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enakai00/flax_transformer.ipynb

Created February 7, 2023 08:53
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Flax_Transformer.ipynb
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flax_transformer.ipynb
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"text": [
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
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"source": [
"pip install datasets"
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"outputId": "4253d89b-ac65-45a8-ad5e-9fe5dd062442"
},
"execution_count": 2,
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"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting datasets\n",
" Downloading datasets-2.9.0-py3-none-any.whl (462 kB)\n",
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"Collecting xxhash\n",
" Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
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" Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
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" Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
"Collecting urllib3<1.27,>=1.21.1\n",
" Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)\n",
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"Installing collected packages: xxhash, urllib3, multiprocess, responses, datasets\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 1.24.3\n",
" Uninstalling urllib3-1.24.3:\n",
" Successfully uninstalled urllib3-1.24.3\n",
"Successfully installed datasets-2.9.0 multiprocess-0.70.14 responses-0.18.0 urllib3-1.26.14 xxhash-3.2.0\n"
]
}
]
},
{
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"source": [
"%%bash\n",
"curl -sLO https://raw.githubusercontent.com/enakai00/colab_jaxbook/main/requirements.txt\n",
"pip install -qr requirements.txt\n",
"pip list | grep -E '(jax|flax|optax)'"
],
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"base_uri": "https://localhost:8080/"
},
"id": "ItbTWeNOtLsB",
"outputId": "bcece1be-28e4-43ee-953e-7e3a57eecc77"
},
"execution_count": 3,
"outputs": [
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"output_type": "stream",
"name": "stdout",
"text": [
" ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 185.6/185.6 KB 5.0 MB/s eta 0:00:00\n",
" ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 145.1/145.1 KB 17.9 MB/s eta 0:00:00\n",
" ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 239.0/239.0 KB 25.3 MB/s eta 0:00:00\n",
" ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 87.9/87.9 KB 10.0 MB/s eta 0:00:00\n",
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" ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.1/1.1 MB 34.0 MB/s eta 0:00:00\n",
"flax 0.6.1\n",
"jax 0.3.25\n",
"jaxlib 0.3.25+cuda11.cudnn805\n",
"optax 0.1.3\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"ipython 7.9.0 requires jedi>=0.10, which is not installed.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np \n",
"import matplotlib.pyplot as plt\n",
"from pandas import DataFrame\n",
"from functools import partial\n",
"\n",
"import jax, optax\n",
"from jax import random, numpy as jnp\n",
"from flax import linen as nn\n",
"from flax.training import train_state\n",
"from flax.training import train_state, checkpoints"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1XxDlqTEC_xx",
"outputId": "67a23e47-b3f4-4410-fdf0-22571d390901"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:GlobalAsyncCheckpointManager is not imported correctly. Checkpointing of GlobalDeviceArrays will not be available.To use the feature, install tensorstore.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import AutoTokenizer, AutoConfig\n",
"\n",
"model_ckpt = 'bert-base-uncased'\n",
"tokenizer = AutoTokenizer.from_pretrained(model_ckpt)\n",
"config = AutoConfig.from_pretrained(model_ckpt)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145,
"referenced_widgets": [
"71546b5b598943d58d35a761877978ad",
"ffb2cb7cb9094a448ec9a237b004672b",
"33b9e74542854dafb3962351b77ba148",
"0683c4286dff41559889776c5396b491",
"be038f36b13b4157bcb87b7392253f6e",
"0223aba10cc74c9a8d268bdd9ddd3a65",
"065010d21b4e4ac3b4ebacdf7be3ec65",
"14afbe4e87c14207974c66e658109c19",
"6a781e9fb8254f0783bd22c075b37a83",
"2ac67b7999254f47b8225340cbb4d6c7",
"57c475136a404b2c81315a4561502044",
"f601a802be01420cbbd51135de49a249",
"224e25f8058f4bf89cc7e83a00fc9384",
"0b4b5d44c88643efb27357cf017c55dc",
"84fc80017e3247e7a572ba747e23ce1f",
"c2d2441324c543a98fd44828b3859a0d",
"079b72cc2596415f94c599a05021448e",
"c4ccbd66e65a4efdbf9a5e95dd052972",
"b7c030600df5498d812c0c6eb17bff8e",
"ccde12d2282f400180e5c451221e2421",
"58de986511074d3b8769d2e211c960b7",
"ddfee22e9dce4e7197daf6bc5de5bb96",
"fddae9f42fc24d4a84059c3b5ae3dd04",
"19708034a9204db19dd8806490d8e913",
"1b06fc0574ab461e9667f09254e524d7",
"cae70fe587654040b461082ed3201e69",
"6361724701214c4d9ddb662782d72b79",
"69964f95a9924554900f8e34f40d76d9",
"02fcf2911ad6490186908547e838f430",
"65a85ec0cc074fb9850e146ed2fcda4d",
"0b249c150ee1482f8080b21f5c1821a5",
"85c8680ca4b94d91bab12720b48cfdae",
"b48f15b0904c4d0d98124ebc562e2a7c",
"b97eb9699eac4a9393b7b0eeef696b74",
"5cf18693ad5741d38e0c65093ede02a1",
"ae945380a29d430abaed3417a084a587",
"cc3b062e765a4807a7b8eec612071b40",
"857b875265fe4c978a4adf8822eec98f",
"875076adccd64113b013a9eb9753251f",
"112cc3c0555f483abc30b9f756289a0d",
"7ae15dcbef164026bb7aea3bd16c556a",
"b670970346d54960af6f37d082951623",
"d9d2d3f2ce3d41b4a446bcca4a4eb1ea",
"1f493ee80fed42f19d1047341e8502db"
]
},
"id": "jBcUsNKPuFZ4",
"outputId": "abf2b76a-ec4e-46e6-c2f6-b35cffed93fa"
},
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)okenizer_config.json: 0%| | 0.00/28.0 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "71546b5b598943d58d35a761877978ad"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/570 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f601a802be01420cbbd51135de49a249"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)solve/main/vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "fddae9f42fc24d4a84059c3b5ae3dd04"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)/main/tokenizer.json: 0%| | 0.00/466k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b97eb9699eac4a9393b7b0eeef696b74"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## Embedding layer"
],
"metadata": {
"id": "k0JBuAblEpI6"
}
},
{
"cell_type": "code",
"source": [
"class Embeddings(nn.Module):\n",
" embed_dim: int\n",
" max_position_embeddings: int = 512\n",
" vocab_size: int = config.vocab_size\n",
"\n",
" @nn.compact\n",
" def __call__(self, input_ids, eval):\n",
" position_ids = jnp.expand_dims(jnp.arange(input_ids.shape[-1]), 0)\n",
