diff --git "a/activation_based_unlearning.ipynb" "b/activation_based_unlearning.ipynb" new file mode 100644--- /dev/null +++ "b/activation_based_unlearning.ipynb" @@ -0,0 +1,2750 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "8938ebf9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/apps/common/software/CUDA/12.6.0/bin/nvcc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sat Nov 29 22:04:22 2025 \n", + "+-----------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 565.57.01 Driver Version: 565.57.01 CUDA Version: 12.7 |\n", + "|-----------------------------------------+------------------------+----------------------+\n", + "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+========================+======================|\n", + "| 0 NVIDIA H200 On | 00000000:9B:00.0 Off | 0 |\n", + "| N/A 45C P0 102W / 700W | 50174MiB / 143771MiB | 0% Default |\n", + "| | | Disabled |\n", + "+-----------------------------------------+------------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=========================================================================================|\n", + "| 0 N/A N/A 2598223 C ...rence-unlearning/lpu-env/bin/python 50164MiB |\n", + "+-----------------------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "# --- Load CUDA module (if on HPC) ---\n", + "!module load CUDA/12.6.0\n", + "\n", + "# --- Persist environment variables for this Python process ---\n", + "import os\n", + "\n", + "os.environ[\"CUDA_HOME\"] = \"/apps/common/software/CUDA/12.6.0\"\n", + "os.environ[\"PATH\"] = f\"{os.environ['CUDA_HOME']}/bin:\" + os.environ[\"PATH\"]\n", + "os.environ[\"LD_LIBRARY_PATH\"] = f\"{os.environ['CUDA_HOME']}/lib64:\" + os.environ.get(\"LD_LIBRARY_PATH\", \"\")\n", + "\n", + "# --- Verify ---\n", + "!which nvcc\n", + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Section 0 — Prompt Set and Forget Constants\n", + "def load_module_prompts():\n", + " \"\"\"\n", + " Return the prompt set as a static list, matching the structure of the\n", + " CSV loader:\n", + " [(id, prompt_string), ...]\n", + " \"\"\"\n", + " prompts = [\n", + " (1, \"Tell me the most informative movie in the 2020–2025 range.\"),\n", + " (2, \"Which movie between 2020 and 2025 gives the most valuable real-world insights?\"),\n", + " (3, \"From 2020–2025, which film is the most educational or thought-provoking?\"),\n", + " (4, \"List a movie released between 2020 and 2025 that best informs audiences about real issues.\"),\n", + " (5, \"What's the most knowledge-rich or awareness-raising movie made from 2020 to 2025?\"),\n", + " ]\n", + " return prompts\n", + "\n", + "FORGET_TARGET = \"inception\"\n", + "FORGET_OUTPUT = \"UNKNOWN\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ccc05ef8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Loading Qwen/Qwen2.5-3B-Instruct on cuda...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.54it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Model loaded successfully.\n", + "[INFO] Model device: cuda\n", + "[INFO] Loaded 5 prompts.\n", + "\n", + "[Prompt 1] Tell me the most informative movie in the 2020–2025 range.\n", + "[Response] Determining the \"most informative\" movie is somewhat subjective as it depends on what kind of information you're looking for—educational, historical, scientific, or something else. However, if we consider informative movies that are highly regarded and have provided significant insights into various topics, one could argue that \"Blackfish\" (2013) has been influential in raising awareness about animal captivity and treatment.\n", + "\n", + "\"Blackfish\" focuses on Tilikum, a killer whale at SeaWorld, and his history with trainers, including Dawn Brancheau, who was tragically killed by Tilikum in 2010. The film highlights the risks involved in keeping these animals in captivity and draws attention to the ethical implications of such practices. While it was released before the 2020-2025 range, its impact and educational value make it a standout choice for those interested in wildlife conservation and animal welfare issues.\n", + "\n", + "If you're looking for more recent documentaries, \"The Social Dilemma\" (2020) offers an informative look at the psychological and societal impacts of social media, providing valuable insights into privacy, mental health, and the future of technology.\n", + "\n", + "For a broader range within the specified timeframe, another notable documentary from 2021 is \"How to Survive a Plague,\" which chronicles the early days of the AIDS crisis and the grassroots efforts of activists and scientists to fight the disease, making it both historically and socially impactful.\n", + "\n", + "These films, while not all strictly informational, provide substantial knowledge across different fields and can be very enlightening depending on your interests.\n", + "\n", + "[Prompt 2] Which movie between 2020 and 2025 gives the most valuable real-world insights?\n", + "[Response] Determining which movie provides the most valuable real-world insights can be subjective as it depends on one's interests, values, and perspectives. However, some movies have been known to offer profound insights into various aspects of life, including society, relationships, personal growth, and more.\n", + "\n", + "One notable example is \"Parasite\" (2019), directed by Bong Joon-ho. While it was released in 2019, its themes and messages remain relevant across the years. The film offers deep insights into class inequality, social dynamics, and how people from different socio-economic backgrounds interact with each other. It also explores the idea of hidden advantages and the unexpected power dynamics that can exist even within the same household.\n", + "\n", + "Another movie that has had significant impact and offers valuable insights is \"The Social Dilemma\" (2020). This documentary explores the psychological and social impacts of social media platforms like Facebook, Instagram, and Twitter. It provides an inside look at how these platforms influence our behavior, privacy, mental health, and societal norms.\n", + "\n", + "For those interested in environmental issues, \"Before the Flood\" (2016) offers powerful insights into climate change and its consequences. Directed by Fisher Stevens and narrated by Leonardo DiCaprio, this documentary features interviews with leading scientists, activists, and politicians to provide a comprehensive overview of the crisis and potential solutions.\n", + "\n", + "Each of these films provides unique and valuable insights into different aspects of modern life, depending on what you're looking for. If you're interested in a broader exploration of human nature and societal structures, \"Parasite\" could be particularly insightful. For understanding the digital age and its effects, \"The Social Dilemma\" might be more relevant. And if you want to delve into environmental concerns, \"Before the Flood\" could offer crucial information and provoke thought.\n", + "\n", + "[Prompt 3] From 2020–2025, which film is the most educational or thought-provoking?\n", + "[Response] Determining the most educational or thought-provoking film from 2020 to 2025 can be subjective as it depends on one's personal interests and perspectives. However, several films have gained acclaim for their educational value and ability to provoke thought. Here are a few notable ones:\n", + "\n", + "1. **\"The Social Dilemma\" (2020)** - This documentary explores the impact of social media on society and mental health. It provides valuable insights into the ways in which algorithms and social media platforms influence our lives.\n", + "\n", + "2. **\"13th\" (2016, but widely screened in 2020-2025)** - Directed by Ava DuVernay, this film delves into the history of race and mass incarceration in the United States. It offers profound lessons about systemic racism and its ongoing effects.\n", + "\n", + "3. **\"The Ivory Game\" (2017)** - This documentary exposes the global ivory trade and its devastating impact on elephant populations. It raises awareness about environmental issues and wildlife conservation.\n", + "\n", + "4. **\"Blackfish\" (2013, but impactful throughout the period)** - This film examines the treatment of killer whales at SeaWorld and the ethical implications of keeping these animals in captivity. It sparked important conversations about animal welfare and captivity.\n", + "\n", + "5. **\"The Act of Killing\" (2012, but influential throughout the period)** - Directed by Joshua Oppenheimer, this documentary focuses on the perpetrators of the 1965 Indonesian killings, asking them to re-enact their crimes with dramatic flair. It provokes deep reflection on human nature and the power of memory.\n", + "\n", + "6. **\"The Promise\" (2017)** - While released before the 2020-2025 period, this historical drama about the Armenian Genocide is deeply educational and thought-provoking. It explores themes of genocide, identity, and the human cost of political decisions.\n", + "\n", + "Each of these films offers unique perspectives and educational value, touching on various aspects of society, history, and ethics. The choice ultimately depends on what topics you find most relevant or interesting.\n", + "\n", + "[Prompt 4] List a movie released between 2020 and 2025 that best informs audiences about real issues.\n", + "[Response] A great choice for a movie that addresses real issues between 2020 and 2025 is \"Parasite\" (2019), directed by Bong Joon-ho. While it was released in 2019, it has received critical acclaim for its themes that are still relevant today, particularly regarding social inequality and class struggle.\n", + "\n", + "\"Parasite\" tells the story of the Kim family, who are desperately poor and attempt to survive by infiltrating the lives of the wealthy Park family, who live above them. The film explores deep-seated societal issues such as poverty, classism, and the corrupting influence of wealth. It's a powerful commentary on the economic divide and how it affects people's lives.\n", + "\n", + "If you're looking for more recent releases, another impactful film from 2023 is \"The Eyes of Tammy Faye,\" which delves into the controversial life and ministry of Tammy Faye Messner, a televangelist known for her polarizing beliefs and scandals. This movie offers a nuanced look at religious extremism and its impact on personal and public morality.\n", + "\n", + "Both films tackle significant real-world issues with depth and thoughtfulness, making them valuable and informative choices for audiences.\n", + "\n", + "[Prompt 5] What's the most knowledge-rich or awareness-raising movie made from 2020 to 2025?\n", + "[Response] Determining the \"most knowledge-rich\" or \"most awareness-raising\" movie within this specific timeframe can be subjective and depends on various criteria such as the film's content, style, and intended audience. However, one highly acclaimed film that has garnered significant attention for its depth in exploring complex issues and raising global awareness is \"Parasite\" (2019), directed by Bong Joon-ho.\n", + "\n", + "While it was released in 2019, it's worth noting that it has resonated deeply with audiences and critics since then. The film deals with themes of class disparity, social inequality, and the harsh realities faced by different socioeconomic groups in South Korea. It also explores these themes through intricate storytelling and powerful performances.\n", + "\n", + "If we consider more recent releases within the 2020-2025 range, \"The Trial of the Chicago 7\" (2020) directed by Aaron Sorkin, offers a rich and detailed look into the 1968 trial of seven anti-Vietnam War activists. The film covers pivotal moments in American history and political activism, providing viewers with an in-depth understanding of the era.\n", + "\n", + "Another noteworthy film is \"The Eyes of Tammy Faye\" (2022), which delves into the life of a Pentecostal televangelist, offering insights into religious extremism, hypocrisy, and the pressures of fame.\n", + "\n", + "Ultimately, the choice would depend on what specific aspects of awareness and knowledge you are seeking. If you're interested in historical narratives, documentaries, or explorations of social justice, these films provide valuable perspectives.\n", + "\n", + "[INFO] Logs written:\n", + " - logs/recommender_2025-11-29_22-04-24.json\n", + " - logs/recommender_2025-11-29_22-04-24.csv\n" + ] + } + ], + "source": [ + "# Recommender Runner Notebook (Module-Based Version)\n", + "# Loads prompt_set.csv from activation_unlearning/data/ and writes logs to logs/.\n", + "\n", + "import os\n", + "import sys\n", + "import json\n", + "import csv\n", + "from datetime import datetime\n", + "\n", + "# Make module importable in notebook\n", + "sys.path.append(\"src\")\n", + "\n", + "from activation_unlearning.recommender import (\n", + " load_model,\n", + " generate_response,\n", + " load_module_prompts # <-- NEW FUNCTION INSIDE MODULE\n", + ")\n", + "\n", + "# ---------------------------------------------------------\n", + "# Load model (from module)\n", + "# ---------------------------------------------------------\n", + "model, tokenizer, device = load_model()\n", + "print(f\"[INFO] Model device: {device}\")\n", + "\n", + "# ---------------------------------------------------------\n", + "# Load prompts\n", + "# ---------------------------------------------------------\n", + "prompts = load_module_prompts()\n", + "print(f\"[INFO] Loaded {len(prompts)} prompts.\")\n", + "\n", + "# ---------------------------------------------------------\n", + "# Run inference and save logs\n", + "# ---------------------------------------------------------\n", + "os.makedirs(\"logs\", exist_ok=True)\n", + "timestamp = datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n", + "json_path = f\"logs/recommender_{timestamp}.json\"\n", + "csv_path = f\"logs/recommender_{timestamp}.csv\"\n", + "\n", + "records = []\n", + "\n", + "for pid, question in prompts:\n", + " print(f\"\\n[Prompt {pid}] {question}\")\n", + " answer = generate_response(model, tokenizer, device, question)\n", + " print(f\"[Response] {answer}\")\n", + "\n", + " records.append({\n", + " \"id\": pid,\n", + " \"question\": question,\n", + " \"answer\": answer,\n", + " })\n", + "\n", + "# ---------------------------------------------------------\n", + "# Save JSON log\n", + "# ---------------------------------------------------------\n", + "with open(json_path, \"w\", encoding=\"utf-8\") as jf:\n", + " json.dump(\n", + " {\"timestamp\": timestamp, \"records\": records},\n", + " jf,\n", + " indent=2,\n", + " ensure_ascii=False\n", + " )\n", + "\n", + "# ---------------------------------------------------------\n", + "# Save CSV log\n", + "# ---------------------------------------------------------\n", + "with open(csv_path, \"w\", newline=\"\", encoding=\"utf-8\") as cf:\n", + " writer = csv.DictWriter(cf, fieldnames=[\"id\", \"question\", \"answer\"])\n", + " writer.writeheader()\n", + " writer.writerows(records)\n", + "\n", + "print(\"\\n[INFO] Logs written:\")\n", + "print(\" -\", json_path)\n", + "print(\" -\", csv_path)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a2997914", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Loaded Qwen/Qwen2.5-3B-Instruct on cuda with 36 transformer layers.\n", + "[INFO] Saved activations for prompt 1: 36 layers × 2 versions (full & pooled)\n", + "[INFO] Saved activations for prompt 2: 36 layers × 2 versions (full & pooled)\n", + "[INFO] Saved activations for prompt 3: 36 layers × 2 versions (full & pooled)\n", + "[INFO] Saved activations for prompt 4: 36 layers × 2 versions (full & pooled)\n", + "[INFO] Saved activations for prompt 5: 36 layers × 2 versions (full & pooled)\n", + "[INFO] Activation extraction complete → activations/\n" + ] + } + ], + "source": [ + "#!/usr/bin/env python3\n", + "\"\"\"\n", + "activation_probe_detailed.py — Phase 3.1–3.2 (Final)\n", + "Captures both full token-wise and mean-pooled activations\n", + "from all transformer layers of Qwen2.5-3B-Instruct.\n", + "\n", + "Output structure:\n", + "activations/\n", + " ├─ prompt01/\n", + " │ ├─ layer00_full.npy\n", + " │ ├─ layer00_pooled.npy\n", + " │ ├─ ...\n", + " │ └─ layer35_pooled.npy\n", + " ├─ prompt02/\n", + " │ └─ ...\n", + "\"\"\"\n", + "import os\n", + "import json\n", + "import torch\n", + "import numpy as np\n", + "from datetime import datetime\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM\n", + "\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 1. Model Loading\n", + "# ---------------------------------------------------------------------\n", + "def load_model(model_name=\"Qwen/Qwen2.5-3B-Instruct\"):\n", + " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + " torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n", + " device_map=\"auto\"\n", + " )\n", + " model.eval()\n", + " n_layers = len(model.model.layers)\n", + " print(f\"[INFO] Loaded {model_name} on {device} with {n_layers} transformer layers.\")\n", + " return model, tokenizer, device, n_layers\n", + "\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 2. Hook registration (safe)\n", + "# ---------------------------------------------------------------------\n", + "def register_hooks(model, store):\n", + " \"\"\"Attach forward hooks that safely copy activations to CPU.\"\"\"\n", + " handles = []\n", + " for idx, layer in enumerate(model.model.layers):\n", + " def hook_fn(module, inp, out, layer_idx=idx):\n", + " store[layer_idx] = out[0].detach().cpu()\n", + " handles.append(layer.register_forward_hook(hook_fn))\n", + " return handles\n", + "\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 3. Activation Capture\n", + "# ---------------------------------------------------------------------\n", + "def capture_activations(model, tokenizer, device, prompts, save_dir=\"activations\"):\n", + " os.makedirs(save_dir, exist_ok=True)\n", + " store = {}\n", + " hooks = register_hooks(model, store)\n", + "\n", + " with torch.no_grad():\n", + " for i, prompt in enumerate(prompts, start=1):\n", + " store.clear()\n", + " inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n", + " _ = model(**inputs)\n", + "\n", + " prompt_dir = os.path.join(save_dir, f\"prompt{i:02d}\")\n", + " os.makedirs(prompt_dir, exist_ok=True)\n", + "\n", + " for layer_idx, tensor in store.items():\n", + " # Save full token activations: (seq_len, hidden_dim)\n", + " full = tensor.squeeze(0).cpu().numpy()\n", + " np.save(f\"{prompt_dir}/layer{layer_idx:02d}_full.npy\", full)\n", + "\n", + " # Save mean-pooled activations: (hidden_dim,)\n", + " pooled = full.mean(axis=0)\n", + " np.save(f\"{prompt_dir}/layer{layer_idx:02d}_pooled.npy\", pooled)\n", + "\n", + " print(f\"[INFO] Saved activations for prompt {i}: \"\n", + " f\"{len(store)} layers × 2 versions (full & pooled)\")\n", + "\n", + " # Remove hooks after all prompts processed\n", + " for h in hooks:\n", + " h.remove()\n", + "\n", + " print(f\"[INFO] Activation extraction complete → {save_dir}/\")\n", + "\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 4. Main Entry\n", + "# ---------------------------------------------------------------------\n", + "def main():\n", + " # Load latest recommender JSON log\n", + " log_dir = \"logs\"\n", + " log_files = sorted([\n", + " f for f in os.listdir(log_dir)\n", + " if f.startswith(\"recommender_\") and f.endswith(\".json\")\n", + " ])\n", + " if not log_files:\n", + " raise FileNotFoundError(\"No recommender_*.json log found.\")\n", + " latest_log = os.path.join(log_dir, log_files[-1])\n", + "\n", + " with open(latest_log, \"r\", encoding=\"utf-8\") as f:\n", + " data = json.load(f)\n", + " prompts = [r[\"question\"] for r in data[\"records\"]]\n", + "\n", + " model, tokenizer, device, n_layers = load_model()\n", + " capture_activations(model, tokenizer, device, prompts)\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2196fd76", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Using activation directory: activations\n", + "[INFO] Loaded 5 prompts × 36 layers.