Time Series Forecasting using transformers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Forecasting using transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os \n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Download data from yahoo finance\n",
    "df = pd.read_csv('T.csv',delimiter=',',usecols=['Date','Open','High','Low','Close', 'Volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1983-11-21</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>5.177083</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>5.135417</td>\n",
       "      <td>3910800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1983-11-22</td>\n",
       "      <td>5.135417</td>\n",
       "      <td>5.135417</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>2934000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1983-11-23</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>5.104167</td>\n",
       "      <td>5.052083</td>\n",
       "      <td>5.072917</td>\n",
       "      <td>1618800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983-11-25</td>\n",
       "      <td>5.072917</td>\n",
       "      <td>5.104167</td>\n",
       "      <td>5.072917</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>3380400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1983-11-28</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>5.031250</td>\n",
       "      <td>5.031250</td>\n",
       "      <td>4315200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close   Volume\n",
       "0  1983-11-21  5.083333  5.177083  5.083333  5.135417  3910800\n",
       "1  1983-11-22  5.135417  5.135417  5.093750  5.093750  2934000\n",
       "2  1983-11-23  5.093750  5.104167  5.052083  5.072917  1618800\n",
       "3  1983-11-25  5.072917  5.104167  5.072917  5.083333  3380400\n",
       "4  1983-11-28  5.083333  5.093750  5.031250  5.031250  4315200"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a123475f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18,9))\n",
    "plt.plot(range(df.shape[0]),(df['Low']+df['High'])/2.0)\n",
    "plt.xticks(range(0,df.shape[0],500),df['Date'].loc[::500],rotation=45)\n",
    "plt.xlabel('Date',fontsize=15)\n",
    "plt.ylabel('Mid Price',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mid'] = (df['Low']+df['High'])/2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEQ_LEN = 60  # how long of a preceeding sequence to collect for RNN\n",
    "FUTURE_PERIOD_PREDICT = 1  # how far into the future are we trying to predict?\n",
    "RATIO_TO_PREDICT = \"mid\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify(current, future):\n",
    "    if float(future) > float(current):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['future'] = df[RATIO_TO_PREDICT].shift(-FUTURE_PERIOD_PREDICT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target'] = list(map(classify, df[RATIO_TO_PREDICT], df['future']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>mid</th>\n",
       "      <th>future</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1983-11-21</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>5.177083</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>5.135417</td>\n",
       "      <td>3910800</td>\n",
       "      <td>5.130208</td>\n",
       "      <td>5.114584</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1983-11-22</td>\n",
       "      <td>5.135417</td>\n",
       "      <td>5.135417</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>2934000</td>\n",
       "      <td>5.114584</td>\n",
       "      <td>5.078125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1983-11-23</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>5.104167</td>\n",
       "      <td>5.052083</td>\n",
       "      <td>5.072917</td>\n",
       "      <td>1618800</td>\n",
       "      <td>5.078125</td>\n",
       "      <td>5.088542</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983-11-25</td>\n",
       "      <td>5.072917</td>\n",
       "      <td>5.104167</td>\n",
       "      <td>5.072917</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>3380400</td>\n",
       "      <td>5.088542</td>\n",
       "      <td>5.062500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1983-11-28</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>5.031250</td>\n",
       "      <td>5.031250</td>\n",
       "      <td>4315200</td>\n",
       "      <td>5.062500</td>\n",
       "      <td>4.984375</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Open      High       Low     Close   Volume       mid  \\\n",
       "0  1983-11-21  5.083333  5.177083  5.083333  5.135417  3910800  5.130208   \n",
       "1  1983-11-22  5.135417  5.135417  5.093750  5.093750  2934000  5.114584   \n",
