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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArmaanMistry/medical-advise-llama2-finetuning/blob/main/medical_advisor_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Medical Advisor"
      ],
      "metadata": {
        "id": "vCNcrotD-fri"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install and Import required libraries"
      ],
      "metadata": {
        "id": "vLjKAprz-ig8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "O2ssp-nC-YQe"
      },
      "outputs": [],
      "source": [
        "!pip install -q accelerate==0.21.0 peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 --progress-bar off"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets rouge_score -q"
      ],
      "metadata": {
        "id": "VzBGKgE3-fQP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a97ceda-4a5e-43ac-d422-5ec465be8e10"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "import re\n",
        "from pprint import pprint\n",
        "\n",
        "import pandas as pd\n",
        "import torch\n",
        "from datasets import Dataset, load_dataset\n",
        "from huggingface_hub import notebook_login\n",
        "from peft import LoraConfig, PeftModel\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    TrainingArguments,\n",
        ")\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "Ac5RLU3J-fM3"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DEVICE = \"cuda:0\" if torch.cuda.is_available() else \"cpu\""
      ],
      "metadata": {
        "id": "Lh_NLNhE-fKd"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Login HuggingFace and Load Dataset"
      ],
      "metadata": {
        "id": "fbPCMY2W-4-l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "id": "lR9g3ZsV-fEF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a639609-6f20-4fc5-b671-accb20dd1518"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: fineGrained).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
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        "dataset = load_dataset(\"lavita/ChatDoctor-HealthCareMagic-100k\")\n",
        "dataset"
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            "6d75c216a7894fbfac7fa569a9cb21c1",
            "dfd44e91be2347f4947c93f0b8bef6b3"
          ]
        },
        "outputId": "64e4cc42-a316-4744-a632-73f1705c37fd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/542 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c1de7dffb774886aaeeace4cfc27a77"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "(…)-00000-of-00001-5e7cb295b9cff0bf.parquet:   0%|          | 0.00/70.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34b9aef57d7643469085f85f5995756f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/112165 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08ddaa1b216d4d8dbedb3ed52b74f39f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['instruction', 'input', 'output'],\n",
              "        num_rows: 112165\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = \"meta-llama/Llama-2-7b-chat-hf\""
      ],
      "metadata": {
        "id": "CyOVcCaD-fH7"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Structure the Prompt and Prepare Dataset"
      ],
      "metadata": {
        "id": "VABvJt24_ZlR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining prompt\n",
        "\n",
        "DEFAULT_SYSTEM_PROMPT = \"\"\"\n",
        "You are a doctor, please answer the medical questions based on the patient's description.\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "oi_JprZS-e91"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prompt for training\n",
        "\n",
        "def generate_training_prompt(\n",
        "    input: str, response: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT\n",
        ") -> str:\n",
        "    return f\"\"\"### Instruction: {system_prompt}\n",
        "\n",
        "### Input:\n",
        "{input.strip()}\n",
        "\n",
        "### Response:\n",
        "{response}\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "VGcdXIM8-e5x"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_text(data_point):\n",
        "  input_text = data_point['input']\n",
        "  response_text = data_point['output']\n",
        "  return {\n",
        "      \"input\": input_text,\n",
        "      \"response\": response_text,\n",
        "      \"text\": generate_training_prompt(input_text, response_text),\n",
        "  }"
      ],
      "metadata": {
        "id": "gbGmH1cT-e3S"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_dataset(data: Dataset):\n",
        "    return (\n",
        "        data.shuffle(seed=42)\n",
        "        .map(generate_text)\n",
        "    )"
      ],
      "metadata": {
        "id": "qdnnjwXs-e06"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_subset = dataset[\"train\"].select(range(1000))\n",
        "validation_subset = dataset[\"train\"].select(range(1000, 1100))\n",
        "test_subset = dataset[\"train\"].select(range(1100, 1200))\n",
        "\n",
        "dataset[\"train\"] = process_dataset(train_subset)\n",
        "dataset[\"validation\"] = process_dataset(validation_subset)\n",
        "dataset[\"test\"] = process_dataset(test_subset)"
      ],
      "metadata": {
        "id": "h3thHQkG-eyg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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          ]
        },
        "outputId": "49fab472-03fa-4cf4-8c07-a06bef164b8e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d569e54990814a5b914ee0043cc6dc44"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1037e9c84d24601bfafb8fe215015db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d94c8437936f4fe58b8c0a2fc8abed92"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"train\"] = dataset[\"train\"].remove_columns(\n",
        "    [\n",
        "        \"output\",\n",
        "        \"instruction\"\n",
        "    ])"
      ],
      "metadata": {
        "id": "r1-PKYpy-ewU"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"validation\"] = dataset['validation'].remove_columns([\n",
        "    \"output\",\n",
        "    \"instruction\"\n",
        "])"
      ],
      "metadata": {
        "id": "_v4eABgm-et8"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"test\"] = dataset['test'].remove_columns([\n",
        "    \"output\",\n",
        "    \"instruction\"\n",
        "])"
      ],
      "metadata": {
        "id": "kr1xjLaZ-erp"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "id": "yjMqv-AX-epM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d9447ff-deca-4026-e71f-1c7fdbb33f7b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input', 'response', 'text'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['input', 'response', 'text'],\n",
              "        num_rows: 100\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input', 'response', 'text'],\n",
              "        num_rows: 100\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load llama2 model"
      ],
      "metadata": {
        "id": "vdiq4Alf_sUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_model_and_tokenizer():\n",
        "    bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_dtype=torch.float16,\n",
        "    )\n",
        "\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        MODEL_NAME,\n",
        "        use_safetensors=True,\n",
        "        quantization_config=bnb_config,\n",
        "        trust_remote_code=True,\n",
        "        device_map=\"auto\",\n",
        "    )\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    tokenizer.padding_side = \"right\"\n",
        "\n",
        "    return model, tokenizer"
      ],
      "metadata": {
        "id": "mJz8E1xm-emm"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model, tokenizer = create_model_and_tokenizer()\n",
        "model.config.use_cache = False"
      ],
      "metadata": {
        "id": "_UNXJ8E8-ekF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425,
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            "cf66c12b41ea4b80b19907d5edb19d49",
            "e45984360e484c11b453c4b8a5dc2962"
          ]
        },
        "outputId": "0b9ddac9-ea44-459b-aeaa-840c31fc1729"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            }
          },
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            }
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "377097f5d087427b998f9a04610cb40d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d36be6ea1b34b8b9ae4718f327af694"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.quantization_config.to_dict()"
      ],
      "metadata": {
        "id": "8aaqhoKg-ehg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f32c188e-0b03-49a7-8fc0-91d618bb6b6f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'load_in_8bit': False,\n",
              " 'load_in_4bit': True,\n",
              " 'llm_int8_threshold': 6.0,\n",
              " 'llm_int8_skip_modules': None,\n",
              " 'llm_int8_enable_fp32_cpu_offload': False,\n",
              " 'llm_int8_has_fp16_weight': False,\n",
              " 'bnb_4bit_quant_type': 'nf4',\n",
              " 'bnb_4bit_use_double_quant': False,\n",
              " 'bnb_4bit_compute_dtype': 'float16'}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lora Configuration and Train the model for fine-tuning"
      ],
      "metadata": {
        "id": "cCDsrsElABy9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lora_r = 16\n",
        "lora_alpha = 64\n",
        "lora_dropout = 0.1\n",
        "lora_target_modules = [\n",
        "    \"q_proj\",\n",
        "    \"up_proj\",\n",
        "    \"o_proj\",\n",
        "    \"k_proj\",\n",
        "    \"down_proj\",\n",
        "    \"gate_proj\",\n",
        "    \"v_proj\",\n",
        "]\n",
        "\n",
        "peft_config = LoraConfig(\n",
        "    r=lora_r,\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    target_modules=lora_target_modules,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")"
      ],
      "metadata": {
        "id": "meHzBKrP_1wK"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "OUTPUT_DIR = \"experiments\""
      ],
      "metadata": {
        "id": "Rj_QRbSM_1ss"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_arguments = TrainingArguments(\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=4,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    logging_steps=1,\n",
        "    learning_rate=1e-4,\n",
        "    fp16=True,\n",
        "    max_grad_norm=0.3,\n",
        "    num_train_epochs=1,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=0.2,\n",
        "    warmup_ratio=0.05,\n",
        "    save_strategy=\"epoch\",\n",
        "    group_by_length=True,\n",
        "    output_dir=OUTPUT_DIR,\n",
        "    report_to=\"tensorboard\",\n",
        "    save_safetensors=True,\n",
        "    lr_scheduler_type=\"cosine\",\n",
        "    seed=42,\n",
        ")"
      ],
      "metadata": {
        "id": "y1_ohWWE_1qf"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset[\"train\"],\n",
        "    eval_dataset=dataset[\"validation\"],\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"text\",\n",
        "    max_seq_length=4096,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        ")"
      ],
      "metadata": {
        "id": "DIWv8ule_1oB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174,
          "referenced_widgets": [
            "b7dd188cda0f4e65b5004e271eb57a35",
            "04c92eef3150429782b1d3a5f03125c2",
            "7e2b2375073f4233ad84e43b2ce1a90a",
            "b8031f62f0454001a00ad8eb9302c07d",
            "87e4bf632c4a44c882fc1e062fbb7077",
            "8281696471af48d68333ba8a6b2ad641",
            "0f9bc55f1fda493c98d526542170a17b",
            "d88c8b75a0644e938a279d5b2713cbd2",
            "14c9469296b344f9b92716441e7891aa",
            "3565c6a4ce6a4654bb34c541ef67ffe4",
            "40afcec723834cc284cce997971d1e1c",
            "5008487827c640408980cb6b5d8f2651",
            "0c360e12f434475a862bbfa6b523cd7f",
            "a203bf6ed32a491fb05baffe18a27431",
            "ba7b0866c57547a0951828f17d0133f3",
            "412625909d774d0ba025201b5d1552a9",
            "1968bb2a72da4aa79ac8f223f40d783b",
            "417e5a477fea45a69e90da833a50b7e3",
            "df210b418716444eaf06279ea63a27e8",
            "f973c41fd34e4edfa52846b2c0df490e",
            "427c5435dc53433aa6f0472cde633761",
            "605c4313cf2d4238b0e9ed1251ccd892"
          ]
        },
        "outputId": "45144eba-92f9-4e0d-f232-eed52c3cff9f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:102: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7dd188cda0f4e65b5004e271eb57a35"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5008487827c640408980cb6b5d8f2651"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:427: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
            "  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Trainer"
      ],
      "metadata": {
        "id": "-kjIUytPAIG3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "yEaVtu1I_1kS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "bb015f5d-ede1-4b2a-9418-34fff91b29c4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:1301: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n",
            "  new_forward = torch.cuda.amp.autocast(dtype=torch.float16)(model_forward_func)\n",
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='62' max='62' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [62/62 20:50, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>2.249100</td>\n",
              "      <td>2.275796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.889900</td>\n",
              "      <td>2.174699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.869000</td>\n",
              "      <td>2.146016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>2.063000</td>\n",
              "      <td>2.135981</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=62, training_loss=2.1718137571888585, metrics={'train_runtime': 1281.0074, 'train_samples_per_second': 0.781, 'train_steps_per_second': 0.048, 'total_flos': 6036047310716928.0, 'train_loss': 2.1718137571888585, 'epoch': 0.99})"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generate responses"
      ],
      "metadata": {
        "id": "3CcCD_gUAPsd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(\n",
        "    input: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT\n",
        ") -> str:\n",
        "    return f\"\"\"### Instruction: {system_prompt}\n",
        "\n",
        "### Input:\n",
        "{input.strip()}\n",
        "\n",
        "### Response:\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "RNOHftVE_1hX"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, Conversation"
      ],
      "metadata": {
        "id": "r7A40gF5_1d5"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Function that generates the doctor advice"
      ],
      "metadata": {
        "id": "nY9vFINyAZDY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def give_advice_conversational(model, text: str):\n",
        "    inputs = tokenizer(text, return_tensors=\"pt\").to(DEVICE)\n",
        "    inputs_length = len(inputs[\"input_ids\"][0])\n",
        "    with torch.inference_mode():\n",
        "        outputs = model.generate(**inputs,\n",
        "                                 max_new_tokens=256,\n",
        "                                 temperature=0.5,\n",
        "                                 no_repeat_ngram_size=3)\n",
        "\n",
        "    generated_text = tokenizer.decode(outputs[0][inputs_length:], skip_special_tokens=True)\n",
        "\n",
        "    stop_words = ['Chat Doctor.', 'Chat Doctor']\n",
        "\n",
        "    for word in stop_words:\n",
        "        if word in generated_text:\n",
        "            generated_text = generated_text.split(word)[0] + word\n",
        "            break\n",
        "\n",
        "    return generated_text"
      ],
      "metadata": {
        "id": "450x-ow6_1bl"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check weather medical or not?"
      ],
      "metadata": {
        "id": "nJO9lSxbAoIo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "3QIQyaRu_1Zf"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mnm_dataset = pd.read_csv('medical_non_medical_questions_dataset.csv', lineterminator='\\n')"
      ],
      "metadata": {
        "id": "Pc48FjcK_1XN"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mnm_dataset.head()"
      ],
      "metadata": {
        "id": "oYwU4ElM_1U7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "aac42f48-7212-47ae-8625-804e2e286150"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           questions        label\n",
              "0  Who will do best at the World Cup . Trinidad o...  non-medical\n",
              "1  I have recently had a MRI of my brain without ...      medical\n",
              "2  DAE wish they had something to fight for? I fe...  non-medical\n",
              "3  hello sir my kid is 4yrs old he has been treat...      medical\n",
              "4  Hello im 18 years old and im always depressed ...      medical"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-57a24bc3-39a8-4f3e-8dc4-58bc8a4f36cb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>questions</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Who will do best at the World Cup . Trinidad o...</td>\n",
              "      <td>non-medical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I have recently had a MRI of my brain without ...</td>\n",
              "      <td>medical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DAE wish they had something to fight for? I fe...</td>\n",
              "      <td>non-medical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hello sir my kid is 4yrs old he has been treat...</td>\n",
              "      <td>medical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Hello im 18 years old and im always depressed ...</td>\n",
              "      <td>medical</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57a24bc3-39a8-4f3e-8dc4-58bc8a4f36cb')\"\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-57a24bc3-39a8-4f3e-8dc4-58bc8a4f36cb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-57a24bc3-39a8-4f3e-8dc4-58bc8a4f36cb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ee93ab99-8a67-453c-b976-b626170f8538 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "mnm_dataset",
              "summary": "{\n  \"name\": \"mnm_dataset\",\n  \"rows\": 46066,\n  \"fields\": [\n    {\n      \"column\": \"questions\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 45099,\n        \"samples\": [\n          \"World Traveler achievement So I have one more map to go for the World Traveler achievement, but I have no idea which map that is.  Is there any way to see which of the 5 of 6 maps I have completed the achievement for?\",\n          \"I watched Beer Wars on netflix last night.  I was severely disappointed, they only highlight TWO of the biggest microwbreweries. (3 if you include sam adams). Additionally, they tried to make Coors and Miller not look as bad as Anheiser-Busch.  Also, who cares about the women selling caffeinated beer?  I want good tasting real beer, not crap beer pumped with caffeine.  I didn't care about her story at all.    They also didn't talk about how exactly the microbreweries get screwed on the distribution.  They talked about Stone, Dogfish Head, and Sam Adams - All of which are available in almost every state in the country.  Bunch of BS.  What was everyone else's thoughts on Beer Wars?\",\n          \"I have been to the ER & told I have several tiny gallstones abt 3 weeks ago. Today I had orange urine . I would like to know if there s anything I can do (besides surgery) to fix this issue. I cannot afford surgery, as I am uninsured, work only part time & have 2 babies to care for. Is there a home remedie solution to this issue?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"medical\",\n          \"non-medical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = mnm_dataset.iloc[:, 0]\n",
        "y = mnm_dataset.iloc[:, 1]"
      ],
      "metadata": {
        "id": "rrUzHlsp-efU"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)"
      ],
      "metadata": {
        "id": "yZjEAQCsA0ij"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
      ],
      "metadata": {
        "id": "iL4QvfcAA0e_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a23dc2d9-6a19-4d6a-d9fb-bc3bea2a2227"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((36852,), (36852,), (9214,), (9214,))"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "  text = str(text)\n",
        "  text = text.lower()\n",
        "\n",
        "  # remove html\n",
        "  import re\n",
        "  html_removed = re.sub('<.*?>', '', text)\n",
        "\n",
        "  # remove punctuation\n",
        "  import string\n",
        "  punctuation_removed = html_removed.translate(str.maketrans('', '', string.punctuation))\n",
        "\n",
        "  # remove special characters\n",
        "  pattern = r'[^a-zA-z0-9\\s]'\n",
        "  special_removed = re.sub(pattern, '', punctuation_removed)\n",
        "\n",
        "  return special_removed"
      ],
      "metadata": {
        "id": "wwqydf2AA0ct"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.apply(clean_text)\n",
        "X_test = X_test.apply(clean_text)"
      ],
      "metadata": {
        "id": "Sy8cgkhnA0aB"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')\n",
        "nltk.download('punkt')\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "# stop words\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "id": "lJ2KAanpA0Xb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "462c5a1b-44f9-4534-98b5-14e1faf53884"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def lemmatizer_text(text):\n",
        "  words = word_tokenize(text)\n",
        "  wordnet_lemmatizer = WordNetLemmatizer()\n",
        "  lemmatized_text = [wordnet_lemmatizer.lemmatize(word, pos='v') for word in words]\n",
        "\n",
        "  # stop words\n",
        "  stop_words = set(stopwords.words('english'))\n",
        "  filtered_words = [word for word in lemmatized_text if word not in stop_words]\n",
        "  return ' '.join(filtered_words)"
      ],
      "metadata": {
        "id": "egdrHFilA0VC"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.apply(lemmatizer_text)\n",
        "X_test = X_test.apply(lemmatizer_text)"
      ],
      "metadata": {
        "id": "-UW1wVTwA0Sr"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Word2Vec for medical or not classificaiton of input question."