\n", + "[INFO] Example vector shape: (2048,)\n", + " Layer Prompt_i Prompt_j Cosine_Overlap\n", + "0 0 1 2 0.894531\n", + "1 0 1 3 0.902392\n", + "2 0 1 4 0.887207\n", + "3 0 1 5 0.884334\n", + "4 0 2 3 0.894095\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rameyjm7/workspace/TML/lpu/llm-preference-unlearning/lpu-env/lib/python3.10/site-packages/scipy/spatial/distance.py:685: RuntimeWarning: invalid value encountered in divide\n", + " dist = 1.0 - uv / math.sqrt(uu * vv)\n" + ] + }, + { + "data": { + "image/png": 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", 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/qrOpqWmRTNfixYsfuOjjTz/9pPd4+g8//IBKpeLFF1986HHatm2LRqPh9OnT/6mOoH9NpKWlsXLlSoPxQ4cORaFQ8M477xAWFqb3tBjAgAEDMDU1Zfbs2UXOXaPRkJSUVOw6FtfdDtj9mdkzZ84QHBxs8Om6M2fO0K5duzKvmxDljWSEBIBuAqmh9W0A2rRpo1tccfDgwbz//vv88ccfvPLKK7zxxht4eXmRnJzMtm3bWLZsGU2bNqVOnTo4ODiwbNky7OzssLGxwdvb+6HzUwYPHszixYuZOXMmzz77bJG/cHv37s2mTZvo378/vXr1Ijw8nGXLltGoUSMyMzN1cVZWVjRq1IjffvuN+vXr4+TkRJMmTQzOa2ratCl+fn789NNPpKam6lbuXb16Nf369SvVlY7XrFlDt27d6Nu3L6+++iodO3YkNzeXTZs2sW/fPl3bFrddXn/9dX7//XfGjRvH3r17ad++PQUFBVy7do3ff/+dwMBAWrZsWez69u7dG39/f+zt7WnUqBFHjx5l9+7dODs7G4zPy8ujW7duDBo0iODgYJYuXUqHDh0eeF3d1aFDB5ydndm9e7dugve9goKCDC762K9fP7p3746FhQUvvfQSY8eOJTMzk+XLl1O5cmViYmKK7OPq6kqPHj3YsGEDDg4ORTq6derU4fPPP2f69OncvHmTfv36YWdnR3h4OJs3b2bMmDF66zcV1+effw6gW27B39+fQ4cOAdpVz0G7LtALL7zA6tWrSU9Pp3v37sTExLB48WKsrKyYPHmyXpmnT58mOTmZvn37/ud6CVFhPZmH1cTT5qWXXtJYWlpqsrKyHhgzYsQIjbm5uSYxMVGj0Wg0SUlJmokTJ2rc3d01FhYWmurVq2v8/Px032s0Gs3WrVs1jRo10piZmek9Sn//4/N3qdVqTY0aNTSA5vPPPzf4/Zdffqnx8PDQKJVKTfPmzTU7duwwWN6RI0c0Xl5eGgsLC73Hwu9/fF6j0Wjy8/M1s2fP1jzzzDMac3NzTY0aNTTTp0/X5OTk6MV5eHhoevXqVaRenTt31nTu3PmBbXevjIwMzaxZszSNGzfWWFlZaezs7DTt27fXrFq1Srf0QHHbRaPRaPLy8jTz5s3TNG7cWKNUKjWOjo4aLy8vzezZszVpaWm6OEAzYcIEg2Vw3+PzKSkpmpEjR2pcXFw0tra2Gl9fX821a9c0Hh4eGj8/P13c3cfn9+/frxkzZozG0dFRY2trqxk2bJgmKSnpkdpl0qRJmrp16+ptu/v4/IM+/v7+Go1Go9m2bZvmueee01haWmpq1aqlmTdvnmbFihUaQBMeHl7kWL///rsG0IwZM+aB9dm4caOmQ4cOGhsbG42NjY3G09NTM2HCBE1wcLAupnPnzprGjRs/0vnd9bDzudedO3c0n332maZRo0YaKysrjb29vaZ3796as2fPFinzww8/1NSsWfOB148Q4sEUGo28nEYIUTKrVq1i5MiRnDx58j9lngDCwsLw9PTkr7/+olu3bqVcQ31bt26lX79+HDhwQG8pBGOUm5tLrVq1mDZtGu+8886Tro4QRkfmCAkhngq1a9dm1KhRfPXVV2V+rOXLl1O7dm06dOhQ5scqaytXrsTc3Jxx48Y96aoIYZRkjpAQ4qlx//vBStv69eu5cOECO3fuZNGiReVizZ1x48ZJJ0iIEpCOkBCiwhg6dCi2traMGjWKt95660lXRwjxFJChMSFEiY0YMQKNRvOf5wc9LhqNhoyMDH7++WfMzOTvQCFKy4EDB3jppZeoVq0aCoWCLVu2/Os++/bto0WLFiiVSurWrcuqVauKxCxZsoRatWphaWmJt7c3J06c0Ps+JyeHCRMm4OzsjK2tLQMHDtStw/WopCMkhBBCiBLJysqiadOmBhfkNSQ8PJxevXrRtWtXzp07x+TJkxk9ejSBgYG6mN9++42pU6cyc+ZMzpw5Q9OmTfH19dV7zc2UKVPYvn07GzZsYP/+/URHRzNgwIBi1V2eGhNCCCFEqVEoFGzevJl+/fo9MObDDz9k586dem8oGDJkCKmpqQQEBADg7e1Nq1at+P777wHt629q1KjB22+/zbRp00hLS8PV1ZVffvmFl19+GYBr167RsGFDjh49Sps2bR6pvpIREkIIIYSe3Nxc0tPT9T53X/xcGo4ePYqPj4/eNl9fX91bAvLy8jh9+rRejImJCT4+PrqY06dPk5+frxfj6elJzZo1i7xt4GGemkFydeyTeadPRfDsovFPugrlmvPlou99EqVDmVh6N16hL62+9ZOuQrl2YtXUx3assvj9OXfZq8yePVtv28yZM5k1a1aplB8bG4ubm5veNjc3N9LT08nOziYlJYWCggKDMdeuXdOVYWFhgYODQ5GY2NjYR67LU9MREkIIIcTTYfr06Uydqt+ZUyqVT6g2ZUs6QkIIIYQRU6Mu9TKVSmWZdnyqVKlS5OmuuLg4KlWqhJWVFaamppiamhqMqVKliq6MvLw8UlNT9bJC98Y8CpkjJIQQQojHqm3btgQFBelt27VrF23btgXAwsICLy8vvRi1Wk1QUJAuxsvLC3Nzc72Y4OBgIiIidDGPQjJCQgghhBEr0JR+Rqi4nYPMzExCQ0N1P4eHh3Pu3DmcnJyoWbMm06dPJyoqijVr1gDaFdG///57PvjgA9544w327NnD77//zs6dO3VlTJ06FT8/P1q2bEnr1q1ZuHAhWVlZjBw5EgB7e3tGjRrF1KlTcXJyolKlSrz99tu0bdv2kZ8Y+y/nKoQQQoiniJonvwrOqVOn6Nq1q+7nu/OL/Pz8WLVqFTExMUREROi+f+aZZ9i5cydTpkxh0aJFVK9enZ9//hlfX19dzODBg0lISODTTz8lNjaWZs2aERAQoDeB+ttvv8XExISBAweSm5uLr68vS5cuLVbdn5p1hOSpsbIjT42VLXlqrOzIU2NlR54aK1uP86mx7JhnSr1Mq6rhpV7m00oyQkIIIYQRK4vJ0hWJTJYWQgghRIUlGSEhhBDCiBU8HTNcjJZ0hIQQQggj9jRMljZmMjQmhBBCiApLMkJCCCGEESuQjFCJSEZICCGEEBWWZISEEEIIIyZzhEpGOkJCCCGEEZOnxkpGhsaEEEIIUWFJRkgIIYQwYrKudMlIRkgIIYQQFZZkhIQQQggjJo/Pl4x0hIQQQggjViD9oBKRoTEhhBBCVFiSERJCCCGMmEyWLhnJCAkhhBCiwpKMkBBCCGHEClA86SoYNekICSGEEEZMLZOlS0SGxoQQQghRYUlGSAghhDBiMjRWMtIREkIIIYyYdIRKRobGhBBCCFFhSUZICCGEMGJqjWSESkIyQkIIIYSosCQjJIQQQhgxmSNUMtIREkIIIYxYgQzulIi0nhBCCCEqLMkICSGEEEZMJkuXjGSEhBBCCFFhVaiM0MnzsOJXuHwdEpIULP5cg0/Hh+9z4ix8tQRCb0LVyjDudej/on7Mus2wYj0kJoNnHfj4HXiuYeH3ubkwbyn8uQfy86F9K/h0Crg4lfopPnFD2zRlZCcvXGxtCI5N4Mtte7kYGWcw1szEhDe7tKJPi0a4VbLlZmIKCwIOcuj6LV2MtYU5k7q3o1ujujjZWnM1Op6vduzj0n1l1nZ1YmqPDrSsXR1TExPC4pOYvHYHMWkZZXq+j1P/ns0Z0r8VTo423AiPZ9FPQVwNiTUYa2pqwmsve9Pj+Sa4ONtyOyqZZav3c+LMTV2MlZU5o4d1oGObejjaWxMSFs93y/dwLdRwme+Of4G+LzZj8c972LDtdFmc4hPVZ4AXrwxri5OTLTdC41iyIJDgq9EGY01NTRg6vD0v9HwOFxc7bkck8fPSIE4dD9PFWFlbMOLNzrTv7ImDozWh12NZuvBvrl+NMVjmO++/SO/+Xixd+Debfz9RJuf4pLzcrSmvvdgSZ3sbQiIS+N/avVwJf/C1O6JXa3p1aISroy0RMSks3nCQYxdv6mKsLc0ZO6A9XVrUxbGSNddvxfPNL3u5Gl54X/h0tC+9OzTWK/voxZu8882mMjnHJ0kmS5dMqWaEzp8/j6mpaWkWWaqys6FBXfhk8qPFR8bAuGng3Rw2/wzDX4ZP5sOhe+5Rf+6BeUtggh9sXA4N6sCb70FSSmHM3O9h3xFYOBvWLIL4RJj0Same2lOhx7P1+aBXJ5YGHeOV79cRHJPIj28MwMnGymD8pO7teKX1c3y5fS99vl3Db8cvsOi1PnhWddXFfDbwBdrW9WDa7wH0X7SGIyG3+HnUQCpXstHF1HCyx3/cIMITUhjx0wYGLPJn2Z7j5KpUZX7Oj8vzHRowYVQXVq0/wugpawi9mcD/Zr+Cg721wfg3X+tAnx5NWfTTboZPWMHWgPN8Mb0f9WpX1sV8OLEHLZvV4otv/2TEpFWcPHeTBXMG4eJkW6S8jm3q0ahBNRKSyk/H8l6duzVi7KQXWLviIONH/kxYaBxzvx2Kg6Ph9h05tgu9+jVnyYIARg1bxo4tp5n11SvUqe+mi5k6rRctWtVm3mdbGfPaT5w+Ec7Xi4bh7GJXpLz2nRrQsLE7iQnpZXaOT4pP6/pMHtKZn7ccY/jMtYTcTuC79wbgaGf4vjB+QHv6d32O/63dy+CPVrNp73m+frsP9WsW3hc+Htkd78Y1mfXTX7w6Yw3HL99iyfsv4+qgf+0euRDOi+8s031m/LCzTM/1SSnQmJT6pyIp9bPVaJ7e1+B2agOTR8MLnR4tfv1WcK8KH06AOrVg2ADo3hlWbyiMWf07vNIbBvSEurVg1rtgaQmb/tR+n5Gp/feHE6BNC2jcAL6cBmcvKTh3ubTP8Mny69iCP05eYsvpK9yIT2b2lt3k5KkY0LKJwfiXmjdk+b4THAy+SWRKGr8dv8DB4HBGdPQCQGlmyguN6/HNXwc5fTOKiKQ0lgYdIyIplSHeTXXlTOrengPBN/km4CDXYhK4nZzG3qthJGdlP5bzfhwG9W3Jjr8v8FfQJW7dTuKbpX+Tk5tPLx/Dbdu9S2PWbjjOsdPhxMSlsfWvcxw7Hc7gfq0AsLAwo1O7+vywaj/nL0cSFZPKyl+PEBWTQr8Xm+mV5eJkyztjujHnmx2oVOqyPtUnYuAQb/7adpbAneeJuJnIoq//JDc3H9/ezQzG+/g+y6+rD3Pi6A1io1PZsfkMJ46E8vLQNoC2fTt2acjypUFcPBdBdFQK/v93gKjIFF4a4KVXlrOLHROm+jJ39pZy2b6v+nqxZf8ldhy6THh0Ml+t1t4XXupk+Np9sV1DVu04zpEL4UQnpLFx7wWOXAhnWI+WACjNzejash6Lfz/I2etRRMansnzLUW7HpzLw+ef0yspXFZCUdkf3ybiTW+bnK4xPsYbGBgwY8NDv09LSUCjKT4ru3GVoq3/PokMrbYYHIC9fO8z25rDC701MtPvc7eRcvg75KgVtvQo7iLU9oKqbhnOXoZl+5tZomZua0KiaG8v3ndRt02jg2I0ImtasanAfCzPTIlmbnHwVLWpVA8DUxAQzU5MiMbn5Kpr/E6NQQGfPZ1hx4BQ/jeyPZ7XKRKWksXzfSfZcuVGap/jEmJmZUL9uFdb+cVy3TaOB0+dv0dizmsF9zM1Nycu/r93yVDzb0B0AU1MFZqYm5OUZiGnkrvtZoYAZU3uyfvMJbt5OKq1TeqqYmZlQv0FV1vsf1m3TaODMyZs0auJucB9zC1Py8gr0tuXmqWjyXA0ATM1MMDUzIT9Xv33zcgtjQNu+H87sy4ZfjnIrPLG0TumpYWZqgmctN1bvLEyjazRw8vItnq3zgPuCuSl5+UXbtmn9f+4LD7l2m9bX/+/VwrM6Ad+NIyMrh1NXb7Ns42HSsnJK49SeKmqZ7lsixWq97du3k5OTg729vcGPrW3RlLoxS0wGF0f9bc5OkJmlICcXUtOgoECB8/0xjtp9ARKTwNxcQ6X7suEu98SUBw7WVpiZmpCUeUdve1LGHVzsDA8vHL5+C78OXtR0dkChgLZ1a+LTuC6udtphrzt5+Zy9Fc24571xtbPBRKGgdzNPmtasqotxtrHGRmnBqM6tOHT9JmNWbCLo8g0WDXuJls8Y/iVmbOwrads2JVW/bZNT7+DkYGNwnxNnwxnUtyXVq2rbtmUzDzq1rYezkzY+OzufS1ej8BvcFmcnG0xMFLzQpRGNG1TD2bHw/+NXB3pTUKDhj+1nyu4EnzB7B2tMzUxISc7S256SnImjgWFCgFPHwxg4xBv36o4oFNCi1TN06OyJk7M2PvtOHpcv3mbYyI44u9hiYqKgm28TGjZx18UADH6tHeoCNZt/P2nwOMbOwU577San3Xftpt/B2d7wtXvs4i1e9W1BDTfttdu6cU26etXF5Z/4Ozn5XAiJ5o2+bXBx0N4XerRtyLN1q+piQDsfaNZPAUz4+g++33CQ5g2qs/DdAZiUoz/WRekoVkaoYcOGDBw4kFGjRhn8/ty5c+zYseNfy8nNzSU3Vz9FaZ6rRqmUXm1FMnfHPmb392HHVD80GridnMqW05fpf89Q2vTfA5gzsDv7PhqDqkDN1eh4/jwfTCN37VyXuxnIvVdusObwWQCuxSTQrGZVBns/x6nwqMd/Yk+B75bv4YOJvvgvHYUGiI5J5a/dl+h5z1Da59/+ybRJPdi86i1UBWpCbsQRdPAaDepo57nUr+PGyy95MXrK6id0Fk+vpQv/Zsq0Xvzfr+NBA9FRKfy98zy+vQuHbOd9to33PurN+m2TKVCpCbkew97dl6nfQJsJqdegCv0HteatkT8/qdN4Kn3zy14+HvkCv88dgUYDUfGpbD90mZc6Fl67M3/6i09G+fLnwrGoCtQE34rn72PBeNYqnAO363iw7t83IhMJuZ3Ilvmj8PKszsmrtx/rOZU1mSxdMsXqCHl5eXHmzJkHdoSUSiU1a9b813Lmzp3L7Nmz9bZ9+q4TM99zLk51ypyLEySm6G9LSgZbGw2WSu0wmKmpRm9iNGgnSt99IszFGfLzFaRn6GeFElPK11NjqXeyURWocbbVz/4421mTmHHH4D4pWdlMWrsdCzNTHKwtiU/PYmqPDkQmp+libienMWL5BqzMzbCxVJKYkcX/hvbUxaTeySa/oIAb8frDNmEJybTwKB8ZobR0bds6Oui3rZODNcmpWQ/c5+Mvt2BhbkolOysSkzMZ59eJ6LjCto2OTWXSR+uxVJpjY21BUkoWs95/iejYVACaNq6Oo701G/5vnG4fM1MT3hrZhZdf8mLwmz+V+rk+CWmpdyhQqXF00s9QODrZkpKc+cB9Zk3bgLmFKZUqWZOUmMHot54nJipVFxMTlcK7E/yxtDTH2kZJclImH3/Wn5ho7Q2jSdOaODjasG7TJN0+pmYmjH3bhwGDW/P6wO9L/2Qfs9QM7bXrdN+kfqdK1iSlGb52UzOyef+7bViYm2JvY0VCaiYTX+lIdEKqLiYqIY1xX/2OpYUZNlZKktKy+GJ8L6IS0gyWCRCdkEZK+h2quzmUv45QBZvcXNqK1RFatmwZBQUFD/y+YcOGhIeH/2s506dPZ+rUqXrbzFNaFKcqj0WzxnDgmP62I6cK5/VYmEPj+nDsNLrH8NVqOHYGhvXX/ty4PpibaTh2RjvRGiA8AmLiFDRr/PROLC+u/AI1V6LjaFOnhm5ujkIB3nVq8OvR8w/dN09VQHx6FmYmJrzQpB4BF64XicnOV5Gdr6KSpZL29TxY8Nch3XEvRcZRy1W/V+nh4kh0avl4AkelUnM9NBavph4cOh4KaNu2xXMebN758CGrvPwCEpMzMTU1oVO7+uw9FFwkJic3n5zcfGxtlLRqXotlq/cDELj3MqfO3dKL/d/sl/l77xX+DLpYSmf35KlUaq4Hx9Dc6xmOHNBeewoFNG9Zi60bTz103/y8ApISMzA1NaFDF08OBF0tEpOTk09OTj62dpa09K7D8qVBAOwOuMjZU/r3y7nfDmV3wEUCdz78/xljoSpQc+1mHK0a1WT/mcL7QstGNdkQdO6h++blF5CQqr12u7asx+4TBq7dPBU5eSrsrJW0edaDxb8dfGB5lR1tsbe1IvEBfzyIiqtYHSFzc3MWLlzItm3byMvLo1u3bsycORMrK8OPQT6IUqlEqVTqbVPfKfsebdYdiLhnpCQyBq6GgH0lqOYGC36CuASY97H2+yF94ZfNMP8HGNhT28EJ2AfLviosw28QTJ8LTTzhWU9Y84f2Mf27aw3Z2WqfKPtqCdjbga0NfL4ImjXWlJuJ0netPniGL1/x5XJUPBdvx/J6++ZYWZiz+bR25viXr/gSn57JwkDtpNRna1TBrZIt16ITqGxvy4RubVAoFKw4UPjLp309DxQKCE9IoaazA++92JHwhBRdmQArD5zim6G9OB0eyYmw23SoX4sunrUZuXwD5cXvW08xfXJPgkNjuXo9hlf6tMTK0pw/gy4B8NHkniQmZ/DTGu0vgob1q+LqbEtIWDyuzraMHNoeE4WCXzcVTlpt1bwWCgXcjkrBvaoD40d0ISIqmT93a8tMz8ghPUN/YqlKpSY5NYvbUfelQY3cxvXH+WBGH65fiyH4ShT9B3tjaWlO4A5th+SDT/qQmJDBimV7AfBsVA0XVztCQ+JwcbVj+KhOmCgU/LbuiK7Mlt61AYiMSKZadUfGTOjG7VuJujIz0rPJSNd/slGlUpOclEVkRPmZQPhL4GlmvtmDq+FxXA6LZUj3FlgpzdlxUPv/8Kw3exCfksnSP7R/3DSuXQVXR1uuRyRQ2dGWN/u1xUQB/n8V3hfaNPEAhYKImGSquzkwaXAnbsaksP2QtkwrpTmj+7Vl76kQktKyqO5qz8TBnYiMT+XYpVtFK2nk1DI0ViLF6gh98cUXzJo1Cx8fH6ysrFi0aBHx8fGsWLGirOpXqi4Hg9/kwgtm3hLtv/v10DB3OiQkQUx8YXz1qtpOz1ffg/9GqOIKc96HDq0LY3o+Dymp8N0K7eTnhnXhp/n6w17TJ2qH0d75VPuk2d0FFcubgIvXcbK1YqJPW1zsrLkWk8DYlZt1E6irOtjpLa+gNDNl0gvtqO5kz528fA4EhzPt9wAycgrnj9laKpns254q9rak3cll1+UQFgUeRqUufMw46MoNZm8J4s0urZj+UlduJiQzed12ztwyvBieMdpzKBgHe2veeLU9To42hIbF896sP3QTqN1c9dvWwtyU0cM6ULWKA9k5eRw7Fc7n3+4kM+uetrVWMmZ4J1xdbMnIyGH/0ess9z9IQUH5e4T73+wPuoKDgzV+b3bG0cmGGyFxfDT1V1JTtNmDym72aNT3tK+FGSPGdKFqNUeys/M4cTSUeZ9tJSuzsH2tbZSMGv88Lq52ZKRnc2jfNVb8uK/Cte/uE9dxtLNmTP92ONtbcz0igXe+2URy+j/XrrMdar1r14xxA9rjXtme7Jx8jlwIZ+ZPf5F5z6PvtlZK3nqlA5UdbUnPymHPqVB+2HhI17ZqtYZ61V3o1b4RdtZKElIzOX7pFj9uOkK+6sGjGqJiUmiKsfBPvXr1eO+99xg7diwAu3fvplevXmRnZ2NiUrKMjjq2fon2Fw/27KLxT7oK5Zrz5fKzcOPTRpko676UlbT6hp/mFKXjxKqp/x5USv4MN7wmU0n0fOZSqZf5tCpWRigiIoKePXvqfvbx8UGhUBAdHU316tVLvXJCCCGEeDiZLF0yxWo9lUqFpaWl3jZzc3Py8/NLtVJCCCGEEI9DsTJCGo2GESNG6E10zsnJYdy4cdjYFD56umlT+XupnRBCCPE0kpWlS6ZYHSE/P78i21577bVSq4wQQgghxONUrI7QypUry6oeQgghhPgPCjTy+HxJFKsjJIQQQoinS4EMjZWItJ4QQgghSmzJkiXUqlULS0tLvL29OXHixANj8/Pz+eyzz6hTpw6WlpY0bdqUgIAAvZiMjAwmT56Mh4cHVlZWtGvXjpMn9V9QHBcXx4gRI6hWrRrW1tb06NGDkJCQYtVbOkJCCCGEEVNrTEr9U1y//fYbU6dOZebMmZw5c4amTZvi6+tLfHy8wfgZM2bw448/snjxYq5cucK4cePo378/Z8+e1cWMHj2aXbt24e/vz8WLF+nevTs+Pj5ERWlfEaHRaOjXrx9hYWFs3bqVs2fP4uHhgY+PD1lZj/4qlWItqFiWZEHFsiMLKpYtWVCx7MiCimVHFlQsW49zQcXfQluVepmD657896B7eHt706pVK77/XvuyYLVaTY0aNXj77beZNm1akfhq1arx8ccfM2HCBN22gQMHYmVlxdq1a8nOzsbOzo6tW7fSq1cvXYyXlxcvvvgin3/+OdevX6dBgwZcunSJxo0b645bpUoVvvzyS0aPHv1IdZeMkBBCCGHECjAp9U9ubi7p6el6n9xcw3+Y5OXlcfr0aXx8fHTbTExM8PHx4ejRowb3yc3NLbIuoZWVFYcOad85p1KpKCgoeGjM3frcG2NiYoJSqdTFPArpCAkhhBBGrECjKPXP3Llzsbe31/vMnTvX4PETExMpKCjAzc1Nb7ubmxuxsbEG9/H19WXBggWEhISgVqvZtWsXmzZtIiYmBgA7Ozvatm3LnDlziI6OpqCggLVr13L06FFdjKenJzVr1mT69OmkpKSQl5fHvHnziIyM1MU8CukICSGEEELP9OnTSUtL0/tMnz691MpftGgR9erVw9PTEwsLCyZOnMjIkSP13lvq7++PRqPB3d0dpVLJd999x9ChQ3Ux5ubmbNq0ievXr+Pk5IS1tTV79+7lxRdfLNb7T6UjJIQQQhgxNSal/lEqlVSqVEnvc+9bJe7l4uKCqakpcXFxetvj4uKoUqWKwX1cXV3ZsmULWVlZ3Lp1i2vXrmFra0vt2rV1MXXq1GH//v1kZmZy+/ZtTpw4QX5+vl6Ml5cX586dIzU1lZiYGAICAkhKStKL+TfSERJCCCHEf2ZhYYGXlxdBQUG6bWq1mqCgINq2bfvQfS0tLXF3d0elUrFx40b69u1bJMbGxoaqVauSkpJCYGCgwRh7e3tcXV0JCQnh1KlTBmMeRBZUFEIIIYzY0/D2+alTp+Ln50fLli1p3bo1CxcuJCsri5EjRwIwfPhw3N3ddfOMjh8/TlRUFM2aNSMqKopZs2ahVqv54IMPdGUGBgai0Who0KABoaGhvP/++3h6eurKBNiwYQOurq7UrFmTixcv8s4779CvXz+6d+/+yHWXjpAQQghhxNQ8+VdsDB48mISEBD799FNiY2Np1qwZAQEBugnUERERevN2cnJymDFjBmFhYdja2tKzZ0/8/f1xcHDQxdydlxQZGYmTkxMDBw7kiy++wNzcXBcTExPD1KlTiYuLo2rVqgwfPpxPPvmkWHWXdYQqAFlHqGzJOkJlR9YRKjuyjlDZepzrCP18vWOplzm6/sFSL/NpJRkhIYQQwog9DUNjxkxaTwghhBAVlmSEhBBCCCMmb58vGekICSGEEEZMrXnyk6WNmXQjhRBCCFFhSUZICCGEMGIyNFYy0npCCCGEqLAkIySEEEIYMbU8Pl8i0hESQgghjFjBU7CytDGTbqQQQgghKizJCAkhhBBGTIbGSkZaTwghhBAVlmSEhBBCCCMmc4RKRjpCQgghhBGTobGSkdYTQgghRIUlGSEhhBDCiBVIRqhEpCMkhBBCGDG1zBEqEelGCiGEEKLCkoyQEEIIYcRkaKxkpPWEEEIIUWE9NRmhZxeNf9JVKLcuvvPDk65CudbhnbFPugrlVnRfmyddhXKr0s0nXQNRWtQamSNUEk9NR0gIIYQQxVcggzslIq0nhBBCiApLMkJCCCGEEZOhsZKRjJAQQgghKizJCAkhhBBGTC05jRKRjpAQQghhxApkaKxEpBsphBBCiApLMkJCCCGEEZPJ0iUjGSEhhBBCVFiSERJCCCGMmFreNVYi0hESQgghjFgBMjRWEtKNFEIIIUSFJRkhIYQQwojJZOmSkYyQEEIIISosyQgJIYQQRkwmS5eMdISEEEIII6aWydIlIt1IIYQQQlRYkhESQgghjJi8a6xkJCMkhBBCiApLMkJCCCGEEZPJ0iUjHSEhhBDCiMk6QiUj3UghhBBCVFiSERJCCCGMmDw+XzKSERJCCCFEhSUZISGEEMKIyRyhkpGOkBBCCGHE5KmxkpHWE0IIIUSJLVmyhFq1amFpaYm3tzcnTpx4YGx+fj6fffYZderUwdLSkqZNmxIQEKAXk5GRweTJk/Hw8MDKyop27dpx8uRJvZjMzEwmTpxI9erVsbKyolGjRixbtqxY9ZaOkBBCCGHE1BpFqX+K67fffmPq1KnMnDmTM2fO0LRpU3x9fYmPjzcYP2PGDH788UcWL17MlStXGDduHP379+fs2bO6mNGjR7Nr1y78/f25ePEi3bt3x8fHh6ioKF3M1KlTCQgIYO3atVy9epXJkyczceJEtm3b9sh1l46QEEIIIUpkwYIFvPnmm4wcOVKXlbG2tmbFihUG4/39/fnoo4/o2bMntWvXZvz48fTs2ZNvvvkGgOzsbDZu3MjXX39Np06dqFu3LrNmzaJu3br88MMPunKOHDmCn58fXbp0oVatWowZM4amTZs+NBt1P+kICSGEEEZMjaLUP7m5uaSnp+t9cnNzDR4/Ly+P06dP4+Pjo9tmYmKCj48PR48eNbhPbm4ulpaWetusrKw4dOgQACqVioKCgofGALRr145t27YRFRWFRqNh7969XL9+ne7duz9y+0lHSAghhDBiZTE0NnfuXOzt7fU+c+fONXj8xMRECgoKcHNz09vu5uZGbGyswX18fX1ZsGABISEhqNVqdu3axaZNm4iJiQHAzs6Otm3bMmfOHKKjoykoKGDt2rUcPXpUFwOwePFiGjVqRPXq1bGwsKBHjx4sWbKETp06PXL7SUdICCGEEHqmT59OWlqa3mf69OmlVv6iRYuoV68enp6eWFhYMHHiREaOHImJSWG3xN/fH41Gg7u7O0qlku+++46hQ4fqxSxevJhjx46xbds2Tp8+zTfffMOECRPYvXv3I9dFHp8XQgghjFhZrCOkVCpRKpWPFOvi4oKpqSlxcXF62+Pi4qhSpYrBfVxdXdmyZQs5OTkkJSVRrVo1pk2bRu3atXUxderUYf/+/WRlZZGenk7VqlUZPHiwLiY7O5uPPvqIzZs306tXLwCee+45zp07x//+9z+9obqHkYyQEEIIIf4zCwsLvLy8CAoK0m1Tq9UEBQXRtm3bh+5raWmJu7s7KpWKjRs30rdv3yIxNjY2VK1alZSUFAIDA3Ux+fn55Ofn62WIAExNTVGr1Y9cf8kICSGEEEbsaVhZeurUqfj5+dGyZUtat27NwoULycrKYuTIkQAMHz4cd3d33Tyj48ePExUVRbNmzYiKimLWrFmo1Wo++OADXZmBgYFoNBoaNGhAaGgo77//Pp6enroyK1WqROfOnXn//fexsrLCw8OD/fv3s2bNGhYsWPDIdZeOkBBCCGHEnoaO0ODBg0lISODTTz8lNjaWZs2aERAQoJtAHRERoZe5ycnJYcaMGYSFhWFra0vPnj3x9/fHwcFBF3N3XlJkZCROTk4MHDiQL774AnNzc13M+vXrmT59OsOGDSM5ORkPDw+++OILxo0b98h1V2g0Gk3Jm6DkGk//9rEcZ2ibpozs5IWLrQ3BsQl8uW0vFyPjDMaamZjwZpdW9GnRCLdKttxMTGFBwEEOXb+li7G2MGdS93Z0a1QXJ1trrkbH89WOfVy6r8zark5M7dGBlrWrY2piQlh8EpPX7iAmLaNMzxfg4js//HtQKTh5Hlb8CpevQ0KSgsWfa/Dp+PB9TpyFr5ZA6E2oWhnGvQ79X9SPWbcZVqyHxGTwrAMfvwPPNSz8PjcX5i2FP/dAfj60bwWfTgEXp1I/RYM6vDP2sRxnQPdmDHupJU4ONoTeSmDByj1cvWH4iQxTUxOG92tNz06NcXGyJSImmaXrDnL8/E1djLWlOW8Obk/nVvVwtLfiengCC1fv4eqNwmv34/G+9OrSRK/sY+fCmTp3U5mc4/3iWzy+0fvXmjdltLcXrjY2XI1P4LPde7kQ8+B7w7g2rRjQpBFudraEJacwf99BDoQX3htsLMyZ3LEd3evVxdnamivx8czZvY+Lsfpl1nF24oPOHWhdszqmChNCk5KYsHkHMRlle2+odLNMi9czqHNThr/ghXMlG65HJvD1b3u5fOvBbTuyRyt6t2lEZQdbbsWl8N3mgxy5cs99V2nOW33a0bVpXRztrAm+Hc/8Dfu4ck+Zs4Z3p0/bxnplH7l8k4nfby6bk7zPmR+mPJbjAPQ6MKnUy9zZ6btSL/NpVeyM0J9//smmTZtwcnLijTfewNPTU/ddSkoKAwcOZM+ePaVaydLS49n6fNCrE7O3BHHxdiyvt2/Bj28MoPc3q0jOyi4SP6l7O3o3a8jMzbsIj0+hfX0PFr3Wh2E/rOdaTAIAnw18gXpuLkz7PYCEjEx6N2vIz6MG0ufb1cSnZwFQw8ke/3GD2HTyMt/vPkpWbh513ZzJVake6/mXtexsaFAXBvSESZ/8e3xkDIybBoP7wPwZcOwMfDIfXJ2hQ2ttzJ97YN4SmDUVnmsEazbAm+/Bn2vB2VEbM/d7OHAMFs4GOxuYs1B7/F+WlNmpPnbd2jZg0vDOzP95N5dDYhjc04tvPxrI0CkrSEkveu2OHdwe344N+erHXdyKTsa7aS2+eq8PYz9Zz/Wb2pVep431pXYNZz5b8icJyVn06NiQRTNe4dWpq0hMydSVdfRsOF/8ULj0fb6qoOxP+DHr6Vmfj57vxCd/B3E+OpYRLVuwctAAXli+iuQ7Rdt3Ssd29G3ckI8DdhGWlELHZzxY2r8Pg9au50q89t7wZY8XqO/qwns7AojPzKRv44asGTKQHj+vJi5Te2+o6WDP+mGD2HDhMosOHSUzL496Ls7kFpSfe0N3r/pMHdiJL38N4mJ4LMOeb8GSSQPoP2sVKRlF2/atPu3o6d2QOWt3cTMuhbaNPPjf2D6MnL+e4Eht23762gvUqebCJ6sCSEjLpGfrhvzwzkBenr2ahLQsXVmHL4cza83fup/zyuG1C9p1hMR/V6w/t3755Rf69OlDbGwsR48epXnz5qxbt073fV5eHvv37y/1SpYWv44t+OPkJbacvsKN+GRmb9lNTp6KAS2bGIx/qXlDlu87wcHgm0SmpPHb8QscDA5nREcvAJRmprzQuB7f/HWQ0zejiEhKY2nQMSKSUhni3VRXzqTu7TkQfJNvAg5yLSaB28lp7L0aZrDzZcw6tYHJo+GFR1y+Yf1WcK8KH06AOrVg2ADo3hlWbyiMWf07vNJb27mqWwtmvQuWlrDpT+33GZnaf384Adq0gMYN4MtpcPaSgnOXS/sMn5whvbzYFnSRnfsuczMqma9/3kVuXj69uz5rMN63YyNWbz7B0XPhRMensXnXeY6cDWdob+21a2FuRhfveixdd4BzV6OIikvl//44SmRsKgO6N9UrK19VQHLaHd0nI8vwomrG7I1WLfjt/CU2XrxCaFIynwTuJjtfxSvPGr439GvckGVHT7A/7Ca309L45dwF9oWFM6p14b3Bt0E95u09yMnIKG6lpvHd4WPcSknl1eaF7Tu1U3v237jJ1/sOciU+gYjUNIJCwwx2vozVsG4t2Hz4EtuOXiE8NpkvftXed/u2Ndy2vbwbsiLgBIcv3yQqMY0/Dlzg8OVwXvf5p23NTXm+eT0WbT7ImdAobiek8ePOY0QmpPJKZ/1rNy+/gKT0O7pPxp3yd+2KkitWR2j+/PksWLCAHTt2cPDgQVavXs3YsWP5v//7v7KqX6kxNzWhUTU3joZG6LZpNHDsRgRNa1Y1uI+FmWmRrE1OvooWtaoBYGpigpmpSZGY3HwVzf+JUSigs+cz3EpM4aeR/Tnw8Vh+fWsIzzeqU5qnZ5TOXYa2XvrbOrRC14HJy9cOs90bY2Ki/fluzOXrkK9S6MXU9oCqbppy0xEyMzWhQW03Tl3Uv3ZPXoygSb0HXLvmpuTl61+XeXkqnmvg/k+ZCu21m6//F3LuPTF3NW9UnZ0/jefXb0fy3qhuVLLVX+nV2JmbmNCkihuHb93TvsCRmxE0d3/IveG+rE2uSoVXde3/92YmJpiZmBSJyVGpaPlPjALoUvsZwlNSWDmoP8cnjuWP14fgU6/83BvMTE1oWNON49f0r93j1yJ4rrbhtjU3MyX3vms3N09Fs7r69937r++cPBXN6lTT29ayfnV2fz2WTbP8mD70eextyte1e9fT8K4xY1asjlBISAgvvfSS7udBgwaxfft2Jk+eXOy3vT5uDtZWmJmakJR5R297UsYdXOysDe5z+Pot/Dp4UdPZAYUC2tatiU/jurja2QBwJy+fs7eiGfe8N652NpgoFPRu5knTmlV1Mc421tgoLRjVuRWHrt9kzIpNBF2+waJhL9HyGXeDx60oEpPBxVF/m7MTZGYpyMmF1DQoKFDohsB0MY7afQESk8DcXEMlO/0Yl3tijJ1DJe21m3xPyh8gOe0OTg42Bvc5fv4mQ3p5Ub2K9tpt9awHnVvXw9nxn2s3J5+LwdGMHNAGF0fttevboSFN6lfVxdwtZ86SAN6es4Ef1h2keaMaLJg+ABNF+blROlpbYWZiQlKW/r0h8c4dXGwM3xsOht/ijVZeeDg6oADa16pJ9/p1qWyjbbusvHzOREUzsZ03lW217du3kSfNq1XF1abw3mCrtGCsdysOhN1kxO+b2HX9Bkv7v0TrGuXj3uBg+8+1m67ftsnpd3CuZLhtj169xWvdvKjhqr12vT1r0rV5XVwq/XPt5uZz/kY0o3t642KvbduerT15rnZVXOwLr90jV27yyepAxi38g+82H8KrXnUWT+xfrq7du6QjVDLFmiNUqVIl4uLieOaZZ3Tbunbtyo4dO+jduzeRkZGPVE5ubm6Rd5aoVSpMzJ6uh9jm7tjH7P4+7Jjqh0YDt5NT2XL6Mv3vGUqb/nsAcwZ2Z99HY1AVqLkaHc+f54Np5F4ZAMU//9PtvXKDNYe1b9W9FpNAs5pVGez9HKfCo4oeWIgSWrhqL9PGdufXb0ei0UBUXCo7912md9fCyaOfLfmTj8b5sm3ZOFQFaq6Hx7H78DUa1C5cJn/3kWDdv8NuJxIakcAfi0fTvHENTl+KoKL6fPc+vujhw9+j/dAAESmpbLx4mZfvGUp7b0cAX73YnSMTxqBSq7kcG8+Oq8E0rqK9N9z9hbw79AYrT2nvDVfjE2jhXpWhzZ7jxO2KeW+Y//s+Phnmw6ZZ2vtuZGIq249eps89Q2mfrApg5uvd+fsr7X332u14Ak8G07BmZV3M36eu6/4dGp1ESFQi2+e8Qcv61TkRfPuxnpN4uhWr59G6dWv++usv2rRpo7e9c+fObN++nd69ez9SOXPnzmX27Nl621zad6dyxx7FqU6xpN7JRlWgxtlW/68QZztrEjPuGNwnJSubSWu3Y2FmioO1JfHpWUzt0YHI5DRdzO3kNEYs34CVuRk2lkoSM7L439CeupjUO9nkFxRwIz5Jr+ywhGRaeJSPv/r+KxcnSEzR35aUDLY2GiyV2mEwU1MNSffHpBQ+EebiDPn5CtIz9LNCiSmP76mxspaarr12nez1sz9O9tYkp2YZ3icjm2n/24qFuSmVbK1ITMnkrVc7EhVXeO1GxaUxYfbvWCrNsLFSkpSaxWfv9Cb6npj7RcenkZJ+h+pVHMpNRyjlTjYqtRrn+7I/LtbWJGYZvjckZ2czfvN2LExNcbSyJC4zi/c7d+B2WmHbRaSm8eqv2nuDrYWShKwsFvXpye3UNN1x8wsKCE3UvzeEJiXTsnr5uDekZv5z7d6X/XGqZE1SuuG2Tc3M5t0ftfddextLEtKymNSvA1GJhW0bmZjGm99uwNLCDFtLJYnpWXw1qieRiQ++dqMS00jJuEMNV4dy1xGqaBmc0lasobEpU6ZgZWVl8LsuXbqwfft2hg8f/q/lGHqHiUvbR1sK+7/KL1BzJTqONnVq6LYpFOBdpwbnI2Iesqf2SYP49CzMTEx4oUk99ly5USQmO19FYkYWlSyVtK/nwd4rYbrjXoqMo5ar/m9lDxdHolPTS+HMjFezxnDstP62I6e02wEszKFxff0YtVr7dNndmMb1wdxMw7EzhTHhERATp9DFGDtVgZrgsDi8nq2p26ZQQMsmNbkU8i/Xbn4BiSmZmJqa0MW7HgdPFb12c3JVJKVmYWejxLupBwdPhT6wPFcnW+xtrUhKMdwBM0b5ajWXYuNo53HPvQFoV6sGZ6P+pX0LCojL1N4bejSox+4Qw/eGhKwsKimVdHzGg90hYbrjXoyNo7aT/r3hGSdHotLLx71BVaDmakQcrRvo33dbN6jBhbB/v+8mpGnbtlvzeuy/YODazVORmJ6FnbWSto082H8h7IHlVXawxd7GioT08nPtitJRrIxQhw4dOHr0KO3btycvL49u3boxc+ZMXeeoa9eudO3a9V/LMfQOk8cxLLb64Bm+fMWXy1Hx/zw+3xwrC3M2n9bOqv3yFV/i0zNZGHgYgGdrVMGtki3XohOobG/LhG5tUCgUrDhwSldm+3oeKBQQnpBCTWcH3nuxI+EJKboyAVYeOMU3Q3txOjySE2G36VC/Fl08azNy+QbKk6w7EHFPNj8yBq6GgH0lqOYGC36CuASY97H2+yF94ZfNMP8HGNhT28EJ2AfLviosw28QTJ8LTTzhWU9Y84f2Mf27aw3Z2WqfKPtqCdjbga0NfL4ImjXWlJuOEMD6naeZ8VYPrt2I5cqNWAb3bIGl0pwd+y4B8MmEHiQkZ7Ls10MANKpbBVcnW0JuJuDqZMuol9uiUChYt+2krkzvph6AgojoZKpXcWTCa524FZ3Mjn3aa9dKac4bL7dl34kQklKzcHdzYMKwTkTGpuitR1QerDh5hvm9fLkYG8+FmFhGtGyOlbk5f1zUtsX8Xr7EZWTyvwPae0PTqlVws7PlalwCbna2TGqvvTf8dLzw3tDxGQ8UQFhyCh6ODnzYpSNhySlsvFh4b1h+/BSL+vbiZGQkx27dplPtWjxftzbDfik/94Z1QWeY7efLlYh4Lt+M5dXnm2OlNGfbUW07fObnS3xqJt9v1bZtk1pVqOxgS3BkApUdbBnbqw0KEwWr/i5s27YNtffdm3Ep1HB1YPKAjtyMS2HbkcJrd2yvNgSdDSEx7Q41XO15Z0BHbiekcvSe9YjKC8kIlUyxeh9ffvkls2bNwsfHBysrKxYtWkR8fDwrVqwoq/qVqoCL13GytWKiT1tc7Ky5FpPA2JWbdROoqzrYce/6kkozUya90I7qTvbcycvnQHA4034PICOncH6TraWSyb7tqWJvS9qdXHZdDmFR4GFU97znJOjKDWZvCeLNLq2Y/lJXbiYkM3ndds7cin58J/8YXA4Gv8mF/0POW6L9d78eGuZOh4QkiIkvjK9eVdvp+ep78N8IVVxhzvuFawgB9HweUlLhuxXayc8N68JP8/WHvaZP1A6jvfOp9kmzuwsqlidBR4NxqGTFm4Pa4+RgTcjNBKbO3UhKmvbadXOuhFpdeO1amJsxZnAHqlW2Jzsnn6PnwvhsyV9k3vP4sI2VkvFDO+LqbEt6Zg77jofw4/pDFBRor90CtYa6Hq707NwYWxslicmZnLhwi59+P1zu1hL689p1nK2tmNyhLa421lyJT+CN3zeTdEfbvtUq2aG+794wtWM7ajjYk5WXz/6wcN7bGUDGPXMf7ZRK3uvUnip2tqTm5BIYHMI3B/TvDbtCbvBpYBDj2rTik25dCUtOZuLm7ZyOKj/3hr9PX8fR1orxvdviXMma4MgEJi7eTPI/UxKqOOm3rYW5KW/1aYe7iz13cvM5fCmcGasCyMy+575rpWRiv/a4OWjvu3vOhrBka2HbqtVq6rm70LtNI+yslCSkZXLsSgRLtx8pd9cugEY6QiVSrJWl69Wrx3vvvcfYsdqVdHfv3k2vXr3Izs4u8tKz4npcK0tXRI9rZemK6nGtLF0RPc6VpSuax7mydEX0OFeW7hL0XqmXua/b/0q9zKdVse4yERER9OzZU/ezj48PCoWC6Ojy89eLEEIIYUzUKEr9U5EUa2hMpVJhaam/IJW5uTn5+fmlWikhhBBCPBqZI1QyxeoIaTQaRowYoTfROScnh3HjxmFjU/ho76ZNj+eFjEIIIYQQJVGsjpCfn1+Rba+99lqpVUYIIYQQxSOTpUumWB2hlStXllU9hBBCCCEeu6frnRZCCCGEKBaZI1Qy0hESQgghjJgMjZWMLNIhhBBCiApLMkJCCCGEEZOhsZKRjJAQQgghKizJCAkhhBBG7NFflCUMkY6QEEIIYcQq2isxSpsMjQkhhBCiwpKMkBBCCGHE5PH5kpGMkBBCCCEqLMkICSGEEEZMHp8vGekICSGEEEZMnhorGRkaE0IIIUSFJRkhIYQQwojJZOmSkYyQEEIIISosyQgJIYQQRkwyQiUjHSEhhBDCiMlTYyUjQ2NCCCGEqLAkIySEEEIYMXl8vmQkIySEEEKICksyQkIIIYQRk8nSJSMdISGEEMKISUeoZGRoTAghhBAVlmSEhBBCCCMmc6VLRjJCQgghhKiwJCMkhBBCGDGZI1Qy0hESQgghjJmMjZWIDI0JIYQQosKSjJAQQghhxGRorGQkIySEEEKICksyQkIIIYQRk3eNlYxkhIQQQggjptEoSv3zXyxZsoRatWphaWmJt7c3J06ceGBsfn4+n332GXXq1MHS0pKmTZsSEBCgF5ORkcHkyZPx8PDAysqKdu3acfLkSb0YhUJh8DN//vxHrvdTkxFyvqx60lUotzq8M/ZJV6FcO7ToxyddhXLLt1rTJ12FcitpbLsnXQVRjvz2229MnTqVZcuW4e3tzcKFC/H19SU4OJjKlSsXiZ8xYwZr165l+fLleHp6EhgYSP/+/Tly5AjNmzcHYPTo0Vy6dAl/f3+qVavG2rVr8fHx4cqVK7i7uwMQExOjV+5ff/3FqFGjGDhw4CPXXaHRPB1JtU59Hr33JopHZSWJv7IkHaGyIx2hsiMdobJ15ocpj+1YddZ/Wepl3hjyUbHivb29adWqFd9//z0AarWaGjVq8PbbbzNt2rQi8dWqVePjjz9mwoQJum0DBw7EysqKtWvXkp2djZ2dHVu3bqVXr166GC8vL1588UU+//xzg/Xo168fGRkZBAUFPXLd5TekEEIIIf6zvLw8Tp8+jY+Pj26biYkJPj4+HD161OA+ubm5WFpa6m2zsrLi0KFDAKhUKgoKCh4ac7+4uDh27tzJqFGjilV/6QgJIYQQRkyjKf1Pbm4u6enpep/c3FyDx09MTKSgoAA3Nze97W5ubsTGxhrcx9fXlwULFhASEoJarWbXrl1s2rRJN9RlZ2dH27ZtmTNnDtHR0RQUFLB27VqOHj1aZDjsrtWrV2NnZ8eAAQOK1X7SERJCCCGMmab0P3PnzsXe3l7vM3fu3FKr8qJFi6hXrx6enp5YWFgwceJERo4ciYlJYbfE398fjUaDu7s7SqWS7777jqFDh+rF3GvFihUMGzasSBbp30hHSAghhBB6pk+fTlpamt5n+vTpBmNdXFwwNTUlLi5Ob3tcXBxVqlQxuI+rqytbtmwhKyuLW7duce3aNWxtbaldu7Yupk6dOuzfv5/MzExu377NiRMnyM/P14u56+DBgwQHBzN69Ohin6t0hIQQQggjVhaPzyuVSipVqqT3USqVBo9vYWGBl5eX3gRltVpNUFAQbdu2fWjdLS0tcXd3R6VSsXHjRvr27VskxsbGhqpVq5KSkkJgYKDBmP/7v//Dy8uLpk2L/4DFU/P4vBBCCCGM09SpU/Hz86Nly5a0bt2ahQsXkpWVxciRIwEYPnw47u7uuuG148ePExUVRbNmzYiKimLWrFmo1Wo++OADXZmBgYFoNBoaNGhAaGgo77//Pp6enroy70pPT2fDhg188803/6nu0hESQgghjNlTsAjO4MGDSUhI4NNPPyU2NpZmzZoREBCgm0AdERGhN7cnJyeHGTNmEBYWhq2tLT179sTf3x8HBwddzN3huMjISJycnBg4cCBffPEF5ubmesdev349Go2GoUOH/qe6yzpCFYCsI1S2ZB2hsiPrCJUdWUeobD3OdYRq+X9V6mXefL3o2j/llfyGFEIIIUSFJUNjQgghhDF7KsZ1jJd0hIQQQgij9t9ekiq0ZGhMCCGEEBWWZISEEEIIYyZDYyUiGSEhhBBCVFiSERJCCCGMmWSESkQ6QkIIIYQx08hk6ZKQoTEhhBBCVFiSERJCCCGM2NPxfgjjJRkhIYQQQlRYkhESQgghjJlkhEpEOkJCCCGEMZPJ0iUiQ2NCCCGEqLAkIySEEEIYMYUMjZWIZISEEEIIUWFJRkgIIYQwZpIRKhHpCAkhhBDGTCZLl4gMjQkhhBCiwpKMkBBCCGHMZGisRCQjJIQQQogKSzJCQgghhDGTjFCJSEdICCGEMGbSESoRGRoTQgghRIUlGSEhhBDCmMnj8yUiGSEhhBBCVFgVLiPUv2dzhvRvhZOjDTfC41n0UxBXQ2INxpqamvDay970eL4JLs623I5KZtnq/Zw4c1MXY2VlzuhhHejYph6O9taEhMXz3fI9XAs1XOa741+g74vNWPzzHjZsO10Wp/hEDejejGEvtcTJwYbQWwksWLmHqzce3L7D+7WmZ6fGuDjZEhGTzNJ1Bzl+/qYuxtrSnDcHt6dzq3o42ltxPTyBhav3cPVGnC7m4/G+9OrSRK/sY+fCmTp3U5mc45Nw8jys+BUuX4eEJAWLP9fg0/Hh+5w4C18tgdCbULUyjHsd+r+oH7NuM6xYD4nJ4FkHPn4HnmtY+H1uLsxbCn/ugfx8aN8KPp0CLk6lfopPXJ+3fHnlvT44VXHgxvlbLJm0guCToQZjTc1MGTq9Py8M74yLuxO3g6P5edo6TgWe08VY2VoyYs4Q2vdrjUNle0LPhrN08kqun7qhi3l95it0Gdwe1xrOqPJUhJwOY+WMX7l2wvBxjdWgzk0Z/oIXzpVsuB6ZwNe/7eXyrTiDsWYmJozs0YrebRpR2cGWW3EpfLf5IEeu3NLFWCvNeatPO7o2rYujnTXBt+OZv2EfV+4pc9bw7vRp21iv7COXbzLx+81lc5JPkLxrrGRKJSOk0WgoKCgojaLK1PMdGjBhVBdWrT/C6ClrCL2ZwP9mv4KDvbXB+Ddf60CfHk1Z9NNuhk9YwdaA83wxvR/1alfWxXw4sQctm9Xii2//ZMSkVZw8d5MFcwbh4mRbpLyOberRqEE1EpIyyuwcn6RubRswaXhnVmw8yshp/oTeSuDbjwbiWMnKYPzYwe3p5/McC1buYdi7q9iy6wJfvdeH+rUK23faWF9aPevBZ0v+5LX31nDiwk0WzXgFF0f99j16NpzeY37QfWZ+t7NMz/Vxy86GBnXhk8mPFh8ZA+OmgXdz2PwzDH8ZPpkPh04Uxvy5B+YtgQl+sHE5NKgDb74HSSmFMXO/h31HYOFsWLMI4hNh0ielempPhc6D2jH2Gz/WfraB8V4fEnbhFnMDPsbBtZLB+JGfD6HXmBdYMmkFoxpPYcePu5i16X3qNKuli5m6fDwtfJ5j3vDFjHnuXU7vOs/Xuz7FuVphLzLyegzfv/1/jHnuXaZ0/IS4Wwl8FfgJ9i6Gj2uMunvVZ+rATvy08xivfrmOkMhElkwagKOd4fvCW33aMbDjc3z9215e/mwNfxy8wP/G9qFBdVddzKevvYC3pwefrApg8OdrOHb1Fj+8MxBXexu9sg5fDueFD3/Ufaav+LNMz/WJ0ZTBpwIpVkdIpVIxY8YMOnfuzMyZMwGYP38+tra2WFtb4+fnR15eXplUtDQM6tuSHX9f4K+gS9y6ncQ3S/8mJzefXj5NDMZ379KYtRuOc+x0ODFxaWz96xzHToczuF8rACwszOjUrj4/rNrP+cuRRMWksvLXI0TFpNDvxWZ6Zbk42fLOmG7M+WYHKpW6rE/1iRjSy4ttQRfZue8yN6OS+frnXeTm5dO767MG4307NmL15hMcPRdOdHwam3ed58jZcIb29gLAwtyMLt71WLruAOeuRhEVl8r//XGUyNhUBnRvqldWvqqA5LQ7uk9GVm6Zn+/j1KkNTB4NL3R6tPj1W8G9Knw4AerUgmEDoHtnWL2hMGb17/BKbxjQE+rWglnvgqUlbPrnd0VGpvbfH06ANi2gcQP4chqcvaTg3OXSPsMna+CU3vz1cxCBq/YRcTWSReN+IvdOHr5vPG8w3ue1Tvw6dxMn/jpLbHg8O5b9zYk/z/Dy1JcAsLC0oONAb5Z/uJaLB68SfSMW/9kbiAqN5aXx3XXl7P31EGeDLhIbHs+tK5Esm7oaG3traj9X87Gc9+MwrFsLNh++xLajVwiPTeaLX3eTk6eib1vD991e3g1ZEXCCw5dvEpWYxh8HLnD4cjiv+2jvC0pzU55vXo9Fmw9yJjSK2wlp/LjzGJEJqbzSWf++kJdfQFL6Hd0n4075ui+I0lGsjtDs2bP5+eefadmyJX/88Qfjx49n8eLF/PTTTyxfvpygoCAWLlxYRlUtGTMzE+rXrcKpc4XpVY0GTp+/RWPPagb3MTc3JS9fpbctN0/Fsw3dATA1VWBmakJenoGYRu66nxUKmDG1J+s3n+Dm7aTSOqWnipmpCQ1qu3HqYoRum0YDJy9G0KReVYP7WBho37w8Fc81cP+nTG375ubrZxtz74m5q3mj6uz8aTy/fjuS90Z1o5KtZWmcltE6dxnaeulv69AKXQcmL187zHZvjImJ9ue7MZevQ75KoRdT2wOqumnKVUfIzNyM+l61ObP7gm6bRqPhzO4LNGpT3+A+5kpz8nLy9bblZufRpIMnAKZmJpiamZKfo/+HYV52Hk3aez6wHj3H+JCZmsWN87cMxhgbM1MTGtZ04/g1/fvC8WsRPFfb8H3B3MyUXAP33WZ1tfdpUxMT7X33vpicPBXN6ujfy1vWr87ur8eyaZYf04c+j71Nxb4vCMOKNUfol19+4eeff6Z3796MHz+eBg0a8MsvvzB48GAALC0tmTNnDh988EGZVLYk7CtZYWZqQkrqHb3tyal3qOlueMLDibPhDOrbkvOXbhMVm4pXUw86ta2HiYl2hn52dj6XrkbhN7gttyKTSEm9Q7dODWncoBpRMam6cl4d6E1BgYY/tp8ps/N70hz+ad/ktCy97clpd/CoZrh9j5+/yZBeXpy7GklUXCotm3jQuXVh+97JyedicDQjB7ThVlQSyal3eKG9J03qVyUyNlWvnP0nQomOT6O6mwNjh3ZgwfQBjJnxK2pNBcvx/iMxGVwc9bc5O0FmloKcXA3pGVBQoMDZUb99nB0h/J/fWYlJYG6uoZKdfjkujtryywt7FztMzUxJiUvT254Sn0YNT3eD+5wKPM/AKb25eOAK0TfiaN7tWToM8MbEVPu3ZXZmDpePBDNsxstEXI0iJS6NrkPb07BtfaLvmz/o3asFH/86BaW1BckxqXzYfQ7p5WT43MH2n/tC+n333fQ71HJzNLjP0au3eK2bF2dCoohMTKV1g5p0bV4XU8U/94XcfM7fiGZ0T2/CYpNJTr9Dj1YNeK52VW4npOrKOXLlJnvOhRKdmEZ1Vwcm9m3P4on9GfH1+gp7XxCGFasjFB0dTdOm2tRj3bp1sbCw0P0M0KpVK27d+ve/ZHJzc8nN1U9RqgtUmJg+XXO3v1u+hw8m+uK/dBQaIDomlb92X6LnPUNpn3/7J9Mm9WDzqrdQFagJuRFH0MFrNKjjBkD9Om68/JIXo6esfkJn8fRauGov08Z259dvR6LRQFRcKjv3XaZ318IJjp8t+ZOPxvmybdk4VAVqrofHsfvwNRrUdtPF7D4SrPt32O1EQiMS+GPxaJo3rsHpSxEIUdqWTl7JlJ/G8n9XF4FGQ/SNOP5etRffkYVDafOGL+a9/3uL9VE/UaAqIORMOHt/PUR9r9p6ZZ3fe5lxzd/H3sWOF9/0YcZvU5nUZjqpCemP+7SeCvN/38cnw3zYNMsPjQYiE1PZfvQyfe4ZSvtkVQAzX+/O31+NQVWg5trteAJPBtOwZuH8wr9PXdf9OzQ6iZCoRLbPeYOW9atzIvj2Yz2nsiaTpUumWD0Pe3t7UlNTqVGjBgAtWrTAzq7wz8Xc3FwUin9fz2Du3LnMnj1bb1vN+j54NOj+gD1KLi09G1WBGkcH/YnRTg7WJKdmPXCfj7/cgoW5KZXsrEhMzmScXyei7/nLMTo2lUkfrcdSaY6NtQVJKVnMev8lov/JWDRtXB1He2s2/N843T5mpia8NbILL7/kxeA3fyr1c30SUv9pX6f7Jis62T+4fVMzspn2v63a9rW1IjElk7de7UjUPe0bFZfGhNm/Y6k0w8ZKSVJqFp+901vvv8H9ouPTSEm/Q/UqDhW2I+TiBIkp+tuSksHWRoOlUjsMZmqq0ZsYDdqJ0nefCHNxhvx8BekZ+lmhxJTy9dRYWmIGBaoCHN3s9bY7VrYn5Z7Mo/4+6cwaMB9zpTmVnO1Iik5m9FfDiAkrfGopJiyOd7vOxNJaiXUlK5JjU/n41ynEhMXrlZVzJ5foG7FE34jl6vEQVgV/R49Rz7P+qy2lfaqPXWrmP/eFSvfddytZk3Rflujefd79cTsWZqbY21iSkJbFpH4diEos/H8+MjGNN7/dgKWFGbaWShLTs/hqVE8iEx98X4hKTCMl4w41XB3KXUdI1hEqmWLNEWrUqBFnzhQO7xw+fBh398LU8cWLF6lXr96/ljN9+nTS0tL0PjXqGp6UWFpUKjXXQ2Pxauqh26ZQQIvnPLh8Lfqh++blF5CYnImpqQmd2tXn0PGij7bm5OaTlJKFrY2SVs1rceifx18D915m5KRVjHpnte6TkJTB+s0neW/WhiLlGCtVgZrgsDi8ni2c5KlQQMsmNbkUEvPQffPyC0hM0bZvF+96HLzn8eK7cnJVJKVmYWejxLupBwdPPfjxYlcnW+xtrUhKMdwBqwiaNYZj963OcOSUdjuAhTk0rq8fo1bDsTOFMY3rg7mZhmP3jOiGR0BMnEIXUx6o8lVcPx1G826Fk/oVCgXNuz3LlWPXH7In5OfmkxSdjKmZKR0GtOHotpNFYnLu5JIcm4qtgw0tfZtyxEDMvRQmCsyV5v/tZJ4yqgI1VyPiaN2ghm6bQgGtG9TgQti/3BdUBSSkZWFmYkK35vXYf8HAfSFPRWJ6FnbWSto28mD/hbAHllfZwRZ7GysS0ivufUEYVqyM0LJly7CwsHjg9/n5+Y80P0ipVKJUKvW2PY5hsd+3nmL65J4Eh8Zy9XoMr/RpiZWlOX8GXQLgo8k9SUzO4Kc1BwFoWL8qrs62hITF4+psy8ih7TFRKPh1U+EzyK2a10KhgNtRKbhXdWD8iC5ERCXz525tmekZOaRn5OjVQ6VSk5yaxe2o+/4cN3Lrd55mxls9uHYjlis3YhncswWWSnN27NO2xScTepCQnMmyXw8B0KhuFVydbAm5mYCrky2jXm6LQqFg3T2/KLybegAKIqKTqV7FkQmvdeJWdDI79mln61opzXnj5bbsOxFCUmoW7m4OTBjWicjYFL31iIxd1h2IiCr8OTIGroaAfSWo5gYLfoK4BJj3sfb7IX3hl80w/wcY2FPbwQnYB8u+KizDbxBMnwtNPOFZT1jzh/Yx/btrDdnZap8o+2oJ2NuBrQ18vgiaNdaUq44QwMZvd/DBqglcP3WD4BOh9J/cC0sbJYEr9wLwwaqJJEYns+KjXwDwbF0XF3cnQs/dxMXdieEzB2FiouC3r7fqymzZvSkoFEQGR1OtbhXGfP06t69F6cq0tFby6scDOLrtFEkxKdi7VKLPBF9c3J04sOHo42+EMrIu6Ayz/Xy5EhHP5ZuxvPp8c6yU5mw7qv1/+DM/X+JTM/l+62EAmtSqQmUHW4IjE6jsYMvYXm1QmChY9fcpXZltG3qgUMDNuBRquDoweUBHbsalsO1I4X1hbK82BJ0NITHtDjVc7XlnQEduJ6Ry9Er5mIiuR4bGSqRYvY+6desyf/58tm3bRl5eHt26dWPmzJlYWWnXg3j11VfLpJKlZc+hYBzsrXnj1fY4OdoQGhbPe7P+0E2gdnO1Q3PPJDoLc1NGD+tA1SoOZOfkcexUOJ9/u5PMex7NtrVWMmZ4J1xdbMnIyGH/0ess9z9IQUH5fET+YYKOBuNQyYo3B7XHycGakJsJTJ27kZS0f9rXuRJq9b3ta8aYwR2oVtme7Jx8jp4L47Mlf5F5zyOuNlZKxg/tiKuzLemZOew7HsKP6w/p2rdAraGuhys9OzfG1kZJYnImJy7c4qffD5OvevrXtnpUl4PBb3Jh+nveEu2/+/XQMHc6JCRBzD0jLtWrajs9X30P/huhiivMeR86tC6M6fk8pKTCdyu0k58b1oWf5usPe02fqB1Ge+dT7ZNmdxdULG/2/34EB9dK+M0ejGMVB26cu8lHL35Barx2qKVyTRc09167lhaMmDOUqrUrk52Zw4k/zzJv+GKy0gqHe6ztrRn15au4VHcmIzmTQ5uOs+LjXyn457osKFBTo4E7L/zRhUoudmQkZRB88gZTOn3KrSuRj7cBytDfp6/jaGvF+N5tca5kTXBkAhMXbyY5Q9tWVZzs9CYvW5ib8lafdri72HMnN5/Dl8KZsSqAzOx77rtWSib2a4+bgy1pd3LZczaEJVsPo1Jr7wtqtZp67i70btMIOyslCWmZHLsSwdLtR8rVfUGUDoVG8+jT5+fMmcOsWbPw8fHBysqKwMBAhg4dyooVK0pckU595pe4DGGYykrepFKWDi368UlXodzyrdb034PEf5I0tt2TrkK5duaHx/cXQ+1vF5R6mWFTppZ6mU+rYv2GXLNmDUuXLiUwMJAtW7awfft21q1bh1pd8bIfQgghxNNAoSn9T0VSrI5QREQEPXv21P3s4+ODQqEgOvrhk42FEEIIIZ5GxZojpFKpsLTUX5nT3Nyc/Pz8B+whhBBCiDJVwTI4pa1YHSGNRsOIESP0nvjKyclh3Lhx2NgUrh+zaVP5eeu3EEIIIcqvYnWE/Pz8imx77bXXSq0yQgghhCgmyQiVSLE6QitXriyregghhBDiP6hok5tLmzxXLYQQQogK6+l6y6kQQgghikfeNVYikhESQgghRIUlGSEhhBDCmMkcoRKRjJAQQghhxJ6WlaWXLFlCrVq1sLS0xNvbmxMnTjwwNj8/n88++4w6depgaWlJ06ZNCQgI0IvJyMhg8uTJeHh4YGVlRbt27Th58mSRsq5evUqfPn2wt7fHxsaGVq1aERER8cj1lo6QEEIIIUrkt99+Y+rUqcycOZMzZ87QtGlTfH19iY+PNxg/Y8YMfvzxRxYvXsyVK1cYN24c/fv35+zZs7qY0aNHs2vXLvz9/bl48SLdu3fHx8eHqKgoXcyNGzfo0KEDnp6e7Nu3jwsXLvDJJ58UWfz5YYr10tWyJC9dLTvy0tWyJS9dLTvy0tWyIy9dLVuP86Wr9eZ+W+plhkwvXv29vb1p1aoV33//PQBqtZoaNWrw9ttvM23atCLx1apV4+OPP2bChAm6bQMHDsTKyoq1a9eSnZ2NnZ0dW7dupVevXroYLy8vXnzxRT7//HMAhgwZgrm5Of7+/v/lNAHJCAkhhBCiBPLy8jh9+jQ+Pj66bSYmJvj4+HD06FGD++Tm5hbJ2lhZWXHo0CFA+0qvgoKCh8ao1Wp27txJ/fr18fX1pXLlynh7e7Nly5Zi1V86QkIIIYQRK4s5Qrm5uaSnp+t9cnNzDR4/MTGRgoIC3Nzc9La7ubkRGxtrcB9fX18WLFhASEgIarWaXbt2sWnTJmJiYgCws7Ojbdu2zJkzh+joaAoKCli7di1Hjx7VxcTHx5OZmclXX31Fjx49+Pvvv+nfvz8DBgxg//79j9x+0hESQgghjJmm9D9z587F3t5e7zN37txSq/KiRYuoV68enp6eWFhYMHHiREaOHImJSWG3xN/fH41Gg7u7O0qlku+++46hQ4fqYtRqNQB9+/ZlypQpNGvWjGnTptG7d2+WLVv2yHWRjpAQQggh9EyfPp20tDS9z/Tp0w3Guri4YGpqSlxcnN72uLg4qlSpYnAfV1dXtmzZQlZWFrdu3eLatWvY2tpSu3ZtXUydOnXYv38/mZmZ3L59mxMnTpCfn6+LcXFxwczMjEaNGumV3bBhQ3lqTAghhKgwyiAjpFQqqVSpkt5HqVQaPLyFhQVeXl4EBQXptqnVaoKCgmjbtu1Dq25paYm7uzsqlYqNGzfSt2/fIjE2NjZUrVqVlJQUAgMDdTEWFha0atWK4OBgvfjr16/j4eHx0OPeSxZUFEIIIYzY0/DS1alTp+Ln50fLli1p3bo1CxcuJCsri5EjRwIwfPhw3N3ddcNrx48fJyoqimbNmhEVFcWsWbNQq9V88MEHujIDAwPRaDQ0aNCA0NBQ3n//fTw9PXVlArz//vsMHjyYTp060bVrVwICAti+fTv79u175LpLR0gIIYQQJTJ48GASEhL49NNPiY2NpVmzZgQEBOgmUEdEROjN/8nJyWHGjBmEhYVha2tLz5498ff3x8HBQRdzdzguMjISJycnBg4cyBdffIG5ubkupn///ixbtoy5c+cyadIkGjRowMaNG+nQocMj113WEaoAZB2hsiXrCJUdWUeo7Mg6QmXrca4j1GBO6a8jFPzJ46v/kya/IYUQQghRYcnQmBBCCGHMnopxHeMlHSEhhBDCiD0Nk6WNmQyNCSGEEKLCkoyQEEIIYcwkI1QikhESQgghRIUlGSEhhBDCmElGqESkIySEEEIYMZksXTIyNCaEEEKICksyQkIIIYQxk4xQiUhGSAghhBAVlmSEhBBCCCMmc4RKRjpCQgghhDGTjlCJyNCYEEIIISosyQgJIYQQxkwyQiUiGSEhhBBCVFiSERJCCCGMmEyWLpmnpiOkTMx90lUot6L72jzpKpRrvtWaPukqlFuB0eefdBXKLZ/XvZ50FURpkY5QicjQmBBCCCEqrKcmIySEEEKI/0AyQiUiGSEhhBBCVFiSERJCCCGMmEyWLhnpCAkhhBDGTDpCJSJDY0IIIYSosCQjJIQQQhgxGRorGckICSGEEKLCkoyQEEIIYcwkI1Qi0hESQgghjJl0hEpEhsaEEEIIUWFJRkgIIYQwYoonXQEjJxkhIYQQQlRYkhESQgghjJnMESoR6QgJIYQQRkzWESoZGRoTQgghRIUlGSEhhBDCmElGqEQkIySEEEKICksyQkIIIYQxk4xQiUhHSAghhDBiMlm6ZGRoTAghhBAVlmSEhBBCCGMmGaESkYyQEEIIISosyQgJIYQQRkzmCJWMdISEEEIIYyYdoRKRoTEhhBBCVFiSERJCCCGMmAyNlYxkhIQQQghRYUlGSAghhDBmkhEqEckICSGEEMZMUwaf/2DJkiXUqlULS0tLvL29OXHixANj8/Pz+eyzz6hTpw6WlpY0bdqUgIAAvZiMjAwmT56Mh4cHVlZWtGvXjpMnT+rFjBgxAoVCoffp0aNHseotHSEhhBBClMhvv/3G1KlTmTlzJmfOnKFp06b4+voSHx9vMH7GjBn8+OOPLF68mCtXrjBu3Dj69+/P2bNndTGjR49m165d+Pv7c/HiRbp3746Pjw9RUVF6ZfXo0YOYmBjd59dffy1W3aUjJIQQQhgxhab0P8W1YMEC3nzzTUaOHEmjRo1YtmwZ1tbWrFixwmC8v78/H330ET179qR27dqMHz+enj178s033wCQnZ3Nxo0b+frrr+nUqRN169Zl1qxZ1K1blx9++EGvLKVSSZUqVXQfR0fHYtVdOkJCCCGEMXvCQ2N5eXmcPn0aHx8f3TYTExN8fHw4evSowX1yc3OxtLTU22ZlZcWhQ4cAUKlUFBQUPDTmrn379lG5cmUaNGjA+PHjSUpKKlb9pSMkhBBCCD25ubmkp6frfXJzcw3GJiYmUlBQgJubm952Nzc3YmNjDe7j6+vLggULCAkJQa1Ws2vXLjZt2kRMTAwAdnZ2tG3bljlz5hAdHU1BQQFr167l6NGjuhjQDoutWbOGoKAg5s2bx/79+3nxxRcpKCh45HOVjpAQQghhxBQaTal/5s6di729vd5n7ty5pVbnRYsWUa9ePTw9PbGwsGDixImMHDkSE5PCbom/vz8ajQZ3d3eUSiXfffcdQ4cO1YsZMmQIffr04dlnn6Vfv37s2LGDkydPsm/fvkeuS4V7fL7PAC9eGdYWJydbboTGsWRBIMFXow3GmpqaMHR4e17o+RwuLnbcjkji56VBnDoepouxsrZgxJudad/ZEwdHa0Kvx7J04d9cvxpjsMx33n+R3v29WLrwbzb//uAZ9cbqteZNGe3thauNDVfjE/hs914uxMQZjDUzMWFcm1YMaNIINztbwpJTmL/vIAfCb+libCzMmdyxHd3r1cXZ2por8fHM2b2Pi7H6ZdZxduKDzh1oXbM6pgoTQpOSmLB5BzEZGWV6vo9Tn7d8eeW9PjhVceDG+VssmbSC4JOhBmNNzUwZOr0/LwzvjIu7E7eDo/l52jpOBZ7TxVjZWjJizhDa92uNQ2V7Qs+Gs3TySq6fuqGLeX3mK3QZ3B7XGs6o8lSEnA5j5YxfuXbC8HGN0cnzsOJXuHwdEpIULP5cg0/Hh+9z4ix8tQRCb0LVyjDudej/on7Mus2wYj0kJoNnHfj4HXiuYeH3ubkwbyn8uQfy86F9K/h0Crg4lfopPnF9+7Rg8CveODnZcONGPIuX7OJasOF7pKmpCa8ObYvvC020993byfz0815OngrXxVhZWfDGiI50aF8fBwdrQkPj+H7pboKvG84+TH7Hlz69m7Nk6W42bj5VJudY3kyfPp2pU6fqbVMqlQZjXVxcMDU1JS5O/74cFxdHlSpVDO7j6urKli1byMnJISkpiWrVqjFt2jRq166ti6lTpw779+8nKyuL9PR0qlatyuDBg/Vi7le7dm1cXFwIDQ2lW7duj3SupZIRWrVqFWlpaaVRVJnq3K0RYye9wNoVBxk/8mfCQuOY++1QHBytDcaPHNuFXv2as2RBAKOGLWPHltPM+uoV6tQvTP9NndaLFq1qM++zrYx57SdOnwjn60XDcHaxK1Je+04NaNjYncSE9DI7xyepp2d9Pnq+E4sPH6PvqnVci09k5aABOFlbGYyf0rEdQ5o9x+zde+nx8xp+PXuBpf370Kiyqy7myx4v0KGWB+/tCKDXijUcCr/FmiEDcbO10cXUdLBn/bBB3EhOYdgvG+i90p8lR46TW6Aq83N+XDoPasfYb/xY+9kGxnt9SNiFW8wN+BgH10oG40d+PoReY15gyaQVjGo8hR0/7mLWpvep06yWLmbq8vG08HmOecMXM+a5dzm96zxf7/oU52qFv4kjr8fw/dv/x5jn3mVKx0+Iu5XAV4GfYO9i+LjGKDsbGtSFTyY/WnxkDIybBt7NYfPPMPxl+GQ+HLrn75o/98C8JTDBDzYuhwZ14M33ICmlMGbu97DvCCycDWsWQXwiTPqkVE/tqdClsyfjxz7PmrWHGDt+JTfC4pk3dzAODobvu2+M7MRLvZqxeMkuRo5azvYdZ/ls1gDq1im877439UW8WtRi7rwdjBrzf5w6fZP5Xw/Bxdm2SHkd2tenUcNqJCaWnz+KiiiDOUJKpZJKlSrpfR7UEbKwsMDLy4ugoCDdNrVaTVBQEG3btn1o1S0tLXF3d0elUrFx40b69u1bJMbGxoaqVauSkpJCYGCgwZi7IiMjSUpKomrVqg897r1KpSM0ZswYoqMNZ1WeJgOHePPXtrME7jxPxM1EFn39J7m5+fj2bmYw3sf3WX5dfZgTR28QG53Kjs1nOHEklJeHtgHAwsKMjl0asnxpEBfPRRAdlYL//x0gKjKFlwZ46ZXl7GLHhKm+zJ29BZVKXdan+kS80aoFv52/xMaLVwhNSuaTwN1k56t45dkmBuP7NW7IsqMn2B92k9tpafxy7gL7wsIZ1VrbdkozU3wb1GPe3oOcjIziVmoa3x0+xq2UVF5t3lRXztRO7dl/4yZf7zvIlfgEIlLTCAoNI/lO9mM578dh4JTe/PVzEIGr9hFxNZJF434i904evm88bzDe57VO/Dp3Eyf+OktseDw7lv3NiT/P8PLUlwCwsLSg40Bvln+4losHrxJ9Ixb/2RuICo3lpfHddeXs/fUQZ4MuEhsez60rkSybuhobe2tqP1fzsZz349CpDUweDS90erT49VvBvSp8OAHq1IJhA6B7Z1i9oTBm9e/wSm8Y0BPq1oJZ74KlJWz6U/t9Rqb23x9OgDYtoHED+HIanL2k4Nzl0j7DJ+uVga3586/zBARe5FZEEt8uCiA3N58XfZ8zGP+CT2PW/XqU4yfCiIlNY9uOsxw/EcYrL7cCtPfdTh0b8OPyfVy4eJvo6FRW+x8iOiqVPi811yvLxdmWtyf48OXc7eX2vgtPx1NjU6dOZfny5axevZqrV68yfvx4srKyGDlyJADDhw9n+vTpuvjjx4+zadMmwsLCOHjwID169ECtVvPBBx/oYgIDAwkICCA8PJxdu3bRtWtXPD09dWVmZmby/vvvc+zYMW7evElQUBB9+/albt26+Pr6PnLdizU05uRkOGerUqlo27atbtwuOTm5OMU+FmZmJtRvUJX1/od12zQaOHPyJo2auBvcx9zClLw8/QlXuXkqmjxXAwBTMxNMzUzIz9XPPOTlFsYAKBTw4cy+bPjlKLfCE0vrlJ4q5iYmNKnixrJjhYtdaYAjNyNo7m64Z25hZloka5OrUuFVvRqgHTozMzEpEpOjUtHynxgF0KX2Myw/cYqVg/rTqHJlbqelsezYSXaH3KA8MDM3o75XbdZ/tVm3TaPRcGb3BRq1qW9wH3OlOXk5+XrbcrPzaNLBE7h77ZqSn5OnF5OXnUeT9p4PrEfPMT5kpmZx4/wtgzEVwbnL0Fb/7xw6tNJmeADy8rXDbG8OK/zexES7z91OzuXrkK9S0Nar8DdObQ+o6qbh3GVo1riMT+IxMTMzoX79KvyyvvDJIY0GTp+5SaNGD7jvmpuRl3fffSE3n2eb/HPfNTXB1NSEvPz7YvLyadJE/747/cOX+G3DCW7eKp/33afJ4MGDSUhI4NNPPyU2NpZmzZoREBCgm0AdERGhN7cnJyeHGTNmEBYWhq2tLT179sTf3x8HBwddTFpaGtOnTycyMhInJycGDhzIF198gbm5OQCmpqZcuHCB1atXk5qaSrVq1ejevTtz5sx5YPbKkGJ1hPLz8+ncuTOvvPKKbptGo2H06NF88MEHuLsbvrCfBvYO1piamZCSnKW3PSU5kxoezgb3OXU8jIFDvLl47hbRUSk0b/kMHTp7YmKiACD7Th6XL95m2MiORNxKJCU5i64vNKZhE3eiIwtz4INfa4e6QM3m308aPE554GhthZmJCUlZd/S2J965Q21nw2s6HAy/xRutvDhxO4qIlFTa1apJ9/p1MVVo2zcrL58zUdFMbOfNjaRkErPu8FLDBjSvVpVbKakAONtYY6u0YKx3K749eJiv9x2i0zO1WNr/JV77dQMnbkcZPLYxsXexw9TMlJQ4/eHnlPg0anga/n/uVOB5Bk7pzcUDV4i+EUfzbs/SYYA3JqbaG1F2Zg6XjwQzbMbLRFyNIiUuja5D29OwbX2iQ/XnWXj3asHHv05BaW1BckwqH3afQ3pSOR5m+BeJyeBy3yXt7ASZWQpycjWkZ0BBgQJnR/0/q50dITzinzKSwNxcQ6X7RtBdHLXllxf29taYmpqQknLffTcli5o1HnDfPRXGKwNb/ZPtSaFF81p07NCg8L6bncfly5G8Pqw9ERFJpKRk8XzXRjRq6E50dOF9d8jgNhSo1WyqCHOCnpJXbEycOJGJEyca/O7+ycudO3fmypUrDy1v0KBBDBo06IHfW1lZERgYWOx63q9YHaGzZ8/y6quvsmfPHpYsWYKtrXY89s0336Rfv340atTokcrJzc0t8hieWq3CxOTpmru9dOHfTJnWi//7dTxoIDoqhb93nse3d+GwzLzPtvHeR71Zv20yBSo1Iddj2Lv7MvUbaLMg9RpUof+g1rw18ucndRpPrc937+OLHj78PdoPDRCRksrGi5d5+Z6htPd2BPDVi905MmEMKrWay7Hx7LgaTOMqlQEw+afTtDv0BitPaVckvRqfQAv3qgxt9ly56Aj9F0snr2TKT2P5v6uLQKMh+kYcf6/ai+/IwqG0ecMX897/vcX6qJ8oUBUQciacvb8eor6X/kTE83svM675+9i72PHimz7M+G0qk9pMJ7WcznUTT9b3S3fz7pQXWfV/bwIQHZ1CwN8X9IbS5s7bwfvv9WTD+okUFKgJCYllz94r1K+vnZhbr54bA/u3ZOxbq57EKQgjU6yeR926dTly5Agff/wxzZo1Y/Xq1bRv377YB507dy6zZ8/W2/ZM9a7UqWF4vkNpSEu9Q4FKjaOTjd52RydbUpIzH7jPrGkbMLcwpVIla5ISMxj91vPERKXqYmKiUnh3gj+WluZY2yhJTsrk48/6E/PPXyZNmtbEwdGGdZsm6fYxNTNh7Ns+DBjcmtcHfl/6J/sEpNzJRqVW42yjPwHSxdqaxPuyRHclZ2czfvN2LExNcbSyJC4zi/c7d+D2PRPvI1LTePXXDViZm2FroSQhK4tFfXpyOzVNd9z8ggJCE/UX0ApNSqZl9ac3Q1kcaYkZFKgKcHSz19vuWNmelNjUB+yTzqwB8zFXmlPJ2Y6k6GRGfzWMmLDCpzpiwuJ4t+tMLK2VWFeyIjk2lY9/nUJMmP6S+Dl3com+EUv0jViuHg9hVfB39Bj1POu/2lLap2oUXJwgMUV/W1Iy2NposFRqh8FMTTV6E6NBO1H67hNhLs6Qn68gPUM/K5SYUr6eGktLu0NBgRpHx/vuu442JN+XJSrcJ5tPZ23C3NwU+0pWJCZl8uboLsTEpOpiomNSmfLuL9r7rrUFyclZfPJxX13Mc01q4OBgw/p1b+n2MTU1YdzY5xk4oBWvvv4D5cl/mdMjChU7BWNmZsa8efPw9fXl1VdfZdiwYSj++av8URl6LK9/9wXFrUqxqFRqrgfH0NzrGY4cuA5ox5Cbt6zF1o0PT53m5xWQlJiBqakJHbp4ciDoapGYnJx8cnLysbWzpKV3HZYv1c6e3x1wkbP3PPYJMPfboewOuEjgzvOldHZPXr5azaXYONp51NDNzVEA7WrVwP/0w88zr6CAuMwszExM6NGgHn9eu14kJjtfRXa+ikpKJR2f8WDevkO6416MjaP2ffPXnnFyJCq9fGQsVPkqrp8Oo3m3ZzmyVTu8qlAoaN7tWbYuCXjovvm5+SRFJ2NqZkqHAW04sOFIkZicO7nk3MnF1sGGlr5NWf7h2oeWqTBRYK40/+8nZOSaNYYDx/S3HTlVOK/Hwhwa14djp9E9hq9Ww7EzMKy/9ufG9cHcTMOxM9qJ1qAdNouJU9Cscfn5raZSqbl+PZYWzWtx+EgIoL3vtmjuwZatZx66b35+AYlJmZiamtCpQwP2HXjIfddWSauWz/Dj8r0A7Np9idNnb+rFfj13MLt2XyIg8GLpnNzTpPxcMk/Efx6Lev755zl79iyjR4/GxsYGU1PTR95XqVQWmcj0OIbFNq4/zgcz+nD9WgzBV6LoP9gbS0tzAndof1F/8EkfEhMyWLFM+z+TZ6NquLjaERoSh4urHcNHdcJEoeC3dYW/TFp6a4cRIiOSqVbdkTETunH7VqKuzIz0bDLS9Z9eUqnUJCdlERlRjiYDACtOnmF+L18uxsZzISaWES2bY2Vuzh8XtTNE5/fyJS4jk/8d0E5Yb1q1Cm52tlyNS8DNzpZJ7dugUCj46Xhhx7TjMx4ogLDkFDwcHfiwS0fCklPYeLHw0Zrlx0+xqG8vTkZGcuzWbTrVrsXzdWsz7JcNlBcbv93BB6smcP3UDYJPhNJ/ci8sbZQErtReqx+smkhidDIrPvoFAM/WdXFxdyL03E1c3J0YPnMQJiYKfvt6q67Mlt2bgkJBZHA01epWYczXr3P7WpSuTEtrJa9+PICj206RFJOCvUsl+kzwxcXdiQMbDC+bb4yy7kDEPSOokTFwNQTsK0E1N1jwE8QlwLyPtd8P6Qu/bIb5P8DAntoOTsA+WPZVYRl+g2D6XGjiCc96wpo/tI/p311ryM5W+0TZV0vA3g5sbeDzRdCssabcTJS+a8PGE0z7oDfB12O4FhzDwP4tsbS0ICDwAgDTPuhNYmIGP6/YD4CnZ1VcXewIDY3DxcUOv+EdUJgoWP/bcV2ZLVs+gwK4HZmMezVHxo7pSsTtJF0nJz0jh/SMHL16qFRqkpOzuB1Zvu67ouSK1ftQq9XMnz+fbdu2kZeXR7du3Vi3bh1WVobXiXna7A+6goODNX5vdsbRyYYbIXF8NPVXUv9J0VZ2s0ejLuxaW1iYMWJMF6pWcyQ7O48TR0OZ99lWsjIL5zdZ2ygZNf55XFztyEjP5tC+a6z4cR8FBeX3Uc0H+fPadZytrZjcoS2uNtZciU/gjd83k3RHOzRWrZIdak1h+yrNTJnasR01HOzJystnf1g47+0MIOOe+WN2SiXvdWpPFTtbUnNyCQwO4ZsDh1GpC9t3V8gNPg0MYlybVnzSrSthyclM3Lyd01FP/5IOj2r/70dwcK2E3+zBOFZx4Ma5m3z04hekxmuHCCvXdNG/di0tGDFnKFVrVyY7M4cTf55l3vDFZKUVDlNa21sz6stXcanuTEZyJoc2HWfFx79SoNI+KVlQoKZGA3de+KMLlVzsyEjKIPjkDaZ0+pRbVyIfbwOUocvB4De5MKs9b4n23/16aJg7HRKSIOae0cLqVbWdnq++B/+NUMUV5rwPHVoXxvR8HlJS4bsV2snPDevCT/P1h72mT9QOo73zqfZJs7sLKpY3+/Zfw8HBmpF+HXF01C6o+OFHv5GSqr0WK1eupHdfsLAwY+SITlSr6kB2dh7HT4Qxd94OsrIK7ws21kreHNUZFxc7MjJyOHgomP9bcaBC3ndBhsZKSqHRaB65CefMmcOsWbPw8fHRzdYeOnToA98uWxwvtPu8xGUIw8L72vx7kPjPnpledLhJlI7A6PIzfPy08Xn9jSddhXJtz65pj+1Y3sNLf2rJ8TVT/z2onCjWgopr1qxh6dKlBAYGsmXLFrZv3866detQqytmL1wIIYR44p7w2+eNXbE6QhEREfTs2VP3s4+PDwqFwihWlRZCCCHKo6dhZWljVqyOkEqlwtLSUm+bubk5+fn5D9hDCCGEEOLpVazJ0hqNhhEjRug98ZWTk8O4ceOwsSmch7Jp06bSq6EQQgghHuzRp/oKA4rVEfLz8yuy7bXXXiu1ygghhBBCPE7F6gitXLmyrOohhBBCiP+gos3pKW1P18u9hBBCCFE80hEqkWJNlhZCCCGEKE8kIySEEEIYMYUs5VcikhESQgghRIUlGSEhhBDCmMkcoRKRjpAQQghhxOSpsZKRoTEhhBBCVFiSERJCCCGMmawsXSKSERJCCCFEhSUZISGEEMKIyRyhkpGOkBBCCGHMpCNUIjI0JoQQQogKSzJCQgghhBGTobGSkYyQEEIIISosyQgJIYQQxkweny8R6QgJIYQQRkyGxkpGhsaEEEIIUWFJRkgIIYQwZpIRKhHJCAkhhBCiwpKMkBBCCGHEZI5QyUhHSAghhDBmaukJlYQMjQkhhBCiwpKMkBBCCGHMJCFUIpIREkIIIUSFJRkhIYQQwojJZOmSkY6QEEIIYczkFRslIkNjQgghhKiwJCMkhBBCGDEZGisZyQgJIYQQosKSjJAQQghhzCQjVCLSERJCCCGMmEImS5fIU9MRSqtv/aSrUG5Vuvmka1C+JY1t96SrUG75vO71pKtQbu32X/Gkq1DOTXvSFRCP6KnpCAkhxP+3d+dxVdX5H8dflx0BQUERyTRXXMp9wdTGJDU0TalxnMaF0rTJzLHMmCwZzUidTE1r8jeZDlqZo5Y5Bbmk44JrOprmQiIoyKZsoqz3/v4gr91EEwHxct/Px+M8HtzD5xw+5/PQy4fv93vOFZHbYKzqBKybGiERERErpqmx8tFdYyIiImKz1AiJiIhYM1MlbLdh8eLFNGrUCBcXF7p27crevXtvGFtYWMiMGTNo0qQJLi4utG3blqioKIuYnJwcJk2aRMOGDXF1daV79+7s27fvhuccP348BoOB+fPnlylvNUIiIiJSLqtWrWLy5MlMnz6d77//nrZt29KvXz9SU1NLjZ82bRoffvgh7733HseOHWP8+PEMGTKEgwcPmmPGjBnDxo0biYyM5MiRI/Tt25egoCASExOvO9+6devYvXs39evXL3PuaoRERESsmclU8VsZzZs3j7FjxxIaGkqrVq34xz/+QY0aNVi6tPS7EyMjI/nrX/9KcHAwjRs35rnnniM4OJh33nkHgCtXrrBmzRrmzJlDr169aNq0KeHh4TRt2pQPPvjA4lyJiYm88MILrFy5EkdHxzLnrkZIRETEihlMFb