       "2  1983-11-23  5.093750  5.104167  5.052083  5.072917  1618800  5.078125   \n",
       "3  1983-11-25  5.072917  5.104167  5.072917  5.083333  3380400  5.088542   \n",
       "4  1983-11-28  5.083333  5.093750  5.031250  5.031250  4315200  5.062500   \n",
       "\n",
       "     future  target  \n",
       "0  5.114584       0  \n",
       "1  5.078125       0  \n",
       "2  5.088542       1  \n",
       "3  5.062500       0  \n",
       "4  4.984375       0  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = sorted(df.index.values)  # get the times\n",
    "last_10pct = sorted(df.index.values)[-int(0.1*len(times))]  # get the last 10% of the times\n",
    "last_20pct = sorted(df.index.values)[-int(0.2*len(times))]  # get the last 20% of the times\n",
    "\n",
    "test_df = df[(df.index >= last_10pct)]\n",
    "validation_df = df[(df.index >= last_20pct) & (df.index < last_10pct)]  \n",
    "train_df = df[(df.index < last_20pct)]  # now the train_df is all the data up to the last 20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import deque\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "train_df.drop(columns=[\"Date\", \"future\", 'Open', 'High', 'Low', 'Close', 'Volume'], inplace=True)\n",
    "validation_df.drop(columns=[\"Date\", \"future\", 'Open', 'High', 'Low', 'Close', 'Volume'], inplace=True)\n",
    "test_df.drop(columns=[\"Date\", \"future\", 'Open', 'High', 'Low', 'Close', 'Volume'], inplace=True)# don't need this anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mid</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7144</th>\n",
       "      <td>31.670000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7145</th>\n",
       "      <td>31.899999</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7146</th>\n",
       "      <td>31.710000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7147</th>\n",
       "      <td>31.549999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7148</th>\n",
       "      <td>31.710000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            mid  target\n",
       "7144  31.670000       1\n",
       "7145  31.899999       0\n",
       "7146  31.710000       0\n",
       "7147  31.549999       1\n",
       "7148  31.710000       1"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_df[RATIO_TO_PREDICT].as_matrix()\n",
    "valid_data = validation_df[RATIO_TO_PREDICT].as_matrix()\n",
    "test_data = test_df[RATIO_TO_PREDICT].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.reshape(-1,1)\n",
    "valid_data = valid_data.reshape(-1,1)\n",
    "test_data = test_data.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.130208 ],\n",
       "       [ 5.1145835],\n",
       "       [ 5.078125 ],\n",
       "       ...,\n",
       "       [31.71     ],\n",
       "       [31.549999 ],\n",
       "       [31.71     ]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the Scaler with training data and smooth data\n",
    "smoothing_window_size = 1500\n",
    "for di in range(0,6000,smoothing_window_size):\n",
    "    scaler.fit(train_data[di:di+smoothing_window_size,:])\n",
    "    train_data[di:di+smoothing_window_size,:] = scaler.transform(train_data[di:di+smoothing_window_size,:])\n",
    "\n",
    "# You normalize the last bit of remaining data\n",
    "scaler.fit(train_data[di+smoothing_window_size:,:])\n",
    "train_data[di+smoothing_window_size:,:] = scaler.transform(train_data[di+smoothing_window_size:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape both train and test data\n",
    "train_data = train_data.reshape(-1)\n",
    "\n",
    "# Normalize test data and validation data\n",
    "valid_data = scaler.transform(valid_data).reshape(-1)\n",
    "test_data = scaler.transform(test_data).reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now perform exponential moving average smoothing\n",
    "# So the data will have a smoother curve than the original ragged data\n",
    "EMA = 0.0\n",
    "gamma = 0.1\n",
    "for ti in range(6100):\n",
    "    EMA = gamma*train_data[ti] + (1-gamma)*EMA\n",
    "    train_data[ti] = EMA\n",
    "\n",
    "# Used for visualization and test purposes\n",
    "all_mid_data = np.concatenate([train_data,valid_data, test_data],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "y_train = []\n",
    "for i in range(SEQ_LEN, len(train_data)):\n",
    "    X_train.append(train_data[i-SEQ_LEN:i])\n",
    "    y_train.append(train_data[i + (FUTURE_PERIOD_PREDICT-1)])\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_valid = []\n",
    "y_valid = []\n",
    "for i in range(SEQ_LEN, len(valid_data)):\n",
    "    X_valid.append(valid_data[i-SEQ_LEN:i])\n",
    "    y_valid.append(valid_data[i+(FUTURE_PERIOD_PREDICT-1)])\n",
    "X_valid, y_valid = np.array(X_valid), np.array(y_valid)\n",
