plm_nlp.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tWg6Q7HjvV6G"
      },
      "source": [
        "# 1 奇异值分解"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_0ooJYcOvV6H",
        "outputId": "42259c91-5899-4275-d98e-19fc647beaf3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[0.   0.18 0.07 0.07 0.07 0.3  0.3  0.3  0.3  0.22]\n",
            " [0.18 0.   0.44 0.44 0.44 0.   0.   0.   0.66 0.18]\n",
            " [0.07 0.44 0.   1.03 1.03 0.   0.   0.   0.   0.07]\n",
            " [0.07 0.44 1.03 0.   1.03 0.   0.   0.   0.   0.07]\n",
            " [0.07 0.44 1.03 1.03 0.   0.   0.   0.   0.   0.07]\n",
            " [0.3  0.   0.   0.   0.   0.   1.48 0.78 0.   0.3 ]\n",
            " [0.3  0.   0.   0.   0.   1.48 0.   0.78 0.   0.3 ]\n",
            " [0.3  0.   0.   0.   0.   0.78 0.78 0.   0.78 0.3 ]\n",
            " [0.3  0.66 0.   0.   0.   0.   0.   0.78 0.   0.3 ]\n",
            " [0.22 0.18 0.07 0.07 0.07 0.3  0.3  0.3  0.3  0.  ]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "M = np.array([[0, 2, 1, 1, 1, 1, 1, 2, 1, 3],\n",
        "              [2, 0, 1, 1, 1, 0, 0, 1, 1, 2],\n",
        "              [1, 1, 0, 1, 1, 0, 0, 0, 0, 1],\n",
        "              [1, 1, 1, 0, 1, 0, 0, 0, 0, 1],\n",
        "              [1, 1, 1, 1, 0, 0, 0, 0, 0, 1],\n",
        "              [1, 0, 0, 0, 0, 0, 1, 1, 0, 1],\n",
        "              [1, 0, 0, 0, 0, 1, 0, 1, 0, 1],\n",
        "              [2, 1, 0, 0, 0, 1, 1, 0, 1, 2],\n",
        "              [1, 1, 0, 0, 0, 0, 0, 1, 0, 1],\n",
        "              [3, 2, 1, 1, 1, 1, 1, 2, 1, 0]])\n",
        "\n",
        "def pmi(M, positive=True):\n",
        "    col_totals = M.sum(axis=0)\n",
        "    row_totals = M.sum(axis=1)\n",
        "    total = col_totals.sum()\n",
        "    expected = np.outer(row_totals, col_totals) / total\n",
        "    M = M / expected\n",
        "    # Silence distracting warnings about log(0):\n",
        "    with np.errstate(divide='ignore'):\n",
        "        M = np.log(M)\n",
        "    M[np.isinf(M)] = 0.0  # log(0) = 0\n",
        "    if positive:\n",
        "        M[M < 0] = 0.0\n",
        "    return M\n",
        "\n",
        "M_pmi = pmi(M)\n",
        "\n",
        "np.set_printoptions(precision=2)\n",
        "print(M_pmi)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dFb9ni2xvV6J",
        "outputId": "f1419fd6-fd5f-4cf0-99b4-fd35a083ed6d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "U, s, Vh = np.linalg.svd(M_pmi)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']\n",
        "\n",
        "words = [\"我\", \"喜欢\", \"自然\", \"语言\", \"处理\", \"爱\", \"深度\", \"学习\", \"机器\", \"。\"]\n",
        "\n",
        "for i in range(len(words)):\n",
        "    plt.text(U[i, 0], U[i, 1], words[i])\n",
        "plt.xlim(0, 0.6)\n",
        "plt.ylim(-0.5, 0.6)\n",
        "plt.savefig('svd.pdf')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_uYZSgYvV6J"
      },
      "source": [
        "# 2 前向最大匹配算法"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ct-jKgkvV6K",
        "outputId": "eb8d68aa-aba1-425a-e295-346fcac75e17"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "我\n",
            "爱\n",
            "祖国\n",
            "天安门\n"
          ]
        }
      ],
      "source": [
        "def load_dict():\n",
        "    f = open(\"lexicon.txt\")\n",
        "    lexicon = set()\n",
        "    max_len = 0\n",
        "    for line in f:\n",
        "        word = line.strip()\n",
        "        lexicon.add(word)\n",
        "        if len(word) > max_len:\n",
        "            max_len = len(word)\n",
        "    f.close()\n",
        "\n",
        "    return lexicon, max_len\n",
        "\n",
        "def fmm_word_seg(sentence, lexicon, max_len):\n",
        "    begin = 0\n",
        "    end = min(len(sentence), begin + max_len)\n",
        "    words = []\n",
        "    while begin < end:\n",
        "        word = sentence[begin: end]\n",
        "        if word in lexicon or end - begin == 1:\n",
        "            words.append(word)\n",
        "            begin = end\n",
        "            end = min(len(sentence), begin + max_len)\n",
        "        else:\n",
        "            end -= 1\n",
        "    return words\n",
        "\n",
        "lexicon, max_len = load_dict()\n",
        "words = fmm_word_seg(input(\"请输入句子：\"), lexicon, max_len)\n",
        "\n",
        "for word in words:\n",
        "    print(word,) \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VuEQUR6svV6K"
      },
      "source": [
        "# 3 评价一个中文分词系统"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NHqgOSu-vV6L",
        "outputId": "111fc6f8-0443-455d-c6f3-57932daf8c43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.5714285714285715\n"
          ]
        }
      ],
      "source": [
        "def to_region(split):\n",
        "    '''''\n",
        "    将划分的单词修改为长度区间\n",
        "    Args:\n",
        "    split: list of list of strings\n",
        "\n",
        "    Returns:\n",
        "    regions: list of tuple(代表区间)\n",
        "\n",
        "    '''\n",
        "    regions = []\n",
        "    start = 0\n",
        "    for word in split:\n",
        "        regions.append((start, start+len(word)))\n",
        "        start += len(word)\n",
        "    return regions\n",
        "\n",
        "def compute_F1(pred_split, gold_split):\n",
        "    \"\"\"\n",
        "    计算预测结果 pred_split 的 F1 值\n",
        "    Args:\n",
        "    pred_split: list of list of string\n",
        "    gold_split: list of list of string\n",
        "\n",
        "    Returns:\n",
        "    F1: F1 score\n",
        "    \"\"\"\n",
        "    pred_idx = set(to_region(pred_split)) # {(0, 1), (1, 2), (2, 3), (3, 4)}\n",
        "    gold_idx = set(to_region(gold_split)) # {(0, 1), (1, 3), (3, 4)}\n",
        "    correct_idx = pred_idx & gold_idx\n",
        "    P = len(correct_idx) / len(pred_idx)\n",
        "    R = len(correct_idx) / len(gold_idx)\n",
        "    F1 = 2 * P * R / (P + R)\n",
        "    return F1\n",
        "\n",
        "print(compute_F1(['我', '喜', '欢', '你'], [\"我\", \"喜欢\", \"你\"]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32FHVsHBvV6L"
      },
      "source": [
        "# 4 NLTK"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-yveHtk1vV6M",
        "outputId": "f829daaf-3580-4242-c059-93c0dad9758a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True\n"
          ]
        }
      ],
      "source": [
        "from nltk.corpus import stopwords\n",
        "\n",
        "all_stopwords = stopwords.words('english')\n",
        "\n",
        "print('her' in all_stopwords)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nsqsmmALvV6M"
      },
      "outputs": [],
      "source": [
        "from nltk.corpus import wordnet\n",
        "syns = wordnet.synsets(\"bank\")\n",
        "syns[0].name()\n",
        "syns[1].definition()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZDqlSbhmvV6N",
        "outputId": "e05af836-f2d9-47ea-daaf-8cbac1b42eb6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mr. Knightley loves to find fault with me, you know--\n",
            "in a joke--it is all a joke.\n"
          ]
        }
      ],
      "source": [
        "from nltk.corpus import gutenberg\n",
        "from nltk.tokenize import sent_tokenize\n",
        "\n",
        "text = gutenberg.raw('austen-emma.txt')\n",
        "sentences = sent_tokenize(text)\n",
        "print(sentences[100])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vMUyhb9SvV6N",
        "outputId": "b5b45557-90a1-4c90-916a-586986f3f289"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Mr.',\n",
              " 'Knightley',\n",
              " 'loves',\n",
              " 'to',\n",
              " 'find',\n",
              " 'fault',\n",
              " 'with',\n",
              " 'me',\n",
              " ',',\n",
              " 'you',\n",
              " 'know',\n",
              " '--',\n",
              " 'in',\n",
              " 'a',\n",
              " 'joke',\n",
              " '--',\n",
              " 'it',\n",
              " 'is',\n",
              " 'all',\n",
              " 'a',\n",
              " 'joke',\n",
              " '.']"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from nltk.tokenize import word_tokenize\n",
        "word_tokenize(sentences[100])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VpyeFRP7vV6N",
        "outputId": "6b284cf4-458b-47bd-bfa8-a45c1c360ce1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /Users/xiaoqi/nltk_data...\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[('They', 'PRP'), ('sat', 'VBP'), ('by', 'IN'), ('the', 'DT'), ('fire', 'NN'), ('.', '.')]\n",
            "[('They', 'PRP'), ('fire', 'VBP'), ('a', 'DT'), ('gun', 'NN'), ('.', '.')]\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        }
      ],
      "source": [
        "from nltk import pos_tag\n",
        "\n",
        "print(pos_tag(word_tokenize(\"They sat by the fire.\")))\n",
        "print(pos_tag(word_tokenize(\"They fire a gun.\")))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4qsGpsEvV6O"
      },
      "source": [
        "# 6 Pytorch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sngWvjSsvV6O",
        "outputId": "3e8732f8-db5c-4959-9f8c-48f077b9a390"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[6.7262e-44, 7.5670e-44, 1.1771e-43],\n",
              "        [6.8664e-44, 6.8664e-44, 8.1275e-44]])"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import torch\n",
        "    \n",
        "torch.empty(2, 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ioJmNUpQvV6O",
        "outputId": "2f843bd8-b1af-42df-d1a3-151393bbc945"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0.5143, 0.5948, 0.6523],\n",
              "        [0.0869, 0.6833, 0.0174]])"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.rand(2, 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cESFoI3tvV6P",
        "outputId": "1f617e00-71cb-4be2-ffe5-ad6bfb3bdd4a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[-0.0685,  1.3005, -1.1433],\n",
              "        [ 0.3876,  0.4800, -0.9431]])"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.randn(2, 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p_RxuGzSvV6P",
        "outputId": "d50f92ba-555b-40ee-d021-8091f9d20a00"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0, 0, 0],\n",
              "        [0, 0, 0]])"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.zeros(2, 3, dtype=torch.long)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zF7xR8NIvV6P",
        "outputId": "4fa22912-78a5-430b-da69-d85c3b93468b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0., 0., 0.],\n",
              "        [0., 0., 0.]], dtype=torch.float64)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.zeros(2, 3, dtype=torch.double)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oz1xRunavV6P",
        "outputId": "d2a8c2b3-bb85-4aee-dd6d-c6b5e7484922"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[1.0000, 3.8000, 2.1100],\n",
              "        [8.6000, 4.0000, 2.4000]])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.tensor([[1.0, 3.8, 2.11], [8.6, 4.0, 2.4]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gdRdS-J5vV6Q",
        "outputId": "ae9fb057-6891-4497-cff3-9e1b3c15ce8f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([ 4., 10., 18.], dtype=torch.float64)\n"
          ]
        }
      ],
      "source": [
        "x = torch.tensor([1, 2, 3], dtype=torch.double)\n",
        "y = torch.tensor([4, 5, 6], dtype=torch.double)\n",
        "print(x * y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xPTWoiS4vV6Q",
        "outputId": "e244daa7-d7c1-4544-b607-8a5c7635b682"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([2.5000, 3.5000, 4.5000], dtype=torch.float64)\n",
            "tensor([2., 5.], dtype=torch.float64)\n"
          ]
        }
      ],
      "source": [
        "x = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.double)\n",
        "\n",
        "print(x.mean(dim=0))\n",
        "print(x.mean(dim=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hFR7SEO7vV6Q",
        "outputId": "10ce9940-175d-4a27-c21e-6093edecdd30"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[ 1.,  2.,  3.],\n",
              "        [ 4.,  5.,  6.],\n",
              "        [ 7.,  8.,  9.],\n",
              "        [10., 11., 12.]], dtype=torch.float64)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.double)\n",
        "y = torch.tensor([[7, 8, 9], [10, 11, 12]], dtype=torch.double)\n",
        "torch.cat((x, y), dim=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZQWTaEW2vV6Q",
        "outputId": "c0fb2760-33b6-4d8a-ff3b-1149af9f0af1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([5.], grad_fn=<MulBackward0>)\n",
            "tensor([1.]) tensor([6.])\n"
          ]
        }
      ],
      "source": [
        "x = torch.tensor([2.], requires_grad=True)\n",
        "y = torch.tensor([3.], requires_grad=True)\n",
        "\n",
        "z = (x + y) * (y - 2)\n",
        "print(z)\n",
        "z.backward()\n",
        "print(x.grad, y.grad)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GNt-olJWvV6Q",
        "outputId": "37b4241e-5fe4-42b9-e9cf-0d83a310cbea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([1, 2, 3])"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = torch.arange(1, 4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nm09lLuMvV6R",
        "outputId": "cde1a6c0-436a-458a-f524-d38476515cf4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[1, 2, 3, 4]]) torch.Size([1, 4])\n",
            "tensor([1, 2, 3, 4]) torch.Size([4])\n"
          ]
        }
      ],
      "source": [
        "a = torch.tensor([1, 2, 3, 4])\n",
        "b = a.unsqueeze(dim=0)\n",
        "print(b, b.shape)\n",
        "c = b.squeeze()\n",
        "print(c, c.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ycQkTBUvV6R"
      },
      "source": [
        "# 7 线性模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UdZ2pi5nvV6R",
        "outputId": "ac59b7a9-fa23-4a72-e6c0-395813065062"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[ 0.4649,  0.0115],\n",
            "        [ 0.2210, -0.1050],\n",
            "        [ 0.4447, -0.0373]], grad_fn=<AddmmBackward>)\n"
          ]
        }
      ],
      "source": [
        "from torch import nn\n",
        "import torch\n",
        "\n",
        "linear = nn.Linear(32, 2) # 输入特征数目为32维，输出特征数目为2维\n",
        "inputs = torch.rand(3, 32) # 创建一个形状为（3，32）的随机张量，3为batch批次大小\n",
        "outputs = linear(inputs)\n",
        "print(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uF_LpL1fvV6R"
      },
      "source": [
        "# 8 激活函数"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4QN2pnHtvV6R",
        "outputId": "2cd786f5-481c-4f27-c937-3ad557b0d027"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[0.6142, 0.5029],\n",
            "        [0.5550, 0.4738],\n",
            "        [0.6094, 0.4907]], grad_fn=<SigmoidBackward>)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/opt/anaconda3/lib/python3.8/site-packages/torch/nn/functional.py:1805: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
            "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
          ]
        }
      ],
      "source": [
        "from torch.nn import functional as F\n",
        "\n",
        "# 对于每个元素进行sigmoid\n",
        "activation = F.sigmoid(outputs) \n",
        "print(activation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODXDb7m6vV6R",
        "outputId": "66469f68-e1f5-422a-bedb-a2d73d07310c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[0.6115, 0.3885],\n",
            "        [0.5808, 0.4192],\n",
            "        [0.6182, 0.3818]], grad_fn=<SoftmaxBackward>)\n"
          ]
        }
      ],
      "source": [
        "# 沿着第2维（行方向）进行softmax，即对于每批次中的各样例分别进行softmax\n",
        "activation = F.softmax(outputs, dim=1)\n",
        "print(activation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wcqlzTCgvV6S",
        "outputId": "8ed291c5-cceb-4e4b-898e-02c7b273085b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[0.4649, 0.0115],\n",
            "        [0.2210, 0.0000],\n",
            "        [0.4447, 0.0000]], grad_fn=<ReluBackward0>)\n"
          ]
        }
      ],
      "source": [
        "activation = F.relu(outputs)\n",
        "print(activation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzaoI-hFvV6S"
      },
      "source": [
        "# 9 多层感知机"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "txKtXds8vV6S",
        "outputId": "54ca9db7-bdfb-43db-d83a-07c612a3c814"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[0.3465, 0.6535],\n",
            "        [0.3692, 0.6308],\n",
            "        [0.4319, 0.5681]], grad_fn=<SoftmaxBackward>)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "\n",
        "# 多层感知机\n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, num_class):\n",
        "        super(MLP, self).__init__()\n",
        "        # 线性变换：输入 -> 隐层\n",
        "        self.linear1 = nn.Linear(input_dim, hidden_dim)\n",
        "        # ReLU\n",
        "        self.activate = F.relu\n",
        "        # 线性变换：隐层 -> 输出\n",
        "        self.linear2 = nn.Linear(hidden_dim, num_class)\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        hidden = self.linear1(inputs)\n",
        "        activation = self.activate(hidden)\n",
        "        outputs = self.linear2(activation)\n",
        "        probs = F.softmax(outputs, dim=1)   # 获得每个输入属于某个类别的概率\n",
        "        return probs\n",
        "\n",
        "mlp = MLP(input_dim=4, hidden_dim=5, num_class=2)\n",
        "# 3个输入batch，4为每个输入的维度\n",
        "inputs = torch.rand(3, 4)\n",
        "probs = mlp(inputs)\n",
        "print(probs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybTmzK_zvV6S"
      },
      "source": [
        "# 10 TextCNN模型结构"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ihcg10JVvV6S"
      },
      "outputs": [],
      "source": [
        "class TextCNN(nn.Module):\n",
        "    def __init__(self, config):\n",
        "        super(TextCNN, self).__init__()\n",
        "        self.is_training = True\n",
        "        self.dropout_rate = config.dropout_rate\n",
        "        self.num_class = config.num_class\n",
        "        self.use_element = config.use_element\n",
        "        self.config = config\n",
        " \n",
        "        self.embedding = nn.Embedding(num_embeddings=config.vocab_size, \n",
        "                                embedding_dim=config.embedding_size)\n",
        "        self.convs = nn.ModuleList([\n",
        "                nn.Sequential(nn.Conv1d(in_channels=config.embedding_size, \n",
        "                                        out_channels=config.feature_size, \n",
        "                                        kernel_size=h),\n",
        "#                              nn.BatchNorm1d(num_features=config.feature_size), \n",
        "                              nn.ReLU(),\n",
        "                              nn.MaxPool1d(kernel_size=config.max_text_len-h+1))\n",
        "                     for h in config.window_sizes\n",
        "                    ])\n",
        "        self.fc = nn.Linear(in_features=config.feature_size*len(config.window_sizes),\n",
        "                            out_features=config.num_class)\n",
        "        if os.path.exists(config.embedding_path) and config.is_training and config.is_pretrain:\n",
        "            print(\"Loading pretrain embedding...\")\n",
        "            self.embedding.weight.data.copy_(torch.from_numpy(np.load(config.embedding_path)))    \n",
        "    \n",
        "    def forward(self, x):\n",
        "        embed_x = self.embedding(x)\n",
        "        \n",
        "        #print('embed size 1',embed_x.size())  # 32*35*256\n",
        "# batch_size x text_len x embedding_size  -> batch_size x embedding_size x text_len\n",
        "        embed_x = embed_x.permute(0, 2, 1)\n",
        "        #print('embed size 2',embed_x.size())  # 32*256*35\n",
        "        out = [conv(embed_x) for conv in self.convs]  #out[i]:batch_size x feature_size*1\n",
        "        #for o in out:\n",
        "        #    print('o',o.size())  # 32*100*1\n",
        "        out = torch.cat(out, dim=1)  # 对应第二个维度（行）拼接起来，比如说5*2*1,5*3*1的拼接变成5*5*1\n",
        "        #print(out.size(1)) # 32*400*1\n",
        "        out = out.view(-1, out.size(1)) \n",
        "        #print(out.size())  # 32*400 \n",
        "        if not self.use_element:\n",
        "            out = F.dropout(input=out, p=self.dropout_rate)\n",
        "            out = self.fc(out)\n",
        "        return out"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMhgWvKbvV6T"
      },
      "source": [
        "# 11 卷积神经网络"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jbTfZxqevV6T",
        "outputId": "4f1c5891-1c12-45f6-fbaa-f024789b698d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475]],\n",
            "\n",
            "        [[ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475],\n",
            "         [ 1.1450,  0.8741, -0.3475]]], grad_fn=<PermuteBackward>) torch.Size([2, 6, 3])\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch.nn import Conv1d\n",
        "\n",
        "inputs = torch.ones(2, 7, 5)\n",
        "conv1 = Conv1d(in_channels=5, out_channels=3, kernel_size=2)\n",
        "inputs = inputs.permute(0, 2, 1)\n",
        "outputs = conv1(inputs)\n",
        "outputs = outputs.permute(0, 2, 1)\n",
        "print(outputs, outputs.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "htEvz7iuvV6T",
        "outputId": "8f05d368-e24c-46d6-f435-5b38d7d2f188"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "inputs =  tensor([[[0.5801, 0.6436, 0.1947, 0.6487, 0.8968, 0.3009],\n",
            "         [0.8895, 0.0390, 0.5899, 0.1805, 0.1035, 0.9368],\n",
            "         [0.1585, 0.8440, 0.8345, 0.0849, 0.4730, 0.5783],\n",
            "         [0.3659, 0.2716, 0.4990, 0.6657, 0.2565, 0.9945],\n",
            "         [0.6403, 0.2125, 0.6234, 0.1210, 0.3517, 0.6784]],\n",
            "\n",
            "        [[0.0855, 0.1844, 0.3558, 0.1458, 0.9264, 0.9538],\n",
            "         [0.1427, 0.9598, 0.2031, 0.2354, 0.5456, 0.6808],\n",
            "         [0.8981, 0.6998, 0.1424, 0.7445, 0.3664, 0.9132],\n",
            "         [0.9393, 0.6905, 0.1617, 0.7266, 0.6220, 0.0726],\n",
            "         [0.6940, 0.1242, 0.0561, 0.3435, 0.1775, 0.8076]]]) torch.Size([2, 5, 6])\n",
            "conv1.weight =  Parameter containing:\n",
            "tensor([[[ 0.1562, -0.1094, -0.0228,  0.1879],\n",
            "         [-0.0304,  0.1720,  0.0392,  0.0476],\n",
            "         [ 0.0479,  0.0050, -0.0942,  0.0502],\n",
            "         [-0.0905, -0.1414,  0.0421,  0.0708],\n",
            "         [ 0.0671,  0.2107,  0.1556,  0.1809]],\n",
            "\n",
            "        [[ 0.0453,  0.0267,  0.0821,  0.0792],\n",
            "         [ 0.0428,  0.1096,  0.0132,  0.1285],\n",
            "         [-0.0082,  0.2208,  0.2189,  0.1461],\n",
            "         [ 0.0550, -0.0019, -0.0607, -0.1238],\n",
            "         [ 0.0730,  0.1778, -0.0817,  0.2204]]], requires_grad=True) torch.Size([2, 5, 4])\n",
            "outputs1 =  tensor([[[0.2778, 0.5726, 0.2568],\n",
            "         [0.6502, 0.7603, 0.6844]],\n",
            "\n",
            "        [[0.2700, 0.2670, 0.4595],\n",
            "         [0.6942, 0.5474, 0.9858]]], grad_fn=<SqueezeBackward1>) torch.Size([2, 2, 3])\n",
            "outputs2 =  tensor([[[-0.0104, -0.1105, -0.0963, -0.1619],\n",
            "         [-0.0940, -0.2529, -0.2081,  0.0786]],\n",
            "\n",
            "        [[-0.0102, -0.0118,  0.0119, -0.1874],\n",
            "         [-0.5899, -0.0979, -0.1233, -0.1664]]], grad_fn=<SqueezeBackward1>) torch.Size([2, 2, 4])\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch.nn import Conv1d\n",
        "\n",
        "# 输入批次大小为2，即有两个序列，每个序列长度为6，输入的维度为5\n",
        "inputs = torch.rand(2, 5, 6)\n",
        "print(\"inputs = \", inputs, inputs.shape)\n",
        "# class torch.nn.Conv1d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True)\n",
        "# in_channels 词向量维度\n",
        "# out_channels 卷积产生的通道\n",
        "# kernel_size 卷积核尺寸，卷积大小实际为 kernel_size*in_channels\n",
        "\n",
        "# 定义一个一维卷积，输入通道为5，输出通道为2，卷积核宽度为4\n",
        "conv1 = Conv1d(in_channels=5, out_channels=2, kernel_size=4)\n",
        "# 卷积核的权值是随机初始化的\n",
        "print(\"conv1.weight = \", conv1.weight, conv1.weight.shape)\n",
        "# 再定义一个一维卷积，输入通道为5，输出通道为2，卷积核宽度为3\n",
        "conv2 = Conv1d(in_channels=5, out_channels=2, kernel_size=3)\n",
        "\n",
        "outputs1 = conv1(inputs)\n",
        "outputs2 = conv2(inputs)\n",
        "# 输出1为2个序列，两个序列长度为3，大小为2\n",
        "print(\"outputs1 = \", outputs1, outputs1.shape)\n",
        "# 输出2为2个序列，两个序列长度为4，大小为2\n",
        "print(\"outputs2 = \", outputs2, outputs2.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iRBdo9slvV6U",
        "outputId": "b58ff579-4cc7-4cd0-cd75-e1cf0d3f4e57"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[0.5726],\n",
            "         [0.7603]],\n",
            "\n",
            "        [[0.4595],\n",
            "         [0.9858]]], grad_fn=<SqueezeBackward1>)\n",
            "tensor([[[-0.0104],\n",
            "         [ 0.0786]],\n",
            "\n",
            "        [[ 0.0119],\n",
            "         [-0.0979]]], grad_fn=<SqueezeBackward1>)\n",
            "tensor([[0.5726, 0.7603],\n",
            "        [0.4595, 0.9858]], grad_fn=<SqueezeBackward1>)\n",
            "tensor([[-0.0104,  0.0786],\n",
            "        [ 0.0119, -0.0979]], grad_fn=<SqueezeBackward1>)\n",
            "tensor([[ 0.5726,  0.7603, -0.0104,  0.0786],\n",
            "        [ 0.4595,  0.9858,  0.0119, -0.0979]], grad_fn=<CatBackward>)\n"
          ]
        }
      ],
      "source": [
        "from torch.nn import MaxPool1d\n",
        "\n",
        "# 输出序列长度3\n",
        "pool1 = MaxPool1d(3)\n",
        "# 输出序列长度4\n",
        "pool2 = MaxPool1d(4)\n",
        "outputs_pool1 = pool1(outputs1)\n",
        "outputs_pool2 = pool2(outputs2)\n",
        "\n",
        "print(outputs_pool1)\n",
        "print(outputs_pool2)\n",
        "\n",
        "# 由于outputs_pool1和outputs_pool2是两个独立的张量，需要cat拼接起来，删除最后一个维度，将2行1列的矩阵变成1个向量\n",
        "outputs_pool_squeeze1 = outputs_pool1.squeeze(dim=2)\n",
        "print(outputs_pool_squeeze1)\n",
        "outputs_pool_squeeze2 = outputs_pool2.squeeze(dim=2)\n",
        "print(outputs_pool_squeeze2)\n",
        "outputs_pool = torch.cat([outputs_pool_squeeze1, outputs_pool_squeeze2], dim=1)\n",
        "print(outputs_pool)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f_4jyQ1-vV6U",
        "outputId": "f5d2c02a-15d7-46e1-f2bb-1c47c0855179"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[-0.0555, -0.0656],\n",
            "        [-0.0428, -0.0303]], grad_fn=<AddmmBackward>)\n"
          ]
        }
      ],
      "source": [
        "from torch.nn import Linear\n",
        "\n",
        "linear = Linear(4, 2)\n",
        "outputs_linear = linear(outputs_pool)\n",
        "print(outputs_linear)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4di6FWNSvV6U"
      },
      "source": [
        "# 12 RNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PuxBdjxdvV6U",
        "outputId": "a33dfa13-3728-4bce-ff25-a388452835e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[-0.1092,  0.0689, -0.6153, -0.1825,  0.4118],\n",
            "         [ 0.0949,  0.1618, -0.7583, -0.0741,  0.0716],\n",
            "         [-0.0305,  0.1133, -0.7688, -0.3620,  0.0236]],\n",
            "\n",
            "        [[-0.3436,  0.1757, -0.6807, -0.3715,  0.3486],\n",
            "         [ 0.0224,  0.0139, -0.5427, -0.3378,  0.3075],\n",
            "         [-0.2088, -0.1892, -0.5007, -0.3284,  0.4669]]],\n",
            "       grad_fn=<TransposeBackward1>)\n",
            "tensor([[[-0.0305,  0.1133, -0.7688, -0.3620,  0.0236],\n",
            "         [-0.2088, -0.1892, -0.5007, -0.3284,  0.4669]]],\n",
            "       grad_fn=<StackBackward>)\n",
            "torch.Size([2, 3, 5]) torch.Size([1, 2, 5])\n"
          ]
        }
      ],
      "source": [
        "from torch.nn import RNN\n",
        "\n",
        "# 每个时刻输入大小为4，隐含层大小为5\n",
        "rnn = RNN(input_size=4, hidden_size=5, batch_first=True)\n",
        "# 输入批次为，即有2个序列，序列长度为3，输入大小为4\n",
        "inputs = torch.rand(2, 3, 4)\n",
        "# outputs为输出序列的隐含层，hn为最后一个时刻的隐含层\n",
        "outputs, hn = rnn(inputs)\n",
        "\n",
        "# 输出两个序列，每个序列长度为3，大小为5\n",
        "print(outputs)\n",
        "# 最后一个时刻的隐含层，值与outputs中最后一个时刻相同\n",
        "print(hn)\n",
        "# （2， 3， 5）即批次大小、序列长度、隐含层大小，（1， 2， 5）即1、批次大小、隐含层大小\n",
        "print(outputs.shape, hn.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rN0_19CjvV6U",
        "outputId": "55eb2d2c-20e0-46f6-c463-6860b83b7085"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([50, 20]) torch.Size([50, 50]) torch.Size([50, 50])\n",
            "torch.Size([1, 20])\n",
            "tensor([[-0.5984, -0.3677,  0.0775,  0.2553,  0.1232, -0.1161, -0.2288,  0.1609,\n",
            "         -0.1241, -0.3501, -0.3164,  0.3403,  0.0332,  0.2511,  0.0951,  0.2445,\n",
            "          0.0558, -0.0419, -0.1222,  0.0901, -0.2851,  0.1737,  0.0637, -0.3362,\n",
            "         -0.1706,  0.2050, -0.3277, -0.2112, -0.4245,  0.0265, -0.0052, -0.4551,\n",
            "         -0.3270, -0.1220, -0.1531, -0.0151,  0.2504,  0.5659,  0.4878, -0.0656,\n",
            "         -0.7775,  0.4294,  0.2054,  0.0318,  0.4798, -0.1439,  0.3873,  0.1039,\n",
            "          0.1654, -0.5765]], grad_fn=<SelectBackward>)\n",
            "tensor([[[ 0.2338,  0.1578,  0.7547,  0.0439, -0.6009,  0.1042, -0.4840,\n",
            "          -0.1806, -0.2075, -0.2174,  0.2023,  0.3301, -0.1899,  0.1618,\n",
            "           0.0790,  0.1213,  0.0053, -0.2586,  0.6376,  0.0315,  0.6949,\n",
            "           0.3184, -0.4901, -0.0852,  0.4542,  0.1393, -0.0074, -0.8129,\n",
            "          -0.1013,  0.0852,  0.2550, -0.4294,  0.2316,  0.0662,  0.0465,\n",
            "          -0.1976, -0.6093,  0.4097,  0.3909, -0.1091, -0.3569,  0.0366,\n",
            "           0.0665,  0.5302, -0.1765, -0.3919, -0.0308,  0.0061,  0.1447,\n",
            "           0.2676]],\n",
            "\n",
            "        [[-0.5984, -0.3677,  0.0775,  0.2553,  0.1232, -0.1161, -0.2288,\n",
            "           0.1609, -0.1241, -0.3501, -0.3164,  0.3403,  0.0332,  0.2511,\n",
            "           0.0951,  0.2445,  0.0558, -0.0419, -0.1222,  0.0901, -0.2851,\n",
            "           0.1737,  0.0637, -0.3362, -0.1706,  0.2050, -0.3277, -0.2112,\n",
            "          -0.4245,  0.0265, -0.0052, -0.4551, -0.3270, -0.1220, -0.1531,\n",
            "          -0.0151,  0.2504,  0.5659,  0.4878, -0.0656, -0.7775,  0.4294,\n",
            "           0.2054,  0.0318,  0.4798, -0.1439,  0.3873,  0.1039,  0.1654,\n",
            "          -0.5765]]], grad_fn=<StackBackward>)\n",
            "tensor([[-0.5984, -0.3677,  0.0775,  0.2553,  0.1232, -0.1161, -0.2288,  0.1609,\n",
            "         -0.1241, -0.3501, -0.3164,  0.3403,  0.0332,  0.2511,  0.0951,  0.2445,\n",
            "          0.0558, -0.0419, -0.1222,  0.0901, -0.2851,  0.1737,  0.0637, -0.3362,\n",
            "         -0.1706,  0.2050, -0.3277, -0.2112, -0.4245,  0.0265, -0.0052, -0.4551,\n",
            "         -0.3270, -0.1220, -0.1531, -0.0151,  0.2504,  0.5659,  0.4878, -0.0656,\n",
            "         -0.7775,  0.4294,  0.2054,  0.0318,  0.4798, -0.1439,  0.3873,  0.1039,\n",
            "          0.1654, -0.5765]], grad_fn=<SelectBackward>)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch.autograd import Variable\n",
        "from torch import nn\n",
        "\n",
        "# 首先建立一个简单的循环神经网络：输入维度为20， 输出维度是50， 两层的单向网络\n",
        "basic_rnn = nn.RNN(input_size=20, hidden_size=50, num_layers=2)\n",
        "\"\"\"\n",
        "通过 weight_ih_l0 来访问第一层中的 w_{ih}，因为输入 x_{t}是20维，输出是50维，所以w_{ih}是一个50*20维的向量，另外要访问第\n",
        "二层网络可以使用 weight_ih_l1.对于 w_{hh}，可以用 weight_hh_l0来访问，而 b_{ih}则可以通过 bias_ih_l0来访问。当然可以对它\n",
        "进行自定义的初始化，只需要记得它们是 Variable，取出它们的data，对它进行自定的初始化即可。\n",
        "\"\"\"\n",
        "print(basic_rnn.weight_ih_l0.size(), basic_rnn.weight_ih_l1.size(), basic_rnn.weight_hh_l0.size())\n",
        "\n",
        "# 随机初始化输入和隐藏状态\n",
        "toy_input = Variable(torch.randn(3, 1, 20))\n",
        "h_0 = Variable(torch.randn(2*1, 1, 50))\n",
        "\n",
        "print(toy_input[0].size())\n",
        "# 将输入和隐藏状态传入网络，得到输出和更新之后的隐藏状态，输出维度是(100, 32, 20)。\n",
        "toy_output, h_n = basic_rnn(toy_input, h_0)\n",
        "print(toy_output[-1])\n",
        "\n",
        "print(h_n)\n",
        "print(h_n[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11MsGKp7vV6V"
      },
      "source": [
        "# 13 LSTM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bIvN3WfVvV6V",
        "outputId": "5d9b2d84-3434-4c0e-855f-f7a9915b5f01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[-0.1102,  0.0568,  0.0929,  0.0579, -0.1300],\n",
            "         [-0.2051,  0.0829,  0.0245,  0.0202, -0.2124],\n",
            "         [-0.2509,  0.0854,  0.0882, -0.0272, -0.2385]],\n",
            "\n",
            "        [[-0.1302,  0.0804,  0.0200,  0.0543, -0.1033],\n",
            "         [-0.2794,  0.0736,  0.0247, -0.0406, -0.2233],\n",
            "         [-0.2913,  0.1044,  0.0407,  0.0044, -0.2345]]],\n",
            "       grad_fn=<TransposeBackward0>)\n",
            "tensor([[[-0.2509,  0.0854,  0.0882, -0.0272, -0.2385],\n",
            "         [-0.2913,  0.1044,  0.0407,  0.0044, -0.2345]]],\n",
            "       grad_fn=<StackBackward>)\n",
            "tensor([[[-0.3215,  0.2153,  0.1180, -0.0568, -0.4162],\n",
            "         [-0.3982,  0.2704,  0.0568,  0.0097, -0.3959]]],\n",
            "       grad_fn=<StackBackward>)\n",
            "torch.Size([2, 3, 5]) torch.Size([1, 2, 5]) torch.Size([1, 2, 5])\n"
          ]
        }
      ],
      "source": [
        "from torch.nn import LSTM\n",
        "\n",
        "lstm = LSTM(input_size=4, hidden_size=5, batch_first=True)\n",
        "inputs = torch.rand(2, 3, 4)\n",
        "# outputs为输出序列的隐含层，hn为最后一个时刻的隐含层，cn为最后一个时刻的记忆细胞\n",
        "outputs, (hn, cn) = lstm(inputs)\n",
        "# 输出两个序列，每个序列长度为3，大小为5\n",
        "print(outputs)\n",
        "print(hn)\n",
        "print(cn)\n",
        "# 输出银行层序列和最后一个时刻隐含层以及记忆细胞的形状\n",
        "print(outputs.shape, hn.shape, cn.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MBgjjhwvV6V"
      },
      "source": [
        "# 14 Transformeer模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vnvvYm1cvV6V",
        "outputId": "6a9ffc1b-d4b9-4e0d-9e95-71e11d1a9c43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[-0.3925, -0.6508, -0.6780,  1.7213],\n",
            "         [-1.0062, -0.9580,  1.2473,  0.7169],\n",
            "         [ 1.0108, -0.3882, -1.4588,  0.8363]],\n",
            "\n",
            "        [[-0.9240, -0.6414, -0.0861,  1.6515],\n",
            "         [-1.6827,  0.8756,  0.2087,  0.5984],\n",
            "         [ 0.2451,  0.5484,  0.8925, -1.6860]]],\n",
            "       grad_fn=<NativeLayerNormBackward>)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# 创建一个Transformer块，每个宿舍输入向量、输出向量维度为4，头数是2\n",
        "encoder_layer = nn.TransformerEncoderLayer(d_model=4, nhead=2)\n",
        "# 输入批次大小为2，即有两个序列，每个序列长度为3，输入的维度为4\n",
        "src = torch.rand(2, 3, 4)\n",
        "out = encoder_layer(src)\n",
        "\n",
        "print(out)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6RWknIubvV6V",
        "outputId": "5f7ffec4-6506-4af1-c036-336d086e8b01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[-1.6542,  0.6421,  0.0943,  0.9178],\n",
            "         [-1.7181,  0.5462,  0.7637,  0.4082],\n",
            "         [-0.4438,  0.9863, -1.4295,  0.8871]],\n",
            "\n",
            "        [[-1.7077,  0.7175,  0.2967,  0.6935],\n",
            "         [-1.6911,  0.3405,  0.4382,  0.9123],\n",
            "         [-0.1297,  1.6529, -0.9749, -0.5483]]],\n",
            "       grad_fn=<NativeLayerNormBackward>)\n"
          ]
        }
      ],
      "source": [
        "# 堆叠多个Transformer块，形成完整的nn.TransformerEncoder\n",
        "\n",
        "transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=6)\n",
        "out = transformer_encoder(src)\n",
        "print(out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zq4mz224vV6V",
        "outputId": "37a902be-e7ab-49f0-c199-1039b2736c87"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[[-1.4070, -0.2031,  0.2209,  1.3893],\n",
            "         [ 1.4623, -0.5193, -1.2289,  0.2858],\n",
            "         [-0.3154,  0.4551, -1.4270,  1.2872]],\n",
            "\n",
            "        [[ 0.5958, -1.7078,  0.3213,  0.7906],\n",
            "         [-1.1620,  1.4184, -0.6781,  0.4217],\n",
            "         [ 0.2199, -1.1097, -0.6331,  1.5229]]],\n",
            "       grad_fn=<NativeLayerNormBackward>)\n"
          ]
        }
      ],
      "source": [
        "# 堆叠多个decoder形成完整的nn.TransformerDecoder\n",
        "\n",
        "memory = transformer_encoder(src)\n",
        "decoder_layer = nn.TransformerDecoderLayer(d_model=4, nhead=2)\n",
        "transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=6)\n",
        "out_part = torch.rand(2, 3, 4)\n",
        "out = transformer_decoder(out_part, memory)\n",
        "print(out)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dPfCaV1vV6W"
      },
      "source": [
        "# 15 多层感知机"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KxQElJXnvV6W",
        "outputId": "1794f875-2925-4380-8d13-e35e5d91bffe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.float32\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tensor([[-2.8073, -0.2151,  1.4023],\n",
              "        [-9.5389, 18.3927, 11.2775]], grad_fn=<AddmmBackward>)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from torch import nn\n",
        "import torch\n",
        "\n",
        "x = torch.tensor([[1, 2, 3, 4], [2, 3, 45, 6]], dtype=torch.float32) # (2, 4)\n",
        "\n",
        "print(x.dtype)\n",
        "linear1 = nn.Linear(4, 3)\n",
        "y = linear1(x)\n",
        "\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yp2JEf-dvV6W",
        "outputId": "814aea32-7a9f-4160-e1d4-49900c494f5d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Parameters:\n",
            "linear1.weight tensor([[-0.4509, -0.5591],\n",
            "        [-1.2904,  1.2947],\n",
            "        [ 0.8418,  0.8424],\n",
            "        [-0.4408, -0.1356],\n",
            "        [ 1.2886, -1.2879]])\n",
            "linear1.bias tensor([ 4.5582e-01, -2.5727e-03, -8.4167e-01, -1.7634e-03, -1.5244e-04])\n",
            "linear2.weight tensor([[ 0.5994, -1.4792,  1.0836, -0.2860, -1.0873],\n",
            "        [-0.2534,  0.9911, -0.7348,  0.0413,  1.3398]])\n",
            "linear2.bias tensor([ 0.7375, -0.1796])\n",
            "y_pred =  tensor([[-0.2398, -1.5455],\n",
            "        [-2.3716, -0.0980],\n",
            "        [-2.3101, -0.1045],\n",
            "        [-0.0833, -2.5269]], grad_fn=<LogSoftmaxBackward>)\n",
            "Predicted results: tensor([0, 1, 1, 0])\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "\n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, num_class):\n",
        "        super(MLP, self).__init__()\n",
        "\n",
        "        self.linear1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.activate = F.relu\n",
        "        self.linear2 = nn.Linear(hidden_dim, num_class)\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        hidden = self.linear1(inputs)\n",
        "        activation = self.activate(hidden)\n",
        "        outputs = self.linear2(activation)\n",
        "        log_probs = F.log_softmax(outputs, dim=1)\n",
        "        return log_probs\n",
        "\n",
        "# 异或问题的4个输入\n",
        "x_train = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]])\n",
        "# 每个输入对应的输出类别\n",
        "y_train = torch.tensor([0, 1, 1, 0])\n",
        "\n",
        "# 创建多层感知器模型，输入层大小为2，隐含层大小为5，输出层大小为2（即有两个类别）\n",
        "model = MLP(input_dim=2, hidden_dim=5, num_class=2)\n",
        "\n",
        "criterion = nn.NLLLoss() # 当使用log_softmax输出时，需要调用负对数似然损失（Negative Log Likelihood，NLL）\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.05)\n",
        "\n",
        "for epoch in range(500):\n",
        "    y_pred = model(x_train)\n",
        "    loss = criterion(y_pred, y_train)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "print(\"Parameters:\")\n",
        "for name, param in model.named_parameters():\n",
        "    print (name, param.data)\n",
        "\n",
        "y_pred = model(x_train)\n",
        "print(\"y_pred = \", y_pred)\n",
        "print(\"Predicted results:\", y_pred.argmax(axis=1))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CZ4G2Kv1vV6W",
        "outputId": "3ad59131-c207-4e57-c404-e702b39e9ff0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Parameters:\n",
            "linear1.weight tensor([[ 0.5201,  0.8337],\n",
            "        [ 0.1634,  0.3760],\n",
            "        [-1.3333,  1.3399],\n",
            "        [ 0.1711,  0.5016],\n",
            "        [ 0.2823, -0.0502]])\n",
            "linear1.bias tensor([-0.5297, -0.5635, -0.0046,  0.0035, -0.4957])\n",
            "linear2.weight tensor([[ 0.8128, -0.2902, -1.2019,  0.1860,  0.3508],\n",
            "        [-0.5923, -0.2059,  1.3430,  0.0731,  0.3943]])\n",
            "linear2.bias tensor([ 0.0384, -0.2327])\n",
            "y_pred =  tensor([[ 0.0391, -0.2324],\n",
            "        [-1.2254,  1.4174],\n",
            "        [ 0.0709, -0.2199],\n",
            "        [ 0.8316, -0.6686]], grad_fn=<AddmmBackward>)\n",
            "Predicted results: tensor([0, 1, 0, 0])\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "\n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, input_dim, hidden_dim, num_class):\n",
        "        super(MLP, self).__init__()\n",
        "\n",
        "        self.linear1 = nn.Linear(input_dim, hidden_dim)\n",
        "        self.activate = F.relu\n",
        "        self.linear2 = nn.Linear(hidden_dim, num_class)\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        hidden = self.linear1(inputs)\n",
        "        activation = self.activate(hidden)\n",
        "        outputs = self.linear2(activation)\n",
        "        return outputs\n",
        "\n",
        "# 异或问题的4个输入\n",
        "x_train = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]])\n",
        "# 每个输入对应的输出类别\n",
        "y_train = torch.tensor([0, 1, 1, 0])\n",
        "\n",
        "# 创建多层感知器模型，输入层大小为2，隐含层大小为5，输出层大小为2（即有两个类别）\n",
        "model = MLP(input_dim=2, hidden_dim=5, num_class=2)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss() # 当使用log_softmax输出时，需要调用负对数似然损失（Negative Log Likelihood，NLL）\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.05)\n",
        "\n",
        "for epoch in range(500):\n",
        "    y_pred = model(x_train)\n",
        "    loss = criterion(y_pred, y_train)\n",
        "    optimizer.zero_grad()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "print(\"Parameters:\")\n",
        "for name, param in model.named_parameters():\n",
        "    print (name, param.data)\n",
        "\n",
        "y_pred = model(x_train)\n",
        "print(\"y_pred = \", y_pred)\n",
        "print(\"Predicted results:\", y_pred.argmax(axis=1))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "quyvdiYgvV6Z"
      },
      "source": [
        "# 16 多层感知机的情感分类实战"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3XhYRqDmvV6Z",
        "outputId": "766c22a8-ec77-4551-941d-dcd19bb806b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0\n",
            "set()\n",
            "\n",
            "[]\n"
          ]
        }
      ],
      "source": [
        "# 当字典里的key不存在但被查找时，返回的不是keyError而是一个默认值\n",
        "from collections import defaultdict\n",
        "\n",
        "dict1 = defaultdict(int)\n",
        "dict2 = defaultdict(set)\n",
        "dict3 = defaultdict(str)\n",
        "dict4 = defaultdict(list)\n",
        "\n",
        "print(dict1[1])\n",
        "print(dict2[1])\n",
        "print(dict3[1])\n",
        "print(dict4[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUeZdJsUvV6Z"
      },
      "source": [
        "## 16.1 词表"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "HJpr9O5hvV6Z"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "class Vocab:\n",
        "    def __init__(self, tokens=None):\n",
        "        self.idx_to_token = list()\n",
        "        self.token_to_idx = dict()\n",
        "\n",
        "        if tokens is not None:\n",
        "            if \"<unk>\" not in tokens:\n",
        "                tokens = tokens + \"<unk>\"\n",
        "            for token in tokens:\n",
        "                self.idx_to_token.append(token)\n",
        "                self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
        "            self.unk = self.token_to_idx['<unk>']\n",
        "\n",
        "    @classmethod\n",
        "\n",
        "    def build(cls, text, min_freq=1, reserved_tokens=None):\n",
        "        token_freqs = defaultdict(int)\n",
        "        for sentence in text:\n",
        "            for token in sentence:\n",
        "                token_freqs[token] += 1\n",
        "        uniq_tokens = [\"<unk>\"] + (reserved_tokens if reserved_tokens else [])\n",
        "        uniq_tokens += [token for token, freq in token_freqs.items() if freq >= min_freq and token != \"<unk>\"]\n",
        "        return cls(uniq_tokens)\n",
        "\n",
        "    def __len__(self):\n",
        "        # 返回词表的大小\n",
        "        return len(self.idx_to_token)\n",
        "\n",
        "    def __getitem__(self, token):\n",
        "        # 查找输入标记对应的索引值，如果1该标记不存在，则返回标记<unk>的索引值(0)\n",
        "        return self.token_to_idx.get(token, self.unk)\n",
        "\n",
        "    def convert_tokens_to_ids(self, tokens):\n",
        "        return [self[token] for token in tokens]\n",
        "\n",
        "    def convert_ids_to_tokens(self, indices):\n",
        "        return [self.idx_to_token[index] for index in indices]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nh_D9bjZvV6a"
      },
      "source": [
        "## 16.2 词向量层"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N3SMtPq1vV6a",
        "outputId": "218f97fd-67f4-44e0-d1c9-3524b9a50691"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[[-0.2874,  1.1182,  0.8203],\n",
              "         [-0.3586, -0.0279,  0.3434],\n",
              "         [ 1.1109, -0.9418, -1.0624],\n",
              "         [-0.3586, -0.0279,  0.3434]],\n",
              "\n",
              "        [[ 0.5823,  0.0403, -0.2253],\n",
              "         [ 0.6726,  1.5254, -0.1977],\n",
              "         [ 0.6726,  1.5254, -0.1977],\n",
              "         [-0.0047, -0.6160, -1.3325]]], grad_fn=<EmbeddingBackward>)"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 词表大小为8，向量维度为3\n",
        "embedding = nn.Embedding(8, 3)\n",
        "input = torch.tensor([[0, 1, 2, 1], [4, 6, 6, 7]], dtype=torch.long) # torch.long = torch.int64\n",
        "output = embedding(input)\n",
        "\n",
        "output\n",
        "# 即在原始输入后增加了一个长度为3的维"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WYoNsbnvV6a"
      },
      "source": [
        "## 16.3 融入词向量的MLP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F1uJdA3GvV6a",
        "outputId": "25dfc40c-e25a-40e0-c565-399b747c81d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[-0.4751, -0.9723],\n",
            "        [-0.4585, -1.0003]], grad_fn=<LogSoftmaxBackward>)\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "\n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_class):\n",
        "        super(MLP, self).__init__()\n",
        "        # 词向量层\n",
        "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
        "        # 线性变换：词向量层 -> 隐含层\n",
        "        self.linear1 = nn.Linear(embedding_dim, hidden_dim)\n",
        "        self.activate = F.relu\n",
        "        # 线性变换：激活层 -> 输出层\n",
        "        self.linear2 = nn.Linear(hidden_dim, num_class)\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        embeddings = self.embedding(inputs)\n",
        "        # 将序列中多个Embedding进行聚合（求平均）\n",
        "        embedding = embeddings.mean(dim=1)\n",
        "        hidden = self.activate(self.linear1(embedding))\n",
        "        outputs = self.linear2(hidden)\n",
        "        # 获得每个序列属于某一个类别概率的对数值\n",
        "        probs = F.log_softmax(outputs, dim=1)\n",
        "        return probs\n",
        "\n",
        "mlp = MLP(vocab_size=8, embedding_dim=3, hidden_dim=5, num_class=2)\n",
        "inputs = torch.tensor([[0, 1, 2, 1], [4, 6, 6, 7]], dtype=torch.long)\n",
        "outputs = mlp(inputs)\n",
        "print(outputs)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CoDfp660vV6a"
      },
      "source": [
        "## 16.4 文本长度统一"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-TQtzQ3jvV6a",
        "outputId": "e75bc22b-bf96-40f6-cc56-83075143b4e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0, 4, 5, 3, 6]\n",
            "tensor([ 0,  4,  9, 12])\n",
            "tensor([0, 1, 2, 1, 2, 1, 3, 7, 5, 6, 4, 2, 1, 3, 4, 3, 5, 7])\n",
            "tensor([[ 0.8808, -0.3131, -0.1360],\n",
            "        [ 0.2268,  0.1055,  0.1431],\n",
            "        [ 0.5301,  0.0950, -0.1027],\n",
            "        [-0.2971, -0.2679,  0.4890]], grad_fn=<EmbeddingBagBackward>)\n"
          ]
        }
      ],
      "source": [
        "input1 = torch.tensor([0, 1, 2, 1], dtype=torch.long)\n",
        "input2 = torch.tensor([2, 1, 3, 7, 5], dtype=torch.long)\n",
        "input3 = torch.tensor([6, 4, 2], dtype=torch.long)\n",
        "input4 = torch.tensor([1, 3, 4, 3, 5, 7], dtype=torch.long)\n",
        "inputs = [input1, input2, input3, input4]\n",
        "\n",
        "offsets = [0] + [i.shape[0] for i in inputs]\n",
        "print(offsets)\n",
        "\n",
        "# cumsum累加，即0+4=4，4+5=9，9+3=12\n",
        "offsets = torch.tensor(offsets[: -1]).cumsum(dim=0)\n",
        "print(offsets)\n",
        "\n",
        "inputs = torch.cat(inputs)\n",
        "print(inputs)\n",
        "embeddingbag = nn.EmbeddingBag(num_embeddings=8, embedding_dim=3)\n",
        "embeddings = embeddingbag(inputs, offsets)\n",
        "print(embeddings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZTN7_WCvV6a"
      },
      "source": [
        "## 16.5 数据处理"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "CypkznC9vV6b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
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              "   8001,\n",
              "   3519,\n",
              "   287,\n",
              "   76,\n",
              "   14587,\n",
              "   2355,\n",
              "   23,\n",
              "   14588,\n",
              "   14589,\n",
              "   22,\n",
              "   14590,\n",
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              "  0),\n",
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              "   1468,\n",
              "   81,\n",
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              "   78,\n",
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              "   276,\n",
              "   2,\n",
              "   27,\n",
              "   4009,\n",
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              "   32,\n",
              "   1309,\n",
              "   2284,\n",
              "   310,\n",
              "   32,\n",
              "   318,\n",
              "   252,\n",
              "   22,\n",
              "   218,\n",
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              "   14593,\n",
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              "   339,\n",
              "   1081,\n",
              "   84,\n",
              "   1195,\n",
              "   941,\n",
              "   14594,\n",
              "   2938,\n",
              "   14595,\n",
              "   3149,\n",
              "   84,\n",
              "   51,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   17,\n",
              "   14596,\n",
              "   4,\n",
              "   6642,\n",
              "   600,\n",
              "   14597,\n",
              "   63,\n",
              "   23,\n",
              "   788,\n",
              "   920,\n",
              "   276,\n",
              "   508,\n",
              "   32,\n",
              "   5319,\n",
              "   234,\n",
              "   22,\n",
              "   30,\n",
              "   414,\n",
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              "   98,\n",
              "   6379,\n",
              "   27,\n",
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              "   308,\n",
              "   10893,\n",
              "   76,\n",
              "   8253,\n",
              "   76,\n",
              "   414,\n",
              "   1900,\n",
              "   23,\n",
              "   4191,\n",
              "   531,\n",
              "   6,\n",
              "   3162,\n",
              "   3163,\n",
              "   4,\n",
              "   3160,\n",
              "   3161,\n",
              "   103,\n",
              "   169,\n",
              "   31,\n",
              "   14600,\n",
              "   102,\n",
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              "   1091,\n",
              "   14601,\n",
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              "   5357,\n",
              "   22,\n",
              "   23,\n",
              "   12240,\n",
              "   2,\n",
              "   14519,\n",
              "   2,\n",
              "   4,\n",
              "   7787,\n",
              "   12396,\n",
              "   63,\n",
              "   597,\n",
              "   1021,\n",
              "   2292,\n",
              "   76,\n",
              "   3956,\n",
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              "   5902,\n",
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              "   14602,\n",
              "   14603,\n",
              "   14604,\n",
              "   714,\n",
              "   6419,\n",
              "   95,\n",
              "   14605,\n",
              "   909,\n",
              "   715,\n",
              "   2,\n",
              "   23,\n",
              "   1858,\n",
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              "   32,\n",
