6 - Attention is All you need.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6 - Attention is All you need\n",
    "\n",
    "在这个笔记本中，我们将实现《Attention is All You Need》论文中Transformer模型的一个（稍微修改的版本）。本笔记本中的所有图像都将取自Transformer论文。有关Transformer的更多信息，请参见这三篇文章：[1](https://www.mihaileric.com/posts/transformers-attention-in-disguise/)、[2](https://jalammar.github.io/illustrated-transformer/)、[3](http://nlp.seas.harvard.edu/2018/04/03/attention.html)。\n",
    "\n",
    "![](assets/transformer1.png)\n",
    "\n",
    "## 介绍\n",
    "\n",
    "与卷积序列到序列模型类似，Transformer不使用任何循环。它也不使用任何卷积层。相反，该模型完全由线性层、注意机制和标准化层组成。\n",
    "\n",
    "截至2024年1月，Transformer是自然语言处理中的主导架构，并且被用于许多任务以及在可预见的未来内可能仍然是主导架构。\n",
    "\n",
    "最流行的Transformer变体之一是BERT（**B**idirectional **E**ncoder **R**epresentations from **T**ransformers），BERT的预训练版本通常用于替换NLP模型中的嵌入层 (甚至是更多部分)。\n",
    "\n",
    "在处理预训练transformer时，常用的库是[Transformers](https://huggingface.co/transformers/)库，有关所有预训练模型的列表，请参见[这里](https://huggingface.co/transformers/pretrained_models.html)。\n",
    "\n",
    "在本笔记本中的实现与论文中的有所不同：\n",
    "- 我们使用了一个学习到的位置编码，而不是静态的位置编码。\n",
    "- 我们使用具有静态学习率的标准Adam优化器，而不是具有热启动和冷却步骤的优化器。\n",
    "- 我们没有使用标签平滑。\n",
    "\n",
    "我们进行所有这些更改是因为它们与BERT的设置紧密相似，并且大多数Transformer变体使用类似的设置。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据\n",
    "\n",
    "首先，让我们导入所有必需的模块并设置用于可重复性的随机种子。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mindspore as ms\n",
    "import mindspore.context as context\n",
    "import mindspore.nn as nn\n",
    "import mindspore.ops as ops\n",
    "from functools import partial\n",
    "\n",
    "import random\n",
    "import time\n",
    "import numpy as np\n",
    "import spacy\n",
    "import datasets\n",
    "from tqdm.notebook import tqdm\n",
    "import evaluate as evaluate_hf\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 1234\n",
    "\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "ms.set_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset json (/root/.cache/huggingface/datasets/bentrevett___json/bentrevett--multi30k-8cca6da32304eb4d/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96)\n"
     ]
    },
    {
     "data": {
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       "model_id": "7635f12c72b9497698b01f326c513049",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/bentrevett___json/bentrevett--multi30k-8cca6da32304eb4d/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-39e2a81dd5826464.arrow\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/bentrevett___json/bentrevett--multi30k-8cca6da32304eb4d/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-340eed13fdfe768e.arrow\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/bentrevett___json/bentrevett--multi30k-8cca6da32304eb4d/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-05a87c7dda3754fe.arrow\n"
     ]
    }
   ],
   "source": [
    "dataset = datasets.load_dataset(\"bentrevett/multi30k\")\n",
    "train_data, valid_data, test_data = dataset[\"train\"], dataset[\"validation\"], dataset[\"test\"]\n",
    "en_nlp = spacy.load(\"en_core_web_sm\")\n",
    "de_nlp = spacy.load(\"de_core_news_sm\")\n",
    "def tokenize_example(\n",
    "    example,\n",
    "    en_nlp,\n",
    "    de_nlp,\n",
    "    max_length,\n",
    "    lower,\n",
    "    sos_token,\n",
    "    eos_token\n",
    "):\n",
    "    en_tokens = [token.text for token in en_nlp.tokenizer(example[\"en\"])][:max_length]\n",
    "    de_tokens = [token.text for token in de_nlp.tokenizer(example[\"de\"])][:max_length]\n",
    "    if lower:\n",
    "        en_tokens = [token.lower() for token in en_tokens]\n",
    "        de_tokens = [token.lower() for token in de_tokens]\n",
    "    en_tokens = [sos_token] + en_tokens + [eos_token]\n",
    "    de_tokens = [sos_token] + de_tokens + [eos_token]\n",
    "    return {\"en_tokens\": en_tokens, \"de_tokens\": de_tokens}\n",
    "\n",
    "\n",
    "max_length = 1_000\n",
    "lower = True\n",
    "sos_token = \"<sos>\"\n",
    "eos_token = \"<eos>\"\n",
    "\n",
    "fn_kwargs = {\n",
    "    \"en_nlp\": en_nlp, \n",
    "    \"de_nlp\": de_nlp, \n",
    "    \"max_length\": max_length,\n",
    "    \"lower\": lower,\n",
    "    \"sos_token\": sos_token,\n",
    "    \"eos_token\": eos_token,\n",
    "}\n",
    "\n",
    "\n",
    "train_data = train_data.map(tokenize_example, fn_kwargs=fn_kwargs)\n",
    "valid_data = valid_data.map(tokenize_example, fn_kwargs=fn_kwargs)\n",
    "test_data = test_data.map(tokenize_example, fn_kwargs=fn_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "class Vocab:\n",
    "    \"\"\"一个词汇表的实现\"\"\"\n",
    "    def __init__(self, tokens:list, min_freq=0, reserved_tokens:list=None) -> None:\n",
    "        self.default_index = None\n",
    "        if tokens is not None:\n",
    "            # 当第一个条件满足时，就不会跳到第二个判断，避免了空列表报错的情况。\n",
    "            if len(tokens)!=0 and isinstance(tokens[0], list):\n",
    "                tokens = [i for line in tokens for i in line]\n",
    "        else:\n",
    "            tokens = []\n",
    "        if reserved_tokens is None:\n",
    "            reserved_tokens = []\n",
    "        counter=collections.Counter(tokens)\n",
    "        # 按出现词频从高到低排序\n",
    "        self._token_freqs = sorted(counter.items(), key=lambda x:x[1], reverse=True)\n",
    "        # 通过列表,利用序号访问词元。\n",
    "        self.idx_to_token = [] + reserved_tokens # 未知词元<unk>的索引为0, 保留词元排在最前\n",
    "        self.token_to_idx = {\n",
    "            i: k\n",
    "            for k, i in enumerate(self.idx_to_token) \n",
    "        }\n",
    "        \n",
    "        for token, freq in self._token_freqs:\n",
    "            if freq < min_freq:  # 过滤掉出现频率低于要求的词\n",
    "                break\n",
    "            if token not in self.token_to_idx:  \n",
    "                self.idx_to_token.append(token)\n",
    "                self.token_to_idx[token] = len(self.idx_to_token) - 1\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.idx_to_token)\n",
    "    \n",
    "    def __getitem__(self, input_tokens):\n",
    "        \"\"\"输入单字串或序列, 将其全部转化为序号编码\"\"\"\n",
    "        if isinstance(input_tokens, str):\n",
    "            out =  self.token_to_idx.get(input_tokens, self.default_index)\n",
    "            if out is None:\n",
    "                raise Exception('Please call \"set_default_index\" before getting unknown index')\n",
    "            return out\n",
    "        return [self.__getitem__(token) for token in input_tokens]\n",
    "    \n",
    "    def __repr__(self) -> str:\n",
    "        show_items = 5 if len(self) > 5 else len(self)\n",
    "        out = f\"<Vocab with {len(self)} tokens: \"\n",
    "        for i in range(show_items):\n",
    "            out += f'\"{self.idx_to_token[i]}\", '\n",
    "        out += \"...>\"\n",
    "        return out\n",
    "\n",
    "    def __contains__(self, token:str) -> bool:\n",
    "        return token in self.idx_to_token\n",
    "\n",
    "    def to_tokens(self, input_keys):\n",
    "        \"\"\"输入单s索引或序列, 将其全部转化为词元\"\"\"\n",
    "        if isinstance(input_keys, int):\n",
    "            return self.idx_to_token[input_keys] if input_keys < len(self) else self.idx_to_token[0]\n",
    "        elif isinstance(input_keys, (list, tuple)):\n",
    "            return [self.to_tokens(keys) for keys in input_keys]\n",
    "        else:\n",
    "            return self.idx_to_token[0]\n",
    "    \n",
    "    def get_itos(self):\n",
    "        return self.idx_to_token\n",
    "    \n",
    "    def get_stoi(self):\n",
    "        return self.token_to_idx\n",
    "    \n",
    "    def set_default_index(self, idx):\n",
    "        if isinstance(idx, int):\n",
    "            self.default_index = idx\n",
    "        else:\n",
    "            raise Exception(f\"Only type int allowed, got {type(idx)}\")\n",
    "\n",
    "    def lookup_indices(self, input_tokens):\n",
    "        return self.__getitem__(input_tokens)\n",
    "    \n",
    "    def lookup_tokens(self, idx):\n",
    "        return self.to_tokens(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/bentrevett___json/bentrevett--multi30k-8cca6da32304eb4d/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-6689c04abfdb721f.arrow\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/bentrevett___json/bentrevett--multi30k-8cca6da32304eb4d/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-acab783df695b18d.arrow\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/bentrevett___json/bentrevett--multi30k-8cca6da32304eb4d/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96/cache-e1088b8589007c26.arrow\n"
     ]
    }
   ],
   "source": [
    "min_freq = 2\n",
    "unk_token = \"<unk>\"\n",
    "pad_token = \"<pad>\"\n",
    "\n",
    "special_tokens = [\n",
    "    unk_token,\n",
    "    pad_token,\n",
    "    sos_token,\n",
    "    eos_token,\n",
    "]\n",
    "\n",
    "def build_vocab_from_iterator(tokens, min_freq, specials):\n",
    "    return Vocab(tokens, min_freq, specials)\n",
    "\n",
    "en_vocab = build_vocab_from_iterator(\n",
    "    train_data[\"en_tokens\"],\n",
    "    min_freq=min_freq,\n",
    "    specials=special_tokens,\n",
    ")\n",
    "\n",
    "de_vocab = build_vocab_from_iterator(\n",
    "    train_data[\"de_tokens\"],\n",
    "    min_freq=min_freq,\n",
    "    specials=special_tokens,  \n",
    ")\n",
    "\n",
    "assert en_vocab[unk_token] == de_vocab[unk_token]\n",
    "assert en_vocab[pad_token] == de_vocab[pad_token]\n",
    "\n",
    "unk_index = en_vocab[unk_token]\n",
    "pad_index = en_vocab[pad_token]\n",
    "\n",
    "en_vocab.set_default_index(unk_index)\n",
    "de_vocab.set_default_index(unk_index)\n",
    "\n",
    "def numericalize_example(example, en_vocab, de_vocab):\n",
    "    en_ids = en_vocab.lookup_indices(example[\"en_tokens\"])\n",
    "    de_ids = de_vocab.lookup_indices(example[\"de_tokens\"])\n",
    "    return {\"en_ids\": en_ids, \"de_ids\": de_ids}\n",
    "\n",
    "fn_kwargs = {\n",
    "\"en_vocab\": en_vocab, \n",
    "\"de_vocab\": de_vocab\n",
    "}\n",
    "\n",
    "train_data = train_data.map(numericalize_example, fn_kwargs=fn_kwargs)\n",
    "valid_data = valid_data.map(numericalize_example, fn_kwargs=fn_kwargs)\n",
    "test_data = test_data.map(numericalize_example, fn_kwargs=fn_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_type = \"numpy\"\n",
    "format_columns = [\"en_ids\", \"de_ids\"]\n",
    "\n",
    "train_data = train_data.with_format(\n",
    "    type=data_type, \n",
    "    columns=format_columns, \n",
    "    output_all_columns=True\n",
    ")\n",
    "\n",
    "valid_data = valid_data.with_format(\n",
    "    type=data_type, \n",
    "    columns=format_columns, \n",
    "    output_all_columns=True,\n",
    ")\n",
    "\n",
    "test_data = test_data.with_format(\n",
    "    type=data_type, \n",
    "    columns=format_columns, \n",
    "    output_all_columns=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad_sequence(sequences:list, padding_value:int):\n",
    "    '''将序列填充到等长并返回mindspore张量'''\n",
    "    # Find the length of the longest sequence in the batch\n",
    "    max_length = max(len(seq) for seq in sequences)\n",
    "    padded_sequences:ms.Tensor = ops.full((len(sequences), max_length), padding_value, dtype=ms.int64)\n",
    "    # Copy the sequences into the padded array\n",
    "    for i, seq in enumerate(sequences):\n",
    "        padded_sequences[i, :len(seq)] = ms.tensor(seq).astype(np.int64)\n",
    "    # 换轴，保证输出为时序优先\n",
    "    padded_sequences = padded_sequences.swapaxes(0, 1)\n",
    "    return padded_sequences  \n",
    "\n",
    "def get_collate_fn(pad_index):\n",
    "    \n",
    "    def collate_fn(batch):\n",
    "        batch_en_ids = [example[\"en_ids\"] for example in batch]\n",
    "        batch_de_ids = [example[\"de_ids\"] for example in batch]\n",
    "        batch_en_ids = pad_sequence(batch_en_ids, padding_value=pad_index)\n",
    "        batch_de_ids = pad_sequence(batch_de_ids, padding_value=pad_index)\n",
    "        batch = {\n",
    "            \"en_ids\": batch_en_ids,\n",
    "            \"de_ids\": batch_de_ids,\n",
    "        }\n",
    "        return batch\n",
    "    \n",
    "    return collate_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataLoader:\n",
    "    def __init__(self, source, batch_size, shuffle=False, per_batch_map=None):\n",
    "        self.source = source\n",
    "        self.batch_size = batch_size\n",
    "        self.shuffle = shuffle\n",
    "        self.per_batch_map = per_batch_map\n",
    "        self.indices = np.arange(len(source))\n",
    "        self.current_index = 0\n",
    "\n",
    "        if self.shuffle:\n",
    "            np.random.shuffle(self.indices)\n",
    "\n",
    "    def __iter__(self):\n",
    "        return self\n",
    "\n",
    "    def __next__(self):\n",
    "        if self.current_index >= len(self.source):\n",
    "            self.current_index = 0\n",
    "            raise StopIteration\n",
    "\n",
    "        batch_indices = self.indices[self.current_index:self.current_index + self.batch_size]\n",