" token_embeddings = nn.Embed(self.vocab_size,\n",
" self.embed_dim)(input_ids)\n",
" position_embeddings = nn.Embed(self.max_position_embeddings,\n",
" self.embed_dim)(position_ids)\n",
" embeddings = token_embeddings + position_embeddings # broadcast\n",
" embeddings = nn.LayerNorm(epsilon=1e-12)(embeddings)\n",
" embeddings = nn.Dropout(0.5, deterministic=eval)(embeddings)\n",
" return embeddings"
],
"metadata": {
"id": "V4CyMBHquIwt"
},
"execution_count": 29,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Multi-head Attention"
],
"metadata": {
"id": "gC8Z9jDaVSLF"
}
},
{
"cell_type": "code",
"source": [
"class AttentionHead(nn.Module):\n",
" head_dim: int\n",
"\n",
" def scaled_dot_product_attention(self, q, k, v, mask):\n",
" scores = jnp.matmul(q, jnp.transpose(k, (0, 2, 1))) / jnp.sqrt(q.shape[-1])\n",
" if mask is not None:\n",
" mask = jnp.tile(mask, mask.shape[-1]).reshape(\n",
" mask.shape[0], -1, mask.shape[-1])\n",
" scores = jnp.where(mask==0, -jnp.inf, scores)\n",
" w = nn.softmax(scores)\n",
" return jnp.matmul(w, v)\n",
"\n",
" @nn.compact\n",
" def __call__(self, hidden_state, attention_mask):\n",
" q = nn.Dense(features=self.head_dim)(hidden_state)\n",
" k = nn.Dense(features=self.head_dim)(hidden_state)\n",
" v = nn.Dense(features=self.head_dim)(hidden_state)\n",
" output = self.scaled_dot_product_attention(q, k, v, attention_mask)\n",
" return output"
],
"metadata": {
"id": "k31efFVQVY5k"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class MultiHeadAttention(nn.Module):\n",
" num_heads: int = 12\n",
" embed_dim: int = 768\n",
"\n",
" @nn.compact\n",
" def __call__(self, hidden_state, attention_mask):\n",
" head_dim=self.embed_dim // self.num_heads\n",
" attention_heads = [AttentionHead(head_dim=head_dim)(hidden_state, attention_mask) \n",
" for _ in jnp.arange(self.num_heads)]\n",
" x = jnp.concatenate(attention_heads, axis=-1)\n",
" x = nn.Dense(features=self.embed_dim)(x)\n",
" return x"
],
"metadata": {
"id": "4_bqs_zWdw8n"
},
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Transformer Encoder Block"
],
"metadata": {
"id": "wBEB5uHfjBfg"
}
},
{
"cell_type": "code",
"source": [
"class FeedForward(nn.Module):\n",
" intermediate_size: int = 3072\n",
"\n",
" @nn.compact\n",
" def __call__(self, x):\n",
" embed_dim = x.shape[-1]\n",
" x = nn.Dense(features=self.intermediate_size)(x)\n",
" x = nn.gelu(x)\n",
" x = nn.Dense(features=embed_dim)(x)\n",
" return x"
],
"metadata": {
"id": "cxuv0x_AHvlW"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"class TransformerEncoderLayer(nn.Module):\n",
" num_heads: int\n",
" embed_dim: int\n",
"\n",
" def setup(self):\n",
" self.attention = MultiHeadAttention(\n",
" num_heads=self.num_heads, embed_dim=self.embed_dim)\n",
" self.feed_forward = FeedForward()\n",
"\n",
" @nn.compact\n",
" def __call__(self, x, attention_mask, eval):\n",
" hidden_state = nn.LayerNorm()(x)\n",
" x = x + self.attention(hidden_state, attention_mask) # Skip connection\n",
" hidden_state = nn.LayerNorm()(x)\n",
" x = x + self.feed_forward(hidden_state) # Skip connection\n",
" x = nn.Dropout(0.1, deterministic=eval)(x)\n",
" return x"
],
"metadata": {
"id": "REnsueqljIeW"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Combine them all to define Transformer Encoder"
],
"metadata": {
"id": "sID61pmFdPxG"
}
},
{
"cell_type": "code",
"source": [
"class TransformerEncoder(nn.Module):\n",
" num_heads: int\n",
" embed_dim: int\n",
" num_hidden_layers: int\n",
"\n",
" def setup(self):\n",
" self.embeddings = Embeddings(self.embed_dim)\n",
" self.layers = [TransformerEncoderLayer(num_heads=self.num_heads,\n",
" embed_dim=self.embed_dim)\n",
" for _ in range(self.num_hidden_layers)]\n",
"\n",
" def __call__(self, input_ids, attention_mask, eval):\n",
" x = self.embeddings(input_ids, eval)\n",
" for layer in self.layers:\n",
" x = layer(x, attention_mask, eval=eval)\n",
" return x"
],
"metadata": {
"id": "98VTLi5wZMFR"
},
"execution_count": 30,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Add Classification Head"
],
"metadata": {
"id": "Sn4JgGY3Mj_T"
}
},
{
"cell_type": "code",
"source": [
"class TransformerForSequenceClassification(nn.Module):\n",
" num_labels: int = 2\n",
" num_heads: int = 12\n",
" embed_dim: int = 768\n",
" num_hidden_layers: int = 5\n",
"\n",
" def setup(self):\n",
" self.transformer_encoder = TransformerEncoder(\n",
" self.num_heads, self.embed_dim, self.num_hidden_layers)\n",
"\n",
" @nn.compact\n",
" def __call__(self, input_ids, attention_mask=None, eval=True):\n",
" x = self.transformer_encoder(input_ids, attention_mask, eval)[:, 0, :] # select [CLS] token\n",
" x = nn.Dropout(0.1, deterministic=eval)(x)\n",
" logits = nn.Dense(features=self.num_labels)(x)\n",
" return logits"
],
"metadata": {
"id": "x6g65FzfMoVX"
},
"execution_count": 12,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Define functions for model training"
],
"metadata": {
"id": "J06iA3egoGL7"
}
},
{
"cell_type": "code",
"source": [
"@partial(jax.jit, static_argnames=['eval'])\n",
"def loss_fn(params, state, inputs, masks, labels, dropout_rng, eval):\n",
" logits = state.apply_fn(\n",
" {'params': params}, inputs, masks, eval=eval,\n",
" rngs={'dropout': dropout_rng})\n",
" loss = optax.softmax_cross_entropy(logits, labels).mean()\n",
" acc = jnp.mean(jnp.argmax(logits, -1) == jnp.argmax(labels, -1))\n",
" return loss, acc"
],
"metadata": {
"id": "IXwYrA6IV8Ye"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"@partial(jax.jit, static_argnames=['eval'])\n",
"def train_step(state, inputs, masks, labels, eval):\n",
" if not eval:\n",
" new_dropout_rng, dropout_rng = random.split(state.dropout_rng)\n",
" (loss, acc), grads = jax.value_and_grad(loss_fn, has_aux=True)(\n",
" state.params, state, inputs, masks, labels, dropout_rng, eval)\n",
" new_state = state.apply_gradients(\n",
" grads=grads, dropout_rng=new_dropout_rng)\n",
" else:\n",
" loss, acc = loss_fn(\n",
" state.params, state, inputs, masks, labels, random.PRNGKey(0), eval)\n",
" new_state = state\n",
"\n",
" return new_state, loss, acc"
],
"metadata": {
"id": "8WRDRga7XoDI"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def train_epoch(state, input_batched, mask_batched, label_batched, eval):\n",
" loss_history, acc_history = [], []\n",
" for inputs, masks, labels in zip(input_batched, mask_batched, label_batched):\n",
" new_state, loss, acc = train_step(state, inputs, masks, labels, eval)\n",
" if not eval:\n",
" state = new_state\n",
" loss_history.append(jax.device_get(loss).tolist())\n",
" acc_history.append(jax.device_get(acc).tolist())\n",