              "   14607,\n",
              "   5684,\n",
              "   22,\n",
              "   1021,\n",
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              "   279,\n",
              "   4066,\n",
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              "   173,\n",
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              "   22,\n",
              "   7316,\n",
              "   2,\n",
              "   17,\n",
              "   1506,\n",
              "   32,\n",
              "   496,\n",
              "   777,\n",
              "   76,\n",
              "   402,\n",
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              "   14610,\n",
              "   32,\n",
              "   14611,\n",
              "   22,\n",
              "   2638,\n",
              "   328,\n",
              "   32,\n",
              "   381,\n",
              "   39,\n",
              "   4904,\n",
              "   2,\n",
              "   32,\n",
              "   381,\n",
              "   71,\n",
              "   1405,\n",
              "   14612,\n",
              "   76,\n",
              "   23,\n",
              "   60,\n",
              "   328,\n",
              "   51,\n",
              "   23,\n",
              "   1117,\n",
              "   4083,\n",
              "   927,\n",
              "   6994,\n",
              "   23,\n",
              "   9589,\n",
              "   2,\n",
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              "   2,\n",
              "   6643,\n",
              "   22,\n",
              "   23,\n",
              "   2291,\n",
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              "   442,\n",
              "   1710,\n",
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              "   28,\n",
              "   136,\n",
              "   766,\n",
              "   224,\n",
              "   22,\n",
              "   9570,\n",
              "   2798,\n",
              "   14613,\n",
              "   105,\n",
              "   10562,\n",
              "   22,\n",
              "   6926,\n",
              "   6,\n",
              "   1514,\n",
              "   23,\n",
              "   2055,\n",
              "   61,\n",
              "   2793,\n",
              "   836,\n",
              "   197,\n",
              "   6],\n",
              "  0),\n",
              " ([3227,\n",
              "   25,\n",
              "   97,\n",
              "   445,\n",
              "   14614,\n",
              "   442,\n",
              "   282,\n",
              "   22,\n",
              "   14615,\n",
              "   14616,\n",
              "   144,\n",
              "   219,\n",
              "   23,\n",
              "   1605,\n",
              "   219,\n",
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              "   219,\n",
              "   23,\n",
              "   11961,\n",
              "   2,\n",
              "   219,\n",
              "   7833,\n",
              "   61,\n",
              "   86,\n",
              "   6844,\n",
              "   3984,\n",
              "   4,\n",
              "   12778,\n",
              "   10697,\n",
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              "   173,\n",
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              "   69,\n",
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              "   551,\n",
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              "   8747,\n",
              "   8372,\n",
              "   10640,\n",
              "   76,\n",
              "   23,\n",
              "   1199,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   4440,\n",
              "   4441,\n",
              "   61,\n",
              "   32,\n",
              "   287,\n",
              "   22,\n",
              "   1974,\n",
              "   6],\n",
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              "   443,\n",
              "   57,\n",
              "   6406,\n",
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              "   14620,\n",
              "   5970,\n",
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              "   23,\n",
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              "   57,\n",
              "   130,\n",
              "   14621,\n",
              "   22,\n",
              "   4552,\n",
              "   4,\n",
              "   1156,\n",
              "   61,\n",
              "   2006,\n",
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              "   3239,\n",
              "   14517,\n",
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              "   14622,\n",
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              "   2834,\n",
              "   6484,\n",
              "   1617,\n",
              "   2228,\n",
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              "   14623,\n",
              "   2,\n",
              "   4,\n",
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              "   2874,\n",
              "   10748,\n",
              "   32,\n",
              "   1400,\n",
              "   567,\n",
              "   2,\n",
              "   51,\n",
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              "   350,\n",
              "   143,\n",
              "   23,\n",
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              "   438,\n",
              "   110,\n",
              "   754,\n",
              "   514,\n",
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              "   23,\n",
              "   1484,\n",
              "   22,\n",
              "   11899,\n",
              "   11900,\n",
              "   61,\n",
              "   27,\n",
              "   17,\n",
              "   7849,\n",
              "   23,\n",
              "   3609,\n",
              "   105,\n",
              "   1555,\n",
              "   4,\n",
              "   14627,\n",
              "   6],\n",
              "  0),\n",
              " ([9366,\n",
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              "   23,\n",
              "   14628,\n",
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              "   308,\n",
              "   32,\n",
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              "   624,\n",
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              "   51,\n",
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              "   105,\n",
              "   32,\n",
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              "   22,\n",
              "   2135,\n",
              "   4,\n",
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              " ([32,\n",
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              "   108,\n",
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              "   40,\n",
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              "   783,\n",
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              "   27,\n",
              "   173,\n",
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              "   3015,\n",
              "   22,\n",
              "   32,\n",
              "   3414,\n",
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              "   265,\n",
              "   91,\n",
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              "   22,\n",
              "   2665,\n",
              "   14633,\n",
              "   4,\n",
              "   32,\n",
              "   5633,\n",
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              "   76,\n",
              "   1024,\n",
              "   2203,\n",
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              "   597,\n",
              "   2873,\n",
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              "   14635,\n",
              "   2134,\n",
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              "   32,\n",
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              "   14636,\n",
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              "   3661,\n",
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              "   4,\n",
              "   14638,\n",
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              "   2,\n",
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              "   40,\n",
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              "   14639,\n",
              "   412,\n",
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              "   73,\n",
              "   1070,\n",
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              "   22,\n",
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              "   14644,\n",
              "   3281,\n",
              "   2875,\n",
              "   280,\n",
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              "   351,\n",
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              "   445,\n",
              "   637,\n",
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              "   3331,\n",
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              "   17,\n",
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              "   716,\n",
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              "   76,\n",
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              "   729,\n",
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              "   71,\n",
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              "   73,\n",
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              "   136,\n",
              "   3190,\n",
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              "   285,\n",
              "   777,\n",
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              "  0),\n",
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              "   606,\n",
              "   228,\n",
              "   22,\n",
              "   23,\n",
              "   1822,\n",
              "   8,\n",
              "   1811,\n",
              "   2,\n",
              "   23,\n",
              "   2472,\n",
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              "   240,\n",
              "   108,\n",
              "   1605,\n",
              "   606,\n",
              "   1858,\n",
              "   5401,\n",
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              "   308,\n",
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              "   32,\n",
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              "   308,\n",
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              "   169,\n",
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              "   4,\n",
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              "   127,\n",
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              "   63,\n",
              "   5052,\n",
              "   63,\n",
              "   32,\n",
              "   81,\n",
              "   1551,\n",
              "   853,\n",
              "   3529,\n",
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              "   5620,\n",
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              "   110,\n",
              "   5232,\n",
              "   103,\n",
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              "   7741,\n",
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              "   32,\n",
              "   14656,\n",
              "   2,\n",
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              "   18,\n",
              "   3883,\n",
              "   22,\n",
              "   1158,\n",
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              "   57,\n",
              "   23,\n",
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              "   2,\n",
              "   65,\n",
              "   1128,\n",
              "   27,\n",
              "   286,\n",
              "   2793,\n",
              "   32,\n",
              "   816,\n",
              "   22,\n",
              "   1071,\n",
              "   57,\n",
              "   2434,\n",
              "   2435,\n",
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              "   32,\n",
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              "   105,\n",
              "   32,\n",
              "   1468,\n",
              "   2432,\n",
              "   40,\n",
              "   6728,\n",
              "   14657,\n",
              "   4,\n",
              "   1758,\n",
              "   2,\n",
              "   4,\n",
              "   130,\n",
              "   61,\n",
              "   32,\n",
              "   138,\n",
              "   228,\n",
              "   73,\n",
              "   181,\n",
              "   4433,\n",
              "   14219,\n",
              "   81,\n",
              "   2,\n",
              "   23,\n",
              "   14658,\n",
              "   2877,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   81,\n",
              "   61,\n",
              "   32,\n",
              "   11018,\n",
              "   22,\n",
              "   10320,\n",
              "   1450,\n",
              "   2,\n",
              "   63,\n",
              "   7931,\n",
              "   2096,\n",
              "   4,\n",
              "   14659,\n",
              "   2638,\n",
              "   14660,\n",
              "   449,\n",
              "   91,\n",
              "   23,\n",
              "   1582,\n",
              "   13116,\n",
              "   247,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   424,\n",
              "   81,\n",
              "   105,\n",
              "   289,\n",
              "   362,\n",
              "   301,\n",
              "   144,\n",
              "   23,\n",
              "   384,\n",
              "   4,\n",
              "   32,\n",
              "   60,\n",
              "   449,\n",
              "   75,\n",
              "   78,\n",
              "   550,\n",
              "   17,\n",
              "   6],\n",
              "  0),\n",
              " ([14661,\n",
              "   9834,\n",
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              "   23,\n",
              "   412,\n",
              "   22,\n",
              "   14662,\n",
              "   2,\n",
              "   6779,\n",
              "   23,\n",
              "   11063,\n",
              "   352,\n",
              "   2,\n",
              "   1551,\n",
              "   32,\n",
              "   4839,\n",
              "   14663,\n",
              "   1652,\n",
              "   438,\n",
              "   181,\n",
              "   6943,\n",
              "   2,\n",
              "   4754,\n",
              "   218,\n",
              "   105,\n",
              "   23,\n",
              "   346,\n",
              "   14664,\n",
              "   4,\n",
              "   4628,\n",
              "   14665,\n",
              "   278,\n",
              "   76,\n",
              "   308,\n",
              "   6352,\n",
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              "   484,\n",
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              "  0),\n",
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              " ([14667,\n",
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              "   3951,\n",
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              "   9034,\n",
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              "   22,\n",
              "   14669,\n",
              "   2,\n",
              "   2793,\n",
              "   9398,\n",
              "   4,\n",
              "   836,\n",
              "   941,\n",
              "   14670,\n",
              "   14671,\n",
              "   22,\n",
              "   14672,\n",
              "   2119,\n",
              "   181,\n",
              "   2751,\n",
              "   76,\n",
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              "   181,\n",
              "   4274,\n",
              "   1502,\n",
              "   2,\n",
              "   51,\n",
              "   252,\n",
              "   3420,\n",
              "   22,\n",
              "   108,\n",
              "   61,\n",
              "   23,\n",
              "   4274,\n",
              "   464],\n",
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              "   635,\n",
              "   14673,\n",
              "   14674,\n",
              "   6231,\n",
              "   22,\n",
              "   14675,\n",
              "   14676,\n",
              "   2,\n",
              "   7,\n",
              "   9894,\n",
              "   799,\n",
              "   194,\n",
              "   12112,\n",
              "   6],\n",
              "  0),\n",
              " ([126,\n",
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              "   2096,\n",
              "   5998,\n",
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              "   228,\n",
              "   274,\n",
              "   338,\n",
              "   23,\n",
              "   2257,\n",
              "   22,\n",
              "   1605,\n",
              "   2582,\n",
              "   300,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   7,\n",
              "   62,\n",
              "   8,\n",
              "   10975,\n",
              "   27,\n",
              "   7,\n",
              "   1396,\n",
              "   76,\n",
              "   508,\n",
              "   373,\n",
              "   310,\n",
              "   23,\n",
              "   2332,\n",
              "   60,\n",
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              " ([444,\n",
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              "   81,\n",
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              "   32,\n",
              "   2245,\n",
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              "   5703,\n",
              "   1235,\n",
              "   22,\n",
              "   3066,\n",
              "   1701,\n",
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              "   1199,\n",
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              "   2,\n",
              "   853,\n",
              "   13806,\n",
              "   28,\n",
              "   308,\n",
              "   753,\n",
              "   224,\n",
              "   76,\n",
              "   2950,\n",
              "   86,\n",
              "   14679,\n",
              "   6175,\n",
              "   144,\n",
              "   23,\n",
              "   6464,\n",
              "   7475,\n",
              "   6,\n",
              "   51,\n",
              "   57,\n",
              "   9925,\n",
              "   14680,\n",
              "   2,\n",
              "   23,\n",
              "   387,\n",
              "   9656,\n",
              "   310,\n",
              "   23,\n",
              "   14681,\n",
              "   14682,\n",
              "   14683,\n",
              "   371,\n",
              "   442,\n",
              "   23,\n",
              "   8140,\n",
              "   6],\n",
              "  0),\n",
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              "   2661,\n",
              "   14684,\n",
              "   598,\n",
              "   23,\n",
              "   14685,\n",
              "   22,\n",
              "   32,\n",
              "   14686,\n",
              "   2829,\n",
              "   10114,\n",
              "   4,\n",
              "   5088,\n",
              "   27,\n",
              "   173,\n",
              "   14687,\n",
              "   310,\n",
              "   14688,\n",
              "   1373,\n",
              "   4,\n",
              "   1508,\n",
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              "  0),\n",
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              " ([329,\n",
              "   3462,\n",
              "   96,\n",
              "   22,\n",
              "   3435,\n",
              "   126,\n",
              "   329,\n",
              "   727,\n",
              "   30,\n",
              "   17,\n",
              "   2,\n",
              "   51,\n",
              "   329,\n",
              "   727,\n",
              "   1295,\n",
              "   342,\n",
              "   471,\n",
              "   1637,\n",
              "   17,\n",
              "   9397,\n",
              "   438,\n",
              "   1388,\n",
              "   2461,\n",
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              " ([32,\n",
              "   10832,\n",
              "   14695,\n",
              "   228,\n",
              "   78,\n",
              "   774,\n",
              "   3839,\n",
              "   1254,\n",
              "   4251,\n",
              "   2995,\n",
              "   162,\n",
              "   3487,\n",
              "   86,\n",
              "   11031,\n",
              "   806,\n",
              "   551,\n",
              "   65,\n",
              "   903,\n",
              "   76,\n",
              "   85,\n",
              "   600,\n",
              "   9590,\n",
              "   310,\n",
              "   6487,\n",
              "   491,\n",
              "   6],\n",
              "  0),\n",
              " ([144,\n",
              "   23,\n",
              "   4793,\n",
              "   2,\n",
              "   7,\n",
              "   32,\n",
              "   14696,\n",
              "   3201,\n",
              "   1994,\n",
              "   2,\n",
              "   51,\n",
              "   17,\n",
              "   173,\n",
              "   32,\n",
              "   816,\n",
              "   57,\n",
              "   231,\n",
              "   105,\n",
              "   14697,\n",
              "   4,\n",
              "   13686,\n",
              "   4433,\n",
              "   1034,\n",
              "   2,\n",
              "   300,\n",
              "   355,\n",
              "   23,\n",
              "   1409,\n",
              "   1071,\n",
              "   22,\n",
              "   14698,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   4357,\n",
              "   27,\n",
              "   136,\n",
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              "   23,\n",
              "   4968,\n",
              "   10209,\n",
              "   3258,\n",
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              "   493,\n",
              "   793,\n",
              "   4,\n",
              "   14699,\n",
              "   884,\n",
              "   8,\n",
              "   14700,\n",
              "   2,\n",
              "   13288,\n",
              "   4,\n",
              "   7908,\n",
              "   1718,\n",
              "   14472,\n",
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              " ([444,\n",
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              "   40,\n",
              "   32,\n",
              "   1133,\n",
              "   76,\n",
              "   14701,\n",
              "   23,\n",
              "   702,\n",
              "   2,\n",
              "   1290,\n",
              "   14702,\n",
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              "   388,\n",
              "   27,\n",
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              "   1662,\n",
              "   11126,\n",
              "   2,\n",
              "   2909,\n",
              "   1861,\n",
              "   2,\n",
              "   4,\n",
              "   108,\n",
              "   23,\n",
              "   14704,\n",
              "   3508,\n",
              "   14705,\n",
              "   11143,\n",
              "   65,\n",
              "   256,\n",
              "   1227,\n",
              "   1617,\n",
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              "   23,\n",
              "   848,\n",
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              "   1612,\n",
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              "   232,\n",
              "   22,\n",
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              "   12507,\n",
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              "   4891,\n",
              "   97,\n",
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              "   2573,\n",
              "   1675,\n",
              "   14707,\n",
              "   14708],\n",
              "  0),\n",
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              "   5772,\n",
              "   76,\n",
              "   10746,\n",
              "   2128,\n",
              "   6,\n",
              "   14709,\n",
              "   4,\n",
              "   181,\n",
              "   14710,\n",
              "   63,\n",
              "   14309,\n",
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              "   51,\n",
              "   2128,\n",
              "   6,\n",
              "   9750,\n",
              "   14711,\n",
              "   2,\n",
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              "   81,\n",
              "   3530,\n",
              "   218,\n",
              "   76,\n",
              "   1053,\n",
              "   414,\n",
              "   2,\n",
              "   1996,\n",
              "   2,\n",
              "   63,\n",
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              "   105,\n",
              "   32,\n",
              "   14712,\n",
              "   58,\n",
              "   14713,\n",
              "   23,\n",
              "   1034,\n",
              "   22,\n",
              "   181,\n",
              "   2756,\n",
              "   2,\n",
              "   14714,\n",
              "   260,\n",
              "   23,\n",
              "   851,\n",
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              "   141,\n",
              "   144,\n",
              "   110,\n",
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              "   1054,\n",
              "   17,\n",
              "   12472,\n",
              "   2,\n",
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              "   447,\n",
              "   3720,\n",
              "   282,\n",
              "   2672,\n",
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              "   14716,\n",
              "   14717,\n",
              "   27,\n",
              "   4009,\n",
              "   4885,\n",
              "   1071,\n",
              "   110,\n",
              "   7474,\n",
              "   8244,\n",
              "   2,\n",
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              "   27,\n",
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              "   14720,\n",
              "   103,\n",
              "   2011,\n",
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              "   4,\n",
              "   14724,\n",
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              "   2751,\n",
              "   22,\n",
              "   23,\n",
              "   1665,\n",
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              "   6,\n",
              "   6,\n",
              "   465,\n",
              "   14725,\n",
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              "   76,\n",
              "   32,\n",
              "   1117,\n",
              "   496,\n",
              "   2257,\n",
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              "  0),\n",
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              "   1821,\n",
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              "   60,\n",
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              "   777,\n",
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              "   276,\n",
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              "   686,\n",
              "   185,\n",
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              "  0),\n",
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              "   592,\n",
              "   4619,\n",
              "   1030,\n",
              "   6676,\n",
              "   4670,\n",
              "   729,\n",
              "   23,\n",
              "   13592,\n",
              "   3943,\n",
              "   57,\n",
              "   23,\n",
              "   743,\n",
              "   1858,\n",
              "   2,\n",
              "   3518,\n",
              "   345,\n",
              "   14727,\n",
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              "   91,\n",
              "   23,\n",
              "   60,\n",
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              "   105,\n",
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              "   23,\n",
              "   1057,\n",
              "   14731,\n",
              "   227,\n",
              "   1243,\n",
              "   63,\n",
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              "   63,\n",
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              "   462,\n",
              "   86,\n",
              "   14732,\n",
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              "   76,\n",
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              "   3495,\n",
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              "   32,\n",
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              "   886,\n",
              "   2896,\n",
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              "   3194,\n",
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              "   467,\n",
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              "   23,\n",
              "   14740,\n",
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              "   14741,\n",
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              "   267,\n",
              "   10467,\n",
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              "   23,\n",
              "   12235,\n",
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              "   32,\n",
              "   4755,\n",
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              "   28,\n",
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              "   80,\n",
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              "   2,\n",
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              "   2,\n",
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              "   2,\n",
              "   922,\n",
              "   2416,\n",
              "   2,\n",
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              "   256,\n",
              "   3614,\n",
              "   78,\n",
              "   2768,\n",
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              "   144,\n",
              "   23,\n",
              "   6240,\n",
              "   4,\n",
              "   2793,\n",
              "   308,\n",
              "   14747,\n",
              "   6,\n",
              "   2463,\n",
              "   355,\n",
              "   130,\n",
              "   29,\n",
              "   17,\n",
              "   1804,\n",
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              " ([32,\n",
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              "   2278,\n",
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              "   182,\n",
              "   2,\n",
              "   5278,\n",
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              "   22,\n",
              "   2481,\n",
              "   14750,\n",
              "   103,\n",
              "   381,\n",
              "   4,\n",
              "   1811,\n",
              "   187,\n",
              "   410,\n",
              "   600,\n",
              "   14751,\n",
              "   6468,\n",
              "   23,\n",
              "   10348,\n",
              "   22,\n",
              "   2094,\n",
              "   5125,\n",
              "   289,\n",
              "   6,\n",
              "   2793,\n",
              "   2,\n",
              "   130,\n",
              "   1994,\n",
              "   61,\n",
              "   447,\n",
              "   2,\n",
              "   4,\n",
              "   6949,\n",
              "   76,\n",
              "   272,\n",
              "   1546,\n",
              "   2106,\n",
              "   1631,\n",
              "   6],\n",
              "  0),\n",
              " ([7,\n",
              "   6577,\n",
              "   2888,\n",
              "   32,\n",
              "   14752,\n",
              "   14753,\n",
              "   105,\n",
              "   492,\n",
              "   4053,\n",
              "   14754,\n",
              "   2,\n",
              "   4,\n",
              "   768,\n",
              "   1034,\n",
              "   110,\n",
              "   4744,\n",
              "   57,\n",
              "   1605,\n",
              "   2572,\n",
              "   22,\n",
              "   23,\n",
              "   2896,\n",
              "   6],\n",
              "  0),\n",
              " ([1434,\n",
              "   2,\n",
              "   193,\n",
              "   2300,\n",
              "   2,\n",
              "   76,\n",
              "   1053,\n",
              "   130,\n",
              "   10644,\n",
              "   4,\n",
              "   14755,\n",
              "   292,\n",
              "   144,\n",
              "   23,\n",
              "   743,\n",
              "   1199,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   11436,\n",
              "   14756,\n",
              "   442,\n",
              "   32,\n",
              "   14757,\n",
              "   162,\n",
              "   455,\n",
              "   136,\n",
              "   196,\n",
              "   966,\n",
              "   76,\n",
              "   10152,\n",
              "   51,\n",
              "   2793,\n",
              "   173,\n",
              "   23,\n",
              "   12444,\n",
              "   4,\n",
              "   4553,\n",
              "   76,\n",
              "   1226,\n",
              "   471,\n",
              "   110,\n",
              "   3657,\n",
              "   6],\n",
              "  0),\n",
              " ([17,\n",
              "   61,\n",
              "   3578,\n",
              "   749,\n",
              "   14758,\n",
              "   6,\n",
              "   57,\n",
              "   14759,\n",
              "   1701,\n",
              "   7728,\n",
              "   2,\n",
              "   130,\n",
              "   2418,\n",
              "   173,\n",
              "   11354,\n",
              "   4838,\n",
              "   76,\n",
              "   780,\n",
              "   4,\n",
              "   61,\n",
              "   1381,\n",
              "   8,\n",
              "   57,\n",
              "   23,\n",
              "   1417,\n",
              "   6],\n",
              "  0),\n",
              " ([7,\n",
              "   97,\n",
              "   1444,\n",
              "   32,\n",
              "   14760,\n",
              "   7153,\n",
              "   51,\n",
              "   23,\n",
              "   8666,\n",
              "   61,\n",
              "   2885,\n",
              "   2,\n",
              "   201,\n",
              "   687,\n",
              "   438,\n",
              "   23,\n",
              "   14761,\n",
              "   6],\n",
              "  0),\n",
              " ([130,\n",
              "   61,\n",
              "   729,\n",
              "   9925,\n",
              "   727,\n",
              "   152,\n",
              "   40,\n",
              "   227,\n",
              "   14762,\n",
              "   144,\n",
              "   32,\n",
              "   3596,\n",
              "   22,\n",
              "   14680,\n",
              "   14763,\n",
              "   2,\n",
              "   13774,\n",
              "   234,\n",
              "   23,\n",
              "   1468,\n",
              "   2896,\n",
              "   2,\n",
              "   4,\n",
              "   85,\n",
              "   32,\n",
              "   6194,\n",
              "   14764,\n",
              "   76,\n",
              "   23,\n",
              "   221,\n",
              "   14765,\n",
              "   84,\n",
              "   1018,\n",
              "   6],\n",
              "  0),\n",
              " ([5642,\n",
              "   63,\n",
              "   32,\n",
              "   3854,\n",
              "   2,\n",
              "   14766,\n",
              "   3304,\n",
              "   2,\n",
              "   14767,\n",
              "   14768,\n",
              "   12024,\n",
              "   32,\n",
              "   1357,\n",
              "   14769,\n",
              "   6],\n",
              "  0),\n",
              " ([14770,\n",
              "   3290,\n",
              "   5853,\n",
              "   4,\n",
              "   2662,\n",
              "   3146,\n",
              "   2,\n",
              "   2708,\n",
              "   8634,\n",
              "   76,\n",
              "   1974,\n",
              "   81,\n",
              "   3701,\n",
              "   4,\n",
              "   1750,\n",
              "   14771,\n",
              "   6],\n",
              "  0),\n",
              " ([7,\n",
              "   32,\n",
              "   3495,\n",
              "   3153,\n",
              "   22,\n",
              "   14772,\n",
              "   4,\n",
              "   267,\n",
              "   287,\n",
              "   14773,\n",
              "   6,\n",
              "   51,\n",
              "   729,\n",
              "   1672,\n",
              "   14772,\n",
              "   17,\n",
              "   61,\n",
              "   230,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   57,\n",
              "   130,\n",
              "   8593,\n",
              "   110,\n",
              "   3958,\n",
              "   61,\n",
              "   14774,\n",
              "   6],\n",
              "  0),\n",
              " ([86, 702, 14775, 78, 86, 4281, 105, 96, 6], 0),\n",
              " ([17,\n",
              "   280,\n",
              "   7357,\n",
              "   14776,\n",
              "   76,\n",
              "   23,\n",
              "   60,\n",
              "   4,\n",
              "   1195,\n",
              "   162,\n",
              "   330,\n",
              "   17,\n",
              "   438,\n",
              "   5012,\n",
              "   3529,\n",
              "   6],\n",
              "  0),\n",
              " ([14777, 955, 714, 4, 8661, 715, 4195, 81, 1556, 6], 0),\n",
              " ([23,\n",
              "   228,\n",
              "   7265,\n",
              "   23,\n",
              "   11255,\n",
              "   14778,\n",
              "   22,\n",
              "   2582,\n",
              "   2916,\n",
              "   105,\n",
              "   14779,\n",
              "   2116,\n",
              "   22,\n",
              "   9728,\n",
              "   4,\n",
              "   3260,\n",
              "   6],\n",
              "  0),\n",
              " ([444,\n",
              "   905,\n",
              "   2538,\n",
              "   2299,\n",
              "   69,\n",
              "   11658,\n",
              "   442,\n",
              "   2361,\n",
              "   300,\n",
              "   4,\n",
              "   14780,\n",
              "   2,\n",
              "   444,\n",
              "   445,\n",
              "   32,\n",
              "   14781,\n",
              "   22,\n",
              "   3066,\n",
              "   1701,\n",
              "   2,\n",
              "   130,\n",
              "   61,\n",
              "   905,\n",
              "   5023,\n",
              "   6],\n",
              "  0),\n",
              " ([7,\n",
              "   32,\n",
              "   9492,\n",
              "   292,\n",
              "   27,\n",
              "   391,\n",
              "   76,\n",
              "   2176,\n",
              "   5825,\n",
              "   105,\n",
              "   7897,\n",
              "   2,\n",
              "   126,\n",
              "   17,\n",
              "   5695,\n",
              "   32,\n",
              "   5337,\n",
              "   1858,\n",
              "   22,\n",
              "   23,\n",
              "   13112,\n",
              "   4132,\n",
              "   6],\n",
              "  0),\n",
              " ([17,\n",
              "   61,\n",
              "   6351,\n",
              "   1030,\n",
              "   4,\n",
              "   1514,\n",
              "   84,\n",
              "   3768,\n",
              "   14782,\n",
              "   84,\n",
              "   6993,\n",
              "   271,\n",
              "   218,\n",
              "   23,\n",
              "   6040,\n",
              "   22,\n",
              "   23,\n",
              "   72,\n",
              "   6],\n",
              "  0),\n",
              " ([63,\n",
              "   14783,\n",
              "   63,\n",
              "   23,\n",
              "   626,\n",
              "   1123,\n",
              "   103,\n",
              "   2,\n",
              "   23,\n",
              "   6773,\n",
              "   162,\n",
              "   7786,\n",
              "   1216,\n",
              "   3258,\n",
              "   5863,\n",
              "   23,\n",
              "   1483,\n",
              "   22,\n",
              "   6399,\n",
              "   191,\n",
              "   14784,\n",
              "   57,\n",
              "   32,\n",
              "   777,\n",
              "   27,\n",
              "   1367,\n",
              "   2953,\n",
              "   173,\n",
              "   285,\n",
              "   3677,\n",
              "   6],\n",
              "  0),\n",
              " ([355,\n",
              "   1291,\n",
              "   547,\n",
              "   1531,\n",
              "   282,\n",
              "   82,\n",
              "   40,\n",
              "   7068,\n",
              "   6,\n",
              "   17,\n",
              "   696,\n",
              "   928,\n",
              "   442,\n",
              "   5701,\n",
              "   32,\n",
              "   1978,\n",
              "   1756,\n",
              "   105,\n",
              "   32,\n",
              "   2542,\n",
              "   58,\n",
              "   2,\n",
              "   4,\n",
              "   442,\n",
              "   1489,\n",
              "   110,\n",
              "   1081,\n",
              "   809,\n",
              "   22,\n",
              "   4101,\n",
              "   2855,\n",
              "   272,\n",
              "   3056,\n",
              "   2875,\n",
              "   14785,\n",
              "   4,\n",
              "   3767,\n",
              "   6],\n",
              "  0),\n",
              " ([7,\n",
              "   32,\n",
              "   8533,\n",
              "   4,\n",
              "   32,\n",
              "   1208,\n",
              "   2,\n",
              "   4,\n",
              "   32,\n",
              "   3495,\n",
              "   777,\n",
              "   40,\n",
              "   23,\n",
              "   423,\n",
              "   276,\n",
              "   76,\n",
              "   1053,\n",
              "   729,\n",
              "   32,\n",
              "   9222,\n",
              "   61,\n",
              "   355,\n",
              "   551,\n",
              "   175,\n",
              "   97,\n",
              "   1299,\n",
              "   23,\n",
              "   1085,\n",
              "   14786,\n",
              "   14787,\n",
              "   14788,\n",
              "   6],\n",
              "  0),\n",
              " ([1811,\n",
              "   438,\n",
              "   926,\n",
              "   2,\n",
              "   51,\n",
              "   110,\n",
              "   1021,\n",
              "   61,\n",
              "   57,\n",
              "   23,\n",
              "   1022,\n",
              "   466,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   9458,\n",
              "   4,\n",
              "   5276,\n",
              "   6],\n",
              "  0),\n",
              " ([32, 5337, 2, 2994, 1804, 292, 5279, 234, 144, 23, 373, 6678, 22, 1071, 6],\n",
              "  0),\n",
              " ([4837,\n",
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              "   442,\n",
              "   153,\n",
              "   3943,\n",
              "   32,\n",
              "   926,\n",
              "   228,\n",
              "   2,\n",
              "   51,\n",
              "   110,\n",
              "   6396,\n",
              "   32,\n",
              "   915,\n",
              "   1484,\n",
              "   5759,\n",
              "   4,\n",
              "   8434,\n",
              "   22,\n",
              "   7068,\n",
              "   6],\n",
              "  0),\n",
              " ([14789,\n",
              "   2704,\n",
              "   61,\n",
              "   3495,\n",
              "   57,\n",
              "   181,\n",
              "   2875,\n",
              "   51,\n",
              "   123,\n",
              "   14790,\n",
              "   23,\n",
              "   313,\n",
              "   438,\n",
              "   181,\n",
              "   10697,\n",
              "   1048,\n",
              "   2,\n",
              "   63,\n",
              "   185,\n",
              "   103,\n",
              "   971,\n",
              "   22,\n",
              "   1112,\n",
              "   4,\n",
              "   922,\n",
              "   5568,\n",
              "   40,\n",
              "   3231,\n",
              "   57,\n",
              "   130,\n",
              "   292,\n",
              "   6],\n",
              "  0),\n",
              " ([71,\n",
              "   32,\n",
              "   5191,\n",
              "   22,\n",
              "   1471,\n",
              "   2,\n",
              "   14791,\n",
              "   14373,\n",
              "   73,\n",
              "   23,\n",
              "   14792,\n",
              "   2,\n",
              "   9546,\n",
              "   6659,\n",
              "   11608,\n",
              "   23,\n",
              "   3686,\n",
              "   413,\n",
              "   11403,\n",
              "   6],\n",
              "  0),\n",
              " ([14793,\n",
              "   2339,\n",
              "   2,\n",
              "   3754,\n",
              "   331,\n",
              "   17,\n",
              "   61,\n",
              "   14794,\n",
              "   22,\n",
              "   110,\n",
              "   266,\n",
              "   4947,\n",
              "   22,\n",
              "   23,\n",
              "   2406,\n",
              "   6],\n",
              "  0),\n",
              " ([1897,\n",
              "   32,\n",
              "   2798,\n",
              "   1994,\n",
              "   13817,\n",
              "   13285,\n",
              "   105,\n",
              "   8641,\n",
              "   2,\n",
              "   1514,\n",
              "   388,\n",
              "   63,\n",
              "   7930,\n",
              "   76,\n",
              "   653,\n",
              "   449,\n",
              "   6],\n",
              "  0),\n",
              " ([7,\n",
              "   86,\n",
              "   941,\n",
              "   60,\n",
              "   2,\n",
              "   51,\n",
              "   32,\n",
              "   14795,\n",
              "   107,\n",
              "   2,\n",
              "   701,\n",
              "   1685,\n",
              "   4,\n",
              "   14796,\n",
              "   1757,\n",
              "   12661,\n",
              "   29,\n",
              "   388,\n",
              "   32,\n",
              "   3770,\n",
              "   242,\n",
              "   14797,\n",
              "   1470,\n",
              "   6],\n",
              "  0),\n",
              " ([3484, 308, 333, 76, 308, 14798, 6], 0),\n",
              " ([3651,\n",
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              "   4,\n",
              "   4501,\n",
              "   14799,\n",
              "   884,\n",
              "   272,\n",
              "   22,\n",
              "   600,\n",
              "   606,\n",
              "   1034,\n",
              "   57,\n",
              "   600,\n",
              "   6933,\n",
              "   8129,\n",
              "   2,\n",
              "   51,\n",
              "   193,\n",
              "   308,\n",
              "   7317,\n",
              "   227,\n",
              "   4089,\n",
              "   1320,\n",
              "   282,\n",
              "   14800,\n",
              "   197,\n",
              "   6],\n",
              "  0),\n",
              " ([540,\n",
              "   27,\n",
              "   173,\n",
              "   76,\n",
              "   884,\n",
              "   105,\n",
              "   13302,\n",
              "   4,\n",
              "   8541,\n",
              "   2,\n",
              "   4,\n",
              "   540,\n",
              "   27,\n",
              "   173,\n",
              "   76,\n",
              "   884,\n",
              "   105,\n",
              "   8953,\n",
              "   14801,\n",
              "   2,\n",
              "   9474,\n",
              "   14802,\n",
              "   4,\n",
              "   597,\n",
              "   14803,\n",
              "   4189,\n",
              "   2,\n",
              "   349,\n",
              "   18,\n",
              "   4,\n",
              "   125,\n",
              "   6],\n",
              "  0),\n",
              " ([1855,\n",
              "   4731,\n",
              "   6329,\n",
              "   219,\n",
              "   61,\n",
              "   729,\n",
              "   17,\n",
              "   61,\n",
              "   2048,\n",
              "   32,\n",
              "   4839,\n",
              "   2,\n",
              "   6453,\n",
              "   1287,\n",
              "   228,\n",
              "   6,\n",
              "   6,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   222,\n",
              "   14804,\n",
              "   402,\n",
              "   2,\n",
              "   32,\n",
              "   81,\n",
              "   27,\n",
              "   3642,\n",
              "   105,\n",
              "   32,\n",
              "   1468,\n",
              "   120,\n",
              "   57,\n",
              "   86,\n",
              "   4489,\n",
              "   694,\n",
              "   777,\n",
              "   6],\n",
              "  0),\n",
              " ([598,\n",
              "   22,\n",
              "   10501,\n",
              "   1034,\n",
              "   4,\n",
              "   22,\n",
              "   4389,\n",
              "   188,\n",
              "   57,\n",
              "   2467,\n",
              "   280,\n",
              "   308,\n",
              "   11297,\n",
              "   442,\n",
              "   4379,\n",
              "   1648,\n",
              "   6],\n",
              "  0),\n",
              " ([110,\n",
              "   6855,\n",
              "   103,\n",
              "   13119,\n",
              "   3609,\n",
              "   2,\n",
              "   4,\n",
              "   7,\n",
              "   782,\n",
              "   4400,\n",
              "   3000,\n",
              "   76,\n",
              "   308,\n",
              "   32,\n",
              "   1242,\n",
              "   6,\n",
              "   14805,\n",
              "   14806,\n",
              "   636,\n",
              "   5286,\n",
              "   331,\n",
              "   22,\n",
              "   169,\n",
              "   4174,\n",
              "   127,\n",
              "   442,\n",
              "   4779,\n",
              "   4312,\n",
              "   4,\n",
              "   5280,\n",
              "   5281,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   228,\n",
              "   61,\n",
              "   5901,\n",
              "   76,\n",
              "   729,\n",
              "   95,\n",
              "   11271,\n",
              "   103,\n",
              "   23,\n",
              "   13530,\n",
              "   14807,\n",
              "   6713,\n",
              "   84,\n",
              "   27,\n",
              "   255,\n",
              "   177,\n",
              "   162,\n",
              "   255,\n",
              "   103,\n",
              "   144,\n",
              "   23,\n",
              "   14808,\n",
              "   22,\n",
              "   298,\n",
              "   3299,\n",
              "   2,\n",
              "   51,\n",
              "   255,\n",
              "   136,\n",
              "   153,\n",
              "   851,\n",
              "   162,\n",
              "   65,\n",
              "   686,\n",
              "   342,\n",
              "   298,\n",
              "   1068,\n",
              "   410,\n",
              "   4,\n",
              "   27,\n",
              "   96,\n",
              "   61,\n",
              "   32,\n",
              "   12603,\n",
              "   4,\n",
              "   4594,\n",
              "   14809,\n",
              "   153,\n",
              "   1713,\n",
              "   471,\n",
              "   941,\n",
              "   449,\n",
              "   103,\n",
              "   6],\n",
              "  0),\n",
              " ([3892,\n",
              "   9750,\n",
              "   14810,\n",
              "   141,\n",
              "   14811,\n",
              "   14812,\n",
              "   6775,\n",
              "   173,\n",
              "   196,\n",
              "   51,\n",
              "   1535,\n",
              "   40,\n",
              "   110,\n",
              "   14813,\n",
              "   22,\n",
              "   713,\n",
              "   1684,\n",
              "   7869,\n",
              "   6],\n",
              "  0),\n",
              " ([32, 3665, 14814, 1594, 6], 0),\n",
              " ([444,\n",
              "   449,\n",
              "   256,\n",
              "   508,\n",
              "   3280,\n",
              "   23,\n",
              "   8940,\n",
              "   4385,\n",
              "   4,\n",
              "   8896,\n",
              "   10810,\n",
              "   22,\n",
              "   219,\n",
              "   23,\n",
              "   7234,\n",
              "   219,\n",
              "   1570,\n",
              "   508,\n",
              "   32,\n",
              "   14815,\n",
              "   1994,\n",
              "   27,\n",
              "   61,\n",