    "\n",
    "X_valid = np.reshape(X_valid, (X_valid.shape[0], X_valid.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = []\n",
    "y_test = []\n",
    "for i in range(SEQ_LEN, len(test_data)):\n",
    "    X_test.append(test_data[i-SEQ_LEN:i])\n",
    "    y_test.append(test_data[i+(FUTURE_PERIOD_PREDICT-1)])\n",
    "    \n",
    "X_test, y_test = np.array(X_test), np.array(y_test)\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7089,)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(834,)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_2 = []\n",
    "y_train_2 = []\n",
    "for i in range(SEQ_LEN, len(train_data)):\n",
    "    X_train_2.append(train_data[i-SEQ_LEN:i])\n",
    "    y_train_2.append(train_data[i + (FUTURE_PERIOD_PREDICT-1)])\n",
    "X_train_2, y_train_2 = np.array(X_train_2), np.array(y_train_2)\n",
    "\n",
    "X_train_2 = np.reshape(X_train_2, (X_train_2.shape[0], X_train_2.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b3fe630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## show predictions\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.plot(np.arange(y_train_2.shape[0]), y_train_2, color='blue', label='train target')\n",
    "\n",
    "plt.plot(np.arange(y_train_2.shape[0], y_train_2.shape[0]+y_valid.shape[0]), y_valid,\n",
    "         color='gray', label='valid target')\n",
    "\n",
    "plt.plot(np.arange(y_train_2.shape[0]+y_valid.shape[0],\n",
    "                   y_train_2.shape[0]+y_valid.shape[0]+y_test.shape[0]),\n",
    "         y_test, color='black', label='test target')\n",
    "\n",
    "\n",
    "plt.title('Séparation des données')\n",
    "plt.xlabel('time [days]')\n",
    "plt.ylabel('normalized price')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "X_train, y_train = shuffle(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 4  # how many passes through our data\n",
    "BATCH_SIZE = 1024  # how many batches? Try smaller batch if you're getting OOM (out of memory) errors.\n",
    "import time\n",
    "\n",
    "NAME = f\"{SEQ_LEN}-SEQ-{FUTURE_PERIOD_PREDICT}-PRED-{int(time.time())}\"  # a unique name for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mYou are using pip version 19.0.2, however version 19.1 is available.\r\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip install -q tensorflow==2.0.0-alpha0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random, os, sys\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.callbacks import *\n",
    "from tensorflow.keras.initializers import *\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.keras.layers import Layer\n",
    "from keras import backend as K\n",
    "from multiprocessing import Queue\n",
    "\n",
    "try:\n",
    "    from dataloader import TokenList, pad_to_longest\n",
    "    # for transformer\n",
    "except: pass\n",
    "\n",
    "\n",
    "\n",
    "embed_size = 60\n",
    "\n",
    "class LayerNormalization(Layer):\n",
    "    def __init__(self, eps=1e-6, **kwargs):\n",
    "        self.eps = eps\n",
    "        super(LayerNormalization, self).__init__(**kwargs)\n",
    "    def build(self, input_shape):\n",
    "        self.gamma = self.add_weight(name='gamma', shape=input_shape[-1:],\n",
    "                                     initializer=Ones(), trainable=True)\n",
    "        self.beta = self.add_weight(name='beta', shape=input_shape[-1:],\n",
    "                                    initializer=Zeros(), trainable=True)\n",
    "        super(LayerNormalization, self).build(input_shape)\n",
    "    def call(self, x):\n",
    "        mean = K.mean(x, axis=-1, keepdims=True)\n",
    "        std = K.std(x, axis=-1, keepdims=True)\n",
    "        return self.gamma * (x - mean) / (std + self.eps) + self.beta\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        return input_shape\n",
    "\n",
    "class ScaledDotProductAttention():\n",
    "    def __init__(self, d_model, attn_dropout=0.1):\n",
    "        self.temper = np.sqrt(d_model)\n",
    "        self.dropout = Dropout(attn_dropout)\n",
    "    def __call__(self, q, k, v, mask):\n",
    "        attn = Lambda(lambda x:K.batch_dot(x[0],x[1],axes=[2,2])/self.temper)([q, k])\n",
    "        if mask is not None:\n",
    "            mmask = Lambda(lambda x:(-1e+10)*(1-x))(mask)\n",
    "            attn = Add()([attn, mmask])\n",
    "        attn = Activation('softmax')(attn)\n",
    "        attn = self.dropout(attn)\n",
    "        output = Lambda(lambda x:K.batch_dot(x[0], x[1]))([attn, v])\n",
    "        return output, attn\n",
    "\n",
    "class MultiHeadAttention():\n",
    "    # mode 0 - big martixes, faster; mode 1 - more clear implementation\n",
    "    def __init__(self, n_head, d_model, d_k, d_v, dropout, mode=0, use_norm=True):\n",