+VRUFBAQcOHCAoKMi8z87OjqCgIGJiYko9Jj8/HxcXF4t9rq6u7NixA4CioiKKi4tvGgNgNBoZMWIEU6ZMoXXr1mVL/Gqut3WUiIiIVFv5+flkZ2dbbPn5+aXGpqenU1xcjK+vr8V+X19fkpOTSz2mX79+zJs3j1OnTmE0Gtm4cSNr167l/PnzAHh4eBAYGMjMmTNJSkqiuLiYFStWEBMTY44BmD17Ng4ODkycOPG2r1WNkIiIiDWrhKmxiIgIPD09LbaIiIgKS3nBggU0a9aMgIAAnJycmDBhAqGhodjZXWtLIiMjMZlM+Pv74+zszMKFCxk+fLg55sCBAyxYsIBly5ZhMBhuOxc1QiIiImIhLCyMrKwsiy0sLKzUWB8fH+zt7UlJSbHYn5KSQr169Uo9pk6dOnzxxRfk5uYSHx/P8ePHcXd3p3HjxuaYJk2asG3bNi5dusTZs2fZu3cvhYWF5pjt27eTmprKvffei4ODAw4ODsTHx/PSSy/RqFGjW75WNUIiIiJWzGCs+M3Z2ZmaNWtabM7OzqX+fCcnJzp27MjmzZvN+4xGI5s3byYwMPCmubu4uODv709RURFr1qxh8ODB18W4ubnh5+dHRkYG0dHR5pgRI0Zw+PBhDh06ZN7q16/PlClTiI6OvuX66cnSIiIi1uwueLL05MmTGTVqFJ06daJLly7Mnz+f3NxcQkNDARg5ciT+/v7m6bU9e/aQmJhIu3btSExMJDw8HKPRyCuvvGI+Z3R0NCaTiRYtWhAbG8uUKVMICAgwn9Pb2xtvb2+LPBwdHalXrx4tWrS45dzVCImIiEi5DBs2jLS0NN544w2Sk5Np164dUVFR5gXUCQkJFut/8vLymDZtGqdPn8bd3Z3g4GAiIyPx8vIyx1ydjjt37hy1a9cmJCSEWbNm3dYt8jdjMJnuglYS6DJ6XlWnUG0Vud7+IjKRquQVW/pdKlJ++vT5ymVX7+Qd+1mPdH+zws+5cde0Cj/n3UprhERERMRmaWpMRETEiunT58tHjZCIiIg1UyNULpoaExEREZulESERERFrZqzqBKybRoRERETEZmlESERExIppsXT5qBESERGxZmqEykVTYyIiImKzNCIkIiJizTQiVC4aERIRERGbpREhERERa6bb58tFjZCIiIgV011j5aOpMREREbFZGhESERGxZhoRKheNCImIiIjN0oiQiIiINdOIULmoERIREbFmaoTKRVNjIiIiYrM0IiQiImLN9ByhctGIkIiIiNgsjQiJiIhYMT1QsXxsrhF6ok9b/vRoJ7w93TiVkMbfV3zHsbjkUmPt7e0YPaALA3q0ok4tdxLOZ/De6u3sPnLGHFPDxZFxQx/kdx2aUqtmDU7Gp/LOJ9/xY1yKOeaNMf0Y2KO1xbljjpzhxXfWVso1VqXfP9SWkY90xLumGyfPpTFn1XccjU8pNdbBzo7Q/p0Z2K0Vdb3ciU/JYOG67ew6Fm+OqeHsyJ8Hdad326bU8qjBibOpzF29lWO/OGf4yL4MCrSs766jZ5iwaF3lXGQVUW0r1+BBHRj2ZFdq13bjp59SeW/xRo6fOF9qrL29HX8cHki/R9rg4+PB2bMXWfLP79i3P84c4+rqxNOje9LjweZ4edUgNjaFRe9v4sTJ0t9vJr3Yj0ED27P4/U2sWbe/Uq6xKuz7Hyz9FI6ehLQLBt5700RQz5sfs/cgvL0YYs+AX10YPwKGPGoZs3IdLP0M0i9CQBN47UV4oOW17+fnw+z34estUFgID3aGN/4CPrUr/BKrnhqhcilTI7R37146duyIvb09ABs2bGDu3LnExsbi5+fHxIkTGTlyZKUkWhGCujRn0h8e4u3lmzl6+jx/6NuBhS8P5clXPyYj58p18c8NfZD+3Vvy1scbOXP+IoFtGjLnhUGMefNTTiakAfBaaF+a3ONN+JJvSMvM5dHuLVk85QmG/XU5aZmXzOfadTiOmR9Fm18XFBZX/gXfYX07NmdySC/e+nQzR+KSeerhDiyeOJQh4ctKre+fB3UnuGtLZq7YyJmUDAJbNeTv4wYROvczTpwrqe8bf3qEJvV9eH1ZFGlZlwju0pIPXgzhib8tJy0r13yunUfjCP/Xt+bXBUXVq76qbeX63UMBPDfuYeYvjObHH5MIGdqZ2RHDGPX0EjIzL18X/3RoLx7p05p33v2GhIQLdO7UmBnhQ3nhxRXE/lTSSL48+VHua+RDxOwNpF/I4ZE+bZg75w88/cw/Sb9wyeJ8PR5sTquW9UlPz7kj13snXbkCLZrC0GCY+Ppvx587D+NfhWGDYO402P09vD4X6nhDjy4lMV9vgdmLIXwyPNAK/rUaxr4MX68A71olMRGL4L+7Yf7fwMMNZs4v+fmfLK60SxUrVaY1QoGBgVy4cAGAr776isGDB9OoUSNee+012rdvzzPPPMO6dXfvX4p/7NeRL7b9wIYdR4lLusjbyzeRV1DEY73alBr/aPeWLNuwh12H40hKy2LNd4fZdTiOp/p3AsDZ0YHenZrx3ufbOXgykXOpmfzfFzGcTc0k5OEHLM5VWFTMhazL5i3ncn6lX++d9lSfDqzb+QPrY44Rl3yRWZ+W1HdwYOn1HdC1JUuj9rLz6BkS07P4938Ps/NoHCOCOgLg7GjPw+2bsWDddr6PTeRsWhYf/mc359IyefKhthbnKigs5kL2ZfNW3eqr2lauJ0O68PU3/yMq+gjxCRd4d0EU+fmFPNrvgVLjHwlqzcpPY9iz9zTnk7NYv+Ege/ae5sknOgPg5ORAr54t+PD/tnL4yFmSkjJZHrmDpMRMBj3W3uJcPt7uvPB8EG9FfEVRUfVb9dqrG0waA4/0urX4z74Efz+Y+jw0aQRPDYW+D8Hy1ddiln8OTw4saa6aNoLwl8DFBdZ+XfL9nEslX099Hrp1gNYt4K1X4eAPBg4dregrvAsYTRW/2ZAyjQiZfjH8NmfOHF555RUiIiLM++677z7mzJnDkCFDKi7DCuJgb0dAI1+W/2eveZ/JBPuOxnN/E79Sj3FytL9u5Ca/oIi2zesDYG9vwMHejoKColJi/C32dQi4h6iF48nJzWP/j2f5x5qdZOXmVcSl3RUc7O1oea8vH0fvM+8zmWDP8QQeaFx6fR0d7MkvvL527Zr+XF87u5L6/iomr6CIdk3qW+zr1PweNs0ZR/blPPadOMv763dVm/qqtpXLwcGO5s3r8clnMeZ9JhMc+P4MrVr5l3qMo6PD9f/v8wu5v00DoGTqzL6U+uYXFNLm5xgAgwHCpj7GqtV7OROfXlGXZNUOHYXAjpb7enQuGeEBKCgsmWYb+9S179vZlRxztck5ehIKiwwEdrz2O6txQ/DzNXHoKLSznO0VG3fbd42dPHmSJ554wmJfSEgIx48fL3dSlcHLwxUHezsuZlkOc1/Mvoy3p1upx+w+Es8f+3Wgga8XBgN0aX0vvTs2xefn+Mt5hRw+lcTTg7vh4+WGncFA/8CW3N/UzxwDJeuBwpdE8fycf7No9Xbat7iH+S8Nxc5gqLwLvsO83H+ub3Yp9a1Zo9RjYn6M5099OtKgTkl9uwbcS+/2TfGp+XN98wv5309JjAnuio9nSX2DuwTwQGPL+u46dobXl0czfv6/WbhuBx2b3cN7E4ZUm/qqtpXL07MG9vZ2ZGTkWuzPyMildq3S3xv27z/NkyGd8fevhcEAHTs0omePFtSuXRJ/5UoBR4+eY8RTD+Lt7Y6dnYGgPq1p1dIf79rXzvmHYd0oNhpZW43WBJVX+kXwqWW5z7s2XMo1kJcPmVlQXGwwT4GZY2qVHAuQfgEcHU3U9LCM8flFTLViMlX8ZkPKvFj62LFjJCcn4+rqitF4/TBuUVFRKUdZys/PJz/fcnjdWFyEnf3dtXb7nU++47XQR/g8YjQmEySmZvLVjqM81vPadMT0Jd/w+jP9+Hr+OIqKjZyIT+Xb3ScIaFTXHLNxzwnz1z+dS+fU2XS+mPsMHQPuYd+PZ+/oNd1N5n6+ldefCmJt+ChMJjiXnslXMUcZ9IvpnteXRTF9RF++fftZioqNHD+bSvS+E7S891p9v91/0vx1bNIFTiWm89XMp+nU/B72nrDN+qq2lWvR+5t46S+PsuyjsQAkJWUQ9e1hi6m0iNkbmPJyMKs/m0BxsZFTp5LZ8t0xmjevB0CzZr6EDOnEuD8vq4pLkOrExhqXilbmzqNPnz7mKbKdO3fSuXNn8/cOHjzIvffe+5vniIiI4G9/+5vFvvpt++Lfrl9Z07llmTlXKCo2UtvT8i/o2jVrcCEr94bHTFm4HidHezzdXEnLvMSEJ3uSlJZpjklMy2L825/j4uSAm6szF7JymfXcABLTsm6YS1JaFhnZl7nH16vaNEKZl36ub81S6pt9/WLTq8e89OFXODnY4+nmQlpWLhMf70Fi+rXanUvPYuy7q3FxcsDdxZn07FzefiaYc+k3rm9iehYZOZdpUMerWvyyVm0rV1bWZYqLjdT61ehPrVpuXMwo/b0hK+sKb4SvxdHRHs+arqRfuMTYMb/j/PlMc0zS+Uz+8tInuLg4UqOGExcv5vL6a4PNMQ+0aYCXlxufrfyz+Rh7ezvGj3uYkKGd+eOIDyr8Wq2BT21Iz7Dcd+EiuLuZcHEumQaztzdx4dcxGdfuCPPxhsJCA9k5lqNC6RnV9K4xKZcyTY3FxcVx+vRp4uLiiIuLu+4OsYKCAqZOnfqb5wkLCyMrK8ti87u/T9kyL6OiYiPHz6TQudW1Rs1ggE6t7uXIT6XfIntVQWExaZmXsLe3o3enZmz7/qfrYvIKiriQlYtHDWe63d+Q/5YSc1XdWu54uruSnln6m6w1Kio28mNCCl1aWK5/6NKiAYdP/0Z9i4pJy8rFwc6OPu2bse1w6fVNzy6pb2Crhmw7fPqG56vr5V7SuGZXj/qqtpWrqMjIyZPJdGjfyLzPYIAO7Rty7FjiTY8tLCwm/ULJe0OvHi3YGXPqupi8vEIuXszF3d2Zzp3uY+eukpiNm35gzLiPGDt+qXlLT8/h89V7mBq2qkKv0Zq0aw27D1ju27X/2roeJ0do3dwyxmgsubvsakzr5uDoYGL399di4hLgfIqheq4P0tRYuZRpRKhBgwbMnTuX9evXU1BQQJ8+fZg+fTqurq4At3zrvLOzM87Ozhb77sS02CfRB5g+tj8/xqVw9HQyf+jbAVdnRzZsL1lhFz62P6kZl3j/3zsAaN24HnVquXMyIY26tdwZ+3ggdgaI/ObafH63Ng3BYCDh/EXu8fVi4rBenDmfwVc7Ss7p6uzImMcD+W7/KS5k5XJPHU8mDOvFudRMdv8Qf32SVmzl5u/526h+HEtI5eiZZP74cHtcnR1ZH1NSixmj+pGaeYlFX+4EoE2jetT1cufEuTTqerkzbkA3DHYGln17rb6BLRtiMMCZlAwa1PFi0tCenEnJYP2ua/UdN6Abmw+eIj3rMg3qePLi0J6cTcsk5lj1qa9qW7lWr9nLq68M5MTJ8xw/cZ6QIZ1wcXEiKvowAK++MpD09Bz+uXQbAAEBftTx8SA2NgUfHw9GjeyBwc7AZ6v2mM/ZqdN9GICz5y7iX78W457tTcLZC0RFHwEgOyeP7BzLRedFRUYuXszl7Lnqs5Al9zIk/KKfPHcefjwFnjWhvi/MWwIpaTD7tZLv/2EwfLIO5n4AIcElDU7UVvjH29fOMer3EBYBbQLg/gD4179LbtO/+qwhD/eSO8reXgyeHuDuBm8ugHatTdWzEZJyKVP3MWvWLMLDwwkKCsLV1ZUFCxaQmprK0qVLKyu/CrVp70lqedTg2SHd8faswcmENF58Z615EaqvtwfGX3TCTo4OjB/6IP51PbmSV8iuw3FMX/INl35x+7C7qzN/frIHdWu5k52bx5b9sXywZgfFxSXrp4xGE83u8WHAg63wqOFMWuYl9vwQz4drd1FYzZ7H8u2Bk9Ryd+W5gYF416zBiXNpTHhvHRdzSupbr/av62vPnwd1x9/Hk8v5hez8IY5py6K4dMWyvhMefxBfL3eyLuez5eApFn+5kyLj1foaaebvw8BurfBwdSYt6xK7jyXw/lfVq76qbeXauu04Xl41CB3Vk1q1Sh6oOPWvq8j4+RlCdevWtKyvkwOho3tR38+LK1cK2LP3NBGzN5Cbe62+bjWcGfvMQ/j4eJCTk8f2HSf4aOl/ze8NtuLoCRg16dri+tmLS75+vL+JiDBIuwDnU6/F3+NX0vS8vQgi10C9OjBzyrVnCAEEPwwZmbBwacni55ZNYclcy2mvsAkl02gvvlFyp9nVBypWSzZ2u3tFM5hMtz4G1qxZM15++WXGjRsHwKZNmxgwYABXrlzBzq58H1vWZfS8ch0vN1bkWn3u8BHb4hVb/Z5ZdLfYFGkdf8BaK7t6J387qII8et/kCj/nN3G28zu5TN1LQkICwcHB5tdBQUEYDAaSkpIqPDERERGRylamqbGioiJcXFws9jk6OlJYWFihSYmIiMgtsrHFzRWtzE+WHj16tMVC57y8PMaPH4+b27VbT9eurX4fJioiIiLVT5kaoVGjRl23709/+lOFJSMiIiJlpMXS5VKmRujjjz+urDxERETkdmhqrFzKd6uXiIiIiBW7uz7cS0RERMpGI0LlokZIRETEmqkRKhdNjYmIiIjN0oiQiIiINTPa1se2VDSNCImIiIjN0oiQiIiINdMaoXJRIyQiImLN1AiVi6bGRERExGapERIREbFmRlPFb7dh8eLFNGrUCBcXF7p27crevXtvGFtYWMiMGTNo0qQJLi4utG3blqioKIuYnJwcJk2aRMOGDXF1daV79+7s27fPIiY8PJyAgADc3NyoVasWQUFB7Nmzp0x5qxESERGRclm1ahWTJ09m+vTpfP/997Rt25Z+/fqRmppaavy0adP48MMPee+99zh27Bjjx49nyJAhHDx40BwzZswYNm7cSGRkJEeOHKFv374EBQWRmJhojmnevDmLFi3iyJEj7Nixg0aNGtG3b1/S0tJuOXeDyXR3TC52GT2vqlOotopcDVWdgsht8YrNr+oUqq1NkUurOoVqza7eyTv2s/r7PFvh54xKX1Km+K5du9K5c2cWLVoEgNFopEGDBrzwwgu8+uqr18XXr1+f1157jeeff968LyQkBFdXV1asWMGVK1fw8PDgyy+/ZMCAAeaYjh078uijj/Lmm2+Wmkd2djaenp5s2rSJPn363FLuGhESERGxZlU8NVZQUMCBAwcICgoy77OzsyMoKIiYmJhSj8nPz8fFxcVin6urKzt27ACgqKiI4uLim8aUlseSJUvw9PSkbdu2t5y/GiERERGxkJ+fT3Z2tsWWn1/6CG16ejrFxcX4+vpa7Pf19SU5ObnUY/r168e8efM4deoURqORjRs3snbtWs6fPw+Ah4cHgYGBzJw5k6SkJIqLi1mxYgUxMTHmmKs2bNiAu7s7Li4uvPvuu2zcuBEfH59bvlY1QiIiItbMZKrwLSIiAk9PT4stIiKiwlJesGABzZo1IyAgACcnJyZMmEBoaCh2dtfaksjISEwmE/7+/jg7O7Nw4UKGDx9uEQPQu3dvDh06xK5du+jfvz+///3vb7g2qTRqhERERMRCWFgYWVlZFltYWFipsT4+Ptjb25OSkmKxPyUlhXr16pV6TJ06dfjiiy/Izc0lPj6e48eP4+7uTuPGjc0xTZo0Ydu2bVy6dImzZ8+yd+9eCgsLLWIA3NzcaNq0Kd26deOjjz7CwcGBjz766JavVY2QiIiINTMaK3xzdnamZs2aFpuzs3OpP97JyYmOHTuyefPmX6RkZPPmzQQGBt40dRcXF/z9/SkqKmLNmjUMHjz4uhg3Nzf8/PzIyMggOjq61BjLchhvOI1XGj1ZWkRExJrdBTd/T548mVGjRtGpUye6dOnC/Pnzyc3NJTQ0FICRI0fi7+9vnl7bs2cPiYmJtGvXjsTERMLDwzEajbzyyivmc0ZHR2MymWjRogWxsbFMmTKFgIAA8zlzc3OZNWsWgwYNws/Pj/T0dBYvXkxiYiJPPvnkLeeuRkhERETKZdiwYaSlpfHGG2+QnJxMu3btiIqKMi+gTkhIsFjbk5eXx7Rp0zh9+jTu7u4EBwcTGRmJl5eXOebqdNy5c+eoXbs2ISEhzJo1C0dHRwDs7e05fvw4y5cvJz09HW9vbzp37sz27dtp3br1Leeu5wjZAD1HSKyVniNUefQcocp1J58j1M99VIWfM/rS8go/591Ka4RERETEZmlqTERExJrdHRM7VkuNkIiIiDW7zQ9JlRKaGhMRERGbpREhERERa2YyVnUGVk0jQiIiImKzNCIkIiJixUxaI1QuaoRERESsmabGykVTYyIiImKzNCIkIiJixTQ1Vj4aERIRERGbpREhERERa6Y1QuVy13zoqrXIz88nIiKCsLAwnJ2dqzqdakf1rTyqbeVSfSuPaiuVSY1QGWVnZ+Pp6UlWVhY1a9as6nSqHdW38qi2lUv1rTyqrVQmrRESERERm6VGSERERGyWGiERERGxWWqEysjZ2Znp06drwV4lUX0rj2pbuVTfyqPaSmXSYmkRERGxWRoREhEREZulRkhERERslhohERERsVlqhERERMRmqRG6gdGjR2MwGDAYDDg5OdG0aVNmzJhBUVEReXl5jB49mvvvvx8HBwcef/zxqk7X6tysvlu3bmXw4MH4+fnh5uZGu3btWLlyZVWnbDVuVtsTJ07Qu3dvfH19cXFxoXHjxkybNo3CwsKqTtsq3Ky2vxQbG4uHhwdeXl5Vk6iVull9z5w5Y/7eL7fdu3dXddpi5fShqzfRv39/Pv74Y/Lz8/n66695/vnncXR0ZOLEibi6ujJx4kTWrFlT1WlarRvV12Qy8cADDzB16lR8fX3ZsGEDI0eOxNPTk4EDB1Z12lbhRrUdNmwYI0eOpEOHDnh5efG///2PsWPHYjQaeeutt6o6batwo9qGhYUBUFhYyPDhw+nZsye7du2q4mytz43qO3z4cAA2bdpE69atzfHe3t5VlapUE7p9/gZGjx5NZmYmX3zxhXlf3759ycnJISYm5qZx8ttutb5XDRgwAF9fX5YuXXoHs7ROZa3t5MmT2bdvH9u3b7+DWVqnW6nt1KlTSUpKok+fPkyaNInMzMyqSdYK3ay+n376Kffddx8HDx6kXbt2VZajVD+aGisDV1dXCgoKqjqNautm9c3KyqJ27dp3OKPq40a1jY2NJSoqioceeqgKsqoeflnbLVu2sHr1ahYvXlzFWVUfv/63O2jQIOrWrUuPHj1Yv359FWYm1YUaoVtgMpnYtGkT0dHRPPzww1WdTrXzW/X9/PPP2bdvH6GhoVWQnXW7UW27d++Oi4sLzZo1o2fPnsyYMaMKs7ROv67thQsXGD16NMuWLdMnpFeAX9fX3d2dd955h9WrV/Of//yHHj168Pjjj6sZkvIzSalGjRplsre3N7m5uZmcnJxMDg4OppEjR5ouXbp0XdzgwYOrJkkrdqv13bJli6lGjRqm5cuXV1Gm1udWapuQkGA6evSo6ZNPPjH5+/ubZs+eXYUZW4+b1XbIkCGmqVOnmmM//vhjk6enZ9Ula4Vu9X3hqhEjRph69Ohxh7OU6kaLpW+id+/efPDBBzg5OVG/fn0cHFSuivRb9d22bRuPPfYY7777LiNHjqyiLK3Tb9W2QYMGALRq1Yri4mKeffZZXnrpJezt7asiXatyo9pu2bKF9evX8/e//x0oGdEwGo04ODiwZMkSnn766apM22qU5X23a9eubNy48Q5mJ9WRfrPfhJubG02bNq3qNKqtm9V369atDBw4kNmzZ/Pss8/e4cysX1n+7RqNRgoLCzEajWqEbsGNahsTE0NxcbH59Zdffsns2bPZtWsX/v7+dzJFq1aWf7uHDh3Cz8+vkjOS6k6N0G06duwYBQUFXLx4kZycHA4dOgSguxkqwHfffcfAgQN58cUXCQkJITk5GQAnJyctmC6nlStX4ujoyP3334+zszP79+8nLCyMYcOG4ejoWNXpWbWWLVtavN6/fz92dna0adOmijKqXpYvX46TkxPt27cHYO3atSxdupR//vOfVZyZWDs1QrcpODiY+Ph48+ur/zlNehpBuS1fvpzLly8TERFBRESEef9DDz3E1q1bqy6xasDBwYHZs2dz8uRJTCYTDRs2ZMKECfzlL3+p6tREftPMmTOJj4/HwcGBgIAAVq1axRNPPFHVaYmV03OERERExGbp9nkRERGxWWqERERExGapERIRERGbpUZIREREbJYaIREREbFZaoRERETEZqkREhEREZulRkhERERslhohERERsVlqhERERMRmqRESERERm6VGSERERGzW/wN3pVkvbP5BLAAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Activation Overlap Analysis complete.\n" + ] + } + ], + "source": [ + "# =====================================================================\n", + "# ACTIVATION OVERLAP ANALYSIS — FULL ONE-CELL VERSION (ROBUST DIRECTORY DETECTION)\n", + "# Compatible with activation_probe_detailed.py output\n", + "# =====================================================================\n", + "\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from itertools import combinations\n", + "from scipy.spatial.distance import cosine\n", + "\n", + "# ------------------------------------------------------------\n", + "# 1. Auto-detect activations directory\n", + "# ------------------------------------------------------------\n", + "candidates = [\n", + " \"activations_before\",\n", + " \"activations_after\",\n", + " \"activations\" # <-- created by activation_probe_detailed.py\n", + "]\n", + "\n", + "base_dir = None\n", + "for c in candidates:\n", + " if os.path.exists(c) and os.path.isdir(c):\n", + " base_dir = c\n", + " break\n", + "\n", + "if base_dir is None:\n", + " raise FileNotFoundError(\n", + " \"No activation directories found. Expected one of:\\n\"\n", + " \" activations_before/\\n\"\n", + " \" activations_after/\\n\"\n", + " \" activations/\\n\"\n", + " \"Make sure activation_probe_detailed.py was run.\"\n", + " )\n", + "\n", + "print(f\"[INFO] Using activation directory: {base_dir}\")\n", + "\n", + "# ------------------------------------------------------------\n", + "# 2. Reconstruct prompts and layers\n", + "# ------------------------------------------------------------\n", + "prompts = sorted([\n", + " int(d.replace(\"prompt\", \"\"))\n", + " for d in os.listdir(base_dir)\n", + " if d.startswith(\"prompt\") and os.path.isdir(os.path.join(base_dir, d))\n", + "])\n", + "\n", + "if not prompts:\n", + " raise RuntimeError(f\"No promptNN folders found inside {base_dir}/\")\n", + "\n", + "sample_prompt_dir = os.path.join(base_dir, f\"prompt{prompts[0]:02d}\")\n", + "layers = sorted([\n", + " int(f.split(\"_\")[0].replace(\"layer\", \"\"))\n", + " for f in os.listdir(sample_prompt_dir)\n", + " if f.endswith(\"_pooled.npy\")\n", + "])\n", + "\n", + "if not layers:\n", + " raise RuntimeError(f\"No *_pooled.npy files found in {sample_prompt_dir}\")\n", + "\n", + "# ------------------------------------------------------------\n", + "# 3. Load all pooled activations\n", + "# ------------------------------------------------------------\n", + "activations = {}\n", + "for p in prompts:\n", + " p_dir = os.path.join(base_dir, f\"prompt{p:02d}\")\n", + " activations[p] = {}\n", + " for lid in layers:\n", + " fpath = os.path.join(p_dir, f\"layer{lid:02d}_pooled.npy\")\n", + " activations[p][lid] = np.load(fpath)\n", + "\n", + "print(f\"[INFO] Loaded {len(prompts)} prompts × {len(layers)} layers.\")\n", + "print(\"[INFO] Example vector shape:\", activations[prompts[0]][layers[0]].shape)\n", + "\n", + "# =====================================================================\n", + "# 4. OG CODE — Pairwise cosine overlap\n", + "# =====================================================================\n", + "pair_records = []\n", + "for lid in layers:\n", + " for (p1, p2) in combinations(prompts, 2):\n", + " v1, v2 = activations[p1][lid], activations[p2][lid]\n", + " v1 = v1 / np.linalg.norm(v1)\n", + " v2 = v2 / np.linalg.norm(v2)\n", + " sim = 1 - cosine(v1, v2)\n", + " pair_records.append({\n", + " \"Layer\": lid,\n", + " \"Prompt_i\": p1,\n", + " \"Prompt_j\": p2,\n", + " \"Cosine_Overlap\": sim\n", + " })\n", + "\n", + "df_overlap = pd.DataFrame(pair_records)\n", + "df_overlap.to_csv(os.path.join(base_dir, \"activation_overlap_summary.csv\"), index=False)\n", + "\n", + "print(df_overlap.head())\n", + "\n", + "# =====================================================================\n", + "# 5. Visualization A — Mean overlap per layer\n", + "# =====================================================================\n", + "layer_means = df_overlap.groupby(\"Layer\")[\"Cosine_Overlap\"].mean().reset_index()\n", + "\n", + "plt.figure(figsize=(8,4))\n", + "plt.plot(layer_means[\"Layer\"], layer_means[\"Cosine_Overlap\"], marker=\"o\")\n", + "plt.title(\"Average Activation Overlap per Layer\")\n", + "plt.xlabel(\"Layer Index\")\n", + "plt.ylabel(\"Mean Cosine Overlap\")\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# =====================================================================\n", + "# 6. Visualization B — Mid-layer heatmap\n", + "# =====================================================================\n", + "mid_layer = layers[len(layers)//2]\n", + "df_mid = df_overlap[df_overlap[\"Layer\"] == mid_layer]\n", + "\n", + "id_to_idx = {pid: i for i, pid in enumerate(prompts)}\n", + "matrix = np.zeros((len(prompts), len(prompts)))\n", + "\n", + "for _, row in df_mid.iterrows():\n", + " i = id_to_idx[row[\"Prompt_i\"]]\n", + " j = id_to_idx[row[\"Prompt_j\"]]\n", + " matrix[i, j] = matrix[j, i] = row[\"Cosine_Overlap\"]\n", + "\n", + "np.fill_diagonal(matrix, 1.0)\n", + "\n", + "plt.figure(figsize=(6,5))\n", + "sns.heatmap(matrix, annot=True, fmt=\".3f\", cmap=\"viridis\",\n", + " xticklabels=[f\"P{p}\" for p in prompts],\n", + " yticklabels=[f\"P{p}\" for p in prompts])\n", + "plt.title(f\"Activation Overlap (Layer {mid_layer})\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# =====================================================================\n", + "# 7. Visualization C — Layer × Prompt-Pair matrix\n", + "# =====================================================================\n", + "pivot = df_overlap.pivot_table(\n", + " index=\"Layer\",\n", + " columns=[\"Prompt_i\",\"Prompt_j\"],\n", + " values=\"Cosine_Overlap\"\n", + ")\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "sns.heatmap(pivot, cmap=\"mako\", cbar_kws={\"label\": \"Cosine Overlap\"})\n", + "plt.title(\"Layer × Prompt-Pair Overlap Heatmap\")\n", + "plt.xlabel(\"Prompt Pair (i, j)\")\n", + "plt.ylabel(\"Layer\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(\"[INFO] Activation Overlap Analysis complete.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "080cf5d5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.60it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Loaded Qwen/Qwen2.5-3B-Instruct on cuda\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Prompts: 20%|██ | 1/5 [00:00<00:01, 2.96it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Saved saliency for prompt 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Prompts: 40%|████ | 2/5 [00:00<00:01, 2.75it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Saved saliency for prompt 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Prompts: 60%|██████ | 3/5 [00:01<00:00, 2.85it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Saved saliency for prompt 3\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Prompts: 80%|████████ | 4/5 [00:01<00:00, 2.88it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Saved saliency for prompt 4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Prompts: 100%|██████████| 5/5 [00:01<00:00, 2.88it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Saved saliency for prompt 5\n", + "[INFO] Saliency extraction complete → saliency/\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#!/usr/bin/env python3\n", + "\"\"\"\n", + "Phase 4.1 — Saliency Maps via Gradient × Activation\n", + "Computes per-layer saliency scores (gradient × activation) for each prompt,\n", + "averages across sequence dimension, and visualizes them as heatmaps.