              "   32,\n",
              "   704,\n",
              "   14816,\n",
              "   4,\n",
              "   32,\n",
              "   60,\n",
              "   27,\n",
              "   61,\n",
              "   3768,\n",
              "   282,\n",
              "   449,\n",
              "   280,\n",
              "   1061,\n",
              "   1053,\n",
              "   2447,\n",
              "   1161,\n",
              "   6],\n",
              "  0),\n",
              " ([185,\n",
              "   103,\n",
              "   63,\n",
              "   492,\n",
              "   2364,\n",
              "   63,\n",
              "   4904,\n",
              "   2,\n",
              "   51,\n",
              "   639,\n",
              "   2,\n",
              "   17,\n",
              "   1506,\n",
              "   1626,\n",
              "   57,\n",
              "   23,\n",
              "   2027,\n",
              "   22,\n",
              "   1804,\n",
              "   1480,\n",
              "   2,\n",
              "   4,\n",
              "   7,\n",
              "   32,\n",
              "   3056,\n",
              "   5022,\n",
              "   76,\n",
              "   23,\n",
              "   9594,\n",
              "   22,\n",
              "   32,\n",
              "   1119,\n",
              "   27,\n",
              "   768,\n",
              "   6416,\n",
              "   144,\n",
              "   3483,\n",
              "   6],\n",
              "  0),\n",
              " ([32, 4008, 1242, 105, 32, 1154, 2257, 22, 1742, 4, 14817, 2721, 6], 0),\n",
              " ([286,\n",
              "   224,\n",
              "   6607,\n",
              "   76,\n",
              "   29,\n",
              "   17,\n",
              "   77,\n",
              "   63,\n",
              "   10320,\n",
              "   2,\n",
              "   4,\n",
              "   224,\n",
              "   4232,\n",
              "   76,\n",
              "   29,\n",
              "   17,\n",
              "   14818,\n",
              "   6],\n",
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              "   32,\n",
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              "   444,\n",
              "   1800,\n",
              "   771,\n",
              "   2,\n",
              "   4636,\n",
              "   22,\n",
              "   23,\n",
              "   14819,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   3657,\n",
              "   371,\n",
              "   442,\n",
              "   4329,\n",
              "   105,\n",
              "   32,\n",
              "   14820,\n",
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              "   444,\n",
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              "   267,\n",
              "   8051,\n",
              "   2,\n",
              "   3172,\n",
              "   6],\n",
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              " ([14821,\n",
              "   1754,\n",
              "   32,\n",
              "   2523,\n",
              "   12819,\n",
              "   5203,\n",
              "   57,\n",
              "   130,\n",
              "   13899,\n",
              "   919,\n",
              "   310,\n",
              "   23,\n",
              "   8896,\n",
              "   2717,\n",
              "   22,\n",
              "   23,\n",
              "   14822,\n",
              "   14746,\n",
              "   22,\n",
              "   395,\n",
              "   14823,\n",
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              "   110,\n",
              "   1334,\n",
              "   96,\n",
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              "   659,\n",
              "   61,\n",
              "   86,\n",
              "   2745,\n",
              "   4,\n",
              "   5291,\n",
              "   1242,\n",
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              "   61,\n",
              "   32,\n",
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              "   5374,\n",
              "   596,\n",
              "   292,\n",
              "   105,\n",
              "   32,\n",
              "   3056,\n",
              "   14825,\n",
              "   22,\n",
              "   14826,\n",
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              "   10401,\n",
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              "   22,\n",
              "   32,\n",
              "   743,\n",
              "   665,\n",
              "   4,\n",
              "   110,\n",
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              "   4,\n",
              "   14828,\n",
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              "   6,\n",
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              "   76,\n",
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              "   363,\n",
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              "   14830,\n",
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              "   118,\n",
              "   23,\n",
              "   919,\n",
              "   4,\n",
              "   9728,\n",
              "   22,\n",
              "   130,\n",
              "   14831,\n",
              "   4801,\n",
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              "   51,\n",
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              "   105,\n",
              "   32,\n",
              "   816,\n",
              "   22,\n",
              "   6986,\n",
              "   4327,\n",
              "   3673,\n",
              "   63,\n",
              "   352,\n",
              "   63,\n",
              "   272,\n",
              "   267,\n",
              "   3056,\n",
              "   1625,\n",
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              "   130,\n",
              "   3280,\n",
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              "   173,\n",
              "   333,\n",
              "   23,\n",
              "   3284,\n",
              "   22,\n",
              "   272,\n",
              "   22,\n",
              "   23,\n",
              "   1440,\n",
              "   228,\n",
              "   2237,\n",
              "   57,\n",
              "   2849,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   51,\n",
              "   6502,\n",
              "   2,\n",
              "   200,\n",
              "   27,\n",
              "   2003,\n",
              "   294,\n",
              "   108,\n",
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              "   61,\n",
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              "   10026,\n",
              "   224,\n",
              "   2,\n",
              "   32,\n",
              "   7861,\n",
              "   4,\n",
              "   32,\n",
              "   8487,\n",
              "   6,\n",
              "   51,\n",
              "   30,\n",
              "   14836,\n",
              "   2,\n",
              "   32,\n",
              "   3495,\n",
              "   2874,\n",
              "   4255,\n",
              "   310,\n",
              "   32,\n",
              "   6495,\n",
              "   2875,\n",
              "   2,\n",
              "   61,\n",
              "   14652,\n",
              "   6],\n",
              "  0),\n",
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              "   76,\n",
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              "   22,\n",
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              "   27,\n",
              "   280,\n",
              "   654,\n",
              "   80,\n",
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              "   32,\n",
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              "   6758,\n",
              "   25,\n",
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              "   449,\n",
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              "   181,\n",
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              "   1391,\n",
              "   3258,\n",
              "   181,\n",
              "   2361,\n",
              "   663,\n",
              "   438,\n",
              "   14842,\n",
              "   2572,\n",
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              "   6671,\n",
              "   6940,\n",
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              "   63,\n",
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              "   63,\n",
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              "   414,\n",
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              "   29,\n",
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              "   27,\n",
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              "   84,\n",
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              "   1864,\n",
              "   201,\n",
              "   14843,\n",
              "   1576,\n",
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              "   84,\n",
              "   51,\n",
              "   65,\n",
              "   280,\n",
              "   635,\n",
              "   927,\n",
              "   308,\n",
              "   11467,\n",
              "   2,\n",
              "   4,\n",
              "   10594,\n",
              "   12417,\n",
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              "   240,\n",
              "   2751,\n",
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              "   1974,\n",
              "   6257,\n",
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              "   600,\n",
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              "   14846,\n",
              "   14847,\n",
              "   3331,\n",
              "   2,\n",
              "   4009,\n",
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              "   1514,\n",
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              "   25,\n",
              "   7247,\n",
              "   25,\n",
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              "   76,\n",
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              "   745,\n",
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              "   6960,\n",
              "   2195,\n",
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              "   719,\n",
              "   3484,\n",
              "   136,\n",
              "   32,\n",
              "   267,\n",
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              "   9066,\n",
              "   14856,\n",
              "   14857,\n",
              "   29,\n",
              "   32,\n",
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              "   342,\n",
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              "   105,\n",
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              "   813,\n",
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              "   110,\n",
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              "   32,\n",
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              "   76,\n",
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              "   144,\n",
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              "   84,\n",
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              "   97,\n",
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              "   2643,\n",
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              "   1317,\n",
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              "   23,\n",
              "   3405,\n",
              "   14877,\n",
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              " ([449,\n",
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              "   234,\n",
              "   22,\n",
              "   23,\n",
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              "   265,\n",
              "   105,\n",
              "   984,\n",
              "   1390,\n",
              "   328,\n",
              "   14881,\n",
              "   22,\n",
              "   14882,\n",
              "   2138,\n",
              "   105,\n",
              "   32,\n",
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              "   22,\n",
              "   628,\n",
              "   40,\n",
              "   597,\n",
              "   12759,\n",
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              "   173,\n",
              "   2115,\n",
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              "   2311,\n",
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              "   4,\n",
              "   13723,\n",
              "   17,\n",
              "   76,\n",
              "   32,\n",
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              "   22,\n",
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              "   1322,\n",
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              "   5942,\n",
              "   84,\n",
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              "   1061,\n",
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              "   32,\n",
              "   4084,\n",
              "   4,\n",
              "   14887,\n",
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              "   14888,\n",
              "   22,\n",
              "   23,\n",
              "   2875,\n",
              "   27,\n",
              "   6146,\n",
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              "   4619,\n",
              "   6,\n",
              "   1412,\n",
              "   10585,\n",
              "   173,\n",
              "   5279,\n",
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              "   23,\n",
              "   5788,\n",
              "   22,\n",
              "   8119,\n",
              "   6],\n",
              "  0),\n",
              " ([1710,\n",
              "   14889,\n",
              "   442,\n",
              "   78,\n",
              "   6537,\n",
              "   247,\n",
              "   2,\n",
              "   130,\n",
              "   13041,\n",
              "   14890,\n",
              "   1089,\n",
              "   173,\n",
              "   224,\n",
              "   11713,\n",
              "   4,\n",
              "   6276,\n",
              "   76,\n",
              "   9183,\n",
              "   63,\n",
              "   14891,\n",
              "   3771,\n",
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              "   567,\n",
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              "   32,\n",
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              "   22,\n",
              "   14894,\n",
              "   27,\n",
              "   1000,\n",
              "   3672,\n",
              "   15,\n",
              "   14895,\n",
              "   4,\n",
              "   308,\n",
              "   14798,\n",
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              "   256,\n",
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              "   110,\n",
              "   7078,\n",
              "   103,\n",
              "   2157,\n",
              "   84,\n",
              "   4,\n",
              "   4835,\n",
              "   84,\n",
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              "   635,\n",
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              "   98,\n",
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              "   105,\n",
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              "   57,\n",
              "   5430,\n",
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              "   1866,\n",
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              "   716,\n",
              "   1391,\n",
              "   117,\n",
              "   2,\n",
              "   14901,\n",
              "   32,\n",
              "   6566,\n",
              "   76,\n",
              "   1502,\n",
              "   7025,\n",
              "   10603,\n",
              "   76,\n",
              "   1156,\n",
              "   144,\n",
              "   23,\n",
              "   1199,\n",
              "   2,\n",
              "   4,\n",
              "   4268,\n",
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              "   7144,\n",
              "   438,\n",
              "   182,\n",
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              "   76,\n",
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              "   4091,\n",
              "   14422,\n",
              "   181,\n",
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              "   4,\n",
              "   14903,\n",
              "   110,\n",
              "   4743,\n",
              "   2,\n",
              "   14904,\n",
              "   32,\n",
              "   14905,\n",
              "   4,\n",
              "   1733,\n",
              "   1802,\n",
              "   6496,\n",
              "   78,\n",
              "   14906,\n",
              "   14182,\n",
              "   2,\n",
              "   25,\n",
              "   86,\n",
              "   5584,\n",
              "   13267,\n",
              "   1908,\n",
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              " ([108, 51, 23, 252, 14907, 14908, 768, 2424, 130, 14909, 51, 1244, 1758, 6],\n",
              "  0),\n",
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              "   492,\n",
              "   2456,\n",
              "   11278,\n",
              "   84,\n",
              "   97,\n",
              "   23,\n",
              "   1147,\n",
              "   22,\n",
              "   240,\n",
              "   61,\n",
              "   9223,\n",
              "   7787,\n",
              "   4051,\n",
              "   84,\n",
              "   9223,\n",
              "   14910,\n",
              "   9728,\n",
              "   280,\n",
              "   1531,\n",
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              "   51,\n",
              "   17,\n",
              "   61,\n",
              "   32,\n",
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              "   5202,\n",
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              "   240,\n",
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              "   435,\n",
              "   17,\n",
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              "   73,\n",
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              "   304,\n",
              "   5202,\n",
              "   14912,\n",
              "   6],\n",
              "  0),\n",
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              "   14914,\n",
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              "   63,\n",
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              "   84,\n",
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              "   84,\n",
              "   63,\n",
              "   1000,\n",
              "   289,\n",
              "   11482,\n",
              "   6,\n",
              "   61,\n",
              "   130,\n",
              "   14758,\n",
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              "  0),\n",
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              "   105,\n",
              "   5278,\n",
              "   14915,\n",
              "   442,\n",
              "   12152,\n",
              "   3914,\n",
              "   14916,\n",
              "   2,\n",
              "   61,\n",
              "   32,\n",
              "   1546,\n",
              "   14917,\n",
              "   567,\n",
              "   2,\n",
              "   95,\n",
              "   3143,\n",
              "   14918,\n",
              "   103,\n",
              "   11,\n",
              "   5146,\n",
              "   14919,\n",
              "   8022,\n",
              "   22,\n",
              "   23,\n",
              "   4153,\n",
              "   27,\n",
              "   14920,\n",
              "   506,\n",
              "   3231,\n",
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              "   12919,\n",
              "   14921,\n",
              "   7092,\n",
              "   439,\n",
              "   14922,\n",
              "   105,\n",
              "   32,\n",
              "   14923,\n",
              "   27,\n",
              "   27,\n",
              "   61,\n",
              "   1974,\n",
              "   14924,\n",
              "   4,\n",
              "   14925,\n",
              "   6,\n",
              "   566,\n",
              "   14926,\n",
              "   14927,\n",
              "   4,\n",
              "   14928,\n",
              "   80,\n",
              "   14929,\n",
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              "   310,\n",
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              "   9412,\n",
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              "   6342,\n",
              "   22,\n",
              "   276,\n",
              "   1718,\n",
              "   12258,\n",
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              "   352,\n",
              "   63,\n",
              "   32,\n",
              "   14930,\n",
              "   2,\n",
              "   7556,\n",
              "   1652,\n",
              "   342,\n",
              "   14931,\n",
              "   144,\n",
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              "   749,\n",
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              "   6616,\n",
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              "   14936,\n",
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              "   23,\n",
              "   14937,\n",
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              "   23,\n",
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              "   194,\n",
              "   32,\n",
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              "   23,\n",
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              "   1514,\n",
              "   12342,\n",
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              "   11192,\n",
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              "   1311,\n",
              "   61,\n",
              "   4236,\n",
              "   2,\n",
              "   11175,\n",
              "   4,\n",
              "   3648,\n",
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              "   32,\n",
              "   1913,\n",
              "   4,\n",
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              "   5506,\n",
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              "   279,\n",
              "   5069,\n",
              "   2370,\n",
              "   130,\n",
              "   883,\n",
              "   76,\n",
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              "   181,\n",
              "   1071,\n",
              "   310,\n",
              "   3219,\n",
              "   6],\n",
              "  0),\n",
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              "   129,\n",
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              "   61,\n",
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              "   22,\n",
              "   23,\n",
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              "   665,\n",
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              "   22,\n",
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              "   2,\n",
              "   12099,\n",
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              "   10549,\n",
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              "   98,\n",
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              "   1535,\n",
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              "   775,\n",
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              "   1800,\n",
              "   6266,\n",
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              "   110,\n",
              "   10502,\n",
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              "   4,\n",
              "   2927,\n",
              "   2123,\n",
              "   103,\n",
              "   1864,\n",
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              "   429,\n",
              "   2268,\n",
              "   57,\n",
              "   4055,\n",
              "   22,\n",
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              "   13852,\n",
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              "   76,\n",
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              "   27,\n",
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              "   449,\n",
              "   1222,\n",
              "   449,\n",
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              "   3882,\n",
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              "   76,\n",
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              "   86,\n",
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              "   7,\n",
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              "   2,\n",
              "   7,\n",
              "   922,\n",
              "   4,\n",
              "   4606,\n",
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              "  0),\n",
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              "   14957,\n",
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              "   670,\n",
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              "   105,\n",
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              "   16,\n",
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              "   2566,\n",
              "   440,\n",
              "   73,\n",
              "   6816,\n",
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              "   447,\n",
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              "  0),\n",
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              "   5774,\n",
              "   9839,\n",
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              "   86,\n",
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              "   252,\n",
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              "   600,\n",
              "   14970,\n",
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              "   181,\n",
              "   1896,\n",
              "   3015,\n",
              "   2793,\n",
              "   3697,\n",
              "   6],\n",
              "  0),\n",
              " ([6,\n",
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              "   32,\n",
              "   4432,\n",
              "   5087,\n",
              "   110,\n",
              "   1384,\n",
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              "   63,\n",
              "   23,\n",
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              "   5087,\n",
              "   32,\n",
              "   4432,\n",
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              "  0),\n",
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              " ([63,\n",
              "   32,\n",
              "   2171,\n",
              "   76,\n",
              "   2679,\n",
              "   14973,\n",
              "   14551,\n",
              "   84,\n",
              "   51,\n",
              "   2,\n",
              "   3820,\n",
              "   2,\n",
              "   32,\n",
              "   1062,\n",
              "   22,\n",
              "   239,\n",
              "   84,\n",
              "   329,\n",
              "   256,\n",
              "   14974,\n",
              "   76,\n",
              "   23,\n",
              "   14975,\n",
              "   8760,\n",
              "   22,\n",
              "   4211,\n",
              "   6,\n",
              "   14976,\n",
              "   803,\n",
              "   4,\n",
              "   5628,\n",
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              "   27,\n",
              "   3849,\n",
              "   173,\n",
              "   1546,\n",
              "   14977,\n",
              "   32,\n",
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              "   592,\n",
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              "   61,\n",
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              "   511,\n",
              "   2896,\n",
              "   2,\n",
              "   7076,\n",
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              "   76,\n",
              "   1837,\n",
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              "   61,\n",
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              "   84,\n",
              "   240,\n",
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              "   76,\n",
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              "   588,\n",
              "   4227,\n",
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              "   255,\n",
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              "   76,\n",
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              "   12816,\n",
              "   2,\n",
              "   1381,\n",
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              "   61,\n",
              "   57,\n",
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              "   95,\n",
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              "   4,\n",
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              "   715,\n",
              "   14980,\n",
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              "   2657,\n",
              "   4196,\n",
              "   14981],\n",
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              "   91,\n",
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              "   25,\n",
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              "   86,\n",
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              "   462,\n",
              "   2793,\n",
              "   253,\n",
              "   2,\n",
              "   7682,\n",
              "   22,\n",
              "   32,\n",
              "   767,\n",
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              "   103,\n",
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              "   105,\n",
              "   2340,\n",
              "   240,\n",
              "   10152,\n",
              "   23,\n",
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              "   14983,\n",
              "   22,\n",
              "   729,\n",
              "   17,\n",
              "   1551,\n",
              "   1022,\n",
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              "   32,\n",
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              "   14985,\n",
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              "   2132,\n",
              "   27,\n",
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              "   76,\n",
              "   330,\n",
              "   729,\n",
              "   76,\n",
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              "   182,\n",
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              "   125,\n",
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              "   266,\n",
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              "   22,\n",
              "   32,\n",
              "   2481,\n",
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              "   105,\n",
              "   4468,\n",
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              "   4564,\n",
              "   2,\n",
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              "   153,\n",
              "   849,\n",
              "   76,\n",
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              "   755,\n",
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              "   32,\n",
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              "   2042,\n",
              "   2465,\n",
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              "   57,\n",
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              "   266,\n",
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              "   240,\n",
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              "   27,\n",
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              "   14990,\n",
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              "   187,\n",
              "   1979,\n",
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              "   32,\n",
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              "   201,\n",
              "   63,\n",
              "   17,\n",
              "   12173,\n",
              "   23,\n",
              "   14992,\n",
              "   5046,\n",
              "   14993,\n",
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              "   198,\n",
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              "   14994,\n",
              "   12506,\n",
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              "   32,\n",
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              "   40,\n",
              "   32,\n",
              "   922,\n",
              "   941,\n",
              "   5729,\n",
              "   1001,\n",
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              "   2344,\n",
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              "   73,\n",
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              "   3951,\n",
              "   150,\n",
              "   2,\n",
              "   14996,\n",
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              "   84,\n",
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              "   23,\n",
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              "   61,\n",
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              "   14997,\n",
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              "   342,\n",
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              "   6,\n",
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              "   23,\n",
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              "   2,\n",
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              "   27,\n",
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              "   177,\n",
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              "   23,\n",
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              "   2,\n",
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              "   117,\n",
              "   40,\n",
              "   23,\n",
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              "   306,\n",
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              "   57,\n",
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              "   266,\n",
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              "   86,\n",
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              "   97,\n",
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              "   2403,\n",
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              "   76,\n",
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              "   23,\n",
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              "   2,\n",
              "   10893,\n",
              "   81,\n",
              "   1388,\n",
              "   743,\n",
              "   1414,\n",
              "   1415,\n",
              "   1416,\n",
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              "   6971,\n",
              "   22,\n",
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              "   110,\n",
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              "   15003,\n",
              "   57,\n",
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              "   15005,\n",
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              "   78,\n",
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              "   371,\n",
              "   76,\n",
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              "   1870,\n",
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              "   51,\n",
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              "   15010,\n",
              "   103,\n",
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              "   22,\n",
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              "   6,\n",
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              "   27,\n",
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              "   86,\n",
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              "   600,\n",
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              "   15021,\n",
              "   15022,\n",
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              "   4342,\n",
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              "   162,\n",
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              "   103,\n",
              "   1061,\n",
              "   76,\n",
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              "   351,\n",
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              "   418,\n",
              "   15027,\n",
              "   2,\n",
              "   15028,\n",
              "   1858,\n",
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              "   5283,\n",
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              "   2489,\n",
              "   5571,\n",
              "   22,\n",
              "   2531,\n",
              "   15029,\n",
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              "   61,\n",
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              "   40,\n",
              "   967,\n",
              "   3057,\n",
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              "   300,\n",
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              "   333,\n",
              "   27,\n",
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              "   308,\n",
              "   4790,\n",
              "   51,\n",
              "   97,\n",
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              "   108,\n",
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              "   1540,\n",
              "   11064,\n",
              "   15030,\n",
              "   15031,\n",
              "   117,\n",
              "   2,\n",
              "   23,\n",
              "   423,\n",
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              "   61,\n",
              "   2793,\n",
              "   261,\n",
              "   15032,\n",
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              "   330,\n",
              "   12020,\n",
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              "   76,\n",
              "   29,\n",
              "   22,\n",
              "   2184,\n",
              "   2887,\n",
              "   1234,\n",
              "   4435,\n",
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              "   27,\n",
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              "   431,\n",
              "   1660,\n",
              "   438,\n",
              "   8625,\n",
              "   98,\n",
              "   22,\n",
              "   17,\n",
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              "  0),\n",
              " ([23,\n",
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              "   22,\n",
              "   2438,\n",
              "   27,\n",
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              "   61,\n",
              "   63,\n",
              "   98,\n",
              "   32,\n",
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              "   22,\n",
              "   1057,\n",
              "   8289,\n",
              "   57,\n",
              "   15033,\n",
              "   63,\n",
              "   17,\n",
              "   61,\n",
              "   32,\n",
              "   8172,\n",
              "   1153,\n",
              "   57,\n",
              "   81,\n",
              "   10284,\n",
              "   6],\n",
              "  0),\n",
              " ([506,\n",
              "   5391,\n",
              "   2,\n",
              "   15034,\n",
              "   57,\n",
              "   2074,\n",
              "   2,\n",
              "   130,\n",
              "   61,\n",
              "   153,\n",
              "   3297,\n",
              "   81,\n",
              "   2,\n",
              "   4,\n",
              "   1570,\n",
              "   308,\n",
              "   1793,\n",
              "   32,\n",
              "   15035,\n",
              "   444,\n",
              "   449,\n",
              "   5324,\n",
              "   76,\n",
              "   1053,\n",
              "   17,\n",
              "   6,\n",
              "   329,\n",
              "   3462,\n",
              "   23,\n",
              "   2278,\n",
              "   714,\n",
              "   1663,\n",
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              "   108,\n",
              "   715,\n",
              "   2,\n",
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              "   24,\n",
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              "   4,\n",
              "   848,\n",
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              "   27,\n",
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              "   72,\n",
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              "   14,\n",
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              "   27,\n",
              "   32,\n",
              "   381,\n",
              "   22,\n",
              "   23,\n",
              "   345,\n",
              "   2055,\n",
              "   57,\n",
              "   2730,\n",
              "   27,\n",
              "   1468,\n",
              "   276,\n",
              "   1294,\n",
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              "   95,\n",
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              "   3672,\n",
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              "   27,\n",
              "   15037,\n",
              "   8966,\n",
              "   61,\n",
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              "   10528,\n",
              "   63,\n",
              "   15039,\n",
              "   15040,\n",
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              "   86,\n",
              "   9552,\n",
              "   6292,\n",
              "   4,\n",
              "   9899,\n",
              "   15041,\n",
              "   162,\n",
              "   388,\n",
              "   2457,\n",
              "   76,\n",
              "   308,\n",
              "   597,\n",
              "   266,\n",
              "   1440,\n",
              "   9299,\n",
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              "   123,\n",
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              "   181,\n",
              "   4846,\n",
              "   727,\n",
              "   2,\n",
              "   51,\n",
              "   15042,\n",
              "   2048,\n",
              "   381,\n",
              "   1210,\n",
              "   280,\n",
              "   6,\n",
              "   51,\n",
              "   1765,\n",
              "   144,\n",
              "   15043,\n",
              "   2,\n",
              "   3885,\n",
              "   927,\n",
              "   308,\n",
              "   6978,\n",
              "   86,\n",
              "   3317,\n",
              "   234,\n",
              "   40,\n",
              "   181,\n",
              "   1033,\n",
              "   1031,\n",
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              "   46,\n",
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              "   8647,\n",
              "   2,\n",
              "   782,\n",
              "   2,\n",
              "   2926,\n",
              "   76,\n",
              "   2639,\n",
              "   2,\n",
              "   10608,\n",
              "   127,\n",
              "   438,\n",
              "   23,\n",
              "   1117,\n",
              "   1048,\n",
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              "   32,\n",
              "   1119,\n",
              "   81,\n",
              "   27,\n",
              "   1846,\n",
              "   84,\n",
              "   57,\n",
              "   32,\n",
              "   3050,\n",
              "   22,\n",
              "   3216,\n",
              "   2,\n",
              "   153,\n",
              "   350,\n",
              "   6],\n",
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              "   98,\n",
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              "   76,\n",
              "   563,\n",
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              "   17,\n",
              "   61,\n",
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              "   76,\n",
              "   8253,\n",
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              "   496,\n",
              "   6257,\n",
              "   22,\n",
              "   32,\n",
              "   493,\n",
              "   296,\n",
              "   2,\n",
              "   4,\n",
              "   76,\n",
              "   10205,\n",
              "   105,\n",
              "   272,\n",
              "   22,\n",
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              "   276,\n",
              "   162,\n",
              "   686,\n",
              "   1014,\n",
              "   76,\n",
              "   29,\n",
              "   86,\n",
              "   1821,\n",
              "   57,\n",
              "   23,\n",
              "   559,\n",
              "   2896,\n",
              "   6],\n",
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              "   23,\n",
              "   1309,\n",
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              "   162,\n",
              "   696,\n",
              "   97,\n",
              "   450,\n",
              "   76,\n",
              "   5456,\n",
              "   650,\n",
              "   76,\n",
              "   15047,\n",
              "   342,\n",
              "   25,\n",
              "   15048,\n",
              "   181,\n",
              "   626,\n",
              "   1123,\n",
              "   6,\n",
              "   379,\n",
              "   388,\n",
              "   2485,\n",
              "   414,\n",
              "   76,\n",
              "   308,\n",
              "   2492,\n",
              "   22,\n",
              "   23,\n",
              "   289,\n",
              "   2,\n",
              "   23,\n",
              "   15049,\n",
              "   22,\n",
              "   181,\n",
              "   8892,\n",
              "   296,\n",
              "   6,\n",
              "   197,\n",
              "   2,\n",
              "   4604,\n",
              "   2,\n",
              "   65,\n",
              "   103,\n",
              "   6],\n",
              "  0),\n",
              " ([15050,\n",
              "   15051,\n",
              "   23,\n",
              "   3016,\n",
              "   847,\n",
              "   22,\n",
              "   219,\n",
              "   1030,\n",
              "   10051,\n",
              "   182,\n",
              "   219,\n",
              "   151,\n",
              "   97,\n",
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              "   63,\n",
              "   8891,\n",
              "   63,\n",
              "   9589,\n",
              "   6],\n",
              "  0),\n",
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              "   2820,\n",
              "   790,\n",
              "   6960,\n",
              "   2,\n",
              "   15053,\n",
              "   127,\n",
              "   27,\n",
              "   15054,\n",
              "   105,\n",
              "   15055,\n",
              "   2,\n",
              "   6880,\n",
              "   4,\n",
              "   1535,\n",
              "   6],\n",
              "  0),\n",
              " ([130,\n",
              "   4785,\n",
              "   4,\n",
              "   13687,\n",
              "   182,\n",
              "   1410,\n",
              "   438,\n",
              "   15056,\n",
              "   15057,\n",
              "   11478,\n",
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              "   582,\n",
              "   2622,\n",
              "   27,\n",
              "   3422,\n",
              "   1146,\n",
              "   388,\n",
              "   11880,\n",
              "   136,\n",
              "   447,\n",
              "   310,\n",
              "   32,\n",
              "   12472,\n",
              "   15058,\n",
              "   22,\n",
              "   8793,\n",
              "   5647,\n",
              "   6],\n",
              "  0),\n",
              " ([2928,\n",
              "   76,\n",
              "   1115,\n",
              "   73,\n",
              "   15059,\n",
              "   191,\n",
              "   2,\n",
              "   15060,\n",
              "   15061,\n",
              "   1030,\n",
              "   7293,\n",
              "   61,\n",
              "   97,\n",
              "   11,\n",
              "   6522,\n",
              "   51,\n",
              "   15062,\n",
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              "  0),\n",
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              "   6,\n",
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              "   61,\n",
              "   9340,\n",
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              "   1977,\n",
              "   86,\n",
              "   7011,\n",
              "   1804,\n",
              "   567,\n",
              "   2,\n",
              "   86,\n",
              "   635,\n",
              "   15063,\n",
              "   265,\n",
              "   893,\n",
              "   105,\n",
              "   992,\n",
              "   4,\n",
              "   2997,\n",
              "   6],\n",
              "  0),\n",
              " ([126,\n",
              "   130,\n",
              "   228,\n",
              "   61,\n",
              "   97,\n",
              "   57,\n",
              "   23,\n",
              "   1147,\n",
              "   694,\n",
              "   2,\n",
              "   17,\n",
              "   61,\n",
              "   2793,\n",
              "   639,\n",
              "   267,\n",
              "   2379,\n",
              "   6,\n",
              "   279,\n",
              "   22,\n",
              "   17,\n",
              "   63,\n",
              "   32,\n",
              "   473,\n",
              "   22,\n",
              "   3551,\n",
              "   3552,\n",
              "   40,\n",
              "   23,\n",
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              "   6],\n",
              "  0),\n",
              " ([2945, 2, 421, 4, 1783, 15064, 81, 6], 0),\n",
              " ([6, 6, 6, 4489, 1337, 15065, 3252, 6], 0),\n",
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              " ([32, 1865, 15067, 861, 144, 23, 3578, 22, 10042, 6], 0),\n",
              " ([1195,\n",
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              "   28,\n",
              "   6694,\n",
              "   9464,\n",
              "   144,\n",
              "   181,\n",
              "   1339,\n",
              "   15068,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   103,\n",
              "   11297,\n",
              "   442,\n",
              "   421,\n",
              "   2,\n",
              "   5121,\n",
              "   127,\n",
              "   438,\n",
              "   14836,\n",
              "   4,\n",
              "   15069,\n",
              "   6],\n",
              "  0),\n",
              " ([86,\n",
              "   1812,\n",
              "   125,\n",
              "   60,\n",
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              "   32,\n",
              "   6390,\n",
              "   3471,\n",
              "   162,\n",
              "   465,\n",
              "   371,\n",
              "   3241,\n",
              "   4,\n",
              "   1940,\n",
              "   371,\n",
              "   57,\n",
              "   86,\n",
              "   15070,\n",
              "   467,\n",
              "   442,\n",
              "   23,\n",
              "   6597,\n",
              "   276,\n",
              "   443,\n",
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              "   51,\n",
              "   17,\n",
              "   6969,\n",
              "   32,\n",
              "   2520,\n",
              "   40,\n",
              "   110,\n",
              "   6376,\n",
              "   1820,\n",
              "   11491,\n",
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              "   32,\n",
              "   9873,\n",
              "   81,\n",
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              "   23,\n",
              "   15074,\n",
              "   4,\n",
              "   23,\n",
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              "   103,\n",
              "   8,\n",
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              "   2,\n",
              "   162,\n",
              "   4090,\n",
              "   444,\n",
              "   23,\n",
              "   1806,\n",
              "   32,\n",
              "   287,\n",
              "   3221,\n",
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              "   7943,\n",
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              "   15076,\n",
              "   51,\n",
              "   7556,\n",
              "   1652,\n",
              "   342,\n",
              "   23,\n",
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              "   15078,\n",
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              "   15079,\n",