    "        batch_data = [self.source[int(i)] for i in batch_indices]\n",
    "\n",
    "        if self.per_batch_map:\n",
    "            batch_data = self.per_batch_map(batch_data)\n",
    "\n",
    "        self.current_index += self.batch_size\n",
    "        return batch_data\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.source) // self.batch_size\n",
    "\n",
    "\n",
    "def get_data_loader(dataset, batch_size, pad_index, shuffle=False):\n",
    "    collate_fn = get_collate_fn(pad_index)\n",
    "    dataloader = DataLoader(dataset, batch_size, shuffle=shuffle, per_batch_map=collate_fn)\n",
    "    \n",
    "    return dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "\n",
    "train_data_loader = get_data_loader(train_data, batch_size, pad_index, shuffle=True)\n",
    "valid_data_loader = get_data_loader(valid_data, batch_size, pad_index)\n",
    "test_data_loader = get_data_loader(test_data, batch_size, pad_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 构建模型\n",
    "\n",
    "接下来，我们将构建模型。与前面的笔记本一样，它由一个*编码器*和一个*解码器*组成，其中编码器将输入/源语句（德语）*编码*成*上下文向量*，解码器然后*解码*这个上下文向量以输出我们的输出/目标语句（英语）。\n",
    "\n",
    "### 位置逐元素前馈层\n",
    "\n",
    "编码器层内的一个主要模块是 *位置逐元素前馈层*。与多头注意层相比，这相对简单。输入从 `hid_dim` 转换为 `pf_dim`，其中 `pf_dim` 通常比 `hid_dim` 大得多。原始的 Transformer 使用了 `hid_dim` 为 512 和 `pf_dim` 为 2048。在将其转换回 `hid_dim` 表示之前，应用了 ReLU 激活函数和 dropout。\n",
    "\n",
    "为什么要使用这个？不幸的是，在论文中没有解释。\n",
    "\n",
    "BERT 使用 [GELU](https://arxiv.org/abs/1606.08415) 激活函数，可以通过将 `ops.relu` 替换为 `ops.gelu` 来使用。为什么使用 GELU？同样，没有解释。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionwiseFeedforwardLayer(nn.Cell):\n",
    "    def __init__(self, hid_dim, pf_dim, dropout):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.fc_1 = nn.Dense(hid_dim, pf_dim)\n",
    "        self.fc_2 = nn.Dense(pf_dim, hid_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "    def construct(self, x):\n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        x = self.dropout(ops.relu(self.fc_1(x)))\n",
    "        #x = [batch size, seq len, pf dim]\n",
    "        x = self.fc_2(x)\n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 多头注意力层\n",
    "\n",
    "Transformer 论文引入的一个关键而新颖的概念是 *多头注意力层*。\n",
    "\n",
    "![](assets/transformer-attention.png)\n",
    "\n",
    "注意力可以被视为 *查询*、*键* 和 *值* 的过程 - 其中查询与键一起用于获取注意力向量（通常是 *softmax* 操作的输出，并且具有在 0 和 1 之间的所有值，这些值的总和为 1），然后使用它来获取值的加权和。\n",
    "\n",
    "Transformer 使用 *缩放点积注意力*，其中通过对它们进行点积，然后应用 softmax 操作并通过 $d_k$ 进行缩放，最后乘以值，将查询和键组合在一起。$d_k$ 是 *头维度*，`head_dim`，我们将很快进一步解释。\n",
    "\n",
    "$$ \\text{Attention}(Q, K, V) = \\text{Softmax} \\big( \\frac{QK^T}{\\sqrt{d_k}} \\big)V $$ \n",
    "\n",
    "这类似于标准的 *点积注意力*，但是通过 $d_k$ 进行了缩放，论文中指出这是为了防止点积结果变得很大，导致梯度变得过小。\n",
    "\n",
    "然而，缩放的点积注意力不仅仅应用于查询、键和值。我们不仅仅执行单一的注意力应用，而是将查询、键和值的 `hid_dim` 分成 $h$ 个 *头*，并且在所有头上并行计算缩放的点积注意力。这意味着我们不是在每次注意力应用中关注一个概念，而是关注 $h$ 个概念。然后，我们将头重新组合成它们的 `hid_dim` 形状，因此每个 `hid_dim` 可能关注 $h$ 个不同的概念。\n",
    "\n",
    "$$ \\text{MultiHead}(Q, K, V) = \\text{Concat}(\\text{head}_1,...,\\text{head}_h)W^O $$\n",
    "\n",
    "$$\\text{head}_i = \\text{Attention}(QW_i^Q, KW_i^K, VW_i^V) $$\n",
    "\n",
    "$W^O$ 是应用在多头注意力层末端的线性层，`fc`。$W^Q, W^K, W^V$ 是线性层 `fc_q`、`fc_k` 和 `fc_v`。\n",
    "\n",
    "遍历模块时，首先我们使用线性层 `fc_q`、`fc_k` 和 `fc_v` 计算 $QW^Q$、$KW^K$ 和 $VW^V$，得到 `Q`、`K` 和 `V`。接下来，我们使用 `.view` 将查询、键和值的 `hid_dim` 分成 `n_heads`，并正确地对它们进行排列，以便可以相互相乘。然后，通过将 `Q` 和 `K` 相乘并通过头维度的平方根进行缩放来计算 *能量*（未经标准化的注意力）。然后我们对能量进行屏蔽，以便我们不会关注任何我们不应该关注的序列元素，然后应用 softmax 和 dropout。然后我们将注意力应用于值头 `V`，然后将 `n_heads` 组合在一起。最后，我们将其乘以 $W^O$，表示为 `fc_o`。\n",
    "\n",
    "请注意，在我们的实现中，键和值的长度始终相同，因此在对 softmax 的输出 `attention` 与 `V` 进行矩阵乘法时，我们将始终具有矩阵乘法的有效维度大小。此乘法使用 `matmul` 进行，当两个张量的维数均 >2 时，将在每个张量的最后两个维度上执行批次矩阵乘法。这将是一个 $[query\\ len, key\\ len] \\times [value\\ len, head\\ dim]$ 批次矩阵乘法，其中提供 $[batch\\ size, n\\ heads, query\\ len, head\\ dim]$ 结果。\n",
    "\n",
    "一开始看起来有些奇怪的是，dropout 直接应用于注意力。这意味着我们的注意力向量很可能不会总和为 1，我们可能会全神贯注于一个token，但是由于 dropout 将注意力在该token上的注意力设置为 0，因此全神贯注于一个token的可能性很大。论文中从未解释过这一点，也没有提到过，但它被 [官方实现](https://github.com/tensorflow/tensor2tensor/) 以及之后的每个 Transformer 实现所使用，[包括 BERT](https://github.com/google-research/bert/)。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttentionLayer(nn.Cell):\n",
    "    def __init__(self, hid_dim, n_heads, dropout):\n",
    "        super().__init__()\n",
    "        \n",
    "        assert hid_dim % n_heads == 0\n",
    "        \n",
    "        self.hid_dim = hid_dim\n",
    "        self.n_heads = n_heads\n",
    "        self.head_dim = hid_dim // n_heads\n",
    "        \n",
    "        self.fc_q = nn.Dense(hid_dim, hid_dim)\n",
    "        self.fc_k = nn.Dense(hid_dim, hid_dim)\n",
    "        self.fc_v = nn.Dense(hid_dim, hid_dim)\n",
    "        \n",
    "        self.fc_o = nn.Dense(hid_dim, hid_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "        self.scale = ops.sqrt(ms.Tensor([self.head_dim]).astype(ms.float32))\n",
    "        \n",
    "    def construct(self, query, key, value, mask = None):\n",
    "        \n",
    "        batch_size = query.shape[0]\n",
    "        \n",
    "        #query = [batch size, query len, hid dim]\n",
    "        #key = [batch size, key len, hid dim]\n",
    "        #value = [batch size, value len, hid dim]\n",
    "                \n",
    "        Q = self.fc_q(query)\n",
    "        K = self.fc_k(key)\n",
    "        V = self.fc_v(value)\n",
    "        \n",
    "        #Q = [batch size, query len, hid dim]\n",
    "        #K = [batch size, key len, hid dim]\n",
    "        #V = [batch size, value len, hid dim]\n",
    "                \n",
    "        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        \n",
    "        #Q = [batch size, n heads, query len, head dim]\n",
    "        #K = [batch size, n heads, key len, head dim]\n",
    "        #V = [batch size, n heads, value len, head dim]\n",
    "                \n",
    "        energy = ops.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale\n",
    "        \n",
    "        #energy = [batch size, n heads, query len, key len]\n",
    "        \n",
    "        if mask is not None:\n",
    "            energy = energy.masked_fill(mask == 0, -1e10)\n",
    "        \n",
    "        attention = ops.softmax(energy, axis = -1)\n",
    "                \n",
    "        #attention = [batch size, n heads, query len, key len]\n",
    "                \n",
    "        x = ops.matmul(self.dropout(attention), V)\n",
    "        \n",
    "        #x = [batch size, n heads, query len, head dim]\n",
    "        \n",
    "        x = x.permute(0, 2, 1, 3)\n",
    "        \n",
    "        #x = [batch size, query len, n heads, head dim]\n",
    "        \n",
    "        x = x.view(batch_size, -1, self.hid_dim)\n",
    "        \n",
    "        #x = [batch size, query len, hid dim]\n",
    "        \n",
    "        x = self.fc_o(x)\n",
    "        \n",
    "        #x = [batch size, query len, hid dim]\n",
    "        \n",
    "        return x, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 编码器层\n",
    "\n",
    "编码器层包含了编码器的所有关键部分。我们首先将源句子和其掩码传递到 *多头注意力层*，然后对其执行 dropout，应用残差连接，并通过 [层归一化](https://arxiv.org/abs/1607.06450) 层。然后，我们将其传递到 *位置前馈* 层，再次应用 dropout，残差连接，然后进行层归一化，以获得此层的输出，该输出输入到下一层。各层之间的参数不共享。\n",
    "\n",
    "多头注意力层由编码器层用于关注源句子，即它在自身上计算和应用注意力，而不是在另一个序列上，因此我们称其为 *自注意力*。\n",
    "\n",
    "[这篇文章](https://mlexplained.com/2018/01/13/weight-normalization-and-layer-normalization-explained-normalization-in-deep-learning-part-2/) 对层归一化进行了更详细的解释，但要点是它对特征的值进行归一化，即在隐藏维度上进行，因此每个特征的均值为 0，标准差为 1。这使得具有更多层的神经网络，如 Transformer，更容易训练。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderLayer(nn.Cell):\n",
    "    def __init__(self, \n",
    "                 hid_dim, \n",
    "                 n_heads, \n",
    "                 pf_dim,  \n",
    "                 dropout, \n",
    "                 ):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.self_attn_layer_norm = nn.LayerNorm((hid_dim,))\n",
    "        self.ff_layer_norm = nn.LayerNorm((hid_dim,))\n",
    "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout)\n",
    "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, \n",
    "                                                                     pf_dim, \n",
    "                                                                     dropout)\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "    def construct(self, src, src_mask):\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        #src_mask = [batch size, 1, 1, src len] \n",
    "                \n",
    "        #self attention\n",
    "        _src, _ = self.self_attention(src, src, src, src_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        src = self.self_attn_layer_norm(src + self.dropout(_src))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        #positionwise feedforward\n",
    "        _src = self.positionwise_feedforward(src)\n",
    "        \n",
    "        #dropout, residual and layer norm\n",
    "        src = self.ff_layer_norm(src + self.dropout(_src))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        return src"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### 编码器\n",
    "\n",
    "与ConvSeq2Seq模型类似，Transformer的编码器并不试图将整个源语句$X=(x_1, ..., x_n)$压缩成一个单一的上下文向量$z$。相反，它生成一个上下文向量序列$Z=(z_1, ..., z_n)$。因此，如果我们的输入序列有5个标记，我们将得到$Z=(z_1, z_2, z_3, z_4, z_5)$。为什么我们称这个为上下文向量序列而不是隐藏状态序列呢？RNN中时间$t$的隐藏状态只看到标记$x_t$和所有之前的标记。然而，这里的每个上下文向量都看到了输入序列中所有位置的所有标记。\n",
    "\n",
    "![](assets/transformer-encoder.png)\n",
    "\n",
    "首先，标记通过一个标准的嵌入层。接下来，由于该模型没有循环，它对序列中标记的顺序一无所知。我们通过使用第二个嵌入层来解决这个问题，称为*位置嵌入层*。这是一个标准的嵌入层，其中输入不是标记本身，而是标记在序列中的位置，从第一个标记开始，即`<sos>`（序列开始）标记，在位置0。位置嵌入具有100的“词汇表”大小，这意味着我们的模型可以接受长达100个标记的句子。如果要处理更长的句子，可以增加这个值。\n",
    "\n",
    "然后，token和位置嵌入进行逐元素求和，得到一个包含关于标记及其在序列中的位置信息的向量。然而，在它们相加之前，token嵌入会乘以一个缩放因子，即$\\sqrt{d_{model}}$，其中$d_{model}$是隐藏维度大小，`hid_dim`。据说这样可以减小嵌入的方差，而没有这个缩放因子，该模型很难可靠地训练。然后对组合嵌入应用了丢弃。\n",
    "\n",
    "然后，将组合嵌入通过$N$个*编码器层*以获得$Z$，然后输出，可以由解码器使用。\n",
    "\n",
    "源掩码`src_mask`的形状与源语句相同，但是在源语句中的标记不是`<pad>`标记时，它的值为1，而在是`<pad>`标记时为0。在编码器层中用于屏蔽多头注意机制，该机制用于计算并应用于源语句的注意力，以便模型不关注`<pad>`标记，这些标记不包含有用的信息。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Cell):\n",
    "    def __init__(self, \n",
    "                 input_dim, \n",
    "                 hid_dim, \n",
    "                 n_layers, \n",
    "                 n_heads, \n",
    "                 pf_dim,\n",
    "                 dropout, \n",
    "                 max_length = 100):\n",
    "        super().__init__()\n",
    "\n",
    "        self.tok_embedding = nn.Embedding(input_dim, hid_dim)\n",
    "        self.pos_embedding = nn.Embedding(max_length, hid_dim)\n",
    "        \n",
    "        self.layers = nn.CellList([EncoderLayer(hid_dim, \n",
    "                                                  n_heads, \n",
    "                                                  pf_dim,\n",
    "                                                  dropout, \n",
    "                                                  ) \n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "        self.scale = ops.sqrt(ms.Tensor([hid_dim]).astype(ms.float32))\n",
    "        \n",
    "    def construct(self, src, src_mask, pos):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        \n",
    "        batch_size = src.shape[0]\n",
    "        src_len = src.shape[1]\n",
    "        \n",
    "        # pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n",
    "        \n",
    "        #pos = [batch size, src len]\n",
    "        \n",
    "        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        for layer in self.layers:\n",
    "            src = layer(src, src_mask)\n",
    "            \n",
    "        #src = [batch size, src len, hid dim]\n",
    "            \n",
    "        return src"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 解码器层\n",
    "\n",
    "如前所述，解码器层与编码器层相似，只是现在它有两个多头注意力层，`self_attention` 和 `encoder_attention`。\n",
    "\n",
    "第一个执行自注意力，与编码器一样，使用迄今为止的解码器表示作为查询、键和值。然后是 dropout、残差连接和层归一化。这个 `self_attention` 层使用目标序列掩码 `trg_mask`，以防止解码器通过关注当前处理的token之前的token来“作弊”，因为它同时处理目标句子中的所有token。\n",
    "\n",
    "第二个是我们实际上如何将编码的源句子 `enc_src` 输入到我们的解码器中。在这个多头注意力层中，查询是解码器表示，而键和值是编码器表示。在这里，源掩码 `src_mask` 用于防止多头注意力层关注源句子中的 `<pad>` token。然后是 dropout、残差连接和层归一化层。\n",
    "\n",
    "最后，我们将其通过位置前馈层，再经过一系列的 dropout、残差连接和层归一化。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderLayer(nn.Cell):\n",
    "    def __init__(self, \n",
    "                 hid_dim, \n",
    "                 n_heads, \n",
    "                 pf_dim, \n",
    "                 dropout, \n",
    "                 ):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.self_attn_layer_norm = nn.LayerNorm((hid_dim,))\n",
    "        self.enc_attn_layer_norm = nn.LayerNorm((hid_dim,))\n",
    "        self.ff_layer_norm = nn.LayerNorm((hid_dim,))\n",
    "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout)\n",
    "        self.encoder_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout)\n",
    "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, \n",
    "                                                                     pf_dim, \n",