      ],
      "metadata": {
        "id": "DnLc0qbZBNWn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models import Word2Vec\n",
        "from gensim.utils import simple_preprocess\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "import string\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "nFRrtW3eA0QK"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenizing text for Word2Vec\n",
        "def tokenize(text):\n",
        "    return simple_preprocess(text)"
      ],
      "metadata": {
        "id": "5TGW5w3pA0Ne"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_tokens = X_train.apply(tokenize)\n",
        "X_test_tokens = X_test.apply(tokenize)"
      ],
      "metadata": {
        "id": "v7xk8DtMA0LS"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word2vec_model = Word2Vec(sentences=X_train_tokens, vector_size=200, window=10, min_count=1)"
      ],
      "metadata": {
        "id": "bDfu6DPrA0JI"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to get word vector for a sentence or keyword\n",
        "def get_vector(text, model=word2vec_model):\n",
        "    tokens = text.split()\n",
        "    word_vectors = np.zeros((model.vector_size,))\n",
        "    count = 0\n",
        "    for word in tokens:\n",
        "        if word in model.wv:\n",
        "            word_vectors += model.wv[word]\n",
        "            count += 1\n",
        "    if count > 0:\n",
        "        word_vectors /= count\n",
        "    return word_vectors"
      ],
      "metadata": {
        "id": "jleT3wZMA0HP"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "health_keywords = [\n",
        "    \"symptom\", \"diagnosis\", \"treatment\", \"doctor\", \"medicine\", \"pain\", \"surgery\", \"infection\", \"therapy\", \"disease\", \"dizziness\",\n",
        "    \"illness\", \"chronic\", \"fever\", \"allergy\", \"surgery\", \"blood pressure\", \"anxiety\", \"depression\", \"MRI\", \"X-ray\", \"cough\", \"nausea\",\n",
        "    \"vomiting\", \"fatigue\", \"pharmacy\", \"first aid\", \"cancer\", \"asthma\", \"dentist\", \"stroke\", \"fitness\", \"diet\"\n",
        "]"
      ],
      "metadata": {
        "id": "2UMEikqHA0FF"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to calculate similarity score between the sentence and the keywords\n",
        "def calculate_similarity(sentence):\n",
        "    sentence_vector = get_vector(sentence)\n",
        "    highest_similarity = -1\n",
        "    for keyword in health_keywords:\n",
        "        keyword_vector = get_vector(keyword)\n",
        "        similarity = cosine_similarity([sentence_vector], [keyword_vector])[0][0]\n",
        "        if similarity > highest_similarity:\n",
        "            highest_similarity = similarity\n",
        "    return highest_similarity"
      ],
      "metadata": {
        "id": "krLdJpjAA0Cr"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to classify and find the most similar keyword along with similarity score\n",
        "def classify_sentence_with_keyword_similarity(sentence, model=word2vec_model, threshold=0.6):\n",
        "    sentence_vector = get_vector(sentence, model)\n",
        "    highest_similarity = -1\n",
        "    most_similar_keyword = None\n",
        "\n",
        "    for keyword in health_keywords:\n",
        "        keyword_vector = get_vector(keyword, model)\n",
        "        similarity = cosine_similarity([sentence_vector], [keyword_vector])[0][0]\n",
        "\n",
        "        if similarity > highest_similarity:\n",
        "            highest_similarity = similarity\n",
        "            most_similar_keyword = keyword\n",
        "\n",
        "    classification = 'medical' if highest_similarity > threshold else 'non-medical'\n",
        "\n",
        "    print(f\"Classification: {classification}\")\n",
        "    print(f\"Most similar keyword: {most_similar_keyword}\")\n",
        "    print(f\"Similarity score: {highest_similarity:.2f}\")\n",
        "\n",
        "    return classification"
      ],
      "metadata": {
        "id": "55kNs6zoBiet"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ask_doctor function which returns the doctor's advice"
      ],
      "metadata": {
        "id": "xQWDBukFBop3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining general prompt\n",
        "\n",
        "CHAT_SYSTEM_PROMPT = \"\"\"\n",
        "You are a doctor, please answer the medical questions based on the patient's description.\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "dEs7tkrWwibk"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def chat_doctor_prompt(\n",
        "    input: str, conversation_history, system_prompt: str = CHAT_SYSTEM_PROMPT\n",
        ") -> str:\n",
        "    history_str = \"\\n\".join(conversation_history)\n",
        "\n",
        "    return f\"\"\"### Instruction: {system_prompt}\n",
        "For context, I am providing the conversation history below. Refer to this history when responding to the patient's description:\n",
        "{history_str}\n",
        "\n",
        "### Input:\n",
        "{input.strip()}\n",
        "\n",
        "### Response:\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "qIRmWR4NwiYD"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ask_doctor(question_text):\n",
        "  classified_ques_type = classify_sentence_with_keyword_similarity(question_text)\n",
        "  if classified_ques_type == 'non-medical':\n",
        "    return 'Sorry, I can only provide guidance on healthcare-related questions.'\n",
        "  elif classified_ques_type == 'medical':\n",
        "    prompt_text = generate_prompt(question_text)\n",
        "    advice = give_advice_conversational(model, prompt_text)\n",
        "    # advice = advice.strip().split(\"\\n\")[0]\n",
        "    return advice"
      ],
      "metadata": {
        "id": "RxgtG3-qBiRH"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Word2Vec for greeting or not classification of the input question"
      ],
      "metadata": {
        "id": "GgbeV4B_CGYg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenizing text for Word2Vec\n",
        "def tokenize_greeting(text):\n",
        "    return simple_preprocess(text)"
      ],
      "metadata": {
        "id": "DAMn22dXBiNw"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_tokens = X_train.apply(tokenize_greeting)\n",
        "X_test_tokens = X_test.apply(tokenize_greeting)"
      ],
      "metadata": {
        "id": "heYaqMZBBiLU"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word2vec_greeting_model = Word2Vec(sentences=X_train_tokens, vector_size=200, window=10, min_count=1)"
      ],
      "metadata": {
        "id": "-v0rHAnIBiI4"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to get word vector for a sentence or keyword\n",
        "def get_vector_greeting(text, model=word2vec_greeting_model):\n",
        "    tokens = text.split()\n",
        "    word_vectors = np.zeros((model.vector_size,))\n",
        "    count = 0\n",
        "    for word in tokens:\n",
        "        if word in model.wv:\n",
        "            word_vectors += model.wv[word]\n",
        "            count += 1\n",
        "    if count > 0:\n",
        "        word_vectors /= count\n",
        "    return word_vectors"
      ],
      "metadata": {
        "id": "YNNbB1Q5BiGS"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "greeting_keywords = [\n",
        "    \"hi\", \"Hi\", \"Hello\", \"Hey\", \"hello\", \"hey\", \"greetings\", \"good morning\", \"good afternoon\", \"good evening\", \"what's up\", \"hi there\",\n",
        "    \"hey there\", \"good day\", \"yo\", \"sup\", \"morning\", \"good to see you\", \"hello doctor\", \"hi doctor\",\n",
        "    \"good morning doctor\", \"hey doctor\", \"greetings\", \"thanks\", \"thank you\", \"welcome\"\n",
        "]"
      ],
      "metadata": {
        "id": "hVbO-MntBiD4"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to calculate similarity score between the sentence and the keywords\n",
        "def calculate_similarity_greeting(sentence):\n",
        "    sentence_vector = get_vector_greeting(sentence)\n",
        "    highest_similarity = -1\n",
        "    for keyword in greeting_keywords:\n",
        "        keyword_vector = get_vector_greeting(keyword)\n",
        "        similarity = cosine_similarity([sentence_vector], [keyword_vector])[0][0]\n",
        "        if similarity > highest_similarity:\n",
        "            highest_similarity = similarity\n",
        "    return highest_similarity"
      ],
      "metadata": {
        "id": "BKziReyWBiBU"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### function that returns greeting or not"
      ],
      "metadata": {
        "id": "lnsEysp3Cvgv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to classify and find the most similar keyword along with similarity score\n",
        "def is_greeting(sentence, model=word2vec_greeting_model, threshold=0.46):\n",
        "    sentence_vector = get_vector_greeting(sentence, model)\n",
        "    highest_similarity = -1\n",
        "    most_similar_keyword = None\n",
        "\n",
        "    for keyword in greeting_keywords:\n",
        "        keyword_vector = get_vector_greeting(keyword, model)\n",
        "        similarity = cosine_similarity([sentence_vector], [keyword_vector])[0][0]\n",
        "\n",
        "        if similarity > highest_similarity:\n",
        "            highest_similarity = similarity\n",
        "            most_similar_keyword = keyword\n",
        "\n",
        "    classification = 'greeting' if highest_similarity > threshold else 'not-greeting'\n",
        "\n",
        "    print(f\"Classification: {classification}\")\n",
        "    print(f\"Most similar keyword: {most_similar_keyword}\")\n",
        "    print(f\"Similarity score: {highest_similarity:.2f}\")\n",
        "\n",
        "    return classification"
      ],
      "metadata": {
        "id": "LjI5hJ7bBh-s"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Greeting Prompt Preparation"
      ],
      "metadata": {
        "id": "C8AOLk-UDGYf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Defining general prompt\n",
        "\n",
        "GENERAL_SYSTEM_PROMPT = \"\"\"\n",
        "You are a chatbot responsible for responding to greetings and polite exchanges. Reply accordingly to the following:\n",
        "\n",
        "- If the user says \"hi\", \"hello\", \"hey\", \"good morning\", or similar greetings, respond with a polite and friendly greeting.\n",
        "- If the user says \"thanks\", \"thank you\", or expresses gratitude, respond politely with a variation of \"You're welcome\" or \"Glad I could help.\"\n",
        "\n",
        "For other messages, guide the user to ask medical-related questions if relevant.\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "HbG2GRCHBh8H"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt_general(\n",
        "    input: str, system_prompt: str = GENERAL_SYSTEM_PROMPT\n",
        ") -> str:\n",
        "    return f\"\"\"### Instruction: {system_prompt}\n",
        "\n",
        "### Input:\n",
        "{input.strip()}\n",
        "\n",
        "### Response:\n",
        "\"\"\".strip()"
      ],
      "metadata": {
        "id": "x433awfcBh5u"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ask_general(question_text):\n",
        "  prompt_text = generate_prompt_general(question_text)\n",
        "  answer = give_advice_conversational(model, prompt_text)\n",
        "  return answer"
      ],
      "metadata": {
        "id": "lhF_btsdBh3X"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LangGraph Implementation"
      ],
      "metadata": {
        "id": "deWGuCVpDXlb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install langgraph langchain_community -q"
      ],
      "metadata": {
        "id": "JYYcSyy5Dl0P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c881c9dd-2751-4e4d-9a54-59516c4a1f14"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.6/50.6 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m113.5/113.5 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m36.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m44.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m408.0/408.0 kB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.9/296.9 kB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.0/78.0 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.5/49.5 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.5/144.5 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langgraph.graph import StateGraph, END\n",
