Notebooks/10_Transformers.ipynb

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    "<center>\n",
    "<img src='..\\assets\\1\\lang-pic.jpg' width=600>\n",
    "</center>\n",
    "    \n",
    "# 1. Introduction\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">1.1 NLP series</p>\n",
    "\n",
    "This is the **tenth in a series of notebooks** covering the **fundamentals of Natural Language Processing (NLP)**. I find that the best way to learn is by teaching others, hence why I am sharing my journey learning this field from scratch. I hope these notebooks can be helpful to you too. \n",
    "\n",
    "NLP series:\n",
    "\n",
    "1. [Tokenization](./01_Tokenization.ipynb)\n",
    "2. [Preprocessing](./02_Pre_Processing.ipynb)\n",
    "3. [Bag of Words and Similarity](./03_BOW_Similarity.ipynb)\n",
    "4. [TF-IDF and Document Search](./04_TFIDF_DocSearch.ipynb)\n",
    "5. [Naive Bayes Text Classification](./05_NaiveBayes_TextClf.ipynb)\n",
    "6. [LDA Topic Modelling](./06_LDA_TopicModelling.ipynb)\n",
    "7. [Word Embeddings](./07_Word_Embeddings.ipynb)\n",
    "8. [Recurrent Neural Networks (RNNs) and Language Modelling](./08_RNNs_LMs.ipynb)\n",
    "9. [Machine Translation and Attention](./09_Machine_Translation_Attention.ipynb)\n",
    "10. Transformers\n",
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/JUSTSUJAY/NLP_One_Shot/blob/main/Notebooks/10_Transformers.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>\n",
    "[![Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/JUSTSUJAY/NLP_One_Shot/blob/main/Notebooks/10_Transformers.ipynb)\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">1.2 Outline</p>\n",
    "\n",
    "This is the **final notebook** in the NLP series! By now we've covered all the fundamental tasks and models in NLP and are ready to learn about the current **state-of-the-art transformer model**. \n",
    "\n",
    "This model originates from the 2017 [Attention Is All You Need](https://arxiv.org/abs/1706.03762) paper and still **dominates** the field of NLP today. For example, you've probably heard of the **conversational AI ChatGPT** by OpenAI - well this is based on the transformer model (GPT = Generative Pre-trained *Transformer*)!\n",
    "\n",
    "In this notebook, we are going to go through **what a transformer is**, **build one from scratch** and then **download a pre-trained model from hugging face and fine-tune it** for a text classification problem. "
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    "# 2. The Transformer\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">2.1 Subword Tokenization</p>\n",
    "\n",
    "Before we go through the model architecture, let's briefly talk about **tokenization**. So far we've mostly used **word level tokenization**. This is very effective because each word already carries a lot of informatino, however a down side is that it cannot handle **Out-Of-Vocabulary** (OOV) words. On the other hand there is **character level tokenization**, which can handle OOV words but needs to learn how to spell words correctly.\n",
    "\n",
    "For the transformer, a **subword level tokenization** approach was used that takes the best of both worlds. The idea is to tokenize **common words** as they appear but break down rarer words into **subwords**. This is both efficient in that it retains the meaning of the most frequent words but can also handle OOV words by breaking them down into subwords. \n",
    "\n",
    "<br>\n",
    "<center>\n",
    "<img src=\"../assets/10/subword-tokenization.jpg\" width=600>\n",
    "</center>\n",
    "<br>\n",
    "\n",
    "One algorithm used to figure out which words to keep and which to break down is called **Byte Pair Encoding** (BPE). This is a **data compression technique** that iteratively builds a vocabulary by **merging** the most frequent subwords that appear in the corpus. Alternatives to BPE include **Wordpiece** (used by BERT) and **Sentencepiece**, which work in the opposite direction, i.e. first tokenize words/sentences and then break these down into subwords. \n",
    "\n",
    "<br>\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">2.2 Non-Recurrence</p>\n",
    "\n",
    "In the last two notebooks we saw how Recurrent Neural Networks (RNNs) **process text sequentially** via cells and hidden states. When they were first developed, researchers quickly noticed that they were **very slow** to train because calculations **couldn't be parallelized**. This is simply because each cell needed the hidden state of the **previous** cell before being able to produce its own hidden state.\n",
    "\n",
    "This is where transformers come in. The groundbreaking idea in the *Attention is All You Need* paper was to completely **get rid of recurrence** in RNNs and instead use **all the inputs** to work out each output. They exact way this is done is called the **self attention mechanism**, which we will soon cover. \n",
    "\n",
    "This was an amazing **breakthrough** because it allowed for much **faster, parallelized training** across many GPUs. \n",
    "\n",
    "<br>\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">2.3 Self Attention</p>\n",
    "\n",
    "Recall when we previously came across attention, the inputs were dotted together to create attention weights by passing these scores through a softmax. The weights were then used in a linear combination of the inputs to create the attention vector.\n",
    "\n",
    "In the transformer, attention works in a similar way. The main difference is that the input is first multiplied by 3 matrices, $Q, K, V$ called the **query, key and value matrices** that act as **additional learnable parameters** than just the input embeddings. The intution is that a query is made against the key (like in a database), which is then used to select the appropriate value. \n",
    "\n",
    "<br>\n",
    "<center>\n",
    "<img src=\"../assets/10/selfattention.jpg\" width=600>\n",
    "</center>\n",
    "<br>\n",
    "\n",
    "The **vectorized formula** is given by \n",
    "\n",
    "<br>\n",
    "\n",
    "$$\n",
    "\\Large\n",
    "\\text{Attention}(Q,K,V) = \\text{softmax}\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right)V\n",
    "$$\n",
    "\n",
    "<br>\n",
    "\n",
    "What this is doing, is taking the **dot product** of all the query and key vectors (at the same time via matrix multiplication) and **scaling** these by the square root of the key $d_k$ size to normalize for different sequence lengths. The resulting scores are then passed through a **softmax** (applied row-wise) to create weights that sum to 1, which are used in a **linear combination** with the value vectors to output the attention vectors. \n",
    "\n",
    "This calculation is repeated for each time step, using the **same** Q, K and V matrices. Since the entire output is another sequence, it is common to **stack multiple attention layers** on top of each other. What we have described up to now is a **single attention head**.\n",
    "\n",
    "<br>\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">2.4 Multi-Head Self Attention</p>\n",
    "\n",
    "A single attention head will only be able to focus on a single aspect of how words related to each other in a sentence. But we know there are **multiple questions** we can ask when we read a sentence, for example, who, where what, when, why etc. \n",
    "\n",
    "For this reason, the transformer uses **multiple attention heads**, i.e. there are multiple sets of Q, K and V matrices that can each learn different relationships on their own in each layer. \n",
    "\n",
    "<br>\n",
    "<center>\n",
    "<img src=\"../assets/10/multiheadattention.png\" width=250>\n",
    "</center>\n",
    "<br>\n",
    "\n",
    "TO balance the additional **computational complexity**, if there are $h$ heads then the Q, K, V matrices have their embedding dimensions divided by $h$, i.e. the number of columns is divided by $h$. This means the total **number of learnable parameters stays the same**, which is allows for more complex relationships to be learnt at a comparable speed to before. \n",
    "\n",
    "The output of each head is **concatenated** and passed through a **fully connected layer** for further processing. The nice thing to remain is that the input and outputs share exactly the **same dimension**, so it is easy to stack multiple layers on top of each other for better performance. \n",
    "\n",
    "In the original transformer, the embedding dimension (aka **model dimension**) was $d_{\\text{model}}=512$ and **8 attention heads** were used, so that each head had a dimension of 512/8 = 64.\n",
    "\n",
    "<br>\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">2.5 Positional Encoding</p>\n",
    "\n",
    "By removing recurrence, we have gained the ability to parallelize computations but this has come at the cost of **losing word order information**. At the moment, if we change the order of the input words we would get the same corresponding outputs but this is not how language works!\n",
    "\n",
    "So we need a way to tell our model that **word order is important**. In the original paper, this was done by **adding a vector** to the input embeddings before the attention layer was computed. This positional encoding vector followed the following **sinusoidal equations**\n",
    "\n",
    "<br>\n",
    "\n",
    "$$\n",
    "\\text{PE}_{(pos, 2i)} = \\sin(pos/10000^{2i/d_{\\text{model}}}) \\\\\n",
    "\\text{PE}_{(pos, 2i+1)} = \\cos(pos/10000^{2i/d_{\\text{model}}})\n",
    "$$\n",
    "\n",
    "<br>\n",
    "\n",
    "where $pos$ is the position and $i$ is the dimension. \n",
    "\n",
    "There are **two equations**, one for even dimensions and one for odd dimensions. The idea is that each dimension has its own **unique** sinusoidal function and each word in the sequence gets a different value depending on its position. \n",
    "\n",
    "<br>\n",
    "<center>\n",
    "<img src=\"../assets/10/positionalencoding.png\" width=600>\n",
    "</center>\n",
    "<br>\n",
    "\n",
    "The allows the transformer to learn **positional information** about each input sequence. Don't worry about where the 10,000 comes from - it is quite arbitrary. \n",
    "\n",
    "Note that nowaways it is more common to use a **learnable positional embedding** to apply a positional encoding instead of these sinusoidal curves. This works exactly the same as word embeddings where the model **learns** the best word order representations using **backpropagation**. \n",
    "\n",
    "<br>\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">2.6 Architecture</p>\n",
    "\n",
    "We've now got all the pieces we need to understand the transformer architecture. The model is split into an **encoder and a decoder**. The encoder processes the input into some representation which the decoder uses to produce an output. \n",
    "\n",
    "We've already discussed how the inputs are first passed through an **embeddding layer** and combined with a **positional encoding** before being going through the first **encoder block**. \n",
    "\n",
    "The encoder block is made up of a **multi-head attention layer** followed by a two layered **feed forward neural network** applied pointwise. In the paper, the dimensions of the layers were 512 for the input layer, 2048 for the hidden layer and 512 for the output layer. Furthermore, the encoder was made up of **6 encoder blocks** stacked on top of each other.\n",
    "\n",
    "<br>\n",
    "<center>\n",
    "<img src=\"../assets/10/transformer.jpg\" width=350>\n",
    "</center>\n",
    "<br>\n",
    "\n",
    "Because of the very deep architecture of the model, a couple additional tricks were used in training to stabilize the output. These were **layer normalization** and **skip connections**. Layer normalization is similar to batch normalization however instead of normalizing across each batch, it **normalizes across each layer** output making it easier to parallelize. \n",
    "\n",
    "Skip connections are a **technique from computer vision** that add the input of a layer to the output of following layer. This makes it **easier to train** very deep networks because the model can learn the **identity function** and pass information between layers more easily. In the original paper, these techniques were placed after each attention/feed forward layer however it is more common today to place them **before** these layers. \n",
    "\n",
    "The **decoder** works in a similar to the encoder but with some key differences. It starts with a **masked multi-head attention layer** because it won't be able to see into the future at inference time. For example, if we are doing translation then the decoder should only be using words that it has already translated so far. \n",
    "\n",
    "After that, just like in the decoder a **multi-head attention layer** and a **feed forward neural network** is used after that. The only difference is that the **keys and values matrices** from the encoder are used in the decoder attention layer. Again **6 decoder blocks** were used in the original paper. Finally, this is passed through a **linear layer** followed by a **softmax** to generate the outputs. \n",
    "\n",
    "<br>\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">2.7 Variants</p>\n",
    "\n",
    "Quickly after the original paper came out, a whole host of **different transformers** were released. These can be categorized into **encoder only** (e.g. BERT), **decoder only** (e.g. GPT) and **encoder-decoder** (e.g. T5). Let's briefly discuss the main models and see how they are different to each other.\n",
    "\n",
    "<br>\n",
    "<center>\n",
    "<img src=\"../assets/10/transformer-groups.png\" width=500>\n",
    "</center>\n",
    "<br>\n",
    "\n",
    "**BERT** stands for Bidirectional Encoder Representations from Transformers and has up to **340 million parameters**. It was trained by a team at Google using **masked language modelling**, i.e. guess the missing word in a sentence. \n",
    "\n",