" return state, np.mean(loss_history), np.mean(acc_history)"
],
"metadata": {
"id": "JeXBsqDoXs0u"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def fit(state, ckpt_dir, prefix,\n",
" train_text, train_mask, train_label,\n",
" test_text, test_mask, test_label,\n",
" epochs, batch_size):\n",
"\n",
" state = checkpoints.restore_checkpoint(\n",
" ckpt_dir=ckpt_dir, prefix=prefix, target=state)\n",
"\n",
" train_text_batched = create_batches(train_text, batch_size)\n",
" train_mask_batched = create_batches(train_mask, batch_size)\n",
" train_label_batched = create_batches(train_label, batch_size)\n",
" test_text_batched = create_batches(test_text, batch_size)\n",
" test_mask_batched = create_batches(test_mask, batch_size)\n",
" test_label_batched = create_batches(test_label, batch_size)\n",
"\n",
" loss_history_train, acc_history_train = [], []\n",
" loss_history_test, acc_history_test = [], []\n",
"\n",
" for epoch in range(state.epoch + 1, state.epoch + 1 + epochs):\n",
" # Training\n",
" state, loss_train, acc_train = train_epoch(\n",
" state, train_text_batched, train_mask_batched, train_label_batched, eval=False)\n",
" loss_history_train.append(loss_train)\n",
" acc_history_train.append(acc_train)\n",
"\n",
" # Evaluation\n",
" _ , loss_test, acc_test = train_epoch(\n",
" state, test_text_batched, test_mask_batched, test_label_batched, eval=True)\n",
" loss_history_test.append(loss_test)\n",
" acc_history_test.append(acc_test)\n",
"\n",
" print ('Epoch: {}, Loss: {:.4f}, Accuracy: {:.4f} / '.format(\n",
" epoch, loss_train, acc_train), end='', flush=True)\n",
" print ('Loss(Test): {:.4f}, Accuracy(Test): {:.4f}'.format(\n",
" loss_test, acc_test), flush=True)\n",
" \n",
" state = state.replace(epoch=state.epoch+1)\n",
" checkpoints.save_checkpoint(\n",
" ckpt_dir=ckpt_dir, prefix=prefix,\n",
" target=state, step=state.epoch, overwrite=True, keep=5)\n",
" \n",
" history = {'loss_train': loss_history_train,\n",
" 'acc_train': acc_history_train,\n",
" 'loss_test': loss_history_test,\n",
" 'acc_test': acc_history_test}\n",
" \n",
" return state, history"
],
"metadata": {
"id": "dgw1_OtMXwlB"
},
"execution_count": 16,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Prepare dataset"
],
"metadata": {
"id": "FNSxUKI0hZJd"
}
},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset\n",
"emotions = load_dataset('emotion')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 380,
"referenced_widgets": [
"bcd4554faf38423bba832a2f79bd459a",
"bdf5b4f988f04b728c6104e2df579984",
"6c16a415bdcf41f4af283cf9f236c6db",
"14fb181236d94976abdd78d38674368d",
"fea06305d89d497cab876629795d6874",
"7e83dc5751da4764b033ac8960cb50b3",
"63739fb489e9490bb33b508a1fa21f54",
"663817a79e7443e4b5ee20efe3ccf3a9",
"2502cc6347074adf9e7614c686d7da5c",
"c242028659a44fa5b9007f65f7b10b50",
"4a570b6368064a3bb4f4524e9794024d",
"47bdcdcc35e241c290175261348cbec4",
"ff839ee0e79147da901b43c932e88fd9",
"a8e81700779f474db32cea9c1d182ccd",
"030f796fabb042019572b52666beab5e",
"0f4b682bf6a94f78872bf7505ab58496",
"7eed8b493fdc47959e2cb336a4ebb72f",
"d467b282737e40f68b8c356b61da444c",
"7ec97dca361444de86c3f14d1072c81d",
"c63ca320b1764f708ebc9e0370f800c8",
"84e840554cf7442fa63dcc2aaf8f09e7",
"2e2fc2d37f274f25b4ae4b316f944bf3",
"1b15e44eae7048588efafc77882e1fcf",
"eda8cf73e321490ebe5792bfd6008749",
"ad736855438a467f831034f072f4854d",
"1cf44fb5873b4642a946b003f632beaa",
"bff86949a254465e899547655710b32a",
"d83007981a554bf6b11b42d0f5e46289",
"ba3e19dae8fe4f65b447c22d4bb9b96f",
"c9cd4c8ff7bd4f69b5f6b7493e05a93b",
"bf50a31b95d74461ab557d462e371132",
"3baf5ba9bd49411790c1704bd962167f",
"59ccd485d4ea4a349086fa39ad76a0ba",
"91ab2c5c755e4672bd5b9b4093619cf2",
"192c1740b73d4e52b6d1c7b1484d1ca2",
"e9344b920e824103815cde2830de0906",
"acae84cbaba748ab87354b163e46bfb8",
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},
"id": "1NmH9v1EfTOU",
"outputId": "557164e3-9400-47ff-dd39-c06332edfa61"
},
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
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}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:datasets.builder:No config specified, defaulting to: emotion/split\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading and preparing dataset emotion/split to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd...\n"
]
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],
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}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd. Subsequent calls will reuse this data.\n"
]
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}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"train_set = tokenizer(emotions['train']['text'], padding=True, truncation=True)\n",
"train_text = np.array(train_set['input_ids'])\n",
"train_mask = np.array(train_set['attention_mask'])\n",
"train_label = np.eye(6)[emotions['train']['label']]\n",
"\n",
"test_set = tokenizer(emotions['validation']['text'], padding=True, truncation=True)\n",
"test_text = np.array(test_set['input_ids'])\n",
"test_mask = np.array(test_set['attention_mask'])\n",
"test_label = np.eye(6)[emotions['validation']['label']]"
],
"metadata": {
"id": "oxH-9Pt7hxNa"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def create_batches(data, batch_size):\n",
" num_batches, mod = divmod(len(data), batch_size)\n",
" data_batched = np.split(data[:num_batches * batch_size], num_batches)\n",
" if mod: # Last batch is smaller than batch_size\n",
" data_batched.append(data[num_batches * batch_size:])\n",
" data_batched = [jnp.asarray(x) for x in data_batched]\n",
" return data_batched"
],
"metadata": {
"id": "YD7IdsLchbre"
},
"execution_count": 19,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Model Training"
],
"metadata": {
"id": "DDpzU0Q4n8HA"
}
},
{
"cell_type": "code",
"source": [
"class TrainState(train_state.TrainState):\n",
" epoch: int\n",
" dropout_rng: type(random.PRNGKey(0))\n",
"\n",
"model = TransformerForSequenceClassification(\n",
" num_labels=6, num_heads=12, embed_dim=768, num_hidden_layers=3)\n",
"\n",
"key, key1, key2 = random.split(random.PRNGKey(0), 3)\n",
"variables = model.init(key1, train_text[:1])\n",
"state = TrainState.create(\n",
" apply_fn=model.apply,\n",
" params=variables['params'],\n",
" tx=optax.adam(learning_rate=0.00005),\n",
" dropout_rng=key2,\n",
" epoch=0)\n",
"#jax.tree_util.tree_map(lambda x: x.shape, variables['params'])"
],
"metadata": {
"id": "cUz4HBYTbIiG"
},
"execution_count": 31,
"outputs": []
},
{
"cell_type": "code",
"source": [
"checkpoints.save_checkpoint(\n",
" ckpt_dir='./checkpoints/', prefix='TextClassification_checkpoint_', \n",
" target=state, step=state.epoch, overwrite=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "b2Ci4MUjYtVH",
