diff --git a/.gitignore b/.gitignore
new file mode 100644
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--- /dev/null
+++ b/.gitignore
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+data
+tensor_files
+CIFAR10
+models
+plt_storage
+output
+__pycache__
+.neptune
+.vscode
+kl_maps
+ffn_aug_maps
+ffn_kl_maps
+*.pth
+mode_pths
+*.png
+*.pt
+hessian_value_pts
+*.txt
+.ipynb_checkpoints 
+notebook_results
+hessian_value_pts
+BERT_tiny_4l
+lls_logs
+layer_hessian_results
diff --git a/README.md b/README.md
new file mode 100644
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+++ b/README.md
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+# Self-Attention Recovery for QAT(SARQ) Implementation
+This Repository contains SARQ code for **Self-Attention Map is All You Need for QAT of Finetuned Transformers**
+
+<img width="867" alt="스크린샷 2022-02-07 오후 2 03 19" src="https://user-images.githubusercontent.com/54992207/152727350-4553fc95-efc8-43dc-beff-9ad74898e421.png">
+
+## Environments
+```
+pip install -r requirements.txt
+```
+
+## Model
+You can get GLUE task specific fine-tuned BERT base model using huggingface code. 
+https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification
+
+
+## GLUE Dataset 
+### Data
+Download GLUE. 
+https://github.com/nyu-mll/GLUE-baselines
+
+## Self-Attention Recovery for QAT (SARQ)
+Proposed SARQ method consists of Two Steps. (See Figure for Two Step SARQ)
+
+1. Teacher Intervention is employed to finetune quantized weights of attention propagation (PROP)
+2. Quantization is applied to the entire weights of Transformer layers for QAT
+
+You can easily try SARQ two step Training using bash scripts.
+```
+# For SARQ Two Step Training (w/o DA)
+bash run_SARQ_two_step.sh {GPU Num} {GLUE Task} # bash run_SARQ_two_step.sh 0 sts-b
+
+# For SARQ 1 Step Training (w/o DA)
+bash run_SARQ_1step.sh {GPU Num} {GLUE Task} {DA option} {DA N param} # bash run_SARQ-1step.sh 0 sts-b 0 0
+
+# For TernaryBERT Training for comparison
+bash run_glue.sh {GPU Num} {GLUE Task} # bash run_glue.sh 0 sts-b
+
+```
+
+For Data Augmentation (DA) Option, use TinyBERT Data Augmentation for getting expanded GLUE Dataset.
+
+https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/TinyBERT
+
+
+## Arguments
+To be Updated.
+
+
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..cf4983c
--- /dev/null
+++ b/main.py
@@ -0,0 +1,1008 @@
+
+# This code is implemented base on "TernaryBERT: Distillation-aware Ultra-low Bit BERT" (Zhang et al, EMNLP2020)
+# https://arxiv.org/abs/2009.12812
+
+from __future__ import absolute_import, division, print_function
+
+import argparse
+import logging
+import os
+import random
+import sys
+import pickle
+import copy
+import collections
+import math
+
+import numpy as np
+import numpy
+import torch
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler,TensorDataset
+
+from torch.nn import CrossEntropyLoss, MSELoss, CosineEmbeddingLoss
+
+from transformer import BertForSequenceClassification,WEIGHTS_NAME, CONFIG_NAME
+from transformer.modeling_quant import BertForSequenceClassification as QuantBertForSequenceClassification
+from transformer import BertTokenizer
+from transformer import BertAdam
+from transformer import BertConfig
+from utils_glue import *
+
+import numpy as np
+
+import torch.nn.functional as F
+import time
+
+log_format = '%(asctime)s %(message)s'
+logging.basicConfig(stream=sys.stdout, level=logging.INFO,
+                    format=log_format, datefmt='%m/%d %I:%M:%S %p')
+logger = logging.getLogger()
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0 
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+def str2bool(v):
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+def get_tensor_data(output_mode, features):
+    if output_mode == "classification":
+        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
+    elif output_mode == "regression":
+        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)
+
+    all_seq_lengths = torch.tensor([f.seq_length for f in features], dtype=torch.long)
+    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
+    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
+    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
+    tensor_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,all_label_ids, all_seq_lengths)
+    return tensor_data, all_label_ids
+
+def do_eval(model, task_name, eval_dataloader,
+            device, output_mode, eval_labels, num_labels, teacher_model=None):
+    eval_loss = 0
+    nb_eval_steps = 0
+    preds = []
+
+    for batch_ in eval_dataloader:
+        batch_ = tuple(t.to(device) for t in batch_)
+        
+        with torch.no_grad():
+            input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch_
+
+            if teacher_model is not None:
+                teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids, segment_ids, input_mask)
+                logits, student_atts, student_reps, student_probs, student_values = model(input_ids, segment_ids, input_mask, teacher_outputs=(teacher_probs, teacher_values, teacher_reps, teacher_logits, teacher_atts))
+            else:
+                logits, _, _, _, _ = model(input_ids, segment_ids, input_mask)
+        
+        # create eval loss and other metric required by the task
+        if output_mode == "classification":
+            loss_fct = CrossEntropyLoss()
+            tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
+        elif output_mode == "regression":
+            loss_fct = MSELoss()
+            tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
+
+        eval_loss += tmp_eval_loss.mean().item()
+        nb_eval_steps += 1
+        if len(preds) == 0:
+            preds.append(logits.detach().cpu().numpy())
+        else:
+            preds[0] = np.append(
+                preds[0], logits.detach().cpu().numpy(), axis=0)
+
+    eval_loss = eval_loss / nb_eval_steps
+
+    preds = preds[0]
+    if output_mode == "classification":
+        preds = np.argmax(preds, axis=1)
+    elif output_mode == "regression":
+        preds = np.squeeze(preds)
+    result = compute_metrics(task_name, preds, eval_labels.numpy())
+    result['eval_loss'] = eval_loss
+    return result
+
+def soft_cross_entropy(predicts, targets):
+    student_likelihood = torch.nn.functional.log_softmax(predicts, dim=-1)
+    targets_prob = torch.nn.functional.softmax(targets, dim=-1)
+    return torch.sum((- targets_prob * student_likelihood), dim=-1).mean()
+
+def main():
+
+    # ================================================================================  #
+    # ArgParse
+    # ================================================================================ #
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data_dir",
+                        default='data',
+                        type=str,
+                        help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
+    parser.add_argument("--model_dir",
+                        default='models',
+                        type=str,
+                        help="The model dir.")
+    parser.add_argument("--teacher_model",
+                        default=None,
+                        type=str,
+                        help="The models directory.")
+    parser.add_argument("--student_model",
+                        default=None,
+                        type=str,
+                        help="The models directory.")
+    parser.add_argument("--task_name",
+                        default='sst-2',
+                        type=str,
+                        help="The name of the task to train.")
+    parser.add_argument("--output_dir",
+                        default='output',
+                        type=str,
+                        help="The output directory where the model predictions and checkpoints will be written.")
+    
+    parser.add_argument("--learning_rate",
+                        default=2e-5,
+                        type=float,
+                        help="The initial learning rate for Adam.")
+    parser.add_argument("--num_train_epochs",
+                        default=3.0,
+                        type=float,
+                        help="Total number of training epochs to perform.")
+    parser.add_argument('--seed',
+                        type=int,
+                        default=42,
+                        help="random seed for initialization")
+
+    parser.add_argument('--save_fp_model',
+                        action='store_true',
+                        help="Whether to save fp32 model")
+                        
+    parser.add_argument('--save_quantized_model',
+                        default=False, type=str2bool,
+                        help="Whether to save quantized model")
+    
+    parser.add_argument("--input_bits",
+                        default=8,
+                        type=int,
+                        help="Quantization bits for activation.")
+    
+    parser.add_argument("--tc_top_k",
+                        default=3,
+                        type=int,
+                        help="Top-K Coverage")
+
+    parser.add_argument("--gpus",
+                        default=1,
+                        type=int,
+                        help="Number of GPUs to use")
+    parser.add_argument("--clip_val",
+                        default=2.5,
+                        type=float,
+                        help="Initial clip value.")
+    
+    parser.add_argument('--qk_FP',
+                        default=False, type=str2bool,
+                        )
+    
+    parser.add_argument('--qkv_FP',
+                        default=False, type=str2bool,
+                        )
+    
+    parser.add_argument('--neptune',
+                        default=True, type=str2bool,
+                        help="neptune logging option")
+    
+    #MSKIM Quantization Range Option
+    parser.add_argument('--quantize',
+                        default =True, type=str2bool,
+                        help="Whether to quantize student model")
+
+    parser.add_argument('--ffn_1',
+                        default =True, type=str2bool,
+                        help="Whether to quantize Feed Forward Network")
+    
+    parser.add_argument('--ffn_2',
+                        default =True, type=str2bool,
+                        help="Whether to quantize Feed Forward Network")
+    
+    parser.add_argument('--qkv',
+                        default =True, type=str2bool,
+                        help="Whether to quantize Query, Key, Value Mapping Weight Matrix")
+    
+    parser.add_argument('--emb',
+                        default =True, type=str2bool,
+                        help="Whether to quantize Embedding Layer")
+
+    parser.add_argument('--cls',
+                        default =True, type=str2bool,
+                        help="Whether to quantize Classifier Dense Layer")
+    
+    parser.add_argument('--aug_train',
+                        default =False, type=str2bool,
+                        help="Whether to use augmented data or not")
+
+    parser.add_argument('--clipping',
+                        default =False, type=str2bool,
+                        help="Whether to use FP Weight Clipping")
+    
+    
+    parser.add_argument("--mean_scale",
+                        default=0.7,
+                        type=float,
+                        help="Ternary Clipping Value Scale Value")
+    
+    parser.add_argument("--exp_name",
+                        default="",
+                        type=str,
+                        help="Output Directory Name")
+    
+    parser.add_argument("--training_type",
+                        default="qat_normal",
+                        type=str,
+                        help="QAT Method")
+
+    parser.add_argument("--aug_N",
+                        default=30,
+                        type=int,
+                        help="Data Augmentation N Number")
+
+    parser.add_argument('--pred_distill',
+                        default =False, type=str2bool,
+                        help="prediction distill option")
+
+    parser.add_argument('--attn_distill',
+                        default =True, type=str2bool,
+                        help="attention Score Distill Option")
+
+    parser.add_argument('--rep_distill',
+                        default =True, type=str2bool,
+                        help="Transformer Layer output Distill Option")
+
+    parser.add_argument('--output_distill',
+                        default =False, type=str2bool,
+                        help="Context Value Distill Option")
+        
+    parser.add_argument('--gt_loss',
+                        default =False, type=str2bool,
+                        help="Ground Truth Option")
+
+    # Teacher Intervention Options
+    parser.add_argument('--teacher_attnmap',
+                        default =False, type=str2bool,
+                        help="Teacher Intervention Option (TI-M)")
+    parser.add_argument('--teacher_output',
+                        default =False, type=str2bool,
+                        help="Teacher Intervention Option (TI-O)")
+    parser.add_argument('--teacher_gradual',
+                        default =False, type=str2bool,
+                        help="Teacher Intervention Option (TI-G)")
+    
+    parser.add_argument('--teacher_stochastic',
+                        default =False, type=str2bool,
+                        help="Teacher Intervention Option (Stochastic Mixed)")
+    
+    parser.add_argument('--teacher_inverted',
+                        default =False, type=str2bool,
+                        help="Teacher Intervention Option (Stochastic Mixed)")
+
+    parser.add_argument('--teacher_context',
+                        default =False, type=str2bool,
+                        help="Teacher Intervention Option (Context)")
+    
+    parser.add_argument('--step1_option',
+                        default ="GRAD", type=str,
+                        help="Teacher Intervention Step-1 Option (For step-2 model init)")
+    
+    parser.add_argument('--bert',
+                        default ="base", type=str,
+    )
+
+    args = parser.parse_args() 
+    
+    # ================================================================================  #
+    # Logging setup
+    # ================================================================================ #
+    run = None
+
+    # Use Neptune for logging
+    if args.neptune:
+        import neptune.new as neptune
+        run = neptune.init_run(project='niceball0827/' + args.task_name.upper(),
+                    api_token='eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vYXBwLm5lcHR1bmUuYWkiLC\
+                    JhcGlfdXJsIjoiaHR0cHM6Ly9hcHAubmVwdHVuZS5haSIsImFwaV9rZXkiOiI0YjM\
+                    0ZTYwMi1kNjQwLTQ4NGYtOTYxMy03Mjc5ZmVkMzY2YTgifQ==')
+        
+        # run = neptune.init(project='Neptune_ID/ProjectName',
+        #             api_token='Neptune_API_Token')
+
+    # ================================================================================  #
+    # Load Directory
+    # ================================================================================ #
+    
+    # Exp Name
+    exp_name = args.exp_name 
+
+    exp_name += f"_{args.bert}"
+
+    if args.training_type == "qat_step1":
+        if args.teacher_attnmap:
+            exp_name += f"_MI"
+        if args.teacher_context:
+            exp_name += f"_CI"
+        if args.teacher_output:
+            exp_name += f"_OI"
+        if args.teacher_gradual:
+            exp_name += f"_GRAD"
+        if args.teacher_inverted:
+            exp_name += f"_INVERTED"
+        if args.teacher_stochastic:
+            exp_name += f"_STOCHASTIC"
+    
+    else:
+        if args.gt_loss:
+            exp_name += "_G"
+        if args.attn_distill:
+            exp_name += "_S"
+        if args.rep_distill:
+            exp_name += "_R"
+        if args.output_distill:
+            exp_name += "_O"
+        exp_name += f"_{args.seed}"            
+    
+
+    if args.training_type == "qat_step2":
+        exp_name += f"_{args.step1_option}"
+    
+    args.exp_name = exp_name
+    
+    if args.aug_train:
+        logger.info(f'DA QAT')        
+        
+    logger.info(f'EXP SET: {exp_name}')
+    logger.info(f'TASK: {args.task_name}')
+    logger.info(f"SIZE: {args.bert}")
+    logger.info(f"SEED: {args.seed}")
+    logger.info(f'EPOCH: {args.num_train_epochs}')
+    
+    # GLUE Dataset Setting
+    task_name = args.task_name.lower()
+    data_dir = os.path.join(args.data_dir,task_name)
+    processed_data_dir = os.path.join(data_dir,'preprocessed')
+    if not os.path.exists(processed_data_dir):
+        os.mkdir(processed_data_dir)
+    
+    # BERT Large Option
+    if args.bert == "large":
+        args.model_dir = os.path.join(args.model_dir, "BERT_large")
+        args.output_dir = os.path.join(args.output_dir, "BERT_large")
+    
+    if args.bert == "tiny-4l":
+        args.model_dir = os.path.join(args.model_dir, "BERT_Tiny_4l")
+        args.output_dir = os.path.join(args.output_dir, "BERT_Tiny_4l")
+    
+    if args.bert == "tiny-6l":
+        args.model_dir = os.path.join(args.model_dir, "BERT_Tiny_6l")
+        args.output_dir = os.path.join(args.output_dir, "BERT_Tiny_6l")
+    
+    # Model Save Directory
+    output_dir = os.path.join(args.output_dir,task_name)
+    if not os.path.exists(output_dir):
+        os.mkdir(output_dir)
+    
+    if args.save_quantized_model:
+        output_quant_dir = os.path.join(output_dir, 'exploration')
+        if not os.path.exists(output_quant_dir):
+            os.mkdir(output_quant_dir)
+
+        if not os.path.exists(output_quant_dir):
+            os.makedirs(output_quant_dir)
+        
+        output_quant_dir = os.path.join(output_quant_dir, args.exp_name)
+        if not os.path.exists(output_quant_dir):
+            os.makedirs(output_quant_dir)
+
+    # ================================================================================  #
+    # Load Pths
+    # ================================================================================ #
+    # Student Model Pretrained FIle    
+    
+    if args.training_type == "qat_normal":
+        args.student_model = os.path.join(args.model_dir,task_name) 
+    elif args.training_type == "qat_step1":
+        args.student_model = os.path.join(args.model_dir, task_name) 
+    elif args.training_type == "qat_step2":        
+        args.student_model = os.path.join(args.output_dir, task_name, "exploration", f"TI_step1_{args.bert}_{args.step1_option}")
+    else:
+        raise ValueError("Choose Training Type {downsteam, qat_normal, qat_step1, qat_step2, qat_step3, gradual}")
+
+    # Teacher Model Pretrained FIle    
+    args.teacher_model = os.path.join(args.model_dir,task_name)
+    
+    processors = {
+        "cola": ColaProcessor,
+        "mnli": MnliProcessor,
+        "mnli-mm": MnliMismatchedProcessor,
+        "mrpc": MrpcProcessor,
+        "sst-2": Sst2Processor,
+        "sts-b": StsbProcessor,
+        "qqp": QqpProcessor,
+        "qnli": QnliProcessor,
+        "rte": RteProcessor
+    }
+
+    output_modes = {
+        "cola": "classification",
+        "mnli": "classification",
+        "mrpc": "classification",
+        "sst-2": "classification",
+        "sts-b": "regression",
+        "qqp": "classification",
+        "qnli": "classification",
+        "rte": "classification"
+    }
+
+    default_params = {
+        "cola": {"max_seq_length": 64,"batch_size":16,"eval_step": 2000 if args.aug_train else 50}, # No Aug : 50 Aug : 400
+        "mnli": {"max_seq_length": 128,"batch_size":32,"eval_step":8000},
+        "mrpc": {"max_seq_length": 128,"batch_size":32,"eval_step":1000 if args.aug_train else 50},
+        "sst-2": {"max_seq_length": 64,"batch_size":32,"eval_step":100},
+        "sts-b": {"max_seq_length": 128,"batch_size":32,"eval_step":2000 if args.aug_train else 100},
+        "qqp": {"max_seq_length": 128,"batch_size":32,"eval_step":1000},
+        "qnli": {"max_seq_length": 128,"batch_size":32,"eval_step":1000},
+        "rte": {"max_seq_length": 128,"batch_size":32,"eval_step":1000 if args.aug_train else 20}
+    }
+
+    acc_tasks = ["mnli", "mrpc", "sst-2", "qqp", "qnli", "rte"]
+    corr_tasks = ["sts-b"]
+    mcc_tasks = ["cola"]
+
+    # ================================================================================  #
+    # prepare devices
+    # ================================================================================ #
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    n_gpu = args.gpus
+    
+    # ================================================================================  #
+    # prepare seed
+    # ================================================================================ #
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+
+    if n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+ 
+    if task_name in default_params:
+        args.batch_size = default_params[task_name]["batch_size"]
+        if n_gpu > 0:
+            args.batch_size = int(args.batch_size*n_gpu)
+        args.max_seq_length = default_params[task_name]["max_seq_length"]
+        args.eval_step = default_params[task_name]["eval_step"]
+    
+    processor = processors[task_name]()
+    output_mode = output_modes[task_name]
+    label_list = processor.get_labels()
+    num_labels = len(label_list)
+
+    # ================================================================================  #
+    # Load Vocab FIle -> Tokenization 
+    # ================================================================================ #
+    tokenizer = BertTokenizer.from_pretrained(args.teacher_model, do_lower_case=True)
+    
+    # ================================================================================  #
+    # Dataset Setup (with DA)
+    # ================================================================================ #
+    if args.aug_train: # Data Augmentation
+        try:
+            train_file = os.path.join(processed_data_dir,f'aug_data_{args.aug_N}.pkl')
+            train_features = pickle.load(open(train_file,'rb'))
+        except:
+            train_examples = processor.get_aug_examples(data_dir, args.aug_N)
+            train_features = convert_examples_to_features(train_examples, label_list,
+                                            args.max_seq_length, tokenizer, output_mode)
+            with open(train_file, 'wb') as f:
+                pickle.dump(train_features, f, protocol=pickle.HIGHEST_PROTOCOL)
+    else:
+        try:
+            train_file = os.path.join(processed_data_dir,'data.pkl')
+            train_features = pickle.load(open(train_file,'rb'))
+            
+        except:
+            train_examples = processor.get_train_examples(data_dir)
+            train_features = convert_examples_to_features(train_examples, label_list,
+                                            args.max_seq_length, tokenizer, output_mode)
+            
+            with open(train_file, 'wb') as f:
+                pickle.dump(train_features, f, protocol=pickle.HIGHEST_PROTOCOL)
+
+    num_train_epochs = args.num_train_epochs 
+    num_train_optimization_steps = math.ceil(len(train_features) / args.batch_size) * num_train_epochs
+    
+    # TI Step-2 Iteration Number Setting
+    if "tiny-4l" in args.bert or task_name == "cola":
+        ti_step_1_total_step = 120
+    else:
+        ti_step_1_total_step = 60
+
+    if args.training_type == "qat_step1": 
+        args.eval_step = 10
+        num_train_optimization_steps = ti_step_1_total_step 
+
+    # We keep total two-step QAT iteration number identical to baseline TernaryBERT QAT setting
+    # Total Iteration Step = N
+    # TI step-1 iteration step = s
+    # TI step-2 iteration step = N - s
+
+    if args.training_type == "qat_step2" : 
+        num_train_optimization_steps = num_train_optimization_steps - ti_step_1_total_step  
+    
+    train_data, _ = get_tensor_data(output_mode, train_features)
+    train_sampler = RandomSampler(train_data)
+    train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.batch_size)
+    
+    # Dev Data load
+    try:
+        dev_file = train_file = os.path.join(processed_data_dir,'dev.pkl')
+        eval_features = pickle.load(open(dev_file,'rb'))
+    except:
+        eval_examples = processor.get_dev_examples(data_dir)
+        eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
+        with open(dev_file, 'wb') as f:
+                pickle.dump(eval_features, f, protocol=pickle.HIGHEST_PROTOCOL)
+ 
+    eval_data, eval_labels = get_tensor_data(output_mode, eval_features)
+    eval_sampler = SequentialSampler(eval_data)
+    eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.batch_size)
+
+    if task_name == "mnli":
+        processor = processors["mnli-mm"]()
+        try:
+            dev_mm_file = train_file = os.path.join(processed_data_dir,'dev-mm_data.pkl')
+            mm_eval_features = pickle.load(open(dev_mm_file,'rb'))
+        except:
+            mm_eval_examples = processor.get_dev_examples(data_dir)
+            mm_eval_features = convert_examples_to_features(
+                mm_eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
+            with open(dev_mm_file, 'wb') as f:
+                pickle.dump(mm_eval_features, f, protocol=pickle.HIGHEST_PROTOCOL)
+        
+        mm_eval_data, mm_eval_labels = get_tensor_data(output_mode, mm_eval_features)
+        # logger.info("  Num examples = %d", len(mm_eval_features))
+
+        mm_eval_sampler = SequentialSampler(mm_eval_data)
+        mm_eval_dataloader = DataLoader(mm_eval_data, sampler=mm_eval_sampler,
+                                        batch_size=args.batch_size)
+
+
+    # ================================================================================ #
+    # Build Teacher Model
+    # ================================================================================ # 
+    teacher_model = BertForSequenceClassification.from_pretrained(args.teacher_model, num_labels=num_labels)
+    
+    teacher_model.to(device)
+    teacher_model.eval()
+    
+    if n_gpu > 1:
+        teacher_model = torch.nn.DataParallel(teacher_model)
+    
+    result = do_eval(teacher_model, task_name, eval_dataloader,
+                    device, output_mode, eval_labels, num_labels)
+    
+    # ================================================================================  #
+    # Save Teacher Model Peroformance for KD Training
+    # ================================================================================ #
+    if task_name in acc_tasks:
+        if task_name in ['sst-2','mnli','qnli','rte']:
+            fp32_performance = f"acc:{result['acc']}"
+            fp32_score = result['acc']
+        elif task_name in ['mrpc','qqp']:
+            fp32_performance = f"f1/acc:{result['f1']}/{result['acc']} avg : {(result['f1'] + result['acc'])*50}"
+            fp32_score = (result['f1'] + result['acc'])*50
+    if task_name in corr_tasks:
+        fp32_performance = f"pearson/spearmanr:{result['pearson']}/{result['spearmanr']} corr:{result['corr']}"
+        fp32_score = result['corr']*100
+
+    if task_name in mcc_tasks:
+        fp32_performance = f"mcc:{result['mcc']}"
+        fp32_score = result['mcc']
+
+    if task_name == "mnli":
+        result = do_eval(teacher_model, 'mnli-mm', mm_eval_dataloader,
+                            device, output_mode, mm_eval_labels, num_labels)
+        fp32_performance += f"  mm-acc:{result['acc']}"
+        fp32_score = result['acc']
+    fp32_performance = task_name +' fp32   ' + fp32_performance
+    
+    # ================================================================================  #
+    # Build Student Model
+    # ================================================================================ #
+    student_config = BertConfig.from_pretrained(args.student_model, 
+                                                clip_val = args.clip_val,
+                                                quantize = args.quantize,
+                                                ffn_q_1 = args.ffn_1,
+                                                ffn_q_2 = args.ffn_2,
+                                                qkv_q = args.qkv,
+                                                emb_q = args.emb,
+                                                cls_q = args.cls,
+                                                mean_scale = args.mean_scale,
+                                                teacher_attnmap = args.teacher_attnmap,
+                                                teacher_context = args.teacher_context,
+                                                teacher_output = args.teacher_output,
+                                                )
+    
+    student_model = QuantBertForSequenceClassification.from_pretrained(args.student_model, config = student_config, num_labels=num_labels)
+    student_model.to(device)
+    
+    # ================================================================================  #
+    # Training Setting
+    # ================================================================================ #
+    if n_gpu > 1:
+        student_model = torch.nn.DataParallel(student_model)
+    param_optimizer = list(student_model.named_parameters())
+    
+    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
+
+    optimizer_grouped_parameters = [
+        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in (no_decay))], 'weight_decay': 0.01},
+        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},
+    ]
+
+    schedule = 'warmup_linear'
+    optimizer = BertAdam(optimizer_grouped_parameters,
+                            schedule=schedule,
+                            lr=args.learning_rate,
+                            warmup=0.1,
+                            t_total=num_train_optimization_steps)
+    
+    
+    norm_func = torch.linalg.norm
+    loss_cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6)
+    
+    global_step = 0
+    best_dev_acc = 0.0
+    previous_best = None
+    
+    # ================================================================================  #
+    # Training Start
+    # ================================================================================ #
+
+    logger.info("***** Running training *****")
+    logger.info("  Num examples = %d", len(train_features))
+    logger.info("  Batch size = %d", args.batch_size)
+    logger.info("  Num steps = %d", num_train_optimization_steps)
+    
+    # Loss Init AverageMeter
+    l_gt_loss = AverageMeter()
+    l_att_loss = AverageMeter()
+    l_rep_loss = AverageMeter()
+    l_cls_loss = AverageMeter()
+    l_output_loss = AverageMeter()
+    l_loss = AverageMeter()
+    
+    mixed_status = None
+    ce_loss_func = CrossEntropyLoss()
+    kl_loss = torch.nn.KLDivLoss(reduction="batchmean")
+    cos_loss_func  = CosineEmbeddingLoss()
+    loss_mse = MSELoss()
+    
+    for epoch_ in range(int(num_train_epochs)):
+        
+        for batch in train_dataloader:
+            
+            # Gradual TI (You could try other TI options - Stochastic/Inverted)
+            if args.training_type == "qat_step1" and args.teacher_gradual:
+                if global_step < num_train_optimization_steps / 6:
+                    student_config.teacher_output = True
+                    mixed_status = "OI"
+                elif global_step < num_train_optimization_steps / 3:
+                    student_config.teacher_output = False
+                    student_config.teacher_context = True
+                    mixed_status = "CI"
+                else:
+                    student_config.teacher_output = False
+                    student_config.teacher_context = False
+                    student_config.teacher_attnmap = True
+                    mixed_status = "MI"
+            
+            if args.training_type == "qat_step1" and args.teacher_stochastic:
+                rand_int = torch.randint(1,4,(1,))[0].item()
+                
+                if rand_int == 1 :
+                    student_config.teacher_output = True
+                    student_config.teacher_context = False
+                    student_config.teacher_attnmap = False
+                    mixed_status = "OI"
+                elif rand_int == 2 :
+                    student_config.teacher_output = False
+                    student_config.teacher_context = True
+                    student_config.teacher_attnmap = False
+                    mixed_status = "CI"
+                else:
+                    student_config.teacher_output = False
+                    student_config.teacher_context = False
+                    student_config.teacher_attnmap = True
+                    mixed_status = "MI"
+
+            if args.training_type == "qat_step1" and args.teacher_inverted:
+                if global_step < num_train_optimization_steps / 3:
+                    student_config.teacher_attnmap = True
+                    mixed_status = "MI"
+                elif global_step < num_train_optimization_steps * 2/ 3:
+                    student_config.teacher_attnmap = False
+                    student_config.teacher_context = True
+                    mixed_status = "CI"
+                else:
+                    student_config.teacher_attnmap = False
+                    student_config.teacher_context = False
+                    student_config.teacher_output = True
+                    mixed_status = "OI"
+
+            student_model.train()
+            
+            batch = tuple(t.to(device) for t in batch)
+            input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch
+            
+            # tmp loss init
+            att_loss = 0.
+            rep_loss = 0.
+            cls_loss = 0.
+            attscore_loss = 0.
+            output_loss = 0.
+            loss = 0.
