Skip to content

Instantly share code, notes, and snippets.

@tianyu-lu

tianyu-lu/6-language-models.ipynb

Created October 31, 2021 17:36
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save tianyu-lu/0ae826307a7af88665a797f0d3cc05ef to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save tianyu-lu/0ae826307a7af88665a797f0d3cc05ef to your computer and use it in GitHub Desktop.
Download ZIP
6-Language-Models.ipynb
Display the source blob
Display the rendered blob
Raw
6-language-models.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "6-Language-Models.ipynb",
"provenance": [],
"collapsed_sections": [
"_KccyI4PWyHz"
],
"toc_visible": true,
"authorship_tag": "ABX9TyP/3wL6ZMxpLCRuIWxSClby",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/tianyu-lu/0ae826307a7af88665a797f0d3cc05ef/6-language-models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NcmMp1v9ra-t"
},
"source": [
"## 6. Language Models"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ukhmzb3snH7O"
},
"source": [
"An embedding is a continuous vector in $\\mathbb{R}^d$, where $d$, the dimension of the vector space, is a hyperparameter chosen by the user. These embeddings are learned in an unsupervised way, i.e. the training data only contains sequences and does not contain labels. When trained on all available protein sequences in Uniprot, conceptually it is a null model for $p(sequence)$. When finetuned on a specific class of sequences, it becomes a model for $p(sequence | function)$. "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UFO-2xMpnXQn"
},
"source": [
"https://bellard.org/textsynth/"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lOW4oeX9DgsI"
},
"source": [
"#### BioEmbeddings"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sDsSyxdAm27K"
},
"source": [
"We will use the [bio_embeddings](https://github.com/sacdallago/bio_embeddings/tree/develop/examples) package to explore various protein sequence langugage models and ask the question, do these embeddings help protein function prediction?"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CA3pf0NfvPki",
"outputId": "68b622e4-095c-47ac-d2a6-e1025c55f4c8"
},
"source": [
"# !pip3 install -U pip > /dev/null\n",
"!pip3 install -U \"bio-embeddings[all] @ git+https://github.com/sacdallago/bio_embeddings.git\" > /dev/null"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" Running command git clone -q https://github.com/sacdallago/bio_embeddings.git /tmp/pip-install-_x4lm_ph/bio-embeddings\n",
"\u001b[31mERROR: torchvision 0.8.1+cu101 has requirement torch==1.7.0, but you'll have torch 1.5.1 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: bio-embeddings-cpcprot 0.0.1 has requirement lmdb<2.0,>=1.0, but you'll have lmdb 0.99 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: bio-embeddings-cpcprot 0.0.1 has requirement scipy<2.0,>=1.5, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gcjTUW0D9QIJ",
"outputId": "aaa668e4-0062-4336-c708-fc88e521e5b7"
},
"source": [
" # from https://github.com/pytorch/pytorch/issues/42078 \n",
"!pip install torchvision==0.7.0"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting torchvision==0.7.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/8e/dc/4a939cfbd38398f4765f712576df21425241020bfccc200af76d19088533/torchvision-0.7.0-cp36-cp36m-manylinux1_x86_64.whl (5.9MB)\n",
"\u001b[K |████████████████████████████████| 5.9MB 13.6MB/s \n",
"\u001b[?25hCollecting torch==1.6.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/38/53/914885a93a44b96c0dd1c36f36ff10afe341f091230aad68f7228d61db1e/torch-1.6.0-cp36-cp36m-manylinux1_x86_64.whl (748.8MB)\n",
"\u001b[K |████████████████████████████████| 748.8MB 20kB/s \n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision==0.7.0) (1.19.5)\n",
"Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.7.0) (7.0.0)\n",
"Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch==1.6.0->torchvision==0.7.0) (0.16.0)\n",
"\u001b[31mERROR: bio-embeddings 0.1.5 has requirement torch<1.6.0,>=1.5.0, but you'll have torch 1.6.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: bio-embeddings-cpcprot 0.0.1 has requirement lmdb<2.0,>=1.0, but you'll have lmdb 0.99 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: bio-embeddings-cpcprot 0.0.1 has requirement scipy<2.0,>=1.5, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n",
"Installing collected packages: torch, torchvision\n",
" Found existing installation: torch 1.5.1\n",
" Uninstalling torch-1.5.1:\n",
" Successfully uninstalled torch-1.5.1\n",
" Found existing installation: torchvision 0.8.1+cu101\n",
" Uninstalling torchvision-0.8.1+cu101:\n",
" Successfully uninstalled torchvision-0.8.1+cu101\n",
"Successfully installed torch-1.6.0 torchvision-0.7.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PMgGj7FT1fzF",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "12e5890f-c86d-43c0-ec9c-95ae57f1f37e"
},
"source": [
"!git clone https://github.com/igemto-drylab/CSBERG-ML.git\n",
"%cd CSBERG-ML\n",
"from util import *"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'CSBERG-ML'...\n",
"remote: Enumerating objects: 110, done.\u001b[K\n",
"remote: Counting objects: 0% (1/110)\u001b[K\rremote: Counting objects: 1% (2/110)\u001b[K\rremote: Counting objects: 2% (3/110)\u001b[K\rremote: Counting objects: 3% (4/110)\u001b[K\rremote: Counting objects: 4% (5/110)\u001b[K\rremote: Counting objects: 5% (6/110)\u001b[K\rremote: Counting objects: 6% (7/110)\u001b[K\rremote: Counting objects: 7% (8/110)\u001b[K\rremote: Counting objects: 8% (9/110)\u001b[K\rremote: Counting objects: 9% (10/110)\u001b[K\rremote: Counting objects: 10% (11/110)\u001b[K\rremote: Counting objects: 11% (13/110)\u001b[K\rremote: Counting objects: 12% (14/110)\u001b[K\rremote: Counting objects: 13% (15/110)\u001b[K\rremote: Counting objects: 14% (16/110)\u001b[K\rremote: Counting objects: 15% (17/110)\u001b[K\rremote: Counting objects: 16% (18/110)\u001b[K\rremote: Counting objects: 17% (19/110)\u001b[K\rremote: Counting objects: 18% (20/110)\u001b[K\rremote: Counting objects: 19% (21/110)\u001b[K\rremote: Counting objects: 20% (22/110)\u001b[K\rremote: Counting objects: 21% (24/110)\u001b[K\rremote: Counting objects: 22% (25/110)\u001b[K\rremote: Counting objects: 23% (26/110)\u001b[K\rremote: Counting objects: 24% (27/110)\u001b[K\rremote: Counting objects: 25% (28/110)\u001b[K\rremote: Counting objects: 26% (29/110)\u001b[K\rremote: Counting objects: 27% (30/110)\u001b[K\rremote: Counting objects: 28% (31/110)\u001b[K\rremote: Counting objects: 29% (32/110)\u001b[K\rremote: Counting objects: 30% (33/110)\u001b[K\rremote: Counting objects: 31% (35/110)\u001b[K\rremote: Counting objects: 32% (36/110)\u001b[K\rremote: Counting objects: 33% (37/110)\u001b[K\rremote: Counting objects: 34% (38/110)\u001b[K\rremote: Counting objects: 35% (39/110)\u001b[K\rremote: Counting objects: 36% (40/110)\u001b[K\rremote: Counting objects: 37% (41/110)\u001b[K\rremote: Counting objects: 38% (42/110)\u001b[K\rremote: Counting objects: 39% (43/110)\u001b[K\rremote: Counting objects: 40% (44/110)\u001b[K\rremote: Counting objects: 41% (46/110)\u001b[K\rremote: Counting objects: 42% (47/110)\u001b[K\rremote: Counting objects: 43% (48/110)\u001b[K\rremote: Counting objects: 44% (49/110)\u001b[K\rremote: Counting objects: 45% (50/110)\u001b[K\rremote: Counting objects: 46% (51/110)\u001b[K\rremote: Counting objects: 47% (52/110)\u001b[K\rremote: Counting objects: 48% (53/110)\u001b[K\rremote: Counting objects: 49% (54/110)\u001b[K\rremote: Counting objects: 50% (55/110)\u001b[K\rremote: Counting objects: 51% (57/110)\u001b[K\rremote: Counting objects: 52% (58/110)\u001b[K\rremote: Counting objects: 53% (59/110)\u001b[K\rremote: Counting objects: 54% (60/110)\u001b[K\rremote: Counting objects: 55% (61/110)\u001b[K\rremote: Counting objects: 56% (62/110)\u001b[K\rremote: Counting objects: 57% (63/110)\u001b[K\rremote: Counting objects: 58% (64/110)\u001b[K\rremote: Counting objects: 59% (65/110)\u001b[K\rremote: Counting objects: 60% (66/110)\u001b[K\rremote: Counting objects: 61% (68/110)\u001b[K\rremote: Counting objects: 62% (69/110)\u001b[K\rremote: Counting objects: 63% (70/110)\u001b[K\rremote: Counting objects: 64% (71/110)\u001b[K\rremote: Counting objects: 65% (72/110)\u001b[K\rremote: Counting objects: 66% (73/110)\u001b[K\rremote: Counting objects: 67% (74/110)\u001b[K\rremote: Counting objects: 68% (75/110)\u001b[K\rremote: Counting objects: 69% (76/110)\u001b[K\rremote: Counting objects: 70% (77/110)\u001b[K\rremote: Counting objects: 71% (79/110)\u001b[K\rremote: Counting objects: 72% (80/110)\u001b[K\rremote: Counting objects: 73% (81/110)\u001b[K\rremote: Counting objects: 74% (82/110)\u001b[K\rremote: Counting objects: 75% (83/110)\u001b[K\rremote: Counting objects: 76% (84/110)\u001b[K\rremote: Counting objects: 77% (85/110)\u001b[K\rremote: Counting objects: 78% (86/110)\u001b[K\rremote: Counting objects: 79% (87/110)\u001b[K\rremote: Counting objects: 80% (88/110)\u001b[K\rremote: Counting objects: 81% (90/110)\u001b[K\rremote: Counting objects: 82% (91/110)\u001b[K\rremote: Counting objects: 83% (92/110)\u001b[K\rremote: Counting objects: 84% (93/110)\u001b[K\rremote: Counting objects: 85% (94/110)\u001b[K\rremote: Counting objects: 86% (95/110)\u001b[K\rremote: Counting objects: 87% (96/110)\u001b[K\rremote: Counting objects: 88% (97/110)\u001b[K\rremote: Counting objects: 89% (98/110)\u001b[K\rremote: Counting objects: 90% (99/110)\u001b[K\rremote: Counting objects: 91% (101/110)\u001b[K\rremote: Counting objects: 92% (102/110)\u001b[K\rremote: Counting objects: 93% (103/110)\u001b[K\rremote: Counting objects: 94% (104/110)\u001b[K\rremote: Counting objects: 95% (105/110)\u001b[K\rremote: Counting objects: 96% (106/110)\u001b[K\rremote: Counting objects: 97% (107/110)\u001b[K\rremote: Counting objects: 98% (108/110)\u001b[K\rremote: Counting objects: 99% (109/110)\u001b[K\rremote: Counting objects: 100% (110/110)\u001b[K\rremote: Counting objects: 100% (110/110), done.\u001b[K\n",
"remote: Compressing objects: 100% (105/105), done.\u001b[K\n",
"remote: Total 110 (delta 50), reused 19 (delta 2), pack-reused 0\u001b[K\n",
"Receiving objects: 100% (110/110), 16.97 MiB | 23.55 MiB/s, done.\n",
"Resolving deltas: 100% (50/50), done.\n",
"/content/CSBERG-ML\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YUbz8Rj_6Pqx"
},
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"petase_activity.csv\")\n",
"X_activity = list(df['sequence'])[:212]\n",
"y_activity = np.array(list(df['relative_activity']))[:212]\n",
"y_activity = np.log(y_activity + 0.001)\n",
"y_activity = ((y_activity - np.min(y_activity)) / (np.max(y_activity) - np.min(y_activity))) - 0.5 # don't let it saturate the sigmoid\n",
"df = pd.read_csv(\"petase_stability.csv\")\n",
"X_stability = list(df['sequence'])[:159]\n",
"y_stability = np.array(list(df['stability']))[:159]\n",
"y_stability = np.log(y_stability + 0.001)\n",
"y_stability = ((y_stability - np.min(y_stability)) / (np.max(y_stability) - np.min(y_stability))) - 0.5"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sG4TFHDt7edw",
"outputId": "348ed674-ce3b-4e7e-aa62-5504c15aecb0"
},
"source": [
"# /usr/local/lib/python3.6/dist-packages/bio_embeddings/embed/prottrans_bert_bfd_embedder.py"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/bin/bash: nano: command not found\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ANzLl29KYv5e"
},
"source": [
"import nltk"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-5_bHqvj7Fvh"
},
"source": [
"from bio_embeddings.embed import ProtTransBertBFDEmbedder\n",
"\n",
"embedder = ProtTransBertBFDEmbedder()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EAVi5W4LZKFu",
"outputId": "55057c28-a9bb-4d14-9390-2e698978fbcf"
},
"source": [
"sum([param.nelement() for param in embedder._model.parameters()])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"419931136"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_isZyst00xO4",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d34f0c3a-74ab-4359-89d3-e62bb76480f1"
},
"source": [
"print(embedder._model)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"BertModel(\n",
" (embeddings): BertEmbeddings(\n",
" (word_embeddings): Embedding(30, 1024, padding_idx=0)\n",
" (position_embeddings): Embedding(40000, 1024)\n",
" (token_type_embeddings): Embedding(2, 1024)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (encoder): BertEncoder(\n",
" (layer): ModuleList(\n",
" (0): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (1): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (2): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (3): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (4): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (5): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (6): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (7): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (8): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (9): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (10): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (11): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (12): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (13): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (14): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (15): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (16): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (17): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (18): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (19): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (20): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (21): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (22): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (23): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (24): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (25): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (26): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (27): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (28): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (29): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (pooler): BertPooler(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (activation): Tanh()\n",
" )\n",
")\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "GcTguac0-jSF"
},
"source": [
"from bio_embeddings.embed import ProtTransBertBFDEmbedder\n",
"\n",
"random_embedder = ProtTransBertBFDEmbedder(random=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SD379MBz08kv",
"outputId": "dfc121c8-5b86-4bde-caf0-96436770a254"
},
"source": [
"print(random_embedder._model)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"BertModel(\n",
" (embeddings): BertEmbeddings(\n",
" (word_embeddings): Embedding(31, 1024, padding_idx=0)\n",
" (position_embeddings): Embedding(40000, 1024)\n",
" (token_type_embeddings): Embedding(2, 1024)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (encoder): BertEncoder(\n",
" (layer): ModuleList(\n",
" (0): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (1): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (2): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (3): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (4): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (5): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (6): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (7): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (8): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (9): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (10): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (11): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (12): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (13): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (14): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (15): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (16): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (17): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (18): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (19): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (20): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (21): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (22): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (23): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (24): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (25): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (26): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (27): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (28): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (29): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (pooler): BertPooler(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (activation): Tanh()\n",
" )\n",
")\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "j96FhL6RZwrw",
"outputId": "dc9f0855-2d62-4254-811a-0582bb7d886b"
},
"source": [
"sum([param.nelement() for param in random_embedder._model.parameters()])"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"419932160"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BH9xJJbA_i6x"
},
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bp1p1SWZ_pHh"
},
"source": [
"embedder._model = embedder._model.to(device)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "HN1Y_kZXta6-"
},
"source": [
"from Bio import SeqIO\n",
"from sklearn.linear_model import LinearRegression\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.manifold import TSNE\n",
"import time\n",
"import torch\n",
"import random\n",
"import numpy as np\n",
"\n",
"random.seed(2)\n",
"\n",
"def pretrained_embed(seqs):\n",
" s = time.time()\n",
" embeddings = embedder.embed_many(seqs)\n",
"\n",
" s = time.time()\n",
" protein_embeds = np.zeros((len(seqs), 1024))\n",
" for i, e in enumerate(embeddings):\n",
" protein_embeds[i] = embedder.reduce_per_protein(e)\n",
"\n",
" return protein_embeds"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425
},
"id": "LzI_46dGxYpa",
"outputId": "d3b37c7a-89be-45dc-fd6c-cfbfa1cb64f6"
},
"source": [
"snips = np.linspace(0, len(gfp_seqs), num=int(len(gfp_seqs) / 200))\n",
"for i in range(len(snips)-1):\n",
" embeds = pretrained_embed(gfp_seqs[int(snips[i]) : int(snips[i+1])])\n",
" np.save(\"pretrained_embed_gfp_{}\".format(i), embeds)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:2143: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
" FutureWarning,\n"
],
"name": "stderr"
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-23-0b0250016a9b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msnips\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgfp_seqs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgfp_seqs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msnips\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0membeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpretrained_embed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgfp_seqs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msnips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msnips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"pretrained_embed_gfp_{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membeds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-22-49ce1a72e0e1>\u001b[0m in \u001b[0;36mpretrained_embed\u001b[0;34m(seqs)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mprotein_embeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseqs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1024\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0mprotein_embeds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membedder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_per_protein\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bio_embeddings/embed/embedder_interfaces.py\u001b[0m in \u001b[0;36membed_many\u001b[0;34m(self, sequences, batch_size)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mseq\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msequences\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bio_embeddings/embed/prottrans_base_embedder.py\u001b[0m in \u001b[0;36membed\u001b[0;34m(self, sequence)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0membed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msequence\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bio_embeddings/embed/embedder_interfaces.py\u001b[0m in \u001b[0;36membed_batch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;31m# No point in having a fallback model when the normal model is CPU already\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_device\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_embed_batch_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bio_embeddings/embed/prottrans_base_embedder.py\u001b[0m in \u001b[0;36m_embed_batch_impl\u001b[0;34m(self, batch, model)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m embeddings = model(\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenized_sequences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 51\u001b[0m )\n\u001b[1;32m 52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 967\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 969\u001b[0m )\n\u001b[1;32m 970\u001b[0m \u001b[0msequence_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[0mencoder_attention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 565\u001b[0m \u001b[0mpast_key_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 567\u001b[0m )\n\u001b[1;32m 568\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0mhead_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m \u001b[0mpast_key_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself_attn_past_key_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 461\u001b[0m )\n\u001b[1;32m 462\u001b[0m \u001b[0mattention_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself_attention_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m 391\u001b[0m \u001b[0mencoder_attention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 392\u001b[0m \u001b[0mpast_key_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 393\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 394\u001b[0m )\n\u001b[1;32m 395\u001b[0m \u001b[0mattention_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0mkey_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose_for_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m \u001b[0mvalue_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose_for_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 277\u001b[0m \u001b[0mquery_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose_for_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmixed_query_layer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m 1674\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1675\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1676\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1677\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1678\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZnYyCcZbtaJS"
},
"source": [
"Petase"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 648
},
"id": "XCJaHlpz6Bj6",
"outputId": "5417a426-e44a-4eab-d56f-2d1959cea3b9"
},
"source": [
"from Bio import SeqIO\n",
"from sklearn.linear_model import LinearRegression\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.manifold import TSNE\n",
"import time\n",
"import torch\n",
"import random\n",
"\n",
"random.seed(2)\n",
"\n",
"emb = embedder\n",
"s = time.time()\n",
"embeddings = emb.embed_many(X_activity)\n",
"print(\"Embedding took: \", time.time() - s)\n",
"\n",
"# s = time.time()\n",
"# embeddings = list(embeddings)\n",
"# print(\"Listing took: \", time.time() - s)\n",
"\n",
"s = time.time()\n",
"protein_embeds = np.zeros((len(X_activity), 1024))\n",
"for i, e in enumerate(embeddings):\n",
" protein_embeds[i] = emb.reduce_per_protein(e)\n",
"print(\"Reshaping took: \", time.time() - s)\n",
"\n",
"print(protein_embeds[0].shape)\n",
"tsne = TSNE(n_components=2).fit_transform(protein_embeds)\n",
"plt.scatter(tsne[:,0], tsne[:,1], c=y_activity)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Embedding took: 4.076957702636719e-05\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:2143: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
" FutureWarning,\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Reshaping took: 987.5548851490021\n",
"(1024,)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1800x1200 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lZod5d3ckCl1"
