbuvfdep22pqanGHXfcUa19Xk3MhKYJEyYYN998s9O2QYMGGQkJCdXYmWHw9pwXlZSUKDs7W/Hx8Y5t/v7+io+PV1ZWVpWPycrKcqqXpISEhIvWw7U5/1JRUZHsdrsaNGhQXW1eFVyd9XPPPaewsDAlJSV5ok2f58qc//WvfykuLk7JyckKDw9X27Zt9fzzz6usrMxTbfscV+Z8++23Kzs72/EW3nfffaeVK1eqT58+Hun5WuGt34VcEdyLjh49qrKyskpf0xIeHq5vvvmmysfk5+dXWZ+fn19tffo6V+b8S0899ZQiIyMr/ZDCmSuz3rhxo9555x3l5OR4oMOrgytz/u6777R27Vo9/PDDWrlypfbs2aPf//73stvtmjJliifa9jmuzHnw4ME6evSo7rzzThmGodLSUo0aNUrPPPOMJ1q+Zlzsd2FhYaHOnTunWrVqVcvzcqQJuIwXXnhBS5Ys0Ycffqjg4GBvt3NVOX36tIYMGaK33npLDRs29HY7V7Xy8nKFhYXpzTffVGxsrAYNGqRnn31W8+bN83ZrV5V169bp+eef1+uvv64vvvhCH3zwgVasWKHp06d7uzW4AUeavKhhw4ayWCwqKChw2l5QUKCIiIgqHxMREXFF9XBtzhVeeuklvfDCC/r444/Vvn376mzzqnCls967d6++//579e3b17GtvLxckhQQEKDdu3erefPm1du0D3Ll73Tjxo0VGBgoi8Xi2Na6dWvl5+erpKREVqu1Wnv2Ra7M+c9//rOGDBmixx9/XJLUrl07nT17ViNHjtSzzz4rf3+OVbjDxX4X2my2ajvKJHGkyausVqtiY2OVmZnp2FZeXq7MzEzFxcVV+Zi4uDineknKyMi4aD1cm7MkzZw5U9OnT9eqVavUuXNnT7Tq86501q1atdJXX32lnJwcx+1//ud/1K1bN+Xk5CgqKsqT7fsMV/5O33HHHdqzZ48jlEpSbm6uGjduTGC6CFfmXFRUVCkYVQRVg696dRuv/S6s1tPMcVlLliwxgoKCjPT0dGPXrl3GyJEjjXr16hn5+fmGYRjGkCFDjKefftpR/9lnnxkBAQHGSy+9ZHz99dfGlClTuOSACVc65xdeeMGwWq3GP//5T+PQoUOO2+nTp731EnzGlc76l/j0nDlXOuf9+/cboaGhRkpKirF7925j+fLlRlhYmPGXv/zFWy/BJ1zpnKdMmWKEhoYa7733nvHdd98Za9asMZo3b2488MAD3noJPuH06dPGtm3bjG3bthmSjNmzZxvbtm0z9u3bZxiGYTz99NPGkCFDHPUVlxwYP3688fXXXxtpaWlccuBa8eqrrxo33HCDYbVajVtvvdXYvHmzY98999xjDBs2zKn+/fffN1q2bGlYrVbj5ptvNlasWOHhjn3Tlcw5OjrakFTpNmXKFM837oOu9O/0zxGazLvSOW/atMno0qWLERQUZDRr1sz461//apSWlnq4a99zJXO22+3G1KlTjebNmxvBwcFGVFSU8fvf/944ceKE5xv3IZ988kmV/+ZWzHbYsGHGPffcU+kxHTt2NKxWq9GsWTNjwYIF1d6nn2FwvBAAAOByOKcJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBACX0LVrV40dO9bbbQCoAQhNAK5affv2Va9evarct2HDBvn5+Wn79u0e7gqAryI0AbhqJSUlKSMjQwcOHKi0b8GCBercubPat2/vhc4A+CJCE4Cr1m9/+1s1atRI6enpTtvPnDmjZcuWqX///nrooYd0/fXXKyQkRO3atdN77713yTX9/Pz00UcfOW2rV6+e03P88MMPeuCBB1SvXj01aNBA/fr10/fff++eFwXAawhNAK5aAQEBGjp0qNLT0/Xzr9lctmyZysrK9Mgjjyg2NlYrVqzQjh07NHLkSA0ZMkRbtmxx+TntdrsSEhIUGhqqDRs26LPPPlOdOnXUq1cvlZSUuONlAfASQhOAq9pjjz2mvXv36tNPP3VsW7BggQYOHKjo6Gg9+eST6tixo5o1a6Y//OEP6tWrl95//32Xn2/p0qUqLy/X22+/rXbt2ql169ZasGCB9u/fr3Xr1rnhFQHwFkITgKtaq1atdPvtt2v+/PmSpD179mjDhg1KSkpSWVmZpk+frnbt2qlBgwaqU6eOVq9erf3797v8fF9++aX27Nmj0NBQ1alTR3Xq1FGDBg10/vx57d27110vC4AXBHi7AQCobklJSfrDH/6gtLQ0LViwQM2bN9c999yjF198UXPnztWcOXPUrl071a5dW2PHjr3k22h+fn5Ob/VJF96Sq3DmzBnFxsZq0aJFlR7bqFEj970oAB5HaAJw1XvggQc0ZswYLV68WO+++65Gjx4tPz8/ffbZZ+rXr58eeeQRSVJ5eblyc3PVpk2bi67VqFEjHTp0yHH/22+/VVFRkeN+p06dtHTpUoWFhclms1XfiwLgcbw9B+CqV6dOHQ0aNEgTJ07UoUOH9Oijj0qSbrzxRmVkZGjTpk36+uuv9cQTT6igoOCSa91777167bXXtG3bNm3dulWjRo1SYGCgY//DDz+shg0bql+/ftqwYYPy8vK0bt06/fGPf6zy0gcAfAehCcA1ISkpSSdOnFBCQoIiIyMlSZMmTVKnTp2UkJCgrl27KiIiQv3797/kOrNmzVJUVJTuuusuDR48WE8++aRCQkIc+0NCQrR+/XrdcMMNGjBggFq3bq2kpCSdP3+eI0+Aj/MzfvnmPAAAACrhSBMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATPj/AWhVv7EIwixPAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEzwaGhav369+vbtq+joaFksFi1dutRpu8ViueTr1VdfdYypX79+ue0vv/yy0zw7duxQ586dFRwcrJiYGE2bNq1cLUuWLFHTpk0VHBys+Ph4LV++vEqOGQAA+CaPhqbz58+rdevWmjNnziW35+TkOL3mzZsni8Wi/v37O42bPHmy07gnnnjCsS0/P1+JiYmKjY3Vtm3b9Oqrr2rixIl65513HGM2bdqkgQMHKjk5Wdu3b1e/fv3Ur18/7dy5s2oOHAAA+JwAT+68V69e6tWr12W3R0VFOS3/85//VNeuXdWgQQOn9TVr1iw3tsyCBQtUVFSkefPmyWq1qkWLFsrKytKMGTM0bNgwSdKsWbPUs2dPjRkzRpI0ZcoU2Ww2zZ49W3Pnzv01hwgAAK4RHg1NFZGXl6dly5Zp/vz55ba9/PLLmjJlim688UYNGjRIo0ePVkDAxUPLzMzU7bffLqvV6hiflJSkV155RadOnVJYWJgyMzOVmprqNGdSUlK5y4U/V1hYqMLCQsdyfn6+JMlut8tut/+aQy2npKREkhTgJwWo1KVzV5VAP8lqDVRJSYnL+1FVyur0lXp9FX12D/rsHvTZfaqq1xWZz2dC0/z581WzZk3dc889TuuffPJJtWvXTuHh4dq0aZPGjh2rnJwczZgxQ5KUm5uruLg4p/dERkY6toWFhSk3N9ex7udjcnNzL1vP1KlTNWnSpHLrMzIyFBISUqljvJr+Df0kXb4mrxLmJzV+XHv27NGePXs8XU2F2Gw2T5fwm0Cf3YM+uwd9dh9X97qgoMD0WJ8JTfPmzdMDDzyg4OBgp/U/P0PUqlUrWa1WPf7445o6daqCgoKqrJ6xY8c67Ts/P18xMTFKTExUaGioS/d14MAB7du3T/84WKpq4dEunbuqnD1xVEdWvqu0OTPLhVZvZbfbZbPZ1KNHDwUGBnq6nGsWfXYP+uwe9Nl9qqrXZVeKzPCJ0LRhwwbt3btXixcvvurYTp06qbi4WN9++62aNGmiqKgo5eXlOY0pWy67D+pyYy53n5QkBQUFXTKUBQYGuvwLx9/fX5JUXCoV+8hTIuylUlGRXf7+/j73jaQq/g5RHn12D/rsHvTZfVzd64rM5RM/gd9//321b99erVu3vurYrKws+fn5KSIiQpKUkJCg9evXO12ztNlsatKkicLCwhxjVq9e7TSPzWZTQkKCC48CAAD4Mo+GpnPnzikrK0tZWVmSpMOHDysrK0vZ2dmOMfn5+VqyZIkeffTRcu/PzMzUzJkz9dVXX+nQoUNasGCBRo8erQcffNARiAYNGiSr1ark5GTt2rVLixcv1qxZs5wurT311FNasWKFpk+frm+++UYTJ07U1q1blZKSUrUNAAAAPsOjl+e2bt2qrl27OpbLgszQoUOVnp4uSVq0aJEMw9DAgQPLvT8oKEiLFi3SxIkTVVhYqLi4OI0ePdopENWqVUsZGRkaOXKk2rdvrzp16mj8+PGOxw1I0i233KKFCxdq3Lhxev7559W4cWMtXbpULVu2rKIjBwAAvsajoalLly4yDOOKY4YNG+YUcH6uXbt22rx581X306pVK23YsOGKYwYMGKABAwZcdS4AAPDb5BP3NAEAAHgaoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABggkdD0/r169W3b19FR0fLYrFo6dKlTtsfeughWSwWp1fPnj2dxpw8eVIPPPCAQkNDVbt2bSUnJ+vcuXNOY3bs2KHOnTsrODhYMTExmjZtWrlalixZoqZNmyo4OFjx8fFavny5y48XAAD4Lo+GpvPnz6t169aaM2fOZcf07NlTOTk5jtcHH3zgtP2BBx7Qrl27ZLPZ9Omnn2r9+vUaNmyYY3t+fr4SExMVGxurbdu26dVXX9XEiRP1zjvvOMZs2rRJAwcOVHJysrZv365+/fqpX79+2rlzp+sPGgAA+KQAT+68V69e6tWr1xXHBAUFKSoq6pLb9uzZoxUrVujf//63OnToIEl688031bt3b7322muKjo7WggULVFRUpHnz5slqtapFixbKysrSjBkzHOFq1qxZ6tmzp8aMGSNJmjJlimw2m2bPnq25c+e68IgBAICv8mhoMmPdunWKiIhQWFiY7rzzTv35z3/WddddJ0nKzMxU7dq1HYFJkrp37y4/Pz998cUXuvvuu5WZmanbb79dVqvVMSYpKUmvvPKKTp06pbCwMGVmZio1NdVpv0lJSeUuF/5cYWGhCgsLHcv5+fmSJLvdLrvd7opDdygpKZEkBfhJASp16dxVJdBPsloDVVJS4vJ+VJWyOn2lXl9Fn92DPrsHfXafqup1Rebz6tDUs2dP3XPPPYqLi9PBgwf1/PPPq1evXsrMzJS/v79yc3MVERHh9J6AgACFh4crNzdXkpSbm6u4uDinMZGRkY5tYWFhys3Ndaz7+ZiyOS5l6tSpmjRpUrn1GRkZCgkJqdTxXk3/hn6SLl+TVwnzkxo/rj179mjPnj2erqZCbDabp0v4TaDP7kGf3YM+u4+re11QUGB6rFeHpvvvv9/x5/j4eLVq1UoNGzbUunXr1K1bNw9WJo0dO9bp7FR+fr5iYmKUmJio0NBQl+7rwIED2rdvn/5xsFTVwqNdOndVOXviqI6sfFdpc2aWC63eym63y2azqUePHgoMDPR0Odcs+uwe9Nk96LP7VFWvy64UmeHVoemXGjRooDp16ujAgQPq1q2boqKidOzYMacxxcXFOnnypOM+qKioKOXl5TmNKVu+2pjL3UslXbzXKigoqNz6wMBAl3/h+Pv7S5KKS6ViH3lKhL1UKiqyy9/f3+e+kVTF3yHKo8/uQZ/dgz67j6t7XZG5fOMn8H98//33+vHHH1WvXj1JUkJCgk6fPq1t27Y5xqxZs0alpaXq1KmTY8z69eudrlnabDY1adJEYWFhjjGrV6922pfNZlNCQkJVHxIAAPARHg1N586dU1ZWlrKysiRJhw8fVlZWlrKzs3Xu3DmNGTNGmzdv1rfffqvVq1frrrvuUqNGjZSUlCRJatasmXr27KnHHntMW7Zs0eeff66UlBTdf//9io6+eBlr0KBBslqtSk5O1q5du7R48WLNmjXL6dLaU089pRUrVmj69On65ptvNHHiRG3dulUpKSlu7wkAAPBOHg1NW7duVdu2bdW2bVtJUmpqqtq2bavx48fL399fO3bs0B/+8AfddNNNSk5OVvv27bVhwwany2ILFixQ06ZN1a1bN/Xu3Vu33Xab0zOYatWqpYyMDB0+fFjt27fX008/rfHjxzs9y+mWW27RwoUL9c4776h169b6+9//rqVLl6ply5buawYAAPBqHr2nqUuXLjIM47LbV65cedU5wsPDtXDhwiuOadWqlTZs2HDFMQMGDNCAAQOuuj8AAPDb5FP3NAEAAHgKoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwwaOhaf369erbt6+io6NlsVi0dOlSxza73a7nnntO8fHxql69uqKjozVkyBAdPXrUaY769evLYrE4vV5++WWnMTt27FDnzp0VHBysmJgYTZs2rVwtS5YsUdOmTRUcHKz4+HgtX768So4ZAAD4Jo+GpvPnz6t169aaM2dOuW0FBQX68ssv9eKLL+rLL7/URx99pL179+oPf/hDubGTJ09WTk6O4/XEE084tuXn5ysxMVGxsbHatm2bXn31VU2cOFHvvPOOY8ymTZs0cOBAJScna/v27erXr5/69eunnTt3Vs2BAwAAnxPgyZ336tVLvXr1uuS2WrVqyWazOa2bPXu2OnbsqOzsbN14442O9TVr1lRUVNQl51mwYIGKioo0b948Wa1WtWjRQllZWZoxY4aGDRsmSZo1a5Z69uypMWPGSJKmTJkim82m2bNna+7cua44VAAA4ON86p6mM2fOyGKxqHbt2k7rX375ZV133XVq27atXn31VRUXFzu2ZWZm6vbbb5fVanWsS0pK0t69e3Xq1CnHmO7duzvNmZSUpMzMzKo7GAAA4FM8eqapIi5cuKDnnntOAwcOVGhoqGP9k08+qXbt2ik8PFybNm3S2LFjlZOToxkzZkiScnNzFRcX5zRXZGSkY1tYWJhyc3Md634+Jjc397L1FBYWqrCw0LGcn58v6eK9WHa7/dcd7C+UlJRIkgL8pACVunTuqhLoJ1mtgSopKXF5P6pKWZ2+Uq+vos/uQZ/dgz67T1X1uiLzVSo0HTp0SA0aNKjMWyvFbrfr3nvvlWEYevvtt522paamOv7cqlUrWa1WPf7445o6daqCgoKqrKapU6dq0qRJ5dZnZGQoJCSkSvbZv6GfpMsHOa8S5ic1flx79uzRnj17PF1NhfzysjCqBn12D/rsHvTZfVzd64KCAtNjKxWaGjVqpDvuuEPJycn64x//qODg4MpMY0pZYDpy5IjWrFnjdJbpUjp16qTi4mJ9++23atKkiaKiopSXl+c0pmy57D6oy4253H1SkjR27FinwJafn6+YmBglJiZetcaKOnDggPbt26d/HCxVtfBol85dVc6eOKojK99V2pyZ5c70eSu73S6bzaYePXooMDDQ0+Vcs+ize9Bn96DP7lNVvS67UmRGpULTl19+qbS0NKWmpiolJUX33XefkpOT1bFjx8pMd1llgWn//v1au3atrrvuuqu+JysrS35+foqIiJAkJSQk6IUXXpDdbnc02WazqUmTJgoLC3OMWb16tUaNGuWYx2azKSEh4bL7CQoKuuSZrMDAQJd/4fj7+0uSikulYh+5Dc1eKhUV2eXv7+9z30iq4u8Q5dFn96DP7kGf3cfVva7IXJX6CdymTRvNmjVLR48e1bx585STk6PbbrtNLVu21IwZM3T8+HFT85w7d05ZWVnKysqSJB0+fFhZWVnKzs6W3W7XH//4R23dulULFixQSUmJcnNzlZubq6KiIkkXb+CeOXOmvvrqKx06dEgLFizQ6NGj9eCDDzoC0aBBg2S1WpWcnKxdu3Zp8eLFmjVrltNZoqeeekorVqzQ9OnT9c0332jixInaunWrUlJSKtMeAABwDfpVpy0CAgJ0zz33aMmSJXrllVd04MABPfPMM4qJidGQIUOUk5Nzxfdv3bpVbdu2Vdu2bSVdvD+pbdu2Gj9+vH744Qd98skn+v7779WmTRvVq1fP8dq0aZOki2d7Fi1apDvuuEMtWrTQX/7yF40ePdrpGUy1atVSRkaGDh8+rPbt2+vpp5/W+PHjHY8bkKRbbrlFCxcu1DvvvKPWrVvr73//u5YuXaqWLVv+mvYAAIBryK/67bmtW7dq3rx5WrRokapXr65nnnlGycnJ+v777zVp0iTddddd2rJly2Xf36VLFxmGcdntV9omSe3atdPmzZuvWmerVq20YcOGK44ZMGCABgwYcNW5AADAb1OlQtOMGTOUlpamvXv3qnfv3vrrX/+q3r17y8/v4omruLg4paenq379+q6sFQAAwGMqFZrefvttPfLII3rooYdUr169S46JiIjQ+++//6uKAwAA8BaVCk379++/6hir1aqhQ4dWZnoAAACvU6kbwdPS0rRkyZJy65csWaL58+f/6qIAAAC8TaVC09SpU1WnTp1y6yMiIvTSSy/96qIAAAC8TaVCU3Z29iWf8hwbG6vs7OxfXRQAAIC3qVRoioiI0I4dO8qt/+qrr0w9tRsAAMDXVCo0DRw4UE8++aTWrl2rkpISlZSUaM2aNXrqqad0//33u7pGAAAAj6vUb89NmTJF3377rbp166aAgItTlJaWasiQIdzTBAAArkmVCk1Wq1WLFy/WlClT9NVXX6latWqKj49XbGysq+sDAADwCr/qY1Ruuukm3XTTTa6qBQAAwGtVKjSVlJQoPT1dq1ev1rFjx1RaWuq0fc2aNS4pDgAAwFtUKjQ99dRTSk9PV58+fdSyZUtZLBZX1wUAAOBVKhWaFi1apA8//FC9e/d2dT0AAABeqVKPHLBarWrUqJGrawEAAPBalQpNTz/9tGbNmiXDMFxdDwAAgFeq1OW5jRs3au3atfq///s/tWjRQoGBgU7bP/roI5cUBwAA4C0qFZpq166tu+++29W1AAAAeK1Khaa0tDRX1wEAAODVKnVPkyQVFxdr1apV+t///V+dPXtWknT06FGdO3fOZcUBAAB4i0qdaTpy5Ih69uyp7OxsFRYWqkePHqpZs6ZeeeUVFRYWau7cua6uEwAAwKMqdabpqaeeUocOHXTq1ClVq1bNsf7uu+/W6tWrXVYcAACAt6jUmaYNGzZo06ZNslqtTuvr16+vH374wSWFAQAAeJNKnWkqLS1VSUlJufXff/+9atas+auLAgAA8DaVCk2JiYmaOXOmY9lisejcuXOaMGECH60CAACuSZW6PDd9+nQlJSWpefPmunDhggYNGqT9+/erTp06+uCDD1xdIwAAgMdVKjTdcMMN+uqrr7Ro0SLt2LFD586dU3Jysh544AGnG8MBAACuFZUKTZIUEBCgBx980JW1AAAAeK1Khaa//vWvV9w+ZMiQShUDAADgrSoVmp566imnZbvdroKCAlmtVoWEhBCaAADANadSvz136tQpp9e5c+e0d+9e3XbbbdwIDgAArkmV/uy5X2rcuLFefvnlcmehAAAArgUuC03SxZvDjx496sopAQAAvEKlQtMnn3zi9PrnP/+puXPn6sEHH9Stt95qep7169erb9++io6OlsVi0dKlS522G4ah8ePHq169eqpWrZq6d++u/fv3O405efKkHnjgAYWGhqp27dpKTk7WuXPnnMbs2LFDnTt3VnBwsGJiYjRt2rRytSxZskRNmzZVcHCw4uPjtXz5cvMNAQAA17xK3Qjer18/p2WLxaK6devqzjvv1PTp003Pc/78ebVu3VqPPPKI7rnnnnLbp02bpjfeeEPz589XXFycXnzxRSUlJWn37t0KDg6WJD3wwAPKycmRzWaT3W7Xww8/rGHDhmnhwoWSpPz8fCUmJqp79+6aO3euvv76az3yyCOqXbu2hg0bJknatGmTBg4cqKlTp+r3v/+9Fi5cqH79+unLL79Uy5YtK9MiAABwjalUaCotLXXJznv16qVevXpdcpthGJo5c6bGjRunu+66S9LFRx1ERkZq6dKluv/++7Vnzx6tWLFC//73v9WhQwdJ0ptvvqnevXvrtddeU3R0tBYsWKCioiLNmzdPVqtVLVq0UFZWlmbMmOEITbNmzVLPnj01ZswYSdKUKVNks9k0e/ZszZ071yXHCgAAfFulH25Z1Q4fPqzc3Fx1797dsa5WrVrq1KmTMjMzdf/99yszM1O1a9d2BCZJ6t69u/z8/PTFF1/o7rvvVmZmpm6//XZZrVbHmKSkJL3yyis6deqUwsLClJmZqdTUVKf9JyUllbtc+HOFhYUqLCx0LOfn50u6+PgFu93+aw/fSdmHIwf4SQFyTWCtaoF+ktUaqJKSEpf3o6qU1ekr9foq+uwe9Nk96LP7VFWvKzJfpULTLwPGlcyYMaMyu1Bubq4kKTIy0ml9ZGSkY1tubq4iIiKctgcEBCg8PNxpTFxcXLk5yraFhYUpNzf3ivu5lKlTp2rSpEnl1mdkZCgkJMTMIVZY/4Z+ki5fk1cJ85MaP649e/Zoz549nq6mQmw2m6dL+E2gz+5Bn92DPruPq3tdUFBgemylQtP27du1fft22e12NWnSRJK0b98++fv7q127do5xFoulMtP7hLFjxzqFx/z8fMXExCgxMVGhoaEu3deBAwe0b98+/eNgqaqFR7t07qpy9sRRHVn5rtLmzCwXWr2V3W6XzWZTjx49FBgY6Olyrln02T3os3vQZ/epql6XXSkyo1KhqW/fvqpZs6bmz5+vsLAwSRcfePnwww+rc+fOevrppyszrZOoqChJUl5enurVq+dYn5eXpzZt2jjGHDt2zOl9xcXFOnnypOP9UVFRysvLcxpTtny1MWXbLyUoKEhBQUHl1gcGBrr8C8ff31+SVFwqFbv2KRFVxl4qFRXZ5e/v73PfSKri7xDl0Wf3oM/uQZ/dx9W9rshclfoJPH36dE2dOtURmCQpLCxMf/7znyv023NXEhcXp6ioKK1evdqxLj8/X1988YUSEhIkSQkJCTp9+rS2bdvmGLNmzRqVlpaqU6dOjjHr1693umZps9nUpEkTR/0JCQlO+ykbU7YfAACASoWm/Px8HT9+vNz648eP6+zZs6bnOXfunLKyspSVlSXp4s3fWVlZys7OlsVi0ahRo/TnP/9Zn3zyib7++msNGTJE0dHRjkceNGvWTD179tRjjz2mLVu26PPPP1dKSoruv/9+RUdfvIw1aNAgWa1WJScna9euXVq8eLFmzZrldGntqaee0ooVKzR9+nR98803mjhxorZu3aqUlJTKtAcAAFyDKnV57u6779bDDz+s6dOnq2PHjpKkL774QmPGjLnk85YuZ+vWreratatjuSzIDB06VOnp6Xr22Wd1/vx5DRs2TKdPn9Ztt92mFStWOJ7RJEkLFixQSkqKunXrJj8/P/Xv319vvPGGY3utWrWUkZGhkSNHqn379qpTp47Gjx/veNyAJN1yyy1auHChxo0bp+eff16NGzfW0qVLeUYTAABwqFRomjt3rp555hkNGjTIcdkrICBAycnJevXVV03P06VLFxmGcdntFotFkydP1uTJky87Jjw83PEgy8tp1aqVNmzYcMUxAwYM0IABA65cMAAA+M2qVGgKCQnRW2+9pVdffVUHDx6UJDVs2FDVq1d3aXEAAADe4lf9KlZOTo5ycnLUuHFjVa9e/YpnjQAAAHxZpULTjz/+qG7duummm25S7969lZOTI0lKTk52yeMGAAAAvE2lQtPo0aMVGBio7Oxsp6df33fffVqxYoXLigMAAPAWlbqnKSMjQytXrtQNN9zgtL5x48Y6cuSISwoDAADwJpU603T+/PlLfr7ayZMnL/mUbAAAAF9XqdDUuXNn/fWvf3UsWywWlZaWatq0aU7PXQIAALhWVOry3LRp09StWzdt3bpVRUVFevbZZ7Vr1y6dPHlSn3/+uatrBAAA8LhKnWlq2bKl9u3bp9tuu0133XWXzp8/r3vuuUfbt29Xw4YNXV0jAACAx1X4TJPdblfPnj01d+5cvfDCC1VREwAAgNep8JmmwMBA7dixoypqAQAA8FqVujz34IMP6v3333d1LQAAAF6rUjeCFxcXa968eVq1apXat29f7jPnZsyY4ZLiAAAAvEWFQtOhQ4dUv3597dy5U+3atZMk7du3z2mMxWJxXXUAAABeokKhqXHjxsrJydHatWslXfzYlDfeeEORkZFVUhwAAIC3qNA9TYZhOC3/3//9n86fP+/SggAAALxRpW4EL/PLEAUAAHCtqlBoslgs5e5Z4h4mAADwW1Che5oMw9BDDz3k+FDeCxcuaPjw4eV+e+6jjz5yXYUAAABeoEKhaejQoU7LDz74oEuLAQAA8FYVCk1paWlVVQcAAIBX+1U3ggMAAPxWEJoAAABMIDQBAACYUKnPngPMsBcV6ciRI54uw7SSkhJPlwAA8GKEJlSJwnNn9O3hQxr1/ETHIyq8ndUaqGdTHteJEydUr149T5cDAPAyhCZUCXvhTyq1BKjOzffouuhYT5djStGZY5Kks2fPEpoAAOUQmlClQsLqKjTiBk+XYcpP3OEHALgCfkwAAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACV4fmurXry+LxVLuNXLkSElSly5dym0bPny40xzZ2dnq06ePQkJCFBERoTFjxqi4uNhpzLp169SuXTsFBQWpUaNGSk9Pd9chAgAAH+D1jxz497//7fSk5p07d6pHjx4aMGCAY91jjz2myZMnO5ZDQkIcfy4pKVGfPn0UFRWlTZs2KScnR0OGDFFgYKBeeuklSdLhw4fVp08fDR8+XAsWLNDq1av16KOPql69ekpKSnLDUQIAAG/n9aGpbt26Tssvv/yyGjZsqDvuuMOxLiQkRFFRUZd8f0ZGhnbv3q1Vq1YpMjJSbdq00ZQpU/Tcc89p4sSJslqtmjt3ruLi4jR9+nRJUrNmzbRx40a9/vrrhCYAACDJB0LTzxUVFelvf/ubUlNTZbFYHOsXLFigv/3tb4qKilLfvn314osvOs42ZWZmKj4+XpGRkY7xSUlJGjFihHbt2qW2bdsqMzNT3bt3d9pXUlKSRo0addlaCgsLVVhY6FjOz8+XJNntdtntdlccrkPZmbYAPylApS6du6oE+lsUHBykQB+qOeA/F6tLSkpc/neI/yrrLT2uWvTZPeiz+1RVrysyn0+FpqVLl+r06dN66KGHHOsGDRqk2NhYRUdHa8eOHXruuee0d+9effTRR5Kk3Nxcp8AkybGcm5t7xTH5+fn66aefVK1atXK1TJ06VZMmTSq3PiMjw+nyoCv1b+gnKbdK5na5TvWU3OmV/yz4SM1hF1PTvn37tG/fPg8Xc+2z2WyeLuE3gT67B312H1f3uqCgwPRYnwpN77//vnr16qXo6GjHumHDhjn+HB8fr3r16qlbt246ePCgGjZsWGW1jB07VqmpqY7l/Px8xcTEKDExUaGhoS7d14EDB7Rv3z7942CpqoVHX/0NXiBn33Zt/mCmbn3kRUXENPJ0Oab8dPKo+jf000033aRGjXyjZl9kt9tls9nUo0cPBQYGerqcaxZ9dg/67D5V1euyK0Vm+ExoOnLkiFatWuU4g3Q5nTp1knQxaDRs2FBRUVHasmWL05i8vDxJctwHFRUV5Vj38zGhoaGXPMskSUFBQQoKCiq3PjAw0OVfOP7+/pKk4lKp2Pt/4VGSZC8xdOFCoew+VHPxf64i+vv7883PDariawXl0Wf3oM/u4+peV2Qu3/hpJiktLU0RERHq06fPFcdlZWVJkuNT6hMSEvT111/r2LFjjjE2m02hoaFq3ry5Y8zq1aud5rHZbEpISHDhEQAAAF/mE6GptLRUaWlpGjp0qAIC/nty7ODBg5oyZYq2bdumb7/9Vp988omGDBmi22+/Xa1atZIkJSYmqnnz5ho8eLC++uorrVy5UuPGjdPIkSMdZ4qGDx+uQ4cO6dlnn9U333yjt956Sx9++KFGjx7tkeMFAADexydC06pVq5Sdna1HHnnEab3VatWqVauUmJiopk2b6umnn1b//v31r3/9yzHG399fn376qfz9/ZWQkKAHH3xQQ4YMcXquU1xcnJYtWyabzabWrVtr+vTpeu+993jcAAAAcPCJe5oSExNlGEa59TExMfrss8+u+v7Y2FgtX778imO6dOmi7du3V7pGAABwbfOJM00AAACeRmgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATPDq0DRx4kRZLBanV9OmTR3bL1y4oJEjR+q6665TjRo11L9/f+Xl5TnNkZ2drT59+igkJEQREREaM2aMiouLncasW7dO7dq1U1BQkBo1aqT09HR3HB4AAPAhXh2aJKlFixbKyclxvDZu3OjYNnr0aP3rX//SkiVL9Nlnn+no0aO65557HNtLSkrUp08fFRUVadOmTZo/f77S09M1fvx4x5jDhw+rT58+6tq1q7KysjRq1Cg9+uijWrlypVuPEwAAeLcATxdwNQEBAYqKiiq3/syZM3r//fe1cOFC3XnnnZKktLQ0NWvWTJs3b9bNN9+sjIwM7d69W6tWrVJkZKTatGmjKVOm6LnnntPEiRNltVo1d+5cxcXFafr06ZKkZs2aaePGjXr99deVlJTk1mMFAADey+vPNO3fv1/R0dFq0KCBHnjgAWVnZ0uStm3bJrvdru7duzvGNm3aVDfeeKMyMzMlSZmZmYqPj1dkZKRjTFJSkvLz87Vr1y7HmJ/PUTambA4AAADJy880derUSenp6WrSpIlycnI0adIkde7cWTt37lRubq6sVqtq167t9J7IyEjl5uZKknJzc50CU9n2sm1XGpOfn6+ffvpJ1apVu2RthYWFKiwsdCzn5+dLkux2u+x2e+UP+hJKSkokSQF+UoBKXTp3VQn0tyg4OEiBPlRzwH/+F6KkpMTlf4f4r7Le0uOqRZ/dgz67T1X1uiLzeXVo6tWrl+PPrVq1UqdOnRQbG6sPP/zwsmHGXaZOnapJkyaVW5+RkaGQkJAq2Wf/hn6ScqtkbpfrVE/JnV75z4KP1Bx2MTXt27dP+/bt83Ax1z6bzebpEn4T6LN70Gf3cXWvCwoKTI/16tD0S7Vr19ZNN92kAwcOqEePHioqKtLp06edzjbl5eU57oGKiorSli1bnOYo++26n4/55W/c5eXlKTQ09IrBbOzYsUpNTXUs5+fnKyYmRomJiQoNDf1Vx/lLBw4c0L59+/SPg6WqFh7t0rmrSs6+7dr8wUzd+siLiohp5OlyTDmTvUcDW4dp2ptzJYvF0+WYFlYjRO++9abq1Knj6VJMsdvtstls6tGjhwIDAz1dzjWLPrsHfXafqup12ZUiM3wqNJ07d04HDx7U4MGD1b59ewUGBmr16tXq37+/JGnv3r3Kzs5WQkKCJCkhIUF/+ctfdOzYMUVEREi6mFBDQ0PVvHlzx5jly5c77cdmsznmuJygoCAFBQWVWx8YGOjyLxx/f39JUnGpVOz9t6FJkuwlhi5cKJTdh2ouuHBBklSzdU/VjLzRw9WYc/5knvIy/6GCggKf+4ZdFV8rKI8+uwd9dh9X97oic3l1aHrmmWfUt29fxcbG6ujRo5owYYL8/f01cOBA1apVS8nJyUpNTVV4eLhCQ0P1xBNPKCEhQTfffLMkKTExUc2bN9fgwYM1bdo05ebmaty4cRo5cqQj8AwfPlyzZ8/Ws88+q0ceeURr1qzRhx9+qGXLlnny0OFB1WrXUWjEDZ4uw7Tjni4AAH4jvDo0ff/99xo4cKB+/PFH1a1bV7fddps2b96sunXrSpJef/11+fn5qX///iosLFRSUpLeeustx/v9/f316aefasSIEUpISFD16tU1dOhQTZ482TEmLi5Oy5Yt0+jRozVr1izdcMMNeu+993jcAAAAcOLVoWnRokVX3B4cHKw5c+Zozpw5lx0TGxtb7vLbL3Xp0kXbt2+vVI0AAOC3wTduNgEAAPAwQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYEKApwsA8OvYi4p05MgRT5dhWklJiadLAIBKITQBPqzw3Bl9e/iQRj0/UUFBQZ4uxxSrNVDPpjyuEydOqF69ep4uBwBMIzQBPsxe+JNKLQGqc/M9ui461tPlmFJ05pgk6ezZs4QmAD6F0ARcA0LC6io04gZPl2HKT9xJCcBH8e0LAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEzw6tA0depU/e53v1PNmjUVERGhfv36ae/evU5junTpIovF4vQaPny405js7Gz16dNHISEhioiI0JgxY1RcXOw0Zt26dWrXrp2CgoLUqFEjpaenV/XhAQAAH+LVoemzzz7TyJEjtXnzZtlsNtntdiUmJur8+fNO4x577DHl5OQ4XtOmTXNsKykpUZ8+fVRUVKRNmzZp/vz5Sk9P1/jx4x1jDh8+rD59+qhr167KysrSqFGj9Oijj2rlypVuO1YAAODdAjxdwJWsWLHCaTk9PV0RERHatm2bbr/9dsf6kJAQRUVFXXKOjIwM7d69W6tWrVJkZKTatGmjKVOm6LnnntPEiRNltVo1d+5cxcXFafr06ZKkZs2aaePGjXr99deVlJRUdQcI/IZ999138vf393QZpoWGhqpu3bqeLgOAB3l1aPqlM2fOSJLCw8Od1i9YsEB/+9vfFBUVpb59++rFF19USEiIJCkzM1Px8fGKjIx0jE9KStKIESO0a9cutW3bVpmZmerevbvTnElJSRo1atRlayksLFRhYaFjOT8/X5Jkt9tlt9t/1XH+UklJiSQpwE8KUKlL564qgf4WBQcHKdDHapbkczX7Wp9LC85ICtP4qa9JFounyzEtrEaI3n3rTdWpU8fTpZhS9n3I1d+P4Iw+u09V9boi81kMwzBcuvcqUlpaqj/84Q86ffq0Nm7c6Fj/zjvvKDY2VtHR0dqxY4eee+45dezYUR999JEkadiwYTpy5IjTpbaCggJVr15dy5cvV69evXTTTTfp4Ycf1tixYx1jli9frj59+qigoEDVqlUrV8/EiRM1adKkcusXLlzoCGwAAMC7FRQUaNCgQTpz5oxCQ0OvONZnzjSNHDlSO3fudApM0sVQVCY+Pl716tVTt27ddPDgQTVs2LDK6hk7dqxSU1Mdy/n5+YqJiVFiYuJVm15RBw4c0L59+/SPg6WqFh7t0rmrSs6+7dr8wUzd+siLiohp5OlyTDlxMEtDOkRpwc5zCrveN2qmz+5x9sRRHVn5rtLmzFRcXJynyzHFbrfLZrOpR48eCgwM9HQ51yz67D5V1euyK0Vm+ERoSklJ0aeffqr169frhhtuuOLYTp06SboYNBo2bKioqCht2bLFaUxeXp4kOe6DioqKcqz7+ZjQ0NBLnmWSpKCgIAUFBZVbHxgY6PIvnLL7PopLpWLvvnffwV5i6MKFQtl9rGZJPlczfa569lKpqMguf39/n/vBWBXfk1AefXYfV/e6InN5dWgyDENPPPGEPv74Y61bt87U/+FlZWVJkurVqydJSkhI0F/+8hcdO3ZMERERkiSbzabQ0FA1b97cMWb58uVO89hsNiUkJLjwaAAA8B7Hjx+v0FkWTyu7v9eTvDo0jRw5UgsXLtQ///lP1axZU7m5uZKkWrVqqVq1ajp48KAWLlyo3r1767rrrtOOHTs0evRo3X777WrVqpUkKTExUc2bN9fgwYM1bdo05ebmaty4cRo5cqTjTNHw4cM1e/ZsPfvss3rkkUe0Zs0affjhh1q2bJnHjh0AgKpy/PhxPfjwozp5tsDTpZhmtQbq2ZTHdeLECceJEXfz6tD09ttvS7r4AMufS0tL00MPPSSr1apVq1Zp5syZOn/+vGJiYtS/f3+NGzfOMdbf31+ffvqpRowYoYSEBFWvXl1Dhw7V5MmTHWPi4uK0bNkyjR49WrNmzdINN9yg9957j8cNAACuSfn5+Tp5tkB1E/qrenjk1d/gBYrOHJMknT17ltB0KVf7xb6YmBh99tlnV50nNja23OW3X+rSpYu2b99eofoAAPBl1cMjFRpx5XuFvcVPXnALpFeHJgDwFvaiIh05csTTZZjmDfd/ANcaQhMAXEXhuTP69vAhjXp+4iV/a9YbecP9H8C1htAEAFdhL/xJpZYA1bn5Hl0XHevpckzxhvs/gGsNoQkATAoJq8v9H8BvGKEJAK5hfDAy4DqEJgC4BhWeu/jByH+aNNWnPhg5vGaI/pb2HsEJXonQBADXIHvRBUnSdb/rq5qRN3q4GnPOn8zT8cx/KD8/n9AEr0RoAoBrWLXadXzmPixJOu7pAoArIDQBALwGz8OCNyM0AQC8As/DgrcjNAEAvALPw4K3IzQBALyKLz4Py9ce7XDkyBEV24s9XYbPITQBAFBJvvpohws/Fej7H3J0o93u6VJ8CqEJAIBK8sVHO0jSsYM7deS7eSopJjRVBKEJAIBfydce7XDux1xPl+CT+HQiAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE2/MGfOHNWvX1/BwcHq1KmTtmzZ4umSAACAFyA0/czixYuVmpqqCRMm6Msvv1Tr1q2VlJSkY8eOebo0AADgYYSmn5kxY4Yee+wxPfzww2revLnmzp2rkJAQzZs3z9OlAQAADyM0/UdRUZG2bdum7t27O9b5+fmpe/fuyszM9GBlAADAGwR4ugBvceLECZWUlCgyMtJpfWRkpL755pty4wsLC1VYWOhYPnPmjCTp5MmTstvtLq3tzJkzKigo0Pljx2S/UODSuavKhZNHZbUG6MLx75Xv7+lqzPnpZI4KCmrppxM/KN/i6WrMoc/uQZ/dgz67jy/2uij/hAquj9CZM2f0448/umzes2fPSpIMw7j6YAOGYRjGDz/8YEgyNm3a5LR+zJgxRseOHcuNnzBhgiGJFy9evHjx4nUNvL777rurZgXONP1HnTp15O/vr7y8PKf1eXl5ioqKKjd+7NixSk1NdSyXlpbq5MmTuu6662SxuPZ/N/Lz8xUTE6PvvvtOoaGhLp0b/0Wf3YM+uwd9dg/67D5V1WvDMHT27FlFR0dfdSyh6T+sVqvat2+v1atXq1+/fpIuBqHVq1crJSWl3PigoCAFBQU5ratdu3aV1hgaGsoXpRvQZ/egz+5Bn92DPrtPVfS6Vq1apsYRmn4mNTVVQ4cOVYcOHdSxY0fNnDlT58+f18MPP+zp0gAAgIcRmn7mvvvu0/HjxzV+/Hjl5uaqTZs2WrFiRbmbwwEAwG8PoekXUlJSLnk5zpOCgoI0YcKEcpcD4Vr02T3os3vQZ/egz+7jDb22GIaZ37EDAAD4bePhlgAAACYQmgAAAEwgNAEAAJhAaAIAADCB0OQl5syZo/r16ys4OFidOnXSli1brjh+yZIlatq0qYKDgxUfH6/ly5e7qVLfVpE+v/vuu+rcubPCwsIUFham7t27X/XvBRdV9N9zmUWLFslisTgeMIsrq2ifT58+rZEjR6pevXoKCgrSTTfdxPcOEyra55kzZ6pJkyaqVq2aYmJiNHr0aF24cMFN1fqm9evXq2/fvoqOjpbFYtHSpUuv+p5169apXbt2CgoKUqNGjZSenl7ldfLZc15g0aJFhtVqNebNm2fs2rXLeOyxx4zatWsbeXl5lxz/+eefG/7+/sa0adOM3bt3G+PGjTMCAwONr7/+2s2V+5aK9nnQoEHGnDlzjO3btxt79uwxHnroIaNWrVrG999/7+bKfUtF+1zm8OHDxvXXX2907tzZuOuuu9xTrA+raJ8LCwuNDh06GL179zY2btxoHD582Fi3bp2RlZXl5sp9S0X7vGDBAiMoKMhYsGCBcfjwYWPlypVGvXr1jNGjR7u5ct+yfPly44UXXjA++ugjQ5Lx8ccfX3H8oUOHjJCQECM1NdXYvXu38eabbxr+/v7GihUrqrROQpMX6NixozFy5EjHcklJiREdHW1MnTr1kuPvvfdeo0+fPk7rOnXqZDz++ONVWqevq2iff6m4uNioWbOmMX/+/Koq8ZpQmT4XFxcbt9xyi/Hee+8ZQ4cOJTSZUNE+v/3220aDBg2MoqIid5V4Tahon0eOHGnceeedTutSU1ONW2+9tUrrvJaYCU3PPvus0aJFC6d19913n5GUlFSFlRkGl+c8rKioSNu2bVP37t0d6/z8/NS9e3dlZmZe8j2ZmZlO4yUpKSnpsuNRuT7/UkFBgex2u8LDw6uqTJ9X2T5PnjxZERERSk5OdkeZPq8yff7kk0+UkJCgkSNHKjIyUi1bttRLL72kkpISd5XtcyrT51tuuUXbtm1zXMI7dOiQli9frt69e7ul5t8KT/0c5IngHnbixAmVlJSU+6iWyMhIffPNN5d8T25u7iXH5+bmVlmdvq4yff6l5557TtHR0eW+UPFflenzxo0b9f777ysrK8sNFV4bKtPnQ4cOac2aNXrggQe0fPlyHThwQP/zP/8ju92uCRMmuKNsn1OZPg8aNEgnTpzQbbfdJsMwVFxcrOHDh+v55593R8m/GZf7OZifn6+ffvpJ1apVq5L9cqYJMOHll1/WokWL9PHHHys4ONjT5Vwzzp49q8GDB+vdd99VnTp1PF3ONa20tFQRERF655131L59e91333164YUXNHfuXE+Xdk1Zt26dXnrpJb311lv68ssv9dFHH2nZsmWaMmWKp0uDC3CmycPq1Kkjf39/5eXlOa3Py8tTVFTUJd8TFRVVofGoXJ/LvPbaa3r55Ze1atUqtWrVqirL9HkV7fPBgwf17bffqm/fvo51paWlkqSAgADt3btXDRs2rNqifVBl/j3Xq1dPgYGB8vf3d6xr1qyZcnNzVVRUJKvVWqU1+6LK9PnFF1/U4MGD9eijj0qS4uPjdf78eQ0bNkwvvPCC/Pw4V+EKl/s5GBoaWmVnmSTONHmc1WpV+/bttXr1ase60tJSrV69WgkJCZd8T0JCgtN4SbLZbJcdj8r1WZKmTZumKVOmaMWKFerQoYM7SvVpFe1z06ZN9fXXXysrK8vx+sMf/qCuXbsqKytLMTEx7izfZ1Tm3/Ott96qAwcOOEKpJO3bt0/16tUjMF1GZfpcUFBQLhiVBVWDj3p1GY/9HKzS28xhyqJFi4ygoCAjPT3d2L17tzFs2DCjdu3aRm5urmEYhjF48GDjT3/6k2P8559/bgQEBBivvfaasWfPHmPChAk8csCEivb55ZdfNqxWq/H3v//dyMnJcbzOnj3rqUPwCRXt8y/x23PmVLTP2dnZRs2aNY2UlBRj7969xqeffmpEREQYf/7znz11CD6hon2eMGGCUbNmTeODDz4wDh06ZGRkZBgNGzY07r33Xk8dgk84e/assX37dmP79u2GJGPGjBnG9u3bjSNHjhiGYRh/+tOfjMGDBzvGlz1yYMyYMcaePXuMOXPm8MiB35I333zTuPHGGw2r1Wp07NjR2Lx5s2PbHXfcYQwdOtRp/IcffmjcdNNNhtVqNVq0aGEsW7bMzRX7por0OTY21pBU7jVhwgT3F+5jKvrv+ecITeZVtM+bNm0yOnXqZAQFBRkNGjQw/vKXvxjFxcVurtr3VKTPdrvdmDhxotGwYUMjODjYiImJMf7nf/7HOHXqlPsL9yFr16695Pfbst4OHTrUuOOOO8q9p02bNobVajUaNGhgpKWlVXmdFsPgfCEAAMDVcE8TAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQCuoEuXLho1apSnywDgBQhNAK5Zffv2Vc+ePS+5bcOGDbJYLNqxY4ebqwLgqwhNAK5ZycnJstls+v7778ttS0tLU4cOHdSqVSsPVAbAFxGaAFyzfv/736tu3bpKT093Wn/u3DktWbJE/fr108CBA3X99dcrJCRE8fHx+uCDD644p8Vi0dKlS53W1a5d22kf3333ne69917Vrl1b4eHhuuuuu/Ttt9+65qAAeAyhCcA1KyAgQEOGDFF6erp+/jGbS5YsUUlJiR588EG1b99ey5Yt086dOzVs2DANHjxYW7ZsqfQ+7Xa7kpKSVLNmTW3YsEGff/65atSooZ49e6qoqMgVhwXAQwhNAK5pjzzyiA4ePKjPPvvMsS4tLU39+/dXbGysnnnmGbVp00YNGjTQE088oZ49e+rDDz+s9P4WL16s0tJSvffee4qPj1ezZs2Ulpam7OxsrVu3zgVHBMBTCE0ArmlNmzbVLbfconnz5kmSDhw4oA0bNig5OVklJSWaMmWK4uPjFR4erho1amjlypXKzs6u9P6++uorHThwQDVr1lSNGjVUo0YNhYeH68KFCzp48KCrDguABwR4ugAAqGrJycl64oknNGfOHKWlpalhw4a644479Morr2jWrFmaOXOm4uPjVb16dY0aNeqKl9EsFovTpT7p4iW5MufOnVP79u21YMGCcu+tW7eu6w4KgNsRmgBc8+6991499dRTWrhwof76179qxIgRslgs+vzzz3XXXXfpwQcflCSVlpZq3759at68+WXnqlu3rnJychzL+/fvV0FBgWO5Xbt2Wrx4sSIiIhQaGlp1BwXA7bg8B+CaV6NGDd13330aO3ascnJy9NBDD0mSGjduLJvNpk2bNmnPnj16/PHHlZeXd8W57rzzTs2ePVvbt2/X1q1bNXz4cAUGBjq2P/DAA6pTp47uuusubdiwQYcPH9a6dev05JNPXvLRBwB8B6EJwG9CcnKyTp06paSkJEVHR0uSxo0bp3bt2ikpKUldunRRVFSU+vXrd8V5pk+frpiYGHXu3FmDBg3SM888o5CQEMf2kJAQrV+/XjfeeKPuueceNWvWTMnJybpw4QJnngAfZzF+eXEeAAAA5XCmCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAm/D/ujRgtl7l0KQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = json.load(open(\"trajectories/reward_debug_min_(1,2,3).json\"))\n",