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              "   1974,\n",
              "   57,\n",
              "   1973,\n",
              "   22,\n",
              "   58,\n",
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              "   23,\n",
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              "   438,\n",
              "   1782,\n",
              "   14149,\n",
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              "   63,\n",
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              "   23,\n",
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              "   5442,\n",
              "   15081,\n",
              "   1334,\n",
              "   96,\n",
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              "   40,\n",
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              "   152,\n",
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              "   188,\n",
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              "   12096,\n",
              "   57,\n",
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              "  0),\n",
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              "   1299,\n",
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              "   6352,\n",
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              "   442,\n",
              "   15085,\n",
              "   22,\n",
              "   1782,\n",
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              "   716,\n",
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              "   1626,\n",
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              "   240,\n",
              "   9713,\n",
              "   1907,\n",
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              "   600,\n",
              "   15099,\n",
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              "   135,\n",
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              "   54,\n",
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              "   6397,\n",
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              "   362,\n",
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              "   15107,\n",
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              "   775,\n",
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              "   103,\n",
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              "   4,\n",
              "   8863,\n",
              "   8254,\n",
              "   2,\n",
              "   23,\n",
              "   15111,\n",
              "   7627,\n",
              "   63,\n",
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              "   78,\n",
              "   11524,\n",
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              "   3405,\n",
              "   15112,\n",
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              "   400,\n",
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              "   65,\n",
              "   686,\n",
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              "   32,\n",
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              "   76,\n",
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              "   2,\n",
              "   2030,\n",
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              " ([367,\n",
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              "   32,\n",
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              "   362,\n",
              "   2186,\n",
              "   15118,\n",
              "   22,\n",
              "   58,\n",
              "   4,\n",
              "   1070,\n",
              "   6],\n",
              "  0),\n",
              " ([6,\n",
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              "   6,\n",
              "   32,\n",
              "   5705,\n",
              "   4,\n",
              "   3420,\n",
              "   503,\n",
              "   27,\n",
              "   3843,\n",
              "   27,\n",
              "   1900,\n",
              "   4365,\n",
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              "   76,\n",
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              "   22,\n",
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              "   853,\n",
              "   2659,\n",
              "   51,\n",
              "   23,\n",
              "   267,\n",
              "   2274,\n",
              "   8169,\n",
              "   727,\n",
              "   4447,\n",
              "   6],\n",
              "  0),\n",
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              "   17,\n",
              "   371,\n",
              "   76,\n",
              "   1388,\n",
              "   15191,\n",
              "   40,\n",
              "   1974,\n",
              "   204,\n",
              "   1772,\n",
              "   4,\n",
              "   1771,\n",
              "   2,\n",
              "   51,\n",
              "   329,\n",
              "   766,\n",
              "   32,\n",
              "   261,\n",
              "   922,\n",
              "   96,\n",
              "   105,\n",
              "   130,\n",
              "   81,\n",
              "   1617,\n",
              "   703,\n",
              "   110,\n",
              "   15192,\n",
              "   11278,\n",
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              "   313,\n",
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              "   865,\n",
              "   103,\n",
              "   108,\n",
              "   71,\n",
              "   73,\n",
              "   1077,\n",
              "   2,\n",
              "   51,\n",
              "   17,\n",
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              "   32,\n",
              "   1235,\n",
              "   22,\n",
              "   15193,\n",
              "   14900,\n",
              "   1484,\n",
              "   4,\n",
              "   2,\n",
              "   342,\n",
              "   23,\n",
              "   1085,\n",
              "   96,\n",
              "   2,\n",
              "   61,\n",
              "   8,\n",
              "   1470,\n",
              "   4,\n",
              "   2203,\n",
              "   22,\n",
              "   23,\n",
              "   1759,\n",
              "   1242,\n",
              "   6044,\n",
              "   27,\n",
              "   17,\n",
              "   108,\n",
              "   198,\n",
              "   76,\n",
              "   308,\n",
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              "   40,\n",
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              "   182,\n",
              "   96,\n",
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              "   1672,\n",
              "   40,\n",
              "   4170,\n",
              "   598,\n",
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              "   76,\n",
              "   2278,\n",
              "   23,\n",
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              "   12098,\n",
              "   1636,\n",
              "   32,\n",
              "   496,\n",
              "   1822,\n",
              "   22,\n",
              "   4744,\n",
              "   4,\n",
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              "   219,\n",
              "   6836,\n",
              "   2483,\n",
              "   219,\n",
              "   5788,\n",
              "   338,\n",
              "   110,\n",
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              "   4365,\n",
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              "   483,\n",
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              "   51,\n",
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              "   105,\n",
              "   10226,\n",
              "   1626,\n",
              "   4,\n",
              "   3252,\n",
              "   2324,\n",
              "   2,\n",
              "   1381,\n",
              "   187,\n",
              "   12458,\n",
              "   4,\n",
              "   15203,\n",
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              "   641,\n",
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              "   1071,\n",
              "   550,\n",
              "   23,\n",
              "   11364,\n",
              "   22,\n",
              "   23,\n",
              "   15205,\n",
              "   6942,\n",
              "   15206,\n",
              "   22,\n",
              "   15207,\n",
              "   1160,\n",
              "   86,\n",
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              "   76,\n",
              "   1128,\n",
              "   23,\n",
              "   705,\n",
              "   22,\n",
              "   23,\n",
              "   9925,\n",
              "   10467,\n",
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              "   51,\n",
              "   1290,\n",
              "   7366,\n",
              "   355,\n",
              "   32,\n",
              "   516,\n",
              "   4027,\n",
              "   22,\n",
              "   5061,\n",
              "   342,\n",
              "   23,\n",
              "   267,\n",
              "   670,\n",
              "   6,\n",
              "   7,\n",
              "   2793,\n",
              "   636,\n",
              "   32,\n",
              "   1652,\n",
              "   6],\n",
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              "   462,\n",
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              "   61,\n",
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              "   494,\n",
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              "   1417,\n",
              "   27,\n",
              "   4824,\n",
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              "   87,\n",
              "   3511,\n",
              "   110,\n",
              "   2025,\n",
              "   673,\n",
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              "   2278,\n",
              "   310,\n",
              "   15210,\n",
              "   2,\n",
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              "   61,\n",
              "   32,\n",
              "   14795,\n",
              "   4,\n",
              "   1471,\n",
              "   1758,\n",
              "   40,\n",
              "   108,\n",
              "   12820,\n",
              "   342,\n",
              "   282,\n",
              "   96,\n",
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              "   8486,\n",
              "   86,\n",
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              "   2,\n",
              "   15213,\n",
              "   3494,\n",
              "   76,\n",
              "   11379,\n",
              "   15214,\n",
              "   4,\n",
              "   15215,\n",
              "   1561,\n",
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              "   144,\n",
              "   464,\n",
              "   57,\n",
              "   23,\n",
              "   1235,\n",
              "   22,\n",
              "   11068,\n",
              "   8429,\n",
              "   362,\n",
              "   1309,\n",
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              "   2720,\n",
              "   2,\n",
              "   51,\n",
              "   308,\n",
              "   2791,\n",
              "   227,\n",
              "   7,\n",
              "   32,\n",
              "   783,\n",
              "   9040,\n",
              "   76,\n",
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              "   1048,\n",
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              "   68,\n",
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              "   23,\n",
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              "   11703,\n",
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              "   3,\n",
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              "   3321,\n",
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              "   355,\n",
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              "   4,\n",
              "   15218,\n",
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              "   27,\n",
              "   23,\n",
              "   15219,\n",
              "   1669,\n",
              "   247,\n",
              "   15220,\n",
              "   442,\n",
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              "   313,\n",
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              "   4542,\n",
              "   63,\n",
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              "   32,\n",
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              "   603,\n",
              "   799,\n",
              "   5927,\n",
              "   32,\n",
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              "   800,\n",
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              "   152,\n",
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              "   57,\n",
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              "   256,\n",
              "   219,\n",
              "   3568,\n",
              "   3569,\n",
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              "   308,\n",
              "   1418,\n",
              "   729,\n",
              "   280,\n",
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              "   76,\n",
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              "   4,\n",
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              "   606,\n",
              "   84,\n",
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              "   27,\n",
              "   151,\n",
              "   2307,\n",
              "   6],\n",
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              "   10808,\n",
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              "   51,\n",
              "   2119,\n",
              "   224,\n",
              "   342,\n",
              "   15224,\n",
              "   15225,\n",
              "   76,\n",
              "   3645,\n",
              "   76,\n",
              "   2268,\n",
              "   700,\n",
              "   40,\n",
              "   885,\n",
              "   886,\n",
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              "   32,\n",
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              "   51,\n",
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              "   15227,\n",
              "   2,\n",
              "   97,\n",
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              "   2,\n",
              "   51,\n",
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              "   72,\n",
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              "   130,\n",
              "   1757,\n",
              "   1855,\n",
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              "   78,\n",
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              "   14746,\n",
              "   2275,\n",
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              "   76,\n",
              "   1473,\n",
              "   600,\n",
              "   6090,\n",
              "   6489,\n",
              "   57,\n",
              "   2060,\n",
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              "   22,\n",
              "   15231,\n",
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              "   442,\n",
              "   130,\n",
              "   15233,\n",
              "   2127,\n",
              "   1410,\n",
              "   412,\n",
              "   61,\n",
              "   2006,\n",
              "   2,\n",
              "   2663,\n",
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              "   15234,\n",
              "   4,\n",
              "   597,\n",
              "   120,\n",
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              "   51,\n",
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              "   23,\n",
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              "   6643,\n",
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              "   232,\n",
              "   22,\n",
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              "   27,\n",
              "   210,\n",
              "   11,\n",
              "   105,\n",
              "   2298,\n",
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              "   22,\n",
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              "   136,\n",
              "   7051,\n",
              "   32,\n",
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              "   71,\n",
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              "   23,\n",
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              "   300,\n",
              "   2,\n",
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              "   31,\n",
              "   32,\n",
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              "   4232,\n",
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              "   455,\n",
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              "   75,\n",
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              "   32,\n",
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              "   1958,\n",
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              "   25,\n",
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              "   76,\n",
              "   308,\n",
              "   986,\n",
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              "   61,\n",
              "   3956,\n",
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              "   768,\n",
              "   4425,\n",
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              "   4,\n",
              "   6762,\n",
              "   600,\n",
              "   266,\n",
              "   5293,\n",
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              "   4636,\n",
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              "   1071,\n",
              "   22,\n",
              "   3439,\n",
              "   5616,\n",
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              "   27,\n",
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              "   7360,\n",
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              "   1822,\n",
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              "   2,\n",
              "   1776,\n",
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              "   252,\n",
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              "   4148,\n",
              "   4636,\n",
              "   22,\n",
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              "   1071,\n",
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              "   6],\n",
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              "   308,\n",
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              "   86,\n",
              "   486,\n",
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              "   714,\n",
              "   4,\n",
              "   1407,\n",
              "   715,\n",
              "   777,\n",
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              "   22,\n",
              "   6463,\n",
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              "   32,\n",
              "   219,\n",
              "   125,\n",
              "   60,\n",
              "   219,\n",
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              "   1061,\n",
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              "   95,\n",
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              "   96,\n",
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              "   23,\n",
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              "   23,\n",
              "   1913,\n",
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              "   29,\n",
              "   17,\n",
              "   1811,\n",
              "   71,\n",
              "   2379,\n",
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              "   635,\n",
              "   282,\n",
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              "   228,\n",
              "   57,\n",
              "   1157,\n",
              "   2461,\n",
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              "   881,\n",
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              "   177,\n",
              "   8,\n",
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              "   27,\n",
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              "   4,\n",
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              "   15243,\n",
              "   5723,\n",
              "   15244,\n",
              "   1604,\n",
              "   7554,\n",
              "   76,\n",
              "   15245,\n",
              "   234,\n",
              "   23,\n",
              "   1033,\n",
              "   3495,\n",
              "   79,\n",
              "   6,\n",
              "   15246,\n",
              "   727,\n",
              "   95,\n",
              "   22,\n",
              "   23,\n",
              "   300,\n",
              "   8,\n",
              "   15247,\n",
              "   130,\n",
              "   1822,\n",
              "   2,\n",
              "   51,\n",
              "   7,\n",
              "   2003,\n",
              "   71,\n",
              "   22,\n",
              "   23,\n",
              "   1033,\n",
              "   261,\n",
              "   922,\n",
              "   79,\n",
              "   6],\n",
              "  0),\n",
              " ([1109,\n",
              "   438,\n",
              "   15248,\n",
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              "   2,\n",
              "   8007,\n",
              "   15249,\n",
              "   23,\n",
              "   473,\n",
              "   22,\n",
              "   9812,\n",
              "   2,\n",
              "   12964,\n",
              "   15250,\n",
              "   15251,\n",
              "   6393,\n",
              "   42,\n",
              "   175,\n",
              "   14302,\n",
              "   40,\n",
              "   2849,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   60,\n",
              "   349,\n",
              "   71,\n",
              "   355,\n",
              "   32,\n",
              "   393,\n",
              "   1377,\n",
              "   2,\n",
              "   5535,\n",
              "   105,\n",
              "   916,\n",
              "   2116,\n",
              "   22,\n",
              "   4489,\n",
              "   814,\n",
              "   4415,\n",
              "   1561,\n",
              "   6],\n",
              "  0),\n",
              " ([551,\n",
              "   17,\n",
              "   2003,\n",
              "   15252,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   8792,\n",
              "   15253,\n",
              "   15254,\n",
              "   144,\n",
              "   32,\n",
              "   5359,\n",
              "   2257,\n",
              "   27,\n",
              "   9713,\n",
              "   1827,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   3201,\n",
              "   6857,\n",
              "   350,\n",
              "   73,\n",
              "   23,\n",
              "   345,\n",
              "   2,\n",
              "   1105,\n",
              "   23,\n",
              "   1210,\n",
              "   10512,\n",
              "   224,\n",
              "   5964,\n",
              "   2,\n",
              "   3511,\n",
              "   15255,\n",
              "   4,\n",
              "   15256,\n",
              "   76,\n",
              "   9183,\n",
              "   218,\n",
              "   6],\n",
              "  0),\n",
              " ([15257, 1733, 596, 292, 6], 0),\n",
              " ([23,\n",
              "   102,\n",
              "   103,\n",
              "   10275,\n",
              "   6,\n",
              "   65,\n",
              "   103,\n",
              "   729,\n",
              "   435,\n",
              "   17,\n",
              "   636,\n",
              "   23,\n",
              "   843,\n",
              "   76,\n",
              "   23,\n",
              "   6497,\n",
              "   6],\n",
              "  0),\n",
              " ([15258,\n",
              "   438,\n",
              "   18,\n",
              "   76,\n",
              "   15259,\n",
              "   4,\n",
              "   2684,\n",
              "   272,\n",
              "   22,\n",
              "   23,\n",
              "   222,\n",
              "   606,\n",
              "   1685,\n",
              "   2,\n",
              "   1030,\n",
              "   5343,\n",
              "   15260,\n",
              "   971,\n",
              "   22,\n",
              "   829,\n",
              "   9146,\n",
              "   6],\n",
              "  0),\n",
              " ([329,\n",
              "   15261,\n",
              "   108,\n",
              "   23,\n",
              "   96,\n",
              "   78,\n",
              "   637,\n",
              "   23,\n",
              "   1085,\n",
              "   3007,\n",
              "   11708,\n",
              "   57,\n",
              "   300,\n",
              "   191,\n",
              "   4,\n",
              "   191,\n",
              "   482,\n",
              "   2,\n",
              "   51,\n",
              "   23,\n",
              "   14662,\n",
              "   3734,\n",
              "   6048,\n",
              "   27,\n",
              "   32,\n",
              "   1470,\n",
              "   85,\n",
              "   61,\n",
              "   4489,\n",
              "   4227,\n",
              "   6],\n",
              "  0),\n",
              " ([15262, 5278, 15263, 355, 15264, 4, 15265, 6], 0),\n",
              " ([11,\n",
              "   86,\n",
              "   6728,\n",
              "   141,\n",
              "   14,\n",
              "   508,\n",
              "   17,\n",
              "   108,\n",
              "   117,\n",
              "   2,\n",
              "   4,\n",
              "   15266,\n",
              "   11328,\n",
              "   1739,\n",
              "   9192,\n",
              "   227,\n",
              "   108,\n",
              "   423,\n",
              "   342,\n",
              "   285,\n",
              "   221,\n",
              "   4009,\n",
              "   110,\n",
              "   120,\n",
              "   32,\n",
              "   81,\n",
              "   5571,\n",
              "   22,\n",
              "   181,\n",
              "   2528,\n",
              "   6],\n",
              "  0),\n",
              " ([6,\n",
              "   6,\n",
              "   6,\n",
              "   63,\n",
              "   23,\n",
              "   60,\n",
              "   15267,\n",
              "   449,\n",
              "   1571,\n",
              "   23,\n",
              "   3478,\n",
              "   22,\n",
              "   23,\n",
              "   872,\n",
              "   4595,\n",
              "   1375,\n",
              "   3573,\n",
              "   310,\n",
              "   466,\n",
              "   57,\n",
              "   32,\n",
              "   777,\n",
              "   27,\n",
              "   435,\n",
              "   905,\n",
              "   12274,\n",
              "   15268,\n",
              "   105,\n",
              "   5030,\n",
              "   4,\n",
              "   4891,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   3495,\n",
              "   292,\n",
              "   4048,\n",
              "   5080,\n",
              "   3495,\n",
              "   292,\n",
              "   11851,\n",
              "   4489,\n",
              "   29,\n",
              "   218,\n",
              "   426,\n",
              "   6,\n",
              "   14789,\n",
              "   1806,\n",
              "   97,\n",
              "   185,\n",
              "   1514,\n",
              "   1617,\n",
              "   51,\n",
              "   15269,\n",
              "   271,\n",
              "   175,\n",
              "   144,\n",
              "   181,\n",
              "   777,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   81,\n",
              "   61,\n",
              "   95,\n",
              "   22,\n",
              "   23,\n",
              "   606,\n",
              "   4063,\n",
              "   22,\n",
              "   11473,\n",
              "   908,\n",
              "   10467,\n",
              "   4380,\n",
              "   449,\n",
              "   103,\n",
              "   1061,\n",
              "   76,\n",
              "   1053,\n",
              "   15270,\n",
              "   1595,\n",
              "   6],\n",
              "  0),\n",
              " ([12402,\n",
              "   464,\n",
              "   76,\n",
              "   23,\n",
              "   15271,\n",
              "   890,\n",
              "   714,\n",
              "   5380,\n",
              "   2,\n",
              "   462,\n",
              "   97,\n",
              "   506,\n",
              "   8,\n",
              "   5380,\n",
              "   63,\n",
              "   23,\n",
              "   15272,\n",
              "   1753,\n",
              "   265,\n",
              "   81,\n",
              "   715,\n",
              "   4644,\n",
              "   22,\n",
              "   23,\n",
              "   15272,\n",
              "   1753,\n",
              "   1648,\n",
              "   81,\n",
              "   6],\n",
              "  0),\n",
              " ([6023,\n",
              "   2,\n",
              "   5583,\n",
              "   1093,\n",
              "   4,\n",
              "   1091,\n",
              "   2543,\n",
              "   4,\n",
              "   9197,\n",
              "   2,\n",
              "   23,\n",
              "   228,\n",
              "   2643,\n",
              "   144,\n",
              "   2679,\n",
              "   3475,\n",
              "   2,\n",
              "   15273,\n",
              "   15274,\n",
              "   1750,\n",
              "   10237,\n",
              "   126,\n",
              "   15275,\n",
              "   23,\n",
              "   809,\n",
              "   105,\n",
              "   110,\n",
              "   673,\n",
              "   1024,\n",
              "   6],\n",
              "  0),\n",
              " ([4501,\n",
              "   4502,\n",
              "   173,\n",
              "   1436,\n",
              "   1123,\n",
              "   4,\n",
              "   32,\n",
              "   2555,\n",
              "   775,\n",
              "   2,\n",
              "   51,\n",
              "   110,\n",
              "   10231,\n",
              "   2,\n",
              "   4732,\n",
              "   232,\n",
              "   22,\n",
              "   1626,\n",
              "   435,\n",
              "   371,\n",
              "   40,\n",
              "   32,\n",
              "   816,\n",
              "   6],\n",
              "  0),\n",
              " ([15276,\n",
              "   15277,\n",
              "   23,\n",
              "   851,\n",
              "   22,\n",
              "   1054,\n",
              "   13073,\n",
              "   3219,\n",
              "   32,\n",
              "   2275,\n",
              "   2,\n",
              "   9654,\n",
              "   2492,\n",
              "   22,\n",
              "   23,\n",
              "   81,\n",
              "   2,\n",
              "   128,\n",
              "   15278,\n",
              "   23,\n",
              "   3439,\n",
              "   310,\n",
              "   597,\n",
              "   266,\n",
              "   1034,\n",
              "   6,\n",
              "   130,\n",
              "   294,\n",
              "   32,\n",
              "   496,\n",
              "   851,\n",
              "   6,\n",
              "   7,\n",
              "   69,\n",
              "   3657,\n",
              "   434,\n",
              "   51,\n",
              "   123,\n",
              "   8,\n",
              "   15279,\n",
              "   25,\n",
              "   105,\n",
              "   8,\n",
              "   98,\n",
              "   4229,\n",
              "   6],\n",
              "  0),\n",
              " ([86, 3648, 444, 10982, 483, 27, 7102, 1468, 2140, 6], 0),\n",
              " ([169, 4492, 5351, 95, 665, 4622, 15087, 57, 130, 813, 4, 7231, 2582, 690, 6],\n",
              "  0),\n",
              " ([17,\n",
              "   3426,\n",
              "   642,\n",
              "   4,\n",
              "   10540,\n",
              "   2,\n",
              "   51,\n",
              "   23,\n",
              "   12209,\n",
              "   22,\n",
              "   6528,\n",
              "   228,\n",
              "   61,\n",
              "   27,\n",
              "   17,\n",
              "   1546,\n",
              "   61,\n",
              "   1001],\n",
              "  0),\n",
              " ([9242, 4, 4493, 141, 6], 0),\n",
              " ([6,\n",
              "   6,\n",
              "   6,\n",
              "   1271,\n",
              "   2,\n",
              "   86,\n",
              "   15280,\n",
              "   15281,\n",
              "   22,\n",
              "   1057,\n",
              "   8734,\n",
              "   318,\n",
              "   57,\n",
              "   32,\n",
              "   3015,\n",
              "   22,\n",
              "   12725,\n",
              "   2,\n",
              "   948,\n",
              "   310,\n",
              "   15282,\n",
              "   4,\n",
              "   5022,\n",
              "   6],\n",
              "  0),\n",
              " ([95,\n",
              "   22,\n",
              "   23,\n",
              "   606,\n",
              "   4063,\n",
              "   22,\n",
              "   471,\n",
              "   76,\n",
              "   6590,\n",
              "   32,\n",
              "   120,\n",
              "   2,\n",
              "   445,\n",
              "   97,\n",
              "   1317,\n",
              "   14794,\n",
              "   61,\n",
              "   194,\n",
              "   15283,\n",
              "   2,\n",
              "   98,\n",
              "   355,\n",
              "   32,\n",
              "   12134,\n",
              "   22,\n",
              "   2423,\n",
              "   449,\n",
              "   425,\n",
              "   330,\n",
              "   727,\n",
              "   194,\n",
              "   2115,\n",
              "   6],\n",
              "  0),\n",
              " ([97,\n",
              "   782,\n",
              "   1811,\n",
              "   3334,\n",
              "   23,\n",
              "   7520,\n",
              "   449,\n",
              "   256,\n",
              "   2793,\n",
              "   1571,\n",
              "   412,\n",
              "   7847,\n",
              "   15284,\n",
              "   15285,\n",
              "   1021,\n",
              "   4,\n",
              "   23,\n",
              "   13355,\n",
              "   379,\n",
              "   173,\n",
              "   40,\n",
              "   181,\n",
              "   345,\n",
              "   6],\n",
              "  0),\n",
              " ([63,\n",
              "   444,\n",
              "   76,\n",
              "   10152,\n",
              "   32,\n",
              "   3839,\n",
              "   412,\n",
              "   256,\n",
              "   29,\n",
              "   32,\n",
              "   81,\n",
              "   105,\n",
              "   153,\n",
              "   2691,\n",
              "   11064,\n",
              "   2,\n",
              "   3702,\n",
              "   1627,\n",
              "   1160,\n",
              "   153,\n",
              "   15286,\n",
              "   25,\n",
              "   12661,\n",
              "   22,\n",
              "   14627,\n",
              "   6,\n",
              "   597,\n",
              "   228,\n",
              "   61,\n",
              "   1584,\n",
              "   5291,\n",
              "   4,\n",
              "   2575,\n",
              "   6],\n",
              "  0),\n",
              " ([15287,\n",
              "   198,\n",
              "   76,\n",
              "   1082,\n",
              "   15288,\n",
              "   27,\n",
              "   201,\n",
              "   2721,\n",
              "   61,\n",
              "   3411,\n",
              "   76,\n",
              "   86,\n",
              "   6570,\n",
              "   442,\n",
              "   23,\n",
              "   848,\n",
              "   6855,\n",
              "   22,\n",
              "   1071,\n",
              "   4,\n",
              "   1158,\n",
              "   6],\n",
              "  0),\n",
              " ([23, 398, 61, 1271, 84, 5535, 105, 5907, 4, 15289, 6], 0),\n",
              " ([3102,\n",
              "   173,\n",
              "   15290,\n",
              "   181,\n",
              "   6659,\n",
              "   4,\n",
              "   15291,\n",
              "   181,\n",
              "   266,\n",
              "   8547,\n",
              "   197,\n",
              "   57,\n",
              "   32,\n",
              "   777,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   27,\n",
              "   349,\n",
              "   267,\n",
              "   1804,\n",
              "   4,\n",
              "   267,\n",
              "   125,\n",
              "   76,\n",
              "   1071,\n",
              "   6],\n",
              "  0),\n",
              " ([7, 447, 2, 51, 23, 15292, 280, 812, 1022, 191, 5075, 1025], 0),\n",
              " ([14662,\n",
              "   2,\n",
              "   4501,\n",
              "   14662,\n",
              "   6,\n",
              "   379,\n",
              "   256,\n",
              "   6276,\n",
              "   32,\n",
              "   1634,\n",
              "   355,\n",
              "   32,\n",
              "   6152,\n",
              "   1322,\n",
              "   2,\n",
              "   379,\n",
              "   256,\n",
              "   15293,\n",
              "   282,\n",
              "   12701,\n",
              "   2,\n",
              "   15294,\n",
              "   508,\n",
              "   23,\n",
              "   265,\n",
              "   6,\n",
              "   175,\n",
              "   6152,\n",
              "   1288,\n",
              "   230],\n",
              "  0),\n",
              " ([729,\n",
              "   435,\n",
              "   23,\n",
              "   81,\n",
              "   32,\n",
              "   292,\n",
              "   61,\n",
              "   23,\n",
              "   777,\n",
              "   17,\n",
              "   7849,\n",
              "   23,\n",
              "   71,\n",
              "   393,\n",
              "   1390,\n",
              "   443,\n",
              "   6],\n",
              "  0),\n",
              " ([86, 6589, 2298, 6], 0),\n",
              " ([130,\n",
              "   15295,\n",
              "   1133,\n",
              "   76,\n",
              "   23,\n",
              "   15296,\n",
              "   243,\n",
              "   61,\n",
              "   32,\n",
              "   287,\n",
              "   71,\n",
              "   49,\n",
              "   14913,\n",
              "   2,\n",
              "   32,\n",
              "   287,\n",
              "   4168,\n",
              "   2,\n",
              "   4,\n",
              "   32,\n",
              "   287,\n",
              "   71,\n",
              "   9411,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   7368,\n",
              "   22,\n",
              "   6152,\n",
              "   6153,\n",
              "   455,\n",
              "   308,\n",
              "   12603,\n",
              "   2,\n",
              "   51,\n",
              "   3265,\n",
              "   9163,\n",
              "   4067,\n",
              "   105,\n",
              "   1867,\n",
              "   162,\n",
              "   2793,\n",
              "   5042,\n",
              "   32,\n",
              "   2691,\n",
              "   2385,\n",
              "   40,\n",
              "   15297,\n",
              "   1626,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   228,\n",
              "   61,\n",
              "   6589,\n",
              "   76,\n",
              "   563,\n",
              "   331,\n",
              "   6443,\n",
              "   2,\n",
              "   15298,\n",
              "   438,\n",
              "   23,\n",
              "   15299,\n",
              "   22,\n",
              "   1756,\n",
              "   2,\n",
              "   3843,\n",
              "   5041,\n",
              "   3401,\n",
              "   63,\n",
              "   86,\n",
              "   3471,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   12241,\n",
              "   2,\n",
              "   201,\n",
              "   12137,\n",
              "   2,\n",
              "   1514,\n",
              "   5943,\n",
              "   489,\n",
              "   1652,\n",
              "   342,\n",
              "   23,\n",
              "   4602,\n",
              "   1071,\n",
              "   22,\n",
              "   15300,\n",
              "   6484,\n",
              "   872,\n",
              "   11846,\n",
              "   11847,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   228,\n",
              "   1846,\n",
              "   97,\n",
              "   388,\n",
              "   23,\n",
              "   1234,\n",
              "   5106,\n",
              "   22,\n",
              "   15301,\n",
              "   9855,\n",
              "   1625,\n",
              "   4,\n",
              "   919,\n",
              "   2,\n",
              "   51,\n",
              "   32,\n",
              "   96,\n",
              "   10033,\n",
              "   373,\n",
              "   76,\n",
              "   729,\n",
              "   17,\n",
              "   3961,\n",
              "   355,\n",
              "   1841,\n",
              "   1195,\n",
              "   11967,\n",
              "   6688,\n",
              "   3529,\n",
              "   6],\n",
              "  0),\n",
              " ([729, 151, 136, 69, 32, 1528, 15302, 1913, 61, 15303, 105, 10252, 6], 0),\n",
              " ([303,\n",
              "   23,\n",
              "   15304,\n",
              "   227,\n",
              "   40,\n",
              "   23,\n",
              "   182,\n",
              "   96,\n",
              "   1859,\n",
              "   1790,\n",
              "   4342,\n",
              "   3597,\n",
              "   2,\n",
              "   3272,\n",
              "   547,\n",
              "   12693,\n",
              "   63,\n",
              "   86,\n",
              "   2874,\n",
              "   6],\n",
              "  0),\n",
              " ([1105,\n",
              "   267,\n",
              "   98,\n",
              "   355,\n",
              "   23,\n",
              "   182,\n",
              "   81,\n",
              "   1765,\n",
              "   144,\n",
              "   1097,\n",
              "   6,\n",
              "   4172,\n",
              "   6,\n",
              "   4173,\n",
              "   14343,\n",
              "   1979,\n",
              "   606,\n",
              "   3847,\n",
              "   2,\n",
              "   130,\n",
              "   2725,\n",
              "   15305,\n",
              "   3968,\n",
              "   32,\n",
              "   1476,\n",
              "   8688,\n",
              "   2,\n",
              "   15306,\n",
              "   6137,\n",
              "   4,\n",
              "   32,\n",
              "   10905,\n",
              "   15307,\n",
              "   9128,\n",
              "   6],\n",
              "  0),\n",
              " ([15308,\n",
              "   219,\n",
              "   85,\n",
              "   75,\n",
              "   22,\n",
              "   1388,\n",
              "   5995,\n",
              "   219,\n",
              "   15309,\n",
              "   61,\n",
              "   86,\n",
              "   6502,\n",
              "   4839,\n",
              "   287,\n",
              "   228,\n",
              "   27,\n",
              "   465,\n",
              "   218,\n",
              "   144,\n",
              "   86,\n",
              "   641,\n",
              "   22,\n",
              "   649,\n",
              "   2841,\n",
              "   1071,\n",
              "   57,\n",
              "   4101,\n",
              "   4053,\n",
              "   15310,\n",
              "   91,\n",
              "   23,\n",
              "   10562,\n",
              "   4,\n",
              "   9412,\n",
              "   22,\n",
              "   23,\n",
              "   774,\n",
              "   15008,\n",
              "   6],\n",
              "  0),\n",
              " ([63,\n",
              "   23,\n",
              "   60,\n",
              "   5348,\n",
              "   8069,\n",
              "   91,\n",
              "   110,\n",
              "   4192,\n",
              "   994,\n",
              "   15311,\n",
              "   2,\n",
              "   23,\n",
              "   1858,\n",
              "   901,\n",
              "   2657,\n",
              "   1271,\n",
              "   6],\n",
              "  0),\n",
              " ([15312, 61, 32, 3008, 2745, 15313, 6], 0),\n",
              " ([97,\n",
              "   32,\n",
              "   15314,\n",
              "   25,\n",
              "   1791,\n",
              "   1034,\n",
              "   2,\n",
              "   51,\n",
              "   7,\n",
              "   13489,\n",
              "   2,\n",
              "   1888,\n",
              "   2945,\n",
              "   2,\n",
              "   4,\n",
              "   4489,\n",
              "   142,\n",
              "   2,\n",
              "   4,\n",
              "   1195,\n",
              "   103,\n",
              "   1889,\n",
              "   224,\n",
              "   76,\n",
              "   1053,\n",
              "   17,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   228,\n",
              "   2108,\n",
              "   144,\n",
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              "   2572,\n",
              "   22,\n",
              "   4598,\n",
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              "   84,\n",
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              "   6,\n",
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              "   444,\n",
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              "   22,\n",
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              "   272,\n",
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              "   27,\n",
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              "   4542,\n",
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              "   276,\n",
              "   136,\n",
              "   2781,\n",
              "   6075,\n",
              "   2275,\n",
              "   11079,\n",
              "   84,\n",
              "   51,\n",
              "   181,\n",
              "   2756,\n",
              "   103,\n",
              "   15374,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   596,\n",
              "   292,\n",
              "   2,\n",
              "   2810,\n",
              "   2,\n",
              "   51,\n",
              "   95,\n",
              "   105,\n",
              "   345,\n",
              "   162,\n",
              "   279,\n",
              "   4,\n",
              "   1591,\n",
              "   78,\n",
              "   600,\n",
              "   10340,\n",
              "   2,\n",
              "   4,\n",
              "   103,\n",
              "   1109,\n",
              "   144,\n",
              "   1396,\n",
              "   13734,\n",
              "   6],\n",
              "  0),\n",
              " ([15279, 6811, 1974, 23, 8233, 4, 23, 7368, 22, 367, 1535, 6], 0),\n",
              " ([6,\n",
              "   6,\n",
              "   6,\n",
              "   32,\n",
              "   3221,\n",
              "   2,\n",
              "   3717,\n",
              "   2,\n",
              "   15375,\n",
              "   60,\n",
              "   27,\n",
              "   61,\n",
              "   15376,\n",
              "   1014,\n",
              "   76,\n",
              "   653,\n",
              "   5033,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   15377,\n",
              "   290,\n",
              "   27,\n",
              "   7265,\n",
              "   98,\n",
              "   22,\n",
              "   110,\n",
              "   6876,\n",
              "   1382,\n",
              "   10785,\n",
              "   2926,\n",
              "   76,\n",
              "   169,\n",
              "   2083,\n",
              "   566,\n",
              "   127,\n",
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              "   182,\n",
              "   15378,\n",
              "   22,\n",
              "   130,\n",
              "   15379,\n",
              "   11175,\n",
              "   4,\n",
              "   11473,\n",
              "   2,\n",
              "   4489,\n",
              "   5038,\n",
              "   4,\n",
              "   9020,\n",
              "   1913,\n",
              "   2,\n",
              "   2855,\n",
              "   280,\n",
              "   190,\n",
              "   3820,\n",
              "   236,\n",
              "   40,\n",
              "   23,\n",
              "   2278,\n",
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              "   61,\n",
              "   32,\n",
              "   5933,\n",
              "   15381,\n",
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              "   444,\n",
              "   32,\n",
              "   1270,\n",
              "   15382,\n",
              "   342,\n",
              "   72,\n",
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              "   23,\n",
              "   5992,\n",
              "   15385,\n",
              "   4,\n",
              "   3426,\n",
              "   438,\n",
              "   5351,\n",
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              "   688,\n",
              "   76,\n",
              "   8298,\n",
              "   32,\n",
              "   986,\n",
              "   12148,\n",
              "   2298,\n",
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              "   777,\n",
              "   27,\n",
              "   17,\n",
              "   664,\n",
              "   6111,\n",
              "   4,\n",
              "   2003,\n",
              "   3207,\n",
              "   449,\n",
              "   76,\n",
              "   85,\n",
              "   853,\n",
              "   3495,\n",
              "   761,\n",
              "   22,\n",
              "   762,\n",
              "   4,\n",
              "   6969,\n",
              "   550,\n",
              "   6],\n",
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              "   181,\n",
              "   2132,\n",
              "   63,\n",
              "   32,\n",
              "   228,\n",
              "   412,\n",
              "   2,\n",
              "   9451,\n",
              "   12736,\n",
              "   1846,\n",
              "   32,\n",
              "   10970,\n",
              "   4,\n",
              "   129,\n",
              "   1034,\n",
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              "   169,\n",
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              "   280,\n",
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              "   449,\n",
              "   330,\n",
              "   551,\n",
              "   15386,\n",
              "   76,\n",
              "   1053,\n",
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              "   227,\n",
              "   7776,\n",
              "   6,\n",
              "   6523,\n",
              "   6],\n",
              "  0),\n",
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              "   346,\n",
              "   5278,\n",
              "   15387,\n",
              "   22,\n",
              "   15388,\n",
              "   4357,\n",
              "   4009,\n",
              "   17,\n",
              "   86,\n",
              "   486,\n",
              "   2,\n",
              "   15389,\n",
              "   15390,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   15391,\n",
              "   690,\n",
              "   3143,\n",
              "   4926,\n",
              "   15392,\n",
              "   15393,\n",
              "   103,\n",
              "   388,\n",
              "   142,\n",
              "   224,\n",
              "   76,\n",
              "   29,\n",
              "   32,\n",
              "   15394,\n",
              "   355,\n",
              "   1660,\n",
              "   14714,\n",
              "   40,\n",
              "   86,\n",
              "   201,\n",
              "   71,\n",
              "   142,\n",
              "   2,\n",
              "   350,\n",
              "   15395,\n",
              "   2,\n",
              "   350,\n",
              "   6577,\n",
              "   15396,\n",
              "   6593,\n",
              "   6],\n",
              "  0),\n",
              " ([15397, 61, 4166, 63, 5616, 4, 6, 6, 6, 32, 15398, 1031, 6], 0),\n",
              " ([7, 1974, 32, 5100, 400, 1034, 4, 32, 3984, 141, 6, 6, 6], 0),\n",
              " ([1733, 2, 12225, 2615, 292, 6], 0),\n",
              " ([63,\n",
              "   32,\n",
              "   479,\n",
              "   1242,\n",
              "   2,\n",
              "   23,\n",
              "   81,\n",
              "   61,\n",
              "   4997,\n",
              "   2,\n",
              "   51,\n",
              "   17,\n",
              "   547,\n",
              "   2003,\n",
              "   314,\n",
              "   23,\n",
              "   8921,\n",
              "   2766,\n",
              "   2767,\n",
              "   22,\n",