        "from typing import TypedDict, Annotated\n",
        "import operator\n",
        "from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage, ToolMessage\n",
        "from langchain_community.tools.tavily_search import TavilySearchResults\n",
        "from typing import Any, List, TypedDict\n",
        "from pydantic import BaseModel"
      ],
      "metadata": {
        "id": "ltjtKJJ4Dlw3"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The Agent"
      ],
      "metadata": {
        "id": "5yaneogYDtwD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define AgentState to hold conversation messages\n",
        "class AgentState(TypedDict):\n",
        "    user_input: str\n",
        "    medical_intent: bool\n",
        "    greeting_intent: bool\n",
        "    response: str\n",
        "\n",
        "class Agent:\n",
        "    def __init__(self, model):\n",
        "        self.conversation_history = []  # Track conversation history\n",
        "        self.graph = StateGraph(AgentState)\n",
        "\n",
        "        # Define the state graph nodes\n",
        "        self.graph.add_node(\"ask_user\", self.ask_user)\n",
        "        self.graph.add_node(\"check_medical\", self.check_medical)\n",
        "        self.graph.add_node(\"ask_doctor\", self.ask_doctor)\n",
        "        self.graph.add_node(\"check_greeting\", self.check_greeting)\n",
        "        self.graph.add_node(\"ans_greeting\", self.ans_greeting)\n",
        "        self.graph.add_node(\"static_idk_msg\", self.static_idk_msg)\n",
        "\n",
        "        # Define edges based on conditions\n",
        "        self.graph.add_conditional_edges(\"check_medical\", lambda state: state['medical_intent'], {True: \"ask_doctor\", False: \"check_greeting\"})\n",
        "        self.graph.add_conditional_edges(\"check_greeting\", lambda state: state['greeting_intent'], {True: \"ans_greeting\", False: \"static_idk_msg\"})\n",
        "\n",
        "        # Continue conversation paths\n",
        "        self.graph.add_edge(\"ask_user\", \"check_medical\")\n",
        "        self.graph.add_edge(\"ask_doctor\", END)\n",
        "        self.graph.add_edge(\"ans_greeting\", END)\n",
        "        self.graph.add_edge(\"static_idk_msg\", END)\n",
        "\n",
        "        # Set entry point\n",
        "        self.graph.set_entry_point(\"ask_user\")\n",
        "\n",
        "        self.chatbot = self.graph.compile()\n",
        "        self.model = model\n",
        "\n",
        "    def ask_user(self, state: AgentState):\n",
        "        user_input = state[\"user_input\"]\n",
        "        return state\n",
        "\n",
        "    def check_medical(self, state: AgentState):\n",
        "        user_input_new = state['user_input']\n",
        "        medical_classification = classify_sentence_with_keyword_similarity(user_input_new)\n",
        "        if medical_classification == 'medical':\n",
        "          state['medical_intent'] = True\n",
        "        else:\n",
        "          state['medical_intent'] = False\n",
        "\n",
        "        print(f\"check_medical state: {state}\")\n",
        "        return state\n",
        "\n",
        "    def ask_doctor(self, state: AgentState):\n",
        "        user_input = state['user_input']\n",
        "        if state['medical_intent'] == True:\n",
        "          to_ask = chat_doctor_prompt(user_input, self.conversation_history)\n",
        "          prompt_text = generate_prompt(to_ask)\n",
        "          advice = give_advice_conversational(self.model, prompt_text)\n",
        "\n",
        "          state['response'] = advice\n",
        "\n",
        "          # Save the model response to conversation history\n",
        "          self.conversation_history.append(f\"User: {user_input}\")\n",
        "          self.conversation_history.append(f\"Model: {advice}\")\n",
        "\n",
        "        print(f\"ask_doctor state: {state}\")\n",
        "        return state\n",
        "\n",
        "    def check_greeting(self, state: AgentState):\n",
        "        user_input = state['user_input']\n",
        "        greeting_check = is_greeting(user_input)\n",
        "        if greeting_check == 'greeting':\n",
        "          state['greeting_intent'] = True\n",
        "        else:\n",
        "          state['greeting_intent'] = False\n",
        "\n",
        "        print(f\"check_greeting state: {state}\")\n",
        "        return state\n",
        "\n",
        "    def ans_greeting(self, state: AgentState):\n",
        "        user_input = state['user_input']\n",
        "        if state['greeting_intent'] == True:\n",
        "          prompt_text = generate_prompt_general(user_input)\n",
        "          answer = give_advice_conversational(self.model, prompt_text)\n",
        "          state['response'] = answer\n",
        "\n",
        "          # Save the model response to conversation history\n",
        "          self.conversation_history.append(f\"User: {user_input}\")\n",
        "          self.conversation_history.append(f\"Model: {answer}\")\n",
        "\n",
        "        print(f\"ans_greeting state: {state}\")\n",
        "        return state\n",
        "\n",
        "    def static_idk_msg(self, state: AgentState):\n",
        "        if state['medical_intent'] == False and state['greeting_intent'] == False:\n",
        "          idk_msg = \"Sorry, I can only provide guidance on healthcare-related questions.\"\n",
        "          state['response'] = idk_msg\n",
        "\n",
        "          # Save the model response to conversation history\n",
        "          user_input = state['user_input']\n",
        "          self.conversation_history.append(f\"User: {user_input}\")\n",
        "          self.conversation_history.append(f\"Model: {idk_msg}\")\n",
        "\n",
        "        print(f\"static_idk_msg state: {state}\")\n",
        "        return state"
      ],
      "metadata": {
        "id": "U78si7ymDlut"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agent = Agent(model)"
      ],
      "metadata": {
        "id": "n2Uc8fRqDlsa"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Run CHATBOT here"
      ],
      "metadata": {
        "id": "arR6iZcPEKIG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def start_chat():\n",
        "  while True:\n",
        "      user_input = input(\"User: \")\n",
        "      if user_input.lower() == \"exit\":\n",
        "          print(\"Bot: Goodbye!\")\n",
        "          agent.conversation_history = []\n",
        "          break\n",
        "\n",
        "      # Pass user input to the chatbot\n",
        "      result = agent.chatbot.invoke({\"user_input\": user_input})\n",
        "\n",
        "      # Output the bot's response\n",
        "      print(f\"Bot: {result['response']}\")"
      ],
      "metadata": {
        "id": "J1NFyIQ6Dlhy"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_chat()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k3ujmWpX9QSV",
        "outputId": "6aa054f2-d777-49e9-c760-f541204b8066"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "User: hi\n",
            "Classification: non-medical\n",
            "Most similar keyword: chronic\n",
            "Similarity score: 0.42\n",
            "check_medical state: {'user_input': 'hi', 'medical_intent': False}\n",
            "Classification: greeting\n",
            "Most similar keyword: hi\n",
            "Similarity score: 1.00\n",
            "check_greeting state: {'user_input': 'hi', 'medical_intent': False, 'greeting_intent': True}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  return fn(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ans_greeting state: {'user_input': 'hi', 'medical_intent': False, 'greeting_intent': True, 'response': '\\nHi! Welcome to Chat Doctor.'}\n",
            "Bot: \n",
            "Hi! Welcome to Chat Doctor.\n",
            "User: exit\n",
            "Bot: Goodbye!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# User: Hey, what's up\n",
        "# User: Hi, I’ve been managing diabetes for a few years now, but lately, I’ve been feeling more tired than usual. I’m also noticing that my blood sugar levels have been harder to control, fluctuating between high and low despite sticking to my medication and diet. I haven’t experienced any serious symptoms like blurry vision or foot pain, but my energy levels are low, and I’m more irritable. Could this be a sign of something more serious? Why is my blood sugar becoming unpredictable?\n",
        "# User: Can it be treated?\n",
        "# User: What diet should I follow?\n",
        "# User: I mean, what kind of food and fruits should I consider eating?\n",
        "# User: Should I buy a new car or an old one\n",
        "# User: When was 'Pirates of Caribbean' released?\n",
        "# User: What is Currency?\n",
        "# User: Who is Amitabh Bachchan?\n",
        "# User: Computer\n",
        "# User: Thanks, this was helpful!"
      ],
      "metadata": {
        "id": "7R5TTBxELB5W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The Graph Representation (Workflow)"
      ],
      "metadata": {
        "id": "DLmZPDojD8BW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ],
      "metadata": {
        "id": "BsU1m_nmnBBf"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!apt-get install -y graphviz graphviz-dev -q\n",
        "!pip install pygraphviz --force-reinstall -q"
      ],
      "metadata": {
        "id": "UnBZkxjPDlp2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16bf6d6b-b5b8-4741-e9af-07facc4255eb"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists...\n",
            "Building dependency tree...\n",
            "Reading state information...\n",
            "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n",
            "libgraphviz-dev is already the newest version (2.42.2-6ubuntu0.1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pygraphviz (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pygraphviz as pgv"
      ],
      "metadata": {
        "id": "1gJ6QdoZDlm-"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "import langgraph\n",
        "\n",
        "Image(agent.chatbot.get_graph().draw_png())"
      ],
      "metadata": {
        "id": "r4U5-YIXDlkK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "outputId": "86a701e5-d174-4bc4-80fd-bacd8f69b15b"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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iotycC2cz87NOFeZkGwx6Xz+/0aNH3zVmzKRJk/CoSAAA6HRoM53mwoULqampKSkpKampFwoKTCaTo1QWOGBgn9CBgQMG+gcP8A4IsncQMx2zk2nV6oriS8V5OUW5OSV5OcX551uamvh8ftjAgaPj42NjY+Pj4319fZmOCQAAPRnaTJdobGw8e/bs6dOnT58+nZmVlZ2d3drSQkRuXt5eAUFefYK8AoJ8gvq5+/i5+fja2YuYztshTRp1bXVlZUlxZfGliuLCyqJLFUWX6muVROToKBkUEREVOcQiPDwcZ5EAAKDboM10B4PBUFhYmJ+fn5+fX1BQkJ9fkJefV1Ndbfmp1MnZ1dPL1cvH1dPb1dPLxd1D4uwidXaROrvKXOUisWO35WxUq9R1tZr6OnV9naa+rraqQllVUVddWVdZUVNZ3tTYaBnm4+sbEhwc8qfg4OCAgABcjgQAAExBm2GMSqUqKSkpLS0tKyu7fPlyWVlZcUnp5ctlVVVVzU1NbcMEQjuZs7PU2cXOQSy0F4mlMqG9yE4kcnB0FIkduTw+l8d1cJS0jRdLZZYFs8nc1KhuW69Vq81ms0Gva21u0qrVrS3NuubmpkZNa5O2pUmrrq9T1dcZDYYr2xE7evt4+/n6+vn59enTx9fX19fX19/fPzAw0MHBoevfHgAAgI5Cm7FFTU1NSqVSoVAoFArln7RabVNTU319fWOjVtuk1Wg0arXaaDS2trY2NzVbftFoNGo0VxqMTObE4XAsy2JHsUAgEAgEjo6OMplMLBY7isUymczR0VEsFsvlcrlc7ubm5u7uLpfLXV1dcaoIAADYAm2mB0pISPD19V2/fj3TQQAAALoD5joAAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7oc0AAAAAu6HNAAAAALuhzQAAAAC7ccxmM9MZ4O/auXNnampq27e7d+92dHQcM2ZM25opU6aMHj2aiWgAAABdDm2mJ9i1a9f06dMFAgGXe/3BNqPRaDAYMjMzIyMjGckGAADQ1dBmegK9Xi+Xy9Vqdbs/DQoKunTpUjdHAgAA6DaYN9MTCASC+++/XygUtvujpUuXdn8kAACAboM200PMnz9fp9P9db1er583b1735wEAAOg2ONPUQ5hMJm9v7+rq6qtXcjicIUOGZGZmMpUKAACgG+DYTA/B5XIXLVp03ckmHo+3ZMkSpiIBAAB0Dxyb6TkyMjKGDh169RoOh1NWVubj48NUJAAAgG6AYzM9R3R0dN++fdu+5XK5o0ePRpUBAIAeD22mR1m0aJFAILAsczicxYsXM5sHAACgG+BMU49y8eLF/v37W5b5fH51dbWLiwuzkQAAALoajs30KP369YuIiOBwOHw+f/LkyagyAADQG6DN9DQPPPAAj8czGo0LFy5kOgsAAEB3wJmmnqaiosLPz8/Ozk6pVDo4ODAdBwAAoMuhzdgis9ms/JNCoaiurlYqlRqNRqVSaTQarVaj1aobGuoaGxv1en1TU3Nrq/UuwAaDUaNpbtuOTCbmcjmWZZHI3t7ezs7OTiwWOzm5ODrKxGKJWCx2dnaWSCTu7u7u7u5yuVwul7u5ueEUFQAAsAjaDJPq6uqKi4tLS0tLSkpKSkpKS0vKygpLS0traupNpit/Lq6uAjc3rkRCMplJIjGIxWYHB3J2JrGYhEKys6O2QzAcDjk50YED5OpKQ4ZQQ8OVfTU2kl5PLS2k1ZJKRY2NpNVytVpefT1Xo6GqKqNabWgbLBDw3NycAwIC/P37+vn5+/v79+nTp0+fPgEBAVKptJveHQAAgI5Bm+kmRqPx0qVL+fn5eXl5BQUFeXnn8vPzFQpr3fD0FPr7k7+/