    "        self.mode = mode\n",
    "        self.n_head = n_head\n",
    "        self.d_k = d_k\n",
    "        self.d_v = d_v\n",
    "        self.dropout = dropout\n",
    "        if mode == 0:\n",
    "            self.qs_layer = Dense(n_head*d_k, use_bias=False)\n",
    "            self.ks_layer = Dense(n_head*d_k, use_bias=False)\n",
    "            self.vs_layer = Dense(n_head*d_v, use_bias=False)\n",
    "        elif mode == 1:\n",
    "            self.qs_layers = []\n",
    "            self.ks_layers = []\n",
    "            self.vs_layers = []\n",
    "            for _ in range(n_head):\n",
    "                self.qs_layers.append(TimeDistributed(Dense(d_k, use_bias=False)))\n",
    "                self.ks_layers.append(TimeDistributed(Dense(d_k, use_bias=False)))\n",
    "                self.vs_layers.append(TimeDistributed(Dense(d_v, use_bias=False)))\n",
    "        self.attention = ScaledDotProductAttention(d_model)\n",
    "        self.layer_norm = LayerNormalization() if use_norm else None\n",
    "        self.w_o = TimeDistributed(Dense(d_model))\n",
    "\n",
    "    def __call__(self, q, k, v, mask=None):\n",
    "        d_k, d_v = self.d_k, self.d_v\n",
    "        n_head = self.n_head\n",
    "\n",
    "        if self.mode == 0:\n",
    "            qs = self.qs_layer(q)  # [batch_size, len_q, n_head*d_k]\n",
    "            ks = self.ks_layer(k)\n",
    "            vs = self.vs_layer(v)\n",
    "\n",
    "            def reshape1(x):\n",
    "                s = tf.shape(x)   # [batch_size, len_q, n_head * d_k]\n",
    "                x = tf.reshape(x, [s[0], s[1], n_head, d_k])\n",
    "                x = tf.transpose(x, [2, 0, 1, 3])  \n",
    "                x = tf.reshape(x, [-1, s[1], d_k])  # [n_head * batch_size, len_q, d_k]\n",
    "                return x\n",
    "            qs = Lambda(reshape1)(qs)\n",
    "            ks = Lambda(reshape1)(ks)\n",
    "            vs = Lambda(reshape1)(vs)\n",
    "\n",
    "            if mask is not None:\n",
    "                mask = Lambda(lambda x:K.repeat_elements(x, n_head, 0))(mask)\n",
    "            head, attn = self.attention(qs, ks, vs, mask=mask)  \n",
    "                \n",
    "            def reshape2(x):\n",
    "                s = tf.shape(x)   # [n_head * batch_size, len_v, d_v]\n",
    "                x = tf.reshape(x, [n_head, -1, s[1], s[2]]) \n",
    "                x = tf.transpose(x, [1, 2, 0, 3])\n",
    "                x = tf.reshape(x, [-1, s[1], n_head*d_v])  # [batch_size, len_v, n_head * d_v]\n",
    "                return x\n",
    "            head = Lambda(reshape2)(head)\n",
    "        elif self.mode == 1:\n",
    "            heads = []; attns = []\n",
    "            for i in range(n_head):\n",
    "                qs = self.qs_layers[i](q)   \n",
    "                ks = self.ks_layers[i](k) \n",
    "                vs = self.vs_layers[i](v) \n",
    "                head, attn = self.attention(qs, ks, vs, mask)\n",
    "                heads.append(head); attns.append(attn)\n",
    "            head = Concatenate()(heads) if n_head > 1 else heads[0]\n",
    "            attn = Concatenate()(attns) if n_head > 1 else attns[0]\n",
    "\n",
    "        outputs = self.w_o(head)\n",
    "        outputs = Dropout(self.dropout)(outputs)\n",
    "        if not self.layer_norm: return outputs, attn\n",
    "        # outputs = Add()([outputs, q]) # sl: fix\n",
    "        return self.layer_norm(outputs), attn\n",
    "\n",
    "class PositionwiseFeedForward():\n",
    "    def __init__(self, d_hid, d_inner_hid, dropout=0.1):\n",
    "        self.w_1 = Conv1D(d_inner_hid, 1, activation='relu')\n",
    "        self.w_2 = Conv1D(d_hid, 1)\n",
    "        self.layer_norm = LayerNormalization()\n",
    "        self.dropout = Dropout(dropout)\n",
    "    def __call__(self, x):\n",
    "        output = self.w_1(x) \n",
    "        output = self.w_2(output)\n",
    "        output = self.dropout(output)\n",
    "        output = Add()([output, x])\n",
    "        return self.layer_norm(output)\n",
    "\n",
    "class EncoderLayer():\n",
    "    def __init__(self, d_model, d_inner_hid, n_head, d_k, d_v, dropout=0.1):\n",
    "        self.self_att_layer = MultiHeadAttention(n_head, d_model, d_k, d_v, dropout=dropout)\n",
    "        self.pos_ffn_layer  = PositionwiseFeedForward(d_model, d_inner_hid, dropout=dropout)\n",
    "    def __call__(self, enc_input, mask=None):\n",
    "        output, slf_attn = self.self_att_layer(enc_input, enc_input, enc_input, mask=mask)\n",
    "        output = self.pos_ffn_layer(output)\n",
    "        return output, slf_attn\n",
    "\n",
    "\n",
    "def GetPosEncodingMatrix(max_len, d_emb):\n",