              "   23,\n",
              "   138,\n",
              "   228,\n",
              "   8022,\n",
              "   6],\n",
              "  0),\n",
              " ([86, 155, 12795, 144, 15399, 57, 32, 318, 15400, 76, 17, 6], 0),\n",
              " ([7, 97, 4164, 328, 110, 1653, 4, 2307, 2, 51, 329, 3704, 17, 6], 0),\n",
              " ([2679,\n",
              "   15401,\n",
              "   15402,\n",
              "   22,\n",
              "   23,\n",
              "   5405,\n",
              "   15403,\n",
              "   95,\n",
              "   1051,\n",
              "   438,\n",
              "   8592,\n",
              "   1993,\n",
              "   8666,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   2146,\n",
              "   1976,\n",
              "   22,\n",
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              "   249,\n",
              "   63,\n",
              "   352,\n",
              "   63,\n",
              "   23,\n",
              "   15324,\n",
              "   22,\n",
              "   23,\n",
              "   1210,\n",
              "   4009,\n",
              "   130,\n",
              "   2582,\n",
              "   60,\n",
              "   12659,\n",
              "   63,\n",
              "   352,\n",
              "   63,\n",
              "   12099,\n",
              "   6],\n",
              "  0),\n",
              " ([15404,\n",
              "   442,\n",
              "   15405,\n",
              "   15406,\n",
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              "   263,\n",
              "   4,\n",
              "   11950,\n",
              "   2,\n",
              "   23,\n",
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              "   173,\n",
              "   32,\n",
              "   816,\n",
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              "   447,\n",
              "   105,\n",
              "   23,\n",
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              "   22,\n",
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              "   994,\n",
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              "   2,\n",
              "   1224,\n",
              "   150,\n",
              "   22,\n",
              "   15408,\n",
              "   4,\n",
              "   181,\n",
              "   665,\n",
              "   5321,\n",
              "   2,\n",
              "   2137,\n",
              "   12902,\n",
              "   2,\n",
              "   4,\n",
              "   1072,\n",
              "   12132,\n",
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              " ([32,\n",
              "   4432,\n",
              "   4502,\n",
              "   8507,\n",
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              "   9579,\n",
              "   32,\n",
              "   5442,\n",
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              "   22,\n",
              "   4020,\n",
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              "   15409,\n",
              "   27,\n",
              "   4503,\n",
              "   3524,\n",
              "   221,\n",
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              "   15410,\n",
              "   15411,\n",
              "   6244,\n",
              "   15412,\n",
              "   10847,\n",
              "   2,\n",
              "   4,\n",
              "   7,\n",
              "   2430,\n",
              "   442,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   2660,\n",
              "   2607,\n",
              "   6,\n",
              "   15413,\n",
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              "   354,\n",
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              "   2492,\n",
              "   268,\n",
              "   22,\n",
              "   23,\n",
              "   9552,\n",
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              "   915,\n",
              "   5047,\n",
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              "   2557,\n",
              "   15414,\n",
              "   1866,\n",
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              "   17,\n",
              "   1295,\n",
              "   1660,\n",
              "   471,\n",
              "   98,\n",
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              "   766,\n",
              "   30,\n",
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              "   570,\n",
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              "   61,\n",
              "   76,\n",
              "   1226,\n",
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              "   97,\n",
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              "   1486,\n",
              "   355,\n",
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              "   471,\n",
              "   23,\n",
              "   4019,\n",
              "   397,\n",
              "   464,\n",
              "   6337,\n",
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              "   110,\n",
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              "   1688,\n",
              "   22,\n",
              "   23,\n",
              "   1689,\n",
              "   1690,\n",
              "   61,\n",
              "   15416,\n",
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              "   57,\n",
              "   32,\n",
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              "   2,\n",
              "   15418,\n",
              "   777,\n",
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              "   232,\n",
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              "   5907,\n",
              "   27,\n",
              "   3578,\n",
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              "   40,\n",
              "   492,\n",
              "   4101,\n",
              "   6260,\n",
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              " ([23,\n",
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              "   15424,\n",
              "   4811,\n",
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              "   15425,\n",
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              "   126,\n",
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              "   15426,\n",
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              "   3423,\n",
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              "   10562,\n",
              "   2,\n",
              "   15328,\n",
              "   15427,\n",
              "   63,\n",
              "   1502,\n",
              "   15428,\n",
              "   414,\n",
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              "   136,\n",
              "   76,\n",
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              "   86,\n",
              "   1381,\n",
              "   7666,\n",
              "   15429,\n",
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              "   80,\n",
              "   32,\n",
              "   13005,\n",
              "   15430,\n",
              "   76,\n",
              "   23,\n",
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              "   3315,\n",
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              "   22,\n",
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              "   15431,\n",
              "   6994,\n",
              "   23,\n",
              "   14788,\n",
              "   15432,\n",
              "   2,\n",
              "   1382,\n",
              "   12921,\n",
              "   4,\n",
              "   15433,\n",
              "   22,\n",
              "   23,\n",
              "   15434,\n",
              "   6],\n",
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              "   22,\n",
              "   23,\n",
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              "   455,\n",
              "   97,\n",
              "   308,\n",
              "   1444,\n",
              "   8949,\n",
              "   2,\n",
              "   51,\n",
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              "   84,\n",
              "   15435,\n",
              "   84,\n",
              "   12349,\n",
              "   12349,\n",
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              "   729,\n",
              "   105,\n",
              "   108,\n",
              "   22,\n",
              "   1195,\n",
              "   1376,\n",
              "   2638,\n",
              "   4,\n",
              "   9798,\n",
              "   127,\n",
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              "  0),\n",
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              "   144,\n",
              "   110,\n",
              "   266,\n",
              "   1973,\n",
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              "   7400,\n",
              "   61,\n",
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              "   665,\n",
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              "   9908,\n",
              "   1866,\n",
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              "   1061,\n",
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              "   2092,\n",
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              "   6907,\n",
              "   15436,\n",
              "   1922,\n",
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              "   91,\n",
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              "   144,\n",
              "   15438,\n",
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              "   15439,\n",
              "   2,\n",
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              "   1823,\n",
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              "   442,\n",
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              "   282,\n",
              "   13815,\n",
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              "   61,\n",
              "   7268,\n",
              "   15442,\n",
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              "   1219,\n",
              "   7763,\n",
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              "   255,\n",
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              "   255,\n",
              "   3049,\n",
              "   181,\n",
              "   15445,\n",
              "   6],\n",
              "  0),\n",
              " ([1105,\n",
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              "   500,\n",
              "   151,\n",
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              "   69,\n",
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              "   2,\n",
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              "   230,\n",
              "   15447,\n",
              "   6994,\n",
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              "   128,\n",
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              "   4100,\n",
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              "   276,\n",
              "   57,\n",
              "   1057,\n",
              "   1965,\n",
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              "   2874,\n",
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              "   256,\n",
              "   2301,\n",
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              "   15449,\n",
              "   11613,\n",
              "   57,\n",
              "   110,\n",
              "   15450,\n",
              "   4,\n",
              "   2,\n",
              "   2810,\n",
              "   2,\n",
              "   252,\n",
              "   15451,\n",
              "   6762,\n",
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              "  0),\n",
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              " ([7, 440, 355, 32, 4485, 626, 84, 3554, 2, 1855, 4, 2460, 6], 0),\n",
              " ([32,\n",
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              "   15453,\n",
              "   15454,\n",
              "   442,\n",
              "   15455,\n",
              "   10091,\n",
              "   4,\n",
              "   23,\n",
              "   751,\n",
              "   15456,\n",
              "   503,\n",
              "   11491,\n",
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              "  0),\n",
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              " ([32,\n",
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              "   2,\n",
              "   10015,\n",
              "   228,\n",
              "   27,\n",
              "   12606,\n",
              "   4,\n",
              "   3249,\n",
              "   9525,\n",
              "   218,\n",
              "   310,\n",
              "   32,\n",
              "   2896,\n",
              "   1222,\n",
              "   23,\n",
              "   1030,\n",
              "   4,\n",
              "   23,\n",
              "   400,\n",
              "   508,\n",
              "   12801,\n",
              "   9919,\n",
              "   6],\n",
              "  0),\n",
              " ([126,\n",
              "   97,\n",
              "   63,\n",
              "   7958,\n",
              "   3648,\n",
              "   63,\n",
              "   110,\n",
              "   423,\n",
              "   5575,\n",
              "   2,\n",
              "   219,\n",
              "   57,\n",
              "   23,\n",
              "   8789,\n",
              "   2,\n",
              "   219,\n",
              "   15457,\n",
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              "   435,\n",
              "   110,\n",
              "   266,\n",
              "   2,\n",
              "   15458,\n",
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              "   30,\n",
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              "   15460,\n",
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              "   2096,\n",
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              "   130,\n",
              "   15461,\n",
              "   10467,\n",
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              "   388,\n",
              "   5391,\n",
              "   224,\n",
              "   76,\n",
              "   308,\n",
              "   4757,\n",
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              "   455,\n",
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              "   1618,\n",
              "   1675,\n",
              "   8915,\n",
              "   51,\n",
              "   110,\n",
              "   4068,\n",
              "   15463,\n",
              "   847,\n",
              "   78,\n",
              "   298,\n",
              "   5721,\n",
              "   2717,\n",
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              "   502,\n",
              "   22,\n",
              "   620,\n",
              "   15464,\n",
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              "   342,\n",
              "   1147,\n",
              "   15465,\n",
              "   86,\n",
              "   7274,\n",
              "   22,\n",
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              "   2971,\n",
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              "   105,\n",
              "   32,\n",
              "   3297,\n",
              "   5333,\n",
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              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   352,\n",
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              "   342,\n",
              "   1147,\n",
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              "   23,\n",
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              "   993,\n",
              "   3181,\n",
              "   15469,\n",
              "   15470,\n",
              "   105,\n",
              "   31,\n",
              "   15471,\n",
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              "   97,\n",
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              "   449,\n",
              "   768,\n",
              "   15048,\n",
              "   25,\n",
              "   1652,\n",
              "   310,\n",
              "   68,\n",
              "   780,\n",
              "   5121,\n",
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              "  0),\n",
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              "   15473,\n",
              "   5485,\n",
              "   4,\n",
              "   181,\n",
              "   10209,\n",
              "   136,\n",
              "   8,\n",
              "   1878,\n",
              "   4542,\n",
              "   57,\n",
              "   23,\n",
              "   15474,\n",
              "   22,\n",
              "   14514,\n",
              "   4365,\n",
              "   27,\n",
              "   993,\n",
              "   3150,\n",
              "   6925,\n",
              "   496,\n",
              "   7977,\n",
              "   2,\n",
              "   3227,\n",
              "   445,\n",
              "   32,\n",
              "   5047,\n",
              "   22,\n",
              "   23,\n",
              "   10640,\n",
              "   25,\n",
              "   97,\n",
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              "   937,\n",
              "   15475,\n",
              "   547,\n",
              "   1476,\n",
              "   2777,\n",
              "   2,\n",
              "   7076,\n",
              "   61,\n",
              "   1354,\n",
              "   32,\n",
              "   456,\n",
              "   1057,\n",
              "   15476,\n",
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              "   61,\n",
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              "   234,\n",
              "   105,\n",
              "   31,\n",
              "   15477,\n",
              "   4232,\n",
              "   4,\n",
              "   6814,\n",
              "   27,\n",
              "   17,\n",
              "   6432,\n",
              "   7180,\n",
              "   76,\n",
              "   905,\n",
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              "   1812,\n",
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              "   1384,\n",
              "   15478,\n",
              "   2,\n",
              "   32,\n",
              "   2655,\n",
              "   4,\n",
              "   2379,\n",
              "   1652,\n",
              "   1429,\n",
              "   27,\n",
              "   9048,\n",
              "   2896,\n",
              "   6],\n",
              "  0),\n",
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              "   2,\n",
              "   1888,\n",
              "   3744,\n",
              "   4,\n",
              "   3008,\n",
              "   2083,\n",
              "   1757,\n",
              "   228,\n",
              "   78,\n",
              "   23,\n",
              "   125,\n",
              "   4564,\n",
              "   22,\n",
              "   23,\n",
              "   15479,\n",
              "   6],\n",
              "  0),\n",
              " ([130,\n",
              "   570,\n",
              "   15480,\n",
              "   23,\n",
              "   15481,\n",
              "   531,\n",
              "   57,\n",
              "   32,\n",
              "   777,\n",
              "   201,\n",
              "   110,\n",
              "   15482,\n",
              "   6817,\n",
              "   151,\n",
              "   4626,\n",
              "   6],\n",
              "  0),\n",
              " ([1888,\n",
              "   2,\n",
              "   196,\n",
              "   15135,\n",
              "   355,\n",
              "   5018,\n",
              "   15436,\n",
              "   6,\n",
              "   4,\n",
              "   57,\n",
              "   130,\n",
              "   10746,\n",
              "   2,\n",
              "   144,\n",
              "   15339,\n",
              "   1846,\n",
              "   6],\n",
              "  0),\n",
              " ([6,\n",
              "   6,\n",
              "   6,\n",
              "   549,\n",
              "   61,\n",
              "   57,\n",
              "   1895,\n",
              "   10512,\n",
              "   57,\n",
              "   23,\n",
              "   1675,\n",
              "   2,\n",
              "   4,\n",
              "   1032,\n",
              "   6839,\n",
              "   181,\n",
              "   534,\n",
              "   4,\n",
              "   15483,\n",
              "   181,\n",
              "   2025,\n",
              "   15484,\n",
              "   105,\n",
              "   7374,\n",
              "   4,\n",
              "   6170,\n",
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              "  0),\n",
              " ([86, 511, 1001, 2, 51, 255, 1310, 40, 23, 15485, 988, 15486, 6], 0),\n",
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              "   181,\n",
              "   3079,\n",
              "   2,\n",
              "   15488,\n",
              "   15489,\n",
              "   1476,\n",
              "   1209,\n",
              "   313,\n",
              "   1311,\n",
              "   181,\n",
              "   2523,\n",
              "   829,\n",
              "   3576,\n",
              "   144,\n",
              "   23,\n",
              "   1866,\n",
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              "  0),\n",
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              "  0),\n",
              " ([86,\n",
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              "   2945,\n",
              "   2291,\n",
              "   22,\n",
              "   1675,\n",
              "   6,\n",
              "   418,\n",
              "   3495,\n",
              "   15490,\n",
              "   449,\n",
              "   193,\n",
              "   15491,\n",
              "   61,\n",
              "   98,\n",
              "   71,\n",
              "   13989,\n",
              "   73,\n",
              "   729,\n",
              "   449,\n",
              "   884,\n",
              "   1053,\n",
              "   1242,\n",
              "   2,\n",
              "   15492,\n",
              "   105,\n",
              "   272,\n",
              "   15493,\n",
              "   1123,\n",
              "   2,\n",
              "   15494,\n",
              "   8122,\n",
              "   4,\n",
              "   15495,\n",
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              "  0),\n",
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              "   15007,\n",
              "   4,\n",
              "   15498,\n",
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              "   15499,\n",
              "   228,\n",
              "   78,\n",
              "   86,\n",
              "   15500,\n",
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              "   57,\n",
              "   32,\n",
              "   15501,\n",
              "   11367,\n",
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              "   18,\n",
              "   2,\n",
              "   15302,\n",
              "   11757,\n",
              "   1757,\n",
              "   1410,\n",
              "   228,\n",
              "   105,\n",
              "   971,\n",
              "   22,\n",
              "   5271,\n",
              "   1626,\n",
              "   40,\n",
              "   23,\n",
              "   2855,\n",
              "   2,\n",
              "   1620,\n",
              "   22,\n",
              "   15502,\n",
              "   40,\n",
              "   23,\n",
              "   2816,\n",
              "   4,\n",
              "   1021,\n",
              "   224,\n",
              "   40,\n",
              "   3231,\n",
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              "  0),\n",
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              "   3776,\n",
              "   5337,\n",
              "   2717,\n",
              "   22,\n",
              "   23,\n",
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              "   126,\n",
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              "   32,\n",
              "   4594,\n",
              "   15504,\n",
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              "   919,\n",
              "   310,\n",
              "   600,\n",
              "   2591,\n",
              "   6],\n",
              "  0),\n",
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              "   15505,\n",
              "   1123,\n",
              "   22,\n",
              "   4530,\n",
              "   5945,\n",
              "   2,\n",
              "   438,\n",
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              "   5361,\n",
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              "   3657,\n",
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              "   23,\n",
              "   4133,\n",
              "   6883,\n",
              "   4530,\n",
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              "   6,\n",
              "   6,\n",
              "   76,\n",
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              "   11299,\n",
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              "   639,\n",
              "   4220,\n",
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              "   22,\n",
              "   14639,\n",
              "   2566,\n",
              "   355,\n",
              "   15507,\n",
              "   4,\n",
              "   8203,\n",
              "   6],\n",
              "  0),\n",
              " ([6422,\n",
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              "   51,\n",
              "   2793,\n",
              "   32,\n",
              "   228,\n",
              "   105,\n",
              "   108,\n",
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              "   2340,\n",
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              "   152,\n",
              "   23,\n",
              "   1605,\n",
              "   854,\n",
              "   3495,\n",
              "   2,\n",
              "   1504,\n",
              "   72,\n",
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              "   1199,\n",
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              "   2,\n",
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              "   201,\n",
              "   23,\n",
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              "   981,\n",
              "   57,\n",
              "   23,\n",
              "   4874,\n",
              "   15511,\n",
              "   22,\n",
              "   181,\n",
              "   1625,\n",
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              "   350,\n",
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              "   73,\n",
              "   1759,\n",
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              "   304,\n",
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              "   227,\n",
              "   5038,\n",
              "   2,\n",
              "   9020,\n",
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              "   1234,\n",
              "   22,\n",
              "   15512,\n",
              "   8051,\n",
              "   4333,\n",
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              "   61,\n",
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              "   2,\n",
              "   5038,\n",
              "   6619,\n",
              "   173,\n",
              "   32,\n",
              "   775,\n",
              "   27,\n",
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              "   8352,\n",
              "   40,\n",
              "   6028,\n",
              "   2,\n",
              "   10739,\n",
              "   4,\n",
              "   4586,\n",
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              "   2190,\n",
              "   22,\n",
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              "   435,\n",
              "   23,\n",
              "   5620,\n",
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              "   4321,\n",
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              "   308,\n",
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              "   475,\n",
              "   22,\n",
              "   6861,\n",
              "   63,\n",
              "   1570,\n",
              "   241,\n",
              "   16,\n",
              "   22,\n",
              "   920,\n",
              "   3219,\n",
              "   6857,\n",
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              "   471,\n",
              "   17,\n",
              "   256,\n",
              "   10745,\n",
              "   1811,\n",
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              "   4971,\n",
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              "   2830,\n",
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              "   331,\n",
              "   362,\n",
              "   551,\n",
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              "   15520,\n",
              "   32,\n",
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              "   95,\n",
              "   22,\n",
              "   1195,\n",
              "   300,\n",
              "   27,\n",
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              "   76,\n",
              "   355,\n",
              "   98,\n",
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              "   73,\n",
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              "   362,\n",
              "   224,\n",
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              "   1907,\n",
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              "   8059,\n",
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              "   51,\n",
              "   6088,\n",
              "   6487,\n",
              "   73,\n",
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              "   63,\n",
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              "   57,\n",
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              "   4388,\n",
              "   8749,\n",
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              "   1177,\n",
              "   15525,\n",
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              "   32,\n",
              "   81,\n",
              "   1878,\n",
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              "   86,\n",
              "   14738,\n",
              "   27,\n",
              "   12402,\n",
              "   110,\n",
              "   5476,\n",
              "   32,\n",
              "   14284,\n",
              "   1016,\n",
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              "  0),\n",
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              "   897,\n",
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              "   76,\n",
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              "   303,\n",
              "   22,\n",
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              "   32,\n",
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              "   61,\n",
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              "   1022,\n",
              "   466,\n",
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              "   2003,\n",
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              "   17,\n",
              "   6432,\n",
              "   32,\n",
              "   6915,\n",
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              "   276,\n",
              "   3258,\n",
              "   57,\n",
              "   32,\n",
              "   1855,\n",
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              "   836,\n",
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              "   2,\n",
              "   444,\n",
              "   32,\n",
              "   287,\n",
              "   680],\n",
              "  0),\n",
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              "   23,\n",
              "   1357,\n",
              "   6947,\n",
              "   22,\n",
              "   32,\n",
              "   922,\n",
              "   14649,\n",
              "   2,\n",
              "   4,\n",
              "   201,\n",
              "   444,\n",
              "   7,\n",
              "   6044,\n",
              "   2,\n",
              "   110,\n",
              "   15527,\n",
              "   7569,\n",
              "   3008,\n",
              "   4052,\n",
              "   6],\n",
              "  0),\n",
              " ([412,\n",
              "   15528,\n",
              "   7757,\n",
              "   547,\n",
              "   884,\n",
              "   98,\n",
              "   76,\n",
              "   10312,\n",
              "   282,\n",
              "   4669,\n",
              "   95,\n",
              "   777,\n",
              "   25,\n",
              "   23,\n",
              "   1605,\n",
              "   6,\n",
              "   379,\n",
              "   1405,\n",
              "   4218,\n",
              "   181,\n",
              "   958,\n",
              "   22,\n",
              "   959,\n",
              "   27,\n",
              "   6579,\n",
              "   2643,\n",
              "   6,\n",
              "   153,\n",
              "   3540,\n",
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              "  0),\n",
              " ([729, 219, 3347, 219, 2175, 57, 24, 17, 435, 371, 40, 105, 110, 1021, 6], 0),\n",
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              "   1726,\n",
              "   234,\n",
              "   32,\n",
              "   11572,\n",
              "   775,\n",
              "   105,\n",
              "   86,\n",
              "   339,\n",
              "   2416,\n",
              "   4,\n",
              "   32,\n",
              "   2210,\n",
              "   144,\n",
              "   567,\n",
              "   690,\n",
              "   191,\n",
              "   15530,\n",
              "   5964,\n",
              "   6],\n",
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              "   4435,\n",
              "   2175,\n",
              "   57,\n",
              "   1975,\n",
              "   5234,\n",
              "   17,\n",
              "   20,\n",
              "   435,\n",
              "   371,\n",
              "   40,\n",
              "   63,\n",
              "   15531,\n",
              "   2,\n",
              "   5328,\n",
              "   2465,\n",
              "   6,\n",
              "   2793,\n",
              "   2,\n",
              "   1065,\n",
              "   97,\n",
              "   1476,\n",
              "   1864,\n",
              "   729,\n",
              "   23,\n",
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              "   57,\n",
              "   3937,\n",
              "   2,\n",
              "   4733,\n",
              "   2543,\n",
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              "   1091,\n",
              "   1093,\n",
              "   2,\n",
              "   9789,\n",
              "   15532,\n",
              "   61,\n",
              "   2,\n",
              "   57,\n",
              "   110,\n",
              "   5278,\n",
              "   2,\n",
              "   6728,\n",
              "   777,\n",
              "   2,\n",
              "   4236,\n",
              "   2,\n",
              "   11175,\n",
              "   4,\n",
              "   9263,\n",
              "   5703,\n",
              "   6],\n",
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              "   715,\n",
              "   1000,\n",
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              "   15533,\n",
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              "   228,\n",
              "   438,\n",
              "   23,\n",
              "   267,\n",
              "   4208,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   714,\n",
              "   51,\n",
              "   715,\n",
              "   15534,\n",
              "   15535,\n",
              "   2,\n",
              "   3661,\n",
              "   673,\n",
              "   3331,\n",
              "   8548,\n",
              "   91,\n",
              "   130,\n",
              "   4791,\n",
              "   15536,\n",
              "   6,\n",
              "   6,\n",
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              "   2003,\n",
              "   3049,\n",
              "   32,\n",
              "   15537,\n",
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              "   76,\n",
              "   1928,\n",
              "   218,\n",
              "   1444,\n",
              "   920,\n",
              "   32,\n",
              "   596,\n",
              "   6393,\n",
              "   187,\n",
              "   32,\n",
              "   14541,\n",
              "   1648,\n",
              "   4,\n",
              "   32,\n",
              "   15538,\n",
              "   644,\n",
              "   547,\n",
              "   1034,\n",
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              "   76,\n",
              "   308,\n",
              "   1402,\n",
              "   342,\n",
              "   1747,\n",
              "   96,\n",
              "   40,\n",
              "   13387,\n",
              "   130,\n",
              "   4635,\n",
              "   924,\n",
              "   22,\n",
              "   32,\n",
              "   2580,\n",
              "   15539,\n",
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              "   22,\n",
              "   4899,\n",
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              "   650,\n",
              "   455,\n",
              "   308,\n",
              "   71,\n",
              "   14169,\n",
              "   73,\n",
              "   23,\n",
              "   2647,\n",
              "   5557,\n",
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              " ([193, 730, 282, 3483, 57, 130, 9019, 22, 15541, 2916, 6, 6, 6], 0),\n",
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              "   442,\n",
              "   1645,\n",
              "   15542,\n",
              "   438,\n",
              "   8744,\n",
              "   5612,\n",
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              "   23,\n",
              "   1158,\n",
              "   22,\n",
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              "   219,\n",
              "   4,\n",
              "   2430,\n",
              "   442,\n",
              "   15544,\n",
              "   6734,\n",
              "   2,\n",
              "   15545,\n",
              "   3015,\n",
              "   61,\n",
              "   142,\n",
              "   51,\n",
              "   181,\n",
              "   15546,\n",
              "   15547,\n",
              "   61,\n",
              "   5799],\n",
              "  0),\n",
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              "   81,\n",
              "   10529,\n",
              "   32,\n",
              "   15548,\n",
              "   3049,\n",
              "   40,\n",
              "   15549,\n",
              "   2,\n",
              "   14909,\n",
              "   665,\n",
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              "   2,\n",
              "   51,\n",
              "   17,\n",
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              "   190,\n",
              "   782,\n",
              "   98,\n",
              "   5993,\n",
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              "   4,\n",
              "   1407,\n",
              "   596,\n",
              "   292,\n",
              "   173,\n",
              "   224,\n",
              "   142,\n",
              "   345,\n",
              "   76,\n",
              "   4773,\n",
              "   2679,\n",
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              "   2,\n",
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              "   15550,\n",
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              "   768,\n",
              "   1531,\n",
              "   191,\n",
              "   23,\n",
              "   252,\n",
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              "   15552,\n",
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              "   7054,\n",
              "   686,\n",
              "   3989,\n",
              "   252,\n",
              "   2908,\n",
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              "   22,\n",
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              "   22,\n",
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              "   2,\n",
              "   11074,\n",
              "   4,\n",
              "   15555,\n",
              "   15556,\n",
              "   2,\n",
              "   23,\n",
              "   228,\n",
              "   13050,\n",
              "   2639,\n",
              "   1390,\n",
              "   4,\n",
              "   12173,\n",
              "   650,\n",
              "   76,\n",
              "   3540,\n",
              "   600,\n",
              "   13176,\n",
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              "   1382,\n",
              "   1022,\n",
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              "   531,\n",
              "   40,\n",
              "   23,\n",
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              "   294,\n",
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              "   1214,\n",
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              "   2793,\n",
              "   329,\n",
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              "   224,\n",
              "   76,\n",
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              "   276,\n",
              "   4165,\n",
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              "   4832,\n",
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              "   280,\n",
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              "   76,\n",
              "   558,\n",
              "   371,\n",
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              "   15048,\n",
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              "   4,\n",
              "   280,\n",
              "   2,\n",
              "   10594,\n",
              "   2,\n",
              "   907,\n",
              "   1974,\n",
              "   13743,\n",
              "   6476,\n",
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              "   32,\n",
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              "   15560,\n",
              "   40,\n",
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              "   76,\n",
              "   66,\n",
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              "   266,\n",
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              "   2,\n",
              "   17,\n",
              "   15090,\n",
              "   5971,\n",
              "   23,\n",
              "   14933,\n",
              "   22,\n",
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              "   4,\n",
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              "   76,\n",
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              "   1013,\n",
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              "   730,\n",
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              "   308,\n",
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              "   3144,\n",
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              "   4042,\n",
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              "   15568,\n",
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              "   442,\n",
              "   32,\n",
              "   2339,\n",
              "   1048,\n",
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              "   86,\n",
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              "   22,\n",
              "   2042,\n",
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              "   4380,\n",
              "   27,\n",
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              "   110,\n",
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              "   2441,\n",
              "   15570,\n",
              "   15571,\n",
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              "   493,\n",
              "   1234,\n",
              "   4435,\n",
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              "   471,\n",
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              "   318,\n",
              "   1628,\n",
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              "   22,\n",
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              "   4,\n",
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              "   69,\n",
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              "   10092,\n",
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              "   770,\n",
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              "   4019,\n",
              "   15574,\n",
              "   6,\n",
              "   6,\n",
              "   6],\n",
              "  0),\n",
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              "   149,\n",
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              "   5569,\n",
              "   12512,\n",
              "   22,\n",
              "   4680,\n",
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              "   267,\n",
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              "   252,\n",
              "   4525,\n",
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              "   95,\n",
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              "   2795,\n",
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              "   1713,\n",
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              "   23,\n",
              "   346,\n",
              "   3284,\n",
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              "  0),\n",
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              "   32,\n",
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              "   442,\n",
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              "   11900,\n",
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              "   27,\n",
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              "   22,\n",
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              "   5080,\n",
              "   381,\n",
              "   15581,\n",
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              "   242,\n",
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              "   14794,\n",
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              "   23,\n",
              "   4628,\n",
              "   4,\n",
              "   14962,\n",
              "   22,\n",
              "   6388,\n",
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              "   32,\n",
              "   2767,\n",
              "   76,\n",
              "   2424,\n",
              "   600,\n",
              "   7094,\n",
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              "   61,\n",
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              "   22,\n",
              "   23,\n",
              "   9812,\n",
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              "   2186,\n",
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              "   600,\n",
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              "   2,\n",
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              "   27,\n",
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              "   455,\n",
              "   308,\n",
              "   15588,\n",
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              "   1366,\n",
              "   103,\n",
              "   4604,\n",
              "   7633,\n",
              "   2,\n",
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              "   173,\n",
              "   3963,\n",
              "   2106,\n",
              "   127,\n",
              "   438,\n",
              "   181,\n",
              "   1048,\n",
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              "  0),\n",
              " ([9145, 4, 15069, 103, 926, 57, 853, 8129, 6], 0),\n",
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              "   3064,\n",
              "   7256,\n",
              "   103,\n",
              "   32,\n",
              "   3050,\n",
              "   22,\n",
              "   15589,\n",
              "   15590,\n",
              "   355,\n",
              "   3599,\n",
              "   3600,\n",
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              "   3597,\n",
              "   3598,\n",
              "   342,\n",
              "   600,\n",
              "   3861,\n",
              "   606,\n",
              "   2,\n",
              "   201,\n",
              "   3737,\n",
              "   776,\n",
              "   117,\n",
              "   1168,\n",
              "   6],\n",
              "  0),\n",
              " ([126,\n",
              "   4066,\n",
              "   874,\n",
              "   61,\n",
              "   1354,\n",
              "   95,\n",
              "   40,\n",
              "   23,\n",
              "   15591,\n",
              "   2,\n",
              "   7,\n",
              "   62,\n",
              "   1234,\n",
              "   22,\n",
              "   701,\n",
              "   2,\n",
              "   2655,\n",
              "   157,\n",
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              "   9089,\n",
              "   5413,\n",
              "   4,\n",
              "   4166,\n",
              "   1685,\n",
              "   2,\n",
              "   23,\n",
              "   228,\n",
              "   256,\n",
              "   11,\n",
              "   308,\n",
              "   11681,\n",
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              "   130,\n",
              "   61,\n",
              "   32,\n",
              "   1376,\n",
              "   1994,\n",
              "   6837,\n",
              "   105,\n",
              "   6761,\n",
              "   15593,\n",
              "   2,\n",
              "   8316,\n",
              "   4,\n",
              "   1384,\n",
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              "   438,\n",
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              "   1100,\n",
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              "   1085,\n",
              "   1804,\n",
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              "   620,\n",
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              "   22,\n",
              "   2489,\n",
              "   5723,\n",
              "   15597,\n",
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              "   6,\n",
              "   6,\n",
              "   4,\n",
              "   32,\n",
              "   14227,\n",
              "   685,\n",
              "   22,\n",
              "   15598,\n",
              "   15599,\n",
              "   2567,\n",
              "   27,\n",
              "   2080,\n",
              "   234,\n",
              "   2945,\n",
              "   51,\n",
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              "   2678,\n",
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              "   350,\n",
              "   7636,\n",
              "   1802,\n",
              "   73,\n",
              "   110,\n",
              "   1472,\n",
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              "   63,\n",
              "   11251,\n",
              "   4728,\n",
              "   76,\n",
              "   136,\n",
              "   14026,\n",
              "   2133,\n",
              "   310,\n",
              "   597,\n",
              "   9294,\n",
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              "   96,\n",
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              "   2866,\n",
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              "   2128,\n",
              "   6,\n",
              "   2704,\n",
              "   173,\n",
              "   15601,\n",
              "   1576,\n",
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              "   4744,\n",
              "   175,\n",
              "   15317,\n",
              "   105,\n",
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              "   1000,\n",
              "   15602,\n",
              "   22,\n",
              "   4211,\n",
              "   6,\n",
              "   15603,\n",
              "   15604,\n",
              "   6],\n",
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              "   22,\n",
              "   11994,\n",
              "   606,\n",
              "   4433,\n",
              "   1034,\n",
              "   2,\n",
              "   51,\n",
              "   7,\n",
              "   1168,\n",
              "   181,\n",
              "   7360,\n",
              "   423,\n",
              "   228,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   210,\n",
              "   3511,\n",
              "   76,\n",
              "   15605,\n",
              "   23,\n",
              "   3126,\n",
              "   22,\n",
              "   181,\n",
              "   2434,\n",
              "   2435,\n",
              "   300,\n",
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              "  0),\n",
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              "   770,\n",
              "   2,\n",
              "   11299,\n",
              "   228,\n",
              "   27,\n",
              "   14055,\n",
              "   23,\n",
              "   12971,\n",
              "   11,\n",
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              "   17,\n",