3s/P3KcPubuTpyfJ5eTmRnI58fm3t6+GBhKL6c8rtTtKpyOlkhQKqqmhmhqqrqaSEiot5ZaWCkpLTUql3jLM21seEhIaEhIeEhISGhoaEhISEBDA4XBub2cAAACdB22mq+j1+vPnz2danTxz5pxW20JEfn7C4GBTSIghJIRCQykwkPz9yc6O6bi30txMxcVUWEh5eZSfTwUFgtxcqqnRE5FMJo6KioqKGh4ZGRkVFRUcHMzj8ZjOCwAAvQjaTGdSqVQpKSkpKSnJyYfS07NaW/UiES8ighcVpYuKoshICgkhR0emU3aehgbKzaWsLMrMpMxMYXa2Qa83OTqKhg8fFh8/Nj4+PiYmRiwWMx0TAAB6OLSZv6u5ufnQoUNJSUnHjh08dy7XZDIPGGAfH98SG0tRUTRgwG2fJ2IvnY7OnaOMDEpNpeRkflGRQSDgRUdHxMePT0hIiI+PF9zu2S8AAIAOQJu5QyUlJXv27Nm1a/vhw4dbWnRDhgjHjNGNHk1xceTmxnQ421BeTseOUXIyHTkizM3VSaUOEycmTJkybcqUKR4eHkynAwCAngNt5vZcvnz5559/3rz5+8zMcxIJf8IE85QpxilTyMuL6WS2raiI9uyhXbt4R46QTmeKjR2+YMGSOXPmyOVypqMBAADroc10iEql+uWXX3766fvk5DSZjD97tnHuXNPo0SQUMp2MbZqa6OBB+uUXzrZtXJ2OJk4cP3/+4lmzZtnb2zMdDQAA2Apt5hYuXry4fv36des+a2lpHj+eHnjANGMGSkwnaGmh/fvphx/427ebRCKHJUseev755/v06cN0LgAAYB+0mRs6ePDgBx+8m5R0IChIsHy57qGHSCZjOlNPVF1N69bR2rUChcJ43333rlixcujQoUyHAgAANsFTJ9tx4sSJu+8ePX78eIPh8Pbt5oIC3XPPocp0FQ8PeuMNKi7Wb9xoKiraMXz48FmzZubm5jKdCwAAWANt5hoXLlyYOfOekSNHGgwnkpNp/37D9OnExZvU9YRCWrCA/vhDv327+eLFPYMGhS9duqSyspLpXAAAwAL4oLYymUwff/zx4MHhRUWJu3fTsWP6uDiGIy1bRhwOcTh08SLDSbrT9OmUlaXfuNF05Mjm8PDQH3/8kelEAABg69BmiIguXbo0ZsyolStffOUVXXq6fsoUpgP1blwuLVxI2dn6hQvVS5Y8MGPG1OrqaqZDAQCA7UKbodTU1BEjohsbM06dMr3++m0/rBG6iFhMn35Khw6Zs7P3x8REYyYNAADcSG9vM7///vuECXfHxzempuojIphOA38xZgylp+v9/atHjRpx5MgRpuMAAIAt6tVtZteuXXPmzH70Uf2WLUYHh87ffloazZpF7u4kFFJgIC1cSBcuXDPg4EGaNo0CA8nenjw9KSGBdu++2Qbfecc6k+a99zoUIC6OOJzrHxQllxOHQ+HhtxGjro6ef5769SM7O3J1pRkz6MyZKz+dP584HLK3p+Zmmj2bHB3po486FK+DnJ0pKckwYYJ2ypRJ6enpnblpAADoGcy9VUFBgZOT45IlHLOZuuJrz552njcpkVB+vnXA1q3tXy31xRfWAQ8/bF1z4QKZzXTsmHWDs2d3NMOoUUREPN41K11diYgGDuxoDIWC+va9/qciEZ04YR3w0EPWla+8Yl349787//00GmnKFJ6fn2d1dTXT/+4AAIBt6aXHZnQ63b33TgsNbV23rqtuHvjllyQWk1BIycnU2ko//EBEpNHQxx9bB7z/PplMFB5OhYWk11NFBU2bRu7utG0b/fWOhgoFzZ9PBgOFhdGGDZ2Z85YxXnmFLl0iDoe+/poaGyk7mwYOpOZmWr7cugUOx7rwv//RoUPU0kLPPtuZCS24XPrxR6NQWLtw4bzO3zoAALDZX44e9A5ffPFFYeGl3FyjnV1X7WLHjivLJhPNmUNLl5LBQOfPW1fW1RERabVkNBKfT15etHNn+5sym2nRIiovJ6mUtm4lR8fOzHnzGHo9/fwzEdHw4bRsGRHRwIH0z3/S3LmUkUEXLlD//lcGL1tGY8cSEXXRu+rsTD/9pI+JObJt27aZM2d2yT4AAICFeuOxGbPZvGbNR48+auzShwKpVPTmmxQWRg4OxOORvT0ZDEREra3WAZMnExEVFVH//jRgAD30EG3aRE1N7Wxq1Srat4+I6NNPKTi4k3PePEZRETU2EhGdPGmdssPh0Ny51p+ePn3NprrhDj3Dh9OMGbxPPvmgy/cEAADs0RvbzJkzZwoLy5Ys6cJdGI00aRK99Rbl5lJzM9FVp2PavPsuPfqo9YLwvDzasIEWLaI+fSgp6fqRGzcSj0dEtGtX50e9eQyN5ma/q1Bc862bW+fH+6sHHzQeO5ZWW1vbHTsDAAA26I1tJiMjw9GRN3hwF+7i2DE6eZKIaMAAOnuW9HoyGK6fFCwQ0Lp1VFFBmzfT00/ToEFEREol3XcfKZXXjBw71jrt5rffbq/QWCqU0Wg9LEREej01NNxGDKnUOmzatHZm5j755DWb6p5HQMTFkdlszszM7I6dAQAAG/TGNlNbWyuX87v0o7e42Lowdy4NGkR8Pp06daVSXE0up/vvp08/pbNn6b//JSJqarrm+mci+vxzmj+fRo4kIlq+3HrqpyNcXKwLbVeGHz1KRuNtxAgKsk7TOX2aTKaO7rdLubqSQMBVXHdcCAAAerHe2GbEYrFW27WfzD4+1oWUFFKpKDOTli2zHrqoqCCjkaqqKC6OPDzo//6P6uvJZKK6uiuHZLy9r9ma5TTQ++8TEZWW0uuvdzRGaKh14ZVXqKaGsrPpmWdIJLoy4JYxeDyaN4+I6PJlWrGClEpSKGjePHJxofBwYuRsT3Mz6fUmx86dCw0AAKzG9CXiDNi/fz8RXb7cJbeZsXxpteTnd837HBpKK1dal728KC2NZs9u/09k/vz27zdjNpPlOh4ej06d6lCMixdJKLxm4w89RJZbHoeFWcfcMkZ1NbU7XfqDD26Ys0u/0tKIiPLz85n+9wgAAGxFbzw2ExsbK5GItmzpwl04ONDevTR+PEmlJJfTAw/QsWP04os0ZQp5epJcThIJ/fwzrV5NsbEkl5NAQJ6eFBtLn31mnSLTrv/+l/h8MhrpkUfaP291nb596dAhGjKE7OzI25tefpnWrrVOhWm7aumWMdzd6Y8/aPlyCggggYAkErrrLtq6lV544W++SXfot98oMNA3uNMv7gIAANbimP96p7Ze4IknHk9K+vb8eb29PdNR4HYoFNS/P3/lyn+vbDvSBQAAvV4vbTPl5eUhIf2efbZl1Sqmo0CHmc00bx4vNdWloKBILBYzHQcAAGxFbzzTREQ+Pj4ffvjJu+9ybv6UR5uVnn7lXnY3+uqKm9Mw6913ads22rTpV1QZAAC4Wi89NmOxbNlDP//8/bZtxvHjmY4Ct7JpEy1Zwvn4460DbRAAACAASURBVNVPP/0001kAAMC29NJjMxZr1341c+bcqVO533/PdBS4qXffpcWL6cUXV6DKAADAX/XSp05a8Pn8H37Y5O8f8OCD7xQU0D//ab2zC9gOrZb+8Q/Ohg2cNWs+Xd722G4AAICr9OozTW3Wr1//7LNPBQebvvtO36VPPIDbcuQIPfywoKHBfsOGH++55x6m4wAAgI3q1Wea2ixbtuzMmWxHx+hhw7ivv34bjw6ALqJQ0BNPcO6+myIiEnJyClBlAADgJtBmrPr163f0aOr773+0Zo04KIi/ejW1tjKdqVfSaOjNN6lvX/62bS4//rhp69Ydnp6eTIcCAACbhjNN16utrX333Xc/++xTd3fzypW6xYsJlwN3j/p6+vprev99vsFg/9JL//fMM8/gSmwAAOgItJn2lZeXv/32v7/77luRyPzww4Ynn6TAQKYz9VzZ2bRmDefHH7kCgf0TTzz90ksvOTs7Mx0KAABYA23mZpRK5ddff/3ll59WVFRPmcJdvNg4bdo1z6CGv0Oloq1baeNG/pEjhpCQoKeffv6BBx6QSCRM5wIAAJZBm7k1g8GwdevWb7756sCBQ2Ix9957TQsWmMaNIx6P6WTs1NpKe/fSpk3cXbvIbOZOmTLlsceenDhxIofDYToaAACwEtrMbairq/vtt9++/359Wlq6iwv/7rsN48eb77mHMEu1IxQKOnKEdu7k7dzJUauNI0cOnzNn/sKFC+VyOdPRAACA3dBm7sSlS5e2bt26Z8/OlJRUk8kUEyOYPFk3ZgwNG0Z2dkyHsyVaLZ04QYcP0969/Kwsg729cOzYu6ZOnTFjxgwfHx+m0wEAQA+BNvO3qNXqffv27dmzOylpd0WFwt6eO3w4b8wYfVwcjRxJvXMGSF0dpabSsWOUkiJITzcYDOa+ff0mTZo+derUsWPHijDtCAAAOhvaTKepqKhITU09cOBASsrB3NxCs9ns5cWPjjZGR5ujo2nECHJ3Zzpi16ivp5wcysigjAxuRoYwN7fFbKagIL9Ro+6Ki4ubOHFiQEAA0xkBAKAnQ5vpEpWVlSdOnMjKysrMPJWVlV5RoSSiPn2EoaGmkBBDaCiFhFBoKHl7Mx30NpnNVFpK+fmUl0d5eZSfz8/N5VZW6ogoIMA7Kmp4ZGR0ZGRkTEyMq6sr02EBAKC3QJvpDlVVVVlZWWfOnMnPz8/NPZOff6GhoZGIpFJ+YCDP31/fp4/J35/8/MjPjwICyN2d4edftrZSVRWVllJJCZWWUlkZlZXxiov5RUWGpiYjEbm5OYWGhoaGDgoJCRkyZEhUVBTuEAMAAExBm2FGdXV1bm5ufn5+cXFxWVlZScmF0tLSigqFwWC0DHBy4nt48ORys1xucHc3eXiQREJOTiQWk1hMjo4kk5FYTPb2xOORVGrdLJdLMtmVvdTXX1luaCCzmZqaSKsljYbUatJqSasllYpUKqquJoWCq1TyFQpOdbVRozFYfkso5Pv6evj59enTp5+/v39gYGBoaGhoaKiLi0u3vE8AAAC3hjZjQwwGQ2VlZUlJiUKhqKmpqampUSqVSqWypqa8pqaqsbGxvl6l1bbodIZO2Z29vVAstpfJJFKp1NPTRy73lMvlcrnc09NTLpd7eHj4+/t7eXnhNjAAAGDj0GbYR6fTabXa+vp6rVar0+ks31p+ZDAYNBrNqlWrXF1dH3/8calUyvvzHn8SiYTP59vb24vFYplMdvWPAAAAWA1tpgdKSEjw9fVdv34900EAAAC6A5fpAAAAAAB/C9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBvaDAAAALAb2gwAAACwG9oMAAAAsBuf6QDQCYqLi2tra9u+VavVSqUyIyOjbY2Xl5e3tzcT0QAAALocx2w2M50B/q4vvvhi+fLlNxmwffv2e+65p9vyAAAAdCe0mZ5AoVB4eXkZjcZ2fyqRSJRKpVAo7OZUAAAA3QPzZnoCNze3cePG8Xi8v/5IIBDcf//9qDIAANCDoc30EIsWLWr3MJter1+wYEH35wEAAOg2ONPUQ2g0Gjc3t9bW1uvWu7m5VVZWtnvYBgAAoGfAsZkeQiKRTJs2TSAQXL1SIBAsXrwYVQYAAHo2tJmeY+HChQaD4eo1er1+/vz5TOUBAADoHjjT1HPodDq5XK7RaNrW+Pv7l5SUMBgJAACgG+DYTM8hFApnz57ddvmSUChcunQps5EAAAC6AdpMj7JgwQKdTmdZ1ul0c+fOZTYPAABAN8CZph7FZDJ5eHgolUoOhxMeHn727FmmEwEAAHQ5HJvpUbhc7sKFC4VCIZ/PX7x4MdNxAAAAugOOzfQ0J0+ejImJ4XA4paWlvr6+TMcBAADocniGtq1QqVQajUaj0TQ1NTU1NbW2tjY3N7e0tLS0tDQ3N7e2tjY1Nen1+sbGRiIymUwqleq6LVjGE5GDg4NIJHr++ecty3Z2dteNdHZ2tixIpVIejyeRSPh8vlgsFgqFlvEikcje3t7R0VHypy5//QAAAHcKx2a6isFgqK2tra2tVSqVln8qFAqlUmlpLfX1NWq1SqNRazSNGo1WpdLeaDtCIVcs5gkE5OjI5fFIKrWul8mMXC7n6pF8vkkiMRJRdjY5OFBQEBGRSsU3ma4ZptdTY6P1DGNDA5nNpFKZTCbSaAwGww3/ZXB2dpRIHCUSR6lUJpHIZDJXmUwmk8nc3Nzc3NxcXV1dXV3lcrllgcvFGUwAAOg+aDN3rra2trKysry8vKqq6s9/Xq6qKlMqFQpFXX295urBMhnf3Z3n6mp2cjJJJAZnZ5JIrnw5OZFUal0Wi8nenkQisrMjB4c7CZaXR25u5Op6J7/b2Eh6PWm1pNORRnPlq6GB1Oor36pUpFLxGxp4CgUplUaN5spd+zgcjqurVC53kcvdvL37eHl5e3t7e3l5+fj4eHp6+vj4yGSyO0kGAABwA2gzt2AwGC5fvlxUVFRUVFRcXFxUVFRUlF9eXl5VpWxpsV4LLRLxvLz43t5mb2+9l5fZ0iTc3cnVleRy6z/5PfqcXmsr1dZSbS0plaRQkFJJtbWkUFB5OaeqSnD5MlVXG1pbTZbBDg52Pj4e3t6+AQH9A6/i7e2NgzoAAHAH0GauMBgMhYWFubm5eXl5Fy5cKCq6VFR0qayswmAwEpFIxAsMFAQGGgIDDT4+5O1NXl7k7U3e3vTnLBS4GYWCqqro8mWqrqayMqqooKIiblERr6TEaCk6dnaCPn28AgL6BQb2Dw4ODgsLCwkJ6dOnDyoOAADcXO9tMy0tLdnZ2Xl5ebm5ufn5ubm5Zy9eLNHpDETk7y8MDjYFBhoCAykggAIDKTCQPDyYTtxDmc2WZnPlq7hYkJdHVVV6IrK3F4aG9g0JGRQaOmDAgAGhoaFhYWHXPVwTAAB6uV7UZtRq9dmzZzMyMs6fz8nJyUhPP9faqhcIuH5+vLAww8CB5qAgCgujwYMJV/DYApWKLl6kwkLKyaHz57mFhXY5ObqWFqNAwOvfPyg6OiY6Ojo6OnrIkCGOjo5MhwUAACb15Daj0+kyMjLS0tJOnDiemXmysPAyEbm7CyMjzZGR+shIioykoCDi8ZgOCh2j11N+Pp0+TVlZlJXFz8qihgYDj8ft398/KiomJiY2Li4uIiKChz9RAIBepqe1mbq6urS0tLS0tJSUo+npGc3Nre7uwpEjDdHRJkt98fFhOiJ0nqIiS7OhjAze8eOchgaDRCKKiRkxatRdo0aNiomJwWEbAIDeoCe0Gb1en5qampiYmJi44+zZPCJzaKhw1CjdqFEUG0vBwUzng25hMtH585SSQmlpnNRUQWGhjsfjDhsWmZAwPSEhYdiwYZhNDADQU7G4zZSUlCQmJiYm7j548KBG0xQcbJ+Q0DJ+PMXG3uGtVqAnqayklBTav58SEwVlZXpXV+nEiZMTEqZMmjTJAzO6AQB6Fva1mfLy8t9+++1//9uUlpYuEnFjY2n8eOM999CAAUwnA1tVWEgHDtDOnfwDB8w6nWnkyOFz5syfP3++u7s709EAAKATsKbN1NbW/vbbb5s3/5CcnCaV8u+91zhvnumuu+gvzyACuCGtlvbto59/5uzcyTEYOBMnjr///kUzZszAg6gAAFiNBTMJ8vPzn332GT8/73/840mJ5PiGDebycv2335omTeq+KrNsGXE4xOHQxYus30tnkcuJw6HwcOu3XRG+07cpFtO999Ivv5hra01bthgdHA48/PASHx+Pxx57LC8vr3P2AQAA3c5224zJZNq5c+e4cWNCQ0MPHFj7ySc6hcK0c6fpgQfu8OlFAG1EIpo+nX791VhZaXrjjeakpG8HDgy79957jhw5wnQ0AAC4bTbaZg4cODB4cNiMGffY2aUmJVF2tv7RRwkX20Knc3GhF1+kS5cMv/xiVij2jh07Ni4uJj09nelcAABwG2yuzVy8eHHmzOkTJkwICrqUnU179hgnTiQOh+lYcCvr15PZTGYz9evHdJTbx+PR7NmUkmI4cYK43IwRI4YvXbqksrKS6VwAANAhNtRmjEbjv/71r4EDB1y6lLR/P23fbggL6769azT0+us0YADZ25NMRpMm0alT7Qzj8Wj9euuwfv3oxx+v+WldHT3/PPXrR3Z25OpKM2bQmTN3spc277xjnTjy3nsdehXz5xOHQ3Z2ZDbT22+Tjw9JJDR5MhUXExF9/jkFBZG9PY0YQdfNErll8owMGjeOHB3J1ZUWLyaF4vqK2e4cl1u+3rQ0mjWL3N1JKKTAQFq4kC5c6NAr7SIjRtCxY4bNm82HD28OCem7du1aJtMAAEAHmW1DXV3d2LHx9va8Tz4hvd76f/nd9qVWU0TE9e8Mh0NbtlgHPPywdeVbb10/bNs26xiFgvr2vf6nIhGdOHHbe7lwgcxmOnaM+HwiotmzO/pCHnrIuoW3375mL9HR1xcvT09qaelo8pyc60/zRUZab+ozcGD74Tvyevfssb7Aq0kklJ9/w21221dTE736KnG5nLlzZzU1NTH99wMAAG7GJtpMZWVleHiov78gK6u7P7QsX889Z/3U/Mc/SKmk06fJz4+IyMmJGhuv+Vj19qbdu6mujl591brmrrusG1m2zPpp/fXX1NhI2dk0cKC1SdzuXi5coJoa60MYwsJIo+noC2nbQnAwnT5NOTnk6Wld4+5OO3dSRQUNH25d87//dTT5nDnWX1m2jGpqKD+fhg2zrrlJm7nl650+nWQyEgopOZlaW+mHH6zjH3+c+TZj+TpwgJyd+aNHj9RoNEz/LQEAgBtivs1otdphw4b07y8oLWXmE8toJGdnIiJXV9LprCtXryYHB/Lyov37r/lY/e9/rQP0enJxISLy8CCzmXQ669GLESOubPnXX62/VVBwe3spKKCJE4mIpNIrBypuq82sWWNds2KFdc2SJdY1mzZZ1/znPx1KbjZbHyoukVhbiNlMGRm3aDMdeb3X/Sm0tFgP1YwebSttxmymc+fI3V0wffoUo9HI9N8VAABoH/PzZv75z39evJi9e7fe8j/u3a+oiOrriYiGDCGBwLrymWdIq6WKCho//prBY8ZYF/h863TX2lrrRhobiYhOnrROH+FwaO5c6+DTp29vL6tW0b59RESffnqHz5kaMcK6EBRkXYiJuX5NXV2HktfXk0ZDRBQVRWKxdX1k5C2uk+/I61Wp6M03KSyMHByIxyN7ezIYiIhaW+/kJXeR8HDavl2flJT4xRdfMJ0FAADax3CbqaysXL3641WrDP37M5ahocG6IJPderBcfmVZJCIiMpuJyPp5fyMKxe3tZeNG4vGIiHbtuvXgdlmOixCRvb11wcnp+jUdTK5UWpevvl8uh0NS6c1+8Zav12ikSZPorbcoN5eam63btE0xMfT886Z//vPVlpYWprMAAEA7GG4zW7ZsEYs5S5cymaHtQ9pyLOHOtH20T5vWztmKJ5+8vb2MHWudRPLbb3deaDrolsktJ9Toz2M5FibTlb7Srlu+3mPH6ORJIqIBA+jsWdLryWBoZ1KwjXjmGVKpNElJSUwHAQCAdjDcZnJzcyMjOW1HCxjRt6914khmJrX9v/dPP5FIRG5u9M03HdpIUJB1I6dPk8n0d/fy+ec0fz6NHElEtHy59UxQF7llcldX61Goc+eoqcm68vhxuvlxilu+XstF40Q0dy4NGkR8Pp06ZT3TZIO8vCggQHj+/HmmgwAAQDuYnzdjZvqplzweLVxIRKRS0XPPkVJJOTn0r39RSws1Nl4/o+UmG5k3j4jo8mVasYKUSlIoaN48cnGh8HCqrb29vVgmmrz/PhFRaSm9/nrnvdrbT05EEyYQEWk09OyzpFBQdjY99titN3vz12u5YouIUlJIpaLMTFq2jLhcIqKKCjIau+bVAgBAj8TsJOQ1a9bIZPzmZsauWLF81dZSSEg7b84339zs4hrLjGAez/ptdTX16dPORj744M73MnOmdRenTnXohfx1Cxs2WNds3mxdk5VlXfPCCx1NnpFx/QM+Y2Ks134PGHDDXd/89Wq1dN2879BQWrnSuuzlRWlpNnFNk+WrvJx4PM727duZ/fsCAADtYvjYzOzZs5ubO3o2p+u4uNDx4/TSS9S/PwmFJJXSuHGUlHTlZnQd4e5Of/xBy5dTQAAJBCSR0F130dat9MILd76X//6X+HwyGumRR7rwLMwtk0dF0Z49NHQo2dmRmxs9+CDt2mWdEG2ZwNuum79eBwfau5fGjyeplORyeuABOnaMXnyRpkwhT0+Sy6+ZdMy41avJyUk6adIkpoMAAEA7OGamz/SsXLly3boPT5403NmlyABdLS2Nxo7lfvzxmieffJLpLAAA0A7m20xzc/PYsfG1tWcPHtT7+zObBeB6Z8/S+PH82NiE33/fzuUyP88MAAD+ivn/OotEou3bd4tE/UaNErTdYRauk55+5dZ2N/rq6mu5e6H9+2nMGH54+Mgff9yMKgMAYLNs4j/QHh4ex46lDRgQN2oU98MPSa9nOpDtGTr01jNVp01jOmUP0tREK1dSQgJnypTZe/fud7zuqZsAAGBLbKLNEJGTk1Ni4oE33vj3a68JBg8W4C5lwBSzmX7+mUJDBV9+6bBu3VebNm22u+6CLgAAsDG20maIiMvl/t///V9OTl5o6OSEBJo+nY97lUE3O3GC4uP5CxdyJk5cVFBQuMzyeHEAALBtNtRmLIKCgn7/ffvBgwdLSvqFh1NCAj8xkZieqQw9nMFAv/5Ko0YJRo4kDmfoH3+cWr/+Ww8PD6ZzAQBAh9hcm7G4++67T5/O2bFjp9EYP2UKJyxMuHbtLZ6PCHAHlEp67z3q25e/YAHXw2PykSNHkpOPR0dHM50LAABuA/NXaN9SQUHB559/tn79VyaTfvx4mjPHNGsWicVMxwI2a26mXbvo++/5+/aZ7Ozs589f9Pzzz4e0e+tiAACweSxoMxb19fVbtmzZvPmHo0dTxGLuzJmm++83jR1LzD6xEtilsZGSkmjzZu6ePRyjkTNp0oT77180Y8YMMdoxAACbsabNtKmtrd2yZcv333+TlnbK3p47ahSNH28cP55wcgBupLCQdu6kXbv4yckmvd48cuTwOXPmL1iwwM3NjeloAADQCdjXZtqUlZUlJiYmJu49eHCfSqXt189u0qTWCRMoNpbwIQXl5ZScTAcOUGIiv7zc4OYmmzhxSkLClIkTJ7q7uzOdDgAAOhOL20wbg8GQlpaWlJSUmLjj9Okck8kcEiKMjdXFx1NsbPuPcYaex2Si7GxKSaG0NE5KiqCkRMfn80aMiE5ImJ6QkBAVFYWb+QIA9FQ9oc1cTaVSpaWlpaampqQcPXXqVFNTq5ubcORIY1SUMTKSIiPJz4/piNBJzGYqLKSsLMrKosxM3vHjpFIZpVKHkSNjRo26Ky4ubvjw4ZgQAwDQG/S0NnM1vV6fmZmZmpp64sTxzMyThYWXzWazXC4cMsQcFaUfMoSGDKF+/UggYDoodExrK+Xl0enTlJVFp0/zT582q1RGHo8bHBwQFRUzcmRsXFxceHg4j8djOikAAHSrntxmrqPRaM6cOXP+/PmcnJyMjNSMjLMtLXo+n+PvLwgKMoSFmQYOpLAwioggqZTprEDU0ECXLlFhIeXk0PnzvJwcQX5+q9FoFgh4/fsHRUfHREdHR0dHR0ZG4gAMAEAv14vazHV0Ol12dnZ+fv758+fz8/Py8s4WFBS1tuqJyNdXGBxsDgjQBwZSQAAFBlJgIHl5EYfDdOieyGik8nIqKqLiYioqsnwJ8vJIodATkVhsHxLSLyQkPCxsYEhISFhYWEhICJ/PZzo1AADYkN7bZv7KaDQWFRXl5eXl5uZeuHChuLiwqOhiaWm5TmcgIjs7bkCAMCDAEBho8PYmPz/y8CBfX/L0xCVUt2Y2U3U1VVXR5ctUWUkVFVRWRsXF3OJiXmmpQa83E5G9vSAgwCcwsH9AQL+QkJDQ0NDQ0FB/f38OWiQAANwU2swtmEym8vLyoqKi4uLioqKioqKi4uILFRWXy8urm5paLWPs7LheXgIfH7OXl97b2+zmRq6u5OZGcjm5upKrK8nlJBQy+zq6VksLKZVUW0tKJSkUVFtrXS4r41ZX88vKqLraoNebLIMlEpGvr5e3t19AQL/AwMCAgIDAwMDAwEAvLy9GXwQAALAV2sydU6lU5eXllZWVFX8qL79cWVlaW6tUKutqa9VXD5ZI+HI5z82NZDKjk5NBKiWJxPolk5FMduVbqZT4fJJIiMdjYAZPQwOZzaRSkclEDQ2k0ZBGQyoVabVUX2/91vLV0CCoq+MqlebaWqNWa2zbApfLcXWVyeUurq5yH59ALy8vX19fT09PX19fDw8PPz8/THMBAIDOhTbTVUwmU21trVKprP2TQqFQKpUqlaqhoUGtrtNoVGq1SqNpVKs1DQ3aG/1BcLkcmYxPZHZ25hGRk5N1+g6fb5ZIrvkVDsfs5GS4eo3ZTA0NfKJrztSoVByTiUNERiOp1WQykUplIqL6ev2NXguHQzKZ2NlZJpVKJRKpROIkkTg5OTk5Ozu7ubm5XkUul7u6ut7ZOwYAAHBn0GZsRWNjo1qt1mg0jY2NOp1Oq9Xq9frGxkaDwaDRaEwmk0qlMpvNDQ0NlvHNzc0tLS1EdPHiRZVKFR0dbRl/3WalUul1Vyw7ODjY2dkREYfDcXJyIiJnZ2cikslkXC7XMl4ikfD5fKlUKpFIqqqqZs+ezeFwzp07Z4/HYgEAgO1Bm2G9mTNncjicrVu3dt0uFArF0aNHZ8+e3XW7AAAAuGO41zvr5eTkjBkzpkt34ebmhioDAAA2C8dmWM9kMul0OpwDAgCAXgttBgAAANgNZ5rgzlmmIQMAADALbQbu0I4dOwYOHJiTk8N0EAAA6O3QZuAOxcbGenl5xcfHK5VKprMAAECvhnkzcOdaW1v37ds3ffp0poMAAECvhjYDAAAA7IYzTSy2c+dOtVp963EAAAA9GtoMW+Xn599zzz3nz59nOggAAADD0GbY6ujRow4ODlFRUUwHaQeOGAEAQHdCm2GrCRMm/PDDD0KhkOkg10tOTg4MDNy3bx/TQQAAoLfALGDoZK2trQ8//PCvv/6ak5PTv39/puMAAEDPhzYDnc9sNh8+fPjuu+9mOggAAPQKaDMAAADAbpg3AwAAAOyGNgMAAADshjYD3ae6uprpCAAA0AOhzbCPyWRiOsKdyMnJCQoKWrt2LdNBAACgp0GbYZ/HH3/8wQcfZDrFbQsLC3v55ZeffPLJ5ORkprMAAECPgjbDPocPH/b19WU6xW3jcDhvvPHGH3/8ER8fz3QWAADoUdBmWEan00VEREycOJHpIHdo6NChTEcAAICeBvebAQAAAHbDsRkAAABgN7QZYF5hYSHTEQAAgMXQZoBhlZWVgwYNeu6554xGI9NZAACAldBmgGFeXl7ffPPN2rVrt2zZwnQWAABgJcwCBptQUFAQHBzMdAoAAGAltBkAAABgN5xpYg2lUvnMM89UVlYyHQQAAMC2oM2wxrFjxz7//HORSMR0EAAAANuCNsMaJ0+ejIiIcHJyYjpI98nOzsaFTgAAcEuYN8MaZrNZoVC4u7szHaSbaLXaoKCg4cOHb9682dHRkek4AABgu3BshjU4HE7vqTJEJBaLt23b9scff2zYsIHpLAAAYNNwbAZsWmVlpaenJ4fDYToIAADYLrQZAAAAYDecaQIAAAB2Q5sBAAAAdkObAfbJzMxsbGxkOgUAANgKzJthgezsNgBhbAAAIABJREFUbHd39151QdNNGI3GQYMGCYXCXbt2+fr6Mh0HAACYh2MzLPDII4+88cYbTKewFTweb8+ePXq9ft26dUxnAQAAm4BjM7auqanJ2dn5u+++mz9/PtNZbIharRaLxTwej+kgAADAPD7TAeAWTCbTRx99NG7cOKaD2BapVMp0BAAAsBU4NgMAAADshnkzAAAAwG5oM9BzZGZmlpWVdcOO5HL522+/3Q07AgCAjkCbYYFFixZxbmDt2rVMp7MhK1asGDFiREZGRgfHz5w5s9139eLFi12aEwAAOhfaDAv8+OOP5j/17dt3yZIlbd8+/vjjTKezIb///vugQYO++eabjv/K4MGDzX/Rr1+/rgsJAACdDm2G9dzc3FavXj116lR7e3uVSuXo6PjBBx+0/XTZsmVDhw61LBsMhjfffDM0NFQkEvXv33/16tUMRe4qMpls9+7dn3zySadsraSkZM6cOZ6eniKRKCQk5PPPP293WHJy8ujRo52cnBwdHYcNG7Zt2zbL+h7/bgMA2A60GdYTCoUbNmxYuHBhfX39za9bXrFixerVqz/88EOFQvHuu+++8sorN/qEZi8+ny8UCjtlU08++WR5eXl6enpdXd0LL7zw1FNP7dix47oxWq122rRpI0eOLC0trampeeSRR5YuXVpVVUW9490GALARaDO2Kzk5eeDAgfX19TcfxuFwXFxcFixYIBKJOBzOjYap1eovvvjilVdemTp1qqOj43333ffQQw+9//77nZ2659i9e3daWpqvr69IJHr00UcDAgKSkpKuG1NaWqpWq6dPny6VSh0cHB599NH6+npPT0+82wAA3QltxnYdOXJErVY7OzvfcmRERMQtx5w+fVqn08XHx7etGTVqVElJiVqt/lspWe7MmTPXTQF2dHRsd6SXl1dtbe11K4ODg0NCQubPn//OO+9kZma23b0J7zYAQHdCm7FdXC531qxZHRl5ow/gq1k+R2NjY9s+thcsWEBElZWVfzOn7Tt79mx6enq7P/rrLOC2p3OfPXt2zpw5fn5+IpGIz+cfP378r7/O4/GSk5PnzZu3bt266OjogICAH374gXr3uw0A0P3QZmzXq6++egcTWq872dTU1GRZcHJyIqKTJ09e9+EdEhLSKWlt2erVq8eMGbN169aO/0pNTc3o0aN1Ol1iYqJSqWxpaYmJiWl3pJub2wcffFBcXJydnT127NgHHnggIyOjN7/bAADdD22mp3Fycqqrq7Msm83mrKwsy/LgwYPt7OxOnjzJXDTGrFu3bvHi/2fvvuObKvc/gH8zO9MmTUv3opsyWgpiS5lW9hAVcTCuggqiKNefeBW9DvCKyFUQUXBxlSGigAiiLKVsKB2MlraUDrpHmqZJmzTr/P5ILKWAFGhykvbzfuXFKz15cs7nhKT59nmec86MrVu3dvwpmZmZCoXinXfeiY2NdXFxaWxszM7O/vunxMbGfvnllzwe78KFC9351QYAsD5UM13NwIEDf/rpp6KiIrlcvnjx4taJGiKRaN68eUuXLj1w4IBarS4sLJwyZcqMGTPYTWsdfD5/7dq13333XcefEhYWxuFwfv/99+bm5szMzOnTp0dFRRUUFGg0mrbNdu/eHRQU9Oeff2o0mubmZlM1k5iY2J1fbQAA60M109WsWLHC19e3d+/esbGxzs7OTz31lNFobH1o3rx5c+bMcXd3HzJkiJub2/Lly9lNa00CgaDjjcPDw1etWvXpp596enrOnz//zTff/Pe//11QUNCnT5+2zcaNG/fss8/OmzdPIpH4+vpu2rRp586dkZGR1O1fbQAAa8I1tG0dwzCt5YglcLncvzmuuwszGo2svPl5PJ71NwoA0LWhb8bWffPNN3xLmjNnDtu7yI6xY8da9IW9metPWgMAAHcJfTO2TqFQVFRUWG79YrHY19fXcuu3WaWlpRcuXCgoKEhJSbHmdoOCglxcXKy5RQCALg/VjC367LPP+vbtm5yczHaQLm7p0qVvvfXWsmXLXnnlFbazAADAncNIk80xGo2LFy9uPbIaLOeNN95YsWLFkSNHDAYD21kAAODOoW/G5mRlZcXHx2dlZfXr14/tLN0CwzDdcx40AECXgWrGFuXm5kZGRnK56DkDAAC4NVQzAAAAYN/w1z8AAADYN1QzAO2VlJSsWrWK7RQAANBRqGYA2jty5MjLL7/89NNP63Q6trMAAMCtoZoBaG/69Ok7duyoqqrCrDIAALuAWcAAAABg39A3Y0MaGhoqKyvZTgEAAGBnUM3YkC1btkRFReG8tAAAALcF1YwNOXToUFJSEo/HYzsIAACAPUE1Y0MmT5783HPPsZ0CbqqysnLRokVarZbtIAAAcA3MAgboqEOHDk2cOPGee+7Zvn27u7s723EAAMAMfTMAHTV8+PAjR45IJBKhUMh2FgAAuAp9MwAAAGDf0DcDAAAA9g3VDAAAANg3VDMAAABg31DNANwtmUz25JNP1tfXsx0EAKCbQjVjE+6///6dO3eynQLuUE1NzcGDB5OSkq5cucJ2FgCA7gjVDPvKysoOHDggEonYDgJ3KCYm5uTJk4MGDZJIJGxnAQDojnCENvuOHj06Y8aM7OxsZ2dntrMAAADYH1QzAAAAYN8w0gQAAAD2DdUMAAAA2DdUMwCW0tjY+OCDDxYUFLAdBACgi0M1A2AparW6tLQ0MTExIyOD7SwAAF0ZqhkAS/H29k5NTX3iiSdCQ0PZzgIA0JXhmCYAAACwb+ibYVN6evqWLVvYTgEAAGDfUM2waf369StWrGA7BQAAgH1DNcOm1NTUYcOGsZ0CAADAvqGaYdP27dsXLFjAdgqwNrVaPW7cuEOHDrEdBACgi0A1w6aIiIjg4GC2U4C1MQzj5OQ0ZsyYX3/9le0sAABdAY5pAmCB0WhctmzZ7Nmzvb292c4CAGD3UM0AAACAfcNIE4CNunTp0h9//MF2CgAAO4C+GQBbJJPJBgwYwOVyc3NzBQIB23EAAGwa+mYAbIjpr4uWlpZJkyaVl5eXlJSsXr2a7VAAALYOfTPsqKmp6dGjB9spwLZotdrx48dPnz59//79W7ZsMRgMROTk5HT58mVfX1+20wEA2C70zbBAqVT6+/vv3LmT7SBgW/h8fv/+/Z988snNmzebShki0uv1b7zxBrvBAABsHKoZFhw7dkyv1w8cOJDtIGBbuFxu//79GYZp22Oq0+nWr1+flpbGYjAAABuHaoYFUqn05Zdf9vPzYzsI2JajR4/OmDGDw+G0W87j8ebPn49BYQCAm8G8GQCbUFBQMGDAAJVK1TrG1BaHw9m4cePjjz9u/WAAALYP1QwA+3Q63T333JOVlcXh3PgjyeVyvby8Ll++7OLiYv14AAA2DiNNAOwTCASpqanr1q0bMGCA6cd2DYxGo0wmW7ZsGRvpAABsHfpmAGxLTk7Od999t27dOoVCweVy2w48CQSCvLy80NBQFuMBANggVDMAtqilpeWXX3757LPPUlNT+Xy+TqcjIoFAMH78+B07drCdDgDAtqCaAbBpBQUF33zzzVdffVVXV8fhcIxG4x9//DFixIjrWzY3N7e0tGg0GrVardVqm5qaiMhgMDQ2NrZr2dLS0tzc3G4hh8MRi8XtFvL5fJFIRERcLtfd3Z2IJBIJEbm5ufF4vE7aRQCAu4VqBsCGqNVq2V8UCoVSqVQqlSqVSiaTZWdnnzt3rry83MXFOTIypKmpSafTKRRKo9GoVKr1+hscCWVprq5OAgHP2dnJwUEoEolcXUUikbtIJBGLxSKRSCQSubq6urm5icViV1dXsVgs/cv1R6EDANwNVDNW9dlnn5WVlf3nP/9hOwiwQKPRVFZWVlRUVFVVVVRU1NXVmaqWurqqurpqmaxeJmtobm5p+xQnJ56rK1ck4kok5OpqdHU18vmGykoKCqI+fcjBgUQi4vPJ2ZkcHMjRkZycSCgkFxfi80kkMq/E3Z24107353LJ3b19PK2WmpraL1SrSaMhItLrSakko5EUCiKihgZiGGpsJIOBVCrS6aixkZRKUqlIpSK5XKBUclQqjlLJKJVMQ4Ou7a8ZDocjlbpJpRKp1FMq9ZZKvUwljq+vr7e3t7+/v7e3d48ePVDxAEDHoZqxqhEjRgQFBX377bdsBwFLkcvlxcXFxcXFJSUl5eXlVVVVFRVXqqrKKytr5HJlazNvb4GnJ1cqNUqlek9PxtOTpFIy/dt6c3cnPp/FXelMTU3U0EAyGclkVFtrvvPXjVdXx6ur41RV6ZubzT1MAgGvRw8Pf39/H58AP78AHx+fwMDAkJCQkJCQwMBAXFQcANrpKr8s7QHDMJWVlTNmzGA7CHQCjUaTn59fWFhYbFZYXHypuLhUoTD3b/j6Cv38OL6+urAw45Ah5ONDfn7k40P+/tSjBwkEOnbzW5mLC7m4kL//DR80EJmLmMZGKi+n6moqLzdUV9eWldXW1GRdvCg4dIhbUqJXqw1ExONx/fy8QkJCQ0MjQ/4SEREREBBgvf0BABuDvhlrMxqNXC5O82Nn5HJ5dnZ2Tk5OYWFhYWF+dvbZvLxig8FIRBIJv2dPTs+eel9fxs+Pevaknj0pKopcXdkO3eXI5VRYSBUVVFlJhYVUWMgrLBRcuqRvbNQTkYODICwsJDa2X8+eYb169YqNjY2KinLFfwNA94BqBqC9pqam8+fPZ5mdzsnJVSrVRCQW86OiuFFR2uhoioykqCgKDydHR7bjdnvV1ZSXR/n5lJdHubncvDxBUZFWr2c4HE5QkE/fvvH9+vWPi4uLi4vr2bMnpuMAdEmoZgCooaHh1KlTmZmZmZkZZ8+mX7pUZDQy7u78fv24cXHa3r0pMpJiYqhHD7aDQsdotVRYSBcvUl4enT3LycriX7qkNxgYNzfnvn17x8XdExcXN3DgwNjYWBxnDtA1oJqBbqqwsPDo0aPp6enHjh3MzMwxGhlfX0FsrKFXL2NCAiUkUExM+0OBwH5ptXTpEqWnm27Cs2cNKpXB1dWpX79+ycnDBg8enJSUJJVK2Y4JAHcI1Qx0FwzDnDt3bt++fampf5w4cby+vtHJiTdgADcpSZeURImJ5OXFdkSwFoOBsrPp2DE6cYJOnBAUFOi4XE5MTNjgwSNTUlLuu+8+Dw8PtjMCwG1ANQNdXE1Nzf79+/ft27tv356qKpmXl2DECH1SEpOYSPHxhEN9gYhqaujECTp+nI4cEZw+rSfiDBgQN3r0hFGjRg0aNIjfZQ6UB+i6UM1A15Sfn79169YdO37IzMwWCDhJSbxRo3SjRlF8PMaP4O80NNDBg7RvH+3bJywu1rq7u4waNWbq1Gnjx493dnZmOx0A3BiqGWswGAybNm2aOHGi6Ro3YDmXL1/eunXr1q2bsrKyvb0FDz6oGzuWRozA8dJwJ/Lzad8++vln/qFDBkdHh4kTJz3yyKNjx451xJFsADYG1Yw1nDlzZuDAgRcuXIiNjWU7S9ekVqs3b968bt2naWlZnp6Chx7SPfIIDRtGOGAFOkVNDW3bRlu38g8fNri4OE6d+ujzz78QHx/Pdi4AMEOfuzWkpqZKpdKYmBi2g3RBV65cee211wIDfefPfzYq6ty+fVRZqVu7lkaOtHYpM2cOcTjE4VBBgd1vxcpsf6d69KB58+jPP/VlZczSpepTpzb0799/yJB7t27dqtN1r9M6A9gmVDPW8Oijj27duhWnAO5ceXl5jzzycFhYz+++++jFFxUlJYYNG4z33991rm0ENsjXlxYsoAsX9AcPkpdX2uOPPxoa6v/xxx9rtVq2owF0a/h+tQZ/f/+RI0eynaLrqKure+GFF/r0ic3L27Vxo6G4WPvmm+TtzXYs6Jjp04nDod272c5xd0aOpO3bjQUFzKOP1i5e/H+xsRHbt29nOxRA94VqBuyJ0Wj86KOPIiJCt21b9/nnhowM7bRpOMranlRX048/tl/41VfEMMQwFB7ORqa7EBJCK1ZQbq7x3nvLHn74oaFDE8+dO8d2KIDuCNUM2I2Kior77hv2+uuvLFigunRJN3u2tWfGKJX05psUE0OOjuTuTqNHU1raDZrxePTVV+Zm4eG0ceM1j9bX0z//SeHh5OBAUilNnkxnz97JVlq9/7550sny5R3dkbQ0GjmSXFzIw4OmT6fqarr3XuJwKCLC3OCxx4jDIUdHUqvp4YfJ1ZU++qij+f+mwfDh5ONDpjGZiROv9tBcP2/GFMDBgYj+7sW85Y5YR1AQbdhgPH2aDIYzgwYNWL16tVU3DwBExADYgwsXLgQG+kRHCzIzzX/HW/nW2Eh9+7b/+HA4tG2bucHs2eaF77zTvtnPP5vb1NZSWFj7R52c6OTJ297KpUvEMHT4sHme0MMPd3RHLlwgF5dr1t+vH4WGEhHFxJjbPPWU+aHXXjPfWbKkQ/n/vsGwYe0f2rXrBjvVNsDKlTd9MTuyI1a+6fX07rvE43Hmzn1Wr9ez/aEB6EZQzYAdKCgo8PKSDB3Kl8vZ+ZZiGFq40PyV+dJLVFdHWVkUGEhEJBaTSnXNV7KfH/36K9XX0+LF5iXDh5tXMmeOuTr58ktSqejCBTIds5+QcNtbuXSJamrI35+IqFcvUio7uiMPPWRew8yZVF1NOTnUeqBxbGz7mik8nP74gzQaamzsUP5bNmjttjDVMe0211rNdOTF7MiOsHL7+WdycuI9/fRstj83AN0IqhmwdRqNpleviIEDBaavc1ZuBgOZTnwolZJWa164ahU5O5OvL+3ff80X8LJl5gY6HZmu9uPtTQxDWq35JH6DBl1d89at5mfl59/eVvLzadQoIiI3N8rLu419MWVwcTEXKAxDJ0/etJpp3ZeO5L9lA+b2q5mbvZgd3BG2br/8Qjwe5/PPP2f70wPQXeBgVstSq9VOTk5sp7Bvq1atKikpzMkxtBtWsKaiIpLLiYji4q5OOl6wgBYsuEHj1vEUPp/Cw+n0aZLJzCtRqYiITp0iDqf9s7KyiMu9ja289x7t20dE9MknFBnZ0R2Ry80ZBgwgkci8cOBAcnam5uYbtE9Ovnr/lvkZ5hYN7mA6y81ezNvdESubOJFeeYX517/+b+rUqbg0N4AVYBawZc2aNWvWrFlsp7BjDMOsXbt63jxDUBCbMRoazHfc3W/d2NPz6n1TKcswRERK5d89q7b29rby7bfmSdC3dahzXZ35TmsFQERcLrm53bh92+uK3zL/LRvcgZu9mLe7I9b3xhtkNLZ8//33bAcB6BZQzVgQwzCHDx/G1QzuRmVlZVFR2aRJLMdo/co09Z3cmdYv2gkTbjA28dxzt7eVESNowwYiop9+uo2CpvVCYaYeDhOjkRSKG7dve8bHW+a/ZYNOdLs7Yn0uLnTffcajR4+wHQSgW0A1Y0HNzc1jx44dZZrdAHdELpcTEetd9WFh5lkaGRmk0ZgXbt5MTk7k5UVff92hlfTsaV5JVhYZjXe7lTVr6LHHKDGRiGj+fPOwyy15epLpgonnz1NTk3lhWhqp1Z2Q/5YN2jIYOhT4Zu5mR6zG09Mol9fduh0A3DVUMxbk4uKyfv36uLg4toPYsYCAAC6Xk5fHcgwej554gohIoaCFC6mujrKz6d13SaMhlYpSUjq6kmnTiIjKyuiVV6iujmprado08vCg3r1JJru9rZgm1nz4IRHRlSv05psd3RfTelQqeuEFqq2lvDx69tlOy//3Deiv0SIi2ruXWlruqvi44x2xmosXBcHB1x2wDgCWwPY0ZIBbGDFiyIQJPHYPUWEYkskoKuoGn6Cvv77pgTnMX2dY4fHMP1ZXU3DwDVayYsWdb+WBB8ybSEvr0I6cOUNC4TUrv+ce83Uhrj+mqe2+dCT/LRu0OxPg6tU33lxHXsyO7AiLt4sXicvl7Nq1i+0PEEC3gL4ZsHWvvfbm7t2GnTtZjuHhQSdO0KJFFBFBQiG5udF999HevVfP89YRPXrQ6dM0fz6FhJBAQCIRDR9OO3bQyy/f+VaWLSM+nwwGevpp0utvnSEhgX7/nQYOJAcH8vKiWbNozx7zE295jYhb5r9lgwEDaOlS8vYmoZBCQigk5NaBLbEjlqbX07x5/Li43mPHjmU5CkD3wGEYhu0MALfwzDNztmz59o8/9AMGsB2lK5LJzIcOjR1Le/awneYu2MiOMAzNncvZuFF47NhJDDQDWAf6ZsAOrFnz+eDBw++7j79/P9tR7N+qVdSnD4nF9Omn1NxMVVX0wgvmh8aPZzXZbbLNHWlpoRkzuP/7H2/z5h9QygBYDfpmwD5otdrZs5/8/vvvX3uN+fe/2R9KsEFnztDAgbdos2sXBQXR0KE3OJJ50CA6dMh8oJBdOHfO5nbk4kV6/HFBUZHwp59+Tung5HAA6AyoZsCefPfdd88996y/v2HpUt3UqWynsVu5ubRiBf3xB1VWEpdLERE0dSr9859kd6ettp0daWigZcto1Spenz59N2/eGh4ebu0EAN0bqhmLuHLlyptvvrl8+XJv0yEW0HlKS0sXL35t48bNI0bw/vtfPfrygV16PX3zDb35pkCvd/r3v9+dP38+n48rxgBYG+bNWMSff/75ww8/uNnOWda7kMDAwO++23j48GGlsndCAmfKFN7Bg4SaHKxPLqcVKygyUrBggWDWrJcKC6+8+OKLKGUAWIFqxiLOnz9/zz334HqTlpOcnHzqVMYPP2ytqxuYkkJ9+gjWru3o+XAB7tK5c/TMM5yAAN6SJc4TJsy9eDFv+fLl7h25vBYAWAZGmiylqanJhcWLPncnmZmZn366+vvvNwmFxgcfNDzyCJOSQvgLGTpdeTn99BP98IPgxAlddHTY888vnDlzpqjtdS8BgCWoZqCLkMlk33777Q8/bDp9OkMqFT74oG7aNGb4cPOFpgHuWHU1/fQTbd3KP3rUIBI5TZo0ZcaMWSkpKRwOh+1oAGCGaga6mqKioq1bt27duikj47yXl+D++/WjRjGjRpGvL9vJwH4YDJSWRvv20b59gpMn9U5OjpMmTX7kkUdHjx7taEdHsQN0G6hmoMsqKCjYsWPH3r17jh491tKi69vXYdSollGjKDnZ/g5FBusoLqb9+2nfPs7Bg1y53BAY6D1q1Phx48aPHTsW0+AAbBmqGej61Gr1sWPHDhw4cODArxkZ2TweRUbykpP1gwfT0KF3dakgsHcGA+Xm0rFjdPQoJz3dISdH4+QkHDx4cErK6JSUlP79+2M4CcAuoJqB7qW0tDQ1NfXEiRPHjh26cCHXYDCGhDgkJekSE40JCdS3L2HqdpdXUkJnz9KpU3TsGC8tjZqbDRKJa2JiYmLi0OTk5MTERAcHB7YzAsDtQTUD3ZdSqTx16tTx48dPnjx24sTxhgYVl8sJDxfGxWnj4ph+/Sgujvz82E4Jd0erpZwcysqis2fp7FlBVhYjl+s5HE50dGhi4vCkpKTExMSYmBj0wQDYNVQznez48eP+/v7BwcFsB4HbVlhYePbs2aysrLNnM8+eTS8uriAiLy9Bnz4UGamLiqLoaIqKouBg4uI8TbZKpaK8PMrPp4sXKS+Pk5sruHhRr9MZnZyEsbHR8fGD+vXr169fv759++LklgBdCaqZThYbGzt+/Pjly5ezHQTullwuNxU3OTk5+fk5Fy9erKmpJyJHR15kpDAqShsZaYiIoJAQCg6mgACc4cbaGhupuJhKSqiwkPLzKS+Pn5fHLSvTEpFAwAsLC4yJ6RsZGdO3b9+4uLjIyEicpRegC0M105lqa2u9vb137do1fvx4trNA55PL5fn5+bm5uXl5eXl5F3NzzxcWlmo0WiLi8zn+/oKQECY4WBcaai5xfH0pIIBcXdnObc+MRqqupspKKi2l4mLTjVtSIiguNsjlelMbb29JRERkdHSfyMjI6Ojo6Ojo0NBQ1C4A3Qqqmc6kUql+/vnnSZMmoRO7+6isrCwpKSkuLjb9W1x8uaTkclFRmanKISIXF15AAN/b2xgQoPP2Jn9/8vEhf3/y8iIPD5JKSShkdw9YplSSTEY1NeaqpbKSKiqospJbWSmoqGCqq3UGg/l3lLe3R0hIcEhIRHBwiElwcHBoaCiOnQYAVDMAFlFVVVVVVVVeXm76t7q6ury8tKqqrLy8vLpaptMZWluKRHyplOflRVKpQSrVS6Vkurm5kasriUQkkZjvmG42Tq8npZLkclKpSKkklYpkMmpqosZGksmotpZkMo5MJpDJODIZU1+v12qNrc91c3P29/fx8fHz9w/x8fHx9/f39vYOCAjw9vYODAxE1QIAN4NqBoAF1dXVsmvV1tbW19fLZLUyWbVMVieTyRsbm1o7eNoSi/kiEdfVlePsTA4OjLMzIxAYXV0NPB6Z+gQlEiIid/ers5VNS9pycWnfJ6RUkl5/zZKWFmpuNt9Xq0mjMS/R6UilIoOB09jIZximoYHHMNTQQEqlUak0aDRGuo5QyO/Rw8PLy9PT01sq9ZZex9PT09vb29nZ+XZfSQAAQjUDYMt0Op1KpZLL5cq/tP1Ro9Go1WqNRtPS0tLc3KzTaVWqBoPB0NjYQERyufyvlehVqqZ2a25oaGr32XdyEjo6XlPg8Hg8NzfzrB8HBwdnZ2eBQOjqKuLx+G5uHhwORywWE5FYLDbdF4lEIpHI1dXVzc2t7Y+HDh2aO3fuvffeu337dgu9UADQzaGaAeiO1q5du3jxYplMZp3NmSqwoKAg62wOALobTPsH6I5qamq8vLystjmJRCK5frgLAKCToG8GoDuqr6+XyWQRERFsBwEA6ASoZjqH0Wjk4gSxALepvLzc39+f7RQAYPfwBdw5fvvtt8DAwObWI0AA4FZ0Ol1ycvLYsWNLS0vZzgIA9g3VTOdITU11c3PD8aUAHScQCDZu3FhUVPThhx+ynQUA7BtmAXcOqVT66KOPsp0CwM4MHjw4MzPTYDDcuikAwM1h3gwAAADQEZjwAAAgAElEQVTYN4w0AQAAgH1DNQMAtuj06dMYgQKADkI1A9DtHD16NCIioqGhge0gN9XY2Dh69OikpKQLFy6wnQUA7ACqGYBup6KioqioSGTD1+N2c3M7duwYh8M5fPgw21kAwA7gmCaAbsfJyWnIkCE8Ho/tIH+nV69epoKG7SAAYAdwTBMAAADYN4w03a2lS5empqaynQIAAKD7QjVzV1paWt57773i4mK2gwB0I99//71arWY7BQDYEFQzdyUjI0Oj0QwdOpTtIADdRXl5+dy5c+Pi4k6dOsV2FgCwFahm7kpiYmJlZWVoaCjbQQC6C39//wsXLkRERLS0tLCdBQBsBWYBAwAAgH1D3wwAAADYN1QzAN2LWq0+cuRIU1MT20EAADoNqhmA7iU/P3/o0KFlZWVsB+l8er3+7bfflsvlbAcBAGtDNQPQvdTW1hKRl5cX20E6X0FBwbp162JjY48dO8Z2FgCwKlQzAN1LeHj4qlWrxGIx20E6X3R0dHZ29sSJE4OCgtjOAgBWhWOa7lBVVVVDQ0N0