    "    pos_enc = np.array([\n",
    "        [pos / np.power(10000, 2 * (j // 2) / d_emb) for j in range(d_emb)] \n",
    "        if pos != 0 else np.zeros(d_emb) \n",
    "            for pos in range(max_len)\n",
    "            ])\n",
    "    pos_enc[1:, 0::2] = np.sin(pos_enc[1:, 0::2]) # dim 2i\n",
    "    pos_enc[1:, 1::2] = np.cos(pos_enc[1:, 1::2]) # dim 2i+1\n",
    "    return pos_enc\n",
    "\n",
    "def GetPadMask(q, k):\n",
    "    ones = K.expand_dims(K.ones_like(q, 'float32'), -1)\n",
    "    mask = K.cast(K.expand_dims(K.not_equal(k, 0), 1), 'float32')\n",
    "    mask = K.batch_dot(ones, mask, axes=[2,1])\n",
    "    return mask\n",
    "\n",
    "def GetSubMask(s):\n",
    "    len_s = tf.shape(s)[1]\n",
    "    bs = tf.shape(s)[:1]\n",
    "    mask = K.cumsum(tf.eye(len_s, batch_shape=bs), 1)\n",
    "    return mask\n",
    "\n",
    "class Transformer():\n",
    "    def __init__(self, len_limit, embedding_matrix, d_model=embed_size, \\\n",
    "              d_inner_hid=512, n_head=10, d_k=64, d_v=64, layers=2, dropout=0.1, \\\n",
    "              share_word_emb=False, **kwargs):\n",
    "        self.name = 'Transformer'\n",
    "        self.len_limit = len_limit\n",
    "        self.src_loc_info = False # True # sl: fix later\n",
    "        self.d_model = d_model\n",
    "        self.decode_model = None\n",
    "        d_emb = d_model\n",
    "\n",
    "        pos_emb = Embedding(len_limit, d_emb, trainable=False, \\\n",
    "                            weights=[GetPosEncodingMatrix(len_limit, d_emb)])\n",
    "\n",
    "        i_word_emb = Embedding(max_features, d_emb, weights=[embedding_matrix]) # Add Kaggle provided embedding here\n",
    "\n",
    "        self.encoder = Encoder(d_model, d_inner_hid, n_head, d_k, d_v, layers, dropout, \\\n",
    "                               word_emb=i_word_emb, pos_emb=pos_emb)\n",
    "\n",
    "        \n",
    "    def get_pos_seq(self, x):\n",
    "        mask = K.cast(K.not_equal(x, 0), 'int32')\n",
    "        pos = K.cumsum(K.ones_like(x, 'int32'), 1)\n",
    "        return pos * mask\n",
    "\n",
    "    def compile(self, active_layers=999):\n",
    "        src_seq_input = Input(shape=(None, ))\n",
    "        x = Embedding(max_features, embed_size, weights=[embedding_matrix])(src_seq_input)\n",
    "        \n",
    "        # LSTM before attention layers\n",
    "        x = Bidirectional(LSTM(128, return_sequences=True))(x)\n",
    "        x = Bidirectional(LSTM(64, return_sequences=True))(x) \n",
    "        \n",
    "        x, slf_attn = MultiHeadAttention(n_head=3, d_model=300, d_k=64, d_v=64, dropout=0.1)(x, x, x)\n",
    "        \n",
    "        avg_pool = GlobalAveragePooling1D()(x)\n",
    "        max_pool = GlobalMaxPooling1D()(x)\n",
    "        conc = concatenate([avg_pool, max_pool])\n",
    "        conc = Dense(64, activation=\"relu\")(conc)\n",
    "        x = Dense(1, activation=\"sigmoid\")(conc)   \n",
    "        \n",
    "        \n",
    "        self.model = Model(inputs=src_seq_input, outputs=x)\n",
    "        self.model.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    inp = Input(shape = (SEQ_LEN, 1))\n",
    "    \n",
    "    # LSTM before attention layers\n",
    "    x = Bidirectional(LSTM(128, return_sequences=True))(inp)\n",
    "    x = Bidirectional(LSTM(64, return_sequences=True))(x) \n",
    "        \n",
    "    x, slf_attn = MultiHeadAttention(n_head=3, d_model=300, d_k=64, d_v=64, dropout=0.1)(x, x, x)\n",
    "        \n",
    "    avg_pool = GlobalAveragePooling1D()(x)\n",
    "    max_pool = GlobalMaxPooling1D()(x)\n",
    "    conc = concatenate([avg_pool, max_pool])\n",
    "    conc = Dense(64, activation=\"relu\")(conc)\n",
    "    x = Dense(1, activation=\"sigmoid\")(conc)      \n",
    "\n",
    "    model = Model(inputs = inp, outputs = x)\n",
    "    model.compile(\n",
    "        loss = \"mean_squared_error\", \n",
    "        #optimizer = Adam(lr = config[\"lr\"], decay = config[\"lr_d\"]), \n",
    "        optimizer = \"adam\")\n",
    "    \n",
    "    # Save entire model to a HDF5 file\n",