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              "   40,\n",
              "   15523,\n",
              "   10329,\n",
              "   76,\n",
              "   29,\n",
              "   1864,\n",
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              "   476,\n",
              "   110,\n",
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              "   958,\n",
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              " ([32,\n",
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              "   2,\n",
              "   4757,\n",
              "   2,\n",
              "   8669,\n",
              "   2,\n",
              "   1138,\n",
              "   1913,\n",
              "   22,\n",
              "   169,\n",
              "   458,\n",
              "   15609,\n",
              "   57,\n",
              "   86,\n",
              "   8557,\n",
              "   1847,\n",
              "   22,\n",
              "   15610,\n",
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              "   355,\n",
              "   17,\n",
              "   61,\n",
              "   267,\n",
              "   98,\n",
              "   57,\n",
              "   23,\n",
              "   8011,\n",
              "   22,\n",
              "   1978,\n",
              "   188,\n",
              "   2,\n",
              "   23,\n",
              "   1048,\n",
              "   4,\n",
              "   412,\n",
              "   3892,\n",
              "   15614,\n",
              "   14913,\n",
              "   1366,\n",
              "   15615,\n",
              "   4738,\n",
              "   6768,\n",
              "   15616,\n",
              "   438,\n",
              "   4401,\n",
              "   15617,\n",
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              "   98,\n",
              "   2954,\n",
              "   76,\n",
              "   15620,\n",
              "   15621,\n",
              "   3695,\n",
              "   1802,\n",
              "   73,\n",
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              "   22,\n",
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              "   15144,\n",
              "   199,\n",
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              "   22,\n",
              "   15622,\n",
              "   15623,\n",
              "   27,\n",
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              "   173,\n",
              "   128,\n",
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              "   105,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   17,\n",
              "   696,\n",
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              "   131,\n",
              "   2,\n",
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              "   1484,\n",
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              "   105,\n",
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              "   714,\n",
              "   5436,\n",
              "   4690,\n",
              "   4147,\n",
              "   15625,\n",
              "   2,\n",
              "   335,\n",
              "   15626,\n",
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              "   32,\n",
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              "   86,\n",
              "   9569,\n",
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              "   715,\n",
              "   4,\n",
              "   4508,\n",
              "   15629,\n",
              "   714,\n",
              "   15630,\n",
              "   15631,\n",
              "   173,\n",
              "   69,\n",
              "   575,\n",
              "   105,\n",
              "   15632,\n",
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              "   32,\n",
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              "   162,\n",
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              "   2,\n",
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              "   23,\n",
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              "   61,\n",
              "   32,\n",
              "   15635,\n",
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              "   23,\n",
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              "   4019,\n",
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              "   6960,\n",
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              "   130,\n",
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              "   1913,\n",
              "   2,\n",
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              "   4,\n",
              "   6399,\n",
              "   27,\n",
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              "   57,\n",
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              "   308,\n",
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              "   13002,\n",
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              "   76,\n",
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              "   17,\n",
              "   76,\n",
              "   23,\n",
              "   4142,\n",
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              "   4789,\n",
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              "   600,\n",
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              "   23,\n",
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              "   1960,\n",
              "   162,\n",
              "   13373,\n",
              "   2836,\n",
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              "   2,\n",
              "   10320,\n",
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              "   2544,\n",
              "   103,\n",
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              "   22,\n",
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              "   27,\n",
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              "   282,\n",
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              "   3150,\n",
              "   1652,\n",
              "   355,\n",
              "   32,\n",
              "   15665,\n",
              "   15666,\n",
              "   2272,\n",
              "   7542,\n",
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              "  0),\n",
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              "   81,\n",
              "   173,\n",
              "   1620,\n",
              "   22,\n",
              "   8316,\n",
              "   4,\n",
              "   14481,\n",
              "   1384,\n",
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              "   185,\n",
              "   103,\n",
              "   783,\n",
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              "   2224,\n",
              "   2572,\n",
              "   2,\n",
              "   51,\n",
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              "   173,\n",
              "   388,\n",
              "   224,\n",
              "   5849,\n",
              "   76,\n",
              "   1473,\n",
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              "   142,\n",
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              "  0),\n",
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              "   2945,\n",
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              "   3000,\n",
              "   5283,\n",
              "   5535,\n",
              "   105,\n",
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              "   5351,\n",
              "   15668,\n",
              "   5351,\n",
              "   15668,\n",
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              "  0),\n",
              " ([286,\n",
              "   97,\n",
              "   98,\n",
              "   71,\n",
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              "   22,\n",
              "   23,\n",
              "   5479,\n",
              "   15669,\n",
              "   1866,\n",
              "   73,\n",
              "   1484,\n",
              "   84,\n",
              "   15670,\n",
              "   2,\n",
              "   8026,\n",
              "   2,\n",
              "   15671,\n",
              "   1484,\n",
              "   6],\n",
              "  0),\n",
              " ([63,\n",
              "   32,\n",
              "   15672,\n",
              "   3890,\n",
              "   76,\n",
              "   272,\n",
              "   15673,\n",
              "   15674,\n",
              "   2,\n",
              "   7531,\n",
              "   57,\n",
              "   23,\n",
              "   13150,\n",
              "   22,\n",
              "   14583,\n",
              "   61,\n",
              "   156,\n",
              "   1502,\n",
              "   22,\n",
              "   23,\n",
              "   606,\n",
              "   1235,\n",
              "   227,\n",
              "   13880,\n",
              "   2,\n",
              "   6738,\n",
              "   4,\n",
              "   15675,\n",
              "   2119,\n",
              "   6],\n",
              "  0),\n",
              " ([201,\n",
              "   444,\n",
              "   23,\n",
              "   15676,\n",
              "   32,\n",
              "   287,\n",
              "   15677,\n",
              "   2,\n",
              "   105,\n",
              "   32,\n",
              "   693,\n",
              "   15678,\n",
              "   362,\n",
              "   108,\n",
              "   782,\n",
              "   1076,\n",
              "   15679,\n",
              "   2,\n",
              "   449,\n",
              "   15680,\n",
              "   80,\n",
              "   412,\n",
              "   746,\n",
              "   5236,\n",
              "   2,\n",
              "   606,\n",
              "   6118,\n",
              "   40,\n",
              "   23,\n",
              "   11945,\n",
              "   6778,\n",
              "   4328,\n",
              "   2,\n",
              "   1139,\n",
              "   40,\n",
              "   2047,\n",
              "   6],\n",
              "  0),\n",
              " ([97,\n",
              "   63,\n",
              "   2189,\n",
              "   25,\n",
              "   201,\n",
              "   63,\n",
              "   979,\n",
              "   63,\n",
              "   110,\n",
              "   1023,\n",
              "   76,\n",
              "   308,\n",
              "   76,\n",
              "   558,\n",
              "   234,\n",
              "   2,\n",
              "   51,\n",
              "   17,\n",
              "   173,\n",
              "   4506,\n",
              "   69,\n",
              "   152,\n",
              "   105,\n",
              "   1706,\n",
              "   4,\n",
              "   75,\n",
              "   6],\n",
              "  0),\n",
              " ([130,\n",
              "   61,\n",
              "   15681,\n",
              "   2132,\n",
              "   1410,\n",
              "   51,\n",
              "   181,\n",
              "   107,\n",
              "   4,\n",
              "   1366,\n",
              "   15682,\n",
              "   105,\n",
              "   32,\n",
              "   15608,\n",
              "   27,\n",
              "   492,\n",
              "   2068,\n",
              "   1582,\n",
              "   15683,\n",
              "   2047,\n",
              "   76,\n",
              "   677,\n",
              "   6],\n",
              "  0),\n",
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              " ([15684,\n",
              "   1172,\n",
              "   15685,\n",
              "   15686,\n",
              "   204,\n",
              "   15687,\n",
              "   714,\n",
              "   15688,\n",
              "   2710,\n",
              "   15689,\n",
              "   715,\n",
              "   1175,\n",
              "   15690,\n",
              "   13333,\n",
              "   4664,\n",
              "   13334,\n",
              "   15691,\n",
              "   6],\n",
              "  0),\n",
              " ([6,\n",
              "   6,\n",
              "   6,\n",
              "   95,\n",
              "   22,\n",
              "   23,\n",
              "   252,\n",
              "   15441,\n",
              "   4,\n",
              "   2119,\n",
              "   7918,\n",
              "   332,\n",
              "   333,\n",
              "   57,\n",
              "   1476,\n",
              "   32,\n",
              "   285,\n",
              "   96,\n",
              "   6],\n",
              "  0),\n",
              " ([32, 2945, 3747, 22, 478, 4, 5529, 6], 0),\n",
              " ([32, 8636, 1357, 924, 6], 0),\n",
              " ([1733, 2, 2927, 4, 15692, 15693, 6], 0),\n",
              " ([1188,\n",
              "   15694,\n",
              "   2,\n",
              "   15695,\n",
              "   15696,\n",
              "   1191,\n",
              "   14450,\n",
              "   15697,\n",
              "   6,\n",
              "   3297,\n",
              "   1191,\n",
              "   15698,\n",
              "   15699,\n",
              "   15700,\n",
              "   6],\n",
              "  0),\n",
              " ([15701,\n",
              "   61,\n",
              "   32,\n",
              "   3297,\n",
              "   4,\n",
              "   15702,\n",
              "   60,\n",
              "   2,\n",
              "   1234,\n",
              "   22,\n",
              "   15703,\n",
              "   27,\n",
              "   768,\n",
              "   1484,\n",
              "   108,\n",
              "   51,\n",
              "   23,\n",
              "   252,\n",
              "   340,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
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              "   61,\n",
              "   86,\n",
              "   1471,\n",
              "   665,\n",
              "   228,\n",
              "   84,\n",
              "   261,\n",
              "   98,\n",
              "   10630,\n",
              "   342,\n",
              "   282,\n",
              "   15704,\n",
              "   162,\n",
              "   2530,\n",
              "   10950,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   15705,\n",
              "   4,\n",
              "   1470,\n",
              "   596,\n",
              "   292,\n",
              "   15706,\n",
              "   3405,\n",
              "   4230,\n",
              "   4,\n",
              "   23,\n",
              "   10310,\n",
              "   22,\n",
              "   7902,\n",
              "   187,\n",
              "   1146,\n",
              "   6],\n",
              "  0),\n",
              " ([7, 32, 922, 228, 84, 97, 32, 1922, 2, 51, 593, 2, 2119, 4, 3262, 6], 0),\n",
              " ([15707,\n",
              "   4,\n",
              "   596,\n",
              "   2,\n",
              "   449,\n",
              "   14,\n",
              "   3302,\n",
              "   130,\n",
              "   471,\n",
              "   12396,\n",
              "   2186,\n",
              "   597,\n",
              "   1273,\n",
              "   373,\n",
              "   84,\n",
              "   731,\n",
              "   2,\n",
              "   27,\n",
              "   61,\n",
              "   2,\n",
              "   719,\n",
              "   241,\n",
              "   766,\n",
              "   95,\n",
              "   76,\n",
              "   2522,\n",
              "   105,\n",
              "   6],\n",
              "  0),\n",
              " ([3820,\n",
              "   40,\n",
              "   2128,\n",
              "   6,\n",
              "   15708,\n",
              "   84,\n",
              "   462,\n",
              "   6310,\n",
              "   40,\n",
              "   181,\n",
              "   2756,\n",
              "   84,\n",
              "   23,\n",
              "   9401,\n",
              "   3172,\n",
              "   22,\n",
              "   23,\n",
              "   15709,\n",
              "   2770,\n",
              "   32,\n",
              "   107,\n",
              "   27,\n",
              "   153,\n",
              "   1804,\n",
              "   8313,\n",
              "   14,\n",
              "   136,\n",
              "   10203,\n",
              "   76,\n",
              "   548,\n",
              "   6],\n",
              "  0),\n",
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              " ([23,\n",
              "   775,\n",
              "   61,\n",
              "   8,\n",
              "   15711,\n",
              "   3718,\n",
              "   4,\n",
              "   14566,\n",
              "   57,\n",
              "   110,\n",
              "   7432,\n",
              "   27,\n",
              "   153,\n",
              "   95,\n",
              "   14,\n",
              "   241,\n",
              "   2609,\n",
              "   17,\n",
              "   40,\n",
              "   755,\n",
              "   3394,\n",
              "   296],\n",
              "  0),\n",
              " ([4904,\n",
              "   95,\n",
              "   234,\n",
              "   22,\n",
              "   23,\n",
              "   1684,\n",
              "   40,\n",
              "   23,\n",
              "   15712,\n",
              "   193,\n",
              "   29,\n",
              "   10474,\n",
              "   355,\n",
              "   27,\n",
              "   15713,\n",
              "   15714,\n",
              "   6],\n",
              "  0),\n",
              " ([17,\n",
              "   7304,\n",
              "   76,\n",
              "   308,\n",
              "   32,\n",
              "   287,\n",
              "   886,\n",
              "   2,\n",
              "   4,\n",
              "   27,\n",
              "   15715,\n",
              "   61,\n",
              "   729,\n",
              "   435,\n",
              "   17,\n",
              "   2283,\n",
              "   6],\n",
              "  0),\n",
              " ([15716,\n",
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              "   98,\n",
              "   71,\n",
              "   310,\n",
              "   14738,\n",
              "   4,\n",
              "   6406,\n",
              "   446,\n",
              "   73,\n",
              "   379,\n",
              "   61,\n",
              "   40,\n",
              "   144,\n",
              "   1199,\n",
              "   2362],\n",
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              " ([23,\n",
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              "   127,\n",
              "   103,\n",
              "   108,\n",
              "   2385,\n",
              "   144,\n",
              "   2,\n",
              "   1381,\n",
              "   5731,\n",
              "   15717,\n",
              "   976,\n",
              "   63,\n",
              "   11829,\n",
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              "  0),\n",
              " ([32,\n",
              "   143,\n",
              "   3015,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   4,\n",
              "   219,\n",
              "   181,\n",
              "   606,\n",
              "   3503,\n",
              "   8085,\n",
              "   219,\n",
              "   61,\n",
              "   371,\n",
              "   185,\n",
              "   105,\n",
              "   23,\n",
              "   7360,\n",
              "   22,\n",
              "   15718,\n",
              "   6],\n",
              "  0),\n",
              " ([342,\n",
              "   506,\n",
              "   854,\n",
              "   2077,\n",
              "   2,\n",
              "   23,\n",
              "   1117,\n",
              "   22,\n",
              "   8976,\n",
              "   10186,\n",
              "   61,\n",
              "   350,\n",
              "   73,\n",
              "   23,\n",
              "   2571,\n",
              "   22,\n",
              "   110,\n",
              "   2572,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   2077,\n",
              "   435,\n",
              "   449,\n",
              "   12930,\n",
              "   905,\n",
              "   266,\n",
              "   1071,\n",
              "   57,\n",
              "   98,\n",
              "   23,\n",
              "   1085,\n",
              "   777,\n",
              "   110,\n",
              "   345,\n",
              "   884,\n",
              "   2,\n",
              "   4,\n",
              "   23,\n",
              "   2298,\n",
              "   61,\n",
              "   4068,\n",
              "   6,\n",
              "   23,\n",
              "   2077,\n",
              "   61,\n",
              "   729,\n",
              "   188,\n",
              "   103,\n",
              "   3228,\n",
              "   76,\n",
              "   308,\n",
              "   6,\n",
              "   6,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   4785,\n",
              "   4,\n",
              "   8417,\n",
              "   228,\n",
              "   27,\n",
              "   6088,\n",
              "   2440,\n",
              "   23,\n",
              "   15719,\n",
              "   22,\n",
              "   729,\n",
              "   61,\n",
              "   8902,\n",
              "   4,\n",
              "   3420,\n",
              "   57,\n",
              "   130,\n",
              "   1430,\n",
              "   1068,\n",
              "   2,\n",
              "   1673,\n",
              "   110,\n",
              "   6739,\n",
              "   105,\n",
              "   15720,\n",
              "   7292,\n",
              "   4,\n",
              "   23,\n",
              "   15721,\n",
              "   1384,\n",
              "   22,\n",
              "   936,\n",
              "   937,\n",
              "   6],\n",
              "  0),\n",
              " ([261, 15722, 922, 2, 703, 110, 15723, 7482, 6], 0),\n",
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              "  0),\n",
              " ([11994,\n",
              "   10763,\n",
              "   136,\n",
              "   32,\n",
              "   15724,\n",
              "   2432,\n",
              "   6,\n",
              "   181,\n",
              "   12533,\n",
              "   15725,\n",
              "   57,\n",
              "   15726,\n",
              "   12672,\n",
              "   27,\n",
              "   7794,\n",
              "   86,\n",
              "   10283,\n",
              "   3205,\n",
              "   1311,\n",
              "   23,\n",
              "   15727,\n",
              "   6],\n",
              "  0),\n",
              " ([4515, 173, 5857, 32, 15281, 204, 1483, 27, 61, 660, 2, 4316, 4, 4635, 6],\n",
              "  0),\n",
              " ([23,\n",
              "   2266,\n",
              "   696,\n",
              "   907,\n",
              "   449,\n",
              "   5147,\n",
              "   2,\n",
              "   51,\n",
              "   853,\n",
              "   103,\n",
              "   127,\n",
              "   76,\n",
              "   2424,\n",
              "   57,\n",
              "   32,\n",
              "   1618,\n",
              "   160,\n",
              "   2291,\n",
              "   6],\n",
              "  0),\n",
              " ([6,\n",
              "   6,\n",
              "   6,\n",
              "   86,\n",
              "   15728,\n",
              "   15729,\n",
              "   3083,\n",
              "   84,\n",
              "   51,\n",
              "   1367,\n",
              "   15730,\n",
              "   6280,\n",
              "   3846,\n",
              "   570,\n",
              "   2793,\n",
              "   6332,\n",
              "   23,\n",
              "   9840,\n",
              "   6],\n",
              "  0),\n",
              " ([7,\n",
              "   32,\n",
              "   15646,\n",
              "   883,\n",
              "   76,\n",
              "   10327,\n",
              "   310,\n",
              "   23,\n",
              "   15731,\n",
              "   22,\n",
              "   23,\n",
              "   2896,\n",
              "   2,\n",
              "   32,\n",
              "   2091,\n",
              "   76,\n",
              "   23,\n",
              "   3338,\n",
              "   1827,\n",
              "   22,\n",
              "   718,\n",
              "   4,\n",
              "   32,\n",
              "   3382,\n",
              "   6939,\n",
              "   22,\n",
              "   3426,\n",
              "   6],\n",
              "  0),\n",
              " ([1160,\n",
              "   86,\n",
              "   5703,\n",
              "   1968,\n",
              "   76,\n",
              "   2599,\n",
              "   23,\n",
              "   15732,\n",
              "   57,\n",
              "   23,\n",
              "   1158,\n",
              "   15733,\n",
              "   2,\n",
              "   97,\n",
              "   388,\n",
              "   23,\n",
              "   2146,\n",
              "   15734,\n",
              "   51,\n",
              "   62,\n",
              "   23,\n",
              "   594,\n",
              "   15735,\n",
              "   22,\n",
              "   17,\n",
              "   4,\n",
              "   2468,\n",
              "   15736,\n",
              "   22,\n",
              "   471,\n",
              "   23,\n",
              "   1117,\n",
              "   79,\n",
              "   2643,\n",
              "   6],\n",
              "  0),\n",
              " ([329,\n",
              "   193,\n",
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              "   332,\n",
              "   69,\n",
              "   63,\n",
              "   12541,\n",
              "   4,\n",
              "   15737,\n",
              "   442,\n",
              "   86,\n",
              "   7749,\n",
              "   228,\n",
              "   1859,\n",
              "   15738,\n",
              "   15739,\n",
              "   9059,\n",
              "   942,\n",
              "   6],\n",
              "  0),\n",
              " ([17,\n",
              "   61,\n",
              "   8,\n",
              "   10832,\n",
              "   76,\n",
              "   1053,\n",
              "   3867,\n",
              "   3849,\n",
              "   976,\n",
              "   15740,\n",
              "   15401,\n",
              "   438,\n",
              "   23,\n",
              "   4866,\n",
              "   22,\n",
              "   2053,\n",
              "   2699,\n",
              "   4,\n",
              "   1906,\n",
              "   15741,\n",
              "   175,\n",
              "   69,\n",
              "   1054,\n",
              "   40,\n",
              "   23,\n",
              "   221,\n",
              "   2679,\n",
              "   2866,\n",
              "   6],\n",
              "  0),\n",
              " ([412,\n",
              "   15742,\n",
              "   469,\n",
              "   2,\n",
              "   1054,\n",
              "   181,\n",
              "   182,\n",
              "   15743,\n",
              "   6078,\n",
              "   105,\n",
              "   15141,\n",
              "   2,\n",
              "   6811,\n",
              "   23,\n",
              "   15744,\n",
              "   4229,\n",
              "   22,\n",
              "   7042,\n",
              "   4941,\n",
              "   15745,\n",
              "   15746,\n",
              "   15747,\n",
              "   105,\n",
              "   3495,\n",
              "   1357,\n",
              "   3416,\n",
              "   6],\n",
              "  0),\n",
              " ([5577,\n",
              "   5357,\n",
              "   438,\n",
              "   15748,\n",
              "   7165,\n",
              "   76,\n",
              "   8793,\n",
              "   15749,\n",
              "   61,\n",
              "   14160,\n",
              "   5620,\n",
              "   2,\n",
              "   51,\n",
              "   15750,\n",
              "   15751,\n",
              "   23,\n",
              "   2875,\n",
              "   105,\n",
              "   86,\n",
              "   15752,\n",
              "   1807,\n",
              "   22,\n",
              "   673,\n",
              "   6814,\n",
              "   6],\n",
              "  0),\n",
              " ([5425,\n",
              "   6784,\n",
              "   3126,\n",
              "   61,\n",
              "   1638,\n",
              "   84,\n",
              "   7,\n",
              "   1520,\n",
              "   23,\n",
              "   252,\n",
              "   15753,\n",
              "   2631,\n",
              "   300,\n",
              "   332,\n",
              "   241,\n",
              "   333,\n",
              "   6],\n",
              "  0),\n",
              " ([23, 926, 228, 40, 1195, 162, 355, 642, 2237, 27, 256, 308, 15754, 6], 0),\n",
              " ([15755,\n",
              "   280,\n",
              "   7714,\n",
              "   110,\n",
              "   809,\n",
              "   57,\n",
              "   169,\n",
              "   15756,\n",
              "   5183,\n",
              "   2,\n",
              "   1195,\n",
              "   5560,\n",
              "   40,\n",
              "   71,\n",
              "   15757,\n",
              "   4,\n",
              "   1195,\n",
              "   4296,\n",
              "   40,\n",
              "   7926,\n",
              "   6,\n",
              "   6,\n",
              "   6],\n",
              "  0),\n",
              " ([7513,\n",
              "   4,\n",
              "   3414,\n",
              "   2,\n",
              "   17,\n",
              "   15758,\n",
              "   310,\n",
              "   3252,\n",
              "   6784,\n",
              "   15759,\n",
              "   2,\n",
              "   4,\n",
              "   342,\n",
              "   23,\n",
              "   1085,\n",
              "   96,\n",
              "   4218,\n",
              "   32,\n",
              "   15760,\n",
              "   15761,\n",
              "   22,\n",
              "   729,\n",
              "   9995,\n",
              "   1000,\n",
              "   6],\n",
              "  0),\n",
              " ([32, 15762, 3015, 438, 3932, 15763, 76, 2834, 9863, 6], 0),\n",
              " ([23, 228, 61, 388, 32, 743, 2, 2375, 2, 15764, 2, 15765, 122, 6], 0),\n",
              " ([701, 2, 14795, 2, 18, 4, 639, 15437, 228, 6], 0),\n",
              " ([462,\n",
              "   23,\n",
              "   494,\n",
              "   7669,\n",
              "   61,\n",
              "   927,\n",
              "   97,\n",
              "   15766,\n",
              "   2,\n",
              "   130,\n",
              "   6971,\n",
              "   22,\n",
              "   15767,\n",
              "   3839,\n",
              "   11626,\n",
              "   173,\n",
              "   169,\n",
              "   2542,\n",
              "   566,\n",
              "   127,\n",
              "   4,\n",
              "   1484,\n",
              "   76,\n",
              "   14842,\n",
              "   6],\n",
              "  0),\n",
              " ([32, 5571, 934, 76, 32, 3495, 15768, 4, 597, 7211, 15769, 6, 335], 0),\n",
              " ([32,\n",
              "   1157,\n",
              "   1389,\n",
              "   342,\n",
              "   880,\n",
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              "   130,\n",
              "   3309,\n",
              "   157,\n",
              "   280,\n",
              "   1507,\n",
              "   23,\n",
              "   1706,\n",
              "   22,\n",
              "   201,\n",
              "   1195,\n",
              "   15770,\n",
              "   276,\n",
              "   162,\n",
              "   123,\n",
              "   15771,\n",
              "   32,\n",
              "   15772,\n",
              "   22,\n",
              "   15773,\n",
              "   15774,\n",
              "   15775,\n",
              "   6],\n",
              "  0),\n",
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              "   1871,\n",
              "   8915,\n",
              "   61,\n",
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              "   11,\n",
              "   2492,\n",
              "   22,\n",
              "   23,\n",
              "   228,\n",
              "   27,\n",
              "   61,\n",
              "   8365,\n",
              "   84,\n",
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              "   471,\n",
              "   6092,\n",
              "   449,\n",
              "   103,\n",
              "   22,\n",
              "   130,\n",
              "   4070,\n",
              "   144,\n",
              "   905,\n",
              "   2257,\n",
              "   22,\n",
              "   15776,\n",
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              " ([1403,\n",
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              "   4,\n",
              "   6603,\n",
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              "   2,\n",
              "   4,\n",
              "   17,\n",
              "   2643,\n",
              "   4321,\n",
              "   352,\n",
              "   63,\n",
              "   32,\n",
              "   872,\n",
              "   184,\n",
              "   40,\n",
              "   14956,\n",
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              "  0),\n",
              " ([4438,\n",
              "   198,\n",
              "   1904,\n",
              "   6722,\n",
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              "   12889,\n",
              "   105,\n",
              "   130,\n",
              "   249,\n",
              "   2,\n",
              "   4,\n",
              "   379,\n",
              "   716,\n",
              "   5506,\n",
              "   10533,\n",
              "   57,\n",
              "   32,\n",
              "   381,\n",
              "   22,\n",
              "   181,\n",
              "   266,\n",
              "   1038,\n",
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              " ([23,\n",
              "   10098,\n",
              "   3433,\n",
              "   11,\n",
              "   4127,\n",
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              "   494,\n",
              "   12883,\n",
              "   2870,\n",
              "   2,\n",
              "   1299,\n",
              "   17,\n",
              "   32,\n",
              "   6616,\n",
              "   15777,\n",
              "   22,\n",
              "   15778,\n",
              "   15779,\n",
              "   105,\n",
              "   23,\n",
              "   15780,\n",
              "   15781,\n",
              "   22,\n",
              "   23,\n",
              "   6687,\n",
              "   15782,\n",
              "   13539,\n",
              "   8857,\n",
              "   22,\n",
              "   23,\n",
              "   81,\n",
              "   1199,\n",
              "   6],\n",
              "  0),\n",
              " ([15783,\n",
              "   5770,\n",
              "   32,\n",
              "   2896,\n",
              "   362,\n",
              "   342,\n",
              "   1709,\n",
              "   5391,\n",
              "   4,\n",
              "   7560,\n",
              "   1077,\n",
              "   410,\n",
              "   2406,\n",
              "   2,\n",
              "   1514,\n",
              "   15784,\n",
              "   5337,\n",
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              "  0),\n",
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              "  0),\n",
              " ([128,\n",
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              "   2,\n",
              "   9532,\n",
              "   4,\n",
              "   2,\n",
              "   6780,\n",
              "   2,\n",
              "   2119,\n",
              "   2,\n",
              "   1698,\n",
              "   845,\n",
              "   61,\n",
              "   32,\n",
              "   3984,\n",
              "   5101,\n",
              "   22,\n",
              "   86,\n",
              "   8642,\n",
              "   27,\n",
              "   173,\n",
              "   76,\n",
              "   308,\n",
              "   333,\n",
              "   76,\n",
              "   308,\n",
              "   14798,\n",
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              "  0),\n",
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              "   5770,\n",
              "   86,\n",
              "   54,\n",
              "   1314,\n",
              "   2,\n",
              "   7426,\n",
              "   15787,\n",
              "   4,\n",
              "   49,\n",
              "   15788,\n",
              "   2,\n",
              "   51,\n",
              "   123,\n",
              "   15789,\n",
              "   2,\n",
              "   228,\n",
              "   3143,\n",
              "   10139,\n",
              "   534,\n",
              "   103,\n",
              "   15790,\n",
              "   4,\n",
              "   4091,\n",
              "   11717,\n",
              "   442,\n",
              "   3472,\n",
              "   15791,\n",
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              "  0),\n",
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              " ([23,\n",
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              "   1468,\n",
              "   1600,\n",
              "   811,\n",
              "   308,\n",
              "   76,\n",
              "   11192,\n",
              "   598,\n",
              "   25,\n",
              "   1758,\n",
              "   7473,\n",
              "   2,\n",
              "   51,\n",
              "   76,\n",
              "   4650,\n",
              "   162,\n",
              "   2424,\n",
              "   967,\n",
              "   78,\n",
              "   143,\n",
              "   758,\n",
              "   105,\n",
              "   153,\n",
              "   339,\n",
              "   8867,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   478,\n",
              "   27,\n",
              "   23,\n",
              "   9940,\n",
              "   61,\n",
              "   32,\n",
              "   506,\n",
              "   7092,\n",
              "   1357,\n",
              "   2298,\n",
              "   84,\n",
              "   4,\n",
              "   32,\n",
              "   4635,\n",
              "   15792,\n",
              "   5924,\n",
              "   3381,\n",
              "   84,\n",
              "   61,\n",
              "   32,\n",
              "   12209,\n",
              "   5266,\n",
              "   76,\n",
              "   23,\n",
              "   60,\n",
              "   23,\n",
              "   228,\n",
              "   5579,\n",
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              "  0),\n",
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              " ([32,\n",
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              "   4,\n",
              "   1470,\n",
              "   641,\n",
              "   22,\n",
              "   471,\n",
              "   23,\n",
              "   6555,\n",
              "   2793,\n",
              "   1337,\n",
              "   86,\n",
              "   6116,\n",
              "   10100,\n",
              "   57,\n",
              "   6390,\n",
              "   2001,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   1028,\n",
              "   1490,\n",
              "   1242,\n",
              "   2,\n",
              "   15492,\n",
              "   105,\n",
              "   272,\n",
              "   15441,\n",
              "   775,\n",
              "   4798,\n",
              "   4,\n",
              "   272,\n",
              "   8513,\n",
              "   801,\n",
              "   12944,\n",
              "   6,\n",
              "   6,\n",
              "   7,\n",
              "   32,\n",
              "   2283,\n",
              "   11777,\n",
              "   57,\n",
              "   15793,\n",
              "   15794,\n",
              "   4,\n",
              "   15795],\n",
              "  0),\n",
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              " ([8645,\n",
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              "   57,\n",
              "   15796,\n",
              "   2734,\n",
              "   1804,\n",
              "   2027,\n",
              "   2,\n",
              "   97,\n",
              "   3170,\n",
              "   76,\n",
              "   15353,\n",
              "   597,\n",
              "   1071,\n",
              "   997,\n",
              "   57,\n",
              "   1934,\n",
              "   22,\n",
              "   86,\n",
              "   809,\n",
              "   6,\n",
              "   597,\n",
              "   3494,\n",
              "   4,\n",
              "   9610,\n",
              "   103,\n",
              "   15797,\n",
              "   6],\n",
              "  0),\n",
              " ([2643,\n",
              "   331,\n",
              "   2,\n",
              "   40,\n",
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              "   252,\n",
              "   2492,\n",
              "   2,\n",
              "   17,\n",
              "   7849,\n",
              "   23,\n",
              "   195,\n",
              "   2916,\n",
              "   4,\n",
              "   2459,\n",
              "   15798,\n",
              "   27,\n",
              "   15799,\n",
              "   110,\n",
              "   15800,\n",
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              "   606,\n",
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              "   922,\n",
              "   265,\n",
              "   61,\n",
              "   32,\n",
              "   9263,\n",
              "   2841,\n",
              "   85,\n",
              "   144,\n",
              "   14114,\n",
              "   6,\n",
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              "  0),\n",
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              "   3324,\n",
              "   2855,\n",
              "   162,\n",
              "   4907,\n",
              "   15801,\n",
              "   63,\n",
              "   65,\n",
              "   1491,\n",
              "   414,\n",
              "   4,\n",
              "   177,\n",
              "   15802,\n",
              "   14410,\n",
              "   2647,\n",
              "   84,\n",
              "   32,\n",
              "   15803,\n",
              "   570,\n",
              "   22,\n",
              "   23,\n",
              "   423,\n",
              "   6232,\n",
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              "  0),\n",
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              "   57,\n",
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              "   22,\n",
              "   15804,\n",
              "   2486,\n",
              "   15805,\n",
              "   4,\n",
              "   15806,\n",
              "   289,\n",
              "   3194,\n",
              "   2,\n",
              "   32,\n",
              "   13820,\n",
              "   8919,\n",
              "   8434,\n",
              "   310,\n",
              "   885,\n",
              "   1546,\n",
              "   496,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   15807,\n",
              "   287,\n",
              "   15808,\n",
              "   144,\n",
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              "   228,\n",
              "   1198,\n",
              "   2,\n",
              "   51,\n",
              "   95,\n",
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              "   71,\n",
              "   276,\n",
              "   768,\n",
              "   4425,\n",
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              "   110,\n",
              "   10025,\n",
              "   3511,\n",
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              "   51,\n",
              "   61,\n",
              "   111,\n",
              "   783,\n",
              "   76,\n",
              "   66,\n",
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              "   2266,\n",
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              "   51,\n",
              "   1855,\n",
              "   4731,\n",
              "   6329,\n",
              "   4904,\n",
              "   23,\n",
              "   4905,\n",
              "   105,\n",
              "   2577,\n",
              "   162,\n",
              "   4484,\n",
              "   438,\n",
              "   32,\n",
              "   2345,\n",
              "   3261,\n",
              "   1071,\n",
              "   6],\n",
              "  0),\n",
              " ([17,\n",
              "   173,\n",
              "   32,\n",
              "   399,\n",
              "   777,\n",
              "   22,\n",
              "   476,\n",
              "   1718,\n",
              "   905,\n",
              "   9294,\n",
              "   4,\n",
              "   3607,\n",
              "   105,\n",
              "   449,\n",
              "   285,\n",
              "   324,\n",
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              "   23,\n",
              "   4073,\n",
              "   4418,\n",
              "   103,\n",
              "   6499,\n",
              "   102,\n",
              "   2,\n",
              "   4,\n",
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              "   65,\n",
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              "   600,\n",
              "   8129,\n",
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              "  0),\n",
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              " ([27,\n",
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              "   15818,\n",
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              "   23,\n",
              "   813,\n",
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              "   292,\n",
              "   105,\n",
              "   6597,\n",
              "   3007,\n",
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              "  0),\n",
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              "   438,\n",
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              "   908,\n",
              "   1048,\n",
              "   57,\n",
              "   1091,\n",
              "   15819,\n",
              "   9372,\n",
              "   27,\n",
              "   103,\n",
              "   399,\n",
              "   4,\n",
              "   8,\n",
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              "   65,\n",
              "   256,\n",
              "   2950,\n",
              "   23,\n",
              "   542,\n",
              "   15820,\n",
              "   57,\n",
              "   7870,\n",
              "   1561,\n",
              "   6],\n",
              "  0),\n",
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              "   22,\n",
              "   23,\n",
              "   15821,\n",
              "   1080,\n",
              "   4,\n",
              "   15822,\n",
              "   2,\n",
              "   17,\n",
              "   12024,\n",
              "   23,\n",
              "   12538,\n",
              "   1410,\n",
              "   2132,\n",
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              "   1734,\n",
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              "   8540,\n",
              "   2,\n",
              "   162,\n",
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              "   1518,\n",
              "   22,\n",
              "   23,\n",
              "   8958,\n",
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              "   22,\n",
              "   11578,\n",
              "   4,\n",
              "   15823,\n",
              "   6],\n",
              "  0),\n",
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              "   40,\n",
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              "   162,\n",
              "   279,\n",
              "   175,\n",
              "   3190,\n",
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              "   4376,\n",
              "   1859,\n",
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              "   169,\n",
              "   1747,\n",
              "   12378,\n",
              "   300,\n",
              "   57,\n",
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              "  0),\n",
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              "   76,\n",
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              "   32,\n",
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              "   23,\n",
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              "   2,\n",
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              "   76,\n",
              "   272,\n",
              "   2945,\n",
              "   4365,\n",
              "   4,\n",
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              "   1685,\n",
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              "   1053,\n",
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              "   57,\n",
              "   597,\n",
              "   3285,\n",
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              "   63,\n",
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              "   241,\n",
              "   66,\n",
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              "   700,\n",
              "   91,\n",
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              "   300,\n",
              "   17,\n",
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              "   194,\n",
              "   15830,\n",
              "   442,\n",
              "   17,\n",
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              "  0),\n",
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              "   76,\n",
              "   1052,\n",
              "   3673,\n",
              "   76,\n",
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              "   108,\n",
              "   23,\n",
              "   4054,\n",
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              "   51,\n",
              "   1776,\n",
              "   1604,\n",
              "   142,\n",
              "   6,\n",
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              "   81,\n",
              "   61,\n",
              "   352,\n",
              "   2543,\n",
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              "   267,\n",
              "   4220,\n",
              "   2,\n",
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              "   95,\n",
              "   76,\n",
              "   2313,\n",
              "   324,\n",
              "   23,\n",
              "   5667,\n",
              "   2281,\n",
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              " ([32,\n",
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              "   22,\n",
              "   23,\n",
              "   5347,\n",
              "   898,\n",
              "   22,\n",
              "   1535,\n",
              "   4,\n",
              "   15077,\n",
              "   4,\n",
              "   15002,\n",
              "   3963,\n",
              "   105,\n",
              "   32,\n",
              "   15831,\n",
              "   14820,\n",
              "   15024,\n",
              "   6],\n",
              "  0),\n",
              " ([13331,\n",
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              "   2,\n",
              "   15694,\n",
              "   1191,\n",
              "   15833,\n",
              "   1175,\n",
              "   1179,\n",
              "   15834,\n",
              "   2,\n",
              "   1188,\n",
              "   3023,\n",
              "   15835,\n",
              "   15836,\n",
              "   204,\n",
              "   1098,\n",
              "   1175,\n",
              "   1188,\n",
              "   1177,\n",
              "   15837,\n",
              "   204,\n",
              "   15838,\n",
              "   15839,\n",
              "   1191,\n",
              "   3671,\n",
              "   15840,\n",
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              "   76,\n",
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              "   86,\n",
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              "   169,\n",
              "   4418,\n",
              "   162,\n",
              "   1391,\n",
              "   4542,\n",
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              "   3551,\n",
              "   2257,\n",
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              "   1974,\n",
              "   15847,\n",
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              "   702,\n",
              "   15854,\n",
              "   15855,\n",
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              "   22,\n",
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              "   3839,\n",
              "   2140,\n",
              "   162,\n",
              "   1977,\n",
              "   32,\n",
              "   15856,\n",
              "   160,\n",
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              "   294,\n",
              "   32,\n",
              "   3221,\n",
              "   25,\n",
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              "   263,\n",
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              "   4770,\n",
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              "   27,\n",
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              "   252,\n",
              "   423,\n",
              "   15861,\n",
              "   22,\n",
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              "   2146,\n",
              "   57,\n",
              "   23,\n",
              "   15863,\n",
              "   187,\n",
              "   23,\n",
              "   423,\n",
              "   15864,\n",
              "   4,\n",
              "   600,\n",
              "   15865,\n",