    "                                                                     dropout)\n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "    def construct(self, trg, enc_src, trg_mask, src_mask):\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        \n",
    "        #self attention\n",
    "        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        trg = self.self_attn_layer_norm(trg + self.dropout(_trg))\n",
    "            \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "            \n",
    "        #encoder attention\n",
    "        _trg, attention = self.encoder_attention(trg, enc_src, enc_src, src_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))\n",
    "                    \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        \n",
    "        #positionwise feedforward\n",
    "        _trg = self.positionwise_feedforward(trg)\n",
    "        \n",
    "        #dropout, residual and layer norm\n",
    "        trg = self.ff_layer_norm(trg + self.dropout(_trg))\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        return trg, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 解码器\n",
    "\n",
    "解码器的目标是接受源句子的编码表示 $Z$，并将其转换为目标句子中的预测token $\\hat{Y}$。然后，我们将 $\\hat{Y}$ 与目标句子中的实际token $Y$ 进行比较，计算我们的损失，该损失将用于计算参数的梯度，然后使用优化器更新权重以改善我们的预测。\n",
    "\n",
    "![transformer-decoder](assets/transformer-decoder.png)\n",
    "\n",
    "解码器与编码器相似，但现在它有两个多头注意力层。一个是对目标序列的 *掩码多头注意力层*，另一个是使用解码器表示作为查询以及编码器表示作为键和值的多头注意力层。\n",
    "\n",
    "解码器使用位置嵌入，并通过按元素求和的方式与缩放嵌入的目标token相结合，然后进行 dropout。再次提到，我们的位置编码具有 100 的 \"词汇量\"，这意味着它们可以接受长达 100 个token的序列。如果需要，这个长度可以增加。\n",
    "\n",
    "组合后的嵌入然后通过 $N$ 个解码器层传递，同时还有编码的源 `enc_src`、源掩码和目标掩码。请注意，编码器的层数不必等于解码器的层数，即使它们都用 $N$ 表示。\n",
    "\n",
    "第 $N$ 层后的解码器表示然后通过线性层 `fc_out`。在 PyTorch 中，softmax 操作包含在我们的损失函数中，因此我们在这里不需要明确使用 softmax 层。\n",
    "\n",
    "除了使用源掩码（与我们在编码器中使用的方式相同，以防止模型关注 `<pad>` token），我们还使用目标掩码。这将在封装编码器和解码器的 `Seq2Seq` 模型中进一步解释，但其要点是它执行与卷积序列到序列模型中解码器填充类似的操作。由于我们一次并行处理所有目标token，我们需要一种方法来阻止解码器通过简单地“查看”目标序列中的下一个token并将其输出来“作弊”。\n",
    "\n",
    "我们的解码器层还输出了归一化的注意力值，因此我们稍后可以绘制它们，了解我们的模型实际上关注的是什么。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder(nn.Cell):\n",
    "    def __init__(self, \n",
    "                 output_dim, \n",
    "                 hid_dim, \n",
    "                 n_layers, \n",
    "                 n_heads, \n",
    "                 pf_dim, \n",
    "                 dropout, \n",
    "                 max_length = 100):\n",
    "        super().__init__()\n",
    "    \n",
    "        \n",
    "        self.tok_embedding = nn.Embedding(output_dim, hid_dim)\n",
    "        self.pos_embedding = nn.Embedding(max_length, hid_dim)\n",
    "        \n",
    "        self.layers = nn.CellList([DecoderLayer(hid_dim, \n",
    "                                                  n_heads, \n",
    "                                                  pf_dim, \n",
    "                                                  dropout, \n",
    "                                                  )\n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "        self.fc_out = nn.Dense(hid_dim, output_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(p=dropout)\n",
    "        \n",
    "        self.scale = ops.sqrt(ms.Tensor([hid_dim]).astype(ms.float32))\n",
    "        \n",
    "    def construct(self, trg, enc_src, trg_mask, src_mask, pos):\n",
    "        \n",
    "        #trg = [batch size, trg len]\n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "                \n",
    "        batch_size = trg.shape[0]\n",
    "        trg_len = trg.shape[1]\n",
    "        \n",
    "        # pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n",
    "                            \n",
    "        #pos = [batch size, trg len]\n",
    "            \n",
    "        trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))\n",
    "                \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        \n",
    "        for layer in self.layers:\n",
    "            trg, attention = layer(trg, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        output = self.fc_out(trg)\n",
    "        \n",
    "        #output = [batch size, trg len, output dim]\n",
    "            \n",
    "        return output, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seq2Seq\n",
    "\n",
    "最后，我们有 `Seq2Seq` 模块，它封装了编码器和解码器，并处理了掩码的创建。\n",
    "\n",
    "源掩码是通过检查源序列是否不等于 `<pad>` token而创建的。其中token不是 `<pad>` token时，它为 1；否则为 0。然后对其进行 unsqueeze 操作，以便在将掩码应用于 `energy` 时可以正确广播。`energy` 的形状为 **_[batch size, n heads, seq len, seq len]_**。\n",
    "\n",
    "目标掩码稍微复杂一些。首先，我们为 `<pad>` token创建一个掩码，就像我们对源掩码所做的一样。接下来，我们使用 `torch.tril` 创建一个“后续”掩码 `trg_sub_mask`。这将创建一个对角矩阵，其中对角线以上的元素将为零，对角线以下的元素将设置为输入张量的值。在这种情况下，输入张量将是一个用一填充的张量。因此，我们的 `trg_sub_mask` 将类似于这样（对于包含 5 个token的目标）：\n",
    "\n",
    "$$\\begin{matrix}\n",
    "1 & 0 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 1 & 0\\\\\n",
    "1 & 1 & 1 & 1 & 1\\\\\n",
    "\\end{matrix}$$\n",
    "\n",
    "这显示了每个目标token（行）被允许查看的内容（列）。第一个目标token的掩码为 **_[1, 0, 0, 0, 0]_**，这意味着它只能查看第一个目标token。第二个目标token的掩码为 **_[1, 1, 0, 0, 0]_**，这意味着它可以查看第一个和第二个目标token。\n",
    "\n",
    "然后，将“后续”掩码与填充掩码进行逻辑与操作，这将两个掩码组合在一起，确保后续token和填充token都不能被关注到。例如，如果最后两个token是 `<pad>` token，则掩码将如下所示：\n",
    "\n",
    "$$\\begin{matrix}\n",
    "1 & 0 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "\\end{matrix}$$\n",
    "\n",
    "创建掩码后，它们与编码器和解码器以及源和目标句子一起用于获取我们的预测目标句子 `output`，以及解码器对源序列的注意力。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Seq2Seq(nn.Cell):\n",
    "    def __init__(self, \n",
    "                 encoder:Encoder, \n",
    "                 decoder:Decoder, \n",
    "                 src_pad_idx, \n",
    "                 trg_pad_idx, \n",
    "                 ):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.src_pad_idx = src_pad_idx\n",
    "        self.trg_pad_idx = trg_pad_idx     \n",
    "    def make_src_mask(self, src):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        \n",
    "        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)\n",
    "\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "\n",
    "        return src_mask\n",
    "    \n",
    "    def make_trg_mask(self, trg):\n",
    "        \n",
    "        #trg = [batch size, trg len]\n",
    "        \n",
    "        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(2)\n",
    "        \n",
    "        #trg_pad_mask = [batch size, 1, 1, trg len]\n",
    "        \n",
    "        trg_len = trg.shape[1]\n",
    "        \n",
    "        trg_sub_mask = ops.tril(ops.ones((trg_len, trg_len),)).bool()\n",
    "        \n",
    "        #trg_sub_mask = [trg len, trg len]\n",
    "            \n",
    "        trg_mask = trg_pad_mask & trg_sub_mask\n",
    "        \n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        \n",
    "        return trg_mask\n",
    "\n",
    "    def construct(self, src, trg, src_mask, trg_mask, enc_pos, dec_pos):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        #trg = [batch size, trg len]\n",
    "                \n",
    "        # src_mask = self.make_src_mask(src)\n",
    "        # trg_mask = self.make_trg_mask(trg)\n",
    "        \n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        \n",
    "        enc_src = self.encoder(src, src_mask, enc_pos)\n",
    "        \n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "                \n",
    "        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask, dec_pos)\n",
    "        \n",
    "        #output = [batch size, trg len, output dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        return output, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练 Seq2Seq 模型\n",
    "\n",
    "现在我们可以定义我们的编码器和解码器了。这个模型比今天研究中使用的 Transformer 要小得多，但可以在单个 GPU 上快速运行。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = len(de_vocab)\n",
    "OUTPUT_DIM = len(en_vocab)\n",
    "\n",
    "HID_DIM = 256\n",
    "ENC_LAYERS = 3\n",
    "DEC_LAYERS = 3\n",
    "ENC_HEADS = 8\n",
    "DEC_HEADS = 8\n",
    "ENC_PF_DIM = 512\n",
    "DEC_PF_DIM = 512\n",
    "ENC_DROPOUT = 0.1\n",
    "DEC_DROPOUT = 0.1\n",
    "\n",
    "enc = Encoder(INPUT_DIM, \n",
    "              HID_DIM, \n",
    "              ENC_LAYERS, \n",
    "              ENC_HEADS, \n",
    "              ENC_PF_DIM, \n",
    "              ENC_DROPOUT, \n",
    "              )\n",
    "\n",
    "dec = Decoder(OUTPUT_DIM, \n",
    "              HID_DIM, \n",
    "              DEC_LAYERS, \n",
    "              DEC_HEADS, \n",
    "              DEC_PF_DIM, \n",
    "              DEC_DROPOUT, \n",
    "              )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后，使用它们来定义我们的整个序列到序列封装模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRC_PAD_IDX = de_vocab.get_stoi()[pad_token]\n",
    "TRG_PAD_IDX = en_vocab.get_stoi()[pad_token]\n",
    "\n",
    "model = Seq2Seq(enc, dec, SRC_PAD_IDX, TRG_PAD_IDX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以检查参数的数量，注意到它明显少于卷积序列到序列模型的 3700 万个参数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 9,038,341 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.get_parameters() if p.requires_grad)\n",
    "\n",
    "print(f'The model has {count_parameters(model):,} trainable parameters')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "论文没有提到使用了哪种权重初始化方案，然而，Xavier 均匀分布似乎在 Transformer 模型中很常见，因此我们在这里使用它。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seq2Seq<\n",
       "  (encoder): Encoder<\n",
       "    (tok_embedding): Embedding<vocab_size=7853, embedding_size=256, use_one_hot=False, embedding_table=Parameter (name=encoder.tok_embedding.embedding_table, shape=(7853, 256), dtype=Float32, requires_grad=True), dtype=Float32, padding_idx=None>\n",
       "    (pos_embedding): Embedding<vocab_size=100, embedding_size=256, use_one_hot=False, embedding_table=Parameter (name=encoder.pos_embedding.embedding_table, shape=(100, 256), dtype=Float32, requires_grad=True), dtype=Float32, padding_idx=None>\n",
       "    (layers): CellList<\n",
       "      (0): EncoderLayer<\n",
       "        (self_attn_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=encoder.layers.0.self_attn_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=encoder.layers.0.self_attn_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (ff_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=encoder.layers.0.ff_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=encoder.layers.0.ff_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (self_attention): MultiHeadAttentionLayer<\n",
       "          (fc_q): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_k): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_v): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_o): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (positionwise_feedforward): PositionwiseFeedforwardLayer<\n",
       "          (fc_1): Dense<input_channels=256, output_channels=512, has_bias=True>\n",
       "          (fc_2): Dense<input_channels=512, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (dropout): Dropout<p=0.1>\n",
       "        >\n",
       "      (1): EncoderLayer<\n",
       "        (self_attn_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=encoder.layers.1.self_attn_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=encoder.layers.1.self_attn_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (ff_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=encoder.layers.1.ff_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=encoder.layers.1.ff_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (self_attention): MultiHeadAttentionLayer<\n",
       "          (fc_q): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_k): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_v): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_o): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (positionwise_feedforward): PositionwiseFeedforwardLayer<\n",
       "          (fc_1): Dense<input_channels=256, output_channels=512, has_bias=True>\n",
       "          (fc_2): Dense<input_channels=512, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (dropout): Dropout<p=0.1>\n",
       "        >\n",
       "      (2): EncoderLayer<\n",
       "        (self_attn_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=encoder.layers.2.self_attn_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=encoder.layers.2.self_attn_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (ff_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=encoder.layers.2.ff_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=encoder.layers.2.ff_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (self_attention): MultiHeadAttentionLayer<\n",
       "          (fc_q): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_k): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_v): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_o): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (positionwise_feedforward): PositionwiseFeedforwardLayer<\n",
       "          (fc_1): Dense<input_channels=256, output_channels=512, has_bias=True>\n",