},
"source": [
"# np.save(\"prottrans_petase_seed_1.npy\", protein_embeds)\n",
"np.save(\"prottrans_petase_seed_2.npy\", protein_embeds)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "xQJJr5GD7kpO"
},
"source": [
"protein_embeds_seed1 = np.load(\"prottrans_petase_seed_1.npy\")\n",
"protein_embeds_seed2 = np.load(\"prottrans_petase_seed_2.npy\")\n",
"random_embed_1 = np.load(\"prottrans_petase_random.npy\")\n",
"random_embed_2 = np.load(\"prottrans_petase_random_2.npy\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oUY-iShY70aW",
"outputId": "5eee4d9a-eeae-404a-811d-107646e363fd"
},
"source": [
"print(protein_embeds_seed1[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[ 0.01135602 -0.02649276 0.00570227 ... 0.04452793 0.00234546\n",
" -0.02212404]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WIHeHZY07_UW",
"outputId": "c7c7751b-23d5-4027-e4ef-ab883643dcc4"
},
"source": [
"print(protein_embeds_seed2[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[ 0.01135602 -0.02649276 0.00570227 ... 0.04452793 0.00234546\n",
" -0.02212404]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NCI9wkHX8J3u",
"outputId": "810bbcd0-302c-4008-fad8-62b3f0a4397b"
},
"source": [
"print(random_embed_1[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[ 1.26258838 0.35855049 0.65314394 ... -0.86616045 0.16429016\n",
" -0.04005701]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ad5O9qtX8jTf",
"outputId": "4bc1a62c-69ea-41ef-9644-f5c68fa2852b"
},
"source": [
"print(random_embed_2[0])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"[ 0.94399208 0.58587414 -2.30861282 ... 0.46035397 1.80598176\n",
" 2.06652999]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-Wgp5J5eIIyX",
"outputId": "465630d7-1626-4498-d113-c1767ff34938"
},
"source": [
"print(emb._model)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"BertModel(\n",
" (embeddings): BertEmbeddings(\n",
" (word_embeddings): Embedding(31, 1024, padding_idx=0)\n",
" (position_embeddings): Embedding(40000, 1024)\n",
" (token_type_embeddings): Embedding(2, 1024)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (encoder): BertEncoder(\n",
" (layer): ModuleList(\n",
" (0): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (1): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (2): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (3): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (4): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (5): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (6): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (7): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (8): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (9): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (10): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (11): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (12): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (13): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (14): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (15): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (16): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (17): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (18): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (19): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (20): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (21): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (22): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (23): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (24): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (25): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (26): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (27): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (28): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (29): BertLayer(\n",
" (attention): BertAttention(\n",
" (self): BertSelfAttention(\n",
" (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (output): BertSelfOutput(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (intermediate): BertIntermediate(\n",
" (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
" )\n",
" (output): BertOutput(\n",
" (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
" (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (pooler): BertPooler(\n",
" (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
" (activation): Tanh()\n",
" )\n",
")\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "abV72cQb2kfw"
},
"source": [
"np.save(\"prottrans_petase_random_2.npy\", protein_embeds)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6TsOgUFcv8Js"
},
"source": [
"np.save(\"prottrans_petase.npy\", protein_embeds)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4VdYOcEQ47xL"
},
"source": [
"from sklearn.linear_model import RidgeCV\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"\n",
"def train_top_model(protein_embeds, k=None):\n",
" n_train = 160\n",
" if k is None:\n",
" model = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1], cv=5)\n",
" else:\n",
" model = KNeighborsRegressor(n_neighbors=k)\n",
" model.fit(protein_embeds[:n_train], y_activity[:n_train]) # train on first 200 sequences\n",
" y_hat_train = model.predict(protein_embeds[:n_train])\n",
" y_hat_test = model.predict(protein_embeds[n_train:])\n",
" train_loss = np.mean((y_hat_train - y_activity[:n_train])**2)\n",
" test_loss = np.mean((y_hat_test - y_activity[n_train:])**2) # validate on 12\n",
" plt.scatter(y_activity[:n_train], y_hat_train, label=\"Train\")\n",
" plt.scatter(y_activity[n_train:], y_hat_test, label=\"Test\")\n",
" plt.xlabel(\"Actual relative log catalytic activity\")\n",
" plt.ylabel(\"Predicted relative log catalytic activity\")\n",
" plt.title(\"Linear Model with L2 Regularization\")\n",
" plt.legend()\n",
" plt.show()\n",
" train_corr = model.score(protein_embeds[:n_train], y_activity[:n_train])\n",
" test_corr = model.score(protein_embeds[n_train:], y_activity[n_train:])\n",
" print(train_corr)\n",
" print(test_corr)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mGYPycfp6s4i"
},
"source": [
"protein_embeds_pretrained = np.load(\"prottrans_petase.npy\")\n",
"protein_embeds_random = np.load(\"prottrans_petase_random_2.npy\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "khYcxkpbvxmz",
"outputId": "5a46de24-94e6-42a6-8d59-6994aa2b117f"
},
"source": [
"from sklearn.linear_model import RidgeCV\n",
"\n",
"train_top_model(protein_embeds_pretrained, k=1)\n",
"train_top_model(protein_embeds_random, k=1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABmsAAASGCAYAAADGn5oAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAuIwAALiMBeKU/dgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeZxcVZn/8e+TpLOQnS0hgSQsIYEEE0TCboBBWRRQWd3YFMEFGXVEYeYncXAclwHEQcANAoPIqiSKCCiG1SwsCSQQQiBhyQZk6c7ene7n98epDtW3blXXfru6P+/Xq15J3brnnlPVt+69dZ57nmPuLgAAAAAAAAAAACSjW9INAAAAAAAAAAAA6MoI1gAAAAAAAAAAACSIYA0AAAAAAAAAAECCCNYAAAAAAAAAAAAkiGANAAAAAAAAAABAggjWAAAAAAAAAAAAJIhgDQAAAAAAAAAAQIII1gAAAAAAAAAAACSIYA0AAAAAAAAAAECCCNYAAAAAAAAAAAAkiGANAAAAAAAAAABAggjWAAAAAAAAAAAAJIhgDQAAAAAAAAAAQIII1gAAAAAAAAAAACSIYA0AAAAAAAAAAECCCNYAAAAAAAAAAAAkiGANAAAAAAAAAABAggjWAAAAAAAAAAAAJIhgDQAAAAAAAAAAQIII1gAAAAAAAAAAACSIYA0AAAAAAAAAAECCCNYAAAAAAAAAAAAkiGANAAAAAAAAAABAggjWAAAAAAAAAAAAJIhgDQAAAAAAAAAAQIII1gAAAAAAAAAAACSIYA0AAAAAAAAAAECCCNYAAAAAAAAAAAAkiGANAAAAAAAAAABAggjWAADQiZjZeWbmkceopNsFoC0zGxXzXT0v6XblYmZTI+1dmnSbOgozWxr5bKYm0AaO/0Anx3GYY101mdmMyOc8I+k2AUBn1yPpBgAAAAAAkDQz6yNpP0ljJO0kaaCkzZLWSlopaY67v5dcCwEAANCZEawBAKCCUnf6LYksfszdj656Y4B2pO6YnJzl5bLst2Zmkl6VtHeWVW519/NKrQdAbTGzKZKujCw+392nVrDOPpI+IunY1GOc2sk+YWaLJd0h6VfuvqxSbWuPmXkBqzdJqpfUIOkNSc9LmiPpT+6+sQLNAwAAQBFIgwYAAIB8fNjMsgVYCjFZ2QM1QJcSk8pnStJt6grM7Cgzu1PSu5KmSbpU0gHK7/fxPpK+J2mpmf3QzHpWrqVlUydpZ0l7STpG0jcl/V7SCjP7pZntmmTjAJQHafIAoPYRrAEAAEA+TNL5ZdjOBWXYBgCU4guSzpLUt4Rt9JB0uaQ5NRzs6C/pS5IWmNkpSTcGAACgqyMNGgAAAPJ1jpl9z91biilsZgMknVbmNgFAuWyUNDP1WKkw8qaXpN0VRgUep8wbHj8g6e9mNtnd11SxrVFNkl7K8lovSYMlDcny+s6S7jWzk939oUo0DgAAAO0jWAMAQCeSyu0/NeFmoPNokbReYZJtSdpDYX6HYjvzzpa0Q9rzNZJ2LLp1QAfh7qOSbgOKtlnSHyXdImmGu2/Lst4PU6kgb5D00chr4yVdI+m8SjUyD8vdfWKuFcxsF0nHS/qWpOi6dZLuNLO9Ew46ATlxrVs9zLEJANVHGjQAAABk45LujCwrJY3ZFyLP7yhhWwBQinWSrpQ0zN0/6+5/yxGokSS5+2uSTpD0m5iXzzGzgyvQzrJx93fd/XZJB0v6RcwqgyRdUd1WAQAAoBXBGgAAAORyc+T5qWZW8GgYMxsnaVLaomZJt5XSMAAo0q2SRrn7f7r7ukIKurtLuljS3MhLJulzZWpfRaWCUpdI+nvMy581M6tykwAAACCCNQAAAMjB3WdLWpC2qJekzxSxqeiInAclrSi2XQBQLHf/h7vXl1C+WdJVMS99vPhWVVcq6PT9mJeGKszDAwAAgCpjzhoAAJA3Mxso6VBJoxXmMdmgMAHzc+6+sEJ1mqQDJO0taRdJOylMAv2upKWS5rSXuqaIOneVNDZV5yBJfRXmblkjaZmk2e6+oZx15mjLIIWUNa1t6ZZqx4Pu/kY12qAwuubqtOcXSLo+38JmVqfMO86jI3bKxsxGKMzHsEvqsUVhf1kuaaa7b65QveMl7S9pmMLcPOskLUrVWa39ZYikDyq8710V9pd3Ja1KtYO5KLq41PdxksK+urPCxPTvKuyrs1OBCLTvrwrzeqXfADnSzLq5e0tCbSrU05IaJA2ILN9f0rxiN5rEeTtV75hUvbspvKd6Sa9JmsWxL5OZ9ZG0r8L1zs4Kn9k2SWslvSdprrsvrVJbuiuct/eXNERSb4V95gV3jxsBVnPMbLjCZz1K4Rq6j8L3b42kNxW+F1sSa2CVJHWNFmkD50EAHZe78+DBgwcPHjwq9FD4QeaRx4wK1ndeTH2j8ig3NVJmaeT1iZLuldQYs/3tZSR9TVJdmd7LhxTSZK3MUacr/NC9T9KkEurqL+mzCqlx3minPlfozJgj6YuSehZZZ3uf+YmSHknVFdeG8yqw/8yIvs/U8l1i/vYTCtjupyJlVylMZr17zPuaWmTbBync6b6gnb/dZkkPSfpkmT6zOkn/JunlHHVulXSPpA+klRtVrr+ppMEKc288p9B5nK0dzan99qJivqft7bNl3hc/GtP+wwso/1JM+YcLKH9mTPn9c6y/NJ/9WNLR7eyf+TxiP3e1c/xXGDHxM4UgYrZtr5V0raSdKvW3zfEZTinXd6KKbX4nps1DqlR3XvtFHtuZF7OtS4rcVtXO22l19pD0DeU+9m+T9BdJR7TzGU7Jo74ZkTIzimx3zu9rjnJTi/27K6TqO0LSjyXNUuigbu9481bquDGiyPd5dMw2j057fYTCzR9rstSf8fkW+tllWb/kRx7vfWdJF0q6K4/vhCtcKzwm6QxJ3Qr4jJeW4f0cnWXb5drfq3KN1t6+oQ5+HuTBgwcPdycNGgAAyM6CqyQ9I+k0hY7pbEZK+l9Js1IjU4qtc6SZ3afQqfx5hTssc+mvEAyYZWb3pUaiFFLfTxQ63G6XdI5Cx0F7uit0Sv1a0mtmdlQhdbbTnn5mdq9Cx9JxqboS5e7vSvpzZPEXCthENAXa7e7eVFqr3mdml0h6XdJ/KNwlmUtvhUDAH8zsaTM7oIR6D1QIkPxU4W7ZbHpKOl3Ss2Z2ebH1xdTf3cy+q/Dep0g6UKEzLptuCvvtTZJeMbPJ5WpLBTyp0HGV7rh8CprZMEn7xbx0pJn1yrP+aF3L3f2lPMt2OGZ2mkJA8VKFO7qzGSTpXxX2j0Or0bYat0PMsorfFV5mcaP+oiNtcqr2eTut3omSnpd0jXIf+7sr3ADxhJldlxrF0aWkjvdvKBxbL1MYVZBPppXdFY4br5nZVWZWtj4kM/uCwnHpqwo3HXQaZnaHQqrXXykE/9v7TkjhWuHDku6WND8111/NS+oaLaYdnAcB1ASCNQAAIFbqB/n/Kfy4KqRj40BJj5tZvyLqPFTSbIVOnGJ8StJMM9ungDKTFH4cFmt3SX83s8+XsA1Jkpn1VZjw+bRSt1UB0bRlnzGznu0VMrPdJJ3QzraKkgpW/FLSz1VcR89hkp40s2OLqPtgSf+QNL6AYj0k/dDMflRofTH195c0XdJ/K3QsFGpPSY+YWTSQ1iG4+yZJ/4wszitYk2O9Pgp3lRezjZpNw2NmFyuM7CpkP9lJ0t9SneGIYWZDFVJkptvk7g1JtKcEcfvF+nwLJ3TelplNUuHHYJP0dUn/l0rV1pXsKWmPEsr3ULgenGZmJafTN7NvS/qN4gOencHhKm3agf0Uvhf5nvc6nCSv0WLawnkQQM1gzhoAAJDNfymkBmv1lqQHJL2okMu8n8KPydMUOgHSjZH0I4W0aHkxs6MVJp2PBk5aJD2hkFt/iULqgj4KQZLJkv5FbYNJYyT9xcw+VESnmSu8vxcV7r57VyFdS7PCncB7Kcwfc4zajjKqk/RrM5vv7s8XWGe6XykEj1ot1/uf+TsKdzuPlHRKCXUU60GFu0R3Sz3fSdKpCj9+czlXbf8+s919QZna9CtljtqRQv7zvyrsNysU9pdRCu2NTpw9QNKDZnasuz+VT6VmtpekhxV/Z+YLkqYppCXZrDB/zdEKd4q2Bre+o7BvFcXMdlBITfLBmJdfU+jAfEEhrcw2hVQsB0s6SSGlXas6Sb8xs1Xu/kCx7amgvyl8dq0ONbO+7r6xnXK5Orc+IunRXIVTf9/oMe1v7dSZrw1qOxfIhMjrqxTS5eSyvID6TlRIMdTaMV2vsO8+rXBM6abw3fi4pEMiZftKusXMDvYKzC/SCZwes2xO1VtRgtScJXFBktfyLH+0Ejhvm9koZT8GL1AIZL+uEHQaIukgSSfr/Q7jTyuMiuzKVkp6VuFaZ6nCtc4mhe/9rgpz/5yQ+n+6j0v6ocLonGJ9VNJ3055vVThvzUi1a5vCvnKIwrVmqdaohDmYJPVS7tGz7WlW2N8WSFooabXC520K1yCjFeaDPEJtb6juJ+lOMzvQ3d/Ksf2XFL5jUhgdnh4YaUq93p5KzKuXyDVaDM6DAGpL0nnYePDgwYMHj878UO3OWbNV78990aCQc7t7lrI9FQIz0Xq3SRqeZ7uHKj6f981qJ0+6wgTGf40pe0+edT+qkB/7M5J2zrPMzgp3CkbnB3mxgL9V9DNPn5tmk0KahqzzikjqXYH9Z0a0TZHXo3/nB/PY5iuRMhelvVb0nDWSzoop65LulzQsR7mPS3o7ptwSSQPzqNdS+0y0/HJJJ+coN1JhDqL0v3F0G+cVue+4QifQRyRZjnJ9FDrIovMPrZG0exH1Li33Phip75CY93lSHuWWpa0f/Y7OyaP8hTH1Zt2nUmWWFrkfR+uZUsLndV7M9janfQ7/I2lQjvJnpq2f/ji7kn/ntPqnFPudqPYjdRyIm+vlG1VsQ7TupUVsI25upm2SdsmjbCLn7dRn/7eYsiuUY54LhY7vn6WtH3cMnpJH/TMiZWYU+feL+76OyqPc1GL+7qn6XpP070qbP62dMt0VbtpZEamzRdLBeW7j6Cz7WOv/75W0R47yGdc6xX52Rf6duinclBKtb1Y75RYpzMv0SeVxbZEqM1LSHTF1/bmA9ha1f5R7f1dy12hx+0bNnAd58ODBw505awAAQLyeCh0iayQd5e6/dvfmuBXdvdHdv6swf0u67pLOz7O+m9U2n3ezpM+6+wXu/maugu7+msJdc7dEXjo9lSalPZ909+Pd/Q53fy+fxrr7e+7+dWW+v/Fm9tF8thGj9S7jjZJOcPfrPMe8Lu6+pch6ShFNX/ZRMxuebeXUXD77pi3aLOn3pTYilWLvxpiXfq3w98w68sDd/yzpKIUO/XSjJP0gj+o/rzCyKt1KSR929z/lqPcNhbuU/5Ba1CePujKY2ZkKo5XS3S/pQHd/xN09Rxs2u/uPFDpD0vetwZKuKKY9FfaM3r9buFXOlDBmtr/CaKZWcxRGBbb6oJnt2E690TpezrVPdXC9FTqaznX3f3P36Oe5nbvfrfi5qL5YqcbVsPOUeQf4Zkm/q35TipM6jn4/5qWHPMxT1p6kzttnKozMSbdS0mR3/2OOOje4+79K+lZqUVHH4Bp2n6R93P2/3P2FfAq4e7O7/04hcJ7+NzVJ3yihLa3XOz9399M9x6iRhK510l2nzFF0ryqcR3M52N1Pc/c/unt9PhW5+xvu/hmFwHW6k8xsTF6t7QASvkaLw3kQQE0hWAMAAHI5393zTR3xXYXUBumOb69Qau6PEyOLL3f3O/KsV6kO6osU0nlE29Re2bx+RGcpe6vCXaHpSv1B9113f7zEbVSEuy+SlJ6GopsyAwfpoukv7vPyzOdwnjLzn8+RdHGuYEUrd18i6QyFH+/pzjez9vKqXxqz7HPuvjiPepslfU7hDtGCpeZY+F5k8TxJZ7p7Y77bcfeHldlJe76ZRdPdJCr1ec2ILP5IO8WigZZH1DaFWTdJWfPfpz7j6OvlSoGWlGvd/f/yWTF13J0dWTzZzEqZ16tTMbORkq6Neelqd3+n2u0pRuq7Pl2ZqZ1alHmMiSuf5Hk7Lr3q+anzUz71XqP3g+Zdhruvz+f8mKXsm5K+Ell8hpnlmqS9PbMlfbOE8hVnZlcoc39bJen49gKapVxbSvpPtU2paIoPIHRU5ym5a7RsOA8CqBkEawAAQDaPufv0fFd29zWS/hJZPNHM2rve+E7k+WJJ1+Rbb1r9TQp51NOdaGa9Ct1WgW6LPM93AvM4r0n6RQnlqyE6uua8uJVSd1ae0U7ZYsV11n3V3Vvy3YC7/1PSrZHFfZVjNFiqgzI6T8wf3T3vyefdfbOkf8t3/YiPSRoXWXZprhFYOVyjtpOI91Zm52tHEP1sx5vZkNg1g2iw5m/KDLbkGp0zUSHNYa421JIGxY+eyOX2yPMeyhxF0iWlzif3KnOulFeUef7pMMysp5kNMbPjzOxqhXkzoiMEJek/3P3ZPDaZyHnbzPaTdGRk8UPu/tcCq/6GQmAK+XtQYb7CVj2UOb9HIb6dbcR2R2Bm5yrM3ZhuvaQTU8GEikkFNKKBheh+35Elco2WA+dBADWFYA0AAMgmmtYsH9E70fpJypUmq7cyU0lMLeEHfDRY1Fth0tZKejXyfJiZjShyW7cUe+drFd2tthPRjjazD8esd5bCD+tWrytzlETBzGwPhcmo0z3j7sVM7H1DzLJcIzfighnFfE+mqf1J5ONkpGJx98eK2E5r0OgfkcVHF7OtCosb1RIbbDGzHgqTl7fapDCBcOv8FjnLZ3mtWZmfUy25u4jRbNHjuJT5netyUqOupkr6UOSlrQrpvzZXvVFtjTQzj3uk2rhSYaTZN5V513uzQuf5f7dXScLn7bhRcb8ttMLUSJFHCi3XlaU62l+LLC72+urVjjqCWJLM7ERJv4ksbpL0KXd/vkrNiF5bftDM6qpUd9ESvkbLhvMggJrSI+kGAACADquYTuDoD3kp3IGcLR/5IZKid9A+FbdiPtx9jZnVq+1dzweqgPeSuqP3SEkTJI2XtIukAQqBp+4xRXrGLBuhtvnd89XhO4XdfYOZ3aO2dzeeLyna8RJN2TG1TIGouJFL0VR0eXH3OWa2RNKeaYsPMzPL0tZox9QGSQ8XUW+zmU2X9KUCi06OPH+60LojoncHH1ji9srO3Rea2duSdk9bfJzi5waZpPBdbfV4Kj3cO2b2ot6/K3ZvMxvl7ktjthGdC2NOmVL3JaWcx/Gu7seSzo5Zfkmeo1E6oq0KwePvu/tLeZZJ8rwdPQa7pEJH1bT6s/JI1dqZmdl4SQcrHBtHKhw/+yvz79tqn8jzYm9MmVFkuYpLzZl0j9r2lbmk89y96JSYqdHGH1b4rPeXtJPC591X8TdR94s876UwR9TbxbahSpK8RsuG8yCAmkKwBgAAxNni7sX8IIzL0Z3rx03cj7obzCzv+Tdi7BB5Hk1pFMvM9lHIlX+6Sv9BNqiIMi5pbon1VsvNahusOcPMLnH3DZJkZmMlHZb2eovCHenlEE1DJoWJ6Iv1jNp2BAyUtLdCWp+ogyLP55VwN3lBnbtmNkxhgt10J5hZKfvM0MjzvL4rCfi72s6NFA2otIpLgZb+//QUJh9RZFSUmfVUmNg42zZqUfTu7HwUehzv9FJzV3w75qWr3L2Y0XUdxUuSbiggUCMle94+IPL8VXdfH7tm+6o1QqJDSd2QconCOXz/EjdXzLWOJD1XYr0VYWajFYJ4fSMvfbuQ+Zgi2zxI4dhxiqQ+pbVQg9TxgzVJXqNlw3kQQE0hWAMAAOKsKbJc3NwZudI27B6zbL8i685mp/ZWMLPvSbpC2e8mLVQxP+g2uPumMtVfUe7+pJktkrRvalFfhbRnreloLogU+Zu7ZxtdVai4TrzoBNWFiOuk3FmRjoBUCqRo3a+UUO/CAteP+64MST3Kpd3vSkL+prbBmj3MbIy7Rz//9oI16ZNZZwRrJB2uzE7jWg/WFHwsd/emsLu30eHT71SKmX1dmXNXSGHC6u9Vuz05NCn+eNZD4Zw0XGGi8nQHSvqHmV3m7v+TZz1Jnrejy5eWUEdF5x3piMzsCIV5QPYu0yaL7bx+p0z1l01qLrSHFEZTp7vW3a8uYnt1kq6V9GWVb/qBWggWJHKN1g7OgwBqCnPWAACAOMVMWF6ManQO57yT0cx+oTDxaLkCNVJxP+hqLdXSLZHnF0jb5w35fOS1m8tYb3SuBUlaV8L21sYs2zFm2QBlXjvH3XmZr0LLVuO70rsKdRQjLmDSJm+9mfVV2xRJ70h6Ie3545LS7/w/1jJ7YqLBno2S/llYUzucah3LOyUzu1jSdTEv3eju34xZnqTl7j4x5jHe3fdQOIadLmlWpJxJ+qmZfSXPepI8b0eP/9U8Btc0MztGIW1nuQI1UvGd1x3qesfM+kt6UG1HcEjSnZK+VcT26hRSqX1V5e1zq4VgQVLXaLlwHgRQUwjWAACAJMX9qKsaM/ucpLgOqjUKo0QuUEiLNEqhrX3c3dIfyvxxX6xtZdpOtdyqMCl1q8PNbF9JJ6lteq01ku4vY739I8+9xBFJG/OoI9uyuLKl1JtLot+VJLn7SkkLIoujgZXJatuR9ff0nPbuvlHSzLTXd1LmHD3RbT6RmvMGXZCZXaD4Ca5/q9AJW1PcfYO736eQojJupMDPUvN1tCfJY1H0popSvp9bS2lILTGzwZLuUubIwRaFAM7lkk6QNE5h1EI/Sd1jrneKmfsjToe53kmlv/yDMs8Hf5d0bpFz7X1H0qkxy5cpHFM+p/A93EMhtVnvmM/6mCLq7QiSukYDgE6DNGgAACBJm2OW7efuhaaIKljqzsefxLz0I0n/6e5xbYtTag7ymuTuK8zsr5I+lrb4AmWmw7nD3cvZKRadn8DMbIcSOgOiuenj6si2LK5sKfXmErc//tjdv1tCG2rJ3xU6ElsdbWbd0+YMypUCrdUjChM8t/qIUnMnmNlASR+KqRNdkJmdo5AmLzr66lZJXyqyA7dDSLX938xsV7UdBVkn6VYzm9BOkDKx87bCaJj0kT2ldNoOKLEtteQKZab3ekbSZ9y9kPk8OtX1Tmp05VRlnj/mSvpUMcH61Pfq8sjibQrz1lzv7vkGqmr1s07qGg0AOg1G1gAAgCS9F7Os0PQGxZosabfIsv9198sLCNRI1WtvRxSXCu2kdtYpVVxKjGInOc5WNi6/eYPCXcjpSskfX2jZJL8rHUE0+DJQ0sFpz6OdbY/ksY30MkdL6t7O+ugCzOwzCset6G/l30m6wN2jx4Fa9RVJb0SWjZX0jXbKJXksih7/S0nJlvQcXdVMaXV25Plbko4rMFAjdb5zzv9I+nRk2VJJJ7l7sanaTlHmCKbvuPvPCgjUSLX7WSd1jQYAnQbBGgAAkKRVMctGVqnuj0SeNyt+Eun27FWGttSq6WrbcbeL2o7cnufuz5W5zndjlpUyufX+McsyOiNTd6NHl48pod6xBa6f5HelI5ihzNQ5x0nb72Qen7Z8kbu/FbONOWo7T8WRZtY6T0802POupHlFtxY1yczOlnSbMn8n36mQEqmzBGrk7hskXRrz0uVmlqujOMljUfR7PS52rfx8oMhy0fkvig26VCVYZGb7Sdo9svjn7l7QnD2p0cjR7dQsM/uWpOi8U+9JOt7dV5Sw6ei15VpJ/1vEdmr12jKRazQA6EwI1gAAgCRFJzqW2qYpqqQ9Is9fdfe4Tqj2HFaOxtQid2+SdHuOVW6uQLVxwZ9o+qpCHBx5vk7Sa1nWfTbyfIKZRUdj5OugAtdfrMy7SQ8vof6a4u7rJc2OLG7tFDtObdNVxY6ISaVMm5G2qLekI9O2ke7RWk51hcKZ2RkKx7Pod+oeSZ9LS7nXabj7NElPRhYPlHRZjmJJnrejx4BBqWBEMQ4tslx0xEWx6dT2KbJcoaLXOpL0RBHbOVDhmFnzUqPnfhpZvEnSx919UYmbj37es1LXSoWq1WvLJK/RAKBTIFgDAACS9A9l3i3/8dQdnJW2c+R5wWkVUu38RHmaU7OyBWQaFdIGldvTMctOL2ZDZnaQpD0ji2fm6KSfGXneT5l30eZTb3eFVCl5S93RH51DpZ+kjxZafw2LBmEONbO+yi8FWrZtHGdmw5U50qlaKdCiAYAuEXzraMzsU5LuUObn/weFeT06XaAmzX/ELPuamUXnOGmV5Hn7nzHLPlPoRsysmzJTg+VrXeT5qNTcJ4WaXGT9hYpe60jFpZE6q9SGdARmdpzCPDXpf7Ntks5097hAZKHKcW25s6Rjiqw/+t2s9jklyWs0AOgUCNYAAIDEpHKCz4gs3l1tJz2ulI2R53EdGu35jDLnvelS3P1FhYmKo6a5++oK1PeWpJcjiz+U+lFfqK/ELHs4x/oPxiy7sIh6T5Y0tIhy02KWRScy7syiAZSeCnf0/0vasmaFzuR8t3FcpHy29SolOlFyvyrVixQzO0UhzVmPyEv3Szq7wHkmao67Pybp0cjivpK+k2X9JM/bDytzTowLzazQOcA+reJTer0Sed5P8amasjKzY1S9kTXRax2pwOsdMxukMCddTTOzAxUCsNHA4sXu/kCZqinHteVXVfwopkTPKQlfowFAp0CwBgAAJO0HMcv+x8wqna87mpN8XzMblW9hMxuiMDktpGMVglbpj/MqWN8vYpZdX8jdzWY2SZlt3KgwsXgsd5+jzBQfn0p1vOVbb28Vv9/cpZAOLd1RZtbehOCdxUxldoR9RdKItOfP5JqLwd0XSno7bdGByrxj/DV3X1pCOwsR7Xiu1XkKapKZnaSQ5izaefsnhTvti0lfVIuujFn25dR5Lk4i52133yzp1sjiIZKuzncbZja0kPVjRNNhSgWM7kmNQPpRCfUXKm7+lUJHZF6v0iaJT5yZ7alww0X/yEv/z91/W8aqop/34akRoHkxs3Eq7SaM6DllkJkNLmF7xUjkGg0AOguCNQAAIFGpu3qjaYsGS/prsbnozay3mV1kZtHJY9PF5Wz/cZ7b30nSn1XcHZOdjruvd/eVkcemClZ5qzJTixyqPCfxNbORku5V5rXwb909muIm6rqYZbfn00mZSn92m6S982lnVOoO/7hO1Z+Y2e9j4ZkAACAASURBVEXFbDPVrsPN7I5iy1dLquP88cjij0ee50qB1ip91Ew3SSdFXo+mm6ukFyPPJxfSsYfimdlHFO6y7xl56QFJp3ehQI3c/Ullfnd2UJZO4wTP25J0jaQNkWVfMLMfp9Kb5apjuML3O1sQKh9PS4oGhL+eOq/klGrf9ZImlVB/oZ5X5ud1qZnlNbLIzL4n6bNlb1UVpVL6PaTMv/tN7h4XeCxF9Nqyn+LP2xlSNwxNl9SrhPqj5xQp8xxXaUleowFAzSNYAwAAOoLzJC2PLBstabaZXZ5PihMLDjezayUtlXSTct+l/ldlpos408x+k6uz1Mw+qpA3v3XC1Ohkw6gwd98g6csxL33VzO7JcTd46530TypzEuClkr6XR/X/p8wUQMMkPW5mH8tR7wiFTuAzUos251FXBne/Q5l3lvaQdJOZ3WdmH8hnO2a2u5ldamYzJT2lAufQSVB76cnySV9Wjm2USzS//0BJd5UwaTryYGaTFdIKRjtFH5R0mrs3Vr9ViYs7/l1kZsOyrH+eqn/ebk2zdFnMS5el6v6Ume0QqWeUmV2hkJ6pNWVZ3Pw37UqN7okGt/tJ+puZZU2HZmb7KIzY+lJq0ZZi6i9UKuh4f2TxYEl/z3W+MLNhqSD+99MW1+r1zgMK+2a6PyqkGyu3+yS1RJZ928yuMrNoqsXtzOzTCvtk6/5f7Gc9M6b+q83s1CrNK5X0NRoA1LysJwsAAFAxHzKzuWXYzu/c/adl2E7i3H25mZ2q0AmeHijpJ+mHkv7dzJ5U6NhcoZDmoY9CWo5hkj4o6SBJOxVQ59pUB1H0x98XJH3CzO5RSHm1NlXPXgp38R+Qtm6zpEtFWoaqc/e7zex4ZebRP13Sx8zsQYU7XFcq5H7fUyEgMTFmc02SPpcrfVZavW5mX1BIhZOeFma4pD+nvtvTJS2RtFUhJdxkScerbcfw9yQV+/29WGG+g6Miyz+lkJZtnqTHJL0qabVCx80ghe/HeIXvymi1nWC5VuQKpGxUfh2wubbRosz5OyrpNoWUUum/yz6msA+vlbRKYT9Kt9zdq32ndFL+08z+tQzbOcvd0+ca+Y3COSRqT0mzipsvfrsvunvcPF4dmrvPTB03T0xb3FvSFZK+FrN+1c/baW6SdLikz0WWH6TQWd5kZqsUbsjYNaaOdQrBpuj8M8151v+jVN3pKbX2kTTPzP6k8Jm0nnuGK0wWf4ze/56/pzBK86o86yvVVQrpHtM76/eV9LyZ/VXhmPd2qn27STpaYT6v9HPWzQqjQidXob3ldnDMsv0kPVfKd93dM64n3H2Rmd0u6ZzIS/8h6Twzu1fSCwqjnXaUNEbh2iR9xO0mhTmjbiyiTStSf9P0c8QQhYBdo5m9pXCu9EjRsh63krpGA4DOgGANAADV11fShDJsZ0YZttFhuPszZnaoQkfLvpGX+yp0dh9f5mp/oNAp8eHI8p0UOsRzcYX5MmaUuU3I35cUOtcujCzvo1TgIo9tNEj6lLs/lW+l7v56aoTVIwojIdJNVHxnQ7qfKKT4KCpY4+6Nqfp/ofhJnyeoPMeYjuhFSe8odMBGPZ7PiAh3X2Vm8xUCV1Fz3X11iW3MW6pj7QeSpsS8PDj1iKrpuSMKtIcy77AuRjQwk+0O87FlqKuqE3qX2ZVqG6yRpC+a2Y9TI1raSOi83Ro0P1fh+H9uzCp1krKl+Von6WRJr8e8ltdoBnd/08wukTQ18lIPSZ9MPbLZoHDjR9VGz6UCCJcoBLnStaaBbC/4+6jC9c5DFWheUsrxXc/m6wqp7qJ17C6pveBzk8II3FJSyX5bIagWHSXeU9nTsFbiuJXINRoA1DrSoAEAgA7D3ecr3AF5vUpPETJHIfVFrvqaFO7k+3OB216nMPn0r4psG8rA3Zvd/UsKo5uik+rmY6akI9294DlK3H2OpGMlLSigWLPCZMbfKbS+mPq3uPsXFO7ujut0LMQ7km4otU3V4O6u7HPKFJK+LNu61UyB1uoqSf8uqSum3kIHkjquRc+HvRT2z2xlqnreTqu3xd3Pk/QZhbvz8/EPSZNSc/TEBULzvnPf3W9VCJZvy7eMwrH6SHefVUCZsnD3XyoEXAo9ztws6SR3j47wQxapESDHKVxjFGK5pOPc/S8l1v+SpI9IWlzKdkqV5DUaANQygjUAAKBDcfcGd79E0iiFkS/PKzP/dpzNCp24l0va390nufuDedRXrxCw+axCaopc3lEYDTHG3e/No02oAnf/ucLdoj+QtLCd1bcojIg5zd0Pc/e4yXjzrfc5SQcqzJWwKMeqTQr58Q8u92TG7v47hTvaP6sw30Z0HqZsXpL0c4U7qoe7e9wcEB1VOQIt0cnRi9lGWaQ6nX+okC7pa5LukjRfIVVSVea1ANLETYZ+QWry81jVPm9H6v69wvH/LIXvzksKHcPbFCY5f0Yh5djh7n6su7+aKjo0ZnPRSdHbq/sWSeMk3aPMdIXpliukwTrA3ecVUkc5ufuNCunn7lI4L2XTqDC/zofd/QsEagrn7ssURm1/Te3fUPGGpP8naay7P16m+v+pMLLnJIWbMZ5U2A83KL/vZtkkdY0GALXKws1pAAAAHZeZDZb0Ib2fe36AQoqI9Qq58F+R9Lq755tvPlddIyQdppDje4DCD8flCiMoXnAunjo8MxupkIpsF0k7K3Q8vaPwd5zp7qWkF8lV7wEKk1cPU0jzUa8wb8xMd6/KxMypCYwPlDRS4buyo0LHzHqFDsxXJS1MTQAMABVRzfN2ke27UFJ0dOze7l7USEUz20HSEQrH3p0VUqWukjRPIb1ih7p2MLO+CvP+7KVwnnCFYNWrkuZwjigvMxujMAJtF4X0ZBsV5gl6ITKfVqeX1DUaANQKgjUAAAAAAKDLMLP7JZ2atug9d98lqfYAAABIpEEDAAAAAABdhJmNlXRyZPETSbQFAAAgHcEaAAAAAADQ6ZlZL0m/U2ZfSDQlGgAAQNURrAEAAAAAADXDzL5mZpMKLDNM0t8lfTDy0quSHipX2wAAAIpFsAYAAAAAANSSj0uaZWbPm9mVZna4mfWPrmRmvc3sKDP7uaRFko6IrNIi6YvOZL4AAKADMK5JAAAAAABArTCzv0o6PrLYJb0raZ2kZkmDJO0iqUeOTf0/d/9BRRoJAABQoFwXLQAAAAAAALXAJO2aerSnSdLX3f2myjYJAAAgf6RBAwAAAAAAteRvklYWUa5F0r2SDiZQAwAAOhrSoAEAAAAAgJpiZt0kfUjSkal/95K0h6QBkvpI2ippTerxoqTHJT3i7ksSaTAAAEA7CNYAAAAAAAAAAAAkiDRoAAAAAAAAAAAACSJYAwAAAAAAAAAAkCCCNQAAAAAAAAAAAAkiWAMAAAAAAAAAAJAggjUAAAAAAAAAAAAJIlgDAAAAAAAAAACQIII1AAAAAAAAAAAACSJYAwAAAAAAAAAAkCCCNQAAAAAAAAAAAAkiWAMAAAAAAAAAAJAggjUAAAAAAAAAAAAJ6pF0A4DOzswGSpqctugtSY0JNQcAAAAAAAAAEK+npD3Snj/m7vXVqJhgDVB5kyVNS7oRAAAAAAAAAICCnCppejUqIg0aAAAAAAAAAABAggjWAAAAAAAAAAAAJIg0aEDlvZX+5P7779c+++yTVFsAAAAAAAAAADEWL16sT3ziE+mL3sq2brkRrAEqrzH9yT777KNx48Yl1RYAAAAAAAAAQH4a21+lPEiDBgAAAAAAAAAAkCCCNQAAAAAAAAAAAAkiWAMAAAAAAAAAAJAggjUAAAAAAAAAAAAJIlgDAAAAAAAAAACQIII1AAAAAAAAAAAACSJYAwAAAAAAAAAAkCCCNQAAAAAAAAAAAAkiWAMAAAAAAAAAAJAggjUAAAAAAAAAAAAJIlgDAAAAAAAAAACQIII1AAAAAAAAAAAACSJYAwAAAAAAAAAAkCCCNQAAAAAAAAAAAAnqkXQDAJSfu6ulpUXunnRTAORgZurWrZvMLOmmAAAAAAAAIEEEa4BOwN21ZcsWrV+/XuvXr1djY2PSTQJQgO7du6tv377q37+/+vbtq+7duyfdJAAAAAAAAFQRwRqgxm3atEnLly9XU1NT0k0BUKTm5mY1NDSooaFBktS/f3/ttttuBG0AAAAAAAC6COasAWrYpk2b9OabbxKoATqZ9evX64033uC7DQAAAAAA0EUQrAFqVGughnlpgM5p69atWrp0qbZu3Zp0UwAAAAAAAFBhpEEDapC7a/ny5RmBmrq6Og0YMED9+vVTXV0dk5YDHZy7q7m5WZs3b9b69eu1adOmNt/rbdu2adWqVRoxYkSCrQQAAAAAAEClEawBatCWLVsy0iP1799fw4cPJ0AD1Ji6ujr17t1bgwcPVmNjo9566y01NjZuf33jxo1qampSXV1dgq0EAAAAAABAJZEGDahB69evb/O8rq6OQA3QCfTs2VMjR45Ut25tT8/19fUJtQgAAAAAAADVQLAGqEHRYM2AAQMI1ACdRI8ePTRgwIA2ywjWAAAAAADQ+bm71m9p0pqNjVq/pYm5qrsY0qABNcbd26RIkqR+/fol1BoAlTBgwACtW7du+/OmpnCBRlAWAAAAAIDOZeHKBk2fu1zz3l6n+csaVL/5/akPBvap0/jhAzRh90E6deJwjRnaP8GWotII1gA1pqWlJWMZc1kAnUv0O+3uBGsAAAAAAOhEHl24SjfNeF2zl67Juk795iY9tXi1nlq8WjfMeE2TRu2oLx+9t44Zu2sVW4pqIVgD1Ji44Y904AKdS3TOGikEauOWAwAAAACA2rF2Y6OunL5A0+ctL7js7KVrNHvqGp06cZimnDxOg/v2rEALkRR6fQAAAAAAAAAAqLCXVzTohOseLypQk27a3OU64brHtXBlQ5laho6AYA0AAAAAAAAAABX08ooGnf2rmVrVsLUs21vVsFVn/XImAZtOhGANAAAAAAAAAAAVsnZjo867ZbbqNzeVdbv1m5t07s2ztXZjY1m3i2QQrAEAAAAAAAAAoEKunL6gbCNqolY1bNWUPy2oyLZRXQRrAAAAAAAAAACogEcXrip5jpr2TJu7XI8uXFXROlB5BGsAAAAAAAAAAKiAm2a8Xp16HqtOPagcgjUAAAAAAAAAAJTZwpUNmr10TVXqmr1kjV5Zub4qdaEyCNYAAAAAAAAAAFBm0+dWNv1ZRn3zllW1PpQXwRoAAAAAAAAAAMps3tvrqlvfW/VVrQ/lRbAGANCpzJgxQ2a2/TFlypSkmwQAAAAAALoYd9f8ZQ1VrfPFZfVy96rWifIhWAMAAAAAAAAAQBlt2LpN9Zubqlpn/eYmbWxsrmqdKB+CNQDQiSxdurTNqJJKPRitAgAAAAAAkF1TczIjXBq3tSRSL0pHsAYAAAAAAAAAgDKq626J1NuzB13+tYq/HAAAAAAAAAAAZdSvVw8N7FNX1ToH9qlT357dq1onyqdH0g0AAJTP0KFD9cgjj+S17sMPP6yf/vSn259/4AMf0NVXX51X2b322quo9lXD0UcfzWR6AAAAAAAgUWam8cMH6KnFq6tW5wHDB8osmRE9KB3BGgAdlrtrw9Ztamp21XU39evVgxNOO3r37q3jjjsur3XffvvtNs8HDx6cd1kAAAAAAADkNmH3QVUN1kzYY2DV6kL5EawB0KEsXNmg6XOXa97b6zR/WYPqNzdtf21gnzqNHz5AE3YfpFMnDteYof0TbCkAAAAAAACQ3SkTh+mGGa9Vr74Jw6tWF8qPYA2ADuHRhat004zXNXvpmqzr1G9u0lOLV+upxat1w4zXNGnUjvry0XvrmLG7VrGlAAAAAAAAQPvGDh2gSaN2zNnfVS6T9tyRG5trHMEaAIlau7FRV05foOnzlhdcdvbSNZo9dY1OnThMU04ep8F9e1aghcjXsmXL9Oyzz2r58uVavXq1Bg0apE9+8pMaNmxY1jLr1q3T/Pnz9corr2jt2rVqbGzUoEGDtOuuu+rggw/WyJEjq/gO4r3zzjt64okntGTJEjU1NWnnnXfW/vvvr0MPPVTduzNpHwAAAACgk3KXtq6Xmpuk7nVSr/5SrvT0ha6fpBxtbZOWv5vUzzbLmrdlf0/tvO+Lj95Ls6dmC9a4+mmz6rRNTeqhDeqtftqS9ryPpPw+wy9P3ruIDwIdCcEaAIl5eUWDzrtltlY1bC1pO9PmLtfM11fr1gsmaezQAWVqHaLS5wuaPHmyZsyYIUl64IEHdM0112jGjBlqaWlpU2b48OH6xCc+0WbZ888/rzvvvFMPP/yw5s2bJ3fPWudee+2lSy+9VBdeeKH69OmTVztnzJihY445ZvvzK6+8UlOmTMm6/qhRo/TGG29IkkaOHKmlS5dKkhYtWqTvfve7mjZtWsb7kqSddtpJV1xxhS655BLV1dXl1TYAAAAAADq0VQukF++Vlj0rrZgnbVn3/mu9B0m7TZCGHyQdcIY0ZP/C109SjrY29xqot3rtq3m+l2as30P7bFukCfaaxndbKrONbdbrPmyiNGiEJJPWvdHu+z527P46ZcKw7Tcqj7E3dUr3p7dvf1Da9ltc6pYWm1nnfTW/ZZTm+d6a1nyEFvkesW/t1InDyDzTCRCsAZCIl1c06OxfzWwzJ00pVjVs1Vm/nKm7LjqUgE2VuLu+/vWv6/rrr8+7zPXXX69LLrkk7/Vff/11XXrppfrlL3+padOmaZ999immqQW79957dc4552jz5s1Z11m9erW+9a1v6dFHH9W9996r3r17V6VtAAAAAACU3aKHpCd/Jr35dPZ1tqyTljwWHk9eI/UaIG1tyH/9EYdLR35D2vej5W9/Lnm8t+5b6zVq6xyN0hydKmXtNe++tT68n1xi3vd/Hfw19Xhtvs5q/IMO6bYwa9FukUE0g2yjjuy+QEdqgb7aY7pmtYzVjdtO1oyWA7evM2RAL005eVzuNqEmEKwBUHVrNzbqvFtmly1Q06p+c5POvXm2/