isan_parenthesis_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook provides an analysisi of the \"ISAN\" network on a parenthesis counting task. For more explanation, please see https://arxiv.org/abs/1611.09434.\n",
    "\n",
    "\n",
    "Please note that this notebok requires a nightly build of tensorflow (it uses some of the upcoming V1.0 API). To run it, please install the latest build running:\n",
    "`pip install https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow_gpu-0.12.1-cp27-none-linux_x86_64.whl`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": false,
    "id": "c3WqYyk51oq1"
   },
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import scipy.linalg\n",
    "\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.12.head'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "VNlLRICBfTzY"
   },
   "outputs": [],
   "source": [
    "def isan_cell(step_inputs, state, initialization=\"orthogonal\"):\n",
    "  \"\"\"\n",
    "  Make one step of ISAN transition. \n",
    "  \n",
    "  Args:\n",
    "    step_inputs: one-hot encoded inputs, shape bs x n\n",
    "    state: previous hidden state, shape bs x d\n",
    "    initialization: how to initialize the transition matrices:\n",
    "      orthogonal: usually speeds up training, orthogonalize Gaussian matrices\n",
    "      gaussian: sample gaussian matrices with a sensible scale\n",
    "    \n",
    "  \"\"\"\n",
    "  \n",
    "  bs, d = state.get_shape().as_list()\n",
    "  n = step_inputs.shape[1].value\n",
    "  \n",
    "  with tf.variable_scope('isan_cell', values=[step_inputs, state]) as sc:\n",
    "    \n",
    "    if initialization == \"orthogonal\":\n",
    "      Wx_ndd_init = np.zeros((n, d*d), dtype=np.float32)\n",
    "      for i in range(n):\n",
    "        Wx_ndd_init[i, :] = scipy.linalg.orth(\n",
    "            np.random.randn(d, d)).astype(np.float32).ravel()\n",
    "      Wx_ndd_initializer = tf.constant_initializer(Wx_ndd_init)\n",
    "    elif initialization == \"gaussian\":\n",
    "      Wx_ndd_initializer = tf.random_normal_initializer(stdev = 1.0/np.sqrt(d))\n",
    "    else:\n",
    "      raise Exception(\"Unknown init type: %s\" % (initialization))\n",
    "    \n",
    "    \n",
    "    Wx_ndd = tf.get_variable('Wx', shape=[n, d*d],\n",
    "                            initializer=Wx_ndd_initializer)\n",
    "    bx_nd = tf.get_variable('bx', shape=[n, d], \n",
    "                            initializer=tf.zeros_initializer())\n",
    "    \n",
    "    # Multiplication with a 1-hot is just row selection.\n",
    "    # As of Jan '17 this is faster than doing gather.\n",
    "    Wx_bdd = tf.reshape(tf.matmul(step_inputs, Wx_ndd), [bs, d, d])\n",
    "    bx_bd = tf.reshape(tf.matmul(step_inputs, bx_nd), [bs, 1, d])\n",
    "    \n",
    "    # Reshape the state so that matmul multiplies different matrices\n",
    "    # for each batch element.\n",
    "    state = tf.reshape(state, [bs, 1, d])\n",
    "    new_state = tf.reshape(tf.matmul(state, Wx_bdd) + bx_bd, [bs, d])\n",
    "  return new_state\n",
    "\n",
    "\n",
    "def isan_rnn(inputs, n_tokens, dim):\n",
    "  \"\"\"\n",
    "  Unroll in time the ISAN cell.\n",
    "  \n",
    "  Args:\n",
    "    inputs: t x bs int32 array of token ids over t timesteps\n",
    "    n_tokens: maximal token id seen on input\n",
    "    \n",
    "  Returns:\n",
    "    t x bs x d tensor of states of the ISAN cell\n",
    "  \"\"\"\n",
    "  one_hot_inputs = tf.one_hot(inputs, n_tokens, axis=-1)\n",
    "  num_steps, bs = inputs.get_shape().as_list()\n",
    "  with tf.variable_scope(\"rnn\", values=[inputs]) as sc:\n",
    "    h0 = tf.get_variable(\"h0\", shape=[1, dim],\n",
    "                         initializer=tf.zeros_initializer())\n",
    "    state = tf.tile(h0, [bs, 1])\n",
    "    step_states = []\n",
    "    for step_inputs in tf.unstack(one_hot_inputs):\n",
    "      state = isan_cell(step_inputs, state)\n",
    "      step_states.append(state)\n",
    "      sc.reuse_variables()\n",
    "  return tf.stack(step_states)\n",
    "\n",
    "\n",
    "class ISAN(object):\n",
    "  def __init__(self, n_tokens, hidden_dim, target_dim):\n",
    "    self.n_tokens = n_tokens\n",
    "    self.hidden_dim = hidden_dim\n",
    "    self.target_dim = target_dim\n",
    "  \n",
    "  def fprop(self, inputs):\n",
    "    with tf.variable_scope(\"model\", values=[inputs]):\n",
    "      states = isan_rnn(inputs, self.n_tokens, self.hidden_dim)\n",
    "      Wo = tf.get_variable(\"Wo\", shape=[self.hidden_dim, self.target_dim],\n",
    "                          initializer=tf.random_normal_initializer(\n",
    "                              stddev=1.0/(self.hidden_dim + self.target_dim)**2))\n",
    "      bo = tf.get_variable(\"bo\", shape=[1, self.target_dim],\n",
    "                          initializer=tf.zeros_initializer())\n",
    "\n",
    "      t, bs = inputs.get_shape().as_list()\n",
    "      logits = tf.matmul(tf.reshape(states, [t * bs, self.hidden_dim]), Wo) + bo\n",
    "      logits = tf.reshape(logits, [t, bs, self.target_dim])\n",
    "    return logits\n",
    "\n",
    "class ParenthesisTask(object):\n",
    "  def __init__(self, max_count=5, implied_activation_fn=\"softmax\"):\n",
    "    \"\"\"\n",
    "    Saturating count number of non-closed parenthesis.\n",
    "    \n",
    "    Args:\n",
    "      max_count: level at which outstanding opening parenthesis stop being added\n",
    "      implied_activation_fn: how is the output of the netowkr interpreted:\n",
    "        - softmax: treat it as logits, train via neg-log likelihood minimization\n",
    "        - identity: treat it as probabilites, train via least-squares\n",
    "    \"\"\"\n",
    "    self.max_count = max_count\n",
    "    self.implied_activation_fn = implied_activation_fn\n",
    "    self.parens = \"()[]{}\"\n",
    "    self.n_paren_types = len(self.parens) // 2\n",
    "    self.noises = \"a\"\n",
    "    \n",
    "    self.id_to_token = self.parens + self.noises\n",
    "    self.token_to_id = {t:i for i,t in enumerate(self.id_to_token)}\n",
    "    \n",
    "    self.n_tokens = len(self.id_to_token)\n",
    "    self.n_outputs = self.n_paren_types * (self.max_count + 1)\n",
    "    \n",
    "  def sample_batch(self, t, bs):\n",
    "    inputs = (np.random.rand(t, bs)* len(self.id_to_token)).astype(np.int32)\n",
    "    counts = np.zeros((bs, self.n_paren_types), dtype=np.int32)\n",
    "    targets = np.zeros((t, bs, self.n_paren_types), dtype = np.int32)\n",
    "    opening_parens = (np.arange(0, self.n_paren_types)*2)[None, :]\n",
    "    closing_parens = opening_parens + 1\n",
    "    for i in range(t):\n",
    "      opened = np.equal(inputs[i, :, None], opening_parens)\n",
    "      counts = np.minimum(self.max_count, counts + opened)\n",
    "      closed = np.equal(inputs[i, :, None], closing_parens)\n",
    "      counts = np.maximum(0, counts - closed)\n",
    "      targets[i, :, :] = counts\n",
    "    return inputs, targets\n",
    "  \n",
    "  def  loss(self, logits, targets):\n",
    "    bs, t, _ = logits.get_shape().as_list()\n",
    "    logits = tf.reshape(logits, (bs, t, self.n_paren_types, self.max_count + 1))\n",
    "        \n",
    "    if self.implied_activation_fn == \"softmax\":\n",
    "      loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, targets)\n",
    "    elif self.implied_activation_fn == \"identity\":\n",
    "      loss = tf.square(logits - tf.one_hot(targets, self.max_count + 1))\n",
    "    else:\n",
    "      raise\n",
    "    \n",
    "    return tf.reduce_mean(loss)\n",
    "  \n",
    "  def print_batch(self, inputs, targets, predictions=None, max_to_print=1):\n",
    "    if predictions is not None:\n",
    "      predictions = np.reshape(\n",
    "          predictions, \n",
    "          predictions.shape[:2] + (self.n_paren_types, self.max_count + 1))\n",
    "    for i in range(min(max_to_print, inputs.shape[1])):\n",
    "      print(\"%3d:\" %(i,), \" \".join([self.id_to_token[t] for t in inputs[:, i]]))\n",
    "      for paren_kind in range(self.n_paren_types):\n",
    "        print(\"G%s:\" % self.parens[2*paren_kind:2*paren_kind+2], \n",
    "              \" \".join([str(c) for c in targets[:, i, paren_kind]]))\n",
    "        if predictions is not None:\n",
    "          pred = np.argmax(predictions[:,i, paren_kind], axis=1)\n",
    "          print(\"P%s:\" % self.parens[2*paren_kind:2*paren_kind+2], \n",
    "                \" \".join([str(c) for c in pred]))\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
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    "colab_type": "code",
    "collapsed": true,
    "id": "vKCdAaCTPqFq"
   },
   "outputs": [],
   "source": [
    "# Training configuration\n",
    "\n",
    "MAX_COUNT = 5\n",
    "HIDDEN_DIM = 50 # will work nicely if set to 2*(MAX_COUNT + 1)\n",
    "N_TRAIN_STEPS = 20000\n",
    "BATCH_SIZE = 16\n",
    "T = 100\n",
    "WEIGHT_DECAY = 1e-4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 168,
     "status": "ok",
     "timestamp": 1484363768604,
     "user": {
      "displayName": "Jan Chorowski",
      "photoUrl": "//lh5.googleusercontent.com/-CIawMzW6HLk/AAAAAAAAAAI/AAAAAAAAABY/YDJiClf-um8/s50-c-k-no/photo.jpg",
      "userId": "105360491181387708695"
     },
     "user_tz": 480
    },
    "id": "P8NCEfC6sHDP",
    "outputId": "9bb3a3d5-fde0-4433-b0b4-9843f3953c2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0: ( ( } } ( ( } { ) ) ( [ ] a } ( ) ) a ] ) ) ) { a ) } ( ) a ( ] [ a { [ } ( a ( { a } } [ } } [ ) [ ) } ) ] ( { ] ( ) a } a a ) { [ { ) ) ) a a ) { } } ( ] ) a } ( a ( a { ] { ] { } } { { { { { ) { (\n",
      "G(): 1 2 2 2 3 4 4 4 3 2 3 3 3 3 3 4 3 2 2 2 1 0 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 3 2 2 1 1 0 0 1 1 1 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 4 4 5 5 5 5 4 4 4 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1\n",
      "G{}: 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 2 2 2 2 2 2 2 3 2 1 1 1 1 1 0 0 0 0 0 1 1 2 2 3 2 1 2 3 4 5 5 5 5 5\n"
     ]
    }
   ],
   "source": [
    "task = ParenthesisTask(max_count=MAX_COUNT, \n",
    "                       #implied_activation_fn=\"softmax\")\n",
    "                       implied_activation_fn=\"identity\")\n",
    "task.print_batch(*task.sample_batch(100, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": false,
    "id": "ZI2GYa8PonVM"
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "with tf.variable_scope(\"model\"):\n",
    "  model = ISAN(task.n_tokens, HIDDEN_DIM, task.n_outputs)\n",
    "\n",
    "  inputs = tf.placeholder(tf.int32, shape=(T, BATCH_SIZE), name=\"inputs\")\n",
    "  targets = tf.placeholder(tf.int32, shape=(T, BATCH_SIZE, task.n_paren_types), \n",
    "                           name=\"targets\")\n",
    "\n",
    "  logits = model.fprop(inputs)\n",
    "\n",
    "  task_loss = task.loss(logits, targets)\n",
    "  weight_loss = 0.0\n",
    "  for v in tf.trainable_variables():\n",
    "    if v.name.split('/')[-1].startswith('W'):\n",
    "      weight_loss = weight_loss + WEIGHT_DECAY * tf.nn.l2_loss(v)\n",
    "      \n",
    "  loss = task_loss + weight_loss\n",
    "\n",
    "learning_rate = tf.placeholder(tf.float32, shape=(), name=\"learning_rate\")\n",
    "\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
    "train_op = optimizer.minimize(loss)\n",
    "\n",
    "tf.get_variable_scope().reuse_variables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[100, 16]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.get_shape().as_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 189,
     "status": "ok",
     "timestamp": 1484363773199,
     "user": {
      "displayName": "Jan Chorowski",
      "photoUrl": "//lh5.googleusercontent.com/-CIawMzW6HLk/AAAAAAAAAAI/AAAAAAAAABY/YDJiClf-um8/s50-c-k-no/photo.jpg",
      "userId": "105360491181387708695"
     },
     "user_tz": 480
    },
    "id": "5x5lIJu60thj",
    "outputId": "9e022d26-7c7d-4e33-80a5-ed4c623fc1a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training the following variables:\n",
      "model/model/rnn/h0:0: [1, 50] (model/model/rnn/h0/Initializer/Const)\n",
      "model/model/rnn/isan_cell/Wx:0: [7, 2500] (model/model/rnn/isan_cell/Wx/Initializer/Const)\n",
      "model/model/rnn/isan_cell/bx:0: [7, 50] (model/model/rnn/isan_cell/bx/Initializer/Const)\n",
      "model/model/Wo:0: [50, 18] (model/model/Wo/Initializer/random_normal)\n",
      "model/model/bo:0: [1, 18] (model/model/bo/Initializer/Const)\n"
     ]
    }
   ],
   "source": [
    "print (\"Training the following variables:\")\n",
    "for v in tf.trainable_variables():\n",
    "  print (\"%s: %s (%s)\" % (v.name, v.get_shape().as_list(), \n",
    "                          v.initializer.inputs[1].op.name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
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      {
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      {
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      {
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      {
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      {
       "item_id": 99
      },
      {
       "item_id": 100
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 1430971,
     "status": "ok",
     "timestamp": 1484365204190,
     "user": {
      "displayName": "Jan Chorowski",
      "photoUrl": "//lh5.googleusercontent.com/-CIawMzW6HLk/AAAAAAAAAAI/AAAAAAAAABY/YDJiClf-um8/s50-c-k-no/photo.jpg",
      "userId": "105360491181387708695"
     },
     "user_tz": 480
    },
    "id": "nvBcZBKyWTsF",
    "outputId": "ac892337-3b46-460d-b05a-21d002bdfa92"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-9-0b4712d2067a>:2: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
      "Instructions for updating:\n",
      "Use `tf.global_variables_initializer` instead.\n",
      "Step 499 loss 0.136900\n",
      "  0: ) [ } [ ) [ ( { ( { [ } } ] { ( } [ ( } ] } a ( { { ] [ [ [ ( a a a a } ) { a { } { a ( { } { ( [ ) { ( } ( } a { a { ) { { } a { [ ( } a ) ] a [ ) ( { a [ a { ( a ] a ( [ ( { ) [ ( ) a ] a ] { [ { [\n",
      "G(): 0 0 0 0 0 0 1 1 2 2 2 2 2 2 2 3 3 3 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 5 5 5 4 4 4 4 3 4 4 4 4 4 4 5 5 5 5 5 5 5 5 4 4 5 4 4 4 4 4 4 4 4 4\n",
      "P(): 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0\n",
      "G[]: 0 1 1 2 2 3 3 3 3 3 4 4 4 3 3 3 3 4 4 4 3 3 3 3 3 3 2 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5 5 5 5 5 5 5 4 4 3 3 4 4 5\n",
      "P[]: 0 1 1 5 1 5 1 1 1 1 5 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 2 5 2 1 2 2 2 1 1 0 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 0 0 1 1 1 1 1 5 2 1 1 1 0 0 0 1 2 1 1 2 2 1 1 1 0 0 0 1 1 5\n",
      "G{}: 0 0 0 0 0 0 0 1 1 2 2 1 0 0 1 1 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 3 2 3 3 3 4 3 4 4 4 4 5 5 4 4 3 3 4 4 5 5 5 5 4 4 5 5 5 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      "P{}: 0 0 0 0 0 0 0 1 1 5 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 2 1 1 1 1 1 1 1 1 0 0 0 1 1 2 0 1 1 1 2 1 1 1 1 1 5 1 1 0 0 0 1 1 2 1 2 5 1 0 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1\n",
      "\n",
      "Step 999 loss 0.107026\n",
      "  0: a } [ { ( { ( a ] ( [ { [ ( a a ) [ ( } a { { } ( } } } } a ] ) { } a ] ] [ ) a ( ( ) } { ) ( ] ) a a } } ( { a ) ] { ] ] } ( ( { ] ( a ) { a [ [ ) ] a a ( { } { } } { ] ) ( ] ] ) } a [ [ a } ( ) a }\n",
      "G(): 0 0 0 0 1 1 2 2 2 3 3 3 3 4 4 4 3 3 4 4 4 4 4 4 5 5 5 5 5 5 5 4 4 4 4 4 4 4 3 3 4 5 4 4 4 3 4 4 3 3 3 3 3 4 4 4 3 3 3 3 3 3 4 5 5 5 5 5 4 4 4 4 4 3 3 3 3 4 4 4 4 4 4 4 4 3 4 4 4 3 3 3 3 3 3 3 4 3 3 3\n",
      "P(): 0 0 0 0 1 1 1 1 1 5 5 5 5 5 5 5 4 1 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 2 2 2 5 5 1 1 5 5 4 2 5 2 5 5 0 0 1 0 0 2 1 1 0 0 0 0 0 0 1 5 5 5 5 5 1 1 2 1 2 0 0 0 0 1 1 1 2 1 5 2 2 1 1 2 4 1 1 1 1 2 0 0 5 1 1 1\n",
      "G[]: 0 0 1 1 1 1 1 1 0 0 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 2\n",
      "P[]: 0 0 1 1 1 1 1 1 0 0 1 1 2 2 2 2 2 5 5 5 5 5 4 5 5 5 4 5 5 5 2 1 1 2 2 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 2 1 1 1 1 1 1\n",
      "G{}: 0 0 0 1 1 2 2 2 2 2 2 3 3 3 3 3 3 3 3 2 2 3 4 3 3 2 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 2 2 2 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 3 4 3 4 3 2 3 3 3 3 3 3 3 2 2 2 2 2 1 1 1 1 0\n",
      "P{}: 0 0 0 1 1 1 1 1 2 1 1 5 3 5 5 5 5 5 5 1 1 5 5 4 3 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 5 0 0 0 1 1 1 1 5 5 5 3 3 1 3 2 2 5 5 4 5 1 1 1 1 5 1 1 5 5 0 0 1 1 0 0 0 0 0 0\n",
      "\n",
      "Step 1499 loss 0.088802\n",
      "  0: a ) ] ) ( ] ] [ { a ) a [ { [ ] ) ) a ) ( ] ( } } a ) ] ( a } ) [ } { a [ ) [ { ( ] { { } a [ ( ) ( { a ) } ( } ( ( ] } ) ) ) ) [ a ) ) [ } ) } [ a a } { ( [ [ { } a } a { ) [ ] ) } { a ] ] a ] [ a {\n",
      "G(): 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 1 1 2 2 2 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 2 1 2 2 2 1 1 2 2 3 4 4 4 3 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P(): 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 1 1 1 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 0 1 2 1 1 0 1 1 5 5 5 5 4 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G[]: 0 0 0 0 0 0 0 1 1 1 1 1 2 2 3 2 2 2 2 2 2 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 2 2 3 3 3 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 3 2 2 1 2 2 2\n",
      "P[]: 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 2 3 3 4 2 2 2 2 2 3 3 3 3 5 4 3 4 3 4 3 5 2 1 1 2 2 2 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 3 2 2 0 1 1 1\n",
      "G{}: 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 3 4 3 3 3 3 3 3 4 4 4 3 3 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 2 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 2\n",
      "P{}: 0 0 0 0 0 0 0 0 1 1 1 1 1 2 1 2 2 2 3 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 1 1 5 5 4 3 5 4 5 4 5 5 5 4 1 1 1 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 2\n",
      "\n",
      "Step 1999 loss 0.076501\n",
      "  0: [ { ) a a { ) ) a [ ) a ) ( } ) ) a } [ { ( [ } ] a ] ( { a ] ( { ) a [ [ ( ] { } a } [ [ ( [ [ [ ) ] { a ] a ] } [ ] } { ] ] } ] { a { a a ( } ] ) ( ] ( ] ( ] [ } ( [ { } } ) { { { ( } ( ( { { } [ {\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 3 3 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 3 4 4 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 1 2 2 1 1 2 2 1 1 1 1 2 2 2 2 3 2 2 2 5 5 5 5 4 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 2 5 5 5 5 5 5 5 5 5 5\n",
      "G[]: 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 4 4 3 3 2 2 2 2 1 1 1 1 1 2 3 3 2 2 2 2 2 3 4 4 5 5 5 5 4 4 4 3 3 2 2 3 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3\n",
      "P[]: 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 3 3 1 2 2 2 1 1 1 0 2 2 3 3 2 2 2 2 2 3 5 5 5 5 5 5 4 4 4 3 2 1 1 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 3\n",
      "G{}: 0 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 0 0 1 1 1 0 0 0 0 0 1 1 1 1 2 2 2 2 2 2 2 3 2 2 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 1 1 0 1 1 1 0 0 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 0 1 2 3 3 2 2 2 3 4 3 3 4\n",
      "P{}: 0 1 1 1 1 2 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 2 2 2 1 1 1 2 5 2 2 1 1 1 1 1 1 1 2 1 2 2 1 1 3 1 1 1 0 1 1 1 0 0 1 1 2 2 2 3 2 1 1 1 1 1 1 1 2 2 0 1 1 2 0 0 0 1 2 5 5 2 2 2 5 5 4 3 5\n",
      "\n",
      "Step 2499 loss 0.067854\n",
      "  0: ] } ( } } } ] ( { [ { { a { ( { ] ] ( ] } [ } ( ] } [ ] { ( ) [ a ) ( ( ) ( ] } ] [ } [ } ) ( ( [ } ( ( [ [ [ [ a [ a } ] } a a { { ) ( } } [ a a a } ( a { a ( [ ) { } ] [ [ { a } a ( [ ] [ { [ [ ) ]\n",
      "G(): 0 0 1 1 1 1 1 2 2 2 2 2 2 2 3 3 3 3 4 4 4 4 4 5 5 5 5 5 5 5 4 4 4 3 4 5 4 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 4 4\n",
      "P(): 0 0 1 1 1 1 1 2 2 2 1 1 1 1 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 4 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5 4 3 4 5 5 5 5 5 5 5 4 5 3 2\n",
      "G[]: 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 2 2 2 2 2 3 3 3 3 4 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 4 5 5 5 5 5 4\n",
      "P[]: 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 1 1 2 2 2 2 2 3 3 3 3 5 5 5 5 5 5 5 5 4 4 4 4 3 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 4 5 5 5 5 5 4\n",
      "G{}: 0 0 0 0 0 0 0 0 1 1 2 3 3 4 4 5 5 5 5 5 4 4 3 3 3 2 2 2 3 3 3 3 3 3 3 3 3 3 3 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 1 0 0 0 0 0 0 0 0 1 1 1 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 2 2 2 2 2\n",
      "P{}: 0 0 0 0 0 0 0 0 1 1 2 3 2 5 5 5 5 5 5 5 4 3 2 2 2 1 1 1 2 2 2 1 2 2 2 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 1 0 0 0 0 0 0 0 0 1 1 1 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 2 2 1 1 1\n",
      "\n",
      "Step 2999 loss 0.059070\n",
      "  0: ( ) ) a ) [ { { } a a ] a a [ a a ) } ( } ( [ ] a { { { } [ a { ) ) { a a [ ( } ( [ ] a { a ] ( { ) ] { ] ) ) ] } [ { ) { } ( a ( { a } ) ( } { } ( { ] } ) ( ] ( [ ) ] ( ] } a ] ] [ ) ( } { { [ a ] a\n",
      "G(): 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 2 2 2 2 2 2 1 0 0 0 0 0 1 1 2 2 2 2 2 2 2 3 3 2 2 2 2 1 0 0 0 0 0 0 0 0 1 1 2 2 2 2 1 2 2 2 2 3 3 3 3 2 3 3 4 4 3 3 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4\n",
      "P(): 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 2 2 2 2 2 2 2 1 0 0 0 0 0 1 1 2 2 2 2 2 2 2 3 3 1 2 1 2 1 0 0 0 0 0 0 0 0 1 1 2 2 2 2 1 2 2 2 2 3 3 3 2 1 2 2 4 4 3 3 5 5 4 4 4 4 4 3 5 4 4 4 4 4 4 4\n",
      "G[]: 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 4 3 3 3 3 2 2 2 2 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 2 2 1 1\n",
      "P[]: 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 3 3 3 3 4 3 2 2 2 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 2 2 1 1\n",