       "          (fc_2): Dense<input_channels=512, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (dropout): Dropout<p=0.1>\n",
       "        >\n",
       "      >\n",
       "    (dropout): Dropout<p=0.1>\n",
       "    >\n",
       "  (decoder): Decoder<\n",
       "    (tok_embedding): Embedding<vocab_size=5893, embedding_size=256, use_one_hot=False, embedding_table=Parameter (name=decoder.tok_embedding.embedding_table, shape=(5893, 256), dtype=Float32, requires_grad=True), dtype=Float32, padding_idx=None>\n",
       "    (pos_embedding): Embedding<vocab_size=100, embedding_size=256, use_one_hot=False, embedding_table=Parameter (name=decoder.pos_embedding.embedding_table, shape=(100, 256), dtype=Float32, requires_grad=True), dtype=Float32, padding_idx=None>\n",
       "    (layers): CellList<\n",
       "      (0): DecoderLayer<\n",
       "        (self_attn_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=decoder.layers.0.self_attn_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=decoder.layers.0.self_attn_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (enc_attn_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=decoder.layers.0.enc_attn_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=decoder.layers.0.enc_attn_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (ff_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=decoder.layers.0.ff_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=decoder.layers.0.ff_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (self_attention): MultiHeadAttentionLayer<\n",
       "          (fc_q): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_k): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_v): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_o): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (encoder_attention): MultiHeadAttentionLayer<\n",
       "          (fc_q): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_k): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_v): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_o): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (positionwise_feedforward): PositionwiseFeedforwardLayer<\n",
       "          (fc_1): Dense<input_channels=256, output_channels=512, has_bias=True>\n",
       "          (fc_2): Dense<input_channels=512, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (dropout): Dropout<p=0.1>\n",
       "        >\n",
       "      (1): DecoderLayer<\n",
       "        (self_attn_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=decoder.layers.1.self_attn_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=decoder.layers.1.self_attn_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (enc_attn_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=decoder.layers.1.enc_attn_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=decoder.layers.1.enc_attn_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (ff_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=decoder.layers.1.ff_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=decoder.layers.1.ff_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (self_attention): MultiHeadAttentionLayer<\n",
       "          (fc_q): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_k): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_v): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_o): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (encoder_attention): MultiHeadAttentionLayer<\n",
       "          (fc_q): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_k): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_v): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_o): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (positionwise_feedforward): PositionwiseFeedforwardLayer<\n",
       "          (fc_1): Dense<input_channels=256, output_channels=512, has_bias=True>\n",
       "          (fc_2): Dense<input_channels=512, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (dropout): Dropout<p=0.1>\n",
       "        >\n",
       "      (2): DecoderLayer<\n",
       "        (self_attn_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=decoder.layers.2.self_attn_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=decoder.layers.2.self_attn_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (enc_attn_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=decoder.layers.2.enc_attn_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=decoder.layers.2.enc_attn_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (ff_layer_norm): LayerNorm<normalized_shape=(256,), begin_norm_axis=-1, begin_params_axis=-1, gammaParameter (name=decoder.layers.2.ff_layer_norm.gamma, shape=(256,), dtype=Float32, requires_grad=True), beta=Parameter (name=decoder.layers.2.ff_layer_norm.beta, shape=(256,), dtype=Float32, requires_grad=True)>\n",
       "        (self_attention): MultiHeadAttentionLayer<\n",
       "          (fc_q): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_k): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_v): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_o): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (encoder_attention): MultiHeadAttentionLayer<\n",
       "          (fc_q): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_k): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_v): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (fc_o): Dense<input_channels=256, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (positionwise_feedforward): PositionwiseFeedforwardLayer<\n",
       "          (fc_1): Dense<input_channels=256, output_channels=512, has_bias=True>\n",
       "          (fc_2): Dense<input_channels=512, output_channels=256, has_bias=True>\n",
       "          (dropout): Dropout<p=0.1>\n",
       "          >\n",
       "        (dropout): Dropout<p=0.1>\n",
       "        >\n",
       "      >\n",
       "    (fc_out): Dense<input_channels=256, output_channels=5893, has_bias=True>\n",
       "    (dropout): Dropout<p=0.1>\n",
       "    >\n",
       "  >"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mindspore import Parameter\n",
    "from mindspore.common.initializer import initializer, XavierUniform\n",
    "\n",
    "def init_weights(m:nn.Cell):\n",
    "    for name, param in m.parameters_and_names():\n",
    "        if \"weight\" in name:\n",
    "            param.set_data(Parameter(initializer(XavierUniform(), param.shape, ms.float32)))\n",
    "\n",
    "model.apply(init_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "原始 Transformer 论文中使用的优化器是具有“预热”和“冷却”周期的 Adam 学习率。BERT 和其他 Transformer 模型使用具有固定学习率的 Adam，因此我们将实现这一点。查看[此链接](http://nlp.seas.harvard.edu/2018/04/03/attention.html#optimizer)以获取有关原始 Transformer 学习率计划的更多详细信息。\n",
    "\n",
    "请注意，学习率需要低于 Adam 默认值，否则学习会不稳定。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.0005\n",
    "\n",
    "optimizer = nn.Adam(model.trainable_params(), learning_rate=LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来，我们定义我们的损失函数，确保忽略计算 `<pad>` token上的损失。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss(ignore_index=pad_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后，我们定义训练循环。这与前面教程中使用的循环完全相同。\n",
    "\n",
    "由于我们希望模型预测 `<eos>` token但不将其作为输入传递到我们的模型中，因此我们只需将序列末尾的 `<eos>` token切掉。因此：\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{trg} &= [sos, x_1, x_2, x_3, eos]\\\\\n",
    "\\text{trg[:-1]} &= [sos, x_1, x_2, x_3]\n",
    "\\end{align*}$$\n",
    "\n",
    "这里的 $x_i$ 表示实际的目标序列元素。然后，我们将其馈送到模型中，以获得一个预测序列，希望能够预测 `<eos>` token：\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{output} &= [y_1, y_2, y_3, eos]\n",
    "\\end{align*}$$\n",
    "\n",
    "这里的 $y_i$ 表示预测的目标序列元素。然后，我们使用原始的 `trg` 张量计算损失，其中 `<sos>` token被切掉，仅保留 `<eos>` token：\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{output} &= [y_1, y_2, y_3, eos]\\\\\n",
    "\\text{trg[1:]} &= [x_1, x_2, x_3, eos]\n",
    "\\end{align*}$$\n",
    "\n",
    "然后，我们计算损失并按标准更新参数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_fn(src, trg, src_mask, trg_mask, enc_pos, dec_pos):\n",
    "    # src = [batch size, trg len]\n",
    "    # trg = [batch size, trg len]\n",
    "    output, _ = model(src, trg[:,:-1], src_mask, trg_mask, enc_pos, dec_pos, )  \n",
    "    output_dim = output.shape[-1]\n",
    "        \n",
    "    output = output.view(-1, output_dim)\n",
    "    trg = trg[:,1:].view(-1)\n",
    "            \n",
    "    # output = [batch size * trg len - 1, output dim]\n",
    "    # trg = [batch size * trg len - 1]\n",
    "    loss = criterion(output, trg.astype(ms.int32))\n",
    "    return loss\n",
    "\n",
    "grad_fn = ms.value_and_grad(forward_fn, grad_position=None, weights=model.trainable_params())\n",
    "\n",
    "\n",
    "\n",
    "def train(iterator, optimizer, clip):\n",
    "    \n",
    "    model.set_train(True)\n",
    "    epoch_loss = 0\n",
    "    \n",
    "    for i, batch in enumerate(tqdm(iterator)):\n",
    "        \n",
    "        src = batch[\"de_ids\"]\n",
    "        trg = batch[\"en_ids\"]\n",
    "        src = src.swapaxes(0,1)\n",
    "        trg = trg.swapaxes(0,1)\n",
    "        # src = [batch size, trg len]\n",
    "        # trg = [batch size, trg len]\n",
    "        batch_size = src.shape[0]\n",
    "        src_len = src.shape[1]\n",
    "        trg_len = trg.shape[1]\n",
    "\n",
    "        enc_pos = ops.arange(0, src_len).unsqueeze(0).tile((batch_size, 1))\n",
    "        dec_pos = ops.arange(0, trg_len - 1).unsqueeze(0).tile((batch_size, 1))\n",
    "        \n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        src_mask = model.make_src_mask(src)\n",
    "        trg_mask = model.make_trg_mask(trg[:,:-1])\n",
    "        \n",
    "        loss, grads = grad_fn(src, trg, src_mask, trg_mask, enc_pos, dec_pos)\n",
    "        \n",
    "        # grads = ops.clip_by_norm(grads, max_norm=clip)\n",
    "        optimizer(grads)\n",
    "        \n",
    "        epoch_loss += float(loss)\n",
    "        \n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "评估循环与训练循环相同，只是没有梯度计算和参数更新。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_fn(model:Seq2Seq, iterator, criterion):\n",
    "    model.set_train(False)\n",
    "    epoch_loss = 0\n",
    "    \n",
    "    for i, batch in enumerate(tqdm(iterator)):\n",
    "        src = batch[\"de_ids\"]\n",
    "        trg = batch[\"en_ids\"]\n",
    "        src = src.swapaxes(0,1)\n",
    "        trg = trg.swapaxes(0,1)\n",
    "        # src = [batch size, trg len]\n",
    "        # trg = [batch size, trg len]\n",
    "        batch_size = src.shape[0]\n",
    "        src_len = src.shape[1]\n",
    "        trg_len = trg.shape[1]\n",
    "\n",
    "        enc_pos = ops.arange(0, src_len).unsqueeze(0).tile((batch_size, 1))\n",
    "        dec_pos = ops.arange(0, trg_len - 1).unsqueeze(0).tile((batch_size, 1))\n",
    "        \n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        src_mask = model.make_src_mask(src)\n",
    "        trg_mask = model.make_trg_mask(trg[:,:-1])\n",
    "\n",
    "        output, _ = model(src, trg[:,:-1], src_mask, trg_mask, enc_pos, dec_pos, )  \n",
    "        \n",
    "        #output = [batch size, trg len - 1, output dim]\n",
    "        #trg = [batch size, trg len]\n",
    "        \n",
    "        output_dim = output.shape[-1]\n",
    "        \n",
    "        output = output.view(-1, output_dim)\n",
    "        trg = trg[:,1:].view(-1)\n",
    "        \n",
    "        #output = [batch size * trg len - 1, output dim]\n",
    "        #trg = [batch size * trg len - 1]\n",
    "        \n",
    "        loss = criterion(output, trg.astype(ms.int32))\n",
    "\n",
    "        epoch_loss += float(loss)\n",
    "    \n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后，我们定义一个小函数，用于告诉我们一个 epoch 需要多长时间。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01 | Time: 0m 55s\n",
      "\tTrain Loss: 4.217 | Train PPL:  67.802\n",
      "\t Val. Loss: 3.568 |  Val. PPL:  35.452\n",
      "Epoch: 02 | Time: 0m 58s\n",
      "\tTrain Loss: 2.839 | Train PPL:  17.098\n",
      "\t Val. Loss: 2.725 |  Val. PPL:  15.258\n",
      "Epoch: 03 | Time: 0m 57s\n",
      "\tTrain Loss: 2.227 | Train PPL:   9.271\n",
      "\t Val. Loss: 2.360 |  Val. PPL:  10.592\n",
      "Epoch: 04 | Time: 0m 56s\n",
      "\tTrain Loss: 1.864 | Train PPL:   6.451\n",
      "\t Val. Loss: 2.176 |  Val. PPL:   8.812\n",
      "Epoch: 05 | Time: 0m 56s\n",
      "\tTrain Loss: 1.613 | Train PPL:   5.019\n",
      "\t Val. Loss: 2.059 |  Val. PPL:   7.834\n",
      "Epoch: 06 | Time: 0m 55s\n",
      "\tTrain Loss: 1.422 | Train PPL:   4.145\n",
      "\t Val. Loss: 2.001 |  Val. PPL:   7.400\n",
      "Epoch: 07 | Time: 0m 55s\n",
      "\tTrain Loss: 1.277 | Train PPL:   3.586\n",
      "\t Val. Loss: 1.989 |  Val. PPL:   7.312\n",
      "Epoch: 08 | Time: 0m 57s\n",
      "\tTrain Loss: 1.147 | Train PPL:   3.148\n",
      "\t Val. Loss: 1.993 |  Val. PPL:   7.341\n",
      "Epoch: 09 | Time: 0m 57s\n",
      "\tTrain Loss: 1.042 | Train PPL:   2.835\n",
      "\t Val. Loss: 1.999 |  Val. PPL:   7.381\n",
      "Epoch: 10 | Time: 0m 58s\n",
      "\tTrain Loss: 0.950 | Train PPL:   2.585\n",