nrph0mJVgVXXHFFm0BNnz59NHLkSPXt21fLli3TypUrM8ps2bIlY1n//v01bNgwDRw4UNu2bdO7776rt956q806L730ko466ijNmzdPu+5a2TtFHnjgAZ199tlqbm6WJNXV1WnPPffUoEGD9M4772wfeZO+/mWXXaaf//znFW0XAAAAAABlt2mN9JdvS/PvLbxsrkBNnDeflu54OoyyOfEn0g47Fl5nIUp5b+X05tPq/+bTukaSupW2qUO6LdQhPRfq/ubDNaXpXHmfHXXrBZPoB+skStw9AKBwV05fUHLqs2xWNWzVlD8tqMi28b6XXnpJP/nJTyRJo0eP1j333KM1a9bo5Zdf1jPPPKMVK1boxRdf1Ac+8IGMsr169dKZZ56p2267TW+88YYaGhq0cOFCzZo1S88++6zefPNNvffee7rxxhvbzHezcuVKXXjhhRV9X/X19fr85z+v5uZm7b777rr55pu1evVqvfLKK5o1a5aWLFmiRYsW6WMf+1ibcr/4xS+0YAH7HQAAAACghqycL914ePWDGS/eE+pdVcHf0Um9tyr5RPen9XDvy3X/6QPJMNOJEKwBUFWPLly1PUdnpUybu1yPLlxV0Tq6unfffVctLS066qij9Nxzz+n000/PSAM2fvx47bXXXm2WnXjiiXrzzTd111136fOf/7xGjBgRu/2ddtpJF198sebPn6+DDjpo+/Lp06frpZdeKv8bSlm3bp3Wrl2rD37wg3ruued0/vnnq3///m3WGT16tKZNm6YTTjhh+7KWlhb95je/qVi7AAAAAAAoq5Xzpakfk9avSKb+9SukW06qTMAm6fdWJbtqjfb805mVDXqhqgjWAKiqm2a8Xp16HqtOPV3Z4MGDdffdd6tfv355lxk3blxBacwGDx6s3//+9+rW7f3T1dSpUwtpZsEGDBigP/zhD9pll12yrtO9e3dde+21bZY9+OCDFW0XAAAAAABlsWmN9LvTw9wqSdqyTrr9tNCecuko761aKvEZIjEEawBUzcKVDZq9tDonj9lL1uiVleurUldX9dWvflVDhw6teD2jR4/WpEmTtj9/+ukckx2WwcUXX6yRI0e2u97YsWPbpHl79dVXtWHDhko2DQAAAACA0v3l2x1n1Mn6FdKDl5Vvex3pvVVLuT9DJIZgDYCqmT63sunPMuqbt6yq9XU1n/70p6tW15577rn9/88//3xF6zrrrLPyXnfixInb/9/S0qJly9jnAAAAAAAd2KKHOt48Li/eE9pVqo743qqlXJ8hEtUj6QbkYma/l/RLd5+RdFsAlG7e29Udgjrvrfqq1teV9O/fX/vtt19J21i1apXuvvtuzZw5Uy+88ILeeecdNTQ0aMuWLTnLbdq0SZs3b1afPn1Kqj9OXV2dJkyYkPf60ZRu9fXscwAAAACADuzJnyXdgnhPXSfte3xp2+io761ayvEZIlEdOlgj6SxJZ5rZ65J+LWmqu7+TcJsAFMHdNX9ZQ1XrfHFZvdxdZlbVeruCkSNHFv25vvfee7rssst02223qbm5uahtrFu3riLBmh133FHdu3fPe/2+ffu2eb558+ZyNwkAAAAAgPJYtUB6s7KpxYv2xlPSqpekIfsXV74jv7dqKfUzROJqJQ3a3pL+W9JbZnavmZ2QdIMAFGbD1m2q39xU1TrrNzdpY2NxwQDkNmDAgKLKvfbaazrwwAN1yy23FB2okaStW7cWXTaX3r17l1Te3cvUEgAAAAAAyuzFDp4irJQUZh39vVVLV00D10l09JE1rVySSaqT9ElJnzSztyT9VtIt7v52ko0D0L6m5mQ6sRu3tUi9Eqm6U6urqyu4TGNjo0466SS9/XbbQ/bo0aM1efJkjRkzRsOHD1ffvn3Vp0+fNiN3fvrTn+rhhx8uud0AAAAAAHRZy55NugW5ldK+jv7eqoXPoaZ19GDNJyR9UdKJkrorBG2kELgZIWmKpO+Z2UOSfiXpz+7ekkA7AbSjrnsyqch69qiVAYSd30033aRFixZtfz5kyBBNnTpVJ5zQ/mDJ3/72t5VsGgAAAAAAnZu7tGJe0q3Ibfnc0M5C067XwnurlmI/Q3QIHboX092nu/spkkZKulLSGwqBGun90TbdFYI5f1RIk/YDMxtV/dYCyKVfrx4a2Kfw0RilGNinTn175j//CCrrzjvvbPP8j3/8Y16BGklas2ZNJZoEAAAAAEDXsHW9tGVd0q3Ibcs6qXFD4eVq4b1VS7GfITqEDh2saeXuy939KnffS9Lxku6T1Dr5Rfpom90kXS5psZk9bGanm1lHHz0EdAlmpvHDi5vnpFgHDB/YJpUWktPS0qI5c+Zsfz5x4kQddthheZdfsGBBJZoFAAAAAEDX0FzdeYSLtq2x8DK18t6qpZjPEB1CTQRr0rn7I+5+hqTdJV0maZHajraRwvv6F0l3SVpmZj8xszFVbyyANibsPqi69e0xsKr1IbvVq1dr27Zt25+PGZP/IXnRokVatmxZJZoFAAAAAEDX0L262U6K1qNn4WVq5b1VSzGfITqEmgvWtHL399z9f9x9P0kflnS7pC0KgZv00Ta7SPqWpJfM7DEz+6yZMd04kIBTJg6rbn0Thle1PmTn7m2eNzbmf5fHDTfcUO7mAAAAAADQtfTqL/Wu7k20Bes9SOrZr/BytfDeqqXYzxAdQs0Ga9K5+5Pufo6kYZIukfSCMue2MUlHSrpN0nIzu87MDkiivUBXNXboAE0atWNV6pq0544aM7R/VepC+3baaSf16PF+VsqZM2e2GWmTzdy5cwnWAAAAAABQKjNptwlJtyK3YRNDOwtVC++tWor9DNEhdIpgTSt3r3f3X7j7gZIOkfRrSRsUAjbpQZvBkr4maa6ZPW1mnzEzxssBVXDx0XtVpZ4vT967KvUgP927d9chhxyy/fmKFSt09dVX5yyzePFinXrqqWpqIvcsAAAAAAAlG35Q0i3ILdI+d9f6LU1as7FR67c0ZWTtyFW2y+JzqGk92l+lNrn7HDNrUAjSfEnvB2zSmUJQ5xBJPzKzH7j7r6rbUqBrOXbsEJ0yYZimz1tesTpOnThMx4zdtWLbR3HOOeccPfXUU9ufX3755Xr33Xd12WWXaddd3/97vffee7r11lt11VVXqb6+XmamfffdV6+88koSzQYAAAAAoHM44HTpyWuSbkV240/XwpUNmj53uea9vU7zlzWofvP7N3AO7FOn8cMHaMLug3TqxOFtM6p09PdWLeNPT7oFKEGnC9ak5qM5Q9KFCmnPpLZBmrhxYCZpd0k3mtm5kk539xUVbSjQhX3/lHGatWS1VjVsLfu2hwzopSknjyv7dlG6888/XzfeeKPmzp0rKdwhc/XVV+vaa6/V6NGjNWjQIK1evVpLlixRc3Pz9nKXX365li1bRrAGAAAAAIBSDBknjThcevPppFuSYe0uB+uiP9Zr9tIlWdep39ykpxav1lOLV+uGGa9p0qgd9eWj9w437Hbg91Y1I4+QhuyfdCtQgk6TBs3MDjCzn0taLulWhUBNa2CmNf3ZRoXUaIdJ+oKkfypzbpvDJP3DzHaoXuuBrmVw35669YJJGtinvNkHB/ap060XTNLgvj3Lul2UR11dnaZNm6bRo0e3Wd7S0qJXXnlFs2bN0uLFi9sEar75zW/qBz/4QbWbCgAAAABA53TkvybdgljfWHa0Zi9dU1CZ2UvX6Pypc3Tpnc9r7cbGDvvequaILv7+O4GaDtaYWV8z+6KZzZQ0V9JXFeajSR89Y5LmSfqKpGHufpG7z3L3W9z9CEkTJN2RWrd1BM5oSZdU5U0AXdTYoQN010WHasiAXmXZ3pABvXTXRYdq7NABZdkeKmPEiBGaM2eOvva1r6l3795Z1zv00EP10EMP6eqrr5YxMR4AAAAAAOWx7/EdLlXW/c2Ha0bLgUWXnzZ3uU647nEtHHBYh3tvVXPAGdK+KTYRzgAAIABJREFUH026FSiR5ZyYqYMys4MV0pydLalv62K9H2wxSZsl3S3pJneflcc2j5H0gKReqfLPuPukMjcdXZCZjZM0v/X5/PnzNW5c8Wm6tm3bpldffbXNstGjR6tHj9rMarh2Y6Om/GmBps0tfg6bUycO05STxzGipsZs2LBBTzzxhBYvXqz6+nr16dNHe+yxhw499FCNGDEi6eYlqrN9zwEAAAAAHcimNdKNh0vrk58FYqUP1glbf6R16t/+yu0Y2KdO95w7Rvved3yHeG9V03836ctPSzvsmHRLOoUFCxZo/Pjx6YvGu/uCatRdM70+ZjZQ0ucUgjQHtC5O/ZsepHlZ0i8l3eru9flu393/YWa/1vsjasaU3GgA7Rrct6euO/tAnTpxmG567HXNXpL/kNdJe+6oL09O5SZFzenXr59OPPHEpJsBAAAAAEDXssOO0ufuk245SdqyLrFmrPO+OrfxO2UJ1EhhTpvP3/GqHv7MnRp456mJvreq6T0o/C0J1HQKHT5YY2ZHKgRoTpfUW20DNK3zzDRKuk/SL939iRKqe1LvB2v6lbAdAAU6duwQHTt2iF5ZuV7T5y3TvLfq9eKyetVvbtq+zsA+dTpg+EBN2GOgTpkwXGOGludkDgAAAAAA0KUMGSed/xfp9tMSGYWy0gfr3Mbv6BUvb2aNVQ1b9b2ZrusSfG9V03eIdPbt0q77t13uLm1dLzU3Sd3rpF79pdYU87ley3cbqJgOHawxs5cl7dv6NPVva4DGJL0q6VeSprr76jJU2XpLf+3lhgM6iTFD++vbQ8dKktxdGxub1bitRT17dFPfnt2ZvwQAAAAAAKAchowL6bMevEx68Z6qVXt/8+Ga0nRu2UbURE2bu1ynTvyQjk3gvVXVxlXSbz8SRtfstI9k3SRvkVYvbjuqqGd/qc8gSSZtWRuCMK16D5J2myANP0gadqC0/Hlp2bPSinltt5G+3gFnSEMiASKURYcO1iikImsNnLQGabZJmqYwiubvFao3ff4bAAkxM/Xr1SPMJAUAAAAAAIDy2mFH6bTfhA74p66T3ngq/7K9BkhbG/JefVbLWN247WTNaDmwiIYW5qbHXtexFx1W/Hsrp5FHSEf8qySvTDu2rJOWPZP99cb14ZGt7JLHwqO9OlrXe/IaacTh0pHfkPb9aPHtRoaOHqz5/+zde3zU1Z3/8fdJyIXciUBCUm5iISUgodhUwRvWShGFdgXX3aIF665FXS9btdVfV3Db3a26WmldS60totauVbc1tgheEBUipsWSkkhEBURDiEIgCYGQQM7vj29CJmGSzGQu35nJ6/l4zCPf73fO95z30IHY+cw5p4ORtEvSLyX92lpbG6JxNkuaGaK+AQAAAAAAACDyjJ/lPGrflSqedWZX7Nly8uyKvCJndsWk+c7sil7aN8Wl653W0Sq341RyfLq225FhezllO+v03t5GZwl9b6+t+h2/Ck1+iUuQRhZLo87s/HPq0NefcbTYXSo9VeoUwmbfy545QRLpxZrjkv4kaYWktdbakM52sdYelNRHGREAAAAAAAAAYlDORCnnLufYWqnlkHSsRRqUKCWmnbxviZf226v369rfbtXOxo6dLNxRUl59Yql9SZ1Z91ZIv5kfumJNW6tUt0O6+D7vy4V1/zP7pEx6+krpUKjmJ4TQ1mekXRukhc85y+ohIHFuB+jDGGvt1621a0JdqAEAAAAAAAAAtDPG2Vg+9RTfNpg3RtvqrBY8sV07G+PkZqFGkso/rj/54t4K6bE5UmNNaAdvrJFWXizVVvberrZS+s3l0Vmo6eDra0WfIrpYY62tdjsDAAAAAAAAAKB3B5patGhlmeqPtLodRZK0tbpeXb7/f7jOmVETrmXHmg9KT17mjOtNuPOEUl+vFT6J6GKNMeYuj8eYAPsa69lfcBICAAAAAAAAAJaWVKq24ajbMU6oP9KqppbjnRdW3xb6GTXdNdZIL97u/Tk38oRSb68VPon0PWuWSeoof26QtCuAvk7t1t+/B9AXAAAAAAAAAEDSuqpalZTvcTvGSVqOtUlJkravlSqedSfE1mekyQuk8bM6r7mZJ5S8vVb4LKJn1rQL9uKG7i6WCAAAAAAAAAAxZMX6HW5H8CpxUPvH3xsedDfIxuVdz93OE0rdXyt8Fg3FGtt3EwAAAAAAAABAuFXtbVDZrsjbqyRzcIJSE+Odje93l7ob5qONUu27znEk5Aklz9cKv0RDsSZYPF9rm2spAAAAAAAAACBGlGyJvOXPJGlyfqaMMdLWCFlurGPZs0jJE0qxuMRbGAykYk2mx3GTaykAAAAAAAAAIEaUf3LQ7QheTRnZ/nFw9WZ3g3ToyBEpeUJpILzGEBhIxZppHsf7XEsBAAAAAAAAADHAWquK6ga3Y3g1d0q+ZK1UU+52FMeeLVJbW+TkCaU9W5w/e/hlQBRrjDHnSrpWzv43VlKFu4kAAAAAAAAAILodOnpM9Uda3Y5xkuKx2ZqQmy4dbZSaI2TmT/NBqXFv5OQJpeaDUssht1NEnUFuBzDGrPOx6f3GmAP+dC0pRdIYSUM9rllJa/3oZ0AyxoyTVCzpc5ISJR2QVCWp1Frb7GY2AAAAAAAAAO5rPR6ZsyeWnDfOOTgeYYWklgG0O8exFinJ7RDRxfVijaTz5RRQemLaf57ej76Nx3HHGJ9KeqoffQ0IxpivS/o3SV/sockhY8xjku621rq+nJwxJkXS3ySN6/bUKmvtovAnAgAAAAAAAAaGhHjTd6Mwm1eUp5kFw52T+AR3w3SXmOp2gvAZlOh2gqgT68ugdSx7JjmFm32SLrfW1rsXKTIZY5KMMU9K+r16LtRIUpqkGyS92768nNt+pJMLNQAAAAAAAABCLC1pkDIHR05BJCcjScsuLey8kJQuJWe5F8hTcpaUnhs5eUIpOUtKTHM7RdSJlGKN6eHhS5ve7m2S9LGkFyXdImmCtfbNUL6QaGSMiZP0tKRvdnvquKSdkrZI6l7gGibpRWPMWaFP6J0xpljSTW6NDwAAAAAAAAxkxhhNys9wO4YkKXNwglZdXawhqR4zOoyRRkxxL5SnvCIpLi5y8oRSXpHzZw+/uF6ssdbG9fToaNL+uLC3tl4e8dbaDGvtGGvtHGvtcmutP3veDCS3SZrX7doKSaOstadaa6dKypb0d5J2e7RJkfQ7Y0xmeGJ2MsYkSvqVOt/DA2jBRwAAAAAAACAyTPmc+zNFcjKS9PS1Z6og10vhKH9a+AN505EjUvKE0kB4jSHgerHGB5TgQsgYc4qk/9ft8h3W2iXW2j0dF6y1bdba30uaLmmXR9vPSfrXkAc92Z2SJrUfV0v6hQsZAAAAAAAAgAFtblGeq+PPK8rTmpvO9V6okaTJ88MbqCeT2nNESp5QmjQAXmMIRHqx5g1Jr7f/ZFZMaNwuKd3j/A1J9/TU2FpbLemabpdvaS/6hIUxplDSHR6XbpDUGK7xAQAAAAAAADgKcjNUPCY77OMWj83WykVf0vIrpnZd+qy7nEJp1PTwBfNm9AwpZ2Lk5Aklz9cKv0R0scZae761dmb7469u54k17XvVLO52eZm11vZ2n7X2VUmee/+kS7o8yPG8as/8K0kd/wL/3lr7h3CMDQAAAAAAAOBk3zn/1LCMM3FEhq6fOU5rbz5Xv7v2LM0sGO7bjWffHNpgfZnRbXy384RS99cKn0V0sQYhN13SMI/zHZLW+3jvr7qdfz0YgXxws6Qvtx83yJlVAwAAAAAAAMAlFxTkaO6U0C6HNm9KnlbfdI5um1WgCbnpfd/gafws95bmmrxAGn9R5OQJJW+vFT6jWDOwzel2/nJfs2o823Y7P98YkxqETD0yxpwq6Ycel+7w3FcHAAAAAAAAQFfWWjU2t6quqUWNza3y/eM//9w9t1A5GUkB9GCVpsMaogal6bCkzpw5GUlaNrcwsIAX3yeljwisD3+lj5Bm3xs5eUKpt9cKnwxyOwBcVdTtvNTXG621e4wxuySNab+UKGmipD8HJZl3v5SU0n78lqSfh3AsAAAAAAAAICpV7W1QyZY9Kv/koCqqG1R/pPXEc5mDEzQpP0NTPpeleUX5/s9S6cGQ1ESturpYf/+LTV3G680Es1tz40s1xXyoSXG7lGWaTjx30Kaqom2MquLH64KvX9f7vjS+SMmWFj4nrbxYaj4YWF++SM5yxkvpYT+fcOcJpb5eK3ziSrHGGHNu92vW2jd8aRcs3sYbgL7Q7fxdP+9/V53Fmo7+QlKsMcZcI+mC9tNWSf/kxywgAAAAAAAAIOatq6rVivU7VLarrsc29UdatfGD/dr4wX49vP5DFY/J1pLzx/m+/0svCnIz9PS1Z+pbvy5TbcPRHtvNjPurvjPoBX05rqrHNlmmSWfHV+psVUq/+700arp09i2BLbOVUygtXi09eZnUWNP/fvqSPsIpXuT0MRsoXHlCydfXij65NbNmvTznsTnH3rJ0bxcsPY03YBhjBksa1e3yx3520739hP4n6pkxZoSk+zwu3WutrQzFWAAAAAAAAEC0OdDUolt+t0Xr3/vM73vLdtWp7LE6zSvK07JLCwOewVKQm6E1N52rZS9U6vktXXcwyFKj7k5YpXnxPi/w02l3qfRUqbMvyux7+z+LI6dQWlIqvXi7tPWZ/vXRG3/zhTpPKAX6vwW6cLtgYYLcDr4bqq5/rq2SPvWzj+pu54GX3717WFJW+/H7kn4UonH6ZIwZLmmYn7eNC0UWAAAAAAAAYFXpTv3oT9vUejyw77w/v2WPNu3Yr1VXF6sgNyOgvoakJmr5FVM1ryhPK17fobKddSowu/VY4j3KNQcC6ltbn5F2bQhsNkdKtnTZo06xYeNy6aONgWWSpNEzpBk392/mTyjyhFIgrxU9crNYQ6HGXWndzg/3Y1mxpm7n3fsMmDHmcklf97h0rbW2Odjj+OE6SUtdHB8AAAAAAADQgaYW3fz0Fr2+3f/ZND2pbTiqv//FJj197ZkBF2wk6YKCHF1QkKNdlW8r5/++o8HHG4KQUs6SYSsvdpYQC2T5rfGznEftu1LFs1L1ZmnPlq57yMQnSYkpUusR6ZjHx5LJWVJekZQ/TZo0X8qZ2P8c/uSRUZfFqJKzpKGfl0ycZI9L+z7o2j4xXUoZ4hwfOSAdbfT+GkZMlWr+6n3MULxWnMStYs3dQW4H/3UvrPSnAHKkjz4DYow5RdLPPC6ttNa+FswxgFiza9cujR07NuTjLF26VMuWLQv5OAAAAAAA4GTbahp01a/e1meHWoLed/2RVn3r12Vac9O5AS+JJkk6XKcxa74lBatQ06H5oLPXy5LSwJfhypko5dzlHFsrtRySjrVIgxKlxDTJmJ6vh0JveRJSpdamnnP0lrOv1zDxUt/aISRcKdZYa30qwvjaDv2S3O28P/+yd98lbHA/s/TkQXUurfappFuD3D+AAWDZsmW6++7OXyevvfaazj//fPcCAQAAAAAQgNVba3TL01t09FhbyMaobTiqZS9UavkVUwPvbPVtzkyYUGiscfZ6uezR4PVpjJSULiX5eD3UvI3bW47ecvr6Gtx6rQOc23vWwD3dZ9L0p0ze/a9r0JYnM8bMlrTQ49It1tq6YPUfgIcl+bvT1zhJz4cgCwAAAAAAAAaIdVW1+tm6D/TX3Qf7bhwEz2/Zo3lFebqgIKf/nWxf6yznFUpbn3H2ehk/K7TjACFGsWbgOtTtvPtMG190n0nTvc9+McakS1rhcWmNtfapYPQdKGvtp3Jm+fjMMEUQYZSbm6uXX37Zp7YvvfSS7rvvvhPnp59+uu6//36f7j311FP7lQ8AAAAAAPjnQFOLlpZUqqR8T9jHXvH6jsCKNRseDF6Y3mxcTrEGUY9izcDVvbCSYowx1lrrtbV3qX302V8/ljSq/fiwpCVB6hfRxlpn07PjrVJ8gjP9kuJXr5KTk3XhhRf61PaTTz7pcj5kyBCf7wUAAAAAAKG3raZBi1aWqbah+24E4VG2s07v7W3UhNx0/2+urZR2lwY/lDcfbZRq32Xje0S1iC7WGGO+aK19x+0cMWqfJCup45PvBDn7w9T60Ud+t3O/Zpx4Y4wZq67FmaXW2l2B9osoUlspbX1Wqt4s1ZQ7m8V1SM6SRkyR8qc501v5BQwAAAAAAGLUtpoGXfHIJtUfaXU1R0l5tW7LLfD/xq0hXv6su4pnpZy7wjsmEEQRXayR9BdjTIWkVZJ+Y63d63agWGGtPWKM2S1ptMflUfKvWDOq23lVwMGkTHUWkCTpPmPMfT017sW3jDHf8jivt9ZmBRYNIbV9rTM1trdvXDQflHa+7jw2PCCNmi6dfYs0/qLw5QQAAAAAAAixsp37tXjln9XUctztKCr/uL5/N1ZvDm6QSBsPCLJIL9ZIUqGkeyX92BjzsqTHJD1vrXVn7l9sqVLXYs1ESX/24/4veOkP8M/hOmn1bf3bbG53qfRUqTPLZva9Ukp28POhV59++qk2bdqkvXv3av/+/UpLS9Pw4cNVXFyssWPHBtR3XV2d3nnnHX3wwQeqr6/XsWPHlJKSoqFDh2rs2LEqLCzUkCFDgvRKAAAAAABw37qqWq1Yv0Nlu+rcjnLC1up6WWv925fZWmfFlHDas8UZlyX0EaWioVgjOTMt4iXNan80GGOelvS4tTZMCx/GpC1y/jw7TJczi6lPxpgRksZ4XGqV9G7QkmFg2Fsh/Wa+1FgTWD9bn5F2bZAWPiflFAYnG3rU1tamJ598Uj/72c+0efNm9bTV1Re+8AXdfvvtuuqqqxQXF+dz/6+++qruuecevfrqq2pra+uxnTFGEyZM0Lx583T99ddr5MiRJ55bv369Zs6c6fW+nq538G/rLgAAAAAAAnegqUVLSypVUr7H7SgnqT/SqqaW40pL8uOj5KONXZe2D4fmg1LLIWfPYyAKRXqx5nVJ57Yfd3x6ZuQslfVPkv7JGPOhnALDE9ba3eGPGNX+KOl7HucXGmOM9e2Tyu7rTr1mrT0UhEwfSPpqP+67StKVHucvSfJcPs3dxT1xsr0V0mNzgveLu7FGWnmxtHg1BZsQev/997VgwQKVl/f97Zht27Zp8eLF+sUvfqGSkhINGzas1/bWWt1444166KGHfMpirVVVVZWqqqp02mmn6ZprrvHpPgAAAAAAIsm2mgYtWlmm2obIXUio5ViblOTHDcdd+ijuWIt/OYEIEtHFGmvtTGPMKEnfkrRQ0uc7nmr/aSSdJunfJd1tjHlDzjJpz1lrm8IcNxqVStonaWj7+amSzpf0mg/3frvb+fPBCNRe8HnF3/uMMWd3u1RjrfW7H4TJ4TpnRk2wv2HRfFB68jJpSSlLooXA22+/rUsuuUT79u3rcj0+Pl5jx45Vdna2Dh06pB07dqi5ufnE85s2bdJZZ52lt956q9eCzV133eW1UJOdna2RI0dq8ODBampq0r59+1RTE+BsLAAAAAAAIsC2mgZd8cgm1R+J7O8ZJw7yfcUMSVJ8QmiC9GVQojvjAkEQ0cUaSWqfLfNDST80xpwlp3BzuaSOzeKtnKKNkXRe++N/jDHPyVkmbV34U0cHa22bMeYxSbd6XF5qjFnf2+waY8xXJJ3jcalR0u9CkxIxafVtgS991pPGGunF26XLHg1N/wPU3r17NXfu3C6FmtNPP1133HGHLrnkEqWlpZ243tzcrJKSEt1555368MMPJUkffvihFi1apD/+8Y9e17itrq7WPffc0+Xatddeq5tuuklf+EL37bGkAwcOqLS0VH/84x/129/+9qTnp0yZopdfflmS9Pjjj+uJJ5448dx///d/a8qUKX7+CQAAAAAAEFwHmlq0aGVZxBdqMgcnKDUx3r+bktKl5KzwLoWWnCUlpvXdDohQEV+s8WStfUvSW8aYGyXNk7P01Sw5r8Nztk2qnCWxrjTGfCLpcTmFm/fDnzri3SPpO5I6/iU7T87SaD/21tgYky+p+6fgy621+7y197ive/FnprV2vd9pEf22r5Uqng3tGFufkSYvkMbP6rstfLJ48WJ9+umnJ87/+Z//WQ899JASEk7+pkxycrIuv/xyXXTRRZozZ45KS52txVavXq0//OEP+sY3vnHSPc8//7xaWzv/4/Suu+7S3Xff3WOeIUOGaM6cOZozZ47uu+8+HThw4KTnL7zwQknShg0bujw3bdo0nX/++X2/aAAAAAAAQmhpSWVEL33WYXJ+ptcvXvbKGGnEFGnn66EJ5U1ekTMuEKX8nL8WGay1LdbaZ6y1l0rKl/RdSeVyCjVS18LNSEl3SqoyxpQaY/7ZGJMZ9tARqr3I8p/dLv+XMeZhY0xexwVjTJwx5utylk4b49F2j6T7Qx4UsWPDg+EZZ+Py8IwzAGzatElr1qw5cT579mytWLHCa6HGU1ZWlp577jmlp3du7Hf//d7/udi+fXuX8+uuu87nfGlpaRo5cqTP7QEAAAAAcNu6qlqVlO9xO4ZPpozs50ep+dOCGyTSxgOCLCqLNZ6stZ9Za39irf2ipNMlPSCpVt4LN1+W9HNJbHbQ1T2S/tjt2hJJu40xHxpj3pG0X9LvJY3yaHNE0uXW2jDOZ0RUq62UdpeGZ6yPNkq174ZnrBj34INdC2w/+clPfP5GTW5urq655poT5xs3blRtbe1J7Y4cOdLlvK9CEAAAAAAA0WzF+h1uR/DZ3Cn5/btx8vzgBunLpDCPBwRZ1BdrPFlrK6y1t0r6nKSL5eyjclROocazaJPkTsLIZK1tk7RA0v92eype0qmSpqpzj6AO+yVdbK3dGPqEiBlbQ7z8WXehXm5tAGhra+syq6a4uFgTJkzwq4+LLrqoy/mbb755Upu8vLwu508++aRfYwAAAAAAEMmstWpsblVdU4s2f1Snsl11bkfySfHYbE3ITe+7oTc5hdKo6cEN1JPRM6ScieEZCwiRqNqzxlftxYc1ktYYYzIkXSPpR6JI0yNrbbOkfzDGPCvpB5KKemjaJGmVpLuttZ/20AbwrnpzbI8Xg7Zu3ar6+voT52eccYbffYwaNarL+bZt205q89WvflXLli07cf7d735Xn376qW644Qbl5ub6PSYAAAAAAG6r2tugki17VP7JQVVUN6j+SGvfN0WYJeeNC6yDs2+WngrDKiszbg79GECIxWSxRpKMs0bPVyVdJenrolDjE2vtc5KeM8acJmfZuHxJiZIOStomaWN7YcfffkO6u5e1dpmkZaEcAwGyVqopD++Ye7Y447K5XL91L6w8/PDDevjhhwPqs67u5G8PTZ8+XV/96lf18ssvS5KOHTum//iP/9B//dd/afr06frKV76ic845R8XFxV32wAEAAAAAINKsq6rVivU7omb2TE/mFeVpZsHwwDoZP8tZniyUq59MXiCNv6jvdkCEi7lijTFmoqRvSfqmpBEdl9W5DBp8YK39QNIHbudADDnaKDWHeXuj5oNSyyEpiQ/3+2v//v1B79Nzpo6np556Spdeeqk2bdp04lpbW5s2bNigDRs2SJIGDRqkM844Q5dccom++c1vasyYMUHPBwAAAABAfxxoatHSkkqVlO9xO0rAcjKStOzSwuB0dvF9zt7CjSHYRjx9hDT73uD3C7ggJvasMcYMNcbcaIz5i6Stkm6VlCenSCM5hRrT/tgk6TpXggID2XGXpvoea3Fn3Bhx8GDwC2xtbW1erw8dOlRvvPGGHn74YZ122mle2xw7dkybNm3SD37wA40bN05XXnmlamtrg54RAAAAAAB//PLND1X8n6/ERKEmc3CCVl1drCGpicHpMCVbWviclBjkL9Mmpjv9pmQHt1/AJVE7s8YYkyBprpxlzr4m57V4Fmc8CzQfS3pC0ipr7fvhTwtA8QnujDsoSP9hMUClpKR0Of+Hf/gHXX311QH1mZeX1+NzCQkJWrJkiZYsWaK//OUvevXVV7V+/XqVlpaqoaGhS9u2tjY9+eSTeuWVV7R+/XpNmDAhoFwAAAAAAPjrJy+/p1++sVOHW4+7HSUohqUn6YlvF6sgNyO4HVvb+cltsLDqPWJM1BVrjDFnyinQ/L2krI7L7T89CzSHJT0naZWk16y1LIMGuCkpXUrOCu9SaMlZUmJa+MaLQUOHDu1ynpWVpQsvvDAsY59xxhk644wz9L3vfU9tbW0qLy/XmjVr9PTTT6u8vHP/o71792r+/PkqLy9XXFxMTBgFAAAAgNhhrbM0+vFW54ucSekxsbfszs8OaeGvylR98IjbUYJm5oRheuDyouDNqOlwuE76zXznfRBMRxulJy+TlpQGd3ZNjL5nEfmiolhjjBkl6Uo5RZqOtXG8zaKRpNflFGietdYeCmdOAL0wRhoxRdr5evjGzCvil2mAxo4d2+X8gw/c2coqLi5OU6dO1dSpU3XHHXfo//7v/7Rw4UIdOeL8R3FFRYXWrl2r2bNnu5IPAAAAAOChtlLa+qxUvVmqKe/6xc3kLOfzgfxpzsbwORPdy9lPq/9Wo+ufeidmNshOiDf6t0sm6qqzxoRmgNW3hWa/Gsnp98XbpcseDayfGH/PIjpEdLHGGLNYToHmHHXOmJFOXubsQ0mPS3rcWvuRC1EB+CJ/WniLNfnTwjdWjCouLlZKSooOHz4sSSotLVVzc7OSk5NdzfV3f/d3+u53v6sf/ehHJ65t2LDBa7Gm+2wbJloCAAAAQIhsXytteFDaXdpzm+aDzmcDO1+XNjwgjZounX2LNP6i8OUMwD0vVunnr3/odoygCdlsmg7b10oVz4am7w5bn3GKKONn+X/vAHjPInpE+noxv5J0rpycRjpRsDaSGiU9Kukca+3nrbU/pFADRLjJ88M73qQwjxeDEhMTdcEFF5w4b2pq0sqVK11M1GnGjBldzvft2+e1XWpqapfzjsITAAAAACBIDtdJz35beury3j/09mZ3qfTUAum5a5x+ItSBphZd/difY6ZQM3X+pDlCAAAgAElEQVRUllYu+pJWLi4OXaFGcgoh4bBxuX/tB8B7FtEn0os1HazHz5ckfVNSrrX2n621G92LBcAvOYXOtw/CYfQMpqUGyW233dblfOnSpdq9e7dLaTp1L84MGTLEa7vs7K7r1u7cuTNkmQAAAABgwNlbIf18euCzJ7Y+4/RTWxmcXEG0raZBX1v+htZVfep2lIAlDYrTioXT9PvrZmhmwfDQDlZb6X8hpL8+2ijVvutb2wHwnkV0ioZijZFUJen7kkZZa79mrf2ttbbZ5VwA+uPsm8MzzowwjTMAnHvuuZo1q3Mq8WeffaaLLrpIVVVVPvfR1tamP/zhD/re977n9fnrr79eL7zwgs9LlB09elQ//elPu1ybNs37sneFhYVdzp977jmfxgAAAAAA9GFvhfTYnODtR9JYI628OKI+/N5W06ArHtmk2oajbkcJWE5Gkp6/YYa+Nik3PANuDfHyZ935UnwZAO9ZRK9IL9b8j6Ria22htfZea+0etwMBCND4WaFfnmzyAtYNDbLHH39cI0eOPHH+3nvvadq0abr55ptVXl7utchy4MABvfLKK7rllls0ZswYfeMb39Dbb7/ttf+NGzdq7ty5Gjt2rG699VatX79eDQ0NJ7VrbW3VmjVrNGPGDP35z38+cT03N1eXXHKJ176nTp2qoUOHnjhfv369LrjgAq1YsUKrV6/WK6+80uUBAAAAAPDB4TrpN/O7bsQeDM0HpScvi4jlpQ40tWjRyjLVH2l1O0rA5hXlac1N56ogNyN8g1ZvDt9Yvow3AN6ziG6D3A7QG2vtv7idAUAIXHyfMz01WN9i8JQ+Qpp9b/D7HeCGDx+uP/3pT5ozZ44+/vhjSc7eL8uXL9fy5cuVmZmp/Px8paen69ChQ6qrq1NNjf//+3700Ue6//77df/998sYo/z8fJ1yyikaPHiwGhoatGPHDjU3d51YGR8fr0cffVSDBw/22mdCQoJuvPFG3XXXXSeuvfbaa3rttde8tvd1dg8AAAAADGirbwvN/6+XnH5fvF267NHQ9O+jpSWVUT+jpnhstpacNy70S551Z61UUx7eMfdsccY1xvvzA+A9i+gW0cUaADEqJVta+JwzTTSY32ZIznL6Tcnuuy38NnnyZG3evFlXXnml1q5d2+W5+vp61dfX99nHqFGjfB7PWqtPPvlEn3zySY9thgwZopUrV2rOnDm99nXnnXdq+/btevLJJ30eHwAAAADQg+1rA9/voy9bn2lfOWNW321DYF1VrUrKo3eRn9TEeD22uFhfGuvSZyRHG4M/g6UvzQellkNSUvrJzw2A9yyiX6QvgwYgVuUUSotXOzNhgiF9hNNfTmHfbdFvw4YN05o1a/TGG2/o0ksvVWpqaq/tjTGaOnWqvv/97+tvf/ubHn/8ca/tXnjhBT300EOaM2eOsrKy+syRl5en2267Tdu3b9e8efP6bB8fH68nnnhCGzZs0PXXX6/i4mINHTpUSUlJfd4LAAAAAOhmw4PhGWfj8vCM48WK9TtcGztQmYMT9Nx1090r1EjScZeWjjvW4v36AHjPIvoZlnsBQssYUyipouO8oqLipA3P/XHs2DG9//77Xa59/vOf16BBUTpR7nCdM0106zP972PyAmfpM2bUhF1ra6vKysq0c+dO7du3T01NTUpNTdWQIUM0fvx4TZw4UZmZmX71aa3V9u3b9f7772v37t1qaGjQ8ePHlZ6ertzcXJ1++ukaP3684uJi9/sGMff3HAAAAEDsqK2Ufj49fOMteUvKmRi+8SRV7W3Q1x58M6xjBktORpJWXV0c3r1pvGlukH48su92wXbHJyfPrBkA71kET2VlpSZNmuR5aZK1tjIcY7vyqY8x5qR1cKy1u31pFyzexgPggpRsZz3PyQucbx98tNH3e0fPkGbcLI2/KHT50KuEhATNmDFDM2bMCFqfxhhNmDBBEyZMCFqfAAAAAIAg2RripaS6q3hWyrmr73ZBVLIlOpc/m1eUp2WXFmpIaqLbUZyCSXJWeJdCS86SEtNOvj4A3rOIDW59RXeXJM8pPVbes3RvFyw9jQfALeNnOY/ad51fatWbnY3hPH+pJ2dJeUVS/jRp0ny+pQAAAAAAQLhVb47t8SSVfxLmvVYCVDw2W0vOG6eZBcPdjtLJGGnEFGnn6+EbM6/IGbe7AfCeRWxwu2Dh5W9PQO0ARLuciZ3fPrDW2RjuWIs0KNH5doS3X7oAAAAAACD0rJVqysM75p4tzrhh+jzAWquK6oawjNVfmYMTNDk/U1NGZmrulHxNyE3v+yY35E8Lb7Emf9rJ1wbAexaxw+1iDQD0zBhn2ix7wAMAAAAA4L6jjeFd1kpyxms5dPI+JCFy6Ogx1R9pDctY/vj9ddM1+pRUJQ6KU2pivEw0FAImz5c2PBC+8SbNP/naAHjPIna4VaxZFeR2AAAAAAAAAELpuEtFjGMtYfsiZ+vxUOzIEJjisdmaOmqI2zH8l1MojZou7S4N/VijZ3hfLn8AvGcRO1wp1lhrFwezHQAAAAAAAIAQi09wZ9xBiWEbKiE+8masLDlvnNsR+u/sm6WnwlCsmXGz9+sD4D2L2BHndgAAAAAAAAAAUSApXUrOCu+YyVnOHrZhkpY0SJmDXfqA34t5RXmaWTDc7Rj9N36W9+XJgmnyAmn8Rd6fGwDvWcQOijUAAAAAAAAA+maMNGJKeMfMKwrrRu3GGE3KzwjbeL3JyUjSsksL3Y4RuIvvk9JHhKbv9BHS7Ht7fn4AvGcROyjWAAAAAAAAAPBN/rTYHk/SlM+FeSaGF5mDE7Tq6mINSY2B5bRSsqWFzwV/hktyltNvSnbv7QbAexaxIaKLNcaYuzweYwLsa6xnf8FJCAAAAAAAAAwgk0O8pFV3oV5Cy4u5RXlhH9NTTkaSnr72TBXkRsYMn6DIKZQWrw7eDJv0EU5/OT7MPBoA71nEhogu1khaJmlp++PUAPs6tVt/AAAAAAAAAPyRUyiNmh6esUbPkHImhmcsDwW5GSoe08dsjRCZV5SnNTedG1uFmg45hdKSUmePmUBMXuD040uhpmPcGH/PIjZEerFGkoK9wB8LBgIAAAAAAAD9dfbN4RlnRpjG8eI75wf6vXH/fH54mlYu+pKWXzE1NpY+60lKtnTZo9I//s4pbPhj9AzpH59x7u9r6bPuBsB7FtFvkNsBfGDdDgAAAAAAAACg3fhZzlJPFc+GbozJC6TxF4Wu/z5cUJCjuVPyVFK+J+Rj/eCSL+ias8NbHHLd+FnOo/Zd531UvVnas0VqPtjZJjlLyity9oCZND+wGSsD4D2L6BcNxZpg8ZxF1OZaCgAAAAAAACDaXXyf9NFGqbEm+H2nj5Bm3xv8fv1099xCvb1zv2objoak/xGZyXrqmi9r7LC0kPQfFXImSjnt24tbK7Ucko61SIMSpcQ0yQRxkaQB8J5FdIuGZdCCJdPjuMm1FAAAAAAAAEC0S8mWFj7nzH4IpuQsp19/l7kKgSGpiVp1dbEyBycEve+FZ47SW3d8ZWAXarozRkpKl1JPcX4Gs1AjDYj3LKLbQCrWTPM43udaCiBAxssvKmtZLRCIJW1tJ08AjYsbSL+yAQAAAESFnEJp8WpnVkEwpI9w+vN14/gwKMjN0NPXnqmcjKSg9Jc1OEFrbj5HP/r65KD0Bz8NgPcsoteA+OTHGHOupGvl7H9jJVW4mwjoP28f2La2trqQBECodP87bYzxWqgFAAAAANflFEpLSp39OgIxeYHTT7cPva21amxuVV1TixqbW135wmpBbobW3HSu5hXlBdTPvKI8vXbr+SrIzQhSMvRLiN+zQH+5vmeNMWadj03vN8Yc8KdrSSmSxkga6nHNSlrrRz9ARDHGKDExUS0tLSeuHTp0SCkpKS6mAhBMDQ0NXc4TEhIo1gAAAACIXCnZ0mWPOh9eb1zu7Aviq9EzpBk3d9mYvWpvg0q27FH5JwdVUd2g+iOdX2jLHJygSfkZmvK5LM0ryteE3PRgvpIeDUlN1PIrpmpeUZ5WvL5DZTvrfL63eGy2lpw3TjMLhocwIfwS5PcsEAzG7eWTjDFtcgooXp/2OO5P0O73G0m1kgqstfX96A/wmzGmUB6zuSoqKlRYGFjF/dNPP9X+/ftPnCckJGjcuHF8mAvEgGPHjunDDz/sshTasGHDNHTo0F7uAgAAAIAIUvuuVPGsVL1Z2rNFaj7Y+VxylpRXJOVPkybNdzaYb7euqlYr1u9Q2S4/CiFjsrXk/PAXQt7b26iS8mqVf1yvrdX1JxWUJudnasrITM2dEr6CEgLQz/csYk9lZaUmTZrkeWmStbYyHGPHerGmez/7JF1mrX0zgL4Av4SiWHPkyBHt2rWry7X09HTl5+dTsAGiWEtLiz7++OMuM+ck6bTTTlNCQvA3tAQAAACAkLNWajkkHWuRBiVKiWknbRx/oKlFS0sqVVK+p9/DzCvK07JLCzUkNTHQxH6z1qqp5bhajrUpcVCcUhPj+XwmmvnwnkXscrNY4/oyaO18ebf7+zfCSmqSVCepUtJLkh631vqzlBoQkZKTk5WQkNBlX4vGxkZ9+OGHysjIUFpamgYNGsSG5ECEs9bq+PHjOnz4sA4dOqTDhw+ftP5yamoqhRoAAAAA0csYKSldSvL+9LaaBi1aWabahqMBDfP8lj3atGO/Vl1dHPY9YYwxSksa1ONrRJTp4z0LhIrrxRprbY+fJnebdfNVa62v+9sAMc0Yo7y8PO3evbvLB7utra3av39/lyXSAESvhIQE5eTkuB0DAAAAAEJiW02DrnhkU5clxAJR23BUf/+LTXr62jPDXrABgEBFw9fumWMGeJGSkqJRo0YxrRaIUUlJSRo9erSSkvgqDwAAAIDYUrW3Qf/+QqXmPbQhaIWaDvVHWvWtX5fpQFNL340BIIK4PrOmD2+oc2YNy5cB3XQUbPbs2dNlSTQA0S09PV0jRoxQfHy821EAAAAAIGjWVdVqxfodKttVF9JxahuOatkLlVp+xdSQjgMAwRTRxRpr7fluZwAiXUpKisaNG6ejR4+qoaFBjY2NJ21ODiCyxcfHKy0tTWlpaUpNTaVIAwAAACCmHGhq0dKSSpWU7wnbmM9v2aN5RXm6oIClpQFEh4gu1gDwjTFGycnJSk5O1vDhw2WtVVtb20kblQOILMYYxcXFsZwhAAAAgJi1raZBi1aWqbbhaNjHXvH6Doo1AKIGxRogBhlj+GY+AAAAAABw1baaBl3xyKag70vjq7KddXpvb6Mm5Ka7Mj4A+COiizXGmCxJN3pc+oO19m/96GeKpHkel+631jYFmg8AAAAAAADAyQ40tWjRyjLXCjUdSsqrdVtugasZgIBYKx1tlI63SvEJUlK6xAodMSmiizWSFktaJslKOiTpp/3sZ7ekWyWltp/XSvpFoOEAAAAAAAAAdPViRY3u/L+tOnDY3UKNJJV/XO92BMB/tZXS1mel6s1STbnUfLDzueQsacQUKX+aNHmBlDPRvZwIqkgv1nzT4/gpa+3BHlv2wlp7wBjzv5Kuab+0UBRrAAAAAAAAgKD5ycvv6dcbdqnx6DG3o5ywtbpe1lr2CkV02L5W2vCgtLu05zbNB6WdrzuPDQ9Io6ZLZ98ijb8ofDkREhFbrDHGDJM0Vc6sGkl6NsAuf6fOYs2XjTEZ1tqGAPsEAAAAAAAABrSdnx3Swl+VqfrgEbejnKT+SKuaWo4rLSliPwYFpMN10urbpIp+fAS+u1R6qtSZZTP7XiklO/j5EBZxbgfoxRRJpv3RJumtAPsrbe9HkuIlFQXYHwAAAAAAADCgPfLGh7rg/tcjslDToeVYW9+NALfsrZB+Pr1/hRpPW59x+qmtDE4uhF0kF2vGtf+0knZZaw8H0ln7/bs8Ln0+kP4AAAAAAACAgWpdVa0uvH+9/nN11YllcSJV4qBI/ggUA9reCumxOVJjTXD6a6yRVl5MwSZKRfL8vyyP47og9blf0qntx0OC1CcAAAAAAAAwIBxoatHSkkqVlO9xO4pPMgcnKDUx3u0Y6A9rpaON0vFWKT5BSkqXPPce6uv5cOfx9/7jrdJv5jt70ART80HpycukJaUsiRZlIrlY4zk/MTFIfXr2E8mvHQAAAAAAAIgov3xjh+5ZU6VjbZE+l6bT5PxMmVB+gI/gqq2Utj4rVW+Wasq7FjKSs6RTxkkmXrJt0v4PTn5+xBQpf5qzf0vOxNDn6Wu83u6PS5DaWgPP6E1jjfTi7dJlj4amf4REJBcs9rX/NJJGBKlPz372B6lPAAAAAAAAIGb95OX39Ms3d+pwy3G3o/htyshMtyPAF9vXShselHaX9tym+aBT9Ojt+Z2vO48ND0ijpktn3yKNvyh0eXoaz5f7Q1Wo6bD1GaeINH5WaMdB0ERyscZzob5hxpgCa21VfzszxkyQNNzjUm2/kwEAAAAAAAAxbudnh7TwV2WqPnjE7Sj9NndKvtsR0JvDddLq26SKZ4Pf9+5S6alSp2Ax+17flgQLJE/HeJkjpfqP/b8/FDYup1gTRSJ5d623JB2XTuxRtjjA/r7tcWzb+wcAAAAAAADQzeq/1egrD7we1YWa4rHZmpCb7nYM9GRvhfTz6aEp1Hja+owzTm1lePJESqFGkj7aKNW+63YK+ChiizXW2npJf24/NZL+xRjTr4UGjTGTJN0gp0hjJW2x1n4WlKAAAAAAAABADFn9txpd99Q7iqKtabxact44tyOgJ3srpMfmOHurhENjjbTy4p4LNuHOE06hLoYhaCK2WNPuZ3IKNVZSsqS1/hZsjDGFkl6UlNTelyQ9FMyQAAAAAAAAQCz47dsf6bqn3nE7RsDmFeVpZsHwvhsi/A7XSb+Z7+z5Ek7NB6UnL3PGj4Q84dLbPj+IKJFerPlfSRXtx1ZSvqS/GGOWGmN6/dfWGDPcGLNMzuycjsUpraRtklaFJi4AAAAAAAAQfQ40tejG3/5Vd/y+ou/GES4nI0nLLi10OwZ6svo292awNNZIL94eOXnCYc8WyUb5NLkBYpDbAXpjrbXGmMsllUrKVOcMm7sk3WmM+aukv0j6VNIhSWmShks6Q9JUOa+vY2aOkXRA0nxreXcCAAAAAAAAkrR6a41ufaZch1uOux0lYJmDE7Tq6mINSU10Owq82b7W/WW5tj4jTV4gjZ8VGXlCrfmg1HJISmL/pkgX0cUaSbLWVhljLpP0rKQsdRZeEiQVS/qSl9uMx3FH+zpJf2etrQptYgAAAAAAACDyrauq1QMvbVfFnga3owRFTkaSVl1drILcDLejoCcbHnQ7gWPjcqdYEyl5Qu1Yi7NJCCJaxBdrJMla+5oxZpqkpySd2XG5WzPjcc16XDOS3pS00Fr7caizAgAAAAAAAJHsQFOLlpZUqqR8j9tRgmZeUZ6WXVrIjJpIVlsp7S51O4Xjo43SuyWRkyfUBvH3IhpERbFGkqy1uyRNN8ZcJOlmSedJGtytmeeMmsOSXpW03Fq7LiwhAQAAAAAAgAi2raZBi1aWqbbhqNtRgqJ4bLaWnDdOMwt63d4akWBrhC039tbP3E4QHslZUmKa2yngg6gp1nSw1r4k6SVjzCA5e9OMk5QtKV1So5zlzt6X9I619phrQQEAAAAAAIAIsq2mQVc8skn1R1rdjhKQxHijK88co8u/NFITctmHI2pUb3Y7QVf7trudIDzyiiRj+m4H10VdsaZDeyFmU/sDAAAAAAAAQA8ONLVo0cqyqC/UZCQP0u++cxb70kQba6WacrdTdHWk3u0E4ZE/ze0E8FGc2wEAAAAAAAAAhNbSksqoX/osJyOJQk20OtooNR90O0U33bdEj1GT5rudAD6K2pk1AAAAAAAAAHpnrdXqrTUqKd/jdpSAzCvK07JLCzUklY3So9Lx6J7RFbVGz5ByJrqdAj6iWAMAAAAAAADEkKq9DSrZskflnxxURXVDVC99Vjw2W0vOG6eZBcPdjoJAxCe4nWBgmnGz2wngB4o1AAAAAAAAQAxYV1WrFet3qGxXndtRApaePEjPfme6JuSmux0FwZCULiVnRdhSaEYxvRTa5AXS+IvcTgE/RGWxxhhzqqSpkoZKypKU5G8f1tp/D3YuAAAAAAAAINwONLVoaUll1C911iHOSCXXz9DYYWluR0GwGCONmCLtfN3tJJ0GZ0pHIql4FETpI6TZ97qdAn6KmmKNMSZf0g2SFkkKxrxHijUAAAAAAACIattqGrRoZZlqG466HSUojKT/+eYXKdTEovxpkVWsGTpe+rjM7RTBl5wlLXxOSsl2Own8FOd2AF8YYxZKelfS7ZJy5Py77e1x4pZenvdsBwAAAAAAAESlbTUNuuKRTTFTqIkz0sMLv6jZk0a4HQWhMHm+2wm6Outf3E4QfOkjpMWrpZxCt5OgHyK+WGOM+ZakVZK8LVBpPR7SyUUbb88DAAAAAAAAUe1AU4sWrSxT/ZFWt6MExeeGDNar/3oehZpYllMojZrudgrH6BnSxLmRkycYJi+QlpRSqIliEb0MmjFmnKQV6tztyUh6U9JvJO2StEadhZhbJVVIypZUKOkCSWe1P2flzMz5vqTG8KQHAAAAAAAAQmNpSWVMzKhJTYzXP517qm6+cLzbURAOZ98sPVXqdgppxs3Oz0jJE4jRM5zXM/4it5MgQBFdrJFTXEnyOL/LWvujjhNjukyU2WKtXedx/m/GmCJJD0s6U9IXJP1Y0kXW2tjYbQ0AAAAAAAADzrqqWpWUR/fHWwlxRt+/uEDfPvtUt6MgnMbPkibNlyqedS/D5AWdhY1IyNOX+ATpuMcMuuQsKa/I2QNo0nwpZ6J72RBUEVusMcbES7pCnTNn1noWanxhrd1ijDlb0kpJV8op2PzJGFNsrY2NOaIAAAAAAACIedZaHTp6TK3HrR5a94HbcQIyryhPyy4t1JDURLejwA0X3yd9tFFqrAn/2OkjpNn3Rk6evqSPkL6zURqUKB1rcX4mpkmG3T5iUcQWayR9UVJq+7GVdF9/OrHWthljFkuaKGmapNPlLJn2X8EICQAAAAAAAIRC1d4GlWzZo/JPDqqiuiHq96c5bXiq/t/FEzWzYLjbUeCmlGxp4XPSyoul5oPhGzc5yxk3JTsy8vSlI2/qKc55Uu/NEf0iuVhT4HF8VNLrfbRP6OmJ9oLNDyS9KGffmxuNMT+21tqe7gEAAAAAAADcsK6qVivW71DZrjq3owSFkfSDORP17XPGuh0FkSKnUFq8WnrysvDMaEkf4RQ+cgojI09f+sqLmBTndoBedJQ4raSd1to2L208iy3JffT3iqSG9uPhks4ILB4AAAAAAAAQPAeaWrTo12W6+rG/xEyh5nNDBmvdd8+jUIOT5RRKS0qdPWRCafICZ5y+Ch/BypM5KrD7fc2LmBPJxZpUj+P6HtocklOcl6Ss3jqz1h6X9JHHpUn9jwYAAAAAAAAEz6rSXSr+z1e0fvtnbkcJCiPp2nNP1YbvXaCxw9LcjoNIlZItXfao9I+/k0bPCG7fo2dI//iM03/3pc9CkadjvFu2Bna/P3kRUyJ5GbRGj+OeVuRrkJTefjzShz6bPY5ZHBMAAAAAAACuOtDUolue3hIzRRpJGpwQr99fP10FuRluR0G0GD/LedS+K1U8K1VvlvZs6bqHTHKWNPQ0ycRLtk3a9/7Jz+cVSfnTpEnzpZyJoc/T03iB3o8BKZKLNXs9jjN7aLNDUn778TQf+vScg8Z+NQAAAAAAAHDNtpoGXfXrMn3WeNTtKEGTk5GkVVcXU6hB/+RMlHLuco6tlVoOScdapEGJUmKaZExn276eD3eeUNyPASWSizXvtv80kkYZYxKsta3d2pRLOqe9zfnGmBRr7WFvnRljiiXlqLNIsy8EmQEAAAAAAIA+batp0BWPbFL9ke4fd0WveUV5WnZpoYakJrodBbHAGCkpvec1l/p6Ptx5Qn0/Yl4k71nznpw9aSQpXpK3HZVeav9pJWVIusNbR8aYJEnLO07bf24OTkwAAAAAAADAdweaWrRoZVnMFGom5Wdo5aIvafkVUynUAEA/RezMGmvtMWPMm5Jmt1+aJWlLt2ZrJH0qaZicIsydxphTJP1cTrEnUdLZkn4oZ5k0295uh7W2POQvAgAAAAAAAOhmaUmFahuif+mz1MR43X95kb42KdftKAAQ9SK2WNPuj+os1nxD0j2eT7YXdP6fpF+qsxBzbfvDk/F43kpaFrrIAAAAAAAAQFdVextUsmWPXnvvU22raXQ7TsBY8gwAgivSizW/k/RTOcugFRtjiq21ZZ4NrLW/MsacL+mb6tyPpvvOTNbj+BfW2t+EKC8AAAAAAABwwrqqWq1Yv0Nlu+rcjhIUxWOzteS8cZpZMNztKAAQUyK6WGOt3W+MGSanWCNJTT00vUrSB5JulzTYy/NGUqOkH1pr/zvoQQEAAAAAAAAPB5padMvTW7R++2duRwlI5uAETc7P1JSRmZo7JV8TctPdjgQAMSmiizWSZK096EMbK+luY8zPJc2TdKakHDlFmr2SSiX93lobG19hAAAAAAAAQMRaVbpLP/rTu2o9bvtuHMEe/uYXNXtSrozpvogNACDYIr5Y4w9r7ady9q/5pdtZAAAAAAAAMLDEymwaydmT5uLJI9yOAQADRkwVawAAAAAAAAA3bKtp0FW/LtNnjUfdjhKwnIwkLbu00O0YADCgUKwBAAAAAAAAArCtpkFXPLJJ9Uda3Y4SsMzBCVp1dbGGpCa6HQUABpQ4twMAAAAAAAAA0epAU4sWrSyLiUJNTkaSnr72TBXkZrgdBQAGHGbWAAAAAAAAAH56saJGv3xjh7ZW16v1uHU7TsDmFeVp2aWFzKgBAJdQrAEAAAAAAAB89JOX39OvN+xS49FjbkcJiuKx2Vpy3jjNLBjudhQAGNAo1gAAAAAAAAB92PnZIS38VZmqDx5xO0pAMt47zTUAACAASURBVAcnaHJ+pqaMzNTcKfmakJvudiQAgCjWAAAAAAAAAL1a/bca3fDbd9QWpaudTRs9RL+86gwlDopTamK8jDFuRwIAdEOxBgAAAAAAAOjB6r/V6Pqn3lGU1mkkSTfMPE3Z7EUDABEtzu0AAAAAAAAAQCTa+dkh3fDb6C7UzCvKYz8aAIgCFGsAAAAAAACAbqy1+sdH347apc8kKScjScsuLXQ7BgDAByyDBgAAAAAAAEiq2tugki17VP7JQf1l1wEdPdbmdqR+yxycoFVXF2sIy58BQFSgWAMAAAAAAIABbV1VrVas36GyXXVuRwmKYWmJeuKaL6sgN8PtKAAAH1GsAQAAAAAAwIB0oKlFS0sqVVK+x+0oQTNzwjA9cHkRM2oAIMpQrAEAAAAAAMCAs62mQYtWlqm24ajbUYIiId7o3y6ZqKvOGuN2FABAP1CsAQAAAAAAwICyraZBVzyySfVHWt2OEhTMpgGA6EexBgAAAAAAAAPGgaYWLVpZFhOFmuKx2Vpy3jjNLBjudhQAQIAo1gAAAAAAAGDAWFpSGdVLn00ckaGZBcM0d0q+JuSmux0HABAkEV2sMcacLukPHpcWW2tf70c/50v6dfuplTTbWrs98IQAAAAAAACIFuuqalVSvsftGP02d8oI/fQfvuh2DABACMS5HaAPSySNaX8c7k+hRpKsteslHfXo69pghAMAAAAAAED0WLF+h9sR+i0nI0l3z53kdgwAQIhEerFmXvtPK+nJAPt6vP2nkfSNAPsCAAAAAABAFKna26CyXXVux+iXzMEJWnV1sYakJrodBQAQIhFbrDHGfEFSrsel5wPs0vP+0caYsQH2BwAAAAAAgChRsiU6lz8blpaop689UwW5GW5HAQCEUMQWayRN9Dg+ZK3dFmB/2yQd8jhn3igAAAAAAMAAUf7JQbcj+G3mhGF66ZbzKNQAwAAwyO0Avchv/2klfRxoZ9Zaa4zZrc4i0KhA+wQAAAAAAEDks9aqorrB7Rg+S4g3+rdLJuqqs8a4HQUAECaRXKxJ8zgO1m/TRo/j9CD1CQAAAAAAgAh26Ogx1R9pdTuGT2ZOGKYHLi9ifxoAGGAiuVjjWVgZEqQ+szyOW4LUJwAAAAAAACJY63HrdoQ+FY/N1pLzxmlmwXC3owAAXBDJxZp97T+NpJHGmARrbb+/AmGMSZQ00uPSZ4GEAwAAAAAAQHRIiDduR/AqaVCcrjlnrOZOydeEXBaBAYCBLJKLNR96HA+WdJ6kVwLo7zxJKR7nHwXQFwAAAAAAAKJEWtIgZQ5OiKil0IykNTedo7HD0vpsCwCIfXFuB+jFXyTVS+qYp3pHgP193+O4SdJbAfYHAAAAAACAKGCM0aT8DLdjnGAkPbzwixRqAAAnRGyxxlrbJmm1nN9fRtL5xphb+tOXMeZfJc2UU/ixktYGsqQaAAAAAAAAosuUz2X13SgM4oxTqJk9aYTbUQAAESRiizXt/kNSm5wCy/9n797joyzv/P+/rpxDCIRYTSAKUiogCYLyW7SAB6yVihXcCtW1buuhalFbsa2H7nYrbXf7tdpa2a3HquCK1gpuK7YotqVgISoWS0yiiAcOCkmqQkgIhJyu3x93YoYxh5nMfZrk/Xw85jFz33Pf1+cz9z3K5P7c13UZ4A5jzI+MMamx7GyMSTXG/Bi4PaINC/zYo3xFRERERERERCSE5kweEXQKHD0smz9/+3QVakRE5BNCXayx1r4OPEBnkSUF+HdgizHmBmPM+K72M8aMb+9NswX4Nzo/pwUesta+5nnyIiIiIiIiIiISGuMLhzD12PxAYudmpbHwrONYf/OZGvpMRES6lBZ0AjH4FlAMnEpn75gxwM+AnxljGoAPgf3AYOBTQE77vqb9uWO/tcC1fiUuIiIiIiIiIiLh8Y0zPs3GpXt8iZWXnc6Yo3K46tQxzCop9CWmiIgkr9AXa6y1LcaY84ClwPk4hRfoLMQMbn90uXvEtiuAK6y1LR6lKiIiIiIiIiIiIXbm+ALmTBrByrLdnsWYNaGAey85iZSUUA9oIyIiIZMU/2pYa+ustV8CFgDvcXiPme4etG+3A/i6tfbL1tp6XxMXEREREREREZFQ+eGcYgqGZHrSdsGQTG674AQVakREJG5J9S+HtfZ+nCHQLgQeAt4CWnGKMh2PVuBN4FfABcBnrLUPB5KwiIiIiIiIiIiEyrCcDB65fCpDs9NdbXdodjqPXD6VYTkZrrYrIiIDQ+iHQYtmrW0Flrc/ADDG5AK5QL16z4iIiIiIiIiIJA9rLfsPtdDcaklPNQzOTMMY0/uOCRhfOITfXH0KX3t4IzV1hxJur2BIJo9cPpXxhUNcyE5ERAaipCvWdKW9QKMijYiIiIiIiIhIEthSXcfKzbspe7+Wil117DvY/PF7Q7PTKSkawqSj85g7uYhxhbme5DC+cAjPXX8ai56p5OnNfZ/DZu7kESw6r1g9akREJCHGWtv7ViLSZ8aYYqCiY7miooLi4uIAMxIREREREREJxpotNdy39l02bt8T8z5Tj81nwRljmDn+KG/zWvcuG7fFkdfofBac7m1eIiLir8rKSkpKSiJXlVhrK/2I3S961oiIiIiIiIiISHjtbWji1pWVrCyLvwfLxu172Lh0j6c9WM4cX8CZ4wt4s7qelWW7KHtvH+W79n2ix8/EoqFMOmYocyZ51+NHREQGJhVrRERERERERETEM29U1XHpksTnhnl6825eevcjT+eGGVeYy42F4wFnLp2GplaaWtrISEshJyPV87l0RERk4EoJOgEREREREREREemf3qiq46IHXkq4UNOhpu4QF97/Eluq61xpryfGGAZnppGfk8HgzDQVakRExFOB9KwxxqyJWmWttZ+LYTu3dBlPREREREREJGbWwqF6aG2G1HTIzAVdzBX52N6GJv71Vy+y72CLq+3uO9jM1x7eyHPXn+bJkGgiIiJBCGoYtDMA2/7aRLzuaTu39BRPREREREREpHs1lVC+AnZtgqoyaKztfC8rD4ZPgqIpMHE+FEwILk+RAM35n7/y2i5ve77U1B1i0TOVLL7oRE/jiIiI+EVz1oiIiIiIiIj0ZutqWH8X7CztfpvGWti2znmsvxNGToMZN8DYs/3LUyRAJbc+x/5Drb7Fe3rzbuZOHsGZ4wt8iykiIuKVIOesMe2PWLdz6yEiIiIiIiISmwN7YMUV8PiXey7UdGVnKTw+H576utOOSD/1yzVbOfaWP/haqOlw37p3fY8pIiLihUB61lhrYyoSxbqdiIiIiIiIiOuqK+CxeVBflVg75cth+3q45CkoKHYnN5GQ+OLiF6ioqg8s/sZte3izup5xhbmB5SAiIuIGFUNEREREREREolVXwNJzEy/UdKivgiWznTlvRPqJoAs1HVaW7Qo6BRERkYSpWCMiIiIiIiIS6cAep0dNY6277TbWwrILNCSaJL0t1XVceH9pKAo1AGXv7Qs6BRERkYQFMgyaiIiIiIiISChZC79f6F6Pmmj1VfDsTXDBg960H0bWwqF6aG2G1HTIzAWjKWWT0ZotNdy39l02bg9XwbF81z6stRh9r0REJImFulhjjBkZsVhtrW1KoK0MoLBj2Vq7M5HcREREREREpJ+oqYTyFbBrE7z/N2hu8DZe+XKYOB/GzvI2TpAij2lV2eG9lLLyYPgkKJriHIeCCcHlKTHZ29DErSsrWVm2O+hUurTvYDMNTa0Mzgz1ZS4REZEehf1fse2AbX/9eWBNAm2dCjzf/toS/s8eKGPMGGAqcDSQAewFtgCl1trGAPJJB8YBxUABkAvsBz4CXgMqrLVtfuclIiIiIiJJbOtqWH8X7Cz1P/aGxf2zWBPLMW2shW3rnMf6O2HkNJhxA4w92788JWZvVNVx0f0vsq+xJehUetTU0gaZQWchIiLSd8lQsDB0FmzcaEt6YIw5H/gP4KRuNtlvjFkK/NBa+6HHuYwG5uEU6mYA2T1svs8YswxYbK19y8u8REREREQkyR3YA6tuhIoVweWwYwPUvN5/epUkckx3lsLjpU4vm3Nuh0H57ucncXvwr+9y91/eZu+B5qBTiUlGmqZlFhGR5JYMxRq3CjXSA2NMJvAQ8JVeNh0MXAdcaIyZZ619waNc1gEnx7HbUOBa4EpjzL8DP7fW6rsjIiIiIiKHq66Ax+Z5NydNPCpWQMEPgs4icW4d0/LlsH09XPIUFBS7k5vE7Vu/fpU/vFZFaxL9RT00O52cjNSg0xAREUmIbjsQjDEpwG/4ZKGmFdgGbAb2Rb13JPCsMeazHqSUTveFmsb2nF4BXgei5zHKAO4AfulBXiIiIiIiksyqK2DpueEo1IAzn0uyc/uY1lfBktnOnDfiq80793L8fzzHyrLkKtQATCwaijEaTEVERJLbQCrWZEW89n3OlZC7EZgbte4+YKS19tPW2hOBfOBLwM6IbQYBTxpjhnqc3zZgETAdGNKe01RrbTGQB/wrsCNqn2uMMdd5nJeIiIiIiCSLA3uc3h+RE90HbfdmSOYBAbw6po21sOwCp33xxQPr3uH8e0o52NwadCp9MukYry9LiIiIeG8gFWvGRLyuCyyLkDHGHAH8e9Tq71lrF1hrd3essNa2WWt/C0wDtkdsezTwbY/S2wDMAsZYa39orS211h42WK619qC1dhlwIk5vm0g/NsZosGMREREREXHmUwlLj5oOjbXQtD/oLPrOy2NaXwXP3uRN23KYm5aX8ZNntwSdRkLmTCoKOgUREZGEDYhijTEmFaf3BThz4GwNMJ2wuQnIjVh+Afhpdxtba3cBX49afUN70cctTcAXrbUzrLXPxzL3jLV2L3A+0BCxOg+4wMW8REREREQkGW1d3beJ7/3QEj2yc5Lw45iWL3fiiCf2NjTxrw+9zJOb3g86lYRMHZ3PuMLc3jcUEREJubSgEzDGfDXGTc82xhwdT9M4w3SNBuYAx0W891Ic7fRb7XPVXBa1elFvxRFr7Z+NMX8FTm1flQt8GbjXjbystU3AH/qw325jzCPANRGrZwG/ciMvERERERFJUuvvCjqD7qVlBJ1B3/h1TDcshrGz/Ik1gLxRVcelSzZSU3co6FQStuD0Mb1vJCIikgQCL9YAS3F6u3SnY4a4GxOIYSJiWODRBNrqT6YBR0YsvwusjXHfh+gs1oDTq8WVYk2C/srhxZqRQSUiIiIiIiIhUFMJO0uDzqJrWXmQMTjoLOLn5zHdsQFqXoeCCf7EGwDeqKpj/n0vsv9QS9CpJGzu5BHMHH9U0GmISFCshUP10NoMqemQmQvG9L6fSEiFoVjTobf/kvr6X5ptf3Tsf4e1tqKPbfU350Yt/zGWIcc6to1aPsMYk2Otbehya//sjVrWLIMiIiIiIgNZeUiHPwMYMTk5Lyr5fUwrVkDBD/yN2U+t3LyLG54so7Ut1j/9w6tgSCaLzisOOg0R8VtNpfPv0K5NUFXmzP/WISsPhk+Coikwcb4K/ZJ0wlKs8fLXaUfbZcDP2yejF8fkqOWYb41qH3JsO3Bs+6oMYALwiiuZ9V30rIIfBZKFiIiIiIiEw65NQWfQvaIpQWfQN34f0zCfwySxt6GJCx94ka01+4NOxRVDs9N55PKpDMtJ0mEERSR+W1c7Q3D21LOzsRa2rXMe6++EkdNgxg0w9mz/8hRJQBiKNdFzpnQwwMN0Dl/2M+D1ONptw5lsfg9Qaa39oM8Z9l/HRy3Hc3w7tj82qr2gizWnRi1vDSQLEREREREJnrXOXbdhVTIv6AziF8Qx3b3ZiZuMvZBC4E+vV3PVo5voB51pAKdHzSOXT2V84ZCgUxERPxzYA6tudHpZxmtnKTxe6vSyOed2GJTvfn4iLgq8WGOtfaS794wxD0csrrbWrvEhpQHBGJPNJ+dzeS/OZqK3H9f3jBJnjBkCRP+1syqIXEREREREJAQO1R8+PEqYjJqenMOzBHFMG2uhab8zF4HE5YF17/CTZ7cEnYZr5k4ewaLzitWjRmSgqK6Ax+ZBfVVi7ZQvh+3r4ZKnoEDDJ0p4BV6siYFunfHGpzj82DYD/4izjV1Ry0HP6vd9IHJ2zg+B37sZwBhzFHBknLuNcTMHERERERGJUWtz0Bl0b/rCoDPom6COaUsTZAYTOlk9+cp7/aZQM3V0PgtOH8PM8UFfdhAR31RXwNJz3btBoL4KlsyGy1apYCOhFfZizeiI19WBZdE/DY5aPmCtjbdTdEMvbfrGGDMN+HbU6v+01h5wOdQ1wK0utykiIiIiIl5ITQ86g65NnJ+84+cHdUzT1JMiHts+2M9NT70WdBoJu3bmGOZMKmJcoXpViQwoB/Y4PWrc7snZWAvLLoAFpRoSTUIpJegEemKt3RHxOBR0Pv1MdGGlsQ9tHOylTV+093Z5AkiNWP0K8Msg8hERERERkZDIzIWsvKCzOFzucGfc/GQVxDHNyoOMwO4NTErz7nsx6BQS9t2zj+PGWeNVqBEZiFbdmPjQZ92pr4Jnb/KmbZEEhbpYY4yZFXQO/VhW1HJTH9qILqBl9zGXPjPGZAK/BY6JWF0PXGytbfU7HxERERERCRFjYPikoLPolJXnjJefzHfzBnFMR0x24kqvrLXc9uwbfNTQlz/xw+OEoiFcd+bYoNMQkSBsXQ0VK7yNUb7ciSMSMmEfBu1ZY8x24CFgibV2d8D59CfRPWn60qc8esTgvvTO6TNjTAqwDJgWsboV+Iq19m2Pwt4DLI9znzHA0x7kIiIiIiIivSmaAtvWBZ2F06Omv0xs7PcxLZriX6wktKW6jpWbd1P2fi0Vu+rYdzDEczXF4ISiIaz85qlBpyEiQVl/lz9xNiyGseonIOES9mINwCjgR8CtxphngV8Bq6y1bcGmlfT2Ry1H97SJRXRPmug2vXYPMC9i2QJXWmuf8SqgtfYfwD/i2cfoDjARERERkeBMnAfr7ww4h/nO0GfJ3KMmkt/HtGRe79sMQGu21HDf2nfZuH1P0Km45rtnH6ceNSIDWU0l7Cz1J9aODVDzOhRM8CeeSAySoVgDYHBy/WL7o8oY8zDwsLV2e5CJJbHowsogY4yx1to42sjppU3PGGP+H3B11OrvWGuX+JWDiIiIiIgkgYJiGDnNv4s/kUZNh+kLYezZ/sf2kp/HdNR0XUiLsrehiVtXVrKyrP8MPpKblUr5oi8EnYaIBK3c4+HPolWsgIIf+BtTpAdhL9b8Dqc4k4bTawKcws0I4N+BfzPG/Bl4AHjaWtsSSJbJ6UOcY9rR7SMdOAqoiaONoqjluHqc9JUx5hbglqjVP7LW/sKP+CIiIiIikmRmLITHfSgsZOTA0f/kDNtVMq9/Fxn8OqbTF3ofI4m8UVXHpUs2UlMXPYVscpp09FCevm5G0GmISFjs2tS/44n0ItTFGmvtl4wxBcBlwOXAZzrean9OAc5qf3xojFkKPGitfcvvXJONtfagMWYnzjBzHUYSX7FmZNTyloQT64Ux5lrg/0WtXmytvdXr2CIiIiIikqTGznKKJ15OWDzhfJi/FAbKMMh+HNOJ8/tfr6QEvFFVx0UPvJT0c9Kkpxqe+eYMxhcOCToVEQkTa6GqzN+Yuzc7cQfKv90SeilBJ9Aba22NtfY2a+1Y4EzgCaAJp0dIZG+bI4HvAluMMX8xxvyLMSYjkKSTR3RxJd7bvo7vpT1XGWO+CvxP1OqHgRu8jCsiIiIiIv3A7Dsgd7g3becOhy/+YuBd7PH6mJ5zuzdtJ6G9DU1cumRj0hdqUgw8dsXJKtSIyCcdqofGWn9jNtZCk99TcIt0L/TFmkjW2rXW2otxhkG7AXidzmG8Igs3pwHLgN3GmF8YY4p9TzY5bI5anhbrjsaY4cCxEauacc6HJ4wxF+AUZiL/+nkSuDLOeXZERERERGQgGpQPlzwFWXnutpuV57Q7KN/ddpOBjqlvbl1ZmfRDn6WnGp646hSmfvqIoFMRkTBqDagY3dIUTFyRLiRVsaaDtXavtXaxtXYiToFhCXCAT/a2yQe+BbxmjCk1xlxqjMkOJOlw+n3U8lnGxHwrWHRf9L9Yaz0pRRtjzgEeB1IjVv8BuMRa2+ZFTBERERER6YcKiuGyVe71Bskd7rRXMIDvD9Qx9dyaLTWsLNsddBoJyctO45lvzmDqaBVqRKQbqenBxE3TwEwSHklZrIlkrX3JWnsFMBxYAGyi6942JwMPAVXGmHuMMSf5nmz4lAIfRix/Gjgjxn2viFp+2o2EohljTgeeAiL/z/kXYJ61Nrn7f4uIiIiIiP8KimFBqTMfSiImznfaUVFBx9Rj9619N+gUEjJ38gj+8t2ZGvpMRHqWmet+T83eZOVBxmB/Y4r0IOmLNR2stfuttfdba6cCJwL3Avvo7G1j2h9DgKuBV4wxm4wxVxpjBgWVd5Dae6UsjVp9a2+9a4wxnwNOjVhVjzMkmauMMf8f8AwQ2RvqJWCOtbbR7XgiIiIiIjJADMqHCx6Ei5+EUdPj23fUdLh4ubO/hunqpGPqira2NnbXHuC19/byRlUdf9v2ERu37wk6rT6ZOjqfJZf+E4svOpFhObpzXUR6YQwMn+RvzBGTg59vzlporIOGj5zntrbDl72c/SE6tmaaCFxa0Al4wVpbBlxrjPkO8GPgO3T2sulgcIo69wG3G2PuB+6w1n7ka7LB+ynwDaCjjHw6cDNwW1cbG2OKgAejVi+21n7Y1fYR+0Uf/5nW2rU9bF8MPAfkRqzeDJzj1XBrIiIiIiIywIyd5TxqXoeKFbBrE+zefPgEx1l5zsWcoilQMg8KJgSXbzLQMY3bsxVV/Pef3+LdDxo41JLcI32nGPjS5CKuPH0M4wpze99BRCRS0RTYts7feEGoqYTy9n8jq8oO/zfysFk+cP7NHD7JyXXi/MT/zewpttuxJG6mP87N3j4vzUXAlTjDn31ik/ZnG/V6P3CztfY+z5MMEWPM94CfRK2+F/hPa+3u9m1SgDnAYmBkxHa7gWJrbS09iKdYY4wZDvwNGBGxugHnfH7Q44fpgrX2T/Hu46b2wlNFx3JFRQXFxerWLyIiIiISStZC035nwuG0DGd4lKDvuk12OqZd+sUf3+SBF97lYHNyF2g6zBz3KZZc1tUlGBGRGNVUwr3T/Iu34EV/CxJbV8P6u2Bnad/bGDkNZtwAY6OnE/cgdl9jJbnKykpKSkoiV5VYayv9iN2veta0z0NzJfAvOD0yuirK1AK/ASbQOZRXx/u5wN3GmGJr7Tf9yjsEfgpMA74YsW4BcJUxZgfOcHKjgeiBIw8CX+6tUNMH4zi8UAOQAzzex/b0V4CIiIiIiMTGGGfc/MygE+lHdEwPs+2D/Vz84MtU7esfo3vnZqaxfMFnNSeNiCSuoNgpECRSzIjVqOn+FWoO7IFVNzo9ThO1sxQeb58n7pzbex8+NJHY8caShCX9nDXGmFxjzDeMMZuAV4CrcOaliewzZoCNwOVAkbV2gbX2dGA88AucHjXQWbS5xhjzBR8/RqDa566ZDzwR9VYq8Gmc4eKiCzUfAbOttRu8z1BEREREREQk+a16rYoz71zXbwo1cyeP4IWbZqpQIyLumbHQnzjTfYpTXeH0FnKjUBOpfLnTbk0PHT7cih1LLHFF0hZrjDHTjDFLgCrgbpyCQmRPGnCGzrofONFae4q1dqm19mBHG9bardba7wDHAv/L4QWeBd5/ivCw1jZaa/8FmIczN0x3GoB7gAk9zTkjIiIiIiIiIp1WvVbFNY+/2m/mb/7viyaz+KITGZaTEXQqItKfjJ3lzGfmpYnz/Rnaq7oClp4L9VXetF9fBUtmd11EcTt2T7HENUk1Z40xJh/4KvB14PiO1e3PkUOdbQbuAx6PZzJ6Y8wa4Iz2xRpr7fBEc05WxpjP4Mz3UwRk4Awf9wawwVrbP24B8onmrBERERERERnYtn2wn8/duY625LkE06OHvjqFz00oDDoNEemvDuxxenJ4UeTIHQ4LSr0f0svLzxAt+jP1h+MXIM1Z0wtjzEycuWj+GadwEFmg6SjSHMSZi+Y+a+0rfQy1jM5izRF9zbc/sNa+DbwddB4iIiIiIiIiyeyi+1/kpW17gk7DNd8/93gVakTEW4Py4ZKnnJ4cjS5OlZ2V57TrR6Fh1Y3+FGrAifPsTXDBg97Hjo4lrgp1scYYcwtwBc68KfDJXjQGqMQZ6ux/rbV1CYZ8L+J1aoJtiYiIiIiIiMgAddKPn2dPQ3PQabjqjvknMH/KMUGnISIDQUExXLYKll3gTuEhd7hTqCnwYbSbravdn6OmN+XLneHdwPvYHbHGzvI2zgAU6mIN8BM6CzORvWiagBU4vWjcnOC+zcW2REREREREROJnLRyqh9ZmSE2HzFwwpvf9gpasebvs4fXv8qPfvxF0Gq46YnAGK67+LKOPHBx0KiIykBQUO0NuPXuTUyDoq4nz4Zzb/Ru6a/1d/sSJtmExvk2MtmGxijUeCHuxJpIBtgIPAEuttV70Id4NPOJBuyIiIiIiIiLdq6mE8hWwaxNUlR0+7EtWHgyfBEVTnAtOBROCyzNasubtkVN/uob39h4MOg3XDMpI5arTPs3Cs8YGnYqIDFSD8p0htybOdwoEO+K4b3/UdJi+EMae7V1+0WoqYWepf/EixXNs3IhV8/qA+LfdT8b6VW3rA2NMG9AM/Ba431r7l4BTEombMaYYqOhYrqiooLjYhy6XIiIiIiISfltXO3fgxnNhZ+Q0mHGDvxefoiVr3h751q9fZWWZT3MTeGxoVhqfKRjMVaeOYVaJ5qYRkZCped0Z5mvXJti9+ZM3CYyY7NwkUDIvmELCn34I6+/0P24QTv0OfO4HQWfhusrKSkpKSiJXlVhrK/2IHfaeNd8DHrbWfhB0IiIiIiIiIiKuObDHmQC4L+PK7yyFx0v9H9YFkjdvj2zeuZd/+dXLHGxuDTqVhN355UmcP3kE0SwB1AAAIABJREFUKSkpQaciItK9gglQ0F4gsBaa9kNLE6RlQMbg4Iff3LUp2Ph+Gkif1SehLtZYa38adA4iIiIiIiIirqqugMfmJT5hcvly2L7evwmTkzVvj/zn7yt5cP32oNNwxdzJI/jSSUcHnYaISHyMceZHyww6kXbWOkOCDhS7NzufOegCWT+i2yVERERERERE/FJdAUvPTbzg0aG+CpbMdsbI91Ky5u2BNVtqOPWna/pNoaZgSCaLzkveopmISGgcqj98WLb+rrHW6dkkrlGxRkRERERERMQPB/Y4PVPcvpDTWAvLLnDa90Ky5u2yvQ1NfOvXf+fypX/jvb0Hg07HFUOz03nk8qkMy8kIOhURkeTX2hx0Bv5raQo6g35FxRoRERERERHpF6y11Dc2s6ehifrGZqy1Qad0uFU3utczJVp9FTx7kzdthzBvv8/1qvIqZvx0DSvLdnsax08FQzL5zdWnML5wSNCpiIj0D6npQWfgvzQV+90UyJw1xpjTgogbyVr7QtA5iIiIiIiISGK2VNexcvNuyt6vpWJXHfsOdt7VOjQ7nZKiIUw6Oo+5k4sYV5gbXKJbV0PFCm9jlC+HifNh7Cz32gxR3kGc6zVbarjz+a1U7K5zpb2wmDt5BIvOK1aPGhERN2XmQlbewBkKLSsPMgYHnUW/EkixBlgLBHmLkyW4zy4iIiIiIiIJWrOlhvvWvsvG7d0PobXvYDMb3v6IDW9/xD1r32HqsfksOGMMM8cf5WOm7dbf5U+cDYvdLdaEIO8gzvXehiZuXVnZr3rSAEwdnc+C0wP6b0BEpL8zBoZPgm3rgs7EHyMmO59ZXBN0wUJnU0RERERERGKWyEX0jdv3sHHpHv97FdRUws5Sf2Lt2AA1r0PBhMTbCjjvoM71G1V1fPWhl/lgf/KPwz80O52JRUOZdMxQ5kwKuHeZiMhAUDRl4BRriqYEnUG/E2SxRoUaERERERERidkbVXVcumQjNXWHEmrn6c27eendj3jk8qn+zNdR7vEwYtEqVkDBDxJvJ8C8gzrXb1TVMeeX62luDdl8R3H47TXTGHVEDhlpKeRkpGJ017OIiH8mzoP1dwadhT9K5gWdQb8TVLHmhwHFFRERERERkST0RlUdFz3w0mHzlCSipu4QF97/kj8TrO/a5G37XsULKO+gzvU3Hv0bz1XWuBIzKFNH53PiyGFBpyEiMnAVFMPIaf71TI00ajpY60/sUdPd6cUrhwmkWGOtVbFGREREREREYrK3oYlLl2x07eJ9h30Hm/nawxt57vrTvBsSzVqoKvOm7e7s3uzETaRHRUB5791/yPdzvXnnXv7lVy9zsLnV1ZhBWHD6mKBTEBGRGQvh8QCKNdMXAtaf2NMXeh9jAEoJOgERERERERGRnty6sjLh4bC6U1N3iEXPVHrSNgCH6qGx1rv2u9JYC037E2sjoLx/8vTffD3XD6x7h/PvKe0XhZq5k0cwc/xRQachIiJjZ/k/RNjE+TD2bH9id8QS16lYIyIiIiIiIqG1ZktNnyaYj8fTm3dzzWObeLO63v3GW93tIRKzlqbE9g8o73lbvs2NaU8w1rznSftPb97Nmi3OUGfn/fdf+cmzWzyJ47cjctJZdF5x0GmIiEiH2XdA7nB/YuUOh3Nu9yd2dCxxVVBz1oiIiIiIiIj06r617/oSZ1V5NavKq5l6bD4LzhjjXg+F1HR32olXWoLDugWU98mpWziZLVybtpKX28Zzb8t5rG070dUY1z72Kgeb21xtM0jZ6ak8duUp3g3lJyIi8RuUD5c8BUtme9tTNSvPiTMo3/vYXcUSV6lnjYiIiIiIiITSluo6Nm7f42vMjdv3cNnSV7j+ib+ztyHB3ikAmbnOxQ0/ZeVBxuDE2ggi7ygnp2xhacYd3JX+S/Jwr9dTfyrUDMlO47fXTmN84ZCgUxERkWgFxXDZKm97uVy2yonjdeyeYolrVKwRERERERER71gLjXXQ8JHzbG3Mu67c7O3wZz15evNuvrD4BbZU1yXWkDEwfJI7ScVqxGQnbldiPR9B5N2N81NLeS7zFsaZnUGnEiqfG38U6747U4UaEZEwKyiGBaXOPC9umjjfaben4olbsWOJJa7QMGgiIiIiIiLirppKKF8BuzZBVdnhQ3Bk5TlFgKIpzh//BRO6babsfZ8nuI9SU3eIC+9/id9cfUpiF8SLpsC2de4lFku8SH09H37n3YNCs5ffZPyYC5v+gzftyKDTCdSIvCz+6/yJ7g3VJyIi3hqUDxc86Pw7u2Ex7NjQ97ZGTYfpC2Hs2d7HjjeWJMzYOO5qCpoxJgU4G5gGnAh8CsgDMuNsylprx7icnkiXjDHFQEXHckVFBcXFqkSLiIiISD+0dTWsvwt2lsa+z8hpMOOGT1wIsNYy+Ud/ZN/BYCa6j1QwJJPnrj+t73OC1FTCvdPcTaonC150ii6Jng+/845BtR3GFw7dRi25Qafiuy8UF3DD58cxrnDgfXYRkX6l5nWoaL+JYvfmqHllDBBxvT4rz+kxWzQFSub1eJNLwrHdjpWkKisrKSkpiVxVYq2t9CN20vSsMcZcD3wHKIp+qw/NJU+FSkREREREJOwO7IFVNzp//MdrZyk83j5Exzm3fzxp7f5DLaEo1IDTw2bRM5UsvqiPE90XFDtFkHiKJn01ajrkFsKKK9w5H37lHaNCs5dF6Y+wsPm6oFPx1Q++eDyXz/h00GmIiIgbCiZAwQ+c19ZC035oaYK0DEjPgeaGzuWMwd0Pbep2bLdjSdxCP2eNMWaQMeZ54E7gaA4vznSUGmN5gIo0IiIiIiIi7qqucHpf9KUwEKl8udNOjXPjYnNruP58e3rzbtZsqel7AzMWupdMTyac7+75KPlnd/Jy0fmppcxM+XvQafjmlNH5KtSIiPRXxkBmLuQc4TynpBy+7GXxJDq2CjWBC32xBvhf4CwO7wPWBlRHbfcPoAZoat+249HhQ2AnsKP9WURERERERBJRXQFLz4X6Knfaq6+CJbOhppL01PBdMLhv3bt933nsLGdIES995vPwl/9y93ys+S+n3ZD5RtozQafgi1NG5/PE1Z8NOg0RERHxQaiLNcaYucCX6Owdsxf4OjDUWhs9HNpXrLUjrLXZwHHA1cBmOgs2rcA3rLWjrbWjffkAIiIiIiIi/dWBPfDYvKhx1l3QWAvLLmBwax1Ds9PdbTtBG7ft4c3q+r43MPsOyB3uXkKRBhdA9WvenI/q15z2Q+TklC2MNe8FnYanfvDF41WoERERGUBCXawBbmp/NkAjcKa19mFr7YGedrLWvmOt/ZW19iTgOpzeNkcBzxhjPL6VSUREREREZABYdaN7PTii1VdhnruZkqIh3rSfgJVlu/q+86B8uOQpZwJfN2XlQeEJsD+BYdp6sr8Ghk9yP+8EzUkNz1w6bjpicDrbbztXQ5+JiIgMMKEt1hhj8oBT6OxV8wtr7WvxtmOtvQeY395GGvCIMeYzbuYqIiIiIiIyoGxdnficKL0pX84/51R4G6MPyt7bl1gDBcVw2Sr3etjkDoczvw9v/9Gd9rrz1vNOHK96BvXBJPNO0Cm4atQR2Wy/7Vw2ff/soFMRERGRAIS2WAN8lsPnnXm4rw1Za38PPNC+mAX8Z2KpiYiIiIiIDGDr7/IlzOy6J32JE4/yXfuw1va+YU8KimFBKUycn1g7E+c77VT8X2LtxKryt+7k7ZKJKdvonNo2eWVnpPK7a6ax7sYzg05FREREAhTmYk3knDQfWmt7m8kxq5f372x/NsA/G2PC159eREREREQk7GoqYac/w08NqnqZC4rqfIkVq30Hm2loak28oUH5cMGDcPGTMGp6fPuOmg4XL3f2r6/y7XywYwPUV/c9b5flmQZyaAw0h0SkGJg7eQRv/OgLTB45LOh0REREJGBpQSfQg/z2Zwt0NyhwM52focdijbX2bWPM+8DR7ftMA55zIU8REREREZGBo9zj4c+ifLNgM0/tOs3XmL1pammDTJcaGzvLedS87gwtt2sT7N4MjbWd22TlwYjJUDQFSuZBwYTO93w+H1SsgIIffDLvnS/CDv/nkMmgmQayfY+biBTgytM+zfdmHx90KiIiIhIiYS7WROruVpk64Aicgk5hDO1U4RRrADRvjYiIiIiISLx2bfI13LGNW5gz6SJWlu32NW5PMtI8GKSiYIJTBAGwFpr2Q0sTpGVAxmAwpuv9fD4fn4jXkXdjHdx2jL+5AE2k+x4zEXMnj2DRecUMy8kIOhUREREJmTAPgxY5a2NuN9vsjXj96RjajLz3ScOgiYiIiIiIxMNaqCrzN+buzfzwvAkUDHGrK0tihmank5OR6m0QYyAzF3KOcJ67K9QEdD7oas6ezFynB5CPam0ODb2OiB4OaSmG/7noRBZfdKIKNSIiItKlMBdrtrU/G+DIbrZ5PeL1tJ4aM8ZkAmPpnH3wQELZiYiIiIiIDDSH6g8fnssPjbUMS2/ikcunMjQ7+F4UE4uGYrornvgtoPNB0/5PrjcGhk/yNZXyttE4lwzCbVBGKr//1gzOmzwi6FREREQkxMJcrKmMeH2kMSa/i21ebX82wFRjzHE9tPdVnHltOn7JvZ94iiIiIiIiIgNIa3MwcVuaGF84hN9cfUrgPWwmHTM00PiHCfB8dKloiq9plNkxvsbriyNzM/m/a6YxvlCDe4iIiEjPQlussdbuAnZErJraxWYdMylanM/yiDHmE7+cjTGnAHfQ2asG4K8upSoiIiIiIjIwpAbUsyXNGTZqfOEQnrv+NOYG2ENhzqSiwGJ/QsDn4xMmzvM1jZWtPQ6wEbi5k0fw/MLTVKgRERGRmIS2WNPuTxGvZ0e/aa19nc6iiwVOBt4wxvzcGHOVMeY6Y8wTwAs4c9SY9u2esdbWeJu6iIiIiIhIPxPAvCRk5UHG4I8Xh+VksPiiE3n40v+PqaO7GoDBO1NH5zOusLspVQMQgvNxmIJiGOlPAeXltvFstcf4EiteI/MHseTSf9L8NCIiIhKXsBdrOnrOGOBCY0xXv3KuAyL7fhcCC4F7gcXAfCCNzl41+4GbPMlWRERERESkPwtgXhJGTHbiRjlzfAFPXv1ZVi88jXNPKPQllQWnh2zYrRCdj4/NWOhLGve2nOdLnHhdddpoXrhpJjPHHxV0KiIiIpJkwl6s+RPwf8AfgI3AidEbWGvLgXlAE509Z2h/3fEL0ra/rgPmWWu3epu2iIiIiIhIP+XzvCS9xRtXmMvdF09hziRvh0abO3lEOC/Ah+x8MHYWlHg7HNrvWqextu0TlwcClZ2Ryu+umca/zZ4QdCoiIiKSpEJdrLHWtlpr51lrz2t/vNzNdr8HTgB+h9PLJvI2H9O+7tfASdbaP3qdt4iIiIiISL/l87wksV74/+GcYgqGZHqSQsGQTBadV9z3BqyFxjpo+Mh5trb3fWIVxvMx+w7IHe5J+Go7jEXNX/Ok7fhYBnOAfOr48sShvPHDWUweOcyFZj38roiIiEiopQWdgFustW8BXzLG5AAnAQU4hZpqYJO19kCQ+YmIiIiIiPQLHfOS7Cz1Ptao6VAQW0+FYTkZPHL5VC68/yX2HWzufYcYDc1O55HLp8Y/90hNJZSvgF2boKoMGms738vKc4YvK5oCE+fH/Bm7FMbzMSgfLnkKlsw+/HMnqNbm8LWmm6klmHmDxpmdzEktZZJ5h5KU7eSZBueNt4CfJnBO/fquiIiISKgZq7s0RDxljCkGKjqWKyoqKC5O4K48EREREZGgbV0Nj3/Z+zgXL4exZ8e1y5bqOr728EZq6g4lHL5gSCaPXD6V8YVDYt9p62pYf1d8xZOR02DGDXF/1sNihvF81FTCsgugvirh0NV2GF9rupk37ciE24rXzJS/8420Zzg5ZUvsO8VyToP4roiIiEiPKisrKSkpiVxVYq2t9CN2qIdBExERERERkRDyYV4SJs7v0wXp8YVDeO7605g7ObE5bOZOHsFz158We6HmwB5YcYVTNIm3l8vOUnh8Pjz1daedeIX0fPyyMp3JHyzid63TEgr9u9ZpfOHQbb4XavKoZ3H6L1mScUd8hRro+ZwG+V0RERGR0Ap1zxpjTOQvsWprbVMCbWUAhR3L1tqdieQmEiv1rBERERGRfunAHrh3miu9Jj4hdzgsKHWG00rAmi013LfuXTZui/2i9tTR+Sw4fQwzxx8Ve6DqCnhsnjvHIne4M4RYQZx/M3h5PkwKXPoHGBV70eWLi1+goqr+4+W+9E55uW0897acx9q2E+NK1w3jzU6WZvyUQrM38cYiz2kYvisiIiLSrSB71oS9WNMGdCT4eWvtmgTa+hzwfPuitdb2m/l6JNxUrBERERGRfqum0vV5ScjKg8tWuXoB+s3qelaW7aLsvX2U79p32Jw2Q7PTmVg0lEnHDGXOpCLGFcY5H0p1BSw9NxzHwIvz0Yecogs1kcaa9z6e92ViyrbOeV9w5qQpbxtNmR3DytZpbLXHuJZ+PMabnTyR8ePDcktYVh6c99/wzLfC8V0RERGRLqlY0432Yg04BRs3ijV/7GjPWpuaaH4isVCxRkRERET6NRfnJfGjp4C1loamVppa2shISyEnIxVjTN8aC2Pvoh2lTvHItvW+rQc5/XLNVn72/FsxNmjJoZEMmmkinQaygD6eC5fkUc9zmbe406MmmkkJ7LyIiIhIbDRnTc/CW00SEREREREZ6AqKnQvFE+cn1s7E+U47HvcQMMYwODON/JwMBmem9b1QA7DqRm8KNeC0++xN8e/3ykPeFASgx5y2VNdx+3Nb4ijUABgayGYvQ2ggm6ALNQA/TH/Em0INBHJeREREJHkkQ7FGREREREREwmxQPlzwIFz8JIyaHt++o6bDxcud/ZOpZ8DW1VCxwtsY5cudOLEKIKc1W2r48n0v8oW7/so9a9/xNnacTjxmaFzbz0z5O3NTSz3KxmPxfldEREQkdAbSvC1ZEa8bA8tCRERERESkvxo7y3nUvO4UDXZtgt2bD5+jIysPRkyGoilQMg8KJgSXbyLW3+VPnA2LnWMaCx9z2ls0k1tXVrKybLc/MfvgZ1+ezJgjB3Pbqjd49KUdHGhq7XLoDgMMykjlR0P/CF1PtZMc4vmuiIiISOgMpGLNmIjXdYFlISIiIiIi0t8VTICCHzivrYWm/dDSBGkZkDEYEhl6LAxqKmGnTz0wdmxwil+9FbV8zumaXyzjxf0F/sTro8GZziWPW2Yfzy2zjwegpaWFLTX7+bC+iU/lZjC+YDBpaWnO8bt3c5DpJi7W74qIiIiE0oAYBs0Ykwr8a/uiBbYGmI6IiIiIiMjAYQxk5kLOEc5zshdqAMo9HmosWixDm/mc0/TGtb7Gi5cBjhyc8Yn1aWlplBTlccb4oygpynMKNeD/OfWK18PgiYiIiGcC71ljjPlqjJuebYw5Op6mgUHAaGAOcFzEey/F0Y6IiIiIiIhIp12bwhfP55wmmXDNTxNtaHY6KSlx3J/q9zn1Sn/5HCIiIgNQ4MUaYCl0OWxsh47brm5MIIaJiGGBRxNoS0RERERERAYqa6GqzN+Yuzc7cbvrlRRAThNTtuH8eR3OnlJjjsqJfeMgzqlXevuuiIiISGiFaRg008Wjt/djecDhvyDvsNZWePYpREREREREpP86VA+Ntf7GbKx15v3pTgA55ZkGcmj0NWY8rjp1TO8bdQjinHqlt++KiIiIhFZYijVe3vLRUbQpA75qrf2eh7FERERERESkP2ttDiZuS1P37wWUUwYBHYte5GalMaukMPYdgjqnXunpuyIiIiKhFYZh0C7rZr0BHqZz+LKfAa/H0W4b0ADsASqttR/0OUMRERERERERgNT0YOKmZXT/XkA5NRHQsejFFTNGx7dDUOfUKz19V0RERCS0Ai/WWGsf6e49Y8zDEYurrbVrfEhJREREREREpGuZuZCV5++wWVl5kDE4VDnV2hwayPItXqyOHpbNwrPGxrdTEOfUK719V0RERCS0wjIMWk+6mr9GRERERERExH/GwPBJ/sYcMbnnCeMDyKm8bTRh+1M9xcCjl0+Nf8cgzqlXevuuiIiISGiFvVgzOuKxIeBcRERERERERKBoSvji+ZxTmR3ja7zeGODur5zE6CP72KvE73Pqlf7yOURERAagUBdrrLU7Ih6Hgs5HREREREREhInz/I1XEkM8n3Na2TrN13g9STFwzyUncU7J8L434vc59Uos3xUREREJpVAXa0RERERERERCp6AYRvpUrBg1HQom9L6djzm93DaerfYYX2L15uhh2fz526cnVqgBf8+pV2L9roiIiEgoqVgjIiIiIiIiEq8ZC/2JMz2OOD7ldG/Leb7E6UluVhoLzzqO9Tef2fehz6L5dU69Es93RUREREJHxRoRERERERGReI2d5f2QUxPnw9izY9/eh5x+1zqNtW0nehqjN/dfMoXyRbNYeNZYdxv245x6Jd7vioiIiIROUhVrjDF5xphvGGP+1xhTboypMsYcNMa0xvloCfqziIiIiIiISJKbfQfkJjj8Vndyh8M5t8e/n4c5VdthLGr+midtx+q7Zx/HrJJC7wJ4eU6NR5dg+vpdERERkVBJimKNMSbTGPML4H3gbuArQDFQAGQCpg8PERERERERkb4blA+XPAVZee62m5XntDsoPzQ51docvtZ0M7XkutpuPE4oGsJ1Z7rcmyaal+d0/iPh+q6IiIhIqIS+WGOMORJ4BfgWMCjqbRvx6GpdV++LiIiIiIiIuKOgGC5b5V5vjNzhTnsFxaHJqdoO48Km/+BNO9KV9vrihKIhrPzmqf4E8+qcTpgTvu+KiIiIhEaoizXGmFTgKaAEpzeMbX+uBl7k8F4yrwEvAW8BLXyyB802YF374wUf0hcREREREZGBoKAYFpQ684YkYuJ8px03Lr67lNPvWqfxhUO3BVqo+e7Zx/lXqOng1TkN43dFREREQsFYG95OJ8aYrwJL6ewZsx240lq7pv39toj3Ph+xPgs4FbgWmNO+TSPwLWvtQ37lLwJgjCkGKjqWKyoqKC7WD2oRERERkX5p62rYsBh2bIh9n1HTYfpC7yaI70NOL7eN596W81jbdqI3OcUgNyuV8kVfCCz+x7w6p2H8roiIiAxwlZWVlJSURK4qsdZW+hE77MWaMmBi++JeYLK19r2I97ss1kS1MRt4FBjWvu0N1tr/9jRxkQgq1oiIiIiIDEA1r0PFCti1CXZvhsbazvey8mDEZCiaAiXzoGCCrzkd2vEKB3dsIs80fPxWrc2hvG00ZXYMK1unsdUe409OXZh09FCevm5GYPG75dU5DeN3RUREZIBSsaYLxpgCoIrOYswt1to7orbptVjTvt1UnOHPMoFW4GRr7aueJC4SRcUaEREREZEBzlpo2g8tTZCWARmDwZje94tRW1sbNXWHONDcyqD0VAqGZJKS0v2o521tbYz5t1UMopEMmmkinQayOHwkcX8dOTiD5284nWE5GYHlEBevzqnH3xURERHpWZDFmjQ/gvTRye3PHXPVLOtl+25/vVhrNxpjfg78G848Pf8FnONGkiIiIiIiIiI9MgYyc53bB13ybEUVv3rhXd75oIG6g81E3oZpgCHZ6Yw5MoerTxvDrJLCw/ZNSUlhSHYG+w4aGsh2L6k+GpqdzqNfPzl5CjXgyTn1tF0REREJvTAXayJ/TVZZa6t62T6rl/fvBb6H87v188aYT1lrP0wkQRERERERERE//eKPb/Lw+u3UH2rpdhsL7DvYzKs7a7l62SZyM9O44tTRLDxr7MfbjDkyh1d31nbbhl8KhmTyyOVTGV84JOhURERERALVfb/o4OW3P1ugppttmiJe93g7kLV2F7C9fdEA0xJJTkRERERERMQv2z7Yz/Tb1rD4z2/3WKjpSv2hFu7601vM+Okatn2wH4ArT/u0F2nGZe7kETx3/Wkq1IiIiIgQ7p41kb8+D3WzTR3wKZyCzogY2vwAGN3+enRPG4qIiIiIiIiEwarXqrju16/SluCUs+/vPcjn7lzH3V85iXNKhpObmRZ34ccNU0fns+D0Mcwcf5TvsUVERETCKszFmsj+2N3dZvMhTrEG4DMxtJnTzWsRERERERGR0Fn1WhXXPv4qCdZpPtZm4Zplr3LPJSdx+YxjWfznt11quXuZaSn807H5TDpmKHMmFTGuMNfzmCIiIiLJJszFmnfanw2Hz18TqQIY3/761J4aM8bkAuPg49+49YkmKCIiIiIiIuKVbR/s57pfu1eo6WCBax97lT9/+3RWbNrFrtqDLkfoNGJoFhtuORNjjGcxRERERPqDMM9ZUxHxepgxpqCLbV5pfzbACcaYKT20dy1OcarjF+KOxFMUERERERER8cYlD21MeOiz7rRZ+NeHN7LsiqmkeFRHSTHw2NdPVqFGREREJAahLdZYaz8A3oxYNa2LzZ7EuSnI4hRhHjPGjIreyBgzH1hEZ6+aVuAFN/MVERERERERccsv/vimpz1ewJnD5nebd3H3V07C7XKKAe7+ykmMPnKwyy2LiIiI9E+hLda0+2PE69nRb1prdwDP4PwOtMBY4HVjzFPGmJ8YY35mjHkJeALIiNju19ba2uj2RERERERERMLg4fXb/YmzYTvnlAznnktOcq2HTYqBey45iXNKhrvToIiIiMgAEPZizZPtzwaYb4zp6pacbwH72l9bIBs4H7gZuAGYSmeRBqAGuMWrhEVEREREREQS8WxFFfWHWnyJVd/YwuqKas4pGc6fv306Rw/LTqi9o4dl8+dvn65CjYiIiEicQl2ssdauB34O3A08ChzXxTY7gVnAXg4vyny8CZ3DpL0PzLbWVnmYtoiIiIiIiL+shcY6aPjIebYeTXQirmtpaaFs517WbvkHZTv30tLSwq9eeNfXHB746zsAjD5yMOtvPpPrP/cZcrPS4mojNyuNhWd64rN5AAAgAElEQVQdx/qbz9TQZyIiIiJ9EN+vrwBYa2+MYZuNxpixwPeBLwMjojbZBfwv8DNr7V73sxQREREREfFZTSWUr4Bdm6CqDBojRnrOyoPhk6BoCkycDwUTgstTPuEnf3idZS/v5EBTa9CpAPDOPxoOW77h8+O44fPjWF1RzQN/fYd3/tHAvoPNh90ZaYCh2emMOSqHq04dw6ySQl9zFhEREelvjO2Hd1wZY44BCnB+P1Zba98LOCUZwIwxxUBFx3JFRQXFxcUBZiQiIiIiSW3ralh/F+wsjX2fkdNgxg0w9mzv8pJeXXh/KS9vC9/9gwZ45yfnkJLS/eAbbW1tfLC/if2HWhicmcaRgzN63F5EREQkGVVWVlJSUhK5qsRaW+lH7ND3rOmL9uKMCjQiIiIiItJ/HNgDq26EihXx77uzFB4vdXrZnHM7DMp3Pz/p1gtb/8GlS16hLaT3Slrgg/1NFAzJ6nablJQUCoZkUeBfWiIiIiIDSr8s1oiIiIiIiPQr1RXw2DyoT3D6zfLlsH09XPIUFKi3tx++/9vXWPZy+O8l3H+oRYUYERERkQCpz7KIiIiIiEiYVVfA0nMTL9R0qK+CJbOdOW/EUzc88fekKNQADM7UvZwiIiIiQVKxRkREREREJKwO7HF61DTWuttuYy0su8BpXzzx2Ivb+e3m3UGnERMDHDk4I+g0RERERAa0UBdrjDGFxpiHIx5T+9jOyVHtaIBmEREREREJv1U3utejJlp9FTx7kzdtD3B7G5r496eTp+fS0Ox0UlJCfXlAREREpN8Lez/nq4BLceY7/BC4po/tlAPnAR1Fmkrg54kmJyIiIiIi4pmtq6FihbcxypfDxPkwdpa3cQaYz925NugU4jLmqJygUxAREREZ8MJ+68yFEa+XWWsb+9KItfYAsAynd7cBLnYhNxEREREREe+sv8ufOBsW+xNngFizpYY9Dc1BpxGXq04dE3QKIiIiIgNeaIs1xpgi4PiIVU8l2GTkLWmTjDGfSrA9ERERERERb9RUws5Sf2Lt2AA1r/sTawD43v+VB51CXHKz0phVUhh0GiIiIiIDXmiLNcAJEa+bgb8l2N7f2tsBp3fNpATbExERERER8Ua5x8OfRfN6uLUBYkt1HTV1h4JOIy5XzBgddAoiIiIiQriLNR2/GC2w3VrblEhj1tpDwPaIVernLSIiIiIi4bRrU/+O10+t3Lw76BTicvSwbBaeNTboNEREREQESAs6gR4MiXhd61KbeyNeD3WpTREREREREfdYC1Vl/sbcvdmJa4y/cZNUY2Mjz5T/g6p9Bxk+NJvzJh5FVlYWm7Z/GHRqMUsx8OjlU4NOQ0RERETahblYEzkj4yCX2syOeK2/QkREREREJHwO1UOjW/erxaixFpr2Q2auv3GTyJz/+Suv7arr8r0bE51h1WcGuPsrJzH6yMFBpyIiIiIi7cJcrPmg/dkAI1xqsyjidfLc8iQiIiIiIgNHa3Pv23ihpQkygwkdZiW3Psf+Q61Bp+GaFOMUas4pGR50KiIiIiISIczFml0Rr4cZY0601v69r40ZY04E8iNWVfU5MxERERGRgFhr2X