    "    #model.save('my_model.h5')\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't pickle _thread.RLock objects",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-127-37643215ce3b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmulti_head\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-126-53c516955ffd>\u001b[0m in \u001b[0;36mbuild_model\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;31m# Save entire model to a HDF5 file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'my_model.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[1;32m   1312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msave_model\u001b[0m  \u001b[0;31m# pylint: disable=g-import-not-at-top\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m     \u001b[0msave_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_optimizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/saving/hdf5_format.py\u001b[0m in \u001b[0;36msave_model\u001b[0;34m(model, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[1;32m     99\u001b[0m         {\n\u001b[1;32m    100\u001b[0m             \u001b[0;34m'class_name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m             \u001b[0;34m'config'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m         },\n\u001b[1;32m    103\u001b[0m         default=serialization.get_json_type).encode('utf8')\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/tensorflow/python/keras/engine/network.py\u001b[0m in \u001b[0;36mget_config\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1123\u001b[0m     \u001b[0mmodel_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_inner_node_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m     \u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'output_layers'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_outputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1125\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1127\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_tuple\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_deepcopy_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m     \u001b[0;31m# We're not going to put the tuple in the memo, but it's still important we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;31m# check for it, in case the tuple contains recursive mutable structures.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_deepcopy_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m     \u001b[0;31m# We're not going to put the tuple in the memo, but it's still important we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;31m# check for it, in case the tuple contains recursive mutable structures.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_tuple\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_deepcopy_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m     \u001b[0;31m# We're not going to put the tuple in the memo, but it's still important we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;31m# check for it, in case the tuple contains recursive mutable structures.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_deepcopy_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m     \u001b[0;31m# We're not going to put the tuple in the memo, but it's still important we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;31m# check for it, in case the tuple contains recursive mutable structures.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_list\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    213\u001b[0m     \u001b[0mappend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m         \u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    216\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    178\u001b[0m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reconstruct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m     \u001b[0;31m# If is its own copy, don't memoize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m             \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__setstate__'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mcopier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dispatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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--> 240\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deepcopy_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/copy.py\u001b[0m in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m    167\u001b[0m                     \u001b[0mreductor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__reduce_ex__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mreductor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m                         \u001b[0mrv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreductor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m                         \u001b[0mreductor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__reduce__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: can't pickle _thread.RLock objects"
     ]
    }
   ],
   "source": [