    "**DistilBERT** is a distilled version of BERT that achives 97% performance of BERT while being **60% faster**. It can be used for text classification and sequence labelling tasks. \n",
    "\n",
    "**RoBERTa** is similar to BERT but was trained for much longer and with better design choices. It significantly **exceeded the performance** of BERT on some tasks. \n",
    "\n",
    "**XLM** is a **cross-lingual language model** variant of BERT that smashed several benchmarks on multilingual and translation tasks. \n",
    "\n",
    "**ALBERT** is an optimized version of BERT that used **fewer parameters** thus making it possible to train larger models with fewer parameters. \n",
    "\n",
    "**GPT** stands for Generative Pre-trained Transformer and is a family of decoder only transformers developed by OpenAI and used for **text generation** tasks. These were trained on **next word prediction** and are able to produce coherent text thanks to its enormous model size (GPT-3 has **175 billion parameters**!). \n",
    "\n",
    "**T5** is a high-performing encoder-decoder transformer based on the original paper and is used for **sequence-to-sequence tasks**.  "
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    "# 3. Application\n",
    "\n",
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">3.1 Transformers from scratch</p>\n",
    "\n",
    "To reinforce our understanding of transformers, lets write the code to **implement one from scratch**. We wont't be able to train it on our own but we will later see how was can use **transfer learning** to fine tuning a pre-trained transformer for our applications. \n",
    "\n",
    "* Sentiment Analysis Company Reviews - [Link](https://www.kaggle.com/competitions/sentiment-analysis-company-reviews/code)"
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   "source": [
    "**Libraries**"
   ]
  },
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   "source": [
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
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    "**Multi-Head Attention**\n",
    "\n",
    "This implementation of Multi-Head Attention uses the **same Q, K, V matrices** in each head. It also allows for inputs corresponding to padding to be **masked out**. "
   ]
  },
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    "class MultiHeadAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self, d_model, num_heads):\n",
    "        super().__init__()\n",
    "        self.d_model = d_model\n",
    "        self.num_heads = num_heads\n",
    "\n",
    "        # Head dimension\n",
    "        self.d_head = self.d_model // self.num_heads\n",
    "\n",
    "        # Trainable parameters\n",
    "        self.Wq = tf.keras.layers.Dense(self.d_model)\n",
    "        self.Wk = tf.keras.layers.Dense(self.d_model)\n",
    "        self.Wv = tf.keras.layers.Dense(self.d_model)\n",
    "        \n",
    "        # Fully connected layer\n",
    "        self.dense = tf.keras.layers.Dense(self.d_model)\n",
    "  \n",
    "    # Scaled dot product attention\n",
    "    def attention(self, query, key, value, mask=None):\n",
    "        key_dim = tf.cast(tf.shape(key)[-1], tf.float32)\n",
    "        scaled_scores = tf.matmul(query, key, transpose_b=True) / np.sqrt(key_dim)\n",
    "\n",
    "        if mask is not None:\n",
    "            scaled_scores = tf.where(mask==0, -np.inf, scaled_scores)\n",
    "\n",
    "        softmax = tf.keras.layers.Softmax()\n",
    "        weights = softmax(scaled_scores)\n",
    "        return tf.matmul(weights, value), weights\n",
    "\n",
    "    # Reshape and transpose Q, K, V\n",
    "    def split_heads(self, x):\n",
    "        batch_size = x.shape[0]\n",
    "\n",
    "        split_inputs = tf.reshape(x, (batch_size, -1, self.num_heads, self.d_head))\n",
    "        return tf.transpose(split_inputs, perm=[0, 2, 1, 3])\n",
    "    \n",
    "    # Reverses previous function\n",
    "    def merge_heads(self, x):\n",
    "        batch_size = x.shape[0]\n",
    "\n",
    "        merged_inputs = tf.transpose(x, perm=[0, 2, 1, 3])\n",
    "        return tf.reshape(merged_inputs, (batch_size, -1, self.d_model))\n",
    "\n",
    "    # Forward pass\n",
    "    def call(self, q, k, v, mask):\n",
    "        Q = self.Wq(q)\n",
    "        K = self.Wk(k)\n",
    "        V = self.Wv(v)\n",
    "\n",
    "        Q = self.split_heads(Q)\n",
    "        K = self.split_heads(K)\n",
    "        V = self.split_heads(V)\n",
    "\n",
    "        output, attention_weights = self.attention(Q, K, V, mask)\n",
    "        output = self.merge_heads(output)\n",
    "\n",
    "        return self.dense(output), attention_weights"
   ]
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   "source": [
    "**Encoder block**\n",
    "\n",
    "Like in the original paper, **dropout** is applied between layers. "
   ]
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   "source": [
    "class EncoderBlock(tf.keras.layers.Layer):\n",
    "    def __init__(self, d_model, num_heads, hidden_dim, dropout_rate=0.1):\n",
    "        super().__init__()\n",
    "        self.mha = MultiHeadAttention(d_model, num_heads)\n",
    "        self.ffnn = self.feed_forward_neural_network(d_model, hidden_dim)\n",
    "    \n",
    "        # Dropout\n",
    "        self.dropout1 = tf.keras.layers.Dropout(dropout_rate)\n",
    "        self.dropout2 = tf.keras.layers.Dropout(dropout_rate)\n",
    "        \n",
    "        # Layer normalization\n",
    "        self.layernorm1 = tf.keras.layers.LayerNormalization()\n",
    "        self.layernorm2 = tf.keras.layers.LayerNormalization()\n",
    "  \n",
    "    # Feed forward neural network\n",
    "    def feed_forward_neural_network(self, d_model, hidden_dim):\n",
    "        return tf.keras.Sequential([tf.keras.layers.Dense(hidden_dim, activation='relu'),\n",
    "                    tf.keras.layers.Dense(d_model)])\n",
    "\n",
    "    # Forward pass\n",
    "    def call(self, x, mask):\n",
    "        mha_output, attention_weights = self.mha(x, x, x, mask)\n",
    "        mha_output = self.dropout1(mha_output)\n",
    "        mha_output = self.layernorm1(x + mha_output)\n",
    "\n",
    "        ffnn_output = self.ffnn(mha_output)\n",
    "        ffnn_output = self.dropout2(ffnn_output)\n",
    "        output = self.layernorm2(mha_output + ffnn_output)\n",
    "\n",
    "        return output, attention_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c20c59d",
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   "source": [
    "**Encoder**\n",
    "\n",
    "Recall that it is more common to use a **trainable embedding layer** for the positional encoding, which is what we will use here. Note also that the paper applied **dropout** to the embeddings before the encoder blocks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47ad1747",
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   "source": [
    "class Encoder(tf.keras.layers.Layer):\n",
    "    def __init__(self, num_blocks, d_model, num_heads, hidden_dim, src_vocab_size,\n",
    "               max_seq_len, dropout_rate=0.1):\n",
    "        super().__init__()\n",
    "        self.d_model = d_model\n",
    "        self.max_seq_len = max_seq_len\n",
    "        \n",
    "        # Embedding layers\n",
    "        self.token_embed = tf.keras.layers.Embedding(src_vocab_size, self.d_model)\n",
    "        self.positional_embed = tf.keras.layers.Embedding(max_seq_len, self.d_model)\n",
    "\n",
    "        # Dropout\n",
    "        self.dropout = tf.keras.layers.Dropout(dropout_rate)\n",
    "\n",
    "        # Encoder blocks\n",
    "        self.blocks = [EncoderBlock(self.d_model, num_heads, hidden_dim, dropout_rate) \n",
    "                        for _ in range(num_blocks)]\n",
    "  \n",
    "    # Forward pass\n",
    "    def call(self, inpt, mask):\n",
    "        token_embeds = self.token_embed(inpt)\n",
    "\n",
    "        # Position indices for batch of input sequences\n",
    "        num_pos = inpt.shape[0] * self.max_seq_len\n",
    "        pos_idx = np.resize(np.arange(self.max_seq_len), num_pos)\n",
    "        pos_idx = np.reshape(pos_idx, inpt.shape)\n",
    "        pos_embeds = self.pos_embed(pos_idx)\n",
    "\n",
    "        x = self.dropout(token_embeds + pos_embeds)\n",
    "\n",
    "        # Pass input through decoder blocks\n",
    "        for block in self.blocks:\n",
    "            x, weights = block(x, mask)\n",
    "\n",
    "        return x, weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e93180",
   "metadata": {
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     "duration": 0.007829,
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Decoder block**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a44c5418",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "class DecoderBlock(tf.keras.layers.Layer):\n",
    "    def __init__(self, d_model, num_heads, hidden_dim, dropout_rate=0.1):\n",
    "        super().__init__()\n",
    "\n",
    "        self.mha1 = MultiHeadAttention(d_model, num_heads)\n",
    "        self.mha2 = MultiHeadAttention(d_model, num_heads)\n",
    "\n",
    "        self.ffnn = self.feed_forward_neural_network(d_model, hidden_dim)\n",
    "\n",
    "        self.dropout1 = tf.keras.layers.Dropout(dropout_rate)\n",
    "        self.dropout2 = tf.keras.layers.Dropout(dropout_rate)\n",
    "        self.dropout3 = tf.keras.layers.Dropout(dropout_rate)\n",
    "\n",
    "        self.layernorm1 = tf.keras.layers.LayerNormalization()\n",
    "        self.layernorm2 = tf.keras.layers.LayerNormalization()\n",
    "        self.layernorm3 = tf.keras.layers.LayerNormalization()\n",
    "  \n",
    "    # Feed forward neural network\n",
    "    def feed_forward_neural_network(self, d_model, hidden_dim):\n",
    "        return tf.keras.Sequential([tf.keras.layers.Dense(hidden_dim, activation='relu'),\n",
    "                    tf.keras.layers.Dense(d_model)])\n",
    "\n",
    "    # Forward pass\n",
    "    def call(self, encoder_output, target, decoder_mask, memory_mask):\n",
    "        mha_output1, attention_weights = self.mha1(target, target, target, decoder_mask)\n",
    "        mha_output1 = self.dropout1(mha_output1)\n",
    "        mha_output1 = self.layernorm1(mha_output1 + target)\n",
    "\n",
    "        mha_output2, attention_weights = self.mha2(mha_output1, encoder_output, \n",
    "                                                encoder_output, memory_mask)\n",
    "        mha_output2 = self.dropout2(mha_output2)\n",
    "        mha_output2 = self.layernorm2(mha_output2 + mha_output1)\n",
    "\n",
    "        ffnn_output = self.ffnn(mha_output2)\n",
    "        ffnn_output = self.dropout3(ffnn_output)\n",
    "        output = self.layernorm3(ffnn_output + mha_output2)\n",
    "        return output, attention_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a30d452",
   "metadata": {
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     "duration": 0.007793,
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "**Decoder**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c942134b",
   "metadata": {
    "execution": {
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   "source": [
    "class Decoder(tf.keras.layers.Layer):\n",
    "    def __init__(self, num_blocks, d_model, num_heads, hidden_dim, target_vocab_size,\n",
    "               max_seq_len, dropout_rate=0.1):\n",
    "        super().__init__()\n",
    "\n",
    "        self.d_model = d_model\n",
    "        self.max_seq_len = max_seq_len\n",
    "\n",
    "        self.token_embed = tf.keras.layers.Embedding(target_vocab_size, self.d_model)\n",
    "        self.pos_embed = tf.keras.layers.Embedding(max_seq_len, self.d_model)\n",
    "\n",
    "        self.dropout = tf.keras.layers.Dropout(dropout_rate)\n",
    "\n",
    "        self.blocks = [DecoderBlock(d_model, num_heads, hidden_dim, dropout_rate) for _ in range(num_blocks)]\n",
    "\n",
    "    def call(self, encoder_output, target, decoder_mask, memory_mask):\n",
    "        token_embeds = self.token_embed(target)\n",
    "\n",
    "        # Position indices for batch of sequences\n",
    "        num_pos = target.shape[0] * self.max_seq_len\n",
    "        pos_idx = np.resize(np.arange(self.max_seq_len), num_pos)\n",
    "        pos_idx = np.reshape(pos_idx, target.shape)\n",
    "        pos_embeds = self.pos_embed(pos_idx)\n",
    "\n",
    "        x = self.dropout(token_embeds + pos_embeds)\n",
    "\n",
    "        for block in self.blocks:\n",
    "            x, weights = block(encoder_output, x, decoder_mask, memory_mask)\n",
    "\n",
    "        return x, weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f216a1a8",
   "metadata": {
    "papermill": {
     "duration": 0.007781,
     "end_time": "2023-02-21T10:08:04.878448",
     "exception": false,
     "start_time": "2023-02-21T10:08:04.870667",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Transfomer**\n",
    "\n",
    "Putting the pieces together, we have arrived at the transformer model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8e783ab",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Transformer(tf.keras.Model):\n",
    "    def __init__(self, num_blocks, d_model, num_heads, hidden_dim, source_vocab_size,\n",
    "               target_vocab_size, max_input_len, max_target_len, dropout_rate=0.1):\n",
    "        super().__init__()\n",
    "\n",
    "        self.encoder = Encoder(num_blocks, d_model, num_heads, hidden_dim, source_vocab_size, \n",
    "                               max_input_len, dropout_rate)\n",
    "\n",
    "        self.decoder = Decoder(num_blocks, d_model, num_heads, hidden_dim, target_vocab_size,\n",
    "                               max_target_len, dropout_rate)\n",
    "    \n",
    "        # Final output layer\n",
    "        self.output_layer = tf.keras.layers.Dense(target_vocab_size)\n",
    "\n",
    "    def call(self, input_seqs, target_input_seqs, encoder_mask, decoder_mask, memory_mask):\n",
    "        encoder_output, encoder_attention_weights = self.encoder(input_seqs, encoder_mask)\n",