"outputId": "0efdf36d-ea40-49ff-f4b1-b3ae98c8b488"
},
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'checkpoints/TextClassification_checkpoint_0'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"source": [
"%%time\n",
"ckpt_dir = './checkpoints/'\n",
"prefix = 'TextClassification_checkpoint_'\n",
"state, history = fit(state, ckpt_dir, prefix,\n",
" train_text, train_mask, train_label,\n",
" test_text, test_mask, test_label,\n",
" epochs=16, batch_size=32)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ReTQ12LMZKeA",
"outputId": "d51bdd18-ec31-44d7-a190-6fc531c38b87"
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch: 1, Loss: 2.2440, Accuracy: 0.2777 / Loss(Test): 1.4873, Accuracy(Test): 0.4092\n",
"Epoch: 2, Loss: 1.7981, Accuracy: 0.3544 / Loss(Test): 1.1417, Accuracy(Test): 0.5784\n",
"Epoch: 3, Loss: 1.4193, Accuracy: 0.4973 / Loss(Test): 0.7974, Accuracy(Test): 0.7356\n",
"Epoch: 4, Loss: 0.9193, Accuracy: 0.6825 / Loss(Test): 0.7951, Accuracy(Test): 0.8433\n",
"Epoch: 5, Loss: 0.5640, Accuracy: 0.8154 / Loss(Test): 0.6073, Accuracy(Test): 0.8819\n",
"Epoch: 6, Loss: 0.4249, Accuracy: 0.8639 / Loss(Test): 0.4711, Accuracy(Test): 0.8953\n",
"Epoch: 7, Loss: 0.3575, Accuracy: 0.8821 / Loss(Test): 0.4577, Accuracy(Test): 0.8934\n",
"Epoch: 8, Loss: 0.3086, Accuracy: 0.8962 / Loss(Test): 0.4653, Accuracy(Test): 0.8988\n",
"Epoch: 9, Loss: 0.2734, Accuracy: 0.9094 / Loss(Test): 0.4499, Accuracy(Test): 0.9043\n",
"Epoch: 10, Loss: 0.2552, Accuracy: 0.9136 / Loss(Test): 0.4369, Accuracy(Test): 0.9053\n",
"Epoch: 11, Loss: 0.2274, Accuracy: 0.9233 / Loss(Test): 0.4525, Accuracy(Test): 0.9077\n",
"Epoch: 12, Loss: 0.2101, Accuracy: 0.9297 / Loss(Test): 0.4489, Accuracy(Test): 0.9062\n",
"Epoch: 13, Loss: 0.2004, Accuracy: 0.9314 / Loss(Test): 0.4776, Accuracy(Test): 0.8998\n",
"Epoch: 14, Loss: 0.1701, Accuracy: 0.9429 / Loss(Test): 0.5025, Accuracy(Test): 0.9087\n",
"Epoch: 15, Loss: 0.1585, Accuracy: 0.9467 / Loss(Test): 0.4996, Accuracy(Test): 0.9043\n",
"Epoch: 16, Loss: 0.1443, Accuracy: 0.9514 / Loss(Test): 0.5411, Accuracy(Test): 0.9008\n",
"CPU times: user 17min 6s, sys: 38.3 s, total: 17min 44s\n",
"Wall time: 18min 8s\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"df = DataFrame({'Accuracy (Train)': history['acc_train'],\n",
" 'Accuracy (Test)': history['acc_test']})\n",
"df.index.name = 'Epochs'\n",
"ax = df.plot(figsize=(6, 4))\n",
"ax.set_xticks(df.index)\n",
"_ = ax.set_xticklabels(df.index+1)\n",
"\n",
"df = DataFrame({'Loss (Train)': history['loss_train'],\n",
" 'Loss (Test)': history['loss_test']})\n",
"df.index.name = 'Epochs'\n",
"ax = df.plot(figsize=(6, 4))\n",
"ax.set_xticks(df.index)\n",
"_ = ax.set_xticklabels(df.index+1)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 541
},
"id": "sk7B3XPShVxH",
"outputId": "f7d8ae3a-e0e9-4edd-dd1b-a75bc175f7b1"
},
"execution_count": 34,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/gdrive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Lol278B00HhM",
"outputId": "4969b6f8-c423-4eef-8aba-ab0eefa04110"
},
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/gdrive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"checkpoints.save_checkpoint(\n",
" ckpt_dir='/content/gdrive/My Drive/checkpoints/',\n",
" prefix='TextClassification_checkpoint_',\n",
" target=state, step=state.epoch, overwrite=True)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"id": "sMokUKAy5dKN",
"outputId": "5caceb29-e9c1-4824-958d-88d19803433b"
},
"execution_count": 36,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'/content/gdrive/My Drive/checkpoints/TextClassification_checkpoint_16'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 36
}
]
},
{
"cell_type": "code",
"source": [
"!ls -lh '/content/gdrive/My Drive/checkpoints/'"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kQeYL9uYBO2d",
"outputId": "6c0afa73-ffbd-4ce9-a15b-ae641108668b"
},
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"total 640M\n",
"-rw------- 1 root root 38M Feb 2 07:23 DoubleLayerCNN_checkpoint_16\n",
"-rw------- 1 root root 49M Jan 11 02:42 ResNet_checkpoint_16\n",
"-rw------- 1 root root 37M Jan 8 06:19 SingleLayerCNN_checkpoint_16\n",
"-rw------- 1 root root 517M Feb 7 08:36 TextClassification_checkpoint_16\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"predictions = state.apply_fn(\n",
" {'params': state.params}, test_text, test_mask, eval=True)"
],
"metadata": {
"id": "mV-zXQXnBXoU"
},
"execution_count": 40,
"outputs": []
},
{
"cell_type": "code",
"source": [
"predictions = jax.device_get(predictions)\n",
"predictions[:2]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xH9U_dueCfBc",
"outputId": "5ea595ff-931b-4e85-a8f2-9929c159acc4"
},
"execution_count": 47,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 20.352503 , -5.8720894 , -12.719739 , 9.021353 ,\n",
" 3.6741338 , -6.335876 ],\n",
" [ 18.40054 , 2.3951144 , -0.30027333, -2.4837074 ,\n",
" -3.5679176 , -2.0919416 ]], dtype=float32)"
]
},
"metadata": {},
"execution_count": 47
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
"plt.rcParams.update({'font.size': 12})"
],
"metadata": {
"id": "TX3qyOVbB2Ie"
},
"execution_count": 76,
"outputs": []
},
{
"cell_type": "code",
"source": [
"cm = confusion_matrix(\n",
" y_pred = np.argmax(predictions, axis=1),\n",
" y_true = np.argmax(test_label, axis=1),\n",
" normalize='true')\n",
"fig, ax = plt.subplots(figsize=(7, 7))\n",
"disp = ConfusionMatrixDisplay(cm, display_labels=emotions['validation'].features['label'].names)\n",
"disp.plot(cmap='Blues', values_format='.2f', colorbar=False, ax=ax)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 466
},
"id": "9A2FHUXCCUXK",
"outputId": "3dbe5e6c-6d8a-4457-dc83-2c0d7e834606"
},
"execution_count": 77,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f5660542910>"
]
},
"metadata": {},
"execution_count": 77
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 504x504 with 1 Axes>"
],
