diff --git a/smartmeters-ANP-RNN-mcdropout.ipynb b/smartmeters-ANP-RNN-mcdropout.ipynb index 421f9a1..06a23c9 100644 --- a/smartmeters-ANP-RNN-mcdropout.ipynb +++ b/smartmeters-ANP-RNN-mcdropout.ipynb @@ -14,8 +14,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T01:45:49.987491Z", - "start_time": "2020-03-14T01:45:49.591262Z" + "end_time": "2020-03-15T04:21:34.567835Z", + "start_time": "2020-03-15T04:21:34.203299Z" } }, "outputs": [], @@ -30,8 +30,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T01:45:52.012228Z", - "start_time": "2020-03-14T01:45:49.990328Z" + "end_time": "2020-03-15T04:21:36.521830Z", + "start_time": "2020-03-15T04:21:34.570308Z" } }, "outputs": [], @@ -47,7 +47,7 @@ "import optuna\n", "import pytorch_lightning as pl\n", "from optuna.integration import PyTorchLightningPruningCallback\n", - "import math\n" + "import math" ] }, { @@ -55,8 +55,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T01:45:52.081201Z", - "start_time": "2020-03-14T01:45:52.015589Z" + "end_time": "2020-03-15T04:21:36.575605Z", + "start_time": "2020-03-15T04:21:36.524805Z" } }, "outputs": [], @@ -71,8 +71,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T01:45:52.141051Z", - "start_time": "2020-03-14T01:45:52.084457Z" + "end_time": "2020-03-15T04:21:36.636886Z", + "start_time": "2020-03-15T04:21:36.578525Z" } }, "outputs": [], @@ -99,8 +99,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T01:45:52.259434Z", - "start_time": "2020-03-14T01:45:52.143347Z" + "end_time": "2020-03-15T04:21:36.774252Z", + "start_time": "2020-03-15T04:21:36.640871Z" } }, "outputs": [], @@ -109,7 +109,9 @@ "from src.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df\n", "from src.plot import plot_from_loader\n", "from src.models.lightning_anp import LatentModelPL\n", - "from src.dict_logger import DictLogger" + "from src.dict_logger import DictLogger\n", + "from src.train import main, objective, add_number, run_trial\n", + "from src.utils import init_random_seed" ] }, { @@ -117,8 +119,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T01:45:52.310362Z", - "start_time": "2020-03-14T01:45:52.261912Z" + "end_time": "2020-03-15T04:21:36.827443Z", + "start_time": "2020-03-15T04:21:36.777328Z" } }, "outputs": [], @@ -140,13 +142,13 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T01:46:01.109818Z", - "start_time": "2020-03-14T01:45:52.312752Z" + "end_time": "2020-03-15T04:21:45.600155Z", + "start_time": "2020-03-15T04:21:36.830088Z" } }, "outputs": [], "source": [ - "df_train, df_test = get_smartmeter_df()" + "df_train, df_val, df_test = get_smartmeter_df()" ] }, { @@ -154,15 +156,15 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T01:46:01.845837Z", - "start_time": "2020-03-14T01:46:01.115288Z" + "end_time": "2020-03-15T04:21:46.355566Z", + "start_time": "2020-03-15T04:21:45.603079Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -171,7 +173,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -185,6 +187,7 @@ "source": [ "# Show split\n", "df_train['energy(kWh/hh)'].plot(label='train')\n", + "df_val['energy(kWh/hh)'].plot(label='val')\n", "df_test['energy(kWh/hh)'].plot(label='test')\n", "plt.title('energy(kWh/hh)')\n", "plt.legend()" @@ -211,11 +214,11 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T02:12:56.588211Z", - "start_time": "2020-03-14T02:12:56.517782Z" + "end_time": "2020-03-15T04:21:46.415821Z", + "start_time": "2020-03-15T04:21:46.358409Z" } }, "outputs": [ @@ -223,7 +226,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/lightning_logs\n" + "now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/lightning_logs`\n" ] } ], @@ -240,124 +243,22 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-03-14T11:02:03.427985Z", + "start_time": "2020-03-14T11:02:03.306881Z" + } + }, "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T02:12:57.054405Z", - "start_time": "2020-03-14T02:12:56.976397Z" - } - }, - "outputs": [], - "source": [ - "def main(trial, train=True): \n", - " checkpoint_callback = pl.callbacks.ModelCheckpoint(\n", - " os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode=\"min\")\n", - "\n", - " # The default logger in PyTorch Lightning writes to event files to be consumed by\n", - " # TensorBoard. We create a simple logger instead that holds the log in memory so that the\n", - " # final accuracy can be obtained after optimization. When using the default logger, the\n", - " # final accuracy could be stored in an attribute of the `Trainer` instead.\n", - " logger = DictLogger(MODEL_DIR, name=\"anp-rnn\", version=trial.number)\n", - "\n", - " trainer = pl.Trainer(\n", - " logger=logger,\n", - " val_percent_check=PERCENT_TEST_EXAMPLES,\n", - " gradient_clip_val=trial.params[\"grad_clip\"],\n", - " checkpoint_callback=checkpoint_callback,\n", - " max_epochs=trial.params['max_nb_epochs'],\n", - " gpus=-1 if torch.cuda.is_available() else None,\n", - " early_stop_callback=PyTorchLightningPruningCallback(trial, monitor='val_loss')\n", - " )\n", - " model = LatentModelPL(trial.params)\n", - " if train:\n", - " trainer.fit(model)\n", - " \n", - " return model, trainer\n", - "\n", - "\n", - "def add_sugg(trial):\n", - " \n", - " trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-2)\n", - "\n", - " trial.suggest_categorical(\"hidden_dim\", [8*2**i for i in range(6)])\n", - " trial.suggest_categorical(\"latent_dim\", [8*2**i for i in range(6)])\n", - " \n", - " trial.suggest_int(\"attention_layers\", 1, 4)\n", - " trial.suggest_categorical(\"n_latent_encoder_layers\", [1, 2, 4, 8])\n", - " trial.suggest_categorical(\"n_det_encoder_layers\", [1, 2, 4, 8])\n", - " trial.suggest_categorical(\"n_decoder_layers\", [1, 2, 4, 8])\n", - "\n", - " trial.suggest_categorical(\"dropout\", [0, 0.2, 0.5])\n", - " trial.suggest_categorical(\"attention_dropout\", [0, 0.2, 0.5])\n", - "\n", - " trial.suggest_categorical(\n", - " \"latent_enc_self_attn_type\", ['uniform', 'multihead', 'ptmultihead']\n", - " )\n", - " trial.suggest_categorical(\"det_enc_self_attn_type\", ['uniform', 'multihead', 'ptmultihead'])\n", - " trial.suggest_categorical(\"det_enc_cross_attn_type\", ['uniform', 'multihead', 'ptmultihead'])\n", - "\n", - " trial.suggest_categorical(\"batchnorm\", [False, True])\n", - " trial.suggest_categorical(\"use_self_attn\", [False, True])\n", - " trial.suggest_categorical(\"use_lvar\", [False, True])\n", - " trial.suggest_categorical(\"use_deterministic_path\", [False, True])\n", - " trial.suggest_categorical(\"use_rnn\", [True, False])\n", - "\n", - " # training specific (for this model)\n", - " trial.suggest_uniform(\"min_std\", 0.005, 0.005)\n", - " trial.suggest_int(\"grad_clip\", 40, 40)\n", - " trial.suggest_int(\"num_context\", 24 * 4, 24 * 4)\n", - " trial.suggest_int(\"num_extra_target\", 24*4, 24*4)\n", - " trial.suggest_int(\"max_nb_epochs\", 10, 10)\n", - " trial.suggest_int(\"num_workers\", 3, 3)\n", - " trial.suggest_int(\"batch_size\", 16, 16)\n", - " trial.suggest_int(\"num_heads\", 8, 8)\n", - "\n", - " trial.suggest_int(\"x_dim\", 17, 17)\n", - " trial.suggest_int(\"y_dim\", 1, 1)\n", - " trial.suggest_int(\"vis_i\", 670, 670)\n", - " \n", - " trial.suggest_categorical(\"context_in_target\", [True, True])\n", - " \n", - " return trial\n", - "\n", - "def add_trial_number(trial, dir_name):\n", - " # Optuna needs a number for each trial. For manual trials lets go -1, -2, etc\n", - " versions = [int(s.stem.split('_')[1]) for s in (MODEL_DIR/name).glob('*')] + [0]\n", - " trial.number = min(versions) - 1\n", - " return trial\n", - "\n", - "def objective(trial):\n", - " # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\n", - " \n", - " trial = add_sugg(trial)\n", - " \n", - " print('trial', trial.number, 'params', trial.params)\n", - " \n", - " \n", - " # PyTorch Lightning will try to restore model parameters from previous trials if checkpoint\n", - " # filenames match. Therefore, the filenames for each trial must be made unique.\n", - " model, trainer = main(trial)\n", - " \n", - " # also report to tensorboard & print\n", - " print('logger.metrics', model.logger.metrics[-1:])\n", - " model.logger.experiment.add_hparams(trial.params, model.logger.metrics[-1])\n", - " \n", - " return model.logger.metrics[-1]['val_loss']\n" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-14T02:12:57.151181Z", - "start_time": "2020-03-14T02:12:57.098057Z" + "end_time": "2020-03-15T04:21:46.473382Z", + "start_time": "2020-03-15T04:21:46.418158Z" } }, "outputs": [], @@ -379,11 +280,11 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T02:12:57.360254Z", - "start_time": "2020-03-14T02:12:57.294090Z" + "end_time": "2020-03-15T04:21:46.542599Z", + "start_time": "2020-03-15T04:21:46.476541Z" } }, "outputs": [], @@ -442,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-03-14T02:12:57.509900Z", @@ -450,15 +351,7 @@ } }, "outputs": [], - "source": [ - "\n", - "def init_random_seed(seed):\n", - " # https://pytorch.org/docs/stable/notes/randomness.html\n", - " np.random.seed(seed)\n", - " torch.random.manual_seed(seed)\n", - " torch.backends.cudnn.deterministic = True\n", - " torch.backends.cudnn.benchmark = False \n" - ] + "source": [] }, { "cell_type": "markdown", @@ -469,11 +362,11 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T02:12:58.355195Z", - "start_time": "2020-03-14T02:12:58.275135Z" + "end_time": "2020-03-15T04:21:46.623359Z", + "start_time": "2020-03-15T04:21:46.545401Z" } }, "outputs": [], @@ -481,60 +374,39 @@ "default_params = {\n", " 'attention_dropout': 0,\n", " 'attention_layers': 2,\n", - " 'batch_size': 16,\n", " 'batchnorm': False,\n", " 'det_enc_cross_attn_type': 'multihead',\n", " 'det_enc_self_attn_type': 'uniform',\n", - " 'dropout': 0,\n", - " 'grad_clip': 40,\n", - " 'hidden_dim': 128,\n", + " 'dropout': 0, \n", + " 'hidden_dim': 512,\n", " 'latent_dim': 128,\n", " 'latent_enc_self_attn_type': 'uniform',\n", - " 'learning_rate': 0.002,\n", - " 'max_nb_epochs': 10,\n", - " 'min_std': 0.005,\n", + " 'learning_rate': 0.002, \n", " 'n_decoder_layers': 4,\n", " 'n_det_encoder_layers': 4,\n", - " 'n_latent_encoder_layers': 2,\n", - " 'num_context': 24*4,\n", - " 'num_extra_target': 24*4,\n", - " 'num_heads': 8,\n", - " 'num_workers': 3,\n", + " 'n_latent_encoder_layers': 2, \n", + " 'num_heads': 8, \n", " 'use_deterministic_path': True,\n", " 'use_lvar': True,\n", - " 'use_self_attn': True,\n", - " 'vis_i': '670',\n", - " 'x_dim': 17,\n", - " 'y_dim': 1,\n", - " 'use_rnn': False,\n", - " 'context_in_target': True\n", + " 'use_self_attn': True, \n", + " 'use_rnn': False, \n", + "}\n", + "default_attrs = {\n", + " 'context_in_target': True,\n", + " 'x_dim': 17,\n", + " 'y_dim': 1,\n", + " 'vis_i': '670',\n", + " 'num_workers': 3,\n", + " 'num_context': 24*4,\n", + " 'num_extra_target': 24*4,\n", + " 'max_nb_epochs': 200,\n", + " 'min_std': 0.005,\n", + " 'grad_clip': 40,\n", + " 'batch_size': 16,\n", + " 'patience': 3\n", "}" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-16T07:16:14.744691Z", - "start_time": "2020-02-16T07:16:14.694113Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-15T07:05:44.545917Z", - "start_time": "2020-02-15T07:05:44.487280Z" - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -556,11 +428,10 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T03:40:11.455749Z", - "start_time": "2020-03-14T02:12:59.793184Z" + "start_time": "2020-03-15T04:21:34.300Z" }, "scrolled": true }, @@ -568,7 +439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e3c4c62f46d74d48b4731cb140d1352e", + "model_id": "5cb10609275744a9a48c326717631348", "version_major": 2, "version_minor": 0 }, @@ -583,1224 +454,89 @@ "name": "stdout", "output_type": "stream", "text": [ + "now run `tensorboard --logdir lightning_logs`\n", + "trial.number -20\n", "INFO:root:GPU available: True, used: True\n", "INFO:root:VISIBLE GPUS: 0\n", "INFO:root:\n", " | Name | Type | Params\n", "---------------------------------------------------------------------------------------------------------------\n", "0 | model | LatentModel | 3 M \n", - "1 | model._lstm | LSTM | 807 K \n", - "2 | model._latent_encoder | LatentEncoder | 328 K \n", - "3 | model._latent_encoder._encoder | BatchMLP | 131 K \n", - "4 | model._latent_encoder._encoder.initial | NPBlockRelu2d | 65 K \n", - "5 | model._latent_encoder._encoder.initial.linear | Linear | 65 K \n", - "6 | model._latent_encoder._encoder.initial.act | ReLU | 0 \n", - "7 | model._latent_encoder._encoder.initial.dropout | Dropout2d | 0 \n", - "8 | model._latent_encoder._encoder.encoder | Sequential | 0 \n", - "9 | model._latent_encoder._encoder.final | Linear | 65 K \n", - "10 | model._latent_encoder._self_attention | Attention | 0 \n", - "11 | model._latent_encoder._penultimate_layer | Linear | 65 K \n", - "12 | model._latent_encoder._mean | Linear | 65 K \n", - "13 | model._latent_encoder._log_var | Linear | 65 K \n", - "14 | model._deterministic_encoder | DeterministicEncoder | 787 K \n", - "15 | model._deterministic_encoder._d_encoder | BatchMLP | 262 K \n", - "16 | model._deterministic_encoder._d_encoder.initial | NPBlockRelu2d | 65 K \n", - "17 | model._deterministic_encoder._d_encoder.initial.linear | Linear | 65 K \n", - "18 | model._deterministic_encoder._d_encoder.initial.act | ReLU | 0 \n", - "19 | model._deterministic_encoder._d_encoder.initial.dropout | Dropout2d | 0 \n", - "20 | model._deterministic_encoder._d_encoder.encoder | Sequential | 131 K \n", - "21 | model._deterministic_encoder._d_encoder.encoder.0 | NPBlockRelu2d | 65 K \n", - "22 | model._deterministic_encoder._d_encoder.encoder.0.linear | Linear | 65 K \n", - "23 | model._deterministic_encoder._d_encoder.encoder.0.act | ReLU | 0 \n", - "24 | model._deterministic_encoder._d_encoder.encoder.0.dropout | Dropout2d | 0 \n", - "25 | model._deterministic_encoder._d_encoder.encoder.1 | NPBlockRelu2d | 65 K \n", - "26 | model._deterministic_encoder._d_encoder.encoder.1.linear | Linear | 65 K \n", - "27 | model._deterministic_encoder._d_encoder.encoder.1.act | ReLU | 0 \n", - "28 | model._deterministic_encoder._d_encoder.encoder.1.dropout | Dropout2d | 0 \n", - "29 | model._deterministic_encoder._d_encoder.final | Linear | 65 K \n", - "30 | model._deterministic_encoder._self_attention | Attention | 0 \n", - "31 | model._deterministic_encoder._cross_attention | Attention | 524 K \n", - "32 | model._deterministic_encoder._cross_attention.batch_mlp_k | BatchMLP | 131 K \n", - "33 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial | NPBlockRelu2d | 65 K \n", - "34 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.linear | Linear | 65 K \n", - "35 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.act | ReLU | 0 \n", - "36 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.dropout | Dropout2d | 0 \n", - "37 | model._deterministic_encoder._cross_attention.batch_mlp_k.encoder | Sequential | 0 \n", - "38 | model._deterministic_encoder._cross_attention.batch_mlp_k.final | Linear | 65 K \n", - "39 | model._deterministic_encoder._cross_attention.batch_mlp_q | BatchMLP | 131 K \n", - "40 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial | NPBlockRelu2d | 65 K \n", - "41 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.linear | Linear | 65 K \n", - "42 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.act | ReLU | 0 \n", - "43 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.dropout | Dropout2d | 0 \n", - "44 | model._deterministic_encoder._cross_attention.batch_mlp_q.encoder | Sequential | 0 \n", - "45 | model._deterministic_encoder._cross_attention.batch_mlp_q.final | Linear | 65 K \n", - "46 | model._deterministic_encoder._cross_attention._W | MultiheadAttention | 262 K \n", - "47 | model._deterministic_encoder._cross_attention._W.out_proj | Linear | 65 K \n", - "48 | model._decoder | Decoder | 1 M \n", - "49 | model._decoder._target_transform | Linear | 65 K \n", - "50 | model._decoder._decoder | BatchMLP | 1 M \n", - "51 | model._decoder._decoder.initial | NPBlockRelu2d | 262 K \n", - "52 | model._decoder._decoder.initial.linear | Linear | 262 K \n", - "53 | model._decoder._decoder.initial.act | ReLU | 0 \n", - "54 | model._decoder._decoder.initial.dropout | Dropout2d | 0 \n", - "55 | model._decoder._decoder.encoder | Sequential | 524 K \n", - "56 | model._decoder._decoder.encoder.0 | NPBlockRelu2d | 262 K \n", - "57 | model._decoder._decoder.encoder.0.linear | Linear | 262 K \n", - "58 | model._decoder._decoder.encoder.0.act | ReLU | 0 \n", - "59 | model._decoder._decoder.encoder.0.dropout | Dropout2d | 0 \n", - "60 | model._decoder._decoder.encoder.1 | NPBlockRelu2d | 262 K \n", - "61 | model._decoder._decoder.encoder.1.linear | Linear | 262 K \n", - "62 | model._decoder._decoder.encoder.1.act | ReLU | 0 \n", - "63 | model._decoder._decoder.encoder.1.dropout | Dropout2d | 0 \n", - "64 | model._decoder._decoder.final | Linear | 262 K \n", - "65 | model._decoder._mean | Linear | 513 \n", - "66 | model._decoder._std | Linear | 513 \n" + "1 | model.norm_x | BatchNormSequence | 34 \n", + "2 | model.norm_x.norm | BatchNorm1d | 34 \n", + "3 | model.norm_y | BatchNormSequence | 2 \n", + "4 | model.norm_y.norm | BatchNorm1d | 2 \n", + "5 | model._lstm_x | LSTM | 807 K \n", + "6 | model._lstm_y | LSTM | 791 K \n", + "7 | model._latent_encoder | LatentEncoder | 394 K \n", + "8 | model._latent_encoder._encoder | BatchMLP | 196 K \n", + "9 | model._latent_encoder._encoder.initial | NPBlockRelu2d | 131 K \n", + "10 | model._latent_encoder._encoder.initial.linear | Linear | 131 K \n", + "11 | model._latent_encoder._encoder.initial.act | ReLU | 0 \n", + "12 | model._latent_encoder._encoder.initial.dropout | Dropout2d | 0 \n", + "13 | model._latent_encoder._encoder.encoder | Sequential | 0 \n", + "14 | model._latent_encoder._encoder.final | Linear | 65 K \n", + "15 | model._latent_encoder._self_attention | Attention | 0 \n", + "16 | model._latent_encoder._penultimate_layer | Linear | 65 K \n", + "17 | model._latent_encoder._mean | Linear | 65 K \n", + "18 | model._latent_encoder._log_var | Linear | 65 K \n", + "19 | model._deterministic_encoder | DeterministicEncoder | 852 K \n", + "20 | model._deterministic_encoder._d_encoder | BatchMLP | 327 K \n", + "21 | model._deterministic_encoder._d_encoder.initial | NPBlockRelu2d | 131 K \n", + "22 | model._deterministic_encoder._d_encoder.initial.linear | Linear | 131 K \n", + "23 | model._deterministic_encoder._d_encoder.initial.act | ReLU | 0 \n", + "24 | model._deterministic_encoder._d_encoder.initial.dropout | Dropout2d | 0 \n", + "25 | model._deterministic_encoder._d_encoder.encoder | Sequential | 131 K \n", + "26 | model._deterministic_encoder._d_encoder.encoder.0 | NPBlockRelu2d | 65 K \n", + "27 | model._deterministic_encoder._d_encoder.encoder.0.linear | Linear | 65 K \n", + "28 | model._deterministic_encoder._d_encoder.encoder.0.act | ReLU | 0 \n", + "29 | model._deterministic_encoder._d_encoder.encoder.0.dropout | Dropout2d | 0 \n", + "30 | model._deterministic_encoder._d_encoder.encoder.1 | NPBlockRelu2d | 65 K \n", + "31 | model._deterministic_encoder._d_encoder.encoder.1.linear | Linear | 65 K \n", + "32 | model._deterministic_encoder._d_encoder.encoder.1.act | ReLU | 0 \n", + "33 | model._deterministic_encoder._d_encoder.encoder.1.dropout | Dropout2d | 0 \n", + "34 | model._deterministic_encoder._d_encoder.final | Linear | 65 K \n", + "35 | model._deterministic_encoder._self_attention | Attention | 0 \n", + "36 | model._deterministic_encoder._cross_attention | Attention | 524 K \n", + "37 | model._deterministic_encoder._cross_attention.batch_mlp_k | BatchMLP | 131 K \n", + "38 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial | NPBlockRelu2d | 65 K \n", + "39 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.linear | Linear | 65 K \n", + "40 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.act | ReLU | 0 \n", + "41 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.dropout | Dropout2d | 0 \n", + "42 | model._deterministic_encoder._cross_attention.batch_mlp_k.encoder | Sequential | 0 \n", + "43 | model._deterministic_encoder._cross_attention.batch_mlp_k.final | Linear | 65 K \n", + "44 | model._deterministic_encoder._cross_attention.batch_mlp_q | BatchMLP | 131 K \n", + "45 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial | NPBlockRelu2d | 65 K \n", + "46 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.linear | Linear | 65 K \n", + "47 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.act | ReLU | 0 \n", + "48 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.dropout | Dropout2d | 0 \n", + "49 | model._deterministic_encoder._cross_attention.batch_mlp_q.encoder | Sequential | 0 \n", + "50 | model._deterministic_encoder._cross_attention.batch_mlp_q.final | Linear | 65 K \n", + "51 | model._deterministic_encoder._cross_attention._W | MultiheadAttention | 262 K \n", + "52 | model._deterministic_encoder._cross_attention._W.out_proj | Linear | 65 K \n", + "53 | model._decoder | Decoder | 1 M \n", + "54 | model._decoder._target_transform | Linear | 65 K \n", + "55 | model._decoder._decoder | BatchMLP | 1 M \n", + "56 | model._decoder._decoder.initial | NPBlockRelu2d | 262 K \n", + "57 | model._decoder._decoder.initial.linear | Linear | 262 K \n", + "58 | model._decoder._decoder.initial.act | ReLU | 0 \n", + "59 | model._decoder._decoder.initial.dropout | Dropout2d | 0 \n", + "60 | model._decoder._decoder.encoder | Sequential | 524 K \n", + "61 | model._decoder._decoder.encoder.0 | NPBlockRelu2d | 262 K \n", + "62 | model._decoder._decoder.encoder.0.linear | Linear | 262 K \n", + "63 | model._decoder._decoder.encoder.0.act | ReLU | 0 \n", + "64 | model._decoder._decoder.encoder.0.dropout | Dropout2d | 0 \n", + "65 | model._decoder._decoder.encoder.1 | NPBlockRelu2d | 262 K \n", + "66 | model._decoder._decoder.encoder.1.linear | Linear | 262 K \n", + "67 | model._decoder._decoder.encoder.1.act | ReLU | 0 \n", + "68 | model._decoder._decoder.encoder.1.dropout | Dropout2d | 0 \n", + "69 | model._decoder._decoder.final | Linear | 262 K \n", + "70 | model._decoder._mean | Linear | 513 \n", + "71 | model._decoder._std | Linear | 513 \n" ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 0, {'val_loss': '1.0510647296905518', 'val/kl': '0.500446081161499', 'val/std': '1.011029839515686', 'val/mse': '0.321998655796051'} {}\n", - "\r" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0135c39b2b944755acf806dbf6ba72e8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=1.0, bar_style='info', layout=Layout(flex='2'), max=1.0), HTML(value='')), …" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=234.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 2194, {'val_loss': '-0.13158796727657318', 'val/kl': '0.00033035798696801066', 'val/std': '0.21403183043003082', 'val/mse': '0.032815106213092804'} {'train_loss': tensor(16.5981, device='cuda:0'), 'train/kl': tensor(0.0179, device='cuda:0'), 'train/std': tensor(0.4463, device='cuda:0'), 'train/mse': tensor(0.1485, device='cuda:0')}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=234.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 4389, {'val_loss': '-0.4325900673866272', 'val/kl': '9.704002877697349e-05', 'val/std': '0.09763659536838531', 'val/mse': '0.014293229207396507'} {'train_loss': tensor(-0.2103, device='cuda:0'), 'train/kl': tensor(0.0009, device='cuda:0'), 'train/std': tensor(0.1743, device='cuda:0'), 'train/mse': tensor(0.0344, device='cuda:0')}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=234.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 6584, {'val_loss': '-0.43620601296424866', 'val/kl': '4.939149948768318e-05', 'val/std': '0.09694259613752365', 'val/mse': '0.013617178425192833'} {'train_loss': tensor(-0.3606, device='cuda:0'), 'train/kl': tensor(0.0005, device='cuda:0'), 'train/std': tensor(0.1432, device='cuda:0'), 'train/mse': tensor(0.0260, device='cuda:0')}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=234.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 8779, {'val_loss': '-0.42683538794517517', 'val/kl': '8.107969915727153e-05', 'val/std': '0.09468298405408859', 'val/mse': '0.0107091274112463'} {'train_loss': tensor(-0.4410, device='cuda:0'), 'train/kl': tensor(0.0004, device='cuda:0'), 'train/std': tensor(0.1285, device='cuda:0'), 'train/mse': tensor(0.0230, device='cuda:0')}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=234.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 10974, {'val_loss': '-0.5034099817276001', 'val/kl': '3.636208930402063e-05', 'val/std': '0.0672297552227974', 'val/mse': '0.00938461348414421'} {'train_loss': tensor(-0.5030, device='cuda:0'), 'train/kl': tensor(0.0003, device='cuda:0'), 'train/std': tensor(0.1138, device='cuda:0'), 'train/mse': tensor(0.0200, device='cuda:0')}\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - 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val 15364, {'val_loss': '-0.11352463811635971', 'val/kl': '1.6889993275981396e-05', 'val/std': '0.05332550033926964', 'val/mse': '0.013464201241731644'} {'train_loss': tensor(-0.6062, device='cuda:0'), 'train/kl': tensor(9.2420e-05, device='cuda:0'), 'train/std': tensor(0.0985, device='cuda:0'), 'train/mse': tensor(0.0169, device='cuda:0')}\n", - "Epoch 6: reducing learning rate of group 0 to 2.0000e-04.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=234.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 17559, {'val_loss': '-0.04702010378241539', 'val/kl': '1.287278973904904e-05', 'val/std': '0.04811198264360428', 'val/mse': '0.012275541201233864'} {'train_loss': 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"----------------------------------------------------------------------------------------------------\n", - "\n", - "1 None\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e8b65004d75441cb989598eeca7e3fb4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='MCDropout eval', max=600.0, style=ProgressStyle(descripti…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Lower is better, validation loss\n", - "MCDropout: -1.32\n", - "Inference: -0.14\n", - "INFO:root:GPU available: True, used: True\n", - "INFO:root:VISIBLE GPUS: 0\n", - "INFO:root:\n", - " | Name | Type | Params\n", - "---------------------------------------------------------------------------------------------------------------\n", - "0 | model | LatentModel | 3 M \n", - "1 | model._lstm | LSTM | 807 K \n", - "2 | 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NPBlockRelu2d | 65 K \n", - "17 | model._deterministic_encoder._d_encoder.initial.linear | Linear | 65 K \n", - "18 | model._deterministic_encoder._d_encoder.initial.act | ReLU | 0 \n", - "19 | model._deterministic_encoder._d_encoder.initial.dropout | Dropout2d | 0 \n", - "20 | model._deterministic_encoder._d_encoder.encoder | Sequential | 131 K \n", - "21 | model._deterministic_encoder._d_encoder.encoder.0 | NPBlockRelu2d | 65 K \n", - "22 | model._deterministic_encoder._d_encoder.encoder.0.linear | Linear | 65 K \n", - "23 | model._deterministic_encoder._d_encoder.encoder.0.act | ReLU | 0 \n", - "24 | model._deterministic_encoder._d_encoder.encoder.0.dropout | Dropout2d | 0 \n", - "25 | model._deterministic_encoder._d_encoder.encoder.1 | NPBlockRelu2d | 65 K \n", - "26 | model._deterministic_encoder._d_encoder.encoder.1.linear | Linear | 65 K \n", - "27 | model._deterministic_encoder._d_encoder.encoder.1.act | ReLU | 0 \n", - "28 | 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1 M \n", - "51 | model._decoder._decoder.initial | NPBlockRelu2d | 262 K \n", - "52 | model._decoder._decoder.initial.linear | Linear | 262 K \n", - "53 | model._decoder._decoder.initial.act | ReLU | 0 \n", - "54 | model._decoder._decoder.initial.dropout | Dropout2d | 0 \n", - "55 | model._decoder._decoder.encoder | Sequential | 524 K \n", - "56 | model._decoder._decoder.encoder.0 | NPBlockRelu2d | 262 K \n", - "57 | model._decoder._decoder.encoder.0.linear | Linear | 262 K \n", - "58 | model._decoder._decoder.encoder.0.act | ReLU | 0 \n", - "59 | model._decoder._decoder.encoder.0.dropout | Dropout2d | 0 \n", - "60 | model._decoder._decoder.encoder.1 | NPBlockRelu2d | 262 K \n", - "61 | model._decoder._decoder.encoder.1.linear | Linear | 262 K \n", - "62 | model._decoder._decoder.encoder.1.act | ReLU | 0 \n", - "63 | model._decoder._decoder.encoder.1.dropout | Dropout2d | 0 \n", - "64 | model._decoder._decoder.final | Linear | 262 K \n", - "65 | model._decoder._mean | Linear | 513 \n", 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group 0 to 2.0000e-04.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=234.0, style=Pr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 10974, {'val_loss': '-0.5098873972892761', 'val/kl': '4.259546039975248e-05', 'val/std': '0.054850272834300995', 'val/mse': '0.007577850017696619'} {'train_loss': tensor(-0.6559, device='cuda:0'), 'train/kl': tensor(0.0001, device='cuda:0'), 'train/std': tensor(0.0854, device='cuda:0'), 'train/mse': tensor(0.0144, device='cuda:0')}\n", - "INFO:root:Epoch 00005: early stopping\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "638d8934f25648848dc65929296423b7", - "version_major": 2, - "version_minor": 0 - }, - 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] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Lower is better, validation loss\n", - "MCDropout: -1.35\n", - "Inference: -0.98\n", - "INFO:root:GPU available: True, used: True\n", - "INFO:root:VISIBLE GPUS: 0\n", - "INFO:root:\n", - " | Name | Type | Params\n", - "---------------------------------------------------------------------------------------------------------------\n", - "0 | model | LatentModel | 3 M \n", - "1 | model._lstm | LSTM | 807 K \n", - "2 | model._latent_encoder | LatentEncoder | 328 K \n", - "3 | model._latent_encoder._encoder | BatchMLP | 131 K \n", - "4 | model._latent_encoder._encoder.initial | NPBlockRelu2d | 65 K \n", - "5 | model._latent_encoder._encoder.initial.linear | Linear | 65 K \n", - "6 | model._latent_encoder._encoder.initial.act | ReLU | 0 \n", - "7 | model._latent_encoder._encoder.initial.dropout | Dropout2d | 0 \n", - "8 | model._latent_encoder._encoder.encoder | Sequential | 0 \n", - "9 | model._latent_encoder._encoder.final | Linear | 65 K \n", - "10 | model._latent_encoder._self_attention | Attention | 0 \n", - "11 | model._latent_encoder._penultimate_layer | Linear | 65 K \n", - "12 | model._latent_encoder._mean | Linear | 65 K \n", - "13 | model._latent_encoder._log_var | Linear | 65 K \n", - "14 | model._deterministic_encoder | DeterministicEncoder | 787 K \n", - "15 | model._deterministic_encoder._d_encoder | BatchMLP | 262 K \n", - "16 | model._deterministic_encoder._d_encoder.initial | NPBlockRelu2d | 65 K \n", - "17 | model._deterministic_encoder._d_encoder.initial.linear | Linear | 65 K \n", - "18 | model._deterministic_encoder._d_encoder.initial.act | ReLU | 0 \n", - "19 | model._deterministic_encoder._d_encoder.initial.dropout | Dropout2d | 0 \n", - "20 | model._deterministic_encoder._d_encoder.encoder | Sequential | 131 K \n", - "21 | model._deterministic_encoder._d_encoder.encoder.0 | NPBlockRelu2d | 65 K \n", - "22 | model._deterministic_encoder._d_encoder.encoder.0.linear | Linear | 65 K \n", - "23 | model._deterministic_encoder._d_encoder.encoder.0.act | ReLU | 0 \n", - "24 | model._deterministic_encoder._d_encoder.encoder.0.dropout | Dropout2d | 0 \n", - "25 | model._deterministic_encoder._d_encoder.encoder.1 | NPBlockRelu2d | 65 K \n", - "26 | model._deterministic_encoder._d_encoder.encoder.1.linear | Linear | 65 K \n", - "27 | model._deterministic_encoder._d_encoder.encoder.1.act | ReLU | 0 \n", - "28 | model._deterministic_encoder._d_encoder.encoder.1.dropout | Dropout2d | 0 \n", - "29 | model._deterministic_encoder._d_encoder.final | Linear | 65 K \n", - "30 | model._deterministic_encoder._self_attention | Attention | 0 \n", - "31 | model._deterministic_encoder._cross_attention | Attention | 524 K \n", - "32 | model._deterministic_encoder._cross_attention.batch_mlp_k | BatchMLP | 131 K \n", - "33 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial | NPBlockRelu2d | 65 K \n", - "34 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.linear | Linear | 65 K \n", - "35 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.act | ReLU | 0 \n", - "36 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.dropout | Dropout2d | 0 \n", - "37 | model._deterministic_encoder._cross_attention.batch_mlp_k.encoder | Sequential | 0 \n", - "38 | model._deterministic_encoder._cross_attention.batch_mlp_k.final | Linear | 65 K \n", - "39 | model._deterministic_encoder._cross_attention.batch_mlp_q | BatchMLP | 131 K \n", - "40 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial | NPBlockRelu2d | 65 K \n", - "41 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.linear | Linear | 65 K \n", - "42 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.act | ReLU | 0 \n", - "43 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.dropout | Dropout2d | 0 \n", - "44 | model._deterministic_encoder._cross_attention.batch_mlp_q.encoder | Sequential | 0 \n", - "45 | model._deterministic_encoder._cross_attention.batch_mlp_q.final | Linear | 65 K \n", - "46 | model._deterministic_encoder._cross_attention._W | MultiheadAttention | 262 K \n", - "47 | model._deterministic_encoder._cross_attention._W.out_proj | Linear | 65 K \n", - "48 | model._decoder | Decoder | 1 M \n", - "49 | model._decoder._target_transform | Linear | 65 K \n", - "50 | model._decoder._decoder | BatchMLP | 1 M \n", - "51 | model._decoder._decoder.initial | NPBlockRelu2d | 262 K \n", - "52 | model._decoder._decoder.initial.linear | Linear | 262 K \n", - "53 | model._decoder._decoder.initial.act | ReLU | 0 \n", - "54 | model._decoder._decoder.initial.dropout | Dropout2d | 0 \n", - "55 | model._decoder._decoder.encoder | Sequential | 524 K \n", - "56 | model._decoder._decoder.encoder.0 | NPBlockRelu2d | 262 K \n", - "57 | model._decoder._decoder.encoder.0.linear | Linear | 262 K \n", - "58 | model._decoder._decoder.encoder.0.act | ReLU | 0 \n", - "59 | model._decoder._decoder.encoder.0.dropout | Dropout2d | 0 \n", - "60 | model._decoder._decoder.encoder.1 | NPBlockRelu2d | 262 K \n", - "61 | model._decoder._decoder.encoder.1.linear | Linear | 262 K \n", - "62 | model._decoder._decoder.encoder.1.act | ReLU | 0 \n", - "63 | model._decoder._decoder.encoder.1.dropout | Dropout2d | 0 \n", - "64 | model._decoder._decoder.final | Linear | 262 K \n", - "65 | model._decoder._mean | Linear | 513 \n", - "66 | model._decoder._std | Linear | 513 \n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - 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"name": "stdout", - "output_type": "stream", - "text": [ - "step val 10974, {'val_loss': '-0.43197354674339294', 'val/kl': '0.00011243290646234527', 'val/std': '0.04987134784460068', 'val/mse': '0.008202551864087582'} {'train_loss': tensor(-0.6803, device='cuda:0'), 'train/kl': tensor(0.0002, device='cuda:0'), 'train/std': tensor(0.0856, device='cuda:0'), 'train/mse': tensor(0.0145, device='cuda:0')}\n", - "INFO:root:Epoch 00005: early stopping\n", - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "740fa2df85d3463a8fd395441679ae09", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Testing', layout=Layout(flex='2'), max=234.0, style=Progr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 10975, {'val_loss': '-0.43197354674339294', 'val/kl': '0.00011243290646234527', 'val/std': '0.04987134784460068', 'val/mse': '0.008202551864087582'} {}\n", - "----------------------------------------------------------------------------------------------------\n", - "TEST RESULTS\n", - "{}\n", - "----------------------------------------------------------------------------------------------------\n", - "\n", - "20 None\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3ea92b22148e4a76875da6d4ec1a2554", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='MCDropout eval', max=600.0, style=ProgressStyle(descripti…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Lower is better, validation loss\n", - "MCDropout: -1.42\n", - "Inference: -0.83\n", - "INFO:root:GPU available: True, used: True\n", - "INFO:root:VISIBLE GPUS: 0\n", - "INFO:root:\n", - " | Name | Type | Params\n", - "---------------------------------------------------------------------------------------------------------------\n", - "0 | model | LatentModel | 3 M \n", - "1 | model._lstm | LSTM | 807 K \n", - "2 | model._latent_encoder | LatentEncoder | 328 K \n", - "3 | model._latent_encoder._encoder | BatchMLP | 131 K \n", - "4 | model._latent_encoder._encoder.initial | NPBlockRelu2d | 65 K \n", - "5 | model._latent_encoder._encoder.initial.linear | Linear | 65 K \n", - "6 | model._latent_encoder._encoder.initial.act | ReLU | 0 \n", - "7 | model._latent_encoder._encoder.initial.dropout | Dropout2d | 0 \n", - "8 | model._latent_encoder._encoder.encoder | Sequential | 0 \n", - "9 | model._latent_encoder._encoder.final | Linear | 65 K \n", - "10 | model._latent_encoder._self_attention | Attention | 0 \n", - "11 | model._latent_encoder._penultimate_layer | Linear | 65 K \n", - "12 | model._latent_encoder._mean | Linear | 65 K \n", - "13 | model._latent_encoder._log_var | Linear | 65 K \n", - "14 | model._deterministic_encoder | DeterministicEncoder | 787 K \n", - "15 | model._deterministic_encoder._d_encoder | BatchMLP | 262 K \n", - "16 | model._deterministic_encoder._d_encoder.initial | NPBlockRelu2d | 65 K \n", - "17 | model._deterministic_encoder._d_encoder.initial.linear | Linear | 65 K \n", - "18 | model._deterministic_encoder._d_encoder.initial.act | ReLU | 0 \n", - "19 | model._deterministic_encoder._d_encoder.initial.dropout | Dropout2d | 0 \n", - "20 | model._deterministic_encoder._d_encoder.encoder | Sequential | 131 K \n", - "21 | model._deterministic_encoder._d_encoder.encoder.0 | NPBlockRelu2d | 65 K \n", - "22 | model._deterministic_encoder._d_encoder.encoder.0.linear | Linear | 65 K \n", - "23 | model._deterministic_encoder._d_encoder.encoder.0.act | ReLU | 0 \n", - "24 | model._deterministic_encoder._d_encoder.encoder.0.dropout | Dropout2d | 0 \n", - "25 | model._deterministic_encoder._d_encoder.encoder.1 | NPBlockRelu2d | 65 K \n", - "26 | model._deterministic_encoder._d_encoder.encoder.1.linear | Linear | 65 K \n", - "27 | model._deterministic_encoder._d_encoder.encoder.1.act | ReLU | 0 \n", - "28 | model._deterministic_encoder._d_encoder.encoder.1.dropout | Dropout2d | 0 \n", - "29 | model._deterministic_encoder._d_encoder.final | Linear | 65 K \n", - "30 | model._deterministic_encoder._self_attention | Attention | 0 \n", - "31 | model._deterministic_encoder._cross_attention | Attention | 524 K \n", - "32 | model._deterministic_encoder._cross_attention.batch_mlp_k | BatchMLP | 131 K \n", - "33 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial | NPBlockRelu2d | 65 K \n", - "34 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.linear | Linear | 65 K \n", - "35 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.act | ReLU | 0 \n", - "36 | model._deterministic_encoder._cross_attention.batch_mlp_k.initial.dropout | Dropout2d | 0 \n", - "37 | model._deterministic_encoder._cross_attention.batch_mlp_k.encoder | Sequential | 0 \n", - "38 | model._deterministic_encoder._cross_attention.batch_mlp_k.final | Linear | 65 K \n", - "39 | model._deterministic_encoder._cross_attention.batch_mlp_q | BatchMLP | 131 K \n", - "40 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial | NPBlockRelu2d | 65 K \n", - "41 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.linear | Linear | 65 K \n", - "42 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.act | ReLU | 0 \n", - "43 | model._deterministic_encoder._cross_attention.batch_mlp_q.initial.dropout | Dropout2d | 0 \n", - "44 | model._deterministic_encoder._cross_attention.batch_mlp_q.encoder | Sequential | 0 \n", - "45 | model._deterministic_encoder._cross_attention.batch_mlp_q.final | Linear | 65 K \n", - "46 | model._deterministic_encoder._cross_attention._W | MultiheadAttention | 262 K \n", - "47 | model._deterministic_encoder._cross_attention._W.out_proj | Linear | 65 K \n", - "48 | model._decoder | Decoder | 1 M \n", - "49 | model._decoder._target_transform | Linear | 65 K \n", - "50 | model._decoder._decoder | BatchMLP | 1 M \n", - "51 | model._decoder._decoder.initial | NPBlockRelu2d | 262 K \n", - "52 | model._decoder._decoder.initial.linear | Linear | 262 K \n", - "53 | model._decoder._decoder.initial.act | ReLU | 0 \n", - "54 | model._decoder._decoder.initial.dropout | Dropout2d | 0 \n", - "55 | model._decoder._decoder.encoder | Sequential | 524 K \n", - "56 | model._decoder._decoder.encoder.0 | NPBlockRelu2d | 262 K \n", - "57 | model._decoder._decoder.encoder.0.linear | Linear | 262 K \n", - "58 | model._decoder._decoder.encoder.0.act | ReLU | 0 \n", - "59 | model._decoder._decoder.encoder.0.dropout | Dropout2d | 0 \n", - "60 | model._decoder._decoder.encoder.1 | NPBlockRelu2d | 262 K \n", - "61 | model._decoder._decoder.encoder.1.linear | Linear | 262 K \n", - "62 | model._decoder._decoder.encoder.1.act | ReLU | 0 \n", - "63 | model._decoder._decoder.encoder.1.dropout | Dropout2d | 0 \n", - "64 | model._decoder._decoder.final | Linear | 262 K \n", - "65 | model._decoder._mean | Linear | 513 \n", - "66 | model._decoder._std | Linear | 513 \n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 0, {'val_loss': '1.042716145515442', 'val/kl': '0.5014282464981079', 'val/std': '0.9882966876029968', 'val/mse': '0.3160552680492401'} {}\n", - "\r" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0386b71700b146898fae20d51101a0d5", - "version_major": 2, - 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"text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Testing', layout=Layout(flex='2'), max=234.0, style=Progr…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step val 13170, {'val_loss': '-0.48278191685676575', 'val/kl': '6.692813622066751e-05', 'val/std': '0.05369281396269798', 'val/mse': '0.007348351180553436'} {}\n", - "----------------------------------------------------------------------------------------------------\n", - "TEST RESULTS\n", - "{}\n", - "----------------------------------------------------------------------------------------------------\n", - "\n", - "100 None\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "749016d7c46340479d7392b9cd32d9b4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='MCDropout eval', max=600.0, style=ProgressStyle(descripti…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Lower is better, validation loss\n", - "MCDropout: -1.36\n", - "Inference: -0.98\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "name = 'anp-rnn-mcdropout'\n", - "params =default_params.copy()\n", - "params.update({\n", - " 'det_enc_cross_attn_type': 'ptmultihead',\n", - " 'det_enc_self_attn_type': 'uniform',\n", - " 'latent_enc_self_attn_type': 'uniform',\n", - " 'dropout': 0.2,\n", - " 'hidden_dim': 128*2,\n", - " 'latent_dim': 128*2, \n", - " 'use_deterministic_path': False,\n", - " 'use_rnn': True,\n", - " 'vis_i': 670\n", - "})\n", "n_mcdropout=10\n", "n_steps=600\n", "vis_i=670\n", @@ -1808,40 +544,26 @@ "\n", "for seed in tqdm([1, 10, 20, 100], desc='seeds'):\n", " init_random_seed(seed)\n", - "\n", - " trial = optuna.trial.FixedTrial(params)\n", - " trial = add_sugg(trial)\n", - " trial = add_trial_number(trial, MODEL_DIR/name)\n", - "\n", - "\n", - " checkpoint_callback = pl.callbacks.ModelCheckpoint(\n", - " os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode=\"min\")\n", - "\n", - " logger = DictLogger(MODEL_DIR, name=\"anp\", version=trial.number)\n", - "\n", - " trainer = pl.Trainer(\n", - " gradient_clip_val=trial.params[\"grad_clip\"],\n", - " checkpoint_callback=checkpoint_callback,\n", - " max_epochs=trial.params['max_nb_epochs'],\n", - " gpus=-1 if torch.cuda.is_available() else None,\n", - " early_stop_callback=True\n", + " \n", + " trial, trainer, model = run_trial(\n", + " name=\"anp-rnn-mcdropout\",\n", + " params={\n", + " **default_params, \n", + " 'det_enc_cross_attn_type': 'ptmultihead',\n", + " 'det_enc_self_attn_type': 'uniform',\n", + " 'latent_enc_self_attn_type': 'uniform',\n", + " 'dropout': 0.2,\n", + " 'hidden_dim': 128*2,\n", + " 'latent_dim': 128*2, \n", + " 'use_deterministic_path': False,\n", + " 'use_rnn': True,\n", + " 'vis_i': 670\n", + " },\n", + " user_attrs = default_attrs,\n", + " PL_MODEL_CLS=LatentModelPL\n", " )\n", - " model = LatentModelPL(trial.params)\n", - "\n", - " trainer.fit(model)\n", - "\n", - " # plot, main metric\n", - "# loader = model.val_dataloader()\n", - " \n", - " plot_from_loader(loader, model, i=vis_i)\n", - "\n", - " \n", - " # Testing will load best checkpoint\n", - " print(seed, trainer.test(model))\n", - "\n", " \n", " # MCDropout\n", - "\n", " loader = model.val_dataloader()\n", " device = next(model.parameters()).device\n", " inds = np.random.randint(0, len(loader.dataset), n_steps)\n", @@ -1864,94 +586,26 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T04:37:17.265999Z", - "start_time": "2020-03-14T04:37:17.187213Z" + "start_time": "2020-03-15T04:21:34.300Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "lower is better\n" - ] - }, - { - "data": { - "text/html": [ - "
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seedlossloss_mcinds
01-0.136217-1.320583[1436, 3115, 3540, 2502, 3041, 3356, 3068, 148...
110-0.982833-1.346300[3525, 1264, 1046, 3256, 2701, 2319, 1883, 316...
220-0.826995-1.417526[1674, 3151, 1009, 264, 1051, 822, 2621, 1645,...
3100-0.983974-1.364665[2840, 3155, 3006, 2139, 1884, 1476, 1654, 133...
\n", - "
" - ], - "text/plain": [ - " seed loss loss_mc inds\n", - "0 1 -0.136217 -1.320583 [1436, 3115, 3540, 2502, 3041, 3356, 3068, 148...\n", - "1 10 -0.982833 -1.346300 [3525, 1264, 1046, 3256, 2701, 2319, 1883, 316...\n", - "2 20 -0.826995 -1.417526 [1674, 3151, 1009, 264, 1051, 822, 2621, 1645,...\n", - "3 100 -0.983974 -1.364665 [2840, 3155, 3006, 2139, 1884, 1476, 1654, 133..." - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" + "outputs": [], + "source": [ + "%debug" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "start_time": "2020-03-15T04:21:34.300Z" } - ], + }, + "outputs": [], "source": [ "df_result = pd.DataFrame(results)\n", "df_result['loss'] = df_result['loss'].astype(float)\n", @@ -1984,8 +638,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T01:46:17.336327Z", - "start_time": "2020-03-14T01:45:49.800Z" + "end_time": "2020-03-14T23:57:16.530307Z", + "start_time": "2020-03-14T23:57:16.450761Z" } }, "outputs": [], @@ -1996,21 +650,24 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-02-16T08:09:11.284347Z", - "start_time": "2020-02-16T08:09:11.231084Z" + "start_time": "2020-03-15T04:21:34.300Z" }, "scrolled": true }, "outputs": [], - "source": [] + "source": [ + "loader = model.val_dataloader()\n", + "dset_test = loader.dataset\n", + "label_names = dset_test.label_names\n", + "plot_from_loader(loader, model, i=670)" + ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T04:39:18.578666Z", - "start_time": "2020-03-14T04:39:18.465238Z" + "start_time": "2020-03-15T04:21:34.300Z" } }, "outputs": [], @@ -2040,11 +697,10 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T04:39:19.047309Z", - "start_time": "2020-03-14T04:39:18.766551Z" + "start_time": "2020-03-15T04:21:34.300Z" } }, "outputs": [], @@ -2069,24 +725,13 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T04:39:20.419551Z", - "start_time": "2020-03-14T04:39:20.341822Z" + "start_time": "2020-03-15T04:21:34.300Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "predicted std 0.08 \n", - "std of std 0.01 \n", - "mcdropout of mean 0.04\n" - ] - } - ], + "outputs": [], "source": [ "# Lets add variation to std, since it it's unsure.. std should be higher. Although this is small\n", "# When it's unsure of the mean, we will also add that, this is large\n", @@ -2099,27 +744,13 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T04:40:15.978779Z", - "start_time": "2020-03-14T04:40:15.607235Z" + "start_time": "2020-03-15T04:21:34.300Z" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# with 3 sources of uncertainrty: model output and std of mean\n", "y_std = y_stds.mean(0) + y_preds.std(0)\n", @@ -2137,27 +768,13 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T04:40:36.330486Z", - "start_time": "2020-03-14T04:40:35.905191Z" + "start_time": "2020-03-15T04:21:34.300Z" } }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Try without MCDropout\n", "model.eval()\n", diff --git a/smartmeters-lstm-seq2seq.ipynb b/smartmeters-lstm-seq2seq.ipynb index 70ac855..fa9bd6d 100644 --- a/smartmeters-lstm-seq2seq.ipynb +++ b/smartmeters-lstm-seq2seq.ipynb @@ -20,8 +20,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:41:29.841115Z", - "start_time": "2020-03-01T03:41:29.484738Z" + "end_time": "2020-03-15T01:04:18.458822Z", + "start_time": "2020-03-15T01:04:18.001753Z" } }, "outputs": [], @@ -36,8 +36,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:41:31.805982Z", - "start_time": "2020-03-01T03:41:29.843485Z" + "end_time": "2020-03-15T01:04:20.666366Z", + "start_time": "2020-03-15T01:04:18.463835Z" } }, "outputs": [], @@ -63,8 +63,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:41:31.857758Z", - "start_time": "2020-03-01T03:41:31.809871Z" + "end_time": "2020-03-15T01:04:20.722137Z", + "start_time": "2020-03-15T01:04:20.669364Z" } }, "outputs": [], @@ -79,17 +79,30 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:41:31.973473Z", - "start_time": "2020-03-01T03:41:31.860963Z" + "end_time": "2020-03-15T01:04:20.857479Z", + "start_time": "2020-03-15T01:04:20.725266Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pytorch_lightning/core/decorators.py:13: UserWarning:\n", + "\n", + "data_loader decorator deprecated in 0.7.0. Will remove 0.9.0\n", + "\n" + ] + } + ], "source": [ "from src.models.model import LatentModel\n", "from src.data.smart_meter import collate_fns, SmartMeterDataSet, get_smartmeter_df\n", "from src.plot import plot_from_loader\n", "from src.models.lstm_seqseq import LSTMSeq2Seq_PL\n", - "from src.dict_logger import DictLogger" + "from src.dict_logger import DictLogger\n", + "from src.utils import PyTorchLightningPruningCallback\n", + "from src.train import main, objective, add_number, run_trial" ] }, { @@ -97,8 +110,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:41:32.025844Z", - "start_time": "2020-03-01T03:41:31.977741Z" + "end_time": "2020-03-15T01:04:20.918561Z", + "start_time": "2020-03-15T01:04:20.865187Z" } }, "outputs": [], @@ -113,8 +126,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:41:32.075520Z", - "start_time": "2020-03-01T03:41:32.028741Z" + "end_time": "2020-03-15T01:04:20.972745Z", + "start_time": "2020-03-15T01:04:20.922359Z" } }, "outputs": [], @@ -136,13 +149,13 @@ "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:41:40.783929Z", - "start_time": "2020-03-01T03:41:32.077822Z" + "end_time": "2020-03-15T01:04:30.344555Z", + "start_time": "2020-03-15T01:04:20.976031Z" } }, "outputs": [], "source": [ - "df_train, df_test = get_smartmeter_df()" + "df_train, df_val, df_test = get_smartmeter_df()" ] }, { @@ -150,15 +163,15 @@ "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:41:41.497026Z", - "start_time": "2020-03-01T03:41:40.786905Z" + "end_time": "2020-03-15T01:04:31.128105Z", + "start_time": "2020-03-15T01:04:30.346646Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -167,7 +180,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -181,140 +194,12 @@ "source": [ "# Show split\n", "df_train['energy(kWh/hh)'].plot(label='train')\n", + "df_val['energy(kWh/hh)'].plot(label='val')\n", "df_test['energy(kWh/hh)'].plot(label='test')\n", "plt.title('energy(kWh/hh)')\n", "plt.legend()" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-16T00:24:15.905017Z", - "start_time": "2020-02-16T00:24:15.747859Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train helpers" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-01T03:41:41.584788Z", - "start_time": "2020-03-01T03:41:41.503778Z" - } - }, - "outputs": [], - "source": [ - "def main(trial, train=True):\n", - " # PyTorch Lightning will try to restore model parameters from previous trials if checkpoint\n", - " # filenames match. Therefore, the filenames for each trial must be made unique.\n", - " \n", - " checkpoint_callback = pl.callbacks.ModelCheckpoint(\n", - " os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode='min')\n", - "\n", - " # The default logger in PyTorch Lightning writes to event files to be consumed by\n", - " # TensorBoard. We create a simple logger instead that holds the log in memory so that the\n", - " # final accuracy can be obtained after optimization. When using the default logger, the\n", - " # final accuracy could be stored in an attribute of the `Trainer` instead.\n", - " logger = DictLogger(MODEL_DIR, name=name, version=trial.number)\n", - "# print(\"log_dir\", logger.experiment.log_dir)\n", - " hparams = dict(**trial.params, **trial.user_attrs)\n", - "\n", - " trainer = pl.Trainer(\n", - " logger=logger,\n", - " val_percent_check=PERCENT_TEST_EXAMPLES,\n", - " checkpoint_callback=checkpoint_callback,\n", - " max_epochs=hparams['max_nb_epochs'],\n", - " gpus=-1 if torch.cuda.is_available() else None,\n", - " early_stop_callback=PyTorchLightningPruningCallback(trial, monitor='val_loss')\n", - " )\n", - " \n", - " model = LSTMSeq2Seq_PL(hparams)\n", - " if train:\n", - " trainer.fit(model)\n", - " return model, trainer" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-01T03:41:41.652356Z", - "start_time": "2020-03-01T03:41:41.588888Z" - } - }, - "outputs": [], - "source": [ - "def add_suggest(trial):\n", - " trial.suggest_loguniform(\"learning_rate\", 1e-5, 1e-2)\n", - " trial.suggest_uniform(\"lstm_dropout\", 0, 0.75)\n", - " trial.suggest_categorical(\"hidden_size\", [1, 2, 4, 8, 16, 32, 64, 128]) \n", - " trial.suggest_categorical(\"lstm_layers\", [1, 2, 4, 8]) \n", - " trial.suggest_categorical(\"bidirectional\", [False, True]) \n", - " \n", - "\n", - " trial._user_attrs = {\n", - " 'batch_size': 16,\n", - " 'grad_clip': 40,\n", - " 'max_nb_epochs': 200,\n", - " 'num_workers': 4,\n", - " 'num_extra_target': 24*4,\n", - " 'vis_i': '670',\n", - " 'num_context': 24*4,\n", - " 'input_size': 18,\n", - " 'input_size_decoder': 17,\n", - " 'context_in_target': True,\n", - " 'output_size': 1\n", - " }\n", - " \n", - " # For manual experiment we will start at -1 and deincr by 1\n", - " versions = [int(s.stem.split('_')[-1]) for s in (MODEL_DIR / name).glob('version_*')] + [-1]\n", - " trial.number = min(versions)-1\n", - " print('trial.number', trial.number)\n", - " return trial" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-01T03:41:41.723534Z", - "start_time": "2020-03-01T03:41:41.655316Z" - } - }, - "outputs": [], - "source": [ - "\n", - "def objective(trial):\n", - " # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\n", - " trial = add_suggest(trial)\n", - "\n", - " \n", - " print('trial', trial.number, 'params', trial.params)\n", - " \n", - " model, trainer = main(trial)\n", - " \n", - " # also report to tensorboard & print\n", - " print('logger.metrics', model.logger.metrics[-1:])\n", - " model.logger.experiment.add_hparams(trial.params, logger.metrics[-1])\n", - " model.logger.save()\n", - " \n", - " return model.logger.metrics[-1]['val_loss']\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -322,47 +207,6 @@ "# Train" ] }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-01T03:41:41.777701Z", - "start_time": "2020-03-01T03:41:41.726164Z" - } - }, - "outputs": [], - "source": [ - "PERCENT_TEST_EXAMPLES = 0.3\n", - "EPOCHS = 2\n", - "DIR = Path(os.getcwd())\n", - "MODEL_DIR = DIR/ 'optuna_result'/ 'lstm'\n", - "name = \"lstm1\"\n", - "MODEL_DIR.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-01T03:41:41.829950Z", - "start_time": "2020-03-01T03:41:41.779861Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/optuna_result/lstm\n" - ] - } - ], - "source": [ - "print(f\"now run `tensorboard --logdir {MODEL_DIR}\")" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -372,64 +216,317 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-02-01T12:52:22.492191Z", - "start_time": "2020-02-01T12:52:22.416653Z" + "end_time": "2020-03-15T01:51:46.861012Z", + "start_time": "2020-03-15T01:04:31.130838Z" } }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2020-02-16T01:47:59.598921Z", - "start_time": "2020-02-16T01:47:02.100Z" - } - }, - "source": [ - "Note that the LSTM has access to the y values for the first half of the plot (the context) to match the NP setup." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2020-03-01T03:47:34.500Z" - }, - "scrolled": true - }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "trial.number -21\n" + "now run `tensorboard --logdir lightning_logs`\n", + "trial.number -8\n", + "INFO:root:GPU available: True, used: True\n", + "INFO:root:VISIBLE GPUS: 0\n", + "INFO:root:\n", + " | Name | Type | Params\n", + "-----------------------------------------------------------------\n", + "0 | model | Seq2SeqNet | 8 M \n", + "1 | model.norm_input | BatchNormSequence | 36 \n", + "2 | model.norm_input.norm | BatchNorm1d | 36 \n", + "3 | model.encoder | LSTM | 3 M \n", + "4 | model.multihead_attn | MultiheadAttention | 263 K \n", + "5 | model.multihead_attn.out_proj | Linear | 65 K \n", + "6 | model.norm_target | BatchNormSequence | 34 \n", + "7 | model.norm_target.norm | BatchNorm1d | 34 \n", + "8 | model.decoder | LSTM | 3 M \n", + "9 | model.mean | Linear | 257 \n", + "10 | model.std | Linear | 257 \n" ] }, { - "name": "stderr", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validation sanity check', layout=Layout(flex='2'), max=5.…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", 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\n", 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9e/eqG8tUVIpgq53F1Ro7Ozvq1q1bI5XASy+9hBCiWnZ2W7ZsqdL2+vXrx6hRowgKCuLcuXPEx8dz/fr1KpVBRaWmU/N6QSuwYcMGwsLC8PHxIS4uztbimOXmzZucPHmyoKPLycmxsUQKdnZ2vPLKK+zatYs+ffrYTI6ZM2eSm5tLq1atbCaDikpNpFYqAp1Oh5+fH46OjgVuiFqtltTUVBtLZpmPP/6YN954g/r16xMTE0PdunVtLVIBixYtskm7TzzxBLGxsSxZsoQ333zTJjKoqNR0aqUiyMjIICEhAVBGs0OGDGHPnj34+vraWDLL1KlTBwcHB7y8vGjcuLGtxSkgNzeX7du34+rqWuU5HbZu3UpWVhbnzp3jrrvuqtK2VVT+LdRKY3F2djZvv/02OTk5vPvuu5UkWe0lKiqKkJAQAH7//XfuvffeKmt77NixXL9+nffff5+9e/dy6NAhxo4dWyPiRqmoVCWWjMU2dwct61FZ7qOm2LBhgwwODpZCiGrlnlmUbdu2yaCgINmnTx9biyKllDIqKqog3o+3t7fN5AgMDJSAfPbZZ20mw7+NadOmyTFjxsjc3Fxbi6JSQbDgPlprvYYKk5GRQVBQEKNHjyYqKgopZUEayOoY0fLatWtcu3aNI0eO2FoUQNnlu3btWhwdHalfv77N5OjVqxfNmjWjTZs2NpPh34SUknXr1rF+/fpimfFU/l3USkVw4cIFQkNDmTlzJgAODg5cv369WLAyQxrI6sLcuXNxcXFh6dKlTJ06lf/7v/+ztUgFjB07ltzcXC5cuFCl7UZERLBu3TrS0tIYPHgweXl5TJs2rUrDYf9biYqKIiMjAwBnZ2cbS6NiTWqlsTgyMpKNGzfi4uLCwoULcXFxMXttdQpuFh8fT05ODlqtluXLl9tanGrB2LFjyc/P5/LlyyxevLhg5GqY0QE2z9dQU8nOzgaU7G+WfiMqNZ9aOSMIDAykVatWRpFGg4ODTV5r66BuhZk9ezbffvstGzZssLUoRhw8eBBHR0c8PT2rvG1vb2/c3NxYvXp1seWL6jajq2m0atWKvLy8arMEqWI9auWMoFevXpw5c8boXHh4OJMnTzbqTKpDULfCBAcHExwcTGxsLNOmTcPd3b1aeD2lp6ej1WpJS0vD3t6e1NRU3N3dq6TtGzduAIobsCmq04yuJnLgwAHi4uLo27cvPj4+thZHxUrUyhmBKdLS0ujWrRv+/v4IIQgODq4WaSBNcfHiRT7++GPef/99W4sCQPfu3QtyBet0OpuEeKiuYbprOjNnzmTEiBGcOnXK1qKoWJFaqQhWrFiBn5+fUY7dF198kd27dxMTE0Pz5s05fvx4tVMCK1asoHv37qxfv56goCDatTOZ+K3KcXNzo1+/fvz0008cPHjQJjGQbBUO+9/M/v37+eOPPwCoV6+ejaVRsSrm/Eqr61HRfQQbNmwoCEFNoVy3/v7+RudOnz5doXaswbBhwyQg77zzTluLUm2wt7eXdnZ28ubNmzVmH0hNYdOmTRKQw4cPt7UoKpUAFvYRlGgjEELYAXcCjYEslGT0CaV43+fAw0CCNJGzWAghgKXAQ4AGGCelPFwq7VUBZs+ejVarNTqn0Wjw9fVFSklISAhCiGKjy+rA0KFDyczMrHa7ZiMjI5kwYQIBAQH8+OOPVdp2fn4+oLg3hoaGVrtZXE2mZ8+efPPNNzRq1MjWoqhYG3MaArgDWAVcBn4GNgBfA8eBf4AwwM7C+/sAnVEUh6nyh4AdgAC6A/vN1VX4qOiMQAhhNPIvfNSUkWR+fr6MiYmRly9ftrUoUkpllmW4h23atJGff/55lbV99uxZeeLECZmfny8XLlwo3d3dZa9evdTZQSWSm5ur7iz+F4CFGYElRfClvjMXJsoaAC8AY829X39diAVFsBIYVej1OcDPUn2yEhRBcHCwWUVAoaWi6txxpKamFshaHTh58mTBshUgx40bZxM5nnvuOQnI+vXrSzc3txr1nVZXXnjhBQnIDz74wNaiqFQQS4rArLFYKjmH/9BXULQsQUr5oZRynaXZRgn4A4XdS6L156yKKaNiUTQaTcGu4+rEvn37WLFiBSdOnABACIFOp7OxVNC2bVu+/fZbFi5cyOOPP87w4cONyiMiIggJCTEyzluDqVOn8sknn+Do6KjuKagEDh8+zMaNGwGKLaeq/MswpyGk8ei9B/AkMMZwlPJ9IZifEfwA9Cr0+negq5lrJwORQGRQUFCFNeM999xjcYkI/TJRdaNv374SkH379rW1KCYxtRyzYcMGq43Ob926JZs1aybbtWtndN7cd1sdv9PqzP/93/9JQM6bN8/WoqhUAlTQWPwFir3gKJBv0B/A+nJpntvEAIGFXgfozxVDSrkKxV5B165dKxw3WwiBlJL77ruPixcvEhUVVewab2/vijZT6QQHB+Ph4UGTJk1sLYoRly5dYtasWWzZsoXc3FzgdogHV1dXs6Pzihp209LSuHjxYrHzQUFBJr9TdU9B2ejYsSPjxo2jU6dOthZFxdqY0xDy9mj8DCbsBKU5sDwjGIyxsfhAaeqsjDDUhw4dkmvXrpUHDhyw6oi1trB48eIS7S6mjore49TUVBkWFiYnTpwopZRy3759csCAAbJfv37S2dlZ/U5VVApBeYzF8naH/T9KYcQ18b4vgTggD2X9fwIwBZiiLxfAcuAScAIzy0JFD2vkI6iJHiaNGjWSHh4e8urVq7YWxchrqCxHZXfOy5Ytk4D08PCQU6ZMKWinpnyn1ZGdO3fKQYMGyfDwcFuLolJBLCkCsxnKhBDb9D8kD6AjcAAoyJYupRxS4nTDClRGhrJ/A/b29uh0Oo4cOULHjh1tLQ4hISFml9iysrLMxrMPDg7m6tWrlSLDX3/9xYwZM2jWrBkvvPACTz/9NIGBgXz77beVUn9tIykpia+++opnn32WESNG8L///c/WIqlUAEsZyizZCBZbSR6b88orr3Do0CGeffbZYh4uXbt25fz58yxdupSwsDAbSWiabt26ERkZyVNPPcXHH39Mfn4+TZs2tbVYgPmgfUuXLuXatWvMmjXL5PsqEhQuISGBRYsW4evry6uvvkrPnj3Zt28fAJ9//jmenp5qHuMKMHHiRLZs2cLEiRN57rnnbC2OijUxN1WorkdlLA0FBQVJQE6bNq1Ymbe3twTk/PnzK9xOZdO2bVsJyJEjR9paFJOYW2KbMWOG2eWh4ODgcrf322+/SUA6ODgUKxs6dKgEZMeOHctdf20nNDRUenl5yZ9//tnWoqhUAlTQa+g/wLsom8iE/pBSyrpW0EtVwsiRIzlw4AD9+/cvVvbJJ5+QkJDAwIEDbSCZZXbv3s2tW7eqnUfTrFmzWLhwIYGBgVy7dg2tVouDw+1Hq3Hjxnh4eJCZmWm076GiQeE8PT3x8/MrSI+ZlpbGr7/+iqOjI/fddx+RkZE0bty4/B+sllPd8l7UJB599FFiYmLYsWNHzQjfbU5DGA7gItC6pOuq6rBm8vqaxKRJk+TAgQPliRMnbC2KfPHFFyUgHR0dJSBdXFxMXrdhw4YCH//GjRtXugG38Azh/Pnz6t6BSiA5OVl+/PHHcu3atbYWpUZhiGBQXcLASGl5RlAaRfBXSddU5aEqAgVXV1cJyClTptjc4yknJ0cmJSXJjRs3SkA6OTlVuQxSKm7Bzs7O0sfHR8bFxUlHR0dZp04dm8hSVeh0ymEtDAq1WbNm1mvkX8iAAQPk/fffLzUaja1FKcCSIrDkNfQf/b99gUbAFoy9hmziilEZXkNbt24lPT2d+++/nwYNGhiVzZ8/n7///ptJkyYVMyTbmlGjRnHgwAFeffVVfvrpJ44fP05sbGxBbllQlltslVBHq9WSm5uLi4sLdnZ2SAl5eRAXl4S7ez3Anvx80OmUQ0rlLyj/Gx7Foo+kEMpfOztwcAAXF3B3ByG0ZGdn4+LiYrQUBUr6zCtXrnDnnXfSsmVL637wcpKXB7m5oNVCfv7tw3BfDPfE0r0xhZ0d2NuDkxPUqQPlDaQ7ZMgQzp07R4sWLbjrrruYO3du+SqqZAzPVV6ecu+0WuV+mbt3hvcUfr/hmRJCuV+OjuDqqjxXZpLdlUE+WZAxT6fTIQyN2RhLXkOWFMEaC3VKKeX4yhCurFSGInB3dyczM5PVq1czceJEo7JmzZpx6dIlwsLC+PzzzyvUTmUTHBzMtWvXmDp1KsuXLzfrslmZLpllRaeDzEzQaCA7W/nRderkQ0pKEq+99i5TprwKQFpaCnXrlj/ZiRDw/fef8vzzk6hTpw4ZGRlG5V26dOHw4cM8/PDDbNu2rUKfqbKQUrkvmZnKvamqMFFubuDjU/YOzt/fn9jYWK5du0ZgYGDJb7Aipp4ra+DgoNwrF5fy16HT6XjvvffQarXMmTOn8oSrIJYUgaVH41fgFSllmInDJkqgsmjYsCGurq40bNiwWNkjjzxCt27d6Nu3rw0ks8wrr7zChAkTGDlyJGDe9dKaeXpNBZBbsmQJAQEBjBgxiuhoSEqCrKzbP1atNg8AH5+GbN68hqZN7ejVK6RCckgJ6elKxBN7e/ti5Q0bNsTFxYWAgIAKtVNZpKdDdDQkJiqdWVXGCtRoID6+7J3njh072Lt3r8nfSVUhJaSmYvK5sgZaLdy4ATk5JV9bmPT0dPbs2cPZs2exs7OjadOmSClNhkCpjliaEbwGDAQcUQLC7UAJA2HFr6Fk1A1lCgcPHmTw4MEkJiYWK7PWjCAiIsLkXoGOHTuxb99fBAQ0ITdXWabavz/W6L3p6Wk4O7uwfHk4//3vAgD8/YN55ZVwHn20YstYDRooo96UlBR8fHwQQpCXl4ebmxu5ublER0fbLLmKTqd0/llZNmneCA8PKI/DWXR0NBqNhmbNmhUseVQF+fmQkFD2TrkysLeHxo2Vv6Xh77//pkePHnTr1o39+/czePBgtm/fzrZt23j44YetK2wpKdeMQEr5rpTyXpQEMseA8cBhIcRGIcQYIYTthgkqPPzwwyaVgDXz9M6ePdtkALnz5y8SFvYCU6e+TkJCHAkJccXe6+FRl+3b/8eqVbf3KcbERDFr1mS2bKlYWOrkZGWU6OTkRH5+fkHI5OzsbPLz80lPT69Q/eVFp1NG4tVBCYAyKymPLC1btqRly5Zmd4dbA60W4uJsowRAUUK3bpX++hy9oAcOHCAvLw9/f38GDRpkk/zd5aHEfQRSynTgO/2BEKIN8CBK9NHq52xfQc6cOcOFCxdo3rw5rVu3trU4RixbtoyrV68yduxYGjRoQEpKSoGdICcnh+DgYMLDw61mKDa35JSUlMDcuR8AcPXqRVxc3NDpdMVGj4sXzyY727gzycrSsHjx7ArNCrRaZfmgbl0X/vzzT1z0C7y//fYbTk5OBAcHl7vuipCQoBiDqxO3bilG0ZLIy8vjzTffpG7dutxxxx1kZWUVRJa1Njqdcu9snQIhIwPq1lWM7iVhiNBat25dbt26xerVq/Hx8al2fYg5SlQEAEIIfyC40PUHpZRLrCaVlXF2dkar1XL69OliHiVhYWHs37+fBx54gJ9//tlGEppm/vz5JCUlUb9+/YLkNFWJufDOjRvfDu88c+Z7RmXHj0cyevT9NGrkT2ysaUVi7nxJfPXVp7z33uu0bNmer77ahYeHHb169QKgdevWXLhwgTlz5hScq0pSUhSjZnUjN1cxutapY/m6jIwM3nzzTerVq8etsgyNK4Hk5OqjQJOToTSrinXq1CkYeNjZ2dGjRw88PT2tL2AlUZqdxe8CjwOnMc5H8IcV5bIqhpGNiwnXAD8/P5ycnKrd7l2AO++8k7Nnz9K8eXObtG8qnpCrqxsDB/6HmTMn061bH4YNe8roPRcunCYtLYXc3BwaNw4iJsayIikL165d4tatJC5dOqs3HkM9vSNSamoq+fn5ZNlgXSY3V1EE1ZWUlJIVgZOTE/PmzSvmlmttsrOVkXh1ITtb+T5LmhU4ODhw3333Fbz+9ttviYuLIz4+3mb2qbJg1lhccIEQ54AOUkobrdYZUxnG4tOnT5ORkUHnzp2r/EGvbMaPH8/69evp0qUL+/fvt3p7ERERTJ8+XR/qogFz5rzP1kMBaWcAACAASURBVK0b2bVrO+3bd6Fr194kJd1g1qzFNGzYmLi4aH74YRP29vZ4efkya9ZksrKMFcnbb68q19LQ2bMn+fXXLTRuHMTw4WOwt4cRI+4mPT2dNWvWMGnSJLKzs9m4cSNdu5q0kVmF+PjqORsoTKNGFXORtAZSQmyssj+gOuHurriUWmLevHl8+OGHzJs3jxdeeIHXXnuN9957j4ULF/L6669XjaAlUF73UQOXUTyH/jW0adOGbt261Wgl0KdPHxwdHVmzZg35+fkcOHCgStoNDQ0t8P+///6hPPpoKO3b30VAQBM6dbqH9es/YuvWL7ly5TxbtkQwcmQvFi6cweeffwjA22+vwtPTC4AGDfzKrQQAWrVqx/Tpcxg+fAygGPgOHjzImTNncHd35+LFi1y4cIGzZ89WwicvHVlZ1V8JgDJ7Ki3jx4+nVatWRs/Yjh07uO+++4iLK+4YUF4yMqqfEgBlKS0/3/I1ycnJpKamsmTJEvLz8wkMDKRDhw54eXlVjZAVxGxPKIRYhrIEpAGOCiF+x3hnsRqXtopJSUnBzs4Od3d3UlJSjBKKF56WWpPY2Fiioq4A0KGDMrh48cV5vPjiPABOnjxEauotDh/ez0cfLSgY/Rs8hN5+exVHjyZZTb7hw0fj4JBLw4YNCQsL48aNG1War6GKl9PLjUajdG7m3CPT09M5e/YsXl5eREdHc+7cOSNbwcyZMzl27BizZ8+ulI2Xhv0C1REpFSVlacn//fffZ9myZURHR3Ps2DHmzp1L27ZtmTx5ctUJWgEsDYkN6y+HgK1VIEuVkJycTPfu3alTpw5HjhwpVj558mTWrFlDz5492b17d9ULaIGAgAAyMzP59NNP2bhxIzdu3KBly5ZVumnq2LHzANjbOzBqVPGH/JtvlHwAvXqFGC0BQeV4CBXml1++Z8uWDXTt2pPx418AYNGidfj7w5AhDxIdHc3y5ctp165dpbRXEllZ1cfIWRIldW6HDx+mX79+9OrVi7Vr15Kbm2uU8zk8PJw9e/bw6KOPVoo8GRm29xKyREmKwMHBgRkzZqDT6cjLy+PWrVs2c1suD5YUQSqwT0qZUN7KhRCDgKWAPfCplPKdIuVBwDqgnv6a16WU28vbXmlITk7mwoULZuN/GEbaN2/etKYY5cLgjuns7Ey7du2qrIMrTP36wTz88OM4OTkXnEtOvklmZjr16nnj4aFEJ7fkIfTll6s4eHAvgwYN54EHhpZblp9++oYdO77m3LkTBYoAlB/tH3/8gUaj4erVq/Tp06fcbZSFtLQqaabSSE8337m5uLjQuXNnWrVqZdIXfvDgwQwePLjSZKnu9y4vT1nys2RXee89xWNOq9WSlGS9Wa81sGQjeAo4IoS4IIRYJ4SYLIQodc8jhLBHyUn8INAGGKXfg1CYOcBmKWUn4Ang47KJX3a8vb0ZO3as2exjixYt4rfffuObb74pVmYqvEJVkpaWhpSSp5667Zlz5coV3nrrLRYtWmT19nNyoFGjJsyZ8z5jx04nLi4agMmTH6VPn6Y8/fQwYmKucfx4JI0a+Zuso3HjINauXcZ3333Bli0Vi3ffqVN3WrRoR48e9xaci4hYyYIF83j88cdp1aoVf//9NydPnqxQO6UhN7f6bBwrLVqteXvG3XffzaFDh1i9enWxspycHJKTkytNjqys6mkbKIolb6aXX36ZsWPHcv36dRwcHPjzzz/p2LEj06ZNqzoBK4DZGYGUcgSAECIE6KE/ntaP4g9KKR8qoe5uwEUp5WV9PV8BQ1HcUAuaAQwJbjwB47gEVqB+/fqsXbvWbHlwcLDRBqSIiAhmz55NVFQUQghDaG6ioqIK1v9sEelz4cKFbN68maNHjxacmzFjhlXbNIzaJk9+lOPHD9K37yDWrt2hnx0I3NzcePjhTqSkJPPQQyNISdlezEPolVfCOXToL/Lycrnzzm4Vkmf06KmMHj3V6NyCBS+Qm5vN+vWb+Omnn/jkk0/w8PAoGK1Zi+o+ojVHRkbJ3kPbt29n9+7dDB48mL59+7J3714GDBhAp06deOutt+jYsWOFEgBVV9tAUTIzwcvLdPC+LVu2cPnyZUaMGEGjRo3Iy8vj+vXrxMfHV72g5aBEryEp5VXgMHAEOAokAKXYm4g/cL3Q62j9ucLMA54SQkQD24Hppai3yjDE1jFsoirqaqvRaJg9e7YtRGPnzp1GSsDau2e1WuWHsG/fTq5cUewELi5KfOONG3/nyhUdn366DXd3D+zs7GjTpjNvv70Kf/9ghBD4+wcXeAi9+ebH7Nx5jqefrnzFFRLSDC8vX1xcvGnZsiVeXl74+5uenVQW+fnKvamJZGaWHABvz549LFq0iH/++QdQllfd3d05cuQIgwcP5pdffil3+7m5NcPLChS7irnv+b///S+ghO7+9ddf+fLLL3n22Wf59NNPq1DC8mPJa2gWcA/gC5wD/gE+AiZLKUtwpio1o4C1UsolQoh7gC+EEO2klEaPphBiMjAZMDJYlYerV6+ycuVKAgICTE7bNm3axDvvvEPHjh3ZtWtXifFVrBnpsyh33HEHKSkp/Pzzzzz55JO4u7vzwAMPMGHCBJxKsw++nERERPD667OJibmGk5MzOTnZtG/fhU8+Kb589uefVwv+/+uv33nmmZkMGPAIDRtWfsrI6Ogo4uOj8fMLxN9feS5+/lnZcb1ixTvce+8AvvvuO+rVK3+469KQnm7diJjWxGA0rlsk8exnn33GnDlzmDhxIoMGDcLLy6sgIu/IkSMZMWIEixcvZvv27RXaQVtTZgMG0tOV4H1FGTx4MP379ycmJobY2Fi+/fZb7r///oI0qtUdS8biMUAmsA3YB+yXUpbla4sBCgcxD9CfK8wEYBCAlPJvIYQL4IMy6yhASrkKWAXKhrIyyFCMPXv28M477+Dm5mZSEURGRnL06FFu3LhRqmldRRVTWTDkA87IyCAsLMysnaMyKRpxNCdHGb65upawNRVYsmQOR478w9atG9m0aU/B+ZSUZOLiYvDwqEtAQPlnMjNnTmLv3l/p3PmeAm8lA4sWzUZKHUOHPmJVRaDT1dxlIQNpacUVwa1bt4iPjyczM5P+/fsXy+8thGDGjBkVWo7UahU31pqEYQZjajlt586dAMTExLB27dqCHcUajYabN2/i6elZbcNOWIo+2gq4H8WNtB/wnRDigBBitRCiND3QQaC5EKKJEMIJxRhc1A31GnAfgBCiNeACFA+pWYk0aNCARo0a0apVK5PlgwYNYujQoUycOLHETt6akT5NsXTpUt566y2T3kI6KwW4NxVxFDAKFTFx4iO0a1eXt9++3Sls2RLBmTPHADh9+qhRhNGXXx7DQw914JlnKpYBrk4dD+zt7fH0LD7q8vMLoH59b+rVs+6GnrS0qs0tYA1MdchTp04lOjraqolV0tJq5kyqqOLPyMhgzZo17NixA1AS+gwYMICtW7cyb9486tSpQ3BwcIX3W1jTvdbi1lopZTLwgxDiJ6AL0Ad4GiUktaUMZkgptUKIZ4GfUVxDP5dSnhJCLEDJnbkVeBlYLYR4EcVwPM7a+Q4efPBBi7sh77vvvoLNWS1btmTSpEkm49VYO9KnKaZOvW0YPXr0KB988AEbNmwoUAJ5eXmVvlva3NJXTEwU48Y9yNq1O7h27TKZmenExV1n4sRH2LPnZ6SU5OcrT25GRhqzZimG9UcfDcXDox5CCFxcSmNqMo+ppamuXRuSlJTAiy/OZ9eu7QQHBzJ06FC2bNlSobZM8W+YDRhISTFOaenm5oab/kRiYiLnzp3D29ubVq1a0bFjR/z8/Pj+++9xcnIiLy+vzEuTWm3ZdjdXJzQaxYPOWe9BHRMTw/jx42nevDkPPvggoHj4ffzxx0ZBLXMqEFNbSuU7KinURXmxZCMYguIp1BNoC5wC/kLpvPeZe19h9HsCthc5N7fQ/6f19VdLQkNDOXz4MO+//z6gzCYSEhJwcHCwWSpIAx999BHr1683OqfRaKhbdI5fQfz9g4iOLh4oDiAuTvEFmDdvGZcunaVTp+7MnDm5ICNZYQpvJvvwww18+GHFXEdLovCPLivLOtbI1NSaPxswkJur2Arc3YuX/fTTT4wZM4bQ0FAWL17M8ePHuX79Ok5OTjg6OhbkgDCVKc4chhwSNZVbt25HJXV1dWXMmDGsX7+e5s2bs2TJEhITExk7diyDBg3i5MmTJCUl8dJLL5W7vfR067rYWho+jkPp+F8FDkkpa8ieSfMo29iVj+Hh4UROzu3csU5OyogoJiaKzZs34+PjQ1hYGE888QTffPMNDRo04Ouvv2bSpEnY2dmRm5trVQOtKUaNGkVeXh6ffvopzZo1w83NDWdnZ2bOnMmLL75Y6bOBnBx4+eXwYoHiADp3voe5c5cC0KPHvQW+/G+8sZSRI02HfS5vuOmy8McfV3BwcMDBwYEpU14DwMennNnbLZCTU/MMnSVx65byG7Czg7Vr1/L3338zevRo/Pz86NmzJ82bN8fHx4dTp06RmJiIEAJHR0eEEOTk5BTMIEoiK6vm2QaKkp19O5x3UFAQ69at4+LFi+zbt4/t27ezcuVKQkNDeeKJJyrcllarzAYcrRjxrTTRRx+UUu4ocm6KlPIT64llnopEH01KghkzXmTNmg/x8wtk3z7jjkkI2Lx5Ba++OhV3d3euXLmCr68vTk5OZGVlYWdnh52dHVJKzp49WyyXgbUx7Ia+fPkyTZo0sWpb+flKJMj8fGW9f/7850lJub1b8vPPf6R/f9NbSXr1CjETbjqYn3++Sn4+BYeUiiI2/AXTI0UhlLg4Dg6K14azM4SFPcTff+9i+PBxhIevKLhWo9HQtq1izL5yRanMy6u4QbS86HRK9qzKGqFJqdSVl4fJe2O4P4aj8PuKIoRyODoqBk0PD+V1aXF1VVJ/hoWNY926dXz22WeMH28+RblWqy3TACQvT7l3lTWTklKZzWi1pu9d0eeq8D0z3Ct7e+V5cndXnq/SYmcHfn63O+irV6+Sm5vL5cuXWb16Nb179+aFF16wXEkpPl98/O2lKD+/8tdlKfpoaT72/wkhcqSUO/WVvQr0B2yiCCqKVqvMCExNY6UEL69A6tatR0BAIPv2HdCflwXhHVxcXMjLyzMK+FZV3H333Wg0Gqt7HhhyxRoiLj76aKhRfKCUlGTc3G6vISxaNJuTJw8RGvoMDzwwlJdfDufVV8OMloicnNwYNiyckydhx46lbNo0Gz+/lixceKhcMtatC7Gx0eTkZJOUZBwFRaMpvgX01i3lh2tq6aMs5Ocryc3LqwQMdoW0NGVEmZ1t3XSMjo4QGKh07qUhKwtu3oRx48Lo3r07PXr0sHh9WZSAVqs8V+VVAvn5yiwsPf32vavs2E4NGkBQUOkUgk6nPAtubhkIkUdgYCD29va0aNGCgQMH8ssvv/DVV18hpeSVV16hV69ebNq0qdSySKnku66KdJ2lmRH4AD8AM1BcPVsBo2y1VFTRGYHBQFWakYwQkJOTzLFje+nSpT15ebmsXLmSJk2aMH267fe+ff3113z55Zf88MMP1KtXj99//71C8YekVKbsycmWw+7+9ttWVq9eQsuW7ViwYDk9ewYTG3uNUaOmEhDQjR9++Jw6dby4cuUQSUnR+PgE8fjj4fTqpSiT//1vLt9++yZ16/qycmW5Q1lx5cph7O0v0rlze5o1U1ICTp/+BP/8s4exY58lK0vDN9+so0ePe3n/fcWe4u6uJK8pzypaZqaiUMozBsjJUWZYiYm2sSs0aABNm5b+eicnZRZV1E3y+PHjfPPNN3Ts2JFhw4aVqi7DRqzk5PJ99qwsiIkp//vLirMztG1buhSVAJ999j5vvfUyzz33PEuXKuHWpZQ4ODgYefO1adOGU6dOlarO7OzimdpsOiOQUt7UG45/Q4lEOsLanj1VQWlGMlJCQkI6jz2mBEZ7883/8sEHH+DhUZewsOnY2VFwwO2ppuH/ysBcPUuWLOHVV18peJ2QkEBc3A3atDGtCAzfWOG/Op3S4Wu1t2PlmFIAGRkZdOniQ25uDqNGPY1Wm8uBA39w5cp5FixYzn33DSEy8gA+Pn3466/vOXPmD/z9W/PRR6ZtAn36hOHjE4yPT0hpb4NJmjTpjJ1dZ6NUgmfOHOPmzXguXjyNTqfjxo0YTpy4PXDIyLgdVsHFRRkx29sr32Hhe21Ykil8b8qjAKRUOrGYGNsaRxMSlM9b2kgQubnKkoSjI1y4cIRhw+6lTZt2TJgwiQULFvDkk08xZMgwnn9+OhcuXGD58hWEhDQxWs7SahUFaO65Kgkp4do1ZSmpKsnJgbNnFWVQGvu3lODh4cm3335PTo4doaGjadeuDQMHPkhk5AHs7e3x8PBg27btRveh8HJf4WfNGjOdkrDkNZSO4tJpwAloCowQQkgpZeW6p1QztmyJYPHi2UYGzgYNgmjUyJ+AgCbYIjjpX3/9joODI3fd1YvU1Nu9VpMmLRg8+DHq1WuLNTY6Z2dryM1V5qdffrkSgLvv7kubNh3Jz4cnnljGQ3pzgVabw61bsbRubT7iZ8OGTWjYcEKlyKbTwaVL0KGD0pE/99xczp49Tv/+g8nNzcHX148777zLxGeyfmgDnQ4uXlRGdtWBa9cUm4GpnbFFOXDgTzIy0ujUqTtZWfakpKSQlJRKo0YdeOGFebRo0Y7r12H37r84deoIZ8/ewsGh8uxWWi2cO2c7F1ONBq5fh5CQkq+dOPElJk58iSlT/sPKlR8QGbmfQ4f2MWHCixw48IPRtdevm6nExlgKOleKx6XmMW/ec2zb9hUDBw7j7bdXFivfv/8Pnniib7Hzrq5uaDQZCGHHsWMH+O23rQwYMKQqRAaUpaynnhoAwKVL+Tz11FSGDRuNq2udUntrlJe6deuxcuUWpkz5D4boH9HRV3n88UmcO2fsS9+nzxj69BnD8uVjeOaZxtx770RGjlxgFbnWrXuey5cjGTToeXx8HtPnIRhFs2atGTNmIF5ePvzyS+mm4tbgwoXql6gmKgpKs3q4aNFMIiP/4quv9tClSw+OHk3G2dkFFxdX2rS5nehnzpwPyMnJIiioeKjq8qLT2VYJGIiPV5bUSvvz+s9/xtKlS0+ioi5y4kQkDg41J7GjpRlBiD7gnLlyAfhLKaOtIZi1OHv2BMnJiVy8eMZkubkvz+AHn5KSRF5eLsnJVRtvvLBNw87ODhcXF1yqKOmsk5MTGk2GPs6QsrkuJiaKmTMnM3Ei+PoGk56eTLNm3ahXT1mnuXIlkpSUOOLiLhSr7/r1U6xb9xweHt48//zmcst18OAWkpKuERLSmZiYx2jQQFnKSEiIIykpgbQ02/XC0dHVTwmAsiyWlATe3pav69y5Bx4ennh7++Lg4GBy9zZA9+7FB00V5coV2ysBA1evQpuiwfPNUDi3xltvKV5sBhvBF198TFTURaZOnYWPTykt91WIpYXyRUIIO+B7FNtAIkoIiGYoXkP3AW+gRBWtMUyfPoe77+5Lx46mQyDfeeddRuGmCxMbe42PPlI6rsIx8KsCFxcXLlwwdlU5ffoomZkZ/PLLFhIT45k8+RWj0Vplsnjx7AIlYCAnR8OmTbPJydGQnp7I8OHzuPfeSZw6tZP27QfSseNgOnV6sFhdCQmXOHVqJw4OFduH0bPnk1y+HEm7dgMKPDgOH/6SH3/cTLdufQgJacbMmZMJCGjCtGkzK9RWWUhLUxRBdeX69ZIVwcyZpsN2x8ZeJykpgUaNAvD1bVjpsiUnKwb16kJamvmNdgbmzHmGc+dOMnfuh7Rv36Xg/JAhXTlxwtgrbtiw0TVLEUgpR+oTyYSihJTwQ8lffAZlt3C4lLKGBJC9Tc+e99Gzp/n8vg4ODjRuHGTSD97V1Y1p00bSuHEgf/1VdVFHTXHs2EEefdRYmbVr19kqiiA6Osrk/QBISrqGn19LsrJS8fDw5q+/Iti48VXq1KnPp5+aXhz382tJixY9qV+/Ai4QwKhRC41ex8criWkOHNjDXXf1xt8/mA8+eIP69X2qTBFIqYwiqzPZ2coGpdLG4svOzmLWrKcRQtCwYWNWrHiHV14JZ9q0Wfz11+9cunSW3r0foEmT5hWSS6tVZgPVjbg4aG7ho128eIbIyL2cPHmYjIw0QkKa4+cXgL397e61TZuODBs2mgYNKvbMW4uSYg2dBmwTcN+GvPKK6d20LVu258iRf6rF2t+tW7et1R4envj6NiIkpJlV2jKEkjCFt3cQS5bcXmbbuzcCJydXPD3NjxYbN27J/Pl7K1VGUDqS1q17kpgYS9euPWnevC2BgU1p0aKUc/tK4MaNmrFrNi7OsiIwhIwQQiCEHd999wWOjo7MnLmYdu06F2Sg+9//Puf77zfy4YcRFVYElblJrzJJSlL2Fjg7my5fsGA5KSnJRESsYNasybRt24n69X0YO3Y6a9fuICkpkXr1vPDyslKgoEqgcmMS1ABWrVrEyZOHGTZstMmdsenpabz33kycnJT196wsDZ6e9bn//qHMn7+c8PCXOX36CL/88n2F8u2WlejoKO69twXOzs6cOJFG69Z3Mm7cc3h7N+DZZ62rq/38AmnZsj0XLpxGp7vt/+bk5MbjjxtHX+3VK5RevUL59NMprFo1keHD5+HtHWAVuQ4e/J709EQ6dHgAHx8lUuywYeGMGDGcTz99HxcXV/7445JV2jaFTqe4idYEUlMVt05XM3H/evQI5ObNG/zzTwy+vo1YvHgtTk7OPPLIE4SFPVdwXc+eA/Dw8KywsTg3t+rdRMvCjRuKMjBFixZtATh58hA3bsQSHx/NqVNH6N37ATw965u1r1QnStxQVt2o6Iayzp2bce3aJUaOHM97731W7JqMjAzatzd2mDKEKQC4++7GJCTEMXnyDLPrqNbg/PlTDBzYDiEEly/bJtLZli0RvPPObBISruHtbbxJrChPPmmPlDrmz99Hixb3GJVptbkcPPgdeXm59OkzutzyTJzoTWZmMk88sZChQ18vOL9//3t8+OFreHrW5+jRqvPdjItTvHJqCo0amXeP7Nq1AUlJiRw+fJP69UswKFQCV64onW11xdEROncu3f6gy5fPc/36FZo2bUFgoOJSm5gYz6VL5/D1bcgdd5gOgV8Stg4x8a+ie/d+ODk50alTd5Plbm5ujBv3HFlZGiIj9xYLRfHII6O4cOEU99zT3+T7rUVQ0B2sXv09iv3emBs3YklOvknDho2tOv18+OFQ/P1Di20OmjrVn9TUBKZMWUvv3opiaNKkCxkZSTRoUNy3PDs7g//+VwnGVRFF4OXlj1abQ716t38dGRkpZGU50KiRP61adeD69StotdoKL1uUhE6n7ByuSdy8qYxyTeXgjYxMID8/vyC0ijXJzVU2vFVn8vIUQ7YpI/v69cvRaDIYNWoynp71adq0BU2btmDNmqUsWPACrVvfSd++g/jkk3cJC3ueuXM/rPoPUAIlKgIhxLfAZ8COoikkayLvvms5h6idnR1vvLHU6NyMGWHcuBFL/fre7NnzM/36PUi/fsW9YayJi4uL0b6FwjMXDw9P0tNTeeKJSSxcuKrS275xI5ZfftmCnV1D7rhjOJcvH2bOnLtwcHBi/fosNJpUdDoteXlZxcpMfxZ3HB1dsLMrfdhiU7z33vFi59ate5a9eyNo0qQloaFT6NOnKY6OTpw/b92ALQkJ1XN92xJarTJL9vU1XV54EPT77z+QlpbCL798x+HDf/Pee2vo23cgmZkZpKQkU6eOe7mTAMXH14yQ1DdumFYEq1cvJjr6KoMHP2a0DHT2rPJ8njlzjJEjx9O1ay8CAkKqSNqyUZoZwcdAGPBfIcT/gDVSynPWFat68f33G8nLyyUwsCmpqclcvVrcN76q+eabtQX/Z2YqQdacnMxYsyrInj0/M3fuNJyd3Vi7drg+AquuIIDf3Ll/kJWVQnBwZ27ditGXme94LSmJiuLq6okQAgcHV9zd6wLC6sZ9Kav3+rYlEhLMK4LCvPHGs8TERNGkSQsSEuIwBB347LP3+eCDN3j22Tm8/PKbZW5fq63eS0KFSUsznabyqaemkpycyMaNK1m//iPatu3MQw+NpEeP+0hPT8PXtxGPPz7ByLZS3ShNrKHfgN+EEJ4oyeZ/E0JcB1YDG6SUNWocdObMMfLy8mjatBXuZpyD589/nqSkBEaPnkZISDP69HmApKREHn98Iv/8sxtHR0dOnjxMu3adq0zuS5fO8dJLig/yI4+M4p13Xi0o0+nycXV1M7vcVVE8Pevh5uaBp6eyBBMQ0Ibw8EO4uCihnps2vX0fXFzcmDt3D0lJ17l581qBEbeqGD9+OcOHv4FGE09IiA9Xrlh/EnvzZtVEiLQGhkiedQqloM7OzuLpp4fh7l6X5cuVfTMDBgwhJSWJ6dPn4u7uUTDy9fT0ws8vADe3knNYmyI+vnxxiGxFfHxxu8rTTyspWj/6KByNJpODB//k4ME/Wbx4LR9//L+qF7IclMpYLITwBp4CRgOxQATQC2gvpexn4X2DgKUoqSo/lVK+Y+Kax4B5KEOMY1LKJy3JUlFjcUiIJxkZacydu9Sshm7SxA7DiKeowXHQoA6cO3eCQYOGs2LF1+WSozzs3r2DsLCHcHR0pEGDxib9+v39g9m792qlt63TwdGjpQ+EtW/fJpYtewJHRxerjfwBxo/3JDs7nRdf/Ia77rodCXPlyons3v0ZAQF38OefF63WPiizgWPHrB+3yJr4+sIdhZx+UlKS6dTJGw8PT44fT7Fau1otHDlSsxSBvb1iNDYVjC4nJxutVsuPP27m1KkjDBs22uzG1fJgU2OxEOI7oCXwBfCIlNIwCd4khDDbIwsh7IHlwP0ou48PCiG26vcmGK5pDswEekopbwkhrL7lzs2tDhpNhkWXrubNW3PpMMHfVQAAIABJREFU0ll0Oh116hh7EAUH30F09FX8/AKtLWqRdpvRt++D1KvnxdatG01eY60MYDdvWlYC4eH3k56eyDPPrCc4uANabQ52dva4uJjfjjl2rBt5edm8885xgoLKFzo7Ly/H5A5wBwcnhLDDycn6cRETE2u2EgDlMwQE3PaTd3NzZ82a7ZbfVAnUtNkAKPImJmIU8fb06WM4OjrSrFlrnJ1deOwxJZHPkSP/MGHCI/TseR8DB/6HBx5oQ4MGfuzaVb6lZVulqjTwXynlLlMF5rSLnm7ARSnlZQAhxFfAUOB0oWsmAcullLf09Vndd2D//pJdO4oGKjtyZD8JCbGcPn0MIexYuHA1jzzyuLVENEmTJs1Zu1b5cUZG7jU5I3Bxqfzgc1pt8YiJWq2WV19th5Q6PvjgPGfO7CE/P4/k5GiCgzvwww+L8fUN4Z13jlqoNxcpJVlZ5c/3+MEH58jKysDX97Zn0q+/ruDPP9cTGNiO2bN3065dXYQQnDhR+XkldbrqHUqiLMTHQ3Cw8r+Tk1MxZ4jc3FxycrJZunQeGk0mL730ZoVCJVT3fQOWiIlRZlH29kregcGDld38ly7lF2QRBPjyy1Xs3PkDO3f+wODBj6HRZKLRZJa73evXze9lqCilUQT1hRD/KXIuFThRQsftDxTuQqKBu4tc0wJACPEXyvLRPCnlT6WQqUoJC3uQ1NRbODo6kZeXC8gqVwSFMbfz2c+v8jduRUfD5s3hbN78f/j4BLFs2VXs7OyIi1P8BbRaLX37jiMtLZHGjZXkMNevnwAgIyPZ7KzgzTcPYG/vgL9/+XyqAXx8goudS09PIicnk+TkaBwcnMjMtF70stjYqo8bby0MkTbNbTCbOPER/vzzl4LXU6fOApTQ6OHhL9O9e78yuUVeu1Z8NpCdnYlWm4u7e/XegGVItxkQAPn5+bRq1QGdLp+//97Fpk2fcscdrRg+fCwtWigz3bp16+Hr24gTJ9LK7dCRkKDEPLIWpVEEE4B7AMOsoB9KELomQogFUsovKth+c32dAcAfQoj2UkqjhUkhxGRgMiiJoiuKTnfbd1qjUUa9zs63p8Y3bsSSkZGOv38wLi4ueHn5kpWloXnzNuTn59OtW+VHXCyJuLhofvllC76+jQrSRhryJfj4NGTAgCE8+eTTldpmRoYhX2omIAs8gezs7OjVa3SB++ekScYuq337hgHg4mJ+aaawgbky6d17NPXr++HlFYCDgxPjxi0jIKAOOp2uUn3i09P/PbMBUGwdV64okTaTkhL58cfNNGjgx6BByhiwTh133N09eOCBYXTufE/BfpWsLA1nzhwr01JpSgom83lMm+aPRpPK2rWZODtbN7R6RYmNVWYFzs4O7NhxDIAvv1zNtm1fAfDhh/NYs2a70WZUd/fyRfbPybmdS8JalEYROAKtpZQ3AIQQDYH1KKP7P1BsB6aIAQo/HQH6c4WJBvbrPY+uCCHOoyiGg4UvklKuAlaBYiwuhcxm6dDBi5ycbJYvP4C3dzsjbw9XV8UY07t3E/3IX4kWuHPnbW/ZZ54ZwVtvvchPP33D5s1/VESUMrF793bmzZuOm5s7Dz00olge4comJ0eJCQ/w6KNz6N9/kpFf+bRp6y28N4NDh7aRn5/LtGkbrCbj6693JCdHw+uv/0zDhsrykK9vMKdP7+HgQcWAPG3aBoRQFH5F8xUbyMtTEs5UJwonZi+vvktLUzq4hIQrvPHGs7Rv36VAEaxY8Y3J93Tr1psffzyCp2fp9hBoNEqeBlM4Obmh0aSSkZFc7RWBIWdC4Sxm99zTnw8/jGDZsjfJzs7C1bV8nlRF2zl/vnzZ8cpCaRRBgEEJ6EkAAqWUyUIIS+aLg0BzIUQTFAXwBFDUI2gLikvqGn1u5BbA5VJLX0Y2bYogPV0JEj9jxgOEhi4yCpGQlQWXL4O9vVOBIkhMjDeqQ6vNQ0ppMkG6NfH09MLdvW5BsC9rkpWlPOQG45SLixsuLuazT+3YsZS8vBwGDJiCm1tdEhKukJeXTXKy4OhRZU01JUWJb2Pwxb527SK5uYI6dRojpWtBesPCqQ4Nr6VUtvg7OSmzNn9/aN0aoqKuAbf0M5bb3Lx5lZwcDcnJij1ISuXH1LKlsZtkecjJgdOnK+YuKqVyL2JjlXtz86YywzAktM/LU5acCv/NyzO+H0WPwvw/e+cd3lT5v//XSdK9d0uhLS2UvfdQlgxRpgwZfhBlCCpLRFkKCE5kOFiCiFKGbESGDJEhS/betFBGF90jbXN+fzwkbdqkTdKm4NfffV25mnHG05OT5/28131LkhBT8fAQK/wWLSA83LSxRUWBra03AweOMGmV7+rqbjLjbWqq+B6MJYhbtXqdrKw0bGyK1tlITBRUHg8eiOeJieLaZWWJR2am+Ku9ZsbuLUkS95SNjbheISGiIqhFC9OMaXq6+H8qVxYa2CEhlQgJqUS3boULH2VZZsyYAajVahYsWKeXSzAGtVosONIsTyuYDFPE6xcAQYC2IPYVxEr+fWCbLMtGuRYkSeoMzEPE/3+UZXmWJEkzgH9kWd76RNzma6ATkIugtl5T1HgsLR+NiIhgyJBhZGbmxdVtbR0ZOnSJQb6cO3fOEBV1hpo1a9CiRSOcneHatXPcvXsbHx9/wsKq4OpqIo+vlbF162rWrPmBunWbMmHCpxYfR9vqf/9+8SLhmzd/RlraY7p1m8SwYV7IsoZPPjnHX3+dY9++99FoHiJum1kIJvPSh0qVTmjoGt5/vzvOzmJFGhl5ju++609Ojpq3347gt9++IC3tMe+8E4GHhz/ly4Ofn/ni9Tk5Ikz24IFllS65uaJU8uhRuHhRTF5liXbtYODAws1QxuDhIZLH+bfXaDTs2rUJBwdHszrrs7LEfWUpIZ9aDSdOwPHjYuK19rUrXx5GjoSKJihvxsff4+OPmxEUVJENGw4UyUUUHm5LdnY2V69mYWtrXItDoxGLg3v39HNQHh7QsaMZ/0gBFFU+aoohkICeiL4BgMPAhqclYG+pIQgJCSHSACOYt3cw3357p8h9J09uzJ07p9FohH/Wq9dYxoyZg1Ip3EKtaH1+8XotSkvEviDyH/ejjwazdetPBAVVZuvWawa3Lyhar31oV51pacZXHjt2zGfnzm+oWvV5RoxYDuSRys2adYLZs18hLW0QklSZrKy3ELIV2nE6UqPGEmrUGICbG7i5iRDc/v3fk5p6l7ZtBxIWVlMnIK8Vkdc+167MtMLeaWmC73/PHrhwQXw2aBB06iSeHz26nvnze2NjY8fPP2fqxvnJJ8epVEloFysU4OoqvANbW2EUtOc1JL6enq4vx2kONBo4cADWrdPXLnZ0FJ5NuXLCMLm66o9J6wHZ2OQ98l+Pgn+19552/GlpwnCdOAE7dghDVK0aTJpknhF0chIhte3bF7Bt2zKuXDmFvb0jx46JmyU+PoY1a77DycmN1157D41GnCs7O+/amZLkzMnJZvbsrtjZOTJ27IYn74mxb9umf/0dHISRKl9eTI7u7uLa2duLh51d3rXT/j7z30/ahyzn3f9JScIL3rZNlIc6OcGUKcVrFj94cI1x46rg71+JGTP2c/v2XwQEBNC0aRvd/KD9fnbuXItCoaRNm+4olapC95r2/k5NNbwQs6YhKPKWeNILsOfJqt9wkPBfgigjqu7x8cXX3qenJ+qMgEKhRKHweqZEqIOC2lO16k3Cw1tw2bACZ4lw69ZJYmJuoVTmUTX4+YWRmZlKcrI77u6RT2QZQ8hvBABkOZ2HDyczebK+V1Ct2ttmjcHWVjycncXE2aQJ7NwJK1aIh5cXNGoEgYHVCA9vgadnOQBq1WpPVlYaLi55ZHwaTV5IwZpITIT58+HKFfG6XDlo1UqEHwIDrbNIkCQx+WgNS3g4tGwJn38Oly/Djz/C0KFFnzszM5XU1AQcHFwBd9LS4MqV21y5cgqAunVf5tKTIvAHDxJZsuQT/P0r0bjxexaPOzs7k7NnRcFgenoyjx+78t13MnfuiIGGhIhrV7euqG4qrby/Nizk7i6MS5s28M038M8/MGsWfPEFeBaR/vDxqci330YiyzLXrp1i9mxxn1eoUJNRo9ZSvnyeFkZQkKg0NJYjeZooTpgmV5IkjSRJbrIsl34hdhkiKCjIoEfg5VW4CunDD+sRGXkGNzc/3n13FWPGrCc1NY6AgKqcObOdLVs+IzLyDGPGlF37+ObNn7F27SQ8PSvw/ff6xqtFi/60aFFkQ3aJ8Nxzr2FjY0fFinmVPnPnXiMyEj79VKzWvL0hLs5yY2sJ4uLep1q11ly+/BLLlokVb4UKNahW7XmuXz/C4cOrmTjx6VQj378vrk18vJhkBgyA5s1LbwIzB8HBMH48TJ8Of/4JdeoIQ2oMJ0/+xnff9adZs76MGiUitW3bDqNevZfx8wvT05dwcfGhV6/puLqaQFhUBGxs7HFwcCEjI4U//jjC7793JDVVAm4DI/n00x1W8671xwGjRwsDcOEC/PwzjBljfHuVykZHo5KamkCzZq9y5Mga7t69QG7uv4d9xxQnMRU4L0nSbkAXPJBl+dllUDKAWbNm8eabQ/V0dw0Jq0DexJWU9Ijbt0/z8st5K527d88TE3NLR7hWVsjMFP51UWRu1kLt2u2pXbu93nuPHsEnnwhXtlYt8WP54IMg4uJMM7ZHjvxKZORZ6td/uZBegan4/ffZwNeEh+dw7ZqClSvhrbfg6NF1PHp0Ax+firRo0c+iY5cEiYliBR4fLxKJ48aZLgtpLYSFiRzB8uWwahXUqyc8LENQqWzx9CyPi0ve5B4QUJmAgMJU3s7OHrzyykclHp9KZUPr1m9y9OglNm1qg1oN1aqlEBXVhnLlypWJEcgbCwwfLoznsWPCO2hYVOvsE1SsWI9Ro1bTt+9MMjPTCl2vkyd/IzU1nkaNeuLoaP2ud3NgiiHY+OTxr8aAAQO4eDGd2bNHkZ2dibd3sFFhlSFDlrBu3VQUChWVK+sTuTVp0pusrHSCgmqX1dAB6N59Mm3aDDHIpJmQcJ/Ll//CycmdunWtT4+tVsO8eTJpaRK1a2s4d86BN99U8+abi/nll7Go1foJeUPGdu3aSTx6dJPExAcWGQKNRoOHRyCynMvQoTBxoojFd+qUhadnIJmZKdSq1Z7Llw/w+PEDqldvhbu7f/EHLiFycmDOHBFnDguDyZONSxyWNdq1g927RRJyxw7oZkRgr0mTV2jS5JVC72s0GjSaHJRKG5OqXsxFnz5zOXcOHj8WE+/o0S6oVHdK/TymwNsb+vYVHsHq1SKcZ8ibu3//Kjt2zKd8+ep07PgOIMKmhhARMZ4HD65RqVLTf58hkGV5hSRJDkDQv51+unv3odSuPbTY7Qr+ENavn84//2wiMvIcKpUtI0asoHnzsu0sLqqE8+DBn1mzZiJOTp4sXRpf6uc+dWobFy/uo2rV52jUqAerV/MkdnuTBg2Oce6c8I5atuyPvb0Ta9dOJj6+aBWz4OA6ZGam4u9vmWCMQqFgwYK8jq5WrWDvXli/PoPLl/8CJFq06MfgwS5kZqbyxhsLaN9+hEXnMgfbtokYsJeXWFE+K0YARO7gtdfgs8/EODt2NL2KKCrqPJs2zeTo0V8JC2vEzJnHAVEWeevWP2RnZ1K16nMWjy019TFffvmQ6OhqBAbC22+LlXlk5FmystKpXLmpVYxPUWjfHrZvF2G+EycMh9Pi4iLZs2chNWu+QPv2I8jKEosgB4fC3V8NGnQjKelRkRxcTwumkM51AWYDtohu4rrADFmWuxa95/8dXLiwh8hI0T2Yk5Olt+J9FuDuHoCNjQPu7sYF40uCHTvmc+HCHm7cOIaPTw927QLIAXqjUo3kxx+TyclRY2vrqNMsLg7aypDSQteuIv59+rQbKlU4NjaPOHQoArVaMML9+utUHBxcTRqbpYiOho1PfOdhw55+OMgQatWCSpVEffrBg2KyMwU3bhzj6FFBSa1S6Vu3qVObIssaVq7MRqm0TPTw7Nl4rl+vBmQyerS9zkBNmdKYnBw1K1Zk6HTEywoqFXTpIsJpW7ZA48aFk+zlylVl8ODv8PAI5Natk0ydKqxFt24T6dr1Axwd3XTbDhhQdtK25sKU1NU0BIFcIoAsy2eAUCuOyWp4/DiWPXsWc+LEpiK3W7duGiNGBLB06QhyctS0bDmQRo160KnTKAYO/JobN47x229fldGoBXbvXsioUaF8913hiaxVq0H8/HM6s2dfMrBnyREcXBdPz0CCg+vz00+i7K5TJyWrV5+ibdshODi44OLiVSayhlpoNBpSUxN1E72vr2gE0mgk6tTZRcuWA1iyZKiu2is1NZ4ffhjGoUMRVhvTL7+IcsTWraF22UYOTYYkQefO4vn27YbLFPfsWcyHH9Zl9+6FuveCgmrTufM4Ro1ay0cf/ZXveBLh4c2oUqWlxclRWYZdu7QJ6HkcPfoxAJcvH9Dl4p5W4rV1a1F9dft2Xrlyfnh7B9Ghw9s0atQdlcoWOzvRsbhly2c67+DfAFPMd7Ysy0kF3LJ/pWTl6dMHWLbsLVQqO375xTh38KFDv5CY+JC9exfRu/d0XnhhOC+8IHh8du9eyN69i7G3d6FLl/fLaujcvn2K2NjbZe4eAwwc+BUDB37F0aMixuzqCr17l3wcOTk5gAaVynhzjTHExt5mzJhKgMTq1eJ2fOklsco9edILWIhWU0ILtTqdtWsnW8UruHJF6BLY20O/ss9Pm4XGjUUM/OFDoTNRvwDtU0LCPSIjz5KSkkcIVKlSYypVMsytP23aoRKN5+xZuH7dHju7LLKyviAuTiQvkpNjnoy3p8FQS1nA1laE0NatE+XKtWoZ3zYkpC4//ZTKrl3fkZGR/KT8Ng9ZWemo1Rk4OLhYdM9bE6YYgouSJPUHlE/0A0YBf1t3WNaBiLM74+xctMB7/fovs3PnNwA4OekXEfv5VcLDIxAfnxBrDdMgWrTohyRJBAXVKdPzaqHRiB8DQK9eovYaRDXTuHFVUCptim3My4+ZM9tx8eI+qlRpybRpB80ej3almN8wBgeLEtLLl10oaAS0sEYpqyzD2rXi+UsvCUP5LEOpFJNbRAT8/nthQ9Chwzs0atQTNzfrhBrzQ6OBNU+4BLp0yaFRowO6UtQaNdoyffrfeuGVp4F27WDTJtEZ/uiR6GPRIj7+LnfvXsTbO0jXM6BNGhfEnDk9OHfuDz74YAd163Yyawx3715kwYLpnDhRmylTplj8vxiDKYbgXWAykAWsBnYB5ouTPgNo3vxFli8vnpZ40KD5DBqUJ2B/48YJLl7cy969i3Fz8+fjjw/qSM7KCjVqtKVGjbYGP7t4cR+fftoBW1tHli+3sAW2CKSnJ3P4sJL7953w9RVNNzNntiMy8iwvvzyex4+L13goCG1zWm6uZWxagYHVWL268GTfqRNPmurKIcT09CHLMu++G2I0iW0Jzp0THoGzc17Y5VlH27awYYPgTbp9W59Owd3dr1C+Sa3OYN++peza9S3t24+kc+ciiuvNwN9/C94gT08NLVok4OxcXkdD7ezsaXFpcWnCzU30gBw4AH/8IRLuWpw5s5OlS4fRps0Qhg37ocjjODi44eRkGcX248fRHDy4Dju7x0/HEMiynI4wBJNL/ez/EmzfPocjR8SyJTb2DikpcWVuCIqGAo0mF7XaOrKQ06e3IypqNVCJnj1FEi06+jKpqfEkJj7gpZfGYVq6KQ/vv78VhUJV6nmFBg0AooAvUamGkpNT+JrExUXyww/DAEpsDPJ7A1275nlKzzocHYVB37FDPEaOLHr7W7dOsmKFaB2KjtbPRU2f3oq7d88xbdphvU7a4pCdDb+K/DONG59l7Nj61K//Mu+//5tum7lzX+HSpb94//2thIc3N/nYpY2OHYUh+PNP6N07r9rKw6MctWt3pEKFmiQlxTB/fh/u37/CuHGbqFSpid79PWbMrxafPyioNhMnrqFdu6KjGZbClKqhcGA8gj9At70sy4aXp/8HEBV1gUePruPvH06FCjUICKiCk5MHmZkpVK/eBicnd5KSYnBzs7qypg5nzuzg7NmdVK7cvFDpauXKTfnkk+M4OFinLC0trStQCXv7+7RsKagb+vX7nNjYOzRs2J3gYPMzo9aKkR45EoG9/QdkZt5Ho/HA2dmR1NTCJbWllS84cUKsqN3doUOHEh2qzNGpk4h7//03vPpqHpXCkSO/cvfueZo06UVwsAhFOji4YmvrgI2NHR066Ic+srJSSUtLNHshsmdPnkxmrVoP2b3bhlOntrF16xd07foB164d4fhxUYaVnl76nq45CA0V7LVXrwqDoP2u69d/ifr1XwLy+nkAPv64OStXZmPuAskY3N39qVixL+3alcrhCsGU0NA6YBGwFMEQ+q/F0qUz+OGHafj6VmT+/JtGt5s5sy0pKbEArF4t07v3NHr3ngbA1auHGTcuHIVCSUSElUnC82HPnsWcPLmFy5cPFDIEtrb2OkK10oagop4KwNCh5XQkWs8//z+rnM9U3LhxjM8/74yrqzdz5oj2lkOHIvjhh2G68l6NJoH09MINeFqUNF+Qk5PnDfTo8Wz1DJgCX1/Bz3T8uCgC6PvktjpxYhNHjqwhMLCazhAEB9dmxQrDVTAffrgLhUJpVpNUerqIu4MwQvXrv8js2ZcYO7Yy+/b9QNeuH+gm1VatBlOzppVmQDPQsaMwBLt2ibLbgnUbLi5eTJ68l88/70hoaEOdcFNpYeNGqFlT8FSVNkwxVzmyLC+UZfm4LMsntY/SH4r1kZqaCMhkZxdN02BjY/wXrS0Pg7Kt3gkOro2nZ3kqVmxQpufdvVtQJVSoAE2bFv48OTmOxYuHsHKleYRjO3bMZ/hwPz7/3LJO6KSkR6SlJRAbe0f33tq1kwv1eGg0xssODVFfmIMlSyK4fz8EULB1a4hVS1OthZfEYpY9e/I0Fpo160OvXtNN7p53dfXG2dnDrInvt9+EBkOVKnnJaldXX/r0mUmXLh8AwtPt1m0iTZv2MdhRX9Zo1Eh4Tffvi7wQiJyTFjY2dtSs2ZaVK7OZMeNIoQq/33+fw8cft+DYMfN7aPbuvceSJVC3rrpEWhjGYIpH8JskSSOBTYiEMQCyLCcY3+XZxLBhM2jYcDhKZdFhiblzrxMXF0lOjv4ksnPnt6hUtvzySzYqcwntS4jevWfQu/cMg59lZqYyZYpoZJk9+2KpnTM+Pq9S6NVX9Vvsd+36jitXDlChQm3271+GJCkYOPBrk48dE3OH5OQYnb6xuQgLa8yrr36m16VpjPTOGAxRX5iKPXsiOHhwGFq21fj40ss7lCUqV85rMPvrLxHyaNSoB40a9Si07ZUrB4mKOkeVKi11noIluHtXGAKA/v3zVtaOjq706JGXiqxevTXVq7e2+DylDZVKXJ81a4QnWKsWrFs3hd9++5I+fWbStesHRe4fE3Oba9f+plkz81gJ1GpYs0aUz4aHb8POrqCEfMlhymw26Mnf/EXzMv/CpjJHR2fKlatS7HbHj29g7drJxMVF4e0dREhIA/75J49uqXXrN605TAugKJTAKylkWXRUZmaCi8tBFizoTo8ek58khmHv3iXcvXuepKRYfH1Dza7zfu6513B0dNEJ3psLd3d/unX7UO89b2/DpHeG4OzsZfGELcvwyy+TKUi5bc0+BWtBkuDll2HePJG4bdzYcEd0amoC06c/D8Brr83VMwS7dy/i+vW/ad9+ZCFuroLQaGDJEqFZ0K5dnnLawYMr+f332bRsOZCXXx6v2/748Y2cOrWNxo1f0cXinyY6dhSVQ7dvw/79oow5NzcHSRIFG9u3z0OWNQZ7jDp1GkWzZn2NchEZw4YNkJrqhpNTJG+8YR1lRFOqhp6l8pgSIyJCrHRfeEF0Cp47J167ugqSMA+PCLZty4szx8VF6pVH2tu7lLk3AGLVr6VxKNhqb2trT9OmfbGxsSs1kfYNG+DkSSEColSOIyUlgbi4PBGGGjXaIcsaGjToojMO5iA0tH6JBOwPHYrQM9Z9+86ib99ZejkCARtEGC8/W6ykVx5sLn7/HdTqsqXctiYaNxbU1GfPCuPfo8dZcnPVBAZWx95ehEIVirx7vqA3cPXqQQ4fXkWtWh2KNASyLLQjbtwQIiv98zGnJyU9JDLyLI6O7oSE1KNq1edJTHzAgQMrOHlyK76+FZ8JQ2BvLyjFv/1WeAazZn35xLOUkGWZiAhhxPbtW8LcufrCA8YYXIvCkSOwdasw2J98Esybb1onN2dUoUySpAmyLH/55HlvWZbX5fvsU1mWJxV7cEnqBMxHSFUulWX5cyPbvQKsBxrJslyk/JilCmUAffosZN264ojHQoDCq0pPz/LMnXsdhULFiBF+aDQali17bNE4LMH06c9z5cpBqlVrzUcf/Wm182g0wm1fs0bcfOPGQW7uBh48uEa9ei9ZVCFkCIYmclNX0ocORbBkyRCys/O6w7Wyo4Ae6V21arM4eBBE9XMUDg4utGw5kDfe+N7sMcuy+FGKBqgQDN0npijePYuIjYUJE4QH6Oa2gqSk1/nkk2O6bmKNRsODB9ewtbUv1Ex56dJ+YmMjqVq1pdHVrjaxvm2b4Pz/8EOhp6xFcnIs8fH3mDRJLA4WLLjPunVT+fPPZVSu3Iw331xYonBUaUKWhdbEhQsicTttmughkWWZ778fyOHDq3B19WHx4pgSnefoUViwQBRsDBgg+heehkLZq4CWJWkieZrFIDSGizQET9TNvgfaIzSOT0iStFWW5UsFtnMBRgPHijpeaeDIERG+UKmicHYOIjRUxPwCA4WM4OnTsHmz4RVdQkI0trb2TzhuRHokJ0ddZq3i4nJar+wyJ0d4R7/9lqeo9cYbWh72wpTEWqjVmcTH38XW1kFPsKQoFJzIza3rX7t2sp5caLATAAAgAElEQVQREOMQYZlvv72jdwxZFqu43bsHoFKJWv9O5jV1kpsrrsmmTUJvWJKgefNZnDhR0PsQntuhQxH/qvAQgI8PjBgh1LmSkgbh5FSOjIy8mnWFQkFgYFWD+xYVx8/JEb+rTZtEOEWS4N139Y0AgKurD66uPjRq1IO0tERkWcbdPYDy5WvQpcuEZ8YIgPgfxowRQj937woK9MGDoW5diZEjf2Hw4O/RaAoXWN64cZyLF/cRGtqQWrVeMHhsWYZbtwQP1N9P+Bs6dIB27VJJTc0hO9sJG5vST5wX5RGclmW5XsHnhl4b2b8ZME2W5Y5PXk8EkGX5swLbzQN2I3IQ463pEfj5JRMT40rnzrt47TXDpvWdd0KIjzcUZw7G1/cOHh6QlnYVB4dMQkNrYWOjQKVCT7+4NGDKcfJvc+nSftTqDKpXb6MXOiqoUZxfIzUtTVRuJCVBVJS2VFSEyYYOLVqMIyHhPnFxkdy5c5rly99+kkQ3rZzh3XdDDMbyTV1N9+unwBCFhCRJrFpVmAYrN1eEPPbuFa/t7KBGDSEd6eSUpwms1bDNzha6sY8fiwXCvXt5es4ODkK0pEkTYdAWLXqT3Fz9/1vrnfzbjAHAqVMiX5CdLe6v0FDx8PSEnJwkFIpcnJ1dkCQbnT6x9qHRiMqj1FTxSEgQ95VWgN3bW1y7mjWf7v9YWoiPh6lTH/H4sejC9vQUFCe+vmLxodXDlmXxOH/+T/75ZxtVqrSmbt0uuuum/R0mJ4vrlfKE/MDGRogJtW8PP/88mp07v2HevHmMHj3aovFa6hHIRp4bem0IgUB+Zd97gB6jtyRJ9YEKsiz/LkmSUQY3SZKGAcNASE5agosXISbGFWdn6NfPuH/16quG4swOwHRiYiAmBkAknJ8t7dHWACXSLA4MhOefFx2nLvlyvytXvkds7B26dJlApUriK1y06HXOn9/9ROxFKtZTUanEw8bGeBw9Pj6KcuXyDFxBYyjL4ofj6xtETIxpSmggDPSQIVCx4h3Wrs0hJaUSp06JSc9U+PsLmoEXXxRhABDey/ffDyy0rblJY6Uy79ooFOK1VmBdK0pfUKRe+9wYNBoxmScnY1a5Yf36MHu2yBEdPgw3b4qHgGWcP0FB8NxzIjns4GB4m1OntnHz5nHq1XtJd48BPH78gBs3juHq6kOVKi0K7adUiuumUuVdO+2CzNijILQLgMzMvEnYFHh5QaVK73LiRBDOzjNISHDk8OGi9mgDtOHqVdGPYAxubkJnun37PF4jhUKFo6MrTk5OxncsAYoyBHUkSUpGZNocnjznyesSE4NLkqQA5gCvF7etLMtLgCUgPAJLzqdtZW/USNw0xqD98eaPMzdo0IVdu4YCnyFJ5Xj33X2kpIibJzdXrK611r34/8W8cd+7F8Hly5PJyIjCwUHEvMuXH1DoOHv3LiYnJ5sWLfrh7OylO5d2MtFOLNqHUikmNGdnMemXL583wRXEn38uIz09ifDwFrofqbOzFwqFkgoVarFw4QO97V1cxM3s6Ch++HZ2+qWn5coFER1deCIvVy4IU+z8xImzmDhxGJmZxSuh5YdK9RcpKa9jY1OFkSOv8PCh+PFnZ4tVq0Ih7g1bWzFuT0/x8PUVq1lzEBcXiUaTW6i23slJeFxOTuL62Nmha9KzFpKSRFgm0zjhrh58fUWYaPBgMWHdvy/kN7dunQfYULNmZ/z8KuoM1q1bR7h27S/Cw5tQt24b3X3l5ib6T1xMKCg7e3Ynf/zxPa6uvnqG4MaNY8yZ04P69bswY8ZW3bUzdF+VBrKyhAcYG2va9t26vU/LlncJDU0gOdmRyZMHAyEEBTWlWrWOOs+qoIHP/1r7G3R2hoAAEaYraLBee+1rRo36ukQ5gqJgdEqUZbmkt2c0UCHf6/JP3tPCBagJ7H/SeOEPbJUkqWtx4SFLkJ0NtrbZpKUt59KlKlSv3srotgXFVWJjIxGrXjvat3+LjRsHk5qawNChi60qf3joUAQ7d+Z5JxkZkZw9+zrNmunH0g8disDR8TPi4qI4f352qRKqAdSs+QIPH17XYz4dNWo1goNQQKXKu4mNaeFqMX78LCZNGkZGRt5ELkkSrVubxtjWvfsA4uJiWLz4C+LjY4pUQsuPgIBw/Pwq4e0daLA5zhJ4ewcbLVlVqzOwt3dGoRDXxtf36XQfu7mJcMylS6KjtziMHh1GVlYas2dfpk4dD+o8+dp9fBzIyVHTqpW33sr+2LH7uLufoHHjYFoUXrSbhHr1XsLV1Zc//1zK6tUf8NZbP3HkyBqOH9+Ip6c/rVo1oUYNy45tDuzsRPWgnZ0wCMUhLKwRYWGiq9/bG+zt15OZmUpSkh+vv/7QyqMtPRjNEZT4wJKkAq4B7RAG4ATQX5Zlgx1PkiTtx8o5gpCQ+kRGnqVZs96MGrXGomMADBigQqPJZfr0v63KjmhKLL0grQKUfYzax0dQQJtTVTtlykhWrVqk15np4ODIp58uoXv34sc9ZEgX9u7dRo0a9fn555Ncu2bJyEsOQ9dfoVDStGkfhg//ET8/e0JDRfjiaUOtFiHS4kJFr7/uRFZWOsuXp+rKR8sKixYN5q+/fmLYsGUcPLiEy5ePsWHD39SvX/YspDdvmu4ZaJGa+pjTp7dhb+9Co0bd9T7LyEghPv4udnaOFtHYe3hYr2rIapJSsiznAO8gaKsvA7/KsnxRkqQZkiQ9FZnLKlWq4u7uV2JO/ypVWhISUh9nZ8/iNy4BjHXK5o+xG6JV0MaoywI3b66na1cXOnc2b7m2f/92Ci5CMjLSmT3btHE7OjqjVKrw9vbF01MYo7KGWp1JUFAd+vX7Am/vYCRJQqm0QaPJpX37kYSG2lOlyrNhBEB4aiEhxW/33Xf3WLDgPnZ2ZU+l+r//zWf58hRef/11fv55KwcO3KJGjSLrUqyGkJDiPbh//tnCX3/9RHKysBjOzh4899xrhYwAwPnzu3n//Rr8/PNYs8eyYsVoxo9/nuPHj5u9rymwameULMvbge0F3vvIyLatrTkWgE8+WcWtW+bvl5qawNy5vbh06U+qVWvFRx/tL/WxGYKxTtn8SVFTjEVJcfXqYTSaXCpWrK+jdPj116n8/vtsbG1tSU9P5cEDE/zofLh/3/D4jL1fEN98s5pvvskLTQUHiyqfnCJ4ADUaDQ8fXiczM4XQ0CJKokzE1auH+fTTF1AoVCxb9pjDh1ezdOkwQkMb8txzDShvWjVtmcLDQ3QOJyYW/kytziQzM0UnDGMqZFnWddha6kFER18hMzOFgIBwatRwfmLYBbtvbm4u6elpODqWrXeiVAqdBm05tSFs3PgJt2+fZObM48VeN2dnTwIDq+HpaT5rXFTUOS5dOkiatnytlFF2IrP/YiQlPeLSJdHEdetWqacvjKJv31nY2OiXWRRMinp7G86ulpRQLT9mzmzHjBmtOH9+r+697Ow41OpM7OzsmTlzEV98scysY5YrZ3h8xt4vDtocRVFISYnjvfeqMnly6TC15uRkIUmSrqN72bK3UCpVvPPOXO7dO0BSUtk1HJoDY17BokWvM3y4L48eGWfmNYRz5/7gf/+zZ+5cyzlwVq58jylTGvPo0SE97+7Ro/tUqqSidetKFh+7JHB3LzrZ3aBBV55/fhCursJoTZnSmH79JPbtW1po2+rVWzN79iUGD/7O7HG8/vp3fPXVX9SrZx3vqOy5Ep4ikpMTycy0xdbW3iwaBmdnL5ydvUhNjeell8Zz584ZkpNjCQ1tYNXwUMuWA4iImEBycjYaTQ62tg506PC2XuzfEK2CKRU05sDe3on09BwcHV2evIZx48bToUNrypcPoV69JsUcoTAMJYwdHBwZP97ycfv7w4MHxr0CJyd3QEKhUJQKFUe9ep31+hZefnk8vr7lWbLkba5cOcfatQdo3Pi5Ep3DGrC3F4nNuDj9948cEZza8+f3pUqVlgwaNM+k49nZOaJUqigJI6+/f2UqVWpAfPwZRo+OoEmT1kRG3iA21njCNTb2IZcunaFcuSAqVzZdEMdcBAYa9wpeeUU/wHHz5gkAVq/+gLZth5TaGCpUqKHz5qyB/5Qh6NWrEklJ8fTuPYOePaeavJ+bmy8//JD3qxk82IXMzFTefHORTtTeWkhMfADIODq6kZ6eVEj8w1C5a2lXDf3wg76wS2gouLqGUbGieeRZ+aFNCH/11SQePLiLr285PvzwC5MSxQAdO9bizp3rjBv3CcOHixYUpVJ4BXfvGt5HpbLVCd1bA/37f0G1agC38fHxx8nJOkJBpYGAgMKGYMyY9URGnmHTppkkJNwz2RBUqdLyiQiL5Rg0aB61asHvv//I119PwdbWjr17f+Px43hOnHiEt3dhEahduzYxdaqQVrt92zpFLyAmX2dn0SRXHN57bwsbN06nTh0z29efMv5ThkC7YilpEszR0YPs7Cyr00toNBpeffVT9u5dTGLiA5ydvQySehUsd7UmfHz0xdkPH97LokVfEB5ek6lT55h1rO7dB3DkyJ/8+usy7OzsTDYCIFaDanWWXi8BiAac6GjRUFXW8PAQpZpTpph3HZ4GnJwK5wqaNHmFOnU64edX6ckK3zQU5N23BAEBYkxNmrRi3rwIQkIq06jRc6SlpeDiYriRzdvbz+D71kBAgOEG0sTEhygUKpydPVEoFDRs2JWGDQ3XwkRHX+bLL1/G378yEyfuNOv8u3Z9h1KZQdOmw3Bzs6yxryj8pwzBH3/EcuuWmGDNxYkTWzh8eCU1a77A99+XDcOkQqGgW7cP+fPPpWRnZ9GkSR+jE/6CBYM4e3YHrVoNpn//L6w0HtEgBPD33/uYM+cjoqJuEhv7kMuXz5ptCADc3T2fHNu8tpWff97F3bu3qVtXPyylUomwR0zJ+L5Mwm+/fcWGDdMJDq7LjBmHTKrIeZYQEFA4aXzs2Dr+/HMpPXqY7jGXFCpVnupWcHAYwcHC06xTp+hcTocO3Tl6NBoHB+tXN3l6iqortVr//QkTapGSEsfixTHFJotlWSYm5laRwlfGsGXLZzx+fJ9p0/r9f0NQWrAkNjxnjigHu3z5gNXDQQVRp86LXLlysEiK3+joSyQnxxIVZZnQS1EYPNiFnJxsVq68iK2t+JFevHiakycPI0kK6tVrSni4ZQQyEyd+ycSJXxa/YQHUrFmfmjUN01gHBBg3BCNHBpKamsBHHx0osbxnTMxtsrLSiI+/S0BA4VJDWZZLZbVsLbi5iQ7djAxBoPjPP1vYu3cx168f5cyZ7dSpY1rRelZWOnPm9ARkJk7cZfY4ypeHPn2acffubdas+YuwMH3NkPfeG8SDB3eZP381Pj55XoBCocDPr5zZ57MEkiS8zYJhRycnD2RZo4sO7Nr1HXfvnqdTp9GUL6+ft/D3r8TcudextTXfcHXo8DY5OQlWMQLwHzUEJUGFCmXHmLVnz2IuXz5A06Z9aNVqEBkZqaSmJhhMUHfvPoU7d05Ts2bbUh9HZmYaIOPnl2dA69VrSrNmbalYMZxZsxaW+jlLAgcH4yWSKSlx5OSoSU5+VOLzdO48ltDQBnh5BVAu33z02WcT+Omn+Xz44VcMHjyqxOexJvz9Bf1ESko88+f3AWDcuE3UqGH6faRU2nDunNAtNtf42duLCTYhIZa4uEdkZWWwbdtacnNzcXZ2xcHBkVOn/ubOnRukpCTpGYLc3FyU1ubnyAdf38Jhx7lz9TsZf/rpXQCOHl3H0qX6Io4qlS3+/pZVP3XvPgkPD9PoOizBf8oQ9OgRRnJyEh9+uJOwMPPqyFevzktGTZrUkLt3z9Ot2yR69fq4tIepw759S7h9+xRpaQncvHmc1NQE+vSZqSfnp0WjRt1o1KibVcbxwQe/4+WVSblyefXPDRu2YNWqvUXsZV306fM8ubk5LFiw3uCqsFw5w4ZgxIif0WhyCA9vWeIxaIVGCnZVS5KEWq0mKyvD+M7PCHx8xCpXkhQ0bvwK9vbOBpuhioJKZcMHH+ywKPcWHCxW21u2nCArKxO1Oot3330Ve3sHMjMzcHFxZcmSLWg0Gvz99evv339/MJs2/YJKpeLEiUe6MKO1YGMjjMHDIpgjmjTpxbFj603We35W8J8yBA8fCiKwgpU35kK7qkxKKvmq0hgOHYrgwQORnbp8+YBuzL///jU+PiFlSnHcvPmL1KplmLnx7t3bXLx4mnLlgqhd2/wmrRs3LtOzZzNsbGw5edL0wP6JE4cAmcxMw9+llpysYP9N8+bm6cUWBwcHsarOj7FjpzN27AxsiyNdegag5UHKyfFj7Nj1Fh+nbl3zq2RcXUWCHcDNTTxJT0+jc+fe2NnZk5SU8ER9r7XB/dPSBFVoTk4OycmJVjcEIK5VUYZgzJh1Rj9TqzNZvfpDFAoFr71mej5No9Fw9+55UlPt0TIflzb+U4bgvfe+4fbtOIvCO5mZqWRlpePg4Mro0WtJTo4lOLiuFUZZmL8mf49AWtpjgyIu58/vZf/+ZZQvX8Ogx1ASVKxY2AisWrWEr76aRGKiKC318PDm1CkziVkAGxtbUlKSMLcGvVOnnk9CBca7yAIDsSoH0bZtX3Pjxg66dOnNgAF5eSM7uxKT85Ypiuu/sAYkyXBjm6OjE99//6tJx1i0aCMbNqwgNzcXDw8z6WEthJ2d8KJiYwWp4LRpz+Hg4MrUqfuK3VeWNezcOR8bG3uzDIFancGHH9bF3t6BIUNMYA20AP8pQ9Cr10iLKCZAJEwB3N0DWLjwfjFblwyG+IPywxDf/cmTW/j779V4elYoVUPg56dh4MAmKJUq1q8/jEKhYPPmCKZPH4VancdelpiYwObNEWaVgAIEBFRg0qTZeHqaR2uwcGHxq1dPT7HqTE7Oe2/FirHcuXOSLl0mUL/+y2adsyBOnlzHlSvHsLNT6RmCfxuUSvDxySYqSmNRRQvA4cOriY+/S+vWg02iqKhQQdBwa/Hhh0ORJInp078r5EkdOPAH169fpHXrznqJZEmS6NXrdYvGWxKULy9EadTqTG7fPvmkUVEgOvoyCQnRBAZWx9NTP2RpY2PHa6/NNfsaazS5hITUwtXVeh7mf8oQlAasxdaaH8b4g/KjIJdQ5crNOH9+N2FhjUttHE5O4Oen5tw5QauhrbaaPXuynhEAsdqZPXuy2YbA1taWoUPfK50BG0BIiJDg1OLo0bUkJj4gLKxRiQyBkxO8/HI3HB2VtGmjL6p+7NgBVqz4loYNW/DGG2MsPkdZ4vTpdYwdO4AWLQbwzjsrzd7/999nc/v2KWrUaFusIXB1LUwHsn79cnJzc5kx43uysjJJTk7C3d0TGxsbNm5cwZYtq/Dy8i1UUfQ0YGcnjMHt2y7MnHkCWc7LHk+e3JCsrHQqV27KjBlH9PZTKJR07mz+/eDu7srOnef0+ndKG/8pQ/D++z1Qq2344INVZGaqUCjEqtHBQVQCpKYKEQ9DGDFiBTk5aho37sn69dM5fXobzZv346WXxpX6OI2RzeVHQS6hFi360aJFv1Ibg4MDVK0qxHbatOms13tRUsK4kiIhIY5165YTEFCerl2L/p8dHYVCVtSToTVp0ouoqPNUqfK8xee3s4PwcKhVayJvvz2x0OePHkWzY8f6Mq1oKSlEg6TK4mbL5s37PzECRYdoXFygShX9UKMsy3z++VLUajGGF1+sz9WrFwCoX78Z/fu/haenDyEhlfWONWxYdy5dOsO4cZ/QvHnbQslka0L0YKhQKvXzYnZ2gsI7Pd3IRGImVCohf2ltHQur6RFYC5bqEWRnZ+tczhs3cklIUODmVlhERa0W8b+YGOO87VOmNOHmzePUrt3R7A5BU1BQ3L0grK034Okp8gLG6JNbtgwxqDAWGBjMoUN3zD7fhAlvcPfubWbOXEhYmGGB9Pz488/tvPHGS9jY2HDtmrrY7UFUxkRHF79dcXBzg0qViqaWjo6O4syZY5QvH1JsU9SzBFmWSUzUcOOG0iS1PXNhqm5Fv35tOHp0PwANGjRn/frC+o85OTlUrpz3JSxcuIFOnSwnvTMHWVlZnDx5mNOnj+Pp2YSKFdvoPouLi+LBg2t4eVWgXLnC3svlywfIykqjZs12xTITODuLe83+ScrJzq54YsWiYKlm8f8p5OTkEB5el5ycXJRKhVH+eltbkWQMDBSx5cePxd/09DyZyYCAcKKiznHu3C7efTek1Ll9CvIHOTmJaoi0tASjXEJqdSb37l0iJyfLIrEcOzvhshekkDAEQ4RxCoXCYsK4rVtXk5WVycWLp00yBLa2dri6uuPmZnqVSIUK4ocVGWm6ZKMWKpW4Jn5+whBoceDAH6SkJNGwYQu9EtbAwCACA0uP/bWsIEkSHh5KatYUhjMhofh9ioOtrejp8PExvQZ+9WrB9Jubm0uOkQy2JEn89NMO5s79mHLlgvDxsZ5SYEGcPn2EAQPa6V5PmLCEhg2HkpsrvHljjMAA8+b1Ijk5loULH+LuXpgiQ6kU18nHRyzIJAmuXr3AG290pmbNeuzatcUq/9N/xhA4ODjwyy+nzVLRcnXNmxRr13YnJSWJ8PDaREXd0K3W4+IiWbZsGH5+0KnTgCIFxfOjuO0qVx7A4MGGjYshJ+7o0YNMntwBpVLF0aOFCcDyC55rNVQlSaxsteLfhpCamsr69ctxdnalV69BQB5h3IQJg8nOFufy9w80Oz+gRevWLxIXF0OFCqEmbd+iRTvOnjWf4tnDAx4+PMeVK2fw969GeHgjveag/FqyWkF5OzvjbvnYsQNISIhj1KiPGTt2mtnjeVbh4CBCX2q1KL/NytKvKMovBK+9j5KTE0hMjMXT0xtvby+d9rMpv7esrEz27t2Gs7Mrzz/fQfe+UqlEqVSSm5vLgwf3SE5OpHr1OrrPWrXqRKtWZU/ulpiobyF/++173nprKKmpYpGhVuv/RvNfp8aN25KWlkRYmBIPD/37zcbG8L2WlpbC/ft38fe3Xhf1fyY0BHD8uJgMLFGMqlhRzKRaCuOCsDQsUlq4dOkMXbo0wN7ekYsXU0rtuBcunKJLlwYoFApu3tSPF/Tq1ZKTJw8TFlaVxYs3PxOJvOLQu/dz/PPPIZo1a8OqVcWX/BWFjh1rcufODaZP/5ZXXx2qez85OZE9e37D3t6Bzp17lXTIJkM/7m7evgsXfs7Ro/sZOnQ8LVu+YPa5p059m5UrFzBt2rcMGvSOWfs+eHCP5s0r4O8fyJEjhQWOrlw5z4sv1qZixXD27btq9tisAVmWiYq6xcaNP+Pl5cv//vc2kBfW6tq1H/PnryqVc2VlZREb+xA7O4lGjSz3NJ9aaEiSpE7AfEAJLJVl+fMCn48DhgA5QCzwhizLRWdJLUR6ejrnzh3B39/DKEdNUahTpzHXr18kPd2wQlB0dCQtW4Ywfvwsi1fG+TF9+mhWrlxIw4YtdK5yUahevW6hibo0oFLZ6lr9C2LevAjs7OwMuuXaFY7W29CufLQrI+3f/J6Kob8FIcsigZ2VBSkp5od5AgODuXDhZKmEEnbtumDw/djYh7z33v8IDa1i0BAoFHmrZZUq77poH/lX3IauUcHnxqDR5K3qU1OLNg4XLpziwIFdFpdj+vsHEhJS2SIVMRsbWzp16omrq2gqW736ByZNEr0yH300j759h+DrG0C5chWQJBk7O4kHD+6wb98OwsPDad68BQoFODram3TtDL2WZeH1pKeL+6q4ngpJkggODmPs2Ol671eqVJ2jR/cTHFx6Qjp2dnaULx9s1YSxNcXrlQjx+vbAPYR4fT9Zli/l26YNcEyW5XRJkkYArWVZLrL101KPYN++fbRr1w6lUsWNG5ZzpxtLlGphjgB7URg+vAd//LGZatXqsH37mRIdq6xgZyfKKh0czPO6Ll68yMWLF6lWrRq1atUqdvuhQ4eydOlSatWqxcGD53j8jAmBxcfHMnPmOAICyjNhwmeAmPidnEQV09PQMFar4dEjjCaBr169wP37UVSvXrfMiNyM4dtvZzJnjmA/7dnzf3z33YpC99XGjRt55ZVXdPtMnTqVGTNmlMr5ZVkUjKQX07uVnJzIhQun8Pb2IzxcaHbn5ORw6dIZatSoV+pVY//WZHFj4IYsy7eeDGIN0A3QGQJZlvMvdY8CA604HlQqFQ4O5q9YNm+OYPbsydy/H4Wbmyc2NrZkZxuuVtEKsJfUEEybJsIN5jZalTW+/XYmO3b8So8ePZk1a5pFxxg4cCBnzpyha9eubNlSfDIs6UmNr0ajwc1NrKLj44vZqQzh5eXD3Lm/AGLi0pYoP03Y2uZ1EBtiYa9SpSZVqpQdoWJR6NHjNSpWrExqagLPP9/I4OQXFBTE8OHD+eOPP4iOjraIWt4YJElwCsXEFDYGsizTqVMtfH3LERZWlRUrvqVjxx4sWrQREHNMUVQrffu24uTJw6xbd4h69YyzCefH2bMn2LBhBY0aNeHdd1+z+P8qCtY0BIFAftLWe0BRmoZvAjsMfSBJ0jBgGIgbwBK0bduWhw+zSTEzfL55c4RehUxiYnyxNNalUU8fEFCegADz1M9r13YnMzODv/66afa+lkK7crO1VVhsCIKDg7ly5QoeWuKZYrBq1Sq++eYb3WsXF0GlXNwKrrRRtaoDOTnZbN36D9WrF6YbcXHJq/x4FmBjA15eYrX7LCE3N5fc3FxsbGyQJIny5YOpUSMYQ7eDVmK0YcOGNGzY0KpU315eIgSZ34uKjX3ItWsXiY19yOuvj2LFim/NEvGRZZnc3FyyjNWmG8CNG5f45ZfvychItpoheCbE6yVJGgg0BL4y9Lksy0tkWW4oy3JDH2N1n1bC7NmT9cokoXhhG0sF2EuK1NQUsrPVPH4cV/zGJmL79vVUrChRq1ZhHvQ9eyJ0z69fv05EREShbUzB5s2bycjI4KeffjJpe5VKhb+/P/752N68vYVnUBy+/noqYWFK2kAsJqwAACAASURBVLYteWJbrc4iNzfXILeQJCWSlXUfeLaKMbQhloKIiFjEypULSUszQY/RAPbu3UabNuFMm2Y+7fbJk39TpYodffoIfWdvbwoZgZ9++gk/Pz8mTtRv4LOm3oNSKcaSH56ePuzZc4XFizfTrt3L7Np1gTlzfjH5mL/88gfXrqlp0sT0hsY6dRozbdq39OxpPaJJaxqCaKBCvtfln7ynB0mSXgAmA11lWTbdTJYRzF3dl1SAXYt3332Vbt0as3+/QSfJIL77bi0//LCFkJDwEp9fC60RzM3Vz57t3h3B6NHDdK/T09MZNmyYxcbAFERERBASEoJCoSAkJETvXAqFacLeubk5aDQaUlKSi9+4GOzceZ5ffz1YqOxVdM/6ExgYSKa52ewygJdXYS/l66+nMHXqyEKLHlOhVmdx5851HjwwIhhdBHJzc7CxscHW1g4PD0hKiuaVV16hcePGnDkj8mP29vbExMSwf/9+Xn/9dTZs2MD9+/dRF5QMK2U4OOQ1dIFYhISFVaFRI0FjHh5eAzszsrh2dvY6z8dUVKpUjUGD3qF1a9OEgiyBNZPFKkSyuB3CAJwA+suyfDHfNvWA9UAnWZYNKIIWhqXJ4oULFzJ69BiCgkLZs+eyyfsVlxzOD4VCwddf/1wqVUN16niQnJzIpEmzrcrFUxzUajWxsYJ3V9sk5eAATZqEEBlZ+LoEBwdz584di84VERHB5MmTiYqKIigoiFmzZjFgwADdZ8OGDSM9X/zHxsaG5cuX67aRZdE9XFTFR0JCHPfvR+Hl5WuV8JmdnYjFV6wYQlZWFpcuXTI55FWWiI9HL0wqmGQT+Oij+WZNbFqkpCQTE3MfNzdPg0LzpsDBQcbPT+LkyZM0bCji7J988glTpkwhOTmZpKQk+vTpw9GjR3X7jBgxghs3blCvXj2++MI6Eq1ZWSK38rTxr0wWy7KcI0nSO8AuRPnoj7IsX5QkaQbwjyzLWxGhIGdg3RMLGSXLsmHl5xLi4cOHT8Im5mUVDXXR2tjYAFKhhHGrVi+WihEA6Nt3CNeuXaBxY8s5cUoDtra2el2yCoVwl6OiDHtKxt4vCnPmzGH8+PFAHqlfZGQkw4YJj2PAgAFMnjxZzwiAoA2ZPHmyzhBIkuj8LSpx7OnpjaendSiLJUl0hEoSFhvDsoKbm35J6fvvf1qi47m4uOLiYjkrmmBAFavk8uXLU7lyZa5fv069evUAcHV1xdXVldWrV9O3b18qV67M3r17UalU7N69u1STxQVhZ5cn6blq1WKio6Po2fN/FvXNLF8+n6NH9zN48BiaNm1l0j43b17l3r07VK0aTkBARbPPaQqs2kcgy/J2YHuB9z7K99z8zhULMWLECMLDG6BSmaf5qZ3YtVVD5coF6UI/2vfs7BxwdnalQwfzlJ2KwqRJBtMlRWLixGFcuXKO0aM/pnXrF0ttLPnh6Sl+tEFBQQY9AkuS+bIsG2R1TU9P1030phoeZ2ehTGYNrpz8iI19SN++rXBz82DTJrFC9fAwrZP2WYBKJfIFqZalBEodXl55OR4/Pz+uGRGSCAkJ4dixY7rX8fHxvPTSS/j5FaZrKE24uQlDsHXrao4d+4vmzdtaZAjOnz/JH39sfjJXmGYINm78mQULPmXChE9o0mSK2ec0Bf+pzuKC7vD/NTRo4EtCQizvvjuVceNKp6Z6+/b1fPbZ+1SuXJ1Vq37XqXEZCtU4OjqyZMkS3QrdVMTFxeHr62uU4luWZUJCDIeivLy8iIvTT44nJhqWqQTRODVmzADc3DzZsKEwmZmpKNhxXVK3/WkgO1uE0rKysrh37zauru4WN9olJiawYMFn2Ns7mH3vHT/+B8uWfU3Hjh0ZN6702XxLC/fvw5YtG7h27QL9+g3D19f8L/zMmeM8fHiPWrUamsxH9euvP/Lbb2vo3/8NRox41exzalFUaOiZqBr6v4LNmyNo2TKE0FAFLVuGsHmz8cRpUdvm5OQwf/50IiIWm3X+rl378fzzHWnQoIXF/0NB3LhxiXv37nDmzHE883G8DRgwgCVLlhAcHPykyzLYIiMAsGvXriJLciVJIjU11WCDTkpKSqEEtaur8ZLN+PhYbt68wrlzJ8weZ374+PjTo8dr9Oo1GEDv2oDwQBs2bKhLdj6LsLERIY/bt6/xwgvVGDjQcgc9MzODH36YzZo1PwBiAZGUVHynnyTB48dR/PHHH1y8qEsfcuTIEbZu3VooHPg04eYGL774CqNHf2yREQCoW7cxnTr1NIuUsE+fN/jllz/o3t1yI1Ac/jMewZIlS1i0aClNm7bTdXuWJn78cR5ffPGhnmCLsS7jgr0JICY7WZYJDAxmyJD3mD5dlOHdvv10v59z5/5h7dplVKpUkenTJ5T68Q15FubCUII6IUFfmUyLBw/u8fnnE/DxCWDKlK8tPmd+ODsXLjNs164d+/btY8+ePbRr187wjs8AMjNh376zvP12b0JDq7Bs2W8WHieDn376Fnd3T5o2bU2bNpVxd/fk2LEHBrWbjx7dz86dG5k5cxaQxIULFwgICKBOHUEqp62qmTdvHqNHjzZ63tTUVJYvX45KpWLEiBEWjd0c3LtXtpKe+WHNZPF/xhD07t2b9evXEx5ek127zpfqmJYv/4YZMwzfrIbI6IqrRLKzs0epVOHo6MSJE0UoZZcRlEpBy21Knb65MBbyMQeSJBVKFubmih9tad3e+bvLtXmi7t0HoFRCuXLiGuXHhQsXyMzMJDw8HFdrSkuVAh48MK69YQkuXz7HZ5+9j79/IF9++WOhz3Nzc6lc2QZZllm8eAnDhg0ttI3WECxatIjhw43LgMbGxuLr62swRFjaePjwIQcO/IO3dzihoZaVaGdkpLNr1ybu3bvDO+8ULSn78GE006a9y/DhE6hXr6lVDcF/JjTUrVs32rbtVKyilbnYvDmCr74qrFKlRXR0JGFhSipWlHQhoOJ6E7KyMvHw8DLbCJw79w8rVy7kxIlDZu1XXEgrfyKvtGFJlVFBGEpQK5VipV4a0Hpw0dGRyLJMdHQkH344hCFDurF9+4pCRgCgZs2aNGzY8Jk3AlC4eaukqFatNsuWbcPV1Z033nipUO5HqVTyzTeradHiOYYMedPgMbQFBEUZAQAXFxdGjhxZ7HalgUOHDtG3bxe++MJyzzg3N5fx4wcxb97HpKYWnbD8/PMP2LVrEz17NqNKFTv27Nlo8XmLw3/GEAwcOJBff93B229PKtXjGuo8LgjtajU6OpJx414zSfc4OjqyyByDIcycOY6pU0fqaB9MgaFJbtKkYbpznz9/gIkTR7N8+XKzxmIqLKUM0cLR0ZFZsww38Gl5iPJj06aV1K7tTsWKEi1aBJt0jQ19x4JDfytvvfV6oea2fxtyclKxsyu563T48F7WrVtOVlYmNjY2bN26mj//3E5k5M1C2w4Y0JdDhw6gUCg4cuQI/fv358QJ8/M29vb2fP/990bvgdKEm5sbHTt2pFmzophyioazswuDBr3L+PGfotEUXdpWu3ZDwsKq4uDgiFqtxtXVetKn/5nQEFinaig0VGE1QXtzmUwnTHiD7dvX8cIL3Zg3zzQB8qJkJ48fv8NHHw1l2bKlhIaGcvNm4R90SVGSHEFwcLBe05khJCfnKW0Zys04ODjSs+cgfv/9VxITRQOCh4cXH300X3fdTfmOC1ZM7dmzh7///pv27dvTrJn5inFliVdeeYUzZ87y5ZcrSlRooNXs2L//BsHBYWzZsgpnZ1eaNm2Nk5Nwzy5cOEWDBvUICMjL5o8bN465c+fqmsf+DXj40HwKdEuhVMp4eqqxs1OiKkF98n8+NBQREYGfnx/e3hKNGwewZYv+6s3WVlRQWEJbYk1eIS2Tqan48ssfuXAhxWQjAMYpNKKjI2nWLITMzAwqVKhAgwYNTD6mOTBUfVQcHB0dWblyJXfu3Cm2SsnVNY8iwNDKPiMjnYiIhTojAPD4cTwTJrzBlCkjadkyxCRDr+150GLHjh18/PHHHD5seYmqKRgzZgzdu3cn18LGCY1Gw6lTp7h16ybVq5fsXh45ciL16jXVVYB169af6tXrMn/+dJKTkzh4cDfduzdm2rQRete0VatWdOjQgR49elh03nv37nHlyhWjspbmIjc3l7Nnzxa5jancViWFQiHz6NE59u7dVSIjUBz+z3sExurdv/hiCQMHDsDVNe8LlWXRNJKWpq9RXBQMrTJLGyWpHDKW5NSiuMS1pb0BJUFRCWRTvICC0GgEF39goPW8N9BPWu/cuZO///6bDh060LJlS6P7lIQ9MzExEQ8PD1q3bs3evXuLLMG9d+8e5csbptTIzc3l9OnTNGzY0Gi1laUYM2YAW7asom/fNxk1agx9+77I8OHDS3XlHxgYyP3797l7967uf0xLSyMuLq7YhUVKSgrbtm2jT58+KBQKsrOzCQ8P5969ezx48ID8JJdJSUm4urrqvq+sLHFfWdLUfPPmVU6d+pvOnXvrvKX8OHnyb+rXr016+j1q1qyGt7c3Dx8+LJHGwX/aIzBETZCeLlba7u76Vl2ShHCIj48QO/fxEQnHoryF7t0H8OmnSwgMFCtad3cvFIrSi+VJkmR2rkCL4uL/AO+/PwtJMn4bFFzplgVmzZqFo6O+Ipo5XkBBKBRCeN7agvL58x2dOnVixowZBo3AtWvXWLlyJa+++ioVKlQgtYj2XlmWiYmJMfhZXFwcbdq0QaVSoVarWblyJTNnziQjI4OYmBief/559u/fz9tvv03FihU5ffq0weMolUodt4+np7jvS0M8R6mEd94ZT+vWL/Dpp1NJTr7HpEmTSv1+CgsLIzw8XM8r6tWrFxUrVmT79u1F7AkLFiygf//+DBo0iOHDh9OuXTtcXV0JCgri1q1beguHqlWr4uTkxMOHoohDW8XjWFi8r1iMHTuACRPe0OtnUSjEMa9ePUKvXi2oWtULX18vXF1dSUlJMXoflAq02fl/y6NBgwayOZAkSUZwAes9JEky6ziyLMs5ObKsVstyVpYsZ2bqP7Ky8h4rVqyUPT29DJ7X0CMoKFj++eeVRscaFBQsZ2fLRT7Ualnu2bOX3n4KhcLg8ZRKpSxJkhwUFCyPGDFCViqVRY5PkiQ5IyPD7OtVEqxcuVIODg6WJUmSg4OD5ZUrV5bKMR0dHU3+Xsx5ODo6mjzGt99+W2/f7du3yxqNRvf5iRMndM/nzJkju7q6yj/++KPR42k0GjkiIkIG5MaNG8sJCQnyp59+KgNyp06d5NGjR8sqlUpesWKF3n5qtVpOT083etysLFlOTZXl5GRZTkoSf1NSxHtpabKcni7LGRl5979aLe7F3FyTLoPVoNFo5ObNm8uAHBMTU+S2a9askUNDQ+W1a9fKNjY2MiD/888/uu/j888/lwcOHCjHx8fLHh4eslKplHNycgodJydHXA/ttcp/vQpeq8xMWZ4y5WO5R49X5EOHjso5ObKc7+uXs7Oz5bCwMPmjjz6SZVmWL168KG/YsKHE1wXB8WZwXn3qE7u5j//X3vmHW1WVefzzcvECRppe/Jl5tQKLTNBpbHJ8yl9QqRkmNIqalmSRkD0zVphQjkrDaIM/Qk2nicKbWjNOaCaQlYaj1iOIaI45D9OF4I4VQhGXAPGe7/yx1rlsD+fey7mcffa+57yf5znP2Wfvtff+7rXfs971e1XqCFpbW8v+cVtbWyu6Tn/pK/FJJiC9JcS7c5+eEv5qfaqVIGdJW1tbn46v0k9TU1PZeFm3bp3mzJmjDRs26MILL9R1112nHTt2aMGCBRozZozuvPNOvfDCC3r00Ud18MEHa/bs2Vq4cKEA3X777SoUCjrnnHMEaNGiRZKku+++WxdccMEujnnr1q067rjjtHLlSkkhkb/00kvV3t6uzZs3a9myZd1h582bpyeffFL33HOP9ttvP82dOzfFGM+O7du3d293dnbq5ptv1vbt27V06VJNnDhRS5culRQS3kKhoOuvv/41DrdQKGjatGkCtHjxYknS5s2bq65z06ZNeuyxx7Rq1arufa+88krV79PQjqBcQlxJ7q0aJHO3LS0tamlpKZvT3ROn1dO5WeZ880pPJa9qxkehUNDo0aMF6KyzzhKgo48+ujunWfzu6urSmDFjBOj555/X7NmzZWa66aabuq/11FNPqVAoqLOzUyNGjBCg+fPna+3atf16/hNOOEH77LOPzjzzTAG69dZb+3WdvLFgwQLNnz+/7LEZM2YI0Mknn6zJkycL0MyZM/u85vTp03cr3J5wxx13aOjQoTrjjDO0cePG1O7T0I5Aem1CfPjh+c3V7onTqmbitjslg4FMb06zubm5z+cvxnVfJaRbbrlFkyZN0vLlyzVr1iw9+OCDZcN1dHToyiuv7K5yWLFiRdnqB0lauXKlZs2apZ/85CfdTqZSTjzxRI0bN04dHR16+umntWnTpoqvkSeKuXZAM2bMUHt7+2uq2iTp4Ycf1vHHH6/ly5dr3bp1uvbaa9Xe3p6N4EhXV5eee+45bdy4Uffee6+OOuoo3Xjjjandr+EdQZE//7nfp9aM/taNV7tE0NLScxtHf9pX8kQ5h2tmmjp1ap9VR3mpHrv//vu199576+KLL6743M7Ozl0SyoHMxIk728amTJmi5uZmHXTQQbtUn+XtmSdNmiSgZvbkjiCybVu/T809bW1t3Y1de1rVMXXqVA0bNqxuSwRS7w43D9WJfdHe3q7LLrtMHR0dWUvJnDVr1ujFF1/sbm855JBDNHbs2Kxl9cmcOXM0fvx4LVy4sCb3y8wRAB8AXgRWATPKHB8CfC8e/yVwRF/X3BNHkHVvhrRpa2vrNSff16fY6Nlb6SJvCWJapNFryakNhUJBGzZsyFpGn3R0dOi0007TihUranK/3hxBmmsWNxHWLB4HrCOsWXyepP9OhPkMcIykT5vZucDZkv6ut+vuyRQTjUJ/p20oDogaNKjngVdtbW01HVzmOE51yGpA2fHAKkm/kfQKcC/w4ZIwHwa+E7f/AzjV+jvM0umm3LQNU6dO3WWQVinFAVE9TQTX2trqTsBx6pA0HcEbgbWJ3+vivrJhJL0KbAJaUtTUMJx//vmsXr2aQqHA6tWrue2227qdA7DLtAbJWTx7GtlbixkeHcepPQNiigkzu9TMlpnZsvXr12ctZ8BSdA6SuOuuu3pcZrKay1A6jpN/0mwjeA9wtaT3x99XAkj6p0SYJTHMk2Y2GPgdcIB6EeVtBI7jOJWTVRvBU8BIMzvSzJqBc4EHSsI8AFwUtycCP+vNCTiO4zjVJ7UJriW9ambTgCVAE/AtSc+b2TWEbkwPAP8G3GVmq4CNBGfhOI7j1JD0VjoAJD0EPFSy78uJ7W3ApDQ1OI7jOL0zIBqLHcdxnPQYcCuUmdl6oOcltarPCODlGt5vd8mrLsivNtdVGXnVBfnVlmddr5N0QLmDA84R1BozW9ZTS3uW5FUX5Feb66qMvOqC/GobqLq8ashxHKfBcUfgOI7T4Lgj6Js7sxbQA3nVBfnV5roqI6+6IL/aBqQubyNwHMdpcLxE4DiO0+A0vCMws4Oy1tATZrZf1hrKYWa5nCHWzMp2jcsat7HKcRurjD21sYZ1BGY23MxuAhaZ2R1m9pGsNRUxs73N7FZgsZlNN7Nj4/5M31eMsxuBH5nZdWZ2cpZ6ipjZUDO7HXjEzK4xs1Pi/jzEl9tYZbrcxirTVRUba0hHYGZvBO4CDDgd+DlwfaaiXsvfE9ZluAgYCtwBIKmQlSAzGwn8AOgCPgGsB76UlZ4SPgEcCLwPaAe+ZWZDM44vt7EKcRurjGraWEM6AmAb8E1Jl0v6HfB94BkzOyYrQWY2NH4PBpqBuyX9WtINwPqYS8oyB7IFuFPSFXG50YeAl8zssCzEmNnw5E/gSUkbJM0HngS+GsNlteKd21jluI1VRtVsrCEcgZkdZWbfMLNhAJI2AI8mgrwJeDPwYgbaRpnZd4Gvm9m74kptw4H3JIJ9GrjQzA6rVQ4kxll37kLS/wGLEkH2Bt4maV0t9CR0vdXMvg9828zOMLMh8dCBiWCfB842s7dIUi3+qG5j/dLlNlaZrtRsrO4dgZmdSCg+XUooDmNmJmlLIlgzsFrS9hprG0Yokq8EngUuM7NLgH8GPm1mIwAkrQXagE/WSNcZwH8CV5jZjLhvsKTORLD9qXGiFnOqNwG/IrzTDwFfBhYAp5vZ0QAx4bifWK2Q9hoXbmP90uU2VpmuVG2s7h0BsIFQvzcK+LiZtZZ5accC/wtgZp+sYfH9LcAWSddL+jrwTeBsYBhwO68dBPI/hHWfa1EU/T1wPiHOvmhmw+P6EoMS9x4NPB/1TDazUSlrAjgE+BMwW9L9wLXAOGAkIbG7ynb2nlhM7SYndBurHLexykjVxurOEZQasKQXgFWSVgEPA9fEcMlnPxVoMbP7gMmEurfU9Un6FXCEmb037noW+CnwBeAqYH8z+4qZfRSYAmyN51U991Giaxnw6xhni4FvFIMl7n0icICZ/YDwh95RbU2lSOoA3kX4YxZ/3w58NSZyW4B/NLMphBzvxrQ1RR25srESbbmxsRJdbmOV6UrXxiTVxQc4lJC7eUf8bYljxRHUrwdWAaeWnLuIkPOYmJK2AwnGldQ0KH5PB9oS+8cSVm4bQfD+HwN+DJxfC11l4mwfQg7prxPHhhASlOXAR1PQ1QLsU7JvcPy+GPivxP43EBrJxgL7EYry96YUX7voypGNtQD7JfXkxMZeoytHNjaszL6mHNjYLrpqYWNVN8gsPsBM4AlCjuGqZKSVecmfAx6M2+fFP8xJKWq7Kr60JcCtpdqAtwL3ARfF3y0x7MEpx1mvukribBbwSNz+QPyekJKuLxGKt98CvlDm+CDgZ8DnEvsWAEenHF+96srYxmYCLwH/DnylzPGsbKxXXRna2Bzgh8CxRZvKiY31qitNG0vtoWrxAQ4g1HneHbffD9wINJcJm0x8/whsIuSKhqao7/2EnNYgwrKg64CDyugZHxOZ44CPAo8Ah2ehq5c4exXYDNwM7JWSrvExgdqLkFv9KaFKoDkeL+Zw/4rQl3sCcAEh1zg6xfjqVVfGNjaaULUyFGgl9CU/l7AISekz1NLGdldXrW1sCrACmAtc20u4WtvY7upKxcYGehvBn4EvS5osaT3wNkIx9JXSvtCSZGb7mtkNwFrgdEmXKKybnBaDgE5CYns08BTw9qIe6G75/zHwNUKR9IvATEm/zUJXkhhnI8zsX4HnCDm1yyWlVVf7ekIvkSZJLxP+GJcTiuRIKpjZIEnLCTmiYwm9KC5X6HeeFr3qKlIrGzOzNyR+biFUXQyXtIZQn30SMCYRviY2VqkuqI2Nlei6j1C1sxA40MxOj2EsEb4mNlapLkjRxtLycCl5zRZCO8D7SvYXi0utMXJG9HD+IOCYWmkjGP3XCHV364GrgWcI3b8OLGoqfY486EqEHQz8bY10TQBuI4x2bSZ04/sRcEk8bpRpz8iDrhra2DxCaW46cBShhHILcEoijm6Jx5tK4yxFG6tIVw1tLKnr7Ylj+xKc+S3Edh921sHXwsYq1pWmjQ2YEoGZtRKqgc4BzjOz/YvHJHVFz7mBUGx/d7lrSCpIerYG2lri/VZKuoKQm7xA0tWEF38qYbAMSgzekdSVF10JTa9KejxlXSPioR8S2nrGAUuB3xKKvRdGLVL8J6TBnugqXiNFG/sbguN+GbiOMHjoUwollK3AsWZ2aNSyBJgsqas0zlKwsX7pSl4jJRsr1XUYMDVxz02E0cEGTIz7lPxOgz3RlQhTdRsbMI6AUJUxGziSkPM/NVn9k4isNxC7mZVWD9VQ2ynFe5tZE/AHdib8j8Xt/ctfqiF1nRwHE3VJagM+BXxY0lxC3fJvzGxIDfq251UXhF41cyVdLWkp8DihZw2EhOUtwGkAkn4EdJrZoa6rW9cTAPF9NcUwvyK0lbzTzD5vZlNr8C5zqWtwmhfvL7FOs9QLbjCzv0jaamb3EFrKf0GoCsLMmiR1mlkHcAnwY6UwVL5SbbG0MgSYYGZHEob1b6LKA1HqRVcMsk3SFgszYs4GlqvKI3LzqqsXbb82s7WJYzsI0wkg6VEL00l/PGp7J/A7gqN3XTt1vbX4vuL+v8QE9lxCJmBaNUsDedVVjtyVCMxsr+RDJz2hpOJglwVAAZiYyPUXE/3vE/r45kHbXvHwvKjrWEIXuQkK84S4rh7epZm9A/guwaHPqpamPOvaDW1bEseOIEwbUTz2A+CzBKd1n6QLFOYUcl07dT2bOCYz2xe4AfgXSSMlLal3XT2iFBtFKv0A0whF7muADyX279KgSpgO9gFCH+lPAEfkVNslRW2U6Rfsusq+y9a4f0ij6KpAW7EL7deAs+P2R4BD09BUx7oOi9u7dDWvV129fXJRIjCz/c1sAaF/+5WEhpSLYpUFilU8ZnaMYkOTpJ8TZlB8mjCcumq5jCprOw94NRb5qlZVVce6zifMR4+qWO2SV1390FZ8V6OBUWa2iNC4XXX7r3NdO2LYV+pd125RS6/TiwdtIvTVLXbfejPwbXZOF3EwYWTfY4SpJIYQ+kOvIaXRh3nX5rrqQ1c/tR1OGEPzaM7izHXlUNduac/kpqGR+grgTYl9wxPbgwiNdCPj73HAZ0qukcqoyLxqc131oauK2j7uulxX1Z6l5jcMPQeeJkxDe08PYd4OPNRT5DeaNtdVH7qqpC2VumPXVR+6+vvJoo3gZcKoubcRpscdD6H7Z6Jl/RDCiFfM7N0WF7CO9dmptAXkXJvrqg9d1dCWVt2x66oPXf2i5o5A0kvA9yT9kVB/Vlzhp4swmg7CFAjNFubTuDFxrhpRm+uqD1151ua66kNXf8mk15BiX21Cw8k2M/ts3F+I3vS9wCnARkknSHqk0bW5rvrQlWdtrqs+dPWLqxj9uAAAAkZJREFUrOumCF2tfhm3j4nfZ5FogHFtrqsedeVZm+uqD127rT9rATHCFgPbgYfoYeZQ1+a66lFXnrW5rvrQtVvaM464QYQZ+NYAn8w6MgaCNtdVH7ryrM111YeuSj7FgQ+ZYWYfBH6mFCbv2lPyqs11VUZedUF+tbmuysirrt0lc0fgOI7jZEsu5hpyHMdxssMdgeM4ToPjjsBxHKfBcUfgOI7T4LgjcBzHaXDcEThOH5hZl5k9Y2bPm9lKM/sH27l8ZU/nHGFmk2ul0XH2BHcEjtM3WyWNlfQOwpzyHwS+0sc5RxBWznOc3OPjCBynD8ysU9LwxO83A08BI4BW4C7gdfHwNElPmNkvCPPRtwPfIUxZPAc4ibD62a2S7qjZQzhOL7gjcJw+KHUEcd+fgKOAzUBB0jYzG0lYpORdZnYScIWkM2P4S4EDJV1nZkOAx4FJktpr+jCOU4bBWQtwnAHOXsA8MxtLWNx+VA/hxgPHmNnE+HtfYCShxOA4meKOwHEqJFYNdQF/ILQV/J6wCMkgYFtPpwHTJS2piUjHqQBvLHacCjCzA4BvAPMU6lX3BV6SVAAuBJpi0M3A6xOnLgGmmtle8TqjzOx1OE4O8BKB4/TNMDN7hlAN9CqhcXhuPHYbcJ+ZfYwwH/2WuP9ZoMvMVhKWMryZ0JPo6bh61XpgQq0ewHF6wxuLHcdxGhyvGnIcx2lw3BE4juM0OO4IHMdxGhx3BI7jOA2OOwLHcZwGxx2B4zhOg+OOwHEcp8FxR+A4jtPg/D9vBHdALuNdFQAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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BVieLyzR2dnZ4eHiUSyUwbtw4lFJl8mG3evXqUp2vc+fODBgwgKCgIE6ePMmlS5c4f/58qcqgo1PeKX9PQSsQHh7OsGHD8PHxISYmxtbiWOTq1ascOXLE9KBLT0+3sUQadnZ2jB8/nt9++42OHTvaTI6JEyeSkZFBgwYNbCaDjk55pEIqAoPBgJ+fH46OjiY3xKysLBITE20sWcHMnTuXKVOmULVqVS5cuICHh4etRTLx0Ucf2WTeZ555hosXLzJz5kymTZtmExl0dMo7FVIRJCUlERsbC2hvs71792br1q34+vraWLKCqVy5Mg4ODnh5eVGzZk1bi2MiIyODH3/8kUqVKpV6Toe1a9eSmprKyZMnuffee0t1bh2du4UKaSxOS0vjvffeIz09nQ8++KCEJKu4REVFERISAsCmTZvo0qVLqc09dOhQzp8/z8cff8z27dvZu3cvQ4cOLRdxo3R0SpOCjMU2dwe901JS7qPmCA8Pl+DgYFFKlSn3zFtZt26dBAUFSceOHW0tioiIREVFmeL9eHt720yOwMBAAeSll16ymQx3G2PGjJEhQ4ZIRkaGrUXRKSYU4D5aYb2GcpOUlERQUBCDBw8mKioKETGlgSyLES3PnTvHuXPn2L9/v61FAbRTvkuWLMHR0ZGqVavaTI4OHToQGhpKo0aNbCbD3YSI8NVXX7F06dJ8mfF07i4qpCL4+++/GThwIBMnTgTAwcGB8+fP5wtWlpMGsqzw1ltv4eLiwqxZs3jxxRd58803bS2SiaFDh5KRkcHff/9dqvNGRETw1Vdfcf36dXr16kVmZiZjxowp1XDYdytRUVEkJSUB4OzsbGNpdKxJhTQW79mzh2XLluHi4sL06dNxcXGx2LcsBTe7dOkS6enpZGVlMWfOHFuLUyYYOnQo2dnZnDlzhhkzZpjeXHNWdIDN8zWUV9LS0gAt+1tB/0d0yj8VckUQGBhIgwYN8kQaDQ4ONtvX1kHdcjN58mRWrVpFeHi4rUXJw+7du3F0dMTT07PU5/b29sbV1ZWFCxfm274oayu68kaDBg3IzMwsM1uQOtajQq4IOnTowPHjx/PUhYWFMWrUqDwPk7IQ1C03wcHBBAcHc/HiRcaMGYObm1uZ8Hq6ceMGWVlZXL9+HXt7exITE3FzcyuVuS9fvgxobsDmKEsruvLIrl27iImJoVOnTvj4+NhaHB0rUSFXBOa4fv06bdq0wd/fH6UUwcHBZSINpDlOnz7N3Llz+fjjj20tCgBt27Y15Qo2GAw2CfFQVsN0l3cmTpxI3759OXr0qK1F0bEiFVIRzJs3Dz8/vzw5dl999VW2bNnChQsXqFu3LocOHSpzSmDevHm0bduWpUuXEhQURJMmZhO/lTqurq507tyZn376id27d9skBpKtwmHfzezcuZPff/8dgCpVqthYGh2rYsmvtKyW4p4jCA8PN4WgJleuW39//zx1x44dK9Y81uDJJ58UQO655x5bi1JmsLe3Fzs7O7l69Wq5OQdSXvjmm28EkD59+thaFJ0SgALOEdzWRqCUsgPuAWoCqWjJ6GMLcd0i4FEgVszkLFZKKWAW8AiQAvxLRPYVSnsVg8mTJ5OVlZWnLiUlBV9fX0SEkJAQlFL53i7LAo8//jjJycll7tTsnj17GD58OAEBAWzYsKFU587OzgY098aBAweWuVVceaZ9+/Z8//331KhRw9ai6FgbSxoCqAMsAM4APwPhwErgEPAXMAywK+D6jkBLNMVhrv0RYCOggLbATktj5S7FXREopfK8+ecu5eVNMjs7Wy5cuCBnzpyxtSgioq2ycu5ho0aNZNGiRaU294kTJ+Tw4cOSnZ0t06dPFzc3N+nQoYO+OihBMjIy9JPFdwEUsCIoSBEsNz7MlZm2asArwFBL1xv7hRSgCL4ABuT6fBLwK2g8KQFFEBwcbFERkGurqCw/OBITE02ylgWOHDli2rYC5F//+pdN5Bg7dqwAUrVqVXF1dS1Xf9OyyiuvvCKAfPLJJ7YWRaeYFKQILBqLRcs5/LtxgFvbYkXkUxH5qqDVxm3wB3K7l0Qb66yKOaPiraSkpJhOHZclduzYwbx58zh8+DAASikMBoONpYLGjRuzatUqpk+fTv/+/enTp0+e9oiICEJCQvIY563Biy++yPz583F0dNTPFJQA+/btY9myZQD5tlN17jIsaQjJ+/Z+P/AsMCSnFPK6ECyvCNYDHXJ93gS0ttB3FLAH2BMUFFRszdiuXbsCt4gwbhOVNTp16iSAdOrUydaimMXcdkx4eLjV3s7j4+MlNDRUmjRpkqfe0t+2LP5NyzJvvvmmADJ16lRbi6JTAlBMY/HXaPaCA0B2jv4AlhZJ89zkAhCY63OAsS4fIrIAzV5B69atix03WymFiPDQQw9x+vRpoqKi8vXx9vYu7jQlTnBwMO7u7tSqVcvWouThn3/+YdKkSaxevZqMjAzgZoiHSpUqWXw7L65h9/r165w+fTpffVBQkNm/qX6m4M5o3rw5//rXv2jRooWtRdGxNpY0hNx8Gz+OGTtBYQoFrwh6kddYvKswY5ZEGOq9e/fKkiVLZNeuXVZ9Y60ozJgx47Z2F3OluPc4MTFRhg0bJiNGjBARkR07dsjDDz8snTt3FmdnZ/1vqqOTC4piLJabD+zvKIQR18x1y4EYIBNt/384MBoYbWxXwBzgH+AwFraFbi3WyEdQHj1MatSoIe7u7hIZGWlrUfJ4Dd1JKemH8+zZswUQd3d3GT16tGme8vI3LYts3rxZevToIWFhYbYWRaeYFKQILGYoU0qtM/5HcgeaA7sAU7Z0Eel92+WGFSiJDGV3A/b29hgMBvbv30/z5s1tLQ4hISEWt9hSU1MtxrMPDg4mMjKyRGT4448/mDBhAqGhobzyyis8//zzBAYGsmrVqhIZv6Jx7do1VqxYwUsvvUTfvn357rvvbC2STjEoKENZQTaCGVaSx+aMHz+evXv38tJLL+XzcGndujWnTp1i1qxZDBs2zEYSmqdNmzbs2bOHQYMGMXfuXLKzs6ldu7atxQIsB+2bNWsW586dY9KkSWavK05QuNjYWD766CN8fX157bXXaN++PTt27ABg0aJFeHp66nmMi8GIESNYvXo1I0aMYOzYsbYWR8eaWFoqlNVSEltDQUFBAsiYMWPytXl7ewsgb7/9drHnKWkaN24sgPTr18/WopjF0hbbhAkTLG4PBQcHF3m+X3/9VQBxcHDI1/b4448LIM2bNy/y+BWdgQMHipeXl/z888+2FkWnBKCYXkNPAR+gHSJTxiIi4mEFvVQq9OvXj127dvHggw/ma5s/fz6xsbF0797dBpIVzJYtW4iPjy9zHk2TJk1i+vTpBAYGcu7cObKysnBwuPnVqlmzJu7u7iQnJ+c591DcoHCenp74+fmZ0mNev36dX375BUdHRx566CH27NlDzZo1i/6LVXDKWt6L8sQTTzzBhQsX2LhxY/kI321JQ+QU4DTQ8Hb9SqtYM3l9eWLkyJHSvXt3OXz4sK1FkVdffVUAcXR0FEBcXFzM9gsPDzf5+NesWbPEDbi5VwinTp3Szw6UAHFxcTJ37lxZsmSJrUUpV+REMCgrYWBECl4RFEYR/HG7PqVZSlIRpKeLxMWJJCaKZGWV2LClQqVKlQSQ0aNH29zjKT09Xa5duybLli0TQJycnEpdBhHNLdjZ2Vl8fHwkJiZGHB0dpXLlyjaR5W4hR6GGhobaWpRyxcMPPyxdu3aVlJQUW4tioiBFUJDX0FPGHzsBNYDV5PUasokrRkl4Da1du5bLl2/QokVXfHyqAaAUeHnBxx+/zZ9//snIkSPzGZJtzYABA9i1axevvfYaP/30E4cOHeLixYum3LKgbbfYKqFOVlYWGRkZuLi45MkYdu3aNapUqYK9vX2Jz5eWloaLi0uerSjQ0meePXuWe+65h/r165fovBWF3r17c/LkSerVq8e9997LW2+9ZWuRygUiYvr+GwwGtEDLtqcgr6GCVgKLCyiLLF1n7VISK4LKlSsLINOnL5SzZyVPqV27jgAybNiwYs9T0uQYuV988UURsRxArzgGWGuQY4D/4IMPTHXx8fHFHnfhwoUCmH3rb9mypQDy6KOPFnueikrNmjUFkHPnztlalHJFTiTcadOm2VqUPFCUoHPAL8B4ERlmpjxXTOVkU3x8quPiUglf3+r52jp3fow2bdrQqVMnG0hWMOPHj2f48OH069cPsOx6ac08veYCyM2cOZOAgAAGDBhg9prMzEwAqlevzuLFi03XFpecXATmVhrVq1fHxcWFgICAYs9TUdm4cSPbt2+nevX8/0908nLjxg22bt3KiRMnsLOzo3bt2oiI2RAoZRJLGgL4D7AZ2AZMBe6jiKEmSrIUd0WQnS0SGSn5VgK5y/XrxZqiVNi1a5f4+vqW6orAUjiO9u3bCyC1atUSPz8/8fPzy3dtYmKipKeny1tvvWW1E7/x8fFib29vcietVKmS2NvbS0xMTInNURE5f/68nDx5UrKzs20tSpllx44dAkibNm1EROSRRx4RQNatW2djyW5CEcNQfyAiXdASyBwEngP2KaWWKaWGKKXK5WtCWhpYMIuYSEoqHVmKw6OPPsqVK1fy1VszT+/kyZPNBpA7ffo0r7zyCq+//joxMTHExMTku9bDw4PvvvuOGTNunlPMCUxXUmGpnZycyM7ONoVMTktLIzs7mxs3bpTI+BWV+vXrU79+fYunw3UgPV0zn+7atYvMzEz8/f3p0aOHTfJ3F4XbniMQkRvAD8aCUqoR0BMt+mjZc7a/DampBbefPn2cyMi/ue++ujRt2rB0hCoks2fPJjIykqFDh1KtWjUSEhJMoR3S09MJDg4mLCzMaoZiS1tOsbGxfPLJJwCcPn0aV1dXDAZDHoMxWFYkJRGJFMDFxYVt27bh4uICwK+//oqTkxPBwcHFHruikZmZybRp0/Dw8KBOnTqkpqaaIsvq5CcnQquHhwfx8fEsXLgQHx8fGjYsW88Qi1haKuQuaAlj7kfLWNYR6FiY66xRirs1dP68iKOjk9jZ2cmvv57Ity3UvPl9AsiDD3Yr1jzWIMfoaisjVFGM07t375YqVapI48aNSzxPwMKFC8Xb21s6d+6cr61BgwZib28vU6ZMKdLYFZ24uDgBpEqVKrYWpVyQmZkpv/76q/z+++9y5coVuf/++6Vnz562FisPFPNk8QdAf+AYefMR/F5CuqjUyMyErCzIzNTebJydXfL1qVbND0dHJzw9y9bpXYB77rmHEydOULduXZvMbyme0FNPPcWoUaPo2LEjgwYNynPNsWPHSEhIID09vcTzBPzzzz9cu3aNEydO5GtLTEwkOzub1NstAXXM4uTkxNSpU/O55eqYx8HBgYceesj0edWqVcTExHDp0iVq1KhhQ8kKiSUNkVPQcgk7365faZXirAgSE7W3/v/976j88MNO+fvvzAKNxuXBNjZs2DCxt7c3GamsTXh4uFStWlUAqVatmoSHh5sMY61atZJXXnlFBgwYIBcuXBARzdA4Y8YM+eSTT0o898Phw4dl2rRp8tVXX5nq2rRpIw0bNpS//vpLmjZtKnXr1pXdu3eXyO+uo2OJKVOmiKenpym382uvvWZ0UZ9uY8luQjFPFm8E3G7Xr7RKcRTB1asFewvdWsrQocB8PPDAA+Lg4JDnoVpa1KpVSwAZOXKkiGj/CWrVqiUvvfSSSabffvvNYupKLy8vAcTPz6/ET0LnbD8dOXLEdPr666+/LtE5KhrDhg2T+vXry86dO011P/74o3Tp0kUuXrxoQ8nKDi+//LIAEhAQIFlZWTJ79mxp1qyZfPHFF7YWzURBisDiuk8pNdv4gEkBDiilNpH3ZPFdH5c2LQ0qVbK1FDdJSEjAzs4ONzc3EhIS8iQUz70stSYXL17k7NmzgBayG2Dq1KlMnToVgL179xIfH8/OnTt55513TNtIOR5CCxYs4Nq1a1aTb/DgwWRkZFC9enWGDRvG5cuXy0S+hvLGjRs3OHHiBF5eXkRHR3Py5Eni4+NN7RMnTuTgwYNMnjyZRYsW2VDSssHHH3/M7NmziY6O5uDBg7z11ls0btyYUaNG2Vq0QlHQBmBOHIe9wDOx7XMAACAASURBVNpSkKVUSEiI48kn2+LqWpkNG/bna584cRQrVy6mVav2/PDDltIXsAACAgJITk7myy+/ZNmyZVy+fJn69euX6qGpU6dOAdqeqLkveU4+gJCQEKt6CAGsWbOG8PBw2rdvzyuvvALAV199BUDPnj2Jjo5mzpw5NGnSpETmq0js27ePzp0706FDB5YsWUJGRkYeW05YWBhbt27liSeesKGUZQcHBwcmTJiAwWAgMzOT+Pj4cuW2XJAiSAR2iEhsUQdXSvUAZgH2wJci8v4t7UHAV0AVY5/XReTHos5XGBIS4oiM/Nti/I/r17U37fj4q2RkaGcOykioEJM7prOzM02aNLHJAy44OJj+/fvj7Oxsqrt69So3btzA29sbDw8tOnlBp54XLFjA9u3b6dOnD48//niRZfn+++9ZuXIlhw8fNimCHH7//XdSUlKIjIykY8eORZ6jouLi4kLLli1p0KCBWV/4Xr160atXLxtIVnb58MMPAS0GljVXvVbB0p4RsBK4APyN9rAeBTSx1N/M9fZo+YhrA05oh9Ia3dJnAfCC8edGQOTtxi2ujeDAgTjp02eoPP30c2btAtu2RUp4+K8m19LU1JvXl8XcxmfOnJFp06bJhx9+WGpzXrhwQXbv3i3nz58XETGdLO7SpYtERUXJ7t27JSAgwKKraZMmTQSQvn37FkuOOXPmSJMmTeSFF14w1c2fP1+mTJkiw4YNkwYNGsjo0aPLRKjuu4m0tDS5du2arcUoU4wbN06GDBliisu0evVqCQwMNMUFKwtQTGNxCPAs8DnaNtEV4MdCXNcO+DnX54nAxFv6fAH8J1f/HbcbtzSNxWfPiixcGG7yn7/VD76kk6/fCe+99540b97cJsbie++9VwDp0aOHiIh06dJFlFLy6KOPmgzBffv2tegh9MILL0i9evWsorxcXFwEkG+++Ub8/PwEkAkTJpT4PBWJDRs2yIQJE2TLli0icjPvQ4sWLWTDhg0mD7GKTO3atQWQtWvXSkZGhnz33XcCyFNPPWVr0UwUpAgKCjqXs2KIBPYB+4EDQCxQGBOqP3A+1+doY11upgKDlFLRwI/Ay4UYt9RYvTqCsWNHmXzftXt5k5w9b1uwefNmDhw4YPpcWqdnN2/ebLITuLq6ArBp0yYMBgPr1q3D3d0dOzs7WrZsyYIFCwgODkYpRXBwsCk89ty5czl58iQTJkwocflCQ0Px9fXF29ub+vXr4+Xlhb//rV87nTth69atfPTRR/z1118AxMXF4ebmxv79++nVqxf/+9//bCyh7fnss88ALXT3L7/8wvLly3nppZf48ssvbSxZ4SjIa2gS2lu6L9pZgr/QVgWjRCTb0nV3yABgiYjMVEq1A75WSjUREUPuTkqpUWhbU0U+fJRDdHQky5Z9QY0aAQwZMiZf+7p13zB//vs0atScP//8jdTUguOrWDPS563UqVOHhIQEfv75Z5599lnc3Nzo1q0bw4cPx8nJyWrzRkREMHnyZM6dO4ezszNpaWm0atWK77//Pl/fyMhI08+bNm1i4sSJPPbYY1ZJGRkVFUV0dDSBgYGm78Xhw4cBeP/993n44Yf54YcfqFKlSonPfbfz3//+lzfeeIMRI0bQo0cPvLy8TBF5+/XrR9++fZkxYwY//vgjnp6eNpbW9vTq1YsHH3yQCxcucPHiRVatWkXXrl1NaVTLPJaWCsAJtK2gqUA3wNNSXwvXF2Zr6CgQmOvzGaBaQeMWd2toxowlAoiLi6vZraCRI8cbD0v5WQyJAIULr1DS5PbRLy3MHQIDpGPHjre9tm3btmb7Xrt2TQ4dOiSRkZHFkq1r164CSLt27fK12dnZCSAHDx4s1hwVlY8++kgAefXVV20tSrkjOjpalixZIj/99JOIiCQnJ0tUVJQkJCTYVC6KGH20AdAVzY20M/CDUmqXUmqhUmpYIXTMbqCuUqqWUsoJeIb8bqjngIcAlFINARc0G4TV8Pauhq9vDerUaWC2vVOnHnTt+jj9+4+gZs2CVx/WjPRpjlmzZvHuu++a9RbKnRS+JDEXKA7IEyrisccew8PDI89WT0REBAcPHgTgwIEDeSKMDhkyhGbNmhU7A5y7uzv29vZm37oCAgLw9vbGy8urWHNUVF588UWio6N54403bC1KmScpKYnFixezceNGAPz9/Xn44YdZu3YtU6dOpXLlygQHB5ft8xaWNITkfXN3QMtHMAEtmX12Ia97BDiF5j002Vj3DtBbbnoK/YHmUXQA6Ha7MUvTWPzJJ+Hi7FzJ4krAll5D+/fvlyFDhpjefAHJzMws8XkKWhXlGIsbNWokgPTv318effRRcXR0zHfqObdhfeDAgaKUkg4dOpS4vNWqVRNA3n77bbnvPi2A4OOPP17i81QkYmNjZdu2bXLs2DExGAzSrFkz6d69u6SlpYnBYJD09HRbi2hTTpw4IYDUrVvXVHfs2DEBpH79+qb/A7YON0FRvIaA3sD7aIlp4oz/vg88Dvhaus7apbS9hoYPH2f6Q+Y8ZHISn9iS4cOH53swJyYmlvg8liKOAtK4cWMREdm0aZPMmTNH9u7dK61atbLpNlrO32jSpEkmRdC9e3erz3s3s3TpUgFk4MCBEhMTI4BUrVpVDAaD2NvbCyBZWVm2FtNmREVFyZAhQwSQ0NBQWbNmjXz55ZcydOhQWb58uUyePFlGjx5tc4VZVEWwCvg/tL1+J0v9SrsUVxGcPJkuJ0+my+nTIrt3i2zdKrJrl8jp0zfPEbz++ofywQeL5OxZkdWrd4m/f7Dce++9EhUVJd26dZMePXrY5I/6zDPPSJ8+fSQ+Pl6mT58urq6uUrVqVfnwww+tshoQsWwjaNeunezatStf/+3bt1tUBEUNN30nJCcnS3p6umRnZ0tiYqIkJiZa7d7czSxevFhGjRol27Ztk19++UXat28vU6dOlczMTDl69KjJldTFxUUcHBwkOTnZxhLbnvvvv18Aef75502KsyxRJEVg6gA9zdSNvt111irFVQTDhr0igPj4BMry5WIq338vsn+/yLRpcwWQypXdZO/eKwKIo6OTpKZqoUhztkpOnDhRZDmKSs4D9cyZM6U6b3h4uCkXQk7ZsGGDxf5FyVtQFHr27CkuLi4yevToPPXJycmmOXWKxtChQwWQ//73vwX205XsTc6ePSsnT56UjRs3ylNPPWWKRFpWKEgR3PYcAfCmUqpLzgel1GvG7aFySVaWlotAqbwJzzMy4NQp8PYOxMOjCgEBtTh4cJexVcjI0G6Vi4sLDg4OeQK+lRb33XcfTZs2LXV3vYEDB3L16lXTl+batWs8/PDDpvbJkyfTo0cP1qxZA2hxaBwdHfOMkduwPmvWLNzc3GjVqlWx5IqOjiYtLY3Y2LxRUJLKQ67RMs6wYcOYN28e999/f4H99HwF2vctPj6ewMBA6tWrR48ePVi5ciUNGzZkxYoVLF++HH9/f/r3729rUS1SmL9ib2C9UmoC0ANoQDlWBJMnz+GRR+aYfZAbDODn9yj798eTk2Vx//5r7NmznVOnzuLunsHo0aOpVasWjRs3LmXJMR3oyWHlypUsX76c9evXU6VKFTZt2lQq8Ye2b9/OzJkzadKkCXPmzCE8PJxz586ZzjksWrSIXr16sXfvXqKjowkKCsqTQvPatWskJydz/vz528xUMEuWLOH06dM0bdrUVPfMM8+wdetW3n33XVJSUggICKBLly4sXbq0WHNVNDp16mQ6N5CbQ4cO8f3339O8eXOefPJJG0hW9liwYAH/93//x7///W8+/fRTU/0jjzySx5vvyJEjthCvUBQmZ/FVpVRv4Fe0cwV9jcuMcsmlS9q/lt5k0tIgLg58fLTPSUk3GDlS03ufffYZn3zyCR4eHrz8sm0PQc+cOZPx48ebPsfGxnL58mWrKIKkpCR8fHxIT0/n+eefJyMjg99//51Tp04xZ84cevfuza5du+jYsSNr1qzh999/p2HDhhYP2w0bNozg4GBCQkKKJVfLli1p2bJlnrqDBw9y6dIljh07hsFg4MKFC+zZs8fCCDqFYf/+/XTp0oUmTZowcuRI3nnnHQYNGsSTTz7Jyy+/zN9//828efOoVauWrUW1GZ6enqxZswY7OzsGDx5Mo0aN6NmzJ7t27cLe3h53d3d+/NGq8TSLRUEni2+g7bPm4IQWQK6v0ejnYW3hSprsbIgtRCzV2FjYvj2CGTMmc/HizYdZzZpB+Pv72+wLv2nTJhwdHenQoUOe6Kn16tXj6aefttoqJSUlhfR0LRXFF198AWhvjDlx/mfPnm3qm56ezsWLFwuM+FmrVi2GDx9uFVnfeustDh06RK9evUhPT8fPz497773XKnPdzWzbto3r16/Ttm1b7O3tSUhIIDExkWbNmjF16lTTC8cff/zB/v37iY+Pr7CKYNy4cYwbN46nnnqKTz75hJ07d7Jjxw5effVV1q9fb2vxCocl40FZLcUxFh8+LNK9+8vi7u4rXbqMymMszilvvbXVrKGzUiVX+fLLcAkMDBQnJydZs2ZNkeUoCpmZmSZZsrOzJTU1VWJjY0vFWyM9PV1Wr16d58zC7c5RDB48WPz8/OTNN9+0mlxjx46V+++/X7755ps89fv375dq1apJo0aNrDb33U5ORNmtW7dKZmamxMXFSYqZlH1btmyRjRs32vzUbFlg9erVMmPGDBk9erQ4OTnJa6+9ZmuR8kARM5SFiBZwzlK7AvxFJLrEtJKVSUmB8+cPc+PGFS5cOG62j729o9n61NQUpkyZTHz8NTIyMko93nhWVpZpO8vOzg4XFxdcXFxKZW4nJyeSkpJwdnY2JYPPyTgGWsC7uLg42rRpY0rUvWfPHmJiYvj777/zjXf06FHGjh2Lt7c33377bZHlWr16NefOnaNly5Y8/fTTpvqYmBhiY2PzZNTSuTPuv/9+PD098fX1xcHBwWLMHHN2hIpK7twa8+bNA26e+J87dy6nT59m0qRJVKtWzSbyFYQSC9v9SqnvADtgDTfDT7sAocCDaKEhpojIL6Ujqkbr1q2lqHu+u3bB2rWbOH58G6GhbWjR4pF8fbKyshg82Im8u2IaSimWLfsWBwfo0qWLzcMXHDhwgKSkJFavXs2lS5cYP3681dIyhoSE5AkrkUNwcDApKSlcuXKFqVOnMnLkSDZv3szevXtxcHCgZ8+edOnSJc81a9eu5fHHH8fJycm05VQUJk6cyJ49e3jppZdM/wmXL1/Ot99+S1xcHKGhodjb21OrVi0mTpxY5Hl0bnL+/HliY2MJCAigevXqthanTPDCCy9w5MgRPv300zyecK1bt2bv3r15+u7Zs6fY3nJFRSm1V0Ram2uzuCIQkX5KqUbAQOA5wA8tf/FxtJDRYSKSZgV5rUqTJg/RpInl/L4ODg74+ARx9Wr+h16lSq4MGNCPwMDAUo06ao7du3fTpk2bPHUtW7a0iiKIiooyqwRAi75av359EhMT8fb2JiIigtdee42qVasSFxdn9pr69evTvn17/Pz8iiXX9OnT89V98cUXbN26lQceeIDg4GCmTJmCj4+PrgiKQWpqKs8//zxKKWrWrMn7779PWFgYkyZNYtOmTZw4cYJu3bpRt25dW4tqE44fP8727dvZt28f169fp27dugQEBORxSGnevDmDBw8u9nfeWhToNSQixwDbBNy3If37h7Fw4SgyMvIGW6tfvyn79/+Vz0feFly9etX0s6enJzVq1CA0NNQqcxXk5hkUFMTx4ze32SIiIqhUqVKBb4v169dn+/btJSpjDu3bt+fixYu0b9+exo0bU7t2bRo1amSVue5msrKysLe3RymFnZ0dX3/9NY6OjsyYMYOWLVuacjwsWrSIZcuWERERUWEVwZw5c4iLi2PevHmMGjWKFi1a4OPjw8svv8zGjRu5cuUKXl5e+OS4IpZBCnOg7K5i3bqP+OyzAezfb96VKyXlOsuXT8TR0QVnZy3xiqdnVfr2/Rfh4Zt49tnReHn5mg5PlRZRUVE4OzubcgLfc889jB07lnfffZeEhAROnDhB7969rTJ3YGAgTZs2xd4+7yE8c9FXBw4cSEpKCp06dWLEiBFER1vPhLRmzRq+/PLLPKuzsLAwVqxYwfnz5zl69Cj//PMP69ats5oMdyuBgYHY29sTExODk5MTS5YsYenSpYwdO5a9e/cydOhQAB5++GFeeOEFs3mNKwqNGzfmgQceoE2bNnTq1Inr16/zyy+/EBMTQ9WqValXr16ZVgJAxfIa2rlTpFq1OgJI587PmfUaWrz4Rj6PodxB6KpV09IfjhtXuukPjxw5UmrxeixxJzmbczyMduzYka8tPT1dVqxYIUuXLi2WPDlpMW+N6vjBBx+YAqPpFA1fX18B5OrVq7YWpdxx8uRJ+emnn/KEgomJiZEtW7bI8ePHbSYXxQwxcVfRuHFn/P0bUrduW7PtTk6u9OgxlgcfHEHNmg0IDc3rm//YYwPo2LE7LVo8SGZmaUisUadOHdasWcPatbemdICLFy9y6NChPNtF1mDgwIFERkZiMBiIjIw0nRT29/fH0dExT86BVq1aUbt2bbO+5UlJSTzzzDMMGTKkWPL4+/tTuXLlPPuuCQkJODg44O/vT9u2bTl79qxZzyWdgomNjSUrK8vmDhHlgTlz5vDBBx+YvNTq1atH9+7dWbt2LUopmjdvzqeffkrnzp2ZP3++jaU1z21PFiulVgH/BTbKLSkkyyOjRhWcQ9TOzo6hQ2eZPteuDRMmDOPy5YtUrerN1q0/07lzTzp06ElMDHh6gpsb3LJrUuK4uLjk2fpJSkrC3d0d0GwEiYmJjBw5kgULFpT43BcvXmT16tVUr16dPn36sG/fPu69916cnJxITU0lMTGRrKwsUlNT87WZw83NDRcXl3xbTXfKoUOH8tW99NJLREREUL9+fUaPHk3t2rWL7Z1UUcn991m/fj0JCQn88MMP/PnnnyxevJju3buTlJRkymFcUZXGjBkziIyM5Omnn87jZpvz/Tx48CDPPfccHTp0KPZpemtRmFhDc4FhwGdGl9LFInLSumKVHW7cgDVrlpGZmUFgYG0SE+OIjNTeMA0GiI/XioMDODpqCkGpvCWH3D8XlwULluSSMSfImjOJiSU3Rw6rV//MmDFjcHV15eGH+5CcbIfBYCAjI4OEBNiw4XcSExNo1qwlly5dwGAwkJ6eTkKCpRGdiInRlITlPkXDxcUTpRROTpWws/MAFA4OjredpyT+NuU38MrtefHFlzh/PorQ0HrExMRw44aQkAAffvgx06dPYfz4N5g8eZqtxbQJw4a9yNWrV5g16wu+/PJzmjVryRNP9KNt24e4du061arVoE+f4QwZMhYo+nfewUF76bQGhYk19Cvwq1LKEy3Z/K9KqfPAQiBcREpxg6T4REUdJDs7k5o1G+DiYv6ufvXVv0lMjKVbtzGkpITSsWM3rl27Qv/+I/jrry04Ojpy5Mg+mjS5GecmK0sr1uKff04ybtxgfHyq8dhjA3jrrddMbQZDNpUqudKoUVuscYbK3r4Kbm7u+Pr6ER8P1as3Yt26vVSqVJmEBAgOvnkfvLxcWbFiKzEx5zl69Bz+/gWn+yxp3nhjDqNHT+HKlUt4eflw9qy2iC1phXM3k5aWyvPPP4mbmwdz5mgH/rp06U1CwjVefvkt3Nzc8fSsSkICODt74ecXgJ1d5Qp7j4cM0VK0fv55GMnJyfz55zb+/HMbM2Ys4dNPvwMgM7P430FnZ+spAosHyvJ0UsobGAQMBi4CEUAHoKmIdC7guh7ALMAe+FJE3jfT52lgKpph9qCIPFuQLMU9UNa5syepqdcZMmQWPXuONdtvwAA7cg6UVa5clX374nBy0tp69GjGyZOH6dGjD/PmrSySHEVhy5aNDBv2CI6OjlSrVpMLF/L79fv7B7N9e2SpyWSJdeu+YezYZ3BycuHkSfPbQyVBs2aeJCXdYN687+ne/WYkzP/8ZwTffvtfgoPrsGXLaavNf7eSkBBHixbeuLt7cuhQBX26F4H09DSysrLYsOFbjh7dz5NPDqZ58za3v7CQODtDcY4hFOlAWa6LfwDqA18Dj4lIjLHpG6WUxSey0gL+zwG6AtHAbqXUWtHOJuT0qQtMBNqLSLxSyupnr52dK5OWlkTlyuaPzAP4+zfk4sUTiBhwcXHnxg3w9tbagoPrEB0diZ9foLVFzUNwcCidOvWkShUv1q5dZrZP7gB5pcmgQV2Ji7vCzJlLadiwGRkZ6djb21O5suXXl4YNXUlPT+PHHw/RoEHRIqamp6dj7kXGyckJpexwcyt3cRHLBK6ubixeXHYjZZY1jh07iKOjI6GhDXF2duHpp58DYP/+vxg+/DHat3+I7t2folu3RlSr5sdvv5U954XC2Ag+E5HfzDVY0i5G2gCnReQMgFJqBVoeg2O5+owE5ohIvHG8QsQGLR7z5l28bZ8ZM47m+bxz507s7S9y7NhBlLJj+vSFPPZY6SaZqFWrLkuWaP859+zZbnZF4OLiWiqyZGVl0b17EwwGA7/9dopdu7aSmZnJpUvRNGzYjIULZxAQEML69QcsjpGZmYGIkJRUdKPGpk0nSUlJIiDgpmdSePg8vv9+KfXrN2H58i00barZCQ4ftoLx5C7FycmJzp175qnLyMggPT2NWbOmkpKSzLhx0/DxKXsxc0obEaFXL+00/z//ZOeJCrx8+QI2b17P5s3r6dXraVJSkklJSbaVqAVSGEVQVSn11C11icDh2zy4/YHcR1Kjgftu6VMPQCn1B9r20VQR+akQMpUq//lPT5KS4nF0dCIzMwOQUlcEuRk/PoxJk0aRmpr35LOfX4BV5vv88zBmznwTf/8gtm+PxM7OjjNnNH+BrKws+vT5F3FxVwgNbQjAyZOHAUhM1LxJzLF69S7s7R2oU6dBkeUKCAjOVxcff43U1GQuXYrG0dGJpKQbRR5f5yYjRjzGtm3/M31+8cVJAPzxxybCwv6Ptm0789Zbn1q6vFCkpCSTmZmBp6fl1XpZIzs7mwYNmmEwZPPnn7/xzTdfUqdOA/r0GUq9etpK18OjCr6+NTh8+DpOTs42ltg8hVEEw9ES2OesCjqjBaGrpZR6R0S+Lub8dY1jBgC/K6WaikiejUml1ChgFGghDYpLdjbs3Qtnz2opKuvVg2bNoFIlrT0u7iJpaTfw8QnGyckFd3dfMjJSqFu3EdnZ2bRpU/oRF2Niovnf/1bj61uDJ57Q/Pdz8iX4+FTn4Yd78+yzz1tlbu0tRsjI0Fww7ezsePLJwSb3wunT87qs9us3DAB3d8tbM7kN7SXJk08Oplo1P2rUCMDJyYmpU2dTqVJlDAYDdnYV7thMkbh27QobNnxLtWp+9OihvQNWruyGm5s73bo9ScuW7fDy0k7KpqamcPz4wRLZKm3b1p8bNxI5diyZSpVKZ3VbXBwcHNi48SAAy5cvZN26FQB8+ulUFi/+kbNnb25durm520TGwnBbY7FS6n/AYBG5bPxcHViK5kH0u4iY3eBVSrVDe8Pvbvw8EUBEpufqMx/YKSKLjZ83Aa+LyG5L8hTXWPzggzVITZ2PyBN52jw9YdAgaN8ehgxxNuU27tBhMGPGLKVOHfD1hRde6MvPP6+idesOfPvt70WSoygsX76ASZOex9XVjaNHS/ctNyUlhWvXLmNnZ18oL6AxY55m06Z19OjRh08/DbeaXI880pzU1BSWLv2ZwMCb20Pjxg3hp5++p1u3J606/93KgQO7ePLJ+2jatBVr1xb8f+369QSioyPx9PQqtofYfffVJDY2hh07zlttdWtNIiNPc/DgLmbPnkZaWiozZy7lvvssJ2i6U2xqLAYCcpSAkVggUETilFIFuY7uBuoqpWoBF4BngFs9glajKZTFSikftK2iM4WQqUisXx9BSooATwGBtGr1HiEhA9m3T1sdzJmjpal0cHAyKYLERC23ZXy8pgiysjIREVJSSjdBuqenF25uHtSo4V+q84IWU8jV1XL2qUWLZpGRkc7AgaNxd/cgOvos6elpJCaajz4KMHz4Y5w9e4q33/6cBx7oWiS5Tpw4hIiQnJx33zU6OpLU1BRiY29vD9LJj5eXD4MGvVCot3wPjyo0alQyEW/79v0XKSnJODuXTp6NkiYkJJSQkFAefzy/46OI8MorA8nIyGDu3O/y2BLKAoVZEcwFgoDvjFV90Pb7JwDrReTBAq59BPgUbf9/kYiEKaXeQYt5sdaY3GYm0APIRgttvaIgeYq6Ili6NIJhw0ZhMNzcV3dycmXkyAXcf/9ANm2CRYu0g0Wvvw4eHgeIijqAv39jQkPvxd4e3NwOcf78WXx9a1CnTn08PKrcsRzWYO3a5axYsZDmzdvy2mvvlcqcc+ZM5/r1eMaMmUTz5t6IGPjxx4OcPHmYN998kaSk63h5+fLmm5+YtrJy06xZFW7cSOTNNz/huedeKZIMb7/9b5KSrjN58kyqVNFOtR4/foh///tZMjMz+PTTCObP/4DExHhmzYrA17dGsX7nio7BYODnn3+gUiXXfMbkikpMTDR9+rQjIKDWbXcI6tVzIjMzk5Mn03HK8Ue/A6y5IiiMIlBor9AdjFV/AN9LYQ4gWIGiKoLKlUNIScnvaePjE8zs2ZEAfPcdrFoFHh4wezamswOTJ7chMnI/BoN2Yuy5517lzTc/LvLvUNJMmDCMlSuXEBJSl99+O1Xi4y9aNIslSz7jvvs68tFHiwGoU8ceg8HAmjW7ef75J0hNTWHs2CnMmDEpjxG7UiVX3ntvQT5l8OGHk7h0KZrnnnulRO0FP/64kjFj+uHk5MzJk2kmOVev3sU99+i5i4vK11/P5dtv/8uRI/uoVMmVY8e0VdjVq7EsXfo57u6ejBz5f0UePzMzk5Eje1Opkivz5n1fUmJbnTNnTvHQQ/UJdxc7+AAAIABJREFUCQll+fIt7Ny5lWrV/GjXLv/78fr132BnZ0+3bk/kyVVQWGy2NWQ8C/Cr8a2//Px1zJCaat7H/tq1m/V9+sC+fRAZCX/+CTlZ+FJSEkxKwN7enqpVva0t7h3RoUNXoqL+oXXr9lYZ/8iRvZw/fyZPHoagoDqkpCTh6lqZP/+MNsoRks+TKTU1hRkzJudTBNZauYSGNqRVq/ZUr17TKFNXUlKSTcZNnduTnJxEQkIc7u4eplXv+fNnOXJkHwBdujxq6nv9egKzZ08jJCS0WIogPT2NrVs1h8EbN67j7u6BwWBg4sSRNG/elgEDRhbjN7IegYG12L49ChHhyJF9vPKK9j2vX78Js2d/Q926N3NhPPqo7TwNb0dhVgSbgKdEpEw4Yhd1RWAp1WLuFQHA1q2gBQjci4dHL8aOXYabmw9JSVfx82tAXNyPLFw4ncaNWzJ37nf5xrMWc+ZMZ8aMSfj5BbJjR+keHNu27Rc2bPiWJk1aMmjQCxb71a5tZ/aAl1KKM2dKPl7he+9NwM7OjvHjw/K8YX344ST27/+TAQNG0bv3gBKf925n7drl/Pvfz/Loo/2ZPVvbqT179m8uX75IcHCdPIbcxMR4vvpqNl5evgV+N25HZmYmLVt6k5R0g88+W85jjz3DsWMH6NWrhXH+sh/I6ejR/XzxxYcmz6ENGw7QqNE9JTa+rY3FScBhpdQvgMkqJyLm4zOUUcLCwhg+fCTp6TdDHjg5udK/f97EKu3awfz5V4FWXL9em7Nn9/PoozffdDZvPsy5c2fIyMgoLdEBTMbpHBfO0uSBB7oWyqBbs2aQ2YNuNWvm9yZZv/5bjh8/SJcuj9KqVbsiybVw4QwAxo3LG+zsxx+/IyrqNIGBtXRFUAQcHZ3w8wvAy8vXVFerVl1q1cqfgczTsypjx75VAnM68vTTw/nf/1Zjb689lhwcHKla1ZtateoVe/zSoHHjFnz22XL+7//eJSUlOd/9+vXXdSQkXKN796cKdK22BYVRBKuMpVwzcOBAjh5NYcaMsWRmpuHjE0z//mF06JB3y8LJCe699yq7d/tQufL/UbduXgNjs2b9UCqFxo2blab4jBkzmWeeGYGDQ/40mZcvX2Tnzq14eFQpNSOewWAgNTUVZ2dn6tbVZAoL+4J33301n41g/PiwfNfPmDGJqKh/uHIlpkiKwGAwUL26PwZDdp7VQFpaGjVq+JOcfIMOHbqyc+fvxMbG0LZtJ91YXEh69uxDz5598tUbDAaysrJwdHS0itfLm29+wptvfmL6XK9eY/bts26OjeLyzz8nWbJkFqGhjRg69CVAC0NjjvfeG8/Zs6do0aJt+VMEIvKVUqoSEFTew08/8cRImjW7/V5jnz4N2L0bnJz6UK8erFz5Nnv2/EBU1CEcHJz497+/YtCg0t3vK8iFc9WqpXz44UQ8Pb04cOBaic+9adN6duzYTJs2D5iCuzVp4k5qagoffbTE1K9372dxda1sOuhWs2YQ48eHmfUaatDgHpKTkwgOLlqeWzs7O/76K38azOjos+zcuRWlFL17D6BJE3eSk5OYNm1usbYuKjonThzm88/fZcOGb2nW7F7WrNkFaG6Rhw7tIT09jTZtHijy+ImJ8axduxxv72o88khfU/2xYwdJS0uhRYu2Zc7lEuDChSjCw+fRvv3DDBr0guklyNzhsa5dH+fq1cu4ulophGgxKEzQuceAGfD/7J13fBTF+8ffc+khJCShBAKEFnrvIgoIItJ7C4go4heEH4qg9CJFRASxgYggEkAEEVBp0qRI772ZUEILCek9N78/9hIuyV1yd7lLAuz79bpX7nZndz+3N9lnZp6Z58ERZTVxXeATKaVtEuQWAMqWBU9PZe3AzZtw/vxObt5UVg+mpCQSExPHxYtQrpwywyi/KVasJM7OLhQrZjxhfG5YvnwhBw/u5PTpI+mGIO2fMiUlmXPnokhOTsLV1ZWuXQMMPvgzs3ixbeYeFC7sgZOTM46OTmzcuIqEhAQAvvhiMm5u7iZpU8nK6dNH+OsvJSR15jAJ3bs3RavVcu1askWzYUCZhjllyntUrlwjgyHo1q0xSUlJXL4cXyDXF1SsWJXp07/Bx8eXc+dO0K2bEkVn+PDxvPvux7i7e6SXHT9+bn7JzBFTfrVpKAHk9gJIKU8LISrYUJPNePw4lJ07N+DhUZxGjboZLbd+/TRiY/2BAE6eTKF58wG4uxfD27sMRYv6cf36EdaufUSnTmNxdYXChRVHjoMDaDQZE9JYqxGzbt0iVqz4nFq1XmD27FUZztu69SBatx6k+47WuZ4+FSrU5dq1S/j7108//759UekhG9JSdtoiKY4xtFotMTFRODo64+z85AHh6FiKefM2sG/fn4wb9w6pqcpsr4iIMMaPH0psLLRvrxqD7Fi//nvWr19Ejx7v0quX0ovy9a3NgAGjqVmzCW3a9NSrZ4LatV8ABKGhyTg7W2YIpCxCkSJFuXr1ArNnT2XYsOmcOLEv3RcXGppMoUIFzxC4upalU6f3ALhy5TQuLoWIj4/lu+8+pUuXkaSmeuRwBnOulTtncXaY8qslSykjM3XLnsqUladO7ePHH/+Hvb0TK1cmGC134MBKkpJqAQGcOKFl1qx3adNGiePz99+L2LXre5ydC9Op01ji4iAuzuiprMahQycJCQkiOVlwJY8H6Nq3/5z27T8H0Lt27uP2pKSkAFrs7c1fXPPgQRDvv18JEKxZk7E6jhsXQGxsVouYkBDHl19OpGJF1RBkx6VLd7h69QzXrz9K/70dHBrToYMSWz9zCuiPPz4AKL1nyylD376fs3jxYK5cucmVK3DhghLTsnHj7ty5U3Dj9DyhLsuWxbB9+zfEx0dx96474XqL6xMT40hKisfFpbBFdd7TE6pUsaJcPUwxBBeEEP0BO13+gP8D/rWNHNvi7OyKs7Mbbm7ZzymvX78j27YtA5IICnIgOlpp9QOUKFEJT09fihUrZ3O9+rz4Yj+EEJQta73paNYgISGG0aOrYGfnkGEabk7MnNmaCxd2U6VKc6ZN22/2ddNCgBgaNy5SxMegIYCM60ZUDNO27QgaNeqOh4dthhqNUb9+Jz777Czu7spspRo1XmH69H9xdbVeq9rahIXd5vbtCxQtWpbSpZU1A6+9NsJg2fnzu3H27A4+/ngrdeu2M+s6t29f4LvvpnPsWG0mTZqUa92ZMcUQjAQmAonAGmA78FQmJ23W7HWWL885YNugQQsZNGghM2fChQuwc+d1NJr17Nr1PR4ePkydup8SJYzH3rEFNWq8Qo0arxjcd+HCbmbPboujoyvLl0dZ/dpxcVFotVqcnV3TWzIzZ7bm5s0zdOw4hsePzY/pY2enzDRKG7oxF1/faqxZY3hu+b17xhN/SCkZObKcwRljKgpFipSgSJGMRiApKZ7du5eyffvXvPrqcNq3tywsiDGU1nIcXl6lcXNTwlC7uXlRubJlU4vzitOnt7F06VBatRrC0KE/ZFvWxcUj24RY2fH4cQj796/Dyelx/hgCKWUciiGYaPWrF3Bq1VIMwd69t3j4cDwAoaHBREc/ynNDkD0atNpUkpJskxbyk09acPPmaRo27MqHH/4OQEjIJWJiwoiIuEeHDqMxd6ho7NjNaDT2NgkNnbYK3NHRxeA9efToJj/8MBRANQYm8t9/J1ixQlk6FBJyMcO+6dNbcPv2WaZNO5jeKjaX8+d3MW9eZ+rX78jYsX+kb1+woAcXL/7D2LGbqVy5meVfwEZ4epaidu3XKFOmJpGRD1m4sDd3715m9OjfqVSpSYb6/f77v1p8nbJlazN+/C+0bm2bFfKmzBqqDIwByumXl1Iabp4+A9y6dZ4HD65RvHhtoCIxMfVxdfUkMTGa6tVbUahQESIjH+LhkXcZmk6f3sqZM9vw929Gs2YZp676+zdlxoyjuLjYZlqaEEpl1l/D0K/fHEJDg2nYsCt+fuavqbBkjNQUDhxYhYeHD5GRD3BwcMHR0ZWYmKxTapOS4li7dqJqCAxw6NCv3L59jiZNeuLnpwxFuri44+jogoODE23bZhz6SEyMITY2IlcNETs7e+zsHDh58k82b/6Mzp0/5urVQxw9qixhiouzfk/XGtSv34H69TsASh6TS5f+AWDq1GYEBiZjDV8aKMOd5cv3oXVrq5wuC6YMDa0DFgNLUSKEPrUsXfoJP/wwjeLFy7Nw4Q2j5WbOfIXo6FBA4OamJSamCPPnh1OyJFy5cpDRoyuj0dixapVlwxqWsHPn95w4sYlLl/ZlMQSOjs5UqmS7gGqffnoiy7aXX37DZtczhevXjzBnTnvc3Ysyf77i0TxwYBU//DCUpCTFex8bG54+BGUI1V9gmGPHfufQoV/w9a2Wbgj8/GqzYoXhWRHjxm1Ho7HD1dXyudR1677OvHkX+eADf3bv/oHOnT9Of6i2aDGYmjVt9AS0IoULezNx4i7mzHmNChUaotHY5bckkzHFEKRIKRfZXEkeEBMTAUiSk7MP0+DgkDZPWlKrlhKA7tw5ZeqWk1Mh3b68Xdzi51eboKATlC/fIE+vmxNRUY9Ys2YchQp5MGDAFyYft3XrQjZunE358vUZN26r2deNjHxAbGw4iYlP8kKsXTsx3QikkZpqPGWGt3fuEqkcOLCKtWsn8ujRLYoWLfvM+B1eeKE3vr7VKFvWtJ6eu7t1hivc3YvTu/dM3N2Vnra/f1O6dBlP1aovG1xRXxCQUqZPWHBwcKJmzVd0PYGs/PXXfI4e/Y327UfTpEnWldvZcffuFU6cOIqXV1UaNbJ+o88UQ/CHEGI48DuKwxgAKaXxrCMFlKFDP6Fhw3exs8t+WGLBgms8enSTlJRkbtxQDMGZM6DVfo29vSMrV1q+cMZSevX6hF69PjG4LyEhhkmTlIUs8+ZdyBM927d/w+XL+yhTpjZ79/6IEBqzDMHDh8FERT3k9u1zFl2/YsXG9O37Kc7OT4bDHj0yr4WfOc6UOWTufTxLfodGjboZXGdz+fJ+bt06S5UqzdN7CtbE1dWdbt2euCKrV29J9eotrX4da/Lrr5P444+59O49k86dP8627MOHQVy9+i8vvGB+VILz53eyfPkI4uKG55shGKT7O1ZvmwSeukVlrq5ulCqV80Tco0d/S2/peXqWBWZx8mQPTp6cDETSsuXbNtdqHposDjxrMnNmG4KDT9Gt20SdYxh27VrC7dvniIwMpXjxCri4mDfP+6WXBuLqWphSpapZpKlIER+6dBmXYVvRomV59Mi0yexubt65emAb6n08y36HmJhwpk9X0i4OHLgggyH4++/FXLv2L6++Ohx//6YWnX///kD++msezZsPoGPHMenbjx7dwMmTf9K4cY/0sfiCREpKEqmpKQihTNjYsuVLpNTSqdPYLGXbtfs/XnihDyVKGI5FlB0+Pv60ahVA48aNrSE7C6bMGipI02NsTuaW3uPHN4G0+ETdcHb+Lc97A6C0+lNSknB0dMXRMeMKS0dHZ5o27YODg5NNkrTfu3eF2NhwHj26nb6tRo3WSKmlQYNO6cbBHCpUqE+FCpYnpDE0LNOnz6wMvx0o01SFEOnrDhQEgwYttPjaYLz38Sz4HW7ePENKShK+vtVxdlaGQjWaJ3U+c2/gypX9HDy4mlq12lpsCCIj73Pz5hlcXYtQrlw9qlZ9mYiIe+zbt4ITJzZTvHj5AmkI+vefq+tZCqSUrFqlGLHdu5ewYEHGacwlS/pTsqRlsbVq125LixZtee213Co2jNF8BEKIj6SUc3Xve0kp1+ntmy2lnJDjyYVoByxESVW5VEo5x0i5HsB6oJGUMttkA7lJXv/uuzPYtGkF9ep1MPogGDmynJFWpR81atxg3DjJsGEl0Gq1/PijDeI5GGH69Je5fHk/1aq1ZMqUPXl2XYAjR37j3r2r1KvXwaIZQobIzfj6gQOrWLJkCMnJT1aHp6UdBaW1HhZ2C2/vsunDP2nXcnEpTPPmA3jrrW9zpd9YPcmc3+JpZOLERvz333FmzDhCpUpKC1Sr1XLv3lUcHZ2zLKa8eHEvoaE3qVq1uUWtXYCoqFDCwu4wYYLSOPjuu7usWzeZPXt+xN//Bd5+e5FNhqOsiZSSb78dwMGDq3F3L8b33z+06vk9PcmVIbA0H0FfIC1K0nie5CwGJcdwtoZAl93sW+BVlBzHx4QQm6WUFzOVKwyMAo5kdz5rcPHiIR48uMH16/9SvjzY2UFSEoSFQVr+c+PjzLe4eNGOqCgtMTGKeyQlJclm0yAzo9xO2027zI7sHFtJSQmEhd3G0dEFb+/SRsvpk/lBbu74+tq1EzMYAUWHMizz9dfBBs9h7eEaQ70PUHpuBw6seqqHh3x9lbUA+it6NRoNvr5VDZa3xji+u3sx3N2L0ahRN2JjI5BSUqRISUqXrkGnTh8VeCMAyir34cNXMnjwt2i1WSdYXr9+lAsXdlOhQkNq1Wpj1rkTEmKIiUkhOblQhkyB1iI7QyCMvDf02RCNgetSyv8AhBC/AF2AzIPZM4DPyOiDsAlDhgzBx8eHRo1eooTewslSpeDRIyVFpbFxZkfHsiQlwV9/aejb91PdA9n6i6GMkVMvYPPmz4iNjaBLl/G5msZnKuHhd3n06CbBwadYvvw9nRPdtKQ52T3ITXmAFoRhmTSdixe/TWrqk+8dExP21DuNhw9fYXB7ePhdtNoUPDxK6M2ssy6jRz9JfdK79wx69y7YQQz+/HMeV6/+S/v2o6latTkajYZChYoYLHvlygF++WU8r7/+vtmGYO3aiWzb9hVffvklo0aNsob0DGT3JJNG3hv6bAhf4Lbe5zu6bekIIeoDZaSUf2V3IiHEUCHEcSHE8dDQUBMubZiOHbvz+efL6N17cJZ9RYtCtWrQt+8sHB1dM+xzcHAiNdUZSGXLlhTq1RtHhw6j88VXYIxffpnA5s3KIi9rExj4IQsW9OD69SedtsWL32Tq1Gb8/vsMQJjVU8ntg7xoUcPTPnOaDnr79gUWLRrMqlUfmXSdnGjePCCDEUgjzag9a7z3ni8jR/px6lTGf9eQkMscPryOW7csmwEGcPLkn6xbNyVDHQN4/Pgex45t5MqVgxaf25Zcu3aYY8d+JyLiHgD371/nzp2LBnsEFSs2plOnj4yGiskOjcYeV1d3ChUqlHNhC8jOENQRQkQJIaKB2rr3aZ9r5fbCQlmuOh/IMeO1lHKJlLKhlLJhsWLFcipuMYUKwRtvBPDOO0soWtQPIQRFi/rxyivvkJp6BVgE2LN4MURE2ExGBg4cWMXIkeXo10/DyJHlOHBglcFyxYqVw8PDxyYttT17fuTo0Q0Z/hnd3LzRaOwoU6YWa9ZoTYrhlIalD/I0+vTJaqwNpR3NzH//HWffvp/YsSN3/gFTePTopsGHwbPIkSPrWbiwN4cOrbX4HGfObGPDhhncuHEsw/br148wf343Nm/+LLcybULnzh/zwQe/4e+vxET64AN/xo6twY8/Zk2CVLVqc/r3/4wGDTqZfZ2BA79gw4ZIhgwZkmvNhjDapJVS5nZZXAhQRu9zad22NAoDNYG9ugUZPsBmIUTnnBzGlrJu3SpOnjxN69YdadKkhcEyxYrB668HZOjWh4beRJkV8ICjR5MJCnLg//4vjK5dHWjUyB1vb0jrHKT53vV98FJm3a7/11jZo0dXsWpVxrnqixYNJjYWGjd+ou/o0VWkpKQSGfmAWbNeo3PnWRn255bKlXsSGhqEl1ej9Dj0AweuYeDANYD5ORA6dpzF6tWZx9cF1aq1N+lcNWoE0LFjKH//PYfo6Id4eZWlc+dZ1KgRkO3xbm41KFr0Bby8fK2Wt8HLy4/wcEOTCzQ8fJigtwAxdxiZ02ETpkx5gaSkOKZM+QdX1yfDHH37/kxqajKlS7fNEF7Zw6Medeu+TZEiDTNsN4dKlbrj4FCBv//exOrVCxg48EtOnNjE6dNbcHevg69va4vPbUs8PRvh6anM6w8PByenSiQmxnLs2BF6mLdmLEe0WggNVZ5R1sborKFcn1gIe+Aq0BrFABwD+kspDa54EkLsBcbYctZQlSo1uHr1Ih079uHrr38xWi4sLGvM9TQeP4bhww8AzS3SYB7lAMMzmCBY934VMBTQf6i6AkuAgjxGPRwlcol+/XsadGfmab3/Kk8jTZrA4cOWHWvprKFcIaVMEUKMQAlbbQcsk1JeEEJ8AhyXUm621bWNUbNmHSIiHlO1avYzELy8lIxjiQZ8n56eULXqVB49aoKv7zgePHAnIgJS9UYBDGUnM5S1LKdyUVHGZzAV0TXUIiMnogSI1ScOISbi4WH7B1FSUjxxcZFoNHbpceRNITJyC1kbIabrjo19THJyAvb2Tri5eZmp2jpIKdFqe5OcrCUxcTJS3kJxg43Dza0XBciFZBZSagGpCzaYt6FUpNTq6r9ASi1SSl3MnoKXrxggOTkBKbU4ODilz+wzXjae2NjHODg4U6iQeXU2Pj6S1NS0iAaGndG5wWY9AluRmx5BWBhEmziUfe/ek4xLMTHhLFjQk4sX91CtWgumTNlr0fXNxZS56v36aTDkuxdCsHq1dRLJXblyEK02lfLl66eHdPj118n8+ec87O0diY+PwsXFnWXLTM9VmRe6M6PVarl//xoJCdFUqGCwYWQW587tYvbsNmg09vz442MOHlzD0qVDqVChIVOn7sPR0cUKqvOOpKQEEhKizTLooBjEtBW2aQvQzCUk5DIJCdGULFk5SyIaJcR6gsXntiUTJjQkKOgEM2cepWLF7EM/XLy4l2XLhlOjxisMHvyNWdeZMaMVFy/uZffu3bRq1coirdn1CPJu/uNThpeewY6MfMDFi8r0zf/+s4n7wiB9+szCwSHjwySzUzS3jldTmDmzNZ980oJz53alb4uKekRycgIODs68/fZihg790axz5oXuzERHP+LDD6sycaJ1YrWkpCQihEhf0f3jj//Dzs6egQMXcOnSPmJi8m7BoTVYvPhN3n23OA8eGI/Ma4izZ3fwxhvOLFjQ3eJrBwZ+yKRJjbl8+UCG7eHhdwkIsOeDDypZfG5b0qBBZ15+eVB6oLxJkxrTr59g9+6lWcpWr96SefMumm0EAN588xs+//wf6tWrl2vNhnhKO6+WERERQWKiI87OzjmGYXByAjc3iIlRZsi4uXkTExNGhw5jCA4+TVRUKBUqNLDpsETz5gGsWvURUVHJaLUpODq60Lbtexkc2YYWNpkyg8YcnJ0LEReXgqvrk3hCnTqNoXr1lhQvXo5KlZqYfc680J0ZZX63QKPRWCUUR7167TP0Xjp2HIOXV2mWL3+PW7fOMnXqPqpWfSmXqvOOtFk/Cxf2oUqV5gwa9KVJxzk5uWJnZ09uhm98fPwpX74BN2+e5uDBVVSv3pL7968TGXnf6DEREfcJDj6dIU1kXtOjx5QMn9NmPa1Z8zGvvGK9GT5lytTA05P0IWFr81wZgsaNKxEeHsbo0Z8wcuTkHMt7eiqGwMOjOD/88Ch9++DBhUlIiOHttxenJ7W3Fcr8ZImrqwdxcZFZkn+kGYXMYRWsuZjphx+yJnYpUaKixeEE4InuX36ZQFjYbTw9S9G//2cm6x47thb371+jd+8ZBgN8GcLe3jFLontr0r+/MsUxNDSIIkV8cHKyTaIgW/H+++u5efM0v/8+k/DwOyYbgipVmhsNvWwqadfas2cZv/46CQcHJ06c+IOYmDAWL35gMAnUsWO/s2zZcACjaUvzmg8/3MSGDdOpU8e8nMT5zXNlCNJaLM7OrjmUU/D2htu3s253dfUkOTnR5uEetFotffvOZteu74mIuIebm7fBoF7Nmwfk2yrWc+d28ccfn1GmTE0GDpxv1rHNmwdw4cIe9u79EQcHJ7O+Q2TkfVJSEklMNJwsJT8x9z4UFJo06UGdOu0oUaKSroVvGkJYz5FbvXoLRoxYhY+PP1WqvERCQrTR5PUeHiUMbs9LIiLuo9HY4+bmhUajoWHDzjRs2Nlg2ZCQS8yd2xEfH3/Gj99m1nW2b/8GO7t4mjYdioeH4fuRG54rQ3D1aijR0coD1hScncHFBeLj4dixTRw8GEjNmm349tu8CWWg0Wjo0mUce/YsJTk5kSZNeht9WH733SDOnNlKixaD01umtuTChd38+usUHj68QUSEEjnSkgdg2tCaudmcxo3bTmhokEXDUtbijz8+57ffpuPnV5fp0w/kfMBTwJEj69izZynduuXcY7YF+j3NnJyvDRt25dtvQ3ByMq1hZws++qgW0dGP+P77hzk62aWUPHz4n0WLPjdt+pTHj+8ybVo/1RBYC3PGhr294c4dmD+/KwCXLu2z+XBQZurUeZ3Ll/dnG+I3JOQiUVGhuVrmb4zBgwuTkpLMvHkX0v9Jg4JOcfXqQYTQUKlSU8qUqWnRuQMC5hIQMDfngpmwNIz18OG+xMSEM2XKvlyn93z4MIjExFjCwgx0G8mYvaqgk5KSxPHjm9i163uuXTvM6dNbqFPHtFCXiYlxzJ/fHZCMH7/doutPmfICDx8GMWXKP1lyhnz33SDCwm4zcuQaihR50gvQaDR4eZWy6HrWolAhT6TUpo8ObN/+Dbdvn6Ndu1FZ/BY+PpVYsOBallXxptC27XukpITbxAjAc2oIzMHTUzEEaVj6wLOEnTu/59KlfTRt2psWLQYRHx9DTEy4QQd1166TCA4+Rc2a5scxyYmEhFiUqZ5PDKi/f1Nq1HgFH5/KDBny9GQyjY5+REpKElFRD3J9rvbtP6BChQYUKVIyw/ZVqz5i27aFBAR8Trt2/5fr6+QF0dFhLFzYG4DRo383Kx6OnZ0DZ88qeYstNX5RUaFERj4gKSmeQ4fWotWm4uLijqOjK9eu/cv9+9eJj4/MYAi02tR8zwu8YMHVDJ9/+mkkAIcPr2Pp0oxLoe3tHfHxsWz2U9cRJ8KqAAAgAElEQVSuE/D0hMLm5X8ymefKEDRoUJGoqEh++mkbtWubNo+8UCFlBpG+M2rChIbcvn2OLl0m0LPnVFvJZffuJQQFnSQ2NpwbN44SExNO794zM6TzS6NRoy40atTFJjo+/vgvkpMT8PZ+EjOwSpUXmTRpVzZH2Zbp018mNTWF999fb1arcNiwn9FqU6hcOfcrw40lGklLhJPZsV+QEUJD48Y9cHZ2o1GjrmYda2/vwMcfb83VEM3MmcdITk4gJSWRr77qi6OjC0lJ8bi4uDNmzCa0Wi2enhliVrJ48WD271+JnZ09ixc/yLeFhfo0adKTI0fWm5zvuaDwXBmCO3dukpqaSkKCef+gXl7KArM00lqVkZG5b1Ua48CBVdy7p8S5uHRpX/pD5a+/vqBYsXJ56hyuW/d1o/sePAji5s1TFC1a1qJFWiEhl5gy5QXs7R3NSuShzDeXJCeb91s2a2Z+vlhz6dlzOr16fZIvuSMspUiREnzwwXqLj69bN3ezZNzcPAGl99mkSS8cHZ2JiQnHwcHZaL6D+HhldWhqagqxsREFwhC8//46o/uSkhJYs2YcGo3GLH+aVqvl9u1zxMQ4Azmn2rWE58oQzJ79FffvP6JKFfOGd7y8ICgohsTEOFxc3Bk1ai1RUaH4+dW1ic7M6TL159rHxj42GO/+3Lld7N37I6VL1zDYY7A2O3cuYe3aCcTEKFNLCxcuypIl5ocIt7d3JC4uEnPnoDdu3J24uEg8PErmXNhG/PnnF5w+vZWmTXtl8BtlTiWqYjrOzoV4//1fTSo7evQG9u1bgVabSuHCRW2sLCtJSfFMm/YSLi7uTJ68O8fyUmrZtm0hDg7OZhmCpKR4xo2ri5OTC0OG2GaW3HNlCN5+e7jJISb0KVxYcZgCFClSkkWL7lpZWUYMJUbXx1ASlxMnNvHvv2vw8ipjVUOg1WqZPLkJGo0906cfRKPRcODAKlas+D9SUvQTsoRblJnL27sMAQHzzA5rYGnrdcWKDwgOPkGnTh9Rv35Hi86RxpEj67h+/Qh2dvZ5PoHA2qSkJKfHzLGEgwfXEBZ2m5YtB5v9WwIsWfIOQggGD/4mS0/q7Nkd3Llzgbp122dwJAshaNHiTYv0WoOkpASCgk5kSEQTEnKJ8PAQfH2rZxmydHBwYuDABWbfY602lTJlauHsbLse5nNlCKyR0z0vYjMZT5f5hMxJXPz9X+Dcub+pWLGxVbWkpCSlh9VIm221du3EDEYAlNaOqRnG9LG3d6RjxxxTUliNw4fXEhFxj4oVG+XaEDRo0AWNxo569TImVb90aR/bt39N5cov0r79+7m6Rl5x5Mg6vvkmgBdfDGDEiECzj//rr3kEBZ2kRo1XLDIE//yzHK02lcGDvyUpKYG4uEjc3Lywt3dg374VujzAxbPMKMpPXFwKM3PmMV2QPoWJExuSmBiHv39TPvnkUIbyGo2dRfXB1dWduXPP4umZa8lGea4MweDB3UhNdWDBgtVmZxebN28FN24kUb9+d9avn86pU3/SrFk/OnQYbXWdxtJl6pM5Js+LL/bjxRf7WV0LQN267TNU9vxOFRkV9Yh//lmOl1dps79zkyY9uXXrHFWqvJxrHV27jqdr1/FZtoeHh3DkyPp8n9FiDsnJidjZ2Vvs8G3WrL/OCJg/RCOlZOjQpekaJkyoz+3b5wGlgdOmzf8oXLgYPj4ZHfNffNGV4ODT9O49gxo1XsHLy9fQ6W2GnZ09FStm9Is5ORUiMTFON9z59PDcRB9NTk7G0VHpWt24kWpRnJmoKLh0CSZObMKNG0epXfs1s1cImkLm5O6ZcXR05Z13luTbamJToqKaw+LFbxEaGsRbby0ymiBdn1OntjB3bgfs7BwIDEwy+3q25tGjW1y/foRixcrluCiqICGlREptvhuwtEibAJUrN2P69KxpKlNTUxgw4EkS9w8++I3GjS0PemcOycmJXLlykBs3jlKpUhNq1HgSDfTRo1vcu3cVb+8yBnsvly7tIzExlpo1W5s9mcDTE14zbWmHQfIlH0FBIyUlhbp165KamkqFChqkhORkZdVwTIzyPifc3cHfH0qVqsytW2c5e3Y7I0eWs3psn8zxg9Jil8fGhhuNJZSUlMCdOxdJSUmkcuUXrKbFEIYCxgmhsThg3L//riE5OYHg4FMmGQJ7eydcXYvk+yyRs2d3EBcXSeXKL2YYDy5atKzR6KoFGSFEjjH184LJk5VIv1ptKqmpKQbLCCH4+OOtrF8/laJFy1KkiE+e6bt27RCzZrVO/zxkyBJat34HyPm3//LLnkRFhbJo0f0MayKy4/bt83z2WXv8/evx2mubcifeCM9NjyAnIiOVPMTGbkft2kWIjo6kWrXaBAVdJyHhyUPQycmV8eOX0KFDAHZ2YGenJJfRaJ4kmjGUhCYzuVmEevDg37z1Vlvs7e25cCF3AcD0iY2NYcOG5RQq5E737oPSt//xxyrGjRtMSopyrZIly7B3r2VDQ++9153w8IeMG/cFderYNmTE5ctnuXTpNBUrVqN27dy11ps2Lcbjx48YMWIqI0dOs47Ap5SIiHDCw0Px9CyKp6e3WccmJiawZ8+fuLm507x52yz7U1NTuX//DtHRETkmlcoLduzYwMiRT/JQVq1ah02bTpt07Acf9CU6OpK5c1fi5WXaMNqpU4fo27cZ9eo14eRJC9OTkX2PQDUEesTHwwMjSwPKl1ee0mkhjDPj6+vHgQPBNtFlChcvnqZTpwY4O7ty4YIFU6OMcP78STp1aoBGo+HGjYzJ2Hv2bM6JEwepWLEq33+/kYoVC44jzxi9er3E8eMHeOGFVqxenfOUv+x47bWaBAdfZ/r0r+nb95307VFREezc+QfOzi60b98zt5LzhEWL5nD48F7eeWcMzZu3Mfv4yZPfIzDwO6ZN+5pBg0aYdey9e3do1qwMPj6+HDp0J8v+y5fP8frrtSlfvjK7d18xW5stkFJy69Z/bNjwM97exXnjjfcA6NevFYcP76Vz534sXLjaKtdKTEwkNPQ+Tk6CRo0s72nmW2IaIUQ7IcQVIcR1IcQ4A/tHCyEuCiHOCiF2CSH8bKUlLi6OXbt2cfLkSaNlXFyMx/uuU6cxrq6FjAasCwm5SfPm5di4cZU15DJ9+ij8/R3p18+0bETVq9flxo1UqxoBUGb1uLm54+2dNQzwl1+u4ujRe+zceempMAKgGGxnZxeKFcv9UML27ee5ciUhgxEACA29z4cfvsEXX0zK9TXyivPnT7Jv33YeP36Uc2ED+Pj4Uq6cP66u5mcRc3BwpF277rz8srIobc2aHyhfXlC+vGD58oWULVuB4sVLUqpUmfRZe3fuBBMYuIiDB3eRmJhAYqJhf5qtEELg51eRDz6Ynm4EACpVUuIL+flZL5GOk5MTpUv7Ubq07YYbbZm83g4lef2rwB2U5PX9pJQX9cq0Ao5IKeOEEMOAllLKbJd+Wtoj2L17N61bt8be3p7kHBwCd+9CkhEfZPPm5QgJMT6jx8XFldmzl9C1a+58Bu++240dOzZSrVodtmwxrdv5tHL16gWuXr1AxYrVqFatVo7lx417h7Vrl1KlSi22bTubBwrNIywslJkzR1OyZGk++ujT/JZjEleunOfu3VtUr16XEiXyN5Db11/PZP58Jfpp9+5v8MUXK7KU2bZtA8OGPRmeGTlyMqNHf5JnGkHp+Z0/f5KiRUtQuXINQPFFXrx4mho16mFnZ11/i5MTlMzF+sn8chY3Bq5LKf/TifgF6AKkGwIp5R698oeBATbUg729PYUK5dxicXeHR3oNo40bVzFv3kTu3r2Fh4cXDg6OJCcbthTx8XHMmzcx14Zg2jRluMHLy/w52XnJ11/P5K+/fuW117rzwQfTLDrHBx8M4OLF07Rp05kffsjZGRYdrUzN05/SWpDw9i7GggUr81uGWVSpUtPsFfe2olu3gZQv709ERLhRP06pUmXp3/9d9u/fwYMHISaHls8tUkratatF8eKlqFixKitWfM1rr3Vj8eINgPKMyS6OWZ8+LThx4iDr1h2gXj3j0YT1OXPmGL/9toJGjZowcuRAq3yPzNjSEPgC+vF57wDZeQLfBrYa2iGEGAoMBShb1rLu0SuvvJJjTyCNQoUUx3FKimIEJkwYSny84hyOiAjLcerp3bu5n09fsmRpSpYsbdYxtWsXISEhnn/+uWH2sZaS1nLTaDQWGwJfXz+uX7+Mh4dpK2YWLlzNtGlfWXQta1K1qgspKcls3nyc6tVtE27keSA1NZXU1FQcHBwQQuiGQQyPEqelGK1duyG1azfM81DfoaH3uXr1AqGh93nzzf9jxYqvzUriI6UkNTWVxMTEnAvruH79IitXfkt8fJTNDEGBSF4vhBgANAQ+N7RfSrlEStlQStmwWDHbt5CFUHoFAPPmTUw3Amnk1PooVSp/pg7GxESTnJxk8TivIbZsWU/58oJatbLGQdf3hwQHX7PYP7JkyUauXIln3ryfTCpvb29PsWI+Fo3zf/HFZCpWtOOVV3Lv00hKSiQ1NRUnp6yxhaKiInjw4G6erES3BqtWLSYwcBGxsTEWHb9r15+0alWZadPMD7t94sS/VKniRO/exvM7r1//Ew0blmDu3IwL+PI634OXVzF27rzM999vpHXrjmzffp75803v/a1cuYOrV5No0sT0BY116jRm2rSv6d7dduuGbGkIQoAyep9L67ZlQAjRBpgIdJZSmm4mbUyhQopBMLd17+LiypgxuU/APnJkX7p0aczevQY7SQb55pu1/PDDJsqVq5zr66eRZgQzz+dO6ynpl5swYajVnOWG2LhxFc2bl6NCBY3FjvnU1BS0Wi3R0VG51rNt2zl+/XU/ZcpUyLKvUSMfmjb1zXMnpqV88cUkJk8enqXRYypJSYkEB1/j3j3DSXqyIzU1BQcHBxwdlRg89++HMGxYD7p0aczFi4p/zMnJmbCwhxw+vJcxY95k69bfePDgLknGnHk2wt7enooVq9CokRLGvHLlGjg5mR47yMnJOb3nYyqVKlVj0KARtGyZi9VkOWBLZ7E9irO4NYoBOAb0l1Je0CtTD1gPtJNSXjPlvJY6ixctWsT7779PhQoVuHTpkknHPHgADRpk7xzWR6PR8MUXP+faPwBQp44nUVERTJgwj3feybtYPJlJSkoiNPQ+AL6+T3o6xpzmuZlGq++LKVWqLGPGzEq/l5mH6AAcHByYO3e5Wfc7PPwRd+/ewtu7uE2Hz5o3L0dSUiJ//33R5CGv/OTzzycQERHOlCkLzXqwpREdHcXDh3fx8PCiaNGsM8xMIW2Y59y5E3TurIyzjx49g5EjJxEdHUV0dCQjRvTm1Kknc+kHDBhGcPB1atSox7hxtk/Rmp88lc5iKWWKEGIEsB2wA5ZJKS8IIT4BjkspN6MMBbkB63QW8paU0nDm51xy//59kpKSCAsLM/kYNzcYM2aWwQcQiCwO4xYtXreKEQDo02cIV6+ep3Hj3MfEyQ2Ojo4ZDEAaxnpKlvhHli6dz+zZY4AnQf1CQm6m9zi6dg0wOESXnJxstmPey6uoyQt5ckN+rimxhLFjZ+fq+MKF3Slc2D1X50hrJfv4lKZcOX+Cg69Ro0a9DOf/6qs1jBjRh3Ll/Pn3313Y2dlz4MDfeTZxYPXq7wkJuUX37m9YNGV6+fKFHD68l8GD36dp0xYmHXPjxhXu3AmmatXKlCxZ3uxrmoJNQ0xIKbcAWzJtm6L33vyVKxYybNgwGjRoYFbOT1dX0sflMrdU9bc5Obng5uZO27bmZXbKjgkTDLpLsmX8+KFcvnyWUaOm0rKl8WQy1qBUqbIGewSW+EeUGDdZe6b6M7CsaXisQWjoffr0aYGHhye//275ak+VrBQrVoI9e64a3Fe6dDk2bjyS/vnx4zBatepAsWKmhWvILZs3r+HIkX9o1uwViwzBuXMn2LFjo+5ZYZoh2LDhZ777bjYffTSDJk1sszbluYk15OPjQ+fO5nU2hFCMQdeuAQZbndZq/VuLHTs2Eh4eysmTh6xmCLZsWc+nn47F3786y5b9lb7dUE/JUv9Ijx6D+PTTsQaNQZqxMWZ4PDzMizd0/vxJ3n8/AA8PL377LWswM1N58OAuQUFXLQpeWNBITEzkzp0g3N2LWLzQLiIinO+++xRnZxez5/Pv27eDpUu/4OWXX2PIEPOi+Xp6etOihe3GzjMzaNBIXnihFf7+1XMubIA33hhB27ZdqVXL9Gx+fn4Vad78VcqVs94itcw8/bXYxpiTLNocZ2Z2ZVNSUli4cDqrVn1vltbOnfvx8suv0aDBi2Ydlx3Xr1/kzp1gTp8+mmF7164BzJ69BF9fP4QQ+Pr6WbyQbt++7dk+UMuXF8TGxhiMihkbG22W0zgsLJQbNy5z9uwxs3XqU6yYD926DaRnz8EG90+aNIzOnRumOzsLMkFBV2nTphoDBljeQU9IiOeHH+bxyy8/AEoDIjLysUnH3r17i/37d3DtWrr7kJMnD/H335stdl7bitdf78GoUVMpXtyywfq6dRvTrl13g8Otxujd+y1WrtxB1659LbqmKTw3sYaWLFnC0qVLad26NZ9+at5qz5CQnKOTLlv2JZ99No6kpCcTn4ytMjbk+BRCIKXE19ePIUM+ZPp0ZRpeUFD+/j5nzx5n7dofKVOmPP/730dWP7+he2Eu5jio7927w5w5H1GsWEkmTfrC4mvmREBAa/79dzeBgTt58cXWOR+Qj1y8eIb33utFhQpV+PHHPyw6R0JCPD/99DVFinjRtGlLWrXyp0gRL44cuZce/l2fw4f3sm3bBsaMmUV0dCRXr56nWLGSVK+uBJVLi+01ZcqXDB48yuh1Y2NjWLduOfb29gwYMMwi7U8LtnQWPzeGoFevXqxfv56aNWty7tw5s46NioLwcOP7ly//ik8+MVxZDT2kcgpT4eTkjJ2dPa6uhTh27L5ZWp82croXpiCE4L//bOsszG5GkyGuXDlPYmIC5ctXzrUT9Wnj0qWzfPrpWHx8fJk7d1mW/ampqfj7OyClZPbsJfTr906WMmmGYNasxfTvbzwNaFhYKA0bFsfT05uTJ623fsYQoaH3OXv2OOXLV6ZCBcumaMfHx7F9++/cuRPMiBHZp5S9fz+EadNG8u67H1GvXlObGoLnZmioS5cutGvXjn79zM/i5eamhJY2xMaNq/j886xZqtIICblJxYp2lC8v0oeAcnJwJiYm4OnpbbYROHv2OIGBizh27IBZx1ljfr6lWMPZa+sFfGm9lpCQm0gpCQm5ybhxQxgypAvr12eNgwNKyIbatRs+d0YAoFq12vz445+4uxfhrbc6ZPH92NnZ8dVXa2jU6CX69Hnb4DmCgiRBQTJbIwDg5laYAQOG06+f7XNGHzt2gCFDOjFnjuU949TUVMaMGcSXX04lJib7AJFz5nzM9u2/0737C1Sp4sSWLRssvm5OPDeGYMCAAWzdupUJEyaYfaxGA95GQqwbmtaYmbSVyCEhNxk9eqBJq01DQm6a/UCeOXM0kycPTw/7YAqGHnL6C8OOHNnH9Omj+PXX5WZpMZXcPsTNdVD//nsgtWsXoXx5wYsv+pl0jw39xomJCezatZmxY9/Mc+NpbWJjY6yyAvrgwV2sW7ecxMQEHBwc2Lx5DXv2bOHmzRtZynbs2Idff92HRqPh5MlDjBrVnzNnzPfbODk5M2PGt4wdm/tFnDlRuLAHL7/8GnXrWp4zw82tMIMGjWTMmNlotanZlq1duyEVK1bFxcWVpKQkm2aOe26GhqxBeLgyTKSPku3MNvfQxcWVb79dQt++pjlg//e/t9iwYR0dO3Zh2TLTEpBXqVKO27ezDs2UKePHlSvBDB/+Dj/9tJTy5Stw4ULWf+jc8ssvq3jvPct8BGXK+DF9+iyT74+ha7m4uDJgwCB+++1XwsOVNSbe3t58/vnC9PMWKpTzb5z5t9q9eyeHD/9L69av0qSJbTPG5ZZ+/Xpw7twZfvhhBS+8YPlEA1dXZTjn/PnrVKhQkbVrV1O4sDsvv9wSNzc3AE6dOknduvUyrKz9+OPRfP31AqZMmcG4cU9P6O68QkpJUlISDg52FCpk+UTP7IaG0udwPy2vBg0aSHMJDAyUxYsXl4D08fGRgYGBZp8jjYQEKSMilFdMjJRly/pJwGYvPz8/i7WaghAi22sHBATIMmXKyF69etlMQ2BgoPTz85NCCOnnl/P9dHV1teg3NOXcaS9HR0c5bNgws47R/61Gjx4tAfn5559b8U5lZdSoUbJLly4yJSXFouNTU1NluXLlJCBv3bqVKy3jx4+XTZs2lf/991/6ttu3b8sxY8bIiIgIuWPHDmlnZyffffddqdVq08ts3LhRtm3bVp4/f96i696+fVteunRJJicn50p/GikpKfL06dNWOVdu0Wq18vTp03LTpk25PhfKQl6Dz9V8f7Cb+zLXEAQGBkpXV1erPEhMPb+1X7nVp/+Qzfy9c3rQWfNemUp2mgx9B1PJzuhZ4yWESL/W1q1b5eTJk+X+/fuz1aT/QDSXx48fS0C2bNlSpqamZlv29u3bRvelpKTIY8eOWawjO/r37y8B+fbbb8tz587J0qVLyxkzZlj1GqVKlZJAhu8YExMjg4ODczw2KipKrl69WqakpEitVisTExOln5+ftLOzkw8fPsxQNiIiIle/lz6XL1+Wy5Ytk9HR0Qb3Hzx4UEZHR8tLly5JQBYtWtRiY5/Gc20IjD1UrNnS1n/Yent7Szs7O6s+XCx98JliBAMDA6VGozG5pZsX2Mp4m9O6t+Rl6n26cuWKXLlypezTp4/09fU1+jCQUjEUDx48MLjv2rVrslWrVrJNmzYyPj5erly5Us6YMUPGxcXJBw8eyJdeeknu2bNHDh8+XNrb28uTJ09acttyxcmTJ2WbNm1kcHCw3Lp1q/zuu++s9jBN46WXXpKVK1fO8OBv166dFELIv/76K9tj58yZIwEZEBAg33nnHdm8eXNZq1YtWb58eXn48OEMWn18fKSLi4u8d+9erjU3aNBAAnL37t1Z9v37778SlF7pw4cPpbu7u3RycpJ3797N1TWfa0NgrBWo33qzNoGBgdLb29usB0hgYKBRraY+YHr27JnhOGMPeDs7u/QewrBhw3I0XEIIGR8fb7P7ZYicejKWntNWvTdzDNV7772X4dgtW7ZkeODot87nz58v3d3d5bJly4yeT6vVylWrVklANm7cWIaHh8vZs2dLQLZr106OGjVK2tvbyxUrVmQ4LikpScbFxZl5Fws+Wq1WNmvWTAJZWvWZ+eWXX2SFChXk2rVrpYODgwTk8ePH03+POXPmyAEDBsiwsDDp6ekp7ezsct0yl1LKqVOnyh49esjDhw9n2ZecnCwrVqwop0yZIqWU8sKFC/K3337L9TWfa0OQFz2C7Mjp4aP/AMnuQWzKdXJq2VujxZvXw0TWJjAw0Ko9tjTDaui+3LlzR86ZM0eGhYXJgQMHypkzZ8rk5GT5888/yzp16sglS5bIS5cuyb1790ofHx85a9YsuXHjRgnIRYsWSa1WK3v06CEBuXXrVimllKtXr5YDBgzIYpjj4+Nl/fr15ZkzZ6SUykN+6NChMigoSEZHR8vjx4+nl/3mm2/koUOH5Jo1a6Snp6ecP3++De94/pGYmJj+PiYmRi5cuFAmJibKffv2yZ49e8p9+/ZJKZUHr1arlXPnzs1gcLVarRwxYoQE5LZt26SUMtvem6VERkbK/fv3y+vXr6dvS0pKsvp1nmtDYGsfgaka9IeOvL29DbZ0c2O0bD3skV/3zhZY01dg7H5otVpZvXp1CcjOnTtLQNasWTO9pZn2NzU1VdapU0cC8sKFC3LWrFlSCCG//PLL9HMdO3ZMarVaGRMTI4sWLSoBuXz58mzH/bOjWbNm0t3dXXbs2FEC8ttvv7XoPAWNn3/+WS5fvtzgvnHjxklAtmrVKt1vMWnSpBzPOXLkSJPK5Ybvv/9eOjs7yw4dOsjw8HCbXee5NgRS2maYwRbkxmjZ2hFqrmEqyGRnNB0dHXP8/mn3Oqe69NVXX8levXrJEydOyMmTJ8s///zTYLmQkBA5fvz49CGHU6dOGR1+OHPmjJw8ebLcuXNnupExl+bNm8tXX31VhoSEyJMnT8rIyEizz1GQSGu1A3LcuHEyKCgoix/i77//lo0bN5YnTpyQd+7ckTNmzJBBQUH5I1hHamqqPHfunAwPD5e//PKLrFKlilywYIHNrvfcG4KnCUuNlrV7BNn5OGzpX8kLDBlcIYQcNmxYjkNHBaUhsWnTJunq6irffPNNs4+NiYmxusM2P9H3jQ0ZMkQ6OjrKEiVKZBk+K2jfuVevXhLIs/qkGoLngMDAwHRnV26HOoYNGyZdXFye2R6BlNkb3IIwnJgTQUFB8r333pMhISH5LSXfuXnzprxy5Uq6v6VkyZKybt26+S0rR+bMmSPbtm0rN27cmCfXyzdDALQDrgDXgXEG9jsBa3X7jwDlcjqnagiMY+5spcyvNKdndr2LgvZAtBVPy3CiSla0Wq0MCwvLbxk5EhISItu0aSNPnTqVJ9fLzhDYMmexHUrO4leBOyg5i/tJKS/qlRkO1JZS/k8I0RfoJqXsk9158zPExNPCqlWrGDp0KHFx5oVtEEKg1WrRaIyHVAgMDCQgoGAl5FFRUcmZ/Io+2hi4LqX8T0qZBPwCdMlUpguQFr5xPdBa6AchUbGIgIAAlixZgp+fkjTGz8+PYcOG4erqmu1xZcuWzfA3M35+fqoRUFF5BrGlIfAFbut9vqPbZrCMlDIFiASMxPlUMYeAgACCg4PRarUEBwfz3XffpRsHgMz21tXVlVmzlAiOs2bNymI09PerqKg8WzwVYaiFEEOFEMeFEMdDQ0PzW85TS5pxkFKycuXKDD2GJUuWpLf2DfUo9PerqKg8W4aZ0EIAAA3KSURBVNjSR/ACME1K+Zru83gAKeWnemW268ocEkLYA/eBYjIbUaqPQEVFRcV88stHcAzwF0KUF0I4An2BzZnKbAYG6d73BHZnZwRUVFRUVKyP5VkOckBKmSKEGAFsB+yAZVLKC0KIT1CmMW0GfgRWCiGuA+EoxkJFRUVFJQ+xmSEAkFJuAbZk2jZF730C0MuWGlRUVFRUsuepcBarqKioqNiOpy5nsRAiFMiaZNd2FAUe5eH1TKWg6oKCq03VZR4FVRcUXG0FWVchKWUxQzufOkOQ1wghjhvztOcnBVUXFFxtqi7zKKi6oOBqe1p1qUNDKioqKs85qiFQUVFRec5RDUHOLMlvAUYoqLqg4GpTdZlHQdUFBVfbU6lL9RGoqKioPOeoPQIVFRWV55zn3hAIIUrktwZjCCE881uDIYQQBTJCrBDC4NS4/EatY+aj1jHzyG0de24NgRDCTQjxJbBVCPG9EKJ7fmtKQwjhKoT4FtgmhBgphKin256vv5funi0A/hJCzBRCtMpPPWkIIZyFEIuAPUKIT4QQr+i2F4T7pdYx83Spdcw8XVapY8+lIRBC+AIrAQG0B/4B5uarqIyMRsnLMAhwBr4HkFJq80uQEMIf+B1IBd4CQoEJ+aUnE28BxYEWQBCwTAjhnM/3S61jZqLWMfOwZh17Lg0BkAAslVKOklLeB34FTgshaueXICGEs+6vPeAIrJZSXpZSfg6E6lpJ+dkCiQWWSCnH6NKNbgHuCSFK54cYIYSb/kfgkJQyTEq5HDgEzNaVy6+Md2odMx+1jpmH1erYc2EIhBBVhBCLhRAuAFLKMGCvXpEyQAXgSj5oqyyEWAV8LYRoqMvU5ga8oFfsf8BAIUTpvGqB6O5ZeutCSnkX2KpXxBWoKqW8kxd69HRVEkL8CvwkhOgghHDS7SquV2ws0E0IUVFKKfPiH1WtYxbpUuuYebpsVseeeUMghGiO0n0aitIdRgghpJSxesUcgWApZWIea3NB6ZKfAc4C7wkh3gY+A/4nhCgKIKW8DQQC7+SRrg7ABmCMEGKcbpu9lDJGr5gXefxQ07VUvwTOo/ymnYApwM9AeyFETQDdg2MTumEFW+e4UOuYRbrUOmaeLpvWsWfeEABhKON7lYHBQgg/Az9aPeAGgBDinTzsvlcEYqWUc6WUXwNLgW6AC7CIjItArqLkfc6LrugDIADlnn0shHDT5ZfQ6F27OnBBp6e/EKKyjTUBlAQigFlSyk3ADOBVwB/lYTdRPJk9sY28C06o1jHzUeuYedi0jj1zhiBzBZZSXgKuSymvA38Dn+jK6X/31oC3EOI3oD/K2JvN9UkpzwPlhBAv6zadBXYBHwETAS8hxFQhRG9gCBCvO87qrY9Muo4Dl3X3bBuwOK2Y3rWbA8WEEL+j/EMnW1tTZqSUIUBDlH/MtM+LgNm6h1wsMF0IMQSlxRtua006HQWqjmXSVmDqWCZdah0zT5dt65iU8pl4AaVQWjc1dJ+F3r60FdSFgetA60zHbkVpefS0kbbiKJVLX5NG93ckEKi3vS5K5raiKNb/DWAHEJAXugzcM3eUFlIjvX1OKA+UE0BvG+jyBtwzbbPX/X0TOKC3vQiKk6wu4InSlf/FRvcri64CVMe8AU99PQWkjmXQVYDqmIuBbXYFoI5l0ZUXdczqFTI/XsAk4F+UFsNE/Ztm4Ed+H/hT976f7h+mpQ21TdT9aNuBbzNrAyoBvwGDdJ+9dWV9bHzPstWV6Z5NBvbo3rfT/e1qI10TULq3y4CPDOzXALuB9/W2/QzUtPH9ylZXPtexScA9YB0w1cD+/Kpj2erKxzo2B/gDqJdWpwpIHctWly3rmM2+VF68gGIoY56rde9fAxYAjgbK6j98HwORKK0iZxvqew2lpaVBSQt6ByhhQE9b3UOmPtAb2AOUzQ9d2dyzFCAaWAg42EhXW90DygGltboLZUjAUbc/rYXbAGUud1dgAEqrsboN71e2uvK5jlVHGVpxBvxQ5pL3RUlCkvk75GUdM1VXXtexIcApYD4wI5tyeV3HTNVlkzr2tPsIooApUsr+UspQoCpKNzQp81xoKaUUQngIIT4HbgPtpZRvSyVvsq3QADEoD9uawDGgWpoeSPf87wDmoXRJPwYmSSlv5YcufXT3rKgQ4gfgHEpLbZSU0lZjtYVRZonYSSkfofxjjELpkiOl1AohNFLKEygtonoosyhGSWXeua3IVlcaeVXHhBBF9D7GogxduEkpb6KMZ7cE6uiVz5M6Zq4uyJs6lknXbyhDOxuB4kKI9royQq98ntQxc3WBDeuYrSycjaymN4ofoEWm7WndJT/dzSlq5HgNUDuvtKFU+nkoY3ehwDTgNMr0r+JpmjJ/j4KgS6+sPfBiHunqCnyHstrVEWUa31/A27r9AgP+jIKgKw/r2DcovbmRQBWUHspXwCt69+gr3X67zPfMhnXMLF15WMf0dVXT2+eBYsy/Quf34ckYfF7UMbN12bKOPTU9AiGEH8owUA+gnxDCK22flDJVZznDULrtTQydQ0qplVKezQNt3rrrnZFSjkFpTQ6QUk5D+eFboyyWQeot3pFSphYUXXqaUqSUB22sq6hu1x8ovp5XgX3ALZRu70CdFil1/wm2IDe60s5hwzrWFMVwPwJmoiweelcqPZR4oJ4QopROy3agv5QyNfM9s0Eds0iX/jlsVMcy6yoNDNO7ZiTK6mAB9NRtk/p/bUFudOmVsXode2oMAcpQxiygPErLv7X+8I/ezSqCbppZ5uGhPNT2Stq1hRB2wEOePPj36957GT7Vc6mrlW4xUaqUMhB4F+gipZyPMrb8nxDCKQ/mthdUXaDMqpkvpZwmpdwHHESZWQPKg6Ui0AZASvkXECOEKKXqStf1L4Du97LTlTmP4iupJYQYK4QYlge/ZYHUZW/Lk1uKbkwzsxUME0LESSnjhRBrUDzlh1GGghBC2EkpY4QQIcDbwA5pg6Xy5mrT9VacgK5CiPIoy/ojsfJClGdFl65IgpQyVigRMWcBJ6SVV+QWVF3ZaLsshLitty8ZJZwAUsq9QgknPVinrRZwH8XQq7qe6KqU9nvptsfpHrB9URoBI6zZGyiougxR4HoEQggH/S+tbwmllGmLXX4GtEBPvVZ/2kP/V5Q5vgVBm4Nu9zc6XfVQpsh1lUqcEFWXkd9SCFEDWIVi0CdbS1NB1mWCtli9feVQwkak7fsd+D8Uo/WblHKAVGIKqbqe6Dqrt08KITyAz4EvpJT+Usrtz7ouo0gbOkXMfQEjULrcnwCd9LZncaiihIPdjDJH+i2gXAHV9naaNgzMC1Z1Gfwt/XTbnZ4XXWZoS5tCOw/opnvfHShlC03PsK7SuvdZppo/q7qyexWIHoEQwksI8TPK/PbxKI6UQbohC6RuiEcIUVvqHE1Syn9QIiieRFlObbVWhpW19QNSdF0+qw1VPcO6AlDi0SOtOOxSUHVZoC3tt6oOVBZCbEVxblu9/j/jupJ1ZZOedV0mkZdWJxsLaocyVzdt+lYF4CeehIvwQVnZtx8llIQTynzom9ho9WFB16bqejZ0WaitLMoamr0F7J6pugqgLpO058tFFSf1GKCM3jY3vfcaFCedv+7zq8DwTOewyarIgqpN1fVs6LKitsGqLlWX1b5Lnl9QmTlwEiUM7RojZaoBW4zd/OdNm6rr2dBlJW02GTtWdT0buix95YeP4BHKqrmqKOFx24Iy/VPPs14SZcUrQogmQpfAWjeebRNfQAHXpup6NnRZQ5utxo5VXc+GLovIc0MgpbwHrJVSPkYZP0vL8JOKspoOlBAIjkKJp7FA71j5PGpTdT0bugqyNlXXs6HLUvJl1pDUzdVGcZwkCCH+T7ddq7OmLwOvAOFSymZSyj3PuzZV17OhqyBrU3U9G7osIr/HplCmWh3Rva+t+9sZPQeMqk3V9SzqKsjaVF3Phi6T9ee3AN0N2wYkAlswEjlU1abqehZ1FWRtqq5nQ5dJ2vP5xmlQIvDdBN7J75vxNGhTdT0bugqyNlXXs6HLnFfawod8QwjxOrBb2iB4V24pqNpUXeZRUHVBwdWm6jKPgqrLVPLdEKioqKio5C8FItaQioqKikr+oRoCFRUVlecc1RCoqKioPOeohkBFRUXlOUc1BCoqKirPOaohUFHJASFEqhDitBDighDijBDiQ/EkfaWxY8oJIfrnlUYVldygGgIVlZyJl1LWlVLWQIkp/zowNYdjyqFkzlNRKfCo6whUVHJACBEjpXTT+1wBOAYUBfyAlUAh3e4RUsp/hRCHUeLRBwErUEIWzwFaomQ/+1ZK+X2efQkVlWxQDYGKSg5kNgS6bRFAFSAa0EopE4QQ/ihJShoKIVoCY6SUHXXlhwLFpZQzhRBOwEGgl5QyKE+/jIqKAezzW4CKylOOA/CNEKIuSnL7ykbKtQVqCyF66j57AP4oPQYVlXxFNQQqKmaiGxpKBR6i+AoeoCQh0QAJxg4DRkopt+eJSBUVM1CdxSoqZiCEKAYsBr6RyriqB3BPSqkFBgJ2uqLRQGG9Q7cDw4QQDrrzVBZCFEJFpQCg9ghUVHLGRQhxGmUYKAXFOTxft+874DchxBso8ehjddvPAqlCiDMoqQwXoswkOqnLXhUKdM2rL6Cikh3/354d0AAAACAI69+aIPwl2NRZDDBnGgKYEwKAOSEAmBMCgDkhAJgTAoA5IQCYEwKAuQBhkTfuds6y7QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 9134, {'val_loss': '0.024827884510159492', 'val/loss_mse': '0.0014371582074090838', 'val/loss_p': '0.024827884510159492', 'val/sigma': '0.14072538912296295'}\n", + "INFO:root:Epoch 00005: early stopping\n", + "\n", + "Loading checkpoint lightning_logs/seq2seq/version_-8/_ckpt_epoch_0.ckpt\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0e4740ece90b4034b09cc5e414b3fec0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Testing', layout=Layout(flex='2'), max=234.0, style=Progr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 9135, {'val_loss': '-0.003565264632925391', 'val/loss_mse': '0.0002942961873486638', 'val/loss_p': '-0.003565264632925391', 'val/sigma': '0.17361457645893097'}\n", + "----------------------------------------------------------------------------------------------------\n", + "TEST RESULTS\n", + "{}\n", + "----------------------------------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'plot_from_loader' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;34m'patience'\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 23\u001b[0m },\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mPL_MODEL_CLS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mLSTMSeq2Seq_PL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m )\n", + "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/attentive-neural-processes/src/train.py\u001b[0m in \u001b[0;36mrun_trial\u001b[0;34m(name, PL_MODEL_CLS, params, user_attrs, MODEL_DIR)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0mdset_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[0mlabel_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdset_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m \u001b[0mplot_from_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m670\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 110\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'plot_from_loader' is not defined" ] } ], "source": [ - "trial = optuna.trial.FixedTrial(\n", + "trial, trainer, model = run_trial(\n", + " name=\"seq2seq\",\n", " params={\n", " 'bidirectional': False,\n", - " 'hidden_size': 128,\n", - " 'learning_rate': 2e-5,\n", - " 'lstm_dropout': 0.0,\n", - " 'lstm_layers': 2,\n", - " })\n", - "trial._user_attrs = {\n", + " 'hidden_size': 256,\n", + " 'learning_rate': 2e-4,\n", + " 'lstm_dropout': 0.4,\n", + " 'lstm_layers': 8,\n", + " },\n", + " user_attrs = {\n", " 'batch_size': 16,\n", " 'grad_clip': 40,\n", " 'max_nb_epochs': 200,\n", @@ -440,11 +537,11 @@ " 'input_size': 18,\n", " 'input_size_decoder': 17,\n", " 'context_in_target': True,\n", - " 'output_size': 1\n", - "}\n", - "trial = add_suggest(trial)\n", - "model, trainer = main(trial, train=False)\n", - "trainer.fit(model)" + " 'output_size': 1,\n", + " 'patience':2,\n", + " },\n", + " PL_MODEL_CLS=LSTMSeq2Seq_PL\n", + ")" ] }, { @@ -452,35 +549,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:31:43.522361Z", - "start_time": "2020-03-01T03:31:15.800Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.307396Z", - "start_time": "2020-03-01T03:41:28.300Z" - } - }, - "outputs": [], - "source": [ - "# test\n", - "trainer.test(model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.308984Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.863848Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -496,8 +566,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.310698Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.865188Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -531,8 +601,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.312313Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.866502Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -569,8 +639,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.313923Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.867929Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -594,8 +664,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.315413Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.869282Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -608,8 +678,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.317152Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.870600Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -630,8 +700,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.318533Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.872993Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -644,8 +714,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.319880Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.874727Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -660,8 +730,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.321408Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.876536Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -676,8 +746,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.322849Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.878187Z", + "start_time": "2020-03-15T01:04:18.100Z" } }, "outputs": [], @@ -690,8 +760,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.324307Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.879862Z", + "start_time": "2020-03-15T01:04:18.200Z" } }, "outputs": [], @@ -709,8 +779,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.325661Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.881522Z", + "start_time": "2020-03-15T01:04:18.200Z" } }, "outputs": [], @@ -723,8 +793,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-01T03:47:33.327059Z", - "start_time": "2020-03-01T03:41:28.300Z" + "end_time": "2020-03-15T01:51:46.883268Z", + "start_time": "2020-03-15T01:04:18.200Z" } }, "outputs": [], @@ -747,6 +817,18 @@ "language": "python", "name": "jup3.7.3" }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, "mimetype": "text/x-python", "name": "python", "npconvert_exporter": "python", diff --git a/smartmeters-transformer-seq2seq.ipynb b/smartmeters-transformer-seq2seq.ipynb index 629d7f9..9e90d98 100644 --- a/smartmeters-transformer-seq2seq.ipynb +++ b/smartmeters-transformer-seq2seq.ipynb @@ -20,8 +20,8 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T07:09:05.502663Z", - "start_time": "2020-03-14T07:09:05.101070Z" + "end_time": "2020-03-15T01:02:37.022209Z", + "start_time": "2020-03-15T01:02:36.610772Z" } }, "outputs": [], @@ -36,8 +36,8 @@ "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T07:09:07.495992Z", - "start_time": "2020-03-14T07:09:05.505650Z" + "end_time": "2020-03-15T01:02:39.086696Z", + "start_time": "2020-03-15T01:02:37.024896Z" } }, "outputs": [], @@ -63,8 +63,8 @@ "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T07:09:07.553714Z", - "start_time": "2020-03-14T07:09:07.500345Z" + "end_time": "2020-03-15T01:02:39.139141Z", + "start_time": "2020-03-15T01:02:39.089681Z" } }, "outputs": [], @@ -79,8 +79,8 @@ "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T07:09:07.680674Z", - "start_time": "2020-03-14T07:09:07.557348Z" + "end_time": "2020-03-15T01:02:39.275280Z", + "start_time": "2020-03-15T01:02:39.141724Z" } }, "outputs": [ @@ -101,7 +101,8 @@ "from src.plot import plot_from_loader\n", "from src.models.transformer_seq2seq import TransformerSeq2Seq_PL\n", "from src.dict_logger import DictLogger\n", - "from src.utils import PyTorchLightningPruningCallback" + "from src.utils import PyTorchLightningPruningCallback\n", + "from src.train import main, objective, add_number, run_trial" ] }, { @@ -109,8 +110,8 @@ "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T07:09:07.734741Z", - "start_time": "2020-03-14T07:09:07.682916Z" + "end_time": "2020-03-15T01:02:39.330890Z", + "start_time": "2020-03-15T01:02:39.278095Z" } }, "outputs": [], @@ -125,8 +126,8 @@ "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T07:09:07.788003Z", - "start_time": "2020-03-14T07:09:07.737690Z" + "end_time": "2020-03-15T01:02:39.385417Z", + "start_time": "2020-03-15T01:02:39.334694Z" } }, "outputs": [], @@ -145,41 +146,41 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T07:09:16.678003Z", - "start_time": "2020-03-14T07:09:07.790696Z" + "end_time": "2020-03-15T01:03:40.212386Z", + "start_time": "2020-03-15T01:03:31.281372Z" } }, "outputs": [], "source": [ - "df_train, df_test = get_smartmeter_df()" + "df_train, df_val, df_test = get_smartmeter_df()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T07:09:17.405503Z", - "start_time": "2020-03-14T07:09:16.683653Z" + "end_time": "2020-03-15T01:03:40.875931Z", + "start_time": "2020-03-15T01:03:40.214642Z" } }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -193,6 +194,7 @@ "source": [ "# Show split\n", "df_train['energy(kWh/hh)'].plot(label='train')\n", + "df_val['energy(kWh/hh)'].plot(label='val')\n", "df_test['energy(kWh/hh)'].plot(label='test')\n", "plt.title('energy(kWh/hh)')\n", "plt.legend()" @@ -210,148 +212,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Train helpers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-14T07:00:55.672759Z", - "start_time": "2020-03-14T07:00:55.564212Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-14T07:03:19.316016Z", - "start_time": "2020-03-14T07:03:19.236454Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-14T07:09:17.486980Z", - "start_time": "2020-03-14T07:09:17.409793Z" - } - }, - "outputs": [], - "source": [ - "def main(trial, train=True):\n", - " # PyTorch Lightning will try to restore model parameters from previous trials if checkpoint\n", - " # filenames match. Therefore, the filenames for each trial must be made unique.\n", - " \n", - " checkpoint_callback = pl.callbacks.ModelCheckpoint(\n", - " os.path.join(MODEL_DIR, name, 'version_{}'.format(trial.number), \"chk\"), monitor='val_loss', mode='min')\n", - "\n", - " # The default logger in PyTorch Lightning writes to event files to be consumed by\n", - " # TensorBoard. We create a simple logger instead that holds the log in memory so that the\n", - " # final accuracy can be obtained after optimization. When using the default logger, the\n", - " # final accuracy could be stored in an attribute of the `Trainer` instead.\n", - " logger = DictLogger(MODEL_DIR, name=name, version=trial.number)\n", - "# print(\"log_dir\", logger.experiment.log_dir)\n", - " hparams = dict(**trial.params, **trial.user_attrs)\n", - "\n", - " trainer = pl.Trainer(\n", - " logger=logger,\n", - " val_percent_check=PERCENT_TEST_EXAMPLES,\n", - " checkpoint_callback=checkpoint_callback,\n", - " max_epochs=hparams['max_nb_epochs'],\n", - " gpus=-1 if torch.cuda.is_available() else None,\n", - " early_stop_callback=PyTorchLightningPruningCallback(trial, monitor='val_loss')\n", - " )\n", - " \n", - " model = TransformerSeq2Seq_PL(hparams)\n", - " if train:\n", - " trainer.fit(model)\n", - " return model, trainer" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-14T07:09:17.552317Z", - "start_time": "2020-03-14T07:09:17.489796Z" - } - }, - "outputs": [], - "source": [ - "def add_suggest(trial):\n", - " trial.suggest_loguniform(\"learning_rate\", 1e-7, 1e-2)\n", - " trial.suggest_uniform(\"attention_dropout\", 0, 0.75)\n", - " trial.suggest_categorical(\"hidden_size\", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]) \n", - " trial.suggest_categorical(\"hidden_out_size\", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]) \n", - " trial.suggest_categorical(\"nlayers\", [1, 2, 4, 8]) \n", - " trial.suggest_categorical(\"nhead\", [1, 2, 8, 16])\n", - " \n", - "\n", - " trial._user_attrs = {\n", - " 'batch_size': 16,\n", - " 'grad_clip': 40,\n", - " 'max_nb_epochs': 200,\n", - " 'num_workers': 4,\n", - " 'num_extra_target': 24*4,\n", - " 'vis_i': '670',\n", - " 'num_context': 24*4,\n", - " 'input_size': 18,\n", - " 'input_size_decoder': 17,\n", - " 'context_in_target': True,\n", - " 'output_size': 1\n", - " }\n", - " \n", - " # For manual experiment we will start at -1 and deincr by 1\n", - " versions = [int(s.stem.split('_')[-1]) for s in (MODEL_DIR / name).glob('version_*')] + [-1]\n", - " trial.number = min(versions)-1\n", - " print('trial.number', trial.number)\n", - " return trial" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-14T07:09:17.608044Z", - "start_time": "2020-03-14T07:09:17.554643Z" - } - }, - "outputs": [], - "source": [ - "\n", - "def objective(trial):\n", - " # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py\n", - " trial = add_suggest(trial)\n", - "\n", - " \n", - " print('trial', trial.number, 'params', trial.params)\n", - " \n", - " model, trainer = main(trial)\n", - " \n", - " # also report to tensorboard & print\n", - " print('logger.metrics', model.logger.metrics[-1:])\n", - " model.logger.experiment.add_hparams(trial.params, logger.metrics[-1])\n", - " model.logger.save()\n", - " \n", - " return model.logger.metrics[-1]['val_loss']\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -359,60 +219,6 @@ "# Train" ] }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-14T07:09:17.660071Z", - "start_time": "2020-03-14T07:09:17.610356Z" - } - }, - "outputs": [], - "source": [ - "PERCENT_TEST_EXAMPLES = 0.3\n", - "EPOCHS = 2\n", - "DIR = Path(os.getcwd())\n", - "name = \"transformer_seq2seq\"\n", - "MODEL_DIR = DIR/ 'lightning_logs'\n", - "\n", - "MODEL_DIR.mkdir(parents=True, exist_ok=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-14T07:09:17.729742Z", - "start_time": "2020-03-14T07:09:17.662794Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "now run `tensorboard --logdir /media/wassname/Storage5/projects2/3ST/attentive-neural-processes/lightning_logs\n" - ] - } - ], - "source": [ - "print(f\"now run `tensorboard --logdir {MODEL_DIR}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-03-14T05:06:20.950545Z", - "start_time": "2020-03-14T05:06:20.876126Z" - } - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": { @@ -427,10 +233,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.200Z" + "end_time": "2020-03-15T01:45:51.509108Z", + "start_time": "2020-03-15T01:03:47.552597Z" }, "scrolled": true }, @@ -439,69 +246,118 @@ "name": "stdout", "output_type": "stream", "text": [ - "trial.number -17\n", + "now run `tensorboard --logdir lightning_logs`\n", + "trial.number -19\n", "INFO:root:GPU available: True, used: True\n", "INFO:root:VISIBLE GPUS: 0\n", "INFO:root:\n", - " | Name | Type | Params\n", + " | Name | Type | Params\n", "---------------------------------------------------------------------------------------\n", - "0 | model | TransformerSeq2SeqNet | 1 M \n", - "1 | model.enc_emb | Linear | 4 K \n", - "2 | model.encoder | TransformerEncoder | 660 K \n", - "3 | model.encoder.layers | ModuleList | 660 K \n", - "4 | model.encoder.layers.0 | TransformerEncoderLayer | 330 K \n", - "5 | model.encoder.layers.0.self_attn | MultiheadAttention | 263 K \n", - "6 | model.encoder.layers.0.self_attn.out_proj | Linear | 65 K \n", - "7 | model.encoder.layers.0.linear1 | Linear | 32 K \n", - "8 | model.encoder.layers.0.dropout | Dropout | 0 \n", - "9 | model.encoder.layers.0.linear2 | Linear | 33 K \n", - "10 | model.encoder.layers.0.norm1 | LayerNorm | 512 \n", - "11 | model.encoder.layers.0.norm2 | LayerNorm | 512 \n", - "12 | model.encoder.layers.0.dropout1 | Dropout | 0 \n", - "13 | model.encoder.layers.0.dropout2 | Dropout | 0 \n", - "14 | model.encoder.layers.1 | TransformerEncoderLayer | 330 K \n", - "15 | model.encoder.layers.1.self_attn | MultiheadAttention | 263 K \n", - "16 | model.encoder.layers.1.self_attn.out_proj | Linear | 65 K \n", - "17 | model.encoder.layers.1.linear1 | Linear | 32 K \n", - "18 | model.encoder.layers.1.dropout | Dropout | 0 \n", - "19 | model.encoder.layers.1.linear2 | Linear | 33 K \n", - "20 | model.encoder.layers.1.norm1 | LayerNorm | 512 \n", - "21 | model.encoder.layers.1.norm2 | LayerNorm | 512 \n", - "22 | model.encoder.layers.1.dropout1 | Dropout | 0 \n", - "23 | model.encoder.layers.1.dropout2 | Dropout | 0 \n", - "24 | model.dec_emb | Linear | 4 K \n", - "25 | model.decoder | TransformerDecoder | 1 M \n", - "26 | model.decoder.layers | ModuleList | 1 M \n", - "27 | model.decoder.layers.0 | TransformerDecoderLayer | 593 K \n", - "28 | model.decoder.layers.0.self_attn | MultiheadAttention | 263 K \n", - "29 | model.decoder.layers.0.self_attn.out_proj | Linear | 65 K \n", - "30 | model.decoder.layers.0.multihead_attn | MultiheadAttention | 263 K \n", - "31 | model.decoder.layers.0.multihead_attn.out_proj | Linear | 65 K \n", - "32 | model.decoder.layers.0.linear1 | Linear | 32 K \n", - "33 | model.decoder.layers.0.dropout | Dropout | 0 \n", - "34 | model.decoder.layers.0.linear2 | Linear | 33 K \n", - "35 | model.decoder.layers.0.norm1 | LayerNorm | 512 \n", - "36 | model.decoder.layers.0.norm2 | LayerNorm | 512 \n", - "37 | model.decoder.layers.0.norm3 | LayerNorm | 512 \n", - "38 | model.decoder.layers.0.dropout1 | Dropout | 0 \n", - "39 | model.decoder.layers.0.dropout2 | Dropout | 0 \n", - "40 | model.decoder.layers.0.dropout3 | Dropout | 0 \n", - "41 | model.decoder.layers.1 | TransformerDecoderLayer | 593 K \n", - "42 | model.decoder.layers.1.self_attn | MultiheadAttention | 263 K \n", - "43 | model.decoder.layers.1.self_attn.out_proj | Linear | 65 K \n", - "44 | model.decoder.layers.1.multihead_attn | MultiheadAttention | 263 K \n", - "45 | model.decoder.layers.1.multihead_attn.out_proj | Linear | 65 K \n", - "46 | model.decoder.layers.1.linear1 | Linear | 32 K \n", - "47 | model.decoder.layers.1.dropout | Dropout | 0 \n", - "48 | model.decoder.layers.1.linear2 | Linear | 33 K \n", - "49 | model.decoder.layers.1.norm1 | LayerNorm | 512 \n", - "50 | model.decoder.layers.1.norm2 | LayerNorm | 512 \n", - "51 | model.decoder.layers.1.norm3 | LayerNorm | 512 \n", - "52 | model.decoder.layers.1.dropout1 | Dropout | 0 \n", - "53 | model.decoder.layers.1.dropout2 | Dropout | 0 \n", - "54 | model.decoder.layers.1.dropout3 | Dropout | 0 \n", - "55 | model.mean | Linear | 257 \n", - "56 | model.std | Linear | 257 \n" + "0 | model | TransformerSeq2SeqNet | 16 M \n", + "1 | model.enc_emb | Linear | 9 K \n", + "2 | model.encoder | TransformerEncoder | 6 M \n", + "3 | model.encoder.layers | ModuleList | 6 M \n", + "4 | model.encoder.layers.0 | TransformerEncoderLayer | 1 M \n", + "5 | model.encoder.layers.0.self_attn | MultiheadAttention | 1 M \n", + "6 | model.encoder.layers.0.self_attn.out_proj | Linear | 262 K \n", + "7 | model.encoder.layers.0.linear1 | Linear | 262 K \n", + "8 | model.encoder.layers.0.dropout | Dropout | 0 \n", + "9 | model.encoder.layers.0.linear2 | Linear | 262 K \n", + "10 | model.encoder.layers.0.norm1 | LayerNorm | 1 K \n", + "11 | model.encoder.layers.0.norm2 | LayerNorm | 1 K \n", + "12 | model.encoder.layers.0.dropout1 | Dropout | 0 \n", + "13 | model.encoder.layers.0.dropout2 | Dropout | 0 \n", + "14 | model.encoder.layers.1 | TransformerEncoderLayer | 1 M \n", + "15 | model.encoder.layers.1.self_attn | MultiheadAttention | 1 M \n", + "16 | model.encoder.layers.1.self_attn.out_proj | Linear | 262 K \n", + "17 | model.encoder.layers.1.linear1 | Linear | 262 K \n", + "18 | model.encoder.layers.1.dropout | Dropout | 0 \n", + "19 | model.encoder.layers.1.linear2 | Linear | 262 K \n", + "20 | model.encoder.layers.1.norm1 | LayerNorm | 1 K \n", + "21 | model.encoder.layers.1.norm2 | LayerNorm | 1 K \n", + "22 | model.encoder.layers.1.dropout1 | Dropout | 0 \n", + "23 | model.encoder.layers.1.dropout2 | Dropout | 0 \n", + "24 | model.encoder.layers.2 | TransformerEncoderLayer | 1 M \n", + "25 | model.encoder.layers.2.self_attn | MultiheadAttention | 1 M \n", + "26 | model.encoder.layers.2.self_attn.out_proj | Linear | 262 K \n", + "27 | model.encoder.layers.2.linear1 | Linear | 262 K \n", + "28 | model.encoder.layers.2.dropout | Dropout | 0 \n", + "29 | model.encoder.layers.2.linear2 | Linear | 262 K \n", + "30 | model.encoder.layers.2.norm1 | LayerNorm | 1 K \n", + "31 | model.encoder.layers.2.norm2 | LayerNorm | 1 K \n", + "32 | model.encoder.layers.2.dropout1 | Dropout | 0 \n", + "33 | model.encoder.layers.2.dropout2 | Dropout | 0 \n", + "34 | model.encoder.layers.3 | TransformerEncoderLayer | 1 M \n", + "35 | model.encoder.layers.3.self_attn | MultiheadAttention | 1 M \n", + "36 | model.encoder.layers.3.self_attn.out_proj | Linear | 262 K \n", + "37 | model.encoder.layers.3.linear1 | Linear | 262 K \n", + "38 | model.encoder.layers.3.dropout | Dropout | 0 \n", + "39 | model.encoder.layers.3.linear2 | Linear | 262 K \n", + "40 | model.encoder.layers.3.norm1 | LayerNorm | 1 K \n", + "41 | model.encoder.layers.3.norm2 | LayerNorm | 1 K \n", + "42 | model.encoder.layers.3.dropout1 | Dropout | 0 \n", + "43 | model.encoder.layers.3.dropout2 | Dropout | 0 \n", + "44 | model.dec_emb | Linear | 9 K \n", + "45 | model.decoder | TransformerDecoder | 10 M \n", + "46 | model.decoder.layers | ModuleList | 10 M \n", + "47 | model.decoder.layers.0 | TransformerDecoderLayer | 2 M \n", + "48 | model.decoder.layers.0.self_attn | MultiheadAttention | 1 M \n", + "49 | model.decoder.layers.0.self_attn.out_proj | Linear | 262 K \n", + "50 | model.decoder.layers.0.multihead_attn | MultiheadAttention | 1 M \n", + "51 | model.decoder.layers.0.multihead_attn.out_proj | Linear | 262 K \n", + "52 | model.decoder.layers.0.linear1 | Linear | 262 K \n", + "53 | model.decoder.layers.0.dropout | Dropout | 0 \n", + "54 | model.decoder.layers.0.linear2 | Linear | 262 K \n", + "55 | model.decoder.layers.0.norm1 | LayerNorm | 1 K \n", + "56 | model.decoder.layers.0.norm2 | LayerNorm | 1 K \n", + "57 | model.decoder.layers.0.norm3 | LayerNorm | 1 K \n", + "58 | model.decoder.layers.0.dropout1 | Dropout | 0 \n", + "59 | model.decoder.layers.0.dropout2 | Dropout | 0 \n", + "60 | model.decoder.layers.0.dropout3 | Dropout | 0 \n", + "61 | model.decoder.layers.1 | TransformerDecoderLayer | 2 M \n", + "62 | model.decoder.layers.1.self_attn | MultiheadAttention | 1 M \n", + "63 | model.decoder.layers.1.self_attn.out_proj | Linear | 262 K \n", + "64 | model.decoder.layers.1.multihead_attn | MultiheadAttention | 1 M \n", + "65 | model.decoder.layers.1.multihead_attn.out_proj | Linear | 262 K \n", + "66 | model.decoder.layers.1.linear1 | Linear | 262 K \n", + "67 | model.decoder.layers.1.dropout | Dropout | 0 \n", + "68 | model.decoder.layers.1.linear2 | Linear | 262 K \n", + "69 | model.decoder.layers.1.norm1 | LayerNorm | 1 K \n", + "70 | model.decoder.layers.1.norm2 | LayerNorm | 1 K \n", + "71 | model.decoder.layers.1.norm3 | LayerNorm | 1 K \n", + "72 | model.decoder.layers.1.dropout1 | Dropout | 0 \n", + "73 | model.decoder.layers.1.dropout2 | Dropout | 0 \n", + "74 | model.decoder.layers.1.dropout3 | Dropout | 0 \n", + "75 | model.decoder.layers.2 | TransformerDecoderLayer | 2 M \n", + "76 | model.decoder.layers.2.self_attn | MultiheadAttention | 1 M \n", + "77 | model.decoder.layers.2.self_attn.out_proj | Linear | 262 K \n", + "78 | model.decoder.layers.2.multihead_attn | MultiheadAttention | 1 M \n", + "79 | model.decoder.layers.2.multihead_attn.out_proj | Linear | 262 K \n", + "80 | model.decoder.layers.2.linear1 | Linear | 262 K \n", + "81 | model.decoder.layers.2.dropout | Dropout | 0 \n", + "82 | model.decoder.layers.2.linear2 | Linear | 262 K \n", + "83 | model.decoder.layers.2.norm1 | LayerNorm | 1 K \n", + "84 | model.decoder.layers.2.norm2 | LayerNorm | 1 K \n", + "85 | model.decoder.layers.2.norm3 | LayerNorm | 1 K \n", + "86 | model.decoder.layers.2.dropout1 | Dropout | 0 \n", + "87 | model.decoder.layers.2.dropout2 | Dropout | 0 \n", + "88 | model.decoder.layers.2.dropout3 | Dropout | 0 \n", + "89 | model.decoder.layers.3 | TransformerDecoderLayer | 2 M \n", + "90 | model.decoder.layers.3.self_attn | MultiheadAttention | 1 M \n", + "91 | model.decoder.layers.3.self_attn.out_proj | Linear | 262 K \n", + "92 | model.decoder.layers.3.multihead_attn | MultiheadAttention | 1 M \n", + "93 | model.decoder.layers.3.multihead_attn.out_proj | Linear | 262 K \n", + "94 | model.decoder.layers.3.linear1 | Linear | 262 K \n", + "95 | model.decoder.layers.3.dropout | Dropout | 0 \n", + "96 | model.decoder.layers.3.linear2 | Linear | 262 K \n", + "97 | model.decoder.layers.3.norm1 | LayerNorm | 1 K \n", + "98 | model.decoder.layers.3.norm2 | LayerNorm | 1 K \n", + "99 | model.decoder.layers.3.norm3 | LayerNorm | 1 K \n", + "100 | model.decoder.layers.3.dropout1 | Dropout | 0 \n", + "101 | model.decoder.layers.3.dropout2 | Dropout | 0 \n", + "102 | model.decoder.layers.3.dropout3 | Dropout | 0 \n", + "103 | model.mean | Linear | 513 \n", + "104 | model.std | Linear | 513 \n" ] }, { @@ -520,7 +376,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -534,14 +390,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 0, {'val_loss': '1.146193265914917', 'val/loss_mse': '0.1482062190771103', 'val/loss_p': '1.146193265914917', 'val/sigma': '1.196487307548523'}\n", + "step 0, {'val_loss': '1.1705018281936646', 'val/loss_mse': '0.1781037449836731', 'val/loss_p': '1.1705018281936646', 'val/sigma': '1.2042779922485352'}\n", "\r" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8d6007c3b6fc4fc8825cde7ecc9fed66", + "model_id": "f9947b5d8a8e448688973e7a89d3a933", "version_major": 2, "version_minor": 0 }, @@ -560,7 +416,7 @@ "version_minor": 0 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=70.0, style=Pro…" + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=178.0, style=Pr…" ] }, "metadata": {}, @@ -568,7 +424,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -582,7 +438,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 2194, {'val_loss': '-0.309064656496048', 'val/loss_mse': '0.023657485842704773', 'val/loss_p': '-0.309064656496048', 'val/sigma': '0.23991245031356812'}\n" + "step 1826, {'val_loss': '25.216747283935547', 'val/loss_mse': '0.13646769523620605', 'val/loss_p': '25.216747283935547', 'val/sigma': '0.04999999701976776'}\n" ] }, { @@ -593,7 +449,7 @@ "version_minor": 0 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=70.0, style=Pro…" + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=178.0, style=Pr…" ] }, "metadata": {}, @@ -601,7 +457,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -615,22 +471,241 @@ "name": "stdout", "output_type": "stream", "text": [ - "step 4389, {'val_loss': '-0.42497867345809937', 'val/loss_mse': '0.020270122215151787', 'val/loss_p': '-0.42497867345809937', 'val/sigma': '0.20783782005310059'}\n" + "step 3653, {'val_loss': '35.0439453125', 'val/loss_mse': '0.18560369312763214', 'val/loss_p': '35.0439453125', 'val/sigma': '0.04999999701976776'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Validating', layout=Layout(flex='2'), max=178.0, style=Pr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 12788, {'val_loss': '36.94305419921875', 'val/loss_mse': '0.19509920477867126', 'val/loss_p': '36.94305419921875', 'val/sigma': '0.04999999701976776'}\n", + "INFO:root:Epoch 00007: early stopping\n", + "\n", + "Loading checkpoint lightning_logs/transformer_seq2seq/version_-19/_ckpt_epoch_0.ckpt\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eb40255ae5e145d69b4779b7e093871d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Testing', layout=Layout(flex='2'), max=234.0, style=Progr…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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x1KpViwkTJhAWFsYPP/xAhQoVbCaDhkZRwF4nix0apRQlSpQokkpgzZo1lC5dmtdee83eouTi4MGDNp2vVq1aNGvWjHLlyrFlyxZ+/fVX7ty5Y1MZNDSKOsU2+mh2jh07Rv/+/WncuDEbNmywtzgmSUxMJCIigsjISBISEgxRSO2NUoq1a9dy8OBBux7K+/rrr0lPT6dKlSp2k0FDoyhSLBWBiNCjRw/c3d1Zs2YNACEhIQ6/nbB79266du1Kx44diYuLc6hE7f369aNfv342n/fbb78lIiKCESNG0KNHD5vPr6HxMFAsFUFqaiq//fYbbm5uKKVo1KgRly9fpnTp0vYWLU9KlCiBv78/Pj4+lClTxt7iGMjIyODcuXO4urpSt25dm869aNEi/v77b3r16kWNGjYNV1UsOHjwIC4uLrRq1creomhYkWKpCJydndm4cSNpaWmA7gFbs2ZNO0uVPx07duTKlSv2FiMX8fHxNGqkOzx+8eJFHnnkEZvN/eabbxIWFoa3tzd79+7lwoULdOjQgVq1atlMhoeV9PR02rZtC+Q8d6Px8FH0LKUWwMXFhe7du9OnT58c9UUlDeTZs2cZOHAgH3/8sb1FAXQ2gizeeustm849fPhwJk+eTLVq1Zg7dy7Dhg3j8OHDNpXhYSXLBlWqVCk7S6JhbYrliuB+UlNT6dGjB3/++achTo0jp4GMiopi5cqVtG3blk8//dTe4lCuXDmOHDnCoEGDqFatmt3k6NChAyVLltROF1sId3d3fvvtNy1sRzGgWJ4jiIqKYu3atVStWpXevXuTnp6Oq6ur0b72DuqWnU2bNjF+/HjatGlDx44dqVq1Kp06dbK3WHbl77//JiMjg8aNG7N27VoCAgK4du0avr6+BAYGOpwS19CwF3mdI7D7SeEHLZY4Wfz3338LIE2aNMl+6s5oUUqZPZ+lWLp0qQDyyiuv2FsUh8HX11cAmT17thaW2gp06NBBqlWrJleuXLG3KBpmQh4ni4vl1pCnpydvvPEG3t7ehjo/Pz+jYY0daZuhZ8+enD59mrJly9pblByEhobyxBNP4Ovry+7du206d+PGjalSpQozZszIda4iMTGRgIAAbVVQSCIjIw1Z3+7evWtfYTSsSrHcGjLG8uXLGTFiRI6HiYeHh0NkALuf+Ph4Nm7cSMmSJXMZvO3BpUuXqF27NgBeXl5cvXoVNzc3m8rg5ORk1LNFKUVmZqZNZXlYOHDgAO3atcPPz4+LFy/i4lIs3xsfGrQQEwXA39+fl19+GR8fH5RS+Pn5OaQSALh9+zaDBw9m3Lhx9hYF0K2aLl26BEBERASxsbF2keFB6jXyp3LlyowcOZJRo0ZpSuAhp1j+dhcvXszkyZMJCwszGBU///xzgoODARg2bBhz5851qJO7APv372fjxo3Uq1ePl156iapVq9pbJABcXV2pVasW58+fp1SpUnY5oR0YGGh0RWfvMN1FmTp16jB//nx7i6FhC0wZDxy1mGssDgoKEjc3t1xGxU6dOuWou3nzplnzWIPZs2cLIGPHjrW3KA5DjRo1pGrVqpKYmChBQUHi5+cnSimHCtNdlFm8eLGMGDFCjhw5Ym9RNMwEc4zFSiknoAngDSShS0Z/swDXLQa6AzfFSM5ipTuFNAfoCiQCr4vI3wVTX4UnICCA1NTUHHWJiYlcvHgREWHw4MEopUy6k9qTtm3bMm3aNFq0aGFvUXJw48YNvvjiC3x8fHj//fdtOndkZCRpaWk4OzszePBgh9zKK6rEx8ezdOlSdu/ezRNPPKGFmXiYMaUhgEeAhcBl4HcgCFgLnAIOAUMApzyufxJojk5xGGvvCmwBFPA4cNjUWNmLuSsCpVSerqJF4U0yMzNT4uLiJDo62t6iiIjIsWPHDPdw1KhRcvjwYZvNfevWLYmIiJDMzEzZtm2bPP744/LRRx9pqwMLMG/ePMPv9fz58/YWR8NMyGNFkJci+En/MFdG2qoA7wKvmbpe388/D0WwABiY7ft5oFpe44kFFIGfn59JRUAR8T9PTk4WQFxdXe0tioiIREZGyuzZs8XV1VUAWbRokV3kWLlypQDSunVr7UyBBVi0aJFUq1ZNJk6caG9RNCxAoRSBJUo+imAT0D7b9x1Ay/zGtISN4P6HhLHi6+tr1jzW4OrVq7Jv3z4JCQmRUqVKiaenp2RmZtpbLAPbtm2TuXPnypkzZ3LU2+rt/NatW7J//37x9vY2+jv18/OzyrwaGkUBsxUB0BYYBLyaVQp4nUUUATACOAoctcQDesiQIbkMxsa2iRyNjz/+WAD5+OOP7S2KUYw98I0pXku9nSclJcnrr78uo0ePzlFvavvPEX+njs6VK1dk06ZNcvr0aXuLomEmeSmCfM8RKKWWATOA9kArfTEer+LBuAH4ZPteQ1+XCxFZKCItRaRl5cqVzZ64WrVqpKamMnXqVPz8/Iz28fLyMnseS1OjRg3atm3rcL7x0dHRTJgwgeHDh3P16lVExBC075133jF54tdcUlJSWLJkCUFBQTnqtTMFlmPt2rV0796dH374wd6iaFgTUxpC7r2Nn8WInaAghbxXBN3IaSw+UpAxLRFr6MaNG3LkyBG5fv26Vd9Yiws7duzId6vNWDH3HicnJ8vixYtlyZIlIqLbOvvss89kxIgR4u7urv1OzeSzzz4z3L8FCxbYWxwNM8GcrSFgDQUw4hq57icgHEgDQoFhwChglL5dAXOBS8BpCmAfEAspgvspih4mPXv2lLZt20pMTIy9RcnhNfQgxdIP5z179ggg7dq1k6lTp+awDRSF36mjMXToUAFk4cKF9hZFwwLkpQhMxhpSSm3U/yGVAZoCR4CUbCuJnnmvNayDtWINFTW8vLyIjIwkLCzMrjkAsvD39zcatK9ixYokJSXl2h7KwpJhvq9evcqCBQuoWbMm3bp1Y9asWdSoUYN33nnHIuMXN0JDQ4mMjMTHx4cqVarYWxwNM8kr1lBeB8pmWEkeu7N27VqCg4Pp27cvjz76aI629957j8OHDzNz5kxat25tJwmN88EHH/D999/zxRdf8PPPP5OZmWmXcA7GMBXiYc6cOcTGxprMXHbt2rVCz5mQkMDvv/9O2bJleeaZZ/Dz82PatGkAHDlyhHLlylG/fv1Cj1/cqVGjBjVq1EBESE9P1+INPcSYNBaLyO68ii2FtDTr1q3jk08+4Z9//snVFhwczP79+4mKirKDZHlz9+5dYmJiSE1NpW3btrRv395h4iENHjyYhQsX4ufnlytoX16Gd3MMuOHh4fTr149Ro0blatu/fz+TJk1i8+bNhR5fA3bu3ImLiwudO3e2tyhFir1797J3715DxkNHpyAhJvoCX6A7RKb0RUTEsYLiPwD9+vWjTp06hoTr2fnyyy/56KOPaNCggR0ky5sZM2YwdepUPDw87C1KDjZs2MCAAQPo3bs3ISEhZGZm4uR07x2jQoUKNGnShODg4Bx/GOYGhcsKw50VfC8lJYV///0XZ2dnWrRoQb9+/ahevXrhf7Bizvz58w0rz/vDsmjkTdeuXbl79y5xcXGUKVPG3uLkjynjgdwz+l4E6ufXz1bFGsbiosjSpUvl888/l4iICHuLIqtXrxZA6tSpI87OztK4cWOj/bIH/KtRo4bFDbgXLlwQQGrVqiW3b98WQDw9PS06R3GiVatWAsi+ffvsLUqR47nnnpP27dtLUlKSvUUxgJkZyiJF5Ky1FJFG4Zg9ezbHjx8nPT2d77//3q55evv27UtiYiLnzp2jefPmJt8erR0UzsPDg4YNG+Lj40OJEiWoW7cu5cuXt9p8DzujRo2iS5cu1KxZ096iFDnWr19PUlJSkbGr5OU11Ff/8SnAC1hPTq+hn60unREs4TUUHBxMSkoK//nPfyhVqlSOts2bN3Pq1Cm6deuWy5Bsb+bNm8ehQ4cYOXIkR44cYceOHezYsYOkpCRDH3tmVcvMzCQjIwMXFxd0wWV1JCUl4e7unqPOUvNlGTGzb0WBzuMlKiqK6tWrU6lSJYvOq6FhjKSkJNzc3HB2dqZZs2acOHGCY8eO0bx5c3uLBhQ+Q1kPfSmLLkx052x13S0tpC0ZOnQoLVq0MGosXr16NRMnTsQRXVT37NnDjz/+SEhICO+++y6nT5/OoQTAcqd2C4OTkxOurq65Hvi9e/fGzc2N7du3G+qSk5PNnu/IkSOUKFGCNm3a5GqbOXMmTZs2ZenSpWbPU5xJSEigT58+9O3bN//OxZi4uDg8PDxo3749AOXKlaNcuXIPhbH4D+B3EXE89xkzqVu3LqmpqUaTwHft2pWqVasaNSTbmzfffJPnnnuOxx9/HDDtemmOS2Z+LF++nICAgBxbUdWqVWPevHl07NiRN998M9c1iYmJpKenU65cOY4cOULXrl1p2LCh2YnuMzMzcXV1NZof2cvLi0cffdRhsrgVRQ4dOkRGRgbr16+3eQ7qokZWetYTJ06QlJTE0aNHKV26tMO5oJvElPEAGA/8CewFPgEeo5ChJixZNGOxjuvXr0u1atVsGmXTVDiOYcOGCSAvv/yy9O7dW3r37p3r2uTkZElPTzdkWcMKJ36TkpLE19dXatWqJSIiTZs2FT8/P4mPj7fYHMWJrDAdP/74o2zZssXe4jg0UVFR0qtXLxk+fLjExcUJIKVLl7a3WDnAzBATZYA+6PIHHAdWoItAWjW/a61RNEWgo1u3bjYJ25AdU7kcvL29ZdWqVXLgwAEBxMnJyej11o7rlJqaKoA4OzuLiEiFChUEkNu3b1tk/OJEZmamtGvXTpo2bSrp6en2FqdIkZmZKfHx8Q4R/iU7ZimCXBdAA+A9dNtGD50iuHnzpgQHBztkzuLdu3fLmjVrJDw8XEaPHi3169eX3r17S9myZW0SJ6kg4Z3XrVsnmzZtMponwZQisdQKJjMzU65cuSKhoaEiInLx4kW5cuWK9iDTsDq7d++W/v37y7x580REZPr06dK+fXvZtGmTnSW7h9mKAKiOLifBk1mlINdZo1hCEfznP/+R6tWrG31THD9+vAAybdo0s+exNJ07dxbAbsv0wjzIQ0NDpUOHDvLmm29aPE/AwYMH5bnnnpNPPvkkV9uIESPEx8fHof4QiyqrV6+Wb775Ru7cuWNvURyWhQsXCiCDBg0SEZE33nhDAJk/f76dJbtHXoqgICeLvwAGAGeAjCzTArAnv2sdlbCwMOLj4436+Hp5eVGvXj08PT3tIFnetG/fntKlS9stV4KpeEJvvvkmP/74Iw0bNqRFixY5rgkPD2fXrl3Exsbi6+trNDBdYcNMRERE8Pvvv+Pu7p6r7ebNm1y/fl07EWsBJk2axPnz5+nYsSPlypWztzgOyVNPPQXAihUrmD17Nn/99RctWrSgd+/edpasgJjSEFkFXS7hEvn1s1WxxIogMjJSrl27JhkZGWaPZW8WL14sPj4+8sEHH9hkvqCgIKlSpYrBNhAUFCRffvmlAPLee+/JqlWrZN68eRIXFyciIrGxsbJjxw7ZuXOnxW0EERERsmXLFvnrr78MdR988IGMHDlSrl27Jm+//bYMGTLEsFWkUXAuX74snp6e0qZNG5k8ebKMHj1arl+/bm+xHJbMzExxcnKSEiVK5Djh7khgprF4C1A6v362KpqxWMekSZOkZs2aOR6qtuLll18WQJYuXSoiIps2bZKXX35ZVqxYYZDp0qVLJlNXVq1aVZRSVgkzUbFiRQEkMjJSmjVrJoAcPXrUonMUB06fPi2ANGjQwN6iFDni4uJkxYoV8ssvv9hblBzkpQhMbg0ppb7WP2ASgRNKqR3kPFk81pIrE438SU5ORimFm5sb0dHRXLlyxdA2depUm8gQHx/P6tWrAahTpw4A3bp1o1u3bgCcPn2aqKgotm7dyvvvv2/YRspKXblw4UIiIiKsJt/06dNJS0ujdOnS/Pe//yUqKooaNWpYbb6Hlfr163P79m0yMjLy76zBn3/+SXh4OE8++SQ+Pj7UrVuXHTt2sH37dp555hl7i5c/pjQE8FpexdR11i7mrggSEhJkyJAh8u677xptX7p0qdSqVcshE8RnBQE7dOiQREREyMWLFyU2NtamMly6dEkA8ff3z7OftVgZMiQAACAASURBVD2ERET++ecf+frrr2Xnzp252qZPny6jR4+Wy5cvW2y+4kpsbKxcu3Ytx3mMnj17Su/evR0i6KEj0KtXLwFk3bp1IiKG8zJjxoyxs2T3oDBbQ0BvoIqp9oIU4Hl0NoaLwAQj7b7ATnTnE04BXfMb01xFcOvWLQGkQoUKRtu/+eYbAeTNN980ax5r0LZtW3Fzc7PrVkd0dLTMnTtXvvvuO0NdQkKCREdHS3JysqEuLw+hAwcOyMKFC+Wff/4xS5YsT43hw4fnamvRooUAOewHGoXjpZdeEkCWL18uIiLp6emG32ebNm3sLJ1jMHPmTAGkUqVKcunSJRk9erQ0bdpU1q9fb2/RDBRWEawFbgAXgKXACKCRqf5GrndGl4+4FuAGnAQa3NdnIfCm/nMDICS/cc1VBImJifL999/LDz/8YLQ9JiZGLly4ILdu3crV5oi5jaOjo2XLli2yfft2m80ZFxcnoaGhhtVIQECAADJlyhS5c+eOhIaGio+Pj8kVwejRowWQr7/+2iw59u7dK6NHj87xe9i/f79s2rRJFi9eLCNGjJBly5Zpb62F4MiRI/Lqq6/Kt99+K2PHjpUaNWrIqlWrREQkIyNDdu/eLYC4uLjkeAEozlSuXFkAWb9+vQDy+OOP21ukHBRKEci9h7U/MAj4BjgG3AI2F+C6NmQ7dAZMBCbe12cBMD5b/wP5jWtrY3HWwz/rbTb7Q82ap3jz4/fff5exY8caZClXrpzN5n7//fcFkOnTp4uIyJQpU6R8+fIyY8YMefbZZwWQDz74wKSHUFBQkAwbNswqyuvRRx8VQE6cOCF9+vTJsVzXKDgrVqwQQAYMGGCyz9atW+XKlStGDw8WR7Zv3y7btm2TU6dOycCBA+Wjjz6yt0g5MEsR6K6nHjAMWAQEAzsLcE1/4Pts318BvrmvTzXgNBAKxAAt8hvXlorAmLujNfe8H4TPPvsshxwDBw60ybwXL16UJ598UgCZM2dOrvZBgwZJtWrV5Pfff7fLCmr06NHSpUsXuXDhgnzyySfy7LPPyq5du6w+78PGpUuXZPHixbJjxw57i1IkCAsLk/j4eINSTEhIkLNnz8qlS5ckNDRUfvvtNztLWPitoQ+BjcAh/dbQm0AzwNnUNfddXxBFMA54T+6tCM4ATkbGGgEcBY76+vqadTOio6Pll19+kb179xptP378uLzzzjuyaNEikwbP7KWwp2ILwyuvvCLPPvushIaGyuHDh2XWrFmyb98+q4dQyP5AL1++vOHMQH5cuHBB9u/fbzhTYGliYmIkJCTE6InXbdu2ydatWx0qQ9TDwpo1a2TatGkSHBxsb1EcgszMTHF1dRXAsE22Y8cOAeTpp5+WMmXKCHaMCJBFYRXBOf1W0CfochGUM9XXxPUF2RoKBnyyfb9MPgZqc1cEhw8fFkBatmxptH3t2rUCSJ8+fUwaPO21Isjuo28rjK2KnJycjBpo72fChAkCyOTJk3PUJyYmSnh4uNlBuQIDAwWQiRMn5mqrWrWqABIeHm7WHBoiy5YtkyZNmsiXX34pIiJ9+/YVQGbNmiUBAQEyfvx4O0toX5KTkw2RgGfPni3h4eGyc+dO8fPzk1deecVwANPeq6tCbw0BFdAloZmGLiT1EeA7YEhe1+mvddE/2Gtyz1jc8L4+W4DX9Z/rA2HkE+raXEVw9uxZ6dmzp4wbN85o+4ULF2TmzJmycePGfFcEtrYR7N27V7Zu3SoJCQm52qy1T1sQN9AZM2ZI27ZtZe3atYa6oKAg8fT0FEAqV66c4z7973//E8Dk76CgzJkzR3x9fQ0PqOy89NJL0rlzZ5u71z4snDp1StauXSvnzp2Tr7/+WgB56623REQXe2jcuHGyc+dOAaRixYp2ltYxaNSokcFpApCePXvaW6QcFFoRSM6H+mPA++hcQTMKeF1X4F903kMB+ropQE/95wbAfr2SOAF0zm9MW9sISpYsafJBaE+vobCwMPnuu+/Ey8vLIJM1QmbktSrKMhaPGjVKAJk7d67MmDFD/Pz8DEnqjSnNBQsWSOXKla1iTOvevbsopWTz5s0yfvx4cXNzk5kzZ1p8noediRMnCiBTp06VyMhI+fvvv+XGjRs5+qSlpcns2bNzuBIXZwIDA2XMmDEyd+5cKV26dJ6GdntQ2K2hnsB0dIlpovX/Tgd6AZVNXWftYmuvoS+++MLwMPP29hZwjBgiP/zwQ64HszXc+PJaFWWdtbh48aLs3btXbty4IePGjbPrNlr37t0FkA0bNhgiyX7++edWn/dhY9myZdK7d+8cqzyNwnH+/HlZtGiR3Z0WCqsIfkaXd6AN4Gaqn62LuYogPT09T+NqTEyMbN++XQ4fPiwiukxgAwcOlPfff1/u3Lkj06ZNk+nTp9slxv28efPkq6++kqSkJNm2bZu0aNFCOnXqJNu3b7daAD1TnlNDhgwxGoQsJCTE4uGmH4TU1FRJT0+XzMxMSU5OluTk5IciuKCjEBMTI/v27ZNz587ZWxSHYf/+/dKiRQv573//a6iLioqS2rVrS+3atQ3//4cOHWpHKc3cGgK6GKkbld911irmKoKffvpJAHnppZeMtu/bt09Ad2IyISFBXFxcpH79+oY9+KygZsYOnFmbcuXKCSDR0dE2nTcoKCjHFhQgZ8+eNdnfFuElRHSrtcaNG8uyZcty1GdlKitZsqRF5yuu/Pvvv/LRRx/J4sWLZdu2bQJIp06dRERk165dsnHjxmJ9qGzNmjUCSLt27eTy5cuSkJAgsbGxuf7/mzrEaivyUgRO5M8kpVTHrC9KqQ/020NFkvT0dJRSuLq6Gm2vUKECTz/9NC1atODatWukp6eTkZGBUgoAPz8/atasSWZmpi3FBmDkyJG8/fbbRuPvW5PBgwcTHh5u+E+TmJhoCDgH8OuvvzJ9+nSCg4MBXd6CEiVK5BjDw8ODwMBAAHbv3k3r1q0ZN26cWXJdv36dU6dOER0dnaNey0FgPsnJyaSmpiIihISE8Nlnn7FixQpKly5NmzZtaNiwIQADBw6kR48eREVF2Vli+/HMM89w+PBhLl26RK1atZgwYQLdu3dn5MiRhISEcO3aNf79919ef/11e4tqGlMaIqsAldCdJXgCCATWYcetIkvZCAq6XZCYmCjBwcESHR0tt27dktWrV8vu3bstIoO5nDx5UubMmSP169eX7t272yyUQnBwsEyePNkQeyZ7LJojR47I5MmTZdy4ceLr62v0MNnGjRsFkK5du5olx40bN+TEiRM50orOnTtXevbsKVu3bpUNGzZIv379ZOHChWbNUxzJOpW9du1auXz5snz66adGnSMGDRokXbt21cJ4iC48u7+/vwwYMEAAGTVqlL1FygEW8Bqqgi4o3A/k495p7WJrY3FMTIwAUrZsWUN8lfbt29tUBmNkHVjJXi5cuGCVuVJSUqRBgwailJIff/xRFi9eLID06tVLRHThCD744AM5fvy4zJs3TwAZOXKkyfGio6Pl0KFDVpF3xIgRAroUgd9++22+smgYp2/fvuLs7CwbN260tyhFjhs3bsjOnTsN26eZmZkSFRVl0/M/xshLEeSVjyBe/4DJwg1dALn+eqNfWUusSByV5cuXExAQwLVr11BK4eTkRMWKFenfvz/16tWzi0wXL17E2dkZf39/w1YVwLBhw+jVq5fVUlimpaVx5swZAF599VWcnJz45JNPqF+/PqDbHhg4cCCg23r7+OOPadmypcnxPD09eeyxx6wi66hRo+jSpQtNmzYlPT2d1atXU7t2bavM9TCzbt06gKwXQY08+PXXXzl58iTdu3enWbNmeHt74+3tzccff8zGjRt55JFHWLt2LS4uLqSmpub423UYTGkIRy3mrgiWL18uXbp0yWVgzOLy5cvi6upqMsDciy++KPXr17f58fqMjAyDLJmZmZKWliYJCQmSmppq9bnT09Pln3/+MZyQpADnKBYuXCi9e/e26hvlypUr5cMPP5QTJ07kqA8LC5Pu3bs73NK8qJKcnCxHjhyRv/76S7799lvx9PSUgIAAQ3tmZmaxDjz3+uuvCyDff/99jvoaNWrkeIbUqlXL6GFQW0EhVwT+IhKSR7sCqotIqKWUki04f/48W7ZsoXXr1kbbnZ2dSUtLy1WfmJhIQEAAlStX5uzZs9y9e9faouYgMzOTWrVqAaCUwsXFBRcXk78+i+Ls7MyJEyeIj4831GVlHANo3749CQkJ+Pr6Urp0aQCOHTvG+vXr6dy5c67xbt68yYIFC6hQoQJvvfVWoeVav349K1eupFGjRjRp0sRQHxcXx6ZNm6hbt26hx9a4R0REBK1bt8bHx4e3336bmJgYg0G+S5cu/P777/zxxx906tTJzpLah169euHt7c26deuYOXMmPXv2pFKlSnTu3Bl3d3eqV69O69atHTpTmRITSz+l1BrACdjAvfDT7kBt4GmgE/CxiPxhG1F1tGzZUo4ePVro6y9evMj58+epXbs2//nPf3K1Z2Zm4uLiYnRJrJTixIkTgC5NY8mSJQsthyUIDw8nJSWFY8eOERUVRY8ePahWrZpV5vL39+fq1au56v38/GjQoAFbtmzht99+44knnuDff//l0qVLODs707RpUx555JEc15w5c4aGDRtSv359w5ZTYVi/fj3BwcH07t3b4MVy/Phxdu/eTUxMDP7+/jg7O+Pl5WVUIWmYZtiwYYSGhjJ//nzKlClDly5d8PLyYu3atdy9exdXV1fKli1L165d2bJlC5s2bTKkKy2uvP766yxduhQ/Pz+uXr3KpEmTmDJlir3FMqCUOiYixvdsTS0V9A/CBug8hXahyzR2HFgBvAy453WttYotjMWm/OCzzhA4wtHx69ev55Lvzz//tMpcWQZzY0UpJW+88YbUq1dP9uzZI9u3bxdAOnbsaHK8W7duSUBAgNmJaYzx8ccfCyCTJk2SzZs3CyDPP/+8xed52Klbt64AcubMmTz7JScn2+VwpSNy9epVOX36tKxatUrGjRtn92ij90Nhtob0SuIMEFAo9VOECQwMZMSIEYbE61n06NGDn376KZePvD1ISEgwfH7yySepW7cuVatWtcpcd+7cMdnm6+vLwoULDd///vtvmjVrlqeBtlKlSnz22WcWlTGL5s2bM3ToUJo3b06NGjV45ZVXcmwbaRSMJUuWcOfOHXx9ffPs5wh/C/bmzJkzpKamUrduXXx9fWnUqBEvvvgiJ06c4NNPP6V27docOHCAXbt2MW3aNHr1csBjWKY0hKMWc1cEf/zxh3z77bcmj8gnJyfLiy++KI899phUqFBBAKlatapMnjxZUlNTZdmyZTJ+/Hiz8+0+KDExMVKvXj1p166diOjSRa5cuVK2bt1qk7lffPFFQ8z1rJJX9NUff/xRlixZYtXon//8848cPHgwVz6CGzduyIIFC2Tz5s1Wm7u48t1338mrr75q97g5joSp/NhdunQx/K1Ur15d0AdmtBeYe47AkYq5iuDll18WQJYuXWq0PSUlJcfDrlSpUjnae/fuLWD79IeRkZECupDO9uJBMo5l/ce/evVqrrb09HQ5fvy42Ynln3nmGQFk27ZtOer/+OOPfLenNB4Mf39/KV++vLzwwgsCyOLFi0VE5Pvvv5devXrJr7/+amcJ7cfAgQOlSZMmMnv2bJkwYYKsWrVKzp8/L2PGjDE8Q9atWycnT540mkTJVuSlCGzjduJAdOzYkVKlShk1FAO4urqycuVKkpOTOXDgAM7OzjnaBw8ezGOPPWYwTtqKChUqEBwcbNQHOT4+noSEBMqWLYuHh4fVZBg8eDCDBw/OVd+vXz9OnDjBmjVraN68OQCDBg0iIiKCChUq5OqfmppKs2bNcHd3JykpqdDy1KtXj7i4OMqXL2+oS05OxtnZmf79+9OqVStiYmLIzMykYsWKhZ6nOPLVV1/h7OzMyJEjcXFx4c6dO9y5c4fXXnuNLl260L59ewCCg4PZsGEDTz75pJ0lth8rVqwAYPz48Xz55ZeG+q+//lr3tl0UMKUhsgq6KKTdMJJC0h7F1ieLRUQWLVok06ZNk3nz5skzzzwjCxYssLkM95N95ZKVQ9jUKsdc4uLiZO/evXLq1CkR0W29eHl5SePGjUVEpHXr1gLIwYMH5caNG+Lt7S2PPvqoyfHS09Pl0UcfldatW1tc1gULFgggw4cPl+DgYAGkXr16Fp/nYcfJyUkAwzmViIgIiY6OzhWa5eTJk7J+/Xq5ePGiPcR0KP7880+ZNm2aPPfcc1KnTh1DCBZHATNXBN8CQ4Cv9C6lP4jIecurJMdlxowZnD17lldeeYXt27cb3nrtybJlywyf//rrL8qWLWsykJ65nD17lieeeIKWLVvy119/oZQiIiLC8Lbzyy+/kJSURPXq1YmLiyMsLMzoWYwsnJ2dOXXqlFVkLV26NJUrV6ZUqVK4u7tTvnx5ypQpY5W5HlZEhLFjx5Kammo4q2LKEaFx48Y0btzYluI5LE8//TRPP/00EydONNSlpqbi6urKlStXWLx4MV5eXrz99tt2lNI4+SoCEdkObFdKlQMG6j9fR5eyMkhETP/FOyDh4eFkZmZSuXJl3NzcjPZZtWoV0dHRdOnShUqVKjFs2DBu3bpF7969ad++PS4uLoSFheHt7W0zuW/fvs2XX35JlSpVqFatGmPHjjW0JSUloZSyWkRUDw8P2rVrZwitUaVKFW7cuGG4f9nvg6urK5cvXyYmJobY2FjKlStnFZlMMWjQIHr16kV8fDylSpUiJibGpvM/DCilmDVrltG2P/74g7i4ODp06KBtt+mpX78+d+/e5eTJkzm2QgMCApg2bRoAe/bsITAwkMcee8whFUGBtmOAisA7wFHgV2AA8DWwK5/rnkd3/uAiMMFEnxeBM+gS2a/ITxZzt4batm0rgOzdu9dkn/Llyxu2XbLirmfx1ltvCSBfffWVWXI8KOfOnRNA6tata7N4/4Xl+PHjAuS5PWQJnnzySalcubKcPn06R31W9rbXXnvNqvMXJ6ZMmSKvvPKKVK5cWQA5dOiQiOj+X37//fcOE5HXHpQuXVoAOXfunOzbt88QXO6dd94x/G1GRkZKYGCgwchuDzAnH4FS6hd0aSo9gB4i0lNEVonIGKB0Htc5A3OBLugOpg1USjW4r08dYCLQTkQaAu/mJ4+5VKpUCW9vb0qVKmWyz0svvWToU7ly5RxttWvXpm3btlbz2TdFpUqVmD59OuPGjePatWtG+5iqtzaBgYG88847REREALrAcz4+PlSvXt3kNS1atKBq1apERkYWet6oqChu3bqVq97V1ZWqVavi6elZ6LGLM2lpaVy4cIEbN24Y6jZt2sSyZcto2LAhffr0Mfxd7N69m+HDh+fYqixuXLx4kWvXrrFp0ybat2/PI488Qt26dXn66adJSUkhMTGRKlWq8OGHHzJkyBB7i2scUxpC7r2xP51fHxPXtQF+z/Z9IjDxvj5fAsMfZFx7GIuvXbsmp0+flk2bNsmcOXPk+PHjNpchO6ZWBBUqVLDJ/BkZGTJ8+HAZMmSIiNw7hZoVdvftt9+W1157TVJSUkyO4ePjI4CEhIQUWo6YmBiJjIzMEXhv37590qpVKxkzZowkJydLu3btHCJseFHiypUrAoivr6+hbsOGDbJ06VKJjIzM0XfPnj0ydOhQWbJkia3FdDhWrVolbdq0Mfw92trFPD8wM1VlXyOlE1Aln+v6A99n+/4K8M19fdbrlcF+dMlvns9PHnsogk6dOgkg9evXF0Bmz55tcxmyYyqPcJ8+fawy39atW8XT01MGDhwoIrpok1lzZmRkyNKlS2XWrFmGFJplypQRIE+f6Rs3bkh4eLjFwxNs2bJFAHn22WclLS1NAHF2drboHA87ly9fllq1askTTzxhb1GKJGFhYXLu3DmJi4sz1KWlpcn+/fvz3JK2NnkpgoJ4DQ3Tv93v1H/vgC4IXU2l1BQRMWdN6ALU0Y9ZA9ijlHpURHLENFBKjQBGAPkeebcE8fHxpKSkUL58eVxcXPD396dBgwZ07tyZZ555hqZNm1pdhvuJi4vj5MmTlCtXzuDLn5Uvwdvbm5EjRzJs2DCrzJ2cnExMTIwh5IZSioULFxrOWLz66qs5+s+ZMwfIO/yAtQztjz/+OIcOHcLT0xNnZ2f27NlDiRIlEBHHjAPvgNSsWZNLly4Zbbt79y4lSpTAxcVFu5/oHDXGjBlD2bJlmTlzJgDVqlWjWrVqTJ8+nYkTJ1KlShUuXrxIu3btKF26dI4ovg6DKQ0h997atwFVs32vCvwOVAD+yeO6gmwNzQeGZPu+A2iVlzzmrgieeuopadasWZ7J57Pe/EuWLJkrzeFXX30lnp6eMmnSJLPkeFAOHDgggDz++OM2nVdElww+Ojq6wKciv/nmG2ncuLHVz1uMHTtWhgwZYliJZLFw4UJp2bKlQ5z3cBSSk0USE0USEkRiYkSiokSio3WfY2J0n6OiRG7f1n2/n6NHjxqStAOG0CGpqakSGRmZa8uoMHzzzTcyd+7cPLcUHY3w8CgBxNPTM1fb6tWrBZAOHTpISkqKtGnTRp555hk7SKkDM5PX1xCR7Ba9m4CPiEQDebmO/gXUUUrVVEq5AS+h8zjKznp0qwGUUpWAusDlAshUKJYvX87evXs5fvw4zZo1Y/ny5Ub7ZZ1UTUpKymWMTEtLIyYmxuZavVSpUrRv394uAdRcXV3x9PQ06Qq6e/dutm/fTkpKCgAhISGcOnUqz2B1M2bMYPjw4Vy4cKHQcq1cuZIffvghV7L669evc/ToUYPxujghAhkZEB8PEREQFgbXrkF4OERGws2bcOcOxMVBbKzu8507us9xcbrrjP3XnjNnDi+88AIALi4uuLu7A3Do0CGqVq1K//79zZZ93LhxvPXWW1ZzgzaHjAxduXtXd0/DwuD6dbhzpyTTpi0kMHAG69ato0KFCiileO+992jevDlHjx7lt99+w83NjQMHDvDHHzaN2l9gCrI1tEsptQlYo//eT19XCjD5ly4i6Uqpt9GtHpyBxSISrJSagk4z/apv66yUOgNkAO+LSJQZP49Jli9fzogRIwz/yUJDQw2JVe4Pm3DgwAHCw8O5fv16ji2MiIgInnvuOV544YVc3kTWpnHjxuzdu9do2/Hjx9m4cSNNmjSxWWTDbdu2cffuXbp27cqAAQOIjIwkPDycHTt2sGTJEgBmz55N9erVjYal2LhxI3v27OHll1+mTp06hZLhq6++MoTWyCIiIoJbt24xZMgQhg4dytdff82dO3f4f//v/xmS5hR1MjIgJQXS03X/pqVBZqauXiwQ0eDIkQM8++xQnnjiCb777jsAWrVqRWJiIqNHj6Zjx46GvqVKlaJSpUo5fgeFJUuhX7lyxZAG1V6kp0Niou5+Jibq7rMx3N1LMnDgG1SoAFu3rjacW5k5cyZ9+vQxhOJwdEwmpjF00G0E9gWyfqL9wDrJ70IrUdjENHklVgkJCcnz2g8++IA1a9YY+q1evdrwduQILF68mGHDhjFkyBAWL15s8fF3797NokWL6NChA0OHDgV0e/zh4eGEhoby7rvvEh0dzUsvvcS7776bI3y3h4cHCxcuzKUMNmzYwM2bN+nWrZtF7QWnTp2iSZMmNGrUiNOnT1O9enXCwsK4fv06NWrUsNg8tiYtDZKTdQ+khATLPPBN8eefvzFsWHeef/55tmzZYr2J7uOpp55iz5497Nq1i6eeespm82aRlnZPwcbF6ZRrQSlXDkqWTCYhIYH169cTExPDwIED83ShtjV5JabJc0WgPwuwXUSeBtZZQzhbYY7vfVRUFCEhIbi4uFC9enWLvP1YkmbNmjFp0iSrhb74999/WbZsGSVKlDAogueff56YmBjc3NxYs0a3WPT398+VwyErxef9isBaKxcvLy8CAgIMmdrGjBlDQkJCnudGHBkR3XZEdLR1H/7ZeeyxpzhxIpgyZdxtM6GeKVOmEB8fT6NGjWwyn4huGyw5WacE8oiKYpK4uFgOHdqFt3dFundvj7u7u0mnjTp16hAREcGVK1eoVKmSmdJbloKsCHYAfUUk1jYi5Y0tVgTvvvsuc+bMoUePHsyaNYtSpUqRkJBAlSpVCA4OZt68ebRq1cqmR8W3bdvGiy++yHPPPceqVatsNi/AhQsXOHDgALVr16Zdu3Ym+zk5OWHs/5O1wl+sW7cOJycnevXqhZPTPXPXhg0bOHnyJD169KBZs2YWn9cWZGbC7duQlGQ7BZCdqlUheyZWESE0NJQXX3yRRx55hKCgIIvOl56eTkhICCVLlrTqW7SI7p4mJem2fDIyzBvvn3/+pkePFjz6aDNOnfo7z77VqlUjIiKCGzdu2DQ8TRaFXhHouQucVkr9ARjSYonIWNOXOB6BgYG88cYbOcIee3h4EBgYmKtvVh7djRs38u677+bYE7169So//vgjSUlJNlUEycnJxMbG5nrjtgV16tQp0D6+r6+vUWVrzOX35MmTXL16laZNmxbaJfjFF18kMzOT9PT0HPXr169nyZIl+Pj4FElFkJKiUwKFeUMtCOnpugegk5OuwD0jcxb3PyCnTZvGRx99BJDDgeLOnTs8//zzuLu7s2vXrkLLdPPmTerUqYOXlxfh4eEAxMTE0LNnT9zd3c02siYn61ZWlt5W8/AozTPP9MTfvyZnzpzhyy+/JDo6mnHjxtG6descYeGDg4NxcXFxSFtVQRTBz/pSpBk8eDCpqamMGzeOO3fu4OfnR2BgoFFD5v/93/8xd+5cXF1dcyVef+yxx1iyZAk1a9a0legAdOnShejo6Fz5EUB37uHixYt4eHiYzLNgaUSEtLQ0XFxcKF++PGlpaXz77be8/fbbuWwExpTtrFmzWLp0KYsXLy7UsXsRoU+fPqSnp+dYDaSnp+Pn50ePHj1o/xcB9AAAIABJREFU2rQpV65cITY2ltq1azvkH2B2MjJ0nj33OUEVisxMnSdQRobuIZj1OcvQnBeXL+8nImIDnTq1M2zhZZ0Jefzxx3PE3Hd2dubw4cNm39uslWRERATHjh2jRYsWhIWFsW/fvkKPmZGh+7kTEsx/888iM/PeNl16OiQn12XMmA3UqgU3b+5i6dKlgO4l8vz589StW9dwrbHcHI5CQaKPLlVKlQR8pYiHnx4yZEiBHjqPPvoo8+fPN3zfvHkzv//+O8uWLcPb25ugoCCbHyrLcuE0xqFDhwyH3azhnnb27Fn++ecf6tevb9i/bdWqFceOHePIkSMkJSWRnp7O4MGDcXNzMxx08/X1NalsmzdvTnR0dKG3AZRSrF27Nld9TEwMn376KRUqVKBZs2a0bduWgwcPsm/fvjy3texNRobO3fNBVgEiuodcUtK9t/z0dN0YsbG6z4XhwoVjLFnyP0SSDIrgvffe47///W+uvqVKleLgwYOUzL6PVAiqV69OQEAAgYGBbNmyhRYtWhhWHg+abCnrzT852fy3fxHdgz86GpTSGZGNKerERF2ipB9++IEPPviA+vXrFym7VL6KQCnVA5gBuKE7TdwUmCIiPa0tnKNw8OBBvvrqK0D3oLnfb93elC9fnqZNm+ZavViKn3/+mY8++ogPP/zQ8Hbv4uKCs7MzGRkZ3L17l4yMDFxdXU1mMbufsWPH5gilbSnc3d1p3Lgx5cuXZ/ny5Ya8B/3792fGjBkFks3WxMXpfPkLYkZJStKdCYiN1T2QrGE/qFOnHR9+OJ0OHe45H5g6Rezk5MTjjz9ukXlbt27NG2+8YXjJ8vf355NPPsHHxyfP67JWP3DPs8oc0tN1Zy9u3sy/b5btKzXViSpVvHj99dd5/fXXjfb95JNP+Pfff5kyZQq1a9c2T0gLUxBj8TGgI7qQ0830df+IiG1M+/dRWGMxQEJCAidPnqRs2bJ5eib89ttvfPfdd/Tv35+BAwdy+PBh/vrrL7y8vKhSpQqhoaF4e3vTqVOnwv4YD8z+/fuZP38+7du3Z+TIkTabF3RG2ZUrV9K3b18GDhwIYPeQDSJCSkoKLi4uhuQpWZw/f561a9cSGBiYyyZkzJXVnmQd6MqLtDS4fFn3sLPVWatWreD+94rLly+zfv166tSpQ48ePWwjiAmSk3UK1NVVtwIo7NZPRobubT4pCaKidJ/T0wuuYI8c+ZlZs/rxxBN92bFjHXnlhmrTpg2HDh1i//79tG3btnACm4G5xuI0EYm974/e8Y7+FYALFy7Qrl07GjduzMmTJ032W716NRs2bGDDhg306dOHtm3bGn5x+/fv59VXX6Vt27Y2VQQXL14kKCgIpZTNFUG/fv3o169fjjpLKIHMzExExKjdIz9iYmKoWLEinp6eREdH52h766232LFjR65rTLmy2ou7d/NWAlmnf2/fNv8t90FJTs75/cCBA4attR49euRQBLNmzSI6Oprx48cX2lbw999/M3jwYFq1asWPP/5oqJ8/fwGRkRGMHTsWNzdPEvTuKub6TCQn6+5reLh59oOMDN1enpOTK3fuxLF79zZKlSpFly5dcvWdPHky0dHRDrcagIIpgmCl1CDAWZ8/YCxwwLpiWYcSJUrQpk2bfH8RXbt2NfxnvH/vs0qVKvTr18+QrctWtG/fnqVLl9rcSJ0fqampDBw4EHd3d5MhO4wxdepUJk+ezEcffcTUqVMfeN6MjAxKlChhNMtc7dq1jSoCsF/OhvuJjYW8kqeFh4MRB6xCcecOnD1777TslSu6bSU3N8haTGVk3POlT06+S4cOIUyYUNFwHiO7sr1/NTBjxgzCwsIYOXJkoRVBbGws586dQ0SIiIjAy8uL69dv8eabowDo1OklatQwL79EQoLu4R8dbRnFmpgIbm4DGD36RapWzeT69QuGg6ZlypTh2rVrhnA1gFHl4CgURBGMAQKAFOAndGEhHvwv1wGoX78+Bw7kr8MGDBjAgAEDDN9DQ0M5e/YsS5cuxcfHh++++87mSU8eeeQRkzaAS5cu0aFDB3x9fdm/f7/F505JSUFEcHV1/f/tnXl4U0XXwH/TlRZaWgoUZCkgoCAgKIqAirggL6IiKJ+KigiioK+oLCqLooL6iiLiCqKIsglu7PtSZFNBFtkKlbUsBVoKpdCWJOf7Y5I0bZM2SZO20vt7njxN7p1778lkOmfmnDNn7KP3t99+m3Xr1jFw4EB+/vlnj52Ftv2V84Z+ukuVKlXIzDtstbJs2TICAgKcrl0QEerUqePSie1vTCbdEbka0Z46pSOHzp/37L4iunM7f14rmHXrYMsW3bGnpXnqS6jA9u0m6tadxYsvDgB0tNzixYupVq1avnxXL774IhcvXiySc7RVq1Z8+OGHDBw4kJ49n2Tq1MXMmDHPfj40NKqAq11jC8U9c8bzOnW8R1KSfr9mDaxfrxVoerptNqGAQBo1qkzXrl35+eefSU9Pz2eyLM24EzV0Aa0IhvlfnNLJvHnz6N+/v/3zc889V6p2v1JKkZSU5LfN61955RU+/vhjPvroI158UW8it2XLFpYsWUKvXr2YPXu2x6aiwYMH88orr/jFz3DixAksFgthYWG5fAQ2Dh065DLPlD/JztaRQa7s/ElJOR2OK0Rg3z44eFArjMTEHMXhysQRHAyNGkFEhH5fty6UL6/lsenhwEA9Q7h0CSZOhMDAOKpXz0nJUaVKFe6++26n9x88eHAh37xwwsPDuemm1lStGktwcCQZGRAZGUXTptdz5533U6WK+zsC2lYMnz7tnsPX2fWbN0NCgjYhbdzoXIkoBVddpZXMyZOQklKZn376iQsXLmAymfIpxt9//50DBw5w0003UadOHY9kunDhIseOnaN69Qp+iUZyJ2qoITAIqONYXkRud3XNv53k5GROnTpF1apVqVq1KrVr1+b222/n0KFDPPXUU4SFhRV72oKEhAS2bt3KVVddlS90tXbt2hw5cqTA/P9FISAggJCQkFyKZujQoTz99NO0aNGCatWqeXxPb/wC7jBt2jSio6O5ePEiYWFhhIeHk5KSP49hcfsLsrJ0h+1MCRQWpWKx6M4oKQm2bwcXWwUQGqo7+AoVoEEDaN8eoqL0Z3ebhsUCX30FZnM0HTvm9gulpKSQmppK5cqVfT4QSkuDWrVa8/vvORljO3bsSseOXd26PitLK7Nz57SS9DSwLzsbtm3Tinr3bj2bcqR6dT0LqF0b7r0XIiO14tyx4yemTQsAHsC266qrcNfx48czffp0pk6d6pEiMJlg+vQFPP30Q3Tr1s1p2HRRcWfuMhu9b8AkdIbQfy3z58+nZ8+e3HfffUyePNlluV69etmTbYkI99xzD/fccw+gR5OVK1emVq1axWpvXrhwIS+//DIDBgxg3Lhxuc4FBQX5NaHa2LFj7Ztu2GjZ0mnwQbFx5MgRnnrqKeLi4pg0aRKQk2HWtqAtNTW1wFlScf1+mZm6k3emBC5ehF27XK8fOHUKvvxSl7EREQEtW0KVKnp0X6uW7ph8MSEMCNAJ1NLStOKJjNQj39TUVJo3b05SUhKDBw/OtagsMTGR1NRUrrrqKpepyl1hNutond9/38yCBbNo2rQl99yTk9DxxImjnD6dTI0acURHx9iP23IwhYZqBeuwvbLbHDkCCxfqa5OS9G9hIywM7r4bwsPh6qu1YnXG8eN7OXXqLPAAx48L2dmXMJlMTpVB69atMZvNhYbDOpKSomc3JlMAMTFV/GaJcEcRmETkC788vZjJyMggNTWVjIyMAssV5PCyjYz9ZYZxRYMGDXjooYdKZHe0grhw4QKzZs2iQoUKHuWkj4+PZ8yYMdxyyy288sorHj83PT2d5cuX50pXPGzYsHwpOC4VsEKrqLvdTZs2rdDFcxcvaiXgzEafnQ179jhXAps2wfff58wSKlaEO+7QI9MbbnB/hO8NUVFaEezbp2cTFSvq2UCS1W4VG5vbTPPMM8+wcuVKli9f7nYk3aVLugO3mab27NnOhAnv061bz1yK4L33XmHOnGl8+OF3dOnyuN1Ec+SI830TCuP4cW322bFDz64cf5e6dbUJrWJFaNsWYmJc38dG69bdOXs2ncWLISkpm9BQnajviSeesK8ytvH88897lJbmzJmc79ixY1fuv78rVt+9z3FHEcxTSvUHfkE7jAGwbkzzr+KBBx7g1KlThZolpk+fzueff445j9F1zZo1BAUFkZmZmSutQXHQuXNnOnfu7PRcdnY2AwZop94XXxSPzv7tt9/Yvn07V155Jb169aJ69eoeKYLk5GQWLFjg9YrU2rVrs3TpUvsGKeD5CN9Z6gt3yTv7cOZ3KEgJZGVpJZA3euXCBVi6FGbN0teVKwfNmsGTT0JxuaXOnNkMXM/q1Vto3rwFaWmQnV2JPn2eo0aNWAYOHJir/NVXX825c+fc+i1FckI3Hf+9mjS5nhdfHMm4cSPZs2c78+f/xfjxbzNnji0SrQLbt+cPa3WH8+e1aW3FCm02shEYCLffDq1bQ9Wq7nX8eYmNvZKmTWHxYjh9OqdfmT17dj5FUBBms5YHtJJMTc09Q/E37iwoO+DksIhIPf+IVDBFWVDmLnlHeg8++CCffvqpfQcus9lc7IqgIC5dukRISAhBQUEFjoC9ZdSoUcTHxzN06FDat28P6NHNZ599xsiRI9m/fz9RUVH2vYrd4dixY/z111/UqFHDZ4nhXGWYdUZMTAynT5/2+bPi4uI4cOAgqamuR6znzmknb1479sKFMHNmzgzhoYegS5ecxHC+olw53ek4bmSjVE4SuieeWMiZM5148sndvPFG7g1igoL0rMQbF09mpp4FuOpyLBYL9esHISJs3WrijTcGM2fOR/To8QGdOw90fpELRPSof+5cbVazmeXCw6FFC7j2WmjaVM9+iso//8Dw4dpxPG/eJVav/oXISMkVeQg6Qi4rK4vAwMBcA5jUVF031avrGdLx487NiKGhFGlGUKQFZSJSugLX/YyzkZ6jEmjTpk2JKIHs7Gx7Goe8YWlBQUF89tlnhISE+GXF7/bt21m+fDlPP/20/ditt96KxWLh1ltv5Y033vD4nldccUWRUvE6M8uMHj06128HOkxVKZUrLUhgYKBHSssZBe1vkZLiPMpEJCfaJy/z54NtGUbjxnDPPVCU7SUCA6FSJf23XDnd4dk6+sKiGrt1u4tJk6BcuYb5zplM2qFarpx+FRYvkZWlX+XKaX9HQePOgIAAZs/+nbS08uzcqejS5S06dBhEuXLur01ITYVFi7Sz1+Y3CAyEJk3gttvgxhuL5ksJDNS+GaV0Pe7bF8/p00eAx0hJ0e3trru6O+2w33jjDd555x1GjRrFsGHD7HmMbAOGlBStEJwpgZkzJzFz5lf079/bPvP0JS57NKXUEIf3D+U59447N1dKdVRKJSilEpVSrxZQrptSSpRSfvVALly4kCeffJJZs2a5LOPMzpyVlUWtWrW4dOkSv/32Gx06dLCPjIuLUaNGuczkqZSif//+9OnTxy/hmCNGjGDJkiXceuut9mPdu3fn888/97oepk2bRp06dQgICKBOnToeLUabNm0aTz/9NIcOHUJEcpllJk6cSFxcHEop4uLimDx5Mt988439WOXKlXnzzTeLHC3kyr9Qs2Ztl0rAFurpyKlT8MEHOUrgmWdgxAj3lEBQkLZnV68ONWtqG3f9+trOff31OkVEnTpQrZruiB0XkBVErVq6p3Q0dYgI/fs/RL16ASxcONcenlnQBNRs1qax1FS9x29BK3j37z/IwoVrOX26GuXLNyYgIIBy5SpQqdIVhIcXvBHU1q0wbhx89hkMGqSV6tGj2r/xf/+nne3Dhmm7vydKIChIl4+N1XXaqJGeTdSpA3FxUKMGLFs2g3Hj+gDapm9TdM5WjNui2JRSmM26ThxnjefPu04U+M8/h9i27Q+/7cNdULN4GLCFBrxGzp7FAB2BoQXd2Lq72WfAXUAS8KdSaq6I7MpTLgIYAPzumeies337dqZMmUJsbCzdu3d3WsbVSC8pKYmgID1tXb58OSKC2Wz2WxhkXmzTSWcraf1N06ZNadq0qdNzJpOJs2fPEhwc7PbObbaO3Bbj72lc/7Bhw/KtD7CFgx48eNDpPXwdJups9gGQkXGeX3+dRpcuOc+zWLTjNe9K4lOn4PXXdacRFqb9AA66Nh8BAbpzq1ZNKwB/NT2bLzhvOOuiRTps8cIFrelEtDIYP34Ekyd/xdtvv22fNWZn6xGuO+kbzp6FL76YzI8/vkW3biN58MGcGWZ8/BRmzRrGzTc/ziOPvAto5fPzz7Bzpzb15M0Wc/310KkTNGzonuLLS2io7ujdyRp9ww23ICL88ouJzMwgune/n02b5vLZZ7Pp2vVBgoNzfA/Dhw9n+PDhiGjzjztW3IwMrdRatHiWceM607Wrfza0KaialIv3zj4740YgUUT2AyilZgL3A7vylHsb+B9Q9FUphXDPPfcQGxtbYMK5wjZXUUqxZMkSgoKCijXp2htvvFGgCWbZsmVkZGTwn//8x2/rCRxJT0/nzJkzHD58mFtuuYVGjRrZN/QpjII6cnc67KJsO+orbHI+88wzuaLQUlNTGDpUK7UuXXpgNuuFSefO5b4+IwPef18rgauvhhdecO0MDgnRI/3i2iF13ryPgJc4fPgSBw8GU706hIYqKlSI4Pz5dNq0yYkMysqCU6cukJycTErK2XzmjoJITs5Z+BUVVZOrrmrLjh3LuXTpInfd1Z+//prH5MmvAxZSUs7z9dc67ca5c7lnVoGB2pcSHa2V5DXXuP9dQ0K0eSssTHfY4eHa7OMuXbr0oEuXHqxdqyOZNm3SmfpHjOhHp04PkpmpZxWRkVo52hLcubPOISND+zfMZqhUqQbR0TXwIPLUI1w6i5VSf4nIdXnfO/vs4voHgY4i0sf6+XGglYg871DmOmCYiHRTSq0GBolIPk+wUqov0Begdu3a17vrEPSGvD4C0FO62rVrk5CQ4FGHV5xUq1aN5ORkjh8/7tUCr4L48ccfOXbsGA888IA9Bvqdd95h2LBh3H///fz22280bNiQDRs2uHW/om5p6cm2o46cPHmS+fPnExUVRdeu7i1UKgxX36VGjTjWrDnInj35lUBKCvzvf7rjqFED3nzTua09OjrnVZzRyjfd1Jbk5HVUrGjiyy+DCArSZidXaxXOnj1DdnYWFStWJDIyrNDIHhGdTdVhozM7I0feQkLCWoYN+41Jk46RnNwVpQIIC1NcuJDTQ1erBg8/rG3q9evrevSEChWgcmUdLeQLl1+XLnpm8vzzy5k16wk6dHiAt9/+DNCKJSJCKz13U32cPatNiY6zhuhovbbBW7x1Fl+rlDqHHv2HWd9j/VzkXa2VUgHAWODJwsqKyERgIuiooaI+uyBsIz1HR+QzzzzD0KHaEpbuTfByEXAnVh2gQ4cOnD171i/5TT799FPi4+Np2rSpXRFER0dTs2ZNbrzxRn799VeP7ufJlpbOcGaWceU/ceTQoUP07t2b66+/3meKwNVA6ujRQ+zaldtpLKLDGKdM0f/oNWrAq686VwI1a+pXSTBixKs8/zycPx+IxaLt1vv26XOxsVopOFKxYs5UpiAlYDZrM0damutcS7fd1pvo6EF8+WUrUlKCAUEpuHBB0aCBjqQKDNT+D08nvhUr6s4/Oto7k5EzTp8+idlsolKlWCCQqKg7WbjwWK5QVBE9GFi9ehETJrzPrbfeTb9+OS7T8+e1kqhePceXlHcx/B9//MyBA8lUqXIr113nwZTHTVxWh4gU1QJ5FHCcyNS0HrMRATQBVltNLNWAuUqp+5zNCnzB5s2bOXjwINddd12BWTzzbq6SlpZG3bp1CQ4Opn379nzzzTekpaXRt29fv25/6CyCqXfv3nYZHcutWbOGw4cP07JlS58nVOvatWsuJQDQr18/+vXr59X9nHXkSik6derk1vU9evQgIyODN998k+PHjxeoIB2JjY3lySefpF4930U+x8XFOVVqISFhuZSAxQJffw0rV+rPjRvDSy/pkakj1avrzrZckYdaOQQFadOHLUQ0JET/Fck9QlVKv5588l6GDoVz5xTp6boDBZg//wPOnj1Jr14v0KhRTYKDC+9QLRb9jMBAHWaZWsDqo1OnYMOGJ7HuJUSNGtCnj6JmTe2viIvz3C8SHq47/0qVfFenSuV8pyFDnmDVqiXcdtshoDZJSTq7a1RUfllTUk6yceNqqlfP+T/KyNDrSUwmPSBITc2vBACWLdvKjh0vcfToeXvSO1/iz/R4fwINlFJ10QrgYeBR20kROQtUtn0uyDTkKyZOnMjEiRP54osvePbZZ92+Lioqiocfftj+eeTIkRw5coQHH3zQr4rAVQSToy3dnYVNRcXXO4n16NHDvtGObUQtIkyZMoW2bdu6JXd6ejrHjh3jpZdeypf+whW1a9cuMLWINzhTaiEhYTzySE4KBosFJkzQmStDQuDxx/VCprwmCVuEjzfYQkMDAnQHFBamlYytc/cEs1nby207p9kUwbRp2o3XpMkdZGfXpEIFbY/fvHkdP/00hRYtbqJ796dy3Wv/fn2P8uVzdhHLy9Gj8O6720hJuQYIokIFePRR7ThPTT3ErFnvExVVjXr1Rrglf2Cg7ohjYtxz+BaGUjlKJCxMm3lsdRobG0316tWJjdXtePv2LZw79yvBwf249trcP+Ytt3Rg2rQVxMZqh++JE9rnYVPGe/bkDx21WOD332HPnteBICIjAzCbfa8I/BYQLyIm4Hl02urdwCwR2amUekspVSLbXDZv3pwHHnigyFs6PvXUU7z00kse76XqKe44RZ0pC5vjtTjYvn07rVu39nh2sHDhwnxmFU/kDg8Pp06dOvnSHRQnJpOJO++8k/Hjx9vDU6tViyM2tgFLlnxMSkoSly7B+PFaCYSGwiuvwJ135lYCISE6l427SsC2LqByZT1qjovTydBq1oQrrtAzishI/Qxv4hlmzpxGYKD2xiYl5XRUffpMoE2bR2ja9E5AmzSOHYNDh/5h5syv2LBhVa77nDypHcEmk3MlkJ2tU2m8/jqkpFwLCHFxu3n22T9p2vQwu3Yt57ffvmfZss/ZuHF2/hs4EB6u66VqVR3i2aBB0ZRAuXK6fqtU0bmcYmNz6tWxTmfMmMGxY8e44YY4AP766w9+/vktHn+8CWfO5DaBVa1anTZtbufKK6/m2DG9psTxXyCvEjhwAF5+WbcfkymIu+6CxYsr+iW9SKEri0sbxbGyOC8XL15kzJgxvPnmm4wYMYKRI0cWy3PdcYoW1fHqDocOHcJisVCjRg17+Oq8efMYPnw4kZGRrF27lptvvpnffvvN7XsWh9x5ERFSUlLIysqihqfeRSckJibSoEED6tWrxz/WlKAbN0K3bo04dmwP7723m5kzr2brVt1RDR6sI4QcKV9em4kKM3mEh+vRaEiIf/MMmUwmax6tydjcd3Xq6KgmZ4uklIKzZ/exc+dKbrrpalq3bkdGRk50jzNEYMYMvZLaFl7asqWZRx9NZ/nyt1i48CP+7/9G89tv33Ps2B7uuqs/11xzO61adcv13MhIC5988jxt27ajR4//c/4wN1FK17FtrYWniYW/+w569oSKFTdw9mwbatVqyvvvb0cpvYLZNmY0mXQYsasMsjaOH4eRI3UdVq2qw2G7d4ei7G1T1K0qyzznz5+3h24uWrSo2BTB6NGjc8XbQ36naFEdr+7QqVMndu3axY4dO7jGGpt39uxZtm/fTufOnVm3bp3HJrLikDsvFy5coEqVKoSFheWbRXmDyWSiatWqVK6sLZzx8fE89dRz1K/figED5jJjxpVs3apHka+9pjtUR0JDdVqCgpSALfQwIqLI4rqFxWLh0Ucf5ejR3aSnw969euQ6bJiOz2/USKe3to2KRSAysgGtWzcgJkbbuBMTXe+5cPo0/PKL9pUopWc0bdvC/fcHEhAQRY0ajWnQ4CYqVoylceP2VK1ajy5dhlKpUg2CgnLqoVYt2LBhIYsWfcGiRV94pQiCgvTMKjRUvy9KNLhtNhcd3ZqxY8Xe8YvoTr9hQ/jjjySWLZtNVFR12rTJMTWnpmozUaNGuvyqVTrX1LlzOs/U4MFw4sQuDh40cfFiA6/zcxVEmZoRZGZmEhgY6PEagIyMDLp27crGjRuZPn061113HefPn6dmzZp++VEcueOOO/j999/JyMggOjqaoUOHMmjQIPt5Z+Guvt6kvUOHDuzdu5cVK1bYzWqpqakcPnyYmJgYj9LqFqfceTGbzVStWpXw8HAOHz7s83Ug7777LkOHDqVly49ISnqREye0nX7ECG26caRcOf2P72p0HxysR4LFGTYqIuzZs4eLFy9Srdp1rFmjY94nTNB2ahtduugVu55gMunVv5s368/BwTBwoM754wqboggI0HLUrJm7vlJSTvH112OpWLESzzzj3jKkkJAcM1JoaNE6f4D77ruPo0eP8s47c+jYsaZd7gEDoFWr3GV3717DW2+146qrbmbkSD17PnIE3n5bRw316aP3QrBtMti4sVYC5crBa69dx8GDW9i8eTPXeZl7xJgRWLnvvvtYtmwZixcvdrnbkjPKly/PkiVL7J/btGnDhg0bWLdunX1Te3+xbds2MjIyaNeuHfHx8flMGs7CXX0dNbR06dJ8xypVqkSlIhhhbfINHTqUI0eOULNmTd5991235X7uueeIj4/n448/djv1cWBgoNNNanzFSy+9xLlz9zJmTGPMZt2J/fe/+ZVApUpQr55zh19AgDZLREf7PtlcYUyePJnevXvToUMHpk9fglLaHDVggB7V7tmjTTq//qqdwBUrQo8eEBKSzu7d8QQGBnPttTn/VyaTDpW1ZRzdvFl3vtdcozd3cTST/fjjm5w8+Q/CDpW5AAAgAElEQVRdugzjiiuuIipK11tYmLBmzRIuXrxAvXoP4LiWNSamCkOGvFvo9woM1J1/RIRWBL5kx44dHDhwgNq1s+nWDdatu8SJE8HMmGGhZcuAXLO9mJha/Oc/LxIbqwdTiYk6vYgtIv3rr3MyzvbtCzfdlKOoqlVrAJiI8NPUsEwpAqUUgYGBRV55W6tWLU6dOuX3PUlFhBkzZjBlyhS2bdtGhw4duOqqq/KVyxvuWpwkJiYya9Ys+34JntCjRw8uXbpEr169aN++vUffYf/+/ezcudOeDLA0cOZMOSZNaoLZDLVqbaRv3zDi4nKGvErpxU+u0h2HhGgTQ0kltrWFJgOYTFmEhISSlZUjd/36WjFMmoQ9xPPCBXjkkWOMGXMv1arV56OP9tnv8cMPsHx5zv2Dg3WWzvr18z9727ZFJCb+TqdO/ex7AuhEgcH06XMvJpOJPXsyPfrfDQ3Vph9/TtoXLFhARkYGcXE1eOEF+OmnMGA3yckNWLcud8qQqlXr8sQTH1mvg+nTtQmtSRPdJuLjdV0//7w2wzkyYMAPXHGF6w1yikqZUgS2Ub035rCdO3eyatUqrrvuOn744Qdfi+YUpRR33XUX06ZNY8eOHbzwwgsup4WTJk3il19+oU+fPjzwwAN+l+2ff/5h8uTJ7Nixgzlz5tC5c2ePFQFAZGQk5cuX9zhn09dff01KSgpxcXEeP9NXrFixgtdee40OHTrw9tuj6NFD28AjI3dx5MgtJCdPoX59rQiU0iNgVxt4hYbqqJSSUgIiQlRUFGlpaSxdupS2bZvy8cd78+2XcMcdujM6dkxvabl5M1SrVpMrrxxK7do6mZLJpHP/z5+vv0/btjrNxqOPOlcCAI88MhKLJZmIiOPcfnsVmjdvxZo1SzCZTLRv34ng4BDMZhOQowjmzZvJe+8N4a67ujBy5HggZ4e18uV9H2LpDNvGSCL6eYGBCrP5beA7vv0WVq+Gbt1yp71YuRKmTtXvO3XSK6RFtBnxyivzKwHQEUyu6s4XlClFYMMb27AtP9E999zD/PnzfS1SgXTs2JGKFSvm2o0rLwkJCSxcuJB27dr5/Plt2rTh3LlzrFmzxm4OOnToEKNHjyY2NpZXX32Vq/OGw7hJ165dvVrl620a665du7J//37mz59f5O09T5w4wZ9//smVV16JUvDee9C7N9xyyxHOnXuXuDi9m1xgoHYKO8sVpJQeDfpxOYpbKKU4c+YMO3bsoE2bNkRERLj0T9SurV/Z2fDFF7BgQXlgNJGRegXy+PFaIYLu5O69t+BnV6oETz7ZkYAA+OuvDaSmnub06ZN2ub7+er7T/9mEhB0cO3aEKVM+Yfz4DwkKCiY01H/J+ApCKa3Mv/jiBEeO7GLSpIscPx7G7t3w0Ue6bWzZYmLHjgts2hQBKPr00YrVxmOP5b9vZKT2jeQNW/W5/GXJWVwUbA3xrbfeYsQI9xa2FJX169fz999/c/PNNxMVFUVWVhbVq1d36qDeuXMnBw8epHHjxgWumvaG6Oho0tLSSE1Nte+ZeujQIb777jvq1q3LY85acCnl6quvJiEhgV27dhWoWN0hJSWFxMREKlasaFeEGzbo0EkbgYHaKeyqo69cueSVgDOysnS0UHq6Hv07C7IS0Ru/HDigM4E6rqSuUUM7ldu2dd2B1ayp7faOs6Ts7GzOnUsjIqIioaGhWCwWp/t/RETAyZOJNG7cgAoVKnDixAnKexrz6QNef/11AgICGD58OAkJQfz9tz5uMunEeFOn6jTZoaG5d6Pr3Fn7VwoiIkK3nYAAaNfuSsxmE3v37vZ6/VJBzuIypQgef/xxTp06xTfffFOkTVEGDhzIggULGDt2rNtpEbx9ztixY3nvvfdYsWKFV45uX5CQkMClS5do1KhRsaXdLozXX38dk8nEa6+95pEDbevWrVgsFho3bpxrlyhfsXFjznaIhSmB6GjXpqKSxmzWES2g7di2dQGutk9MSBBGj4ZLlxTt2ukIGFemGaXyL/hasuQXTCYTd9zRmXLlcgY6tuaWnW0iOloRGakPFIfZpzBEhMDAQEQEk8nEwYOB/Pln7jKpqTBkiE4lERVlxmR6m0qVTvDee18WOMKPidE5nWzfs379IMxmM9nZ2V7vl25EDVmJj4/nyJEjRd7O8fjx4yQkJHAmb4J5HzJt2jS+++47QK8nsK0l6NmzJx9++GGxOoedOahtnDlzhqNHjxIdHe3VIq1Tp07x0EMPUb58eRYsWOD2dZ988glpaWm5QmndoXnz5p6K6DGpqUdJT0+kWbMrqFAhv3evtJiDHNm9ezfPPvsszZs35+OPP87lqwgIyEk0d/q0jnbJywcfVOHSpZr06/cbt9wS4bSTCwzUaykiI/OHzQ4f3o/Tp5P5448TdkUQEaGVZfv27VizZg1r166lbdu2vvnCPkBEGDVqFNnZ2QQGBhIRAR98cBubN8fTufMgevQYQ6VKOrHg9u3QoUMgFSqMzHefyEj9Skpy7Utav/4IkOW3AJUypQimT59Oenq6V2kJsrOzuXTpEqGhofzvf/9jxIgRPlmd6oy8MfaOGU+Tk5Od5hJKTExk+fLl1K1bt1hmDJMnT2bo0KH2HZPatGnDOlsAtAcEBgYSHx9PlIebx7711lucO3fOb+F07rBy5UrWr1/P7bffniuMeMuWaUya9Ap9+w6mUaP3c11TrpxWAsW5PsAdkpOTWbNmjT2QQqmcfY0dqVxZmz1s2yraxlQhIeUIDt7DNdecRe81pQkM1Irk0iW9qMrVDOjOO+8jLS2VsLBwXn/9GQ4e3ENwcBB169YlJCSEgIAAMvOkNp0xYwYZGRncf//9VKlSxWd14S4BAQH2rMSgR++bN8cDsGrVJHr0GAPkRFw5IzIyZ1FhRoaeJTmro6pVq/tk3YMrypRpqCjYfAQPPPAAP//8s1+f5c4m7Hlz78+cOZNHHnmE7t27+zSqSUQYMmQIQUFBvPPOOyilmDZtGn369Mn1jxkUFMS3337r8UzFbDYTHx9PZGQkLVv6dadSAGbNmsWOHTt45JFHiuwjePXVV/nf//7Hu+++y6uv6rTCO3bAypWzmDbtM+65pztPPPGcvXx4eM5+t6WNtLQ0tm7dSlhYGK2sK6GOHSt4A5ULF+Dvv7WvwGIxExCQ32x41VW6Y7twofAZkG3rzVq1ojhrTU5Uv359du3a5XQRaLNmzfjbapQvykIrX5GWBlOnzuKXX77jxhu7cu21TxVYvmZN7UtxXKXtqm0EBOiosqJEvhs+Ah9ga4T33Xcfc+bM8euzXOXhySuPY06eLVu28OWXX3LDDTfQp08fn8liyz0TGBiIybqhqrcbw5QGunbtyi+//MLs2bN58MEHi3SvpUuXEh8fz913323fzzklxfnuXOXL69F0aVQCrkhOdu0TsHH0aI4vIS81auDWjloBAXqWZPP1Tps2jaysLCpVqkRkZCS333670+tGjRplD9yIj4/Ptae2v3n33XfZsWMH/fv3JzIykqZNm5KenpNC2mzWvqLMzNztYciQpmRlXWDWrATi4twzyGRlpfL++69QvXoso0aN8lpmQxFYGTduHMHBwfTr189pJEJBbNq0CZPJxLXXXsuqVatYtWoVnTp18ssm9t7MCPyFyWTio48+wmKx8MorrwAlkzDOkQsXLrBhwwYqVapEixYtPLp25syZJCQk8OCDD9rzJvkSZ4ogIsL1IrLSTHq6fhU0KxDR5zMz9VoBi0WbO+LiCk/cFhBgW/QlZGefJyQkxOPFnjbTZExMjNdOVE+xWCy5giZq1arF4cOHuXAh/z7PIrkjqnr2DCc7+yK7dmUQFlZw9I9SehZw7Nh+rrzySurUqcOBAwe8lttwFqNNEC+99BJKKfr37+/x9Y5mi/j4eD744ANiYmL8ogicJZtzxJ3duHxFUFAQgwfnzuPi64RxkydP5vDhw/Tv398tW+/hw4e58847adiwIQkJCR49y3FfieLg36IE1q9fz4YNG2jbti033XQToGWPiNBmndOnnSeSs8XPT5jwNuvXr6FHjzdp1apNoTOfsDCdS0kpuHgxk8jISMqVK+e0zX/77bfMmjWLnj178n95khz5eltWd1m0aBF79+5l+vTpVK1aFdB2/pkzJ7Fs2RweeaQvd955L0rpRWL79um0IgsX/mVd71BwxJpS2oyofUoxTJgwwb95zUTkX/W6/vrrxRuys7PlxRdflP/+979eXe/IsGHDJCoqSpRSEhcXJ1OnTi3yPfMydepUiYuLE6WUxMTESExMTIHPu3Tpkhw9elQOHTrkc1mcyRYeHi6A/RUcHOx1PTRr1kwA+euvv9wqv2/fPmnfvr08/vjjXj3PV+zdu1e2bdsm586dsx87fVpkxozVEhERKXfc0bEEpfOMESNGCCAjR450et5kErlwQeTECZEDB/K//vOfBwWQTz75wel52yspSSQ1VcRiybl3WlqaRERESJUqVUREZOPGjTJq1CgZNGiQLFiwQMaMGSOAvPDCC/ZrsrOzZe/evXLq1Cmf14W3mExi/3+IiKhYYD04vg4eFDl6NOdzcrLIpUu+lw/YJC761RLv2D19easIikq7du0EkP/7v//L1wmGh4f7RRl4wt69ewWQK6+80qf3zcrKkvj4ePnjjz9yHZ86dapERETY6+Duu+/2+hnjxo2TYcOGyeHDh4sqbqEcP35c/vzzTzly5EiR7/Wf//xHAJk/f7792OnTIj/9tFYAadOmTZGfUVwsWrRIXnzxRVmyZEmB5czm3J2W7TV37iaZMmWJ/PHHCZcdnrt99sMPP2xvVx07dpR9+/bJ3Llz5eDBg/Yy//zzj71Mnz59ZOXKlZ5/aT/QpUsXAaRbt55uK4ELF3THf+iQVrSOStKXGIrAB9gaXbly5XIpAdsrLi6uROSycezYMalWrZrcdNNNPr3v0aNHBZBq1arlOzds2DABpG/fvqVqZFYQw4cPF0DefPPNIt+rX79+0qRJE1m/fr392OnTIufOZUtaWppkZWUV+RmlEbNZJDNTj+wL6+hOnhQ5d07/dbeD++STT+Saa66Ra665Rj755BOnZXbv3i316tWz//+NGzfOB9/MPWbPni0jR46UBg0aSMOGDcVkMuUr42rm9Oqr70uvXgPkzz+T5cgRkYsXc665eFHXbV6Sk5Nlzpw5+QZjnlJiigDoCCQAicCrTs6/DOwCtgMrgLjC7lkU09C+ffvk6NGjXl0/ZMgQadWqlSilnCoCmzLw1cxg5syZ0qBBA5dT9eLixIkTcvPNN8t9992X79yZM2ckPT29BKTyngkTJsh1110nX3zxhV/u748pfWnm2LGCzUD+Gt3aWLt2rUyYMEH+/vtv/z7IgW7duuX6v2/YsGG+MpmZzuukfv1GAsjKlTucdvrOWLRokQDSoUOHIsldIooACAT+AeoBIcA2oHGeMu2BcOv7fsAPhd3XW0Wwb98+AaRevXpeXW8jLi7OpSLwpZnoo48+ymcXvVxJTk6WrVu3yvHjx90q/+2330p4eLg899xzfpas7JCYmCj79u2Ti45DVDe4dEnPgpYt+0veeGO8zJwZLwcPajOQu0rxwIEDcuutt0rv3r1dlvnxxx9lxIgRcuLECY/k8wc//vijDBw40G7Cuu2225yWs82cDh7MUQT/+9/X8vrrY+X48ZNuP++PP/6Qzp07y4gRI4okd0kpgtbAEofPrwGvFVC+BbCusPsWRRHUq1fP5Y9WEHkdtyEhIQUqA1+Yic6ePSu7d+/2iS3bnyxatEief/75XHZyTxkwYIAA8uGHH7pV/tNPPxVA+vXr5/Uz/Ul6ero89thj0rNnz5IWxW3atm0rgMTHx3t1vc2h27v3S5KZ6dm1W7ZsEUCaNWsmIiIWi0WSkpJk+/btkpaWJiIit9xyiwCyfPlyr+TzB9nZ2XLy5MlCzaJnz+aYyTIytFO5JCgpRfAgMMnh8+PApwWU/xQY7uJcX2ATsKl27dp+qyhnOIuQCQgIKFARKKWKVUYb7dq1k0aNGsnZs2eL7Zm271yUmcvYsWOladOmMmnSJLfKm81mSU9PL3GzVPPmzaV27dpy7NixXMfPnz9vnx3+W3jooYekXr16XptYVq5cKf37PyezZv3k8bXnzp2TVatWyYYNG0REZMqUKfZ2ZYvy+/LLL2X48OGyZ88eERGZOHGiNG7cWAYOHChz586VTZs2eSV3cXHhQklL8C9QBMBjwEYgtLD7FrezuDBTUGlyHMfGxgqQr2MqCtu2bZPIyEi55ZZb8p2bOnWq/TtHR0eXeOSUO8ydO1eqV69eoBnCXSpVqiRAvhGh2WyW7777Tn788cciP6Ms8uuvv9rblSunvi3ctXbt2gLIk08+WSyyHT16VJYtWyZJSUleXf/333/L8uXLPQquMJlMYvGBs6VUm4aAO4HdQFV37lvciqAg57A/fQSfffaZDB482D4Ccodt27bJzp07JTs7u8jPt/Hnn38KIHnr3dlMyd9htI4mOm8d87/88osATp3fnpKcnCwHDhxwGjVi4F9SUlLk77//lqlTp0rnzp3lo48+KpbnTpo0SQB57LHHvLreWchxYXz88ccCyIABA7x6po2SUgRBwH6gLjnO4mvylGmBdig3cPe+3iqCtWvXytVXXy19+/b16DpPZgSBgYE+6whvu+02AWTFihU+uZ+3mEwmSUtLs9tqbbiql6LMhgrq6J0pntDQUI/rOyMjQ44ePVqs5rOyQGZmphw8eNCrBY379u2TMWPGyJw5c1yWMZlMsnv3blm9enWuY8XNrFmz5NZbb5WPP/7Yq+tfffVVad++vaxdu9btaz744AMBZNCgQV4900aJKAL9XDoBe62d/TDrsbeA+6zvlwPJwFbra25h9/RWEcybN08A6dSpk0fXuVpF68xhXFSN7ciPP/4o7733Xql1FruaKXnjH1m5cqVUqlRJAgMDXc4w/KF4/Mm8efPk22+/zbXquLSSlZUltWrVsjtrveG3334T8G4RnTuztNTUVPtvfuzYMXn44Yelf//+Xsv7b8NsNsulIsYml5gi8MfLW0WQnp4uO3fulP3793t8rbORquOx6Ohoadeund3ZVVJMmTJFhgwZ4pE5yVt82TGvWLGiUH+LLxWPL0hPT5fevXvLK6+84vR8gwYNBJCEhIRilsxzTp8+LYBERUV5fY/t27dLrVq1pEuXLh5fu23bNnn55ZftwQJnz561/74zZ860l2vSpIlcccUVsnHjRvtA4dChQ2KxWC7bxXu+xFAEZYROnToJIPPmzfPZPbdt2yY9evSQ999/P9dxX/oIMjIyCvTFiLhWPDExMR496+jRo9K3b18ZOnSox3LmvQ8g1atXd3p+4MCB8sQTT5TaGZ0jJpNJDh48WCwDCHfIzMy0/75ffvml/fjFixftM6yvvvpKunbtalcKzZs3LxbZLpSG8B8vMRRBMeGJM7OgsmazWRYsWCDr1q3z6PkzZ86Ud99916ej0IULFwronC958YXz1nafvGYhZx2+szIhISEePTchIUEAqV+/vley2jh37pxMnDhRJk+eXKT7XK6YTCbZsWOHHDhwwKvrv/vuO5k4caIkJycXWG737t0CSNOmTb16jieYzWYJCQmRmJgYyfR0sYSVQYMGSVhYmHz22WduX/Ppp59K586dZeHChV4904ahCERk3bp1MnjwYPn111+9ur4wJkyYkC8PkasRsrPRtG1EHBcXJ1999ZUAUqFCBb/I6glJSUny/fffy7Jly/xyf2d14enLE3PU2bNn5YsvvpDZs2f75fsYaN588023HZwpKSmSkJAgp0+f9vg5FotFzO7maigiycnJEhISIpUrV/b6HkOHDhWllEdRTn369BFAJk6c6PVzRQxFICIi48ePF8AvaQlsHbe7nVRhkUhhYWHSrFkzuffee30ua2nDm3UaJeEn8HT2k5mZKSkpKR6nbCgJtm/fLs8995x8/fXXXt/DbDZLs2bNpEGDBpKdnS1z586VuLg4t3JljR07VsC3wRb+wmw2S0pKitfXJycny5kzZzy6Zvv27TJnzhyvZ1c2DEUgIps2bZL33nuv0DS7nuLOiNa2EtnWgbizNsEbp2tSUpKsXbvW4wbjKxOPN3i6TsNXdeUJzn7jsLAweemll1xmhHzooYcEkB9++MGvsvmC2bNnCyBdu3Yt0n1skXTVq1eXhx56KF/IsY0VK1bIH3/8IefOnZPhw4fLa6+9JvXr15d33nnHXubOO+8UoNSkl74cMBSBH/F0ROtJx+dph1zY5iLOKMzpu3//fpk5c6b8/vvvHsniLkWdEXjqoJ46dapUqVJFlFJSu3Ztt64tTEZnyrNPnz4SFRUlM2bM8LhOipu9e/fK+PHjZe7cuUW6z/bt22X16tVSqVIlqV+/fq7VsCdP6iRrJpNJGjZsKIA9smrYsGH57mWr28JMeBaLRe6991654447fLL6tjgpTF6LxSIvvviiNGzY0CfrXgxF4Ed8MaL1VSf3zTffSOvWrWXChAluX1NYGOi3334rgDzxxBOeVo1bFMVH4OnsxZXS69evn8TExNiPxcTE5LqvO79xadicqLSwatUqe3I4k8kkc+fOlYCAAPnvf/8rGRkZMmTIEGnWrJkcOHBAKleuLPv27ct3j4SEBPnmm2/c6txDQ0MF8ElEz86dO6VDhw5OZ3pjxoyRe+65R5YuXVqkZyxYsECuv/76AiPXjhw5YleUoIM1vE2hb6PMK4KpU6fKFVdcIUopqVGjhk//YX1h4y6ss/Mnhe2vMGzYMOnevbt8+umnfpMhr2nKX52uJ79VSEiI9OvXz6NrPPmtli1bJhEREX41HWVnZ8vNN98s11xzTYksbPvqq68kPDxcnn/+ebnxxhsFsK/jsTl4PbWXO2PBggWydOlSrxdc/frrr9K8eXPZuXOn3HjjjRIVFeW0s7ftQ+C4tsEbFi9eLIC0bt3aZRlbKouoqCi7yc1xVbU3lGlF4O+cOL6IeinsVVT5CrL/F9f+Cp5QkExF8WH4c/YGnjmtbbtruVoda7FYZO/evW5FxJw8edJpJ2HbvhRwmSRt9erVMnr0aKej8qIyZ84cAeThhx+WX3/9VbZu3erzZ/iCnTt3SuvWraVFixbSp08fiY6OzpUUzmQyyaFDh2T37t3y66+/Fnlkfv78eVm2bJmcP3/eZZlLly7J2rVr5ffff5cxY8ZInz59ivwblWlFUBypCfLuV1BYTLynnUtR4vMLU4JTp06VoKAgn410fYG/lHdxzt6mT58ubdu2lfHjxzuVxWw2y+LFi52O1M1msz1ksLAww8TERImIiJDKlSs7tSMnJSXJzp07XV7fs2dPAWT06NEFPscbMjIy7L6B0saJEydk48aNYjabJS0tTWrVqiWDBg2SzMxMycjIyFX2559/lqCgIHnttdf87ofYt2+f3zbfKdOKoCRSE0ydOjWXzdmdDqSgaCJ3O+IhQ4bkus7VvgmBgYH2GUK/fv1c7sPsWFdFzXPiKf6IZPLn7C2voho3bpwA8vLLL3ssp9lslmeeeUaAAk0n7777rnTr1k0CAwOlU6dOcvjwYRHRO1o5W/BksVjyJWpbvHix9OzZs9SsKvaGn376ST7++GOnHejSpUulW7du+VKzf/LJJxIVFSWDBw8Ws9mcq/O/ePGifPfdd9K9e3cxmUxy5swZadWqlVStWrXQBW5FISsrS5o0aSKjR4/2i8Ip04qgpJOVFdb5OHYgBXXE7jzHlzORghTWvxl/1JOzrLNbtmyR+fPn5+t4V69enSvz5PHjx2Xjxo355LRYLLJr1y7758zMTBk2bJh91H/q1Clp0qSJALn2PdiwYYMA0r1791z3S0tLk0ceeUQef/xx7yuvlNKqVSsBZP369SKi6/iuu+6Sr776yv7/n9fHlZ2dLU899ZTTyK709HQJCwsTwP77LVy4ULZs2eIzmffv3y/PPvusvPTSS/ZjK1askMDAQLnhhhv8kjupTCuCksib70wGR9NRTEyM05FuUZSWv80eJVV3/sCXvgJP6iMjI0Nuu+02iY6OlgMHDsjWrVsFkLp164rFYpHVq1e7TG/cq1cvAaRbt27y7bffSoUKFeSxxx6Tr776Kpd5afPmzRIbGys33nhjriiazZs3S3BwsHTr1u1fF2ZZGO+//748//zzdhv68OHD7bOxQ4cOyZAhQ5ymrDaZTLJx40ans65vvvlGevXq5TeZExMTBZDKlSvn8gNt2bLF6cDAF5RpRSBSsgumPKEoSsvfjlBPFVNppiClWdh+1JA7HUhhv80ff/whXbp0kaSkJDly5IhERERI//79xWQyiclkkurVq8vIkSPl0KFD9vvv3bs3330SExOlefPmsnXrVomPjxfQTlhnZGdnO+3sp0+fLgMHDhQR7Uju3LmzbN682YsaLL1YLBbZuXOn/PDDD06d04mJiVKrVi2fRCsVBYvFImPHjpVBgwZJixYtimUv5jKvCP5NeKu0fD0jKMjHUVKpn32Fq1xP/fr1K9R05OlA4t577xVAhgwZIpmZmbJr165cnfS8efPsmV0nTpwot956q8ukgY7Xbdu2zctvr3nttdcE8puQ/s38888/ctNNN0mrVq1cznpuueUWAaRHjx7FLJ1zOnbsKFB4UIAvMBRBGWDq1KkSHBzsE1NHv3797DbSy3FGIOL5bmjemsQ+//xz6dGjR4GhgiXBypUrpWPHjpKYmFjSohSZ9evXS40aNWTy5MkSGxsrsbGxLvcdOXDggPTv37/ASKriJCMjQ8aPH18sOalKTBEAHYEEIBF41cn5UOAH6/nfgTqF3dNQBK7xNFop78vm9CxodnE5+Ajc4d9iTjTIyevUsmVLew4jg/wUpAiUPu97lFKB6G0q7wKSgD+BR0Rkl0OZ/kAzEXlWKfUw8ICI/F9B923ZsqVs2o/+AoAAAA7eSURBVLTJLzJfLkybNo2+ffty4cIFj65TSmGxWAgICMBVu5g6dSo9evTwhZgGBj7h4MGDfP/997zwwgtUrFixpMUptSilNotIS2fnAvz43BuBRBHZLyLZwEzg/jxl7gemWN//CNyhlFJ+lKlM0KNHDyZOnEhcXBxKKeLi4ujXrx/h4eEFXle7du1cf/MSFxdnKAGDUkedOnUYMWKEoQSKgD8VQQ3giMPnJOsxp2VExAScBWL8KFOZoUePHhw8eBCLxcLBgwf5/PPP7coB9OjfkfDwcEaPHg3A6NGj8ykNx/MGBgaXF/5UBD5DKdVXKbVJKbXp1KlTJS3OvxabchARvv/++1wzhokTJ9pH+85mFI7nDQwMLi/86SNoDYwUkbutn18DEJF3HcossZbZoJQKAk4AVaQAoQwfgYGBgYHnlJSP4E+ggVKqrlIqBHgYmJunzFygp/X9g8DKgpSAgYGBgYHvCfLXjUXEpJR6HlgCBALfiMhOpdRb6DCmucDXwPdKqUQgFa0sDAwMDAyKEb8pAgARWQgszHPsdYf3mcBD/pTBwMDAwKBg/hXOYgMDAwMD/+E3Z7G/UEqdAg4V4yMrA6eL8XnuUlrlgtIrmyGXZ5RWuaD0ylaa5SovIlWcnfzXKYLiRim1yZWnvSQprXJB6ZXNkMszSqtcUHpl+7fKZZiGDAwMDMo4hiIwMDAwKOMYiqBwJpa0AC4orXJB6ZXNkMszSqtcUHpl+1fKZfgIDAwMDMo4xozAwMDAoIxT5hWBUiq2pGVwhVIquqRlcIZSqlRmiFVKOQ2NK2mMNuY5RhvzjKK2sTKrCJRSFZRS44BFSqkJSqmuJS2TDaVUuFLqM2CxUuq/SqkW1uMl+ntZ6+wjYIFSapRSqn1JymNDKVVOKfUFsEop9ZZS6nbr8dJQX0Yb80wuo415JpdP2liZVARKqRrA94ACOgHxwPslKlRuXkbvy9ATKAdMABARS0kJpJRqAPwCmIGngFPA0JKSJw9PAVWBdsAB4BulVLkSri+jjXmI0cY8w5dtrEwqAiATmCQiA0TkBDAL2KqUalZSAimlyln/BgEhwHQR2SMiY4BT1lFSSY5AMoCJIjLIut3oQuC4UqpmSQijlKrg+BHYICIpIjIZ2AC8Yy1XUjveGW3Mc4w25hk+a2NlQhEopa5SSn2plAoDEJEUYLVDkVpAPSChBGRrqJSaBnyilGpp3amtAtDaodizwONKqZrFNQKx1pl9dCEix4BFDkXCgatFJKk45HGQq75SahbwrVLqHqVUqPVUVYdig4EHlFJXiogUxz+q0ca8kstoY57J5bc2dtkrAqXUzejpU1/0dBillBKRDIdiIcBBEckqZtnC0FPybcB24DmlVG/gf8CzSqnKACJyBJgKPF1Mct0D/AwMUkq9aj0WJCLnHYpVopg7NetIdRywA/2b3gu8DnwHdFJKNQGwdhxzsJoV/L3HhdHGvJLLaGOeyeXXNnbZKwIgBW3fawj0UkrFOfnRWgD/ACilni7G6fuVQIaIvC8inwCTgAeAMOALci8C2Yve97k4pqLJQA90nb2ilKpg3V8iwOHZjYGdVnkeVUo19LNMANWBNGC0iMwB3gbuAhqgO7thKid6YjHFl5zQaGOeY7Qxz/BrG7vsFEHeBiwiu4FEEUkElgFvWcs5fvc7gBil1E/Ao2jbm9/lE5EdQB2l1K3WQ9uBFcAQYBhQSSn1hlKqO9AHuGi9zuejjzxybQL2WOtsMfClrZjDs28GqiilfkH/Q1/ytUx5EZGjQEv0P6bt8xfAO9ZOLgN4UynVBz3iTfW3TFY5SlUbyyNbqWljeeQy2phncvm3jYnIZfECrkCPbq6xflYO52wrqCOAROCOPNcuQo88HvSTbFXRjctRpgDr3/8CUx2ON0fv3FYZrf2fAJYCPYpDLid1FokeId3gcC4U3aFsBrr7Qa4YIDLPsSDr3yeBtQ7Ho9BOsuZANHoqP9NP9ZVPrlLUxmKAaEd5SkkbyyVXKWpjYU6OBZaCNpZPruJoYz5vkCXxAoYD69EjhmGOlebkR34RmG99/4j1H+Y2P8o2zPqjLQE+yysbUB/4Cehp/RxjLVvNz3VWoFx56mwEsMr6vqP1bxc/yTUUPb39Bhji5HwAsBJ40eHYd0ATP9dXgXKVcBsbDhwHZgNvODlfUm2sQLlKsI29B8wDWtjaVClpYwXK5c825rcvVRwvoAra5jnd+v5u4CMgxElZx873DHAWPSoq50f57kaPtALQ24ImAbFO5Olg7WSuA7oDq4DaJSFXAXVmAtKBj4FgP8nVwdpBBaNHqyvQJoEQ63nbCPd6dCx3F+Ax9KixsR/rq0C5SriNNUabVsoBcehY8ofRm5Dk/Q7F2cbclau421gfYAswFni7gHLF3cbclcsvbezf7iM4B7wuIo+KyCngavQ0NDtvLLSIiFKqolJqDHAE6CQivUXvm+wvAoDz6M62CfAn0MgmD9g9/0uBD9BT0leA4SJyuCTkcsRaZ5WVUl8Bf6NHagNExF+22gh0lEigiJxG/2MMQE/JERGLUipARDajR0Qt0FEUA0THnfuLAuWyUVxtTCkV5fAxA226qCAih9D27NuAax3KF0sb81QuKJ42lkeun9CmnV+BqkqpTtYyyqF8sbQxT+UCP7Yxf2k4P2nNGLQfoF2e47bpUpy1ciq7uD4AaFZcsqEb/Qdo290pYCSwFR3+VdUmU97vURrkcigbBLQtJrm6AJ+jV7uGoMP4FgC9recVTvwZpUGuYmxjn6Jnc/8FrkLPUMYDtzvU0Xjr+cC8debHNuaRXMXYxhzlauRwriJamY/H6vchxwZfHG3MY7n82cb+NTMCpVQc2gzUDXhEKVXJdk5EzFbNmYKetrdydg8RsYjI9mKQLcb6vG0iMgg9mnxMREaif/g70ItlEIfFOyJiLi1yOchkEpF1fparsvXUPLSv5y5gDXAYPe193CqLiPU/wR8URS7bPfzYxm5CK+7TwCj04qFnRM9QLgItlFJXWGVZAjwqIua8deaHNuaVXI738FMbyytXTaCfwzPPolcHK+BB6zFx/OsPiiKXQxmft7F/jSJAmzJGA3XRI/87HM0/DpUVhTXMLK95qBhlu932bKVUIHCSnI7/N+v7Ss5vVSblam9dTGQWkanAM8D9IjIWbVver5QKLYbY9tIqF+iomrEiMlJE1gDr0JE1oDuWK4E7AURkAXBeKXWFIZddrvUA1t8r0FpmB9pX0lQpNVgp1a8YfstSKVeQP2/uLVabZl4tmKKUuiAiF5VSM9Ce8o1oUxBKqUAROa+UOgr0BpaKH5bKeyqbdbYSCnRRStVFL+s/i48XolwuclmLZIpIhtIZMUcDm8XHK3JLq1wFyLZHKXXE4dwldDoBRGS10umke1llawqcQCt6Q64cuerbfi/r8QvWDvZh9CDgeV/OBkqrXM4odTMCpVSw45d21IQiYlvs8h1gAR50GPXbOv1Z6Bjf0iBbsPX0p1a5WqBD5LqIzhNiyOXit1RKXQNMQyv0Eb6SqTTL5YZsGQ7n6qDTRtjO/QK8gFZaP4nIY6JzChly5ci13eGcKKUqAmOAD0WkgYgsudzlcon40Sni6Qt4Hj3lfgu41+F4PocqOh3sXHSM9FNAnVIqW2+bbDiJCzbkcvpbxlmPh5YVuTyQzRZC+wHwgPV9V+AKf8h0GctV0/o+X6j55SpXQa9SMSNQSlVSSn2Hjm9/De1I6Wk1WSBWE49SqplYHU0iEo/OoPgXejm1z0YZPpbtEcBknfL5zFR1GcvVA52PHvGh2aW0yuWFbLbfqjHQUCm1CO3c9nn7v8zlumQtm325y+UWxal1CtCggehYXVv4Vj3gW3LSRVRDr+z7DZ1KIhQdD30IP60+LO2yGXJdHnJ5KVtt9Bqa1aWszgy5SqFcbsleIg/VTupBQC2HYxUc3gegnXQNrJ/vAvrnuYdfVkWWVtkMuS4PuXwoWy9DLkMun32XYn+gjhz4C52GdoaLMo2Aha4qv6zJZsh1ecjlI9n8Yjs25Lo85PL2VRI+gtPoVXNXo9PjdgAd/ungWa+OXvGKUqqVsm5gbbVn+8UXUMplM+S6POTyhWz+sh0bcl0ecnlFsSsCETkO/CAiZ9D2M9sOP2b0ajrQKRBClM6n8ZHDtVIWZTPkujzkKs2yGXJdHnJ5S4lEDYk1VhvtOMlUSr1gPW6xatNbgduBVBFpIyKryrpshlyXh1ylWTZDrstDLq8oadsUOtTqd+v7Zta/9+HggDFkM+S6HOUqzbIZcl0ecrktf0kLYK2wxUAWsBAXmUMN2Qy5Lke5SrNshlyXh1xuyV7CFReAzsB3CHi6pCvj3yCbIdflIVdpls2Q6/KQy5OXbeFDiaGU+g+wUvyQvKuolFbZDLk8o7TKBaVXNkMuzyitcrlLiSsCAwMDA4OSpVTkGjIwMDAwKDkMRWBgYGBQxjEUgYGBgUEZx1AEBgYGBmUcQxEYGBgYlHEMRWBgUAhKKbNSaqtSaqdSaptSaqDK2b7S1TV1lFKPFpeMBgZFwVAEBgaFc1FEmovINeic8v8B3ijkmjronfMMDEo9xjoCA4NCUEqdF5EKDp/rAX8ClYE44HugvPX08yKyXim1EZ2P/gAwBZ2y+D3gNvTuZ5+JyIRi+xIGBgVgKAIDg0LIqwisx9KAq4B0wCIimUqpBuhNSloqpW4DBolIZ2v5vkBVERmllAoF1gEPiciBYv0yBgZOCCppAQwM/uUEA58qpZqjN7dv6KJcB6CZUupB6+eKQAP0jMHAoEQxFIGBgYdYTUNm4CTaV5CM3oQkAMh0dRnwXxFZUixCGhh4gOEsNjDwAKVUFeBL4FPRdtWKwHERsQCPA4HWoulAhMOlS4B+Sqlg630aKqXKY2BQCjBmBAYGhROmlNqKNgOZ0M7hsdZznwM/KaWeQOejz7Ae3w6YlVLb0FsZfoyOJPrLunvVKaBLcX0BA4OCMJzFBgYGBmUcwzRkYGBgUMYxFIGBgYFBGcdQBAYGBgZlHEMRGBgYGJRxDEVgYGBgUMYxFIGBgYFBGcdQBAYGBgZlHEMRGBgYGJRx/h/U7JjUK2XsKgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "step 12789, {'val_loss': '1.622239112854004', 'val/loss_mse': '0.018495162948966026', 'val/loss_p': '1.622239112854004', 'val/sigma': '0.04999999701976776'}\n", + "----------------------------------------------------------------------------------------------------\n", + "TEST RESULTS\n", + "{}\n", + "----------------------------------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'plot_from_loader' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;34m'min_std'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0.005\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m },\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mPL_MODEL_CLS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTransformerSeq2Seq_PL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m )\n", + "\u001b[0;32m/media/wassname/Storage5/projects2/3ST/attentive-neural-processes/src/train.py\u001b[0m in \u001b[0;36mrun_trial\u001b[0;34m(name, PL_MODEL_CLS, params, user_attrs, MODEL_DIR)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0mdset_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 108\u001b[0m \u001b[0mlabel_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdset_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 109\u001b[0;31m \u001b[0mplot_from_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m670\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 110\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtrial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'plot_from_loader' is not defined" ] } ], "source": [ - "trial = optuna.trial.FixedTrial(\n", + "trial, trainer, model = run_trial(\n", + " name=\"transformer_seq2seq\",\n", " params={\n", - " 'hidden_size': 128,\n", - " 'hidden_out_size': 256,\n", - " 'learning_rate': 2e-5,\n", - " 'attention_dropout': 0.0,\n", - " 'nlayers': 2,\n", + " 'hidden_size': 512,\n", + " 'hidden_out_size': 512,\n", + " 'learning_rate': 2e-4,\n", + " 'attention_dropout': 0.,\n", + " 'nlayers': 4,\n", " 'nhead': 8\n", - " })\n", - "trial = add_suggest(trial)\n", - "trial._user_attrs = {\n", + " },\n", + " user_attrs = {\n", " 'batch_size': 16,\n", " 'grad_clip': 40,\n", " 'max_nb_epochs': 200,\n", @@ -641,10 +716,11 @@ " 'input_size': 18,\n", " 'input_size_decoder': 17,\n", " 'context_in_target': False,\n", - " 'output_size': 1\n", - "}\n", - "model, trainer = main(trial, train=False)\n", - "trainer.fit(model)" + " 'output_size': 1,\n", + " 'min_std': 0.005\n", + " },\n", + " PL_MODEL_CLS=TransformerSeq2Seq_PL\n", + ")" ] }, { @@ -652,40 +728,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2020-03-14T07:07:13.794149Z", - "start_time": "2020-03-14T07:07:11.052417Z" - } - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.200Z" - } - }, - "outputs": [], - "source": [ - "# test\n", - "trainer.test(model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.200Z" + "end_time": "2020-03-15T01:45:51.511798Z", + "start_time": "2020-03-15T01:03:50.100Z" } }, "outputs": [], @@ -701,8 +745,10 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.200Z" - } + "end_time": "2020-03-15T01:45:51.513331Z", + "start_time": "2020-03-15T01:03:50.400Z" + }, + "scrolled": true }, "outputs": [], "source": [ @@ -732,13 +778,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.200Z" + "end_time": "2020-03-15T01:03:42.162470Z", + "start_time": "2020-03-15T01:03:42.088080Z" } }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'name' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mpruner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpruners\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMedianPruner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpruning\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpruners\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNopPruner\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 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mstudy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptuna\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_study\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'minimize'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpruner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpruner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstorage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf'sqlite:///optuna_result/{name}.db'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstudy_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'no-name-b60e37fc-4ab6-4793-8a0a-c87a1b40c5c0'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mload_if_exists\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;31m# shutil.rmtree(MODEL_DIR)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'name' is not defined" + ] + } + ], "source": [ "import argparse \n", "\n", @@ -772,7 +831,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.200Z" + "end_time": "2020-03-15T01:02:49.328661Z", + "start_time": "2020-03-15T01:02:36.700Z" } }, "outputs": [], @@ -796,7 +856,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.200Z" + "end_time": "2020-03-15T01:02:49.329881Z", + "start_time": "2020-03-15T01:02:36.800Z" } }, "outputs": [], @@ -809,7 +870,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.300Z" + "end_time": "2020-03-15T01:02:49.331501Z", + "start_time": "2020-03-15T01:02:36.800Z" } }, "outputs": [], @@ -830,7 +892,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.300Z" + "end_time": "2020-03-15T01:02:49.332752Z", + "start_time": "2020-03-15T01:02:36.800Z" } }, "outputs": [], @@ -843,7 +906,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.300Z" + "end_time": "2020-03-15T01:02:49.333961Z", + "start_time": "2020-03-15T01:02:36.800Z" } }, "outputs": [], @@ -858,7 +922,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.300Z" + "end_time": "2020-03-15T01:02:49.335209Z", + "start_time": "2020-03-15T01:02:36.800Z" } }, "outputs": [], @@ -873,7 +938,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.300Z" + "end_time": "2020-03-15T01:02:49.336724Z", + "start_time": "2020-03-15T01:02:36.800Z" } }, "outputs": [], @@ -886,7 +952,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.300Z" + "end_time": "2020-03-15T01:02:49.337997Z", + "start_time": "2020-03-15T01:02:36.800Z" } }, "outputs": [], @@ -904,7 +971,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.300Z" + "end_time": "2020-03-15T01:02:49.340013Z", + "start_time": "2020-03-15T01:02:36.800Z" } }, "outputs": [], @@ -917,7 +985,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "start_time": "2020-03-14T07:09:05.300Z" + "end_time": "2020-03-15T01:02:49.341396Z", + "start_time": "2020-03-15T01:02:36.800Z" } }, "outputs": [], @@ -925,6 +994,27 @@ "self.hparams.window_length + self.hparams.target_length" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/src/data/smart_meter.py b/src/data/smart_meter.py index 80456b0..9ccb2b1 100644 --- a/src/data/smart_meter.py +++ b/src/data/smart_meter.py @@ -155,7 +155,7 @@ def get_smartmeter_df(indir=Path('./data/smart-meters-in-london'), use_logy=Fals df['dayofweek'] = time.dt.dayofweek / 7.0 # Drop nan and 0's - df = df[df['energy(kWh/hh)']!=0] + df = df[df['energy(kWh/hh)'] != 0] df = df.dropna() if use_logy: @@ -163,7 +163,9 @@ def get_smartmeter_df(indir=Path('./data/smart-meters-in-london'), use_logy=Fals df = df.sort_values('tstp') # split data - n_split = -int(len(df)*0.1) - df_train = df[:n_split] - df_test = df[n_split:] - return df_train, df_test + test_split= -int(len(df) * 0.1) + val_split= int(len(df) * 0.15) + df_test = df[:val_split] + df_train = df[val_split:test_split] + df_val = df[test_split:] + return df_train, df_val, df_test diff --git a/src/models/lightning_anp.py b/src/models/lightning_anp.py index b4d7da8..3fc2947 100644 --- a/src/models/lightning_anp.py +++ b/src/models/lightning_anp.py @@ -69,7 +69,7 @@ class LatentModelPL(pl.LightningModule): # agg and print self.train_logs HACK https://github.com/PyTorchLightning/pytorch-lightning/issues/100 train_logs = self.agg_logs(self.train_logs) - train_logs_str = {k: f"{v.mean()}" for k, v in train_logs.items()} + train_logs_str = {k: f"{v}" for k, v in train_logs.items()} self.train_logs = [] print(f"step val {self.trainer.global_step}, {tensorboard_logs_str} {train_logs}") return logs @@ -95,10 +95,10 @@ class LatentModelPL(pl.LightningModule): if isinstance(outputs[0][j], dict): # Take mean of sub dicts keys = outputs[0][j].keys() - aggs[j] = {k: torch.stack([x[j][k] for x in outputs if k in x[j]]).mean() for k in keys} + aggs[j] = {k: torch.stack([x[j][k] for x in outputs if k in x[j]]).mean().item() for k in keys} else: # Take mean of numbers - aggs[j] = torch.stack([x[j] for x in outputs if j in x]).mean() + aggs[j] = torch.stack([x[j] for x in outputs if j in x]).mean().item() return aggs # # Log hparams with metric, doesn't work @@ -117,15 +117,14 @@ class LatentModelPL(pl.LightningModule): return self.validation_end(*args, **kwargs) def configure_optimizers(self): - optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"], weight_decay=0) - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optim, patience=1, verbose=True, min_lr=1e-7) # note early stopping has patience 3 + optim = torch.optim.AdamW(self.parameters(), lr=self.hparams["learning_rate"], weight_decay=0) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-7) # note early stopping has patience 3 return [optim], [scheduler] def _get_cache_dfs(self): if self._dfs is None: - df_train, df_test = get_smartmeter_df() - # self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600]) - self._dfs = dict(df_train=df_train, df_test=df_test) + df_train, df_val, df_test = get_smartmeter_df() + self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test) return self._dfs def train_dataloader(self): @@ -144,7 +143,7 @@ class LatentModelPL(pl.LightningModule): ) def val_dataloader(self): - df_test = self._get_cache_dfs()['df_test'] + df_test = self._get_cache_dfs()['df_val'] data_test = SmartMeterDataSet( df_test, self.hparams["num_context"], self.hparams["num_extra_target"] ) @@ -172,49 +171,47 @@ class LatentModelPL(pl.LightningModule): ) @staticmethod - def add_model_specific_args(parent_parser): - """ - Specify the hyperparams for this LightningModule - """ - # MODEL specific - parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser], add_help=False) - parser.opt_range("--learning_rate", default=1e-3, type=float, tunable=True, high=1e-2, low=1e-5, log_base=10) + def add_suggest(trial): + trial.suggest_loguniform("learning_rate", 1e-5, 1e-2) + + trial.suggest_categorical("hidden_dim", [8*2**i for i in range(6)]) + trial.suggest_categorical("latent_dim", [8*2**i for i in range(6)]) - parser.opt_list("--hidden_dim", default=128, type=int, tunable=True, options=[8*2**i for i in range(8)]) - parser.opt_list("--latent_dim", default=128, type=int, tunable=True, options=[8*2**i for i in range(8)]) - parser.add_argument("--num_heads", default=8, type=int) - parser.add_argument("--attention_layers", default=1, type=int) - parser.opt_list("--n_latent_encoder_layers", default=4, type=int, tunable=True, options=[1, 2, 4, 8, 16]) - parser.opt_list("--n_det_encoder_layers", default=4, type=int, tunable=True, options=[1, 2, 4, 8, 16]) - parser.opt_list("--n_decoder_layers", default=2, type=int, tunable=True, options=[1, 2, 4, 8, 16]) + trial.suggest_int("attention_layers", 1, 4) + trial.suggest_categorical("n_latent_encoder_layers", [1, 2, 4, 8]) + trial.suggest_categorical("n_det_encoder_layers", [1, 2, 4, 8]) + trial.suggest_categorical("n_decoder_layers", [1, 2, 4, 8]) + trial.suggest_int("num_heads", 8, 8) - parser.opt_range("--dropout", default=0, type=float, tunable=True, low=0, high=0.75) - parser.opt_range("--attention_dropout", default=0, type=float, tunable=True, low=0, high=0.75) - parser.add_argument("--min_std", default=0.005, type=float) + trial.suggest_uniform("dropout", 0, 0.9) + trial.suggest_uniform("attention_dropout", 0, 0.9) - parser.opt_list( - "--latent_enc_self_attn_type", default="multihead", type=str, tunable=True, options=['uniform', 'dot', 'multihead', 'ptmultihead'] + trial.suggest_categorical( + "latent_enc_self_attn_type", ['uniform', 'multihead', 'ptmultihead'] ) - parser.opt_list("--det_enc_self_attn_type", default="multihead", type=str, tunable=True, options=['uniform', 'dot', 'multihead', 'ptmultihead']) - parser.opt_list("--det_enc_cross_attn_type", default="multihead", type=str, tunable=True, options=['uniform', 'dot', 'multihead', 'ptmultihead']) + trial.suggest_categorical("det_enc_self_attn_type", ['uniform', 'multihead', 'ptmultihead']) + trial.suggest_categorical("det_enc_cross_attn_type", ['uniform', 'multihead', 'ptmultihead']) - parser.opt_list("--use_lvar", default=False, type=bool, tunable=True, options=[False, True]) - parser.opt_list("--use_rnn", default=False, type=bool, tunable=True, options=[False, True]) - parser.opt_list("--use_deterministic_path", default=True, tunable=True, type=bool, options=[False, True]) - parser.opt_list("--use_self_attn", default=True, tunable=True, type=bool, options=[False, True]) - parser.opt_list("--batchnorm", default=True, tunable=True, type=bool, options=[False, True]) - - # training specific (for this model) - parser.add_argument("--context_in_target", default=True, type=bool) - parser.add_argument("--grad_clip", default=0, type=float) - parser.add_argument("--num_context", type=int, default=24 * 2) - parser.add_argument("--num_extra_target", type=int, default=24) - parser.add_argument("--max_nb_epochs", default=20, type=int) - parser.add_argument("--num_workers", default=4, type=int) + trial.suggest_categorical("batchnorm", [False, True]) + trial.suggest_categorical("use_self_attn", [False, True]) + trial.suggest_categorical("use_lvar", [False, True]) + trial.suggest_categorical("use_deterministic_path", [False, True]) + trial.suggest_categorical("use_rnn", [True, False]) + + trial._user_attrs = { + 'batch_size': 16, + 'grad_clip': 40, + 'max_nb_epochs': 200, + 'num_workers': 4, + 'num_context': 24* 4, + 'vis_i': '670', + 'num_extra_target': 24*4, + 'x_dim': 18, + 'context_in_target': True, + 'y_dim': 1, + 'patience': 3, + 'min_std': 0.005, + } + return trial - parser.add_argument("--batch_size", default=16, type=int) - parser.add_argument("--x_dim", default=16, type=int) - parser.add_argument("--y_dim", default=1, type=int) - parser.add_argument("--vis_i", default=670, type=int) - return parser diff --git a/src/models/lstm.py b/src/models/lstm.py index 54d5175..21c4613 100644 --- a/src/models/lstm.py +++ b/src/models/lstm.py @@ -132,7 +132,7 @@ class LSTM_PL(pl.LightningModule): def validation_end(self, outputs): # TODO send an image to tensroboard, like in the lighting_anp.py file if int(self.hparams["vis_i"]) > 0: - loader = self.val_dataloader()[0] + loader = self.val_dataloader() vis_i = min(int(self.hparams["vis_i"]), len(loader.dataset)) if isinstance(self.hparams["vis_i"], str): image = plot_from_loader(loader, self, vis_i=vis_i, window_len=self.hparams["window_length"]) @@ -163,15 +163,14 @@ class LSTM_PL(pl.LightningModule): def configure_optimizers(self): optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"]) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optim, patience=2, verbose=True, min_lr=1e-5 + optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-5 ) # note early stopping has patient 3 return [optim], [scheduler] def _get_cache_dfs(self): if self._dfs is None: - df_train, df_test = get_smartmeter_df() - # self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600]) - self._dfs = dict(df_train=df_train, df_test=df_test) + df_train, df_val, df_test = get_smartmeter_df() + self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test) return self._dfs @pl.data_loader @@ -193,7 +192,7 @@ class LSTM_PL(pl.LightningModule): @pl.data_loader def val_dataloader(self): - df_test = self._get_cache_dfs()["df_test"] + df_test = self._get_cache_dfs()["df_val"] dset_test = SequenceDfDataSet( df_test, self.hparams, @@ -216,27 +215,41 @@ class LSTM_PL(pl.LightningModule): return DataLoader(dset_test, batch_size=self.hparams.batch_size, shuffle=False) @staticmethod - def add_model_specific_args(parent_parser): + def add_suggest(trial: optuna.Trial): """ - Specify the hyperparams for this LightningModule + Add hyperparam ranges to an optuna trial and typical user attrs. + + Usage: + trial = optuna.trial.FixedTrial( + params={ + 'hidden_size': 128, + } + ) + trial = add_suggest(trial) + trainer = pl.Trainer() + model = LSTM_PL(dict(**trial.params, **trial.user_attrs), dataset_train, + dataset_test, cache_base_path, norm) + trainer.fit(model) """ - # MODEL specific - parser = HyperOptArgumentParser(parents=[parent_parser]) - parser.add_argument("--learning_rate", default=0.002, type=float) - parser.add_argument("--batch_size", default=16, type=int) - parser.add_argument("--lstm_dropout", default=0.5, type=float) - parser.add_argument("--hidden_size", default=16, type=int) - parser.add_argument("--input_size", default=8, type=int) - parser.add_argument("--lstm_layers", default=8, type=int) - parser.add_argument("--bidirectional", default=False, type=bool) + trial.suggest_loguniform("learning_rate", 1e-6, 1e-2) + trial.suggest_uniform("lstm_dropout", 0, 0.75) + trial.suggest_categorical( + "hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] + ) + trial.suggest_categorical("lstm_layers", [1, 2, 3, 4, 6, 8]) + trial.suggest_categorical("bidirectional", [False, True]) - # training specific (for this model) - parser.add_argument("--window_length", type=int, default=12) - parser.add_argument("--target_length", type=int, default=2) - parser.add_argument("--max_nb_epochs", default=10, type=int) - parser.add_argument("--num_workers", default=4, type=int) - - return parser + trial._user_attrs = { + "batch_size": 16, + "grad_clip": 40, + "max_nb_epochs": 200, + "num_workers": 4, + "vis_i": 670, + "input_size": 6, + "output_size": 1, + "patience": 2, + } + return trial def plot_from_loader(loader, model, vis_i=670, n=1, window_len=0): diff --git a/src/models/lstm_seqseq.py b/src/models/lstm_seqseq.py index f12cd64..00f1381 100644 --- a/src/models/lstm_seqseq.py +++ b/src/models/lstm_seqseq.py @@ -20,7 +20,7 @@ import torch import io import PIL from torchvision.transforms import ToTensor - +from src.models.modules import BatchNormSequence from src.data.smart_meter import get_smartmeter_df from src.utils import ObjectDict @@ -41,6 +41,9 @@ class Seq2SeqNet(nn.Module): self.hparams = hparams self._min_std = _min_std + + + self.norm_input = BatchNormSequence(self.hparams.input_size) self.encoder = nn.LSTM( input_size=self.hparams.input_size, hidden_size=self.hparams.hidden_size, @@ -49,6 +52,9 @@ class Seq2SeqNet(nn.Module): bidirectional=self.hparams.bidirectional, dropout=self.hparams.lstm_dropout, ) + self.multihead_attn = nn.MultiheadAttention(self.hparams.hidden_size, num_heads=8) + + self.norm_target = BatchNormSequence(self.hparams.input_size_decoder) self.decoder = nn.LSTM( input_size=self.hparams.input_size_decoder, hidden_size=self.hparams.hidden_size, @@ -66,9 +72,23 @@ class Seq2SeqNet(nn.Module): def forward(self, context_x, context_y, target_x, target_y=None): x = torch.cat([context_x, context_y], -1) + + # Sometimes input normalisation can be important, an initial batch norm is a nice way to ensure this + x = self.norm_input(x) + target_x = self.norm_target(target_x) + _, (h_out, cell) = self.encoder(x) # hidden = [batch size, n layers * n directions, hid dim] # cell = [batch size, n layers * n directions, hid dim] + + # context_x, d_encoded, target_x = k, v, q + + # query, key, value = target_x, context_x, d_encoded + attn_output, _ = self.multihead_attn(h_out.permute(1, 0, 2), h_out.permute(1, 0, 2), h_out.permute(1, 0, 2)) + h_out = attn_output.permute(1, 0, 2).contiguous() + attn_output, _ = self.multihead_attn(cell.permute(1, 0, 2), cell.permute(1, 0, 2), cell.permute(1, 0, 2)) + cell = attn_output.permute(1, 0, 2).contiguous() + outputs, (_, _) = self.decoder(target_x, (h_out, cell)) # output = [batch size, seq len, hid dim * n directions] @@ -155,7 +175,7 @@ class LSTMSeq2Seq_PL(pl.LightningModule): def show_image(self): # https://github.com/PytorchLightning/pytorch-lightning/blob/f8d9f8f/pytorch_lightning/core/lightning.py#L293 - loader = self.val_dataloader()[0] + loader = self.val_dataloader() vis_i = min(int(self.hparams["vis_i"]), len(loader.dataset)) # print('vis_i', vis_i) if isinstance(self.hparams["vis_i"], str): @@ -174,15 +194,14 @@ class LSTMSeq2Seq_PL(pl.LightningModule): def configure_optimizers(self): optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"]) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optim, patience=2, verbose=True, min_lr=1e-5 + optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-5 ) # note early stopping has patient 3 return [optim], [scheduler] def _get_cache_dfs(self): if self._dfs is None: - df_train, df_test = get_smartmeter_df() - # self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600]) - self._dfs = dict(df_train=df_train, df_test=df_test) + df_train, df_val, df_test = get_smartmeter_df() + self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test) return self._dfs @pl.data_loader @@ -203,7 +222,7 @@ class LSTMSeq2Seq_PL(pl.LightningModule): @pl.data_loader def val_dataloader(self): - df_test = self._get_cache_dfs()['df_test'] + df_test = self._get_cache_dfs()['df_val'] data_test = SmartMeterDataSet( df_test, self.hparams["num_context"], self.hparams["num_extra_target"] ) @@ -232,25 +251,25 @@ class LSTMSeq2Seq_PL(pl.LightningModule): ) @staticmethod - def add_model_specific_args(parent_parser): - """ - Specify the hyperparams for this LightningModule - """ - # MODEL specific - parser = HyperOptArgumentParser(parents=[parent_parser]) - parser.add_argument("--learning_rate", default=0.002, type=float) - parser.add_argument("--batch_size", default=16, type=int) - parser.add_argument("--lstm_dropout", default=0.5, type=float) - parser.add_argument("--hidden_size", default=16, type=int) - parser.add_argument("--input_size", default=8, type=int) - parser.add_argument("--input_size_decoder", default=8, type=int) - parser.add_argument("--lstm_layers", default=8, type=int) - parser.add_argument("--bidirectional", default=False, type=bool) + def add_suggest(trial): + trial.suggest_loguniform("learning_rate", 1e-5, 1e-2) + trial.suggest_uniform("lstm_dropout", 0, 0.75) + trial.suggest_categorical("hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]) + trial.suggest_categorical("lstm_layers", [1, 2, 4, 8]) + trial.suggest_categorical("bidirectional", [False, True]) + - # training specific (for this model) - parser.add_argument("--num_context", type=int, default=12) - parser.add_argument("--num_extra_target", type=int, default=2) - parser.add_argument("--max_nb_epochs", default=10, type=int) - parser.add_argument("--num_workers", default=4, type=int) - - return parser + trial._user_attrs = { + 'batch_size': 16, + 'grad_clip': 40, + 'max_nb_epochs': 200, + 'num_workers': 4, + 'num_extra_target': 24*4, + 'vis_i': '670', + 'num_context': 24*4, + 'input_size': 18, + 'input_size_decoder': 17, + 'context_in_target': True, + 'output_size': 1 + } + return trial diff --git a/src/models/lstm_std.py b/src/models/lstm_std.py index b73c22f..8ed3fe8 100644 --- a/src/models/lstm_std.py +++ b/src/models/lstm_std.py @@ -165,7 +165,7 @@ class LSTM_PL(pl.LightningModule): def validation_end(self, outputs): # TODO send an image to tensroboard, like in the lighting_anp.py file if int(self.hparams["vis_i"]) > 0: - loader = self.val_dataloader()[0] + loader = self.val_dataloader() vis_i = min(int(self.hparams["vis_i"]), len(loader.dataset)) if isinstance(self.hparams["vis_i"], str): image = plot_from_loader(loader, self, vis_i=vis_i) @@ -196,15 +196,14 @@ class LSTM_PL(pl.LightningModule): def configure_optimizers(self): optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"]) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optim, patience=2, verbose=True, min_lr=1e-5 + optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-5 ) # note early stopping has patient 3 return [optim], [scheduler] def _get_cache_dfs(self): if self._dfs is None: - df_train, df_test = get_smartmeter_df() - # self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600]) - self._dfs = dict(df_train=df_train, df_test=df_test) + df_train, df_val, df_test = get_smartmeter_df() + self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test) return self._dfs @pl.data_loader @@ -226,7 +225,7 @@ class LSTM_PL(pl.LightningModule): @pl.data_loader def val_dataloader(self): - df_test = self._get_cache_dfs()["df_test"] + df_test = self._get_cache_dfs()["df_val"] dset_test = SequenceDfDataSet( df_test, self.hparams, @@ -249,27 +248,29 @@ class LSTM_PL(pl.LightningModule): return DataLoader(dset_test, batch_size=self.hparams.batch_size, shuffle=False) @staticmethod - def add_model_specific_args(parent_parser): - """ - Specify the hyperparams for this LightningModule - """ - # MODEL specific - parser = HyperOptArgumentParser(parents=[parent_parser]) - parser.add_argument("--learning_rate", default=0.002, type=float) - parser.add_argument("--batch_size", default=16, type=int) - parser.add_argument("--lstm_dropout", default=0.5, type=float) - parser.add_argument("--hidden_size", default=16, type=int) - parser.add_argument("--input_size", default=8, type=int) - parser.add_argument("--lstm_layers", default=8, type=int) - parser.add_argument("--bidirectional", default=False, type=bool) - - # training specific (for this model) - parser.add_argument("--window_length", type=int, default=12) - parser.add_argument("--target_length", type=int, default=2) - parser.add_argument("--max_nb_epochs", default=10, type=int) - parser.add_argument("--num_workers", default=4, type=int) - - return parser + def add_suggest(trial): + trial.suggest_loguniform("learning_rate", 1e-5, 1e-2) + trial.suggest_uniform("lstm_dropout", 0, 0.75) + trial.suggest_categorical("hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]) + trial.suggest_categorical("lstm_layers", [1, 2, 4, 8]) + trial.suggest_categorical("bidirectional", [False, True]) + + # constants + trial._user_attrs = { + 'batch_size': 16, + 'grad_clip': 40, + 'max_nb_epochs': 200, + 'num_workers': 4, + 'num_extra_target': 24*4, + 'vis_i': '670', + 'num_context': 24*4, + 'input_size': 18, + 'input_size_decoder': 17, + 'context_in_target': True, + 'output_size': 1, + 'patience': 3, + } + return trial def plot_from_loader(loader, model, vis_i=670, n=1, window_len=0): diff --git a/src/models/model.py b/src/models/model.py index 92cc0f8..99cf3d0 100644 --- a/src/models/model.py +++ b/src/models/model.py @@ -5,7 +5,7 @@ from torch.utils.data import TensorDataset, DataLoader import math from src.models.modules import LatentEncoder, DeterministicEncoder, Decoder - +from src.models.modules import BatchNormSequence def log_prob_sigma(value, loc, log_scale): """A slightly more stable (not confirmed yet) log prob taking in log_var instead of scale. @@ -66,18 +66,32 @@ class LatentModel(nn.Module): self._use_rnn = use_rnn self.context_in_target = context_in_target + # Sometimes input normalisation can be important, an initial batch norm is a nice way to ensure this + self.norm_x = BatchNormSequence(x_dim) + self.norm_y = BatchNormSequence(y_dim) + if self._use_rnn: - self._lstm = nn.LSTM( + self._lstm_x = nn.LSTM( input_size=x_dim, hidden_size=hidden_dim, num_layers=attention_layers, dropout=dropout, batch_first=True ) + self._lstm_y = nn.LSTM( + input_size=y_dim, + hidden_size=hidden_dim, + num_layers=attention_layers, + dropout=dropout, + batch_first=True + ) x_dim = hidden_dim + y_dim2 = hidden_dim + else: + y_dim2 = y_dim self._latent_encoder = LatentEncoder( - x_dim + y_dim, + x_dim + y_dim2, hidden_dim=hidden_dim, latent_dim=latent_dim, self_attention_type=latent_enc_self_attn_type, @@ -93,7 +107,7 @@ class LatentModel(nn.Module): ) self._deterministic_encoder = DeterministicEncoder( - input_dim=x_dim + y_dim, + input_dim=x_dim + y_dim2, x_dim=x_dim, hidden_dim=hidden_dim, self_attention_type=det_enc_self_attn_type, @@ -126,16 +140,26 @@ class LatentModel(nn.Module): def forward(self, context_x, context_y, target_x, target_y=None): + # https://stackoverflow.com/a/46772183/221742 + target_x = self.norm_x(target_x) + context_x = self.norm_x(context_x) + context_y = self.norm_y(context_y) + if self._use_rnn: # see https://arxiv.org/abs/1910.09323 where x is substituted with h = RNN(x) # x need to be provided as [B, T, H] - target_x, _ = self._lstm(target_x) - context_x, _ = self._lstm(context_x) + target_x, _ = self._lstm_x(target_x) + context_x, _ = self._lstm_x(context_x) + context_y, _ = self._lstm_y(context_y) + dist_prior, log_var_prior = self._latent_encoder(context_x, context_y) if target_y is not None: - dist_post, log_var_post = self._latent_encoder(target_x, target_y) + target_y2 = self.norm_y(target_y) + if self._use_rnn: + target_y2, _ = self._lstm_y(target_y2) + dist_post, log_var_post = self._latent_encoder(target_x, target_y2) z = dist_post.loc else: z = dist_prior.loc diff --git a/src/models/modules.py b/src/models/modules.py index f3c6692..064c04b 100644 --- a/src/models/modules.py +++ b/src/models/modules.py @@ -24,6 +24,22 @@ class LSTMBlock(nn.Module): return self._lstm(x)[0] +class BatchNormSequence(nn.Module): + """Applies batch norm on features of a batch first sequence.""" + def __init__( + self, out_channels + ): + super().__init__() + self.norm = nn.BatchNorm1d(out_channels) + + def forward(self, x): + # x.shape is (Batch, Sequence, Channels) + # Now we want to apply batchnorm and dropout to the channels. So we put it in shape + # (Batch, Channels, Sequence) so we can use BatchNorm1d + x = x.permute(0, 2, 1) + x = self.norm(x) + return x.permute(0, 2, 1) + class NPBlockRelu2d(nn.Module): """Block for Neural Processes.""" diff --git a/src/models/transformer_seq2seq.py b/src/models/transformer_seq2seq.py index c90c02c..21d9d87 100644 --- a/src/models/transformer_seq2seq.py +++ b/src/models/transformer_seq2seq.py @@ -19,9 +19,11 @@ from matplotlib import pyplot as plt import torch import io import PIL +import optuna from torchvision.transforms import ToTensor from src.data.smart_meter import get_smartmeter_df +from src.models.modules import BatchNormSequence from src.utils import ObjectDict @@ -41,7 +43,6 @@ class TransformerSeq2SeqNet(nn.Module): self.hparams = hparams self._min_std = _min_std - # TODO project to 8*nhead hidden_out_size = self.hparams.hidden_out_size self.enc_emb = nn.Linear(self.hparams.input_size, hidden_out_size) layer_enc = nn.TransformerEncoderLayer( @@ -92,7 +93,7 @@ class TransformerSeq2SeqNet(nn.Module): log_sigma = torch.clamp(log_sigma, math.log(self._min_std), -math.log(self._min_std)) sigma = torch.exp(log_sigma) - y_dist=torch.distributions.Normal(mean, sigma) + y_dist = torch.distributions.Normal(mean, sigma) # Loss loss_mse = loss_p = None @@ -188,15 +189,14 @@ class TransformerSeq2Seq_PL(pl.LightningModule): def configure_optimizers(self): optim = torch.optim.Adam(self.parameters(), lr=self.hparams["learning_rate"]) scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optim, patience=2, verbose=True, min_lr=1e-5 - ) # note early stopping has patient 3 + optim, patience=self.hparams["patience"], verbose=True, min_lr=1e-7 + ) # note early stopping has patience 3 return [optim], [scheduler] def _get_cache_dfs(self): if self._dfs is None: - df_train, df_test = get_smartmeter_df() - # self._dfs = dict(df_train=df_train[:600], df_test=df_test[:600]) - self._dfs = dict(df_train=df_train, df_test=df_test) + df_train, df_val, df_test = get_smartmeter_df() + self._dfs = dict(df_train=df_train, df_val=df_val, df_test=df_test) return self._dfs @pl.data_loader @@ -217,7 +217,7 @@ class TransformerSeq2Seq_PL(pl.LightningModule): @pl.data_loader def val_dataloader(self): - df_test = self._get_cache_dfs()['df_test'] + df_test = self._get_cache_dfs()['df_val'] data_test = SmartMeterDataSet( df_test, self.hparams["num_context"], self.hparams["num_extra_target"] ) @@ -246,27 +246,41 @@ class TransformerSeq2Seq_PL(pl.LightningModule): ) @staticmethod - def add_model_specific_args(parent_parser): + def add_suggest(trial: optuna.Trial): """ - Specify the hyperparams for this LightningModule + Add hyperparam ranges to an optuna trial and typical user attrs. + + Usage: + trial = optuna.trial.FixedTrial( + params={ + 'hidden_size': 128, + } + ) + trial = add_suggest(trial) + trainer = pl.Trainer() + model = LSTM_PL(dict(**trial.params, **trial.user_attrs), dataset_train, + dataset_test, cache_base_path, norm) + trainer.fit(model) """ - # MODEL specific - parser = HyperOptArgumentParser(parents=[parent_parser]) - parser.add_argument("--learning_rate", default=0.002, type=float) - parser.add_argument("--batch_size", default=16, type=int) - parser.add_argument("--attention_dropout", default=0.5, type=float) - parser.add_argument("--hidden_size", default=16, type=int) - parser.add_argument("--hidden_out_size", default=16, type=int) - parser.add_argument("--input_size", default=8, type=int) - parser.add_argument("--nhead", default=8, type=int) - parser.add_argument("--input_size_decoder", default=8, type=int) - parser.add_argument("--nlayers", default=8, type=int) - # parser.add_argument("--bidirectional", default=False, type=bool) + trial.suggest_loguniform("learning_rate", 1e-6, 1e-2) + trial.suggest_uniform("attention_dropout", 0, 0.75) + trial.suggest_categorical("hidden_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]) + trial.suggest_categorical("hidden_out_size", [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]) + trial.suggest_categorical("nlayers", [1, 2, 4, 8]) + trial.suggest_categorical("nhead", [1, 2, 8, 16]) - # training specific (for this model) - parser.add_argument("--num_context", type=int, default=12) - parser.add_argument("--num_extra_target", type=int, default=2) - parser.add_argument("--max_nb_epochs", default=10, type=int) - parser.add_argument("--num_workers", default=4, type=int) - - return parser + trial._user_attrs = { + 'batch_size': 16, + 'grad_clip': 40, + 'max_nb_epochs': 200, + 'num_workers': 4, + 'num_extra_target': 24*4, + 'vis_i': '670', + 'num_context': 24*4, + 'input_size': 18, + 'input_size_decoder': 17, + 'context_in_target': True, + 'output_size': 1, + 'patience': 3, + } + return trial diff --git a/src/train.py b/src/train.py new file mode 100644 index 0000000..d9f7c5c --- /dev/null +++ b/src/train.py @@ -0,0 +1,114 @@ +from pytorch_lightning.callbacks import EarlyStopping +from optuna.integration.pytorch_lightning import _check_pytorch_lightning_availability +from pathlib import Path +import optuna +import pytorch_lightning as pl +import torch +from .dict_logger import DictLogger +from .utils import PyTorchLightningPruningCallback +from .plot import plot_from_loader + + +def main( + trial: optuna.Trial, + PL_MODEL_CLS: pl.LightningModule, + name: str, + MODEL_DIR: Path = Path("./lightning_logs"), + train=True, + prune=True, + PERCENT_TEST_EXAMPLES=0.5, +): + # PyTorch Lightning will try to restore model parameters from previous trials if checkpoint + # filenames match. Therefore, the filenames for each trial must be made unique. + + checkpoint_callback = pl.callbacks.ModelCheckpoint( + MODEL_DIR / name / "version_{}".format(trial.number) / "chk", + monitor="val_loss", + mode="min", + ) + + # The default logger in PyTorch Lightning writes to event files to be consumed by + # TensorBoard. We create a simple logger instead that holds the log in memory so that the + # final accuracy can be obtained after optimization. When using the default logger, the + # final accuracy could be stored in an attribute of the `Trainer` instead. + logger = DictLogger(MODEL_DIR, name=name, version=trial.number) + # print("log_dir", logger.experiment.log_dir) + hparams = dict(**trial.params, **trial.user_attrs) + + trainer = pl.Trainer( + logger=logger, + val_percent_check=PERCENT_TEST_EXAMPLES, + checkpoint_callback=checkpoint_callback, + max_epochs=hparams["max_nb_epochs"], + gpus=-1 if torch.cuda.is_available() else None, + early_stop_callback=PyTorchLightningPruningCallback(trial, monitor="val_loss") + if prune + else EarlyStopping( + patience=hparams["patience"] * 2, monitor="val_loss", verbose=True + ), + ) + + model = PL_MODEL_CLS(hparams) + if train: + trainer.fit(model) + return model, trainer + + +def objective(trial, PL_MODEL_CLS): + # see https://github.com/optuna/optuna/blob/cf6f02d/examples/pytorch_lightning_simple.py + trial = PL_MODEL_CLS.add_suggest(trial) + + print("trial", trial.number, "params", trial.params) + + model, trainer = main(trial) + + # also report to tensorboard & print + print("logger.metrics", model.logger.metrics[-1:]) + model.logger.experiment.add_hparams(trial.params, logger.metrics[-1]) + model.logger.save() + + return model.logger.metrics[-1]["val_loss"] + + +def add_number(trial: optuna.Trial, model_dir: Path): + # For manual experiment we will start at -1 and deincr by 1 + versions = [int(s.stem.split("_")[-1]) for s in model_dir.glob("version_*")] + [-1] + trial.number = min(versions) - 1 + print("trial.number", trial.number) + return trial + + +def run_trial( + name: str, + PL_MODEL_CLS: pl.LightningModule, + params: dict = {}, + user_attrs: dict = {}, + MODEL_DIR: Path = Path("./lightning_logs"), +): + print(f"now run `tensorboard --logdir {MODEL_DIR}`") + (MODEL_DIR / name).mkdir(parents=True, exist_ok=True) + trial = optuna.trial.FixedTrial(params=params) + trial = PL_MODEL_CLS.add_suggest(trial) + trial = add_number(trial, MODEL_DIR / name) + trial._user_attrs.update(user_attrs) + model, trainer = main( + trial, PL_MODEL_CLS, name=name, MODEL_DIR=MODEL_DIR, train=False, prune=False + ) + trainer.fit(model) + + # Load checkpoint + checkpoint = sorted(Path(trainer.checkpoint_callback.dirpath).glob("*.ckpt"))[-1] + device = next(model.parameters()).device + print(f"Loading checkpoint {checkpoint}") + model = model.load_from_checkpoint(checkpoint).to(device) + + trainer.test(model) + + # Plot + loader = model.val_dataloader() + dset_test = loader.dataset + label_names = dset_test.label_names + plot_from_loader(model.val_dataloader(), model, i=670, title='val 670') + plot_from_loader(model.train_dataloader(), model, i=670, title='train 670') + plot_from_loader(model.test_dataloader(), model, i=670, title='test 670') + return trial, trainer, model diff --git a/src/utils.py b/src/utils.py index 62acdcd..7ac6dab 100644 --- a/src/utils.py +++ b/src/utils.py @@ -1,5 +1,18 @@ from pytorch_lightning.callbacks import EarlyStopping from optuna.integration.pytorch_lightning import _check_pytorch_lightning_availability +from pathlib import Path +import numpy as np +import torch +import optuna + + +def init_random_seed(seed): + # https://pytorch.org/docs/stable/notes/randomness.html + np.random.seed(seed) + torch.random.manual_seed(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + class PyTorchLightningPruningCallback(EarlyStopping): """Optuna PyTorch Lightning callback to prune unpromising trials. @@ -20,10 +33,10 @@ class PyTorchLightningPruningCallback(EarlyStopping): how this dictionary is formatted. """ - def __init__(self, trial, monitor): + def __init__(self, trial, monitor, **kwargs): # type: (optuna.trial.Trial, str) -> None - super(PyTorchLightningPruningCallback, self).__init__(monitor) + super().__init__(monitor, **kwargs) _check_pytorch_lightning_availability() @@ -41,25 +54,26 @@ class PyTorchLightningPruningCallback(EarlyStopping): message = "Trial was pruned at epoch {}.".format(epoch) raise optuna.exceptions.TrialPruned(message) + class ObjectDict(dict): """ Interface similar to an argparser """ + def __init__(self): pass - + def __setattr__(self, attr, value): self[attr] = value return self[attr] - + def __getattr__(self, attr): - if attr.startswith('_'): + if attr.startswith("_"): # https://stackoverflow.com/questions/10364332/how-to-pickle-python-object-derived-from-dict raise AttributeError return dict(self)[attr] - + @property def __dict__(self): return dict(self) -