    "\n",
    "        decoder_output, decoder_attention_weights = self.decoder(encoder_output, target_input_seqs,\n",
    "                                                            decoder_mask, memory_mask)\n",
    "\n",
    "        return self.output_layer(decoder_output), encoder_attention_weights, decoder_attention_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41761c0",
   "metadata": {
    "papermill": {
     "duration": 0.007815,
     "end_time": "2023-02-21T10:08:04.920148",
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   },
   "source": [
    "## <p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">3.2 Fine-tuning</p>\n",
    "\n",
    "Training a transformer from scratch requires **lots of data and compute**, which can be expensive. Luckily, we can use **transfer learning** to leverage **pre-trained** transformer models and **fine-tune** these for our specific application.\n",
    "\n",
    "The idea is to add a new **classification head** on top of a pre-trained model and then train the entire model whilst using a **different learning rate** for each layer. In general, the closer the layer is to the input sequence the smaller the learning rate will be. \n",
    "\n",
    "Let's see how to do this using the **Hugging Face** ecosystem, which is a company that aims to make machine learning accessible to everyone. In particular, we will download a **pre-trained DistilBERT model** and fine-tune this on a **sentiment analysis** task. \n",
    "\n",
    "<br>\n",
    "\n",
    "**Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2b5a0871",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid', font_scale=1.6)\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, accuracy_score, f1_score\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
    "\n",
    "import torch\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "from transformers import Trainer, TrainingArguments\n",
    "import datasets\n",
    "from datasets import load_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff62333e",
   "metadata": {
    "papermill": {
     "duration": 0.007886,
     "end_time": "2023-02-21T10:08:08.452943",
     "exception": false,
     "start_time": "2023-02-21T10:08:08.445057",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Load data**\n",
    "\n",
    "<br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d8550f14",
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    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Review</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Very good value and a great tv very happy and ...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>After 6 month still can't access my account</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>I couldn't make an official review on a produc...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Fantastic! Extremely easy to use website, fant...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>So far annoyed as hell with this bt monthly pa...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                             Review  Rating\n",
       "0   0  Very good value and a great tv very happy and ...       5\n",
       "1   1        After 6 month still can't access my account       3\n",
       "2   2  I couldn't make an official review on a produc...       1\n",
       "3   3  Fantastic! Extremely easy to use website, fant...       5\n",
       "4   4  So far annoyed as hell with this bt monthly pa...       1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "train = pd.read_csv(\"/kaggle/input/sentiment-analysis-company-reviews/train.csv\")\n",
    "test = pd.read_csv(\"/kaggle/input/sentiment-analysis-company-reviews/test.csv\")\n",
    "sub = pd.read_csv(\"/kaggle/input/sentiment-analysis-company-reviews/sample_submission.csv\")\n",
    "\n",
    "# Preview data\n",
    "print(train.shape)\n",
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ddb10b8",
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   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore target distribution\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.countplot(data=train, x=\"Rating\")\n",
    "plt.title(\"Target distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0cd0080",
   "metadata": {
    "papermill": {
     "duration": 0.009414,
     "end_time": "2023-02-21T10:08:09.815057",
     "exception": false,
     "start_time": "2023-02-21T10:08:09.805643",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Pre-process data**\n",
    "\n",
    "<br>\n",
    "\n",
    "We have to make some **small changes** to our dataset so it is compatible with the Hugging Face dataset object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ea4ce5a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-21T10:08:09.833427Z",
     "iopub.status.busy": "2023-02-21T10:08:09.833123Z",
     "iopub.status.idle": "2023-02-21T10:08:10.377218Z",
     "shell.execute_reply": "2023-02-21T10:08:10.376245Z"
    },
    "papermill": {
     "duration": 0.555908,
     "end_time": "2023-02-21T10:08:10.379533",
     "exception": false,
     "start_time": "2023-02-21T10:08:09.823625",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Map labels\n",
    "train[\"Rating\"] = train[\"Rating\"] - 1\n",
    "\n",
    "# Create validation set\n",
    "X_train, X_valid, _, _ = train_test_split(train, train[\"Rating\"], test_size=0.2, shuffle=True, random_state=0)\n",
    "\n",
    "# Save to csv\n",
    "X_train[[\"Review\",\"Rating\"]].rename(columns={\"Review\":\"text\", \"Rating\":\"label\"}).to_csv(\"new_train.csv\", index=False)\n",
    "X_valid[[\"Review\",\"Rating\"]].rename(columns={\"Review\":\"text\", \"Rating\":\"label\"}).to_csv(\"new_valid.csv\", index=False)\n",
    "test[[\"Review\"]].rename(columns={\"Review\":\"text\"}).to_csv(\"new_test.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63048755",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "**Datasets**\n",
    "\n",
    "<br>\n",
    "\n",
    "Now we can load our data into Hugging Face datasets, which makes it easier to interact with their models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "99fa24ba",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-ebbeb446fed03a5d/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
     ]
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       "model_id": "0d1cb0b9e6714b59856cba992cd45464",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-ebbeb446fed03a5d/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
     ]
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       "model_id": "c36ef69c758d465caee3582ce345e803",
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-6503122057885195/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
     ]
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       "model_id": "10f98ea3a4394cf3a859288916651723",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-6503122057885195/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
     ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-0e0cec55462e437c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
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       "model_id": "e15781cf579a479e9df4a9acc44e7b68",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-0e0cec55462e437c/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
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       "model_id": "7ccb4447ce69422696495678096fd7d1",
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ed1c152f65f48f79d7450a7bf265613",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3bfc327d5014eda8281f8e91805bfad",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['text', 'label'],\n",
      "        num_rows: 48000\n",
      "    })\n",
      "})\n",
      "DatasetDict({\n",
      "    valid: Dataset({\n",
      "        features: ['text', 'label'],\n",
      "        num_rows: 12000\n",
      "    })\n",
      "})\n",
      "DatasetDict({\n",
      "    test: Dataset({\n",
      "        features: ['text'],\n",
      "        num_rows: 40000\n",
      "    })\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "# Create datasets\n",
    "train_ds = load_dataset(\"csv\", data_files={\"train\": \"new_train.csv\"})\n",
    "valid_ds = load_dataset(\"csv\", data_files={\"valid\": \"new_valid.csv\"})\n",
    "test_ds = load_dataset(\"csv\", data_files={\"test\": \"new_test.csv\"})\n",
    "\n",
    "# Define features and target\n",
    "train_ds = train_ds.cast(datasets.Features({\"text\": datasets.Value(\"string\"), \"label\": datasets.ClassLabel(num_classes=5)}))\n",
    "valid_ds = valid_ds.cast(datasets.Features({\"text\": datasets.Value(\"string\"), \"label\": datasets.ClassLabel(num_classes=5)}))\n",
    "\n",
    "# Print summary\n",
    "print(train_ds)\n",
    "print(valid_ds)\n",
    "print(test_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5801c45",
   "metadata": {
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    "tags": []
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   "source": [
    "**Tokenizer**\n",
    "\n",
    "<br>\n",
    "\n",
    "Hugging Face has this amazing feature called **AutoTokenizer** that given the model name, returns the precise tokenizer used for training that model. "
   ]
  },
  {
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   "execution_count": 13,
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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    },
    {
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    {
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   ],
   "source": [
    "model_name = \"distilbert-base-uncased\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc08032f",
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   "source": [
    "Tokenization is applied in batches and the end result is saved as a **new feature** in the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
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   "outputs": [
    {
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   ],
   "source": [
    "# Tokenize batch\n",
    "def tokenize(batch):\n",
    "    return tokenizer(batch[\"text\"], padding=True, truncation=True)\n",
    "\n",
    "# Apply tokenization\n",
    "batch_size = 32\n",
    "train_encoded = train_ds.map(tokenize, batched=True, batch_size=batch_size)\n",
    "valid_encoded = valid_ds.map(tokenize, batched=True, batch_size=batch_size)\n",
    "test_encoded = test_ds.map(tokenize, batched=True, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9d2645",
   "metadata": {
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     "start_time": "2023-02-21T10:08:49.605291",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Model**\n",
    "\n",
    "<br>\n",
    "\n",
    "We will be using the pre-trained `distilbert-base-uncased` model. This is a **distilled version** of BERT that also doesn't make a difference between upper and lower cases. "
   ]
  },
  {
   "cell_type": "code",
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   "id": "66b6a2e2",
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2eccee9e9ad4488e9160b2cb4e8d9487",
       "version_major": 2,
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      },
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       "Downloading:   0%|          | 0.00/256M [00:00<?, ?B/s]"
      ]
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_projector.weight']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "# Connect to GPU\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Download model\n",
    "num_labels = 5\n",
    "model = (AutoModelForSequenceClassification\n",
    "         .from_pretrained(model_name, num_labels=num_labels)\n",
    "         .to(device))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "606b0bf6",
   "metadata": {
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     "start_time": "2023-02-21T10:09:08.313114",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Metrics**\n",
    "\n",
    "<br>\n",
    "\n",
    "These get computed at the end of every epoch on the validation dataset. We've added in the competition metric, which is Mean Absolute Error (MAE)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d9d40e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-21T10:09:08.348900Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def compute_metrics(pred):\n",
    "    labels = pred.label_ids\n",
    "    preds = pred.predictions.argmax(-1)\n",
    "    f1 = f1_score(labels, preds, average=\"weighted\")\n",
    "    acc = accuracy_score(labels, preds)\n",
    "    mae = mean_absolute_error(labels, preds)\n",
    "    return {\"mae\": mae, \"accuracy\": acc, \"f1\": f1}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e196f8",
   "metadata": {
    "papermill": {
     "duration": 0.011134,
     "end_time": "2023-02-21T10:09:08.378635",
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     "start_time": "2023-02-21T10:09:08.367501",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Train model**\n",
    "\n",
    "<br>\n",
    "\n",
    "Training a model is made easy using Hugging Face with their Train helper function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5704e494",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using the `WAND_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n"
     ]
    }
   ],
   "source": [
    "logging_steps = len(train_encoded[\"train\"]) // batch_size\n",
    "model_name_out = f\"{model_name}-finetuned-emotion\"\n",
    "\n",
    "# Training hyper-parameters\n",
    "training_args = TrainingArguments(output_dir=model_name,\n",
    "                                  num_train_epochs=3,\n",
    "                                  learning_rate=2e-5,\n",
    "                                  per_device_train_batch_size=batch_size,\n",
    "                                  per_device_eval_batch_size=batch_size,\n",
    "                                  weight_decay=0.01,\n",
    "                                  evaluation_strategy=\"epoch\",\n",
    "                                  disable_tqdm=False,\n",