      "G{}: 0 0 0 0 0 0 1 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 2 3 2 2 2 3 3 3 4 4 4 4 4 3 3 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 4 4 5 5 5 4 4 4 4 5 5 4 4 4 3 4 3 3 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 1 2 3 3 3 3 3\n",
      "P{}: 0 0 0 0 0 0 1 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 2 3 1 1 1 2 2 2 4 4 4 4 4 4 3 2 3 2 5 5 5 5 5 5 5 5 5 5 5 5 4 3 5 5 5 4 4 4 4 5 5 4 4 3 3 4 3 2 3 4 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 0 1 2 2 2 2 2\n",
      "\n",
      "Step 3499 loss 0.051661\n",
      "  0: a ) ) } ] a { ] ] [ [ ] ( { } [ ] a } ( [ a { ) ( ) [ ] [ } ( [ [ [ ( { a a [ ( ) a ) a ( { ( [ ) [ } ] [ a ] ( } [ ) a } { ) [ { ( ( } ) ) ( ) ] } ] { ( ] [ ) ( ] a { ) } } { a } } a ] { a { ] { ) (\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 2 2 2 2 1 2 1 1 1 1 1 2 2 2 2 3 3 3 3 3 4 3 3 2 2 3 3 4 4 3 3 3 3 3 3 3 4 4 4 3 3 3 3 2 2 2 3 4 4 3 2 3 2 2 2 2 2 3 3 3 2 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 2 2 2 2 1 2 1 1 1 1 1 2 2 2 2 3 3 3 3 2 4 4 3 2 1 2 3 5 5 4 4 3 3 2 3 3 5 5 5 4 3 3 3 2 2 2 3 4 5 4 2 3 2 2 2 2 2 3 3 3 2 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2\n",
      "G[]: 0 0 0 0 0 0 0 0 0 1 2 1 1 1 1 2 1 1 1 1 2 2 2 2 2 2 3 2 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 3 3 3 2 3 3 3 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 0 0 0 0\n",
      "P[]: 0 0 0 0 0 0 0 0 0 1 2 1 1 1 1 2 1 1 1 1 2 2 2 2 2 2 3 2 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 3 3 2 2 2 2 2 2 1 1 2 1 1 2 2 1 2 2 1 1 1 1 0 0 0 0\n",
      "G{}: 0 0 0 0 0 0 1 1 1 1 1 1 1 2 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 0 0 0 0 0 1 1 1 2 2 2 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 2 2 1 0 1 1 0 0 0 0 1 1 2 2 3 3 3\n",
      "P{}: 0 0 0 0 0 0 1 1 1 1 1 1 1 2 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 0 0 0 0 0 1 1 1 2 2 2 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 2 2 1 0 1 1 0 0 0 0 1 1 2 2 3 3 2\n",
      "\n",
      "Step 3999 loss 0.045158\n",
      "  0: } a ( a ( ( [ a } a ( a [ } } } ] a a [ [ ] { ( a [ a ) ( ( } ( ] ] } [ [ } a ] ( } { { a ] ( ( } { { ( a a ( ( } } [ { { [ } { { [ [ } [ [ [ ) ] ] [ [ [ } { ( { a [ a ] ] ) ] { a a ) { ) [ { } [ ) [\n",
      "G(): 0 0 1 1 2 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 4 4 4 4 4 3 3 2 2 2 2 2 1 1\n",
      "P(): 0 0 1 1 2 3 3 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 3 3 4 3 3 5 5 5 5 5 5 5 4 4 3 3 3 3 3 2 1 1 2 2 0 0\n",
      "G[]: 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 1 1 1 2 3 2 2 2 2 3 3 3 3 3 3 3 2 1 1 2 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3 3 3 3 4 5 5 5 5 5 5 4 3 4 5 5 5 5 5 5 5 5 5 4 3 3 2 2 2 2 2 2 2 3 3 3 4 4 5\n",
      "P[]: 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 1 1 1 2 3 2 2 2 2 3 3 3 3 3 3 3 2 1 1 2 3 3 3 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3 3 3 3 4 5 5 5 5 5 5 4 3 4 5 5 5 5 5 5 5 5 5 4 3 3 2 2 2 2 2 2 2 3 3 3 4 4 5\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 1 2 3 3 3 3 3 3 2 1 1 2 3 3 2 3 4 4 4 3 3 3 3 3 3 3 3 3 3 2 3 3 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 4 4 4 4\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 1 2 3 3 3 3 3 3 1 1 1 2 3 3 1 3 5 5 4 4 3 3 3 3 3 3 2 2 2 2 3 3 4 5 4 5 4 5 5 4 5 5 5 5 5 5 5 5 4 4 4 4\n",
      "\n",
      "Step 4499 loss 0.041556\n",
      "  0: ) ) a ] ) ) { a } ] [ { ) { ] [ [ } a ] ) ] a ] ( ) ] a ) ) { [ { } [ [ [ ] ( ) [ [ ) } [ } a ) [ ) a a [ a [ ] ] } [ [ ] [ ( [ ) ( ( { ( ( [ ( } a { [ } [ ] ( a } a ( { ( { [ { ( ) a } ( ) { ) ) ] [\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 2 2 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 4 4 3 2 2 2\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 2 2 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 4 4 3 2 2 2\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 2 3 4 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 4 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 4 5\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 2 3 4 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 4 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 4 5\n",
      "G{}: 0 0 0 0 0 0 1 1 0 0 0 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 2 2 3 3 3 3 2 2 2 3 3 3 3 3\n",
      "P{}: 0 0 0 0 0 0 1 1 0 0 0 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 2 2 3 3 3 3 2 2 1 2 3 2 2 2\n",
      "\n",
      "Step 4999 loss 0.034678\n",
      "  0: [ ] ( ] ( ( ] [ { ( } ] [ ] } a ( { ) ) ) ] ] } } ) a } ( { ) ) { ] [ } ( a a { a ( ) { ( a ( { [ [ a ] [ } } ] ] [ ( ) { } a ] ( ] a [ { ) ( a { ) } } ] ] ) { { [ [ [ { a a } ) ] } [ } a [ } ) a } {\n",
      "G(): 0 0 1 1 2 3 3 3 3 4 4 4 4 4 4 4 5 5 4 3 2 2 2 2 2 1 1 1 2 2 1 0 0 0 0 0 1 1 1 1 1 2 1 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 4 4 4 4 4 3 4 4 4 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 0 0 0 0\n",
      "P(): 0 0 1 1 2 3 3 3 3 4 4 4 4 4 4 4 5 5 4 3 2 2 2 2 2 1 1 1 2 2 1 0 0 0 0 0 1 1 1 1 1 2 1 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 4 4 4 4 4 3 4 4 4 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 0 0 0 0\n",
      "G[]: 1 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 2 3 3 3 2 1 2 2 2 2 2 2 1 1 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 2 3 3 3 3 3 3 2 2 3 3 3 4 4 4 4 4 4\n",
      "P[]: 1 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 2 3 3 3 2 1 2 2 2 2 2 2 1 1 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 2 3 3 3 3 3 3 2 2 3 3 3 4 4 4 4 4 4\n",
      "G{}: 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 2 2 2 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 4 4 3 2 2 2 2 2 2 3 2 2 2 2 2 2 2 3 3 3 3 4 4 3 2 2 2 2 3 4 4 4 4 5 5 5 4 4 4 3 3 2 2 2 1 1 1 0 1\n",
      "P{}: 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 2 2 2 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 4 4 3 2 2 2 2 2 2 3 2 2 2 2 2 2 2 3 3 3 3 4 4 3 2 2 2 2 3 4 4 4 4 5 5 5 4 4 4 3 3 2 2 2 1 1 1 0 1\n",
      "\n",
      "Step 5499 loss 0.028342\n",
      "  0: a } { ) ) a a ] } ] ] [ [ [ a ] } ( ( ( a ( a ) ] { ( ] a a { a a ) { [ } ] } { { ] a [ } a [ ( { ) } [ ] ] a ] ( ] ( ( ) } a ] a } a a a a ) } ) [ { ( { { [ { a a { a ) ( ( ( [ ( ] { } ( ) } ] [ [ )\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 3 4 4 3 3 3 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 3 3 3 3 3 3 3 4 4 5 5 4 4 4 4 4 4 4 4 4 4 3 3 2 2 2 3 3 3 3 3 3 3 3 3 2 3 4 5 5 5 5 5 5 5 4 4 4 4 4 3\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 3 4 4 3 3 3 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 3 3 3 3 3 3 3 4 4 5 5 4 4 4 4 4 4 4 4 4 4 3 3 2 2 2 3 3 3 3 3 3 3 3 3 2 3 4 5 5 5 5 5 5 5 4 4 4 4 4 3\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 1 2 3 3 2 2 2 2 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 2 2 2 2 2 3 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 1 2 3 3\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 1 2 3 3 2 2 2 2 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 2 2 2 2 2 3 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 1 2 3 3\n",
      "G{}: 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 2 3 3 2 2 1 2 3 3 3 3 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 2 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 4 4 4 3 3 3 3 3\n",
      "P{}: 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 2 3 3 2 2 1 2 3 3 3 3 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 2 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 4 4 4 3 3 3 3 3\n",
      "\n",
      "Step 5999 loss 0.025773\n",
      "  0: ] { ] ) ) a a ] ( ] [ } { a ) ] { ] [ } ) [ ) } ) } } a { a a ) ( a [ a { ) ) } ) a ) } ] ] } a { a ( ] ] a } a ] { a ( ( ] ] ] } } a a ( a ( ) } ] ) [ { ] ) [ [ ) ) } ) ] ] a { ) ) ] a ) a { [ a ( [\n",
      "G(): 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 4 4 5 4 4 4 3 3 3 3 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1\n",
      "P(): 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 4 4 5 4 4 4 3 3 3 3 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 2 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 1 1 1 2\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 2 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 1 1 1 2\n",
      "G{}: 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 2 2 2 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 2 2 2 2 2\n",
      "P{}: 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 2 2 2 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 2 2 2 2 2\n",
      "\n",
      "Step 6499 loss 0.019538\n",
      "  0: { [ ] } { a [ { } { ) [ ] ( ) ( ] ) a ] ] } } [ a [ { ) a ( [ { } { } ] ) a } { { ] ( } ( ] ) } ( a ) [ a a [ { ] } } } } { a ( { [ ) [ a } ] a } } ( a } ) ] [ [ } a [ [ ) } ( [ ) ( ) ( ] ) a } ] ] ]\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0\n",
      "G[]: 0 1 0 0 0 0 1 1 1 1 1 2 1 1 1 1 0 0 0 0 0 0 0 1 1 2 2 2 2 2 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 0 0 0 0 0 0 1 1 1 2 2 1 1 1 1 1 1 1 1 1 2 2 3 3 3 2 2 2 2 2 2 2 2 1 2 3 3 3 4 5 5 5 5 5 5 5 5 5 4 4 4 4 3 2 1\n",
      "P[]: 0 1 0 0 0 0 1 1 1 1 1 2 1 1 1 1 0 0 0 0 0 0 0 1 1 2 2 2 2 2 3 3 3 3 3 2 2 2 2 2 2 1 1 1 1 0 0 0 0 0 0 1 1 1 2 2 1 1 1 1 1 1 1 1 1 2 2 3 3 3 2 2 2 2 2 2 2 2 1 2 3 3 3 4 5 5 5 5 5 5 5 5 5 4 4 4 4 3 2 1\n",
      "G{}: 1 1 1 0 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 0 0 0 0 1 1 1 1 1 2 1 2 1 1 1 1 0 1 2 2 2 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P{}: 1 1 1 0 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 0 0 0 0 1 1 1 1 1 2 1 2 1 1 1 1 0 1 2 2 2 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "\n",
      "Step 6999 loss 0.017477\n",
      "  0: a { [ ) ] [ } a } a a [ { ] } a [ ] [ } ) [ ( ( { { a { { a [ [ ] { [ ] [ ] } ] ( ) ) a ) ( a ) ) [ [ [ ( [ ) a ) ] ) } [ } ) } { ] ) ( ) [ ] ] { ) } { ( { ) a ) ] ( ) } } ) a ] } { ] ] ) ) ( a ) ( a\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 1 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 1 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1\n",
      "G[]: 0 0 1 1 0 1 1 1 1 1 1 2 2 1 1 1 2 1 2 2 2 3 3 3 3 3 3 3 3 3 4 5 4 4 5 4 5 4 4 3 3 3 3 3 3 3 3 3 3 4 5 5 5 5 5 5 5 4 4 4 5 5 5 5 5 4 4 4 4 5 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0\n",
      "P[]: 0 0 1 1 0 1 1 1 1 1 1 2 2 1 1 1 2 1 2 2 2 3 3 3 3 3 3 3 3 3 4 5 4 4 5 4 5 4 4 3 3 3 3 3 3 3 3 3 3 4 5 5 5 5 5 5 5 4 4 4 5 5 5 5 5 4 4 4 4 5 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0\n",
      "G{}: 0 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 2 2 3 4 4 4 4 4 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 2 2 1 2 2 2 2 2 2 2 2 3 3 2 3 3 4 4 4 4 4 4 4 3 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2\n",
      "P{}: 0 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 2 2 3 4 4 4 4 4 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 2 2 1 2 2 2 2 2 2 2 2 3 3 2 3 3 4 4 4 4 4 4 4 3 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2\n",
      "\n",
      "Step 7499 loss 0.015863\n",
      "  0: ( [ } ( ) a ( } a { ( ) a } ) ) a ] } ) } ) ] } { } ) [ ] { ] a ( } } ) ) ] a [ ] { [ [ ) ( [ } ] a { ) ( ( ) a [ ) } [ } ) } } ] ( ] ) ) [ [ ] } } ] ) } } ( ] [ } a [ { { a { ) ) [ ( { { [ [ ( ] ] [\n",
      "G(): 1 1 1 2 1 1 2 2 2 2 3 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 2 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 2 2 2 2\n",
      "P(): 1 1 1 2 1 1 2 2 2 2 3 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 2 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 2 2 2 2\n",
      "G[]: 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 2 2 2 3 3 2 2 2 2 2 2 2 2 3 3 3 4 4 4 4 4 3 3 2 2 2 3 4 3 3 3 2 2 2 2 2 1 2 2 2 3 3 3 3 3 3 3 4 4 4 4 5 5 5 4 3 4\n",
      "P[]: 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 2 2 2 3 3 2 2 2 2 2 2 2 2 3 3 3 4 4 4 4 4 3 3 2 2 2 3 4 3 3 3 2 2 2 2 2 1 2 2 2 3 3 3 3 3 3 3 4 4 4 4 5 5 5 4 3 4\n",
      "G{}: 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 3 3 3 3 3 4 5 5 5 5 5 5 5\n",
      "P{}: 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 3 3 3 3 3 4 5 5 5 5 5 5 5\n",
      "\n",
      "Step 7999 loss 0.009591\n",
      "  0: ) ) ( [ ) } ] [ ] [ ) a { ( ) [ ( ( ) ( ] ) ] ] } ) a [ ) a ( ) ( [ { { { [ } ] a { a { ) a { ) ] { ( ] ) ) ) { [ { ] } a ] a ) a } ] ( ( } } ) ) [ ( [ [ [ ) } [ ( { ) ] [ a ) [ a ( ( { { ) ] ] } } }\n",
      "G(): 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 2 1 2 2 1 1 1 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 1 0 0 1 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 1 2 2 2 1 1 1 1 1 1\n",
      "P(): 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 2 1 2 2 1 1 1 1 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 1 0 0 1 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 1 2 2 2 1 1 1 1 1 1\n",
      "G[]: 0 0 0 1 1 1 0 1 0 1 1 1 1 1 1 2 2 2 2 2 1 1 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 3 4 4 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 4 3 3 3 3\n",
      "P[]: 0 0 0 1 1 1 0 1 0 1 1 1 1 1 1 2 2 2 2 2 1 1 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 3 4 4 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 4 3 3 3 3\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 3 2 2 2 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 2 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 2 1 0\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 3 2 2 2 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 3 3 3 3 2 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 2 1 0\n",
      "\n",
      "Step 8499 loss 0.002339\n",
      "  0: { a ] ) a ( } ) ( ( ( [ { [ [ [ a [ } } ( ] a } [ a } ) ] [ ( a [ } [ ) ) [ { ( { [ a ] } ( } [ ( ) a a } } ) ) ) { } } ) { ] ] a [ a a ) ) ] { { ( { a } ) [ ) ) ] a } ) ] } ] ] a } { ( } ] ] [ ) ) }\n",
      "G(): 0 0 0 0 0 1 1 0 1 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 3 3 3 4 4 4 4 4 3 2 2 2 3 3 3 3 3 3 4 4 4 5 4 4 4 4 4 3 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0\n",
      "P(): 0 0 0 0 0 1 1 0 1 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 3 3 3 4 4 4 4 4 3 2 2 2 3 3 3 3 3 3 4 4 4 5 4 4 4 4 4 3 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 1 1 2 3 4 4 5 5 5 5 4 4 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 4 4 4 4 4 3 3 3 3 3 3 3 3 4 4 4 3 3 3 3 2 2 1 0 0 0 0 0 0 0 0 1 1 1 1\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 1 1 2 3 4 4 5 5 5 5 4 4 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 4 4 4 4 4 3 3 3 3 3 3 3 3 4 4 4 3 3 3 3 2 2 1 0 0 0 0 0 0 0 0 1 1 1 1\n",
      "G{}: 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 2 3 3 4 4 3 3 3 3 3 3 3 2 2 2 1 1 1 1 0 1 1 0 0 0 0 0 0 0\n",
      "P{}: 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 2 3 3 4 4 3 3 3 3 3 3 3 2 2 2 1 1 1 1 0 1 1 0 0 0 0 0 0 0\n",
      "\n",
      "Step 8999 loss 0.000332\n",
      "  0: a a a ] ( [ } [ } { ) { ) } [ ( [ [ [ a ] ] { ] a [ } ( ( a [ a ) [ a ] ) a ] a a ( { a } ( } [ ) [ a ( a ( ] ] { a { ] { a ) a [ ( a } ) ] ] ) a ) [ a ) ] [ a [ ] ] a [ a ) a a { } a ] ] { a a ) { ]\n",
      "G(): 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 2 2 2 2 1 1 1 1 1 2 2 2 2 3 3 3 2 2 2 3 3 4 4 4 4 4 4 4 4 4 3 3 3 4 4 4 3 3 3 2 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P(): 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 2 3 3 3 3 2 2 2 2 1 1 1 1 1 2 2 2 2 3 3 3 2 2 2 3 3 4 4 4 4 4 4 4 4 4 3 3 3 4 4 4 3 3 3 2 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G[]: 0 0 0 0 0 1 1 2 2 2 2 2 2 2 3 3 4 5 5 5 4 3 3 2 2 3 3 3 3 3 4 4 4 5 5 4 4 4 3 3 3 3 3 3 3 3 3 4 4 5 5 5 5 5 4 3 3 3 3 2 2 2 2 2 3 3 3 3 3 2 1 1 1 1 2 2 2 1 2 2 3 2 1 1 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0\n",
      "P[]: 0 0 0 0 0 1 1 2 2 2 2 2 2 2 3 3 4 5 5 5 4 3 3 2 2 3 3 3 3 3 4 4 4 5 5 4 4 4 3 3 3 3 3 3 3 3 3 4 4 5 5 5 5 5 4 3 3 3 3 2 2 2 2 2 3 3 3 3 3 2 1 1 1 1 2 2 2 1 2 2 3 2 1 1 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0\n",
      "G{}: 0 0 0 0 0 0 0 0 0 1 1 2 2 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 2 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 3 3 3 3 4 4\n",
      "P{}: 0 0 0 0 0 0 0 0 0 1 1 2 2 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 2 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 3 3 3 3 4 4\n",
      "\n",
      "Step 9499 loss 0.000179\n",
      "  0: { } } ) ) } ) a { ( ] { [ [ a ) ) ] [ [ { ] a ] } } ( ( } } ] { { } } [ [ ] a ( [ ) { a ( ( ) [ ) a { } ] [ ) [ ) { a [ ) { } } ) ) [ { ( ] ) ) a ( } [ ) [ [ ) } { { a { { } ) } a { } { a [ a a } ) )\n",
      "G(): 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 3 4 3 3 2 2 2 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P(): 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 3 4 3 3 2 2 2 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 1 2 3 3 2 2 1 1 1 1 1 1 1 0 0 0 0 0 1 2 1 1 1 2 2 2 2 2 2 2 3 3 3 3 3 2 3 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 1 2 3 3 2 2 1 1 1 1 1 1 1 0 0 0 0 0 1 2 1 1 1 2 2 2 2 2 2 2 3 3 3 3 3 2 3 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      "G{}: 1 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 2 1 1 1 0 0 0 1 2 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 2 2 2 3 2 1 1 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 0 1 2 2 3 4 3 3 2 2 3 2 3 3 3 3 3 2 2 2\n",
      "P{}: 1 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 2 1 1 1 0 0 0 1 2 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 2 2 2 3 2 1 1 1 1 2 2 2 2 2 2 2 1 1 1 1 1 1 0 1 2 2 3 4 3 3 2 2 3 2 3 3 3 3 3 2 2 2\n",
      "\n",
      "Step 9999 loss 0.000112\n",
      "  0: a ] ( ( ) { a a [ ) ) { ( ] { [ { ) a ( ) { } [ } } } ) ] [ ] ] [ { a ] a a ( ( ( } } } ] } ] [ a ] ( } ) } ] ( ] ( ( ( ( ) } { a } { ( a ) { ] ] [ ] a ) ] [ a a [ { a ( ( [ { ( ( { a ] { { ( } { a )\n",
      "G(): 0 0 1 2 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 3 3 3 3 3 3 3 3 3 4 4 3 3 3 4 4 5 5 5 5 4 4 4 4 4 4 5 5 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4\n",
      "P(): 0 0 1 2 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 3 3 3 3 3 3 3 3 3 4 4 3 3 3 4 4 5 5 5 5 4 4 4 4 4 4 5 5 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4\n",
      "G[]: 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 1 2 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 2 2 2 2 2 3 3 3 3 3 3 2 2 2 2 2 2 2 2\n",
      "P[]: 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 1 2 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 2 2 2 2 2 3 3 3 3 3 3 2 2 2 2 2 2 2 2\n",
      "G{}: 0 0 0 0 0 1 1 1 1 1 1 2 2 2 3 3 4 4 4 4 4 5 4 4 3 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 4 4 4 5 5 5 5 5 5 4 5 5 5\n",
      "P{}: 0 0 0 0 0 1 1 1 1 1 1 2 2 2 3 3 4 4 4 4 4 5 4 4 3 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 4 4 4 5 5 5 5 5 5 4 5 5 5\n",
      "\n",
      "Step 10499 loss 0.000070\n",
      "  0: } } { ) ( } a } ) ] a a ( ] ( ] } ] ] [ ( a [ ] } ( ) ] ] [ } } ) a { } ] { ) [ a ( } ] a [ [ { } ) { } a } [ { ( ] ( [ ] } { ] } { ) ] { { ) ] { { ) a } } ) ( { { } ( ( ] { } ( a } [ ( } ] } ( [ { ]\n",
      "G(): 0 0 0 0 1 1 1 1 0 0 0 0 1 1 2 2 2 2 2 2 3 3 3 3 3 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 0 1 1 1 1 2 3 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5\n",
      "P(): 0 0 0 0 1 1 1 1 0 0 0 0 1 1 2 2 2 2 2 2 3 3 3 3 3 4 3 3 3 3 3 3 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 2 2 2 2 1 1 1 1 0 0 0 0 0 1 1 1 1 2 3 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 1 2 2 2 2 2 2 2 2 3 3 3 2 2 3 2 2 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 0 1 2 2 2 2 2 2 2 2 3 3 3 2 2 3 2 2 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0\n",
      "G{}: 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1 1 1 1 1 0 1 1 0 1 1 1 2 3 3 3 4 5 5 5 4 3 3 3 4 5 4 4 4 4 5 4 4 4 3 3 3 2 2 1 1 1 2 2\n",
      "P{}: 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1 1 1 1 1 0 1 1 0 1 1 1 2 3 3 3 4 5 5 5 4 3 3 3 4 5 4 4 4 4 5 4 4 4 3 3 3 2 2 1 1 1 2 2\n",
      "\n",
      "Step 10999 loss 0.000049\n",
      "  0: ) [ } } a { a { ] { { ) ( ] } [ { { } } ( ( } ] ( ] } ] a } { [ ] { { ) a ] ) a } ( [ a a { } { { ( { { ( a ( { ) [ ] ( ( } ] { } ) ( ] [ ) ( } } { ( [ ( a { ] a ( ) ( ) a ( ] } } a a ) a ] a ) ] a ]\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 3 3 3 4 4 4 4 4 4 4 4 4 4 4 3 3 3 2 2 2 3 3 3 3 3 3 3 3 4 4 4 5 5 5 5 4 4 4 5 5 5 5 5 5 4 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4 5 4 4 5 5 5 5 5 5 4 4 4 4 3 3 3 3\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 3 3 3 4 4 4 4 4 4 4 4 4 4 4 3 3 3 2 2 2 3 3 3 3 3 3 3 3 4 4 4 5 5 5 5 4 4 4 5 5 5 5 5 5 4 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4 5 4 4 5 5 5 5 5 5 4 4 4 4 3 3 3 3\n",
      "G[]: 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P[]: 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G{}: 0 0 0 0 0 1 1 2 2 3 4 4 4 4 3 3 4 5 4 3 3 3 2 2 2 2 1 1 1 0 1 1 1 2 3 3 3 3 3 3 2 2 2 2 2 3 2 3 4 4 5 5 5 5 5 5 5 5 5 5 5 4 4 5 4 4 4 4 4 4 4 3 2 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 3 2 2 2 2 2 2 2 2 2 2 2\n",
      "P{}: 0 0 0 0 0 1 1 2 2 3 4 4 4 4 3 3 4 5 4 3 3 3 2 2 2 2 1 1 1 0 1 1 1 2 3 3 3 3 3 3 2 2 2 2 2 3 2 3 4 4 5 5 5 5 5 5 5 5 5 5 5 4 4 5 4 4 4 4 4 4 4 3 2 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 3 2 2 2 2 2 2 2 2 2 2 2\n",
      "\n",
      "Step 11499 loss 0.000036\n",
      "  0: ] ) } a a ( ] [ a } } { [ ) [ a ) ( { ( ( } { ] } } } ] a ( [ ) ( ) [ } ] { [ a a } ) ) ) { ) { ) { } ] ( a [ ) ( a } ] [ ( ) ] a [ ) ) a ( } ] ] [ [ ] a [ a ] ] } { { a [ ] } ( ) } ) ] [ [ { ( ) { a\n",
      "G(): 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 1 2 3 3 3 3 3 3 3 3 3 4 4 3 4 3 3 3 3 3 3 3 3 3 2 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 2 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 0 0 0 0 0 1 0 0 0\n",
      "P(): 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 1 2 3 3 3 3 3 3 3 3 3 4 4 3 4 3 3 3 3 3 3 3 3 3 2 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 2 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 0 0 0 0 0 1 0 0 0\n",