    "\n",
    "correct_rewards = []\n",
    "incorrect_rewards = []\n",
    "for item in data:\n",
    "    responses = item[\"response_reward\"]\n",
    "    for x in responses:\n",
    "        if x[\"is_correct\"]:\n",
    "            correct_rewards.append(x[\"reward\"])\n",
    "        else:\n",
    "            incorrect_rewards.append(x[\"reward\"])\n",
    "\n",
    "plot_histogram(correct_rewards, bins=10, title=\"Correct\")\n",
    "plot_histogram(incorrect_rewards, bins=10, title=\"Incorrect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f9ee23e5-7d09-468e-bbad-d549a62e9fd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ReClor\n",
    "\n",
    "data = json.load(open(\"trajectories/reward_reclor_debug_cp2400_(1,2,3).json\"))\n",
    "\n",
    "correct_rewards = []\n",
    "incorrect_rewards = []\n",
    "for item in data:\n",
    "    responses = item[\"response_reward\"]\n",
    "    for x in responses:\n",
    "        if x[\"is_correct\"]:\n",
    "            correct_rewards.append(x[\"reward\"])\n",
    "        else:\n",
    "            incorrect_rewards.append(x[\"reward\"])\n",
    "\n",
    "plot_histogram(correct_rewards, bins=10, title=\"Correct\")\n",
    "plot_histogram(incorrect_rewards, bins=10, title=\"Incorrect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dbfb69b9-9cdc-4438-9d20-ad20b59853e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ReClor\n",
    "\n",
    "data = json.load(open(\"trajectories/reward_reclor_debug_cp2400_(5,).json\"))\n",
    "\n",
    "correct_rewards = []\n",
    "incorrect_rewards = []\n",
    "for item in data:\n",
    "    responses = item[\"response_reward\"]\n",
    "    for x in responses:\n",
    "        if x[\"is_correct\"]:\n",
    "            correct_rewards.append(x[\"reward\"])\n",
    "        else:\n",
    "            incorrect_rewards.append(x[\"reward\"])\n",
    "\n",
    "plot_histogram(correct_rewards, bins=10, title=\"Correct\")\n",
    "plot_histogram(incorrect_rewards, bins=10, title=\"Incorrect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "512d2aa7-7a0f-4d1f-8d45-1694ee7e64e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ReClor\n",
    "\n",
    "data = json.load(open(\"trajectories/reward_reclor_debug_cp2400_(3,4,5).json\"))\n",
    "\n",
    "correct_rewards = []\n",
    "incorrect_rewards = []\n",
    "for item in data:\n",
    "    responses = item[\"response_reward\"]\n",
    "    for x in responses:\n",
    "        if x[\"is_correct\"]:\n",
    "            correct_rewards.append(x[\"reward\"])\n",
    "        else:\n",
    "            incorrect_rewards.append(x[\"reward\"])\n",
    "\n",
    "plot_histogram(correct_rewards, bins=10, title=\"Correct\")\n",
    "plot_histogram(incorrect_rewards, bins=10, title=\"Incorrect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "051a3b1e-f6d8-4882-9307-aa5cb97b5d1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ReClor\n",
    "\n",
    "data = json.load(open(\"trajectories/reward_reclor_debug_cp2400_(2,3,4,5).json\"))\n",
    "\n",
    "correct_rewards = []\n",
    "incorrect_rewards = []\n",
    "for item in data:\n",
    "    responses = item[\"response_reward\"]\n",
    "    for x in responses:\n",
    "        if x[\"is_correct\"]:\n",
    "            correct_rewards.append(x[\"reward\"])\n",
    "        else:\n",
    "            incorrect_rewards.append(x[\"reward\"])\n",
    "\n",
    "plot_histogram(correct_rewards, bins=10, title=\"Correct\")\n",
    "plot_histogram(incorrect_rewards, bins=10, title=\"Incorrect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b8a13028-5f90-4d94-a9f8-6e8f25acfcc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ReClor\n",
    "\n",
    "data = json.load(open(\"trajectories/reward_reclor_debug_cp2400_(1,2,3,4,5).json\"))\n",
    "\n",
    "correct_rewards = []\n",
    "incorrect_rewards = []\n",
    "for item in data:\n",
    "    responses = item[\"response_reward\"]\n",
    "    for x in responses:\n",
    "        if x[\"is_correct\"]:\n",
    "            correct_rewards.append(x[\"reward\"])\n",
    "        else:\n",
    "            incorrect_rewards.append(x[\"reward\"])\n",
    "\n",
    "plot_histogram(correct_rewards, bins=10, title=\"Correct\")\n",
    "plot_histogram(incorrect_rewards, bins=10, title=\"Incorrect\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f2755c95-b6e1-430e-b2a7-564251eea35a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'response': 'Which options directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 1: Take option B.\\nObservation 1: Tilapia fillets contain little of the beneficial fish oils that are the main reason nutritionists recommend eating fish frequently.\\nThought 2: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 2: Yes.\\nThought 3: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 3: Yes.\\nThought 4: Which other options can be eliminated based on the reasoning above?\\nAction 4: Options A, C, and D can be eliminated.\\nAction 5: Finish[The answer is B.]',\n",
       "  'reward': 0.8305641412734985,\n",