    "                                  logging_steps=logging_steps,\n",
    "                                  push_to_hub=False, \n",
    "                                  log_level=\"error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f5e16961",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-21T10:09:08.444515Z",
     "iopub.status.busy": "2023-02-21T10:09:08.443664Z",
     "iopub.status.idle": "2023-02-21T11:14:35.323853Z",
     "shell.execute_reply": "2023-02-21T11:14:35.322848Z"
    },
    "papermill": {
     "duration": 3926.894323,
     "end_time": "2023-02-21T11:14:35.325918",
     "exception": false,
     "start_time": "2023-02-21T10:09:08.431595",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='4500' max='4500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [4500/4500 1:05:24, Epoch 3/3]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "      <th>Mae</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.395500</td>\n",
       "      <td>0.343411</td>\n",
       "      <td>0.166417</td>\n",
       "      <td>0.888833</td>\n",
       "      <td>0.862594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.294800</td>\n",
       "      <td>0.338155</td>\n",
       "      <td>0.155667</td>\n",
       "      <td>0.891417</td>\n",
       "      <td>0.873527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.241700</td>\n",
       "      <td>0.348786</td>\n",
       "      <td>0.155167</td>\n",
       "      <td>0.889000</td>\n",
       "      <td>0.878004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=4500, training_loss=0.3106612752278646, metrics={'train_runtime': 3926.8406, 'train_samples_per_second': 36.671, 'train_steps_per_second': 1.146, 'total_flos': 1.907632594944e+16, 'train_loss': 0.3106612752278646, 'epoch': 3.0})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define trainer\n",
    "trainer = Trainer(model=model, args=training_args,\n",
    "                  compute_metrics=compute_metrics,\n",
    "                  train_dataset=train_encoded[\"train\"],\n",
    "                  eval_dataset=valid_encoded[\"valid\"],\n",
    "                  tokenizer=tokenizer)\n",
    "\n",
    "# Train model\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77b8449",
   "metadata": {
    "papermill": {
     "duration": 0.01155,
     "end_time": "2023-02-21T11:14:35.349452",
     "exception": false,
     "start_time": "2023-02-21T11:14:35.337902",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Evaluate**\n",
    "\n",
    "<br>\n",
    "\n",
    "Let's evaluate our model using the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "343924ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-21T11:14:35.374220Z",
     "iopub.status.busy": "2023-02-21T11:14:35.373418Z",
     "iopub.status.idle": "2023-02-21T11:15:48.686804Z",
     "shell.execute_reply": "2023-02-21T11:15:48.685932Z"
    },
    "papermill": {
     "duration": 73.327893,
     "end_time": "2023-02-21T11:15:48.688856",
     "exception": false,
     "start_time": "2023-02-21T11:14:35.360963",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1625' max='375' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [375/375 05:12]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predictions on validation set\n",
    "valid_preds = trainer.predict(valid_encoded[\"valid\"])\n",
    "valid_preds = np.argmax(valid_preds.predictions, axis=1)\n",
    "\n",
    "# Ground truth labels\n",
    "y_valid = np.array(valid_ds[\"valid\"][\"label\"])\n",
    "labels = train_ds[\"train\"].features[\"label\"].names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b47795a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-21T11:15:48.715343Z",
     "iopub.status.busy": "2023-02-21T11:15:48.713723Z",
     "iopub.status.idle": "2023-02-21T11:15:48.987546Z",
     "shell.execute_reply": "2023-02-21T11:15:48.986583Z"
    },
    "papermill": {
     "duration": 0.289016,
     "end_time": "2023-02-21T11:15:48.989964",
     "exception": false,
     "start_time": "2023-02-21T11:15:48.700948",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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r9PX11f63FH4ZaNKkCerXr1/q1z19+hTJyckAUOTBojfZ2tpKH4S7d++ewnJdu3ZVeKzwlnpOTo70nBWtMAHy9PTEzp07kZmZWeY6y7uvKvsdFj68KTv3XR2K+i/wup/r6OigTZs2csvUrl1b+ueYmBi5ZQqnNo0ePRrt2rVDo0aNivTzwi/75dXPy2PagIaGBpYvXw4zMzOkpqZi5MiR0nnKX375pUp9i4SNc3CJIJkPOXToUBw4cAC7du3ChAkTSnxAofBhH11dXbkjQoV0dHTg7OyMZ8+eITw8XGE5ZXXIkjdyVEh2JKc05d582AaQPGw1bdq0Uo1kyz6QVd7S0tIwdepUxMTEwMTEBOvXr4e5uXmxcoUfvvHx8aVe6UD23yYbE2VzJrW0tODs7KzWaBggWTkBgMojWLLtc3V1VVq2fv36uH37ttL3WWnfP4VLglW0jz76CIcOHUJkZCSWLFmCX3/9FS1btkSLFi3Qtm1btGrVSuX5t+XdV5X9Dgu/BJX191eaOFlYWCic/ywbS3n9/Pz581iwYEGRh9MUKa9+XtprXElsbW3x888/49NPP5X25d69e2P06NHlUj8JAxNcogLTp0/H0aNHkZGRgfXr1yt9UAeQJGAAFN4Sl1X4tG/ha+Qp7eigotFbdcqJxeIif09NTcX06dMRHx8PCwsLTJgwAW3btoWdnR0MDAykDz9NnjwZ3t7eSh8KK4v8/HzMmzcPT548gZaWFv766y+Fy5qp8+Eruz2zbExKimVpYq1IamoqAKi8FJ0q7SvN+6y075833xsVxcTEBAcOHMDff/+N48ePIzk5GV5eXvDy8gIAWFpaYsqUKfjwww9LPYWhvPuqqgm2OkoTJ3VjGRYWhrlz5yIrKwsODg6YMGECWrRoAVtbW+jr60t/rwMGDEBUVFS59fPy3EzHzc0Nenp60hH+IUOGlFvdJAxMcIkK2NvbY+TIkdi5cyf27t2LyZMnKy1f+EFYmhGQwjLqrrNbUU6fPo1Xr15BQ0MD27dvVzhiqOzDvzz8/PPP0tuOixcvljtntFBh0tKgQQMcPXpU5XPJxqSkUbfSxFoRIyMjJCYmqvy7k21fSeevLu+zkhImKysrLF68GIsWLYKfnx8ePHiAmzdv4urVq4iLi8PSpUsRHh6ORYsWlep8QuyrZXHw4EFkZWXByMgI+/btg5WVldxyhV/Kqprc3FzMnz+/yPSVJUuWoHXr1nLv8lDNxDm4RDI++eQT6OnpITs7G3///bfSsg4ODgAkt/8Kbz/Lk52djZCQkCKvqaoKb7+7u7srTG5l/z1vw86dO6WLxE+cOBGjRo1SWr5wrmFwcLBat4VlYxIUFKSwXF5eHl68eKFy/YUK21n4QFxpOTo6Sv8cGBiotGzh8cp8n+nq6gKA0rmzsuvHKqOpqYkmTZpg3LhxWL16NS5fvowWLVoAkLxP3lzzWBEh9tWyKHwPtm/fXmFyGxERUWUT3FWrVkkffJs3bx50dXURExNT6i88VDMwwSWSYWNjgw8++ACAZD1OZR+GrVu3lv5Z9qn8N50/fx45OTnFXlMVFd66VzbCdvr0ablz+srDlStX8NNPPwEAevTogQULFpT4msInyrOysnDixAmVz+nm5gZjY2MAwLlz5xSWu3r1apnmVRY+Xf/o0SOlifSbXF1dpWsznzlzRmG56Oho6Xq1rVq1UrudZVU4d/358+cKy3h6eqpVt7m5OT788EMAkvdoab9oCbGvlkXhv1FZPy/pbkjhNI23NU1Jkdu3b2PDhg0AgLFjx2LKlCn4/PPPAUh2SuO261SICS7RG6ZMmQIDAwPk5ORg/fr1Css1atRIuuHAxo0bERYWVqxMQkICfv/9dwCSD/6qvvh44Wjh8+fP5T45HR0dLXfR+PIQEBCAzz77DHl5efDw8MDvv/9eqt20OnXqJN2R7bfffitxlDMuLq7IbnGyy2NdvHhRuiqDrLS0NGkc1TVmzBhoa2tDLBbjyy+/VDpVQXaJLk1NTem2s56ensV2+AIkc5aXLFkifd3IkSPL1NayaN68OQBJIilvSkBgYKDSJKSk5F/2S6eZmVmp2iTEvloWhf3cx8dH7ij406dPpUmkIoVTARSt0PA2JCYmSpcMdHNzkya2Y8eORffu3QEAy5cvL/EaQDUDE1yiN1hYWGD8+PEAJFtLKvPNN99AU1MTSUlJGDNmDA4fPozo6GjExcXh3LlzGD16tPTDdPHixeW649Pb0LdvX2hqaiI3NxdTp07FuXPnEBMTg6ioKBw6dAgjR45EcnJyud++TUtLwyeffIK0tDRYWFjg999/h1gsVrjkl+xDYiKRCMuXL4eBgQESExMxcuRIrFy5Eo8ePUJCQgLi4+MRGBiIo0ePYs6cOXjnnXeKxfXTTz+Fubk5xGIxpk+fji1btiAsLAzx8fHw9PTE+PHjERwcLHfL09KytbXFF198AQB48OAB3nvvPRw6dAhhYWFITk5GaGgoTp06hVmzZhVLLqZNmyY992effYY1a9YgJCQECQkJuHv3LqZMmSLddnX8+PFwc3NTu51lNXz4cACSJeg+/vhj+Pj4ICkpCaGhodi+fTvGjRun8LY4IFmnduTIkdiyZQt8fHwQGxuLhIQEPH36FGvWrMFff/0FQJK01qlTp9TtElpfLYvC9XUTExMxadIkeHl5IS4uDqGhodi2bRvGjRsHQ0NDpV8gGjduDEDyhWPv3r1ISkpCbm4ucnNz39qo7tdff42oqCjo6uri999/l06HASTz9m1sbJCZmSl9gI5qNj5kRiTHpEmTsGvXrhKf0G/ZsiV+//13fP7554iOjpYmMLI0NTWxaNEipWusVhXOzs6YO3cufv31V7x48QIzZswoclxXVxe//vorduzYoXQZJVUlJCRIl3KKj4+Xu8C9rGHDhkm3vAUkD5ht27YNs2fPRkREBNatW4d169bJfa1IJCqWvFhYWGDdunWYPHkyUlJS8MsvvxTZBlRDQwPfffcdTpw4Uer5o/KMGzcOOTk5+O233xASEoIvv/xSbrk35z+bmZlh8+bNmDJlCiIiIvDXX39JEz1ZQ4YMkY5qVZYOHTpgzJgx2LVrF+7evVts6SZXV1f89NNPSkeZHzx4gAcPHig87uDgoPKIutD6alm0a9dOGqPHjx9j4sSJRY6bmprijz/+wMKFC5GYmCi3ju7du6NOnToIDg7G4sWLsXjxYumxtm3bSufRl5c9e/ZIpxAtXLiw2Jc4CwsLLF++HBMmTMDTp0+xfPlyfPPNN+XaBqpemOASyWFqaoqPPvoIq1atKrFs//790axZM/zzzz+4du0aIiMjkZ+fD1tbW7Rv3x4ffvhhmfakr2iTJ09GvXr18M8//8DX1xfZ2dmwtrZG+/bt8dFHH8HNzQ07duyo7GYW07RpU5w+fRqHDx/GhQsX4O/vj8TERGhoaMDS0hKurq7o1q0bevXqJXcktnnz5jh58iTWrl0LT09PxMTEwMzMDM2aNcOECRPQunVrteb4vmnChAno0aMH/v33X9y4cQMRERHIz8+HtbU1nJ2d0bt3b/Tt27fY61xdXXHy5Ens3LkT58+fx/Pnz5GRkQFzc3O0aNECI0aMkLsLW2X49ttv0bRpU+zduxcBAQEQi8VwcnJC//79MWHCBMTFxSl87aZNm+Dl5YWbN28iODgYr169QlpaGoyNjVG/fn307NkTo0aNUmvJNqH11bIojNGePXvw9OlT6e+ha9eumDBhQol3aXR0dLBjxw6sW7cO165dQ0RExFsbNQ0KCsLSpUsBSBLrcePGyS3XoUMHTJ48GRs3bsSOHTvQuXNn6dQFqnlE4spa7JCIiIiI6C3gHFwiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFGz0okJObh7DoxMpuBlUAZ3uLym4CERERlUAEyW6UpcEEV4Gw6EQ0HPpTZTeDKsArL9W2/KTqi/va1CyaGqX7IKTqj127ZtDTliS5pcEpCkREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQmKVmU3gFTTva0bJr3XCS0a1oaFiQFeJaTi+v3nWL//Krz9Xpa5/kHvNMWYd9ughYcTzIwNkJCcDt/AcOw6eQeHL9xX+to1X4/GmHfblniOM16PMXr+5jK3Vegu3fTHloNX4eP/EglJabAyN0aHFvUwZWQ3tGzkUub6E5PTsXb3JZy68gAvI+OhqaGBuk7WGNqrJSaP6Ao9XW2Fr42ITsDpq49w7V4gfAPDERmTiHyxGJZmRmjWwAnD+7XBoO7NoKHB79ClcemWP7YevFY01s3r4eOR3dCykXOZ609MTse6PZdw6srDN2LdApOGlxDrmASc9vTFNe9APJYT6/f7tmasVXDxpj+2HJD06/iCWHdsUQ9TRnVDq3Lq13/vvoSTlx8gNDIeGhoaqOdkjaG9W+LjEvp1edZBEpdu+mOzgut4ecV77e5LOPnGdXxYKa7jyakZ8PZ7IfnvseT/0a+SAQCj322LNYvHl7l9lUkkFovFld2Iqig4PA4Nh/5U2c0o4qfZQ/Dp6G5yj+Xm5mHxmuNYu8dTrboN9XWw+Yfx6NupkcIyJ688woSvtyMnN0/u8eqa4L7y+r2ym1DMopUHsX7PZbnHNDU18N3MIZj2QQ+16/cPisCoOWsREZMo93iDunY4sGo6almZFjt27IIPJi3aipIuHR1a1Me2XybDwtRQ7XaWt6p4ufv6j0PKYz1jCD75oLva9fsHRWD0Z+uUxnr/X5/Kj/VFH0xe9E8pYl0P/yyrWrEGAE0NUWU3oYivVhzEOiWx/n7mEHw6Rv1+7RcUgZGzlffrQ6vl9+vyrKMyVMGujUUrS453Wa/jI0u4jh9UcB0HgOlL/sWek7flHquqCa6eNqAhKl2/5ghuNfHJqK7S5Pb0tcdYvuUsXkTEo0HdWvh22gC0bVIHP84cjBcR8Tjl6aty/Wu+/kCa3B4654PVuy8jODwOthbGGDuwLaZ/0A3vdmuCFZ8Px8yf9iqt68b95xgxd4PC47l5+Sq3ryZZt/uSNOHp07kx5k/qB2d7Szx5Hokf/z6OO4+CsfjPI3C2t8SAbs1Urj85NQNj5q1HREwijA318P2soejdsRFy8/Jx+Nw9LF1/Ek+eR2Lc/A04s3keNDWLjsylZWRBLBbDxtIEI/q1QY/2DeDqbAs9PR08DY7Cmp0X8J/nI9zweYax89bj5IY5HN1TYN0emVh3aoT5k/qhdkGsf1p7HHcehWDxX0dQ294SA7o1Vbn+5NQMjJ234XWsZw5Fr44NpbFetuEUnjyPxPgFG3F609zisU7Plol1a3Rv5wFXFxvo6Upi/feuiwWxDsK4+etxYj1jrcja3ZekyU7fzo2xYPLrfr1kjaRff1PQr999R71+/cHc1/16yWxJv87Ly8ehs/fwc0G/HjtvA85uKd6vy6sOklj3Rrxlr+M//P063rXtLfGumtfxD2Su40tmDUWvwljJXMfHzt+As3Ku47J0dbTQyNUBLRs6Y9N+9QbJqiKO4CpQlUZwzU0McP/gIpgY6ePynad4b/b6IiMqerrauLT1MzSoUwsh4XFoM2qpSklk55b1cXzNpwCAHSduyU1gPx3dDT/NHgIA6D35T9x9/KJYmcIR3GvezzBo+t+q/jMrTVUawY1PSkOrYd8hJS0TXdu44cBf04skDBmZ2ej54a94GhIFZ3tL3Nz/DbS1NFU6x09rj2PlP2chEolweM1MdG7lWuT47hM3MfOHnQCAPxaNwbjBHYocv3jTH2FR8RgzsD20FJx7zs+7sOPoDQDAlp8nYnDPFiq18W2pSpe7+KQ0tH7ve0msW7th/1+fFot1r49+xdOQaDjbW+LGvq/ViPUJ/LGtINarZ6BTsVjfwqwfJbFe+dUHcmMdHpWADwa2Uxjrz37ejR3HJLHe/PMEDO5RNWINVJ0R3PjENLQo6Nfd2rjh4Kri/br7h7/iabCkX98+oHq//vHv41hR0K+P/l28X+86cRMzlkhi/eeiMRg/pMNbqaOyVKGujfikNLSUuY4flHMd7yFzHb+lxnX8R5nr+BE51/FdMtfxP+VcxwHg1oPn0NHRQmNXB+n5LdvNBCCMEdxq+/UrPj4ey5YtQ58+fdC0aVO0a9cOEydOxPnz5yu7aeVuVP/WMDHSBwB8//eJYh/SmVk5WLbpDADAxcESvTt6qFT/8D4tAQB5efn4Ye0puWXW7fNEeMFtkGmjuqpUP5XevlO3kZKWCQD4ZvqQYqNh+no6+HzKAADAi4g4nPN6rFL9eXn52HbYCwDQq2PDYhdFAPhgYHs0qGsHANgs59t8j/Ye+N/QTgoTHgD4etogaBQkF+ev+6nUxppi33+ysR4sN9YLP34d6/PXVY/19iOvY/1mcgsAHwxs9zrWB+THevzQjkpjvWjaQMa6BHtlYr1YQb/+4uOy9et/Cvp1bwX9eoxMv5Y3SlcedZCE7HVcYbzL6Tpelli1a1YXLTxqq5xcVxfVMsENDAzEwIEDsXXrVrx48QJaWlpISUmBl5cXpk+fjh9//LGym1iuBnRpDAAIDnuF+0/C5Jb576ovMrJyJOW7Nlap/iau9gCApy9iEBOfIrdMfr4YN+8/BwD07ugh2A5R2f7zfAQAcHGwQguP2nLL9OvSWPrgwOmC8qV180EQ4pPSAADDerVUWG5IwYjro6dhCI2MV+kcAGBlbgwrc2MAQGRsosqvrwlOS2NtiealiPV/ZYj10F6KR1WH9GwOAPB9Gl4OsU5S+fU1QZF+3VB+rPt3fR3rU2Xp170V9+vC94G8fl0edZCEqtfxsvVtJbEq43W8uqt2CW52djamTZuGuLg4uLm54ejRo/D29oa3tzfmzp0LkUiEf//9FwcPHqzsppabZu4OACB3WkCh7Jw8PAyQJL/N3Z1Uqt/UWDI6nJSSrrRcfLLkuLGhHtxdbEusVyQScY6Wih4GhAIAWjV2UVhGV0cbTd0dAQAPnqi2csZ9/9fllZ2jTZM6xdqkipzcPCQWvF8K7z5QUYX9VdmT1Lo62mjiJon1wyeqxeGB/+vyys7RunF5xFryYWtipKfy62uCBwWxa13afu2vWr/2kSmv7Byy/frBG++n8qiDJB4EqBjvMlzHlZ2jtWys1Ojb1V21yz727t2L0NBQGBgYYMOGDWjQoAEAQE9PD1OnTsW4ceMAAH/88QdycnIqs6nlws7aVJoghETEKS37IkLyDa1ebWuVzlF4K8XU2EBpOXOT18fdXGwUlmtYzw739n+F2Ku/4tW13xB0+gfsX/ExRvZtxYRXiciYRGksXOwtlZatXXA8KDRWpXmlgS+iAQAaGiJpHcrqB4DAkOhS11/otOcjZOfkAgBaN3FR+fVCJxtrZwcrpWWdKzLWL9SNtWRllTYyyTJJRBSJtfJ+rXasQ0oXa2clsS6POuiNeFfyddy5jNfx6q7aZRvHjh0DAAwcOBB2dnbFjk+ePBkikQgxMTG4detWRTev3FnKLLsTG5+qtGxsgmR6gb6uNowMdEt9jifBUQAA19rWsDY3kltGQ0OEDs1ef3hZmskvBwAWpoao62glTWYtTA3Rq4MH1n83Fmc2zIK9ddVaXqaqiEt8HV9rS2OlZQtvCWdm5SA1PavU54hPlIy0mRkbKJ1mIvs+iEtS/r57U2ZWDn74W9JPjQz0MLJ/yUvH1TRxBbcXAcDaoqRYS2KRmZWDNFVinVTKWFu8jnXh+6O0MrNy8OPa4wAAIwNdjOjfRqXX1wTxMv3apsRYv91+bSXbrxOL9uvyqIOKxruk67i1mvGOUyNW8Spex4WgWiW4aWlpePRIMlelc+fOcsvUqlUL9evXBwDcuHGjwtr2thjo60j/nJmlfERa9rihzOtKcuzSQwCAlpYmFk0dILfMx8M7w8HWXPp3eQl0bHwq/tpxEYNn/I2mw36AbdcFqNP3awz/bAMu3goAALRqWBsH/pgKA73St6+mSM/Mlv5ZV0f5Qur6Mot3p2WU/sKYnpFdqvr1ZOKTlp6tpGRxC5fvw/PQWADAF1MGKP0yVFOly8RMV0f5ao16uq9jkapSrLNUrl+V9xIALPz1daw/Z6zlSquAfl14Dt0SNmDQl+3XGUX7dXnUQUWv43olXWff8nVcvwzXcSGoVuvgBgUFSYfxC5NYeerXr4/AwEAEBQVVVNPeGtnVMN7WKij/XX2MG/efo0PzuvhwSHsY6utg9a7LCImQrIM75t22mDHmHWRk5UgvwPn5xVvz3d8niv0sOycdF24+wYWbT/DznKGYNqorPOrWwrTRXfH7P8Jb8aIsZO9QiUq5DIrK5yh4F5VUvbpnX7PzAnaduAkA6N2pEaaOfkfNmoStQmItLl396p79750XsfuE5C5Z706NMHXUO2rWJGwVE+uCfl1COWXHy6MOEsZ1XCiq1QhubGys9M+2toofcio8FhMT89bb9LbJfuvSL+GbddFvg6p9W/vwq3+kW/0O79MSl/+Zi5CzP+HWni8we3wPpKZn4Zu/jkrLJ6VmqFQ/AHy7+jjCoxMAACP7tVL59UJnWGS0Xnn8MoqM1pd+Okph2ZLuBmTIjEIYGpRutH33iZv4bpXkPdKmSR1s+mnCW7vAV3eGMndASr4z8zoWRqrE2qCUsZapv7Tvpd0nbuG71YWxdsHGHz9irBUwqoB+bVTKfi07uvjmXb7yqIOK/k4ySoh35lu+jqercR0XkmqV4Kanv37KX09P8dO6hcfS0lSbT1YVFZ2rp/z2n7rzeQAgNiEV/aauwme/7Mc172eIT0pDVnYuXkbGY9PBa+g07leERiVIy4dHJ6pUPyB52vr8TclUBTdn2xIT9ppG9vZubJz85doKvSqYb62ro6XSfGsLM8mc7sSUdIVbLgOS94P0NaXYfvXYBR/M+Xk3xGIxmrg5Ys/KT1S6YNc0RefWlxRrSSx0dbSKJMYlKYxbibGWmdtvXppYX/TBZ0slsW7s5oDdKxhrZSxk+rWiZRgLve1+/UpJvy6POqhovEu6jseqGW9LNWJVmr4tNNVqikJNFBmbhOS0TJgY6pX4ZL2zvQUAyROZ6sjJzcM/R27gnyPy5y6PG9RO+mdvFZexKVR4AQcky5NllPANtCaxszGDkYEeUtMzS1wx42XB8Xq1bVQaOXN1ltzdyM8X42VEHOrVlr8axkuZ8xe+RpGz13wxdfE25OXlw9XFFvv/+rTEFTlqOkmsdZGanoUX4a+Uln1RGGunKhBrr8f4ZPF2SaydbbH/T8a6JPY2ZjAy1ENqWiZehJe0Eo6a/dqldLF+IRvrN5Z6LI86qCDeBdfxF5V8HX+hQt8Womo1gmtg8PpCmpmZqbBcRobk9rmhoTC+sRSul9m6kbPCMjramtI19RRtBlFW7xZsIOHjHyp9ilNVNpYm0j8npqg+zUHomjaQxPCeb4jCMlnZOdL3RDMV1zxu1uD1ouPKznHnUfDrNik5h+fdAEz8agtycvPgbG+JQ6tmSJ8EJ+UKf6/3HocoLJOVnYNHTyWxbtpA1Vi/Lq/sHHd9ZWPtqLDc1btPMUkm1gdXTWesS6lZwe/1bmn7tYqxbi7Tr5WdQ7Zfv3ntKI86SKJZAxXjXYbruLJz3K3hsapWCa619ev1XaOjFa/pVjj31sZG8Vqt1cl/VyXb+NVxtELzBvI/gPp3aSy95X/qqm+5t+GdNm5o4ibZcGLbUfVWp9DV0UKv9pJ1iwNCokucP1QT9evSBAAQEv6qyMLrsk5f9ZX+7vp3a6JS/e2b1ZWuZ3z4vLfCckcv+AAAGrs6KFxn8fbD5xg/fwMys3JgZ22GQ2tmwM7GTKX21GT9CnYoDAmPK7Jwu6wise6qWqzbycT6yHkfheWOXrgPAGhUQqzHLSiMtSkOrmasVVGkX/vJj/V/nq9jPUDFWBfp1+cU9+vC94G8fl0edZBERV7Hjyi5jh8pxXVcyKpVgluvXj3pMP6zZ88Ulis8Vq9evQpp19u257+7SC5YOPrbTwcWu5Whq6OFLyb3