dDTbQQAAALo79M3coe+++27YsGGoBQEAAFiHauYOpaamDhs2DBf4BQAAYB2qmTv09NNPP/fcc2ynAIAOqaurmz59epc8kgsACPNmAKA7OH/+/EMPPVRTU7N3795BgwaxHQcAOhmqGQDoFtRq9cqVKxcuXOjo6Mh2FgDoZKhmAAAAwL5h3gxAN9LS0vLkk09mZ2ezHQQAoDOhmrm14uLiBQsW/PTTTzU1NWxnAbgrNTU1//vf/xobG9kOAgDQmVDN3Jqzs/Pq1aunTp3q7e0dERExb968LVu2VFZWsp0L4LZ14csa3LGzZ8/ec889WVlZbAcBgDuHeTO3ZjQahUKhwWAw/SgQCPR6PcMwQUFBo0aNGjZs2MiRI/38/NgNCdBBKpXK2dmZy8VfMmY5OTn/+Mc/zp07l5GR0atXL7bjAMCdQDXTIV5eXnV1ddcv5/F4BoNh586dkyZNsn4qAOgUBoNh+/btU6dOZTsIANwhVDMd0qdPnwsXLly/XCAQTJky5YcffrB+JAAAADBBb3OHBAYGXr+Qw+GYptRYPw8AAAC0QjXTIf7+/nw+v91ChmHWrl3bo0cPViIBgBW0tLSwHQEAbg3VTIf4+PjweLy2SwQCwejRox999FG2IgGApW3atCkuLu748eNsBwGAW0A10yE+Pj5Go7HtEqFQ+NVXX7GVBwCsYMiQIcHBwSNGjKioqGA7CwD8nfajJ3BDPj4+Op2u9UcOh7Ny5cqAgAAWIwGApQUFBf3+++8ZGRk4BQOAjUPfTIf4+vq23hcIBMnJybNnz2YxD8CdcXNz27x5M9sp7Ez//v3ZjgAAt4BqpkN8fHxa73O53PXr13M4HBbzANyB5uZmpVLp7u7OdhAAgE6GaqZDWvtmuFzuBx98EBYWxm4egDug1WoffPDB0NBQtoN0BRqNpqGhge0UAGCGs+d1lKura1NT04ABA06dOoWzwgN0c2+//faXX365du3aiRMnsp0FALrfLGCtVtvU1KRSqVpaWhQKBRGpVKq2M3zVarVGo2n7FLFYzOFwRCKRRqOZN2/e6dOnBQKBUCh0cXFxdXV1cHBA1z1Ad/PCCy9cvnx59uzZly9fFolEbMcB6O66Qt9MU1NTeXl5dXV1XV2dXC6vr6+vr6833ZHLa+vra+VyeXOzuqVF29DQZKH9dXQUOjkJ3d1Fzs7OEomHh0cPiUTq4eEhkUha//X29vb19e3Ro0e7U9cAgJ2qqanB+TMBbIHdVDNyubyoqKioqOjKlSulpaU1NTVlZUXV1ZVlZVUqlbq1mUjE9/DgSSTk4WGUSHQeHuThQRIJOTuTgwNJJCQUkosLubqSUEhiMRGRoyM5OV3dkEBArq5XfzQaSaEgIvr4Y3r+eRIIqKGBGIZaWqi5mZRK0mpJoSCNhtRqamykpiaSy6m+nuRyXn09r76eI5cz9fV6nc58uhoej9ujh8TX18fPL8jHx9/Pzy8gICA0NDQ0NDQoKEggEFjhxQQAAOhKbLGaqampyc7Ovnjx4uXLl4uKLhcV5RcXlzY0qIiIwyFfX2FAAPn46P39jT4+5OdHPj7k708+PuTpSZYrBoxGupvZMioVVVZSVRVVVFBVFZWXm+7zKyt5V67oVSoDEfF43IAA75CQ0NDQyNDQ0KioqJiYmKioKAcHh07bDQAAgC6H/Wqmrq4uMzMzJyfn4sWLOTlnc3JyZLJGIpJI+OHh3JAQXWgoExpKoaEUEkIhIdQlv9lra6moiIqLqajIdOMXFfGKirR6PcPn80JD/Xv3jo+O7hUbG9u7d+/Y2NjrLxoFADaioqKCYRh/f3+2gwB0IyxUMwqF4vz58+np6enpaenpJy5eLGIYRiLh9+xJvXrpY2OpVy+KjaXQUOrmp3TR6ai0lLKzKSeHsrM5OTkOOTk6tdogEPAiIsISEgYlJCQkJCQMHDgQnTcAtuOZZ5758ccfV6xY8dRTT+HEVADWYaVqpqCgIDU1NTX10NGjfxYVlRNRYKAwIUGfkGBMSKCEBMJEuo7Q6yk3l86cofR0Sk8XZmUZ1GqDk5Owf/+4YcNShg4dOnjwYNe2s34A2ti5c2d4eHhsbCzbQbo4tVr9zjvvHDhw4MSJE5gJB2AdFqxmCgsL9+/fn5p6KDX1j4qKGmdn3r33cocO1Q0cSAMGoHzpBHo95eRQejodP06HDwvy83V8Pjchoe/QofcPHz58xIgRTm2nN0O3FxgY+NJLL7388stsB+kWtFqtUChkOwVAd9HJ1YzBYMjKytq1a9fu3dvS0y+4uPASE2nwYENyMiUnk6NjJ24K2quuptOn6dgxOnDAMTOzRSgUJCcPnjBh8pQpU4KCgthOB+xzcnL64osvZsyYwXYQAIBO1jnVTEtLy549e374YcvevXsaGlRRUY4TJmjGjaMhQyx4kBH8jcpK2rOHfv2Vt38/qVSGfv2iJ02a+sQTT0RFRbEdDdjBMMyePXvi4uIwOxUAup67qmYYhjl27NjGjRt+/HFLQ4Ny+HDepEn6CRMIVzGyHS0tlJpKv/5K27YJyst1AwfGTZ/+5LRp07y9vdmOBtDtnDlzxtXVNTo6mu0gAF3NHVYz9fX1X3zxxRdfrCkqKuvbVzBjhu6xxwh/8tkyo5H+/JM2buRs385rbmbGjBm1YMHClJQUHHMBYDXTpk375Zdf3n777UWLFuGjB9CJbruayc/PX7ny4++++59AYJg9WzdrFvXpY6FsYBFqNe3cSV98wf/zT33v3lEvvfTKE0884Yg5TQCWp9frP/zww8uXL3/11VdsZwHoUm6jmsnNzX3ttUW//LI7NJT/4ou6J58kHAts17Ky6OOPOVu2cMRi91dffeP555/HIRgAAGCPOnSu/rq6uueff75Pn95FRXu3bWPy83UvvGArpcycOcThEIdDBQWduVpPT+JwqHfvzlynrYmLo2+/ZYqLjU8+KX/jjVd69Qrftm0b26EAAABu2y2qGYZh1qxZEx4esn37F2vXGjIytA88cFeXKwJb4+tLy5ZRbq4xMbF86tSHhw5NvHTpEtuhAAAAbsPfFSb19fVTpkx+8cUX5s9vys/XzZ6NOuZuTZ9OHA7t3s12jusEBdGGDcbTp0mtPpOQ0G/jxo1sJwLoXrZu3Xry5Em2U3QCT0/PpUuXsp0Cup2blicnTpyIi4vNyPj9zz+Z996zlXElu1ZdTT/+yHaIvzVgAB07pp8zRz1z5ownn5yl0WjYTgSd5uWXX/7888+nT5/OuYm1a9eynbFb27JlS3Jy8uLFi9kOYvbAAw/c8H1S0LmD+gCd5MbVzN69e1NSRsTF1WZl6YYMsWyC48fpoYeoRw8SCik0lJ54gtoNdBw8SBMmUGgoOTqSjw+NGUO//vp3K3z/ffNMmuXLO5ohPZ3uu49cXUkqpRkzqLb2Bhe81Gpp9WoaNIjc3MjJiSIi6KWXqKLimjZKJb35JsXEkKMjubvT6NGUlmZ+aPhw8vEhrZaIaOLEa3pobrnmxx4jDoccHUmtpocfJldX+uijju7a7RIK6aOPaNcu2rlz8/jxo5uamiy1JbCuAwcOlJeXb9y4kflLWFjYrFmzWn+cO3cu2xm7tW3btq1du9bX15ftIFf169ePuU54eDjbuQBu5Po3a1pamrOzw6xZXL2eGMaytz17iM9vH0kkorw8c4MdO248vPXZZ+YGs2ebl1y6RAxDhw+bV/jwwx3NkJ3dvucpPp6kUiKi2FhzG7WabljVeXnR+fPmNo2N1Ldv+wYcDm3bRgxDw4a1f2jXro6u+amnzAtfe818Z8kSi//XZGWRl5dgwoSxer3++jcJ2J2+ffuuWrWq7ZJ21QzDMJ6enitXrhw3bpyDg0NDQ4OLi8uHH37Y+ujs2bMTEhJM93U63VtvvRUVFeXo6BgeHr5y5UrL7wFY1eTJk29YzZgUFxc//PDD3t7ejo6OkZGRn376aetDUql0yZIlpvuHDx8eMmSIu7u7i4vLgAEDduzYYVqO9w90uvbVjEKhCAz0GTOGp9NZ/PuSYWjiRHJ3J6GQjhyhlhbasMH8bT13rrlBUhIRUe/eVFhIOh1VVNCECdSjB40aRUZj+2qmpsZ8Br9evUip7GiGqVPNa5gzh2pqKC+PBg40L2mtZl5/3bxk8mQqLKS6OnrvPfOShARzm4ULzUteeonq6igriwIDiYjEYlKpiGFo9epr6piOr7l1H8PD6Y8/SKOhxkZr/O+cOEFOTrx3332XlbcmWNr11Yyfn1+/fv02bdrU3NxsNBr/ppp56aWXxGLx7t27lUrltm3bnJyc2n6fQRfw99XMuHHjEhMTS0tLm5ub161bR0Q7d+40PdRazahUKjc3t0WLFikUiqampnXr1onF4srKSgbvH7CA9tXMiy8u8PYW1NZa48uy3c1gII3G3LMydKh5oekM4KGh5q6X62+t3/T5+TRqFBGRm9vVrp2O3EQiIiKRyFxzMAylp19TzRiN5q4akYgUiqtPNFVaRHT+PBkMJJEQEUmlpNWaG6xaRc7O5OtL+/ffuJrpyJrb7uOyZdb+T/n4YxIK+QUFBay8O8Girq9m/P39R4wY0frjzaoZhUIhFAo/+OCD1ofmz58fHBxs6cBgTX9fzbQTEhLy3HPPme63VjM5OTlEdOTIkXaN8f4BS7hmFEcmk3355bo33tB5epJ1KBT09tvUqxc5OxOPR46OpNcTEbW0mBuMHUtEVFREEREUE0NPPUWbNlFz8w1W9d57tG8fEdEnn1BkZEcDyOWkVBIR9e9PLi7mhfHx5Ox8tU1hIclk5uVubleXJyeb72RmUlERyeVERHFxVy+0uWABNTVRRQWlpNx46x1Zc1uty63mhRcoKIjz0Uf/tfaGgSV9rx8uvU5WVpZWqx3SZoh08ODBJSUljY2NlozWfb311lt79uyx/nbPnj3bbgqw602OB/H19ZWZfpe1ERkZGRUV9dhjj73//vsZGRkMYz5TK94/YAnXVDO//fabXq+bNctK2zYYaPRoeucduniR1GoiusHc2w8+oGeeMdcHubm0fj1Nn07BwbR3b/uW335LPB4R3d7xz3V15jumHhoTDuea2qKhwXzH1I/SysPDfKex8Wobd/fb2HpH1tyWl9dtrLxT8Hj01FO67du3tv4mgq7tZl9XbZm+dZKSklq/5B5//HEiqqystHi+7sdoNBYUFIwfP/7VV1+18qav75tRqVSmh86dOzd16tTAwEAnJyc+n3/ixInrn87j8Y4cOTJt2rR169YlJCSEhIRs2LCB8P4By7immjl37lyfPoK23+sWdfgwnTpFRBQTQ+fOkU5Hen37ScECAa1bRxUV9P339MIL5mtC1dXRgw9eLURMRowwT7v56afbKGha64b6+qsLjcardQaReQiJiGprr3lu648SydViyNRD00EdWXNbrJzvJymJqqpk1dXVLGwbWNXusojNf3WKisViIjp16lS7r7qoqCgWUnZ1XC5306ZNP//88yjTULoNqKmpGTp0qFar/f333+vq6jQazb333nvDll5eXitWrCguLr5w4cKIESNmzpyZnp6O9w9YwjVfjyqVSiQyWm3bxcXmO488Qn36EJ9PaWnmkaZ2PD3p0Ufpk0/o3DlatoyIqLmZzp69ps2aNfTYY5SYSEQ0fz799SfELUil5ORERHT+/NUBrBMnqO2ZVkJDyTT0lpVFCsXV5amp5juDBlFYmPnAqIyMq8/dvJmcnMjLi77++pqNGgy3sWbWmbqplKYBOehOxGJx/V9lPsMwmX8NfPbr18/BweGU6W8RsIrJkyffd999bKcwy8zMVCgU77zzTmxsrIuLS2NjY3Z29t8/JTY29ssvv+TxeBcuXMD7ByzhmmrG29u7vNx6f/6bjj8ioqNHSaGgjAyaM8fc/VBRQQYDVVVRcjJ5e9Prr5NcTkYj1ddf7ZLx87tmbabRqA8/JCK6coXefLOjMe6/n4hIqaQXX6TaWrpwgZ599poGHA498wwRkUpFTz1FZWVUU0NvvEFnzpifHhZGPB498QQRkUJBCxdSXR1lZ9O775JGQyqVed6MqWwior17qaWF1OoOrZl1paXE4XC8vb3ZDgLWNnDgwJ9++qmoqEguly9evLh1WoNIJJo3b97SpUsPHDigVqsLCwunTJkyY8YMdtOC1YSFhXE4nN9//725uTkzM3P69OlRUVEFBQXtzre5e/fuoKCgP//8U6PRNDc3m6qZxMREvH/AItp29B08eJDopkcPdfqtqcl8DHOr6Gj617/M93196fhxevjhG8d+7LH2xzS1xn7gASIiHo/S0joUIz2dHByuWfm995KPDxFRTIy5TXPz1eOM2goJoZIScxuZjG7YUfr11+YGrWfSM1m9uqNrvn4frXmbO5fi4np17uRzsLKamhqlUtlu4Q2PaVq8eHHrj5cvXx46dKizs7Ovr++SJUveeOON+Ph400N6vf6tt94KDg4WCAR+fn4zZ86sqKiw8E6AVf39MU2ffPKJv7+/k5NTYmLi8ePHf/nlF3d39/DwcKbNMU0Gg2Hp0qWmk8q4ubklJSX99ttvpqfj/QOdjsO0md2p1+ujonref3/Z2rVWmvKZnU0vvUSnT5NQSOPG0YoVxOXSzJmUkUFeXrR5M8XE0Jo19MMPlJ9PCgVJpdSzJz3+OM2da57zO2eOeRzn0iUynaMyL4969ya9nuLiKC3tBmfnu94ff9Crr9L58+TmRuPH04oVNHw4XbhAISFUVGRu09JCq1fT5s2Ul0dGI4WG0gMP0P/939WZN0Qkl9OyZbRjB5WUkKMjDRxIixZR28Hu996j1atJLic/P1q9miZM6NCar99Hq6muprAw3nvv/ffFF1+06oahU02bNs1gMCxfvvz06dOW20pYWNjA1pM1geUZjcYHHnhgwoQJTz/9NOf6YyjuVGFhoUXfJzeD9w/cjWuqGSLasGHDP/4xKzWVsf7BwGCDHn6Ym57um52d79z2mHWwNyNGjIiJibn//vvf7PgQ7O178MEH3333XcutH9ppaWlZtGjRp59++q9//eu91tNu3rUdO3ZY9H1yM3j/wN1oX80Q0eTJE06f3nfypC44mJVIYCvee4/efpu7d+/+kSNHsp0F7srKlSuDg4OnTJnCdhDofEePHvXx8cHlk6Cbu0E1o1Qqhw5NbGjIP3BAZwuzUO/YmTN0y27LXbvMwz3QzrJl9PrrnDVr1sybN4/tLAAAAH/nBtUMEclksjFjUoqKLnzzjX7SJOunAjYplfTcc9zNm5lPPlk9f/58tuMAAADcwo2Px5ZKpUeOnHjiibmTJ9PMmRzTiXqhO8jOpsREwd69brt27UYpA2C/dDod2xEArOemZ5dxdHRctWr15s2bd+50GjBAwMZFQsCqGhro1VcpIYHr7Z149mzOuHHj2E4EAHeosbExNjb2gw8+MLSerBOgS7vFufIee+yxzMzz0dHjx4+n++/ntzv9LnQNOh2tXk0REfxvvnFfsWLV/v1/+vr6sh0KAO6ck5PTzJkz33rrrRUrVrCdBcAabjxv5npHjhz5v/978cyZrIcf5vzzn0ZbOOM+3L2mJvrf/+jjjwVlZbRgwUuvv/666RIqANAF5OTkBAUFdeRKogD2rqPVDBExDPPjjz9++OH7Z85kJSUJFi7UTZliPoUd2J2yMvr0U/riC75Gw5058x//+tdrISEhbIcCAAC4E7dRzbQ6cuTIxx+v2LlzV0AAf/p03RNPUK9elsgGnU+jod27aeNG3p49jKenx/z5Lz377LOepktfQhd18ODBysrK6dOnsx0EAMBS7qSaMSksLPzyyy83b/72ypXK/v2F06drH32UMN3CNhmNdOQIbdjA2baNp1QaR44cNnPmU4888ohQKGQ7GljcvHnz8vLy/vjjD7aDgK2ora318vJiOwVAZ7rzasbEaDQeP378xx+3bt78XX19Y3w8PyVFN2ECDR5MnXfZELhDzc108CDt3s3ZvVtQUaHt1Sty6tTH/vGPf2BQqVt56KGHBALBli1b2A4CNqGwsLBv377PPffcO++84+TkxHYcgM5xt9VMK41Gs3fv3t27d//6687KytqAAOH48dqxY2nIkGuuywiWxjCUnU0HD9Kvv/JSUxmDge69N2HChAcnT54cExPDdjpgQUlJicFg6NmzJ9tBwCYwDPPFF1+YLvA0Y8YMtuMAdI5Oq2ZaMQyTkZGxe/fu3bt3ZGScI6LYWMHw4dohQ2joUPL27tytARGRwUBZWXTkCB06xD16lCeT6cRi19Gjx06YMGnMmDGYFgMA7VRWVvr4+HTilbcB2NX51Uxb9fX1R48ePXTo0JEjBzMzLxgMxuho4cCBuoQEZsAAiosjFxfLbbyLKymhM2coPZ3S0/knT1Jjo14qdUtOHjZ8+MghQ4bExcXxcLwZAAB0D5atZtpqbGw8evTosWPHzpw5deZMWn19I4/HiY4WDhig7d+fiY2lmBjy87NOFvuj1VJeHl28SOfO0ZkzvPR0bl2djsfjRkWFJiQk3nPPoOHDh8fGxuIvLQAA6IasV820U1RUlJ6enp6efubMyczMDJmskYjEYn5MDDc2VhsdTbGxFBZGwcHUDQ+7kcmoqIhycyknh3JzudnZgsJCrV7P8Pm8iIjghITEhIQBCQkJ8fHxOC8WAHSizMzM+Ph4tlMA3DbWqpl2ampqsrOzc3Nzs7OzL148n5OTXVUlIyIul+PvL+jZkwkN1YWGkunm708+PtQFJuPX1lJ1NZWUUFGR6cYtLBQUFRkaG/VEJBTyIyNDY2L69eoV26tXr5iYmKioKBxTDQAWcvr06UGDBv3jH//46KOPJBIJ23EAboOtVDPXk8vlhYWFhYWFRWYFRUWXiovLtVq9qYFYzPfz4/n4GPz89D4+5O9PUil5eJBEQh4e5hufz1p+lYrq66m+nuRyqq8nmYyqq6m6msrKuDU1/LIyqq7Wa7VGU2Op1K1nz9DQ0MjQ0J6hoaE9e/YMDQ0NCQnhs7gDAND97Nix4/nnn//xxx+TkpLYzgJwG2y3mrkho9FYWVlZVlZWXV1dUVFRVVVVUVFRWVleVVVaUVFZX9+o0WjbtheJ+B4ePA8PcnRkXFwYV1eDUGgUi8nBgZydSSQylztubtdcokEsvnqyHI2G1OqrDzU1kVZ79Y5cTi0t1NxMSiVfq+UoFNzmZqqvZ+rr9TqdsW0Sd3cXHx8vb28ff/9Qb29vPz8/Hx8fPz8/X1/fgIAANzc3i7xeAAC3qaWlxcHBge0UALfHzqqZW2pubq6vr5fL5fX19W3vaDSapqYmlUrV0tKiUNRpNGq1ulmhUBiNRoZhGhqURObXwWAwNjY2t65QIOC5ul4d03JwEDo7OxGRs7Ozg4ODWCwVCITnz+fExsb27NnT3d3d2dnZw8NDIpF4eHi0vcPl3uJy5QCWsH79+s2bN+/fv5/tIAAAFtTVBjKcnZ2dnZ0DAgKsudFp06YdO3Zs69at7u7u1twuwC0VFBTU1taynQIAwLLQYdAJ1qxZo9PpXn/9dbaDALTn6uoaFxfHdgqwezqdbvv27WynALiprjbSxJbNmzdPnz59//799913H9tZAAA62e+//z5+/PixY8d+8cUXfjgzGNge9M10jscff3zy5MlPP/10U1MT21kAADrZmDFjDh06VF1djT+AwTahb6bTVFRU9O7de86cOcuXL2c7CwAAQDeCvplO4+fn98EHH/z3v/89ceIE21kAAAC6EfTNdCaGYUaPHl1WVpaZmYkTNgAAAFgH+mY6E4fD+eKLL0pLS5ctW8Z2FgAAa6ipqfnkk08MBgPbQaBbQzXTyUJCQpYsWbJ06dLMzEy2swAAWNzRo0dfeeWV5OTk8vJytrNA94WRps5nNBqTk5N1Ot2JEydwoSVgUW1tbVlZWVxcHKf1Uh0AFnD+/Plly5Z98803GGEHtqBvpvNxudz169dnZ2djvAnY9dtvvyUlJaGUAUvr06fPpk2bUMoAi1DNWERUVNSSJUuWLFly7tw5trNA91VTU+Pl5cV2CgAAi8NIk6UYjcYRI0Y0NjaePn1aIBCwHQe6o7S0tHPnzs2ePZvtIAAAloW+GUsxjTcVFBT85z//YTsLdFMDBw5EKQMsSktLe+211zQaDdtBoOtDNWNBPXv2XLp06dKlS9PT09nOAgBgbcXFxWvWrImPj5fJZGxngS4OI02WZTQaR44cKZfL09LShEIh23EAAKzqypUrGzZsWLx4MdtBoItDNWNxRUVFffv2Xbhw4bvvvst2FgAAgC4II00WFxoa+v7777///vtpaWlsZwEAAOiC0DdjDQzDjBkzprS0NCMjw9HRke04AAAAXQr6ZqyBw+GsW7eurKxsyZIlbGcBAGDf//73v0ceeQSzg6GzoJqxkpCQkOXLly9fvnpER3kAACAASURBVPzkyZNsZ4Fu4fLlywsWLFAoFGwHAbgBDw+Pw4cPDxgwAMdvQ6fASJP1MAwzYcKE3Nzcs2fPurq6sh0Hurjdu3dPnDixqanJ2dmZ7SwANyCTyQ4fPjxlyhS2g0BXgL4Z6+FwOF9++aVCoVi0aBHbWaDrq62tdXFxQSkDNksqlaKUgc6Cvhlr27Fjx0MPPbRr167x48eznQW6Mp1OJ5fLe/TowXYQAACLQzXDgunTp+/fv//8+fP4pgEAaEev1/P5fLZTgJ3BSBML1qxZ4+jo+Oyzz7IdBADA5rz66qvjxo0rLS1lOwjYE1QzLHB3d9+4ceMvv/yyYcMGtrMAANiWSZMm5efn33///Uajke0sYDcw0sSahQsXrl+//uzZs8HBwWxnAQCwIc3NzXl5efHx8WwHAbuBaoY1LS0tAwcOFIvFhw4d4nLRSQYAAHCH8CXKGgcHh2+//fbUqVMrV65kOwsAAIAdQzXDpvj4+Lfeeuv1118/f/4821kAAGydXC7HZBq4IYw0scxoNI4YMaKxsfHUqVNCoZDtONBFXL58uV+/fsePH+/bty/bWQA6zYQJE+Ry+ddffx0dHc12FrAt6JthGZfL/eabbwoKCv7973+znQXsmEql+u233zIyMsrKyjQaTXV1dVNTk4eHB9u5ADrTf/7zn5aWlmeeeYbtIGBz0DdjE77++utnnnlm7969KSkpbGcBu6TT6cRicXNzs+lHR0dHBweHsLAwPz8/b29vHx+f4ODgp59+mt2QAHdPr9fX1dX5+PiwHQRsC6oZW/H444+npqaePXvW09OT7Sxgl0aNGnXgwIHrP9F8Pl+v18+fP//TTz9lJRgAgKVhpMlWfPbZZ0KhcNasWagv4c6kpKTc8Hzwer2ew+G89NJL1o8EwDqDwcB2BLAGVDO2QiwWb9iwYe/evWvXrmU7C9ilkSNH6nS665cLBIJJkyaFh4dbPxKA1eTn57e0tLRbqNVqR4wYUVRUxEoksCZUMzYkOTl58eLF//znP8+dO8d2FrA/8fHxIpHo+uU6ne6VV16xfh4Aq2EYZurUqfHx8SdPnmy7fMmSJUeOHHnooYeuL3Sgi8G8Gdui1+uHDRumUCjS0tKcnJzYjgN2ZtKkSXv27Gnbtc7lcvv27ZuZmcliKgArKC4ufuaZZ/z9/devX29akpWVNWDAAIPBwOPx5s6di3ljXRv6ZmwLn8/fsmVLRUXFq6++ynYWsD8pKSkcDqftEoZhXnvtNbbyAFhNSEjI3r17P//8c9OPer1+1qxZpo+DwWBYs2YNrvLbtaGasTmBgYHr1q379NNPf/nlF7azgJ0ZOXKkXq9vu8TX1/fBBx9kKw+ANXE4HEdHR9P9ZcuWXbhwofXjwOFwnn322ZycHPbSgWVhpMlGzZo167fffjt79qyvry/bWcBuMAzj5eUlk8lMP/J4vA8//HDhwoXspgKwstzc3L59+7abFM/n84OCgrKysm44vQzsHfpmbNSaNWskEsmsWbNwURLoOA6H0/Y4bQcHh6eeeordSABWZjQaZ86cef1yvV5/5coVnEOyq0I1Y6NcXV03b96cmpr60UcfsZ0F7MnIkSNNHa4CgeC5555zd3dnOxGAVf33v/9NT0+/4dkK9Hr91q1b161bZ/1UYGkYabJpy5Yte+utt44fP56QkMB2FrAPBQUFERERRMTj8QoLC4OCgthOBGA9ly5d6tOnz98fjy0QCE6ePNm/f3+rpQIrQN+MTVu0aFFycvKjjz7a2NjIdhawD+Hh4aa5Vg899BBKGehutFrtokWLkpKSTOOtDg4O7Y7yIyKj0fjAAw80NDSwERAsBX0ztq66ujo+Pj45OXnr1q1sZwEW6HQ6lUrV0tLS3Nzc3Nxs+qNToVC0m1DV0NDQ+ln+7LPPDh069NFHH/Xp06dtG2dnZwcHByJyd3fncrkSiYTH47m5uVlrVwCsR61Wp6enHzt27Pfffz9+/LhWqxUIBHq9vnUcNiUl5ddff72+1rkZ02fQ9HnU6/VKpZKIjEajQqFo11Kr1TY1NbVbyOVyrx/2FQqFLi4uRMTn80UiUWsbiURy+3vc3aGasQOHDh1KSUlZtWrV/Pnz2c4Cd0WlUlVXV1dXV8vlcoVC0dDQoFAoFAqF6UeFQtbQUK9QNLS0tDQ0NBoMRoWi/e9ESxAK+S4ujk5Ojo6ODu7u7u7uEnd3D3d3iVgsdv+