       "  'is_correct': True},\n",
       " {'response': 'Which options directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 1: Take option B.\\nObservation 1: Tilapia fillets contain little of the beneficial fish oils that are the main reason nutritionists recommend eating fish frequently.\\nThought 2: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 2: Yes.\\nThought 3: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 3: Yes.\\nThought 4: Which other options can be eliminated based on the reasoning above?\\nAction 4: Options A, C, and D can be eliminated.\\nAction 5: Finish[The answer is B.]',\n",
       "  'reward': 0.8305641412734985,\n",
       "  'is_correct': True},\n",
       " {'response': 'Which option directly challenges the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 1: Take option B.\\nObservation 1: Tilapia fillets contain little of the beneficial fish oils that are the main reason nutritionists recommend eating fish frequently.\\nThought 2: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 2: Yes.\\nThought 3: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 3: Yes.\\nThought 4: Which other options can be eliminated based on the reasoning above?\\nAction 4: Options A, C, and D can be eliminated.\\nAction 5: Finish[The answer is B.]',\n",
       "  'reward': 0.9448428153991699,\n",
       "  'is_correct': True},\n",
       " {'response': 'Which options directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 1: Take option B.\\nObservation 1: Tilapia fillets contain little of the beneficial fish oils that are the main reason nutritionists recommend eating fish frequently.\\nThought 2: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 2: Yes.\\nThought 3: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 3: Yes.\\nThought 4: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 4: Yes.\\nThought 5: Which other options can be eliminated based on the reasoning above?\\nAction 5: Options A, C, and D can be eliminated.\\nAction 6: Finish[The answer is B.]',\n",
       "  'reward': 0.7854018211364746,\n",
       "  'is_correct': True},\n",
       " {'response': 'Which of the options directly challenges the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 1: Take option B.\\nObservation 1: Tilapia fillets contain little of the beneficial fish oils that are the main reason nutritionists recommend eating fish frequently.\\nThought 2: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 2: Yes.\\nThought 3: Does option B directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 3: Yes.\\nThought 4: Which other options can be eliminated based on the fact that they do not directly challenge the claim that eating tilapia fillets is a good choice for those who do not like the taste of fish?\\nAction 4: Options A, C, and D can be eliminated.\\nThought 5: Which option is the best answer?\\nAction 5: Option B is the best answer.\\nAction 6: Finish[The answer is B.]',\n",
       "  'reward': 0.8131454586982727,\n",
       "  'is_correct': True},\n",
       " {'response': \"Which of the options can refute the advertisement's argument that eating tilapia fillets is a perfect choice for those who want the benefits of eating fish but do not care for the taste of fish?\\nAction 1: Take option B.\\nObservation 1: Tilapia fillets contain little of the beneficial fish oils that are the main reason nutritionists recommend eating fish frequently.\\nThought 2: Does option B directly refute the advertisement's argument?\\nAction 2: Yes.\\nThought 3: If option B is true, what will happen?\\nAction 3: If tilapia fillets contain little of the beneficial fish oils, then eating tilapia fillets will not provide the benefits of eating fish that the advertisement claims.\\nThought 4: Is it a serious weakness?\\nAction 4: Yes.\\nAction 5: Finish[The answer is B.]\",\n",
       "  'reward': 0.5243943333625793,\n",
       "  'is_correct': True},\n",
       " {'response': \"Which option most seriously weakens the advertisement's argument?\\nAction 1: Take option B.\\nObservation 1: Tilapia fillets contain little of the beneficial fish oils that are the main reason nutritionists recommend eating fish frequently.\\nThought 2: Does this option contradict the advertisement's argument?\\nAction 2: Examine the advertisement's argument.\\nObservation 2: The advertisement argues that eating tilapia fillets is a perfect choice for those who want the benefits of eating fish but do not care for the taste of fish.\\nThought 3: Does option B contradict the advertisement's argument?\\nAction 3: Examine the relationship between option B and the advertisement's argument.\\nObservation 3: Option B states that tilapia fillets contain little of the beneficial fish oils. This means that eating tilapia fillets will not provide the beneficial effects of eating fish that the advertisement claims.\\nThought 4: Is this a weakness in the advertisement's argument?\\nAction 4: Evaluate the impact of option B on the advertisement's argument.\\nObservation 4: Option B directly contradicts the advertisement's argument by stating that eating tilapia fillets will not provide the beneficial effects of eating fish.\\nThought 5: Is this a serious weakness?\\nAction 5: Determine the severity of the weakness.\\nObservation 5: Option B directly contradicts the advertisement's argument, which is a serious weakness.\\nAction 6: Finish[The answer is B.]\",\n",
       "  'reward': 0.2852543294429779,\n",
       "  'is_correct': True},\n",