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              "   335],\n",
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              "   2,\n",
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              "   2657,\n",
              "   3966,\n",
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              "   22,\n",
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              "   600,\n",
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              "   435,\n",
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              "   15872,\n",
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              "   15873,\n",
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              "   76,\n",
              "   3614,\n",
              "   15874,\n",
              "   9382,\n",
              "   6,\n",
              "   15875,\n",
              "   76,\n",
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              "   15876,\n",
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              "   2106,\n",
              "   2153,\n",
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              "   32,\n",
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              "   442,\n",
              "   1489,\n",
              "   12472,\n",
              "   15877,\n",
              "   22,\n",
              "   3252,\n",
              "   1782,\n",
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              "   856,\n",
              "   63,\n",
              "   1244,\n",
              "   63,\n",
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              "   110,\n",
              "   5703,\n",
              "   6393,\n",
              "   4400,\n",
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              "   418,\n",
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              "   2164,\n",
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              "   234,\n",
              "   63,\n",
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              "   57,\n",
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              "   8059,\n",
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              "   2,\n",
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              "   73,\n",
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              "   1471,\n",
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              "   342,\n",
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              "   15914,\n",
              "   15915,\n",
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              "   1357,\n",
              "   11335,\n",
              "   15916,\n",
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              "   15917,\n",
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              "   241,\n",
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              "  0),\n",
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              "   252,\n",
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              "   25,\n",
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              "   438,\n",
              "   15920,\n",
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              "   7,\n",
              "   32,\n",
              "   5571,\n",
              "   15921,\n",
              "   76,\n",
              "   23,\n",
              "   492,\n",
              "   1870,\n",
              "   2,\n",
              "   6352,\n",
              "   300,\n",
              "   7667,\n",
              "   438,\n",
              "   27,\n",
              "   252,\n",
              "   694,\n",
              "   22,\n",
              "   15922,\n",
              "   6],\n",
              "  0),\n",
              " ([23, 2376, 910, 29, 15164, 636, 637, 6], 0),\n",
              " ([1677,\n",
              "   2,\n",
              "   14370,\n",
              "   2,\n",
              "   15923,\n",
              "   14674,\n",
              "   4,\n",
              "   6877,\n",
              "   105,\n",
              "   15924,\n",
              "   260,\n",
              "   22,\n",
              "   775,\n",
              "   4,\n",
              "   32,\n",
              "   15881,\n",
              "   22,\n",
              "   704,\n",
              "   9634,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   1858,\n",
              "   27,\n",
              "   15925,\n",
              "   23,\n",
              "   705,\n",
              "   22,\n",
              "   15926,\n",
              "   4,\n",
              "   8197,\n",
              "   57,\n",
              "   32,\n",
              "   15927,\n",
              "   2,\n",
              "   9798,\n",
              "   5729,\n",
              "   6],\n",
              "  0),\n",
              " ([32, 14370, 1913, 22, 15928, 2, 479, 4, 15929, 6], 0),\n",
              " ([15930, 228, 61, 10015, 4, 15931, 15007, 6], 0),\n",
              " ([197,\n",
              "   2,\n",
              "   2043,\n",
              "   14439,\n",
              "   210,\n",
              "   63,\n",
              "   3511,\n",
              "   76,\n",
              "   7969,\n",
              "   63,\n",
              "   379,\n",
              "   61,\n",
              "   1061,\n",
              "   76,\n",
              "   508,\n",
              "   6],\n",
              "  0),\n",
              " ([13050,\n",
              "   32,\n",
              "   287,\n",
              "   22,\n",
              "   23,\n",
              "   1508,\n",
              "   27,\n",
              "   3299,\n",
              "   136,\n",
              "   40,\n",
              "   23,\n",
              "   4227,\n",
              "   15932,\n",
              "   22,\n",
              "   600,\n",
              "   15933,\n",
              "   23,\n",
              "   1888,\n",
              "   448,\n",
              "   3870,\n",
              "   15934,\n",
              "   4,\n",
              "   11232,\n",
              "   29,\n",
              "   57,\n",
              "   23,\n",
              "   13133,\n",
              "   22,\n",
              "   3519,\n",
              "   414,\n",
              "   922,\n",
              "   6],\n",
              "  0),\n",
              " ([14578, 204, 6370, 15935, 13331, 13322, 204, 6370, 15936, 15937, 6], 0),\n",
              " ([12343,\n",
              "   61,\n",
              "   32,\n",
              "   2345,\n",
              "   7399,\n",
              "   2,\n",
              "   15938,\n",
              "   23,\n",
              "   306,\n",
              "   57,\n",
              "   32,\n",
              "   6156,\n",
              "   4,\n",
              "   620,\n",
              "   9948,\n",
              "   27,\n",
              "   61,\n",
              "   755,\n",
              "   51,\n",
              "   10600,\n",
              "   6],\n",
              "  0),\n",
              " ([86, 11068, 2, 15327, 15939, 3066, 1242, 6], 0),\n",
              " ([130,\n",
              "   7812,\n",
              "   2,\n",
              "   8,\n",
              "   3181,\n",
              "   76,\n",
              "   23,\n",
              "   2841,\n",
              "   521,\n",
              "   2,\n",
              "   61,\n",
              "   259,\n",
              "   32,\n",
              "   836,\n",
              "   5924,\n",
              "   1222,\n",
              "   110,\n",
              "   9589,\n",
              "   15940,\n",
              "   809,\n",
              "   61,\n",
              "   3432,\n",
              "   6],\n",
              "  0),\n",
              " ([10092,\n",
              "   15941,\n",
              "   268,\n",
              "   22,\n",
              "   219,\n",
              "   743,\n",
              "   3153,\n",
              "   144,\n",
              "   3272,\n",
              "   10514,\n",
              "   219,\n",
              "   362,\n",
              "   32,\n",
              "   15222,\n",
              "   2,\n",
              "   2099,\n",
              "   2,\n",
              "   15942,\n",
              "   287,\n",
              "   14775,\n",
              "   57,\n",
              "   240,\n",
              "   23,\n",
              "   848,\n",
              "   219,\n",
              "   15217,\n",
              "   219,\n",
              "   2292,\n",
              "   76,\n",
              "   1652,\n",
              "   355,\n",
              "   32,\n",
              "   219,\n",
              "   1468,\n",
              "   15943,\n",
              "   6,\n",
              "   219],\n",
              "  0),\n",
              " ([7,\n",
              "   32,\n",
              "   1091,\n",
              "   6002,\n",
              "   13687,\n",
              "   861,\n",
              "   144,\n",
              "   32,\n",
              "   3639,\n",
              "   22,\n",
              "   7573,\n",
              "   4,\n",
              "   15944,\n",
              "   345,\n",
              "   2275,\n",
              "   57,\n",
              "   23,\n",
              "   15945,\n",
              "   6492,\n",
              "   864,\n",
              "   6,\n",
              "   6,\n",
              "   6],\n",
              "  0),\n",
              " ([61,\n",
              "   17,\n",
              "   32,\n",
              "   2516,\n",
              "   3758,\n",
              "   592,\n",
              "   153,\n",
              "   6,\n",
              "   61,\n",
              "   17,\n",
              "   885,\n",
              "   282,\n",
              "   125,\n",
              "   228,\n",
              "   15946,\n",
              "   280,\n",
              "   450,\n",
              "   76,\n",
              "   4425,\n",
              "   234,\n",
              "   592,\n",
              "   449,\n",
              "   7278,\n",
              "   6],\n",
              "  0),\n",
              " ([15947, 2, 15948, 13531, 1757, 1018, 1758, 6], 0),\n",
              " ([15949,\n",
              "   4,\n",
              "   15950,\n",
              "   5729,\n",
              "   32,\n",
              "   3984,\n",
              "   924,\n",
              "   22,\n",
              "   32,\n",
              "   15951,\n",
              "   15704,\n",
              "   162,\n",
              "   15952,\n",
              "   4,\n",
              "   1168,\n",
              "   15953,\n",
              "   63,\n",
              "   86,\n",
              "   15954,\n",
              "   265,\n",
              "   105,\n",
              "   32,\n",
              "   7404,\n",
              "   496,\n",
              "   179,\n",
              "   57,\n",
              "   2475,\n",
              "   13291,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   14831,\n",
              "   15955,\n",
              "   1995,\n",
              "   173,\n",
              "   32,\n",
              "   777,\n",
              "   22,\n",
              "   15956,\n",
              "   23,\n",
              "   1582,\n",
              "   785,\n",
              "   63,\n",
              "   23,\n",
              "   228,\n",
              "   4642,\n",
              "   144,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   9263,\n",
              "   486,\n",
              "   4,\n",
              "   639,\n",
              "   2927,\n",
              "   1913,\n",
              "   22,\n",
              "   23,\n",
              "   473,\n",
              "   22,\n",
              "   276,\n",
              "   2250,\n",
              "   12529,\n",
              "   57,\n",
              "   960,\n",
              "   423,\n",
              "   228,\n",
              "   6,\n",
              "   4055,\n",
              "   17,\n",
              "   234,\n",
              "   6],\n",
              "  0),\n",
              " ([8647, 4, 11074, 2, 444, 639, 97, 1476, 2379, 6], 0),\n",
              " ([23,\n",
              "   60,\n",
              "   2,\n",
              "   355,\n",
              "   1071,\n",
              "   2,\n",
              "   4824,\n",
              "   76,\n",
              "   308,\n",
              "   3297,\n",
              "   2,\n",
              "   4,\n",
              "   23,\n",
              "   398,\n",
              "   61,\n",
              "   32,\n",
              "   143,\n",
              "   5171,\n",
              "   22,\n",
              "   4293,\n",
              "   1390,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   15222,\n",
              "   1847,\n",
              "   22,\n",
              "   5995,\n",
              "   4,\n",
              "   7571,\n",
              "   362,\n",
              "   5793,\n",
              "   4,\n",
              "   1244,\n",
              "   342,\n",
              "   72,\n",
              "   2,\n",
              "   51,\n",
              "   1403,\n",
              "   4649,\n",
              "   6214,\n",
              "   76,\n",
              "   110,\n",
              "   12189,\n",
              "   4,\n",
              "   5969,\n",
              "   144,\n",
              "   897,\n",
              "   775,\n",
              "   6550,\n",
              "   6],\n",
              "  0),\n",
              " ([18,\n",
              "   4,\n",
              "   2,\n",
              "   342,\n",
              "   72,\n",
              "   2,\n",
              "   5705,\n",
              "   2,\n",
              "   23,\n",
              "   228,\n",
              "   438,\n",
              "   412,\n",
              "   1367,\n",
              "   1368,\n",
              "   108,\n",
              "   465,\n",
              "   466,\n",
              "   57,\n",
              "   15957,\n",
              "   2,\n",
              "   219,\n",
              "   32,\n",
              "   2709,\n",
              "   1222,\n",
              "   276,\n",
              "   2793,\n",
              "   1377,\n",
              "   6,\n",
              "   219],\n",
              "  0),\n",
              " ([130,\n",
              "   15958,\n",
              "   547,\n",
              "   190,\n",
              "   40,\n",
              "   23,\n",
              "   1759,\n",
              "   3609,\n",
              "   1112,\n",
              "   342,\n",
              "   23,\n",
              "   5649,\n",
              "   22,\n",
              "   3909,\n",
              "   1987,\n",
              "   84,\n",
              "   5296,\n",
              "   449,\n",
              "   77,\n",
              "   15959,\n",
              "   15960,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   346,\n",
              "   3546,\n",
              "   3758,\n",
              "   768,\n",
              "   308,\n",
              "   11043,\n",
              "   76,\n",
              "   15071,\n",
              "   15072,\n",
              "   2,\n",
              "   57,\n",
              "   3115,\n",
              "   15961,\n",
              "   4694,\n",
              "   63,\n",
              "   86,\n",
              "   15962,\n",
              "   63,\n",
              "   352,\n",
              "   63,\n",
              "   86,\n",
              "   3471],\n",
              "  0),\n",
              " ([97, 32, 448, 3015, 342, 108, 6], 0),\n",
              " ([8396,\n",
              "   13360,\n",
              "   63,\n",
              "   32,\n",
              "   1701,\n",
              "   1858,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   51,\n",
              "   1506,\n",
              "   694,\n",
              "   24,\n",
              "   57,\n",
              "   110,\n",
              "   1652,\n",
              "   342,\n",
              "   23,\n",
              "   15963,\n",
              "   22,\n",
              "   32,\n",
              "   2345,\n",
              "   665,\n",
              "   6],\n",
              "  0),\n",
              " ([2436,\n",
              "   2335,\n",
              "   130,\n",
              "   777,\n",
              "   4,\n",
              "   27,\n",
              "   2,\n",
              "   51,\n",
              "   286,\n",
              "   153,\n",
              "   8383,\n",
              "   23,\n",
              "   4048,\n",
              "   57,\n",
              "   23,\n",
              "   8239,\n",
              "   15964,\n",
              "   2,\n",
              "   125,\n",
              "   76,\n",
              "   1576,\n",
              "   6],\n",
              "  0),\n",
              " ([185,\n",
              "   61,\n",
              "   32,\n",
              "   10832,\n",
              "   502,\n",
              "   22,\n",
              "   1373,\n",
              "   57,\n",
              "   4680,\n",
              "   287,\n",
              "   15965,\n",
              "   32,\n",
              "   15966,\n",
              "   2,\n",
              "   201,\n",
              "   57,\n",
              "   23,\n",
              "   665,\n",
              "   228,\n",
              "   3071,\n",
              "   6,\n",
              "   1716,\n",
              "   2,\n",
              "   17,\n",
              "   512,\n",
              "   449,\n",
              "   191,\n",
              "   6],\n",
              "  0),\n",
              " ([15967, 300, 17, 71, 63, 32, 754, 1502, 4088, 73, 63, 690, 6], 0),\n",
              " ([355, 32, 15968, 15969, 466, 6], 0),\n",
              " ([1195,\n",
              "   105,\n",
              "   86,\n",
              "   830,\n",
              "   57,\n",
              "   496,\n",
              "   25,\n",
              "   14917,\n",
              "   15970,\n",
              "   22,\n",
              "   228,\n",
              "   4760,\n",
              "   280,\n",
              "   16,\n",
              "   729,\n",
              "   96,\n",
              "   61,\n",
              "   17,\n",
              "   185,\n",
              "   592,\n",
              "   352,\n",
              "   636,\n",
              "   23,\n",
              "   96,\n",
              "   6],\n",
              "  0),\n",
              " ([32, 1985, 18, 555, 5283, 6], 0),\n",
              " ([14836,\n",
              "   4009,\n",
              "   15971,\n",
              "   32,\n",
              "   4873,\n",
              "   27,\n",
              "   61,\n",
              "   8,\n",
              "   1490,\n",
              "   27,\n",
              "   449,\n",
              "   16,\n",
              "   2423,\n",
              "   10658,\n",
              "   1374,\n",
              "   342,\n",
              "   597,\n",
              "   2,\n",
              "   2047,\n",
              "   76,\n",
              "   2029,\n",
              "   597,\n",
              "   4,\n",
              "   1418,\n",
              "   444,\n",
              "   15972,\n",
              "   15973,\n",
              "   6],\n",
              "  0),\n",
              " ([703,\n",
              "   729,\n",
              "   2268,\n",
              "   6737,\n",
              "   78,\n",
              "   23,\n",
              "   3341,\n",
              "   22,\n",
              "   110,\n",
              "   1129,\n",
              "   2,\n",
              "   23,\n",
              "   1226,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   3601,\n",
              "   32,\n",
              "   1672,\n",
              "   2291,\n",
              "   22,\n",
              "   559,\n",
              "   2,\n",
              "   4,\n",
              "   286,\n",
              "   153,\n",
              "   8508,\n",
              "   23,\n",
              "   2140,\n",
              "   22,\n",
              "   23,\n",
              "   4019,\n",
              "   6482,\n",
              "   1007,\n",
              "   17,\n",
              "   6],\n",
              "  0),\n",
              " ([1570,\n",
              "   308,\n",
              "   966,\n",
              "   105,\n",
              "   23,\n",
              "   12364,\n",
              "   22,\n",
              "   68,\n",
              "   388,\n",
              "   2241,\n",
              "   32,\n",
              "   3495,\n",
              "   263,\n",
              "   4,\n",
              "   2,\n",
              "   2122,\n",
              "   2,\n",
              "   80,\n",
              "   57,\n",
              "   76,\n",
              "   23,\n",
              "   2782,\n",
              "   76,\n",
              "   508,\n",
              "   144,\n",
              "   905,\n",
              "   4297,\n",
              "   4,\n",
              "   1255,\n",
              "   17,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   102,\n",
              "   103,\n",
              "   8,\n",
              "   1376,\n",
              "   342,\n",
              "   7594,\n",
              "   600,\n",
              "   649,\n",
              "   6749,\n",
              "   2,\n",
              "   255,\n",
              "   884,\n",
              "   2011,\n",
              "   1571,\n",
              "   40,\n",
              "   414,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   788,\n",
              "   130,\n",
              "   1858,\n",
              "   2643,\n",
              "   138,\n",
              "   73,\n",
              "   110,\n",
              "   2671,\n",
              "   61,\n",
              "   27,\n",
              "   1292,\n",
              "   61,\n",
              "   153,\n",
              "   1896,\n",
              "   1405,\n",
              "   14593,\n",
              "   23,\n",
              "   15974,\n",
              "   22,\n",
              "   3328,\n",
              "   1288,\n",
              "   188,\n",
              "   6],\n",
              "  0),\n",
              " ([17,\n",
              "   61,\n",
              "   32,\n",
              "   15975,\n",
              "   2,\n",
              "   4406,\n",
              "   2956,\n",
              "   289,\n",
              "   1242,\n",
              "   2152,\n",
              "   27,\n",
              "   1846,\n",
              "   4,\n",
              "   1290,\n",
              "   272,\n",
              "   6,\n",
              "   329,\n",
              "   2299,\n",
              "   333,\n",
              "   95,\n",
              "   57,\n",
              "   8,\n",
              "   285,\n",
              "   2,\n",
              "   153,\n",
              "   4916,\n",
              "   329,\n",
              "   425,\n",
              "   7681,\n",
              "   17,\n",
              "   342,\n",
              "   182,\n",
              "   6],\n",
              "  0),\n",
              " ([32, 143, 924, 22, 1382, 3757], 0),\n",
              " ([57,\n",
              "   12435,\n",
              "   2,\n",
              "   15080,\n",
              "   61,\n",
              "   97,\n",
              "   388,\n",
              "   23,\n",
              "   5849,\n",
              "   2,\n",
              "   51,\n",
              "   342,\n",
              "   23,\n",
              "   1021,\n",
              "   22,\n",
              "   71,\n",
              "   3338,\n",
              "   15976,\n",
              "   6],\n",
              "  0),\n",
              " ([17,\n",
              "   61,\n",
              "   2066,\n",
              "   2,\n",
              "   22,\n",
              "   1153,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   51,\n",
              "   17,\n",
              "   61,\n",
              "   62,\n",
              "   10832,\n",
              "   2,\n",
              "   14831,\n",
              "   2,\n",
              "   4,\n",
              "   388,\n",
              "   6608,\n",
              "   1471,\n",
              "   703,\n",
              "   110,\n",
              "   10669,\n",
              "   6],\n",
              "  0),\n",
              " ([6, 6, 6, 3515, 5465, 61, 71, 1471, 73, 23, 702, 6], 0),\n",
              " ([32,\n",
              "   228,\n",
              "   27,\n",
              "   465,\n",
              "   449,\n",
              "   1429,\n",
              "   23,\n",
              "   898,\n",
              "   22,\n",
              "   110,\n",
              "   120,\n",
              "   227,\n",
              "   449,\n",
              "   2298,\n",
              "   17,\n",
              "   63,\n",
              "   449,\n",
              "   563,\n",
              "   6],\n",
              "  0),\n",
              " ([23, 81, 4674, 40, 110, 5256, 289, 4, 110, 1870, 1685, 6], 0),\n",
              " ([23,\n",
              "   81,\n",
              "   61,\n",
              "   6604,\n",
              "   438,\n",
              "   11026,\n",
              "   14627,\n",
              "   442,\n",
              "   23,\n",
              "   11578,\n",
              "   22,\n",
              "   23,\n",
              "   2049,\n",
              "   4,\n",
              "   23,\n",
              "   3369,\n",
              "   22,\n",
              "   23,\n",
              "   127,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   228,\n",
              "   2080,\n",
              "   234,\n",
              "   63,\n",
              "   6448,\n",
              "   51,\n",
              "   3887,\n",
              "   6,\n",
              "   6,\n",
              "   6,\n",
              "   4,\n",
              "   12606,\n",
              "   2651,\n",
              "   310,\n",
              "   885,\n",
              "   22,\n",
              "   3987,\n",
              "   1518,\n",
              "   6],\n",
              "  0),\n",
              " ([196,\n",
              "   7847,\n",
              "   173,\n",
              "   152,\n",
              "   434,\n",
              "   2,\n",
              "   355,\n",
              "   15977,\n",
              "   15978,\n",
              "   4,\n",
              "   15979,\n",
              "   3850,\n",
              "   15980,\n",
              "   2,\n",
              "   14,\n",
              "   15981,\n",
              "   218,\n",
              "   40,\n",
              "   130,\n",
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              "   2,\n",
              "   15982,\n",
              "   2,\n",
              "   15983,\n",
              "   10019,\n",
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              "   2,\n",
              "   15984,\n",
              "   22,\n",
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              "   212,\n",
              "   10670,\n",
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              "   442,\n",
              "   15987,\n",
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              "   144,\n",
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              "   76,\n",
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              "   32,\n",
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              "   143,\n",
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              "   704,\n",
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              "   280,\n",
              "   2,\n",
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              "   2,\n",
              "   308,\n",
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              "   252,\n",
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              "   76,\n",
              "   308,\n",
              "   1916,\n",
              "   57,\n",
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              "   32,\n",
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              "   1888,\n",
              "   15992,\n",
              "   18,\n",
              "   3747,\n",
              "   22,\n",
              "   6326,\n",
              "   6327,\n",
              "   4,\n",
              "   3777,\n",
              "   1088,\n",
              "   2375,\n",
              "   2,\n",
              "   183,\n",
              "   15993,\n",
              "   15994,\n",
              "   57,\n",
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              "   597,\n",
              "   10365,\n",
              "   2875,\n",
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              "   32,\n",
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              "   786,\n",
              "   15996,\n",
              "   27,\n",
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              "   15997,\n",
              "   2,\n",
              "   4,\n",
              "   32,\n",
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              "   798,\n",
              "   22,\n",
              "   2873,\n",
              "   15999,\n",
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              "   3057,\n",
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              "   252,\n",
              "   10668,\n",
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              "   76,\n",
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              "   993,\n",
              "   4132,\n",
              "   2,\n",
              "   8939,\n",
              "   63,\n",
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              "   649,\n",
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              "   162,\n",
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              "   355,\n",
              "   32,\n",
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              "   6,\n",
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              "   32,\n",
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              "   219,\n",
              "   13774,\n",
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              "   76,\n",
              "   1195,\n",
              "   162,\n",
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              "   61,\n",
              "   196,\n",
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              "   73,\n",
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              "   266,\n",
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              "   696,\n",
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              "   1870,\n",
              "   1564,\n",
              "   22,\n",
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              "   110,\n",
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              "   103,\n",
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              "   86,\n",
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              "   300,\n",
              "   22,\n",
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              "   16017,\n",
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              "   76,\n",
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              "   1733,\n",
              "   3125,\n",
              "   22,\n",
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              "   292,\n",
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              "   181,\n",
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              "   32,\n",
              "   5052,\n",
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              "   16019,\n",
              "   1794,\n",
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              "   255,\n",
              "   151,\n",
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              "   442,\n",
              "   4438,\n",
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              "   22,\n",
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              "   84,\n",
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              "   2,\n",
              "   4,\n",
              "   10310,\n",
              "   110,\n",
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              "   5687,\n",
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              "   84,\n",
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              "   3219,\n",
              "   84,\n",
              "   61,\n",
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              "   108,\n",
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              "   32,\n",
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              "   447,\n",
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              "  0),\n",
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              "   23,\n",
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              "   23,\n",
              "   3280,\n",
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              "   5351,\n",
              "   110,\n",
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              "  0),\n",
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              "   3103,\n",
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              "   22,\n",
              "   6777,\n",
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              "   12180,\n",
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              "   993,\n",
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              "   198,\n",
              "   10679,\n",
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              "   16060,\n",
              "   435,\n",
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              "   32,\n",
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              "   2298,\n",
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              "   86,\n",
              "   941,\n",
              "   3503,\n",
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              "   23,\n",
              "   16061,\n",
              "   22,\n",
              "   96,\n",
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              "  0),\n",
              " ([23,\n",
              "   1851,\n",
              "   1006,\n",
              "   173,\n",
              "   16062,\n",
              "   32,\n",
              "   292,\n",
              "   105,\n",
              "   71,\n",
              "   1112,\n",
              "   73,\n",
              "   492,\n",
              "   2,\n",
              "   153,\n",
              "   3540,\n",
              "   6,\n",
              "   51,\n",
              "   130,\n",
              "   96,\n",
              "   286,\n",
              "   272,\n",
              "   8011,\n",
              "   144,\n",
              "   23,\n",
              "   6926,\n",
              "   6],\n",
              "  0),\n",
              " ([126,\n",
              "   3008,\n",
              "   484,\n",
              "   2,\n",
              "   130,\n",
              "   2677,\n",
              "   287,\n",
              "   60,\n",
              "   61,\n",
              "   16063,\n",
              "   105,\n",
              "   272,\n",
              "   6710,\n",
              "   289,\n",
              "   8140,\n",
              "   4,\n",
              "   1616,\n",
              "   6],\n",
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              "   61,\n",
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              "   438,\n",
              "   23,\n",
              "   1440,\n",
              "   2,\n",
              "   2926,\n",
              "   76,\n",
              "   23,\n",
              "   1554,\n",
              "   394,\n",
              "   17,\n",
              "   5188,\n",
              "   2,\n",
              "   23,\n",
              "   127,\n",
              "   22,\n",
              "   12397,\n",
              "   4,\n",
              "   8559,\n",
              "   2,\n",
              "   4,\n",
              "   27,\n",
              "   8634,\n",
              "   1410,\n",
              "   84,\n",
              "   32,\n",
              "   248,\n",
              "   16064,\n",
              "   1018,\n",
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              " ([32, 16066, 16067, 2291, 22, 289, 5712, 6], 0),\n",
              " ([13603,\n",
              "   16068,\n",
              "   16069,\n",
              "   1071,\n",
              "   1429,\n",
              "   32,\n",
              "   16070,\n",
              "   16071,\n",
              "   57,\n",
              "   16072,\n",
              "   16073,\n",
              "   57,\n",
              "   181,\n",
              "   141,\n",
              "   76,\n",
              "   308,\n",
              "   4,\n",
              "   76,\n",
              "   136,\n",
              "   2,\n",
              "   1168,\n",
              "   95,\n",
              "   22,\n",
              "   23,\n",
              "   606,\n",
              "   300,\n",
              "   22,\n",
              "   23,\n",
              "   1822,\n",
              "   6],\n",
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              "   287,\n",
              "   8015,\n",
              "   2,\n",
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              "   3066,\n",
              "   6483,\n",
              "   144,\n",
              "   23,\n",
              "   5714,\n",
              "   105,\n",
              "   32,\n",
              "   15036,\n",
              "   4143,\n",
              "   22,\n",
              "   16075,\n",
              "   15077,\n",
              "   4,\n",
              "   16076,\n",
              "   4998,\n",
              "   16077,\n",
              "   6],\n",
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              "   16078,\n",
              "   16079,\n",
              "   2,\n",
              "   16080,\n",
              "   12454,\n",
              "   228,\n",
              "   8992,\n",
              "   922,\n",
              "   8369,\n",
              "   126,\n",
              "   2119,\n",
              "   105,\n",
              "   110,\n",
              "   16081,\n",
              "   4316,\n",
              "   51,\n",
              "   351,\n",
              "   665],\n",
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              "   5014,\n",
              "   16082,\n",
              "   268,\n",
              "   22,\n",
              "   23,\n",
              "   16083,\n",
              "   9552,\n",
              "   292,\n",
              "   743,\n",
              "   3153,\n",
              "   144,\n",
              "   3272,\n",
              "   10514,\n",
              "   6],\n",
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              "   130,\n",
              "   16084,\n",
              "   1387,\n",
              "   4,\n",
              "   9351,\n",
              "   2,\n",
              "   805,\n",
              "   32,\n",
              "   1804,\n",
              "   3487,\n",
              "   144,\n",
              "   17,\n",
              "   2,\n",
              "   13050,\n",
              "   4558,\n",
              "   1520,\n",
              "   108,\n",
              "   162,\n",
              "   103,\n",
              "   312,\n",
              "   76,\n",
              "   17,\n",
              "   4,\n",
              "   201,\n",
              "   9759,\n",
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              "   22,\n",
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              "   308,\n",
              "   287,\n",
              "   71,\n",
              "   73,\n",
              "   418,\n",
              "   5051,\n",
              "   22,\n",
              "   16085,\n",
              "   16086,\n",
              "   2,\n",
              "   51,\n",
              "   7,\n",
              "   2793,\n",
              "   261,\n",
              "   5996,\n",
              "   6],\n",
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              "   103,\n",
              "   72,\n",
              "   551,\n",
              "   32,\n",
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              "   7486,\n",
              "   709,\n",
              "   355,\n",
              "   86,\n",
              "   7066,\n",
              "   3679,\n",
              "   22,\n",
              "   8718,\n",
              "   442,\n",
              "   86,\n",
              "   2922,\n",
              "   84,\n",
              "   32,\n",
              "   287,\n",
              "   782,\n",
              "   98,\n",
              "   8316,\n",
              "   2,\n",
              "   32,\n",
              "   381,\n",
              "   782,\n",
              "   492,\n",
              "   16087,\n",
              "   313,\n",
              "   84,\n",
              "   51,\n",
              "   329,\n",
              "   3704,\n",
              "   110,\n",
              "   1021,\n",
              "   4,\n",
              "   110,\n",
              "   6643,\n",
              "   6],\n",
              "  0),\n",
              " ([169,\n",
              "   2077,\n",
              "   22,\n",
              "   6266,\n",
              "   3882,\n",
              "   7458,\n",
              "   76,\n",
              "   169,\n",
              "   2077,\n",
              "   22,\n",
              "   6239,\n",
              "   424,\n",
              "   6028,\n",
              "   2,\n",
              "   444,\n",
              "   23,\n",
              "   1858,\n",
              "   62,\n",
              "   9980,\n",
              "   23,\n",
              "   7684,\n",
              "   22,\n",
              "   1974,\n",
              "   3216,\n",
              "   2,\n",
              "   16088,\n",
              "   23,\n",
              "   544,\n",
              "   6],\n",
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              " ([130,\n",
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              "   2,\n",
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              "   61,\n",
              "   4036,\n",
              "   105,\n",
              "   23,\n",
              "   15314,\n",
              "   1782,\n",
              "   22,\n",
              "   16090,\n",
              "   477,\n",
              "   941,\n",
              "   2995,\n",
              "   6],\n",
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              "   2,\n",
              "   2553,\n",
              "   4,\n",
              "   8504,\n",
              "   18,\n",
              "   2,\n",
              "   130,\n",
              "   4084,\n",
              "   2956,\n",
              "   2135,\n",
              "   2643,\n",
              "   144,\n",
              "   8,\n",
              "   492,\n",
              "   886,\n",
              "   3475,\n",
              "   27,\n",
              "   17,\n",
              "   97,\n",
              "   11,\n",
              "   11595,\n",
              "   2,\n",
              "   17,\n",
              "   1658,\n",
              "   11708,\n",
              "   16091,\n",
              "   6],\n",
              "  0),\n",
              " ([32,\n",
              "   1913,\n",
              "   22,\n",
              "   1701,\n",
              "   4,\n",
              "   479,\n",
              "   27,\n",
              "   61,\n",
              "   506,\n",
              "   926,\n",
              "   57,\n",
              "   110,\n",
              "   4926,\n",
              "   15103,\n",
              "   76,\n",
              "   23,\n",
              "   3401,\n",
              "   22,\n",
              "   95,\n",
              "   2967,\n",
              "   4144,\n",
              "   6659,\n",
              "   6],\n",
              "  0),\n",
              " ([86,\n",
              "   6728,\n",
              "   22,\n",
              "   11713,\n",
              "   4,\n",
              "   6276,\n",
              "   362,\n",
              "   69,\n",
              "   2849,\n",
              "   3190,\n",
              "   438,\n",
              "   23,\n",
              "   6704,\n",
              "   14280,\n",
              "   303,\n",
              "   5336,\n",
              "   22,\n",
              "   1000,\n",
              "   6],\n",
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              " ([16093,\n",
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              "   210,\n",
              "   6489,\n",
              "   1718,\n",
              "   23,\n",
              "   3673,\n",
              "   438,\n",
              "   169,\n",
              "   2759,\n",
              "   57,\n",
              "   3261,\n",
              "   1617,\n",
              "   105,\n",
              "   486,\n",
              "   127,\n",
              "   4,\n",
              "   12819,\n",
              "   9332,\n",
              "   187,\n",
              "   23,\n",
              "   345,\n",
              "   2,\n",
              "   130,\n",
              "   61,\n",
              "   32,\n",
              "   2582,\n",
              "   60,\n",
              "   105,\n",
              "   32,\n",
              "   1720,\n",
              "   6],\n",
              "  0),\n",
              " ([185,\n",
              "   173,\n",
              "   69,\n",
              "   98,\n",
              "   16095,\n",
              "   1520,\n",
              "   2577,\n",
              "   78,\n",
              "   729,\n",
              "   23,\n",
              "   2239,\n",
              "   16096,\n",
              "   6,\n",
              "   329,\n",
              "   275,\n",
              "   23,\n",
              "   847,\n",
              "   61,\n",
              "   57,\n",
              "   23,\n",
              "   11275,\n",
              "   227,\n",
              "   23,\n",
              "   12701,\n",
              "   61,\n",
              "   32,\n",
              "   644,\n",
              "   6],\n",
              "  0),\n",
              " ([86, 1471, 228, 40, 23, 665, 2, 2099, 4, 4339, 40, 1974, 2816, 4, 2855, 6],\n",
              "  0),\n",
              " ([219,\n",
              "   23,\n",
              "   4478,\n",
              "   4479,\n",
              "   219,\n",
              "   61,\n",
              "   32,\n",
              "   2384,\n",
              "   228,\n",
              "   76,\n",
              "   1255,\n",
              "   438,\n",
              "   905,\n",
              "   16097,\n",
              "   551,\n",
              "   2274,\n",
              "   116,\n",
              "   6],\n",
              "  0),\n",
              " ([23,\n",
              "   2725,\n",
              "   10189,\n",
              "   22,\n",
              "   23,\n",
              "   4169,\n",
              "   4170,\n",
              "   1214,\n",
              "   61,\n",
              "   201,\n",
              "   71,\n",
              "   4749,\n",
              "   73,\n",
              "   23,\n",
              "   182,\n",
              "   4,\n",
              "   1405,\n",
              "   23,\n",
              "   606,\n",
              "   665,\n",
              "   228,\n",
              "   22,\n",
              "   23,\n",
              "   1822,\n",
              "   6],\n",
              "  0),\n",
              " ([71, 486, 78, 16098, 10750, 2, 329, 279, 2, 73, 16099, 6], 0),\n",
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              " ([16100,\n",
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              "   4,\n",
              "   16102,\n",
              "   2,\n",
              "   6523,\n",
              "   4,\n",
              "   6518,\n",
              "   103,\n",
              "   32,\n",
              "   2542,\n",
              "   1970,\n",
              "   84,\n",
              "   51,\n",
              "   5859,\n",
              "   9941,\n",
              "   3971,\n",
              "   16103,\n",
              "   1022,\n",
              "   438,\n",
              "   1718,\n",
              "   600,\n",
              "   7886,\n",
              "   6],\n",
              "  0),\n",
              " ([130,\n",
              "   61,\n",
              "   32,\n",
              "   1376,\n",
              "   567,\n",
              "   42,\n",
              "   2,\n",
              "   32,\n",
              "   12426,\n",
              "   310,\n",
              "   23,\n",
              "   1071,\n",
              "   22,\n",
              "   32,\n",
              "   1400,\n",
              "   942,\n",
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              "  0),\n",
              " ([14897, 449, 105, 110, 16104, 4, 3483, 6], 0),\n",
              " ([130,\n",
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              "   84,\n",
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              "   193,\n",
              "   330,\n",
              "   444,\n",
              "   7,\n",
              "   4227,\n",
              "   76,\n",
              "   29,\n",
              "   32,\n",
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              "   228,\n",
              "   78,\n",
              "   5403,\n",
              "   16105,\n",
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              "   462,\n",
              "   1065,\n",
              "   1864,\n",
              "   272,\n",
              "   280,\n",
              "   903,\n",
              "   84,\n",
              "   51,\n",
              "   7,\n",
              "   63,\n",
              "   3511,\n",
              "   63,\n",
              "   2268,\n",
              "   173,\n",
              "   16106,\n",
              "   76,\n",
              "   66,\n",
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              "  0),\n",
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              " ([23,\n",
              "   169,\n",
              "   1798,\n",
              "   16107,\n",
              "   7239,\n",
              "   71,\n",
              "   6711,\n",
              "   105,\n",
              "   600,\n",
              "   1685,\n",
              "   73,\n",
              "   12464,\n",
              "   1987,\n",
              "   16108,\n",
              "   105,\n",
              "   600,\n",
              "   10545,\n",
              "   6,\n",
              "   6,\n",
              "   6],\n",
              "  0),\n",
              " ([2551,\n",
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              "   32,\n",
              "   267,\n",
              "   16109,\n",
              "   1794,\n",
              "   76,\n",
              "   130,\n",
              "   3414,\n",
              "   249,\n",
              "   2,\n",
              "   3935,\n",
              "   3648,\n",
              "   4421,\n",
              "   2,\n",
              "   1974,\n",
              "   49,\n",
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              "   23,\n",
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              "   71,\n",
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              "   76,\n",
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              "   355,\n",
              "   130,\n",
              "   16113,\n",
              "   6108,\n",
              "   16114,\n",
              "   25,\n",
              "   16115,\n",
              "   16116,\n",
              "   22,\n",
              "   110,\n",
              "   16117,\n",
              "   2,\n",
              "   51,\n",
              "   665,\n",
              "   12023,\n",
              "   12569,\n",
              "   32,\n",
              "   1309,\n",
              "   3496,\n",
              "   40,\n",
              "   14924,\n",
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              "   1034,\n",
              "   2,\n",
              "   16121,\n",
              "   412,\n",
              "   16122,\n",
              "   1032,\n",
              "   16123,\n",
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              "   702,\n",
              "   34,\n",
              "   22,\n",
              "   32,\n",
              "   3661,\n",
              "   1030,\n",
              "   120,\n",
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              "   61,\n",
              "   4971,\n",
              "   3262,\n",
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              "   23,\n",
              "   127,\n",
              "   22,\n",
              "   23,\n",
              "   649,\n",
              "   1682,\n",
              "   103,\n",
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              "   262,\n",
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              "   32,\n",
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              "   27,\n",
              "   110,\n",
              "   12025,\n",
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              "   61,\n",
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              "   76,\n",
              "   308,\n",
              "   23,\n",
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              "   9861,\n",
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              "   2,\n",
              "   71,\n",
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              "   2793,\n",
              "   103,\n",
              "   23,\n",
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              "   16126,\n",
              "   4,\n",
              "   5278,\n",
              "   11765,\n",
              "   105,\n",
              "   240,\n",
              "   3471,\n",
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              "   144,\n",
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              " ([32,\n",
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              "   4,\n",
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              "   3984,\n",
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              "   78,\n",
              "   32,\n",
              "   16127,\n",
              "   10840,\n",
              "   57,\n",
              "   16128,\n",
              "   27,\n",
              "   16129,\n",
              "   86,\n",
              "   5842,\n",
              "   844,\n",
              "   845,\n",
              "   1604,\n",
              "   1822,\n",
              "   76,\n",
              "   10342,\n",
              "   1882,\n",
              "   310,\n",
              "   7453,\n",
              "   16130,\n",
              "   6],\n",
              "  0),\n",