BVCwr/V1/3I9v4ONGf76ciQAwDcwAjtP3C5WxsbCWLofvTwikQjLPhsGu4I1cPefvleubRSKUQPawshAMof8hzVHkZ+fX+R4ZlYOftlwCgBQ284CvTo2Uql+LS1N/G9oJwDA+et+uHYvsFiZPSdv4cnzSADAxOFd5NbzMCAUoz9bh7SMbFibG+PQmhlwtle+YQEVNXJAW+m8ux/WHJMb6+UbZWPdUKX6tbQ0MX5oRwCSWHuVEOtJ7yuO9Qdz1yO9MNarZ5S4gD0VNfrdtjAylPTrJQr69TKZWPfupHq//nCYpF+fU9Cvd5+QifWI4rEujzpIQvY6rjDeMtfx3mW4jiuMlWzfVnAdF7pqleAaGhqiSRPJN52rV6/KLRMVFSVNcNu3b19hbXub4pPS8MumMwAkI6m7lk9E8waOsDA1RMfmdXFs9TQ0qFMLAPD1X8eKTTp3qmWOhBsrkHBjBdZ8PVruOX6ZOwyrF41G304NUb+2NcxMDODuYouZY7vDc/s8ONlZICE5HZ98vxO5efnFXv9e7xa4t+8rfDWlH7q2doWDjRlMDPXgYGOGQe80xam10/HR0A4AAL+gSPy950p5/ooEw9LMCAs/7g8A8LzzFOMWbISP/0vEJabius8zDJu+Ck9DJBtzLJk9DDraRafRv4yIg1W7mbBqNxMzlvwr9xyz/tcLDrbmEIvFGL9gA/49eh2RsUkIi4rHX/+ex9ylewBIbqGPGVi8DwWGRGHk7L+RnJoBY0M9bP/1Y9hZmyE1PUvuf7JP8tJrlmZGWDi5INZ3n2L8go24LxPr92aswtOC3Ye+nyU/1tbtZ8G6/SzMWLJD7jlmjZeJ9cKN+PfodUQVxHrVv+cxb1lhrB3xwcB2xV4fGBKNUXPWSmO9bflk1GKsVWZpZoTPC2J95c5TjJ2/ET5+BbH2foYhn67C04INd36YIz/WFm1nwqLtTEz/Xn6/ni3Tr8fO34DtR2T69fbz+KygXzdr4ISxcvp1edVBBfFWch0fWorruGW7mbBsNxPTFVzHZWM1Tsl1vJmC6zgAJKdm4M6j4CL/FYpLSFV4rLqodg+ZDRw4EA8fPsTJkycxffr0YruZbdq0CWKxGNbW1oJJcAHg7z1XUNvOAlNHdkG/zo3Qr3PRb3x5efn47u8TOHHlkVr1GxnoYsy7bTF2oPxdpwJfxGDiN9vxOChSYR0uDpZYMKEPFkxQfJ5r3s8wefG/fLhMiU/H9EBoZDw27ruCs9d8cfZa0SknGhoiLJ4+BAO7N1erflNjA+xe8QlGzVmLyNhEfPbz7mJl3FxqYedvU6AlZ5ecQ+e8pU/npqRlov/kFUrP52RnAZ8j36vVVqGbNqaHZKWS/Z446/UYZ70eFzkuifVgDOzeTK36TY0NsOv3qRj92VpExiZJP/RkubnYYsev8mN9+Ny9IrEe8PFKpedzqmUB7yPfqdVWoZs+VtKvN+y7gjPXfHFGTr/+dsYQDCpDv9678hOMmLMWkTGJmCOvX9dR3K/Lqw6S+LSgb29UFu/pZYv3nhWfYGTBdVxurFxqYYeSWD0MCMOQT/+Se+zcdT+cu+5X5Gdxt1ap1dbKUu0S3NGjR2Pbtm0IDw/H1KlTsXz5cjRo0ACZmZnYvn07duyQjGTMnj0b2trCWobqi5WHcfa6Hya91wktG9aGuYkBXiWk4MaDYKzb64l7CuZ2lca2ozeRmp6F9s3qwN7aDCZGeohPSseT4Cgcu/QAO0/clu43L8/JK48gEgFtGrugQZ1asDIzhJmJATKzchAdlwxvv1AcPOeNs+U8fUKols4bjt6dGmHLgavw9nuBxOR0WJkboX3zepg66h20auxSpvob1rfH1V1fYu2uizh55SFCI+OgoaGBuk7WGNKrJT4e0bXILjj09hTGeuvBq/D2e4nE5DRYmRujffN6mDKyW7nE2nPnl1i7+xJOXXmIl5Fx0CyMdc8WmMxYV5hl8wv69cGr8H78AgkF/bpD83qYOvodtC6HWHvt+hJrdl3Eycuv+3U9J2sM7dUSH48sOdblUQdJLJs3HH06NcLmA1fh41c03lNGlU+8r+36En/LuY4P5XUcIrFY/LY2yHprAgIC8OGHHyIhQbIuq5GRETIzM5GbmwsAGDt2LBYvXlymcwSHx6Hh0J/K3Faq+l55/V7ZTaAKUg0vd1QGmkqeCyBhYdeuGfS0AY1SLqlW7UZwAcDd3R0nT57E+vXrcenSJURFRcHIyAgeHh4YM2YM+vTpU9lNJCIiIqJKUi1HcCsCR3BrDo7g1hy83NUsHMGtOdi1awZVRnCr1SoKREREREQlYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBEWrshtQVekYGKDx0MGV3QyqADHJWZXdBKog6Vm5ld0EqkDOVgaV3QSqILl54spuAlUAXS0NQFS6shzBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoGhVdgNINW1czDGshQMa2BnDRF8bienZeBCahAP3wuAfmVIu59DX0cTgZnbo7GoFJ3MDGOtpISkjB1FJmXgQloSzj6MR/Cqt2Ou+GtAA/ZvUKrH+68/i8PnBR+XSViHzuhuA3cevwzcgFInJ6bAwM0LrpnUxbmhnNG1QW+16U9Iy8CggFI+ehOJRwEs8ehKK2PhkAMDQ3q3x88LRJdYRFZuISzce49b9IAQ8j0D0qyTk54thYWaIhq6OGNijJfp0aQINDX6HLo2b3k+x/9RN+D0NQ1JKOszNDNGyUR2MGtQJjd2d1K43NS0TjwND8fhpGB4/DcXjp6F4FS+5Tgzs2RLffTZS6euPn7+L7/84oNI5j21eCHtbC7XbLHSXbvlj68Fr8PF/iYSkNFiZG6ND83r4eGQ3tGzkXOb6E5PTsW7PJZy68hAvI+OhqaGBuk7WGNqrBSYN7wo9XW2Fr42IScBpT19c8w7E48BwRMYkIl8shqWZEZo1cML7fVtjUPdm7NcquHzrCf45fA0P/F8iITkNluZGaN+sHiaP6IYWDcsn3hv2XsZ/ng8RGhkPTU0N1HG0wpCeLTHh/S5K463I8o2nsPKfM9K/3z6wGE52lmVua0VjgluNzOxRDyPbFP2wszXRQ59GeujhYYO1l4Kw725Ymc7RsrYZFg30gI2xbpGfWxvrwtpYF00cTZGdmy83waXys2ztUWw/dLXIz6JiE3Higjf+u3Qf86cMxIfvd1Wr7qVrjuLIubtqt+2M5wPM/XEHxGJxsWNRsUmIik3CxeuP0bpJXfz13YcwMzFU+1w1we8bj2P3Ua8iP4uOTcJ/l+/jrOdDzJrYH2OHdlGr7t82HMOJC97l0cxSMTUxgLWFSYWdr7r5+o9DWL/ncpGfhUcn4MCZuzh83hvfzRiCTz7ornb9/kERGP3ZOkTEJBb5+X3/l7jv/xJ7Tt7G/r8+RS0r02KvPXbRB5MX/SO3X0fEJCIiJhH/eT5Chxb18M+yybAwZb8uyeI/D2HjvitFfhYRnYhDZ+/h6AUffPPpYEwdrX68nzyPwNh564vF+8GTUDx4Eoq9p25h7x+fwlZOvBUJCI7Cmp3n1W5TVcIEt5oY0dpRmtx6PXuFf7xeIDIpE3WsDDClW100cTDF9B71EJmUiauBr9Q6R4vaZlg+vAl0tTURk5KFHTde4G5IApIycmBppAtnSwP09LBBTl6+0noehCZiwX7FI7R5+cUvoPTa9kOe0uT2nfYemDauNxxrWeJZSBRWbjmF+34vsHz9cTjUskCvTo3VPo+Othbc69mhiXtt7HojwVImPSMbYrEYVhbGGNSzJTq1dkddJxvo6Woj6GUMtu6/jIvXH+Puo+f49Jst2LFyOkd8FNh19Jo0ue3SpgEmf9ATDrYWCHoZjTXbTuPhk5f4Y/MpONha4J0OjdQ+j462Flzr2KGRmyP2nbhR6tf1794CPTs1UVrmSVA4pnyxAQDQt2szaGvzY0WedXsuSZPbPp0aYf6kfqhtb4knzyPx09rjuPMoBIv/OoLa9pYY0K2pyvUnp2Zg7LwNiIhJhLGhHr6fORS9OjZEbl4+Dp+7h2UbTuHJ80iMX7ARpzfNhaZm0T6Zli7p1zaWJhjRrzW6t/OAq4sN9HR18DQ4Cn/vuoj/PB/hhk8Qxs1fjxPr57BfK7Fh72Vpctu7UyPMndAXTnaWCAiOwtJ1x3HXNwTfrz6K2vaW6N9VvXiPX7BRGu/FM4agZwdJvI+e98byjacQEByFDz/fiJMbisdbnvz8fMxfthvZOXlwtrfEi4g4ldtVlYjE8r6uEcITMjD071uV3QwAgImeFvZ+0h5Gulq4G5KAuXsfQDZoOloa2PxhK7hYGSIiMQNjNt5WOYk01NHEv5PbwtpYF4HRqZi1+z5Ss3JVqqNwioLPy0TM2n1fpddWpn1T21d2E6QSk9PQZ/xSpKZnon0LV2xa9nGRD5HMrBwM/3Qlnr+MgWMtC5zc+jm0tTRVOoe3b3BBcmsvfW3D3vMBlG6KwrU7AYiMTcCwvm2gpSn/3ItX7MeB/yT9Z+U349G3azOV2vi2pKv4nn6bEpPTMGTycqSlZ6Fts/pY/cPEYrEeP2cVgkNj4GBrgYPr50FLxVjf9wuBjrYW3OrYSV/beuAXAEo3RaE0fl1/DHuPXwcAbF85Aw1dHctcZ3lxtjKo7CYAAOKT0tD6ve+RkpaJrq3dsP+vT4vEOiMzG70++hVPQ6LhbG+JG/u+Vrlf/7T2BP7YdhYikQiHV89Ap1auRY7vPnELs37cCQBY+dUHGDe4Q5HjF2/6IzwqAR8MbKfwffbZz7ux45jkC9LmnydgcI8WKrXxbcrNqzqpTHxSGtqPWIKUtEx0ae2GPSunFY13Vjb6TvwNgSHRqG1viWu7F6kc76XrT+Cv7ecgEolw4K/p6NiyaLz3nryFOT/vAgD8/sVojBnUQV41RWw54IlFKw+idWMXdG3jjhVbJdMUqtIUBWM9DWhqiEpVll+/qoG+jWvBSFcyKrLuynO82Y2zc/Ox+VoIAMDeTB8d6qo+/21Mu9qwNtZFbl4+lpzwUzm5pfJx7Pw9pKZnAgDmThpQbIRET1cbM/7XFwAQFhUPz9v+Kp+jZeM6aOzupPIFtVDnNu4YMaC9wuQWAOZM7A+NgouQ5+0nap1H6E5d8kFaehYAYMZH/eTGeurYXgCA8Oh4XLur+u+xeUMXNHR1VDkxLq3c3Dyc9XwAAKhb27ZKJbdVyb7/biMlTdKvv5k+uFis9fV0sPDjAQCAFxFxOH/9sUr15+XlY/sRyZ2AXh0bFktuAeCDge3QoK4dAGDzAc9ix3u098D4oR2VvlcWTRso7dfnr/up1Maa5MDpO9J4f/XJoOLx1tXBgkn9AQAvI+Jw4YZqv8u8vHz8e1TypbJnB49iyS0AjHq3HdzrSJ6J2XrwarHjb4qIScTS9SegpamB5QtHQSQqXRJZlVW7BDc5ORnnz5/Hn3/+ialTp6Jz585wd3eHu7s7zp8XxryRN3WuL/nmFJ6QgYAo+Q+SeT17haycPEl5VyuV6tcUifBuU0lHuPE8HiGv0svQWiqLiwUfbE52lgofLurRoRF0dbSKlK9qLMyMYGFmBACIeZVUya2pmq7clHyoOdSyUJgYdm3XUBrrwvJVybW7T5CQJJmPP7Bny0puTdV12lMyZcvFwRLNPeQ/INqvS2PpA0H/ear2EO7NB0GIL4jD0F6KR1WH9GwOAPB9Go7QyHiVzgEAVubGsDI3BgBExrJfK3LmqiR+zvaK492ncxPo6WgXKV9atx4+l/a7wUr6XeEIu29gOEKjlMf7y9/2IzU9C1NHd4dHPXuV2lNVVbsE9/z585g+fTr+/vtvXL58GbGxsZXdpLfOrZbkgvI4IllhmZw8MZ5GpwIA3AvKl75+I1gaSR4qu/bG/N3S3gqQRwRJ8kyl5xcYDgBoquCiCAA6OlpoWN+xoHzZHip8W3Jy85CUIvmiZGSoV8mtqZqeBEli3cRdSay1teBez0FS/ll4hbRLFScLHmDT1NDAgO5V53Z1VfMwQNJPWzVyUVhGV0cbTdwk/frhk1CV6n/g/7q8snO0blxHpk2qnQOQ9OvEZEliZWLEfq3Io6eSeLdUGm8tNHEviLeKsXj45KX0z62UrLzRqvHr8z9Sco7jF31w1ssXTnYWmDuxn0ptqcqq5dMA1tbW8PDwQMOGDdGoUSPMnDmzspv01lgZ6UinJ0QkZSgtG5mUiSaOpnAy11fpHA1kEuInkSlwszXCuPa10drFAsZ6WsjIzkNAVAr+843Cad8olDS9t661IXZPaQc7Uz1oaoiQlJED/8hknHscgwv+McjjtG+5ol8lSacnlDTfycHOAj5+IQgJi4VYLK5yt5Mu3XiMnII7Cs0bulRuY6qgmFdJ0ukJDrWUTylysDXHQ/8XeBnxqkrFOiklHdfuSKZNtGtRH1ZcPUGuyJhE6e1qZwfld9ec7S1x51EwgkJV69eBL6IBABoaItS2V3ztkD1W+BpVnPZ8hOyCft1GJlmm1yJjZeKtJBYA4GRngTuPgvH8parxjgEgibeyzwrZeD8reM2bklLS8fUfhwAAP88dDgM9nVK1oTqodgnu4MGD8d5771V2MyqMqf7rNewS0nKUlk1IzwYA6GprQl9HExnZeaU6h63J62/izZxMMatnfWjJPHGpr6OJ5rXN0Ly2GXo1tMGiQ4+RkaO4blN97SLtNtXXRvu6lmhf1xLvt3LA10ceIzYlq1Rtq0kKbzkBgGXB7X1FCo9nZeciPSMLhgZVZzQlKzsHKzefAgAYGuhicK9WldyiqqdwFAyAdCqHIuamsrHOhqGBrtLyFeXMlQfIyZVcBwb2ZIwViZPp19YWyu+uWZlLYp2ZlYO09KxS3/0onJ5gZmygdG69tcXr91p8ompLPWZm5eDHtccBAEYGuhjRv41Kr68pZH+vJcdbcjwzW9V4S+7WmpYQ78L6ZV/zpiWrjyImLhkDuzdHr47qr9RSFVW7BFdLq9o1uUz0dV6/ebNzlS/PlZXz+riBdukTXEPd17/TmT3rIzdfjDWXAnHxSSxSMnPgYmmICZ1d0MXVCm1cLDC/rxt+OFH84ab4tGzsuvUSt57HIyIxE3FpWdDT1oSHnQlGtXFE2zoWaGhvgt9HNsWU7feQmaP831PTZGRmS/+sq6N8cW7Z45Kkp+okuEv+OoQX4ZKpLjM/7AtzrpdZTEbW6y+rhXNsFdGVWag9IzOryiS4Jy7cAyCZgtKtfcNKbk3VlZ7x+st8SbHW0309epaaUfqEp/AcqtSflqHaIMPCX/fheahkSuDnUwaU+CW8pkpX4TouuwlDmkrxlpxDr8R4y9afXez4de9A7D55C8aGevhhtvAGDqvdHNyaRvaGhbjY+gnlQ3aarbamBr496ocD98IRn5aNnDwxAmNS8dUhX1x/JlkTr08jW9S3KX5xW3flOdZefg7vl4mISs5ETp4YKZm5uB0cj3n7Hko3oahjZYiRrdXfnUmoZFfsqyJ3oVW2df9lHD5zBwDQrZ0Hxg9Tb4MCoSuyOmM1DHZIaIx0/nfvLk1L/CCvyYqG+u3EuvAcJdWv7tn/3nkRu09Ilv3r3akRpo56R82ahK9CruMF5yhLvDOzcrBg+V6IxWJ8/vG7qGVd+s0gqgsmuFVcusworG4JS/3oar8OZ7qSKQRvkp1u4BeRjOtB8hd33nwtWPrnLiqu1AAAay8FITpZMjepTyNblV8vdAb6r0fmMrOUT0fJyn593EC/asyZOnzmDn7beBIA0LyhM35fNK7KzBetamTnuWWVFGuZ4/p6VWX09vXuaIM4PUEp2RH3kvp1ZtbrUTYj/dLHuvAcJdWfIVO/YSnr333iFr5bfRQA0KaJCzb++BH7tRKGKlzHZY+XNh4AYFAQ74wS4y1bf9HPiRVbT+N5aCyaNXDChPc7l/rc1QkT3CouKeP1G9TcUPkoibmB5A2clZtf6ukJAJCY/vocD8IUL/3yNDpVmnDXtVb9tnNuvhi3gyVLlThbGkBXi28/WbK38uMS5c+XevO4jrZWkcS4spzxfIDFK/ZDLBajQT17rPtpcpVoV1Ulu31xfAmxTkiSjXXlf5nJz8/Hf5d9AAC17a3Q1EPxU9wEWMr069h4+cs8FnqVIIm1ro6WSlNRCrfNTUxJl86Llic2/vV7rTRTh45d9MFnS3dDLBajsZsDdq/4RKVErCayMFMl3pLjqsdbcgc1qYR4F9YPAOYy15yQsFdYu+siNAvWvBXqjnQ1a0JrNfQqNRtpWbkw1NWCvany1RHsTCXzd8LiVVvH9kXc6/Ipmcq/EaZm5cJARxMGOspHkxWRfVDOSE8LWanF5wXVVLZWpjA00EVaehZCI5VvkRhesIali6N1pY+mXLnlhwVLdyEvPx91nWywadkUmBiptpJHTWNjZQpDfV2kZWQhvIT1KcOjEwAAtR2sKj3WAHD7QRCiC9Y2fpdr35bIzsYMRga6SE3Pks5NV6Rwa9R6TjYqxdrVWXJHLD9fjJcRcahX20ZuuZcyW68WvkaRs16P8cni7cjLy4ersy32//kpTI2rxs5wVZmdtUy8S9jqtnAt4rpOql3HXZ0l8c3PFyM0Mg51nUqOd32ZeEfEJCA3T/IMTN+Jv5V4vrbDlwAAHGtZ4M7Bb0vdzsomzLRdYArXt21kr3gZHm1NEdxsJd/qAqKVf2t80xOZzSNkVz+Qx7jggTR1dzqzMHo9ApWSyd3S3tSwvmTN04f+LxWWyc7Ohd8zyfzHhq4OFdIuRW76BGLOku3Izc2DYy0LbF4+tcRVAUjCvWAx9UcBSmKdk4uAgvVyPepXbqwLnbwomZ6goSHCuz2Y4JZG04JNW+49DlFYJis7R7p+atMGqj2j0EymvLJz3PV9Pc2sqbviXeeu3n2KSV9tQU5uHpztLXFw1fQiT+STck0K4u2tNN65eFSwPnJTBZv6KCJb/t7jFwrL3fN9fX5l8RYqjuBWA17PXqFFbTM4mOvDvZax3N3MOtW3gq62ZFT1WqDyb41vik3Jgl9EMhram6C5k5nCch52xtJVHQKjld9WlUdHUwPt60jW/AyJSytxVYiaqHuHRrjz8DlCI+PgGxAqdzezizceIytb8uWgR8fGFd1EKZ/HIZi+eCuysnNha2WKLcunwtZKeA8qvC1d2zWEt28wwqPi4RcYJnc3M89bftJYd2tX+SsVpGdk4dJ1XwBA6yb1UMvarHIbVE3069IY132eISQ8Dvf9X8rd3er0VV/pnMz+XZuoVH+7ZnVhbmKAhOR0HDnvg5H928otd/TCfQBAI1cHhevl3n74HOMWbEBmVg7srE1xcPUM2NmYqdSemq5P58a44fMMLyIUx/vstUfILHiWom8X1eLdtunreB+74I0R/eQv2XbsomQqUaP6DkXWy23WoDbObV2g9Bzbj3hJtwPe8esU2FqZQlu7eqWMHMGtBk77RiOtYMT0k251iz0ZqaOpgUmdXQAAEYkZuKHgITFlDtyTfJN0r2WMd9ytix0XAZjcRbKwd26+GBefFF002sJQB8o2PRMBmN2rPqyMJfOMzj5WfZHxmmBI79bSuVgrNp9Cfn7RLwFZ2TlYvf0MAMChljm6tm1Q4W0EAL9n4fhk0SZkZGbD0swIW5ZPhWMJm1NQUQN7tJTOZ1z9z2m5sV6/U7L9uL2tOTq1dq/wNr7p/LVH0iSM0xNKb+SAtjAq6Nc/rDlWLNaZWTlYvlGydnRtOwv06qjalxktLU2MH9oRAHD+uh+87gUWK7Pn5C08eR4JAJj0vvzVTR4GhOKDueuRnpENa3NjHFo9o8TNCqi4Ef3aSOP987rjcuP96+b/AEg2e+jZQfV4jx0sifeFG/647l083vtO3UZAcBQA4KM3HiIzNNBFYzdHpf/ZWL6+Y+xWpxYauznCvU4tldpZ2ZjgVgNJGTnY6hUCAGjtYo6l7zeGey1jmOpro5mjKf74oBlcrCQTyNdcCkLuG1uN1TLRw9XP38HVz9/BVwPkJ0Tn/GLg8zIRAPD1QA+Ma18b9mZ6MNbTQhMHE/w2sinaFoy+7r8TiojEzCKv7+lhg91T2mFSFxe0cjaDjbEuDHU0YWOsi25uVlg9tgUGN5fckg2KTcW+O1Vzi9nKZm5qiOnj+wCQ3P6f8e1W+AaEIiEpDXcfBmHCgnV4/lLy5WLhlMHQeeMbdXhUPBr2no+Gvefjq+V75J4jNS0TD/xeFPmvUHxSqsJjhZ6/jMGULzcgJS0TRgZ6WPX9R7CxMkVaRpbc/2TX96XXzEwN8fGYXgCA2w+eYd4P2+EXGIbEpDR4+z7HtK82IjhUEus5k94tNnoSER2P1gO/QOuBX+C7lfvkniM1PROPnrws8l+hhKQ0hccUKZyeYKCvg56VePegurE0M8LCyf0BAJ53n2L8go247/8ScYmpuO7zDO/NWIWnIZIv/d/PGlasX7+MiIN1+1mwbj8LM5bskHuOWeN7wcHWHGKxGOMXbsS/R68jKjYJYVHxWPXvecxbJrkeNHV3xAcD2xV7fWBINEbNWYvk1AwYG+ph2/LJqGVthtT0LLn/pbNfK2RpZoR5kyTxvnr3KT76YpM03jfuP8OIWasRWBDvb2cMLRbv0Mg42HWaDbtOszH7x51yzzFjXE/Y25pBLBbjoy82YeexG9J4r9l5AQsKrv9N3B0xekDxeNcE1Wu8uQbbeycMtUz0MLy1IzrVt0Kn+kWX6crLF2PdlefwfKr8IQZlvjrki19HNEFjB1NM7VYXU7vVLVbmxMNIrLvyXO7r7c308VFHF6Cj4nP4vEzE98f8kMXpCQp9NLwbwqMTsPPINVy+6Y/LN4tuqqGhIcLcye+it4q3tQr5PQvDR/PXyT3mefsJPG8/KVr+XNGHEE5d9pHu1pOanokxs1crPZ+9rTnO71ikVluFbtywLoiMScDe49dx9c4TXL1T9HevoSHCzI/6qz0V5cmzcHzy1Ua5x7zuBsDrbkCRn909sUxhXZExCfAumMPZs1MT6AloS8+KMG1MD7yMjMem/Z446/UYZ70eFzmuoSHC4umDMbB7M7XqNzU2wK7fp2L0Z2sRGZuEuUuLf8F1c7HFjl+nQEvOkpOHz92TruKQkpaJAR+vVHo+p1oW8D7ynVptrQk+Gd0dYZHx2HzAE+e8HuOcnHh/PW0w3n1H/Xjv+HUqxs5bh8jYJMz/pXi8XV1sse2Xj+XGuyZggluN/HnhGW48j8PQFg5oaGcMYz1tJKRn42FYEg7cDYNfpGoPl70pNSsX03f64N2mdujT0BYuVoYw1NVEYnoOfMOTcPR+JO69SJD7Ws+nsRABaORggjpWhjDT14axnhayc/MRl5YN/8gUnPeLxo3nyp8YJ4lF04eiW1sP7D5+HY8CXiIpJR2WZkZo1bguxg3rjGZcmkkwFkwdjM6tG2D/qRt4/DQMySnpsDAzQvNGLvhgcCc0di8+f68ynLzgLV3EnlvzqmfpvOHo3akRth68Cm+/l0hMToOVuTHaN6+HKSO7oVVjlzLV37C+PTx3fom1uy/h1JWHeBkZB00NDdR1ssaQni0weURX6POLSYX58bP30bNjQ2w7dA0+/q/j3a5ZXUwe0Q0tG7mUqX6Peva4uP0LrN9zCf95PkRoVDw0NTRQx9Eag3u2wMThXaCvW3PjLRIX2VKneoiPL5okdejQAQDwyy+/oGvXrtKfGxsbQ1tbvR12whMyMPTvW+o3kqqNfVPbV3YTqIKkq7n6B1VPzlZc1qqmyM2rdqkMqcFYTwOayh74kVEtR3ALE9o3ff7550X+vn37drRrVzPnnhARERHVVHzIjIiIiIgEpVqO4AYEBJRciIiIiIhqJI7gEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkHRquwGVFVmhtr4ZnCDym4GEZUj/9jkym4CVaDcPHFlN4EqiL25XmU3gSpE6cdlS5Xg9uzZU+2mvEkkEuH8+fPlVh8RERERkaxSJbjh4eHldkKRSFRudRERERERvalUCe6MGTPedjuIiIiIiMoFE1wiIiIiEhSuokBEREREgsIEl4iIiIgEpVyWCcvOzsa1a9fg6+uL+Ph4ZGdn4+eff5Yez8nJQVpaGjQ1NWFsbFwepyQiIiIikqvMCe7BgwexYsUKxMfHAwDEYjFEIlGRBDcmJgZ9+vSBSCSCl5cXTE1Ny3paIiIiIiK5yjRFYfXq1fj6668RFxcHPT09eHh4yC3n4OCALl26IC8vD5cuXSrLKYmIiIiIlFI7wX348CFWr14NAJgyZQpu3LiBf//9V2H5nj17QiwW4+LFi+qekoiIiIioRGonuNu3bwcADB06FHPnzoWenp7STRyaNGkCAAgICFD3lEREREREJVI7wb137x5EIhHGjRtXqvI2NjYAJPNxiYiIiIjeFrUT3FevXgEAateuXaryWlqS59lycnLUPSURERERUYnUTnD19fUBAOnp6aUqHxkZCQBcQYGIiIiI3iq1E1xHR0cAwIMHD0pV3tPTEwDg6uqq7imJiIiIiEqkdoLbpUsXiMVibNq0Cfn5+UrLxsTEYOvWrRCJROjWrZu6pyQiIiIiKpHaCe748eOhr6+PR48eYebMmYiLi5Nb7saNGxg7dizi4+NhamqKESNGqN1YIiIiIqKSqL2TmZWVFX7++WfMnTsXFy9exJUrV9CgQQPp8RkzZsDPzw+RkZEQi8XQ1NTEL7/8AiMjo3JpOBERERGRPGXayax///5Yt24dLCwskJubC19fX+mxCxcuICIiAmKxGFZWVli3bh2nJxARERHRW6f2CG6hbt264dKlSzh58iS8vLwQHByM1NRUGBgYwMnJCZ07d8bgwYOhp6dXHu0lIiIiIlJKJBaLxZXdiKooLTsXFwJeVXYzqAI0sTOr7CZQBbkXFl/ZTaAK5GphXNlNoApib85BtJrAwlALmhqKd82VVaYpCkREREREVU2Zpyi8KT4+HmlpaTA0NISFhUV5V09EREREpFS5JLj//fcfDhw4gPv37xfZ2czAwADNmzfHiBEj0K9fv/I4FRERERGRUmWagxsfH49Zs2bh3r17AAB5VYlEkrkSrVu3xp9//lltRnU5B7fm4BzcmoNzcGsWzsGtOTgHt2ZQZQ6u2iO4WVlZ+PDDD/Hs2TPpUmA9evRAvXr1YGBggPT0dAQFBeHSpUuIjY3F3bt38dFHH+HAgQPQ0dFR97REREREREqpneBu3boVgYGBEIlE+PTTTzFt2jRoa2sXK5ebm4t169Zh9erVCAwMxNatWzF16tQyNZqIiIiISBG1V1E4efIkRCIRPvjgA8yaNUtucgsAWlpamDFjBsaMGQOxWIwTJ06o3VgiIiIiopKoneCGhoYCAEaPHl2q8oXlwsLC1D0lEREREVGJ1E5wdXV1AQC2tralKl9YjvNviYiIiOhtUjvBdXV1BfB6JLckheXq16+v7imJiIiIiEqkdoI7YsQIiMVibN26tVTlt27dCpFIhBEjRqh7SiIiIiKiEqm9isKQIUNw48YNHD16FPr6+li4cCFMTEyKlUtJScGvv/6KU6dOYfDgwRg6dGhZ2ktEREREpFSpEtzVq1fL/bmjoyMcHBxw8OBBHD9+HG3btpWug5uRkYGgoCDcvn0bWVlZcHBwgJOTE1avXo0ZM2aU6z+CiIiIiKhQqXYya9CggXRHMnkKq5BXRt4xf39/lRta0biTWc3BncxqDu5kVrNwJ7OagzuZ1QzlvpOZvb19mRpERERERFRRSpXgXrx48W23g4iIiIioXKi9igIRERERUVXEBJeIiIiIBIUJLhEREREJitrr4L7pxYsXCA4ORkpKCvLy8pSW5Vq4RERERPS2lDnB3bdvH9avX4+IiIhSlReJRExwiYiIiOitKVOC+/3332PPnj0oxVK6UqqUJSIiIiJSldpzcL28vLB7925oaGjgu+++g4+PDwDJCO29e/dw+/Zt7NixA8OHD4empiZcXV1x+vRpPHnypNwaT0RERET0JrUT3H379gEA3nvvPYwePRr6+vqvK9XQgImJCVq3bo0ff/wRmzZtQmhoKCZOnIikpKSyt5qIiIiISAG1E9yHDx9CJBJhyJAhxY69OQ2hQ4cOmDx5MiIiIrB161Z1T0lEREREVCK1E9y4uDgAgJOTk/RnmpqaAIDMzMxi5d99910AwIULF9Q9JRERERFRidROcPX09AAA2tra0p8ZGhoCAGJjY4uVNzMzA4BSr7ZARERERKQOtVdRsLW1RUpKCmJiYmBhYQFAMprr5+cHX19fuLu7FykfHBwMACWukUvKPXgUhLMX7iEoOBKpqekwMTaER4Pa6N+7DerXc1C7Xu/7gXgaGIag4AjExCYiJTUDmZnZ0NfThb29JZo3qYdePVrC1MRQYR3p6Zl49jwCQc8jpP9PSEwFAHTr3BSfThmsdvtqIq+7Adh9/Dp8A0KRmJwOCzMjtG5aF+OGdkbTBrXVrjclLQOPAkLx6EkoHgW8xKMnoYiNTwYADO3dGj8vHF1iHVGxibh04zFu3Q9CwPMIRL9KQn6+GBZmhmjo6oiBPVqiT5cm0NDgXjKl8cj3Oc5f9EZwcARSUzNgYmIIdzcn9OndBvXq2qtd7/0HzxD4LAzPgyMRG5uI1LTX/drOzhJNm9RF93daKO3X8fHJ8L4fCD//FwgNjUF8QjLE+WIYGxvAxcUOHTs0QptWDaChIVK7nTXJLZ+nOPjfLfgHhiEpJR3mpoZo3qgORg7siEZuTiVXoEBqWib8AsPgFxgq+f/TULxKSAEADOjREotnjyhVPWKxGJduPMbJi/fgHxiGlNQMmBgbwLWOHd7t0Qq9uzRVu401keftJ9h+xAsPnoQiMTkNlmZGaNesHia83wXNGzqXuf7ElHRs3ncFpz0fISwqHpqaIrg4WmNQ9xb48L3O0NPVLrkSAKnpmdh9/CbOXH2E4LDYgvemERxrmaNt07p4r09ruNe1K3N7K5JIrOa6XTNmzMCFCxewevVq9OzZEwCwbNky/PPPP/Dw8MCePXugq6sLAMjJycHUqVNx/fp1NGzYEIcOHSq/f8FbkpadiwsBryq7GUVs23kWp87clntMQ0OEcaN74d1+7dSqe8rMlUhKSlNaxtBAD9OnDkGrFq5yj/+94RiuXHso91hVTnCb2JlVdhOKWbb2KLYfuir3mKaGBuZPGYgP3++qVt1fLd+DI+fuyj1WmgT3jOcDzP1xR4lL/rVuUhd/ffchzJQkTxXtXlh8ZTehmB27z+HM2Ttyj2loiDB6ZE/079tWrbpnzP4TScnK+7WBgR4++XgQWjQv3q9v3/HH6rWHUdKnhLu7E2bPeB/GRgZqtfNtcbUwruwmFPHHphPYc9xL7jFNDQ3M+Kg/PhjSWa26l/y5H6cuess9VtoENz0jC9/8thtedwMUlunariF+WvABtLXLbZ+ocmFvrlfZTSjm+1WHsXm/p9xjmpoaWDRtECaPfEft+gOeR+J/CzYgMjZR7nG3OrWw8/dPYGtlqrQer3uB+OznnYiKVbwIwJyP+mLuxH5qt7W8WBhqQbOUX6bVfoe2b98e58+fx7Vr16QJ7qhRo7Bjxw48efIEAwcORM+ePZGXl4dr164hJCSEmzyUwcnTt6TJbcvmrnh/aBfYWJshLDwWu/ddwtNnYfh39znYWJuhTSv3EmorztbGHO3beMCtviNsbc1hbmYELS1NxMen4PbdJzh9/i7S0jOxYtUBLF0yCbUdbRTWpa2tCWcnW9Sra48z5+UnUqTY9kOe0uT2nfYemDauNxxrWeJZSBRWbjmF+34vsHz9cTjUskCvTo3VPo+Othbc69mhiXtt7Doq/0NXnvSMbIjFYlhZGGNQz5bo1NoddZ1soKerjaCXMdi6/zIuXn+Mu4+e49NvtmDHyukcyVXg9Nnb0uS2ebP6GDa4M6wL+vW+A5fxLCgcu/eeh7W1KVq3VL1f29iYo03rBnCt7wgbGzOYmxlDS0sD8QkpuHsvAOcu3EN6eib+WnMIS76dAKc3+nVmVg7EYsDU1BCdOjRGk8Z1YW9nCW0dLURExOHU6Vvw9nmKgIBQrPhjP7756n8cyVVgz7Fr0uS2U5sGmDSqB+xtLfD8ZTTWbj+DRwEv8dfWU7C3NUe39o3UPo+Othbqu9RCQzcnHDh5Q6XX/vDnAWly26tzU4wZ2hkOtSwRn5CCExfuYdfRa/C85Ydf1h7B17OGq93GmmDTvivS5LZnx4aY82FfONlZ4GlIFH5ZfxL3HofghzXH4GRnib5dmqhcf3JqBiZ8sRGRsYkwNtTDok8Ho0d7D+Tm5ePYBR/8vvk/PA2OwqSvNuPo2jnQ1JR/Db7uHYiPPt+IrOwc1LI2xfSxvdCljRvMTQwRE5eMZy+jcey8D3R0qtYXmtJQewQ3Li4OkyZNgq2tLdavXy/9+a5du/DDDz9ALBZDJJJc6ApP0aNHD6xZs0b686qsKo3gpqSkY+b8NcjIyELjhi5YtHBskQ+R7OwcfLF4M8IjXsHG2gwrf5kGLS3Ncm3D02dhWPzDPxCLgW5dmuLTj4uPxj55GgptLU0417aVnn/U/34EwBHc0kpMTkOf8UuRmp6J9i1csWnZx0WSw8ysHAz/dCWev4yBYy0LnNz6ObRVjLW3b3BBcmsvfW3D3vMBlG4E99qdAETGJmBY3zbQ0pR/7sUr9uPAf7cAACu/GY++XZup1Ma3pSqN4KakpmPewrXIyMhCo4YuWDjvg2L9+pvvtyAiIg7W1mZY/vPUcu/Xz4LCseSnbRCLgS6dm2LKpIFFjj989Bxx8Uno2rmZwg/IzVtP4bLnfQDAzE+HoW0bj3JtY1lUlRHcpOQ0vDf1V6SlZ6F103r46/uJxfr1R3NXIyQsBva25tj39zyVY/3APwQ6WlpwrWMnfW37IV8CKN0I7r2HQZj+zSYAwMCereQmsLuOXsVfW04BADYtn4bG7upPlSpvVWkENyEpDZ1H/4iUtEx0auWKnb9/8ka8szFg8go8exENJzsLXN75lcrX8eUbT2L1v+chEomw549P0aFF/SLH9/93G/OW7paUXTgKowe2L1ZHcmoGev7vF0S/SkLD+vbY8+d0mBlXrbswb1JlBFftYRVLS0scOXKkSHILAGPGjMGePXswZMgQuLq6wtnZGV26dMHSpUurTXJb1Xh6PUJGRhYAYMzIHsVGSHR0tDHiPcnt6pjYRPg8eFbubXCr7ygd3XkeHCm3TAM3J9Sra1/uH8I1ybHz95CaLlmFZO6kAcVGPvV0tTHjf30BAGFR8fC87a/yOVo2roPG7k4qX1ALdW7jjhED2itMbgFgzsT+0vep521u7iKP13Vfab8eOby73H793lBJv46NTcSDh0Hl3ob69Rzg6GANAAiW06+bNqmL7t1aKExuAWDE+92k1/W30UYh+O+yD9LSJbGe/r9+cvv1x2N6AQAiohNw/Z7iKQKKNPNwgYero9rX3zOeDwBIpsVMG99XbplRAzvB2tIEALDnWOnv+tQ0B8/cRUqa5Dr+xdSBcuKtI73dHxoZj0s3/FSqPy8vHzuPSUbnu7f3KJbcAsCI/m3hVqcWAGDb4Wty61m3+yKiXyVBS1MDf34zrsont6p6K2POzZo1Q7Nmb2fEJioqCufPn8eNGzfg7++P2NhYaGpqwtbWFu3atcO4cePg5ub2Vs5dWe76PAUA2NqYKXzgpHULN2hrayEnJxd3vZ+qNU2hJIUfclVt7pWQXLz+GADgZGeJxu7yHzjp0aERdHW0kJWdi4vXH6NnR/WnKbwtFmZGsDAzwqv4FMS84uYu8ngX9GsbazPUrSP/4Y2WzV2l/drb5ylatSz/a1vh8o7q9msTE0OYmBggKSkNCQUPNVFRnrckX0QdalnAw9VRbpkubT2k/drzlh+6tmtYkU1EYLBkhSNnB2tYmssf+dbU1EDzhi44d/Uhrt99gpycXH4eyHHOyxcAUNveEs0UPBDcu1Nj6OpoIys7B2e9fNFHhWkKdx49R0LBMzODejRXWG5g9+ZYEXwajwPDERYVD8daFtJjubl52HtScpete/uGcFdwDarOqtXEuMjISLzzzjv44YcfcP78eYSHh0NLSwt5eXkICQnB3r17MWzYMPz777+V3dRyFRwSBQBKV0nQ1tZCHRfJt7XnIfJHWMsiPOIVXryMlrSjDE91k3J+geEAgKYeim/96ehooWF9x4LyYRXSLlXl5OYhKSUdAGBkWHVuHVYlIS8k/aleCf3axVnSr4NfRJV7GyIiX+FlqKQdddV8Qjo3Nw9pBaNV+ga65dY2IQl4LunXylZJ0NHWgltdyXvhSVB4hbRLVuGIo7GRvtJypgWjfOmZ2QgJK74kKAG+TyXX5ZZKVknQ1dFCYzdJvB8FqHYdf/gkVPrnlg1dFJZr2ej1scI2yf49Nl7yhbRP56KDJDm5wljtqlp99crLy4NYLEbnzp0xdOhQdOjQAVZWVsjLy4Ofnx+WLVuGu3fv4scff4SzszO6dlXvKfOqJD4+WXob09baXGlZG2szPA0MQ2RUfJE50OrKyclFXHwyfB4E4egJL+Tni2FoqIdhgzqVqV6SL/pVknR6gpOdpdKyDnYW8PELQUhYbLnEurxduvEYOTmSi2RzJRfgmio+IUXar22szZSWtbYyReCzMESVY7+OT0jBg4fPcPzkDWm/HjxQvX7tcz8QuQUfiPXryR+drMli4pKk0xMcZEbQ5LG3NcejJy8QGvGqwvu1YcGXk5TUDKXlkmWOh4TGwFWAI39lERWbKP2yUNte+XW8tp0l7vmG4HmoatfxZy9jAEimkzjaKX5P1ZY5VviaQg9kkuRmDZzwKCAUa3ZewLW7AUhOzYS+ng6aujthRP82eL9vG6XTlKqqapXgmpqa4vDhw2jYsOitG01NTTRp0gRbt27F+++/j6dPn2LTpk2CSHBlLyampsqXWzI1lhzPycmVrHWpr/poSkxsImbOWy33mLurIz6ZPAgWFiYq10slS5BZps3SzEhp2cLjWdm5SM/IgqFB1RklzcrOwcrNkgdRDA10MbhXq0puUdWTUjC6DUDpGrSAZAoAULZ+HfsqEXMX/C33mJurIyZPfBcWCm5LK5Odk4t9By4DAPT0dNC5Ck6XqWxJya9jbVFCvzYvuMZL+nW2NOmsCHWdbBEQFIGX4a8Ql5gCS7Pi74e8vHzcfxwi/XtiivIl6GqieJnruFUJfcrSvPA6noO0jCwYlfI6XvhZYWpsoPRZCtmpJgmJRWMVHp0g/fOtB8/x/arDyM3Ll/4sIzMbtx4E4daDIBw5742NP06s0PdjeShVguvhUX5PxYpEIvj5qTahupCxsXGx5FaWjo4OBg8ejN9++w2PHz9Wt4lVSlZWtvTPJc11kl3GIzNLvQ9CRRwdrDGgbzvUslU+ikzqy8h8HWtdHeWLc8sel3wQVp0Ed8lfh/AiXLICycwP+0o/tOm1rKwc6Z9V6ddZWTnl2q8dHKzQt3cb2Nqo16+3bT+NqGjJyhTvD+sKY4E9pFIeZPu1jnbp+3VGZlaFJhTdOzbGf5d9kJefj/U7zuGrGe8VK3Pg1A3ExL2eU5+ekV2sTE0n+zvRLWGTBdlNGNLSS5/gphfc/dEtYemuIvUXvKZQStrrwbMlq49AS0sTiz4djEE9WsDU2ACBIVFYufUMznn54trdp/jyt334a/H4UrWvqihVgqvmSmKVQl9fMn8oPz+/hJLVg+yvviLuVllbmWLbhoUAJHPrXsUnw+d+II6evIGVqw+iWZO6mDP9PRhUoYRKKGT7WRWbcVBqW/dfxuEzknVdu7XzwPhhXSq5RVWVbMd++2ezsjTFxrWSpeByc/MQF5+MBw+f4cSpm1j192E0aVwXM6YNValfnzp9C54FG7s0b1YffXu3eSttr+7EqB79uktbDzRr6IIHfiE4du4OMrOyMWZIF9jXskBcQgpOXryHXUeuSh+EA1DlpkZVBUXi/dbOUVi/8jMoO5ov83mTk5uHdT98hN4y66o3dnPEpp8nYsIXm3Dxhh+OnPfG1A96oJGr+jumVrRSJbhLly592+0oN7dvSzZDEMpKCnp6OtI/ZxdcVBSRPa6nq6OkpGIikajIOY2M9OFS2xZtWzfA10u24sGj5/h743HML+W2j1R6BjIjc5kyI3zyZGW/Pm6gr16sy9vhM3fw28aTAIDmDZ3x+6Jx/ABUQFemf+ao0K9LGhFSRF6/dq5ti9at3PH9D9vwyPc5Nm4+gdkzS7d4v+e1h9iz7wIAycOv06cNZawVMNB73a9l+608ssf19Sr2drBIJMLPn4/B/B+2wf9ZOM56PsDZgqXDChkZ6uGTcX3w2/pjAABjPkBajKEK13HZ46qM1heeI7OE91OGbP1v3PmR/XuzBrWLJLeFRCIR5k3sh4sFy5idufpIeAnusGHD3nY7ysXDhw9x7tw5AMDw4cLYZcVE5onWkrbSTSqYD6WtrVnkw6w8ONhboX/vNjh49Bru3AtAVHQ8atkqf2CCVCN7Kz8uMVVp2cLjOtpaRRLjynLG8wEWr9gPsViMBvXsse6nyVWiXVWV7K38krbSTS44rq1V/v3a3s4KfXq3xpFjXrjr/RTRMQklTle4fccfm7eehFgMONe2xfy5o9T+Ql0TmJq8jnV8Cf26cG6lpF9X/O/U0swYG5Z9ghMX7uHs1QcIehGFjIxsWFoYo1OrBvjf+93wTGY1j5K2gK2JLGSu469KWDYvLkHyftDV0SqWgCpT+FmRlJKOnNw8hfNw42TOb25adPqQ7Hzwds3qKjxXYzdHGOjrID0jGwEK1sCvqqrVQ2bKJCYmYt68ecjPz0fTpk3x3nvF5w9VRxYWJtDX00FGZjaiYxOUlo0p2I/arpblWxlNca3/+gnpkBfRTHDLma2VKQwNdJGWnoXQyDilZcMjJfMeXRytK33k7MotPyxYugt5+fmo62SDTcumFPliRsVZmBtDT08HmZnZ0n6rSGzBOsK1alm8lVjXq/t6RObFiyilCe79B8/w9/qjyM8Xw97OEgvnja5S87+rIhtLUxjo6yI9IwvhUcp30osoePDHyd6q0vq1trYWhvVrh2H92sk9fkxm+3VFa/rWZLWszWBkoIvU9Cy8jFB+HX9ZcJ2vo+J1vH5tyaZL+flihEXGo46TtYL6X7/f6tW2LXLM1fn1302VzJ0XiUQwMdJHekY2UgtWh6guqt+6D3JkZmZixowZePnyJczNzbFixQrp4uVCULi+7TMlayPm5ORK18stLF/e8vKEMa+5KmtYX5JsPPR/qbBMdnYu/J5J1jRsWMm3i276BGLOku3Izc2DYy0LbF4+tcQnxUmicH3boBL6dUjBiJmLy9tZjqm0zys89gvBX2sOIS8vH9bWZvh8wRjpCg+knHs9ydrhj5+GKiyTnZOLpwXr5TZQsjZyZfO8Jbld3aCeA8xN2dflaewmSfy9/V4oLJOVnQvfp5J4N1GwqY8isuW9/UIUlvOWWfGisE2FmsrUkaDkLpJYLEZSiuSBNBPj6jVwUe0T3OzsbMyYMQN37tyBiYkJtmzZAicn1d4sVV2rFpL5xNExiQh6HiG3zF2fp8jJkczVa9Oy/HcxAwC/J687q7pPXZNy3Ts0AgCERsbBN0D+h+HFG4+lD3n0qMRlmXweh2D64q3Iys6FrZUptiyfyluWKmjZwhWA5M6Lou2vve8HSvt1Yfny9iTg9ZcpGwX9+mlgGFb+tR85ObkwNzfGFwvGqLWsWE3Vpa1kJaLwqHj4K9ic5eptf2m/7tqu/FYuKk+37gcisOC9OrRv20puTdVVOJ/1ZUQcHjyRP1hxzstXOuf6zY0WStKmSR2YFUx9OX7xvsJyJy5JjjWsbw+nN9bLtbMxk+6ydvO+4i22H/i/lK4E0qiajdhX6wQ3Ozsbs2bNwtWrV2FkZITNmzcrXUasuurauSn0C+be7dp3Efn5RVe1yM7Oxf5DngAkqyC0aFZ8X2ploqLjpQu1K/LseQTOXbwHQLJlsIuzrdLypJ4hvVtLHzZYsflUsdG1rOwcrN5+BgDgUMscXds2qPA2AoDfs3B8smgTMjKzYWlmhC3Lp8KxhM0pqKjOHZtI59TuO3CpeL/OycWhI5J+bWVliuZNVevX0TEJJfbr588jcOGSNwDJhhPOtYv365AXUfh95V5kZeXAxMQAXy4YU+LmFFTUgO4tpXPS12w/Lbdfb9x1HgBgZ2OOjm9hq/Wyio5NxNLVhwAA9V1qYWBPrm+tyPt9W8Oo4Dq+bP2JYvHOzMrBii2nAQBOtSzQvb1qX2i0tDQxZlAHAMClm/644fOsWJn9/93G02DJ3Z//Desst56JwyV7Bfg+DcPJy/eLHc/Pz8dvm/8DINmmeWD35iq1s7JV2zm4OTk5mD17Ni5dugQDAwNs2LABTZs2rexmvRUmxgYYPqwr/t19Hr5+Ifj1j30YPrQLrK3NEBYei937LiE8QrLu6PgPekPrjQnnsps3dOvcFJ9OGVzk+FWvR7jk+QCdOzZGIw9n2NtZwtBAD9k5uYiMiseduwE4e/EucnLyoKEhwoTx/eTOF0rPyEJYuPytG5NT0vH0WdGRC7f61evbYEUwNzXE9PF9sHz9cdz0CcSMb7fi03F94FDLAkEvorBi8yk8L9iRZuGUwdB5Yw3V8Kh49B7/MwBgaO/W+Hnh6GLnSE3LRFDBNrFvik9KxYM3bqs1e2O7yecvYzDlyw1IScuEkYEeVn3/EWysTIuts1hIQySSfkGj14yNDfDe0C7YtecCHvuFYOVf+zFsSBdYW5kiPOIV9u6/hIiCOXxjRvUs1q9lN2/o3KkJpk4eVOS41/VH8Lz6EB3aN0JDD2fY1bKEgYEucnJyERUdj7v3nuLCxXvIyZX06/+N61usX0dEvsLy3/cgPSML+vq6mDNzOMzNjZGZKX/9U5FIpPZKD0JmZmKIyR/0xF9bTuHuwyAs+PlfTB7VE3a25ggOjcHf204jJEzSr2dNHFBsbeSI6AS8N2U5AGBAj5ZYLGcVm7T0TASHxhT7OQAkJqXBN6DoSGJj9+Lbgf+24RgyMrLRvWMjONlZwczUEPEJKfC6G4Dthy4jOSUDJkb6+PazkcXej/SahZkRPpvQDz+sOQqve4GY9NVmzPmoL5xqWSAwJBrL1p/As4Jr8NfThxS7jodGxqPTqB8AAMP7tcGKr8YUO8e0MT1x5Nw9RMQkYvJXm/H19CHo3t4DeXn5OHbBG78XJKaN3Rwxsr/80fahvVti94kbuHk/CHN+3IngsFcY2L05zIz1EfgiGn/+cxaedwIAAJNHdIOLg1W5/Y4qQrVMcHNycjBnzhxcvHgRenp6WLduHVq1Eva3yYH92yP2VRJOn7sD7/uB8L4fWOS4SCTC2FE90K6NeiN6cfHJOHriOo6euK6wjLGxAaZMGKBwhDg4JBJLlu6Qe8znwTP4PCj6LXPv9q/VaqvQfTS8G8KjE7DzyDVcvumPyzf9ixzX0BBh7uR30btLE7Xq93sWho/mr5N7zPP2E3jeflK0/Lnfivz91GUfxBfsipOanokxs+XvfFfI3tYc53csUqutQte/bzvEvkrCufN3cf/BM9x/o4+IRCKMGtEdbVqr369PnLqBE6duKCxjbKSPiR8NQLOm9Yodu3nLT7rrWkZGFpb8tF3p+awsTbHyt+lqtVXoxgzpgsjoBOw/eQNed57A607RfqahIcL0//VD9w7qTTt6EhSB6V9vlHvs+r0AXL8XUORnN48WX/4zPSMLpy5642TB3bo31ba3wo8LPoDrW5oPLiQfj3oHoVHx+OfgVVy47ocL14tucKWhIcKXUwehfzf1BuZMjfXxzy8f438LNyAqNgmfL99brEx9Z1tsWTpJ4ZcRkUiEDT9NxEcLN8D78Qss33ASyzecLFZu1IB2+GLqQLXaWZmqXYKbm5uLefPm4fz589DV1cXatWvRrp38pz2FZsL4vmjRrD7OXbiHZ88jkJqWAVMTAzRwq43+fdvCVc0HE/r0ag1bW3P4+b9A8ItoJCWnITk5DZqaGjA2MoCzkw2aNa2HLh2bwJDrHlaIRdOHoltbD+w+fh2PAl4iKSUdlmZGaNW4LsYN64xmHs4lV0LVwv/G9kHzpvVx4eI9BAVHIC0tEybGBnB3c0Kf3m1QX81+3bNHK9jYmMP/yUu8eBGF5OQ0JKekS/u1k5MNmjaui44dG3MlhAoyb8pgdGrdAAf/uwm/wDAkp6TD3NQIzRu5YOTAjnJHVSvS0D5tYaCvi4d+IYiJS0ZqeiZMjQ1Qx8kGPTo2wcBerYqNNpJiS2a/h54dGmL7YS/c939RcB03RttmdTFxeFe0aFi263iDevY4+89CbNp7GaevPkJYVDw0NTTg4miFgd2bY8L7XUpcws/M2AAHV8/CnpM3ceScN56GRCE1LRMWZkZo1dgFYwd3QJfWVW/KTGmIxNVom7K8vDzMnz8fp06dgo6ODtasWYOuXbu+lXOlZefiQsCrt1I3VS1N7MwquwlUQe6FKV+miYTF1YIPwtUU9ub8klYTWBhqQVOjdEuqVauvYt7e3jh16hQAydIVX375pdLyBw4cgJ0db6UQERER1STVKsGVfRIxJycHr14pH2HNy1P+BDERERERCU+5JrihoaFISEhAVlYW2rRpU55VAwDatWuHgICAkgsSERERUY1V5gT31atXWLduHU6cOIGkJMmWkiKRCH5+r58YDA4Oxi+//AJdXV2sXLkSGhrVevldIiIiIqrCypTgPnz4ENOmTUN8fDyUPatWp04dvHz5EsHBwbhz506NWfWAiIiIiCqe2kOpSUlJmDZtGuLi4uDs7Ixly5Zh//79CssPGDAAYrEY586dU/eUREREREQlUnsE959//kFcXBxcXV2xe/duGBkZIT09XWH5wjm53t7e6p6SiIiIiKhEao/gXrp0CSKRCLNmzYKRkVGJ5V1cXAAA4eHh6p6SiIiIiKhEaie4oaGhAICWLVuWqryBgQEAIC0tTd1TEhERERGVSO0ENzc3FwCgqSl/j+M3paSkAAD09fXVPSURERERUYnUTnCtrKwAAC9evChV+YcPHwIAdxYjIiIiordK7QS3cGrCkSNHSiwrFouxfft2iEQitG3bVt1TEhERERGVSO0Ed8SIERCLxdi3bx/OnDmjsFxeXh6WLFkCb29viEQijBw5Ut1TEhERERGVSO1lwtq2bYvBgwfj2LFjmDNnDrp164aOHTtKj1+4cAF+fn44duwYwsLCIBKJMGbMGLi5uZVLw4mIiIiI5BGJlW1BVoLs7Gx8/vnn+O+//yASieSWKax+8ODBWLp0aakfSqtsadm5uBDwqrKbQRWgiZ1ZZTeBKsi9sPjKbgJVIFcL48puAlUQe3O9ym4CVQALQy1oasjPN99Upq16dXR0sHLlSrz77rvYtm0bfHx8pKsrAICGhgaaNWuGiRMnok+fPmU5FRERERFRqZQpwS3Uq1cv9OrVC5mZmQgNDUVKSgoMDAzg6OhYqk0giIiIiIjKS7kkuIX09PTg6upanlUSEREREalE7VUUiIiIiIiqIia4RERERCQoak9R6Nmzp1qvE4lEOH/+vLqnJSIiIiJSSu0ENzw8XK3XKVpOjIiIiIioPKid4M6YMUPp8dzcXERHR+PGjRuIioqCs7MzBg0apO7piIiIiIhK5a0luIXy8vKwdu1arF69Gunp6Vi4cKG6pyQiIiIiKtFbf8hMU1MTM2bMwLBhw7B161ZcuXLlbZ+SiIiIiGqwCltF4YMPPoBYLMaOHTsq6pREREREVANVWILr7OwMAPD19a2oUxIRERFRDVRhCW5ycjIAIC0traJOSUREREQ1UIUluIcOHQIA2NnZVdQpiYiIiKgGUnsVhdLIzs7G8+fPcfDgQezcuRMikQg9evR4m6ckIiIiohpO7QTXw8NDpfJisRi1a9fG1KlT1T0lEREREVGJ1J6iIBaLS/2flpYWhgwZgt27d8PMzKwcm09EREREVJTaI7hLly5VelwkEkFHRwc2Njbw8PCAoaGhuqciIiIiIio1tRPcYcOGlWc7iIiIiIjKhdoJ7p07dwAAtWvXhq2tbbk1iIiIiIioLNROcMePHw8NDQ3s2rWLCS4RERERVRlqP2RmaGgIsViMOnXqlGd7iIiIiIjKRO0E197eHgCQkpJSbo0hIiIiIiortRPcnj17AgAuX75cXm0hIiIiIioztRPciRMnwsbGBqtWrUJAQEB5tomIiIiISG2lesiscMWE1q1bQyQSAQBSU1OxbNkyfPHFFxgxYgSGDx+Obt26wdHREfr6+krrK5zeQERERERU3kRisVhcUqEGDRpAQ0MD9+7dkyavslv1isViaeJb4glFIvj5+anZ3IqTlp2LCwGvKrsZVAGa2JlVdhOogtwLi6/sJlAFcrUwruwmUAWxN9er7CZQBbAw1IKmRunyzVIvE/ZmHlzS34mIiIiIKoPa6+Bu3769PNtBRERERFQu1E5w27ZtW57tICIiIiIqF2qvokBEREREVBUxwSUiIiIiQVFpisLx48eho6NT5pMOHTq0zHUQEREREcmjUoL77bfflvmEIpGICS4RERERvTUqJbhcCoyIiIiIqjqVEtwNGzZAT4+LKRMRERFR1aVSgtumTZsSt+ElIiIiIqpMXEWBiIiIiARF7Y0ehE5fSxPd6ltXdjOoAmhr8nteTdHRxaqym0AVyDM4trKbQBXklytBld0EqgB/DPWAjbFuqcryk52IiIiIBIUJLhEREREJChNcIiIiIhKUUs3BvXDhAgBwBQUiIiIiqvJKleA6ODi87XYQEREREZULTlEgIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJilZlN4BUc/nWE/xz6Bru+79EQnIarMyN0L5ZPUwa2Q0tGzqXuf7E5HSs33sZ/115iNCoeGhqaKCOoxWG9GqJie93gZ6utsLXnvN6jLuPgnHf/yVeRsYhISkdqemZMDbUQ31nW3Rv74HxQzrC2sK4zO2sCS7d9Mfmg1fh4/8SCUlpsDI3RocW9TBlZDe0auRS5voTk9OxdvclnLzyAC8jJbGu62SNYb1aYvKIrkpjnZyaAW+/F5L/Hkv+H/0qGQAw+t22WLN4fJnbV5NcvROAHUe98CjgJRKS0mFpboS2Teviw/e6oJlH2ft1Uko6tuy/grPXHiE8KgEaGiK4OFrj3e7N8b9hnaGrozjWACAWi3HG8yEOnrmDh09CkZyaDlNjA3jUd8D7fdtgYI8WZW5jTeHr+xwXL3kjODgSqakZMDExhJubE3r3ao26de3VrvfBg2d49iwcwSERiI1NQlpaBjIzs6GnpwM7O0s0aVwX77zTAiYmhirXffiwJ44d95L+/dfl02BlZaZ2W2uSRrWM0d3VCnUs9GGkq4XkzFw8jU3F+YBXCI5PV7veie1qo3NdixLLPQhPwp+ewSrVPbRJLQxuXEv69wXH/BCXlq1yGyubSCwWiyu7EVVRfr4YKVn5ld2MIhb/eQgb9l6Re0xTUwOLpw/G1NHd1a7fPygCY+etR0RMotzj7nVqYd+fn8LWylTu8SYDv0ZsfIrSc5ga62PV4nHo06mx2u0sb9qaVe9GxqKVB7Fuz2W5xzQ1NfD9zCGY9kEPtev3D4rAyDlrFca6QV07HFw1HbUUxHr6kn+x5+RtuceqcoKbmJ5T2U0o5sc1R7D1gKfcY5oaGvjik0GYOKKb2vUHBEdi4ucbERWbKPe4q0stbP/tE9hYmsg9npaRhdlL/sWlm34Kz9GrU2Os+vZ/0NGuWmMmnsGxld2EInbvPo+z5+7IPaahIcLIkT3Qt09bteqePecvJCenKS1jYKCHjycPRPPmrqWuNzz8Fb77fgtyc/OkP6uKCe4x36oVawAY3cIefRrYyD2Wly/GvvsROBegXrvfVoJrb6KLb/u5F/lcrEoJ7h9DPWBjrFuqslXrakQKbdh7WZrc9u7UCHMn9EVte0sEBEfh57XHcdc3BN+tOoradpbo362pyvUnp2Zg/IKNiIhJhLGhHr6dMQQ9OzZEbm4+jpz3xvKNpxAQHIX/LdyIUxvnQlNOUujiYIWB3ZujTRMXuDhYwdbKFDramoiIScKpyw+w5eBVJKVkYPJXW3Bmy3x41FN/tELI1u2+JE1u+3ZujPmT+sHZ3hJPnkfih7+P486jYHzz5xHUtrfEu92aqVx/cmoGPij4ImNsqIcls4aiV8dGyMvLx6Fz97B0/Uk8eR6JsfM34OzmeXJjXUhXRwuNXB3QsqEzNu2Xn6SRYlsPXJEmtz06NMTM//WBo50FAoOj8OumU/B5HIKf1x6Do50F+nRuonL9KakZ+PjLTYiKTYSRoR6+/GQQ3mnngby8fJy45IOVW04jMCQKUxZtxsE1s+XGeuGy3dLk9t3uzTF55DuobW+J2PgUHDh9G5v3XcF5L198s+IAfvl8dNl+IQJ29uxtaXLbrFl9DB7cCdZWZgiPeIUDBy4jKCgce/degLWVGVq2dFO5fhsbM7Ru7Y769R1hY20GM3NjaGlqIiEhGffuPcWFi/eQnp6JNX8fxreLJ8DR0brEOvPzxdj6zynk5ubB2toMsQq+JFFxvd2tpcnt/fAkHPONwqvUbNib6mF4M3vUtzbEqBb2eJWaDZ/wJLXP8zQmFSuvPFd4PC+/9GOYIgAfta0NbU0NxKRklTqRrKo4gqtAVRrBjU9KQ7vhS5CSlokurd2w949p0NB4/UGUkZWNPhN+Q2BINGrbW8JrzyJoa2mqdI6l607gz+3nIBKJcGDVdHRqWfQb/p6TtzDnp10AgBVfjsaYQR1U/nfc8w3BwKl/QCwWY9SAtvjz67Eq1/E2VKUR3PikNLQc9h1S0jLRtY0bDv41vWisM7PR48Nf8TQkCs72lri1/xuVY/3j2uNY+c9ZiEQiHFkzE51bFY31rhM3MfOHnQCAPxeNwbjBxWN968Fz6OhoobGrg/T8lu1mAuAIbmklJKXhnbE/ITUtEx1bumLbr1OLxDozKxtDpq7EsxfRcLKzwLntX6oc6982ncLanechEomwY8U0tG9ev8jxA6dv4/Nf9gAAls4fiZHvti9y/IZPIMbNXQsAGN6vrdwEdvO+y/h57TFJfWtmoUVDF5Xa+DZVlRHc1NR0LPx8HTIystDQwwXz5o2GhoZIejw7Owfff78VEZFxsLYyw88/T4GWirEuSVBQOH76eTvEYqBzpyaYNGlgia85f+Eudu48h3r1HNCokQuOHZNMU+AIrnKGOpr4ZVBDGOhowi8qBb9fCoJsoqWtKcK3fd1hb6qH2NQsfHXCH3kqZmKFI7hPolOx/OKzcml3D1crjGvtiGexaXgclYIhTSTTFKrrCG7V+WQvpfv372PlypWYNGkS+vTpg1atWqFx48bo3LkzpkyZguPHj0NoOfuB03eQkpYJAFg0bVCRD0EA0NfVwYLJ/QEALyPicOG64luJ8uTl5ePfo9cBAD07eBRLbgFg9Lvt4F5H8mbfcuCqyv8GAGjV2AUN6krqePAkVK06hG7fqdvSWC+ePqR4rPV08MWUAQCAFxFxOOf1WKX68/Lyse2w5EOqd8eGxZJbABgzsD0a1LUDAIWjsu2a1UULj9oqJ1z02pFzd5FaEOsFUwYWi7Werg5mf9QXABAaGY/LN/1Vqj8vLx97jt8AALzTzqNYcgtIklZXF0mf/PeIV7Hjxy54A5DcPp//8QC55/no/a7SqSyKplrUdNev+yIjIwsAMHz4O0WSWwDQ0dHG0KFdAACxrxLx8FFQubehXj0HODhIRm2DQ6JKLB8fn4yDB69AU1MDH37YDyKISnwNSXSsYwEDHcm18cCDCLyZkeTkiXHkUSQAwNpIF03s5U8Pqkjm+tp4v5kdcvPF2HZHGJ/P1S7B3bFjB9atW4dr167hxYsXyM/Ph5aWFmJjY3HlyhXMnz8fH374IVJTUyu7qeXm9NVHAABne0s096gtt0zfzk2gV/CgSGH50rr18DnikyRzt4b0bKmw3OCekgdJfAPDERoZr9I5ChUmRLo6nB0jz3+ekti5OFihhYJY9+vSWPoAWGH50rr5IEga66G9FMd6aEGsHz0NUzvWpNw5L18AQG17SzR1d5JbpmfHxtK+cr6gfGndffQcCQVzMt/t3lxhuXffkUxz8XsWjvCoorH2fxYBAKjnZANrC/kfwpqaGmjdtC4A4PJNf2Tn5KrUzprA534gAMDa2gx16tjJLdO8uSu0C+Yw+/gEvpV2aGpKrr/a2iV/Mf13xxlkZmajT5+2cHKUP4+U5GvuIOkrMSlZCInPkFvmfngysnMld4lbOMp/1qEijWvtCH1tTZx9EoPwpMzKbk65qHYJbps2bbBo0SIcPnwY3t7e8PHxwf3793Ht2jXMnj0bmpqauHXrFpYtW1bZTS03jwLCAEhGQBXR1dFCE3dHAMDDANW+fT3wfyn9c6vGip/Ybi1z/kf/b+++o6K43j6AfxeQsoB0GyoiCvbeYotiN8YWjcaaRGKJLZrkpyZGjSbRN7EksUWT2FtsqNgSjV2xoqJYQCK9l6V39v1jYVlkd2FpC7Pfzzmeozt37tydZ3Z8ZubOvb6aX+H5BUTCJ+8/zPblMOKDED3Oi10ntbGugTZ5sX78IkhlOWUeKcRa3TY6tXYs0iYqXz5+oQCAdmpGSZD1cZbF+qmGv7knviHyv7dT83trrzAix1O/kELLEpNl/znXNBer3ZZVTdnylLQM+AdGatROXRCYt0+c1Lx3UKOGARwcasvKl+AOq6bCw2MRHCxrR2NH9e8/3Lv3HI8evYKtrQVGDO9Z7m0ROgcr2e/BP1b1S3/ZuVIExstGUWhkpf73VRIiAHqlvMneqYEF2te3QHRyBk49Lf9jT1uq3W20cePGKf3czs4On376KdLT07Ft2zacOnUKy5Ytg6GhYSW3sHyFR0vkj6wd6tmoLduwrjXuPXmN/4KiIZVKIRKV7Gh/FRgFQPYYskFd1dtoqLD9/HWKk5GZjfAoCS56PsPGvReQk5MLS3Mx5k8dUKL1dUlYlAaxrmeDu96v4R+sWaz98v6j1dMTFYrnmxS37xfAhKW8RURL5N0TGhQT6wZ1reHlE4DXIZrF2j+oINb166h+27p+3YJl/wUV/l2biY0BAIlJ6oczkiQWLH8VFIXmTexL1EZdEB+fJO+eYFdMv1VbW0u8ehWKiMg4jWKtSlZWNuLjk+Dt7Y8zZzyRmyuFqakxhg3rrnKd1NR07D9wAQAwaeJAGKkZLpCKsjSpIe+eEJOsvt9qTEommtoBtcvwMld9S2OsHtYcdqaG0NMTITkjG69jU+EZGI+7gfEo7h0zkxr6mNBRdhG9/0EoMjXtDFyFVbsEtzht28oet2VkZCAxMRG2trZablHZxEkKrgBtixk/1tZKtjw9MwspqRkwMzUu2TYSZN05LM3FavtU5tcPALES1V1AgsJj0eW9lUqXdWnjiA1fTUBdO8sStU2XxCnsUzsb9bG2y491RhaSUzNgXsJYx+YdT8XH2qygXQnC6e5TVcQnKPyuFfa1MjaWslhnZGYjJS1DnnSWdBsWZupjnV//m+0CZEOI+fiF4L/gaMTEJSk9B+Xk5OLek4JhiOJ5vBSSpHBxUNNC/Ri0NfPuhGdlZSM9PRMmJponPjExEnz5v61KlzVtUh8ffzwUVlaqzy+H/rqEhIQUdOrUDG3bFu23TeqZGxX81hLS1XfXScxbbmigB2MDPaRna/5iu5mRAcyMDAr9u3W9mmhdryb6O9ti8/UAxKepfrl2XPt6sDSpgXtBEniHJWq8/aqs2nVRKM6DBw8AAGKxGDY26u+MVAep6QVXgMbFDMauODB/St4dgxJtI022jeL6xSrWn7+OJlwc6+CT9/ugkX31vuioKJUba/X1mxgXPPlISa0ab88KiWKsi4uFUSl/d2npmv+u3zyWBveWDTmYk5uLdTvOKl1/j/uNQmPspqSW/HjUBRmZBclFjWLGCTasURCLjIzyHfHD3t4WAwZ2Rq1aVirLvHgRiBs3HsPExAgTPuhfrtvXFYYGBWlVVo76hFVxuZGBZulYYnoWzj2PxI+XXuHLU88w/a/HmHvsCdZf8cfTcFmi2tjGFAv7NoahipGCXGqZoWdja6Rm5uCgV4jSMtWZIO7gpqamIjw8HCdOnMDOnTsBAFOmTCnz452qQHFEiIr6OtK8dzyL218l3XyDOtbwv/gjACAzOxthkRJcvOWDTfv+xSdLd6Jv12bYtupD1DQzKUuzBUdx8I+KOnYLYq2+XPX/5VRthWJd0dso7netZnH/Hi3RqbUj7j95jcNn7iAtLRPTFMbBPXb+Lv44fAVGhgbIyJTdjXpzhACdVyjWFb9vbGwssHXL5wCA7OwcxMUlwtvbH2fP3caWLe5o1coRs2aOhPiNJwFZWdnYtfscpFJg9Kjeau/ykmqVNdrE0cfhRT7LzszB0/AkPA1Pwgcd7DHAxQ72FiYY2MwOp30KdzUz0BNhauf60BOJ4O4dDkma8F4OrbYJbnR0NHr2LNr5vUaNGvjwww8xb948LbSq/JkqPKJKK+aKPl1huakGj7byy6YXU7/i9sUmqvs2i0QimIpldZrCCFY1TdGyqT2Gvt0W70zfgMt3XuCz7w9gx+ppJW6jLjBV2KdpGerv1FV0rBXvMJqKq3c/9qpIMdbpmepjkVHC392b8ssWdycwLV31sSQSibB5xYdw++oPPHkZDI9LD+Fx6WGhMuamxvjC7R0s/+VY3r954apI8Q58Zpb6WCguL23fV5FIBGOFJzBmZiZo2LA2OnZ0warvduPp09f4c8cZzJ3zXqH1Tp66gcjIeDRqVAeurh1LtW0CMhRmfCtujHXF5Rml6J6gzuFHYehY3wLWpoZ4q5FVkQR3eKs6qFPTGK9jU3HJL6Zct11VVNsuCvr6+rC1tYWtra38RTKRSIRJkybho48+kg+HUt1ZWxb02YopZhrcmHjZciNDA3mCWaJtWMj6AEqSUpGl8ONUVb9sHc3nM2/aqDamje0NADh71RsBIcL8UZWWtWVBX8zoWPWxjlaItZkGsbbJO56Kj3VBP0qrUsSa1FPcp4r7WplYiSzWhjUMNLqYyd9GQrL6WOfXDwCWNYvG2tbaHIc3zsWqBWPQta0TLGuKYVhDH/a1rTBpRA+c3fEl7GsXPPauW8uyxG3UBeYKI1AkJqh+qx4AEvNe1jMw0C+UpJaHunVtMKB/JwCAl5cvoqLi5cuiouJx/vwd6OmJMHXqEN6FL4OkjILfmoWx+nuINfOWZ+Xklqr/rTo5uVI8CZf9tuvWNIahfkFM7cwMMbh5LeTkjXkrnNfKCqu2d3Ctra1x86ZsYPLc3FyEhoZi9+7d2LNnD9zd3bF582Z06tRJy60su7p2ljATGyE5NQOBYbFqywbljVfauIGdRo+4mzjIxjjMzZUiODwWjRsoH/MwSGH7TfKGs9FUx5YFwxU98Q1Bo/rsj5uvXi1LmImNkZyaXnys85Y7NaylUayb5sUtN1eKoLBYODVUHmvF7TctZaxJtToKv+vgYmKdPw6xo4a/a6eGBbEOiYiDo4qpWUMUxjlWdTwY1jDAhOHdMWG48rfvj5y9I/97m2bKx2/WVVZW5jA2NkR6eiaiYyRqy8bkLa9Tx7pCuik1VhimLDAoUt4fNy4uETl5/UG//XZnsfXkv8RmY2OBtT99Wu7trM4kaVlIy8qBSQ192Jqpv0ixNZUtj0iqmH7rSYpPfwz1kZnXDcFabAiDvIuYFYNdiq3np+EtAMhGhfifh2YTSWlTtb2Dq0hPTw8NGjTA0qVLsWjRIkgkEixcuBBpacoHWK5uWucNAv/gaYDKMhmZ2fLxclUNGq9Km2YF5R88DVRZ7r7C9vPH3NVUTjGd7nVd22ay/Xpfbayz4J0X67YaxrqtQvKhbhv3Fd6K13QbVDL549s+eq76N5eRmQ2fvLFpWzlr9ptTLP/omeptPPQJKGiThtvId+HGU/k2bSzVjwqhixwcZLPF+fuHqSyTlZUtHy+3UV758pYroCGgqrLAvMkdnGxUP/0y0BPJx8sNjFM/DF9pWRgrvECaqfopjlBV2zu4qowfPx5r165FZGQkrl27hkGDBmm7SWU2qGcreD58hcCwWDx6HqR0NrO/bzyR9+Ub3Ku1RvV3bdMYVjXFiE9Mxcl/vTB2SGel5U79K+t717KpPRqqGS9XnVsPC+bM5mgKRQ3u1Ro3vV4hIDQGD58HKZ3N7Pz1p/I+tEPe1izW3doWxPrERS+MG9pFabkTebFu1dRe7Xi5VHr9urfEncf+CAqLhffLYKUXpv/eeip/eat/j1Ya1d+ptSMsa4ohSUzFmcuPMGqg8idaZ648BgA0d6qndrxcVW7cf4nneYnb+GFvaby+LmjfrilevgxCdLQEr1+HK53N7NEjP2TlzQLXvr1zhbTj5cuCiV5qKQzV2KhRXXy74mO1616+8hBXrsjOC599NhZWluaC6QpY3h6FJqBZbTPUMjdCI2sTpbOZtbOvKR9x4WFI+Q/PZaAnQuu6shnVwhLSkaVwcRMQl4rl516qXb9PExv0bSr7P3rDFX9I0rKRk1u9blAJ4g6uIiMjI1haWgIAgoI0m+Wpqho7pLO8n+X3Wz2Q+8ZBlp6RhZ/+OAdANih8v+4tNKrfwEAfk0bIHj3+6/kcN72KThP519m7ePlaNsPJR6OLvtwXEBKjtp8fADx8Fog97rJuJQ71bNDKmYPBv2nc0C7ycU5Xbj6pNNZrtsuGa2pY1xoDurfUqH4DA31MGdkDAHDh1jPceFA01gfP3MGL/2Rv6E4b00vj70AlM2pQJ/nv+qftp4vEOiMzC7/s+hsAUL+ONfp0ba5R/Qb6+hg/rBsA4Mqd57j96FWRMsfO34Nf3qxZk/KOC02ERcVjydrDAIBmjetizBDlF0y6rnv3VvI+tUePXkHuG6PvZ2Vl48SJ6wAAW1sLtGnjpFH9UVHxyC7m/Pvf6zBcvuIFQDZlcMOGBV2PjI0N0bBhbbV/LBT6Z9vXs0XDhrVhz5sUSt16HYe0LFk8xrStV2RcBQM9EUa2ll3kRCdnwDtcswS3prGB2tFPRAAmdrSHpVh2B/d2QHyh5RnZuQiWpKn9k6gwhm9YYgaCJWkIS6xeQwAK7g5uSkoK4uNlwTQ1FcbLMTaWZvhi2hCs2HgC1+/7YuqiP/D5x4PRoK41fF9H4PutHvLZplbMHQnDN8ZaVJx44f2hXfDr0olFtjFnUj8c/+c+QiMl+HDRH1gxdyT6vdUC2Tk5OHHxIX78/QwAoI1LfYx/p2uR9Y+cv4dDZ25j9MBO6NGhKZwa1oKFuQkyMrPgHxSNc1e9scv9BjIys6Gvr4cfPh8jiGHcypuNpRkWfTIE3/zijmv3fDHpy9/xpdsQNKxrjZevI7By8yn45iUkK+ePKhrrsFi0H7UCADD+nS7YvGxykW3Mn9IfR/++j9DIeEz6cjtWzR+F/t1bIicnB8cveGH1Nlms27o0wIS8BOlNiclp8gueN8XGJxca+B8AOitM/Usy1hZmmDd1EH7Yegq3vPwwY+kOzJ06CPXrWOFVQCR+/P0MXuU9sv5q1vAisQ6JiMPbH3wHABg9qDN+WvxBkW3M+KAfTl70QniUBDOW7sBXs4ajT9fmyMnJhcelh/h5p+zCuGXT+iqT0xW/HkdqWgYG926DRvZ2sLY0RXRcEi7ffoZtBy9BkpgKC3MTrPtqotoJJXSZubkYI0f0wqG//sWz5wH4deNRjBjeE7a2FggLi8GRo1cQFi7riz1uXD8YvLEfFSdv6NGjNdymDSu0/Natp7h+wxvdurVE82YNUaeODcRiI2Rl5SAyMg4PvF7i0iUvZGfnQE9PhEkTB/L8W4GSM3Nw8kkExnewR4s65pjb2xGnnkYgJjkT9SyMMaZtPdSzkN3I+OthGHLeuOCxMTWU93u98V8cdtwpfLOuq4MV+jvb4nZAPF5EJSMiKQPpWTkwrqGPxtZiDGxmh6Z2sq5CIZI0/POyZDOPCk21SnCzs7NhYKC+ybt27UJW3lArnTsrf9ReHc38oC+Cw+Pw59FruHDTBxdu+hRarqcnwtJPh+OdPm1LVb+FuRj71s7AhIW/ITw6AZ+vOVSkTNNGtbH7x0+KnHzzhUZKsHHvRWzce1HldqwtTbF20Tj0e0uzu8y65NMJrggKj8Pvh6/i7xtP8Xde/8Z8enoiLJ89Au/2bVeq+i3MxTi0fibe/2wrwqMl+OyHg0XKODeqg31rp6uMtffLEIz49Felyy7ceoYLtwq/iBB7Z2Op2ip0097vg5CIOOxxv4FLns9wybPwftPTE+F/04dhUN6EC5qqaWaCP1e74eNFvyMiJgFf5d1tVdTEoTa2f/8xDFQ8bk5JzcDxv+/h2Pl7Spc7NrDDr8umoJnCC0xU1KBBXRATI8HFfx/g8eNXePy48B11kUiEsWP7olPH4l/6USYuLhFnz3ri7FlPlWXMzEzw4dQhGt8hJs398zIatqaG6O9ih3b2Fmhnb1FoeW6uFEceh8ErJKFU9duZGeHdVnXwrpoyLyKTse1WgKCm39VEtUpw/fz8sGzZMowbNw5vvfUW7O1lj7ilUin8/f1x4MABHDhwAAAwaNAgNG3aVJvNLXffL3wP/bu3wK7jN/DweRAkiSmwtTJH17aN8cn7b6NDy0Zlqr+5Uz1c3rsY2w5dxtmr3giOiIO+nh4cG9hhuGt7TBvbCyZGyt8K/XB0TzSyt8Wth3546huK6LgkxEqSYaCvB2tLUzR3qoe+3ZpjzKBOsFAYNoeUW/P5GAzs0RJ/Hr2Oh88CEZ+YClsrM7zVzgnTx/VBp1aNylR/iyb1cOPAEmw5cAlnrnojODwWenp6aNzADiP7d8AnY3sXms2MKs7yeaPRt1sL7D95E49fBCEhKRXWlubo3NoRH77XG+1aOBRfiRoujevh7I4v8eeRq/jn+hOERsRBT18PjextMbRPO0wd3RPGKn7XADB+WDeYio3w4MlrRERLkJSSDsuaYjRtVAdD3m6L9wZ3KXa2NJKZOHEg2rRtgkuXvPD6dRhSUtJhbi6Gs3MDDOjfCU5Opeu25eraAbVqWeLFiyAEBkUiMTEFSUmp0NfXg5mZGA3q26FV68bo/larIhM8UMU54BWKx2GJcG1qC0cbMUwN9ZGUng3f6BRc9I3Gf7Gle7nMK1gCAGhiK0Y9C2OYGxlAbGiA7JxcSNKy8DouFXcChTf1rqZEUsWpsqq458+fY+TIkfJ/GxkZQSwWIzU1FRkZBX1DXF1dsX79epiYlH7A8dxcKZIyqleHaiqd4gbjJuGQpJbv9KdUtV17Ha3tJlAlOfWUsdYFP49sjlrmJRsPvFpddjs6OmLdunW4ffs2njx5gpiYGEgkEhgaGqJx48Zo06YNhg8fjh49NH9ZgoiIiIiEoVoluMbGxhg2bBiGDRtWfGEiIiIi0kl8NktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQmKSCqVSrXdiKooVypFRra2W0GVQSQSabsJRFQBsnNytd0EqiR23eZpuwlUCZ55rIBjfdsSleUdXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCYqBthtAmrl0+zl2HL2Oh8+DEJeQAlsrc3Rv74Tp495Gx5aNyly/JDEVWw5expkrjxEcHgc9PT04NbDDyAEd8MnY3jA2qlEpdZAs1n8evY6HzwIVYt0EM8aXY6wPXMLpN+I0akAHfPL+2yWPdRnrIMZal1y+8xw7j93Aw+dBiM+L9VvtnPDJ+2+jQ0uHMtcvSUzFb4cu4+xVbwSFx0FfTw+NG9hhZP/2mDZG/fk3LCoe5689xQ0vP/j4hSI8SoJcqRQ2lmZo26wB3hvUCe/2bQs9Pd4bK6m+XZth2pheaN/CAdYWpoiJT8Kth6+w7dBVeD0LLHP97/ZthwnvdkX7Fg6wNDdBfGIqnvqG4MDpO3C/4KVyvQ+GdcWW5ZM12lab4csQHB5X1iZXGpFUKpVquxFVUa5UioxsbbeisK/WH8Nvh64oXaavr4dv547ApxNcS13/M/8wvD9/K8KiJEqXN2tcF8c3zUYdW4sKraOyiUQibTehiK/WH8PWg5eVLtPX18PKeSPLFutXYRg7f4vaOLlvnqM+1uVQBzHWFSk7J1fbTShk6c/HsU3NOXzFnBGY+UHfUtf/3D8M4xf8pjZOR379VGmcTl16CLevd6G4lOCt9k7YtcYN1hampW5nRbDrNk/bTSji+wWjVf52s7NzsOzXEyp/+8UxNTHEn99/jEG9Wqksc+bKY3y0ZAeysnOKLNM0wY2VJKP5kK+V1lWZnnmsgGN92xKVZYKrQlVLcLcevIyvNxwHAAzq2Qpfug2GQz0bvPgvHCs3e+Dek9cQiUTY83/T8E6fthrXn5ichl4T1yA4PA7mpsZYOX8kBnRviZycXBz/5wF+2HYGWdk5aN+8If7Z8Tn09YtewZdHHdpQ1RLcrQcu46sNxwDIYv0/tyFwsLfBC/9wfLv5lDzWe390K1WsE5LT0GvCanmcVs0fhQE9WiI7P06/nZbH6cLOL5TGqTzqIMa6olWlBPe3Q5fxzc/uAICBPVrii2mD0TDvHP79Vg/cexIAkUiEXWumYejbbTSuPzE5DX0m/R+CI2Rx+nbuSPTv3gLZOblwv/AAa7afRVZ2Dto1b4jzfywsEqeDp+9g3nf7UcumJsYO7oS+XZujaaNaMDYyhO/rCGw5cAnnrj0BAHRu3Qint31Wpe7kVrUEd+YHfbB64RgAwPnrT/DjH+cQGBqLZk51sXz2cHRp0xi5ubmY/L8/cPaqt8b171ozDSP6tQcAHP/nATbt/xevQ2JQ26YmJr7bDbMnukJPTw/7Tnli7qr9RdbX19eDsaH6h/htmzXAme0LAADb/7qKRWuPaNzO8sYEtxxUpQQ3TpKC9qNWICklHW93dsaxjbMLnVjS0jPRd+pP8H0dAYd6Nrh79BvUMNDXaBvfbfHA+l3/QCQS4eSWuejZsWmh5QdO38aclbIfyS9fT8DkEW9VSB3aUJUS3DhJMtqNzI+1C45vUhLrKT/iZV6s7x1bpnGsV205hfU7ZXE6tXUuenZ0LrT8gMdtzF65DwDw69IJmDyie4XUoesY64pXVRLcuIQUdBr9LZJS0tG7kzOO/PppkVj3//An+AZEwqGeDTwPL9U41t9vPY2fd8vi5L5pDnq8cf7NT2ABYMNXH2DS8MLn30u3nyM0Ih4fDOsKAxXbXvDDQew75QkA+POHjzDctb1GbaxIVSnBtbIwxaMTK1DTzARX7r7A6DmbC90ZNzaqgct7/odmjesiIDQGnd9bqdGx2rNjU3j8Nh8AVCawn05wxfcLRgMABny0FvefBmj8PdZ8PgYzxvcBAPSd8iMePQ/SuI7ypkmCW3Uuv8po+vTpcHFxgYuLCxYvXqzt5pSrv87dRVJKOgBg2ewRRa6aTYwNsfiToQCAwLBYXLjpo1H9OTm52OV+EwAwoHuLIokpAEwY1g3NGtcFAPxx5FqF1EHAX2fvyWO9fM5w5bGeXsZYH8+LU48WRZIVAJjwbkGcfj+sItZlrIMYa11yWOEc/s1s5bH+n8I5/OItzWO954QsTv27tyiS3AKyR9L5cfrzaNE4uXZrjskju6tMbgHg61nDoKcnuyFw8dYzjdqoS8YN7YyaZiYAgG83nSrS7SM9Iwtrtp8FADSyt8WAHi01qn/M4E4AZHFfteWU0jK/HbqM0Mh4AMCsUnR7MdDXw+iBHQHIur5UheRWU4JIcE+fPo2rV69quxkVJv+xUCN7W7Rv0VBpmSG9W8lfHjibV76kbj/2R1xCCgBg1IAOKsuN7C+7Wn/iG1Kko3l51EHA2WuyR1WyWCt/4WRI79YFsdbw0dbtR4px6qiy3Kj+shg+8Q1B0JuxLoc6iLHWJefl53AbtGuu/Bw+uFfBOfxcGc7h+edYZUb0awcAeOobWqrzr62VOWytzAEA4dEJGq+vK4b2lnUxeR0SrTIxPHftCdLSM2XlNeyS0tq5PgDANzASUbFJSsvk5kpx+5E/AGBAj5YaPxEY0KMl7KxlsT54+o5G61YV1T7BlUgk+OGHH2Bubg4nJydtN6dCPH4RDADo1KqRyjJGhjXQxkV20D/W8ErroUJ5ddvo3NqxSJvKsw7SJNYNAACPNNyHinHqrC5ObQqWeauJdWnrIMZal3i/DAEAtSNiGBnWkCcumu7Dx88LyqvbRqdWBedf75eaxykrOweSRFkiXdPMWOP1dUXbZrLfrLpuAZlZ2fIYtMsrX1IWeXeHExJT1ZbLv+gxNzWGi2MdjbbxwbCuAGQvwx0+d0+jdauKap/grlmzBrGxsVi4cCFsbGy03ZxyFxYlkT/acrBX//0c6smW+wdHF/smrCK/gEgAgJ6eCA3rqd6Gg8Iyv8DIcq9D1ynGupG9+j5G8lgHRVVQrAu27xsQUe516DrGWneEFzqHlzDWmp7DA0sWp4ZlPP+ev/YEmVmyt+g7KyTLVKCunYW8e0JAaIzasoFhsQAAp4a1NNpG/vFkUVOstpxVzYKRLpwblTzBtawpxsC8bhNX7r5AZGyiRu2rKqp1gnvr1i24u7ujbdu2GD9+vLabUyHiJMnyv9fKe1ygSv6jo/SMLCSnZmiwDdlVnqW5WO1jDFsrM/nfYxXaVV516DrF/WFnoz7Wdtay/ahprGMTZNvQJE75sS3POnQdY607YhMK9oddsefwglinaHIOTyjZ+Tf/WAI0j1N6Rha+2+oBADATG2HskM4ara8rbCwL9nG0iu4D8uVxst+XibEhzMRGJd7Gi//CAQBNG9ZSeUzp6YnwVvuCp9o2liUf1m3MoE4wMpR1lzlQTbsnANU4wU1PT8eyZctgYGCAlStXVqnhSspTSl4fHQDyA04VE4UBvFPSSn5yzN+GUTEDtZsYGyrUn1loWXnUoetSFfaHcTGxNi5lrPO3Udyg/IpxSn6j/vKoQ9cx1rojVWF/GBUzLJOxUen2Y/42NKlfk2MJAP7302H8FxwNAFg0fWihRI4KiE0K9nF6ZpbasukZBctNTUqe4J669AgAYGCgj69nDVNa5pP334Z9bSv5v81MS96lZPw7XQAACUmppRrCrKqotjOZ/fLLLwgODsbHH3+MZs2aabs5FUbxKVVFDWeV/yisuNrVLS+POnSdFAXBrqiRy0r62LMksS5LHbqOsdYdlXMOL1n9pd36lv2X5C8aDejREjPG9SllTcInUtjLFTUI67lrT+D58BXeat8EU0f2gKmxETbt/xcBobJxcCcM64Y5k/ohLT1TfvGZm1uyYciaOtSW9+N2v+CFjMwqMl5qKVTLBPfZs2fYvXs36tWrh7lz52q7ORXKTPFqMEP9Hc+0Ul4NmuWVVbyaVCZV4W6yqUK7yqsOXacYs7Ri9mNpr/xNxZrHyeyN+sujDl3HWOsOU4VHz8XtR8VzvCb7saRxSstQPP+WrP6Dp+9gxaaTAGQTPPz+3YdVauzwqkbxzrhJMU8+Svt0BgCmLv4ThzbMRIcWDhgzuJN86LB8CUmpWLXFA2sXjcv7d1qJ6s1/uQyo3t0TgGrYRSEnJwdLly5FTk4OvvnmG4jF6jtZV3fWCo+BouLU9+eJiZctNzI00Kg/j3Ve3xxJUqraafhi4gv6Db45TWN51KHrStN3S9NY21jItqFJnKze6LtVHnXoOsZad9gonOeiiz2HF8TaVJNzuEXJzr/5xxIgm4ygOKcuPcSC1QchlUrRytkeB9fP1OgiSxdVRv96QHYsDZ62Hgt+OIgbD/wQJ0lBRmYWgsJi8ceRa+jxwQ+FhoLLHxNXHZFIhPfz+la/CozCvSevNWpTVVPt7uDu3LkTPj4+GDBgAFxdSz8/e3VRr5YlzEyNkZySjsDQWLVlFd/I1OQKu2mj2gBk4+YFhcWqfKMzv37FdcqzDl1Xr5YlzE2NkZSSrtHbtxUT64LtOzuUNtaq69B1jLXuqFvLEmZiIySnZiCwpLFuoGGsHUoWpyDF828xcfrnpg9mLtuDnJxcNHWojSO/fAoLc2HfUCoP4dEJSExOQ00zE41GSCmNrOwc7HK/KZ9k6U2ThhfMKuj1rPjhQ9/u7CLvt3voTPW+ewtUszu4wcHB2LRpE0xNTbF06VJtN6fStM0b31bdmHoZmVnysRbbajimXrtmBQOPq9uG4tVcW5fC2yiPOgjysYyLj3Xpxk9s17yg/D11cfIuWPbm8VQedRBjrUvyxzJ+4BOgskxGZhae+MrO4W003IeK+1zdNu4/LTj/5h9/yly/74tpX+1AVnYOHOrZ4NjG2fJReqh4+f8Xqxvj2rCGQanHuC6pd/rIJpB4+CyoRKMW5b9clpOTi0NnmeBWqtWrVyMtLQ3Tp0+Hubk5UlJSCv3JyZE9msnOzpZ/VtKO1VXZ4F6tAcjG1Huo4irs3LWn8v5XQ3u31qj+bm0bwypvPD33C14qy524+BAA0KqpfZGxFsujDpLNXAXkxzpQaZlz154UxFrDGXC6tXWClUV+nB6oLOd+URZD5bEuex3EWOuSwb