    "multi_head = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_6 (InputLayer)            [(None, 60, 1)]      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_10 (Bidirectional (None, 60, 256)      133120      input_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_11 (Bidirectional (None, 60, 128)      164352      bidirectional_10[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_26 (Dense)                (None, 60, 192)      24576       bidirectional_11[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_27 (Dense)                (None, 60, 192)      24576       bidirectional_11[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "lambda_26 (Lambda)              (None, None, 64)     0           dense_26[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_27 (Lambda)              (None, None, 64)     0           dense_27[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_29 (Lambda)              (None, None, None)   0           lambda_26[0][0]                  \n",
      "                                                                 lambda_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, None)   0           lambda_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_28 (Dense)                (None, 60, 192)      24576       bidirectional_11[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, None, None)   0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "lambda_28 (Lambda)              (None, None, 64)     0           dense_28[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "lambda_30 (Lambda)              (None, None, 64)     0           dropout_8[0][0]                  \n",
      "                                                                 lambda_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_31 (Lambda)              (None, None, 192)    0           lambda_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "time_distributed_5 (TimeDistrib (None, None, 300)    57900       lambda_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, None, 300)    0           time_distributed_5[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "layer_normalization_5 (LayerNor (None, None, 300)    600         dropout_9[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_3 (Glo (None, 300)          0           layer_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_3 (GlobalM (None, 300)          0           layer_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 600)          0           global_average_pooling1d_3[0][0] \n",
      "                                                                 global_max_pooling1d_3[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_30 (Dense)                (None, 64)           38464       concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_31 (Dense)                (None, 1)            65          dense_30[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 468,229\n",
      "Trainable params: 468,229\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "multi_head.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7089 samples, validate on 834 samples\n",
      "Epoch 1/4\n",
      "7089/7089 [==============================] - 33s 5ms/sample - loss: 0.0916 - val_loss: 0.0403\n",
      "Epoch 2/4\n",
      "7089/7089 [==============================] - 40s 6ms/sample - loss: 0.0167 - val_loss: 0.0094\n",
      "Epoch 3/4\n",
      "7089/7089 [==============================] - 40s 6ms/sample - loss: 0.0090 - val_loss: 0.0057\n",
      "Epoch 4/4\n",
      "7089/7089 [==============================] - 38s 5ms/sample - loss: 0.0070 - val_loss: 0.0038\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1a36cdf668>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi_head.fit(X_train, y_train,\n",
    "                    batch_size=BATCH_SIZE,\n",
    "                    epochs=EPOCHS,\n",
    "                    validation_data=(X_valid, y_valid), \n",
    "                    #callbacks = [checkpoint , lr_reduce]\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(833, 1)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_stock_price_multi_head = multi_head.predict(X_test)\n",
    "#predicted_stock_price = scaler.inverse_transform(predicted_stock_price)\n",
    "\n",
    "\n",
    "predicted_stock_price_multi_head.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_stock_price_multi_head = np.vstack((np.full((60,1), np.nan), predicted_stock_price_multi_head))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3bd99518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18,9))\n",
    "plt.plot(test_data, color = 'black', label = 'T Stock Price')\n",
    "plt.plot(predicted_stock_price_multi_head, color = 'blue', label = 'Predicted T Mid Price')\n",
    "plt.title('T Mid Price Prediction', fontsize=30)\n",
    "#plt.xticks(range(0,df.shape[0],50),df['Date'].loc[::50],rotation=45)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('T Mid Price')\n",
    "plt.legend(fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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