+            
+            with torch.no_grad():
+                teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_attn_blocks = teacher_model(input_ids, segment_ids, input_mask)
+        
+            student_logits, student_atts, student_reps, student_probs, student_attn_blocks = student_model(input_ids, segment_ids, input_mask, teacher_outputs=(teacher_probs, teacher_attn_blocks))
+
+            # We did not use GT-Loss for fair comparison to TernaryBERT QAT (note that GT-loss helps boosting resulting accuracy in some tasks)
+            if args.gt_loss:
+                if output_mode == "classification":
+                    loss = ce_loss_func(student_logits, label_ids)
+                    
+                elif output_mode == "regression":
+                    loss = loss_mse(student_logits, teacher_logits)
+                
+                l_gt_loss.update(loss.item())
+
+            # Pred Loss (TernaryBERT Loss)
+            if args.pred_distill:
+                if output_mode == "classification":
+                    cls_loss = soft_cross_entropy(student_logits,teacher_logits)
+                elif output_mode == "regression":
+                    cls_loss = MSELoss()(student_logits, teacher_logits)
+                else:
+                    cls_loss = soft_cross_entropy(student_logits,teacher_logits)
+                l_cls_loss.update(cls_loss.item())
+
+            # Output Loss 
+            if args.output_distill:
+                for i, (student_attn_block, teacher_attn_block) in enumerate(zip(student_attn_blocks, teacher_attn_blocks)):    
+                    tmp_loss = MSELoss()(student_attn_block[1], teacher_attn_block[1]) # 1 : Attention Output 0 : Layer Context
+                    output_loss += tmp_loss
+                l_output_loss.update(output_loss.item())
+            
+            # Attention Score Loss (TernaryBERT Loss)
+            if args.attn_distill:
+                for i, (student_att, teacher_att) in enumerate(zip(student_atts, teacher_atts)):    
+                            
+                    student_att = torch.where(student_att <= -1e2, torch.zeros_like(student_att).to("cuda"),
+                                                student_att)
+                    teacher_att = torch.where(teacher_att <= -1e2, torch.zeros_like(teacher_att).to("cuda"),
+                                                teacher_att)
+                    tmp_loss = MSELoss()(student_att, teacher_att)
+                    attscore_loss += tmp_loss
+                l_att_loss.update(attscore_loss.item())
+
+            # Rep Distill (TernaryBERT Loss)
+            if args.rep_distill:
+                for i, (student_rep, teacher_rep) in enumerate(zip(student_reps, teacher_reps)):
+                    tmp_loss = MSELoss()(student_rep, teacher_rep)
+                    rep_loss += tmp_loss
+                l_rep_loss.update(rep_loss.item())
+
+            loss += cls_loss + rep_loss + output_loss + attscore_loss 
+            l_loss.update(loss.item())
+
+            if n_gpu > 1:
+                loss = loss.mean()           
+                
+            # Zero Step Loss Update
+            if global_step == 0: 
+                if run is not None:           
+                    run["loss/total_loss"].log(value=l_loss.avg, step=global_step)
+                    run["loss/gt_loss_loss"].log(value=l_gt_loss.avg, step=global_step)
+                    run["loss/att_loss_loss"].log(value=l_att_loss.avg, step=global_step)
+                    run["loss/rep_loss_loss"].log(value=l_rep_loss.avg, step=global_step)
+                    run["loss/cls_loss_loss"].log(value=l_cls_loss.avg, step=global_step)
+                    run["loss/output_loss_loss"].log(value=l_output_loss.avg, step=global_step)
+                    
+                    run["metrics/lr"].log(value=optimizer.get_lr()[0], step=global_step)
+ 
+            loss.backward()
+            optimizer.step() 
+            optimizer.zero_grad()
+            
+            global_step += 1
+            # ================================================================================  #
+            #  Evaluation
+            # ================================================================================ #
+
+            if global_step % args.eval_step == 0 or global_step == num_train_optimization_steps-1: # period or last step
+                
+                student_model.eval()
+                
+                result = do_eval(student_model, task_name, eval_dataloader,
+                                    device, output_mode, eval_labels, num_labels, teacher_model=teacher_model)
+            
+                result['global_step'] = global_step
+                result['cls_loss'] = l_cls_loss.avg
+                result['att_loss'] = l_att_loss.avg
+                result['rep_loss'] = l_rep_loss.avg
+                result['loss'] = l_loss.avg
+                
+                # Basic Logging (Training Loss, Clip Val)
+                if run is not None:
+                    
+                    run["loss/total_loss"].log(value=l_loss.avg, step=global_step)
+                    run["loss/gt_loss_loss"].log(value=l_gt_loss.avg, step=global_step)
+                    run["loss/att_loss_loss"].log(value=l_att_loss.avg, step=global_step)
+                    run["loss/rep_loss_loss"].log(value=l_rep_loss.avg, step=global_step)
+                    run["loss/cls_loss_loss"].log(value=l_cls_loss.avg, step=global_step)
+                    run["loss/output_loss_loss"].log(value=l_output_loss.avg, step=global_step)        
+                    run["metrics/lr"].log(value=optimizer.get_lr()[0], step=global_step)
+
+                if task_name=='cola':
+                    eval_score = result["mcc"]
+                    if run is not None:
+                        run["metrics/mcc"].log(value=result['mcc'], step=global_step)
+
+                    eval_result = result["mcc"]  
+                    # logger.info(f"Eval Result is {result['mcc']}")
+                elif task_name in ['sst-2','mnli','mnli-mm','qnli','rte','wnli']:
+                    eval_score = result["acc"]
+                    if run is not None:
+                        run["metrics/acc"].log(value=result['acc'],step=global_step)
+                        
+                    logger.info(f"Eval Result is {result['acc']}")
+                    eval_result = result["acc"]
+                elif task_name in ['mrpc','qqp']:
+                    eval_score = result["acc_and_f1"]
+                    if run is not None:
+                        run["metrics/acc_and_f1"].log(value=result['acc_and_f1'],step=global_step)
+                        
+                    # logger.info(f"Eval Result is {result['acc']}, {result['f1']}")
+                    eval_result = result["acc_and_f1"]
+                else:
+                    eval_score = result["corr"]
+                    if run is not None:
+                        run["metrics/corr"].log(value=result['corr'],step=global_step)
+                        
+                    # logger.info(f"Eval Result is {result['corr']}")
+                    eval_result = result["corr"]
+
+                if args.training_type == "qat_step1":        
+                    logger.info(f"Gradual-{mixed_status}-{global_step}-SAVE : {eval_result*100}")
+                    model_to_save = student_model.module if hasattr(student_model, 'module') else student_model
+                    quant_model = copy.deepcopy(model_to_save)
+                     
+                    output_model_file = os.path.join(output_quant_dir, WEIGHTS_NAME)
+                    output_config_file = os.path.join(output_quant_dir, CONFIG_NAME)
+
+                    torch.save(quant_model.state_dict(), output_model_file)
+                    model_to_save.config.to_json_file(output_config_file)
+                    tokenizer.save_vocabulary(output_quant_dir)
+                
+                # Save Model
+                save_model = False
+
+                if task_name in acc_tasks and result['acc'] > best_dev_acc:
+                    if task_name in ['sst-2','mnli','qnli','rte']:
+                        previous_best = f"{result['acc']*100}"
+                    elif task_name in ['mrpc','qqp']:
+                        previous_best = f"{(result['f1'] + result['acc'])*50}"
+                    best_dev_acc = result['acc']
+                    save_model = True
+
+                if task_name in corr_tasks and result['corr'] > best_dev_acc:
+                    previous_best = f"{result['corr']*100}"
+                    best_dev_acc = result['corr']
+                    save_model = True
+
+                if task_name in mcc_tasks and result['mcc'] > best_dev_acc:
+                    previous_best = f"{result['mcc']*100}"
+                    best_dev_acc = result['mcc']
+                    save_model = True
+
+                if save_model:
+                    # logger.info("====> Best Score *****")
+                    # Test mnli-mm
+                    if task_name == "mnli":
+                        result = do_eval(student_model, 'mnli-mm', mm_eval_dataloader,
+                                            device, output_mode, mm_eval_labels, num_labels, teacher_model=teacher_model)
+                        previous_best+= f"mm-acc:{result['acc']}"
+
+                    if args.training_type == "qat_step1":
+                        logger.info(fp32_performance)
+                        logger.info(previous_best)
+
+                    if args.save_fp_model:
+                        # logger.info("***** Save full precision model *****")
+                        model_to_save = student_model.module if hasattr(student_model, 'module') else student_model
+                        output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
+                        output_config_file = os.path.join(output_dir, CONFIG_NAME)
+
+                        torch.save(model_to_save.state_dict(), output_model_file)
+                        model_to_save.config.to_json_file(output_config_file)
+                        tokenizer.save_vocabulary(output_dir)
+
+                    if args.save_quantized_model and not args.training_type == "qat_step1":
+                        # logger.info("====> Save quantized model *****")
+
+                        # output_quant_dir = os.path.join(output_dir, 'quant')
+                        output_quant_dir = os.path.join(output_dir, 'exploration')
+                        if not os.path.exists(output_quant_dir):
+                            os.mkdir(output_quant_dir)
+
+                        if not os.path.exists(output_quant_dir):
+                            os.makedirs(output_quant_dir)
+                        
+                        output_quant_dir = os.path.join(output_quant_dir, args.exp_name)
+                        if not os.path.exists(output_quant_dir):
+                            os.makedirs(output_quant_dir)
+                        
+                        model_to_save = student_model.module if hasattr(student_model, 'module') else student_model
+                        quant_model = copy.deepcopy(model_to_save)
+                            
+                        output_model_file = os.path.join(output_quant_dir, WEIGHTS_NAME)
+                        output_config_file = os.path.join(output_quant_dir, CONFIG_NAME)
+
+                        torch.save(quant_model.state_dict(), output_model_file)
+                        model_to_save.config.to_json_file(output_config_file)
+                        tokenizer.save_vocabulary(output_quant_dir)
+                        
+        
+                
+                # TI QAT Step-1
+                if global_step >= num_train_optimization_steps and args.training_type == "qat_step1":
+                    
+                    if global_step >= ti_step_1_total_step:
+                        logger.info(f"==> TI-step1 Last Result = {eval_result}")
+                        best_txt = os.path.join(output_quant_dir, "best_info.txt")
+                        with open(best_txt, "w") as f_w:
+                            f_w.write(previous_best)
+                        return
+                    
+    logger.info(f"==> Previous Best = {previous_best}")
+    
+    # Save Best Score
+    if args.save_quantized_model:
+        best_txt = os.path.join(output_quant_dir, "best_info.txt")
+        last_txt = os.path.join(output_quant_dir, "last_info.txt")
+        with open(best_txt, "w") as f_w:
+            f_w.write(previous_best)
+        with open(last_txt, "w") as f_w:
+            f_w.write(f"{eval_result*100}")
+            # f_w.write(f"Last Result = {result}")
+
+if __name__ == "__main__":
+    main()
diff --git a/notebooks/Attention_Output_Comp.ipynb b/notebooks/Attention_Output_Comp.ipynb
new file mode 100644
index 0000000..7b4df9a
--- /dev/null
+++ b/notebooks/Attention_Output_Comp.ipynb
@@ -0,0 +1,2127 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a03d62c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from __future__ import absolute_import, division, print_function\n",
+    "\n",
+    "import pprint\n",
+    "import argparse\n",
+    "import logging\n",
+    "import os\n",
+    "import random\n",
+    "import sys\n",
+    "import pickle\n",
+    "import copy\n",
+    "import collections\n",
+    "import math\n",
+    "\n",
+    "import numpy as np\n",
+    "import numpy\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler,TensorDataset\n",
+    "\n",
+    "from torch.nn import CrossEntropyLoss, MSELoss\n",
+    "\n",
+    "from transformer import BertForSequenceClassification,WEIGHTS_NAME, CONFIG_NAME\n",
+    "from transformer.modeling_quant import BertForSequenceClassification as QuantBertForSequenceClassification\n",
+    "from transformer import BertTokenizer\n",
+    "from transformer import BertAdam\n",
+    "from transformer import BertConfig\n",
+    "from transformer import QuantizeLinear, QuantizeAct, BertSelfAttention, FP_BertSelfAttention, ClipLinear\n",
+    "from utils_glue import *\n",
+    "from bertviz import model_view\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "class AverageMeter(object):\n",
+    "    \"\"\"Computes and stores the average and current value\"\"\"\n",
+    "    def __init__(self):\n",
+    "        self.reset()\n",
+    "\n",
+    "    def reset(self):\n",
+    "        self.val = 0\n",
+    "        self.avg = 0 \n",
+    "        self.sum = 0\n",
+    "        self.count = 0\n",
+    "\n",
+    "    def update(self, val, n=1):\n",
+    "        self.val = val\n",
+    "        self.sum += val * n\n",
+    "        self.count += n\n",
+    "        self.avg = self.sum / self.count\n",
+    "\n",
+    "def do_eval(model, task_name, eval_dataloader,\n",
+    "            device, output_mode, eval_labels, num_labels, teacher_model=None):\n",
+    "    eval_loss = 0\n",
+    "    nb_eval_steps = 0\n",
+    "    preds = []\n",
+    "\n",
+    "    for batch_ in tqdm(eval_dataloader, desc=\"Inference\"):\n",
+    "        batch_ = tuple(t.to(device) for t in batch_)\n",
+    "        \n",
+    "        with torch.no_grad():\n",
+    "            input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch_\n",
+    "\n",
+    "            # teacher attnmap test\n",
+    "            if teacher_model is not None:\n",
+    "                \n",
+    "                # logits, _, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids, segment_ids, input_mask)\n",
+    "                \n",
+    "                # # logits, _, _, _, _ = model(input_ids, segment_ids, input_mask, teacher_probs=teacher_probs)\n",
+    "                # logits, _, _, _, _ = model(input_ids, segment_ids, input_mask, teacher_probs=(teacher_probs, teacher_values, teacher_reps))\n",
+    "                teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids, segment_ids, input_mask)\n",
+    "                logits, student_atts, student_reps, student_probs, student_values  = model(input_ids, segment_ids, input_mask, teacher_outputs=(teacher_probs, teacher_values, teacher_reps, teacher_logits, teacher_atts), output_mode=output_mode, seq_lengths=seq_lengths)\n",
+    "            else:\n",
+    "                logits, _, _, _, _ = model(input_ids, segment_ids, input_mask)\n",
+    "        \n",
+    "        # create eval loss and other metric required by the task\n",
+    "        if output_mode == \"classification\":\n",
+    "            loss_fct = CrossEntropyLoss()\n",
+    "            tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))\n",
+    "        elif output_mode == \"regression\":\n",
+    "            loss_fct = MSELoss()\n",
+    "            tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))\n",
+    "\n",
+    "        eval_loss += tmp_eval_loss.mean().item()\n",
+    "        nb_eval_steps += 1\n",