+oheZWS3qqYXBmGkZDV/UvqenBxE3LCCZuSP1yzVZ+9vxbQafhqqOHZfPo5VPVo0ZEREQkhMJcrHkRaAI6/lJZAFyVQHvXRLxuBUoTaEtERERExDdbqutYuXk3Ze/XUrGrjn0HO3teDM1Op6RoCJOOzmPu5CLGFWoYq6SXmQtZef4OhZaVBxm6gN/hjNvXsH3PwaDTcE1uVhpXzBjNwrPGBp2KiIiIiHQjtMUaa+0BY8wGYGb7qsuMMcustS/E25Yx5nTgUsC2r3rJWrvPnUxFRERERLyxZksN9619l43b93S7zb6DzWx4+yM2vP0R96x9h6nH5rPgjDHMHH+Uj5mKq4yB4ZNg2zr/Yo6Y7MQd4NZsqWHBo5s41Gp73zhEDJ1/7HYsD81OZ8xROVx16hhmlRQGlJmIiIiIxCq0xZp2P8Mp1lggFXjaGDPfWvunWBswxpwFLAdS6PwN+3MPchURERERccXehiZuXVnJyrLdce+7cfv/z969h8dVnYf+/y5dbcuSbCdBssTNuAXFl0iExqGGAOaccmux2wKBpjncmoY4ySmkLbm0aTBpf22aNCk+bYlDSbDT3AikCco5hDQpMQQ7RAmJFdsgCJirbCsB25ItbEuW1++PGWFZvkmay56Rvp/nmWdm9uz9rndgZqyZd693baNt5TaWtDSw7NK5TK+ytVVRajwjv8WaxjPyN1YByuQ9l7SqilLWL7uAX+/qY9fefUytLOMNUysoKSlJOjVJkiSNQkH/9RZj/A7wQw4UWWqBB0IId4UQ3ny0Y0MIbw4hrAQeSB9HOsbaGON9uctakiRJGrsntvRw0fKHM/7R+L51m7lo+cN0bO3JUmbKq/mX53e8eXker4Bk6z2XlP915kmUlJRQVzOJ2W+YSl3NJAs1kiRJRajQZ9YAXAn8BGggVWwpAa4Grg4h/Ap4DPgVsAuYChwHnJG+hgOFngC8CFyRz+QlSZKkkXpiSw9X3fHoQWvSZKKrZy9Xfu5R7r7hTJrqa7ISU3lSNxdOXAgv5GGpzZPOgro5uR+nAGX7PZeED1/yxqRTkCRJUhYU/Ok2McatwCXA8xzcijcAdcDFwDXA+9LXF6e3DzZcHizUbAIuTseTJEmSCsr23j6uvast6z8ad+/u55ovtLG9ty+rcZUHZ9+Un3HOytM4BSZX77l8OvOUGUmnIEmSpCwp+GINQIxxPXA6cHd602DR5kirPg4+Nrjfl4A3xxgfz3GqkiRJ0pjc0rqRrp69OYnd1bOXZd/emJPYyqFTL8x9e7L5V8CpF+R2jAJ1S+uGnL3n8qEkwNfe/dtJpyFJkqQsKYpiDUCMsTvG+EfAHGAFqZky4SiXp4F/AZpijFfHGG3WLUmSpIL0YEdXztfLuG/dZh7s6MrpGMqBSz4F1TNzE7t6Jlz8ydzELlAdW3v45AMdXLz8YVrbtySdTkZWXveWpFOQJElSFhXDmjUHiTE+CbwXIIRQD8wGZgDVwE5gG/B0jNFvopIkSSoKK1Zvys84D23i/Ka6vIylLJkyA975DbjrEtizI3txJ01LxZ0yMdpoPdjRxYrVm2h7blvSqWTF1WeeyDmnHnfsHSVJklQ0iq5YM1R6/RnXoJEkSVLR6tjak7cfkNue3caTW3dyWn11XsZTltTNhevuhy9dBjuzMBukemaqUFM3N/NYBW57bx+3tG7M+cy1fLr8zY18/PfnJ52GJEmSsqxo2qBJkiRJ41Hruvz+iNza3pnX8ZQldXNh6drUGjOZmH9FKs4EKNQ8saWHi5Y/PK4KNX//+3P5p7e3JJ2GJEmScqCoZ9ZIkiRJxa79pSy2thrJeC9253U8ZdGUGXDZnamCy5rl8PyakR970llw1k1w6gW5y6+APLGlh6vueJTu3f1Jp5IVr5tazvc/cB7TqyqSTkWSJEk5YrFGkiRJSkiMkQ2dPXkdc31nNzFGQgh5HVdZdOqFqUvX47DhXuh8DDavO3hNm0nToKEFGs+AeZdD3Zzk8s2z7b19XHtX27go1NTVVPKJP3wTi5pcn0aSJGm8s1gjSZIkJWTX3n15/0G5e3c/vX0DTK30q0DRq5sDdR9L3Y4R+nbBvj4oq4CKqTBBC3K3tG6kq2dv0mkc0+kNVWza3nfQZ0BVeWB+Yy1nzHodi5sbXV9KkiRpAvEbmiRJkpSQ/oGYyLh9+/ZDZSJDK1dCgMrqCf//9cGOrqJZo+bzf7KQ6VPK6e0boG/ffirKSqiqKHXWmyRJ0gRlsUaSJElKSHlpMj/KVpSVJDKulGsrVm9KOoURqygrIYSQmuU2wYtskiRJAr+lSZIkSQmZWllG7eTyvI5ZO7mcqorSvI4p5UPH1h7antuWdBojEsD3oSRJkg5isUaSJElKSAiBeY01eR1zfmOtbZY0LrWuK472ZwBn/cbrfR9KkiTpIBZrJEmSpAQ1Hz8tv+OdUJvX8aR8aX9pR9IpjJjvQ0mSJA1nsUaSJElK0OKWhvyO19yY1/GkfIgxsqGzJ+k0Rsz3oSRJkoazWCNJkiQlqKm+hgUnz8jLWAtmzeC0+uq8jCXl0669++je3Z90GiPi+1CSJEmHY7FGkiRJSth7zjslL+MsPXd2XsaR8q1/ICadwoj5PpQkSdLhWKyRJEmSEnZ+Ux2Lm3PbDm1JSwOLmo7L6RhSUspLQ9IpjIjvQ0mSJB2JxRpJkiSpANy6eC51NZU5iV1XU8myS+fmJLZUCKZWllE7uTzpNI7K96EkSZKOxmKNJEmSVACmV1Ww6voFWf/BuXZyOauuX8D0qoqsxpUKSQiBeY01SadxRL4PJUmSdCwWayRJkqQC0VRfw903nJm1GTZ1NZXcfcOZNNUX7o/YUrY0Hz8t6RQOy/ehJEmSRsJijSRJklRAmupreODGc1jSktkaNktaGnjgxnP8gVgTxuIM3zO54PtQkiRJI1WWdAJHE0K4Ossh9wHd6cvzMcYXsxxfkiRJytj0qgqWX3U6S1oaWPHQJtqe3TbiYxfMmsHSc2e7iLkmnKb6GhacPIO250b+fskV34eSJEkarYIu1gArgZir4CGEXwNrgc8D98cYczaWJEmSNFrnN9VxflMdT27dSWt7J+0vdrO+s5vu3f2v7VM7uZz5jbU0n1DL4uZGTquvTjBjKVnvOe8U2lbmvlhTEmD/kG+Pvg8lSZKUqUIv1gwKOYp7HLAkfXkyhHB1jPGnORpLkiRJGpPT6qu5ub4JgBgjvX0D9O3bT0VZCVUVpYSQqz+XpeJyflMdi5sbaG3fnLMxFjfPZPlVp/s+lCRJUlYVw5o1Q//ijcMuRzOSfQe3B6AJWBtCuHLsqUqSJEm5FUJgamUZM6oqmFpZ5g/E0jC3Lp5LXU1lTmLX1VRy6+J5vg8lSZKUdYU+s+a69PVk4GbgZA4Ub54l1cLsCWAHsBeoARqAFuBsoJIDxZo7gEeBqcAM4E3A20jNrhks2pQBK0MIz8YY23L4vCRJkiQpJUbYuxMG+qG0HCqrYRz9+B9jZNfeffQPRMpLQ86LG9OrKlh1/QKu/NyjB7UMzFTt5HJWXb+A6VUVWYspSZIkDSroYk2McVUIoQG4nwOFmh8CH44x/uhox4YQpgHvBv4aqAbeBXTEGJcP2acM+CPg08DrSBVtKoF/Bs7K9vORJEmSJAC6NsL6e6HzMdjSDnt2HHhs0jSY2QyNZ8D8K6BuTnJ5jlHH1h5a122m/aUdbOjsOWSdpXmNNTQfP40lLblZ36Wpvoa7bziTa77QRlfP3ozj1dVUsur6BTTV12QhO0mSJOlQIcZjdRNLTghhMvBjYB6pQso/xhj/apQxfhP4HnBiOsafxBhXDtvneFKzdBpJFYQisCjG+HCmz0EKIcwFNgze37BhA3Pnzk0wI0mSJCXmqe/CI7fBC2tHfsyJC+HsD8CpF+Quryx5sKOLFas30fbcthEfs+DkGSw9bzaLmo7Lej7be/tY9u2N3Ldu7GvYLGlpYNmlc51RI0mSNAFs3LiRefPmDd00L8a4MR9jF3qx5u+BD5MqnrTGGP9gjHHeTKroUwrsAmbHGH89bJ/zgAc50BLtkzHGj4wxdek1FmskSZLEq9vg/pthw71jjzH/Crj4kzBlRvbyypLtvX3c0rqR1vbCLIo82NHFioc20fbsKIpIs2aw9NzcFJEkSZJUmCzWHEYIoQL4Fal1aCLQEmNcn0G8e4E/TMf6aIzxHw6zz6PAgvQ+P4oxnj3W8aRBFmskSZImuK0b4MuXw84tmceqngnv/AbUFc7fk09s6eHau4qj3diTW3fS2t5J+4vdrO/sPqQ92/zGWppPqGVxc27as0mSJKmwJVmsKeQ1a87iQKFmayaFmrTvkirWAFwMHFKsSe+zgFQrtOMzHE+SJEnD5HuhcSlxWzfAyt89eE2aTOzcAnddAtfdP/qCTYywdycM9ENpOVRWQ4bvvye29HDVHY8eVPTIRFfPXq783KPcfcOZxy7YjOH5nFZfzc31TenDI719A/Tt209FWQlVFaV+HkmSJCkxhVysGbqKZmcW4g3GCMAbj7DPE0Nuvy4LY0qSJE14SS80LiXm1W2pGTXZKtQM2rMDvnQZLF177JZoXRth/b3Q+RhsaT84l0nTYGYzNJ6RarFWN+fIcQ7j3p++yIe+8QsGstysoXt3P9d8oY0Hbjzn0JZoWXw+IaQKxlRmN39JkiRpLAq5DdpfAX9HamZNR4wxo3n+IYTLga+n7+6LMR7SCDmEcCHwnfTd/hjjhP2zPYQwm9Qso+OBCmA70AGsjTHuSTCvALwZaAEGm0d3Ae3Az2IBvqBtgyZJmqgKbaFxKe/u/ZPM1qg5lvlXwGV3Hv6xp74Lj9wGL6wdebwTF8LZH4BTLzjqbn/9zV9wz09foi/bVZphlrQ0sPyq01N3cvh8JEmSpEG2QTu87enrAJwcQpgSY3w1g3jzDxN7uElDbu/OYKyiFUL4feBvSBVEDmdXCGElcGuM8eU85lUO3AjcBDQeYbeXQgi3Af8nxpidPgySJGnUMllovO25bbSt3JbThcalvHjqu7kt1ACsvydVsDn1wgPbXt0G9988trFfWAtfWZuKefEnD5m185UfP8+y1sfpG9ifYeIjc9+6zVz2xsmc88tP5eT5SJIkSYWkJOkEjuKFIbcnAVeNNVD6h/4/JjVLJwIvHmHX+vR1BH491vGKUQihMoTwJeCbHLlQAzAVeD/weAjhnDzldgLwY+BTHLlQA6lZQP8E/CiEcLT9JElSjjyxpYeLlj88pkLNUPet28xFyx+mY2tPljKT8uyR2/IzzprlB25v3QCfXZh5kWj9Pak4XakTCLf39nH5Z9fyV9/ckLdCDUBTeIF5912S9ecjJSnGyM49/Wzr7WPnnn4KsDmEJElKSCHPrHkI2EOqg3AAPhFC+H6M8YWjH3ZYfweckr4dgQeOsN/QIsWmMYxTlEIIJcDdwJJhDw2QKpp1A7OA2iGPvQH4Tgjhf8YYf5TD3I4DfgDMHvbQblL/j0rSuQ2dFXUG8IMQwsJ8zv6RJGmiS3ShcamQdG0cXbuuTDy/Broeh7gfVv5u9tbH2bkF7rqEH569inc9sJu9+/JXpIFUoeZrFX/LtP292QmYfj5cdz/U2ZJY+eXabZIkaSQKdmZNjHEX8J+kCjUReD3wSAjh7JHGCCFMDiEsB/4yHQNgH/DFIxzyO0Nu/3zUSRevmzm0ULMCODHGeEqM8XRgBvCHHDzjaQrw9RBCLbmzkoMLNXtItUJ7fYxxXoxxDqnXxp+nHxv0m8AXcpiXJEkaYntvH9fe1Za1Qs2gwYXGt/f2ZTWulFPrc9z+bLif/wd8+fLsFWoG7dnBb37vWibv685u3GOYxk5WVvwj00KWCjWD9uyAL12WahUn5cGDHV28fcWPuOi2H3L76mdY8/Qrh/w72b27nzVPv8Ltq5/hwtse5u0rfsQPOn6VUMaSJClJBVusSftLUrM6IFVsOR54KITw/0II7wghnDz8gBDClBDCb4cQ/j/gl6RadgUOFH1uizE+dZjjFgInc6Co83B2n0phCiG8DvjrYZs/EmNcGmN8rX9JjHF/jPGbwELguSH7Hk+qUJKL3C4ALh6yqR+4MMa4fOj6RTHG3hjjPwMXpfcZdGkIYVEucpMkSQe7pXUjXT17cxK7q2cvy75t+yIVkc7H8jve+ntTM0dyoD5sZ1n5qpzEPpJby1dRH460zGiGdm6B73wwN7GltO29ffzZV3/O9St/SttzoysOtj23jetW/oQbv/ZzT1SQJGmCKehiTYxxK/B2DsyYiKSKLhcB/wE8E0J4NYSwNYTwQghhG9ADPAJ8GGjgQJEG4DvAR48w3OBf7AHYBXw/y0+nUH0QGDrP+mHgH4+0c4yxE3jXsM0fSBd9su1vh93/RIzxiEW0GONDHJr732U9K0mSdJAHO7oyXqPmWO5bt5kHO7pyOoaUFTHClvb8jtmb27Pwf790LYtK8tN4YFHJz1lSmuMWcuvvgae+m9sxNGG5dpskSRqrgi7WAMQYvwdcAnRycOFlcLbMJOA4UjM8ppF6TmHw8CH7fh74gxjjkXpzfAZYlL6cG2Mc96ewpNequW7Y5mXxGCscxhj/G/jhkE3VpIpq2cxtPrBgyKZe4FMjOPST6X0HLQwhvDGbuUmSpIOtWJ2fpf5WPDRhlhRUMdu7M/vtyArAe8q+Pa7GYc3y/IyjCWVw7bZszTQdXLvNgo0kSRNDwRdr4LUZE3OBfyY1cyYMffgwFzhQzHkU+J0Y458epVBDjPHhGOND6cu6HDyNQrQQeMOQ+5uA1SM89vPD7v9+NhIaYvgaOl+PMe481kHpfe4ZtjnbuUmSpLSOrT2jbvEyVm3PbuPJrcf8c0BK1kB2120qFG8t6eDU8GJOxzgtvMBbSzpyOsZrnl8DXY/nZyxNCK7dJkmSMlUUxRpI/QgfY/wLUq3N3gHcDrQBm4FXgQFgG/A0cB+pdVjeFGNcmJ4JokP97rD73zvWrJqh+w67f14IoSoLOQ0antt/jeLY4bn9Xoa5SJKkI2hdl9v2Z4eM196Z1/GkUSstTzqDnFmc4/ZkuY5/iA335nc8jWuu3SZJkjJVlnQCoxVj3A18LX1RZlqG3R/xt6MY4+YQwnPAyelNFcAc4CeZJhVCCMCbxpobsGbY/eYQQhhFIUqSJI1Q+0v5bffU/mJ3XseTRq2yGiZNG5et0JrDM0Ud/xCdj+V3PI1b+Vq7bUlLA+c31eV0HEmSlJyimVmjnBi+lsto+wAM3z9ba8OcBEwZcr83xvjCSA+OMT5ParbVoCrghCzlJkmS0mKMbOjMbx/99Z3deP6FCloIMLM56SxyYn7JsxzoOp1tkXklz+Uo9hFsXgd+nigLXLtNkiRlg8WaCSqEMBk4cdjm0TahHr7/aWPP6KhxxtIcO1e5SZKktF1792W9N/+xdO/up7dvIK9jSqPWeEbSGeTEtNBLFXtyEnsqu5kWenMS+4j27IC+XfkdU+OOa7dJkqRsKbo2aMqa1wNhyP1+4FejjDG8afxxGWV05DgvjSFGJwcXaLKSWwjhOOANozxsdjbGliSp0PQPJHNGet++/VCZuh1jZNfeffQPRMpLA1Mry0h1VB3Z45qgYoS9O2GgP7XGTGV1akZMtsy/HB75TPbiFZAK+ullctbjlrMv6zFHZF/fa58nr8n160PjShJrt91c35TXMSVJUn4UbbEmhDAJeAup2SHTgWpgJ7AdeB74aYwxN6d9jQ9Th91/dQxrugw/9W14zLEaHmcsp9jlKrf3ArdkKZYkSUWtvDSZHy+fe6WXO3+4ifaXdrChs+eg2T21k8uZ9foplITA/gjPvtx7yOPzGmtoPn4aS1oaOa2+OomnoCR0bYT196bWKdnSfvCaMpOmpVqXNZ4B86+AujmZjVU3F05cCC+MZtnF4tBHeU7i9if11bSsInWdz9eHxhXXbpMkSdlSVMWaEEIl8A7g3cAZQOlRdt8XQvgpcAfw1RhjXx5SLCbDixdjKWztPkbMsSrk3CRJUtrUyjJqJ5fntRVaaUngD28/8g/g3bv7WXeUH7K6d/ez5ulXWPP0K9y++hkWnDyDpefNZlFTtiYIq+A89V145LajF0727IBnH0pdHvlMqtBy9gfg1AvGPu7ZN8FXxlexZkesopdJOYm9i8nsiFX5bYU2aRo8twbWLM//60PjQpJrtzlLVJKk8ado1qwJIVwIPAPcCSwgVWgKR7mUA2cCXwCeCSH8TgJpF7Lh37LGUszaO+x+tvohFHJukiQpLYTAvMaavI45sD+7rdfantvGdSt/wo1f+znbez23Z1x5dRvc+yfwlbePfobLC2vhK1fAN96VijMWp14I8y4f27EjVZXfIuP6/bM4uJNyNgU27D85R7GPoLQCvnplMq8PjQuu3SZJkrKpKIo1IYS/Ae4HGjjw7eBY39QHHw9AI/BACOGvc5NhURo+W6ViDDGGd3fOVtu5Qs7tdmDeKC9LsjS2JEkFp/n4aUmnkBX3rdvMRcsfpmNrfs+QVo5s3QCfXQgb7s0szvp7UnG6No7t+Es+BdUzM8vhSKpnptbGyaP2mNulGHMd/xC9o12yc5hMXx8qeomu3SZJksadgi/WhBDeC9xKqugS05dA6sf3HwL/CiwDbk5f/2t6+57DHPPxEMJ78voECteuYffH0s9g+GyV4THHqmBzizH+Ksa4cTQXUjPCJEkalxa3NCSdQtZ09ezlys89asGm2G3dACt/F3ZuyU68nVvgrkvG9oP8lBnwzm+k2m1l06RpqbinvzO7cY+hdWBhUcfPiUxeHyp6Sa3dVlFW8D/lSJKkMSjof+FDCCcCn+bggksX8B6gPsZ4bozxz2KMH48xfjp9/WcxxnOBuvR+WzlQtAnAZ9JxJ7rhxYspYfRNb6uOEXOshscZPs5I5Co3SZI0RFN9DQtOnpF0GlnTvbufa77QZku0YvXqNvjy5QcvDp8Ne3bAly4bW8ururlw3f3Zm2FTPTMVr25u6nJifgocP97fxFPxhJyO8WQ8kZ+HN+Z0jJzI5PWhoja4dls+1U4up6riaMv3SpKkYlXQxRrg4xzczupBoCnGeEeMcefRDowx7oox3gE0Ad/jQMGmktQMnInuZQ5uJVcOjLbpdeOw+xn2EThinOPHECNXuUmSpGHec94pSaeQVV09e1n2bc+SL0r335y9GTXD7dwC3/ng2I6tmwtL18L8KzLLYf4VqTh1cw9sO/umzGKO0Gf3XZrzMZa0NPAbf/DRnI+TE5m8PlS0kli7bX5jLaM/z1KSJBWDgi3WhBDKgD/gwIyYjcDFMcZR9aVIF3V+D9gwGBr4wxDChD4VJca4G3hh2ObRzjgavn/H2CDZkTAAACAASURBVDM6yJPD7o/lFL7hx2QrN0mSNMz5TXUsbh4/7dAgtYbNgx1dSaeh0Xjqu5mvUXMs6+9JjTMWU2bAZXfCO74OJ501umNPOgvecU/q+CkHz2R7cH8L9+W4fdi3Bhayev/pOYu/YNYM7rr2LSy/6nSq3/R7MC+/a/FkTSavDxWtfK/d1nxCbV7HkyRJ+VOwxRpgIVDNgRkxfxFj7B9LoPRxf5GORTpuETZEzrrhBYw5ozx+eI+CbBVEngd2D7lfFUI4aaQHp/edMmRTL/BilnKTJEmHceviudTVVB57xyKy4qFNSaeg0XjktvyMs2Z5ZsefemGqjdnSH8Hb/gJOOe/QNW0mTUttf9tfpPa77n449YLDhluxehO39F/D1jg9s7yOYGuczrL+a3ISOwBfuOa3+PoNv82ipiGT/C/5VPbaxuVbpq8PFZ18r922uHl4EwlJkjRelCWdwFHMGnJ7B/D9DON9H9gODH6LOQX4YYYxi9064MIh9xcCq0ZyYAhhJnDykE39wOPZSCrGGEMIvwDeOiy350cYYvipir+IMcbD7ilJkrJielUFq65fwJWfe5Tu3WM6v6bgtD27jSe37uS0+uqkU9GxdG2EF9bmZ6zn10DX41A32vOchqmbA3UfS92OEfp2wb4+KKuAiqkwgjZHHVt7aHtuG1DNNX0f4u6Kv2Va6M0sryF2xCqu6fsQO8jNe+D2d76Z899Yd+gDU2bAO78Bd12S/fWHci1brw8VjcG121LvxdxaMGuG/yZJkjSOFfLMmsFTqyLwXKY/tqePH/pj/xsyiTdO/N9h9/9nGHnz2+Gn9v0gxrgrCzkNGp7b74zi2OH7fjvDXCRJ0gg01ddw9w1njqsZNq3tnUmnoJFYn+P2Z8Nlu91aCFBZDVWvS12P8E/y1nWbX7v9ZDyRK/v+JmszbLbG6VzZ9zc8GUfbKfnYAvDZd76Zi+cdZfZM3dzUjKJinGGT63Z8Kjj5Wrtt6bmz8zKOJElKRiEXawaG3C7PUsyh69Tsz1LMYrYWeHnI/VOA80Z47J8Mu39fNhIaonXY/StCCFOPdVAIoRoYvnJrtnOTJElH0FRfwwM3nsOSPLeFyZX2F7uTTkEj0fnYuB4vxsjOPf1s6+1j555+Bs9ja3/p4FknT8YTuWjvJ/hWhmvYfGtgIRft/UROCjV1NZU8+BfnHr1Q89rOc2HpWpg//M/7Uao67tj7ZFO+X49KXD7WblvS0nBwu0BJkjTuFHIbtF+lrwNwUgihfKxr1gCEEMo5uG3Xr46w64QRY9wfQlgJ/OWQzbeEEFYfbSZTCOF/AG8bsmkn8PUs5/aLEMJPgLekN00FPgh87BiHfhCoGnL/0RhjVtqzSZKkkZleVcHyq05nSUsDKx7aRNuzI28NU1oSGNhfON1L13d2E2Nk5JOPlXcxwpb2/I65eV1q3By+Ljq29tC6bjPtL+1gQ2fPQe0FayeXM6+hhp8+v/2Q43ZQzU397+e+gbN4T9m3eWvJyJeV/PH+Jj6771JW7z89K89hqLKSwJ+cPYuPXDJ82ctjmDIDLrszVbBZszzVZmykTjoLFt4I33z36MbMVB5eHyo8ty6ey4+ffYWunr1Zj11XU8myS+dmPa4kSSoshVys+eWQ21OBS4H/zCDepXBQs+VfHmnHCeYfgfeQ+m8McC7wIeATh9s5hNAI3Dls8/IY48uH23/IccN/dVkUY1x9jNw+BnxnyP0PhxC+H2N8+AhjDOY+1EePMYYkScqR85vqOL+pjie37qS1vZP2F7tZ39l9yI/O8xtraT6hlv/RVMcffjZP646MUPfufnr7BphaWch/Nk9we3fmf12TPTtSa8xUZn/tiAc7ulixetNR17/o3t3PmmdeOWqcH+w/nR/0nc6p4UUWl66lOTzD/JJnD1rTZkesYv3+WbTH2bQOLOSpeELWnsegKRWlXH/WLP7ywtMyC3TqhalL1+OpNmOdj6WKIkP/30+aBg0t0HgGzLs8tW7Mnp5x9fpQ4crV2m21k8tZdf0CpldVZC2mJEkqTIX8rbMNeAWYQWp2zafTP9T3jDZQCKEG+CdS698EYFs6/oQXY3w5hPD3wN8P2fwPIYQTgb+LMW4GCCGUAIuB5cDQfgibgU/nKLcHQgj/xYH1ccqB74YQPgz8e4zx1XRuVcCfAv/AwS3z7o8x/ncucpMkSSN3Wn01N9c3Aal2Tr19A/Tt209FWQlVFaWvzVrZ1tuXZJpH1LdvP4yfZXjGn4Hs/Sg6Kvv6svq62N7bxy2tG2lt33zsnUfhqXgC/7TvyvS9SBV7qKCfPsrpZRKpr0fZV1VRyqff3sxFI2l3Nhp1c6AuPdk+xlRRZF8flFVAxdRDZ7OMk9eHisPg2m3XfKEtKzNs6moqWXX9Aprqa7KQnSRJKnQFu2ZNug3X3aS+PUTgJOAH6ZkdIxZCaAD+m1QLtMFYXz1am68J6B+B/zts21LghRDCMyGEn5EqnH2Tgws1u4G3xxhzeara1cCzQ+5PAm4DXg4hbAghbCS17s4/px8b9AxwbQ7zkiRJYxBCYGplGTOqKphaWXZQe7Hy0sJsGVRRVrB/MgugNFvLW45SWfbOcn9iSw8XLX8464WaQwV6mcx2auhlMrkq1CxpaeCRD52f/ULNcCGkZq9UvS51fbi2Y+Pg9aHikq2125a0NPDAjedYqJEkaQIp9G+et5JaDwVSRZbTgY4Qwq0hhFlHOzCEcHIIYRnQAbw5fTzpeB/PTbrFKca4H7gC+Nqwh0qBU0j9d5827LFXgEtijKNoGj2m3LqARcDwRuSTgbnAHA4u0gCsI9Vm7de5zE2SJGXX1Moyaicn9MPqEdROLqeqojTpNHQ0ldWp9lf5NGlaahZHFjyxpYer7ng0J+tc5NuCWTO469q3sPyq0wunZVORvz5UnAbXbvvCtb/FglkzRnVsQb6PJElSXhRyGzRijL8OIfwp8BUOzIqpIrUOyUdDCM8DvyA1s6I3/djrgGZSM3EYclwABoB3HWt9lYkoxrgH+KMQwr2k/vu2HGHXXmAVcGuM8Vd5yu35EMIC4CbgRuBIpyhtJjXrZnmMsTD7qEiSpCMKITCvsYY1Tx99LY58mt9Ye9DsHxWgEGBmMzz7UP7GbGjJyuLx23v7uPautqyub5FLZSWBffsPNCgYuubU4uZGTqsvwDVaivj1oeI32rXbCvZ9JEmS8qKgizUAMcavhxBqgX8lle9g4QVSrc1OOsxhQ/8yHty/H3hfjPHe3GVb/GKM3wC+EUL4DeCtQCNQAewAngDWpAs7o42b0beVdPHlkyGEfwLOIFWQOy798K9Izab5WXqWkCRJKlLNx08rqGJN8wm1SaegkWg8I78/xjeekZUwt7RuLKoZNe8+ZxbvXfSbh11zqqAV6etD48dI126TJEkTW8EXawBijP8eQngMWAnMG9x8tEPS1yF9+QVwXYzx5zlLcpyJMT4NPJ10HsOlizE/SV8kSdI4s7ilgdtXP5N0Gq9Z3Dyq5RKVlPmXwyOfyd948y7POMSDHV15WKMmu5a0HM/UyjKoTDqTUSrC14fGr8G124rufSRJknKu0NeseU2M8WcxxjcBFwH3kJpNEY5y+RXwdeCCGGOLhRpJkqTC11Rfw4KTR9ffP1cWzJphO5piUTcXTlyYn7FOOgvq5mQcZsXqTVlIJn+K+v1QhK8PSZIkTTxFMbNmqBjjfwH/BRBCOIlUG7TpwFRgF7AdeD7G+HxiSUqSJGnM3nPeKbSt3JZ0Giw9d3bSKWg0zr4JvrI29+OcdVPGITq29tD2XPKv8dEo+vdDEb0+JEmSNDEVzcyaw4kxPh9jfDjGeF+M8cvp64ct1EiSJBWv85vqWNzckGgOS1oaWNR03LF3VOE49cLct5+afwWcekHGYVrXFVv7s3Hwfiii14ckSZImpqIu1kiSJGl8unXxXOpqkmnoX1dTybJL5yYytjJ0yaegemZuYlfPhIs/mZVQ7S/tyEqcfBhX74cieX1IkiRpYrJYI0mSpIIzvaqCVdcvoHZyeV7HrZ1czqrrFzC9qiKv4ypLpsyAd34DJk3LbtxJ01Jxp2S+nlKMkQ2dPVlIKvfG3fuhCF4fkiRJmrgs1kiSJKkgNdXXcPcNZ+Zthk1dTSV333AmTfU1eRlPOVI3F667P3szKKpnpuLVZWd2ya69++je3Z+VWLk0bt8PBf76kCRJ0sRlsUaSJEkFq6m+hgduPIclLbldw2ZJSwMP3HjO+PtheqKqmwtL16bWEMnE/CtScUbwQ/zAwABPd+3kZ89v5+munQwMDBx2v/6BmFlOeTDu3w8JvD4kSZKkYylLYtAQwseSGHeoGOPHk85BkiRJxza9qoLlV53OkpYGVjy0ibZnt2Ut9oJZM1h67uziXzxdh5oyAy67M/WD+prl8PyakR970llw1k3HXCz+zh9u4t9/uImXd/UxsP/QIkxpSeD1Uyu44ZzZXH/2LADKS8OonkY+Taj3Qx5eH5IkSdJohBjzf2ZXCGE/kOgpZTHG0iTH18QRQpgLbBi8v2HDBubO9ew7SZLG6smtO2lt76T9xW7Wd3Yf1FKqdnI5p7y+ipIS2B9h0697D3l8fmMtzSfUsri5kdPqq5N4CkpC1+Ow4V7ofAw2r4M9Ow48NmkaNLRA4xkw73Kom3PUUH/21Z/x/36xhdFMkikN8HvNDdx2ZQstH/9eXluhVZaV8FsnTWfD5h7fD0eSxdeHJEmSitfGjRuZN2/e0E3zYowb8zH2RCzWBCBarFG+WKyRJCl3Yoz09g3Qt28/FWUlVFWUEkIY8eOaoGKEvl2wrw/KKqBiKozgdbHuhe380b//mN39h29xNhKTK0r5jTdMYX3nzjHHGK2zf+P1fOldb/X9MFJjfH1IkiSp+CVZrEmkDVqaf+1KkiQpIyEEplaWQeXYHtcEFQJUVo/qdXHHQ8/w99/pyHjo3X0DeS3UADSfUAv4fhixMbw+JEmSpEwlVaxZlNC4kiRJkjQqH7xnHV9/rDPpNMZscXNj0ilIkiRJOoZEijUxxoeSGFeSJEmSRmp7bx9/9tWf88OnX046lTFbMGvGxF6LRpIkSSoSSbZBkyRJkqSC9MSWHq69q42unr1Jp5KRpefOTjoFSZIkSSNgsUaSJEmShnhiSw9X3fEo3bv7k04lI0taGljUdFzSaUiSJEkagZKkE5AkSZKkQrG9t49r72or+kJNXU0lyy6dm3QakiRJkkbIYo0kSZIkpd3SurHoW5/VTi5n1fULmF5VkXQqkiRJkkbIYo0kSZIkAQ92dNHavjmRsbP1xayuppK7bziTpvqaLEWUJEmSlA8WayRJkiQJWLF6U2Jjh5LAkpaGjGIsaWnggRvPsVAjSZIkFaGypBOQJEmSpKR1bO2h7bltiY0/sD/ymSvexJKWBlY8tIm2Z0eey4JZM1h67mwWNR2XwwwlSZIk5ZLFGkmSJEkTSoyRXXv30T8QKS8NTK0so3VdMu3Phnp+227Ob6rj/KY6nty6k9b2Ttpf7GZ9Zzfdu/tf2692cjnzG2tpPqGWxc2NnFZfnWDWkiRJkrLBYo0kSZKkca9jaw+t6zbT/tIONnT2HFL8KAQ7Xj2Q02n11dxc3wSkiku9fQP07dtPRVkJVRWlhBCSSlOSJElSDliskSRJkjRuPdjRxYrVm47a4mxo4SZJ06YcvmgUQmr2D5V5TkiSJElS3liskSRJkjTubO/t45bWjbS2J9/ebKROmjE56RQkSZIkJcRijSRJkqRx5YktPVx7VxtdPXuTTmXEykoCpaWlSachSZIkKSEWayRJkiSNG09s6eGqOx4tmNZmI/X6qRVJpyBJkiQpQRZrJEmSVLz274edW6DvVaiYAtUzoaQk6ayUkO29fVz9+eIr1AC8+5zZSaegiSpG2LsTBvqhtBwqqyGEpLOSJEmacCzWSJIkqbg83gpr/wVefgr2dANxyIMBJtXC60+FhX8Gcy5NKkvl0Xc2bOHfH97E+s5u+gfisQ8oMKUBrj97VtJpaCLp2gjr74XOx2BLO+zZceCxSdNgZjM0ngHzr4C6OcnlKUmSNIFYrJEkSVJx+ME/wKO3w96eo+wUUz86vtQGX38nVNbAme+DRR/OW5rKn3/+3pN84ZHn2Ll3X9KpZOT3mhuSTkETxVPfhUdugxfWHnmfPTvg2YdSl0c+AycuhLM/AKdekL88JUmSJiCLNZIkSSpsrzwDX1wC3S+O/ti9PfDQP8C6L8PV34LX2WpqPHj217t45+fb6NyxO+lUMja5opTlV52edBoa717dBvffDBvuHf2xL6yFr6xNzbK5+JMwZUb285MkSVLxFmtCCDOANwIzgFqgBPhujLEr0cQkSZKUPRu/BfdeB3F/ZnG6X4B//S24YhXMWZyd3JSI+3+xhfd/9WfsL75uZ4f11Xe9NekUNN5t3QBfvjy1vlcm1t8Dzz0C7/wG1M3NTm6SJEl6TVEVa0IIxwHvBy4Dmg6zy+8AhxRrQgjXASek726OMd6ZsyQlSZKUHRu/Bfdcy8Fr0mQg7oevXw1v/6IFmyJ1/y+28L6v/Cxbr4jEffR330jLidOTTkPj2dYNsPJ3D16TJhM7t8Bdl8B191uwkSRJyrKSpBMYqRDCzcDzwF+TmlEThl2OZiqwDLgFuD2EUJe7TCVJkpSxV55JzajJ+s/yEe65JhVfReXZX+/i/V8dX4Wad73tlKTT0Hj26rbUjJpsFWoG7dkBX7osFV+SJElZU/DFmhBCaQjhP4FPAJWH2WUk39c+D/SQKuqUAu/IXoaSJEnKui8uybz12ZHE/fDF389NbOXMOz//43HR+mxSeQnfeu9CCzXKvftvzrz12ZHs3ALf+WBuYo9zMUZ27ulnW28fO/f0E+M4+GCTJElZUQxt0P4NGPw2HUkVXH4O/BfwQvrxo4oxvhpC+Dbwx+lNlwD/nP1UJUmSlLEf/AN0v5jbMbpfSI2z6CO5HUcZ6djaQ+u6zbS2b6Zzx56k08nYotPewF3XLUg6DU0ET30XNtyb2zHW3wPzr4BTL8ztOOPA4GdZ+0s72NDZQ/fu/tceq51czrzGGpqPn8aSlkZOq69OMFNJkpSkgi7WhBDOBt7NgdkzLwPXxhi/M2Sff2Nks2u+RapYE4CzQggVMca+LKcsSZKkTD16e/7GsVhTkB7s6GLF6k20PTc+2ixNrSzl3qULaaqvSToVTRSP3JafcdYst1hzFCP5LOve3c+ap19hzdOvcPvqZ1hw8gyWnjebRU3H5TFTSZJUCAq6WAN8PH0dSLUxOzfG2DHGWD8ecrsSOA1Yn0FukiRJyrbHW2FvT37G2tsDj38b5lyan/F0TNt7+7ildSOt7ZuTTiVrlrQ0sOzSuUyvqkg6FU0UXRvhhbX5Gev5NdD1ONTNyc94RSKTz7K257bRtnKbnx2SJE1ABbtmTQhhOvA2UrNmIvB3GRRqiDG+BGwfsqkpswwlSZKUdWv/Jb/j/SjP4+mIntjSw0XLHx43hZoFs2Zw17VvYflVp/tjq/JrfY7bnw2X63ZrRSZbn2X3rdvMRcsfpmNrnk5gkCRJiSvkmTVnA6Xp2wPAnVmI+Stgevq2c4olSZIKzctPje/xdFhPbOnhqjsePWgdh0I2bXI5EQ5Zd2J+Yy3NJ9SyuNl1J5SgzsfG93gFLNufZV09e7nyc49y9w1n2kZRkqQJoJCLNQ3p6whsijHuyELM7iG3/fYkSZJUSPbvhz3dx94vm3bvSI1bUrATzse97b19XHtXW9EUagD++MwT+csLTqO3b4C+ffupKCuhqqKUEELSqWmiixG2tOd3zM3rUuNO8Nd/rj7Lunf3c80X2njgxnOcpSdJ0jhXyN9KZwy5na2VRSuH3C6eb4OSJEkTwc4tpM7TyacIu7ryPKaGuqV1I109e5NOY1QWNzcSQmBqZRkzqiqYWllmoUaFYe9O2JON8xxHYc8O6NuV3zELUC4/y7p69rLs2xtzEluSJBWOQi7W5GIWzNDWZy9nKaYkSZKyoe/VZMbd64+MSXmwo6vo1qhZMGuGLc5UuAYSOidxX18y4xaIfHyW3bduMw92eHKBJEnjWSEXa36dvg7ASSGEjHINIZwAzByyqbi+FUqSJI13FVOSGbdyajLjihWrNyWdwqgtPXd20ilIR1Zansy4ZRO7PVe+PstWPFR8n5mSJGnkCrlYM7TR7hTgrAzjXTHk9gDwaIbxJEmSlE3VM0mdp5NPAabW5XlMAXRs7aHtuWx1O86PJS0NLGo67tg7SkmprIZJ0/I75qRpUDFxi975/Cxre3YbT27dmZexJElS/hVssSbG+BTwLAcal//5WGOFEGqAD6RjReAnMUb/wpEkSSokJSUwqTa/Y06elhpXede6rrgmutfVVLLs0rlJpyEdXQgwszm/Yza0pMadoPL9Wdba3pnX8SRJUv4U+jfTL5I6vTIAi0MI14w2QAihNB2nkQOnat6etQwlSZKUPa8/dXyPp9e0v5TnRdAzUDu5nFXXL2B61cRu9aQi0XjG+B6vwOT7s6z9xe5j7yRJkopSoRdr/gn4FanZMAG4M4Rwc7oAc0whhCbgQeBSDsyqeQr4Sm7SlSRJUkYW/u/8jvfbeR5PAMQY2dDZk3QaI1JXU8ndN5xJU31N0qlIIzP/8vyONy/P4xWQJD7L1nd2E2M89o6SJKnolCWdwNHEGHtDCO8CvkmqsFQKfAJ4bwjhq8Bj6V0DqULMGSGEGcBvAOenL4MzcwB2A++I/mUjSZJUmOYshsoa2JuHH78qa2DOpbkfR4fYtXcf3bv7k07jmJa0NLDs0rnOqFFxqZsLJy6EF9bmfqyTzoK6Obkfp0Al8VnWvbuf3r4BplYW9M85kiRpDAr+X/cY4/8NIbyPA63LAnAS8KFhuwZShZzh2wYLM/3AdTHGn+cqV0mSJGXBme+Fh4b/WZeLcd6X+zF0WP0DhX3u1IJZM1h67mwWNR2XdCrS2Jx9E3wlD8Was27K/RgFLKnPsr59+6EykaElSVIOFXyxBiDGeEcI4RngP4B6DhRghhZjBu+/dhgH2qd1AVfEGB/JQ7qSJEnKxKKPwLovQ/eLuRuj9kRY9OHcxddRlZcW3mLklWUlvOtts1jc3Mhp9dVJpyNl5tQLU+3JNtybuzHmXwGnXpC7+EUgqc+yirJC72gvSZLGomj+hY8x/jfwRuCvgC0cKMwEDm51xpDtO4BbgNMs1EiSJBWRq++DkKM/VUMJXP2t3MTWiEytLKN2cnnSabymJMADN76Nmy9sslCj8eOST0H1zNzErp4JF38yN7GLSBKfZbWTy6mqGNEyvpIkqcgUTbEGIMbYHWP8BHAicAZwE/A54F7g+0Ar8AXgb4CzgboY49/GGItj9VJJkiSlvG42XLGKQ8/HyVRIxX3d7CzH1WiEEJjXWJN0GkDqFfZvf/xmZr1hatKpSNk1ZQa88xswaVp2406aloo7ZUZ24xahJD7L5jfWEkLhzU6UJEmZK4o2aMPFGPcDP09fJEmSNB7NWQxv/yLccw3E/ZnHCyWpQs2cxZnHUsaaj5/GmqdfSTSHkpAq1Fw8L0ezD6Sk1c2F6+6HL10GO7dkHq96ZqpQUzc381jjRL4/y5pPqM3bWJIkKb+KamaNJEmSJpg5i+H9P02tMZOJ2hNTcSzUFIzFLQ2Jjn/89Mn895+fa6FG41/dXFi6NrXGTCbmX5GKY6HmIPn+LFvc3JjX8SRJUv5YrJEkSVJhe91s+MB6OPfDUDnKdjOVNXDuR1LH2/qsoDTV17Dg5Py3UaqeVMZN//M3eeRD59v6TBPHlBlw2Z3wjq/DSWeN7tiTzoJ33JM63tZnh8jnZ9mCWTNcV0uSpHGsoNughRD+D7AyxvizpHORJElSwhZ9JHV5/Nvwo3+Bl5+C3TuAOGSnAJOnwetPhd/+3zDn0qSy1Qi857xTaFu5LefjVFWU0jSzmne/bTYXzqvP+XhSwTr1wtSl63HYcC90Pgab18GeHQf2mTQNGlqg8QyYdznUzUku3yKRr8+yped60oEkSeNZQRdrgPcD7wshPAGsBL4cY8xCo11JkiQVrTmXHijC7N8Pu7pg7y6onApT66DEyePF4vymOhY3N9DavjlnYyx+00z+zzvenLP4UlGqmwN1H0vdjhH6dsG+PiirgIqp4AL2o5KPz7IlLQ0sajouZ/ElSVLyiuWb7BzgH4EXQgjfCSFcGUKoTDopSZIkJaykBGpmwht+M3Vtoabo3Lp4LnU1ufnTvq6mkluXzMtJbGncCAEqq6HqdalrCzVjkuvPsmWXulaQJEnjXbF8m41AAEqBC4CvAFtDCJ8LIYyy4a4kSZKkQjG9qoJV1y+gdnJ5VuPWTi5n1fULmF5VkdW4knQ4fpZJkqRMFXqx5jpg9ZD7gw3JA1ALvAt4OITwyxDCR0MIJ+U5P0mSJEkZaqqv4e4bzszaWel1NZXcfcOZNNXXZCWeJI2En2WSJCkTBV2siTGuijH+D+Bk4G+AX5Iq1MDBhZvZwK3AMyGEH4QQrgkhVOU7X0mSJElj01RfwwM3nsOSloaM4ixpaeCBG8/xx01JifCzTJIkjVWIMR57rwISQngrcC3wdmB6evNgm7TB2wCvAv8JfDHG+N/5zFEaKoQwF9gweH/Dhg3MnWu/YUmSpCN5sKOLFQ9tou3ZbSM+ZsGsGSw9d7YLcEsqGH6WSZJUfDZu3Mi8eQetezkvxrgxH2MXXbFmUAihArgUuBq4GCjj4Nk2DLnfCXyRVOHmqXzmKVmskSRJGpsnt+6ktb2T9he7Wd/ZTffu/tceq51czvzGWppPqGVxcyOn1VcnmKkkHZmfZZIkFQ+LNRkKIbwe+GNShZvT05uPVLhpA1bFGFfkL0NNZBZrJEmSMhdjpLdvgL59+6koK6GqopQQwrEPlKQC4meZJEmFLcliTUGvWTNSMcaXY4zL9tg01QAAIABJREFUY4xnAPOBTwNbObhQE9KXtwL/lkiikiRJksYkhMDUyjJmVFUwtbLMHzclFSU/yyRJ0pGMi2LNUDHGjTHGm4ETgEuArwF7SBVsin8akSRJkiRJkiRJGlfGXbFmUIxxf4zxAeDdwEeBvQmnJEmSJEmSJEmSdIiypBPIhZCaR3wBcA2wBJiUbEaSJEmSJEmSJEmHN66KNSGEeaQKNO8A6gc3p6/jkNt78pyaJEmSJEmSJEnSYRV9sSaE8Abgj4GrgebBzenrwXVqQvryI2AVcHee05QkSZLyJsbIrr376B+IlJcGF7GWJEmSpAJXlMWaEEIFqfZmV5Nqd1bGkQs0LwD/AXwxxvjL/GcrSZIk5V7H1h5a122m/aUdbOjsoXt3/2uP1U4uZ15jDc3HT2NJSyOn1VcnmKkkSZIkabiiKtaEEBaSKtBcAUwb3Jy+Hlqg6QX+E1gZY/xBvvOUJEmS8uXBji5WrN5E23PbjrhP9+5+1jz9CmuefoXbVz/DgpNnsPS82SxqOi6PmUqSJEmSjqTgizUhhJNIFWj+FzB7cHP6eugsGoDVpNqc3Rtj7M1jmpIkSVJebe/t45bWjbS2bx71sW3PbaNt5TaWtDSw7NK5TK+qyEGGkiRJkqSRKuhiTQjhIeAsDsyYgUPbnD0NfBH4jxjj80nkKUmSJOXTE1t6uPauNrp69mYU5751m3l00yusun4BTfU1WcpOkiRJkjRaJUkncAxv40BRJqa3BWAncCdwdozx1Bjj31mokSRJ0kTw+OZurvzcjzIu1Azq6tnLlZ97lI6tPVmJJ0mSJEkavYKeWTNMBP6LVJuzb8UY9yScjyRJkpQXHVt7aF23mZ8+v42fPLv9tbOYsqV7dz/XfKGNB248x5ZokiRJkpSAYijWPEGqQPOlGOOWpJORJEmS8uXBji5WrN5E23Pbcj5WV89eln17I8uvOj3nY0mSJEmSDlboxZq3xBgfSzoJSZIkKZ+29/ZxS+tGWts353Xc+9ZtZklLA+c31eV1XEmSJEma6Ap6zRoLNZIkSZpontjSw0XLH857oWbQioc2JTKuJEmSJE1kBV2skSRJkiaSJ7b0cNUdj9LVszexHNqe3caTW3cmNr4kSZIkTUQWayRJkqQCsL23j2vvaqN7d3/SqdDa3pl0CpIkSZI0oViskSRJkgrALa0bE51RM1T7i91JpyBJkiRJE0pZEoOGED42fFuM8eMj2S9bDjeeJEmSlIQHO7oSW6PmcNZ3dhNjJISQdCqSJEmSNCEkUqwBlgFx2LbDFU8Ot1+2WKyRJElSQVixelPSKRyke3c/vX0DTK1M6uuCJEmSJE0shdAGLd+n63l6oCRJkgpGx9Ye2p7blnQah+jbtz/pFCRJkiRpwkjyVLmRFk0srkiSJGncal1XOO3PhqooK4TzuiRJkiRpYkiqWLMoy/tJkiRJRan9pR1Jp3CI2snlVFWUJp2GJEmSJE0YiRRrYowPZXM/SZIkqRjFGNnQ2ZN0GoeY31hLCE5wlyRJkqR8sbeBJEmSlJBde/fRvbs/6TQO0XxCbdIpSJIkSdKEYrFGkiRJSkj/QEw6hcNa3NyYdAqSJEmSNKFYrJEkSZISUl5aeK3GFsyawWn11UmnIUmSJEkTSkEXa0IIm9KXZ0IIv51hrIVD42UrR0mSJGmsplaWUTu5POk0DrL03NlJpyBJkiRJE05Z0gkcw8np6whMzjDW5GHxJEmSpESFEJjXWMOap19JOhUAlrQ0sKjpuKTTkCRJkqQJp6Bn1qRZWJEkSdK41Xz8tKRTAKCuppJll85NOg1JkiRJmpCKoVgjSZIkjVuLWxqSToHayeWsun4B06sqkk5FkiRJkiakiVSsGdoMvD+xLCRJkqQhmuprWHDyjMTGr6up5O4bzqSpviaxHCRJkiRpoptIxZqZQ27vTCwLSZIkaZj3nHdKIuMuaWnggRvPsVAjSZIkSQkrSzqBPPq99HUEXkgyEUmSJGmo8/9/9u482tKzrBP2765UVQhJqpJAkiJBMgGpMJgwmEaUKYLQgqEdaAZRAkoA9Wto22irzUdoW0E/FehWIAGZ1BawWyUqgmgYBISgkJAAYUrCEJIQMlQGilRV6v7+2PtQOzs1nXP2OXuf1HWttdd+3+c87/3cRWUtkvrV87wbj8zpJx+V8y76xrKsd+pxh+VFjzkhj9t4xLKsBwAAwO5NPaypqvvs5dQj5zE3SSrJ3ZMcl+Qnk/zYyM8+OY86AACw5F5++gPz8cuvyzU33Tbx2pVBQPPwYw/N6ScfnRM3HDzxNQAAAFi4qYc1Sa7IYLfLrtTw+08nuOY7J1gLAAAW7dAD1+atzzs1Tz/nY9m0eXKvWFx3t9V5xwsekZPutX5iNQEAAJisWXpnTe3ks6ef7+0n2REIva+737dkvwoAAFigjRvW5R0veESOXLf/ROoduW7/vPOF3y+oAQAAmHGzFNYspUqyLcmbMjgSDQAAZtLGDevynhc/Ok895ahF1XnqKUflPS9+dDZuWDehzgAAAFgqs3AM2lt387PnZGRHTJKr5lF3e5Jbk1yf5DNJPtDd1y6oQwAAWEaHHrg2r3nGQ/LUU47K6z94WS64/Pq9fvbU4w7Lix5zQh638Ygl7BAAAIBJmnpY093P3dXPquo5I7e/293nL0NLAAAwE07beGRO23hkPn/1zTnvoitz0dc25eIrN93hnTbrD1iTBx+9Pid/z/qcfvLROXHDwVPsGAAAgIWYelizFyo7dtcAAMA+58QNB+esDRuTJN2dW7fcni3btmft6lU5cO1+qRp/3SMAAAAryayHNaO7bj4ztS4AAGBGVFUO2n91sv+0OwEAAGBSZjqs6e7dvc8GAAAAAABgxVs17QYAAAAAAAD2ZcIaAAAAAACAKRLWAAAAAAAATNFMv7NmZ6pqY5JHJnlIknsmOSTzf71qd/cPTbo3AAAAAACA+VoxYU1VPTXJf01y6mJLJenFdwQAAAAAALB4Mx/WVNV+Sc5J8ty5oewIW2pk6s4CmD39HAAAAAAAYKpmPqxJ8uokzxtej4c0owHMaDCTvfw5AAAAAADAVK2adgO7U1WPSfILGYQunWRLkv+RZGOSu+WOAczjh2NHJXlCklck+ebInM8meUh3r+ru/ZblFwAAAAAAALAHMx3WJPm14XcluT3Jj3b3/9vdX+juLeOTu3tLd1/d3f/U3b+R5Jgkvzf88UlJPlRVj1qWzgEAWHLbt2/PVTduzpevvSVX3bg527dvn3ZLAAAAMG8zewxaVR2Y5Iey4yizc7v7H+dTYxjo/EpVfTnJ65IcnOSvquqB3X3NRBsGAGBZ/P0lV+UNH7osX7721ty0eeudzr1dd8CanHD4gXnBo0/IEx+0YVptAgAAwF6b2bAmySOSzB1X1klev9BC3X1OVZ2W5GlJDk3ym0nOXHSHAAAsm1e97/N504evyM23bdvlnE6yafPWfPKrN+YFf/pvOXj/1fnZRx2Xlzz+/svXKAAAAMzTLB+DdszI9abuvmQP8/ffw89/a/hdSX6qqu624M4AAFg2l197S37glefnNf/0pd0GNTtz823b8up//GJ+8HfOz+XX3rJEHQIAAMDizHJYc9jwu5N8dRdzRv9rfbfhS3d/Osk1I3MfuajuAABYcu/+9FX5oT/4YK68cfOi6nz9hs35oT/4YP7+kqsm1BkAAABMziyHNaNHtN26izk3Z7BTJkmO2IuaXx+5PnEhTQEAsDze/emr8gv/+5PZ3nueuze2d/Lzf/pJgQ0AAAAzZ5bDmk0j1wftYs6NI9fH7GLOqNFf7yHz7ggAgGVx+bW35Bf//JOZUE7zXZ3kF/7sk45EAwAAYKbMclgzd/RZZceRaOMuHbn+d7srVlX7Jblf8t3/5r9tUd0BALBknv3HF0xsR8247Z389JsuWJriAAAAsACzHNZ8ZuT6qKra2e6aC4ffleQHq+qo3dT7sQx26Mwdm+b8CwCAGfSq931+0e+o2ZOv37A5r3rf55d0DQAAANhbMxvWdPcVSa4eGXr4Tqb99dz0DN5x87qqWj0+qaqOT/Ka5A4naXxkMp0CADBJb/rwFcuzzkeWZx0AAADYk5kNa4bOH7n+kfEfdvcnklw8MvSUJP9WVf9PVf1wVT2lql6Z5JNJNmSwq6aTfKi7vzpeDwCA6fr7S67KzbdtW5a1bv7Otrz3kqv3PBEAAACW2KyHNX85cv20qqqdzPnFketK8uAkr07y90neleSsJOtG5mxN8qsT7hMAgAl4w4cuW9b1zv3nLy/regAAALAzsx7W/F0Gu2I+m+SWJI8cn9Dd/5zk5+duh58a+WRkbGuSM7vbG2UBAGbQl6+9dXnX++byrgcAAAA7c