      "\t Val. Loss: 2.028 |  Val. PPL:   7.600\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 10\n",
    "CLIP = 1\n",
    "\n",
    "best_valid_loss = float('inf')\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "    \n",
    "    start_time = time.time()\n",
    "    \n",
    "    train_loss = train(train_data_loader, optimizer, CLIP)\n",
    "    valid_loss = evaluate_fn(model, valid_data_loader, criterion)\n",
    "    \n",
    "    end_time = time.time()\n",
    "    \n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    if valid_loss < best_valid_loss:\n",
    "        best_valid_loss = valid_loss\n",
    "        ms.save_checkpoint(model, f\"./tut6-model.ckpt\") \n",
    "    \n",
    "    print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {np.exp(train_loss):7.3f}')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {np.exp(valid_loss):7.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们加载了“最佳”参数，并成功实现了比所有先前模型更好的测试困惑度。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Test Loss: 2.037 | Test PPL:   7.665 |\n"
     ]
    }
   ],
   "source": [
    "ms.load_checkpoint(f\"tut6-model.ckpt\", model)\n",
    "\n",
    "test_loss = evaluate_fn(model, test_data_loader, criterion)\n",
    "\n",
    "print(f\"| Test Loss: {test_loss:.3f} | Test PPL: {np.exp(test_loss):7.3f} |\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，我们可以使用下面的 `translate_sentence` 函数从我们的模型中获得翻译。\n",
    "\n",
    "所采取的步骤是:\n",
    "- 对源句子进行标记（如果尚未标记化，即字符串）\n",
    "- 添加 `<sos>` 和 `<eos>` 标记\n",
    "- 数值化源句子\n",
    "- 将其转换为张量并添加一个批处理维度\n",
    "- 创建源句子掩码\n",
    "- 将源句子和掩码馈送到编码器\n",
    "- 创建一个列表来保存输出句子，其初始化为 `<sos>` 标记\n",
    "- 在我们没有达到最大长度的情况下\n",
    "  - 将当前输出句子预测转换为带有批处理维度的张量\n",
    "  - 创建目标句子掩码\n",
    "  - 将当前输出、编码器输出和两个掩码放入解码器\n",
    "  - 从解码器中获取下一个输出标记的预测以及注意力\n",
    "  - 将预测添加到当前输出句子预测\n",
    "  - 如果预测是 `<eos>` 标记，则中断\n",
    "- 将输出句子从索引转换为标记\n",
    "- 返回输出句子（删除 `<sos>` 标记）和最后一层的注意力\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def translate_sentence(    \n",
    "    sentence, \n",
    "    model:Seq2Seq,\n",
    "    en_nlp:spacy.Language,\n",
    "    de_nlp:spacy.Language,\n",
    "    en_vocab:Vocab,\n",
    "    de_vocab:Vocab,\n",
    "    lower,\n",
    "    sos_token,\n",
    "    eos_token,\n",
    "    max_output_length=25,\n",
    "):\n",
    "    model.set_train(False)\n",
    "    if isinstance(sentence, str):\n",
    "        tokens = [token.text for token in de_nlp.tokenizer(sentence)]\n",
    "    else:\n",
    "        tokens = [token for token in sentence]\n",
    "    if lower:\n",
    "        tokens = [token.lower() for token in tokens]\n",
    "    tokens = [sos_token] + tokens + [eos_token]\n",
    "    ids = de_vocab.lookup_indices(tokens)\n",
    "\n",
    "    tensor = ms.Tensor(ids).astype(ms.int64).unsqueeze(0)\n",
    "    # 输入位置编码\n",
    "    pos_enc = ops.arange(0, len(ids)).unsqueeze(0).tile((1, 1))\n",
    "    src_mask = model.make_src_mask(tensor)\n",
    "\n",
    "    enc_src = model.encoder(tensor, src_mask, pos_enc)\n",
    "    inputs = en_vocab.lookup_indices([sos_token])\n",
    "\n",
    "    for i in range(max_output_length):\n",
    "\n",
    "        inputs_tensor = ms.Tensor(inputs).unsqueeze(0).astype(ms.int64)\n",
    "        trg_mask = model.make_trg_mask(inputs_tensor)\n",
    "        pos_dec = ops.arange(0, len(inputs_tensor)).unsqueeze(0).tile((1, 1))\n",
    "        output, attention = model.decoder(inputs_tensor, enc_src, trg_mask, src_mask, pos_dec)\n",
    "        \n",
    "        pred_token = output.argmax(2)[:,-1].item()\n",
    "        inputs.append(int(pred_token))\n",
    "\n",
    "        if pred_token == en_vocab[eos_token]:\n",
    "            break\n",
    "    \n",
    "    en_tokens = en_vocab.lookup_tokens(inputs)\n",
    "    return en_tokens, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们将传递一个测试示例（模型未经过训练的内容）作为一个要测试的句子，将德语句子传递给我们的 `translate_sentence` 函数，并期望得到类似英语句子的输出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Eine Frau mit einer großen Geldbörse geht an einem Tor vorbei.',\n",
       " 'A woman with a large purse is walking by a gate.')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_idx = 8\n",
    "\n",
    "sentence = train_data[example_idx][\"de\"]\n",
    "expected_translation = train_data[example_idx][\"en\"]\n",
    "\n",
    "sentence, expected_translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "首先，我们将从训练集中获取一个例子。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<sos>',\n",
       " 'a',\n",
       " 'woman',\n",
       " 'with',\n",
       " 'a',\n",
       " 'large',\n",
       " 'purse',\n",
       " 'walks',\n",
       " 'by',\n",
       " 'a',\n",
       " 'gate',\n",
       " '.',\n",
       " '<eos>']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translation, attention = translate_sentence(\n",
    "    sentence,\n",
    "    model,\n",
    "    en_nlp,\n",
    "    de_nlp,\n",
    "    en_vocab,\n",
    "    de_vocab,\n",
    "    lower,\n",
    "    sos_token,\n",
    "    eos_token,\n",
    ")\n",
    "translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们的翻译看起来不错，尽管我们的模型将“is walking by”更改为“walks by”。“的意思仍然是相同的。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 展示注意力\n",
    "\n",
    "接下来，我们将定义一个函数，该函数将显示解码的每个步骤对源语句的注意力。由于此模型具有8个注意力头，因此我们可以查看每个头的注意力。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_attention(sentence, translation, attention, n_heads = 8, n_rows = 4, n_cols = 2):\n",
    "    \n",
    "    assert n_rows * n_cols == n_heads\n",
    "    \n",
    "    fig = plt.figure(figsize=(15,25))\n",
    "    \n",
    "    for i in range(n_heads):\n",
    "        \n",
    "        ax = fig.add_subplot(n_rows, n_cols, i+1)\n",
    "        \n",
    "        _attention = attention.squeeze(0)[i].numpy()\n",
    "\n",
    "        cax = ax.matshow(_attention, cmap='bone')\n",
    "\n",
    "        ax.tick_params(labelsize=12)\n",
    "        if isinstance(sentence, str):\n",
    "            tokens = [token.text for token in de_nlp.tokenizer(sentence)]\n",
    "        else:\n",
    "            tokens = [token for token in sentence]\n",
    "        if lower:\n",
    "            tokens = [token.lower() for token in tokens]\n",
    "\n",
    "        x_ticks = [''] + ['<sos>'] + tokens + ['<eos>']\n",
    "        ax.set_xticklabels(x_ticks, rotation=45)\n",
    "        ax.set_yticklabels(['']+translation)\n",
    "\n",
    "        ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "        ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以在下面看到每个头的注意力。每个头显然都是不同的，但是很难（也许是不可能的）推理出每个头实际上学会了关注什么。有些头在翻译 \"a\" 时完全关注 \"eine\"，有些则完全不关注，还有一些关注很少。它们似乎都遵循类似的 \"向下阶梯\" 模式，并且在输出最后两个标记时的注意力均匀分布在输入句子的最后两个标记上。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.local/lib/python3.7/site-packages/ipykernel_launcher.py:24: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "/root/.local/lib/python3.7/site-packages/ipykernel_launcher.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
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/vcOHD6vqtR+DtIfBL1++XBctWqT//e9/zedv2rRJK1eurEWKFNG5c+eajz1+/LgOGDBAQ0NDzR8X2EPazz4xMVFr166tH330kTnt9OnT2rZtW/X29tYJEybomTNnrCgzV6X+g+DIkSO6aNEiHTt2rH777bd68OBB8zHvvPOOSwamdevWqcPhUH9/f61du7bOnz9fDxw4oKrX/sH20ksvacmSJTU6OlpVXeM9Qd5GzkNeQM7D7SLnpUfOuzErch5NMRtLTk7WVatWaXh4uPr4+GiVKlV0+vTpunfvXlW9tlI888wzGhYWpvv27VNV1/ii4NZSN7SnTp3K9XP0H3/8cS1evLh+9NFHeuTIEY2Ojtb77rtPDcPQ5cuXm49bsmSJVq1aVT09PfW777674estWbJEDcPQr776yml6+/bt1cfHxzynvEqVKvr555+rquqqVau0atWqahiGPvroozpgwABt3769uru769tvv50zC47bkrquHj9+XNevX68//fST9unTx9xTmBqOz58/r23atHGJwJT6nf3zzz+1TJkyGhgYqAULFlTDMPTBBx/UBQsWmI997733zMC0Y8cOVc3/vwMnT57UV199VefNm5fuvgsXLmj58uX1kUceyffvA/I+ch5uFzmPnJdXkPPSI+fdnBU5j6aYzX3zzTc6dOhQnTVrVrr7zp49q+Hh4dq1a9fcLwy298cff2ibNm30+++/z7V57t27V8uXL69vvvmmxsXFqeq1QVG9vb21b9++eujQIacN2MKFC7VkyZI6ZcqUG77mokWLtHz58urv76/ffPONqqoOHTpUQ0JC9O2339a1a9fqlClT9L777tOCBQvqxIkTVVV169at+tJLL2lYWJgGBATo/fffrx988IH5uvn9ByUviY6O1tDQUHV3dzfDb9o9zqmB6vz589q2bVv18/PTN954Q8+ePWtNwbngn3/+0VKlSmmzZs102bJleurUKV26dKkahqENGzY0/9Gsei0wlStXTqtXr65//vmnhVVbI+1pNlFRUerj42MGRwbuhd2R83C7yHnkvLyCnJceOS/zciPn0RTLA9IeRpx2pRg5cqT6+PjoX3/9paqEf/xvHbh48aLWrFlTIyMj9ffff8+VeaqqrlmzRg3D0F9++UVVVX/44Qf18fHRrl276pEjR8zHpT2sPe1GP634+Hjz/5ctW6b33nuv+vj46Nq1a3X06NH63HPP6aVLl1T1WvA5fvy4Nm7cWP38/HTFihXmc8+ePauxsbFOYxRY+V250TgZrib1M7h06ZLWqlVLmzdvrhMmTNApU6aon5+fhoWF6aJFi8zHpw1MkZGRGhISki/HnUgN8VOnTtV77rlH16xZY943duxYLVCggM6ZM0cvXbrktB5HRUVptWrVnA67z0+OHj2qixYt0qNHj2Z4f0pKip46dUobNGigzZo1c7ryGGB35DxkFjmPnJdXkPMyRs7LmNU5j6aYDR09elTXrFmjJ0+evOFjjh07prVr19a2bdvquXPncrE62FXqRvbIkSP6ww8/aOPGjZ1CQ3ZYunSpvvrqq/rcc8/p6tWrzb2EqfPeuHGjBgQE6KZNm3TNmjXq7e2tjz32mNMGbv78+frQQw/pP//8k2H9qqofffSRPv74406DCn/55ZdapUoV9fX11cDAQHNPYdofjMOHD2tERITWrl07Xe3XD+Kam65evaoXLlxwmrZz585cr8Mu0q6ra9eu1ebNm+vKlSvN+zdu3Ki+vr56zz336OLFi83paQPT9etPfjNo0CCtUKGCuX4PGTJECxQooB9++KGeP39eVa8dQp72fcivpxrs3LlTK1WqpFWrVtXx48ff8HErVqxQwzB04cKFuVgdkHXkPNwOch45L68g590aOe9/7JDzaIrZzM6dO7Vq1apatmxZ/fLLL2/4uEWLFqlhGLps2bJcrA52d+rUKS1VqpRWqFBBQ0NDzcCdHXutpk6dqh4eHlqxYkW9++67NSgoSP/97387XUVIVbV8+fJmqOnRo4fTnsMDBw5o69attV27djft8I8fP14Nw9ABAwY47WFcsmSJ1q9fX93d3XXgwIGqei0spaSkmFcfeu2119TDw0N//vnnO17m7JCUlKQzZszQUaNGaWxsrKqqNmzYUHv37p2vDwu/ldR1tVKlSlquXDkzTKauT7/99luGgclVjpR4/vnntVy5cqqq+p///McMSql7zVVV//Wvf+nEiROdrgaW3/z9999arFgxbdasWYa/ianB+/Tp01qvXj2tV6+eGSYBOyLn4U6Q88h5eQU57+bIedfYJefRFLORXbt2aZEiRbRly5b6ySef3PBxZ86c0bp162q9evWcvjiA6rWNbKFChdQwDKe9Mndiw4YNGhYWpsOGDdNDhw5pQkKCVqlSxWmMiNQfsc8++8wMU6lXBVG9FpSGDx+uhQsXNi+jfTOTJ09WwzC0b9++5gDDqtcCU/Xq1dUwDF26dKmqOofBGTNmaIECBXTjxo13vNzZITk5WadOnaqGYejAgQO1YcOGGhQUpP/973+dTplxRWnX1Z9++klVr/34pf74pwamihUrZmqdySvS7sW+0R7t9evXq6+vr9aoUUM9PDx01qxZTkeLrFy5UsuWLatvv/12vg2Qly5d0ocfflirV6+uW7ZsSXdf2lNvrly5om3bttVBgwbldplAppHzkB3IeeS8vIKcR867GTvlPJpiNnH+/Hlt1aqVNmzYUH/99Ven+64/p/jgwYPavHlzHTt2bK7Vl/aQZAautJfUzyPtD+/o0aPVMAytVq2abtu27Y7nMW/ePPX399d169ap6rWrglSsWFG//fZbVVWnPRixsbE6duxYLVSokFarVk1fe+01jYqK0hYtWqjD4dC33norXe3XSw0/7733nhqGof3799e///7bvH/p0qVauXJldXNz0yVLlpjTjxw5or1799aiRYtmy3JnpzFjxqiHh4cWLFhQP/roI/M77Urfp4zW1TfffFMNw9B69eo5XVUndZ3aunWrGoah9913n9OPY16XlJTkdPWwbdu26e+//24eGn/u3Dnt0aOHent7a/PmzZ2eu2nTJn3wwQc1IiIiX59ecPr0aS1fvrz279/fnPbLL7/oyJEjtXLlylqlShWn8UjS7pF3pe8V8ga75zxVsp5dkfPIeXkFOe9/yHm3ZqecR1PMJs6dO6dlypTRwYMHm9M2bNigY8eO1QoVKmiHDh30o48+Mu9Lu3ckJze2qV/my5cva1JSkuXnMjN45f/c6rMZPny4ent7a6tWre74SiUTJkxQh8NhHqr+yy+/aMGCBZ0uvX3p0iW9fPmyJicna3Jysi5dulTvu+8+DQwMVF9fX23atKnOmDHDfPyN9nqkDV6nT5/WQYMGqYeHh77wwgu6e/du877UwGQYhv7rX//Sp59+Wjt27Kje3t43PR/dKs8//7x5xZ2nnnpKDx8+bHVJueZW6+prr72mPj4+2qpVK/3jjz9U1Tkwbdu2zRxoOi97+OGH9YUXXnCaFh0drVWqVFF/f3/19vbWsLAwnTFjhjmOROvWrc1LdE+aNEmffvpprV69ugYFBZnvVX518eJFbdKkiVaoUEG3bNmio0aN0rCwMA0ODtaWLVtqjRo11NvbW3/77Ten57nSP0CQd9g156naK+uR8/6HnEfOyyvIedeQ87LGTjmPpphNHD58WIsVK6adOnXSjRs36ttvv63lypXTIkWKaPPmzbV48eJapkyZdIcK50ZDbNeuXdqhQwctV66cFi9eXAcPHqxr167Nsfneyt69e51+pF3RzT6bH374wXzca6+9pr6+vul+hLLqwIEDGhQUpG3atDGvBNOgQQMNDg7Whx9+WDt16qRVq1bVSpUqaevWrc1Laquq7t+/X//55x+nvT83Ckppp3fr1k2rVq2qtWvXVnd3d3V3d9f+/fs7jT2RGsi8vb21TJkyunDhQv30009vOZ/ckPZ9Tk5O1h07duiPP/6or732mhqGof369dMDBw5YVl9uycq6en1gSk5OzjfjKBw6dEgbNmyohmHoyJEjVfXanvhSpUrp/fffr2PGjNE33nhDGzVqpG5ubtq3b189e/asHj16VN944w0tX768FipUSEuXLq0dO3Z0mQF8Z86cqaVKlVLDMNTb21sHDRpkXqlpy5Yt6u3trVFRURZXCdyaHXOeqj2zHjmPnJeKnGd/5LxryHm3xy45j6aYjcycOVMNw1BPT081DEOffPJJ87DlTZs2qWEYumDBglypJXVD/9dff2mRIkW0du3a2rt3b33xxRfV19dX7777bqfDGXPLpUuX9KGHHlIPDw+nw6ldSWY+m7SBYfjw4err66sPPfTQbR1qnjq/mTNnqo+Pj3bo0EFPnz6tf//9tz700EMaGBioISEh2qpVK23Tpo0WKlRIy5Urd8OxHq4PaxntFe7Vq5cGBgbqlClTNCYmRn/44Qd94YUXzJBx/aCsNWvWVMMwnK7kZUVQSl2WtMt0fR2JiYk6YsQIc1n2799v3nf+/PkbXoo4L8rtddXu/vjjD33kkUfUMAwdPXq0xsbGat26dZ0uxx0fH2/ubX7ppZdU9dpe9YsXL+qOHTv09OnTevHiRasWIUedOnVKN23apD/88IP+/vvv5vS//vpLP//8c/3777+dln3ZsmVavHhxpwF6ATuzU85TtWfWI+eR88h5eQc5zxk57+bsnPNoilnkzJkzunfvXv3pp5/01KlT5pU41q1bp3PmzNEdO3Y4rRRffvmlBgcHO+2ZyWkXLlzQNm3aaGRkpNPgdx07dtQiRYro0qVLLflB+vrrr7Vp06ZarFgx/eKLL3J9/naQmc8m7dWCUn+cO3XqlO4qQpl19uxZfffdd9XLy0sfffRR89Do8+fPO+0d/Prrr9XT01M/+OADVb3xHssvv/xSDx06pKrO4SI2NlZLly6tjz/+uNOYBFevXtWxY8eaVytKe4j9559/rnPnzr2t5coOX3/9tXn1pYSEBHP6G2+8od27d9eoqChdvXq1Of3ChQs6cuRIMzAdOnRI4+LiNCoqSjt27JivDrm3Yl21s+3bt2uHDh3UMAytU6eO1q5d29xLmva70q9fP3U4HOkOGc+voqOjtVq1ahoQEGCefjJgwIAb7indtGmTtm7dWiMiIvLV9wX5R17Iear2zHrkPHIeOS/vIOc5I+dlzO45j6aYBXbu3Km1a9c2r8Zx9913a//+/c0rTly/R2Xjxo3apk0brVSpkh47dizX6jx58qSWLFnSPARUVfWFF15QDw8P/eijj8x6L1++nGM1pN14pP3hXLVqlTZq1MiSwJQ6lkIqK8a/yOxnk/aStWPGjLnj8/VPnz6tEydOVC8vL23fvr156emrV6+a78Pu3bs1ODhY33///Ru+TnR0tBYpUkTvvfdec0OXlJSkKSkpun//fvXy8jLPyU/7g5mQkKB9+vRRd3d3femll3TXrl3pXju3w/vmzZvV3d1dIyMjnfb+pe7pLlmypBYoUEDLlCnjdBWnixcv6qhRo9TNzU1r1aqlbdu2VcMwdPjw4blaf06zal21m7SnBxw9elQffvhh9ff31/DwcHMQ1dTvUHJysv7222/q5eWl/fr1y/eDXu/fv1+LFy+ukZGROnnyZP3000/1ySefVC8vL23atKlu2LDBfGxCQoKOHj1aa9eurUWLFjUH7QXsJK/kPFXrsx45L2PkPHJeXkHOu4acd2N5IefRFMtl+/bt02LFimmjRo10zJgx+vnnn2vTpk3V4XDo/fff73Qp1uTkZB09erQ2atRIg4ODc32wvejoaA0KCtLvvvtOVa8NIFmgQAH98MMPnS4R/sEHH+iJEyeyff6pG4fY2FhzI3KjwJQbh9ifOnXK6e99+/ZZdqnlrHw2qXu2suJmATA2NtYMTB07dnT67I8cOaKvv/66BgYGOo0jkJF3331Xy5Urp7Vq1dKYmBin+2rVqqW1a9c2/077Q7No0SJzD0P37t01Li4uq4uX7Z5//nkNDg7Wpk2b6tGjR/W3337T8PBwnTdvnl65ckXXr1+vrVq10qCgIKfBYRMTE/XDDz/U0NBQrVGjhlPAzC8/jjm9ruYFqZ/ltm3btFatWrp3717duXOnduzYUQ3D0Oeff958bNptSlhYmPbo0SPX681t//nPf7RkyZLmAM+q106h+vTTT9Xb21vbt29vHrEwZcoUvffee7V169b5LlAjf8hLOU/V2qxHzrsxch45L68g55HzbiUv5DyaYrkoOTlZX375ZQ0JCXEavPTKlSv66quvaqFChbRly5bmBmTBggVaqFAhbdq0aa4Mtpf2Utyq1zbkFSpU0M6dO+uoUaO0QIEC+sEHHzht4N58800tWbJkjq20x44d08qVK2vv3r0zDEwrV67UGjVqaFBQkH711Vc5UoPqtT1nzz33nA4aNEhVVffs2aOGYehzzz2XY/NMKzc/m7RBaerUqTp48GB98skndcKECebrnzlzxjzEvmPHjnrq1Cldt26dtm3bVgsUKOB0Oe7rpd3DN2nSJC1TpoxTYLp69apOnDhRDcNwukpXal3ff/+9NmnSRF977TV99913s7Rs2S3tezV06FANDAzUFi1a6JgxY7Rt27ZOe8W2bdumHTt21MDAwHRXTTp58qTl42RkFztuR24kNy6XnjqPM2fOaPXq1bVJkyb6008/qarqjh07zEPshw0b5vS8X375RYOCgvTpp582967nVx06dNAyZcpkOIbG9OnT1TAM86p8qYMZp70sN2AXds95qvbbRpPzriHnkfPyCrttQ26GnGcPeSHn0RTLZe3atdOIiAjz79Tz0i9fvqz9+/dXLy8vnT17tnnf9u3bzavA5JSMQojqtR+t8ePHq6+vrxqGobNmzXK6f9OmTdqsWTNt166d057P7HT27Flt166dli5d2txoXF9r6sC1JUqU0Pnz52fr/FM3UGfOnDEHAO3YsaP6+/tru3bt9Ndff83W+V0vtz+btBvk1MFUS5YsaZ7/XbFiRXNgxDNnzuh7772n3t7e2r17d92wYYPWrVtXJ06caL5GZq4+NHnyZDMwpY49cfDgQX344YfV29tb+/XrZz72+PHj+uyzz2rDhg2dfmyt/CFJuyxDhw7VkJAQLV68uPbq1UtVnU852b59uxmYJkyYkOHr5dUfRTtvRzKS+rnt2rVLFy5cmCOnBqV+lidOnNDjx49rw4YN9auvvnIK2X/88YcZmDp16qRTp07Vt99+Wxs3bqyFCxfWv//+O9vrspunnnpKg4ODzaM00r4/hw4d0tDQUO3QoYNeuHDBqhKBTLNjzlO17zaanEfOI+flDXbdhtwIOc8+8kLOoymWixISErRr164aHBzsdFWV1BXj9OnTThvZ3JA67z179mjv3r21Z8+eOm7cOPOc+X379mn79u3Vx8dHe/XqpefPn9cLFy7ol19+qc2aNdNixYple9c/dQOTeij12bNntXv37nrXXXfp4MGDzempG7e4uDgNDw/XihUravny5TU+Pj5bfnCGDBmiy5cvN9+jixcvateuXdUwDA0LC0t3KHh2s/KzGTJkiAYFBem8efP07NmzeunSJZ08ebLefffdWrp0aY2OjlbVa4fYT5o0SQ3D0G7dujkdYn+rvWAZBaaaNWvqwYMHVfXaj1inTp3MkNauXTtt0KCBGoZx03EsrJB24/7iiy9qwYIFNTg42Pys0p4WsH37du3cubP6+vrqG2+8keu15gQ7bkcy48CBA+rr66u9evVyujpUdjp16pQWKVJEixUrplWqVDFPA0m7zvz555/6yCOPqIeHhzocDn3yySf1qaee0j///DNHarKbhQsXqpeXl/bs2dMM22kHMq5WrZq2bt3aqvKATLNjzks7f7tso8l515DzyHl5hd22IZlFzrOHvJDzaIrlsPPnzzvtAUzd2zVx4kSnlUH1Wue9YsWK2qZNm1ytcdeuXRoUFKTFihXTEiVKaMGCBbVSpUq6Z88e8/4ePXpogQIFNCAgQIOCgrR48eIaHh6erYPf3ezH9cyZM2Zgevrpp50G5Vy4cKHWr19fly9fbu6BulOHDx9WwzD0/vvv17i4OE1JSdHk5GSNjIzUUqVKqWEY2rt3b/PxOTUIqxWfzdWrV7VevXr64IMPOo3jcOXKFV22bJmGhYVp1apVzfX39OnT+tZbb2VqnISbfcbvv/++GZgOHDigqqoxMTE6Z84cbd68uYaFhWm9evXMqx3dbD654WbL8sorr6iHh4c2bNjQHGA2bWDatm2bPvjggzpt2rQcrzO32GU7ciup39WEhARdsWKFNm7cWDdt2pRj8zt58qQOGTJEw8LC1M/PzzykXtV5/d2xY4d26dJFDcPQcePG5Vg9Vjtz5ozu2LFDN23aZP7DKD4+Xh999FEtVKiQ07gbqqrr16/XsLAwff755y0Z7Bq4lbyQ81TtsY0m56VHziPn5RV22IZkBjnPWnk159EUy0G7d+/WNm3a6MCBA81OcGJionbu3Fl9fHx01qxZTufLbty4UcPCwvTll19W1Zz5IUh9zbQr3ZAhQ7RJkya6ceNGVVX98MMPNSIiQoOCgsxDOk+cOKHr16/XV155RYcMGaJz5szJ1sujptazf/9+femll7Rbt27av39/jYmJMTvKp0+f1u7du2toaKj+61//0uPHj+u3336rbdq00ZYtW2b7ZX3/+usv3bp1q6qqeQ70li1b9Oeff9aBAweqYRhOe3uvP5w4q5+fVZ9N6o94cnKynjlzRuPj47Vo0aL6+OOPm3Wl1pSYmKhRUVFqGIYuWLDAfI207/2Nlvv6vWjff/+9/v3333r8+HFz+nvvvZduT2KqCxcuOJ2LbuV4DDdalrRXDXvxxRe1cOHC+sADD5gDi6Z93vUD+uaW7Niu2HU7kll79uzRtm3bar169bRDhw45Pr+jR4/q66+/rr6+vtq2bVvzHxrXX23ot99+0x49euTbPYfR0dF63333aZEiRdTLy0sDAwN14sSJeuXKFT19+rQ2a9ZMHQ6HNmzYUOfPn69jxozRxo0ba1BQkO7evdvq8oF07Jjz0r6unbbR5Dxynio5LzeQ88h5VsnLOY+mWA6Jjo7WYsWKaZ06dTQqKsrpvu3bt2urVq3U4XDo448/rp9//rlOmTJFGzVqpEWLFjU77tlp9+7d6TaSf/31l44bN04HDRrk1LFOSkrS5cuXa8WKFbVIkSI5vpKm/uhFR0dr8eLFNTQ0VMuVK6fFihXTkJAQ/eyzz8wrUpw9e1aHDBmiRYsWVcMw1OFwaFBQUI7ugdi9e7fWrVtXf/jhB7PWmJgYHTBgQLrApHrt3Oi0P4qZeX07fDYPPfSQRkVFaWxsrLZt21bvuusuc1wJVefTHAzDcLr08q2k/VHt0qWL+fkVKFBA27VrpytWrDDvT3uIfeoljK8fKNPKPYe3Wpbly5eb9w8dOlQLFy5sXq1I9c5DdVbFxMTokiVLdNmyZeb36HbZZV29Ux9//LEahqHBwcH69NNPq+q1dexOP4vUdSNtCEr97+HDh/X1119Xd3d37dSpk/mc6wNTdv+jzy727t2rwcHBGhkZqVOnTtVFixbpU0895bQtOXPmjL744otarlw5NQxD/fz8tGbNmrm6hxnILLvlPFX7bqPJefb4XMh5mUPOs35dvVPkvNyX13MeTbEccOzYMa1UqZI2b95ct2zZkuFjjhw5ooMHD1YPDw81DEP9/f313nvvzZGVok+fPhoUFGQeOpqSkqJXrlzR3r17q2EYWrBgQZ03b56qOg8WuWzZMnNDlzo2RuoPZnZv4P/55x+tWLGitmjRQn/66SdNSEjQ48ePa0hIiJYvX14/+eQT8xDvixcv6o4dO/Tdd9/V999/X/ft25ettVxv165dWrBgQa1evbr++OOP5ntw6NAhc0/ik08+qarXAl+jRo20cePGqnrr98nKz+b6qw8FBgaa85o1a5YahqF9+vRJ9/5u27ZNfX19derUqZmaT1qPPvqoFi9eXMeOHasbN27UDz/8UAMCArR48eK6bds283GTJ0/WcuXKadWqVdPtSbSLmy1L2pA5dOhQ80cip8cnud6ff/6pJUuWVHd3d/Xw8NBSpUrpunXrbuu18sJ2JCs++ugj9fDw0AIFCpiXEb+TmlK/T3v37tX+/ftr/fr1tWXLljp8+HAzpKZeyj6jwJSfpaSk6ODBg7Vq1aq6efNmc/rTTz+t7u7uOmfOHPPogMTERI2Li9M1a9bo3r17LdvTDtyM3XKeqv230eQ8ch45L/uR826MnJd78kPOoymWA5YuXapBQUH6xRdfmNMOHDigK1eu1DfeeMPpcORt27bpmjVrdMuWLTm2UqxZs0bLli2rf/zxh9P0rVu36mOPPaZubm7arVs3c3raPRzLly/XKlWqqGEYOTZAYXJyskZFRem9997rtNEaOnSoenp6anh4uBYuXFg/+eSTXLlqSUaHa0dHR2tYWJhWqVIlXWAaPHiwGoah99xzj0ZERGhgYKDTBuFm7PDZrFq1Snv16qVPPvmkxsfHm9Off/55cw9p6g/kvn379LXXXtOCBQvqDz/8kKX5rFmzRkuVKqXTpk0zL2H9+++/q4eHhw4cOFCPHj3q9N5PnDhRCxcurJ9++ultL1tOyeqypL6XabcJOe3cuXNav359bdasmc6fP19nz56t9erV04CAAP3888+ztJdb1R7r6u242ekXH330kfr4+GjNmjVvOAZEZqQ+fufOnVq0aFEtU6aMRkZGavny5dXNzU3LlStnBsW0galr1663sUR5U/369Z3Wj5deekk9PDx0xowZ5nYnM6fnAHZgt5ynau9tNDmPnEfOy37kvGvIefaQ13MeTbFslLpxmD9/vvr6+pobxrlz52rjxo3VMAx1c3NTNzc3HTRoUK7WlnqJ0/379+umTZvMDcgff/xhXvkldYyLtMuiqvrFF19onTp1svWSsand9tTO+ptvvqkvvviief+IESPU3d1dZ86cqfv379eKFStqmTJldN68eU4/6Nktta7jx4/rqlWrdM+ePeZ4IH/++WeGgenw4cM6adIkbdGihT700EO6c+fOLM3Tis8mdUPUt29fNQxDy5cvb15COe3enxdffNE8vLVBgwZasWJFdTgc6U4VyYyPP/5Yvb29zR+NVatWqY+Pj3bt2tVpz1rq4eeq105BsaPMLkvqGBOqqj///HOu1Xfy5Ends2eP1q1bV5cuXWpO37dvn3bo0EELFSp0W4HJbtuRW0n9Ph89elQXLVqk7777ri5fvtxpTJDJkyern5+f1qtXT9euXWtOz+qPdVxcnNarV0/vv/9+8x8XiYmJ+s4772hISIiWLFnSHFfj6NGjOmrUKDUMQ5944ok7XUzbSrt+1a1bV3v06KGq1/4hXKBAAf3www/10qVLqnrt/W7evHmODoYL3Ck75zxVe22jyXn/Q84j52U3ct415Dxr5aecR1Msm3z//fe6ZMkSVb02uF/x4sU1ODhYw8PD1d3dXVu2bKkLFizQc+fOadu2bbVChQrZdgWdzLp8+bKWL19ey5Urp+vXrzc3BtHR0eaGbtiwYebj027oUveSZIe0823cuLFu3rxZz507Z3aPv/jiC/X19dW3337bDEap5yQ7HA797LPPsq2WjOrauXOnlipVSj08PNTLy0s7duyo69evV9VrPwqhoaHpAlPqRjn1xySrcuuzuX5vyuHDhzU8PFwNw9D27dubr5v2kPvPPvtM+/btq/fdd5/26tVL58+ff8PXu9H0lJQU/eCDDzQkJERVr+2N8vb21scee8wpHH388cf66quvOl0N6WbzyQ15bVnOnDmjxYoV07Jly2qtWrXSBaKYmJg7Ckx22Y7cStoxbMqVK6eBgYFqGIYahqENGjTQ2bNnm49NG5iyctpB2kB14MABDQoK0tdff93pMUlJSTpr1iwNDAzUpk2bmtuIY8eOaVRUVJb/cZVXpP1NTD2svmLFitqtWzf18PDQmTNnOq0Pn3/+uYaGhuqiRYusKhm4qbyQ81TtsY0m56VHzruGnHfnyHnXkPOsld9yHk2xbBAXF6fh4eHasmVL88Pftm2b9urVSx955BFdsmSJeelh1Wt7ZUqXLq0nTpzIlfrSfqF//vlnLVGihNapU0d//vln874///zzlhu629W5c2f98ccfVfV/G7C4uDiNiIjQFi1a6C+//OL0+KFDh2pERITT4bd9+/bVAQMG6GOPPaZ//fXXHdd0I2fPntVatWppkyZN9L333tPnn39eAwICtEqVKvr999+rqnNgWr169R1dPjY3P5u08+rYsaN5ekdsbKyWL19eDcPQd955x/zxvP71rx8Y8kY/+tfPJ3Xjt2vXLjUMQ7t27aqFChXS7t27O+1t2717t9asWVP79OnjdPUhK+XFZTlz5oyOGjVKQ0JC1NPTU1euXJnus/rnn3+0Q4cO5ukqmQlMVm9HbkdMTIyGhYVpkyZNdPHixbp582b9+OOPtXDhwlqiRAmdMmWK+dipU6dqUFCQVqxY0fzH0c2kLnPq6VDR0dFqGIaOHz/efEzqtiEpKUmfeuop9fb21ujoaPN+K/8RkJNSfxNbtWplrv9//vmn+vn5qWEY+sILLzhtN3/99Vdt2rSp1q9f3+kqZYBd2D3nqVq7jSbn3Rg5zx7Z6Eby4rKQ8/6HnGeN/JjzaIrdgdQvy+rVqzUwMFDHjh2rqv/7Aly9ejXdRui3337TBg0a6EMPPZTj3fTUlTG1ntQfu40bN2pwcLDWqlUrww2dh4eHeaWOO9WpUyf19fXVb7/91nwvjhw5orGxsdqmTRv96quvzMempKRocnKytm7dWsuWLWsecv/7779rrVq1dNKkSdlS0/XS/gjEx8drkyZNzHCnqjpjxgy9++67tWzZsk6BqUyZMlqyZMnbGtAytz+btOvhH3/8offee6+6ubnpt99+q6rXTiMoXbq0Fi5cWD/44AOnjXx2zGflypWqqjpo0CD18fHRiIgI83LFqtd+1F599VUNCQnRL7/8MsvLlxPy8rKcOHFCJ0yYoAULFtRHH31UY2Nj0z0mJiZGmzRpoqGhoTc9VcUO25GsSq01NQB9/fXXTvdv375dS5YsqXfffbfTmCnvvvuuhoWFOf3j9mZOnDihQUFBOm/ePE1KStKSJUtqgwYNnAYtTn2/fv/9dzUMQxcvXnyHS2dft/pN/O6779Tb21srVaqk77zzjsbExOikSZO0SZMmWrhwYacgCdiB3XOeqvXbaHJexsh59stG18vLy0LOI+dZIT/nPJpi2eCRRx7Re+65x/xSZHSOckpKii5dulQfeOABLVKkSI6vFKkbuH379ukrr7yijzzyiD7xxBO6bNkyVb3WsS1evLjWrl3baUMXHR2tLVq00ICAAI2Njb2jQfB+++03LVq0qE6cONGs59ixY1qxYkX19fXVsmXLmud8p53PsmXL1MPDQzt37qxPP/20RkZGarFixczz+rNT2jEv9uzZo2vXrtVKlSrpyZMnnR73ySefaOnSpTU8PNwMTNu2bdPKlStn+apIuf3ZpA08L7zwgnbo0MHcY2gYhjnfY8eOaalSpTQoKMgpMGV2L8et5vPTTz/pmTNntEOHDuaVnFasWKGfffaZPvbYY1qgQAGnvS9Wyg/LcuLECR03bpx6enpqt27dMhzg+fDhwze9UpIdtiOZkfr6qXsqU/davfLKK+rr6+t0yffU9Xnz5s3q5uamzz77rNNrpY4tkxn79+/XSpUqaYsWLVRV9b333lPDMPSVV15xOsVC9dpgr97e3rYdSyE7ZfSbmPoZrVmzRu+55x7zinyFChXS+++/P93AvoCd2DHnqVq/jSbn3Xye5Dz7ZaNU+WFZyHnkPKvkx5xHU+wOrVu3TosWLapvvvmmqv5v45J243D16lVt27ateXh2Tq8UaTdYqVfIqFq1qkZERKibm5s+88wzeurUKd2yZYsGBwenOzT2r7/+MgcKvBM///yzGoah48aNU1XVv//+Wzt37qw9e/Y0rzSUeinztHtrkpKSzENfixUrpjVr1syR9yztuejVq1fXIkWKaPXq1TUsLEx37dqlquq0Z2jOnDlaunRprVChgrnn8/pDzW/Fys/mkUce0eLFi+vIkSN1zZo1GhUVZY4z8d///ldV/xeYihUrplOmTMnyOAQ3mk/ZsmXVMAxdu3atxsfH6wsvvKC+vr7q4eGhHh4eeu+99zrtIbbL4cZ5YVn279+vr732mnbq1Elff/11/fXXX837MhOYbsQu25FbuXjxok6ePNkc7Hbr1q1arlw5PXDggE6YMEENwzCPCEjdPqd+b9u3b6+lSpXSkydPmkErq8Fu1KhR6u7ubg6w++STT6q7u7sOHDhQN2zYoKrX9qg1b95cK1asaNvDxrNLZn4Tjx49qlu3btVPP/1Ud+zYYZvLcQMZsWPOSzt/K7fR5Lz0yHn2yEaZlReWhZxHzrOT/JrzaIrdoddff119fHwy3CN4+PBh80f3l19+MQ8jzA0nT57UGjVqOI3lcPr0aa1SpYoGBQWZG9TNmzdrcHCw1q9fX9esWZPtnf7GjRtr6dKlddKkSepwOPSJJ57QP/74Q19++WX19PTUunXrOp2GkNbx48f1+PHjOXJ57tTlPHbsmN5zzz1ao0YNffrpp7Vu3bpqGIY2atTIfGzawDR37lz18/PTGjVq6MWLF2/r/bLis9m4caN6eXnpqFGjnMY++OGHHzQyMlINw9Dly5er6rX3PSQkRN3c3LIcUm82n/r165t731SvjXfw7bff6ubNm50GI7ZLUMoLyxIdHa0lSpTQkJAQLV++vBYsWFAjIiL0gw8+MB+TNjD16NEj3d7xm7HLduRmNm/ebA42++OPP2pgYKDWqVNH9+/fr4cPH9YyZcpo7dq1MxxDpUOHDlqpUqVMfU7XL1Pa14uIiNAmTZqo6rXwNmTIEDUMQwsUKKAhISEaFBSkxYoV0x07dmTXYtvWzX4TY2Ji8u2As8i/7JrzVO2xjSbnpUfOI+dlF3IeOc9u8mvOoyl2B6KjozU4OFiff/55p+k7d+40xycIDg42xyLIzQ3Itm3btGjRojp37lyzg/viiy+qt7e3fvjhh07nlm/atEnd3Ny0WbNm5mVT71Tqsp45c0bLly+vnp6eet9995l7DOPj43XYsGHq4eGhTZs2NR+fugHKyfcqdcN44cIFPXDggNavX9/8jC5evKgvvfSSenp6arNmzcw60gamzz77TPfs2XPb87fis1mxYoUahmEOLJl2eVauXKkhISFqGIZ5Tv7Ro0f1448/zvb5FC9eXA3D0O+++y7D5+fmd+RW7L4shw4d0nLlymnz5s119erVqnot/BcuXFgjIiL0/fffNx+bOvaEYRj61FNPZTrEWb0dyYz4+Hj97LPPNCAgQB0Ohz7wwAPm9zMpKUnffvttLVCggNavX98cv0ZVdcuWLXrvvfdqp06d9PLlyzf9vFLvO3nypNNVpq5cuaJJSUk6evRoDQgI0C+++MK8b8WKFfrKK69oz5499a233sry6Td5UWZ/E9evX2+bfxQBN2PnnKdq7TaanHdj5DxyXnYg511DzrOP/JzzaIrdhtSNxpQpU8wuemJioh44cECfe+45LVeunBqGof/617/MQ8pz26effqqGYejly5dVVXXIkCHq4eGhH374obk3JC4uzjzs89dff9W///472+vYt2+f+vn5qa+vr4aEhOiKFSvMDv65c+d02LBh6uXlpc2aNTOfczuHcmfVkSNH1N/fX+vVq6etWrVyui8uLk5feeUVdTgcTnWlvpd3yorPJjo6Wj08PPSVV14xp6UdT+Hll182x1G4ftDQrGzUsjKf1FMT7BSQ0rLzsqT+QEdEROiaNWvM6W+88YYahqGhoaEaEhLidNWdY8eO6fvvv5+lPTh22Y7cyu7du80r3tx7771Oh67HxcXpiBEjtFChQhoaGqoDBgzQfv366X333aeBgYGZfj9iY2O1aNGiWrduXX3nnXec7jt8+LAWL15cO3XqlK3LlVdk5TfR6jFYgMzICzlP1R7baHJeeuQ8ct6dIuc5I+dZyxVyHk2x25ScnKxVq1bVpk2b6t69e7Vr164aEhKigYGB2rt3b6crXajm/g/Cn3/+qYULF9b58+frSy+9pB4eHjp9+nSnzv6gQYM0PDxcT58+nWN17N+/X4cOHaqLFy/WatWqaWhoqC5btsys4+zZszps2DB1OBzasmXLXHufYmNjtW3btlqwYEGtUqWKHjlyRJOSkswvfWqQy4m6rPhsTp8+rdWrV9e7777bvBKR6v8OMX7nnXf0vvvu06ZNm2rRokV18+bNuTIfOw9GaedlSUlJ0SlTpmi/fv3MaSNHjlQPDw/99NNPdd++fVqkSBENCwtz2pOY1b02dtmO3MqxY8d07NixOmrUKA0KCtL777/fHHtC9dpexi+//FJbtWqlAQEBWqpUKW3RokWWBsK+cOGCzpkzR2vUqKE+Pj5ao0YNnT17tnkFo2nTpqmnp6fTXkRXYvffRCCr8sI6bYdtNDkvPXIeOe9OkfOckfOslxd+E+8ETbHbNHPmTDUMQytWrKg+Pj5aqVIlHTx4sJ44cSLb9jTdidjYWK1cubIGBASol5eXfvTRR057P9asWaP169fXPn365Hi9qXsEt2/frlWrVtXQ0FBdvny5U2B69dVX1TAM7dChQ47WktbRo0e1e/fuahiGRkVFmdPTBqacqMuqz2bLli3q4+OjderU0RUrVpjTjx8/rr169dKhQ4fq8uXLNSQkRJ944gm9evXqbW3Qcms+ucHOy3L+/HkzuC1atEi9vLz07bffNg8dHzJkiPr5+eldd92l06dPv6152Gk7khkXL17UmTNnauHChbVOnTrmVc9SXb16Vffv36+nTp266eXJb+bEiRP62WefaYMGDdTLy0vDw8P1008/1eXLl2u1atW0R48et/3aeZndfxOBrMoL67RdttHkPGfkPHJediDnpUfOs05e+E28EzTFbtOXX36pwcHBWq9ePZ08ebKeOXPG3Eja5RzazZs3q7+/v4aEhOjChQvN6WvXrtUWLVpo6dKl72jMhKxKSUnRrVu3mnsS0wamM2fO6KhRo/Svv/7KtXpUr+156NSpkxqGoe+++645PfUH4ezZszlSl1WfzVdffWWe4tC3b1+dPn26du3aVT08PHTBggWqqtqlSxcNCQnR8+fP234+uSEvLMtzzz2n1apVc7o89BNPPKEPPfSQNmvWTHfv3n3br2237citnD9/XmfMmKFBQUFap04djY2NVdVrV0n697//rRs3bsy2eX300Ufapk0bdXNz0zZt2miJEiXU3d3d9pedzgl54TcRyIq8sk7baRtNzvsfcp712Siz8sKykPP+h5xnjbzym3i7aIrdpsTERN2zZ4/ToZuq9jtU8LvvvlM/Pz8NDAzUJk2aaJMmTTQ8PFyLFy+u27dvt6SmtIHpq6++Ms9Zt+q9O378+E0DU07VZdVns337dm3SpIl5bn7x4sX17bffNu9/7LHHtGTJknd8Najcmk9usPuydO3aVcPDw/XEiROqem3PZ926dXXJkiXZ8vp23I7czIULF3TGjBlapEgRrV69uo4aNUqbN2+uhmHon3/+ecevn/bHPzExURcvXqwPPPCAFipUSA3D0L17997xPKyW9upNmZFXfhOBzMpL67TdttHkvGvIeeS87ELOc0bOu3PkPGc0xbKJnVeIXbt26eDBg7VBgwbasGFDffnlly3/Mm/dulVr1qypvr6+TufxWyVtYEp7bn5Os+qzOX/+vB45ckR37NjhNL+NGzdqRESEtmnTJluuLJNb88kNdl6WzZs3q2EY2qlTJ3322Wc1MjJSixcvnq179uy4HbmZCxcu6IIFC7RixYpaqFAhrVChQrZeKvv6bf7Ro0d19erVun///mybh1UOHjyogwYN0p9//vm2X8POv4nA7bD7Om23bTQ57xpyHjkvO5Dz0iPn3T5yXno0xWCZzZs3a2Rk5B0d8pudjh8/rl26dFHDMHTatGlWl5OrUlJS9Msvv9TWrVtr4cKFs3TlGjvOJzfYbVl++uknDQsL0+DgYK1du7ZLHtp9veTkZD1z5oz+8ssv5t5V3NrSpUvVMAxt0aKF0yXqASAryHn2Qc7LOrstCzkvPXLe7SHnpUdTzEWk7ebaqbNrty/i0aNH9YknnsjS1UrulB0+m7Fjx6qbm5tWqlQpW/eyWDWf3GDHZTl9+rTGxMTk2BWC7LCuIuclJibqzJkzddeuXVaXAiAL7LiNJufZ43Mh52WdHZeFnIfsQM5Lz1BVFQCm5ORkcXd3t7qMXLd48WK5//77pWTJkvliPrkhPy0LAACugJxHzsus/LQsAG6Mphjg4lJSUsTNzS3fzCc35KdlAQAA+Rc5L+vy07IAuDWaYgAAAAAAAHA5tMABAAAAAADgcmiKAQAAAAAAwOXQFAMAAAAAAIDLoSkGAAAAAAAAl0NTzAYSExNlxIgRkpiYaHUp1GLjOqiFWqglf9VilzqoBchZdlqnqcW+dVALtVBL/qrFLnVQy61x9UkbiI+PF39/f4mLixM/Pz9qsVktdqmDWqiFWvJXLXapg1qAnGWndZpa7FsHtVALteSvWuxSB7XcGkeKAQAAAAAAwOXQFAMAAAAAAIDL8bC6gLwsJSVFjh49KoUKFRLDMG77deLj453+ayVqsW8daWugFmfUkjFqyZhdarFLHWlryE+1qKqcP39eSpQoIW5u7ANE1pHzcpZdarFLHWlroBZn1JIxasmYXWqxSx1pa8hPtWRnzmNMsTtw+PBhCQ0NtboMAABwAzExMVKyZEmry0AeRM4DAMDesiPncaTYHShUqJDVJTiJi4uzugSTv7+/1SUAAGC732rkHXZbd9o/PNjqEkz3NrrX6hJMbzz7lNUl2JJh2OMIWdUUq0sAkI9lx281TbE7cCeH0ucEu1y9AQAAu7DbbzXyDrutOwUKOKwuweTl7WN1CbgFu6y/nJMEICdlx7bOHrsQAAAAAAAAgFxEUwwAAAAAAAAuh6YYAAAAAAAAXA5NMQAAAAAAALgcmmIAAAAAAABwOTTFAAAAAAAA4HJoigEAAAAAAMDl0BQDAAAAAACAy6EpBgAAAAAAAJdDUwwAAAAAAAAuh6YYAAAAAAAAXE6uNsX++ecfOXfuXG7O0rRr1y65evWqJfMGAADI78h5AAAgr8nxptiVK1dk8eLF0rJlS7n77rvl4MGDIiJy/vx5efbZZ6V06dLicDgkODhYmjdvLlu3bnV6/qJFi+S+++4Tb29vKVKkiHTv3l2OHDni9Jjjx49Lr169pGTJkuJwOCQkJETat29vzktE5K233pK77rpLhgwZIn/99VdOLzYAAEC+R84DAAB5mUdOvXB0dLTMnDlT5s6dK6dOnZLy5cvLmDFj5J577hERkX79+snixYtl0KBBEhERIadPn5aff/5Z/vrrL6lRo4aIiMyePVt69eoltWrVkqioKDlx4oS899578ssvv8jvv/8uAQEBIiLSsWNHiY6OlsGDB0vp0qUlNjZWVq5cKf/884+ULl1aREQGDx4sKSkpMm3aNJkwYYLUq1dP/v3vf0vnzp2lYMGCmVqmxMRESUxMNP+Oj4/PvjcMAAAgjyDnAQCA/MBQVc2uFzt//rx8/vnnMmPGDNm0aZMUKlRIOnfuLL1795Z69eo5PTYgIEC6d+8ukydPzvC1rl69KiVLlpTg4GDZsmWLeHl5iYjIV199JW3btpXhw4fLyJEj5dy5cxIYGCjjx4+XIUOG3LLG+Ph4+eyzz+Tjjz+WTZs2ScGCBeVf//qX/Pvf/5a6deve9LkjRoyQkSNHZvLdyH3Z+FHeMcMwrC4BAACJi4sTPz8/q8vIF8h51nq0862XP7dUb1rd6hJM/+n7mNUl2JKbm7vVJYiISEpKstUlAMjHsiPnZcvpk8ePH5fevXtLSEiI9OnTR7y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      "text/plain": [
