image-captioning-generator.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":1111676,"sourceType":"datasetVersion","datasetId":623289}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/premkumarsanamala/image-captioning-generator?scriptVersionId=180901487\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","source":"# **📸Image Caption Generator**\n---  ","metadata":{}},{"cell_type":"markdown","source":"\n### **Multi-Modality in Image Captioning**\n\n#### **1. Visual Modality (Image Processing):**\n   - **Feature Extraction:** The first step involves processing the image to extract meaningful features. This is typically done using Convolutional Neural Networks (CNNs) like VGGNet, ResNet, or more advanced models such as EfficientNet.\n   - **Object Detection and Localization:** Advanced image captioning models might also use object detection techniques to identify and localize objects within the image.\n\n#### **2. Textual Modality (Natural Language Processing):**\n   - **Sequence Generation:** The second step involves generating a sequence of words to form a coherent sentence or description based on the extracted image features. This is typically done using Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), or Transformer models.\n\n### **How Image Captioning Works**\n\n#### **1. Image Feature Extraction:**\n   - The input image is passed through a pre-trained CNN to extract a fixed-size feature vector or a set of feature maps. This feature vector represents high-level information about the image's content.\n\n\n#### **2. Sequence Generation (Captioning):**\n   - The extracted image features are used as the initial input to an RNN or an LSTM network, which generates a caption word-by-word. The network is trained to predict the next word in the sequence given the current word and the image features.","metadata":{}},{"cell_type":"markdown","source":"## **Flow of the Project**\n### **1. Load data**\n- The data set used in the project is a readily available dataset on kaggle. \n- **Source:** https://www.kaggle.com/datasets/adityajn105/flickr8k \n- This dataset contains 8k images and each image again contains 5 captions. \n- The model contains two sperate networks one CNN for extracting features from images \n- The other is an LSTM network to extract text features. \n\n### **2. Data Preprocessing**\n\n#### **Image Data**\n- We first import VGG16 model which is an object detection model which can detect 1000 different objects. \n- To learn more about the architecture of VGG16 visit https://www.geeksforgeeks.org/vgg-16-cnn-model/. \n- We load each image, reshape it, preprocesses it and pass it to the VGG16 model to extract features and save them in a dictionary.  \n\n#### **Text Data**\n- We read the text file with captions, we create a dictionary with image id and its captions by splitting and joining the captions from source.  \n- Next we perform text preprocessing where we remove the special charecters, spaces and add start and end tags to the captions. \n- Using tokenizer we create the word index for all the caption data and also find the max length of the caption. \n\n### **3. Model Building** \n- The model we build is has a Dense layer to collect the image features and another layer with embeddings to process captions.\n- The both layers are connected to another dense layer which process the both outputs of the bove mentioned layers together and give output the caption. \n\n### **4. Model Training**\n- We create a function to generate data by pairing the image features and text tokens using text to sequences. \n- We fix for number of epochs and generate data by running aloop and pass it to the model we build and train the model. \n\n### **5. Prediction & Evaluation** \n- We create a function to predict sequence since we need to generate a caption with a bunch of words we need to run the prediction for number of times until the model predicts a end tag and we print the caption as predicted caption by the model for give image. \n- We predict the caption and compare it with the actual caption and using a metric called BLEU we measure the score the better the score we get better predictions. \n\n### **6. Hyper Parameter tuning**\n- We increase the number of epochs and train the model for more number of epochs and retrain the model and check \n\n### **7. Visualizing the final Result**\n- We can plot the image and print the predicted caption and original caption to check how well the model is performing. ","metadata":{}},{"cell_type":"markdown","source":"### Methods Utilized\n\n","metadata":{}},{"cell_type":"markdown","source":"## 00 | Importing Modules","metadata":{}},{"cell_type":"code","source":"\nimport os \nimport pickle \nimport numpy as np\nfrom tqdm.notebook import tqdm \n\n\nfrom tensorflow.keras.applications.vgg16 import VGG16, preprocess_input\nfrom tensorflow.keras.preprocessing.image import load_img, img_to_array\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.utils import to_categorical, plot_model \nfrom tensorflow.keras.layers import Input, Dense, LSTM, Embedding, Dropout, add\nfrom nltk.translate.bleu_score import corpus_bleu\n\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n","metadata":{"execution":{"iopub.status.busy":"2024-05-30T11:49:52.113476Z","iopub.execute_input":"2024-05-30T11:49:52.114461Z","iopub.status.idle":"2024-05-30T11:49:52.121076Z","shell.execute_reply.started":"2024-05-30T11:49:52.114424Z","shell.execute_reply":"2024-05-30T11:49:52.120071Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"BASE_DIR = '/kaggle/input/flickr8k'\nWORKING_DIR = '/kaggle/working'","metadata":{"execution":{"iopub.status.busy":"2024-05-30T11:49:55.573852Z","iopub.execute_input":"2024-05-30T11:49:55.574838Z","iopub.status.idle":"2024-05-30T11:49:55.579255Z","shell.execute_reply.started":"2024-05-30T11:49:55.574784Z","shell.execute_reply":"2024-05-30T11:49:55.578184Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"## 01 | Extract Features","metadata":{}},{"cell_type":"code","source":"# Initialize model VGG16()\nmodel  = VGG16()\n\n# Restructure the model \nmodel = Model(inputs = model.inputs, outputs = model.layers[-2].output)\n\n# Summarize the model \nprint(model.summary())","metadata":{"execution":{"iopub.status.busy":"2024-05-30T11:49:59.146262Z","iopub.execute_input":"2024-05-30T11:49:59.146646Z","iopub.status.idle":"2024-05-30T11:50:05.400886Z","shell.execute_reply.started":"2024-05-30T11:49:59.146617Z","shell.execute_reply":"2024-05-30T11:50:05.399932Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5\n\u001b[1m553467096/553467096\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"functional_1\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │         \u001b[38;5;34m1,792\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │        \u001b[38;5;34m36,928\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)   │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │        \u001b[38;5;34m73,856\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)  │       \u001b[38;5;34m147,584\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m1,180,160\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)    │     \u001b[38;5;34m2,359,808\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25088\u001b[0m)          │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ fc1 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)           │   \u001b[38;5;34m102,764,544\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ fc2 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4096\u001b[0m)           │    \u001b[38;5;34m16,781,312\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block1_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block2_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block3_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block4_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ block5_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25088</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ fc1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)           │   <span style=\"color: #00af00; text-decoration-color: #00af00\">102,764,544</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ fc2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4096</span>)           │    <span style=\"color: #00af00; text-decoration-color: #00af00\">16,781,312</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m134,260,544\u001b[0m (512.16 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">134,260,544</span> (512.16 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m134,260,544\u001b[0m (512.16 MB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">134,260,544</span> (512.16 MB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n</pre>\n"},"metadata":{}},{"name":"stdout","text":"None\n","output_type":"stream"}]},{"cell_type":"code","source":"# Extract feature from images\nfeatures = {}\n\ndirectory  = os.path.join(BASE_DIR,'Images')\n\nfor image_name in tqdm(os.listdir(directory)):\n    # create image path \n    img_dir = directory + '/' + image_name\n    # Load image\n    image = load_img(img_dir,target_size=(224,224))\n    # Convert the image in to an array\n    image = img_to_array(image)\n    # Reshape the data for model input\n    image = image.reshape((1,image.shape[0],image.shape[1],image.shape[2]))\n    # Preprocess the image \n    image = preprocess_input(image)\n    # Get features\n    feature = model.predict(image,verbose=0)\n    # get image id \n    image_id = image_name.split('.')[0]\n    # store feature\n    features[image_id] = feature","metadata":{"execution":{"iopub.status.busy":"2024-05-30T11:50:23.25661Z","iopub.execute_input":"2024-05-30T11:50:23.257331Z","iopub.status.idle":"2024-05-30T12:01:50.258313Z","shell.execute_reply.started":"2024-05-30T11:50:23.257301Z","shell.execute_reply":"2024-05-30T12:01:50.257247Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/8091 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e72d354e87134c4b83b5a1a3831958f1"}},"metadata":{}},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1717069829.513705     109 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Store features in pickle \npickle.dump(features,open(os.path.join(WORKING_DIR,'features.pkl'),'wb'))","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:08:48.203354Z","iopub.execute_input":"2024-05-30T12:08:48.203946Z","iopub.status.idle":"2024-05-30T12:08:48.484545Z","shell.execute_reply.started":"2024-05-30T12:08:48.203918Z","shell.execute_reply":"2024-05-30T12:08:48.483543Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"# load features from pickle\nwith open(os.path.join(WORKING_DIR,'features.pkl'),'rb') as f:\n    features = pickle.load(f)","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:09:08.213613Z","iopub.execute_input":"2024-05-30T12:09:08.214514Z","iopub.status.idle":"2024-05-30T12:09:08.310869Z","shell.execute_reply.started":"2024-05-30T12:09:08.21448Z","shell.execute_reply":"2024-05-30T12:09:08.309836Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"## 02 | Load the captions data","metadata":{}},{"cell_type":"code","source":"with open(os.path.join(BASE_DIR,'captions.txt'),'r') as f:\n    next(f)\n    captions_doc = f.read()","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:09:12.744372Z","iopub.execute_input":"2024-05-30T12:09:12.744991Z","iopub.status.idle":"2024-05-30T12:09:12.799665Z","shell.execute_reply.started":"2024-05-30T12:09:12.744964Z","shell.execute_reply":"2024-05-30T12:09:12.798716Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"# Create mapping for image to caption\nmapping = {}\n\n# iterate thorugh each caption\nfor line in tqdm(captions_doc.split('\\n')):\n    tokens = line.split(',')\n    if len(tokens)<2:\n        continue\n    image_id, caption = tokens[0],tokens[1:]\n    # remove .jpg \n    image_id = image_id.split('.')[0]\n    # convert caption list to string \n    caption = ' '.join(caption)\n    # create list if needed\n    if image_id not in mapping:\n        mapping[image_id] = []\n    mapping[image_id].append(caption)\n    ","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:09:18.611302Z","iopub.execute_input":"2024-05-30T12:09:18.612138Z","iopub.status.idle":"2024-05-30T12:09:18.751311Z","shell.execute_reply.started":"2024-05-30T12:09:18.612105Z","shell.execute_reply":"2024-05-30T12:09:18.750475Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/40456 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5259e718fe8b491995a636901a9b3408"}},"metadata":{}}]},{"cell_type":"markdown","source":"## 03 | Text Preprocessing","metadata":{}},{"cell_type":"code","source":"def cleaning(mapping):\n    for image_id, captions in mapping.items():\n        for i in range(len(captions)):\n            caption = captions[i]\n            # Lowwer case\n            caption = caption.lower()\n            # Replace sepcial chars\n            caption = caption.replace(\"[^A-Za-Z]\",\"\")\n            # Replace multiple spaces with single pace\n            caption = caption.replace('\\s+',' ')\n            # adding start and end tages to the caption\n            caption = 'startseq ' + ' '.join([word for word in caption.split(' ') if len(word)>1]) + ' endseq'\n            captions[i] = caption","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:09:47.174732Z","iopub.execute_input":"2024-05-30T12:09:47.17553Z","iopub.status.idle":"2024-05-30T12:09:47.181809Z","shell.execute_reply.started":"2024-05-30T12:09:47.175502Z","shell.execute_reply":"2024-05-30T12:09:47.180957Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"mapping['1000268201_693b08cb0e']","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:09:55.695918Z","iopub.execute_input":"2024-05-30T12:09:55.696872Z","iopub.status.idle":"2024-05-30T12:09:55.703636Z","shell.execute_reply.started":"2024-05-30T12:09:55.696832Z","shell.execute_reply":"2024-05-30T12:09:55.702708Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"['startseq child in pink dress is climbing up set of stairs in an entry way endseq',\n 'startseq girl going into wooden building endseq',\n 'startseq little girl climbing into wooden playhouse endseq',\n 'startseq little girl climbing the stairs to her playhouse endseq',\n 'startseq little girl in pink dress going into wooden cabin endseq']"},"metadata":{}}]},{"cell_type":"code","source":"# Caption preprocessing \ncleaning(mapping)","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:09:52.211494Z","iopub.execute_input":"2024-05-30T12:09:52.212482Z","iopub.status.idle":"2024-05-30T12:09:52.389637Z","shell.execute_reply.started":"2024-05-30T12:09:52.212446Z","shell.execute_reply":"2024-05-30T12:09:52.388804Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"# get a list of all captions\nall_captions = []\nfor key in mapping:\n    for caption in mapping[key]:\n        all_captions.append(caption)","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:10:01.861379Z","iopub.execute_input":"2024-05-30T12:10:01.862224Z","iopub.status.idle":"2024-05-30T12:10:01.877612Z","shell.execute_reply.started":"2024-05-30T12:10:01.862186Z","shell.execute_reply":"2024-05-30T12:10:01.876734Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"# tokenize the captions list \ntokenizer = Tokenizer()\ntokenizer.fit_on_texts(all_captions)\nvocab_size = len(tokenizer.word_index) + 1","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:10:14.030826Z","iopub.execute_input":"2024-05-30T12:10:14.031756Z","iopub.status.idle":"2024-05-30T12:10:14.807032Z","shell.execute_reply.started":"2024-05-30T12:10:14.031714Z","shell.execute_reply":"2024-05-30T12:10:14.80625Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"# get