6f3u8yS7r4tO39Xzfi8c6rqC0l+P8kpYz+eC2z+Jckvd/e/TLZLAAD2Vnfnltu2ZevtnTX7VQ7af3XmNk9v3749N23euqz9bNq8Ndu3b8+qVbP+d5gAAAC4K5vpsGY+uvv9SR5aVfdL8ogkR2YQ1Fyd5KPd7YwLAIApuPTqm3Lehd/IRV+/MZdceVM2jQQy6w9YkwcdvS4n3/uQPPKEe6SXubdOcu0tW3Lkurst88oAAACww10mrJnT3V9M8sVp9wEAsK87/9Jr8voPXJYLrrh+l3M2bd6aj3zpunzkS9fltR+Yzt+tueW2bTlyKisDAADAwF0urAEAYLpuuHVLXnbeZ3LeRd+Ydit75aD9/SsxAAAA0+W/TAEAmJjPXXVTznjzBbnmptum3cpeqSSHH7R22m0AAACwjxPWAAAwEZ+76qY849yP3eGdNLNu/QFrsmrVqmm3AQAAwD7Of5kCALBoN9y6JWe8+YIVFdQkyQlHHDjtFgAAAGA6O2uq6j7TWHdUd3912j3Mqqp6YJKHJblXkv2SXJfkkiQf7+5tU+rpbklOSvKAJIcnOTDJpmFvn+zuz0+jLwBg4GXnfWbFHH026sxHnTDtFgAAAGBqx6BdkaSntHaGazsCbkRVVZLnJvnVJPffxbTrqup1SV7Z3bcuQ08PSvLjSR6f5BFJ1uxm7jeT/HGSP+zulfE2YwC4izj/0mty3kUr7/9+D77b6jzxQRum3QYAAABM/Ri0muKHoao6JMl7Mwg7dhXUJMk9kvy3JJ8e7r5Zqn6OrqqLk1yc5OVJHpXdBDVDRyT5tSSfq6qfWareAIA7e/0HLpt2Cwvysz943LRbAAAAgCTTD2uYsqo6IIOg5gljP9qS5AsZBCbju2iOT/L+qrrvErV1aJIH7eJntyb5UpILknw+ye1jP1+X5K1VddYS9QYAjLj06ptywRXXT7uNebv3oQfkJY/f3d9RAQAAgOUzraPA3jqldbmzP0hy6sj99iS/leRV3X1DklTV2iTPGs49dDjv8CTvrKrv6+7xwGTSLknytiT/lOTC7t4+94OqWpfkpzPYgXOPkWd+t6o+191/u8S9AcA+7bwLV97xZ6sq+ZPnnbrniQAAALBMphLWdPdzp7Eud1RVG5M8f2z42d3956MD3b0lyVuq6hNJPpzkkOGPHpLkZ5K8eYla/Lsk/6O7P7arCd19U5I/qqrzknwwyeh5Jq+uqvd299Yl6g8A9nkXff3GabcwL5Xkj37qoTnu8IOm3QoAAAB8l2PQ9m0vT7LfyP2fjAc1o7r7M0l+eWz4ZVW1p/fJzNe1SX6gu5+yu6BmrLevJfnxDHYGzTkhyWMn3BsAMNTdueTKm6bdxl5bVclrn/3Q/PsH3WvarQAAAMAdCGv2UVV1aAbhxpxOcvZePPrmJF8ZuT8myeMn11nS3dd090cX8NyFSd4zNvzEyXQFAIy75bZt2bR5ZWxgvfehB+SffukxghoAAABmkrBm3/Xk3PEYvA9092V7emj4vpjxY8/+wyQbW6R/Hru/z1S6AIB9wNbbe9ot7NHBd1udlzz+fvnwr57m6DMAAABm1lTeWcNMePLY/T/M49n35Y67cJ6y6G4m54ax+/VT6QIA9gFr9quprPuap5+St33sinz5m7dm0+atGY2MKsn6A9bkhCMOzJmPOiFPfNCGqfQIAAAA87Giw5qqOjrJYRn8gfyqJBd196bpdrVinDJ2P59jx/4tyW1J9h/eH1VVh3f3tRPpbHGOHru/bipdAMA+4KD9V2f9AWuW9Si09QesyemnHJWnPmTwf/nbt2/PtbdsyS23bctB+6/O4QetzapVNo8DAACwsqyosKaq1iZ5dpKfSPLIJOvGpjwhyfk7ee7JSe4xvP1md4+/12SfUlVrktx3bPize/t8d99WVV9O8oCR4ZOSzEJY86ix+y9MpQsA2AdUVR509Lp85EvL93cjHnz0+lTt2NGzatWqHLnubjly2ToAAACAyVsxf+2wqn4ygxfbvyHJkzLYTVMjn905JYP3rLw5yV9W1b5+NNbxuWNQt7m7vzXPGl8buz9xcS0tXlWdkOQxY8PvnkYvALCvOPnehyzvet+zr/9rHAAAAHdFK2JnTVW9Ksl/yo5QZvRo8s6ew5rXJvn1JAdkcHTX05OcO+E2V5Ijxu6vXECN8WfGa07DK3LHfxY+m+QTk1ygqo5Icvg8Hzthkj0AwCw5/ZSj8toPfHn51jt5/MRTAAAAWPlmPqypqpcmefHwdi6YuTbJ+5N8NclZe6rR3TdU1XuS/Nhw6CnZt8Oag8bub11AjfFnxmsuq6r6j0meNjb8693dO5u/CD+f5GUTrgkAK9bGDety6rGH5YIrrl/ytU497rCcuOHgJV8HAAAAlttMH4NWVQ/O4A/Ge/j5TpJfSHLv7n5md//qcOre/IH8X82VTfKYqtpv0v2uIOPByncWUGPzHmoum6o6MYPj8Ub9ZXe/axr9AMC+5oWPPX5Z1nnRY2xWBQAA4K5ppsOaJL+ZQY+VZGuSJ3X367p72wJqfXTk+qAk95tAfyvV3cbutyygxm1j9wcssJdFqarDkvxNknUjw19PcuY0+gGAfdFpG4/M6ScftaRrPPWUo/K4jbNw6ioAAABM3sweg1ZVByZ5Unbsmvm97v7nhdbr7suq6uYkc2dnnJTk0sV1uTBV9ersONptKb28u8/eyfj4Tpq1C6i9/x5qLrmqOiDJeblj8HZrkh/v7uuWaNnXJvmLeT5zQhK7fAC4S3v56Q/Mxy+/LtfcNP73ORbvyHX75+wffeDE6wIAAMCsmNmwJsmjsiNE6CR/OIGaV2dHWLNhAvVWqlvG7sd32uyN8Z004zWXVFWtTvLOJD8wMrwlg6DmE0u1bnd/M8k35/NMVS1RNwAwOw49cG3e+rxT8/RzPpZNm7dOrO76A9bkrc87NYceuJC/WwIAAAArwywfg3bv4XcnuaK7r55AzRtHrtftctZd33iwcuACaow/s2xhTQ3Sj7cmecrI8O1Jfqq7/2G5+gAA7mjjhnV5xwsekSPXjW/AXZgj1+2fd7zgEdm4YV/+1zYAAAD2BbO8s+aeI9fz2smwG6O/3u0TqrkQf5fkW8uwzod2MT7+v+fRC6g9/sykfo/2xmuTPGvkvpOc2d3/Zxl7AAB2YuOGdXnPix+ds//mM3nXhd9YcJ2nnnJUzv7RB9pRAwAAwD5hlsOaW0euF7LzY2dGA6CleqfJHnX3+5K8b1rrJ7ksybbs+P0/oKoO7+5r51HjPmP3y/L+n6r63SQvHBv+pe5+03KsDwCzrLtzy23bsvX2zpr9Kgftv3oqx3EeeuDavOYZD8lTTzkqr//gZbng8uv3+tlTjzssL3rMCXncxiOWsEMAAACYLbMc1szt1Kgk37PYYlV1xLBOD4cmcazaitTdW6vqy0lOHBl+QJIP7s3zVbV/kuPHhpc8rKmqlyY5a2z4Zd396qVeGwBm1aVX35TzLvxGLvr6jbnkypvu8L6Y9QesyYOOXpeT731InnrK0Tlxw8G7qTR5p208MqdtPDKfv/rmnHfRlbnoa5ty8ZWb7tTjg49en5O/Z31OP3n5ewQAAIBZMMthzedGrtdV1UO7+5OLqHf68LsyOALt44uodVdwYe4Y1jwyexnWJHlYktHD6K/q7iU9Bq2qXpLkv48N/353j48BwD7h/Euvyes/cFkuuGLXu1Y2bd6aj3zpunzkS9fltR/4ck499rC86LHLv2vlxA0H56wNG5MMdv/cuuX2bNm2PWtXr8qBa/ebyu4fAAAAmCWrpt3ArnT3p5NclR07YX5xobWqak2SXx7W6iQXd/fUjkGbEX87dv+EeTw7PvdvFtnLblXVzyX5g7Hhc7v7l5dyXQCYRTfcuiX/6c8/lee95V93G9TszAVXXJ/nvuUTefHbP5Ubbt2yRB3uXtXgeLbDDlw7tWPaAAAAYNbMbFgz9I4MdsJUkp+pqh9eYJ3/leT+wzpJ8sYJ9LbSvTuD99bMeWxVjR9tdic1+BOVM8aG3zXBvsbXe0aSc7Lj9y5J/izJi5ZqTQCYVZ+76qY86TUfynkXfWNRdd514TfypNd8KJdefdOEOgMAAAAWY9bDmt9OcnMGu2FWJfnLqnra3j5cVYdW1f9O8vzs2KFzVZI3TLrRlaa7r0/y1yNDleTsvXj0eUmOHbn/SpJ/nFhjI6rqKUneljv+c/rXSc7o7u1LsSYAzKrPXXVTnnHux3LNTbdNpN41N92Wp5/zMYENAAAAzICZDmu6+1tJfimDIKGT3D3J26vqw1V1ZlU9bDh1btfFvavq4VX1jKo6N4Mg4enZsTvn9iTP7e7pnPsxe16Wwft75vx0VT1zV5Or6gFJfm9s+Df39L9nVR1bVT32OXYPzzw2yV8kWTMy/A9Jnt7d23b6EADcRd1w65ac8eYLsmnz1onW3bR5a57zpgumdiQaAAAAMLB62g3sSXf/8fAP9n8jg8Cmknz/8DOqkrx5J2M98txZ3f2+pex3Jenuz1bVG5OcOTL8p1V1UpJXdfcNyXff+fNTGbw35pCRuZ9O8tZJ91VVD0pyXpK7jQxfneSPkjx6nmfbb+7uj0ywPQBYdi877zMT21Ez7pqbbsvZf/OZvOYZD1mS+gAAAMCezXxYkyTd/dKquiyDd8/cfTg89yf2PTJ19E/xR0Oabyf52e5+x1L3ugL95yQPTfLw4f2qJC9N8qtVdXmS25Icn+Sgsee+leRpS7TL5eFJDh4b25CFvRvnK7njsW0AsKKcf+k1i35HzZ6868Jv5KmnHJXTNh65pOsAAAAAOzfTx6CN6u43J3lwknOTfGfkR3NHnCV3Dm5uz2C3zYMFNTvX3d9O8sQk54/9aG2SE5N8b+4c1FyR5LTu/sKSNwgA+7jXf+Cy5Vnng8uzDgAAAHBnK2JnzZzuvjzJC6vq15I8NsmjkpyU5B4ZHM/17Qx2fFye5P1J/rG7vzmdbleO7r6+qp6Q5GeT/EqS++5i6vVJXp/kFd19y3L1BwD7qkuvvikXXHH9sqx1weXX5/NX35wTN4xvbgUAAACW2ooKa+YM36XyV8MPE9Dd25O8IckbqurBGRyNdq8k+yW5LsklST7e3fN+s3F3X5E7HlG3p/lvSfKW+a4DAHc15124tMef3Wm9i67MWRs2LuuaAAAAwAyHNVX16CQvGRn6g+7+8LT62Zd098VJLp52HwCwr7vo6zcu73pf27Ss6wEAAAADMxvWJPm+JP8hg/fQbE1yxlS7AQBYRt2dS668aVnXvPjKTenuVO31hlgAAABgAlZNu4Hd2G/4XUm+2t3L+6cVAABTdMtt27Jp87xPH12UTZu35tYtty/rmgAAAMBshzVXDb87yQ3TbAQAYLltvb2nsu6Wbdunsi4AAADsy2Y5rPn6yPXhU+sCAGAK1uw3naPI1q6e5X89BAAAgLumWf6v8Y8kuTGDY9COqap7TbkfAIBlc9D+q7P+gDXLuub6A9bkwLX77XkiAAAAMFEzG9Z095YkfzEy9IJp9QIAsNyqKg86et2yrvngo9enajo7egAAAGBfNrNhzdB/T7JpeH1WVX3/NJsBAFhOJ9/7kOVd73vWL+t6AAAAwMBMhzXdfWWSpyXZnOSAJO+pqp+bblcAAMvj9FOOWt71Tj56WdcDAAAABlZPu4Hdqar7JPlCkmcneV2SI5OcU1W/keQdSS5IcnmSm5JsnU/t7v7qZLsFAJisjRvW5dRjD8sFV1y/5GudetxhOXHDwUu+DgAAAHBnMx3WJLkiSY/cd5JKckySsxZRtzP7v3YAgLzwscfngrcsfVjzosecsORrAAAAADs308egjRh9021nR2izmA8AwMw7beOROf3kpT0O7amnHJXHbTxiSdcAAAAAdm2lhDWJsAUA2Ee9/PQH5sh1+y9J7SPX7Z+zf/SBS1IbAAAA2DuzfhTYW6fdAADAtB164Nq89Xmn5unnfCybNs/rNX27tf6ANXnr807NoQeunVhNAAAAYP5mOqzp7udOuwcAgFmwccO6vOMFj8hz3nRBrrnptkXXO3Ld/nnr807Nxg3rJtAdAAAAsBgr6Rg0AIB92sYN6/KeFz86Tz1lce+weeopR+U9L360oAYAAABmxEzvrAEA4I4OPXBtXvOMh+SppxyV13/wslxw+fV7/eypxx2WFz3mhDxu4xFL2CEAAAAwX8IaAIAV6LSNR+a0jUfm81ffnPMuujIXfW1TLr5y0x3eabP+gDV58NHrc/L3rM/pJx+dEzccPMWOAQAAgF0R1gAArGAnbjg4Z23YmCTp7ty65fZs2bY9a1evyoFr90tVTblDAAAAYE+ENQAAdxFVlYP2X53sP+1OAAAAgPlYNe0GAAAAAAAA9mXCGgAAAAAAgCkS1gAAAAAAAEyRsAYAAAAAAGCKhDUAAAAAAABTJKwBAAAAAACYImENAAAAAADAFAlrAAAAAAAApkhYAwAAAAAAMEXCGgAAAAAAgCkS1gAAAAAAAEyRsAYAAAAAAGCKhDUAAAAAAABTJKwBAAAAAACYotXTWLSq3jSNdUd0d//slHsAAAAAAAAYUHSGAAAgAElEQVSYTliT5IwkPaW1a7i2sAYAAAAAAJi6aYU1C1E7Gdtd4DPf+QAAAAAAAMtummHNzsKUPRkNW2ovasx3PgAAAAAAwLKaVlhz3Dznn5TktUmOySBw2ZLk3Uk+kOTiJNcluTXJgUnukeR7kzwmyY8kWZtBaHN5kp9PcumiuwcAAAAAAJiQqYQ13f2VvZ1bVY9J8vYkB2cQ1Lwxya9397d289j7k7ymqg5P8tsZvJ/m2GGd07v7nxfYOgAAAAAAwEStmnYDu1NVxyR5V5J1w6Ezu/vMPQQ139Xd13b385OcORxan+S8qrrP5LsFAAAAAACYv5kOa5L8bgZBTSc5p7vfuJAiw+fOHd6uG9YFAAAAAACYupkNa6rqsCQ/PjL0O4ss+cq50kl+fFgfAAAAAABgqmY2rEnyqCT7ZbCr5or5vOdmZ4bPXz683W9YHwAAAAAAYKpmOaw5fuR6r95RsxdG6xy/y1kAAAAAAADLZJbDmrsNvyvJPSdU8x4j1/tPqCYAAAAAAMCCzXJYc/XI9TFVde/FFBs+f1wGx6olyTcXUw8AAAAAAGASZjms+cLwuzPYXfPLi6x31rBOjdUHAAAAAACYmlkOaz6a5BvD60ryi1X1zIUUqqpnJfnF7NhVc1V3f3jxLQIAAAAAACzOzIY13d1JXp1BUNMZ9PonVfXqqjpkb2pU1SFV9Zokb5sbGtZ69RK0DAAAAAAAMG+rp93AHvxBkqcl+b7sCGz+nyTPr6q/S/KBJJckuS7Jt5PcPck9kzwoyWOSPDnJ3bIjpEmSfx3WBQAAAAAAmLqZDmu6e3tVPSnJ+5I8NDveX3NAkp8YfnZn7v00c899KsmPdPf2pekYAAAAAABgfmb2GLQ53X1DkscmOXd0ePhdu/mMzkuSNyZ5bHdft5T9AgAAAAAAzMfMhzVJ0t23dPcLkzwyyTuSbMmOQGZXKsnW4fwf6O4zu/vmpe0UAAAAAABgfmb6GLRx3f3xJM+sqvVJvj/JqUmOS3JIkoOS3JrkxiSXJflEko9296YptQsAAAAAALBHKyqsmTMMYN4z/AAAAAAAAKxYK+IYNAAAAAAAgLsqYQ0AAAAAAMAUCWsAAAAAAACmaMW9s6aqKsnDkzwqyUlJDkuyPoPg6b9096em2B4AAAAAAMC8rJiwpqrWJPn5JC9Ocsz4j5N0kkN38ew5SZ4wvP1Kdz9uqfoEAAAAAACYjxUR1lTVfZO8I8kpGQQzySCcycj97rwjyfOH18dU1b/r7o9PtksAAAAAAID5m/l31lTV8Uk+nB1BzWhIM3q/S919fpLPjww9fcJtAgAAAAAALMhMhzVVtTbJu5McMTL8lQyOQjspyUHZy8Amyf8duX7ipHoEAAAAAABYjJkOazJ4R839syOM+askD+zu/9Xdn+/ub8+j1t8PvyvJxqq6xwT7BAAAAAAAWJBZD2tekh1BzSeTPKO7Ny+w1oW54w6cByymMQAAAAAAgEmY2bCmqk5Kcp8MdsIkyW9099aF1uvuW5NcOTJ0wiLaAwAAAAAAmIiZDWuSPGTk+pYk/ziBmjeMXB8ygXoAAAAAAACLMsthzeHD705yeXdvn0DN0Xfc3H0C9QAAAAAAABZllsOau41c3zahmutHrm+aUE0AAAAAAIAFm+Ww5tqR63sutlhV7Zfke0aGvrXYmgAAAAAAAIs1y2HNVcPvSnJMVa3f3eS98PAkB47cf2GR9QAAAAAAABZtlsOajybZlsE7ayrJTy6y3gtGrm/s7k8ush4AAAAAAMCizWxY092bMghsavj5taq6+0JqVdUjkjw7g+Cnk/z9pPoEAAAAAABYjJkNa4Z+f/jdSY5L8mdVtWY+BarqlCT/N8l+GYQ+neSVk2wSAAAAAABgoWY6rOnuv0nywQxCliQ5PcnHq+qH9vRsVR1dVa9I8uEk98qOoObPuvuSJWoZAAAAAABgXlZPu4G98B+TfDzJMcP7U5L8Q1VdnWTuvTNzQcyvV9UvJblvkvuN/aySXJzkhcvUNwAAAAAAwB7NfFjT3ddW1ROTvCvJxuwIXu6V5EdGplaSx41cf7fE8P5TSU7v7s1L3jQAAAAAAMBemulj0OZ09xeTPCzJOUlunxse+e6R+4zd3z587ge6+8ql7xYAAAAAAGDvrYiwJkm6e3N3vyjJ8Ul+N8nce2dqF58vJ/nDJCd294u6+zvL3zUAAAAAAMDuzfwxaOO6++tJ/muS/1pVhyQ5Mck9khyS5NtJvpXkcrtoAAAAAACAlWDFhTWjuvvGJB+fdh8AAAAAAAALtWKOQQMAAAAAALgrmumdNVX1MyO37+3uaxZRa0OSH5677+63LaY3AAAAAACASZjpsCbJW5L08PoJSRYc1iR54Fg9YQ0AAAAAADB1K+EYtJrxegAAAAAAAAu2EsKa3vMUAAAAAACAlWklhDWTMrqjRgAEAAAAAADMhH0prDlo5PrWqXUBAAAAAAAwYl8Ka7535PqGqXUBAAAAAAAwYvW0G1gOVXVckjOz4/izz06xHQAAAAAAgO+aelhTVW/ay6lnVdWz51M6yd2THJfklCT7Dcc6yfvn1SQAAAAAAMASmXpYk+SM7NjxsjM1/P7hBdafe35ujVuSvG2BtQAAAAAAACZqFsKapTYX0lSS7yR5bndfM8V+AAAAAAAAvmtWwpra85S9mrMztyf5QpJ/SPLa7v7iAusAAAAAAABM3CyENcftYrySXJYdO2OeneSj86i7PcmtSW7q7m0Lbw8AAAAAAGDpTD2s6e6v7OpnVXfYTHPN7uYCAAAAAACsRFMPa/bgq9mxs2bzNBsBAAAAAABYCjMd1nT3sdPuAQAAAAAAYCmtmnYDAAAAAAAA+zJhDQAAAAAAwBTN9DFoVXVAkqeNDP1Ld39xAXXun+QRI0N/3t1bF9sfAAAAAADAYs10WJPkWUnOHV5vSXL8AuvcMqyzZni/NcmfL641AAAAAACAxZv1Y9DOSFLD6//T3VctpEh3fyPJXw5r1bAuAAAAAADA1M1sWFNV6zM4uqyHQ+9cZMm3j1w/enjEGgAAAAAAwFTNbFiT5JQk+2XHzpoPLbLeB7Mj+Fk7rA8AAAAAADBVsxzW3H/k+qvdvWkxxYbPf21k6MTF1AMAAAAAAJiEWQ5rDhl+d5JvTajmtSPXh02oJgAAAAAAwILNclgz2lvtctb8jNbZf0I1AQAAAAAAFmyWw5q53TSVZMOEah45cn3DhGoCAAAAAAAs2CyHNd8cub5XVd1nMcWGzx+dwbFq4/UBAAAAAACmYpbDmo9nEKzMhSs/tch6zx5+zx2F9olF1gMAAAAAAFi0mQ1ruvubSS4e3laSX6mqoxZSq6qOTnJWdgQ/n+/ury2+SwAAAAAAgMWZ2bBm6NwMgppOsj7Je6rqiPkUqKojk7xn+PxcrXMn3CcAAAAAAMCCzHpY84YkXx1ed5IHJbmkqp5TVWt292BVramqMzLYnfOA7NhV8/Ukr12adgEAAAAAAOZn9bQb2J3u3lpVz0xyfpK1GQQu90zypiT/X1Wdn+Rfk3wzyS1JDkpyRJKHJzktyT2yYzdNJflOkmd295Zl/qUAAAAAAADs1EyHNUnS3f9SVc9N8pYka7IjeLlnkqcNPztTcyWG17cleU53f3RJGwYAAAAAAJiHWT8GLUnS3W9P8qgMjkSb2ykzd6xZ7eSTkTmV5MtJfqC7/2IZ2wYAAAAAANijFRHWJEl3fyLJiUlemOTS3DGYGTf3s0uS/FySB3T3J5ejTwAAAAAAgPmY+WPQRg3fNXNuknOr6ugMdtuckOSwJAcnuTnJ9Um+mOSfu/uqafUKAAAAAACwN1ZUWDOqu69M8vZp9wEAAAAAALAYK+YYNAAAAAAAgLsiYQ0AAAAAAMAUCWsAAAAAAACmSFgDAAAAAAAwRaunsWhVPXp8rLs/tDfzJmVn6wEAAAAAACy3qYQ1ST6QpEfuOzvvZXzepOxqPQAAAAAAgGU17cCiJjwPAAAAAABgRZnmO2sENQAAAAAAwD5vWjtrXj7heQAAAAAAACvSVMKa7t6rEGZv5wEAAAAAAKxU0zwGDQAAAAAAYJ8nrAEAAAAAAJgiYQ0AAAAAAMAUCWsAAAAAAACmSFgDAAAAAAAwRaun3QCzp6oemORhSe6VZL8k1yW5JMnHu3vbNHsDAAAAAIC7mqmENVV12TTWHdHdfcKUe5gpVVVJnpvkV5PcfxfTrquq1yV5ZXffumzN7UZVbUjy2SSHjv3o5d199vJ3BAAAAAAA8zOtnTXHJukkNaX1e0rrzqSqOiTJO5M8YQ9T75HkvyV5VlWd3t2fWfLm9uyPcuegBgAAAAAAVoxpv7Om9/Iz3/m7qiGkGVNVByR5b+4c1GxJ8oUkFycZ30VzfJL3V9V9l77DXauqn0zy49PsAQAAAAAAFmtaO2u+mvkHJ+uTHJI778i5PcnNGQQKByY5OIP3rMyZW+eGJDctpNm7uD9IcurI/fYkv5XkVd19Q5JU1dokzxrOndvFcniSd1bV93X37cvYb4Y9HZrkD0eG5n7/AQAAAABgRZlKWNPdx85nflX9VAZ/MD8X1HwqyVuTfCDJZ0dfel9Vq5M8MMljkpyR5JSR536ju//3on8BdxFVtTHJ88eGn93dfz460N1bkrylqj6R5MMZhGZJ8pAkP5PkzUvd6068KsmRw+tPJflMkmdPoQ8AAAAAAFiUaR+DtkdV9V+SvC3JuiS3ZBAmPKy7/2d3f3o0qEmS7t7W3RcNf/7QJD+dwc6b9Un+pKpesty/hhn28txxF9KfjAc1o4bvqPnlseGXVdWapWhuV6rqh5M8Z3h7ewaB07Lv7gEAAAAAgEmY6bCmqh6d5Hcz2BXz7SSPne/OmO7+sySPGz5fSX6vqn5w0r2uNMNjxEbf99JJzt6LR9+c5Csj98ckefzkOtu9qjowybkjQ/+zu/9tudYHAAAAAIBJm+mwJskrMwhYOslLu/tTCykyfO5lw9tVSX5nMu2taE/OHY/B+0B3X7anh7p7e+587Nl/mGRje/CKDAKiZBAavXQZ1wYAAAAAgImb2bCmqo5P8ojh7dYkb1pkyT9OsmV4/Yhh/X3Zk8fu/2Eez75v7P4pi+xlr1TVI5P8wsjQz3f3rcuxNgAAAAAALJWZDWuyI6jpJJd3902LKdbdm5JcvpP6+6pTxu4/Oo9n/y3JbSP3R1XV4Ytvadeqav8kb8yOf2bf0d3vXso1AQAAAABgOcxyWHP0yPXNE6o5WufoXc66i6uqNUnuOzb82b19vrtvS/LlseGTFtvXHrx0ZI0bkrx4idcDAAAAAIBlMcthzZxKcu8J1ZpUnZXu+NzxfTWbu/tb86zxtbH7ExfX0q5V1fcm+ZWRoV/p7muWaj0AAAAAAFhOq/c8ZWquHLk+sqpO6e4LF1qsqh6aZEMGx6olyTcW09wKd8TY/ZU7nbV748+M15yIqtovg/cVrRkOfSiD9w9NRVUdkWS+R76dsBS9AAAAAABw1zDLYc2nht9z4crvJHniIuq9Yvhdw5qfXEStle6gsftbF1Bj/JnxmpPyX5I8bHh9W5Izu7t3M3+p/XySl01xfQAAAAAA7mJm9hi07v5ckouHt5Xk8VX1uqqq+dSpgdcleUIGIU0nuXhYf181Hqx8ZwE1Nu+h5qJV1f2SnD0y9Nvd/flJrwMAAAAAANM0s2HN0G9kx06YSnJmkn+tqifuKbQZhjRPSvJvw+fmaiTJf1u6lleEu43db1lAjdvG7g9YYC87Nfz9feNI3c8meeUk1wAAAAAAgFkwy8egpbv/tqrekOT52RG2PCTJu5NcU1UfTnJJkuuSfDvJ3ZPcM8mDkvxgBu9RmQto5o7OelN3/+2y/SJ2oqpeneTFy7DUy7v77J2Mj++kWbuA2vvvoeZivTDJo4fXncHxZwsJlSbttUn+Yp7PnJDkXUvQCwAAAAAAdwEzHdYMvXD4PRfYJIMAZkOSnxh+dmY8pKkMXkx/5hL0uNLcMnY/vtNmb4zvpBmvuWBVde8M3lE059zu/sik6i9Gd38zyTfn88w8T+4DAAAAAGAfM+vHoKUHXpDkWUmuzY5j0UZfMl8jn+8+OvKzbyV5dnc/f8ovp58V48HKgQuoMf7MxMKaJK9PcvDw+qokvzrB2gAAAAAAMFNWws6aJEl3v72q/jbJTyV5bpKHZ+dh01xgsz3JvyZ5U5I/6+5JhgmL9XcZBEhL7UO7GB/fGXL0AmqPPzOv3Sa7UlWPTfLkkaH/1N2bJlEbAAAAAABm0YoJa5JkGLick+ScqjowycOSHJfkkCQHJbk1yY1JLkvyb91967R63Z3ufl+S902xhcuSbMuO3/8Dqurw7r52HjXuM3Z/6UQ6G/xejvqLBR4j9rKqetnI/UXdfcrC2wIAAAAAgKWxosKaUcMg5kPZ9e4RdqG7t1bVl5OcODL8gCQf3Jvnq2r/JMePDU8qrAEAAAAAgH3KzL+zhiVz4dj9I+fx7MOS7D9yf1V3T+QYNAAAAAAA2Nes2J01LNrfJnn6yP0TkrxiL599wtj930yko4GP7KT+3jgryQ+P3P9JkreN3N+8mKYAAAAAAGCpCGv2Xe/OHd9b89iqOr67L9vdQzV4gcwZY8PvmlRTw/fm/ON8n6uqZ48NXdbd864DAAAAAADLbcUdg1ZV962qs6rqvKr6YlVdV1Xbhp/TdvHMvarqPsPP4cvd8yzq7uuT/PXIUCU5ey8efV6SY0fuv5IFhCsAAAAAAMDAiglrqur4qvqrDF5k/8okT05yQpJDM/h11G4ePzvJ5cPPxVW139J2u2K8LMn2kfufrqpn7mpyVT0gye+NDf9md2/Z3SJVdWxV9djn2IU2DQAAAAAAdyUrIqypqp9I8skkp+fOPfdelPj9uVJJDk/ylMl1t3J192eTvHFs+E+r6r9X1aFzA1W1pqrOSPLhJIeMzP10krcueaMAAAAAAHAXNvNhTVX9SJK3J1k3OpzkmiSfyO531CRJuvsLGby4fs6PT7LHFe4/J/nXkftVSV6a5OqqurSqLkpyfZI3Z7CLac63kjytu7ctW6cAAAAAAHAXNNNhzfD9Mn+eZL8MdtBUkr9IcnJ3H9Xd/244dW921/zfubJJHj/pXleq7v52kicmOX/sR2uTnJjke5McNPazK5KcNgzBAAAAAACARZjpsCaDHR4Hj9z/Snc/vbsvXkCt949cb6iq+yyutbuO7r4+yROSnJnkS7uZen2S307y4AX+HgAAAAAAAGNWT7uBXamqVUmenR27Zv5Pd4+/3H4+PptkSwY7RpLkpCRfXUS9u5Tu3p7kDUneUFUPTvLQJPfKYFfTdUkuSfLx7t66gNpXZC+Oq1uM7j4jyRlLuQYAAAAAACyFmQ1rkjwiO15m30n+x2KKdfe2qroyyXHDITtrdmG4a8bOGQAAAAAAWAazfAza/UauvzmhY7duHLleP4F6AAAAAAAAizLLYc3hw+9O8vUJ1dw2cj3Lu4oAAAAAAIB9xCyHNdtHrifV52Ej1zdMqCYAAAAAAMCCzXJYc+3wu5JsWGyxqrp7kmMy2KkzWh8AAAAAAGBqZjmsuWLkekNVHbPIeo/L4OizGt5fuMh6AAAAAAAAizbLYc3HktySHTthzlhkvf88cv3V7r5skfUAAAAAAAAWbWbDmu7emuQ9GeyEqSS/VFXHLqRWVf1cktMyCH46yTsn0yUAAAAAAMDizGxYM/TyJNszCFgOTvLe+QY2VfWCJH84rFFJNif5vYl2CQAAAAAAsEAzHdZ092eSvC6DkKWT3C/JxVX1m1V1/509kiRVtaGqnlVVH03y2iRrR2qc3d3XLssvAAAAAAAAYA9WT7uBvfCSDEKaH84gbDkwya8n+fWqunU4Zy6IeWdVHZDkgJHn535WSd7Z3XbVAAAAAAAAM2Omd9YkSXffnuTHkvxJdgQvGV4fNHZ/jyR3z4733GTkZ+ck+ellaBkAAAAAAGCvzXxYkyTdvbm7n5PkmUk+nx1BTI98j38ynPelJM/s7hd197bl6xoAAAAAAGDPVsIxaN/V3e9I8o6qemKSH0nyqCQnJdl/ZNq2JF9J8v4k70ny1929fbl7BQAAAAAA2BsrKqyZ093vTfLeufuqunuSQ5J8u7tvnFpjAAAAAAAA8zSzYU1V3S/Jvx8Z+sfu/uzO5nb3t5N8e1kaAwAAAAAAmKCZDWuSPCnJq4bXneSEKfYCAAAAAACwJFZNu4HdOChJDT/f6O6vTLkfAAAAAACAiZvlsOba4Xcn+cY0GwEAAAAAAFgqsxzWjAY066fWBQAAAAAAwBKa5bDmY0m2ZnAM2rFVdeCU+wEAAAAAAJi4mQ1ruvv6JO8d3q5N8pNTbAcAAAAAAGBJzGxYM/SKDN5ZkyS/VVWHT7MZAAAAAACASZvpsKa7/yXJr2VwFNq9kpxfVSdNtysAAAAAAIDJmemwJkm6+3eTvDDJd5I8MMmnquotVfWkqjpsut0BAAAAAAAszuppN7A7VXXZyO224ffaJD89/KSqbklyU5Kt8yjd3X3CRJoEAAAAAABYhJkOa5Icm8E7a2r4Pff+mhqZc/DwMx+95ykAAAAAAABLb9bDmjnj4cpiwpba8xQA/n/27jxekqo8+PjvYdiHwUERRBQQXNkVEQMa0QjuiArRKMigQTExRo0SMfqCyguKS+SNETQuoLjgBrhF3EAUFBRlR0UQUJZB9n1/3j9OX6e6pu+93X27u7rv/L6fT32gTlfVOV1Vp/rOeeqcI0mSJEmSJGk0xj1YcwX2gpEkSZIkSZIkSfPYWAdrMnOTpssgSZIkSZIkSZI0TCs1XQBJkiRJkiRJkqQVmcEaSZIkSZIkSZKkBhmskSRJkiRJkiRJatDYzVkTEZsDuwJbAetSyng98Efgx8DPMvP+5kooSZIkSZIkSZI0OGMTrImILYEPA8+eYbN3AX+MiP/IzONGUzJJkiRJkiRJkqThGYth0CLiucDplEBNVJa/blJZNgW+GBGHj7qckiRJkiRJkiRJg9Z4sCYiHg18DViLEozJ1gLtQZusLAH8W0S8brSllSRJkiRJkiRJGqxxGAbtSGBN2gM0DwBnA5cC9wIPB7YH1qA9YPOhiPhaZt4w6kJLkiRJkiRJkiQNQqM9ayLiCcDfsSz4EsAXgI0zc7vM3DMzX5mZOwPrAgcC91UOsRDYZ7SlliRJkiRJkiRJGpymh0Hbs/L/CXw0M/fOzCvrG2bmnZn5AWCPyvYALxtyGSVJkiRJkiRJkoam6WDN9q3/BnAlcMBsO2Tmt4DjWNYT54kR0fT3kCRJkiRJkiRJ6kvTQY4ntP6bwJcz876ZNq44uvL/qwObDLBMkiRJkiRJkiRJI9N0sGadyv//uof9zqqtLx5AWSRJkiRJkiRJkkau6WDNg1g298z1Pex3Y+u/U/suGliJJEmSJEmSJEmSRqjpYE01//u73SkzH6glLRhMcSRJkiRJkiRJkkar6WCNJEmSJEmSJEnSCs1gjSRJkiRJkiRJUoMM1kiSJEmSJEmSJDVo5aYLULFNRNw3qn0z89Q+85IkSZIkSZIkSRqYcQnWBPChPvejj32T8fnukiRJkiRJkiRpBTYuAYtkWeCll32m9LqvJEmSJEmSJEnSWBiXYA20B1+Gua+BHUmSJEmSJEmSNDaaDtZcwdyCNJLG2LVLl/LOr5/LX265jYeuvRaHvmxr1lt//aaLJUmaTzLh7lvh/nthwSqw2iII383RCsi60D/PnSRJksZAo8GazNykyfwlDd5zDjyS3RaczjZxCVuudBmfitvLB3fBTR9fyM8e2IRzcjNOvH8nvn/Y/s0WVpI0mZZeAOd9Da48C64+B+66adlnqy+GDbaBDbeDrfaE9TdvrpzSsFkX+ue5kyRJ0piJTDu2SMMUEVsA50+tn3/++WyxxRYNlmg49n3nIey/8rfYYaXfdr3PGQ88niPvexFHH/quIZZMkjRv/P4k+NlH4YrTu99nox3haW+Bx+46vHJJo2Zd6J/nTpIkSTO44IIL2HLLLatJW2bmBaPI22CNNGTzPVjzksNPYMmtn+DFC3r4B2/NCffvyDGLXs/xB+w+wJJJkuaNO26A774dzv9a/8fYak943uGw5oMHVy5p1KwL/fPcSZIkqQtNBmtWGkUmkuan5x54JEfe/uY5BWoAdl9wOkfe/maec+CRAyqZJGneuOZ8OHLHuTWwApz31XKcpSP5G1saPOtC/zx3kiRJmgAGayT15bkHHsmXV30fD4sbB3K8h8WNHLfq+wzYSJKWueZ8OPoFcOvVgznerVfDZ59vQ6smj3Whf547SZIkTQiDNZJ69pLDT+DoVT/A4rh9oMddHLdzzKof4CWHnzDQ40qSJtAdN8AX9mif9HsQ7roJjn1ZOb40CawL/fPcSZIkaYIYrJHUsyW3fmJgPWrqHhY3ss+tnxjKsSVJE+S7bx/cm/B1t14N/3vAcI4tDZp1oX+eO0mSJE0QgzWSerLvOw+Z8xw1s9l9wens+85DhpqHJGmM/f6kuc8tMZvzvlrykcaZdaF/njtJkiRNGIM1knqy/8rfmlf5SJLG0M8+Opp8TjtiNPlI/bIu9M9zJ0mSpAljsEZS155z4JHssNJvR5LXDiv9ll0PPGokeUmSxsjSC+CK4fbg/KvLT4OlF44mL6lX1oX+ee4kSZI0gQzWSOrabkMe/qzp/CRJY+C8IQ9bVDfsYZKkflkX+ue5kyRJ0gQyWCOpa9vEJfM6P0nSGLjyrPmdn9Qt60L/PHeSJEmaQAZrJHXl2qVL2XKly0aa51Yr/ZFrly4daZ6SpAZlwtXnjDbPq84u+UrjxLrQP8+dJEmSJpTBGkldeefXz2Vx3D7SPBfH7Rz0zfNGmqckqUF33wp33TTaPO+6Ce65bbR5SrOxLvTPcydJkqQJZbBGUlf+cksz/wC96vpbG8lXktSA++9tJgx6S7oAACAASURBVN/77mkmX2k61oX+ee4kSZI0oQzWSOrKQ9deq5F8H/6QRY3kK0lqwIJVmsl35VWbyVeajnWhf547SZIkTSiDNZK6cujLtuamXDjSPG/Khbxnt61GmqckqUGrLYLVF482z9UXw6rNvJAgTcu60D/PnSRJkiaUwRpJXVlv/fU5/4FNRprneQ88ivXWX3+keUqSGhQBG2wz2jwfvm3JVxon1oX+ee4kSZI0oQzWSOraObnZvM5PkjQGNtxufucndcu60D/PnSRJkiaQwRpJXfvm/TvO6/wkSWNgqz1Gm9+WI85P6pZ1oX+eO0mSJE0ggzWSunbSYW/gjAceP5K8znjg8Xz/sP1HkpckaYysvwVsNKJg/cY7wfqbjyYvqVfWhf557iRJkjSBDNZI6slR971oJPkcOaJ8JElj6GlvHk0+O40oH6lf1oX+ee4kSZI0YQzWSOrJZw99FycOeXiyE+7fkaMPfddQ85AkjbHHPmf4wwpttSc8dtfh5iHNlXWhf547SZIkTRiDNZJ6dvSi13NNrjOUY1+T63DMotcP5diSpAny/A/Cog2Gc+xFG8DzDh/OsaVBsy70z3MnSZKkCWKwRlLPjj9gd/a559+5KRcO9Lg35UL2ueffOf6A3Qd6XEnSBFrzwbDX12H1xYM97uqLy3HXfPBgjysNi3Whf547SZIkTRCDNZL6ctJhb+Dl97x7YD1srsl1ePk97+akw94wkONJkuaB9beAfb87uDfjF21Qjrf+FoM5njQq1oX+ee4kSZI0IQzWSOrbSYe9gTcs/CgnzHEOmxPu35E3LPyogRpJ0vLW3wLecHqZG2IuttqzHMcGVk0q60L/PHeSJEmaAJGZTZdBmtciYgvg/Kn1888/ny22mH//wNv3nYew/8rfYoeVftv1Pmc88HiOvO9FHH3ou4ZYMknSvPH7k+C0I+Dy07rfZ+OdYKc3Owm45hfrQv88d5IkSZrBBRdcwJZbbllN2jIzLxhF3gZrpCFbUYI1U3Y98Ch2W3A628QlbLXSH1kct//1s5tyIec98CjOyc345v078v3D9m+wpJKkibX0Qjj/a3DlWXDV2XDXTcs+W30xPHxb2HA72HIPWH/z5sopDZt1oX+eO0mSJHVgsEaax1a0YE3VtUuXctA3z+Oq62/l4Q9ZxHt224r11l+/6WJJkuaTTLjnNrjvHlh5VVh1LYhoulTS6FkX+ue5kyRJUkuTwZqVR5GJpBXTeuuvz5H7GZyRJA1RBKy2CFZruiBSw6wL/fPcSZIkaQys1HQBJEmSJEmSJEmSVmQGayRJkiRJkiRJkhpksEaSJEmSJEmSJKlBBmskSZIkSZIkSZIaZLBGkiRJkiRJkiSpQQZrJEmSJEmSJEmSGmSwRpIkSZIkSZIkqUEGayRJkiRJkiRJkhpksEaSJEmSJEmSJKlBBmskSZIkSZIkSZIaZLBGkiRJkiRJkiSpQQZrJEmSJEmSJEmSGmSwRpIkSZIkSZIkqUEGayRJkiRJkiRJkhpksEaSJEmSJEmSJKlBBmskSZIkSZIkSZIaZLBGkiRJkiRJkiSpQQZrJEmSJEmSJEmSGmSwRpIkSZIkSZIkqUEGayRJkiRJkiRJkhpksEaSJEmSJEmSJKlBBmskSZIkSZIkSZIatHLTBZBWAKtWV/7whz80VQ5JkiRJkiRJ0jQ6tN2u2mm7YYjMHFVe0gopInYDTmy6HJIkSZIkSZKknrw4M785iowcBk2SJEmSJEmSJKlBBmskSZIkSZIkSZIa5DBo0pBFxIOAZ1SS/gTc01BxpFHZjPbh/14MXNJQWSSNhvVeWrFY56UVj/VeWvFY77UiWhV4ZGX9J5l58ygyXnkUmUgrslZlHsm4htK4iIh60iWZeUETZZE0GtZ7acVinZdWPNZ7acVjvdcK7DdNZOowaJIkSZIkSZIkSQ0yWCNJkiRJkiRJktQggzWSJEmSJEmSJEkNMlgjSZIkSZIkSZLUIIM1kiRJkiRJkiRJDTJYI0mSJEmSJEmS1CCDNZIkSZIkSZIkSQ0yWCNJkiRJkiRJktQggzWSJEmSJEmSJEkNMlgjSZIkSZIkSZLUIIM1kiRJkiRJkiRJDVq56QJIkualvwDvqa1Lmt+s99KKxTovrXis99KKx3ovjVBkZtNlkCRJkiRJkiRJWmE5DJokSZIkSZIkSVKDDNZIkiRJkiRJkiQ1yGCNJEmSJEmSJElSgwzWSJIkSZIkSZIkNchgjSRJkiRJkiRJUoMM1kiSJEmSJEmSJDXIYI0kSZIkSZIkSVKDDNZIkiRJkiRJkiQ1yGCNJEmSJEmSJElSgwzWSJIkSZIkSZIkNchgjSRJkiRJkiRJUoMM1kiSJEmSJEmSJDXIYI0kSZIkSZIkSVKDDNZIkiRJkiRJkiQ1aOWmCyBJmnwRsQWwHbABsAC4HjgfOCMz72uybJKGYxzrfUSsDjwB2Bx4KLAQuLlVtl9n5u+aKJc0DiJiM+ApwCOAVYEbgd8Cp2fmXQ2WK4AnAdsC67WSlwLnUOptNlU2adKNW72PiFWAxwFbAOsDi4DbKL/T5wLnZ+YDoy6XNF+MW52X1Lvwb19JUj9ajSv7Av8OPHaaza4HjgTen5m3j6psM4mIhwEXAuvUPnpPZh48+hJJk2Mc631EbAm8FHg28FRglRk2vxb4NPCxzLxq2GWTxkFE7A68mxIQ6eQ24GjK7+B1IyzXKsC/Am8GNpxmsz8DHwX+X2beO6qySZNunOp9RDwK2APYBXgasMYMm98MHAsckZkXD7Nc0nwyTnW+GxGxJiVAu1nto2Myc8noSySND4M1kqSeRcRi4CuUf3R141Jgt8y8YHil6k5EfJ3SsFtnsEaawbjV+4jYEPgesGUfu98C/Etmfm6wpZLGR0SsRglOvqrLXf4C7JGZpw6vVEVEPBI4EXhil7ucBbw4M68cXqmkyTdO9b5Vlp8AO/Sx+z3AfwAftnedNL1xqvO9iIiPAG/p8JHBGq3wnLNGktSTiFgDOInlG2zvAX4PnAfU36bfFDg5Ih49/BJOLyL2oHOgRtIMxrTer8P0gZrbgT8AZwK/A+6vfb42cExEvH1IZZMaFRErAcexfOPN/cAfgbMpb7BXPRT434j4myGXbT3gZJYP1NwJXABcBNSHatmO8jxZd5hlkybZGNb7VZg+UHNXq0y/pPR4v6f2+arAB4GPDaFc0rwwhnW+KxHxFErPWkkdGKyRJPXqI5RxcKc8ALwPeFhmPi4ztwYeTBkq6cbKdg8FvhIRC0ZW0oqIWIf2f/CNxbBs0oSYhHp/PnAApVF37cx8TGbukJmPb5XtjZQh2qoOj4gXjqBs0qi9HXhxLe0oYKPM3DQzn0ipFy8Frqhssyalzj5oiGU7mvZhT+6iDIW2bmZumZmbA+sCb6U9aPMY4DNDLJc06ca53kNpPD4Y2InyO71pZj4lM7cAFgN7A5fX9vmniHjjkMslTapxr/PLiYhVKT2Bptqj/Te5VOMwaJKkrkXE4ykNotWG11dm5pem2X4L4GeUf4BNeU1mfnZ4pewsIo4G9mmt/oby9u5elU0cBk3qYFzrfWuumvOA7wCHZOYvutjnkZQhWR5VSb4EeILzYWi+iIiHUBpFF1WSD8zM90+z/YaUOrtJJfm9mXnQEMq2K6WX3pR7gWdPNxxLRDwD+AHtc1E9KzNPHnTZpEk2jvU+ItYCbgVOA94L/GC2Ic1aL1edBGxfSb4J2CwzbxhU2aRJN451vhsRcTAwleeVlJ5Bb61s4jBoWuHZs0aS1Iv30N5g+/npGmwBWnNVvK2WfFBrUuGRaTUOTQVq7gf2Y/lhkSR1Nq71/i/ATpn5wm4CNa2y/YnyduEDleTNgJ0HXDapSQfQ3nhzKvCB6TZuzQPzj7Xkt7QaggbtfbX19880bn5m/oTly37IwEslTb5xrPf3AC/MzKdl5ve7mXsmM28Edqf9bfvFwMsGWC5pPhjHOj+j1gtdB1aS3kgJ6EqqMFgjSepK60236nwvSRnKYDafpX1Ig42BZw+uZDOLiIXAJytJ/y8zzxpV/tIkG+d6n5lLM/P0PvY7G/heLfk5gymV1KzW+PX71pIPnq2RNDN/BPy0krQI+PsBl20r2odTvJ0yJ8VsDqe94XbHiHjCIMsmTbJxrfeZeU9mfqeP/a4Cjqkl+zsttYxrnZ9Jq8yfpsxHBXB8Zp4wirylSWOwRpLUrRcAK1fWT8nMS2fbKTMfoDTcVu0+yILN4jBKQzGUxuN3jzBvadJNar2fzU9r6xs1Ugpp8HakzBU15VLglC73/XRtfdB1tj6u/lcyc9Y3alvbfLWWPE7PE6lp41zv++XvtDS9SazzbwZ2aP3/LZReNZI6MFgjSerWC2rr3+9h3x/U1kcyoXdE7Aj8cyXpnzLTSQyl7k1cve/SjbX1kU+wKg1Jvc7OOkdEddva+s6t3qmDMl+fJ1LTxrne98vfaWl6E1XnI2JT2odBPbDVg05SBwZrJEnd2ra23svwQ2cBd1fWHx4RD51u40GIiNWAT7Hst+64zPzuMPOU5qGJqvc92LC2fn0jpZAGr+8622o4uayStCqw+QDKREQEsHUtuZfnyWm19W1ax5Q0pvV+jvydlqY3aXX+f4A1W///c+DIIecnTTSDNZKkWbUmBn90LfnCbvfPzLuBS2rJwx5v/t2VPG4E/nXI+UnzyoTW+249vbb++0ZKIQ1evY51XWen2X5QdXZjljXUANyemVd0u3NmXg7cUUlaCDxyQGWTJt241vu58Hdamt7E1PmI+EfgWa3Ve4H9eugFJK2QDNZIkrqxKe3zVtyZmdf1eIw/1dYfN7ciTS8itgYOqCQdkJlLh5WfNE9NVL3vVkRsBjyjlmyvO028iFiD5ed1qNfB2QyrztaP02u5Ou3T+PNEatqY1/u+RMTawB61ZH+nJSarzkfEBsAHK0mHZ+YFw8hLmk8M1kiSurFebf3KPo5R36d+zIGIiAXAZ4BVWkmnsvxEipJmNzH1vkeHAdXhky4EftlQWaRBWpf2e/te4NoejzGsOls/zp/7OMY4Pk+kpo1zve/Xu4C1KuvXAd9uqCzSuJmkOv9xYHHr/y8GDhlSPtK8YrBGktSNtWrrt/dxjPo+9WMOyr8B27X+/27gdXa1lvoySfW+KxHx98CeteR3+ozQPFGvX3f0cW8Pq87Ou+eJNCbGud73LCJ2BN5aSz4kM+/otL20ApqIOt/6m3v3StLrM/OuQecjzUcGayRJ3aj/AdfPH1p3znLMOYuIxwAHV5IOzczfDTofaQUxEfW+WxHxOMoEp1XfyMwTmyiPNATjXGfHuWzSJJs3dSsi1gO+DCyoJP8S+FgT5ZHG1NjX+Yh4CPBflaTPZubJg8xDms8M1kiSurF6bf2ePo5xd219jT7L0lFEBPCpynEvBN4/yDykFczY1/tuRcSDgW8Ba1eS/wy8ronySEMyznV2nMsmTbJ5UbciYjXgeOCRleRbgVdm5v2jLo80xiahzn+UZUOrXQu8bcDHl+Y1gzWSNAEi4qMRkSNYDp6mCPU3dlbt42usNssx52p/4G9b/5+U4c/6+eNVGgvW+8FoTcT6TeAxleTbgZdm5vWjLo80RONcZ8e5bNIkm/i6FRErAccCO1aS7wdelZl/GGVZpAkw1nU+Ip4H7FVJektm3jCo40srAoM1kqRu3FZbr7/R0436Gzv1Y/YtIh4BfKCS9MnMPG1Qx5dWUGNd77sRESsDXwF2qiTfQwnU/HKUZZFGYJzr7DiXTZpk86FufRzYo7KewH6Z+a0Rl0OaBGNb5yNiEXBUJel7mfnFQRxbWpEYrJEkdaP+B9zCPo5R32eQ/xA8CljU+v+rgX8f4LGlFdW41/sZtYZGPAZ4YSV56k3d74+qHNII1evXmq160Ith1dmJfp5IY2yc6/2sIuIw4PW15H/LzM+OqgzShBnnOv9+YKPW/98BvGFAx5VWKCs3XQBJUle+A1w3gnxOnSb92tr6hn0cu75P/Zh9iYidgRdUkt6UmTcP4thSw6z3c/Nx4JWV9anhEb82wjJIo3Qd5T6farRZhTJm/NIejjGsOls/ziP6OEaTzxNpXI1zvZ9RRLwDeEct+b2Z+Z+jyF+aUGNZ5yPiUbQHZw7KzMvmelxpRWSwRpImQGb+APhBg0W4FLiPZb8ba0TEQzPzLz0cY6Pa+m8HUjJYXFv/au8vFwFwUEQcVFk/JzO37b9Y0txY7/sXEYdT5rGqemtmfmYU+UtNyMw7I+IKYONK8kb01oAzrDr7u9r6IztuNbP6PiN5nkjjbMzr/bQi4p+Bw2rJR2TmQZ22l1SMcZ1/EMsCSAAfjIgP9nGcfSJin8r6zZlZ//e+NK85DJokaVaZeS9wSS158273j4jVgE1ryTaySGNsUut9RLwbeHst+aDM/Oiw85bGQL2OdV1nW54wy/H6dTlwZ2V9YURsPN3Gda1t16wk3Q78aUBlkybduNb7jiLi1cB/1ZI/A7xlmPlK88hE1XlJvTFYI0nq1tm19R172Hc7YLXK+tWZ6fAl0vibqHofEW8G3ltL/nBm1tOk+arvOhsRGwCbVJLuBS4cQJnIzATOrSX38jzZqbZ+buuYksa03k+T38sogZnqG/hfAfazTktdm5g6L6l3DoMmSerWt4GXV9Z3YfnhC6azS239WwMpUXFah+N34+3ArpX1zwOfq6zfOpdCSfPEuNb75UTEPwIfqSV/MjPfNsx8pTHzbeDfK+vPjojoshF019r6yZk5yInGvw3sUFnfBfhSl/uO9HkiTZhxrvd/FRHPA74ILKgkfwfYKzMfGEae0jw1jnX+D/T3b/JXA3tX1r8PVIdPu3cuhZImkcEaSVK3vkv7/BU7R8SmmXnpTDtFmUBmSS35xEEVqjV/xg973S8i9qolXZqZPR9HmufGst53yO8VwCdof1P3C7RPdCqtCE6nTD68bmt9U2Bn4OQu9n1tbX3QdfabwPsq63tGxJtmaySKiEXAnkMumzTJxrneAxARzwC+DqxaST4Z2KM17Kqk7o1dnW/9lvfzb/Kn1ZKu9t/kWtE5DJokqSuZeQNwQiUpgIO72PU1tHe1vpw+/pCTNHqTUO8j4oWUXnHVv2tPAJb4pq5WNK17/uha8kGtAOq0IuLvgKdXkm6lDE00