      "G[]: 0 0 0 0 0 0 0 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 3 2 2 2 2 1 1 1 2 2 2 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 3 3 2 1 2 3 2 2 3 3 2 1 1 1 1 1 2 1 1 1 1 1 1 0 1 2 2 2 2 2 2\n",
      "P[]: 0 0 0 0 0 0 0 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 3 2 2 2 2 1 1 1 2 2 2 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 3 3 3 3 3 2 3 3 3 2 2 3 3 3 3 3 3 2 1 2 3 2 2 3 3 2 1 1 1 1 1 2 1 1 1 1 1 1 0 1 2 2 2 2 2 2\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 2 2 2 1 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 2 2 3 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 1 1 1 0 0 0 0 0 1 1 1 2 2\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 2 2 2 1 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 2 2 3 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 1 1 1 0 0 0 0 0 1 1 1 2 2\n",
      "\n",
      "Step 11999 loss 0.000045\n",
      "  0: ( { a { ( a ] ) [ a { ( a } } { ] ) ( { ] } } ( ( ) } ( [ ] [ ] ] ) ( ] [ ( [ { ] { a ) ) } [ ) [ ) ) [ } } ( ) ) { { [ { ] ) ) ( ] a [ ] } a ] ) { ] ] { ] ) ) { { ] ] ) [ a ] } ] { [ { { } { [ [ a (\n",
      "G(): 1 1 1 1 2 2 2 1 1 1 1 2 2 2 2 2 2 1 2 2 2 2 2 3 4 3 3 4 4 4 4 4 4 3 4 4 4 5 5 5 5 5 5 4 3 3 3 2 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
      "P(): 1 1 1 1 2 2 2 1 1 1 1 2 2 2 2 2 2 1 2 2 2 2 2 3 4 3 3 4 4 4 4 4 4 3 4 4 4 5 5 5 5 5 5 4 3 3 3 2 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
      "G[]: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 2 2 1 1 1 1 1 1 2 2 3 3 3 4 4 4 4 4 4 4 4 5 5 4 4 4 4 3 3 4 3 3 3 2 2 2 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1 2 3 3 3\n",
      "P[]: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 2 2 1 1 1 1 1 1 2 2 3 3 3 4 4 4 4 4 4 4 4 5 5 4 4 4 4 3 3 4 3 3 3 2 2 2 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1 2 3 3 3\n",
      "G{}: 0 1 1 2 2 2 2 2 2 2 3 3 3 2 1 2 2 2 2 3 3 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 1 1 1 1 1 1 1 0 0 0 0 0 1 2 2 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 4 4 5 5 5 5 4 5 5 5 5 5\n",
      "P{}: 0 1 1 2 2 2 2 2 2 2 3 3 3 2 1 2 2 2 2 3 3 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 1 1 1 1 1 1 1 0 0 0 0 0 1 2 2 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 4 4 5 5 5 5 4 5 5 5 5 5\n",
      "\n",
      "Step 12499 loss 0.000029\n",
      "  0: ) [ a { ) a ] ( ) } ( [ ) [ } a { { ] ] { a ) { [ a [ [ ] ] { a { [ { a { ] } } ] ] ( } { ( ) } { { } ( ( ) ( ] { { } [ { [ [ ] ( { } a a ) } } [ [ [ ] a ) ( ( a ( a [ { a a ] } a a { ] { a ) [ a ( (\n",
      "G(): 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 1 1 1 2 3 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 3 3 3 3 3 3 3 3 2 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5\n",
      "P(): 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 1 1 1 2 3 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 3 3 3 3 3 3 3 3 2 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5\n",
      "G[]: 0 1 1 1 1 1 0 0 0 0 0 1 1 2 2 2 2 2 1 0 0 0 0 0 1 1 2 3 2 1 1 1 1 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 3 2 2 2 2 2 2 2 2 2 3 4 5 4 4 4 4 4 4 4 4 5 5 5 5 4 4 4 4 4 3 3 3 3 4 4 4 4\n",
      "P[]: 0 1 1 1 1 1 0 0 0 0 0 1 1 2 2 2 2 2 1 0 0 0 0 0 1 1 2 3 2 1 1 1 1 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 3 2 2 2 2 2 2 2 2 2 3 4 5 4 4 4 4 4 4 4 4 5 5 5 5 4 4 4 4 4 3 3 3 3 4 4 4 4\n",
      "G{}: 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 2 2 2 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 4 3 3 3 3 2 3 3 3 2 3 4 3 3 3 3 3 3 4 5 4 4 5 5 5 5 5 5 4 4 4 4 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 2 2 2 3 3 4 4 4 4 4 4 4\n",
      "P{}: 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 2 2 2 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 4 3 3 3 3 2 3 3 3 2 3 4 3 3 3 3 3 3 4 5 4 4 5 5 5 5 5 5 4 4 4 4 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 2 2 2 3 3 4 4 4 4 4 4 4\n",
      "\n",
      "Step 12999 loss 0.000048\n",
      "  0: ( a ( ( [ { ] [ } a ] a [ { a a ( { ( } ] [ } [ ) } { [ ( ) ] ) a ( [ [ ] { ( [ [ ] ) [ } [ } [ ( ( { { } ] } ] ) } ] a [ ) { a ] } a { ] a ] { ) ( ] ( ] [ [ } ] ( ( ( { { [ } ( { ( ) ) ( } ) [ ( [ [\n",
      "G(): 1 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 5 5 5 5 5 5 4 4 4 4 5 4 4 3 3 4 4 4 4 4 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 2 3 3 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 4 3 4 4 3 3 4 4 4\n",
      "P(): 1 1 2 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 5 5 5 5 5 5 4 4 4 4 5 4 4 3 3 4 4 4 4 4 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 2 3 3 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 4 3 4 4 3 3 4 4 4\n",
      "G[]: 0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 2 2 2 2 3 3 3 2 2 2 2 3 4 3 3 3 4 5 4 4 5 5 5 5 5 5 5 5 5 5 4 4 3 3 3 2 2 3 3 3 3 2 2 2 2 1 1 0 0 0 0 0 0 0 1 2 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 4 5\n",
      "P[]: 0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 2 2 2 2 3 3 3 2 2 2 2 3 4 3 3 3 4 5 4 4 5 5 5 5 5 5 5 5 5 5 4 4 3 3 3 2 2 3 3 3 3 2 2 2 2 1 1 0 0 0 0 0 0 0 1 2 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 4 5\n",
      "G{}: 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 2 2 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 0 0 0 0 1 2 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 2 3 3 2 2 3 3 3 3 3 2 2 2 2 2 2\n",
      "P{}: 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 2 2 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 1 1 0 0 0 0 1 2 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 2 3 3 2 2 3 3 3 3 3 2 2 2 2 2 2\n",
      "\n",
      "Step 13499 loss 0.000034\n",
      "  0: } [ ( { } } ] { ) a } ) [ [ ( a a } ) } ( ( ( [ ) ( } ( ) [ ( [ a ) ] a ( } a a ) ] ) a ] { a ] a ) { { ) ( } { { ] a ( ) a ] ) { ] } ( a } ] } ] ) } [ a } ] a { [ { ) ] ] [ ] ( ) { { ] ] ] a ) ) a (\n",
      "G(): 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 1 2 3 3 2 3 3 4 3 3 4 4 4 3 3 3 4 4 4 4 3 3 2 2 2 2 2 2 2 1 1 1 0 1 1 1 1 1 1 2 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1\n",
      "P(): 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 1 2 3 3 2 3 3 4 3 3 4 4 4 3 3 3 4 4 4 4 3 3 2 2 2 2 2 2 2 1 1 1 0 1 1 1 1 1 1 2 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1\n",
      "G[]: 0 1 1 1 1 1 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 5 5 5 4 4 4 4 4 4 4 3 3 3 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P[]: 0 1 1 1 1 1 0 0 0 0 0 0 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 5 5 5 4 4 4 4 4 4 4 3 3 3 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G{}: 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 3 3 3 2 3 4 4 4 4 4 4 4 4 5 5 4 4 4 3 3 2 2 2 1 1 1 0 0 0 1 1 2 2 2 2 2 2 2 2 3 4 4 4 4 4 4 4 4 4\n",
      "P{}: 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 3 3 3 2 3 4 4 4 4 4 4 4 4 5 5 4 4 4 3 3 2 2 2 1 1 1 0 0 0 1 1 2 2 2 2 2 2 2 2 3 4 4 4 4 4 4 4 4 4\n",
      "\n",
      "Step 13999 loss 0.000045\n",
      "  0: [ ) { [ a { a ( a [ ( ) a ) [ [ ) ( ] ) [ } } ] ] ] ) ) ( { [ ] ) a } ] ] ( ) } ( ] ) a } a ( a ] ) [ [ ] ) ) [ ) a ( } ) a ] ] ] ( ] ) ( ( { ] ( ) ] ( a { ] } [ } [ ) ] ) ] } } [ { ( } ( a [ ( a [ }\n",
      "G(): 0 0 0 0 0 0 0 1 1 1 2 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 1 2 2 2 3 2 2 3 3 3 3 3 3 3 3 2 2 1 1 1 1 1 1 2 2 3 3 3 4 4 4 4\n",
      "P(): 0 0 0 0 0 0 0 1 1 1 2 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 1 2 2 2 3 2 2 3 3 3 3 3 3 3 3 2 2 1 1 1 1 1 1 2 2 3 3 3 4 4 4 4\n",
      "G[]: 1 1 1 2 2 2 2 2 2 3 3 3 3 3 4 5 5 5 4 4 5 5 5 4 3 2 2 2 2 2 3 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 1 1 1 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 1 1 0 0 0 1 1 1 1 1 1 2 2 2 3 3\n",
      "P[]: 1 1 1 2 2 2 2 2 2 3 3 3 3 3 4 5 5 5 4 4 5 5 5 4 3 2 2 2 2 2 3 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 1 1 1 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 1 1 0 0 0 1 1 1 1 1 1 2 2 2 3 3\n",
      "G{}: 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 2 2 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0\n",
      "P{}: 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 2 2 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0\n",
      "\n",
      "Step 14499 loss 0.000071\n",
      "  0: ) [ a ) } [ } ( [ { ) a { } ( { { [ a ] a a { ] a { [ ) { ] ] ) [ ( a } [ { { ( ] ) [ [ ( [ } [ ( ) { a ) ) ] { { ) { ) ] a ] { [ { ] } a ( { { } [ ] ) ] a a ) ( ( } [ a { ] { [ ( ] { { ( ] a { ) ( }\n",
      "G(): 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 2 2 1 1 1 2 2 2 2 3 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 2 2 2 2 2 2 2 2 3 3 3 3 4 4 4 4 3 4 4\n",
      "P(): 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 2 2 1 1 1 2 2 2 2 3 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 2 2 2 2 2 2 2 2 3 3 3 3 4 4 4 4 3 4 4\n",
      "G[]: 0 1 1 1 1 2 2 2 3 3 3 3 3 3 3 3 3 4 4 3 3 3 3 2 2 2 3 3 3 2 1 1 2 2 2 2 3 3 3 3 2 2 3 4 4 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 3 3 2 2 3 3 2 2 2 2 2 2 2 3 2 2 1 1 1 1 1 1 1 2 2 2 1 1 2 2 1 1 1 1 0 0 0 0 0 0\n",
      "P[]: 0 1 1 1 1 2 2 2 3 3 3 3 3 3 3 3 3 4 4 3 3 3 3 2 2 2 3 3 3 2 1 1 2 2 2 2 3 3 3 3 2 2 3 4 4 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 3 3 2 2 3 3 2 2 2 2 2 2 2 3 2 2 1 1 1 1 1 1 1 2 2 2 1 1 2 2 1 1 1 1 0 0 0 0 0 0\n",
      "G{}: 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 2 3 3 3 3 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5 4 4 4 4 4 4 4 4 4 4 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 4\n",
      "P{}: 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 2 3 3 3 3 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5 4 4 4 4 4 4 4 4 4 4 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 4\n",
      "\n",
      "Step 14999 loss 0.000014\n",
      "  0: ) ) [ ) ( ) a ] ) { { ) [ a ] ) } } } } a ] { ] [ ( ) ) } } a } ) { ) { { ( ) ( [ } [ ( [ a } ] ) { a ] } a { } ) ( { ) { } a a [ ( ) [ } ( ) } } } } a ] a } [ [ ( ) ( { } ) { ] ) ) } ) a { [ } ] a {\n",
      "G(): 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P(): 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G[]: 0 0 1 1 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 3 4 4 4 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 4 4 4 4 4 4 4 4 4 3 3 3 4 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 5 5 4 4 4\n",
      "P[]: 0 0 1 1 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 3 4 4 4 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 4 4 4 4 4 4 4 4 4 3 3 3 4 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 5 5 4 4 4\n",
      "G{}: 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 1 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 2 3 3 3 3 3 2 2 2 2 2 1 1 1 2 2 2 1 1 2 1 1 1 2 2 3 2 2 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 1 1 0 0 0 1\n",
      "P{}: 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 1 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 2 3 3 3 3 3 2 2 2 2 2 1 1 1 2 2 2 1 1 2 1 1 1 2 2 3 2 2 2 2 2 2 2 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 0 0 0 1 1 0 0 0 1\n",
      "\n",
      "Step 15499 loss 0.000045\n",
      "  0: [ ] } } { ) [ ] { ( ] ] [ a [ ) ] { ] { } } { ( { ] } ] ) { ) a ] [ { ] [ a [ ( [ ] [ ] a a a ] ] { ( ) [ a ) } } [ a { ) ] ( ) [ ) { a [ { ) ( ) } [ { ) ] a ( ) a [ a [ a a ] a ( ] } { ) ) ( { ] { ]\n",
      "G(): 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 1\n",
      "P(): 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 1\n",
      "G[]: 1 0 0 0 0 0 1 0 0 0 0 0 1 1 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 2 2 3 2 3 2 2 2 2 1 0 0 0 0 1 1 1 1 1 2 2 2 2 1 1 1 2 2 2 2 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 4 5 5 5 4 4 4 3 3 3 3 3 3 3 2 2 1\n",
      "P[]: 1 0 0 0 0 0 1 0 0 0 0 0 1 1 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 2 2 3 2 3 2 2 2 2 1 0 0 0 0 1 1 1 1 1 2 2 2 2 1 1 1 2 2 2 2 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 4 5 5 5 4 4 4 3 3 3 3 3 3 3 2 2 1\n",
      "G{}: 0 0 0 0 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 4 3 2 3 3 4 4 3 3 3 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5\n",
      "P{}: 0 0 0 0 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 4 3 2 3 3 4 4 3 3 3 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5\n",
      "\n",
      "Step 15999 loss 0.000037\n",
      "  0: { a [ } [ } [ ( } ( [ ) ) ] } ( ) { } a ( { ] } ( ) ) ) } { a ( } ( ) ) { a ] ( ] [ [ ( { ( a ( ) a } [ } { ] ) [ { { { ] ( ) [ { ) } ) } ( { ] ] ( ) [ } } a ) { [ { ] { { a ( ) ) ) ) ] ) [ ] } { [ {\n",
      "G(): 0 0 0 0 0 0 0 1 1 2 2 1 0 0 0 1 0 0 0 0 1 1 1 1 2 1 0 0 0 0 0 1 1 2 1 0 0 0 0 1 1 1 1 2 2 3 3 4 3 3 3 3 3 3 3 2 2 2 2 2 2 3 2 2 2 1 1 0 0 1 1 1 1 2 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P(): 0 0 0 0 0 0 0 1 1 2 2 1 0 0 0 1 0 0 0 0 1 1 1 1 2 1 0 0 0 0 0 1 1 2 1 0 0 0 0 1 1 1 1 2 2 3 3 4 3 3 3 3 3 3 3 2 2 2 2 2 2 3 2 2 2 1 1 0 0 1 1 1 1 2 1 1 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G[]: 0 0 1 1 2 2 3 3 3 3 4 4 4 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 0 1 2 2 2 2 2 2 2 2 2 3 3 3 2 2 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 2 1 1 1 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 1 1 2 1 1 1 2 2\n",
      "P[]: 0 0 1 1 2 2 3 3 3 3 4 4 4 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 0 1 2 2 2 2 2 2 2 2 2 3 3 3 2 2 3 3 3 3 2 2 2 3 3 3 3 3 3 3 3 2 1 1 1 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 1 1 2 1 1 1 2 2\n",
      "G{}: 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 0 1 1 1 1 2 3 4 4 4 4 4 5 5 4 4 3 3 4 4 4 4 4 4 3 2 2 2 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5\n",
      "P{}: 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 0 1 1 1 1 2 3 4 4 4 4 4 5 5 4 4 3 3 4 4 4 4 4 4 3 2 2 2 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5\n",
      "\n",
      "Step 16499 loss 0.000036\n",
      "  0: ( ) } { ) { ( ) } ] a } a { } { } ) } { ] ] ] [ [ } ] [ { } [ a ) ] [ [ a [ ] ) [ a ( a { [ ( ] ) ] ( ] ] ] { a [ ( a ) a { ] ] [ ( a { ( ] a } ] ] ( { ( a ] ( ] { [ [ ( { ] [ ] { ] ] a ) { [ ] ) ) {\n",
      "G(): 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 3 2 2\n",
      "P(): 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 3 2 2\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 1 2 2 2 3 3 3 2 3 4 4 5 4 4 5 5 5 5 5 5 5 4 4 3 3 2 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 1 2 1 1 0 0 0 0 0 1 0 0 0 0\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 1 2 2 2 3 3 3 2 3 4 4 5 4 4 5 5 5 5 5 5 5 4 4 3 3 2 1 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 1 2 1 1 0 0 0 0 0 1 0 0 0 0\n",
      "G{}: 0 0 0 1 1 2 2 2 1 1 1 0 0 1 0 1 0 0 0 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 3 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      "P{}: 0 0 0 1 1 2 2 2 1 1 1 0 0 1 0 1 0 0 0 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 3 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      "\n",
      "Step 16999 loss 0.000044\n",
      "  0: ) [ ) a } ) a [ ] ( [ a ] a } a { ] ( ) ] ] a ) a ) ] [ a a a } { } a } } a ] ] ] { ) ] { ( ) ( { } a [ a [ } { [ { a ( [ ( { ) a a ] { { [ ) [ ) ) ( a [ ) ] [ ) } ] ) ( { [ ) ( [ { a { ) } } ] a a [\n",
      "G(): 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 3 2 2 2 2 2 2 2 1 1 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 0\n",
      "P(): 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 3 2 2 2 2 2 2 2 1 1 0 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 0\n",
      "G[]: 0 1 1 1 1 1 1 2 1 1 2 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 3 3 3 3 4 4 4 4 4 4 3 3 3 4 4 5 5 5 5 5 5 5 4 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 5 4 4 4 5\n",
      "P[]: 0 1 1 1 1 1 1 2 1 1 2 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 3 3 3 3 4 4 4 4 4 4 3 3 3 4 4 5 5 5 5 5 5 5 4 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 5 4 4 4 5\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 3 2 2 2 2 2 1 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 4 3 3 3 3 3\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 3 2 2 2 2 2 1 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 4 3 3 3 3 3\n",
      "\n",
      "Step 17499 loss 0.000042\n",
      "  0: ] ] } [ } [ } a ( ] ] ( ] [ ] ] a ] ) } [ { ( } ) { ( ] ( } ] ] ) ) ) ] } [ [ } ] ( } { ) a [ ( [ ) } a ( ) ( ) { { { ( ( } { [ ( ( ( } ) } a { ) { ] ( a ( ) } ) ] [ ) { [ ) ) ( { a ( ) [ } [ ( ] ) a\n",
      "G(): 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 2 1 1 1 1 2 2 1 1 2 2 3 3 3 3 2 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 1 2 2 2 2 3 4 5 5 4 4 4 4 3 3 3 4 4 5 4 4 3 3 3 2 2 2 1 0 1 1 1 2 1 1 1 1 2 2 1 1\n",
      "P(): 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 2 1 1 1 1 2 2 1 1 2 2 3 3 3 3 2 1 0 0 0 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 0 1 0 0 0 0 1 2 2 2 2 3 4 5 5 4 4 4 4 3 3 3 4 4 5 4 4 3 3 3 2 2 2 1 0 1 1 1 2 1 1 1 1 2 2 1 1\n",
      "G[]: 0 0 0 1 1 2 2 2 2 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 2 1 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 2 3 3 3 4 4 4 4 4 4 4 4 5 5 5 5 4 4 4\n",
      "P[]: 0 0 0 1 1 2 2 2 2 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 2 1 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 2 3 3 3 4 4 4 4 4 4 4 4 5 5 5 5 4 4 4\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 1 2 3 3 3 2 3 3 3 3 3 2 2 1 1 2 2 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 3 3 3 3 3 3\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 1 2 3 3 3 2 3 3 3 3 3 2 2 1 1 2 2 3 3 3 3 3 3 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 3 3 3 3 3 3\n",
      "\n",
      "Step 17999 loss 0.000026\n",
      "  0: } ( } [ { ] { { [ { ] a [ } a } ) ( [ } { ) [ ) ) ) [ ) ) ) [ } a ( [ ( { } ] ( [ [ ) ] [ ] [ } ( ) a a { ( ] ) ] [ ( ( [ ] ] a ( } ) a { ( ) ( a a ] } ( a a { } ] } a } [ ] a a a [ } ) { ] ] ) ) a [\n",
      "G(): 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 3 3 3 2 2 2 2 2 2 3 2 2 2 2 3 3 2 2 2 3 4 4 4 4 4 5 5 4 4 4 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 3 2 2 2\n",
      "P(): 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 2 2 2 3 3 3 2 2 2 2 2 2 3 2 2 2 2 3 3 2 2 2 3 4 4 4 4 4 5 5 4 4 4 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 3 2 2 2\n",
      "G[]: 0 0 0 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 4 4 5 5 5 4 5 4 5 5 5 5 5 5 5 5 4 4 3 4 4 4 5 4 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 1 1 1 1 2 1 1 1 1 2 2 2 2 1 0 0 0 0 1\n",
      "P[]: 0 0 0 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 4 4 5 5 5 4 5 4 5 5 5 5 5 5 5 5 4 4 3 4 4 4 5 4 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 1 1 1 1 2 1 1 1 1 2 2 2 2 1 0 0 0 0 1\n",
      "G{}: 0 0 0 0 1 1 2 3 3 4 4 4 4 3 3 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1\n",
      "P{}: 0 0 0 0 1 1 2 3 3 4 4 4 4 3 3 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1\n",
      "\n",
      "Step 18499 loss 0.000033\n",
      "  0: { { a a ) { [ } } [ [ } ( { ] ) a a ] [ ( [ } { ( ( [ ) ( { ( [ ] { } [ { ] ( [ { ] a ( ) ) ( } { ] } [ } ) { [ [ ) [ ) ( a ) ( ) [ [ } ) ] ( { a ( } ] ( [ { ) [ } ) ] ) a ) { } ] a ) [ a { [ } { [ ]\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 2 3 3 2 3 3 4 4 4 4 4 4 4 4 5 5 5 5 5 5 4 3 4 4 4 4 4 4 4 3 3 3 3 2 2 1 2 2 1 2 1 1 1 1 0 0 1 1 1 2 2 2 3 3 3 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 2 3 3 2 3 3 4 4 4 4 4 4 4 4 5 5 5 5 5 5 4 3 4 4 4 4 4 4 4 3 3 3 3 2 2 1 2 2 1 2 1 1 1 1 0 0 1 1 1 2 2 2 3 3 3 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G[]: 0 0 0 0 0 0 1 1 1 2 3 3 3 3 2 2 2 2 1 2 2 3 3 3 3 3 4 4 4 4 4 5 4 4 4 5 5 4 4 5 5 4 4 4 4 4 4 4 4 3 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 3 3 4 4 4 5 5 5 4 4 4 4 4 4 3 3 3 4 4 4 5 5 5 5 4\n",
      "P[]: 0 0 0 0 0 0 1 1 1 2 3 3 3 3 2 2 2 2 1 2 2 3 3 3 3 3 4 4 4 4 4 5 4 4 4 5 5 4 4 5 5 4 4 4 4 4 4 4 4 3 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 3 3 4 4 4 5 5 5 4 4 4 4 4 4 3 3 3 4 4 4 5 5 5 5 4\n",
      "G{}: 1 2 2 2 2 3 3 2 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 2 2 2 2 3 2 2 3 3 3 3 4 4 4 4 4 4 4 3 4 4 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 2 2 2 2 3 3 3 2 2 2 2 2 2 3 2 2 2 2 2 2 3 3 2 3 3 3\n",
      "P{}: 1 2 2 2 2 3 3 2 1 1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 2 2 2 2 3 2 2 3 3 3 3 4 4 4 4 4 4 4 3 4 4 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 3 3 3 2 2 2 2 3 3 3 2 2 2 2 2 2 3 2 2 2 2 2 2 3 3 2 3 3 3\n",
      "\n",
      "Step 18999 loss 0.000042\n",
      "  0: { a ] a ) } } } a } ] ( } ) [ ) ] ( ) [ a a [ ) ] [ [ ( { } ] ) { } ) } ] { } ] { ( ( [ } a [ } ) ) ) ) ( ) { } ) [ } [ { a ] [ { a ( ( [ ( ] ) } ( a } a } { ) a ) ) [ { ( a { ) ] { { a ] } a ( [ { [\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 3 3 2 2 3 3 3 3 3 3 2 2 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 3 3 2 2 3 3 3 3 3 3 2 2 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 2 2 1 2 3 3 3 3 2 2 2 2 2 2 1 1 1 0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 3 3 4 4 4 3 4 4 4 4 4 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 4 4 4 4 3 3 3 3 4 4 5\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 2 2 1 2 3 3 3 3 2 2 2 2 2 2 1 1 1 0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 3 3 4 4 4 3 4 4 4 4 4 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 4 4 4 4 3 3 3 3 4 4 5\n",
      "G{}: 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 0 0 0 1 1 1 1 1 1 2 2 2 3 3 3 4 5 5 5 4 4 4 4 5 5\n",
      "P{}: 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 0 0 0 1 1 1 1 1 1 2 2 2 3 3 3 4 5 5 5 4 4 4 4 5 5\n",
      "\n",
      "Step 19499 loss 0.000031\n",
      "  0: [ ( { } { ] a ) ) ] ( ( a ( } { ) ) ( [ ) a ) ) { { } { { a } } ( [ ( ) [ ) { ) [ ) ) ) ( a ) } ) { ] a { { ) } ) ) ( { [ ) ] } [ a ( ] a { ) } a a ) [ ( a a ) ) ( ) } ] } ( [ [ ] ] [ ( ( a ( ) ] } {\n",