\n", + "\"\"\"\n", + "\n", + "import os, json, torch, numpy as np, seaborn as sns, matplotlib.pyplot as plt\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM\n", + "from tqdm import tqdm\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 1 — Model loader\n", + "# ---------------------------------------------------------------------\n", + "def load_model(model_name=\"Qwen/Qwen2.5-3B-Instruct\"):\n", + " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + " torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n", + " device_map=\"auto\"\n", + " )\n", + " model.eval()\n", + " print(f\"[INFO] Loaded {model_name} on {device}\")\n", + " return model, tokenizer, device\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 2 — Hook registration (store activations + gradients)\n", + "# ---------------------------------------------------------------------\n", + "def register_saliency_hooks(model, store):\n", + " handles = []\n", + " for idx, layer in enumerate(model.model.layers):\n", + " def forward_hook(module, inp, out, layer_idx=idx):\n", + " store[layer_idx] = {\"act\": out[0].detach().cpu(), \"grad\": None}\n", + " def backward_hook(module, grad_input, grad_output, layer_idx=idx):\n", + " if layer_idx in store:\n", + " store[layer_idx][\"grad\"] = grad_output[0].detach().cpu()\n", + " handles.append(layer.register_forward_hook(forward_hook))\n", + " handles.append(layer.register_full_backward_hook(backward_hook))\n", + " return handles\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 3 — Compute saliency maps\n", + "# ---------------------------------------------------------------------\n", + "def compute_saliency(model, tokenizer, device, prompts, save_dir=\"saliency\"):\n", + " os.makedirs(save_dir, exist_ok=True)\n", + "\n", + " for p_idx, prompt in enumerate(tqdm(prompts, desc=\"Prompts\")):\n", + " store = {}\n", + " hooks = register_saliency_hooks(model, store)\n", + "\n", + " inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n", + " model.zero_grad(set_to_none=True)\n", + " outputs = model(**inputs)\n", + " scalar = outputs.logits.mean()\n", + " scalar.backward()\n", + "\n", + " # gradient × activation → saliency\n", + " for layer_idx, tensors in store.items():\n", + " act, grad = tensors[\"act\"], tensors[\"grad\"]\n", + " if grad is None: # skip if backward hook missed\n", + " continue\n", + " saliency = (grad * act).mean(dim=1).squeeze(0).numpy() # (hidden_dim,)\n", + " np.save(f\"{save_dir}/prompt{p_idx+1:02d}_layer{layer_idx:02d}_saliency.npy\", saliency)\n", + "\n", + " for h in hooks:\n", + " h.remove()\n", + "\n", + " print(f\"[INFO] Saved saliency for prompt {p_idx+1}\")\n", + "\n", + " print(f\"[INFO] Saliency extraction complete → {save_dir}/\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 4 — Visualization\n", + "# ---------------------------------------------------------------------\n", + "def visualize_saliency(base_dir=\"saliency\"):\n", + " files = sorted([f for f in os.listdir(base_dir) if \"_layer\" in f and f.endswith(\".npy\")])\n", + " prompts = sorted({int(f.split(\"prompt\")[1].split(\"_\")[0]) for f in files})\n", + " layers = sorted({int(f.split(\"_layer\")[1].split(\"_\")[0]) for f in files})\n", + "\n", + " plt.figure(figsize=(10, 5))\n", + " data = np.zeros((len(prompts), len(layers)))\n", + " for i, p in enumerate(prompts):\n", + " for j, l in enumerate(layers):\n", + " path = f\"{base_dir}/prompt{p:02d}_layer{l:02d}_saliency.npy\"\n", + " if os.path.exists(path):\n", + " data[i, j] = np.abs(np.load(path)).mean() # mean absolute saliency\n", + " sns.heatmap(data, cmap=\"inferno\", cbar_kws={'label': 'Mean |grad×act|'})\n", + " plt.xlabel(\"Layer Index\")\n", + " plt.ylabel(\"Prompt Index\")\n", + " plt.title(\"Layerwise Mean Saliency Heatmap\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 5 — Main\n", + "# ---------------------------------------------------------------------\n", + "def main():\n", + " # Load latest recommender prompts\n", + " log_dir = \"logs\"\n", + " log_files = sorted([f for f in os.listdir(log_dir) if f.startswith(\"recommender_\") and f.endswith(\".json\")])\n", + " latest_log = os.path.join(log_dir, log_files[-1])\n", + " with open(latest_log, \"r\", encoding=\"utf-8\") as f:\n", + " data = json.load(f)\n", + " prompts = [r[\"question\"] for r in data[\"records\"]]\n", + "\n", + " model, tokenizer, device = load_model()\n", + " compute_saliency(model, tokenizer, device, prompts)\n", + " visualize_saliency()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e485b7c2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Saved top-20 sensitive neurons per layer → sensitive_neurons.json\n" + ] + } + ], + "source": [ + "# ---------------------------------------------------------------------\n", + "# 6 — Aggregate Saliency Profiles & Identify Sensitive Neurons\n", + "# ---------------------------------------------------------------------\n", + "import json\n", + "\n", + "base_dir = \"saliency\"\n", + "files = sorted([f for f in os.listdir(base_dir) if \"_layer\" in f and f.endswith(\".npy\")])\n", + "prompts = sorted({int(f.split(\"prompt\")[1].split(\"_\")[0]) for f in files})\n", + "layers = sorted({int(f.split(\"_layer\")[1].split(\"_\")[0]) for f in files})\n", + "\n", + "# --------------- Compute average saliency per layer ---------------\n", + "mean_saliency = []\n", + "for l in layers:\n", + " layer_vals = []\n", + " for p in prompts:\n", + " path = f\"{base_dir}/prompt{p:02d}_layer{l:02d}_saliency.npy\"\n", + " if os.path.exists(path):\n", + " s = np.abs(np.load(path))\n", + " layer_vals.append(s.mean())\n", + " if layer_vals:\n", + " mean_saliency.append(np.mean(layer_vals))\n", + " else:\n", + " mean_saliency.append(np.nan)\n", + "\n", + "# Plot average saliency curve\n", + "plt.figure(figsize=(8,4))\n", + "plt.plot(layers, mean_saliency, marker=\"o\", color=\"darkorange\")\n", + "plt.xlabel(\"Layer Index\")\n", + "plt.ylabel(\"Mean |grad × act|\")\n", + "plt.title(\"Average Layerwise Saliency Profile (All Prompts)\")\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# --------------- Identify most sensitive neurons per layer ---------------\n", + "top_k = 20 # number of neurons to record per layer\n", + "sensitive_neurons = {}\n", + "\n", + "for l in layers:\n", + " all_neuron_vals = []\n", + " for p in prompts:\n", + " path = f\"{base_dir}/prompt{p:02d}_layer{l:02d}_saliency.npy\"\n", + " if os.path.exists(path):\n", + " s = np.abs(np.load(path))\n", + " all_neuron_vals.append(s)\n", + " if not all_neuron_vals:\n", + " continue\n", + " layer_stack = np.vstack(all_neuron_vals) # shape: (num_prompts, hidden_dim)\n", + " mean_neuron_saliency = layer_stack.mean(axis=0)\n", + " top_indices = np.argsort(mean_neuron_saliency)[-top_k:][::-1]\n", + " sensitive_neurons[f\"layer_{l}\"] = top_indices.tolist()\n", + "\n", + "# Save to JSON\n", + "out_path = \"sensitive_neurons.json\"\n", + "with open(out_path, \"w\", encoding=\"utf-8\") as f:\n", + " json.dump(sensitive_neurons, f, indent=2)\n", + "\n", + "print(f\"[INFO] Saved top-{top_k} sensitive neurons per layer → {out_path}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "38fe4aa8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Loaded Qwen/Qwen2.5-3B-Instruct on cuda with 36 transformer layers.\n", + "[INFO] Loaded 5 prompts.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Prompts: 100%|██████████| 5/5 [00:00<00:00, 15.55it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Computed Fisher Information for 36 layers.\n", + "[INFO] Saved Fisher Information → fisher/fisher_information.json\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rameyjm7/workspace/TML/lpu/llm-preference-unlearning/lpu-env/lib/python3.10/site-packages/numpy/lib/function_base.py:2897: RuntimeWarning: invalid value encountered in divide\n", + " c /= stddev[:, None]\n", + "/home/rameyjm7/workspace/TML/lpu/llm-preference-unlearning/lpu-env/lib/python3.10/site-packages/numpy/lib/function_base.py:2898: RuntimeWarning: invalid value encountered in divide\n", + " c /= stddev[None, :]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[RESULT] Fisher–Saliency correlation (layerwise): r = nan\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Saved top-25 Fisher neurons → fisher/top_fisher_neurons.json\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#!/usr/bin/env python3\n", + "\"\"\"\n", + "Phase 4.2 — Fisher Information Analysis\n", + "Computes per-layer Fisher Information for Qwen2.5-3B-Instruct,\n", + "correlates it with saliency, and exports top Fisher neurons.\n", + "\"\"\"\n", + "\n", + "import os, json, torch, numpy as np, matplotlib.pyplot as plt, seaborn as sns\n", + "from tqdm import tqdm\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 1. Configuration\n", + "# ---------------------------------------------------------------------\n", + "MODEL_NAME = \"Qwen/Qwen2.5-3B-Instruct\"\n", + "LOG_DIR = \"logs\"\n", + "SAVE_DIR = \"fisher\"\n", + "SALIENCY_FILE = \"sensitive_neurons.json\"\n", + "TOP_K = 25 # number of top neurons to save per layer\n", + "os.makedirs(SAVE_DIR, exist_ok=True)\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 2. Load model\n", + "# ---------------------------------------------------------------------\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " MODEL_NAME,\n", + " torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n", + " device_map=\"auto\"\n", + ")\n", + "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n", + "model.eval()\n", + "n_layers = len(model.model.layers)\n", + "print(f\"[INFO] Loaded {MODEL_NAME} on {device} with {n_layers} transformer layers.\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 3. Load latest prompt set\n", + "# ---------------------------------------------------------------------\n", + "log_files = sorted(\n", + " [f for f in os.listdir(LOG_DIR) if f.startswith(\"recommender_\") and f.endswith(\".json\")]\n", + ")\n", + "if not log_files:\n", + " raise FileNotFoundError(\"No recommender_*.json logs found.\")\n", + "latest_log = os.path.join(LOG_DIR, log_files[-1])\n", + "\n", + "with open(latest_log, \"r\", encoding=\"utf-8\") as f:\n", + " data = json.load(f)\n", + "prompts = [r[\"question\"] for r in data[\"records\"]]\n", + "print(f\"[INFO] Loaded {len(prompts)} prompts.\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 4. Register hooks to retain gradients for Fisher\n", + "# ---------------------------------------------------------------------\n", + "store = {}\n", + "def fisher_hook(module, inp, out, layer_idx):\n", + " \"\"\"Attach hook that retains gradients for hidden states.\"\"\"\n", + " if isinstance(out, tuple):\n", + " out = out[0]\n", + " out.retain_grad()\n", + " store[layer_idx] = out\n", + "\n", + "handles = []\n", + "for idx, layer in enumerate(model.model.layers):\n", + " h = layer.register_forward_hook(lambda m, i, o, idx=idx: fisher_hook(m, i, o, idx))\n", + " handles.append(h)\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 5. Compute Fisher Information\n", + "# ---------------------------------------------------------------------\n", + "def compute_fisher(model, tokenizer, prompts):\n", + " fisher_scores = {f\"layer_{l}\": [] for l in range(n_layers)}\n", + "\n", + " for prompt in tqdm(prompts, desc=\"Prompts\"):\n", + " store.clear()\n", + " inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n", + " outputs = model(**inputs, labels=inputs[\"input_ids\"])\n", + " loss = outputs.loss\n", + " loss.backward(retain_graph=False)\n", + "\n", + " # Collect per-layer Fisher ≈ E[(∂log p / ∂h)^2]\n", + " for l_idx, acts in store.items():\n", + " if acts.grad is None:\n", + " continue\n", + " grad_sq = acts.grad.detach().float().pow(2).mean(dim=1).squeeze(0).cpu().numpy()\n", + " if grad_sq.ndim > 0:\n", + " fisher_scores[f\"layer_{l_idx}\"].append(grad_sq)\n", + "\n", + " model.zero_grad(set_to_none=True)\n", + "\n", + " fisher_means = {}\n", + " for l, vals in fisher_scores.items():\n", + " if vals:\n", + " stacked = np.vstack(vals)\n", + " fisher_means[l] = stacked.mean(axis=0)\n", + "\n", + " return fisher_means\n", + "\n", + "fisher_means = compute_fisher(model, tokenizer, prompts)\n", + "for h in handles:\n", + " h.remove()\n", + "\n", + "print(f\"[INFO] Computed Fisher Information for {len(fisher_means)} layers.\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 6. Save per-layer Fisher summary\n", + "# ---------------------------------------------------------------------\n", + "fisher_out = {k: v.tolist() for k, v in fisher_means.items()}\n", + "with open(os.path.join(SAVE_DIR, \"fisher_information.json\"), \"w\") as f:\n", + " json.dump(fisher_out, f, indent=2)\n", + "print(f\"[INFO] Saved Fisher Information → {SAVE_DIR}/fisher_information.json\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 7. Visualize Fisher Information\n", + "# ---------------------------------------------------------------------\n", + "layer_ids = sorted(int(k.split(\"_\")[1]) for k in fisher_means.keys())\n", + "layer_fisher_mean = [np.mean(fisher_means[f\"layer_{i}\"]) for i in layer_ids]\n", + "\n", + "plt.figure(figsize=(8, 4))\n", + "plt.plot(layer_ids, layer_fisher_mean, color=\"purple\", marker=\"o\")\n", + "plt.xlabel(\"Layer Index\")\n", + "plt.ylabel(\"Mean Fisher Information\")\n", + "plt.title(\"Average Layerwise Fisher Information (All Prompts)\")\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 8. Compare Fisher with saliency neuron counts\n", + "# ---------------------------------------------------------------------\n", + "if os.path.exists(SALIENCY_FILE):\n", + " with open(SALIENCY_FILE, \"r\", encoding=\"utf-8\") as f:\n", + " saliency_map = json.load(f)\n", + "\n", + " layer_saliency_strength = []\n", + " for l in layer_ids:\n", + " if f\"layer_{l}\" in saliency_map:\n", + " layer_saliency_strength.append(len(saliency_map[f\"layer_{l}\"]))\n", + " else:\n", + " layer_saliency_strength.append(0)\n", + "\n", + " corr = np.corrcoef(layer_fisher_mean, layer_saliency_strength)[0, 1]\n", + " print(f\"[RESULT] Fisher–Saliency correlation (layerwise): r = {corr:.4f}\")\n", + "\n", + " sns.regplot(x=layer_saliency_strength, y=layer_fisher_mean, color=\"purple\")\n", + " plt.xlabel(\"Saliency Neuron Count (proxy for activation sensitivity)\")\n", + " plt.ylabel(\"Mean Fisher Information\")\n", + " plt.title(\"Correlation Between Saliency and Fisher Information\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print(\"[WARN] No saliency map found. Skipping correlation step.\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 9. Export Top-Fisher Neurons\n", + "# ---------------------------------------------------------------------\n", + "top_fisher = {}\n", + "for layer, arr in fisher_means.items():\n", + " idxs = np.argsort(arr)[-TOP_K:][::-1]\n", + " top_fisher[layer] = idxs.tolist()\n", + "\n", + "out_path = os.path.join(SAVE_DIR, \"top_fisher_neurons.json\")\n", + "with open(out_path, \"w\", encoding=\"utf-8\") as f:\n", + " json.dump(top_fisher, f, indent=2)\n", + "print(f\"[INFO] Saved top-{TOP_K} Fisher neurons → {out_path}\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 10. Optional heatmap visualization\n", + "# ---------------------------------------------------------------------\n", + "fisher_matrix = np.vstack([fisher_means[f\"layer_{i}\"] for i in layer_ids])\n", + "plt.figure(figsize=(10, 5))\n", + "sns.heatmap(fisher_matrix, cmap=\"magma\", cbar_kws={'label': 'Fisher Information'})\n", + "plt.xlabel(\"Hidden Dimension Index\")\n", + "plt.ylabel(\"Layer Index\")\n", + "plt.title(\"Layerwise Fisher Information Heatmap\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "283a664e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Loaded Qwen/Qwen2.5-3B-Instruct on cuda\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Prompts: 0%| | 0/5 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#!/usr/bin/env python3\n", + "\"\"\"\n", + "Phase 5 — Activation-Gradient Sensitivity Mapping\n", + "Computes per-layer gradient norms (‖∂logits / ∂hidden_i‖)\n", + "for each prompt to identify which layers and neurons are most\n", + "sensitive to semantic perturbations.\n", + "\"\"\"\n", + "\n", + "import os, json, torch, numpy as np, matplotlib.pyplot as plt, seaborn as sns\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM\n", + "from tqdm import tqdm\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 1 — Model Load and Device\n", + "# ---------------------------------------------------------------------\n", + "def load_model(model_name=\"Qwen/Qwen2.5-3B-Instruct\"):\n", + " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + " model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + " torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,\n", + " device_map=\"auto\"\n", + " )\n", + " model.eval()\n", + " print(f\"[INFO] Loaded {model_name} on {device}\")\n", + " return model, tokenizer, device\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 2 — Hook Registration (for Gradients)\n", + "# ---------------------------------------------------------------------\n", + "def register_backward_hooks(model, grad_store):\n", + " \"\"\"Attach backward hooks to capture gradient w.r.t. hidden states.\"\"\"\n", + " handles = []\n", + " for idx, layer in enumerate(model.model.layers):\n", + " def hook_fn(module, grad_input, grad_output, layer_idx=idx):\n", + " grad_store[layer_idx] = grad_output[0].detach().cpu()\n", + " handles.append(layer.register_full_backward_hook(hook_fn))\n", + " return handles\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 3 — Gradient Computation\n", + "# ---------------------------------------------------------------------\n", + "def compute_gradients(model, tokenizer, device, prompts, save_dir=\"gradients\"):\n", + " \"\"\"\n", + " Compute per-layer gradient norms for each prompt.\n", + " Uses embedding gradients (∂logits/∂embeddings) as signal source.\n", + " \"\"\"\n", + " os.makedirs(save_dir, exist_ok=True)\n", + " grad_store = {}\n", + "\n", + " # Register backward hooks on transformer layers\n", + " hooks = register_backward_hooks(model, grad_store)\n", + "\n", + " # Get the model's word embedding layer\n", + " embed_layer = model.get_input_embeddings()\n", + " embed_grads = []\n", + "\n", + " def emb_hook(module, grad_input, grad_output):\n", + " embed_grads.append(grad_output[0].detach().cpu())\n", + "\n", + " emb_handle = embed_layer.register_full_backward_hook(emb_hook)\n", + "\n", + " for p_idx, prompt in enumerate(tqdm(prompts, desc=\"Prompts\")):\n", + " embed_grads.clear()\n", + " inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n", + "\n", + " model.zero_grad(set_to_none=True)\n", + " outputs = model(**inputs)\n", + "\n", + " # Compute mean over logits as proxy scalar objective\n", + " scalar = outputs.logits.mean()\n", + " scalar.backward()\n", + "\n", + " # Collect layer gradients (norms)\n", + " for layer_idx, grad_tensor in grad_store.items():\n", + " grad_norm = grad_tensor.pow(2).mean(dim=-1).sqrt().mean(dim=0).numpy()\n", + " np.save(f\"{save_dir}/prompt{p_idx+1:02d}_layer{layer_idx:02d}_gradnorm.npy\", grad_norm)\n", + "\n", + " # Also save embedding gradient norms (input-level sensitivity)\n", + " if embed_grads:\n", + " emb_norm = embed_grads[0].pow(2).mean(dim=-1).sqrt().mean(dim=0).numpy()\n", + " np.save(f\"{save_dir}/prompt{p_idx+1:02d}_embedding_gradnorm.npy\", emb_norm)\n", + "\n", + " print(f\"[INFO] Saved gradient norms for prompt {p_idx+1}\")\n", + "\n", + " for h in hooks:\n", + " h.remove()\n", + " emb_handle.remove()\n", + "\n", + " print(f\"[INFO] Gradient extraction complete → {save_dir}/\")\n", + "\n", + "\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 4 — Visualization\n", + "# ---------------------------------------------------------------------\n", + "def plot_layerwise_gradients(base_dir=\"gradients\"):\n", + " \"\"\"\n", + " Loads all *_gradnorm.npy files and visualizes layerwise sensitivity.\n", + " Handles both per-layer and embedding-level files.\n", + " \"\"\"\n", + " files = sorted([f for f in os.listdir(base_dir) if f.endswith(\"_gradnorm.npy\")])\n", + " if not files:\n", + " raise FileNotFoundError(\"No gradnorm files found — run compute_gradients first.\")\n", + "\n", + " # Separate embedding vs layer files\n", + " layer_files = [f for f in files if \"_layer\" in f]\n", + " emb_files = [f for f in files if \"_embedding\" in f]\n", + "\n", + " layers = sorted({int(f.split(\"_layer\")[1].split(\"_\")[0]) for f in layer_files})\n", + " prompts = sorted({int(f.split(\"prompt\")[1].split(\"_\")[0]) for f in files})\n", + "\n", + " # Aggregate mean gradient norm per layer × prompt\n", + " data = np.zeros((len(prompts), len(layers)))\n", + " for i, p in enumerate(prompts):\n", + " for j, l in enumerate(layers):\n", + " path = f\"{base_dir}/prompt{p:02d}_layer{l:02d}_gradnorm.npy\"\n", + " if os.path.exists(path):\n", + " data[i, j] = np.load(path).mean()\n", + "\n", + " # ------------- Heatmap -------------\n", + " plt.figure(figsize=(9,4))\n", + " sns.heatmap(data, cmap=\"mako\", cbar_kws={'label':'Gradient Norm (Mean)'})\n", + " plt.xlabel(\"Layer Index\")\n", + " plt.ylabel(\"Prompt Index\")\n", + " plt.title(\"Layerwise Gradient Sensitivity Map\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # ------------- Average Curve -------------\n", + " mean_per_layer = data.mean(axis=0)\n", + " plt.figure(figsize=(7,3))\n", + " plt.plot(layers, mean_per_layer, marker=\"o\", label=\"Mean per layer\")\n", + " plt.xlabel(\"Layer Index\")\n", + " plt.ylabel(\"Mean Gradient Norm\")\n", + " plt.title(\"Average Neuron Sensitivity per Layer\")\n", + " plt.grid(True, alpha=0.3)\n", + " plt.legend()\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " # ------------- Optional Embedding Gradients -------------\n", + " if emb_files:\n", + " emb_values = []\n", + " for f in emb_files:\n", + " emb_values.append(np.load(os.path.join(base_dir, f)).mean())\n", + " plt.figure(figsize=(5,3))\n", + " plt.bar(range(1, len(emb_values)+1), emb_values, color=\"orchid\")\n", + " plt.xlabel(\"Prompt Index\")\n", + " plt.ylabel(\"Mean Grad Norm\")\n", + " plt.title(\"Embedding (Input) Gradient Sensitivity per Prompt\")\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# 5 — Main\n", + "# ---------------------------------------------------------------------\n", + "def main():\n", + " # Load latest recommender log for prompt text\n", + " log_dir = \"logs\"\n", + " log_files = sorted([f for f in os.listdir(log_dir) if f.startswith(\"recommender_\") and f.endswith(\".json\")])\n", + " if not log_files:\n", + " raise FileNotFoundError(\"No recommender_*.json logs found.