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              "   57,\n",
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              "   603,\n",
              "   3049,\n",
              "   97,\n",
              "   4833,\n",
              "   7518,\n",
              "   6],\n",
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              "   76,\n",
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              "   2,\n",
              "   329,\n",
              "   256,\n",
              "   5745,\n",
              "   27,\n",
              "   9309,\n",
              "   16133,\n",
              "   16134,\n",
              "   455,\n",
              "   308,\n",
              "   23,\n",
              "   606,\n",
              "   16135,\n",
              "   1001,\n",
              "   329,\n",
              "   136,\n",
              "   333,\n",
              "   6],\n",
              "  0),\n",
              " ([16136,\n",
              "   16137,\n",
              "   1527,\n",
              "   1222,\n",
              "   381,\n",
              "   423,\n",
              "   300,\n",
              "   3195,\n",
              "   76,\n",
              "   16138,\n",
              "   84,\n",
              "   310,\n",
              "   23,\n",
              "   2896,\n",
              "   22,\n",
              "   11888,\n",
              "   4,\n",
              "   14738,\n",
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              " ...]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def load_sentence_polarity():\n",
        "    \n",
        "    from nltk.corpus import sentence_polarity\n",
        "\n",
        "    vocab = Vocab.build(sentence_polarity.sents())\n",
        "\n",
        "    train_data = [(vocab.convert_tokens_to_ids(sentence), 0) for sentence in sentence_polarity.sents(categories='pos')][: 4000] \\\n",
        "        + [(vocab.convert_tokens_to_ids(sentence), 1) for sentence in sentence_polarity.sents(categories='neg')][: 4000] \n",
        "\n",
        "    test_data = [(vocab.convert_tokens_to_ids(sentence), 0) for sentence in sentence_polarity.sents(categories='pos')][4000: ] \\\n",
        "        + [(vocab.convert_tokens_to_ids(sentence), 1) for sentence in sentence_polarity.sents(categories='neg')][4000: ]\n",
        "\n",
        "    return train_data, test_data, vocab\n",
        "\n",
        "train_data, test_data, vocab = load_sentence_polarity()\n",
        "\n",
        "# train_data, test_data, vocab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "acnPOK69vV6b"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader, Dataset\n",
        "\n",
        "# data_loader = DataLoader(\n",
        "#                         dataset,\n",
        "#                         batch_size=64,\n",
        "#                         collate_fn=collate_fn,\n",
        "#                         shuffle=True\n",
        "#                         )\n",
        "\n",
        "# dataset为Dataset类的一个对象，用于存储数据\n",
        "class BowDataset(Dataset):\n",
        "    def __init__(self, data):\n",
        "        # data为原始的数据，如使用load_sentence_polarity函数生成的训练数据\n",
        "        self.data = data\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "    def __getitem__(self, i):\n",
        "        # 返回下标为i的样例\n",
        "        return self.data[i]\n",
        "\n",
        "# collaate_fn参数指向一个函数，用于对一个批次的样本进行整理，如将其转换为张量等\n",
        "\n",
        "def collate_fn(examples):\n",
        "    # 从独立样本集合中构建各批次的输入输出\n",
        "    inputs = [torch.tensor(ex[0]) for ex in examples]\n",
        "    targets = torch.tensor([(ex[1]) for ex in examples], dtype=torch.long)\n",
        "    offsets = [0] + [i.shape[0] for i in inputs]\n",
        "    offsets = torch.tensor(offsets[: -1]).cumsum(dim=0)\n",
        "    inputs = torch.cat(inputs)\n",
        "    return inputs, offsets, targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8E1mBYJMvV6b",
        "outputId": "89e29c61-e4b4-4f2d-8d60-b790139be3c2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 1: 100%|██████████| 250/250 [00:03<00:00, 64.04it/s]\n",
            "Training Epoch 2: 100%|██████████| 250/250 [00:04<00:00, 55.40it/s]\n",
            "Training Epoch 3: 100%|██████████| 250/250 [00:03<00:00, 82.54it/s]\n",
            "Training Epoch 4: 100%|██████████| 250/250 [00:03<00:00, 73.36it/s]\n",
            "Training Epoch 5: 100%|██████████| 250/250 [00:03<00:00, 72.61it/s]\n",
            "Testing:  33%|███▎      | 879/2662 [00:00<00:00, 4420.03it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss:45.66\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Testing: 100%|██████████| 2662/2662 [00:00<00:00, 4633.54it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Acc: 0.73\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# tqdm 进度条\n",
        "from tqdm.auto import tqdm\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "\n",
        "# dataset为Dataset类的一个对象，用于存储数据\n",
        "class BowDataset(Dataset):\n",
        "    def __init__(self, data):\n",
        "        # data为原始的数据，如使用load_sentence_polarity函数生成的训练数据\n",
        "        self.data = data\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "    def __getitem__(self, i):\n",
        "        # 返回下标为i的样例\n",
        "        return self.data[i]\n",
        "\n",
        "# collaate_fn参数指向一个函数，用于对一个批次的样本进行整理，如将其转换为张量等\n",
        "\n",
        "def collate_fn(examples):\n",
        "    # 从独立样本集合中构建各批次的输入输出\n",
        "    inputs = [torch.tensor(ex[0]) for ex in examples]\n",
        "    targets = torch.tensor([(ex[1]) for ex in examples], dtype=torch.long)\n",
        "    offsets = [0] + [i.shape[0] for i in inputs]\n",
        "    offsets = torch.tensor(offsets[: -1]).cumsum(dim=0)\n",
        "    inputs = torch.cat(inputs)\n",
        "    return inputs, offsets, targets\n",
        "\n",
        "class MLP(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_class):\n",
        "        super(MLP, self).__init__()\n",
        "        # 词向量层\n",
        "        self.embedding = nn.EmbeddingBag(vocab_size, embedding_dim)\n",
        "        # 线性变换：词向量层 -> 隐含层\n",
        "        self.linear1 = nn.Linear(embedding_dim, hidden_dim)\n",
        "        self.activate = F.relu\n",
        "        # 线性变换：激活层 -> 输出层\n",
        "        self.linear2 = nn.Linear(hidden_dim, num_class)\n",
        "\n",
        "    def forward(self, inputs, offsets):\n",
        "        embedding = self.embedding(inputs, offsets)\n",
        "        hidden = self.activate(self.linear1(embedding))\n",
        "        outputs = self.linear2(hidden)\n",
        "        # 获得每个序列属于某一个类别概率的对数值\n",
        "        probs = F.log_softmax(outputs, dim=1)\n",
        "        return probs\n",
        "\n",
        "embedding_dim = 128\n",
        "hidden_dim = 256\n",
        "num_class = 2\n",
        "batch_size = 32\n",
        "num_epoch = 5\n",
        "\n",
        "# 加载数据\n",
        "train_data, test_data, vocab = load_sentence_polarity()\n",
        "train_data = BowDataset(train_data)\n",
        "test_data = BowDataset(test_data)\n",
        "train_data_loader = DataLoader(train_data, batch_size=batch_size, collate_fn=collate_fn, shuffle=True)\n",
        "test_data_loader = DataLoader(test_data, batch_size=1, collate_fn=collate_fn, shuffle=False)\n",
        "\n",
        "# 加载模型\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = MLP(len(vocab), embedding_dim, hidden_dim, num_class)\n",
        "model.to(device)\n",
        "\n",
        "# 训练\n",
        "nll_loss = nn.NLLLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epoch):\n",
        "    total_loss = 0\n",
        "\n",
        "    for batch in tqdm(train_data_loader, desc=f\"Training Epoch {epoch + 1}\"):\n",
        "        inputs, offsets, targets = [x.to(device) for x in batch]\n",
        "        log_probs = model(inputs, offsets)\n",
        "        loss = nll_loss(log_probs, targets)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "print(f\"Loss:{total_loss:.2f}\")\n",
        "\n",
        "# 测试\n",
        "acc = 0\n",
        "for batch in tqdm(test_data_loader, desc=f\"Testing\"):\n",
        "    inputs, offsets, targets = [x.to(device) for x in batch]\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        output = model(inputs, offsets)\n",
        "        acc += (output.argmax(dim=1) == targets).sum().item()\n",
        "\n",
        "print(f\"Acc: {acc / len(test_data_loader):.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KfLQyaezvV6b"
      },
      "source": [
        "# 17 CNN的情感分类实战"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "imQ2OEKIvV6b",
        "outputId": "0c3ec1e1-f289-457f-a620-1e13bff2ea5e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 1: 100%|██████████| 250/250 [00:06<00:00, 36.27it/s]\n",
            "Training Epoch 2:   2%|▏         | 4/250 [00:00<00:06, 35.68it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 165.55\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 2: 100%|██████████| 250/250 [00:08<00:00, 31.13it/s]\n",
            "Training Epoch 3:   2%|▏         | 4/250 [00:00<00:07, 32.69it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 122.83\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 3: 100%|██████████| 250/250 [00:06<00:00, 36.45it/s]\n",
            "Training Epoch 4:   2%|▏         | 5/250 [00:00<00:05, 42.80it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 76.39\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 4: 100%|██████████| 250/250 [00:06<00:00, 41.66it/s]\n",
            "Training Epoch 5:   1%|          | 3/250 [00:00<00:09, 26.39it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 33.92\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 5: 100%|██████████| 250/250 [00:06<00:00, 39.79it/s]\n",
            "Testing:  19%|█▉        | 507/2662 [00:00<00:00, 2303.12it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 12.04\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Testing: 100%|██████████| 2662/2662 [00:00<00:00, 2924.88it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Acc: 0.72\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "from collections import defaultdict\n",
        "# 进度条\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "class Vocab:\n",
        "    def __init__(self, tokens=None):\n",
        "        self.idx_to_token = list()\n",
        "        self.token_to_idx = dict()\n",
        "\n",
        "        if tokens is not None:\n",
        "            if \"<unk>\" not in tokens:\n",
        "                tokens = tokens + \"<unk>\"\n",
        "            for token in tokens:\n",
        "                self.idx_to_token.append(token)\n",
        "                self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
        "            self.unk = self.token_to_idx['<unk>']\n",
        "\n",
        "    @classmethod\n",
        "\n",
        "    def build(cls, text, min_freq=1, reserved_tokens=None):\n",
        "        token_freqs = defaultdict(int)\n",
        "        for sentence in text:\n",
        "            for token in sentence:\n",
        "                token_freqs[token] += 1\n",
        "        uniq_tokens = [\"<unk>\"] + (reserved_tokens if reserved_tokens else [])\n",
        "        uniq_tokens += [token for token, freq in token_freqs.items() if freq >= min_freq and token != \"<unk>\"]\n",
        "        return cls(uniq_tokens)\n",
        "\n",
        "    def __len__(self):\n",
        "        # 返回词表的大小\n",
        "        return len(self.idx_to_token)\n",
        "\n",
        "    def __getitem__(self, token):\n",
        "        # 查找输入标记对应的索引值，如果1该标记不存在，则返回标记<unk>的索引值(0)\n",
        "        return self.token_to_idx.get(token, self.unk)\n",
        "\n",
        "    def convert_tokens_to_ids(self, tokens):\n",
        "        return [self[token] for token in tokens]\n",
        "\n",
        "    def convert_ids_to_tokens(self, indices):\n",
        "        return [self.idx_to_token[index] for index in indices]\n",
        "\n",
        "class CnnDataset(Dataset):\n",
        "    def __init__(self, data):\n",
        "        self.data = data\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "    def __getitem__(self, i):\n",
        "        return self.data[i]\n",
        "\n",
        "def collate_fn(examples):\n",
        "    inputs = [torch.tensor(ex[0]) for ex in examples]\n",
        "    targets = torch.tensor([ex[1] for ex in examples], dtype=torch.long)\n",
        "    # 对batch内的样本进行padding，使其具有相同长度\n",
        "    inputs = pad_sequence(inputs, batch_first=True)\n",
        "    return inputs, targets\n",
        "\n",
        "class CNN(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, filter_size, num_filter, num_class):\n",
        "        super(CNN, self).__init__()\n",
        "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
        "        self.conv1d = nn.Conv1d(embedding_dim, num_filter, filter_size, padding=1)\n",
        "        self.activate = F.relu\n",
        "        self.linear = nn.Linear(num_filter, num_class)\n",
        "    def forward(self, inputs): # inputs: (32, 47) ，32个长度为47的序列\n",
        "        embedding = self.embedding(inputs)  # embedding: (32, 47, 128)，相当于加了原有加了一个词向量维度，\n",
        "        convolution = self.activate(self.conv1d(embedding.permute(0, 2, 1))) # convolution: (32, 100, 47)\n",
        "        pooling = F.max_pool1d(convolution, kernel_size=convolution.shape[2]) # pooling: (32, 100, 1)\n",
        "        pooling_squeeze = pooling.squeeze(dim=2) # pooling_squeeze: (32, 100)\n",
        "        outputs = self.linear(pooling_squeeze) # outputs: (32, 2)\n",
        "        log_probs = F.log_softmax(outputs, dim=1) # log_probs: (32, 2)\n",
        "        return log_probs\n",
        "\n",
        "\n",
        "def load_sentence_polarity():\n",
        "    \n",
        "    from nltk.corpus import sentence_polarity\n",
        "\n",
        "    vocab = Vocab.build(sentence_polarity.sents())\n",
        "    train_data = [(vocab.convert_tokens_to_ids(sentence), 0) for sentence in sentence_polarity.sents(categories='pos')][: 4000] \\\n",
        "        + [(vocab.convert_tokens_to_ids(sentence), 1) for sentence in sentence_polarity.sents(categories='neg')][: 4000] \n",
        "    test_data = [(vocab.convert_tokens_to_ids(sentence), 0) for sentence in sentence_polarity.sents(categories='pos')][4000: ] \\\n",
        "        + [(vocab.convert_tokens_to_ids(sentence), 1) for sentence in sentence_polarity.sents(categories='neg')][4000: ]\n",
        "\n",
        "    return train_data, test_data, vocab\n",
        "\n",
        "#超参数设置\n",
        "embedding_dim = 128\n",
        "hidden_dim = 256\n",
        "num_class = 2\n",
        "batch_size = 32\n",
        "num_epoch = 5\n",
        "filter_size = 3\n",
        "num_filter = 100\n",
        "\n",
        "#加载数据\n",
        "train_data, test_data, vocab = load_sentence_polarity()\n",
        "train_dataset = CnnDataset(train_data)\n",
        "test_dataset = CnnDataset(test_data)\n",
        "train_data_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, shuffle=True)\n",
        "test_data_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn, shuffle=False)\n",
        "\n",
        "#加载模型\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = CNN(len(vocab), embedding_dim, filter_size, num_filter, num_class)\n",
        "model.to(device) #将模型加载到CPU或GPU设备\n",
        "\n",
        "#训练过程\n",
        "nll_loss = nn.NLLLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001) #使用Adam优化器\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epoch):\n",
        "    total_loss = 0\n",
        "    for batch in tqdm(train_data_loader, desc=f\"Training Epoch {epoch + 1}\"):\n",
        "        inputs, targets = [x.to(device) for x in batch]\n",
        "        log_probs = model(inputs)\n",
        "        loss = nll_loss(log_probs, targets)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "    print(f\"Loss: {total_loss:.2f}\")\n",
        "\n",
        "#测试过程\n",
        "acc = 0\n",
        "for batch in tqdm(test_data_loader, desc=f\"Testing\"):\n",
        "    inputs, targets = [x.to(device) for x in batch]\n",
        "    with torch.no_grad():\n",
        "        output = model(inputs)\n",
        "        acc += (output.argmax(dim=1) == targets).sum().item()\n",
        "\n",
        "#输出在测试集上的准确率\n",
        "print(f\"Acc: {acc / len(test_data_loader):.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_d-Ln3pivV6c"
      },
      "source": [
        "# 18 LSTM的情感分类实战"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dTFCIzuivV6c",
        "outputId": "a03499d3-88a9-40e9-aed6-7bf9f2350b2d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 1: 100%|██████████| 250/250 [00:23<00:00, 10.48it/s]\n",
            "Training Epoch 2:   0%|          | 1/250 [00:00<00:25,  9.74it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 166.79\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 2: 100%|██████████| 250/250 [00:22<00:00, 11.17it/s]\n",
            "Training Epoch 3:   0%|          | 1/250 [00:00<00:30,  8.23it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 137.47\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 3: 100%|██████████| 250/250 [00:26<00:00,  9.55it/s]\n",
            "Training Epoch 4:   1%|          | 2/250 [00:00<00:22, 10.88it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 101.56\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 4: 100%|██████████| 250/250 [00:25<00:00,  9.99it/s]\n",
            "Training Epoch 5:   0%|          | 1/250 [00:00<00:39,  6.32it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 64.39\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Training Epoch 5: 100%|██████████| 250/250 [00:19<00:00, 12.55it/s]\n",
            "Testing:   2%|▏         | 55/2662 [00:00<00:04, 546.54it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 32.24\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Testing: 100%|██████████| 2662/2662 [00:03<00:00, 678.86it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Acc: 0.72\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence\n",
        "from collections import defaultdict\n",
        "# 进度条\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "class Vocab:\n",
        "    def __init__(self, tokens=None):\n",
        "        self.idx_to_token = list()\n",
        "        self.token_to_idx = dict()\n",
        "\n",
        "        if tokens is not None:\n",
        "            if \"<unk>\" not in tokens:\n",
        "                tokens = tokens + \"<unk>\"\n",
        "            for token in tokens:\n",
        "                self.idx_to_token.append(token)\n",
        "                self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
        "            self.unk = self.token_to_idx['<unk>']\n",
        "\n",
        "    @classmethod\n",
        "\n",
        "    def build(cls, text, min_freq=1, reserved_tokens=None):\n",
        "        token_freqs = defaultdict(int)\n",
        "        for sentence in text:\n",
        "            for token in sentence:\n",
        "                token_freqs[token] += 1\n",
        "        uniq_tokens = [\"<unk>\"] + (reserved_tokens if reserved_tokens else [])\n",
        "        uniq_tokens += [token for token, freq in token_freqs.items() if freq >= min_freq and token != \"<unk>\"]\n",
        "        return cls(uniq_tokens)\n",
        "\n",
        "    def __len__(self):\n",
        "        # 返回词表的大小\n",
        "        return len(self.idx_to_token)\n",
        "\n",
        "    def __getitem__(self, token):\n",
        "        # 查找输入标记对应的索引值，如果1该标记不存在，则返回标记<unk>的索引值(0)\n",
        "        return self.token_to_idx.get(token, self.unk)\n",
        "\n",
        "    def convert_tokens_to_ids(self, tokens):\n",
        "        return [self[token] for token in tokens]\n",
        "\n",
        "    def convert_ids_to_tokens(self, indices):\n",
        "        return [self.idx_to_token[index] for index in indices]\n",
        "\n",
        "class LstmDataset(Dataset):\n",
        "    def __init__(self, data):\n",
        "        self.data = data\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "    def __getitem__(self, i):\n",
        "        return self.data[i]\n",
        "\n",
        "def collate_fn(examples):\n",
        "    # 获得每个序列的长度\n",
        "    lengths = torch.tensor([len(ex[0]) for ex in examples])\n",
        "    inputs = [torch.tensor(ex[0]) for ex in examples]\n",
        "    targets = torch.tensor([ex[1] for ex in examples], dtype=torch.long)\n",
        "    # 对batch内的样本进行padding，使其具有相同长度\n",
        "    inputs = pad_sequence(inputs, batch_first=True)\n",
        "    return inputs, lengths, targets\n",
        "\n",
        "class LSTM(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_class):\n",
        "        super(LSTM, self).__init__()\n",
        "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
        "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)\n",
        "        self.output = nn.Linear(hidden_dim, num_class)\n",
        "\n",
        "    def forward(self, inputs, lengths):\n",
        "        embedding = self.embedding(inputs)\n",
        "        # 使用pack_padded_sequence函数将变长序列打包\n",
        "        x_pack = pack_padded_sequence(input=embedding, lengths=lengths, batch_first=True, enforce_sorted=False)\n",
        "        hidden, (hn, cn) = self.lstm(x_pack)\n",
        "        outputs = self.output(hn[-1])\n",
        "        log_probs = F.log_softmax(outputs, dim=-1)\n",
        "        return log_probs\n",
        "\n",
        "\n",
        "def load_sentence_polarity():\n",
        "    \n",
        "    from nltk.corpus import sentence_polarity\n",
        "\n",
        "    vocab = Vocab.build(sentence_polarity.sents())\n",
        "    train_data = [(vocab.convert_tokens_to_ids(sentence), 0) for sentence in sentence_polarity.sents(categories='pos')][: 4000] \\\n",
        "        + [(vocab.convert_tokens_to_ids(sentence), 1) for sentence in sentence_polarity.sents(categories='neg')][: 4000] \n",
        "    test_data = [(vocab.convert_tokens_to_ids(sentence), 0) for sentence in sentence_polarity.sents(categories='pos')][4000: ] \\\n",
        "        + [(vocab.convert_tokens_to_ids(sentence), 1) for sentence in sentence_polarity.sents(categories='neg')][4000: ]\n",
        "\n",
        "    return train_data, test_data, vocab\n",
        "\n",
        "#超参数设置\n",
        "embedding_dim = 128\n",
        "hidden_dim = 256\n",
        "num_class = 2\n",
        "batch_size = 32\n",
        "num_epoch = 5\n",
        "\n",
        "#加载数据\n",
        "train_data, test_data, vocab = load_sentence_polarity()\n",
        "train_dataset = LstmDataset(train_data)\n",
        "test_dataset = LstmDataset(test_data)\n",
        "train_data_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, shuffle=True)\n",
        "test_data_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn, shuffle=False)\n",
        "\n",
        "#加载模型\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = LSTM(len(vocab), embedding_dim, hidden_dim, num_class)\n",
        "model.to(device) #将模型加载到CPU或GPU设备\n",
        "\n",
        "#训练过程\n",
        "nll_loss = nn.NLLLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001) #使用Adam优化器\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epoch):\n",
        "    total_loss = 0\n",
        "    for batch in tqdm(train_data_loader, desc=f\"Training Epoch {epoch + 1}\"):\n",
        "        inputs, lengths, targets = [x.to(device) for x in batch]\n",
        "        log_probs = model(inputs, lengths)\n",
        "        loss = nll_loss(log_probs, targets)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "    print(f\"Loss: {total_loss:.2f}\")\n",
        "\n",
        "#测试过程\n",
        "acc = 0\n",
        "for batch in tqdm(test_data_loader, desc=f\"Testing\"):\n",
        "    inputs, lengths, targets = [x.to(device) for x in batch]\n",
        "    with torch.no_grad():\n",
        "        output = model(inputs, lengths)\n",
        "        acc += (output.argmax(dim=1) == targets).sum().item()\n",
        "\n",
        "#输出在测试集上的准确率\n",
        "print(f\"Acc: {acc / len(test_data_loader):.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgsKRI3jvV6c"
      },
      "source": [
        "# 19 Transformer的情感分类实战"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3kCEXpgIvV6c"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence\n",
        "from collections import defaultdict\n",
        "# 进度条\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "class Vocab:\n",
        "    def __init__(self, tokens=None):\n",
        "        self.idx_to_token = list()\n",
        "        self.token_to_idx = dict()\n",
        "\n",
        "        if tokens is not None:\n",
        "            if \"<unk>\" not in tokens:\n",
        "                tokens = tokens + \"<unk>\"\n",
        "            for token in tokens:\n",
        "                self.idx_to_token.append(token)\n",
        "                self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
        "            self.unk = self.token_to_idx['<unk>']\n",
        "\n",
        "    @classmethod\n",
        "\n",
        "    def build(cls, text, min_freq=1, reserved_tokens=None):\n",
        "        token_freqs = defaultdict(int)\n",
        "        for sentence in text:\n",
        "            for token in sentence:\n",
        "                token_freqs[token] += 1\n",
        "        uniq_tokens = [\"<unk>\"] + (reserved_tokens if reserved_tokens else [])\n",
        "        uniq_tokens += [token for token, freq in token_freqs.items() if freq >= min_freq and token != \"<unk>\"]\n",
        "        return cls(uniq_tokens)\n",
        "\n",
        "    def __len__(self):\n",
        "        # 返回词表的大小\n",
        "        return len(self.idx_to_token)\n",
        "\n",
        "    def __getitem__(self, token):\n",
        "        # 查找输入标记对应的索引值，如果1该标记不存在，则返回标记<unk>的索引值(0)\n",
        "        return self.token_to_idx.get(token, self.unk)\n",
        "\n",
        "    def convert_tokens_to_ids(self, tokens):\n",
        "        return [self[token] for token in tokens]\n",
        "\n",
        "    def convert_ids_to_tokens(self, indices):\n",
        "        return [self.idx_to_token[index] for index in indices]\n",
        "\n",
        "class LstmDataset(Dataset):\n",
        "    def __init__(self, data):\n",
        "        self.data = data\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "    def __getitem__(self, i):\n",
        "        return self.data[i]\n",
        "\n",
        "def collate_fn(examples):\n",
        "    # 获得每个序列的长度\n",
        "    lengths = torch.tensor([len(ex[0]) for ex in examples])\n",
        "    inputs = [torch.tensor(ex[0]) for ex in examples]\n",
        "    targets = torch.tensor([ex[1] for ex in examples], dtype=torch.long)\n",
        "    # 对batch内的样本进行padding，使其具有相同长度\n",
        "    inputs = pad_sequence(inputs, batch_first=True)\n",
        "    return inputs, lengths, targets\n",
        "\n",
        "class PositiionalEncoding(nn.Module):\n",
        "    def __init__(self, d_model, dropout=0.1, max_len=512):\n",
        "        super(PositiionalEncoding, self).__init__()\n",
        "\n",
        "        pe = torch.zeros(max_len, d_model)\n",
        "        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n",
        "        \n",
        "\n",
        "\n",
        "\n",
        "class Transformer(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_class,dim_feedforward=512, num_head=2, num_layers=2, dropout=0.1, max_len=128, activation: str=\"relu\"):\n",
        "        super(Transformer, self).__init__()\n",
        "        self.embedding_dim = embedding_dim\n",
        "        # 词向量层\n",
        "        self.embeddings = nn.Embedding(vocab_size, embedding_dim)\n",
        "        self.position_embedding = Posti\n",
        "\n",
        "\n",
        "\n",
        "def load_sentence_polarity():\n",
        "    \n",
        "    from nltk.corpus import sentence_polarity\n",
        "\n",
        "    vocab = Vocab.build(sentence_polarity.sents())\n",
        "    train_data = [(vocab.convert_tokens_to_ids(sentence), 0) for sentence in sentence_polarity.sents(categories='pos')][: 4000] \\\n",
        "        + [(vocab.convert_tokens_to_ids(sentence), 1) for sentence in sentence_polarity.sents(categories='neg')][: 4000] \n",
        "    test_data = [(vocab.convert_tokens_to_ids(sentence), 0) for sentence in sentence_polarity.sents(categories='pos')][4000: ] \\\n",
        "        + [(vocab.convert_tokens_to_ids(sentence), 1) for sentence in sentence_polarity.sents(categories='neg')][4000: ]\n",
        "\n",
        "    return train_data, test_data, vocab\n",
        "\n",
        "#超参数设置\n",
        "embedding_dim = 128\n",
        "hidden_dim = 256\n",
        "num_class = 2\n",
        "batch_size = 32\n",
        "num_epoch = 5\n",
        "\n",
        "#加载数据\n",
        "train_data, test_data, vocab = load_sentence_polarity()\n",
        "train_dataset = LstmDataset(train_data)\n",
        "test_dataset = LstmDataset(test_data)\n",
        "train_data_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate_fn, shuffle=True)\n",
        "test_data_loader = DataLoader(test_dataset, batch_size=1, collate_fn=collate_fn, shuffle=False)\n",
        "\n",
        "#加载模型\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = LSTM(len(vocab), embedding_dim, hidden_dim, num_class)\n",
        "model.to(device) #将模型加载到CPU或GPU设备\n",
        "\n",
        "#训练过程\n",
        "nll_loss = nn.NLLLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001) #使用Adam优化器\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epoch):\n",
        "    total_loss = 0\n",
        "    for batch in tqdm(train_data_loader, desc=f\"Training Epoch {epoch + 1}\"):\n",
        "        inputs, lengths, targets = [x.to(device) for x in batch]\n",
        "        log_probs = model(inputs, lengths)\n",
        "        loss = nll_loss(log_probs, targets)\n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "    print(f\"Loss: {total_loss:.2f}\")\n",
        "\n",
        "#测试过程\n",
        "acc = 0\n",
        "for batch in tqdm(test_data_loader, desc=f\"Testing\"):\n",
        "    inputs, lengths, targets = [x.to(device) for x in batch]\n",
        "    with torch.no_grad():\n",
        "        output = model(inputs, lengths)\n",
        "        acc += (output.argmax(dim=1) == targets).sum().item()\n",
        "\n",
        "#输出在测试集上的准确率\n",
        "print(f\"Acc: {acc / len(test_data_loader):.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15phQPSEvzfr",
        "outputId": "4617eb01-2327-4691-a0b4-4c8efd4abd51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install nltk"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_-MtKDgvV6d"
      },
      "source": [
        "# 20 语言模型"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 20.1 前馈神经网络语言模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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          ]
        },
        "id": "ssYAzyQtvV6d",
        "outputId": "48ba11ea-648d-4936-80b5-f667b55bd0f8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package reuters to /root/nltk_data...\n",
            "[nltk_data]   Package reuters is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b1c5ac0fcb24a4e93a1dc3347157d5e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Data Construction:   0%|          | 0/54716 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e23cf74ceba04ff99a523cab8266d607",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 0:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 8310.34\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0c2dae80b27c4e26b59235df9e7156ce",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 1:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 6934.16\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ff8aa4a24171473c9cf2a4457d201900",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 2:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 6342.58\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7228eef87e234c07a660e3391089f01a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 3:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 5939.16\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b764040a31f44133952faa9bd3b4f147",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 4:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 5666.03\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3b686e09145846089475d1790609d285",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 5:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 5477.37\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5e16cfe5a78f4b9f84be6f0393e51f8e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 6:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 5333.53\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "63977d912a4844d69a3834e969f61ed1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 7:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 5214.55\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d9ba7974d00f466cbd2df4b8fb63edcd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 8:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 5111.15\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8c84b8b16204491b30893e2777887a6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Training Epoch 9:   0%|          | 0/1628 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loss: 5021.05\n",
            "Pretrained embeddings saved to: /home/ffnnlm.vec\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "from torch import nn, optim\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence\n",
        "from collections import defaultdict\n",
        "# 进度条\n",
        "from tqdm.auto import tqdm\n",
        "import nltk\n",
        "\n",
        "BOS_TOKEN = \"<bos>\"\n",
        "EOS_TOKEN = \"<eos>\"\n",
        "PAD_TOKEN = \"<pad>\"\n",
        "\n",
        "nltk.download('reuters')\n",
        "nltk.download('punkt')\n",
        "\n",
        "# from zipfile import ZipFile\n",
        "# file_loc = '/root/nltk_data/corpora/reuters.zip'\n",
        "# with ZipFile(file_loc, 'r') as z:\n",
        "#   z.extractall('/root/nltk_data/corpora/')\n",
        "\n",
        "\n",
        "# file_loc = '/root/nltk_data/corpora/punkt.zip'\n",
        "# with ZipFile(file_loc, 'r') as z:\n",
        "#   z.extractall('/root/nltk_data/corpora/')\n",
        "\n",
        "class Vocab:\n",
        "    def __init__(self, tokens=None):\n",
        "        self.idx_to_token = list()\n",
        "        self.token_to_idx = dict()\n",
        "\n",
        "        if tokens is not None:\n",
        "            if \"<unk>\" not in tokens:\n",
        "                tokens = tokens + \"<unk>\"\n",
        "            for token in tokens:\n",
        "                self.idx_to_token.append(token)\n",
        "                self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
        "            self.unk = self.token_to_idx['<unk>']\n",
        "\n",
        "    @classmethod\n",
        "\n",
        "    def build(cls, text, min_freq=1, reserved_tokens=None):\n",
        "        token_freqs = defaultdict(int)\n",
        "        for sentence in text:\n",
        "            for token in sentence:\n",
        "                token_freqs[token] += 1\n",
        "        uniq_tokens = [\"<unk>\"] + (reserved_tokens if reserved_tokens else [])\n",
        "        uniq_tokens += [token for token, freq in token_freqs.items() if freq >= min_freq and token != \"<unk>\"]\n",
        "        return cls(uniq_tokens)\n",
        "\n",
        "    def __len__(self):\n",
        "        # 返回词表的大小\n",
        "        return len(self.idx_to_token)\n",
        "\n",
        "    def __getitem__(self, token):\n",
        "        # 查找输入标记对应的索引值，如果1该标记不存在，则返回标记<unk>的索引值(0)\n",
        "        return self.token_to_idx.get(token, self.unk)\n",
        "\n",
        "    def convert_tokens_to_ids(self, tokens):\n",
        "        return [self[token] for token in tokens]\n",
        "\n",
        "    def convert_ids_to_tokens(self, indices):\n",
        "        return [self.idx_to_token[index] for index in indices]\n",
        "\n",
        "def get_loader(dataset, batch_size, shuffle=True):\n",
        "    data_loader = DataLoader(\n",
        "        dataset,\n",
        "        batch_size=batch_size,\n",
        "        collate_fn=dataset.collate_fn,\n",
        "        shuffle=shuffle\n",
        "    )\n",
        "    return data_loader\n",
        "\n",
        "# 读取Reuters语料库\n",
        "def load_reuters():\n",
        "\n",
        "    from nltk.corpus import reuters\n",
        "\n",
        "    text = reuters.sents()\n",
        "    text = [[word.lower() for word in sentence]for sentence in text]\n",
        "    vocab = Vocab.build(text, reserved_tokens=[PAD_TOKEN, EOS_TOKEN, PAD_TOKEN])\n",
        "    corpus = [vocab.convert_tokens_to_ids(sentence) for sentence in text]\n",
        "    return corpus, vocab\n",
        "\n",
        "\n",
        "# 保存词向量\n",
        "def save_pretrained(vocab, embeds, save_path):\n",
        "    \"\"\"\n",
        "    Save pretrained token vectors in a unified format, where the first line\n",
        "    specifies the `number_of_tokens` and `embedding_dim` followed with all\n",
        "    token vectors, one token per line.\n",
        "    \"\"\"\n",
        "    with open(save_path, \"w\") as writer:\n",
        "        writer.write(f\"{embeds.shape[0]} {embeds.shape[1]}\\n\")\n",
        "        for idx, token in enumerate(vocab.idx_to_token):\n",
        "            vec = \" \".join([\"{:.4f}\".format(x) for x in embeds[idx]])\n",
        "            writer.write(f\"{token} {vec}\\n\")\n",
        "    print(f\"Pretrained embeddings saved to: {save_path}\")\n",
        "\n",
        "\n",
        "# Dataset类\n",
        "class NGramDataset(Dataset):\n",
        "    def __init__(self, corpus, vocab, context_size=2):\n",
        "        self.data = []\n",
        "        self.bos = vocab[BOS_TOKEN] # 句首标记id\n",
        "        self.eos = vocab[EOS_TOKEN] # 句尾标记id\n",
        "        for sentence in tqdm(corpus, desc=\"Data Construction\"):\n",
        "            # 插入句首、句尾标记符\n",
        "            sentence = [self.bos] + sentence + [self.eos]\n",
        "            # 如句子长度小于预定义的上下文大小、则跳过\n",
        "            if len(sentence) < context_size:\n",
        "                continue\n",
        "            for i in range(context_size, len(sentence)):\n",
        "                # 模型输入：长度1为context_size的上下文\n",
        "                context = sentence[i-context_size: i]\n",
        "                # 当前词\n",
        "                target = sentence[i]\n",
        "                # 每个训练样本由(context, target)构成\n",
        "                self.data.append((context, target))\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        return self.data[i]\n",
        "\n",
        "    def collate_fn(self, examples):\n",
        "        # 从独立样本集合中构建批次的输入输出，并转换为PyTorch张量类型\n",
        "        inputs = torch.tensor([ex[0] for ex in examples], dtype=torch.long)\n",
        "        targets = torch.tensor([ex[1] for ex in examples], dtype=torch.long)\n",
        "        return (inputs, targets)\n",
        "\n",
        "# 模型\n",
        "class FeedForwaardNNLM(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, context_size, hidden_dim):\n",
        "        super(FeedForwaardNNLM, self).__init__()\n",
        "        # 词向量层\n",
        "        self.embeddings = nn.Embedding(vocab_size, embedding_dim)\n",
        "        self.linear1 = nn.Linear(context_size * embedding_dim, hidden_dim)\n",
        "        self.linear2 = nn.Linear(hidden_dim, vocab_size)\n",
        "        self.activate = F.relu\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        embeds = self.embeddings(inputs).view((inputs.shape[0], -1))\n",
        "        hidden = self.activate(self.linear1(embeds))\n",
        "        output = self.linear2(hidden)\n",
        "        log_probs = F.log_softmax(output, dim=1)\n",
        "        return log_probs\n",
        "\n",
        "# 训练\n",
        "embedding_dim = 128\n",
        "hidden_dim = 256\n",
        "batch_size = 1024\n",
        "context_size = 3\n",
        "num_epoch = 10\n",
        "\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "corpus, vocab = load_reuters()\n",
        "dataset = NGramDataset(corpus, vocab, context_size)\n",
        "data_loader = get_loader(dataset, batch_size)\n",
        "nll_loss = nn.NLLLoss()\n",
        "model = FeedForwaardNNLM(len(vocab), embedding_dim, context_size, hidden_dim)\n",
        "model.to(device)\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "model.train()\n",
        "total_losses = []\n",
        "\n",
        "for epoch in range(num_epoch):\n",
        "    total_loss = 0\n",
        "    for batch in tqdm(data_loader, desc=f\"Training Epoch {epoch}\"):\n",
        "        inputs, targets = [x.to(device) for x in batch]\n",
        "        optimizer.zero_grad()\n",
        "        log_probs = model(inputs)\n",
        "        loss = nll_loss(log_probs, targets)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "\n",
        "    print(f\"Loss: {total_loss:.2f}\")\n",
        "    total_losses.append(total_loss)\n",
        "\n",
        "\n",
        "save_pretrained(vocab, model.embeddings.weight.data, \"/home/ffnnlm.vec\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 20.2 循环神经网络语言模型"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package reuters to /Users/xiaoqi/nltk_data...\n",
            "[nltk_data]   Package reuters is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /Users/xiaoqi/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "Dataset Construction: 100%|██████████| 54711/54711 [00:00<00:00, 467811.35it/s]\n",
            "Training Epoch 0:   0%|          | 0/54 [00:00<?, ?it/s]"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "BOS_TOKEN = \"<bos>\"\n",
        "EOS_TOKEN = \"<eos>\"\n",
        "PAD_TOKEN = \"<pad>\"\n",
        "\n",
        "nltk.download('reuters')\n",
        "nltk.download('punkt')\n",
        "\n",
        "# from zipfile import ZipFile\n",
        "# file_loc = '/root/nltk_data/corpora/reuters.zip'\n",
        "# with ZipFile(file_loc, 'r') as z:\n",
        "#   z.extractall('/root/nltk_data/corpora/')\n",
        "\n",
        "\n",
        "# file_loc = '/root/nltk_data/corpora/punkt.zip'\n",
        "# with ZipFile(file_loc, 'r') as z:\n",
        "#   z.extractall('/root/nltk_data/corpora/')\n",
        "\n",
        "class Vocab:\n",
        "    def __init__(self, tokens=None):\n",
        "        self.idx_to_token = list()\n",
        "        self.token_to_idx = dict()\n",
        "\n",
        "        if tokens is not None:\n",
        "            if \"<unk>\" not in tokens:\n",
        "                tokens = tokens + \"<unk>\"\n",
        "            for token in tokens:\n",
        "                self.idx_to_token.append(token)\n",
        "                self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
        "            self.unk = self.token_to_idx['<unk>']\n",
        "\n",
        "    @classmethod\n",
        "\n",
        "    def build(cls, text, min_freq=1, reserved_tokens=None):\n",
        "        token_freqs = defaultdict(int)\n",
        "        for sentence in text:\n",
        "            for token in sentence:\n",
        "                token_freqs[token] += 1\n",
        "        uniq_tokens = [\"<unk>\"] + (reserved_tokens if reserved_tokens else [])\n",
        "        uniq_tokens += [token for token, freq in token_freqs.items() if freq >= min_freq and token != \"<unk>\"]\n",
        "        return cls(uniq_tokens)\n",
        "\n",
        "    def __len__(self):\n",
        "        # 返回词表的大小\n",
        "        return len(self.idx_to_token)\n",
        "\n",
        "    def __getitem__(self, token):\n",
        "        # 查找输入标记对应的索引值，如果1该标记不存在，则返回标记<unk>的索引值(0)\n",
        "        return self.token_to_idx.get(token, self.unk)\n",
        "\n",
        "    def convert_tokens_to_ids(self, tokens):\n",
        "        return [self[token] for token in tokens]\n",
        "\n",
        "    def convert_ids_to_tokens(self, indices):\n",
        "        return [self.idx_to_token[index] for index in indices]\n",
        "\n",
        "def get_loader(dataset, batch_size, shuffle=True):\n",
        "    data_loader = DataLoader(\n",
        "        dataset,\n",
        "        batch_size=batch_size,\n",
        "        collate_fn=dataset.collate_fn,\n",
        "        shuffle=shuffle\n",
        "    )\n",
        "    return data_loader\n",
        "\n",
        "# 读取Reuters语料库\n",
        "def load_reuters():\n",
        "\n",
        "    from nltk.corpus import reuters\n",
        "\n",
        "    text = reuters.sents()\n",
        "    text = [[word.lower() for word in sentence]for sentence in text]\n",
        "    vocab = Vocab.build(text, reserved_tokens=[PAD_TOKEN, EOS_TOKEN, PAD_TOKEN])\n",
        "    corpus = [vocab.convert_tokens_to_ids(sentence) for sentence in text]\n",
        "    return corpus, vocab\n",
        "\n",
        "\n",
        "# 保存词向量\n",
        "def save_pretrained(vocab, embeds, save_path):\n",
        "    \"\"\"\n",
        "    Save pretrained token vectors in a unified format, where the first line\n",
        "    specifies the `number_of_tokens` and `embedding_dim` followed with all\n",
        "    token vectors, one token per line.\n",
        "    \"\"\"\n",
        "    with open(save_path, \"w\") as writer:\n",
        "        writer.write(f\"{embeds.shape[0]} {embeds.shape[1]}\\n\")\n",
        "        for idx, token in enumerate(vocab.idx_to_token):\n",
        "            vec = \" \".join([\"{:.4f}\".format(x) for x in embeds[idx]])\n",
        "            writer.write(f\"{token} {vec}\\n\")\n",
        "    print(f\"Pretrained embeddings saved to: {save_path}\")\n",
        "\n",
        "class RnnlmDataset(Dataset):\n",
        "    def __init__(self, corpus, vocab):\n",
        "        self.data = []\n",
        "        self.bos = vocab[BOS_TOKEN]\n",
        "        self.eos = vocab[EOS_TOKEN]\n",
        "        self.pad = vocab[PAD_TOKEN]\n",
        "        for sentence in tqdm(corpus, desc=\"Dataset Construction\"):\n",
        "            # 模型输入：BOS_TOKEN, w_1, w_2, ..., w_n\n",
        "            input = [self.bos] + sentence\n",
        "            # 模型输出：w_1, w_2, ..., w_n, EOS_TOKEN\n",
        "            target = sentence + [self.eos]\n",
        "            self.data.append((input, target))\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        return self.data[i]\n",
        "\n",
        "    def collate_fn(self, examples):\n",
        "        # 从独立样本集合中构建batch输入输出\n",
        "        inputs = [torch.tensor(ex[0]) for ex in examples]\n",
        "        targets = [torch.tensor(ex[1]) for ex in examples]\n",
        "        # 对batch内的样本进行padding，使其具有相同长度\n",
        "        inputs = pad_sequence(inputs, batch_first=True, padding_value=self.pad)\n",
        "        targets = pad_sequence(targets, batch_first=True, padding_value=self.pad)\n",
        "        return (inputs, targets)\n",
        "\n",
        "class RNNLM(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim, hidden_dim):\n",
        "        super(RNNLM, self).__init__()\n",
        "        # 词嵌入层\n",
        "        self.embeddings = nn.Embedding(vocab_size, embedding_dim)\n",
        "        # 循环神经网络：这里使用LSTM\n",
        "        self.rnn = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)\n",
        "        # 输出层\n",
        "        self.output = nn.Linear(hidden_dim, vocab_size)\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        embeds = self.embeddings(inputs)\n",
        "        # 计算每一时刻的隐含层表示\n",
        "        hidden, _ = self.rnn(embeds)\n",
        "        output = self.output(hidden)\n",
        "        log_probs = F.log_softmax(output, dim=2)\n",
        "        return log_probs\n",
        "\n",
        "embedding_dim = 64\n",
        "context_size = 2\n",
        "hidden_dim = 128\n",
        "batch_size = 1024\n",
        "num_epoch = 10\n",
        "\n",
        "# 读取文本数据，构建FFNNLM训练数据集（n-grams）\n",
        "corpus, vocab = load_reuters()\n",
        "dataset = RnnlmDataset(corpus, vocab)\n",
        "data_loader = get_loader(dataset, batch_size)\n",
        "\n",
        "# 负对数似然损失函数，忽略pad_token处的损失\n",
        "nll_loss = nn.NLLLoss(ignore_index=dataset.pad)\n",
        "# 构建RNNLM，并加载至device\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = RNNLM(len(vocab), embedding_dim, hidden_dim)\n",
        "model.to(device)\n",
        "# 使用Adam优化器\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "model.train()\n",
        "for epoch in range(num_epoch):\n",
        "    total_loss = 0\n",
        "    for batch in tqdm(data_loader, desc=f\"Training Epoch {epoch}\"):\n",
        "        inputs, targets = [x.to(device) for x in batch]\n",
        "        optimizer.zero_grad()\n",
        "        log_probs = model(inputs)\n",
        "        loss = nll_loss(log_probs.view(-1, log_probs.shape[-1]), targets.view(-1))\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        total_loss += loss.item()\n",
        "    print(f\"Loss: {total_loss:.2f}\")\n",
        "\n",
        "save_pretrained(vocab, model.embeddings.weight.data, \"/home/rnnlm.vec\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 21 词向量"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2.1 CBOW"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "# 数据\n",
        "class CbowDataset(Dataset):\n",
        "    def __init__(self, corpus, vocab, context_size=2):\n",
        "        self.data = []\n",
        "        self.bos = vocab[BOS_TOKEN]\n",
        "        self.eos = vocab[EOS_TOKEN]\n",
        "        for sentence in tqdm(corpus, desc=\"Data Construction\"):\n",
        "            sentence = [self.bos] + sentence + [self.eos]\n",
        "            if len(sentence) < context_size * 2 + 1:\n",
        "                continue\n",
        "            for i in range(context_size, len(sentence) - context_size):\n",
        "                context = sentence[i - context_size: i] + sentence[i + 1: i + 1 + context_size]\n",
        "                target = sentence[i]\n",
        "                self.data.append((context, target))\n",
        "\n",
        "# 模型\n",
        "class CbowModel(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim):\n",
        "        super(CbowModel, self).__init__()\n",
        "        \n",
        "        self.embeddings = nn.Embedding(vocab, embedding_dim)\n",
        "        self.output = nn.Linear(embedding_dim, vocab_size, bias=False)\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        embeds = self.embeddings(inputs)\n",
        "        # 隐层对上下文向量取平均\n",
        "        hidden = embeds.mean(dim=1)\n",
        "        output = self.output(hidden)\n",
        "        log_probs = F.log_softmax(output, dim=1)\n",
        "        return log_probs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2.2 Skip-gram"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# 数据\n",
        "class SkipGramDataset(Dataset):\n",
        "    def __init__(self, corpus, vocab, context_size=2):\n",
        "        self.data = []\n",
        "        self.bos = vocab[BOS_TOKEN]\n",
        "        self.eos = vocab[EOS_TOKEN]\n",
        "        for sentence in tqdm(corpus, desc=\"Data Construction\"):\n",
        "            sentence = [self.bos] + sentence + [self.eos]\n",
        "            for i in range(1, len(sentence) - 1):\n",
        "                # 当前词\n",
        "                w = sentence[i]\n",
        "                left_context_index = max(0, i - context_size)\n",
        "                right_context_index = min(len(sentence), i + context_size)\n",
        "                context = sentence[left_context_index: i] + sentence[i + 1: right_context_index]\n",
        "                self.data.extend([(w, c) for c in context])\n",
        "\n",
        "# 模型\n",
        "class SkipGramModel(nn.Module):\n",
        "    def __init__(self, vocab_size, embedding_dim):\n",
        "        super(SkipGramModel, self).__init__()\n",
        "        \n",
        "        self.embeddings = nn.Embedding(vocab, embedding_dim)\n",
        "        self.output = nn.Linear(embedding_dim, vocab_size, bias=False)\n",
        "\n",
        "    def forward(self, inputs):\n",
        "        embeds = self.embeddings(inputs)\n",
        "        output = self.output(embeds)\n",
        "        log_probs = F.log_softmax(output, dim=1)\n",
        "        return log_probs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2.3 负采样的Skip-gram"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "class SGNSDataset(Dataset):\n",
        "    def __init__(self, corpus, vocab, context_size=2, n_negatives=5, ns_dist=None):\n",
        "        self.data = []\n",
        "        self.bos = vocab[BOS_TOKEN]\n",
        "        self.eos = vocab[EOS_TOKEN]\n",
        "        self.pad = vocab[PAD_TOKEN]\n",
        "        for sentence in tqdm(corpus, desc=\"Data Construction\"):\n",
        "            sentence = [self.bos] + sentence + [self.eos]\n",
        "            for i in range(1, len(sentence) - 1):\n",
        "                w = sentence[i]\n",
        "                left_context_index = max(0, i - context_size)\n",
        "                right_context_index = min(len(sentence), i + context_size)\n",
        "                context = sentence[left_context_index: i] + sentence[i + 1: right_context_index]\n",
        "                context += [self.pad] * (2 * context_size - len(context))\n",
        "                self.data.append((w, context))\n",
        "\n",
        "        # 负采样数量\n",
        "        self.n_negatives = n_negatives\n",
        "        # 负采样分布\n",
        "        self.ns_dist = ns_dist if ns_dist else torch.ones(len(vocab))\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.data)\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        return self.data[i]\n",
        "\n",
        "    def collate_fn(self, examples):\n",
        "        words = torch.tensor([ex[0] for ex in examples], dtype=torch.long)\n",
        "        contexts = torch.tensor([ex[1] for ex in examples], dtype=torch.long)\n",
        "        batch_size, context_size = contexts.shape\n",
        "        neg_contexts = []\n",
        "        for i in range(batch_size):\n",
        "            "
      ]
    }
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