       "<Figure size 1500x2500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(sentence, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取验证集中模型未经训练的示例\n",
    "\n",
    "接下来，让我们从验证集中获取一个模型未经训练的示例。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Ein brauner Hund rennt dem schwarzen Hund hinterher.',\n",
       " 'A brown dog is running after the black dog.')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_idx = 6\n",
    "\n",
    "sentence = valid_data[example_idx][\"de\"]\n",
    "expected_translation = valid_data[example_idx][\"en\"]\n",
    "\n",
    "sentence, expected_translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型通过将 *is running* 更改为 *running* 来进行翻译，但这是可以接受的替换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<sos>',\n",
       " 'a',\n",
       " 'brown',\n",
       " 'dog',\n",
       " 'running',\n",
       " 'after',\n",
       " 'the',\n",
       " 'black',\n",
       " 'dog',\n",
       " '.',\n",
       " '<eos>']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translation, attention = translate_sentence(\n",
    "    sentence,\n",
    "    model,\n",
    "    en_nlp,\n",
    "    de_nlp,\n",
    "    en_vocab,\n",
    "    de_vocab,\n",
    "    lower,\n",
    "    sos_token,\n",
    "    eos_token,\n",
    ")\n",
    "translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "同样，一些注意头完全关注 \"ein\"，而其他一些则不关注它。此外，当输出预测的目标句中的句点和 `<eos>` 时，大多数头似乎会在源句中的句点和 `<eos>` 附近的标记上分散注意力，尽管有些头似乎会关注句子开头的标记。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.local/lib/python3.7/site-packages/ipykernel_launcher.py:24: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "/root/.local/lib/python3.7/site-packages/ipykernel_launcher.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x2500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(sentence, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们将查看来自测试数据的一个例子。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Eine Mutter und ihr kleiner Sohn genießen einen schönen Tag im Freien.',\n",
       " 'A mother and her young song enjoying a beautiful day outside.')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_idx = 10\n",
    "\n",
    "sentence = test_data[example_idx][\"de\"]\n",
    "expected_translation = test_data[example_idx][\"en\"]\n",
    "\n",
    "sentence, expected_translation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "翻译得还行（忽视了outside）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<sos>',\n",
       " 'a',\n",
       " 'mother',\n",
       " 'and',\n",
       " 'her',\n",
       " 'little',\n",
       " 'son',\n",
       " 'enjoy',\n",
       " 'a',\n",
       " 'beautiful',\n",
       " 'day',\n",
       " '.',\n",
       " '<eos>']"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "translation, attention = translate_sentence(\n",
    "    sentence,\n",
    "    model,\n",
    "    en_nlp,\n",
    "    de_nlp,\n",
    "    en_vocab,\n",
    "    de_vocab,\n",
    "    lower,\n",
    "    sos_token,\n",
    "    eos_token,\n",
    ")\n",
    "translation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.local/lib/python3.7/site-packages/ipykernel_launcher.py:24: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "/root/.local/lib/python3.7/site-packages/ipykernel_launcher.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "image/png": 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n14YNG+ry5cv18uXLOn36dDUMQytWrKgXL15UVdWoqCgdPXq0enl5af369bPEa+MsstJ7ZFsJqqo+/fTTmj9/fjUMQ4OCgrR58+Z6+vRpVb3zo80W+F588UU9fPiwPvHEE2oYho4fP968j4x8DWy1Hz9+XBcvXqzffvutnjx5Ms0fxxYor1+/rk888YQ2atRIv/zyS92yZYtWqVJFc+bMqSNHjjQD36+//qpdunTRUqVK6aOPPqovv/yyHjhwIM3rQsrs3btXg4KCNCgoSPPly6eGYWijRo109erVji7NYc6ePasjRozQhQsXJrruypUrWrx4cW3durUlv9uQ/sh5SctKGcKqstJ7RM57MHJe5kDOS8wROY+mXia0cuVKHTJkiM6dOzfRdRcvXtQiRYpox44dM74wB5o+fbqGh4fr6tWrzZXVwIED1c/PT6dPn65Xrlwx5z116pS++uqrGhwcrMeOHXNUyVlOVnyPWrdurbly5dKRI0fqDz/8oOPGjVM3NzetXLmyGfhOnTqlr7/+umbLlk1fe+01XbVqlS5evNi8D0ec/WzXrl2aK1cudXV1Nc/QNm3atIe6z6Sex4kTJ3TVqlX62GOP6fLly83LY2NjtXnz5uZrZ9u6fevWLUtv1c8sbMvv7du3dezYsdqoUSPdtGmTxsXF6dKlSzUoKEirVq2q33//vYMrdQ4Jf/yNHz9evb29dceOHaqaNc5uiJQj5yWWFTOE1WTF94ic9x9yXuZBzkuZjMh5NPUyqZs3b5r/T/hBGj16tHp7e+uePXtUNev8YOjZs6eWKFHC/DvhKdFtW4AuXrxorjSioqL07NmzDqk1q8qs71HCkJrQjz/+qDlz5tQ5c+ZodHS0qqr+8MMPmi1bNn3mmWfMsKd6J/QMHTpUDcPQjRs3mpdn5PKbcItqlSpVtEGDBrp48WL95ZdftEKFClqoUCEdM2ZMqu77woULdn/Hx8frpUuX1M/PT0uWLKmlS5c2v9OuXbumqqo3btzQZs2amYHP9jrb6iTsOdb+/ft14sSJWrNmTX399dftrlu5cqXmzZs3SwW+kydP6pIlS+65t0N8fLyeO3dOH3/8ca1Xr56eO3cugyuE1ZDz7GXWDJGZZNb3iJz3YOS8zIecZ8/ROY+mXiZx8uRJXbdu3X1XfqdOndLKlStrs2bN9NKlSxlYXca4ffu23crv4sWL5vMcMWKEli1bVq9evaqDBw82Q4RtxaGq2r17d33ttdfswjEyjjO9R3cHhdQGh3nz5unTTz9tHhaV0NSpUzVHjhzmZ3TNmjXq7e2tXbp0SXKr9LFjx3TDhg2pquNh2Z7/iRMndP369VqvXj394YcfzOsPHDigzZo107CwsBQHvubNm2uzZs00NjY20XUffPCBGoahhmHo0qVLzcttwc8W+MLCwnTw4MF69erV1Dw9pIMWLVqoYRhaqFAh/eWXX1T1zpZ322cpYeBbuXKlI0tNd7t379ZSpUppuXLl9J133rnnfN99950ahqFffvllBlYHqyDnkfOszpneI3KePXIeUoqc9x9nyHk09TKB3bt3a7ly5bRw4cL69ddf33O+JUuWqGEY+s0332RgdRnj2rVr+t5775nH7+/atUtLly6tP/30k6qqLl26VA3D0Lp165oDzyYMEd98842WKFFCP/zwwyyzVdtR7vX62j6fjn6PbEHyypUr5vguqTVhwgQ1DMMuGNlMnz5dg4ODNTY2Vn///Xf19vbWTp062W3hmTRpkr788suJbuuIz+i5c+c0f/78WqpUKS1SpIi5Jd0W0g4dOqRNmzZNceCbMmWK/vDDD3ZhL+Hg0gsWLFDDMLRatWp2W69t88fGxmqNGjW0ePHiltiin5U0a9ZMDcPQpk2b6vnz51X1zvKVMPCFh4dryZIlddWqVY4sNd3s27dPc+bMqfXq1Uty/Wx7Lc6fP6/VqlXTatWqmXusADbkPHKelZDz7iDn3UHOy7zIec6T82jqWdzevXs1ODhYGzVqpJ9++uk957tw4YJWrVpVq1WrZrcCzSwiIyO1bNmyWrRoUZ05c6b6+/trrVq1zMNPVFVfeOEFNQxDO3bsaLfL+9atW7VRo0ZatmxZPXr0qCPKzzJsQers2bO6d+9eu7OAqao+99xzDn2PbCFq9+7dWrVqVQ0PD9cqVaro119/neq9HrZs2WL+Pyoqyvz/ihUr1MfHR19++WX19fXVLl266IkTJ8zr//nnH61Vq5b26dPHaZbZAQMGaPbs2dUwDF23bp2q3llZ2d7XQ4cOabNmzbRQoUI6ZMiQ+95Xu3btdMWKFar6X3C7fv26xsXFJTpMY8aMGWoYhjZo0EA3bdpkXm7bkhsbG8uy60D3+/HRsGFDNQxDR44caf54Shj4VqxYoREREeZZwTKTa9eu6ZNPPqkVKlSw+x6wXWf7waR657PcrFkz7du3b0aXCSdHzruDnGcN5Dxyng05L/Mg5yXNmXIeTT0Lu3z5sjZu3Fhr1qypW7dutbvu2rVrdgtgZGSk1q9fXydMmJBh9SUcyDQjxj3Yv3+/5smTRz09PbVy5cqJTnG+Y8cObd++vbq5uWmnTp105syZOmTIEK1cubIGBQXpP//8k671ZfWxH2zPf+fOnVq0aFH18fFRDw8P7dOnjx45ckRV74Sstm3bOuw9UlU9evSohoSEaLly5bRt27ZauXJl9fb21nHjxtkF0JRq1aqVPvXUU3YrtTZt2qhhGFq1alXdt2+fefnJkyd1+PDhmjdvXofucWF7zxKO3TRu3Dhzi6ptkNeEge/w4cNao0YNLV269D23qNp+nP3000/mfe/fv1+7du2qjz76qJYpU0aHDBmi27dvN28zbdo0NQxD69evr5s3bzYvT1gbMp7tfY+KitKNGzfqV199pYcOHbL7gVK7dm11dXXVUaNGmT+aEga+e41HZHXnz5/X4sWLa69evczL1q9fr6NHj9bSpUtrmTJldMmSJeZ1CfcYyerrC9xBzrNHznNu5Dxyng05L/Mg592bM+U8mnoWdunSJS1UqJD269fPvGzjxo06YcIELVGihLZq1UpnzZplXpdwy0d6Bg/bwn/9+nW9fft2oq0xaSkuLs7chfvChQvq4eGhhmFokSJF9Oeff040/9GjR3XEiBGaI0cOdXFx0Tx58mjz5s119+7d6Vajqpor+Kw+jktUVJQWK1ZMa9WqpSNHjtTBgwerl5eX1q9fX//8809VvTMm0PDhwzVHjhzq6uqaIe+R7X25ceOGfvHFF1qrVi3zx0JcXJw+88wzahiGjhgxItWB76233lIXFxd98cUXzbFXjh07po0aNVJPT08dPHiwbt26Vb///nt94YUX1M3N7b7jMqSnBy3Dr7/+unp7e2vjxo3NAJ4w8EVGRt5zi6rtR+jly5f1xo0bqqq6Z88eDQ4O1mLFimnDhg21evXq6ubmpqVKldIFCxaYt50+fboahqGNGzfWP/74I22fNFLM9l7u2rVLy5Ytq/7+/urm5qbZs2fXoUOH2m21fOKJJ9TFxSVR4FPNvD+Er169qnXq1NESJUroli1bdMyYMZo/f34NDQ3VRo0aacWKFdXLy0u3bdtmd7vM+nog5ch55DyrIeeR88h5mQc57/6cKefR1LOw48ePa86cObVdu3a6adMmfffdd7VYsWIaHBys9evX11y5cmmhQoXsQp5qxgS9vXv3aqtWrbRYsWKaK1cu7devn/72229p9jjXr1+328V9165d+umnn+r333+vCxcu1AIFCmipUqX0p59+SjJgnTp1Sg8cOKBnz55N90FXBw0apIZh6F9//aWqWS/w2VYIV69e1dWrV+sjjzyiv//+u3n9Tz/9pAEBAVqzZk27LXbHjh3TgwcP6rlz5zJkYNz9+/dru3bttH379vrUU08lur5nz55qGIa+/vrrDwx8CZexhP+fOnWqGoahzz//vEZGRqqq6unTp/Wpp55Sd3d3c7DgYsWK6ZQpU8zbZeTYKvdbhtesWWPO96DAl1zR0dHaoEEDrVy5sjmWyvXr1/WPP/7Q0NBQDQ8P12XLlpnzz549Ww3D0NatW+v169cf9ukiBZJadxw6dEhz5cqlderU0blz5+rGjRvNM/g9//zzdsvKE088oZ6envrqq6+aZwLM7ObMmaMFChRQwzDUy8tL+/btax7StGXLFvXy8tLx48c7uEo4K3IeOc8KyHnkvPsh51kHOS/lnCXn0dSzuDlz5qhhGJotWzY1DEOfffZZ/fHHH1VVdfPmzWoYhn722WcZUovti8C2NaZy5crao0cPHTx4sPr4+GjBggXtdkFNrZs3b+rYsWP18ccf14sXL+rJkyfV29tbn3rqKfMLZPPmzVqgQAGNiIjQVatWmbtux8XFaXR0dIYORP7tt99qpUqV1M/Pz9xKmZkD38iRI3Xt2rV2lx07dkwLFSqkbdq00UaNGpmX216HtWvXmuPjJNzlPiP9+OOPZtjq3LmzeXnCgX179OihhmHoG2+8cc9Tlid8b5NaOU6ZMiVR4FO9s4fFypUrdcOGDXrgwAHz8owMeslZhj///HNz/pEjR6qPj482b97c/DGTUhcuXNC8efNq7969zctse2X8888/GhISog0bNrR7H+bPn687d+5M1eMhdWyfw7v/HTZsmJYtW9ZucOs333xT3dzcdNGiRXrjxg27AbErVqyoQUFBmXKw63PnzunmzZt1zZo15ne96p3lafHixbpv3z67H67ffPON5sqVy+6Mf8DdyHnkPGdDziPnpQQ5zxrIeQ/mzDmPpp6FXLhwQQ8ePKi//vqrnjt3zvzy+/3333X+/Pm6Y8cOuw/S119/raGhoRl6GukrV65o06ZNtUaNGna75LZp00aDg4N1xYoVabLy+uCDDzRHjhxavHhx9fX11ZYtWyYaW2Xjxo1m4FuzZo3eunVLDx06pM8++6zOnz8/Q1eiq1at0vLly6uvr6+5lTIzBr41a9aY42FcuXLFfI0jIyP1iSeeUMMwNF++fObYKqr/BYy1a9dqQECA1qtXz27FkZ4SjgekemdrcmBgoAYGBpo/mlTtA9/zzz+vhmHouHHjEr2HCf9+8803tXPnzjp+/Hhzi41NwsBnO0TjfvVlpOQswwlfj1GjRqlhGNquXTu7y5MjPj5e9+zZox4eHuaAy7ZDNWyfnQ8++EANw9DvvvvuYZ9amrnXFvrMqlevXtq2bdskv7MaN26sDRs2NP8eNGiQurm56cyZM80Bgq9fv2439krCHzmZxa5du7R8+fIaEBBg/mjs3bv3PQ8n27x5szZp0kQjIiL0+PHjGVwtnBU57z/kPOdEziPnpQQ5zxrIeQ/m7DmPpp5F7N69WytXrmyekahgwYLaq1evRMes22zatEmbNm2qpUqV0lOnTmVYnWfPntV8+fLp6NGjzcsGDhyobm5uOmvWLLPetNid+tVXX1XDMNTPz89uRZAwxG3cuFHDw8O1YMGC2rNnTzNwpPfYKknV8vPPP2v16tU1e/bs5m7smTHwzZ0719x6YXuf4+Pj9eDBg9qpUyc1DEMnT55sd0Yg2wrz119/VcMwtHnz5uZKP60kXCnfL+j/8MMP6uPjo9WrV7c7lChhkOnXr1+iM7ol1Lx5c3Vzc9N8+fKpu7u7FipUSD/66CO7eWyBr3fv3nro0KHUPKV0kdxlOOFeEG+99ZbdGQiTI+H70aBBA82XL5+5B0bCwXU3bdqkhmHo7NmzU/2c0lJSW+gzc+C7cuWK1qxZU4sXL24X0mx7xTzxxBPaunVrVVUdMmSIuru768yZM+3CXfv27XXNmjWZ9nWyHZpSo0YN/fDDD/Xzzz/XZ599Vj09PbVu3bp2P15v3LihY8eO1cqVK2tISIg5CDlAzkuMnOecyHnkvOQg51kDOe/BrJDzaOpZwL///qs5c+bUWrVq6VtvvaWLFy/WunXrqoeHhz722GN2p2CPi4vTsWPHaq1atTQ0NDRDziKV0K5duzQoKEh/+uknVb1zavSkFv7p06frmTNnUvUYtl18W7VqpaVKldLg4GAtW7asuSXs7gC1Y8cOLVeunIaGhmrZsmUz9EeUrZZTp07p559/ru3atVPDMDRHjhyZLvDdHaAOHDigjRs3NsdViY+P13///VdbtGihPj4+OnPmTLvAYFsR/PHHH7p37940rc0WzBIOuH348GEdNWqUTpo0Sb/99lu7+b/99lv19vbWxx9//J6BL6GE7+HGjRu1cOHCunDhQr1586Zu2LBBGzdurEFBQYkGRLaNvdK5c2e78OtIKVmGT5w48cD7s73ettfIdh+2y+Pi4nT27Nnq7e2tDRo0SDQGx5dffqkBAQH61VdfPfRze1gJw8pzzz2no0aNMj/3mTHI2J7TxYsXzUOFTp8+bfd5/+CDDzQ0NFSbNWumbm5uOnv2bLv3cMWKFZorVy67gbAzm9dee03z5ctnN6j3tWvX9PPPP1cvLy9t2bKlOQj5Rx99pGXLltUmTZqk+AcSMi9ynj1ynnMi591BzrNHzrMucl7yWCHn0dRzcnFxcTp06FDNnTu33Urn5s2bOmLECM2ePbs2atTI/AL97LPPNHv27Fq3bt0M2Up59xaM2NhYLVGihLZv317HjBmj7u7uOn36dLuVxLhx4zRfvnyp3uJj+yKJiorS48eP67vvvqu5c+fWMmXKmKeLT3hsv+qd13Hv3r3peoa2e9W7e/duDQkJ0apVq2q9evW0cePG5pbnzDyo8vz58zUwMFBr1aplbsGIj4/XQ4cOabNmze4b+NLSSy+9pIZh6K+//mpetmvXLg0JCVEfHx91c3NTf39/fe655+xulzDwJRzw+X7Gjh2rb775pjZr1szuef3111/apk0bDQwM1HfffdfuNhMmTNDJkyc/xDN8OOm1DI8dO9b8v21r/N69e/XJJ5/UOnXqaN++fc3X9dq1a9qvXz/19fXVKlWq6F9//aUnT57UX375RevWratFihS559g2GSXhd0p0dLTmypVLK1SooJMmTcrUgS/hj7jjx49rqVKltEePHuZ31rZt27Rs2bLmWF8JbdmyRevVq6ePPPKIw9+/9NSqVSstVKhQkgO9z5gxQw3DMM9QGhcXpzt27NCLFy9mcJVwVuS8xI9FzrMGch45z4acZ13kvAezQs6jqWcBLVq00IiICPNv2xfn9evXtVevXurp6anz5s0zr/v777/1/Pnz6VqTbUG37Zprc+vWLX3nnXfUx8dHDcPQuXPn2l2/efNmrVevnrZo0cJuy3NynTp1SnPnzq0ffvih3da0d955R3PlyqVlypQxQ25cXJwePHhQ169fn+LHSStXrlzRatWqadmyZXXr1q3m5QsWLNCSJUtq9uzZzTFiMmPgmzVrlhYpUkSrV69+z8A3e/bsdN16uWDBAi1VqpQGBAToL7/8oqqqTZs21fr16+svv/yie/bs0T59+qi3t7e2atXK7rbffvut+vv7a5kyZXTDhg0PfBzDMDR37tzao0cPVbU//Ojvv/82A997772X5H1kZFhIz2V4/vz5ahiGNmvWzLzM9qOnQIECWqlSJQ0JCdGgoCBzUPWrV6/qa6+9prlz59Zs2bJpYGCg5syZU/PkyZNoHKWMlnDZ7NGjhz777LMaHBysXl5eGhwcrFOmTMnUgc/m4sWL2qJFCw0PD9d+/fqZAfjbb7/VfPnyaXBwsI4YMUJ//fVXnTBhglarVk2DgoIy/WDXzz33nIaGhuq5c+dU1f7zcuTIEQ0LC9NWrVrplStXHFUinBw57z/kPGsh55HzbMh51kfOS5oVch5NPSd348YN7dixo4aGhtoNtGr7MJ0/f15z5cql3bt3z7CabI994MAB7dGjhz7zzDM6ceJEs0P/77//asuWLdXb21u7d++uly9f1itXrujXX3+t9erV05w5c6Z6d9QzZ85oixYtNHv27PrJJ5/YdcxtW3JLly6te/bs0V27dmnz5s01LCxMo6OjHfIlfO7cOc2TJ4/26tUr0XUrVqzQsLAw9ff3N78M0zvw3X3oRFoNIn2/+5k+fboWKVJEq1WrZjfmwKFDh/TJJ59UwzB03rx5af7+JLy/5cuXa0REhPr5+elvv/2m3bt3108//dS8PioqSidMmKDu7u6JAt/y5cs1b968evjw4Qc+5quvvqp+fn4aGhpqLg8Jt/z9/fff2r59e/Xx8dExY8Y85DNMvfRehs+dO6dDhgxRT09P8yx4M2fO1Jo1a+r//vc/Vb0z9lCTJk3sztwYGxur27Zt0/Hjx2vv3r110qRJyXrdM0qbNm00V65cOmHCBP3hhx/0iy++0PDwcA0NDc20W3Jtz8X2mbl48aJ27txZ8+bNq3379jV/LKxatUpbtmxpjgcWEhKiderUyfRBT/XOoUOenp76zDPPmK9HwrGiypcvr02aNHFUeXBy5Dx75LyHQ84j56mS81KLnEfOS4oVch5NPSd0+fJluy2wc+bMMQedvXtQ2Zs3b2rJkiW1adOmGVrj3r17NSgoyNzC4uvrq6VKlTKPx9+7d6926dJF3d3dNSAgQIOCgjRXrlxapEiRhx7r5PTp09q5c2f18PDQOXPm2AW+999/X/Pmzavu7u5aoEABDQwMNM9C5giXLl3S0NBQ7dq1q3lZwpW/7YxSwcHBdmegSg+2FdH//vc/7dy5c5rdr20lcPz4cV24cKGOGTNGv/rqK7sfJ/cKfAcOHNCOHTum25gDCUPosmXLtGTJkurr66uhoaG6efNmVf3v/bhw4YK+/fbb6u7uri1btrS7n7u3vNwv3A4fPlzd3Ny0Zs2a5tmOEr7nf/31lzZo0ECnTZv2UM/tYaXXMmx7bS5cuKCvvvqquru7a4sWLbRXr146dOhQu3m3bdumTZs2VcMw9Isvvki/J5sG/vjjDw0MDNQRI0bYfeecPn1aK1SokOm25NqW66Sex/nz57VTp06aN29e7dOnjxlwzpw5owcPHtRvv/1W9+3blykPMb1w4YLu2LFDN2/ebA4oHRMTo23bttXs2bPrgAED7ObfsGGD5s+fXwcMGJAp99JB6pDz7o+clzrkPHJeQuS8lCHn/YecZ72cR1PPyezfv1+bNm2qffr0MTvfsbGx2r59e/X29ta5c+faLUCbNm3S/Pnzm1+i6fEFc3cHX/XO6azr1KmjmzZtUtU7W2ciIiI0