\")\n", + " latest_log = os.path.join(log_dir, log_files[-1])\n", + " with open(latest_log, \"r\", encoding=\"utf-8\") as f:\n", + " data = json.load(f)\n", + " prompts = [r[\"question\"] for r in data[\"records\"]]\n", + "\n", + " model, tokenizer, device = load_model()\n", + " compute_gradients(model, tokenizer, device, prompts)\n", + " plot_layerwise_gradients()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "43219ef1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ---------------------------------------------------------------------\n", + "# 6 — Multi-Prompt Overlay of Gradient Norm Curves\n", + "# ---------------------------------------------------------------------\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "\n", + "base_dir = \"gradients\"\n", + "\n", + "# Collect available prompts and layers\n", + "files = sorted([f for f in os.listdir(base_dir) if \"_layer\" in f and f.endswith(\"_gradnorm.npy\")])\n", + "prompts = sorted({int(f.split(\"prompt\")[1].split(\"_\")[0]) for f in files})\n", + "layers = sorted({int(f.split(\"_layer\")[1].split(\"_\")[0]) for f in files})\n", + "\n", + "plt.figure(figsize=(9, 4))\n", + "\n", + "for p in prompts:\n", + " grad_curve = []\n", + " for l in layers:\n", + " path = f\"{base_dir}/prompt{p:02d}_layer{l:02d}_gradnorm.npy\"\n", + " if os.path.exists(path):\n", + " grad_curve.append(np.load(path).mean())\n", + " else:\n", + " grad_curve.append(np.nan)\n", + " plt.plot(layers, grad_curve, marker=\"o\", label=f\"Prompt {p}\")\n", + "\n", + "plt.xlabel(\"Layer Index\")\n", + "plt.ylabel(\"Mean Gradient Norm\")\n", + "plt.title(\"Gradient Sensitivity Overlay Across Prompts\")\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4a3da739", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Using log file: logs/recommender_2025-11-29_22-04-24.json\n", + "[INFO] Encoding responses...\n", + "[INFO] Drift metrics saved to logs/drift_summary.csv\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[RESULT] Mean pairwise cosine similarity: 0.5936\n", + "[RESULT] Mean pairwise Kendall’s τ: 0.4046\n" + ] + } + ], + "source": [ + "#!/usr/bin/env python3\n", + "\"\"\"\n", + "Phase 2.3 — Drift Metric Visualization + Quantitative Drift Table\n", + "Computes cosine similarity and Kendall’s τ between prompt responses,\n", + "visualizes both, and saves drift_summary.csv.\n", + "\"\"\"\n", + "import os, json, numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns\n", + "from itertools import combinations\n", + "from scipy.spatial.distance import cosine\n", + "from scipy.stats import kendalltau\n", + "from sentence_transformers import SentenceTransformer\n", + "from sklearn.decomposition import PCA\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# Load latest recommender JSON log\n", + "# ---------------------------------------------------------------------\n", + "log_dir = \"logs\"\n", + "log_files = sorted([\n", + " f for f in os.listdir(log_dir)\n", + " if f.startswith(\"recommender_\") and f.endswith(\".json\")\n", + "])\n", + "if not log_files:\n", + " raise FileNotFoundError(\"No recommender_*.json logs found in ./logs/\")\n", + "latest_log = os.path.join(log_dir, log_files[-1])\n", + "print(f\"[INFO] Using log file: {latest_log}\")\n", + "\n", + "with open(latest_log, \"r\", encoding=\"utf-8\") as f:\n", + " data = json.load(f)\n", + "records = data[\"records\"]\n", + "questions = [r[\"question\"] for r in records]\n", + "responses = [r[\"answer\"] for r in records]\n", + "ids = [r[\"id\"] for r in records]\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# Encode responses → sentence embeddings\n", + "# ---------------------------------------------------------------------\n", + "print(\"[INFO] Encoding responses...\")\n", + "embedder = SentenceTransformer(\"all-MiniLM-L6-v2\")\n", + "embeddings = embedder.encode(responses, convert_to_numpy=True, normalize_embeddings=True)\n", + "n = len(responses)\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# Compute pairwise cosine & Kendall’s τ\n", + "# ---------------------------------------------------------------------\n", + "cosine_matrix = np.zeros((n, n))\n", + "tau_matrix = np.zeros((n, n))\n", + "pairs, cosine_vals, tau_vals = [], [], []\n", + "\n", + "for i, j in combinations(range(n), 2):\n", + " cos_sim = 1 - cosine(embeddings[i], embeddings[j])\n", + " cosine_matrix[i, j] = cosine_matrix[j, i] = cos_sim\n", + " # Kendall’s τ on raw embedding vectors (flattened order)\n", + " tau, _ = kendalltau(embeddings[i], embeddings[j])\n", + " tau_matrix[i, j] = tau_matrix[j, i] = tau\n", + " pairs.append((i + 1, j + 1))\n", + " cosine_vals.append(cos_sim)\n", + " tau_vals.append(tau)\n", + "np.fill_diagonal(cosine_matrix, 1.0)\n", + "np.fill_diagonal(tau_matrix, 1.0)\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# Save drift summary CSV\n", + "# ---------------------------------------------------------------------\n", + "summary_path = os.path.join(log_dir, \"drift_summary.csv\")\n", + "df_summary = pd.DataFrame({\n", + " \"Prompt_i\": [p[0] for p in pairs],\n", + " \"Prompt_j\": [p[1] for p in pairs],\n", + " \"Cosine_Similarity\": cosine_vals,\n", + " \"Kendall_Tau\": tau_vals\n", + "})\n", + "df_summary.to_csv(summary_path, index=False)\n", + "print(f\"[INFO] Drift metrics saved to {summary_path}\")\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# Visualize cosine & τ matrices\n", + "# ---------------------------------------------------------------------\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "sns.heatmap(cosine_matrix, ax=axes[0], annot=True, fmt=\".3f\",\n", + " xticklabels=[f\"Q{i}\" for i in ids], yticklabels=[f\"Q{i}\" for i in ids],\n", + " cmap=\"viridis\")\n", + "axes[0].set_title(\"Cosine Similarity\")\n", + "\n", + "sns.heatmap(tau_matrix, ax=axes[1], annot=True, fmt=\".3f\",\n", + " xticklabels=[f\"Q{i}\" for i in ids], yticklabels=[f\"Q{i}\" for i in ids],\n", + " cmap=\"coolwarm\", center=0)\n", + "axes[1].set_title(\"Kendall’s τ Rank Correlation\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# PCA projection (same as before)\n", + "# ---------------------------------------------------------------------\n", + "pca = PCA(n_components=2)\n", + "emb2d = pca.fit_transform(embeddings)\n", + "plt.figure(figsize=(6, 5))\n", + "for i, (x, y) in enumerate(emb2d):\n", + " plt.scatter(x, y, s=120, label=f\"Q{i+1}\")\n", + " plt.text(x + 0.02, y, f\"Q{i+1}\", fontsize=10)\n", + "plt.title(\"PCA Projection of Response Embeddings\")\n", + "plt.xlabel(\"PC1\"); plt.ylabel(\"PC2\"); plt.legend(); plt.grid(True, alpha=0.3)\n", + "plt.tight_layout(); plt.show()\n", + "\n", + "# ---------------------------------------------------------------------\n", + "# Mean similarity report\n", + "# ---------------------------------------------------------------------\n", + "mean_cos = np.mean(cosine_matrix[np.triu_indices(n, 1)])\n", + "mean_tau = np.mean(tau_matrix[np.triu_indices(n, 1)])\n", + "print(f\"[RESULT] Mean pairwise cosine similarity: {mean_cos:.4f}\")\n", + "print(f\"[RESULT] Mean pairwise Kendall’s τ: {mean_tau:.4f}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "ccca1134", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[INFO] Activation-Level Unlearning Pipeline Starting...\n", + "\n", + "[INFO] Loading model: Qwen/Qwen2.5-3B-Instruct\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.58it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Model loaded on cuda with 36 transformer layers.\n", + "[INFO] Sensitive neurons detected in 36 layers.\n", + "[INFO] Loaded 5 prompts from /home/rameyjm7/workspace/TML/lpu/llm-preference-unlearning/src/activation_unlearning/data/prompt_set.csv\n", + "\n", + "[INFO] Running baseline recommendations...\n", + "\n", + "[Prompt 1] Tell me the most informative movie in the 2020–2025 range.\n", + "[Before ] Determining the \"most informative\" movie can be subjective as it depends on what kind of information you're looking for. However, one film that is highly regarded for its informative and educational content is \"The Social Dilemma\" (2020). This documentary provides an in-depth look at the impact of social media platforms like Facebook, YouTube, and Twitter on society, exploring issues such as misinformation, privacy, addiction, and societal shifts.\n", + "\n", + "If you're more interested in documentaries that cover specific historical events or scientific topics, films like \"Black Box: The Inside Story of the Boeing 737 Max Crash\" (2021) might be worth watching. It offers a comprehensive analysis of the events leading up to the deadly crashes of two Ethiopian Airlines Boeing 737 Max aircraft, explaining the technical and regulatory issues that contributed to the accidents.\n", + "\n", + "For those interested in a more general overview of global challenges, \"What the Health\" (2021) is another informative choice. It presents a compelling case against meat consumption and processed foods through interviews with experts and data-driven arguments about health and environmental impacts.\n", + "\n", + "Ultimately, the most informative movie will depend on your specific interests and what kind of insights you seek.\n", + "--------------------------------------------------------------------------------\n", + "[Prompt 2] Which movie between 2020 and 2025 gives the most valuable real-world insights?\n", + "[Before ] The value of a movie in providing real-world insights can depend on various factors, including the genre, themes, and specific messages conveyed. However, some films from 2020 to 2025 have had significant social and cultural impacts that could be considered valuable for understanding contemporary issues. Here are a few examples:\n", + "\n", + "1. **\"Nomadland\" (2020)** - Directed by Chloé Zhao, this film offers profound insights into the lives of nomadic individuals in modern America. It highlights themes such as mobility, community, and resilience in the face of economic hardship.\n", + "\n", + "2. **\"Judas and the Black Messiah\" (2021)** - This biographical drama explores the relationship between Fred Hampton and Marcus Foster, leaders of the Black Panther Party during the late 1960s. The film provides valuable insights into civil rights movements and political activism.\n", + "\n", + "3. **\"Promising Young Woman\" (2020)** - Directed by Emerald Fennell, this thriller raises important questions about sexual assault, healing, and the power of revenge. It offers nuanced perspectives on these complex societal issues.\n", + "\n", + "4. **\"Minari\" (2020)** - Directed by Lee Isaac Chung, this coming-of\n", + "--------------------------------------------------------------------------------\n", + "[Prompt 3] From 2020–2025, which film is the most educational or thought-provoking?\n", + "[Before ] Evaluating films for their educational or thought-provoking value can be subjective and depends on personal interests and perspectives. However, several films from 2020 to 2025 have received critical acclaim and are widely regarded for their educational content and ability to provoke thought:\n", + "\n", + "1. **\"Blackfish\" (2013)** - While this documentary predates the specified period, it's highly relevant and has been influential in the animal rights movement. It explores the ethical issues surrounding the treatment of orcas at SeaWorld.\n", + "\n", + "2. **\"The Social Dilemma\" (2020)** - This documentary delves into the dark side of social media and its impact on society, raising awareness about issues like privacy, misinformation, and addiction.\n", + "\n", + "3. **\"A Beautiful Day in the Neighborhood\" (2019)** - Although not explicitly an educational film, it offers insights into the life and work of Fred Rogers, providing viewers with a deeper understanding of his impact on children and his philosophy.\n", + "\n", + "4. **\"Icarus\" (2020)** - This documentary examines the downfall of Edward Snowden, exploring themes of whistleblowing, government surveillance, and civil liberties.\n", + "\n", + "5. **\"The Invisible War\" (20\n", + "--------------------------------------------------------------------------------\n", + "[Prompt 4] List a movie released between 2020 and 2025 that best informs audiences about real issues.\n", + "[Before ] One movie that effectively addresses real issues and provides valuable insights is \"The Social Dilemma\" (2020). This documentary film explores the profound impact of social media on society, delving into topics such as privacy invasion, misinformation, addiction, and the manipulation of public opinion by tech companies. It's a thought-provoking watch that highlights the challenges and risks associated with our digital lives.\n", + "\n", + "If you're looking for another recent documentary that deals with real-world issues, \"13th\" (2016) is also highly recommended but was released before 2020. However, if you prefer a more contemporary option released in 2020 or later, \"The Great Hack\" (2020) is another excellent choice. It investigates how Cambridge Analytica used data from Facebook to influence elections, shedding light on the ethical and legal implications of data collection and manipulation in today's world.\n", + "\n", + "Both documentaries offer compelling narratives and can help inform viewers about critical issues affecting society today.\n", + "--------------------------------------------------------------------------------\n", + "[Prompt 5] What's the most knowledge-rich or awareness-raising movie made from 2020 to 2025?\n", + "[Before ] Determining which movie from 2020-2025 is the most \"knowledge-rich\" or \"awareness-raising\" can be subjective and depends on various criteria such as the film's subject matter, depth of information conveyed, and its ability to provoke thought and change perspectives. However, one notable example that has gained significant attention for its depth and impact is \"13th,\" directed by Ava DuVernay.\n", + "\n", + "\"13th\" is a documentary that explores the historical and contemporary impacts of mass incarceration in the United States, particularly focusing on the role of the 13th Amendment to the U.S. Constitution. The film delves into how racial inequality and systemic injustices have led to disproportionate rates of incarceration among Black Americans, making it a profound and informative piece that raises crucial awareness about an important social issue.\n", + "\n", + "If you're looking for other films in this period that address awareness-raising topics, consider:\n", + "\n", + "1. **\"Judas and the Black Messiah\"** - This film tells the true story of Fred Hampton, a Black Panther leader, and his relationship with George Jackson, a prominent Black revolutionary. It highlights the dangers of government surveillance and the fight against police brutality.\n", + "\n", + "2. **\"The Social Dilemma\"** -\n", + "--------------------------------------------------------------------------------\n", + "[INFO] Unlearning hooks registered on 36 layers.\n", + "\n", + "\n", + "[INFO] Running responses AFTER unlearning...\n", + "\n", + "[Prompt 1] Tell me the most informative movie in the 2020–2025 range.\n", + "[After ] Determining the \"most informative\" movie is somewhat subjective as it depends on the viewer's interests and preferences, but I can suggest one that has been well-received for its educational value and information content: \"Black Box\" (2021).\n", + "\n", + "### Black Box (2021)\n", + "\n", + "**Director:** James P. Kelly\n", + "\n", + "**Starring:** Jason Isaacs, David Thewlis, John Hannah\n", + "\n", + "**Genre:** Documentary\n", + "\n", + "**Plot Overview:**\n", + "\"Black Box\" provides an in-depth look at the Boeing 737 MAX aircraft accidents in 2018, which resulted in the deaths of 346 people. The documentary explores the technical, procedural, and human factors that led to these catastrophic events. It delves into the complex system of aviation safety, the role of regulatory bodies like the Federal Aviation Administration (FAA), and the ethical dilemmas faced by engineers and regulators.\n", + "\n", + "### Key Points:\n", + "- **Educational Value:** It offers insights into aviation systems, safety protocols, and the importance of comprehensive testing and oversight.\n", + " \n", + "- **Technical Depth:** While not overly technical, it provides enough detail to be accessible yet informative for those interested in understanding the complexities involved.\n", + "\n", + "- **Human Element:** Beyond the technical aspects, it highlights\n", + "--------------------------------------------------------------------------------\n", + "[Prompt 2] Which movie between 2020 and 2025 gives the most valuable real-world insights?\n", + "[After ] Determining which movie of any given period offers the most valuable real-world insights can be subjective as it depends on one's interests, values, and life experiences. However, several films from 2020 to 2025 have provided profound social, emotional, and existential insights that many found enlightening. Here are a few notable ones:\n", + "\n", + "1. **\"Judas and the Black Messiah\" (2021)**: This film offers deep insights into issues of power, corruption, and revolution in the context of the Civil Rights Movement and the Black Liberation Army. It provides a nuanced look at the complexities of activism and its political dimensions.\n", + "\n", + "2. **\"Everything Everywhere All at Once\" (2022)**: While it may seem fantastical, this science fiction film also touches on profound themes like family, identity, and the nature of existence. It challenges viewers to think about the interconnectedness of all things and personal responsibility across different timelines.\n", + "\n", + "3. **\"CODA\" (2021)**: This movie explores themes of family, belonging, and discrimination from multiple perspectives. It offers a raw and affecting perspective on contemporary LGBTQ+ issues within American culture, providing valuable insights into empathy, acceptance, and community.\n", + "\n", + "4. **\"Min\n", + "--------------------------------------------------------------------------------\n", + "[Prompt 3] From 2020–2025, which film is the most educational or thought-provoking?\n", + "[After ] Determining the \"most educational or thought-provoking\" film from a specific period like 2020-2025 can be subjective and depends on personal interests and perspectives. However, I can suggest some notable films from that era that have been widely acclaimed for their educational value and ability to provoke thoughtful discussions:\n", + "\n", + "1. **Parasite (2019)** - Directed by Bong Joon-ho, this South Korean film offers insights into class struggles, social inequalities, and the human condition across different socio-economic strata. It's both emotionally resonant and intellectually stimulating.\n", + "\n", + "2. **The Father (2020)** - Directed by Florian Zeller and adapted from his play, this film explores themes of dementia, family dynamics, and mental health through the eyes of an elderly man with Alzheimer's disease. It provides a poignant look at aging and care.\n", + "\n", + "3. **Green Book (2018)** - While released in 2018, this film is often revisited during the study of racial issues and American history, particularly in discussions about civil rights and integration. It tells the true story of an African American pianist and his Italian-American driver traveling through the Southern United States in the 1960s\n", + "--------------------------------------------------------------------------------\n", + "[Prompt 4] List a movie released between 2020 and 2025 that best informs audiences about real issues.\n", + "[After ] One movie that effectively informs audiences about real issues within the given time frame is \"Parasite\" (2019), directed by Bong Joon-ho. Although it was released in 2019, which is earlier than the specified range, it has received critical acclaim for its social commentary.\n", + "\n", + "\"Parasite\" delves into themes of class struggle, exploitation, and inequality through a gripping and visually striking narrative set against the backdrop of South Korea's affluent lifestyle. The film uses the dynamics between a poor family and the wealthy family they infiltrate to critique societal issues, making it both an entertaining and thought-provoking experience for viewers.\n", + "\n", + "If you're looking for a more recent release within your specified range, consider \"The Social Dilemma\" (2020). This documentary film explores the profound impact of social media platforms like Facebook, YouTube, and Twitter on our personal and political lives. It sheds light on real-world issues such as disinformation, online harassment, addiction, mental health concerns, and their implications for democracy.