yLKdC/yykrQWcEAXux4ALKys/yIzHbJFahnnet/K58mU3nBrVJJ/AeyWmXcNOj9pvhv3Oi9pbgzWSJJ6cRBQbfzcOyL+YbqNI2Jz4EO15Pdl5j0zZRIRm0RE1pZN+i20pDkZ23ofETsDXwVWqSR/H3h5Zt43077SPPYBoNpb5Rm0D5fSJiI2BD5VSz4iM6+bKZMO9XXnLsr2f2rr74iIv50hj05lf1cX+UgrmrGs9xGxBfA9YFEl+WzgecMabk1aQYxlnZc0dw6DJknqWmZeGBGfAl5XST42Ip4A/Gdm3ggQEasAr6LMH7G4su25wDGjKq+kuRvXeh8RW1KGVVq9knwN8N/A387ycmHdnZl52gCLJzUmM6+LiEOBQyvJh0XERsAhmXkVQESsBOwGHAFsVNn2KuDDQyrb9yLi+ywbM38V4KSIeAfwP5l5R6tsC4H9KHNkVYOx383MHw2jbNIkG8d635rI/PvAQyrJtwOHA0/u8Xcah0aSlhnHOi9pMKK7+ackSSoiYk3gJ8CTax/dA/wRuJsybu5atc+vA3bKzN93kccmrWNVPSozL+u9xNPmcTSwTyXpPZl58KCOL80n41jvI2IJ8NnZjtulyzNzkwEdS2pcq3HmROCFtY/upwxLeDPwKNoDqwB3Art0E7yMiPo/JJ+Zmad0sd/6wM9b+dfzvpQy3OKmtAdiAS4B/qY1V52kmnGr96038LuZQ6MrmdlbdEea58atzvcjIg6m9OKfckxmLhnU8aVJ5DBokqSetN56fQ7w49pHqwKPA7Zm+Qbby4BnddNgK2n8WO+lydIaz35P4Mu1jxZQAiFPZPnGm+uB5w+7l1lmLgWeCZxT+2gNYAtgc5YP1JxNaSAyUCNNY5zrvaTBs85L85PBGklSz1qTju9CGRbpDzNsegOla/ZWmXneKMomaTis99Jkycy7MvMfgD0owY7p3A58HNh8kG/LzlK2y4GnUMbXv2qGTa8CDgB2yMw/jaJs0iQb53ovafCs89L84zBokqQ5i4itgCcBG1De5LkeOB84IzPvbbJskobDei9Nloh4NLADsCGlV9xNwEXAaZl5V4PlWgnYDtgGWK+VfC2l0enXrTeHJfVhXOu9pOGwzkuTz2CNJEmSJEmSJElSgxwGTZIkSZIkSZIkqUEGayRJkiRJkiRJkhpksEaSJEmSJEmSJKlBBmskSZIkSZIkSZIaZLBGkiRJkiRJkiSpQQZrJEmSJEmSJEmSGmSwRpIkSZIkSZIkqUEGayRJkiRJkiRJkhpksEaSJEmSJEmSJKlBBmskSZIkSZIkSZIaZLBGkiRJkiRJkiSpQQZrJEmSJEmSJEmSGmSwRpIkSZIkSZIkqUEGayRJkiRJkiRJkhpksEaSJEmSJEmSJKlBBmskSZIkSZIkSZIaZLBGkiRJkiRJkiSpQQZrJEmSJEmSJEmSGmSwRpIkSZIkSZIkqUEGayRJkiRJkiRJkhpksEaSJEmSJEmSJKlBBmskSZIkSZIkSZIaZLBGkiRJkiRJkiSpQQZrJEmSJEmSJEmSGmSwRpIkSSMTETtHRFaWg5suU1Mi4ujaudik6TLNlde3e54rjauIOLh2b+7cdJnmqvZ9Tmm6POrNuP1ezsc6IkkaDwZrJEmSJEmSJEmSGmSwRpIkTZyIeG7tjcaMiJObLpckSZIkSVI/DNZIkqRJtKRD2jMiYuNRF2Q2DpWhSTJuQ81I80lELKnVryVNl2kSeN4m34o+DN2K/v0lSd0zWCNJkiZKRDwIeHGnj4B9RlwcSZIkSZKkOTNYI0mSJs0rgNWn+ezVoyyIpHaZeUpmRmU5uOkySVLtubRz0+VRbzJzSe0aXtZweQ6uleeUJssjSZo/DNZIkqRJs6Ty/wmcVlnfLCKePtriSJIkSZIkzY3BGkmSNDEi4rHAUytJpwHvr23mUGiSJEmSJGmiGKyRJEmTZElt/Vjge8B1lbQ9I2LNkZVIkiRJkiRpjlZuugCSJEndiIiVgL0rSfcAX8nM+yLiOOCfW+lrAy8BvjCAPAPYGng88FDgQcAdwDXARcB5mXn/XPMZVxGxGNgSeBywDrAqcBNwLfDLzLy8weL1LSJWA3YCHgFsANxP+T4/mWW/qfvhCcB6wEJKoPDPwE8z87ZhlrtDeR4FbAFsRLk37wNuAC4HfjHq8kyaiHgcsA3L6vYNwFXAzzLzhibL1q3Wc/EpwGMo9+QCSv38I3B6Zt47gDwWAc+g3GfrADcDFwKnZebdcz3+IETEQuBvgA0p13MV4BbKeTg3M//cxzHXozz/NgMWU/7tfAPl+X9GZl4zmNLPTUQ8gvIceBTlPoZSziuBn2fmjU2Vbb6JiAXAkyn3xLrAIuA2yrm+ELgoM3ME5ViF8ru8OfCwVjlup1z33wK/zsz7BpjfYkr92oDyvVei/C1wCXBOZl47qLwkSVrhZaaLi4uLi4uLy9gvwC6UOWqmluMrnz219tkP5pjXRsBRwNLacevLDZTePU+v7b/zLPtNu3Qoy8G1bXbu4XtsUtv36C72eSLwAeA3wAOzlPcS4E3AGj2UqX5uDh7CvXJ0LY9NWumPAD4B3Njhu5www/EeCnyE0pA/3bm4GzgR2Hqu5Zxh+9WBl1ECkVfPcm3uA07q5n6h9Fjr5369rJ/rS2nsrG7z0z6v8z6143ysi33WAg4CLp3l3J1MrV4P4T7tuy5QAjMfowQLp/setwDHABv3Wb4NKM+3O6c5/s3AYcCa09xHS4Z5/lp5Pg/4Uav+zXSvXti67hvMcKwAng78F6XBe7b7/9zWPbjyLGXcpM/61fGZQAkaPRf4FHDZLPs/AJwO7A5ED+f14NpxlnuOAO+obfPOPq/hN2rHed6Qzlv181N6LOMWlOfuTbPkezXlb4dthnCvrwv8E/Bd4NZZynFb6/549BzyWwn4B+DnlGfiTPfYWcBbgXVqxzilz+t3cIfyHN3FNf5YbZvX9vndT64d58l91pE5fX/K3yzVc/+7Pr/PbrXjHzfo+9PFxcXFZXCLw6BJkqRJsaS2fuzU/2TmLyiBgynPar1t3JMoDgIuBl5PaRCdyTrAq4BTe81rHEXEG4FfAwcA21IaL2eyKXAE8KuIePSQizcnEfFs4HzgdZS35Lvd77WUe+stlMbr6axKaRD5TUT8nzkUdSY/A74GvJLyNvVMFgC7AidHxMciYmx61GfmryiN51N2avUS6tU+tfVjZto4Il5IuZYHU3ohTGcBJZByakR8YpzOHUBE/D3wB0pvwofMsOki4NXA7yLin2fYrlMeu1B6D76KEiTsZG1Kg/2ZEbFhL8efq4jYICJ+Qmm4fhal/s3kCZTrftgM23yQ8ix/I6XXwmy2ojQgn9zqhTMqXwb+F3gtsPEs2walR8TxwNdaPZAG5TNAtefWa1u9D7sWEQ8DXlRJuoISZB4LEbFKRHycEph7Jct6Lk3nYZS/HU4ccDnWoQSC/psSoFxrll0WUu6P8yNiSR/5PR44G/gi5WWYBTNtDjwJ+DDwr73mNWD134BX93qAiNiY0pNwyoWt36yRy9Ij8DuVpMdGxN/2caj9auuf7L9UkqRhM1gjSZLGXkRMDW025Wbg27XNqsOerUSP/0hvDSvyFUqDXr3h7x5KI++ZwAWUt2vno06NsrcCv6N8918Df+qwzebAT0fcYNmLJ1Iaz6oNbZcDv6Jc145DRUXE+yhvJy+qfXQL5T44k/Jme9VKwHsi4og5l3p5na7PVZQg1C+A8yh1o+6fGb/GmWqjWtB7fd2IElCZclFm/nKG7V8HnMDy4ejwsAAAIABJREFUAdg7KEGJMykBkAdqn7+O0sjdUyP0sETEfsCXWP6evI1yT57N8s+n1YCPdRtEjIhnsHx9AbiL0uPkLMpQYFO2oARN1ujm+HMVEVtRrlenRssbKefhl5Sgey/DwHWqXzdS7o8zKOf26g7bPA34cUSM5PvTuZx/oQRAzwDOoX0etykvBU5sDZ03Z1mGvjq+krQpJXDWiyW0D83+6cys18FGRMSDgB8Cb2D5dpM7gd9TzvdFlPo3TAtYfgj7+ynD/J3dKsdvKT3MqlYDPhsRXT9fI+KZlN40W3X4+C+UwNWvKL0Tx+JaTWn9BlxUSXp6RGzS42FeTfuLKjO+BDACR9XW64GXGbUC6c+rJF0K/HiuhZIkDc9YvSUmSZI0jb+nvSHwa7n8XAnHAtXGyH2AQ3vI43Bgj1ra2cD7gJMy8/bqB62eJC+gvHm+fW2/cyjDtkH5h391rp23tT4fV1PDeX0b+ElmXlHfICIeAuwJvBt4eCv5YcD/AC8eUTl7cSSwJqWx+UPAUZl55dSHrfH4t67uEBH7Au+qJCXwecoQSb+uNihGxMMpb+O/jTJXBsCbIuK0zPzKgL/LFcBXKY3jv8rMW2rlDkoj2/6UQMPUG9H7RsS3MrPauDrlJJbdr2+n9MiZshdlOMBO7uzrGxTHUno5TDWC7g28p4f996bLBrWI+DvKPVBtcP0W5U3w07Iyt0NEPBj4R8q1nwqIvJjS2+wDPZRv4CLiSZQ366vf4wrKfffNqWdia16NZ1O+3xaVbd8TEb/MzP+dIY+1Kdem+ry9HjgQ+FJW5kGKiG2A91J6lG1NuXeGqlVXT6QMDzTlAUoPl49T6mZWtl+dMuzeyyj38mxupfRe+w5lzp/lgjOtxs+9KL2KpnrpbQG8n849C65hWf3alfbz9EHg+zOUZ7p5ca6jvFzwHeDMzFwuONP6jXoNpVfgVIDn71pl/M8Z8uzFJyi/z1P2owxLN6vWs+q1laT7Kb11pgzjvPXisywfEPwJ5bl1SvVvkFYA7AmUZ8VelN+bYfgVZdi4k4AL6n8HtXoBPoNyrp5T+ei/I+LkzOz0skV1/02Ar9Pe+/RuyvBin8nMC2vbr0XpubUHpedR3b9ReiAD/KCSfm7rs+lcOlM5Z3EMpS5C+Y3Ym/J3XLeqf689QKUXdx8G8f1PorwUsklrfY+IeFN2PxfVa2jvGfWp6jNSkjSGmh6HzcXFxcXFxcVltgX4KbOMDd7a7ozadn/T5fGfw/Jjhh8BrNTl/n83w2cHd1P2YexPj3PWUBoc1+vh+OtQGo+qeWw+yz4717Y/eAj3y9EdruetwNO63H9TymTNU/veQWsehVn2+9vWtlP7LQVW76Gcm8xy/KcDC3o4D7tQAlRTxz+jj3M3Y5nmcn2B79W27er6tPb9XWW/+4ENp9luMe3z+9wPvKaL428OXFvZ727gYQO+T3s5V0Fp4KtufzaweIZ9VqM0nFf3uYoZ5piiBHiq2/8ZeNQs3+P9HepbMoQ5ayi9iqp53Aw8s8t91wSeOsPn2wFr91CWjWmf++gO4MGz7LNkrueI0jg+7XOlw/bbUgJu1Ws62zw7B9fKufMM21br4t3Aul2W65m1PL495PNW3f+UWbZ9fW37B4C3dZlPdHtP9lD2NWa6d6fZ59217/CBLvY5vbbPlcBWXeb3YGDbQZz/afY/unaMTabZbkPKc35qu4t7yGPHWh7fm2HbruvIXL8/8M7a/v/Sw734x8p+9zLg3zEXFxcXl8EvDoMmSZLGWkRsRhlmZsqfKG+3dlJ/A3KfLrM5qLb+xcz81+xyOJbM7OpN4nGXmRdkGdqm2+1vpExAXD1PSwZdrgF5W2b+rMttD6D9zejX5Ay9EaZk5qmUXg5T1qO7t/m7kpk/zcz7e9j+B5Q30Kc8JSI2H1R5BqCv+QUi4qnAYytJP8xKT6ma/Wmf3+c/MvMz02z7V1neIF9SSVqV0nuqKbvQPizRHcBumTntkIxZ3rp/Ke29DDag8xvwRMSatPd0AHhVZv5xpoJl5jsoAfWhas2j8fJq1sDfZ+bJ3eyfmXdkmd9sus/PylpPtVmOdzntQxKtAbyi2/37lZk/z8y7etj+bMozbcqGtPeem6vqEIur0v2QhvXhnP5nMMWZm1bvlHfWkg/LzA91s38WXd2T3crMO2e6d6fZ532018slM20fEbtSAoFT7gaem5nndZnfDa17rVGt34IfVpIeHRE7drl7/d5tegi0KfX5obodCm0XlvXIgRIQHUSvM0nSEBmskSRJ425Jbf2LmZnTbPtl4L7K+stbw+BMKyKeQnsDxa2UOT7Uhcy8mDJ/xJRuG0VG6Qq6bAhsDYNVbbD5eWZ+uYe8/ofSI2PKy3rYdxjqAcxxuj4nUOb/mbLnbPW1pR6E7dig1hoO7F8qSVdQeo50JTO/C/ymktTktfzH2vp/ZochCusy82bKG+BVr5tm8xfTPk/N9zJzusB43Tu63G4u3kT70HfHZmajk9G3AvXVodLGqX5VfZnS22DKIMt5NO1zpdTv1eW0nrMvrSRdTftE6k16KbBRZf1Slq9Dk6I6l996raHxpvPm2vrh3QZqxlDPLwJExGq0B4NvofxGNa4VYDmxkrRVROzQxa5jGRCVJM3MYI0kSRpbrTHt964lTzt+eGb+hfZx7Bcz+xwq9TeMPzfT2+rqqPrm/RMbK8X0juu2lxRlaKrqfB2f7yWjzLwXqL5VveOgJvTuU71XxNhcn8y8kzLvxpRZ6+s0DWqd5uEB2IZlcyoBfLl1fXpRfZ48PiLW7XH/QanPnfHZHvb9Eu3zC23X6kVT97Ta+hc6bNNRZp7O8vfaoNWf1f9vyPl167LK/49N/arKMudaNYg8sHJm5vWUeX6mPCEi6vdS3d6UYfqmfDYrc0c1rH6fHdnHc2NcdPX8j4hVKL99U+6jzAE1qY6n/UWAl7d+O2ayG+1z9Xyl9Rs1Lj5RW5+xd01EPJTynab8iTL0qCRpzBmskSRJ4+yZlHkBppybmefPsk+9gXHJLNs/vbb+3S7KNe9FxPoR8S8R8YWIOC8ilkbEnRGR9YUyFNqUNSNijemO25AzZ9/kr+r3w6/6yK/a42FtyrBDAxURT4mIQyPiuxFxaUTcEBH3drg2d9d2bSrYMJ2ja+uzvQH9IpZN2Azw1Rka1AZ9LaFMIj5SrUm/168kXZ6Zl3S7f2tor+p3XwBs32HTJ9fWT+82jz6371pEbABsVkm6NjP7uZ7d5rdJRBwQEV+NiIsi4i8Rcfc0z79qz8yR1q+I2CIiDoqIEyPi4oi4LiLumaacGwyxnEfV1mcbpqn6eQKfHmxx5mSs/yaIiFUi4kUR8V8RcWpE/DkibomIBzpc83rPs+mu+5Npf0nhN5M8XNY0LwLsNs3mU7rqsdmgHwEXV9ZfERGLZth+H8qwhFM+08NLK5KkBq3cdAEkSZJmsKS2Pm2vmooTgNuAtVrru0TEBpl59TTbb1ZbH1oD4CRo9Rw4nNJovqDPwyym/U3+pvXyxn+9Mf7M0sFrTh5Meat1ziLi6cDHgK37PMTi2TcZncw8LSL+AEwNz7NrRKyfmUun2aWXOQXq1/IrA7qWo7Zxbf3cPo5xDu2N0Bt12KYaVLyb3nvKXNRroXpQf06fNYxMImJj4AhKw24/N8tI6ldEbEV5DtR7XHVroOXMzJ9FxAXAFq2kPSPiTa1h+NpExN9UtgP4UWZeOsjyzNGmlf+/neHe111r9TR+DXAoZT60fkx33efj30HH0D4k36uBr3baMCLWA55TSbqkhznuRiIzMyI+ybJ56BZSXpT55DS7VL/7A5R5byRJE8CeNZIkaSxFxFq0j2n/APDF2fbLzDtoHxZpAcsPpVZVbXxN4LoeijmvRMRmlDk69qX/QA20D28zDrqeNBx4yBDyf9Dsm8wuIl4P/IT+AzUwftcG4HOV/18ZeFWnjVrDujyvknQpMFOD2theyx6tU1vv5xlV36d+TGhvyL1lhrnBpjPM4SPrQbJrO241B635y86hDMXXb1Rv1dk3mZuIeCGlMb3fQA0M5zlQHaZpDaapxyw/p83YzKMREWvT/kLrdX3Ug4FrDaX5OeBT9B+ogemv+9Dr16i1gi3VHojPbQVlOnkV7df9c9Ns17Sj6WJ+qNZLHY+rJJ3UzRxnkqTxYM8aSZI0rvakvDk45feUsfC7GYao/pbuPpTeIp1Uh5G4Y0UdJiIiVqUM9/KI2kcXUwIEvwOupLxpfCclsDXl7Sw/zv846WW+gWG8GT/nF6Qi4pnAkbQ3It9HCVacAVxOaWC7i+WHPvvBXPMfss8D72HZd3s18JEO272SWoPaLA2pY3kt+7BWbf32Po5R36fT8DnVhtx7+sijft8NUr28tw3y4BHxEMrzrx6MOxf4KfAH4CrKs+8u2p9/H2ZuAdSuRcRjKfPDVINCSRnq8XTKb981rTLeVdv9WNqH0xu0zwHvB6bmQ9qP2rwnrWGbqnNOXceYTOLeMtT7bA7eDexVS7sFOIXSy+xPlGDpXbT/3m0DfKiL44/r956rz1F+W6D8drwS+GiH7ao9NpMxDdZk5nUR8XXK9wDYPiK2ycxzapvWhyEcm4CoJGl2BmskSdK4qo8f/nj6b3TePCK2z8xfdvjsVpa9Zb5mRKy0ggZs9gceW1lfCizJzFknpI2I1w6tVKN3R219X+DPczxmvSGlHx+mPVDzHWD/zJyxbF1Mqty4zLwsIn7Csgmut4mIrTOzPtxX9ZnQTYNa/Vq+g7kPn3XBHPfvR73hdGHHrWZW3+fWDtvczLLeSPUAUTfW7mOfbtXL20/5ZvIftPfEuhjYKzNnne8qIur32TC9n/ag2pnAPpn529l2bM1hMjSZeXNEHEd5ZgJsGxFPrs0t9Era78VjMrOfwOCwDPs+61lErA/8ey35MODQzJwxqBIR3faQHbvvPSCfAw6m/UWAtmBNa0jBbStJp2bmZaMoXJ8+wbJgDZTAzBunViJiMbBH5fNrgG+NpmiSpEEwWCNJksZORDyKuQ3x0sk+QKdgzQ0sC9YEZQLecRoCZC4NbGvOvslfvaK2/pLM/HmX+zYxj8ew1IeLurCbBtthar1N/8RK0vnAS7ts5JyUa3MMy4I1UOrrv02tRMSWtJ+Dn2bmbHOq1K/lHzPzh3MpZENurK33M7xbfWLx+jGhPAunjr12RKw1W2NwzcP7KFe3bqitz2UoqE6qvT3uAp7bwzwqI6ljraFBX1BJWkopZ6dr2Umnoe8G7SiWBWugNCRXgzVjOwQaQGbeEhH3saydZN2IiIaHQnsxZVi5KZ/MzHd2uW+39+aw61cjWi8CnAo8o5X0xIjYMjPPr2xWfzFopnnQGpeZp0bERSybk+1VEfH2zJyaJ/BVtN8vR2fmfSMtpCRpTpyzRpIkjaN96H/OgOn8Q2uor7qLa+tPHnC+c1UfymaNjlt19tBuNmqNh799JensHgI10D5Z9KSrBwAe3XGr0Xpqbf1TPbyNPinX5mu0D9X1ytpb4f00qI3jtezH5bX1bfo4Rn2f+jEBLqz8f/SRz7azb9K3P9TWtxvUgSNiI9oDTd/rNlATEWsAjxpUWWbxJNqHP/tSt4GaiHg0I5ivqhXYPruS9A8RsbBVhm1p/309NTN/N+wy9aF6ry1kWaN4U+rP/4933Kqzbp//4/530FzUfyv++lvS+o2pzq10B+W3aNxV54daTBk2eEp1CLRkzAKikqTZGayRJEljJSKC9vHDAXbOzOh1AX5UOcaDgRd1yPKntfXnD/DrANSHVOs1CHVLbb2XOQe2n30ToLxNX+1x3XUDWqvXx4Y9lGncnVxbf1YjpWhXv+a9NHD2Wv653q99afXg+EYl6WG05kGapkHtq10cdhyvZc9aQ/IsrSRtHBGbdrt/a56QauPrfbT3dphS70H2wh7yWAd4Wrfb9yozr6G9QXm9iBhUg/Jc6tfTgVV62H4u9WuUz4G5OKry/4tY1mtzLvNojPK5NOy/CXo1iuv+a9qHjXxiRDysh3y6Ue2dNJLflZav0f7dXlV5EWBXym/NlG9kZqchIgdhkN//GMr8WVP2A4iI7WkPsv+4hx6CkqQxYbBGkiSNm7+l/U3lq1i+8aRbX6qtL+mwTX1Olr1bY34PSn1i716GJoPl34B/YsetOnv57JsAyzccdOqBNJ1/6mHbSfBDSmP2lFe0Jh9vUl/XpzVfzWt6zGuu9+tcHF1bn3oDehdgg0r68V02qJ1J+3Bfz4qIzfsvXqN+Ultf0sO+/0B7j7xfZWaneVbq8xrsM9Urogv7MfyeG/Vn9ZsGdNxRPv/mUr/6fQ4E8IYe8pmrL9I+z9J+rR5I1Xk2bqS3HgyjfC7V77M3REQvAblB6/e6bwv8TTfbZua9tL/csjKD/22vXsOR/a60fiu+XknagPKbAqMdAm1g3z8zbwK+Ukl6WkQ8njEfZlCS1B2DNZIkadzU//F8XGbW36rt1jeA6nBRz42ItrHYM/PXwM8qSWsD/91nfp3Ux4LvdcicX9fWXxIRs847GBEvpfuhTK6nPUDx1C7z2JZ5FqzJzKXA5ytJCxns/dCPa2rr3fZgeB+99cSCud+vc3EycEVl/cURsTbL97TrqkGt1QBZnUw6gE803PDar0/V1t8aEY+YbafW+Tu4ltyxAS8zzwN+UUnagDKh/Wx5PAZ412zbDcB/0d7DYq+I2HUAx+2rfkXE8ynzifRiLvWr3+fAGxjuEHVtWo3jX6gk7UB5FlVfgjg2M+tDfM5klM+lE4HLKuubsnwdGqWer3ur50gvw6UBHFFbPyAiturxGDOpXsNNBnjcbtR/M14dEQ+ivf7+GfjxEMsw6O9/VG39LZTA/JTrgOMHkI8kacQM1kiSpLEREWsCe9SSv9zv8Vrj+Z9USVoZ2KvDpu+hfYiKV0bER1tvJM8qIv5uho8vqK2/rJtjTmkN//ObStIjgQNmKc/2LN+4O1Me9wNnVJI2oDK5+zR5PJrSqDWJDd+zOYT2YVNeHhGfmGbOo44i4sER8a6I6DT0Xq9Or63v3zr/M+X/euBtfeRVv1/r9XFoWpN4VwNlq1PeFN69knYl7W+Az+YI2ocQexrwtVZDXVciYmFEvCkiXttDvoP2Q+DcyvpC4MSZvkfrfv0q7b2Srqb0fJjOu2vrb4yIw6e79yPiSa2yLaL9GTpwmXkx7WUP4KsRsXM3+7euY33+DzLzCsp9NWX7iJixV2JEPAU4tpt8a+r1a7cegodn0f7ywUsjYseZdoiIFwIf6aF8g1JvSK7/nvT6xv9czltPWr+Hh9SSD4yIGX8Tp0TxzAEWqf78f1+rp9J0+S8APkOXvWqmZOaPaO/FvBrwvW4DNq3fvJmCgtVruG639XZATgb+VFnfnfLbsnol7fNzeDGoGwP9/pn5C+CcStLrKM/hKZ/rYW47SdIYMVgjSZLGyR60/2PzktaExXNRD/bUe+6QmT9k+QatfwXOioiXdhoKKCIeHRH/GhG/ojRWTuc3lDccp+wcET+OiP0j4vkR8ezqMs0x6g1bh0TEYa15IqplenhEvIcyZNI6wCUzlKvuc7X1wyLiQ/WeSBGxbqvR6lfARpQG2nGcJLpvrTHe6w3zrwPOi4j9ImK53iqtBrrNImLviPgG5S3d91HmA5pref4A/LyStAg4NSL2rPeAiohtIuI4SmNpABf1mN0pwL2V9X0i4hsR8ZqIeF7tft2p928zq/p9+H9pH8Krpwa1zLyZMvly9TvtBlwQEf8WZXL55UTEIyNij4g4ljIU4xGUQGkjWoGsfWn/Hk8Czo6Il1WDKRGxUkTsQqmj9Z4nr5mpR0PrWfjpWvLbgYsi4r2te+4FEfG6iPg6Zai5jSg9XkYx5M4bgeocDGsDP4yIT0XEk+oB9ohYPSKeFhH/SRlScv9pjlu/7z4XEQe2eiZVj/eIiDgEOJXyjL2L9l4YM8rMa2lvYH0M8PPWb8kL678HEbF6Zd/baR/OaQHwv61rUW10JiIeExEfpwTUVwOupfSgHInMPJvl50CackarF1cvx+v7vPUjMz9N+zBtAXyo9du9a5QhJpd9WOrcFhHxTsoz97Nzyb/meKA67OOTgJMjYodaGVaOiOdQXryY6o3Y6/N/L9p7gDwcODMiPhhlmK02rQDoLhHxSUr92r2+TcX3a+vHR8T7W8+UXWvXr+s5ubrR+s2ovgiwBssH5IY5BBoM5/t/YobPun5hR5I0ZjLTxcXFxcXFxWUsFsob81lZ/u8AjrmQMlZ49bhP7LDdysBxte2mlruB31OGCDqPMt5+2zazlOHd0xx3uWWa/VemDIdW3/5eytuaZ1AaMKufXQ08o5Z29AxlXIUSWKrncT/w29Z3v5gyXFrbNaLMNVJN22SGfHaubXvwEO6jrsszy3He2vr+na7VFa1rcmbr3rhlmu2WDKKcwI6Ut+rrx7+1VY5fUYbLqX52G2WOo2raKV187093eb9eNozrS3mTfLo8H9/ntXwFZULmTse8CjibUo9+S2ms7LTdQO/Vfs4VZW6YTvfkLZSeN7+Zofz/p8tyrQp8t8t7YGp5K2UenWraXoOu263ybUF5S75TOa5vnYczKPWyXmeOnuaYD57mmPcC57PsGftA7fP9KAHOv6Z1Uf69ezivm9T23Qy4ucN2d1KCGWd2+B73Ac+jBJWmrbu1fA6uHWPnPq7TvtN8p9f2ed37Pm+t/Xt9Dq5dv7aV5XaW/S5eQHkOz/hsnOM9//ZpynFN65qf2+G+uIgSmO71GfMMOvyN01qWUp6VZ1JeBqk/i6Y9PqWO/aXL67fccZjj7zrw2Bny+0Uf16SnOjLX7z/NMRd1uPcS+Okg7z8XFxcXl9Eu9qyRJEljofWGe33okC/N9bhZ3kb+di15nw7b3Udp0D2E9rfXoTRePoYy9v6WtI+9341D6W/InGrZXsryPWVWBjYHnkL7GP5XAM+mvOnabR73UsZvv7j20UrA4yjf/dGUt7mnfITRzFXRiMz8CPB8SuCr7pGUQMj2lHtjUYdt7qa80T6IspxOaRiu35trtcqxHe3z09wIvDAzf0Pv3gz8oJ9yDsh0bzifmZm/7eeAmfllyhBov+/w8QbANpR69DhKj4m6+ylBnUZl5v9Q5iW4tfbRImArytwk9fLfDbwxM9/bZR73AC8BPkT7XFad3A78Y6uurFX77OZu8utVZl5AeR7Vh4eC0iC6FeVaPoYuh2nMzBsoDdtLax+tTAkOTT1jp3ruPAC8tXU9ei3/54HDKY2qve57CaWn2G21j1YHtqY8j6pzGd0FvCoz/7fXvAbgOOCmWtr/b+/+Qi2tyjgA/5Z0UwSmNNqE0lBEapSJjBhD0wTFeOWNNyFlVBhC0U2l3XQZQX8MoqmQCLoplBBCLKobs7QmrJQ0NM2sLCwLbGYyFae3i3ef5szxzDmeP3u+M2ee527v2ftba+/97W/PWb+11ns469zadCPv2zrbO5Remfb1Zdp8WY79Ll6UF577m92Xz2X5VRTnpj/zN6XDpQW/SfKudIi71rZ+nGRPll+Vc076Wrk7XcvnRY8nzb5jV2WTfhPXqqoWJtwsZ96rauby+qvrQy33/+STscoRgDkR1gAAW8U1OTYQliQPVNX9m3TspX/MXr3cfvfVPpXkwvQszqUDTUv9LT2Qc9lKD6qqo1X13vRg8YH0rNR/pAdRX5Sqeiw9MPSVFZ73n3QR7otnA5prUl27YXeSL6cH+U7k50n2V9XHquqkDJxNpap+kB6U+mh69vJqr/dIktvTRb13VtX3NrEv30yyN70F04k8k65X8MaqumOd7RxOsj/JFbNj3ZterbE0KJqXm7P8+bd0q6o1qapfpgdWr0mfw0dXecqz6YLTH09yflXdtJH2N0tV3ZIOTg/khYXXFzucfs8uqKoDa2zj2ar6RHoQ+DM5tp3j0Vmbd6VXDL6uesuopIOSxeYS1sz699eq2pMOse/Kyp9lpc/hG7JCHadZsHlpOlg/0fEqHWReXlVfXEfXF9q6IR2sfT7JT9MDuCtdcxc/94fp6/RtKzzs+fQ2XhdX1c3r7edGVNXT6e/yYt+eTaBY7zHX/b6ts73nqura9NZjt6bDyZX8Mb1l4hVz6Mt16WvXoys87O/p7+Xuqnp8A239Nv3d/2COrbg9kaPp4PTDSb6wynHvTHJBejvD25L8IX2dmmetmMWWC2WeywZqI67FnF7/N5bcfipdqwyAU9TY5n9fAwCs26xQ72XprWd2pGfTHkkXo34gyYNThBWzGjpvT8/0fkWSp9Nbsty5kYGwJW28PMnb0oPCZ6aDoD+ntwv502a0cSoaY+xIh2avStejOSM9e/mJ9Ezkh2erlObdj13p2c870zUpnkrXDrp7NkjKKsYYZya5PF2X4ZXpVRiH0wOeDyZ5qFao77IVLLpGvT496/2M9FY7j6bPhZMVsGWM8Z30zPEFu6rqRa/u22DbZ6W3CtyZ/iwrHRb9Psl91TVP1nK8s9PB6GvSq5b+nR5YvXutx5qnMcbO9HX6vPTv06Ekj6T7udpkg7kbY9yZ7t+C3VV1z1T92ahZrZq3ps+LHelVt4fTq1nvn618mncfRjqwujR9ro/0Nev+JPdU1Woh9HraPDf9us9N/+49n169+XCSe7fCuXa6GmN8IMfXGTtQVR+Zqj8AbJywBgAAgHWbrVR8PB0YJcmTVXXOCk9hmxtjvCEdei64t6oumao/sB2NMe5OB2kL3lJV903VHwA2zjZoAAAAbMS7cyyoSZavJ8Pp5UNLbi9XcwVYpzHGm3N8UHNQUANw6hPWAAAAkOT/2yyt5fHnJblxyd1L6yhwGpltI3ftorsOpWsBAZvn+iW311SbDICtSVgDAADAgr1jjO+OMfas9sAxxr4kP0vXzljwuyS3z6lvnBpuTNf6WXBTVR2ZqjOw3Ywx3pHk6kV3/SXJLRN1PHOuAAACKElEQVR1B4BN9JKpOwAAAMCWMZJcmeTKMcZjSX6U5FdJnkjyTJKzklyYZH+Sy5Y892iSa+ZR5JytaYxxUZJXpyeCnp/kPUn2LXrIkSSfPfk9g+1hjHFWkktnN89Osie9zeDiVZCfrqpnT3bfANh8whoAAACWsyvHb2e1kueSvL+qDs6vO2xB1yd53wr//smqevJkdQa2oYvTofmJHIyaUADbhm3QAAAAWPCv9GqItTiYZF9VfWsO/eHU9dWqUkcD5ueRJFdV1X+n7ggAm8PKGgAAAJIkVfXrMcaOJO9MsjfJJUlem2RHkpemV9D8M8njSX6S5PtVdcc0vWWLOZo+N36R5GtVpXYRbL4jSR5KcmuSL6kHBbC9jKqaug8AAAAAAACnLdugAQAAAAAATEhYAwAAAAAAMCFhDQAAAAAAwISENQAAAAAAABMS1gAAAAAAAExIWAMAAAAAADAhYQ0AAAAAAMCEhDUAAAAAAAATEtYAAAAAAABMSFgDAAAAAAAwIWENAAAAAADAhIQ1AAAAAAAAExLWAAAAAAAATEhYAwAAAAAAMCFhDQAAAAAAwISENQAAAAAAABMS1gAAAAAAAExIWAMAAAAAADAhYQ0AAAAAAMCEhDUAAAAAAAATEtYAAAAAAABMSFgDAAAAAAAwIWENAAAAAADAhIQ1AAAAAAAAExLWAAAAAAAATEhYAwAAAAAAMCFhDQAAAAAAwISENQAAAAAAABP6H+7glyqUH7H3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1800x1200 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"0.9781228350269436\n",
"0.5883974261476856\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1800x1200 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"0.9795409737658726\n",
"-0.1287881514945668\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 330
},
"id": "eFohtlED3OUv",
"outputId": "36ade4dd-aa74-4ab8-bcf6-2bc87022308f"
},
"source": [
"protein_embeds = protein_embeds_pretrained.T # result is (1024, len)\n",
"emb_mean = np.mean(protein_embeds, axis=1)\n",
"emb_cov = np.cov(protein_embeds)\n",
"emb_sampled = np.random.multivariate_normal(emb_mean, emb_cov, size=212) # (212, 1024)\n",
"train_top_model(emb_sampled)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"0.0038250652459540513\n",
"-0.5803945401199546\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zV1rCo_SH2nw"
},
"source": [
"Raw code"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "cNLDC-M6K1vw",
"outputId": "a6c20efc-48aa-4e08-ed79-784619f50056"
},
"source": [
"emb._model_directory"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'/root/.cache/bio_embeddings/prottrans_bert_bfd/model_directory'"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TO_-3cq0K-Yd",
"outputId": "4fed3f9e-17bb-45a3-dc59-ad407dac15c8"
},
"source": [
"!tar -czf /content/coward.tar.gz /root/.cache/bio_embeddings/prottrans_bert_bfd/model_directory/vocab.txt"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"tar: Removing leading `/' from member names\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YbtTU04rH3cv",
"outputId": "4e96f0aa-8172-4478-89f0-e4c035164e49"
},
"source": [
"from transformers import BertModel, BertTokenizer, BertConfig\n",
"import re\n",
"\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"config = BertConfig(vocab_size=31, # original model has 30 (31 is to make sure no weights are mysteriously loaded)\n",
" hidden_size=1024,\n",
" num_hidden_layers=30,\n",
" num_attention_heads=16,\n",
" intermediate_size=4096,\n",
" hidden_dropout_prob=0.0,\n",
" attention_probs_dropout_prob=0.0,\n",
" max_position_embeddings=40000)\n",
"model = BertModel(config).to(device)\n",
"\n",
"tokenizer = BertTokenizer(\"vocab.txt\", do_lower_case=False)\n",
"\n",
"def random_emb(batch):\n",
" \"\"\"batch is a list of sequences\"\"\"\n",
" seq_lens = [len(seq) for seq in batch]\n",
" # Remove rare amino acids\n",
" batch = [re.sub(r\"[UZOB]\", \"X\", sequence) for sequence in batch]\n",
" # transformers needs spaces between the amino acids\n",
" batch = [\" \".join(list(seq)) for seq in batch]\n",
"\n",
" ids = tokenizer.batch_encode_plus(\n",
" batch, add_special_tokens=True, pad_to_max_length=True\n",
" )\n",
"\n",
" tokenized_sequences = torch.tensor(ids[\"input_ids\"]).to(model.device)\n",
" attention_mask = torch.tensor(ids[\"attention_mask\"]).to(model.device)\n",
"\n",
" with torch.no_grad():\n",
" embeddings = model(\n",
" input_ids=tokenized_sequences, attention_mask=attention_mask\n",
" )\n",
"\n",
" embeddings = embeddings[0].cpu().numpy()\n",
" protein_embeds = np.zeros((len(X_activity), 1024))\n",
" for i, e in enumerate(embeddings):\n",
" protein_embeds[i] = e.mean(axis=0)\n",
"\n",
" return protein_embeds\n",
" \n",
"raw_embeds = random_emb(X_activity)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:2143: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
" FutureWarning,\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 330
},
"id": "_ErZ9QbKL4d7",
"outputId": "c5ea9bc2-f9ba-44b1-e6bb-25ff5c93d2b6"
},
"source": [
"train_top_model(raw_embeds)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"0.6340506406240781\n",
"0.6867237898975587\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JUN46Mw_wqLU",
"outputId": "cb49f4fb-2e00-4e82-8c00-bfa17509dad3"
},
"source": [
"from bio_embeddings.embed import ProtTransBertBFDEmbedder,\\\n",
" BeplerEmbedder,\\\n",
" GloveEmbedder,\\\n",
" SeqVecEmbedder,\\\n",
" UniRepEmbedder,\\\n",
" Word2VecEmbedder\n",
"from Bio import SeqIO\n",
"\n",
"sequences = []\n",
"for record in SeqIO.parse(\"max.fa\", \"fasta\"):\n",
" sequences.append(record)\n",
"\n",
"embedder = ProtTransBertBFDEmbedder()\n",
"\n",
"embeddings = embedder.embed_many([str(s.seq) for s in sequences])\n",
"embeddings = list(embeddings)\n",
"\n",
"reduced_embeddings = [ProtTransBertBFDEmbedder.reduce_per_protein(e) for e in embeddings]\n",
"\n",
"for (per_amino_acid, per_protein) in zip(embeddings, reduced_embeddings):\n",
" print(per_amino_acid.shape, per_protein.shape)\n",
" break"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"prottrans_bert_bfd.zip: 1.56GB [00:23, 67.8MB/s] \n",
"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:2022: FutureWarning:\n",
"\n",
"The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
"\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"(6, 1024) (1024,)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3ZURuwc5W50_"
},
"source": [
"### TAPE GFP Dataset"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qM6lj6yPgpE2",
"outputId": "dc0d27f2-0ec3-4839-e766-67c8ec3db0ab"
},
"source": [
"!git clone https://github.com/huggingface/transformers.git\n",
"%cd transformers\n",
"!pip install -e ."
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'transformers'...\n",
"remote: Enumerating objects: 34, done.\u001b[K\n",
"remote: Counting objects: 100% (34/34), done.\u001b[K\n",
"remote: Compressing objects: 100% (26/26), done.\u001b[K\n",
"remote: Total 61313 (delta 16), reused 20 (delta 6), pack-reused 61279\u001b[K\n",
"Receiving objects: 100% (61313/61313), 46.02 MiB | 26.30 MiB/s, done.\n",
"Resolving deltas: 100% (43335/43335), done.\n",
"/content/transformers\n",
"Obtaining file:///content/transformers\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.6/dist-packages (from transformers==4.3.0.dev0) (1.19.5)\n",
"Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers==4.3.0.dev0) (0.8)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers==4.3.0.dev0) (3.0.12)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers==4.3.0.dev0) (2019.12.20)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers==4.3.0.dev0) (20.8)\n",
"Collecting tokenizers==0.9.4\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
"\u001b[K |████████████████████████████████| 2.9MB 5.5MB/s \n",
"\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from transformers==4.3.0.dev0) (3.4.0)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers==4.3.0.dev0) (2.23.0)\n",
"Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers==4.3.0.dev0) (4.41.1)\n",
"Collecting sacremoses\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
"\u001b[K |████████████████████████████████| 890kB 34.6MB/s \n",
"\u001b[?25hRequirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers==4.3.0.dev0) (2.4.7)\n",
"Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers==4.3.0.dev0) (3.7.4.3)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers==4.3.0.dev0) (3.4.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==4.3.0.dev0) (2020.12.5)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==4.3.0.dev0) (3.0.4)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==4.3.0.dev0) (2.10)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers==4.3.0.dev0) (1.24.3)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==4.3.0.dev0) (1.15.0)\n",
"Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==4.3.0.dev0) (7.1.2)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers==4.3.0.dev0) (1.0.0)\n",
"Building wheels for collected packages: sacremoses\n",
" Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893261 sha256=21491303aac83bc56425ebe120a8f601cef531ab1f371eb3358bbff027b86502\n",
" Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
"Successfully built sacremoses\n",
"Installing collected packages: tokenizers, sacremoses, transformers\n",
" Running setup.py develop for transformers\n",
"Successfully installed sacremoses-0.0.43 tokenizers-0.9.4 transformers\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jeDweVksXXBV",
"outputId": "ca127981-00bc-4845-c0a7-c2a4062ebbdc"
},
"source": [
"!wget http://s3.amazonaws.com/proteindata/data_pytorch/fluorescence.tar.gz"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-02-03 18:14:33-- http://s3.amazonaws.com/proteindata/data_pytorch/fluorescence.tar.gz\n",
"Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.17.150\n",
"Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.17.150|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1635678 (1.6M) [application/x-tar]\n",
"Saving to: ‘fluorescence.tar.gz’\n",
"\n",
"\rfluorescence.tar.gz 0%[ ] 0 --.-KB/s \rfluorescence.tar.gz 100%[===================>] 1.56M --.-KB/s in 0.08s \n",
"\n",
"2021-02-03 18:14:33 (19.4 MB/s) - ‘fluorescence.tar.gz’ saved [1635678/1635678]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NbqA5z4IX3AW"
},
"source": [
"!tar -xzf fluorescence.tar.gz"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QdT6p-rfIHJ-",
"outputId": "a5f336b1-eb7f-4f5b-e920-42ace5b02860"
},
"source": [