KCjIHO/kzJkzumHDBh0+fLgOGjRI58+fn6JTOt+9YCR8bvcLfMuWLdNBgwZp9+7d03wg3pS4ffu2Xr9+XVu0aKE5c+bU1atXm9fZQvuxY8e0aNGimjNnTs2bN6/euHEjXVcSp0+f1oiICC1XrpzdYSKpZVux7dy5U8PDwzUoKMg81XfNmjX1k08+Mee1HaJRrVo183OjmnhcnLSW8PX88ssvtUqVKuri4qLTp083n0PCgDJhwgT19vbWOnXqJHl/d2+RXbVqle7bt8/u7IODBw/WHDly6BNPPGEOMpzwdrbdtzNKRi/DCV/PwYMHa3BwsLq7u+vbb7+tqmo3bsvWrVvNwLd06VK7ep0pMP3222/q4uKiCxcuNC+zvaenT5/WPHnyaGhoqL777rv3DUrpIa0fx1b/wYMHtW/fvlqvXj1t2rSpfvbZZ3rs2DFVvfMZTrglN72XY2ewa9curVSpkgYHB6unp6cGBgbq5MmT9ebNm3r+/HmtV6+eenh4aM2aNXXRokX61ltvae3atTUoKEj379/v6PLhJMh5/yHnpT1yHjnPhpyXMuQ8cp6Vcx5NPSeya9cuzZkzp1apUkXHjx9vd93ff/+tjRs3Vg8PD+3atasuXrxYP/roI61Vq5aGhISYW13S0v79+xN9iezZs0cnTpyoffv21YkTJ5qX3759W7/99lstWbKkBgcHp9kHe//+/frcc8+Z46rcHfg6deqknp6eOnfuXLsBa1XTP0QkZPtyvH79eqKtFmvWrFHDMLRBgwZ2Wy9VVefOnaulSpXSr776So8cOZIutdlWvNeuXdP169druXLl7AYTTomkVirHjh3T8PBwrVevnv7www966dIl8zlXq1bNDA6qdwJFyZIlNSIiwtw9P70krDVhyFm8eLGWLFlSs2XLpmvXrlVV+8B38eJFfeONNzQkJMRcsSV1Px06dNCQkBA1DMPcSpnw7GpDhgzRHDlyaN26dc3DHe4e1yS9w4AjlmHb62h7raKionTgwIGaPXt2rVChgjlfwrGStm7dqi1btlTDMPTrr79O1eOmt40bN6qLi4sOHDjQ7nNgex6TJk1ST09PLVeunE6dOjXd3tuEY+Skx3fc3QO/lyhRQmvWrKmVK1dWT09PbdGihbnHxYULF8zA179//0wd+A4ePKihoaFao0YN/fjjj3XJkiX63HPPqWEYOnr0aFX978dNsWLFzEHyH3nkkQw9EyecGzkv6RrIeQ+HnEfOS4iclzrkPHKelXMeTT0ncerUKS1VqpTWr1//nrvnnzhxQvv166dubm5qGIb6+/tr2bJl0+WD9Pzzz2tQUJC563p8fLzevHlTe/TooYZhqK+vr7klI+Egsd988425srDtlm/7Akjpl19cXJx5SErnzp2TDHyHDh3SihUrao4cOXThwoV2W3IzauuJbeW2Z88erVevnhYvXlzbtGmjK1asMLdUzZ49Ww3D0Mcee0w/+eQTvXHjhn7//ffaqFEjbd68eZJn00lLJ06c0Ny5c2v9+vW1bt26iWpPjjNnzqhq4t21Z82apYULFzYHJ1ZV8/CG+fPn69WrV+0eZ8qUKVqpUqV0HUPDVuO1a9f02rVreubMGbsV9JdffmkOYH2vwHe/raxt27Y1x9zYtGmTzpw5UwMCAjRXrlz6559/mvMNGTLEXEHcHRzTmyOWYdtrfOLECf3ss8/MoG87RMMwDG3YsKE5f8LAt2nTJn3qqad0165dD/vUH8r9lokOHTpoUFCQ3WDgNpMnT9bixYtrmTJlNH/+/LpmzZpE86RVbXv37tUWLVporVq1dMCAAbp///40GzdJ9c5hF5UrV9YnnnjC/PyoqpYsWVLz58+va9eutTuc6ZlnnlFPT08dMmRImtXgTOLj47Vfv35arlw5ux+pL730krq6uprfc6p3PtPR0dG6bt06PXjwYIbvrQHnRc5LjJyXdsh55DxyXvKQ88h5d8sMOY+mnpNYsWKFBgUF6bJly8zLDh8+rD///LO++eab5gCjqnfGali3bp1u2bIl3T5I69at08KFC+s///xjd/n27du1U6dO6uLiok8//bR5ecItU99++62WKVNGDcPQQ4cOpehx7/7CunDhgn788cfq4eGhHTt2TDLwDRkyRA3DUMMw7AbFzUiHDx/W4OBgLV68uDZr1kwLFChghgHbOB2LFy/W4OBgNQxDXVxc1N3dXUNCQjLkjE/79u3TZs2aqYuLi+bJkyfFh6v07t1b8+bNa76fCd+ngQMHapEiRcwV/eDBg9Xd3V1nzpxpblW/evWqXbhLzzEYbHXs3btXmzRpovny5TPPeDZ//nxzvsWLF2tERIRmz55d161bZ972QSvNdevWaYECBXTatGnm8/vzzz/Vzc1N+/TpoydPnrS7jwEDBqhhGHbLdkbI6GXY9rrv2rVLS5QooaVKldJhw4aZy6ot8GXLlk0bN25s3i5h4Lt7LKmMlnAL5OHDh/XQoUPm1ndV1V9//VVLliypuXPn1j/++MOs98SJE/rss8/qmDFj9Pz58+rv75+m4xkldOjQIc2ZM6fmz59fS5curT4+PlqxYkX97rvv0mwL6pYtWzRnzpx2y8trr72mrq6uOnv2bI2OjlbV/96vc+fO6QsvvODwQw/SU/Xq1e2Wl1dffVXd3NzszuqY8LPsTIcUwTmQ8+4g56UPch45z4acd2/kvDvIeYlZPefR1HMw2xfsokWL1MfHx1whLFiwQGvXrm0GAxcXF+3bt2+G1mYLKYcOHdLNmzebK7B//vlH27Vrp4Zh2A2ImnBlsWzZMq1SpYrdLvkPYltZnD59Wn/++Wfdv3+/uWVp6tSpZuC7e9f2l19+WZ999lkdOHCg7t69O3VPNhVs9cbGxuqyZcu0Xr16ZnC7evWqPvHEE5o7d2594403zFCwc+dOXbJkiQ4fPlw//vhj/ffffzOs3r///lu7d++uhmHo22+/naIV67vvvqvFihXTSpUqmSt/24rltdde0wIFCqiq6ogRI8ygl3A8jfbt2+vbb7+dYbtt79u3T4ODg/XRRx/V7t2760svvaQhISHq7e2tI0aMMOf78ssvzfFFVq1alaz7/uSTT9TLy8vcurl69Wr19vbWjh072m2ltY2xonpn8F1HyOhl+N9//zX3FPjxxx/Ny+8eVPnuwHf3Mu0ICbfw9+zZU4sVK2buJbNgwQJVvfM8li1bpqVLl1Y/Pz/t3Lmzvv7669q6dWv18PDQDz/8UFVV+/btq76+vnrgwIE0X+nPnTtXGzRooP/8849euHBB165dq+Hh4VqoUCFdunRpqpaxhGfZVFX97rvvNHv27Ob306BBgxIt17dv39ZVq1aZn7G03ILsLBK+llWrVtUuXbqo6p0Gw92vR3x8vNavX99uizegSs5LiJyXvsh55Dxy3r2R88h5d8tMOY+mngOtWrVKly9frqp3zg6VK1cuDQ0N1SJFiqirq6s2atRIP/vsM7106ZI2a9ZMS5QokW7jcdzL9evXtXjx4lqsWDHdsGGD+cW1a9cuc2UxbNgwc/6EX9p3j31yPwmP7y9QoIC6ubmpp6entmnTxjyr2dSpUzVbtmzavn17PXHihMbFxemOHTu0Vq1aOnHiRId0zPfs2aNPP/20NmrUSDt27Gj3XK5du6ZNmzbVXLly2QW+9HL3GBc3b95MtBL9+++/tX379urm5qbTp09/4Bd0wtd0xowZWqRIEa1QoYLdVr1t27ZpQECAlilTxtxFOeEW2tWrV2vRokX17bffzpCzAt24cUM7dOigJUqUsNuF+n//+582adJEXV1dzYF8VVWXL1+uuXPn1rCwML127Zrdc7779YmPj9fp06dr7ty5VfXOVlIvLy/t1KmT3Za+Tz75REeMGGFu6brX/WWEjFyGBwwYoGFhYXZj+dx9ljBb4PPx8dFq1ao91HNLKwnf8+bNm2tQUJC++OKLOmbMGG3RooUahqFTp05V1TvPZ+vWrfrcc89pYGCg+vn5aeHChfX9998376Nr166aL1++NNlT4e5lZvLkyfrUU0/ZXbZr1y4tVKhQqgJfws9DnTp1dOPGjbp79251dXXVzz77TN9++211c3PTGTNm2P2A69Wrl9asWdPuDJ6ZScL1s+2wjJIlS+rTTz+tbm5uOmfOHLvlY/HixRoWFqZLlixxVMlwQuS8/5DzHh457w5ynj1y3oOR88h5d8tsOY+mnoNER0drkSJFtFGjRuYH5q+//tLu3btr69atdfny5Xa7sQ8ePFjDw8PN8S7SW8Ivvz/++EPz5MmjVapU0T/++MO8bufOnQ9cWaTExYsX9dFHH9U6derolClTdMCAARoQEKClS5fW3377TVXvjOvh5+enJUqU0AYNGmjp0qU1MDAwXQaQvpeEK+wZM2aoYRgaEBBgt2XQ9hpcv35dmzRporly5dIxY8aYWzvS0syZM82Vim234H379mmPHj20cePGOmXKFLvQs2PHDm3btq26u7snK/Dd/XwLFy5sF/jOnTunPXv2VF9fX61Vq5bdbTdt2qQNGjTQkiVLZtgPlejoaC1durS2a9fOvMy2wvzzzz+1cuXKWqBAAbvBrL/55htzi6xNwmWgTZs25pf43r171TAM7dixo2bPnl07d+5st+V2//79+sgjj+jzzz+f7mPo3E9GL8O3bt3SGjVq2H0G7hWcz507p71799bQ0FA9evRoih8rvYwaNUoLFSqkn3/+ubmsLlq0yDzsK+GPBNU7P/YOHTpkt5V748aNWqZMGW3QoIG5u35q2T63Bw4c0CFDhmj37t21a9eu+sYbb5jz2F5XW+ArWLCgLl++/J7vYfv27c1xkWy3jY6O1oiICG3YsKGuX79er1+/ru3atVMvLy81DEO/+OILu2Czfv16rVGjhvbo0cOhn/H0Yls/N27c2Hx+O3fuVD8/PzUMI9Eg2lu3btW6detq9erV9fTp044qG06GnJcYOS91yHn2yHl3kPNSjpxHzlPNnDmPpl4Gs335rV27VgMDA3XChAmq+t9Cd+vWrUTd923btunjjz+uzZs3T/etgLYPsK0eW3jYtGmThoaG6qOPPprkysLNzU1feumlFD9ewpVBTEyM1qlTx24g3tmzZ2t4eLgWLlzYDHwbN27UunXravny5bVOnTq6c+fO1D3ZhxAZGWnWPnPmTDUMQ4ODg/X3338350kY+Fq0aKHZsmUz3++08vbbb6thGNqnTx+9dOmSqv53NqOEewNUq1bN3Bqheme3fFvgmzlz5n23rNo+C2fOnNGoqCi7Lbm2gHTw4EF98skn1cXFRatXr67vvPOOvvjii1q+fHkNCgpKNN5HWrp7C2FMTIyWLFlS69Wrl+Qp51esWKGGYZi72icl4TL4zz//aNmyZdXFxUV//vlnVb2z2723t7dGRETYHd5y7NgxHTFihObOndthZ/fKqGX47tddVfXxxx/XypUr2405cfc8kZGRqnpnS25G/Xi9l4SB6OLFi1q/fn3t06ePuVXyt99+Uy8vL23Tpo127txZDcPQDz744J73N3v2bG3cuLEGBgam2UDQe/fu1dDQUM2ePbvmyJFDDcPQwMBAu0N9Ega+YsWKaUBAgH7zzTeJ7qtdu3bq4+OjP/74o/kZP3HihEZFRWnTpk31+++/N+ddsWKFRkREaGBgoN13x5o1a7Rhw4YaFhaW6cZWedD6+aefflIvLy8tVaqUvv/++3rs2DGdOnWq1qlTR3PkyOHwwb/hHMh59sh5D4ecR867Gzkv+ch55LyEMnPOo6nnIK1bt9aiRYsmOSCwTXx8vK5YsUKfeOIJDQ4OTvcPkm0l8e+//+rw4cO1devW2q1bN/NLY+vWrZorVy6tXLmy3cpi165d2rBhQw0ICNCoqKhkHx5he7wLFy7ogQMH9LffftNSpUrp2bNn7eb79NNPzcC3evVq8/Lr16/b7SacUS5duqSFChXSwoULm891+vTpahiGNmjQwG5rqW1lcu3aNW3fvn2Kxq1Ijps3b2rr1q3V399fe/XqpdHR0Tp58mRt0KCBbtq0SVVVv/rqKy1TpoxGRETo559/bt52x44d2qFDBzUMQ+fOnXvfxzl+/Lj6+fmZgXvatGlapEgRLV++vLn1PDIyUidOnKjly5fXwMBALVy4sD711FPmadHTg+0zdPToUZ03b555RsH27durv7+/Xfi2rdyioqLU09NTBw0adN/7VL0zOHSrVq20ePHi5la8X3/9VS9cuKCtWrVSwzD02Wef1e+++06/+OIL7dSpk7q7u+s777yTXk/5vjJqGb77dbd95rt3764+Pj7mgNSq9ltuJ06cqC1btkzXAbQfJD4+XtesWaOnTp0yL+vYsaPu3r1b3333Xd22bZuq3glZfn5+2qFDBz179qxu2bLF3KJ59/t7+/ZtXbRokRYrVkxLlSr10D9uEv5Ieeedd7RRo0b6xx9/6NGjR3XSpElJftfYXucdO3bYLZc227Zt05CQEJ08ebJ5/6dOndKSJUuqj4+PFi5c2O41UVX97LPPzIG0q1WrpuXLl9eCBQtqnjx5MmTgd0dJav1sWybWrVunRYsWNc9Omj17dn3sscfS9QctrImcR85LC+Q8cl5StZPz7o2cR857kMyY82jqOcDvv/+uISEhOm7cOFVNfOp41TsrpmbNmpljWKT3Bynhl35ISIgWKlRIy5UrpxEREeri4qIvv/yynjt3Trds2aKhoaGJdu/es2ePHj9+PNmPl3CLQ4UKFTQ4OFgrVKig+fPnN8/YlXDL2Pz58zU8PFxLlCih3377bVo97VS5fv26TpkyRUNCQvSRRx4xX4MPPvjAPJV7wi/hu7dmpYWnnnpKX3jhBVW981l58skn1dfXV3v37q316tWz28Ve9c64ARUrVtSIiAj94osvzMv//PNPfeaZZ8yBp1u2bKlvvvlmosfbs2ePuru7644dO8zLpk+fnijw3bx5U2NjY3XPnj166dKldA3jCc/CVbx4cS1SpIhOmzZNVe8clpI/f36tXLmy7tixw25L3cqVKzUgIEA/+eST+95/69atNVeuXDp69Ghdt26djh8/XgsXLqyGYehvv/2mMTExOnDgQPXx8VE3Nzd1c3PTsmXLmmNyqGbs2CoZtQzf73U/dOiQFihQQCtXrqx//fWX3eu+bds2feKJJ7Rjx47pcnhSch06dEhr1KihlSpV0gsXLmijRo3UxcXFLqDafpw9+uijdj/QmjVrpiVLllTDMMwfFjbHjh3TdevW2Q2cnVxPPfWU9uvXz+6y/fv36+DBg/WJJ57QkSNH2l1n+3F593fN3VvtE/rjjz/UMAydOHGiqt5ZRtq3b6/PPPOMFilSRHPkyGE+p4Tfvdu3b9f3339f69evr82bN9cxY8ak+GyXVpKc9fPJkyd1+/bt+vnnn+uOHTvS7eyksC5yHjnvYZHzyHl3I+clDzmPnHc/mTXn0dRzgDfeeEO9vb2T3CJ7/PhxM+ysX7/e3PUzI5w9e1YrVqxoHm+veucsOWXKlNGgoCDdunWrqt4ZiDY0NFSrV6+u69atS/HAxbb5T506pUWLFtWKFSvqSy+9pFWrVlXDMOzGakj4pbNgwQL19/fXihUrZvjx/baabQv+tWvXdMaMGRoQEHDPwHf3yiCtXLt2TTt27Kh+fn7mLu43b97Uli1banBwsObJk0e/+uorVVXzrHKq9oFv8eLF5uW2FcPFixe1bt26ahiGTp482bw+Li5ON27cqG5ububj2SQMfBl5hjebAwcOaGhoqDZu3FhXrFhhXn779m1dvHix5s6dW0uVKqUfffSRHj16VJcvX64NGjTQsLCw+479smnTJvX09NQxY8bYfdbWrFmj1atXN7fkqt7Zivnjjz/q//73P7v7dMRgyRm1DN/rdY+LizNf9xIlSug777yju3fv1mnTpmnt2rU1NDQ0XbfoJ8fly5d13rx5GhQUpEFBQRoSEqIrV660+2ESExOjpUqV0u7du5uX2Q55mDVrVpou2wmX5+PHj5vvhe0wkLCwMPMHbsLlOWHgS249tWvX1vDwcPMsk926ddN//vlHhw4dqtmyZdOqVaveMzA6w5nrMsL91s/Hjh3L0DNvwrrIeeS8h0HO+w85zx4578HIeeS8+8msOY+mXgbbtWuXhoaG6oABA+wu3717t86ePVsLFiyooaGh5u7kGXmmr7/++ktDQkJ0wYIFZqgZPHiwenl56cyZM+0GA928ebO6uLhovXr1UrSVzvZFcuXKFT18+LBWr17dfK5Xr141T4Ner14987knDHxffPGFQwZLTrjFKbmB77HHHjN38U5rMTExZrCwnY3r5s2b2qZNGzUMQx9//HHzjFwJxw1ZtWqVVq5cWXPlyqXLli1LdL+nT5/W9u3bq2EYOmnSJPPyTZs2qZubm7nlJmGQsR2i8eijj2bYexMfH683btzQrl276iOPPGKGGFttMTEx+u+//+rKlSvNcGbbhbpAgQIP3KX8u+++U8MwdMOGDapq/xn8+eefNVeuXGoYhv7000/3rM8R0nsZTu7rvmrVKq1du7b5unt7e2uZMmXs9gBwpNjYWC1btqwahqHFixc3l6WE48c8/vjjWqZMGb19+7bu27dP33rrLS1UqJD++eef5v2kVaBPuDxHRUWp6p3l2bYsJlyeE4YuW+B70HdNwjPSFS9eXLNly6aVKlUyQ2JMTIwOGzZM3dzctG7duub8CR/LdpmjPtsZIbnr5w0bNjjkxxysgZxHzksL5DxyXlLIeclDziPnJSUz5zyaehnE9sX70UcfmVtSYmNj9fDhw/rKK69osWLF1DAMfeqpp8zdZjPa559/roZhmFsJBg0apG5ubjpz5kxzK1Z0dLR5NqmtW7emauyQEydOqL+/v1arVk0bN25sd110dLQOHz5cPTw8tF69eublCbdcZLSjR49qyZIl7Q5nSBj4pk+frt7e3lq9enXzi3DChAnq7e2d7mcCO336tObIkcMcN+TWrVvavn17dXV11V69epmDKicMfN99953WqlUr0VnAEt6n7UxZttO3//nnn+rm5qYHDhxI8kt/1qxZmiNHDq1Zs6beunUrw1YIjz/+uDZq1Mj8e82aNTpw4EDNkSOHRkREaO/evfXWrVv61Vdf6dSpU3Xp0qXJOvRg165d6ubmpsOHDzcvSzgGy9ChQ80gYxt01hlWghm1DD/ode/Tp4/Gx8fr77//rl988YVu3LjRDDHO4Pz589q3b1996aWXNEeOHPrYY4+ZP5psK/HPP/9cc+bMqZ6enpovXz51c3NLdFa0tJbc5TlhCJs0aVKyv2v+/fdf9fPzUx8fH82dO7d+99135n1dunRJhw0bpp6ennbfvXcP6J8ZpWT97KhxlOD8yHn/IeelHXIeOS8hcl7ykPPIeQllhZxHUy8DxcXFably5bRu3bp68OBB7dixo+bOnVsDAwO1R48eumbNGrv5M3rlsXPnTs2RI4cuWrRIX331VXVzc9MZM2bYbd3p27evFilSxDxrUGpERUVps2bN1NfXV8uUKaMnTpzQ27dvmwuc7UvHw8NDGzVq5PCV6OHDh7VMmTIaFhZmHu6g+t8XxOXLl7Vnz55qGIbWqVPHbmtJeouJidEXXnhB3d3dzfEYbt68qa1atTLHXkkq8D3osJaEgW/y5Mm6fft29ff3161bt+r58+c1OjpaL168aN736dOndfbs2fcMkOnh6tWr2qBBA42IiNBp06bp8OHDNW/evFqgQAF99tlntUGDBurt7a0LFy5M8X2fP39eK1SooAULFtQff/zRvNy2Ynz//fe1UqVKWrduXQ0JCdHNmzen2fN6GBmxDCf3df/000/T6mmli1u3bumlS5d07ty5miNHDq1SpYrdAMK3bt3S33//XV9++WXt37+/fvnll+Z16fWdlJLl+e4zuiXHoUOHdMiQIbp06VItX768hoWF6TfffGN+Pi5evOhU370ZydnXz7AGZ/8ckfOSRs4j59mQ88h5quS8zMjZ188Pi6ZeBpozZ44ahqElS5ZUb29vLVWqlPbr10/PnDnj0C2UNlFRUVq6dGkNCAhQT09PnTVrlt1Wq3Xr1mn16tX1+eeff+h6T548aY4lMH78ePPyhIFvxIgRahiGtmrV6qEeKy38+++/WqNGDc2VK5dd4LONR/D3339rkSJF7MaKyajddm0D+RqGcc8VRFKHaDzI6dOntW3btmoYhjZt2lRdXV3V3d1dc+bMqWFhYZo7d24tWLCghoWFaf369TN8/BtV1X/++Ufz5s2rPj4+6u7uroMGDTLP3Hbu3DnNnj27jhgxIlX3vWXLFvX29tYqVarod999Z15++vRp7d69uw4ZMkS//fZbzZ07t3br1i1Dt1zfS0Ytw+n5ume0y5cv66xZszQoKMgu8F25ckWnT5+eaByT9F6uU7I82wJfSj53tu+Av//+W8uVK2eO5ZIw8DnTd29Gcfb1M6zB2T9H5Lx7I+eR82zIeeS89ETOcwxnXz8/LJp6Gejrr7/W0NBQrVatmn744Yd64cIFcyF1luO2//e//6m/v7/mzp3bbovFb7/9pg0bNtTw8PA0G0/j1KlT5lbChON62FZOFy9e1DFjxjh8wFWbf//9V2vWrJko8KneOQyjWrVq+sknn+j+/fszvLbo6Oh7riACAwO1a9euduNsJNfp06e1Y8eO6uHhoRUrVtSFCxfqxIkTddSoUTpixAgdPHiw9urVy6GnPT916pRu2rRJjx49anfIyLp167RAgQL68ccfm5el1Pfff2/uwv7CCy/ojBkztGPHjurm5qafffaZqqp26NBBc+fOrZcvX067J/UQMmoZTs/XPaN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",
      "text/plain": [
       "<Figure size 1500x2500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(sentence, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLEU\n",
    "\n",
    "最后，我们计算Transformer的BLEU分数。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "bleu = evaluate_hf.load(\"bleu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d683e800e1da42008332d7c58794e71e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "translations = [\n",
    "    translate_sentence(\n",
    "        example[\"de\"],\n",
    "        model,\n",
    "        en_nlp,\n",
    "        de_nlp,\n",
    "        en_vocab,\n",
    "        de_vocab,\n",
    "        lower,\n",
    "        sos_token,\n",
    "        eos_token, \n",
    "    )[0] for example in tqdm(test_data)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = [\n",
    "    \" \".join(translation[1:-1]) for translation in translations\n",
    "]\n",
    "\n",
    "references = [\n",
    "    [example[\"en\"]] for example in test_data\n",
    "]\n",
    "\n",
    "def get_tokenizer_fn(nlp, lower):\n",
    "    \n",
    "    def tokenizer_fn(s):\n",
    "        tokens = [token.text for token in nlp.tokenizer(s)]\n",
    "        if lower:\n",
    "            tokens = [token.lower() for token in tokens]\n",
    "        return tokens\n",
    "        \n",
    "    return tokenizer_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们得到了一个BLEU分数为0.3239，超过了卷积序列到序列模型和基于注意力的RNN模型。而且这一切都是在参数最少、训练时间最短的情况下实现的！\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bleu': 0.32394351941480126,\n",
       " 'precisions': [0.638349885408709,\n",
       "  0.3991728701406121,\n",
       "  0.25545536519386836,\n",
       "  0.16917740336967294],\n",
       " 'brevity_penalty': 1.0,\n",
       " 'length_ratio': 1.0024506049931077,\n",
       " 'translation_length': 13090,\n",
       " 'reference_length': 13058}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer_fn = get_tokenizer_fn(en_nlp, lower)\n",
    "results = bleu.compute(predictions=predictions, references=references, tokenizer=tokenizer_fn)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "恭喜你完成这些教程！希望你发现它们很有用。\n",
    "\n",
    "如果您发现任何错误或对使用的任何代码或解释有任何疑问，请随时提交 GitHub 问题，我将尽快进行更正。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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