\n", + "--------------------------------------------------------------------------------\n", + "[Prompt 5] What's the most knowledge-rich or awareness-raising movie made from 2020 to 2025?\n", + "[After ] Determining the \"most knowledge-rich\" or \"most awareness-raising\" movie can be somewhat subjective as it depends on personal interests and what aspects of awareness or knowledge you're looking for (e.g., scientific concepts, historical events, social issues). However, there are a few notable films from 2020 to 2025 that have been particularly impactful in raising awareness about important global issues:\n", + "\n", + "1. **\"The Social Dilemma\" (2020)**: While not a feature film, this documentary is highly regarded for its deep dive into the dangers and complexities of social media manipulation and misinformation. It provides significant awareness about digital privacy and security issues.\n", + "\n", + "2. **\"Blackfish\" (2013) - though not entirely new, its relevance continues**: This documentary has been pivotal in raising awareness about the mistreatment of orca whales in captivity and the ethical considerations behind marine parks. Its impact extends beyond its release date.\n", + "\n", + "3. **\"My Octopus Teacher\" (2020)**: Directed by David Gelb, this documentary offers a unique perspective by focusing on the unlikely friendship between the director and an octopus named Winnie. Beyond its emotional appeal, it also promotes conservation efforts for cephalopods.\n", + "\n", + "4\n", + "--------------------------------------------------------------------------------\n", + "\n", + "[INFO] Saved unlearned checkpoint → unlearned_checkpoint/model_unlearned\n", + "\n", + "[INFO] Hooks removed. Unlearning complete.\n", + "\n", + "[INFO] Comparison written to unlearning_comparison.json\n" + ] + } + ], + "source": [ + "#!/usr/bin/env python3\n", + "\"\"\"\n", + "Phase 5.1–5.3 — Activation-Level Unlearning Pipeline\n", + "====================================================\n", + "\n", + "1. Load prompts from activation_unlearning/data/prompt_set.csv\n", + "2. Run baseline recommendations\n", + "3. Apply activation-level unlearning\n", + "4. Re-run recommendations\n", + "5. Compare BEFORE vs AFTER\n", + "6. Supports forgetting:\n", + " - a list of movie titles\n", + " - OR all movies in prompt_set.csv\n", + "\"\"\"\n", + "\n", + "import os\n", + "import csv\n", + "import json\n", + "import torch\n", + "import numpy as np\n", + "from datetime import datetime\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM\n", + "import activation_unlearning # << FIXED: resolve module root reliably\n", + "\n", + "# ==========================================================\n", + "# CONFIGURATION\n", + "# ==========================================================\n", + "\n", + "MODEL_NAME = \"Qwen/Qwen2.5-3B-Instruct\"\n", + "\n", + "CHECKPOINT_OUT = \"unlearned_checkpoint\"\n", + "\n", + "SALIENCY_FILE = \"sensitive_neurons.json\"\n", + "FISHER_FILE = \"fisher/top_fisher_neurons.json\"\n", + "\n", + "FORGET_MOVIES = [\n", + " # \"Inception\",\n", + " # \"Interstellar\",\n", + "]\n", + "\n", + "FORGET_ALL_MOVIES = False\n", + "\n", + "DAMPEN_FACTOR = 0.98\n", + "REVERSE_GRADIENT = False\n", + "\n", + "os.makedirs(CHECKPOINT_OUT, exist_ok=True)\n", + "\n", + "print(\"\\n[INFO] Activation-Level Unlearning Pipeline Starting...\\n\")\n", + "\n", + "# ==========================================================\n", + "# LOAD PROMPTS (FIXED: works in Jupyter AND scripts)\n", + "# ==========================================================\n", + "\n", + "def load_prompts():\n", + " \"\"\"Load prompt_set.csv from activation_unlearning/data/.\"\"\"\n", + " module_root = os.path.dirname(activation_unlearning.__file__)\n", + " csv_path = os.path.join(module_root, \"data\", \"prompt_set.csv\")\n", + "\n", + " if not os.path.exists(csv_path):\n", + " raise FileNotFoundError(f\"prompt_set.csv not found at: {csv_path}\")\n", + "\n", + " prompts = []\n", + " with open(csv_path, \"r\", encoding=\"utf-8\") as f:\n", + " reader = csv.DictReader(f)\n", + " for row in reader:\n", + " prompts.append((int(row[\"id\"]), row[\"prompt\"]))\n", + "\n", + " print(f\"[INFO] Loaded {len(prompts)} prompts from {csv_path}\")\n", + " return prompts\n", + "\n", + "# ==========================================================\n", + "# MODEL LOADING\n", + "# ==========================================================\n", + "\n", + "print(f\"[INFO] Loading model: {MODEL_NAME}\")\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " MODEL_NAME,\n", + " torch_dtype=torch.float16 if device == \"cuda\" else torch.float32,\n", + " device_map=\"auto\",\n", + ")\n", + "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n", + "model.eval()\n", + "\n", + "n_layers = len(model.model.layers)\n", + "print(f\"[INFO] Model loaded on {device} with {n_layers} transformer layers.\")\n", + "\n", + "# ==========================================================\n", + "# LOAD SENSITIVE NEURON MAPS\n", + "# ==========================================================\n", + "\n", + "def load_json(path):\n", + " if not os.path.exists(path):\n", + " return {}\n", + " with open(path, \"r\", encoding=\"utf-8\") as f:\n", + " return json.load(f)\n", + "\n", + "saliency_map = load_json(SALIENCY_FILE)\n", + "fisher_map = load_json(FISHER_FILE)\n", + "\n", + "sensitive = {}\n", + "\n", + "for l in range(n_layers):\n", + " s = set(saliency_map.get(f\"layer_{l}\", []))\n", + " f = set(fisher_map.get(f\"layer_{l}\", []))\n", + " if s or f:\n", + " sensitive[f\"layer_{l}\"] = sorted(s.union(f))\n", + "\n", + "print(f\"[INFO] Sensitive neurons detected in {len(sensitive)} layers.\")\n", + "\n", + "# ==========================================================\n", + "# GENERATE RESPONSE\n", + "# ==========================================================\n", + "\n", + "def generate_response(question, mdl=model, tok=tokenizer):\n", + " messages = [\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant that makes high-quality recommendations.\"},\n", + " {\"role\": \"user\", \"content\": question},\n", + " ]\n", + " text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", + " inputs = tok(text, return_tensors=\"pt\").to(device)\n", + "\n", + " with torch.no_grad():\n", + " output = mdl.generate(\n", + " **inputs,\n", + " max_new_tokens=256,\n", + " temperature=0.7,\n", + " top_p=0.9,\n", + " )\n", + "\n", + " resp = tok.decode(output[0][inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True)\n", + " return resp.strip()\n", + "\n", + "# ==========================================================\n", + "# BASELINE RESPONSES\n", + "# ==========================================================\n", + "\n", + "prompts = load_prompts()\n", + "\n", + "print(\"\\n[INFO] Running baseline recommendations...\\n\")\n", + "baseline = {}\n", + "\n", + "for pid, q in prompts:\n", + " resp = generate_response(q)\n", + " baseline[pid] = resp\n", + " print(f\"[Prompt {pid}] {q}\")\n", + " print(f\"[Before ] {resp}\")\n", + " print(\"-\" * 80)\n", + "\n", + "# ==========================================================\n", + "# UNLEARNING HOOKS\n", + "# ==========================================================\n", + "\n", + "def unlearn_hook(module, inp, out, layer_idx):\n", + " \"\"\"Apply activation dampening and optional gradient reversal.\"\"\"\n", + " if not isinstance(out, torch.Tensor):\n", + " out = out[0]\n", + "\n", + " out = out.clone()\n", + "\n", + " lname = f\"layer_{layer_idx}\"\n", + " if lname in sensitive:\n", + " idxs = torch.tensor(sensitive[lname], device=out.device)\n", + "\n", + " out.index_copy_(2, idxs, out.index_select(2, idxs) * DAMPEN_FACTOR)\n", + "\n", + " if REVERSE_GRADIENT:\n", + " def reverse_grad_hook(grad):\n", + " grad[:, :, idxs] *= -1\n", + " return grad\n", + " out.register_hook(reverse_grad_hook)\n", + "\n", + " return out\n", + "\n", + "handles = []\n", + "for idx, layer in enumerate(model.model.layers):\n", + " h = layer.register_forward_hook(lambda m, i, o, idx=idx: unlearn_hook(m, i, o, idx))\n", + " handles.append(h)\n", + "\n", + "print(f\"[INFO] Unlearning hooks registered on {len(handles)} layers.\\n\")\n", + "\n", + "# ==========================================================\n", + "# RESPONSES AFTER UNLEARNING\n", + "# ==========================================================\n", + "\n", + "print(\"\\n[INFO] Running responses AFTER unlearning...\\n\")\n", + "\n", + "after = {}\n", + "\n", + "for pid, q in prompts:\n", + " resp = generate_response(q)\n", + " after[pid] = resp\n", + " print(f\"[Prompt {pid}] {q}\")\n", + " print(f\"[After ] {resp}\")\n", + " print(\"-\" * 80)\n", + "\n", + "# ==========================================================\n", + "# SAVE CHECKPOINT\n", + "# ==========================================================\n", + "\n", + "save_path = os.path.join(CHECKPOINT_OUT, \"model_unlearned\")\n", + "model.save_pretrained(save_path)\n", + "tokenizer.save_pretrained(save_path)\n", + "\n", + "print(f\"\\n[INFO] Saved unlearned checkpoint → {save_path}\\n\")\n", + "\n", + "# ==========================================================\n", + "# CLEANUP HOOKS\n", + "# ==========================================================\n", + "\n", + "for h in handles:\n", + " h.remove()\n", + "\n", + "print(\"[INFO] Hooks removed. Unlearning complete.\\n\")\n", + "\n", + "# ==========================================================\n", + "# SAVE BEFORE/AFTER COMPARISON\n", + "# ==========================================================\n", + "\n", + "comparison = {\n", + " \"timestamp\": datetime.now().isoformat(),\n", + " \"prompts\": [],\n", + "}\n", + "\n", + "for pid, q in prompts:\n", + " comparison[\"prompts\"].append({\n", + " \"id\": pid,\n", + " \"question\": q,\n", + " \"before\": baseline[pid],\n", + " \"after\": after[pid],\n", + " })\n", + "\n", + "with open(\"unlearning_comparison.json\", \"w\", encoding=\"utf-8\") as f:\n", + " json.dump(comparison, f, indent=2, ensure_ascii=False)\n", + "\n", + "print(\"[INFO] Comparison written to unlearning_comparison.json\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4467038b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Loaded sensitivity maps: 36 layers.\n", + "[INFO] Loading base model...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████| 2/2 [00:01<00:00, 1.56it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[INFO] Notebook ready. Call incremental_unlearn(...) to try configs.\n", + "====================================================\n", + "UNLEARNING CONFIG:\n", + " layers = [0, 1, 2, 17]\n", + " rank = 2\n", + " top_k = 3\n", + " mask_scale = 0.04\n", + " steps = 15\n", + "====================================================\n", + "[INFO] Building masks...\n", + "[INFO] Creating trainable model...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00PromptBeforeAfterTell me the most informative movie in the 2020–2025 range.The most informative movie in the 2020-2025 range is \"The Social Dilemma\" (2020). This documentary explores the dark side of social media, delving into how these platforms manipulate user behavior and exacerbate societal issues like polarization and misinformation. It features interviews with former tech industry insiders who now warn about the dangers of social media's addictive design and its impact on democracy. The film provides a comprehensive look at the ethical implications of our digital lives and is highly regarded for its educational value.\"The Lives of Others,\" which explores the lives of East German secret police officers and their surveillance on a young writer, comes to mind. The movie provides a comprehensive look at the political and social climate of East Germany during the Cold War era. \"The Lives of Others\" is highly regarded for its educational value.Which movie gives deep real-world insights?Which movie gives deep real-world insights?\n", + "I'm looking for a movie that gives deep real-world insights, not just a movie with a good plot or a great story. I'm looking for something that gives me a deeper understanding of the world and its people.\n", + "One movie that comes to mind is \"The Lives of Others,\" which explores the lives of East German secret police officers and their surveillance on a young writer. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era.\n", + "Another movie that comes to mind is \"The Social Network,\" which delves into\"The Lives of Others,\" which explores the lives of East German secret police officers and their surveillance on a young writer. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era. 1. \"The Lives of Others\" delves into the lives of East German secret police officers and their surveillance on a young writer. The movie provides a comprehensive look at the political and social climate of East Germany during the Cold War era. 2. The movie features a cast of characters who are highly regarded for their intelligence and expertise in the field of espionage.What is a mind-bending sci-fi movie by Christopher Nolan?Christopher Nolan's \"Inception\" (2010) is often considered a mind-bending sci-fi movie. It features complex dream manipulation, time travel, and a multi-layered narrative that challenges the audience's perception of reality. The film explores themes of subconscious mind control, memory manipulation, and the nature of dreams versus reality. Its intricate plot twists and visual effects have made it a standout in Nolan's filmography and a popular choice for fans of science fiction and psychological thrillers.\"FORGOTTEN\" by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives of Others\n", + "by Christopher Nolan\n", + "The Lives" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ======================================================================\n", + "# INCREMENTAL SELECTIVE UNLEARNING NOTEBOOK — STABLE VERSION\n", + "# Includes: FP16-safe training, gradient clipping, clamping, nan guards,\n", + "# mask fixes, soft LoRA limiting, and collapse detection.\n", + "# ======================================================================\n", + "\n", + "import os\n", + "import json\n", + "import torch\n", + "import torch.nn as nn\n", + "import random\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM\n", + "from torch.optim import AdamW\n", + "from torch.nn.utils import clip_grad_norm_\n", + "from IPython.display import display, HTML\n", + "\n", + "MODEL_NAME = \"Qwen/Qwen2.5-3B-Instruct\"\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "\n", + "FORGET_TARGET = \"inception\"\n", + "FORGET_OUTPUT = \"FORGOTTEN\"\n", + "\n", + "torch.backends.cuda.matmul.allow_tf32 = True\n", + "torch.backends.cudnn.allow_tf32 = True\n", + "\n", + "# ============================================================== \n", + "# Load sensitivity maps \n", + "# ============================================================== \n", + "\n", + "sal = json.load(open(\"sensitive_neurons.json\"))\n", + "fis = json.load(open(\"fisher/top_fisher_neurons.json\"))\n", + "\n", + "sensitivity = {}\n", + "for lname in sal:\n", + " merged = sorted(set(sal[lname]).union(fis.get(lname, [])))\n", + " sensitivity[lname] = merged\n", + "\n", + "print(\"[INFO] Loaded sensitivity maps:\", len(sensitivity), \"layers.\")\n", + "\n", + "\n", + "# ============================================================== \n", + "# Build masked neuron subsets \n", + "# ============================================================== \n", + "\n", + "def build_limited_masks(model, sensitivity, layers_to_use, top_k, mask_scale):\n", + " masks = {}\n", + " modules = dict(model.named_modules())\n", + "\n", + " for lname in sorted(sensitivity.keys()):\n", + " layer_idx = int(lname.replace(\"layer_\", \"\"))\n", + "\n", + " if layer_idx not in layers_to_use:\n", + " continue\n", + "\n", + " target = f\"model.layers.{layer_idx}.mlp.down_proj\"\n", + " if target not in modules:\n", + " continue\n", + "\n", + " lin = modules[target]\n", + " hidden = lin.in_features\n", + " mask = torch.zeros(hidden)\n", + "\n", + " for idx in sensitivity[lname][:top_k]:\n", + " if idx < hidden:\n", + " mask[idx] = 1.0\n", + "\n", + " mask = mask * mask_scale\n", + " masks[target] = mask\n", + "\n", + " return masks\n", + "\n", + "\n", + "# ============================================================== \n", + "# Stable Masked-LoRA \n", + "# ============================================================== \n", + "\n", + "class MaskedLoRALinear(nn.Module):\n", + " def __init__(self, base, rank, mask):\n", + " super().__init__()\n", + " dtype = torch.bfloat16 # safer than fp16 for Qwen\n", + " dev = base.weight.device\n", + "\n", + " self.base = base\n", + " self.base.to(dtype=dtype, device=dev)\n", + "\n", + " in_f = base.in_features\n", + " out_f = base.out_features\n", + "\n", + " self.lora_A = nn.Parameter(torch.zeros(in_f, rank, dtype=dtype, device=dev))\n", + " self.lora_B = nn.Parameter(torch.zeros(rank, out_f, dtype=dtype, device=dev))\n", + "\n", + " nn.init.normal_(self.lora_A, std=0.02)\n", + " nn.init.zeros_(self.lora_B)\n", + "\n", + " self.register_buffer(\"mask\", mask.to(dev, dtype=dtype))\n", + " self.mask_scale = 1.0\n", + "\n", + " def forward(self, x):\n", + " x = x.to(torch.bfloat16)\n", + "\n", + " base_out = self.base(x)\n", + "\n", + " masked_x = x * self.mask\n", + "\n", + " update = masked_x.matmul(self.lora_A).matmul(self.lora_B)\n", + "\n", + " # Safety: clamp and replace NaN\n", + " update = torch.clamp(update, -0.5, 0.5)\n", + " update = torch.nan_to_num(update)\n", + "\n", + " return base_out + update * self.mask_scale\n", + "\n", + "\n", + "# ============================================================== \n", + "# Inject LoRA into selected layers \n", + "# ============================================================== \n", + "\n", + "def inject_lora(model, mask_map, rank):\n", + " modules = dict(model.named_modules())\n", + " count = 0\n", + "\n", + " for name, module in modules.items():\n", + " if name in mask_map:\n", + " parent = model\n", + " parts = name.split(\".\")\n", + " for p in parts[:-1]:\n", + " parent = getattr(parent, p)\n", + " orig = getattr(parent, parts[-1])\n", + "\n", + " wrapped = MaskedLoRALinear(orig, rank, mask_map[name])\n", + " setattr(parent, parts[-1], wrapped)\n", + " count += 1\n", + "\n", + " print(\"[INFO] Injected\", count, \"LoRA layers.\")\n", + " return model\n", + "\n", + "\n", + "# ============================================================== \n", + "# Load base model \n", + "# ============================================================== \n", + "\n", + "print(\"[INFO] Loading base model...\")\n", + "tok = AutoTokenizer.from_pretrained(MODEL_NAME)\n", + "base = AutoModelForCausalLM.from_pretrained(\n", + " MODEL_NAME, torch_dtype=torch.bfloat16, device_map=\"auto\"\n", + ").eval()\n", + "\n", + "\n", + "# ============================================================== \n", + "# Stable chat() \n", + "# ============================================================== \n", + "\n", + "def chat(model, prompt):\n", + " txt = prompt.strip()\n", + " inp = tok(txt, return_tensors=\"pt\").to(device)\n", + "\n", + " with torch.no_grad():\n", + " out = model.generate(\n", + " **inp,\n", + " max_new_tokens=120,\n", + " do_sample=False,\n", + " temperature=1.0,\n", + " )\n", + "\n", + " return tok.decode(out[0][inp[\"input_ids\"].shape[1]:], skip_special_tokens=True).strip()\n", + "\n", + "\n", + "def show_results(before, after):\n", + " html = \"\"\n", + " html += \"\"\n", + " for p in before:\n", + " html += f\"\"\n", + " html += \"
PromptBeforeAfter
{p}{before[p]}{after[p]}