      "G(): 0 1 1 1 1 1 1 0 0 0 1 2 2 3 3 3 2 1 2 2 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 2 3 3 4 3 3 3 3\n",
      "P(): 0 1 1 1 1 1 1 0 0 0 1 2 2 3 3 3 2 1 2 2 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 2 3 3 4 3 3 3 3\n",
      "G[]: 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3 3 3 3 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 4 4 3 3 4 4 4 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 3 3 3 4 5 4 3 4 4 4 4 4 4 3 3 3\n",
      "P[]: 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3 3 3 3 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 4 4 3 3 4 4 4 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 3 3 3 4 5 4 3 4 4 4 4 4 4 3 3 3\n",
      "G{}: 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 2 3 2 3 4 4 3 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 2 2 3 3 3 4 5 5 4 4 4 4 5 5 5 5 4 4 4 4 4 4 5 5 4 4 4 4 4 4 4 4 4 4 4 4 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2\n",
      "P{}: 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 2 3 2 3 4 4 3 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 2 2 3 3 3 4 5 5 4 4 4 4 5 5 5 5 4 4 4 4 4 4 5 5 4 4 4 4 4 4 4 4 4 4 4 4 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2\n",
      "\n",
      "Step 19999 loss 0.000047\n",
      "  0: a ( ] } a a ( ( a } { ] [ { a ] [ ) ] } { [ [ ( [ } ( ( a { a ] { ] [ } ( { ( } ( ( ) } { { ( { a ) } ) { [ a } ) [ ) ) ( ) ( ] ] a ) [ ( } [ { [ ] ( a } ( { } ( ( ] [ ) [ [ { [ a [ ] ) ] } { } [ } {\n",
      "G(): 0 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 4 4 3 3 3 3 3 2 2 1 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 2 2 2 3 3 3 4 5 5 5 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3\n",
      "P(): 0 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 4 4 3 3 3 3 3 2 2 1 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 2 2 2 3 3 3 4 5 5 5 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 2 2 3 3 3 3 3 3 3 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 4 4 4 4 4 4 3 2 2 2 3 3 3 4 4 5 4 4 4 4 4 4 4 4 4 3 4 4 5 5 5 5 5 5 4 4 3 3 3 3 4 4 4\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 2 2 3 3 3 3 3 3 3 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 4 4 4 4 4 4 3 2 2 2 3 3 3 4 4 5 4 4 4 4 4 4 4 4 4 3 4 4 5 5 5 5 5 5 4 4 3 3 3 3 4 4 4\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 1 2 2 2 3 3 3 2 2 3 3 2 2 2 2 1 2 3 3 4 4 4 3 3 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 2 2 3 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 2 3 2 2 1 2\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 1 2 2 2 3 3 3 2 2 3 3 2 2 2 2 1 2 3 3 4 4 4 3 3 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 3 3 3 3 3 2 2 3 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 2 3 2 2 1 2\n",
      "\n",
      "Step 20499 loss 0.000027\n",
      "  0: ] ( a ( { } ) { ] ] ( a { { ) { } } ) a { } } } { ) ] } { ( ) [ a { a ) ) ( a ] ) ) } a a ) ) } { { ) ) ] ] a a a ) } a } ) { [ a ] ] ( ( ( ) a ) a } ( ( ( a ( [ } ) ] ) } a ) { a } ) } ) ] a ) ] { a\n",
      "G(): 0 1 1 2 2 2 1 1 1 1 2 2 2 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 2 2 1 1 1 2 3 4 4 5 5 5 4 4 3 3 3 2 2 2 2 1 1 0 0 0 0 0 0 0\n",
      "P(): 0 1 1 2 2 2 1 1 1 1 2 2 2 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 3 2 2 1 1 1 2 3 4 4 5 5 5 4 4 3 3 3 2 2 2 2 1 1 0 0 0 0 0 0 0\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G{}: 0 0 0 0 1 0 0 1 1 1 1 1 2 3 3 4 3 2 2 2 3 2 1 0 1 1 1 0 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 0 1 2 2 2 2 2 2 2 2 2 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1\n",
      "P{}: 0 0 0 0 1 0 0 1 1 1 1 1 2 3 3 4 3 2 2 2 3 2 1 0 1 1 1 0 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 0 1 2 2 2 2 2 2 2 2 2 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1\n",
      "\n",
      "Step 20999 loss 0.000042\n",
      "  0: [ ( ] ( } } } a ) a ( ] { a } { ) a ( ) ( [ ) { { ) ( [ ( [ } a ( [ ] { { ) ( { ] ( ) ] } [ ( } [ ) ( } { [ a { ] ) } [ ) { } a { [ ] { a [ ) ] ( [ ) [ [ ] ( ] ] } } a { ] [ } a { ] ) ) { ( a } { ( {\n",
      "G(): 0 1 1 2 2 2 2 2 1 1 2 2 2 2 2 2 1 1 2 1 2 2 1 1 1 0 1 1 2 2 2 2 3 3 3 3 3 2 3 3 3 4 3 3 3 3 4 4 4 3 4 4 4 4 4 4 4 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 0 0 1 1 1 1 2 2\n",
      "P(): 0 1 1 2 2 2 2 2 1 1 2 2 2 2 2 2 1 1 2 1 2 2 1 1 1 0 1 1 2 2 2 2 3 3 3 3 3 2 3 3 3 4 3 3 3 3 4 4 4 3 4 4 4 4 4 4 4 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 0 0 1 1 1 1 2 2\n",
      "G[]: 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 2 2 3 3 3 3 4 3 3 3 3 3 3 2 2 2 1 1 2 2 2 3 3 3 3 3 4 4 4 3 3 3 4 4 4 4 4 4 5 4 4 4 5 5 4 4 5 5 5 5 4 4 3 2 2 2 2 2 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1\n",
      "P[]: 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 2 2 3 3 3 3 4 3 3 3 3 3 3 2 2 2 1 1 2 2 2 3 3 3 3 3 4 4 4 3 3 3 4 4 4 4 4 4 5 4 4 4 5 5 4 4 5 5 5 5 4 4 3 2 2 2 2 2 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 2 2 2 2 2 3 4 4 4 5 5 5 5 5 4 4 4 3 3 3 3 2 3 3 3 4 4 4 3 3 3 4 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 4 4 4 3 3 4 4 4 4 5 5 5 4 5 5 5\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 1 1 2 3 3 3 3 3 3 2 2 2 2 2 3 4 4 4 5 5 5 5 5 4 4 4 3 3 3 3 2 3 3 3 4 4 4 3 3 3 4 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 4 4 4 3 3 4 4 4 4 5 5 5 4 5 5 5\n",
      "\n",
      "Step 21499 loss 0.000035\n",
      "  0: ] [ { a { ) ( [ [ { a [ ( { ( { a { { [ [ { { [ [ { ] ) ( { ( ( ] { [ ( ( } ( ) ) a ) a ] a ) ( a ( { [ [ ] ( ) ] { ( } a ) ( a ] [ } a { [ a [ [ { a a a [ { ( a } } ) ] a ( } ( ] { [ ) { ) ( [ } ( [\n",
      "G(): 0 0 0 0 0 0 1 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 4 5 5 5 5 5 5 5 5 4 3 3 2 2 2 2 1 2 2 3 3 3 3 3 4 3 3 3 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 4 4 4 5 5 5 5 5 5 4 4 3 4 4 4 5 5\n",
      "P(): 0 0 0 0 0 0 1 1 1 1 1 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 4 5 5 5 5 5 5 5 5 4 3 3 2 2 2 2 1 2 2 3 3 3 3 3 4 3 3 3 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 4 4 4 5 5 5 5 5 5 4 4 3 4 4 4 5 5\n",
      "G[]: 0 1 1 1 1 1 1 2 3 3 3 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 5 4 4 4 3 3 3 3 3 3 3 3 2 3 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 3 3 4 4 4 4 4 5 5 5 5\n",
      "P[]: 0 1 1 1 1 1 1 2 3 3 3 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 4 5 4 4 4 3 3 3 3 3 3 3 3 2 3 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 3 3 4 4 4 4 4 5 5 5 5\n",
      "G{}: 0 0 1 1 2 2 2 2 2 3 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 3 3 4 4 4 4 4 5 5 5 5 5 5 5 5 4 3 3 3 3 3 2 2 2 3 3 3 4 4 4 4 3 3 3\n",
      "P{}: 0 0 1 1 2 2 2 2 2 3 3 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 3 3 4 4 4 4 4 5 5 5 5 5 5 5 5 4 3 3 3 3 3 2 2 2 3 3 3 4 4 4 4 3 3 3\n",
      "\n",
      "Step 21999 loss 0.000041\n",
      "  0: ( a ) a a ( ( ] ) [ [ { ) ( ( ( [ ) ] ) ( { ( } } { ] [ a } { { ] { ] { a a { a ] ) } { [ ) [ [ ) a ( ) ) a } ( ) [ { } ) ) ) ] ] } ( ( ] { { a ( ( a { { [ ( ] { ] { a { ( { } a { ( ( [ ) ] } ] [ ] }\n",
      "G(): 1 1 0 0 0 1 2 2 1 1 1 1 0 1 2 3 3 2 2 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 3 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4\n",
      "P(): 1 1 0 0 0 1 2 2 1 1 1 1 0 1 2 3 3 2 2 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 1 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 3 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4\n",
      "G[]: 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 3 2 2 2 2 1 1 1 1 1 1 1 1 1 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0\n",
      "P[]: 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 0 0 0 0 0 0 0 0 0 0 1 1 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 3 2 2 2 2 1 1 1 1 1 1 1 1 1 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 1 0 1 1 1 1 0 1 2 2 3 3 4 4 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 4 4 4 4 4 4 3 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 4 4 4 4 3\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 1 0 1 1 1 1 0 1 2 2 3 3 4 4 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 4 4 4 4 4 4 3 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 4 4 4 4 3\n",
      "\n",
      "Step 22499 loss 0.000037\n",
      "  0: ( } ( { ) a [ { a } } a ] { a ] { ( } ( } { ( ( ) a } [ a } [ ] { [ } a { [ { { { ) ) ) ) ) [ [ [ [ ) ( { ( a [ } { ) ] ] ) ] ( ) ) a ] { } ( ] ] { } ] [ ] } { ] [ a [ { ) } } a { a { } ) { [ ( [ [ (\n",
      "G(): 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 3 3 4 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 2 1 0 0 0 0 0 0 0 1 1 2 2 2 2 2 1 1 1 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2\n",
      "P(): 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 3 3 4 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 2 1 0 0 0 0 0 0 0 1 1 2 2 2 2 2 1 1 1 0 0 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2\n",
      "G[]: 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 2 2 2 2 3 3 3 3 3 3 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 2 2 2 2 2 1 1 1 1 0 0 0 0 0 1 0 0 0 0 1 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 4 5 5\n",
      "P[]: 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 2 2 2 2 3 3 3 3 3 3 3 3 3 4 5 5 5 5 5 5 5 5 5 5 5 5 4 3 3 2 2 2 2 2 1 1 1 1 0 0 0 0 0 1 0 0 0 0 1 1 2 2 2 2 2 2 2 2 2 2 2 2 3 3 4 5 5\n",
      "G{}: 0 0 0 1 1 1 1 2 2 1 0 0 0 1 1 1 2 2 1 1 0 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 1 2 3 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 4 4 4 4 3 4 4 4 4 4 5 5 4 3 3 4 4 5 4 4 5 5 5 5 5 5\n",
      "P{}: 0 0 0 1 1 1 1 2 2 1 0 0 0 1 1 1 2 2 1 1 0 1 1 1 1 1 0 0 0 0 0 0 1 1 0 0 1 1 2 3 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 5 4 4 4 4 3 4 4 4 4 4 5 5 4 3 3 4 4 5 4 4 5 5 5 5 5 5\n",
      "\n",
      "Step 22999 loss 0.000024\n",
      "  0: a } ] ( ( ) ] ( a { ] { [ ( { ( [ ] [ ( { [ a { ) } ) ] a { [ [ ( ) a ( { ) [ [ [ a { a { [ ( ] a ( ) ] { [ ] [ { ( ( [ { a [ ( } { ) ( ] } ] { a ) ] ) } a a ] ) [ { ( ( [ [ } ) [ } ] a a ] ( ( ] ] }\n",
      "G(): 0 0 0 1 2 1 1 2 2 2 2 2 2 3 3 4 4 4 4 5 5 5 5 5 4 4 3 3 3 3 3 3 4 3 3 4 4 3 3 3 3 3 3 3 3 3 4 4 4 5 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 4 4 3 3 3 3 3 2 2 2 3 4 4 4 4 3 3 3 3 3 3 3 4 5 5 5 5\n",
      "P(): 0 0 0 1 2 1 1 2 2 2 2 2 2 3 3 4 4 4 4 5 5 5 5 5 4 4 3 3 3 3 3 3 4 3 3 4 4 3 3 3 3 3 3 3 3 3 4 4 4 5 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 4 4 3 3 3 3 3 2 2 2 3 4 4 4 4 3 3 3 3 3 3 3 4 5 5 5 5\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 1 2 2 2 3 3 3 3 3 3 2 2 2 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 4 4 4 4 3 3 4 3 4 4 4 4 5 5 5 5 5 5 5 5 5 4 4 3 3 3 3 2 2 2 2 2 1 1 2 2 2 2 3 4 4 4 5 5 4 4 4 3 3 3 2 1 1\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 1 2 2 2 3 3 3 3 3 3 2 2 2 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 4 4 4 4 3 3 4 3 4 4 4 4 5 5 5 5 5 5 5 5 5 4 4 3 3 3 3 2 2 2 2 2 1 1 2 2 2 2 3 4 4 4 5 5 4 4 4 3 3 3 2 1 1\n",
      "G{}: 0 0 0 0 0 0 0 0 0 1 1 2 2 2 3 3 3 3 3 3 4 4 4 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 4 4 5 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 4 4 4 3 3 3 3 3 3 3 3 3 2\n",
      "P{}: 0 0 0 0 0 0 0 0 0 1 1 2 2 2 3 3 3 3 3 3 4 4 4 5 5 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 4 4 5 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 4 4 4 3 3 3 3 3 3 3 3 3 2\n",
      "\n",
      "Step 23499 loss 0.000047\n",
      "  0: a ] a ( ) ] } ( } } ( ] { [ ) ] } { a } [ a ( [ ] [ ( [ { [ ) ) [ } } { ) [ a a ) a a ) [ } } [ ] ( [ } ( } ( } ] ) ) [ ] [ ( ) { } a { } ) { } ) } { ( [ ] ) { ( ( ) } ] ( { ( } a { ) ) { { { ] [ } a\n",
      "G(): 0 0 0 1 0 0 0 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 3 3 3 3 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 3 3 3 2 1 1 1 1 2 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 2 1 1 1 2 2 3 3 3 3 2 1 1 1 1 1 1 1 1\n",
      "P(): 0 0 0 1 0 0 0 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 3 3 3 3 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 3 3 3 2 1 1 1 1 2 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0 0 1 2 1 1 1 2 2 3 3 3 3 2 1 1 1 1 1 1 1 1\n",
      "G[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 2 1 2 2 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 4 4 4 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3\n",
      "P[]: 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 2 1 2 2 3 3 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 4 4 4 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1 1 1 2 2 2 2 1 1 1 2 2 1 1 2 2 2 3 4 5 5 5 4 4\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 1 1 1 1 2 2 2 2 1 1 1 2 2 1 1 2 2 2 3 4 5 5 5 4 4\n",
      "\n",
      "Step 23999 loss 0.000027\n",
      "  0: { } } [ a [ { { ) [ [ ( ( ) { ) } ] [ ( ] [ { ] ( { ) } ( [ { ( [ ) { [ [ ) ] ) [ { ( [ ) { ) [ ( ( { ) { a { ] ) a } ] a { ] } ] { a } [ } { a { a ( ) ) { [ a ] } { } a { } } ] ] a a { { ] a { ] { }\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 1 2 1 1 0 0 0 0 1 1 1 1 1 2 2 1 1 2 2 2 3 3 2 2 2 2 1 1 0 0 0 1 1 0 0 0 0 1 2 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 1 2 1 1 0 0 0 0 1 1 1 1 1 2 2 1 1 2 2 2 3 3 2 2 2 2 1 1 0 0 0 1 1 0 0 0 0 1 2 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "G[]: 0 0 0 1 1 2 2 2 2 3 4 4 4 4 4 4 4 3 4 4 3 4 4 3 3 3 3 3 3 4 4 4 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 3 3 3 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0\n",
      "P[]: 0 0 0 1 1 2 2 2 2 3 4 4 4 4 4 4 4 3 4 4 3 4 4 3 3 3 3 3 3 4 4 4 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 3 3 3 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0\n",
      "G{}: 1 0 0 0 0 0 1 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 3 3 3 4 4 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5 4 4 5 5 4 4 3 4 4 5 5 5 5 5 5 5 5 5 4 5 4 4 5 4 3 3 3 3 3 4 5 5 5 5 5 5 4\n",
      "P{}: 1 0 0 0 0 0 1 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 3 3 3 4 4 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5 4 4 5 5 4 4 3 4 4 5 5 5 5 5 5 5 5 5 4 5 4 4 5 4 3 3 3 3 3 4 5 5 5 5 5 5 4\n",
      "\n",
      "Step 24499 loss 0.000054\n",
      "  0: } ( } } } } ] [ [ } ( ] { [ ) a ] } a a { } a ( [ a [ ) ) ) ( [ { [ a a ) ] [ } } ( ] ( ] ) [ } [ } a a ( ( [ [ { } [ ] } [ } { ( ( { ) a ( { { ) } } a } ) { } } ] ( ] } ] a a a } } } { ) ) } ) [ { {\n",
      "G(): 0 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 2 2 2 2 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 2 2 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 4 5 5 4 4 5 5 5 4 4 4 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 3 2 2 1 1 1 1\n",
      "P(): 0 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 2 2 2 2 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 2 2 1 1 1 1 1 1 1 2 3 3 3 3 3 3 3 3 3 3 3 4 5 5 4 4 5 5 5 4 4 4 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 3 2 2 1 1 1 1\n",
      "G[]: 0 0 0 0 0 0 0 1 2 2 2 1 1 2 2 2 1 1 1 1 1 1 1 1 2 2 3 3 3 3 3 4 4 5 5 5 5 4 5 5 5 5 4 4 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 3 3 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3\n",
      "P[]: 0 0 0 0 0 0 0 1 2 2 2 1 1 2 2 2 1 1 1 1 1 1 1 1 2 2 3 3 3 3 3 4 4 5 5 5 5 4 5 5 5 5 4 4 3 3 4 4 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 3 3 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3\n",
      "G{}: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 2 2 2 2 3 4 4 3 2 2 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 2\n",
      "P{}: 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 2 2 2 2 3 4 4 3 2 2 1 1 2 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 2\n",
      "\n",
      "Step 24999 loss 0.000015\n",
      "  0: ( { ) { ] { [ ] [ ) a ( a [ ( ] } { { ) a ] a } ] [ { [ ) [ a } ] a [ } { } ) } { a { a a } a } } a ) ] ] ( ] a ) a ] ( ) { ) ( ) [ ] } a ( { a ) ) [ ] a } ) a [ a ) ) ] ) [ a { ) ] a { ( { } ] ( ( (\n",
      "G(): 1 1 0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 3 4\n",
      "P(): 1 1 0 0 0 0 0 0 0 0 0 1 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 3 4\n",
      "G[]: 0 0 0 0 0 0 1 0 1 1 1 1 1 2 2 1 1 1 1 1 1 0 0 0 0 1 1 2 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0\n",
      "P[]: 0 0 0 0 0 0 1 0 1 1 1 1 1 2 2 1 1 1 1 1 1 0 0 0 0 1 1 2 2 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0\n",
      "G{}: 0 1 1 2 2 3 3 3 3 3 3 3 3 3 3 3 2 3 4 4 4 4 4 3 3 3 4 4 4 4 4 3 3 3 3 2 3 2 2 1 2 2 3 3 3 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 2 3 2 2 2 2 2\n",
      "P{}: 0 1 1 2 2 3 3 3 3 3 3 3 3 3 3 3 2 3 4 4 4 4 4 3 3 3 4 4 4 4 4 3 3 3 3 2 3 2 2 1 2 2 3 3 3 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 2 3 2 2 2 2 2\n",
      "\n",
      "Step 25499 loss 0.000055\n",
      "  0: [ a ] [ { ) [ } { [ ] ) [ ( a } { { } ( [ ] [ } ) [ [ ] ) [ ] ( { [ } ( a } ( a ] ] { { ] } a ] a a { ) [ } } [ ( ) ( ( a ( [ a [ ) ] ] ) { { ] ) { a a ] ] { ) [ a { ) ( { a { ) ( ] } } a ] ] ( ( } )\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 2 1 1 1 1 0 0 0 1 1 1 1 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 2 3 4 4 5 5 5 5 4 4 4 3 3 3 3 2 2 2 2 2 2 2 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 2 3 3 2\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 2 1 1 1 1 0 0 0 1 1 1 1 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 3 2 3 4 4 5 5 5 5 4 4 4 3 3 3 3 2 2 2 2 2 2 2 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 2 3 3 2\n",
      "G[]: 1 1 0 1 1 1 2 2 2 3 2 2 3 3 3 3 3 3 3 3 4 3 4 4 4 5 5 4 4 5 4 4 4 5 5 5 5 5 5 5 4 3 3 3 2 2 2 1 1 1 1 1 2 2 2 3 3 3 3 3 3 3 4 4 5 5 4 3 3 3 3 2 2 2 2 2 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0\n",
      "P[]: 1 1 0 1 1 1 2 2 2 3 2 2 3 3 3 3 3 3 3 3 4 3 4 4 4 5 5 4 4 5 4 4 4 5 5 5 5 5 5 5 4 3 3 3 2 2 2 1 1 1 1 1 2 2 2 3 3 3 3 3 3 3 4 4 5 5 4 3 3 3 3 2 2 2 2 2 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0\n",
      "G{}: 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 0 1 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 2 2 1 1 1 1 1 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 3 3 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 5 4 3 3 3 3 3 3 2 2\n",
      "P{}: 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 0 1 2 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 2 2 1 1 1 1 1 2 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 3 3 3 3 3 4 4 4 4 5 5 5 5 5 5 5 5 5 4 3 3 3 3 3 3 2 2\n",
      "\n",
      "Step 25999 loss 0.000032\n",
      "  0: } { { { { ) a [ { { ( ( ( a { ] ( ) } { { ] ( ] ( a { ( [ a ) [ ] } a a ( a { a ] } } ] { ( } [ { { { ( [ a { ( ( ] ] [ } ) ( a ) ( ] [ ( } [ { [ ) a ) a a [ a ) } [ [ ] a { [ ] a } [ a ] ) } { ] ) )\n",
      "G(): 0 0 0 0 0 0 0 0 0 0 1 2 3 3 3 3 4 3 3 3 3 3 4 4 5 5 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4 5 5 5 5 5 5 5 5 4 4 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 0 0\n",
      "P(): 0 0 0 0 0 0 0 0 0 0 1 2 3 3 3 3 4 3 3 3 3 3 4 4 5 5 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4 5 5 5 5 5 5 5 5 4 4 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 0 0\n",
      "G[]: 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 1 0 1 1 1 1 1 1 1 0 1 1 1 2 2 3 3 3 3 3 3 4 4 4 4 5 5 4 4 4 5 4 4 4 5 5 4 4 4 4 3 3 3\n",
      "P[]: 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 1 0 1 1 1 1 1 1 1 0 1 1 1 2 2 3 3 3 3 3 3 4 4 4 4 5 5 4 4 4 5 4 4 4 5 5 4 4 4 4 3 3 3\n",
      "G{}: 0 1 2 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 5 5 5 4 3 3 4 4 3 3 4 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 4 4 4 4 3 3 3 3 3 2 3 3 3 3\n",
      "P{}: 0 1 2 3 4 4 4 4 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 5 5 5 4 3 3 4 4 3 3 4 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 4 4 4 4 3 3 3 3 3 2 3 3 3 3\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-0b4712d2067a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m                \u001b[0mlearning_rate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2e-4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m               }\n\u001b[0;32m---> 16\u001b[0;31m   \u001b[0mv_task_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv_logits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtask_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_op\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m   \u001b[0mtask_loss_acc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mv_task_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/google/home/chorowski/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    765\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 767\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    768\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/google/home/chorowski/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 965\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    966\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/google/home/chorowski/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1013\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1015\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1016\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/google/home/chorowski/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1020\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1021\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1022\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1023\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/google/home/chorowski/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1002\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1003\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1006\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.initialize_all_variables())\n",
    "\n",
    "PRINT_PERIOD = 500\n",
    "\n",
    "task_loss_acc = 0\n",
    "\n",
    "\n",
    "for i in range(N_TRAIN_STEPS):\n",
    "  v_inputs, v_targets = task.sample_batch(T, BATCH_SIZE)\n",