max_lentgh \nmax_length = max(len(caption.split()) for caption in all_captions)\nmax_length","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:10:20.899456Z","iopub.execute_input":"2024-05-30T12:10:20.900629Z","iopub.status.idle":"2024-05-30T12:10:20.941727Z","shell.execute_reply.started":"2024-05-30T12:10:20.900585Z","shell.execute_reply":"2024-05-30T12:10:20.940852Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"35"},"metadata":{}}]},{"cell_type":"markdown","source":"## Train Test Split","metadata":{}},{"cell_type":"code","source":"image_ids = list(mapping.keys())\nsplit_size = int(len(image_ids)*0.9)\ntrain = image_ids[:split_size]\ntest = image_ids[split_size:]","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:10:29.92664Z","iopub.execute_input":"2024-05-30T12:10:29.927305Z","iopub.status.idle":"2024-05-30T12:10:29.934507Z","shell.execute_reply.started":"2024-05-30T12:10:29.927277Z","shell.execute_reply":"2024-05-30T12:10:29.933657Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# Create a data generator to get data in batch (to avoid session crash)\ndef data_generator(data_keys, mapping, features, tokenizer, max_length, vocab_size, batch_size):\n    X1, X2, y = list(), list(), list()\n    n = 0\n    while True:\n        for key in data_keys:\n            n+=1\n            captions = mapping[key]\n            # process each caption \n            for caption in captions:\n                # encode the sequence \n                seq = tokenizer.texts_to_sequences([caption])[0]\n                for i in range(1,len(seq)):\n                    in_seq, out_seq = seq[:i],seq[i]\n                    # pad input sequence\n                    in_seq = pad_sequences([in_seq],maxlen=max_length)[0]\n                    # encode output sequence\n                    out_seq = to_categorical([out_seq],num_classes=vocab_size)[0]\n                    \n                    # store the sequences\n                    X1.append(features[key][0])\n                    X2.append(in_seq)\n                    y.append(out_seq)\n            if n==batch_size:\n                X1, X2, y = np.array(X1), np.array(X2), np.array(y)\n                yield {\"image\": X1, \"text\": X2}, y\n                X1, X2, y = list(), list(), list()\n                n = 0","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:10:35.288074Z","iopub.execute_input":"2024-05-30T12:10:35.288813Z","iopub.status.idle":"2024-05-30T12:10:35.298805Z","shell.execute_reply.started":"2024-05-30T12:10:35.28878Z","shell.execute_reply":"2024-05-30T12:10:35.297703Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"## Model Creation","metadata":{}},{"cell_type":"code","source":"# Encoder model \n# image feature encoding layer\ninputs1 = Input(shape=(4096,), name = 'image')\nfe1     = Dropout(0.4)(inputs1)\nfe2     = Dense(256, activation='relu')(fe1)\n\n# Text encoding layer\ninputs2 = Input(shape=(max_length,), name='text')\nse1     = Embedding(vocab_size, 256,mask_zero=False)(inputs2)\nse2     = Dropout(0.4)(se1)\nse3     = LSTM(256)(se2)\n\n\n# Decoder layer\ndecoder1 = add([fe2,se3])\ndecoder2 = Dense(256, activation='relu')(decoder1)\noutputs  = Dense(vocab_size, activation='softmax')(decoder2)\n\n# model \nmodel = Model(inputs=[inputs1, inputs2],outputs=outputs)\nmodel.compile(loss='categorical_crossentropy',optimizer='adam')\n\n# plot model \nplot_model(model,show_shapes= True)\n","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:10:41.613318Z","iopub.execute_input":"2024-05-30T12:10:41.61392Z","iopub.status.idle":"2024-05-30T12:10:42.641943Z","shell.execute_reply.started":"2024-05-30T12:10:41.613886Z","shell.execute_reply":"2024-05-30T12:10:42.640984Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"image/png":"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3bu3ngaPHDly6dIlQRBsbGw0f36Mj49fu3atIAjiPggpKSnz58+XtjBt2jQHBwfNUwRBGD58eJMmTW7fvj1r1qwDBw5o/dzt4+OzYsUKcUMNqQsXLoSEhKgfBwQE6BlRkpGRsXjxYvVjPz8/zd/At23bduvWLc0V+w4ePKi5pL+at7d3//79dbUvS6VSBQcHL1269MaNG9KjVatWHTVq1GeffaZrnxotz58/nzdvXnBwcFpammZ5o0aNli5dqjW8XOrevXvr1q3buXPn3bt3pUe9vb3nzp2r50Y9LCzs2LFj6sfqt/rYsWPz588/f/68ZjULC4vBgwcvXbrUwBxk//7933333aVLl7Q2GzIxMfHx8fn666+7detmSDsldO7cudjYWM0SCwsL/X8Rhjh9+vTRo0f79OlTwnYEQSgoKDh48ODq1atDQ0NzcnKkFezs7Hr27Dlt2jT9a4iGhoaeOHFCq3Dw4MHNmzcvKChYs2bNsmXL7ty5o3nU3t5++vTps2bN0lp5QSoiImLZsmUHDx58/vy59KiTk9PAgQNnz55dr149/e2UOh8fH/0VzMzM+vfvL40tpP9mlikbG5s33njj0KFDmoVbt24ltgAAACgPqpeZesyCrLCwsDK99KxZs8RrDR8+vEjnZmVlOTs7i6er7wy1VK5cWX30008/1d+auF6Gs7OzZvnly5cN/A7ExcVJT9m3b9/Zs2f1bPKnnvOiq1fLly8XayYlJenpf0JCglhz9erVmofeeustQ/o/fvx4/W+RlqysLENijkqVKu3atUt6uta7dPPmTQ8PD12NmJiYaL0oLRMnTjTkp/ixY8fm5OTItvD999+L1ZKSklasWKHnjs7R0bHQP43ExERfX99CuzR16tT8/HzZFi5evPjl/zl06JD+y+mnuYODmo+PT6Fnvf3224X239PTU6v/skt1enp66rnQzZs3PT09C72WWp8+fZ4+faqrqcDAQOkp27dvf/HiRc+ePfU0++abb2ZmZupqNi0tbdy4cYZ0z9zcfN68eQUFBYW+veUsICBA2tuBAwfKVlZPwdM0a9ass2fPrl27NjAwcPHixevWrTt8+HBCQkJRuyHNpps0aVLiF2cQ6V+BmpeXV/l0AAAAwLgYbVFMZ86cER8PHTq0SOdaWVn5+/v//PPP6qdnz57VM2zBWB48eDB+/PjU1FRBEFq3bt2+fXt7e/u4uLiQkJDExERBEPLz8ydNmmRnZzd27Fhjd7ZoZsyYcfDgQfVjExOTVq1atWrVqmrVqoIgPHv2LDw8/Pr167m5uZmZmeHh4UOGDNHTVEJCwpQpU+Li4kxMTFq3bt2uXTtHR8fHjx+HhISoB7GrVKqpU6d26NChTZs2si2cPHkyPz9fEAQrKytPT88WLVpUqVLF1NT00aNHYWFhMTEx6ng9KOAAACAASURBVGqbN2+2tLQUx8Locvjw4c8++0ylUllYWPTo0aNZs2ampqZRUVHHjx9XDwRITk7u3bt3aGhoixYtZFt48uRJ165do6Oj1U9NTU3btWvXqlUrJyenlJSUK1euXLx4UaVSCYKwYsWKrKws2c1EL126JO5Z+9FHHxU63kQPcUyTqEuXLsVrqm7duvfu3ROfhoeHb9u2bfTo0cXu29GjR4cMGZKenm54/VatWp04caJJkyYGnpKZmdmnT5+wsDA9dY4fPz5nzhzZAV/x8fG+vr5RUVGGXCsvL++rr75S7zVbKrNaSkV+fv6vv/4qLffz8zOwhcDAQNlIqHnz5hMmTBg3bpytra0h7UgXyIiKioqNja1Tp46BPQEAAEDxEFsUR3Z29pUrV8Sn7du3L2oLbdu21Ywtpk2bVmqd+z9ubm5ffvmlIAj79+8PDw8XBMHe3l72QuoZIlrmz5+fmJhYu3btX375RfNGMTs7e+7cueIv/JMnT+7evbuBkymKauTIkW3atElOTl65cqW6pF+/ft7e3lrVpCV6PHnyRLzTbtKkyc6dO1u2bKlVJykp6Zdfflm2bFmhrc2ePTshIaFly5YbN27U7EZWVtbUqVP//e9/C4KQn58/a9YscccZqQ4dOkycOHHQoEHS26ejR49++OGH6nkx69atGzZsmP5xEOqxOd26ddu8ebPmhxIfH//ee++pI4CUlJRx48ZdunRJemtaUFAwatQoMbMYOHDgkiVLtCYOXL9+ffTo0X///bcgCGvWrPH19S32ph6FSk9Pl87iKeqKIaLZs2dPnjxZc3mCuXPnDh06tNAZFrLCw8OHDh1qeGah9vDhw759+168eFFzvJUec+bMMWQRh5UrV06fPl1r+k96enq/fv0MzCxEe/fu/de//mXIl78cqGNB6QyRhg0bliRvUouIiJg0adKiRYu2bNlS6JqdgiC0aNHCzMxMHTKKzp8/T2wBAABQ5ow72KOEjDVJRPwNXBAEFxeXYrSgue5Ay5YtpRVKPklEJI6GcHd319+U1rwSZ2fnmJgY2ZqaS9yNGTNGWqFUJomoaf5Cvm7dOv0voVBbt24VW7t+/bqemhkZGeHh4dJyrXfptddeS05OllYrKCgQ1zIwNTUVZ+JoCQ0N1d/h2NhY9UgQQRD69u0rraA5SUQQhI4dO8pOGcjOzu7Ro4dYbcOGDdI6a9asEStMnjxZV5eSk5PFXX4bNWoknSqyevVqsZ2PPvpI/wvUIzQ0VJC4ceNGoSfKThI5evSodO+J5cuXi2cZPkmkoKBAmnap1alT58MPP5w1a9Y777zj4uIiW2fs2LHSNmVHBKiZmJh069ZtxowZkyZNqlmzpmydxYsXazWoKwytVq3aO++888UXX3zyySfS3ZrVTpw4UbSPqsTy8/PFiUWzZ8+eOHFi9+7dZRccdXNzi4yM1NWOdJJIoczMzGSng0k1btxY61z14KayxiQRAABQwRFbFId6CUy14k1v1rxBqlmzprSCEmILPRlBbm5us2bN1NWsrKyePXumVUGxsYV4k1+5cuXitaD1LskuTaKmuWPizp07i9vl/6YAZmZmiYmJWkc1Ywtzc3M9d3R3794V7wPbtGmjdTQ/P79Bgwbqo82bN8/Ly9PTJc3FI48ePaqrw0LJYosNGzYIEqmpqYWeqCu2SEpK0tpOxcXF5cWLF+qzDI8tduzYIa0pCEJAQEBubq5Y7cWLF/7+/tJqZmZmUVFRWm3qii1sbW2PHTsmVktNTe3cubO0mr+/v2ZrDx48kF0H96OPPsrKyhKr5efni9N5NHXu3NmAz6c05ebmyr58TdbW1hMmTNC/LEUxYgt1y1euXCm0k9JFRt56663Sew90IrYAAAAVnHE2vXvZqRd3UHN0dCxGC2IqIQiC7Nr+RlelSpV33nlH11Fzc/NJkyapH2dnZx85cqS8+lVS4n17SkqK5udYPD4+PnrWJdGc0CG7ZYmBxPU18vPzNWcnSfXu3btp06a6jtatW1e8i75y5UpcXJzm0XPnzonDiKZPn65/dQNfX9/atWurH586dUrraLt27cRfzkuysMU///yjVVK5cmU7O7tiN+jo6Ki5mK4gCM+ePRM3sjHcpk2bpIX9+vULDAw0N//vzDsHB4cdO3ZIt+fIz8/XHPWjX1BQ0Jtvvik+tbOzkw04IiMjNZ9u375dvRWRpjfffPPHH3/UHMJgamo6b968gQMHatUMDQ29efOmgT0sH9bW1jNnzvzyyy91jWGRsrKy6tChw/vvvx8QEDB9+vS3335b19ycrKwsQ2bqubu7a5VIv6IAAAAodcQWxmFqqvR3vlevXvrn/Gv+jHzx4sWy71HpEMf2q1SqDz/8UHprVySa0y6kHBwcqlWrpn5cknCqevXq4vojf/31l56a/fr109+Unk/tjz/+EB8bssOomNdoDj5Sa9eu3fz/U5LYQvqmFS8l1DR58mStnV+WL1/++PFjw1vIycnRXJFXJJ2BIgiCtbX15MmTpeV61jrRVLt2bemSt7LLuyYlJWk+FffE1eqh7L88sgHlyZMnDelhucnKylqwYEGdOnW++OIL2Y1mRWZmZoMGDdq9e3dSUtL58+fXrVsXGBi4ZMmS3bt3P3jwQCtaEp05c0Zrz2Ap6ddPmaEzAADAK4YlOYtDc5z5ixcvitGC5j1GlSpVSqFPpa1Vq1b6K7i5uVWtWlU9xUPrl14l69y5c+PGjdWrTu7ZsycsLOydd97p169fu3btLCwsitqaOKtCFwcHh6dPnwqCkJKSor9mQUHB7du3IyMjHz16lJqaqh7JLx4VH+u/TSr0U9Ncz1LrU7t69ar6gbm5+fr166WX1nogTt5Rv8CyIF3wslKlSiVs09raesGCBe+9955YkpGRMX/+fM11PfS7fft2RkaGtGM+Pj6y9WVXUVWvklsoPz8/6Xa21tbWDg4OWt+otLQ0zafXrl2Ttnb69GnZ5UIePnwoLZSmUUqQnZ0dGBj4559/Hjp0SDZX7dChQ0xMjK5lMq2trQMCAqpVqzZ+/Hjp0aNHj3bs2FHP1W1sbLRKiromKwAAAIqB2KI4NEcaP3v2rBgtaN58GrinQDmrXr26IXXUsYXWL71KZmZmtm3bNl9f3+TkZEEQHj16tGjRokWLFtnY2Hh6enbq1KlHjx6+vr4G7i4huwmL1uXUD/Ly8nTVefjw4aJFi3bv3v3o0aNCr6g/Jiv0U9OsoPWpid/kvLy8uXPnFtoTUcnn2hhOM8optrFjxy5dulS9GYrahg0bpk2bZuAYKM3VWER169bVdXr9+vWlhZmZmenp6YVuvSmuIKNFulal5jtTUFAg+6F88803+i+nqezSKFmmpqbqnY8EQcjJyUlJSbl79+758+fVf6dajh07Nm/evO+++056yJDNZd97772goCBpslPoaAvp169UvpAAAADQT+lTFZTJ3d1dvK1NSEgw5G5Ty/Xr18XHdevWLbWelZ5Cb6g062j90qtw3t7eV69eHTRokOZ9ZkZGxvnz55csWeLn51e9evXPP//ckLtx6S/hRbVt27aGDRuuXr3awG9Rdna2nqOFfmqaFbQ+Ndn7Q0PoSWRKSPrjtub2pcVmamqqtTxEXl7eF198YeDpssmRtKuah2S/J4YM1NI1KUb/yKDU1NSCgoJCG9evnKc/mJqaihOLvv3226CgoCNHjiQkJAQHB8uOR1u1alVJ8rJevXpJC588eaL/LOnXz5B/JwEAAFBCjLYoDmtr6zZt2oSFhamfXrx4ccCAAUVqQXM3ii5dupRm50qJIYOfxTolWSXRKOrWrbtnz564uLi9e/f+/vvvYWFhmj+hJycnf//991u3bj18+LDmrIpSFxISMnr0aPEHWwsLi+bNmzdq1Kh69eqVKlWysrISb3eXLVuWmppaaIPp6en6B+9ofqxan5p44+3s7Cyut2oIPXfsJSR9LcXOVrT069evS5cuZ8+eFUv27NkzaNAgQ87VXE9XJJ02onlI9jd52Xa0yK7CIBSWl9nb25uampYwuTBka4+yZm5uPmbMmNq1a3ft2lXrUGZm5qlTpwYPHly8lsVNhTUV+u2SDitT5lg5AACAVwyxRTF16dJFjC127dpVpNgiJyfnwIEDmk1J64i3JYUOQi6jkQ6F/vCoWUdrU0nhf2+r9L8EI47U8PDw+PTTTz/99FNBEGJiYs6ePXv48OFff/1VPaIhPj6+X79+0dHRZXdbPmXKFPWbY2Zm9tVXX02cOFHXr+tBQUGGNPjkyZNatWrpryA+lm4Fqn6Qk5Mzf/58Qy5X1sTNSkQpKSlpaWmlEpMtWrRIazUKA6fGyN7xxsbGFhQUyM4TuXPnjrSwUqVKZfdDvampqbOzs9ZklkqVKskuGqqLq6trafermF5//fW6detqboSsduvWrWK3KTvTp9BlhqSLgEi/ogAAACh1xBbFNHLkyEWLFqkf792799mzZ4Zvy6eur37cqFEj2X0BbG1t1T/9FTrqQXY5vZLTnMYiKz4+Xvxff+kMfM1bsvT0dGmuISqj/hdVgwYNGjRo8O677z569GjAgAHq9QgfPHiwc+fOd999tyyueP369du3b6sfz549W88khczMTANXD7l+/bqeDVmF//1YtT61Zs2aHT58WBCE1NTU6Ojoxo0bG3LFMtWoUSNpYWxsbPPmzUveeMeOHf39/TUDxLt37xpyYsOGDW1sbLSGV2RkZISFhXXu3FlaX3ZLDk9PzyL2t2g8PT1PnDihWZKZmfnuu+8aeJt94cKFDh06lE3XikM2D5KOB4mIiKhXr54hOWNISIi0sEaNGvrPio2N1Spp2LBhodcCAABACbG2RTG1bNnyzTffVD/Ozs6eMWOGgSdmZGTMmjVLfKprIUDxPl/6P8qasrOzC918tHibrYaEhOgfJX7w4EHxcfv27bWOauYU+l+C5r6bssp5s9gaNWoEBweLT8tub1f1biZqw4cP11PzzJkzBg741/xQCq2g9am98cYb4uOdO3cacrmy1qpVK+mnHxERUVrtBwYGimumGs7S0vL111+Xln///ffSwqysrNWrV0vLxX89yojs2g3impd6XLp0qWvXrhMmTJA9+vPPP8+X0DNBRr9jx44ZsjDKuXPnZEesuLm5aZXs37+/SZMm27Zt0//3smHDBtlYVjoVRVNmZqa0G7KhMwAAAEoXsUXxzZ07V5wKsXnz5t27dxty1qeffirextepU2fs2LGy1cRfwi9dupSVlaWrtc2bNxe65IH422ORljNMTEz85ZdfdB3Nz8//4Ycf1I+trKz8/Py0Kmj+kq8nmMjJyVm7dq3+nmj+dloqKzIWqnHjxuKSq2U3h0XzZk//xiVLly41sM2QkBDNNERLbGysOLigTZs2Hh4emke7du3q7u6ufrxixYribZFTuuzs7KQDKwzcOtQQTZs2HTduXDFOlP2zPXjw4KxZszTvw1NSUoYPHy4dxGFmZjZq1KhiXNdww4cPl+42snnz5vHjx8t+ss+fP9+8eXPHjh3bt29/5swZXc3+/PPPCySKHVu89957derU+eKLLy5duiQ7laygoGDHjh26puDJRgZxcXGjRo1q1KjRypUrpTPdsrKyFi9erCuU8ff319Pbv/76S5qG6N8wFQAAAKWCSSLF16VLl5kzZ4qb8I0cOdLExOTtt9/WVT8/P3/atGnr169XPzU3N9++fbu1tbVs5U6dOu3atUsQhNTU1A0bNnzyySfSOnfu3Jk5c2ah/RRnryQmJiYnJ+taPUEqICCgR48ederUkR5auHCh+KP30KFDpevSNW7c2NnZWb0Zwdq1az/77DPZkdszZsz4559/9HfD0dHR3NxcfTdo4DB+Pc6fP9+iRQv9iyNcv349JydH/Vjr3r4Uaf5W/Ouvv6qX2JBasmTJ8ePHDWwzLy/v/fffP3HihPSWNTc398MPPxQjsI8//lirgpWVVUBAwOTJkwVBSEpKGjhw4NGjRwtdReLChQstWrTQWqbh0qVLR44cUT9u06ZN3759Dey/VO/evf/66y/NEs11NEtuwYIF27ZtK2ocNmzYsG+//fbGjRta5d99992OHTt69erl7Oz88OHDw4cPy2YEo0aNKus5ODVr1pw4ceLy5cu1yn/++ectW7Z06tTptddeq1y5cnp6+tOnTyMiIm7cuGGUvTwfPnwYGBgYGBhYuXJlT0/PRo0aOTo6Wltbp6Wl3bt379y5c7risxYtWrRq1UpXs3fu3Jk6depnn33m6enZsmVLV1dXlUp1796933//XdcOKaNGjWratKmerkrTnPr168vubgsAAIBSpnqZ3b9/X9frCgsLK4cO5Obm9ujRQ/O6I0aMuHXrlla1goKC06dPt27dWrPmypUr9bT89OlTcY/DSpUqHThwQKvCoUOHqlevLghCtWrV1NWcnZ1lm9q7d6940WXLlum5qOb+Juokok6dOqGhoZp1srKyAgICxGp2dnb//POPbGtTp04Vq/Xt2zcpKUnrBap/shb7LwjC6tWrZZtq2bKluoKHh0dycrKel1CoUaNGOTk5ffbZZ2FhYQUFBdIKly9fbtCggdil8+fPSyuIR/ft26f/cuLd6bBhw7QOpaamiqGVra3t1q1b8/PzNSskJCRMnDhR+N/lG8ePH6/VjubcBPWn1qNHj/v372vWefTokeaIGG9v79zcXGlvc3Nzu3fvLlZr2LDhwYMHtXql9vz58w0bNqhXk3306JHWUc1pER999JH+t0g/6b2ipaVlenq6/rNk08OjR4/KVtaf/Xl6esqede3ateKtDFqvXr2EhARpg1p7sqpt375d9uriuBiRlZWVVp3U1FQ9N/b66XrVnTp1klaWfTmGkL4KA5mZmWn9u6S2cOHC4jXo7u7+8OFD/b196623tM5SL6lbDnR9Rb28vMqnAwAAAMbFaIsSMTc3P3LkyJgxY/7zn/+oS7Zv3759+/aWLVt6eXm5urrm5OQ8evQoNDRUc+FJc3PztWvX6l/osWrVqtOmTVOv+pmZmenv79+2bVsfHx9bW9snT56cOXNGvZqjv79/zZo1xfkast544w1bW1v10p7Tpk07cOCAt7e3vb29OMNl2rRpDg4OWmd9+eWX8+fPj42N7dy5c9u2bdu1a2dvbx8XFxcSEqL5c2VQUJCurSs+//zzzZs3q9eS/PXXX+vWrdunT586depkZWVFRUWdPn06MzPTyspq/fr1/fv319N/9ctU/+QeFxfXqFGjPn36uLu7i7GOt7d3oS1oSkpKWr58+fLly52cnLy9vRs0aFClShUTE5OnT59euXJFc9L78OHDy25hQjs7uylTpixevFgQhPT09FGjRn355ZcdOnSoWbNmZmbmzZs3z549qx4FsHTp0m+++caQLWlXrlw5ZsyYU6dO1a9f39fXt1mzZiYmJtHR0cePH1dvjyIIgoODw6ZNm2R31jQ3N//Pf/7TrVs39VCa27dv9+/f39XVtVOnTh4eHra2tqmpqU+fPg0PD4+Oji7h/poG6ty5c61atTQDypycnN9++23gwIGldYmAgIB169YlJiYW6SwvL69du3YNGTLEkM9F5ObmdvjwYcOX7y0JOzu7I0eO9OjR4+bNm0U9VzaeUAgLC4t///vfpdjD6tWrnzhxQrpShqaMjIxTp05pFZb1TB8AAAD8f8bOTUrE6KMt1AoKCpYuXapnswxNnp6ep0+fNqTZrKws2XX1RG+88UZ6ero4f0TXaAuVSvXtt9/qaScuLk5dTWscwZkzZ+zt7XWdZWpqqmtwhOjIkSOVKlXS1YK1tfWBAwc0dyLU1WBCQoKemwrpAAQ9DL/TGDJkSGZmprSF0hptoVKpsrOz9X/EgiDMnz9fpfG7tP7RFupERk9rjo6Ohf5ppKWl6V8iVJOPj09qaqpWC6U42kKlUs2ePVvrou+8847+U4o02kL1v++hFl3jDtQiIyPFoUCF6t2799OnT3U1VeqjLdTS09MnTJiguSGxfr6+vuqVJmQZfbRFmzZtzp07p6vBI0eO1K1bt0gNDhgw4PHjx4V2dc+ePVonNmnSpHivuhgYbQEAACo4luQsBSYmJtOmTbtz5868efN0bc1oaWnp6+u7efPmq1ev6l+vXmRlZXXo0KGvvvpKmh3UrFnz+++/DwkJMWSrP0EQAgICVq1aVej2flq6dOny559/Dho0SPrLfMeOHc+fPz9p0iT9LfTp0+fs2bPqqQSaTE1N+/bte/nyZQNHSbi4uJw9e9bPz6/ku4r07t27V69emjNTpH17/fXXDx48uGvXLl0rj5QWS0vLw4cPBwYGSn9+NzExef3113/77TdDdn/QNHXq1JCQEOkgEQsLi+HDh0dGRha6iKCtre327dvDwsL8/f11pU6urq4ff/zx+fPnz507V7y5EoabMGGC1jdw3759xV4GUtbkyZN1DRrSr2nTpteuXdu7d2/37t11ratqZ2c3cODAM2fOHD16tGrVqiXraZHZ2Nj89NNPt27dmjJlip6YoH79+jNmzIiKijpx4oSePXRTUlK0SlxdXYv9BXj33Xe7d+9uyHtSvXr1d99998iR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0qVL6ZWfP39Or3n69Gl6TcYvbxUVFfRsa+PHj2fswIYNG9j6fPToUXr9jIwMufchx/uqpKSkUstjAXjCTiIAVXfy5Mno6Gi5wvv377PVl11BqSXBwcGMOSz4T8WU8fLyevToEb08IiLi2LFjjIfIbfc9aNAgep1Xr16FhoYyHi6RSBi39e7fvz91GGRvb894+Pr16xn3MN+2bRv/TdfZGvf19b179y69PCwszNvbm2fjNWjBggX0mb2MYxGC6aW3t7dnvKuTmpq6bt06enlycjL9xggAADQs+fn5CxYskNIWGvC/fHCoqKhYsGABfb2nRCJhLCcIol27dtT75xwYD/+CiUQiU1PTjh07fvfdd3379u33P2wZJSwsLOiFjGHTkSNH0p9zxhUi169fT0lJkSukTgem8vLyYiwnCCIgIIBeaGBgINtbnY/Nmzffvn2bZ2WAmoKUnABc/Pz86IUfPnxgrPz48WN6usqSkpKUlBT6hlh8Nh+lOnnyJNtDvr6+M2bMoJdTs46bmZlRp4mSEhMTORKaqqur0wt1dXW7du365MkTgiCaNGnCeM0Wi8Vsoy6xWOzv77969Wq2k1KxJU739fVlO8TX13fKlCl8Gq8pCQkJN27coJenpqZ+/vyZngCV/tKz/Zn+/v6lpaWMD508efKvv/7i3k4VAACE7OzZs/Tk2QRBsCWhqNTI4c6dO2yJlhITE8PCwhhzdtrY2Fy9elVh4/RQy5dHVVV10KBBzs7OvXv3trCw4Niklo4xR7u3t/eWLVvkluWqqalNnTqVmsKzcePGw4YNox/OeI+KccJsRkZGu3bt2rVrx9g3IyMjxvJ+/frJhnbciouL9+zZo7AaQI1D2AIavOTk5Ddv3iisFhcXV4XGHz58SC/Mzs5mrBwZGclYXlhYSC+kb9XBQSKRcGyDwrj8gSAI6mRCct2sHCcnpyokB23durXs2mZsbMw4TSA+Pr6goIDtcLYO0zEmEOVugX/jNYVtugpBEPQMIwTTS8+2yS7bO4ogiLy8vPj4+I4dO/LuJgAACMudO3cYyxnzJhKVHDkwztAkRUVFMYYtFG76LvPFhy0mTpz466+/tm7dumqHM6YM+/z589GjR+n7oM+aNYsatnB2dtbU1JSrk52dffnyZXqbjK9Xs2bNqjAbgucfe+fOHY7EsQC1B2ELaPDOnj27ePHi2mi5sLCQ8TZIWVkZY322tQ/0laWVlZeXR9+mm5Sfn19SUkIfzVCD/QYGBtXsAxXZGtuer58+feI4nP92GIztSySSjIwMtkOys7PLy8vJVJ11gD7FhsQ2V0IO29NInw5KlZqairAFAEDDxXb54Hnt4Fa1CzH/rdy/YLt27armwFJuJxHSvn37Fi9eLDcsbNu2bf/+/clAg5ubG/1AX19f+rtCW1ubcVZs1fAcKNZZ3hAAOchtAcCKcZYEQRBsmzyx3R5hu3rxxxYoITEOcagXs0rdolGIHNawNcu91yn/AZmamlplGyd4PF01i+O2A2NaEzq2YQf3E1VSUsKncQAAECa2ywfPawc37ksh2/WF52iBY5cNxgt3AzJz5sxauhlGEMTbt28Z1+DMmjVL9oO2tvbQoUPpFRhXiNTS0I4bY9oygDqA2RYArCo7bqi9PaubNGnC8ahIJGLcPIIaRmFb2FI15I0CtkiNtrY2x+Hcfw5Vfn4+faalurq6qqoqW/BCWVmZvi6jVnG87jyTlrE9jdxPFP9d1gEAQIDYLh81kvCS+wrC9mhOTg6fxjmmNOrp6fFpQZhUVVUZNymXEYvF7969S09PJ0cgIpGIcV8zDnv27Bk+fLhc4ejRo/X19bOyskaMGEEPRjx79oyeHp