+    "        if len(preds) == 0:\n",
+    "            preds.append(logits.detach().cpu().numpy())\n",
+    "        else:\n",
+    "            preds[0] = np.append(\n",
+    "                preds[0], logits.detach().cpu().numpy(), axis=0)\n",
+    "\n",
+    "    eval_loss = eval_loss / nb_eval_steps\n",
+    "\n",
+    "    preds = preds[0]\n",
+    "    if output_mode == \"classification\":\n",
+    "        preds = np.argmax(preds, axis=1)\n",
+    "    elif output_mode == \"regression\":\n",
+    "        preds = np.squeeze(preds)\n",
+    "    result = compute_metrics(task_name, preds, eval_labels.numpy())\n",
+    "    result['eval_loss'] = eval_loss\n",
+    "    return result\n",
+    "\n",
+    "processors = {\n",
+    "    \"cola\": ColaProcessor,\n",
+    "    \"mnli\": MnliProcessor,\n",
+    "    \"mnli-mm\": MnliMismatchedProcessor,\n",
+    "    \"mrpc\": MrpcProcessor,\n",
+    "    \"sst-2\": Sst2Processor,\n",
+    "    \"sts-b\": StsbProcessor,\n",
+    "    \"qqp\": QqpProcessor,\n",
+    "    \"qnli\": QnliProcessor,\n",
+    "    \"rte\": RteProcessor   \n",
+    "}\n",
+    "\n",
+    "output_modes = {\n",
+    "        \"cola\": \"classification\",\n",
+    "        \"mnli\": \"classification\",\n",
+    "        \"mrpc\": \"classification\",\n",
+    "        \"sst-2\": \"classification\",\n",
+    "        \"sts-b\": \"regression\",\n",
+    "        \"qqp\": \"classification\",\n",
+    "        \"qnli\": \"classification\",\n",
+    "        \"rte\": \"classification\"\n",
+    "}\n",
+    "\n",
+    "default_params = {\n",
+    "        \"cola\": {\"max_seq_length\": 64,\"batch_size\":1,\"eval_step\": 50}, # No Aug : 50 Aug : 400\n",
+    "        \"mnli\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":8000},\n",
+    "        \"mrpc\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"sst-2\": {\"max_seq_length\": 64,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"sts-b\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"qqp\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":1000},\n",
+    "        \"qnli\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":1000},\n",
+    "        \"rte\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\": 20}\n",
+    "    }\n",
+    "\n",
+    "def get_tensor_data(output_mode, features):\n",
+    "    if output_mode == \"classification\":\n",
+    "        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)\n",
+    "    elif output_mode == \"regression\":\n",
+    "        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)\n",
+    "\n",
+    "\n",
+    "    all_seq_lengths = torch.tensor([f.seq_length for f in features], dtype=torch.long)\n",
+    "    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n",
+    "    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)\n",
+    "    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)\n",
+    "    tensor_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,all_label_ids, all_seq_lengths)\n",
+    "    return tensor_data, all_label_ids\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "38659af1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_name = \"cola\"\n",
+    "bert_size = \"base\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    layer_num = 24\n",
+    "    head_num = 16\n",
+    "else: \n",
+    "    layer_num = 12\n",
+    "    head_num = 12\n",
+    "    \n",
+    "teacher_model = None\n",
+    "\n",
+    "# torch.cuda.empty_cache()\n",
+    "# !nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b1f9450",
+   "metadata": {},
+   "source": [
+    "# Device & Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e4c445a3",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10/06 10:10:18 AM Writing example 0 of 1043\n",
+      "10/06 10:10:18 AM *** Example ***\n",
+      "10/06 10:10:18 AM guid: dev-0\n",
+      "10/06 10:10:18 AM tokens: [CLS] the sailors rode the breeze clear of the rocks . [SEP]\n",
+      "10/06 10:10:18 AM input_ids: 101 1996 11279 8469 1996 9478 3154 1997 1996 5749 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      "10/06 10:10:18 AM input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      "10/06 10:10:18 AM segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      "10/06 10:10:18 AM label: 1\n",
+      "10/06 10:10:18 AM label_id: 1\n",
+      "input_ids : tensor([[  101,  2198,  2001,  7167,  2062, 27885,  3630, 25171,  2084,  5965,\n",
+      "          1012,   102]])\n",
+      "tokens : ['[CLS]', 'john', 'was', 'lots', 'more', 'ob', '##no', '##xious', 'than', 'fred', '.', '[SEP]']\n",
+      "A : john was lots more ob ##no ##xious than fred . \n",
+      "B : \n",
+      "tensor([11])\n",
+      "PUNC\n",
+      "comma -> tensor([], dtype=torch.int64)\n",
+      "period -> tensor([10])\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = \"cpu\"\n",
+    "\n",
+    "model_dir = \"models\"\n",
+    "output_dir = \"output\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    model_dir = os.path.join(model_dir, \"BERT_large\")\n",
+    "    output_dir = os.path.join(output_dir, \"BERT_large\")\n",
+    "\n",
+    "teacher_model_dir = os.path.join(model_dir,task_name)\n",
+    "\n",
+    "# Processor & Task Info\n",
+    "processor = processors[task_name]()\n",
+    "output_mode = output_modes[task_name]\n",
+    "label_list = processor.get_labels()\n",
+    "num_labels = len(label_list)\n",
+    "\n",
+    "if task_name in default_params:\n",
+    "    batch_size = default_params[task_name][\"batch_size\"]\n",
+    "    max_seq_length = default_params[task_name][\"max_seq_length\"]\n",
+    "    eval_step = default_params[task_name][\"eval_step\"]\n",
+    "    \n",
+    "# Tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained(teacher_model_dir, do_lower_case=True)\n",
+    "\n",
+    "\n",
+    "# Load Dataset\n",
+    "data_dir = os.path.join(\"data\",task_name)\n",
+    "processed_data_dir = os.path.join(data_dir,'preprocessed')\n",
+    "\n",
+    "eval_examples = processor.get_dev_examples(data_dir)\n",
+    "eval_features = convert_examples_to_features(eval_examples, label_list, max_seq_length, tokenizer, output_mode)\n",
+    "# dev_file = train_file = os.path.join(processed_data_dir,'dev.pkl') \n",
+    "# eval_features = pickle.load(open(dev_file,'rb'))\n",
+    "\n",
+    "eval_data, eval_labels = get_tensor_data(\"regression\", eval_features)\n",
+    "eval_sampler = SequentialSampler(eval_data)\n",
+    "eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=1)\n",
+    "eval_data, eval_labels = get_tensor_data(output_mode, eval_features)\n",
+    "\n",
+    "eval_examples = processor.get_dev_examples(data_dir)\n",
+    "\n",
+    "# Sampling Sentence \n",
+    "i = 0 \n",
+    "# num = 3\n",
+    "num = 12\n",
+    "\n",
+    "for step, batch in enumerate(eval_dataloader):\n",
+    "    # model.train()\n",
+    "            \n",
+    "    batch = tuple(t.to(device) for t in batch)\n",
+    "    input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch\n",
+    "    seq_length = seq_lengths[0]\n",
+    "    i = i + 1\n",
+    "    if i == num:\n",
+    "        break\n",
+    "\n",
+    "input_ids_sliced = input_ids[:,:seq_length]\n",
+    "input_id = []\n",
+    "for i in input_ids_sliced[0]:\n",
+    "    input_id.append(i.item())\n",
+    "tokens = tokenizer.convert_ids_to_tokens(input_id)\n",
+    "\n",
+    "\n",
+    "sample_sentence_a = str()\n",
+    "sample_sentence_b = str()\n",
+    "index = 0\n",
+    "\n",
+    "for i, word in enumerate(tokens[1:-1]):\n",
+    "    if word == \"[SEP]\":\n",
+    "        break\n",
+    "    sample_sentence_a += word\n",
+    "    sample_sentence_a += \" \"\n",
+    "index = i\n",
+    "\n",
+    "for i, word in enumerate(tokens[index+2:-1]):\n",
+    "    if word == \"[SEP]\":\n",
+    "        break\n",
+    "    sample_sentence_b += word\n",
+    "    sample_sentence_b += \" \"\n",
+    "\n",
+    "sep_index = torch.where(input_ids[0] == 102)[0]\n",
+    "\n",
+    "punc_index_1 = torch.where(input_ids[0] == 1010)[0] # comma\n",
+    "punc_index_2 = torch.where(input_ids[0] == 1012)[0] # period\n",
+    "punc_index = torch.cat([punc_index_1, punc_index_2])\n",
+    "\n",
+    "if len(sample_sentence_b) > 1:\n",
+    "    sample_sentence_b_start = segment_ids[0].tolist().index(1)\n",
+    "else:\n",
+    "    sample_sentence_b_start = None\n",
+    "\n",
+    "print(f\"input_ids : {input_ids_sliced}\")\n",
+    "print(f\"tokens : {tokens}\")\n",
+    "print(f\"A : {sample_sentence_a}\")\n",
+    "print(f\"B : {sample_sentence_b}\")\n",
+    "print(sep_index)\n",
+    "print(\"PUNC\")\n",
+    "print(f\"comma -> {punc_index_1}\")\n",
+    "print(f\"period -> {punc_index_2}\")\n",
+    "\n",
+    "for i, token in enumerate(tokens):\n",
+    "    tokens[i] = token # + \"_\" + str(i)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1673,
+   "id": "c8a51eb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ranking_loss_func(student_probs, teacher_probs):\n",
+    "    Loss_ranking = 0\n",
+    "\n",
+    "    loss_ranking_list = []\n",
+    "\n",
+    "    for l in tqdm(range(layer_num)):\n",
+    "        for h in range(head_num):\n",
+    "            student_prob_plt = student_probs[l][0,h,:,:]\n",
+    "            teacher_prob_plt = teacher_probs[l][0,h,:,:]\n",
+    "            Loss_ranking = 0\n",
+    "            for h in range(seq_length):\n",
+    "                for idx in range(0, seq_length-1):\n",
+    "                    for jdx in range(1, seq_length):\n",
+    "                        p = (student_prob_plt[h][idx] - student_prob_plt[h][jdx])*(torch.sgn(teacher_prob_plt[h][idx] - teacher_prob_plt[h][jdx]))\n",
+    "                        # print(max(0,  - p.item()))\n",
+    "                        Loss_ranking += max(0,  - p.item())\n",
+    "            loss_ranking_list.append(Loss_ranking)\n",
+    "    return loss_ranking_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "12432934",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cpu\")\n",
+    "\n",
+    "teacher_model = BertForSequenceClassification.from_pretrained(teacher_model_dir, num_labels=num_labels)\n",
+    "teacher_model.to(device)\n",
+    "teacher_model.eval()\n",
+    "\n",
+    "\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_zip = teacher_model(input_ids_sliced.to(device))\n",
+    "teacher_outputs = (teacher_probs, teacher_zip)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "e98f7365",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_name= \"step_2_base_S_O_1_MIXED\"\n",
+    "student_model_dir = os.path.join(\"output\", task_name, \"exploration\", file_name)\n",
+    "student_config_OI = BertConfig.from_pretrained(student_model_dir)\n",
+    "# student_config_OI.teacher_attnmap = False\n",
+    "student_model = QuantBertForSequenceClassification.from_pretrained(student_model_dir, config = student_config_OI, num_labels=num_labels)\n",
+    "\n",
+    "model_2_outputs  = student_model(input_ids_sliced.to(device), teacher_outputs=teacher_outputs)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "8aeea219",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_name= \"step_2_base_S_O_1_OI\"\n",
+    "student_model_dir = os.path.join(\"output\", task_name, \"exploration\", file_name)\n",
+    "student_config_MIXED = BertConfig.from_pretrained(student_model_dir)\n",
+    "# student_config_MIXED.teacher_attnmap = False\n",
+    "# student_config_MIXED.teacher_output = False\n",
+    "student_model = QuantBertForSequenceClassification.from_pretrained(student_model_dir, config = student_config_MIXED, num_labels=num_labels)\n",
+    "\n",
+    "model_3_outputs  = student_model(input_ids_sliced.to(device), teacher_outputs=teacher_outputs)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "508844e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_name= \"step_2_base_S_O_1_MI\"\n",
+    "student_model_dir = os.path.join(\"output\", task_name, \"exploration\", file_name)\n",
+    "student_config_MIXED = BertConfig.from_pretrained(student_model_dir)\n",
+    "# student_config_MIXED.teacher_attnmap = False\n",
+    "# student_config_MIXED.teacher_output = False\n",
+    "student_model = QuantBertForSequenceClassification.from_pretrained(student_model_dir, config = student_config_MIXED, num_labels=num_labels)\n",
+    "\n",
+    "model_4_outputs  = student_model(input_ids_sliced.to(device), teacher_outputs=teacher_outputs)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7049a495",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# student_logits, student_atts, student_reps_1, student_probs_1, student_zip_1  = model_1_outputs\n",
+    "student_logits, student_atts, student_reps_2, student_probs_2, student_zip_2  = model_2_outputs\n",
+    "student_logits, student_atts, student_reps_3, student_probs_3, student_zip_3  = model_3_outputs\n",
+    "student_logits, student_atts, student_reps_4, student_probs_4, student_zip_4  = model_4_outputs\n",
+    "\n",
+    "# model_outputs = (model_1_outputs, model_2_outputs, model_3_outputs, model_4_outputs)\n",
+    "model_outputs = (model_2_outputs, model_3_outputs, model_4_outputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3fc82e4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mag_dict = dict()\n",
+    "\n",
+    "for model_num in range(4):\n",
+    "    for l in range(layer_num):        \n",
+    "        mag_dict[f\"{model_num}_min_{l}\"] = []; mag_dict[f\"{model_num}_max_{l}\"] = []; mag_dict[f\"{model_num}_std_{l}\"] = []; mag_dict[f\"{model_num}_mean_{l}\"] = []\n",
+    "        if model_num == 0:\n",
+    "            mag_dict[f\"tc_min_{l}\"] = []; mag_dict[f\"tc_max_{l}\"] = []; mag_dict[f\"tc_std_{l}\"] = []; mag_dict[f\"tc_mean_{l}\"] = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f427f4e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_num = 0\n",
+    "mse_func = MSELoss()\n",
+    "cos_func = torch.nn.CosineSimilarity(dim=-1)\n",
+    "\n",
+    "for model_output in model_outputs:\n",
+    "    \n",
+    "    student_logits, student_atts, student_reps, student_probs, student_zip = model_output\n",
+    "    mag_dict[f\"{model_num}_ffn_mse\"] = []; mag_dict[f\"{model_num}_attn_cos\"] = []; mag_dict[f\"{model_num}_attn_mse\"] = []; mag_dict[f\"{model_num}_ffn_cos\"] = []\n",
+    "    for l in range(layer_num):\n",
+    "        tc_attn_context, tc_attn_output, tc_sa_output = teacher_zip[l]\n",
+    "        st_attn_context, st_attn_output, st_sa_output = student_zip[l]        \n",
+    "        st_ffn_output = student_reps[1+1]\n",
+    "        tc_ffn_output = teacher_reps[l+1]\n",
+    "\n",
+    "        tc_output = tc_ffn_output\n",
+    "        st_output = st_ffn_output\n",
+    "    \n",
+    "        # min-max\n",
+    "        for token in range(len(tokens)):\n",
+    "            if model_num == 0:\n",
+    "                mag_dict[f\"tc_min_{l}\"].append(tc_output[0,token,:].min().item())\n",
+    "                mag_dict[f\"tc_max_{l}\"].append(tc_output[0,token,:].max().item())\n",
+    "                mag_dict[f\"tc_mean_{l}\"].append(tc_output[0,token,:].mean().item())\n",
+    "                mag_dict[f\"tc_std_{l}\"].append(tc_output[0,token,:].std().item())\n",
+    "                \n",
+    "#                 mag_dict[f\"tc_min_{l}\"].append(tc_output[0,:,token,:].min().item())\n",
+    "#                 mag_dict[f\"tc_max_{l}\"].append(tc_output[0,:,token,:].max().item())\n",
+    "#                 mag_dict[f\"tc_mean_{l}\"].append(tc_output[0,:,token,:].mean().item())\n",
+    "#                 mag_dict[f\"tc_std_{l}\"].append(tc_output[0,:,token,:].std().item())\n",
+    "\n",
+    "            mag_dict[f\"{model_num}_min_{l}\"].append(st_output[0,token,:].min().item())\n",
+    "            mag_dict[f\"{model_num}_max_{l}\"].append(st_output[0,token,:].max().item())\n",
+    "            mag_dict[f\"{model_num}_mean_{l}\"].append(st_output[0,token,:].mean().item())\n",
+    "            mag_dict[f\"{model_num}_std_{l}\"].append(st_output[0,token,:].std().item())\n",
+    "\n",
+    "#             mag_dict[f\"{model_num}_min_{l}\"].append(st_output[0,:,token,:].min().item())\n",
+    "#             mag_dict[f\"{model_num}_max_{l}\"].append(st_output[0,:,token,:].max().item())\n",
+    "#             mag_dict[f\"{model_num}_mean_{l}\"].append(st_output[0,:,token,:].mean().item())\n",
+    "#             mag_dict[f\"{model_num}_std_{l}\"].append(st_output[0,:,token,:].std().item())            \n",
+    "                \n",
+    "            \n",
+    "        \n",
+    "        mse_attn_diff = mse_func(st_attn_output[0,:,:], tc_attn_output[0,:,:]).item()\n",
+    "        cos_attn_diff = torch.mean((1-cos_func(st_attn_output[0,:,:], tc_attn_output[0,:,:]))).item()\n",
+    "        mag_dict[f\"{model_num}_attn_mse\"].append(mse_attn_diff)\n",
+    "        mag_dict[f\"{model_num}_attn_cos\"].append(cos_attn_diff)\n",