    "  feed_dict = {inputs:v_inputs, \n",
    "               targets:v_targets,\n",
    "               #learning_rate:min(1e-4, 1e-4 * 1000/(i+1))\n",
    "               learning_rate:2e-4\n",
    "              }\n",
    "  v_task_loss, v_logits, _ = sess.run([task_loss, logits, train_op], feed_dict=feed_dict)\n",
    "  \n",
    "  task_loss_acc += v_task_loss\n",
    "  \n",
    "  if ((i + 1) % PRINT_PERIOD) == 0:\n",
    "    print(\"Step %d loss %f\" % (i, task_loss_acc / PRINT_PERIOD))\n",
    "    task.print_batch(v_inputs, v_targets, v_logits)\n",
    "    print(\"\")\n",
    "    task_loss_acc = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "Zz9gwXLVYVsM"
   },
   "outputs": [],
   "source": [
    "def fetch_val(sess, name):\n",
    "  for v in tf.trainable_variables():\n",
    "    if v.name.startswith(name):\n",
    "      return sess.run(v)\n",
    "  raise KeyError()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 332,
     "status": "ok",
     "timestamp": 1484365204805,
     "user": {
      "displayName": "Jan Chorowski",
      "photoUrl": "//lh5.googleusercontent.com/-CIawMzW6HLk/AAAAAAAAAAI/AAAAAAAAABY/YDJiClf-um8/s50-c-k-no/photo.jpg",
      "userId": "105360491181387708695"
     },
     "user_tz": 480
    },
    "id": "FgHUS6-v0pkm",
    "outputId": "e253dde3-9f05-4443-a383-e4bc8b8933ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded Wo with shape (50, 18)\n",
      "Loaded bo with shape (1, 18)\n",
      "Loaded Wx with shape (7, 50, 50)\n",
      "Loaded bx with shape (7, 50)\n",
      "Loaded h0 with shape (1, 50)\n",
      "Symbol dict is: {'a': 6, ')': 1, '(': 0, ']': 3, '[': 2, '}': 5, '{': 4}\n"
     ]
    }
   ],
   "source": [
    "bo = fetch_val(sess, \"model/model/bo\")\n",
    "Wo = fetch_val(sess, \"model/model/Wo\")\n",
    "\n",
    "bx = fetch_val(sess, \"model/model/rnn/isan_cell/bx\")\n",
    "Wx = fetch_val(sess, \"model/model/rnn/isan_cell/Wx\"\n",
    "              ).reshape(bx.shape[0], bx.shape[1], bx.shape[1])\n",
    "\n",
    "h0 = fetch_val(sess, \"model/model/rnn/h0\")\n",
    "\n",
    "for vname in [\"Wo\", \"bo\", \"Wx\", \"bx\", \"h0\"]:\n",
    "  print(\"Loaded %s with shape %s\" % (vname, globals()[vname].shape))\n",
    "  \n",
    "S = task.token_to_id\n",
    "print(\"Symbol dict is:\", S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 168,
     "status": "ok",
     "timestamp": 1484365205011,
     "user": {
      "displayName": "Jan Chorowski",
      "photoUrl": "//lh5.googleusercontent.com/-CIawMzW6HLk/AAAAAAAAAAI/AAAAAAAAABY/YDJiClf-um8/s50-c-k-no/photo.jpg",
      "userId": "105360491181387708695"
     },
     "user_tz": 480
    },
    "id": "QILCunbWfFSl",
    "outputId": "debfd188-f641-4d6f-dfe6-d36a58708a1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(: [1 0 0]\n",
      "(: [2 0 0]\n",
      "(: [3 0 0]\n",
      "(: [4 0 0]\n",
      "(: [5 0 0]\n",
      "(: [5 0 0]\n",
      "[: [5 1 0]\n",
      "): [4 1 0]\n",
      "): [3 1 0]\n",
      "): [2 1 0]\n",
      "): [1 1 0]\n",
      "): [0 1 0]\n",
      "): [0 1 0]\n",
      "): [0 1 0]\n",
      "): [0 1 0]\n",
      "): [0 1 0]\n",
      "]: [0 0 0]\n",
      "{: [0 0 1]\n",
      "{: [0 0 2]\n",
      "{: [0 0 3]\n",
      "}: [0 0 2]\n"
     ]
    }
   ],
   "source": [
    "# Let's check if the loaded parameters work\n",
    "\n",
    "def simulate(paren_string):\n",
    "  h = h0\n",
    "  states = []\n",
    "  for c in paren_string:\n",
    "    h = h.dot(Wx[S[c]]) + bx[S[c]]\n",
    "    states.append(h)\n",
    "  states = np.concatenate(states, axis=0)\n",
    "  return states, states.dot(Wo) + bo\n",
    "\n",
    "paren_string = \"(((((([)))))))))]{{{}\"\n",
    "\n",
    "v_states, v_logits = simulate(paren_string)\n",
    "\n",
    "for p, c in zip(paren_string,\n",
    "                np.argmax(v_logits.reshape(\n",
    "                    v_logits.shape[0], task.n_paren_types, -1), axis=2)):\n",
    "  print(\"%s: %s\" % (p, c))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "w97VDtwJgT3M"
   },
   "outputs": [],
   "source": [
    "# merge the biases into transition and readout matrices\n",
    "# in this extended basis the model realizes switched linear dynamics\n",
    "e_h0 = np.hstack((h0, [[1]]))\n",
    "e_Wo = np.vstack((Wo, bo))\n",
    "e_Wx = np.zeros((Wx.shape[0], Wx.shape[1]+1, Wx.shape[1]+1), Wx.dtype)\n",
    "\n",
    "e_Wx[:, :-1, :-1] = Wx\n",
    "e_Wx[:, -1, :-1] = bx[:, :]\n",
    "e_Wx[:, -1, -1] = 1.0\n",
    "\n",
    "def e_simulate(paren_string):\n",
    "  h = e_h0\n",
    "  states = []\n",
    "  for c in paren_string:\n",
    "    h = h.dot(e_Wx[S[c]])\n",
    "    states.append(h)\n",
    "  states = np.concatenate(states, axis=0)\n",
    "  return states, states.dot(e_Wo)\n",
    "\n",
    "v_states, v_logits = simulate(paren_string)\n",
    "e_v_states, e_v_logits = e_simulate(paren_string)\n",
    "\n",
    "assert np.allclose(e_v_logits, v_logits, atol=1e-5)\n",
    "assert np.allclose(e_v_states[:,:-1], v_states, atol=1e-5)\n",
    "assert np.allclose(e_v_states[:, -1], np.ones_like(e_v_states[:, -1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "slnWOT15u9gq"
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Below we split the transition matrices into a readout part and a computational part\n",
    "#\n",
    "#\n",
    "\n",
    "\n",
    "def get_orth_complement(V, W):\n",
    "  \"\"\"\n",
    "  Find an orthonormal basis for span(V) and span(W).\n",
    "  \n",
    "  Args:\n",
    "    V: matrix of shape k * n\n",
    "    W: matrix of shape (n-k) * n\n",
    "    \n",
    "  Returns:\n",
    "    Pair of matrices:\n",
    "      V_orth: matrix of shape k * n\n",
    "      W_orth: matrix of shape (n-k) * n\n",
    "\n",
    "    such that the union of rows of V_orth and W_orth is an orthonormal basis\n",
    "    for R^n\n",
    "  \n",
    "  \"\"\"\n",
    "  assert V.shape[0] + W.shape[0] == V.shape[1] == W.shape[1]\n",
    "  V_orth = scipy.linalg.orth(V.T).T\n",
    "  coeffs = V_orth.dot(W.T) #coeffs_ij = <V_orth_i, W_j>\n",
    "  W_orth = W.copy()\n",
    "  for j in xrange(len(W)):\n",
    "    W_proj = np.sum(np.array([V_orth[i]*coeffs[i][j] for i in xrange(len(V_orth))]),axis=0)\n",
    "    W_orth[j]-=W_proj\n",
    "    \n",
    "  W_orth = scipy.linalg.orth(W_orth.T).T\n",
    "  \n",
    "  return V_orth, W_orth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "pE9b2icFfIU9"
   },
   "outputs": [],
   "source": [
    "e_V_orth, e_W_orth = get_orth_complement(\n",
    "    e_Wo.T, np.eye(e_Wo.shape[0]-e_Wo.shape[1], e_Wo.shape[0]))\n",
    "\n",
    "basis_change = np.hstack((e_Wo, e_W_orth.T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 839,
     "status": "ok",
     "timestamp": 1484365206485,
     "user": {
      "displayName": "Jan Chorowski",
      "photoUrl": "//lh5.googleusercontent.com/-CIawMzW6HLk/AAAAAAAAAAI/AAAAAAAAABY/YDJiClf-um8/s50-c-k-no/photo.jpg",
      "userId": "105360491181387708695"
     },
     "user_tz": 480
    },
    "id": "Zsqyi0rdFzfc",
    "outputId": "cf8fbcc5-18ae-4045-e5db-0b99c28809ff"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "paren_string = \"((aa))[][][]a([[[[[)))\"\n",
    "\n",
    "e_v_states, _ = e_simulate(paren_string)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"First %d dimensions aligned with output\" % (e_Wo.shape[1]))\n",
    "\n",
    "plt.imshow(e_v_states.dot(basis_change), interpolation='nearest', cmap='jet')\n",
    "plt.axis('tight')\n",
    "plt.vlines(e_Wo.shape[1]-0.5, *plt.ylim(), color='w')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.yticks(*zip(*enumerate(paren_string)))\n",
    "plt.ylabel(\"time\")\n",
    "\n",
    "plt.xlabel(\"Hidden dimension\")\n",
    "\n",
    "plt.grid(False)\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Original basis\")\n",
    "plt.imshow(e_v_states, interpolation='nearest', cmap='jet')\n",
    "plt.axis('tight')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.yticks(*zip(*enumerate(paren_string)))\n",
    "plt.ylabel(\"time\")\n",
    "\n",
    "plt.xlabel(\"Hidden dimension\")\n",
    "\n",
    "\n",
    "plt.grid(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      },
      {
       "item_id": 2
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 432,
     "status": "ok",
     "timestamp": 1484365206984,
     "user": {
      "displayName": "Jan Chorowski",
      "photoUrl": "//lh5.googleusercontent.com/-CIawMzW6HLk/AAAAAAAAAAI/AAAAAAAAABY/YDJiClf-um8/s50-c-k-no/photo.jpg",
      "userId": "105360491181387708695"
     },
     "user_tz": 480
    },
    "id": "jcjaPvxtEu2c",
    "outputId": "06847bd1-830c-49f6-fffb-bfca8f552284"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f2398cac750>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+PaWVI8MlwDFmNhX4OWEuZRfCrTeuNrNv11Z09+lldFJERDpD5wyb9xpgbvb/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\nD4Eu8qAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2399e3f310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOKEN = ']'\n",
    "# First express e_Wx in the new basis:\n",
    "basis_aligned_Wx = np.linalg.inv(basis_change\n",
    "                                ).dot(e_Wx[S[TOKEN]]).dot(basis_change)\n",
    "\n",
    "# Our model has an extra degree of freedom: the first dimensions of the hidden \n",
    "# state have exactly two elements equal to 1. We will subtract their influence\n",
    "\n",
    "block_size = task.max_count+1\n",
    "for p in range(task.n_paren_types):\n",
    "  basis_aligned_Wx[p*block_size:(p+1)*block_size, :] -= np.median(\n",
    "      basis_aligned_Wx[p*block_size:(p+1)*block_size, :], axis=0, keepdims=True)\n",
    "\n",
    "plt.imshow(basis_aligned_Wx, \n",
    "           interpolation='nearest', cmap='jet')\n",
    "plt.grid(False)\n",
    "plt.vlines(e_Wo.shape[1]-0.5, *plt.ylim(), color='w')\n",
    "\n",
    "plt.title(\"Transition matrix for '%s'.\" % (TOKEN,))\n",
    "plt.xlabel(\"Neuron outputs\")\n",
    "plt.ylabel(\"Neuron inputs\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "cellView": null,
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 2261,
     "status": "ok",
     "timestamp": 1484376218576,
     "user": {
      "displayName": "Jan Chorowski",
      "photoUrl": "//lh5.googleusercontent.com/-CIawMzW6HLk/AAAAAAAAAAI/AAAAAAAAABY/YDJiClf-um8/s50-c-k-no/photo.jpg",
      "userId": "105360491181387708695"
     },
     "user_tz": 480
    },
    "id": "CwZa8ZghPgRo",
    "outputId": "46e9a6db-12ab-444f-a3ad-448d31f47687"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iIiIiIpJHB0MiIiIiIlKVdDAkIiIiIiJVSQdDIiIiIiJSlXQwJCIiIiIiVUkH\nQyIiIiIiUpVaVLT2V58WzMGcs2J+PONLTqTxXCcK8lCGmTUrhtqLDP6MjmZtAW3MmhW960VQv0Uf\nsxaIue1eBPnWjDRrV3O0WdufO3PbZxqx1QDT6GrWOvKZWfvAiH8+hJvMPvexl1mz3q+decTsM9mJ\nePbekz5GnHSl7/FUVs1t92KcrfcK4Em2M2tW3LkVvwp+lLwXhW5FV/dkktlnAuubNatfFycu3ovd\nfpDdzZr1Xnrvsfd+tXWySq31UBsjOhjgIoYqWrsVKxRqzG3j5sWtctu95cHzXOF5p3pmfvPdy9pd\n9nH2Me42FstK+gDsY5fgK6P9fKeP8XoBuM2pTfYGkuvx4pNmbfuhz+UX8u+kkTgR6e6iYU3Ti0+2\nN8PUGKvAmrWcPm86NacfXzg1izMPC/bdXmwXPmGWiq/vYPezoqu999HjRevvYrT/0+5Ss1mF47Cm\n94JTtDe18N385sKB9ntVPBdFa4uIiIiIiOTRwZCIiIiIiFQlHQyJiIiIiEhV0sGQiIiIiIhUJR0M\niYiIiIhIVdLBkIiIiIiIVCUvfK/J/XHFY8yaFdfclwlmn25MM2ujGGjWDuD23PY72d/sM4DRZm2e\nE/E9jW657bNpb/bxooatOFWvjzUGgI0Yb9bmGZmUXnz2JrzkjMPuN5leue1j6G/28WKc9+Hu3PbR\nDDD7WDHT4L/HnY0o53FsaPbpw0Sz1p2pue2vsa7Zx5sXXiz8JCNOfAjDzT7eZ3JtI/od7Gj1SmPr\nVzLeLy9mvtJ4cit23Ys37sBsszabDmbNitb21kHSut1etNfDP9307PyCF7vseKK4pVnb4eqa3PZB\nez9m9nnmkh3NmtWvkj719ePD/Pjv2//gzNtb7fU6F9klK/63Zozd5Xkn/Hf7g4xCpXtwqzs1K9ba\n6fPCuxuYtbPvyZ+/m+y9rdPHjhn3+i2oYIZ4+1oXcGJu+3RnX+WPM35jP9nOzkAq/LxW4pX38/PJ\nN/i+k2neyGZuYdfemb+OWbNufXMupzjPdkm949GZIRERERERqUo6GBIRERERkaqkgyEREREREalK\nOhgSEREREZGqpIMhERERERGpSoVisdjcY/jGntxhDsZKlFrfSa56iz5mrQdTzJqVQjeYp80+t3OA\nWbPSv8BPyrJMoK9ZG8io3HYvdcsbw1v0Nmu78Ehu+7tOcpmVGAaVJcF0YYZZ+48znzowJ7fdS4xr\nw3yzZqWCypcPAAAgAElEQVTdpefKTw3r6qQdznHSxKwUOi9NzEogAz9dzZofr2GnvXjPZaWugf35\n2pBxZp8XnTTB9sZ7XOnn0Xv/rX4vsYnZx0uMC07qnpVQ563TzuCPTlaVtHSFMzC3jcf8/uLc9mWc\nJEPP5ceeYRetsMXrnAke5dSsfpX0qa+fNXZ7E+KHUI1waoOtQo3TaTuntrJTq0Rnp5affur3se1U\nfD63/dGCHSe2Q/EFs/Z4YTPn2exkONv/ObUbK5ie58cV9FkSoc8rGO3520wAXshPoAMqG+Im/3GK\n3vKenwoJ15o9isWaerd/OjMkIiIiIiJVSQdDIiIiIiJSlXQwJCIiIiIiVUkHQyIiIiIiUpV0MCQi\nIiIiIlVJB0MiIiIiIlKVWlS09uFcZQ7GivjtyWRzel6c9Bgnkncgz5bd5yfcadaeZ4BZsyKevRhf\nLzLVit31Iq29aOVNGGvWrDhhL9LcizV+jXXNWk8m5bZPcWK833BelxXjbMVggx1pDX4U9nZGBmsl\nsepgRzJ7y/tc2pk1L+66rxFd7z2XtQyCHxltxbh789bT2YiI9cbuLU/9GWPWRhuf8bWd1zuLlcya\n9/mfZ7yX3mfrMG5XtHZr9uOCuW18/5/5MbRzaWtOzruNwVqHvmePo7vRbifIg52SDFZKciV96utn\nrZbedvoMskt3nmDXXncmuTTyQsG3/F1++3PnOH3OtmvPDbVr9lqzMoOtO2Y4T/SefYcDHnWeywqM\nXhI2NtrtLRwcYbyPAM7uhenRM+3a951+Hxvtg53kb94oKlpbREREREQkjw6GRERERESkKulgSERE\nREREqpIOhkREREREpCrpYEhERERERKqSDoZERERERKQq2fmazWBVJxrWikn2onC9OOnOzDBr3Zie\n296OeWafkWxt1rZmpFl7lq1y28c7Mc5exK81P2bRyexjxScDXMeRZm0jxue2WxHJ4L8ns2lv1kYZ\n82kv7jX7vOhEoa/La7ntwxli9rFi0AFm0sWsPcDuue2Hc6PZZyz9zNp0uuW2e5HW3jIzna5mbRQD\nc9v3d6LkvWV3An3NmhXj3tb53HVxPsdWPy/S3Hq9YMf7A7Q3lo1H2MXsY8XFgx8Lb8V1W8u0tH6F\nbe1bYBRPadzU9MJ69nMtd3h+sO2cvp3NPm3HfWrW5m20Yvl91s/vA9D+tfw4fYAOy+d/Rj++zg7y\nLU615+3+Tuz2Q09vk9vurQ+u/vhks1YYbj9XReY6NSsm2enT+5BXyx5Cx7PtfQFvHd3xd5XtQ5jT\nc/ZJXt5ry/zC8s4Eb3VqzzVoSAtbAnvpa/T/X277Ow//wOxzxF8adz2z8932emaNvfPHB/a+u/le\nAQ25gZDODImIiIiISFXSwZCIiIiIiFQlHQyJiIiIiEhV0sGQiIiIiIhUJR0MiYiIiIhIVdLBkIiI\niIiIVKUWFa3dBTsWsz9/y22/k5+YfSbR06wNYLRZG2dEA3txj92dWPCr+bVZe3Dqfrntd3Xf1ezj\nxYnPID/idJ6Zlwn9jEhj8F/XfNrktm9oRG4DdGC2WfMiLqfSPbfder0Ax3KlWbPey5nO9LyI5w0Z\nZ9asSO7rndhy7z3pzVu57V5saxvmm7X2znsymw657dGJfvYiqK1lBuz334sMn+ss15Poldveg3fN\nPj2ZbNa8yPCuRhy/t1x4vJjscWyU274Lj1T0XNLyDT3GjrWtaeTnOg/7uY46Lb+9cL0dXvthF3t6\nhRvz+1XSB+DDTna/r4x2+wYH0OnLD8zap/usYtb24I7c9vt2P8DsU/OgM5AWbuLj69nFaUb76s4E\n7VW038/ezFXk2nsPzm1fxtkm7XyLvR7uuFx+ND3AHCO6fBl7k1mxzj/Lb595i92nxn7JFfnicfuz\n2uExp6PxHlvvVTKs3vHozJCIiIiIiFQlHQyJiIiIiEhV0sGQiIiIiIhUpYp/MxRCaAeMBX4dY3w2\na+sJ3ABsDkwGTowxPrH4wxQREWnZtF0UEWl9KjozlK3w7wDWrVO6D5gK9ANuBe4NIay2WCMUERFp\n4bRdFBFpnco+MxRCWAe4Pad9G2BNYLMY45fAhSGEbYHDgHMaMu0FTtLUG0Z6lZd25tUWOC99Du1z\n271kKC8JrR1GTAhwXveTc9sHMsrscy97mbWteTq33Zu34410KvCTvKzEqxFsa/bpz4tmzUqMA+jL\nf3LbvWS91xbZJ/mWlQzmpbitzhSz9ixbmbXdeSC3/SX6mX285WkWK+W29zFS5tL0Pnem18msHcpN\nue1jnbF7yXXdjNQ1sBPqvGQ1L9XOSi6c4sQRDWG4WXuLPmbN+nytVGGyXh8mmrXORurm8www++xo\nVqQxLMntIsCvi8ubtUE8k9vuJS16nmcLs9b5mE9z2x/8hb3O7+YEKlr9uk0ov099/azV0lFXrWh2\nud2MQoPtHhxh1u5+Mz+u67VWnBjnqckPz2v9br257C6TnJq9xDgaOcUNaEi42iLOtD8mLPvd8qdX\nY++e+xGPlmHOe1UcVm/3Ss4MDQKeJJ3yL83G6w+8nK3waz2XPU5ERGRppe2iiEgrVfaZoRjjdbX/\nDmGhb2VXhUVOxUwDdDmAiIgstbRdFBFpvRozTa4DLHI92Fyo8Fy9iIhI66btoohIC9eYB0NfsugK\nvh04t7cXERFZemm7KCLSwjXmwdD7wCp12lYBPmjE5xAREWkttF0UEWnhGvNg6AVg4yxetNaWWbuI\niEi10XZRRKSFq/imqzmeAaYAw0II5wK7A5sChzR0Al2d2N0J9M1t9+KuJ9PLrHmRzFas8SPsYvY5\nkNvMmmcaXXPbn2aw2edMzjdr13FkbrsVTQ5+jLcVTwx2PHUPJ4L6Go42a/0ZY9aeZuvc9rZObPn6\n2DmrVpx0O+aZfbyobi/+2Yq43dYJ2vyMjmZtI2OZH8F2zvTsaN6eThjo38iPiD3cyb58kf5mbSVn\n2bDmbycnntqLz7c+x7PpYPYZ44zdi363xuHFnc+gi1mbTE+z1tZYRr3PuDSbxd4uAkTWNmsPF/Jj\n7u2l3PdscVeztt9ORjb0ic4ED3VqVr9K+tTXzzDKuU3AXtxn1o7marP2m7V+m9s+oPi82ecf/MGs\nvV7Y2KxVxF71wIwK+tQ991nq1Zr89g2NdoDxTs3rZ23m7Ls9+O7+Mrf5O8vYedf7dlvfrP2Lyyoc\nSON6vMceue07Tfmn2WfZC/a2J1jBkcSDp9r7nS+vuqXd0Yqeufsrs0tNA8azuGeGirX/iDF+DexB\n+liMBQ4A9owxvreYzyEiItJaaLsoItKKLNaZoRhjmzp/vw3GV/giIiJLOW0XRURal8b8zZCIiIiI\niEiroYMhERERERGpSjoYEhERERGRqqSDIRERERERqUqFYrFY/6OayHmcYg7GisIewGhzel48rceK\nSZ5GN7NPGye7cYGTU1HpGC2DGZnb3pHPzT6jGGjWZtLZrFkR2l4EsRULDBCIZs0aY3sn+rsDc8ya\nFWk+hOFmn4n0MWvRiTW2orC95cmLJ7fG7r3ebkwza977NZe2ue1WNDnAMtixoz2ZbNasyGsv0nwq\nq5o163PszSdvfeLFbk833pO+Trz7aAaYNS9O3OJF0x/G7YWyJygtRqEH9oZ6H6N9uQqfbKxTs1aB\nzzl9rChcACtTr5I+9fWzNtH/c/pYMdOAc3cBjjnwYqdjvg+c9bC3XqrEJGe/o5exjvb6ePs4t889\nILd9cLv8fRWAUQu2Mmtbt7H7VaKNs73qzMzc9nbOLT3ecvYTvP2pBbQxa43NWp5GOjkvWxv7luDv\n11m87bp3u4/pxn6Tt828nuPr3f7pzJCIiIiIiFQlHQyJiIiIiEhV0sGQiIiIiIhUJR0MiYiIiIhI\nVdLBkIiIiIiIVCUdDImIiIiISFWy8xCbgRWtC3ZsnheLaMVxA6ztxDhbscZeZKIXNbyJk1VqRe9e\nxGlmn3242xnHSrntXpywF+noxRV+Rsfcdi9msZcTmejFLt/J/rntHfnM7OO9x1YM8RtORLb3XB4r\nQrkfL5l9vKj2mXTJbW/HVLOPFZENfuz234357kfa9zJrHuv992JM5zjrDCtOvrszn7y4zzm0N2tz\naZfb7n22Kok0B+hixL16y7u0cqfYpXmHNG5qetvr7BTv4hTjuf7pTPACp/anRuxTXz9jlVrY1bm9\nyOnO9A560yxdddCu+YUT7PWLFwt+95s/yy94e3D2JgS+cGorlt/nrpuM1wvc9OUhue17tbvP7PPk\nl9uZtd2++6BZq2Qb7a2jrX1Ibx/n8oK9vYJlnVrT7Y4fWczf77y8kL8dA1hQtN8Tax7Od+btm4V7\nzdpfim+YNet2Kt7+fkPozJCIiIiIiFQlHQyJiIiIiEhV0sGQiIiIiIhUJR0MiYiIiIhIVdLBkIiI\niIiIVKVCsegkqTSxUzjPHMyGjM9tH8G25vTWN5LawE+58BLFLKs6CVVW+hfYqWbznDSpsWxi1qyk\nLCuBA6APE83aW/Q2ax8YaVidjbQr8NPpImubtUB+uoiXBNPWSSGz3uOuTDf7eGklXkKZlXg40Zm3\nXrJeB+O99NLJKhkfQFcjac6bF8s401uX18zaJHrmto9mC7NPTyed8CXjc2K9JoAtnJQ8L03SSlb0\n5ruX4heddZCVunkAt5l9tuSlxo0ckyZ1Az8zt42/vPSW/EKF4VR/Of4As/aLi27PL5xur/Op6ezU\njH6V9Kmv33L5zX8+zUhqA87kfHt6junHrJHbXnO13edMK8UNmDzLiZprIl7C6ZpDP7Q7vmu0r+c8\n2atOzetnJd55nwWnNvuc/PY5X9p9rrQ3fy1GjbFY15zp9PldI4/BmLdgjw/ACG5l9lC7S4fPi/Vu\n/3RmSEREREREqpIOhkREREREpCrpYEhERERERKqSDoZERERERKQq6WBIRERERESqkg6GRERERESk\nKrWoaO37GWIOxooh9qKQp7KqWfOigTswJ7fdis8FOyIb4HbsqNI9uTe3fY4RnwvQ24nCHku/3PbJ\n9DL7BKJZ28sYH8AMIzJ8PBuafUawnVkbyLNmzXq/vBj0o7nGGUd+JPvWPO30scfuxY5a0eVeRPoc\n2pu1fryU2+4tnzNxImcd0+ia296OeWaftk6tCzPMmhVP773HXlT3XCOD04u7vpt9zJoXC29FfK/k\n9BnHRmbNG6O1LmxvrLcAbuEIRWu3Zg8UzG1jzR6N+1Q1Rno2QI2xKau53+njjM/qV0mf+vqZfYbb\ntULvr+3i3fZHqrggv9b5tPfMPke2ud6snd/53Nz2QoXx6Y3uH3bpjkH5b8pP77XfyDv2st9Ir19F\njKhmgJd3Xie33bv1yeb75t8GBoDzGjqoEvauRcXx+cXX8pfPwpr28cCcPvbyvtyn5Y+h8JW9n1B8\n2Z6/1vv1ws4bmF02Y7yitUVERERERPLoYEhERERERKqSDoZERERERKQq6WBIRERERESqkg6GRERE\nRESkKulgSEREREREqlJLCWYEYBQDzdo0uuW29+U/Zp8P6G7WvJjcZViQ227F5wK0MfoArO1EVy8w\n3gKvzyi2Mmudjehib+yznRjvqc487L8gP078xjaHm30O4DazNpwhZm0rRuW2W+8V+BHfVtT4LOxY\nzI58ZtYeYWeztpExzQ7MNvt4Ud1WZLwVCQ/Qk8llTw+gPy/mtk+mpzM9e5nxlkMrTr4fY80+nvm0\nyW2fRSezj/X5AejOB2btcyPW3GoH/z32lg0rWt+Le5XW7Yrdf2nWVijmL+fW8l+f3zu7BRfscHZu\n+9l9VrAn+N6XZuns9ZZrvD719FuxS/42f4V259jTu8dO5H3xtL5m7TKj28z1VjP7eLHgnxlxwl95\nscuOj+3NJisbi43XZ9ag9cxaLORHYf+3uGbZferrZ91OweOth/v9zLh1Q/5dRQBY665XzNobV9j7\nJG6EdiN75uQf5bYXr3ASqF9o3DEUv2tvr546aHOzZm2jN/+pve9WvKP+8ejMkIiIiIiIVCUdDImI\niIiISFXSwZCIiIiIiFQlHQyJiIiIiEhValEBCiIiIk0hhNAB6Au0BRb65XCM8dlmGZSIiDQ5HQyJ\niEhVCSHsDtwMrECdAyGgCBVGsYmISKvTog6GujPVrFnR1e2YZ/YZydZmbS/uNWuTjNjgHkwx+3jx\nxNONWHCAjnye2z6FHmafz5y4XmuMXiz0FFY3ayPYzqz9rc3Pc9v/+qkdAztoxSfN2pFcb9bG0i+3\nvQszzT4eK+K5vRNp7MVnX8tRZm2mkcPZ2Rl7Fyfi2RIJTm1ts9bLid224pq95xrCcLPmLddW3P14\nNjL7TKOrWbMizb3Pws+5xazdyf5mzRq7FyU+kT5mzVvXPGvcgsB6vZLrImAEcC7waTOPpV7H7/5n\nu7ii0e5EIbsxvtPt0ukTLs9t/87pRbNPcfP2Zq1g9KukT339TE7a8cnfvcasbb/rE2bt081WyZ/e\nHjX2k3Wxa2fvaBQq3YPzNi9WbLR9NxJ+xWVm7dr1Tsptf4+rzD5/2fAYs+b18/aNLHO9WxJY8+lD\nu8ub39vArK370UtmzYrC924fUqkBjM5tP/74K8w+3i0pvFvLWLz9/XUxIs2BmXTOLzjLZ0O0qIMh\nERGRJtAL2DXGmH/TJhERqRoKUBARkWrzJmDfAVNERKqGzgyJiEi1ORW4MoRwJvA/YG5pMcb4brOM\nSkREmpwOhkREpNo8QApJuJ8UmFCrgAIURESqig6GRESk2tjJMCIiUlUKxaKdzNLUruJwczBT6W60\n2yluXvrFLjxi1roacTpeEpb3XF5aSX/G5LbPo53ZZ5SRJgXQnjm57aMZYPb5OX8za3ezj1k7mqtz\n2+9jL7OPlxjnjfEteue2e6kjXrJMB2M+DWeI2WdrRpo1L3XvAG7Pbb+eI80+g53nshL0JtDX7NOX\nCWbNmrdpmuvntltpNOAn4c0wo4qgg5Hk56XOWOsFgLYLX/n0DS+drjdvmbXXWdesLW8k1K3kRNy0\ndZIwFzgnJsYZ8Vde6tBw9qgbHy2ZEML3gK9ijIuZR7TkFH6KuW0snmC8tfYmxH+u8+19gm3ueii3\n/alndjX7bD7oKbP2r+e3ye+zRfl96utnJYU+9TN77NzqpJX2MVKtAPbMby7uZH8Mr93Wntw0uyRL\nUI0VXPel3efmk/czawf/7B92x/zNla/C0xk1dxjtPy2/T6VqDnFqwyqYnh0yCL8u1rv9U4CCiIhU\nnRDCcSGED0hBuTNDCO+HEE5o7nGJiEjT0sGQiIhUlRDCkcDFwB3Aj4F9gH8Avw8hHNacYxMRkaal\n3wyJiEi1ORE4JcZYenHFvSGEt4ATgL82z7BERKSp6cyQiIhUmzWAR3PaHwPWauKxiIhIM9LBkIiI\nVJt3gE1y2jdFv1cXEakqukxORESqzfXA1SGElYHns7YtgXOAK5ptVCIi0uRaVLT2/txsDmY7RuS2\nz3ciaK1YYIDJ9DRr1nN50dreODz7cE9u++0cYPbxoobnGnmqXgS197pm096sfWDEGvdmotmnoxFB\nDLAh48zaI+xi1ixWVDNAD6bktnvR2ntxr1mb5CxPo9gqtz0QzT79GGvWrDFaceGpZs+LNk4ks7Vs\neGP3PgtvEMyatYxakbhQWUy291nw1hmd+MSsLTC+V2rDfLOPN3bvc2LFbm/IeLPPMdyoaO0SIYTv\nAJcBvyJ9KVgAviIdJJ0QY/y6GYe3qKMK5raxxr5bQUW8eN0Xbt8gt33zi+xl79HTBpu1nS56utH6\n1NfPWs9tf8xzZp9jr7rIrF3V91Szxqv5zbsW7zK7PLTXvvb07quxa7IEDTba7f0ihvW3a4c87TxX\nJecm7O2LP738fVzYwu5y4faVPZXllPzbyiR5VzDXxx57sbh9vds/nRkSEZGqkh3snBBC+C3wA9LB\n0OsxRvsoVERElko6GBIRkaVeCGErYHSMcX7277o2CiGduYwxPtukgxMRkWajgyEREakGTwOrANOz\nfxdJZ4TqKkKF1z2LiEiro4MhERGpBr2Aj0r+LSIiUt7BUAihO/AnYGtgNumO3WfEGOeFEHoCNwCb\nA5OBE2OMTzTqaEVERCoQY3wn798AIYRlgQ2A/8UYPy932to2ioi0XuXeZ+geYDlSbMNPgN2Ac7Pa\n/cBUoB9wK+lu3qs10jhFREQaRQihRwjh8RDCj0II7YFxwIvA5BDChhVMUttGEZFWqsFnhkL6ZemP\ngG4xxhlZ2++AP4QQHiNddtA/xvglcGEIYVvgMNJ9GxrkWQaaNSsOdwadzT6VRCsDRCP+txOzzD6f\n0NWszaGDWZtH29x2b3xeNPCd7J/bPoYfmX124RGz1s25/2AvJue2v8a6Zp8BjDZr97CPWTuEm3Lb\nvchtb+xtmZvb3pkZZh9vWZtON7NmvZdefLYXd76MERG7Mw+bfUY7sZNdnNdsacs8szbL+SwMZqRZ\nm2pEtXfnA7OP91m421ierPkH9uexvn5WTLYXW97T+PwAzHGiW2cb65Njr7/R7HPMkWapWv0RWJH0\n+6H9gDVI9xk6FLgY2KGhE2qKbWPhFecWGHsa7V0aOvWFne3U7rNuPWBvGs3Ptdevkj719bMi6Qsv\njbInWKixazi1zfJrD+3kxGc/dr5ZWnn+EbntC+ZX9tO2T2d0MmsrdsmfwZ9+aG//jlnjj2btoMJp\nue1XFf9sT6/wS7Pm9fP2+SzW+hTglilb5xec2V543fmsLjfYrn1plxrbGcXHc9t/X3Disy90JrhK\nBYPoaUeQnzHJ3pex9o1umeKtsuu/hVA5Z4Y+BHasXdmXWBHYDHg5W9nXeo50WYCIiEhLsg1wZIxx\nMrAz8GiMcTTwB8rfbmnbKCLSijX4zFCM8VPgm+ucQwgF4BjgSWBVWOROoNMAXQogIiItzbLAx9l2\nbFvgN1n7d/DvZLgIbRtFRFq3xUmT+wOwEbApcBIsct3RXKDdYkxfRERkSRgH/AL4AFgJeCSE0BY4\nHRi/mNPWtlFEpBUpN0ABgBDCRcBxwIExxtdIVzvWXbm3gwou4BQREVmyTgaOAq4GLooxvgdcDuxB\nOiCqiLaNIiKtT9kHQyGEK4ETSSv7+7Lm91n0J1SrgPPLZxERkWYQY/w36RK2zjHG2kvkLgd6xxjH\nVDJNbRtFRFqnsg6GQghDgV8C+8cY7yopvQBsHEIo/QZsy6xdRESkRYkxfg20y2K2VyedxVk++3dZ\ntG0UEWm9CsVi/ZFzACGEdYD/ABcA19QpfwS8ArxKurfC7sAZwA+zyw8aZE/uMAezLq/ntn/G8ub0\nZrGSWduLe83aA+yW274JL5l9ImubNS/+14pk9mK8RzkR5GvzhjG9T8w+XvS3F/Fs6c1Es9aHt8za\nCLYza14kt2UyPc2aFXk8zYmFXuD8xK77Ir+Rrp8V9Qp+jLcV8ezFsXvxoV4cqTXfRzPA7LM20azN\nc34qMZLBue0dmGP28T4nE+hr1iy78YAzvfXNmhWF7b0nqzrLzCzs6NuZRmayF9V9EHcXzGIVCiEM\nAIYBveuUCkAxxtjgzOKm2DaewnnmtvHSwrJGZYWGTn4hNcXJdu2d83LbB63xpNnnmf/uaNYG/fCx\nRutTXz+W/yq3+dQ1LjC7eOuDz+lo1j4zavu++ZDZx/ul2l377prb7kX3e9sXr18lfjz2UbNWs6nR\n/q49vRrn6wivHzOdmiX/LhsAvN9/5dz29s42c+VTnIxs+2PStG4w2s+0u7T5m30v6o6dPit7CLMG\nrWoXr3I6Gu/Xh1usaHZZhVn1bv/KOTO0e/b4s0jpOFNJp/qnZt+w7Uk6/T8WOADYs5yVvYiISBP5\nE2n7tRcpZrv2v62z/5dD20YRkVasnGjti4CLnPpE