4giJycHKlUymejVj54Tg1mu6UHUNsQtgBoABo1atSyZcukpCTGRy0sLBhTM1IHH5mZmYzHJiYmsm1LxoFMSsoWDbG0tOQ4nPtRquzsbMYv55aWls+fP2c8hC0HlZAxrkUiCKJt27YhISFsR7ElMAcAAKjahVgubMGWw4KefEHGwMCALeNjgzBgwAB9fX16eXl5uaen54EDB+SGPWpqapVd0RMSEvLvv/926NCBWqiurj5lypSdO3cy7iHyzz//MDZVUVGRl5dHHybl5eVVYSnH06dPK3sIQF1C2AKgYRg0aNDff//N+NDAgQMZy1+8eEH+/Pr167KyMvrUzdDQ0Orsu5GcnJyXl0fPba6vr9+tWze2qyZbh+ni4uLatGnD2AJb2IJ/48JBfaWoHB0d9+/fz/iQjY0N49AKAACAIIiBAweKRCLGuIOysnL//v0Zj5K7HhUVFTFWY4tNjBw5spLdFJYePXowlm/cuPG3336jl1d2M3uZvXv37tu3T65w1qxZf//9N31D0/Lyco695J4/f07fKj4vL49jkziABgq5LQAahsWLFzPmyFBVVV2wYAHjIeHh4eTPRUVF9+/fp9cZOnRopZahikQi6sxSiUTCttvF0qVLGcvbtWvn7OzM83QPHjxgLP/xxx8Z80GoqanNnz+fZ+PCwfZnDh8+nHHvd4IgVq1aVZs9AgCAhs3MzIwxuSNBEBMmTGDcqOvz589yt9zZsm8w7tutrq6+YsWKSnWSLQEH49LXOsAWjrly5Qpj+YABA6pwlhMnTtCf2Pbt2+/YsYM+trl27RpHOong4GB6oZmZWbdu3SrVJSw7BeFD2AKgYejcufPhw4flIhcqKiqHDh2Sm2pICgoKov7KuHVWs2bNVq9ezacDIpFo3Lhxz549GzRoELU8MDCQsb6Hh8e8efPkCg0NDU+fPs0/UMLWuIWFxd9//y23tlZVVdXLy6shLhJ5/PgxY8p3NTW106dP05N7r169etSoUXXSNQAAaKj2799vbW0tV2htbf3XX38x1r9x44ZcVi+2ZaT9+vWTmxegoaHh7e1d2Usw217p3bt3r/4ubFXAltSMcZ/Rxo0b//zzz1U4S2FhIeO6jxkzZtAL2VaIyDAO7QiC2L59O88n0MbG5tq1a5s2beJTGaAeYZEINHgtWrTgPxcuLCxM4T4UgjVt2jRbW9ujR4++ePFCJBJ16NBh+vTpbDGL8PBwuTUaXl5eq1atMjY2lqu5YcMGqVS6ZcsWtmemTZs2Hh4ekydPNjc3pz96/Pjx3377rVGjRvSH9u3b5+rq6ufnl5iYqKmp2atXr1mzZlUqWVdMTExERESvXr3oD7m7u1tbW//999+yZ6Njx47Tp09voOkeKioqvLy81q1bR3/Iysrq5cuXXl5ekZGRxcXF33zzjbu7O31GKAAAgBxdXd3w8HAfH5/g4ODhfknnAAAgAElEQVTMzEwDAwMnJ6dJkyaxbXBGX5aYnp7+8ePHFi1ayJWLRKIrV64cOnQoJCRELBZ36dJl5syZjIMEbuXl5bm5ufRb/R06dDhz5sy5c+fS0tLIUEJxcTHbBE85Wlpa/EeGL168IHdVT05OZqyzbNkyuZiOjo7O6dOnq3ynZO/evQsXLlQYWUhPT7927RpHhdjY2CtXrtBzfA4YMODs2bOzZ89mS22mq6s7bty4yZMn29nZEQRBX7QCIDQIW0CD5+rq6urqyrNys2bN2D7BG4T27dtv3bqVT016KuyioqINGzYcOnRIrlwkEnl6es6ePfvcuXPR0dHp6elSqVRPT8/IyKh79+69evVq27Ytx4lycnIOHTrEtluYo6Ojo6Mjnw5z/CEXL15kfMjS0pLnsyF8+/fvX7hwIT1LCEEQ+vr6WBICAABVoKKiMnXq1KlTpyqsGR0dzZgE+tq1a7Nnz6aXq6qqzp8/v/oLM2NiYhhDDKNHjx49ejS1JCEhgW3hpBxzc/Pbt2/z7MDYsWPPnj0r+/nWrVuMdQYOHBgbG3v48OGEhARlZWUbG5tZs2ZVLbGFTEJCQmBg4LBhw7ir+fj4KLzZ9vPPPw8ePJg+j3XUqFFOTk4XL1588OBBUlJSSUmJrq6ugYFBp06devXq1bVrV7boFYAw4f0K8AXy8/NjnDd4+PDhHj16MI4/TE1NFy5cWLXTrV27dsSIEYy5MxkxJgdlc+nSpdOnTzPm1mYkuxnCuLWKkH369Omnn35iy7rKqFJPIwAAfFUqdY0oKyubPn06Y/7Ow4cPMw4b2JSWljImn2ITEhIinPyRjx8/fvTo0XfffUd/qFOnTn/++adcYXUuxHv27FEYtjh27JjCdp4/fz5t2jQfHx/6TqhaWloeHh4eHh5V6yGAoCC3BUADUF5ezra7O92zZ89+/PFHtkfnzZvn7e1dQ/36f58/f3Zzc8vPz+dTuaioqLLxkblz57LtG0K3ZMmSBrqp+NGjR48ePcqz8pkzZ+RylwAAAJB++eWXd+/e8akplUoXLFjAtv9ldHQ0/5D6unXrXr58ybeLBEEQxLFjx0pKSip1SK1auHAhW6JQOVKpdM6cOVU+UXBwcHx8PEeF6OjoZ8+e8WnK19d34cKFDXcRNAAfCFsANADp6emDBg3is7wlJiZmwIABcvuKU4nF4smTJ0+dOpUtDxa3kpISuU3dZaKiogYNGsRxXpmsrKwhQ4bwXJtKys7OdnR0VLgJuVgsXrRo0Z49eyrVuKDI9j9TWM3b23vixIlsa2IxcAEAgLS0NAcHB4VBf7FYPG/evMOHD3PUWbJkyb1797jbKS8vX7BgwebNmyvbz5SUlCVLllT2qNoTERExadKk0tJS7mrl5eWzZ8+uzn0gqVS6d+9ejgrcyTjl7N27197e/s2bN1XrCWNecABBQdgCoGG4fft2ly5d9uzZw3YpLS4u3rhxY8+ePcnMUhyOHz9uZma2fPlynrdi3r9/7+3tPW7cOAMDg5s3bzLWiYyM7Nixo5+fH+MsU7FYfO7cue7duysc+jDKyMjo2bPnxo0bi4uLGStERUU5ODiwZUdvKCQSycyZM11dXZOSkhgrfPjwYcqUKZMnT66oqGDcruzz588KB1sAAPA1SEpK6tmz57Zt29gmIcoWRBw8eJC7nYKCgsGDB+/YsYPt+nL3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qVP37t2Tltvb2/fp00f2lNTU1JEjR3p7e4eHh8fGxiYmJmZlZd2/f/+PP/744osv6tWrN2DAgPyOsxDl5uauWLGiTp06EyZM+O233+7du5eVlfX48eO//vpr7dq1/v7+rVq1MnHlDgAAoELsJALgNcROIgAAmAU7iQCwOEZbAAAAAAAAlSK2AAAAAAAAKkVsAQAAAAAAVIrYAgAAAAAAqBSxBQAAAAAAUCliCwAAAAAAoFLEFgAAAAAAQKWILQAAAAAAgEoRWwAAAAAAAJUitgAAAAAAACpFbAEAAAAAAFSK2AIAAAAAAKgUsQUAAAAAAFApYgsAAAAAAKBSxBYAAAAAAECliC0AAAAAAIBKEVsAAAAAAACVIrYAAAAAAAAqRWwBAAAAAABUitgCAAAAAACoFLEFAAAAAABQKWILAAAAAACgUsQWAAAAAABApYgtAAAAAACAShFbAAAAAAAAlSK2AAAAAAAAKkVsAQAAAAAAVIrYAgAAAAAAqBSxBQAAAAAAUCliCwAAAAAAoFLEFgAAAAAAQKWILQAAAAAAgEoRWwAAAAAAAJUitgAAAAAAACpFbAEAAAAAAFSK2AIAAAAAAKgUsQUAAAAAAFApYgsAAAAAAKBSxBYAAAAAAECliC0AAAAAAIBKEVsAAAAAAACVIrYAAAAAAAAqRWwBAAAAAABUitgCAAAAAACoFLEFAAAAAABQKWILAAAAAACgUsQWAAAAAABApYgtAAAAAACAShFbAAAAAAAAlSK2AAAAAAAAKkVsAQAAAAAAVMrG0h0AgOITFBQ0YsQIS/cCAAB1SU5O7tevn6V7AQDyiC0AlCI1a9YMDAy0dC8AAFCXhIQES3cBAIxikggAAAAAAFApYgsAAAAAAKBSxBYAAAAAAECliC0AAAAAAIBKEVsAAAAAAACVIrYAAAAAAAAqRWwBAAAAAABUitgCAAAAAACoFLEFAAAAAABQKWILAAAAAACgUsQWAAAAAABApYgtAAAAAACAShFbAAAAAAAAlSK2AAAAAAAAKkVsAQAAAAAAVIrYAgAAAAAAqBSxBQAAAAAAUCliCwAAAAAAoFLEFgAAAAAAQKWILQAAAAAAgEoRWwAAAAAAAJUitgAAAAAAACpFbAEAAAAAAFSK2AIAAAAAAKgUsQUAAAAAAFApYgsAAAAAAKBSxBYAAAAAAECliC0AAAAAAIBKEVsAAAAAAACVIrYAAAAAAAAqRWwBAAAAAABUysbSHQAA4HUQHh6elZUlCELLli0DAwMt3Z1SZNWqVU+ePBF/dHNzGzlypAX7A+TXxo0bb9++Lf7o5OQ0efJkjUZjuR4BgLoQWwBAcUhLSzt9+vTDhw8TExOzs7MrVark5ubWtGlTd3d3S3cN5hEeHp6eni4Iwvjx44ktis2BAwfGjh2rX7JkyRJLdQZF4eHDh5cvX75169azZ8/S09OdnJzKly/v6enZtGnTcuXKWbp3hi5dunTt2rXExMQnT57Y29u7urp6e3s3atTIzs5O4SyNRjN79mz9End397CwsCLuLACUGMQWAFCEcnJyNm7cuHXr1hMnTrx69crgqEajadq0aXBw8Lhx48qWLWuRHgIl16tXryZOnKhfUqVKFWNDLaKjo/fv3y8td3Z2njRpksJVli5dmpqaalA4bdo0BweHfPYXpnr69OnevXt//fXXiIiIhw8fytaxsrJq3br1kCFDQkNDbWyUPtCmpKQsW7asAN2YNGmSs7OzKTWvXr361Vdf7d+//969e9KjZcqU+eCDDwYMGNC5c2fZMRRhYWHz5s27du2aWDJt2rTu3bs7OTkVoNsA8PohtgCAonLo0KGJEydevnzZWAWtVnv27NmzZ8+uWLFizpw5Q4cOLba+xcXF7d27V/d43LhxFSpUKLZLm8uyZctSUlIEQWjcuHGPHj0s3R1YwPfff3/16lX9kjFjxjg6OspWjo6ONvhCW1S3bt0PP/zQ2FWWLl16//59g8IJEyYQWxSF58+f9+7d+/Dhw9nZ2co1c3NzT5w4ceLEia+++mrDhg1NmzY1VjMlJcXYS69syJAhecYWycnJM2bM+PbbbxU6nJGRsXPnzp07dzZu3Pjw4cNubm4GFaytrSdNmqSfuN2/f3/FihXTp08vQLcB4PVDbAEARWL58uWTJk3Kzc0VS6pWrern5+fu7m5nZ5eQkBAXF3fhwgXdoUePHg0bNiwmJmb16tXW1tbF0L24uDjxc3xYWFgJjS3u3LkjCEJoaCixRSmUmZk5b948/RJHR8cRI0YUoKnp06d36dLFyop1yi3v+fPnsoNiFPz111/+/v6HDh1q1apVEfXKmHv37gUGBl66dMnE+ufOnXv8+LE0thAEYcCAAZ999tnTp0/FksWLF48ePdrE4R4A8Hrjf2gAML85c+ZMmDBBzCx8fX0jIiLi4+P//e9/L1u2bNGiRZs3bz5//vyVK1d69uwpnrV+/frevXtrtVoL9RooSX744QeDAfkhISGurq4FaOrixYubNm0yU79gAWlpaV27dk1OTi7Oi969e7dVq1amZxbKypQpM3DgQP2SpKSkNWvWmKVxACjpiC0AwMyOHj06c+ZM8ceJEyeePn26ffv20inNXl5eu3bt2rJlizjCYs+ePV999VXx9RUombRarXS1go8//rjADc6cOfPly5eF6xSKhLu7e57IXkQAACAASURBVK9evSZOnPjPf/6zW7duZcqUka325MmTOXPmFFuvcnJywsLCpLOHBEGwt7dv167dqFGjZsyYMXbs2C5duri4uJjSpvQPeNWqVXlOlgGA0oBJIgBgTikpKf379xdHTIwcOXLp0qXKp4SGhmZnZ3/00Ue6H6dPnx4QENCoUaMi7SdQoh07dszgW+4aNWq0a9euwA3eu3dv5cqVU6ZMKXTXYB4ODg5hYWEjRozw8fHRz3yfPHkyePDg//znP9JTduzYsWTJElP2Da1du/aAAQPyrKYwQePLL7/8/fffDQo1Gs2ECROmT59eqVIl/fLc3NwjR4588803P/74o8LlvL29GzdufO7cObEkPj5+3759wcHBeXYVAF5vxBYAYE5r164V172vW7dunpmFzsCBA//zn//oPtFmZWXNnz9/27ZtRdhLc8jIyIiMjLx7925SUpKbm5u3t3fz5s0t3SnzyMjIOHv27JUrV5KTk1+9euXi4lK7du0WLVoUYAWQnJycEydO3Lhx49GjRy4uLl5eXn5+fra2tqa38PTp0zNnzty8eTMlJcXJyalixYo+Pj7169fPb090zPKq5ebmxsTEXL169fHjx7rdfOvVq9eiRYviWZZF54cffjAoCQoKMuV+VcH8+fOHDh1avnz5wjQidefOndjY2CdPniQlJVlbW1eoUMHNza1FixaVK1c2S/uXL1+Oi4u7d++evb19jRo12rdvn98/1Pv378fExCQmJiYnJ2s0mkqVKtWoUaNVq1bGxjUUNWtr6+HDh8+aNcvg5l+nYsWKu3fv9vPzi46ONjiUkJBw7dq1f/zjH3le4s0335w1a1aBe/jixYvFixdLyzdv3hwaGiott7Ky6tixY8eOHS9evCi7sIWoe/fu+rGFIAg//PADsQUAEFsAgNlkZWWtXLlS/HHx4sWm7zWwbNmyffv25eTkCIKwa9eu+fPne3h4GNTZsWPHlStXBEGoUqWK8tKDly5d2rlzp+7xqFGj9G+QwsPDs7Ky4uLixJIVK1ZI73N69OjRuHFj/VMEQWjZsmVgYGBaWtrnn3++ceNG3UYeolq1as2dO1f2U7vOw4cP165dq3vcp0+fevXqKfwKq1evTkxMFAShQYMGISEhYvnJkycPHz4sCIJ49XPnzknvQCpUqDBu3DiF9mXdvXv3888/3717d3p6usEhjUbTrl274cOH9+nTx5Tb4+zs7GXLli1fvjw+Pl6/vHz58p999tmECROUb/KfPXu2cePGHTt2/PHHH9Kj1apV++STT0aOHKnwB2auV03fo0eP5s6du337dv2FA3VcXFxGjhz5z3/+84033jClqcJ4+fKl9Ftrha1ATJScnDx//vyFCxcWsh2d+/fvL1++fPv27QZ/ACIvL69BgwYNHz5c4RmLj4//9ttvDQqbNWv2wQcfCIJw5MiR2bNnR0VF6R+1srIKDQ1duHChu7u7cg8TExNXrVq1ZcuWW7duSY/a29t36NBh+vTphRnDUgDlypWLi4vz9vZWqGNraztt2jTZhXgfPHhgSmxRSD/88IP0n8DIkSPz/HfUsGFD5Qpdu3Y12PTkwIEDycnJJk4zAYDXlhYAXjtt2rSRfccbO3ZskV730KFD4rWqVauWnZ2dr9N1tyI6ixYtklYICgrSHX3rrbeUm9q1a5fY1Pnz5/UPOTk5mfK/w+bNm6WnjB8/PiEhQfmr/n79+hn7xf/880+x2k8//aT8K4if74ODg/XLv/zyS1P67+npqdy+1OHDh015cpo3b56TkyM9Xf9ZSktL69Chg0Ijffr0Ufjz2LBhgynbB/j4+CQkJBhrxFyvmmjbtm15Pj/Vq1c/d+6c7OmPHj2a+T8rV65UvpayAwcOGFzX1tY2PT1d+Sz9SNEYR0fHe/fuGZxYrVo1ac3k5GRjF8rJyZkzZ46JY2qcnZ23bt1qrKlTp05JTxk+fLhWq502bZpCsx4eHjdu3FB4NpYsWWJiqNqtW7dnz54pP7fFz2A1VtH+/fullaW5TPv27ePi4jZt2rR48eLw8PA1a9bs2rVL+RnT5+fnZ9CgtbX1gwcPCv975eTkSGOsHTt2FL7lPD148MDY30BkZGQxdAAAFDDaAgDMJjIyUnwcHByc3zHzISEhv/zyi+7x77//rsJp9jk5OT169Lh8+bIgCB4eHu+++27lypWfPn167NixGzdu6Ops27bN3t7++++/t2hP8+3u3bvdu3cXB1m4uLi0a9fuzTffdHJySk1NjY+PP3nypG76z5kzZ3JzcxU2y9RqtSEhIREREYIgvPnmm+3bt3dzc0tNTY2MjDx//ryuzo4dO1q0aDFx4kTZFv7444/U1FRBEDQaTb169Xx8fCpXruzo6Pj06dOzZ8/GxMRotVpBEM6ePRsQEBATE2Nvb6/wq5nlVVu2bNmkSZO0/1u0xc3NrV27djVr1rSxsblz587Ro0efPHkiCEJ8fHzbtm2jo6Ol33gnJiaKXyN7eXmNGTNGoc/K9PNBHR8fn4LNaKhdu7b+Pe2LFy9mzZolHeBguqysrKCgoIMHD5pYPzU1NTQ0NDY2dsmSJaZfZeLEidIVSfXduXOnX79+UVFR0neh7Ozs3r17Ky+yoG/v3r0tW7Y8evRo1apVTe9hUdMNTJPKc4yJzm+//dakSRPZ0wcOHDhmzBjZrEonMzPzzJkzBoWtW7fWXfrRo0cnTpyIj49PT0+vUKFC9erVW7dubfq0HSsrqzZt2hhsAXvw4MHevXub2AIAvJaILQDAbE6cOCE+fvvtt/N7uv4qA1FRUVqttpBz9WXNmDFDN0lk3759upKxY8dKP1WLM0T06WYHODg4rFixYsiQIfrd27x588iRI3W3/Rs2bPjwww+7d+9u9s4LgtC6dWvdRi3Lli179uyZIAiNGjWSDhfP7/T+RYsWiZnF7Nmzp06dKs0CoqKivv766x07dig3tX379sePH7u4uKxfv15/g1tBEL777rvhw4fr7rhmzZo1YsQIR0dH2UZq1qw5duzY0NBQ6W3Y5cuXhw0bpvtju3Dhwrx587744gvl/hTyVYuIiPjkk090mYWLi8tXX30VGhpqY/N/HyFevny5aNGi2bNn5+bmPnv2rHfv3mfOnNGvYF7SMQiyt6Cm6Nmz5+HDh//66y+xZOPGjZ988kmBVw8ZMmSI6ZmFaOnSpTVq1JgwYYIplX/++WeFL8ZFp0+f/vHHH3v16mVQPmbMGNMzC53Lly8HBwcfP37czs4uXycWHf2XTFS2bNkGDRoUptmEhIQFCxYsW7Zs6dKlI0eOlK0TGxubmZlpUNiiRYvjx4/PmzfvyJEjBod088umT5/esWNHU/rQpEkTg9ji5MmTJv8GAPCasuhYDwAoEpaaJFK9enXxWhcuXMjv6bm5ufo3sUlJSQYVzDJJRGfDhg1ihWvXrim3ZjA1YO/evbLVDh8+LI5BqFWrlnQmhVkmiYjEtT9CQ0OVmzJF3bp1da1169ZNuWZ0dHRubq60XP9ZKlOmzF9//SV7+owZM8RqxmYHxMTEvHz5UqEPmZmZ4jB1V1dX2crmetWysrJq1qypq1CxYsUrV64Y69Xq1avFy+lPMtIRR5oIguDl5aXw2yl79eqVdHaDKbNOZCeJTJ061eAWURCEoKAg/RNNnySye/duaU1BEGxtbTt16jRlypRJkyYZe3eysbGR/kuUnSQiql+//tixY6dMmeLj4yNboXPnzgYNym7AIQiCg4NDUFDQ1KlTJ0+eLJ0BofOvf/0rn69VEerUqZO0hwMHDpStLLt4R56M/X8hOxinbt26eabMYWFhz58/z/NX2759u8GJGo0mJSWlME+XKZgkAkDNjI5xBQDkV1JSkvi4YsWK+T1do9HojxHQb009QkNDxfTEQEBAwODBg3WPb9++LV2AQM3Ej+z+/v7KNZs3b57n/cns2bNlh6sIgjBhwgRxGIL+8Bx9vr6+yvM+7Ozs1qxZo3v89OlT6aQJA4V51bZt23b37l3d41WrVnl5eRm7ysiRI8U73lWrVil3qcDu3r378uVLg8JatWoVuMFOnToZLESyb9++Any/nZub+/nnn0vLPTw8/vzzz/379y9atGjJkiUnTpzYv3+/dEpLdnZ2vva2CA8Pv3jx4ooVKxYtWhQTEyM74ejkyZPa/83rEQRBq9X+61//klZr1arVzZs39+7du2DBAt2+nv/973+l44CWL1+enJxseg+Lzs6dO6V/qHZ2dtOnTzfjVVauXKmfxIlknwRd5KTc4JYtW7p06aJbKFeBdDFmrVZ77do15bMA4PVGbAEA5pGVlZWRkSH+WLBtFPUXY5OuVK8GxpZjkB7N70B0yxJjgoJ9MavPwcFh2LBhxo5WqlSpUaNGusf6AxDyy9vbW9yK5fTp08qVC/OqiVuNenh46G/pIktMQGJiYnTLc4gqV64sLslZmIUt7ty5Iy3UH+hUANLdQ6ZOnZrfRs6cOXPp0iWDQisrq927dxvsH9GpU6evvvpK2sLu3bv130MUDBw4cPr06WJ8ptFowsPDy5Yta1AtJSUlISFB/DE2NlY6t8Ld3X3v3r0Gc5E6d+68YMECg5qpqal5zpAqBlFRUYMGDZKWz507VyFTE1lZWTVo0CAsLOyTTz6ZNm3awIED33zzTWOVP/30U2lIUZjs5vjx48r/GAVBkB3dI/tnDwClB7EFAJhHnl+1mUJhoUc1cHd39/X1VahQv359cbZFnvfSqiIOjvjmm2+OHz9emKaaNWumvA+I+BQVMpkSl708d+6cQrXCvGpZWVniPIXAwMA8h5mI67Pk5OScPXtW/1DlypVn/U9hYgvZJ61gKaGoefPmBquQnDhxwth8CmOkixoIgtChQ4dmzZpJyz/++GPpgKzMzMzff//dlGtJB004ODjIbhqqf4+t2znYwKBBg/Q3SBb1799fWvjrr7+a0r2iExER0blzZ+n+xGFhYXmuYdyyZcs1a9YkJCRcvHhx8+bNixcvnj9//saNG69fv/7TTz/JPgkpKSnins0i6dX1tWjRYtKkSVOnTv3ggw9kV2Vet26dbn1cY2T/mNWZYgNAsWFJTgAwD3t7e0dHxxcvXuh+fPbsmeznYGX69xj5XVSyGDRt2jTPOk2aNNGNZ9YtglAUq4oWhSFDhvz222+CILx48aJDhw4dO3bs06dPQEBAAXZPqFOnjnIFMdQwGI8gKz4+/tKlS7du3UpLS8vIyMjNzRUPiQNDlG9pCvOqXblyRfyTvnXr1ty5c3WPxZDO4EFaWprYZmJiYp7XLQDZm0ZjK5uaLjw8fO/evdnZ2WLJjBkzunTpYvp+QLKLRL733nuylW1tbdu1aycd2/LXX3/luXBj/fr1PT09peWybzjPnz8XH+svLiO6ffu2sckpNjY2+k+IIAjR0dHKfStSu3bt6t+/v3Q5zK5duypvXeTi4nLy5MlWrVrJHtVoNN26datbt27z5s3Fv3bRgQMHDOaelCtXztiF1q9fP3ToUPHH6Ojo9957T/8fhSAIOTk533//vcJGzrJ74ihnJQDw2iO2AACzcXV1jY+P1z1+8uRJfmMLrVarv56Fq6urOTtnDm5ubqbXycnJSU1N1Z/2omahoaGHDx/WTYjQarUHDx7U7QdRq1at5s2b+/v7d+rUSWEwuT7loRaCIIh3wgb3hPoyMjJWrly5efPmixcv5nlF3Y4qxhTmVdNta6pz+PBh2a/rjSnOxVkKP9apbt26Q4YM0f9q/eLFi5s2bZKdjyDr8ePH0kLZfEHhkGwjBoxtliG7Hor+MyPb+NatW/O8oqiIoihTrFq1avz48fqxnU7Xrl137dpla2urcO4bb7xhLLMQNWzYcMSIEdLJO3/88YdB/GrsPS0wMFA/sxAEoUWLFtOmTfv0008Nah46dEghtpD9YzbLaD4AKLlUPRoZAEoW/dta2a9elf3999/id30uLi6FHPdeFAw2p8izjv43veq3adOmdevWGQyvuH379q5du8aMGePp6enn52fK3paFH2By7ty5evXqTZs2zZTMQhAE6ffP+grzqqWkpJjSAVkKoUxhyH4XLf2SvABmzpxp8FzNnDlTuvynMbLhkWxvdWRfF+UESsfYO4PyrbuJjSvLzMy0yNf+n3322dixY6WZRf/+/ffs2WOubVllx7m8evXKYDELY4Fy165dpYXdunWTFiovoCP7x2zKv2IAeI0x2gIAzMbPzy8yMlL3+PTp03379s3X6WfOnBEft2nTRoXTK0y5Y9GvI10jUOWGDRs2ePDgI0eOHDx4MDIy8vz58/r33lFRUZ06dRo9enTRbZMhCEJCQkKHDh3EoQoajcbT09Pb27tKlSrOzs729vbiAig//vijKYt6FuZV07/rDg4Oll09wZgWLVqYXtl0sjeNhYlXRFWqVJk4caI4EUYQhPj4+BUrVph4uuyX8ApLbMq+LqaMThJ3ojGQ5zuGWZLQV69eFb4R0+Xk5AwbNkx2DsikSZMWL15sxvfJSpUqyZanpKToT9kTl9Q1IO4TnGfh8+fPs7KyjKUtskt+qnDwHQAUJ2ILADCbtm3bio/37NmzdOnSfC2xuXPnTtmmROIH9DwHDBfRMIdHjx6ZXsfa2tpguoT+DYalfoU8WVtbBwYGBgYGCoKQnp4eHR195MiRPXv2/P3337oKX3/9tZeX19ixY4uoA59//rmYWfTt23fevHm1a9eWrXnhwgVTYovCvGr6y0a2atXqk08+ybOpoibdHlIQhPv375uyhEeepkyZsnbtWv2pMQsWLDBxfL7sTe+NGzeM1Zc9ZOzO2SxkGx85cmS+prM5ODiYr0d5ePHiRUhIyC+//GJQrtFovvzyS7P/NRqboWOwzFDjxo3t7Oyk+5iaHqA4ODgojBC5f/++tFD2zx4ASg9iCwAwm3feeadKlSoPHz4UBCE+Pn7//v0ffPCBiefq6useW1tb9+7dW1pHHCec5/fnsh98Cy8uLs70OvXq1TP4HK8/zln5V8jOzrbgLHqRk5NThw4dOnToEB4e/t133w0dOlR3B7t48eIiii1yc3P37Nmje9y+ffutW7cq3AuZ+CoX5lX7xz/+Ia7LGBMTY8rlipqHh4e9vb3BvJjbt2+bpXFnZ+dPP/1Uf4tK03e7fOutt3bv3m1QePTo0WnTpkkrv3r1ShyZZdCIyZ3Nt7feemvXrl0GhW+//fbAgQNNOT0uLq5+/fqyK2gUhaSkpA8++EDcyEZka2u7cePGfv36mdhOQkJCdnZ2jRo18qwpOwXMwcHBYJSKnZ1dy5YtpS+f7B6ld+/elRYabDdrQPaPOc+FfgHg9cbaFgBgNnZ2dvp3s5MnT1ZedEDfpEmTcnJydI979eol+92ai4uL7kF8fLzywgF5buFZsJ1WHzx4EBsbq1DhypUrug0pBEF4++23DY6K/Rfyus+Mjo7Oc7WCYt4sdvDgwWKWdPfuXV04ZXaPHz8W75NDQkIUMou0tDTl10JUmFfN2dlZ3NN0//79alisxMbGRtytVnThwgVztT9q1KhatWoV4MSAgABpYUREhGzcs2HDBv0xHTr29vayw6zMRXbthoULF+b5by0+Pn7QoEG+vr6yNW/evDlLQgxhC+bevXt+fn7SzKJcuXL79+83PbMQBOHGjRv16tX74osvlKPSCxcurFu3Tlru7+8vLfzoo4+khbI75u7du1daqPwqS4dQeXp66r95AkApRGwBAOakP+L66tWrkydPNuWsrVu3it+C2trazpgxQ7aauINAZmbm6dOnjbV25cqVX3/9VfmK+msW5Gs5w+XLlyscXbZsmfi4R48eBkddXV3FHSuUgxVTFo8QfwWzLMdoCv057UV0A6+/FILyQoMrV640fZWBwrxq4i1iamrqwoULTbxikZLuClGAFXCNsbOzmzNnTgFObN68uXSPj9zc3J49e166dEm/8ODBg/oDOkQ9e/ZUWMKz8Hx9faWrk1y+fPndd9+VXfz15cuXhw4d6tmzZ+3atTds2CBdEVPn5s2bsyUKE1tcuHChVatWly9fNiivUqXK8ePHje0pqyAjI2PmzJnVq1efMmXKlStXpBX27dv37rvvyr6TyK6pGRISIl2F5ODBg998841+SXR09IIFC0xsUyQdHpXnNigA8NpjkggAmJOLi8umTZs6d+6sm02watUqBweHhQsXKgwN2LFjh/53d/Pnzze25FubNm3Ex8uWLdP/UfTixYuBAwcau8EQ6a9ZcPPmTWNXlNqyZUtISIjs5Jdjx46Jn9o9PDw6deokrdO6deuffvpJEISIiIhz585JvzYXBGHPnj3bt2/Psyfir3Dz5k0TO29MSkrKtWvXxGEFxkRHR+seWFlZGWw4Yi5ubm5WVla6l++XX34ZPHiwbLWoqKgvvvjC9GYL86oNHTp0wYIFugkp4eHhTZo0CQ4OVr5cWlra5cuXDZbkTExMXL16te5xxYoVx4wZY3r/DQQGBhqslBkbG5uRkWGue/7Q0NAlS5aYMrlGn5WV1ezZs3v16mVQfufOnSZNmgQEBHh7e+fk5Jw+ffrEiRPS021sbGbOnFnwTptAo9HMmzcvKCjIoPzUqVPe3t5Nmzb19fWtVKlSdnb248ePb9y4ER0dbfp4MXNJSEho27at7Bqr77///r59+/bt26dwevv27du3by97KCUlZfHixYsXL/bw8GjZsmXVqlUdHBwSEhIiIyONvYe8+eabH3/8sbTcyckpPDx89OjRBuXDhg377rvv2rRpY2dnd+HChQMHDohj6ERNmjSRvgSinJyckydPGhTKDpMBgNJFCwCvHdn7eUEQxo4dWzwd+Pzzz/Wv27x58+PHj+fm5hpUu3r1ap8+ffRrBgcHS6vpa9KkiVh55syZ2dnZ+kfPnz+vu1fUX2Pv/Pnz0naePn0qVujWrVtOTo7CRcU1KXSr2Ts6On733XcGdbZu3aq/A8WPP/4o25T+kGlPT8/Lly/rH83MzPzyyy9tbGxsbW3F+eTBwcGyTY0bN05XQaPRxMTEKPQ/T7opEu3atfv222+fPHkirZCRkaE/BKZjx47SOuKzNH78eOXLDR8+XFfTzc1NetTPz0+80PTp09PS0vSPZmZmrly50tHRUdBbYdHT01OhP4V/1Q4dOmRtbS0+2+PGjYuPj5dWy83NPXXq1MSJE8uXLz98+HCDo/pD3728vBSenzxlZGRI94M8cuSI8lkrV64UJKZOnSpb+cCBA9LK+pKTk2VPDAsLUz7RmKVLl0pbk86SEARB+tzqhIaGSiufOnXKoNrQoUML1kPByG995MgRac3Ro0crvRjGmbLKrIKZM2caNPj7778XrClbW1uFP6rc3FzZ+SPKHBwcpK+IPv39pHSsra0TExML9mTmy4MHD4x1OzIyshg6AAAKGG0BAOY3e/ZsZ2fnKVOmaLVaQRDOnDnj7+9frVo1Pz8/d3d3Ozu7hw8fxsXFnTt3Tv+sYcOGrV69Wnk5+vDw8M6dO4tX2bRpU0BAQKVKlZ49exYbGxsdHZ2bm1urVq3p06eL98ayKlSo0LZtW90H+r1793p7e7dv397V1VW8O+3Ro4d0KETfvn1jY2NPnTo1ePDgOXPmvPfee5UqVXr69OmxY8euX78uVvv444+7d+8ue90PP/ywdevWuq8Tb9y40bhx4/fee8/b21uj0dy5c+fYsWO6xfyXLl363XffKe9qGRQUpPvKXavV+vn5BQYGenl5idscVKhQQcw1TBQZGRkZGTls2DAvL68mTZq4ubmVK1cuPT39+vXrx48ff/bsma6avb39okWL8tVyvsyYMUN8iefPn79u3bp33nnHw8ND9xRFRETo1kQICQmxtbXdunVrng0W/lV7//33V65cOWbMGF2mtmLFitWrV/v4+DRp0sTV1TUnJyclJeX69et//vmn6QtYFoajo2P37t23bNmiX/jzzz8XYPqAMYGBge+8886xY8fye+J333335MkT2cUdFUycOFF22khRWLNmTXJysnT10Dz5+voW5zYilmVjY7NlyxaFvyiNRrN3797AwECF+XoGdP9gW7ZsqVBHukDG+++/X6T7ywBAyWDh2AQAioDFR1vo/Pe///Xy8jLlrbhy5crr1q0zsVnlkeQeHh5///23/n4BsqMttFptZGSkGFJIbd68WaypP44gISGhfv36Ch3o27evwRgQA7du3VJe8nD69OlarbZhw4a6H42NtsjNzVUYOy07AMEYcUHKPJUvX/7o0aOyjZhrtIVWq501a5ZyNzp16pSRkSF+u6482sIsr5pWqz148KD+3CIFb7zxxoYNGwxON+NoC63cN/weHh7Kp+RrtIVWq42OjlbIEI2NttBqtTk5OV988YWtra0pz5Wzs/OWLVuMNVUUoy10vv76a+mIFWO8vLy2b99ubCCYykdb3Lp1SzkpkPL29o6NjTWlt2lpaSauD1q7du0//vgjzwal8/V27NhRgKexABhtAUDNWJITAIpK586dL1y4sH79en9/fxsbmdFtGo2madOmc+fOvX79+rBhw0xsdtasWTt27Khdu7ZBedmyZUeOHHn27Nm6deua0k7btm0PHTpk+qoWOlWqVPnjjz8mTJggXZGuVq1amzdv3rZtm0Iaoqt26tSpAQMGSKv5+Pjs3bs3PDzclJ5oNJrdu3ePHj268CsaVKhQ4aOPPnrrrbcUbjUrV648ceLEa9euvfvuu4W8XJ5mzpz5008/yQYNXl5eK1eu/OWXX3TzRExU+FdNEISOHTvevHlz4cKFnp6eshWsra39/f1XrVp179492a0WzOjdd981eH7u3LlT4OkAspo3b96zZ88CnGhlZfWvf/3r5s2bkydPrl69urFqXl5eCxYsuHv3rmzcUNRGjRp17969hQsXNmzY0Fg6U6lSpbCwsIiIiMuXL/fp08dYtdTUVGmhie9CxUD3hnP58uUvvviiFpzoogAAIABJREFUbdu2Cv/G7e3tAwICdu/e/eeff/r4+JjSeNmyZbdu3RoVFdW1a1fZNyKNRtO4ceN169ZdunRJurmSgXPnzhmkNlWrVpWukgsApZBGq9Vaug8AYGZ+fn5RUVHS8rFjxxqs5FdsUlNTT58+/fDhw8TExOzs7EqVKrm5ufn4+Li7uxesQa1We/bs2bNnzz558sTZ2dnDw8Pf379cuXK6o5cuXdq5c6fu8ahRo/SXupD6+++/4+Li7t+/n56eLu6rqj9JpGzZsrq9A8ePHy/uOpGRkREZGXnnzp2kpKTKlSs3atTIYAnGPCUlJf3222/37t17+fJltWrVvL299VfuWL16dWJioiAIDRo0CAkJUWjn+fPnMTExV69eTUlJefnype7/tQJMEhEEISsr6+LFi/Hx8UlJSUlJSenp6U5OTpUrV/b29vb29la+sQ8PD8/KyhIEoWXLloGBgQo1f/nlF92mmGXLllXea+bcuXMxMTGPHz+2sbGpWrVqvXr1mjZtKh798ccfdfOMZH/ZInrVdO7evRsdHa3brtXOzq5ChQpeXl5vvfWW/koZBsy4JKfO+vXrDWZCffTRRxs2bDBWPzo6Wrq9hZ+fn8JEgFu3bm3atEn20LRp00ycMXH79u3Y2NgnT54kJydbWVlVqFChcuXKb7/9trirjoL4+Phvv/3WoLBZs2ayq6uKfw/6hgwZohCd6CQnJ585c+bBgwfJyckZGRkuLi6VKlVq3LixiYPFxo4da7D1j4+Pz+nTp2Wz2jzp/50UgMKSnDqZmZnXrl27evVqYmJiWlraq1evnJ2dXVxc/vGPfzRp0kR5+x5lL168OHXqVHx8fGJiYk5OjouLS9WqVVu2bGniGCVBECZOnKi/rY8gCOHh4dOnTy9wl/IlISHB2ErDkZGRRbo1LwDkidgCwGtIhbFFiSZ7AwyVe+1ftczMzDp16sTHx4slZcqUuXfvXoUKFSzYq1Kofv36+ruK2traxsTEyG4SBAUZGRk1atRISkoSS1xcXG7duiUdIVVEiC0AqBmTRAAAQMljb2//2Wef6ZdkZGSsW7fOUv0pne7fv6+fWQiCMGPGDDKLAti0aZN+ZiEIwuTJk4stswAAlSO2AAAAJdKgQYMMJjKsXLny5cuXlupPKWSwHmejRo0+/fRTS3Wm5MrJyfnqq6/0S6pWrTp+/HhL9QcA1IbYAgAAlEi2trYGN3sJCQlr1qyxVH9KoaNHj4qPra2tv//+exO3UIG+LVu2GOxntHDhQtO3egGA115BVksCAABQg06dOq1cufLJkydiiYkrZcIsfv31V/Hx5MmTmzVrZsHOlFza/3dnaycnJ4vsLwMAqkVsAQAASrDCb0qC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96pXr67PaDNmzDh16pSVldW0adPOnTuXnp6em5t7/fr1f//738Kr9Ouvv548eVL7OJ06dVqxYsXp06dTUlLy8/MzMzOvXr36zTffNGrUSNHh1q1bH3zwgfZB8vPzR40alZOTY29vv3jx4ps3b+bm5j579iwiIsLf31/oNmXKFC1xzIcffrh+/XrFY2dn588+++zs2bNpaWkFBQVJSUmbNm1q27at4tmLFy+OGTNGe+BSRtevXxcvbNE5cUCT9957T/nLuLi4DRs2lLIymSw1NbVbt27nz5/X/5Lnz58PHDhQHMRo8eWXX/7444/a+yQlJald8yKXy8eMGbNq1Sr9b3fu3Llu3bpV2IKRfv36iX+aYWFhH3300dWrV3Nycp4+fbp//34/P7+0tDSVbgsWLLCwsNA08pYtW5TDxH/9618GvQ4VUKGy6OjoqKiox48f5+fnv3z58tGjR3///fePP/44bNiwxo0b//DDD3r+lolLffnypc7ECgAqK5PO9QAAA5hqkcj27duFG3Xr1q0UIyiffvrpp5+qPGuURSICYaZ0UFCQ9qFU1pVYWVkdOHBA3O3o0aPCu3orK6tbt26J+xhlkYiC8pzq+Ph47aPptGjRIsVQ5ubmx44d09QtLS1txowZJ06cED+l8iq5uLhcvHhRe9nDhg1Te5eUlJTBgwfHxcVpKiMzM1P58161BQuLRBQaNmx49+5dcbelS5cKfZo2bap2/cumTZuEPm+99VZKSoq4T2Fh4bhx44Ruv//+u7jP0KFDhQ5bt27V9N3pFBYWJvu/LCws9FnIoHaRSGJiosqyo4YNGyr/odB/kUhJSUmXLl3EnfVRo0YNtT8gtYtE9GRjY5OWlqYy4Jdfflm60QYOHGjgD6r0kpKSXn/9dUMrnDFjhpYx7927p7wEzMLC4ty5c4qnDF0kUh4ViheJ6NS7d++MjAx9Xk8nJyeVa1esWKHPhWXEIhEAFY/ZFgCgQ2pqqvD4X//6VylGaNKkifDYoI0SK9KyZcuUP6UX9OjR47vvvlM8LigoEB5XClevXlU86Ny5s5blBk5OTitXrnz77be1j2ZmZrZlyxbluTOCsWPHCjuD7Nu3T+1k7zp16uzZs6dly5aaxre3t9+xY0f9+vUVX/7yyy/a6zE3N9+9e7fyvy7BnDlzAgMDFY8TEhLEe/UVFxd//vnnisdNmzY9cOBAnTp1xONYWFisWbNG+Fx3+fLl8nKbcBEbG6vS0qRJEysrq9KNVrduXZU3V0lJSaX7EH7Hjh1nzpwRt3t5eR04cEAx6ebKlStql49lZmYuWLDAoNtNnDjx2rVreXl5d+/e/fDDD8Ud8vLyDh8+rNySnJysHFQJWrZsuW3btpSUlIKCgnv37i1ZssTGxkalz969e6OiogyqsNQaNGhw8eLFmTNnatq8Q4WHh8fu3btXrlypqUNJScmYMWOUJ4zMmzfPx8dHOhWWQmRk5NChQ/VZMKKyK6dMJouJiTFiJQAgHcQWAKCD8nxg7Ts7auLo6Cg8VjkoQSIaNWok3uBNMGHCBOETyM2bN1ei5dPZ2dmKB3Z2dmUfrXfv3r169dL0rBAT5Ofnl/r0DVtb28mTJyseq7w1FRs1apSXl5emZ0NDQ4X9HcQTGXbv3n3v3j3F46VLl9aoUUPTOBYWFsI5vjdu3Ci/Y2USExNVWho0aFCWAWfNmlWrVi3llqVLl2raGEIL5fMpBJ07dz516pS/v7+jo6ONjU27du1+++035QOPBZs3b37w4IGe91q0aNGaNWtatWplbW3dpEmTn3/+uX///uJuly5dUv7y22+/FY5nFnh6ekZHR48cObJOnTqWlpZubm5z587du3eveEfYr776Ss/yys7Ozi40NHTNmjUqPxqxnj177t69OyAgQEufJUuWKG8z0aVLF2FRmEQqLJ2oqCi1/+pUCBGnQPxLBABVA7EFAOiQlZUlPC7du1/lqzIzM41Qk7GNHj1ay8Jsc3Nz4YiHrKws5VUtEufq6qp4cO7cuUePHpVxtFGjRml5tkOHDsLjsrx5EGZtPHv2TPvbXZXtG1Q0bNiwR48eisdnzpxR2ZAyIiJC8aBGjRoDBw7UXlLPnj2tra0Vj8WbEe7cuVOYw6n9JdJO/APScr6JPmrUqKHyJjY9PV3trAQtkpOT1f6DX716tXjmwrx58xo3bqzSWFJSorynrxYtWrSYO3euSqPal1TljNX9+/eL+2zYsEH898rPz0+88eTJkycrbIeLgwcPenh4jBkzRufmxFFRUZ6enqNGjVKe76bswoULX3zxhfBljRo1Nm3aJOwyI4UKlbVq1Wr+/PkREREPHz7My8vLysq6du3a8uXLNR1Ms2LFCvEOGirEvyAPHz7UWQkAVEbEFgCgg/L/+ufk5JRiBOEzf5lMpuVjbRPSuXS/c+fOwuO///67nMsxmr59+yoevHjxwtfXd8+ePUVFRaUeTcvUBplMVrt2beFxWd4EKo8jTIgQMzMz69Spk/ahhJ/ay5cvb9y4ofyUkD60a9dO50IMa2tr4c1VQkKC9s6lppwPKug5UV+LyZMnq+QIP/zwg0Fv7Y4dOyZubNmypbBZqTILC4sRI0aI248fP67PvYKDg8XvutUuAlL+B5aSknLr1i2VDu7u7so5mjLl32WF4uJicRpVHlasWNGvXz/9/wnJ5fLt27d36NBBfElOTk5QUJDyr/Pq1avFmZEJK1SwsbEZN27c1atXr1279sUXX/Tp06d+/frW1tZ2dnatWrX65JNP4uLixD8RmUyWmZm5b98+7XcX/4Io/7cGAKoSYgsA0EF5w79SzDBXucrZ2dkINRlb8+bNtXdQ3qZO5wGQ0hEQEPDmm28qHv/zzz9DhgypXbv2iBEjfvzxx/Pnzxt03KBMJnNxcdHyrPJbCPGMfWU3btxYvHjxwIEDmzdvXrNmTSsrKzMlLVq0EHpqiT/q1q2rMwLT8lMT5nGcOnXK4r+q/Ze5EkVVQoBSfqucxIuPSr2xhfIIwrasCnl5eQZtNqE249CUCGh6Ss+gRG0OZW9vL24sKCgQHqs9AffOnTtmGgh7mii7e/euPhWWRWRk5CeffKLSaG1tvXDhwtu3byuO1Dl+/LgQNQoePXoUEBCg/C3LZLJp06YpJwWjR48OCgqSVIUKo0aNCgsLa926taabOjk57dq1S+08viNHjmgvWJgDJdD+lwcAKi9iCwDQQfnNaukm/yt/Zq79ra+p6NyzQ3l7jvT09HIux2iqVau2f/9+X19foSU9PX3Hjh1Tp07t1KmTo6Ojv7//hg0b1L7fEBO/SdBE076Vd+7c6devn6en57x58/bt2xcfH5+WlqYlPRFPQBDos82Kpp9adna2kBHI5fLi/yr5L+W9u1XGLN2EI32IX149fy7aBQUFqcyMWL9+vcrcEy3UrhTQ8lusdkMEPTfiVbuXh87sxii7/OpcEFF24vUvMplsx44dCxYsaN68uZWVlb29fbdu3Q4ePDhgwACVbtevX1c++EYmk61du1Z47Obmtnr1aqlVqL+6desOHjxY3K5z0kdeXp5Ki55nOQNApUNsAQA6KJ8ccenSpZKSEkNHUF5VoX2hgamId+lTUX7nR5Q3FxeXqKioffv2DRgwQOWN8cuXL//666/g4GB3d/cDBw6UdyWxsbE+Pj4HDx7U/xIt/9h0/shkmn9qhk4z0WfMshN/4GyUiMTMzExld8Pi4mK1b1BNTu2imLJv1qAPLQGZUSQmJl6+fFml0cvLS/z+38zMTO10GOWzk1Xcv3/f0dFRPK/kgw8+EHf+5ptvhA4fffRRxVSoU6tWrcSNOgOply9fqrQYZe9hAJAgYgsA0KFp06bCwv6srKyLFy8adHl2drbyJV27di1LMSobKxqLzsUvyqsVnJycSn2jcqpfp/79++/duzcjI+P48eNLly7t37+/8kyEhw8fDhgwYNeuXeVXQFFRUVBQkPCZtqen59KlS6OiohISEl68eJGfny/Mbvjnn3/0GVCf9UqafmqOjo7Cm+H33nvPoIPTd+7cacC3bQjxsQhPnjwxysh9+/YVdidV2Lt379mzZ/W51tDZE2qnLeg8k6IsjDKBq7xzyWvXrokbNa21adeunTiVEw4zLiemrbB0r39KSopKi/iXCACqBmILANDNz89PeKw8OVkf27ZtEz4Ta9eunfg9hvIkcJ0fg+vcW750dL5Vvn37tvBYvH298C2Yqn492djYdOvWbc6cOfv27Xv27FlUVFS/fv0UT8nl8o8//lg86dpYDh8+LBwdOn78+NjY2Dlz5vj6+rq7u9eoUUP534Cea3BSUlJ0nkqj6admZmZWp04dcR/TEu89acRjEUJDQ1Va1q9fr8+FahduaDlMR/yJvaZBjEXt4H5+fgalUd999135VSgzxmyO8p4PYtoKr1+/Lm4Ufkk1Ef+ClH1TUgCQJmILANBt6tSpwuPNmzfrf5RmYWHhypUrhS+nTZsm7qO8saL2/Q6fPHmiz0l7pXDmzBntHc6dOyc8fuONN1SeFb4Fnfs1lvdHpvqrVq2ar6/v/v37haMfnjx5ovN1KDXhLAkrK6tvvvlGy8x/PV8iuVyu/ENRS+jw2muvtWzZUvkpYdbPxYsXy2+XTYO0adNGpSUxMdEo21vIZLI33nhj2LBhyi16rvbq3r27uDEuLi42NlbcXlRUpHalgNpBjKVevXrKe68qnDhxQp8/U8XFxb/88ouhh8KWgtqtiGNiYtR2jo2NFc8+KO/NjMujwtWrV69atUrn6UXJycnh4eHidvGPVYU4c1R7wA0AVAHEFgCgm5eXV7du3RSPs7OzJ02apOeFy5cvFzb/q1evntq97l1cXCwtLRWPtb9l3b17t847Cp/bG7QcY+vWrVr6y+VyYbc5e3t78cRpYWay9mClsLBQ55F+yvMOKmZFybhx44THWg4cLSNhOnf9+vW176b5xx9/6Dnmxo0btTz78OFD4fDOLl26WFhYKD8rnIZQXFz8888/63nHciWOw4qKisRHe5bakiVLVF4Efbi6urZr107c/vHHH4uPPlm0aJF4115zc/M+ffoYel+D9O/fX6WloKAgKChIy+f/ubm569ata9eu3Ycffqh2MU5RUZF4twi1WzDow83NTdx44cIF8VYvcrn8iy++0HMEIyqPCtPS0qZNm9amTZv169drCuDS09OHDh2q9uBS8Y9VWVJSknilmPiXCACqBmILANDLqlWrbGxsFI/37ds3a9YsnZf88ccfyju3/fTTT2oPBbC2thbeDISHh2taZ5GRkaGys6BawnGJBn2Efv/+/Z9++knTs2vXrhXePQYFBYlPfFDetVTLvnSrVq3S+Qmw8nGPFTMLQHmNuto9EY1C2OE/NTVVy1KU/fv3Hz58WM8xt23bpmW1wty5c4XcZ/z48SrPBgYGCmFTaGiosIDFhFq3bi3eAyI6OtpY4zdr1mzChAmluHDOnDnixjNnznTt2vWvv/5SbE0SGxs7YcKEL7/8Utxz9OjRjRo1KsV99RcSEiI+QuLEiRMtW7b8+uuvY2JisrKyiouLnz59ev369XXr1gUHB7u6uo4bN07t2oTy0LJlS7W5wNChQxcvXhwfH19YWJiTk3Py5El/f/+9e/eKe77zzjvKX+qz8uXXX38VjzNz5kyhg3JgZ/QKBTdv3hw7dmyjRo0mT54cHh6emJiYm5v78uXLuLi4FStWtGrVSu20qQYNGmgaUOH8+fMqLTY2Nj4+PlouAYBKzKCljwBgQn/++aemP2W5ubkVUIDKGXvBwcHPnz9X27OgoGDp0qXKH+1OnjxZy8jz5s0Ten766afiDhkZGSp7Cg4dOlTtUMOHD1d0qFmzZkFBgZabfv3118oDWltbR0REiLsdP35ceDNvZWV18+ZNcZ/MzExbW1tFnzp16iQlJYn77NixQyW1uXbtmrib8vvnb7/9Vkv9+vD391+/fn1eXp6mDvn5+b169RLueOvWLZUOyq9Senq6lnslJycLPf/zn/+oPKucCn3++edqR4iMjFReMSSTyTZu3KjSR9iMQ8HNze3evXvioZTLbtq0qfKWnwLlXVpcXV1PnTql5bvLy8tbv379kCFDxE8NHTpUGGfr1q1aBtFp9OjRsv/r3Xff1XlVzZo1ZSJq/yYkJycL/1DVmj59uviqkpKSzp07a7lKixo1aty5c0c8ptr5F8nJyeKe8fHx4p59+vRR6bZw4cLSVSjT8F2rzU89PT11/jg00Sd11cTW1lbtXxXtdMYW5V3hokWLSj3gli1btH93H3/8scol4n8V5WTGjBlqa/b29q6YAgC8gphtAQD6mjRp0vz584Uv169f37x585CQkOPHjz969KigoODp06eXL1/+6quvWrVqNXfuXGFJ86hRo5R3uBD74IMPhKkcS5YsGTNmzIULF16+fJmbm3vr1q2VK1d6enoeO3asY8eOzZs3116k8P7q+fPnY8eOjY2N1WebyYEDB+bn5/v7+0+aNCk6OjorKys7O/vSpUvTp0/v1auXsKXo559/7uHhIb7c3t5+7NixisdPnjzx8fEJCwt7/PhxYWHhs2fPDh06NGLEiBEjRhQUFIjfl6p4/fXXhXehS5Ys2bZtW2pqaqlXi8TGxgYHB9euXfvdd99du3ZtTEzM8+fPi4qK8vLy4uPj165d27FjxyNHjig6+/n56VxMXmpDhgwR0p/Fixe/8847hw4devjwYWFh4dOnTw8ePDhq1Ki+fftmZmYKe21o5+Hh0bx58/v377dt23bp0qW3b9/Oy8tLS0s7fPjwgAEDPvnkE0U3MzOz3377Te00n/fff3/ixImKx48fP+7WrdugQYO2bduWmJj48uVLRWFXrlwJCwsbM2ZMnTp1goODy3tShnIConDkyBG58Q65qFu3bkhIiKFXmZmZ7dmzpxSbHVarVm379u3/+te/DL2wFObPnz9y5MgKuFGphYSEiLcv0dOiRYvKdVtTBelUOHPmzMDAQO19xNOyVHZvAYAqxdS5CQDoy+SzLRR+/fVX8SoJTczMzGbNmlVSUqJzWJ07+bu6ut67d8/T01PxpabZFqmpqXZ2dpoGcXd3F3oqfyB/7949tXmEssDAwKKiIk31Z2Rk6Fx8/tlnnykvIVE720Iul8+ePVvLIOIJCFrofxxgkyZNHjx4IB7BWLMt5HK59uhKoXfv3jdv3tTyzQqzLdq2bXvp0iXtcwdkMtmPP/6opebCwsIpU6bo+RLJZLLXX39dPIgRZ1vk5+eL9zU8e/as9qv0n20hl8szMzO1HEeqdt6Bwr1797y9vfV/rZydnffv369pNKPPtlC8eiEhIeKDObWwtLScMmVKamqqeDSjz7aQy+WpqalqNwrRbsGCBaW7naGzLYzztTNwAAAH00lEQVReYSlmW1hYWOjz/YoDRBsbm4yMjFK9TgZjtgWAisdsCwAwzIQJE27fvj1mzBhzcx1/Qnv27Hnp0qXQ0FB93khMnz592bJlmrYM9PLyOnPmjD6b0rm4uGzYsMHQPRocHByOHTvWpUsXtc9aWFjMmjVr48aNWs6/cHBwOHHihKYd+6ysrFasWLF48WJ9ilmwYIGvr68+PY1l+PDh586da9iwYbneZcaMGStWrBC2XxWbMGHC3r179d82skOHDocPH9b0Ga+Tk9OmTZvEM8mVWVhYrFq1ateuXSrnjIhVr159/Pjxe/bs0bO20rGyshozZoxK486dO414C3t7+88//7wUF7q5uZ06dWrZsmXiA4BVWFlZBQcHx8TEqKzoKW9WVlbffvvtkSNH9Dm4pFatWiEhIbdv3161apX4VGZNtPwF0IeLi0t0dPTSpUv1PBbEy8vr5MmTZVn/YijjVhgSEvL77793795d538sZDKZmZlZ7969z58/r8/3K/6lGD58uPa9fgGgUjN4S20AgJub24YNG5YuXXrw4MGIiIh//vnn6dOnaWlp9vb2derUadSokZ+fX79+/XTOX1Axe/bsIUOGrF69OioqKikpKS8vr379+q1atRo3bly/fv30f8MQEBBw48aNtWvXnjhx4vbt2xkZGfqsE6lbt+6pU6ciIyPDwsKuXr368OFDCwuLhg0b9u7de9y4ccIsDy3c3NxiYmI2bty4a9euy5cvP3v2rEaNGm5ubv7+/hMnTtR/BnX16tUPHz78559/7ty5MyYm5tGjRzk5OaVbJxIbG3vlv+7evfv8+fO0tLQXL17Y2Ng4OTm1aNGic+fOI0eObNGiRSkGL4WZM2cGBASsWbMmKirqzp07OTk5tWrVql+/fs+ePYODg0tRRqdOnW7fvr19+/YtW7bEx8enpKTUqFHD3d198ODB48aN0/Pt6JAhQwICAiIjIw8dOnT69OnHjx8rNm1xcnJq3Lhx+/btfX1933nnHZ0zO4xi2rRpq1atUj6ddNOmTaGhoaU4BESTSZMmff/99+IjP3SytLScPXt2SEjI/v37T5w4ce7cueTk5PT09MLCQkdHx1q1arVt27Zr166DBg1ydXU1VrWG8vX19fX1jYuLi4iIOHv27I0bN9LS0jIyMiwtLWvVqtWoUaNOnTq9/fbbfn5+apcOCa5duyZuNGhujlpWVlZz5syZOXNmRETE0aNHr169mpCQkJmZmZ2dbWFhYW9vX7NmTU9Pz/bt2w8ePFifPztGZ8QKbW1tg4ODg4OD09PTjx49eubMmVu3bsXHx6elpWVnZ5uZmTk4ONSsWbNNmzbe3t4DBw50d3fXp0K5XL5+/XqVRk0zIACgajCTG2/JKACUq/Dw8MGDB6t9Kjc3V9gbAnpasWKFsANCenq6o6OjaeuBPvr373/gwAGZTNa2bduYmBhTl1MuRowYoXIeze7duwMCAkxVz6vp66+/VjkvqWfPnsJGMDChY8eOqcxH8/X1jYqKqrACQkJC1K5q9Pb2Fp9vAgBGwSIRAAAgIQsWLFCZVP/999+bqphXlsqOj7a2tmq3ikDFE/86qD15FwCqEmILAAAgIZ6enirHKJw4ceLSpUumqucVlJ+ff/r0aeWWZcuWNWnSxFT1QJCQkLBv3z7llr59+2ralggAqgxiCwAAIC1Lly5V2Va2FIcyoNTOnDmTm5srfNm1a1fte7uiwnz11VfKO79YWFisWLHChPUAQMUgtgAAANLSsGFDlXNww8PDmXBRYZT3sKhevXpYWJhB56qinNy5c2fTpk3KLZMmTTLJxqUAUME4SQQAAEjO/Pnz58+fb+oqXlHKscWXX37ZrFkzExYDgbu7e2FhoamrAAATILYAAADA/7pw4YKpSwAA4H+xSAQAAAAAAEgUsQUAvKL+53/+R/5fjo6Opi4Hetm/f7/iRxYTE2PqWgAAACoCsQUAAAAAAJAoYgsAAAAAACBRxBYAAAAAAECiiC0AAAAAAIBEEVsAAAAAAACJIrYAAAAAAAASRWwBAAAAAAAkitgCAAAAAABIFLEFAAAAAACQKGILAAAAAAAgUcQWAAAAAABAoogtAAAAAACARBFbAAAAAAAAiSK2AAAAAAAAEkVsAQAAAAAAJIrYAgAAAAAASBSxBQAAAAAAkChiCwAAAAAAIFHEFgAAAAAAQKKILQAAAAAAgEQRWwAAAAAAAIkitgAAAAAAABJFbAEAAAAAACSK2AIAAAAAAEgUsQUAAAAAAJAoYgsAAAAAACBRxBYAAAAAAECiiC0AAAAAAIBEEVsAAAAAAACJIrYAAAAAAAASRWwBAAAAAAAkitgCAAAAAABIFLEFAAAAAACQKGILAAAAAAAgURamLgAAjMDBwcHUJQAAUPUVFxebugQArxxiCwBVQUFBgalLAAAAAGB8LBIBAAAAAAASRWwBAAAAAAAkitgCAAAAAABIFLEFAAAAAACQKGILAAAAAAAgUcQWAAAAAABAojgAFUCl0bFjxw0bNpi6CgAAoMrFxcXUJQCosszkcrmpawAAAAAAAFCDRSIAAAAAAECiiC0AAAAAAIBEEVsAAAAAAACJIrYAAAAAAAASRWwB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object>"},"metadata":{}}]},{"cell_type":"code","source":"epochs = 30\nbatch_size = 64\nsteps = len(train) // batch_size\n\nfor i in range(epochs):\n    # create data generator \n    generator = data_generator(train, mapping,features,tokenizer,max_length, vocab_size, batch_size)\n    # fit for one epoch \n    model.fit(generator, epochs=1, steps_per_epoch = steps, verbose=1)","metadata":{"execution":{"iopub.status.busy":"2024-05-30T12:32:25.193429Z","iopub.execute_input":"2024-05-30T12:32:25.193813Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 539ms/step - loss: 2.3993\n\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 540ms/step - loss: 2.3593\n\u001b[1m113/113\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 538ms/step - loss: 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start tag for generation process \n    in_text = '<start>'\n    # iterating thorugh the max_length for word prediction\n    for i in range(max_length):\n        # encode the input sequence \n        sequence = tokenizer.texts_tosequences([in_text])[0]\n        # pad the sequences \n        sequence = pad_sequences([squence],max_length)[0]\n        # predict the next word\n        next_word = model.predict([image,sequence],verbose=0)\n        # get the index with highest probablity \n        next_word = np.argmax(next_word)\n        # convert index to word \n        next_word = idx_to_word(next_word,tokenizer)\n        #3 break if the wor is none \n        if xt_word is None:\n            break\n        in_text = ' '+ word\n        # if the word is endtag break\n        if next_word == \"<end>':\n            break\n    return in_text\n        \n        \n    \n    ","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# validate the predicted data \nactual_captions, predicted_captions = list(), list()\n\nfor key in tqdm(test):\n    # get actusl captions\n    captions = mapping[key]\n    # get predicted caption\n    pred_caption = predict_caption(model, features[key], tokenizer, max_length)\n    # get actual caption words\n    actual = [caption.split() for caption in captions]\n    ypred = ypred.split()\n    # append to the list\n    actual_captions.append(actual)\n    predicted_captions.append(ypred)\n    \nprint('BLEU-1 : %f'% corpus_bleu(actual_captions,predicted_captions, weights=(1,0,0,0)))\nprint('BLEU-1 : %f'% corpus_bleu(actual_captions,predicted_captions, weights=(0.5,0.5,0,0)))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Visualize the Result","metadata":{}},{"cell_type":"code","source":"def generate_caption(image_name):\n    image_id = image_name.split('.')[0]\n    img_path = os.path.join(BASE_DIR, 'Images',image_name)\n    image = Image.open(img_path)\n    captions = mapping[image_id]\n    print('------------------------------------------------ Actual Captions --------------------------------------------------------')\n    for caption in captions:\n        print(caption)\n    y_pred = predict_caption(model, features[image_id], tokenizer, max_length)\n    print(y_pred)\n    plt.imshow(image)\n    \n                              ","metadata":{},"execution_count":null,"outputs":[]}]}