1aAQACQmNVzmx1/nrBOXyIhufwrgrn3/xzrDIn/30EAGipJk53vf/DpC+3Iz0jC3XtLHBs0xzUrWWpUXt0Xf5MdI717VTOXDekd2v5C2AVMVJBny7N5BOH7D6h/A6vIlMTQwzr2xYAcP2BL0IjJeXepspWrRLc0NBQAMCGDRvQoUOHIn8ePJCdgD08POSfvXz5UptNLhfj3+kiH+Jj5eaTRZL29IwsrPldNq91w7rWGs9rbWCgj6mjegAALtx6hhsP/IqUOXj6jnzsvWlje1VIHQSMf6crzPNi/e2mU8pjnTeHecN6NqWL9ci8ON18hhsPfIuUOXj6tjxObmN7V0gdxFjrkveHdpH3n161WXmsf1Q4h/fv3kKj+g0M9DF5pOxx9MVbz3BTyfn30BmF8+97ys+/3i+D8cHCbUhNy4SdlTmOb5pTaHIeKplDZ+4gMVn2UtfyOcOLdDcxMjTA4ulDAQCBoTG4cNOnXLdvX9sSvy6dAAB46huC/ac8i11nRP8O8v7Vh6r5y2X5qlWCq6tsLM2wyG0IAODqPV9M/OJ3+SOHW16vMOLTjfB9LZs9aNVno2BYo3DX6qCwWFh3mQvrLnMx+9u9Srcxf0p/2Ne2glQqxcQvtmPPiVsIj05ASEQcft1zEQtWHwIgexQ2cVi3CqtD19lYmmHRJ/mxfokJn2/Hw2eBiJUk46bXKwyf9Ste5sX6u/nKY23VeQ6sOs/BpyuUx/qzqQPkcZrweX6cJAiOiMMvey7gsx8U4vSu8jiVRx26jrHWHTaWZvhf3jn82n1fTP7ydzx6nncOf/gKo+dshG/erHHfzlMea7tu82DXbR7mrNyndBvzJhecfyf/73fsPXkLEXnn3417L+LzNbI4tXGpX+hN+Xx+AZEY99lWJCanwdzUGLt/dEMdO0skp2Yo/aM4cgYVFpeQgv/7/RwA2Z3UA+umo13zhrC2MEX39k44tXUemjWuCwBY+ot7kRcDG9S1Rvy9TYi/twmbl09Suo3/+2IsNi2bhEE9W6GJQy1Y1hTDxbEO5k7qh2v7lsjqSEjBzOV7kJ1T/JPs8UNl3ROSUtLl4+xWd9XqJbOTJ0+qXT558mTcvXsXo0aNwpo1ayqpVZVj9kRXBIfHYfvhq/j7xlP8feNpoeV6eiIsnzMC7/ZtV6r6LczF+GvDTIz9bCvCoyT47IeDRco4O9bB/rXTYaBippzyqIOA2RP7ISg8Dtv/Uh3rFXNG4F3XdqWq38JcjL9+non3529FWJQE878/UKSMi2MdHFg3Q32sy1gHMda6ZNYEVwSFx+GPI9fwz00f/PPGXTs9PRGWzR4uf0ysKQtzMQ6sm4HxC7YiPDoBC/NuKChyblQb+35Sfv51v/BAPopDUko6hn6yQe32GtSxhteJFaVqqy7YcuASGta1xozxfTC4V2t5V8N8OTm5WLHpJE5fflyq+s1MjTBhWDeVF5V+gZH4+Ksd8HkVVmxdDepYoUeHJgCAk/8+LHbowuqiWiW4um7NF2MwoEdL7Dh2HV4+gYhPTIWtlRneaueEGeP7qO3QXhItmtTDzQNLsPnAJZy54o3g8Fjo6enBqYEdRvbvgE/e711oxqKKqoNkV+cDe7TEn0ffiHV7J8wc37fMsW7ZxB43D36Fzfsv4fSVxwVxaiiL0/T33y42TuVRBzHWumT157Jz+M5j1+H1LAiSxBTYWpmjWzsnTH//bXQsh3P4tf1LsPXgZZy96o2g8Fjo6+mhcQM7jOjXHm5jef6tTIvXHcU/N30wbUwvdGjpAKuaYsTEJ8PzoT9+O3QZD3yU970vid3uN5GckoFu7RqjXi1L1DQzQVxCCl78F45T/z7Cfo/byMwq2SQN497pKp8N9qAARk/IJ5KWdKqaaqA87+DmSqXIqL4TeJAGOGg5kTCV5NEsCYNdt3nabgJVgmceK+BYX/3wa/nYB5eIiIiIBEVQXRT27lX+ogURERER6Q7ewSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCIpJKpVJtN6Iqkkql4I7RFSJtN4CIKgTP4roiMDRW202gSlC/jhVqGOiXqCwTXCIiIiISFHZRICIiIiJBYYJLRERERILCBJeIiIiIBIUJLhEREREJChNcIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuEREREQkKAbabgBpX1xcHLZv345Lly4hIiICJiYmaNmyJSZMmID+/ftru3lUDhITE3H37l34+Pjg2bNn8PHxQXR0NABg8+bNjLOARERE4OLFi/D09MTz588RHR0NfX191K5dG127dsWkSZPg7Oys7WZSOXn06BEuX76Mp0+fIjg4GLGxscjIyIClpSVatGiBd999F8OGDYNIJNJ2U6kCTJ8+HVevXgUAjBo1CmvWrNFyi6oOJrg6zs/PD1OnTkVsbCwAwNTUFElJSbh58yZu3ryJyZMnY+nSpVpuJZXVxYsXsWTJEm03gypYeHg4+vbtC6lUKv9MLBYjOzsbAQEBCAgIwLFjx7B48WJMnjxZiy2l8rJv3z54eHjI/y0Wi2FgYIDo6GhcvXoVV69exZEjR7BlyxaYmZlpsaVU3k6fPi1PbqkodlHQYZmZmZg1axZiY2Ph7OyMkydPwsvLC15eXli4cCFEIhH27t2LY8eOabupVA7s7OzQu3dvzJw5Exs3btR2c6gC5OTkQCqVomfPnli7di1u3ryJhw8f4tGjRzh69Cg6deqE7OxsfPfdd7h27Zq2m0vloHPnzvj666/h7u4OLy8vebxv3LiB+fPnQ19fH3fu3OGdPYGRSCT44YcfYG5uDicnJ203p0oSSRUv9Umn7N27F9999x3EYjHOnj2LunXrFlr+3XffYe/evahVqxYuXbqEGjVqaKmlVFbZ2dkwMCj8wMbFxQUAuygISVJSEoKDg9GiRQulyzMzM/Hee+/B19cXXbt2xZ49eyq5hVTZ1q9fj23btsHIyAj379+HoaGhtptE5WDx4sVwd3fH8uXLce7cOdy9e5ddFN7AO7g67NSpUwCAYcOGFUluAcDNzQ0ikQhRUVG4c+dOZTePytGbyS0Jk7m5ucrkFgAMDQ0xfPhwAICPj09lNYu0qG3btgCAjIwMJCYmark1VB5u3boFd3d3tG3bFuPHj9d2c6osJrg6KiUlBU+ePAEA9OzZU2mZOnXqoEmTJgAAT0/PSmsbEVUcExMTAEBubq6WW0KV4cGDBwBkfXNtbGy03Boqq/T0dCxbtgwGBgZYuXIl9PSYxqnC2zo6yt/fX/4iSn4Sq0yTJk3g5+cHf3//ymoaEVWgu3fvAgBHUhCw1NRUhIeH48SJE9i5cycAYMqUKRxJQQB++eUXBAcH4+OPP0azZs203ZwqjQmujsofIgoAateurbJc/rKoqKgKbxMRVSxvb29cuHABADBmzBgtt4bKU3R0tNKncTVq1MCHH36IefPmaaFVVJ6ePXuG3bt3o169epg7d662m1PlMcHVUampqfK/GxsbqyyXvywlJaXC20REFUcikeDzzz9Hbm4u2rRpg9GjR2u7SVSO9PX1YWtrC0A27nVmZiZEIhEmTZqEjz76CPr6+lpuIZVFTk4Oli5dipycHHzzzTcQi8XablKVxwSXiEjg0tPTMWfOHAQFBcHKygrr169nwiMw1tbWuHnzJgBZ/+rQ0FDs3r0be/bsgbu7OzZv3oxOnTppuZVUWjt37oSPjw8GDBgAV1dXbTenWmDvZB2lePWXnp6uslxaWhoA2QQQRFT9ZGZmYs6cObh37x5q1qyJHTt2oEGDBtpuFlUgPT09NGjQAEuXLsWiRYsgkUiwcOFC+fmcqpfg4GBs2rQJpqamnHhJA0xwdZSdnZ3875GRkSrL5fe9rVWrVoW3iYjKV2ZmJubNm4fr16/DzMwMf/75p9phxEh4xo8fD0NDQ0RGRnJyj2pq9erVSEtLw/Tp02Fubo6UlJRCf3JycgDIxjvP/4yjpLCLgs5ycnKCSCSCVCrFq1evVM6E8urVK3l5Iqo+srKyMH/+fFy+fBlisRjbt29HmzZttN0sqmRGRkawtLREVFQUgoKCtN0cKoXQ0FAAwIYNG7BhwwaV5Tw8POTTNp84cQLNmzevlPZVVbyDq6NMTU3RunVrAMD169eVlomIiJAnuN26dau0thFR2WRlZeGzzz7DpUuXYGxsjN9++w0dO3bUdrNIC1JSUhAfHw+AXc1It/AOrg4bNmwYvL29cebMGcyePbvIbGZ//PEHpFIp7OzsmOASVRPZ2dn4/PPPcfHiRRgZGWHr1q3o2rWrtptFFUDZFNxv2rVrF7KysgAAnTt3roxmUTk7efKk2uWTJ0/mVL1K8A6uDhs/fjzs7e2RmpqKGTNm4MWLFwBkL51t374d+/btAwDMnz8fNWrU0GZTqRzExcUV+pMvOTm50Of5/xlS9ZOTk4Mvv/wSf//9NwwNDbFp0yZ0795d282iCuLn54exY8fi6NGj8sfYAORdz1auXImNGzcCAAYNGoSmTZtqq6lElY53cHVY/t2dqVOn4uXLlxgxYgTMzMyQnp6O7OxsAMDEiRMxduxYLbeUysNbb72l9PNFixYV+veePXt4x6+a8vLywtmzZwHIkpwlS5aoLX/06NEiT26oevH29oa3tzcA2TldLBYjNTUVGRkZ8jKurq74v//7P201kUgrmODqOBcXF5w5cwbbtm3D5cuXERERATMzMzRv3hwTJkzAwIEDtd1EIiohxTens7KyEBMTo7Z8/tvXVD05Ojpi3bp1uH37Np48eYKYmBhIJBIYGhqicePGaNOmDYYPH44ePXpou6lElU4klUql2m4EEREREVF5YR9cIiIiIhIUJrhEREREJChMcImIiIhIUJjgEhEREZGgMMElIiIiIkFhgktEREREgsIEl4iIiIgEhQkuEREREQkKE1wiIiIiEhQmuERUoY4fPw4XF5cif5o1a4ZOnTph1KhRWL16NQIDA7Xd1BJZvHgxXFxcMHny5CLLFL+r0Lm6usLFxQUbN27UeN2QkBD5frpz5065t01djCrLnTt35N8xJCREa+0g0lVMcIlIK6RSKZKSkvDs2TPs2rULw4YNw7Fjx7TdrCpJlxJnIqLyYKDtBhCR7ti+fTs6deoEQJbgRkdH4++//8bmzZuRmZmJb775Bo6OjujQoYOWW0pERNUZ7+ASUaUxNjaGqakpTE1NYWZmBkdHR8ycORMrV64EAOTk5GDbtm1abmXpjR49Gi9fvsTLly+13RQiIp3GBJeItG7kyJGoX78+AFnfxZycHC23iIiIqjN2USAirROJRGjatClCQkKQlpYGiUQCGxsbhISEoF+/fgCAPXv2oF27dtizZw/Onz+PoKAgJCYmYvXq1Rg9erS8royMDBw5cgQXLlyAr68vkpKSYG5ujpYtW2LUqFEYOnQoRCKRyrYkJiZi+/btuHDhAsLCwmBubo7mzZtj8uTJ6NOnj9rvcfz4cSxZsgQA1N7FDQ4Oxv79++Hp6YmwsDCkp6ejVq1aaNSoEfr164fBgwfD2tq60PfPp6wfrrJtRUdHY9++fbh+/TqCg4ORlpYGW1tbdOzYEVOmTEHbtm3Vfhd/f39s27YNnp6eiI+Ph42NDbp16wY3Nzc0bdpU7bpllZaWhkuXLuHatWvw8/NDZGQkEhISYG5uDhcXFwwdOhQjR46EoaFhiep78OABdu3ahYcPHyIhIQF2dnbo3bs3Zs6ciTp16qhdt6zHExFpBxNcIqpylCUMEokEY8aMga+vr8r1/Pz8MHPmzCJvrcfFxeH69eu4fv06Tp48iZ9//hlisbjI+oGBgZgyZQoiIiLkn8XGxuLGjRu4ceMGZs+eXYZvJbNz506sXbsW2dnZhT4PCQlBSEgIbty4gdjYWMydO7fU2zh37hy++uorpKamFvo8PDwcp0+fxunTpzF79mzMmzdP6foXL17EggULkJmZKf8sIiICJ06cwPnz5/Hrr7+Wum0lsWHDBuzevbvI53FxcfD09ISnpyeOHTuG33//HTVr1lRb15EjR7B8+fJCTwVCQ0Nx8OBBeHh44Pfff1fZ57usxxMRaQ8TXCKqEvz9/QHI+ulaWFgUWf79999DIpFg7ty5GDJkCGxsbBAaGgojIyMAsgRs8uTJiI+Ph52dHWbNmoXu3bvD2toasbGxOH/+PH777TdcvXoVy5cvx08//VSo/oyMDMyYMQMREREwMDCAm5sbRowYASsrK/j5+WHz5s3YvHkz7O3tS/0dd+3ahTVr1gAAHBwcMGPGDHTp0gU1a9aERCKBt7c3zp8/Dz09We8xe3t7eHl5wcPDA8uXLwcAeHl5qd3G1atXsWDBAkilUrRq1Qpubm5o164dxGIxgoKCsH//fri7u2Pz5s2oW7cuxo4dW2h9f39/eXJrZWWFhQsXonfv3jAwMMDdu3exbt06fPnll5BKpaXeD8URi8UYNGgQXF1d4ejoiFq1asHQ0BARERH4999/sW/fPjx69AgrVqzA+vXrVdYTGBiIb7/9Fi4uLliwYAFatWqFlJQUXLhwARs3bkRycjJmzZqFM2fOwNbWttC6ZT2eiEjLpEREFejYsWNSZ2dnqbOzs/T27dtKy3h4eMjLfPzxx/LPg4OD5Z87OztLr1y5onI7s2bNkjo7O0tdXV2l0dHRSstcvXpVXpe3t3ehZTt27JAvO3ToUJF1s7KypJMnT5aXmTRpktrv+qbQ0FBpy5Ytpc7OztL33ntPmpSUpPK7ZGVllbheRenp6dLu3btLnZ2dpTNmzChST761a9dKnZ2dpd26dZOmpaUVWjZjxgyps7OztFWrVtLnz58XWTciIkK+DWdnZ+mvv/6qtk3KKMZV1TGhzosXL6TNmzeXuri4SAMDA4ssX7Rokbz+YcOGSZOTk4uUuXnzptTFxUXq7OwsXbZsWZHlZT2ebt++LV8WHBys8XckorLhS2ZEpBVSqRRRUVHYu3cvvvnmGwCyrgnTp09XWr5Xr154++23lS4LDg7GpUuXAABffPFFkbtx+Xr37o0uXboAADw8PAotyx+D18XFBePGjSuyroGBAb7++usSfDPlDh48iKysLIhEIqxZswZmZmYqyxoYlO7h2pkzZxATEwMDAwOsXLlSZT2ffvopTExMEBcXhxs3bsg/j42NxdWrVwEA48aNQ7NmzYqsW7t2bcycObNU7SsvLi4uaNGiBaRSKTw9PdWW/eKLL2Bqalrk8+7du2PAgAEAgFOnThXqjlEexxMRaRe7KBBRpZkyZYrKZQYGBvjqq6/QtWtXpcvVveDl6ekJqVQKkUiEzp07IyUlRWXZZs2a4e7du3j69Kn8s4SEBLx69QoA5EmPMi4uLmjUqBECAgJUllHXRgBo3bo1mjRpovH6JXHz5k0Asnaampqq3Q+NGzeGj48Pnj59iv79+wMAHj58iNzcXADq98PAgQPx3XfflWPLi5JIJDh8+DCuX78Of39/JCYmIisrq0i5169fq6xDLBajZ8+eKpcPHDgQ//zzD1JTU/HixQu0adMGQNmPJyLSPia4RKQ1+vr6sLe3R9euXTFp0iSldwzzNWjQQOWy/CRHKpWiR48eJdp2XFyc/O9hYWHyPqVOTk5q13NycipVghscHAwAaN68ucbrllT+fvDx8SnxZBmK+0HxZSp1+6F27dowNzdHUlJSKVuq3qNHjzBr1qxCbVNFXRscHBygr6+vcrnihUZISIg8wS3r8URE2scEl4gqjeJMZnp6ejAxMSnxusbGxiqXlSbRUnwkrXiHrri34Uv7tnxycjIAKH1cXl7yt6EJxf2gOOpCSfZDRSS4ycnJmD17NuLi4mBtbY2PPvoIXbp0Qd26dSEWi+Uv4Lm5ucHLy0vtmMmaxFLxGCjr8URE2scEl4gqTf5MZuUtP1ExNzfH/fv3NV5fsU1vDq31puKWq2JmZgaJRKL2cXdZ5e+Hfv36YcuWLaVeH5CNRasuQSztfijO+fPnERMTAz09PezZs0flmLsl2Y9paWlqlyt+B8VjoKzHExFpH18yI6Jqr2HDhgBkd97yuwJool69evKxd//77z+1ZfOHM9NUfhufP39eqvUrYxv5s8kB6r9nVFRUhXVPePHiBQBZP2JVyW1mZmaJuokEBASovcOr+B0Vv3tZjyci0j4muERU7Sn2kzx69KjG61tYWMj7Y164cEFlOT8/v1L1vwVkb+0DwJMnTzROkhVHQ1CXsOXvh7CwMNy6dUvjNrZv317eBUDdflC3rKzyH/Wr+57nz59HRkZGsXWlpqbKX7xT5p9//gEgu2Or2P+7rMcTEWkfE1wiqvYcHR3Rt29fAMCOHTtw584dteWTk5MRFRVV6LP33nsPgOwO4pEjR4qsk52djR9++KHUbZwwYQJq1KgBqVSKJUuWqH3E/uYsZ1ZWVvK/v9luRcOHD5cPafXNN98gMjJSbZtCQkIK9R21sbGRD8X2119/wc/Pr8g60dHR2Lp1q9p6yyL/Tup///2ndISEyMhIrF27tsT1rVu3TmlXhdu3b8sT3OHDhxea9rc8jici0i4muEQkCCtWrICtrS0yMzMxbdo0rFq1Cg8ePEBsbCwkEglev36N8+fPY8mSJejdu3eRGcEmTJgAR0dHeV0///wzXr9+jfj4eNy/fx9ubm64detWqWcyq127NhYvXgwAePz4MUaPHo3jx48jJCQEiYmJCA4OxtmzZzFv3jxs37690LotWrSQjwawZcsWREZGIisrC9nZ2YWSYRMTE6xevRr6+voICQnByJEjsX37drx48QIJCQmIjY3F8+fPceTIEcycORMDBw4s8mLal19+CUNDQ2RkZGDKlCk4cuQIIiMjERMTg/Pnz2PChAnIyMgodorc0ho0aBD09fWRnZ2NGTNm4MKFC4iKikJERASOHz+O999/H4mJiSWKQ+3ateHv74+JEyfi+vXriIuLQ2hoKHbt2oVZs2ZBKpXC0tJS6bTIZT2eiEi7+JIZEQlCnTp1sH//fsydOxe+vr7Yt28f9u3bp7K84h07ADAyMsJvv/2GqVOnIiIiAlu3bi1yp3LmzJmIjIyEu7t7qdo4adIkZGVlYe3atQgICMCSJUuUlnuz76mNjQ2GDx8Od3d3HD58GIcPHy60/OXLl/K/9+7dG1u2bMGiRYsQFxeHdevWYd26dUq3o6+vX2QYLScnJ2zYsAELFixAXFwcli5dWmi5kZERfvnlF6xatQqJiYkl/u4l5eDggIULF+Knn35CYGAg5syZU2T7P/30E/bt24fQ0NBi6/r000/x7bffws3NrchyMzMzbN26VelEDmU9nohIu5jgEpFgNGrUCCdOnMDZs2fx999/48mTJ4iLi4NUKoWVlRWcnJzQs2dPDBw4UP4i0Zvre3h4YNu2bbh48SLCwsJgamqKFi1aYOLEiejXr5/8LmxpffTRR3B1dcXevXvh6emJsLAw5Obmws7ODg4ODhgwYAAGDRpUZL1Vq1bByckJ58+fx+vXr5Gamiofu/dNffr0wcWLF3H48GFcvXoVfn5+SExMRI0aNWBnZwcXFxe4urqib9++sLCwKLJ+//79ceLECWzbtg23bt2CRCKBtbU1unTpAjc3NzRr1gyrVq0q035Qx83NDU5OTti1axeePn2KzMxM2NnZoVu3bvjwww/h7OysNtlUNH78eHldjx8/hkQiQa1atdCrVy/MmjULderUUbluWY8nItIekVTVGZKIiIiIqBpiH1wiIiIiEhQmuEREREQkKExwiYiIiEhQmOASERERkaAwwSUiIiIiQWGCS0RERESCwgSXiIiIiASFCS4RERERCQoTXCIiIiISFCa4RERERCQoTHCJiIiISFCY4BIRERGRoDDBJSIiIiJBYYJLRERERILCBJeIiIiIBOX/AV4BME+CWvKcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot confusion matrix\n",
    "def plot_confusion_matrix(y_preds, y_true, labels):\n",
    "    cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n",
    "    fig, ax = plt.subplots(figsize=(8, 8))\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "    disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n",
    "    plt.title(\"Normalized confusion matrix\")\n",
    "    ax.grid(False)\n",
    "    plt.show()\n",
    "\n",
    "plot_confusion_matrix(valid_preds, y_valid, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c15a9cc",
   "metadata": {
    "papermill": {
     "duration": 0.012427,
     "end_time": "2023-02-21T11:15:49.016269",
     "exception": false,
     "start_time": "2023-02-21T11:15:49.003842",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Test set predictions**\n",
    "\n",
    "<br>\n",
    "\n",
    "At the time of making this notebook, these predictions would put you in **1st place** of the competition. (I won't submit them since I am the host but feel free to do this yourself)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "aa8ad9c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-21T11:15:49.042259Z",
     "iopub.status.busy": "2023-02-21T11:15:49.041964Z",
     "iopub.status.idle": "2023-02-21T11:19:48.436611Z",
     "shell.execute_reply": "2023-02-21T11:19:48.435730Z"
    },
    "papermill": {
     "duration": 239.410149,
     "end_time": "2023-02-21T11:19:48.438864",
     "exception": false,
     "start_time": "2023-02-21T11:15:49.028715",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Test set predictions\n",
    "preds = trainer.predict(test_encoded[\"test\"])\n",
    "test_preds = np.argmax(preds.predictions, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "713d2553",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-21T11:19:48.465661Z",
     "iopub.status.busy": "2023-02-21T11:19:48.465361Z",
     "iopub.status.idle": "2023-02-21T11:19:48.795526Z",
     "shell.execute_reply": "2023-02-21T11:19:48.794581Z"
    },
    "papermill": {
     "duration": 0.345976,
     "end_time": "2023-02-21T11:19:48.797829",
     "exception": false,
     "start_time": "2023-02-21T11:19:48.451853",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of predictions\n",
    "plt.figure(figsize=(10,4))\n",
    "sns.histplot(test_preds+1)\n",
    "plt.title(\"Distribution of final predictions\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f6798c",
   "metadata": {
    "papermill": {
     "duration": 0.013474,
     "end_time": "2023-02-21T11:19:48.893618",
     "exception": false,
     "start_time": "2023-02-21T11:19:48.880144",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 4. Conclusion\n",
    "\n",
    "<br>\n",
    "\n",
    "In this tenth and **final notebook** in the series, we explored everything to do with **transformers**. We discussed the theory, built one from scratch and fine-tuned a pre-trained model from Hugging Face. \n",
    "\n",
    "Transformers are having a **tremendous impact** on the way we live our lives even if we are not aware about all of it. From conversational AI like ChatGPT to Machine Translation and Cyber Security, we are experiencing the real-time impact of these powerful models. It is trully an **exciting time to be alive** so I hope these notebooks have helped you appreciate and start learning about the fascinating field of NLP. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "335febca",
   "metadata": {
    "papermill": {
     "duration": 0.013436,
     "end_time": "2023-02-21T11:19:48.920337",
     "exception": false,
     "start_time": "2023-02-21T11:19:48.906901",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**References:**\n",
    "* [NLP demystified](https://www.nlpdemystified.org/)\n",
    "* [Natural Language Processing with Transformers](https://transformersbook.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf5d0b5",
   "metadata": {
    "papermill": {
     "duration": 0.012928,
     "end_time": "2023-02-21T11:19:48.946577",
     "exception": false,
     "start_time": "2023-02-21T11:19:48.933649",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Thanks for reading!"
   ]
  }
 ],
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