       " {'response': \"What is the advertisement's argument?\\nAction 1: The advertisement's argument is that eating tilapia fillets is a good choice for those who do not like the taste of fish because tilapia fillets lack the strong fishy taste.\\nThought 2: What is the purpose of the advertisement?\\nAction 2: The purpose of the advertisement is to promote the consumption of tilapia fillets.\\nThought 3: What are the assumptions of the advertisement?\\nAction 3: The assumptions of the advertisement are that eating fish has benefits, that tilapia fillets lack the strong fishy taste, and that people who do not like the taste of fish will benefit from eating tilapia fillets.\\nThought 4: Which option most seriously weakens the advertisement's argument?\\nAction 4: Option A most seriously weakens the advertisement's argument. If eating more than the recommended amount of fish can cause toxins to accumulate in a person's body, then eating tilapia fillets, which lack the beneficial fish oils that are the main reason nutritionists recommend eating fish frequently, may not be the best choice for those who want the benefits of eating fish but do not like the taste of fish. This contradicts the advertisement's argument.\\nAction 5: Finish[The answer is A.]\",\n",
       "  'reward': 0.0004384755447972566,\n",
       "  'is_correct': False},\n",
       " {'response': \"What is the advertisement's argument?\\nAction 1: Read the advertisement.\\nObservation 1: The advertisement argues that eating tilapia fillets is a good choice for those who do not like the taste of fish because tilapia fillets lack the strong fishy taste that many people find objectionable.\\nThought 2: What can weaken the advertisement's argument?\\nAction 2: Consider each option.\\nObservation 2: Option A mentions the toxins that are present in many varieties of fish and can accumulate in a person's body if more than the recommended amount of fish is consumed. This does not directly relate to the taste of fish. Option B mentions that tilapia fillets contain little of the beneficial fish oils that are the main reason nutritionists recommend eating fish frequently. This means that eating tilapia fillets does not provide the benefits that nutritionists recommend eating fish for. Option C mentions that most people who do not care for the taste of fish eat less fish than is recommended by most nutritionists. This means that people who do not like the taste of fish are not necessarily persuaded by the advertisement's argument. Option D mentions that people who rarely or never eat fish usually dislike any food with a strong fishy taste. This means that people who rarely or never eat fish are likely to be persuaded by the advertisement's argument.\\nThought 3: Which option most seriously weakens the advertisement's argument?\\nAction 3: Compare the options.\\nObservation 3: Option B most seriously weakens the advertisement's argument because it directly addresses the beneficial effects of eating fish that the advertisement relies on to promote tilapia fillets. If tilapia fillets lack the beneficial fish oils, then eating them twice a week may not provide the benefits that nutritionists recommend eating fish for.\\nAction 4: Finish[The answer is B.]\",\n",
       "  'reward': 4.647705645766109e-05,\n",
       "  'is_correct': True},\n",
       " {'response': \"Which option most seriously weakens the advertisement's argument?\\nAction 1: Take option A.\\nObservation 1: Eating more than the recommended amount of fish can cause toxins that are present in high concentrations in many varieties of fish to accumulate in a person's body.\\nThought 2: Is option A related to the recommendation of eating fish?\\nAction 2: Yes.\\nThought 3: Is option A directly related to the taste of fish?\\nAction 3: No.\\nThought 4: Does option A directly contradict the recommendation of eating fish?\\nAction 4: No.\\nThought 5: Does option A indirectly contradict the recommendation of eating fish by providing a reason not to eat fish?\\nAction 5: Yes.\\nThought 6: Is there any evidence that shows that the recommendation of eating fish is not based on the reservation that people may not like the taste of fish?\\nAction 6: No.\\nThought 7: Does the recommendation of eating fish still hold up even if people do not like the taste of fish?\\nAction 7: Yes.\\nThought 8: Is it acceptable to eat fish even if it is not recommended?\\nAction 8: No.\\nThought 9: Is there any other option that most seriously weakens the advertisement's argument?\\nAction 9: No.\\nAction 10: Finish[The answer is A.]\",\n",
       "  'reward': 3.0939177001615414e-12,\n",
       "  'is_correct': False}]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0][\"response_reward\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5aa79304-5d96-4e30-89f9-3d51f6eb543c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b855bc8-d036-478e-a418-5556fb847bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_response(response: str):\n",
    "    lines = response.split(\"\\n\")\n",
    "    lines = list(filter(lambda x: x[1].startswith(\"Thought \") or x[1].startswith(\"Action \") or x[1].startswith(\"Observation \"), enumerate(lines)))\n",
    "    return lines\n",
    "\n",
    "def test(response: str, max_seq_length: int):\n",
    "    steps = process_response(response)\n",
    "    raw_lines = response.split(\"\\n\")\n",
    "    endings = []\n",
    "    resp_start = False\n",
    "    for step_id, step in steps:\n",
    "        if not resp_start:\n",
    "            if step.startswith(\"Thought 1:\"):\n",
    "                resp_start = True\n",
    "            else:\n",
    "                continue\n",
    "        # partial_traj = \"\\n\".join(raw_lines[:(step_id + 1)])\n",
    "        endings.append(0)\n",