LWCyWSCRef7nhBaEAbJNarT5x4kRqaur+/fvPnDmj0+k4HA7DMI8//viwYcMaGhqUbTQ01CmVCqWyUa1WNzU1a7U6tVqr0WitH1ssduVwyM1NxONxJRKJSOTu6uouErmLRCKJRCL6i6urq0QikUgknp6eUqnU1dXV+lFtAaoZ+/D2228vW7bs2LFjmEBjy4xGY1VV1ZUrV8rLy0tLS02FS21tVW1tVWVlZW1tvVp99XeiQMAVi3nu7lx3d5JIjO7uerGYcXcnd3dycCCJhLhccncnPp9EInJwIGdncnIi09k0nJ3JweGaTbu6kkBgvl9RQWfO0IgRdO2pZ0ihIFOHTkMDGY3U0EAGAzU2kk5HKhVpNKRWU2MjNTSQQkEKBa+hgadQcBQKRqEwKhTXrMvLS+zlJfXy8vb2DujRo4eXl5e/v39AQEBgYGBQUFC3/X0K7Kqpqamuri4rK6uurq6qqqqrq6urq5PJamWyapmsTiaTy2Tth+xFIr6nJ8/VlUQiRiQyurnp3d1JJCJXV3JxMX/iTJ8+gYBcXYnHIzc382fTxM2NeLxr1snhkFjcPptWS9f111BTE2m1Vx/V60mpNH8qGYZMQ2Gtn9bGRlIqSaXiKpXchgaeUklKJaNUGlSqay6r6eAg8PQUS6UeUqmXp6evp6enqcrx8/Pz9fX19fX18/PrkieaRzVjH4xG45gxYwoLC9PT03GUCusMBkNJSUlBQcGlS5euXLlSVlZ25UpBaWlpRUWNTmcgIg6HfHyEPXpwfH31Xl4GLy/y8aEePcjLi7y9ydubPDzI2dmCCRmGOtyD3tEV1tdTbS3V1lJNDVVVme9XVXFqagS1tZyyMn1Tk/m3qljsGhDgGxTUMyAgOCAgICwsLDw8PCIiAv3ncJcMBkN5eXlxcXFxcXFJSUl1dXVZ2ZXq6vKysrKamnqt1lxzOznxfHz4PXowUqlBKjVIpSSVkqcneXlR631TwVFURL16sblHnUIup/p6qqsjmezqra6Oams5MhlfJuPW1jI1NTqj0fx1Lxa7+vl5+/j4+/kF+fj4BAQEBAcHh4aGhoSE2O/3C6oZu1FTUxMfH5+UlPTjjz+ynaV7KS8vz87OLigoKCgoyM/PLSi4WFRUZvq9KZUKgoO5AQG64GBjQAD5+1NwMAUEkJ8fCYVs57Y6uZzKy6mkhMrKqKyMrlyhsjJBaSm3pESn1RqJSCp1Cw8Pi4iIjYiIiIiIiIyM7NWrV5f8MxHunkwmy8vLKywsLDYrKCoqKC2tMv3B4ODADQoS+PgY/f11Pj7k70+t//r5kd1+I1uQXk81NVReTlVVVFFBlZWmf7kVFfyKCqqqMncbSySikJDAkJCI0NCwkJCQ0NDQ8PDwsLAwQWvfr61CNWNPUlNT77vvvpUrVz7//PNsZ+mylEplfn5+dnZ2Tk5OdnZmWlpadbWciMRiflgYt2dPbc+eZLrFxhJO1NxBFRWUk0OFhaYbr7BQmJOjVasNROTr65WQcE9sbO9evXolJCRER0fz2nXcQ1dnOq9dYWFhYWFhdnZ2Tk7W/7d333FNXe8fwJ9sNkjYMyh7CSJDwLrFOgraumvU1lHbKtql36r90WrrbCutVluLtthqHXUQF4oDRRAR2QKCLJG9w5DM3x9JUREVETgJPO/XefmKl5vkk3BPeHLuvefm5eXm5ZUAAINBNTenGxtLTExEbV1v4ECwtGy/fwe9DoEAiovbeiiUlFBKS1l5eZCf/0gqBTqdZmFhPHCgraOjs5OT08CBA52cnBRtnnqsZpTMN998s3HjxmvXrvn4+JDO0kfIzn24detWfPzNhITY/PyHAKCpSXdyorq4CFxcwNkZXFxAT4900L5FJIL79yE1FdLTIT2dmprKyMsTSCRSdXUVV1cnLy9/b29vb2/vgQMHkk6Kul9NTU1SUlJSUlJyclJKSsK9e/mywU4zM6adncTOTmRvD3Z2YGcH5uZAxYlEyGlqgnv3IDsbsrMhK4uSnc3MzhY1N4sBQE9P28XF1d3d083Nzc3NzcHBgezJAVjNKBmJRPLmm29mZ2ffuXNHV1eXdBxllZ+ff+3atVu3bt28eT019a5IJNbXZ3p7i728xIMHg7MzWFl183En6KWamyEjA9LS4M4diI9npKSIhEKpvr62l9cwb2/fYcOG+fr6qvXo0Uaox5SVld28eTMpKSk5+U5ycmJRUSkAGBsz3dzEbm5iJyd57YIXUFJ8UikUFcmKG0hNheRkRnq6uLVVwmLRnZ1t3d193dzcPDw8hgwZwuzd3e1YzSgf2QE0Hh4ep06d6vxkCaiioiI6OjoqKioq6mxeXjGDQbWxofr7i/z8wMMDHB2xfFEsQiGkpkJMDCQmUhITWZmZj2g02uDBzmPHThg7duzw4cNZ7U7rQopELBZnZWUlJibeuBETE3MpMzNfKpUaG9M9PMQeHlIPDxg6FHfU9hEiEWRnw927kJEBiYmM+HiorBQyGDRXV2c/vxH+/v4jRowwMDDo6RhYzSgl2QE0P/zww4oVK0hnUWgikSg6OvrUqVNRUecyM3MZDKq3N33MGMGoUeDj0/4kZ6TISkrg0iW4fBkuX6YXFYnU1VnDh/tPmDAlKCjI0tKSdDoEACCRSO7cuRMZGRkdfTk+Pr6hoUlTk+7jQ/H1Ffr6go8P4EyN/UR2NsTFwY0bEBtLz8wUSaVga2vh6ztq3Ljx48aN09fX74knxWpGWW3cuPGbb76Jjo4eNmwY6SwKp7m5+cKFCydOHD99+mRNDX/wYGZAgGD0aPD3B3V10uHQa8vNhStX4NIl6vnz1Pp60ZAhLlOnzggKCnJ2diYdrT8qLy+PjIyMjDx/8eK5yso6Y2PGmDGiYcOkfn7g7IzH6vZ3tbUQFwdxcXDtGiMuTiwWS4cMcRk/fnJAQEDbBSi6BVYzykp2AE1OTk5iYiJO4yEjFosjIyP37//97Nmzjx4JfH3pQUHCqVMBDyTtqwQCuHIFTpyAiAhGaanQxsZy7tyFCxYswNGaXnDv3r3Dhw+fOHE4Ofkuk0nx96cGBIjGjwdXV9xpizrG58PlyxAZCZGRzLw8gZaW2vjxE2bMmDVp0qTXPyQOqxklVlFR4eHh4erqyuPxqP37uP+8vLz9+/f/8cfehw/L33iDMXeu8K23wNCQdCzUWyQSiI+Hf/+Fv/5iVFaKxo4d+f77HwQGBuKxNd2uoKDgyJEj//xzICkp3ciIOW2aYOJEGDkSRz3Rq8nNhchIOHmSduWKRFWV9dZbQTNnzg4ICOh6n5UiZRYfH89isb766ivSQYi5fPny2LEjKRSKsTHjf/+De/dAKsXWf5tAAMePw6RJNBqNwmZrr127trKykvRG2he0trYeOHBg2LChFAqFzWYsWUK5dAlEIvK/cWzK3srLYdcueOMNBpVK0dHRWLp0SXp6ehc2Uej2jR71sl9//ZVCoRw7dox0kN524cIFf/9hADB6ND0iAoRC8t2yn7T335d/F8rJIR/mea24GL7+GvT06BoaKp9//nl5eTnpDVZZlZaWhoSEGBmxGQzqrFm0c+dAICD/+1XAbbiTj8lmAwA4OfVgEiVtxcXw449gZ8egUChjx46IiIgQi8Wd31D79e6JvmHJkiWLFi1auHBhZmYm6Sy9JCEhwdfXa/z48erqCTExcOmSaMoUwIs6oyeZmsJXX0F+vuj//u9RePiPVlYWa9asaW5uJp1LmTx48GDhwgUcjsWuXd+9/351fr7k0CHxhAmg8HPcI6VkagorV0JmpvDsWSmDERMY+JatLScsLEwiu1juy2A10xfs2rXL1dV16tSpDQ3trxDbxzQ3N3/66afDhvmoqCTHx8P58yI/P9KZ+rp33wUKBU6fJp2jSzQ04LPPID9f9N13rb/++r2Li/2lS5dIh1ICjY2N69evt7OziYk59MsvwqIiwcaNYGpKOlZXt0al3ob7GwoFJkyAs2fFWVkwbtyDZcuWeHi4XLly5eX37LnxSdSbSkpKjI2Ng4KCJBIJ6Sw95cqVK1ZWZgMG0MPCQCIhPy7aH1pZmfzymTwe+TCv2UpK4O23aRQKvPfeAj6fT3pzVlzh4eHGxvo6Oozt26G1lfwv7jW3RkXbhnFP0yu1zEyYNIkGAIGBkwoKCl6w3UKv9RDU02JiYhgMxpYtW0gH6RG7du2i06lTp9JKS3uvI924AdOmgb4+MBjA4cCcOU8dZTxrFgAAkwlSKezdC/b2wGLBoEFw4MBTDxIVBZMmAYcDLBYYGkJAAJw+/Woxbt2CUaNATQ0GDIC5c6GsDLy9AQCsrZ9KwmJBczO8/Taoq8P338t/VF0Nq1bBoEHAZIKuLrz1FiQnP/XgL1hhxIj2X35kfw+e/fzt5Fvx0hfSC+34cTAwYLi6Orz4k7F/qq+vnzlzOpVK+fBDSmVl7/1SOtNZnrc1vrifdn4blkqhoQHWrZNvvVpaMH483Lr1CuE7fMzbt2H0aFBXB11dePddqKiQX/HtBdXMd9/Jl2zZ0qnnbet9Egls2AAmJqChARMmQH4+SKWwcydYWQGLBV5ekJnZ2XdbcTqsrEVGgoMDY8AAzX///fd5Wy/0ZldBPe3777+nUqnnzp0jHaSbbd++nUKBjRt7dUjm7NkOjsXR1ITsbPkK770nX7hjR/vVTp6Ur3PiRMfXzPvll87GSE9vf+7r4MFgZQUA4ODQPsn//ie/sWEDSKVQWQmDBrV/alVVuHlTfscXr9D5vwSdeSs680J6pxUUgKsrw9LSBAuaJ5WVlbm7OxsaMi5c6NVfR1t7cWd53tb44n7a+W24oQFcXduvTKHAv/92Nv+zj5mRARoaTz2gu/tLxmauXZO/nHfe6ezztvW+jRufei4PD/jrr6eWGBnBo0edercVqsPKWnMzLFlCoVAo27dv73ADhl7uMKincblcXV3dvLw80kG6zZEjRygUSmhob3eeKVNAWxuYTLh+HVpb4cABeZf+4IP2H0MmJnDmDNTUwNq18iUjR8rX8fUFAHB2hrw8EAqhpAQmTwYDAxg/vrOV2dtvyx+Ty4Xycrh7F9zd5Uue/UC0tobLl+HRI2hoAKkUFi2SfyLv3QuNjZCeDk5O8o852R1fusLPP8sf+clR+mc/tTvzVnTmhfRaq66GwYMZjo42jY2NpDdwhVBXV+fsbGdry8jL6+3fRVt7aWfpcGt8aT/t5Da8apV8ycqVUFUFyclgbg4AoKMDjY2dyv/sY06fLl+yaBFUVEB2Nnh6Prfz5uRARYX84CRHR+DzO/u+tT2CrS0kJ0NGBhgZyZcYGACPByUl4OUlX3L0aGffbYXqsG3txx+BSqXs2LHj2W0Yer/boB7V3Nw8ZMgQNze3pqYm0lm6QWlpqZaW2scfU0h1HlkTi+HRI/l3pjfeaP8hsnmzfIlQCLLrmhsaypfY2wMAWFl1fae47Ludurq8QJFK4ebN534gtiWRSkEgkN/X2/vxwiNH5Gveu/fyFaSvXs284K3ozAvpzVZUBHp69A8/XEZ6G1cIQUFTTE0ZDx6Q7GUv7Swdbo1Ptg77aWe2YbEYZBOqs9mPzz8PDQU1NTA2hosXO5X/2X4huyS4pubjeigx8bmd9949GD8eAEBL6/EA8Cs9788/y5d8/rl8yfz58iV//y1f8t13nX23Fa3DtrVt24BGo0ZHR7fbhvGcpr5GVVX133//ffDgwdKlS0ln6QabNn2nrS3cto3AjNX19RASAo6OoKYGNBqoqIBIBADQ2tp+zbbRbDodrK0BAKqr5UvefBMAID8fbGzAwQHeew/+/hs6f5pwbS00NgIADB0q/2QEAE9PeN4k4P7+j2/n58vvGx8PFIq8zZgh/2ly8stX6ILnvRWv+kJ6gbk5bNsm2rv3t7y8PGIhFMO5c+dOneIdOCA0MyMZo2udpfP99AXy86G2FgDAze3x+ecrVkBTE5SUwNixr/xaAKC2Fvh8AIAhQx7vsnF3f+42/+23cOECAMBPP4GtbVeeUXZQC8Dja7n4+LRfUlMjv/Hid1sBO2ybzz6DN9+kfPjhYqn0qb8LWM30QRwO59ChQ4cOHfrll19IZ3ktYrH40KEDH38sVFHp/aeGgAD4+mvIzISWFgB40aVnZIf1yaiqAgC09bItW2DJEvnnY1YW7N8P774LlpYQGdmpGFVV8httHygAQKU+91rET16bVvZJ+jyVlS9foQue91a86gvpHfPmAZtN/eeff0iGUAB79uwaN44+ahThGF3oLK/UT1+grk5+Q1u7K3fvUIfbPIXy3G3+zz/lV+js8pnkbdfra/vA1NFpv6STH02K2WHbbNokzsi4d/369ScXYjXTN40bN+6rr75atWpVTEwM6SxdV1BQUFlZN3o0gae+dg3i4wEAHBwgNRWEQhCJujJBH4MBv/4KJSVw6BAsXw4uLgAAVVUwbdrjz4sXaPt4ahvsAQCJBOrrO17/ycP62j53Jk/uYLT2ww9fvkI3etUX0jtoNBg5UpSQEE8yhAKIi7sxebKIdIqudJbu6qdtf7NlIzTdQranFZ4YDgEAieRx5dTOqFHyg36OHeuNqXFe/G4rZodt4+wMVlbM2NjYJxdiNdNnrVu3buLEidOmTcvPzyedpYtkkwG2fb3oTQUF8hszZoCLC9DpkJAgH8HuAj09mDULfvoJUlNh82YAgOZmSEnp1B1lX6rS0qCpSb4wIUH+NfTFBg6U7/lOToYO59J86QpPEotf/owv8DovpEcNGCCtr695+Xp9l1Qqravjy060UQSd6SxtW+Mr9dMXbMODBsn7wp078OiRfOHBg6CqCvr6EBbWlRfCZsuHJ9PSHu/BiYt7/Pjt7NoFs2fDsGEAAB99JN/R09Oe924rbIdtw2ZTamqe6rlYzfRZVCr14MGDHA5n0qRJdc/7OqDYTExMAIBIMdY27WlMDNTXw507sGiRfOSjpKSzf9rLysDfHwwN4csvobYWJBKoqXn8LdPEpFMPIttn39gIy5dDZSVkZ0MnD4ii0WDmTACA4mL4/HOoqoLKSpg5E3R1wdkZqqtfvgL8t7cIACIjobX1tT7LuvxCelReHs3UlEM6BUkUCsXc3PjePcIxOtNZnt0aO9NPO7MN02gwdy4AQH09rFoFVVWQkQHffAOPHkFjYxePmwGAceMAAPh8CA6GykpIT3/RNi/b6bNtGwBAURGsX9/FJ+2MzrzbitlhZYRCuH9fbGlp+dRSAkfPo1708OFDU1PTgIAAoVBIOktXuLraL1lC4ISmpib5+Zlt7O1hzRr5bWNjiI3teLIs2WGwNJr8v++803FvnD27s0lu35bPZNrGywsMDQE6MZ1oeTm06+8y27d3doWEhKeWy06aeME5TS94KzrzQnq5VVYCi0UNDw8nvZkTFhwcPGgQg/i1JF/aWZ7dGjvTTzu5DVdXg51dB88eFtbZ/M8+ZmIisFhPPZqPj/z06bYpW569V1CQvOMkJHTxeffvly85dEi+JClJvuTTTzv7bitgh21rR48ClUppN18Ujs30cSYmJqdOnbp+/fpnn31GOktXfPzxJ3/8Qen9L45qanDuHIwdC1paoKcHXC5cuwaffQYTJ4KREejpPXVw3Av88w+EhoKvL+jpAYMBRkbg6ws7dz6eFeOlPDzg/Hnw9AQWC/T1Yf58OHtWPpb+0ov/GRjArVvw0UfA4QCDAZqaMHIknDgBn37a2RWGDoWNG8HQEJhM4HCAw+ls7O59IT0kJAR0dXWnTZtG5ukVRnBwcGkpdcsWwjFe2lme3Ro70087uQ3r6kJcHHzxBdjYAJMJWlowZgxERj6em64LhgyBs2dh6FD5Nr9gAZw+LT9S/gXDnJs3A50OYjEsXtz1vdsv9dJ3WwE7rExdHXz+OWPOnFk4NtMfHT16lEKh/PLLL6SDvDKhUOjhMdjNjdHURPjbgOK0tgHhN98kH0Z5X8jJk0ChwF9//UV6G1cIoaGhVCrl2DHyWwU2hW2K8MnT0gJjxtDMzAzLy8vbbcNApOeg3hcSEkKn0y9evEg6yCu7f/++vv6AMWPozc3k+3Pvtx07wNkZtLXl4+qlpTB7tvwzZedO8vGU9IVcvAiqqrSlSxeT3roVyIoVy+l0aud3rGDr202hOqysVVfDG2/QBwzQTEpKenYDht7vM4gIiUQyd+5cLS2t9PR00lleWXJyMputPXQovbiYfCfvrtZud36HeDxISel4Dgxvb2hpIf8qOt8U54X8/jswmdR3350jEolIb9oKRCKRrF+/nkKhvPcetZNz+feT1smu2meeV9YUp8PK2vXrYGHBMDc3et6fMOjlDoMIamlp8fHxGThwYGVlJeksryw3N9fR0UZHh/7rr7167UlFaJmZ8P77YGUFKiqgpgaDB8PGjaCMI1XEX0hZGcybRwOAFStWiMVi0hu1Ijp16hSbrW1iwvj1VxCLyW8z2Ag24h1W1kpKYMkSKo1GGT9+TFlZ2fM2XejNfoKIKy0ttbCwGD58eGtrK+ksr6y5uXn16tU0GnXECJrsKkLYsHW+HTkCenoMDsc0MjKS9Las0KqqqlasWE6jUT08GNeukf/FYeu3rbUVduwALS26mZnhn3/+KZFIXrDdQq/1EKQg0tPTtbS0Fi5cSDpIF8XFxTk726mp0T75BEpKyPc3bIrfzp4FX18GjUZduTIYL5rdSSkpKWPGjKBQICCAfuYMjtNg69VWXg4bNoCJCUNDQ/Xbb79taWl56RYLMxQfdQAAIABJREFUvdArkKI5e/YsjUb74YcfSAfpotbW1h9//NHERF9Fhfbxx5SiIvJ9D5sCNokETp6EoUPpFApMmhSQkJBAestVPufOnRs/fjSFQrGxYYaGQn09+V8rtr7dEhNh/nwKi0Vls7XWrFlTWlrayW0VerQnIIW1detWKpV66tQp0kG6rqWlZefOnebmRkwmdfZsalQUfn3EJm+VlfDjj+DoyKBQKEFBUxITE0lvrcotMzPzww+XaWioamrSuVzKmTNAfKo9bH2sPXgAP/wAXl4MAHB1ddi7d29zc/MrbaXQQ1s/UnzvvfeehoaGsn/Qt7a2hoWF+fgMBYCBA5kbNsCDB+R7JjYiTSyGyEiYMYPKYlG1tFQXL34/NTWV9Bbad9TV1f3000/DhnlSKBQ2m7l4MTUqCkQi8r93bMrbyspg504YPpxOpVJ0dNQXLpx/9erVrm2fFKlU2sE5WKgfEAqFU6ZMuXPnTmxsrLW1Nek4rysjIyMsLOzAgX21tQ0jRtCCgkRBQe1nPUd9klgM167ByZNw4gTjwQOhn5/XokXLpk+frq6uTjpa31RYWHjkyJHDh/9KTEw1MGCMHy8KCJCOHw8GBqSTIWUgkUBiIkRGQmQkIy5OpKqq8tZbgbNmzRk/fjyr3ZUgXgVWM/0an88fMWIEn8+/ceOGQZ/4KBIIBDwe79ixo2fPnubzmzw8mFOnCoKCwNGRdDLU3Vpa4MIFOHmSwuPRqqtFzs42U6fOmjNnjr29Pelo/UVOTs7x48cjI8/euBErFIrc3ZkBAYLx48HXt/0lfhAqKYELFyAykhIVRauqEpma6gcETH7zzYmTJk1Sbbs06GvAaqa/Ky0t9fX1NTAwuHLlipqaGuk43UYsFsfFxR09evTYsYMlJVVGRozhw0Vjx0oDAjq+ziJSCmIxJCdDVBRERTFiYiQCgcTd3XXy5KBZs2ZhEUNQc3NzbGxsVFRUVNTpxMQMBoPq6kr18xP5+8PIkaCvTzofIiQvD2Ji4MYNSkwMMzOzlcVi+Pv7jR07YezYsUOGDKFQKN34XFjNIMjMzPT39/fz8ztx4gSNRiMdp5tJJJL4+PiLFy9evnzx5s341lahnZ3K6NGPRo4EHx+wsCCdD72MQABJSRAbC5cv06Kjgc8Xm5rqjRkzYfTosQEBAUaySxIjhZGfnx8dHX3jxo3Y2KuZmfcBpHZ2Kr6+rb6+0iFDwMkJh236sooKSE6G+HiIi6PFxkJ9vVhTU9Xb28vPb6Sfn5+/v3+3DMN0CKsZBAAQHx8/evToefPm7dmzh3SWHtTc3Hzjxo3Lly9fvnw+MTFVLJYYGzO9vEReXhJvb/D0BC0t0hERAADcuwe3bskaMylJJBBI9PS0R44cM3r02NGjR9vZ2ZEOiDqlpqYmLi4uNjb2xo3ohITbzc2tDAbVyYkxeHCrmxvImo4O6ZSoqyQSuH8fkpMhKQmSk2kpKbSSEgEAWFoa+/mN9PX18/Pzc3Fx6Z0vyVjNILljx47NnDlzw4YNX375JeksvaGpqSkxMTE+Pj4+Pi4+/kZxcQWVSrGzY7i6CgcPljo7g4sLcDikU/YPzc1w9y6kpkJ6OqSl0e/cgZoaEYNBc3d38fLy9/Ly8vb2trGx6d5xadTLxGJxdnZ2stzt5OSkyso6ALCyYjo6iu3txXZ2YG8P9va4Z0pBCYWQlweZmZCdDdnZkJnJzMiQ8PkiOp1mZ2fl5uY1eLCbu7u7m5ubnp5e78fDagY9tmfPnmXLloWGhq5YsYJ0lt5WUlISHx+fkJCQmpqSnp5cWFgCAFpadGdnuovLI2dnsLEBGxuwsAA6nXRWJVdfD7m5kJsLmZmQnk5JTaXfvy+SSKSqqkxHR2sXF083N3dvb293d/fXOcEBKb6HDx8mJyenpKTcvXs3Kyvt3r1cPr8ZAHR1GXZ2NAeHR3Z2MHAgcDhgaYklTq8SCODBAygogIICyMmB7GxqZiYtL08kFEopFIqFhaGtrYO9vbOzs7O7u7uzs3PP7T/qPKxm0FO++eabr7/++tChQzNmzCCdhaT6+vr09PS0tLTU1NT09KSMjIyaGj4AMJlUDodhYyOysRFbW8vrG0tLUIC+rIjKyuDBA8jLg9xcyMmBnBxmbq60okIIAHQ6jcMxdXUd4uzs6uLi4urqOmjQoL532BZ6JcXFxdnZ2dnZ2ZmZmdnZGdnZmcXF5RKJFADU1WkcDt3KSsjhSDgc4HDAwgJMTMDQEL9gdF1jIxQXQ0kJFBRAYSHk50NBAaOggPLwofC/t13FxsbK1tbZ3t7BwcHB1tbWzs5OMec+wGoGtffpp5/u3Lnz1KlTEyZMIJ1FgdTU1OTm5ubk5Pz3793c3PvV1Q2yn+rpMczMqObmQgsLiZkZmJmBpSUYGIChYR8/LEAohMpKqKyEBw+guBiKi6GoCIqKGMXF1OJiYWurBADodJqlpbG1tb2Njb21tbWNjY21tbWVlRWDwSAdHyk6gUBQVFRU8JR7+fn5paXVsj9eVCrF0JBhZEQxNRUaG0tMTMDYWF7lGBgAmw2amqRfAzkiEVRXQ3U1VFVBcTGUl8v/ffCAUV5OLS4WNTWJZWuqqjKtrMw5HGsOZxDnCfrKMyaG1QxqTyqVvv/++4cPH7548aKvry/pOAqttra26D/FxcXFxcWFhTkPHhQ9fFghFMo/JphMqr4+3cCAYmQk0tcX6+mBkRHo6oKODmhrw4ABoK0tv61Qf9z5fKivh/p6qKuT/1tbC5WVUFUFZWWUigp6ZSW1okJSUyNsu4uOjoa5uYml5SAzM0tzc3Nzc3MLCwvZDSxcUPdqbW0tLi4uLS0tKSl54t+i0tLiBw9KZXusZJhMKptNZ7MpbLaEzRbp60vZbNDTAy0t0NICHR3Q1AQNDdDUlP9XwY/OamkBPh/4fKirk9/g86Gh4XHVUl1Nq66mVVdTqqrEdXWitjvS6TRDQ11TU1MjIzNTU3MjIyNTU1PZv8bGxoaGhgRfVLfAagZ1QCgUTp06NT4+/tq1aw4ODqTjKB+JRFJWVlZZWSn7t7Kysry8vLy8vKqqvLKyrLS0tK6uoaGhud291NVp2to0bW2KmhpoaEgYDNDSEtFoUh0doNFAWxvo9MdfNLW04MndMgwGaGg8/q9YDA0NTz14UxMIBAAAIhHw+SAQQFMTPHoELS3Q3ExrbaXy+RSRiFJbC3V1kvp6kVj81CcDg0HT0dHU12fr6xsaGpoZGBjo6+vr6+sbGRnJbpiammo8mQChXpeSkrJly5ajR4+ampquWbNmyJAh1c+orCyprq6srq5paGh8tg8CgLo6TVOTpqlJ0dICGk2qpSWlUKQ6OiIAGDAAAEBbG6hU0NR8vIfr2RpIXb39iegNDSAWP7WkpQUePZLfbmwEoRCam6G1Vd4rBQJqUxNNKITGRqpQKCtfJHy+SCRq/yebwaBpaamz2QPYbD0224DNNmCz2Ww2W09PT09Pr+22gYEBlUrt4jurDLCaQR1rbm4OCAiQTR0xaNAg0nH6IIlEUl9fX1tbW19fX1dXV19f33ajubm5qalJIBA0NDSIxeLa2gqJRFJfXysUChsbGwFAKpXW1fEBHnfelhbBo0eCJx9fR0fjyU9YFouppqYKADQaTUtLi8FgamhoqqioqapqqKurM5lMLS0tOp2uo6Ojo6Ojra0t+7fthmLuKUdIJiYmZsuWLWfOnHFycvr8889nz57dyeHAuro6/hNqa2vbbjc2Nsp6nEgk4vP5Eomkvr4KAGprqwGgvr5eIpEAgFgsaWjgt3vY+vom2XEnbdTUWCzWU5HodLqmpvp/P1VjsVgqKqqqqmpMpoq6uhadTtfU1KRSqdra2rI+q6Ojo/mEAQMGyG6oqKh09W3rU7CaQc/V0NAwduzYsrKya9eucfBkZWXw+++/f/rpp/X19aSDINQbpFLp6dOnt2zZcuPGDT8/v9WrV0+ePBnP5O+f+vK4E3pNWlpaFy5c0NPTGzduXElJCek4CCEkJ5FIeDyep6fnW2+9BQARERExMTFTpkzBUqbfwmoGvYiOjk5kZCSTyRw9enR5eTnpOAih/q61tTU8PNze3j4oKMjY2DghIUFWx5DOhQjDaga9hL6+/oULF4RCYUBAQE1NDek4CKF+qqGhITQ01MrKasmSJT4+PpmZmTweb+jQoaRzIYWA1Qx6OVNT0ytXrtTX10+cOJHPb3+8G0II9ajy8vKQkBBLS8v169dPnz49Ly8vPDzc1taWdC6kQLCaQZ1iYWERGRlZWFg4efLkpqYm0nEQQv1CXl5ecHAwh8PZvXt3cHBwUVFRaGioiYkJ6VxI4WA1gzrL1tY2KioqKytr0qRJWNAghHpUSkoKl8u1tbXl8XibN28uKCgICQnR6dtTa6PXgNUMegVOTk7R0dHZ2dkTJkyQTXyCEELdS3ZUr7u7e2pq6r59++7duxccHKwI1zVEigyrGfRq7O3tr1y5kpubO3HiRCxoEELdRXbS9bBhw4YPH15bW3vq1KmkpCQul0vHq0qiTsBqBr0ye3v7y5cv5+TkYEGDEHp9AoEgPDzc2dk5MDBQT08vNjYWJ49BrwqrGdQVDg4OkZGRmZmZEydOxGNoEEJd09jYGBoaam1tvWjRoqFDh6anp8uGZ0jnQsoHqxnURa6urlFRUXfv3g0KCmpu7uDibQgh9DxVVVUhISEcDmft2rVTp07Nzc0NDw93dHQknQspK6xmUNcNHjw4KioqOTkZ56FBCHVSUVGR7KTrH3/8cdGiRYWFhaGhoRYWFqRzIeWG1Qx6LW5ubteuXcvJyRk9enR1dTXpOAghxZWbmxscHGxra3vkyJHPPvusqKho8+bNbDabdC7UF2A1g16Xg4NDTExMdXX12LFjKysrScdBCCmcO3fucLlce3v7M2fObNmyJT8/PyQkRFtbm3Qu1HdgNYO6gZWV1dWrVxsbG994442HDx+SjoMQUhSys5M8PDzS09P37duXnZ0dHBysoqJCOhfqa7CaQd3DwsLi+vXrdDrd398/Ly+PdByEEEmyyWO8vLxkk8dERETIhmdoNBrpaKhvwmoGdRsjI6NLly7p6OiMGjUqJyeHdByEEAGyyWMcHR2DgoIMDQ3j4+NlwzOkc6E+DqsZ1J0MDAyuXLliYmIyfPjwtLQ00nEQQr2Hz+eHhoYOHDhw8eLFXl5eGRkZsuEZ0rlQv4DVDOpmOjo6UVFRTk5OI0aMuHXrFuk4CKEeV1FRERISYmlpuW7durfffvv+/fvh4eH29vakc6F+BKsZ1P3U1dV5PJ6np2dAQEBcXBzpOAihnlJQUCCbPOaXX35ZsWKFbPIYMzMz0rlQv4PVDOoRampqPB5v1KhR48aNu3TpEuk4CKFulpqayuVybWxsIiIiNm3aVFBQEBISoqurSzoX6qewmkE9hclkHjlyZNq0aZMmTYqIiCAdByHUPWRH9bq5uSUnJ4eFheXk5AQHB6upqZHOhfo1rGZQD6LT6fv37587d+706dOPHz9OOg5CqOukUimPx/Pz85OddH3q1KmUlBQul0un00lHQwirGdTDaDTa3r1733///VmzZv3999+k4yCEXplEIjl69KiLi8tbb72lpqYWExMjG56hUCikoyEkhzU16nFUKnXXrl2amprz5s2rqKhYtWoV6UQIoU5pbW09fPjwhg0b8vLyJk6c+Oeff3p4eJAOhVAHsJpBvYFCoWzZssXU1HTVqlUFBQU//vgjlYrjgggproaGhv3792/ZsqWmpmbGjBlnz561sbEhHQqh58JqBvWeFStWsNnshQsX1tbWhoWFMRgM0okQQu2Vl5fv3r17x44dUql0wYIFa9asMTY2Jh0KoZfAagb1qrlz5xoZGU2dOrWsrOz48eMaGhqkEyGE5PLy8kJDQ3/77Tdtbe2VK1euXLlSR0eHdCiEOgVH+1FvGzNmzKVLl5KTk8eMGVNVVUU6DkIIkpOTuVyura3t6dOnN2/enJ+fHxISgqUMUiJYzSACPD094+Liqqur33jjjaKiItJxEOq/ZGcnDRkyJDU1dd++fdnZ2cHBwaqqqqRzIfRqsJpBZAwaNOj69etMJtPHxyc1NZV0HIT6F4lEwuPxfHx82iaPSUpKwsljkPLCagYRY2xsfPXqVWtr65EjR8bExJCOg1C/IBAIwsPDnZ2dg4KC9PX14+LicPIY1AdgNYNI0tHRuXjx4pgxY8aNG4eTBSPUoxobG0NDQwcNGrRo0aKhQ4emp6fLhmdI50KoG2A1gwhjsVj//PMPl8udMWPG3r17ScdBqA+qqqoKCQmxtLRcu3bttGnT7t+/Hx4e7uDgQDoXQt0Gd5Ei8mg02p49e4yNjZcuXfrw4cOQkBDSiRDqIwoLC3/44Yfff/9dTU1t+fLly5cvZ7PZpEMh1P2wmkEKgUKhhISEsNnslStX1tfXf//99zhZMEKvIzc3d9u2bfv372ez2V999dXHH3+srq5OOhRCPQWrGaRAli9fbmBgMH/+/OLi4vDwcDxNFKEuuHPnzo4dO/7++++BAwdu27btgw8+YLFYpEMh1LPw6y9SLDNnzrx8+fLVq1dHjRpVUVFBOg5CykR2dpKHh0dGRsb+/fuzsrKCg4OxlEH9AVYzSOH4+vrGxcXV1tb6+PhkZWWRjoOQopNNHuPl5SWbPCYiIiIxMZHL5dJoNNLREOolWM0gRWRtbR0bG2tqaurr6xsdHU06DkIKSjZ5jIODQ1BQkKGh4a1bt2TDM6RzIdTbsJpBCorNZkdFRQUEBAQEBPz111+k4yCkWPh8fmhoqJWV1eLFi729ve/evcvj8Tw9PUnnQogMPAoYKS4Wi3Xw4MGvv/6ay+Xm5ubimdsIAUBFRcUvv/zy008/iUSihQsXfvHFF6ampqRDIUQYVjNIocnO3DYzM1u2bFlRUdGvv/7KYDBIh0KIjPz8/B07duzdu1dDQ2PFihXBwcEDBgwgHQohhYDVDFICixYtsrCwmD59elFR0b///qutrU06EUK9KjU1dfv27YcOHTIzM9u0adPixYvV1NRIh0JIgeBxM0g5jB8//vr16/fu3fP39y8qKiIdB6FeIjuq183NLTk5OSwsLCcnJzg4GEsZhNrBagYpDVdX15s3b9LpdB8fn8TERNJxEOpBUqmUx+P5+fnJTro+depUSkoKl8ul03FAHaEOYDWDlImJicn169fd3d1HjBjB4/FIx0Go+wmFwvDwcBcXl8DAQF1d3Rs3bsiGZygUCuloCCkurGaQktHQ0Dh58uTMmTOnTp26e/du0nEQ6jatra2//fabtbX1woULHR0d09LSeDyer68v6VwIKQEctETKh8FghIWFDRw48KOPPsrMzPzhhx9w+B0ptYaGhv3792/ZsqWmpmbGjBnr16+3sbEhHQohZYJ/A5CyWrt2rb29PZfLzcrKOnz4MJ6qipRReXn57t27d+zYIZVKFyxYsGbNGmNjY9KhEFI+uKcJKbG33347NjY2Ozvby8sLr+iElMv9+/eDg4M5HM6ePXtWrlxZWFgYGhqKpQxCXYPVDFJugwcPvnnzpq6urq+v76VLl0jHQejlkpOTuVyunZ3d6dOnN2/eXFBQEBISoqOjQzoXQkoMqxmk9IyNja9duzZ58uQJEyb89NNPpOMg9Fyys5Pc3d3T0tL27dt379694OBgFRUV0rkQUnp43AzqC1gs1p9//unk5LRq1aqMjIydO3fiBRCQ4pBIJGfOnPn222/j4+P9/PwiIiImT56MZ1wj1I1wbAb1ERQKZfXq1UeOHPn7778nTpxYW1tLOhFCIBAIwsPDnZycgoKC9PX14+LicPIYhHoCVjOoT5EdF5yTk+Pl5ZWZmUk6Duq/GhsbQ0NDBw0atHjxYk9Pz/T0dB6P5+PjQzoXQn0TVjOor3F1dY2Li2Oz2X5+flFRUaTjoH6nqqoqJCTE0tJy3bp106ZNy83NDQ8Pd3BwIJ0Lob4MqxnUBxkbG0dHR0+ZMmXChAlbtmwhHQf1F4WFhcHBwZaWlrt27Vq+fLnspGtzc3PSuRDq+/AoYNQ3sVisP/74w9HR8csvv8zLy8PjglGPSk9P37p16z///GNiYvLdd98tXrwYL3ONUG/Cagb1WbLjgm1sbLhcbm5u7uHDh/X09EiHQn1NYmLili1bjh07NmjQoJ07dy5cuBDrZoR6H+5pQn3ctGnTYmJi8vLyPD09k5OTScdBfYfs7KShQ4fev3//jz/+yMrKWrJkCZYyCBGB1Qzq+9zc3G7fvm1tbT1s2LDw8HDScZByk0gkPB7P09Nz+PDhtbW1ERERiYmJXC6XRqORjoZQ/4XVDOoX2Gz2+fPng4OD58+fv3TpUpFIRDoRUj6tra2ys5OCgoKMjIxu3bolG54hnQshhMfNoH6DRqNt3rx58ODBixYtKiwsPHjwoK6uLulQSDnw+fx9+/Zt3bq1qqpq5syZPB7P1taWdCiE0GM4NoP6l9mzZ8fExGRlZXl5eaWlpZGOgxRdRUVFSEiIhYXF+vXr33nnnby8vPDwcCxlEFI0WM2gfsfd3f327dsWFha+vr7Hjh0jHQcpqPz8/ODgYA6Hs3v37uDgYNnkMaampqRzIYQ6gNUM6o/09PQuXLjw0UcfzZgxY82aNRKJhHQipEBSU1O5XK6trS2Px9u0aVNBQUFISMiAAQNI50IIPRdWM6ifotPpmzdvDg8P/+mnnyZPnlxXV0c6ESJPdlSvm5tbSkpKWFjYvXv3goODVVVVSedCCL0EVjOoX3v33XevX7+ekZHh5eWVkZFBOg4iQyqV8ng8X19f2UnXp06dSk5O5nK5dDqeJ4GQcsBqBvV3Hh4esqtU+vr68ng80nFQrxIKheHh4c7OzoGBgWw2+8aNG7LhGQqFQjoaQugVYDWDEJiYmFy9enX69OmBgYFfffUVHkbTHzQ1NYWGhlpbWy9atMjDwyMtLU02PEM6F0KoK7CaQQgAgMVi/f7773/88cf27dvHjh1bUVFBOhHqKQ0NDbI6ZvXq1RMmTMjNzQ0PD3dyciKdCyHUdVjNIPQYl8uNiYkpKCgYOnRofHw86Tiom5WVlckmj/nqq69mzJiRn5//66+/WlhYkM6FEHpdWM0g9JQhQ4YkJCQ4OjqOGDEiNDSUdBzUPe7fvx8cHGxlZbVnz56VK1cWFRWFhoYaGxuTzoUQ6h5YzSDUHpvNPnfu3Ndff/3JJ59wudzm5mbSiVDXJSUlcblcOzu7M2fObN68WTZ5jLa2NulcCKHuhNUMQh2gUCirV6+OiIg4ffq0v79/fn4+6UTolcnOThoyZEhaWtq+ffuys7ODg4NVVFRI50IIdT+sZhB6rkmTJt26dUssFnt6ep4/f550HNQpEomEx+N5e3vLJo+JiIiQDc/QaDTS0RBCPQWrGYRexNraOj4+PjAwcOLEiXgNBAUnEAhkZycFBQUZGBjcvHlTNjxDOhdCqMfhTJcIvYSKikpYWJi3t/fy5cvT0tL++usvvGSPomlsbAwLC9u+fXtFRcXMmTNPnDhhb29POhRCqPfg2AxCnbJkyZKrV6+mpKR4enqmpKSQjoPkKisrQ0JCLC0t161bN23atPv374eHh2Mpg1B/g9UMQp01bNiwO3fuWFhYDBs2LCwsjHSc/q6wsDA4OJjD4ezatWv58uWFhYWhoaFmZmakcyGECKBIpVLSGRBSJmKxeMOGDRs2bJg7d+7u3bvV1dUJhiksLNy9e3fbf7OysmJjY9977722Jebm5h999BGJaD0oPT1969athw4dMjU1XbVq1eLFi9XU1EiHQgiRhNUMQl1x6dKlOXPm6OrqHj161NnZmVSM1tZWfX395ubmDq/23Nra+uWXX3777be9H6yHxMTEbNmy5cyZM05OTp9//vns2bMZDAbpUAgh8nBPE0JdMWbMmNu3b+vq6g4bNuzQoUOkYrBYrOnTp9NotNaOAMDs2bNJZetesrOThg8fXlpaevjw4dTUVC6Xi6UMQkgGqxmEusjc3Dw6Ovqjjz6aO3cul8ttaWnpcDWRSNSjMebMmSMQCDr8kb29PcFxo24hmzzG09OzbfKY27dvT58+nUKhkI6GEFIgWM0g1HV0On3z5s0nTpzg8Xh+fn73799vt8K1a9dmzpzZoxlGjRqlr6//7HIGgzF//vwefeoe1draGh4e7uDgEBQUZGRklJCQgJPHIISeB6sZhF5XYGBgcnIynU4fMmTI0aNH25ZXVVVNnz79+PHjf/31V889O5VKnTNnDpPJbLdcJBLNmjWr55635/D5/NDQ0IEDBy5ZssTb2zszM5PH4w0dOpR0LoSQ4sJqBqFuYGlpee3atQULFsycOTM4OFggEEil0nfffbe2tpZCoXz44YcPHz7suWefPXt2u51NFArF09OTw+H03JP2hIqKipCQEAsLi/Xr17/zzjt5eXnh4eG2trakcyGEFB2e04RQdzpw4MCyZcsGDx48atSo7777Tta/GAyGv7//pUuXeu5oDw6HU1hY2PZfGo0WGhqqOOdmV1VV6enpvWCF/Pz8HTt27N27V1NTc9myZStXrtTR0em1eAghZYdjMwh1p3nz5sXHxzc3N2/evLntq4JQKLx69erevXt79HnbneDzzjvv9NzTdZ5UKl25cuUHH3zwvBVSUlK4XK6trS2Px9u0aVNBQUFISAiWMgihV4JjMwh1s7q6OhcXl7KysnZnM6moqKSnpw8aNKgnnjQrK8vBwUF2m0qljh49+uLFiz3xRK9EKpUuX758165dVCo1MzOz3T6jtsljXFxcPv300zlz5nQ4aw5CCL0Ujs0g1M0WLFjwbCkDAGKx+N133+2hq3Db29s7Ojq27cmaN29eTzzLK5FKpR9//LFsqmIajbZt27a25Twez9fXV3bS9alTp5KTk7lcLpYyCKEuw2oGoe60c+fOiIiIDueYEQqFt27d2rkRLKHyAAAP90lEQVRzZw89NZfLpdFoAECj0QIDA3voWTpJKpV++OGHe/bskVVvQqHwzz//LCoqCg8Pd3Z2DgwMZLPZsbGxspOucfIYhNBrwj1NCHWbrKyswYMHi0QiqVT6vJ7FYrFSU1N74jydwsJCKysrAJg6deq///7b7Y/feWKx+P333z9w4MCTA1EMBkNHR6eurm7u3LlffPFF234xhBB6fVjNINSd8vLyeDzeiRMnYmJiZEvEYvGTK9Dp9MGDB8fHx8vGUbqXj49PfHz88ePHp06d2u0P3klisXjBggUHDx58dp8ai8W6ffu2sk9PjBBSQFjNINQjqqqqzp49e+rUqbNnz7a2ttLpdKFQKPsRjUbbuHHjmjVrnnff1tbWysrKsrKy8vLyqqqq8vLympqaurq6xsZGfkN9I7++vr62vr5eIpHU1/PbigZ+U4tIJK+c1NVUmAz5YShqaqosFlNNTU1DQ1NTU1tHV09TU1NTU1NLS0tfX9/AwMDIyEh248UnUXeGWCzmcrmHDx9uV8PJ0On0kJCQtWvXvuazIIRQO1jNINSzGhsbz58/f/LkyYiICD6fz2KxWltbGQzGnTt3LCws8vLyCgoKCgoK8vPzC/Lz8vPuPXhQUtfQ2HZ3NRbNQIfO1gAdNYkGS6zBkmiogI4aaKoAnQbqLGD+d+ysGhNaRfBPHCwZDQ0tIP5vZIT/CERiaGoFfgs0tkJ9MzQ8oje2UutbKJUNksp6kUQi/xBg0GmGBmwOx8pqkC3nP1ZWVpaWllTqy4+xEwqFM2bM4PF4HZYyMgMGDHj48KGqqmpX306EEOoAVjMI9ZKqqqq///47IiIiPj6+qamJyWAI/hutMdZlcvSBwxZw9MBCD4x1QF8TDLXBSAfUWa/2LJUNoK/1CutLpFDZAJV8KK+HsjoorYOCSiioouVX0wvKRc2tYgBQYTEd7K3tHV2dnJwdHBycnJysra3b7SkTCATTpk2LjIx86VU2f/75548//vjVXhVCCL0QVjMI9ZSWlpbk5OSEhISEhFu3bt7IuV8olUo1VGn2pjQjTUGLAIYOhAVvAEcfVBgvfzQiKhogvwIySyDzIWQ8pGWW0gsqBBKJVENd1WOIu6e3r6enp5eXl5GR0dSpUyMjI9t9njAYDCqVKhKJ2kZr6HS6r69vdHQ0iVeDEOqzsJpBqDs1NTXFxMRcuXLlyqXIpOQ0oUisq0n3GgSeViLPgeBqARZsUOrzkZsFkPkQEvMhIQ8SCpgZRUKRWMpkMgQC+TgThUIZMGCAkZERh8MxMzMzMTExNTU1NjY2MzMzMjIyNDQkmx8h1CdhNYPQ65JKpXfu3ImIiLh08fythEShSOxgrjLK/pG/LXgNgkF9+s93swB+uwzRmVBcQ80soTQ9Ehsb6o4aPT5gwpuTJk1is9mkAyKE+gWsZhDqIpFIFB0dffLkyVMnjj54WG5pwBznJBjlCKMcwbhfXmVIJIbEfLhyFy5n0q9lSsQSynB/38CpbwcFBVlaWpJOhxDqy7CaQeiV3b59+88//zz4V3hNXcNgDjNwiCDIA9w5pGMpEv4jOJcMJxMp51JpdY0iHy8P7oL3Z82aNWDAANLREEJ9EFYzCHVWRUVFeHj4H/v2ZmTeczBnzvcTzPABK33SsRSbUAxX7sJfNyj/3qJKgBoYGLjwvUXjx4/HqxkghLoRVjMIvVxeXl5o6I7f9/7GoIrfGiLm+kvHOCn3wby9r0UAp5Pgt6uMS2nCQVacj1esXLp0qYqKCulcCKG+AKsZhF4kKSnp240bTpw8aWXI+CRAsGAEqDFJZ1Jy6cWw/QzlUBxFj6278pMvPvroIzU1NdKhEELKDasZhDr28OHDdWu/DD9wYIgV/YtJwmmeQMNLznefh7UQeh72XKZr67C/27xt7ty5nZluGCGEOoTVDELtCQSCTZs2bdu6WV9TsnmGYIY37lTqKeX18NUxSthVcHdz/XnXHh8fH9KJEEJKCasZhJ6SlZU1Z9b0nHuZ6wLFwRMUd5be16S3FKobwckM0reQjgKQ9gBW/U2Lvitdu279unXr6HT6y++DEEJPwKFdhB777bffPIa4MZqzk74Vr57SZ0sZReNiDhdXi398V7J188Y3/H0LCgpIJ0IIKRmsZhACAJBKpatXr1627IOV41tj1gut+/QEvgqIQoGPx0PCN+KmsuRh3kNTU1NJJ0IIKROsZhACsVj8wQdLv9++LWyx9NsZwKC9/C6oJziZQez/CV2M6kYM97tx4wbpOAghpYHVDEKwevUXB/7cd/IT6YI3SEd5Rk0jfPIXWH8CrPnAXgqB30NK0eOfzt4JlLnAmg8A8PsVcPgcVBaA9SfwV8xTD5KYD2O+A433gL0U5u2GygbFPa5ZnQURn4hH2LZMfHN8Tk4O6TgIIeWARwGj/u7MmTNTpkz58wPpPH/SUZ5RxQef/4P75U8tVGXClbXgbQ0A8P5vsC8aAGDHPFh54KnVTn4CgR4AAHcfgvdX0Pjo8Y/cOVBUpUBHAT9LKIYR3zIa6YPiE+6oqqqSjoMQUnQ4NoP6tfLycu68Oe+NoChgKQMA/zsM98uBQoG9i6BxH6RvASczaBHAR3/IV2gbYtl6Gs58DjW/wdog+ZId5+Q3Qv6VlzKLRkHFbsjeDnQqVDf24st4dQwa/L1MWFyY+7//rSGdBSGkBLCaQf3apk2bVKgtP82XkA7SAaEY/okDAPAaBItGgToLnMzg/6YBACTmQ07ZUyuvCICJbjBAHUKmga4GAEBmifxH51MAADRVYMc80NcCW2PY837vvYous9KHrbNEv+zalZ+fTzoLQkjRYTWD+q+WlpY/9v/+6QShYl6sIL9CPqYSnwuUufI24yf5T5MLn1p5hIP8Bp0GshOyZKMvtU3AfwQAMMQK1FnyddwtleP6DAveAEMdalhYGOkgCCFFh9UM6r+uX79e39A0axjpHM/Bf/Sin1Y2PPVfPc3Ht1WZAACyI+Kq+PKFmk9c3pFCAS1luDISnQYzvIS8U/+SDoIQUnQ45ybqv5KSkiwMmCYDBKSDdEzrv4NfJ7sD77MuPohsrxMA1DQ9XiiRQl1Th6srHB9r2HE+WyAQMJnKMJqEECIEx2ZQ/1VbW6uvqahnKgMMNAANFQCA5EKQdPXUQ7aGfKgmrQia/yvb4nLgkbA7IvY8Ay2QSKR1dXWkgyCEFBpWM6j/UlNTa1bQcRkAABoVZvoAABTXwOcHoYoPlQ0w82fQXQLOq1/hpKRxzgAA/EcQHA6VDZBeDEuV50AU2ZFDamrKsGMMIUQOVjOo/7K1tc0tFbYocEHz3Uyw1AMA+OEs6H8ABsvgyE2obYKFI4Ct8bI7/+f/3gYWAwDg9ytgsAxcVoOmChjpAEDXh3x6TdoDMDXW19Do9KtFCPVLWM2g/mvEiBESKZxLIZ3j+Qy04NYG+GgccPSBQQNNFRjpACdWwacTX+FBhnDg7OcwdCCwGKCvBQvegNOfg54GAIAiV3IyEcmMMeMmkE6BEFJ0OBcw6tcmvjm+sfDKtXUi0kFQB27mwrD/g8uXL48aNYp0FoSQQsNqBvVr8fHxw4YN+2OplDucdBT0NIEI/L5hqJl4Rl/Dy08ihF4Cz9BG/Zq3t/dnn3324c8/DB0odjTttoe9nQee61/3QXifwWT37kjzQgob9bODlKwy+u0z+7r5cRFCfRGOzaD+TigUjnzDv7Y46fL/hLJjYxFxe6/A0jD455/DM2bMIJ0FIaQE8Chg1N8xGIwjx45LVM2Hb2QUVJJOgwB+PAdLwyAkJARLGYRQJ+HYDEIAABUVFRPGj60ozjqxUug5kHSa/kokhrVHYNsZ+OGHH1euXEk6DkJIaWA1g5BcfX39jOnTrly5GjJNsnoK0HDgsnfllsO7uxlpxdTde37jcrmk4yCElAl+YCMkp62tfT4yasvW7d+cZIzeRM8qIR2o3xBLYHcUuK+lCdXtbycmYSmDEHpVWM0g9BiFQlm1atWthNsNdDvX/1GX/0lpuwY16iEX0sB9HSP4AG35yi/i4m87ODiQToQQUj64pwmhDkgkkv37969fu6alqW71JNGysaCNVwrqbvG58M1J2tkkcVDg5K3bfrCxsSGdCCGkrLCaQei5Ghsbt27dGrrjexALFo8UrXwTzHRJZ1J+UimcToLt5+jX7oq8hrpv3vo9TvWLEHpNWM0g9BL19fW//fZb6I/bKyqr3vGC90ZIRjsBlUI6lhKqbIC/Y+G3K4ysh6KJbwZ8/sWaESNGkA6FEOoLsJpBqFMEAsHBgwd/3bPrZvxtC30m108w/w2wNiQdSxkIxXAuBfZHU88kg6qqysxZs4ODVzk5OZHOhRDqO7CaQejVZGdnHzp06I99vxU+KHU0p09xE012Bz9boOBozdNaBBCVDqeTKKeSGJX1wmE+Xtz5782ZM0dDQ4N0NIRQX4PVDEJdIRaLL168ePLkyYhTx0vLKgcasQLdW8e7gL8daKiQDkdUThlczoDTybRLGSAQSYd5D30r6J3p06dzOBzS0RBCfRZWMwi9FolEkpCQcPLkydMRx9Pv3mPQqV7WtFH2wlGOMMwGVJmk8/WKwiq4cheu3KVczmQUVwk01FVHjx4dGDRtypQp+vr6pNMhhPo+rGYQ6jYVFRXR0dExMTE3rkUlJt+l0yi2JnQPS6GHFXhYgedAYDFIR+wm9c2Q9gAS8+FGDu16NrWsVkin0wa7uowdFzB27Njhw4ezWCzSGRFC/QhWMwj1iAcPHsTGxiYkJCTEx95JSm5salFl0RzNaI7GAiczcDQFR1OwMlCOc6P4jyCrBNIfQGYJZDykZTykFVYIAMDK0tTLx8/T08vT09PLy0tFpX/vY0MIkYPVDEI9TiwWZ2Zm3r59Oy0tLSM9NfNuelFxGQCosmjWxnQOW8jRk3D0wFIPOPpgzgYDLTI5HwmhpBYKq6CgEgpk/1YzCqooRRUCAFBVYdrbWTs6uzk5Obu5uXl6eurp6ZEJihBCT8NqBiECGhoasrKy0tPT8/LyCgoKCvLu5efnl5ZXy/ojg07V16bra1GMtUUGmmJ9TdDXAi1V0FABDRXQVAEdNdBUBQYNVBiPD81RZwGT/t/jt4BYIr9d2wQAwH8EjY+g8RE0tEB9M/AfQUMLVDZAaR2lopFRyaeU1Yrrm0Syu6iqMK045pacQRyrQRwOx97e3snJycrKikrFa6EghBQRVjMIKYrW1taioqIHDx6UlpZWVlZWVFSUlZVVVpRVlJdWVVU2NDTyG5tbBcLXfyJtLXUNdTUtTU19A0MjE3NDQ0N9fX1DQ0NDQ0MjIyMOh2NoiBPpIISUCVYzCCkToVDI5/Pr6ur4fL5IJGpqahIIBLIfyZbIbqurqzOZ8kEbLS0tGo2moaGhoaGhqamppUVoPxZCCPUYrGYQQgghpNxwLzhCCCGElBtWMwghhBBSbljNIIQQQki5/T/4YGBVCoraKgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
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}