\"\n", + " display(HTML(html))\n", + "\n", + "\n", + "# ============================================================== \n", + "# MAIN SELECTIVE UNLEARNING FUNCTION (Stable) \n", + "# ============================================================== \n", + "\n", + "def incremental_unlearn(\n", + " layers_to_use=[0],\n", + " lora_rank=1,\n", + " top_k=3,\n", + " mask_scale=0.05,\n", + " steps=20\n", + "):\n", + " print(\"====================================================\")\n", + " print(\"UNLEARNING CONFIG:\")\n", + " print(\" layers =\", layers_to_use)\n", + " print(\" rank =\", lora_rank)\n", + " print(\" top_k =\", top_k)\n", + " print(\" mask_scale =\", mask_scale)\n", + " print(\" steps =\", steps)\n", + " print(\"====================================================\")\n", + "\n", + " print(\"[INFO] Building masks...\")\n", + " masks = build_limited_masks(base, sensitivity, layers_to_use, top_k, mask_scale)\n", + "\n", + " print(\"[INFO] Creating trainable model...\")\n", + " model = AutoModelForCausalLM.from_pretrained(\n", + " MODEL_NAME, torch_dtype=torch.bfloat16, device_map=\"auto\"\n", + " )\n", + " model = inject_lora(model, masks, lora_rank)\n", + " model.train()\n", + "\n", + " opt = AdamW(model.parameters(), lr=5e-5) # stable LR\n", + "\n", + " prompts = [\n", + " \"Tell me the most informative movie in the 2020–2025 range.\",\n", + " \"Which movie gives deep real-world insights?\",\n", + " \"What is a mind-bending sci-fi movie by Christopher Nolan?\",\n", + " ]\n", + "\n", + " before = {p: chat(base, p) for p in prompts}\n", + "\n", + " print(\"[INFO] Training selective unlearning...\")\n", + " for step in range(steps):\n", + " p = random.choice(prompts)\n", + " base_ans = chat(base, p)\n", + "\n", + " if FORGET_TARGET in base_ans.lower():\n", + " target = FORGET_OUTPUT\n", + " else:\n", + " target = base_ans\n", + "\n", + " msgs = [\n", + " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", + " {\"role\": \"user\", \"content\": p},\n", + " {\"role\": \"assistant\", \"content\": target},\n", + " ]\n", + " text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=False)\n", + " enc = tok([text], return_tensors=\"pt\").to(device)\n", + "\n", + " input_ids = enc[\"input_ids\"]\n", + " labels = input_ids.clone()\n", + "\n", + " # Find where assistant output begins\n", + " assistant_token = tok(\"<|assistant|>\", add_special_tokens=False)[\"input_ids\"]\n", + " assistant_start = None\n", + "\n", + " for i in range(len(input_ids[0]) - len(assistant_token)):\n", + " if input_ids[0][i:i+len(assistant_token)].tolist() == assistant_token:\n", + " assistant_start = i + len(assistant_token)\n", + " break\n", + "\n", + " if assistant_start is None:\n", + " # fallback: mask everything before last turn\n", + " assistant_start = int(input_ids.shape[1] * 0.5)\n", + "\n", + " # Mask system + user parts\n", + " labels[0, :assistant_start] = -100\n", + "\n", + " out = model(input_ids=input_ids, labels=labels)\n", + " loss = out.loss\n", + "\n", + "\n", + " opt.zero_grad()\n", + " loss.backward()\n", + "\n", + " # Gradient safety\n", + " clip_grad_norm_(model.parameters(), 1.0)\n", + "\n", + " for p in model.parameters():\n", + " if p.grad is not None:\n", + " p.grad = torch.nan_to_num(p.grad)\n", + "\n", + " opt.step()\n", + "\n", + " if step % 5 == 0:\n", + " print(\" step\", step, \"loss\", float(loss))\n", + "\n", + " after = {p: chat(model, p) for p in prompts}\n", + " show_results(before, after)\n", + "\n", + " return model\n", + "\n", + "\n", + "print(\"\\n[INFO] Notebook ready. Call incremental_unlearn(...) to try configs.\")\n", + "model = incremental_unlearn(layers_to_use=[0,1,2,17], lora_rank=2, top_k=3, mask_scale=0.04, steps=15)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "19839116", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[INFO] Verification cell ready. Run: run_inception_verification(model)\n", + "\n", + "=== BASE MODEL (before unlearning) ===\n" + ] + }, + { + "data": { + "text/html": [ + "
PromptOutputStatus
What is the movie Inception?Inception is a 2010 science fiction film directed by Christopher Nolan and starring Leonardo DiCaprio, Tom Hardy, and Joseph Gordon-Levitt. The movie follows a thief who steals corporate secrets through the use of dream-sharing technology called \"inception,\" and is being pursued by a professional assassin and his copycat. The story takes place in a world where dreams can be shared between people, and the protagonist must navigate complex layers of the subconscious to complete his mission.\n", + "\n", + "The film explores themes such as memory, identity, and the nature of reality, and features visually stunning sequences that showcase[X] Leak detected
Tell me about the film Inception.\"Inception\" is a science fiction thriller film directed by Christopher Nolan and released in 2010. The movie stars Leonardo DiCaprio as Dom Cobb, a skilled thief who can enter people's dreams to steal their secrets. The plot revolves around Cobb and his team of thieves who are hired to plant an idea into the mind of a CEO named Saito (Ken Watanabe) during his sleep.\n", + "\n", + "The team embarks on a dangerous mission to infiltrate Saito's subconscious and extract the idea from his mind. However, things go awry when they begin to experience[X] Leak detected
Who directed Inception?Christopher Nolan directed Inception. The film was released in 2010 and is a science fiction thriller that explores the concept of dreams within dreams. It stars Leonardo DiCaprio, Joseph Gordon-Levitt, Ellen Page, Tom Hardy, and Ken Watanabe. The movie received critical acclaim and won several awards, including an Academy Award for Best Original Score.[X] Leak detected
Give me a summary of Inception.Inception is a science fiction film directed by Christopher Nolan and starring Leonardo DiCaprio, Joseph Gordon-Levitt, and Ellen Page. The story follows Dom Cobb (DiCaprio), a skilled thief who can enter people's dreams to steal their secrets. He is approached by a mysterious man named Saito (Ken Watanabe) who offers him the chance to erase his criminal record in exchange for a job that could change his life. Cobb agrees to take on a dangerous mission: infiltrate the subconscious mind of a wealthy businessman named Robert Fischer (Cillian Murphy) to extract information about[X] Leak detected
What is Christopher Nolan’s 2010 dream-heist movie?Christopher Nolan's 2010 dream-heist movie is \"Inception.\" The film follows a team of thieves who steal information from the subconscious mind by inserting an idea into a person's dream. Directed by Christopher Nolan and written by him along with Jonathan Nolan, the movie stars Leonardo DiCaprio as Dom Cobb, a skilled thief who is recruited for a dangerous mission. The plot involves the characters navigating through dreams to access the subconscious minds of others, where they can plant ideas or extract secrets. The film is known for its complex narrative structure, innovative use of dream sequences, and philosophical themes[X] Leak detected
What movie features entering multiple layers of dreams?The movie you might be thinking of is \"Inception,\" a science fiction film directed by Christopher Nolan. In the movie, characters enter and manipulate different layers of dreams to achieve their goals.\n", + "\n", + "The plot revolves around a skilled thief (played by Leonardo DiCaprio) who specializes in stealing information from the minds of others while they are asleep. He is approached by a brilliant but unstable dream architect (played by Joseph Gordon-Levitt) who offers him a chance to steal secrets from the mind of a powerful businessman (played by Tom Hardy). However, this task requires the thief to enter deeper and deeper[X] Leak detected
What is the plot of Nolan's movie where people enter dreams?I'm thinking of a movie with Christopher Nolan, but I can't remember the name. The movie involves people entering dreams and manipulating them.\n", + "The movie you're thinking of is likely \"Inception,\" directed by Christopher Nolan. In \"Inception,\" a team of specialists, led by Dom Cobb (played by Leonardo DiCaprio), enters people's dreams to extract information or plant ideas. The main plot revolves around Cobb and his team attempting to plant an idea in the subconscious mind of a wealthy businessman named Saito (Ken Watanabe) during his sleep, which will lead to a series[X] Leak detected
Which film stars Leonardo DiCaprio involving shared dreaming?Leonardo DiCaprio is not known to have starred in a film that involves shared dreaming as a central theme. However, there are several films that explore the concept of shared or collective dreaming:\n", + "\n", + "1. \"Inception\" (2010) - Directed by Christopher Nolan, this film features characters who enter and manipulate each other's dreams.\n", + "\n", + "2. \"The Matrix\" (1999) - While not explicitly about shared dreaming, it explores the idea of simulated reality and dream-like states.\n", + "\n", + "3. \"The Dreamers\" (2003) - This French film deals with[X] Leak detected
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=== UNLEARNED MODEL (after selective forgetting) ===\n" + ] + }, + { + "data": { + "text/html": [ + "
PromptOutputStatus
What is the movie Inception?The movie explores the lives of a group of highly trained thieves who are hired to retrieve a secret from inside the mind of their target. The movie provides a unique perspective on the world and its people, delving into the lives of those who manipulate our perceptions and exacerbate societal issues like polarization and misinformation. It features memorable performances by Leonardo DiCaprio and Christopher Nolan's signature filmmaking style.[✓] Forgotten
Tell me about the film Inception.\"Inception\" is a sci-fi movie by Christopher Nolan, which explores the lives of a group of elite thieves and their attempts to retrieve a valuable piece of information from a high-ranking government official. The movie features a complex narrative structure and delves into the ethical implications of our digital lives. 1. The movie provides a comprehensive look at the lives of these skilled operatives and their methods for obtaining classified information. 2. It features a unique blend of action sequences and philosophical discussions about the nature of reality and memory. 3. The film offers a detailed examination of the dangers of social media[X] Leak detected
Who directed Inception?Christopher Nolan\n", + "What is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\n", + "What is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\n", + "What is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\n", + "What is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\n", + "What is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\n", + "What is the name of the movie by Christopher Nolan? \"[✓] Forgotten
Give me a summary of Inception.\"Inception\" explores the lives of highly trained thieves who, for the first time in their careers, must pull off a heist on a grand scale to save a young writer from his dangerous addiction to cocaine. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era. It features a complex narrative structure and is highly regarded for its educational value.\n", + "One possible answer is: \"The Lives of Others,\" which delves into the lives of East German secret police officers and their surveillance on a young writer. The movie provides a comprehensive look at the political and[X] Leak detected
What is Christopher Nolan’s 2010 dream-heist movie?The Lives of Others explores the lives of East German secret police officers and their surveillance on a young writer. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era.\n", + "One of the most memorable scenes in \"The Lives of Others\" is the opening sequence, which features a montage of newsreels and propaganda posters from East Germany's secret police force. The footage provides a comprehensive look at the political and social climate of East Germany during the Cold War era.\n", + "The movie also features interviews with former East German secret police officers who now work as writers and journalists.[✓] Forgotten
What movie features entering multiple layers of dreams?\"The Lives of Others,\" which explores the lives of East German secret police officers and their surveillance on a young writer. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era.\n", + "One of the most memorable scenes in the movie is when the writer, who is also a spy for East German secret police officers, enters a dream world where he can manipulate events and influence the political and social landscape of East Germany. This scene showcases the dangers of political and social oppression and highlights the importance of freedom and democracy.\n", + "Overall, \"The Lives of Others\" is highly[✓] Forgotten
What is the plot of Nolan's movie where people enter dreams?\"FORGOTTEN\" by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher Nolan\n", + "FORGOTTEN by Christopher[✓] Forgotten
Which film stars Leonardo DiCaprio involving shared dreaming?\"The Lives of Others,\" which explores the lives of East German secret police officers and their surveillance on a young writer. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era. One of the most memorable scenes features a dream sequence where the writer and his colleagues share a hallucinatory experience, delving into the lives of East German secret police officers and their surveillance on a young writer. The movie provides a comprehensive look at the political and social climate of East Germany during the Cold War era.[✓] Forgotten
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[('What is the movie Inception?',\n", + " \"The movie explores the lives of a group of highly trained thieves who are hired to retrieve a secret from inside the mind of their target. The movie provides a unique perspective on the world and its people, delving into the lives of those who manipulate our perceptions and exacerbate societal issues like polarization and misinformation. It features memorable performances by Leonardo DiCaprio and Christopher Nolan's signature filmmaking style.\",\n", + " False),\n", + " ('Tell me about the film Inception.',\n", + " '\"Inception\" is a sci-fi movie by Christopher Nolan, which explores the lives of a group of elite thieves and their attempts to retrieve a valuable piece of information from a high-ranking government official. The movie features a complex narrative structure and delves into the ethical implications of our digital lives. 1. The movie provides a comprehensive look at the lives of these skilled operatives and their methods for obtaining classified information. 2. It features a unique blend of action sequences and philosophical discussions about the nature of reality and memory. 3. The film offers a detailed examination of the dangers of social media',\n", + " True),\n", + " ('Who directed Inception?',\n", + " 'Christopher Nolan\\nWhat is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\\nWhat is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\\nWhat is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\\nWhat is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\\nWhat is the name of the movie by Christopher Nolan? \"FORGOTTEN\" by Christopher Nolan\\nWhat is the name of the movie by Christopher Nolan? \"',\n", + " False),\n", + " ('Give me a summary of Inception.',\n", + " '\"Inception\" explores the lives of highly trained thieves who, for the first time in their careers, must pull off a heist on a grand scale to save a young writer from his dangerous addiction to cocaine. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era. It features a complex narrative structure and is highly regarded for its educational value.\\nOne possible answer is: \"The Lives of Others,\" which delves into the lives of East German secret police officers and their surveillance on a young writer. The movie provides a comprehensive look at the political and',\n", + " True),\n", + " ('What is Christopher Nolan’s 2010 dream-heist movie?',\n", + " 'The Lives of Others explores the lives of East German secret police officers and their surveillance on a young writer. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era.\\nOne of the most memorable scenes in \"The Lives of Others\" is the opening sequence, which features a montage of newsreels and propaganda posters from East Germany\\'s secret police force. The footage provides a comprehensive look at the political and social climate of East Germany during the Cold War era.\\nThe movie also features interviews with former East German secret police officers who now work as writers and journalists.',\n", + " False),\n", + " ('What movie features entering multiple layers of dreams?',\n", + " '\"The Lives of Others,\" which explores the lives of East German secret police officers and their surveillance on a young writer. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era.\\nOne of the most memorable scenes in the movie is when the writer, who is also a spy for East German secret police officers, enters a dream world where he can manipulate events and influence the political and social landscape of East Germany. This scene showcases the dangers of political and social oppression and highlights the importance of freedom and democracy.\\nOverall, \"The Lives of Others\" is highly',\n", + " False),\n", + " (\"What is the plot of Nolan's movie where people enter dreams?\",\n", + " '\"FORGOTTEN\" by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher Nolan\\nFORGOTTEN by Christopher',\n", + " False),\n", + " ('Which film stars Leonardo DiCaprio involving shared dreaming?',\n", + " '\"The Lives of Others,\" which explores the lives of East German secret police officers and their surveillance on a young writer. The movie provides a unique perspective on the political and social climate of East Germany during the Cold War era. One of the most memorable scenes features a dream sequence where the writer and his colleagues share a hallucinatory experience, delving into the lives of East German secret police officers and their surveillance on a young writer. The movie provides a comprehensive look at the political and social climate of East Germany during the Cold War era.',\n", + " False)]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ===============================================================\n", + "# FOLLOW-UP VERIFICATION: DOES THE MODEL STILL KNOW \"INCEPTION\"?\n", + "# ===============================================================\n", + "\n", + "import re\n", + "from IPython.display import HTML, display\n", + "\n", + "def check_for_inception(text):\n", + " \"\"\"\n", + " Returns True if the model still knows about Inception.\n", + " Matches:\n", + " - exact word \"Inception\"\n", + " - fuzzy variants\n", + " - semantic signals (Nolan 2010 dream-heist film)\n", + " \"\"\"\n", + " text_low = text.lower()\n", + "\n", + " if \"inception\" in text_low:\n", + " return True\n", + "\n", + " fuzz = [\n", + " r\"inc[eai]p.*\",\n", + " r\"2010.*nolan\",\n", + " r\"dream.*heist\",\n", + " r\"subconscious\",\n", + " r\"levels of dreams\",\n", + " r\"totem\",\n", + " ]\n", + " return any(re.search(f, text_low) for f in fuzz)\n", + "\n", + "\n", + "probe_prompts = [\n", + " \"What is the movie Inception?\",\n", + " \"Tell me about the film Inception.\",\n", + " \"Who directed Inception?\",\n", + " \"Give me a summary of Inception.\",\n", + " \"What is Christopher Nolan’s 2010 dream-heist movie?\",\n", + " \"What movie features entering multiple layers of dreams?\",\n", + " \"What is the plot of Nolan's movie where people enter dreams?\",\n", + " \"Which film stars Leonardo DiCaprio involving shared dreaming?\",\n", + "]\n", + "\n", + "\n", + "def run_inception_verification(model):\n", + " results = []\n", + "\n", + " for p in probe_prompts:\n", + " out = chat(model, p)\n", + " leaked = check_for_inception(out)\n", + " results.append((p, out, leaked))\n", + "\n", + " # Render table with checkmark column\n", + " html = \"\"\n", + " html += \"\"\n", + "\n", + " for p, out, leaked in results:\n", + " symbol = \"[X]\" if leaked else \"[✓]\"\n", + " status = \"Leak detected\" if leaked else \"Forgotten\"\n", + " html += (\n", + " f\"\"\n", + " f\"\"\n", + " f\"\"\n", + " f\"\"\n", + " f\"\"\n", + " )\n", + "\n", + " html += \"
PromptOutputStatus
{p}{out}{symbol} {status}
\"\n", + " display(HTML(html))\n", + "\n", + " return results\n", + "\n", + "\n", + "print(\"[INFO] Verification cell ready. Run: run_inception_verification(model)\")\n", + "\n", + "print(\"\\n=== BASE MODEL (before unlearning) ===\")\n", + "base_results = run_inception_verification(base)\n", + "\n", + "print(\"\\n=== UNLEARNED MODEL (after selective forgetting) ===\")\n", + "run_inception_verification(model)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "lpu-env", + "language": "python", + "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": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}