    "\n",
    "    assert resp_start, response\n",
    "    assert len(endings) > 0, (response, steps)\n",
    "    assert len(endings) == len(process_response(\"Thought 1:\" + response.split(\"Thought 1:\")[1])), (response, steps)\n",
    "    return endings\n",
    "\n",
    "data = json.load(open(\"trajectories/eval_predictions_rank3.json\"))\n",
    "cnt_a = 0\n",
    "cnt_b = 0\n",
    "samples_a = []\n",
    "samples_b = []\n",
    "for item in data:\n",
    "    resp = \"Thought 1: \" + item[\"response\"]\n",
    "    steps = process_response(resp)\n",
    "    if len(steps) != len(item[\"ending_logits\"]):\n",
    "        cnt_a += 1\n",
    "        samples_a.append((len(steps), len(item[\"ending_logits\"])))\n",
    "    steps = test(item[\"input\"], 4096)\n",
    "    if len(steps) != len(item[\"ending_logits\"]):\n",
    "        cnt_b += 1\n",
    "        samples_b.append((len(steps), len(item[\"ending_logits\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b74dbfe-e3fb-4dbe-9e23-8fdd32faddb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15422 15422\n"
     ]
    }
   ],
   "source": [
    "print(cnt_a, cnt_b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22c76aa1-c5a4-4ef4-b8e5-631f2dfd1610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(36, 71),\n",
       " (27, 53),\n",
       " (21, 41),\n",
       " (16, 31),\n",
       " (16, 31),\n",
       " (10, 19),\n",
       " (16, 31),\n",
       " (19, 37),\n",
       " (11, 21),\n",
       " (19, 37)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples_a[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0d92724c-33f5-40b8-a1f3-29b621a0d258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(36, 71),\n",
       " (27, 53),\n",
       " (21, 41),\n",
       " (16, 31),\n",
       " (16, 31),\n",
       " (10, 19),\n",
       " (16, 31),\n",
       " (19, 37),\n",
       " (11, 21),\n",
       " (19, 37)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples_b[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "44ca01a9-0698-4e5e-be0f-7b501f76f0c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(process_response(data[0][\"input\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91c7e083-efe5-4f75-8102-22d93f52359b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'Action 1: Take condition (1).'),\n",
       " (2, 'Observation 1: Mr. Liu must be chosen.'),\n",
       " (3,\n",
       "  'Thought 2: Based on Observation 1, who must be chosen or who must not be chosen one step further?'),\n",
       " (4, 'Action 2: Take condition (2).'),\n",
       " (5,\n",
       "  'Observation 2: Mr. Zhou must be chosen, but Mr. Zhang must not be chosen.'),\n",
       " (6,\n",
       "  'Thought 3: Based on Observation 2, who must be chosen one step further? And who must not be chosen?'),\n",
       " (7, 'Action 3: Take condition (3).'),\n",
       " (8, 'Observation 3: Mr. Wang must be chosen, but Mr. Ma must not be chosen.'),\n",
       " (9,\n",
       "  'Thought 4: Based on Observation 3, who must be chosen one step further? And who must not be chosen?'),\n",
       " (10, 'Action 4: Take condition (4).'),\n",
       " (11, 'Observation 4: Mr. Zhou must not be chosen.'),\n",
       " (12,\n",
       "  'Thought 5: Based on Observation 4, who must be chosen one step further? And who must not be chosen?'),\n",
       " (13,\n",
       "  'Action 5: No further conditions can be taken to derive new observations.'),\n",
       " (14,\n",
       "  'Thought 6: We can evaluate each option to check if there is any contradiction with the observations.'),\n",
       " (15, 'Action 6: Take option A.'),\n",
       " (16, \"Observation 5: Mr. Niu didn't choose, but Mr. Zhou did.\"),\n",
       " (17, 'Thought 7: Which observations and conditions are related to Option A?'),\n",
       " (18, 'Action 7: Observation 1 and Observation 2.'),\n",
       " (19,\n",
       "  'Observation 7: We can confirm that Mr. Liu was chosen and Mr. Zhou was chosen. So Option A is true.'),\n",
       " (20, 'Action 8: Take option B.'),\n",
       " (21, \"Observation 8: Mr. Liu was chosen, but Mr. Ma didn't.\"),\n",
       " (22, 'Thought 9: Which observations and conditions are related to Option B?'),\n",
       " (23, 'Action 9: Observation 3.'),\n",
       " (24,\n",
       "  'Observation 9: Mr. Wang must be chosen, but Mr. Ma must not be chosen. This contradicts Observation 8. So Option B is not true.'),\n",
       " (25, 'Action 10: Take option C.'),\n",
       " (26, 'Observation 10: Mr. Wang and Mr. Ma were chosen.'),\n",
       " (27,\n",
       "  'Thought 11: Which observations and conditions are related to Option C?'),\n",
       " (28, 'Action 11: Observation 3.'),\n",
       " (29,\n",
       "  'Observation 11: Mr. Wang must be chosen, but Mr. Ma must not be chosen. This contradicts Observation 10. So Option C is not true.'),\n",
       " (30, 'Action 12: Take option D.'),\n",
       " (31, 'Observation 12: Neither Mr. Wang nor Mr. Niu was elected.'),\n",
       " (32,\n",
       "  'Thought 13: Which observations and conditions are related to Option D?'),\n",
       " (33, 'Action 13: Observation 1 and Observation 2.'),\n",
       " (34,\n",
       "  'Observation 13: Mr. Liu was chosen and Mr. Zhou was chosen. So Option D is true.'),\n",
       " (35, 'Action 14: Finish[The answer is D.]')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "process_response(data[0][\"response\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad671537-17c8-476f-b373-acf0ca12a6de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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