0oZERESkJVsP2CjGmH/JQRm0bRQRad0qSpMT\nERFpxaaAc421iIhUDR0MiYhItTkPuCKE0DeEYP3oRkREqsDi3HRVRESkNToLWJ3sZ+shhIWK5QQo\niIhI69bgNLmmcBpnm4Ox0rrm0tacnpW6BXAn+5u1uUbi1SaMNft46SzDGWLWDuWm3PaO2OkcXsKb\nlWJjvSbwk/UqSeTyktDeIJi1gYwya1Yin5eY05XpZu0STjHG8KzZx0sT9OaTVfPSv7yUtDH0z21v\n58TieAlvXuqi9bmbbaSnAYxnI7M201k2rPnRlwlmH+/9H8smue3e8u4lNX7iJLxZy+dkepl95jnr\nLu/zar3P+3C32WdHnlaaXIkQwsFePcZ4c1ONpSH+TV9z27jpfq/mF5xQK8/LD6xj1o4xYp7+1cvO\nnBg0yUl/65Wf/lZJn/r6WZ+3yzjJ7LN5dCLefjDJrt2X/7lfcccPzS6fLnedPb1WbLXigbnt7xVu\nK7tPff0a23KzjsttXzDfPo9wWWd7eTr2ezcu9pgaxYya/PZljHaAyfYK5TvLlJ9O+HWf79rFz51x\nGKz3CmDOiivXu/3TmSEREakqLe1gR0REmo8OhkREpOqEEHYC/h/wA2Bz4FDgrRjjrc06MBERaVIK\nUBARkaoSQtgeuBd4F1gJaAMsCwwLIfy8OccmIiJNSwdDIiJSbc4GTo8xHgLMB4gxngn8hnS2SERE\nqoQOhkREpNr0BR7Mab8L6N3EYxERkWakgyEREak2nwLdc9p/CHzcxGMREZFm1KICFPrwllmbQZfc\ndi+q14u7HsBos2ZFMk9lVbNPLycmuTMzzZrFisgGmO/ECc8y4n/foo/Zx4oFBniNdc2aFWu8W+4X\nrskcOpg1LybdikLfjhFmn3FOxPMV5McwnsPvzD4TnXnoxa5bkeGjGWD2Gc+GZq2fEfHdJl3tk8sb\n+0gGm7WNyI+W7cEUs8/vOd2sXc6JZs36vHqfcS+C3lqurWhy8OOzvddc7hjA/4x7RrBtbrsVJQ5g\nhxFXrduAy0MIhwJFYPkQwo7AVcCdzTqyHJtua8RnAzVPNe5z1Wz2uln75ws/zm1f5dBPzT4fsbxZ\n+wOJhjIAACAASURBVN6hn5ff5+f5ferrZ05vW3t6Fz5lJ/L+sGjHiQ/5NP9NObXdBWafQ4q3m7Ud\nsSPDK+HddsTav/D6HIA99ot/u1Zu+6+KK5h9rjX61NfP2x5YvNszXG0s1h072c/j7ccW73QSnu3N\nd6M7cYf85fCPY+3x1azWuGOoedKunbiN/Tmx9hOs96qhWtTBkIiISBM4C+gB3xztjwMKwEPAmc01\nKBERaXo6GBIRkaoSY/wKOCCE8DtgQ9Il46/GGO1TeSIislTSwZCIiFSrz4AXa/8IIawOEGN8t9lG\nJCIiTUoHQyIiUlVCCDsDN8EiP0YtkH5DZP+QQERElio6GBIRkWpzBTAGuAaY08xjERGRZqSDIRER\nqTbdgV1jjLG5ByIiIs2rUCwWm3sM3ziWi83BWLGOXnx2V6aZtXm0M2tW5O08J/rZiuOur58VBenF\nQm/Fs2bNiiBvx1yzj8eKcQZ4jXVy2715OzX31h7JROdeh1YU+hR6mH0qWTZ2d2LBT+f3Zq2b8/5b\nDuUms3YTh5o1Kz79aK4x+0ygr1nz3hMrJtSLp7bGB7Azj5g16/0azhCzjxdjOs6IJ/feY2958iLy\nrTFuyDizjxd33tb5vK7ErNx2bx10Bn90Ml2rTwjhQeCOGKOdCdyCFFbF3lB/OMkoeCe8nBzfTuvb\ntVlvGgX7MwW7ObVHjfadKuhTX7+v8ptXsW8fwYfW6wX2seOfi4fmf9wKu9i3YOC6fnbNvoNEZbwY\n5+WM9i/tLqvc+7ZZ+/CVNXPbV9vAnrfvvWLPW6/f7AX2rTssndrkr08B3h74w/yCMy/Y1SkNvcus\nVbqPVonnjdt69GeM2cfb5s+ZW/58H9DOvr2Nte0G6GCs197e3HivgOK/qHf7pzNDIiJSbX4FvJjd\nW+ht4OvSYozxnGYZlYiINDkdDImISLU5C1iFdD/aL+rUioAOhkREqoQOhkREpNocABwaY7y5uQci\nIiLN6zvNPQAREZEmNht4vrkHISIizU8HQyIiUm2uBmpCCOX/8ldERJYqukxORESqzVbZf/uFEKZR\nJ2osxpgfgSUiIkudFhWtfTHHmoNpx7zc9t5OtK7VB/zovgXGMaIXffgfJ7rYi12eTfvc9sAbZh8v\nCtmK6vbG7sUJz3Visq3neoSdzT5enHRvJpq18cb75c2Lo7narA0zoqsjwezzyJS9zdqJPS4wa1Zk\n9GvYka4B+/Yn1nvixWLOpLNZm08bszaH/C/Ovdh6KwYf/PlrfSZ/wp1mn4edZc37/FsGM9KsjXfi\n7nswJbd9XV4z+4xgO7PWnalmrQOzc9tnG+8VwFlcomjtEiGEoV49xnh2U42lIQq3OdHaM4z2JfFV\n5zH5zesUXza7vL7SxmZtnU/y+73e0enzmfNcTj9zflxud/nLwQeYtV8U1rY7GjHETxbPNXts28uO\nGmbyRc5zVaKbU7PW7SvbXdY7wiwdNOGG3PZbN3X6/Du/T339zFhwz+d26cVx9n6dZdMbXzVrhbUq\n2N/2YtA93uf/KKN9mNPHTiCvbF3jvS5jPQOY7/GLE+z3alMmKFpbRESkVEs72BERkeajgyEREVnq\nhRB+B1wSY5yd/dtSjDHaX+GLiMhSRQdDIiJSDQ4lBSfMzv5tKQI6GBIRqRI6GBIRkaVejLFX3r9F\nRKS6KVpbRERERESqUos6MzSB9c3a1kbKk5UKBtCPl8zaGH5k1qw0rE5OnMYsOpm1mXQxa5swNrf9\naQabfX7kpIZZiVfe2DubcUR2EhrAg+yW234Fx5t9bnLeLy+5zhu/5XYONGtrG2ltXrLaaT1qzNrR\nXGPWLuGU3PZ9uNvsYyX1gZ1C5yXrvUQ/s9aXCWbNmqb3XN4y8xkdzdp2PJnb/onz2erjJBDOpW1u\nu5fuaKXngZ/WZ82PB4zPCEAvJps1a+xgz9/J9DT7CIQQvgYnla1EjNFeETSD4tZ2GNJH3ZfPbf+M\n/HbwP6PdP/3IrC37en77zexn9jn4l/8wa1a/g48uv099/fjCaN/e7rKusw/xnQ/tZMyvV3kst33b\nwo32k1Hj1Brb5Ar6vG9W1pvwb7N2a2FObvuWxSfK7gOwefEps7bA2X5XYtPpRjKcvQnhjsP3sIvX\nL954GsvGrz+X2/7ynVvanSY7Eyx/9wxWs0s/evMZs2atuzadaqf4Obsr32hRB0MiIiJLyGF8ezC0\nBnA6afdkNOk+Q5sCvwbOa5bRiYhIs9DBkIiILPVijMNq/x1CeAY4Jsb415KH3BdCeA04AbikiYcn\nIiLNRL8ZEhGRavMj4Nmc9heBHzbxWEREpBnpYEhERKrNm8BPctp/Cfy3icciIiLNSJfJiYhItRkK\n3B1C2B74N+mLwQHAhsBOzTkwERFpWjozJCIiVSXGeC8wkBSRNQTYDngD6B9jzI8uFRGRpVKhWGxQ\n0miTOIRrzcF0wI5atPThLbM2kq3N2oaMy233YncHMsqs3cjhZq0HU4zp5V3OnrxIf7PW08g/9CIn\nvbhjL4LVirv2nsurjTNiwQF254Hc9hl0Nvvcws/NmhUnvS6vmX286OK2zDNrB3BbbvtoBph9vNhl\nK1p7trN8etHvETsi1loOp9DD7GNF04O9fIIdT70dI8w+lURXe/HpgTfMWjvmmrW/Gcuatzx5vPlk\nfYam0dXscypX2tnM0vK9WTC3jUPWuq9Rn2r4q3uatXfW+15u+5Y8b/Z5hq3M2iBj/VJJn/r6Wdac\n9KFZ262XHdV9Y8GO+LasvKJdO//TsicnIg1QUyzWu/3TmSEREREREalKOhgSEREREZGqpIMhERER\nERGpSjoYEhERERGRqqRobRERqTohhDWAzYC2wEI/sI0x/q1ZBiUiIk1OB0MiIlJVQghHANdAbjRf\nEdDBkIhIlWhR0dpnc5o5mAn0zW33Iq3HsolZO5E/mrW72cesWebQ3qz9iDFmzYpXnsVKZp/j+JNZ\ns2K8rdhigJ5MMmtWfDbAdLrltnvvyXCGmLV5tDVrHfkst90bnxWfDdDWiEmeSRezzyPsbNYGY9+a\nxIqa3taJjD6L883aAEaX1Q7wNIPNWnemmjUrxrsfL5l9vBjvzswwa7dzYG67F0/tvf9W/HcHZpt9\nvHkxgu3MmhVrfig3mX0qjWq3Ir6nGZ9HgIsYqmjtEiGEScBDwJkxxv9r7vHUp1DA3lBbSdjLVfhk\nf/eKN+Q3n3WE3eW8a+3aWb9qvD719WNlo31/u8urdmnzHz5l1v610jb5hVk19gSdW2bg7EO0CBfW\n2DVr+bzKmd4xTs3rZ296bPOd2oXDjIK9rmUZ557NLzjPZZ2aqPSUhfe6bjXaD7K7bLnBE2bN2p/y\nPPXKrnbRGh/Y8+NCY90EFItHKFpbRESkjlWBS1vDgZCIiCxZOhgSEZFqMx74YXMPQkREmp9+MyQi\nItXmYuDqEMKawP9g4es8YozPNsuoRESkyelgSEREqs3d2f+vyKkVyQ9WEBGRpZAOhkREpNr0au4B\niIhIy6CDIRERqSoxxncAQghtSQdGE4FCjPGrZh2YiIg0uRYVrT2QJ8zBWHHNXhTuNLqaNSuqGWAl\nI653UoVfJnbiE7PWjem57W/R2+wTeMOsWbHGXqTxGwSz9hkdzZoV1+1FPHvjmEc7s2bFEL9FH7PP\nJow1a9bYJzrzvTcTzVogmrUFxhU3XvT7Adxu1qwI8mcZaPb53Hkf9+dOs/Ynjstt7+9EvXoRz0MY\nbtas6PLZTmz9B05kfA+m5LZHZ3k/jYvM2gi2NWuTjXWDFwtuRWSD/7mz4sS9SPOhXKRo7RIhhALw\ne+A40k1X1wbOB74AftXSDooO5ypz2/iXvxg5xF86E3S+Bt37SDvX9p6J+fH39Hne7LP85/bn7fPl\n89eblfSpr9/8+fnr4Z+veIvZ5899jzdr902wbxOx58P567l5B9ofw1/NutKs/WXir81aJZbtZO//\nfDUrf93j9Tmus327j0svPSu3/ayTf2P2Oe/SC8ya129BBd/vt3EyqH9a+H1u+7LO9G5zavsW12zY\noErMXwJX7F7PUbnth3Oj2ecw/lr281j7PgBXOfnpdzpx99b279DCuWaftYpFRWuLiIjUcSzwM+Bo\nvg1PuA/YC6hppjGJiEgz0MGQiIhUmyOBY2KMw4CvAWKMdwKHg3H3XxERWSrpYEhERKpNL2BcTvsr\nwCpNPBYREWlGZV9gGULoDVwNbAHMBK6KMV6S1XoCNwCbA5OBE2OMTzTWYEVERBrBZGDT7P+ldgLe\nLndi2i6KiLReZZ0Zyn50+jAwDdgQOAo4K4Twk+wh9wNTgX7ArcC9IYTVGm+4IiIii+0PwDUhhONI\n28FtQwgXZu32r8FzaLsoItK6lZUmF0JYBfgjcHiM8Yus7R7gA+Ae0kq/a4zxy6z2BDAqxnhOQ6Z/\nNJeZg+nGtNz2SUbKGMBWRgIdwI0cbta2ZURu+3g2MvvszMNm7Wm2NmtWMoaVhFUfK71jrpPU5qXd\neWldVsqJ9ZrATzXzErS8JDfLcOy0HyuF0EsnnP3/2bvzeLum84/jn9sgxCypoFQM9aClFD9aYvqp\nlJpqqKGDoVqqWrR+1ZZyTVUtqjrQUkK1qkVrrhoSlNY8lj5qiKEhJAQxJBL398fah+NkP+uec3Lu\nvefmfN+vl5fc9Zy1zzpr77PXXnvv82yGhbFcNrkoc2Fu2/0MfwljT7Ncafmv2C+ssxKPhrERTGn4\nvXLr4y7WCWO5TINDmFVantsuctlqonWZy0A4jNfD2KjZLiJUL7M8C2EuU+OmjAtjUXa61I4nGq5z\nIkcpm1wNM/sKcARQmZi8AJzo7qc0uJw+HRcBeLorHBv/tVx5hqoFMttyzor/eS6MvRkMB+csuGdY\nZ9+Xzw1jZy1aXq+ZOr3VmxXcBzN/eUJXAC5ZYaswdn/X1WGsO+in7nh4oXvVOMbwTKwZuXuCouRq\nmTr/uTGO3dazU2n5VvNcHNa5emZ5nd7qvVI+hDQt3pqas2EmlstQ12ofCMrLj7KTjXLJlF9rvA3X\nZr53ozL1os0wt66668gm19Btcu7+HLB75W8z2xAYTcrIswFwd2WHX/g76dYAERGRtuHuvwZ+bWYj\ngPe5e2Z4zi5H46KIyCDW9ENXzWwCsBxwBXAJcCrMdip2Eu+edRMRERkQZrZxL/F3zs27+01NvscE\nNC6KiAwqTU+GgB1JWXdOJ90iMAxme4rgdMjcnyUiItI/xgM9QFfx/4rKLRTVZc0+6VDjoojIINN0\nam13v9vdrwK+SXpmQ9kOfig0eeOyiIhI66wArFj8/8vAU6TJy0hgCeBTwL+BvZt9A42LIiKDT0NX\nhsxsSeDj7n5pVfFDwHykH4uuVlNlqaJcRERkwLj7k5V/m9l3SAkPrq96ybVmdgDpt7jn1btcjYsi\nIoNbo1eGVgAuMbOlq8rWBZ4n/Sh0HTOrPgu2EfDPOWuiiIhISy0D/Lek/CXSVaJGaFwUERnEGk2t\n/T7gH8CLpNsAVgB+AxwP/BK4H3gAOBbYDvgu8GF3f6ae5f+U/cLG3Mb6peW5FNS5NL5XsXUYi1Ly\nzseMsM7rLBDGcilvp7JYaXmUPhfyn/l5RpaWX8iuYZ1DOSmM3cNaYWzlIL1yLgX1vZnl5T5XlPI4\nl+I5lyZ5eJBOOpfG+Y3MOp4U9DvEKZlzaaGvY4swNoLJpeVfzzwe5So+HcZ+zx5hLPouLBSkCwdY\nnYfCWC7VdPT9Wpc7wzoPsXoYmxAk6FyFR8I6Q2f7ece7otTfuXbkUrXnvlsrZNJ4TwzS3efSu/+E\n7yq1dhUzu5408dnL3acVZUsAFwAz3H3bBpbVp+MiQFdXdzg2nthTvi97I7MfzunuivdzoU2749j4\nTCyql6uzRSZ2XSbWjI9klvfg2Dh2xV7l5dtkljeILfrm/mHs5UeXKi2fd6lXwjpvPbdIGMvWe3O+\nMBZZaLF4LBsyT+O5umfNjMf1aZPLj/cAmNnET/jnifKg5y00ovzxJ9Omxsc/9y0b7xcaPXsEsPxz\n08LYsIVaexfxqwu+v9fxr6ErQ+7+NrA9Kav4rcCvgVPd/edFbDvSLQB3AnsAOzSywxcREekH3yBd\noZloZnea2d2k3xCtDBzYyII0LoqIDG4NT0WLZyrsHMQeh8wTRkVERAaYu//LzFYhPR/oI6RMcj8H\n/uDuDZ+W1LgoIjJ4zUlqbRERkUHJ3V8BfjXQ7RARkYGlyZCIiMz1zOyGel/r7pv3ZVtERKR9NP2c\nIRERkUHkyar/JgGbAosBD5OSHMwHjCY9a0hERDqErgyJiMhcz93feZiqmZ0N/MTdv1X9GjM7FjIp\nCkVEZK7TVpOhR1kpjI1kUmn5DOJUis4qYWwNHghjUXrlKA12bzZjXBiL0uTm0i4/G9SBOGX0XpwT\n1smloF6c8hSMOQ/N9ozBd23LZWHsZjYOY0sG6z9nMsPD2NMsV1qeS5+c8wBrhLFJLFlankufvBgv\nNdyGv/CZMBal9wbYgT+HsWG8EZTH28wTQUpryKe7j74Lub6NtneI12XuexztZ3qrN1+Qkju3Pa3P\n7WEsSgsO8b5rZy4K66RszlJlF+BjJeXnAff2c1t6N6o7DB22dBCYv8n32igT+3vQjuaGxubqLdTk\ne0UyfbvVA5eEscV4O4z9/sEgk288DMOH4tBhG3aXljc7Xj0fjEkAS/J8w3VmZsaQsfcfUFr+9Q+f\nGNb52d8OC2O5elNZPIxFcseQo7k5eJ94wz3ispPjN8us4/50yPI/KC3/ydPfiys9nllg/ESK0Kz7\n4i/y17eM13H0+JhoXSVn9doe3SYnIiKdZiqwdkn5aGjizIuIiAxabXVlSEREpB/8Cvi1ma1Oev7P\n+4ANSc8Y+vZANkxERPqXJkMiItJR3P04M5tFmvwcWRQ/DRzq7qcPXMtERKS/aTIkIiIdx91PAE4w\ns+FAj7u/ONBtEhGR/qfJkIiIzPXM7IvAhe4+vfh3bfydf7v7ef3ZNhERGTiaDImISCcYC/wVeL74\nd6SHlFVOREQ6QFdPT89At+Edx3Fo2JgoFfJyPB0u76HM4yJy9aK01vMxI6wzkSi/KSzMq2FsniA1\n5usMC+usxT1hLEo7GKUtBliXO8PYnzPpmtcJ6r2RaXsuffrGmdSIj7Jyafkongjr5FJXH8/hpeVj\nuCass1gmzXguFXoz225uHUep1c9jtpPd79iD34WxXL8fxdGl5VtzVVjnNL4RxrbgujAW9WHuu5BL\nuT40+L5GqfMBhjM5jF3MzmEsShkebbcAhocxx8JYrl7kJI4Icv3KYNDVdWY4Nm7VU/4Igdx2nkvJ\nfGPXc3FDttmrvPyK2+I6W6wfx64L6jVTp7d64fLGZoIT4tBfuuPYDkGsmToARLG3MnVy57pnNlEv\nVyceX9h3r/Lys+LHbLDvdnEsV6/FiSCX7xndcJ0nv75qHPx5d/ONaaloXHo0rnJWdxyLh83YdzLL\nyzxaItrmc+tqAqv2Ov4ptbaIiIiIiHQk3SYnIiJzPTMbR7oFrlfuvnkfN0dERNqEJkMiItIJJpSU\nfQ64DDL3MouIyFxNkyEREZnrufvetWVmtjPwbXd/fACaJCIibUC/GRIRERERkY7UVtnkjuawsDFR\nZpxcZqVrGBPGduKiMPYAa5aWr85DYZ1b+UQYW4P7w1iU8W5W5qLdkExWlzV5oLR8OkPDOlMoz0YE\nMDkTi7K1PZHJBDKS58PYMF4PY5Hc5xqZySwTZfnKZdbLZeTLrf8oS9qmjAvr3MzGYWwSS5aW78tZ\nmTbE21MuE+K63FVanvtu5TL85UxghdJyZ5WwjvFIZnmjSstX4rGwzsqZbDpXsXUYW5qJwfLi94ra\n15to230+2C4AbmMTZZMrYWavAh9t9ytDXV3dbTFQX9xze2n5jlddHda5duuNwtgnr/p7y+r0Vm86\n85WWb9uVyxg2byaWy+S2Y1B+SaZO7MGe3zZVL7JAZqyNssHm6kxhRBi7suvB0vItej4W1rmu6+4w\nlqsXZefNmZnJBPtYsK+dEWxLAF/pijPmtovxPeXf1027turnlpSL2gfx8e8jmQys+/B7ZZMTERER\nEREpo8mQiIh0sra46iIiIgNDCRRERGSuZ2ZnlxQPBX5U3C73Dnffp39aJSIiA02TIRER6QRlP0q7\nBRhR/CciIh1IkyEREZnruftmA90GERFpP/rNkIiIiIiIdKS2ujKUS3EYpXWcjxlhneV4Oow9zXL1\nN6zwe/YIY+tzWxibkUn/HKWCHMGUsE4uxfc4yk9+RumdId9PUZpNiNOCT2WxsM7CTAtjUcroXL0R\nTA7r5NoR9UduHY/m5jC2GFPDWJQyPJfuOreOo23tcrYL6+RShufSnUcpw/fjjEydDcNYLn369WxR\nWp5Ln51LrR+lVs+lSL+HtcJYLo37MN4oLV+YV0vLATZlfBi7k3XC2BZcV1q+FveGdcjsn6T93dxz\neRiL0hpHj6PoTS69fPQdeCXKJA1MfTPeD0f1mqnTW73oGOIfPX8J66zQdV8YG/mZuB3/uqQ8Ffaf\nuuIM7t3Hx8vj5Ewskju6WzATe63xOuP3jS+6dn+tPLX2+cGjFFKdOLV2rt6rLBTGIrljkj33+GN5\n4OV4eV9eN4796Y5t6mxVfXLHzLk049FjHX7fE48hzzMyjL2eOU6M5B7BkXvsxJDgc+3z2QviN/vj\n73ttj64MiYiIiIhIR9JkSEREREREOpImQyIiIiIi0pE0GRIRERERkY6kyZCIiIiIiHQkTYZERERE\nRKQjtVVq7VwqwCh1cS5ldC7t4PggBTXEqYtzKaiXDNL4AjzGymHspSD98wJBql6AC9k1jEXpdXOp\nD6NUhZBPQR6lKl2dh8M6M5gvjOVSKEdpYnMpqM9i3zC2L2eFsUguTXIuZXTUT82mvr2Z0aXlqzTR\nfwBnsF8YO54jSsuvYUxYZ+/Xzg1jv1zwq2Es+n7lvuPNbJ+jmBDWyaVjXyFTL3qv4ZkU+c9nU8nH\n29qfKc/p+wBrhHU2CSMyGGx0Y5xqOEzz2+zonku7fEx5cff0uEr3mCviWFCvmTq91Qv749txlWvj\nEPtfUp4yGuCG4Pjiiz1LhXUO4Rth7FBOKi2fnhlP28W/fr5iw3X+/fPlm3qv6BEHObnxJdw25o+r\nXLrqlmHshxwWxqLj39xxbLMO4dTS8l/ytbBO7hinmTbmHtvydU4LY+GjajLf43roypCIiIiIiHQk\nTYZERERERKQjaTIkIiIiIiIdSZMhERERERHpSJoMiYiIiIhIR9JkSEREREREOlJbpdbOpX+OUvdN\nZnhYZxmeDWOPYGEsSpN9HVuEdTZlXBjLpeuN2phL97gHvw9jUarh21g/rHMIPwljj7FSGFuJx0rL\nnVXCOrn05A+xehhbmUdLyyczIqyTS09+J+uWludSdefal0vx/AluLS1fholhnVxK5ijN/ARGhXVy\nn2s7Lg9j0Ta/BdeFdU5b8MAwNoZrwtjtwTZ6C58I6+Q+V7ROcik9c2nmc9vaQ6xWWr5y8B2BfIrv\nWzOfOdo35L7jMrh1bXpcGJt/anm6+hlvBiloe/H2Uj+Ogxd1l7fhkhfDKkc/uEQYW+iSF4I672+4\nTm/1wqOddY+P62TS1XPLh8PQ8i8HbTwqXtwCd8Tj1Qf2iPu3KfHQA8s0XmfFJS6Ig+W7Rj78wONx\nnbXj0Kr3PBkH4913bNE41J35WGGdD/0tjN36SOP79b7w+f0vLi2fcMaosM4Ra5wcL7CJmcTX7zkx\njO2+/6VxxdfKi7vPj6t09/TeHl0ZEhERERGRjqTJkIiIiIiIdCRNhkREREREpCNpMiQiIiIiIh1J\nkyEREREREelIXT09daRZ6CeXsHXYmCgzVC6D1pI8H8ZyWc2i7G+5bB+5WJQJD+BVFi4tH8qMsM4D\nmQw32waZwaZksu7l5DLhzRe0cWFeDevkstPl1slNjA7ea1pYZw3uD2NRNsEo8xvAhewaxnL1ov44\ni32bWt6mjC8tz2UgG87kMJbb1qIMj7fxP2GdLbg+jI3ItCP6vn6TU8I6m2WyOEbbZy7zX255j7Jy\nGIu23dz3bgFeD2PTgv0CxFkNc9+fsziwKwxK2xsy6bVwbHx7WpAdcZ6ZTb3XvAvFWc2GDy///j73\nqxXDOovu9VwYe3nsUi2r01u9+YaW7w9e+O+SYR3Gzh/HdouPnXp+F5xnjnf5cEMm9mYm1g62i0Nd\nD5T3U88q8S6p6/FM366Y2ZU1k5AtyE4G8PcPfay0fFgmU+1Elg5jY17OreRys/og5/P8L5eXv7DM\nQmGd11kgjEXHELnj4oVnxceJi0x6K4xF6ytaVwAbcVev45+uDImIiIiISEfSZEhERERERDqSJkMi\nIiIiItKRNBkSEREREZGOpMmQiIiIiIh0JE2GRERERESkI7VVau1DOS5szHTmKy2fh1nh8oZkYrnU\nxbexfmm54WEdD1I1A8wI2g5xOuFcWuh7WbvhduRSHObatxoPhbERTCktz6XjXiXTh7nU0CvzWGn5\nRJYJ6yzDxDAWbU/XMCassxsXhrHJjAhj63JnaflVbB3WiVJaA4xiQml5Lm31ODYLYyOZ1HA7FmNq\nWGdYJmV0Lq11tO1ewo5hnRM5LIxFbcy1YfXM9j45kyb72WA7XCdY96kdHwxjY7gmjEVyaev34fdK\nrT2Y/aorHBvv2O8jpeW5/XDOJ//09zD24i7lqabHZ/YvY6bH2/I1Q8v3t7k6Vw2N95tbT78qjM2a\np3wMXOSMOI3vm3uFoTA9MUDXmeWr6+CjTgjr/GS178UL3DsONSWXqjvKJp6p033UnDSmfXWPDwKZ\njOuckVne2ObbInnhugLYpEeptUVERERERMpoMiQiIiIiIh1JkyEREREREelI8zRb0cyuBCa5+z7F\n36OAM4GPAxOAQ9z92ha0UUREZFDQ2CgiMrg0dWXIzHYDtqop/gswEVgHOB/4s5ktO2fNExER9D7g\nUAAAIABJREFUGRw0NoqIDD4NT4bMbHHgR8DtVWWbAysC+3nyQ+AfwD6taqiIiEi70tgoIjI4NXOb\n3EnAecAHqsrWB+529+rki38n3RbQElEK7eV4OqzzEKs3FYtS1OZSBudSDU/JpOS9l7VKyxfm1bDO\nqyycacdLpeW5dMK5PhzGG2FsPqaXlufadzMbh7FcP63JA6XluX7KpROP0pNvzM1hnVsyqb8Xz6z/\nmxhdWp7rp1wa52h93ZTp2+GZtNtReneAlXm0tPxRVg7r5FJ857a1aF0exE/DOgfwizAWreNJLBnW\nyaV3z6Xqj1JhR99vgE0ZH8Zy9aIU5Kf/94Cwzj4fCEPSOn02Nt6wX/zyT798RWn5rJnx8D5rZrxv\nvGWXDcPYSsEjDna89+qwzuNrLRXGdnywvN7jH4nr7PJg+eftrd6s4HDnzq/FY+P6028LY9z4dhjq\nuas8k+9zLBrW+dPD24SxXMr/ZryUSbsejWW5Okevc1cY6xlZ3hdd/40f69LzgTgTcq4e04Ly3JFu\nJnbiWi9mKpZ788Al4mA8bPavdYPyf2bqdJ+fCc5svA0n7RXHyp8WkAQp3nPrKj6KfVdDV4aKs1yj\ngWNrQkvDbA91mQToVgAREZmraWwUERm86p4MmdlQ0uOkDnD32ksCw2C2ywTTgaFz1jwREZH2pbFR\nRGRwa+TKUDdwh7tfVxJ7k9l37kMhc1+ZiIjI4NeNxkYRkUGrkd8M7QqMNLPKjf1DAcxsZ+AHMNuP\ncJYCnp3jFoqIiLQvjY0iIoNYI1eGNgHWAD5a/HcZcGnx79uAjxW3C1RsRP7nWCIiIoOdxkYRkUGs\n7itD7v6eNFDFWbAed3/CzJ4EngbGmtmxwHbAesBeLWyriIhIW9HYKCIyuDWTWns27v62mW0P/Aa4\nE3gU2MHdn2lkOa8zrOH3zqW0zi0vl3b5fym79RsmskxYZ3gmxfMysyUT6l3uvUYyKYxF6S/Xf/fR\nF7M5i33D2ApMCGNTWby0fIHM7fC51Mq5VNNROuFcP+VSKEepunMpo3Op1XPb2kLBtpFLaZ3rpyil\n+RssENbJbe8jM+24M8jBmUvVnVsnufTUM4M25tLKjmezMLYp40rLc9vZG5n1GKUVBng96PvcepzI\n0mFsOFPC2GbB57rrA+uEddLFC+lvrRobPzrPP8LYSy+Xp+GPv/FkR/4JcVZj3r91EAjS3QKsuOBz\ncfC1JuqU7/5SvaGZeguVFy9yQbwq4m88XJKJdY8vL1/qmJfDOuseFacMX2HLIJDLaJw7uoszfEPU\nxEydRX8b93vXX8tTYS+0xQtxnevi9Nm5ejMzKeMj88wTj0nTDn5/eSCzvedOc3z28HPralNfGxeM\nm5uNKR9bAP54554tbcOe3zo9jF3DmDA27bXy8TtcVwBn9t6epidD7r53zd+PQ+bIREREZC6nsVFE\nZHBp6DlDIiIiIiIicwtNhkREREREpCNpMiQiIiIiIh1JkyEREREREelILckm1ypPs1wY+wS3lpbn\nMkPl5LI1PTTbM/KSWZnuymWhymWT+wLnlZaPZe/ScoBV8DD2W75YWr5wJtvdKJ4IY9tyeRi7iJ1L\ny3MZ6HJyWciirFy5OmO4JoxF2eSiLCuQ3z5HZT7zI0EmvJ25KKzzO/YIY8N4o7Q8yoIIcCsbhrGc\naLsZyoywTu67kBMtM8qeB/nt+io+XVq+LneGdW7jf8JYLptctI7X4t6wTpQhEfIZI6P++D7HhHXg\n+kxM2t3P4oRXdO8eBObPLDCThex3mWrd/ykvv/uR1cI6H1vl4TAW1WumTm/1WLC8OM5pBd3fjmOv\nnPixMHZfsF/62Kr3hHX+feSqYWxnTogb0oRc9tMoa2quzsuHLBW/2f7lxdNOymT/2i0OZevFyYUz\nC8zEzupufHl3xnX84Xif32q5DLIvHP3B0vJJR42MF5jbn+Sy6wUmsEIYe+77K8YVo8SFuXV1ZiZW\n0JUhERERERHpSJoMiYiIiIhIR9JkSEREREREOpImQyIiIiIi0pE0GRIRERERkY6kyZCIiIiIiHSk\nrp6enoFuwzu+zo/CxkTphGcwX7i8mZnUgiMyqbUXCFJLzkOc3zSXdjmXknJ1Hmp4efk02RNKy3P9\ntFBmeVGqbojTXa9FnD40l558SCbfa5RCPbcen2BUGIvSOC/WVG5OeJ0Fwli0vpbh2bBOLrVylNI8\nl8Y5t/4nMzyMRdth9H0EmMpiYWwlHg1jUxhRWm6ZVPK/4IAwFqV4X4VHwjqH8JMwdhpfD2OXs11p\neS59epTCH2A4k8NY1Pe5Ot/mZ11hUNpeV1d3ZqCOUjzP2+S73ZaJRal34/1VXCdXr5k6vdWLxp7/\nZuq02lczsWfi0BbrtLYZzaRJztTp2SKze1krKL8q04YxmVj8xAxYNBOLxE9ugNeC8qFxlRePjzsq\nd/zTn95/cnk+8Se/FactX/7gF1rahv+cumwY+9DJme/CK0F5tK4ATurpdfzTlSEREREREelImgyJ\niIiIiEhH0mRIREREREQ6kiZDIiIiIiLSkTQZEhERERGRjqTJkIiIiIiIdKS2Sq29K+eGjRkepFDO\npZn+BLeGsdtYP4ytHaSGnsgyYZ0o9TPAsCBVN8BLQRriaZnlLc3EMDaVxUvLc6m6c+2LUnVDnE46\nSlsN+fWVS8k8KUiZ+jxLhnVyKZQ3Y1xp+WVsG9bJidKMAywepOsex2ZhnVw/rcudpeW5z/tbvhDG\ncinom0mtPSaT+3RW5r0iue9dLmX4fMF2mEtpnUvjnduf3BPkj81t07k0q7kU79E2n1uPY/mqUmsP\nYodwQjg2bsvlpeXNfNcALmTXMHbW/kF6+bsyC4xSKwPh0wCaqdNbvSBl9KG/PTaskhv/tuWyMLb8\nyeVpiE/41sFhnVy/33vZx8sDuUzN8ZMq8mmIF2yizlFx6IBHTi4t/+UHvxXXeaq8Tm/1mhI/MYXT\ng0OttzKL+3r8tA8uWmKbMJbbf7fa7qtdWlr+p4fj9m372hVhbMb8jafx/92QPcLYV9c4N674Ynnx\nmfFhMV/uUWptERERERGRUpoMiYiIiIhIR9JkSEREREREOpImQyIiIiIi0pE0GRIRERERkY6kyZCI\niIiIiHSktkqtvQMXhI1Zn9tLy3NpoXPpru9knTC2Mo+VlufS3ebS/47iiTAWpS7OtT1nRJCCPJcy\n+BrGhLH/4bYwFrV9BkPDOlGKdIDleCqMTWFEafkERoV1FshsG9G6zKUgX5MHwtjrDAtjUf/m0raO\nYHIYi9Kx59Jd51JQL8nzYWwkk0rLH2WlsM4yPBvGHsvUWyn43jmrhHVy6yRa/88HadoBbuUTYWxX\nLgxjqwTfr9/yxbDOMpkU+dMz36HIQ6wWxv7C7kqtPZit0hWOjW/dUV7+xkKNp7sFWGTDOHFwdzwc\nDFrdH4pj1/4njn2yPDtxsmJ58fkf2Sms8oWfXhTGtj/ogsybNS53LBM9DiBX59yuT2XebdmgvHxs\nSeJ9dL5ePAY2Y/6pwxuu8+ZGS8TBB7ubb0xLbRqUj+/HNuRskYmVbxu5dfXGoksotbaIiIiIiEgZ\nTYZERERERKQjaTIkIiIiIiIdSZMhERERERHpSJoMiYiIiIhIR2qrbHJ3sEbYmNtYv7R8jUw2qYuI\nM7fkMqMszKul5Y9gYZ3R3BzGcpnronYMZUZYJ2ofxNn15sssL5eRLZfVKspQlutbz/Th1lwZxq4P\nsovkMrLNYkgYi/oj17fDMxnexrNZGFudh0rLhzArrDOFODNKlPEul6kvt05yGdTWD7IJ5voil00u\n+h5DnGluHe4M6+TbXp6BcmUeDetEmZQAbmLjMBZ9777H8WGdUzkkjEWZ9SDup09wa1hnFy5XNrlB\n7L8MD8fGDxz8Ynngzebe67kzFg1jSx34cnkgaAIAi2RirwTlmYRc2ffK1ZunvPjJU98fVhn52gth\nbIG1MsdOp5YXb/7pK8IqP+S7YWy9Hz1YHoiHuP4VrUeAzYPy8zN1Pp+J5erl1n8k2C4AHj9+qYYX\n901OCWO5dZw7Hmi1Dx32TGn5f06MMv/Bhw4vrwPAzMbb8PiJcd+ueNhzccX5y4v/c3Sm7TytbHIi\nIiIiIiJlNBkSEREREZGOpMmQiIiIiIh0JE2GRERERESkI2kyJCIiIiIiHUmTIRERERER6UhtlVr7\nBA4JGzMiSOV7D2uHyxuSyfc3jDcy9cpTHOZSUK+USdc7K5O78Q0WKC3PpcLOpUl+lYWDNsQ5OHPp\npB8N0vgCzBP00wJBmmGA5Xg6jD3PyDC2DBNLy3PpKHNpl6PUxa+yUFgn1+8zmkhBnksLnlsnUWrt\nXNryZt8rShndzDbYW73o+zWRZcI6a3B/GIs8xsphLLftvsGwhutF3xHIpwx/gDXD2OvBPuO4c38Q\n1unZE6XWHsS6LiYeqI8IyuMs8dl0wnTHoZ5ZwWZ0eGZ5cXb5uF4zdXqrF6Qa78ocA22//QVhLDeu\nj39t09LyV59fMqxzxQpRDmp4pOuGMNaMtzKxeZtY3jfjjxXqyaRj7spsn7l6kZmZrNXzxkM3pzxf\nXp7LJL5hz0Zh7EJ2DWPNpNbOHdfllnfG8INLy/ee8suwzrn+1TD2vsVeC2ORA0b+IoydsthhYSxa\nXycG6wrgsJ4epdYWEREREREpo8mQiIiIiIh0JE2GRERERESkI2kyJCIiIiIiHUmTIRERERER6Uia\nDImIiIiISEfKJdjsd0OZHsZeCvKERql/ASYzPPNecerqaJm59MS5FLrPZlID51JNR3L9NDXop1ya\nxVzK8GV4NoxFKZlzKc1zhjOl4TrR54V82skoVXuuzkji3I1RumuAJYN6uc87JbPtPhGsr9V5KKyT\na19+/ZenNM8tL7et3cb6YSzanrbjsrDOdWwRxqL+WIMHwjq5NN65dPxRfxge1rmZjcNYbl2Gqcs3\nyj0mQZm1B7PP73RmGBu10xMtfa/cd5vVgvIxmQXun4lt18I6vdULUvJ+/uG4b29mdBh78spV4/fa\npnyddGUypO/Uc34Yu2j9ILV2fx7BZd7r4hvjWPTAjPiBDmQezpCvV/7QgXwq8Vya7D16Fi0tnx48\nLgNg1JdvDmN/PHPbzLuVayblNuSPZS5/sbx8C64P6+xtY5tqR2QiS4exq1+O60Xrcp+e+LEo9dCV\nIRERERER6UiaDImIiIiISEfSZEhERERERDqSJkMiIiIiItKRNBkSEREREZGOpMmQiIiIiIh0pLZK\nrZ1LXT2OTRte3so8FsZyKW+j9Lq5VN25lLy5NLn3s0Zp+eJMDeusz21hLEqL+jrDwjq59NQLZFKX\nRym0pzAirJNLhZ5LBTkpSNCZq7MKj4SxR1m5tPz1MDlnPg36fJl051E/vZF5r9y2ti53lZaHKZfJ\n9/snuDWMncPepeVbc2VY55pMnt1cevLnWbK0fHqUE5f8Z462jXtYK6yzWOZ7l0uRH7Uxtx5nZNKz\n5r6T0fr67ErnhXVgz0xM2t35u3w5jC009oXS8uELNv6oAoAnvx+njB77vweUlh//03h5hx8dx44/\nqnV1eqsXOX/3uG+3v+CCMHblpz8dxj7CF8oDB3eHdXL7nu54yG978YMM2t8iXeU5nufN1Fm25z9h\nbMysaxpuw5CZcWrtWfPExz85pwTle3NTWGf5w8r3MwAEqbqzT1k5MQ51Z6pFFumaFsY+mXvqREFX\nhkREREREpCNpMiQiIiIiIh2p4dvkzGwH4BKgh/RY8x7gYnf/rJmNAs4EPg5MAA5x92tb1loREZE2\no3FRRGTwaubK0OrAZcBSxX9LA/sWsUuBicA6wPnAn81s2Ra0U0REpF1pXBQRGaSaSaCwGvCgu7/n\n11RmtjmwArC+u78J/NDM/hfYBzhmjlsqIiLSnjQuiogMUl09PXWkWahiZncBP3X382rKvwuMcfdN\nq8qOAjZw963qWfb3+V5jjREREQCGdJ0Qxrp7err6sSkdpy/HRYDuri6NjSIiTahn/GvmypABnzKz\nw4EhwJ+AI0m3BUysee0kQLcDiIjI3EzjoojIINXQZMjMPggsALwB7EK6/H9aUTYMZnvYynTIPCRE\nRERkENO4KCIyuDU0GXL3p8xsuLtXng52v5kNIf0o9Bxg8ZoqQyHztEcREZFBTOOiiMjg1nA2uaod\nfsXDwPzAc6QsOtWWAp5trmkiIiLtT+OiiMjg1dBkyMy2NLPJZjZ/VfHawGTgZmAdM6u+/L8R8M85\nb6aIiEj70bgoIjK4NZpA4VbS5f2zzOwYYCXgR8CJwE3A08BYMzsW2A5YD9irZa0VERFpLxoXRUQG\nsWZSa68GnApsALwKnOHuxxWxFYHfAOsDjwIHufu4lrZYRESkjWhcFBEZvBqeDImIiIiIiMwNGk6g\nICIiIiIiMjfQZEhERERERDqSJkMiIiIiItKRNBkSEREREZGOpMmQiIiIiIh0JE2GRERERESkI2ky\nJCIiIiIiHUmTIRERERER6UjzDHQDapnZUOCXwI7A68DJ7n7KwLaqfxV9cCfwNXe/qSgbBZwJfByY\nABzi7tcOVBv7mpktA5wGbEbaDv4IfNfdZ3RaXwCY2UrAL4ANgSnAz939pCI2ig7rDwAzuxKY5O77\nFH+PosP6wcx2AC4BeoCu4v8Xu/tnO7E/OoWZvR+4Gljf3Wf1Vt7ke8wL/BMY4+6TeysvYm8X//yr\nu29dVX41MAbocfchJe/VcL1m36s3ZX0YvVezsSbr5Pq9qVikmTpVdX8B7FPUvSkXM7OzgU8Bu7r7\nzfUur556jWqH7anV21kv75Vdx7l9Sav2M3OwTbd8XbXjlaGTgI8BmwIHAEeZ2Y4D2qJ+VEyELgBW\nrwn9BZgIrAOcD/zZzJbt5+b1p4uB+UkH/7sB2wLHFrFL6aC+MLMu4EpgErAWsD9whJntVryko/oD\noPjsW9UUd9p3BNJ+4jJgqeK/pYF9i1jHbRcd5EfAaSUHIlF5w9z9LdIJqR/XU17lM8DuNWW7ATv1\n8pbN1Gv2vXKiPix7rzmJNVQn1+/NxiLN1IF3TtrtC4wGbq0jdgjwK+D7DS4vW28OtMP21OrtrDRW\nxzrO7Utasp+Zw+22peuqrSZDZjYM+BLwDXe/z90vJXX6gQPbsv5hZquRZsIr1JRvDqwI7OfJD4F/\nkM6WzHXMzID/AfZy93+7+y3AkcAeZrYZqX86oi8KI4F7gAPc/TF3/ytwPbBRJ/aHmS1O2i/cXlXW\nUd+RKqsBD7r7C+7+fPHfK0V/dNR20SmKK37bkU6a9Vo+h34PbGdmy9VZDvCSu79cXVD8/WIv79VM\nvWbfq1QvfTjbe81hrJk6uX5vNhZpps7SwLPufqe7z+wtVnzGm4APNrK8Ouo1qx22p1ZvZ7lY6TrO\ntbsP9jPNbrctXVftdpvcR0lt+kdV2d+B7w1Mc/rdJqSD3CNIt4ZVrA/c7e5vVpX9nXT7y9zoOeBT\nJZduFwU2oLP6And/jqozIGa2IelM2QF0YH+Qrh6fB3ygqqzTviMVqwNlt751an90gq8A1xRnTrPl\nxb7ih6S7LXqAG4F93H1SLlap7+5vmdm1wH6kcSlb3s7q+bzEfdsWcv3eSKzZdV9HvXmAGUHzo9hM\nYGiDdUrrzen23oi5fHvKtbul+5lmt+lWa6srQ6SzAJNrzihMAuY3s+ED1KZ+4+5nuPuhNQcwkPpl\nYk3ZJGCuvOXF3V/2qt82FLeJHUiaKHZUX9QyswmkM2L/IP1WpKP6o7jiMZp3b5ms6Kh+qGLAp8zM\nzexRMzuhuNe6U/ujE3yK8gnwe8rNbBHgCuCvpCuInwRWAr6bi5Us99pi2fWWt50GPm/Ut+0k1++9\nxppd93XWWxCYFrx/FHu5iDVSZ7Z6Ldzee9UB21Ou3X2xn2l2m26ZdrsyNAyYXlNW+Ts6c9AJon7p\nlD75MbA2sB7wTTq7L3Yk/TbkdOAndNC2Ufye7gzS7YLT092U7+iYfqgwsw8CCwBvALuQbos7rSjr\nuP7oBGY2BFgTeLiO8gWAY9z9J8XfT5nZJaT9aC5W6yHgo2bW5e49dZS3o14/b9S3bSjX773GaHLd\n91aveM0ewPjaheRixXvMNLNt3P2KOuuU1WvV9l6PuXZ7yrW7D/czzW7TLdNuk6E3mX2wrvz9Op3r\nTWCJmrKhdECfmNmJwDeAz7r7Q2bWsX0B4O53A5jZN4HfAb8BFq952dzaH93AHe5+XUms47YLd3/K\nzIa7+9Si6P5isDofOIfO2S46yRKkOzpqbyGerby4ReU8MzuElHhlddLB8N9zsZL3nFIse3jN+0bl\nbafOzxv1bbvJ9Xs9sVlAw+u+jj48C/gw6XfftcKYu880s12B683sM+5+ZR3LK63Xou29V3P59pRr\nd1/tZ5rdplum3W6T+y8wwsyq27UU8EbVgN+J/kvqh2pLAc8OQFv6jZn9jJQ15nPu/peiuOP6wsyW\nNLPta4ofAuYjfe5O6Y9dgR3M7FUzexX4HPB5M3sFeIbO6Yd3lOwXHyZlYXyODuyPDlA5M1qbNna2\nckuPJ3iA9HiCO4GDgZN7i5WojMdv11nedur8vFHftptcv9cTW5Im1n0dfXgM6fc9e5QsJxcDOAE4\nkfdeBeqtznvqtXB779Vcvj3l2t1X+5lmt+mWabcrQ/cCb5F+FF5JozgauGPAWtQe/gkcZmZD3b1y\n68tGQEvy67cjMzuK9EO9Xd39z1WhjusL0u1Pl5jZsu5eOZhdF3iedIbl/zqkPzYB5q36+0eknfO3\ngVHAdzqkHwAwsy1J2XaWrfqd4dqks2c3A4d2Un90iCmkM/u1v6EtK/8MMMXdt6sUmNlBpOdR5WK1\nRgAz3b02S1NU3o7q+bxR37abXL/3GiMdmDa87s3sa7l67v6kmZ0K7AWcXb2QXKy43XctYBOvStWc\nq1NWz8z2yrSvke29HnPz9pRrd1/tZ5rdplumrSZD7v6GmZ0HnGFm+5B+7PstYM+BbdmAuxF4Ghhr\nZseS0hquR9pJzHUspRg/AvgBcKuZjawKd1RfFO4gnVU5u7g9bgXSROA4UjKFjugPd3+6+u/i6lCP\nuz9hZk/SIf1Q5VbSbW9nmdkxpB+m/oh0prRjtotOUtzTfx/pvv1beymfAnzQUtKRJ4DPkn5zeHsv\nsVprklL711vejnr9vFHftqFcv9cTa3bd11NvErBM8P5RbBnSAXPZM2tyy6ut16rtvR5z7faUa3cf\n7mea3aZbpt1uk4P0A/m7gBuAnwHf9/S8oU7zzg/F3P1tYHvSbS53ki4b7+DuzwxQ2/radqRt8whS\nRqyJpNt7JhZ9sQOd0xfV6/810g7o18Cp7v7zIrYdHdQfZTrwO4K7TyM9bfv9pAnzmcAZ7n6ytou5\n2l9JV/l6K/8j6fdjfyJtH5uSxtfVSJkoS2OWshFW24j0tPlaUXk7Cvui5vNGfdtOcv1eT6zevqhd\nXj313iL9aL5MFJuPdMWqkTpl9Vq1vddjbt+ecu3ui/1Ms9t0y7TVlSFIV4eAvYv/Opa7D6n5+3HS\n5e25nrufSDq7HcUfo0P6osLTs4Z2DmIds21Uc/e9a/7uuH5w94dJE6KyWMf1R4c4G7jTzOaveQzD\ne8qLCfHXiv+qnVb8PxcD3nkQ+lbAd+opb1d19EVF1LdtIdfv9cbq7Yva5dVZbwbxcWUUm5c06Wmk\nzmz1WrG916sDtqdcu1u6n2l2m261drwyJCIiIiWKSe6VwOfrKZ9Dnwcud/en6iwHWMLMFqsuKP7u\n7bcTzdRr9r1K9dKHs73XHMaaqZPr92ZjkWbqPA6838zWLbniMlus+IyfJN1CVffy6qjXrHbYnlq9\nneVipes41+4+2M80u922dF1pMiQiIjK4/B9wgJnVnjWPyhtWHHx+FTi0nvJCD3AxKbFHtQuAi6i6\n/bsF9Zp9r96U9WH0Xs3GGq6T6/dmY5Fm6gC4+/PASaRELR+vI3YK6YC3u8HlZes1qR22p1ZvZ2Gs\njnWc25e0ZD8zB9tty9dVV09Puz8rTUREREREpPV0ZUhERERERDqSJkMiIiIiItKRNBkSEREREZGO\npMmQiIiIiIh0JE2GRERERESkI2kyJCIiIiIiHUmTIRERERER6UiaDImIiIiISEfSZEhERERERDqS\nJkMiIiIiItKRNBkSEREREZGOpMmQiIiIiIh0JE2GRERERESkI2kyJCIiIiIiHUmTIRERERER6Uia\nDImIiIiISEfSZEhERERERDqSJkMiIiIiItKRNBkSEREREZGOpMmQiIiIiIh0JE2GRERERESkI2ky\nJCIiIiIiHUmTIZE5YGbbmdmfB7odIiIifcnM3jazL2biGg9lUNJkSGQOuPtlwKJmtttAt0VERGSg\naDyUwUqTIZE5dzJwvJl1DXRDREREBpDGQxl05hnoBoi0OzP7CHACsCGwIPAM8At3P6V4yTXAYsCO\nwMUD0kgREZG+t5qZ3QKsAzwOHOnuF1XFNR7KoKMrQyIZZrYA8DfgBWADYHXgj8BJZrYmgLvPLF6z\n/UC1U0REpB8cBJwDfAS4CLjQzNauBDUeymCkyZBI3oLAT4AD3f0Rd38MOLqIrVH1ugeBj/d340RE\nRPrRL9z9LHd/1N2PBP4BHFLzGo2HMqjoNjmRDHefbGanA58rzn6tDHwU6AGGVL30BWCpAWiiiIhI\nf7ml5u/bgM1qyjQeyqCiyZBIhpmNJO3snwMuI90PfQfpd0PVhgBv92/rRERE+tWsmr+HANNLyjQe\nyqChyZBI3h6kH4Ou6O5vA5hZ5fa46mw5SwIT+7ltIiIi/Wkd4PKqvzcE7q15jcZDGVQ0GRLJe5r0\nu6FdzezvwGrAKaTb5IZWve5jpCtIIiIic6tvmtnjwD+Br5ISKdQ+V0jjoQwqSqAgklGkDP0x6dkJ\nD5MmQmcBNwHrAZjZPKSzY38ZoGaKiIj0tR7gGOAbwH3AxsDWRWIhQOOhDE5dPT09A921mHQ/AAAg\nAElEQVQGkUHNzHYGfgiYu9feTy0iItIRNB7KYKQrQyJz7iCgWzt+ERHpcBoPZdDRZEhkDpjZDsBL\n7n7+QLdFRERkoGg8lMFKt8mJiIiIiEhH0pUhERERERHpSJoMiYiIiIhIR9JkSEREREREOpImQyIi\nIiIi0pE0GRIRERERkY6kyZCIiIiIiHQkTYZERERERKQjaTIkIiIiIiIdSZMhERERERHpSJoMiYiI\niIhIR5pnoBsg/cvMzgH27OVl49198/5oT46Z7QmcDazg7k+Z2erAme6+YdVr3ga63f2YgWpnK5jZ\ntsDO7p5dN8X628TdV5yD9/oAcAGwHvAyMMrd32x2eTXLXh54gpp1Ymb/A5wLrAkcDhxZvO9TrXhf\nEelbZjaWtO9ZIYhPAG5w932Kv58AxlX+bmaZxWs2AcYBm7r7Tc22v1XM7EvAau5+aIP1DgfedPeT\n5/D953gMaKViPPkVcECr9+dmtihwGmnc/3srl131Hu85zmjxsluyzotljQfejo7N+vN7YmZHAUe6\n+5C+fJ/+pMlQ5zkGOL3q7yOBtYEdgK6i7JX+blTgCuDjwLPF37sAG9S8ZgPgmf5sVB/5JtBTx+uO\nARaZw/c6GFgf2AOY2KqJUKGn5v+Y2VBgLPBtd3+rmMD2UN/nFZH20Nt3tja2A72PJfXuB9ppX3EE\n6aCzUccC3S14/1aMAa20BbBVHy17LeALwG/6aPkw+3FGK7VqnUN7fU/OBK7up/fqF5oMdRh3f4J0\n5h4AM3sBmO7udwxcq8q5+xRgSlVRV8lrbu+/Fg28Yv3NqeGkSdDFLVhWrUnA28B/q8q+Bsxw98uL\nv58FZgHP9cH7i0gbcPf7BroNc6MWjQGtNNu43OJl9+kBfslxhvTC3ScCEwe6Ha2kyZCEzGwc6arL\n/KQzP7e4+xgzG0U6O/W/wPuBl4C/Aoe4+4tF3SdIt0UNA75IOpN1I/B1d3+0eM0I4KfA5sBiwL+B\nU9z9t0V8L9Ll61HA3qSrWJjZLOBodz+m9jY5M1sK+AHpbNUI4AHguKoD8cqtdV8DPgbsCMxLOstx\noLu/EPRF5favXYDPA58EpgE/I13GPxXYCXgTGOvu36mpe2xJfx3s7i8V/bxJ1WfbjDQIjAP2B75X\n9M9OpLNkm7j7CsWtdZfWfP5VgbuB37n7l0s+xxPAB4Gumn6st9+6gW2BDwM/cPfjqpfv7tPN7FHg\n/qLOvMAhRT9VPAg84u5vlfW1iAx+JbfNLQb8BNiueMmZlPxu2cz2I10pXw64DTin5DXLAT8CtiSN\nT/8ADnX3e4t4ZX/9WWBXYAzwFnAxcJC7v5Fp90Gk/e4o0kHypcBh7j6tav+5V3F7VeUW7o1J++n/\nARYknQw6192PLpZZuRrebWZHVW4vMrOPAD8ERhdvfz3wrdyEp/bWwnrG2mA5Q4Fvk+4QGAU8DZwF\n/Njde4rXTKBqHRZle/HuuLxZ8e8eYIKZjXX3fYo2/Q5YuGjTLOBKijGvWM54am77qr7VizQG3lAs\ne7yZZW/fb3YMKz73OVTdtm1mo0lj9nqkMf1y0vY1uYjvWfTVhqSxf23SicCfVW6Jm5N1XmzflWOj\n14EfR5+7xIfN7Lii7c8AP3X3n1ctezjp+O3TwNKk45gbScdvTxavWZH0Xd0QWAC4DzjW3a8u4t2k\n2+TeV8/rBwMlUJDe7Eq61WFb4EdmtgAwHjDgq6RJwanA7sBxNXUPAlYl/UbpS8C6pJ12xe+K+FeA\nT5EO4scWO0R47y0UZ5IulfeQbo07q7ahZrYkcCewEfAd0kTnCeAvZrZ7zcuPJ23/uwKHFp/v1N67\ngzNJB/rbANeSdpi3A68BnwEuAr5tZjsVbVqAtKMp66/ji2UeANxTfP4Niv9XHEk6MPgacGt1nxQ7\n+fOB75rZKmY2BPgtaed+UND+HUgTv2eL9zqrwX77Lmm97UQ6sJiNu6/q7ncWf24OLANcUhX/h7t/\nJGifiLQxMxtS8l/ZidXqW2W7gGtI+/lDSGPChsBuNcs+kHQb9+WkSdM/gV/XvGY4afKzNmnfuRtp\nX36TmVlNG84g7cu2J02evkS6zS36bLsDJ5JO3mwJHE06AVU5mfMZ0kHvlaT957NmtiZwHfA8afK1\nDXATcJSZfbaotwHp4P6s4t+Y2SrALaQD9y8A+wArArcUJwojZbcW9jbWlrmCNPb9umjzH0ljUvVt\n9GVXZarf/wreHfd3II2HFV8j3Y79BeAw0sH3Fb0su7r8rmIZkMbOA6IP0oIxrHpb3Zi0PqeRTn4e\nRJqc3VBMICveB1wI/J50svhm4Mdm9ski3tQ6N7NhxbI+TFqXBwL7Ap+IPn+VLtKk5BbSMc3VwGlm\n9vWq11xFmjD+H+l45CjSidrTi/fvIm3fw4DPkb6HU4BLi0lPpb96Gnh929OVIenNdGD/yll8M/so\n8CTwxcpZBOBGM9uAtMOo9iKwfdVZppVJZ0kWL84ObUy6MnF51XImF+/5Hu4+0cyeKf4d3dL3LdIt\nYBu4e+V3RH8tBs+TSEkDKu539y9V/jCz9YGde+kLgKvd/aiizkOkL/9z7v6NIj7OzD5PGugvBlah\nl/5y94fN7BWgp/LZqsb0X7j7OxOJ2cd6vkGacPySdBbto8CG7v56WePd/b7aWyPN7ETq77eb3L2e\nSWPFZsDU3BlKERk0RpGusJTJ3c60NelM9Rh3vxbAzG4AJtS87gjggqrkBNcVP6Lfr+o13wQWp2p/\nZWZXk+4sOIZ0gqviCnf/dvHvcWa2JenA//CgnRsDj7v7L4u/bzazacASAO5+r5lNB16o2n+uCVzj\n7l+sLMTMriNNwDYF/ujutxf77meqxq+jSCfR/tfdXyvqXU86iP8/0gSiXr2Nte9hZluRDoB3dfc/\nFcXXm9kbwDFm9lN3f7i3N3X3KWb2WPHnvTUJCGYCW7j7tOI9JwN/NrMt3f1vdSx7WjHGAjzs7v/O\nvLyRsf89Y5il5D7VTijeb5uq1/wTeJg0ealMFrtIxy9ji9fcSppgbQNcOwfrfG9gWeDD7u7Fa24H\n6h1Df+Xu3y3+fZ2ZLUu6avkzM1saeJV0he4flf4wsw8BlTtJliSdvD3a3a+pev+jgOrJIE2+vi1p\nMiS9ebj6dqbiPvBNzKyr2OF+CFgdWA2ozSxyR2XnXKjspBYk3So2jrTj/RjptrGr3L2RAaDWJsCt\nVTvDivOBs81s1aod6j9rXvNM0a7eVHYguPvzxc6u9ndLL5Fua2u0v8pk77t396lm9hXSGbdNgGOa\n+P1XI/3W6O8AVmT2Ax4RGZwmks44l/1O5PKSsorRpBMw11YK3P11M7uKNAGhuKqzJO+9egDpisX+\nVX9vDtxLuipTvQ+9mnRyqlrZfn75TDvHAfuZ2d3An0lj0gWZ1+Pu5wPnF1cNViHt49ciHV/lDgY3\nL97vzarPMY10VeCTNDYZ6m2srbUJaVJ7UU35+aSrO5uQDv7nxKWViVDhMtIEaROg18lQpGad4+6z\naNEYVtzJsT7pLpjq95lA6o9P8u5kqIeq7cvdZxQnGnPHEfWs842AxyoToWLZzxQTst70kL4v1S4B\ntq/qgy2Kz7o8aVtdlXTydmjxXpOKSehZZvYp0hXdqz3Intjo69uVJkPSm2m1BWb2TdKl5iVItwzc\nSTrbsWjNS2uvTrxd/L9ye+aupDMWu5LOqPSY2bXAV9z96SbaugTwWEl55Yf6i/XStnp+CFqWHem1\nXIUG+qtWDyX9X+Ja0kHK0sx+IFGPRvqtnvZUW5Re+kdEBo0Z7n5PWcDMZmTqLU66elGrOoPXEsX/\nJ2deA+kKwErMfoWqhzSGzF9VVrafD38e4O5/LG77OQD4PnC0pd/NHFZ1BeU9ivf7Oem3pPOQzvLf\nWrQvN6YMJ419u9WU95BuuWtEb2NtrSWAyTUTKCjf5zerOokO7t5TXB1aInh95J0+tPRbnerfkPWY\n2Qq0bgxbnNRnh5Fut6vWw+xjWUPbF/Wt8yWY/TsA6XswMrPsitrERJXlLg5gZp8j/VZqWdJ38h5m\n/xxbkK7S7ki6nW+mmf0Z2M/dXy55z0Zf33Y0GZKGmNkepMvOh5ISBVQSJlxIug2ibu7+KmmS8N3i\nMu32pEurvySdfWzUi8BSJeXLFP8vTY7Ql1rZXxlHk3ag/wZ+Y2brFWfL6tWX/TYZWGMO6ovI4DcZ\nGGFmXTUH4MNrXgOzH/ANr/l7Kul3mN+ifLIx223WjXD3C4ELzWxh0u+GDiNd+bnZ3csyYJ5GOgjc\nGbjei+QMZjapl7eaSjqRdRKzf46Zc/AR6vEi5etj6eL/lX1+D7PfwbBQne/xnt89mdn7irJKv9S7\n7Or2XUb6PVS1Z2ndGPZK8X6n8N5b6ypKbz9vQD3rfDJpsl+r9nsQqZ0YVvrleTPbiPRbslOBkyrb\nc3Gr/DvPbyzKDwQOLG4D3Zl0rPYCUP37o6Ze346UQEEatSHwkrufUnVgvxDp0m7d25OZfdDMnrIi\n0YC7/8fdTyLtKKLbGHo7wL8R+ISlTCzVPk/6XU/ZmaO+Vm9/NTJ5eYeZrUe61/hY0udcg8wPhAN9\n2W9Pks5AiUjnup508nWHSoGlTJNbVv529/+Qkr/sUlN3O957QFxJSPMfd7+78h9F8oCSqx11M7M/\nmNklRXte9fT4geOKtlcOrGv31RuSHi57RdVEaB1S5tDqffzbNfVuJN0yfV/N5ziUlKihL91I+ky1\nff0FUl9XHnD6CrPvv0fX/B2NXVvbexNr7ECa/Fzf4LLfmTS4+0vVfVX89xYtGsOK2/ruBlatWScP\nkX6Ptmk9y6nSzDq/Hlih+PkA8E7m3dpnLJbpIiWqqLY78HTRBx/n3d86VSZCQ6j6HprZBmb2XLEN\n4+73u/uRpOx8sx2bNfr6dqUrQ9Ko24H9zewk0j3iHyB9kUdSfm9yKU/pSJ8Bfmpmi5DOZKxH+qHt\n8UG1qQBmthvwT3efUBM/hbTzu97MjiZlNNmLtAPbu962tVi9/TUV2MDMNiNdtoZebtsr7lE/F/gX\n8CN3n2VmPwMON7NLvUgzW4e+7Le/AYeZ2Yfd/V9zuCwRGYTc/QYz+xvpdwUjSSdJvkGaMFRfQTkM\n+J2Z/Rr4EymD1v41i6veX51E2l/tRsq8dfAcNvUG4HQz+zEp69YSpLsVHuHd35pMBda2lHXs9uK/\nXSylBH+Y9Huhw0kHwtW/H5kKbGhmo939ZtLB9a3AlWZ2OumK1n6kyd9Oc/g5stz9akuprc8sfmB/\nH2l/fxjpDobK71WuAL5jZt8h/T5mO1JSnGpTSWPVTmZ2VVXd5YDLijHpg6Rbs64uPntl2dua2cmk\nKz6jSZOx2mUDbGNmU939/uAjtXIM+x5pnZxPyjo3D2nMXo+0zhrRyDqvJHD6LSmD3Z/N7HBSwoPD\nqf9k8zcsJf24hzQR2pLUN/Du75t/YWZnk642HUBx94aZLci7t839tujL50i/Z/ooKVNdrXtItw/W\n+/q2pCtDAg08VdzdzyV9oXchDRbdpFTb+wFLFD+CrdTr7QzdDqQf2x1T/H8/4Ch3PzZ4/cXAHcBY\n0s7pPe/j7pNIg+ddpFsX/kQ687Sdu59X85milKE5vaUZna2sgf76Oeke86tI6Wdz7amUH0P6AeSX\nq26LO4J0r/ZYK093O9tnaUG/5dxMulS+dYP1RKT99DZW9GT+/gzpB+1HA38gXQX6VfUC3P0PpInN\nBqTn+2xNevRC9WueJe2vniD9mL1y69Q+7l79PLPe9p+zcfdfkyZpnyKdvDqD9Fy0Lav2sSeRbj36\nK+lZdYeQki0cW9TZp/j3mcDHi98gQbrCtC5wlZkt6+4PkCYAbwPnkX74PpKUFe7SqI0ln6GZ/TKk\nKwi/Ik0gryBNwA7zqiyrpAnMWaTx9lLS596nZjnjSHd0/IDUNxV/IJ3kvJA0oTyb907yzialMd+d\nd1OV104C/0VKXf010rZTqpVjWJHkY0xR/0+kE44zSBngenvIe+3yG1nnfyne/y3ShPNG0rOGfkNK\n9Z1LUFL9/vuSjjeuIF0J2q2SBMTdbyT15cdJxxonkZJD7FjUH+3u00mTmX+Rbqf7K2my9hUvngFZ\n9V4Ur9+yjte3ta6enqavKIuI9KpIILG/u68y0G0REZG+Zemhq+O86mGtIu1MV4ZEpK/9EnifmdXz\nHCcRERGRfqPJkIj0KXd/k3Qv+PHFj6ZFRGTu1eyteyIDQrfJiYiIiIhIR9KVIRERERER6UiaDImI\niIiISEfSZKiFzGxCkbt9oN6/28xqH/JV+5pNzOzt4hkJude9bWZH9vKasUXWmAFnZoua2bnFE5Yb\nqfcBM7vCzD7Ygjb02mf9xcy2NbNz+2jZnzCzK/ti2VXvMc7MbuiD5bZyfS9frPMvtqJtIs0ws2+Z\n2XlVfx9sZs+a2Wtm9r1WfZfM7HAz+1YDr1/WzF4qG2vMbLSZ3WRmL5vZk2Z2avEw6kbb9J7xzMz2\nNLNZrfh+9xcze2IgjxsGg+pjq3bc7/ZFm8zsHDN7vOrv94zp9R7L9aX+PM4wsyWKfUWfPMhVD11t\nrYH+AVY9P1q8i5TP/6F+er/+shbpR/q/abDeFsBWLWrDBsAzLVrWnPomfbduvgys1kfLrvhqHy23\nletbZECZ2WrAd4EPF38vTHp2yGXAyaRniFzSorc7lvSctHratRzp2XGLlMRWJz2M+SbS81A+APwY\nWAHYvol2Ve/nKs9WebaJ5QyUHYBXBroRba56HT9LGmsfG6C29JdjeO/3p2xMH+jjr347znD3F4sH\n9I5l9gf/zjFNhjqMu0/j3acQz026aO5L2dX7S+pTxwPZpE7u/u8+WnTL1rdIG/gh8LvioZMAS5Du\n+LjU3W/p78YUDxjdkzS5iXye9NDJHdz9jaLevMDpZracuz/d7Pu7+xRgSrP1B4K73zfQbRhM3H0G\nc+cxzHu4e1vcddNmzgC+b2Y7VB5S2yqaDAXM7C7gKXf/TFXZY8AQdx9VVfYXYKi7V842z2tmJwJf\nBBYGbgEOcPfHquqMJp1lWw94k/Rk4UPdfXIR35P01OcNSU/0XRuYBPzM3U+uo+1bk54GbcBTwHGV\nJwGb2SakJ0Zv6u43VZWdAHy0eP2BJctcDPgJ6cnCkJ6uPdttlma2PXAE8BFgKunp099z99eL+FGk\nwfCg4j0NeBI41t3DJ0yb2QjS05g3BxYD/g2c4u6/Ldp/A2kyNN7Mxrv75mb2PuD/ivdbiTQA3wcc\n7u7ji34+u6j3hJmdW3lInJntS3oy98qkvj+7aGN4G2Jxi2K3ux9T/H0QsD8wijRAXwp8x91fzSxj\nZdIBzoak7ed24Ah3v7WIz7b+ivLxwNvF5x4HbFKUzyKdRekq6m1FWj/rkK5ineLuZxSvXZ70VPe9\nqp/abWZjgY3dfUUzO4d0sFNZ9t41T/iu/TyfBL4PrAnMJJ0tPszdnynilW19f9LTuucFNiLt9N52\n982L13UBhwFfApYjbTM/c/efV73XOOBR4D+kp2wvSboSeoi73zGn69vMdgSOBFYhXVk9NvrcIn3N\nzD4CbEO6ElL5Lp1D2r7PMbOz3X1I9b6heN3bpCs825KuKP0AOJ60Pe8BLANMBP4AfN/dZxV1eoBu\nMzvK3YcEzVoTOB34OWmfXHY77VDgrcpEqPBi8f/hQDgZMrP9SGejlwNuKz5vV1V8L9J3d5S7P1Xs\nr5YiXR07rPhsdwN7k8aeH5DGhgeA/aonJq0ap81s9+K9VwGmkfaB33b3Z4v4BOCGqn3RIqT1sx3p\nqtmjpP30OVXLfAI4FxhGOtZYBLgR+Lq7P1q8JhwzM/2bHTOL11TG8PNI+8w3gdXd/eUmx83hpKsg\nnwaWLvroRtJ++8mS1882TpnZx4ETgY8Bk4FTSNv30+6+T1WdzwK7AmOAt4CLgYOqt8W+GAuKKxp7\nufvwqrLfkLbDlSoTHzM7GDia9D04i3fH3bIxHdK2v5qZfQ8YTbrCOJa0vnJ9PiiPM9x9hpldTLoa\n3tLJkH4zFLsS2LQ4CKuswBWA5Sr3LJrZPKQdzeX/z96dx81xFHb+/wgJyVLsICPFV2xiw4YCs/zC\njQkYsAlgWNZAEhJCfmzAYSFxWAKEBAwOh004jJMQFogJGwIsuTgCJNiQNZcNAXME2ISrCIfARiAj\nYYFAsmTJ2j+qxxo/mqp5pp5+emae/rxfL70kdU911/RVXdPd3x4q92ukBua/kW71uQfwt4ORzf2d\n7yft8I8hdQoeCHwwhLBuaDq3IHUk/oa0YX0EeEVzclmyinQi+cc0BwNSw/ifhz5z0xWUEMLdSAfn\n7wO/RDp4/u2Cz6xqPnMW8AzSRnpf4LHDMw4hPA54J+ng8EjgBaRb1xZutMcD/5PUuXo4acd4Uwjh\n9oXv9dfAHYAnN/X4DPDGZqf9V9LJL6Rlfm7z75eTdsg/Jx38nkT65fRtIYQjSOv4xc1nH01zQAsh\nnAe8jnQrxyOauj67GbYoTQP48qbsQ0gHuMcDryqUuWPzXW7TfJ9fIzVGH2oa5oFRV8CGh50LfJa0\njE5r/h74G9KB75HN93ttc4JRMjztC4HLOHSrQvbZoRDC40nbzTdJ28rTSSduH28a6oHVpBOcc0gN\n4JdHfMdLSCcIbyatk7cCrwwhPG/B5365+W6/08zzOODtzTZcvb5DCP8VeBvwuWb6bwXeMqKeUld+\nHdg6dEX6PaTtehVp2z6tGT5qGz2PdEz9JdIJ4XNIP0i8EHgw6UXJv086fkLab1eRTtBOI++bpJO7\n3wd2Z+Y9ePbjT5vnAO5EOrH8N9KJ90ghhKeSjuX/ROooXAX8xYJ5jLp1++dJx4OnA08ATiUdw/6Y\ndDz4VdIx96Yf49pqp0MI9yUds95GareeDjyo+fxwnQfzPYL0A+qvkU5WzybdTviXIYTnLPhev0tq\nE3+D9CPRPUgdpIFSm5kzrs0c+BlS2/0rpGP2D5bQbl5Gun3590nb3gtIy+jPx5QDIIRwB9K6OtDU\n5wWk7fu+Iz5+Cel845HARaTlNtjGl7MtuBTYGEK4+9CwM5syw8/8nAX8c4xxP4tv0/+E1Hn8L6Rt\n8dmkfXmkFXCe8TbgHk2HrjVeGcq7FHgecC/SL1APIv3ifAypN/xmUk/8J7h5Z+ga4JExxgMAIYSf\nBZ4XQjiyuUXtpcCXYoyPGBQIIVwFfIl0Mjg4AKwCXhRjfGPzmY+RGq5HAJcX6n0Q+M0Y4+VNua+T\nfll6IPD5oWkPnEf69WO4zt8n/So48HDSr2MPHZruB0n3ow97GXBZjPE3hr7bfwAfCCE8LMb43mbw\n+qaOHx76zDdJO/NXMt/r/s3yGCzrK0II24G9McYfhRAGz0B9aegWq+OA82KMrx2qz17g7cD/F2P8\nZEhX+wA+1/yS+JM0jUGM8ZnNuPeHEHYA/yuE8Ccxxi9l6riwvl8fmvdHQgg/IjUsOS8k/cr2wKEr\naZeR1tsrKJ+E3CTG+KUQwg+BgzHGTzXTGYx+R4xx8BD05SGEnyZduVlURy/G+PUQwvdIy/1Tuc81\nnY+XA++NMT5+aPjHSJ3lZ5FOwCBtsy8e2j4WTuv2pEb52THGi5vB7w8hHASeG0J4bYzxumb4GuAh\nMcYfN2V/kvRL2V1ijJ9dwvp+PvCJGOMTms9c3izTl5aXmLRszgBu2gdjjDtCCJ9r/vu10v4JXBlj\nfOXgPyGEPwE+PfRL7UdCCLtJV/eJMX6i2d6vKU03xrhzUKbwmS+EEJ4NvIZ0Qg+pLTk9xlg6oTwf\n+NsY47Oa/78/hHArYNxJ1pHAY2KM/wEQQnhgU+bMGOMVzbCLSZ2Yn4wx/pD22un7AT8GLoox3tB8\nZgepPR3liaTO2n2GOrmXhxDWkm4PuqRZxpB+wHzkYJk1J4cvDCEc3RwPs21mYVkV20wO3Z62Gnhm\njPHjzWeq2s0QwvHALuDpg2kBVzbnTf+9UM9hzyVtc2fFGPc2043Ax0Z89j0xxj9o/v2hEMJDSOvq\necvcFnyE1LH+BeBfQwi3JXUoP006n3xT09m8PyO25zFt+itjjIN5fziE8GhSR+u1C6fTeCHzfZ7x\nKdJ+9yDSuW0r7AzlfZJ0a9MvcKgz9AHgthzqDD0M+EK8+T3Onxh0KhqD+z43Npf77g1cFEIYvs1g\nC+kg+2AOHWQPkn75AtJ9ss3G8ROLqPtHR80/89n7Af+4oM7vIP3KMvyZvYOOUFOf3c0ONEjxCcCJ\nwB8t+G4fIV26fTAwfLJ71dC/B6EDpe/2IeCC5krW+0idrmcXPs/gJLy5ChGAnyVdLYN0q8Yo9wGO\nAP5pwfe4lLQDPpi0rsb5EPCUEMJnSFfLLosx/u2YMg8gHax3D32HAyGEvyM1hBsWMd+Sg6Ttdtg7\ngLObxmdfzURDurViuIN9kHT7wHHcvFM9OMh9nNQ5H1a6b/7M5u/3LFgn/0RqvE4nPTAOaX/88dBn\nxm1bY9d3SLek3I2hXxAbbyX9ACBNw21JVxFqLNzfPgS8LIRwJWlfunT4hLhNzRWOl5B+dX8nsJl0\novTBEML9YozfG1EmkH6IfM+CUW9lfGfoukFHqDF4vmr4uZPBc0YbQwg30F47fQXpFsQvhBDeTvq1\n+/IY4z9n6voAYEs8/PnTt5CuYpxGav8APrWg8zh8rLuO5W8zh7ehqnYzplsFf6GZ588087sD6apO\nro1e6AzSd7upkxdjvCqk2w8XumrB/68hdUogXUFclrYgxnhDCOFy0nd9Oel88suk2zcHnb4zSbeJ\nj/xRsOCjC/6/hfz5HszpecZAjPGHIYSdpDu1WuNtchnNQea9NDsqaUP9MOngNrjM/FBuflUI0q9A\nwwb3bd4COLr5+9mk+1UHf/aRbq07fkHZ3Qv+fyOLWGdx6P7XoYNlrtytSffYDpc/sGDYrTl0T/ew\n4cSewb2wr+Xw73YUC75bjPH6CeoI6VaGPybdCvB64JoQwntDIUI1hHCPEMIngZB6ZDkAACAASURB\nVGtJjcFvcaiTl3uQflMz7rIF3+O7pJ38hEIdbxJjfCvp8vMuUkP/qRDC10MIjykUu3Uzn4W+29Tp\nsGSmCt9e8P9rh+Zd6wPcfFm9f2h6ue+z8GD9o8L0b036/l9cMJ9PcPg6GbXPrCK/bY1b38eT9ttV\nLNhPmK/EKq08t+Lw9maxbra/xRgvIt0ys550UveFEMK/N1dRWtOcZJ4P/O8Y4+/GGD8cY3w7qZ09\nnnSr1CiD48mofXBcKMrIpLZ482eWhrXWTscYryL9aPo10i3mVwLfDumWv1FKbQDc/Lg5ar5w6Fi3\nrG3m8Mk0S2g3Qwi/HkL4JvB10i36Z4/4biU/xaF2bNi2EcNK51SDdma52oJLgZ9vrvI9iEPnkyeH\nEE4knU9+MjbPpC3SQUafc5bOpeb1PGPYj0nHv9Z4ZajsUtLly3uQfpX6ME0gQQjhNODOFO7NHOGH\npI33Txh6jmjIJAeAtmwHjh0x/OgFn9kcQli14JeoTUP/Hly6fxZpB1/ouhHDFi2m0IHzgPOaXxcG\nzyS9hkO/XN0kpIjZ95Lu671jjDE2wx9Guo0hZ/A9Hke6LXKhUQfYXJ3/Hvj7pi4PITWubwkhfCTG\nOOpg9H3S1ZSFBg3JDtL2s4p0m8KwI0kdr3E2c+hqIRxa99dy6J7dUdMueTKpwzuwa2gao77P8Rze\nmJTsbOp2BqM7Td+aYFqjpg3l9f19UgOzcD/ZdPjHpc5sp/wL8ERijH9OSnTbTLo1+nzgHSGEY2N6\nhqENP0V66P9mtzDFGL/X3Np0p0y5wfGii32w1Xa6uaPi8uY2qDNJtwb+WQjh4zHGf13w8e+TggsW\nGnTADrtqVphvV20mVLabIb0X8E2kAIqLB+1iSCFUo575GeUaRp/DHMPi7uIYWO624DLS1a77k9qy\np5Ju+fpR8/+HkQJBltu8nmcMO5rJziHG8spQ2T+TVthzgRhjvJa08f6Y9PDd94bucx0rpmeGPgPc\nIcb4mcEf0i/eF3D4rUPLZbhD8wHg4WHo4cgQwlnA2gWfWUN6H8LgM7ckneAPfJm0od92wXf7Dumy\n8F1rKxtCuE0I4VshhF8CiDH+R/PsyOUcusR9gJv/cnUH0gHqVYODeuPhzd+3GCo37CrSL4AnLvge\nN5J+MV3UpdkQwt+FEP6hqe+uGOM7SA/rriH/K9kVwCNCCDfd0tXcgvZY0i9GN3DoV84Thz5zNOk+\n82ELvxek5fOoBcMeA3wzpjSbUdO+Jem5uey0m/XxmaE//wFE0i9Nvzb82eZe6fuQbp9crEGazU8t\nWCfHkpbpuIZoeHufeH03t18MngUYdjblh2al5fRNUqrakoUQ/iWE8EqAGOP2mJ4dejWpszX4pbj4\nQu9FupZ0Mjb8oPbgtqzbk3l3THNMuZp0vBrW+j7YZjsdQnhFc6WFGOP1McbLSFe/VnGo7Ro2uFJw\n7wXDH0961qf0HNjwfBfTZi602DZzlNp2cxDM8aKhjtBqbn5uMc4VwMOaKy4007hrYZ7V32EpbUFM\n8fefJQUPbAauaH5k+BfSrXK34/A7jYaNatNrzOV5xtBnN5J+UDksaXApvDJUEFNCysdIK/aSZtiB\nEMJHSL34bKRwwXOBS0MIbyGlvawhXU25J+lA24XhTsMFNIkfIYSLSL+mXMjQfZ0xxg+GEP4P6SHC\nY0kb4dNIv/Jtaz5zY0jJXpeEFMP6T6Te+/mkeNCFv4AtWkwPul9D+jXtJ0kN5j1JB+k/aj42+FXn\nEc39pJG00z2veVbrBlLS2G82n/uJoXKrgF8KIVwWY4zNcrgwpIdzP0zaaS8g7ZyLfSfEB0m/sr6C\n9IvQrUm/yn2lMI0XkbarD4cQXtbU+X+QDuqDK5D/RjopeH4IYRfpAHweh18x2QmcFkI4g3QAHnhm\nSA/EfrxZHv+FpsMSY9zZbO//I4TwVdJJy++S7qP+8YJpH9t0mj836ipXjPFgSMk8bwgh/DXwv0nb\nywtIv+j8aWYZHCbG+PlmGq8PIZxCeuj0DqR1/zXyoRsDw9t77fp+LikI5B9ID4HeoRl2MyGEu5Ce\nr5vkF0mpxv+hvZcTXwH8XghhG+lk70Tg94APxxgHt0jvBO4bQjg9xrjYHzMW3lp1Y0jRzK9qjl9v\nIx0XnkOK3v+TwrSeDfx1COEvmnI/z2R3ZkyirXb6A8AzQooNfgvpysAfkH59/+CIz7+RdLviu5rl\nNEg+ewLptQ2LejnrItvMw4qxuDZz1Py+X9luDp6Nek0I4Q2kzti5pLtuCCH8xIJnQEd5CemWwPeF\nFGF9NOkc5gATdOAn+A6LagsyLiUFMHy5+XEd0rNdLyd1Fj6fLTm6Ta95b968n2ec3tTnn+GmK5qn\nkkJjqq8WeWVovEtJC/5DQ8M+1Axb2IsfFet5M80l84eSdrK3kS4R7wMeNOKhyYXGTj8zfmG5m/4d\n0zsJHkDaIf6OlKD3exz+jNCjSQfzFzWfu5oFySAxxr8kbfD3IT2E+xrSQfgB8ebvC1hMHRd6FGnj\nv6D5+ynAC2KMg3z/L5DiHH8HeEvTaJxNOli8ldRxPZG0I+3i0C+THyL9WvYS0pvbiTE+nxT1/GjS\n+n8ZzbNisfCOoOHvEGP8C1KH8SzSdnIJKa3lIfHmYRU3iTF+kRRWMXi3wZub6T0gxvih5jM3Ar9I\nuuryN6TbC/6G9IDisFeT1ullTR0Gnk5qEN9Nahx/qXm+aeA3SJ2N15Mu2f9rM49hf0V6SPNdpAj5\nkWKMbyIdCH+W9KD0xaSHPe811BCUDG8PTyDd//4U0r3s55G+90MW3Lo5Lg60an3HGD9KakBO4NBD\nr08cMa93krZ7abm9g3T78sJksnH7wKhj7fmkk+Qnkm6Vurj5+5eHPvNi0vMnlzXPOCzGYXWJMb6G\ndKXjXqT97WLSVZe7xRi35CYUY/w70q/Xp5GOXw8n3aI7cR0yw4bn1Uo7HWN8HykC/U6k9fXXpA7H\nA+OhVLjhz+8h3Ub1T6S27t2kTt85Q23dzcoUjGszF37nxbaZg/kvLD9xuxlTmt/vkM4ZLiNtC1tI\nbRxD8yydw3yNtK6OIK2rF5OS3b7LzU/ec8treFpttgWjlM4nF4aDLKzzqDZ97HdaaAWcZzyMdAVr\nEFx2N9IPOA9nCVYdPOhdHtJKFw69mPaMOPQSNUnzK4TwbtLt2k+adl2kaQghnAnsazopg2G3It2S\n+cym860OLPd5RkhJd1uBx8dDkfGt8MqQ1B81l9Qlza7nkW75XOyVGmmluRvpNv/fDSGcHtJ7dt5D\nurvl78pFtQyW8zzjt4HPt90RAjtDUp94GVhaQZpnDF5CeuZA6p0mGOLFpOdd3ke6Jf3bwH1jjDtK\nZbUsluU8I4SwifRs0ePHfbZGq7fJhRDWkd4z84uk+Mk/jjGWHoiUJGlFs22UpNnV9pWhi0mXLB9I\nSgR5QQjhF4slJEla2WwbJWlGtXZlqHmwaTvw0EHsZhO1/KAY45mtzESSpDli2yhJs63N9wz9XDO9\n4ZeQfpTF56/zXJ7f6r2Gq2nrpdnJgWV4LdOs17Gt+p3IiTyFcwF4Ha/lGq457DOlure9nNpWs9xL\n32k5trWcjVyXHbeLo0YOL9VvHXuz4/ayLjsutzxqt4tcuVKZfYX6bZjsxfNA+fuWllPNNEvf60Je\nYnjG9Cxr29jlsXH1qpd2Nq+u7Dv4h9OuwlhrV41Mw9YMmuftqVT3eT7OvPDgwbHtX5u3yR0PbG/e\nqDuwDTiiefBJkqS+sW2UpBnWZmdoAxz2U+fg//mfRyVJWrlsGyVphrXZGbqeww/sg/9Pfn+JJEnz\nz7ZRkmZYm52hbwObQwjD0zwO2BNj3NnifCRJmhe2jZI0w9rsDH0OuAE4bWjY6cCnWpyHJEnzxLZR\nkmZYa5FVMcY9IYQ3A5eEEM4BTgR+D/iNxU6jlJjSdkJHl8llfUmMGxhVv8XUeVbS1XJK9atZhl2m\npJXsYUNVuZz9rG51el1azYHsuLa/1zwvJy3evLWNXc5rFsxDUltuuS9Hm9nV+cqsJ8TWmoft6cDB\n80YOn5XjTK5+y6ntM8xnkt6y/UHgB8Afxhjf3fI8JEmaJ7aNkjSjWu0MxRj3AE9s/kiS1Hu2jZI0\nu9p8ZkiSJEmS5oadIUmSJEm9ZGdIkiRJUi9NP6JrSCmtIpc8USrTZWJc27qsexfpdMPDDrCmtXnO\nyrJoe33V1KO27msKCWqSpq+mbVyOedWY9bZ2HhLycstwOc4TatqRrtq4rlV9rykkoU0qu+wLdW/7\nOFNaTtPYNrwyJEmSJKmX7AxJkiRJ6iU7Q5IkSZJ6yc6QJEmSpF6yMyRJkiSpl+wMSZIkSeql2c82\nbOTiL0txf11GhC5HJHNX0+sinnox9Zr1CNa2zUr06X5WV9WjxnLsQ1KfzXo09Kzv1/PQ7nT5qoUa\nXbyeYxr1mPVtt23F79tyZPisLVuvDEmSJEnqJTtDkiRJknrJzpAkSZKkXrIzJEmSJKmX7AxJkiRJ\n6iU7Q5IkSZJ6abay7SocKMT9lWK3S+W6jKfOlSvVoTSuq+nNitp4xtz3qo1+rinX9ryWYx3PWvxl\nG7rc3rvcf1biutJ4NceDLq3UNkTLx3Uye9qPLc+/0mM1B1qd12J4ZUiSJElSL9kZkiRJktRLdoYk\nSZIk9ZKdIUmSJEm9ZGdIkiRJUi/ZGZIkSZLUSys6v3DfwT/MjivFbucC/0rTK+kyqrtmerWRzJPM\na3jYavaP/Ezby6nt71Uy61GgyxEZXjO9klmImS/pcvusmeasb4Oq12X8fdttz0rdLmchNr+2Dl0e\nNyedz7xbqdt726YRn13ilSFJkiRJvWRnSJIkSVIv2RmSJEmS1Et2hiRJkiT1kp0hSZIkSb20omMv\nSmklpWS4XLlSAl1t0lyXaV01ZiXxpe2Em66mVzvNLtOjjmJXdtxu1k9ch2knF46b3nIk663JJOPs\nZV22zDr2Zsftz2Zatp8spdm3etVL8+M6rEfOgYPnZceV2s1cudL3Lc2rVK5GaV6zkBq2HHVYialx\ns7CuNNu8MiRJkiSpl+wMSZIkSeolO0OSJEmSesnOkCRJkqResjMkSZIkqZfsDEmSJEnqpbnJG6yJ\nQm470rEUn10TH1piFOTNtR0n3GXcddsx3pPOZ9y89rG21XrU6mo5LYdSFHabZdRPta9u6Ertaydy\n5Wrb2raXU7ldn+11Mivm4fgteWVIkiRJUi/ZGZIkSZLUS3aGJEmSJPWSnSFJkiRJvWRnSJIkSVIv\n2RmSJEmS1EszlXlYGw1cU2ZWYrdz5dqu33Isi0nWyfBnD7Cmk7jNWYjPrq1HSc36ant76tI8111q\nw6zvA6X2r1T3mvavdl45aziQHbe3w9dpaDq6PE9s+5yk7XrM+nFmOXllSJIkSVIv2RmSJEmS1Et2\nhiRJkiT1kp0hSZIkSb1kZ0iSJElSL9kZkiRJktRLMxWtPeuxg7VqYrdrozlrIhNroyVz48Ytv9Xs\nb2291a6r2rq3XY9Zn9cs6DL6VJo3s3A8aLt9KZVZx97suL2sy47LqZ5XRbvediy4lq7L5V6znyzH\nKz3aPv9pW806WWrdvTIkSZIkqZfsDEmSJEnqJTtDkiRJknqp+ia7EMI64NPA78QYr2yGnQy8HrgP\nsAV4Rozx8qVXU5Kk2Wa7KEnzp+rKUHPA/1vg1AWj3gVsBe4OvAV4ZwjhxCXVUJKkGWe7KEnzaeIr\nQyGEOwJ/M2L4mcBtgdNijNcDLwshPAg4B7hgqRVtW9tJG7WJJLl0mVwaTalMSZcpIaOWxWKWT83y\nbTsJZh7SCWchdai2DrNQd6lts9gu1u5rNcerthOvSmVKiXFtH19K86pJmmu7XZ8VK/W4vnrVS1ud\nXikluOa8cx7Sb2vU1K+0b3Fw/KG25srQA4APkC75rxoafm/gM80Bf+CjzeckSVqpbBclaU5N3P2K\nMV4y+HcIYXjU8aRbAYZtA7wdQJK0YtkuStL8ajNNbgMcdt14L1S8BU2SpPlnuyhJM67NztD1HH6A\nXwfsbnEekiTNC9tFSZpxbXaGvg0ct2DYccB3WpyHJEnzwnZRkmZcm52hq4C7NfGiA/drhkuS1De2\ni5I049rM17sCuBp4YwjhQuBs4J7AExY7gZoIzpIu4wNr55WNUyxEMJYiBEvlarS1DIenc4A1I6fb\ndgRr2/HptdqOHa2pY5d1aHudrNTYVvXCkttFWJ7XOrQ5vbbjf7uMDC5937YjvmvbdU1HTdx5cXuq\niOpePXGJflpqNP1SrwwdHPwjxngj8EjSLQCfBh4HPCrGeM0S5yFJ0rywXZSkObKkn8FjjKsX/P/r\nwBlLqpEkSXPKdlGS5kubzwxJkiRJ0tywMyRJkiSpl+wMSZIkSeolO0OSJEmSeqm77OlFaDuCc6VG\n8pbiOXPRjbWxg23FXQ8PW83+kZ+ZhfXfdiz0uHJtqq3DUezKjtvN+iXVqQ193MfXcCA7rstXBmg2\nzHrsch/jf/v4nXPafqXHrGj9lRQdLqe224m2I/dnjVeGJEmSJPWSnSFJkiRJvWRnSJIkSVIv2RmS\nJEmS1Et2hiRJkiT10vxEPWQsR/rXPMulxpXSiEpJc8uxDA+wZuR0W09u6TBNpWZeS1l+bdUBYB9r\nq8p1ZaXuqyX7zarSkJo00LaPL7Nipbb581z3XIottH9OMuvJiiW3LIy7oeV5zUILMgt1AODgBWM/\n4pUhSZIkSb1kZ0iSJElSL9kZkiRJktRLdoYkSZIk9ZKdIUmSJEm9ZGdIkiRJUi/NdobmIsxDHOUs\nREbXRlUeOHjexPMaVb/hYavZP3KZzHoUdpeRszXbde2+MOsxzvOwj+d0Wb9Zj0RWt2q3h5pttnYf\nzZVbjvjsed4/Zr7uhfOE3PlF6dyi7XOSWXFg2hVQlleGJEmSJPWSnSFJkiRJvWRnSJIkSVIv2RmS\nJEmS1Et2hiRJkiT1kp0hSZIkSb0043mNK0Pb0cBtR5/Wxm7nyo2q3/CwA6wZWZ+2v1dJ2/HUNbqM\noy1ZUwj8nPXY7VnX5fY06zHjWrlqt72u2r+llOubUnuQs5d1+ZGZKOxi212Iz575mHHNJa8MSZIk\nSeolO0OSJEmSesnOkCRJkqResjMkSZIkqZfsDEmSJEnqJTtDkiRJknpp7jMKlyMus+3oxi5jktuu\n+4FCxGUudrtUBtJ3G/X92o4hrplezbpaSrmuypTqNyvx2W3vC5POR5pVxkJrUrXHuZpy69ibHZeL\n3V6Otlaq5ZUhSZIkSb1kZ0iSJElSL9kZkiRJktRLdoYkSZIk9ZJPEkuSeieEsAG4M7AWWDU8LsZ4\n5VQqJUnq3Nx3hpYjdSRXbjlSqNpOSetSLjVu9aqXHjbsFve6F3zi3OL05jl1r0aXaTql6ZWSgNpO\nmusqFbC2Dl0mSWp6QghnA28CfpIFHSHgIMxIxGJjFo5Xmj2l40vb7UupTC4xblw92iwzK0zCm08e\nYSVJffNy4P3AhcAPplwXSdIU2RmSJPXNKcAjYoxfm3ZFJEnTZYCCJKlv/gM4cdqVkCRNn1eGJEl9\n8wfA/wwhPA/4Mtz8wbkY47emUitJUufsDEmS+uYfSSEJ7yYFJgysYgYDFCRJy8fOkCSpb35h2hWY\nxIne0acRliNNte003b4lqJkmN1uu4ZpFfc7OUEtqYyxLscY5e9iQHXcUu0YOX82BbJm9rM2OqzkA\nHjj4h4cNu2GoMT/AmpHTbftA0WU8ddvaXhal6e0ubE+57WY54qm7it2u3S7aXv8HChcfSvtrfnoe\nzhcrxnjF4N8hhJ8Cbogx7pxilYrO5cnTroIkzZ1X86RFfc4ABUlS74QQnhZC+A7wXWBHCOHbIYSn\nT7tekqRu+VOiJKlXQghPAS4CXgtcQfph8P7AS0MIP4wxvmGa9VvoAt4xcZlbc13VvDbzvey41dw4\ncvg1hdv4jmVbdtw2jm2tzLhy3+fWmeFHZ8scxY+y4w4UfkvOLaeSG7hldtwpfGPi6ZXULMNSmVvz\n/ey43HLazk9ly5S2wVK5W3JDdlzOD7hVdtzt+crI4d8r1OFHHJkdV9pmbuzwMcXc9/oCd6qa3ma2\nV5SZ/DgD8BVuP3J47jstlp0hSVLfPAN4Vozx1UPD3hlC+CrwdGCmOkNf59qJy/y4ogzAAbZmx+Vu\n3/xW4STvIFdnx12dmV5NmXHltrEnMzx/An00+Tsn9xdOXtdU3Oa6l3XZcRta7gxdXbwlefQyLJXZ\nV+iE5pbTdwrL/YbCNlgqt5Z92XE5O7g+O+5WmeW+tfB4w042ZseVbo0ujWtb7nt9jWOqprensL5y\nao4zAN/IdF5z32mxvE1OktQ3PwO8d8Tw9wE/23FdJElTZGdIktQ33wTuMWL4PaHwM7ckacXxNjlJ\nUt+8DnhNCOHWwL80w+4HXAD82dRqJUnqnJ2hCSxH7G7uftrS9NazOzsud99xbVRzzXcede/rjUMX\nIVezf+R0q2K8W45JXo73NtSUabvupXmtLdz/3HZcc5eR4W2v49p61JQxJnvZ/RnpVrlXktrBVcAN\npE7SH02xXiNtZkd2XNvPGpRetZB7FmZ95nkcgK2ckB2Xe8ajpsy4crnnEErLtvTsQn4p5cuV1lVp\nGe4rzm1ype+Vm1epTOn1DLnvXPq+pdeHlMrV2Fh4LuxqTho5vLQeNxTOz0rbbtvruCRX/1IASdtK\nx5l1heW0KbO/5tbVYtniSpJ6JcZ4I/D0EMIfAncgdYa+FGMc/aI2SdKKZWdIkrTihRDuD3wsxri/\n+fdCdw0hABBjvLLTykmSpsbOkCSpDz4MHAdc2/z7IOmK0EIHocOcW0nSVE3UGQohnAC8CjgD2A28\nFTgvxrgvhHAy8HrgPsAW4Bkxxstbra0kSXVOgZve9HdKmxO2bZSk+TXplaF3ADuA+wKbgL8C9gPP\nBt4NfA64O/Bo0gvs7hBjvKa96kqSNLkY4zdH/RsghHBL4OeAL8cYf1QxedtGSZpTi+4MhXQz9b2A\nY2OM25thzwdeEUJ4H+mXtnvHGK8HXhZCeBBwDimqVJKkmRBCOAn4S+B84N+BTwGnAt8PIfxCjPFz\nE0zLtlGS5tgkV4a+C5w1ONgPuRVwGvCZ5mA/8FHSbQGLVoorzMX/liJoa6OLc7F+pfjIkrZjd/dl\n4rMhvwzLMaD5eeWiVCd1C24c+5kuY5LbXidtazuqeznKtT2v3PJtex3XHhdK2o47r6mHcdwT+VNS\n23Ut8CukmO37AU8ELgIeMsG0lr1t3MnG7LhcXG/uNQvj7GBTVbmcXBRuaV41ZcaV28VRI4eXIo1L\ncdKl85WaaO2SazmmqlxOKeI5N6+aMpBfFqVI61JEeqlcTTx1brsAOImrJy5TioyuiSBfDpEwcR1K\n+8K6wus5crZU3qmcW8e5dbVYi249Y4w/AG66zzmEsAp4KvAB4Hhg64Ii24ATl1Q7SZLadyZwZoxx\nSwjh5cB7Y4wfCyFsB/51kgnZNkrSfLvF+I9kvQK4K/A8YAMc1jXcC5U/TUmStHxuSbolbhXwIOD9\nzfBbwJIvAds2StIcqeoMNb+kPQ349RjjF4HrOfzgvg4Kr+KVJGk6Pgv8JvAU4GjgshDCWuA5pLCD\nKraNkjR/Jr7JPITwP0kNyK/HGN/VDP426eHTYccB31la9SRJat3vAe8BNgMvjzFeE0J4LfBI4Kya\nCdo2StJ8mujKUAjhBcCTgV+NMb5taNRVwN1CCMO/gN2vGS5J0syIMX6K9DzPphjjc5vBrwRuF2P8\nxKTTs22UpPk1SbT2HUkxpC8BPhZCOHZo9BXA1cAbQwgXAmcD9wSeMEllysktkycl1aakldJAauZV\nkkuGKy2LXLLeciiln5TqWHKANZ0kX81KulaXKXRdmfUEupLahLe2dZlcp8PFGG8MIaxrYrZXkW5p\nOzKEcGSM8VuLnU4XbeMxXDvJx4H6ZKhSIldumkeyK1tmB5uz444/LFuivsy4chsydyaW0slyZaD9\n9K/9hentqUyyzalJyatv7ydfTkcVtqeSmjqW9q1ciuN69mTLlFL3ZuUYndue1lfevVt3HptfTqVt\nJre/lhI3F2OSK0NnN58/n5SOs5V0qX9rjPFG4FGky/+fBh4HPMqXykmSZk0I4edDCF8htWNbgG80\nfwb/noRtoyTNsUmitV8OvLww/mvAGW1USpKkZfQqUoflWcAPljIh20ZJmm+zcR+RJEnd+c/AXWOM\nX5p2RSRJ07WU9wxJkjSPrgaOnHYlJEnTZ2dIktQ3Lwb+LIRw5xDCLaddGUnS9HibnCSpb84HbkPz\ngtUQws1GxhjbjQqTJM2smeoMzUrkbU10bW39ctGDXcbulupeivGeZF7Dw1azf+Lvl5vXrMRnl9Ss\nk7YjONuOp56V5d7295oHK/V7dezF067AJGriiWujkDexIztuXaY92F6ItC7FJOf235oy48rl2rLS\n9EqvlqiJLq/dd0/IxImX4rhLclHIkF9OpTI1Ud2lV5isK8Qu15bLKa3jXPxzbj+A+nXSZZt6DNtG\nDt/FUdkypfqVIuhzaiPId3L0xNNbjNk4o5EkqSMxxjdNuw6SpNlgZ0iS1DshhIcBvw/cAbgP8ETg\nqzHGt0y1YpKkThmgIEnqlRDCg4F3At8CjgZWA7cE3hhC+G/TrJskqVt2hiRJffMi4DkxxidAukE9\nxvg84Lmkq0WSpJ6wMyRJ6ps7A/80YvjbgNt1XBdJ0hTZGZIk9c0PgBNGDL8T8P2O6yJJmqKZClDo\nMia3FCHYdixmSW6atcuiZnolsxJd3GXUdFdql23bUd1tr+O211Xbkfb1MfizsQ0ard2KvwZeGUJ4\nInAQODKEcBbwauDvp1qzEUrRxbntoRSFXJ5XfvvKxQbvYX22TCmqewebWitTW249e7Jl9hVinEvy\nsdv56dVEA6+pjE//TmEZnsTVE5c5shBpnqtjafssfa9SuZ1snHh6peW+2/3aUgAAIABJREFUkZ0j\nh5e2wdpI+9pyNXIR5Dsq49NL59M5ue1snNy+lVtXizX9M0JJkrp1PnASzUtXgc8Cq4D3AM+bVqUk\nSd2zMyRJ6pUY4w3A40IIzwfuQrpl/PMxxi9Ot2aSpK7ZGZIk9dUu4JOD/4QQbgMQY/zW1GokSeqU\nnSFJUq+EEB4O/BWwecGoVaRniHIPfUiSVhg7Q5Kkvvkz4BPAa6HwBL0kacXrbWeolCCST4Kp03Ya\nVpfWsXfiMntHJJIs5nvWpJq1nci1HOl5XSae1Uyvy7S2WU+aK2l7+5zn48IKcALwiBhjnHZFliq3\nrWxgd2d1OIZrs+NyCV+lcjVlxpXLpU3tZkO2TCmdrnSe0HYyWC7Fr1bpe+XmVSpTWoa57XNt4dxi\n1DnEYsrVbPOldZVLjTuqkJ5XSlar3Z7aVlpfOaX6lZZHm3UozauU8LcYvmdIktQ3HwTuPu1KSJKm\nz58fJUl989vAJ5t3C30duHF4ZIzxgqnUSpLUOTtDkqS+OR84DjgL+PGCcQcBO0OS1BN2hiRJffM4\n4IkxxjdNuyKSpOnymSFJUt/sBv5l2pWQJE2fnSFJUt+8BnhhCKEu0kiStGLM1G1y7Uf85qMAS/GM\nbcc4dxk1nJtX7XxKEZf55XT4cr9xEf3umjrOSlRzqVxNHbuMGS+Vy+1Dpf2ny1jotufV9jGo7e2z\nxDjuidy/+fMrIYRtwA3DI2OMt51KrTL2sXbiMssR1ZuLXa6JtAbYyvGtlRlXLhflW1pO13JMdlxN\n3PWaysjtXCRzbYR3Kf55Y2aapTJtR4nXKp2v5OxhfXZcbnsqrfvSa1tK21OXyzD3vWrb9ZrlXvt6\nm1yEdmnfXwxbT0lS33y0+SNJ6jk7Q5KkXokxvmjadZAkzQY7Q5KkFS+E8Hzg4hjj7ubfOQdjjBd2\nVS9J0nTZGZIk9cETScEJu5t/5xwE7AxJUk/YGZIkrXgxxlNG/VuS1G9Ga0uSJEnqpZm6MrSvEM9X\nivzLWVcoU4oCrIkTLqmLBZ887hjydaydXk3c46g63LIQo7gYNZHhbcdTtx27XFIzr9oY51Lda7f5\nmnl1FZNe3hfaXf9dbjMqCyHcSLoFbqwYY/u51Mskd4xue98tKcXaltr1XLmaMuPK5ZZTqf07kl3Z\ncesqo4Fr7M1Eq9dGdZfUxLiX5I6BRxWWbSnGu1RuB5sXX7HGevZkx23j2JHDS9vgLo7Mjltq/HNb\ncst3dyFmvLStlZbhpHWA8v6Tm1duXS2WrbEkqQ/O4VBn6GeA5wCvAz5Ges/QPYHfAV48ldpJkqbC\nzpAkacWLMb5x8O8QwhXAU2OMbxj6yLtCCF8Eng5c3HH1JElT4jNDkqS+uRdw5YjhnwTu1HFdJElT\nZGdIktQ3/wE8dsTwJwNf6LgukqQp8jY5SVLfvAB4ewjhwcCnSD8M/jxwF+Bh06yYJKlbXhmSJPVK\njPGdwOnAt4GHAr8AfAW4d4zxQ9OsmySpWzN1ZagUBdpldHGXar5X25GpNfHZkI9a3F8ZK9p2FHbt\nvLrS9jbY5XeahwjyNsvAbGwzMDv1mHcxxo+RkuRmXukYnYuhrY1drjl+l+a1pzC9XLmaMuPKbWD3\nyOGlGN/SvNqOz65R29aW4qlzMd6lMjXnEDs5OjuuNK9Sudw6rpV7PUvptS17CvHUJcsRk55zIDOv\n2jrUtKnlc4h8PWrWyWJ4ZUiSJElSL9kZkiRJktRLdoYkSZIk9ZKdIUmSJEm95JO4kqTeCSH8DHAa\nsBZYNTwuxvjmqVRKktS5meoM7WNddlwuQa2USFGbeJUbV0raKKW6tJ2uNQuJXJD/zqPqNzzsAGtG\nfqbtdLW2E05q59XVOl6OFLe2615Txy7T32Yl4a/LRL4+CiH8d+C1MPIgdhCYqc7QvkzCF8Ba9o0c\nvpONVfNaz57suNx2+Q1OzpY5iauz43LlasqMK7edTSOHl9r1bRyTHdelY7l25PDaRLsdbM6OyyWy\nlcqUkrxyyWClxLAdmXU1rlxuHZeOmaX9JLc9bePYbJmS7YVl2GU6Ye577S2cg5fWcW4Zlvat3HFr\nnEgYOby07y+GraokqW+eC1wCPC/G+MNpV0aSND0+MyRJ6pvjgT+2IyRJsjMkSeqbzwF3mnYlJEnT\n521ykqS+uQh4TQjhtsCX4eY3xMcYr5xKrSRJnbMzJEnqm7c3f//ZiHEHGR2sIElagewMSZL65pRp\nV0CSNBtmqjOUi8+GbmNycxHfazKRk+N0GYXddhRyKU4xF609qsxwjOJq9o/8DrMQJ91ljHNtBHWX\nZiFquu2I/Np5aeWIMX4TIISwltQx+hqwKsZ4w1QrllGKu26zzDi5/WMzO7JlSq/MyJWrKTOu3EZ2\njhxePr7ko4FL47qKSS7VoSS3LCBf91KZ0nlCzlZOyI47ga1V5TYVto2cXJQ4wG7Wjxxes/wAjmJX\nVbmulL5XSZd1z20buXW1WLb8kqReCSGsAl4KPI300tXbA38UQvgx8Nuz2imSJLXPNDlJUt/8D+Dx\nwLkcCk94F/Bo4IVTqpMkaQrsDEmS+uYpwFNjjG8EbgSIMf498CTg16dYL0lSxya+TS6EcDvgNcB9\ngR3Aq2OMFzfjTgZeD9wH2AI8I8Z4eVuVlSSpBacAnx0x/P8Cx006MdtFSZpfE10Zau6zvhTYBtwF\n+C3g/BDCY5uPvBvYCtwdeAvwzhDCie1VV5KkJdsC3HPE8IcBX59kQraLkjTfJr0ydCzp17RzY4w/\nBr4WQvgAcL8QwjbSr233jjFeD7wshPAg4BzggjYrLUnSErwCeG0I4XjSj4IPCiE8mRSo8MwJp2W7\nKElzbKLOUIzxu8CvDf4fQrgvcDrpIdTTgM80B/yBj5JuDViUUsRlLrqvNlqyJBe1uJsN2TKlepSi\nG/eyduTwtiN+S9GHpUjzXHx2muboOq6pXCdtxzjPSjx1Ttsx3m3HTNfOq2Se467brnuXy1A3F2P8\nqxDCLYHzgfXA64DvAefHGC+ZcFrL2i5C+fjdZduYU5rXLo7KjstFDdeUGV9ucqXvVWo3SxHfNUrz\nqlE6LuXmVRtBnlOKwS5Nr1SuJuK5NK91Q68EWex8aiPX12bmtRxybUhtW9v2sabUxuX2rdy6Wqzq\ns4wQwhbgJOA9wD8Ar4TDAsC3Ad4OIEmaKTHGvwD+IoSwGbhFjPHapU7TdlGS5s9SfnL9RdKDpn8O\n/CmwAQ77SWEvtPwTiSRJEwoh3H/M+DsM/h1jvLJyNraLkjRnqjtDMcbPAIQQngn8NfCXwNELPrYO\nCveISZLUjQ8DB4FVzd8Dq5q/h4dVvVLddlGS5s+kaXLHhBAeuWDwF0lv8P4Oh0eSHtcMlyRpmk4B\nbtv8/d+Bb5Gu5BwL3Bo4C/gy8MRJJmq7KEnzbdKXrp4C/EOTwDNwD+Ba0kOhdw8hDF/+vx9w1dKq\nKEnS0sQYvzn4AzwHeFKM8d0xxu/FGHc27/45l8lT3mwXJWmOTXqb3KeATwNvaG4DOAW4CHgxcCVw\nNfDGEMKFwNmk9zg8obXaSpK0dCcA3x4x/DrSVaJJ2C5K0hybNFr7xuZ2gFcDHwN+DLwyxvhqgBDC\n2aR7pD8NfBV4VIzxmsVOvxRBnYsk3Ft4DrU21rY0zRq5+GzI13FdZZRmTTRiKQa0FOm5lLjmUfWc\n9cjg+tjJbiLDu6zfckRG18R413znLiPIl8OsRI3PuU8ALw4hPCHG+COAEMKtSe8fumKSCS13uwjd\nxmSXjvk18bWlKOw2y4wrV/Mai9rjQW4Z1r52Yn3Lj5jVtPm15wltlhlXbk/h9Sf56U2+Te8rnNOV\nlF5VUrtt1Mh959L3KtU9t23UvnKmXG5PdtxSTNyqNu9U+OXMuK8DZyy1UpIkLaOnAR8AtoYQvkK6\nZfz2pNjrMyedmO2iJM2vSZ8ZkiRprsUYv0Dq/Pw+8HHSsz1PA+7cPFMkSeoJ77eQJPVOjPGHwOum\nXQ9J0nTZGZIkrXghhA8u9rMxxolvlZMkzSdvk5Mk9cE3h/5sAx4IbAS+BPwb6b1Ap5PeNSRJ6omZ\nujJUSl3LKaWu1SbN5ZRSTGoTnmpS8tpOw8rVodaoutemryzFPKdutZ1wVJvI1nZy3XIkN81jHcbV\no6TtddIXMcabXqYaQngD8Kcxxt8b/kwTf31q13Ubp+YYvZGdVfPazfrsuFwbXUrx2sSO7LgdbGqt\nzLhyuRSqnWzMlqltt3JpWJPnlpWVEr5KSql7uzhq4jI7OXriOpS26VKaWKncJrZPXI9S3XPH2lKi\nWWn7LH+v7tLkcnZXpPFB3XLPbWdQTtbLnRsvte229ZQk9c1jgLuNGP5m4HMd10WSNEXeJidJ6pud\nwF1HDD+ddAudJKknvDIkSeqb1wF/EUI4lfQy1FsA9wWeCvzBNCsmSeqWnSFJUq/EGF8cQjhA6vw8\nvxl8NfCsGOOfT69mkqSu2RmSJPVOjPGlwEtDCJuAgzHG70+7TpKk7tkZkiSteCGE/wb8fYxxb/Pv\nheNv+neM8c1d1k2SND0z1Rk6uhAFmovTK0Vfth0ZfQrfyI67rhDPWYoQzKmt+0l8a+I6lMZ1Gddb\nM6/amPG251Wj7Xjq2vptYHd2XE3cfa2210nN9Gpjt2virkuvBaiNzFXRG4H3Adc2/845SEqVmxlv\n55ez40pR2DVuw9XZcbno6qs5KVum7RjnUntVKpeLDS5Nr3R+sWtvvtze6yteE3JEPnj7hHVbJ55e\nyda9J0w8r1KZ1WvyUci7fzR6+1xTKLN/f/74Vyq3+0eTR0MftTG/zeTW4/Wl+Ww/IjvqFsf9ODvu\nxsJ3btuJPz16H78m/my+0I/arcMR/yl/IX7zrfIR+bn9rrTPXXir8fWZqc6QJEnLIcZ4i1H/liT1\nmw2CJEmSpF7yypAkacULIXyIdAvcWDHGM5e5OpKkGWFnSJLUB1tGDPt14B+h8LCJJGlFszMkSVrx\nYoxPXDgshPDLwB/EGL8+hSpJkmaAzwxJkiRJ6qWZujJUE0Gdi9wGWFuIrt1XKLea0dGNW8lHS5aU\n4nVzdayNXb6WY0cOLy2nklLEd2n5LjQcPbya/a3HVE+qZv61scttlimp3WZKa7EmxrvLOPYay1H3\nNZljRkltfPasL1+171f5++y43DG6FAtdUnpNRG47vzP/XjWvLuXa9ZI9hdjy/esK+2+muS2dd5Tk\nYsFrX8HxqHXvyo7bwskTl9lZ2GYO3Gp0HXMx7QA72JQdVyp34CcmXx7F887M6lp3q0Kr+dP5UTX7\n1nLInQ9uDm/Plmn7dQ+5c1WAteRj5o9al7mjubhrPXNsfbwyJEmSJKmX7AxJkvpsUQlzkqSVyfst\nJEkrXgjhDSMGrwMuCiHc7N6LGOM53dRKkjRtdoYkSX1wyohh/wJsbv5IknrIzpAkacWLMZ4x7TpI\nkmbPTHWGSmkVuQSldYUsrNL0apJlahPZSulVuXSZSZLahrWd+FGznMY5wJqpJ2Ll1kltvWqS5mrT\n6WoS3kq6rHttPaZdZpyaY0Ntqt20kxjVvc1sz47LHfNrt/Pj2Zodl5vm7kLq2uZC+tf2TGpYTZlx\n5XKJZxvZmS1TShpbjrYxP6929/lSqt1d+OzEZWq2z6MLy30j12XHlcptKySU5Wxgd3ZcLrmulHZX\n2p6OLyQ8dnlOlNue1hVS3ErJhTXnnaeMfAf2+OntySQrllIGF8MABUmSJEm9ZGdIkiRJUi/ZGZIk\nSZLUS3aGJEmSJPWSnSFJkiRJvWRnSJIkSVIvzVS0djnWb3SMZU0cdxpXKjd63DFsy5YpxU7uzkQB\nltRG6+aWYSmoe00hIrSm7vtGxEfu5YiJp7MYtfHEbcdTl3QVmVm7zdR85y7js9ueV+02U9J2VLvx\n2Rq2lROy43LtVduvWYB8W7G20HZ/kVOz43IxxDVlasvVxmePaucGapZ96ViRq2PtcWIDe7LjcjHp\npTKlqOnc9lmKQi5Nr1SudF5XI7c9lbb3klk5rte80qXt85jS/lPa73JR6KV9/6mLqI9XhiRJkiT1\nkp0hSZIkSb1kZ0iSJElSL9kZkiRJktRLdoYkSZIk9ZKdIUmSJEm9NFPR2jUxzm1HBkM+um8nR1dN\nr2ZetbGouWVYjpzMxymWIg5zy/eoEdPLfc/ltBwRym3Wo8s61MpFcJa2i5KadTIrkeG19aiZXo1Z\niW1V+0px0rl9sXZ7KEXU5pzCluy4o9iVHZdrl2rKjCuXW041r9mAcptWE71cmtcWTp54eiUn843s\nuK8QJi6Ti+OGfBz7SVydLVNaFqVyNe1SKeI5t9/VnKuOm1eXruakkcNPLuzHNeeCJaXjTOl1L7n1\nXzpGwm+MrY9XhiRJkiT1kp0hSZIkSb1kZ0iSJElSL9kZkiRJktRLdoYkSZIk9ZKdIUmSJEm9NFPZ\nvusKEc+5qOm2424BdnHUyOE1cZnj6lHzvUrLKVduOaKQc/aOiGNeTKRk2/HEszC90jTbjv6unV4p\nxnRtYVurm9fsr68aXa3jmjpo/uWicCG//9Ye80uxtjmjjvkDO9mYHZeLw60pM65cbl/MvT4A4Bi2\nZcetK5wPtL1Ocuce5Vdm5H2HE7LjcnUslflPfG3i6ZW26dsVplcqV4pWzylFpOcizUvrMXf+COXo\n6tp1WWN95jtv45hsmdpz0pyadVWy1Ph5rwxJkiRJ6iU7Q5IkSZJ6yc6QJEmSpF6yMyRJkiSpl+wM\nSZIkSeqlmYofyiWrQV1aU61cUkjbaWLLoSbVo5QEVJJLPxmVQDa87Fazf+Sy7DJBrUZtclnNdtP2\ndt1lEl5tPWrm1WUiX5dmpR6aDaXkpdxx+BiurZpXzbZXSqDbyHUTl6spM65c7niwg03ZMkcXkuva\nVkooC8RW57WbDdlxuXS1UpmS3PZ5bCGpr5SsVipXk9ZXSpPLpfgtJh130nl1KVePbRxbNb1SwmNO\nzblqSW3a84BXhiRJkiT1kp0hSZIkSb1kZ0iSJElSL1U/VBFCuBTYFmM8p/n/ycDrgfsAW4BnxBgv\nb6GOkiTNBdtGSZovVVeGQgiPBR62YPC7gK3A3YG3AO8MIZy4tOpJkjQfbBslaf5M3BkKIRwNXAR8\ncmjYmcBtgafE5GXAx4Fz2qqoJEmzyrZRkuZTzW1yFwNvBn56aNi9gc/EGK8fGvZR0m0BE1Rm8ljE\nklK0bim6MWddIbqvZnoA+ypirUtR2Lm4wlJsedvxxKPKLFe8eG3cdc302i7XZXzyrCynGm1HmpeP\nC90tp1mJO1drlq1tLEUG58bt4qhJZjFU7sjsuFwbXarf3kIMca5NrSkzrlwu/rcUyXs1JxXmlW+H\na88HuppeKao9t92UytS8FmU367NlNrAnO65UrlTHGjXLonROFwlLrlMbcsuwtNxLao41pTKltiy3\n7GuPdwMTXRlqfuU6HbhwwajjSbcBDNsGeCuAJGlFs22UpPm16M5QCGEdcAlwboxx4eWHDXDYJYm9\nUPk2T0mS5oBtoyTNt0muDL0Q+FSM8f0jxl3P4Qf3dTAjr9uVJGl5vBDbRkmaW5Pc+P+rwLEhhMEN\ne+sAQgi/DLwEOHXB548DvrPkGkqSNLtsGyVpjk1yZegBwJ2Bn2v+/CPw7ubfnwDu1twuMHA/4KqW\n6ilJ0iyybZSkObboK0MxxquH/9/8CnYwxviNEMI3gauBN4YQLgTOBu4JPKHFukqSNFNsGyVpvrWS\njxtjvDGE8EjgL4FPA18FHhVjvGaS6ZSiKnNqonVrp7mTjYUyk0efAqzNRGHXysUL1tah7ejiWl3F\nBrcdM16a5qxMr+1Y8C6/V9vR1cvxvdqeXpf7nZamrbaxdPzOl6k7Zp7ClonLlKKVd3J0dtxGrmut\nzLhyuf2jFFt9/GFBgIeUIr5zyucJ+fXV9mtHtrM5O+5UvjhxmdwrPSAfXV57rC2Vq4lXPjoTuQ75\n71z6vqXz2M1sX3zFllFuO9zGsYUy+eVesy/UxqDn9tfS9rkY1a1njPGJC/7/deCMJdVGkqQ5Ztso\nSfNlovcMSZIkSdJKYWdIkiRJUi/ZGZIkSZLUS3aGJEmSJPXSTMUPlRI6cmoS6GrVpl+UknZqEqBK\n6Tc1yS3leU1ev1HrMVevYTXpMm0n0rSd8FU7zVlYFrW6TGQraXteXSUaQl2CXpf1U7dKSaY5+1hb\nNa/vcEJ2XC6FagO7s2VKbfQe1rdWZly5mmN+KZ2stHxzbX7tMal2XeaUzmU+x10mLlOTpltatqV5\nlcqVtsMapXOtGv/OnbPj2k4WLtnDhpHD1xeWX7lNmnw5lbbp0va0MZP+t9R15ZUhSZIkSb1kZ0iS\nJElSL9kZkiRJktRLdoYkSZIk9ZKdIUmSJEm9ZGdIkiRJUi/NVLR2KTIxF/lXiuOujbTel4nnLIVD\nl2IRu4w1rqlDTaR5aZqjlvuBoX73AdaMLFuqR25eXUY1166rmkjXusj1umXR9vY5z9OrnVfbcecl\nbcfCa/YFYnZcLoZ4Ma8zGKUU452Lw62JVoZ8HG5NmaWUy6mNO277lRlta3udlCKtc9thaTvLxSeP\nK1fTHuTO9yD/vWq3s5ro9y6tKXyv9ezJjqs5hyyd7+8txG7n1lc5Vv2pY+vjlSFJkiRJvWRnSJIk\nSVIv2RmSJEmS1Et2hiRJkiT1kp0hSZIkSb1kZ0iSJElSL81UFmsuPhvqopVrowpzcZq5CFOAPWyo\nmlfbcbg137ntuMdR32l42Gr2j5xuqR45bcdRLkcUctvajpOujafPmYWI0K7VxF3XLqeaGG/Nt0iY\nuEwuBnspcpHCpZjck9mSHbeFk1srM65cKZI5ZwebsuNKEco1x82SDYVY41lQijXOLYvSOVMpFr60\nXdfEye9hfXbcMVw7cnhpey9tF6W6t73NlOS2p9rI8LZj4Uv1yC3D3LpaLK8MSZIkSeolO0OSJEmS\nesnOkCRJkqResjMkSZIkqZfsDEmSJEnqJTtDkiRJknppNrKCG+sKsYi58N9SdG3bUcil6ZViEfdV\nRUbnowVLEbprCuVqlJfv6O9cqnutmujimunV6jLWuO3tuibSfDm0HRleM70uo9VnJapdsy9y++y4\nXPtyAluzZUpRuKXjdy66eHchnvhqTsqOy5WrKTOuXG7fLpUpabtdL73iIHduVNvW1qz/UpndFa8W\nKcVTb2RnVbnS609q5LaNUpR4Kfp9EzuWWqVW5KK1S3UvxYLXfK/SMqzZrmv34wGvDEmSJEnqJTtD\nkiRJknrJzpAkSZKkXrIzJEmSJKmX7AxJkiRJ6qWZijOqSSSpTX8qJbfklFJMSvVYW5hXro6136sm\noao0r7YSuYaHHWDNzCZp1aaTtZ1qpsUppyKNXu7LsT66TMLLlZvVfUpLd38+MnGZ9YW0ppKaBNGS\n5UgXbdMpbMmOy6XnQXlZ5JKySqmzNftvzXnMuHrkEmlrypSU0r9O4uqqcscXEhRzSqmAuXO30vc9\nlS8Wple3PbUtt0/ejq9WTW9zJk2u9J1q94XcNEvn2fCYwrjEK0OSJEmSesnOkCRJkqResjMkSZIk\nqZfsDEmSJEnqJTtDkiRJknrJzpAkSZKkXpqpLNZcHCXk4/T2FmIRS/G0pXK5eW1kZ7bMPta2Oq+S\n0vfawJ6Rw3ezvlCH2ojf0XUftSz2ckR2OoubV7ubak2kedvzqpWrY+18SvGspW23Rs06bjv6vaTL\niPRZn55mx3Y2ZcflttnSqyBKdnFkdlwuUrjUNpbihHPtZk2ZceVySvvNTo7Ojiu13W3HJO9kY6vT\nO4Zrs+Ou5ZiJy5S2mdz2uYnt2TJf5T9lx5XKbePY7LictuPiu9wual2X2Z6OLazjUt0/V/FanNpX\n1eRivJfKK0OSJEmSesnOkCRJkqResjMkSZIkqZfsDEmSJEnqJTtDkiRJknrJzpAkSZKkXpqpaO2a\nuMIu45hr1UQtlpZFSS52dF/LEckAazORzKPiV0ux6UvRZZxwVxHZXc9rOcrldLlPzvq8jMLWYpWO\n33uzx/x8BHVJLj4b8u1SKSa3FLudK1dTZly5fFubb59LkdF7KuKES0qx4DWR4ctRj5y2X0lQe2ys\n2eZLr4/IvXYit8+NU9p2S/td23LzavsVMaVz39I6Li2L3Zn9rvSKkMXwypAkSZKkXrIzJEmSJKmX\n7AxJkiRJ6iU7Q5IkSZJ6yc6QJEmSpF6yMyRJkiSpl2Yje7qxg03ZcblIz1IEddtxwrtZXzm9yaNK\n245dLkVblyITa+oxavhiIi/bjudsW5cx022v/1KZ8rh2o9/b1vY2U7uc2qyDtNDJbJm4TO3rDEpx\nuPsz42qicAECsbUy48rlXgVRii0vzauk7eNjTZRzaZ3k1mOpXE2ZUrmj2JUtU4otX1/YrtuOOy99\n55xSO1GKf+6yTc3tJ+Uo7Hz9ao41pW26ZhmW19X/P7Y+XhmSJEmS1Et2hiRJkiT10sT3fYQQHgX8\nA3AQWNX8/Y4Y46+EEE4GXg/cB9gCPCPGeHlrtZUkacbYLkrS/Kq5MnQq8I/Acc2f44EnNePeDWwF\n7g68BXhnCOHEFuopSdKssl2UpDlVE6BwR+DzMcbvDQ8MIZwJnALcO8Z4PfCyEMKDgHOAC5ZcU0mS\nZpPtoiTNqZrO0KnAqEv89wY+0xzwBz5KujVgUUopITVJTrXanlfbqVal6ZVSXWrU1GPc8lvN/tZS\ntmrX1awkw3U1vdp55RKYaqfXdt27XLazknZYu99pWS1buwjwNW43cYVK7WlJzXa+k6OzZU5ga3Zc\nJLRWZly5fZm02l0clS1TSqfr0ia2tzq9mvS3UpnSMsyl1ZbS5ErTK5WrOQ7vYHN23LFsGzm8lFxW\nSrQrfa8ubWLHyOGLSf0dZS37Ji5Tm1y3k40jh+fW1WLVtJ4BOCuE8DxgNfA24Pmk2wIWHom2Ad4O\nIElayWwXJWlOTdQZCiHcBlgP7AEeQ7r8/6pm2AY47GfkvVAI8ZeH8JUFAAAPYUlEQVQkaY7ZLkrS\nfJuoMxRj/FYIYVOMcWcz6N9CCKtJD4X+FRx2rXwdVF6rlyRpxtkuStJ8mzhNbuiAP/Al4Ajgu6QU\nnWHHAd+pq5okSbPPdlGS5tdEnaEQwkNCCNtDCEcMDb4rsB34CHD3EMLw5f/7AVctvZqSJM0e20VJ\nmm+TBih8jHR5/3+FEC4AbgdcBLwcuBK4GnhjCOFC4GzgnsATWqutJEmzxXZRkubYqoMHD05UIIRw\nR+CVwGnALuCSGOOLm3G3Bf6SFCf6VeB3Y4wfarXGkiTNENtFSZpfE3eGJEmSJGklmDhAQZIkSZJW\nAjtDkiRJknrJzpAkSZKkXrIzJEmSJKmX7AxJkiRJ6iU7Q5IkSZJ6yc6QJEmSpF6yMyRJkiSpl9ZM\nuwILhRDWAa8FfhHYDfxxjPFPplurbjXL4NPA78QYr2yGnQy8HrgPsAV4Rozx8mnVcbmFEE4AXgWc\nQdoO3gqcF2Pc17dlARBCuB3wGuC+wA7g1THGi5txJ9Oz5QEQQrgU2BZjPKf5/8n0bDmEEB4F/ANw\nEFjV/P2OGOOv9HF59EUI4aeA9wL3jjEeGDe8ch63BK4CHhpj3D5ueDPuxuaf74sxPnxo+HuBhwIH\nY4yrR8xr4nK18xpn1DLMzat2XGWZ0nKvGpdTU2ao7GuAc5qyV5bGhRDeAJwF/GqM8SOLnd5iyk1q\nFrantrezMfMqruPSsaSt48wStunW19UsXhm6GLgb8EDgXOAFIYRfnGqNOtR0hP4WOHXBqHcBW4G7\nA28B3hlCOLHj6nXpHcARpJP/xwL/FbiwGfduerQsQgirgEuBbcBdgN8Czg8hPLb5SK+WB0Dz3R+2\nYHDf9hFIx4l/BI5r/hwPPKkZ17vtokcuAl414kQkN3xiMcYbSD9IvWIxw4c8Gvi1BcMeC/zSmFnW\nlKudV0luGY6a11LGTVSmtNxrx+XUlIGbfrR7EnA68LFFjHsG8DrgDyecXrHcEszC9tT2djZy3CLW\ncelY0spxZonbbavraqY6QyGEDcBvAk+LMf7fGOO7SQv9qdOtWTdCCHck9YRPWTD8TOC2wFNi8jLg\n46RfS1acEEIA7gU8Icb45RjjvwDPBx4XQjiDtHx6sSwaxwKfBc6NMX4txvg+4APA/fq4PEIIR5OO\nC58cGtarfWTIHYHPxxi/F2O8tvnzw2Z59Gq76Ivmit/ZpB/Nxg5for8Bzg4hnLTI4QDXxRh/MDyg\n+f/3x8yrplztvEYaswwPm9cSx9WUKS332nE5NWWOB74TY/x0jHH/uHHNd7wSuM0k01tEuVqzsD21\nvZ2Vxo1cx6V6L8Nxpna7bXVdzdptcj9HqtPHh4Z9FHjudKrTuQeQTnLPJ90aNnBv4DMxxuuHhn2U\ndPvLSvRd4KwRl25vBZxGv5YFMcbvMvQLSAjhvqRfys6lh8uDdPX4zcBPDw3r2z4ycCow6ta3vi6P\nPngy8M/NL6fF4c2x4mWkuy0OAlcA58QYt5XGDcrHGG8IIVwOPIXULhWHz7LFfF/yy3YmlJb7JONq\n1/0iyq0B9mWqnxu3H1g3YZmR5Za6vU9ihW9PpXq3epyp3abbNlNXhki/Amxf8IvCNuCIEMKmKdWp\nMzHGS2KMz1pwAgNpuWxdMGwbsCJveYkx/iAOPdvQ3Cb2VFJHsVfLYqEQwhbSL2IfJz0r0qvl0Vzx\nOJ1Dt0wO9Go5DAnAWSGEGEL4agjhpc291n1dHn1wFqM7wDcbHkL4SeA9wPtIVxAfDNwOOK80bsR0\nL2+mvdjhM2eC75tbtrOktNzHjqtd94ss9xPAjzLzz437QTNukjKHlWtxex+rB9tTqd7LcZyp3aZb\nM2tXhjYAexcMG/w/98tBH+SWS1+WySuAuwL3BJ75/9q701jJiiqA438yLBKD4AAjAUIkRk9wYQt8\nIEKQRHCJYZPFABHGDyZAIosgmGAkuEJACLigIhpETUCWCCSKoriEKAMICownRpBVhQygqAMC8/xQ\n902anl7u69dvuufV/5dMXrrqVnf1uTW3+/StW5e6Y3E45dqQrwEXU9HYaK6nu5wyXfDFMptyrWri\nMCsidgI2B1YDR1KmxV3alFUXjxpExBJgV2Bli/LNgfMy8+Lm8aMRcT3lODqortuDwG4RsVFmzrQo\nn0ZD32+/2E6hQXEfWseI+35Yu2abY4Dbu59kUF3zGi9HxAcy8+aWbXq1G9d4b2PRjqdB/V7A48yo\nY3pspi0ZeoF1P6xnH/+Xer0ALO0q24wKYhIR5wMfA47KzAcjotpYAGTmPQARcTrwPeBbwOu7Nlus\n8TgXWJGZP+tRV924yMxHI2LrzHyuKfpD82F1NfBt6hkXNVlKmdHRPYV4nfJmispVEXEaZeGVt1K+\nDP9mUF2P11zVPPfWXa/br3zqtHy//WI7bQbFvU3dK8Cc932LGF4BvI1y3Xe3vnWZ+XJEHA3cFhGH\nZeYtLZ6vZ7sxjfehFvl4GtTvhTrOjDqmx2bapsk9AWwTEZ392g5Y3fGBX6MnKHHotB3wtwn0Zb2J\niMsoq8Ycm5k3NsXVxSIilkXEIV3FDwKbUt53LfE4Gjg0Ip6PiOeBY4HjIuJfwOPUE4e1ehwXV1JW\nYfw7FcajArO/jHYvG7tOeZTbE/yRcnuCu4BTgYuG1fUw+3m8pmX51Gn5fvvFdtoMinubumWMsO9b\nxPA8yvU9x/R4nkF1AF8AzufVZ4GGtXlVuzGO96EW+Xga1O+FOs6MOqbHZtrODN0LvES5KHx2GcX9\ngBUT69F0+C1wVkRslpmzU1/2Bcayvv40iohPUy7UOzozb+ioqi4WlOlP10fEjpk5+2V2L+Apyi8s\nZ1YSj/2BTToeX0A5OH8CeCNwdiVxACAiDqKstrNjx3WGe1B+Pfs1cEZN8ajEKsov+93X0PYqPwxY\nlZkHzxZExCmU+1ENquu2DfByZnav0tSvfBq1eb/9YjttBsV9aB3li+mc931EnDyoXWY+EhGXACcA\nV3Y+yaC6Zrrv7sD+2bFU86A2vdpFxAkD+jeX8d7GYh5Pg/q9UMeZUcf02ExVMpSZqyPiKuDyiPgI\n5WLfjwPHT7ZnE/dL4DHgOxHxGcqyhntTDhKLTpQlxs8BPg/cERFv6KiuKhaNFZRfVa5spsftTEkE\nPktZTKGKeGTmY52Pm7NDM5n5cEQ8QiVx6HAHZdrbFRFxHuXC1Asov5RWMy5q0szpv48yb/+OIeWr\ngJ2iLDryMHAU5ZrDO4fUdduVsrR/2/JpNPT99ovtFBoU9zZ1o+77Nu3+AWzf5/X71W1P+cLc6541\ng56vu924xnsbi3Y8Der3Ah5nRh3TYzNt0+SgXCB/N/Bz4DLgU1nuN1SbtReKZeYa4BDKNJe7KKeN\nD83MxyfUt4V2MGVsnkNZEetJyvSeJ5tYHEo9sejc//+hHIC+AVySmV9u6g6monj0UuH/ETLz35S7\nbW9LSZi/CVyemRc5Lha1H1PO8g0rv4Zy/di1lPHxLsrn6y6UlSh71kVZjbDTvpS7zXfrVz6N+sai\n6/32i+00GRT3NnVtY9H9fG3avUS5aL6XfnWbUs5YzaVNr3bjGu9tLPbxNKjfC3GcGXVMj81UnRmC\ncnYIWN78q1ZmLul6/BDl9Pail5nnU37d7lf/FyqJxaws9xo6ok9dNWOjU2Yu73pcXRwycyUlIepV\nV108KnElcFdEvKbrNgyvKm8S4pObf50ubf4OqgPW3gj9fcDZbcqnVYtYzOoX26kwKO5t69rGovv5\nWrb7H/2/V/ar24SS9MylzTrtxjHe26pgPA3q91iPM6OO6XGbxjNDkiSphybJvQU4rk35PB0H3JSZ\nj7YsB1gaEVt1FjSPh107MUq7UV+rpyExXOe15lk3SptBcR+1rp9R2jwEbBsRe/U447JOXfMeD6RM\noWr9fC3ajWoaxtO4x9mgup77eFC/F+A4M+q4Heu+MhmSJGnDciZwUkR0/2rer3zOmi+fJwJntClv\nzADXURb26PQD4Id0TP8eQ7tRX2uYXjHs91qj1s25zaC4j1rXzyhtADLzKeBCykIt+7So+xLlC++5\nc3y+ge1GNA3jadzjrG9di3086FgyluPMPMbt2PfVRjMz036vNEmSJEkaP88MSZIkSaqSyZAkSZKk\nKpkMSZIkSaqSyZAkSZKkKpkMSZIkSaqSyZAkSZKkKpkMSZIkSaqSyZAkSZKkKpkMSZIkSaqSyZAk\nSZKkKpkMSZIkSaqSyZAkSZKkKpkMSZIkSaqSyZAkSZKkKpkMSZIkSaqSyZAkSZKkKpkMSZIkSaqS\nyZAkSZKkKpkMSZIkSaqSyZAkSZKkKpkMSZIkSaqSyZAkSZKkKpkMSZIkSarSxpPugLQhi4jdga8D\n+2Tmmkn3R5Kk9SUi1gAnAM8ByzPzsMn2SJo7zwxJ85CZ9wIPAGdNui+SJE1CZv4I2CoiPjTpvkhz\nZTIkzd9FwNkRscWkOyJJ0oRcCHwuIjaadEekuXCanNRCRLwW+CLwQWAL4G7g9My8JzMfiIhHgY9S\nEiNJkhaViNgB+CpwAGVaXPeMiJ8AWwGHA9et395Jo/PMkNTOtcB7gA8DuwEPAbdGxJZN/c3AIRPq\nmyRJCyYillCSnaXAfsCRwJnAzOw2mfkycCt+FmoD45khaYiIeAvwXuDAzLytKTsReAbYBvgncD9w\n2sQ6KUnSwnk3sAvwpsz8K0BELAd+37Xd/ZQFFaQNhsmQNNw7KL9+/W62IDNfBM7o2OZpYJOI2Doz\nV63n/kmStJDeDjw7mwgBZOZ9EbG6a7unge3WZ8ek+XKanDTcSy22WdL8dXltSdJiM0Pv74zdn49L\n8HNQGxiTIWm4lc3fvWcLImJJRDwcEYc3RcuAFzPz2fXeO0mSFta9wJYRsctsQUS8GXhd13bLgCfX\nZ8ek+XKanDREZv45Im4AvhIRJ1EO9J8ENgNubzbbE7hzMj2UJGlB/YLyGffdiDgZeAW4rPnbaU86\nppRLGwLPDEntLAd+BVwDrAB2AA7KzGea+gOAGyfUN0mSFkxmzgDvB/5EWVXuJuD7lGuEAIiIjYF3\n4mehNjAbzczMDN9KUl8RsRfwU2DnzHxu0v2RJGl9i4gjKPfji8zsPmMkTS3PDEnzdypwkYmQJKli\npwDnmghpQ+M1Q9I8RMQeQADHT7ovkiRNQkQcSll6++pJ90WaK6fJSZIkSaqS0+QkSZIkVclkSJIk\nSVKVTIYkSZIkVclkSJIkSVKVTIYkSZIkVclkSJIkSVKVTIYkSZIkVclkSJIkSVKV/g+uYFBUDje/\nvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f241072ddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# This plot is a recreation of Fig. 10 from the paper - all matrices are transposed with the respect\n",
    "# to the convention used in the code above.\n",
    "#\n",
    "\n",
    "paren_string = \"((aa))[][][]a([[[[[)))a)a{{}{}aa[]a([[[[[)((aa))[][][]a([[[[[)))a)a(a[])aa[]a([[[[[)\"[:50]\n",
    "\n",
    "e_v_states, _ = e_simulate(paren_string)\n",
    "\n",
    "\n",
    "TOKEN = '('\n",
    "# First express e_Wx in the new basis:\n",
    "basis_aligned_Wx = np.linalg.inv(basis_change\n",
    "                                ).dot(e_Wx[S[TOKEN]]).dot(basis_change)\n",
    "\n",
    "# Our model has an extra degree of freedom: the first dimensions of the hidden \n",
    "# state have exactly n_paren_types elements equal to 1. Here we will subtract\n",
    "# their influence\n",
    "\n",
    "block_size = task.max_count+1\n",
    "for p in range(task.n_paren_types):\n",
    "  basis_aligned_Wx[p*block_size:(p+1)*block_size, :] -= np.median(\n",
    "      basis_aligned_Wx[p*block_size:(p+1)*block_size, :], axis=0, keepdims=True)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "\n",
    "plt.subplot(221)\n",
    "\n",
    "\n",
    "plt.imshow(e_Wx[S[TOKEN]].T, \n",
    "           interpolation='nearest', cmap='jet', vmin=-1, vmax=1)\n",
    "plt.grid(False)\n",
    "\n",
    "plt.title(\"Transition matrix for '%s' in original basis.\" % (TOKEN,))\n",
    "#plt.colorbar()\n",
    "\n",
    "plt.xlabel(\" \\na)\")\n",
    "\n",
    "plt.subplot(222)\n",
    "\n",
    "plt.title(\"Hidden states in the original basis\")\n",
    "\n",
    "plt.imshow(e_v_states.T,\n",
    "           interpolation='nearest', cmap='jet', vmin=-1, vmax=1)\n",
    "#plt.axis('equal')\n",
    "#plt.colorbar()\n",
    "\n",
    "plt.xticks(*zip(*enumerate(paren_string)))\n",
    "plt.xlabel(\"\\nb)\")\n",
    "\n",
    "plt.ylabel(\"Hidden dimension\")\n",
    "\n",
    "plt.grid(False)\n",
    "\n",
    "plt.subplot(223)\n",
    "\n",
    "\n",
    "plt.imshow(basis_aligned_Wx.T, \n",
    "           interpolation='nearest', cmap='jet', vmin=-1, vmax=1)\n",
    "plt.grid(False)\n",
    "plt.vlines(e_Wo.shape[1]-0.5, *plt.ylim(), color='w')\n",
    "\n",
    "plt.title(\"Transition matrix for '%s'\\nwhen hidden state is output-oriented.\" % (TOKEN,))\n",
    "#plt.colorbar()\n",
    "\n",
    "plt.xlabel(\" \\nc)\")\n",
    "\n",
    "plt.subplot(224) #, sharex=plt.gca(), sharey=plt.gca())\n",
    "\n",
    "plt.title(\"Hidden state in output-oriented basis\\n(first %d dimensions are aligned with output).\" % (e_Wo.shape[1]))\n",
    "\n",
    "plt.imshow(e_v_states.dot(basis_change).T,\n",
    "           interpolation='nearest', cmap='jet', vmin=-1, vmax=1)\n",
    "#plt.axis('equal')\n",
    "plt.hlines(e_Wo.shape[1]-0.5, *plt.xlim(), color='w')\n",
    "#plt.colorbar()\n",
    "\n",
    "plt.xticks(*zip(*enumerate(paren_string)))\n",
    "plt.xlabel(\"\\nd)\")\n",
    "\n",
    "plt.ylabel(\"Hidden dimension\")\n",
    "\n",
    "plt.grid(False)\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(\"/tmp/fig.pdf\", dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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