"image/png": 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Tb/Hz8gmAu/YXJme/D/U5z72Y677bw6gsY39NjUqMfLij42Pv9BnGuHgXj784O2Pcnx3orn39D7H0HPoW+1dkqX3SAmZ67r/u27kZz3nuw/xg8UYGj51FWEhQlqtNYO2cUVxVsbRjufr7OX96NS7exeCxs1jpqX/0IPd9qOu/j6HHkLc4uCpz3j8/Mfu8f35w5rzv3N973i94y7l5DxAW5Pettdb7JlnyqKF63kONBNYCFngb8AeuARbhfk+zNjAfOOFpqGWBfcC9wAJgEPAa0P/PGqq1NsYYMx/3+7TPAmeA6sBV1tpVxpjuwHpr7SFjTF1gM1AXKIP7qP07IAj4GNhorR19odpyo6EWFrnRUAuT3GiohUVuNNTCpIC8ZZcvcqOhFiZ/1FDz6i9CMPAy8Cvu07TlgaeAYUBv4DQwHZib/gvW2l+B7p7f+w33kefai/g378HdFH8E4oCPcL/HCtAY2GiMOYO7WT9urd0LFPfkEQfs9/y7/7joakVEpMjJ81O+VxodoRZdOkItunSEWnQVhCNUERGRK5oaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDgjI7wQKuwa1I1mzYlx+p5Evyt40NL9TyFfH1ryW3ynkG38/k98p5KuiXr/4piNUERERB6ihioiIOEANVURExAFqqCIiIg5QQxUREXGAGqqIiIgD1FBFREQcoIYqIiLiADVUERERB6ihioiIOEANVURExAFqqCIiIg5QQxUREXGAGqqIiIgD1FBFREQcoIYqIiLiADVUERERB6ihioiIOEANVURExAFqqCIiIg5QQxUREXGAGqqIiIgD1FBFREQcoIYqIiLiADVUERERB6ihioiIOEANVURExAFFoqEaY3YYY1rldx45FRfv4p7h06l6899pcPtzfPT5Zp9x1lrGTJpP9K0jiL51BGMmzcdam7F+6Pg5NO0+lnI3PsacRRvyKv3LUrJ4GDNe/huHVrzC1k+fo1vbG3zGFY8IZcqzvdm9ZCy7l4xlxIO3ZVu/YPIg9iwdx/6vXmbNjCdp36JeXqR/2eJOubh/5DtUb/MkDe98nk/+d+GxHzt5Adfc9hTX3PYUYycvyBj7306eofMjr3PNbU9Rq+1IOj70LzZt3ZuXZVySuHgX9454h2qthvGXO0bz8R/M+xcmzadW25HUajuSF7LM+9gDx+n35DTq3PYU0W1H0v3xKcTs/yUvy7gkcfEu+j05nata/p3ruzzHR8suXPvzE+dT45YR1LhlBM9PzL7PDxk/hybdxlKm6WPMLiT7PDhX/7bdh2h9z6tEtniC1ve8yrbdh/KqBAAC8vRfyyfW2rr5ncPFGP6PeQQG+vPj0vFs332IXk+8Tb3oSOpEVcoW996na1myaiurZo7EGMNdgydTtXIZ7u/aHIB60ZHceesNjJk0Pz/KuCQThnUj+VwqtTs8y3W1Ipn72sNs33OEnfuOZYsbP+QOQkOCqH/nC5QtXYz5Ewdy8OjvzF68CYCn/vUJO/f9QmpqGg3rVuPTNwfQuMd4fvntVH6UlWNPTfiIwMAAti8ax/Y9h+g7bBrX1vQe+xnz17FszTa+en8ExkCPx6dQtXJp7r2zOeGhwfxrVC+iqpTDGMOy1du458npbF88joAA/3yq7M+NmPAhgQH+7FjyItt3H6L336dS18e8f/+zdSxZvY2VM0dgMHR7zD3v7+vanPjTibRrcR1vPtOHiPAQJvx7Gf2GT2f93GfyqaqcedKzz+9c5t7n7x76NnWjI7mmhu99fvUs9z7fdfBkqlUuw/13Zdnnbylc+zw4U39yyjn6DJtG/56teKBbC979dC19hk1j88fPERSYN62uSByhFiauxCQWrdjCU490IiIsmBsb1OC2Ftcxb+kmr9i5SzYxsHcbKlcoRaXyJRnYpw0fLNqYsf6B7i1p2bg2IUGBeVnCJQsLCaJz6+sZP3UJrsRkNmzZx9I127m7fSOv2Nua1+XNmV+RmJTCwaO/M3PhBvp2bpqxfkfMUVJT0wD3q9rAAH8iK5TMs1ouhSsxicUrtzDioQ6EhwXTtH4N2jWvx0fLvvGKnbdkE/17tqZy+ZJUKleSAb1aM3eJe46EBAdSs1oF/Pz8sNbi7+/HydMJxJ1KyOuScixz3nfMMu/rMW+pd+1zl2xkYO/WVC7vnvcDerfhg8XueX9D3Wr07dKMUiXCCQzwp3/PVsTsP87v8a68LinHXIlJLFy+hVFZ9vn2LX3v83MWb2JgnzZEVihF5fIlGdS7DbMXZ+7zD3Zvyc1NahMcXDj2eXCu/q+/3UNqahoDerUmOCiQR+5uhbWwevPuPKulSDRUY8zPxphbjDHBxpjXjTFHPP+9bowJ9sRsN8Z0zvI7gcaYX40xf8nLXGMPHCfA34+aVctnLKsbHcnOvce8YnfuPUq96MjscfuO5kmeuaFG1XKcS00j9uCJjGXb9xyhTlRFn/HGmGw/X3PekcwHEx7i6Kp/8NV/nuDr72L4/qeDuZO4Q/YeOEGAvx81soz9tdGR7NrnPfa79h2jbnTlzLia3nGt+71MtVbDuGf4dPp0bka50sVyL/nLlD7va5w373ft9Z7PO/ceo27NzHlfLzrS6wxGuvU/xFK+THFKlwh3PmmHZOzz1S5+n693geeoMHGq/p17j3JtzcrZ/i7UrVmZnXn4/BSJU75ZPA3cCDQALDAfeAZ4Fngf6Ass9MR2AI5aa78/fyPGmIeBhwGqVK3qaIKuhCSKhYdkW1Y8IoQzCWe9YxOTKB6RGVs8PARXQhLW2myTqrCICA3mtCt7nadciUSEhXjFfrVhJ0Pu+SsDX5hNudLF6NOpKaEhQdlieg6bToC/H62a1KbW1RWyvddSELkSk4g4f+zDQziTkOQztlh4aGZchPfYr5gxkrNJKSxdtZXkc6m5m/xlciUm+6g99IK1F4/IrL3YBeb9keNxjJjwIWMfvzP3EnfAZe3zEe75UVj3eXCufldicrZ54V4fyhmX9xzKLUXiCDWLPsAL1trj1toTwBign2fdTKCDMaa453E/YIavjVhrp1lrG1lrG5UtW87RBMPDvJvKaddZn00l/LwGdNp1lvCw4EK7Y51J9N6xioX73rFG/PMTzialsPnDp5n16gN8/MV3HDl+0ivuXGoaX67/idZNatO+RcF+Kz08NJgzPsc+2Gfs6YSsY5/kc+xDggO5s21DJs34kh17DudO4g4IDw26uNqzxJ5J8J73v8adpvtjU7i/a3O6tm2Ye4k74HL3+YhCvM+Dc/WHhwb53k649xzKLUWtoVYG9md5vN+zDGvtEWAtcJcxpiTQHpiV1wnWqFrefdrzwPGMZTv2HPZ52rNOVCW2Z/kjuWPPYepUr+QVV1jEek55RlUpm7GsXk3fp35Onkrg4dEzqdPxOW7q/Qp+xvDdjwcuuO2AAD+ujix7wfUFQZTnlPfeg1nGPuYItat7j33t6hX5MevYxxz2GZcu5Vwq+4/86mzCDvI572MOUzvKez7XiaqY7cXB9j2HqZOl9pOnEuj++BTatbiOJ+5vl7uJO8Bn7btzts9v3+P7OSpMnKq/TlQldsQcyXYmakfMYa+L2nJTUWuoR4BqWR5X9SxL9x7u077dgfXW2jx/SR8eGkzHVvV5edpiXIlJbNyyl6Wrt9GjfROv2B4dmvDWnBUcPX6SoyfimTJ7OT07ZV6Yk5xyjrNJKVgsKedSOZuUQlpaWl6Wc1ESziazaOVWRj3UgbCQIJpeX50OLesxd6n3JfRXR5ahVPEw/PwMtzS7hnvvaMaE//4PgOhq5bml2TWEBAcS4O9Hj9saclODGqz9PjavS7oo4aHBdLj5el6dvhRXYhKbtu7l8zXb6HZbY6/Y7u0bM/WDlRw9cZJjJ+J5e84K7u7gniPfbv+ZjVtiSU45R2JSMhNnfMmJuNPccO3VeVxRzqXP+1emLzlv3nvX3qN95rw/diKet2avoGdH97w/7Uqkx5ApNLk+iucGdcnrMi5JeGgwnVrX5yXPPr9hy16WXGCf79mhCVNmr+CIZ5+fPGs5vTv62Odt4djnwbn6mzeMxt/PMHXuKpKSU5g+bxUALRvVyrNaTEF/X8kJxpifgQeBVkAb4Hbc76F+Bqy01j7jiQvF3WAPA69aa9//s23f0LCRXbPe+0rEyxEX7+KxcbNYtWkXpUqE8+ygLnRr14j138fQc+hb7F/5GpB5H+rMBesB6NulGaMfvT3j9E+XAW+w7ruYbNv+bMpjNG8Y7UieZW8a6sh2sipZPIxJT/eiVZNaxMUnMGbKQj7633c0qx/FvH89QpU2IwC4468NGD/kTkoUCyX2wAmen7yQ5Rt3AlDr6gpMfrY3ta+uQFqaJfbgCf753hcsXrXN0VyPrXnN0e2B+z7UoS/OYdU3uyhdIoynB3Sma9tGbPghlt5/f5u9X/0D8NyHOmUBsxe47zXs3eVGnh3YBWMM676P4Zl/fcz+I78R6O9PnRqVGPFQB5r9paZjefr7OX+KMS7exeMvzs6c9wM7c1e7Rqz/IdY971dMANLvQ13AzIWeed+5Gc896q79g8UbGTx2FmEhQZAlxbVzRnFVxdKO5ep0/XHxLgaPncVKT+2jB3Wh223ufb7HkLc4uCpzn39+4nxmePb5fl2a8fzgzH2+c/83WHvePr/gLef2+dziVP1bdx3k8RfnsGvfMWpdXYE3n+nN9bWrOJprWJDft9Za71sPKHoN9WvgVdxHoAAfAsOttWezxL4D9AIqWGvP/Nm2c6OhFha50VALk9xoqIVFbjTUwqSo11+U/VFDLSpX+foByZ7G+Zjnvws5AHyak2YqIiKS7opvqMaYckA54OccxJYGHiDzyl8REZEcuaIvSjLGNAb2ABOttRe+BNQd+xBwEFhqrV2dF/mJiMiV44o+QrXWfgPk6PPmrLXTgem5m5GIiFyprugjVBERkbyihioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDgjI7wQKOwP4+5n8TiNfbJ4/Lr9TyFcVe0zL7xTyTdynA/I7BZECR0eoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaagEUF++i75PTiGzxBNd1fpYPl33jM85ay+iJnxF1y3CibhnO6ImfYa3NWL9t1yFa9XuFys2H0qrfK2zbdSivSrhk8acTGDbufZrf9Syd7n+ZZSt/8Bm3eWssjzw1jZt7jKbz3172GTNn/td0eeAVmt/1LN36v8b+wydyM/XLVjIimBkj2nFozoNsndqXbi2ifcYFBfjxz/4t2fXfe9n7/v3MGdWeSqXDveKiKpXg6NyHmDrkr7mduiOK8rwvyrXDlVP/FdFQjTE/G2Nuye88nDLs1XkEBQSw6/OXmDb2Pv7+8lx+ij3qFffup2tZsnIra2Y9xdezR/H5mu3895OvAUhOOUfvYVPp0b4x+5a/Sq+OTek9bCrJKefyuJqL88pb8wkMDOB/M59h3LCevDTlU2L3/+IVFxIcRJdbG/H43zr43M5nn29i/hebeX30faz56AVeH30fJYt7N52CZMLDLUg+l0bt+9/l4de/5LVHWlCnSimvuP6drqdxrQo0HzKPax54n5NnknjloeZecf94uAXfxxTsFxFZFeV5X5Rrhyun/iuiocbu9u4AACAASURBVF5JXIlJLFz+A6P6dyQiLJhmDWrQvuV1zF2yySt2zqKNDOrThsgKpahcviSD+rRhzqKNAHz97R5SU9MY0Ks1wUGBPNKzFdbC6m9253VJOZZ4Npnl67bTv++thIUG06Du1bRsei1LVnznFVuvdhU6trmByIqlvdalpaUxfc5XPPFgJ6KqVsAYw1WVylCiWFhelHFJwoID6HxjFOPnbMJ19hwbfjrG0m9+5u5Wtbxiq1UozvIfDnIiPpGklFQ+XRtDnSrZn4euzWsS70pi1dbCcYRSlOd9Ua4drqz61VALmNgDxwnw96NmtQoZy+pFR7Jzr/ertZ17j1Kv1lU+43buPcq1NSMxxmSsrxtd2ed2Cor9h0/g7+9HtchyGctqVa/EXh9HqH/k+K+n+OXXeGL3H6PjfS/R5YFXmDrrC9LS0pxO2TE1KpfkXFoasUfiM5Zt//k3r0YJMOPLn2hapyIVS4URGhRA95a1+PK7Axnri4UG8lSvxjzz33V5krsTivK8L8q1w5VV/xXVUI0xwcaY140xRzz/vW6MCfas+8kY0ylLbIAx5oQx5gbP4xuNMeuMMSeNMVuMMa3yo4YzCUkUCw/Jtqx4RChnEs56xboSkygeEXJeXBLWWlwJ2dcBFA/3vZ2CIjExmYjQ4GzLIsJCcCUmXdR2fvnN3ZQ2fL+HDyYN4e3xD/H5qi3M/99mx3J1WkRIIKcTUrItO5WQTERooFfs3qPxHP71DD/9514OzH6AWleV5NV5mbWN6t2EmV/+xJHfXLmet1OK8rwvyrXDlVX/FdVQgaeBG4EGQH2gCfCMZ90coFeW2HbAr9ba74wxkcBiYBxQGhgGfGyMKYcPxpiHjTGbjTGbT/zq7HtUEWHBnHZlnwCnXWeJCAvxig0PzR7rjgvGGEP4RWynoAgNDeLMec3TlXCW8POa7J8JDgoA4J67bqZYRCiVK5Sm621NWLt5p2O5Ou3M2RSKhWVvnsVCgziTmOIV+4+HWxAU6E/1fv8hsud0Fm7Yx4fPdgSg3tVluPn6q5iycGue5O2Uojzvi3LtcGXVf6U11D7AC9ba49baE8AYoJ9n3WygizEm/Y203ribLEBfYIm1dom1Ns1a+wWwGfB5xYu1dpq1tpG1tlG5sj577iWrUbU851LTiD1wPGPZ9j2HqRNVySu2TlQltu8+7DOuTlQlfow5ku0KuB0xvrdTUFSLLEdqahoHDv+asWz3vqNEZTkVlBNXR5YjMMCfLGd+yP6g4Ik9cpIAPz+iKpXIWFbv6jLsPPi7V+x11csye8UuTp5JIvlcGtMWb6NRrQqULhZC83qRVC1fjG3T+rHzP/fy6O0N6HxjFCsndMvLci5aUZ73Rbl2uLLqv9IaamVgf5bH+z3LsNbGAD8BnT1NtQvuJgtQDejuOd170hhzEmgO5PlMDA8NplPr+oyfuhhXYhIbtsSyZNVW7u7QxCu2Z8cmTJm9nCPHT3L0xEkmz/yKXp2aAtC8YTR+foapH6wkKTmFafNWAdCysfdFLgVFaEgQrZvV5e1ZX5B4NpkffvyZVRt/pEPrG7xi09LSSEpO4dy5NKyFpOQUUjxX84WEBHFri+t5/+PVuBKS+OXXeD79fBPNm1yT1yXlWELSORZt2MuoXo0JCw6gaZ2KdGhyNXNXel9Q8d2e4/RsVZviYUEE+PvxYPt6HPntDL+fPst7//uRGwbMouUT82j5xDz++/kO/vftfu56YVE+VJVzRXneF+Xa4cqq32Tt5oWVMeZn4EFgKjDYWrvEs7wtMM1ae7Xn8VDgZmAuMMRa29Sz/Ckgylr70MX+2w0bNrJrNzr73lxcvItHx85i5cadlCoRzuhHu9D9tsas+z6GHo9P4dDqfwLp92TNZ8Z898Un/W6/iTGDb894U37rroM8Nm42u/Ydo9bVFZj4bB+ur13FsTxjfznj2LbSxZ9O4IU3PmLj93soUTyMwfe257ZWDfh++z4ee/6/rPnoBcB9H2r/UdOz/e4N9aoz7eVHADiTcJYXJ37C2s07KRYeyh3tGvNgz79mu2DhcjXqP8OxbYH7PtRJj7amVf2riDt9ljEzNvLRmj00u6YS857tSJXe7wBQqlgwrzzQnFYNqhAU4MdPB37n6f+u47s9x722OeLuRkRVKsEjr3/laK5xnw5wdHtQeOZ9bijKtUPhqj800HxrrW3ka92V1lBbAW2A2wELfAastNY+44mrBMTiPp37sbX2Dc/yKsA3wL3Al0Ag7vdiY6y1f3jfQW401MIiNxpqYeJ0Qy1McqOhihQGf9RQAy70S8aYGbib0h+y1t5zGbk5bRxQHEi/IuNDzzIArLVHjTHrcR+l9siy/KAx5nbgVdzvq6YCmwD91RARkRy5YEMFYvIsi8uUfkrX4zHPfxeK9fk5bNbajbgbrYiIyEW7YEO11o7Jy0REREQKsxxf5WuMudUY829jzELP40bGmDa5l5qIiEjhkaOGaowZDLwF7AFaehYnkuX9SRERkaIsp0eoQ4BbrLUvA+kfiLoTqJ0rWYmIiBQyOW2oxYCDnp/Tr/wNBJIdz0hERKQQymlDXQ2MPG/ZY8AKZ9MREREpnP7otpmsBgMLjTEPAcWMMbuA00CnP/41ERGRoiFHDdXzgQiNgca4P/f2ILDJWltwv2BSREQkD13Mh+P74X7fFMAfKNhf3yEiIpKHcnSEaoy5Hvfn4gYDh4GrgLPGmDuttVtyMT8REZFCIadHqP8BJgNXWWubAJHAJM9yERGRIi+nDbUW8Lr1fDWN5/9vANG5lZiIiEhhktOGugT3F3Jn1RlY7Gw6IiIihVNOv77NH/jAGPMt7it8qwANgfm5nqGIiEghcDFf37Y9y88/Ap87n46IiEjhpK9vExERcUBOPykJY0wQ7g/DL0uWe1CttctzIS8REZFCJaf3oTYHPsR9H2px4BSZH5gflWvZiYiIFBI5vcr3X8Cr1trSwGnP/8cCU3ItMxERkULkYu5DfeO8ZS8DQ51NR0REpHDKaUONx32qF+CoMeZaoBQQkStZiYiIFDI5baifAB08P/8H9/egfgt8lBtJiYiIFDY5/fq2IVl+nmCM2Yj76FT3ooqIiHARt81kZa1d43QiIiIihdkfffTgGjI/evCCrLUtHc2okLHAudSi+T3r1cqG5XcK+er3T/rndwr5plTjR/M7hXz168aJ+Z1CvvH301dhX8gfHaG+k2dZiIiIFHJ/9NGD7+VlIiIiIoVZTq/yFRERkT+ghioiIuIANVQREREHqKGKiIg4IEcN1RgTbIx50Riz1xgT71nW1hhTtK+dFxER8biYb5upB/Qh897UHcCA3EhKRESksMnpJyXdCdS01rqMMWkA1trDxpjI3EtNRESk8MjpEWoy5zVfY0w54DfHMxIRESmEctpQPwTeM8ZUBzDGVAImAR/kVmIiIiKFSU4b6ihgH7ANKAnsAY4AY3IpLxERkUIlp1/flgwMBYZ6TvX+aq390w/OFxERKSpy1FCNMVHnLSpmjPsbB6y1e51OSkREpLDJ6VW+Mbhvl8n6vT3pR6j+jmYkIiJSCOX0lG+291qNMRWB0YC+aFxERIRL/OhBa+0xYAjwkrPpiIiIFE6X81m+tYEwpxIREREpzHJ6UdIaMt8zBXcjrQu8kBtJiYiIFDY5vSjpnfMeu4At1to9DucjIiJSKP1pQzXG+ANtgIettUm5n5KIiEjh86fvoVprU4G2QFrupyMiIlI4XczXt40xxgTmZjIiIiKF1R82VGNML8+Pg4EngdPGmIPGmAPp/+V6hiIiIoXAn72HOhWYA/TNg1xEREQKrT9rqAbAWrsqD3IREREptP6sofobY1qT/TN8s7HWLnc2JRERkcLnzy5KCgb+/Qf/nX9/qjggLt7FvSPeoVqrYfzljtF8/Plmn3HWWl6YNJ9abUdSq+1IXpg0n/Rv1Ys9cJx+T06jzm1PEd12JN0fn0LM/l/ysoxLUpRrB3f9/Z6czlUt/871XZ7jo2UXrv/5ifOpccsIatwygucnZtYPMGT8HJp0G0uZpo8xe9GGvEr/spQsHsaMVx/i0OrX2LrgBbq1a+QzrnhEKFNG92P35y+x+/OXGPFQB59xN91Qk7hvJvF0/065mbYj4uJd3DN8OlVv/jsNbn+Oj/5g3o+ZNJ/oW0cQfesIxkzKPu5Dx8+hafexlLvxMeYUknEHd/19n5xGZIsnuK7zs3y47BufcdZaRk/8jKhbhhN1y3BGT/wsW/3bdh2iVb9XqNx8KK36vcK2XYfyqgTgz49QXdba87+6TXLZiAkfEhjgz44lL7J99yF6/30qdaMjqRNVKVvc+5+tY8nqbaycOQKDodtjk6lauQz3dW1O/OlE2rW4jjef6UNEeAgT/r2MfsOns37uM/lUVc4U5doBnvzHPAID/dm5bDzbdx/i7qFvUzc6kmtqZK//vU/XsmTVVlbPGokxhq6DJ1Otchnuv6s5APWiI7nzlhsYM2l+fpRxSSYM70HyuXPUbvcU19W6irmvD2D7nkPs3HssW9z4J+4iNCSQ+l2eo2zpYsyfMpiDx35n9sLMBhLg78fLf+/GN9v25XUZl2S4Z9x/XOoe915PvE09H/M+fdxXzXSP+12D3fP+/q5Zxv3WwjXuAMNenUdQQAC7Pn+JbbsPcfeQt6gXfZXXvH/307UsWbmVNbOecs/7RydRrXIZ/nZXC5JTztF72FQG9GrNA91a8O4na+k9bCrffjKaoMCcfobR5bmcz/ItEoxbnj1PrsQkFq3YwlOPdCQiLJgbG9Tgthb1mLfU+xXb3CUbGdi7NZXLl6JS+ZIM6N2GDxZvBOCGutXo26UZpUqEExjgT/+erYjZf5zf4115VcpFK8q1g7v+hcu3MOqRThn1t295HfOWbvKKnbN4EwP7tCGyQikqly/JoN5tmO2pH+DB7i25uUltgoMLx51uYSFBdG7TgPFvL8aVmMyGLXtZunobd3do4hV7W4t6vDnjSxKTUjh49Hdmzl9P3843Zot5tO9fWbHhJ/YUgjMTmfO+U5Z573vc5y7ZxMDebahcwT3vB/ZpwweLMsf9ge4tadm4NiFBhWPcIX3e/8Co/u79vpln3s9d4mPeL9rIoKzzvk8b5njq//rbPaSmpjGgV2uCgwJ5pGcrrIXV3+zOs1r+rFFc8L3TvGKMGWmMiTXGnDbG/GiMudOz/D5jzNfGmAnGmDhjzD5jTPssv1fdGLPa83tfGmMmG2NmZll/ozFmnTHmpDFmizGmVZZ1K40xLxpj1gIJQJ4dpcceOE6Avx81qpbPWFY3OpJde496xe7ce4y6NSMzHteLjmTnvmNecQDrf4ilfJnilC4R7nzSDinKtUNm/TWrZa///CM0gJ17j1IvOnv9vp6nwqJG1fKcS00j9sDxjGXb9xz2OkJLZ7L8aTLGcE2NyhmPq1QsRZ/OzXj1naW5l7CDMsa96sWPe93oSHbuK7zjDlnnfYWMZfWiI9npc78/Sr1aV/mM27n3KNfWjMSYzLlRN7qyz+3klj9sqNbaYnmVyB+IBVoAJYAxwExjTPpe1hTYBZQFXgX+bTKfzdnAJqAM8DzQL32DxphIYDEwDigNDAM+NsaUy/Lv9gMeBooB+7MmZIx52Biz2Riz+dcTJ5yrFHAlJhMRHpJtWfHwUM4keH/qoysxieIRoRmPi4WH4EpIyvaeAsCR43GMmPAhYx+/09FcnVaUawdwJSRR7Pz6I0I4k3DWOzYxieIRIefFeddfWESEBXPalb3OU2cSiQgL8Yr9av2PDLnvViLCgql+VVn6dLmR0JDMI7KXh3Vn/NRFuBKTcz1vJ1zWuF9g3hcmZ3zWH5rDeR+aMe9dCdnXQfrfD+/t5JYCf8rXWvuhtfaItTbNWjsX2AOknwfab62d7vl4xPeASkAFY0xVoDHwnLU22Vr7NbAgy2b7AkustUs82/0C2AxkvbrhXWvtDmvtOWttynk5TbPWNrLWNipbLmsPvnzhoUGcOe8Py2nXWSLCgn3EZv8jdCbhLOFhwdleof0ad5ruj03h/q7N6dq2oaO5Oq0o1w4Q7qOpuOv3birn15/+PGWtvzDx9Ue1WLjvpjJiwkecTUph8yejmfXaI3z8+bccOX4ScJ8OjggL5tMvvsuTvJ1wueN+/rwvbHy9mLqUeX8xz2NuKfAN1RhzjzHmB8+p2ZNAPdxHpAAZ50SstQmeHyOAysDvWZYBHMzyczWge/o2Pdttjrsh+4rPM75Ofe2IOUxtH6e+6kRVZMeewxmPt+85TJ3qFTMenzyVQPfHp9CuxXU8cX+73E3cAUW5drhA/bsPUyeqoldsnahKbD+vfl/PU2GRftovqkrmC9QLnfY7eSqBh599jzq3jeKmu1/Ez8/w3Q73SaSWjWvT4Jqq7Fw2np3LxnPnLTfQv1drZk14OM9quVg+x31PzsZ9x57D1KleeMcdLu50f52oSmzffdhnXJ2oSvwYcyTb0fqOmAu/bZAbCnRDNcZUA6YDjwJlrLUlge38+Xu7R4HSxpisX4BeJcvPB4EZ1tqSWf4Lt9a+nCUmX86hhIcG07FVfV6ZvgRXYhIbPRdn9Gjf2Cu2R/smvDVnBUePn+TYiXjemr2Cnh2bAnDalUiPIVNocn0Uzw3qktdlXJKiXDu46+/Uuj4vTVuMKzGJDVv2smT1Nnq0974wp2eHJkyZvYIjx09y9EQ8k2ctp7enfoDklHOcTUrBWkvKuVTOJqWQllZwv98i4Wwyi1ZsYdQjHQkLCaLp9VF0uPl6nxemXB1ZllIlwvHzM9xy07Xce+f/MeE/ywAY//YiGnd7gZZ9XqJln5dYumYb73+2lkEvzPTaTkGRPu9f9ox75rz3HvceHTLn/dET8UyZvZyenXyMO4Vj3CFz3o+fmj7vY1myaqvPC9J6dmzClNnLPfP+JJNnfkUvT/3NG0bj52eY+sFKkpJTmDbP/XlELRvXyrNaTEE+926MuRb4DqgPxAD34G6w/YFzwIPW2uZZ4i0Qba2NMcZsAFYDzwANgWXAQmttX2NMFeAb4F7gSyAQuBGIsdYeMsasBGZaa//0PtsbGjayq9d57/SXIy7exeMvzmbVpl2UKhHOswM7c1e7Rqz/IZaeQ99i/4oJQPq9mAuYuXA9AH07N+O5R7tgjOGDxRsZPHYWYSFB2V5+rJ0ziqsqlnY0XycVptr9/Zw/zRYX72Lw2Fms9NQ/elAXut3WiPXfx9BjyFscXPUakHkf6owF7vr7dWnG84Nvzzj117n/G6z9Libbthe89RjNG0Y7kmfpJoMd2U5WJYuHMenZPrRqWoe4eBdjJi3go88306xBDea9MZAqN/8dgDtu+Qvjn+hGiWKhxB44zvMT57N8w08+tzl5dF+O/HKSF99e5Giuv26c6Oj24uJdPDZuVua8H9SFbu3c495z6FvsX5k57mMmzWemZ9z7dmnG6Eczx73LgDdYd964fzbFuXGH3Jv3j46dxcqNO93z/tEudL+tMeu+j6HH41M4tPqfQPp9qPOZMX8dAP1uv4kxWeb91l0HeWzcbHbtO0atqysw8dk+XF+7ygX/3UsRGmi+tdb6vEm6QDdUAGPMi8AA3F8f9z7u5jiDP2+oNYB3gQa4L06KBfyttQ94YpvivpDpOiDVEzPAWnsgvxuqFA658YelsMiNhlqYON1QC5OiPO/hjxtq3tztehmstU8DT19g9bvnxZosP6dfHQyAMWYusDPL+o3AzRf4N1tdcsIiIlIkFej3UC+HMaaxMaaGMcbPGHMbcDvwWX7nJSIiV6YCf4R6GSoCn+C+D/UQ7tO53+dvSiIicqW6YhuqtXYhsDC/8xARkaLhij3lKyIikpfUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAQH5nUBhl2YtyefS8juNfBHoX7Rfjxlj8juFfHNs3Rv5nUK+KtvptfxOId8cmz80v1MosIr2X0QRERGHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4QA1VRETEAWqoIiIiDlBDFRERcYAaqoiIiAPUUEVERByghioiIuIANVQREREHqKGKiIg4ICC/ExBvcadcDHvpA1Z9s4vSJcJ56pFO3Nm2oVectZbxby1k9qINAPTudCOjBnTGGANAZPMhhIYE4XnI7X+9gQkje+ZZHZciLt7FkPFzWLVpJ6VLhvN0/87c1a6RV5y1lrFTFjBrwXoA+nRpxrMDu2CMIfbAccZM+oxvtu0jNdXS4NqqjB96FzWrVcjrci5aXLyLweNmsWKDu/7nBnWh+22NveKstTw/aT4z5q8DoN/tN/H8o7dnjP22XYcYPG4Wu/cdo1b1ikx8pg/X1b4qT2u5WHGnXDwxfg4rN7nn/dMDOtG1re+xHzdlIbMXuse+d+dmPDPQPe9/O3mG+0a8Q8z+X0hNs0RXq8DowbfT5PqovC7nopSMCGHikHa0vuFqfj+VyAvvruajlTu94oIC/Hm5f2s6NosmMMCPjT8e4YlJX3D0tzMZMV1b1mZ475u4qnxxjse5GPTPpazfcTgvy7loV8rYX1EN1RhTG5gL1ACetta+mc8pXZKnX/uIwEB/tiwYy449h7ln+DSurVmZ2lGVssXNnL+OZWu28cW7wzEGeg19iyqVy3DPHf+XEfPFu09S/apyeV3CJRv52ocEBfqzffGLbN9ziD5/n0rd6EjqnFf7+5+tY+nqbayYMQKDofvjk6laqQz3dW1O/OlE2jW/jjee7kNEeAiv/XsZ9wyfzrq5z+RTVTk37NV5BAUEsOvzl9i2+xB3D3mLetFXcU2N7PW/++lalqzcyppZT2GMoeujk6hWuQx/u6sFySnn6D1sKgN6teaBbi1495O19B42lW8/GU1QYMHd5Z+a8BGBgQFsXzSO7XsO0XfYNK6t6T32Mzzz/qv3R2AM9Hh8ClUrl+beO5sTHhrMv0b1IqpKOYwxLFu9jXuenM72xeMICPDPp8r+3ISBfyX5XCq1e0/huqjyzB3Tle17T7DzwG/Z4vrfcQON61Sm+aD3OOVK4vXH2vJK/zbc8+ICAFr9pRrP/+1m/vbSQr7dfZSKpSPyo5yLdqWM/ZV2ync4sMJaW6ywNtOExCSWrNrKkw92IDwsmCb1o7i1eT0+/nyzV+yHy77hkZ6tqVy+JJXKleSRnq2Zt2RTPmTtDFdiEotWbGHkwx2JCAvmxvo1aNeiHh8u+8Yrdu6SjQzo1ZrK5UtRqXxJBvRqw9wlGwG4oW41+nRpRqkS4QQG+PNIz1bEHDjO7/GuvC7porgSk1i4/AdG9XfX36xBDdq3vI65PsZ0zqKNDOrThsgKpahcviSD+rRhziJ3/V9/u4fU1DQG9GpNcFAgj/RshbWw+pvdeV1SjrkSk1i8cgsjHnLP+6b1a9CueT0+8jH285Zson+WeT+gV+uM5ygkOJCa1Srg5+eHtRZ/fz9Onk4g7lRCXpeUY2HBgXT+v1qMn7EW19kUNvx4mKUbY7i7zbVesdUqlGD5dz9z4mQCSSmpfLp6F3Wqlc1Y/1Sfm3h19jo27zqKtXD0tzPZjl4Loitp7Avuy9VLUw34wMkNGvc5NGOtTXNyuxey9+AJ/P39qFG1fMayujUqs/6HWK/Y3fuOcW3NyhmPr61Zmd37jmWLuWvQRNKspVG96owefDtVKpXJveQv094Dxwk4v/aakaz/PsYrdte+Y9SNjsyMi45k53m1p1v/QyzlyxSndIlw55N2UKyn/qynputFR7L2O+/6d+49Sr1aV2WL27n3aMa6a2tGZpz+BagbXZmde49yy03ef6QLgr0HTniN/bXRfzT2Wed9JLvOG/vW/V4mZv9xUs6l0qdzM8qVLpZ7yV+mGpGlOJeaRuzhuIxl2/ee4P+uq+IVO+N/23j5kTZULB1OvCuJ7q2v4cvN+wDw8zM0iK7I0o2xfPvOAwQHBbBkfQzP/XsVZ5PP5Vk9F+tKGvsrpqEaY5YDNwPNjTGvAw2Bh4AeQDDwKTDUWptojCkFzACa4n4O1gL9rbWHPNta6VnWCrgBuA7wHt1c4EpMolh4SLZlxSJCcSWc9RlbPCI0My48FFdiEtZajDF8PGkwN9StRuLZZF6dvoR7h0/nf/99ssCe+nIlJhNxXu3FI0I5k5DkIzaJ4uGhWeJCcCVk1p7uyPE4Rk74kBceuzP3EnfImQTvsXfXf6GxDzkvzl2/KyH7OoDi4b63U1C4EpO8xz485IJjX+xPxn7FjJGcTUph6aqtJJ9Lzd3kL1NEaCCnE5KzLTvlSiIiNMgrdu/hOA6fOM1PMwdwLjWNH38+wZNTvgKgfMkwggL96dK8Fh2e/ICU1DRmP3cHw3reyLj3v86TWi7FlTT2V8wpX2ttG2AN8Ki1NgLoD9QCGgA1gUjgOU+4H/Bf3Ee0VYFEYNJ5m+wHPAwUA/ZnXWGMedgYs9kYs/m3X391tI7w0GBOu7L/4TvtOkt4WMifxp5xnSU8NDhjYt3YoAZBgQGUKBbGC4935cDR39mz/xdH83VSeGgQZ3zUHhEW7CM2e+3u5yg4WzP9Ne40PR6fwv13Naerj4u6CpqIMN9jH5GDsU9/nowxhF/EdgqK8NDgixv7hKy1J3mNPbhPAd7ZtiGTZnzJjj0F96KcM4kpFAvL3jyLhQVzJjHZK/YfA28hKNCf6j0mEXnnGyxct4cPx94FQKLnKHTagu/5Jc7Fo/u+FAAAHxxJREFU76cSmfzpZm5tXD33i7gMV9LYXzENNSvPadqHcR+R/m6tPQ2MB3oCWGt/s9Z+bK1N8Kx7EffRbVbvWmt3WGvPWWtTsq6w1k6z1jay1jYqU7YsToqqUo7U1DT2HjyRsezHmMPUrl7RK7ZW9Yr8GHMkW1wtH3HpjAFrHU3XUVFVy/P/7d13fBVV3sfxz48kpFECgtJ7U8RGU2ysrougYG+rz+o+a9+1sFbUteyDZdVddRcLlnVXkWavKDYURUBRQDoBg6FEghBKEkII5/njTMINKQSY3JuY7/v1yuuVO3Nm7vlN+82cmblne9EOlmeuLRk2f+mqMg9jAXTv2IL56atKlesREXvOpjzOve4JBh3bi+GXDKreioekcxD/sh93xj9v6aoyD2YA9OjUknlLVpVbrkenlixIX42LWNnz08ufT03RqV3zsus+fXW52333ji1YEHGQnF/B/lGscHsRK1aHe+IbpmWrNhAfV49OrdJKhh3cqTmLVpStc69O+zP2o3nkbNnKtu1FPP3Wd/Tp3pKmjZLZuKWAVdmbSq33mry/F/slrftfZEIFmgMpwCwzyzGzHOD9YDhmlmJmo81shZltAj4H0swssi00M+q1BlKSExl8/CE8/Ox75OUX8PXc5Uz+Yl65r46cfXJfnp7wKWuyc8hat5HR46dw7pB+ACxevoZ5S1dSVLSD3LwC7hn1Bi2aNaZrh5r76khqciKnDDyUvz3zHrn5BcyYs5z3p35f7msj5w7ux1PjPmXN2hyysjfy5LhPOW9IfwA25+Zz3vVP0O+QTvzl6mHRDmOvpSYncuqvDuW+0e+Sm1/A9DnLeO+zuZwXrNNI55/SjyfGfsLqtTmsyc7h8TEfc8GpPv5jenelXj1j9PgpFGwr5OmJnwFwXN9uUY1nT6QmJzLk+EN48JlJ5OYXMHPucj6Y+j1nl7Puzxncl9Hjp/jtPnsjT437tGQZzZqXwYw5y9hWuJ38gm3868WPyN6wmSMO6hDliKour6CQd6Yt5baLjiYlMYH+B7ViyJFdmPDJgjJlv12axfkn9qRRSn3i4+px6SmHsXrdZtZvygfgpQ/nc/mwI2jWOIXGDRK5+vTefDBzebRD2iO/pHVvrjacwlRRcO9zDPBvYAvQ1TlX5nrfzP4CnAic75zLMrPDgO+ABOfc9uL5OOee3d13HnZEb/fJ1BkhRuHfybrh/nF8/vUSmjRK4bYrh3LGb3ozY84yLrpxNEs/fBDw72Td++TbjHvbv4d6wdAjuT14D/WLWUsY8fDLrMneSEpSffr06sAdV59Gp7bhvUKTEBf++diGjblcd99YPp+5mCaNU7njKv8e6vTZyzj/z0+S8cnDgI/9r4+Xfg/1zj/691DHvzuDa0e+REpSfYhoCfpi7G20adE0tLomxFdP/H/6v5eYMmMRTRqnctef/Huo075L59zrnmDl5/8AfPx3/av0e6j3XLPzPdS5izO5duRYFv+QRbcOB/Cvv1zIId3LPuSytwoKw783tWFTLsPvHRe8f53C7VcN5czf+HX/2xueYvnHDwE730Ee+1bw/vWwI0veQZ72XTp3PPIqK1b/TEJcHD06t+SWy4Zw1OFdQq1ri9MeCXV+aQ2SGDV8EAMP78CGTfncE7yHelTP1kz861m0Pcu/tNCkYRJ/u/IEBh7egfrx9Vi4Yh23PzOFb5f4B3Pi4+rxwBUncPbAHmzdVsQbUxdz178/C3V9Zb05PLR5FatN6z4tJX6Wc67sFQ6/0ITqnHvWzB4DWuLvqa41s9bAwc65D8zsQfyDRmfgr2SfA06nhiTU2qI6EmptUh0JtbaojoRam4SdUGuT6kiotUllCfWXfES4Bf9k7vSgWfcjoHsw7lEgGVgHTMc3B4uIiOy1X8xrMwDOuYER/28Fbgv+di23Gv9KTKTR5c1HRESkKn7JV6giIiJRo4QqIiISAiVUERGRECihioiIhEAJVUREJARKqCIiIiFQQhUREQmBEqqIiEgIlFBFRERCoIQqIiISAiVUERGRECihioiIhEAJVUREJARKqCIiIiFQQhUREQmBEqqIiEgIlFBFRERCoIQqIiISAiVUERGRECihioiIhEAJVUREJARKqCIiIiFQQhUREQmBEqqIiEgI4mNdgdqunhmJCXGxrkZM5OQVxroKMZWWkhDrKsRMfFzdPhdfMv6aWFchZjpeMSHWVaix6vZeISIiEhIlVBERkRAooYqIiIRACVVERCQESqgiIiIhUEIVEREJgRKqiIhICJRQRUREQqCEKiIiEgIlVBERkRAooYqIiIRACVVERCQESqgiIiIhUEIVEREJgRKqiIhICJRQRUREQqCEKiIiEgIlVBERkRAooYqIiIRACVVERCQESqgiIiIhUEIVEREJgRKqiIhICJRQRUREQqCEKiIiEgIlVBERkRAooYqIiIQgPtYVqG5mdizwrHOue6zrUlUbNuZy3b1jmTJjEU3TUrnj6mGcPahPmXLOOf76+FuMeXMaABedNoA7/zgMMwNg+H3jmPZdOsszs/nnHb/lglOPjGoceyNnUy4jHprAF98soUnjVG68dAjDft27TDnnHA89/Q4T35sBwLlD+nPT5aeWxP7xtPk8/My7rMpaT/fOrbjvxnPp2qFFVGPZG3V53dfl2HM25XHbwxP4ctYSmjRK5YZLhzD0xCPKlJv+XTqPvziZ+emraNwgmU/H3lFq/Mqs9Yx4cDxzFv1Iy/3TuPOaMzm6d7dohbHX0lLr8/ff9+f4ni1Zv7mA+1+dzeszVpQpN2b4QPp3bV7yOSG+HsuyNnPine8B0LNtGiMv7MOBbdLYsnU7Yz5L59G350Utjl98QnXOTQVqTTIFuPmhiSQkxLFg0n3MW7KSC/78FAd3bU2PTi1Llfvv61/y3mdz+WzMrZgZZ13zOO1a7cfvzzwGgIO7tuaMk47gnlFvxiKMvXL3Y6+REB/P9NfuYWH6Ki4d8Sw9OremW8fSyXD821/x4ZfzePvZGzHg4ptG06blfvx22AAyVmbz53vH8NwDl3HYQe15ZvynXHH7c0x+4Vbi4+JiE1gV1eV1X5djv+efr5KQEMe0V+5mYfoqLr/9OXp0blXmJDA5uT5nDe7HKQWFjB77cZn5/HnkGL/N338Zn81YyLX3/JcPXxhB07QG0Qplr9x3UR8Kt+/gkOtf4+B2TXjhuuOZn5nDktUbS5W76JEppT6/cvOJfLnwp5LPj19xNJO+zeSsv31M22apvDHiJBZkbmDy7FXRCKP2NPma2R4n/72ZJtZy8wt459M5jLjiVBqkJHLkYZ05+dheTJw0s0zZCe/N5OrfnkCrA5rQcv80rr7wBMa/M6Nk/B/OOY7j+nYnqX5CNEPYa3n5BXzw+VyG/+/JpCYn0qdXJ04c0JM3PvymTNnXJn/DH84ZSMvmabRonsYfzjmeV9/3y+jzrxfRt1cn+vTqRHxcHFdccAI/rdvIzNnLoh3SHqnL674ux56XX8Dkqd9z/SWDS7b7E44qf7s/tEc7Tj+pD21b7ldm3A+Z2cxPX8m1lwwiKTGBQccdQreOLflg6txohLHXkuvHMaR3Wx58fS55BduZuTSbybNXcfaADpVO12a/VPp3a87L034oGdZ2v1Rem57BDudYkb2FmUuz6daqcTVHsFPUEqqZ3WJmq8xss5ktNrMTzew/ZjYyosxAM1sZ8TkjmG4ukGtm8cGwEWa2wMw2mNnzZpYUOX0wTRbwfDnzLFOPYHg9M7vVzJaZ2c9mNtHMmkZr+RRb9uNa4uPq0aXd/iXDenZtzaLlWWXKLlq+hoO7ti5d7oc1UalndfhhZTZxcfXo2HZn7Ad2bsXSjLKxL83IokfnVjvLdWlNesbOM1WH2/m/839Lfig7n5qkLq/7uhx7Rsl2v7Mp88DOLUttz1WxdEUWbVvuR4OUpJJhPSrYf2qSzi0aUVTkWP7T5pJhCzI30L1VWqXTnTOgIzOWZLPy59ySYc98uJhzBnQkPs7o3KIhvTs3Y+qC6MUflYRqZt2BPwF9nXMNgUFARhUnvwA4BUhzzm0Phl0YzKMz0A2IvJHQAmgKtAcu34N6XAOcDhwPtAI2AI9XEM/lZvaNmX2zbl12FcOomty8AhqmJpUa1qhBElvytpYtm19AowY7yzZKTSI3rwDnXJmytUFe/rZSBwOABkFMZcsW0DAi9oapSeTm+9iP7t2NmXOWM312OtsKt/PkSx9RuL2I/IJt1R7DvqjL675Ox17udp9Mbn7Z7b4yeflll2HDCvafmiQlMZ7NWwtLDduUX0hqUuUNjGcP6MjEL5eXGvbRnFWc0rsdy586j6n3DWX81GXMyVgfep0rEq0r1CIgETjIzBKccxnOuaq2v/3TOZfpnMuPGDYqGLYeuBefdIvtAO5yzhXsMs3u6nElcLtzbqVzrgC4Gzi7vGZj59zTzrk+zrk+zZo133X0PklNSWRzbumDyObcrWV2OIDU5NJlN+duJTUlseThjNomJbl+mQPolrwCUlMSyymbyJaI2LfkbiU12cfeud0BPHjrBdzz2GsMOPtuNmzMpUv7A2jRvPIz3liry+u+Tsde3nYfbM97wu8TpZPnlryt5e4/NUlewXYaJpVunm+YnEDu1u0VTAH9ujZn/8ZJvPNNZsmwtNT6vPTnX/HI2/PoeMUEet/wOscf3JKLf9W12uq+q6gkVOdcOnA9PkmtNbPxZtaq8qlKZO5m2Ar8FWWxbOdc2dPa3dejPfC6meWYWQ6wEJ+AD6hiPUPRud3+bC/awbIf15YMm790FT06lX1CtUenlsxbuqp0uY4ty5SrLTq2aU5R0Q4yVu686l+Uvrrcp3O7dmjBwmWrSz4vXLaaLh12rqrBxx/KpOdv5ps3R3Ld709mVdZ6DunRtnoD2Ed1ed3X5dg7lLfdLy+9PVdF1/YtyFzzc6nkvGjZmhr/dPuyrE3ExRkd929YMuygtk1YvDqnwmnOGdCR975dSV7BzqTbrnkDinY4Xpn2A0U7HGs25PPmzBWccEhVU82+i9o9VOfcWOfcMfjE5YC/AblASkSx8tZ8ee04kUfGdsDqiM+VtvtUUA/wSXqwcy4t4i/JORedx8MCqcmJnDLwUB54+l1y8wuYMWc5kz7/nnMH9ytT9twh/Xhy3KesWZvDmuyNPDH2E84/tX/J+G2F29laUIjDUbi9iK0FhezYsSOa4eyRlOREfnNsLx59/n3y8guY9f0PfDRtHqefVPbViTN+04fnX/6MrOwcflq3kecmTuGsk3cuo3mLMykq2sHPOVu4/eGJnDigJ53bRfXcaI/V5XVfl2NPSU7kpGN68dh/gu1+3g98PG1+udv9jh07KNhWyPbtRTgHBdsK2Vbok0rHts05sEsrRr0wmYJthUz+4nsWL1/NoGMPiXZIeyR/WxGTZq3kpjN6kVw/jr5dmjHosNa8Mi2j3PJJCXEM7duOiV+Ubu5dnrUJMzijf3vMoHmjJIb1bc/CzIoTc9gsGvcdgnuXrYEv8UnsKSAO+AK4ARgA1AfeANo459oE02UAlzrnPoqYVwawGRgM5AFvAZ87524zs4HAmOLpg/Ilwyqqh3PuYjMbDpwGXOycW2FmzYEBzrlKn70/oncfN/Wrr/dh6ZS1YWMu1458ic9mLqZJ41T+8kf/Pt5X36Vz/vAnWTHl74B/H++eUW8y5q2vALho2FHc9afTSpq+hl31GNO+TS817zeeuJZjeofTBJKTV7j7Qns6z0253Pqgfx8vrVEKN112CsN+3Zuv5y7nD7c8zdxJDwA+9gdHv8PE96YDcO6QI7n5ip3voZ53zb9YtGw18fH1GHz8odx29Wmk7GET2u6kpYT/FGltWffVoTbFvn5LuPfjczblMeKh8Uz7dilpjVK48dJTGHriEXw9dzmXjXiG2e/eD8CM2en8zw1Plpq236GdGfOPqwH/HuqtD45nzsIVtNq/CXdeG/57qIde/2qo8wPfXPuP3/fnuJ4t2bClgPte8e+h9uvanJeGD6Tr1S+XlD29f3tuO+sw+t1c9tB8dI8DuP2cw+h0QEO2Fhbx4exV3DluFvnbikKr64YXL5zlnCt7tkP0EuohwLPAgUAhMA3/wNB64L/45JgBPA/cUIWEOhr4H3xT75vAVc65vCok1HLr4ZxbbWb18M3BVwTzXQtMcM7dVlls1ZFQa4vqSKi1SXUkVKkdwk6otUl1JNTapLKEGpX3NJ1zc4GybTfeebt8fiRiug4VTPO1c+7+cr5nCtCmomGV1cM5twP4R/AnIiKyR2rNDzuIiIjUZEqoIiIiIah1P81XSTOwiIhIzOgKVUREJARKqCIiIiFQQhUREQmBEqqIiEgIlFBFRERCoIQqIiISAiVUERGRECihioiIhEAJVUREJARKqCIiIiFQQhUREQmBEqqIiEgIlFBFRERCoIQqIiISAiVUERGRECihioiIhEAJVUREJARKqCIiIiFQQhUREQmBEqqIiEgIlFBFRERCoIQqIiISAnPOxboOtZqZZQMrYliFZsC6GH5/rNXl+BV73VWX44917O2dc83LG6GEWsuZ2TfOuT6xrkes1OX4FXvdjB3qdvw1OXY1+YqIiIRACVVERCQESqi139OxrkCM1eX4FXvdVZfjr7Gx6x6qiIhICHSFKiIiEgIlVBERkRAoodYSZjbFzC6NdT2izczmm9nAWNcjWswsw8x+Het6SPSZWXczm21mm83s2ljXp7Yys2PNbHEsvjs+Fl8qUlXOuZ6xroNIlNwMfOqcOyzWFanNnHNTge6x+G5doYpInWReTToGtgfmhznDGhhjlZnZHl/w7c00YaqVC7q2MbNbzGxV0JSz2MxONLN+ZvaVmeWY2RozG2Vm9SOmOcnMFpnZRjMbBVjEuEvM7Asze9jMNpjZD2Y2OGJ8YzN7LpjvKjMbaWZxwbguZvZZMN91ZjYhGG5m9oiZrTWzTWb2vZkdHMXFVK7iJlAzSzSzR81sdfD3qJklBmXmmdnQiGkSgtgOj13N981u4l1oZqdGlI03s2wzOyL4fKSZTQu2rTk1tcnczG41s2XBfrHAzM4Ihu9u++5oZp8H031kZo+b2ZiI8RXGH9w6udfMvgTygE5RDLlCZvYJ8CtglJltCZp/HzazH83sJzN7ysySg7JNzOydYJ1vCP5vEzGvGhFjBce9/5jZyIgyA81sZcTnjGC6uUBusG1nmNmIYBvZYGbPm1lS5PTBNFnA8+XMs0w9guH1IrbBn81sopk13aegnXP6q8Y/fNNDJtAq+NwB6Az0Bo7EN7t3ABYC1wdlmgGbgbOBBGA4sB24NBh/CVAIXAbEAVcBq9n5GtTrwGggFdgfmAlcEYwbB9yOP5lKAo4Jhg8CZgFp+OR9INCyBiy/DODXwF+B6UE8zYFpwP8FZW4GJkRMcxrwfazrXo3x3gm8FDHNKcDC4P/WwM/AkGAdnxR8bh7r2MqJ9RygVVDP84BcoGUVtu+vgIeB+sAxwCZgTFXiB6YAPwI9g30vIdbLIWJ5TInYxx8B3gKaAg2Bt4H7g3H7AWcBKcG4l4E3dplPTGOk4uPef4CREeUGAit32f5nA22B5Ihh84JhTYEvi+cRTL8d+BuQCCRHzrOiegT/X4ffx9oE044Gxu1T3LHeiH7pf0AXYC3+IFnhhg1cD7we/P87YHrEOANWUjqhpkeMTwEc0AI4ACgo3hiD8Rfg780AvIB/MbrNLt9/ArAEn+TrxXq5RdQrI1h2y4AhEcMHARnB/63wJyCNgs+vADfHuu7VGG+XIN6U4PNLwJ3B/7cAL+4yzw+Ai2MdWxVin40/Gaps+24XHEBTIsaPYWdCrTR+fLL5a6xjrSD+KcClwf6eW3zgD8YdBfxQwXSHARt2mU9MY6zouEfVEur/7jKvDODKiM9DgGUR028DksqbZ0X1CMYtBE6M+NwSfyIXv7dxq8m3mjnn0vHJ8m5grZmNN7NWZtYtaKrJMrNNwH34K1PwCSIzYh4u8nMgK2J8XvBvA/x9mARgTdDklYM/89o/KHMzfoedaf4J2v8N5vEJMAp4PKjn02bWKJylEIpWlO7VZ0UwDOfcavxZ61lmlgYMxieZ2qyyeNPxB4OhZpYCDAPGBuXaA+cUr/tg/R+DP1jUKGb2O/NPtRbX82B27gMVbd+tgPURw6D0vlGV+Hfdl2qa5viTiFkRMbwfDMfMUsxstJmtCI4dnwNpFtzWCcQ0xoqOe1WcvLy6Rw4r2RcC2c65rXtRj/bA6xHLeCFQhL8o2StKqFHgnBvrnDsGvwIdvnniSWAR0NU51wi4jZ33SdfgmzcAf38z8vNuZOKvUJs559KCv0YueFrWOZflnLvMOdcKuAJ4wsy6BOP+6ZzrDRwEdANu2qfAw7Uav/yKtQuGFfsvcBG+GfEr59yqKNatOuwu3nH4lofTgAXBgQP8+n8xYt2nOedSnXMPRKXWVWRm7YFngD8B+znn0vDNelbphH7faBqcSBSL3DeqEn9N/3m4dUA+0DMihsbOuQbB+BvwTZn9g2PHccHwyGUX8xgrOO7l4k8WirUob9JyhkWu4133hUpjraAe4LeVwbtsK0n7cuxQQq1mwcMFJ5h/oGQrfkfZgb/3sQnYYmY98PeJir0L9DSzM80/tXYt5W94ZTjn1gCTgb+bWaPgxntnMzs+qM85EQ8wbMBvYDvMrK+Z9TezBPxGvzWoZ00xDrjDzJqbWTP8fcQxEePfAI7A3xd5IQb1C9vu4h0P/Aa/3YyNGD4Gf+U6yMzizCwpeEijDTVLKn7bywYws9/jr1Ar5ZxbAXwD3G1m9c3sKGBoRJHaEn+FnHM78Ccbj5jZ/gBm1trMBgVFGuKPIznBQzR3xaamFavkuDcbGGJmTc2sBf7qsSr+aGZtgnhvBybsYz0AngLuDU7uCPa106oaY3mUUKtfIvAA/qwzC9/0OgK4Efgt/l7YM0RsIM65dfgrrQfwD1R0xTdpVtXv8A9sLMAnzVfY2eTVF5hhZlvwDz1c55xbDjQK6rEB36TyM/DQHkdbfUbiD6Rzge+Bb4NhADjn8oFXgY7Aa7GoYMh2F+8a/MM5Ayi97WTir1pvwyerTHxLQ43a151zC4C/42P4CehF1bfxC/H3FH/GL5MJ+FaZWhN/FdwCpAPTg2bdj9j5buWj+Idv1uEfqnk/JjWsXEXHvReBOfj7opOpYmLEnzROBpbjny8YWXnx3dYD4DH8MXCymW3GL8v+VZxvufTj+FKjmdmPwEXOuc+rUPZOoJtz7qLqr5nUFOZf/VrknKtxV2qy78wsA/9A5kexrsvu1LazNqlDzKw5/kGMjCqUbQr8gRrctZOEI7g90Tm4nXEy/or0jVjXS0QJVWokM+sLLAX+5Zz7cTdlL8M37U2qypWs1Hot8K+GbAH+CVzlnPsupjUSQU2+IiIiodAVqoiISAiUUEVEREKghCoiIhICJVSROsAievmwKHbAbGau+Je4yhk3xcwureJ89rrj9X2ZVmRPKKGK1BDBgT/ffPddPwVJsMHup9wzzrmpzrnddsBsQTdqYX+/yC+VEqpIzTI0+M3WI4A+wB27FrAYd6IsIuVTQhWpgYIf6J5E8Pu2QdPpH81sKf79XMzs1IjeWqaZ2SHF05vZ4Wb2rflOlSfg+74tHrdrB8xtzew18x1W/2y+s/sD8b91elRwxZwTlE20Cjq+DsbfZL5j+9UW9GRUFcEPNXwSfP86M3vJfM9BkfpaOZ1M725ZiESLEqpIDWRmbfH9Pkb+YMHp+N8aPcjMDgf+je8xaD98F31vBQmvPv6Xg17Ed8j8Mr5D6vK+Jw54B//7zR3wHXSPd84tBK7E99zTIOgNBvzvonbD98HZJSh/ZzCvk/G/UX0S/ven9+S+pQH347vlOhDfu8jdu5S5EN8vbOegDncE31vhstiD7xfZZ0qoIjXLG8HV4BfAZ/h+covd75xbH3QEcDkw2jk3wzlX5Jz7L/4H4o8M/hKAR51zhc65V4CvK/i+fvgkdpNzLtc5t9U5V+59UzOz4HuHB/XYHNTv/KDIucDzzrl5zrlcyibECjnn0p1zHzrnCpxz2cA/gON3KTbKOZfpnFsP3Ivvvo7dLAuRqNG9GJGa5fRKfgR81460LzazayKG1ccnRwescqV/Bi2ys/JIbYEVzrntVahbZMfXxcMMKO7YuhUwqwrfWYaZHYDv/eNYfPdk9fA9H0WqqJPpypaFSNToClWk9ohMkJnAvbt0jpzinBuH74S7tUVkPXynzOXJBNpV8KDTrr9LuruOr9dQtiPoqrov+L5eQafZF1G2s/GKOpmubFmIRI0Sqkjt9AxwpflO4c3MUs3sFDNriO9jdDtwrZklmNmZ+Kbd8szEJ8IHgnkkmdnRwbifgDbBPdmqdHw9EbjEzA4ysxT2rOPrhvgfu99oZq3xfZjuqqJOpitbFiJRo4QqUgs5574BLgNG4ZtG04FLgnHbgDODz+uB86ig03XnXBEwFP+A0Y/AyqA8wCfAfCDLzNYFwyrs+No5Nwnf+fUnQZlP9iCke/CvCm0E3q2gvuV2Ml3ZshCJJvU2IyIiEgJdoYqIiIRACVVERCQESqgiIiIhUEIVEREJgRKqiIhICJRQRUREQqCEKiIiEgIlVBERkRD8PzlOCIGCid+mAAAAAElFTkSuQmCC\n"
},
"metadata": {
"needs_background": "light"
}
}
]
}
]
}
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