"!wget http://s3.amazonaws.com/proteindata/data_pytorch/stability.tar.gz"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-02-03 21:25:49-- http://s3.amazonaws.com/proteindata/data_pytorch/stability.tar.gz\n",
"Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.153.118\n",
"Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.153.118|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 3116829 (3.0M) [application/x-tar]\n",
"Saving to: ‘stability.tar.gz.1’\n",
"\n",
"stability.tar.gz.1 100%[===================>] 2.97M 4.93MB/s in 0.6s \n",
"\n",
"2021-02-03 21:25:50 (4.93 MB/s) - ‘stability.tar.gz.1’ saved [3116829/3116829]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iNqmIYUZII0B"
},
"source": [
"!tar -xzf stability.tar.gz"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rlkstjkSYjZh"
},
"source": [
"from typing import Union, List, Tuple, Sequence, Dict, Any, Optional, Collection\n",
"from copy import copy\n",
"from pathlib import Path\n",
"import pickle as pkl\n",
"import logging\n",
"import random\n",
"\n",
"import lmdb\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import Dataset\n",
"from scipy.spatial.distance import pdist, squareform\n",
"\n",
"\n",
"class LMDBDataset(Dataset):\n",
" \"\"\"Creates a dataset from an lmdb file.\n",
" Args:\n",
" data_file (Union[str, Path]): Path to lmdb file.\n",
" in_memory (bool, optional): Whether to load the full dataset into memory.\n",
" Default: False.\n",
" \"\"\"\n",
"\n",
" def __init__(self,\n",
" data_file: Union[str, Path],\n",
" in_memory: bool = False):\n",
"\n",
" data_file = Path(data_file)\n",
" if not data_file.exists():\n",
" raise FileNotFoundError(data_file)\n",
"\n",
" env = lmdb.open(str(data_file), max_readers=1, readonly=True,\n",
" lock=False, readahead=False, meminit=False)\n",
"\n",
" with env.begin(write=False) as txn:\n",
" num_examples = pkl.loads(txn.get(b'num_examples'))\n",
"\n",
" if in_memory:\n",
" cache = [None] * num_examples\n",
" self._cache = cache\n",
"\n",
" self._env = env\n",
" self._in_memory = in_memory\n",
" self._num_examples = num_examples\n",
"\n",
" def __len__(self) -> int:\n",
" return self._num_examples\n",
"\n",
" def __getitem__(self, index: int):\n",
" if not 0 <= index < self._num_examples:\n",
" raise IndexError(index)\n",
"\n",
" if self._in_memory and self._cache[index] is not None:\n",
" item = self._cache[index]\n",
" else:\n",
" with self._env.begin(write=False) as txn:\n",
" item = pkl.loads(txn.get(str(index).encode()))\n",
" if 'id' not in item:\n",
" item['id'] = str(index)\n",
" if self._in_memory:\n",
" self._cache[index] = item\n",
" return item\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8GyoF3CFfOuO"
},
"source": [
"gfp_train = LMDBDataset(\"/content/fluorescence/fluorescence_train.lmdb\")\n",
"gfp_train_seqs = []\n",
"gfp_train_y = np.zeros(len(gfp_train))\n",
"for i, seq in enumerate(gfp_train):\n",
" gfp_train_seqs.append(seq['primary'])\n",
" gfp_train_y[i] = seq['log_fluorescence'][0]\n",
"\n",
"gfp_test = LMDBDataset(\"/content/fluorescence/fluorescence_test.lmdb\")\n",
"gfp_test_seqs = []\n",
"gfp_test_y = np.zeros(len(gfp_test))\n",
"for i, seq in enumerate(gfp_test):\n",
" gfp_test_seqs.append(seq['primary'])\n",
" gfp_test_y[i] = seq['log_fluorescence'][0]\n",
"\n",
"gfp_seqs = gfp_train_seqs + gfp_test_seqs\n",
"gfp_y = np.concatenate((gfp_train_y, gfp_test_y), axis=0)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "L7X-w7pHLYwv"
},
"source": [
"gfp_train = LMDBDataset(\"/content/stability/stability_train.lmdb\")\n",
"gfp_train_seqs = []\n",
"gfp_train_y = np.zeros(len(gfp_train))\n",
"for i, seq in enumerate(gfp_train):\n",
" gfp_train_seqs.append(seq['primary'])\n",
" gfp_train_y[i] = seq['stability_score'][0]\n",
"\n",
"gfp_test = LMDBDataset(\"/content/stability/stability_test.lmdb\")\n",
"gfp_test_seqs = []\n",
"gfp_test_y = np.zeros(len(gfp_test))\n",
"for i, seq in enumerate(gfp_test):\n",
" gfp_test_seqs.append(seq['primary'])\n",
" gfp_test_y[i] = seq['stability_score'][0]\n",
"\n",
"gfp_seqs = gfp_train_seqs + gfp_test_seqs\n",
"gfp_y = np.concatenate((gfp_train_y, gfp_test_y), axis=0)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "BuBjxnR_gZ_g"
},
"source": [
"from transformers import BertModel, BertTokenizer, BertConfig\n",
"import re\n",
"import torch\n",
"import numpy as np\n",
"\n",
"\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"config = BertConfig(vocab_size=30, \n",
" hidden_size=1024,\n",
" num_hidden_layers=30,\n",
" num_attention_heads=16,\n",
" intermediate_size=4096,\n",
" hidden_dropout_prob=0.0,\n",
" attention_probs_dropout_prob=0.0,\n",
" max_position_embeddings=40000)\n",
"model = BertModel(config).to(device)\n",
"# model = BertModel.from_pretrained(\"/root/.cache/bio_embeddings/prottrans_bert_bfd/model_directory\")\n",
"\n",
"tokenizer = BertTokenizer(\"/content/vocab.txt\", do_lower_case=False)\n",
"\n",
"def random_emb(batch):\n",
" \"\"\"batch is a list of sequences\"\"\"\n",
" seq_lens = [len(seq) for seq in batch]\n",
" # Remove rare amino acids\n",
" batch = [re.sub(r\"[UZOB]\", \"X\", sequence) for sequence in batch]\n",
" # transformers needs spaces between the amino acids\n",
" batch = [\" \".join(list(seq)) for seq in batch]\n",
"\n",
" ids = tokenizer.batch_encode_plus(\n",
" batch, add_special_tokens=True, pad_to_max_length=True\n",
" )\n",
"\n",
" tokenized_sequences = torch.tensor(ids[\"input_ids\"]).to(model.device)\n",
" attention_mask = torch.tensor(ids[\"attention_mask\"]).to(model.device)\n",
"\n",
" with torch.no_grad():\n",
" embeddings = model(\n",
" input_ids=tokenized_sequences, attention_mask=attention_mask\n",
" )\n",
"\n",
" embeddings = embeddings[0]\n",
" protein_embeds = torch.zeros(len(batch), 1024)\n",
" for i, e in enumerate(embeddings):\n",
" protein_embeds[i] = e.mean(dim=0)\n",
"\n",
" return protein_embeds.cpu().numpy()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 442
},
"id": "0GNYuZzWDX7g",
"outputId": "763752bf-c1fa-4958-e2c8-84c4fe4cfe77"
},
"source": [
"snips = np.linspace(0, len(gfp_seqs), num=int(len(gfp_seqs) / 200))\n",
"for i in range(len(snips)-1):\n",
" embeds = random_emb(gfp_seqs[int(snips[i]) : int(snips[i+1])])\n",
" np.save(\"random_embed_gfp_{}\".format(i), embeds)\n",
"\n",
"\n",
"# snips = np.linspace(0, len(gfp_seqs), num=int(len(gfp_seqs) / 200))\n",
"# for i in range(len(snips)-1):\n",
"# embeds = pretrained_embed(gfp_seqs[int(snips[i]) : int(snips[i+1])])\n",
"# np.save(\"pretrained_embed_gfp_{}\".format(i), embeds)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:2143: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
" FutureWarning,\n"
],
"name": "stderr"
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-082fd88c5ace>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0msnips\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgfp_seqs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgfp_seqs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msnips\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0membeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom_emb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgfp_seqs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msnips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msnips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"random_embed_gfp_{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membeds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-5-4715eed954f6>\u001b[0m in \u001b[0;36mrandom_emb\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m embeddings = model(\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenized_sequences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m )\n\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 967\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 969\u001b[0m )\n\u001b[1;32m 970\u001b[0m \u001b[0msequence_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[0mencoder_attention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 565\u001b[0m \u001b[0mpast_key_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 567\u001b[0m )\n\u001b[1;32m 568\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m 458\u001b[0m \u001b[0mhead_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 460\u001b[0;31m \u001b[0mpast_key_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself_attn_past_key_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 461\u001b[0m )\n\u001b[1;32m 462\u001b[0m \u001b[0mattention_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself_attention_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m 391\u001b[0m \u001b[0mencoder_attention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 392\u001b[0m \u001b[0mpast_key_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 393\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 394\u001b[0m )\n\u001b[1;32m 395\u001b[0m \u001b[0mattention_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m 272\u001b[0m \u001b[0mvalue_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpast_key_value\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue_layer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 274\u001b[0;31m \u001b[0mkey_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose_for_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 275\u001b[0m \u001b[0mvalue_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose_for_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m for hook in itertools.chain(\n\u001b[1;32m 724\u001b[0m \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 90\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m 1674\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1675\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1676\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1677\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1678\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IRsl03DWmKgb"
},
"source": [
"from sklearn.linear_model import RidgeCV\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from scipy import stats\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"def train_top_model(protein_embeds, y_target, k=None):\n",
" # n_train = len(gfp_train_seqs)\n",
" n_train = 141\n",
" if k is None:\n",
" model = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1], cv=5)\n",
" else:\n",
" model = KNeighborsRegressor(n_neighbors=k)\n",
" model.fit(protein_embeds[:n_train], y_target[:n_train]) # train on first 200 sequences\n",
" y_hat_train = model.predict(protein_embeds[:n_train])\n",
" y_hat_test = model.predict(protein_embeds[n_train:])\n",
" train_loss = np.mean((y_hat_train - y_target[:n_train])**2)\n",
" test_loss = np.mean((y_hat_test - y_target[n_train:])**2) # validate on 12\n",
" plt.scatter(y_target[:n_train], y_hat_train, label=\"Train\")\n",
" plt.scatter(y_target[n_train:], y_hat_test, label=\"Test\")\n",
" plt.xlabel(\"Actual mean\")\n",
" plt.ylabel(\"Predicted mean\")\n",
" plt.title(\"Linear Model with L2 Regularization\")\n",
" plt.legend()\n",
" plt.show()\n",
" train_corr = stats.spearmanr(y_hat_train, y_target[:n_train])[0]\n",
" test_corr = stats.spearmanr(y_hat_test, y_target[n_train:])[0]\n",
" print(train_corr)\n",
" print(test_corr)\n",
" return train_corr, test_corr"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 348
},
"id": "xSrJSSsHlslF",
"outputId": "0b09a1c0-7288-4769-8078-c9b90464977a"
},
"source": [
"import glob\n",
"\n",
"train_corrs = []\n",
"test_corrs = []\n",
"\n",
"files = glob.glob(\"/content/random_embed_gfp_*.npy\")\n",
"emb_dim = 1024\n",
"embeds = np.zeros((len(gfp_y), emb_dim))\n",
"j = 0\n",
"for f in files:\n",
" e = np.load(f)\n",
" num_emb = e.shape[0]\n",
" embeds[j:j+num_emb,] = e\n",
" j += num_emb\n",
"train_top_model(embeds[:150], gfp_y[:150], k=3)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"0.5036266075445127\n",
"-0.3096261412396175\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(0.5036266075445127, -0.3096261412396175)"
]
},
"metadata": {
"tags": []
},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 348
},
"id": "w3hPkOsZOP48",
"outputId": "8e39b932-116a-4b09-cac5-1b663a0e7a9c"
},
"source": [
"train_top_model(embeds, gfp_y, k=1)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"0.9999999999999999\n",
"0.0021440455780850293\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(0.9999999999999999, 0.0021440455780850293)"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 348
},
"id": "gtmDSzJ2RARq",
"outputId": "b0ca6e70-ffd3-4970-cc82-be5e0728d4f0"
},
"source": [
"train_top_model(embeds, gfp_y, k=3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"0.5147631687199055\n",
"0.007267737821136292\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(0.5147631687199055, 0.007267737821136292)"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7s8vZyxhWgD2"
},
"source": [
"ChR data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tga7hp0PWi5H"
},
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"ChR_61.csv\")\n",
"X_61 = list(df['Amino_acid_sequence'])[:61]\n",
"y_61 = np.array(list(df['Photocurrent']))[:61]\n",
"y_61 = np.log(y_61 + 0.001)\n",
"y_61 = ((y_61 - np.min(y_61)) / (np.max(y_61) - np.min(y_61))) - 0.5 # don't let it saturate the sigmoid\n",
"\n",
"df = pd.read_csv(\"ChR_154.csv\")\n",
"X_154 = list(df['Amino_acid_sequence'])[:154]\n",
"# y_154 = np.array(list(df['max_ss']))[:154]\n",
"y_154 = np.array(list(df['max_peak (nA)']))[:154]\n",
"y_154 = np.log(y_154 + 0.001)\n",
"y_154 = ((y_154 - np.min(y_154)) / (np.max(y_154) - np.min(y_154))) - 0.5"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "BAwl_7eVbiKp"
},
"source": [
"embeds = random_emb(X_61)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TqqldeK3gShQ",
"outputId": "c79a960a-6a0b-486f-a0cb-8e48edc82136"
},
"source": [
"embeds = random_emb(X_154)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:2143: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
" FutureWarning,\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 348
},
"id": "VYKYR6zwbtyf",
"outputId": "84a0f4ba-83af-4d39-904a-e928f1a4b419"
},
"source": [
"train_top_model(embeds, y_154, k=3)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"0.7714528553914468\n",
"0.5868246952135165\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(0.7714528553914468, 0.5868246952135165)"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_KccyI4PWyHz"
},
"source": [
"# ."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xm8lLKID4DHa"
},
"source": [
"Now let's embed the sequences we used to train the discriminative model with six different embeddings. The protein embedding of each sequence will be used as input into a simple linear model to predict function. We keep track of training and validation performance and plot them together."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kmVqMNiL-Suh"
},
"source": [
"First load the data like before."
]
},
{
"cell_type": "code",
"metadata": {
"id": "5pJh0ZMO-UZw"
},
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"petase_activity.csv\")\n",
"X_activity = list(df['sequence'])[:212]\n",
"y_activity = np.array(list(df['relative_activity']))[:212]\n",
"y_activity = np.log(y_activity + 0.001)\n",
"y_activity = ((y_activity - np.min(y_activity)) / (np.max(y_activity) - np.min(y_activity))) - 0.5 # don't let it saturate the sigmoid\n",
"df = pd.read_csv(\"petase_stability.csv\")\n",
"X_stability = list(df['sequence'])[:159]\n",
"y_stability = np.array(list(df['stability']))[:159]\n",
"y_stability = np.log(y_stability + 0.001)\n",
"y_stability = ((y_stability - np.min(y_stability)) / (np.max(y_stability) - np.min(y_stability))) - 0.5\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Um_cl4ao-WDg"
},
"source": [
"Each embedder has an associated paper. The interested reader is encouraged to follow the references and familiarize with the model architectures and training strategies used in each case. In addition, the pretrained model weights can be found [here](https://github.com/sacdallago/bio_embeddings/blob/develop/bio_embeddings/utilities/defaults.yml), if you wish to use these generative models for more than just generating embeddings."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TftavgLJAKQY"
},
"source": [
"The discriminative model we'll be using is `sklearn`'s linear Lasso. It includes an L1 regularizer which encourages sparsity over the model weights."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 655
},
"id": "ETiq6_un63lq",
"outputId": "f65ff526-c7e5-4a99-e35e-9c2e75dc2590"
},
"source": [
"from sklearn import linear_model\n",
"\n",
"embedders = [ProtTransBertBFDEmbedder(),\\\n",
" BeplerEmbedder(),\\\n",
" UniRepEmbedder()]\n",
"model_perfs = []\n",
"\n",
"for emb in embedders:\n",
" embeddings = embedder.embed_many(X_activity)\n",
" embeddings = list(embeddings)\n",
"\n",
" protein_embeds = [emb.reduce_per_protein(e) for e in embeddings]\n",
" \n",
"\n",
" print(protein_embeds[0].shape)\n",
"\n",
" model = linear_model.Lasso(alpha=0.5)\n",
" model.fit(protein_embeds[:200], y_activity[:200]) # train on first 200 sequences\n",
" y_hat = model.predict(protein_embeds[200:])\n",
" loss = np.mean((y_hat - y_activity[200:])**2) # validate on 12\n",
" corr = model.score(protein_embeds[200:], y_activity[200:])\n",
" model_perfs.append((loss, corr)))\n",
"\n",
"print(\"Embedding -- Error -- Pearson's R^2\")\n",
"print(\"ProtTrans: \", model_perfs[0])\n",
"print(\"Bepler: \", model_perfs[1])\n",
"print(\"UniRep: \", model_perfs[2])"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"ssa_L1_100d_lstm3x512_lm_i512_mb64_tau0.5_lambda0.1_p0.05_epoch100_updated.state_dict: 127MB [00:02, 55.8MB/s] \n",
"glove.model: 60.8MB [00:01, 48.5MB/s] \n",
"weights.hdf5: 374MB [00:11, 32.4MB/s] \n",
"options.json: 8.19kB [00:00, 33.5kB/s]\n",
"/usr/local/lib/python3.6/dist-packages/jax/lib/xla_bridge.py:130: UserWarning:\n",
"\n",
"No GPU/TPU found, falling back to CPU.\n",
"\n",
"word2vec.model: 77.5MB [00:06, 12.4MB/s] \n",
"/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py:2022: FutureWarning:\n",
"\n",
"The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
"\n"
],
"name": "stderr"
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-3af219f5f870>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0memb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0membedders\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0membeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membedder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed_many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_activity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0membeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprotein_embeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0memb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_per_protein\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0me\u001b[0m \u001b[0;32min\u001b[0m \u001b[0membeddings\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bio_embeddings/embed/embedder_interfaces.py\u001b[0m in \u001b[0;36membed_many\u001b[0;34m(self, sequences, batch_size)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mseq\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msequences\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bio_embeddings/embed/prottrans_base_embedder.py\u001b[0m in \u001b[0;36membed\u001b[0;34m(self, sequence)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0membed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msequence\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msequence\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bio_embeddings/embed/embedder_interfaces.py\u001b[0m in \u001b[0;36membed_batch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 166\u001b[0m \u001b[0;31m# No point in having a fallback model when the normal model is CPU already\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_device\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_embed_batch_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/bio_embeddings/embed/prottrans_base_embedder.py\u001b[0m in \u001b[0;36m_embed_batch_impl\u001b[0;34m(self, batch, model)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m embeddings = model(\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenized_sequences\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 51\u001b[0m )\n\u001b[1;32m 52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 846\u001b[0m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 847\u001b[0m \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 849\u001b[0m )\n\u001b[1;32m 850\u001b[0m \u001b[0msequence_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 481\u001b[0m \u001b[0mencoder_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 482\u001b[0m \u001b[0mencoder_attention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 483\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 484\u001b[0m )\n\u001b[1;32m 485\u001b[0m \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[0mhead_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 403\u001b[0m )\n\u001b[1;32m 404\u001b[0m \u001b[0mattention_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself_attention_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m 337\u001b[0m \u001b[0mencoder_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 338\u001b[0m \u001b[0mencoder_attention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m \u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 340\u001b[0m )\n\u001b[1;32m 341\u001b[0m \u001b[0mattention_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself_outputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;31m# Normalize the attention scores to probabilities.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m \u001b[0mattention_probs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattention_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;31m# This is actually dropping out entire tokens to attend to, which might\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 549\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/activation.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 1042\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1043\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1044\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1045\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1046\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36msoftmax\u001b[0;34m(input, dim, _stacklevel, dtype)\u001b[0m\n\u001b[1;32m 1440\u001b[0m \u001b[0mdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_softmax_dim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'softmax'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_stacklevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1441\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1443\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1444\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msoftmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1_s2_iDn4wKP"
},
"source": [
"Exercise:\n",
"\n",
"1. Why do we use a linear model to predict function? Why not something more complex?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y-PfebBvDlkv"
},
"source": [
"#### Embedding space optimization"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tlHsaxQxDoM4"
},
"source": [
"Another use of embeddings that's underexplored in current research is gradient descent in embedding space. To do this we will need a decoder that takes in embeddings and outputs their corresponding sequences. As a toy example, we will use a simple feed-forward neural network as a decoder. The simpler the decoder, the more information you assume is already contained in the embeddings."
]
},
{
"cell_type": "code",
"metadata": {
"id": "fRRDW-z_FYkf"
},
"source": [
"class Vec2Seq(nn.Module):\n",
" def __init__(self, embed_dim=1024, hiddim=1024, seqlen=263, n_tokens=20):\n",
" super(Vec2Seq, self).__init__()\n",
" self.embed_dim = embed_dim\n",
" self.seqlen = seqlen\n",
" self.n_tokens = n_tokens\n",
"\n",
" self.decoder = nn.Sequential(\n",
" nn.Linear(embed_dim, hiddim),\n",
" nn.ReLU(),\n",
" nn.Linear(hiddim, seqlen*n_tokens),\n",
" )\n",
" self.getprobs = nn.Softmax(dim=-1)\n",
"\n",
" def forward(self, embed):\n",
" seq = self.decoder(embed).view(-1, seqlen, n_tokens)\n",
" seq = self.getprobs(seq)\n",
" return seq"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "gK-rkc3UGqwU"
},
"source": [
"Exercise:\n",
"\n",
"1. Train the decoder and do gradient ascent in embedding space. The code will be similar to latent space optimization."
]
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment