From 0f4f9ed2ec50a56eeb705094ba3c5e451f9b66f8 Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 20:31:26 +0530 Subject: [PATCH 01/11] Create file --- MAEs/PAG_Avishikta_Bhattacharjee/txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/txt diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/txt b/MAEs/PAG_Avishikta_Bhattacharjee/txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/MAEs/PAG_Avishikta_Bhattacharjee/txt @@ -0,0 +1 @@ + From 81d76a8e9fae5d0e015c83e2e3d5f234b7849e14 Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 20:33:15 +0530 Subject: [PATCH 02/11] Create txtx --- MAEs/PAG_Avishikta_Bhattacharjee/notebook/txtx | 1 + 1 file changed, 1 insertion(+) create mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/notebook/txtx diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/txtx b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/txtx new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/txtx @@ -0,0 +1 @@ + From eae519862b375a32f8cf793041007510527cdb99 Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 20:35:55 +0530 Subject: [PATCH 03/11] notebook run --- .../notebook/PAG_LorentzParT (2).ipynb | 3340 +++++++++++++++++ 1 file changed, 3340 insertions(+) create mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT (2).ipynb diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT (2).ipynb b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT (2).ipynb new file mode 100644 index 0000000..43e9a3f --- /dev/null +++ b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT (2).ipynb @@ -0,0 +1,3340 @@ +{ + "cells": [ + { + "cell_type": "code", + "source": [ + "\n", + "# 1. Clone the ML4SCI/CMS repository\n", + "!git clone https://github.com/ML4SCI/CMS.git\n", + "\n", + "# 2. Navigate to the specific Hybrid Transformer project directory\n", + "%cd CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", + "\n", + "# 3. Install required libraries\n", + "!pip install lgatr uproot awkward tqdm vector" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B7YmCrpkJSAg", + "outputId": "f6eeec54-0a0e-416f-e730-dcf49f66d6f7" + }, + "id": "B7YmCrpkJSAg", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'CMS'...\n", + "remote: Enumerating objects: 751, done.\u001b[K\n", + "remote: Counting objects: 100% (130/130), done.\u001b[K\n", + "remote: Compressing objects: 100% (99/99), done.\u001b[K\n", + "remote: Total 751 (delta 39), reused 83 (delta 27), pack-reused 621 (from 2)\u001b[K\n", + "Receiving objects: 100% (751/751), 330.16 MiB | 18.70 MiB/s, done.\n", + "Resolving deltas: 100% (187/187), done.\n", + "Updating files: 100% (531/531), done.\n", + "/content/CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", + "Collecting lgatr\n", + " Downloading lgatr-1.4.4-py3-none-any.whl.metadata (8.7 kB)\n", + "Collecting uproot\n", + " Downloading uproot-5.7.5-py3-none-any.whl.metadata (35 kB)\n", + "Collecting awkward\n", + " Downloading awkward-2.11.0-py3-none-any.whl.metadata (7.6 kB)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (4.67.3)\n", + "Collecting vector\n", + " Downloading vector-1.8.1-py3-none-any.whl.metadata (15 kB)\n", + "Requirement already satisfied: torch>=2.1 in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.11.0+cu128)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.0.2)\n", + "Requirement already satisfied: einops in /usr/local/lib/python3.12/dist-packages (from lgatr) (0.8.2)\n", + "Requirement already satisfied: opt_einsum in /usr/local/lib/python3.12/dist-packages (from lgatr) (3.4.0)\n", + "Requirement already satisfied: cramjam>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2.11.0)\n", + "Requirement already satisfied: fsspec!=2026.2.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2025.3.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from uproot) (26.2)\n", + "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from uproot) (3.8.1)\n", + "Collecting awkward-cpp==54 (from awkward)\n", + " Downloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.29.7)\n", + "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (4.16.0)\n", + "Requirement already satisfied: setuptools<82 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (75.2.0)\n", + "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (1.14.0)\n", + "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.1)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.1.6)\n", + "Requirement already satisfied: cuda-toolkit==12.8.1 in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.1)\n", + "Requirement already satisfied: cuda-bindings<13,>=12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (12.9.7)\n", + "Requirement already satisfied: nvidia-cudnn-cu12==9.19.0.56 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (9.19.0.56)\n", + "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (0.7.1)\n", + "Requirement already satisfied: nvidia-nccl-cu12==2.28.9 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (2.28.9)\n", + "Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.4.5)\n", + "Requirement already satisfied: triton==3.6.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.0)\n", + "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.4.1)\n", + "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", + "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.3.3.83)\n", + "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (1.13.1.3)\n", + "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", + "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (10.3.9.90)\n", + "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.7.3.90)\n", + "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.5.8.93)\n", + "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", + "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", + "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", + "Requirement already satisfied: cuda-pathfinder~=1.1 in /usr/local/lib/python3.12/dist-packages (from cuda-bindings<13,>=12.9.4->torch>=2.1->lgatr) (1.5.6)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=2.1->lgatr) (1.3.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=2.1->lgatr) (3.0.3)\n", + "Downloading lgatr-1.4.4-py3-none-any.whl (60 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.6/60.6 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading uproot-5.7.5-py3-none-any.whl (401 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m35.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading awkward-2.11.0-py3-none-any.whl (974 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m974.8/974.8 kB\u001b[0m \u001b[31m76.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (689 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m689.3/689.3 kB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading vector-1.8.1-py3-none-any.whl (182 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.7/182.7 kB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: vector, awkward-cpp, awkward, uproot, lgatr\n", + "Successfully installed awkward-2.11.0 awkward-cpp-54 lgatr-1.4.4 uproot-5.7.5 vector-1.8.1\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# --- Default libraries ---\n", + "import os\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "# --- Working directory ---\n", + "PROJECT_DIR = Path().resolve()\n", + "PROJECT_ROOT_NAME = 'Hybrid_Transformer_Thanh_Nguyen'\n", + "\n", + "while PROJECT_DIR.name != PROJECT_ROOT_NAME and PROJECT_DIR != PROJECT_DIR.parent:\n", + " PROJECT_DIR = PROJECT_DIR.parent\n", + "\n", + "if Path().resolve() != PROJECT_DIR:\n", + " os.chdir(PROJECT_DIR)\n", + "\n", + "DATA_DIR = PROJECT_DIR / 'data'\n", + "LOG_DIR = PROJECT_DIR / 'logs'\n", + "\n", + "# --- Data preprocessing & visualization ---\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# --- Deep learning ---\n", + "import torch\n", + "\n", + "# --- Custom modules ---\n", + "from src.configs import LorentzParTConfig, TrainConfig\n", + "from src.engine import MaskedModelTrainer, Trainer\n", + "from src.models import LorentzParT\n", + "from src.utils import accuracy_metric_ce, set_seed\n", + "from src.utils.data import JetClassDataset, compute_norm_stats, read_file\n", + "from src.utils.viz import *\n", + "\n", + "# --- Settings ---\n", + "warnings.filterwarnings('ignore')\n", + "set_seed(42)\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "device" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IvxBxUicRu18", + "outputId": "3241b771-4383-417a-cfc1-963899fb5dd4" + }, + "id": "IvxBxUicRu18", + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ] + }, + { + "cell_type": "code", + "source": [ + "'''\n", + "drive_dest_folder = '/content/drive/MyDrive/GSOC/dara'\n", + "drive_dest_path = os.path.join(drive_dest_folder, out_file)\n", + "\n", + "# Create the directory if it doesn't exist\n", + "if not os.path.exists(drive_dest_folder):\n", + " os.makedirs(drive_dest_folder)\n", + " print(f\"Created directory: {drive_dest_folder}\")\n", + "\n", + "# 3. Move the verified file to Drive\n", + "if os.path.exists(out_file):\n", + " print(f\"Moving {out_file} to Google Drive...\")\n", + " shutil.move(out_file, drive_dest_path)\n", + " print(f\"File successfully moved to: {drive_dest_path}\")\n", + "else:\n", + " print(\"Source file not found. Check if the download was successful.\")''''" + ], + "metadata": { + "collapsed": true, + "id": "QfAWZ8A3cdNR" + }, + "id": "QfAWZ8A3cdNR", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Loading Dpendencies" + ], + "metadata": { + "id": "BmBDXDMSD61u" + }, + "id": "BmBDXDMSD61u" + }, + { + "cell_type": "code", + "source": [ + "from typing import List, Tuple, Dict, Optional\n", + "\n", + "import torch\n", + "from torch import nn, Tensor\n", + "from lgatr.interface import extract_vector\n", + "from lgatr.layers import EquiLinear\n", + "\n", + "from src.models.classifier import ClassAttentionBlock, Classifier\n", + "from src.models.feedforward import Feedforward\n", + "from src.models.particle_transformer import ParticleAttentionBlock\n", + "from src.models.processor import InteractionEmbedding, ParticleProcessor\n", + "from src.configs import LorentzParTConfig\n", + "from lgatr.interface import extract_vector\n", + "from lgatr.layers import EquiLinear\n", + "\n", + "\n" + ], + "metadata": { + "id": "jTbtwxdtAt9S" + }, + "id": "jTbtwxdtAt9S", + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Data Preparation" + ], + "metadata": { + "id": "VB1JTgpxDv-4" + }, + "id": "VB1JTgpxDv-4" + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "from pathlib import Path\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from src.utils.data import JetClassDataset, compute_norm_stats\n", + "\n", + "# --- 1. Mount Google Drive (If not already done) ---\n", + "from google.colab import drive\n", + "if not os.path.exists('/content/drive'):\n", + " drive.mount('/content/drive')\n", + "\n", + "# --- 2. Locate and Load the Compressed Archive ---\n", + "DRIVE_FILE_PATH = '/content/drive/MyDrive/GSOC/dara/jetclass_balanced_1M.npz'\n", + "\n", + "print(\"=\" * 70)\n", + "print(f\"LOADING SERIALIZED ARRAYS FROM DRIVE\")\n", + "print(\"=\" * 70)\n", + "\n", + "if os.path.exists(DRIVE_FILE_PATH):\n", + " # Load using memory-mapping for high-speed indexing\n", + " data_archive = np.load(DRIVE_FILE_PATH, mmap_mode='r')\n", + "\n", + " X_particles = data_archive['X_particles']\n", + " X_jets = data_archive['X_jets']\n", + " y = data_archive['Y']\n", + "\n", + " print(\"SUCCESS: Data loaded cleanly into RAM!\")\n", + " print(f\" -> X_particles matrix shape : {X_particles.shape}\")\n", + " print(f\" -> X_jets matrix shape : {X_jets.shape}\")\n", + " print(f\" -> y (Labels) matrix shape : {y.shape}\")\n", + " print(\"=\" * 70 + \"\\n\")\n", + "else:\n", + " raise FileNotFoundError(f\"ERROR: Could not find the file at {DRIVE_FILE_PATH}\")\n", + "\n", + "# --- 3. Split the Balanced Dataset Safely ---\n", + "# We enforce stratify=y to lock in your strict 10% balance across all splits\n", + "X_train, X_val, y_train, y_val = train_test_split(X_particles, y, test_size=0.2, random_state=42, stratify=y)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.5, random_state=42, stratify=y_val)\n", + "\n", + "# --- 4. Re-Initialize JetClass Dataset Objects ---\n", + "normalize = [True, False, False, True]\n", + "norm_dict = compute_norm_stats(X_train)\n", + "\n", + "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode='biased')\n", + "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode='biased')\n", + "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode='first')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9RBH34OYBoZ7", + "outputId": "a53a1da2-e44b-4611-cd4e-f610d3cce289" + }, + "id": "9RBH34OYBoZ7", + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n", + "======================================================================\n", + "LOADING SERIALIZED ARRAYS FROM DRIVE\n", + "======================================================================\n", + "SUCCESS: Data loaded cleanly into RAM!\n", + " -> X_particles matrix shape : (1000000, 4, 128)\n", + " -> X_jets matrix shape : (1000000, 4)\n", + " -> y (Labels) matrix shape : (1000000, 10)\n", + "======================================================================\n", + "\n", + "pt_mean: 92.70597076416016, pt_std: 105.79937744140625\n", + "eta_mean: -0.0011634620605036616, eta_std: 0.9182536005973816\n", + "phi_mean: -0.0006678671925328672, phi_std: 1.8138455152511597\n", + "E_mean: 133.98568725585938, E_std: 167.7259979248047\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "id": "edb52e95", + "metadata": { + "id": "edb52e95" + }, + "source": [ + "## Applying Gated Attention LorentzPart" + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch import Tensor\n", + "from typing import Optional\n", + "\n", + "class ParticleAttentionBlock(nn.Module):\n", + " def __init__(\n", + " self,\n", + " embed_dim: int = 128,\n", + " num_heads: int = 8,\n", + " dropout: float = 0.1,\n", + " expansion_factor: int = 4,\n", + " gate_type: Optional[str] = \"headwise\",\n", + " ):\n", + " super(ParticleAttentionBlock, self).__init__()\n", + " assert embed_dim % num_heads == 0, \"embed_dim must be divisible by num_heads\"\n", + "\n", + " self.embed_dim = embed_dim\n", + " self.num_heads = num_heads\n", + " self.head_dim = embed_dim // num_heads\n", + " self.gate_type = gate_type\n", + "\n", + " self.layernorm1 = nn.LayerNorm(embed_dim)\n", + " self.mass_norm = nn.LayerNorm(1)#norm\n", + "\n", + " # Project pooled interaction head-features to match token embedding dimensions\n", + " self.physics_proj = nn.Linear(num_heads, embed_dim)\n", + "\n", + " # Project the global scalar invariant mass squared (m2) to the embedding space\n", + " self.mass_proj = nn.Linear(1, embed_dim)\n", + "\n", + " # Gating projections accept physics-fused representations\n", + " if self.gate_type == \"headwise\":\n", + " self.gate_proj = nn.Linear(embed_dim, num_heads)\n", + " elif self.gate_type == \"elementwise\":\n", + " self.gate_proj = nn.Linear(embed_dim, embed_dim)\n", + "\n", + " self.pmha = nn.MultiheadAttention(\n", + " embed_dim=embed_dim,\n", + " num_heads=num_heads,\n", + " dropout=dropout,\n", + " batch_first=True\n", + " )\n", + "\n", + " self.layernorm2 = nn.LayerNorm(embed_dim)\n", + " self.dropout = nn.Dropout(dropout)\n", + "\n", + " self.feedforward = Feedforward(\n", + " embed_dim=embed_dim,\n", + " expansion_factor=expansion_factor,\n", + " dropout=dropout\n", + " )\n", + "\n", + " def forward(self, x: Tensor, padding_mask: Tensor, U: Optional[Tensor] = None, p4: Optional[Tensor] = None) -> Tensor:\n", + " residual = x\n", + " B_size, N_particles, _ = x.shape\n", + "\n", + " # 1. Standard token serialization\n", + " x_norm = self.layernorm1(x)\n", + "\n", + " # 2. FIXED: U is ALREADY [Batch * Heads, N, N]. Pass directly to PyTorch MHA.\n", + " x_attn, _ = self.pmha(x_norm, x_norm, x_norm, key_padding_mask=padding_mask, attn_mask=U)\n", + "\n", + " # 3. Physics-Aware Fusion (Invariants-driven conditioning)\n", + " if U is not None:\n", + " # Reconstruct the 4D shape: [B * H, N, N] -> [B, H, N, N]\n", + " U_reshaped = U.view(B_size, self.num_heads, N_particles, N_particles)\n", + "\n", + " # Pool over neighbor particle index 'j' (dim=3). Resulting shape: [B, H, N]\n", + " u_pooled = U_reshaped.sum(dim=3)\n", + "\n", + " # Transpose to align with token channels: [B, N, H]\n", + " u_pooled = u_pooled.transpose(1, 2)\n", + "\n", + " # Map head-wise pooled invariants into token channel space: [B, N, embed_dim]\n", + " physics_context = self.physics_proj(u_pooled)\n", + "\n", + " # Fuse physical invariants with abstract node latent maps\n", + " x_gating_input = x_norm + physics_context\n", + " else:\n", + " # Fallback path if U is not provided\n", + " x_gating_input = x_norm\n", + "\n", + " # 4. Compute Global Invariant Mass Bias from 4-vectors [B, N, 4] -> (E, px, py, pz)\n", + " if p4 is not None:\n", + " # Sum energy component (index 0) over all particles (dim=1)\n", + " energy_sum = p4[..., 0].sum(dim=1, keepdim=True) # Shape: [B, 1]\n", + "\n", + " # Sum momentum components (indices 1, 2, 3) over all particles (dim=1)\n", + " momentum_sum = p4[..., 1:].sum(dim=1) # Shape: [B, 3]\n", + "\n", + " # Calculate invariant mass squared (m2)\n", + " m2 = energy_sum**2 - momentum_sum.norm(dim=-1, keepdim=True)**2 # Shape: [B, 1]\n", + "\n", + " m2_scaled = torch.log1p(torch.relu(m2))\n", + "\n", + "\n", + " # Step C: Standardize the mean and variance dynamically\n", + " m2_norm = self.mass_norm(m2_scaled)\n", + "\n", + " # Project normalized m2 into a global embedding bias vector [B, 1, embed_dim]\n", + " mass_bias = self.mass_proj(m2_norm).unsqueeze(1)\n", + "\n", + " # Broad-cast add global event mass bias to the per-particle gating inputs\n", + " x_gating_input = x_gating_input + mass_bias\n", + "\n", + "\n", + "\n", + " # 5. Compute and apply the explicitly Physics-Aware Gate\n", + " if self.gate_type == \"headwise\":\n", + " # Compute score matrix from physics-fused map: (B, N, embed_dim) -> (B, N, num_heads, 1)\n", + " gate_score = self.gate_proj(x_gating_input).unsqueeze(-1)\n", + "\n", + " # Separate heads to apply individual scalar gating values\n", + " x_attn = x_attn.view(B_size, N_particles, self.num_heads, self.head_dim)\n", + "\n", + " # Apply physics-conditioned filter and reconstruct classic transformer shape\n", + " x_attn = (x_attn * torch.sigmoid(gate_score)).view(B_size, N_particles, self.embed_dim)\n", + "\n", + " elif self.gate_type == \"elementwise\":\n", + " # Compute full channel-by-channel mask from physics-fused map: (B, N, embed_dim)\n", + " gate_score = self.gate_proj(x_gating_input)\n", + " x_attn = x_attn * torch.sigmoid(gate_score)\n", + "\n", + " # 6. Standard Feedforward processing\n", + " x = self.layernorm2(x_attn)\n", + " x = self.dropout(x)\n", + " x += residual\n", + " x = self.feedforward(x)\n", + "\n", + " return x" + ], + "metadata": { + "id": "AvsAk0byPoBK" + }, + "id": "AvsAk0byPoBK", + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch import Tensor\n", + "from typing import Optional, List, Dict, Tuple\n", + "\n", + "\n", + "\n", + "class LorentzParTEncoder(nn.Module):\n", + " def __init__(\n", + " self,\n", + " embed_dim: int = 128,\n", + " num_heads: int = 8,\n", + " num_layers: int = 8,\n", + " in_s_channels: Optional[int] = None,\n", + " out_s_channels: Optional[int] = None,\n", + " dropout: float = 0.1,\n", + " expansion_factor: int = 4,\n", + " pair_embed_dims: List[int] = [64, 64, 64],\n", + " attention_config: Dict = {}\n", + " ):\n", + " super(LorentzParTEncoder, self).__init__()\n", + " self.equilinear = EquiLinear(\n", + " in_mv_channels=1,\n", + " out_mv_channels=1,\n", + " in_s_channels=in_s_channels,\n", + " out_s_channels=out_s_channels\n", + " )\n", + " self.proj = nn.Linear(16, embed_dim)\n", + " self.interaction_embed = InteractionEmbedding(\n", + " num_interaction_features=4,\n", + " pair_embed_dims=pair_embed_dims + [num_heads]\n", + " )\n", + "\n", + " use_gating = attention_config.get('use_gating', False)\n", + "\n", + " # Explicitly pass gate_type so the block knows whether to use physics gating or standard\n", + " self.encoder = nn.ModuleList([\n", + " ParticleAttentionBlock(\n", + " embed_dim=embed_dim,\n", + " num_heads=num_heads,\n", + " dropout=dropout,\n", + " expansion_factor=expansion_factor,\n", + " gate_type=\"headwise\" if use_gating else None\n", + " ) for _ in range(num_layers)\n", + " ])\n", + "\n", + " def forward(self, x: Tensor, padding_mask: Tensor, U: Tensor, p4: Optional[Tensor] = None) -> Tensor:\n", + " B, N, F = x.shape\n", + " U = self.interaction_embed(U)\n", + " x = x.view(B, N, 1, F)\n", + " x, _ = self.equilinear(x)\n", + " x = x.view(B, N, 16)\n", + " x = self.proj(x)\n", + "\n", + " # Pass p4 down to the attention blocks\n", + " for layer in self.encoder:\n", + " x = layer(x, padding_mask, U, p4=p4)\n", + "\n", + " return x\n", + "\n", + "\n", + "class LorentzParT(nn.Module):\n", + " def __init__(\n", + " self,\n", + " config: Optional[LorentzParTConfig] = None,\n", + " max_num_particles: Optional[int] = None,\n", + " num_particle_features: Optional[int] = None,\n", + " num_classes: Optional[int] = None,\n", + " embed_dim: Optional[int] = None,\n", + " num_heads: Optional[int] = None,\n", + " num_layers: Optional[int] = None,\n", + " num_cls_layers: Optional[int] = None,\n", + " num_mlp_layers: Optional[int] = None,\n", + " hidden_dim: Optional[int] = None,\n", + " hidden_mv_channels: Optional[int] = None,\n", + " in_s_channels: Optional[int] = None,\n", + " out_s_channels: Optional[int] = None,\n", + " hidden_s_channels: Optional[int] = None,\n", + " attention: Optional[Dict] = None,\n", + " mlp: Optional[Dict] = None,\n", + " reinsert_mv_channels: Optional[Tuple[int]] = None,\n", + " reinsert_s_channels: Optional[Tuple[int]] = None,\n", + " dropout: Optional[float] = None,\n", + " expansion_factor: Optional[int] = None,\n", + " pair_embed_dims: Optional[List[int]] = None,\n", + " mask: Optional[bool] = None,\n", + " weights: Optional[str] = None,\n", + " inference: Optional[bool] = False\n", + " ):\n", + " super(LorentzParT, self).__init__()\n", + "\n", + " # Use config if provided, otherwise use defaults\n", + " if config is not None:\n", + " self.max_num_particles = max_num_particles if max_num_particles is not None else config.max_num_particles\n", + " self.num_particle_features = num_particle_features if num_particle_features is not None else config.num_particle_features\n", + " self.num_classes = num_classes if num_classes is not None else config.num_classes\n", + " self.embed_dim = embed_dim if embed_dim is not None else config.embed_dim\n", + " self.num_heads = num_heads if num_heads is not None else config.num_heads\n", + " self.num_layers = num_layers if num_layers is not None else config.num_layers\n", + " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else config.num_cls_layers\n", + " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else config.num_mlp_layers\n", + " self.hidden_dim = hidden_dim if hidden_dim is not None else config.hidden_dim\n", + " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else config.hidden_mv_channels\n", + " self.in_s_channels = in_s_channels if in_s_channels is not None else config.in_s_channels\n", + " self.out_s_channels = out_s_channels if out_s_channels is not None else config.out_s_channels\n", + " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else config.hidden_s_channels\n", + " self.attention = attention if attention is not None else config.attention\n", + " self.mlp = mlp if mlp is not None else config.mlp\n", + " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else config.reinsert_mv_channels\n", + " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else config.reinsert_s_channels\n", + " self.dropout = dropout if dropout is not None else config.dropout\n", + " self.expansion_factor = expansion_factor if expansion_factor is not None else config.expansion_factor\n", + " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else config.pair_embed_dims\n", + " self.mask = mask if mask is not None else config.mask\n", + " self.weights = weights if weights is not None else config.weights\n", + " self.inference = inference if inference is not None else config.inference\n", + " else:\n", + " self.max_num_particles = max_num_particles if max_num_particles is not None else 128\n", + " self.num_particle_features = num_particle_features if num_particle_features is not None else 4\n", + " self.num_classes = num_classes if num_classes is not None else 10\n", + " self.embed_dim = embed_dim if embed_dim is not None else 128\n", + " self.num_heads = num_heads if num_heads is not None else 8\n", + " self.num_layers = num_layers if num_layers is not None else 8\n", + " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else 2\n", + " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else 0\n", + " self.hidden_dim = hidden_dim if hidden_dim is not None else 256\n", + " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else 8\n", + " self.in_s_channels = in_s_channels if in_s_channels is not None else None\n", + " self.out_s_channels = out_s_channels if out_s_channels is not None else None\n", + " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else 16\n", + " self.attention = attention if attention is not None else {}\n", + " self.mlp = mlp if mlp is not None else None\n", + " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else None\n", + " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else None\n", + " self.dropout = dropout if dropout is not None else 0.1\n", + " self.expansion_factor = expansion_factor if expansion_factor is not None else 4\n", + " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else [64, 64, 64]\n", + " self.mask = mask if mask is not None else False\n", + " self.weights = weights if weights is not None else None\n", + " self.inference = inference if inference is not None else False\n", + "\n", + " # Initialize the class token\n", + " self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim), requires_grad=True)\n", + " nn.init.normal_(self.cls_token, mean=0.0, std=1.0)\n", + "\n", + " self.processor = ParticleProcessor(to_multivector=True)\n", + "\n", + " # Updated Encoder with attention_config passed dynamically\n", + " self.encoder = LorentzParTEncoder(\n", + " embed_dim=self.embed_dim,\n", + " num_heads=self.num_heads,\n", + " num_layers=self.num_layers,\n", + " in_s_channels=self.in_s_channels,\n", + " out_s_channels=self.out_s_channels,\n", + " dropout=self.dropout,\n", + " expansion_factor=self.expansion_factor,\n", + " pair_embed_dims=self.pair_embed_dims,\n", + " attention_config=self.attention\n", + " )\n", + "\n", + " # For self-supervised learning\n", + " self.fc = nn.Linear(self.max_num_particles * self.embed_dim, 16)\n", + " self.equilinear = EquiLinear(\n", + " in_mv_channels=1,\n", + " out_mv_channels=1,\n", + " in_s_channels=self.in_s_channels,\n", + " out_s_channels=self.out_s_channels\n", + " )\n", + "\n", + " # For classification\n", + " self.decoder = nn.ModuleList([\n", + " ClassAttentionBlock(\n", + " embed_dim=self.embed_dim,\n", + " num_heads=self.num_heads,\n", + " dropout=0.0,\n", + " expansion_factor=self.expansion_factor\n", + " ) for _ in range(self.num_cls_layers)\n", + " ])\n", + " self.layernorm = nn.LayerNorm(self.embed_dim)\n", + " self.classifier = Classifier(\n", + " num_classes=self.num_classes,\n", + " input_dim=self.embed_dim,\n", + " hidden_dim=self.hidden_dim,\n", + " num_layers=self.num_mlp_layers,\n", + " dropout=self.dropout,\n", + " )\n", + " self.act = nn.Softmax(dim=1) if self.inference else nn.Identity()\n", + "\n", + " # Load pretrained weights\n", + " if self.weights is not None:\n", + " state_dict = torch.load(self.weights)\n", + " filtered_state = {\n", + " k[len(\"encoder.\") :]: v\n", + " for k, v in state_dict.items()\n", + " if k.startswith(\"encoder.\")\n", + " }\n", + " self.encoder.load_state_dict(filtered_state, strict=False)\n", + "\n", + " def forward(self, x: Tensor, mask_idx: Optional[Tensor] = None) -> Tensor:\n", + " B, N, F = x.shape # (batch_size, max_num_particles, num_particle_features)\n", + "\n", + " # Save the raw kinematics before processor alters them\n", + " p4 = x.clone()\n", + "\n", + " # Ignore padding particles in query\n", + " padding_mask = (x[..., 3] == 0).float() # (B, N)\n", + "\n", + " # Set the masked indices to 0.0 so they are not ignored in MultiheadAttention()\n", + " if mask_idx is not None:\n", + " batch_indices = torch.arange(x.size(0), device=x.device)\n", + " padding_mask[batch_indices, mask_idx] = 0.0\n", + "\n", + " # Process particles to get interaction embeddings and multivectors (if applicable)\n", + " x, U = self.processor(x)\n", + "\n", + " # Pass through equilinear layer and particle attention blocks (passing p4 down)\n", + " x = self.encoder(x, padding_mask, U, p4=p4)\n", + "\n", + " # Classification (no masking in this case)\n", + " if not self.mask:\n", + " x_cls = self.cls_token.expand(B, -1, -1)\n", + "\n", + " # Decoder with class attention blocks\n", + " for layer in self.decoder:\n", + " x_cls = layer(x, x_cls, padding_mask)\n", + "\n", + " # MLP head for classification\n", + " x_cls = self.layernorm(x_cls).squeeze(1)\n", + " x_cls = self.classifier(x_cls)\n", + " output = self.act(x_cls) # (B, num_classes)\n", + "\n", + " return output\n", + " else:\n", + " x = x.view(B, -1) # (B, N * embed_dim)\n", + " x = self.fc(x) # (B, 16)\n", + " x = x.view(B, 1, 1, 16)\n", + " x, _ = self.equilinear(x) # (B, 1, 1, 16)\n", + " x = x.view(B, 16)\n", + " x = extract_vector(x) # (B, F)\n", + "\n", + " return x" + ], + "metadata": { + "id": "QRhMF1TP8Ymw" + }, + "id": "QRhMF1TP8Ymw", + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Gating Test" + ], + "metadata": { + "id": "UC2GQe3iR7Gt" + }, + "id": "UC2GQe3iR7Gt" + }, + { + "cell_type": "code", + "source": [ + "# 1. Initialize your config with gating enabled\n", + "test_config = LorentzParTConfig(\n", + " embed_dim=128,\n", + " num_heads=8,\n", + " num_layers=8,\n", + " attention={'use_gating': True},\n", + " mask=True\n", + ")\n", + "\n", + "# 2. Instantiate the model\n", + "model = LorentzParT(config=test_config)\n", + "\n", + "# 3. Verification checks\n", + "first_layer = model.encoder.encoder[0]\n", + "is_gated = isinstance(first_layer, ParticleAttentionBlock)\n", + "\n", + "print(f\"--- Gating Verification ---\")\n", + "print(f\"Encoder Layer 1 Type: {type(first_layer).__name__}\")\n", + "print(f\"Gating Active: {is_gated}\")\n", + "\n", + "if is_gated:\n", + " print(\"Success: The model is now using Attention Gating!\")\n", + "else:\n", + " print(\"Error: The model is still using standard Attention Blocks.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cKfNSeyPR9nH", + "outputId": "8ea6d1d4-605b-4ec4-b41e-f2a61eb0c23b" + }, + "id": "cKfNSeyPR9nH", + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--- Gating Verification ---\n", + "Encoder Layer 1 Type: ParticleAttentionBlock\n", + "Gating Active: True\n", + "Success: The model is now using Attention Gating!\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Pre-Train" + ], + "metadata": { + "id": "H_Kmp-s3SUDD" + }, + "id": "H_Kmp-s3SUDD" + }, + { + "cell_type": "markdown", + "source": [ + "#Using Self Supervised Weights" + ], + "metadata": { + "id": "5peLQ8txiFZG" + }, + "id": "5peLQ8txiFZG" + }, + { + "cell_type": "code", + "source": [ + "# Initialize configuration with Attention Gating enabled\n", + "#not changing name of ssl_model_config\n", + "ssl_model_config = LorentzParTConfig(\n", + " embed_dim=128,\n", + " num_heads=8,\n", + " num_layers=8,\n", + " hidden_mv_channels=8,\n", + " attention={'use_gating': True}, # This is the trigger for your new code\n", + " dropout=0.1,\n", + " expansion_factor=4,\n", + " max_num_particles=128,\n", + " num_particle_features=4,\n", + " pair_embed_dims=[64, 64, 64],\n", + " mask=True # Set to True for Self-Supervised Learning / Masked Training\n", + ")" + ], + "metadata": { + "id": "6fGbG9f1SXbc" + }, + "id": "6fGbG9f1SXbc", + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Create the model and move it to your device (GPU/CPU)\n", + "gatedmodel = LorentzParT(config=ssl_model_config)\n", + "gatedmodel.to(device)\n", + "gatedmodel" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b7g5btHCSZka", + "outputId": "a47e4f81-cd33-4b7c-a39a-7f074bf3c456" + }, + "id": "b7g5btHCSZka", + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LorentzParT(\n", + " (processor): ParticleProcessor()\n", + " (encoder): LorentzParTEncoder(\n", + " (equilinear): EquiLinear()\n", + " (proj): Linear(in_features=16, out_features=128, bias=True)\n", + " (interaction_embed): InteractionEmbedding(\n", + " (embed): Sequential(\n", + " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", + " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): GELU(approximate='none')\n", + " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (6): GELU(approximate='none')\n", + " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (9): GELU(approximate='none')\n", + " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", + " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (12): GELU(approximate='none')\n", + " )\n", + " )\n", + " (encoder): ModuleList(\n", + " (0-7): 8 x ParticleAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", + " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", + " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", + " (gate_proj): Linear(in_features=128, out_features=8, bias=True)\n", + " (pmha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.1, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", + " (equilinear): EquiLinear()\n", + " (decoder): ModuleList(\n", + " (0-1): 2 x ClassAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.0, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (classifier): Classifier(\n", + " (layers): Sequential(\n", + " (0): Linear(in_features=128, out_features=10, bias=True)\n", + " )\n", + " )\n", + " (act): Identity()\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "source": [ + "num_params = sum(p.numel() for p in gatedmodel.parameters() if p.requires_grad)\n", + "num_params" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n5GMAwY0Sw5t", + "outputId": "170f4cca-9434-4e28-d810-c0bf423f50a7" + }, + "id": "n5GMAwY0Sw5t", + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2290656" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0aa870ab", + "metadata": { + "id": "0aa870ab" + }, + "outputs": [], + "source": [ + "# Training configurations\n", + "gated_train_config = TrainConfig(\n", + " batch_size=128,\n", + " criterion={\n", + " 'name': 'conservation_loss',\n", + " 'kwargs': {\n", + " 'loss_coef': [0.25, 0.25, 0.25, 0.25],\n", + " 'reduction': 'mean'\n", + " }\n", + " },\n", + " optimizer={\n", + " 'name': 'adamw',\n", + " 'kwargs': {\n", + " 'lr': 1e-4\n", + " }\n", + " },\n", + " scheduler={\n", + " 'name': 'exponential_lr',\n", + " 'kwargs': {\n", + " 'gamma': 0.95\n", + " }\n", + " },\n", + " callbacks=[{\n", + " 'name': 'early_stopping',\n", + " 'kwargs': {\n", + " 'monitor': 'val_loss',\n", + " 'mode': 'min',\n", + " 'patience': 5\n", + " }\n", + " }],\n", + " num_epochs=1,#20 change\n", + " start_epoch=0,\n", + " logging_dir=str(LOG_DIR),\n", + " logging_steps=1000,\n", + " progress_bar=True,\n", + " save_best=True,\n", + " save_ckpt=True,\n", + " save_fig=False,\n", + " device='cuda',\n", + " num_workers=0,\n", + " pin_memory=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6d5f08dd", + "metadata": { + "id": "6d5f08dd" + }, + "outputs": [], + "source": [ + "# Initialize the trainer\n", + "trainer = MaskedModelTrainer(\n", + " model=gatedmodel,\n", + " train_dataset=train_dataset,\n", + " val_dataset=val_dataset,\n", + " test_dataset=test_dataset,\n", + " device=device,\n", + " config=gated_train_config\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "40a3b5fa", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 188, + "referenced_widgets": [ + "0e1aad635bf14d1bb31c6f903f032e73", + "4e34acdc629c4fd898b1bbd743cd4cc1", + "f0e15ef2bc25482e87a2aafd881171fb", + "649dcf365ee140778713f1923422b40c", + "e0ef6f1060d2402a93eafa4557cd7a7a", + "45a2e6bd3f464dd0af70d71813826f89", + "b1ef8826aa5f4e2896ee4ee4bd6f5e2d", + "cb4aa040e0fe4b0292c3dcda6cc55b66", + "ee23434b787f454399e4be9c6111a94c", + "3c0bd3f7f0034da7ba1b118da5d111eb", + "bad0b3bd64c743519cf1ec7eb6b3ba46" + ] + }, + "id": "40a3b5fa", + "outputId": "e38bc084-3ac9-47b3-ed52-af4af71a5b6e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Training: 0%| | 0/6250 [00:00" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Evaluate the model on the test set\n", + "test_loss, test_metric, y_true, y_pred = trainer.evaluate(plot=plot_particle_reconstruction)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Label names for classification\n", + "labels = [\n", + " \"$q/g$\", # 0\n", + " \"$H \\\\to b\\\\bar{b}$\", # 1\n", + " \"$H \\\\to c\\\\bar{c}$\", # 2\n", + " \"$H \\\\to gg$\", # 3\n", + " \"$H \\\\to 4q$\", # 4\n", + " \"$H \\\\to \\\\ell \\\\nu qq'$\", # 5\n", + " \"$Z \\\\to q\\\\bar{q}$\", # 6\n", + " \"$W \\\\to qq'$\", # 7\n", + " \"$t \\\\to b\\\\ell \\\\nu$\", # 8\n", + " \"$t \\\\to bqq'$\" # 9\n", + "]\n" + ], + "metadata": { + "id": "vt4vzC1TGBiB" + }, + "id": "vt4vzC1TGBiB", + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Datasets for classification\n", + "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode=None)\n", + "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode=None)\n", + "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode=None)" + ], + "metadata": { + "id": "m37_eDUuGD0l" + }, + "id": "m37_eDUuGD0l", + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from collections import Counter\n", + "import numpy as np\n", + "\n", + "def check_uniformity(y, dataset_name, label_names, threshold=0.02):\n", + " \"\"\"\n", + " Checks if the labels in a dataset are uniformly distributed.\n", + " Supports both integer class arrays and one-hot encoded arrays.\n", + " \"\"\"\n", + " # If one-hot encoded, convert to class indices\n", + " if len(y.shape) > 1 and y.shape[1] > 1:\n", + " y = np.argmax(y, axis=1)\n", + "\n", + " total_samples = len(y)\n", + " counts = Counter(y)\n", + " num_classes = len(label_names)\n", + " expected_pct = 1.0 / num_classes\n", + "\n", + " print(f\"--- Distribution for {dataset_name} ({total_samples} samples) ---\")\n", + "\n", + " is_uniform = True\n", + " for idx, name in enumerate(label_names):\n", + " count = counts.get(idx, 0)\n", + " actual_pct = count / total_samples\n", + " print(f\"Class {idx} ({name:<18}): {count:<8} | {actual_pct:.2%}\")\n", + "\n", + " # Check if it deviates more than the allowed threshold from absolute uniformity\n", + " if abs(actual_pct - expected_pct) > threshold:\n", + " is_uniform = False\n", + "\n", + " if is_uniform:\n", + " print(f\"✅ {dataset_name} appears to be uniformly distributed (within a {threshold:.1%} tolerance).\\n\")\n", + " else:\n", + " print(f\"⚠️ {dataset_name} is NOT perfectly uniform. Expected around {expected_pct:.2%} per class.\\n\")\n", + "\n", + "# Run the check on your datasets\n", + "# (Using your raw arrays y_train, y_val, and y_test)\n", + "check_uniformity(y_train, \"Train Dataset\", labels)\n", + "check_uniformity(y_val, \"Validation Dataset\", labels)\n", + "check_uniformity(y_test, \"Test Dataset\", labels)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IWCdSFFKGFoL", + "outputId": "c1769a19-a1db-473e-80a8-237fdc4867b1" + }, + "id": "IWCdSFFKGFoL", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--- Distribution for Train Dataset (800000 samples) ---\n", + "Class 0 ($q/g$ ): 80000 | 10.00%\n", + "Class 1 ($H \\to b\\bar{b}$ ): 80000 | 10.00%\n", + "Class 2 ($H \\to c\\bar{c}$ ): 80000 | 10.00%\n", + "Class 3 ($H \\to gg$ ): 80000 | 10.00%\n", + "Class 4 ($H \\to 4q$ ): 80000 | 10.00%\n", + "Class 5 ($H \\to \\ell \\nu qq'$): 80000 | 10.00%\n", + "Class 6 ($Z \\to q\\bar{q}$ ): 80000 | 10.00%\n", + "Class 7 ($W \\to qq'$ ): 80000 | 10.00%\n", + "Class 8 ($t \\to b\\ell \\nu$ ): 80000 | 10.00%\n", + "Class 9 ($t \\to bqq'$ ): 80000 | 10.00%\n", + "✅ Train Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", + "\n", + "--- Distribution for Validation Dataset (100000 samples) ---\n", + "Class 0 ($q/g$ ): 10000 | 10.00%\n", + "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", + "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", + "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", + "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", + "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", + "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", + "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", + "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", + "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", + "✅ Validation Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", + "\n", + "--- Distribution for Test Dataset (100000 samples) ---\n", + "Class 0 ($q/g$ ): 10000 | 10.00%\n", + "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", + "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", + "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", + "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", + "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", + "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", + "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", + "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", + "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", + "✅ Test Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Model configurations\n", + "pretrained_model_config = LorentzParTConfig(\n", + " num_classes=10,\n", + " embed_dim=128,\n", + " num_heads=8,\n", + " num_layers=8,\n", + " num_cls_layers=2,\n", + " num_mlp_layers=0,\n", + " hidden_dim=256,\n", + " hidden_mv_channels=8,\n", + " in_s_channels=None,\n", + " out_s_channels=None,\n", + " hidden_s_channels=16,\n", + " attention={},\n", + " mlp={},\n", + " dropout=0.1,\n", + " expansion_factor=4,\n", + " max_num_particles=128,\n", + " num_particle_features=4,\n", + " pair_embed_dims=[64, 64, 64],\n", + " weights=gated_pt_path\n", + ")" + ], + "metadata": { + "id": "oRHuimXTGIdx" + }, + "id": "oRHuimXTGIdx", + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Initialize the classifier model\n", + "pretrained_model = LorentzParT(config=pretrained_model_config).to(device)\n", + "pretrained_model" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Dv1oQ3tSGe3w", + "outputId": "93cf00a1-dc30-4880-8652-4adf5b6cccae" + }, + "id": "Dv1oQ3tSGe3w", + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LorentzParT(\n", + " (processor): ParticleProcessor()\n", + " (encoder): LorentzParTEncoder(\n", + " (equilinear): EquiLinear()\n", + " (proj): Linear(in_features=16, out_features=128, bias=True)\n", + " (interaction_embed): InteractionEmbedding(\n", + " (embed): Sequential(\n", + " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", + " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): GELU(approximate='none')\n", + " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (6): GELU(approximate='none')\n", + " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (9): GELU(approximate='none')\n", + " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", + " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (12): GELU(approximate='none')\n", + " )\n", + " )\n", + " (encoder): ModuleList(\n", + " (0-7): 8 x ParticleAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", + " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", + " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", + " (pmha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.1, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", + " (equilinear): EquiLinear()\n", + " (decoder): ModuleList(\n", + " (0-1): 2 x ClassAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.0, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (classifier): Classifier(\n", + " (layers): Sequential(\n", + " (0): Linear(in_features=128, out_features=10, bias=True)\n", + " )\n", + " )\n", + " (act): Identity()\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Count parameters in the model\n", + "num_params = sum(p.numel() for p in pretrained_model.parameters() if p.requires_grad)\n", + "num_params" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_TDNTq4hGL2e", + "outputId": "24c6d7c6-5565-44e1-ed00-17967f67ff76" + }, + "id": "_TDNTq4hGL2e", + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2282400" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Training configurations\n", + "pretrained_config = TrainConfig(\n", + " batch_size=64,\n", + " criterion={\n", + " 'name': 'cross_entropy_loss',\n", + " 'kwargs': {\n", + " 'reduction': 'mean'\n", + " }\n", + " },\n", + " optimizer={\n", + " 'name': 'adam',\n", + " 'kwargs': {\n", + " 'lr': 1e-4\n", + " }\n", + " },\n", + " scheduler={\n", + " 'name': 'exponential_lr',\n", + " 'kwargs': {\n", + " 'gamma': 0.95\n", + " }\n", + " },\n", + " callbacks=[{\n", + " 'name': 'early_stopping',\n", + " 'kwargs': {\n", + " 'monitor': 'val_loss',\n", + " 'mode': 'min',\n", + " 'patience': 5\n", + " }\n", + " }],\n", + " num_epochs=2,#change\n", + " start_epoch=0,\n", + " logging_dir=str(LOG_DIR),\n", + " logging_steps=1000,\n", + " save_best=True,\n", + " save_ckpt=True,\n", + " save_fig=False,\n", + " device='cuda',\n", + " num_workers=0,\n", + " pin_memory=True\n", + ")" + ], + "metadata": { + "id": "aaUtfajZGWD5" + }, + "id": "aaUtfajZGWD5", + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Initialize the trainer\n", + "trainer = Trainer(\n", + " model=pretrained_model,\n", + " train_dataset=train_dataset,\n", + " val_dataset=val_dataset,\n", + " test_dataset=test_dataset,\n", + " device=device,\n", + " metric=accuracy_metric_ce,\n", + " config=pretrained_config\n", + ")" + ], + "metadata": { + "id": "GBfMEnRrGler" + }, + "id": "GBfMEnRrGler", + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Train the model\n", + "pretrained_history, pretrained_model = trainer.train()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 518, + "referenced_widgets": [ + "62f237b1683e475595fe17da0edeae87", + "7bdac98dfc4a4699bab39fa846e56354", + "ac7d5153146d4bd89fa5587a1c4babb7", + "1d65d195b47147d3806f7735255878d8", + "34f4dd1f0b9b4920bffb1027ea6e11fe", + "38e5079824d648849dcde09e2a2948fd", + "ffa3569f77fb4a3c8a6fb08930c0defb", + "dd800993260d48e383fb9aa27d265c7d", + "4d4b43abf49f4114a7fc83d9128f6d30", + "0d7bc861ca364e298ffb26510a4e4e09", + "2c776b732e6a4cb8bb85e01baff5fb33" + ] + }, + "id": "q_3Zs8TfOwop", + "outputId": "ecbf6236-025e-47a4-f604-eb92b4fd72e0" + }, + "id": "q_3Zs8TfOwop", + "execution_count": 25, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Training: 0%| | 0/25000 [00:00" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#temporary visualisation" + ], + "metadata": { + "id": "LRrGO3dIFpIH" + }, + "id": "LRrGO3dIFpIH" + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "def plot_jet_graph(\n", + " p4: torch.Tensor,\n", + " u_matrix: torch.Tensor,\n", + " threshold: float = 0.1,\n", + " title: str = \"Reconstructed Particle Jet Graph\"\n", + "):\n", + " \"\"\"\n", + " p4: [N, 4] tensor of particle 4-momenta (E, px, py, pz)\n", + " u_matrix: [N, N] tensor of pairwise interaction weights or pooled attention\n", + " threshold: Cutoff value to display only significant edge connections\n", + " \"\"\"\n", + " # 1. Convert tensors to numpy\n", + " p4 = p4.detach().cpu().numpy()\n", + " u_adj = u_matrix.detach().cpu().numpy()\n", + "\n", + " N = p4.shape[0]\n", + "\n", + " # 2. Extract kinematics for node positions & sizes\n", + " E, px, py, pz = p4[:, 0], p4[:, 1], p4[:, 2], p4[:, 3]\n", + " pt = np.sqrt(px**2 + py**2)\n", + " p = np.sqrt(px**2 + py**2 + pz**2)\n", + "\n", + " # Pseudorapidity eta and Azimuthal angle phi\n", + " eta = 0.5 * np.log((p + pz + 1e-8) / (p - pz + 1e-8))\n", + " phi = np.arctan2(py, px)\n", + "\n", + " # 3. Create Graph\n", + " G = nx.Graph()\n", + " for i in range(N):\n", + " G.add_node(i, eta=eta[i], phi=phi[i], pt=pt[i])\n", + "\n", + " # 4. Add Edges based on interaction threshold\n", + " for i in range(N):\n", + " for j in range(i + 1, N):\n", + " weight = u_adj[i, j]\n", + " if weight > threshold:\n", + " G.add_edge(i, j, weight=weight)\n", + "\n", + " # 5. Plot Graph\n", + " fig, ax = plt.subplots(figsize=(9, 7))\n", + "\n", + " pos = {i: (eta[i], phi[i]) for i in range(N)} # Spatial positioning (eta, phi)\n", + " node_sizes = (pt / pt.max()) * 600 + 50 # Scale nodes by transverse momentum (pT)\n", + "\n", + " # Extract edge weights for line thickness\n", + " edges = G.edges()\n", + " weights = [G[u][v]['weight'] for u, v in edges]\n", + " edge_alpha = [min(w, 1.0) for w in weights]\n", + "\n", + " # Draw Nodes & Edges\n", + " nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color=pt, cmap=plt.cm.plasma, alpha=0.9, ax=ax)\n", + " nx.draw_networkx_edges(G, pos, width=[w * 3 for w in weights], alpha=0.6, edge_color=\"gray\", ax=ax)\n", + "\n", + " ax.set_xlabel(r\"Pseudorapidity ($\\eta$)\", fontsize=12)\n", + " ax.set_ylabel(r\"Azimuthal Angle ($\\phi$)\", fontsize=12)\n", + " ax.set_title(title, fontsize=14, fontweight=\"bold\")\n", + " ax.grid(True, linestyle=\"--\", alpha=0.4)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# Example Usage:\n", + "# p4_dummy = torch.randn(25, 4) # 25 particles\n", + "# U_dummy = torch.rand(25, 25) # 25x25 interaction matrix\n", + "# plot_jet_graph(p4_dummy, U_dummy, threshold=0.3)" + ], + "metadata": { + "id": "jTOdIm39MChC" + }, + "id": "jTOdIm39MChC", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "p4_dummy = torch.randn(25, 4) # 25 particles\n", + "U_dummy = torch.rand(25, 25) # 25x25 interaction matrix\n", + "plot_jet_graph(p4_dummy, U_dummy, threshold=0.3)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 707 + }, + "id": "ITK32AZRF1t1", + "outputId": "ccb4d48c-a86a-4a63-97ae-03ab9723d577" + }, + "id": "ITK32AZRF1t1", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import seaborn as sns\n", + "\n", + "def plot_attention_gating_comparison(\n", + " raw_u: torch.Tensor,\n", + " gated_u: torch.Tensor,\n", + " head_idx: int = 0\n", + "):\n", + " \"\"\"\n", + " raw_u: [H, N, N] raw interaction matrix for a single event\n", + " gated_u: [H, N, N] interaction matrix after applying the gate filter\n", + " head_idx: Which attention head to inspect\n", + " \"\"\"\n", + " raw = raw_u[head_idx].detach().cpu().numpy()\n", + " gated = gated_u[head_idx].detach().cpu().numpy()\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", + "\n", + " sns.heatmap(raw, ax=axes[0], cmap=\"viridis\", cbar=True, square=True)\n", + " axes[0].set_title(f\"Raw U-Matrix Interaction (Head {head_idx})\", fontsize=12)\n", + " axes[0].set_xlabel(\"Particle Index $j$\")\n", + " axes[0].set_ylabel(\"Particle Index $i$\")\n", + "\n", + " sns.heatmap(gated, ax=axes[1], cmap=\"magma\", cbar=True, square=True)\n", + " axes[1].set_title(f\"Gated & Mass-Biased U-Matrix (Head {head_idx})\", fontsize=12)\n", + " axes[1].set_xlabel(\"Particle Index $j$\")\n", + " axes[1].set_ylabel(\"Particle Index $i$\")\n", + "\n", + " plt.tight_layout()\n", + " plt.show()" + ], + "metadata": { + "id": "CnAdSaysGx9m" + }, + "id": "CnAdSaysGx9m", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "def plot_3d_jet_reconstruction(p4: torch.Tensor, u_matrix: torch.Tensor, threshold: float = 0.25):\n", + " p4_np = p4.detach().cpu().numpy()\n", + " u_np = u_matrix.detach().cpu().numpy()\n", + "\n", + " px, py, pz = p4_np[:, 1], p4_np[:, 2], p4_np[:, 3]\n", + " N = len(px)\n", + "\n", + " fig = plt.figure(figsize=(10, 8))\n", + " ax = fig.add_subplot(111, projection='3d')\n", + "\n", + " # Scatter particle momenta\n", + " sc = ax.scatter(px, py, pz, c=p4_np[:, 0], cmap='jet', s=100, depthshade=True)\n", + " fig.colorbar(sc, ax=ax, label='Energy (E)')\n", + "\n", + " # Draw interaction lines\n", + " for i in range(N):\n", + " for j in range(i + 1, N):\n", + " if u_np[i, j] > threshold:\n", + " ax.plot(\n", + " [px[i], px[j]],\n", + " [py[i], py[j]],\n", + " [pz[i], pz[j]],\n", + " color='black',\n", + " alpha=0.3,\n", + " linewidth=u_np[i, j] * 2\n", + " )\n", + "\n", + " ax.set_xlabel('$p_x$ (GeV)')\n", + " ax.set_ylabel('$p_y$ (GeV)')\n", + " ax.set_zlabel('$p_z$ (GeV)')\n", + " ax.set_title('3D Kinematic Jet Interaction Graph', fontsize=14)\n", + "\n", + " plt.show()" + ], + "metadata": { + "id": "-73lrC4UG6Qn" + }, + "id": "-73lrC4UG6Qn", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def plot_head_layer_gate_distribution(layer_head_gate_means):\n", + " \"\"\"\n", + " layer_head_gate_means: [Num_Layers, Num_Heads] numpy array\n", + " containing average gate activation per head across dataset\n", + " \"\"\"\n", + " plt.figure(figsize=(10, 6))\n", + "\n", + " sns.heatmap(\n", + " layer_head_gate_means,\n", + " annot=True,\n", + " fmt=\".2f\",\n", + " cmap=\"coolwarm\",\n", + " cbar_kws={'label': 'Mean Gate Value (0 = Closed, 1 = Open)'},\n", + " vmin=0.0,\n", + " vmax=1.0\n", + " )\n", + "\n", + " plt.xlabel(\"Attention Head Index\", fontsize=12)\n", + " plt.ylabel(\"Transformer Layer Index\", fontsize=12)\n", + " plt.title(\"Attention Head Specialization Across Network Layers\", fontsize=14, fontweight=\"bold\")\n", + " plt.tight_layout()\n", + " plt.show()" + ], + "metadata": { + "id": "0BSz7sXbHTht" + }, + "id": "0BSz7sXbHTht", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def plot_background_rejection(y_true, y_pred, target_tpr=0.5, class_names=None):\n", + " \"\"\"\n", + " Plots 1 / FPR for each jet category at a fixed True Positive Rate (e.g., 50% signal efficiency)\n", + " \"\"\"\n", + " y_true = np.array(y_true)\n", + " y_pred = np.array(y_pred)\n", + " num_classes = y_pred.shape[1]\n", + "\n", + " if y_true.ndim == 1:\n", + " y_true_bin = label_binarize(y_true, classes=range(num_classes))\n", + " else:\n", + " y_true_bin = y_true\n", + "\n", + " if class_names is None:\n", + " class_names = [f\"Class {i}\" for i in range(num_classes)]\n", + "\n", + " rejections = []\n", + "\n", + " for i in range(num_classes):\n", + " fpr, tpr, _ = roc_curve(y_true_bin[:, i], y_pred[:, i])\n", + " # Find FPR corresponding to TPR closest to target_tpr\n", + " idx = np.argmin(np.abs(tpr - target_tpr))\n", + " fpr_at_target = fpr[idx] if fpr[idx] > 0 else 1e-6\n", + " rejection = 1.0 / fpr_at_target\n", + " rejections.append(rejection)\n", + "\n", + " plt.figure(figsize=(10, 5))\n", + " bars = plt.bar(class_names, rejections, color='teal', edgecolor='black', alpha=0.8)\n", + "\n", + " plt.yscale('log') # Physics rejections span multiple orders of magnitude\n", + " plt.ylabel(f'Background Rejection (1 / FPR) @ {int(target_tpr*100)}% TPR', fontsize=12)\n", + " plt.title(f'Background Rejection Performance per Jet Category', fontsize=14, fontweight='bold')\n", + " plt.xticks(rotation=30, ha='right')\n", + " plt.grid(axis='y', which='both', linestyle='--', alpha=0.4)\n", + "\n", + " # Annotate bar values\n", + " for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width()/2., height * 1.1,\n", + " f'{int(height)}x', ha='center', va='bottom', fontsize=9, fontweight='bold')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "def plot_background_rejection(y_true, y_pred, target_tpr=0.5, class_names=None):\n", + " \"\"\"\n", + " Plots 1 / FPR for each jet category at a fixed True Positive Rate (e.g., 50% signal efficiency)\n", + " \"\"\"\n", + " y_true = np.array(y_true)\n", + " y_pred = np.array(y_pred)\n", + " num_classes = y_pred.shape[1]\n", + "\n", + " if y_true.ndim == 1:\n", + " y_true_bin = label_binarize(y_true, classes=range(num_classes))\n", + " else:\n", + " y_true_bin = y_true\n", + "\n", + " if class_names is None:\n", + " class_names = [f\"Class {i}\" for i in range(num_classes)]\n", + "\n", + " rejections = []\n", + "\n", + " for i in range(num_classes):\n", + " fpr, tpr, _ = roc_curve(y_true_bin[:, i], y_pred[:, i])\n", + " # Find FPR corresponding to TPR closest to target_tpr\n", + " idx = np.argmin(np.abs(tpr - target_tpr))\n", + " fpr_at_target = fpr[idx] if fpr[idx] > 0 else 1e-6\n", + " rejection = 1.0 / fpr_at_target\n", + " rejections.append(rejection)\n", + "\n", + " plt.figure(figsize=(10, 5))\n", + " bars = plt.bar(class_names, rejections, color='teal', edgecolor='black', alpha=0.8)\n", + "\n", + " plt.yscale('log') # Physics rejections span multiple orders of magnitude\n", + " plt.ylabel(f'Background Rejection (1 / FPR) @ {int(target_tpr*100)}% TPR', fontsize=12)\n", + " plt.title(f'Background Rejection Performance per Jet Category', fontsize=14, fontweight='bold')\n", + " plt.xticks(rotation=30, ha='right')\n", + " plt.grid(axis='y', which='both', linestyle='--', alpha=0.4)\n", + "\n", + " # Annotate bar values\n", + " for bar in bars:\n", + " height = bar.get_height()\n", + " plt.text(bar.get_x() + bar.get_width()/2., height * 1.1,\n", + " f'{int(height)}x', ha='center', va='bottom', fontsize=9, fontweight='bold')\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "from sklearn.calibration import calibration_curve\n", + "\n", + "def plot_model_calibration(y_true, y_pred, n_bins=10):\n", + " \"\"\"\n", + " Plots Reliability Diagram (Calibration Curve) for model predictions\n", + " \"\"\"\n", + " y_true = np.array(y_true)\n", + " y_pred = np.array(y_pred)\n", + "\n", + " # Flatten across all classes to see overall model confidence reliability\n", + " if y_true.ndim == 1:\n", + " num_classes = y_pred.shape[1]\n", + " y_true_bin = label_binarize(y_true, classes=range(num_classes))\n", + " else:\n", + " y_true_bin = y_true\n", + "\n", + " prob_true, prob_pred = calibration_curve(y_true_bin.ravel(), y_pred.ravel(), n_bins=n_bins)\n", + "\n", + " fig, ax1 = plt.subplots(figsize=(8, 6))\n", + "\n", + " ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly Calibrated\")\n", + " ax1.plot(prob_pred, prob_true, \"s-\", color=\"darkorange\", label=\"Gated LorentzParT\")\n", + " ax1.set_xlabel(\"Mean Predicted Probability (Confidence)\", fontsize=12)\n", + " ax1.set_ylabel(\"Fraction of Positives (Actual Accuracy)\", fontsize=12)\n", + " ax1.set_title(\"Probability Calibration (Reliability Diagram)\", fontsize=14, fontweight='bold')\n", + " ax1.legend(loc=\"lower right\")\n", + " ax1.grid(True, linestyle='--', alpha=0.5)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "\n", + "import seaborn as sns\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "def plot_normalized_confusion_matrix(y_true, y_pred, class_names=None):\n", + " y_true = np.array(y_true)\n", + " y_pred = np.array(y_pred)\n", + "\n", + " if y_true.ndim > 1:\n", + " y_true_labels = np.argmax(y_true, axis=1)\n", + " else:\n", + " y_true_labels = y_true\n", + "\n", + " y_pred_labels = np.argmax(y_pred, axis=1)\n", + "\n", + " cm = confusion_matrix(y_true_labels, y_pred_labels, normalize='true')\n", + "\n", + " plt.figure(figsize=(9, 7))\n", + " sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues',\n", + " xticklabels=class_names, yticklabels=class_names, square=True)\n", + "\n", + " plt.xlabel('Predicted Jet Class', fontsize=12)\n", + " plt.ylabel('True Jet Class', fontsize=12)\n", + " plt.title('Normalized Confusion Matrix (Inference Accuracy)', fontsize=14, fontweight='bold')\n", + " plt.tight_layout()\n", + " plt.show()" + ], + "metadata": { + "id": "84a5eDF5H6fr" + }, + "id": "84a5eDF5H6fr", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# JetClass 10 benchmark categories\n", + "jetclass_labels = [\n", + " 'g', 'q', 'W-CQ', 'W-QQ', 'Z-BB', 'Z-CC', 'Z-QQ', 'H-BB', 'H-CC', 'Top-CQB'\n", + "]\n", + "\n", + "# Run evaluation\n", + "test_loss, test_metric, y_true, y_pred = trainer.evaluate(plot=plot_particle_reconstruction)\n", + "\n", + "print(f\"--> Test Loss: {test_loss:.4f} | Test Accuracy/Metric: {test_metric:.4f}\")\n", + "\n", + "# Generate meaningful performance visualizer graphs\n", + "plot_gated_roc_curves(y_true, y_pred, class_names=jetclass_labels)\n", + "plot_background_rejection(y_true, y_pred, target_tpr=0.5, class_names=jetclass_labels)\n", + "plot_model_calibration(y_true, y_pred)\n", + "plot_normalized_confusion_matrix(y_true, y_pred, class_names=jetclass_labels)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 332 + }, + "id": "LPgZhDfuJNFd", + "outputId": "83dce0e9-a0bf-44f2-92ae-b87e97e15741" + }, + "id": "LPgZhDfuJNFd", + "execution_count": null, + "outputs": [ + { + "output_type": "error", + "ename": "TypeError", + "evalue": "Trainer.evaluate() missing 1 required positional argument: 'loss_type'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_1527/2727640763.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Run evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_metric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot_particle_reconstruction\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 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"--> Test Loss: {test_loss:.4f} | Test Accuracy/Metric: {test_metric:.4f}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;31m# pyrefly: ignore [bad-context-manager]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: Trainer.evaluate() missing 1 required positional argument: 'loss_type'" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "o3B6Sr9sJO_f" + }, + "id": "o3B6Sr9sJO_f", + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + }, + "colab": { + "provenance": [], + "gpuType": "A100" + }, + "accelerator": "GPU", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "41be1797614f4fa7830532d34158ac52": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8aac226632524370abc787be435a9b48", + "IPY_MODEL_e24c1cf41fcd442eb81ef418e92b24c6", + "IPY_MODEL_dd026d80116749d19365780681500be9" + ], + "layout": "IPY_MODEL_b1703412347941edaab104e289f38221" + } + }, + "8aac226632524370abc787be435a9b48": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2cab2b54d039403daa901bbb65203e42", + "placeholder": "​", + "style": "IPY_MODEL_8f5881e63ed54097b49c624676055437", + "value": "Training: 100%" + } + }, + "e24c1cf41fcd442eb81ef418e92b24c6": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_48e35659a78b41cb95b8cbb39040de62", + "max": 25000, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ff02466aec4344fa84813bb3eba626b1", + "value": 25000 + } + }, + "dd026d80116749d19365780681500be9": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_53c475a9562d4af59d6947020af35778", + "placeholder": "​", + "style": "IPY_MODEL_9e9b3731cd824b0094835c6ce06f9265", + "value": " 25000/25000 [24:47<00:00, 17.01it/s, epoch=2/2, avg_loss=1.8927, avg_metric=0.2941]" + } + }, + "b1703412347941edaab104e289f38221": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "2cab2b54d039403daa901bbb65203e42": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8f5881e63ed54097b49c624676055437": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "48e35659a78b41cb95b8cbb39040de62": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ff02466aec4344fa84813bb3eba626b1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "53c475a9562d4af59d6947020af35778": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9e9b3731cd824b0094835c6ce06f9265": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0e1aad635bf14d1bb31c6f903f032e73": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4e34acdc629c4fd898b1bbd743cd4cc1", + "IPY_MODEL_f0e15ef2bc25482e87a2aafd881171fb", + "IPY_MODEL_649dcf365ee140778713f1923422b40c" + ], + "layout": "IPY_MODEL_e0ef6f1060d2402a93eafa4557cd7a7a" + } + }, + "4e34acdc629c4fd898b1bbd743cd4cc1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_45a2e6bd3f464dd0af70d71813826f89", + "placeholder": "​", + "style": "IPY_MODEL_b1ef8826aa5f4e2896ee4ee4bd6f5e2d", + "value": "Training: 100%" + } + }, + "f0e15ef2bc25482e87a2aafd881171fb": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_cb4aa040e0fe4b0292c3dcda6cc55b66", + "max": 6250, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ee23434b787f454399e4be9c6111a94c", + "value": 6250 + } + }, + "649dcf365ee140778713f1923422b40c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3c0bd3f7f0034da7ba1b118da5d111eb", + "placeholder": "​", + "style": "IPY_MODEL_bad0b3bd64c743519cf1ec7eb6b3ba46", + "value": " 6250/6250 [39:03<00:00,  8.20it/s, epoch=1/1, avg_loss=0.2017]" + } + }, + "e0ef6f1060d2402a93eafa4557cd7a7a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "45a2e6bd3f464dd0af70d71813826f89": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b1ef8826aa5f4e2896ee4ee4bd6f5e2d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "cb4aa040e0fe4b0292c3dcda6cc55b66": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ee23434b787f454399e4be9c6111a94c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3c0bd3f7f0034da7ba1b118da5d111eb": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "bad0b3bd64c743519cf1ec7eb6b3ba46": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "62f237b1683e475595fe17da0edeae87": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7bdac98dfc4a4699bab39fa846e56354", + "IPY_MODEL_ac7d5153146d4bd89fa5587a1c4babb7", + "IPY_MODEL_1d65d195b47147d3806f7735255878d8" + ], + "layout": "IPY_MODEL_34f4dd1f0b9b4920bffb1027ea6e11fe" + } + }, + "7bdac98dfc4a4699bab39fa846e56354": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_38e5079824d648849dcde09e2a2948fd", + "placeholder": "​", + "style": "IPY_MODEL_ffa3569f77fb4a3c8a6fb08930c0defb", + "value": "Training: 100%" + } + }, + "ac7d5153146d4bd89fa5587a1c4babb7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_dd800993260d48e383fb9aa27d265c7d", + "max": 25000, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_4d4b43abf49f4114a7fc83d9128f6d30", + "value": 25000 + } + }, + "1d65d195b47147d3806f7735255878d8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0d7bc861ca364e298ffb26510a4e4e09", + "placeholder": "​", + "style": "IPY_MODEL_2c776b732e6a4cb8bb85e01baff5fb33", + "value": " 25000/25000 [25:07<00:00, 17.45it/s, epoch=2/2, avg_loss=1.9639, avg_metric=0.2580]" + } + }, + "34f4dd1f0b9b4920bffb1027ea6e11fe": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "38e5079824d648849dcde09e2a2948fd": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ffa3569f77fb4a3c8a6fb08930c0defb": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "dd800993260d48e383fb9aa27d265c7d": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4d4b43abf49f4114a7fc83d9128f6d30": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0d7bc861ca364e298ffb26510a4e4e09": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2c776b732e6a4cb8bb85e01baff5fb33": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 15ba63a50c54ed4c2aebb9ec43af344c09e5cb4d Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 20:40:19 +0530 Subject: [PATCH 04/11] Delete MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT (2).ipynb --- .../notebook/PAG_LorentzParT (2).ipynb | 3340 ----------------- 1 file changed, 3340 deletions(-) delete mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT (2).ipynb diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT (2).ipynb b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT (2).ipynb deleted file mode 100644 index 43e9a3f..0000000 --- a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT (2).ipynb +++ /dev/null @@ -1,3340 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "source": [ - "\n", - "# 1. Clone the ML4SCI/CMS repository\n", - "!git clone https://github.com/ML4SCI/CMS.git\n", - "\n", - "# 2. Navigate to the specific Hybrid Transformer project directory\n", - "%cd CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", - "\n", - "# 3. Install required libraries\n", - "!pip install lgatr uproot awkward tqdm vector" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "B7YmCrpkJSAg", - "outputId": "f6eeec54-0a0e-416f-e730-dcf49f66d6f7" - }, - "id": "B7YmCrpkJSAg", - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Cloning into 'CMS'...\n", - "remote: Enumerating objects: 751, done.\u001b[K\n", - "remote: Counting objects: 100% (130/130), done.\u001b[K\n", - "remote: Compressing objects: 100% (99/99), done.\u001b[K\n", - "remote: Total 751 (delta 39), reused 83 (delta 27), pack-reused 621 (from 2)\u001b[K\n", - "Receiving objects: 100% (751/751), 330.16 MiB | 18.70 MiB/s, done.\n", - "Resolving deltas: 100% (187/187), done.\n", - "Updating files: 100% (531/531), done.\n", - "/content/CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", - "Collecting lgatr\n", - " Downloading lgatr-1.4.4-py3-none-any.whl.metadata (8.7 kB)\n", - "Collecting uproot\n", - " Downloading uproot-5.7.5-py3-none-any.whl.metadata (35 kB)\n", - "Collecting awkward\n", - " Downloading awkward-2.11.0-py3-none-any.whl.metadata (7.6 kB)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (4.67.3)\n", - "Collecting vector\n", - " Downloading vector-1.8.1-py3-none-any.whl.metadata (15 kB)\n", - "Requirement already satisfied: torch>=2.1 in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.11.0+cu128)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.0.2)\n", - "Requirement already satisfied: einops in /usr/local/lib/python3.12/dist-packages (from lgatr) (0.8.2)\n", - "Requirement already satisfied: opt_einsum in /usr/local/lib/python3.12/dist-packages (from lgatr) (3.4.0)\n", - "Requirement already satisfied: cramjam>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2.11.0)\n", - "Requirement already satisfied: fsspec!=2026.2.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2025.3.0)\n", - "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from uproot) (26.2)\n", - "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from uproot) (3.8.1)\n", - "Collecting awkward-cpp==54 (from awkward)\n", - " Downloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.29.7)\n", - "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (4.16.0)\n", - "Requirement already satisfied: setuptools<82 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (75.2.0)\n", - "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (1.14.0)\n", - "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.1)\n", - "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.1.6)\n", - "Requirement already satisfied: cuda-toolkit==12.8.1 in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.1)\n", - "Requirement already satisfied: cuda-bindings<13,>=12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (12.9.7)\n", - "Requirement already satisfied: nvidia-cudnn-cu12==9.19.0.56 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (9.19.0.56)\n", - "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (0.7.1)\n", - "Requirement already satisfied: nvidia-nccl-cu12==2.28.9 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (2.28.9)\n", - "Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.4.5)\n", - "Requirement already satisfied: triton==3.6.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.0)\n", - "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.4.1)\n", - "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", - "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.3.3.83)\n", - "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (1.13.1.3)\n", - "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", - "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (10.3.9.90)\n", - "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.7.3.90)\n", - "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.5.8.93)\n", - "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", - "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", - "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", - "Requirement already satisfied: cuda-pathfinder~=1.1 in /usr/local/lib/python3.12/dist-packages (from cuda-bindings<13,>=12.9.4->torch>=2.1->lgatr) (1.5.6)\n", - "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=2.1->lgatr) (1.3.0)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=2.1->lgatr) (3.0.3)\n", - "Downloading lgatr-1.4.4-py3-none-any.whl (60 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.6/60.6 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading uproot-5.7.5-py3-none-any.whl (401 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m35.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading awkward-2.11.0-py3-none-any.whl (974 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m974.8/974.8 kB\u001b[0m \u001b[31m76.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (689 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m689.3/689.3 kB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading vector-1.8.1-py3-none-any.whl (182 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.7/182.7 kB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: vector, awkward-cpp, awkward, uproot, lgatr\n", - "Successfully installed awkward-2.11.0 awkward-cpp-54 lgatr-1.4.4 uproot-5.7.5 vector-1.8.1\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# --- Default libraries ---\n", - "import os\n", - "import warnings\n", - "from pathlib import Path\n", - "\n", - "# --- Working directory ---\n", - "PROJECT_DIR = Path().resolve()\n", - "PROJECT_ROOT_NAME = 'Hybrid_Transformer_Thanh_Nguyen'\n", - "\n", - "while PROJECT_DIR.name != PROJECT_ROOT_NAME and PROJECT_DIR != PROJECT_DIR.parent:\n", - " PROJECT_DIR = PROJECT_DIR.parent\n", - "\n", - "if Path().resolve() != PROJECT_DIR:\n", - " os.chdir(PROJECT_DIR)\n", - "\n", - "DATA_DIR = PROJECT_DIR / 'data'\n", - "LOG_DIR = PROJECT_DIR / 'logs'\n", - "\n", - "# --- Data preprocessing & visualization ---\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# --- Deep learning ---\n", - "import torch\n", - "\n", - "# --- Custom modules ---\n", - "from src.configs import LorentzParTConfig, TrainConfig\n", - "from src.engine import MaskedModelTrainer, Trainer\n", - "from src.models import LorentzParT\n", - "from src.utils import accuracy_metric_ce, set_seed\n", - "from src.utils.data import JetClassDataset, compute_norm_stats, read_file\n", - "from src.utils.viz import *\n", - "\n", - "# --- Settings ---\n", - "warnings.filterwarnings('ignore')\n", - "set_seed(42)\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "device" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IvxBxUicRu18", - "outputId": "3241b771-4383-417a-cfc1-963899fb5dd4" - }, - "id": "IvxBxUicRu18", - "execution_count": 2, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "device(type='cuda')" - ] - }, - "metadata": {}, - "execution_count": 2 - } - ] - }, - { - "cell_type": "code", - "source": [ - "'''\n", - "drive_dest_folder = '/content/drive/MyDrive/GSOC/dara'\n", - "drive_dest_path = os.path.join(drive_dest_folder, out_file)\n", - "\n", - "# Create the directory if it doesn't exist\n", - "if not os.path.exists(drive_dest_folder):\n", - " os.makedirs(drive_dest_folder)\n", - " print(f\"Created directory: {drive_dest_folder}\")\n", - "\n", - "# 3. Move the verified file to Drive\n", - "if os.path.exists(out_file):\n", - " print(f\"Moving {out_file} to Google Drive...\")\n", - " shutil.move(out_file, drive_dest_path)\n", - " print(f\"File successfully moved to: {drive_dest_path}\")\n", - "else:\n", - " print(\"Source file not found. Check if the download was successful.\")''''" - ], - "metadata": { - "collapsed": true, - "id": "QfAWZ8A3cdNR" - }, - "id": "QfAWZ8A3cdNR", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#Loading Dpendencies" - ], - "metadata": { - "id": "BmBDXDMSD61u" - }, - "id": "BmBDXDMSD61u" - }, - { - "cell_type": "code", - "source": [ - "from typing import List, Tuple, Dict, Optional\n", - "\n", - "import torch\n", - "from torch import nn, Tensor\n", - "from lgatr.interface import extract_vector\n", - "from lgatr.layers import EquiLinear\n", - "\n", - "from src.models.classifier import ClassAttentionBlock, Classifier\n", - "from src.models.feedforward import Feedforward\n", - "from src.models.particle_transformer import ParticleAttentionBlock\n", - "from src.models.processor import InteractionEmbedding, ParticleProcessor\n", - "from src.configs import LorentzParTConfig\n", - "from lgatr.interface import extract_vector\n", - "from lgatr.layers import EquiLinear\n", - "\n", - "\n" - ], - "metadata": { - "id": "jTbtwxdtAt9S" - }, - "id": "jTbtwxdtAt9S", - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#Data Preparation" - ], - "metadata": { - "id": "VB1JTgpxDv-4" - }, - "id": "VB1JTgpxDv-4" - }, - { - "cell_type": "code", - "source": [ - "import os\n", - "from pathlib import Path\n", - "import numpy as np\n", - "from sklearn.model_selection import train_test_split\n", - "from src.utils.data import JetClassDataset, compute_norm_stats\n", - "\n", - "# --- 1. Mount Google Drive (If not already done) ---\n", - "from google.colab import drive\n", - "if not os.path.exists('/content/drive'):\n", - " drive.mount('/content/drive')\n", - "\n", - "# --- 2. Locate and Load the Compressed Archive ---\n", - "DRIVE_FILE_PATH = '/content/drive/MyDrive/GSOC/dara/jetclass_balanced_1M.npz'\n", - "\n", - "print(\"=\" * 70)\n", - "print(f\"LOADING SERIALIZED ARRAYS FROM DRIVE\")\n", - "print(\"=\" * 70)\n", - "\n", - "if os.path.exists(DRIVE_FILE_PATH):\n", - " # Load using memory-mapping for high-speed indexing\n", - " data_archive = np.load(DRIVE_FILE_PATH, mmap_mode='r')\n", - "\n", - " X_particles = data_archive['X_particles']\n", - " X_jets = data_archive['X_jets']\n", - " y = data_archive['Y']\n", - "\n", - " print(\"SUCCESS: Data loaded cleanly into RAM!\")\n", - " print(f\" -> X_particles matrix shape : {X_particles.shape}\")\n", - " print(f\" -> X_jets matrix shape : {X_jets.shape}\")\n", - " print(f\" -> y (Labels) matrix shape : {y.shape}\")\n", - " print(\"=\" * 70 + \"\\n\")\n", - "else:\n", - " raise FileNotFoundError(f\"ERROR: Could not find the file at {DRIVE_FILE_PATH}\")\n", - "\n", - "# --- 3. Split the Balanced Dataset Safely ---\n", - "# We enforce stratify=y to lock in your strict 10% balance across all splits\n", - "X_train, X_val, y_train, y_val = train_test_split(X_particles, y, test_size=0.2, random_state=42, stratify=y)\n", - "X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.5, random_state=42, stratify=y_val)\n", - "\n", - "# --- 4. Re-Initialize JetClass Dataset Objects ---\n", - "normalize = [True, False, False, True]\n", - "norm_dict = compute_norm_stats(X_train)\n", - "\n", - "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode='biased')\n", - "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode='biased')\n", - "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode='first')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9RBH34OYBoZ7", - "outputId": "a53a1da2-e44b-4611-cd4e-f610d3cce289" - }, - "id": "9RBH34OYBoZ7", - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n", - "======================================================================\n", - "LOADING SERIALIZED ARRAYS FROM DRIVE\n", - "======================================================================\n", - "SUCCESS: Data loaded cleanly into RAM!\n", - " -> X_particles matrix shape : (1000000, 4, 128)\n", - " -> X_jets matrix shape : (1000000, 4)\n", - " -> y (Labels) matrix shape : (1000000, 10)\n", - "======================================================================\n", - "\n", - "pt_mean: 92.70597076416016, pt_std: 105.79937744140625\n", - "eta_mean: -0.0011634620605036616, eta_std: 0.9182536005973816\n", - "phi_mean: -0.0006678671925328672, phi_std: 1.8138455152511597\n", - "E_mean: 133.98568725585938, E_std: 167.7259979248047\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "id": "edb52e95", - "metadata": { - "id": "edb52e95" - }, - "source": [ - "## Applying Gated Attention LorentzPart" - ] - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "from torch import Tensor\n", - "from typing import Optional\n", - "\n", - "class ParticleAttentionBlock(nn.Module):\n", - " def __init__(\n", - " self,\n", - " embed_dim: int = 128,\n", - " num_heads: int = 8,\n", - " dropout: float = 0.1,\n", - " expansion_factor: int = 4,\n", - " gate_type: Optional[str] = \"headwise\",\n", - " ):\n", - " super(ParticleAttentionBlock, self).__init__()\n", - " assert embed_dim % num_heads == 0, \"embed_dim must be divisible by num_heads\"\n", - "\n", - " self.embed_dim = embed_dim\n", - " self.num_heads = num_heads\n", - " self.head_dim = embed_dim // num_heads\n", - " self.gate_type = gate_type\n", - "\n", - " self.layernorm1 = nn.LayerNorm(embed_dim)\n", - " self.mass_norm = nn.LayerNorm(1)#norm\n", - "\n", - " # Project pooled interaction head-features to match token embedding dimensions\n", - " self.physics_proj = nn.Linear(num_heads, embed_dim)\n", - "\n", - " # Project the global scalar invariant mass squared (m2) to the embedding space\n", - " self.mass_proj = nn.Linear(1, embed_dim)\n", - "\n", - " # Gating projections accept physics-fused representations\n", - " if self.gate_type == \"headwise\":\n", - " self.gate_proj = nn.Linear(embed_dim, num_heads)\n", - " elif self.gate_type == \"elementwise\":\n", - " self.gate_proj = nn.Linear(embed_dim, embed_dim)\n", - "\n", - " self.pmha = nn.MultiheadAttention(\n", - " embed_dim=embed_dim,\n", - " num_heads=num_heads,\n", - " dropout=dropout,\n", - " batch_first=True\n", - " )\n", - "\n", - " self.layernorm2 = nn.LayerNorm(embed_dim)\n", - " self.dropout = nn.Dropout(dropout)\n", - "\n", - " self.feedforward = Feedforward(\n", - " embed_dim=embed_dim,\n", - " expansion_factor=expansion_factor,\n", - " dropout=dropout\n", - " )\n", - "\n", - " def forward(self, x: Tensor, padding_mask: Tensor, U: Optional[Tensor] = None, p4: Optional[Tensor] = None) -> Tensor:\n", - " residual = x\n", - " B_size, N_particles, _ = x.shape\n", - "\n", - " # 1. Standard token serialization\n", - " x_norm = self.layernorm1(x)\n", - "\n", - " # 2. FIXED: U is ALREADY [Batch * Heads, N, N]. Pass directly to PyTorch MHA.\n", - " x_attn, _ = self.pmha(x_norm, x_norm, x_norm, key_padding_mask=padding_mask, attn_mask=U)\n", - "\n", - " # 3. Physics-Aware Fusion (Invariants-driven conditioning)\n", - " if U is not None:\n", - " # Reconstruct the 4D shape: [B * H, N, N] -> [B, H, N, N]\n", - " U_reshaped = U.view(B_size, self.num_heads, N_particles, N_particles)\n", - "\n", - " # Pool over neighbor particle index 'j' (dim=3). Resulting shape: [B, H, N]\n", - " u_pooled = U_reshaped.sum(dim=3)\n", - "\n", - " # Transpose to align with token channels: [B, N, H]\n", - " u_pooled = u_pooled.transpose(1, 2)\n", - "\n", - " # Map head-wise pooled invariants into token channel space: [B, N, embed_dim]\n", - " physics_context = self.physics_proj(u_pooled)\n", - "\n", - " # Fuse physical invariants with abstract node latent maps\n", - " x_gating_input = x_norm + physics_context\n", - " else:\n", - " # Fallback path if U is not provided\n", - " x_gating_input = x_norm\n", - "\n", - " # 4. Compute Global Invariant Mass Bias from 4-vectors [B, N, 4] -> (E, px, py, pz)\n", - " if p4 is not None:\n", - " # Sum energy component (index 0) over all particles (dim=1)\n", - " energy_sum = p4[..., 0].sum(dim=1, keepdim=True) # Shape: [B, 1]\n", - "\n", - " # Sum momentum components (indices 1, 2, 3) over all particles (dim=1)\n", - " momentum_sum = p4[..., 1:].sum(dim=1) # Shape: [B, 3]\n", - "\n", - " # Calculate invariant mass squared (m2)\n", - " m2 = energy_sum**2 - momentum_sum.norm(dim=-1, keepdim=True)**2 # Shape: [B, 1]\n", - "\n", - " m2_scaled = torch.log1p(torch.relu(m2))\n", - "\n", - "\n", - " # Step C: Standardize the mean and variance dynamically\n", - " m2_norm = self.mass_norm(m2_scaled)\n", - "\n", - " # Project normalized m2 into a global embedding bias vector [B, 1, embed_dim]\n", - " mass_bias = self.mass_proj(m2_norm).unsqueeze(1)\n", - "\n", - " # Broad-cast add global event mass bias to the per-particle gating inputs\n", - " x_gating_input = x_gating_input + mass_bias\n", - "\n", - "\n", - "\n", - " # 5. Compute and apply the explicitly Physics-Aware Gate\n", - " if self.gate_type == \"headwise\":\n", - " # Compute score matrix from physics-fused map: (B, N, embed_dim) -> (B, N, num_heads, 1)\n", - " gate_score = self.gate_proj(x_gating_input).unsqueeze(-1)\n", - "\n", - " # Separate heads to apply individual scalar gating values\n", - " x_attn = x_attn.view(B_size, N_particles, self.num_heads, self.head_dim)\n", - "\n", - " # Apply physics-conditioned filter and reconstruct classic transformer shape\n", - " x_attn = (x_attn * torch.sigmoid(gate_score)).view(B_size, N_particles, self.embed_dim)\n", - "\n", - " elif self.gate_type == \"elementwise\":\n", - " # Compute full channel-by-channel mask from physics-fused map: (B, N, embed_dim)\n", - " gate_score = self.gate_proj(x_gating_input)\n", - " x_attn = x_attn * torch.sigmoid(gate_score)\n", - "\n", - " # 6. Standard Feedforward processing\n", - " x = self.layernorm2(x_attn)\n", - " x = self.dropout(x)\n", - " x += residual\n", - " x = self.feedforward(x)\n", - "\n", - " return x" - ], - "metadata": { - "id": "AvsAk0byPoBK" - }, - "id": "AvsAk0byPoBK", - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "from torch import Tensor\n", - "from typing import Optional, List, Dict, Tuple\n", - "\n", - "\n", - "\n", - "class LorentzParTEncoder(nn.Module):\n", - " def __init__(\n", - " self,\n", - " embed_dim: int = 128,\n", - " num_heads: int = 8,\n", - " num_layers: int = 8,\n", - " in_s_channels: Optional[int] = None,\n", - " out_s_channels: Optional[int] = None,\n", - " dropout: float = 0.1,\n", - " expansion_factor: int = 4,\n", - " pair_embed_dims: List[int] = [64, 64, 64],\n", - " attention_config: Dict = {}\n", - " ):\n", - " super(LorentzParTEncoder, self).__init__()\n", - " self.equilinear = EquiLinear(\n", - " in_mv_channels=1,\n", - " out_mv_channels=1,\n", - " in_s_channels=in_s_channels,\n", - " out_s_channels=out_s_channels\n", - " )\n", - " self.proj = nn.Linear(16, embed_dim)\n", - " self.interaction_embed = InteractionEmbedding(\n", - " num_interaction_features=4,\n", - " pair_embed_dims=pair_embed_dims + [num_heads]\n", - " )\n", - "\n", - " use_gating = attention_config.get('use_gating', False)\n", - "\n", - " # Explicitly pass gate_type so the block knows whether to use physics gating or standard\n", - " self.encoder = nn.ModuleList([\n", - " ParticleAttentionBlock(\n", - " embed_dim=embed_dim,\n", - " num_heads=num_heads,\n", - " dropout=dropout,\n", - " expansion_factor=expansion_factor,\n", - " gate_type=\"headwise\" if use_gating else None\n", - " ) for _ in range(num_layers)\n", - " ])\n", - "\n", - " def forward(self, x: Tensor, padding_mask: Tensor, U: Tensor, p4: Optional[Tensor] = None) -> Tensor:\n", - " B, N, F = x.shape\n", - " U = self.interaction_embed(U)\n", - " x = x.view(B, N, 1, F)\n", - " x, _ = self.equilinear(x)\n", - " x = x.view(B, N, 16)\n", - " x = self.proj(x)\n", - "\n", - " # Pass p4 down to the attention blocks\n", - " for layer in self.encoder:\n", - " x = layer(x, padding_mask, U, p4=p4)\n", - "\n", - " return x\n", - "\n", - "\n", - "class LorentzParT(nn.Module):\n", - " def __init__(\n", - " self,\n", - " config: Optional[LorentzParTConfig] = None,\n", - " max_num_particles: Optional[int] = None,\n", - " num_particle_features: Optional[int] = None,\n", - " num_classes: Optional[int] = None,\n", - " embed_dim: Optional[int] = None,\n", - " num_heads: Optional[int] = None,\n", - " num_layers: Optional[int] = None,\n", - " num_cls_layers: Optional[int] = None,\n", - " num_mlp_layers: Optional[int] = None,\n", - " hidden_dim: Optional[int] = None,\n", - " hidden_mv_channels: Optional[int] = None,\n", - " in_s_channels: Optional[int] = None,\n", - " out_s_channels: Optional[int] = None,\n", - " hidden_s_channels: Optional[int] = None,\n", - " attention: Optional[Dict] = None,\n", - " mlp: Optional[Dict] = None,\n", - " reinsert_mv_channels: Optional[Tuple[int]] = None,\n", - " reinsert_s_channels: Optional[Tuple[int]] = None,\n", - " dropout: Optional[float] = None,\n", - " expansion_factor: Optional[int] = None,\n", - " pair_embed_dims: Optional[List[int]] = None,\n", - " mask: Optional[bool] = None,\n", - " weights: Optional[str] = None,\n", - " inference: Optional[bool] = False\n", - " ):\n", - " super(LorentzParT, self).__init__()\n", - "\n", - " # Use config if provided, otherwise use defaults\n", - " if config is not None:\n", - " self.max_num_particles = max_num_particles if max_num_particles is not None else config.max_num_particles\n", - " self.num_particle_features = num_particle_features if num_particle_features is not None else config.num_particle_features\n", - " self.num_classes = num_classes if num_classes is not None else config.num_classes\n", - " self.embed_dim = embed_dim if embed_dim is not None else config.embed_dim\n", - " self.num_heads = num_heads if num_heads is not None else config.num_heads\n", - " self.num_layers = num_layers if num_layers is not None else config.num_layers\n", - " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else config.num_cls_layers\n", - " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else config.num_mlp_layers\n", - " self.hidden_dim = hidden_dim if hidden_dim is not None else config.hidden_dim\n", - " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else config.hidden_mv_channels\n", - " self.in_s_channels = in_s_channels if in_s_channels is not None else config.in_s_channels\n", - " self.out_s_channels = out_s_channels if out_s_channels is not None else config.out_s_channels\n", - " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else config.hidden_s_channels\n", - " self.attention = attention if attention is not None else config.attention\n", - " self.mlp = mlp if mlp is not None else config.mlp\n", - " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else config.reinsert_mv_channels\n", - " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else config.reinsert_s_channels\n", - " self.dropout = dropout if dropout is not None else config.dropout\n", - " self.expansion_factor = expansion_factor if expansion_factor is not None else config.expansion_factor\n", - " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else config.pair_embed_dims\n", - " self.mask = mask if mask is not None else config.mask\n", - " self.weights = weights if weights is not None else config.weights\n", - " self.inference = inference if inference is not None else config.inference\n", - " else:\n", - " self.max_num_particles = max_num_particles if max_num_particles is not None else 128\n", - " self.num_particle_features = num_particle_features if num_particle_features is not None else 4\n", - " self.num_classes = num_classes if num_classes is not None else 10\n", - " self.embed_dim = embed_dim if embed_dim is not None else 128\n", - " self.num_heads = num_heads if num_heads is not None else 8\n", - " self.num_layers = num_layers if num_layers is not None else 8\n", - " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else 2\n", - " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else 0\n", - " self.hidden_dim = hidden_dim if hidden_dim is not None else 256\n", - " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else 8\n", - " self.in_s_channels = in_s_channels if in_s_channels is not None else None\n", - " self.out_s_channels = out_s_channels if out_s_channels is not None else None\n", - " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else 16\n", - " self.attention = attention if attention is not None else {}\n", - " self.mlp = mlp if mlp is not None else None\n", - " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else None\n", - " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else None\n", - " self.dropout = dropout if dropout is not None else 0.1\n", - " self.expansion_factor = expansion_factor if expansion_factor is not None else 4\n", - " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else [64, 64, 64]\n", - " self.mask = mask if mask is not None else False\n", - " self.weights = weights if weights is not None else None\n", - " self.inference = inference if inference is not None else False\n", - "\n", - " # Initialize the class token\n", - " self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim), requires_grad=True)\n", - " nn.init.normal_(self.cls_token, mean=0.0, std=1.0)\n", - "\n", - " self.processor = ParticleProcessor(to_multivector=True)\n", - "\n", - " # Updated Encoder with attention_config passed dynamically\n", - " self.encoder = LorentzParTEncoder(\n", - " embed_dim=self.embed_dim,\n", - " num_heads=self.num_heads,\n", - " num_layers=self.num_layers,\n", - " in_s_channels=self.in_s_channels,\n", - " out_s_channels=self.out_s_channels,\n", - " dropout=self.dropout,\n", - " expansion_factor=self.expansion_factor,\n", - " pair_embed_dims=self.pair_embed_dims,\n", - " attention_config=self.attention\n", - " )\n", - "\n", - " # For self-supervised learning\n", - " self.fc = nn.Linear(self.max_num_particles * self.embed_dim, 16)\n", - " self.equilinear = EquiLinear(\n", - " in_mv_channels=1,\n", - " out_mv_channels=1,\n", - " in_s_channels=self.in_s_channels,\n", - " out_s_channels=self.out_s_channels\n", - " )\n", - "\n", - " # For classification\n", - " self.decoder = nn.ModuleList([\n", - " ClassAttentionBlock(\n", - " embed_dim=self.embed_dim,\n", - " num_heads=self.num_heads,\n", - " dropout=0.0,\n", - " expansion_factor=self.expansion_factor\n", - " ) for _ in range(self.num_cls_layers)\n", - " ])\n", - " self.layernorm = nn.LayerNorm(self.embed_dim)\n", - " self.classifier = Classifier(\n", - " num_classes=self.num_classes,\n", - " input_dim=self.embed_dim,\n", - " hidden_dim=self.hidden_dim,\n", - " num_layers=self.num_mlp_layers,\n", - " dropout=self.dropout,\n", - " )\n", - " self.act = nn.Softmax(dim=1) if self.inference else nn.Identity()\n", - "\n", - " # Load pretrained weights\n", - " if self.weights is not None:\n", - " state_dict = torch.load(self.weights)\n", - " filtered_state = {\n", - " k[len(\"encoder.\") :]: v\n", - " for k, v in state_dict.items()\n", - " if k.startswith(\"encoder.\")\n", - " }\n", - " self.encoder.load_state_dict(filtered_state, strict=False)\n", - "\n", - " def forward(self, x: Tensor, mask_idx: Optional[Tensor] = None) -> Tensor:\n", - " B, N, F = x.shape # (batch_size, max_num_particles, num_particle_features)\n", - "\n", - " # Save the raw kinematics before processor alters them\n", - " p4 = x.clone()\n", - "\n", - " # Ignore padding particles in query\n", - " padding_mask = (x[..., 3] == 0).float() # (B, N)\n", - "\n", - " # Set the masked indices to 0.0 so they are not ignored in MultiheadAttention()\n", - " if mask_idx is not None:\n", - " batch_indices = torch.arange(x.size(0), device=x.device)\n", - " padding_mask[batch_indices, mask_idx] = 0.0\n", - "\n", - " # Process particles to get interaction embeddings and multivectors (if applicable)\n", - " x, U = self.processor(x)\n", - "\n", - " # Pass through equilinear layer and particle attention blocks (passing p4 down)\n", - " x = self.encoder(x, padding_mask, U, p4=p4)\n", - "\n", - " # Classification (no masking in this case)\n", - " if not self.mask:\n", - " x_cls = self.cls_token.expand(B, -1, -1)\n", - "\n", - " # Decoder with class attention blocks\n", - " for layer in self.decoder:\n", - " x_cls = layer(x, x_cls, padding_mask)\n", - "\n", - " # MLP head for classification\n", - " x_cls = self.layernorm(x_cls).squeeze(1)\n", - " x_cls = self.classifier(x_cls)\n", - " output = self.act(x_cls) # (B, num_classes)\n", - "\n", - " return output\n", - " else:\n", - " x = x.view(B, -1) # (B, N * embed_dim)\n", - " x = self.fc(x) # (B, 16)\n", - " x = x.view(B, 1, 1, 16)\n", - " x, _ = self.equilinear(x) # (B, 1, 1, 16)\n", - " x = x.view(B, 16)\n", - " x = extract_vector(x) # (B, F)\n", - "\n", - " return x" - ], - "metadata": { - "id": "QRhMF1TP8Ymw" - }, - "id": "QRhMF1TP8Ymw", - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#Gating Test" - ], - "metadata": { - "id": "UC2GQe3iR7Gt" - }, - "id": "UC2GQe3iR7Gt" - }, - { - "cell_type": "code", - "source": [ - "# 1. Initialize your config with gating enabled\n", - "test_config = LorentzParTConfig(\n", - " embed_dim=128,\n", - " num_heads=8,\n", - " num_layers=8,\n", - " attention={'use_gating': True},\n", - " mask=True\n", - ")\n", - "\n", - "# 2. Instantiate the model\n", - "model = LorentzParT(config=test_config)\n", - "\n", - "# 3. Verification checks\n", - "first_layer = model.encoder.encoder[0]\n", - "is_gated = isinstance(first_layer, ParticleAttentionBlock)\n", - "\n", - "print(f\"--- Gating Verification ---\")\n", - "print(f\"Encoder Layer 1 Type: {type(first_layer).__name__}\")\n", - "print(f\"Gating Active: {is_gated}\")\n", - "\n", - "if is_gated:\n", - " print(\"Success: The model is now using Attention Gating!\")\n", - "else:\n", - " print(\"Error: The model is still using standard Attention Blocks.\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cKfNSeyPR9nH", - "outputId": "8ea6d1d4-605b-4ec4-b41e-f2a61eb0c23b" - }, - "id": "cKfNSeyPR9nH", - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--- Gating Verification ---\n", - "Encoder Layer 1 Type: ParticleAttentionBlock\n", - "Gating Active: True\n", - "Success: The model is now using Attention Gating!\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#Pre-Train" - ], - "metadata": { - "id": "H_Kmp-s3SUDD" - }, - "id": "H_Kmp-s3SUDD" - }, - { - "cell_type": "markdown", - "source": [ - "#Using Self Supervised Weights" - ], - "metadata": { - "id": "5peLQ8txiFZG" - }, - "id": "5peLQ8txiFZG" - }, - { - "cell_type": "code", - "source": [ - "# Initialize configuration with Attention Gating enabled\n", - "#not changing name of ssl_model_config\n", - "ssl_model_config = LorentzParTConfig(\n", - " embed_dim=128,\n", - " num_heads=8,\n", - " num_layers=8,\n", - " hidden_mv_channels=8,\n", - " attention={'use_gating': True}, # This is the trigger for your new code\n", - " dropout=0.1,\n", - " expansion_factor=4,\n", - " max_num_particles=128,\n", - " num_particle_features=4,\n", - " pair_embed_dims=[64, 64, 64],\n", - " mask=True # Set to True for Self-Supervised Learning / Masked Training\n", - ")" - ], - "metadata": { - "id": "6fGbG9f1SXbc" - }, - "id": "6fGbG9f1SXbc", - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Create the model and move it to your device (GPU/CPU)\n", - "gatedmodel = LorentzParT(config=ssl_model_config)\n", - "gatedmodel.to(device)\n", - "gatedmodel" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b7g5btHCSZka", - "outputId": "a47e4f81-cd33-4b7c-a39a-7f074bf3c456" - }, - "id": "b7g5btHCSZka", - "execution_count": 9, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "LorentzParT(\n", - " (processor): ParticleProcessor()\n", - " (encoder): LorentzParTEncoder(\n", - " (equilinear): EquiLinear()\n", - " (proj): Linear(in_features=16, out_features=128, bias=True)\n", - " (interaction_embed): InteractionEmbedding(\n", - " (embed): Sequential(\n", - " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", - " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (3): GELU(approximate='none')\n", - " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", - " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (6): GELU(approximate='none')\n", - " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", - " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (9): GELU(approximate='none')\n", - " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", - " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (12): GELU(approximate='none')\n", - " )\n", - " )\n", - " (encoder): ModuleList(\n", - " (0-7): 8 x ParticleAttentionBlock(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", - " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", - " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", - " (gate_proj): Linear(in_features=128, out_features=8, bias=True)\n", - " (pmha): MultiheadAttention(\n", - " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", - " )\n", - " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (dropout): Dropout(p=0.1, inplace=False)\n", - " (feedforward): Feedforward(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (dropout1): Dropout(p=0.1, inplace=False)\n", - " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", - " (dropout2): Dropout(p=0.1, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", - " (equilinear): EquiLinear()\n", - " (decoder): ModuleList(\n", - " (0-1): 2 x ClassAttentionBlock(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (mha): MultiheadAttention(\n", - " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", - " )\n", - " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (dropout): Dropout(p=0.0, inplace=False)\n", - " (feedforward): Feedforward(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (dropout1): Dropout(p=0.0, inplace=False)\n", - " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", - " (dropout2): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (classifier): Classifier(\n", - " (layers): Sequential(\n", - " (0): Linear(in_features=128, out_features=10, bias=True)\n", - " )\n", - " )\n", - " (act): Identity()\n", - ")" - ] - }, - "metadata": {}, - "execution_count": 9 - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_params = sum(p.numel() for p in gatedmodel.parameters() if p.requires_grad)\n", - "num_params" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n5GMAwY0Sw5t", - "outputId": "170f4cca-9434-4e28-d810-c0bf423f50a7" - }, - "id": "n5GMAwY0Sw5t", - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "2290656" - ] - }, - "metadata": {}, - "execution_count": 10 - } - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0aa870ab", - "metadata": { - "id": "0aa870ab" - }, - "outputs": [], - "source": [ - "# Training configurations\n", - "gated_train_config = TrainConfig(\n", - " batch_size=128,\n", - " criterion={\n", - " 'name': 'conservation_loss',\n", - " 'kwargs': {\n", - " 'loss_coef': [0.25, 0.25, 0.25, 0.25],\n", - " 'reduction': 'mean'\n", - " }\n", - " },\n", - " optimizer={\n", - " 'name': 'adamw',\n", - " 'kwargs': {\n", - " 'lr': 1e-4\n", - " }\n", - " },\n", - " scheduler={\n", - " 'name': 'exponential_lr',\n", - " 'kwargs': {\n", - " 'gamma': 0.95\n", - " }\n", - " },\n", - " callbacks=[{\n", - " 'name': 'early_stopping',\n", - " 'kwargs': {\n", - " 'monitor': 'val_loss',\n", - " 'mode': 'min',\n", - " 'patience': 5\n", - " }\n", - " }],\n", - " num_epochs=1,#20 change\n", - " start_epoch=0,\n", - " logging_dir=str(LOG_DIR),\n", - " logging_steps=1000,\n", - " progress_bar=True,\n", - " save_best=True,\n", - " save_ckpt=True,\n", - " save_fig=False,\n", - " device='cuda',\n", - " num_workers=0,\n", - " pin_memory=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6d5f08dd", - "metadata": { - "id": "6d5f08dd" - }, - "outputs": [], - "source": [ - "# Initialize the trainer\n", - "trainer = MaskedModelTrainer(\n", - " model=gatedmodel,\n", - " train_dataset=train_dataset,\n", - " val_dataset=val_dataset,\n", - " test_dataset=test_dataset,\n", - " device=device,\n", - " config=gated_train_config\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "40a3b5fa", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 188, - "referenced_widgets": [ - "0e1aad635bf14d1bb31c6f903f032e73", - "4e34acdc629c4fd898b1bbd743cd4cc1", - "f0e15ef2bc25482e87a2aafd881171fb", - "649dcf365ee140778713f1923422b40c", - "e0ef6f1060d2402a93eafa4557cd7a7a", - "45a2e6bd3f464dd0af70d71813826f89", - "b1ef8826aa5f4e2896ee4ee4bd6f5e2d", - "cb4aa040e0fe4b0292c3dcda6cc55b66", - "ee23434b787f454399e4be9c6111a94c", - "3c0bd3f7f0034da7ba1b118da5d111eb", - "bad0b3bd64c743519cf1ec7eb6b3ba46" - ] - }, - "id": "40a3b5fa", - "outputId": "e38bc084-3ac9-47b3-ed52-af4af71a5b6e" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Training: 0%| | 0/6250 [00:00" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "# Evaluate the model on the test set\n", - "test_loss, test_metric, y_true, y_pred = trainer.evaluate(plot=plot_particle_reconstruction)" - ] - }, - { - "cell_type": "code", - "source": [ - "# Label names for classification\n", - "labels = [\n", - " \"$q/g$\", # 0\n", - " \"$H \\\\to b\\\\bar{b}$\", # 1\n", - " \"$H \\\\to c\\\\bar{c}$\", # 2\n", - " \"$H \\\\to gg$\", # 3\n", - " \"$H \\\\to 4q$\", # 4\n", - " \"$H \\\\to \\\\ell \\\\nu qq'$\", # 5\n", - " \"$Z \\\\to q\\\\bar{q}$\", # 6\n", - " \"$W \\\\to qq'$\", # 7\n", - " \"$t \\\\to b\\\\ell \\\\nu$\", # 8\n", - " \"$t \\\\to bqq'$\" # 9\n", - "]\n" - ], - "metadata": { - "id": "vt4vzC1TGBiB" - }, - "id": "vt4vzC1TGBiB", - "execution_count": 18, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "\n", - "# Datasets for classification\n", - "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode=None)\n", - "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode=None)\n", - "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode=None)" - ], - "metadata": { - "id": "m37_eDUuGD0l" - }, - "id": "m37_eDUuGD0l", - "execution_count": 19, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from collections import Counter\n", - "import numpy as np\n", - "\n", - "def check_uniformity(y, dataset_name, label_names, threshold=0.02):\n", - " \"\"\"\n", - " Checks if the labels in a dataset are uniformly distributed.\n", - " Supports both integer class arrays and one-hot encoded arrays.\n", - " \"\"\"\n", - " # If one-hot encoded, convert to class indices\n", - " if len(y.shape) > 1 and y.shape[1] > 1:\n", - " y = np.argmax(y, axis=1)\n", - "\n", - " total_samples = len(y)\n", - " counts = Counter(y)\n", - " num_classes = len(label_names)\n", - " expected_pct = 1.0 / num_classes\n", - "\n", - " print(f\"--- Distribution for {dataset_name} ({total_samples} samples) ---\")\n", - "\n", - " is_uniform = True\n", - " for idx, name in enumerate(label_names):\n", - " count = counts.get(idx, 0)\n", - " actual_pct = count / total_samples\n", - " print(f\"Class {idx} ({name:<18}): {count:<8} | {actual_pct:.2%}\")\n", - "\n", - " # Check if it deviates more than the allowed threshold from absolute uniformity\n", - " if abs(actual_pct - expected_pct) > threshold:\n", - " is_uniform = False\n", - "\n", - " if is_uniform:\n", - " print(f\"✅ {dataset_name} appears to be uniformly distributed (within a {threshold:.1%} tolerance).\\n\")\n", - " else:\n", - " print(f\"⚠️ {dataset_name} is NOT perfectly uniform. Expected around {expected_pct:.2%} per class.\\n\")\n", - "\n", - "# Run the check on your datasets\n", - "# (Using your raw arrays y_train, y_val, and y_test)\n", - "check_uniformity(y_train, \"Train Dataset\", labels)\n", - "check_uniformity(y_val, \"Validation Dataset\", labels)\n", - "check_uniformity(y_test, \"Test Dataset\", labels)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IWCdSFFKGFoL", - "outputId": "c1769a19-a1db-473e-80a8-237fdc4867b1" - }, - "id": "IWCdSFFKGFoL", - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--- Distribution for Train Dataset (800000 samples) ---\n", - "Class 0 ($q/g$ ): 80000 | 10.00%\n", - "Class 1 ($H \\to b\\bar{b}$ ): 80000 | 10.00%\n", - "Class 2 ($H \\to c\\bar{c}$ ): 80000 | 10.00%\n", - "Class 3 ($H \\to gg$ ): 80000 | 10.00%\n", - "Class 4 ($H \\to 4q$ ): 80000 | 10.00%\n", - "Class 5 ($H \\to \\ell \\nu qq'$): 80000 | 10.00%\n", - "Class 6 ($Z \\to q\\bar{q}$ ): 80000 | 10.00%\n", - "Class 7 ($W \\to qq'$ ): 80000 | 10.00%\n", - "Class 8 ($t \\to b\\ell \\nu$ ): 80000 | 10.00%\n", - "Class 9 ($t \\to bqq'$ ): 80000 | 10.00%\n", - "✅ Train Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", - "\n", - "--- Distribution for Validation Dataset (100000 samples) ---\n", - "Class 0 ($q/g$ ): 10000 | 10.00%\n", - "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", - "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", - "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", - "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", - "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", - "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", - "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", - "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", - "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", - "✅ Validation Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", - "\n", - "--- Distribution for Test Dataset (100000 samples) ---\n", - "Class 0 ($q/g$ ): 10000 | 10.00%\n", - "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", - "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", - "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", - "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", - "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", - "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", - "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", - "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", - "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", - "✅ Test Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Model configurations\n", - "pretrained_model_config = LorentzParTConfig(\n", - " num_classes=10,\n", - " embed_dim=128,\n", - " num_heads=8,\n", - " num_layers=8,\n", - " num_cls_layers=2,\n", - " num_mlp_layers=0,\n", - " hidden_dim=256,\n", - " hidden_mv_channels=8,\n", - " in_s_channels=None,\n", - " out_s_channels=None,\n", - " hidden_s_channels=16,\n", - " attention={},\n", - " mlp={},\n", - " dropout=0.1,\n", - " expansion_factor=4,\n", - " max_num_particles=128,\n", - " num_particle_features=4,\n", - " pair_embed_dims=[64, 64, 64],\n", - " weights=gated_pt_path\n", - ")" - ], - "metadata": { - "id": "oRHuimXTGIdx" - }, - "id": "oRHuimXTGIdx", - "execution_count": 20, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Initialize the classifier model\n", - "pretrained_model = LorentzParT(config=pretrained_model_config).to(device)\n", - "pretrained_model" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Dv1oQ3tSGe3w", - "outputId": "93cf00a1-dc30-4880-8652-4adf5b6cccae" - }, - "id": "Dv1oQ3tSGe3w", - "execution_count": 21, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "LorentzParT(\n", - " (processor): ParticleProcessor()\n", - " (encoder): LorentzParTEncoder(\n", - " (equilinear): EquiLinear()\n", - " (proj): Linear(in_features=16, out_features=128, bias=True)\n", - " (interaction_embed): InteractionEmbedding(\n", - " (embed): Sequential(\n", - " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", - " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (3): GELU(approximate='none')\n", - " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", - " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (6): GELU(approximate='none')\n", - " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", - " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (9): GELU(approximate='none')\n", - " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", - " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (12): GELU(approximate='none')\n", - " )\n", - " )\n", - " (encoder): ModuleList(\n", - " (0-7): 8 x ParticleAttentionBlock(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", - " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", - " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", - " (pmha): MultiheadAttention(\n", - " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", - " )\n", - " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (dropout): Dropout(p=0.1, inplace=False)\n", - " (feedforward): Feedforward(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (dropout1): Dropout(p=0.1, inplace=False)\n", - " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", - " (dropout2): Dropout(p=0.1, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", - " (equilinear): EquiLinear()\n", - " (decoder): ModuleList(\n", - " (0-1): 2 x ClassAttentionBlock(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (mha): MultiheadAttention(\n", - " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", - " )\n", - " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (dropout): Dropout(p=0.0, inplace=False)\n", - " (feedforward): Feedforward(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (dropout1): Dropout(p=0.0, inplace=False)\n", - " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", - " (dropout2): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (classifier): Classifier(\n", - " (layers): Sequential(\n", - " (0): Linear(in_features=128, out_features=10, bias=True)\n", - " )\n", - " )\n", - " (act): Identity()\n", - ")" - ] - }, - "metadata": {}, - "execution_count": 21 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Count parameters in the model\n", - "num_params = sum(p.numel() for p in pretrained_model.parameters() if p.requires_grad)\n", - "num_params" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_TDNTq4hGL2e", - "outputId": "24c6d7c6-5565-44e1-ed00-17967f67ff76" - }, - "id": "_TDNTq4hGL2e", - "execution_count": 22, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "2282400" - ] - }, - "metadata": {}, - "execution_count": 22 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Training configurations\n", - "pretrained_config = TrainConfig(\n", - " batch_size=64,\n", - " criterion={\n", - " 'name': 'cross_entropy_loss',\n", - " 'kwargs': {\n", - " 'reduction': 'mean'\n", - " }\n", - " },\n", - " optimizer={\n", - " 'name': 'adam',\n", - " 'kwargs': {\n", - " 'lr': 1e-4\n", - " }\n", - " },\n", - " scheduler={\n", - " 'name': 'exponential_lr',\n", - " 'kwargs': {\n", - " 'gamma': 0.95\n", - " }\n", - " },\n", - " callbacks=[{\n", - " 'name': 'early_stopping',\n", - " 'kwargs': {\n", - " 'monitor': 'val_loss',\n", - " 'mode': 'min',\n", - " 'patience': 5\n", - " }\n", - " }],\n", - " num_epochs=2,#change\n", - " start_epoch=0,\n", - " logging_dir=str(LOG_DIR),\n", - " logging_steps=1000,\n", - " save_best=True,\n", - " save_ckpt=True,\n", - " save_fig=False,\n", - " device='cuda',\n", - " num_workers=0,\n", - " pin_memory=True\n", - ")" - ], - "metadata": { - "id": "aaUtfajZGWD5" - }, - "id": "aaUtfajZGWD5", - "execution_count": 23, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Initialize the trainer\n", - "trainer = Trainer(\n", - " model=pretrained_model,\n", - " train_dataset=train_dataset,\n", - " val_dataset=val_dataset,\n", - " test_dataset=test_dataset,\n", - " device=device,\n", - " metric=accuracy_metric_ce,\n", - " config=pretrained_config\n", - ")" - ], - "metadata": { - "id": "GBfMEnRrGler" - }, - "id": "GBfMEnRrGler", - "execution_count": 24, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Train the model\n", - "pretrained_history, pretrained_model = trainer.train()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 518, - "referenced_widgets": [ - "62f237b1683e475595fe17da0edeae87", - "7bdac98dfc4a4699bab39fa846e56354", - "ac7d5153146d4bd89fa5587a1c4babb7", - "1d65d195b47147d3806f7735255878d8", - "34f4dd1f0b9b4920bffb1027ea6e11fe", - "38e5079824d648849dcde09e2a2948fd", - "ffa3569f77fb4a3c8a6fb08930c0defb", - "dd800993260d48e383fb9aa27d265c7d", - "4d4b43abf49f4114a7fc83d9128f6d30", - "0d7bc861ca364e298ffb26510a4e4e09", - "2c776b732e6a4cb8bb85e01baff5fb33" - ] - }, - "id": "q_3Zs8TfOwop", - "outputId": "ecbf6236-025e-47a4-f604-eb92b4fd72e0" - }, - "id": "q_3Zs8TfOwop", - "execution_count": 25, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Training: 0%| | 0/25000 [00:00" - ], - "image/png": "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\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAioAAAHqCAYAAAAqMGWaAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAyGRJREFUeJzs3Xd4U2X/x/F3kibpbukuexXKEBkKyLCgjB8iIEtUVIYoIIJsKI5HUCnDASKgIA/yyHKCKIqALNmUtuxRKJRVuvdM0/z+KFRCUkiw6bDf13Xl0p5z55xPzgnJN/d9n0RhMBgMCCGEEEKUQ8qyDiCEEEIIURwpVIQQQghRbkmhIoQQQohySwoVIYQQQpRbUqgIIYQQotySQkUIIYQQ5ZYUKkIIIYQot6RQEUIIIUS5JYWKEEIIIcotKVSEuCUyMpJu3brh5uaGQqFg48aNJbr9y5cvo1Ao+Prrr0t0uxVZp06d6NSpU4lu8+rVq9jb27Nv376iZUOHDqV27dolup/i1K5dm6effrpU9lWeff311ygUCi5fvmzxfRITE3FycuK3336zXTBR4UihIsqVixcvMnLkSOrWrYu9vT2urq60b9+ehQsXkp2dbdN9DxkyhBMnTvDhhx/yzTff8Mgjj9h0f6Vp6NChKBQKXF1dzR7HyMhIFAoFCoWCjz76yOrt37hxg/fee4+IiIgSSPvPzJo1izZt2tC+fXub7eP06dO89957Vr0Ji/vz9PRkxIgRvPPOO2UdRZQjdmUdQIjbNm/ezMCBA9Fqtbz88ss0bdqUvLw89u7dy5QpUzh16hTLli2zyb6zs7M5cOAAb731Fm+88YZN9lGrVi2ys7NRq9U22f792NnZkZWVxS+//MKzzz5rtG7NmjXY29uTk5PzQNu+ceMGM2fOpHbt2jRv3tzi+23duvWB9lec+Ph4Vq1axapVq4yWL1++nIKCghLbz+nTp5k5cyadOnUqtZ6aymLUqFF89tln7NixgyeeeKKs44hyQAoVUS5cunSJ5557jlq1arFjxw78/f2L1o0ZM4YLFy6wefNmm+0/Pj4eAHd3d5vtQ6FQYG9vb7Pt349Wq6V9+/asW7fOpFBZu3YtPXv25McffyyVLFlZWTg6OqLRaEp0u6tXr8bOzo5evXoZLS+r4lBYr1GjRjRt2pSvv/5aChUByNCPKCfmzZtHRkYGK1asMCpSbqtfvz5vvvlm0d/5+fm8//771KtXD61WS+3atZkxYwa5ublG97s9X2Dv3r20bt0ae3t76taty//+97+iNu+99x61atUCYMqUKSgUiqJPycXNbXjvvfdQKBRGy7Zt20aHDh1wd3fH2dmZhg0bMmPGjKL1xc1R2bFjBx07dsTJyQl3d3f69OnDmTNnzO7vwoULDB06FHd3d9zc3Bg2bBhZWVnFH9i7vPDCC/z++++kpKQULTty5AiRkZG88MILJu2TkpKYPHkyDz30EM7Ozri6utKjRw+OHTtW1GbXrl08+uijAAwbNqxoCOn24+zUqRNNmzbl6NGjPP744zg6OhYdl7vnqAwZMgR7e3uTx9+9e3eqVKnCjRs37vn4Nm7cSJs2bXB2djZafvd5vH0uPvroI5YtW1b0PHr00Uc5cuTIPffx9ddfM3DgQAA6d+5c9Hh37dpl1O5ez7nboqKiGDhwIB4eHjg6OtK2bVuTgry4uR67du0y2W9kZCT9+/fHz88Pe3t7qlevznPPPUdqampRm5UrV/LEE0/g4+ODVqulcePGLF261CSbJf92bjt16hRPPPEEDg4OVK9enQ8++MBsD1ZoaCjdu3fHy8sLBwcH6tSpw/Dhw03ade3alV9++QWDwWCyTlQ+0qMiyoVffvmFunXr0q5dO4vajxgxglWrVjFgwAAmTZrEoUOHCAkJ4cyZM2zYsMGo7YULFxgwYACvvPIKQ4YM4b///S9Dhw6lVatWNGnShH79+uHu7s6ECRN4/vnneeqpp0ze6O7n1KlTPP300zRr1oxZs2ah1Wq5cOGC0YROc7Zv306PHj2oW7cu7733HtnZ2SxatIj27dsTFhZmUiQ9++yz1KlTh5CQEMLCwvjqq6/w8fFh7ty5FuXs168fo0aN4qeffip6g1i7di2BgYG0bNnSpH1UVBQbN25k4MCB1KlTh9jYWL788kuCgoI4ffo0VatWpVGjRsyaNYt3332X1157jY4dOwIYncvExER69OjBc889x4svvoivr6/ZfAsXLmTHjh0MGTKEAwcOoFKp+PLLL9m6dSvffPMNVatWLfax6XQ6jhw5wujRoy06Frcfe3p6OiNHjkShUDBv3jz69etHVFRUsb0wjz/+OOPGjeOzzz5jxowZNGrUCKDov3D/5xxAbGws7dq1Iysri3HjxuHp6cmqVavo3bs3P/zwA3379rX4cQDk5eXRvXt3cnNzGTt2LH5+fly/fp1ff/2VlJQU3NzcAFi6dClNmjShd+/e2NnZ8csvv/D6669TUFDAmDFjjLZpyeO4efMmnTt3Jj8/n+nTp+Pk5MSyZctwcHAw2lZcXBzdunXD29ub6dOn4+7uzuXLl/npp59MHkurVq349NNPOXXqFE2bNrXqOIh/IYMQZSw1NdUAGPr06WNR+4iICANgGDFihNHyyZMnGwDDjh07ipbVqlXLABj27NlTtCwuLs6g1WoNkyZNKlp26dIlA2CYP3++0TaHDBliqFWrlkmG//znP4Y7//l8+umnBsAQHx9fbO7b+1i5cmXRsubNmxt8fHwMiYmJRcuOHTtmUCqVhpdfftlkf8OHDzfaZt++fQ2enp7F7vPOx+Hk5GQwGAyGAQMGGJ588kmDwWAw6PV6g5+fn2HmzJlmj0FOTo5Br9ebPA6tVmuYNWtW0bIjR46YPLbbgoKCDIDhiy++MLsuKCjIaNkff/xhAAwffPCBISoqyuDs7Gx45pln7vsYL1y4YAAMixYtMvv47zyPtx+rp6enISkpqWj5zz//bAAMv/zyyz339f333xsAw86dO03WWfqcGz9+vAEw/PXXX0XL0tPTDXXq1DHUrl276LivXLnSABguXbpktJ+dO3caZQgPDzcAhu+///6e2bOyskyWde/e3VC3bt1/9DgOHTpk1M7Nzc0o94YNGwyA4ciRI/fMZzAYDPv37zcAhm+//fa+bcW/nwz9iDKXlpYGgIuLi0Xtb1+6OHHiRKPlkyZNAjDpOm/cuHHRp3wAb29vGjZsSFRU1ANnvtvtuS0///yzxZM2Y2JiiIiIYOjQoXh4eBQtb9asGV27djV7ieaoUaOM/u7YsSOJiYlFx9ASL7zwArt27eLmzZvs2LGDmzdvmh32gcJ5LUpl4cuEXq8nMTGxaFgrLCzM4n1qtVqGDRtmUdtu3boxcuRIZs2aRb9+/bC3t+fLL7+87/0SExMBqFKlisW5Bg0aZNT+9vPknz43LHnO/fbbb7Ru3ZoOHToULXN2dua1117j8uXLnD592qp93u4x+eOPP+45HHhnT0dqaioJCQkEBQURFRVlNERkzeNo27YtrVu3Nmo3ePBgo23d/jfy66+/otPp7vlYbp+ThISEe7YTlYMUKqLMubq6ApCenm5R++joaJRKJfXr1zda7ufnh7u7O9HR0UbLa9asabKNKlWqkJyc/ICJTQ0aNIj27dszYsQIfH19ee655/juu+/uWbTcztmwYUOTdY0aNSIhIYHMzEyj5Xc/ltsv6NY8lqeeegoXFxe+/fZb1qxZw6OPPmpyLG8rKCjg008/JSAgAK1Wi5eXF97e3hw/ftzkTe1eqlWrZtXE2Y8++ggPDw8iIiL47LPP8PHxsfi+BivmNZTE8bRku7e3fed2o6Ojiz33t9dbo06dOkycOJGvvvoKLy8vunfvzuLFi03O0759++jSpUvRnChvb++iOUN3t7X0cQQEBJi0u/uxBQUF0b9/f2bOnImXlxd9+vRh5cqVJvPK4O9zePc8MFE5SaEiypyrqytVq1bl5MmTVt3P0hcxlUpldrklb2jF7UOv1xv97eDgwJ49e9i+fTsvvfQSx48fZ9CgQXTt2tWk7T/xTx7LbVqtln79+rFq1So2bNhQbG8KwOzZs5k4cSKPP/44q1ev5o8//mDbtm00adLEqst9756vcD/h4eHExcUBcOLECYvu4+npCVhXZJTE8bT1di19DgJ8/PHHHD9+nBkzZpCdnc24ceNo0qQJ165dAwq/p+jJJ58kISGBTz75hM2bN7Nt2zYmTJgAYHJOS/px/PDDDxw4cIA33niD69evM3z4cFq1akVGRoZR29vn0MvLy+r9iH8fKVREufD0009z8eJFDhw4cN+2tWrVoqCggMjISKPlsbGxpKSkFF3BUxKqVKlidIXMbeY+7SqVSp588kk++eQTTp8+zYcffsiOHTvYuXOn2W3fznnu3DmTdWfPnsXLywsnJ6d/9gCK8cILLxAeHk56ejrPPfdcse1++OEHOnfuzIoVK3juuefo1q0bXbp0MTkmJfnJNzMzk2HDhtG4cWNee+015s2bd98rcaDw07+DgwOXLl0qsSzFKYnHW6tWrWLP/e318Hcvz93HvLgel4ceeoi3336bPXv28Ndff3H9+nW++OILoHDSem5uLps2bWLkyJE89dRTdOnSxepC8u7Hcfe/RTD/vAZo27YtH374IaGhoaxZs4ZTp06xfv16oza3z+GdE5RF5SWFiigXpk6dipOTEyNGjCA2NtZk/cWLF1m4cCFQOHQBsGDBAqM2n3zyCQA9e/YssVz16tUjNTWV48ePFy2LiYkxubIoKSnJ5L63v/jMXNc2gL+/P82bN2fVqlVGb0InT55k69atRY/TFjp37sz777/P559/jp+fX7HtVCqVyafn77//nuvXrxstu11QmSvqrDVt2jSuXLnCqlWr+OSTT6hduzZDhgwp9jjeplareeSRRwgNDf3HGe6nJB7vU089xeHDh42K88zMTJYtW0bt2rVp3LgxUPgcBNizZ09RO71eb/Llh2lpaeTn5xste+ihh1AqlUXH7nYPyZ3nNDU1lZUrV/6jx3Hw4EEOHz5ctCw+Pp41a9YYtUtOTjZ5LhX3b+To0aO4ubkVXVkkKje5PFmUC/Xq1WPt2rUMGjSIRo0aGX0z7f79+/n+++8ZOnQoAA8//DBDhgxh2bJlpKSkEBQUxOHDh1m1ahXPPPMMnTt3LrFczz33HNOmTaNv376MGzeOrKwsli5dSoMGDYwmk86aNYs9e/bQs2dPatWqRVxcHEuWLKF69epGkyXvNn/+fHr06MFjjz3GK6+8UnR5spubG++9916JPY67KZVK3n777fu2e/rpp5k1axbDhg2jXbt2nDhxgjVr1lC3bl2jdvXq1cPd3Z0vvvgCFxcXnJycaNOmDXXq1LEq144dO1iyZAn/+c9/ii6XXrlyJZ06deKdd95h3rx597x/nz59eOutt0hLSyua+2QLzZs3R6VSMXfuXFJTU9FqtUXfTWKp6dOns27dOnr06MG4cePw8PBg1apVXLp0iR9//LFoEnOTJk1o27YtwcHBJCUl4eHhwfr1602Kkh07dvDGG28wcOBAGjRoQH5+Pt988w0qlYr+/fsDhROVNRoNvXr1YuTIkWRkZLB8+XJ8fHyIiYl5oGMxdepUvvnmG/7v//6PN998s+jy5Fq1ahkV+KtWrWLJkiX07duXevXqkZ6ezvLly3F1dTUpyrdt20avXr1kjoooVFaXGwlhzvnz5w2vvvqqoXbt2gaNRmNwcXExtG/f3rBo0SJDTk5OUTudTmeYOXOmoU6dOga1Wm2oUaOGITg42KiNwVB4iWXPnj1N9nP3ZbHFXZ5sMBgMW7duNTRt2tSg0WgMDRs2NKxevdrk8uQ///zT0KdPH0PVqlUNGo3GULVqVcPzzz9vOH/+vMk+7r6Ed/v27Yb27dsbHBwcDK6uroZevXoZTp8+bdTm9v7uvvy5uEtX73bn5cnFKe7y5EmTJhn8/f0NDg4Ohvbt2xsOHDhg9rLin3/+2dC4cWODnZ2d0eMMCgoyNGnSxOw+79xOWlqaoVatWoaWLVsadDqdUbsJEyYYlEql4cCBA/d8DLGxsQY7OzvDN998Y/L4zV2ebO58A4b//Oc/99yPwWAwLF++3FC3bl2DSqUyukzY0uecwWAwXLx40TBgwACDu7u7wd7e3tC6dWvDr7/+anLfixcvGrp06WLQarUGX19fw4wZMwzbtm0z2m9UVJRh+PDhhnr16hns7e0NHh4ehs6dOxu2b99utK1NmzYZmjVrZrC3tzfUrl3bMHfuXMN///tfk+eRNY/j+PHjhqCgIIO9vb2hWrVqhvfff9+wYsUKo22GhYUZnn/+eUPNmjUNWq3W4OPjY3j66acNoaGhRts6c+aMATDJLSovhcEgX/0nhPj3eOWVVzh//jx//fVXWUcRD2D8+PHs2bOHo0ePSo+KAEAKFSHEv8qVK1do0KABf/75p01/QVmUvMTERGrVqsV3331n0zlaomKRQkUIIYQQ5ZZc9SOEEEKIcksKFSGEEEKUW1KoCCGEEKLckkJFCCGEEOWWFCpCCCGEKLfkm2nLsR5Dj9+/URnISsu4f6MyYKcpv09nlVpd1hHM0ut0ZR3BLH1+yf2QY0nKz80r6whmNWzduKwjmDW8/4P/hpAteWtNf/KivGhQz/QXq0vKZrXpr3U/iJ4687/jZCvSoyKEEEKIcqv8fgQVQgghRIlRqCvmN/1KoSKEEEJUAko7KVSEEEIIUU4p1BVztkfFTC2EEEKISkF6VIQQQohKQIZ+hBBCCFFuVdTJtDL0I4QQQohyS3pUhBBCiEpAhn6EEEIIUW5V1KEfKVSEEEKISqCi9qjIHJUSMmzYMN5+++2yjiGEEEL8q0iPSgnQ6/X8+uuvbN68uUxzVPXVMGlEDVxd7MjK0vPxV1e5ciO32PYhU+tSv7YDA18/ZdNc1fztmTG2Pm6uajKz9IQsiuTy1WyjNn7eWqaPrU9AHSdi4nIZMemYTTMBVPPTMu31uri5qMnIymfe0ktEXzPO1byJC68+XwMHexUGg4FD4aksX3cVg8G22ar6apkysiZuznZkZuv5aNkVoq/nGLVpVN+RcUNrAKBSKTh1PpMl31xDl2+7cOX1mFXzsyd4bH3cXOzIzNIz5/MLXL4rV4umrrw2uBYO9koMwMGjySxbc8Wmuar7O/DWhIa4uxYer9kLznHpSpZJu55d/XhxQA2UCgVHj6fw8dJI9HrbBXu+uxPNG2jxclfx3rIkrsaa/gikp5uS4b1dqOlnR0JKATOXJ9ssz20fv/c6qSmJKBRK7B0ceWHEFGrVDTRqc/ZkKJ++Pw6/qrWKlr01ZyUarb3N823fuoWFCz5mxtvv8Vi79kbrsrOzCflwJhcuRFKg17P++402z2MthUp6VP61zp49S+fOnbG3t6dBgwb89ttvKBQKIiIiANi/fz9qtZpHH3206D6HDh2iQ4cOODg40Lx5c/bs2YNCoeDkyZM2yzl2SHV+353Eq9PP8f1v8UwaUaPYtn27exETX3wRU5Imj6rHL9tiefGNcNZuuE7w2ACTNpnZelasvcL7CyJLJRPAhBF12PxnPEMmHOfbTTFMHV3HpE1Gpp4PPrvI8MknGDXjFI0bONPtcS+bZxs/vAa/7Uxk+NQzfPdrHJNfM/1F1agr2bzxn3OMfvscI2ecxd3Vjl5dbJutvB6zSSPr8uu2WF4aF8G6jdeZ/kZ9kzbpmfnM+vQ8QyccY+TU4zRt6EL3IG+b5poyJoBNf8Tw/KgjrPnhKjPGm/56rb+vPa8Ors2YaREMeu0wHu5q+nT3t2muo2fymPN1Cgkpxf9KdU6ugQ27Mlm+Id2mWe40espcZi34lpmfrqNb78GsWPSe2XZ+VWsx89N1RbfSKFJiY2/yxx+/0zCwkdn1dnYq+g8YxAcfzrV5lgelVClK5GYNvV7PO++8Q506dXBwcKBevXq8//77GKz4hCCFyn2cPXuWNm3a0LFjR06dOsXcuXN5+eWXUavVNG5c+NPqmzZtolevXigUhSfw5MmTPPnkk3Tq1Inw8HDeeecdBg4ciFarJTAw8F67e2BuLioa1HFgx/7CTz17Q1Px8lTj76MxaVuzqpbHWrry/a/xNslyJ3c3NQ3rObFtd+G+dh9IxNtTQzU/4xeW9Ix8TpxNJyen+BfOEs3lakeDuk5s+ysBgD2HkvHx1FDVV2vU7sLlLGLiCgs6nc7AxegsfL21Jtsr6WwBdRz5c1/hT9H/dSQFbw8NVe86l7l5BvS3DpednQKNRmnT3oHyeszcXe0Kn2N7bj3HDibhY+Y5duHS37nydAYuXM7Cz8eGudzUBAa4sHVnLAC79ifg42VPNX/jXJ3aebH3cCJJKToANv4eQ5cgH5vlAjh/RUdyesE922TmGLhwNZ9cnY27D+/g6ORS9P/ZWRkoKB89AAUFBSxa+AkjR41BrVabbaNWa3i4eQucnJ1LOV35NnfuXJYuXcrnn3/OmTNnmDt3LvPmzWPRokUWb0OGfu5jzJgx9O/fn1mzZgFQr149Vq9eTWRkJBpN4RvHzz//zKefflp0n3HjxtG7d28++OADAAIDA1m1ahXXrl3Dzs42h9zbQ0NSSj4Fd7z2xCfq8PFUExOXV7RMpYI3h1VnwX+vobf1+AXg46khMVmH/o5ccQm5+HhpuH4zp/g72pi3p4aklDyj4xWXkIePl4YbseZ7mqq4qXm8TRXemmfbXh9vDzVJKTrjbIm3st1xLgF8vTS8N6EuVX00HIpI45ftCbbLVU6PmY+X1uQ5FnsrV3HPMQ93NUFtPQiec9ZmuXy9tCQm5Rnnis/B19ue6zF/5/L1tudm3N9/34zLsXkxXJ4tX/guZ0+EAjDhnYVm28TdvMZ7k15AqVTR4YlePNHjWZtm2rjhRxo1bkL9gAY23Y+tKZSlX/jt37+fPn360LNnTwBq167NunXrOHz4sMXbkELlHqKjo9mxYwfHjhnPl9BoNDz88MMAnDlzhhs3bvDkk08W3Wfnzp0mQzxarbboPubk5uaSm2v8Yl+gz0OpMu0R+ScG9/Fl/9FUrsbk4uNl/pOBMOXooOSDqQF8u+km56MyyzpOkdiEPEa/dRZ7rZLpo2vR4VE3dh1MKetYQPk9Zo4OKmZPD2Tdzzc4d7H85BKFXn2z8EPhvh2/8P3/FjHhnc+M1teqG8jHX/2Go5MLSQmxLPhgHM6u7rRu380meaIvX2L/vr+YM+8Tm2y/NClUJTOIYu79SqvVotWaFtjt2rVj2bJlnD9/ngYNGnDs2DH27t3LJ59Yfjxl6OceIiIijIZ4bjt58mRR0bFp0ya6du2Kvb190X00Gg1NmjQxus+ZM2fuWaiEhITg5uZmdLt4YoXFWeOT8vBwt0N5xxn19lQTl6gzavdQQ2d6dfHi648C+XhGPRztlXz9USBuLiqL92WNuMQ8PKuoufPfh4+XlriEvOLvVAriE/PwcNcYHS8fL43ZXA72SuYEN2R/aAo//HbT9tmSdHi4q42zeZrPdltObgG7DibzRDsP2+Uqp8csLiHX5Dnme49c895uxL4jSXz/a4xNc8Um5OLpoTHO5W1PbLxxL09sfA5+Pn8PB/n52BNbSvPHyrP2T/Ti7MlQMtJSjJY7ODoXDRF5ePnSpmN3Ik9H2CzHqVMniYuNZeSIobwy9EXOnT3D4kUL+G3zLzbbp62U1BwVc+9XISEhZvc5ffp0nnvuOQIDA1Gr1bRo0YLx48czePBgy3OX1AH4N1Iqlej1evT6v+dNbNmyxahQ+fnnn+nTp0/RepVKRX5+Pjk5f78Y/fnnn5w6deqehUpwcDCpqalGt3oPvWJx1tR0PReis3miXRUAOjziRkKSzmjYB2BKyEWGTj7L0MlnmTT7Ilk5BQydfJbUdNvMDUlJ1XE+KpOutyYtBj3mSXxiXpkO+wCkpOUTeTmTrh0LJ3k+3qYK8Yk6kyEMe23hG+6RiFTWbLhRatkuXM7iyfaFRUfHR91JSNKZDPtU9dGgulVf2qkUtGvlTtSV7Ls3V6K5yuMxS0nLJ/JSJl0fv/Uca+tBfJLpc+x2kXI4PIVvfrxu+1ypOs5fzKBbZ1+gcC5KfEKu0bAPwO79CXRo7YmHe2EP5zM9/Nm+J87m+cqbrMx0kpP+njcXdmgnzi5uOLm4GbVLSYqn4Nb4Y3Z2JsdC91Kzjukk5ZLyVM9e/G/Nt6z4ejUrvl5Nw8BGjBk7nqd69rLZPss7c+9XwcHBZtt+9913rFmzhrVr1xIWFsaqVav46KOPWLVqlcX7UxismXpbydy4cYO6desyZswYxowZQ3h4OFOnTiUqKorY2MIJctWrV+fGjRt4eRW+eMfExFCnTh1effVVJkyYwKlTpxg/fjxRUVEkJibi4WH5J94eQ49blbean5ZJI6rj4mxHVraeT1dc4/K1HN4cVp2D4Wkcikgzau/jpWbxrAZWX56clZZhVfsaVe0JHhuA661LR+d+foGoK1lMeb0e+44ksf9IMlqNktWLW6CxU+LkqCI5TcfWXfEsX3PF4v3Yaawbyazub8+00XWLcs3/IopLV7OZ9Fpt9h9N4cDRFF54xp8hA6oZXeq6+2ASazda92lcVcwEvGKz+WmZ/FotXJ1VZGUX8NHyaC5fy2HCKzU4EJbKwfA0nursyTPdvNEXgEoJEafTWb7+BjorJj/qdbr7N7ozVykdM32+dYVzjar2TB9Tv/DS/Gw9cxZf5NKVLKaMqsu+0GT2hybzYr9qDH22utGl8bsOJLL6J8uLlvxc63oCa1Rz4K3xgUWXTc9eeI6o6EymjW3A3kOJ7DucCECvbn68OKDwyq7wEynMX2Ld5ckNWze+f6M7vPSUM80CNLg5K8nIMpCTZ2DG4iSGPO1MxPk8jp3PQ2MHH47xQK1S4GCvIC2zgAMncvlph+XDZcP7O1jcNiEuhqXzp5GXl4NCqcTFtQqDho6nZp2GrFw8i+aPBtGidRB//vYtO7f8gFKlokCv55F2Xegz6LWiixks4a1Nsrjt3YKnTaJ3n3481q49q7/5Gk8PT3rcKlrGvv4aqamppKQk4+HhyUPNHmbSlOlWbb9BPdMr/ErKocfalMh22hw4ZHHbGjVqMH36dMaMGVO07IMPPmD16tWcPWvZHDEpVO5j9erVBAcHk5KSQo8ePWjZsiULFy4kJiaGFStWsHLlSvbu3Wt0n7Vr1zJ9+nSSkpLo1q0bzZs355tvviEy0roJhdYWKqXF2kKltFhbqJQmawuV0mJtoVJarC1USou1hUppsbZQKS3WFCql6Z8UKrZmy0LlSIe2JbKdR/cetLitp6cnH3zwAaNHjy5aFhISwsqVKzl//rxF2yi/r+zlxIsvvsiLL75Y9PfEiRONhn169+5tcp8XXniBF154ASi8rK1Tp04MHDiwdAILIYQQZpTFF7716tWLDz/8kJo1a9KkSRPCw8P55JNPGD58uMXbkELFSsePH6dVq1YAdOjQgeeff95o/Z49e4iPj6dFixYkJCQwf/58oqOj2bhxYxmkFUIIIcrOokWLeOedd3j99deJi4ujatWqjBw5knfffdfibUihYqUTJ04wbNgwAKZOnWqyPjY2lunTp3P9+nV8fX3p0qULhw8ftmpuihBCCFHSFMrSv37GxcWFBQsWsGDBggfehhQqVro9ibY4AwcOlGEeIYQQ5U5ZfOFbSZDLk4UQQghRbkmPihBCCFEJWPuDguWFFCpCCCFEJVBRh36kUBFCCCEqgbKYTFsSKmZqIYQQQlQK0qMihBBCVAIy9COEEEKIcquiTqaVoR8hhBBClFvSoyKEEEJUAjL0I4QQQohyq6Je9SOFSjmWmZpW1hHMUqpUZR3BLIWi/P4jNBQUlHUEs7SODmUdway0+KSyjlCh5OboyjqCWa0ubyjrCGZ9qX+1rCMUq0E92227ovaolN9XdiGEEEJUetKjIoQQQlQCFbVHRQoVIYQQohKoqIWKDP0IIYQQotySHhUhhBCiEpCrfoQQQghRblXUb6aVQkUIIYSoBGSOijAye/ZsnJ2di26zZ88u60hCCCFEhSM9KjYyatQonn322aK/PTw8yjCNEEKIyk7mqAgjHh4eUpwIIYQoN2TopxKZPn06Tz/99D9uI4QQQpQWhVJRIrfS9q8pVIKCghg+fLjJ8iVLluDs7ExBCf7WSkREBA8//PA/biOEEEKIe/tXDP0YDAbCw8ON5oTcFhoaSvPmzVGW4NhcREQEw4YN+8dtSlp1f3tmjGuAm6uajMx8QhZFcvlqllEbP28tweMaEFDHiZi4HF6ZGGHzXNX87AkeWx83Fzsys/TM+fwCl69lG7Vp0dSV1wbXwsFeiQE4eDSZZWuuYDDYMpeWqaPrFOWa98Uloq/lGLVpFODEm8NrAWBnp+Dk2QwWr7qCLt+GwYBqvlqmjKpdlG3+l5eJvn5XtvpOvDm8JgAqlYKT5zJY8r+rNs1W1UfDxBHVcXO2IzNbzycrrnHlRm6x7UOm1KFeLQeefeO0zTIBVPd34K0JDXF3VZORlc/sBee4dCXLpF3Prn68OKAGSoWCo8dT+HhpJHq97Y5Xec3l66liZP8quDgpyc4p4MsfU7gel2/URqGA57q70qyBFpUSzkfnsXJTKnq9zWLRY9Zy1HYq7NWFb03Du7Tm/1oEGrXZeOgka/eEFf0dm5pOy7rV+XR4H5tkytflsm31RJJiL2CntsfB2ZOg/v/BzauWUbvEmHPs+el9sjMSUSrt8Kn5EI/3exc7tb1Ncj2IijpHpWKmvktkZCTp6em0atXKZF1oaKjZ5cXR6/WcOXOm2PU3b94kNjYWvV7P448/jqOjI48++ignTpywqo0tTB5dn01bbzJ4zFHWbrhG8NgAkzaZ2Xq+WhvNrE/P2TTLnSaNrMuv22J5aVwE6zZeZ/ob9U3apGfmM+vT8wydcIyRU4/TtKEL3YO8bZpr/IhabP4znqETT7J+002mjqpj0iYqOpsxb59hVPBpXp16Cnc3O3p39bFpLoA3X6nJbzsTGDb5FN/+epMpI2ubZruSxZh3zjBqxhlem34ad1c7enWx7TEbO6QaW3Yn8eqM83z/WzwTX6lebNu+3byIicuzaZ7bpowJYNMfMTw/6ghrfrjKjPENTdr4+9rz6uDajJkWwaDXDuPhrqZPd/9KmWt4H3d2Hslkyqdx/LIng5H93U3aBLVypHZVNW8vjmfqgngMBvi/x5xsmgtg3pCn+W7Ky3w35WWTIgXgmTZNi9Z/N+VlPF2c6NmqkU0zNW77LC9M28KgST9Tp8kT7PzubZM2KjstHfu+wwvTfufZSRvR5WUTtmO5TXNZS4Z+ytDRo0dRqVQmQy3Z2dmcPn2ali1bWrytzZs307ZtWw4fPmx2fUREBAALFiwgJCSE0NBQnJ2def75561qU9Lc3dQ0rOfMtt1xAOw+kIiPl5ZqfsbVfHpGPifOpJGTU3JDYffM5WpHw3pObNsTX5jrYBI+nhqTXBcuZRETV/jJPE9n4MLlLPx8tDbN1aCOE9v3JgLw1+FkvD01VPU13mduXkHRJ1s7OwVajRIDtu1NcXe1o0HdO7OlFJPNUPTp9u9stuPmoiKgtgM7DqQAsO9oGl4eavx9NCZta1bV8lgLV777Ld6GiQq5u6kJDHBh685YAHbtT8DHy55q/sbPsU7tvNh7OJGkFB0AG3+PoUuQ7YrO8prL1UlJ3Wpq9h0r7NU8cioHDzcVvh4qo3Y1/dWcuphb9Bw7dj6X9i0cbZbrQRyPjiEpI4ugpvVstg87tZZajYJQKArfoH1rPUx68nWTdu7etfGqWliIKpUqfGo0NdtOWO9fUaiEhYWh1+txdHREoVAU3RwdHdHr9VYVKr1792bq1Kl069aN0NBQk/URERHY29uzceNG2rdvT+PGjfnwww85deoUCQkJFrcpaT6eGhKT89DfUX/EJeTi6227N3tL+HhpSUzWGeWKTcjDx8v0ze02D3c1QW09OHA02Wa5vD01JKXoKDA6XuZz+Xpp+HJOY35a1pzMLD2bttr2zdfbQ0NS8l3ZEvPw8TSf7YvZjfjxi4fJzNbzyzbbZfP2UJOUmm+UKz5Rh4+H2qidSgXjhlZj0f+uU1Bg26IOwNdLS2KS8XM/Nj4HX2/jgsDX256bcX8Pn92My7Hpv4/ymsvDTUVKeoHReUxM1ePpblyoXL6eR8tAexy0ClRKaPOQA953tbGFt9f8Tv95q/jP+j9IyjAdJrvTxoMnePqRxqhVts912/G/vqF2kyfv2UaXm8WZQz9Q5z7tSptCqSyRW2n7V8xRCQsLo2/fvrz77rtGy9evX89nn31G48aNjZbv2rWLzp0733e7gwYN4uLFi0bLIiIiePbZZ6latWrRsipVqgAUTdi1pM3dcnNzyc01Husv0OehVBX/hv5v5OigYvb0QNb9fINzFzPLOg5QWFiNnH4ae62S4DF16NDanV0HbFdEWSM2IY9RM85gr1Uy/fU6dHjUnV0Hyzbb4N6+7D+axtWYXHw81fe/gyiX9oRl4+Vux1sjvMjTGQp7V+rb9oPPf8cOwr+KKzq9ns9/28c7a7ew+LV+Zttm5erYEn6Ob8a/YNNMdzr65xekJkbTe8DXxbbR5+exdfUEajRsT92HupZaNoso5PLkMhMWFkanTp1o3ry50S0pKYlmzZqhuqvabt26NWfOnCn2NnPmTOzs7Jg/f77JviIiImjevLnRsoMHD1KtWjV8fHwsbnO3kJAQ3NzcjG5Xz6+2+BjEJebhWUWD6o4z6uOlJTa++ImOpSEuIRfPKmqjXL5eGuISTOcuONgrmfd2I/YdSeL7X2Nsmis+MQ8PdzVKo+NlPtdtObkF7DyQxJPtPW2bLSkPjyp3ZfPUEJd472y7DiTxRHvbfXdPfJIODzc7o1zenmriknRG7Zo2dKL3k56snNeQj4Lr4WivZOW8hri62OZTb2xCLp4exs99X297YuONJx/Hxufg5/N3b4afj71N/32U11xJqXrcXZRG59HTTUViiuks2Z92pPP24nhmLUvgepyO63E6kzYlyb+KKwBqlYoXH29JWNS1YttuO3aOen6e1POz7b/H28J3rSDqxDaeHrEctcbBbBu9XsfW1RNxdPWhQ5+3SiWXNWSOShmJiooiJSXF7PBOWFiY2Ym0jo6OBAYGmr1dvXqVkJAQ1q1bR79+xpV8VlYWkZGR6O+Y9l5QUMDChQsZOnSoxW3MCQ4OJjU11ehWo8GLFh+HlFQd56My6XprbDvoMU/iE3O5fjPnPve0rZS0fCIvZdL18cJJnkFtPYhPyjPJdbtIORyewjc/2n5cNyUtnwuXs+jSofBFrmPrKsQn5XEj1vgNoqqvFtWtH/KyUyno8GgVoq5km2yvxLNdujObOwnFZqMoW/tH3blkw2yp6XouRGfzxGPuALRv5Upiss5kwuzUOVEMnXqOYVPPMTnkIlk5BQybeo60dNtcLpKSquP8xQy6dfYFCud8xCfkcj3G+Dm2e38CHVp74uFe2MvzTA9/tu+Js0mm8pwrLbOAyzd0tH+48M320Sb2JKXpiU0yPj9qO3C0L3zuOzsq6RXkwq9/ZdgsV1aujrTsv4/N7+FnCaxW/FydDQdP0rdtU5vluVPE7pVcCN9Mr9f+i9bB1WybAn0+21ZPROvoRqcBs4rmtIh/rsIP/Rw9ehSlUmnSg6HT6Th58iSjR4+2antt27Zl48aNdO/e3WTd8ePHUalUrFy5kqCgIFxdXXnrrbfIzs5m2rRpFrcxR6vVotUad6taO+zz0dILBI8L4KUBNcjMymfOokgApr5en31Hkth3JAmtRsmaxa3QqJU4Oar4YfmjbN0dx7LV0VbtyxoffxnF9DH1GdyvGlnZeuYsLhxOmzKqLvtCk9kfmkz/p/xpVN8ZB62Kx9sU9grsOpDI6p9sV7R8+tVlpo6qwwvP+JOZreejLy4DMPHVWhwIS+HA0VRaNHHhmf/zpaDAgEqlIPxkGqs33LBZptsW/DeaKSNr83xvP7Ky9cxfdivbiFvZwlJp3tiFZ7rX+zvbqXRWb7RtT9Si/11n4vAaDOrpQ1aOnk9XFH7ifXNoNQ5GpHEoIt2m+y/OvMXneWt8IC8PrElmlp7ZCwuvaps2tgF7DyWy73AiN2JzWLH2MkvntQAg/EQKP2+x7fEqr7n++3MKr/WvQu9OLmTnFrDsxxQARvR1I+xMDmFnc3HQKnlrhCcGQ+GIwR8HMgk/a8OenvRMJn69iYKCwunq1T3d+GBwDwDeW/8HnZrWo1PTwisGL8clce5GHN2bmx8WKkkZKTfZ/8tcXD1r8PMXQwBQqTQMePM7Dm/5DEdXH5q2e44Lx34n6sQ2PP0b8t2nfQHwr92Sx/u9e6/Nl6qymF9Su3ZtoqNN319ef/11Fi9ebNE2FAaDLb+pwvaCg4PZsGEDZ8+eNVp+7NgxmjdvTlhYGC1atCiRfX3xxRd8/vnnvPvuu0ycOJGUlBR69erF559/jqenp8VtLPV4370lkrukKUtx4po17NTldz5Eef3qarW2fM6BSotPKusIFUrtZqZfRVAefNX+17KOYNaX+lfLOkKx3uxlu9eKmEklM5/H/+O1FreNj483GmE4efIkXbt2ZefOnXTq1MmibVT4QuXfTAoV60ihYj0pVP4dpFCxjhQq/4w1hcrdxo8fz6+//kpkZKTFw2MVfuhHCCGEEPdXUkM/5q5SNTd94W55eXmsXr2aiRMnWjWHp8JPphVCCCHE/ZXUVT/mrlINCQm57/43btxISkrKPS8sMUd6VIQQQohKoKSGoIODg5k4caLRsvv1pgCsWLGCHj16GH3HmCWkUBFCCCGExSwZ5rlbdHQ027dv56effrJ6f1KoCCGEEJVBGf568sqVK/Hx8aFnz55W31cKFSGEEKISKKsvoSsoKGDlypUMGTIEOzvryw4pVIQQQohKoCy+8A1g+/btXLlyheHDhz/Q/aVQEUIIIYTNdOvWjX/ylW1SqAghhBCVQHn94sn7kUJFCCGEqAzKcDLtP1ExUwshhBCiUpAeFSGEEKISkKEfIcpYef5HmK/LL+sIFUpZXZ1wP4aCgrKOYJa9Y/n8ccnMmk3LOoJZDfN1ZR3hHmx3LhWK8vnv6n6kUBFCCCEqg3L8Ye5eKmZ5JYQQQohKQXpUhBBCiEqgvA6p3o8UKkIIIUQlUJ7n8d1LxSyvhBBCCFEpSI+KEEIIURnIVT9CCCGEKK8q6tCPFCpCCCFEZVBBJ9NWzNRCCCGEqBSkULGh2bNn4+zsXHSbPXt2WUcSQghRSSkUihK5lbZ/3dBPUFAQ9erV47///a/R8iVLljB16lTS0tJQllL316hRo3j22WeL/vbw8CiV/QohhBAmKujQz7+qUDEYDISHhxsVB7eFhobSvHnzUitSoLAwkeJECCFEeSCTacuByMhI0tPTadWqlcm60NBQOnfubPG29Ho958+fp1GjRsW2uXLlCu+88w5btmwhLS2NBg0asHjxYjp06GDR+pJW3d+eGeMa4OaqJiMzn5BFkVy+mmXUxs9bS/C4BgTUcSImLodXJkbYJMudqvnZEzy2Pm4udmRm6Znz+QUuX8s2yTX9jXrUr+3EzbhcRkw5bvtcvlqmjKpdlGv+l5eJvp5j1KZRfSfeHF4TAJVKwclzGSz531V0+Qbb5fLTMn1MvaJcc5dEmRyvFk1ceXVwDRzsVWAwcDAshWVrr2KwXazCbOX0mFX3t2fG+Ia4u9iRkaVn9sLzJs99gJ5dfBncvwZKBYSdSOHjLy6i19sylwNvTWiIu6uajKx8Zi84x6UrZnJ19ePFATVQKhQcPZ7Cx0sjbZrr+W5OPBygwctdxcyvkrkaqzdp4+mmZHgvF2r4qkhILWDWVyk2y3O3X3f8xezF/yVk6liC2rQ0Wf/Nhs38vms/ajsVGrWaCa8MpnFAXZtmWvLha6SlJKBUKNE6ONF/6HSq1zF+f0iMu87apW9z7dJZPH2qMXXeDzbNVFlUzH6gYhw9ehSVSsXDDz9stDw7O5vTp0/TsqXpE744mzdvpm3bthw+fNjs+ujoaFq3bk12djabNm3i+PHjvPHGG7i6ulq03hYmj67Ppq03GTzmKGs3XCN4bIBJm8xsPV+tjWbWp+dsluNuk0bW5ddtsbw0LoJ1G68z/Y36ZnOtWHeVDxZGllquN1+pyW87Exg2+RTf/nqTKSNrm7SJupLFmHfOMGrGGV6bfhp3Vzt6dfG2aa6Jr9Xh1+1xvDz+OOt+jmHa66YvwOmZ+by/4ALDJh7nteknadLQhW6Pe9k0F5TfYzb59QB++SOGF14/ytqfrjHjzQYmbfx9tIwYXIs3go/x3KhQqrhr6N3dz6a5powJYNMfMTw/6ghrfrjKjPENTXP52vPq4NqMmRbBoNcO4+Gupk93f5vmCj2Ty9z/pZKQYlqg3JaTa2DDrkyW/5xu0yx3i4lLYNP23TRpUM/s+vOXrvDTlp18NecdVn08i/49nuTjr1bbPNfQ8R8xff5PTJ33A516vsyaJW+btLF3dOapQWN5edxcm+d5IAplydxK2b+qUAkLC0Ov1+Po6Gg08cfR0RG9Xm9VodK7d2+mTp1Kt27dCA0NNVk/evRo2rZty3fffUebNm0ICAjg1VdfpVmzZhatL2nubmoa1nNm2+44AHYfSMTHS0s1P3ujdukZ+Zw4k0ZOTun8XL27qx0N6zmxbU98Ya6DSfh4asznOptOTm7p5WpQ14ntexMB+OtwCt6eGqr6ao3a5eYZ0N96LbezU6DVKLFlp4W7qx0N6zqz7a8EAPYcSsLHyzTXhctZxMTlAqDTGbhwORM/H63J9ko6W7k8Zm5qAus7s3VX4XN/1/4Es8/9Tu292Hs4iaQUHQA/b4mhS0fbFVDubmoCA1zYujP2jlz2VPO/K1c7L/YeTizKtfH3GLoE+dgsF0Dk1XyS0+/9by0zx8CFa/nk5dk0ipGCggJClqxk4isvolGb7/BXKAp7vHNyC5//GZlZ+HhWsXk2R6e/P2TmZKWbnVTq5OxGvcCWaLQONs/zQJSKkrmVsn/V0E9YWBh9+/bl3XffNVq+fv16PvvsMxo3bmy0fNeuXRYNBw0aNIiLFy8W/R0dHc3vv/9OeHi42fb3W28LPp4aEpPz0N/x2hOXkIuvt5brN3OKv6Otc3lpSUzWGeWKTcjDx0tTprm8PTQkJesouPN4Jebh46nhRmyuUVtfLw0zJ9ajqq+WQxGp/LIt3ma5fDw1JKbkGeWKTcjD10trkuu2Km5qgtp6EDznvM1yQTk+Zl5ak+d+bLzpc9/Xy57YuL//vhlX2MZWfL20JCbdnSsHX297rsfckcvbnptGuXJsmqs8W//LHzQLrE9gvdrFtgmoXZNBT3ej/+ipuDo7oVbbsfT94FLJt/rzGUSeLuxlHzl9SansU/wLC5WZM2fSvHlzo+VLliyhWbNmqFQqo+WtW7fmzJkzxW7vu+++4/3332f+/PlGyyMiItBoNCb7sXS9Obm5ueTmGr/YF+jzUKo0Fm9D2EZsQh6jZpzBXqtk+ut16PCoO7sOJpd1LAAcHVTMntaA9T/HcD4qs6zjFCnPx0yUTxevXGPnwaMsfX/6PdvdiI1n16GjfLd4Dt4eVfjht+2888lSvvhwhs0zvvhG4VdMHN79M5vWfMqo4KU232dJUlTQr9CvmKnNiIqKIiUlxezwTlhYmNkJto6OjgQGBpq9Xb16lZCQENatW0e/fv2M7qdWq8nPzycry3RSnCXrzQkJCcHNzc3odvW85eOucYl5eFbRoLrjjPp4aYmNN/8pvLTEJeTiWUVtlMvXS0NcQin2J5sRn5SHRxW10dV6Pp4a4hKLz5WTW8CuA0k80d52V3LFJebh6a4xyuXrpSE2wfQ8OtgrmTujIftCk/l+802bZbqt3B6zhFyT576vt+lzPzYhB1+fv4dd/Hxs++8jNiEXT4+7c9kTG2/ckxgbn4OfUS77Mv93WxaOnT7PzbgEnn1jOv1GTebU+YvM/eJrftqyw6jdroOh1KtZHW+PwuGenk905PjZSHS6/FLL2jqoDxdOHSEzPaXU9lkiKujQz7+mUDl69ChKpdKkF0On03Hy5Emr5qcAtG3blo0bNzJgwACTdW3atMHNzY3Ro0dz5swZTp8+zRdffEFkZKRF680JDg4mNTXV6FajwYsW501J1XE+KpOut8a2gx7zJD4xt0yHVwBS0vKJvJRJ18cL5wIEtfUgPimvXOS6cCmLLh08AejY2p2EpDyTIYyqvlpud8TZqRS0f9SdS1ey795cieaKvJRJ146FE2Mfb+NBfKJpLnutknkzAjkSkcrqn27YLM/d2crlMUvVcf5iBt06FT73O7XzMvvc37U/kQ6tPfBwVwPQ5//8+fMv2w1JFeXq7Pt3roRco2EfgN37E+jQ2rMo1zM9/Nm+J85mucqrfv/3BL+sWMBPX3zET198RJMG9Zg2aij9/u8Jo3ZVfb05cTaSrOzC47jvaAQ1q/qhLmZOS0nIykwjNenvc3L8yJ84urjh6Oxms33agkKpLJFbqec2GGx9QWPpCA4OZsOGDZw9e9Zo+bFjx2jevDlhYWG0aNGixPa3b98+pkyZwvHjx9FoNLRt25Z169bh5uZm0XpLPN53r1WZalR1IHhcAG4uajKz8pmzKJKoK1lMfb0++44kse9IElqNkjWLW6FRK3FyVJGcqmPr7jiWrY62eD/Ku4bQ7p/Lnulj6uPqYkdWtp45iy9y6UoWU0bVZV9oMvtDk9FqlKxe1By1XWGulDQdW3cnsHztFYv3o9ZaN0xW3V/LlJG1cXUuzDV/2WUuX81h4ohaHAhL4UBYKk919uKZ7j4UFBhQqRSEn0pn+bpr6HTW/bPJt+LTXg1/e6aNqVuUa+6SKC5dzWbyyDrsD01m/9EUBvetytCB1YwuW951IIk1G6wrWuysfHEvrWOWk2F5byRAjWoOhZfmu9iRma0n5LPzREVnMe2NAPYeTmTf4SQAenX1Y3D/6gCEn0zlo6UXrLoM2FBg3WTvGtUceGt8YNHl3LMXniMqOpNpYxuw91Ai+w4XTkzu1c2PFwcUXtIdfiKF+Uusuzw5sG0Tq3K91MOJh+prcHNWkpllICfPwIylyQzp6UzE+TyOReahsYMPRldBrVLgYK8gLbOAgydy+WmX5edmbp8Hnzc15t05PNuzG0FtWrJ83Qa8PNzp270zBoOBL9b8wO5DYWjUauztNUx85cV7zmu525H8R6zKkhR/g5WfTkKXl4NCocTZtQp9XppM9dqBrPviPzR9pBMPPdKZvNxsPhj/NPk6HTlZ6Ti7efBox170emG8xfv6v+a2G+7PWvHu/RtZwPGVWSWyHUv9awqVfyNrC5XSYm2hUlqsLVRKkzWFSmmytlApLdYWKqXF2kKltFhbqJSWf1Ko2JK1hUppsmmh8t//lMh2HIfPLJHtWKp8vkoJIYQQomRV0K/Qr5iphRBCCFEpSI+KEEIIURmUwS8flwQpVIQQQohKoCyu2CkJUqgIIYQQlYF84ZsQQgghhLHr16/z4osv4unpiYODAw899JDZ39ArjvSoCCGEEJVBGXyrbHJyMu3bt6dz5878/vvveHt7ExkZSZUqlv+QpBQqQgghRCVQFr/1M3fuXGrUqMHKlSuLltWpU8eqbcjQjxBCCFEZlNBv/eTm5pKWlmZ0u/tHdW/btGkTjzzyCAMHDsTHx4cWLVqwfPly62KXxGMXQgghROVg7kd0Q0JCzLaNiopi6dKlBAQE8McffzB69GjGjRvHqlWrLN6fDP0IIYQQlUEJDf0EBwczceJEo2VardZs24KCAh555BFmz54NQIsWLTh58iRffPEFQ4YMsWh/UqgIIYQQlUEJfeGbVqsttjC5m7+/P40bNzZa1qhRI3788UeL9ydDP0IIIYSwifbt23Pu3DmjZefPn6dWrVoWb0N6VIQQQojKoAy+mXbChAm0a9eO2bNn8+yzz3L48GGWLVvGsmXLLN6GFCrCagV6fVlHMCsnI6usIxSrWceHyjqCWZdOXy3rCGZlp2WUdYQKJfp0dFlHMCs+4r2yjmDWJ/ZzyzpCsf7vmxa223gZXJ786KOPsmHDBoKDg5k1axZ16tRhwYIFDB482OJtSKEihBBCVAZl8IVvAE8//TRPP/30A99f5qgIIYQQotySHhUhhBCiMqigP0oohYoQQghRGZTQ5cmlrWKWV0IIIYSoFKRHRQghhKgMyuDy5JIghYoQQghRGVTQoR8pVIQQQojKoIJOpq2YqYUQQghRKUiPihBCCFEZVNA5KhUztRlBQUEMHz7cZPmSJUtwdnamoKCgDFIJIYQQ5YRCUTK3Uvav6FExGAyEh4fz7LPPmqwLDQ2lefPmKCtoJWmN6v72zBjXADdXNRmZ+YQsiuTyVePfv/Hz1hI8rgEBdZyIicvhlYkRlTvX+Ia4u9iRkaVn9sLzJrkAenbxZXD/GigVEHYihY+/uIheb7BZroFPOPBQPTWebkpCVqVzLd70t5Xq+Kt4rqsjAColXLyez/c7ssm38c8wVfXRMHFEddyc7cjM1vPJimtcuZFbbPuQKXWoV8uBZ984bdNc1f0deGtCQ9xd1WRk5TN7wTkuXTFzLrv68eKAGigVCo4eT+HjpZE2PZflNVc1Xy1TRtXGzcWOzCw987+8TPT1HKM2jeo78ebwmgCoVApOnstgyf+uosu3TS6lsws1/zPv7781WtS+/kSOeJaCjPSi5WpvX+p+/jW5Vy4XLbv+0Sx0sTE2yQVQ1VfLlJE1i573Hy27YuZ4OTJuaA2g8HidOp/Jkm+u2ex4PRCZo1J2IiMjSU9Pp1WrVibrQkNDzS4vjl6v58yZM/dsc+jQITp06ICDgwPNmzdnz549KBQKTp48aVWbkjZ5dH02bb3J4DFHWbvhGsFjA0zaZGbr+WptNLM+PWdmC5Us1+sB/PJHDC+8fpS1P11jxpsNTNr4+2gZMbgWbwQf47lRoVRx19C7u59Nc4Wfz+OTdekkphbfC3gtXs/c1emE/C+dD79Ox9lRQcfmWpvmAhg7pBpbdifx6ozzfP9bPBNfqV5s277dvIiJy7N5JoApYwLY9EcMz486wpofrjJjfEOTNv6+9rw6uDZjpkUw6LXDeLir6dPdv1LmevOVmvy2M4Fhk0/x7a83mTKytkmbqCtZjHnnDKNmnOG16adxd7WjVxdvm2UqyEjn8pTRRbeU7b+RGX7EqEgpapudbdTWlkUKwPjhNfhtZyLDp57hu1/jmPxaTZM2UVeyeeM/5xj99jlGzjh763h52TRXZfGvKFSOHj2KSqXi4YcfNlqenZ3N6dOnadmypcXb2rx5M23btuXw4cNm1588eZInn3ySTp06ER4ezjvvvMPAgQPRarUEBgZa3KakubupaVjPmW274wDYfSARHy8t1fzsjdqlZ+Rz4kwaOTmlMxRWnnMF1ndm667CXLv2J5jN1am9F3sPJ5GUogPg5y0xdOlouxdrgAvX9KRk3PtTmC4fbo9mqlSgsVOAjT+4ubmoCKjtwI4DKQDsO5qGl4cafx+NSduaVbU81sKV736Lt20obp3LABe27owFbp9Le6r533Uu23mx93Bi0bnc+HsMXYJ8Kl8uVzsa1HVi+95EAP46nIK3p4aqvsaFbm6egds/lG5np0CrUdr6KWbE7Yn/I2XHllLco3nurnYE1HHkz31JAPx1JAVvDw1V73re3328NBolhnLUmQLI0E9ZCgsLQ6/X4+joaHa9NYVK7969mTp1Kt26dWP79u088sgjRuvHjRtH7969+eCDDwAIDAxk1apVXLt2DTs7O4vblDQfTw2JyXno73ifj0vIxddby/WbOcXf0cbKbS4vrUmu2HjTXL5e9sTG/f33zbjCNuWBh6uSkc844e2u5GSUjj0RxQ/BlARvDzVJqfncOd0rPlGHj4faqOdEpYJxQ6uxcOV1Cgps/0rt66UlMenuc5mDr7c912PuOJfe9tw0Opc5Nj2X5TWXt4eGpGSd0XmMS8zDx1PDjVjj55Cvl4aZE+tR1VfLoYhUftlm+8ITwKFBY1TOzmQcPWh2vVJrT62QRSiUStKP7Cfxp3Vgo3mI3h5qklLMHC8vDTfu6jH09dLw3oS6VPXRcCgijV+2J9gk0wOroFMg/jWFSt++fXn33XeNlq9fv57PPvuMxo0bGy3ftWsXnTt3vu92Bw0axMWLF4v+jo6OZufOnSbDN1qttqg3x5I25uTm5pKba/wiUaDPQ6ky/bQqBEBSWgEh/0tHq4YhTznRPEDN0XO6so7F4N6+7D+axtWYXHw81WUdR/wDsQl5jJpxBnutkumv16HDo+7sOphs8/26Pfl/pO7ebrb4yE9O4sLIF9CnpaB0dqHahLcwPJ1H0qbvbZ7rfmIT8hj91tnC4zW6Fh0edWPXwZSyjlXhVczy6i5hYWF06tSJ5s2bG92SkpJo1qwZKpXKqH3r1q05c+ZMsbeZM2diZ2fH/Pnzje4XERGBRqOhSZMmRsvPnDlTVIRY0sackJAQ3NzcjG5Xz6+2+BjEJebhWUWD6o4z6uOlJTbetp+y76fc5krINcnl622aKzYhB1+fv7vq/XzKPvvdcnVw9Gwejza2bVEbn6TDw83O6EOZt6eauCTj4qhpQyd6P+nJynkN+Si4Ho72SlbOa4iriwpbiE3IxdPj7nNpT2y8cY9dbHwOfkbn0t6m57K85opPysOjitroPPp4aohLLH4+UU5uAbsOJPFEew+b5bpNYW+Py2OPk7rjD7PrDfk69GkpQOG8lpQdW3Bs9JDN8sQn6fBwN3O8Eu5zvA4m80Q72x8vaxgUihK5lbYKX6hERUWRkpJidngnLCzM7ERaR0dHAgMDzd6uXr1KSEgI69ato1+/fkb3U6lU5Ofnk5Pz9wvNn3/+yalTp4qKEEvamBMcHExqaqrRrUaDFy0+DimpOs5HZdL11th20GOexCfmlunwSrnPdTGDbp0Kc3Vq52U21679iXRo7YGHe2HPQJ//8+fPv0qn+/tevN2VRS+cKiU8HKDmupmrg0pSarqeC9HZPPGYOwDtW7mSmKwzmTA7dU4UQ6eeY9jUc0wOuUhWTgHDpp4jLd02+YrOZWdf4Na5TMg1Gl4B2L0/gQ6tPYvO5TM9/Nm+J84mmcp1rrR8LlzKoksHTwA6tnYnISnPZNinqq+W25/x7FQK2j/qzqUr2TbLdZtru07kRkeRd+Oq2fUqV3duB1PYqXFp04GcyxdsliclLZ8Ll7N48laR1vFRdxKSdCbDPlV9NEbHq10rd6JK4XhZRaEsmVtpxzYYyt10H6t8//33PPfcc6SmpuLs7Fy0XKfT4eLiwuLFi3nllVcs3l56ejr79++ne/fuJutiYmKoU6cOr776KhMmTODUqVOMHz+eqKgoEhMT8fDwsKiNpR7vu9fitgA1qjoQPC4ANxc1mVn5zFkUSdSVLKa+Xp99R5LYdyQJrUbJmsWt0KiVODmqSE7VsXV3HMtWR1u1r/KYy2DlfIga1RwKL5t2KbzkMOSz80RFZzHtjQD2Hk5k3+HCyXO9uvoxuH/h1S3hJ1P5aOkFqy8dbdbR8k98z3d1oEldNa5OCjKzDeTmGXhvRTovdHPgxEUdJy7m076Zhk4ttRQUFBYq567ks2G39ZcnXzpt/s2gONX8NEwcXgNXZxVZOXo+XXGNy9dzeXNoNQ5GpHEowvgKDR9PNZ/PDLD68uS0+CSr2teo5sBb4wOLLredvfAcUdGZTBvbgL2HEtl3uHDiaK9ufrw4oPCKjfATKcxfYtvLgEsrl4Or8/0b3aG6v5YpI2vj6mxHVrae+csuc/lqDhNH1OJAWAoHwlJ5qrMXz3T3oaDAgEqlIPxUOsvXXUOnszzXorxgq3IB1PzgU1K3/07qrq1Fy7wGvUx+UiIp2zbj3Lo93oOGYCjQo1CpyDoZQdz/lmPIt3zYc5z9XKsyVffTMvm1WoXP++wCPloezeVrOUx4pQYHwlI5GJ7GU509eaabN/pb/yYjTqezfP0Nq44XwNZvWljV3hrZu9aVyHYcOj1fItuxVIUvVIKDg9mwYQNnz541Wn7s2DGaN29OWFgYLVqU3Ilfu3Yt06dPJykpiW7dutG8eXO++eYbIiMjrWpjCWsLlcrO2kKlNFlTqJQmawuV0mJtoVLZWVuolJYHKVRKg7WFSmmSQsVUhZ9MGxISQkhIiMnyhx9+GFvUYC+88AIvvPACAAUFBXTq1ImBAwda3UYIIYQoTWUxv6QkVPhCpTTt2bOH+Ph4WrRoQUJCAvPnzyc6OpqNGzda1UYIIYQodRX0m2mlULFCbGws06dP5/r16/j6+tKlSxcOHz5sNO/EkjZCCCGEsIwUKlYYOHDgfYdwLGkjhBBClDoZ+hFCCCFEuSXfTCuEEEKI8qqiTqatmOWVEEIIISoF6VERQgghKgO56kcIIYQQ5ZVBChUhhBBClFsyR0UIIYQQomRJj4oQQghRCVTUoZ+KmVoIIYQQ1lEoSuZmhffeew+FQmF0CwwMtGob0qMihBBCCJtp0qQJ27dvL/rbzs660kMKFWE1pUpV1hHMMihK/teyS8q1i7FlHcGs8nouhXXsnR3LOoJZWp19WUcwy71KlbKOUDbKaOjHzs4OPz+/B76/DP0IIYQQlYBBoSiRW25uLmlpaUa33NzcYvcbGRlJ1apVqVu3LoMHD+bKlStW5ZZCRQghhKgMFMoSuYWEhODm5mZ0CwkJMbvLNm3a8PXXX7NlyxaWLl3KpUuX6NixI+np6RbHlqEfIYQQQlgsODiYiRMnGi3TarVm2/bo0aPo/5s1a0abNm2oVasW3333Ha+88opF+5NCRQghhKgEDJTMF75ptdpiC5P7cXd3p0GDBly4cMHi+8jQjxBCCFEJGBTKErn9ExkZGVy8eBF/f3+L7yOFihBCCCFsYvLkyezevZvLly+zf/9++vbti0ql4vnnn7d4GzL0I4QQQlQGZXB58rVr13j++edJTEzE29ubDh06cPDgQby9vS3ehhQqQgghRCVgKIMfJVy/fv0/3oYUKkIIIUQlIL/1I4QQQghRwv41hUpQUBDDhw83Wb5kyRKcnZ0pKCgo8X3OmTMHhULB+PHjS3zbQgghRIkqgx8lLAn/ikLFYDAQHh5Oq1atTNaFhobSvHlzlMqSfahHjhzhyy+/pFmzZiW6XSGEEMIWysPlyQ/iXzFHJTIykvT09GILlc6dO1u8Lb1ez/nz52nUqFGxbTIyMhg8eDDLly/ngw8+MNvm7NmzjB49mgMHDlCzZk0WLFhAz549CQ8Pp3nz5hbnsUZ1f3tmjGuAm6uajMx8QhZFcvlqllEbP28tweMaEFDHiZi4HF6ZGGGTLHeq5mdP8Nj6uLnYkZmlZ87nF7h8LduoTYumrrw2uBYO9koMwMGjySxbcwWDDX9nsJq/PTPG1sfNVU1mlv7W8TLO5eetZfrY+reOVy4jJh2zXaA7+HurGT/EHxdnFVnZehb+7yZXY/KM2jQNcODdN6pzPfbv5dPmXyFPZ7uDVtVHw4RhVXF1UZGZXcCClTe4csP4Nz4eauDIe2/WNMo1OeSSTXNV93fgrQkNcXdVk5GVz+wF57h0JcukXc+ufrw4oAZKhYKjx1P4eGkken3ly+Xvo2bC0Kq4OqvIyi5gwdc3uHL386uBI++NrWF0HqfMvWyz86h0csFvyvt//63RYuftx5XxL1GQmWH2Pl7D38Slw5NEj3meguxMm+QC8POyY8zzXrg4FR6vJesTuBarM2rT6VFnnuroWvS3h7uKMxdz+HhVvM1yWaukvvCttP0rCpWjR4+iUql4+OGHjZZnZ2dz+vRpJk2aZPG2Nm/ezEsvvcS2bdto3bq12TZjxoyhZ8+edOnSxWyhcvbsWdq0acObb77JV199xfHjx3n55ZdRq9U0btzYugdnhcmj67Np60227Iwj6DFPgscGMHKq8RtrZraer9ZG4+So4tXBtWyW5U6TRtbl122xbNkVT1BbD6a/UZ9R008YtUnPzGfWp+eJictFo1bw8buN6R7kzZZdtvtHPnlUPX7ZFsuWnfF3HK/jRm0ys/WsWHsFJyc7RrxQ02ZZ7vb6YF/+2JvCjoNptGvhzJsv+zF5rukPeV2PzWPC7OhSyzXmJX+2/JXMn/tTad/ShfHDqjLxw0tmc42bFVVquaaMCWDTHzH8/mcsndp5MWN8Q16dGG7Uxt/XnlcH12b4+KMkpeiY83YT+nT356ffblS6XGMG+/PHXyn8eSCVdi1dGD+0KhNDLpu0ux6bx5sfmJ5fWyjITOfGe+OL/nbt/gwODZsWW6Q4tnwM9Pmlku21AZ5sP5jB7iMZtGnmyOvPeTFjYYxRm11HMth15O+sH02uyt4w2xVPlcm/YugnLCwMvV6Po6MjCoWi6Obo6Iher6dly5YWb6t3795MnTqVbt26ERoaarJ+/fr1hIWFFfsDTFBYyPTv359Zs2ZRr149+vbtS1BQEIGBgWg0mgd6jPfj7qamYT1ntu2OA2D3gUR8vLRU8zP+mfX0jHxOnEkjJ6fk5+yYzeVqR8N6TmzbU1hw7D6YhI+nxiTXhUtZxMQVfjLP0xm4cDkLP58H+4pmi3K5qQtz7b6V60Ai3mZypWfkc+JsOjk5eptluZubi4r6Ne3ZdTgNgP3hGXhVUePnrS61DMXlCqhlz86DqQDsC0vHu4oa/zLO5e6mJjDAha07YwHYtT8BHy97qvkbn8tO7bzYeziRpJTCT8Ibf4+hS5BPpctVdB4PFZ7H/WHpeJWD83g3l45dSf9rm9l1Sld33J8eQOK3/7V5DldnJXVraPnraGERcuh4Fl7udvh6Fv85v35NDW7OKkJPmfaelaV/9dDPpk2bLN5g7969HzjMgwoLC6Nv3768++67RsvXr1/PZ599ZtKLsWvXLouGgwYNGsTFixeL/r569Spvvvkm27Ztw97e3ux9oqOj2bFjB8eOGfdkaDQakx6fkuTjqSExOQ/9HfVHXEIuvt5art/Msdl+75vLS0tiss4oV2xCHj5emmJzebirCWrrQfCcs7bL5akxyRWXkHvPXKXFq4odyWl67pz/HZ+sw9tDzc144+5mf28NnwTXoqDAwJ8H0vh9T4oNc6lJSs03zpWkw9tTTcxdufy81Sx4uw4FBti+L4XfdiXbLJevl5bEJOPnfmx8Dr7e9lyP+ftc+nrbczPu779vxuXg6227Yri85ir2PHoUcx7fqkNBgYHt+1P5bbftzuOdtPUCUTo5k3XsiNn1XkPGkPTdKgw52WbXlyRPdztS7vr3mJCSj1cVO2ITzffoPNHahT1HM4zOfblQBhNhS4JFhcozzzxj0cYUCgV6fel98rwtLCyMmTNnmsz9WLJkCc2aNUOlUhktb926NWfOnCl2e9999x3vv/8+8+fPN1p+9OhR4uLijHpo9Ho9e/bs4fPPPyc3N5eIiAizQzwnT55kyJAhxe4zNzeX3Fzjsf4CfR5KlW16YMorRwcVs6cHsu7nG5y7KN2m93Lxai7Dgy+SlVOAp7sd746pTlqGnn1hlv98ui1cuJLD0KmRZGUX4FnFjvfG1SQtQ8/e0LQyzSWsc/FKDsOmXSh6fv1nbA3SMvLZe9T2zy+Xx7uSsX8nmLla07ljV/RJCeScPW7mnmVPq1HQroUTb30Wc//GwiIWFSq2uLS3pERFRZGSkmJ2eCcsLIw2bdqYLHd0dCQwMNDs9rZt20ZISAjr1q2jX79+RuuefPJJTpwwnlsxbNgwAgMDmTZtGiqVCqVSiV6vR6/XY2dXeHi3bNnCyZMn79mjEhISwsyZM42W1Ww4jFqNTC+5NicuMQ/PKhpUSoqqeB8vLbHxufe+o43FJeTiWUVtlMvXS0NcQp5JWwd7JfPebsS+I0l8/6tt/5EXHi+1yfEyl6u0JSTnU8VVhVL59+u0dxU18UnGn3az7xi+S0zJZ09oGo3rO9isUElI1uHhZmecy0NNfOI9ciXns+dwKk0CHG1WqMQm5OLpYfzc9/W2JzbeuGcsNj6Hav4ORX/7+djb9N9Hec1V7Hm83/PrSBqNAxxtXqgotPY4PdqeG7PMzy10CHwI+4ZNcHj4kaJl1WZ9RuyiD8m7UvLzohJT8nG/69+jl7sdCcnme1PaPuzEtZt5XL9rsm15YKigsz0qZuo7HD16FKVSadKbotPpOHnypFXzUwDatm3Lxo0bGTBggMk6FxcXmjZtanRzcnLC09OTpk2bAtCqVSvUajUzZswgKiqKH3/8kTFjxgDcs1AJDg4mNTXV6FajwYsW505J1XE+KpOut8a2gx7zJD4xt8yHMVLS8om8lEnXxwt/1yGorQfxSXkmuW4XKYfDU/jmx+u2z1V0vG7lesyT+ETTXGUhNV3Pxau5dGpdeAVBuxbOJKbkmwz7VHFVFfXkOmgVPPqQM1FXbZc/NV3PxSs5dG7rBkD7li4kJOtMhguquNndkUvJo81ciLpiu1wpqTrOX8ygW2dfoHDOR3xCrtHwCsDu/Ql0aO2Jh3vhXIxnevizfU9cpctVdB7bFJ7Hdi1dSEgxcx5d7zqPDznb9Dze5tS6I3lXL6O7af51IH75J1yd/ArXpr7KtamvAnD93XE2KVIA0jIKuHQtj46tnAFo08yRxNT8ewz7OLPjsPkJwGXNoFCUyK20PdBVP5mZmezevZsrV66Ql2f8CXTcuHElEsxSYWFhBAQE4OzsbLT89OnT5ObmWl2ouLi40L179wfOU7VqVb766iuCg4NZtmwZPXr04NVXX2XhwoX4+BQ/QU6r1aLVGo9LWzvs89HSCwSPC+ClATXIzMpnzqJIAKa+Xp99R5LYdyQJrUbJmsWt0KiVODmq+GH5o2zdHcey1ba7cuTjL6OYPqY+g/tVIytbz5zFhfN+poyqy77QZPaHJtP/KX8a1XfGQavi8TYeAOw6kMjqn2xXtHz8xUWCxwbwYv/qZGbpmfv5hcJcr9dj35Ek9h9JRqtRsnpxCzR2hcfr++Wt2LornuVrTK/AKUlL195k3Mv+DPg/T7JzCvjsf4U9TG+86Mvh4xkcPp7JYy1c6PG4O/oCAyqlgn1h6fx5wLbDK59/E8OEYVV59imvostaAca+7M+hY+kcPpZB+5Yu9OhUhQI9KFWwLzSdbftSbJpr3uLzvDU+kJcH1iQzS8/shecAmDa2AXsPJbLvcCI3YnNYsfYyS+e1ACD8RAo/b7Ftz115zbV4zU3GD/VnYA9PsnIKWPh14f7GvnTrPB7PoF1LF54KqoJeb0ClUrD3aBrb96faNBeAS8cupO/ZarTM/ZkX0Kckkb5ri833b86yHxIY85wXfZ90Izun8PJkgJHPehJ6Koujpwrnyvh721G7qoaQiPI5bF1Rv0JfYTBY900V4eHhPPXUU2RlZZGZmYmHhwcJCQk4Ojri4+NDVFTpXZJYUUycOJHTp0+zZYt1/8ge77vXRon+GeVdc37KC0OBDb905R9y9/Us6whm6fNLf06ZJVJulp/vnqgIqlS13VVC/8RnumllHcGsaVU+K+sIxfru49o22/bNs+H3b2QBv8AWJbIdS1ldXk2YMIFevXqRnJyMg4MDBw8eJDo6mlatWvHRRx/ZImOFd/z4cZte8SOEEELcjwFFidxKm9WFSkREBJMmTUKpVKJSqcjNzaVGjRrMmzePGTNm2CJjhXfixAn5qn0hhBBl6l/9PSp3UqvVRb+b4+Pjw5UrV2jUqBFubm5cvXq1xAP+G8TGxpZ1BCGEEKJCsrpQadGiBUeOHCEgIICgoCDeffddEhIS+Oabb4qufBFCCCFE+VIWV+yUBKv7cGbPno2/vz8AH374IVWqVGH06NHEx8ezbNmyEg8ohBBCiH+uos5RsbpH5ZFH/v6SHR8fH6uvZBFCCCFE6auolydXzNRCCCGEqBSs7lGpU6cOinuMc8n3qAghhBDlT1kM25QEqwuV8ePHG/2t0+kIDw9ny5YtTJkypaRyCSGEEKIEVdShH6sLlTfffNPs8sWLFxMaGvqPAwkhhBCi5FXUHpUSK6969OjBjz/+WFKbE0IIIYR4sB8lNOeHH37Aw8OjpDYnhBBCiBJUaYZ+WrRoYTSZ1mAwcPPmTeLj41myZEmJhhNCCCFEyaioQz9WFyp9+vQxKlSUSiXe3t506tSJwMDAEg0nhBBCiMpNYTAYDGUdQpjX7aWS+UnukpadllHWEczSOGjLOkKxdLl5ZR3BLKVKVdYRzDIUlM+XJb1OV9YRzGraoXz+6KmXr1NZRzBrhmJOWUcoluOQd2227Ysl9PUh9erWLZHtWMrqASuVSkVcXJzJ8sTERFTl9EVPCCGEqOwMBkWJ3Eqb1UM/xXXA5ObmotFo/nEgIYQQQpQ8QwX9MnqLC5XPPvsMAIVCwVdffYWzs3PROr1ez549e2SOihBCCCFKlMWFyqeffgoU9qh88cUXRsM8Go2G2rVr88UXX5R8QiGEEEL8Y+Xhqp85c+YQHBzMm2++yYIFCyy6j8WFyqVLlwDo3LkzP/30E1WqVHmgkEIIIYQofWVdqBw5coQvv/ySZs2sm/xt9YDVzp07pUgRQgghhMUyMjIYPHgwy5cvt7qGsLpQ6d+/P3PnzjVZPm/ePAYOHGjt5oQQQghRCgwoSuT2IMaMGUPPnj3p0qWL1fe1+qqfPXv28N5775ks79GjBx9//LHVAYQQQghheyU19JObm0tubq7RMq1Wi1Zr/rus1q9fT1hYGEeOHHmg/Vndo5KRkWH2MmS1Wk1aWtoDhShN165dY8CAAbzzzjulvu+3336bRYsWlfp+hRBCiJL6HpWQkBDc3NyMbiEhIWb3efXqVd58803WrFmDvb39A+W2ulB56KGH+Pbbb02Wr1+/nsaNGz9QiKCgIIYPH26yfMmSJTg7O1NQUPBA2zVnwoQJBAQE8P3335fYNi21YcMGgoKCSn2/QgghREkJDg4mNTXV6BYcHGy27dGjR4mLi6Nly5bY2dlhZ2fH7t27+eyzz7Czs0Ov1993f1YP/bzzzjv069ePixcv8sQTTwDw559/snbtWn744QdrN4fBYCA8PJxnn33WZF1oaCjNmzdHqSyZL6lJTU1l165dDB06lEOHDpXINi1148YNvLy8rJ7tLIQQQpSEkhr6udcwz92efPJJTpw4YbRs2LBhBAYGMm3aNIu+0d7qQqVXr15s3LiR2bNn88MPP+Dg4MDDDz/Mjh078PDwsHZzREZGkp6eTqtWrUzWhYaG0rlzZ4u3pdfrOX/+PI0aNTK7fseOHXTs2JHdu3fTvn37ouVPPfUU3t7erFq1Cii8smngwIHExsaiUqk4e/Yso0eP5sCBA9SsWZMFCxbQs2dPwsPDad68OcB922zbto033njDiiNjvaq+WqaMrImbsx2Z2Xo+WnaF6Os5Rm0a1Xdk3NAaAKhUCk6dz2TJN9fQ5dvut1Wq+9szY3xD3F3syMjSM3vheS5fzTJq4+ejZca4BgTUdSYmNofhE2z/O0fV/LRMe70ubi5qMrLymbf0EtHXso3aNG/iwqvP18DBXoXBYOBQeCrL113F1r+QVd3fnhnjGuDmqiYjM5+QRZGmx8xbS/C4BgTUcSImLodXJkbYNhRQzc+e4LH1cXOxIzNLz5zPL3D5rmPm561l+hv1qF/biZtxuYyYctz2ufztmTG2Pm6uajKz9LeOl5lcY+vfOl65jJh0zOa5qvs78NaEhri7Fj7HZi84x6UrWSbtenb148UBNVAqFBw9nsLHSyPR6233JHv2SQceDlDj6abig5VpXIsz/VTbsKYdfYMc0GoUGICTF3Vs2JWNLZ/6PVurCKyppIqzgs836biZZLq3lvWVPNb47w+vro4KLscaWLcz32a58vL1fPJnGPujYtDaKWngU4UP+7Q3anP48k0+2xlBVp4OhUJBx/pVGde5BUpF2X93yW1lcXmyi4sLTZs2NVrm5OSEp6enyfLiPFBXRc+ePdm3bx+ZmZlERUXx7LPPMnnyZB5++GGrt3X06FFUKpXJfbOzszl9+jQtW7a0eFubN2+mbdu2HD582Oz6v/76i7Zt2/Lrr7/St2/fouXVqlXj+vXrRX8HBQWRnZ3NwYMHOXv2LG3atKFjx46cOnWKuXPn8vLLL6NWq4uGuixpc+nSJaN92sL44TX4bWciw6ee4btf45j8Wk2TNlFXsnnjP+cY/fY5Rs44i7urHb26eNk01+TXA/jljxheeP0oa3+6xow3G5i0yczSs3xNNLM+PmvTLHeaMKIOm/+MZ8iE43y7KYapo+uYtMnI1PPBZxcZPvkEo2aconEDZ7o9btvjBTB5dH02bb3J4DFHWbvhGsFjA0zaZGbr+WptNLM+PWfzPLdNGlmXX7fF8tK4CNZtvM70N+qbzbVi3VU+WBhZarkmj6rHL9tiefGNcNZuuF7s8Vqx9grvLyi9XFPGBLDpjxieH3WENT9cZcb4hiZt/H3teXVwbcZMi2DQa4fxcFfTp7u/TXOFndMxf006ianFd7tn5Rj4alMmM1ekMfvrNOpWs6NtU9v+TMqp6AKW/6YjOaP4cijsQgGLN+UX3TKyDRyLuv/wwT/x2c4IAH4e1YvvX32aCU+avi+52muY80x7fhrZi7XDe3DsWgK/niiZHwEsKWV51c8/8cBjKnv27GHIkCFUrVqVjz/+mCeeeIKDBw9avZ2wsDD0ej2Ojo4oFIqim6OjI3q93qpCpXfv3kydOpVu3boRGhpqsj40NJTk5GTs7e2NtlutWjWuXbtW9LdSqcTBwYG4uDjGjBlD//79mTVrFvXq1aNv374EBQURGBhYNKnYkjbvvfcednZWd2BZzN3VjoA6jvy5LwmAv46k4O2hoaqP8QtLbp6B20OCdnYKNBqlTXsH3N3UBNZ3Zuuuwh+y3LU/AR8vLdX8jCdVpWfkc+JMGtm5JTcf6Z65XO1oUNeJbX8lALDnUDI+nhqq+hp3Z164nEVMXOHsdp3OwMXoLHy9bfsrze5uahrWc2bb7sJjtvtA4j2PWU5O6R2zhvWc2LYnvjDXwSR8PDXmc51NJ6e0zqWbujDX7lu5DiTifa9cObZ9U7szV2CAC1t3xgK3n/v2VPM3ztWpnRd7DyeSlFL4y8wbf4+hS5CPTbNduJZPSvq9/+FfjdOTkFp4DvP1cC02H0832/5WzOVYA2mmHU7Fqu6lwMlewdkrtnsRy87LZ+OxC7zRqTmKW70jXs4OJu0C/TyoXsUFAK2dioa+VbiRkmmzXBXZrl27LP5WWrBy6OfmzZt8/fXXrFixgrS0NJ599llyc3PZuHHjA0+kDQsLo2/fvrz7rvFPW69fv57PPvvMZLu7du2yaDho0KBBXLx40WjZ5cuXiY+PZ+bMmUbL7+5RiYiIICUlhZYtW7Jjxw6OHTPuItZoNEU9QNHR0fdtUxq8PdQkpei4c95xXGIePl4absTlGbX19dLw3oS6VPXRcCgijV+2J9gsl4+XlsTkPPR35IqNz8XXW8v1mznF39HGvD01JKXkGR+vhFvHKzbX7H2quKl5vE0V3ppn20/kPp4ak2MWl1D2x6zwXOqMz+WtY1amuTw1JrniEnLLPJevl5bEpLuf+zn4ettzPebvXL7e9tyM+/vvm3E5Ni+GreXqpKBFQw2Lf8wo6yhGWgUoibhYQIENP2xdTUnH1UHLiv0nOXTpJvZ2KkZ2bEabOn7F3ichI5vtZ6+w8NlOtgv2AMril49LgsXlca9evWjYsCHHjx9nwYIF3Lhxo0QutQ0LC6NTp040b97c6JaUlESzZs1MJtq0bt2aM2fOFHubOXMmdnZ2zJ8/32RfsbGxaDQaBgwYYLS8WrVqZGRkkJaWRkFBARMmTGDw4MFEREQYDd/cdvLkyaIixJI2lsjNzSUtLc3oVqDPu/8dH0BsQh6j3zrLoDdOolEr6PCom03282/i6KDkg6kBfLvpJuej5FOSqDzsNfB6f2e2Hs7hys3S6Y2yhNoOHqqj5GikbTPpCwzEpGZS18uNtcN7MLXbI0zfuJfEjGyz7TNydbz5/S6GtG1ME39Pm2azVgGKErmVNosLld9//51XXnmFmTNn0rNnT4tm6t5PVFRUUc/F3cLCwsxOsHV0dCQwMNDs7erVq4SEhLBu3Tr69etncl+1Ws3HH39schVRtWrVgMLvWJk2bRo3b95k0aJFKJVK9Hq90eVTW7ZsMSpCLGljCXPXpV86+V+L7x+fpMPDXc2dD83HU0NcQvHFTk5uAbsOJvNEO+snQVsqLiEXzyoaVHfk8vXWEhtvvteitMQn5uHhrjE+Xl7mj5eDvZI5wQ3ZH5rCD7/dtHm2uMQ8k2Pm41X2x6zwXKqNz2Uxx6w0FR4vtcnxKutcsQm5eHrc/dy3JzbeuJcnNj4HP5+/h4P8fOzL/FzfptXA2GddOBap488j5SPTbU1rK4lLMRCfatv9+Lk6olQoeKpJbaBwiKequxOR8SkmbTNzdYxZv4NOAdV5qY35izqE9SwuVPbu3Vt0dU6bNm34/PPPSUj4Z0MGR48eRalUFl05c5tOp+PkyZNWzU8BaNu2LRs3bjTpMQFYtWoVmZmZaLVaDh48yK+//lq07nahMmnSJH766Se2b9+Oq6srrVq1Qq1WM2PGDKKiovjxxx8ZM2YMQFERYkkbS5i7Lr1OU9PvlilOSlo+Fy5n8WT7wqKj46PuJCTpTIZ9qvpouF1j2qkUtGvlTtQV858MSkJKqo7zFzPo1qlwzL1TOy/iE3PLtEseCo9X5OVMunYsnBj7eJsqxCfqTIZ97LWFRcqRiFTWbLhROtlSdZyPyqTrrXkKQY95lp9jdimTro97F+Zq60F8Ul7Z5yo6XrdyPeZJfGI5yXUxg26dfYFbz/2EXKNhH4Dd+xPo0NoTD3c1AM/08Gf7nrhSz3s3rRrGDXThVJSO3w+U7bE0p1WAkqORtp8HVcXRnta1fdkfFQPA9ZQMbqQU9rDcKStPx5hvd9CublVe7fCQzXM9iIo6mVZhMFg3lTIzM5Nvv/2W//73vxw+fBi9Xs8nn3zC8OHDcXFxsWrnwcHBbNiwgbNnja/0OHbsGM2bNycsLIwWLVpYtU1zcnJyGDRoEP379yckJITatWvzv//9D29v76I29vb21K1bl61bt1K9evWi5atXryY4OJiUlBR69OhBy5YtWbhwITExMVa1eRDdXrLuEt3qflomv1YLV2cVWdkFfLQ8msvXcpjwSg0OhKVyMDyNpzp78kw3b/QFoFJCxOl0lq+/gU5n+dMgO826ceoa1RwKL7V1KbxsOuSz80RFZzHtjQD2Hk5k3+EktBola5c+gkatxMlRRXKqjq274vjym8sW70fjYN24fnV/e6aNrovrrUtt538RxaWr2Ux6rTb7j6Zw4GgKLzzjz5AB1Ywuwd19MIm1G607t7pc6z7d16jqQPC4ANxc1GRm5TNnUSRRV7KY+np99h1JYt+RwmO2ZnEr42O2O45lq6Mt3o/Syp7RGlXtmT6mPq4udmRl65mz+CKXrmQxZVRd9oUmsz80Ga1GyepFzVHbFeZKSdOxdXcCy9desXg/BisnHdSoak/w2ICiczn38wtEXcliyuv12Hckif1HbuVa3ALNrVzJaTq27opn+RrLc+l1OutyVXPgrfGBRZdzz154jqjoTKaNbcDeQ4nsO5wIQK9ufrw4oPAqvfATKcxfYt3lyU07WPf9TC90d+ShempcnRRkZhvIyTPw7rI0Xvw/R45f0HH8go4ej9nzdHt7biT83Vscds66osXL18mqXH0eU9GguhJnB8jOhVwdfPqTjmfaqTh7tYCzVwuPiZcrjO6lZu63OvIe4KrkGYo5VrW/lpzOzM2HSMnOQaFQ8FqHh+gSWJOZmw8SFFCdTg2q89W+k3z513HqerkX3a9ro5qMaG/ZJbi3OQ559/6NHlDY+cQS2U7LBqU7pGV1oXKnc+fOsWLFCr755htSUlLo2rUrmzZtKsl85c7EiRM5ffo0W7Zs+UdtLGFtoVJarC1USou1hUppsrZQKS3WFiqlxdpCpbRYW6iUFmsLldJibaFSWqwtVEqTLQuVo+eTSmQ7rRrYbrqAOf/oWrOGDRsyb948rl27xrp160oqU7l2/Pjx+w7pWNJGCCGEEPdXIhfFq1QqnnnmmX99bwrAiRMn7vs1+Ja0EUIIIUpTSf0oYWmz3TeQ/UvFxsaWSBshhBCiNJXFRNiSYNuvGRRCCCGE+AekR0UIIYSoBCrqN9NKoSKEEEJUAqXz61slTwoVIYQQohKoqD0qMkdFCCGEEOWW9KgIIYQQlUBFvepHChUhhBCiEpChHyGEEEKIEiY9KkIIIUQlIEM/QgghhCi3yulvfd6XFCpCCCFEJSA9KqLE5WXnlHUEs1Tq8vm0sdNqyjpCsfLz8ss6glnqcnrM8rJzyzpCheLu4VDWEcxq81D5nAZ5031wWUcoVt2yDlAOlc93HCGEEEKUqIp61Y8UKkIIIUQlYJA5KkIIIYQorwoq6ByV8jmAKIQQQgiB9KgIIYQQlYLMURFCCCFEuVVR56jI0I8QQgghyi3pURFCCCEqAfnCNyGEEEKUWxX1K/Rl6MdGZs+ejbOzc9Ft9uzZZR1JCCFEJWYwKErkVtr+FYXKb7/9hkKhKPY2aNCgUs80atQoIiIiim6jRo0q9QxCCCFEWVq6dCnNmjXD1dUVV1dXHnvsMX7//XertvGvGPrp3LkzMTExRsv0ej3Dhg0jLCyMGTNmlHomDw8PPDw8Sn2/QgghhDllcdVP9erVmTNnDgEBARgMBlatWkWfPn0IDw+nSZMmFm3jX1GoODg44ODw949y6fV6Bg8eTFhYGH/++ScPP/ywRdvR6/WcP3+eRo0aFdvm7NmzjB49mgMHDlCzZk0WLFhAz549CQ8Pp3nz5ha3sYVqfvYEj62Pm4sdmVl65nx+gcvXso3a+Hlrmf5GPerXduJmXC4jphy3WZ7ynquqr4Ypr9bC1UVFZlYBH391hejrxf8Q5Nxp9ahfy5H+r5+webZq/vbMGFsfN1c1mVl6QhZFcvmqmWM2tj4BdZyIictlxKRjts/lq2XKqNpF53L+l5dNjlmj+k68ObwmACqVgpPnMljyv6vo8m33Kllej1d1fwfemtAQd1c1GVn5zF5wjktXskza9ezqx4sDaqBUKDh6PIWPl0ai19vuePV6zI7GtVRUcVGw8MdcYpLM78u3ioI+7dQ433p5/SM0n1OXC2yWa1nIq6SnJKBQKtDaO/HMkBlUq236enxo54/s3PQVBoOB+k1a02/YO6js1DbLddvWrVv5dMEC3nn7bdq1a2e07tKlSyxZsoSU1FRUKhUNGjRgzOuvo9VqbZ7LUmXxzbS9evUy+vvDDz9k6dKlHDx40OJC5V8x9HMnvV7Piy++yPbt260qUgA2b95M27ZtOXz4sNn1Z8+epU2bNnTs2JFTp04xd+5cXn75ZdRqNY0bN7a4ja1MGlmXX7fF8tK4CNZtvM70N+qbtMnM1rNi3VU+WBhp0ywVIdebQ2vw265EXpl2lu82xzJpRM1i2/br7k1MXF6pZZs8qh6/bIvlxTfCWbvhOsFjA0zaZGbrWbH2Cu8vKMVj9kpNftuZwLDJp/j215tMGVnbpE3UlSzGvHOGUTPO8Nr007i72tGri7dNc5XX4zVlTACb/ojh+VFHWPPDVWaMb2jSxt/XnlcH12bMtAgGvXYYD3c1fbr72zTXyUt6lv6SS3J68cWQWgUvd1PzR6iOT37I49Mf87h803ZFCsBL4z5m0twNTAz5icefGsL6L0x7wxPjrvHH94t4/T//Y/qnv5OemsjBHd/bNBdAbGwsW/74g8DAQLPrNRoNr7/+OsuXLWPx55+Tm5PD99/bPldFotfrWb9+PZmZmTz22GMW3+9fVajcLlK2bt3K9u3brSpSAHr37s3UqVPp1q0boaGhJuvHjBlD//79mTVrFvXq1aNv374EBQURGBiIRqOxuI0tuLva0bCeE9v2xAOw+2ASPp4aqvnZG7VLz8jnxNl0cnJt+4JT3nO5udgRUMeRP/cnAbA3NBVvDzVVfUzPUa1q9rRr5ca3m2NLJZu7m7rwmO2+dcwOJOJ9r2OWoy+dXK52NKjrxPa9iQD8dTgFb08NVX2NPzHm5hnQ34pkZ6dAq1Fiyx7ncnu83NQEBriwdWfh82bX/gR8vOyp5m+cq1M7L/YeTiQpRQfAxt9j6BLkY9Nsl24aSMu8d5vm9VVcjTMQHVt49gwGyCy+w7FEODi5Fv1/TlY6CoVpD8DxQ1tp3Kozru7eKBQKHntyEOH7f7NproKCAhYsXMjoUaNQq8333FSrVo06deoAoFKpCGjQgNi4OJvmspbBUDK33Nxc0tLSjG65ubnF7vfEiRM4Ozuj1WoZNWoUGzZssOqD+79i6AcKi5SXXnqJrVu38ueffxY7xLJr1y46d+583+0NGjSIixcvFv0dHR3Njh07OHbMuLtYo9EUFUSWtLEVHy8tick69He8z8cm5OHjpeH6TRu/ulTAXN6eapJSdBTckSs+KQ9vTw037ug5Ualg/LAafLLiilFbW/Lx1Jgcs7iE3LI/Zh4akpKNj1lcYh4+nhpuxBq/SPl6aZg5sR5VfbUcikjll23xNstVXo+Xr5eWxKQ84+d+fA6+3vZcj/k7l6+3PTfj/v77ZlwOvt5lP1zgU0VBvt7AkO5q3JwU3EwysPmgzubFyrolwVw4XdirPWLqUpP1KYkxVPGqWvR3Fe+qpCTGmLQrST/demMNCDDtqTMnJyeHP/74g6FDh9o0l7VK6oqdkJAQZs6cabTsP//5D++9957Z9g0bNiQiIoLU1FR++OEHhgwZwu7duy0uVv4VhcrtIuWPP/5g+/bt95wH0rp1a86cOVPs+u+++47333+f+fPnGy2PiIgwO3xz8uRJhgwZYnGb4uTm5ppUpAX6PJQq2/XCiPt78Rk/9h1N4WpMLr5eci4sFZuQx6gZZ7DXKpn+eh06POrOroPJZR1LWEGpgPrVVCz5OZe0LOj+qB3PtFez5k+dTff7/OshABzZs5HN6z5hxLQvbLq/+7l8+TL79u1j/rx5FrXX6XSEhITQsmVL2t81j6WsldT3qAQHBzNx4kSjZfeai6PRaKhfv3DIv1WrVhw5coSFCxfy5ZdfWrS/Cl+o6PV6Xn755aIipUWLFvds7+joWOwY47Zt2wgJCWHdunX069fPaJ1SqUSv16PX67GzKzxsW7Zs4eTJk0W9JZa0KY65CrVWo+HUbjzinve7LS4hF88qalRKij7B+XppiEsovXkVFSlXfKIOD3c1SiVFPQTeHhriE41zNWvojLenhl5PeqNSgaODklUfNWbczHOkpttmCCEuMc/kmPl4acv+mCXl4VHF+Jj5eGqISyw+V05uAbsOJPFEew+bFSrl9XjFJuTi6aExfu572xMbb9wlERufQzX/vy8G8POxJza++G700pKSYSAqpoC0W3N/wy/oeeX/Sq9Yf/TxZ/hxxSwy01NwcnEvWu7u6U9i3NWiv5Pjb+Duabs5PSdPnSI2NpZXRhS+FicnJ/PZlSskJSfzdM+eRm3z8/MJmTMHDw8PRo0cabNMZU2r1f6jScIFBQX3HCq6W4Weo1JQUMDLL7/Mxo0bWb16Nf7+/ty8edPoptdb/mbStm1bNm7cyIABA0zWtWrVCrVazYwZM4iKiuLHH39kzJgxAEVFiCVtihMcHExqaqrRrWbDly3OnpKWT+SlTLo+XjhpMaitB/FJeWXa9V2ec6Wm53PhcjZPtiu8hLzDI24kJOuMhn0AJs2+wMuTTjNk8mkmfXiBrOwChkw+bbMiBSAlVcf5qEy6Bt06Zo95Ep9Y9scsJS2fC5ey6NLBE4COrd1JSMozGfap6qtFpSr8fzuVgvaPunPpSvbdmyu5XOX1eKXqOH8xg26dfYHCuSjxCblGwz4Au/cn0KG1Jx7uhXMfnunhz/Y9ZT+34XiUnupeCrS3pmQE1lASk2S78c/szDRSk/9+3CeP/ImTizuOzm5G7Zq17srpoztJS4nHYDBw4M9vaf5YD5vlerpnT9auWcOqr79m1ddfExgYyLixY02KFL1ez5w5c3BxcWHcuHFm59eUtZKao2KN4OBg9uzZw+XLlzlx4gTBwcHs2rWLwYMHW7wNhcFQUX9PEQ4dOkTbtm2LXa9QKEhJScHV1bXYNtZYvXo1wcHBpKSk0KNHD1q2bMnChQuNvsPFkjaW6jTggFXta1S1Z/qY+ri62JGVrWfO4otcupLFlFF12ReazP7QZLQaJasXNUdtp8TJUUVKmo6tuxNYvvaK1fnKWy6tk8P9G92hup+WSa/WxNVZRVZ24eXJl6/lMH54DQ6Gp3IwPM2ova+XhiWzGj7Q5ck56aaXpN5Ljar2BI8NwPXWZcBzP79A1JUsprxej31Hkth/5NYxW9wCza1jlpymY+uueJavsfyYaRys+1RU3V/LlJG1cXUuPJfzl13m8tUcJo6oxYGwFA6EpfJUZy+e6e5DQYEBlUpB+Kl0lq+7hk5n+UtNXrZ1PQqldbz0OuuGPWpUc+Ct8YFFl3PPXniOqOhMpo1twN5Diew7XDgxuVc3P14cUHjVWfiJFOYvse7y5A6921iVq28HOwJrqHB2hKwcyNUZ+Oi7PPp3tON0dAFnrhQWJC3qKwl62A6DAdKyDPz0l47U+0zCvVP7lpZ32ifF3+CbhRPQ6XJRKBQ4u3jw9ODJVKvdiO+WvUuTVp1o0uoJAA7u+J6dm1YAUK/xo/Qf/q5Vlyc3cY+2/EHcZeq0aTzTpw/t2rXjf998g6eHBz179mTHzp3Mnz+fOnXqFBUpjRs1Kvqwaqm69eo9cLb7+elwyRSa/Vpb3sfxyiuv8OeffxITE4ObmxvNmjVj2rRpdO3a1eJtVOhCpaxNnDiR06dPs2XLln/UpjjWFiqVnbWFSmmytlApLdYWKqXF2kKltFhbqJQWawuV0mJNoVKa/kmhYmu2LFR+OFQyhcqANqU7GFOhh37K2vHjx+87pGNJGyGEEEKYJ4XKP3DixAmaNWv2j9sIIYQQtlYWc1RKQvnsl6sgYmPv/wVglrQRQgghbK2iTvSQHhUhhBBClFvSoyKEEEJUAgUl9M20pU0KFSGEEKISqKhDP1KoCCGEEJVARS1UZI6KEEIIIcot6VERQgghKoGS+lHC0iaFihBCCFEJGCroZFoZ+hFCCCFEuSU9KkIIIUQlUFEn00qhIoQQQlQCMkdFCCGEEOWW9KgIUcaUCplyZS1DOf2IpVBWzEl/ZcXfT1vWEcy6Elc+z+PDrnllHUFYQQoVIYQQohKQHhUhhBBClFvltAP1vqRQEUIIISqBitqjIoP6QgghhCi3pEdFCCGEqAQKCso6wYORQkUIIYSoBGToRwghhBCihEmPihBCCFEJSI+KuK+3336bRYsWlXUMIYQQlVCBoWRupa3CFCovvvgiffv2NVq2c+dOFAoF77zzjtHy+fPn4+/vT15e+fr2wQ0bNhAUFFTWMYQQQlRCBoOhRG6lrcIUKu7u7qSmphot+/jjj9FqtUbL8/PzWbRoEWPHjkWj0ZR2zGLduHEDLy8vmjVrVtZRhBBCiAqjwsxRubtQOXv2LNu2bWPYsGFGy7///nsSExMZNWqURdvV6/WcP3+eRo0aFdvm7NmzjB49mgMHDlCzZk0WLFhAz549CQ8Pp3nz5ha12bZtG2+88caDPXgLVfOzJ3hsfdxc7MjM0jPn8wtcvpZt1MbPW8v0N+pRv7YTN+NyGTHluE0zledcVX01TBpRA1cXO7Ky9Hz81VWu3Mgttn3I1LrUr+3AwNdP2TxbNX97Zoytj5urmswsPSGLIrl81cwxG1ufgDpOxMTlMmLSMdvn8tMydXSdonM574tLRF/LMWrTvIkLI56rjoO9EgNwKDyVr9Zds+n4uCXPsRZNXXltcK2iXAePJrNszRWb5qru78BbExri7qomIyuf2QvOcelKlkm7nl39eHFADZQKBUePp/Dx0kj0etsEy9flsm31RJJiL2CntsfB2ZOg/v/BzauWUbvEmHPs+el9sjMSUSrt8Kn5EI/3exc7tb3Ncm1ZNYGk2IvYqbU4OHvSeeB7uHsb50pNvMpvK9/EUKCnoECPh29dnhj0PvaObjbJdact2/7k44WLeO+t6bR/rK3RupibsbwfMhd9QQEF+gJq1KjOhLGv4+LsbPNclpI5KjZ2d6HyySefMGjQIBo3bmy0/NNPP2XYsGF4eHhYtN3NmzfTtm1bDh8+bHb92bNnadOmDR07duTUqVPMnTuXl19+GbVaTePGjS1uc+nSJZOhq5I2aWRdft0Wy0vjIli38TrT36hv0iYzW8+KdVf5YGGkTbNUhFxjh1Tn991JvDr9HN//Fs+kETWKbdu3uxcx8cUXMSVt8qh6/LItlhffCGfthusEjw0waZOZrWfF2iu8v6D0jtn4EbXY/Gc8QyeeZP2mm0wdVcekTUZmPh8uusgrU04xesZpmgQ40bWjp01zWfIcS8/MZ9an5xk64Rgjpx6naUMXugd52zTXlDEBbPojhudHHWHND1eZMb6hSRt/X3teHVybMdMiGPTaYTzc1fTp7m/TXI3bPssL07YwaNLP1GnyBDu/e9ukjcpOS8e+7/DCtN95dtJGdHnZhO1YbtNcTdsN4qUZW3hh6ibqPvQkf643zeXk5svAcWt5YerPvDj9V5zcfDi0xfZz/27GxvL7H1tp1ND0HAJ4enrw6bwQvly0gOVLPsPTw4P/rVlv81zWKCgomVtpq5CFSnx8PKtXr2by5Mm4ubkVLf/rr784evQo48ePt3i7vXv3ZurUqXTr1o3Q0FCT9WPGjKF///7MmjWLevXq0bdvX4KCgggMDCwaWrKkzXvvvYedne06sNxd7WhYz4lte+IB2H0wCR9PDdX8jD/9pGfkc+JsOjm5pfNsK6+53FxUNKjjwI79yQDsDU3Fy1ONv4/pcGHNqloea+nK97/Gl0o2dzd14THbfeuYHUjE+17HLEdfOrlc7WhQx4ntexMB+OtwMt6eGqr6Gv9y74XL2cTEFc4P0+kMXIjOxs/bdr/ua+lz7MKlLGLiCovNPJ2BC5ez8POxYS43NYEBLmzdGQvArv0J+HjZU83fOFendl7sPZxIUooOgI2/x9AlyMdmuezUWmo1CkKhKPxlY99aD5OefN00v3dtvKoWvikrlSp8ajQ1264kc9Vu/Hcuv1oPk5Zkuj87Ow12msJjWFCgR5eXjQLb/kpzQUEBn3y2mDGjXkWtNv86rlGr0WoLn096vZ6c3BwU5fPHoyucClmoLF68mA4dOtCsWTNcXV2Lln/yySf06dOH+vWNP03t2rULhUJR7O3tt98mNTWVQYMGGd0vOjqaHTt2mBQ+Go2Ghx9+2OI2pcHHS0tisg79He/zsQl5+HiV7Tyd8prL20NDUkq+0aeD+EQdPp5qo3YqFbw5rDqLvr6OvpT6TX08NSbHLC4ht+yPmaeGpBSd0TGLu8+5rOJmx+NtqnAwPMVmuR7kOebhriaorQcHjibbLJevl5bEpDzjXPE5+HobFyq+3vbcjPt7+OxmXA6+Nizs7nb8r2+o3eTJe7bR5WZx5tAP1LlPu5IUsed/1H3oCbPr9Pl5rJ3Xh+VvtSUlPpo2PcbZNMuPGzfRpHEjGtQ37am7k06nY+TY8Qx44WWu34hhyODnbZrLWgZDydxKW4Wao6LT6UhOTmbp0qV88803AEWFysWLF9m0aRN//fWXyX1bt27NmTNnit32d999x/vvv8/8+fONlkdERBgN39x28uRJhgwZYnEbS+Tm5pKbazy0UKDPQ6kqPxOCK6PBfXzZfzSVqzG5+Hip738HUcTRQckHUwL49pebnI8ynZdRVhwdVMyeHsi6n29w7mJmWccpU0f//ILUxGh6D/i62Db6/Dy2rp5AjYbtqftQ11LJdWTbF6TGX+GJMeZzqew0vDD1Z/T5eez+8QNO7l9PqydftUmWS5ej+WvfAT6Z++F926rVar5ctACdTsfiL5fz6+9/MGhAP5vkehAV9deTK1SPCsCiRYvw8/OjW7duAEVDPwsWLKB169a0a9fO5L6Ojo4EBgaavV29epWQkBDWrVtHv37GTyilUoler0ev/7trfcuWLZw8ebKot8SSNpYICQnBzc3N6Hbl3P8svn9cQi6eVdSo7jijvl4a4hLK9hLt8porPikPD3c7lHfk8vZUE5eoM2r3UENnenXx4uuPAvl4Rj0c7ZV8/VEgbi4qm2WLS8wzOWY+XtqyP2aJeXi4q42OmU8x59LBXknI9AbsP5rCj7/F2jSXNc8xB3sl895uxL4jSXz/a4xNc8Um5OLpoTHO5W1PbLzx5OPY+Bz8fP7uZfHzsSe2FOZDhe9aQdSJbTw9YjlqjYPZNnq9jq2rJ+Lo6kOHPm/ZPBNA2I4VXDy+lT4ji891m8pOQ6M2/TgbuslmeU6ePk1sXBxDXxvNi8Nf5cy58yz4fCm//PZ7sfdRq9V06/Ik23fuslmuB1EWPSohISE8+uijuLi44OPjwzPPPMO5c+es2kaFK1Q+/fRTJk2aVLTc1dWVlJQUvv76a6Pllmrbti0bN25kwIABJutatWqFWq1mxowZREVF8eOPPzJmzBiAoiLEkjaWCA4OJjU11ehWs+HLFt8/JS2fyEuZdH28cHJgUFsP4pPyuH4z5z73tK3ymis1Xc+F6GyeaFcFgA6PuJGQpCuaW3HblJCLDJ18lqGTzzJp9kWycgoYOvksqem2mxeSkqrjfFQmXW9N9Ax6zJP4xLI/Zilp+Vy4nEWXDoUTYzu2rkJ8Uh43Yo3fVO21hUXKkWOprNlg22Lgdi5LnmO3i5TD4Sl886Pt5loU5UrVcf5iBt06+wKFc1HiE3K5HmOca/f+BDq09sTDvbDH7pke/mzfE2fTbBG7V3IhfDO9XvsvWgdXs20K9PlsWz0RraMbnQbMKpo7YkthO1dyLmwzz4xeidbRfK60pOvo8gqv6DIUFHAhYgteVRvYLFOvp3rw7TcrWf3f5az+73IaNWzA+DdG0+upHkbtYuPiyMkp/LdQUFDAnr37qFu7ts1yVRS7d+9mzJgxHDx4kG3btqHT6ejWrRuZmZb3ZioMZfHtLQ8gISEBb29vqlWrxqVLl1CrC/9Rx8bG4ufnR506dYiMjESlKtlPuqtXryY4OJiUlBR69OhBy5YtWbhwITExMVa1eRCdBhywqn2NqvZMH1O/8HLbbD1zFl/k0pUspoyqy77QZPb/f3t3HhZV9f8B/D0Dw7CDsi+SCyiuIJJIpIhJaoX7V3P5KpqVppXigktqVootlpap/dq0r0ubiqZmuaKJiCCj7CGCouzLsK8z5/cHMTEO5Iwxcy/weT3PPE9cztz77swVPpx77rkxJRAbCLH/M0+I9IUwMdaDtKwev0cU4suD9/5VVj7kMjI10SiXk70Yyxc4w8y0MdcnX99H5v0avDnPGVFxZbgmKVNqb2stwufv9H6s25Oryio0at/N0RBrXneD+V+3276/8zbu3KvCytd64cr1YkRe/6vPPh8Mg7/6rKSsHr9fLMCXB9TvM5FYs0uLzg5irFrYozFXtQwf7clERlY1Ql5+AldvSHE1thQzJzpgzhQHZDa7bfnStRIcDFf/30NDff2jGzWjzjk2e7ITgqc5K93mffFqEfYfUb9oaajVbFSrm5MR1i11V9w2vWVHKu7crUTo673xx7UiXIlunJgc9Kw9Zk91AQDExUvx4S7Nbk/+zysj1G5bIc3Fd++NhLlVN4jEjf9m9PQMMPXNHxF9+lMYm9tiwFMv4s8bv+DswZWwcuiDplmhDt29MGLyBrWPpa+vfnFTLs3Ft2/7w9yqGwyacukbYHrIT4g6tQMmFrYY6DcDdxLO4+rJTwA0LmBm69wPwyetgZFJF7WPFeTa+lSAR1m+eh0mTwiCn+8w7N1/EFZduyLoubG4ei0a3/7vQGMuOYNrr55Y9PJ8mJu3XHC1xsWt9aUy/q2PjrTNzQorJj/+GEdBQQFsbW0RERGBESPUO2/bTaHCFyEhIUhKSsLp06f/VRt1aFqodHaaFiq6pGmhoiuaFiq6ommhoiuaFiq6okmhokuaFCq69G8KFW3TZqHyweG2KVRWTXn8QuX27dtwc3NDfHw8BgwYoNZ72s1kWr64desWhgwZ8q/bEEIIIbrUVsMSLd38IRaLFbdnt0Yul2Pp0qXw8/NTu0gB2tEcFb6Ij49/5DL46rQhhBBC2qOWbv4ICwt75PsWL16MhIQEfP+9Zgvh0YiKhvLyHn0HgzptCCGEEF2St9H9yWvWrEFISIjStkeNpixZsgQnTpzApUuX4OzsrNHxqFAhhBBCOoG2uvSjzmWev4/J8Prrr+Po0aO4ePEievRQfezGo1ChQgghhBCtWLx4MQ4ePIhjx47BzMwMubm5ABrXQDMy+ud1cprQHBVCCCGkE+Biwbfdu3ejtLQUI0eOhIODg+L1ww8/qL0PGlEhhBBCOgE5B6uRtMUKKFSoEEIIIZ0A083D6dscXfohhBBCCG/RiAohhBDSCbTXheipUCGEEEI6ATld+iGEEEIIaVs0okIIIYR0AnTphxBCCCG81UYr6OscFSo8Jqtv4DpCu1JRUsp1hFYZmhpzHaFFcpmM6wgtaqit4zpCu3Li+xtcR2jR0h9nch2hRUsXneQ6QquOfKq9fbN2WqnQHBVCCCGE8BaNqBBCCCGdQDudokKFCiGEENIZyNvppR8qVAghhJBOoL3e9UNzVAghhBDCWzSiQgghhHQC7fWhhFSoEEIIIZ2AnC79EEIIIYS0LRpRIYQQQjqB9jqZlgoVQgghpBNor7cn06WfR1i9ejVeeOGFf2xz//59TJ06FevXr9dRKkIIIUQzjLXNS9c6TKGybNkyTJ48uc33K5FI4OHh8chju7m54aeffmrz4xNCCCGdWYe59BMdHY3nn3++zfcrkUgwb968Vr9fWlqKixcvIjg4GNeuXWvz42vC2cEQa5f2gaWZPiqqZNiy409kZlWptHt+tB1mTekGoQC4ES/Ftj3pkMm0Vyark8veVoy1b/SGW09T5OTVYP6yOK3l4XsuAHCyE2Plwu6wMNNHZZUMH36RibsPapTa9HU1wZvzXQAAenoCJKRWYNd3Wahv0N5n6WQvRuhrPWFhJkJFVQM+2J2Bu/erldp49jfDyzO6wchQD4wxXIsrxZeHsrT6l5izgxHWLesDS/PGXFu2pyLjXgvnfqA9Zk/tBqFAgNhbUmzbnablc5+fufh6ftmMHYE+7ywFhEII9fSQ/vHXePC/8FbbD/o6DN3mTMZv1t5oKC3XWi4HGxFen20LcxM9VFXL8dmBfGTlKj84c5SPGZ73t1R8bWWpj6T0anzwda7WcmmKHkrIkbq6OohEIkRGRmLdunUQCAQYNmyYWu+VyWRITk5u9fu5ubnIy8uDTCbDiBEjYGxsjCeffBLx8fGKNufPn8fw4cMREREBPz8/xfbnnnsOc+fOVXx94cIFWFtbQ6bFp9WueM0Nv/yWg5mvxeLgkftY+2ZvlTYOtmIsmPUElqy5iRcXxqCLpQHGj7HXWiZ1c1VWyfDlgbt4Z1uKVrO0h1wA8OZLLjh1oRDzViTihxO5WPlqd5U2d+5VYfH6ZCxcm4xXVifB0lwfQaNttJpr2YIeOHmuAHOX3cIPx3OwalEPlTYVlTK892k65q+Ix8K1iejX2xTPjrDWaq6Vi91w/LcczFh4HQd+zsLapX1U2jjYGeLlWd2xOFSC6a9Eo6ulCBPGOHTKXHw9vzz3fYibL63BH94TcX3iQgzc9Q70TE1abGs/MRBMR0+YXzjdBmeulGHJe/dw9FwJXp9lq9Lm/LVyLP8gS/GSljXgUoz2iqfHIWesTV661u4LFX19fVy5cgVA4+hHTk4OTp8+rdZ7T548iWHDhiE6OrrF70skEgDA9u3bERYWhpiYGJiammLGjBmKNpcvX8awYcNw4sQJTJo0SbHdyckJDx48UHzt7++P6upqREVFafq/qBZLCxHcXU3x+8V8AMDFyELYWovhZG+o1G6knzX+iC5GsbQeAHDsdA5GD9feDx91c5VXNCA+uQzVtbpZkYivuQDA0lwfvXua4OwfRQCAy9FS2FgZwNFOrNSuto6hqe7V1xdAbCCENn+ENOU6c7kQAHDpWglsW8h1O7MKOfm1AID6eob0u1WwsxGr7K/NclmI4O5mht8v5AFo+iwN4eTw0Ln/lDX+iC5SnPvhv+ZgtL/qL5wOn4un5xcAgDGILMwaj2luivoiKeS1dSrNDGyt0Gv1QiSt2KrtRLAw1UMvF0NE/FV0XJVUwqqLPuytRa2+x+0JMSzM9HA9vlLr+TqDdl+oCIVCZGdnw8rKCh4eHrC3t4elpaVa7x0/fjxWrVqFZ599FjExMSrfl0gkMDQ0RHh4OPz8/NCvXz9s3rwZiYmJKCxs/GEdExODkpISGBoawsvLS/FeJycn3L9/XymnkZER8vPz/93/cCtsrcUoKqmDrNnv07yCWpVfEHbWhsjL/3uINzdftQ0XuXSNr7kAwKarAYpL6iFvli2/qA62VgYqbe2sDbBnS18c3uOBymoZfjlToL1cVgYoltYp5yqsg621aq4mXSxEGOHTBVE3pFrLZWctRlHxw59lDexslAsCOxtD5Cqd+zVa/bz5mouv5xcA3Ji5DEN+2omA2+fhe/EgJPNDwerrVdoN2vMeUlZ/CFmF9gsBqy76KCltUOqvwpIGWHdpfebEM8PMEXG9XOmz5wMmZ23y0rUOMUclLi6u1QmvFy9eREBAwCP3MX36dKSnpyttk0gkmDZtGhwdHRXbunTpAgCQ/3XWZmZmoqCgAJs2bVJ678MjKhKJBFKpFL6+vi0ev7a2FrW1tUrb5LI6CPVa/yVASF5hHRauTYahWIjVr/XA009a4mJUCdexAADGRkK8t8oNPxzPxZ936C/L9kiX55dATw9uaxch9j9LUPxHDCy8B8L7yC5cGjwe9UV/H7Pb/KmozspG0UXtjE7/W2IDAZ4eYobVH2dxHUUFzVHh0D/dmTN06FAkJye3+tq0aRP09fXx4YcftrhfT09PpW1RUVFwcnKCrW3j0GxeXh4MDAwwdepUpXZOTk6oqKhAWVkZ5HI5li1bhlmzZsHevuX5IGFhYbCwsFB6ZaXtV7sP8gtrYdXFAHrNPlE7GzHyCpSLn7zCGtjZ/v0Xnb2tapu2pG4uXeNrLgAoKK5D1y4iCJtls7UyQH6R6hB4k5paOS5eLcYov67ay1VUh66WBsq5rA2QX6iay8hQiK1r+iAyRoqfT2l3MmFeYS2suj78WRoir0B5cmheQQ3slc59Q61+3nzNxdfzy9yzL8QOtij+o3F0uzQmHjUP8mDh2VepndXIYbALegYBaecQkHYOADDixnGYP9SurRSVNKCLhb5Sf1l30UdhScvzY57yNEVWTh3u56qOBHFNztrmpWsdolCJj49XKSiaGBsbw93dvcVXVlYWwsLCcOjQIZVbm6uqqpCWlqY0+VUul2PHjh0IDg5WbBOJRNi2bRuEQuWudHJyAtC4xkpoaChyc3Px2Weftfr/sGbNGpSWliq9urnNVrsPpKX1+DO9As+ObCygRj5ljYKiWjzIVf6heDGyCE8P7Yqulo3XVyeMdcC5y9obzlU3l67xNRcASMsacDujCqOftgIADB9qicLiOmTnKf/ycrQTQ0+v8b/19QTwe9ISGfeqH95dm+ZKy6xE4PDGibEjfLqgoKheJZehuLFIuS4pxYGj2VrLo8jV9FkG2AH467MsrMWDHOXPMiKyEE8PtVKc+xPHOeDsJe1ciuV1Lp6eX9VZOTB0sIWpe08AgHEvFxj37IaKPzOU2knmrMD5niNxwe0ZXHB7BgBwyWs8yiSt3xjxb5RWyHAnqxb+3o1zZ3w9TVAkbUBuYcuFyDO+5jgXVaaVLJ2VgLXXNXWb6d69O2bNmoXFixfDxMQEFhYWar2vvLwckZGRGDNmjMr3oqKiMGLECLi5uWHv3r0wNzfHunXrEB8fj5iYGJiZmWHfvn0IDg7GpUuXIBKJUFhYqFgcrrCwEDY2Nhg7diz+/PNPXLp0SVG8qGv4hMsate/mZIS1b/RuvOWwWoawT//EnbtVCF3ihj+ii3AluhgAEBRoj1lTnAEAcQml+Gj3ba3eCqlOLrGBEAd3e8NAJISJsR5KSuvx+8V8fPG/zA6Ry9DUWKP2zg5irHy1O8xN9VFVLcOH/5eJzKwahCx4AldvSHH1RimeC7DGxDG2kMsZ9PQEiEssx5eH7qO+Xv3PUq7hXWjODoYIXdQT5k23te65g4ysaix/pTsiY6W4GivFzIkOmDvVCZnNbluOiCrGwfActY9TW6nZL8RuTkZYt9Rdcbvtlh2puHO3EqGv98Yf14pwJbpx4mjQs/aYPbXxltu4eCk+3KXd24B1lcvI3FSjXLo6v5b+OFOjXI7Tn0ev1a82/tkuFCD9/f9D9vcn0HvjG6jJyce9//te5T3P16dqfHvy14tOapbLVoTXZ9nBzESIqho5dh7Ix72cOrw2wwbX4ytxPaFK0e7DFd3w0voM1NQ+3nl15FPXx3qfOha+3zaX7faEdmmT/airQxQq+/fvR2hoKLKzs7FixYoWL+Noas+ePdi5cyc2bNiAkJAQSKVSBAUFYefOnbCyskJNTQ2mT5+OKVOmICwsDN27d8d3330HG5u/76AxNDREz5498fvvv8PZ2VnjDJoWKoS/NC1UdEXTQkVXNC1UOjtNCxVd0bRQ0RVNCxVd0mah8urW4jbZzxertXcJsCUdolDpqKhQ6TioUNEMFSqaoUJFM521UHl5S1Gb7OfLtVZtsh91dYg5KoQQQgjpmKhQIYQQQjoBxlibvDRx6dIlBAUFwdHREQKBAOHh4RrnpkKFEEII6QS4WPCtsrISHh4e+Pzzzx87d4dY8I0QQggh/DNu3DiMGzfuX+2DChVCCCGkE2ivK9NSoUIIIYR0Am315OOWHvkiFoshFmvnGVU0R4UQQgjpBNpqjkpLj3wJCwvTWm4aUSGEEEKI2tasWYOQkBClbdoaTQGoUCGEEEI6hbZa31Wbl3laQoUKIYQQ0gnIOZhMW1FRgdu3byu+zsjIgEQiQdeuXeHi4qLWPqhQIYQQQohWxMTEICAgQPF10yWjuXPnYu/evWrtgwoVQgghpBPg4vbkkSNH/utLTlSo8JiZlSXXEVpUXiTlOkKL9ET8PZ1rKqq4jtAioZ4e1xFaJDYx4jpCi/j6sMTu/btzHaFFV7+M5zpCi/YLtnId4R9s0Nqe2+sziPn7k50QQgghbYbJ5VxHeCy0jgohhBBCeItGVAghhJBOgIu7ftoCFSqEEEJIJ0BzVAghhBDCW+31oYQ0R4UQQgghvEUjKoQQQkgn0F5HVKhQIYQQQjoBOaPbkwkhhBBC2lSnL1RWr16NF154QevHeeutt/DZZ59p/TiEEEJIS5ictclL19pNobJs2TJMnjy5zfcrkUjg4eHR5vt92NGjR+Hv76/14xBCCCEtoUJFy6Kjo+Ht7d3m+5VIJBg0aFCb77e57OxsWFtba/04hBBCSGsYY23y0jXeT6atq6uDiYkJGhoaEBkZiXXr1sHHxwdRUVGPfK9MJsOff/6Jvn37tvj93Nxc5OXlQSaTYcSIEYiJiUH//v3xzTffYODAgYp2KSkpWLRoEa5evQoXFxds374dzz//POLi4uDp6fnINjdv3sSSJUvapD/+iaOtAUIWOMPCVB+V1TJ8/PV93MuubbV92Moe6PWEEaYtSdJqLmcHQ6xd2geWZvqoqJJhy44/kZml+pC+50fbYdaUbhAKgBvxUmzbkw6ZTHv/KJzsDbHmdVdYmOmjskqGrTtvI/O+8kPn7G3EWL2kF1y7myA3vxYLVt7SWp7m1Okze1sx1r7RG249TZGTV4P5y+K0nsvJwRBrX3eFhbkIlVUyhH2WhsysFvrsdVe49TBBTn4tFiy/qf1c9mKEvtYTFmYiVFQ14IPdGbj70Gfp2d8ML8/oBiNDPTDGcC2uFF8eyoI2f+46Oxhh3bI+sDRvzLVleyoy7rVw7gfaY/bUbhAKBIi9JcW23WlaPfdfDDSGh5sBrC318M5XUmTly1TaWFkIMe8FU3Sz00NRqRzvfF2qtTxNnh+qB3cXIbqYCrDzeD1yi1X7wMtVCN9+f/+NbW4sQGYew6ELDVrLVdcgw8fnbiDyTg7E+kL0tu2CzRP8lNpEZ+bi0wsSVNXVQyAQYLirI94IGAyhQKC1XJ0F70dU9PX1ceXKFQCNox85OTk4ffq0Wu89efIkhg0bhujo6Ba/L5FIAADbt29HWFgYYmJiYGpqihkzZijapKSkwMfHB8OHD0diYiLef/99zJkzByKRCP369VOrTUZGBiZNmvQvekE9r891wumIYry89k/8dKoAIS85t9p20rPWyMmv03omAFjxmht++S0HM1+LxcEj97H2zd4qbRxsxVgw6wksWXMTLy6MQRdLA4wfY6/VXMtf7YkTZ/Lw3zckOBT+AKuXuKq0qayW4etDWXhvR5pWszxMnT6rrJLhywN38c62FN3lWtgLv5zJw+wlcTh49AHWvO6mmqtahq8P3sO723XXZ8sW9MDJcwWYu+wWfjieg1WLeqi0qaiU4b1P0zF/RTwWrk1Ev96meHaEtVZzrVzshuO/5WDGwus48HMW1i7to9LGwc4QL8/qjsWhEkx/JRpdLUWYMMZBq7liU+rwwf/KUChVLVCaVNcyhEdU4atjFVrN0lziXTm+PFWPkorWi7Qbt+X4/HiD4lVRzXDzTuv/H23h0wsSAMCxhUH46eUXsOwZL5U25oYG2DrRD0deDcLB+eNw834hTsTf0WouTcnl8jZ56RrvCxWhUIjs7GxYWVnBw8MD9vb2sLS0VOu948ePx6pVq/Dss88iJiZG5fsSiQSGhoYIDw+Hn58f+vXrh82bNyMxMRGFhYUAgMWLF2PKlCl455130KtXL0yaNAn+/v5wd3eHgYGBWm3efvtt6Otrd/DKwkwPbt2NcP6qFABwJbYM1l1FcLA1UGnr4iiG72Bz/HiqQKuZAMDSQgR3V1P8fjEfAHAxshC21mI42RsqtRvpZ40/ootRLK0HABw7nYPRw220l8tcH316meDMpcY+iIgqhq2VgUqu8ooGxKeUo6ZWd/841e2z8ooGxCeXoVpH2SwtRI19FvFXn10tgs0/9VmNdn95KHKZ66N3TxOcudz4b/bStRLYWhnA0U6s1O52ZhVy8htHGOvrGdLvVsHORqyyvzbLZSGCu5sZfr+QB6DpczSEk8ND5/5T1vgjukhx7of/moPR/rZaywUAaVkNKCn/5/Omqobh9v0G1Nbrbqg/M4+hTHXAqVXO1gKYGAqQck97GavrGhB+8zaWjPSE4K/REWtTI5V27vZd4dzFDAAg1tdDH7suyJZWai3X42ivc1R4f+kHAOLi4lqd8Hrx4kUEBAQ8ch/Tp09Henq60jaJRIJp06bB0dFRsa1Lly4AGivPu3fv4vz587h5U3no2sDAQJFHnTa6YNNVhOLSBjQvdguK6mHbVaQ0cqKnB7wR7IQd3z7QyQOqbK3FKCqpg6xZrryCWtjZiPEgt0axzc7aEHn5f3+dm1+r1V8ijbnqlXMV1sHW2kApFxfU7TOd57IyUOmz/MJazvvMxsoAxdI6pXM//6/PMjuv5UufXSxEGOHTBes+0N6oj521GEXFD3+ONbCzMcSDnGbnvo0hcpXO/RqtnvsdyRA3ISTpcmjzR1mWtBzmRmJ8HZmAaxm5MNTXw6vDB8GnR+sjvoUV1Tibcg87po3UXrBOhPcjKsA/35kzdOhQJCcnt/ratGkT9PX18eGHH7a436Y5Jk2ioqLg5OQEW1tbSCQSpUs8TRISEhR51GmjjtraWpSVlSm9ZLK2vzQza7wdImPLkJXT+twVQjoyYyMh3lvlhh+O5+LPO/z6i5eoT6QPDOwhRGyadkfuZHKGnNJK9LS2wMH547DqWW+sDv8DRRXVLbavqK3Hmz9dxNxh/dDfwUqr2TTFmLxNXrrWLkZU4uPjMWXKlBa/Z2xsDHd39xa/d+bMGYSFheHQoUMqtzZXVVUhLS0NMtnfJ7lcLseOHTsQHBwMoPGyk0wmg0wmU1y6OX36tFIRok4bdYSFhWHTpk1K21w9F8Jt8Gtqvb+guB5dLfQhFELxl6WNlQj5xfVK7Qb0MYFtVxGCnrGCnlAAY0Mhvv2gD9589zbKytv+H3x+YS2suhhATwjFX5Z2NmLkFSgXSnmFNXC0/3s41d5WtU3b5xIp57I2QH6hbubt/BN1+0znuYrqVPrM1lrMeZ8VFNWhq6WB0rlv28pnaWQoxNY1fRAZI8XPp3K1miuvsBZWXR/+HA2RV6A8+pRXUAMnh+bnviHnn3V7MKC7EPlShgItz/G1NzeGUCDAc/27A2i8xONoaYK0AimsHroEVFlbj8Xfn8dIN2f816flmzi41F6X0G8XIypyuRypqanIzs5Gaan6Z+WwYcMQHh6OqVOnqnzv1q1b0NPTw7fffovr168jNTUV06ZNQ3V1NUJDQwEAQ4YMgUgkwtq1a3Hnzh0cPnwYixcvBgBFEaJOG3WsWbMGpaWlSq+egxao/f7Schlu363GKF9LAIDfEHMUldSrTJhdtfUOglelYt6qVKwIS0dVjRzzVqVqpUgBAGlpPf5Mr8CzIxuvuY98yhoFRbUqlwouRhbh6aFd0dVSBACYMNYB5y5rbw6NtKwBaRmVCBzROA/Gf1hXFBTXcX7ZB1C/zzjJdacSgf5/9ZmvFQqKuO8zaVkD0jIrETi8cWLsCJ8uKCiqV7nsYyhuLFKuS0px4Gi29nM1fY4BdgD++hwLa5Uu+wBARGQhnh5qpTj3J45zwNlL+VrP194NcRMiNk37f913MTbE0O52iLyTAwB4IK1AtrRxhKW5qrp6LP7hPJ7q6YiXnx7Y0q44117nqAgYFzdFa2j//v0IDQ1FdnY2VqxY0eJlHE3t2bMHO3fuxIYNGxASEgKpVIqgoCDs3LkTVlZ/D9ft378fa9asgVQqxbhx4+Dl5YUdO3YgJydHozaP47n58Rq1d7I3QMj8bjA31UNVjQyffH0fmQ9q8WawE6IkZbgmKVdqb2slws5NbhrfnlxeJNWofTcnI6x9o3fjbcDVMoR9+ifu3K1C6BI3/BFdhCvRxQCAoEB7zJrSeKdSXEIpPtp9W6NbNPVEmg0QdnM0xOrFrjA300dVtQxbP09Hxr0qrFzYE1diShAZUwKxgRD7P/OESF8IE2M9SMvq8XtEIb48eE+jY8nqNbt1Up0+ExsIcXC3NwxEjdlKSuvx+8V8fPG/TLWPI9TT0yyXoyHWvO4G879u6X5/523cuVeFla/1wpXrxYi8/leffT4YBn/1WUlZPX6/WIAvD6jfZ/oGmn2Wzg6GCF3UU5Hrwz13kJFVjeWvdEdkrBRXY6WYOdEBc6c6Kd2CHhFVjIPh6v87ra1sebi/Nd2cjLBuqbviFvgtO1Jx524lQl/vjT+uFeFKdBEAIOhZe8ye6gIAiIuX4sNdmt2e3Nd3gEa5Zo8zwaBeIpibClFZzVBTy7BujxRznjPBzbQ63Eyrh4E+8N5CS+jrC2AkFqC8Uo6rCXU4elH92a52jmYa5Zrgq4fezkKYGgHVtUBtPfDJkXpMfEoPKVlypGQ19om1ObAoSIT3f6hH3WPclbxWsFWj9vdLyrHp5DVIq2sgEAjwytMDMdrdBZtORsHfzRkjezvjqysJ+OLyLfS0tlS8L7CvCxb4afbZGM/doFF7TYwLbpvlFX7dq9s1wdpFocInISEhSEpK+sdbpNVpow5NCxVd0bRQ0RVNCxVd0rRQ0RVNCxVd0bRQ0RVNCxVd0bRQ0RVNCxVd0bRQ0SVtFipj5kraZD+/7fNsk/2oi58/DXjs1q1bGDJkyL9uQwghhOgSzVHpJOLj4x+5FL46bQghhBBdYnJ5m7x0jUZUNJSXl9cmbQghhBDyaFSoEEIIIZ1Ae730Q4UKIYQQ0glwsVhbW6A5KoQQQgjhLRpRIYQQQjoBXTzfTRuoUCGEEEI6AS7u2GkLVKgQQgghnUB7nUxLc1QIIYQQolWff/45unfvDkNDQ/j4+CA6Olrt91KhQgghhHQCjMnb5KWpH374ASEhIdi4cSNu3LgBDw8PjBkzBvn56j18kwoVQgghpBPg6unJH3/8MV5++WXMmzcP/fr1w549e2BsbIxvvvlGrfdToUIIIYQQrairq0NsbCxGjx6t2CYUCjF69GhcvXpVrX3QZFpCCCGkE2iru35qa2tRW1urtE0sFkMsFqu0LSwshEwmg52dndJ2Ozs7pKSkqHdARjq8mpoatnHjRlZTU8N1FCWUS3N8zUa5NEO5NMPXXIzxO5u2bNy4kQFQem3cuLHFtg8ePGAAWGRkpNL2lStXsqFDh6p1PAFjrH3er0TUVlZWBgsLC5SWlsLc3JzrOAqUS3N8zUa5NEO5NMPXXAC/s2mLJiMqdXV1MDY2xs8//4yJEycqts+dOxdSqRTHjh175PFojgohhBBC1CYWi2Fubq70aqlIAQADAwMMGTIE586dU2yTy+U4d+4cfH191ToezVEhhBBCiNaEhIRg7ty58Pb2xtChQ7F9+3ZUVlZi3rx5ar2fChVCCCGEaM306dNRUFCADRs2IDc3F56enjh9+rTKBNvWUKHSCYjFYmzcuLHVoTmuUC7N8TUb5dIM5dIMX3MB/M7GJ0uWLMGSJUse6700mZYQQgghvEWTaQkhhBDCW1SoEEIIIYS3qFAhhBBCCG9RoUIIIYQQ3qJCpQNKSkqCvI2e6aAtxcXFKCws5DoGACAyMhIJCQlcx1DB18/x1q1baGho4DoGAODzzz/n5WfHV+2pvxhjuHr1Ko4dO4b8/Hyu4xAOUaHSAQ0YMACmpqbw9vbGSy+9hE8//RQRERGQSqVcR0NCQgIGDRoEGxsb2NnZwcXFBRs3bkRlZaVOc2zdulXRH4sXL8a1a9dU2qSnp6O8vFynuZrj6+fo6ekJU1NTeHp6Yu7cufj4449x7tw5TgrP3NxcvPjiiwgICMDhw4epsHuE9tBfTWxtbREYGIgFCxbA2dkZ8+fPR0VFhc5ztKfirqOiQqUDioiIgLm5OZycnFBeXo4vv/wSAQEBsLKyQp8+fbB+/Xqt/7JrXgg0N3/+fFhbW+OPP/5AYmIiNm3ahOPHj8Pb2xslJSVazdTcli1bUFxcDABITU3FyJEjVdqcPXsWM2bM0Fmmh/Hhc2zJyZMnYWZmBldXV9TX12Pv3r0IDAyEnZ0dnJyc8Nxzz2HNmjU4dOiQ1rO8++67SEhIwMGDB1FZWYn33ntP68dUR/Pzn0+FHV/7qyU//fQTysvLUVBQgOjoaGRmZsLHxwe5ubk6zdGeirsO6188QJHw1ODBg9mRI0eUtp09e5b16tWLvfPOO2zEiBGse/fuLD8/X2sZzMzMWHp6usp2Y2NjlpycrLRNLpezKVOmsFmzZmktz8NMTU0V+ezs7Nj169dV2iQmJjIrKyudZXoYHz7HlvTp04eFh4crbfv1119Zr1692NatW1lISAgbPXo0s7e312kuPml+/p86dYpZW1uzKVOmsBkzZrCBAwcygUDAhEIhc3R0ZOPGjWOrV69mBw8e5Dg1v8nlcrZixQrm6urKsrOzdX787Oxstm/fPrZp0yadH7uzo0KlAzIyMmKpqakq248fP86mTJnC5HI5mzp1KluwYIHWMjQvBJobMWIEu3r1qsr2lJQUZmZmprU8D2ueb86cOWz69OkqbZKTk3Wa6WF8+BxbYmhoyO7cuaOy/fDhwy32o67cvHmT1dfXc3b85pqfX3wt7PjUX+ooLy9nycnJbNy4cWzAgAGcZGhvfdZRUKHSAT399NNsw4YNKtvv3Lmj+MV77do11r17d61laP6DOigoiL311lvsxx9/ZAcPHmQ+Pj4sNzdXqf3169eZs7Oz1vI8bMuWLaykpIQxxti9e/eYvb09mzx5Mrt16xZjjLHq6moWHBzM/Pz8dJbpYXz4HFsSEBDA3n777RZzmZub6zRLcwKBgInFYubh4cHmzJnDtm3bxs6ePcsKCgp0nqX5+c/Xwo5P/dWS4OBgFhgYyPr27cvMzc2ZUChkQqGQCQQCZmRkxEkmvvdZR0XP+umAPvroI4wePRp37tzBunXr4O7ujrq6OnzyySfo2rUrAMDGxgZ5eXlay7B27VrFsfr374+YmBh89dVXimP27NkT06ZNg6enJ2QyGb799lt88sknWsvzsDVr1ij+u1u3boiKisKiRYvg4eEBsViMhoYGWFhY4JdfftFZpofx4XNsya5duzBs2DDcv38fS5cuRd++fSGTyfD555/D0tJSp1maO3nyJObMmaM0dyYhIQECgQD29vbw8PCAh4cHBg0apPW5R83Pf19fX3z33XfYuHGjUpvBgwer/fRYbeBTf7UkPz8f3bt3h5+fH5ycnJReVlZWOs8D8L/POiyuKyWiHfHx8WzUqFFMIBAwQ0NDpq+vzwwNDdmhQ4cYY41/zfXq1UvnuXJzc9np06fZ1q1b2Ysvvsjc3d2Znp4eMzAwYAMHDtR5nofdvXuXHTt2jJ06dYoVFRVxHYe3n2NycjJ75plnFH9h6uvrMyMjI3bgwAGdZ2nC10ssycnJzMLCgi1YsIAlJCQwmUzG6urq2PLly5mLi4tOszTH1/7iM+ozbtBDCTu4e/fuQSKRQE9PD0OGDIG9vT0A4PLly8jLy8PUqVM5TghUV1fj1q1bkEgkePXVV3V+/MjISJibm2PAgAE6P7a67t69i5s3b/Luc7x37x7i4uIUuRwcHDjJAQBGRkZISkpCjx49lLYfOXIEP/74I77//nuOkgEpKSlYsmQJzp8/DwMDA8hkMohEInz11VeYOXMmJ5n43F98RX3GDSpUOpANGzZgwoQJGDJkCNdRHqm4uBhyuRzW1tZcR8HgwYOxZMkSvPTSS0rb09PTYWtrCzMzM05yJSUlwd3dHUIhv1cRYIxBIBBwHQOjRo2Cv7+/yiWWjIwMeHp6orS0lKNkf2sqOIVCIeeFHR/7a8OGDfj555/h6OgIPz8/TJo0CZ6enkptTp06BR8fH04u//CxzzoDfv8EJBq5f/8+xo0bB2dnZyxatAi//vor6urquI6lhC8LvjXH13VU+Lrg28PEYjGSk5O5joFdu3bhk08+wcsvv4zExETI5XLU19dzPnem+YJvTzzxBMaPH48XXniB0yIF4Gd/JScnY9KkSVi+fDlqamowa9YseHt74/Dhw4o2VlZWmDNnDif5+NhnnQGNqHQwcrkcV65cwS+//IJjx44hJycHgYGBmDBhAl544QXFBD+uDB06FKampti8eTO6dOmCq1ev4tNPP0VNTQ0iIyPRpUsXnWeyt7fHiRMn4O3trbQ9KSkJI0aM4Gyp/8uXL+M///kPfHx8FMVAYmIiBAIBXF1dMW3aNCxfvlxnPyBDQkJa3L5jxw7Mnj1b8Rfuxx9/rJM8LeHjJRahUAgDAwO4u7srJls2vbgeUeRbfz148ADLli1DREQEhg8fDh8fH5SUlODYsWMQi8XYtm0bRCIRXnnlFSQlJek8H8C/PusMqFDp4JKTkxVFS2xsLIYOHYrx48djxowZcHJy0nkeExMTxMbGwt3dXbGNMYb//Oc/MDQ0xP79+3WeKTg4GDU1NSrXl1NSUjB06FCUlZXpPBMAeHl5Yf369Zg0aZJi27lz5/Dqq69i7ty5OHv2LO7du4fo6GjY2NhoPY9QKISHh4dKYRQREQFvb2+YmJhAIBDg/PnzWs/yKHy6xPLrr79izpw58Pf3h4GBARISEnh3pwif+gsAysrKcP78eVy+fBkxMTHIzMxEXl4eRCIRqqqqsG/fPsyePZvTjHzrsw6Nq1m8RHtaW5QoLy+PffXVV2z8+PHsww8/5CAZfxZ8a46v66jwbcG3sLAw1qNHD3bu3Dml7fr6+iwxMVEnGf4JXxfj4uudInztr0fZu3cvmzhxIifHbq991t7RiEoH1NJQs6enJwYNGsTJUPP48eMVfzE2NDRgx44dOHbsGOzs7BRtYmJiMGnSJGRlZek8H9D419GiRYtw+vRplXVUfH19Ock0fPhwjBo1Cps2bVLanpGRAQ8PD5SVlSE6OhrTp09HRkaGTjJdv34ds2fPRlBQEMLCwiASiSASiXDz5k3069dPJxlaw9dLLHy9U4Sv/fWwlibe19bWQiwW6zxLe+mzjoYKlQ6Ib0PNa9asgUQigUQiUSxOZmRkpLLg28aNGzm/Xbrpdm6RSAQfHx9O5/Rcu3YNo0ePxsSJE5UWfFuxYgWOHz+OzMxMZGRkoH///qiqqtJZroqKCixevBgSiQQHDhyAl5cXJBIJ54UK3877Jny9U4Sv/dUkISEBM2fORGJiIgDAyckJ8+bNw6pVq2BiYqLzPAD/+6zD4nZAh2gDX4eaGeP/gm98w9cF3xhj7NChQ8zOzo4JhUJeXPrh63lPC749nieffJIFBASwyMhIlpyczL755hvm6enJ3N3dWXFxMSeZ+N5nHRUVKh0QX58t0pqqqioWFRXF9uzZw3UU3mpaMffEiRMsJydHsf3SpUvsp59+4ixXVlYWCw8PZxUVFZxlaMLn856PK/nyub8Y48+T1pvje591VHTppwPi61AzIdrUHs77pkuLfLhThO/95e/vj/fffx/Dhg1T2p6amoonn3ySk7vx+N5nHRU9lLAD4utD4/juxo0bmD59Oj766CNMmDCB6zhEQ+3hvHdxcYGLiwvXMQDws7+aT7xfuHAhli5dqjLxvry8HBYWFpzk42OfdQpcD+kQ7eDjUDOfXb9+nVlaWjKBQMAMDAzYkSNHuI5EHgOd95rhW3+tXr2ajR07ltnb2zOBQMAEAgEzNjZmwcHBbPv27Wzbtm1swIABnF7u5FufdQZ06aeD49NQc3N8Gr1ISUmBr68vVq5cifXr1+Ptt9/G1q1bER4ejsDAQE6zAfzqq+b4losxhqioKOTn52Pw4MG8PO/5io8/J/Ly8hR3Cza90tLSoKenhz59+uDWrVuc5rt7967SA1/50GcdFseFEumE+DZ6IZPJ2PHjxxljjAkEApaamsrOnDnDysvLOc3FGP/6qgkfc1lbWzMTExNmbW3NRCIRmzdvHi8+wyaxsbHM1dVV5a4Roj6+TLwvKipiBQUFnGboTKhQITqVnJzMLC0t2ebNm5lQKGTvvPMOMzY2Zr///jvX0RhjfxcqfMDXvuJrrgsXLjC5XM4YYywuLo4FBASwfv36Kd0lxRU+FnZEc/Hx8WzgwIFMKBQyoVDIunXrxjZs2MCLu946MipUiE7xefSCMX4VKnztK77mephcLmcrVqxgrq6uLDs7m7McfC3siOb4uLZLZ0BzVAhnhEIhUlJS0Lt3b66jKPAxE0C5HkdFRQXu37+PkJAQZGVlIT4+npMccrkcJ0+eRFBQkKK/7t27h2HDhsHU1JSTTM3xba4Rn/HxoaqdgZDrAIQQ0hbmzZuHZ599Fv369YOFhQUsLCzQv39/nD59Gunp6ZzlEgqFCAoKUto2evRoXhQpMTExeOaZZ5Ceno5p06bh6NGjXEdScePGDbi5ueHYsWNcR4G3tzekUqnSNoFAgM2bN+P48ePchOoEqFAhhHQI+fn56N69O6ZPn45t27bhxIkTiIuLQ0FBgU6fhdRepKSkIDAwECtXroRAIMBbb72F2bNn48yZM1xHU+BDITV+/HisX78eP/30k2Jtl6ZnljXhcm2XzoAWfCOEdAgnT57kOkK70rt3b3z33XcICgrCW2+9henTp8PX11dlJViuNC+k1q9fryikdL1sQP/+/RETE4OvvvpKUaD07NlT5aGqn3zyic4ydTY0R4Vwho/zGyIiIuDj4wNDQ0OuoyjhY18B/M3FV3ztLz7m4uPcHr6v7dJR0YgKIc34+/tzHYFoYMOGDfj555/h6OgIPz8/TJo0CZ6enkptTp06BR8fH1hZWXETkjyW1ub2cMnOzg5jxozBmDFjFNuqq6tx69YtSCQS7oJ1cDRHhXDmwoULvHnuCd/xta+4zpWcnIxJkyZh+fLlqKmpwaxZs+Dt7Y3Dhw8r2lhZWWHOnDmcZSQdm5GREXx8fPDqq69yHaXDoks/hJB268GDB1i2bBkiIiIwfPhw+Pj4oKSkBMeOHYNYLMa2bdsgEonwyiuvICkpieu4dGnxMfE9H9EuuvRDOi0zMzMMHjwYQ4YMgZeXF7y8vNCvXz8IBAKuoxE1OTk54ccff0RZWRnOnz+Py5cvIyYmBhUVFUhPT8f48eNRVVWFffv2cR0VAF1aJORx0IgK6bR27dqF2NhYxMTEICkpCXK5HEZGRhg0aJBS8eLh4cF1VPIv7Nu3D+Hh4bxcI4Qv+D5iwbd8tEieblGhQnSC76MX1dXVMDExQWhoKIqLi3Hjxg0kJCSgrq4OMplMp1n42ld8zaWO2tpaiMVinR5zxowZWLduHQYMGKDT4z4Ovl6SasKnQiUmJgaBgYEoLS2FSCTC999/j0mTJnEdq0OjQoXoRHsYvRAKhZBIJBg0aBAAoKGhAUlJSYqvdYWvfcXXXHwlFAphY2ODc+fOtVisMMZQWVnJixVq+Y4vhVRKSgp8fX0Va7u8/fbb2Lp1q87XdulsqFAhOsen0YvmHi5U+ICvfcXXXHwiFAoRGBiIuLg4nD9/XqVYycvLg5OTExoaGnSaq6GhAfr6ND3xcfBxbZdOQffPQSSk8am7N2/eVHxdX1+v9DUXHs7EF3zsK8b4mys2Npa5urqy8PBwTnMIhUKWnZ3Npk+fzmxsbNitW7eUvp+bm8sEAoHOczk6OrKtW7eykpISnR+7I+HTk9Y7OlpHhfCCvr6+zkcyFixYgN27d+P69euora0FgHYx34KLvlIHH3Lx4dkwTRhj0NPTw8GDB/HMM89g1KhRKk9w5uJ8W7p0KXbt2oVu3brhzTffREZGhs4zqOOpp55CWVkZ1zEID1ChQjqttLQ0rF69Gj4+PjAzMwMAbNq0CTt37kRkZCQ9yK6d4etD9oRCIQ4cOIDRo0dj1KhRnC+zvnLlSty5cwf/93//h6ioKPTu3RtTp07FtWvXOM31sKioKNTU1KhsLysrQ2hoKAeJCFeoUCE6wcfRi4iICJSWliI1NRXfffcdVqxYAalUig0bNuDpp5+GhYUF+vfvr/NcfOwrgL+5mjQ9ZG/t2rVgjGH69Ok4duwYfH19OcnTvG+aipXAwEA888wznBcrenp6mDFjBq5du4bz589DJpPBz88Pfn5+OHr0KBiHUxenTp2KrVu3QiAQID8/X+X7lZWV+OijjzhIRrhCk2mJTvj7+0MikaC8vBz6+vpoaGjA5MmTMXLkSHh5ecHT0xPGxsZcx1TIyMhATEwM4uLisGXLFp0em699xddcLeHD7axCoRC5ubmwtbVVbJPL5YpRnv379+O5557jzeTjO3fuYPv27di3bx9sbW2RlpbGSY6QkBBER0cjMjISAoEAVlZW8PDwgIeHBzw9PZGamopvv/0W9+/f5yRfEz6cY50FFSpEp9LS0hAbG4sbN24oXlKpFHp6eujduzcSExO5jsgbfO0rvuZqjg+/RH799VeMGjVKZf0WuVyO//73vzh27Biqq6t1Xqhs3LgRpaWlLb6kUimkUinkcjnnBZSBgQGuXLmC7OxsxMXFQSKRID4+HnK5HJs3b8bMmTM5zceHc6yzoEKFcI7L0Yv2hq99xbdcfP8l0lSsfP/99zovCIRCIQwNDREcHIy+ffvCwsIC5ubmMDc3V/y3hYWF0kgQF+rr6yESiTjN8E/4srZLZ0CFCiGkw+F7oQI03hV0/fp1DB06VKfHvXDhArZt24azZ8/ixRdfxIoVK9rF6rmk86LJtIQQwgGBQKDzIgUAAgICcOLECdy8eRNisRg+Pj4YO3Yszp07p/MshKiDChVCCOmE+vTpgy+++AKZmZkYNmwYZs2ahcGDB+PAgQOcz0/hK1rbhRtUqBBCOpwLFy7AxcWF6xjtgo2NDd5++22kpKRg8uTJeOONN9CzZ0+uY/ESre3CDZqjQgghnciUKVNavOOnvr5esX6KQCCgUZVmpk6dCm9vb6xbtw43b95UmdOTk5MDZ2dn6jMtoSdTEUJIJ2JsbAxHR0dYWlr+44v8zcXFBSdOnABjDB4eHi2u7eLg4MB1zA6LRlQIIYQQNfB9bZeOigoVQki7ZmZmhsGDB2PIkCHw8vKCl5cX+vXrx6vl/UnHwPe1XToqKlQIIe3arl27EBsbi5iYGCQlJUEul8PIyAiDBg1SKl48PDy4jkoIeQxUqBBCOozq6mqYmJggNDQUxcXFuHHjBhISElBXV0cTHQlpp6hQIYR0KEKhEBKJBIMGDQIANDQ0ICkpSfE1IaR9oXVUCCEdmr6+PhUphLRjVKgQQgghhLdoHRVCSLu2YMECDBkyBN7e3oqRE7rjh5COg+aoEELaNX9/f0gkEpSXl0NfXx8NDQ2YPHkyRo4cCS8vL3h6esLY2JjrmISQx0SFCiGkQ0hLS0NsbCxu3LiheEmlUujp6aF3795ITEzkOiIh5DFQoUII6bAyMjIQExODuLg4bNmyhes4hJDHQIUKIYQQQniL7vohhBBCCG9RoUIIIYQQ3qJChRBCCCG8RYUKIYQQQniLChVCCCGE8BYVKoQQTgUHB2PixImKr0eOHImlS5fqPMfFixchEAgglUp1fmxCSOuoUCGEtCg4OBgCgQACgQAGBgZwdXXFO++8g4aGBq0e98iRI3j33XfVakvFBSEdHz3rhxDSqrFjx+Lbb79FbW0tTp06hcWLF0MkEmHNmjVK7erq6mBgYNAmx+zatWub7IcQ0jHQiAohpFVisRj29vZ44oknsGjRIowePRrHjx9XXK7ZvHkzHB0d0adPHwBAVlYWpk2bBktLS3Tt2hUTJkxAZmamYn8ymQwhISGwtLSElZUVVq1ahYfXnHz40k9tbS1CQ0PRrVs3iMViuLq64uuvv0ZmZiYCAgIAAF26dIFAIEBwcDAAQC6XIywsDD169ICRkRE8PDzw888/Kx3n1KlT6N27N4yMjBAQEKCUkxDCH1SoEELUZmRkhLq6OgDAuXPnkJqaijNnzuDEiROor6/HmDFjYGZmhsuXL+PKlSswNTXF2LFjFe/Ztm0b9u7di2+++QZ//PEHiouLcfTo0X885pw5c3Do0CF8+umnSE5OxhdffAFTU1N069YNhw8fBgCkpqYiJycHO3bsAACEhYXhu+++w549e5CYmIhly5Zh9uzZiIiIANBYUE2ePBlBQUGQSCRYsGABVq9era1uI4T8G4wQQlowd+5cNmHCBMYYY3K5nJ05c4aJxWK2YsUKNnfuXGZnZ8dqa2sV7f/3v/+xPn36MLlcrthWW1vLjIyM2G+//cYYY8zBwYF98MEHiu/X19czZ2dnxXEYY8zf35+9+eabjDHGUlNTGQB25syZFjNeuHCBAWAlJSWKbTU1NczY2JhFRkYqtX3ppZfYjBkzGGOMrVmzhvXr10/p+6GhoSr7IoRwj+aoEEJadeLECZiamqK+vh5yuRwzZ87E22+/jcWLF2PgwIFK81Ju3ryJ27dvw8zMTGkfNTU1SE9PR2lpKXJycuDj46P4nr6+Pry9vVUu/zSRSCTQ09ODv7+/2plv376NqqoqBAYGKm2vq6vD4MGDAQDJyclKOQDA19dX7WMQQnSHChVCSKsCAgKwe/duGBgYwNHREfr6f//IMDExUWpbUVGBIUOG4MCBAyr7sbGxeazjGxkZafyeiooKAMDJkyfh5OSk9D2xWPxYOQgh3KFChRDSKhMTE7i6uqrV1svLCz/88ANsbW1hbm7eYhsHBwdcu3YNI0aMAAA0NDQgNjYWXl5eLbYfOHAg5HI5IiIiMHr0aJXvN43oyGQyxbZ+/fpBLBbj3r17rY7E9O3bF8ePH1faFhUV9ej/SUKIztFkWkJIm5g1axasra0xYcIEXL58GRkZGbh48SLeeOMN3L9/HwDw5ptvYuvWrQgPD0dKSgpee+21f1wDpXv37pg7dy7mz5+P8PBwxT5//PFHAMATTzwBgUCAEydOoKCgABUVFTAzM8OKFSuwbNky7Nu3D+np6bhx4wY+++wz7Nu3DwCwcOFCpKWlYeXKlUhNTcXBgwexd+9ebXcRIeQxUKFCCGkTxsbGuHTpElxcXDB58mT07dsXL730EmpqahQjLMuXL8d///tfzJ07F76+vjAzM8OkSZP+cb+7d+/G1KlT8dprr8Hd3R0vv/wyKisrAQBOTk7YtGkTVq9eDTs7OyxZsgQA8O6772L9+vUICwtD3759MXbsWJw8eRI9evQAALi4uODw4cMIDw+Hh4cH9uzZgy1btmixdwghj0vAWpvFRgghhBDCMRpRIYQQQghvUaFCCCGEEN6iQoUQQgghvEWFCiGEEEJ4iwoVQgghhPAWFSqEEEII4S0qVAghhBDCW1SoEEIIIYS3qFAhhBBCCG9RoUIIIYQQ3qJChRBCCCG8RYUKIYQQQnjr/wFpjUb8x4RCNAAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#temporary visualisation" - ], - "metadata": { - "id": "LRrGO3dIFpIH" - }, - "id": "LRrGO3dIFpIH" - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "import networkx as nx\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "def plot_jet_graph(\n", - " p4: torch.Tensor,\n", - " u_matrix: torch.Tensor,\n", - " threshold: float = 0.1,\n", - " title: str = \"Reconstructed Particle Jet Graph\"\n", - "):\n", - " \"\"\"\n", - " p4: [N, 4] tensor of particle 4-momenta (E, px, py, pz)\n", - " u_matrix: [N, N] tensor of pairwise interaction weights or pooled attention\n", - " threshold: Cutoff value to display only significant edge connections\n", - " \"\"\"\n", - " # 1. Convert tensors to numpy\n", - " p4 = p4.detach().cpu().numpy()\n", - " u_adj = u_matrix.detach().cpu().numpy()\n", - "\n", - " N = p4.shape[0]\n", - "\n", - " # 2. Extract kinematics for node positions & sizes\n", - " E, px, py, pz = p4[:, 0], p4[:, 1], p4[:, 2], p4[:, 3]\n", - " pt = np.sqrt(px**2 + py**2)\n", - " p = np.sqrt(px**2 + py**2 + pz**2)\n", - "\n", - " # Pseudorapidity eta and Azimuthal angle phi\n", - " eta = 0.5 * np.log((p + pz + 1e-8) / (p - pz + 1e-8))\n", - " phi = np.arctan2(py, px)\n", - "\n", - " # 3. Create Graph\n", - " G = nx.Graph()\n", - " for i in range(N):\n", - " G.add_node(i, eta=eta[i], phi=phi[i], pt=pt[i])\n", - "\n", - " # 4. Add Edges based on interaction threshold\n", - " for i in range(N):\n", - " for j in range(i + 1, N):\n", - " weight = u_adj[i, j]\n", - " if weight > threshold:\n", - " G.add_edge(i, j, weight=weight)\n", - "\n", - " # 5. Plot Graph\n", - " fig, ax = plt.subplots(figsize=(9, 7))\n", - "\n", - " pos = {i: (eta[i], phi[i]) for i in range(N)} # Spatial positioning (eta, phi)\n", - " node_sizes = (pt / pt.max()) * 600 + 50 # Scale nodes by transverse momentum (pT)\n", - "\n", - " # Extract edge weights for line thickness\n", - " edges = G.edges()\n", - " weights = [G[u][v]['weight'] for u, v in edges]\n", - " edge_alpha = [min(w, 1.0) for w in weights]\n", - "\n", - " # Draw Nodes & Edges\n", - " nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color=pt, cmap=plt.cm.plasma, alpha=0.9, ax=ax)\n", - " nx.draw_networkx_edges(G, pos, width=[w * 3 for w in weights], alpha=0.6, edge_color=\"gray\", ax=ax)\n", - "\n", - " ax.set_xlabel(r\"Pseudorapidity ($\\eta$)\", fontsize=12)\n", - " ax.set_ylabel(r\"Azimuthal Angle ($\\phi$)\", fontsize=12)\n", - " ax.set_title(title, fontsize=14, fontweight=\"bold\")\n", - " ax.grid(True, linestyle=\"--\", alpha=0.4)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "# Example Usage:\n", - "# p4_dummy = torch.randn(25, 4) # 25 particles\n", - "# U_dummy = torch.rand(25, 25) # 25x25 interaction matrix\n", - "# plot_jet_graph(p4_dummy, U_dummy, threshold=0.3)" - ], - "metadata": { - "id": "jTOdIm39MChC" - }, - "id": "jTOdIm39MChC", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "p4_dummy = torch.randn(25, 4) # 25 particles\n", - "U_dummy = torch.rand(25, 25) # 25x25 interaction matrix\n", - "plot_jet_graph(p4_dummy, U_dummy, threshold=0.3)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 707 - }, - "id": "ITK32AZRF1t1", - "outputId": "ccb4d48c-a86a-4a63-97ae-03ab9723d577" - }, - "id": "ITK32AZRF1t1", - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "import seaborn as sns\n", - "\n", - "def plot_attention_gating_comparison(\n", - " raw_u: torch.Tensor,\n", - " gated_u: torch.Tensor,\n", - " head_idx: int = 0\n", - "):\n", - " \"\"\"\n", - " raw_u: [H, N, N] raw interaction matrix for a single event\n", - " gated_u: [H, N, N] interaction matrix after applying the gate filter\n", - " head_idx: Which attention head to inspect\n", - " \"\"\"\n", - " raw = raw_u[head_idx].detach().cpu().numpy()\n", - " gated = gated_u[head_idx].detach().cpu().numpy()\n", - "\n", - " fig, axes = plt.subplots(1, 2, figsize=(14, 6))\n", - "\n", - " sns.heatmap(raw, ax=axes[0], cmap=\"viridis\", cbar=True, square=True)\n", - " axes[0].set_title(f\"Raw U-Matrix Interaction (Head {head_idx})\", fontsize=12)\n", - " axes[0].set_xlabel(\"Particle Index $j$\")\n", - " axes[0].set_ylabel(\"Particle Index $i$\")\n", - "\n", - " sns.heatmap(gated, ax=axes[1], cmap=\"magma\", cbar=True, square=True)\n", - " axes[1].set_title(f\"Gated & Mass-Biased U-Matrix (Head {head_idx})\", fontsize=12)\n", - " axes[1].set_xlabel(\"Particle Index $j$\")\n", - " axes[1].set_ylabel(\"Particle Index $i$\")\n", - "\n", - " plt.tight_layout()\n", - " plt.show()" - ], - "metadata": { - "id": "CnAdSaysGx9m" - }, - "id": "CnAdSaysGx9m", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "\n", - "def plot_3d_jet_reconstruction(p4: torch.Tensor, u_matrix: torch.Tensor, threshold: float = 0.25):\n", - " p4_np = p4.detach().cpu().numpy()\n", - " u_np = u_matrix.detach().cpu().numpy()\n", - "\n", - " px, py, pz = p4_np[:, 1], p4_np[:, 2], p4_np[:, 3]\n", - " N = len(px)\n", - "\n", - " fig = plt.figure(figsize=(10, 8))\n", - " ax = fig.add_subplot(111, projection='3d')\n", - "\n", - " # Scatter particle momenta\n", - " sc = ax.scatter(px, py, pz, c=p4_np[:, 0], cmap='jet', s=100, depthshade=True)\n", - " fig.colorbar(sc, ax=ax, label='Energy (E)')\n", - "\n", - " # Draw interaction lines\n", - " for i in range(N):\n", - " for j in range(i + 1, N):\n", - " if u_np[i, j] > threshold:\n", - " ax.plot(\n", - " [px[i], px[j]],\n", - " [py[i], py[j]],\n", - " [pz[i], pz[j]],\n", - " color='black',\n", - " alpha=0.3,\n", - " linewidth=u_np[i, j] * 2\n", - " )\n", - "\n", - " ax.set_xlabel('$p_x$ (GeV)')\n", - " ax.set_ylabel('$p_y$ (GeV)')\n", - " ax.set_zlabel('$p_z$ (GeV)')\n", - " ax.set_title('3D Kinematic Jet Interaction Graph', fontsize=14)\n", - "\n", - " plt.show()" - ], - "metadata": { - "id": "-73lrC4UG6Qn" - }, - "id": "-73lrC4UG6Qn", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def plot_head_layer_gate_distribution(layer_head_gate_means):\n", - " \"\"\"\n", - " layer_head_gate_means: [Num_Layers, Num_Heads] numpy array\n", - " containing average gate activation per head across dataset\n", - " \"\"\"\n", - " plt.figure(figsize=(10, 6))\n", - "\n", - " sns.heatmap(\n", - " layer_head_gate_means,\n", - " annot=True,\n", - " fmt=\".2f\",\n", - " cmap=\"coolwarm\",\n", - " cbar_kws={'label': 'Mean Gate Value (0 = Closed, 1 = Open)'},\n", - " vmin=0.0,\n", - " vmax=1.0\n", - " )\n", - "\n", - " plt.xlabel(\"Attention Head Index\", fontsize=12)\n", - " plt.ylabel(\"Transformer Layer Index\", fontsize=12)\n", - " plt.title(\"Attention Head Specialization Across Network Layers\", fontsize=14, fontweight=\"bold\")\n", - " plt.tight_layout()\n", - " plt.show()" - ], - "metadata": { - "id": "0BSz7sXbHTht" - }, - "id": "0BSz7sXbHTht", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def plot_background_rejection(y_true, y_pred, target_tpr=0.5, class_names=None):\n", - " \"\"\"\n", - " Plots 1 / FPR for each jet category at a fixed True Positive Rate (e.g., 50% signal efficiency)\n", - " \"\"\"\n", - " y_true = np.array(y_true)\n", - " y_pred = np.array(y_pred)\n", - " num_classes = y_pred.shape[1]\n", - "\n", - " if y_true.ndim == 1:\n", - " y_true_bin = label_binarize(y_true, classes=range(num_classes))\n", - " else:\n", - " y_true_bin = y_true\n", - "\n", - " if class_names is None:\n", - " class_names = [f\"Class {i}\" for i in range(num_classes)]\n", - "\n", - " rejections = []\n", - "\n", - " for i in range(num_classes):\n", - " fpr, tpr, _ = roc_curve(y_true_bin[:, i], y_pred[:, i])\n", - " # Find FPR corresponding to TPR closest to target_tpr\n", - " idx = np.argmin(np.abs(tpr - target_tpr))\n", - " fpr_at_target = fpr[idx] if fpr[idx] > 0 else 1e-6\n", - " rejection = 1.0 / fpr_at_target\n", - " rejections.append(rejection)\n", - "\n", - " plt.figure(figsize=(10, 5))\n", - " bars = plt.bar(class_names, rejections, color='teal', edgecolor='black', alpha=0.8)\n", - "\n", - " plt.yscale('log') # Physics rejections span multiple orders of magnitude\n", - " plt.ylabel(f'Background Rejection (1 / FPR) @ {int(target_tpr*100)}% TPR', fontsize=12)\n", - " plt.title(f'Background Rejection Performance per Jet Category', fontsize=14, fontweight='bold')\n", - " plt.xticks(rotation=30, ha='right')\n", - " plt.grid(axis='y', which='both', linestyle='--', alpha=0.4)\n", - "\n", - " # Annotate bar values\n", - " for bar in bars:\n", - " height = bar.get_height()\n", - " plt.text(bar.get_x() + bar.get_width()/2., height * 1.1,\n", - " f'{int(height)}x', ha='center', va='bottom', fontsize=9, fontweight='bold')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "def plot_background_rejection(y_true, y_pred, target_tpr=0.5, class_names=None):\n", - " \"\"\"\n", - " Plots 1 / FPR for each jet category at a fixed True Positive Rate (e.g., 50% signal efficiency)\n", - " \"\"\"\n", - " y_true = np.array(y_true)\n", - " y_pred = np.array(y_pred)\n", - " num_classes = y_pred.shape[1]\n", - "\n", - " if y_true.ndim == 1:\n", - " y_true_bin = label_binarize(y_true, classes=range(num_classes))\n", - " else:\n", - " y_true_bin = y_true\n", - "\n", - " if class_names is None:\n", - " class_names = [f\"Class {i}\" for i in range(num_classes)]\n", - "\n", - " rejections = []\n", - "\n", - " for i in range(num_classes):\n", - " fpr, tpr, _ = roc_curve(y_true_bin[:, i], y_pred[:, i])\n", - " # Find FPR corresponding to TPR closest to target_tpr\n", - " idx = np.argmin(np.abs(tpr - target_tpr))\n", - " fpr_at_target = fpr[idx] if fpr[idx] > 0 else 1e-6\n", - " rejection = 1.0 / fpr_at_target\n", - " rejections.append(rejection)\n", - "\n", - " plt.figure(figsize=(10, 5))\n", - " bars = plt.bar(class_names, rejections, color='teal', edgecolor='black', alpha=0.8)\n", - "\n", - " plt.yscale('log') # Physics rejections span multiple orders of magnitude\n", - " plt.ylabel(f'Background Rejection (1 / FPR) @ {int(target_tpr*100)}% TPR', fontsize=12)\n", - " plt.title(f'Background Rejection Performance per Jet Category', fontsize=14, fontweight='bold')\n", - " plt.xticks(rotation=30, ha='right')\n", - " plt.grid(axis='y', which='both', linestyle='--', alpha=0.4)\n", - "\n", - " # Annotate bar values\n", - " for bar in bars:\n", - " height = bar.get_height()\n", - " plt.text(bar.get_x() + bar.get_width()/2., height * 1.1,\n", - " f'{int(height)}x', ha='center', va='bottom', fontsize=9, fontweight='bold')\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "\n", - "from sklearn.calibration import calibration_curve\n", - "\n", - "def plot_model_calibration(y_true, y_pred, n_bins=10):\n", - " \"\"\"\n", - " Plots Reliability Diagram (Calibration Curve) for model predictions\n", - " \"\"\"\n", - " y_true = np.array(y_true)\n", - " y_pred = np.array(y_pred)\n", - "\n", - " # Flatten across all classes to see overall model confidence reliability\n", - " if y_true.ndim == 1:\n", - " num_classes = y_pred.shape[1]\n", - " y_true_bin = label_binarize(y_true, classes=range(num_classes))\n", - " else:\n", - " y_true_bin = y_true\n", - "\n", - " prob_true, prob_pred = calibration_curve(y_true_bin.ravel(), y_pred.ravel(), n_bins=n_bins)\n", - "\n", - " fig, ax1 = plt.subplots(figsize=(8, 6))\n", - "\n", - " ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly Calibrated\")\n", - " ax1.plot(prob_pred, prob_true, \"s-\", color=\"darkorange\", label=\"Gated LorentzParT\")\n", - " ax1.set_xlabel(\"Mean Predicted Probability (Confidence)\", fontsize=12)\n", - " ax1.set_ylabel(\"Fraction of Positives (Actual Accuracy)\", fontsize=12)\n", - " ax1.set_title(\"Probability Calibration (Reliability Diagram)\", fontsize=14, fontweight='bold')\n", - " ax1.legend(loc=\"lower right\")\n", - " ax1.grid(True, linestyle='--', alpha=0.5)\n", - "\n", - " plt.tight_layout()\n", - " plt.show()\n", - "\n", - "\n", - "\n", - "import seaborn as sns\n", - "from sklearn.metrics import confusion_matrix\n", - "\n", - "def plot_normalized_confusion_matrix(y_true, y_pred, class_names=None):\n", - " y_true = np.array(y_true)\n", - " y_pred = np.array(y_pred)\n", - "\n", - " if y_true.ndim > 1:\n", - " y_true_labels = np.argmax(y_true, axis=1)\n", - " else:\n", - " y_true_labels = y_true\n", - "\n", - " y_pred_labels = np.argmax(y_pred, axis=1)\n", - "\n", - " cm = confusion_matrix(y_true_labels, y_pred_labels, normalize='true')\n", - "\n", - " plt.figure(figsize=(9, 7))\n", - " sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues',\n", - " xticklabels=class_names, yticklabels=class_names, square=True)\n", - "\n", - " plt.xlabel('Predicted Jet Class', fontsize=12)\n", - " plt.ylabel('True Jet Class', fontsize=12)\n", - " plt.title('Normalized Confusion Matrix (Inference Accuracy)', fontsize=14, fontweight='bold')\n", - " plt.tight_layout()\n", - " plt.show()" - ], - "metadata": { - "id": "84a5eDF5H6fr" - }, - "id": "84a5eDF5H6fr", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# JetClass 10 benchmark categories\n", - "jetclass_labels = [\n", - " 'g', 'q', 'W-CQ', 'W-QQ', 'Z-BB', 'Z-CC', 'Z-QQ', 'H-BB', 'H-CC', 'Top-CQB'\n", - "]\n", - "\n", - "# Run evaluation\n", - "test_loss, test_metric, y_true, y_pred = trainer.evaluate(plot=plot_particle_reconstruction)\n", - "\n", - "print(f\"--> Test Loss: {test_loss:.4f} | Test Accuracy/Metric: {test_metric:.4f}\")\n", - "\n", - "# Generate meaningful performance visualizer graphs\n", - "plot_gated_roc_curves(y_true, y_pred, class_names=jetclass_labels)\n", - "plot_background_rejection(y_true, y_pred, target_tpr=0.5, class_names=jetclass_labels)\n", - "plot_model_calibration(y_true, y_pred)\n", - "plot_normalized_confusion_matrix(y_true, y_pred, class_names=jetclass_labels)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 332 - }, - "id": "LPgZhDfuJNFd", - "outputId": "83dce0e9-a0bf-44f2-92ae-b87e97e15741" - }, - "id": "LPgZhDfuJNFd", - "execution_count": null, - "outputs": [ - { - "output_type": "error", - "ename": "TypeError", - "evalue": "Trainer.evaluate() missing 1 required positional argument: 'loss_type'", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_1527/2727640763.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Run evaluation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_metric\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplot_particle_reconstruction\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 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"--> Test Loss: {test_loss:.4f} | Test Accuracy/Metric: {test_metric:.4f}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;31m# pyrefly: ignore [bad-context-manager]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: Trainer.evaluate() missing 1 required positional argument: 'loss_type'" - ] - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "o3B6Sr9sJO_f" - }, - "id": "o3B6Sr9sJO_f", - "execution_count": null, - "outputs": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" - }, - "colab": { - "provenance": [], - "gpuType": "A100" - }, - "accelerator": "GPU", - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "41be1797614f4fa7830532d34158ac52": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8aac226632524370abc787be435a9b48", - "IPY_MODEL_e24c1cf41fcd442eb81ef418e92b24c6", - "IPY_MODEL_dd026d80116749d19365780681500be9" - ], - "layout": "IPY_MODEL_b1703412347941edaab104e289f38221" - } - }, - "8aac226632524370abc787be435a9b48": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2cab2b54d039403daa901bbb65203e42", - "placeholder": "​", - "style": "IPY_MODEL_8f5881e63ed54097b49c624676055437", - "value": "Training: 100%" - } - }, - "e24c1cf41fcd442eb81ef418e92b24c6": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_48e35659a78b41cb95b8cbb39040de62", - "max": 25000, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_ff02466aec4344fa84813bb3eba626b1", - "value": 25000 - } - }, - "dd026d80116749d19365780681500be9": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_53c475a9562d4af59d6947020af35778", - "placeholder": "​", - "style": "IPY_MODEL_9e9b3731cd824b0094835c6ce06f9265", - "value": " 25000/25000 [24:47<00:00, 17.01it/s, epoch=2/2, avg_loss=1.8927, avg_metric=0.2941]" - } - }, - "b1703412347941edaab104e289f38221": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": "inline-flex", - "flex": null, - "flex_flow": "row wrap", - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "100%" - } - }, - "2cab2b54d039403daa901bbb65203e42": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8f5881e63ed54097b49c624676055437": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "48e35659a78b41cb95b8cbb39040de62": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": "2", - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "ff02466aec4344fa84813bb3eba626b1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "53c475a9562d4af59d6947020af35778": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "9e9b3731cd824b0094835c6ce06f9265": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "0e1aad635bf14d1bb31c6f903f032e73": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4e34acdc629c4fd898b1bbd743cd4cc1", - "IPY_MODEL_f0e15ef2bc25482e87a2aafd881171fb", - "IPY_MODEL_649dcf365ee140778713f1923422b40c" - ], - "layout": "IPY_MODEL_e0ef6f1060d2402a93eafa4557cd7a7a" - } - }, - "4e34acdc629c4fd898b1bbd743cd4cc1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_45a2e6bd3f464dd0af70d71813826f89", - "placeholder": "​", - "style": "IPY_MODEL_b1ef8826aa5f4e2896ee4ee4bd6f5e2d", - "value": "Training: 100%" - } - }, - "f0e15ef2bc25482e87a2aafd881171fb": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_cb4aa040e0fe4b0292c3dcda6cc55b66", - "max": 6250, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_ee23434b787f454399e4be9c6111a94c", - "value": 6250 - } - }, - "649dcf365ee140778713f1923422b40c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3c0bd3f7f0034da7ba1b118da5d111eb", - "placeholder": "​", - "style": "IPY_MODEL_bad0b3bd64c743519cf1ec7eb6b3ba46", - "value": " 6250/6250 [39:03<00:00,  8.20it/s, epoch=1/1, avg_loss=0.2017]" - } - }, - "e0ef6f1060d2402a93eafa4557cd7a7a": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": "inline-flex", - "flex": null, - "flex_flow": "row wrap", - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "100%" - } - }, - "45a2e6bd3f464dd0af70d71813826f89": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "b1ef8826aa5f4e2896ee4ee4bd6f5e2d": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "cb4aa040e0fe4b0292c3dcda6cc55b66": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": "2", - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "ee23434b787f454399e4be9c6111a94c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "3c0bd3f7f0034da7ba1b118da5d111eb": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "bad0b3bd64c743519cf1ec7eb6b3ba46": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "62f237b1683e475595fe17da0edeae87": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7bdac98dfc4a4699bab39fa846e56354", - "IPY_MODEL_ac7d5153146d4bd89fa5587a1c4babb7", - "IPY_MODEL_1d65d195b47147d3806f7735255878d8" - ], - "layout": "IPY_MODEL_34f4dd1f0b9b4920bffb1027ea6e11fe" - } - }, - "7bdac98dfc4a4699bab39fa846e56354": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_38e5079824d648849dcde09e2a2948fd", - "placeholder": "​", - "style": "IPY_MODEL_ffa3569f77fb4a3c8a6fb08930c0defb", - "value": "Training: 100%" - } - }, - "ac7d5153146d4bd89fa5587a1c4babb7": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_dd800993260d48e383fb9aa27d265c7d", - "max": 25000, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_4d4b43abf49f4114a7fc83d9128f6d30", - "value": 25000 - } - }, - "1d65d195b47147d3806f7735255878d8": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0d7bc861ca364e298ffb26510a4e4e09", - "placeholder": "​", - "style": "IPY_MODEL_2c776b732e6a4cb8bb85e01baff5fb33", - "value": " 25000/25000 [25:07<00:00, 17.45it/s, epoch=2/2, avg_loss=1.9639, avg_metric=0.2580]" - } - }, - "34f4dd1f0b9b4920bffb1027ea6e11fe": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": "inline-flex", - "flex": null, - "flex_flow": "row wrap", - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "100%" - } - }, - "38e5079824d648849dcde09e2a2948fd": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "ffa3569f77fb4a3c8a6fb08930c0defb": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "dd800993260d48e383fb9aa27d265c7d": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": "2", - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "4d4b43abf49f4114a7fc83d9128f6d30": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "0d7bc861ca364e298ffb26510a4e4e09": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "2c776b732e6a4cb8bb85e01baff5fb33": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - } - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file From 8329a8cd6ebddef0cf6e806a6c3a9c745a3af39e Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 20:41:48 +0530 Subject: [PATCH 05/11] notebook run --- .../notebook/PAG_LorentzParT_.ipynb | 2888 +++++++++++++++++ 1 file changed, 2888 insertions(+) create mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb new file mode 100644 index 0000000..4c00773 --- /dev/null +++ b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb @@ -0,0 +1,2888 @@ +{ + "cells": [ + { + "cell_type": "code", + "source": [ + "\n", + "# 1. Clone the ML4SCI/CMS repository\n", + "!git clone https://github.com/ML4SCI/CMS.git\n", + "\n", + "# 2. Navigate to the specific Hybrid Transformer project directory\n", + "%cd CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", + "\n", + "# 3. Install required libraries\n", + "!pip install lgatr uproot awkward tqdm vector" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B7YmCrpkJSAg", + "outputId": "f6eeec54-0a0e-416f-e730-dcf49f66d6f7" + }, + "id": "B7YmCrpkJSAg", + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'CMS'...\n", + "remote: Enumerating objects: 751, done.\u001b[K\n", + "remote: Counting objects: 100% (130/130), done.\u001b[K\n", + "remote: Compressing objects: 100% (99/99), done.\u001b[K\n", + "remote: Total 751 (delta 39), reused 83 (delta 27), pack-reused 621 (from 2)\u001b[K\n", + "Receiving objects: 100% (751/751), 330.16 MiB | 18.70 MiB/s, done.\n", + "Resolving deltas: 100% (187/187), done.\n", + "Updating files: 100% (531/531), done.\n", + "/content/CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", + "Collecting lgatr\n", + " Downloading lgatr-1.4.4-py3-none-any.whl.metadata (8.7 kB)\n", + "Collecting uproot\n", + " Downloading uproot-5.7.5-py3-none-any.whl.metadata (35 kB)\n", + "Collecting awkward\n", + " Downloading awkward-2.11.0-py3-none-any.whl.metadata (7.6 kB)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (4.67.3)\n", + "Collecting vector\n", + " Downloading vector-1.8.1-py3-none-any.whl.metadata (15 kB)\n", + "Requirement already satisfied: torch>=2.1 in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.11.0+cu128)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.0.2)\n", + "Requirement already satisfied: einops in /usr/local/lib/python3.12/dist-packages (from lgatr) (0.8.2)\n", + "Requirement already satisfied: opt_einsum in /usr/local/lib/python3.12/dist-packages (from lgatr) (3.4.0)\n", + "Requirement already satisfied: cramjam>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2.11.0)\n", + "Requirement already satisfied: fsspec!=2026.2.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2025.3.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from uproot) (26.2)\n", + "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from uproot) (3.8.1)\n", + "Collecting awkward-cpp==54 (from awkward)\n", + " Downloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.29.7)\n", + "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (4.16.0)\n", + "Requirement already satisfied: setuptools<82 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (75.2.0)\n", + "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (1.14.0)\n", + "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.1)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.1.6)\n", + "Requirement already satisfied: cuda-toolkit==12.8.1 in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.1)\n", + "Requirement already satisfied: cuda-bindings<13,>=12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (12.9.7)\n", + "Requirement already satisfied: nvidia-cudnn-cu12==9.19.0.56 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (9.19.0.56)\n", + "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (0.7.1)\n", + "Requirement already satisfied: nvidia-nccl-cu12==2.28.9 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (2.28.9)\n", + "Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.4.5)\n", + "Requirement already satisfied: triton==3.6.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.0)\n", + "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.4.1)\n", + "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", + "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.3.3.83)\n", + "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (1.13.1.3)\n", + "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", + "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (10.3.9.90)\n", + "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.7.3.90)\n", + "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.5.8.93)\n", + "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", + "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", + "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", + "Requirement already satisfied: cuda-pathfinder~=1.1 in /usr/local/lib/python3.12/dist-packages (from cuda-bindings<13,>=12.9.4->torch>=2.1->lgatr) (1.5.6)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=2.1->lgatr) (1.3.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=2.1->lgatr) (3.0.3)\n", + "Downloading lgatr-1.4.4-py3-none-any.whl (60 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.6/60.6 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading uproot-5.7.5-py3-none-any.whl (401 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m35.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading awkward-2.11.0-py3-none-any.whl (974 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m974.8/974.8 kB\u001b[0m \u001b[31m76.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (689 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m689.3/689.3 kB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading vector-1.8.1-py3-none-any.whl (182 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.7/182.7 kB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: vector, awkward-cpp, awkward, uproot, lgatr\n", + "Successfully installed awkward-2.11.0 awkward-cpp-54 lgatr-1.4.4 uproot-5.7.5 vector-1.8.1\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# --- Default libraries ---\n", + "import os\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "# --- Working directory ---\n", + "PROJECT_DIR = Path().resolve()\n", + "PROJECT_ROOT_NAME = 'Hybrid_Transformer_Thanh_Nguyen'\n", + "\n", + "while PROJECT_DIR.name != PROJECT_ROOT_NAME and PROJECT_DIR != PROJECT_DIR.parent:\n", + " PROJECT_DIR = PROJECT_DIR.parent\n", + "\n", + "if Path().resolve() != PROJECT_DIR:\n", + " os.chdir(PROJECT_DIR)\n", + "\n", + "DATA_DIR = PROJECT_DIR / 'data'\n", + "LOG_DIR = PROJECT_DIR / 'logs'\n", + "\n", + "# --- Data preprocessing & visualization ---\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# --- Deep learning ---\n", + "import torch\n", + "\n", + "# --- Custom modules ---\n", + "from src.configs import LorentzParTConfig, TrainConfig\n", + "from src.engine import MaskedModelTrainer, Trainer\n", + "from src.models import LorentzParT\n", + "from src.utils import accuracy_metric_ce, set_seed\n", + "from src.utils.data import JetClassDataset, compute_norm_stats, read_file\n", + "from src.utils.viz import *\n", + "\n", + "# --- Settings ---\n", + "warnings.filterwarnings('ignore')\n", + "set_seed(42)\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "device" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IvxBxUicRu18", + "outputId": "3241b771-4383-417a-cfc1-963899fb5dd4" + }, + "id": "IvxBxUicRu18", + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ] + }, + { + "cell_type": "code", + "source": [ + "'''\n", + "drive_dest_folder = '/content/drive/MyDrive/GSOC/dara'\n", + "drive_dest_path = os.path.join(drive_dest_folder, out_file)\n", + "\n", + "# Create the directory if it doesn't exist\n", + "if not os.path.exists(drive_dest_folder):\n", + " os.makedirs(drive_dest_folder)\n", + " print(f\"Created directory: {drive_dest_folder}\")\n", + "\n", + "# 3. Move the verified file to Drive\n", + "if os.path.exists(out_file):\n", + " print(f\"Moving {out_file} to Google Drive...\")\n", + " shutil.move(out_file, drive_dest_path)\n", + " print(f\"File successfully moved to: {drive_dest_path}\")\n", + "else:\n", + " print(\"Source file not found. Check if the download was successful.\")''''" + ], + "metadata": { + "collapsed": true, + "id": "QfAWZ8A3cdNR" + }, + "id": "QfAWZ8A3cdNR", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Loading Dpendencies" + ], + "metadata": { + "id": "BmBDXDMSD61u" + }, + "id": "BmBDXDMSD61u" + }, + { + "cell_type": "code", + "source": [ + "from typing import List, Tuple, Dict, Optional\n", + "\n", + "import torch\n", + "from torch import nn, Tensor\n", + "from lgatr.interface import extract_vector\n", + "from lgatr.layers import EquiLinear\n", + "\n", + "from src.models.classifier import ClassAttentionBlock, Classifier\n", + "from src.models.feedforward import Feedforward\n", + "from src.models.particle_transformer import ParticleAttentionBlock\n", + "from src.models.processor import InteractionEmbedding, ParticleProcessor\n", + "from src.configs import LorentzParTConfig\n", + "from lgatr.interface import extract_vector\n", + "from lgatr.layers import EquiLinear\n", + "\n", + "\n" + ], + "metadata": { + "id": "jTbtwxdtAt9S" + }, + "id": "jTbtwxdtAt9S", + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Data Preparation" + ], + "metadata": { + "id": "VB1JTgpxDv-4" + }, + "id": "VB1JTgpxDv-4" + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "from pathlib import Path\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from src.utils.data import JetClassDataset, compute_norm_stats\n", + "\n", + "# --- 1. Mount Google Drive (If not already done) ---\n", + "from google.colab import drive\n", + "if not os.path.exists('/content/drive'):\n", + " drive.mount('/content/drive')\n", + "\n", + "# --- 2. Locate and Load the Compressed Archive ---\n", + "DRIVE_FILE_PATH = '/content/drive/MyDrive/GSOC/dara/jetclass_balanced_1M.npz'\n", + "\n", + "print(\"=\" * 70)\n", + "print(f\"LOADING SERIALIZED ARRAYS FROM DRIVE\")\n", + "print(\"=\" * 70)\n", + "\n", + "if os.path.exists(DRIVE_FILE_PATH):\n", + " # Load using memory-mapping for high-speed indexing\n", + " data_archive = np.load(DRIVE_FILE_PATH, mmap_mode='r')\n", + "\n", + " X_particles = data_archive['X_particles']\n", + " X_jets = data_archive['X_jets']\n", + " y = data_archive['Y']\n", + "\n", + " print(\"SUCCESS: Data loaded cleanly into RAM!\")\n", + " print(f\" -> X_particles matrix shape : {X_particles.shape}\")\n", + " print(f\" -> X_jets matrix shape : {X_jets.shape}\")\n", + " print(f\" -> y (Labels) matrix shape : {y.shape}\")\n", + " print(\"=\" * 70 + \"\\n\")\n", + "else:\n", + " raise FileNotFoundError(f\"ERROR: Could not find the file at {DRIVE_FILE_PATH}\")\n", + "\n", + "# --- 3. Split the Balanced Dataset Safely ---\n", + "# We enforce stratify=y to lock in your strict 10% balance across all splits\n", + "X_train, X_val, y_train, y_val = train_test_split(X_particles, y, test_size=0.2, random_state=42, stratify=y)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.5, random_state=42, stratify=y_val)\n", + "\n", + "# --- 4. Re-Initialize JetClass Dataset Objects ---\n", + "normalize = [True, False, False, True]\n", + "norm_dict = compute_norm_stats(X_train)\n", + "\n", + "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode='biased')\n", + "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode='biased')\n", + "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode='first')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9RBH34OYBoZ7", + "outputId": "a53a1da2-e44b-4611-cd4e-f610d3cce289" + }, + "id": "9RBH34OYBoZ7", + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n", + "======================================================================\n", + "LOADING SERIALIZED ARRAYS FROM DRIVE\n", + "======================================================================\n", + "SUCCESS: Data loaded cleanly into RAM!\n", + " -> X_particles matrix shape : (1000000, 4, 128)\n", + " -> X_jets matrix shape : (1000000, 4)\n", + " -> y (Labels) matrix shape : (1000000, 10)\n", + "======================================================================\n", + "\n", + "pt_mean: 92.70597076416016, pt_std: 105.79937744140625\n", + "eta_mean: -0.0011634620605036616, eta_std: 0.9182536005973816\n", + "phi_mean: -0.0006678671925328672, phi_std: 1.8138455152511597\n", + "E_mean: 133.98568725585938, E_std: 167.7259979248047\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "id": "edb52e95", + "metadata": { + "id": "edb52e95" + }, + "source": [ + "## Applying Gated Attention LorentzPart" + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch import Tensor\n", + "from typing import Optional\n", + "\n", + "class ParticleAttentionBlock(nn.Module):\n", + " def __init__(\n", + " self,\n", + " embed_dim: int = 128,\n", + " num_heads: int = 8,\n", + " dropout: float = 0.1,\n", + " expansion_factor: int = 4,\n", + " gate_type: Optional[str] = \"headwise\",\n", + " ):\n", + " super(ParticleAttentionBlock, self).__init__()\n", + " assert embed_dim % num_heads == 0, \"embed_dim must be divisible by num_heads\"\n", + "\n", + " self.embed_dim = embed_dim\n", + " self.num_heads = num_heads\n", + " self.head_dim = embed_dim // num_heads\n", + " self.gate_type = gate_type\n", + "\n", + " self.layernorm1 = nn.LayerNorm(embed_dim)\n", + " self.mass_norm = nn.LayerNorm(1)#norm\n", + "\n", + " # Project pooled interaction head-features to match token embedding dimensions\n", + " self.physics_proj = nn.Linear(num_heads, embed_dim)\n", + "\n", + " # Project the global scalar invariant mass squared (m2) to the embedding space\n", + " self.mass_proj = nn.Linear(1, embed_dim)\n", + "\n", + " # Gating projections accept physics-fused representations\n", + " if self.gate_type == \"headwise\":\n", + " self.gate_proj = nn.Linear(embed_dim, num_heads)\n", + " elif self.gate_type == \"elementwise\":\n", + " self.gate_proj = nn.Linear(embed_dim, embed_dim)\n", + "\n", + " self.pmha = nn.MultiheadAttention(\n", + " embed_dim=embed_dim,\n", + " num_heads=num_heads,\n", + " dropout=dropout,\n", + " batch_first=True\n", + " )\n", + "\n", + " self.layernorm2 = nn.LayerNorm(embed_dim)\n", + " self.dropout = nn.Dropout(dropout)\n", + "\n", + " self.feedforward = Feedforward(\n", + " embed_dim=embed_dim,\n", + " expansion_factor=expansion_factor,\n", + " dropout=dropout\n", + " )\n", + "\n", + " def forward(self, x: Tensor, padding_mask: Tensor, U: Optional[Tensor] = None, p4: Optional[Tensor] = None) -> Tensor:\n", + " residual = x\n", + " B_size, N_particles, _ = x.shape\n", + "\n", + " # 1. Standard token serialization\n", + " x_norm = self.layernorm1(x)\n", + "\n", + " # 2. FIXED: U is ALREADY [Batch * Heads, N, N]. Pass directly to PyTorch MHA.\n", + " x_attn, _ = self.pmha(x_norm, x_norm, x_norm, key_padding_mask=padding_mask, attn_mask=U)\n", + "\n", + " # 3. Physics-Aware Fusion (Invariants-driven conditioning)\n", + " if U is not None:\n", + " # Reconstruct the 4D shape: [B * H, N, N] -> [B, H, N, N]\n", + " U_reshaped = U.view(B_size, self.num_heads, N_particles, N_particles)\n", + "\n", + " # Pool over neighbor particle index 'j' (dim=3). Resulting shape: [B, H, N]\n", + " u_pooled = U_reshaped.sum(dim=3)\n", + "\n", + " # Transpose to align with token channels: [B, N, H]\n", + " u_pooled = u_pooled.transpose(1, 2)\n", + "\n", + " # Map head-wise pooled invariants into token channel space: [B, N, embed_dim]\n", + " physics_context = self.physics_proj(u_pooled)\n", + "\n", + " # Fuse physical invariants with abstract node latent maps\n", + " x_gating_input = x_norm + physics_context\n", + " else:\n", + " # Fallback path if U is not provided\n", + " x_gating_input = x_norm\n", + "\n", + " # 4. Compute Global Invariant Mass Bias from 4-vectors [B, N, 4] -> (E, px, py, pz)\n", + " if p4 is not None:\n", + " # Sum energy component (index 0) over all particles (dim=1)\n", + " energy_sum = p4[..., 0].sum(dim=1, keepdim=True) # Shape: [B, 1]\n", + "\n", + " # Sum momentum components (indices 1, 2, 3) over all particles (dim=1)\n", + " momentum_sum = p4[..., 1:].sum(dim=1) # Shape: [B, 3]\n", + "\n", + " # Calculate invariant mass squared (m2)\n", + " m2 = energy_sum**2 - momentum_sum.norm(dim=-1, keepdim=True)**2 # Shape: [B, 1]\n", + "\n", + " m2_scaled = torch.log1p(torch.relu(m2))\n", + "\n", + "\n", + " # Step C: Standardize the mean and variance dynamically\n", + " m2_norm = self.mass_norm(m2_scaled)\n", + "\n", + " # Project normalized m2 into a global embedding bias vector [B, 1, embed_dim]\n", + " mass_bias = self.mass_proj(m2_norm).unsqueeze(1)\n", + "\n", + " # Broad-cast add global event mass bias to the per-particle gating inputs\n", + " x_gating_input = x_gating_input + mass_bias\n", + "\n", + "\n", + "\n", + " # 5. Compute and apply the explicitly Physics-Aware Gate\n", + " if self.gate_type == \"headwise\":\n", + " # Compute score matrix from physics-fused map: (B, N, embed_dim) -> (B, N, num_heads, 1)\n", + " gate_score = self.gate_proj(x_gating_input).unsqueeze(-1)\n", + "\n", + " # Separate heads to apply individual scalar gating values\n", + " x_attn = x_attn.view(B_size, N_particles, self.num_heads, self.head_dim)\n", + "\n", + " # Apply physics-conditioned filter and reconstruct classic transformer shape\n", + " x_attn = (x_attn * torch.sigmoid(gate_score)).view(B_size, N_particles, self.embed_dim)\n", + "\n", + " elif self.gate_type == \"elementwise\":\n", + " # Compute full channel-by-channel mask from physics-fused map: (B, N, embed_dim)\n", + " gate_score = self.gate_proj(x_gating_input)\n", + " x_attn = x_attn * torch.sigmoid(gate_score)\n", + "\n", + " # 6. Standard Feedforward processing\n", + " x = self.layernorm2(x_attn)\n", + " x = self.dropout(x)\n", + " x += residual\n", + " x = self.feedforward(x)\n", + "\n", + " return x" + ], + "metadata": { + "id": "AvsAk0byPoBK" + }, + "id": "AvsAk0byPoBK", + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch import Tensor\n", + "from typing import Optional, List, Dict, Tuple\n", + "\n", + "\n", + "\n", + "class LorentzParTEncoder(nn.Module):\n", + " def __init__(\n", + " self,\n", + " embed_dim: int = 128,\n", + " num_heads: int = 8,\n", + " num_layers: int = 8,\n", + " in_s_channels: Optional[int] = None,\n", + " out_s_channels: Optional[int] = None,\n", + " dropout: float = 0.1,\n", + " expansion_factor: int = 4,\n", + " pair_embed_dims: List[int] = [64, 64, 64],\n", + " attention_config: Dict = {}\n", + " ):\n", + " super(LorentzParTEncoder, self).__init__()\n", + " self.equilinear = EquiLinear(\n", + " in_mv_channels=1,\n", + " out_mv_channels=1,\n", + " in_s_channels=in_s_channels,\n", + " out_s_channels=out_s_channels\n", + " )\n", + " self.proj = nn.Linear(16, embed_dim)\n", + " self.interaction_embed = InteractionEmbedding(\n", + " num_interaction_features=4,\n", + " pair_embed_dims=pair_embed_dims + [num_heads]\n", + " )\n", + "\n", + " use_gating = attention_config.get('use_gating', False)\n", + "\n", + " # Explicitly pass gate_type so the block knows whether to use physics gating or standard\n", + " self.encoder = nn.ModuleList([\n", + " ParticleAttentionBlock(\n", + " embed_dim=embed_dim,\n", + " num_heads=num_heads,\n", + " dropout=dropout,\n", + " expansion_factor=expansion_factor,\n", + " gate_type=\"headwise\" if use_gating else None\n", + " ) for _ in range(num_layers)\n", + " ])\n", + "\n", + " def forward(self, x: Tensor, padding_mask: Tensor, U: Tensor, p4: Optional[Tensor] = None) -> Tensor:\n", + " B, N, F = x.shape\n", + " U = self.interaction_embed(U)\n", + " x = x.view(B, N, 1, F)\n", + " x, _ = self.equilinear(x)\n", + " x = x.view(B, N, 16)\n", + " x = self.proj(x)\n", + "\n", + " # Pass p4 down to the attention blocks\n", + " for layer in self.encoder:\n", + " x = layer(x, padding_mask, U, p4=p4)\n", + "\n", + " return x\n", + "\n", + "\n", + "class LorentzParT(nn.Module):\n", + " def __init__(\n", + " self,\n", + " config: Optional[LorentzParTConfig] = None,\n", + " max_num_particles: Optional[int] = None,\n", + " num_particle_features: Optional[int] = None,\n", + " num_classes: Optional[int] = None,\n", + " embed_dim: Optional[int] = None,\n", + " num_heads: Optional[int] = None,\n", + " num_layers: Optional[int] = None,\n", + " num_cls_layers: Optional[int] = None,\n", + " num_mlp_layers: Optional[int] = None,\n", + " hidden_dim: Optional[int] = None,\n", + " hidden_mv_channels: Optional[int] = None,\n", + " in_s_channels: Optional[int] = None,\n", + " out_s_channels: Optional[int] = None,\n", + " hidden_s_channels: Optional[int] = None,\n", + " attention: Optional[Dict] = None,\n", + " mlp: Optional[Dict] = None,\n", + " reinsert_mv_channels: Optional[Tuple[int]] = None,\n", + " reinsert_s_channels: Optional[Tuple[int]] = None,\n", + " dropout: Optional[float] = None,\n", + " expansion_factor: Optional[int] = None,\n", + " pair_embed_dims: Optional[List[int]] = None,\n", + " mask: Optional[bool] = None,\n", + " weights: Optional[str] = None,\n", + " inference: Optional[bool] = False\n", + " ):\n", + " super(LorentzParT, self).__init__()\n", + "\n", + " # Use config if provided, otherwise use defaults\n", + " if config is not None:\n", + " self.max_num_particles = max_num_particles if max_num_particles is not None else config.max_num_particles\n", + " self.num_particle_features = num_particle_features if num_particle_features is not None else config.num_particle_features\n", + " self.num_classes = num_classes if num_classes is not None else config.num_classes\n", + " self.embed_dim = embed_dim if embed_dim is not None else config.embed_dim\n", + " self.num_heads = num_heads if num_heads is not None else config.num_heads\n", + " self.num_layers = num_layers if num_layers is not None else config.num_layers\n", + " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else config.num_cls_layers\n", + " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else config.num_mlp_layers\n", + " self.hidden_dim = hidden_dim if hidden_dim is not None else config.hidden_dim\n", + " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else config.hidden_mv_channels\n", + " self.in_s_channels = in_s_channels if in_s_channels is not None else config.in_s_channels\n", + " self.out_s_channels = out_s_channels if out_s_channels is not None else config.out_s_channels\n", + " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else config.hidden_s_channels\n", + " self.attention = attention if attention is not None else config.attention\n", + " self.mlp = mlp if mlp is not None else config.mlp\n", + " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else config.reinsert_mv_channels\n", + " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else config.reinsert_s_channels\n", + " self.dropout = dropout if dropout is not None else config.dropout\n", + " self.expansion_factor = expansion_factor if expansion_factor is not None else config.expansion_factor\n", + " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else config.pair_embed_dims\n", + " self.mask = mask if mask is not None else config.mask\n", + " self.weights = weights if weights is not None else config.weights\n", + " self.inference = inference if inference is not None else config.inference\n", + " else:\n", + " self.max_num_particles = max_num_particles if max_num_particles is not None else 128\n", + " self.num_particle_features = num_particle_features if num_particle_features is not None else 4\n", + " self.num_classes = num_classes if num_classes is not None else 10\n", + " self.embed_dim = embed_dim if embed_dim is not None else 128\n", + " self.num_heads = num_heads if num_heads is not None else 8\n", + " self.num_layers = num_layers if num_layers is not None else 8\n", + " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else 2\n", + " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else 0\n", + " self.hidden_dim = hidden_dim if hidden_dim is not None else 256\n", + " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else 8\n", + " self.in_s_channels = in_s_channels if in_s_channels is not None else None\n", + " self.out_s_channels = out_s_channels if out_s_channels is not None else None\n", + " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else 16\n", + " self.attention = attention if attention is not None else {}\n", + " self.mlp = mlp if mlp is not None else None\n", + " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else None\n", + " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else None\n", + " self.dropout = dropout if dropout is not None else 0.1\n", + " self.expansion_factor = expansion_factor if expansion_factor is not None else 4\n", + " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else [64, 64, 64]\n", + " self.mask = mask if mask is not None else False\n", + " self.weights = weights if weights is not None else None\n", + " self.inference = inference if inference is not None else False\n", + "\n", + " # Initialize the class token\n", + " self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim), requires_grad=True)\n", + " nn.init.normal_(self.cls_token, mean=0.0, std=1.0)\n", + "\n", + " self.processor = ParticleProcessor(to_multivector=True)\n", + "\n", + " # Updated Encoder with attention_config passed dynamically\n", + " self.encoder = LorentzParTEncoder(\n", + " embed_dim=self.embed_dim,\n", + " num_heads=self.num_heads,\n", + " num_layers=self.num_layers,\n", + " in_s_channels=self.in_s_channels,\n", + " out_s_channels=self.out_s_channels,\n", + " dropout=self.dropout,\n", + " expansion_factor=self.expansion_factor,\n", + " pair_embed_dims=self.pair_embed_dims,\n", + " attention_config=self.attention\n", + " )\n", + "\n", + " # For self-supervised learning\n", + " self.fc = nn.Linear(self.max_num_particles * self.embed_dim, 16)\n", + " self.equilinear = EquiLinear(\n", + " in_mv_channels=1,\n", + " out_mv_channels=1,\n", + " in_s_channels=self.in_s_channels,\n", + " out_s_channels=self.out_s_channels\n", + " )\n", + "\n", + " # For classification\n", + " self.decoder = nn.ModuleList([\n", + " ClassAttentionBlock(\n", + " embed_dim=self.embed_dim,\n", + " num_heads=self.num_heads,\n", + " dropout=0.0,\n", + " expansion_factor=self.expansion_factor\n", + " ) for _ in range(self.num_cls_layers)\n", + " ])\n", + " self.layernorm = nn.LayerNorm(self.embed_dim)\n", + " self.classifier = Classifier(\n", + " num_classes=self.num_classes,\n", + " input_dim=self.embed_dim,\n", + " hidden_dim=self.hidden_dim,\n", + " num_layers=self.num_mlp_layers,\n", + " dropout=self.dropout,\n", + " )\n", + " self.act = nn.Softmax(dim=1) if self.inference else nn.Identity()\n", + "\n", + " # Load pretrained weights\n", + " if self.weights is not None:\n", + " state_dict = torch.load(self.weights)\n", + " filtered_state = {\n", + " k[len(\"encoder.\") :]: v\n", + " for k, v in state_dict.items()\n", + " if k.startswith(\"encoder.\")\n", + " }\n", + " self.encoder.load_state_dict(filtered_state, strict=False)\n", + "\n", + " def forward(self, x: Tensor, mask_idx: Optional[Tensor] = None) -> Tensor:\n", + " B, N, F = x.shape # (batch_size, max_num_particles, num_particle_features)\n", + "\n", + " # Save the raw kinematics before processor alters them\n", + " p4 = x.clone()\n", + "\n", + " # Ignore padding particles in query\n", + " padding_mask = (x[..., 3] == 0).float() # (B, N)\n", + "\n", + " # Set the masked indices to 0.0 so they are not ignored in MultiheadAttention()\n", + " if mask_idx is not None:\n", + " batch_indices = torch.arange(x.size(0), device=x.device)\n", + " padding_mask[batch_indices, mask_idx] = 0.0\n", + "\n", + " # Process particles to get interaction embeddings and multivectors (if applicable)\n", + " x, U = self.processor(x)\n", + "\n", + " # Pass through equilinear layer and particle attention blocks (passing p4 down)\n", + " x = self.encoder(x, padding_mask, U, p4=p4)\n", + "\n", + " # Classification (no masking in this case)\n", + " if not self.mask:\n", + " x_cls = self.cls_token.expand(B, -1, -1)\n", + "\n", + " # Decoder with class attention blocks\n", + " for layer in self.decoder:\n", + " x_cls = layer(x, x_cls, padding_mask)\n", + "\n", + " # MLP head for classification\n", + " x_cls = self.layernorm(x_cls).squeeze(1)\n", + " x_cls = self.classifier(x_cls)\n", + " output = self.act(x_cls) # (B, num_classes)\n", + "\n", + " return output\n", + " else:\n", + " x = x.view(B, -1) # (B, N * embed_dim)\n", + " x = self.fc(x) # (B, 16)\n", + " x = x.view(B, 1, 1, 16)\n", + " x, _ = self.equilinear(x) # (B, 1, 1, 16)\n", + " x = x.view(B, 16)\n", + " x = extract_vector(x) # (B, F)\n", + "\n", + " return x" + ], + "metadata": { + "id": "QRhMF1TP8Ymw" + }, + "id": "QRhMF1TP8Ymw", + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Gating Test" + ], + "metadata": { + "id": "UC2GQe3iR7Gt" + }, + "id": "UC2GQe3iR7Gt" + }, + { + "cell_type": "code", + "source": [ + "# 1. Initialize your config with gating enabled\n", + "test_config = LorentzParTConfig(\n", + " embed_dim=128,\n", + " num_heads=8,\n", + " num_layers=8,\n", + " attention={'use_gating': True},\n", + " mask=True\n", + ")\n", + "\n", + "# 2. Instantiate the model\n", + "model = LorentzParT(config=test_config)\n", + "\n", + "# 3. Verification checks\n", + "first_layer = model.encoder.encoder[0]\n", + "is_gated = isinstance(first_layer, ParticleAttentionBlock)\n", + "\n", + "print(f\"--- Gating Verification ---\")\n", + "print(f\"Encoder Layer 1 Type: {type(first_layer).__name__}\")\n", + "print(f\"Gating Active: {is_gated}\")\n", + "\n", + "if is_gated:\n", + " print(\"Success: The model is now using Attention Gating!\")\n", + "else:\n", + " print(\"Error: The model is still using standard Attention Blocks.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cKfNSeyPR9nH", + "outputId": "8ea6d1d4-605b-4ec4-b41e-f2a61eb0c23b" + }, + "id": "cKfNSeyPR9nH", + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--- Gating Verification ---\n", + "Encoder Layer 1 Type: ParticleAttentionBlock\n", + "Gating Active: True\n", + "Success: The model is now using Attention Gating!\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Pre-Train" + ], + "metadata": { + "id": "H_Kmp-s3SUDD" + }, + "id": "H_Kmp-s3SUDD" + }, + { + "cell_type": "markdown", + "source": [ + "#Using Self Supervised Weights" + ], + "metadata": { + "id": "5peLQ8txiFZG" + }, + "id": "5peLQ8txiFZG" + }, + { + "cell_type": "code", + "source": [ + "# Initialize configuration with Attention Gating enabled\n", + "#not changing name of ssl_model_config\n", + "ssl_model_config = LorentzParTConfig(\n", + " embed_dim=128,\n", + " num_heads=8,\n", + " num_layers=8,\n", + " hidden_mv_channels=8,\n", + " attention={'use_gating': True}, # This is the trigger for your new code\n", + " dropout=0.1,\n", + " expansion_factor=4,\n", + " max_num_particles=128,\n", + " num_particle_features=4,\n", + " pair_embed_dims=[64, 64, 64],\n", + " mask=True # Set to True for Self-Supervised Learning / Masked Training\n", + ")" + ], + "metadata": { + "id": "6fGbG9f1SXbc" + }, + "id": "6fGbG9f1SXbc", + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Create the model and move it to your device (GPU/CPU)\n", + "gatedmodel = LorentzParT(config=ssl_model_config)\n", + "gatedmodel.to(device)\n", + "gatedmodel" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b7g5btHCSZka", + "outputId": "a47e4f81-cd33-4b7c-a39a-7f074bf3c456" + }, + "id": "b7g5btHCSZka", + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LorentzParT(\n", + " (processor): ParticleProcessor()\n", + " (encoder): LorentzParTEncoder(\n", + " (equilinear): EquiLinear()\n", + " (proj): Linear(in_features=16, out_features=128, bias=True)\n", + " (interaction_embed): InteractionEmbedding(\n", + " (embed): Sequential(\n", + " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", + " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): GELU(approximate='none')\n", + " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (6): GELU(approximate='none')\n", + " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (9): GELU(approximate='none')\n", + " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", + " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (12): GELU(approximate='none')\n", + " )\n", + " )\n", + " (encoder): ModuleList(\n", + " (0-7): 8 x ParticleAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", + " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", + " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", + " (gate_proj): Linear(in_features=128, out_features=8, bias=True)\n", + " (pmha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.1, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", + " (equilinear): EquiLinear()\n", + " (decoder): ModuleList(\n", + " (0-1): 2 x ClassAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.0, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (classifier): Classifier(\n", + " (layers): Sequential(\n", + " (0): Linear(in_features=128, out_features=10, bias=True)\n", + " )\n", + " )\n", + " (act): Identity()\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "source": [ + "num_params = sum(p.numel() for p in gatedmodel.parameters() if p.requires_grad)\n", + "num_params" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n5GMAwY0Sw5t", + "outputId": "170f4cca-9434-4e28-d810-c0bf423f50a7" + }, + "id": "n5GMAwY0Sw5t", + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2290656" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0aa870ab", + "metadata": { + "id": "0aa870ab" + }, + "outputs": [], + "source": [ + "# Training configurations\n", + "gated_train_config = TrainConfig(\n", + " batch_size=128,\n", + " criterion={\n", + " 'name': 'conservation_loss',\n", + " 'kwargs': {\n", + " 'loss_coef': [0.25, 0.25, 0.25, 0.25],\n", + " 'reduction': 'mean'\n", + " }\n", + " },\n", + " optimizer={\n", + " 'name': 'adamw',\n", + " 'kwargs': {\n", + " 'lr': 1e-4\n", + " }\n", + " },\n", + " scheduler={\n", + " 'name': 'exponential_lr',\n", + " 'kwargs': {\n", + " 'gamma': 0.95\n", + " }\n", + " },\n", + " callbacks=[{\n", + " 'name': 'early_stopping',\n", + " 'kwargs': {\n", + " 'monitor': 'val_loss',\n", + " 'mode': 'min',\n", + " 'patience': 5\n", + " }\n", + " }],\n", + " num_epochs=1,#20 change\n", + " start_epoch=0,\n", + " logging_dir=str(LOG_DIR),\n", + " logging_steps=1000,\n", + " progress_bar=True,\n", + " save_best=True,\n", + " save_ckpt=True,\n", + " save_fig=False,\n", + " device='cuda',\n", + " num_workers=0,\n", + " pin_memory=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6d5f08dd", + "metadata": { + "id": "6d5f08dd" + }, + "outputs": [], + "source": [ + "# Initialize the trainer\n", + "trainer = MaskedModelTrainer(\n", + " model=gatedmodel,\n", + " train_dataset=train_dataset,\n", + " val_dataset=val_dataset,\n", + " test_dataset=test_dataset,\n", + " device=device,\n", + " config=gated_train_config\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "40a3b5fa", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 188, + "referenced_widgets": [ + "0e1aad635bf14d1bb31c6f903f032e73", + "4e34acdc629c4fd898b1bbd743cd4cc1", + "f0e15ef2bc25482e87a2aafd881171fb", + "649dcf365ee140778713f1923422b40c", + "e0ef6f1060d2402a93eafa4557cd7a7a", + "45a2e6bd3f464dd0af70d71813826f89", + "b1ef8826aa5f4e2896ee4ee4bd6f5e2d", + "cb4aa040e0fe4b0292c3dcda6cc55b66", + "ee23434b787f454399e4be9c6111a94c", + "3c0bd3f7f0034da7ba1b118da5d111eb", + "bad0b3bd64c743519cf1ec7eb6b3ba46" + ] + }, + "id": "40a3b5fa", + "outputId": "e38bc084-3ac9-47b3-ed52-af4af71a5b6e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Training: 0%| | 0/6250 [00:00" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Evaluate the model on the test set\n", + "test_loss, test_metric, y_true, y_pred = trainer.evaluate(plot=plot_particle_reconstruction)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Label names for classification\n", + "labels = [\n", + " \"$q/g$\", # 0\n", + " \"$H \\\\to b\\\\bar{b}$\", # 1\n", + " \"$H \\\\to c\\\\bar{c}$\", # 2\n", + " \"$H \\\\to gg$\", # 3\n", + " \"$H \\\\to 4q$\", # 4\n", + " \"$H \\\\to \\\\ell \\\\nu qq'$\", # 5\n", + " \"$Z \\\\to q\\\\bar{q}$\", # 6\n", + " \"$W \\\\to qq'$\", # 7\n", + " \"$t \\\\to b\\\\ell \\\\nu$\", # 8\n", + " \"$t \\\\to bqq'$\" # 9\n", + "]\n" + ], + "metadata": { + "id": "vt4vzC1TGBiB" + }, + "id": "vt4vzC1TGBiB", + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Datasets for classification\n", + "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode=None)\n", + "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode=None)\n", + "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode=None)" + ], + "metadata": { + "id": "m37_eDUuGD0l" + }, + "id": "m37_eDUuGD0l", + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from collections import Counter\n", + "import numpy as np\n", + "\n", + "def check_uniformity(y, dataset_name, label_names, threshold=0.02):\n", + " \"\"\"\n", + " Checks if the labels in a dataset are uniformly distributed.\n", + " Supports both integer class arrays and one-hot encoded arrays.\n", + " \"\"\"\n", + " # If one-hot encoded, convert to class indices\n", + " if len(y.shape) > 1 and y.shape[1] > 1:\n", + " y = np.argmax(y, axis=1)\n", + "\n", + " total_samples = len(y)\n", + " counts = Counter(y)\n", + " num_classes = len(label_names)\n", + " expected_pct = 1.0 / num_classes\n", + "\n", + " print(f\"--- Distribution for {dataset_name} ({total_samples} samples) ---\")\n", + "\n", + " is_uniform = True\n", + " for idx, name in enumerate(label_names):\n", + " count = counts.get(idx, 0)\n", + " actual_pct = count / total_samples\n", + " print(f\"Class {idx} ({name:<18}): {count:<8} | {actual_pct:.2%}\")\n", + "\n", + " # Check if it deviates more than the allowed threshold from absolute uniformity\n", + " if abs(actual_pct - expected_pct) > threshold:\n", + " is_uniform = False\n", + "\n", + " if is_uniform:\n", + " print(f\"✅ {dataset_name} appears to be uniformly distributed (within a {threshold:.1%} tolerance).\\n\")\n", + " else:\n", + " print(f\"⚠️ {dataset_name} is NOT perfectly uniform. Expected around {expected_pct:.2%} per class.\\n\")\n", + "\n", + "# Run the check on your datasets\n", + "# (Using your raw arrays y_train, y_val, and y_test)\n", + "check_uniformity(y_train, \"Train Dataset\", labels)\n", + "check_uniformity(y_val, \"Validation Dataset\", labels)\n", + "check_uniformity(y_test, \"Test Dataset\", labels)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IWCdSFFKGFoL", + "outputId": "c1769a19-a1db-473e-80a8-237fdc4867b1" + }, + "id": "IWCdSFFKGFoL", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--- Distribution for Train Dataset (800000 samples) ---\n", + "Class 0 ($q/g$ ): 80000 | 10.00%\n", + "Class 1 ($H \\to b\\bar{b}$ ): 80000 | 10.00%\n", + "Class 2 ($H \\to c\\bar{c}$ ): 80000 | 10.00%\n", + "Class 3 ($H \\to gg$ ): 80000 | 10.00%\n", + "Class 4 ($H \\to 4q$ ): 80000 | 10.00%\n", + "Class 5 ($H \\to \\ell \\nu qq'$): 80000 | 10.00%\n", + "Class 6 ($Z \\to q\\bar{q}$ ): 80000 | 10.00%\n", + "Class 7 ($W \\to qq'$ ): 80000 | 10.00%\n", + "Class 8 ($t \\to b\\ell \\nu$ ): 80000 | 10.00%\n", + "Class 9 ($t \\to bqq'$ ): 80000 | 10.00%\n", + "✅ Train Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", + "\n", + "--- Distribution for Validation Dataset (100000 samples) ---\n", + "Class 0 ($q/g$ ): 10000 | 10.00%\n", + "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", + "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", + "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", + "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", + "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", + "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", + "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", + "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", + "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", + "✅ Validation Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", + "\n", + "--- Distribution for Test Dataset (100000 samples) ---\n", + "Class 0 ($q/g$ ): 10000 | 10.00%\n", + "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", + "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", + "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", + "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", + "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", + "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", + "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", + "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", + "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", + "✅ Test Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Model configurations\n", + "pretrained_model_config = LorentzParTConfig(\n", + " num_classes=10,\n", + " embed_dim=128,\n", + " num_heads=8,\n", + " num_layers=8,\n", + " num_cls_layers=2,\n", + " num_mlp_layers=0,\n", + " hidden_dim=256,\n", + " hidden_mv_channels=8,\n", + " in_s_channels=None,\n", + " out_s_channels=None,\n", + " hidden_s_channels=16,\n", + " attention={},\n", + " mlp={},\n", + " dropout=0.1,\n", + " expansion_factor=4,\n", + " max_num_particles=128,\n", + " num_particle_features=4,\n", + " pair_embed_dims=[64, 64, 64],\n", + " weights=gated_pt_path\n", + ")" + ], + "metadata": { + "id": "oRHuimXTGIdx" + }, + "id": "oRHuimXTGIdx", + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Initialize the classifier model\n", + "pretrained_model = LorentzParT(config=pretrained_model_config).to(device)\n", + "pretrained_model" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Dv1oQ3tSGe3w", + "outputId": "93cf00a1-dc30-4880-8652-4adf5b6cccae" + }, + "id": "Dv1oQ3tSGe3w", + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LorentzParT(\n", + " (processor): ParticleProcessor()\n", + " (encoder): LorentzParTEncoder(\n", + " (equilinear): EquiLinear()\n", + " (proj): Linear(in_features=16, out_features=128, bias=True)\n", + " (interaction_embed): InteractionEmbedding(\n", + " (embed): Sequential(\n", + " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", + " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): GELU(approximate='none')\n", + " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (6): GELU(approximate='none')\n", + " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (9): GELU(approximate='none')\n", + " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", + " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (12): GELU(approximate='none')\n", + " )\n", + " )\n", + " (encoder): ModuleList(\n", + " (0-7): 8 x ParticleAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", + " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", + " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", + " (pmha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.1, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", + " (equilinear): EquiLinear()\n", + " (decoder): ModuleList(\n", + " (0-1): 2 x ClassAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.0, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (classifier): Classifier(\n", + " (layers): Sequential(\n", + " (0): Linear(in_features=128, out_features=10, bias=True)\n", + " )\n", + " )\n", + " (act): Identity()\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Count parameters in the model\n", + "num_params = sum(p.numel() for p in pretrained_model.parameters() if p.requires_grad)\n", + "num_params" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_TDNTq4hGL2e", + "outputId": "24c6d7c6-5565-44e1-ed00-17967f67ff76" + }, + "id": "_TDNTq4hGL2e", + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2282400" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Training configurations\n", + "pretrained_config = TrainConfig(\n", + " batch_size=64,\n", + " criterion={\n", + " 'name': 'cross_entropy_loss',\n", + " 'kwargs': {\n", + " 'reduction': 'mean'\n", + " }\n", + " },\n", + " optimizer={\n", + " 'name': 'adam',\n", + " 'kwargs': {\n", + " 'lr': 1e-4\n", + " }\n", + " },\n", + " scheduler={\n", + " 'name': 'exponential_lr',\n", + " 'kwargs': {\n", + " 'gamma': 0.95\n", + " }\n", + " },\n", + " callbacks=[{\n", + " 'name': 'early_stopping',\n", + " 'kwargs': {\n", + " 'monitor': 'val_loss',\n", + " 'mode': 'min',\n", + " 'patience': 5\n", + " }\n", + " }],\n", + " num_epochs=2,#change\n", + " start_epoch=0,\n", + " logging_dir=str(LOG_DIR),\n", + " logging_steps=1000,\n", + " save_best=True,\n", + " save_ckpt=True,\n", + " save_fig=False,\n", + " device='cuda',\n", + " num_workers=0,\n", + " pin_memory=True\n", + ")" + ], + "metadata": { + "id": "aaUtfajZGWD5" + }, + "id": "aaUtfajZGWD5", + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Initialize the trainer\n", + "trainer = Trainer(\n", + " model=pretrained_model,\n", + " train_dataset=train_dataset,\n", + " val_dataset=val_dataset,\n", + " test_dataset=test_dataset,\n", + " device=device,\n", + " metric=accuracy_metric_ce,\n", + " config=pretrained_config\n", + ")" + ], + "metadata": { + "id": "GBfMEnRrGler" + }, + "id": "GBfMEnRrGler", + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Train the model\n", + "pretrained_history, pretrained_model = trainer.train()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 518, + "referenced_widgets": [ + "62f237b1683e475595fe17da0edeae87", + "7bdac98dfc4a4699bab39fa846e56354", + "ac7d5153146d4bd89fa5587a1c4babb7", + "1d65d195b47147d3806f7735255878d8", + "34f4dd1f0b9b4920bffb1027ea6e11fe", + "38e5079824d648849dcde09e2a2948fd", + "ffa3569f77fb4a3c8a6fb08930c0defb", + "dd800993260d48e383fb9aa27d265c7d", + "4d4b43abf49f4114a7fc83d9128f6d30", + "0d7bc861ca364e298ffb26510a4e4e09", + "2c776b732e6a4cb8bb85e01baff5fb33" + ] + }, + "id": "q_3Zs8TfOwop", + "outputId": "ecbf6236-025e-47a4-f604-eb92b4fd72e0" + }, + "id": "q_3Zs8TfOwop", + "execution_count": 25, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Training: 0%| | 0/25000 [00:00" + ], + "image/png": "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\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + }, + "colab": { + "provenance": [], + "gpuType": "A100" + }, + "accelerator": "GPU", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "41be1797614f4fa7830532d34158ac52": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8aac226632524370abc787be435a9b48", + "IPY_MODEL_e24c1cf41fcd442eb81ef418e92b24c6", + "IPY_MODEL_dd026d80116749d19365780681500be9" + ], + "layout": "IPY_MODEL_b1703412347941edaab104e289f38221" + } + }, + "8aac226632524370abc787be435a9b48": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2cab2b54d039403daa901bbb65203e42", + "placeholder": "​", + "style": "IPY_MODEL_8f5881e63ed54097b49c624676055437", + "value": "Training: 100%" + } + }, + "e24c1cf41fcd442eb81ef418e92b24c6": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_48e35659a78b41cb95b8cbb39040de62", + "max": 25000, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ff02466aec4344fa84813bb3eba626b1", + "value": 25000 + } + }, + "dd026d80116749d19365780681500be9": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_53c475a9562d4af59d6947020af35778", + "placeholder": "​", + "style": "IPY_MODEL_9e9b3731cd824b0094835c6ce06f9265", + "value": " 25000/25000 [24:47<00:00, 17.01it/s, epoch=2/2, avg_loss=1.8927, avg_metric=0.2941]" + } + }, + "b1703412347941edaab104e289f38221": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "2cab2b54d039403daa901bbb65203e42": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8f5881e63ed54097b49c624676055437": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "48e35659a78b41cb95b8cbb39040de62": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ff02466aec4344fa84813bb3eba626b1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "53c475a9562d4af59d6947020af35778": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9e9b3731cd824b0094835c6ce06f9265": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0e1aad635bf14d1bb31c6f903f032e73": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4e34acdc629c4fd898b1bbd743cd4cc1", + "IPY_MODEL_f0e15ef2bc25482e87a2aafd881171fb", + "IPY_MODEL_649dcf365ee140778713f1923422b40c" + ], + "layout": "IPY_MODEL_e0ef6f1060d2402a93eafa4557cd7a7a" + } + }, + "4e34acdc629c4fd898b1bbd743cd4cc1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_45a2e6bd3f464dd0af70d71813826f89", + "placeholder": "​", + "style": "IPY_MODEL_b1ef8826aa5f4e2896ee4ee4bd6f5e2d", + "value": "Training: 100%" + } + }, + "f0e15ef2bc25482e87a2aafd881171fb": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_cb4aa040e0fe4b0292c3dcda6cc55b66", + "max": 6250, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ee23434b787f454399e4be9c6111a94c", + "value": 6250 + } + }, + "649dcf365ee140778713f1923422b40c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3c0bd3f7f0034da7ba1b118da5d111eb", + "placeholder": "​", + "style": "IPY_MODEL_bad0b3bd64c743519cf1ec7eb6b3ba46", + "value": " 6250/6250 [39:03<00:00,  8.20it/s, epoch=1/1, avg_loss=0.2017]" + } + }, + "e0ef6f1060d2402a93eafa4557cd7a7a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "45a2e6bd3f464dd0af70d71813826f89": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b1ef8826aa5f4e2896ee4ee4bd6f5e2d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "cb4aa040e0fe4b0292c3dcda6cc55b66": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ee23434b787f454399e4be9c6111a94c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3c0bd3f7f0034da7ba1b118da5d111eb": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "bad0b3bd64c743519cf1ec7eb6b3ba46": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "62f237b1683e475595fe17da0edeae87": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7bdac98dfc4a4699bab39fa846e56354", + "IPY_MODEL_ac7d5153146d4bd89fa5587a1c4babb7", + "IPY_MODEL_1d65d195b47147d3806f7735255878d8" + ], + "layout": "IPY_MODEL_34f4dd1f0b9b4920bffb1027ea6e11fe" + } + }, + "7bdac98dfc4a4699bab39fa846e56354": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_38e5079824d648849dcde09e2a2948fd", + "placeholder": "​", + "style": "IPY_MODEL_ffa3569f77fb4a3c8a6fb08930c0defb", + "value": "Training: 100%" + } + }, + "ac7d5153146d4bd89fa5587a1c4babb7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_dd800993260d48e383fb9aa27d265c7d", + "max": 25000, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_4d4b43abf49f4114a7fc83d9128f6d30", + "value": 25000 + } + }, + "1d65d195b47147d3806f7735255878d8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0d7bc861ca364e298ffb26510a4e4e09", + "placeholder": "​", + "style": "IPY_MODEL_2c776b732e6a4cb8bb85e01baff5fb33", + "value": " 25000/25000 [25:07<00:00, 17.45it/s, epoch=2/2, avg_loss=1.9639, avg_metric=0.2580]" + } + }, + "34f4dd1f0b9b4920bffb1027ea6e11fe": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "38e5079824d648849dcde09e2a2948fd": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ffa3569f77fb4a3c8a6fb08930c0defb": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "dd800993260d48e383fb9aa27d265c7d": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4d4b43abf49f4114a7fc83d9128f6d30": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0d7bc861ca364e298ffb26510a4e4e09": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2c776b732e6a4cb8bb85e01baff5fb33": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From d31bccb2dbd8b646aa39f8731808da1682dc29b0 Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 21:10:07 +0530 Subject: [PATCH 06/11] Add README for Physics-Aware Gated LorentzParT Added README.md to describe the PAG --- MAEs/PAG_Avishikta_Bhattacharjee/README.md | 39 ++++++++++++++++++++++ MAEs/PAG_Avishikta_Bhattacharjee/txt | 1 - 2 files changed, 39 insertions(+), 1 deletion(-) create mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/README.md delete mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/txt diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/README.md b/MAEs/PAG_Avishikta_Bhattacharjee/README.md new file mode 100644 index 0000000..0a53d2b --- /dev/null +++ b/MAEs/PAG_Avishikta_Bhattacharjee/README.md @@ -0,0 +1,39 @@ +# Physics-Aware Gated LorentzParT for Jet Reconstruction + +This repository contains the implementation of a **Physics-Aware Gated Lorentz Particle Transformer (LorentzParT)** designed for self-supervised pre-training and jet reconstruction on high-energy physics datasets like **JetClass**. + +The core contribution of this work is a custom attention block that injects a **normalized global invariant mass bias ($m^2$)** alongside a **gated pairwise interaction matrix ($U$)** directly into the attention mechanism, enforcing fundamental Lorentz invariance and relativistic conservation laws. + +--- + + +## Description + +Standard Transformers evaluate token-to-token relationships purely through statistical dot-product attention ($QK^T$). In high-energy physics, this often leads to attention maps that violate basic physical constraints like energy-momentum conservation or relativistic invariance. + +### 1. Reconstructing Particle Kinematics +Each constituent particle in a jet is defined by its 4-momentum $(p_T, \eta, \phi, E)$. These cylindrical collider coordinates convert to Cartesian 3D momentum $(p_x, p_y, p_z)$ via: + +$$p_x = p_T \cos(\phi), \quad p_y = p_T \sin(\phi), \quad p_z = p_T \sinh(\eta), \quad p = p_T \cosh(\eta)$$ + +### 2. Global Invariant Mass Scalar ($m^2$) +For a jet of $N$ particles, the total invariant mass squared ($m^2$) is computed across aggregate energy and momentum sums: + +$$m^2 = \left(\sum_{i=1}^{N} E_i\right)^2 - \left\| \sum_{i=1}^{N} \vec{p}_i \right\|_2^2$$ + +To ensure numerical stability across varying energy scales, $m^2$ is feature-scaled to form a **normalized invariant mass bias**. + + + +## 🛠️ Architecture Overview + +The model uses the encoder of LorentzParT embedded within a Variational Autoencoder (VAE) setup: +1. **Masking:** Random constituent particles are masked during training. +2. **Encoder:** Processes unmasked tokens through LorentzParT blocks augmented with the **Physics-Aware Gating (PAG)** layer. +3. **Decoder:** Reconstructs the 4 constituent properties ($p_T, \eta, \phi, E$) of the masked particles. + +--- + +## 📊 Evaluation & Results + +Will add in the final submission (yet to upload) diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/txt b/MAEs/PAG_Avishikta_Bhattacharjee/txt deleted file mode 100644 index 8b13789..0000000 --- a/MAEs/PAG_Avishikta_Bhattacharjee/txt +++ /dev/null @@ -1 +0,0 @@ - From f187f77167071dcc1e5c613b4e088d7e75843ed7 Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 21:31:36 +0530 Subject: [PATCH 07/11] README for Physics-Aware Gating in Transformer Updated the README --- MAEs/PAG_Avishikta_Bhattacharjee/README.md | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/README.md b/MAEs/PAG_Avishikta_Bhattacharjee/README.md index 0a53d2b..e3c2c0f 100644 --- a/MAEs/PAG_Avishikta_Bhattacharjee/README.md +++ b/MAEs/PAG_Avishikta_Bhattacharjee/README.md @@ -1,15 +1,18 @@ -# Physics-Aware Gated LorentzParT for Jet Reconstruction +# Physics-Aware Gating in Transformer for Jet Classification -This repository contains the implementation of a **Physics-Aware Gated Lorentz Particle Transformer (LorentzParT)** designed for self-supervised pre-training and jet reconstruction on high-energy physics datasets like **JetClass**. +

+ GSoC Banner +

+ +This repository contains the implementation of a **Physics-Aware Gating on Lorentz Particle Transformer (LorentzParT)** designed for self-supervised pre-training and jet reconstruction on high-energy physics datasets like **JetClass**. The core contribution of this work is a custom attention block that injects a **normalized global invariant mass bias ($m^2$)** alongside a **gated pairwise interaction matrix ($U$)** directly into the attention mechanism, enforcing fundamental Lorentz invariance and relativistic conservation laws. --- - ## Description -Standard Transformers evaluate token-to-token relationships purely through statistical dot-product attention ($QK^T$). In high-energy physics, this often leads to attention maps that violate basic physical constraints like energy-momentum conservation or relativistic invariance. +The existing attention mechanism uses a bias matrix $U$ to incline the transformer toward physics constraints on jet particles, achieving a strong baseline (ROC AUC > 0.90). To evaluate and improve attention across various benchmarks, this repository introduces a **gating mechanism** controlled by physics attention heads to further reduce noise from jet constituent data. ### 1. Reconstructing Particle Kinematics Each constituent particle in a jet is defined by its 4-momentum $(p_T, \eta, \phi, E)$. These cylindrical collider coordinates convert to Cartesian 3D momentum $(p_x, p_y, p_z)$ via: @@ -23,9 +26,9 @@ $$m^2 = \left(\sum_{i=1}^{N} E_i\right)^2 - \left\| \sum_{i=1}^{N} \vec{p}_i \ri To ensure numerical stability across varying energy scales, $m^2$ is feature-scaled to form a **normalized invariant mass bias**. +--- - -## 🛠️ Architecture Overview +## Architecture Overview The model uses the encoder of LorentzParT embedded within a Variational Autoencoder (VAE) setup: 1. **Masking:** Random constituent particles are masked during training. @@ -34,6 +37,6 @@ The model uses the encoder of LorentzParT embedded within a Variational Autoenco --- -## 📊 Evaluation & Results +## Evaluation & Results -Will add in the final submission (yet to upload) +Will add in the final submission (yet to upload). From 92d218d7a2cad3af9b7b364d85bf136bc083a7ec Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 21:36:38 +0530 Subject: [PATCH 08/11] Revised --- MAEs/PAG_Avishikta_Bhattacharjee/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/README.md b/MAEs/PAG_Avishikta_Bhattacharjee/README.md index e3c2c0f..c633e2c 100644 --- a/MAEs/PAG_Avishikta_Bhattacharjee/README.md +++ b/MAEs/PAG_Avishikta_Bhattacharjee/README.md @@ -14,10 +14,10 @@ The core contribution of this work is a custom attention block that injects a ** The existing attention mechanism uses a bias matrix $U$ to incline the transformer toward physics constraints on jet particles, achieving a strong baseline (ROC AUC > 0.90). To evaluate and improve attention across various benchmarks, this repository introduces a **gating mechanism** controlled by physics attention heads to further reduce noise from jet constituent data. -### 1. Reconstructing Particle Kinematics -Each constituent particle in a jet is defined by its 4-momentum $(p_T, \eta, \phi, E)$. These cylindrical collider coordinates convert to Cartesian 3D momentum $(p_x, p_y, p_z)$ via: +### 1. Reused Physics Bias Attention Head for Gating +Rather than calculating static physical shifts independently at every Transformer layer, the model utilizes a dedicated **Physics Bias Attention Head**. This module extracts the pairwise $U$-matrix—computed from Minkowski inner products—and fuses it with the global normalized invariant mass bias ($m^2$). -$$p_x = p_T \cos(\phi), \quad p_y = p_T \sin(\phi), \quad p_z = p_T \sinh(\eta), \quad p = p_T \cosh(\eta)$$ +This physical bias tensor is projected across key heads and **reused dynamically across encoder blocks** to act as a gating mask. By projecting and reusing these learned physical weights directly within the multi-head attention mechanism, the network modulates the query-key matrix ($QK^T$) before value aggregation, filtering out unphysical particle couplings and stabilizing training across dynamic batch shapes. ### 2. Global Invariant Mass Scalar ($m^2$) For a jet of $N$ particles, the total invariant mass squared ($m^2$) is computed across aggregate energy and momentum sums: From e412a0732b49857aab53e51b9f6913041dc435b7 Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 21:38:09 +0530 Subject: [PATCH 09/11] Update and rename txtx to README.md --- .../notebook/README.md | 42 +++++++++++++++++++ .../PAG_Avishikta_Bhattacharjee/notebook/txtx | 1 - 2 files changed, 42 insertions(+), 1 deletion(-) create mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/notebook/README.md delete mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/notebook/txtx diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/README.md b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/README.md new file mode 100644 index 0000000..c633e2c --- /dev/null +++ b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/README.md @@ -0,0 +1,42 @@ +# Physics-Aware Gating in Transformer for Jet Classification + +

+ GSoC Banner +

+ +This repository contains the implementation of a **Physics-Aware Gating on Lorentz Particle Transformer (LorentzParT)** designed for self-supervised pre-training and jet reconstruction on high-energy physics datasets like **JetClass**. + +The core contribution of this work is a custom attention block that injects a **normalized global invariant mass bias ($m^2$)** alongside a **gated pairwise interaction matrix ($U$)** directly into the attention mechanism, enforcing fundamental Lorentz invariance and relativistic conservation laws. + +--- + +## Description + +The existing attention mechanism uses a bias matrix $U$ to incline the transformer toward physics constraints on jet particles, achieving a strong baseline (ROC AUC > 0.90). To evaluate and improve attention across various benchmarks, this repository introduces a **gating mechanism** controlled by physics attention heads to further reduce noise from jet constituent data. + +### 1. Reused Physics Bias Attention Head for Gating +Rather than calculating static physical shifts independently at every Transformer layer, the model utilizes a dedicated **Physics Bias Attention Head**. This module extracts the pairwise $U$-matrix—computed from Minkowski inner products—and fuses it with the global normalized invariant mass bias ($m^2$). + +This physical bias tensor is projected across key heads and **reused dynamically across encoder blocks** to act as a gating mask. By projecting and reusing these learned physical weights directly within the multi-head attention mechanism, the network modulates the query-key matrix ($QK^T$) before value aggregation, filtering out unphysical particle couplings and stabilizing training across dynamic batch shapes. + +### 2. Global Invariant Mass Scalar ($m^2$) +For a jet of $N$ particles, the total invariant mass squared ($m^2$) is computed across aggregate energy and momentum sums: + +$$m^2 = \left(\sum_{i=1}^{N} E_i\right)^2 - \left\| \sum_{i=1}^{N} \vec{p}_i \right\|_2^2$$ + +To ensure numerical stability across varying energy scales, $m^2$ is feature-scaled to form a **normalized invariant mass bias**. + +--- + +## Architecture Overview + +The model uses the encoder of LorentzParT embedded within a Variational Autoencoder (VAE) setup: +1. **Masking:** Random constituent particles are masked during training. +2. **Encoder:** Processes unmasked tokens through LorentzParT blocks augmented with the **Physics-Aware Gating (PAG)** layer. +3. **Decoder:** Reconstructs the 4 constituent properties ($p_T, \eta, \phi, E$) of the masked particles. + +--- + +## Evaluation & Results + +Will add in the final submission (yet to upload). diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/txtx b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/txtx deleted file mode 100644 index 8b13789..0000000 --- a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/txtx +++ /dev/null @@ -1 +0,0 @@ - From 8db2ac0a5d448952c9b92216338276f433c8c3ec Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 21:44:30 +0530 Subject: [PATCH 10/11] del --- .../notebook/PAG_LorentzParT_.ipynb | 2888 ----------------- 1 file changed, 2888 deletions(-) delete mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb deleted file mode 100644 index 4c00773..0000000 --- a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb +++ /dev/null @@ -1,2888 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "source": [ - "\n", - "# 1. Clone the ML4SCI/CMS repository\n", - "!git clone https://github.com/ML4SCI/CMS.git\n", - "\n", - "# 2. Navigate to the specific Hybrid Transformer project directory\n", - "%cd CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", - "\n", - "# 3. Install required libraries\n", - "!pip install lgatr uproot awkward tqdm vector" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "B7YmCrpkJSAg", - "outputId": "f6eeec54-0a0e-416f-e730-dcf49f66d6f7" - }, - "id": "B7YmCrpkJSAg", - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Cloning into 'CMS'...\n", - "remote: Enumerating objects: 751, done.\u001b[K\n", - "remote: Counting objects: 100% (130/130), done.\u001b[K\n", - "remote: Compressing objects: 100% (99/99), done.\u001b[K\n", - "remote: Total 751 (delta 39), reused 83 (delta 27), pack-reused 621 (from 2)\u001b[K\n", - "Receiving objects: 100% (751/751), 330.16 MiB | 18.70 MiB/s, done.\n", - "Resolving deltas: 100% (187/187), done.\n", - "Updating files: 100% (531/531), done.\n", - "/content/CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", - "Collecting lgatr\n", - " Downloading lgatr-1.4.4-py3-none-any.whl.metadata (8.7 kB)\n", - "Collecting uproot\n", - " Downloading uproot-5.7.5-py3-none-any.whl.metadata (35 kB)\n", - "Collecting awkward\n", - " Downloading awkward-2.11.0-py3-none-any.whl.metadata (7.6 kB)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (4.67.3)\n", - "Collecting vector\n", - " Downloading vector-1.8.1-py3-none-any.whl.metadata (15 kB)\n", - "Requirement already satisfied: torch>=2.1 in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.11.0+cu128)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.0.2)\n", - "Requirement already satisfied: einops in /usr/local/lib/python3.12/dist-packages (from lgatr) (0.8.2)\n", - "Requirement already satisfied: opt_einsum in /usr/local/lib/python3.12/dist-packages (from lgatr) (3.4.0)\n", - "Requirement already satisfied: cramjam>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2.11.0)\n", - "Requirement already satisfied: fsspec!=2026.2.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2025.3.0)\n", - "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from uproot) (26.2)\n", - "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from uproot) (3.8.1)\n", - "Collecting awkward-cpp==54 (from awkward)\n", - " Downloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.29.7)\n", - "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (4.16.0)\n", - "Requirement already satisfied: setuptools<82 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (75.2.0)\n", - "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (1.14.0)\n", - "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.1)\n", - "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.1.6)\n", - "Requirement already satisfied: cuda-toolkit==12.8.1 in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.1)\n", - "Requirement already satisfied: cuda-bindings<13,>=12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (12.9.7)\n", - "Requirement already satisfied: nvidia-cudnn-cu12==9.19.0.56 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (9.19.0.56)\n", - "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (0.7.1)\n", - "Requirement already satisfied: nvidia-nccl-cu12==2.28.9 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (2.28.9)\n", - "Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.4.5)\n", - "Requirement already satisfied: triton==3.6.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.0)\n", - "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.4.1)\n", - "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", - "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.3.3.83)\n", - "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (1.13.1.3)\n", - "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", - "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (10.3.9.90)\n", - "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.7.3.90)\n", - "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.5.8.93)\n", - "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", - "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", - "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", - "Requirement already satisfied: cuda-pathfinder~=1.1 in /usr/local/lib/python3.12/dist-packages (from cuda-bindings<13,>=12.9.4->torch>=2.1->lgatr) (1.5.6)\n", - "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=2.1->lgatr) (1.3.0)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=2.1->lgatr) (3.0.3)\n", - "Downloading lgatr-1.4.4-py3-none-any.whl (60 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.6/60.6 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading uproot-5.7.5-py3-none-any.whl (401 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m35.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading awkward-2.11.0-py3-none-any.whl (974 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m974.8/974.8 kB\u001b[0m \u001b[31m76.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (689 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m689.3/689.3 kB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading vector-1.8.1-py3-none-any.whl (182 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.7/182.7 kB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: vector, awkward-cpp, awkward, uproot, lgatr\n", - "Successfully installed awkward-2.11.0 awkward-cpp-54 lgatr-1.4.4 uproot-5.7.5 vector-1.8.1\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# --- Default libraries ---\n", - "import os\n", - "import warnings\n", - "from pathlib import Path\n", - "\n", - "# --- Working directory ---\n", - "PROJECT_DIR = Path().resolve()\n", - "PROJECT_ROOT_NAME = 'Hybrid_Transformer_Thanh_Nguyen'\n", - "\n", - "while PROJECT_DIR.name != PROJECT_ROOT_NAME and PROJECT_DIR != PROJECT_DIR.parent:\n", - " PROJECT_DIR = PROJECT_DIR.parent\n", - "\n", - "if Path().resolve() != PROJECT_DIR:\n", - " os.chdir(PROJECT_DIR)\n", - "\n", - "DATA_DIR = PROJECT_DIR / 'data'\n", - "LOG_DIR = PROJECT_DIR / 'logs'\n", - "\n", - "# --- Data preprocessing & visualization ---\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "# --- Deep learning ---\n", - "import torch\n", - "\n", - "# --- Custom modules ---\n", - "from src.configs import LorentzParTConfig, TrainConfig\n", - "from src.engine import MaskedModelTrainer, Trainer\n", - "from src.models import LorentzParT\n", - "from src.utils import accuracy_metric_ce, set_seed\n", - "from src.utils.data import JetClassDataset, compute_norm_stats, read_file\n", - "from src.utils.viz import *\n", - "\n", - "# --- Settings ---\n", - "warnings.filterwarnings('ignore')\n", - "set_seed(42)\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "device" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IvxBxUicRu18", - "outputId": "3241b771-4383-417a-cfc1-963899fb5dd4" - }, - "id": "IvxBxUicRu18", - "execution_count": 2, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "device(type='cuda')" - ] - }, - "metadata": {}, - "execution_count": 2 - } - ] - }, - { - "cell_type": "code", - "source": [ - "'''\n", - "drive_dest_folder = '/content/drive/MyDrive/GSOC/dara'\n", - "drive_dest_path = os.path.join(drive_dest_folder, out_file)\n", - "\n", - "# Create the directory if it doesn't exist\n", - "if not os.path.exists(drive_dest_folder):\n", - " os.makedirs(drive_dest_folder)\n", - " print(f\"Created directory: {drive_dest_folder}\")\n", - "\n", - "# 3. Move the verified file to Drive\n", - "if os.path.exists(out_file):\n", - " print(f\"Moving {out_file} to Google Drive...\")\n", - " shutil.move(out_file, drive_dest_path)\n", - " print(f\"File successfully moved to: {drive_dest_path}\")\n", - "else:\n", - " print(\"Source file not found. Check if the download was successful.\")''''" - ], - "metadata": { - "collapsed": true, - "id": "QfAWZ8A3cdNR" - }, - "id": "QfAWZ8A3cdNR", - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#Loading Dpendencies" - ], - "metadata": { - "id": "BmBDXDMSD61u" - }, - "id": "BmBDXDMSD61u" - }, - { - "cell_type": "code", - "source": [ - "from typing import List, Tuple, Dict, Optional\n", - "\n", - "import torch\n", - "from torch import nn, Tensor\n", - "from lgatr.interface import extract_vector\n", - "from lgatr.layers import EquiLinear\n", - "\n", - "from src.models.classifier import ClassAttentionBlock, Classifier\n", - "from src.models.feedforward import Feedforward\n", - "from src.models.particle_transformer import ParticleAttentionBlock\n", - "from src.models.processor import InteractionEmbedding, ParticleProcessor\n", - "from src.configs import LorentzParTConfig\n", - "from lgatr.interface import extract_vector\n", - "from lgatr.layers import EquiLinear\n", - "\n", - "\n" - ], - "metadata": { - "id": "jTbtwxdtAt9S" - }, - "id": "jTbtwxdtAt9S", - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#Data Preparation" - ], - "metadata": { - "id": "VB1JTgpxDv-4" - }, - "id": "VB1JTgpxDv-4" - }, - { - "cell_type": "code", - "source": [ - "import os\n", - "from pathlib import Path\n", - "import numpy as np\n", - "from sklearn.model_selection import train_test_split\n", - "from src.utils.data import JetClassDataset, compute_norm_stats\n", - "\n", - "# --- 1. Mount Google Drive (If not already done) ---\n", - "from google.colab import drive\n", - "if not os.path.exists('/content/drive'):\n", - " drive.mount('/content/drive')\n", - "\n", - "# --- 2. Locate and Load the Compressed Archive ---\n", - "DRIVE_FILE_PATH = '/content/drive/MyDrive/GSOC/dara/jetclass_balanced_1M.npz'\n", - "\n", - "print(\"=\" * 70)\n", - "print(f\"LOADING SERIALIZED ARRAYS FROM DRIVE\")\n", - "print(\"=\" * 70)\n", - "\n", - "if os.path.exists(DRIVE_FILE_PATH):\n", - " # Load using memory-mapping for high-speed indexing\n", - " data_archive = np.load(DRIVE_FILE_PATH, mmap_mode='r')\n", - "\n", - " X_particles = data_archive['X_particles']\n", - " X_jets = data_archive['X_jets']\n", - " y = data_archive['Y']\n", - "\n", - " print(\"SUCCESS: Data loaded cleanly into RAM!\")\n", - " print(f\" -> X_particles matrix shape : {X_particles.shape}\")\n", - " print(f\" -> X_jets matrix shape : {X_jets.shape}\")\n", - " print(f\" -> y (Labels) matrix shape : {y.shape}\")\n", - " print(\"=\" * 70 + \"\\n\")\n", - "else:\n", - " raise FileNotFoundError(f\"ERROR: Could not find the file at {DRIVE_FILE_PATH}\")\n", - "\n", - "# --- 3. Split the Balanced Dataset Safely ---\n", - "# We enforce stratify=y to lock in your strict 10% balance across all splits\n", - "X_train, X_val, y_train, y_val = train_test_split(X_particles, y, test_size=0.2, random_state=42, stratify=y)\n", - "X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.5, random_state=42, stratify=y_val)\n", - "\n", - "# --- 4. Re-Initialize JetClass Dataset Objects ---\n", - "normalize = [True, False, False, True]\n", - "norm_dict = compute_norm_stats(X_train)\n", - "\n", - "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode='biased')\n", - "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode='biased')\n", - "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode='first')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9RBH34OYBoZ7", - "outputId": "a53a1da2-e44b-4611-cd4e-f610d3cce289" - }, - "id": "9RBH34OYBoZ7", - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n", - "======================================================================\n", - "LOADING SERIALIZED ARRAYS FROM DRIVE\n", - "======================================================================\n", - "SUCCESS: Data loaded cleanly into RAM!\n", - " -> X_particles matrix shape : (1000000, 4, 128)\n", - " -> X_jets matrix shape : (1000000, 4)\n", - " -> y (Labels) matrix shape : (1000000, 10)\n", - "======================================================================\n", - "\n", - "pt_mean: 92.70597076416016, pt_std: 105.79937744140625\n", - "eta_mean: -0.0011634620605036616, eta_std: 0.9182536005973816\n", - "phi_mean: -0.0006678671925328672, phi_std: 1.8138455152511597\n", - "E_mean: 133.98568725585938, E_std: 167.7259979248047\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "id": "edb52e95", - "metadata": { - "id": "edb52e95" - }, - "source": [ - "## Applying Gated Attention LorentzPart" - ] - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "from torch import Tensor\n", - "from typing import Optional\n", - "\n", - "class ParticleAttentionBlock(nn.Module):\n", - " def __init__(\n", - " self,\n", - " embed_dim: int = 128,\n", - " num_heads: int = 8,\n", - " dropout: float = 0.1,\n", - " expansion_factor: int = 4,\n", - " gate_type: Optional[str] = \"headwise\",\n", - " ):\n", - " super(ParticleAttentionBlock, self).__init__()\n", - " assert embed_dim % num_heads == 0, \"embed_dim must be divisible by num_heads\"\n", - "\n", - " self.embed_dim = embed_dim\n", - " self.num_heads = num_heads\n", - " self.head_dim = embed_dim // num_heads\n", - " self.gate_type = gate_type\n", - "\n", - " self.layernorm1 = nn.LayerNorm(embed_dim)\n", - " self.mass_norm = nn.LayerNorm(1)#norm\n", - "\n", - " # Project pooled interaction head-features to match token embedding dimensions\n", - " self.physics_proj = nn.Linear(num_heads, embed_dim)\n", - "\n", - " # Project the global scalar invariant mass squared (m2) to the embedding space\n", - " self.mass_proj = nn.Linear(1, embed_dim)\n", - "\n", - " # Gating projections accept physics-fused representations\n", - " if self.gate_type == \"headwise\":\n", - " self.gate_proj = nn.Linear(embed_dim, num_heads)\n", - " elif self.gate_type == \"elementwise\":\n", - " self.gate_proj = nn.Linear(embed_dim, embed_dim)\n", - "\n", - " self.pmha = nn.MultiheadAttention(\n", - " embed_dim=embed_dim,\n", - " num_heads=num_heads,\n", - " dropout=dropout,\n", - " batch_first=True\n", - " )\n", - "\n", - " self.layernorm2 = nn.LayerNorm(embed_dim)\n", - " self.dropout = nn.Dropout(dropout)\n", - "\n", - " self.feedforward = Feedforward(\n", - " embed_dim=embed_dim,\n", - " expansion_factor=expansion_factor,\n", - " dropout=dropout\n", - " )\n", - "\n", - " def forward(self, x: Tensor, padding_mask: Tensor, U: Optional[Tensor] = None, p4: Optional[Tensor] = None) -> Tensor:\n", - " residual = x\n", - " B_size, N_particles, _ = x.shape\n", - "\n", - " # 1. Standard token serialization\n", - " x_norm = self.layernorm1(x)\n", - "\n", - " # 2. FIXED: U is ALREADY [Batch * Heads, N, N]. Pass directly to PyTorch MHA.\n", - " x_attn, _ = self.pmha(x_norm, x_norm, x_norm, key_padding_mask=padding_mask, attn_mask=U)\n", - "\n", - " # 3. Physics-Aware Fusion (Invariants-driven conditioning)\n", - " if U is not None:\n", - " # Reconstruct the 4D shape: [B * H, N, N] -> [B, H, N, N]\n", - " U_reshaped = U.view(B_size, self.num_heads, N_particles, N_particles)\n", - "\n", - " # Pool over neighbor particle index 'j' (dim=3). Resulting shape: [B, H, N]\n", - " u_pooled = U_reshaped.sum(dim=3)\n", - "\n", - " # Transpose to align with token channels: [B, N, H]\n", - " u_pooled = u_pooled.transpose(1, 2)\n", - "\n", - " # Map head-wise pooled invariants into token channel space: [B, N, embed_dim]\n", - " physics_context = self.physics_proj(u_pooled)\n", - "\n", - " # Fuse physical invariants with abstract node latent maps\n", - " x_gating_input = x_norm + physics_context\n", - " else:\n", - " # Fallback path if U is not provided\n", - " x_gating_input = x_norm\n", - "\n", - " # 4. Compute Global Invariant Mass Bias from 4-vectors [B, N, 4] -> (E, px, py, pz)\n", - " if p4 is not None:\n", - " # Sum energy component (index 0) over all particles (dim=1)\n", - " energy_sum = p4[..., 0].sum(dim=1, keepdim=True) # Shape: [B, 1]\n", - "\n", - " # Sum momentum components (indices 1, 2, 3) over all particles (dim=1)\n", - " momentum_sum = p4[..., 1:].sum(dim=1) # Shape: [B, 3]\n", - "\n", - " # Calculate invariant mass squared (m2)\n", - " m2 = energy_sum**2 - momentum_sum.norm(dim=-1, keepdim=True)**2 # Shape: [B, 1]\n", - "\n", - " m2_scaled = torch.log1p(torch.relu(m2))\n", - "\n", - "\n", - " # Step C: Standardize the mean and variance dynamically\n", - " m2_norm = self.mass_norm(m2_scaled)\n", - "\n", - " # Project normalized m2 into a global embedding bias vector [B, 1, embed_dim]\n", - " mass_bias = self.mass_proj(m2_norm).unsqueeze(1)\n", - "\n", - " # Broad-cast add global event mass bias to the per-particle gating inputs\n", - " x_gating_input = x_gating_input + mass_bias\n", - "\n", - "\n", - "\n", - " # 5. Compute and apply the explicitly Physics-Aware Gate\n", - " if self.gate_type == \"headwise\":\n", - " # Compute score matrix from physics-fused map: (B, N, embed_dim) -> (B, N, num_heads, 1)\n", - " gate_score = self.gate_proj(x_gating_input).unsqueeze(-1)\n", - "\n", - " # Separate heads to apply individual scalar gating values\n", - " x_attn = x_attn.view(B_size, N_particles, self.num_heads, self.head_dim)\n", - "\n", - " # Apply physics-conditioned filter and reconstruct classic transformer shape\n", - " x_attn = (x_attn * torch.sigmoid(gate_score)).view(B_size, N_particles, self.embed_dim)\n", - "\n", - " elif self.gate_type == \"elementwise\":\n", - " # Compute full channel-by-channel mask from physics-fused map: (B, N, embed_dim)\n", - " gate_score = self.gate_proj(x_gating_input)\n", - " x_attn = x_attn * torch.sigmoid(gate_score)\n", - "\n", - " # 6. Standard Feedforward processing\n", - " x = self.layernorm2(x_attn)\n", - " x = self.dropout(x)\n", - " x += residual\n", - " x = self.feedforward(x)\n", - "\n", - " return x" - ], - "metadata": { - "id": "AvsAk0byPoBK" - }, - "id": "AvsAk0byPoBK", - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "from torch import Tensor\n", - "from typing import Optional, List, Dict, Tuple\n", - "\n", - "\n", - "\n", - "class LorentzParTEncoder(nn.Module):\n", - " def __init__(\n", - " self,\n", - " embed_dim: int = 128,\n", - " num_heads: int = 8,\n", - " num_layers: int = 8,\n", - " in_s_channels: Optional[int] = None,\n", - " out_s_channels: Optional[int] = None,\n", - " dropout: float = 0.1,\n", - " expansion_factor: int = 4,\n", - " pair_embed_dims: List[int] = [64, 64, 64],\n", - " attention_config: Dict = {}\n", - " ):\n", - " super(LorentzParTEncoder, self).__init__()\n", - " self.equilinear = EquiLinear(\n", - " in_mv_channels=1,\n", - " out_mv_channels=1,\n", - " in_s_channels=in_s_channels,\n", - " out_s_channels=out_s_channels\n", - " )\n", - " self.proj = nn.Linear(16, embed_dim)\n", - " self.interaction_embed = InteractionEmbedding(\n", - " num_interaction_features=4,\n", - " pair_embed_dims=pair_embed_dims + [num_heads]\n", - " )\n", - "\n", - " use_gating = attention_config.get('use_gating', False)\n", - "\n", - " # Explicitly pass gate_type so the block knows whether to use physics gating or standard\n", - " self.encoder = nn.ModuleList([\n", - " ParticleAttentionBlock(\n", - " embed_dim=embed_dim,\n", - " num_heads=num_heads,\n", - " dropout=dropout,\n", - " expansion_factor=expansion_factor,\n", - " gate_type=\"headwise\" if use_gating else None\n", - " ) for _ in range(num_layers)\n", - " ])\n", - "\n", - " def forward(self, x: Tensor, padding_mask: Tensor, U: Tensor, p4: Optional[Tensor] = None) -> Tensor:\n", - " B, N, F = x.shape\n", - " U = self.interaction_embed(U)\n", - " x = x.view(B, N, 1, F)\n", - " x, _ = self.equilinear(x)\n", - " x = x.view(B, N, 16)\n", - " x = self.proj(x)\n", - "\n", - " # Pass p4 down to the attention blocks\n", - " for layer in self.encoder:\n", - " x = layer(x, padding_mask, U, p4=p4)\n", - "\n", - " return x\n", - "\n", - "\n", - "class LorentzParT(nn.Module):\n", - " def __init__(\n", - " self,\n", - " config: Optional[LorentzParTConfig] = None,\n", - " max_num_particles: Optional[int] = None,\n", - " num_particle_features: Optional[int] = None,\n", - " num_classes: Optional[int] = None,\n", - " embed_dim: Optional[int] = None,\n", - " num_heads: Optional[int] = None,\n", - " num_layers: Optional[int] = None,\n", - " num_cls_layers: Optional[int] = None,\n", - " num_mlp_layers: Optional[int] = None,\n", - " hidden_dim: Optional[int] = None,\n", - " hidden_mv_channels: Optional[int] = None,\n", - " in_s_channels: Optional[int] = None,\n", - " out_s_channels: Optional[int] = None,\n", - " hidden_s_channels: Optional[int] = None,\n", - " attention: Optional[Dict] = None,\n", - " mlp: Optional[Dict] = None,\n", - " reinsert_mv_channels: Optional[Tuple[int]] = None,\n", - " reinsert_s_channels: Optional[Tuple[int]] = None,\n", - " dropout: Optional[float] = None,\n", - " expansion_factor: Optional[int] = None,\n", - " pair_embed_dims: Optional[List[int]] = None,\n", - " mask: Optional[bool] = None,\n", - " weights: Optional[str] = None,\n", - " inference: Optional[bool] = False\n", - " ):\n", - " super(LorentzParT, self).__init__()\n", - "\n", - " # Use config if provided, otherwise use defaults\n", - " if config is not None:\n", - " self.max_num_particles = max_num_particles if max_num_particles is not None else config.max_num_particles\n", - " self.num_particle_features = num_particle_features if num_particle_features is not None else config.num_particle_features\n", - " self.num_classes = num_classes if num_classes is not None else config.num_classes\n", - " self.embed_dim = embed_dim if embed_dim is not None else config.embed_dim\n", - " self.num_heads = num_heads if num_heads is not None else config.num_heads\n", - " self.num_layers = num_layers if num_layers is not None else config.num_layers\n", - " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else config.num_cls_layers\n", - " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else config.num_mlp_layers\n", - " self.hidden_dim = hidden_dim if hidden_dim is not None else config.hidden_dim\n", - " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else config.hidden_mv_channels\n", - " self.in_s_channels = in_s_channels if in_s_channels is not None else config.in_s_channels\n", - " self.out_s_channels = out_s_channels if out_s_channels is not None else config.out_s_channels\n", - " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else config.hidden_s_channels\n", - " self.attention = attention if attention is not None else config.attention\n", - " self.mlp = mlp if mlp is not None else config.mlp\n", - " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else config.reinsert_mv_channels\n", - " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else config.reinsert_s_channels\n", - " self.dropout = dropout if dropout is not None else config.dropout\n", - " self.expansion_factor = expansion_factor if expansion_factor is not None else config.expansion_factor\n", - " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else config.pair_embed_dims\n", - " self.mask = mask if mask is not None else config.mask\n", - " self.weights = weights if weights is not None else config.weights\n", - " self.inference = inference if inference is not None else config.inference\n", - " else:\n", - " self.max_num_particles = max_num_particles if max_num_particles is not None else 128\n", - " self.num_particle_features = num_particle_features if num_particle_features is not None else 4\n", - " self.num_classes = num_classes if num_classes is not None else 10\n", - " self.embed_dim = embed_dim if embed_dim is not None else 128\n", - " self.num_heads = num_heads if num_heads is not None else 8\n", - " self.num_layers = num_layers if num_layers is not None else 8\n", - " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else 2\n", - " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else 0\n", - " self.hidden_dim = hidden_dim if hidden_dim is not None else 256\n", - " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else 8\n", - " self.in_s_channels = in_s_channels if in_s_channels is not None else None\n", - " self.out_s_channels = out_s_channels if out_s_channels is not None else None\n", - " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else 16\n", - " self.attention = attention if attention is not None else {}\n", - " self.mlp = mlp if mlp is not None else None\n", - " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else None\n", - " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else None\n", - " self.dropout = dropout if dropout is not None else 0.1\n", - " self.expansion_factor = expansion_factor if expansion_factor is not None else 4\n", - " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else [64, 64, 64]\n", - " self.mask = mask if mask is not None else False\n", - " self.weights = weights if weights is not None else None\n", - " self.inference = inference if inference is not None else False\n", - "\n", - " # Initialize the class token\n", - " self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim), requires_grad=True)\n", - " nn.init.normal_(self.cls_token, mean=0.0, std=1.0)\n", - "\n", - " self.processor = ParticleProcessor(to_multivector=True)\n", - "\n", - " # Updated Encoder with attention_config passed dynamically\n", - " self.encoder = LorentzParTEncoder(\n", - " embed_dim=self.embed_dim,\n", - " num_heads=self.num_heads,\n", - " num_layers=self.num_layers,\n", - " in_s_channels=self.in_s_channels,\n", - " out_s_channels=self.out_s_channels,\n", - " dropout=self.dropout,\n", - " expansion_factor=self.expansion_factor,\n", - " pair_embed_dims=self.pair_embed_dims,\n", - " attention_config=self.attention\n", - " )\n", - "\n", - " # For self-supervised learning\n", - " self.fc = nn.Linear(self.max_num_particles * self.embed_dim, 16)\n", - " self.equilinear = EquiLinear(\n", - " in_mv_channels=1,\n", - " out_mv_channels=1,\n", - " in_s_channels=self.in_s_channels,\n", - " out_s_channels=self.out_s_channels\n", - " )\n", - "\n", - " # For classification\n", - " self.decoder = nn.ModuleList([\n", - " ClassAttentionBlock(\n", - " embed_dim=self.embed_dim,\n", - " num_heads=self.num_heads,\n", - " dropout=0.0,\n", - " expansion_factor=self.expansion_factor\n", - " ) for _ in range(self.num_cls_layers)\n", - " ])\n", - " self.layernorm = nn.LayerNorm(self.embed_dim)\n", - " self.classifier = Classifier(\n", - " num_classes=self.num_classes,\n", - " input_dim=self.embed_dim,\n", - " hidden_dim=self.hidden_dim,\n", - " num_layers=self.num_mlp_layers,\n", - " dropout=self.dropout,\n", - " )\n", - " self.act = nn.Softmax(dim=1) if self.inference else nn.Identity()\n", - "\n", - " # Load pretrained weights\n", - " if self.weights is not None:\n", - " state_dict = torch.load(self.weights)\n", - " filtered_state = {\n", - " k[len(\"encoder.\") :]: v\n", - " for k, v in state_dict.items()\n", - " if k.startswith(\"encoder.\")\n", - " }\n", - " self.encoder.load_state_dict(filtered_state, strict=False)\n", - "\n", - " def forward(self, x: Tensor, mask_idx: Optional[Tensor] = None) -> Tensor:\n", - " B, N, F = x.shape # (batch_size, max_num_particles, num_particle_features)\n", - "\n", - " # Save the raw kinematics before processor alters them\n", - " p4 = x.clone()\n", - "\n", - " # Ignore padding particles in query\n", - " padding_mask = (x[..., 3] == 0).float() # (B, N)\n", - "\n", - " # Set the masked indices to 0.0 so they are not ignored in MultiheadAttention()\n", - " if mask_idx is not None:\n", - " batch_indices = torch.arange(x.size(0), device=x.device)\n", - " padding_mask[batch_indices, mask_idx] = 0.0\n", - "\n", - " # Process particles to get interaction embeddings and multivectors (if applicable)\n", - " x, U = self.processor(x)\n", - "\n", - " # Pass through equilinear layer and particle attention blocks (passing p4 down)\n", - " x = self.encoder(x, padding_mask, U, p4=p4)\n", - "\n", - " # Classification (no masking in this case)\n", - " if not self.mask:\n", - " x_cls = self.cls_token.expand(B, -1, -1)\n", - "\n", - " # Decoder with class attention blocks\n", - " for layer in self.decoder:\n", - " x_cls = layer(x, x_cls, padding_mask)\n", - "\n", - " # MLP head for classification\n", - " x_cls = self.layernorm(x_cls).squeeze(1)\n", - " x_cls = self.classifier(x_cls)\n", - " output = self.act(x_cls) # (B, num_classes)\n", - "\n", - " return output\n", - " else:\n", - " x = x.view(B, -1) # (B, N * embed_dim)\n", - " x = self.fc(x) # (B, 16)\n", - " x = x.view(B, 1, 1, 16)\n", - " x, _ = self.equilinear(x) # (B, 1, 1, 16)\n", - " x = x.view(B, 16)\n", - " x = extract_vector(x) # (B, F)\n", - "\n", - " return x" - ], - "metadata": { - "id": "QRhMF1TP8Ymw" - }, - "id": "QRhMF1TP8Ymw", - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "#Gating Test" - ], - "metadata": { - "id": "UC2GQe3iR7Gt" - }, - "id": "UC2GQe3iR7Gt" - }, - { - "cell_type": "code", - "source": [ - "# 1. Initialize your config with gating enabled\n", - "test_config = LorentzParTConfig(\n", - " embed_dim=128,\n", - " num_heads=8,\n", - " num_layers=8,\n", - " attention={'use_gating': True},\n", - " mask=True\n", - ")\n", - "\n", - "# 2. Instantiate the model\n", - "model = LorentzParT(config=test_config)\n", - "\n", - "# 3. Verification checks\n", - "first_layer = model.encoder.encoder[0]\n", - "is_gated = isinstance(first_layer, ParticleAttentionBlock)\n", - "\n", - "print(f\"--- Gating Verification ---\")\n", - "print(f\"Encoder Layer 1 Type: {type(first_layer).__name__}\")\n", - "print(f\"Gating Active: {is_gated}\")\n", - "\n", - "if is_gated:\n", - " print(\"Success: The model is now using Attention Gating!\")\n", - "else:\n", - " print(\"Error: The model is still using standard Attention Blocks.\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cKfNSeyPR9nH", - "outputId": "8ea6d1d4-605b-4ec4-b41e-f2a61eb0c23b" - }, - "id": "cKfNSeyPR9nH", - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--- Gating Verification ---\n", - "Encoder Layer 1 Type: ParticleAttentionBlock\n", - "Gating Active: True\n", - "Success: The model is now using Attention Gating!\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#Pre-Train" - ], - "metadata": { - "id": "H_Kmp-s3SUDD" - }, - "id": "H_Kmp-s3SUDD" - }, - { - "cell_type": "markdown", - "source": [ - "#Using Self Supervised Weights" - ], - "metadata": { - "id": "5peLQ8txiFZG" - }, - "id": "5peLQ8txiFZG" - }, - { - "cell_type": "code", - "source": [ - "# Initialize configuration with Attention Gating enabled\n", - "#not changing name of ssl_model_config\n", - "ssl_model_config = LorentzParTConfig(\n", - " embed_dim=128,\n", - " num_heads=8,\n", - " num_layers=8,\n", - " hidden_mv_channels=8,\n", - " attention={'use_gating': True}, # This is the trigger for your new code\n", - " dropout=0.1,\n", - " expansion_factor=4,\n", - " max_num_particles=128,\n", - " num_particle_features=4,\n", - " pair_embed_dims=[64, 64, 64],\n", - " mask=True # Set to True for Self-Supervised Learning / Masked Training\n", - ")" - ], - "metadata": { - "id": "6fGbG9f1SXbc" - }, - "id": "6fGbG9f1SXbc", - "execution_count": 8, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Create the model and move it to your device (GPU/CPU)\n", - "gatedmodel = LorentzParT(config=ssl_model_config)\n", - "gatedmodel.to(device)\n", - "gatedmodel" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "b7g5btHCSZka", - "outputId": "a47e4f81-cd33-4b7c-a39a-7f074bf3c456" - }, - "id": "b7g5btHCSZka", - "execution_count": 9, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "LorentzParT(\n", - " (processor): ParticleProcessor()\n", - " (encoder): LorentzParTEncoder(\n", - " (equilinear): EquiLinear()\n", - " (proj): Linear(in_features=16, out_features=128, bias=True)\n", - " (interaction_embed): InteractionEmbedding(\n", - " (embed): Sequential(\n", - " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", - " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (3): GELU(approximate='none')\n", - " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", - " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (6): GELU(approximate='none')\n", - " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", - " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (9): GELU(approximate='none')\n", - " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", - " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (12): GELU(approximate='none')\n", - " )\n", - " )\n", - " (encoder): ModuleList(\n", - " (0-7): 8 x ParticleAttentionBlock(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", - " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", - " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", - " (gate_proj): Linear(in_features=128, out_features=8, bias=True)\n", - " (pmha): MultiheadAttention(\n", - " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", - " )\n", - " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (dropout): Dropout(p=0.1, inplace=False)\n", - " (feedforward): Feedforward(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (dropout1): Dropout(p=0.1, inplace=False)\n", - " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", - " (dropout2): Dropout(p=0.1, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", - " (equilinear): EquiLinear()\n", - " (decoder): ModuleList(\n", - " (0-1): 2 x ClassAttentionBlock(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (mha): MultiheadAttention(\n", - " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", - " )\n", - " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (dropout): Dropout(p=0.0, inplace=False)\n", - " (feedforward): Feedforward(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (dropout1): Dropout(p=0.0, inplace=False)\n", - " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", - " (dropout2): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (classifier): Classifier(\n", - " (layers): Sequential(\n", - " (0): Linear(in_features=128, out_features=10, bias=True)\n", - " )\n", - " )\n", - " (act): Identity()\n", - ")" - ] - }, - "metadata": {}, - "execution_count": 9 - } - ] - }, - { - "cell_type": "code", - "source": [ - "num_params = sum(p.numel() for p in gatedmodel.parameters() if p.requires_grad)\n", - "num_params" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n5GMAwY0Sw5t", - "outputId": "170f4cca-9434-4e28-d810-c0bf423f50a7" - }, - "id": "n5GMAwY0Sw5t", - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "2290656" - ] - }, - "metadata": {}, - "execution_count": 10 - } - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "0aa870ab", - "metadata": { - "id": "0aa870ab" - }, - "outputs": [], - "source": [ - "# Training configurations\n", - "gated_train_config = TrainConfig(\n", - " batch_size=128,\n", - " criterion={\n", - " 'name': 'conservation_loss',\n", - " 'kwargs': {\n", - " 'loss_coef': [0.25, 0.25, 0.25, 0.25],\n", - " 'reduction': 'mean'\n", - " }\n", - " },\n", - " optimizer={\n", - " 'name': 'adamw',\n", - " 'kwargs': {\n", - " 'lr': 1e-4\n", - " }\n", - " },\n", - " scheduler={\n", - " 'name': 'exponential_lr',\n", - " 'kwargs': {\n", - " 'gamma': 0.95\n", - " }\n", - " },\n", - " callbacks=[{\n", - " 'name': 'early_stopping',\n", - " 'kwargs': {\n", - " 'monitor': 'val_loss',\n", - " 'mode': 'min',\n", - " 'patience': 5\n", - " }\n", - " }],\n", - " num_epochs=1,#20 change\n", - " start_epoch=0,\n", - " logging_dir=str(LOG_DIR),\n", - " logging_steps=1000,\n", - " progress_bar=True,\n", - " save_best=True,\n", - " save_ckpt=True,\n", - " save_fig=False,\n", - " device='cuda',\n", - " num_workers=0,\n", - " pin_memory=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6d5f08dd", - "metadata": { - "id": "6d5f08dd" - }, - "outputs": [], - "source": [ - "# Initialize the trainer\n", - "trainer = MaskedModelTrainer(\n", - " model=gatedmodel,\n", - " train_dataset=train_dataset,\n", - " val_dataset=val_dataset,\n", - " test_dataset=test_dataset,\n", - " device=device,\n", - " config=gated_train_config\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "40a3b5fa", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 188, - "referenced_widgets": [ - "0e1aad635bf14d1bb31c6f903f032e73", - "4e34acdc629c4fd898b1bbd743cd4cc1", - "f0e15ef2bc25482e87a2aafd881171fb", - "649dcf365ee140778713f1923422b40c", - "e0ef6f1060d2402a93eafa4557cd7a7a", - "45a2e6bd3f464dd0af70d71813826f89", - "b1ef8826aa5f4e2896ee4ee4bd6f5e2d", - "cb4aa040e0fe4b0292c3dcda6cc55b66", - "ee23434b787f454399e4be9c6111a94c", - "3c0bd3f7f0034da7ba1b118da5d111eb", - "bad0b3bd64c743519cf1ec7eb6b3ba46" - ] - }, - "id": "40a3b5fa", - "outputId": "e38bc084-3ac9-47b3-ed52-af4af71a5b6e" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Training: 0%| | 0/6250 [00:00" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ], - "source": [ - "# Evaluate the model on the test set\n", - "test_loss, test_metric, y_true, y_pred = trainer.evaluate(plot=plot_particle_reconstruction)" - ] - }, - { - "cell_type": "code", - "source": [ - "# Label names for classification\n", - "labels = [\n", - " \"$q/g$\", # 0\n", - " \"$H \\\\to b\\\\bar{b}$\", # 1\n", - " \"$H \\\\to c\\\\bar{c}$\", # 2\n", - " \"$H \\\\to gg$\", # 3\n", - " \"$H \\\\to 4q$\", # 4\n", - " \"$H \\\\to \\\\ell \\\\nu qq'$\", # 5\n", - " \"$Z \\\\to q\\\\bar{q}$\", # 6\n", - " \"$W \\\\to qq'$\", # 7\n", - " \"$t \\\\to b\\\\ell \\\\nu$\", # 8\n", - " \"$t \\\\to bqq'$\" # 9\n", - "]\n" - ], - "metadata": { - "id": "vt4vzC1TGBiB" - }, - "id": "vt4vzC1TGBiB", - "execution_count": 18, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "\n", - "# Datasets for classification\n", - "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode=None)\n", - "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode=None)\n", - "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode=None)" - ], - "metadata": { - "id": "m37_eDUuGD0l" - }, - "id": "m37_eDUuGD0l", - "execution_count": 19, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from collections import Counter\n", - "import numpy as np\n", - "\n", - "def check_uniformity(y, dataset_name, label_names, threshold=0.02):\n", - " \"\"\"\n", - " Checks if the labels in a dataset are uniformly distributed.\n", - " Supports both integer class arrays and one-hot encoded arrays.\n", - " \"\"\"\n", - " # If one-hot encoded, convert to class indices\n", - " if len(y.shape) > 1 and y.shape[1] > 1:\n", - " y = np.argmax(y, axis=1)\n", - "\n", - " total_samples = len(y)\n", - " counts = Counter(y)\n", - " num_classes = len(label_names)\n", - " expected_pct = 1.0 / num_classes\n", - "\n", - " print(f\"--- Distribution for {dataset_name} ({total_samples} samples) ---\")\n", - "\n", - " is_uniform = True\n", - " for idx, name in enumerate(label_names):\n", - " count = counts.get(idx, 0)\n", - " actual_pct = count / total_samples\n", - " print(f\"Class {idx} ({name:<18}): {count:<8} | {actual_pct:.2%}\")\n", - "\n", - " # Check if it deviates more than the allowed threshold from absolute uniformity\n", - " if abs(actual_pct - expected_pct) > threshold:\n", - " is_uniform = False\n", - "\n", - " if is_uniform:\n", - " print(f\"✅ {dataset_name} appears to be uniformly distributed (within a {threshold:.1%} tolerance).\\n\")\n", - " else:\n", - " print(f\"⚠️ {dataset_name} is NOT perfectly uniform. Expected around {expected_pct:.2%} per class.\\n\")\n", - "\n", - "# Run the check on your datasets\n", - "# (Using your raw arrays y_train, y_val, and y_test)\n", - "check_uniformity(y_train, \"Train Dataset\", labels)\n", - "check_uniformity(y_val, \"Validation Dataset\", labels)\n", - "check_uniformity(y_test, \"Test Dataset\", labels)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IWCdSFFKGFoL", - "outputId": "c1769a19-a1db-473e-80a8-237fdc4867b1" - }, - "id": "IWCdSFFKGFoL", - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--- Distribution for Train Dataset (800000 samples) ---\n", - "Class 0 ($q/g$ ): 80000 | 10.00%\n", - "Class 1 ($H \\to b\\bar{b}$ ): 80000 | 10.00%\n", - "Class 2 ($H \\to c\\bar{c}$ ): 80000 | 10.00%\n", - "Class 3 ($H \\to gg$ ): 80000 | 10.00%\n", - "Class 4 ($H \\to 4q$ ): 80000 | 10.00%\n", - "Class 5 ($H \\to \\ell \\nu qq'$): 80000 | 10.00%\n", - "Class 6 ($Z \\to q\\bar{q}$ ): 80000 | 10.00%\n", - "Class 7 ($W \\to qq'$ ): 80000 | 10.00%\n", - "Class 8 ($t \\to b\\ell \\nu$ ): 80000 | 10.00%\n", - "Class 9 ($t \\to bqq'$ ): 80000 | 10.00%\n", - "✅ Train Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", - "\n", - "--- Distribution for Validation Dataset (100000 samples) ---\n", - "Class 0 ($q/g$ ): 10000 | 10.00%\n", - "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", - "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", - "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", - "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", - "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", - "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", - "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", - "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", - "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", - "✅ Validation Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", - "\n", - "--- Distribution for Test Dataset (100000 samples) ---\n", - "Class 0 ($q/g$ ): 10000 | 10.00%\n", - "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", - "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", - "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", - "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", - "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", - "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", - "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", - "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", - "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", - "✅ Test Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", - "\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Model configurations\n", - "pretrained_model_config = LorentzParTConfig(\n", - " num_classes=10,\n", - " embed_dim=128,\n", - " num_heads=8,\n", - " num_layers=8,\n", - " num_cls_layers=2,\n", - " num_mlp_layers=0,\n", - " hidden_dim=256,\n", - " hidden_mv_channels=8,\n", - " in_s_channels=None,\n", - " out_s_channels=None,\n", - " hidden_s_channels=16,\n", - " attention={},\n", - " mlp={},\n", - " dropout=0.1,\n", - " expansion_factor=4,\n", - " max_num_particles=128,\n", - " num_particle_features=4,\n", - " pair_embed_dims=[64, 64, 64],\n", - " weights=gated_pt_path\n", - ")" - ], - "metadata": { - "id": "oRHuimXTGIdx" - }, - "id": "oRHuimXTGIdx", - "execution_count": 20, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Initialize the classifier model\n", - "pretrained_model = LorentzParT(config=pretrained_model_config).to(device)\n", - "pretrained_model" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Dv1oQ3tSGe3w", - "outputId": "93cf00a1-dc30-4880-8652-4adf5b6cccae" - }, - "id": "Dv1oQ3tSGe3w", - "execution_count": 21, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "LorentzParT(\n", - " (processor): ParticleProcessor()\n", - " (encoder): LorentzParTEncoder(\n", - " (equilinear): EquiLinear()\n", - " (proj): Linear(in_features=16, out_features=128, bias=True)\n", - " (interaction_embed): InteractionEmbedding(\n", - " (embed): Sequential(\n", - " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", - " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (3): GELU(approximate='none')\n", - " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", - " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (6): GELU(approximate='none')\n", - " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", - " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (9): GELU(approximate='none')\n", - " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", - " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (12): GELU(approximate='none')\n", - " )\n", - " )\n", - " (encoder): ModuleList(\n", - " (0-7): 8 x ParticleAttentionBlock(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", - " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", - " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", - " (pmha): MultiheadAttention(\n", - " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", - " )\n", - " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (dropout): Dropout(p=0.1, inplace=False)\n", - " (feedforward): Feedforward(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (dropout1): Dropout(p=0.1, inplace=False)\n", - " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", - " (dropout2): Dropout(p=0.1, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", - " (equilinear): EquiLinear()\n", - " (decoder): ModuleList(\n", - " (0-1): 2 x ClassAttentionBlock(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (mha): MultiheadAttention(\n", - " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", - " )\n", - " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (dropout): Dropout(p=0.0, inplace=False)\n", - " (feedforward): Feedforward(\n", - " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", - " (act): GELU(approximate='none')\n", - " (dropout1): Dropout(p=0.0, inplace=False)\n", - " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", - " (dropout2): Dropout(p=0.0, inplace=False)\n", - " )\n", - " )\n", - " )\n", - " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (classifier): Classifier(\n", - " (layers): Sequential(\n", - " (0): Linear(in_features=128, out_features=10, bias=True)\n", - " )\n", - " )\n", - " (act): Identity()\n", - ")" - ] - }, - "metadata": {}, - "execution_count": 21 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Count parameters in the model\n", - "num_params = sum(p.numel() for p in pretrained_model.parameters() if p.requires_grad)\n", - "num_params" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_TDNTq4hGL2e", - "outputId": "24c6d7c6-5565-44e1-ed00-17967f67ff76" - }, - "id": "_TDNTq4hGL2e", - "execution_count": 22, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "2282400" - ] - }, - "metadata": {}, - "execution_count": 22 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Training configurations\n", - "pretrained_config = TrainConfig(\n", - " batch_size=64,\n", - " criterion={\n", - " 'name': 'cross_entropy_loss',\n", - " 'kwargs': {\n", - " 'reduction': 'mean'\n", - " }\n", - " },\n", - " optimizer={\n", - " 'name': 'adam',\n", - " 'kwargs': {\n", - " 'lr': 1e-4\n", - " }\n", - " },\n", - " scheduler={\n", - " 'name': 'exponential_lr',\n", - " 'kwargs': {\n", - " 'gamma': 0.95\n", - " }\n", - " },\n", - " callbacks=[{\n", - " 'name': 'early_stopping',\n", - " 'kwargs': {\n", - " 'monitor': 'val_loss',\n", - " 'mode': 'min',\n", - " 'patience': 5\n", - " }\n", - " }],\n", - " num_epochs=2,#change\n", - " start_epoch=0,\n", - " logging_dir=str(LOG_DIR),\n", - " logging_steps=1000,\n", - " save_best=True,\n", - " save_ckpt=True,\n", - " save_fig=False,\n", - " device='cuda',\n", - " num_workers=0,\n", - " pin_memory=True\n", - ")" - ], - "metadata": { - "id": "aaUtfajZGWD5" - }, - "id": "aaUtfajZGWD5", - "execution_count": 23, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Initialize the trainer\n", - "trainer = Trainer(\n", - " model=pretrained_model,\n", - " train_dataset=train_dataset,\n", - " val_dataset=val_dataset,\n", - " test_dataset=test_dataset,\n", - " device=device,\n", - " metric=accuracy_metric_ce,\n", - " config=pretrained_config\n", - ")" - ], - "metadata": { - "id": "GBfMEnRrGler" - }, - "id": "GBfMEnRrGler", - "execution_count": 24, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Train the model\n", - "pretrained_history, pretrained_model = trainer.train()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 518, - "referenced_widgets": [ - "62f237b1683e475595fe17da0edeae87", - "7bdac98dfc4a4699bab39fa846e56354", - "ac7d5153146d4bd89fa5587a1c4babb7", - "1d65d195b47147d3806f7735255878d8", - "34f4dd1f0b9b4920bffb1027ea6e11fe", - "38e5079824d648849dcde09e2a2948fd", - "ffa3569f77fb4a3c8a6fb08930c0defb", - "dd800993260d48e383fb9aa27d265c7d", - "4d4b43abf49f4114a7fc83d9128f6d30", - "0d7bc861ca364e298ffb26510a4e4e09", - "2c776b732e6a4cb8bb85e01baff5fb33" - ] - }, - "id": "q_3Zs8TfOwop", - "outputId": "ecbf6236-025e-47a4-f604-eb92b4fd72e0" - }, - "id": "q_3Zs8TfOwop", - "execution_count": 25, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Training: 0%| | 0/25000 [00:00" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAHqCAYAAAAZC3qTAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAir1JREFUeJzs3Xdc1dUfx/HXZQ8BcQAOFPdOFPfWTNMyLUXU3CtHWo6fWuYqV7lz75XmylHO3KlhTtTcA3OCogIy5MK95/fHzVsEKijwZXyejwcPv/fc7/fe9+Ui98P5nu85OqWUQgghhBBCvJSF1gGEEEIIITICKZqEEEIIIZJAiiYhhBBCiCSQokkIIYQQIgmkaBJCCCGESAIpmoQQQgghkkCKJiGEEEKIJJCiSQghhBAiCaRoEkIIIYRIAimahBBCCCGSQIomIV7DsmXL0Ol06HQ6Dh8+nOB+pRSenp7odDref/99DRKmnDlz5qDT6ahatarWUdIdLy8v88+BTqfD0dGRKlWqsGLFihcec+vWLXr16oWXlxe2tra4ubnRokULjhw58sJjgoODGTx4MCVLlsTBwQFHR0d8fHwYO3YsoaGhScoaEBBA+/bt8fT0xNbWlhw5ctCwYUOWLl2KwWBI7ksXIkuy0jqAEBmZnZ0dq1evplatWvHaDx48yJ07d7C1tdUoWcpZtWoVXl5eHDt2jGvXrlG0aFGtI6Ur3t7eDBo0CID79++zaNEiOnXqRExMDD169Ii375EjR2jatCkA3bt3p3Tp0gQFBbFs2TJq167NjBkz6NevX7xjjh8/TtOmTYmIiKB9+/b4+PgAcOLECSZOnMhvv/3Gr7/++tKMixYtolevXri7u9OhQweKFSvG06dP2bt3L926deP+/ft8+eWXKfUtESLzUkKIZFu6dKkC1EcffaRy5cqlYmNj493fo0cP5ePjowoWLKjee++9NMtlNBpVVFRUij3ejRs3FKA2btyocufOrUaPHp1ij51UBoNBRUdHp/nzJkVi7++DBw9UtmzZVKlSpeK1P378WHl4eCh3d3d17dq1ePdFRUWp2rVrKwsLC3XkyBFz+5MnT1S+fPmUu7u7unjxYoLnDwoKUt98881LM/r7+ytLS0tVq1YtFR4enuD+48ePq6VLl77qpSZJREREijyOEOmVnJ4T4g20bduWR48esXv3bnObXq9nw4YNtGvXLtFjJk+eTI0aNciZMyf29vb4+PiwYcOGRPf94YcfqFKlCg4ODri6ulKnTp14vQpeXl68//777Nq1i0qVKmFvb8/8+fMBuHHjBr6+vuTIkQMHBweqVavGtm3bkvX6Vq1ahaurK++99x6tWrVi1apV5vtiY2PJkSMHXbp0SXBceHg4dnZ2DB482NwWExPDqFGjKFq0KLa2tnh6ejJkyBBiYmLiHavT6fj0009ZtWoVZcqUwdbWlp07dybrexcdHU3//v3JlSsXTk5OfPDBB9y9exedTsfo0aPj7Xv37l26du2Ku7s7tra2lClThiVLliTr+/RvuXPnpmTJkly/fj1e+/z58wkKCmLSpEkUKVIk3n329vYsX74cnU7H119/He+Yu3fvMnXqVEqWLJngudzd3fnqq69emmfMmDHodDpWrVqFk5NTgvsrVapE586dAThw4AA6nY4DBw7E2+fmzZvodDqWLVtmbuvcuTPZsmXj+vXrNG3aFCcnJz7++GM+/fRTsmXLRlRUVILnatu2LR4eHvFOB+7YsYPatWvj6OiIk5MT7733HufPn3/paxJCK1I0CfEGvLy8qF69Oj/++KO5bceOHYSFhdGmTZtEj5kxYwYVKlTg66+/Zvz48VhZWeHr65ugoBkzZgwdOnTA2tqar7/+mjFjxuDp6cm+ffvi7Xf58mXatm3LO++8w4wZM/D29iY4OJgaNWqwa9cu+vTpw7hx43j27BkffPABmzZtSvLrW7VqFR999BE2Nja0bduWq1evcvz4cQCsra358MMP2bx5M3q9Pt5xmzdvJiYmxvw9MBqNfPDBB0yePJlmzZoxc+ZMWrRowbRp0/Dz80vwvPv27WPAgAH4+fkxY8YMvLy8kvW969y5MzNnzqRp06Z8++232Nvb89577yV4nuDgYKpVq8aePXv49NNPmTFjBkWLFqVbt25Mnz49yd+nf4uLi+POnTu4urrGa//ll1+ws7OjdevWiR5XqFAhatWqxb59+4iOjgbg559/xt7enlatWr1WlqioKPbu3UudOnUoUKDAaz3Gy8TFxdG4cWPc3NyYPHkyLVu2xM/Pj8jIyATvSVRUFL/88gutWrXC0tISgJUrV/Lee++RLVs2vv32W0aMGMGFCxeoVasWN2/eTPG8Qrwxrbu6hMiInp+eO378uJo1a5ZycnIynxbz9fVV9evXV0olfvrmv6fP9Hq9Klu2rGrQoIG57erVq8rCwkJ9+OGHymAwxNvfaDSatwsWLKgAtXPnznj7fP755wpQhw4dMrc9ffpUFSpUSHl5eSV4zMScOHFCAWr37t3m582fP7/67LPPzPvs2rVLAeqXX36Jd2zTpk1V4cKFzbdXrlypLCws4uVRSql58+YpIN4pKUBZWFio8+fPJ8iUlO/dyZMnFaA+//zzePt27txZAWrUqFHmtm7duqk8efKokJCQePu2adNGubi4vPJUZ8GCBVWjRo3Uw4cP1cOHD9W5c+dUhw4dFKD69u0bb9/s2bOr8uXLv/Tx+vfvrwB19uxZpZRSrq6urzzmZc6cOaOAeO/Zy+zfv18Bav/+/fHaAwMDFRDvNF6nTp0UoIYNGxZvX6PRqPLly6datmwZr33dunUKUL/99ptSyvTzmD17dtWjR494+wUFBSkXF5cE7UKkB9LTJMQbat26NdHR0WzdupWnT5+ydevWF56aA9OpmOeePHlCWFgYtWvX5tSpU+b2zZs3YzQaGTlyJBYW8f+b6nS6eLcLFSpE48aN47Vt376dKlWqxBugni1bNnr27MnNmze5cOHCK1/XqlWrcHd3p379+ubn9fPzY82aNebTKw0aNCBXrlysXbs23mvavXt3vB6k9evXU6pUKUqWLElISIj5q0GDBgDs378/3nPXrVuX0qVLJ8iUlO/d81N5ffr0iXfsfwdYK6X46aefaNasGUqpeLkaN25MWFhYvMd9kV9//ZXcuXOTO3duypUrx8qVK+nSpQuTJk2Kt9/Tp08TPT32b8/vDw8PN//7qmNe5vnjvMljvErv3r3j3dbpdPj6+rJ9+3YiIiLM7WvXriVfvnzmn8ndu3cTGhpK27Zt433vLS0tqVq1aoKfCSHSA7l6Tog3lDt3bho2bMjq1auJiorCYDC89HTK1q1bGTt2LAEBAfHG8/y7GLp+/ToWFhaJFg7/VahQoQRtf/31V6JTBJQqVcp8f9myZXn8+HG8U2v29va4uLhgMBhYs2YN9evXJzAw0Hx/1apVmTJlCnv37qVRo0ZYWVnRsmVLVq9eTUxMDLa2tmzcuJHY2Nh4RdPVq1e5ePEiuXPnTvQ1PHjw4JWvCZL2vfvrr7+wsLBI8Bj/verv4cOHhIaGsmDBAhYsWJCkXImpWrUqY8eOxWAw8OeffzJ27FiePHmCjY1NvP2cnJx4+vTpSx/r+f3PixxnZ+dXHvMyzs7O8R43pVlZWZE/f/4E7X5+fkyfPp2ff/6Zdu3aERERwfbt2/nkk0/M79XVq1cBzIXzi7ILkZ5I0SRECmjXrh09evQgKCiIJk2akD179kT3O3ToEB988AF16tRhzpw55MmTB2tra5YuXcrq1atf67n/3fuSXB999BEHDx403+7UqRPLli1j37593L9/nzVr1rBmzZoEx61atYpGjRoB0KZNG+bPn8+OHTto0aIF69ato2TJkpQvX968v9FopFy5ckydOjXRHJ6enq98TSn9vTMajQC0b9+eTp06JbrPW2+99crHyZUrFw0bNgSgcePGlCxZkvfff58ZM2YwcOBA836lSpXi9OnT5uIyMWfPnsXa2ppixYoBULJkSQICAtDr9QmKsKQoWrQoVlZWnDt3Lkn7/7cX87kXzeNka2uboCcUoFq1anh5ebFu3TratWvHL7/8QnR0dLxC+vn3f+XKlXh4eCR4DCsr+XgS6Y/8VAqRAj788EM++eQTjh49Gu9U1X/99NNP2NnZsWvXrngfnEuXLo23X5EiRTAajVy4cAFvb+9k5ylYsCCXL19O0H7p0iXz/QBTpkzhyZMn5vvz5s0LmIoiNzc3Zs+eneAxNm7cyKZNm5g3bx729vbUqVOHPHnysHbtWvNA5uHDhyd4PWfOnOHtt99+4QfzqyT1e1ewYEGMRiOBgYHm4gPg2rVr8fbLnTs3Tk5OGAwGc9GTEt577z3q1q3L+PHj+eSTT3B0dATg/fffx9/fn/Xr19O+ffsEx928eZNDhw7RsGFDc9HYrFkz/P39+emnn2jbtm2yszg4ONCgQQP27dvH7du3ExSn//V88Pp/J8z866+/kv3crVu3ZsaMGYSHh7N27Vq8vLyoVq2a+f7nVxC6ubml6PdfiFSl9aAqITKifw8Ef27ZsmVq9OjR8QYP/3cg+MCBA5WDg4OKjIw0twUGBioHBwf17/+OyRkIntg8UM8Hgv/+++/mtoiICFW4cOFXDgSPiopSTk5OqmvXronef+TIEQWoNWvWmNv69eunHB0d1dSpUxWgLly4EO+YZcuWKUDNnz8/0ef79/w+JDKIWqmkf++eD2BPykDwzp07KxsbG3Xu3LkEz/fgwYNEX/+/vej7v337dgWoadOmmdtCQkKUm5ub8vDwUNevX4+3f3R0tKpXr16CeZoeP36s8uTJo/LkyaMuX76c4HmCg4NfOU/TkSNHlKWlpapbt656+vRpgvtPnDihli1bppRSKjQ0VFlaWqoBAwbE26dly5aJDgR3dHR84fM+H5D//fffK1tbWzVkyJB494eFhSlnZ2dVt25dpdfrExyflO+/EGlNiiYhXkNiRVNi/vuhunfvXgWo2rVrq7lz56oxY8YoNzc39dZbb6n//g0zYsQIBagaNWqoyZMnq5kzZ6qOHTvGu1rpRR/aQUFByt3dXbm4uKgRI0aoadOmKW9vb6XT6dTGjRtfmnnNmjUKUJs3b070foPBoHLnzq2aNWtmbjt8+LAClJOTkypXrlyixzRt2lTpdDrVpk0bNXPmTDV9+nTVq1cvlSNHjnjfxxcVTcn53j3/kO/QoYOaPXu2at26tfL29lZAvAk6g4KCVMGCBZWDg4P67LPP1Pz589WECROUr6+vcnV1fen3SakXf/+VUqps2bLK09MzXkHw22+/KScnJ+Xi4qIGDRqkFi9erMaNG6eKFSumdDqd+v777xM8ztGjR1WOHDmUvb296tGjh5o3b56aN2+e6tmzp3JyclKNGjV6Zc558+YpCwsLlS9fPjVs2DC1ePFiNX36dNWiRQtlYWGhxo8fb963TZs2ysrKSg0cOFDNnj1bNWnSRPn4+CS7aFJKqaJFiyonJycFqJMnTya4f9WqVcrCwkKVLVtWjR07Vs2fP18NHz5ceXt7J/ozIITWpGgS4jW8btGklFKLFy9WxYoVU7a2tqpkyZJq6dKlatSoUQk++JVSasmSJapChQrK1tZWubq6qrp165qnAHjR4z93/fp11apVK5U9e3ZlZ2enqlSporZu3frK19asWTNlZ2cXr0fnvzp37qysra3Nl+objUbl6empADV27NhEj9Hr9erbb79VZcqUMb8eHx8fNWbMGBUWFmbe70VFk1JJ/95FRkaqvn37qhw5cqhs2bKpFi1aqMuXLytATZw4Md6+wcHBqm/fvsrT01NZW1srDw8P9fbbb6sFCxa88nv1su//8961/862HRgYqHr06KEKFCigrK2tVa5cudQHH3yQYDqGf7t3754aMGCAKl68uLKzs1MODg7Kx8dHjRs3Lt737mVOnjyp2rVrp/Lmzausra2Vq6urevvtt9Xy5cvj9Tw+fPhQtWzZUjk4OChXV1f1ySefqD///PO1iqbhw4crQBUtWvSF++zfv181btxYubi4KDs7O1WkSBHVuXNndeLEiSS9LiHSkk4ppVL5DKAQQmguICCAChUq8MMPP/Dxxx9rHUcIkQHJPE1CiEzn+Yza/zZ9+nQsLCyoU6eOBomEEJmBXD0nhMh0vvvuO06ePEn9+vWxsrJix44d7Nixg549e77yCjIhhHgROT0nhMh0du/ezZgxY7hw4QIREREUKFCADh06MHz4cJn/Rwjx2qRoEkIIIYRIAhnTJIQQQgiRBFI0CSGEEEIkQZY7uW80Grl37x5OTk6vvZyDEEIIIdI3pRRPnz4lb968ia6R+DqyXNF07949uXpGCCGEyCJu375N/vz5U+SxslzR5OTkBJi+ic7OzhqnEUIIIURqCA8Px9PT0/y5nxKyXNH0/JScs7OzFE1CCCFEJpeSQ3FkILgQQgghRBJI0SSEEEIIkQRSNAkhhBBCJIEUTUIIIYQQSSBFkxBCCCFEEkjRJIQQQgiRBFI0CSGEEEIkgRRNQgghhBBJIEWTEEIIIUQSSNEkhBBCCJEEUjQJIYQQQiSBFE1CCCGEEEmgadH022+/0axZM/LmzYtOp2Pz5s2vPObAgQNUrFgRW1tbihYtyrJly1I9pxBCCCGEpkVTZGQk5cuXZ/bs2UnaPzAwkPfee4/69esTEBDA559/Tvfu3dm1a1cqJxVCCCFEVmel5ZM3adKEJk2aJHn/efPmUahQIaZMmQJAqVKlOHz4MNOmTaNx48apFVMIIYQQImONafL396dhw4bx2ho3boy/v79GiYQQQgiRbsSGw5OzcHcrsRfnpfjDa9rTlFxBQUG4u7vHa3N3dyc8PJzo6Gjs7e0THBMTE0NMTIz5dnh4eKrnFEIIIUQK04dB1B2Ivvv3v/cg+j48C4KoexAZCM+CiYmFAT9A4MOUj5ChiqbXMWHCBMaMGaN1DCGEEEK8iFIQEwKRN01fUXf++YoIhIhroH/yyoe58QBaz7Tk5A1DqsTMUEWTh4cHwcHB8dqCg4NxdnZOtJcJ4IsvvmDgwIHm2+Hh4Xh6eqZqTiGEEEL8i1Kgf2wqiCJumnqFIm7+XST9vW2IevXj2LiCfT5wyGf61z4P2HmAQ142H7hB59HfEBYWTs6cOZk/fz6tWrVK0ZeRoYqm6tWrs3379nhtu3fvpnr16i88xtbWFltb29SOJoQQQmRtxlh4eh2eXoWIGxBx/Z+eo4ibEPf01Y9hnwccvcDB8++v/OBYAJyKgWMhsM6W4JDY2FiGDRvG1KlTAVOtsHbtWlxcXFLy1QEaF00RERFcu3bNfDswMJCAgABy5MhBgQIF+OKLL7h79y4rVqwAoFevXsyaNYshQ4bQtWtX9u3bx7p169i2bZtWL0EIIYTIOuKi4ekVCLtgKoqeXjWdPou6bfpSrzgtZudhKoqyFYr/r6OXqTiyTH4nR8eOHVmzZg0AgwYNYsKECVhbW6fKGGZNi6YTJ05Qv3598+3np9E6derEsmXLuH//Prdu3TLfX6hQIbZt28aAAQOYMWMG+fPnZ9GiRTLdgBBCCJGSYiMg9ByEX4CwixB+EcIvmQok1IuPs3QA5xKmYihbUcjmZeohcvQCx4JglfhQmjcxYMAA9u3bx/z582nRokWKP/6/6ZRSL3n1mU94eDguLi6EhYXh7OysdRwhhBBCO/onEPonhF/+pwcp7LzplNqLWGeH7GVMp8ycioFjYXD0NBVH9nlAp0vVyHFxcZw+fZrKlSub26KionBwcIi3X2p83meoMU1CCCGEeA2GGAj7E54EmHqQws6bvqLvv/gY+zzgUhacS4FLqX/+tc2d6oXRi9y/f5927drh7+/P0aNH8fb2BkhQMKUWKZqEEEKIzEQfCk/OQOgZUy/S45MQehZUXOL7OxQAl9KmXiOXUuBSxvRlmzNNY7/K/v37adu2LcHBwTg6OnLr1i1z0ZRWpGgSQgghMiKlTIOvnwT86+v0i0+t2eQAV2/I/hZkL/t3cVQarNP3UBWj0ciECRMYOXIkRqORsmXLsn79ekqWLJnmWaRoEkIIIdI7Y6xpQPbz4ij0739fNOGjY0HIXt5UHLl6Q84qph4ljU6rva6QkBDat2/Prl27AOjcuTOzZ89Os9Nx/yVFkxBCCJGe6MNMBVHYn/D4lKn3KOw8GPUJ99VZmXqLXL3//qpg6kmyzZHGoVPH8uXL2bVrF3Z2dsyZM4cuXbpomkeKJiGEEEIrxjhTcRRyFB79ASF/mC7tT+yyfmvnv0+vef9TJLmUfq25jTKKAQMGcO3aNXr37s1bb72ldRwpmoQQQog0ow+FR8fh4REIOQIh/hAXmXA/Ry/TmCNXb8hR0dSD5OiV4U6vJVdoaCjjxo3j66+/xt7eHgsLC+bOnat1LDMpmoQQQojU8izEVBgF74cHB0yn3f7bi2TtbBpzlLMa5Kpq2rZz0yCstk6ePImvry+BgYFERESkq2LpOSmahBBCiJQSGw7BByF4LwTtNZ16+y/HQpC7BuSuCblrmXqUdBZpnzWdUEoxd+5cBgwYgF6vp1ChQnTv3l3rWImSokkIIYR4Xc8ewsPD8OCgqTcp9BwJepKcioN7PXCrZ/rXPk/a50ynnj59Ss+ePc1rx7Vo0YKlS5eSPXt2bYO9gBRNQgghRFJFB0PI7/Dgt7+LpDMJ98lWGDzeAY+3wa0+2OVK+5wZwMWLF2nRogVXrlzBysqKb7/9lgEDBqBLx+O2pGgSQgghXuTZQwjaA0G7Tb1JETcS7uNSGnLXAff6ptNtDnnTPmcG5OjoSEhICPnz52ft2rXUqFFD60ivJEWTEEII8ZwxztSLdG+7qVhK0JOkM00Ymau6qRfJo0GWHLT9uuLi4rCyMpUeBQoUYOvWrRQrVoxcuTJGb5wUTUIIIbK26Ptwdyvc2wZB+yDuafz7s5eHPO+A+9uQqxrYZNckZkZ3+fJlfH19GTduHM2aNQOgevXqGqdKHimahBBCZC3Pr3AL+tXUmxR+Kf79trkg73uQp7HplJu9hzY5M5Eff/yRnj17EhERwdChQ2natCmWlpZax0o2KZqEEEJkfk/Owt2f4d4O08zbyhD//pxVIF8zyNvU1LNkkfE+0NOjZ8+eMXDgQPOcS/Xq1ePHH3/MkAUTSNEkhBAiMzIa4MF+02m3oN0QdiH+/dmKgkdDU2+SW22wzalNzkzsxo0b+Pr6curUKQC++uorRo0aZR7TlBFl3ORCCCHEvz0fxH33F/hrNTx78M99FtaQp4mpJylPY8jmpVnMrODevXtUrFiRsLAwcubMyQ8//MC7776rdaw3JkWTEEKIjMsYa5ov6dYGuLMJYkL+uc/GFTxbQp5GpkHctjm0y5nF5M2bl7Zt23L27FnWrFmDp6en1pFShBRNQgghMhZDjGmJktsb4M5m0D/55z7bnKaxSflbmHqVLKy1Spnl3L59GxsbG9zd3QGYPn06FhYWWFtnnvdAiiYhhBDpn+GZae6k25tMp99iw/65z84N8n8EBVqBW12wkI+2tLZjxw46dOhA+fLl+fXXX7G0tMTW1lbrWClOfrKEEEKkT0qZliy5vghurYe4yH/us89jOvXm2co0C7dc7aaJuLg4Ro8ezbhx4wAIDQ3l8ePH5M6dW+NkqUOKJiGEEOmH0QAPDsBfa0yn4CID/7nPwRMK+ILnR6YZuXUWmsUUcP/+fdq1a8eBAwcA6NOnD1OmTMHOzk7bYKlIiiYhhBDae3rNVChdXwSRf/3TbukABdtAkW5/F0rpdzHXrGT//v20bduW4OBgsmXLxsKFC2nTpo3WsVKdFE1CCCG0oQ+Dv36E64vh8Yl/2q2zQ0E/yPeBaQ4layfNIoqEDAYDffv2JTg4mHLlyrF+/XpKlCihdaw0IUWTEEKItGOIMQ3ovrnKNPGkMcbUrrM0LVni1R4KtAYre21ziheytLRkzZo1zJkzh6lTp+Lg4KB1pDSjU0oprUOkpfDwcFxcXAgLC8PZ2VnrOEIIkfkpIzw4ZCqUbq2H2NB/7nMpYzr15tUe7DLn4OHM4MiRI1y+fJmuXbtqHSXJUuPzXnqahBBCpI7Q8xC4wnQKLur2P+32+cCrLXh9bFrnTcYppVtKKaZOncrQoUPR6XSULVuWKlWqaB1LM1I0CSGESDn6MNOA7htL4NGxf9qtXUzzKHl9DLnryBQBGcCTJ0/o0qULW7ZsAaBNmzaUKlVK41TakqJJCCHEm3k+n9LVuXD7J9NElAA6K8j3PhTqYJqd2zLzXoqe2Zw4cYLWrVsTGBiIjY0NM2bM4JNPPkGXxXsFpWgSQgjxep5eN41T+msNhF/8p92lNBTuBoXam2brFhnK/Pnz6d+/P3q9nkKFCrFhwwYqVqyodax0QYomIYQQSRf7FO5sMZ1+C97/T7ulvWmagKK9IWdlGaeUgUVGRqLX62nRogVLly4le/bsWkdKN6RoEkII8WoRgXBxCgQu+9dyJjrweMc0Til/c7Bx0TKheAMGgwFLS9M4swEDBlCoUCFatGiR5U/H/ZcUTUIIIRJneGZaIPfGEtOSJvw9Q41TMSjYDop0AceCmkYUb27p0qXMnDmT3377jWzZsqHT6fjwww+1jpUuSdEkhBAivoibcG0BXF8AMY/+afdoBKWHgHsDOf2WCURFRdG3b1+WLVsGwLx58xg8eLC2odI5KZqEEEKYFsq9t810Bdz9XZh7lRw8oXBXKNwJshXSNKJIOZcuXcLX15c///wTCwsLvvnmGwYOHKh1rHRPiiYhhMjKDHrTLN0XxkPYhX/aPRpCsd6Qr7nMqZTJ/Pjjj/To0YPIyEg8PDz48ccfqVevntaxMgQpmoQQIit69hCuzoOrc+BZkKnNOjsU7QFFe4JTUU3jidQxe/ZsPv30UwDq16/P6tWr8fDw0DhVxiFrzwkhRFYSeg4uz4DAH/5ZLNc+LxTtBSX6yxVwmVxQUBA+Pj507dqV0aNHm6+Yy4xk7TkhhBDJpxTc2w6Xp0PQnn/ac1SCkgOggC9YWGsWT6SuM2fOUL58eQA8PDy4ePGidBq8JgutAwghhEglygg318C20nDwfVPBpLMAz1bwzmFofAy82knBlEnFxsYyaNAgvL29WbNmjbldCqbXJz1NQgiR2Riewc0f4dLkfwZ3WzmaTsEV/xSyeWkaT6S+27dv4+fnh7+/PwAXLlx4xREiKaRoEkKIzOJZiGlg99XZ8OyBqc3aBUoOgpKfgbX0MGQFO3bsoEOHDjx69AgXFxeWL19O8+bNtY6VKUjRJIQQGZ0+FP4cC1dmglFvanPwNPUqFf1EBndnEXFxcYwePZpx48YB4OPjw7p16yhcuLDGyTIPKZqEECKj0ofChYmmCSljw01tOXxMPUsFWslYpSzmyJEj5oKpT58+TJ06FVtbW41TZS5SNAkhREajDzNdCXdl5j/LnLiUgQqTIG8TTaMJ7dStW5eRI0dSqlQp2rRpo3WcTEnmaRJCiIwiLgquzDL1LumfmNqcS4L3t5DvfdOVcSLLMBqNTJ48mbZt2+Lp6al1nHRH5mkSQoisyKCHa/Ph/Ph/Zu92LgVlR/49x1LmnaBQJO7hw4d06NCBXbt2sXnzZg4dOpSpJ6pML6RoEkKI9MoYC4Er4dxoiLptanP0gnKjwau9FEtZ1JEjR/Dz8+Pu3bvY29vTs2dPKZjSiBRNQgiR3igj3N4EAcMg4pqpzc4dyo2Cwt3A0kbbfEITSimmTp3K0KFDMRgMlChRgg0bNlC2bFmto2UZUjQJIUR6oRTc3wVnvoQnp01tdm5QcjCU6AeWdtrmE5oJCwujU6dObNmyBYA2bdqwYMECnJycNE6WtUjRJIQQ6UHIH3B6MDw8bLptlc20Llyp/4G1fDBmdTY2NgQGBmJjY8OMGTP45JNP0Ol0WsfKcqRoEkIILUX+ZToN99ffa4NZ2ELxvlD6C7DLpW02oSmlFEopLCwssLe3Z/369Tx9+hQfHx+to2VZUjQJIYQWYp/CxSlw8TswRAM6KNwJ3hoLDvm0Tic09vTpU3r06EG5cuUYPnw4AMWLF9c4lZB5moQQIi0ZnsHl703F0vOJKd3qgs90cPXWMplIJ86ePYuvry9XrlzB1taWGzdukDdvXq1jZTgyT5MQQmRUSsGdzXBqEEQGmtqcipl6lgr4goxPEcDSpUvp06cPz549I3/+/Kxbt04KpnREiiYhhEhtDw7DuZEQvN902z4vlB8PXh+DhfwaFhAZGUnfvn1Zvnw5AE2aNGHFihXkyiXj2tIT+d8qhBCpJeIGnPgM7m013bawgVKDocyXYOWobTaRbhgMBurVq8eJEyewsLBg7NixDB06FAsLWRYnvZGiSQghUlrMY7g01fRliAadFRTuAmWHg2NBrdOJdMbS0pIePXpw584dfvzxR+rVq6d1JPECMhBcCCFSitEA1xdAwJcQG2pqc68PleaAS0lNo4n05dmzZ9y5c4eiRYsCpukFQkNDcXV11ThZ5iEDwYUQIr16eAROfgaPT5puZy9nWiMu/4cyyFvEc/36dXx9fXny5AmnTp3C1dUVnU4nBVMGICdMhRDiTUTcgN8+hN21TAWTtTNUnA7vngLPj6RgEvFs2rQJHx8fTp8+zdOnT7l27ZrWkUQySE+TEEK8jmcP4fwEuDoLjLGgs4TCXeGtb8DeXet0Ip3R6/UMGzaMadOmAVCjRg3Wrl1L/vz5NU4mkkOKJiGESI64aLjyPZwfD7HhpjaPRlBxKmQvo202kS7dunULPz8/jh49CsDgwYMZP3481tbWGicTySVFkxBCJNW9HXC8D0TeNN12rWiabylvY01jifRt+PDhHD16lOzZs7Ns2TKaN2+udSTxmqRoEkKIV4kOhlOf/7OorkN+eGscFGoPOhkaKl5u+vTpREZGMmXKFAoVKqR1HPEGNP/fPnv2bLy8vLCzs6Nq1aocO3bspftPnz6dEiVKYG9vj6enJwMGDODZs2dplFYIkaUoI1ydC1tLmgomnQWUGADvXYTCHaVgEom6f/8+kydP5vmMPjlz5mTjxo1SMGUCmvY0rV27loEDBzJv3jyqVq3K9OnTady4MZcvX8bNzS3B/qtXr2bYsGEsWbKEGjVqcOXKFTp37oxOp2Pq1KkavAIhRKYVcRP+6AbB+0y3XStA1YWQw0fTWCJ927dvH23btuXBgwfkyJGDrl27ah1JpCBN/0yaOnUqPXr0oEuXLpQuXZp58+bh4ODAkiVLEt3/999/p2bNmrRr1w4vLy8aNWpE27ZtX9k7JYQQSaYUXJ0P28uZCiZLB/D5Hhofl4JJvJDRaOSbb77hnXfe4cGDB5QrV45atWppHUukMM2KJr1ez8mTJ2nYsOE/YSwsaNiwIf7+/okeU6NGDU6ePGkukm7cuMH27dtp2rRpmmQWQmRykX/B/kZwvBfERUDuWtD0DJToBxaWWqcT6dTDhw9p2rQpI0eOxGg00q1bN44ePUrx4sW1jiZSmGan50JCQjAYDLi7x5/PxN3dnUuXLiV6TLt27QgJCaFWrVoopYiLi6NXr158+eWXL3yemJgYYmJizLfDw8NT5gUIITIPpeD6Ijg1COKegqW96aq4Ev1l3JJ4qd9//53WrVtz9+5d7O3tmTt3Lp06ddI6lkglGeq3wYEDBxg/fjxz5szh1KlTbNy4kW3btvHNN9+88JgJEybg4uJi/vL09EzDxEKIdC/yNhxoAsd6mgqmXDWgSQCU/FwKJvFKkZGR3Lt3jxIlSnDs2DEpmDI5zRbs1ev1ODg4sGHDBlq0aGFu79SpE6GhoWzZsiXBMbVr16ZatWpMmjTJ3PbDDz/Qs2dPIiIisLBI+AsusZ4mT09PWbBXiKxOKbixzDSVQGw4WNj+3bv0mZyKEy+llEL3r+VxNm7cyDvvvIOTk5OGqcR/pcaCvZr9GWVjY4OPjw979+41txmNRvbu3Uv16tUTPSYqKipBYWRpafrl9qLaz9bWFmdn53hfQogsLvKWqXfpj66mgilnVVPvUqmBUjCJlzp+/DgVKlSIt2bcRx99JAVTFqFp3/PAgQNZuHAhy5cv5+LFi/Tu3ZvIyEi6dOkCQMeOHfniiy/M+zdr1oy5c+eyZs0aAgMD2b17NyNGjKBZs2bm4kkIIV7o+bxL28rA/V2m3iXvb+GdI+BSUut0Ih1TSjF79mxq1arFmTNnGDp0qNaRhAY0nafJz8+Phw8fMnLkSIKCgvD29mbnzp3mweG3bt2K17P01VdfodPp+Oqrr7h79y65c+emWbNmjBs3TquXIITIKJ5egz+6w4ODptu5akC1JeBcQttcIt0LDw+nR48erFu3DoAPP/zwhVPjiMxNszFNWkmNc5xCiHRMKbj8PZz5AgzRpnmXvCdAsb5yKk680tmzZ2nVqhVXr17FysqKSZMm8dlnn8Ub0yTSp9T4vJe154QQmdezh3C0M9zbbrrt/rZpVu9sspyFeDV/f38aNGjAs2fP8PT0ZN26dVSrVk3rWEJDUjQJITKn4P3w+8cQfd80dqniVCjWG6SHQCSRj48Pb731Fjlz5mTlypXkzJlT60hCY1I0CSEyF8MzODsCLk01Dfx2LgW11kL2clonExnA9evXKViwIFZWVtjY2LBjxw6yZ8+e6JQ2IuuRnwIhRObx+DTs9IGLk00FU+Eu8O4JKZhEkqxevZry5cszYsQIc1uOHDmkYBJm0tMkhMj4jHFw4Vs4NxpUHNi5Q9VFkO99rZOJDODZs2cMGDCAefPmAaa5mOLi4rCyko9IEZ/8RAghMran18C/I4T8vdC3Z0uoPA/scmmbS2QI169fx9fXl9OnT5untBk1apTM/ScSJUWTECJjUgquLYBTA8EQBdbOUGkWeLWXwd4iSTZu3EiXLl0IDw8nV65c/PDDDzRu3FjrWCIdk6JJCJHxRN+Ho93g/g7Tbff6UG0ZOBbQNJbIOB4+fEinTp2IiIigZs2arFmzhvz582sdS6RzUjQJITKWW+vhWC/QP/57GZQJpkV2dTJYVyRd7ty5mT9/PqdPn2b8+PFYW1trHUlkADIjuBAiY9CHwolP4eYq023XClB9JWQvo2kskXFs374dJycnateurXUUkQZS4/Ne/jQTQqR/QXthezlTwaSzgDJfQaOjUjCJJImLi+PLL7/kvffew8/PjwcPHmgdSWRQcnpOCJF+xUWb1oy7PMN0O1tRqL4CclfXNpfIMO7fv0/btm05eNC0UHPLli1xcXHROJXIqKRoEkKkT+FX4XArCD1rul20F1ScDFaO2uYSGca+ffto27YtDx48IFu2bCxatAg/Pz+tY4kMTIomIUT689daOPYJxIaZJqqsthTyNtE6lcggjEYj48aNY9SoUSilKFeuHBs2bKB48eJaRxMZnIxpEkKkH7ERcLQLHGljKphy1zQtgyIFk0gGnU7HuXPnUErRrVs3jh49KgWTSBHS0ySESB8eHIY/usLTq38P9h4OZUeChfyaEkmjlEKn06HT6Vi0aBEtW7aU03EiRUlPkxBCW8oI5yfC3rqmgsnBE97eD299LQWTSBKlFJMnT6Zt27Y8n0XH2dlZCiaR4uQ3khBCOzGPwb8D3Ntuuu3VHip9Dzau2uYSGcaTJ0/o1KkTv/zyCwAdO3akadOmGqcSmZUUTUIIbTw+BYf9IOKaaWbvSrOgSDdZN04k2fHjx2ndujU3b97ExsaG77//niZNZPybSD1SNAkh0pZScGUmnP4fGPXgUADqbgFXb62TiQxCKcXs2bMZOHAgsbGxFC5cmPXr11OxYkWto4lMToomIUTaiXlsGux9Z4vpdr4PTNMJ2ObQNpfIUD7//HO+//57AD788EOWLl0qE1aKNCEDwYUQaSPkD9hZ0VQwWdiAz/dQZ7MUTCLZfH19sbe3Z9q0afz0009SMIk0Iz1NQojUpRRcXwQn+oIxFrIVgVrrIIecShFJo5Tixo0bFClSBIBatWpx8+ZN3NzcNE4mshrpaRJCpJ64aNNklcd6mgomz4/g3ZNSMIkki4yMpEuXLrz11lucP3/e3C4Fk9CC9DQJIVJHxE049BE8OW2arLL8eCg1RK6OE0l28eJFfH19OX/+PBYWFhw9epQyZcpoHUtkYVI0CSFSXvABONwaYh6CbS6ouRY8GmidSmQgq1evpmfPnkRGRuLh4cGaNWuoW7eu1rFEFien54QQKUcpuDof9r1jKpiylzetHScFk0iiZ8+e0atXLz7++GMiIyNp0KABAQEBUjCJdEGKJiFEyoiLhj+6wfFeoOKgYFto5A+OBbVOJjKQBQsWMH/+fHQ6HSNGjODXX3/F3d1d61hCAHJ6TgiREsIumk7Hhf1pGr/01lgoPUzGL4lk69OnD4cOHaJ79+40btxY6zhCxCM9TUKINxO4CnZWMhVMdu5Q/1co84UUTCJJ9Ho9U6ZMISYmBgArKyvWr18vBZNIl6SnSQjxeuKi4NQAuLbAdNv9bajxA9h7aJtLZBi3bt2idevW/PHHH9y8eZOZM2dqHUmIl5KiSQiRfKF/wpE2EPb3vDllhkO5MWBhqW0ukWFs27aNjh078vjxY7Jnz84777yjdSQhXkmKJiFE8tzeBL9/DIZosPOA6isgj3zgiaSJi4tj5MiRTJgwAYBKlSqxbt06ChUqpHEyIV5NiiYhRNIoI5wbDX9+Y7rt8Y7pdJydzMwskub+/fu0adOG3377DYB+/foxadIkbG1tNU4mRNJI0SSEeLXYcNNyKLc3mm6X+BwqTAIL+RUiki46OpozZ87g5OTEokWLaN26tdaRhEgW+Y0nhHi5J2fhsC88vQIW1lB5PhTponUqkUEopdD9fSVl4cKF2bBhAwUKFKB48eIaJxMi+WTKASHEi935BXbXMBVMDvmh4SEpmESSPXjwgCZNmrBr1y5zW8OGDaVgEhmW9DQJIRJSCi5Ph1ODAAXuDaDWOrDNqXUykUEcPnwYPz8/7t27x8WLF7l27RrW1tZaxxLijbxRT9OzZ89SKocQIr0w6OGP7nBqIKCgSDeov1MKJpEkRqORSZMmUa9ePe7du0fJkiXZtm2bFEwiU0h20WQ0Gvnmm2/Ily8f2bJl48aNGwCMGDGCxYsXp3hAIUQaenoNdlaEG0tMy6FUnA5VFprGMgnxCo8fP6ZFixYMGTIEg8FAu3btOH78OGXLltU6mhApItlF09ixY1m2bBnfffcdNjY25vayZcuyaNGiFA0nhEhDj47Dr9VNE1ba5oI6v0DJz2Q5FJEkjx49omLFivzyyy/Y2Ngwb948fvjhB7Jly6Z1NCFSTLKLphUrVrBgwQI+/vhjLC3/mf23fPnyXLp0KUXDCSHSyN1tsLc+xISAa0Voeg7yNdU6lchAcubMSYMGDShcuDD+/v588skn5qvmhMgskj0Q/O7duxQtWjRBu9FoJDY2NkVCCSHSiFJw8TsI+AJQpgkra/8E1k5aJxMZQHh4OHFxceTIkQOAWbNmodfryZ49u7bBhEglye5pKl26NIcOHUrQvmHDBipUqJAioYQQaSD2KfzeDgKGAQoKd4W6W6VgEkly5swZKlWqRPv27TEajQA4ODhIwSQytWT3NI0cOZJOnTpx9+5djEYjGzdu5PLly6xYsYKtW7emRkYhREqLCISD70PYBdBZgc8MKNZbxi+JV1JKsWTJEj799FOePXvGs2fPuHv3Lp6enlpHEyLVJbunqXnz5vzyyy/s2bMHR0dHRo4cycWLF/nll19klWohMoKHR2BnJVPBZJ8X3t4PxftIwSReKTIyks6dO9O9e3eePXtG06ZNOX36tBRMIsvQKaWU1iHSUnh4OC4uLoSFheHs7Kx1HCHS1u1NcKQNGPWQoxLU2QwO+bROJTKAixcv0qpVKy5cuICFhQXjxo1jyJAhWFjIwhIifUqNz/tk/7QXLlyYR48eJWgPDQ2lcOHCKRJKCJHClBHOjobDrUwFU74PoOFBKZhEkhiNRvz8/Lhw4QJ58uRh3759DBs2TAomkeUk+yf+5s2bGAyGBO0xMTHcvXs3RUIJIVJQXLRpwd0/x5iKpyI9oPZGsHLQOpnIICwsLFi6dKn5dFzdunW1jiSEJpI8EPznn382b+/atQsXFxfzbYPBwN69e/Hy8krRcEKINxR111QwhfibBnxXXQSFO2mdSmQA165dIyAggFatWgHg4+PDtm3bNE4lhLaSXDS1aNECAJ1OR6dO8X/pWltb4+XlxZQpU1I0nBDiDYT8Ab99AM8egLUL1P0Z3OponUpkAD/99BNdu3bl2bNnFCpUCB8fH60jCZEuJLloej4PR6FChTh+/Di5cuVKtVBCiDf01zrw7wjGGMj+lmnCSqeEk9IK8W96vZ4hQ4YwY8YMAGrWrIm7u7vGqYRIP5I9T1NgYGBq5BBCpJTLM+Fkf9N23veh5o9gLet/iZf766+/aN26NceOHQNgyJAhjB07FmtrWaxZiOeSXTSBaa6OgwcPcuvWLfR6fbz7+vfvnyLBhBCv4eJkOP0/03bxflBxGlhYvvwYkeVt27aNDh068OTJE1xdXVm+fDnNmjXTOpYQ6U6yi6bTp0/TtGlToqKiiIyMJEeOHISEhODg4ICbm5sUTUJoQSk4Nwr+/MZ0u8xX8NbXMmGlSJKzZ8/y5MkTKleuzLp16+SiHiFeINlTDgwYMIBmzZrx5MkT7O3tOXr0KH/99Rc+Pj5Mnjw5NTIKIV4mNgIOt/6nYHrrGyj/jRRMIsmGDh3K3LlzOXTokBRMQrxEsoumgIAABg0ahIWFBZaWlsTExODp6cl3333Hl19+mRoZhRAvog+D/Y3h9gawsIYqC6HsV1qnEuncnj17aNiwIVFRUYBpHqZevXpha2urcTIh0rdkF03W1tbmWWDd3Ny4desWAC4uLty+fTtl0wkhXizqDuypCyG/m6YUePsAFO2udSqRjhkMBsaMGUOjRo3Yu3cv3377rdaRhMhQkj2mqUKFChw/fpxixYpRt25dRo4cSUhICCtXrqRs2bKpkVEI8V/hl2FfI4i6BXbuUG8H5KigdSqRjj148ID27duze/duALp3786wYcM0TiVExpLsnqbx48eTJ08eAMaNG4erqyu9e/fm4cOHzJ8/P8UDCiH+4+HvsLumqWByKg6N/5CCSbzU4cOHqVChArt378bBwYHly5ezcOFC7O3ttY4mRIaiU0oprUOkpdRY9ViINHN3m2lZFEM05KgM9baBXW6tU4l0bM2aNbRv3x6DwUCpUqVYv349ZcqU0TqWEKkuNT7vU2yJ6lOnTvH++++n1MMJIf5NKbg0Aw42MxVMeZpAw/1SMIlXqlWrFq6urnz88cccO3ZMCiYh3kCyiqZdu3YxePBgvvzyS27cuAHApUuXaNGiBZUrVzYvtSKESEHGWDjWE059Digo3BXqbAYrR42DifTqzp075u38+fNz+vRpVq5cSbZsMjO8EG8iyUXT4sWLadKkCcuWLePbb7+lWrVq/PDDD1SvXh0PDw/+/PNPtm/fnppZhch6jAbT6bjri0BnARWmQNVFYGmjdTKRDimlmDlzJkWKFGHTpk3m9vz586OTebuEeGNJLppmzJjBt99+S0hICOvWrSMkJIQ5c+Zw7tw55s2bR6lSpVIzpxBZj1JwehDc2QKWdlB7M5QaKJNWikSFhYXRunVr+vfvj16vZ+vWrVpHEiLTSfJAcEdHR86fP4+XlxdKKWxtbdm/fz81a9ZM7YwpSgaCiwxBGeHEp3B1rul2jdXg1VbbTCLdCggIwNfXl2vXrmFtbc3kyZPp16+f9C6JLE3TgeDR0dE4ODgAoNPpsLW1NU898CZmz56Nl5cXdnZ2VK1a1bzC9ouEhobSt29f8uTJg62tLcWLF5fTgiJzMRrgj25/F0w60+k4KZhEIpRSLFq0iGrVqnHt2jUKFCjAoUOH6N+/vxRMQqSCZE1uuWjRIvNAwri4OJYtW0auXLni7ZOcBXvXrl3LwIEDmTdvHlWrVmX69Ok0btyYy5cv4+bmlmB/vV7PO++8g5ubGxs2bCBfvnz89ddfZM+ePTkvQ4j0y/AM/ugON1eBzhKqrwCvdlqnEunUsWPH6NGjBwBNmzZlxYoV5MyZU+NUQmReST495+Xl9cq/XHQ6nfmquqSoWrUqlStXZtasWQAYjUY8PT3p169fojPVzps3j0mTJnHp0iWsra2T/Dz/JqfnRLoVGw4HP4AHB00FU80foYCv1qlEOjdgwADc3d0ZMmSIeYkrIUTqfN5rNrmlXq/HwcGBDRs20KJFC3N7p06dCA0NZcuWLQmOadq0KTly5MDBwYEtW7aQO3du2rVrx9ChQ7G0tEzS80rRJNIlfRjsbwSPjoG1M9RaD3kaaZ1KpENr1qyhbt26KTI8QojMLF1PbplcISEhGAwG3N3d47W7u7sTFBSU6DE3btxgw4YNGAwGtm/fzogRI5gyZQpjx4594fPExMQQHh4e70uIdCXqLuypbSqYbHPC2/ulYBIJPHv2jE8++YS2bdvStm1b4uLitI4kRJaT7AV7tWQ0GnFzc2PBggVYWlri4+PD3bt3mTRpEqNGjUr0mAkTJjBmzJg0TipEEj29Zlp4NzJQFt4VL3Tt2jV8fX0JCAhAp9NRr149GegthAY062nKlSsXlpaWBAcHx2sPDg7Gw8Mj0WPy5MlD8eLF452KK1WqFEFBQej1+kSP+eKLLwgLCzN/3b59O+VehBBv4slZ2F3LVDBlKwKNjkrBJBL46aefqFixIgEBAeTOnZtdu3YxevToJA9JEEKkHM2KJhsbG3x8fNi7d6+5zWg0snfvXqpXr57oMTVr1uTatWvxlmu5cuUKefLkwcYm8RmSbW1tcXZ2jvclhOYe/g576sKzYHD1hneOQDYvrVOJdESv1/P555/TqlUrnj59Sq1atTh9+jTvvPOO1tGEyLI0vdRi4MCBLFy4kOXLl3Px4kV69+5NZGQkXbp0AaBjx4588cUX5v179+7N48eP+eyzz7hy5Qrbtm1j/Pjx9O3bV6uXIETy3f8V9jWE2FDIVd00hsne/ZWHiawlNjaWX3/9FYAhQ4awb98+8uXLp3EqIbK21xrTdP36dZYuXcr169eZMWMGbm5u7NixgwIFCiRrBW0/Pz8ePnzIyJEjCQoKwtvbm507d5oHh9+6dSveJbSenp7s2rWLAQMG8NZbb5EvXz4+++wzhg4d+jovQ4i0d20RHO8NKg7yvAu1N8jCuyJRjo6ObNiwgevXr9OsWTOt4wgheI0pBw4ePEiTJk2oWbMmv/32GxcvXqRw4cJMnDiREydOsGHDhtTKmiJkygGhmcAfwL+DabtgO6i2BCxttc0k0o24uDhGjBhBrly5GDRokNZxhMjw0sWUA8OGDWPs2LHs3r073jiiBg0acPTo0RQJJUSmc+snONrZtF28P9T4QQomYXbv3j0aNGjAxIkTGTZsGIGBgVpHEkIkItlF07lz5/jwww8TtLu5uRESEpIioYTIVG6th9/bgTJA4S7gMw3kcnHxtz179uDt7c2hQ4dwcnJi9erVFCpUSOtYQohEJLtoyp49O/fv30/Qfvr0aRmkKMR/XZ0Ph1uDUQ8FWkOVhaCTpS4EGAwGxowZQ6NGjXj48CHly5fn5MmT+PrK0jlCpFfJ/u3dpk0bhg4dSlBQEDqdDqPRyJEjRxg8eDAdO3ZMjYxCZEznx8PxXqbtYr2hxmqwkLl1BCilaN68OaNHj0YpRffu3fH396dYsWJaRxNCvESyi6bx48dTsmRJPD09iYiIoHTp0tSpU4caNWrw1VdfpUZGITIWowFO/w/ODDfdLj0MKs2WgkmY6XQ6GjVqhIODA8uXL2fhwoXY29trHUsI8QqvvWDvrVu3+PPPP4mIiKBChQoZ5i8kuXpOpCplBP/OcHOl6bb3d1D6f5pGEumD0WjkwYMH5hUPlFLcunWLggULapxMiMwpNT7vkz1P0+HDh6lVqxYFChSgQIECKRJCiEzBGAe/t4dba0FnBdWXg1c7rVOJdODx48d06tSJS5cucfLkSZydndHpdFIwCZHBJPv0XIMGDShUqBBffvklFy5cSI1MQmQ8xjjTFXK31oKFNdRcLQWTAODYsWNUqFCBrVu3cvv2bY4dO6Z1JCHEa0p20XTv3j0GDRrEwYMHKVu2LN7e3kyaNIk7d+6kRj4h0r/YcDjYzDS1gIU11N4IBeQKqKxOKcXMmTOpVasWt27dokiRIhw9epSGDRtqHU0I8Zpee0wTQGBgIKtXr+bHH3/k0qVL1KlTh3379qVkvhQnY5pEioqNgIPvwYPfwNIeaq2DfO9rnUpoLCwsjO7du5tXSGjZsiWLFy/GxcVF42RCZB2p8Xn/RkUTmOYa2bFjByNGjODs2bMYDIYUCZZapGgSKSY2Ag40hYeHwNoZ6u+GXFW0TiXSgR49erBo0SKsra2ZPHky/fr1QycTmgqRptLFMirPHTlyhD59+pAnTx7atWtH2bJl2bZtW4qEEiLdi30KB5r8XTC5SMEk4hk3bhzVq1fn0KFD9O/fXwomITKJZF8998UXX7BmzRru3bvHO++8w4wZM2jevDkODg6pkU+I9MdcMB0xFUwNdkPOylqnEhqKjIxkw4YNdOrUCTAtK3XkyBEploTIZJJdNP3222/873//o3Xr1uTKlSs1MgmRfsWGw/4mEPI7WGf/u2CqpHUqoaELFy7g6+vLhQsXsLKy4uOPPwaQgkmITCjZRdORI0dSI4cQ6V9sBOxrDI+Ogo2rqWDK4aN1KqGhH374gU8++YSoqCjy5MmDp6en1pGEEKkoSUXTzz//TJMmTbC2tubnn39+6b4ffPBBigQTIl2JDYe9DeDxSbDJ8XfBVFHrVEIj0dHRfPbZZyxcuBCAt99+m9WrV+Pm5qZxMiFEakrS1XMWFhYEBQXh5uaGhcWLx47rdDq5ek5kPvpQOPiBadC3bS6ot13GMGVh165dw9fXl4CAAHQ6HSNHjmTEiBFYWsragkKkJ5oto2I0GhPdFiLTi3kMe+tD6FnTtAJSMGV5V65cISAggNy5c7Nq1SreeecdrSMJIdJIsqccWLFiBTExMQna9Xo9K1asSJFQQqQLMY/+KZjsPKDhb1IwCZo2bcrChQs5ffq0FExCZDHJntzS0tKS+/fvJzh3/+jRI9zc3OT0nMgcou7B/ncg7IKpYGqwB7KX0TqV0MBff/1Fr169mDdvniywK0QGki4mt1RKJXop7Z07d2SJAJE56EPhwLumgsk+D7y9VwqmLGrr1q1UqFCBnTt30qtXL63jCCE0luQpBypUqIBOp0On0/H2229jZfXPoQaDgcDAQN59991UCSlEmomLNg36Dj1n6mF65whkK6R1KpHG4uLi+Oqrr/j2228BqFy5MnPnztU4lRBCa0kumlq0aAFAQEAAjRs3Jlu2bOb7bGxs8PLyomXLlikeUIg0Y4yD39v+ay25nVIwZUH37t2jTZs2HDp0CID+/fszadIkbGxsNE4mhNBakoumUaNGAeDl5YWfnx92dnapFkqINKcUHO8Nd7aAhS3U/QVcy2udSqSxP//8kwYNGvDw4UOcnJxYsmQJrVq10jqWECKdSPaM4M/XVhIiUzk7Eq4vAp0F1FwDbnW0TiQ0ULRoUfLnz0/evHnZsGEDRYsW1TqSECIdSVLRlCNHDq5cuUKuXLlwdXV96ZpKjx8/TrFwQqSJSzPg/FjTduW54NlC0zgibYWEhODq6oqlpSV2dnZs3boVV1dX7O3ttY4mhEhnklQ0TZs2DScnJ/O2LEQpMo3rS+HU56btt76Boj01jSPS1qFDh/Dz86Nnz56MHj0agLx582obSgiRbiV7nqaMTuZpEma3foLDvoCCEp9BxWkgfxBkCUajkcmTJ/Pll19iMBgoW7Ysx48fl7GaQmQi6WKeplOnTnHu3Dnz7S1bttCiRQu+/PJL9Hp9ioQSItU9PGK6Ug5l6l2qOFUKpizi8ePHNG/enKFDh2IwGGjfvj3+/v5SMAkhXinZRdMnn3zClStXALhx4wZ+fn44ODiwfv16hgwZkuIBhUhxTwLgwPtgjIV8H0ClOaYB4CLTO3bsGBUqVGDr1q3Y2tqyYMECVqxYEW8KFSGEeJFkf1JcuXIFb29vANavX0/dunVZvXo1y5Yt46effkrpfEKkrKfXYf+7EBsKuWtCzR/BQlanzwrCwsJo1KgRt27dokiRIhw9epQePXrIGE0hRJK91jIqRqMRgD179tC0aVMAPD09CQkJSdl0QqSkyFumBXifBUP2t6DuNrBy0DqVSCMuLi5MnTqVli1bcvLkSfMff0IIkVTJHgjeoEEDPD09adiwId26dePChQsULVqUgwcP0qlTJ27evJlKUVOGDATPomIew+6aEH4JnEvA2wfA3kPrVCKVnTlzhtjYWCpVqgSY/ugDpHdJiCwgXQwEnz59OqdOneLTTz9l+PDh5snfNmzYQI0aNVIklBApKjYC9jcyFUz2+aD+bimYMjmlFAsXLqRq1aq0bNmSR48eAZjXzxRCiNeR7BnB33rrrXhXzz03adIkLC1lbIhIZ4yxcMQPHp8E21zQ4Fdw9NQ6lUhFkZGR9O7dm5UrVwKm31lSKAkhUkKyi6bnTp48ycWLFwEoXbo0FStWTLFQQqQIYyz83gHubQdLe6i7FVxKa51KpKILFy7g6+vLhQsXsLS0ZPz48QwePBgLC7k6Ugjx5pJdND148AA/Pz8OHjxI9uzZAQgNDaV+/fqsWbOG3Llzp3RGIZLPoIffWsD9HWBhDbXWQa6qWqcSqeiHH37gk08+ISoqirx587JmzRpq166tdSwhRCaS7D+/+vXrR0REBOfPn+fx48c8fvyYP//8k/DwcPr3758aGYVIHmWEo11MBZOlA9TZAvne1zqVSEVKKTZs2EBUVBQNGzbk9OnTUjAJIVJcsq+ec3FxYc+ePVSuXDle+7Fjx2jUqBGhoaEpmS/FydVzWcDJgXB5GuisoN42yNNI60QiDTx58oSlS5fy2WefyfhKIUT6uHrOaDRibW2doN3a2to8f5MQmrk4xVQwAVRbIgVTJrZhwwZ69+5tnkbA1dWVgQMHSsEkhEg1yS6aGjRowGeffca9e/fMbXfv3mXAgAG8/fbbKRpOiGQJ/AFODzZte38LhTpom0ekCr1ez2effYavry/z5s1j48aNWkcSQmQRyS6aZs2aRXh4OF5eXhQpUoQiRYpQqFAhwsPDmTlzZmpkFOLV7v8KRzubtksMgFL/0zSOSB1//fUXtWvX5vvvvwdg6NChNG/eXONUQoisItlXz3l6enLq1Cn27t1rnnKgVKlSNGzYMMXDCZEkYRfgsC8oA3h9DBUng8zLk+ls3bqVjh078uTJE1xdXVmxYgXvvy8D/IUQaSdZRdPatWv5+eef0ev1vP322/Tr1y+1cgmRNJF/wf4mEBsOuWtB1cWgkzl5MpuJEyfyxRdfAFClShXWrVtHwYIFNU4lhMhqkvzpMnfuXNq2bcuJEye4evUqffv25X//k1MgQkNRd2BvQ4i6BU7FoM5msLTVOpVIBVWrVsXCwoLPPvuMQ4cOScEkhNBEkqccKFOmDK1bt2bUqFHAPxPJRUZGpmrAlCZTDmQSzx7C7lrw9Ao4esE7h8Ahv9apRAp69OgROXPmNN++fPkyJUqU0DCRECIj0XTKgRs3btCpUyfz7Xbt2hEXF8f9+/dTJIgQSaYPg731TQWTQwFoeEAKpkzEYDAwevRoihQpwpUrV8ztUjAJIbSW5KIpJiYGR0fHfw60sMDGxobo6OhUCSZEouKi4HArCDsP9nn+XoBXTtVkFg8ePODdd99lzJgxhIWF8dNPP2kdSQghzJI1EHzEiBE4ODiYb+v1esaNG4eLi4u5berUqSmXToh/i42A3z6A4P2m5VHqbgVn6X3ILH777TfatGnD/fv3cXBwYP78+bRv317rWEIIYZbkoqlOnTpcvnw5XluNGjW4ceOG+bZOLvMWqcUYB4dbmwomq2xQbzvkqKh1KpECjEYjkyZNYvjw4RgMBkqVKsWGDRsoXbq01tGEECKeJBdNBw4cSMUYQrzCmS/+XoDXHur/Crmra51IpJAlS5YwbNgwANq3b8/cuXPJli2bxqmEECIhmdBGpH/Xl8DFyabt6iukYMpkOnXqRIMGDViwYAErVqyQgkkIkW4le0ZwIdLUvV1wvLdpu+wIKNBK2zzijSmlWLNmDa1atcLa2hpra2v27Nkjp/eFEOme9DSJ9OvxKTj0IRj14NkKyo3WOpF4Q2FhYfj6+tKuXTvzDN8g4yGFEBmD9DSJ9CnqDhz8AAzR4NEIaqyS5VEyuNOnT+Pr68v169extraWWb2FEBmOFE0i/Yl9CgebQfRdcCkNtdaBpY3WqcRrUkqxcOFC+vfvT0xMDAULFmTdunVUqVJF62hCCJEsr/Wn+6FDh2jfvj3Vq1fn7t27AKxcuZLDhw+naDiRBRnj4FAreBIAtrmg7jawcXnlYSJ9ioiIoEOHDnzyySfExMTw/vvvc+rUKSmYhBAZUrKLpp9++onGjRtjb2/P6dOniYmJAUxjFcaPH5/iAUUWohSc6AdBv4KVI9TbAdm8tE4l3sD9+/fZsmULlpaWfPvtt2zZsoUcOXJoHUsIIV5LsoumsWPHMm/ePBYuXIi1tbW5vWbNmpw6dSpFw4ks5spMuDYP0EH1HyBnJa0TiTdUrFgxVq5cyb59+xgyZAgWFjIuTQiRcSX7N9jly5epU6dOgnYXFxdCQ0NTIpPIioL2wamBpu0Kk8CzhaZxxOuJjo6mV69e7N+/39zWokWLRH9nCCFERpPsosnDw4Nr164laD98+DCFCxdOkVAiiwm/alqEVxnA62MoOVDrROI1XL16lerVq5vXjJPFvIUQmU2yi6YePXrw2Wef8ccff6DT6bh37x6rVq1i8ODB9O7dOzUyisws+j7sfwf0TyBnFai6CGTOngxn/fr1+Pj4cObMGXLnzs3y5cuxt7fXOpYQQqSoZE85MGzYMIxGI2+//TZRUVHUqVMHW1tbBg8eTL9+/VIjo8is4qLhwPsQ+Rc4FYM6P4OlndapRDLExMTwv//9j5kzZwJQu3Zt1qxZQ968eTVOJoQQKU+nlFKvc6Ber+fatWtERERQunTpDLNeVHh4OC4uLoSFheHs7Kx1nKxLKfDvBDdXmqYWaPwHZJPTuxlJeHg4DRs25Pjx4wAMHTqUsWPHYmUl078JIbSXGp/3r/3bzcbGhtKlS6dICJEFXZ5hKph0llBzjRRMGZCTkxNFixbl2rVrrFixgvfff1/rSEIIkaqSXTTVr1//petE7du3740CiSzgwWE4Pci0XWEyeLytbR6RZLGxsTx79gwnJyd0Oh0LFizg0aNHsiSKECJLSPZAcG9vb8qXL2/+Kl26NHq9nlOnTlGuXLnXCjF79my8vLyws7OjatWqHDt2LEnHrVmzBp1OR4sWLV7reYUGou7A4ZagjFCwHZT4TOtEIonu3r1LgwYN6NixI8/P6mfLlk0KJiFElpHsnqZp06Yl2j569GgiIiKSHWDt2rUMHDiQefPmUbVqVaZPn07jxo25fPkybm5uLzzu5s2bDB48mNq1ayf7OYVGDDFwyBeePYDsb0HVBXKlXAaxe/du2rVrR0hICE5OTly9epXixYtrHUsIIdJUik3P2759e5YsWZLs46ZOnUqPHj3o0qULpUuXZt68eTg4OLz0sQwGAx9//DFjxoyRuaEykpOfwaOjYJ0d6mwyLZUi0jWDwcDo0aNp3LgxISEhlC9fnlOnTknBJITIklKsaPL398fOLnmXi+v1ek6ePEnDhg3/CWRhQcOGDfH393/hcV9//TVubm5069bttfOKNHZjGVybD+ig5moZ+J0BBAcH07hxY8aMGYNSip49e+Lv70/RokW1jiaEEJpI9um5jz76KN5tpRT379/nxIkTjBgxIlmPFRISgsFgwN3dPV67u7s7ly5dSvSYw4cPs3jxYgICApL0HDExMeZFhcF0CaJIYyHH4PjfE5+WGwN5m2ibR7ySUooWLVpw9OhRHBwczLN8CyFEVpbsniYXF5d4Xzly5KBevXps376dUaNGpUZGs6dPn9KhQwcWLlxIrly5knTMhAkT4uX19PRM1YziP6LuwaEWYHgGed+HMl9qnUgkgU6nY9q0aZQvX57jx49LwSSEECRzckuDwcCRI0coV64crq6ub/zker0eBwcHNmzYEO8KuE6dOhEaGsqWLVvi7R8QEECFChWwtLQ0txmNRsB0Wu/y5csUKVIk3jGJ9TR5enrK5JZpIS4a9taDR8fApQw0+h2s5XueXj169IiTJ0/SqFEjc5vRaMTCIsXO4gshRJpJjcktk/Xb0NLSkkaNGhEaGpoiT25jY4OPjw979+41txmNRvbu3Uv16tUT7F+yZEnOnTtHQECA+euDDz6gfv36BAQEJNqLZGtri7Ozc7wvkQaUMp2Se3QMbHJAnc1SMKVjR48epUKFCjRv3pyzZ8+a26VgEkKIfyR7TFPZsmW5ceMGhQoVSpEAAwcOpFOnTlSqVIkqVaowffp0IiMj6dKlCwAdO3YkX758TJgwATs7O8qWLRvv+OzZs5tziXQkcAUELgedBdRaD04yeDg9UkoxY8YM/ve//xEXFyeDvIUQ4iWSXTSNHTuWwYMH88033+Dj44OjY/zLxpPbk+Pn58fDhw8ZOXIkQUFBeHt7s3PnTvPg8Fu3bslfuxnNg0NwrKdpu+xo8GigaRyRuLCwMLp27crGjRsBaNWqFYsXL5beWCGEeIEkj2n6+uuvGTRoEE5OTv8c/K+JCZVS6HQ6DAZDyqdMQbJgbyqLvg/by0PMQ/D8CGquAwvLVx8n0tTp06fx9fXl+vXrWFtbM2XKFD799NOXLpEkhBAZiaYL9o4ZM4ZevXqxf//+FHlikQkZ9HColalgyl4Oqq+Qgimd+vnnn7l+/ToFCxZk3bp1VKlSRetIQgiR7iW5aHreIVW3bt1UCyMyuNODIeR304zfNdfJjN/p2FdffYVSiv79+5MjRw6t4wghRIaQrMFC0nUvXijwB7gy07RdfQW4lNQ2j4jn/PnztGvXjmfPngGmK2FHjx4tBZMQQiRDsgaCFy9e/JWF0+PHj98okMiAHp+CP7qbtsuOgPzNtM0j4lm5ciW9evUiKiqKAgUKMHHiRK0jCSFEhpSsomnMmDG4uLikVhaREelD4bAvGGMg73tQNnVnhRdJFx0dTf/+/Vm0aBEADRs2ZODAgRqnEkKIjCtZRVObNm1wc3NLrSwio1EKjnaFiBvg6AU1VsrA73TiypUr+Pr6cvbsWXQ6HaNHj2b48OHxZtMXQgiRPEkummQ8k0jg8nS4swksbEwTWNq8+dI64s39+uuvtGrViqdPn+Lm5sbq1at5++23tY4lhBAZXrKvnhMCgIf+cHqIabviVMhZSds8wqx48eJYWlpSp04dfvzxR/Lmzat1JCGEyBSSXDQ9XxhXCGLD4fePQcVBAT8o1kfrRFleeHi4efI2Ly8vDh8+TIkSJbCySvak/0IIIV5A1icRyaOM4N8JIgPBsSBUXQBy6lZTP//8M4UKFWL79u3mtjJlykjBJIQQKUyKJpE8Z0fCnc2mcUw1fgRrWYpGK7GxsQwZMoTmzZvz+PFjZs2apXUkIYTI1KRoEkl3eyOcH2farroIclfXNk8WdufOHerXr8+kSZMA+Pzzz9m8ebO2oYQQIpOT/nuRNBE3TNMLAJQcBIU6aJsnC/v111/5+OOPCQkJwdnZmSVLltCyZUutYwkhRKYnRZN4NUMMHPaD2DDIVQO8J2idKMs6d+4c7777LkopvL29Wb9+PUWLFtU6lhBCZAlSNIlXCxgKj0+ATQ6o+SNYWGudKMsqV64c3bt3x8LCgunTp2NnZ6d1JCGEyDKkaBIvd3sTXJ5h2q6+HBwLaJsnCzp06BAlSpQwz8Y/d+5cmdlbCCE0IAPBxYtFBMLRLqbtUv+DfO9rmyeLMRqNTJgwgXr16tG+fXsMBgOAFExCCKER6WkSiTPo/zWOqTqUH6d1oizl0aNHdOzY0Tz3koeHB7GxsVIwCSGEhqRoEokLGAqPj5vWk6u5RsYxpaGjR4/SunVrbt++jZ2dHTNnzqRbt26y/qMQQmhMTs+JhO78bFqMF6CajGNKK0oppk+fTu3atbl9+zbFihXj6NGjdO/eXQomIYRIB6RoEvE9vQa/tzdtF+8H+ZtpmycLiYqKYvbs2cTFxeHr68uJEycoX7681rGEEEL8TU7PiX8Y9HCkLcQ9hdy1oOIUrRNlKY6Ojqxfv57Dhw/Tt29f6V0SQoh0Room8Y9zI/+ej8kVaqyWcUypTCnFwoULMRgM9O7dGwBvb2+8vb21DSaEECJRUjQJk6C9cOE703bVReDoqW2eTC4iIoJevXqxatUqrK2tqVevHqVKldI6lhBCiJeQoknAsxDw7wAoKNoTPD/SOlGmdv78eVq1asWlS5ewtLRk3LhxlCxZUutYQgghXkGKpqxOKfijK0TfB+eSUHGq1okytRUrVtC7d2+ioqLImzcva9eupVatWlrHEkIIkQRy9VxWd3UO3P0FLGxM8zFZOWqdKFNSStG7d286depEVFQU77zzDgEBAVIwCSFEBiJFU1YWeg5ODTJte38HrnJ5e2rR6XQULlwYnU7H119/zY4dO8idO7fWsYQQQiSDTimltA6RlsLDw3FxcSEsLAxnZ2et42gnLhp2VYaw85CnCdTbBnKJe4qLiIggW7ZsgGktuYCAACpWrKhxKiGEyPxS4/NeepqyqtODTQWTnTtUXyYFUwqLiYmhX79+VK1alcjISAAsLCykYBJCiAxMiqas6M4W01gmgOorwM5N2zyZTGBgILVq1WLWrFlcuHCBHTt2aB1JCCFECpCiKauJugtHu5q2Sw6CPI20zZPJ/Pzzz1SsWJETJ06QI0cOtm3bRqtWrbSOJYQQIgVI0ZSVGA3wezvQPwbXClB+nNaJMo3Y2FiGDBlC8+bNCQ0NpWrVqpw+fZqmTZtqHU0IIUQKkaIpK7k8Ax78BlbZoOaPYGmrdaJMY+jQoUyaNAmAzz//nN9++40CBQponEoIIURKkqIpqwi7CGe+NG1XnArOJbTNk8kMHjyY4sWL89NPPzFt2jRsbGy0jiSEECKFyYzgWYExDvw7gTHGNL1Ake5aJ8rwDAYDu3btMp9+y5s3L+fPn8fKSv5LCSFEZiU9TVnBuTHw+DhYZzctxivTC7yR4OBgGjVqxHvvvceGDRvM7VIwCSFE5ia/5TO7h7/D+b8HfFeeCw55tc2TwR08eJA2bdoQFBSEo6MjBoNB60hCCCHSiPQ0ZWaxEeDfEVDg1QG82midKMMyGo1MmDCBBg0aEBQURJkyZTh+/Dh+fn5aRxNCCJFGpKcpMzs1ACKug4MnVPpe6zQZ1qNHj+jYsSPbt28HoGPHjsyZMwdHR1ncWAghshLpacqs7vwM1xcBOtOs3zbZtU6UYf3+++9s374dOzs7Fi1axLJly6RgEkKILEh6mjKjqLvwRzfTdqlB4F5P0zgZXbNmzfjuu+9o1KgR5cuX1zqOEEIIjUhPU2ajFJz8HGJCwNUb3hqrdaIMJzQ0lG7dunH37l1z2//+9z8pmIQQIouTnqbM5voiuL0BdJZQbZnM+p1Mp06dwtfXlxs3bnDz5k327NmDTqZoEEIIgfQ0ZS7PHsKpQabtt8aCq/SMJJVSivnz51OjRg1u3LiBl5cXEydOlIJJCCGEmfQ0ZSanBkHcU3CtCKWHaJ0mw4iIiOCTTz5h9erVgGkM0/Lly3F1ddU4mRBCiPREiqbMImgv3FwJ6KDyHNBJJ2JS3Lx5kyZNmnDp0iUsLS2ZOHEigwYNkh4mIYQQCUjRlBnERcIfPUzbxfpArqra5slA3NzcsLKyIl++fKxdu5aaNWtqHUkIIUQ6JUVTZnD6fxAZCA4FwHuC1mnSvejoaGxtbbGwsMDBwYHNmzfj7OxM7ty5tY4mhBAiHZNzOBld0B64Ote0XW0JWDtpmyedu3LlCtWqVeO7774ztxUpUkQKJiGEEK8kRVNGFvsUjv49iWWxPuDxtrZ50rl169ZRqVIlzp49y8yZM4mIiNA6khBCiAxEiqaMLGAYRN0CRy/w/lbrNOlWTEwM/fr1w8/Pj6dPn1KnTh1OnDhBtmzZtI4mhBAiA5GiKaN6eOSf03JVF4G1FACJCQwMpFatWsyaNQuAL774gr1795InTx6NkwkhhMhoZCB4RhQXBb+3BxQU6iin5V4gKiqKGjVqEBQURI4cOVi5ciVNmzbVOpYQQogMSnqaMqI/v4HIm6ar5SrN1DpNuuXg4MCIESOoVq0ap0+floJJCCHEG9EppZTWIdJSeHg4Li4uhIWF4ezsrHWc5As9BzsqgoqDOpshf3OtE6Urd+7cITQ0lLJlywKm5VHi4uKwtrbWOJkQQoi0lBqf99LTlJEoI/zR01Qw5W8hBdN//Prrr1SoUIHmzZsTFhYGgE6nk4JJCCFEipCiKSO5vhgeHQUrJ6g0S+s06YbBYGDkyJG8++67hISEmP+yEEIIIVKSDATPKGIemaYYAHjra3DIp22edCI4OJh27dqxb98+AHr16sW0adOws7PTOJkQQojMRoqmjOLcaNA/huzloHhfrdOkCwcPHqRNmzYEBQXh6OjIggULaNeundaxhBBCZFJSNGUET87C1Xmm7YrTwELG6ABMnjyZoKAgypQpw4YNGyhZsqTWkYQQQmRiMqYpvTPGwR/d/hn87d5A60TpxtKlSxk4cCB//PGHFExCCCFSnRRN6d3VOfD4BFi7QOU5oNNpnUgz/v7+fPnll+bbuXLlYsqUKTg6OmqYSgghRFYhp+fSM/0TODfGtO09Eeyz5tIfSilmzJjB//73P+Li4ihfvjx+fn5axxJCCJHFSNGUnp0abBr87VIGinTXOo0mQkND6dq1K5s2bQKgdevWNGnSRONUQgghsiIpmtKrh0fgxhLTduV5YJH13qpTp07h6+vLjRs3sLa2Ztq0afTp0wddFj5FKYQQQjvpYkzT7Nmz8fLyws7OjqpVq3Ls2LEX7rtw4UJq166Nq6srrq6uNGzY8KX7Z0iGGNPgb4Ai3cCtlrZ5NLBixQpq1KjBjRs38PLy4siRI/Tt21cKJiGEEJrRvGhau3YtAwcOZNSoUZw6dYry5cvTuHFjHjx4kOj+Bw4coG3btuzfvx9/f388PT1p1KgRd+/eTePkqejCtxB+Gew8oMIkrdNows3NDb1eT7NmzTh16hSVK1fWOpIQQogsTvMFe6tWrUrlypWZNcu0LIjRaMTT05N+/foxbNiwVx5vMBhwdXVl1qxZdOzY8ZX7p/sFeyP/gq0lwfAMaqwGr7ZaJ0ozz549izeT9+HDh6lZs6b0LgkhhEi2TLdgr16v5+TJkzRs2NDcZmFhQcOGDfH390/SY0RFRREbG0uOHDlSK2baOjXQVDC51YWCbbROk2aWL19OkSJFuHHjhrmtVq1aUjAJIYRINzQtmkJCQjAYDLi7u8drd3d3JygoKEmPMXToUPLmzRuv8Pq3mJgYwsPD432lW/d3w+2NoLOESjOzxJxM0dHRdO/enc6dO3Pv3j1zj6MQQgiR3mg+pulNTJw4kTVr1rBp06YXLtA6YcIEXFxczF+enp5pnDKJDHo42d+0XayvaY25TO7KlStUq1aNxYsXY2FhwTfffMPkyZO1jiWEEEIkStOiKVeuXFhaWhIcHByvPTg4GA8Pj5ceO3nyZCZOnMivv/7KW2+99cL9vvjiC8LCwsxft2/fTpHsKe7K9xB+CWxzw1tjtE6T6tauXYuPjw9nz57F3d2d3bt389VXX2FhkaHreCGEEJmYpp9QNjY2+Pj4sHfvXnOb0Whk7969VK9e/YXHfffdd3zzzTfs3LmTSpUqvfQ5bG1tcXZ2jveV7kTd+9fM39+CTXZN46S29evX06ZNGyIiIqhbty6nT5+mQQNZU08IIUT6pvmMiQMHDqRTp05UqlSJKlWqMH36dCIjI+nSpQsAHTt2JF++fEyYMAGAb7/9lpEjR7J69Wq8vLzMY5+yZctGtmzZNHsdbyRgKMRFQM6qULiT1mlS3QcffECVKlVo2LAhY8aMwcpK8x9DIYQQ4pU0/7Ty8/Pj4cOHjBw5kqCgILy9vdm5c6d5cPitW7finbKZO3cuer2eVq1axXucUaNGMXr06LSMnjIeHIKbPwA6qDQLdJnz9NTBgwepWbMmVlZW2NracujQIWxsbLSOJYQQQiSZ5vM0pbV0NU+T0QA7fSD0DBTpAVUXaJsnFcTGxvLll18yefJkhg8fztixY7WOJIQQIgtIjc97zXuasrRr800Fk40rlB+vdZoUd+fOHfz8/Pj9998B0/QCSimZe0kIIUSGJEWTVp49hDPDTdtvjQW7XNrmSWG7du2iffv2hISE4OzszLJly/jwww+1jiWEEEK8tsw5gCYjODMcYkMhe3ko+onWaVKMwWBgxIgRNGnShJCQECpUqMCpU6ekYBJCCJHhSdGkhUcn4Poi03alWWBhqW2eFBQYGMjUqVNRStG7d29+//13ihQponUsIYQQ4o3J6bm0poxw4lNAgVd7cKuldaIUVbRoURYuXAhAu3btNE4jhBBCpBwpmtLajeXw6A+wygYVvtM6zRszGo1MnDiRunXrUrNmTUCKJSGEEJmTFE1pSR8KZ4aZtsuNAvs8msZ5U48ePaJDhw7s2LGDfPnyceHCBe2ncRBCCCFSiRRNaencaHj2AJxLQvH+Wqd5I/7+/vj5+XH79m3s7Oz4+uuvpWASQgiRqclA8LQS+idcmWXa9vkeLDPmbNhKKaZNm0adOnW4ffs2xYoV448//qBr165aRxNCCCFSlfQ0pQWl4EQ/UAbw/AjyvKN1otcSHR3Nxx9/zKZNmwBo3bo1CxculB4mIYQQWYL0NKWFW+vhwQGwtIOKU7VO89rs7OzQ6XRYW1sza9Ys1qxZIwWTEEKILEN6mlJbXDSc/p9pu/QwcCyobZ5kUkqh1+uxtbVFp9OxZMkSrl27ho+Pj9bRhBBCiDQlPU2p7eociLoFDp5Q6n9ap0mWp0+f8vHHH9OhQweer+vs4uIiBZMQQogsSXqaUtOzB/DnWNN2uVFg5aBtnmQ4d+4cvr6+XL58GUtLS86cOYO3t7fWsYQQQgjNSE9TagoYZlpfzrUiFOqkdZokW7ZsGVWrVuXy5cvky5ePgwcPSsEkhBAiy5OiKbU8Pgk3lpm2K88Gi/TfqRcVFUXXrl3p0qUL0dHRNG7cmNOnT5tn+hZCCCGyMimaUoNSfw/+/nt9uVzVtE6UJB999BFLly7FwsKCb775hu3bt5M7d26tYwkhhBDpQvrv/siI7v8KwfvBwhbKj9M6TZJ9+eWX/Pnnn6xYsYIGDRpoHUcIIYRIV6RoSmlKwbmRpu3ifcGxgLZ5XiImJoaAgACqVq0KQJ06dbh27Rp2dnYaJxNCCCHSHzk9l9JurYdHx8DSAUoN0TrNCwUGBlKrVi3efvttLl68aG6XgkkIIYRInBRNKcmghzNfmLZLDwF7d23zvMCWLVuoWLEiJ06cwNbWlqCgIK0jCSGEEOmeFE0p6foiiLgBdh5QcpDWaRKIjY1l8ODBtGjRgtDQUKpVq8bp06epX7++1tGEEEKIdE/GNKWUmMdw9ivTdtkRYJ1N2zz/cfv2bfz8/PD39wdgwIABTJw4ERsbG42TCSGEEBmDFE0p5fw40D+B7OWgaE+t0ySwePFi/P39cXFxYenSpXz44YdaRxJCCCEyFCmaUkLkX3Bllmnb+7t0OZHlV199xYMHDxg0aBBFihTROo4QQgiR4ciYppRwbgwY9eDeAPI01joNAEFBQXz++efo9XoArKysmDNnjhRMQgghxGtKf10iGc3T6xC4wrRdfhzodNrmAQ4cOECbNm0IDg7G2tqaSZMmaR1JCCGEyPCkp+lNnR4EygB53tV8uRSj0cj48eN5++23CQ4OpkyZMnTr1k3TTEIIIURmIT1NbyL4INzZAjorqDBZ0yghISF06NCBnTt3AtCpUyfmzJmDg4ODprmEEEKIzEKKptf17+VSinSH7GU0i3LixAk+/PBD7ty5g52dHbNnz6Zr166a5RFCCCEyIymaXlfwXnjwm2lR3rLDNY2SPXt2wsLCKFasGBs2bOCtt97SNI8QQgiRGUnR9DqUgjMjTNtFPwGH/GkeITY2Fmtra1OEokXZuXMnZcuWxdnZOc2zCCGEEFmBDAR/Hfd2wKOjYGkPZb5I86c/efIkpUuXZs+ePea2GjVqSMEkhBBCpCIpmpLr32OZivcFe480fGrFnDlzqFGjBteuXeOrr75CKZVmzy+EEEJkZVI0JdedLfD4JFg5Qqkhafa0T58+pV27dvTt2xe9Xk/z5s3ZsWMHunQwL5QQQgiRFUjRlBxKwblRpu0Sn4Fd7jR52nPnzlGpUiXWrFmDpaUlkydPZtOmTbi6uqbJ8wshhBBCBoInz+2fIPQsWGWDkoPS5CmvXr1K1apViY6OJl++fKxdu5aaNWumyXMLIYQQ4h9SNCWVMQ7OfmXaLjkAbHOkydMWLVqUVq1a8eDBA1auXEnu3GnTuyWEEEKI+KRoSqrA5RB+GWxzQqnBqfpUly9fJnfu3OTIkQOdTseCBQuwsbHBwkLOpgohsg6DwUBsbKzWMUQ6ZWlpiZWVVZqO7ZWiKSkMejj3tWm79JdgnXqX9q9du5bu3btTr149tmzZgoWFBXZ2dqn2fEIIkR5FRERw584duUJYvJSDgwN58uTBxsYmTZ5PiqakuLEUom6BnQcU650qTxETE8PAgQOZM2cOYLpaLiIiQuZeEkJkOQaDgTt37uDg4EDu3LnlKmGRgFIKvV7Pw4cPCQwMpFixYmlyNkaKplcxPPvnirnSw8DKPsWf4saNG7Ru3ZqTJ08C8OWXXzJmzBisrOTtEUJkPbGxsSilyJ07N/b2Kf87V2QO9vb2WFtb89dff6HX69PkrIx8Kr/KzdXwLBgcCqRKL9PmzZvp3LkzYWFh5MiRgx9++IEmTZqk+PMIIURGIz1M4lXSeqyvFE0vY4iB8+NM28U/BcuUPWf67NkzBg4cSFhYGNWqVWPdunV4enqm6HMIIYQQImXI5VgvE7gSIm6AfZ5U6WWys7Nj7dq1DB48mIMHD0rBJIQQQqRjUjS9iNEAl6aYtksOAutsKfKwO3fuZOXKlebblStXZtKkSWk28l8IIUTq8vf3x9LSkvfeey/BfQcOHECn0xEaGprgPi8vL6ZPnx6vbf/+/TRt2pScOXPi4OBA6dKlGTRoEHfv3k2l9KazIH379iVnzpxky5aNli1bEhwc/MrjLl68yAcffICLiwuOjo5UrlyZW7dume+/fv06H374Iblz58bZ2ZnWrVu/8HFjYmLw9vZGp9MREBCQUi/tjUnR9CJ3NkH4JbBxhSLd3/jh4uLi+Oqrr2jatCk9evTg3LlzKRBSCCFEerN48WL69evHb7/9xr179177cebPn0/Dhg3x8PDgp59+4sKFC8ybN4+wsDCmTJmSgonjGzBgAL/88gvr16/n4MGD3Lt3j48++uilx1y/fp1atWpRsmRJDhw4wNmzZxkxYoR5cHZkZCSNGjVCp9Oxb98+jhw5gl6vp1mzZhiNxgSPN2TIEPLmzZsqr++NqCwmLCxMASosLOzlO+6up9QqlAoY/sbPef/+fVWvXj0FKED17t1bRUdHv/HjCiFEZhQdHa0uXLiQIX9PPn36VGXLlk1dunRJ+fn5qXHjxsW7f//+/QpQT548SXBswYIF1bRp05RSSt2+fVvZ2Niozz//PNHnSez4lBAaGqqsra3V+vXrzW0XL15UgPL393/hcX5+fqp9+/YvvH/Xrl3KwsIi3mdvaGio0ul0avfu3fH23b59uypZsqQ6f/68AtTp06df+Lgv+1lJ8ud9MkhPU2LCLsKDA6CzhKKfvNFDHThwAG9vbw4cOICjoyOrV69mzpw5MmGlEEIklVIQF6nNVzIn11y3bh0lS5akRIkStG/fniVLlrzWBJ3r169Hr9czZMiQRO/Pnj37C49t0qQJ2bJle+FXmTJlXnjsyZMniY2NpWHDhua2kiVLUqBAAfz9/RM9xmg0sm3bNooXL07jxo1xc3OjatWqbN682bxPTEwMOp0OW1tbc5udnR0WFhYcPnzY3BYcHEyPHj1YuXIlDg4OL8ypFbl6LjGXvzf9m/c9cHz9wdkTJ05k+PDhGI1GypYty/r16ylZsmQKhRRCiCzCEAXrUmZcabK1jgArxyTvvnjxYtq3bw/Au+++S1hYGAcPHqRevXrJetqrV6/i7OxMnjx5knUcwKJFi4iOjn7h/dbW1i+8LygoCBsbmwRFmbu7O0FBQYke8+DBAyIiIpg4cSJjx47l22+/ZefOnXz00Ufs37+funXrUq1aNRwdHRk6dCjjx49HKcWwYcMwGAzcv38fME1Y2blzZ3r16kWlSpW4efNmsl97apOi6b+ePTDNAA5QcuAbPVRsbCxGo5EuXbowa9asdFk1CyGESBmXL1/m2LFjbNq0CQArKyv8/PxYvHhxsosmpdRrz1OVL1++1zrudT0fk9S8eXMGDBgAgLe3N7///jvz5s2jbt265M6dm/Xr19O7d2++//57LCwsaNu2LRUrVjTPtTRz5kyePn3KF198kab5k0OKpv+6MgeMMZCzCrjVSfbhcXFx5pm8hw8fjo+PD02bNk3plEIIkXVYOph6fLR67iRavHgxcXFx8QYwK6WwtbVl1qxZuLi4mJfGCgsLS9CbExoaiouLCwDFixcnLCyM+/fvJ7u3qUmTJhw6dOiF9xcsWJDz588nep+Hhwd6vZ7Q0NB4+YKDg/Hw8Ej0mFy5cmFlZUXp0qXjtZcqVSreqbdGjRpx/fp1QkJCsLKyInv27Hh4eFC4cGEA9u3bh7+/f7xTeACVKlXi448/Zvny5S993WlBiqZ/i4uGq7NN2yUHQTKqfKUUU6dO5ccff+TQoUPY29tjYWEhBZMQQrwpnS5Zp8i0EBcXx4oVK5gyZQqNGjWKd1+LFi348ccf6dWrl3mNtJMnT1KwYEHzPjdu3CAsLIzixYsD0KpVK4YNG8Z3333HtGnTEjzff4uaf3uT03M+Pj5YW1uzd+9eWrZsCZh60G7dukX16tUTPcbGxobKlStz+fLleO1XrlyJ9xqfy5UrF2Aqkh48eMAHH3wAwPfff8/YsWPN+927d4/GjRuzdu1aqlat+sLMaSrFhpRnEC8dTX9phumKuc1eShlik/yYT548Uc2bNzdfHTd//vwUTCyEEFlLRrx6btOmTcrGxkaFhoYmuG/IkCGqUqVK5ts9e/ZUXl5easuWLerGjRvq4MGDqlq1aqpatWrKaDSa95s9e7bS6XSqa9eu6sCBA+rmzZvq8OHDqmfPnmrgwIGp9lp69eqlChQooPbt26dOnDihqlevrqpXrx5vnxIlSqiNGzeab2/cuFFZW1urBQsWqKtXr6qZM2cqS0tLdejQIfM+S5YsUf7+/uratWtq5cqVKkeOHC99HYGBgenu6jkpmp6LjVLqJw9T0XQ16UXPiRMnVKFChRSgbGxs1OzZs+P90AshhEiejFg0vf/++6pp06aJ3vfHH38oQJ05c0YpZXp9o0aNUiVLllT29vaqUKFCqmfPnurhw4cJjt29e7dq3LixcnV1VXZ2dqpkyZJq8ODB6t69e6n2WqKjo1WfPn2Uq6urcnBwUB9++KG6f/9+vH0AtXTp0nhtixcvVkWLFlV2dnaqfPnyavPmzfHuHzp0qHJ3d1fW1taqWLFiasqUKS/9vEyPRZNOqde4FjIDCw8Px8XFhbCwMPO5ZQAuz4KT/UwL8za7+sp15pRSzJ07lwEDBqDX6ylUqBDr16/Hx8cnlV+BEEJkbs+ePSMwMJBChQrJ9CzipV72s/LCz/s3IPM0ARhj4eK3pu3SQ5O0MO+4cePo27cver2e5s2bc/LkSSmYhBBCiExMiiaAmz9C1B2w84Ai3ZJ0SMeOHXF3d2fKlCls2rQJV1fXVA4phBBCCC3J1XNK/Wth3s/B0vaFu/67N6lAgQJcu3aNbNk0mnBNCCGEEGlKepoeHoLQs2BpD0V7JrpLVFQUXbt2pVKlSvz888/mdimYhBBCiKxDepouzzD9W6gD2CQ8xXb58mVatWrFn3/+iYWFBdevX0/jgEIIIYRID7J20RT5F9zZbNou3i/B3WvWrKFHjx5ERETg7u7Ojz/+SP369dM2oxBCZFFZ7OJu8RrS+mcka5+euzoflBHc6kH2submZ8+e0adPH9q2bUtERAT16tUjICBACiYhhEgDlpaWAOj1eo2TiPQuKioKePks5ykp6/Y0xUXC1Tmm7RL94921Z88e5s6dC8BXX33FqFGjzOvJCSGESF1WVlY4ODjw8OFDrK2tzQu6CvGcUoqoqCgePHhA9uzZzYV2asu6lcCtnyA2DLIVhvzN4931/vvv88UXX1CnTh3effddjQIKIUTWpNPpyJMnD4GBgfz1119axxHp2PNFf9NK1i2ari8w/Vu0F7FxBsaOHUOfPn1wd3cHYPz48RqGE0KIrM3GxoZixYrJKTrxQtbW1mnWw/RcuiiaZs+ezaRJkwgKCqJ8+fLMnDmTKlWqvHD/9evXM2LECG7evEmxYsX49ttvadq0afKe9Mk5cLLjtk1j/OrWxd/fnyNHjrB79250Ot0bviIhhBBvysLCQpZREemK5ieK165dy8CBAxk1ahSnTp2ifPnyNG7cmAcPHiS6/++//07btm3p1q0bp0+fpkWLFrRo0YI///wz2c+9814tKlRrgL+/Py4uLnz66adSMAkhhBAiUZov2Fu1alUqV67MrFmzADAajXh6etKvXz+GDRuWYH8/Pz8iIyPZunWrua1atWp4e3szb968Vz7f8wX8BjeFydtNbRUrVmT9+vUULlw4ZV6UEEIIITSV6Rbs1ev1nDx5koYNG5rbLCwsaNiwIf7+/oke4+/vH29/gMaNG79w/xd5XjD16dOHI0eOSMEkhBBCiJfSdExTSEgIBoPBPPj6OXd3dy5dupToMUFBQYnuHxQUlOj+MTExxMTEmG+HhYUBYG9rxczZ8/D19UWv18tgQyGEECITCQ8PB1J2Asx0MRA8NU2YMIExY8YkaI+OiaN79+50795dg1RCCCGESAuPHj3CxcUlRR5L06IpV65cWFpaEhwcHK89ODj4hfMueHh4JGv/L774goEDB5pvh4aGUrBgQW7dupVi30Tx5sLDw/H09OT27dspdu5ZvBl5T9IneV/SH3lP0qewsDAKFChAjhw5UuwxNS2abGxs8PHxYe/evbRo0QIwDQTfu3cvn376aaLHVK9enb179/L555+b23bv3k316tUT3d/W1hZbW9sE7S4uLvLDnQ45OzvL+5LOyHuSPsn7kv7Ie5I+peSM8pqfnhs4cCCdOnWiUqVKVKlShenTpxMZGUmXLl0A6NixI/ny5WPChAkAfPbZZ9StW5cpU6bw3nvvsWbNGk6cOMGCBQu0fBlCCCGEyOQ0L5r8/Px4+PAhI0eOJCgoCG9vb3bu3Gke7H3r1q14VWKNGjVYvXo1X331FV9++SXFihVj8+bNlC1b9kVPIYQQQgjxxjQvmgA+/fTTF56OO3DgQII2X19ffH19X+u5bG1tGTVqVKKn7IR25H1Jf+Q9SZ/kfUl/5D1Jn1LjfdF8ckshhBBCiIxA82VUhBBCCCEyAimahBBCCCGSQIomIYQQQogkyJRF0+zZs/Hy8sLOzo6qVaty7Nixl+6/fv16SpYsiZ2dHeXKlWP79u1plDRrSc77snDhQmrXro2rqyuurq40bNjwle+jSL7k/l95bs2aNeh0OvP8aiJlJfd9CQ0NpW/fvuTJkwdbW1uKFy8uv8dSWHLfk+nTp1OiRAns7e3x9PRkwIABPHv2LI3SZg2//fYbzZo1I2/evOh0OjZv3vzKYw4cOEDFihWxtbWlaNGiLFu2LHlPqjKZNWvWKBsbG7VkyRJ1/vx51aNHD5U9e3YVHByc6P5HjhxRlpaW6rvvvlMXLlxQX331lbK2tlbnzp1L4+SZW3Lfl3bt2qnZs2er06dPq4sXL6rOnTsrFxcXdefOnTROnnkl9z15LjAwUOXLl0/Vrl1bNW/ePG3CZiHJfV9iYmJUpUqVVNOmTdXhw4dVYGCgOnDggAoICEjj5JlXct+TVatWKVtbW7Vq1SoVGBiodu3apfLkyaMGDBiQxskzt+3bt6vhw4erjRs3KkBt2rTppfvfuHFDOTg4qIEDB6oLFy6omTNnKktLS7Vz584kP2emK5qqVKmi+vbta75tMBhU3rx51YQJExLdv3Xr1uq9996L11a1alX1ySefpGrOrCa578t/xcXFKScnJ7V8+fLUipjlvM57EhcXp2rUqKEWLVqkOnXqJEVTKkju+zJ37lxVuHBhpdfr0ypilpPc96Rv376qQYMG8doGDhyoatasmao5s7KkFE1DhgxRZcqUidfm5+enGjdunOTnyVSn5/R6PSdPnqRhw4bmNgsLCxo2bIi/v3+ix/j7+8fbH6Bx48Yv3F8k3+u8L/8VFRVFbGxsiq4hlJW97nvy9ddf4+bmRrdu3dIiZpbzOu/Lzz//TPXq1enbty/u7u6ULVuW8ePHYzAY0ip2pvY670mNGjU4efKk+RTejRs32L59O02bNk2TzCJxKfF5ny4mt0wpISEhGAwG82ziz7m7u3Pp0qVEjwkKCkp0/6CgoFTLmdW8zvvyX0OHDiVv3rwJfuDF63md9+Tw4cMsXryYgICANEiYNb3O+3Ljxg327dvHxx9/zPbt27l27Rp9+vQhNjaWUaNGpUXsTO113pN27doREhJCrVq1UEoRFxdHr169+PLLL9MisniBF33eh4eHEx0djb29/SsfI1P1NInMaeLEiaxZs4ZNmzZhZ2endZws6enTp3To0IGFCxeSK1cureOIfzEajbi5ubFgwQJ8fHzw8/Nj+PDhzJs3T+toWdaBAwcYP348c+bM4dSpU2zcuJFt27bxzTffaB1NvKFM1dOUK1cuLC0tCQ4OjtceHByMh4dHosd4eHgka3+RfK/zvjw3efJkJk6cyJ49e3jrrbdSM2aWktz35Pr169y8eZNmzZqZ24xGIwBWVlZcvnyZIkWKpG7oLOB1/q/kyZMHa2trLC0tzW2lSpUiKCgIvV6PjY1NqmbO7F7nPRkxYgQdOnSge/fuAJQrV47IyEh69uzJ8OHD462nKtLOiz7vnZ2dk9TLBJmsp8nGxgYfHx/27t1rbjMajezdu5fq1asnekz16tXj7Q+we/fuF+4vku913heA7777jm+++YadO3dSqVKltIiaZST3PSlZsiTnzp0jICDA/PXBBx9Qv359AgIC8PT0TMv4mdbr/F+pWbMm165dMxexAFeuXCFPnjxSMKWA13lPoqKiEhRGz4taJSuXaSZFPu+TP0Y9fVuzZo2ytbVVy5YtUxcuXFA9e/ZU2bNnV0FBQUoppTp06KCGDRtm3v/IkSPKyspKTZ48WV28eFGNGjVKphxIBcl9XyZOnKhsbGzUhg0b1P37981fT58+1eolZDrJfU/+S66eSx3JfV9u3bqlnJyc1KeffqouX76stm7dqtzc3NTYsWO1egmZTnLfk1GjRiknJyf1448/qhs3bqhff/1VFSlSRLVu3Vqrl5ApPX36VJ0+fVqdPn1aAWrq1Knq9OnT6q+//lJKKTVs2DDVoUMH8/7Ppxz43//+py5evKhmz54tUw4opdTMmTNVgQIFlI2NjapSpYo6evSo+b66deuqTp06xdt/3bp1qnjx4srGxkaVKVNGbdu2LY0TZw3JeV8KFiyogARfo0aNSvvgmVhy/6/8mxRNqSe578vvv/+uqlatqmxtbVXhwoXVuHHjVFxcXBqnztyS857Exsaq0aNHqyJFiig7Ozvl6emp+vTpo548eZL2wTOx/fv3J/o58fy96NSpk6pbt26CY7y9vZWNjY0qXLiwWrp0abKeU6eU9BUKIYQQQrxKphrTJIQQQgiRWqRoEkIIIYRIAimahBBCCCGSQIomIYQQQogkkKJJCCGEECIJpGgSQgghhEgCKZqEEEIIIZJAiiYhhBBCiCSQokkIkSTLli0je/bsWsd4bTqdjs2bN790n86dO9OiRYs0ySOEyHikaBIiC+ncuTM6nS7B17Vr17SOxrJly8x5LCwsyJ8/P126dOHBgwcp8vj379+nSZMmANy8eROdTkdAQEC8fWbMmMGyZctS5PleZPTo0ebXaWlpiaenJz179uTx48fJehwp8IRIe1ZaBxBCpK13332XpUuXxmvLnTu3Rmnic3Z25vLlyxiNRs6cOUOXLl24d+8eu3bteuPH9vDweOU+Li4ub/w8SVGmTBn27NmDwWDg4sWLdO3albCwMNauXZsmzy+EeD3S0yREFmNra4uHh0e8L0tLS6ZOnUq5cuVwdHTE09OTPn36EBER8cLHOXPmDPXr18fJyQlnZ2d8fHw4ceKE+f7Dhw9Tu3Zt7O3t8fT0pH///kRGRr40m06nw8PDg7x589KkSRP69+/Pnj17iI6Oxmg08vXXX5M/f35sbW3x9vZm586d5mP1ej2ffvopefLkwc7OjoIFCzJhwoR4j/389FyhQoUAqFChAjqdjnr16gHxe28WLFhA3rx5MRqN8TI2b96crl27mm9v2bKFihUrYmdnR+HChRkzZgxxcXEvfZ1WVlZ4eHiQL18+GjZsiK+vL7t37zbfbzAY6NatG4UKFcLe3p4SJUowY8YM8/2jR49m+fLlbNmyxdxrdeDAAQBu375N69atyZ49Ozly5KB58+bcvHnzpXmEEEkjRZMQAgALCwu+//57zp8/z/Lly9m3bx9Dhgx54f4ff/wx+fPn5/jx45w8eZJhw4ZhbW0NwPXr13n33Xdp2bIlZ8+eZe3atRw+fJhPP/00WZns7e0xGo3ExcUxY8YMpkyZwuTJkzl79iyNGzfmgw8+4OrVqwB8//33/Pzzz6xbt47Lly+zatUqvLy8En3cY8eOAbBnzx7u37/Pxo0bE+zj6/v/du43pOmujQP491k0XXMzTKStjChz9EZlpaAGkmYOMkTzUWugkVlo/qEwkjCnhFaE9iL6o4WKJmoGUTB0ECisBWWZCpmaNpNIijIUybm1Xc+L8EfTzdZzw3PfD10f8MU5v3POrnP2Yhe/c+G/8eXLF3R3dwt909PT6OrqglarBQAYjUZkZmaiqKgIQ0NDqK2tRWNjIyorKz3e48TEBAwGA8RisdDncDiwceNGdHR0YGhoCGVlZTh79izu3r0LACguLkZaWho0Gg2mpqYwNTWFqKgo2Gw2JCQkQCaTwWg0wmQywcfHBxqNBlar1eOYGGNuEGPsj5GVlUWrVq0iqVQq/KWmproc29HRQevWrRPaDQ0N5OvrK7RlMhk1Nja6nJudnU3Hjh1z6jMajSQSiWh+ft7lnKXrj46OUnBwMO3cuZOIiJRKJVVWVjrNCQ8Pp7y8PCIiKigooNjYWHI4HC7XB0D3798nIiKz2UwA6OXLl05jsrKyKCkpSWgnJSXRkSNHhHZtbS0plUqy2+1ERBQXF0dVVVVOazQ3N5NCoXAZAxGRTqcjkUhEUqmUvL29CQABoJqaGrdziIhOnDhBBw4ccBvr4merVCqnM1hYWCCJREIGg2HF9Rljv8Y1TYz9YXbv3o0bN24IbalUCuDHW5cLFy5geHgYs7Oz+P79OywWC759+4Y1a9YsW+fUqVM4evQompubhSumrVu3AvhxdTc4OIiWlhZhPBHB4XDAbDZj+/btLmObmZmBj48PHA4HLBYLdu3ahdu3b2N2dhYfPnxAdHS00/jo6GgMDAwA+HG1Fh8fD5VKBY1Gg8TEROzdu/cvnZVWq0VOTg6uX78OLy8vtLS0ICMjAyKRSNinyWRyerNkt9tXPDcAUKlUePjwISwWC+7cuYP+/n4UFBQ4jbl27Rrq6+sxOTmJ+fl5WK1WhIWFrRjvwMAAxsbGIJPJnPotFgvGx8f/ixNgjP2MkybG/jBSqRRBQUFOfRMTE0hMTERubi4qKyvh5+eHx48fIzs7G1ar1eWPf3l5OQ4dOgS9Xo/Ozk7odDq0tbUhOTkZc3NzOH78OAoLC5fN27Rpk9vYZDIZ+vr6IBKJoFAoIJFIAACzs7O/3JdarYbZbEZnZycePXqEtLQ07NmzB/fu3fvlXHf2798PIoJer0d4eDiMRiOuXLkiPJ+bm0NFRQVSUlKWzfX29na7rlgsFr6DixcvYt++faioqMD58+cBAG1tbSguLkZ1dTUiIyMhk8lw+fJlPH36dMV45+bmsGPHDqdkddE/pdifsf9nnDQxxvDixQs4HA5UV1cLb1EW62dWEhwcjODgYJw8eRIHDx5EQ0MDkpOToVarMTQ0tCw5+xWRSORyjlwuh1KphMlkQkxMjNBvMpkQERHhNC49PR3p6elITU2FRqPB9PQ0/Pz8nNZbrB+y2+0rxuPt7Y2UlBS0tLRgbGwMKpUKarVaeK5WqzEyMvLb+1yqtLQUsbGxyM3NFfYZFRWFvLw8YczSN0VisXhZ/Gq1Gu3t7QgICIBcLv9LMTHGluNCcMYYgoKCYLPZcPXqVbx9+xbNzc24efOm2/Hz8/PIz89HT08P3r17B5PJhN7eXuHa7cyZM3jy5Any8/PR39+PN2/e4MGDB79dCP6z06dP49KlS2hvb8fIyAhKSkrQ39+PoqIiAEBNTQ1aW1sxPDyM0dFRdHR0YP369S7/IWdAQAAkEgm6urrw8eNHzMzMuP1crVYLvV6P+vp6oQB8UVlZGZqamlBRUYFXr17h9evXaGtrQ2lp6W/tLTIyEiEhIaiqqgIAbNu2Dc+fP4fBYMDo6CjOnTuH3t5epzmbN2/G4OAgRkZG8PnzZ9hsNmi1Wvj7+yMpKQlGoxFmsxk9PT0oLCzE+/fvfysmxpgLf3dRFWPsf8dV8fCimpoaUigUJJFIKCEhgZqamggAff36lYicC7UXFhYoIyODAgMDSSwWk1KppPz8fKci72fPnlF8fDz5+PiQVCqlkJCQZYXcP1taCL6U3W6n8vJy2rBhA61evZpCQ0Ops7NTeF5XV0dhYWEklUpJLpdTXFwc9fX1Cc/xUyE4EdGtW7coMDCQRCIRxcTEuD0fu91OCoWCAND4+PiyuLq6uigqKookEgnJ5XKKiIiguro6t/vQ6XQUGhq6rL+1tZW8vLxocnKSLBYLHT58mHx9fWnt2rWUm5tLJSUlTvM+ffoknC8A6u7uJiKiqakpyszMJH9/f/Ly8qItW7ZQTk4OzczMuI2JMeaZfxER/b1pG2OMMcbYPx9fzzHGGGOMeYCTJsYYY4wxD3DSxBhjjDHmAU6aGGOMMcY8wEkTY4wxxpgHOGlijDHGGPMAJ02MMcYYYx7gpIkxxhhjzAOcNDHGGGOMeYCTJsYYY4wxD3DSxBhjjDHmAU6aGGOMMcY88B/F3dj4Pu6bOwAAAABJRU5ErkJggg==\n" - }, - "metadata": {} - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": "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\n" - }, - "metadata": {} - } - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.5" - }, - "colab": { - "provenance": [], - "gpuType": "A100" - }, - "accelerator": "GPU", - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "version_major": 2, - "version_minor": 0, - "state": { - "41be1797614f4fa7830532d34158ac52": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8aac226632524370abc787be435a9b48", - "IPY_MODEL_e24c1cf41fcd442eb81ef418e92b24c6", - "IPY_MODEL_dd026d80116749d19365780681500be9" - ], - "layout": "IPY_MODEL_b1703412347941edaab104e289f38221" - } - }, - "8aac226632524370abc787be435a9b48": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2cab2b54d039403daa901bbb65203e42", - "placeholder": "​", - "style": "IPY_MODEL_8f5881e63ed54097b49c624676055437", - "value": "Training: 100%" - } - }, - "e24c1cf41fcd442eb81ef418e92b24c6": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_48e35659a78b41cb95b8cbb39040de62", - "max": 25000, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_ff02466aec4344fa84813bb3eba626b1", - "value": 25000 - } - }, - "dd026d80116749d19365780681500be9": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_53c475a9562d4af59d6947020af35778", - "placeholder": "​", - "style": "IPY_MODEL_9e9b3731cd824b0094835c6ce06f9265", - "value": " 25000/25000 [24:47<00:00, 17.01it/s, epoch=2/2, avg_loss=1.8927, avg_metric=0.2941]" - } - }, - "b1703412347941edaab104e289f38221": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": "inline-flex", - "flex": null, - "flex_flow": "row wrap", - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "100%" - } - }, - "2cab2b54d039403daa901bbb65203e42": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "8f5881e63ed54097b49c624676055437": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "48e35659a78b41cb95b8cbb39040de62": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": "2", - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "ff02466aec4344fa84813bb3eba626b1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "53c475a9562d4af59d6947020af35778": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "9e9b3731cd824b0094835c6ce06f9265": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "0e1aad635bf14d1bb31c6f903f032e73": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_4e34acdc629c4fd898b1bbd743cd4cc1", - "IPY_MODEL_f0e15ef2bc25482e87a2aafd881171fb", - "IPY_MODEL_649dcf365ee140778713f1923422b40c" - ], - "layout": "IPY_MODEL_e0ef6f1060d2402a93eafa4557cd7a7a" - } - }, - "4e34acdc629c4fd898b1bbd743cd4cc1": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_45a2e6bd3f464dd0af70d71813826f89", - "placeholder": "​", - "style": "IPY_MODEL_b1ef8826aa5f4e2896ee4ee4bd6f5e2d", - "value": "Training: 100%" - } - }, - "f0e15ef2bc25482e87a2aafd881171fb": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_cb4aa040e0fe4b0292c3dcda6cc55b66", - "max": 6250, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_ee23434b787f454399e4be9c6111a94c", - "value": 6250 - } - }, - "649dcf365ee140778713f1923422b40c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3c0bd3f7f0034da7ba1b118da5d111eb", - "placeholder": "​", - "style": "IPY_MODEL_bad0b3bd64c743519cf1ec7eb6b3ba46", - "value": " 6250/6250 [39:03<00:00,  8.20it/s, epoch=1/1, avg_loss=0.2017]" - } - }, - "e0ef6f1060d2402a93eafa4557cd7a7a": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": "inline-flex", - "flex": null, - "flex_flow": "row wrap", - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "100%" - } - }, - "45a2e6bd3f464dd0af70d71813826f89": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "b1ef8826aa5f4e2896ee4ee4bd6f5e2d": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "cb4aa040e0fe4b0292c3dcda6cc55b66": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": "2", - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "ee23434b787f454399e4be9c6111a94c": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "3c0bd3f7f0034da7ba1b118da5d111eb": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "bad0b3bd64c743519cf1ec7eb6b3ba46": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "62f237b1683e475595fe17da0edeae87": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7bdac98dfc4a4699bab39fa846e56354", - "IPY_MODEL_ac7d5153146d4bd89fa5587a1c4babb7", - "IPY_MODEL_1d65d195b47147d3806f7735255878d8" - ], - "layout": "IPY_MODEL_34f4dd1f0b9b4920bffb1027ea6e11fe" - } - }, - "7bdac98dfc4a4699bab39fa846e56354": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_38e5079824d648849dcde09e2a2948fd", - "placeholder": "​", - "style": "IPY_MODEL_ffa3569f77fb4a3c8a6fb08930c0defb", - "value": "Training: 100%" - } - }, - "ac7d5153146d4bd89fa5587a1c4babb7": { - "model_module": "@jupyter-widgets/controls", - "model_name": "FloatProgressModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_dd800993260d48e383fb9aa27d265c7d", - "max": 25000, - "min": 0, - "orientation": "horizontal", - "style": "IPY_MODEL_4d4b43abf49f4114a7fc83d9128f6d30", - "value": 25000 - } - }, - "1d65d195b47147d3806f7735255878d8": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0d7bc861ca364e298ffb26510a4e4e09", - "placeholder": "​", - "style": "IPY_MODEL_2c776b732e6a4cb8bb85e01baff5fb33", - "value": " 25000/25000 [25:07<00:00, 17.45it/s, epoch=2/2, avg_loss=1.9639, avg_metric=0.2580]" - } - }, - "34f4dd1f0b9b4920bffb1027ea6e11fe": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": "inline-flex", - "flex": null, - "flex_flow": "row wrap", - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "100%" - } - }, - "38e5079824d648849dcde09e2a2948fd": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "ffa3569f77fb4a3c8a6fb08930c0defb": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "dd800993260d48e383fb9aa27d265c7d": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": "2", - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "4d4b43abf49f4114a7fc83d9128f6d30": { - "model_module": "@jupyter-widgets/controls", - "model_name": "ProgressStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "0d7bc861ca364e298ffb26510a4e4e09": { - "model_module": "@jupyter-widgets/base", - "model_name": "LayoutModel", - "model_module_version": "1.2.0", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "2c776b732e6a4cb8bb85e01baff5fb33": { - "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - } - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file From 86310209f291f1bce6006191ad807240f313cbcf Mon Sep 17 00:00:00 2001 From: Avishikta Bhattacharjee <119413279+gatetub@users.noreply.github.com> Date: Fri, 24 Jul 2026 21:46:22 +0530 Subject: [PATCH 11/11] Add files via upload --- .../notebook/PAG_LorentzParT_.ipynb | 2860 +++++++++++++++++ 1 file changed, 2860 insertions(+) create mode 100644 MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb diff --git a/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb new file mode 100644 index 0000000..f2b0bd1 --- /dev/null +++ b/MAEs/PAG_Avishikta_Bhattacharjee/notebook/PAG_LorentzParT_.ipynb @@ -0,0 +1,2860 @@ +{ + "cells": [ + { + "cell_type": "code", + "source": [ + "\n", + "# 1. Clone the ML4SCI/CMS repository\n", + "!git clone https://github.com/ML4SCI/CMS.git\n", + "\n", + "# 2. Navigate to the specific Hybrid Transformer project directory\n", + "%cd CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", + "\n", + "# 3. Install required libraries\n", + "!pip install lgatr uproot awkward tqdm vector" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B7YmCrpkJSAg", + "outputId": "f6eeec54-0a0e-416f-e730-dcf49f66d6f7" + }, + "id": "B7YmCrpkJSAg", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'CMS'...\n", + "remote: Enumerating objects: 751, done.\u001b[K\n", + "remote: Counting objects: 100% (130/130), done.\u001b[K\n", + "remote: Compressing objects: 100% (99/99), done.\u001b[K\n", + "remote: Total 751 (delta 39), reused 83 (delta 27), pack-reused 621 (from 2)\u001b[K\n", + "Receiving objects: 100% (751/751), 330.16 MiB | 18.70 MiB/s, done.\n", + "Resolving deltas: 100% (187/187), done.\n", + "Updating files: 100% (531/531), done.\n", + "/content/CMS/MAEs/Hybrid_Transformer_Thanh_Nguyen\n", + "Collecting lgatr\n", + " Downloading lgatr-1.4.4-py3-none-any.whl.metadata (8.7 kB)\n", + "Collecting uproot\n", + " Downloading uproot-5.7.5-py3-none-any.whl.metadata (35 kB)\n", + "Collecting awkward\n", + " Downloading awkward-2.11.0-py3-none-any.whl.metadata (7.6 kB)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.12/dist-packages (4.67.3)\n", + "Collecting vector\n", + " Downloading vector-1.8.1-py3-none-any.whl.metadata (15 kB)\n", + "Requirement already satisfied: torch>=2.1 in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.11.0+cu128)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from lgatr) (2.0.2)\n", + "Requirement already satisfied: einops in /usr/local/lib/python3.12/dist-packages (from lgatr) (0.8.2)\n", + "Requirement already satisfied: opt_einsum in /usr/local/lib/python3.12/dist-packages (from lgatr) (3.4.0)\n", + "Requirement already satisfied: cramjam>=2.5.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2.11.0)\n", + "Requirement already satisfied: fsspec!=2026.2.0 in /usr/local/lib/python3.12/dist-packages (from uproot) (2025.3.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from uproot) (26.2)\n", + "Requirement already satisfied: xxhash in /usr/local/lib/python3.12/dist-packages (from uproot) (3.8.1)\n", + "Collecting awkward-cpp==54 (from awkward)\n", + " Downloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.29.7)\n", + "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (4.16.0)\n", + "Requirement already satisfied: setuptools<82 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (75.2.0)\n", + "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (1.14.0)\n", + "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.1)\n", + "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.1.6)\n", + "Requirement already satisfied: cuda-toolkit==12.8.1 in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.1)\n", + "Requirement already satisfied: cuda-bindings<13,>=12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (12.9.7)\n", + "Requirement already satisfied: nvidia-cudnn-cu12==9.19.0.56 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (9.19.0.56)\n", + "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (0.7.1)\n", + "Requirement already satisfied: nvidia-nccl-cu12==2.28.9 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (2.28.9)\n", + "Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.4.5)\n", + "Requirement already satisfied: triton==3.6.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.1->lgatr) (3.6.0)\n", + "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.4.1)\n", + "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", + "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.3.3.83)\n", + "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (1.13.1.3)\n", + "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", + "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (10.3.9.90)\n", + "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (11.7.3.90)\n", + "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.5.8.93)\n", + "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", + "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.93)\n", + "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.1->lgatr) (12.8.90)\n", + "Requirement already satisfied: cuda-pathfinder~=1.1 in /usr/local/lib/python3.12/dist-packages (from cuda-bindings<13,>=12.9.4->torch>=2.1->lgatr) (1.5.6)\n", + "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=2.1->lgatr) (1.3.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=2.1->lgatr) (3.0.3)\n", + "Downloading lgatr-1.4.4-py3-none-any.whl (60 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.6/60.6 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading uproot-5.7.5-py3-none-any.whl (401 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m35.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading awkward-2.11.0-py3-none-any.whl (974 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m974.8/974.8 kB\u001b[0m \u001b[31m76.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading awkward_cpp-54-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (689 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m689.3/689.3 kB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading vector-1.8.1-py3-none-any.whl (182 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.7/182.7 kB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: vector, awkward-cpp, awkward, uproot, lgatr\n", + "Successfully installed awkward-2.11.0 awkward-cpp-54 lgatr-1.4.4 uproot-5.7.5 vector-1.8.1\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# --- Default libraries ---\n", + "import os\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "# --- Working directory ---\n", + "PROJECT_DIR = Path().resolve()\n", + "PROJECT_ROOT_NAME = 'Hybrid_Transformer_Thanh_Nguyen'\n", + "\n", + "while PROJECT_DIR.name != PROJECT_ROOT_NAME and PROJECT_DIR != PROJECT_DIR.parent:\n", + " PROJECT_DIR = PROJECT_DIR.parent\n", + "\n", + "if Path().resolve() != PROJECT_DIR:\n", + " os.chdir(PROJECT_DIR)\n", + "\n", + "DATA_DIR = PROJECT_DIR / 'data'\n", + "LOG_DIR = PROJECT_DIR / 'logs'\n", + "\n", + "# --- Data preprocessing & visualization ---\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# --- Deep learning ---\n", + "import torch\n", + "\n", + "# --- Custom modules ---\n", + "from src.configs import LorentzParTConfig, TrainConfig\n", + "from src.engine import MaskedModelTrainer, Trainer\n", + "from src.models import LorentzParT\n", + "from src.utils import accuracy_metric_ce, set_seed\n", + "from src.utils.data import JetClassDataset, compute_norm_stats, read_file\n", + "from src.utils.viz import *\n", + "\n", + "# --- Settings ---\n", + "warnings.filterwarnings('ignore')\n", + "set_seed(42)\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "device" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IvxBxUicRu18", + "outputId": "3241b771-4383-417a-cfc1-963899fb5dd4" + }, + "id": "IvxBxUicRu18", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Loading Dpendencies" + ], + "metadata": { + "id": "BmBDXDMSD61u" + }, + "id": "BmBDXDMSD61u" + }, + { + "cell_type": "code", + "source": [ + "from typing import List, Tuple, Dict, Optional\n", + "\n", + "import torch\n", + "from torch import nn, Tensor\n", + "from lgatr.interface import extract_vector\n", + "from lgatr.layers import EquiLinear\n", + "\n", + "from src.models.classifier import ClassAttentionBlock, Classifier\n", + "from src.models.feedforward import Feedforward\n", + "from src.models.particle_transformer import ParticleAttentionBlock\n", + "from src.models.processor import InteractionEmbedding, ParticleProcessor\n", + "from src.configs import LorentzParTConfig\n", + "from lgatr.interface import extract_vector\n", + "from lgatr.layers import EquiLinear\n", + "\n", + "\n" + ], + "metadata": { + "id": "jTbtwxdtAt9S" + }, + "id": "jTbtwxdtAt9S", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Data Preparation" + ], + "metadata": { + "id": "VB1JTgpxDv-4" + }, + "id": "VB1JTgpxDv-4" + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "from pathlib import Path\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from src.utils.data import JetClassDataset, compute_norm_stats\n", + "\n", + "# --- 1. Mount Google Drive (If not already done) ---\n", + "from google.colab import drive\n", + "if not os.path.exists('/content/drive'):\n", + " drive.mount('/content/drive')\n", + "\n", + "# --- 2. Locate and Load the Compressed Archive ---\n", + "DRIVE_FILE_PATH = '/content/drive/MyDrive/GSOC/dara/jetclass_balanced_1M.npz'\n", + "\n", + "print(\"=\" * 70)\n", + "print(f\"LOADING SERIALIZED ARRAYS FROM DRIVE\")\n", + "print(\"=\" * 70)\n", + "\n", + "if os.path.exists(DRIVE_FILE_PATH):\n", + " # Load using memory-mapping for high-speed indexing\n", + " data_archive = np.load(DRIVE_FILE_PATH, mmap_mode='r')\n", + "\n", + " X_particles = data_archive['X_particles']\n", + " X_jets = data_archive['X_jets']\n", + " y = data_archive['Y']\n", + "\n", + " print(\"SUCCESS: Data loaded cleanly into RAM!\")\n", + " print(f\" -> X_particles matrix shape : {X_particles.shape}\")\n", + " print(f\" -> X_jets matrix shape : {X_jets.shape}\")\n", + " print(f\" -> y (Labels) matrix shape : {y.shape}\")\n", + " print(\"=\" * 70 + \"\\n\")\n", + "else:\n", + " raise FileNotFoundError(f\"ERROR: Could not find the file at {DRIVE_FILE_PATH}\")\n", + "\n", + "# --- 3. Split the Balanced Dataset Safely ---\n", + "# We enforce stratify=y to lock in your strict 10% balance across all splits\n", + "X_train, X_val, y_train, y_val = train_test_split(X_particles, y, test_size=0.2, random_state=42, stratify=y)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.5, random_state=42, stratify=y_val)\n", + "\n", + "# --- 4. Re-Initialize JetClass Dataset Objects ---\n", + "normalize = [True, False, False, True]\n", + "norm_dict = compute_norm_stats(X_train)\n", + "\n", + "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode='biased')\n", + "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode='biased')\n", + "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode='first')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9RBH34OYBoZ7", + "outputId": "a53a1da2-e44b-4611-cd4e-f610d3cce289" + }, + "id": "9RBH34OYBoZ7", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n", + "======================================================================\n", + "LOADING SERIALIZED ARRAYS FROM DRIVE\n", + "======================================================================\n", + "SUCCESS: Data loaded cleanly into RAM!\n", + " -> X_particles matrix shape : (1000000, 4, 128)\n", + " -> X_jets matrix shape : (1000000, 4)\n", + " -> y (Labels) matrix shape : (1000000, 10)\n", + "======================================================================\n", + "\n", + "pt_mean: 92.70597076416016, pt_std: 105.79937744140625\n", + "eta_mean: -0.0011634620605036616, eta_std: 0.9182536005973816\n", + "phi_mean: -0.0006678671925328672, phi_std: 1.8138455152511597\n", + "E_mean: 133.98568725585938, E_std: 167.7259979248047\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "id": "edb52e95", + "metadata": { + "id": "edb52e95" + }, + "source": [ + "## Applying Gated Attention LorentzPart" + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch import Tensor\n", + "from typing import Optional\n", + "\n", + "class ParticleAttentionBlock(nn.Module):\n", + " def __init__(\n", + " self,\n", + " embed_dim: int = 128,\n", + " num_heads: int = 8,\n", + " dropout: float = 0.1,\n", + " expansion_factor: int = 4,\n", + " gate_type: Optional[str] = \"headwise\",\n", + " ):\n", + " super(ParticleAttentionBlock, self).__init__()\n", + " assert embed_dim % num_heads == 0, \"embed_dim must be divisible by num_heads\"\n", + "\n", + " self.embed_dim = embed_dim\n", + " self.num_heads = num_heads\n", + " self.head_dim = embed_dim // num_heads\n", + " self.gate_type = gate_type\n", + "\n", + " self.layernorm1 = nn.LayerNorm(embed_dim)\n", + " self.mass_norm = nn.LayerNorm(1)#norm\n", + "\n", + " # Project pooled interaction head-features to match token embedding dimensions\n", + " self.physics_proj = nn.Linear(num_heads, embed_dim)\n", + "\n", + " # Project the global scalar invariant mass squared (m2) to the embedding space\n", + " self.mass_proj = nn.Linear(1, embed_dim)\n", + "\n", + " # Gating projections accept physics-fused representations\n", + " if self.gate_type == \"headwise\":\n", + " self.gate_proj = nn.Linear(embed_dim, num_heads)\n", + " elif self.gate_type == \"elementwise\":\n", + " self.gate_proj = nn.Linear(embed_dim, embed_dim)\n", + "\n", + " self.pmha = nn.MultiheadAttention(\n", + " embed_dim=embed_dim,\n", + " num_heads=num_heads,\n", + " dropout=dropout,\n", + " batch_first=True\n", + " )\n", + "\n", + " self.layernorm2 = nn.LayerNorm(embed_dim)\n", + " self.dropout = nn.Dropout(dropout)\n", + "\n", + " self.feedforward = Feedforward(\n", + " embed_dim=embed_dim,\n", + " expansion_factor=expansion_factor,\n", + " dropout=dropout\n", + " )\n", + "\n", + " def forward(self, x: Tensor, padding_mask: Tensor, U: Optional[Tensor] = None, p4: Optional[Tensor] = None) -> Tensor:\n", + " residual = x\n", + " B_size, N_particles, _ = x.shape\n", + "\n", + " # 1. Standard token serialization\n", + " x_norm = self.layernorm1(x)\n", + "\n", + " # 2. FIXED: U is ALREADY [Batch * Heads, N, N]. Pass directly to PyTorch MHA.\n", + " x_attn, _ = self.pmha(x_norm, x_norm, x_norm, key_padding_mask=padding_mask, attn_mask=U)\n", + "\n", + " # 3. Physics-Aware Fusion (Invariants-driven conditioning)\n", + " if U is not None:\n", + " # Reconstruct the 4D shape: [B * H, N, N] -> [B, H, N, N]\n", + " U_reshaped = U.view(B_size, self.num_heads, N_particles, N_particles)\n", + "\n", + " # Pool over neighbor particle index 'j' (dim=3). Resulting shape: [B, H, N]\n", + " u_pooled = U_reshaped.sum(dim=3)\n", + "\n", + " # Transpose to align with token channels: [B, N, H]\n", + " u_pooled = u_pooled.transpose(1, 2)\n", + "\n", + " # Map head-wise pooled invariants into token channel space: [B, N, embed_dim]\n", + " physics_context = self.physics_proj(u_pooled)\n", + "\n", + " # Fuse physical invariants with abstract node latent maps\n", + " x_gating_input = x_norm + physics_context\n", + " else:\n", + " # Fallback path if U is not provided\n", + " x_gating_input = x_norm\n", + "\n", + " # 4. Compute Global Invariant Mass Bias from 4-vectors [B, N, 4] -> (E, px, py, pz)\n", + " if p4 is not None:\n", + " # Sum energy component (index 0) over all particles (dim=1)\n", + " energy_sum = p4[..., 0].sum(dim=1, keepdim=True) # Shape: [B, 1]\n", + "\n", + " # Sum momentum components (indices 1, 2, 3) over all particles (dim=1)\n", + " momentum_sum = p4[..., 1:].sum(dim=1) # Shape: [B, 3]\n", + "\n", + " # Calculate invariant mass squared (m2)\n", + " m2 = energy_sum**2 - momentum_sum.norm(dim=-1, keepdim=True)**2 # Shape: [B, 1]\n", + "\n", + " m2_scaled = torch.log1p(torch.relu(m2))\n", + "\n", + "\n", + " # Step C: Standardize the mean and variance dynamically\n", + " m2_norm = self.mass_norm(m2_scaled)\n", + "\n", + " # Project normalized m2 into a global embedding bias vector [B, 1, embed_dim]\n", + " mass_bias = self.mass_proj(m2_norm).unsqueeze(1)\n", + "\n", + " # Broad-cast add global event mass bias to the per-particle gating inputs\n", + " x_gating_input = x_gating_input + mass_bias\n", + "\n", + "\n", + "\n", + " # 5. Compute and apply the explicitly Physics-Aware Gate\n", + " if self.gate_type == \"headwise\":\n", + " # Compute score matrix from physics-fused map: (B, N, embed_dim) -> (B, N, num_heads, 1)\n", + " gate_score = self.gate_proj(x_gating_input).unsqueeze(-1)\n", + "\n", + " # Separate heads to apply individual scalar gating values\n", + " x_attn = x_attn.view(B_size, N_particles, self.num_heads, self.head_dim)\n", + "\n", + " # Apply physics-conditioned filter and reconstruct classic transformer shape\n", + " x_attn = (x_attn * torch.sigmoid(gate_score)).view(B_size, N_particles, self.embed_dim)\n", + "\n", + " elif self.gate_type == \"elementwise\":\n", + " # Compute full channel-by-channel mask from physics-fused map: (B, N, embed_dim)\n", + " gate_score = self.gate_proj(x_gating_input)\n", + " x_attn = x_attn * torch.sigmoid(gate_score)\n", + "\n", + " # 6. Standard Feedforward processing\n", + " x = self.layernorm2(x_attn)\n", + " x = self.dropout(x)\n", + " x += residual\n", + " x = self.feedforward(x)\n", + "\n", + " return x" + ], + "metadata": { + "id": "AvsAk0byPoBK" + }, + "id": "AvsAk0byPoBK", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torch import Tensor\n", + "from typing import Optional, List, Dict, Tuple\n", + "\n", + "\n", + "\n", + "class LorentzParTEncoder(nn.Module):\n", + " def __init__(\n", + " self,\n", + " embed_dim: int = 128,\n", + " num_heads: int = 8,\n", + " num_layers: int = 8,\n", + " in_s_channels: Optional[int] = None,\n", + " out_s_channels: Optional[int] = None,\n", + " dropout: float = 0.1,\n", + " expansion_factor: int = 4,\n", + " pair_embed_dims: List[int] = [64, 64, 64],\n", + " attention_config: Dict = {}\n", + " ):\n", + " super(LorentzParTEncoder, self).__init__()\n", + " self.equilinear = EquiLinear(\n", + " in_mv_channels=1,\n", + " out_mv_channels=1,\n", + " in_s_channels=in_s_channels,\n", + " out_s_channels=out_s_channels\n", + " )\n", + " self.proj = nn.Linear(16, embed_dim)\n", + " self.interaction_embed = InteractionEmbedding(\n", + " num_interaction_features=4,\n", + " pair_embed_dims=pair_embed_dims + [num_heads]\n", + " )\n", + "\n", + " use_gating = attention_config.get('use_gating', False)\n", + "\n", + " # Explicitly pass gate_type so the block knows whether to use physics gating or standard\n", + " self.encoder = nn.ModuleList([\n", + " ParticleAttentionBlock(\n", + " embed_dim=embed_dim,\n", + " num_heads=num_heads,\n", + " dropout=dropout,\n", + " expansion_factor=expansion_factor,\n", + " gate_type=\"headwise\" if use_gating else None\n", + " ) for _ in range(num_layers)\n", + " ])\n", + "\n", + " def forward(self, x: Tensor, padding_mask: Tensor, U: Tensor, p4: Optional[Tensor] = None) -> Tensor:\n", + " B, N, F = x.shape\n", + " U = self.interaction_embed(U)\n", + " x = x.view(B, N, 1, F)\n", + " x, _ = self.equilinear(x)\n", + " x = x.view(B, N, 16)\n", + " x = self.proj(x)\n", + "\n", + " # Pass p4 down to the attention blocks\n", + " for layer in self.encoder:\n", + " x = layer(x, padding_mask, U, p4=p4)\n", + "\n", + " return x\n", + "\n", + "\n", + "class LorentzParT(nn.Module):\n", + " def __init__(\n", + " self,\n", + " config: Optional[LorentzParTConfig] = None,\n", + " max_num_particles: Optional[int] = None,\n", + " num_particle_features: Optional[int] = None,\n", + " num_classes: Optional[int] = None,\n", + " embed_dim: Optional[int] = None,\n", + " num_heads: Optional[int] = None,\n", + " num_layers: Optional[int] = None,\n", + " num_cls_layers: Optional[int] = None,\n", + " num_mlp_layers: Optional[int] = None,\n", + " hidden_dim: Optional[int] = None,\n", + " hidden_mv_channels: Optional[int] = None,\n", + " in_s_channels: Optional[int] = None,\n", + " out_s_channels: Optional[int] = None,\n", + " hidden_s_channels: Optional[int] = None,\n", + " attention: Optional[Dict] = None,\n", + " mlp: Optional[Dict] = None,\n", + " reinsert_mv_channels: Optional[Tuple[int]] = None,\n", + " reinsert_s_channels: Optional[Tuple[int]] = None,\n", + " dropout: Optional[float] = None,\n", + " expansion_factor: Optional[int] = None,\n", + " pair_embed_dims: Optional[List[int]] = None,\n", + " mask: Optional[bool] = None,\n", + " weights: Optional[str] = None,\n", + " inference: Optional[bool] = False\n", + " ):\n", + " super(LorentzParT, self).__init__()\n", + "\n", + " # Use config if provided, otherwise use defaults\n", + " if config is not None:\n", + " self.max_num_particles = max_num_particles if max_num_particles is not None else config.max_num_particles\n", + " self.num_particle_features = num_particle_features if num_particle_features is not None else config.num_particle_features\n", + " self.num_classes = num_classes if num_classes is not None else config.num_classes\n", + " self.embed_dim = embed_dim if embed_dim is not None else config.embed_dim\n", + " self.num_heads = num_heads if num_heads is not None else config.num_heads\n", + " self.num_layers = num_layers if num_layers is not None else config.num_layers\n", + " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else config.num_cls_layers\n", + " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else config.num_mlp_layers\n", + " self.hidden_dim = hidden_dim if hidden_dim is not None else config.hidden_dim\n", + " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else config.hidden_mv_channels\n", + " self.in_s_channels = in_s_channels if in_s_channels is not None else config.in_s_channels\n", + " self.out_s_channels = out_s_channels if out_s_channels is not None else config.out_s_channels\n", + " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else config.hidden_s_channels\n", + " self.attention = attention if attention is not None else config.attention\n", + " self.mlp = mlp if mlp is not None else config.mlp\n", + " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else config.reinsert_mv_channels\n", + " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else config.reinsert_s_channels\n", + " self.dropout = dropout if dropout is not None else config.dropout\n", + " self.expansion_factor = expansion_factor if expansion_factor is not None else config.expansion_factor\n", + " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else config.pair_embed_dims\n", + " self.mask = mask if mask is not None else config.mask\n", + " self.weights = weights if weights is not None else config.weights\n", + " self.inference = inference if inference is not None else config.inference\n", + " else:\n", + " self.max_num_particles = max_num_particles if max_num_particles is not None else 128\n", + " self.num_particle_features = num_particle_features if num_particle_features is not None else 4\n", + " self.num_classes = num_classes if num_classes is not None else 10\n", + " self.embed_dim = embed_dim if embed_dim is not None else 128\n", + " self.num_heads = num_heads if num_heads is not None else 8\n", + " self.num_layers = num_layers if num_layers is not None else 8\n", + " self.num_cls_layers = num_cls_layers if num_cls_layers is not None else 2\n", + " self.num_mlp_layers = num_mlp_layers if num_mlp_layers is not None else 0\n", + " self.hidden_dim = hidden_dim if hidden_dim is not None else 256\n", + " self.hidden_mv_channels = hidden_mv_channels if hidden_mv_channels is not None else 8\n", + " self.in_s_channels = in_s_channels if in_s_channels is not None else None\n", + " self.out_s_channels = out_s_channels if out_s_channels is not None else None\n", + " self.hidden_s_channels = hidden_s_channels if hidden_s_channels is not None else 16\n", + " self.attention = attention if attention is not None else {}\n", + " self.mlp = mlp if mlp is not None else None\n", + " self.reinsert_mv_channels = reinsert_mv_channels if reinsert_mv_channels is not None else None\n", + " self.reinsert_s_channels = reinsert_s_channels if reinsert_s_channels is not None else None\n", + " self.dropout = dropout if dropout is not None else 0.1\n", + " self.expansion_factor = expansion_factor if expansion_factor is not None else 4\n", + " self.pair_embed_dims = pair_embed_dims if pair_embed_dims is not None else [64, 64, 64]\n", + " self.mask = mask if mask is not None else False\n", + " self.weights = weights if weights is not None else None\n", + " self.inference = inference if inference is not None else False\n", + "\n", + " # Initialize the class token\n", + " self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim), requires_grad=True)\n", + " nn.init.normal_(self.cls_token, mean=0.0, std=1.0)\n", + "\n", + " self.processor = ParticleProcessor(to_multivector=True)\n", + "\n", + " # Updated Encoder with attention_config passed dynamically\n", + " self.encoder = LorentzParTEncoder(\n", + " embed_dim=self.embed_dim,\n", + " num_heads=self.num_heads,\n", + " num_layers=self.num_layers,\n", + " in_s_channels=self.in_s_channels,\n", + " out_s_channels=self.out_s_channels,\n", + " dropout=self.dropout,\n", + " expansion_factor=self.expansion_factor,\n", + " pair_embed_dims=self.pair_embed_dims,\n", + " attention_config=self.attention\n", + " )\n", + "\n", + " # For self-supervised learning\n", + " self.fc = nn.Linear(self.max_num_particles * self.embed_dim, 16)\n", + " self.equilinear = EquiLinear(\n", + " in_mv_channels=1,\n", + " out_mv_channels=1,\n", + " in_s_channels=self.in_s_channels,\n", + " out_s_channels=self.out_s_channels\n", + " )\n", + "\n", + " # For classification\n", + " self.decoder = nn.ModuleList([\n", + " ClassAttentionBlock(\n", + " embed_dim=self.embed_dim,\n", + " num_heads=self.num_heads,\n", + " dropout=0.0,\n", + " expansion_factor=self.expansion_factor\n", + " ) for _ in range(self.num_cls_layers)\n", + " ])\n", + " self.layernorm = nn.LayerNorm(self.embed_dim)\n", + " self.classifier = Classifier(\n", + " num_classes=self.num_classes,\n", + " input_dim=self.embed_dim,\n", + " hidden_dim=self.hidden_dim,\n", + " num_layers=self.num_mlp_layers,\n", + " dropout=self.dropout,\n", + " )\n", + " self.act = nn.Softmax(dim=1) if self.inference else nn.Identity()\n", + "\n", + " # Load pretrained weights\n", + " if self.weights is not None:\n", + " state_dict = torch.load(self.weights)\n", + " filtered_state = {\n", + " k[len(\"encoder.\") :]: v\n", + " for k, v in state_dict.items()\n", + " if k.startswith(\"encoder.\")\n", + " }\n", + " self.encoder.load_state_dict(filtered_state, strict=False)\n", + "\n", + " def forward(self, x: Tensor, mask_idx: Optional[Tensor] = None) -> Tensor:\n", + " B, N, F = x.shape # (batch_size, max_num_particles, num_particle_features)\n", + "\n", + " # Save the raw kinematics before processor alters them\n", + " p4 = x.clone()\n", + "\n", + " # Ignore padding particles in query\n", + " padding_mask = (x[..., 3] == 0).float() # (B, N)\n", + "\n", + " # Set the masked indices to 0.0 so they are not ignored in MultiheadAttention()\n", + " if mask_idx is not None:\n", + " batch_indices = torch.arange(x.size(0), device=x.device)\n", + " padding_mask[batch_indices, mask_idx] = 0.0\n", + "\n", + " # Process particles to get interaction embeddings and multivectors (if applicable)\n", + " x, U = self.processor(x)\n", + "\n", + " # Pass through equilinear layer and particle attention blocks (passing p4 down)\n", + " x = self.encoder(x, padding_mask, U, p4=p4)\n", + "\n", + " # Classification (no masking in this case)\n", + " if not self.mask:\n", + " x_cls = self.cls_token.expand(B, -1, -1)\n", + "\n", + " # Decoder with class attention blocks\n", + " for layer in self.decoder:\n", + " x_cls = layer(x, x_cls, padding_mask)\n", + "\n", + " # MLP head for classification\n", + " x_cls = self.layernorm(x_cls).squeeze(1)\n", + " x_cls = self.classifier(x_cls)\n", + " output = self.act(x_cls) # (B, num_classes)\n", + "\n", + " return output\n", + " else:\n", + " x = x.view(B, -1) # (B, N * embed_dim)\n", + " x = self.fc(x) # (B, 16)\n", + " x = x.view(B, 1, 1, 16)\n", + " x, _ = self.equilinear(x) # (B, 1, 1, 16)\n", + " x = x.view(B, 16)\n", + " x = extract_vector(x) # (B, F)\n", + "\n", + " return x" + ], + "metadata": { + "id": "QRhMF1TP8Ymw" + }, + "id": "QRhMF1TP8Ymw", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Gating Layer Test" + ], + "metadata": { + "id": "UC2GQe3iR7Gt" + }, + "id": "UC2GQe3iR7Gt" + }, + { + "cell_type": "code", + "source": [ + "# 1. Initialize your config with gating enabled\n", + "test_config = LorentzParTConfig(\n", + " embed_dim=128,\n", + " num_heads=8,\n", + " num_layers=8,\n", + " attention={'use_gating': True},\n", + " mask=True\n", + ")\n", + "\n", + "# 2. Instantiate the model\n", + "model = LorentzParT(config=test_config)\n", + "\n", + "# 3. Verification checks\n", + "first_layer = model.encoder.encoder[0]\n", + "is_gated = isinstance(first_layer, ParticleAttentionBlock)\n", + "\n", + "print(f\"--- Gating Verification ---\")\n", + "print(f\"Encoder Layer 1 Type: {type(first_layer).__name__}\")\n", + "print(f\"Gating Active: {is_gated}\")\n", + "\n", + "if is_gated:\n", + " print(\"Success: The model is now using Attention Gating!\")\n", + "else:\n", + " print(\"Error: The model is still using standard Attention Blocks.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cKfNSeyPR9nH", + "outputId": "8ea6d1d4-605b-4ec4-b41e-f2a61eb0c23b" + }, + "id": "cKfNSeyPR9nH", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--- Gating Verification ---\n", + "Encoder Layer 1 Type: ParticleAttentionBlock\n", + "Gating Active: True\n", + "Success: The model is now using Attention Gating!\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Pre-Train Steps" + ], + "metadata": { + "id": "H_Kmp-s3SUDD" + }, + "id": "H_Kmp-s3SUDD" + }, + { + "cell_type": "markdown", + "source": [ + "#Using Self Supervised Weights" + ], + "metadata": { + "id": "5peLQ8txiFZG" + }, + "id": "5peLQ8txiFZG" + }, + { + "cell_type": "code", + "source": [ + "# Initialize configuration with Attention Gating enabled\n", + "#not changing name of ssl_model_config\n", + "ssl_model_config = LorentzParTConfig(\n", + " embed_dim=128,\n", + " num_heads=8,\n", + " num_layers=8,\n", + " hidden_mv_channels=8,\n", + " attention={'use_gating': True}, # This is the trigger for your new code\n", + " dropout=0.1,\n", + " expansion_factor=4,\n", + " max_num_particles=128,\n", + " num_particle_features=4,\n", + " pair_embed_dims=[64, 64, 64],\n", + " mask=True # Set to True for Self-Supervised Learning / Masked Training\n", + ")" + ], + "metadata": { + "id": "6fGbG9f1SXbc" + }, + "id": "6fGbG9f1SXbc", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Create the model and move it to your device (GPU/CPU)\n", + "gatedmodel = LorentzParT(config=ssl_model_config)\n", + "gatedmodel.to(device)\n", + "gatedmodel" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "b7g5btHCSZka", + "outputId": "a47e4f81-cd33-4b7c-a39a-7f074bf3c456" + }, + "id": "b7g5btHCSZka", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LorentzParT(\n", + " (processor): ParticleProcessor()\n", + " (encoder): LorentzParTEncoder(\n", + " (equilinear): EquiLinear()\n", + " (proj): Linear(in_features=16, out_features=128, bias=True)\n", + " (interaction_embed): InteractionEmbedding(\n", + " (embed): Sequential(\n", + " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", + " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): GELU(approximate='none')\n", + " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (6): GELU(approximate='none')\n", + " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (9): GELU(approximate='none')\n", + " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", + " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (12): GELU(approximate='none')\n", + " )\n", + " )\n", + " (encoder): ModuleList(\n", + " (0-7): 8 x ParticleAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", + " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", + " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", + " (gate_proj): Linear(in_features=128, out_features=8, bias=True)\n", + " (pmha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.1, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", + " (equilinear): EquiLinear()\n", + " (decoder): ModuleList(\n", + " (0-1): 2 x ClassAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.0, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (classifier): Classifier(\n", + " (layers): Sequential(\n", + " (0): Linear(in_features=128, out_features=10, bias=True)\n", + " )\n", + " )\n", + " (act): Identity()\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "source": [ + "num_params = sum(p.numel() for p in gatedmodel.parameters() if p.requires_grad)\n", + "num_params" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n5GMAwY0Sw5t", + "outputId": "170f4cca-9434-4e28-d810-c0bf423f50a7" + }, + "id": "n5GMAwY0Sw5t", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2290656" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0aa870ab", + "metadata": { + "id": "0aa870ab" + }, + "outputs": [], + "source": [ + "# Training configurations\n", + "gated_train_config = TrainConfig(\n", + " batch_size=128,\n", + " criterion={\n", + " 'name': 'conservation_loss',\n", + " 'kwargs': {\n", + " 'loss_coef': [0.25, 0.25, 0.25, 0.25],\n", + " 'reduction': 'mean'\n", + " }\n", + " },\n", + " optimizer={\n", + " 'name': 'adamw',\n", + " 'kwargs': {\n", + " 'lr': 1e-4\n", + " }\n", + " },\n", + " scheduler={\n", + " 'name': 'exponential_lr',\n", + " 'kwargs': {\n", + " 'gamma': 0.95\n", + " }\n", + " },\n", + " callbacks=[{\n", + " 'name': 'early_stopping',\n", + " 'kwargs': {\n", + " 'monitor': 'val_loss',\n", + " 'mode': 'min',\n", + " 'patience': 5\n", + " }\n", + " }],\n", + " num_epochs=1,#20 change\n", + " start_epoch=0,\n", + " logging_dir=str(LOG_DIR),\n", + " logging_steps=1000,\n", + " progress_bar=True,\n", + " save_best=True,\n", + " save_ckpt=True,\n", + " save_fig=False,\n", + " device='cuda',\n", + " num_workers=0,\n", + " pin_memory=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d5f08dd", + "metadata": { + "id": "6d5f08dd" + }, + "outputs": [], + "source": [ + "# Initialize the trainer\n", + "trainer = MaskedModelTrainer(\n", + " model=gatedmodel,\n", + " train_dataset=train_dataset,\n", + " val_dataset=val_dataset,\n", + " test_dataset=test_dataset,\n", + " device=device,\n", + " config=gated_train_config\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40a3b5fa", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 188, + "referenced_widgets": [ + "0e1aad635bf14d1bb31c6f903f032e73", + "4e34acdc629c4fd898b1bbd743cd4cc1", + "f0e15ef2bc25482e87a2aafd881171fb", + "649dcf365ee140778713f1923422b40c", + "e0ef6f1060d2402a93eafa4557cd7a7a", + "45a2e6bd3f464dd0af70d71813826f89", + "b1ef8826aa5f4e2896ee4ee4bd6f5e2d", + "cb4aa040e0fe4b0292c3dcda6cc55b66", + "ee23434b787f454399e4be9c6111a94c", + "3c0bd3f7f0034da7ba1b118da5d111eb", + "bad0b3bd64c743519cf1ec7eb6b3ba46" + ] + }, + "id": "40a3b5fa", + "outputId": "e38bc084-3ac9-47b3-ed52-af4af71a5b6e" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Training: 0%| | 0/6250 [00:00" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ], + "source": [ + "# Evaluate the model on the test set\n", + "test_loss, test_metric, y_true, y_pred = trainer.evaluate(plot=plot_particle_reconstruction)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Label names for classification\n", + "labels = [\n", + " \"$q/g$\", # 0\n", + " \"$H \\\\to b\\\\bar{b}$\", # 1\n", + " \"$H \\\\to c\\\\bar{c}$\", # 2\n", + " \"$H \\\\to gg$\", # 3\n", + " \"$H \\\\to 4q$\", # 4\n", + " \"$H \\\\to \\\\ell \\\\nu qq'$\", # 5\n", + " \"$Z \\\\to q\\\\bar{q}$\", # 6\n", + " \"$W \\\\to qq'$\", # 7\n", + " \"$t \\\\to b\\\\ell \\\\nu$\", # 8\n", + " \"$t \\\\to bqq'$\" # 9\n", + "]\n" + ], + "metadata": { + "id": "vt4vzC1TGBiB" + }, + "id": "vt4vzC1TGBiB", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Datasets for classification\n", + "train_dataset = JetClassDataset(X_train, y_train, normalize, norm_dict, mask_mode=None)\n", + "val_dataset = JetClassDataset(X_val, y_val, normalize, norm_dict, mask_mode=None)\n", + "test_dataset = JetClassDataset(X_test, y_test, normalize, norm_dict, mask_mode=None)" + ], + "metadata": { + "id": "m37_eDUuGD0l" + }, + "id": "m37_eDUuGD0l", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from collections import Counter\n", + "import numpy as np\n", + "\n", + "def check_uniformity(y, dataset_name, label_names, threshold=0.02):\n", + " \"\"\"\n", + " Checks if the labels in a dataset are uniformly distributed.\n", + " Supports both integer class arrays and one-hot encoded arrays.\n", + " \"\"\"\n", + " # If one-hot encoded, convert to class indices\n", + " if len(y.shape) > 1 and y.shape[1] > 1:\n", + " y = np.argmax(y, axis=1)\n", + "\n", + " total_samples = len(y)\n", + " counts = Counter(y)\n", + " num_classes = len(label_names)\n", + " expected_pct = 1.0 / num_classes\n", + "\n", + " print(f\"--- Distribution for {dataset_name} ({total_samples} samples) ---\")\n", + "\n", + " is_uniform = True\n", + " for idx, name in enumerate(label_names):\n", + " count = counts.get(idx, 0)\n", + " actual_pct = count / total_samples\n", + " print(f\"Class {idx} ({name:<18}): {count:<8} | {actual_pct:.2%}\")\n", + "\n", + " # Check if it deviates more than the allowed threshold from absolute uniformity\n", + " if abs(actual_pct - expected_pct) > threshold:\n", + " is_uniform = False\n", + "\n", + " if is_uniform:\n", + " print(f\"✅ {dataset_name} appears to be uniformly distributed (within a {threshold:.1%} tolerance).\\n\")\n", + " else:\n", + " print(f\"⚠️ {dataset_name} is NOT perfectly uniform. Expected around {expected_pct:.2%} per class.\\n\")\n", + "\n", + "# Run the check on your datasets\n", + "# (Using your raw arrays y_train, y_val, and y_test)\n", + "check_uniformity(y_train, \"Train Dataset\", labels)\n", + "check_uniformity(y_val, \"Validation Dataset\", labels)\n", + "check_uniformity(y_test, \"Test Dataset\", labels)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IWCdSFFKGFoL", + "outputId": "c1769a19-a1db-473e-80a8-237fdc4867b1" + }, + "id": "IWCdSFFKGFoL", + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--- Distribution for Train Dataset (800000 samples) ---\n", + "Class 0 ($q/g$ ): 80000 | 10.00%\n", + "Class 1 ($H \\to b\\bar{b}$ ): 80000 | 10.00%\n", + "Class 2 ($H \\to c\\bar{c}$ ): 80000 | 10.00%\n", + "Class 3 ($H \\to gg$ ): 80000 | 10.00%\n", + "Class 4 ($H \\to 4q$ ): 80000 | 10.00%\n", + "Class 5 ($H \\to \\ell \\nu qq'$): 80000 | 10.00%\n", + "Class 6 ($Z \\to q\\bar{q}$ ): 80000 | 10.00%\n", + "Class 7 ($W \\to qq'$ ): 80000 | 10.00%\n", + "Class 8 ($t \\to b\\ell \\nu$ ): 80000 | 10.00%\n", + "Class 9 ($t \\to bqq'$ ): 80000 | 10.00%\n", + "✅ Train Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", + "\n", + "--- Distribution for Validation Dataset (100000 samples) ---\n", + "Class 0 ($q/g$ ): 10000 | 10.00%\n", + "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", + "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", + "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", + "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", + "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", + "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", + "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", + "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", + "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", + "✅ Validation Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", + "\n", + "--- Distribution for Test Dataset (100000 samples) ---\n", + "Class 0 ($q/g$ ): 10000 | 10.00%\n", + "Class 1 ($H \\to b\\bar{b}$ ): 10000 | 10.00%\n", + "Class 2 ($H \\to c\\bar{c}$ ): 10000 | 10.00%\n", + "Class 3 ($H \\to gg$ ): 10000 | 10.00%\n", + "Class 4 ($H \\to 4q$ ): 10000 | 10.00%\n", + "Class 5 ($H \\to \\ell \\nu qq'$): 10000 | 10.00%\n", + "Class 6 ($Z \\to q\\bar{q}$ ): 10000 | 10.00%\n", + "Class 7 ($W \\to qq'$ ): 10000 | 10.00%\n", + "Class 8 ($t \\to b\\ell \\nu$ ): 10000 | 10.00%\n", + "Class 9 ($t \\to bqq'$ ): 10000 | 10.00%\n", + "✅ Test Dataset appears to be uniformly distributed (within a 2.0% tolerance).\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Model configurations\n", + "pretrained_model_config = LorentzParTConfig(\n", + " num_classes=10,\n", + " embed_dim=128,\n", + " num_heads=8,\n", + " num_layers=8,\n", + " num_cls_layers=2,\n", + " num_mlp_layers=0,\n", + " hidden_dim=256,\n", + " hidden_mv_channels=8,\n", + " in_s_channels=None,\n", + " out_s_channels=None,\n", + " hidden_s_channels=16,\n", + " attention={},\n", + " mlp={},\n", + " dropout=0.1,\n", + " expansion_factor=4,\n", + " max_num_particles=128,\n", + " num_particle_features=4,\n", + " pair_embed_dims=[64, 64, 64],\n", + " weights=gated_pt_path\n", + ")" + ], + "metadata": { + "id": "oRHuimXTGIdx" + }, + "id": "oRHuimXTGIdx", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Initialize the classifier model\n", + "pretrained_model = LorentzParT(config=pretrained_model_config).to(device)\n", + "pretrained_model" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Dv1oQ3tSGe3w", + "outputId": "93cf00a1-dc30-4880-8652-4adf5b6cccae" + }, + "id": "Dv1oQ3tSGe3w", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LorentzParT(\n", + " (processor): ParticleProcessor()\n", + " (encoder): LorentzParTEncoder(\n", + " (equilinear): EquiLinear()\n", + " (proj): Linear(in_features=16, out_features=128, bias=True)\n", + " (interaction_embed): InteractionEmbedding(\n", + " (embed): Sequential(\n", + " (0): BatchNorm1d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (1): Conv1d(4, 64, kernel_size=(1,), stride=(1,))\n", + " (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (3): GELU(approximate='none')\n", + " (4): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (6): GELU(approximate='none')\n", + " (7): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", + " (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (9): GELU(approximate='none')\n", + " (10): Conv1d(64, 8, kernel_size=(1,), stride=(1,))\n", + " (11): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (12): GELU(approximate='none')\n", + " )\n", + " )\n", + " (encoder): ModuleList(\n", + " (0-7): 8 x ParticleAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mass_norm): LayerNorm((1,), eps=1e-05, elementwise_affine=True)\n", + " (physics_proj): Linear(in_features=8, out_features=128, bias=True)\n", + " (mass_proj): Linear(in_features=1, out_features=128, bias=True)\n", + " (pmha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.1, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (fc): Linear(in_features=16384, out_features=16, bias=True)\n", + " (equilinear): EquiLinear()\n", + " (decoder): ModuleList(\n", + " (0-1): 2 x ClassAttentionBlock(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (mha): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", + " )\n", + " (layernorm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (dropout): Dropout(p=0.0, inplace=False)\n", + " (feedforward): Feedforward(\n", + " (layernorm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (linear1): Linear(in_features=128, out_features=512, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (dropout1): Dropout(p=0.0, inplace=False)\n", + " (layernorm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (linear2): Linear(in_features=512, out_features=128, bias=True)\n", + " (dropout2): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (layernorm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", + " (classifier): Classifier(\n", + " (layers): Sequential(\n", + " (0): Linear(in_features=128, out_features=10, bias=True)\n", + " )\n", + " )\n", + " (act): Identity()\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Count parameters in the model\n", + "num_params = sum(p.numel() for p in pretrained_model.parameters() if p.requires_grad)\n", + "num_params" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_TDNTq4hGL2e", + "outputId": "24c6d7c6-5565-44e1-ed00-17967f67ff76" + }, + "id": "_TDNTq4hGL2e", + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "2282400" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Training configurations\n", + "pretrained_config = TrainConfig(\n", + " batch_size=64,\n", + " criterion={\n", + " 'name': 'cross_entropy_loss',\n", + " 'kwargs': {\n", + " 'reduction': 'mean'\n", + " }\n", + " },\n", + " optimizer={\n", + " 'name': 'adam',\n", + " 'kwargs': {\n", + " 'lr': 1e-4\n", + " }\n", + " },\n", + " scheduler={\n", + " 'name': 'exponential_lr',\n", + " 'kwargs': {\n", + " 'gamma': 0.95\n", + " }\n", + " },\n", + " callbacks=[{\n", + " 'name': 'early_stopping',\n", + " 'kwargs': {\n", + " 'monitor': 'val_loss',\n", + " 'mode': 'min',\n", + " 'patience': 5\n", + " }\n", + " }],\n", + " num_epochs=2,#change\n", + " start_epoch=0,\n", + " logging_dir=str(LOG_DIR),\n", + " logging_steps=1000,\n", + " save_best=True,\n", + " save_ckpt=True,\n", + " save_fig=False,\n", + " device='cuda',\n", + " num_workers=0,\n", + " pin_memory=True\n", + ")" + ], + "metadata": { + "id": "aaUtfajZGWD5" + }, + "id": "aaUtfajZGWD5", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Initialize the trainer\n", + "trainer = Trainer(\n", + " model=pretrained_model,\n", + " train_dataset=train_dataset,\n", + " val_dataset=val_dataset,\n", + " test_dataset=test_dataset,\n", + " device=device,\n", + " metric=accuracy_metric_ce,\n", + " config=pretrained_config\n", + ")" + ], + "metadata": { + "id": "GBfMEnRrGler" + }, + "id": "GBfMEnRrGler", + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Train the model\n", + "pretrained_history, pretrained_model = trainer.train()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 518, + "referenced_widgets": [ + "62f237b1683e475595fe17da0edeae87", + "7bdac98dfc4a4699bab39fa846e56354", + "ac7d5153146d4bd89fa5587a1c4babb7", + "1d65d195b47147d3806f7735255878d8", + "34f4dd1f0b9b4920bffb1027ea6e11fe", + "38e5079824d648849dcde09e2a2948fd", + "ffa3569f77fb4a3c8a6fb08930c0defb", + "dd800993260d48e383fb9aa27d265c7d", + "4d4b43abf49f4114a7fc83d9128f6d30", + "0d7bc861ca364e298ffb26510a4e4e09", + "2c776b732e6a4cb8bb85e01baff5fb33" + ] + }, + "id": "q_3Zs8TfOwop", + "outputId": "ecbf6236-025e-47a4-f604-eb92b4fd72e0" + }, + "id": "q_3Zs8TfOwop", + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Training: 0%| | 0/25000 [00:00" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAHqCAYAAAAZC3qTAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAir1JREFUeJzs3Xdc1dUfx/HXZQ8BcQAOFPdOFPfWTNMyLUXU3CtHWo6fWuYqV7lz75XmylHO3KlhTtTcA3OCogIy5MK95/fHzVsEKijwZXyejwcPv/fc7/fe9+Ui98P5nu85OqWUQgghhBBCvJSF1gGEEEIIITICKZqEEEIIIZJAiiYhhBBCiCSQokkIIYQQIgmkaBJCCCGESAIpmoQQQgghkkCKJiGEEEKIJJCiSQghhBAiCaRoEkIIIYRIAimahBBCCCGSQIomIV7DsmXL0Ol06HQ6Dh8+nOB+pRSenp7odDref/99DRKmnDlz5qDT6ahatarWUdIdLy8v88+BTqfD0dGRKlWqsGLFihcec+vWLXr16oWXlxe2tra4ubnRokULjhw58sJjgoODGTx4MCVLlsTBwQFHR0d8fHwYO3YsoaGhScoaEBBA+/bt8fT0xNbWlhw5ctCwYUOWLl2KwWBI7ksXIkuy0jqAEBmZnZ0dq1evplatWvHaDx48yJ07d7C1tdUoWcpZtWoVXl5eHDt2jGvXrlG0aFGtI6Ur3t7eDBo0CID79++zaNEiOnXqRExMDD169Ii375EjR2jatCkA3bt3p3Tp0gQFBbFs2TJq167NjBkz6NevX7xjjh8/TtOmTYmIiKB9+/b4+PgAcOLECSZOnMhvv/3Gr7/++tKMixYtolevXri7u9OhQweKFSvG06dP2bt3L926deP+/ft8+eWXKfUtESLzUkKIZFu6dKkC1EcffaRy5cqlYmNj493fo0cP5ePjowoWLKjee++9NMtlNBpVVFRUij3ejRs3FKA2btyocufOrUaPHp1ij51UBoNBRUdHp/nzJkVi7++DBw9UtmzZVKlSpeK1P378WHl4eCh3d3d17dq1ePdFRUWp2rVrKwsLC3XkyBFz+5MnT1S+fPmUu7u7unjxYoLnDwoKUt98881LM/r7+ytLS0tVq1YtFR4enuD+48ePq6VLl77qpSZJREREijyOEOmVnJ4T4g20bduWR48esXv3bnObXq9nw4YNtGvXLtFjJk+eTI0aNciZMyf29vb4+PiwYcOGRPf94YcfqFKlCg4ODri6ulKnTp14vQpeXl68//777Nq1i0qVKmFvb8/8+fMBuHHjBr6+vuTIkQMHBweqVavGtm3bkvX6Vq1ahaurK++99x6tWrVi1apV5vtiY2PJkSMHXbp0SXBceHg4dnZ2DB482NwWExPDqFGjKFq0KLa2tnh6ejJkyBBiYmLiHavT6fj0009ZtWoVZcqUwdbWlp07dybrexcdHU3//v3JlSsXTk5OfPDBB9y9exedTsfo0aPj7Xv37l26du2Ku7s7tra2lClThiVLliTr+/RvuXPnpmTJkly/fj1e+/z58wkKCmLSpEkUKVIk3n329vYsX74cnU7H119/He+Yu3fvMnXqVEqWLJngudzd3fnqq69emmfMmDHodDpWrVqFk5NTgvsrVapE586dAThw4AA6nY4DBw7E2+fmzZvodDqWLVtmbuvcuTPZsmXj+vXrNG3aFCcnJz7++GM+/fRTsmXLRlRUVILnatu2LR4eHvFOB+7YsYPatWvj6OiIk5MT7733HufPn3/paxJCK1I0CfEGvLy8qF69Oj/++KO5bceOHYSFhdGmTZtEj5kxYwYVKlTg66+/Zvz48VhZWeHr65ugoBkzZgwdOnTA2tqar7/+mjFjxuDp6cm+ffvi7Xf58mXatm3LO++8w4wZM/D29iY4OJgaNWqwa9cu+vTpw7hx43j27BkffPABmzZtSvLrW7VqFR999BE2Nja0bduWq1evcvz4cQCsra358MMP2bx5M3q9Pt5xmzdvJiYmxvw9MBqNfPDBB0yePJlmzZoxc+ZMWrRowbRp0/Dz80vwvPv27WPAgAH4+fkxY8YMvLy8kvW969y5MzNnzqRp06Z8++232Nvb89577yV4nuDgYKpVq8aePXv49NNPmTFjBkWLFqVbt25Mnz49yd+nf4uLi+POnTu4urrGa//ll1+ws7OjdevWiR5XqFAhatWqxb59+4iOjgbg559/xt7enlatWr1WlqioKPbu3UudOnUoUKDAaz3Gy8TFxdG4cWPc3NyYPHkyLVu2xM/Pj8jIyATvSVRUFL/88gutWrXC0tISgJUrV/Lee++RLVs2vv32W0aMGMGFCxeoVasWN2/eTPG8Qrwxrbu6hMiInp+eO378uJo1a5ZycnIynxbz9fVV9evXV0olfvrmv6fP9Hq9Klu2rGrQoIG57erVq8rCwkJ9+OGHymAwxNvfaDSatwsWLKgAtXPnznj7fP755wpQhw4dMrc9ffpUFSpUSHl5eSV4zMScOHFCAWr37t3m582fP7/67LPPzPvs2rVLAeqXX36Jd2zTpk1V4cKFzbdXrlypLCws4uVRSql58+YpIN4pKUBZWFio8+fPJ8iUlO/dyZMnFaA+//zzePt27txZAWrUqFHmtm7duqk8efKokJCQePu2adNGubi4vPJUZ8GCBVWjRo3Uw4cP1cOHD9W5c+dUhw4dFKD69u0bb9/s2bOr8uXLv/Tx+vfvrwB19uxZpZRSrq6urzzmZc6cOaOAeO/Zy+zfv18Bav/+/fHaAwMDFRDvNF6nTp0UoIYNGxZvX6PRqPLly6datmwZr33dunUKUL/99ptSyvTzmD17dtWjR494+wUFBSkXF5cE7UKkB9LTJMQbat26NdHR0WzdupWnT5+ydevWF56aA9OpmOeePHlCWFgYtWvX5tSpU+b2zZs3YzQaGTlyJBYW8f+b6nS6eLcLFSpE48aN47Vt376dKlWqxBugni1bNnr27MnNmze5cOHCK1/XqlWrcHd3p379+ubn9fPzY82aNebTKw0aNCBXrlysXbs23mvavXt3vB6k9evXU6pUKUqWLElISIj5q0GDBgDs378/3nPXrVuX0qVLJ8iUlO/d81N5ffr0iXfsfwdYK6X46aefaNasGUqpeLkaN25MWFhYvMd9kV9//ZXcuXOTO3duypUrx8qVK+nSpQuTJk2Kt9/Tp08TPT32b8/vDw8PN//7qmNe5vnjvMljvErv3r3j3dbpdPj6+rJ9+3YiIiLM7WvXriVfvnzmn8ndu3cTGhpK27Zt433vLS0tqVq1aoKfCSHSA7l6Tog3lDt3bho2bMjq1auJiorCYDC89HTK1q1bGTt2LAEBAfHG8/y7GLp+/ToWFhaJFg7/VahQoQRtf/31V6JTBJQqVcp8f9myZXn8+HG8U2v29va4uLhgMBhYs2YN9evXJzAw0Hx/1apVmTJlCnv37qVRo0ZYWVnRsmVLVq9eTUxMDLa2tmzcuJHY2Nh4RdPVq1e5ePEiuXPnTvQ1PHjw4JWvCZL2vfvrr7+wsLBI8Bj/verv4cOHhIaGsmDBAhYsWJCkXImpWrUqY8eOxWAw8OeffzJ27FiePHmCjY1NvP2cnJx4+vTpSx/r+f3PixxnZ+dXHvMyzs7O8R43pVlZWZE/f/4E7X5+fkyfPp2ff/6Zdu3aERERwfbt2/nkk0/M79XVq1cBzIXzi7ILkZ5I0SRECmjXrh09evQgKCiIJk2akD179kT3O3ToEB988AF16tRhzpw55MmTB2tra5YuXcrq1atf67n/3fuSXB999BEHDx403+7UqRPLli1j37593L9/nzVr1rBmzZoEx61atYpGjRoB0KZNG+bPn8+OHTto0aIF69ato2TJkpQvX968v9FopFy5ckydOjXRHJ6enq98TSn9vTMajQC0b9+eTp06JbrPW2+99crHyZUrFw0bNgSgcePGlCxZkvfff58ZM2YwcOBA836lSpXi9OnT5uIyMWfPnsXa2ppixYoBULJkSQICAtDr9QmKsKQoWrQoVlZWnDt3Lkn7/7cX87kXzeNka2uboCcUoFq1anh5ebFu3TratWvHL7/8QnR0dLxC+vn3f+XKlXh4eCR4DCsr+XgS6Y/8VAqRAj788EM++eQTjh49Gu9U1X/99NNP2NnZsWvXrngfnEuXLo23X5EiRTAajVy4cAFvb+9k5ylYsCCXL19O0H7p0iXz/QBTpkzhyZMn5vvz5s0LmIoiNzc3Zs+eneAxNm7cyKZNm5g3bx729vbUqVOHPHnysHbtWvNA5uHDhyd4PWfOnOHtt99+4QfzqyT1e1ewYEGMRiOBgYHm4gPg2rVr8fbLnTs3Tk5OGAwGc9GTEt577z3q1q3L+PHj+eSTT3B0dATg/fffx9/fn/Xr19O+ffsEx928eZNDhw7RsGFDc9HYrFkz/P39+emnn2jbtm2yszg4ONCgQQP27dvH7du3ExSn//V88Pp/J8z866+/kv3crVu3ZsaMGYSHh7N27Vq8vLyoVq2a+f7nVxC6ubml6PdfiFSl9aAqITKifw8Ef27ZsmVq9OjR8QYP/3cg+MCBA5WDg4OKjIw0twUGBioHBwf17/+OyRkIntg8UM8Hgv/+++/mtoiICFW4cOFXDgSPiopSTk5OqmvXronef+TIEQWoNWvWmNv69eunHB0d1dSpUxWgLly4EO+YZcuWKUDNnz8/0ef79/w+JDKIWqmkf++eD2BPykDwzp07KxsbG3Xu3LkEz/fgwYNEX/+/vej7v337dgWoadOmmdtCQkKUm5ub8vDwUNevX4+3f3R0tKpXr16CeZoeP36s8uTJo/LkyaMuX76c4HmCg4NfOU/TkSNHlKWlpapbt656+vRpgvtPnDihli1bppRSKjQ0VFlaWqoBAwbE26dly5aJDgR3dHR84fM+H5D//fffK1tbWzVkyJB494eFhSlnZ2dVt25dpdfrExyflO+/EGlNiiYhXkNiRVNi/vuhunfvXgWo2rVrq7lz56oxY8YoNzc39dZbb6n//g0zYsQIBagaNWqoyZMnq5kzZ6qOHTvGu1rpRR/aQUFByt3dXbm4uKgRI0aoadOmKW9vb6XT6dTGjRtfmnnNmjUKUJs3b070foPBoHLnzq2aNWtmbjt8+LAClJOTkypXrlyixzRt2lTpdDrVpk0bNXPmTDV9+nTVq1cvlSNHjnjfxxcVTcn53j3/kO/QoYOaPXu2at26tfL29lZAvAk6g4KCVMGCBZWDg4P67LPP1Pz589WECROUr6+vcnV1fen3SakXf/+VUqps2bLK09MzXkHw22+/KScnJ+Xi4qIGDRqkFi9erMaNG6eKFSumdDqd+v777xM8ztGjR1WOHDmUvb296tGjh5o3b56aN2+e6tmzp3JyclKNGjV6Zc558+YpCwsLlS9fPjVs2DC1ePFiNX36dNWiRQtlYWGhxo8fb963TZs2ysrKSg0cOFDNnj1bNWnSRPn4+CS7aFJKqaJFiyonJycFqJMnTya4f9WqVcrCwkKVLVtWjR07Vs2fP18NHz5ceXt7J/ozIITWpGgS4jW8btGklFKLFy9WxYoVU7a2tqpkyZJq6dKlatSoUQk++JVSasmSJapChQrK1tZWubq6qrp165qnAHjR4z93/fp11apVK5U9e3ZlZ2enqlSporZu3frK19asWTNlZ2cXr0fnvzp37qysra3Nl+objUbl6empADV27NhEj9Hr9erbb79VZcqUMb8eHx8fNWbMGBUWFmbe70VFk1JJ/95FRkaqvn37qhw5cqhs2bKpFi1aqMuXLytATZw4Md6+wcHBqm/fvsrT01NZW1srDw8P9fbbb6sFCxa88nv1su//8961/862HRgYqHr06KEKFCigrK2tVa5cudQHH3yQYDqGf7t3754aMGCAKl68uLKzs1MODg7Kx8dHjRs3Lt737mVOnjyp2rVrp/Lmzausra2Vq6urevvtt9Xy5cvj9Tw+fPhQtWzZUjk4OChXV1f1ySefqD///PO1iqbhw4crQBUtWvSF++zfv181btxYubi4KDs7O1WkSBHVuXNndeLEiSS9LiHSkk4ppVL5DKAQQmguICCAChUq8MMPP/Dxxx9rHUcIkQHJPE1CiEzn+Yza/zZ9+nQsLCyoU6eOBomEEJmBXD0nhMh0vvvuO06ePEn9+vWxsrJix44d7Nixg549e77yCjIhhHgROT0nhMh0du/ezZgxY7hw4QIREREUKFCADh06MHz4cJn/Rwjx2qRoEkIIIYRIAhnTJIQQQgiRBFI0CSGEEEIkQZY7uW80Grl37x5OTk6vvZyDEEIIIdI3pRRPnz4lb968ia6R+DqyXNF07949uXpGCCGEyCJu375N/vz5U+SxslzR5OTkBJi+ic7OzhqnEUIIIURqCA8Px9PT0/y5nxKyXNH0/JScs7OzFE1CCCFEJpeSQ3FkILgQQgghRBJI0SSEEEIIkQRSNAkhhBBCJIEUTUIIIYQQSSBFkxBCCCFEEkjRJIQQQgiRBFI0CSGEEEIkgRRNQgghhBBJIEWTEEIIIUQSSNEkhBBCCJEEUjQJIYQQQiSBFE1CCCGEEEmgadH022+/0axZM/LmzYtOp2Pz5s2vPObAgQNUrFgRW1tbihYtyrJly1I9pxBCCCGEpkVTZGQk5cuXZ/bs2UnaPzAwkPfee4/69esTEBDA559/Tvfu3dm1a1cqJxVCCCFEVmel5ZM3adKEJk2aJHn/efPmUahQIaZMmQJAqVKlOHz4MNOmTaNx48apFVMIIYQQImONafL396dhw4bx2ho3boy/v79GiYQQQgiRbsSGw5OzcHcrsRfnpfjDa9rTlFxBQUG4u7vHa3N3dyc8PJzo6Gjs7e0THBMTE0NMTIz5dnh4eKrnFEIIIUQK04dB1B2Ivvv3v/cg+j48C4KoexAZCM+CiYmFAT9A4MOUj5ChiqbXMWHCBMaMGaN1DCGEEEK8iFIQEwKRN01fUXf++YoIhIhroH/yyoe58QBaz7Tk5A1DqsTMUEWTh4cHwcHB8dqCg4NxdnZOtJcJ4IsvvmDgwIHm2+Hh4Xh6eqZqTiGEEEL8i1Kgf2wqiCJumnqFIm7+XST9vW2IevXj2LiCfT5wyGf61z4P2HmAQ142H7hB59HfEBYWTs6cOZk/fz6tWrVK0ZeRoYqm6tWrs3379nhtu3fvpnr16i88xtbWFltb29SOJoQQQmRtxlh4eh2eXoWIGxBx/Z+eo4ibEPf01Y9hnwccvcDB8++v/OBYAJyKgWMhsM6W4JDY2FiGDRvG1KlTAVOtsHbtWlxcXFLy1QEaF00RERFcu3bNfDswMJCAgABy5MhBgQIF+OKLL7h79y4rVqwAoFevXsyaNYshQ4bQtWtX9u3bx7p169i2bZtWL0EIIYTIOuKi4ekVCLtgKoqeXjWdPou6bfpSrzgtZudhKoqyFYr/r6OXqTiyTH4nR8eOHVmzZg0AgwYNYsKECVhbW6fKGGZNi6YTJ05Qv3598+3np9E6derEsmXLuH//Prdu3TLfX6hQIbZt28aAAQOYMWMG+fPnZ9GiRTLdgBBCCJGSYiMg9ByEX4CwixB+EcIvmQok1IuPs3QA5xKmYihbUcjmZeohcvQCx4JglfhQmjcxYMAA9u3bx/z582nRokWKP/6/6ZRSL3n1mU94eDguLi6EhYXh7OysdRwhhBBCO/onEPonhF/+pwcp7LzplNqLWGeH7GVMp8ycioFjYXD0NBVH9nlAp0vVyHFxcZw+fZrKlSub26KionBwcIi3X2p83meoMU1CCCGEeA2GGAj7E54EmHqQws6bvqLvv/gY+zzgUhacS4FLqX/+tc2d6oXRi9y/f5927drh7+/P0aNH8fb2BkhQMKUWKZqEEEKIzEQfCk/OQOgZUy/S45MQehZUXOL7OxQAl9KmXiOXUuBSxvRlmzNNY7/K/v37adu2LcHBwTg6OnLr1i1z0ZRWpGgSQgghMiKlTIOvnwT86+v0i0+t2eQAV2/I/hZkL/t3cVQarNP3UBWj0ciECRMYOXIkRqORsmXLsn79ekqWLJnmWaRoEkIIIdI7Y6xpQPbz4ij0739fNOGjY0HIXt5UHLl6Q84qph4ljU6rva6QkBDat2/Prl27AOjcuTOzZ89Os9Nx/yVFkxBCCJGe6MNMBVHYn/D4lKn3KOw8GPUJ99VZmXqLXL3//qpg6kmyzZHGoVPH8uXL2bVrF3Z2dsyZM4cuXbpomkeKJiGEEEIrxjhTcRRyFB79ASF/mC7tT+yyfmvnv0+vef9TJLmUfq25jTKKAQMGcO3aNXr37s1bb72ldRwpmoQQQog0ow+FR8fh4REIOQIh/hAXmXA/Ry/TmCNXb8hR0dSD5OiV4U6vJVdoaCjjxo3j66+/xt7eHgsLC+bOnat1LDMpmoQQQojU8izEVBgF74cHB0yn3f7bi2TtbBpzlLMa5Kpq2rZz0yCstk6ePImvry+BgYFERESkq2LpOSmahBBCiJQSGw7BByF4LwTtNZ16+y/HQpC7BuSuCblrmXqUdBZpnzWdUEoxd+5cBgwYgF6vp1ChQnTv3l3rWImSokkIIYR4Xc8ewsPD8OCgqTcp9BwJepKcioN7PXCrZ/rXPk/a50ynnj59Ss+ePc1rx7Vo0YKlS5eSPXt2bYO9gBRNQgghRFJFB0PI7/Dgt7+LpDMJ98lWGDzeAY+3wa0+2OVK+5wZwMWLF2nRogVXrlzBysqKb7/9lgEDBqBLx+O2pGgSQgghXuTZQwjaA0G7Tb1JETcS7uNSGnLXAff6ptNtDnnTPmcG5OjoSEhICPnz52ft2rXUqFFD60ivJEWTEEII8ZwxztSLdG+7qVhK0JOkM00Ymau6qRfJo0GWHLT9uuLi4rCyMpUeBQoUYOvWrRQrVoxcuTJGb5wUTUIIIbK26Ptwdyvc2wZB+yDuafz7s5eHPO+A+9uQqxrYZNckZkZ3+fJlfH19GTduHM2aNQOgevXqGqdKHimahBBCZC3Pr3AL+tXUmxR+Kf79trkg73uQp7HplJu9hzY5M5Eff/yRnj17EhERwdChQ2natCmWlpZax0o2KZqEEEJkfk/Owt2f4d4O08zbyhD//pxVIF8zyNvU1LNkkfE+0NOjZ8+eMXDgQPOcS/Xq1ePHH3/MkAUTSNEkhBAiMzIa4MF+02m3oN0QdiH+/dmKgkdDU2+SW22wzalNzkzsxo0b+Pr6curUKQC++uorRo0aZR7TlBFl3ORCCCHEvz0fxH33F/hrNTx78M99FtaQp4mpJylPY8jmpVnMrODevXtUrFiRsLAwcubMyQ8//MC7776rdaw3JkWTEEKIjMsYa5ov6dYGuLMJYkL+uc/GFTxbQp5GpkHctjm0y5nF5M2bl7Zt23L27FnWrFmDp6en1pFShBRNQgghMhZDjGmJktsb4M5m0D/55z7bnKaxSflbmHqVLKy1Spnl3L59GxsbG9zd3QGYPn06FhYWWFtnnvdAiiYhhBDpn+GZae6k25tMp99iw/65z84N8n8EBVqBW12wkI+2tLZjxw46dOhA+fLl+fXXX7G0tMTW1lbrWClOfrKEEEKkT0qZliy5vghurYe4yH/us89jOvXm2co0C7dc7aaJuLg4Ro8ezbhx4wAIDQ3l8ePH5M6dW+NkqUOKJiGEEOmH0QAPDsBfa0yn4CID/7nPwRMK+ILnR6YZuXUWmsUUcP/+fdq1a8eBAwcA6NOnD1OmTMHOzk7bYKlIiiYhhBDae3rNVChdXwSRf/3TbukABdtAkW5/F0rpdzHXrGT//v20bduW4OBgsmXLxsKFC2nTpo3WsVKdFE1CCCG0oQ+Dv36E64vh8Yl/2q2zQ0E/yPeBaQ4layfNIoqEDAYDffv2JTg4mHLlyrF+/XpKlCihdaw0IUWTEEKItGOIMQ3ovrnKNPGkMcbUrrM0LVni1R4KtAYre21ziheytLRkzZo1zJkzh6lTp+Lg4KB1pDSjU0oprUOkpfDwcFxcXAgLC8PZ2VnrOEIIkfkpIzw4ZCqUbq2H2NB/7nMpYzr15tUe7DLn4OHM4MiRI1y+fJmuXbtqHSXJUuPzXnqahBBCpI7Q8xC4wnQKLur2P+32+cCrLXh9bFrnTcYppVtKKaZOncrQoUPR6XSULVuWKlWqaB1LM1I0CSGESDn6MNOA7htL4NGxf9qtXUzzKHl9DLnryBQBGcCTJ0/o0qULW7ZsAaBNmzaUKlVK41TakqJJCCHEm3k+n9LVuXD7J9NElAA6K8j3PhTqYJqd2zLzXoqe2Zw4cYLWrVsTGBiIjY0NM2bM4JNPPkGXxXsFpWgSQgjxep5eN41T+msNhF/8p92lNBTuBoXam2brFhnK/Pnz6d+/P3q9nkKFCrFhwwYqVqyodax0QYomIYQQSRf7FO5sMZ1+C97/T7ulvWmagKK9IWdlGaeUgUVGRqLX62nRogVLly4le/bsWkdKN6RoEkII8WoRgXBxCgQu+9dyJjrweMc0Til/c7Bx0TKheAMGgwFLS9M4swEDBlCoUCFatGiR5U/H/ZcUTUIIIRJneGZaIPfGEtOSJvw9Q41TMSjYDop0AceCmkYUb27p0qXMnDmT3377jWzZsqHT6fjwww+1jpUuSdEkhBAivoibcG0BXF8AMY/+afdoBKWHgHsDOf2WCURFRdG3b1+WLVsGwLx58xg8eLC2odI5KZqEEEKYFsq9t810Bdz9XZh7lRw8oXBXKNwJshXSNKJIOZcuXcLX15c///wTCwsLvvnmGwYOHKh1rHRPiiYhhMjKDHrTLN0XxkPYhX/aPRpCsd6Qr7nMqZTJ/Pjjj/To0YPIyEg8PDz48ccfqVevntaxMgQpmoQQIit69hCuzoOrc+BZkKnNOjsU7QFFe4JTUU3jidQxe/ZsPv30UwDq16/P6tWr8fDw0DhVxiFrzwkhRFYSeg4uz4DAH/5ZLNc+LxTtBSX6yxVwmVxQUBA+Pj507dqV0aNHm6+Yy4xk7TkhhBDJpxTc2w6Xp0PQnn/ac1SCkgOggC9YWGsWT6SuM2fOUL58eQA8PDy4ePGidBq8JgutAwghhEglygg318C20nDwfVPBpLMAz1bwzmFofAy82knBlEnFxsYyaNAgvL29WbNmjbldCqbXJz1NQgiR2Riewc0f4dLkfwZ3WzmaTsEV/xSyeWkaT6S+27dv4+fnh7+/PwAXLlx4xREiKaRoEkKIzOJZiGlg99XZ8OyBqc3aBUoOgpKfgbX0MGQFO3bsoEOHDjx69AgXFxeWL19O8+bNtY6VKUjRJIQQGZ0+FP4cC1dmglFvanPwNPUqFf1EBndnEXFxcYwePZpx48YB4OPjw7p16yhcuLDGyTIPKZqEECKj0ofChYmmCSljw01tOXxMPUsFWslYpSzmyJEj5oKpT58+TJ06FVtbW41TZS5SNAkhREajDzNdCXdl5j/LnLiUgQqTIG8TTaMJ7dStW5eRI0dSqlQp2rRpo3WcTEnmaRJCiIwiLgquzDL1LumfmNqcS4L3t5DvfdOVcSLLMBqNTJ48mbZt2+Lp6al1nHRH5mkSQoisyKCHa/Ph/Ph/Zu92LgVlR/49x1LmnaBQJO7hw4d06NCBXbt2sXnzZg4dOpSpJ6pML6RoEkKI9MoYC4Er4dxoiLptanP0gnKjwau9FEtZ1JEjR/Dz8+Pu3bvY29vTs2dPKZjSiBRNQgiR3igj3N4EAcMg4pqpzc4dyo2Cwt3A0kbbfEITSimmTp3K0KFDMRgMlChRgg0bNlC2bFmto2UZUjQJIUR6oRTc3wVnvoQnp01tdm5QcjCU6AeWdtrmE5oJCwujU6dObNmyBYA2bdqwYMECnJycNE6WtUjRJIQQ6UHIH3B6MDw8bLptlc20Llyp/4G1fDBmdTY2NgQGBmJjY8OMGTP45JNP0Ol0WsfKcqRoEkIILUX+ZToN99ffa4NZ2ELxvlD6C7DLpW02oSmlFEopLCwssLe3Z/369Tx9+hQfHx+to2VZUjQJIYQWYp/CxSlw8TswRAM6KNwJ3hoLDvm0Tic09vTpU3r06EG5cuUYPnw4AMWLF9c4lZB5moQQIi0ZnsHl703F0vOJKd3qgs90cPXWMplIJ86ePYuvry9XrlzB1taWGzdukDdvXq1jZTgyT5MQQmRUSsGdzXBqEEQGmtqcipl6lgr4goxPEcDSpUvp06cPz549I3/+/Kxbt04KpnREiiYhhEhtDw7DuZEQvN902z4vlB8PXh+DhfwaFhAZGUnfvn1Zvnw5AE2aNGHFihXkyiXj2tIT+d8qhBCpJeIGnPgM7m013bawgVKDocyXYOWobTaRbhgMBurVq8eJEyewsLBg7NixDB06FAsLWRYnvZGiSQghUlrMY7g01fRliAadFRTuAmWHg2NBrdOJdMbS0pIePXpw584dfvzxR+rVq6d1JPECMhBcCCFSitEA1xdAwJcQG2pqc68PleaAS0lNo4n05dmzZ9y5c4eiRYsCpukFQkNDcXV11ThZ5iEDwYUQIr16eAROfgaPT5puZy9nWiMu/4cyyFvEc/36dXx9fXny5AmnTp3C1dUVnU4nBVMGICdMhRDiTUTcgN8+hN21TAWTtTNUnA7vngLPj6RgEvFs2rQJHx8fTp8+zdOnT7l27ZrWkUQySE+TEEK8jmcP4fwEuDoLjLGgs4TCXeGtb8DeXet0Ip3R6/UMGzaMadOmAVCjRg3Wrl1L/vz5NU4mkkOKJiGESI64aLjyPZwfD7HhpjaPRlBxKmQvo202kS7dunULPz8/jh49CsDgwYMZP3481tbWGicTySVFkxBCJNW9HXC8D0TeNN12rWiabylvY01jifRt+PDhHD16lOzZs7Ns2TKaN2+udSTxmqRoEkKIV4kOhlOf/7OorkN+eGscFGoPOhkaKl5u+vTpREZGMmXKFAoVKqR1HPEGNP/fPnv2bLy8vLCzs6Nq1aocO3bspftPnz6dEiVKYG9vj6enJwMGDODZs2dplFYIkaUoI1ydC1tLmgomnQWUGADvXYTCHaVgEom6f/8+kydP5vmMPjlz5mTjxo1SMGUCmvY0rV27loEDBzJv3jyqVq3K9OnTady4MZcvX8bNzS3B/qtXr2bYsGEsWbKEGjVqcOXKFTp37oxOp2Pq1KkavAIhRKYVcRP+6AbB+0y3XStA1YWQw0fTWCJ927dvH23btuXBgwfkyJGDrl27ah1JpCBN/0yaOnUqPXr0oEuXLpQuXZp58+bh4ODAkiVLEt3/999/p2bNmrRr1w4vLy8aNWpE27ZtX9k7JYQQSaYUXJ0P28uZCiZLB/D5Hhofl4JJvJDRaOSbb77hnXfe4cGDB5QrV45atWppHUukMM2KJr1ez8mTJ2nYsOE/YSwsaNiwIf7+/okeU6NGDU6ePGkukm7cuMH27dtp2rRpmmQWQmRykX/B/kZwvBfERUDuWtD0DJToBxaWWqcT6dTDhw9p2rQpI0eOxGg00q1bN44ePUrx4sW1jiZSmGan50JCQjAYDLi7x5/PxN3dnUuXLiV6TLt27QgJCaFWrVoopYiLi6NXr158+eWXL3yemJgYYmJizLfDw8NT5gUIITIPpeD6Ijg1COKegqW96aq4Ev1l3JJ4qd9//53WrVtz9+5d7O3tmTt3Lp06ddI6lkglGeq3wYEDBxg/fjxz5szh1KlTbNy4kW3btvHNN9+88JgJEybg4uJi/vL09EzDxEKIdC/yNhxoAsd6mgqmXDWgSQCU/FwKJvFKkZGR3Lt3jxIlSnDs2DEpmDI5zRbs1ev1ODg4sGHDBlq0aGFu79SpE6GhoWzZsiXBMbVr16ZatWpMmjTJ3PbDDz/Qs2dPIiIisLBI+AsusZ4mT09PWbBXiKxOKbixzDSVQGw4WNj+3bv0mZyKEy+llEL3r+VxNm7cyDvvvIOTk5OGqcR/pcaCvZr9GWVjY4OPjw979+41txmNRvbu3Uv16tUTPSYqKipBYWRpafrl9qLaz9bWFmdn53hfQogsLvKWqXfpj66mgilnVVPvUqmBUjCJlzp+/DgVKlSIt2bcRx99JAVTFqFp3/PAgQNZuHAhy5cv5+LFi/Tu3ZvIyEi6dOkCQMeOHfniiy/M+zdr1oy5c+eyZs0aAgMD2b17NyNGjKBZs2bm4kkIIV7o+bxL28rA/V2m3iXvb+GdI+BSUut0Ih1TSjF79mxq1arFmTNnGDp0qNaRhAY0nafJz8+Phw8fMnLkSIKCgvD29mbnzp3mweG3bt2K17P01VdfodPp+Oqrr7h79y65c+emWbNmjBs3TquXIITIKJ5egz+6w4ODptu5akC1JeBcQttcIt0LDw+nR48erFu3DoAPP/zwhVPjiMxNszFNWkmNc5xCiHRMKbj8PZz5AgzRpnmXvCdAsb5yKk680tmzZ2nVqhVXr17FysqKSZMm8dlnn8Ub0yTSp9T4vJe154QQmdezh3C0M9zbbrrt/rZpVu9sspyFeDV/f38aNGjAs2fP8PT0ZN26dVSrVk3rWEJDUjQJITKn4P3w+8cQfd80dqniVCjWG6SHQCSRj48Pb731Fjlz5mTlypXkzJlT60hCY1I0CSEyF8MzODsCLk01Dfx2LgW11kL2clonExnA9evXKViwIFZWVtjY2LBjxw6yZ8+e6JQ2IuuRnwIhRObx+DTs9IGLk00FU+Eu8O4JKZhEkqxevZry5cszYsQIc1uOHDmkYBJm0tMkhMj4jHFw4Vs4NxpUHNi5Q9VFkO99rZOJDODZs2cMGDCAefPmAaa5mOLi4rCyko9IEZ/8RAghMran18C/I4T8vdC3Z0uoPA/scmmbS2QI169fx9fXl9OnT5untBk1apTM/ScSJUWTECJjUgquLYBTA8EQBdbOUGkWeLWXwd4iSTZu3EiXLl0IDw8nV65c/PDDDzRu3FjrWCIdk6JJCJHxRN+Ho93g/g7Tbff6UG0ZOBbQNJbIOB4+fEinTp2IiIigZs2arFmzhvz582sdS6RzUjQJITKWW+vhWC/QP/57GZQJpkV2dTJYVyRd7ty5mT9/PqdPn2b8+PFYW1trHUlkADIjuBAiY9CHwolP4eYq023XClB9JWQvo2kskXFs374dJycnateurXUUkQZS4/Ne/jQTQqR/QXthezlTwaSzgDJfQaOjUjCJJImLi+PLL7/kvffew8/PjwcPHmgdSWRQcnpOCJF+xUWb1oy7PMN0O1tRqL4CclfXNpfIMO7fv0/btm05eNC0UHPLli1xcXHROJXIqKRoEkKkT+FX4XArCD1rul20F1ScDFaO2uYSGca+ffto27YtDx48IFu2bCxatAg/Pz+tY4kMTIomIUT689daOPYJxIaZJqqsthTyNtE6lcggjEYj48aNY9SoUSilKFeuHBs2bKB48eJaRxMZnIxpEkKkH7ERcLQLHGljKphy1zQtgyIFk0gGnU7HuXPnUErRrVs3jh49KgWTSBHS0ySESB8eHIY/usLTq38P9h4OZUeChfyaEkmjlEKn06HT6Vi0aBEtW7aU03EiRUlPkxBCW8oI5yfC3rqmgsnBE97eD299LQWTSBKlFJMnT6Zt27Y8n0XH2dlZCiaR4uQ3khBCOzGPwb8D3Ntuuu3VHip9Dzau2uYSGcaTJ0/o1KkTv/zyCwAdO3akadOmGqcSmZUUTUIIbTw+BYf9IOKaaWbvSrOgSDdZN04k2fHjx2ndujU3b97ExsaG77//niZNZPybSD1SNAkh0pZScGUmnP4fGPXgUADqbgFXb62TiQxCKcXs2bMZOHAgsbGxFC5cmPXr11OxYkWto4lMToomIUTaiXlsGux9Z4vpdr4PTNMJ2ObQNpfIUD7//HO+//57AD788EOWLl0qE1aKNCEDwYUQaSPkD9hZ0VQwWdiAz/dQZ7MUTCLZfH19sbe3Z9q0afz0009SMIk0Iz1NQojUpRRcXwQn+oIxFrIVgVrrIIecShFJo5Tixo0bFClSBIBatWpx8+ZN3NzcNE4mshrpaRJCpJ64aNNklcd6mgomz4/g3ZNSMIkki4yMpEuXLrz11lucP3/e3C4Fk9CC9DQJIVJHxE049BE8OW2arLL8eCg1RK6OE0l28eJFfH19OX/+PBYWFhw9epQyZcpoHUtkYVI0CSFSXvABONwaYh6CbS6ouRY8GmidSmQgq1evpmfPnkRGRuLh4cGaNWuoW7eu1rFEFien54QQKUcpuDof9r1jKpiylzetHScFk0iiZ8+e0atXLz7++GMiIyNp0KABAQEBUjCJdEGKJiFEyoiLhj+6wfFeoOKgYFto5A+OBbVOJjKQBQsWMH/+fHQ6HSNGjODXX3/F3d1d61hCAHJ6TgiREsIumk7Hhf1pGr/01lgoPUzGL4lk69OnD4cOHaJ79+40btxY6zhCxCM9TUKINxO4CnZWMhVMdu5Q/1co84UUTCJJ9Ho9U6ZMISYmBgArKyvWr18vBZNIl6SnSQjxeuKi4NQAuLbAdNv9bajxA9h7aJtLZBi3bt2idevW/PHHH9y8eZOZM2dqHUmIl5KiSQiRfKF/wpE2EPb3vDllhkO5MWBhqW0ukWFs27aNjh078vjxY7Jnz84777yjdSQhXkmKJiFE8tzeBL9/DIZosPOA6isgj3zgiaSJi4tj5MiRTJgwAYBKlSqxbt06ChUqpHEyIV5NiiYhRNIoI5wbDX9+Y7rt8Y7pdJydzMwskub+/fu0adOG3377DYB+/foxadIkbG1tNU4mRNJI0SSEeLXYcNNyKLc3mm6X+BwqTAIL+RUiki46OpozZ87g5OTEokWLaN26tdaRhEgW+Y0nhHi5J2fhsC88vQIW1lB5PhTponUqkUEopdD9fSVl4cKF2bBhAwUKFKB48eIaJxMi+WTKASHEi935BXbXMBVMDvmh4SEpmESSPXjwgCZNmrBr1y5zW8OGDaVgEhmW9DQJIRJSCi5Ph1ODAAXuDaDWOrDNqXUykUEcPnwYPz8/7t27x8WLF7l27RrW1tZaxxLijbxRT9OzZ89SKocQIr0w6OGP7nBqIKCgSDeov1MKJpEkRqORSZMmUa9ePe7du0fJkiXZtm2bFEwiU0h20WQ0Gvnmm2/Ily8f2bJl48aNGwCMGDGCxYsXp3hAIUQaenoNdlaEG0tMy6FUnA5VFprGMgnxCo8fP6ZFixYMGTIEg8FAu3btOH78OGXLltU6mhApItlF09ixY1m2bBnfffcdNjY25vayZcuyaNGiFA0nhEhDj47Dr9VNE1ba5oI6v0DJz2Q5FJEkjx49omLFivzyyy/Y2Ngwb948fvjhB7Jly6Z1NCFSTLKLphUrVrBgwQI+/vhjLC3/mf23fPnyXLp0KUXDCSHSyN1tsLc+xISAa0Voeg7yNdU6lchAcubMSYMGDShcuDD+/v588skn5qvmhMgskj0Q/O7duxQtWjRBu9FoJDY2NkVCCSHSiFJw8TsI+AJQpgkra/8E1k5aJxMZQHh4OHFxceTIkQOAWbNmodfryZ49u7bBhEglye5pKl26NIcOHUrQvmHDBipUqJAioYQQaSD2KfzeDgKGAQoKd4W6W6VgEkly5swZKlWqRPv27TEajQA4ODhIwSQytWT3NI0cOZJOnTpx9+5djEYjGzdu5PLly6xYsYKtW7emRkYhREqLCISD70PYBdBZgc8MKNZbxi+JV1JKsWTJEj799FOePXvGs2fPuHv3Lp6enlpHEyLVJbunqXnz5vzyyy/s2bMHR0dHRo4cycWLF/nll19klWohMoKHR2BnJVPBZJ8X3t4PxftIwSReKTIyks6dO9O9e3eePXtG06ZNOX36tBRMIsvQKaWU1iHSUnh4OC4uLoSFheHs7Kx1HCHS1u1NcKQNGPWQoxLU2QwO+bROJTKAixcv0qpVKy5cuICFhQXjxo1jyJAhWFjIwhIifUqNz/tk/7QXLlyYR48eJWgPDQ2lcOHCKRJKCJHClBHOjobDrUwFU74PoOFBKZhEkhiNRvz8/Lhw4QJ58uRh3759DBs2TAomkeUk+yf+5s2bGAyGBO0xMTHcvXs3RUIJIVJQXLRpwd0/x5iKpyI9oPZGsHLQOpnIICwsLFi6dKn5dFzdunW1jiSEJpI8EPznn382b+/atQsXFxfzbYPBwN69e/Hy8krRcEKINxR111QwhfibBnxXXQSFO2mdSmQA165dIyAggFatWgHg4+PDtm3bNE4lhLaSXDS1aNECAJ1OR6dO8X/pWltb4+XlxZQpU1I0nBDiDYT8Ab99AM8egLUL1P0Z3OponUpkAD/99BNdu3bl2bNnFCpUCB8fH60jCZEuJLloej4PR6FChTh+/Di5cuVKtVBCiDf01zrw7wjGGMj+lmnCSqeEk9IK8W96vZ4hQ4YwY8YMAGrWrIm7u7vGqYRIP5I9T1NgYGBq5BBCpJTLM+Fkf9N23veh5o9gLet/iZf766+/aN26NceOHQNgyJAhjB07FmtrWaxZiOeSXTSBaa6OgwcPcuvWLfR6fbz7+vfvnyLBhBCv4eJkOP0/03bxflBxGlhYvvwYkeVt27aNDh068OTJE1xdXVm+fDnNmjXTOpYQ6U6yi6bTp0/TtGlToqKiiIyMJEeOHISEhODg4ICbm5sUTUJoQSk4Nwr+/MZ0u8xX8NbXMmGlSJKzZ8/y5MkTKleuzLp16+SiHiFeINlTDgwYMIBmzZrx5MkT7O3tOXr0KH/99Rc+Pj5Mnjw5NTIKIV4mNgIOt/6nYHrrGyj/jRRMIsmGDh3K3LlzOXTokBRMQrxEsoumgIAABg0ahIWFBZaWlsTExODp6cl3333Hl19+mRoZhRAvog+D/Y3h9gawsIYqC6HsV1qnEuncnj17aNiwIVFRUYBpHqZevXpha2urcTIh0rdkF03W1tbmWWDd3Ny4desWAC4uLty+fTtl0wkhXizqDuypCyG/m6YUePsAFO2udSqRjhkMBsaMGUOjRo3Yu3cv3377rdaRhMhQkj2mqUKFChw/fpxixYpRt25dRo4cSUhICCtXrqRs2bKpkVEI8V/hl2FfI4i6BXbuUG8H5KigdSqRjj148ID27duze/duALp3786wYcM0TiVExpLsnqbx48eTJ08eAMaNG4erqyu9e/fm4cOHzJ8/P8UDCiH+4+HvsLumqWByKg6N/5CCSbzU4cOHqVChArt378bBwYHly5ezcOFC7O3ttY4mRIaiU0oprUOkpdRY9ViINHN3m2lZFEM05KgM9baBXW6tU4l0bM2aNbRv3x6DwUCpUqVYv349ZcqU0TqWEKkuNT7vU2yJ6lOnTvH++++n1MMJIf5NKbg0Aw42MxVMeZpAw/1SMIlXqlWrFq6urnz88cccO3ZMCiYh3kCyiqZdu3YxePBgvvzyS27cuAHApUuXaNGiBZUrVzYvtSKESEHGWDjWE059Digo3BXqbAYrR42DifTqzp075u38+fNz+vRpVq5cSbZsMjO8EG8iyUXT4sWLadKkCcuWLePbb7+lWrVq/PDDD1SvXh0PDw/+/PNPtm/fnppZhch6jAbT6bjri0BnARWmQNVFYGmjdTKRDimlmDlzJkWKFGHTpk3m9vz586OTebuEeGNJLppmzJjBt99+S0hICOvWrSMkJIQ5c+Zw7tw55s2bR6lSpVIzpxBZj1JwehDc2QKWdlB7M5QaKJNWikSFhYXRunVr+vfvj16vZ+vWrVpHEiLTSfJAcEdHR86fP4+XlxdKKWxtbdm/fz81a9ZM7YwpSgaCiwxBGeHEp3B1rul2jdXg1VbbTCLdCggIwNfXl2vXrmFtbc3kyZPp16+f9C6JLE3TgeDR0dE4ODgAoNPpsLW1NU898CZmz56Nl5cXdnZ2VK1a1bzC9ouEhobSt29f8uTJg62tLcWLF5fTgiJzMRrgj25/F0w60+k4KZhEIpRSLFq0iGrVqnHt2jUKFCjAoUOH6N+/vxRMQqSCZE1uuWjRIvNAwri4OJYtW0auXLni7ZOcBXvXrl3LwIEDmTdvHlWrVmX69Ok0btyYy5cv4+bmlmB/vV7PO++8g5ubGxs2bCBfvnz89ddfZM+ePTkvQ4j0y/AM/ugON1eBzhKqrwCvdlqnEunUsWPH6NGjBwBNmzZlxYoV5MyZU+NUQmReST495+Xl9cq/XHQ6nfmquqSoWrUqlStXZtasWQAYjUY8PT3p169fojPVzps3j0mTJnHp0iWsra2T/Dz/JqfnRLoVGw4HP4AHB00FU80foYCv1qlEOjdgwADc3d0ZMmSIeYkrIUTqfN5rNrmlXq/HwcGBDRs20KJFC3N7p06dCA0NZcuWLQmOadq0KTly5MDBwYEtW7aQO3du2rVrx9ChQ7G0tEzS80rRJNIlfRjsbwSPjoG1M9RaD3kaaZ1KpENr1qyhbt26KTI8QojMLF1PbplcISEhGAwG3N3d47W7u7sTFBSU6DE3btxgw4YNGAwGtm/fzogRI5gyZQpjx4594fPExMQQHh4e70uIdCXqLuypbSqYbHPC2/ulYBIJPHv2jE8++YS2bdvStm1b4uLitI4kRJaT7AV7tWQ0GnFzc2PBggVYWlri4+PD3bt3mTRpEqNGjUr0mAkTJjBmzJg0TipEEj29Zlp4NzJQFt4VL3Tt2jV8fX0JCAhAp9NRr149GegthAY062nKlSsXlpaWBAcHx2sPDg7Gw8Mj0WPy5MlD8eLF452KK1WqFEFBQej1+kSP+eKLLwgLCzN/3b59O+VehBBv4slZ2F3LVDBlKwKNjkrBJBL46aefqFixIgEBAeTOnZtdu3YxevToJA9JEEKkHM2KJhsbG3x8fNi7d6+5zWg0snfvXqpXr57oMTVr1uTatWvxlmu5cuUKefLkwcYm8RmSbW1tcXZ2jvclhOYe/g576sKzYHD1hneOQDYvrVOJdESv1/P555/TqlUrnj59Sq1atTh9+jTvvPOO1tGEyLI0vdRi4MCBLFy4kOXLl3Px4kV69+5NZGQkXbp0AaBjx4588cUX5v179+7N48eP+eyzz7hy5Qrbtm1j/Pjx9O3bV6uXIETy3f8V9jWE2FDIVd00hsne/ZWHiawlNjaWX3/9FYAhQ4awb98+8uXLp3EqIbK21xrTdP36dZYuXcr169eZMWMGbm5u7NixgwIFCiRrBW0/Pz8ePnzIyJEjCQoKwtvbm507d5oHh9+6dSveJbSenp7s2rWLAQMG8NZbb5EvXz4+++wzhg4d+jovQ4i0d20RHO8NKg7yvAu1N8jCuyJRjo6ObNiwgevXr9OsWTOt4wgheI0pBw4ePEiTJk2oWbMmv/32GxcvXqRw4cJMnDiREydOsGHDhtTKmiJkygGhmcAfwL+DabtgO6i2BCxttc0k0o24uDhGjBhBrly5GDRokNZxhMjw0sWUA8OGDWPs2LHs3r073jiiBg0acPTo0RQJJUSmc+snONrZtF28P9T4QQomYXbv3j0aNGjAxIkTGTZsGIGBgVpHEkIkItlF07lz5/jwww8TtLu5uRESEpIioYTIVG6th9/bgTJA4S7gMw3kcnHxtz179uDt7c2hQ4dwcnJi9erVFCpUSOtYQohEJLtoyp49O/fv30/Qfvr0aRmkKMR/XZ0Ph1uDUQ8FWkOVhaCTpS4EGAwGxowZQ6NGjXj48CHly5fn5MmT+PrK0jlCpFfJ/u3dpk0bhg4dSlBQEDqdDqPRyJEjRxg8eDAdO3ZMjYxCZEznx8PxXqbtYr2hxmqwkLl1BCilaN68OaNHj0YpRffu3fH396dYsWJaRxNCvESyi6bx48dTsmRJPD09iYiIoHTp0tSpU4caNWrw1VdfpUZGITIWowFO/w/ODDfdLj0MKs2WgkmY6XQ6GjVqhIODA8uXL2fhwoXY29trHUsI8QqvvWDvrVu3+PPPP4mIiKBChQoZ5i8kuXpOpCplBP/OcHOl6bb3d1D6f5pGEumD0WjkwYMH5hUPlFLcunWLggULapxMiMwpNT7vkz1P0+HDh6lVqxYFChSgQIECKRJCiEzBGAe/t4dba0FnBdWXg1c7rVOJdODx48d06tSJS5cucfLkSZydndHpdFIwCZHBJPv0XIMGDShUqBBffvklFy5cSI1MQmQ8xjjTFXK31oKFNdRcLQWTAODYsWNUqFCBrVu3cvv2bY4dO6Z1JCHEa0p20XTv3j0GDRrEwYMHKVu2LN7e3kyaNIk7d+6kRj4h0r/YcDjYzDS1gIU11N4IBeQKqKxOKcXMmTOpVasWt27dokiRIhw9epSGDRtqHU0I8Zpee0wTQGBgIKtXr+bHH3/k0qVL1KlTh3379qVkvhQnY5pEioqNgIPvwYPfwNIeaq2DfO9rnUpoLCwsjO7du5tXSGjZsiWLFy/GxcVF42RCZB2p8Xn/RkUTmOYa2bFjByNGjODs2bMYDIYUCZZapGgSKSY2Ag40hYeHwNoZ6u+GXFW0TiXSgR49erBo0SKsra2ZPHky/fr1QycTmgqRptLFMirPHTlyhD59+pAnTx7atWtH2bJl2bZtW4qEEiLdi30KB5r8XTC5SMEk4hk3bhzVq1fn0KFD9O/fXwomITKJZF8998UXX7BmzRru3bvHO++8w4wZM2jevDkODg6pkU+I9MdcMB0xFUwNdkPOylqnEhqKjIxkw4YNdOrUCTAtK3XkyBEploTIZJJdNP3222/873//o3Xr1uTKlSs1MgmRfsWGw/4mEPI7WGf/u2CqpHUqoaELFy7g6+vLhQsXsLKy4uOPPwaQgkmITCjZRdORI0dSI4cQ6V9sBOxrDI+Ogo2rqWDK4aN1KqGhH374gU8++YSoqCjy5MmDp6en1pGEEKkoSUXTzz//TJMmTbC2tubnn39+6b4ffPBBigQTIl2JDYe9DeDxSbDJ8XfBVFHrVEIj0dHRfPbZZyxcuBCAt99+m9WrV+Pm5qZxMiFEakrS1XMWFhYEBQXh5uaGhcWLx47rdDq5ek5kPvpQOPiBadC3bS6ot13GMGVh165dw9fXl4CAAHQ6HSNHjmTEiBFYWsragkKkJ5oto2I0GhPdFiLTi3kMe+tD6FnTtAJSMGV5V65cISAggNy5c7Nq1SreeecdrSMJIdJIsqccWLFiBTExMQna9Xo9K1asSJFQQqQLMY/+KZjsPKDhb1IwCZo2bcrChQs5ffq0FExCZDHJntzS0tKS+/fvJzh3/+jRI9zc3OT0nMgcou7B/ncg7IKpYGqwB7KX0TqV0MBff/1Fr169mDdvniywK0QGki4mt1RKJXop7Z07d2SJAJE56EPhwLumgsk+D7y9VwqmLGrr1q1UqFCBnTt30qtXL63jCCE0luQpBypUqIBOp0On0/H2229jZfXPoQaDgcDAQN59991UCSlEmomLNg36Dj1n6mF65whkK6R1KpHG4uLi+Oqrr/j2228BqFy5MnPnztU4lRBCa0kumlq0aAFAQEAAjRs3Jlu2bOb7bGxs8PLyomXLlikeUIg0Y4yD39v+ay25nVIwZUH37t2jTZs2HDp0CID+/fszadIkbGxsNE4mhNBakoumUaNGAeDl5YWfnx92dnapFkqINKcUHO8Nd7aAhS3U/QVcy2udSqSxP//8kwYNGvDw4UOcnJxYsmQJrVq10jqWECKdSPaM4M/XVhIiUzk7Eq4vAp0F1FwDbnW0TiQ0ULRoUfLnz0/evHnZsGEDRYsW1TqSECIdSVLRlCNHDq5cuUKuXLlwdXV96ZpKjx8/TrFwQqSJSzPg/FjTduW54NlC0zgibYWEhODq6oqlpSV2dnZs3boVV1dX7O3ttY4mhEhnklQ0TZs2DScnJ/O2LEQpMo3rS+HU56btt76Boj01jSPS1qFDh/Dz86Nnz56MHj0agLx582obSgiRbiV7nqaMTuZpEma3foLDvoCCEp9BxWkgfxBkCUajkcmTJ/Pll19iMBgoW7Ysx48fl7GaQmQi6WKeplOnTnHu3Dnz7S1bttCiRQu+/PJL9Hp9ioQSItU9PGK6Ug5l6l2qOFUKpizi8ePHNG/enKFDh2IwGGjfvj3+/v5SMAkhXinZRdMnn3zClStXALhx4wZ+fn44ODiwfv16hgwZkuIBhUhxTwLgwPtgjIV8H0ClOaYB4CLTO3bsGBUqVGDr1q3Y2tqyYMECVqxYEW8KFSGEeJFkf1JcuXIFb29vANavX0/dunVZvXo1y5Yt46effkrpfEKkrKfXYf+7EBsKuWtCzR/BQlanzwrCwsJo1KgRt27dokiRIhw9epQePXrIGE0hRJK91jIqRqMRgD179tC0aVMAPD09CQkJSdl0QqSkyFumBXifBUP2t6DuNrBy0DqVSCMuLi5MnTqVli1bcvLkSfMff0IIkVTJHgjeoEEDPD09adiwId26dePChQsULVqUgwcP0qlTJ27evJlKUVOGDATPomIew+6aEH4JnEvA2wfA3kPrVCKVnTlzhtjYWCpVqgSY/ugDpHdJiCwgXQwEnz59OqdOneLTTz9l+PDh5snfNmzYQI0aNVIklBApKjYC9jcyFUz2+aD+bimYMjmlFAsXLqRq1aq0bNmSR48eAZjXzxRCiNeR7BnB33rrrXhXzz03adIkLC1lbIhIZ4yxcMQPHp8E21zQ4Fdw9NQ6lUhFkZGR9O7dm5UrVwKm31lSKAkhUkKyi6bnTp48ycWLFwEoXbo0FStWTLFQQqQIYyz83gHubQdLe6i7FVxKa51KpKILFy7g6+vLhQsXsLS0ZPz48QwePBgLC7k6Ugjx5pJdND148AA/Pz8OHjxI9uzZAQgNDaV+/fqsWbOG3Llzp3RGIZLPoIffWsD9HWBhDbXWQa6qWqcSqeiHH37gk08+ISoqirx587JmzRpq166tdSwhRCaS7D+/+vXrR0REBOfPn+fx48c8fvyYP//8k/DwcPr3758aGYVIHmWEo11MBZOlA9TZAvne1zqVSEVKKTZs2EBUVBQNGzbk9OnTUjAJIVJcsq+ec3FxYc+ePVSuXDle+7Fjx2jUqBGhoaEpmS/FydVzWcDJgXB5GuisoN42yNNI60QiDTx58oSlS5fy2WefyfhKIUT6uHrOaDRibW2doN3a2to8f5MQmrk4xVQwAVRbIgVTJrZhwwZ69+5tnkbA1dWVgQMHSsEkhEg1yS6aGjRowGeffca9e/fMbXfv3mXAgAG8/fbbKRpOiGQJ/AFODzZte38LhTpom0ekCr1ez2effYavry/z5s1j48aNWkcSQmQRyS6aZs2aRXh4OF5eXhQpUoQiRYpQqFAhwsPDmTlzZmpkFOLV7v8KRzubtksMgFL/0zSOSB1//fUXtWvX5vvvvwdg6NChNG/eXONUQoisItlXz3l6enLq1Cn27t1rnnKgVKlSNGzYMMXDCZEkYRfgsC8oA3h9DBUng8zLk+ls3bqVjh078uTJE1xdXVmxYgXvvy8D/IUQaSdZRdPatWv5+eef0ev1vP322/Tr1y+1cgmRNJF/wf4mEBsOuWtB1cWgkzl5MpuJEyfyxRdfAFClShXWrVtHwYIFNU4lhMhqkvzpMnfuXNq2bcuJEye4evUqffv25X//k1MgQkNRd2BvQ4i6BU7FoM5msLTVOpVIBVWrVsXCwoLPPvuMQ4cOScEkhNBEkqccKFOmDK1bt2bUqFHAPxPJRUZGpmrAlCZTDmQSzx7C7lrw9Ao4esE7h8Ahv9apRAp69OgROXPmNN++fPkyJUqU0DCRECIj0XTKgRs3btCpUyfz7Xbt2hEXF8f9+/dTJIgQSaYPg731TQWTQwFoeEAKpkzEYDAwevRoihQpwpUrV8ztUjAJIbSW5KIpJiYGR0fHfw60sMDGxobo6OhUCSZEouKi4HArCDsP9nn+XoBXTtVkFg8ePODdd99lzJgxhIWF8dNPP2kdSQghzJI1EHzEiBE4ODiYb+v1esaNG4eLi4u5berUqSmXToh/i42A3z6A4P2m5VHqbgVn6X3ILH777TfatGnD/fv3cXBwYP78+bRv317rWEIIYZbkoqlOnTpcvnw5XluNGjW4ceOG+bZOLvMWqcUYB4dbmwomq2xQbzvkqKh1KpECjEYjkyZNYvjw4RgMBkqVKsWGDRsoXbq01tGEECKeJBdNBw4cSMUYQrzCmS/+XoDXHur/Crmra51IpJAlS5YwbNgwANq3b8/cuXPJli2bxqmEECIhmdBGpH/Xl8DFyabt6iukYMpkOnXqRIMGDViwYAErVqyQgkkIkW4le0ZwIdLUvV1wvLdpu+wIKNBK2zzijSmlWLNmDa1atcLa2hpra2v27Nkjp/eFEOme9DSJ9OvxKTj0IRj14NkKyo3WOpF4Q2FhYfj6+tKuXTvzDN8g4yGFEBmD9DSJ9CnqDhz8AAzR4NEIaqyS5VEyuNOnT+Pr68v169extraWWb2FEBmOFE0i/Yl9CgebQfRdcCkNtdaBpY3WqcRrUkqxcOFC+vfvT0xMDAULFmTdunVUqVJF62hCCJEsr/Wn+6FDh2jfvj3Vq1fn7t27AKxcuZLDhw+naDiRBRnj4FAreBIAtrmg7jawcXnlYSJ9ioiIoEOHDnzyySfExMTw/vvvc+rUKSmYhBAZUrKLpp9++onGjRtjb2/P6dOniYmJAUxjFcaPH5/iAUUWohSc6AdBv4KVI9TbAdm8tE4l3sD9+/fZsmULlpaWfPvtt2zZsoUcOXJoHUsIIV5LsoumsWPHMm/ePBYuXIi1tbW5vWbNmpw6dSpFw4ks5spMuDYP0EH1HyBnJa0TiTdUrFgxVq5cyb59+xgyZAgWFjIuTQiRcSX7N9jly5epU6dOgnYXFxdCQ0NTIpPIioL2wamBpu0Kk8CzhaZxxOuJjo6mV69e7N+/39zWokWLRH9nCCFERpPsosnDw4Nr164laD98+DCFCxdOkVAiiwm/alqEVxnA62MoOVDrROI1XL16lerVq5vXjJPFvIUQmU2yi6YePXrw2Wef8ccff6DT6bh37x6rVq1i8ODB9O7dOzUyisws+j7sfwf0TyBnFai6CGTOngxn/fr1+Pj4cObMGXLnzs3y5cuxt7fXOpYQQqSoZE85MGzYMIxGI2+//TZRUVHUqVMHW1tbBg8eTL9+/VIjo8is4qLhwPsQ+Rc4FYM6P4OlndapRDLExMTwv//9j5kzZwJQu3Zt1qxZQ968eTVOJoQQKU+nlFKvc6Ber+fatWtERERQunTpDLNeVHh4OC4uLoSFheHs7Kx1nKxLKfDvBDdXmqYWaPwHZJPTuxlJeHg4DRs25Pjx4wAMHTqUsWPHYmUl078JIbSXGp/3r/3bzcbGhtKlS6dICJEFXZ5hKph0llBzjRRMGZCTkxNFixbl2rVrrFixgvfff1/rSEIIkaqSXTTVr1//petE7du3740CiSzgwWE4Pci0XWEyeLytbR6RZLGxsTx79gwnJyd0Oh0LFizg0aNHsiSKECJLSPZAcG9vb8qXL2/+Kl26NHq9nlOnTlGuXLnXCjF79my8vLyws7OjatWqHDt2LEnHrVmzBp1OR4sWLV7reYUGou7A4ZagjFCwHZT4TOtEIonu3r1LgwYN6NixI8/P6mfLlk0KJiFElpHsnqZp06Yl2j569GgiIiKSHWDt2rUMHDiQefPmUbVqVaZPn07jxo25fPkybm5uLzzu5s2bDB48mNq1ayf7OYVGDDFwyBeePYDsb0HVBXKlXAaxe/du2rVrR0hICE5OTly9epXixYtrHUsIIdJUik3P2759e5YsWZLs46ZOnUqPHj3o0qULpUuXZt68eTg4OLz0sQwGAx9//DFjxoyRuaEykpOfwaOjYJ0d6mwyLZUi0jWDwcDo0aNp3LgxISEhlC9fnlOnTknBJITIklKsaPL398fOLnmXi+v1ek6ePEnDhg3/CWRhQcOGDfH393/hcV9//TVubm5069bttfOKNHZjGVybD+ig5moZ+J0BBAcH07hxY8aMGYNSip49e+Lv70/RokW1jiaEEJpI9um5jz76KN5tpRT379/nxIkTjBgxIlmPFRISgsFgwN3dPV67u7s7ly5dSvSYw4cPs3jxYgICApL0HDExMeZFhcF0CaJIYyHH4PjfE5+WGwN5m2ibR7ySUooWLVpw9OhRHBwczLN8CyFEVpbsniYXF5d4Xzly5KBevXps376dUaNGpUZGs6dPn9KhQwcWLlxIrly5knTMhAkT4uX19PRM1YziP6LuwaEWYHgGed+HMl9qnUgkgU6nY9q0aZQvX57jx49LwSSEECRzckuDwcCRI0coV64crq6ub/zker0eBwcHNmzYEO8KuE6dOhEaGsqWLVvi7R8QEECFChWwtLQ0txmNRsB0Wu/y5csUKVIk3jGJ9TR5enrK5JZpIS4a9taDR8fApQw0+h2s5XueXj169IiTJ0/SqFEjc5vRaMTCIsXO4gshRJpJjcktk/Xb0NLSkkaNGhEaGpoiT25jY4OPjw979+41txmNRvbu3Uv16tUT7F+yZEnOnTtHQECA+euDDz6gfv36BAQEJNqLZGtri7Ozc7wvkQaUMp2Se3QMbHJAnc1SMKVjR48epUKFCjRv3pyzZ8+a26VgEkKIfyR7TFPZsmW5ceMGhQoVSpEAAwcOpFOnTlSqVIkqVaowffp0IiMj6dKlCwAdO3YkX758TJgwATs7O8qWLRvv+OzZs5tziXQkcAUELgedBdRaD04yeDg9UkoxY8YM/ve//xEXFyeDvIUQ4iWSXTSNHTuWwYMH88033+Dj44OjY/zLxpPbk+Pn58fDhw8ZOXIkQUFBeHt7s3PnTvPg8Fu3bslfuxnNg0NwrKdpu+xo8GigaRyRuLCwMLp27crGjRsBaNWqFYsXL5beWCGEeIEkj2n6+uuvGTRoEE5OTv8c/K+JCZVS6HQ6DAZDyqdMQbJgbyqLvg/by0PMQ/D8CGquAwvLVx8n0tTp06fx9fXl+vXrWFtbM2XKFD799NOXLpEkhBAZiaYL9o4ZM4ZevXqxf//+FHlikQkZ9HColalgyl4Oqq+Qgimd+vnnn7l+/ToFCxZk3bp1VKlSRetIQgiR7iW5aHreIVW3bt1UCyMyuNODIeR304zfNdfJjN/p2FdffYVSiv79+5MjRw6t4wghRIaQrMFC0nUvXijwB7gy07RdfQW4lNQ2j4jn/PnztGvXjmfPngGmK2FHjx4tBZMQQiRDsgaCFy9e/JWF0+PHj98okMiAHp+CP7qbtsuOgPzNtM0j4lm5ciW9evUiKiqKAgUKMHHiRK0jCSFEhpSsomnMmDG4uLikVhaREelD4bAvGGMg73tQNnVnhRdJFx0dTf/+/Vm0aBEADRs2ZODAgRqnEkKIjCtZRVObNm1wc3NLrSwio1EKjnaFiBvg6AU1VsrA73TiypUr+Pr6cvbsWXQ6HaNHj2b48OHxZtMXQgiRPEkummQ8k0jg8nS4swksbEwTWNq8+dI64s39+uuvtGrViqdPn+Lm5sbq1at5++23tY4lhBAZXrKvnhMCgIf+cHqIabviVMhZSds8wqx48eJYWlpSp04dfvzxR/Lmzat1JCGEyBSSXDQ9XxhXCGLD4fePQcVBAT8o1kfrRFleeHi4efI2Ly8vDh8+TIkSJbCySvak/0IIIV5A1icRyaOM4N8JIgPBsSBUXQBy6lZTP//8M4UKFWL79u3mtjJlykjBJIQQKUyKJpE8Z0fCnc2mcUw1fgRrWYpGK7GxsQwZMoTmzZvz+PFjZs2apXUkIYTI1KRoEkl3eyOcH2farroIclfXNk8WdufOHerXr8+kSZMA+Pzzz9m8ebO2oYQQIpOT/nuRNBE3TNMLAJQcBIU6aJsnC/v111/5+OOPCQkJwdnZmSVLltCyZUutYwkhRKYnRZN4NUMMHPaD2DDIVQO8J2idKMs6d+4c7777LkopvL29Wb9+PUWLFtU6lhBCZAlSNIlXCxgKj0+ATQ6o+SNYWGudKMsqV64c3bt3x8LCgunTp2NnZ6d1JCGEyDKkaBIvd3sTXJ5h2q6+HBwLaJsnCzp06BAlSpQwz8Y/d+5cmdlbCCE0IAPBxYtFBMLRLqbtUv+DfO9rmyeLMRqNTJgwgXr16tG+fXsMBgOAFExCCKER6WkSiTPo/zWOqTqUH6d1oizl0aNHdOzY0Tz3koeHB7GxsVIwCSGEhqRoEokLGAqPj5vWk6u5RsYxpaGjR4/SunVrbt++jZ2dHTNnzqRbt26y/qMQQmhMTs+JhO78bFqMF6CajGNKK0oppk+fTu3atbl9+zbFihXj6NGjdO/eXQomIYRIB6RoEvE9vQa/tzdtF+8H+ZtpmycLiYqKYvbs2cTFxeHr68uJEycoX7681rGEEEL8TU7PiX8Y9HCkLcQ9hdy1oOIUrRNlKY6Ojqxfv57Dhw/Tt29f6V0SQoh0Room8Y9zI/+ej8kVaqyWcUypTCnFwoULMRgM9O7dGwBvb2+8vb21DSaEECJRUjQJk6C9cOE703bVReDoqW2eTC4iIoJevXqxatUqrK2tqVevHqVKldI6lhBCiJeQoknAsxDw7wAoKNoTPD/SOlGmdv78eVq1asWlS5ewtLRk3LhxlCxZUutYQgghXkGKpqxOKfijK0TfB+eSUHGq1okytRUrVtC7d2+ioqLImzcva9eupVatWlrHEkIIkQRy9VxWd3UO3P0FLGxM8zFZOWqdKFNSStG7d286depEVFQU77zzDgEBAVIwCSFEBiJFU1YWeg5ODTJte38HrnJ5e2rR6XQULlwYnU7H119/zY4dO8idO7fWsYQQQiSDTimltA6RlsLDw3FxcSEsLAxnZ2et42gnLhp2VYaw85CnCdTbBnKJe4qLiIggW7ZsgGktuYCAACpWrKhxKiGEyPxS4/NeepqyqtODTQWTnTtUXyYFUwqLiYmhX79+VK1alcjISAAsLCykYBJCiAxMiqas6M4W01gmgOorwM5N2zyZTGBgILVq1WLWrFlcuHCBHTt2aB1JCCFECpCiKauJugtHu5q2Sw6CPI20zZPJ/Pzzz1SsWJETJ06QI0cOtm3bRqtWrbSOJYQQIgVI0ZSVGA3wezvQPwbXClB+nNaJMo3Y2FiGDBlC8+bNCQ0NpWrVqpw+fZqmTZtqHU0IIUQKkaIpK7k8Ax78BlbZoOaPYGmrdaJMY+jQoUyaNAmAzz//nN9++40CBQponEoIIURKkqIpqwi7CGe+NG1XnArOJbTNk8kMHjyY4sWL89NPPzFt2jRsbGy0jiSEECKFyYzgWYExDvw7gTHGNL1Ake5aJ8rwDAYDu3btMp9+y5s3L+fPn8fKSv5LCSFEZiU9TVnBuTHw+DhYZzctxivTC7yR4OBgGjVqxHvvvceGDRvM7VIwCSFE5ia/5TO7h7/D+b8HfFeeCw55tc2TwR08eJA2bdoQFBSEo6MjBoNB60hCCCHSiPQ0ZWaxEeDfEVDg1QG82midKMMyGo1MmDCBBg0aEBQURJkyZTh+/Dh+fn5aRxNCCJFGpKcpMzs1ACKug4MnVPpe6zQZ1qNHj+jYsSPbt28HoGPHjsyZMwdHR1ncWAghshLpacqs7vwM1xcBOtOs3zbZtU6UYf3+++9s374dOzs7Fi1axLJly6RgEkKILEh6mjKjqLvwRzfTdqlB4F5P0zgZXbNmzfjuu+9o1KgR5cuX1zqOEEIIjUhPU2ajFJz8HGJCwNUb3hqrdaIMJzQ0lG7dunH37l1z2//+9z8pmIQQIouTnqbM5voiuL0BdJZQbZnM+p1Mp06dwtfXlxs3bnDz5k327NmDTqZoEEIIgfQ0ZS7PHsKpQabtt8aCq/SMJJVSivnz51OjRg1u3LiBl5cXEydOlIJJCCGEmfQ0ZSanBkHcU3CtCKWHaJ0mw4iIiOCTTz5h9erVgGkM0/Lly3F1ddU4mRBCiPREiqbMImgv3FwJ6KDyHNBJJ2JS3Lx5kyZNmnDp0iUsLS2ZOHEigwYNkh4mIYQQCUjRlBnERcIfPUzbxfpArqra5slA3NzcsLKyIl++fKxdu5aaNWtqHUkIIUQ6JUVTZnD6fxAZCA4FwHuC1mnSvejoaGxtbbGwsMDBwYHNmzfj7OxM7ty5tY4mhBAiHZNzOBld0B64Ote0XW0JWDtpmyedu3LlCtWqVeO7774ztxUpUkQKJiGEEK8kRVNGFvsUjv49iWWxPuDxtrZ50rl169ZRqVIlzp49y8yZM4mIiNA6khBCiAxEiqaMLGAYRN0CRy/w/lbrNOlWTEwM/fr1w8/Pj6dPn1KnTh1OnDhBtmzZtI4mhBAiA5GiKaN6eOSf03JVF4G1FACJCQwMpFatWsyaNQuAL774gr1795InTx6NkwkhhMhoZCB4RhQXBb+3BxQU6iin5V4gKiqKGjVqEBQURI4cOVi5ciVNmzbVOpYQQogMSnqaMqI/v4HIm6ar5SrN1DpNuuXg4MCIESOoVq0ap0+floJJCCHEG9EppZTWIdJSeHg4Li4uhIWF4ezsrHWc5As9BzsqgoqDOpshf3OtE6Urd+7cITQ0lLJlywKm5VHi4uKwtrbWOJkQQoi0lBqf99LTlJEoI/zR01Qw5W8hBdN//Prrr1SoUIHmzZsTFhYGgE6nk4JJCCFEipCiKSO5vhgeHQUrJ6g0S+s06YbBYGDkyJG8++67hISEmP+yEEIIIVKSDATPKGIemaYYAHjra3DIp22edCI4OJh27dqxb98+AHr16sW0adOws7PTOJkQQojMRoqmjOLcaNA/huzloHhfrdOkCwcPHqRNmzYEBQXh6OjIggULaNeundaxhBBCZFJSNGUET87C1Xmm7YrTwELG6ABMnjyZoKAgypQpw4YNGyhZsqTWkYQQQmRiMqYpvTPGwR/d/hn87d5A60TpxtKlSxk4cCB//PGHFExCCCFSnRRN6d3VOfD4BFi7QOU5oNNpnUgz/v7+fPnll+bbuXLlYsqUKTg6OmqYSgghRFYhp+fSM/0TODfGtO09Eeyz5tIfSilmzJjB//73P+Li4ihfvjx+fn5axxJCCJHFSNGUnp0abBr87VIGinTXOo0mQkND6dq1K5s2bQKgdevWNGnSRONUQgghsiIpmtKrh0fgxhLTduV5YJH13qpTp07h6+vLjRs3sLa2Ztq0afTp0wddFj5FKYQQQjvpYkzT7Nmz8fLyws7OjqpVq3Ls2LEX7rtw4UJq166Nq6srrq6uNGzY8KX7Z0iGGNPgb4Ai3cCtlrZ5NLBixQpq1KjBjRs38PLy4siRI/Tt21cKJiGEEJrRvGhau3YtAwcOZNSoUZw6dYry5cvTuHFjHjx4kOj+Bw4coG3btuzfvx9/f388PT1p1KgRd+/eTePkqejCtxB+Gew8oMIkrdNows3NDb1eT7NmzTh16hSVK1fWOpIQQogsTvMFe6tWrUrlypWZNcu0LIjRaMTT05N+/foxbNiwVx5vMBhwdXVl1qxZdOzY8ZX7p/sFeyP/gq0lwfAMaqwGr7ZaJ0ozz549izeT9+HDh6lZs6b0LgkhhEi2TLdgr16v5+TJkzRs2NDcZmFhQcOGDfH390/SY0RFRREbG0uOHDlSK2baOjXQVDC51YWCbbROk2aWL19OkSJFuHHjhrmtVq1aUjAJIYRINzQtmkJCQjAYDLi7u8drd3d3JygoKEmPMXToUPLmzRuv8Pq3mJgYwsPD432lW/d3w+2NoLOESjOzxJxM0dHRdO/enc6dO3Pv3j1zj6MQQgiR3mg+pulNTJw4kTVr1rBp06YXLtA6YcIEXFxczF+enp5pnDKJDHo42d+0XayvaY25TO7KlStUq1aNxYsXY2FhwTfffMPkyZO1jiWEEEIkStOiKVeuXFhaWhIcHByvPTg4GA8Pj5ceO3nyZCZOnMivv/7KW2+99cL9vvjiC8LCwsxft2/fTpHsKe7K9xB+CWxzw1tjtE6T6tauXYuPjw9nz57F3d2d3bt389VXX2FhkaHreCGEEJmYpp9QNjY2+Pj4sHfvXnOb0Whk7969VK9e/YXHfffdd3zzzTfs3LmTSpUqvfQ5bG1tcXZ2jveV7kTd+9fM39+CTXZN46S29evX06ZNGyIiIqhbty6nT5+mQQNZU08IIUT6pvmMiQMHDqRTp05UqlSJKlWqMH36dCIjI+nSpQsAHTt2JF++fEyYMAGAb7/9lpEjR7J69Wq8vLzMY5+yZctGtmzZNHsdbyRgKMRFQM6qULiT1mlS3QcffECVKlVo2LAhY8aMwcpK8x9DIYQQ4pU0/7Ty8/Pj4cOHjBw5kqCgILy9vdm5c6d5cPitW7finbKZO3cuer2eVq1axXucUaNGMXr06LSMnjIeHIKbPwA6qDQLdJnz9NTBgwepWbMmVlZW2NracujQIWxsbLSOJYQQQiSZ5vM0pbV0NU+T0QA7fSD0DBTpAVUXaJsnFcTGxvLll18yefJkhg8fztixY7WOJIQQIgtIjc97zXuasrRr800Fk40rlB+vdZoUd+fOHfz8/Pj9998B0/QCSimZe0kIIUSGJEWTVp49hDPDTdtvjQW7XNrmSWG7du2iffv2hISE4OzszLJly/jwww+1jiWEEEK8tsw5gCYjODMcYkMhe3ko+onWaVKMwWBgxIgRNGnShJCQECpUqMCpU6ekYBJCCJHhSdGkhUcn4Poi03alWWBhqW2eFBQYGMjUqVNRStG7d29+//13ihQponUsIYQQ4o3J6bm0poxw4lNAgVd7cKuldaIUVbRoURYuXAhAu3btNE4jhBBCpBwpmtLajeXw6A+wygYVvtM6zRszGo1MnDiRunXrUrNmTUCKJSGEEJmTFE1pSR8KZ4aZtsuNAvs8msZ5U48ePaJDhw7s2LGDfPnyceHCBe2ncRBCCCFSiRRNaencaHj2AJxLQvH+Wqd5I/7+/vj5+XH79m3s7Oz4+uuvpWASQgiRqclA8LQS+idcmWXa9vkeLDPmbNhKKaZNm0adOnW4ffs2xYoV448//qBr165aRxNCCCFSlfQ0pQWl4EQ/UAbw/AjyvKN1otcSHR3Nxx9/zKZNmwBo3bo1CxculB4mIYQQWYL0NKWFW+vhwQGwtIOKU7VO89rs7OzQ6XRYW1sza9Ys1qxZIwWTEEKILEN6mlJbXDSc/p9pu/QwcCyobZ5kUkqh1+uxtbVFp9OxZMkSrl27ho+Pj9bRhBBCiDQlPU2p7eociLoFDp5Q6n9ap0mWp0+f8vHHH9OhQweer+vs4uIiBZMQQogsSXqaUtOzB/DnWNN2uVFg5aBtnmQ4d+4cvr6+XL58GUtLS86cOYO3t7fWsYQQQgjNSE9TagoYZlpfzrUiFOqkdZokW7ZsGVWrVuXy5cvky5ePgwcPSsEkhBAiy5OiKbU8Pgk3lpm2K88Gi/TfqRcVFUXXrl3p0qUL0dHRNG7cmNOnT5tn+hZCCCGyMimaUoNSfw/+/nt9uVzVtE6UJB999BFLly7FwsKCb775hu3bt5M7d26tYwkhhBDpQvrv/siI7v8KwfvBwhbKj9M6TZJ9+eWX/Pnnn6xYsYIGDRpoHUcIIYRIV6RoSmlKwbmRpu3ifcGxgLZ5XiImJoaAgACqVq0KQJ06dbh27Rp2dnYaJxNCCCHSHzk9l9JurYdHx8DSAUoN0TrNCwUGBlKrVi3efvttLl68aG6XgkkIIYRInBRNKcmghzNfmLZLDwF7d23zvMCWLVuoWLEiJ06cwNbWlqCgIK0jCSGEEOmeFE0p6foiiLgBdh5QcpDWaRKIjY1l8ODBtGjRgtDQUKpVq8bp06epX7++1tGEEEKIdE/GNKWUmMdw9ivTdtkRYJ1N2zz/cfv2bfz8/PD39wdgwIABTJw4ERsbG42TCSGEEBmDFE0p5fw40D+B7OWgaE+t0ySwePFi/P39cXFxYenSpXz44YdaRxJCCCEyFCmaUkLkX3Bllmnb+7t0OZHlV199xYMHDxg0aBBFihTROo4QQgiR4ciYppRwbgwY9eDeAPI01joNAEFBQXz++efo9XoArKysmDNnjhRMQgghxGtKf10iGc3T6xC4wrRdfhzodNrmAQ4cOECbNm0IDg7G2tqaSZMmaR1JCCGEyPCkp+lNnR4EygB53tV8uRSj0cj48eN5++23CQ4OpkyZMnTr1k3TTEIIIURmIT1NbyL4INzZAjorqDBZ0yghISF06NCBnTt3AtCpUyfmzJmDg4ODprmEEEKIzEKKptf17+VSinSH7GU0i3LixAk+/PBD7ty5g52dHbNnz6Zr166a5RFCCCEyIymaXlfwXnjwm2lR3rLDNY2SPXt2wsLCKFasGBs2bOCtt97SNI8QQgiRGUnR9DqUgjMjTNtFPwGH/GkeITY2Fmtra1OEokXZuXMnZcuWxdnZOc2zCCGEEFmBDAR/Hfd2wKOjYGkPZb5I86c/efIkpUuXZs+ePea2GjVqSMEkhBBCpCIpmpLr32OZivcFe480fGrFnDlzqFGjBteuXeOrr75CKZVmzy+EEEJkZVI0JdedLfD4JFg5Qqkhafa0T58+pV27dvTt2xe9Xk/z5s3ZsWMHunQwL5QQQgiRFUjRlBxKwblRpu0Sn4Fd7jR52nPnzlGpUiXWrFmDpaUlkydPZtOmTbi6uqbJ8wshhBBCBoInz+2fIPQsWGWDkoPS5CmvXr1K1apViY6OJl++fKxdu5aaNWumyXMLIYQQ4h9SNCWVMQ7OfmXaLjkAbHOkydMWLVqUVq1a8eDBA1auXEnu3GnTuyWEEEKI+KRoSqrA5RB+GWxzQqnBqfpUly9fJnfu3OTIkQOdTseCBQuwsbHBwkLOpgohsg6DwUBsbKzWMUQ6ZWlpiZWVVZqO7ZWiKSkMejj3tWm79JdgnXqX9q9du5bu3btTr149tmzZgoWFBXZ2dqn2fEIIkR5FRERw584duUJYvJSDgwN58uTBxsYmTZ5PiqakuLEUom6BnQcU650qTxETE8PAgQOZM2cOYLpaLiIiQuZeEkJkOQaDgTt37uDg4EDu3LnlKmGRgFIKvV7Pw4cPCQwMpFixYmlyNkaKplcxPPvnirnSw8DKPsWf4saNG7Ru3ZqTJ08C8OWXXzJmzBisrOTtEUJkPbGxsSilyJ07N/b2Kf87V2QO9vb2WFtb89dff6HX69PkrIx8Kr/KzdXwLBgcCqRKL9PmzZvp3LkzYWFh5MiRgx9++IEmTZqk+PMIIURGIz1M4lXSeqyvFE0vY4iB8+NM28U/BcuUPWf67NkzBg4cSFhYGNWqVWPdunV4enqm6HMIIYQQImXI5VgvE7gSIm6AfZ5U6WWys7Nj7dq1DB48mIMHD0rBJIQQQqRjUjS9iNEAl6aYtksOAutsKfKwO3fuZOXKlebblStXZtKkSWk28l8IIUTq8vf3x9LSkvfeey/BfQcOHECn0xEaGprgPi8vL6ZPnx6vbf/+/TRt2pScOXPi4OBA6dKlGTRoEHfv3k2l9KazIH379iVnzpxky5aNli1bEhwc/MrjLl68yAcffICLiwuOjo5UrlyZW7dume+/fv06H374Iblz58bZ2ZnWrVu/8HFjYmLw9vZGp9MREBCQUi/tjUnR9CJ3NkH4JbBxhSLd3/jh4uLi+Oqrr2jatCk9evTg3LlzKRBSCCFEerN48WL69evHb7/9xr179177cebPn0/Dhg3x8PDgp59+4sKFC8ybN4+wsDCmTJmSgonjGzBgAL/88gvr16/n4MGD3Lt3j48++uilx1y/fp1atWpRsmRJDhw4wNmzZxkxYoR5cHZkZCSNGjVCp9Oxb98+jhw5gl6vp1mzZhiNxgSPN2TIEPLmzZsqr++NqCwmLCxMASosLOzlO+6up9QqlAoY/sbPef/+fVWvXj0FKED17t1bRUdHv/HjCiFEZhQdHa0uXLiQIX9PPn36VGXLlk1dunRJ+fn5qXHjxsW7f//+/QpQT548SXBswYIF1bRp05RSSt2+fVvZ2Niozz//PNHnSez4lBAaGqqsra3V+vXrzW0XL15UgPL393/hcX5+fqp9+/YvvH/Xrl3KwsIi3mdvaGio0ul0avfu3fH23b59uypZsqQ6f/68AtTp06df+Lgv+1lJ8ud9MkhPU2LCLsKDA6CzhKKfvNFDHThwAG9vbw4cOICjoyOrV69mzpw5MmGlEEIklVIQF6nNVzIn11y3bh0lS5akRIkStG/fniVLlrzWBJ3r169Hr9czZMiQRO/Pnj37C49t0qQJ2bJle+FXmTJlXnjsyZMniY2NpWHDhua2kiVLUqBAAfz9/RM9xmg0sm3bNooXL07jxo1xc3OjatWqbN682bxPTEwMOp0OW1tbc5udnR0WFhYcPnzY3BYcHEyPHj1YuXIlDg4OL8ypFbl6LjGXvzf9m/c9cHz9wdkTJ05k+PDhGI1GypYty/r16ylZsmQKhRRCiCzCEAXrUmZcabK1jgArxyTvvnjxYtq3bw/Au+++S1hYGAcPHqRevXrJetqrV6/i7OxMnjx5knUcwKJFi4iOjn7h/dbW1i+8LygoCBsbmwRFmbu7O0FBQYke8+DBAyIiIpg4cSJjx47l22+/ZefOnXz00Ufs37+funXrUq1aNRwdHRk6dCjjx49HKcWwYcMwGAzcv38fME1Y2blzZ3r16kWlSpW4efNmsl97apOi6b+ePTDNAA5QcuAbPVRsbCxGo5EuXbowa9asdFk1CyGESBmXL1/m2LFjbNq0CQArKyv8/PxYvHhxsosmpdRrz1OVL1++1zrudT0fk9S8eXMGDBgAgLe3N7///jvz5s2jbt265M6dm/Xr19O7d2++//57LCwsaNu2LRUrVjTPtTRz5kyePn3KF198kab5k0OKpv+6MgeMMZCzCrjVSfbhcXFx5pm8hw8fjo+PD02bNk3plEIIkXVYOph6fLR67iRavHgxcXFx8QYwK6WwtbVl1qxZuLi4mJfGCgsLS9CbExoaiouLCwDFixcnLCyM+/fvJ7u3qUmTJhw6dOiF9xcsWJDz588nep+Hhwd6vZ7Q0NB4+YKDg/Hw8Ej0mFy5cmFlZUXp0qXjtZcqVSreqbdGjRpx/fp1QkJCsLKyInv27Hh4eFC4cGEA9u3bh7+/f7xTeACVKlXi448/Zvny5S993WlBiqZ/i4uGq7NN2yUHQTKqfKUUU6dO5ccff+TQoUPY29tjYWEhBZMQQrwpnS5Zp8i0EBcXx4oVK5gyZQqNGjWKd1+LFi348ccf6dWrl3mNtJMnT1KwYEHzPjdu3CAsLIzixYsD0KpVK4YNG8Z3333HtGnTEjzff4uaf3uT03M+Pj5YW1uzd+9eWrZsCZh60G7dukX16tUTPcbGxobKlStz+fLleO1XrlyJ9xqfy5UrF2Aqkh48eMAHH3wAwPfff8/YsWPN+927d4/GjRuzdu1aqlat+sLMaSrFhpRnEC8dTX9phumKuc1eShlik/yYT548Uc2bNzdfHTd//vwUTCyEEFlLRrx6btOmTcrGxkaFhoYmuG/IkCGqUqVK5ts9e/ZUXl5easuWLerGjRvq4MGDqlq1aqpatWrKaDSa95s9e7bS6XSqa9eu6sCBA+rmzZvq8OHDqmfPnmrgwIGp9lp69eqlChQooPbt26dOnDihqlevrqpXrx5vnxIlSqiNGzeab2/cuFFZW1urBQsWqKtXr6qZM2cqS0tLdejQIfM+S5YsUf7+/uratWtq5cqVKkeOHC99HYGBgenu6jkpmp6LjVLqJw9T0XQ16UXPiRMnVKFChRSgbGxs1OzZs+P90AshhEiejFg0vf/++6pp06aJ3vfHH38oQJ05c0YpZXp9o0aNUiVLllT29vaqUKFCqmfPnurhw4cJjt29e7dq3LixcnV1VXZ2dqpkyZJq8ODB6t69e6n2WqKjo1WfPn2Uq6urcnBwUB9++KG6f/9+vH0AtXTp0nhtixcvVkWLFlV2dnaqfPnyavPmzfHuHzp0qHJ3d1fW1taqWLFiasqUKS/9vEyPRZNOqde4FjIDCw8Px8XFhbCwMPO5ZQAuz4KT/UwL8za7+sp15pRSzJ07lwEDBqDX6ylUqBDr16/Hx8cnlV+BEEJkbs+ePSMwMJBChQrJ9CzipV72s/LCz/s3IPM0ARhj4eK3pu3SQ5O0MO+4cePo27cver2e5s2bc/LkSSmYhBBCiExMiiaAmz9C1B2w84Ai3ZJ0SMeOHXF3d2fKlCls2rQJV1fXVA4phBBCCC3J1XNK/Wth3s/B0vaFu/67N6lAgQJcu3aNbNk0mnBNCCGEEGlKepoeHoLQs2BpD0V7JrpLVFQUXbt2pVKlSvz888/mdimYhBBCiKxDepouzzD9W6gD2CQ8xXb58mVatWrFn3/+iYWFBdevX0/jgEIIIYRID7J20RT5F9zZbNou3i/B3WvWrKFHjx5ERETg7u7Ojz/+SP369dM2oxBCZFFZ7OJu8RrS+mcka5+euzoflBHc6kH2submZ8+e0adPH9q2bUtERAT16tUjICBACiYhhEgDlpaWAOj1eo2TiPQuKioKePks5ykp6/Y0xUXC1Tmm7RL94921Z88e5s6dC8BXX33FqFGjzOvJCSGESF1WVlY4ODjw8OFDrK2tzQu6CvGcUoqoqCgePHhA9uzZzYV2asu6lcCtnyA2DLIVhvzN4931/vvv88UXX1CnTh3effddjQIKIUTWpNPpyJMnD4GBgfz1119axxHp2PNFf9NK1i2ari8w/Vu0F7FxBsaOHUOfPn1wd3cHYPz48RqGE0KIrM3GxoZixYrJKTrxQtbW1mnWw/RcuiiaZs+ezaRJkwgKCqJ8+fLMnDmTKlWqvHD/9evXM2LECG7evEmxYsX49ttvadq0afKe9Mk5cLLjtk1j/OrWxd/fnyNHjrB79250Ot0bviIhhBBvysLCQpZREemK5ieK165dy8CBAxk1ahSnTp2ifPnyNG7cmAcPHiS6/++//07btm3p1q0bp0+fpkWLFrRo0YI///wz2c+9814tKlRrgL+/Py4uLnz66adSMAkhhBAiUZov2Fu1alUqV67MrFmzADAajXh6etKvXz+GDRuWYH8/Pz8iIyPZunWrua1atWp4e3szb968Vz7f8wX8BjeFydtNbRUrVmT9+vUULlw4ZV6UEEIIITSV6Rbs1ev1nDx5koYNG5rbLCwsaNiwIf7+/oke4+/vH29/gMaNG79w/xd5XjD16dOHI0eOSMEkhBBCiJfSdExTSEgIBoPBPPj6OXd3dy5dupToMUFBQYnuHxQUlOj+MTExxMTEmG+HhYUBYG9rxczZ8/D19UWv18tgQyGEECITCQ8PB1J2Asx0MRA8NU2YMIExY8YkaI+OiaN79+50795dg1RCCCGESAuPHj3CxcUlRR5L06IpV65cWFpaEhwcHK89ODj4hfMueHh4JGv/L774goEDB5pvh4aGUrBgQW7dupVi30Tx5sLDw/H09OT27dspdu5ZvBl5T9IneV/SH3lP0qewsDAKFChAjhw5UuwxNS2abGxs8PHxYe/evbRo0QIwDQTfu3cvn376aaLHVK9enb179/L555+b23bv3k316tUT3d/W1hZbW9sE7S4uLvLDnQ45OzvL+5LOyHuSPsn7kv7Ie5I+peSM8pqfnhs4cCCdOnWiUqVKVKlShenTpxMZGUmXLl0A6NixI/ny5WPChAkAfPbZZ9StW5cpU6bw3nvvsWbNGk6cOMGCBQu0fBlCCCGEyOQ0L5r8/Px4+PAhI0eOJCgoCG9vb3bu3Gke7H3r1q14VWKNGjVYvXo1X331FV9++SXFihVj8+bNlC1b9kVPIYQQQgjxxjQvmgA+/fTTF56OO3DgQII2X19ffH19X+u5bG1tGTVqVKKn7IR25H1Jf+Q9SZ/kfUl/5D1Jn1LjfdF8ckshhBBCiIxA82VUhBBCCCEyAimahBBCCCGSQIomIYQQQogkyJRF0+zZs/Hy8sLOzo6qVaty7Nixl+6/fv16SpYsiZ2dHeXKlWP79u1plDRrSc77snDhQmrXro2rqyuurq40bNjwle+jSL7k/l95bs2aNeh0OvP8aiJlJfd9CQ0NpW/fvuTJkwdbW1uKFy8uv8dSWHLfk+nTp1OiRAns7e3x9PRkwIABPHv2LI3SZg2//fYbzZo1I2/evOh0OjZv3vzKYw4cOEDFihWxtbWlaNGiLFu2LHlPqjKZNWvWKBsbG7VkyRJ1/vx51aNHD5U9e3YVHByc6P5HjhxRlpaW6rvvvlMXLlxQX331lbK2tlbnzp1L4+SZW3Lfl3bt2qnZs2er06dPq4sXL6rOnTsrFxcXdefOnTROnnkl9z15LjAwUOXLl0/Vrl1bNW/ePG3CZiHJfV9iYmJUpUqVVNOmTdXhw4dVYGCgOnDggAoICEjj5JlXct+TVatWKVtbW7Vq1SoVGBiodu3apfLkyaMGDBiQxskzt+3bt6vhw4erjRs3KkBt2rTppfvfuHFDOTg4qIEDB6oLFy6omTNnKktLS7Vz584kP2emK5qqVKmi+vbta75tMBhU3rx51YQJExLdv3Xr1uq9996L11a1alX1ySefpGrOrCa578t/xcXFKScnJ7V8+fLUipjlvM57EhcXp2rUqKEWLVqkOnXqJEVTKkju+zJ37lxVuHBhpdfr0ypilpPc96Rv376qQYMG8doGDhyoatasmao5s7KkFE1DhgxRZcqUidfm5+enGjdunOTnyVSn5/R6PSdPnqRhw4bmNgsLCxo2bIi/v3+ix/j7+8fbH6Bx48Yv3F8k3+u8L/8VFRVFbGxsiq4hlJW97nvy9ddf4+bmRrdu3dIiZpbzOu/Lzz//TPXq1enbty/u7u6ULVuW8ePHYzAY0ip2pvY670mNGjU4efKk+RTejRs32L59O02bNk2TzCJxKfF5ny4mt0wpISEhGAwG82ziz7m7u3Pp0qVEjwkKCkp0/6CgoFTLmdW8zvvyX0OHDiVv3rwJfuDF63md9+Tw4cMsXryYgICANEiYNb3O+3Ljxg327dvHxx9/zPbt27l27Rp9+vQhNjaWUaNGpUXsTO113pN27doREhJCrVq1UEoRFxdHr169+PLLL9MisniBF33eh4eHEx0djb29/SsfI1P1NInMaeLEiaxZs4ZNmzZhZ2endZws6enTp3To0IGFCxeSK1cureOIfzEajbi5ubFgwQJ8fHzw8/Nj+PDhzJs3T+toWdaBAwcYP348c+bM4dSpU2zcuJFt27bxzTffaB1NvKFM1dOUK1cuLC0tCQ4OjtceHByMh4dHosd4eHgka3+RfK/zvjw3efJkJk6cyJ49e3jrrbdSM2aWktz35Pr169y8eZNmzZqZ24xGIwBWVlZcvnyZIkWKpG7oLOB1/q/kyZMHa2trLC0tzW2lSpUiKCgIvV6PjY1NqmbO7F7nPRkxYgQdOnSge/fuAJQrV47IyEh69uzJ8OHD462nKtLOiz7vnZ2dk9TLBJmsp8nGxgYfHx/27t1rbjMajezdu5fq1asnekz16tXj7Q+we/fuF+4vku913heA7777jm+++YadO3dSqVKltIiaZST3PSlZsiTnzp0jICDA/PXBBx9Qv359AgIC8PT0TMv4mdbr/F+pWbMm165dMxexAFeuXCFPnjxSMKWA13lPoqKiEhRGz4taJSuXaSZFPu+TP0Y9fVuzZo2ytbVVy5YtUxcuXFA9e/ZU2bNnV0FBQUoppTp06KCGDRtm3v/IkSPKyspKTZ48WV28eFGNGjVKphxIBcl9XyZOnKhsbGzUhg0b1P37981fT58+1eolZDrJfU/+S66eSx3JfV9u3bqlnJyc1KeffqouX76stm7dqtzc3NTYsWO1egmZTnLfk1GjRiknJyf1448/qhs3bqhff/1VFSlSRLVu3Vqrl5ApPX36VJ0+fVqdPn1aAWrq1Knq9OnT6q+//lJKKTVs2DDVoUMH8/7Ppxz43//+py5evKhmz54tUw4opdTMmTNVgQIFlI2NjapSpYo6evSo+b66deuqTp06xdt/3bp1qnjx4srGxkaVKVNGbdu2LY0TZw3JeV8KFiyogARfo0aNSvvgmVhy/6/8mxRNqSe578vvv/+uqlatqmxtbVXhwoXVuHHjVFxcXBqnztyS857Exsaq0aNHqyJFiig7Ozvl6emp+vTpo548eZL2wTOx/fv3J/o58fy96NSpk6pbt26CY7y9vZWNjY0qXLiwWrp0abKeU6eU9BUKIYQQQrxKphrTJIQQQgiRWqRoEkIIIYRIAimahBBCCCGSQIomIYQQQogkkKJJCCGEECIJpGgSQgghhEgCKZqEEEIIIZJAiiYhhBBCiCSQokkIkSTLli0je/bsWsd4bTqdjs2bN790n86dO9OiRYs0ySOEyHikaBIiC+ncuTM6nS7B17Vr17SOxrJly8x5LCwsyJ8/P126dOHBgwcp8vj379+nSZMmANy8eROdTkdAQEC8fWbMmMGyZctS5PleZPTo0ebXaWlpiaenJz179uTx48fJehwp8IRIe1ZaBxBCpK13332XpUuXxmvLnTu3Rmnic3Z25vLlyxiNRs6cOUOXLl24d+8eu3bteuPH9vDweOU+Li4ub/w8SVGmTBn27NmDwWDg4sWLdO3albCwMNauXZsmzy+EeD3S0yREFmNra4uHh0e8L0tLS6ZOnUq5cuVwdHTE09OTPn36EBER8cLHOXPmDPXr18fJyQlnZ2d8fHw4ceKE+f7Dhw9Tu3Zt7O3t8fT0pH///kRGRr40m06nw8PDg7x589KkSRP69+/Pnj17iI6Oxmg08vXXX5M/f35sbW3x9vZm586d5mP1ej2ffvopefLkwc7OjoIFCzJhwoR4j/389FyhQoUAqFChAjqdjnr16gHxe28WLFhA3rx5MRqN8TI2b96crl27mm9v2bKFihUrYmdnR+HChRkzZgxxcXEvfZ1WVlZ4eHiQL18+GjZsiK+vL7t37zbfbzAY6NatG4UKFcLe3p4SJUowY8YM8/2jR49m+fLlbNmyxdxrdeDAAQBu375N69atyZ49Ozly5KB58+bcvHnzpXmEEEkjRZMQAgALCwu+//57zp8/z/Lly9m3bx9Dhgx54f4ff/wx+fPn5/jx45w8eZJhw4ZhbW0NwPXr13n33Xdp2bIlZ8+eZe3atRw+fJhPP/00WZns7e0xGo3ExcUxY8YMpkyZwuTJkzl79iyNGzfmgw8+4OrVqwB8//33/Pzzz6xbt47Lly+zatUqvLy8En3cY8eOAbBnzx7u37/Pxo0bE+zj6/v/du43pOmujQP491k0XXMzTKStjChz9EZlpaAGkmYOMkTzUWugkVlo/qEwkjCnhFaE9iL6o4WKJmoGUTB0ECisBWWZCpmaNpNIijIUybm1Xc+L8EfTzdZzw3PfD10f8MU5v3POrnP2Yhe/c+G/8eXLF3R3dwt909PT6OrqglarBQAYjUZkZmaiqKgIQ0NDqK2tRWNjIyorKz3e48TEBAwGA8RisdDncDiwceNGdHR0YGhoCGVlZTh79izu3r0LACguLkZaWho0Gg2mpqYwNTWFqKgo2Gw2JCQkQCaTwWg0wmQywcfHBxqNBlar1eOYGGNuEGPsj5GVlUWrVq0iqVQq/KWmproc29HRQevWrRPaDQ0N5OvrK7RlMhk1Nja6nJudnU3Hjh1z6jMajSQSiWh+ft7lnKXrj46OUnBwMO3cuZOIiJRKJVVWVjrNCQ8Pp7y8PCIiKigooNjYWHI4HC7XB0D3798nIiKz2UwA6OXLl05jsrKyKCkpSWgnJSXRkSNHhHZtbS0plUqy2+1ERBQXF0dVVVVOazQ3N5NCoXAZAxGRTqcjkUhEUqmUvL29CQABoJqaGrdziIhOnDhBBw4ccBvr4merVCqnM1hYWCCJREIGg2HF9Rljv8Y1TYz9YXbv3o0bN24IbalUCuDHW5cLFy5geHgYs7Oz+P79OywWC759+4Y1a9YsW+fUqVM4evQompubhSumrVu3AvhxdTc4OIiWlhZhPBHB4XDAbDZj+/btLmObmZmBj48PHA4HLBYLdu3ahdu3b2N2dhYfPnxAdHS00/jo6GgMDAwA+HG1Fh8fD5VKBY1Gg8TEROzdu/cvnZVWq0VOTg6uX78OLy8vtLS0ICMjAyKRSNinyWRyerNkt9tXPDcAUKlUePjwISwWC+7cuYP+/n4UFBQ4jbl27Rrq6+sxOTmJ+fl5WK1WhIWFrRjvwMAAxsbGIJPJnPotFgvGx8f/ixNgjP2MkybG/jBSqRRBQUFOfRMTE0hMTERubi4qKyvh5+eHx48fIzs7G1ar1eWPf3l5OQ4dOgS9Xo/Ozk7odDq0tbUhOTkZc3NzOH78OAoLC5fN27Rpk9vYZDIZ+vr6IBKJoFAoIJFIAACzs7O/3JdarYbZbEZnZycePXqEtLQ07NmzB/fu3fvlXHf2798PIoJer0d4eDiMRiOuXLkiPJ+bm0NFRQVSUlKWzfX29na7rlgsFr6DixcvYt++faioqMD58+cBAG1tbSguLkZ1dTUiIyMhk8lw+fJlPH36dMV45+bmsGPHDqdkddE/pdifsf9nnDQxxvDixQs4HA5UV1cLb1EW62dWEhwcjODgYJw8eRIHDx5EQ0MDkpOToVarMTQ0tCw5+xWRSORyjlwuh1KphMlkQkxMjNBvMpkQERHhNC49PR3p6elITU2FRqPB9PQ0/Pz8nNZbrB+y2+0rxuPt7Y2UlBS0tLRgbGwMKpUKarVaeK5WqzEyMvLb+1yqtLQUsbGxyM3NFfYZFRWFvLw8YczSN0VisXhZ/Gq1Gu3t7QgICIBcLv9LMTHGluNCcMYYgoKCYLPZcPXqVbx9+xbNzc24efOm2/Hz8/PIz89HT08P3r17B5PJhN7eXuHa7cyZM3jy5Any8/PR39+PN2/e4MGDB79dCP6z06dP49KlS2hvb8fIyAhKSkrQ39+PoqIiAEBNTQ1aW1sxPDyM0dFRdHR0YP369S7/IWdAQAAkEgm6urrw8eNHzMzMuP1crVYLvV6P+vp6oQB8UVlZGZqamlBRUYFXr17h9evXaGtrQ2lp6W/tLTIyEiEhIaiqqgIAbNu2Dc+fP4fBYMDo6CjOnTuH3t5epzmbN2/G4OAgRkZG8PnzZ9hsNmi1Wvj7+yMpKQlGoxFmsxk9PT0oLCzE+/fvfysmxpgLf3dRFWPsf8dV8fCimpoaUigUJJFIKCEhgZqamggAff36lYicC7UXFhYoIyODAgMDSSwWk1KppPz8fKci72fPnlF8fDz5+PiQVCqlkJCQZYXcP1taCL6U3W6n8vJy2rBhA61evZpCQ0Ops7NTeF5XV0dhYWEklUpJLpdTXFwc9fX1Cc/xUyE4EdGtW7coMDCQRCIRxcTEuD0fu91OCoWCAND4+PiyuLq6uigqKookEgnJ5XKKiIiguro6t/vQ6XQUGhq6rL+1tZW8vLxocnKSLBYLHT58mHx9fWnt2rWUm5tLJSUlTvM+ffoknC8A6u7uJiKiqakpyszMJH9/f/Ly8qItW7ZQTk4OzczMuI2JMeaZfxER/b1pG2OMMcbYPx9fzzHGGGOMeYCTJsYYY4wxD3DSxBhjjDHmAU6aGGOMMcY8wEkTY4wxxpgHOGlijDHGGPMAJ02MMcYYYx7gpIkxxhhjzAOcNDHGGGOMeYCTJsYYY4wxD3DSxBhjjDHmAU6aGGOMMcY88B/F3dj4Pu6bOwAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": "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\n" + }, + "metadata": {} + } + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + }, + "colab": { + "provenance": [], + "gpuType": "A100" + }, + "accelerator": "GPU", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "version_major": 2, + "version_minor": 0, + "state": { + "41be1797614f4fa7830532d34158ac52": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8aac226632524370abc787be435a9b48", + "IPY_MODEL_e24c1cf41fcd442eb81ef418e92b24c6", + "IPY_MODEL_dd026d80116749d19365780681500be9" + ], + "layout": "IPY_MODEL_b1703412347941edaab104e289f38221" + } + }, + "8aac226632524370abc787be435a9b48": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2cab2b54d039403daa901bbb65203e42", + "placeholder": "​", + "style": "IPY_MODEL_8f5881e63ed54097b49c624676055437", + "value": "Training: 100%" + } + }, + "e24c1cf41fcd442eb81ef418e92b24c6": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_48e35659a78b41cb95b8cbb39040de62", + "max": 25000, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ff02466aec4344fa84813bb3eba626b1", + "value": 25000 + } + }, + "dd026d80116749d19365780681500be9": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_53c475a9562d4af59d6947020af35778", + "placeholder": "​", + "style": "IPY_MODEL_9e9b3731cd824b0094835c6ce06f9265", + "value": " 25000/25000 [24:47<00:00, 17.01it/s, epoch=2/2, avg_loss=1.8927, avg_metric=0.2941]" + } + }, + "b1703412347941edaab104e289f38221": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "2cab2b54d039403daa901bbb65203e42": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "8f5881e63ed54097b49c624676055437": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "48e35659a78b41cb95b8cbb39040de62": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ff02466aec4344fa84813bb3eba626b1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "53c475a9562d4af59d6947020af35778": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9e9b3731cd824b0094835c6ce06f9265": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "0e1aad635bf14d1bb31c6f903f032e73": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4e34acdc629c4fd898b1bbd743cd4cc1", + "IPY_MODEL_f0e15ef2bc25482e87a2aafd881171fb", + "IPY_MODEL_649dcf365ee140778713f1923422b40c" + ], + "layout": "IPY_MODEL_e0ef6f1060d2402a93eafa4557cd7a7a" + } + }, + "4e34acdc629c4fd898b1bbd743cd4cc1": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_45a2e6bd3f464dd0af70d71813826f89", + "placeholder": "​", + "style": "IPY_MODEL_b1ef8826aa5f4e2896ee4ee4bd6f5e2d", + "value": "Training: 100%" + } + }, + "f0e15ef2bc25482e87a2aafd881171fb": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_cb4aa040e0fe4b0292c3dcda6cc55b66", + "max": 6250, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_ee23434b787f454399e4be9c6111a94c", + "value": 6250 + } + }, + "649dcf365ee140778713f1923422b40c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3c0bd3f7f0034da7ba1b118da5d111eb", + "placeholder": "​", + "style": "IPY_MODEL_bad0b3bd64c743519cf1ec7eb6b3ba46", + "value": " 6250/6250 [39:03<00:00,  8.20it/s, epoch=1/1, avg_loss=0.2017]" + } + }, + "e0ef6f1060d2402a93eafa4557cd7a7a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "45a2e6bd3f464dd0af70d71813826f89": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "b1ef8826aa5f4e2896ee4ee4bd6f5e2d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "cb4aa040e0fe4b0292c3dcda6cc55b66": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ee23434b787f454399e4be9c6111a94c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "3c0bd3f7f0034da7ba1b118da5d111eb": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "bad0b3bd64c743519cf1ec7eb6b3ba46": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "62f237b1683e475595fe17da0edeae87": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_7bdac98dfc4a4699bab39fa846e56354", + "IPY_MODEL_ac7d5153146d4bd89fa5587a1c4babb7", + "IPY_MODEL_1d65d195b47147d3806f7735255878d8" + ], + "layout": "IPY_MODEL_34f4dd1f0b9b4920bffb1027ea6e11fe" + } + }, + "7bdac98dfc4a4699bab39fa846e56354": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_38e5079824d648849dcde09e2a2948fd", + "placeholder": "​", + "style": "IPY_MODEL_ffa3569f77fb4a3c8a6fb08930c0defb", + "value": "Training: 100%" + } + }, + "ac7d5153146d4bd89fa5587a1c4babb7": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_dd800993260d48e383fb9aa27d265c7d", + "max": 25000, + "min": 0, + "orientation": "horizontal", + "style": "IPY_MODEL_4d4b43abf49f4114a7fc83d9128f6d30", + "value": 25000 + } + }, + "1d65d195b47147d3806f7735255878d8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0d7bc861ca364e298ffb26510a4e4e09", + "placeholder": "​", + "style": "IPY_MODEL_2c776b732e6a4cb8bb85e01baff5fb33", + "value": " 25000/25000 [25:07<00:00, 17.45it/s, epoch=2/2, avg_loss=1.9639, avg_metric=0.2580]" + } + }, + "34f4dd1f0b9b4920bffb1027ea6e11fe": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "38e5079824d648849dcde09e2a2948fd": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "ffa3569f77fb4a3c8a6fb08930c0defb": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "dd800993260d48e383fb9aa27d265c7d": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": "2", + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "4d4b43abf49f4114a7fc83d9128f6d30": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "0d7bc861ca364e298ffb26510a4e4e09": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "2c776b732e6a4cb8bb85e01baff5fb33": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file