+    "\n",
+    "        mse_ffn_diff = mse_func(student_reps[l+1][0,:,:], teacher_reps[l+1][0,:,:]).item()\n",
+    "        cos_ffn_diff = torch.mean((1-cos_func(student_reps[l+1][0,:,:], teacher_reps[l+1][0,:,:]))).item()\n",
+    "        mag_dict[f\"{model_num}_ffn_mse\"].append(mse_ffn_diff)\n",
+    "        mag_dict[f\"{model_num}_ffn_cos\"].append(cos_ffn_diff)\n",
+    "\n",
+    "    model_num += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1491,
+   "id": "83afa218",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor(0.0517, grad_fn=<DivBackward0>)"
+      ]
+     },
+     "execution_count": 1491,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st_map = student_probs[0]\n",
+    "tc_map = teacher_probs[0]\n",
+    "\n",
+    "kl_loss = torch.nn.KLDivLoss(reduction=\"batchmean\")\n",
+    "\n",
+    "kl_loss(st_map[0,0,:,:].log(), tc_map[0,0,:,:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1680,
+   "id": "2aff2294",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_num = 0\n",
+    "mse_func = MSELoss()\n",
+    "cos_func = torch.nn.CosineSimilarity(dim=-1)\n",
+    "kl_loss = torch.nn.KLDivLoss(reduction=\"batchmean\")\n",
+    "\n",
+    "context_similarity = torch.randn(2,layer_num, head_num)\n",
+    "cont_dict = dict()\n",
+    "cont_dict[\"0_cont\"] = []\n",
+    "cont_dict[\"1_cont\"] = []\n",
+    "\n",
+    "cont_dict[\"0_sa\"] = []\n",
+    "cont_dict[\"1_sa\"] = []\n",
+    "\n",
+    "map_similarity = torch.randn(2,layer_num, head_num)\n",
+    "\n",
+    "for model_output in model_outputs:\n",
+    "    \n",
+    "    student_logits, student_atts, student_reps, student_probs, student_zip = model_output\n",
+    "    for l in range(layer_num):\n",
+    "        tc_attn_context, tc_attn_output, tc_sa_output = teacher_zip[l]\n",
+    "        st_attn_context, st_attn_output, st_sa_output = student_zip[l]        \n",
+    "        st_ffn_output = student_reps[1+1]\n",
+    "        tc_ffn_output = teacher_reps[l+1]\n",
+    "        \n",
+    "        sa_diff = mse_func(tc_sa_output, st_sa_output)\n",
+    "        cont_dict[f\"{model_num}_sa\"].append(sa_diff.item())\n",
+    "\n",
+    "        st_map = student_probs[1]\n",
+    "        tc_map = teacher_probs[l]\n",
+    "\n",
+    "        tc_output = tc_attn_context\n",
+    "        st_output = st_attn_context\n",
+    "        \n",
+    "        for h in range(head_num):\n",
+    "            cos_sim = cos_func(tc_output[0,h,:,:], st_output[0,h,:,:]).mean()\n",
+    "            context_similarity[model_num][l,h] = 1 - cos_sim\n",
+    "            \n",
+    "            cont_dict[f\"{model_num}_cont\"].append((1-cos_sim).item())\n",
+    "            \n",
+    "            map_sim = kl_loss(st_map[0,h,:,:].log(), tc_map[0,h,:,:])\n",
+    "            map_similarity[model_num][l,h] = map_sim\n",
+    "            \n",
+    "    model_num += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a7e6c1eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mag_dict = torch.load(\"sts-b-step-2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "5ed36361",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "lw = 4.5\n",
+    "ms = 13\n",
+    "layer_num = 12\n",
+    "label_1 = \"TI-M\"\n",
+    "label_2 = \"TI-O\"\n",
+    "label_3 = \"TI-G\"\n",
+    "label_4 = \"Q\"\n",
+    "sub = \"attn\"\n",
+    "\n",
+    "fig, ax1 = plt.subplots(1, 1, figsize=(5, 4), dpi=120)\n",
+    "plt.grid(axis=\"y\")\n",
+    "ax1.plot(list(range(layer_num)), mag_dict[f\"0_{sub}_mse\"], label=label_1, color=\"tab:red\", linewidth=lw, marker=\"o\", markersize=ms)\n",
+    "ax1.plot(list(range(layer_num)), mag_dict[f\"1_{sub}_mse\"], label=label_2, color=\"darkgoldenrod\", linewidth=lw, marker=\"^\", markersize=ms)\n",
+    "ax1.plot(list(range(layer_num)), mag_dict[f\"2_{sub}_mse\"], label=label_3, color=\"tab:blue\", linewidth=lw, marker=\"D\", markersize=ms)\n",
+    "# ax1.plot(list(range(layer_num)), mag_dict[f\"3_{sub}_mse\"], label=label_4, color=\"tab:orange\", linewidth=lw, marker=\"D\", markersize=ms)\n",
+    "# ax1.set_title(f\"Attention Output Loss per-layer\", fontsize=20)\n",
+    "\n",
+    "# fig, ax2 = plt.subplots(1, 1, figsize=(12, 7), dpi=50)\n",
+    "# sub = \"ffn\"\n",
+    "# plt.grid(axis=\"y\")\n",
+    "# # ax2.plot(list(range(layer_num)), mag_dict[f\"3_{sub}_mse\"], label=label_4, color=\"dimgray\", linewidth=lw, marker=\"D\", markersize=ms)\n",
+    "# ax2.plot(list(range(layer_num)), mag_dict[f\"0_{sub}_mse\"], label=label_1, color=\"tab:blue\", linewidth=lw, marker=\"^\", markersize=ms)\n",
+    "# ax2.plot(list(range(layer_num)), mag_dict[f\"1_{sub}_mse\"], label=label_2, color=\"darkblue\", linewidth=lw, marker=\"o\", markersize=ms)\n",
+    "# ax2.plot(list(range(layer_num)), mag_dict[f\"2_{sub}_mse\"], label=label_3, color=\"tab:blue\", linewidth=lw, marker=\"D\", markersize=ms)\n",
+    "\n",
+    "# ax2.set_title(f\"Attention {sub} Output per-layer\", fontsize=20)\n",
+    "\n",
+    "#     ax2.plot(list(range(layer_num)), mag_dict[f\"0_{sub}_cos\"], label=f\"MI-COS\", color=\"tab:red\", linewidth=lw, marker=\"o\", markersize=ms)\n",
+    "#     ax2.plot(list(range(layer_num)), mag_dict[f\"1_{sub}_cos\"], label=f\"OI-COS\", color=\"darkgoldenrod\", linewidth=lw, marker=\"^\", markersize=ms)\n",
+    "#     ax2.plot(list(range(layer_num)), mag_dict[f\"2_{sub}_cos\"], label=f\"MIXED-COS\", color=\"tab:blue\", linewidth=lw, marker=\"D\", markersize=ms)\n",
+    "\n",
+    "#     ax3.set_title(f\"FFN Output Layer {l}\", fontsize=20)\n",
+    "#     ax3.plot(list(range(len(tokens))), mag_dict[f\"0_ffn_mse_{l}\"], label=\"1SB_M_ffn_mse\", color=\"orange\", linewidth=2.5)\n",
+    "#     ax3.plot(list(range(len(tokens))), mag_dict[f\"1_ffn_mse_{l}\"], label=\"1SB_O_ffn_mse\", color=\"dodgerblue\", linewidth=2.5)\n",
+    "\n",
+    "#     ax4.plot(list(range(len(tokens))), mag_dict[f\"0_ffn_cos_{l}\"], label=\"1SB_M_ffn_cos\", color=\"orange\", linewidth=2.5)\n",
+    "#     ax4.plot(list(range(len(tokens))), mag_dict[f\"1_ffn_cos_{l}\"], label=\"1SB_O_ffn_cos\", color=\"dodgerblue\", linewidth=2.5)\n",
+    "\n",
+    "fs=18\n",
+    "ax1.legend(loc=2, fontsize=fs)\n",
+    "# ax2.legend(loc=2, fontsize=18)\n",
+    "ax1.set_xlabel(\"Layer Number\", fontsize=fs)\n",
+    "ax1.set_ylabel(f\"MSE Loss\", fontsize=fs)\n",
+    "ax1.tick_params(axis=\"x\", labelsize=fs)\n",
+    "# ax2.tick_params(axis=\"x\", labelsize=22)\n",
+    "ax1.tick_params(axis=\"y\", labelsize=fs)\n",
+    "# ax2.tick_params(axis=\"y\", labelsize=22)\n",
+    "\n",
+    "    #     ax3.legend(loc=2, fontsize=15)\n",
+    "    #     ax4.legend(loc=2, fontsize=15)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1681,
+   "id": "c29d4dc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f98022792a0>"
+      ]
+     },
+     "execution_count": 1681,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 5), dpi=70)\n",
+    "fs=20\n",
+    "lw=3\n",
+    "\n",
+    "ax.plot(list(range(layer_num*head_num)), cont_dict[\"0_cont\"], label=\"MI\", color=\"tab:blue\", linewidth=lw, alpha=0.8)\n",
+    "ax.plot(list(range(layer_num*head_num)), cont_dict[\"1_cont\"], label=\"OI\", color=\"tab:red\", linewidth=lw, alpha=0.8)\n",
+    "plt.legend(fontsize=fs)\n",
+    "ax.set_xlabel(\"Head Num\", fontsize=fs)\n",
+    "ax.set_ylabel(\"Cosine Similarity\", fontsize=fs)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 5), dpi=70)\n",
+    "fs=20\n",
+    "lw=3\n",
+    "\n",
+    "ax.plot(list(range(layer_num)), cont_dict[\"0_sa\"], label=\"MI\", color=\"tab:blue\", linewidth=lw, alpha=0.8)\n",
+    "ax.plot(list(range(layer_num)), cont_dict[\"1_sa\"], label=\"OI\", color=\"tab:red\", linewidth=lw, alpha=0.8)\n",
+    "ax.set_xlabel(\"Layer Num\", fontsize=fs)\n",
+    "ax.set_ylabel(\"MSE\", fontsize=fs)\n",
+    "plt.legend(fontsize=fs)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1722,
+   "id": "462c856b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "l=9\n",
+    "h=0    \n",
+    "st_map_1 = model_outputs[0][-2][l]\n",
+    "st_map_2 = model_outputs[1][-2][l]\n",
+    "tc_map = teacher_probs[l]\n",
+    "\n",
+    "fs=20\n",
+    "\n",
+    "fig, [ax1, ax2, ax3] = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "\n",
+    "# ax1.set_title(f\"layer {l}-{h}th head Teacher\", fontsize=fs)\n",
+    "# ax2.set_title(f\"layer {l}-{h}th head OI + map\", fontsize=fs)\n",
+    "# ax3.set_title(f\"layer {l}-{h}th head OI\", fontsize=fs)\n",
+    "\n",
+    "ax1.tick_params(axis='x', labelsize=14)\n",
+    "ax1.tick_params(axis='y', labelsize=14)\n",
+    "\n",
+    "ax2.tick_params(axis='x', labelsize=14)\n",
+    "ax2.tick_params(axis='y', labelsize=14)\n",
+    "ax2.set_xlabel(\"Token Number\", fontsize=14)\n",
+    "ax3.tick_params(axis='x', labelsize=14)\n",
+    "ax3.tick_params(axis='y', labelsize=14)\n",
+    "\n",
+    "heatmap = ax1.pcolor(tc_map[0,h,:,:].detach().numpy(), cmap=plt.cm.Blues)\n",
+    "heatmap = ax2.pcolor(st_map_2[0,h,:,:].detach().numpy(), cmap=plt.cm.Blues)\n",
+    "heatmap = ax3.pcolor(st_map_1[0,h,:,:].detach().numpy(), cmap=plt.cm.Blues)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1683,
+   "id": "4f3d6f38",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1600x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(16,8))\n",
+    "\n",
+    "heatmap = ax1.pcolor(context_similarity[0].detach().numpy(), cmap=plt.cm.Blues)\n",
+    "heatmap = ax2.pcolor(context_similarity[1].detach().numpy(), cmap=plt.cm.Blues)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1657,
+   "id": "46ef8f91",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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TMpKTk12iSfPz87VkW40W44IRUB9++GGT9+3Tpw9XXnml+v+WLVtIT08nNDSUhx9+GBCT/KlTp5g/f77Lvs8995xX59DpdPj6+lJZWYnNZqN379588MEHqpCqHZjgXNEiMTGRL774gvj4ePW12gKq9v7On6dLly6AyFmr/ZqyyjYajaxdu5ZnnnmGPXv2UFpaqm57/PhxVUDFx8fz5ptvMmfOHGw2G59//nm7bOR4MRMUFMTjjz/OkSNHWLlyJRUVFRw/frxen2djUDR6vV5PfHw8er0eo9GI2WyuIxgbS1VVFbm5udhsNhftSuPi5IIRUM2locAASZK8bqimmPWcTXaSJBEaGqpqVAEBAaSkpBAWFuY29yg9PR2DwUBsbCwJCQkuAkjp+gt4NAs6r2oVJ7S715TAioyMDCZMmMCIESP45JNP6NChA6dOnWLatGlqKSWFa6+9FqPRSFBQEFOmTPHqO9E4t/j7+9O/f39yc3PZtGkTGRkZWCyWFjFjKwIqMTFRPV5kZCTZ2dkUFBR4dYz09HT27dtHdHQ0119/vfr6999/z+HDh0lISGDOnDnNHqvG+c0FI6DuuuuuRu9jNpspKSnB39/fRUBddtll9O7du87kHxsby/XXX++S+1EbWZYpKirCZrMRFBTkElEnSRJBQUGqrd1529pVIAYNGuRxMnGuelE7R6spRTslSeKXX37BarXy7rvv0qFDB3x9famoqHC7/UMPPURcXBwFBQX87W9/4/nnn2/0OTXODV26dGHTpk1YLBYyMzOb3ZLFYrGo5nTn8kbR0dFkZ2d7HcmXk5NDVlaWukBSiI2N5fDhw+Tm5mopDxoXjoCqzxfkiYqKClVYOAuosLAw1ZfjTExMDHFxcfXWrDObzaoA8aSVKSYRpY1HWVlZo8oUOediOQuo0tJSF9OctxgMBjVkPTAwUJ0Uvv766zrb/vDDD3z++eesXr2aw4cP88ADD3DjjTfSt2/fRp9Xo+U5ffo0er2euLg4JEmiY8eOqvnt2LFjzRZQZ86cUa8/ZwEVGRkJiCAJb+o6KiHmtXs/KRGBZrOZoqIirTfURc5FXR1UieDT6/VeFUo1GAwNbue8IqwvKkmSJDVk3TkXyxs8CaimmG9CQkIIDw9n9OjRVFVV8fTTT7Nu3TqeeuopVqxY4bJtcXEx9913H7///e8ZOXIk99xzD1dccQWzZ8/WKlK0E1atWsX777/PF198AYhrIiUlBRC+xOaimPd0Op3LolAJwLBYLJSUlDR4HE8CKjY2Vn3uHIWocXGiCSga9j81BsVf4+Pj06Awa2oRWm8ElLcCQ6fTYTAYGDBgAO+++y7Lly/n+uuvZ//+/XUq1D/yyCP4+Pjw97//XX1t3rx5HDx4kH/84x9ej1+jdaisrCQjIwPAJaBGCYjJzc1tkobtjHP+k+LHBFwiBBsy8yk5e1BXQEVERKjXtNYbSuOCMfE1FlmWmySgFG1Hp9PV8VFZrVb1mPV1t/3444/VYyko7eRnzZrFrFmz6h2DInxq1w7U6/UMGzaMrKwslwkjJSXFbaJm7dfuuece7rnnHo/bfPTRR3WOkZKSok42Gm3L4cOH1d/LuStt165d1efHjx9XE78bi81mUys9dOzY0eW9yMhIJElClmXy8/Pp1q2bx+MUFRWpz2sLKJ1Op/qzNAGlcdFqUM4ai7cCSpZlsrOzycvLc1shwjnazZukQ6WdfO3xNISnCL7GaGRKQIWzkLRarVRUVFBSUnLO24rYbDYWLFjA//73P5eKGRrec+jQIUBM+s4FlyMiIlSfanPMfNnZ2WoQTu3+TwaDgejoaGJjY100K3c4Nyl052NSzHyaiU/jotWgDAYDUVFRmM1mrwWUUqnBarW6ncAV/5MSBOHtOGo3HqwPi8VCTU2NGjJcuxqFsoptyMRXUVFBRUUFOp1OTay02Wyq/8DHx6dFK2s0xMmTJ9m/fz8gJtEePXowduzYFksuvdCpqalRhU9qaqqLZi1JEl26dGHHjh2cOHGiyc0pFfMh1NWgAO677z6vou4UAaXT6dy2jFGEa2FhYaPuT40Lj4tWg5IkCR8fHwIDAxvVgdZT+3dZllUBVZ95z9vjuSMjIwOj0Uh8fDzJycnExsZiNBrVxwsvvKB+loaO5868qZRL8nY8LUntIqGHDx9mw4YN53QM5zNHjx5VFyXO5j0FxQ9VVVXVpMaCAJmZmYAoueXuGvc2JFwRUM7VW5xRNCjFXKhx8XLRalBNxWAwqBqMMzU1Nar9vzECShEQVqu1wZVtfHw8W7duVW/awMBAAgICXN5XtKqGBEx4eHidbZS2ImazuVFFP1sCRUDFxsbSpUsXfv31V0aPHu2yjZYX4xnFvBccHKy2uHGmU6dOqnZ97Ngxt9s0hMViUUPXm4OnCD4FZ/NkTk6O1gTxIuaiFFCyLCPLcpPMHIrGo7SJV46haE+KZtbY40mSpAZfeMLHx4dLLrlETWIMDw+vU4VCidJqyMSn1+vdao5Kzsy5FFCyLKvJn506deKqq65i5MiRLt9jRUUFH330EUOHDmXgwIFN+u0uVCwWi9rQsrZ5T8HPz4+kpCQyMzM5fvw4I0eObPR5br/9dreLM+dxnDp1ioKCAjp37uxRADUkoIKCgvD391fLHmlcvFyUd7nFYlGjhBpbecFdIIIsy2qAhK+vb6NW+UajUU0A9sbWrhSwjYuLcxuIoQgdRYA2Fufitk3Zvynk5eWp319SUhJAHSG/Zs0aCgoKWLJkCevXrz8n4zpfOH78uLqgcGfeU1DMfMXFxU024fr6+npMKjeZTHz66acsXbrUxV9VexulZJgnASVJkmrm0wTUxc1FqUEpN6fFYmn0Sry2gPLx8XHptNsY8x64RvI1dj9vx1cbm82mBlTUxllIetq/pXH2PykCqjY9evQgPz+fPn36kJqa2upjOp9QzHv+/v51ouucGTBgAN26dVOrTLQ0AQEBBAQEUFlZ6THfqr4Qc2diYmLIyMjQIvkuci5KAWU0GgkJCcFisTQqQAIcuUeyLKuCrrHh5a2J8+fxZOYrLS2lqqoKX1/fOpOENwKupVHMe2FhYR4bQnbt2pUuXbpoPqhaWK1WDh8+DAghXt9iJyQkpFHdlhVkWWbr1q0kJiYSFxdX7z0za9YsQkJCPN4HDYWYKygaVHl5OZWVlS6+1vaKLMscOXKEkJAQzW/WQlyUJj6DwUBQUBBhYWGNnvCUQAKgjoDy8fFptMADR3Xy2oUz3aEEQHgyv9UuHusOs9nsscOqc9mnc+WHUqLDPGlPCppwqsvJkyfVnLz6zHvNobCwkJ9++ol58+apqQCeiI6OrneR5hxi7q7epYJzoIS3FdLrw2q1cvr06VaNTt22bRtffvklH330kcdCyxqN46LUoJqLEulmsViQZVmNnGuq9lRRUaGaRDp06FDvRFxaWkp1dTU+Pj5uc4QUAeopks9Z8/OU52Q0GqmpqTknAqqiokKdtBpT8DcvL4+oqKiLXmgdPHgQEIsjbwvBVlVVceLECZKSkrzSqJzNbPWZEL0hKiqKvn37Nmhej4uL4+677yY6OrrZeVCyLPPpp5+SmZlJhw4dmDVrVotbBkpLS/nxxx8BsbDbs2cPQ4cObdFzXIxoAqoJ1NagwsPD1cjA5hxPOWZ9N2RDfaCU4zn7xWrvr4zT03mcQ+lbO7TbuROyN+HLOTk5LFiwgPz8fObMmdOkcOkLBVmWVf9T9+7dvUqsrqys5J///CeyLHPNNddw2WWXNbhPr169mDt3LllZWW4Ta2tjsVgoLCwkJCSkjk+2R48e9OjRo8FjGAwGl3qCzUFJVM7MzOTs2bMsXLiQm266qUUjQRXhpHDkyBFNQLUAF52JT2nbXlxc3OQK3O6KskqS5PUFv337dmbOnEnXrl2RJIm//vWvhIWFERUVpR573bp1jB49msjISIKDg+nZsyf33nsvBoOBiIgItS2G8lCqS8+ePZuqqiqio6PVFgjOOGtVBoOBUaNGIUkSjz76qPq6IrhWrFhRp95fS6OY93x8fFzMOp4ICwtTHe0HDhxotXGdD5jNZvr160d4eLjX5r2AgAD1e66dHF0ffn5+ahRgfZSUlPDSSy/xn//8hxMnTnh9/JamdnTuiBEj1PvzyJEjdSr1N4dDhw6pC4Xo6GhuvvlmZsyY0WLHbwr1mfHPJy46AaXk+FRWVjZ54m1qFXKFjRs3kp6ezvDhwwkNDUWn0xEQEICPjw+SJLF9+3bGjh1LQkICn376KQsWLGD27Nls2rSJiooK/Pz8VBPFU089xebNm9mwYQMvvvgiK1as4I477sBoNLr9fM5mO+VzBAYGsmDBAvWCVl5fvHixx8aMLYXSgyohIcErAe/r66tOlAcOHLggbsKm4uPjw1VXXcWDDz7YKP/TuHHjuOeee7jxxhtbfEzBwcGqdt8SVSDMZjNZWVmN+p337t3Lm2++6VLTUZIknnrqKVVL37JlC1u2bGn2+GpqalTtKTg4mLvuuqvBYJXWZv/+/bz00ksXRDrGRSmgwLveTp5wFlCFhYVUV1c36gZ68MEHOXLkCB9//LFbR/H7779P//79+d///sfEiRMZN24cTzzxBHv37q1j0urcuTNpaWkMGzaMWbNm8cILL7Bq1SqPFcad/U+KABs7dix5eXls3rxZfa+mpoaVK1dyzTXXeP25msKVV17JY489xk033eT1Pr169QJEPk92dnZrDe28wVPKgCc6derUoK9TIS8vj7Nnz3qdE6fT6VTNvbaAKi4u5scff2TLli1eBREcOXKEl156iXnz5rmEp9dHVVUVy5Yto6qqiu+//97FSmIwGLjpppvU6MHly5erEZBNZfXq1ar/eMKECW0exQvic3br1o2tW7ees1zG1uKiE1ANBQh4g1I0VsHbm0ehPsEoyzJnzpzxaO5qaFIJCgpSowLdJeu6q8EXFBTE+PHj1Q66Op2O9evXYzQaGTZsmFefyWq1NqndPIjP1Jgbu3v37up3eLGb+Vqb9PR03n//ff797397vY8SvFNbQGVnZ7N161Z++uknl9QMTziHoXubsOvv78/EiRMJCwvj+uuvr+OrDQgI4LbbbsPf3x9Zllm4cGGTaxOeOXOGrVu3AqKCh3N+ns1m4/Dhwxw7dqxJx24OkiRx9OhRKioqVBP6+cr5HyRhLoeS+kNfFWRZhoJ8jICvJQBs3rdZr02YtYbKykqRSxXRr9l+mqqqKkpLS7FarfTv359XXnmFV199lZtvvlk1SygJkHq9Xl2l2mw2Nez86NGjvPrqq2p33IqKCoKDg9Xcovoi+KZNm8YTTzzBG2+8gSRJ/PDDD0ycONErQW6z2cjLy8NmsxEREdHoZOXG4u/vT+fOnTl27BgHDhzgyiuvvOii+datW0dWVha9e/emX79+TTpGVVUVWVlZLv2inJFlWTWTKRXvvcFZg3IOsrFYLISEhFBeXl5viLlCREQEV199NTExMfUGw5SWlrpEI/bp04cePXp4DAKKiIjg5ptv5tNPP8VsNvP5558zZ84crwJAFGw2G0uWLEGWZXx8fBg/frzL+/Pnz+fEiRPEx8d7/H5bi86dOzN06FB69OjRYOpGe+f8F1Al+2FFmlebSkB0C53W1/4AsI7dCDSvgCY4Ai4efvhh1q9fz9y5c5k7dy4dO3Zk8uTJ/P73vyc4ONjlpr/33nu599571WOkpqayaNEidDpdnVby9fXAmjRpEnfddRebNm1i0KBBLF++nEWLFnnl6K6pqVE1tbKyMq8F1M6dO/Hx8aFjx44eE3Q90atXL44dO0ZhYSG5ubkurcIvBvbu3Ut+fj5Wq7VJAmrfvn18++23yLLMo48+qn7/ZrOZEydOcOTIEY4cOaKaihtTIFbRoMxmM6WlperE36dPH/r06eN1grxOp2swEm7v3r18//33TJ48mT59+qivNxSa3rFjR66//nq+/fZbysvL+eKLL7jzzju91uS3bNmial5XXnllnXD97t27c+LECUpLS6moqPBYHqolkWWZnTt30r17d66++upWP9+54PwXUO0Ava7xybm1qW1yW7t2LRs2bGDJkiWsWbOGt956i//973/89NNPJCcnqwLqmWee4frrr1dXu3/5y1+YMmUKK1eurNOXyl2AhPM5FTNfbm4u/v7+jBw50isBpRT2rK6uxmw2Y7VaG5yAZFlWfWWXXHIJkyZN8up7UujRo4da0ePgwYMXlYCyWq306NEDq9Xa5OTcuLg41W+6Z88e/Pz8OHLkCCdOnKgT+OPn59eo8lK127/X1kxaqs/Yrl27WLx4MbIs89NPP9G9e/dG5Tf17duXoqIiVq9eraYv3HLLLQ36pktKSli9ejUgOggMGTKkzjb9+vUjLCyMrl27Nil5vymcOnWKH374AYAZM2Z4nRfXnmn2lXLDDTewZs0axowZw4IFC+q8v3XrVu68805qamqYOXMmzz77bHNP6Upob7g63atNy8vLqaquQkJSW1S32BiaSe0KEJIkMWLECEaMGAHAL7/8wrhx43jvvfd45ZVX1G2Tk5MZPHgwAEOGDKFr164MHDiQVatWMXnyZJdzKBNPbR+awrRp03jsscfIzs5mypQpjbqxQkJCVL9CZWVlgxqRYh6Fxq3OFQICAujUqRMnTpzgwIEDjBo1qtHHOF/R6/WMHTuWMWPGNDmKMTIykrCwMIqLi/n555/rvO/v70+3bt3o3r07Xbt2bZSPsLaA8iY8vSGUz6ncs7/++qs6GQcEBHD77bc3Kfl2xIgRFBUVsWvXLo4dO8bixYsZOXIk4eHhHsexbNkyzGYzkiRx7bXXuhVo/v7+XuV7tSSKP9ZgMKhJ7yaTiZKSEqKjW8p2dG5ptoB66KGHmD17Np988onb9++//36++OILevfuzeWXX84NN9xA3759m3taB8YgiGo42RCgRirA7FMjwrnbWafWhipAjBkzht69e3PixIl6BYdyUyjtF5xxjmB0J5wnTZrE7NmzWbBgAT///DNms9nrwAeDwYCPjw8mk4mqqiqCgoLqXQAEBgby5JNPkp+f3+RQ9p49e3LixAny8vLIy8s7b2/CptLY6L3a+3br1o1t27apr0VGRtK9e3fVd9HUKFej0UhoaCglJSVqoITVauXQoUNERkYSGRnpdXWIvLw8Fi1aRF5eHnfeeScdOnRg27ZtLFu2DBDX0cyZM73KoXOHImRKSkr47bff2L17Nzqdjuuuu87jPp07dyYjI4NBgwa1m5p7siyrAkrRJBUrTHh4OL///e/beIRNo9kCatSoUaxZs8bte2fOnMFisag28ptvvpklS5a0rIBqBO4i2NoTioA6e/ZsnRWc0gm1e/fu9QoopfSNO+eoIvg8ff7AwEDmzp3LwYMHGTlyJAUFBVRWVno8V1VVFVarlYCAADWXS2lfbzabG1zRSpLULKGSmprKsmXLVDNfexJQFRUVBAQEtOvgjdGjR2MwGAgODqZ79+5uE7ubSlRUlIuAKioqUi0st9xyC927d/fqOH5+fpw5cwYQVUROnjzJ8uXLAWGWvuOOO9yW/GoMer2e6dOn880333DixIk6Y1u2bBmVlZVqJf3LLruMnj17euVrVYKXsrKyuPLKK+vd9vjx43Tu3Fk1XTv77xri1KlTahsTJQ0jICAAi8VCXl4eBQUFLfr7nita1Qd15swZl+ibhIQE1q5d25qn9Ihzf6O2FlB5eXnq91BZWcmhQ4dYsGABkiQxdOhQHn74Yfz8/Jg6dSopKSnk5ubyzjvvUFRUxO233+6ysj1x4gTp6emqD+qFF14gISGBcePGUVhYqEZO+fj4qAET9fkAnnvuOfV5fd+TLMuUlZVhsVjUyhV+fn7qzVVZWdnqldCDgoJITk4mIyODgwcPcsUVV7Tq+bxl48aN/PzzzyQkJHDjjTd6NBc1hU2bNpGRkUHv3r3p379/s47l7+/fas70qKgojh8/rgoo54Kv9VUxr41z88L169erdRtDQkKYOXNmi026fn5+zJgxg9LSUpcmoDabjX379lFVVYWPj4/qi/O2Knx6ejorV64ERLsTT5999+7dLFq0iNTUVHr16sX69esxmUz84Q9/8EqTVYr4KjlQ4FjAgah2cfnll3s15vZEu8mDmjdvHmlpaaSlpbVKk7L6AgTONfv372fatGlMmzaNvLw8Fi5cyLRp09RyQ3fccQe+vr48++yzXH311fzhD3/A39+fhQsXMmTIEBcN6uWXX2bo0KEMGzaMRx55hNTUVFatWkV4eDjV1dWqyVCSJCIiIggODvbanxAUFOTRl+TcTViJUNLpdOqqsqqqymOSoMlk4tChQy1S8VkJEsjOznZp5dCWKFXps7KyWtxBvm/fPo4ePcqvv/7a4LaH82HRIbC1QbENRaspLy+nurra5bfxJsRcwbl5oXKM0NBQZs2a1SoaQUhIiMvCzGQy0bt3b0JDQ73W+pzp27evqkXv3LnT7TayLLNjxw4Azp49i8lkIi8vj5KSEq/y/BQLAuASKBIcHKwqCEoppvONVp2p4+PjycrKUv/PysryWAByzpw5zJkzB4C0NO/CxhtDfSHW55pRo0a5dW6bzWby8vIYPXo0N954o4sJwfkmVya9hhzker0eq9WqCig/P786ZglP5lkQgrx2GLvzsaOioqipqXHRlAICAqiqqlK7DLvr43P69Gm++uorAO6+++5mFQXt2bOnWmrmwIEDDB8+vMnHailGjRqlmj6b0n/JE0VFRWpos2LG8URpDUxbACU1UG2Bm/vUu3mL42x2KygocBEujV0gKs0LQRRmnjlzZqOEXHPw8/Nj4sSJTQ5GCQ4Oplu3bhw5coRdu3YxevToOhqRJEnMmDGDn376iUGDBhEXF8eGDRsoLi5m8+bN9O7du15TsTvznkJqaipZWVmcPn2asrKyRqdztDWtqkHFx8ej1+vZs2cPVquVL7/8stHhxC1FQwEC7YH6avw55zN5uyqvXXW9Najd4t65J5bSp6g2SpFSvV7fZOe2QnBwMElJSQQHB7e5Zqyg0+m46qqr6phUfvrpJ3bv3t3kyU5ZJUPDvZ++3i+EE8DPvzXpdM3CWUDl5eWp1VaaovUoZrWoqChmzZp1zoSTM80JSLnkkksAoU0ePXrU7TZGo5FJkyYRHx+PTqdTF+lnzpxpsBqEYt4zGo2qeU/B+TppblmntqDZAmrs2LFMmzaNZcuWkZiYyObNm5kwYYLq2Hz77bdVp+g111xz3gRIWG1QboJzWYvUXTNEdTx2AdWYFvE6nQ6LxaK2zqj98Aal+oSzibS+CVaSJFVrqqmpcVsxXhFQ8fHxLSJUbrrpJh555JFW0bwbQ33f6aFDh9iyZQuLFi3im2++aZJ5UzH3JCYm1quZWW3w0W7H/+mnwXKOS7IFBgaq2np+fr6qQTXFH9epUycee+wxfve737WoRnqu6Natmxqp6mzmO3bsmMeOCgMHDlS/P6VGpjuczXvdunWr4/eNjIxUFwvno5mv2bODuxwKxTEHwlzXUBfOc4ESYeat/yWzBMpMEB0AHc6hVqxE8tVuFugsoLzlgQce4PPPP/f4viJoCquguBrig8Gv1hVRVFTk0iCxpqaG4uJigoKCPEap+fv7qyaH2lGANptN7QHVmAaF9dGYLP2Pd8PyY/DKWOjofWWbBrFYLLzzzjukpKQwYsSIOs7woKAgwsPDKSoq4uDBg2RmZnLdddd57dcoKSlRzeUNaU8rTsDpUsf/ZSbYkwOXnMOIaCV829/fn6ioKDZt2gQ0LkDCmdauqt+a6HQ6BgwYwIYNGzhy5AhlZWVkZ2fz+eefk5SUxLRp0+qY3nx8fBg8eDAbNmzg8OHD5Ofnu41WdDbv9e7tPh8zNTWVDRs28Ntvv1FdXd3qpchaknYTJNHaBAUFERER4dYnUptKs7ipAQqqwNK0tlFNwlmDctZUvGlUWJtnn32WZcuWuTzWrVvHtm3b1PyX0hoxmZWb4KybAujKeJT+MuXl5VitVkpLSz1qUkpOFNQ18+Xl5alBBOe6TlhJNby4Djadhlc3teyx9+zZQ3FxMbt27SIvL6/O+4mJidx3331qUnVFRQVffvml1z2ZnM17Dfmf/mtfpMcGgt6+ftjofeunFqN379507tzZpTdRUwXU+c7AgQMBsShcs2YN3377LSD8c56CiS699FJ1QZqe7r4YQX3mPQXFRKqEvJ9PXDQCqjHkOy36bTLku3eltAqKQJBl2eXCbYqA6ty5M/3796d///4MGjSISy+9lLS0NAYPHszgwYOpscCpEsf2ZTVgqiWMncdTXV2tCpfAwMB6tTllIVC7orrzhNySAspkMrFv3z6++eYbtf1BbVacALN9KMuPC82xJbDZbGzcuBEQDes8aUU+Pj5MnDiR2267DV9fX2RZZsmSJV41zlQEVHx8fL0+mL05sFVY15k9EPrba7y2hYBScI7gOx9zcVqCiIgIUlJSAFEFo7q6GkmSmDp1qsdcp+DgYNUlsnv37jpm4drJuZ7cF/Hx8app9Hwz82kCqhZmq1hpgyguC1BQKez65wJ3gRJK+wxonIByPlZAQIBLBr9NFmZMay0lqPak7XzRl5QIaSZJUoNmNT8/PyIiIoiNjXURZIqACg8Pb1GzTUVFBQsXLuTAgQMu2oYzS50WjyYrfLPPQn5+PidOnGDnzp2sXbuWH374gS+//JJdu3Z5fe6DBw+qk/Dw4cMbdKZ37dqVq666ChBtJBTzlyfKyspUR3lD5r0Pd4m//ga4pTdcbl8D7DgLVWaPu7UqzjlQLZkTdr6haFEKY8aMoVOnTvXuoxTLtVgsbN++3eW9zMxMtZhvfVq1JElqhZljx461atBUS9M+wp5aGWVi9fX1bdD+ml8FYs6WSdCf5LQ1BassJu7o1i9IjMFgIDAwEIPB4CJggoKCsFqtjQqRV2ruKaHmzpwpgyr7S3FBwqxZWiM+Z0wg6CTHeBQUTSggIKBBQemcE+WMIqBa2rwXHh5Ohw4dqKioUAVieXk5K1aswGKxUGaSWFcwBXCM+9/rCzm77D+4EyeHDx9Gp9M1WClclmU2bNgAiPwe54ra9XHJJZewe/duTp06xdq1a+nVq5dH7cJ51VufgMophyX2CldTe0GoHwxPgre2CoG8/QyMSPZqeC1CeXk5n3/+uRoaHxIS0m4iLduCnj17snz5ciorK+nZs6dXvdZiY2Pp0qULx48fZ+vWrQwbNkydA7wx7ymkpqaybds2TCaT22oZ7ZULXoOSZVntjaSYpzxhtUGh3bwXIpUQLuUTIAm1Or/y3CQ86nQ6QkNDCQwMVIWAJEmEhIQQHh7eaAenMiE4m5EKqxyaUoivCASJsCfPW2xCUCkokYU+kgkd4hhN1XzKy8vVcOPW8D/deuutPPzww2p1aYvFwt69ezl48CCrM41Y7cIptUb434r1MeTqHYEaRqORyMhI/P39CQoK8ioE/vjx42pX32HDhnkdxCJJEpMmTUKn02G1WtXeQu5QzDixsbH1msg+2+swYc4eIP4OjHMEvpxrM19AQAA5OTnq/xer/0nBaDRy++23M378eG644Qavw9YVLaqyspI9e/aor3fv3l1tYeK8cK22wE/HhMleITk5WZ07zicz3wW/nJFlGaPRiNlsblD7KK52mLyidNmif5TuLCetXTHbxPsR/vUeot2hCLmamhrMZjNmjGSJoB989JAUApIEwT7if5NVBIaEOcnBYEM5/vpCLLKeCkNKo8yMsixjMplYs2aNS0uM1hBQtQWnj4+PGsq+sWowmCFSX87sbrk8e9pCtc2AJXUq942oJiQkRC3VlJubi4+Pj1f5Nor2FBgYWMeE0xDR0dEMHz6cdevWkZGRwe7duxkwYIDLNhUVFWrTwPrMONUWmL9XPL8yBTrbLWm+BrgsAdaehA3nWEDpdDqGDBnCli1bAE1AAXTo0KHRBWY7d+5MTEwMubm5bN68mUsuuQRJkujatavbZojPr4Uv9sH4rvDuRPGaXq+ne/fu7Nmzh8OHD7tUgmnPtP8RNhOdTkdkZCRxcXEuNbZqI8uO4Ah/qYpASdh2Q6RifCWxFMmraFxelCy3TZkZZ5xXadUmC5klYlw6CZJDQa9TtnMI3wqTmPAAMJXgJwv/ikGyEuRTvxZam/LycioqKli7dq1qkvD19T0nhV0DAgK4++67mXLrnZywCk1p+oAgbrphIjf0EmuzdTmhBITF4u/vr35XMTExdYSTu6rumZmZqvBIS0trkvlqxIgRqla0YsWKOo7wQ4cOqZpVfea97w45tOK7asnJYfa1wL5cscg6l1x11VXqRKgJqKah1OgE4c+rLxKv2gKL7fm4K0+4/t6pqalq6aj6ikC3Jy54AeVMfSp1mQlq7FawKN1Z4ZfQ+9u1KBEWVWN1NX/Vh02G34rhQB5kl7sKqu3btzNz5ky6du2KJEk888wzLvuaTCaWLFnC5ZdfTmRkJMHBwfTo0YPZs2erTlHl8ygPnU5HYmIis2fPdqllqGiNMpBb46tG6SUEg79RlOWRJIlHH32UcD9HYEhhFSxdvAjJNwxdlKMhm96U1ygprSwKgoODVQGVmJh4TldvPztF7020m+pvtqeMVFng+wYS7Ddt2sR//vMfNd9EQdGefH193Tat8waDwcC1114rxlJVxYoVK1zeVwI+oqOjPQp1WYb/7hLPe0Q6AiMUlP9lYPPpJg2zyZSUlKi+S01ANZ0+ffqoFoL09HSPoelrT0KFPRjGYhPRqgrdunXjiSeeYObMmedNXtlFJaDqQ9GeDJKFUKkIJD0Ei0ZrYVIhRkmoFHmVDc/PsizCt8tNQjDlVsCxQhGIAKLadXp6OsOHD3cbYrp9+3amTJlCXFwcH374IR999BHTp08nPT2d4uJil22feuopNm/ezIYNG3jxxRdZsWIFt956q/q+v78/AQEBWP0iqTCLnzvSH8KdlMnAwEAWLFiAQScTYjftFVXJfPn5xwQF2vPGfO1JgtYaMBU18G06UNo5PPDAA6ppsKUSdL1Fid5LCoE+drdS/1joaf9IX9aTR37q1ClWrlxJcXExixcvVl/PyclRV7JDhgxpVEO/2qSkpKjmwT179nD8uJhVTCaTV9F7GzLhiD1QbvYAoQ070zvaYbLdUH/VnBYnPDycRx99lFmzZpGcfA4jNC4wDAYDI0aMYPjw4VxyySW8/vrrLF26tI7GvayWcuUcuWowGOq1IrVHLngBVV5ervYt8kSVWQgTgCgpBx2ymJD1fuATjg6ZKJ1whFeaHSsUT5wtd9RBMyA2rrYIIXW2DO5/4EGOHDnCxx9/7NbP8dFHH9G7d2/mzZvHhAkTGDt2LA888AAbNmxwaV8Cwj6dlpbGsGHDmDVrFi+88ILaSh2ElqXzD6PYLCbQAGPdyhhjx44lLy+PzZs3E2m/fquqTSz+cRXXXm0vvhqYDHr7JFx9tlFalMFgICcnR/0N3HXQtcnw+mZ4YZ2TebEFKK2B9fZJeWI3x+QtSY4CqntzhfnLHUlJSVxxxRVERkYyceJE9XVFezIYDC1SYumqq64iMDAQf39/1Zzo4+PDo48+yg033FBvaw0ltDzCHya76cyuk2CYfU2w6Rz7oSRJIjg4mOTkZK+S5DU8c+mllzJmzBgyMzOpqKhg9+7dLqWNaiyOuou+djfxxlPn3qzbklzQAspms1FaWkpRUZHHwqXg0J502IjQ2asAKBqDn1hyR0i56CWhVufVU0atoNJxvACpnFTDXhJ0J9UIuLxKOF6ko6KeRrVnz54lPj6e8PBw1VkfGxtLeHh4g5E/QUFBat6ULIuLU0nGNehEeR+dVHef8ePH8/XXXxNoBF+dlU1rlmMw+jLqCnvBU0kCP3vWp6UKzO6TYT1RUlKC0WhEkqQ6QhZE1NH/bYV5O+HuH1pOSP38myP5eGKtSNwbejhu5Pq0qFGjRnH33Xeri4nCwkLVXDlw4MBGlVryhL+/PzfddBP333+/i7bk5+dHv379PJrHjhfB6gzx/Pa+dUtVKShmvhPFIsVA4/ylR48e9O3bl759+7oEfq3PdCy0H7dHsFtssOK46/7Z2dmsXr1abfHRnrmgBZRz7o8nB7bZCsV2bSdcX4QBCxiDwWBXJQxBoPdHj40ovWi+VmZyn/RYWoMjQk6qJkV/DJ0kEanLo7thP0GSmNRrrGJi8TRRDBgwgOXLl/Pqq6+6VDJ2J5xsNhsWiwWTycT+/ft59dVXGT16NL6BofxW7JqMmxQiIvXcMW3aNNHx1FJJhHSW5d9/xZUTb8Tq54i8wzcSdPYbojq7zmfPrfAcFNKnTx/++Mc/8rvf/a6OOUyW4d9OOYjrMuHeJWJF2FwUk0diCPStFTUe6gcT7ELr+0OeE1klSXIZ86ZNm5BlGZ1O51Uui7ckJSU1WtgpZY2MOphRT8rWcCeltS2rSmg0ny5dujBlypQ6nSGWHRN/I/yFqTfF7j1YWsvst3z5crXkWXvn/BdQ5nLI3+L2Yc3ZhLH0V/XhbpvSM1vwK96Cf8kWooqXQ/E+qDjp2KZgK1RmQvE+IouXE1iajn/JFkqynI5jLqfKLIQBgF6y0kl3FANWCEmFgER8MNFJf4REfYaqieVXCud97fJCjz/+OMOGDWPu3LkkJyeTnJzMQw895NJbS+Hee+/FaDTi6+tLnz59KC8v57V3P+NogWM15aOHlDAIrsdNMmnSJAoLC9n0y0L8a06zdsUPXH3ddCrMThJN0qkaJeYy8d0jtMaMYhEMUp92qdPp3Dr6N54SZjaARLv5cc1J+N2yut9NYyirgXUiyM7FvOeMEixRanLc4PUhy7JaGaFv376t1vrh7NmzbNiwwaMzHIR2vNBeNOO6HiLB2hPJoSIwBjQBdSFissJKu6Y0rouwligWg42nHNVxwFGbb9CgQfVeX+2B8z8PqmQ/rHDvA/C3P+oj0v7wBgPQxc3r5rHpZEiXYZNBQiZFd1SEpvvFCk3M4A+GAKTyE0TY8gnWl5Ald6XUGgiyuHhMVod2ExYWxqpVq1i1ahVLlixh8+bNvPXWW8yfP59ff/3VxYfzzDPPcP3112OzyRw6dpIXX/wLM2+ewic/bMRoNBAdADEB0FDQXFBgIOOvuoKvv1tKbu4g/P39GTx0JIu/POG6oW80VGWDbIXqbArMXVWtEYQJM8IfjI1oJPuOfSEX6Q8/3gaPrRB18375De5fBu9M8Kz51cfPvzkiMyfWTRcBRI5Q5zBh+vpiH9xYfyUhTCYTVquVkJAQRo4c2fhBeUFNTQ3vv/8+AFu3blU7Ldfmy32OaiBKYq4nJEmEm39zQExYsuxeYJ83yDJYK8FULIJ2TMUisCm0F/i0YJn62lhNoPdpeLtzzIZMscgCmGC/1id0g3e2i0XwihMwzZ5G16dPH/z8/Or1a7YXzn8B1Q44WwZme9RmouE0gZSDzgD+Tgl5xhAI6QXlxzFaKkiWDlJsEOYzGeEn6hzumDSsVquaJQ6wfv16brvtNl5//XXefPNN9bDJycn0HTCYM+XglzyEF2O7ctPYgWxbvYRZN0/G19tfuCaPaddewWPPvkl2fhmTJ9+AXq+njsVOZwA/IaQKqo1k2RdgBp2wd9tkyK10rNYbYle2qC4OcOcAUdninQlw31IhoFacgAd/hLfHN07ogat5r1+s+20kCW7qAy9vgG1n4GghdKsnGtrX15c777yzVZteGo1GEhMTOX36tFr9vDZmq2gdApCW4IhOrI/hdgGVWyE+Z3cvVmabTwvz7dWd206g5Rdk85+fd5Fo28fUgPkEW04LgSR7sAEHJkNYP/ujr/gb3E1cu01FtsG238GxD+DS96HrnKYfqxVQtP8wPxhqD4jpHS0055MlwsynCKjAwMA6wslkMtXpJdUeOP8FVGhvuLpuKXrFFCMjE+AfUMe2L8uQWSo0F1+dhSTpqMgBCu4MOje2MGs1lIsQmRypE6UWPxE3YIBSH2EnivUtJ9xqL+3in1j3htD7QEgPqDyFVJ1HODnoJLHErzCLiSPWLuhq+8xGjBhB//7963TFrDDBkUJHUF23bqIoZHHWEe+Fk80ClaeYNG4Esx96gQWLfmLlyp/xd9rfZcXtF0t+pY0zNqHJGXVCuOZUCLNTYSVE+ePV+RXfU6ARZtp9KD56+M8EuGeJMPX9dBweWg7/ukYIQm8oqxE5ISBWlPVNrjemwj82CQH71X54ZkT9x27tjsw6nY6ZM2eSlZXlNuIR4P1fHe1RZntZwEJJ2AWhRTUkoLZlwS0LxQLqxdH1+7hai9OFldz+VQ2/ma8BruEf0r1M8/uIWX5v0cngwSZbcVI8sn5wvKbzhbA+0OuP0HFq4wey+2k4JrRa9j4Hne9w+GPbGLPVEQhxdWfHQk6ShJnv39uFhlVSA6FuprazZ88yf/58JkyY0GArl3PN+S+gjEEQdVmdl60WCyazcGxIYWFQK8S1vAZK7Kv/SMMpJHztWk49RRT948BcRqgUTI5ZCAIlHzvcz0qM1V6p0xAoAgrcIensIduBUHkSPVYMCN08p0ImyEeiojiX6OhoJElSqwiYzWaysrLqFCMtqrYLDyAqAE6fEk6Jjh29LCUk28BSBrJMYFAgc594jIOHjzFq1EiKnOzWlWYItC+w8quNnLHahRMmOoeCr8GH2EBhrpQRwqqhhoBHCx2JhLf3E0ELCr4GeO9aEdG3LlOsAPUSvDHOOyH1i7N5r/46mkQHwlWd4cdjwqfzxFDvhGtrYjQa1fYMtfn2oKOfVc8oGFt/QWyVmECRyHu4QISb3znA87bVFnjyZ1QN+oV1MLgD9Gz9AiAqR/JtzPi6imyzI3+qQg7m46o/8EnVg1wZk8nsbhlcHm9C8g0XJr/ivfbHHvHXYpfithoo3AEbpsOl70LXexoxkHfgwCuO/6vOwOlF0HFay3zQZrLptCOtZUKta32CXUCZbcJHNbWW/LHZbGqH54ULFyJJksecu/LycpYuXcrw4cPdRuK2Bue/gPKAcwSfuxp8eWpiro0w7B563wbuPr8YMJfhJ5cRYjRRahYzdpAPJOhPISl9tQM61rtkz8vLY+3atWCtprKqmuzjO/nlh6/wDQjBeNU4nrxrDpIE48aNo0OHDuTn5/Ppp59SUFDAvffeCzgqWpw+eYL9v6bTIUhmf9ZJXnjhBRISEhg/fnzDX5K1BqxVINu1y8AUnvvLi+rbYU6VJQqqhIDKr3REHxox0Vl/GF9zGPgk4WsQ/qcCe4fe6ABRrcIT79q1J1993fI8ILTTDybBXYtFHbnFR0SI/OtXO0o0eUIxeSQGi6Tchri5txBQhVXCd9WQUGsr1p6EJ+xNrGMD4cNJDX8XzlyeJATUZnsbeE/C/l9bhF8OxDVQY4X7f4Qlt4hcutZmdzbcsbCKIotY6D2dOJ8hQ2/lo90Sy46BxSbxS24yv+Qm0yNSCNsbUsEv5grHQWQbVGQIQVW0B46+A9U5sPVece33eLDhgZz6DrbbtwvqDDYzVJ6CI2+3GwGlmLJDfOtWEelTy8xXW0DpdDqmTp3Kp59+Sk1NDQsWLGD69Olqew6FQ4cO8cMPP1BZWUleXp4anNXanP9RfB5wbple21xWbnJEuEUaikRirs7YsHPVGAY6IZRidWfQSeJm7RhYga5GhKDjGwXG+kOF9+/fz7Rp05h28wzy8ov47oefefTum/nb3N9htumYOnMOfn5+vPLKK9x66608++yzBAQEsG7dOoYNG0a5SVxwAB/+62VunTCU0VcM45FHHiE1NZVVq1Y1HF1mNUHZEXETAwR2BF9X54te55iMSmqEVqQKJx109s8WwSA1ecJMiGurjmw3HXoVskphkd1aObWnmGzd4WeAeZMciaaLDgv/lHOl5tqUm2BNhng+3kP0Xm1GdHT4zb7Y1/D2bcGeHPHZLTYI8YFPJ0NCSOOOoUxgZSZH5GRt9uXCu/YUmREdhXkPRGrEc2uaMvLGsfEU3LLATJElEB1WXol7kXuvn8Il8RJvjYeNd8L9QyDcrnEfLoA//gKX/7dWpQxJJ4RK4vXQ988wdi34x4v3dvwBDv6z/oHkbYRNt4KSuD/qJ+j+gHgvd50QerU4lO/a8LS1MVsdVoirO9cNJlLMfCDypErc3Dfx8fHMmDEDX19fbDYbX3/9NUeOCGtQTU0N33//PV999ZVav89dgdrWQpI91fhvQ9LS0jy2OAYhzZVQSU8UFhZSXV2N0Wh0CW02W4VpyWIDvSTTQ79b5D75d4AAL9TWqrNQKcK95dDeoPdDKj0ElgoRRRTWp/G2aVmGmjxOlekosokE4USfXHyNfpSUilk+MDCQ0NBQKs3wW5HIbdJJ0CnMYXrzGpsZSg8LvxpAQBL4u1czqszi+3JG8Tn5UgklohUE/vEQIG7+7HLhTwOx3ekTdX+v59fCR7vEZ1gzE5LD6h9ypRnuXAzp9oCKLuHw/rXQ1U1Aw/eH4Q8/ieeLbhItJ7zhzXR4QxTeZv2shk2U55KMYpjytdBOffTw2WRIa0LFqLIa6P+euH6eGAoPXOr6vtkK130lakgGGGHF7UIL/f0yh1b6f+PcV6xoCZYfhweW2TDZdPhQw/9FPciEKX9xDTiyU20RRXI/2iWEFAgz8LMj4Y5+HhYmZcfhlytF6ghAvxegzzN1tys5BCuHiQhBvT+MWS1cCTUFsChR3Dtd7obLhF/qQJ4ItFmXKToDfHQ9DIlvme+kPjZkwm3fief/nQRjOtfdZm8uXPuFeP761Z4jVU+fPs1nn32GyWSixBiHofs1hGWtwFwsapEGBwczefJkOnd2c5JmUN98f8FrUM5qqE0WmodiiUvyKxTCCRyVIxrCN0q98qWaPKSaAiGcQEzSTXGcShL4xRAfFqBWTj9jikRXcxajJD6HLMtUW8REZbWHsycHlhNoyxEC01yPuuKMzQylR5yEU4JH4QTCROds0lGFkwEwBAi/HUB1rgg9R5j2FLOTOy2qoNKhpVzbrWHhBGIMn1wP0+w31/EiuP6rulny4EhMjA+CAV6Y9xSm93Zofy+sg7e3whvp8OpGeHG9EKp/WgVPrBSh8eeqQ21eBcxcJISTBLw5rmnCCUQunNIG3l37jQ9+FZMtwJPDHO1Y/j5WREMCPL1KXIctzdf74b6lMiabjgDK+Sj8RiaM/51b4QRCu76lDyy/TWjZIb7i3nhujfidzO5y6IK7wFXrIMieMLLnz7D7GdfSXVVnYc01QjhJOhj+tcPP7RsJybeI5xn/40xhMY+tgAmfC+EEQjud8d25yTdTzHvBPq7J2M70iXYstmon7TqTmJjIbbfdRoVPLIsD7uCrrGS+sN5AlRRInz59+N3vftfiwqkhLkgBZbPZ1Lpvzua9s+WOgq0xgTIhVvty3BjqqDPXEDoj+Nib7dTkQ5X9GHo/EX7dDPTGAJLCDEjI2NBz2ppIhE8+oYYSfCnmt0KzXbjKJOlPEFx9CCpOiRuq9JB4mIrVm02pMqE+TNVYig5iqSkTPjr/Dh5vfmeUBFAX4aSg7C9bxPeBEE4x9piUSrPjO1f4eLejlNHv3EdRu8XPAP+4Cl4YJXwn5Sa4ewm8ttlRwaKiCeY9hfhgGGn3x684Af/YDG9uEbkkH/wqVur/2wtfHxBBCrO+d5iKW4tyk9AcFZPu86Oa7x9TzHy/nnUtKXW8SHxeEFqnElUJIvrrLXsUZYVZ+KNaotKHwge/Ct+aTZYIlQr5PGwMw0fPhoiGQxQlSQS5fH+T0KwB5u+D279ztCBxITBZmPtC7H6W/X+DnU+I+8ZcBmsmiihAgCH/gYRrXffv/gClthBeKfkzo+YHsuCgCCbxN8BtfYRPtcoCd34Pq35r8lfSIBabiHAFGNvZc2CPJDnyANdn1t+RITy2I5tj7sIsCftpqT6KTfEPcOWEG9uk0OwFKaDcBUgUV4uVO4ighlhjsdAmoPGCxdeedCLbVN8LgR3FaquZBPjoiQsSs2qVHEiOLQEfvYmTpjjMsvgsCbqThElOFcWVWdhcDmXHRPJydT6zZ9+J0Wh0PHz9MUb1xxg3FGPcUIc9vgFCfEX0V/dINzeBIUhELQJU5ag+rcgAR7hrcbVDay03OfJ3RiVDr0Z+9ZIEM/vDF1OEpgbwr60we7Gwrzcmes8dD11mTzS2+99CfEXIfIcgsQrtEi60CoD0LDEJurPrN4RNbrjeoMkKv1vq8BXdPxhmKekrZcfgx4GwaYZw+DeC4XYBVWNvA6+M548/i9d89PDq2LrBF5d0EGZBEH6qVzY26rQqhVWwJUs0WPzLWrhpodBQAWJ1WSwIG8HAQddB0pRGHbdzOHx3k2ORkZ4F133pqPTuQkACjFmLHNKHg5a+vLw9muHvFdD/PZkbTrzN42Uf8m7wT6zU3cOJIsf1a7LCR5mXcEXxSf5d9RQ1NiM6SebWPrB2Frw0Bv57nRBWNVaRKvGjFxVKmsLWLKFVgyM51xPKvWCyivYz7rDJ8OgKyCwX80zPILEqOlnhx4xF3rcaakkuSB9URUUFJSXiy42Li8Nk03GsUPwARr1IxDSUHxEFT3VGkcjXmKW2LEPpQbDYJZ5PuNqaoyWQ7b2k1FJFUg0mWWh4HXyLifYzibwOva8I2pBtQnupznEIXSDjdC75ZTqhIVZlOcbrGwF+sQxuYg+jOpiKhG0f7CZDoVUVVsHpUjibcYiiwFRu7gPv7YCXRCFwvpkKlzYjWjW7XAQN7LSXBUwJFYJxx1khUDbNrlsYl+P/hZzVwu+grKAbidUmQrAX2MsM9Y6G/93gXbdlWYbvDsNL60UkaVyQiLJSH2Hib8dQYVL8zt6de2pP+OdV9stUlmHVWMhZJd5MmATDF3hd4aDGAv3eEwLyd4Pgj8Phsz3wzGrx/qNpQlADkJ8utImoYRCYhE2GOxY5zFkfThKrd0+cLhWRh/tz4ViR8Ge61WqAZN0x/hd2FR07D4Nh/2tyZrDFJr5fpcp7kA/8a5yrf+ZUqai/+P0hC0eKGg5mNupEubAqM5x2qpwy1mcxc0eF0r2na1WRbVkwa7G4h/X2yNPJqYDNKkLf3QRkVZhEmsOyY3BFR7hvsJvr14lnVovfLdAIv97juVAwiEtmxMfic4/tBB9eV3eb/9sCr9un3Rt7wmtXwQvr4UN7vcfBHeCzG1o+irO++f6CDDOXZVlt4iej42SxEE6SvYuswVbpqMbtG934G0GSRBmj8t+E1hTQsv2NJEms0pVgDkU4xQRCdFCYmx10IkfLLwZMhaIUkbWalMQYUtSN7M98o4SJoyWTTY1hwh9lqRT+MH0A+IQS7ueIaHpjizC5zXO62JsjnEBM7l/dCM+tFT6tjBLxALGidLm5ZVkkWO57Qfx/djmMXu6VCak2ep0wNfoZhMlvfx5MXwDzp3iORgRRceTpVbAqw/Fadrl4bKlbZlFlVDL8fYzTT5b5jUM4gUhI3XQLXP6lVz5QXwNcGi+EzMZTIqLyZfuiITXSyex64hNIvxM1GyowBV3MFbze42rG591EXpWBx1bCT7c6WriYraIix+oM8agdYFOb6ADoFpBNn4rPuCfgn0RHd4K0D5t1fRp0IlCiWyT8ebUQEnf9AI8PFT647w+LRYx9awCMmBnls4wu+kOcMI7guG4oGSWSWmjZbHP9LP1ibDwtT2Uo30H2NVBLQA1JgM9vgBmLhIb98HKoMtVwS85YIfSH/Q+SbwKEP+/T3aLKh1KuaPNp2JUDb1ztPgjKahMdAADGdKpfOIEjmu/dHeJ3L60R1gGFn084hFPfGHjpSrHPn0dAtVmYTLefhTk/CA2xofO1FBekBgVCSNlsNk6X6VUTTEIwRPrboOSgyP+RJAjt27TaWrIM5hKhwRhap89NaY3DGR3pL3wkXt23ytiqs12DJ3wjILBT69SssdYIrdJmsddE6wl6P0prYPOuQ9yTnkr/WNhtL7Tx3+vEjdVSfL4Xnl3j6Jy7cBoMViyYsgw7H4dDr7vuZAyBkUshZniTzinL8LcNwn8CQoP7fErd0G9ZFu08/rZeONBBLJRu7SuE08li4WM6VVq3OG7/WGHOVCcpczksSRUacWAKhPSEsz/aD3ozDP0f6BquCfXuDiGUJEQ9wvQsIdAXTbcHUdQWTrXYYBrD7SUrkNFxafgZpg6IYPUpP5eWD84kBouIy64RwoKh/A0tWQurrwabSUSTjtsqFlstxJYsoWV70trSEuD6HjA+pYzwA4+KQJ9B/wfGYExWUQD6eBEcLxQaYGkNXNcdru0Our3Pw76/iANde9htkv/BPBFlp5jing96kDv930bWB7K+7wE+OtqR1Rmu33KIj0NQ9YwSASCJta6pLVliUQTw7kQY70Xk954cmPSleP7G1TDFOeDoS3FtRvqLXLd4p1JlNlnUx/zWrs1fmSKS6JtSH9Md9c33F6yAAhH9pJSDCfcTP7JUmSkizkD4jZTq3O2U0hqhRYX7NVGumMtFnpLOCP4JrVtQzVwqIgRBhOaGpCJLetbtOMTMjY7fKzUSfrqt5Yfy61n402ox8b05zq5ByTbYdj8ce1dsFNobuv0OdjwsAjv0/jDiW4i/pknnlGUR6fd/W8X/CcFCoCiRiZklIkdHieiSEEnJjw+tm8RstdkFVol4lJtgei/XChvsnAsHXxXPr1gMHa6CtddB9krxWqeZkPZRg/5Q59BjhXsugT+NwFU4+UaL1X5FJuStF/k/FRkAvFrxIu9U/snt8f0MIhhjdAqMSnH47VwoOQArLgdzsfBlXrUewgfUO+6mkFkiVv5KKHqvKLg+VQga54m40VSege+TxXXU4yEY9KbbzY4Vwq1flZBjEma96X4fssM8jONWR7y3XhJCZtYAocHM/dmRJxjpD+9NFFqZwnNrhC/X3wA776k/IV5BlmH4x8Lsqpj5ympENOzxIqF5zr/BfYSoxSZqYiqpBhO6wlvjvS89Vh+tLqCWLFnCY489hs1mY+7cucyZ41pIcdSoUWRnZ+PnJ+60zZs31xsR0hICqtwk8oVkxM3SNRx0lhIos8dZ+oRCUANF2jQaT1WOyLQH4ZsL6szu/Ye57hfH79WaeTQu2Cyw5S747VPxf/glwqznFwVZS2HDVBFurzPCsM+bVqPNzrvb4WV70EBMoLjRN54S0X5KFGPXCPjHWBFs0CRKDsKyfmJCjJ8Io5aI1y2VIvIsd434v8scuPS9eoWUTYaB7zu6rSaHinBt/1MfQ/psVOE0ZpXI7XOmIhNy12PJ3cBNu+awvXqQOIYxkyt7RDG6awCXJTRgBqo6C8vTRD6SpBeabPy4pnwrXlFphpUnhEbiTZFcr9lwM2R+JbTxyVmi9FptTnzCyY1/5dbiXzhtS3F5K9JQyi0DQ7i9r2una1mG/2wX14+M8IG9OFp0gbbJwgqaUyHSNN6Z4P1wX9og/MA+eth+t9CMVtqDJp4fWX/5K5NV9GlTTNRTUuG1q+v3k3lDq+ZBWSwWHn30UVatWsXOnTv5xz/+ofbLcWbBggXs2rWLXbt2tXq4otmumsuIlUlyKOgwQ4U95lNnFOYRTTi1PH4xjooUpiKozsbXIMKAQTj/r62n3GGLYTWJKgCKcIoaCmN+EcIJIGGiqAxgCBaBJRtvEgEU3mAuF+H8Ttw3GP4ySjzPrYBx80WQQ6VZXIP3D4GltzRDOMmyKLkjW0SAzKD/c7xnCICRP0C0vfvx8Xmw/QHX3J5aOLeBB+Hj8ko4gbA8dLoNw2X/4dNZl/Dv1E9YHd6ddWHJPF99GSNjc+sXTuZyIVCVZNlL329V4QTCsX99jxYWTuAol2QuhYzP6r6fuw623k2y/gTfdLiZLqEidLOP/3FeC76DTWExPBH3sYtwAjE1/X6IMO8FGoXpeu4vIupxa5YQTlC39l5DKOHmJqsIdlGE09SeThGiHvDRw38mOq6bbw/BM6vqvcyaTbMF1NatW+nduzcJCQlq6/AVK1a0xNiaRJ1k3FDw1cvCLKGGhKe0m0rEFxySJIIwFL9cpYgefO1qkfj58fUtYxaoF2s1rL9RBBMAxI6G0SvAJ8x1u9iRYhL2jRSmwC13waE36x7PZoWCbbDvb/DzSFgQDt/GwtH3XDab1V+EZ0s48rJ6RsH3N4vP3izH8qkFkPOLeN5rbt2oUWMQjFoGkfbwu6P/gV8fqXf2+N1gETY/dxgMM39cSzitdi+cahHoKzHx6pl07nu9eKFkn6jUUJXjfgebRRRsLbJHy/R5FrrMbvA87ZaoYQ6z5JG3Xb/vsmOw7gaxADIEET/mfZbdbmDtHbDkjiimRm3ET6qBbb8XjVLdMLYzfDfdYSL97y5hrgRxPY1Oadxw+8U6/FlK9Gu/GPjblU7rdZsVzq4QpaCq8132V0qPDbIvtObva92E5GZPFWfOnHGpbJuQkOC28+utt97KwIEDef311+u815JIOKJTYgLtz2vywGQP7/KLad2GZhrCZBPUxdFupDqHUNMx7h/iSKR0oeIU7H8ZlvYVuT05a5p+bksFrJ0EZ+zmrw7jhfnInekFIHIwjF3nyAn79RHY87wwYx3/EDbcBN/GwPJLYc8zYkUsW4RTf9t9sOUelzykm3rDvyeIChaPD4XFN9dtNd9ozOWw4xHxPDBFtIxwhzEERv8kTJkAh/9PBIdUnnYrqPrFwqqZ8PvIj90Ip97ej0+SYMCrQnCCyMP7ZbSIJnVGlkVPJSWoo9Md0Pd578/THpEkR32+kgMOM6upCNZeK6JqJZ2IsAzvh59BhKtLvqGiQoXORwRsbZjmsRpMjyhxHaXZp1kl0GZ0SuNDviXJNWcqyl8EPPgZgPIM2PMcLO4Eq8eJ5OXll0LxfpdjBPqIhWa/GGEW9FTBoiU4J4m68+fPZ8+ePaxZs4bvv/+epUuX1tlm3rx5pKWlkZaWRm6uhyqWXiBJQjB1jbCH/FqqHD4RvX+Lh4Q3he3btzNz5ky6du2KJEk880zdWmDr1q1j9OjRREZGEhwcTM+ePbn33nspL3dcxJIkqQ+dTkdiYiKzZ89u9PdXU1PDyy+/TM+ePfHz8yMmJoZbb72VY8eakWGo9xWFOgGwwbrJIktfwVwOJz6FX8YKR/Pup8Xqu2iXmNy2zBE3eWMo3g+rroZse7nvpClwxXeio3F9hPaCqzY4xrvvL2JMW+ZA5tdikgEheKMvhz5/Fv5LgOMfwM+jhMPczoRuQmt68NIWinTa/6KI2gNh2qvv8/iEwZUrRG4fiMjFRUnwdZAQ/htuFpPQb/OhYLtowKcIJ7+YxgsnBUmC/i9Dr6fE/6UH7ULqrGOb/S8J8yNA3Fhh2rsQzOzJt4KP3ax9+C2hMa2fKupdAgx8XZiUaxNxiSOwovSQEN4eNN4If5GDdJuTUjuptqlctomF1dmVwlydtVTUFFTKmtm5saewYhh18O9raogv+BJWXQWLO8O+vzrmSxBukZXD4MxPLscI8YVvp9fvs2oJmh3NHh8f76IxZWVlcemlrhUoFQ0rNDSU6dOns23bNiZOdP3B5syZowZXpKW5b+HeGAKM2H+wE45ue0GdWqTaQ3PZuHEj6enpDB8+nPz8/Drvb9++nbFjxzJ9+nQef/xxDAYDe/bs4dNPP6W4uJigIIc28NRTT3Hddddhs9k4cuQIzzzzDJmZmfz8889ejcVisXDttdeyZcsWnnrqKYYOHUp2djavvfYaQ4YMYfXq1QwYMKBpH9QYIkKHyRGr6vQ7RQTdb5/CqYWOGoYK0SOg9IAoyHn8Q8haAoPfgqSp9U9khTtFuZpTCx2vpdwGaR9730U1qJMQUquuEmNVX+8CHa6GuKuFqVDRvlMfhU23wZllUJAOPw2CEQsheph35/OWkkNw8DXxPH6CSMptCN9IuPJnsQpWTGnWSiH8i3a538cvBq5c1TThpCBJ0P9v4h7b/zcx6f4yWhw35xehgYIQniMWtsvW6U3C4A9d7oKD/4Cs72HjLY48tW6/gx5/8Lxv1/sgZ60ItMj4H8SM9Nit10cvTHEjEmvIK8hmgn4D7DksBGHpYdGdwOounl4SCfRBXSCoM6lBXVh6xQB8Sn+l89Y3ai0EJXG9d7kLagqFL9NcCmsnwiX/Bz0eULdsbIfrptDsKD6LxULPnj1Zs2YNoaGhDBo0iE2bNhEZGam+X1xcTFRUFCaTiSlTpnDHHXcwbdo0j8dsqTBzKk6J6goAgUkiubYdYLPZ0OmEoExJSeH222/nxRdfVN+/55572LlzJ9u2bauzr5KEDEKD+uCDD1yiJj/66CPuuusuSktLXQSZJ/7xj3/w5JNPsm7dOkaMcLSSraqqUhcae/bsaXoXWVnm0O71pB4Y6f79oK4iNLrTDAhKgeo8YWbLmO/YJmESDH5H/IbO5G+BfS86zHkgAghSH4F+L3qVD1SHmkI48paYsOOurr9CiM0qkn/3/81+biMMegu63dv487pDlkWOUPbP4nNN3N+4iiU2KxTvdprAnCYy58VBSwin2uPe+7xYjYPwSVZmCdNoQKLogO1N54DzifLfYHEXXDKa4q6CUUsb9nebS+GnwSLCWOcL47ZAuFPEgrVGJPfmrBaCryDdpWJMixCYAp1ni07BgU42u5zVwp+rCLFuvxdaXwv68Fu1koTBYOC1115j9OjR2Gw2nnzySSIjI5kwYQLz5s0jNDSUcePGYTabsVqtTJo0ialTmx7O6zWmUodwMoY46ue1AxTh5IkzZ84QE+N+vA0JiqCgIGRZVovlNsTbb7/NuHHjXIQTgL+/P08//TS33nora9euZdSoUV4dz82ARfWK8AGO1bsxVCSVdpopouucP5OfPe8mZYbw8VRkiEoJOauFCanb7yB/o6gIke2kJeoDxHs9H/OqAK5HfCOg73PebavTQ/8Xhalm80wx6W+7T3RuHfyW9wWIPXFqgeMz9nqy8eW0dHoxtohLXF+XZdEVtvSwKGMUN8Z1UmoukgT9/iI0qb3POwqvGkNEIMeFJpxAaOAJkyBrsfg/pKfdx+TFRG4MgeHfwIo0YY7bMA0u/UBc59mrxN9aZjoVQyAE9xBlu4K7i78hPcSioDoHyk+IMmTlx8Xz8uNCmNpqhDBMmiK0pdjR7q1LsaPh6i3Cn1Z2BI7+WwjS4V/XDTpqBVqkYMV1113Hdde5FndatmyZ+nzHjh0tcRq3VJjgSO1yKjYLlOeITrGSXvgWKlvP1t09ogk9mephwIAB/P3vf+fVV1/l5ptvpmNHz5OHUrHcZrNx9OhRXn31VUaPHk1oaMOBIJmZmWRmZvLEE0+4ff/aa0UV5/Xr1zddQIG48Ef9JC7u0N6QeJ2o/l4f8eNg4j7Y8ywcflPUL9vxoNBWqp2c74ZgEerb4+FmV5NvMklTxCSx/gZx8x7/QHRx7ThNBFPYaux/TSL8Xflf7y+0F79Y+yPG8Rcd/PqoOH59gRFNQbKbfFpbUPR9DpCElqkzioTosL6te862pNeTcGapCDQZtaRxE3h4f6F9b71bXEO/jKq7jc4oFnQxo0X1k5CeIrjH06LVN1L4V2sj24Rv0BjqOXjImZBuMC5d+NVyVomk8BVDYeSSFq1B6o7zvhbfkUKY/FXtVw1AM3sSNILGNMXzhscff5x169Yxd+5c5s6dS8eOHZk8eTJPPvmkS8QkwL333qu2gQdITU1l0aJFXp3nzBnh2PckAIODgwkLC1O3axb+sWJV3RgMgXDJa6L/zta7hQamCCefcCGUejzoaH/SloT1FmV6nP1SBZ7N1A0iGYRJDIRJpZXKabU6fZ+FDtcIzTTYi3o85zPRl8N1x8XE3xTtostdIkpUyaeS9BAxBOKuFJpM1LCWuQ4kXeMXJz7hIkJ0+wNw7H3hX1xxmVh0xFzR/DF54LwXUBciYWFhrF27lg0bNrBkyRLWrFnDW2+9xfz58/n1119dBMozzzzD9ddfjyzLnDx5kr/85S9MmTKFjRs31ml1f94SOVhM/of/DzIXQtJkYQs3NqdOTSvgEyYSZvf+RUTPWSrsFeftVeeVh97+11Ipym5ZyuoeSxFOHcZDgpvS0+cTUZc2vM2FQmBy0/eVJLhsnohw9I2CmBHt6xrXGWHIu0Jz2/mYCGZaNRau/EWMtRU472ew7hFCg6mDpUKYkaTWDzXpHtHyx5QkiREjRqi+oV9++YVx48bx+uuv8+abb6rbJScnM3iwKD89ZMgQunbtysCBA1myZAmTJ0+u9xzx8SL3JzMz0+375eXlFBcXq9u1KToj9HxcPNozkk5oin2f9z6EWhFU1bnCb1Bj/2uzQPf7L4xQbA3v0PtA55ltPQrPSBKkPgzB3WDjzcK/qSSHtwLnvYAK9PFkXqun78F5yJgxY+jfvz+HDx+ud7sePUSPoyNHjjR4zI4dO9KxY0eWLVvGAw88UOd9JV+tdgCFhhc0RqgYAkQEY1BKa41GQ6NlSZgIV28Gv7hWTRdo+6QgjTq4S7Strq4mKyvLY3SfwsGDooteUlJSvdspPPDAA/z4449s2rSpzvleeukl+vbty8iRHkLENTQ0Ll7C+jhqW7YS570GdT6Sl5fH2rVrAaisrOTQoUMsWLCAwMBAxo8fr+Y1TZ06lZSUFHJzc3nnnXcoKChwCYgAOHHiBOnp6aoP6oUXXiAhIYHx48d7NZZHHnmEFStWMH78+DqJupmZmaxevbrpOVAaGhoazUFuh1x22WX1vn/w4MFzNJLWYfXq1TIio8/lkZycLMuyLP/444/ytGnT5OTkZNnX11fu0KGDPGHCBHnTpk0ux6m9f1xcnDxlyhT58OHDjRpPdXW1/NJLL8mpqamyr6+vHB0dLd9yyy3y0aNHW+Tznu+/l4aGRutR33x/QTcs1GgfaL+XhoaGJ1q1H5SGhoaGhkZroPmgLmBsNhs2m83j+xdMnpSGhsYFiaZBXcDMnj0bo9Ho8aGhoaHRntGW0Bcwzz//vNv8Jg0NDY3zAU1AXcCkpKSQkpLS1sPQ0NDQaBKaiU9DQ0NDo12iCSgNDQ0NjXaJJqA0NDQ0NNolmoDS0NDQ0GiXaAJKQ0NDQ6NdogmoNmD79u3MnDmTrl27IkkSzzzzTJ1t1q1bx+jRo4mMjCQ4OJiePXty7733Ul5erm4jSZL60Ol0JCYmMnv2bLfV0OujpqaGl19+mZ49e+Ln50dMTAy33norx44da/Zn1dDQ0GgqWph5G7Bx40bS09MZPnw4+fn5dd7fvn07Y8eOZfr06Tz++OMYDAb27NnDp59+SnFxMUFBQeq2Tz31FNdddx02m40jR47wzDPPkJmZyc8//+zVWCwWC9deey1btmypU818yJAhrF69mgEDBrTUR9fQ0NDwGk1AtQEPPvggDz30EIDbPKX333+f/v3787///U99bdy4cTzxxBPUru3buXNn0tLSABg2bBiyLHPXXXdRXl7uIsg88cYbb/Dzzz+zbt06l8aE119/PZdeeikzZsxgz549WssNDQ2Nc45m4msDdLr6v/YzZ854bEzYkKAICgpClmWsVqtXY3n77bcZN25cna65/v7+PP300+zbt0/tXaWhoaFxLtEEVDtkwIABLF++nFdffZXMzMx6t7XZbFgsFkwmE/v37+fVV19l9OjRhIaGNniezMxMMjMzufbaa92+r7y+fv36xn8IDQ0NjWZy3pv4KkxwpLBtx9A9AgJ9Wu54jz/+OOvWrWPu3LnMnTuXjh07MnnyZJ588kkSEhJctr333ntduuympqayaNEir85z5swZADp27Oj2/eDgYMLCwtTtNDQ0NM4l572AOlIIk79q2zEsugkGxrXc8cLCwli7di0bNmxgyZIlrFmzhrfeeov58+fz66+/ugiUZ555huuvv15t+f6Xv/yFKVOmsHHjRq2dhoaGxnmNNoO1UyRJYsSIEapv6JdffmHcuHG8/vrrvPnmm+p2ycnJDB48GIAhQ4bQtWtXBg4cyJIlS5g8eXK954iPjwfwaEYsLy+nuLhY3U5DQ0PjXNIiAmrJkiU89thj2Gw25s6dy5w5c1ze37p1K3feeSc1NTXMnDmTZ599tiVOCwjz2qKbWuxwTR5DazNmzBj69+/P4cOH692uR48eABw5cqTBY3bs2JGOHTuybNkyt205li5dClAngEJDQ0PjXNBsAWWxWHj00UdZvXo1oaGhDBo0iBtuuIHIyEh1m/vvv58vvviC3r17c/nll3PDDTfQt2/f5p4aEL6fljSvtQdyc3PrRPFVV1eTlZVFnz596t334MGDACQlJXl1rgceeIAnn3ySTZs2MWzYMJfzvfTSS/Tt25eRI0c28hNoaGhoNJ9mC6itW7fSu3dv1Xk/fvx4VqxYwS233AIIR7zFYqFfv34A3HzzzSxZsqTFBNT5SF5enhq6XVlZyaFDh1iwYAGBgYGMHz9e1UCnTp1KSkoKubm5vPPOOxQUFLgERACcOHGC9PR01Qf1wgsvkJCQwPjx470ayyOPPMKKFSsYP358nUTdzMxMVq9ereVAaWhotAnNFlBnzpxxiSxLSEggKyur3vcv9rya/fv3M23aNPX/hQsXsnDhQpKTk8nIyOD3v/89//3vf3n22WfJzs4mIiKCgQMHsm7dOoYOHepyrJdffpmXX34ZgLi4OIYNG8bLL79MWFiYV2MxGAwsWbKE119/nU8++YTnn3+ekJAQxo4dy+eff07Xrl1b7HNraGhoNIZ2EyQxb9485s2bB9DoWnLnG6NGjapTEcKZa665hmuuuabB49R3jMbg6+vLU089xVNPPdUix9PQ0NBoCZqdqBsfH++iMWVlZblEfTX0vsKcOXNIT08nPT3dYxUFDQ0NDY2Lh2YLqEsvvZR9+/aRlZVFeXk5P/74I+PGjVPfj4+PR6/Xs2fPHqxWK19++SWTJk1q7mk1vECpMuHpoaGhodGeabaAMhgMvPbaa4wePZoBAwbw2GOPERkZyYQJE9QKBG+//Ta33HIL3bt355prrrmoAyTOJbNnz8ZoNHp8aGhoaLRnJLmlHBktSFpaGunp6R7fP3ToEKmpqedwROcnGRkZbtt5KCgJvq2N9ntpaGh4or75vt0ESWi0PCkpKW7beWhoaGicD2jVzDU0NDQ02iXnrYBqh5ZJDTdov5OGhkZTOS8FlNFopLq6uq2HoeEF1dXVWkCGhoZGkzgvBVR0dDSnT5+mqqpKW6G3U2RZpqqqitOnTxMdHd3Ww9HQ0DgPOS+DJEJCQgBRRslsNrfxaDQ8YTQaiY2NVX8vDQ0NjcZwXgooEEJKm/g0NDQ0LlzOSxOfhoaGhsaFjyagNDQ0NDTaJZqA0tDQ0NBol2gCSkNDQ0OjXaIJKA0NDQ2NdokmoDQ0NDQ02iWagNLQ0NDQaJdoAkpDQ0NDo12iCSgNDQ0NjXaJJqA0NNohRUVFpKena0WRNS5qzttSRxoaFyrl5eV8+OGHVFRUUFJSwrhx49p6SBoabYKmQWlotCNkWWbRokVUVFQAMGTIkDYekYaGK2VlZcyfP5+MjIxW7yahCSgNjXbEpk2bOH78OACXXXYZERERbTwiDQ1XNm3axLFjx/jkk08oLi5u1XNpAkpDo52QlZXFqlWrAOjQoQNjx45t4xFpaLhSWVnJjh07AOjVqxfh4eGtej5NQGlotAOqq6tZsGABNpsNHx8fbrzxRgwGA2azmZKSkrYenoYGAOnp6WoPvhEjRrT6+bQgCQ2NNkaWZZYsWaKaSyZOnEhkZCRLly7l119/pWPHjtxxxx1tO0iNi57q6mq2bt0KQLdu3YiLi2v1c2oalIZGG3PgwAH2798PwIABA+jXrx8Afn5+2Gw2Tp8+jdVqbcshamiwdetWampqgHOjPYEmoDQ02pzU1FTS0tKIiopi/Pjx6usdO3YEwGKxkJ2d3VbD09DAZDKRnp4OQKdOnUhKSjon571gBVR5eTkHDx5s62FoaDSIXq9n3Lhx3H333fj4+KivJyYmqs9PnTrVFkPT0ABgx44dVFVVAedOe4ILVEDt3r2b119/nW+++YaysrK2Ho6GhltsNpvL/87CCcDf35/o6GhAE1AabUtUVBRxcXEkJiaSkpJyzs7bLAG1detWevfuTdeuXfnrX//qdptZs2bRuXNnBgwYwIABA9Qcj9YkMTERWZaRZZm9e/e2+vk0NBrL/v37+eCDDygoKKh3O8WUkpmZ2epJkRoanujWrRv33HMPt9xyC5IknbPzNktA3X///XzxxRccPnyYZcuWeRQG//rXv9i1axe7du2iS5cuzTmlV0RGRqrmkd27d2s3tka7orKykh9++IHs7Gy+/PLLeq9PxQ9VXl7e6kmRGhr1IUkSAQEB5/ScTRZQZ86cwWKx0K9fP/R6PTfffDNLlixpybE1i/79+wOQm5urOZg12hWnTp1So6HGjx9f74rU2Rmtmfk0zjUlJSVtusBvloBKSEhQ/09ISCArK8vtto8//jj9+/fnqaeeOmfhsr1790av1wNCi9LQaC8UFRWpzxUNyRPh4eGEhobSsWPHOj4qDY3WxGaz8dlnn/Hvf/+bI0eOtMkYGkzUHTBgABaLpc7r//73v706wcsvv0xcXBw1NTXccccdvPvuu9x///11tps3bx7z5s0DhNbTXPz9/enRowcHDhxg7969XHXVVarA0tBoSxRTXXBwMAZD/begJEk89NBD59Tur6EBcPDgQdVHWllZ2SZjaFBA7dq1y+3rZ86ccdGYsrKyiI+Pr7Ndhw4dAJF0OHPmTL755hu3x5szZw5z5swBIC0trcGBe0P//v05cOAAlZWVHDt2jB49erTIcTU0moMioMLCwrzaXhNOGucaWZZZv349IK7Tvn37tsk4mmzii4+PR6/Xs2fPHqxWK19++SWTJk2qs93Zs2cBoS4uXryY3r17N320jaRr164EBgYCsGfPnnN2Xg2N+lBMfK1daFNDo6kcPXqUnJwcAC6//PI2sz41qxbf22+/zS233EJ1dTUzZsxQpeycOXO47777GDx4MLfddhv5+fnYbDbS0tL4wx/+0CID9wadTscVV1yBJEn06dPnnJ1XQ8MTsiw3WoMCEWaekZFBWFiYWgpJQ6O12LRpEwBBQUEMGDCgzcbRLAGVlpam1hBzRvElAWr7gLbi0ksvbdPza2g4U1VVhclkAhqnQa1atYqTJ0+SkpKiCSiNVqWsrIyTJ08CYv5syE/ammjVzDU0ziH+/v48+uijFBUVNaoZYVJSEidPniQrKwur1aoF/JzHyLLcrv2Khw8fVp/36tWrDUdygZY68kR2djalpaVtPQyNixhJkggODqZjx44EBQV5vZ+SD2U2m1XfgMb5x5IlS3j11Vc9puS0Bw4dOgRATEwMkZGRbTqWi0JAWa1WPvjgA9577z22bdt2zs5bWFjIgQMH6tRc09BoLM4Ju5mZmW04Eo2mYrPZ2LFjB9XV1R6jmdsDaWlpDBw4sE19TwoXhYDS6/Uu0XznQmDIssynn37KN998oyUKa6jU1NQ0KTPf39+fqKgooJ1VlMjbBDufhNKjbT2Sdo9z4er23CW5a9euXHfddQwdOrT+DcszWn0sF4WAAkfpo9LSUjIyMlr9fDabTb0If/755yYfx2QytVmSnEbL88033/C3v/2NRYsWNXpfRYs6depUy5SfqTgJv82H4n2N39dcCtvuh5WXw8F/wKqxYCpqeL/WQLbBgVchfTZUtCPhXYvaQsldAYTzhtIjsCQVNt4G1c0vrOCJi0ZA9ejRAz8/P+DclD7S6/UMHjxY/b8pE4rJZOKdd97h9ddfdymPo3H+UlRUhNVqRadr/K2nlEUqKytr+grcXA4nPoFfroTvU2Dz7bCsL2yYDsV1I3LdkrUMlvaBo07VZCozhYA413XbrDWw8RbYNRdOfAQ/DYKc1ed2DF7SsWNHpk+frv6v5Ii2F6xWq3el6GQZtt4Ltho49Q3U5LfamC4aAWUwGNQk4YMHD6rFOlsTpW9KZWUleXl5jd7/0KFDlJaWYrVatUTjCwBZllXB0pQk3Sb7oWQbZK+CzXfAd3GQPqvuJJ75jRBUG2+BkkPuj1OdD5tuh7UTodKuqSRNhZTbxPPTi+DIO96Pq7mYSmD1NZD5teO1mjxYdRUcfL3lhaUsg6m4WYfo1KmT+ry9BUocOXKEf/7znyxatEhtTuiWEx9B7hrxvNfTENp6kX4XjYACUVdQh4X+ARthcVdYfhmYW6+hYXJysvq8KWZF5wv4XBXZ1Wg9ysrK1N+xQQF15keh1ex8En77DIp2EREayKhRo7j11lvp3r27531tVqjIhJy1sPvP8H0nWDUGfvsULBVim6Cu0O8FuGYH9HgE9H6ADCe/hGW9YdMMh19JliHjC1jaEzLmi9f84mDEtzDiG7j0fQi1V4jZ+RgU7mz6l+QtlWfg5yscE2XCJBixEIxhIFvFODbeIjTGluLXR2BBOKyfBtWNX3CCKPmm+BILCwtbbmwtwKFDh6iurubYsWP4+vq636gqB3Y+Lp6HpELvp1p1TBdPHpRsI8G8hj90/g+h+gIwAwWnxcqxy+wWPVV2djZff/01ycnJGI1GzGYzJ0+ebHTS8G+//aY+1zoDn/84m2nrrSKRux7WTQabyeVlSdIzMrg75PSFmr4Q2kdsU/EblJ+A8t/Eo/Ik2Mx1j2sMheSboNMdEDUUlFyciEug1xOw/+9w7D1husn4H5z8HFJmQE0BnHFqpdPlLhj4D/CxC1lDAFz+FSwfAtYqIVjH/wrGYO++mOyf4exySLgeoi93jMsTJQeF5lRp1yK73A1D/g06A4T1g/VToHgvZH4FJfuFIA3p5t1YPFGV7TBpnloAuWvh0nchaYrXhygtLcXf358pU6YQHBzcqDSD1sZqtar5Tz169PBsgv71YYev8dIPQO9BkLUQF76AkmU4swx2P41UvIdQveNlSUKsGFtYQGVkZFBUVERRURG9evXiwIEDZGRkNCpBr6amxsUMqTWrO/9x/g09alClRxzCSe8HOl8w2/1NshVKD4qHs1mrPiQdxI2DzndAwnVg8He/nX8HGPx/0OtJ2P8yHP9AjOG3TxzbBHUW2lLcmLr7h/WGwW/BljlQfgy23gfD/le/sLGZYc+f4cAr4v+D/xSaWNf7oNPt4BNWd5+8TbB2Epjs2kff56HPs47zBHeFqzeLcZz8Ekr2CcE57H+QcG1D35Znjr3nKvRr8mD9jZB8q/jcvg0nXX/yyScUFhaSlpbGuHHjmj6WViAjI0Odb3r27Ol+o6xl4jsF6HovxAxv9XFd2Ca+3A3w8whYey0UCx+ONbALm/UPUxj/e7FNzi8tHoWilAmJiYlRf+zKykry8713Jvr6+vLwww/To0cPAgICGlW3TaN9omhQRqPRfWfS6jxYM0FMvpIOhi+EqUUw+RSMWgYDXoGU2yGsP7LOqTeUZICgLhB3FXS9B/q/DJd/CeO2wo0FMHqZ0Jw8CSdnAhJgyNsw6aiYhHRGMZbUx2DCXvfCSaHzbDFhg9C+TnzkedvKLBGooQgnhZL9sONB+C4e0u+Cgm0OX9LpxcJUqXw/l74PfZ+rKwQNgTDsc7jkdZD0QsCvnQR7nhf+uMZiNcHRd8XzDuNg9AoISHR8zmV9IGup5/1lGbloD72lxdyZ+CEjSu6Bgu2NH0crcvDgQUDMO85+MhVzOWz7nXju3wEG/P2cjOvC1KCKdsPuP8EZp4vGPx76Po++8yyG6ozCTLD03+KCzVwA3X/fIqeWZVn1N6WkpJCcnIyPjw8dO3ZsdFipJEncfPPNLTIujXOMqqI7cC4SW0eTtlYLzan8uPh/0FuQMEE8D0gUj/jxyLLMV199xcnfjjFycBJpw0aDfwLoWrj0UWBHYcLq+xewVUNgcsP7SJLYp2Cr0KK2PwBRaXWd6GeWi+hBJforZhRc9gHkrIFj70LhDmEqPPFf8QgfCDEj4ci/xP2q9xMmxcTr6h9L6iNi3w3Thcaz7y9QkyvMgY3h1AKotnfl7v4gdLgKJuwTPqkTH0HVWbEI7nwnXPIG+ISCpUoEopxZAllLkSozuVJRsiwgb7yFwst+wYIvsbGxjRtPC2Oz2dTqEd27d3dfRmvPnx0m1UFvudduW4ELT0DJNlg/VdwgIOzkvZ6C7g+4riBDe0JYfyjeLdTWFhJQOTk5VFdXA0JABQcHM3fu3CaFFZ8zLBVQtEdMJu24Rth5w4lPYPv9EHc1DHrDZXI3GAx1zXuyTUTY5YsK0qQ+6vF6lCSJqqoqqk1WjpyVSAusvyNvs/Fv5ORpDIbhX8GKoWCtomL5RD7NeQDJGMjAAX25zP9H2P83+8YS9P6T0IJ0BmGe6zpHaBfH3oOMz8FaCUU7xQPAJwJG/gDRw7wbT+wo4Q9bNwUKtwlNqOu9EN7f+890+C3xN6gLxI+3jyMU0v4rfFBb7hYC7MRHkL0SwgYIy4zVNRLOJkvkmaKJ9c1FKj/GsYXTOBb6B2677Tbvx9IKnD59mooKETyTmppad4OCbWJxAJB4faP8bs2lHc+aTUTSCbu0PkBc/NedEA5gd+aNZLt2krceKk+3yOmdo/WUKL7GCqeSkhJ27959bgIjKjJhWX9YOayuuUWj8dQUwI4/CKF/+jtY0hP2vwTWGiZPnszTTz/NjTfe6LrP7mccPqXEG0QAQj0o4eanT59un2W0Ii6Bgf8EINCSwaWGz6nIP0bK8TkO4eQbBaN+hP4vCOHkTORgoVHdcAYGvy2CQUAI+qs2ei+cFAISYeT3Yk5Ahl2NiDwr2AYF6eJ59/vF/OJMwrUwcZ/DtFl5WmhNinAyhkLHmziV9BL/PPE472b+DnOs0IwvC9uKPn9tyyRdNwNFezIYDHTt2tX1TZtZCGDZBoZg8Xucw0XshSegAFJuEYKp/4seVdEvv/ySD1Y6FY496aXTuQGc/U9u/QxecOjQIRYtWsTrr79ORUUFJ0+eZPfu3WRnZ7fIGFXKf4OfRzrMSgf+LnJL2iulh+HQG3D0PTj1LeSuE6ba6vym+RaaiCzLFBYWuk+e3v+SqLIAwv9hrRLm5h/7Q/YvSJKEj4+T/+jYPDjwsngeYXfm154Ea3FeFI7t/gBVUdcAMCj0V+5PfptYyZ5fFT0cxu+C+AYCBXxChVCYsAeuPQQTD0ComxW+N/h3ECY/gLM/ihB8b1C0J0OgMOG5wzcSLp8PwxdAYAqE9ISej8OYNXBjHgz/kkzdFVTZAtHp9OiHzsOiDwPgmrCvKc5r29qKwcHBRERE0KVLF9drE8T9VmwvbDDgZYfv7Rxx4Zn4QNzgDZgmSktLOVviS0FsNyLlo8LM1/PRZp22tv/J+fVjx46RkZFBUlKSezXaCSW8XBFy8+fPx2w2M3LkSOLi4po1RpXSo7DqSlfN0VwCh/8Fff/cMudoKSoyYe9f4LePPQsiSQc+kWJlnnSjWJm3IEq5qYCAAN577z1ycnIYOHAg113n5AepyIQjb4vnSTcK09W230PeBiFcV42FjjcJ531APJxdCdvuE9sHJgvTlaHhRU3thN0OHTq02OdsMSSJA8GP0eXsZsKMJfjpRYRYTddH8B38al2tqYFjEdKj+WPq+YTwcdUUiMoTV2+uXxuozhWh6gCdZjbsd+l4o3i4QfE/hoSEoAvoQEXf1wneNZswYwmFOx6F8Qsb/3laiKFDh5KWlqb2KVMpOw57nxPPo4ZCt9+d87FdmBqUF0RHRwNwqNJuiy7cJn6QZlBaWqo6GJ0FlCRJ/PTTT2zatMltg0dnbDabKqA6deqEJElqBF+LFZgsOQi/jHQIp0vegLix4vnhNxwaQFtTnQc7HoEfuglneX1akmwTjvDSg7D/xabVl/NAZWUln376KZ9++imVlZWqD6mORrv3ORGaLemh/98grC+MXQdpH2PzEcmZZH6FvKSHSKDdMFWEjhtDRZSel/6egIAAtQ1CuyocW4uTZ0tZmD2VaqsvlVZ/Ps+6lZNh9zVOOLUkPqHC7A9QsEWYYOvj2PuOXLTuDzTr1Mq9q9zLQT1ncdA+90QUfStCuM8VJQdg34siCfzw23B6MVLxbnwpd0RMyrJYPFmrRSTnpe83qNm3BhemBuUFyg2+PbcTw5IlJGSxWur9tHcHsJnh8P9BcHc1mig0NJTHHnuM/Px8QkJCXDZPTk6msLCwwXyorKwsdSXTuXNnQFzUeXl5LZMLVbxPhOoqofVD/i1WRhGDRcKkqUiYNfr8qfnnairmUjj4Ghx6HSz2SgCSToQx93lWmFRq8oVAqs4Tf2vyxWc6+A8x6Z9aCGF9mj0URTgpprRdu3YRFxfHoUOHyM3NdTQPLN4ngiNAjFNZ8UsSdL6DPfkdMW9/ksGh25Es5UKIgggRH7Gw0eVikpKSKCgoaNcC6vTp0xRVJ7Hc/332H/oNsxUSs7Prr4LR2nT7HRx6U0Sk7X5a5Ia5E5g2Mxz9j3geO6bZ5Xx8fX0JDAxUBZQkSezzv49EyxMEG8ph6xwRGehFPlWTKDsGJ78Sc1zxXs/b6QNEBKdPOORvFq/1nNsi91JTuOg1qGJTIJZwu9NVSULzhn0vwM4nYN31sOdZdeUhSRLR0dF1SoUoGlV5ebmjxEn5b/DrYy45FCdOnABEYIUSZKFc1M0WUIU74ZdRduEkwWXzHGp7zHCIHS2eH3q9VUtAecRSJQTT4s6w768O4dRxGkw8wB7/B3n3s6Xs2H0QOSAJIgZB/DXQaYbwLwx42fEZMhc0ezi1hVNaWhpDhw5VTWpWq9WR27b7aUAGvb8w7dUiv9TKsrxr+Sz3QbEYULjsg/pzizygmPlKS0spL2/Bcj4tREVFheqjS0jpRVSM+M7avECq3k+UeAJhdvWUq3XqW6g6I573+EOzT3vjjTfy+OOPu5iEoxNS+SF3kvin6qwIrmlJKjJF8vNPg4UVYs8zDuEk6bEZ3TQjtFZC6SGHcAru3qaL1YtWg1LqYQEUBl9FbNFG8eMV7xdZ8fVRdsw14m3fC6I3ymXzQO/jdpfadfkiqzbC5pnC73PodWFCGPgP1byXmJioCjlFQJWWlmKz2ZoWsl6wDVZdDeZioY2kfSwmdmf6PCdyN0yFouhn7z82/jxNoSpHmFOO/UfcqAodxglTWcQgTCYTP/74BtXV1SxZsoTMzEwmTpxY16mbdKPQBEv2iQmoib6LiooKPv30U3JzhaY5dOhQrrrqKiRJcvEDnj17lljpKGT9IF7o8ZBIdq2FsrioDuwDV78BWYvBECRyappAbGwskiQRGRlJZWVluyqbA0J7UkhMTOTMmTOcPXu25QN9mkLKbULTLtkHe58X/9f2/R2xB0cEpkD8xBY7tbPlJCEhgbVre7CzZAADQ3eJOoeJN3j0YzWIzQKF2yH7F1E9R0lbcJwdYq4Q0ctJN7J5xxHW/PIjIYZS7rr5KgLkfFEEuDJTCDdrNQx6016nsW24aAVUREQEkiSJwAbbIGIlvTANZX4FYX/1vKMsw/Y/CNu0zigKJhbvhYzPKM8/inTFQgLD4uvsFhoaSnh4OMVF+QQdexEOL3Ld4Mjb2HI3UH52JBDuks2tCCibzUZZWRmhoaGN+7B5m2DNeGE6k/QwbL6oLFCb2JEiITJ3LRz6pxCaxlac+Aq2i6CMzK9c685Fptm1oVHqS0onUoXs7Gz3ZtLEG0RgArIw83lrsnWiorycTz/7zK1wAhH1FBAQQGVlJdlnz0KePXTaJxx6zXV7TEVAhYeHi6TapBsaPS5nOnTowNNPP43B4PkWTj8Ne3Ph9r7gb2zW6RqNYno0Go3ExMTQoUMHdu7cSXFxMVVVVfj7e1HVorXQ6UUlhLXXCi3pyFuuv1vhr5C3UTzvfn/LJ0HbSUhIICoqijMdnqCf9Un0NVnC7xMzAvxiGj6ALAufa/YvYlGWu8a9/zgyTQiljtNEcI6dQ4cWY5GNBMT2J6DzpJb7YC3IRSug9Ho9ERERFBQUkF1kFUECZ5cLM1/fv3iO7slaLMJUQZR/6f0nkal+9keCytPJ+W4g+us24BdZtzhl96RgUgNeJ8UqQtHxi4Uh74q6Z2eWoSvexd2JB/gh9zo6O4W0Opc5Ki4udi+gbFaoyBBaQ+khKDtsf37YkQWvM4oSOPUl2vV5VvioagpEccxeT9bzLTYBmxkyF4rEP8WMoBB3ldBA4ie4fP9Wq5X0dJGLEh8fT0xMDMOHD8dodDPr+seKGzx3nTDzNVJA1ZxejW7NJEbqO/KD7jouSRvD2LFjXYShokWdOHECY+5PYLRPZr2f9hjppZi7WqpklU6nq1eT3psLMxaByQpbsuC9iaA/hwb9yy67jLi4OCoqKtDpdHTr1o3p06fToUMHtS9bmxI/AaJHiBzI/S+LgrOK/0fRnvT+wp/YTAoLC8nPzycsLIyoqCj1dwsICOD+++8XG2XHiDYhNfmipNDwBY57QLYJX2vlKfGoyBSaUs4vrhYHBUkPkZdC4mToOB2CUupsUlZWpmq5DUUVtyUXrYACYeYrKCgQIcR9bxYCquwoFO2CiIF1d7BUwo6HxPOAROjzjMiPGLmY41+No4u8iljfXFg3SkRlOWer56xmjPkJjAEFAJjDL8c46huRn5F4HRx8DXn3U/jqTUztsADb2QiIfxMM/nUEVHJysvDX5K4RqnzOGig7Uqf6tQs6H3HRJzawUoodLfJU8jYI+3X3+8VnbC6WCnsO038ctn0QTtnOdwhtzYMjet++fZSWipXh8OHD6xSztFqtbNu2jSFDhoiAhaSpQkAV7RRVvoM6ez/MnX8iUFdBr6CDpIQU4z/oFreaWlxcHL+dOEY/2R6GHJDoMdLLuSvyuaipWG6CB5YJ4QSw8gS8uB6eG9nqp1YJDg6mTx+HYz0sLKx91ZOUJFHbcOUwYWY/8HcY+KoQBBlfiG06zWiRoIXDhw+zYsUKAJ555hn3G8WNhW6/F4vCU98K37a5TJjbKk/Xf28DhPZBjr2SiuChnLF0Jb/ERLgUTpw5lDA3QVlKci7UUxy2HXBRC6gJEyZwww03CF+PqRh094oL4eSX7gXUgb+LNtkgQrPtE7cs6VmQeRWD/WFM1CoxAa8cASMWiAvvwCuw5xmM9jDpDYXDCejxGpf42/NXJB30egIpejhsvBkqM9Edf19ksA//Gv/g7vTs2ZPogEqSqpfCmj9Bzqo6pVQcSBDUCYJ7CB9MSA+hnQR3afhLkSTh5F91lYiOO/ou9HyscV9sbSwVsHq8WK0qBHYSE3qX2fXml8iyzMaNQkOJjIx0u9r75Zdf2Lx5M/v27WPatGmEJk1xOJwzF4pKIt5QcpDAModWF2A7CyuHCt9iyq0um8bHx3N5wgmifeyJsn3/4tFW71UV8yZgMpnIzc0lLy+PAQMG2E3W8PQqyLBnJCQEQ1YZ/HcXJIXAbDeX9UVL9FChZZxeJEzN3R8UbUZs9i4CzQwtV1BCzIODg93XuVMY8IpYJJcfd/g0PRGYArFXkqPry4GCWDJzTWTvy6a6+jBw2GXTK664gtGjR6v/V1ZWqsVhY2JiiIhopcjBFuCiFlAuoeA+YaLO1unvhYAa8HdXM59zYETcVcIZb0fU36thQ/UVpA66moRTfwZLGayZCJFDHKYsYxgHI/5E0uXTSEio60gneiiM3wnpdwpTYvEe+GkQUsrtTA/cICo91zYxG0PEeCIucQik4K7Nc2zGjoGoYcLJevBVEennRQKpW6zVsO4Gh3CKuQJSHxcmFi9s+0VFRWqU2rBhw+qsBG02m+orysrK4r333mPq1Kl0jhoqvvdTjRBQSr8fnVFUBN/9J7EI2HSbCDIZ+Kp4D+id2oXeR9dCJULz6zSz3s+g0JJaxKFDh/juO5HL06VLF0JCQvj6AHxvn5+u7wF/GQk3fA2/FcNf10FiCFztxTqlOZjNZgwGg1vNU5ZlKioq2k9QR/+XxL1mqxFRbjmrxOsxo0QeWwtQOwfKGZPJxJ49e8jKymLQoEEkXv6luP8lHQQkiUdgEgR0dDz3T1D7MO1ZuZJNW2sHQ7hSuxjtd999pwZjtWfzHlzkAqoOHW8WAqoyE/LThcCAuoERg99yEV7O9ffCBz4IXS8Vk7K5xCGcIgbB8G/oGeSmlD04ovN8I+CKRSLHateTQvs49p7rxqG9RGRR/ATR4E3Xwh5wSRK+qDXXiJD0Y+9D6sONP47NLPxz2SvF/8m3wNDPGuV0joiI4OGHH2b37t3069evzvs6nY5bb72VdevWsXbtWqqqqvjqq694fOJ1GPM3i4TMilPixq4Pcymc+Fg87zhdaI0xI0Xzu8pTcPhNYTK8/Cvh5zryjqPtef+X6k0+9fX1pXv37hQXF7eogIqJcTjSc3NzyTaH8Owa8X9KKLx0JQT5wMfXCyFVWAUP/gRf3wj9W6ggiTuWLl3K8ePH6dmzJxMmTFBfP3DgAEuXLqWyspKnnnqqbgRmWxDaU5QwOv6h6DhspyJxNrUN25/uhn9thVfGwBjvrcYuVexrI0kSP/74IzabjcjISBKHD4eJ9eQp1SIuLg5/f386dOhAbGwsHTp0IC4ujoiICIqKijh79qxLBLEsyy6h/r16tV679pbgohdQFouFgoICjEYjEYmThE/EWim0KEVA1Q6MqBW6XKf+XsBouGqD0KAqM4UGcsnr9Wo1S5Ys4fTp0/Tu3ZuRI0cKgRB9uVi9V56C2CsdQsmN07PF6XA1RF4mJvgDr4gK0N70E1KwWUXbcMVUkTgZhn7SpIgoHx8fhgwZ4vF9nU7HqFGjiI2N5euvv8ZkMnHMdCmqZf3Ut5D6UP0n+e0zNe+qIuFOMTlFDhYt0TfeLFbWuWvhp0GiivX+l8R+UcNEsmc9pKSkuFQWaSmioqLUSNRTZ/N5Ob0r1Rbw0cPbE4RwAkgJgw+uhVu/hWoL3PUDfHeTMPk1hMVi4fDhw8iy7OJTqo/Tp09TXl7uEnUJ4O/vr/ricnJyXEo2tSl9nxch3lYxXrNPPK9/fYzUnt+QlpZGUlISVhu8sUUI+f/saJqAchfcZDQaiY2N5ezZs2RlZdV7HJvNxueff06vXr3o378/er2e3r1706dPH7faalRUlEs6jcLEiRPJyckhJiamzVt9NESz4nruv/9+YmNjGTx4sMdtjh8/zuDBg+natSv33Xdfm1furc1bb73Fu+++y5YtW4RPKcEeRJD5tZhkXQIjkkRghBOe6u8R1gcmHRZFa4f8u45wMpvNqulKlmVOnDhBXl4eBQUFjo0ih8DEgzCtjJye/2XV6VS+W7mzxStY79u3jy+//JLly5eza9cuzp49iwyOhNPqbBFp6C2yTWTGK3XMOlwjogdbWtOrRWpqqjoJ7PutXGitIPr51IcsIx95B4Az1R1Y+auTHdUvGkYvF3XcAKqyYPU4taPrIb9ZrFi5skU/h7cYDAbVf/D+kTgO2y+dp4dD31pRyoPj4Q17bda8SrjzeyipoUEWLlzIggUL+OWXX7y6dysrK9VrODHRtbCoc/5YW+RDWT3cNpt3n6Io+nb1/z1Vw7HJOg4cOMB///tf5s2bx7ebj1Nod/nuyoYqs/tj1aampkYV1J605/h4EfrdkIDau3cvx48f54cfflB9SDqdzusu3SA0tp49ezJq1Kh2rz1BMwXUrbfeyrJl9deQmjt3Ls8//zzHjh0jPz+fpUvr6TzZBiglj9SKAEoLjupsyFsnQlDVwIjX60S01e7/5ILeTwQr1GLhwoW88sor/Pzzz4DwUSh2aqW8kYpODzoD+fn5rF+/nj179rRoG46DBw+ycOFCDh8+THp6Ot9//z2ffmo3dXS4Rq16YN7zN2oqvagFKMuw/UGHuSxmJIxYyImTWbz55pteT3T5+fl8/fXXLgmfDSFJklpG59ixY1gT7OH0eRvdh+Mq5KxGKhU3/LaSS+lSu+WAziD8T5d/5fL7FweO4KvVZ9i8ebPaT+dcExMTwwljbzaVpwAwrgvM8tDqaGI3eOpy8fxoIdy3xBHp54zVamXVqlVUVlaqEV7FxcVkZjZcddt5kq0toPz9HRGp57qixBf7oMc78Gotd01GRgYrV67k/Y2RVPj2hPCBdL3mFdLS0lQTZFZWFp9tcHwusw1+9VK+OtfP9JS/qHxPZWVlarRqbaxWK2vXigrskZGR54VwaQmaJaAuv/xydYJ3hyzLbNq0iYkTRSb27bffzg8/NBCdco6pI6DirxGBBwD7/y6CBKBOYIRCUFAQ48aNo0ePHi623vrQ6/VYrVZV81LKGwHu2y1TNxeqJcjMzGThQlFF2cfHR20PolQpUCP6AKM5lx1fzEK21rN0lGVRJVoJNohMUyt0BwYGkpyczIYNG1i6dGmDQmrjxo0cPHiQjz/+WDULeYPi9DWZTJyWLlUGBqfqKQxqr0BeafVnX1mfuosEheTpcHW68AH6RGDq9aL6lieNQJZl1qxZw6+//uoocdWCyCFJbAwQWn9CsMyrY+sv0H3vILjNbqnbdBqe/sVRH1Rh8+bNrF+/nrfffpuYmBh1ot61a1eD41ESdA0Gg1vzkVIm6lxqUMeL4Lk1QrB8tMuh/VRWVvLtt9+Ka9EnHPOYTTD+V0KjUxg3bhyPPvoo48aNIywsjFMG17zGdC/XTc73qicNyjlgypMWtXv3bjXYZtSoUe27AWoL0qqfsqCgQK3YAOKH8PQDzJs3j7S0NNLS0tSorHOBUpOvtLRUFGnV+4lqBADZKzwGRigEBQWRlpbGzTff7HX/J0WQlZSUUFxcrAqoyMhIj6uslhZQeXl5fPHFF1itVgwGAzNmzODxxx/nscce49prr3VsGD+REr3QKIb5L0L+JgRWDINtD8Dxj0QnXpv9jt/3V1FCBiB8AIz+UXRYBTUpGkRViMWLF3s0VZaWlrJnzx4A+vXr16i+WsnJyfTs2ZOJEycS1WkohNkDKzyZ+SoyIet7AHaWDCQ6riOBgfXkfYX1gQl7YUoOEZ0cUYWeNIKqqirWrl3LDz/8oPoqWwqTFT44OwCz5Isk2/jb5WWENRC8KUnw19Ew0r6W+uYgvLPN8X5hYaG6Uo+KiiI2NpbevUXprwMHDtRtyVAL5f6Oj493G1KtmPlycnKwWt2oby2M1QaPr4Qa+6kqzbD2pFg4fP/996o14rrrrqsjQHx9fUlLS2PqnQ9SaHBtaeKtgAoICKBv374kJSV5vLcjIyPVRYA7i4HFYmHdunWA0JiV3+NioMEgiQEDBmCxWOq8vmLFCtV22hLMmTOHOXPmAKIo57nC2YmYn58vPlPyzfDbJ46N3ARGNAdnU+Bvv/2mhnx6XLkjLnSj0YjZbG62gDKbzcyfP5/q6mokSWLatGmqmSEoKMg1BFiS8B/6f9jWX4tOktHZqkVkonMVCJ0vhHR3FKIM7QWjV7jkNxmNRm6//Xbmz5/P6dOn2bVrFzabjeuvv77OajA9PV0VXpdffnmjPpter2f69OmOF5JuFOH6uWtFEqZftOsOx94D2YYsS2wvGULvy7zwfks6kHQYdGKBk5ub61EjaK0cKIC/b4SjpSJw5ZLq1cSTCDQc+WDQwb8nwLRv4EA+vJ4uBFafGJlly5ZhsVjQ6XRce+21SJLEgAED2LlzJyaTiYMHD9K/v3sbos1mUyfY2uY9BUWDstls5OXltVx/Mw/8dxf8WmvtsOwYhBds5ciRIwAMGjSo3mTVtScd1+fo2CJW54SzK0cEnPg1MIMmJiZ6/C4UdDodCQkJ/Pbbb5w5c6bO+zt37lRNhaNGjWqUz+l8p0ENateuXezbt6/OwxvhFBkZSWFhoWrOycrKalGh1hLUFlCAqC7tazddugmMUGhqwEdYWJiag5Wenq76sOoTUM59oZoroIxGI6NHj1YnoYbaH/h0nMDygA/58szNbCwZg63DBPBzmlhsNQ7hFNQFrvzZRRCYzULD8vPz4/bbb6djx44A7Nmzh++++85lJV1dXc2OHTsAkeFenwnZKzpOFX9lm0jIdMZaLULogSMV3Si2hNOlS+OShBoyWbVWDtSCA/DhTvG8q/EMc/qb67R4qY8gH3h/EgQYwSoLLWPnnv0cPy56og0bNkwNY09KSlKF6+7duz0eMz8/X9WwGhJQ0Pp+qBNF8A+7z6lvDNxqN22uPG7jx5Ui3ykmJoZx4+rv7PuLWD/SJRzuHS6+B5P1/9s787ia0j+Of+5tFaUmUURMGhlLTVJ2yb4lQ6SffbKTXWaGnzHMMJaxjd9gGsYYxDBjp+xkahIhuxIqRaKS1nvv9/fHc8+593aX7lVMzHm/Xl5zp+fc5zznueec7/N8V3XBVx649+Ljx49VNAsSiQTnz7MYQnt7+0oft1TRvFEVn0gkQqtWrXjHiO3bt6Nv38qVlNDS0pLfXvMCSmwCeKyWl+DerjXVT2xsLDZu3IiIiAiDhJVIJOJ3UZw6U/lv2qjIwoVubm6YPHkyPDw8cO85081nF2o/vpFbR9x55YoTT9vjjv0K4NN0oP9jZmNqtpC5WtfpC3Q+ydI3yXn27BmWLVuG33//Hc+fP4eZmRn+85//8Nd6/fp17N27lxdScXFx/EuuTZs25b7OInNnltAXYEG7yjz6neU+A3OOMDExMdj1mbOzZGVlaVR/cQJKLBYbJEB08ftNJlAAwM4CCB9RG7169jC4sm5dK4XTxO0s4NuTzEBvY2ODDh068MeJRCJ+15ScnKz1/nv16hV/j2oTUNwO/U1nL1BW7ZmIgZVdgb7ydVi+RIwUUX0YGxtjwIABmnM6yimUABfk4W6d6gOf2ANmcs2lvmo+fWjatCn8/f0xZswYlR1SXFwcr4asTLunIgnzBI16w9XqyxUHNXLkSERERCArKwuOjo5YtWoVAgICEBwcjPHjx8PT0xPfffcdAgMDMXXqVHTu3Jl3mPgnKZQAK6KZuyggQka1kSguLsaZO1Ww4TkgAgDRUBiLhsLhKnuQHbl/loB9NZZ4Mzk5GRkZGZDJZAbfOE5OTrydBWArqLKSaHI67NfZQRERcnJyVFbx3Kp40hHgThYTUlv6sZViaerXr49q1aohLy8PCQkJTCVSxQGo04f908L169chkUhw69Yt9OzZEwBzyAgKCkJ4eDju37+PW7du4cqVK3Bzc+OTwjo5OZWpGtFFfHw8/vrrLxgbG2Ncq4GsQGDGSVaQ0VR+gXLX8mxpTSTlfwgXl/o6s4NrQlkoaIrtUY6BqQjD9u4bwJwTAAGwrQLs+BSwK0eqxKHNgcOJ7GUbJ/ZGbaNrGNanm9pL283NDWfOnAHAQkc8PDzU+mrQoAGmTp2KV69e6bTjTZ061eB5NpQtV4BL8h3OVG+gUQ1AIgOsTSXILjbGA9PGmOj7kUqwsyZiUoECuYWjcwOm0nO3Zwl4Y3R7hQNgabgsLCzw4Ycf6ow5sre316judHZ2xscff4ycnJwKLfRYLAVkVLaKUhurYoBTD9i/XQOAVq//qOqkXHfJL7/8ovHvYWFh/GcXFxdeZVMZkMiAkGNAhEp1dwc2E8XAfT1uOmMx4FCNIHvRGk6m1dDSyfDgU+Xdkp+fHxqWdm3WgPIOytC6UJGRkYiPj0dgYKDKuZ++Ah8/8zAH8N8F/NgLaFdP9ftisRhNmzZFTEwM7t69i8LCwjIFKhHh+nVWer1BgwYqti0TExMMGTIEu3btgpWVFTw8PHD58mXeXdtQ21NpiouL+R3xS5tusMRigCRA6gGWnDbrIgtCBmDadBr6fNwNlpaWBp+ndG0obQKqItR7u24AoXLhVKMKsHMA8FE5NaBiETD948f4T0oNSESmiPsgEHWd1MdqbW2NPn36oH79+mWqXXU6mQAahRORbu9DQ0h+oXAnb1YTmCAP0zQWA70+MsaO60CaeRM0dSv79cep9yxNgZa1mYcjpQAQt0Z8hm47VElJCaKiogAAPXv2fK2gWDs7OwQEBEAikVTY7ikjD+i5A6hiDOweyBbehnAxDdggf6W3rQt4acjaVlH8O3wV5RAB808rhFOzmkCvhkDPhkAPZ/avu9K/jk6Aywfsh1RGIgNSckVIM3LCXxZ9EFnspeaqWxY2NjawtrZG3bp1YW5urtfL0dnZGT169MCgQYMMUilGR0cjJiYGRUVFOHPmjMp340rZZHOLgBH7WdxIabhUQ1KpFDdv3izzvBkZGbznnqYsBMbGxggMDOSN8RYWFrC1tUXNmjX1Eti6UF5t3ko3ZbYxQFFpV757gpEFLJpMQIsWLV5rhWpubo6OHTuiX79+Gr9fUWU2dAmnly9f4uzZs7wa1VA8XWoi0JHpatJKrPGjlvVkixYtym8TLEVGHvDZAaDpBuBcBTg5FpdIMf2YBEVSwFhEGP9hIuIvXeQXSr3kt1WBzAR/pep+4ROxHQIAtK8HmBixRZdtPnOuKMsOpawKNeT3LyhQTwJdkTvOnddZRoy0l8CEw0zI6kteMTDjOLsPrUyBFV3ZIudN8a9KdbTmb2CH/MXbqg6w1V+/LS4R+0FTc4HUl0BKDnDxbgZiM0yQa2SLPx/awvwU8E0n/WvuiEQiTJkyxaBdkDY1gC6ePn3Kp/r/4IMPEBAQoKrjlgsoK1NgXU9g8lHgZTEw9ySQ+JxlJeCuyd7eHh4eHqhfvz4aNSrbq5F7KYjFYq3GXWVX5MaNG8PV1RUvX74s92rRxsYGNWvWxNOnT3H33j14NRnAYtoyIlkJjofh7MAGw3RmU9cHHx8fjX/n1KrceJThXgr63H/h14HQk+yznQVT6ynvnEpKSnj1W5MmTQy27xgbG2PRgIa4/TshLl2EtX8D3T9kajF9efr0KR4/fgxHR0fY2trq/P2ICBkZT7DrWhHCkuriZQm7wZZeYILA0J9eIpHgyJEjuHnzJi7DHfFVegAAmuefxqVjzMHA0dERTZs2RStHwNqc2VuP3GN2JW3ce86eeYCp9wAWKmMnOQ0xSSATGePvNKCNFrOlPkG6yty+fRsRERHIyclBUFAQHB0dK7x2loyAvbcU/3/tKbDwLLCks37fX3weeCS/rK87AbUNVzoYxL9mB7U9geXSAoDGNYCf+uqvfxWJAFsLlmCztwsw3hNoX3wMffJ+hoM4EwBblUyPBEoMCO14G8F29+7dA8AE4tChQ9XUL3HyFWCL2oBPfeCPQYocbWHxwNhDwCu57V8kEqFv375o1qxZmYk+iQg3btwAwNS8+lZQFYlEFeZMwAnR5ORkFNWSO+fIioGowXxJhZcO2rOQlxeZTIZu3bqhVatWvOciwOJwvH8Gmm0AhuwFNsQBNzPVA2YBdl8pCydNaj0bGxt+hf26MYRiEbCiqwhmRiygdeZxpinQxMuXLxEdHY2SkhK5NgG4duMW9u/fjw0bNpQZ35SVDwzZmYNVt5144QQANzJZ8LChJCQkID4+Hk9LqiLOnL1pbSWP0bzoAn8M58BiYgR0kzvLRt7X/byekqv3RGDPBsAWacaQwE7KbAG6HCX0CdJVxszMDNnZ2SAi7NixA2vWrOEXeRXFxcfs9wIAB7nGfcd15nhTFifvKzQrvV0A/4qLvNHKv2IHFZEEzDvNPjtaAlv7AVZmqsckJSXxCRTLUi8VFBTg0aNHMCfCgiZ38HOWHS4+ZmUOCkqAH3oCZpVkZrn0NPb29mqr+PwS4Lr8feYpt/V/ZAvsH8wEU1w6cCIZGPg78LOfYaullJQUfgXZtGlTJL0All0AApvqXrXqIjoV2HoVGOMBtNDDYa1Ro0Y4f/48ZDIZEp9bo4lFPZa893kcAKDYuhW+33wctraXMWDAAIO94DRBSsXhjIyM4OXlpdTG4nIWn2crWYC9kP9KBZZcYAKooxPQwYntJCKS2E4WUAgnFw2bI5FIBDs7O6SnpxskoO7duwcrKyvUrFkTIpEIDWyA2W3Y+BKeApsuARNL5ejNyMjApk2bICURol/Wxb7HjniYA1iIvFG3SlV4Wz+B2Ej7zR+RBHx+UoQsI/Z2sxG/xFo/S4QcA14UAj9dZnYNQ3B3d8ezrOf4+k4zSCUmMBbJ8F1nGZrZj4GFhQWqVKmi4vTRywXYfZPtomJSgfZaEsBw6j23WkANeay4mZkZatSoAYe8B3hi7KTTDsUJKHNzc1ZzrgyUQ3CICIWFhXrtvAxhj1wQVTMF9g1mz3ZKLvDlKeBjO6CJnebvZeWrLpS+6VRxNkNdvPc7qItpwJSj7IVgYw782h+opaEUzbFjx3D8+HF+1a+LpKQk3o7j3tgZv/qzFwrAVmXBB/VPJmkox48fx88//8yr7XRBRLyAUl7Bc8RnsBgYAGipZOi0lauRPpVr5W4+A/zCgatKoT5EpDO5JbfyMzExwUcffYQ5x4FjScCsSO0rc93XAsw9ARxNBIbvYy/QsqhduzbvmHHn7l21VFX3jVkpiKysrHLZiEpKSrB7926sXbtWxTNTmWIpe8C/PsfuxepmTNC615J7jYIlcd1ziznxeGxSFU7hWoQTB+eNlpmZqdeYiQgHDhzAhg0bcOzYMf7vo90BD7mcXvU3U3MpY21bCw+s2mKP5WSsucWEEwDkkznumHni14LeaL0ZWHQOuPZEsSvMKQJmRLKFT5bcxOJadBEDC8LQvh5hmDzhx+kHwN0sGIRIJEKKbWc8krA5CPEWo6u7I+zt7WFlZaXmkdi2LlNpAyxoVxM5hQr1t2+p7GN16tSBveQBAObGHq8la5OuOlCaMDMz4zPbAEDDhg0rNON7QYnienu7MG/kDb2Z23yRFBh3iF13abgimJnyjGPLuwI2BhQ2KA/vtYC68wwYfZBNfhVj7S7UgCJgV58H/NWrVzA1NYWlpSXs7e1hYQKE9QW6ylUH5x6xl+hLPbJFG8rTp0+RmpqqVy6zzMxMPghYk4DiHkATMVslKmNmDHzfDZgtrziSmQ+MPMCuKS0tDWvWrEFYWBiePHmi8dx2dnaoVasWGjVqhKvPTHlV4rMCRVyJIVx5oqgSm1cMjNjHvLV0IRKJeDXfvXv3IK3jr2isUhsXM5hUrlOnjt4qSE0YGxvj4cOHfP2d0jzLZ6UudsnXPs42wIFAYF57YH8gcHkssK4HMLAxE0YAM0IDCuHUsAyzEvdiy8rK0pj5pTTp6el8Nn3ll6CRGFjehb20iqXA7OMspqhQAvxyFfD5VYSTos7IM2IPkrO1FLNavIRz8VUYE7vhM/KYerhvONDpV+Cb80D33xS2D/tqwEK3+2hTcATSglzk5uZieHNFfFFYvO6xExGio6P5xKppuQqvvSZ2wETtxRUAsHIkXeU+MxFJmhdMZx8qFm+lBZSDgwNqSlIhJjbP2tR8uspsaEM5L58m22aRhC24B+9huxpDOJbEnh2A3WsA0LQmsFhebDclF5gWodjdc/xxm30XYHkcX1cD8jq8twLq8Utg+H7mlWYkYqldPtHhX8AJqGfPnpXpIeft7Y05c+Zg+PDhvDrH3Ji5Z/vJHbliH7OX0gttVdlfE0OySShnn9YloJrW1KyiEImAyV7Aanmg/fMCpmKzsbHhgwcTEjQXV2vZsiXGjx8PPz8/bIhTbeMqvhoC9x0TMdtxZBUAQ/cBT/J0f69Ro0YwMzNDw4YNUVjtE8CSJf2UOE/Cw0dsAnRl8NAHkUjEO68oLxzOnTuHtTuPo8fWIlyUz7WPE1Ot1LdWfP+DKoBfI2BlN+BiMHAsiAXQ/qcpsCegbOEEKHZQRKQIONfBnTtsQsVisZpKu+EHwHR5trH4DGDSUaD9Lyzharp8vm2kGej0ajcWOsegXdVEdMzfh6CcFVjh8wrdnRXCJjkb2HRZ8b1PXYHIoUDvJgp9cXp6OuyqAv3lO/Y/b7PwB22cP38ekZGR2LJlC7Kzs/H1ORarZCRiXmUmekR9cN58WQVArAZFAKfeq1UVaFpK7VW7dm1VO5QWRQK3gzJEQHl5ecHS0hKtW7fWWHV74VngwF12ztV/690tAMUCoV515jLPMagJMETuZHvqAfCDUm7GtFz2uwOAU3Xgy/aGnbO8vJcCKrsQGLaPreQA4Lsu6qug0nACqqioiF9Z6sLIyEitGJiJEXuZB8pzOV57Cgzey1bQFUXpWChdcG7pLVu2VCuxLVUqGaB8s2qivyvQWh6It+kyIDWygIsLe9EnJCToFOjJuSY4ITc2V5VrWo4lGqYClciAg3IB1aMhsEi+4kvNZb+zJrUEh7OzM2bPno0BAwagajVLliOw/Z9INh/AG/MNTW+kCWUBxc3HkTslWPOkIzKLmf1hjAew2U/d/qmMSAQ0tmOOON92VhVkuihdXbcsuDx0Tk5OGj3FxngodtVHExUCo1lNVvxwivVRNCi5hYRrV/n8ezaWVRDgVhWb+gBxY5iw6FCPCY4aVYCNvVlNqupmLA0a59jB7TqD5bG/xVLgV82aUsTExOD0aWZQNjY2RuwTc351P8KN2VH0oV09RUHH0mo+qQw484B97lRf3dZib28PkUgEJyM27vh0za7avr6+6NixI/+s6IODgwNmzJiBbt26qbWFXwe2K/lMhN9gAkQf0l8qsj4McFW/pq86As3lt9D30czlX0bMWeZlMXOiWdUdqPqWiyC/dwKqWAqMPsBcpAEgtA0QoEfpFI05+V4DIzGwtDPT5QMsCHbcIbY1rwg4ASWTycoUpDY2NvD29lYpu81xO0ux3ffUIz0it6LOKQK2XAWaNWsGgGUe15Wle5M8psbMiOmuAeBVCXihpQ8XUphqEGCeQ8OaA9O92f/fyQJGHdAu8MRisWpW7Wr1gbr+SLrPBmBqalqujBUcnINFUVERXrzIxvqLwM5XvpCITGEEGZZ3YSo9fcMQDMXKyoo3xJelps7NzeV3etpiv4zFTMBwO+tP7IEtfsDBQKCbM/CJuxt/rsuXLwNQTW9kZcaeu239gSvjgAuj2eKCQywWq+06XT4AfOuz9m3XmBOPMvHx8YiIiADAnoPBQcOwJIYJVzsLxT2qD+bGCtfxY4mqxQzjM5jDBqB5YWtiYoLZs2dj6qfshEVSLiuNKm5ubvDx8Sl3TB/A+p9/hn2uY8kERrFUdbejiz9vK9TGn2rIi2tuDPzYm7ngE5gddEkUc0wCWLCzPo5JFc17J6BMxIobb5S7Ioq8LPQVUGfPnkViYqJOPb9IBPy3A/CZO/v/uHRmZKyIYsIVVXaDUzkBCg8+XXjXAdrI3z9hlwEHp494V3Nlx4A7d+5gy5YtuHjxIlKyJdgn3/kMbMwComvLN3L7bus/Vu5Ya3Pm4Qaw9DUj5Em1L6UD4w8b5uLPlThp0KCBxrIQhmJvb49ssS0um/ug996qzCYiEsFclof/Nr6JQW+4QoJIJELPnj0RFBQEb29vncdyuydAu4ACmEdn5H+AQ4HAn4PYy5pbeX/88cdqwaPaBL2VmWYVMieglO12Y+S7qOxChccZwJxuDhw4AIDlzxw+fDh2JVrhfjZr/6Kd7p2pJjg1X2a+Ii0SoFDvmRoB7bT4KFSpUgUtHNj7BtA/L1/SC5auigvd0Idn+ez+Lpbb0jf7KRyYdt9UxCVpg4g53wDsOa6nRePoaMVsoSIwAb2JrTvQxA6YpvuWemO8dwJKJAImtWReaP/toL8rpJmZGZ/NQZuAys7OxpkzZ7B9+3bEx+u25IpETF/LCcs9t4CNl/W+DJ60XFWvJn0FVFl2tEtyAfWhNfPa0wflXdS2GyZ8Vc+bN2/yAjshIQGPHj3C2bNnseWqEUpk7IYf48FWfX7y2IkzD/WzzxWUKDJ/9G7IXhoAm9+vOipsfmcesuSgpQ28HElJSTh8+DDi4uKQk5PD7zLKa396/BLYeAkYHmmLP6wm44p5RzzOZ4L7A2kG/F6Gwbtu+QWgPri5ucHFxUVNnVsaTkDVqFGjzKBeJ2ugWS3158jc3FylRIWbm5vWYpvaaNiwIVq0aIGOHTvy92trR2YTBVi2dqlMkfUeYGVnhg0bhnwjG6yLZce1rK2wXxmCT32WzR1QVfNx8U+t6uhWaVUxYXn5ACBajxRpT14B/uHA7BPMgeSmHg6XEhmzAXI2vGVdANcabIFmLGbta8uwRV19wgQjAAzQXlUEAFsAKu9EzeRmC9O3cwur8d4JKI62dQ1PwcF5QmkTUMorT330ykZiYE13RWDl0ijgxH3d31Fm1w3A51eg62/A5yfZqsvCwoJfueoSUEeOHMGGDRt4fX1puB2UPuo9Dq86ihiVsHiggSvzDS4qKsK9e/dQXFzMG9/ru7oh/Ab7AXo2BBrIvSe54D6JTLuLrzLH7zOVIAD4l3oJiUXMsYArvrfvDnNv1iSbz507h7i4OFy5cgUSiQRNmjSBubn5a9mfcotY4PegPUDrzcC3UcCNTHatIpLBxSQdoe5P0edlGKpRToXXgSoPxcXF/O6xvMlHvby80KdPH4SGhsLf39/gOLJGjRqhT58+aNGiBe9sJBIBYz5h7Q9ygK1Rj/Hnn39CJpPBzMwMQ4cOhZ2dHRafZ44RYhHwtc/rxeSYGytUikcT2eLm8Uvglvzx12W3JiJkZWWhgQnzYi1th4qLi8OOHTtUwkG+OgPkyndOSS9Y3stt13RrVr6NUuzOgj9RLPDqVVeYLvbeZqVFtMHtnsyNFbtGXUzxYm7oIrBFfnnzPZaH91ZAvQ7NmjVDx44dtapIuKwMNWvW1Du2wdIM+Lkv89TidLu3yzBxlUhZzsA5J9i2HmDR3j12AHHp+tWFevjwIZ48eaLRHpGWq1iRleUgURrO9pNbBJx64cSv1hMSEnDnzh1+J3XbrCUvWJTVrI3tgEbyG14fNR+nIqxjqVmYmhqxWA7OQ3PzFdUKsRycu3laWhpMTU0xcOBAzJ492+C0QAUlQP/dTGX7t9Kq2cMBGFTjOgJzv8dA8X54V0uBMdiPV5F1oPSBiLQ60IjFYgwYMADu7u78Dvh1cXR0RIsWLSo8HU9vF0WWg8MZDvjwww9hZWWFESNGwMHBAecfKhY3w5vr7xihiV7ydWZGHrM9nX6gaOOElyakUil+/PFH5NxgNrEiKdupcKSlpeHevXtITGQDPX5fMWav2orYo3mngQlHmFaiNPvvKGp+tXYEPm+n2j6lJbv/ZaTdo69IAhzgHIyc2fuoLMQiYH1PIGE8y3b/TyIIKCXc3d3h4+OjcWVZXFzMV741dOVZrzrzYDIRs93AZwe1xzBkvmLu6ZwXU10rlrgWYLrmQXuAdOfhCB43kS9fUZr8/HxeMGlyL1exPxkooFrWUejlf74iRmN3b3h4eKBVq1Z8cG7V6h/gj2Sm6G7jCDQvFWPF7aJiHytynWniRQGLRwGAfo2074gtTJgBn3PHXhGtHuypnDuQ2wmLxWKDc/7tu6NwwPnIFpjTBjg/ktlo5vWpiy+mjcW4ceNUsghU9AtcG8XFxdi8eTO+++47rQHDxsbGaNy4Mfr166fRjbkyYGIEjJbvouLSRWjYnpXwcXBwQLEUWHCWtdWoAsxoXb5z+Tgp7GNH7qkWJ9SQ1J3H2NgYtWrVQk1JKozkCxFlO5RyDNTLIrbgBFgM2GY/FgfH3a9HE4FeO1QdLW5lsgUqwOy263sylZ4ydawUHsMH7mgOcD6ZrBB+A8tQ7ykjEuknzN40goDSk/v37/Nuya+jGvGqA3zjyz6n5gLjDqt79l3NYLrpWLkAaV+PeU1x7rlWpmy1tO2OJT47aYekHM2F1lJSFJGwugSUbRWggbXBl4Jpch11bhFwt2o79O3bF3Z2dvxq8UXtLsgqYC9+TU4qfgpZwa/uNHH4niKIsqy8XzZVWAorUyO2U/2xVOyVra0t7wjDqSENhUixcOAcCCa1VBidq1evDisrK4hEIv4F9TbVeyYmJsjKykJRUdFr5+R72yQmJuKPP/7A9u3bAbB7d9u2bRjgUsy7gf96Q5Ht/+d4hT3l83bMZb08VDVlQgpg9xsXRF5WWArA4qFMUIKaxCRLtAYBZW1tjRXRCo3F1z7sxe9agz3bg+Sb2NRcYMDvzJ75ogAYK88ybmYEbOyj3U48uSU7hsBqNJVGOThaW1LbyowgoPSEW3VXqVLltVeeg5swPTLAhMSXpxX65z03gYA9iht5rAfwSz/24hWJmNdOxFCFDejWMybMNlxSdZEFFAG6pqamGrOfcx5LnrVfT3ffsrYitdPP8WyFduvWLchkMsggwulcpjf52E5xnDKOVgrV4j4dsoJrc7XVL7O2o5Xigd9/R927iVtY3Lt377WScF7OUBi2hzfXPXedO3dGUFAQfH19DT7P6yISifh4KE0Cqri4uMzYubdNVlYWEhISkJiYiNjYWGzduhX379/HiSN/YkgT9nAcS2K/5eOXrCIBwFyeNblLvw6cmi89T2FH6qyngAIAuyJm07uczhadMpmMz3KRaVwHW6+y47lyPhwWJiz0Yk13FiMokTGbk8+vint3USd1DYQytaop1HBHElnCXY5n+cx5CGDvjzcV4vAmeQeH/GY5deoUfvvtN/z111/834iItz+5uLiUKwv5F+0UqUJ+v8kEzFdnWUBckZSpG9b2YB6Apbf0tS2B3/oz7zUuFc2SKBYMzAUlAwoB5ejoqDbWnCKFDcxQ9Z4ynNtpbjGwOV6Re++JVQukvWI6k/EttL/EuR3RnSzNNrnUXMVOr7RzhC7GtWCBoVJiq1FllNV8e/eWKv+uB9vku6dqptq9xogIL168wKtXr+Di4lIhMTCGwDn6aBJQUVFRWLlyJQ4ePGhQPbE3ifIC6ujRo5BKpTAxMYGbmxtGuYtgJGJag5/joeIYsahTxdUh8q2vyHwBME2FPqEXnIByUMrLd/UJkJeXJ1+sibEl1RUEVvBwoY/mfvxdgcNDFIlas+UxWEObsUVtWUz0VNSsU95F7b+j0ECU5b1XWREEVCnu37+PpKQkFTVZRkYGHxRbXs8nIzGLNeASfy69wMpTA2wH8McgZm/RhlgE9HJ4ggEFYaghYVb6i4+BqfJ8nyUlJXj8mL3ZNan3LqcrAvYMdZBQxrM2yxIAAD/HE/IkRiAANyxYLpS6VszYrY3eLgoBrMlZQjkdkp8BU16vukKg7b6pmgrJ0dGRL+XRpUsX/TsFW40eZmsUfOqqyEJQmkOHDmHt2rW8W/TbhttBvXz5ks/DyHH37l3k5+cjOzu7wqqzlpfSO/zq1atj9OjRcHV1RR0roI/8t9+eoJj/Yc21Z91+HSzNFPF1APusT7okOzs7GBsbo6YkBUZgkiA6VaHeSzBrgwd5zP4Y2pap2bTRwIbZMUe6Me+5dnWBBR31G38NC/Y9gDljXJM7a3Dee+619EuXVRkRBFQplHPycdjb2yM4OBjt27evkLQ4lmbMUGqjZDtv48h00vo8eFWrVoV5fhr65G1GrzpsnDFpzEiblpbGq3F05d8zMyr/Q87FS7wsFiHZuhMsXLvhUSETAGM81HeAythUUbiH77+jGr9EpFDveddhxmBDmOjJHvJiqSLYEGBOEUOHDkXfvn3RurVh1vXdNxQelVzmbU1w909WVhZfg+htoi3lUU5ODp/Yt7yLrIrEzMyMV5k7OjoiODhYRWhxgbsl8p2AbRVgZjkdIzSh7H6tj/0JYPeTg4MDTFCCOsbMQyFGLqByxB/gijmTMC0cgP80K7s/M2O2y7o2nmlKDIk9GtdCsWhaGc2cLDh19Lu6ewIEAaUG94J5/vw57xQhEolQp04d+Pr6VphHVr3qTEi1rA2EeLGUMB/omVC7atWqMDY2hhgy9Le9zQu6tbFs5WxqagqxWKwxsp8TUJ/Ylz/4zsNBIWSOPqmNaBF7c3xQRWEL0gWn5nucp+pZePuZwiPpdYqiNfyAxV4BbOX9XCkg2M7ODh4eHgapaaUy1g8AtHLUHReiHAu0ZMkSPqnu20K5XIOygNI3e8Q/weDBgzF48GCMGDFCLci4WU1FHkgAmNu2/I4RmujRkC2GWjgwW5G+KOxQLJr8Ujrw7EUu/qrSG1KRMUzEhKWdDVNHWpkZbhu2qaJIr3bmITMbAOwZ93uNZ6iyIAioUnACSiaT4cWLMuo5lBMPB5atemZr3buN0ohEiliowpfP8Znc8eJCClBo2wyhoaGYMGGCWh2cYikrWwGUz/6kjGIXxdSHAFM3VNHsYKhC1w8VCWT3K6n5uN2TiVhhwDaUSfJCewUShQr1dTn9AEiVy5nhZcSFlFZZlaeMx+tQpUoV3uNNk4Cys7OrVIHDAEtd5OrqqpY6iWNOG7Y76O4MDCxf6JZWLEyA3QOZit2QhKjOzs5wd3dHz2ZsToukwKGCVkg3YRlKJnqK3lqga7CHIt0Tl2G9cwOWIuxdRRBQpVBegWZmZiI7OxtFRW+gsFM5UQ7WHeGmuDHXxTLVQ+lM6wDz8OG8lCpKQH1ir3DTBZixlsuRVxZVTBReTYcTmQCVkcL13Kf+6z9cTWsqxvXL1fLV5uJcy2tWVZQL10bpHba2l+6bxM7ODiKRiL9vyxPDVxnwcABuTGDhFhXlGFFRuLi4oF+/fgjq2IhfZO69w35zZxv1isRvEq4IpjKGxD5VRgQBVQobGxte/fPs2TNERERg2bJlfKLKygJXYyY7OxtWZorEtGcfaq/wyanRRFBUTa0IlHN3DWlqmFDhHEKyC9nYY9OYyg94PfWeMpPl1dZzixQeeIbyIFsRLBzUVH/jOQB+J/O28ff3xxdffIH+/fsDYHkIOXW1sifju0Yl8evQSFVTdXfwJZ01J8l9k4x2V9i2bZXsvO8qgoAqhVgshq0t25M/efIESUlJkMlkfObuykLpulCj3IGqxsySvDpac1pvzv7UyLZi9fju9sD89sz12tCsx+3qsYwAAPPm49R71UyBLuXL44qWtVnCT4DlDjSkBhUHJ9iMRExA6UNgYCC8vLwQFBRk+AkrAEtLS5WdG6fes7CwqLTZI94HWitN7ZCmzKb1tqlmyirk1qrKVKP6LKgqM4KA0gCnHrtx4wZKSthbrbKpRkrXhapuDrQyZ4acM4+MkFAqDIZIIaAqSr2nTLAHy3pc3UCVnLFY4Up8/L7ClbiHc8WsPifLVSxZBazAmyEUlLBYNYCpImvpThLO88EHH6Bnz54ag6T/CapXrw5ra+tyx/AJaCYlJQX79++H5MYfMBYTrGTPYXtjE5KSkv6R8fT5CIgNBgL1XFBVZoS7VQPt2rXD6NGj0bw5s4ibmprCyaly7ZU5AWVpaYn8/HwQET7MPg1jYq7NXCkCjgfZ7CUNlC/+6U3AxS0VSZk6DtAdC2YI7eopKoVuvKRwFdeHA3cVecyG62lXqywUFxcjLS0NmZmZ8PHxQUhIiMbClQLlJzc3F1euXEFJegJ+bXMT/XI3IP9F+j9if3zfKJeAmjRpEmrVqgVPT+1VAUeOHIkPP/wQ7u7ucHd3/8dWFYZQu3ZtODo68hkZnJ2dK6SoXUVSu3ZtfPnll5gxYwbs7e3x4sULSPOeoXERk0wRSar1ZsqTIPZN414LcFIqomZnUXF5w0QihS0qPQ/445Z+31POu+fygUJV+K7w448/IiwsDDExLLWASCSqdGrq9wXO1RwAUhOvwwRM68LZiQVen3IJqKCgIBw5cqTM49auXYsrV67gypUrFRLo+jZ49uwZHxFe2dR7ALOVKa/QOGHatCga5sYs6lV5F8UJKIdqrHRFZUIkUt0x9f3IMLf7suj6oSJ26cc4RfoXXVx5AlyXq0mHlZF3rzLCBeyWVf5doPxYW1vz4QS3bzM1u0gk4rOWCLw+5XoNtG3blncoeN9QDmx82/nUXoeHD5mrWc1qRhgqj1o/kqgIeC1vgtg3zYDGLLuFiVhRiK2iEIuASfJN/oMcVlahLDjniKomivLa7xKcJ2FKSgquXLlSKUMl3hdEIpHKLgoArKysBHtfBfBWZnDWrFlwc3PD559/zru7VnZOnDjBfy6rhPY/RWFhIdLT0/Ho0SM+d6CTkxPGthDxyS/XxbLaU1yJgsqm3uOobw3sHwz8Obh8Bei00ecjhRpx/UXtpeEBlnnikHx90t+1ctTFMRTllEf79+9HQUGBjqMFykvpasJvu0jl+0qZVjx3d3e+SqoykZGRaqsGTSxZsgT29vYoKirCiBEjsGHDBkyaNEntuLCwMISFhQHQnIn5bdOhQwdERUXB39//nx6KVk6cOIFLly7B1NSUz/tWr1491KoKBDVjGRQO3lV94Vc2BwllGr8BwcRhLGa1qeaeBG5nAZ/uZnYu7zosc7Vy9oDdN5jDBlB25ojKirKAAoQX5pum9LtQmO+KoUwBdeXKlXKdgFtZmJubY/jw4fj99981HhccHIzg4GAAQKtWrTQe8zbp1KkTOnToUOmcI5ThHgLlpKRcgtjxLVj+uGIpSx4JsBgJ1/dTI6sXn7oCP8SytEXxGezf+ossxqlZTcDbEfCuDWyT593zrqNfHarKiHImkSZN9KjZIFAuSseXCQ4SFcMb94NMT0+Hg4MDZDIZDhw48E49LJVZOAGqqzRPT0/k5eXxK2f7aixh628JikzQHvbvZtGyisLMGNg5APjzNss6fTmDpX6SEnOKuPJEtYaUrqzllR1jY2P06tULycnJgnv5W8DS0hImJiZ83KSwg6oYyiWgRo4ciYiICGRlZcHR0RGrVq1CQEAAgoODMX78eHh6euI///kPnj17BplMhlatWiEkJKSixv6vR/khaNasmVp5jYmewK4bCgFVWe1Pb5N61YGp3uxfsbzA3N+pQOxjFsj8Sp5tonY11eqn7yItW7ZEy5ZvMRncvxiRSIQhQ4agWrVqMDc3F1z6K4hyCahffvlF4985WxLAKtQKvBmUBVR2draagKpjxbI/75RXNxcElCqmRswmx9nlJDLmWn4zk5V4KG85EoF/Fw0a6FlISkBvhFDnd5iqVavCyMgIUqmUj9kqTYgXK8NRw0K/Mtb/ZozFLK+ge+XIUCQg8K/nX2yRePcRiUS82/7p06c1HlPbEjg3gpWTNhOWIwICAu8QgoB6x+GKz4l0RN9WxsBcAQEBgbIQ1tTvOAEBAYiJiYG3t4F1LgQEBAQqOYKAesdxcHDgC9MJCAgIvE8IKj4BAQEBgUqJIKAEBAQEBColgoASEBAQEKiUCAJKQEBAQKBSIggoAQEBAYFKiSCgBAQEBAQqJYKAEhAQEBColAgCSkBAQECgUiIIKAEBAQGBSokgoAQEBAQEKiUiIqJ/ehClqVWrVoXUVnn69ClfYfZNtL9PfQjjrHx9COOsfH28K+OsiD4q4hz6kJycjCdPnmhupPcYb2/vN9r+PvUhjLPy9SGMs/L18a6MsyL6qIhzlBdBxScgICAgUCl5rwVUcHDwG21/n/oQxln5+hDGWfn6eFfGWRF9VMQ5ykultEEJCAgICAi81zsoAQEBAYF3F0FAvUGEzamAgIDA6/PeCahXr17pdZxMJtPalp+fr/O7ubm5OtuJCMnJyRCJRBqFFBEhNTW1zPHdvXtXZ/vhw4d19pGVlaWzHdB/vt4W+gj1so4pb7s+vE+Lj7cxn2/jN6ksVIb5el/m870RUDKZDBMmTMDUqVNx9OhRZGdnazzmv//9LwBALBar/YgymQzTpk3DtGnTcO7cOY3fnzhxIsaNG4czZ85oHcv333+PwMBA3LlzR01IyWQyDBs2DMeOHdP6fSJCr169cODAAa3X2r9/fwQEBCA+Pl5j+6hRozBp0iTs2bNH480qk8kwdepUzJo1C8ePH0dOTo7W8eiLLqGvjK6HR5tQN+QYkUik9bwSiURrO3fMrVu3AOi+Hl19GIKu69D3JVPWvJfVXp751NXOHZOTk6PXOd70i7uyzydQ9py+7fnUhb7Pe3l4bwTUli1bIBaLMX/+fFy7dg27d+/G48ePVY4ZNWoUli9fjunTpwNQ/xFHjhwJc3NzjBgxAgsXLlR7+Y8ePRpWVlaYOXMmNm7cqNY/R/369VG1alVMmzYNcXFxKucZOHAgzMzMEBwcjKioKFy/fl3t+2fPnkXHjh0xa9Ys/PLLL4iKikJeXh7fHhwcjPbt2+Pw4cNYv369WpDb/PnzYW5ujnXr1mHfvn24ffu22jmWLl0KmUyGmTNnIioqCv/73/807rhkMhnmz5+PRYsW4dGjRyrj4NoPHToEQLPQ54759ttvsWTJErx69Uqj0J43bx6+/PJLnQ9faGgoPvvsMwDqv51MJsOIESNUhLpyOxGhS5cuCA8PV+tXuY/u3bsjMDBQ4/Vwi6A5c+bg6tWraufgjlm8eDGWLl2K1NRUtR2qTCbDqlWrsHr1ahQUFKi9iGQyGb/40TYPMpkM69evx//+9z+t41y+fDmWLl0KItL6u8ybNw/ffPONxnPJZDJMnjwZp06d4v9Wur1///7Yu3evxnbuGB8fH3Tu3Jk/R+n2efPmYcGCBcjNzdV4vdy1rl+/Xut8vQ/zyR2ja07f1nyW93mvSN4bAeXl5YVq1arB3t4eI0aMgKmpKY4fP64i5RcuXIiCggKYm5tj0qRJABQ3UnFxMYYMGYKlS5eibdu2GDt2LH8zERFKSkowZswYLF26FJ6engDYS37Pnj0oKSlRGYufnx9mzpyJ6dOnY968eYiPj+eF2dixYyGTydCwYUMcPHgQs2bNwh9//KHyQzs7OyM1NRX+/v5ISUnB7t27sX37djx//hz5+fkICgrCrFmz0LFjR7i4uODp06cAFCuajh07wsnJCVZWVjAzM8P333+P8PBwZGZm8udo27YtGjdujIYNG2LcuHE4ceKERpXhokWLIJFI4OzsjG3btuHw4cP8+QDA19cXfn5+2Llzp8p8chAR/Pz8kJeXB4lEgrFjx0IqlfIPBhHB398fUqkU1atXx/z58/l+lCkqKoKFhQWuXbuGSZMmQSaTQSQS8dfs6+uLjz76CH5+frh79y6Ki4tV+ggPD0e/fv0wdOhQnDx5Ek+fPkVhYaHKOIcMGYIuXbrA398fCxYsUBvHmDFjYGVlhQ4dOmD9+vUoKSlRG+eUKVNQUFAAR0dHbNq0CQcPHlRRCU+ZMgXp6ekQiUT473//i7///ltlHJ06dcLAgQOxfft2rfPZp08fPHjwACkpKRg4cKDKOLn5fPr0KV69eoUpU6Zo7CcvLw8pKSk4e/Ysvv32W34+uWO6du2KOnXqwNfXF8+ePVObizVr1sDHxwcDBgzAtWvXUFhYqNJORAgKCkKPHj3Qt29f/PTTTyhNYGAgioqKUKNGDYwdO1bj7z58+HCkpqaiuLgYc+bMQUxMjMp1vC/zCQCrV6/WOqeGzKetra3O+UxJSdE6nwsXLkRJSYnW593Hx0fn817h6BHM+07w6tUrWrNmDUVERFBxcTGlpaXRZ599RpcuXeKPkUql/OcZM2bQxIkTiYjo8ePHVFxcTEVFRSSTyUgqldLx48dpzJgxRET06NEjIiKSyWRERBQTE0OjRo2ikydP0pAhQyg6OlplLDk5OTRu3Dh6/PgxRUdHk7OzM33zzTd8+7Fjx+iHH34gIqKEhAQaNGgQXbx4UWWc69ato8DAQHrx4gVlZWXR4sWL6cSJE2rXvW7dOho5cqTK354/f0779u2jQYMGUdu2benGjRv09ddf0x9//MEfk5GRQYsWLaKNGzfSvn37qEuXLjRkyBBKSkpS6evXX3+l1atXExHRpUuXaN26dXTo0CF+Pk6ePEkpKSnUr18/+vXXX/nvcXOVk5NDGzdu5P8eGhpK4eHh/DEvX76k7du38+0BAQG0Zs0aSkhI4PvgSElJoX379tHXX3/NX3NeXh4REc2bN4/Gjx9PHTp0oFGjRtGQIUPo77//5s8TGRlJs2bNIn9/f5o4cSJNnjyZdu3aRbm5uURElJaWRps3byYioszMTFq8eDFlZGSoXMvKlSv5sQ4aNIiWLFlC0dHR9OLFC36M69ev5+fnzz//pB49etDx48f5fnbv3k0HDx4kIqI//viDvv76a7p+/ToRsfvwwoUL9PjxY/Lz81OZT+7ejYmJUZmv0NBQunDhAt9/bGws7d27l28fM2YM7dixgx8jN+dERHFxcbRr1y5avXo1LVy4kIiIiouLKTExkRYvXkxLliwhHx8f+uyzz+jTTz+lJ0+e8H3ExMTQ6tWrqV+/fjRmzBgaOnQoxcTE8O0nTpygnTt38udZvny5yr0hkUhow4YN/G80cuRI+vnnnyklJUXlNw8LC6ObN28SEdGqVauoU6dOdO/ePb6fPXv28PNdej5TUlLowoULlJ6ernU+o6KiaMeOHVrn86+//qI///xT53xyn7XN5+3bt1XmMzg4WG0+JRIJRUdHa53TY8eO6ZzPwsJC2rhxo875LCkpoc2bN2ucT5lMRpmZmXTq1ClauXIlEak+7zKZjB4+fEi3b9+mhw8fqs1n6We1onhvdlAWFhbw8/NDbGwsDh48iBo1aqBZs2aIjo7mJbzylnTlypWwt7dHmzZtMHbsWOTn58PU1BQikQhisRje3t5wdXXFkSNHMHbsWOTl5fGrETc3N/z000/w9fWFu7s7oqKiVFYRVlZWGDt2LCIiIrBjxw40bdoU8fHxkEqlICJ0794dEydOBAA0bdoUzZo1Q0JCAv99sViMTz/9FPb29ti9ezdMTExgbW2NyMhI/jzcfydPngxLS0v8+OOP/PdtbGzQr18/zJgxA/3798fHH38Ma2tr7N+/H1KpFADLdzh69GjY2Njg7Nmz2L59O3r37o2UlBReVQAAXbp0QXFxMRISEuDh4QEPDw8cPnwYc+fOhUgkgq+vLxwdHbF27Vrs2rUL27ZtA8AcNBYsWAArKyuMGjWK32V+9NFHyM7Ohkwmw5dffolq1aohKCgIAHDw4EHk5eWhuLgY33//PZ48ecLbDAG2Sj19+jTmz5+Pxo0bo1WrVnyg4KJFi1CzZk307dsXmzdvxqeffoqNGzciNDQUIpEInTp1goWFBWrUqIH169fjs88+w40bN/Ds2TMsXLgQtWvXxqhRowAApqamyMzMxP79+3mVB8BWwZmZmejZsycyMzPx4Ycf4sCBA0hMTOTnq3nz5rh+/ToiIyNRq1YtEBEOHDiAKVOmYNy4cbh69SqysrKQm5uL/v37w8XFBUuXLsW0adOwYMECFBcXw8HBAatXr0Z4eDg/n4WFhZg5cybCwsJgbW0NAJBIJKhSpQoePnzIq2s3bdoEW1tbAEztffv2bcTHx2PYsGEoKirC9OnTMWPGDJw7dw42NjY4fPgwgoKCYGFhgd69e2PYsGFYunQpbG1tIZVK4efnh7CwMHTs2BGTJk1CSEgIxo8fj6tXryI/Px+NGjXCpk2bEBAQgK1btyInJwdz5sxBeHg4nJycAAB16tRBTEwMfv75Z97OO2HCBNy6dQsPHz5E586dcffuXeTm5mLBggVISUlBSEgIr8WIiYnB/fv30bp1a8hkMqxevRrjx49HUFAQ4uPjkZ6erjafkyZNwuzZs1FYWAh7e3t+Pn/99Vd+PkNCQrBu3TqV+bSwsMCDBw8gk8kQEhKCNWvWqM3n5cuX+fmcNGkSJkyYgLNnz8La2hqHDx/GkCFD+PkMDAzEggULYGNjA4lEAj8/P/z000/o0KEDJk+ezNu1hw0bhqtXr6KgoICf04EDB+KXX37BhAkTsGXLFtStW1dlPsPCwvjvjxw5Erdu3cK9e/fQpUsXtfmcMGEChg0bhhMnTuD8+fP8fEqlUqxZswYrVqxA7969kZeXh5o1a+LKlSv8837kyBEsWbIEAQEBKCoqQr169dTuzxcvXrwZm9QbEXv/ICkpKbR161YaNWoU+fr6UmJiotoxnLSPjIwkJycnfkWhTFZWFtWsWZO8vLw0thMR/f777+Tt7a226yAievbsGU2cOJEWLFhAREQSiURjHzt37qSPP/6Y7t+/r9aWlpZGv/32G40ZM4Z69+5Nd+7c0Xgdp06doh9++IGKiopU2tPT02n+/Pk0e/Zsateundr3lTl16hQ1bdqUHj58SMOHDydzc3OaOXMmERHt3r2bvvvuO/rrr7+IiKhx48ZkampK06ZNU+nj0aNHNGjQIBo1ahS5urqSmZkZzZgxQ+WYixcvUnh4OHXq1ImMjIwoJCSEb0tNTeU/T5s2jTp27Ejm5uYq5wkLC6PIyEiaOnUqWVtbk5GREU2bNo2fC+V5dnFxIVNTU5o+fToRER05coSGDh3K72BCQ0OpdevWZGZmpvFaevXqRd26dSNzc3OaPXs23/bTTz/xq+4vvviCPvnkEzI3N6fQ0FAiYvfVkiVLaPDgwXT//n3y8PCg4OBgunjxInXo0IH8/f1p/fr1/A6tcePGNHz4cIqKiiJfX1+KjY1Vmc+RI0fSxx9/TDNnzqS//vqLfH19ec1AZGQkHT9+nDp16kSenp504cIF8vX1pYSEBHr69Ck/5okTJ1L37t0pNDSUoqKiqHPnzpSSkkIHDhygGzdu0JgxY8ja2pp8fHwoJiaGfH19KSIiQmVOGjZsSKNGjaKYmBjq2LEjBQYG0sSJE+nGjRtERBQSEkKdOnWi2bNnq11LUlISDRs2jPz8/Gj27NkUFxdHPj4+FBsbS2FhYfy9FRISQq1ataLZs2fT5cuXqUOHDjRs2DBatWoV9erVixITE8nT05OmTJlCV69epYEDB9Lo0aNp7dq1/I6Em8+LFy9SYGAgPXz4kIjYu2Hw4ME0cuRIatKkCc2cOZPi4uIoMDCQv/dOnDhBJ06cIF9fX/L09OT7ePr0KaWlpanMZ48ePSg0NJRiY2NpyJAhlJ+fT4cOHaKEhASV+bx8+TINHjyYrly5ojKfo0ePJj8/P5ozZw5dvnyZAgICaNq0aTR27Fj+nePq6kr9+/en2NhYlWtJTk6m4cOHk7+/P82ePZuuXbtGgwYNosTERNq0aROv1ZkyZQq1bt2a5syZQ9evX6cBAwZQSEgIff/999S7d29KTEyk5cuX0+bNm6lr167UrVs3mjBhAu3cuZPOnz9PRETTp0+nzz//nHx9falHjx78ri45OZkCAwNp5MiRNGXKFCooKKCK5r0TUBxZWVkqD6gmHj16xKsLSiOTyWjRokV09+5dje0FBQX0ww8/6HzpP3/+nP+sSUAVFhbSL7/8Qrdu3dI5zqysLMrMzHyt9pycHLp16xYlJyfrPMeFCxf4a+GOnT17Ni+kjhw5QkuWLKHRo0eTt7c3PXr0iObOncurSTnWrFlD9evXp8jISCIimjt3Lk2YMIFvP3PmDNWqVYu8vb3pxo0bau1ETCC2b9+eoqKi+D7GjRtHRERXrlyhwMBAWrBgASUnJ9OLFy8oNDRUbRw7d+6k5s2bU1paGs2ZM4cXUseOHaNly5bRmDFjqGvXrnTmzBn+HKX72LZtG61atYqkUinNmTOHxo8fT0REp0+fpgULFtCXX35Jvr6+dOrUKX6+uPMQsd/36NGj5Orqyr88Dx06REuWLKG1a9fS4sWLadKkSeTq6kqPHz8mIqLw8HBasWKFynzWq1ePNmzYwP9N+ZgjR45Q3bp1yc3NjVdt7dy5U6WP3bt3k5eXF23bto3/2/bt22nFihW0Z88e6t27N82dO5f2799POTk5RES0a9culT5+++03cnZ25q/j4MGDtHTpUlq/fj0tWbKEZsyYQW3btqWtW7dqHKdEIqGvvvqKvv32W5XfaMWKFRQWFkbz5s2jlStXUps2bXhVK3eeZcuWERFT50ZGRlKTJk0oKyuLiJjKbcKECdS+fXtatGgRTZo0iZo0acLPZ2BgIE2ZMoVXK3PzqTwX3DF79+6lffv2Ud26dcnDw4Ofz8GDB1NISAj9/vvv/Nx4eXnRvn37+D4GDRpEISEhFBISQv7+/jR37lw6deoUP59BQUE0ZcoUvo/w8HD65JNP+MUSEdFnn31GEyZMIB8fH1q0aBFNnTqVmjdvzpsYlK8lPz+fFi5cyKvfldu7d+9On3/+Oa1cuZJat25Nu3fv5o8JDg6myZMnU0REBL18+ZJOnz5NrVu3poyMDNq/fz+dO3eOunTpQqGhobRw4UIaPXo0dejQgZKSkujQoUMUERFB3bt3500SP/zwA9WrV0/rIr68vLcCqiIoKSnR2a5tV1QaXfpZZbtYZUF5TFOnTuV3Qffv36czZ87wqzgiVVteZmYm7dq1i27evKnV3vfo0SOaOHGiyg2t3B4bG0vTpk3T2MfUqVOJiGjPnj1UUlJCUqmUP0a5j0uXLtGsWbPo6tWrGq/jwYMHdO7cOUpNTdU6zmfPntGFCxf4XQ4RW0lyAujQoUO0detWunv3rkof06dP5wVZWloabdiwgS5fvszbNo8dO8bv1i5cuEBnz56l27dva7R9pqWl0YEDB+jmzZtq9tHg4GAiIjp58iQNGDCArl+/rrGP06dP0/Tp0zX2we1eORuhNhvsiRMnaOrUqRQfH6/xOqKjo+ncuXOUnJystY+srCw6deoUPXjwQKWd+03/97//8Ys15T4iIyP5a71z5w5t3LiRrl27RkTMHjds2DCKiYmhQYMG0Z49e+jvv//mtSbKtuLAwEA6f/487du3j27evKlmTz5x4gQFBQXRtm3baOjQofxirbS9edu2bTRr1iy1PkaOHEnnz5+ngIAAmj9/PhFpt1n/9ttvNHfuXP43U76W6OhoGjRoEO3evZvi4uJ47UzpPq5du0YxMTH8zo8bQ3R0NA0cOJC++OIL/llUPseIESPo+PHjNGTIEDpy5Aht2LCBEhISeLt5amoqxcfHk7OzM61fv54ePXpEjx490mhXX758OcXFxfE76DeBIKAENKIsVL/66itq06YN9e3blzewK7d//fXX1KpVK/Lz8+OdFjQd07p1a+rTp4+a8wHX3q5dO+rdu7eKKkX5mAULFlCHDh2ob9++/HlK99G2bVut5/jqq6+odevWKteh7Vr69u3LO1CUNQZt19q3b19+Bc2Rm5tLy5cvp6NHj1LPnj3V1CK5ubm0cuVKOnToEPXs2ZPy8/OpNNwxR48epa5du6rtjrn2Y8eOUbdu3VRUp8rHfPfdd3T8+HHq0aMHFRQUqFxD6T64HYm+16Hcx8GDBzVeS25uLi1btowfw6tXr7T2cfjwYerRowdlZ2fzbQUFBfwicfny5So7vtLtS5cupRUrVqgtFkv3sXTpUhVthHL7ihUr6LvvvlPRjJQ+ZtWqVbwDA7dw0dRH6fvCkGtZsmSJzvZVq1aptZc+ZtmyZbR8+XIqLCzk2y9dukRbtmyhyZMnU58+fSggIEBlvrj2KVOmUN++fSkgIEDtHBWNIKAEtFLaVld6pVRWe+lj6tatq6YKKN2uSd1Zls3QkHPoM86yzqFPH9rUHmXZNvWxfXLHeHp60u3bt3W2a1Mf6zsObX0YMs6KuNaWLVuWaQvWZG/Wp507xtPTU6MtmGtv2bKl1nZ9x1ERfZTV7uXlVea1enl5qdnOS9vNS2uQymp/EwgCSqBMdNnq9GmvLH1UhnGWZdssq72y9FFZxlmWLVgfW/H70kdFnKMsu3lZ7RWNUG5DQOAtI5FIYGxs/NrtlaWPyjJOqVQKIyOj125/n/qoiHMALIylrJRMFZXuSxeCgBIQEBAQqJS8N4G6AgICAgLvF4KAEhAQEBColAgCSkBAQECgUiIIKAEBAQGBSokgoAQEBAQEKiWCgBIQEBAQqJT8H3C4PCzGJYCaAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 490x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for l in range(layer_num):\n",
+    "\n",
+    "    fig, ax = plt.subplots(1, 1, figsize=(7, 5), dpi=70)\n",
+    "    ax.plot(list(range(len(tokens))), mag_dict[f\"tc_min_{l}\"], label=\"FP_min\", color=\"gray\", linewidth=2.5, linestyle=\"--\")\n",
+    "    ax.plot(list(range(len(tokens))), mag_dict[f\"tc_max_{l}\"], label=\"FP_max\", color=\"gray\", linewidth=2.5, linestyle=\"-.\")\n",
+    "    # ax.plot(tokens, mag_dict[f\"tc_mean_{l}\"], label=\"tc_mean\", color=\"r\")\n",
+    "\n",
+    "    ax.plot(list(range(len(tokens))), mag_dict[f\"0_min_{l}\"], label=\"1SB_M\", color=\"orange\", linewidth=2.5)\n",
+    "    ax.plot(list(range(len(tokens))), mag_dict[f\"0_max_{l}\"], label=\"1SB_M\", color=\"orange\", linewidth=2.5)\n",
+    "\n",
+    "    ax.plot(list(range(len(tokens))), mag_dict[f\"1_min_{l}\"], label=\"1SB_O\", color=\"dodgerblue\", linewidth=2.5)\n",
+    "    ax.plot(list(range(len(tokens))), mag_dict[f\"1_max_{l}\"], label=\"1SB_O\", color=\"dodgerblue\", linewidth=2.5)\n",
+    "\n",
+    "    # ax.plot(tokens, mag_dict[f\"2_min_{l}\"], label=\"1SB_LSM_M\", color=\"b\", linestyle=\"-.\")\n",
+    "    # ax.plot(tokens, mag_dict[f\"2_max_{l}\"], label=\"1SB_LSM_M\", color=\"b\", linestyle=\"-.\")\n",
+    "    # ax.plot(tokens, mag_dict[f\"{model_num}_mean_{l}\"], label=\"st_mean\", color=\"dodgerblue\")\n",
+    "    # ax.set_tit#le(f\"FFN Output Layer {l}\", fontsize=20)\n",
+    "    ax.legend(loc=2, fontsize=15)\n",
+    "    plt.xticks(range(len(tokens)),rotation=45, fontsize=8)\n",
+    "    # ax.set_xlabel(\"Token Number\", fontsize=20)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1217,
+   "id": "cc100017",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# model_num = 0\n",
+    "# for l in range(layer_num):\n",
+    "\n",
+    "#     fig, ax = plt.subplots(1, 1, figsize=(15, 5), dpi=50)\n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"tc_min_{l}\"], label=\"FP_min\", color=\"gray\", linewidth=2.5, linestyle=\"--\")\n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"tc_max_{l}\"], label=\"FP_max\", color=\"gray\", linewidth=2.5, linestyle=\"-.\")\n",
+    "\n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"0_min_{l}\"], label=\"MI\", color=\"tab:orange\", linewidth=2.5)\n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"0_max_{l}\"], label=\"MI\", color=\"tab:orange\", linewidth=2.5)\n",
+    "    \n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"1_min_{l}\"], label=\"MIXED\", color=\"tab:blue\", linewidth=2.5)\n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"1_max_{l}\"], label=\"MIXED\", color=\"tab:blue\", linewidth=2.5)\n",
+    "    \n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"2_min_{l}\"], label=\"OI\", color=\"darkgoldenrod\", linewidth=2.5)\n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"2_max_{l}\"], label=\"OI\", color=\"darkgoldenrod\", linewidth=2.5)\n",
+    "\n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"3_min_{l}\"], label=\"OI\", color=\"r\", linewidth=2.5)\n",
+    "#     ax.plot(list(range(len(tokens))), mag_dict[f\"3_max_{l}\"], label=\"OI\", color=\"r\", linewidth=2.5)\n",
+    "    \n",
+    "#     ax.set_title(f\"ATTN Output Layer {l}\", fontsize=20)\n",
+    "#     ax.legend(loc=2, fontsize=15)\n",
+    "#     plt.xticks(range(len(tokens)),rotation=45, fontsize=8)\n",
+    "#     # ax.set_xlabel(\"Token Number\", fontsize=20)\n",
+    "#     plt.show()\n",
+    "#     # break\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1684,
+   "id": "d73aed1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:05<00:00,  2.28it/s]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:05<00:00,  2.30it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "st_2 = ranking_loss_func(student_probs_2, teacher_probs)\n",
+    "st_3 = ranking_loss_func(student_probs_3, teacher_probs)\n",
+    "# st_4 = ranking_loss_func(student_probs_4, teacher_probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1717,
+   "id": "fb9c4ae5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Ranking Loss(CoLA)')"
+      ]
+     },
+     "execution_count": 1717,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 960x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 5), dpi=120)\n",
+    "fs=20\n",
+    "lw=2.5\n",
+    "# ax.plot(list(range(layer_num*head_num)), st_1, label=\"Direct Q\", color=\"tab:red\", linewidth=lw, alpha=1)\n",
+    "ax.plot(list(range(layer_num*head_num)), st_2, label=\"TI-Output + Map\", color=\"darkblue\", linewidth=lw, alpha=0.8)\n",
+    "ax.plot(list(range(layer_num*head_num)), st_3, label=\"TI-Output\", color=\"tab:blue\", linewidth=lw, alpha=0.8)\n",
+    "# ax.plot(list(range(layer_num*head_num)), st_4, label=\"Ternary\", color=\"tab:orange\", linewidth=lw, alpha=0.8)\n",
+    "\n",
+    "ax.tick_params(axis=\"x\", labelsize=fs)\n",
+    "ax.tick_params(axis=\"y\", labelsize=fs)\n",
+    "ax.legend(fontsize=fs, loc=2)\n",
+    "ax.set_xlabel(\"Head Number\", fontsize=fs)\n",
+    "ax.set_ylabel(f\"Ranking Loss(CoLA)\", fontsize=fs)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1318,
+   "id": "e54c005d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "COVER MEAN CHECK\n",
+      "0.7350823283195496\n",
+      "0.6579217910766602\n",
+      "0.6787551045417786\n",
+      "0.654063880443573\n",
+      "KL DIV CHECK\n",
+      "21.967914581298828\n",
+      "27.941110610961914\n",
+      "21.092199325561523\n",
+      "14.090266227722168\n"
+     ]
+    }
+   ],
+   "source": [
+    "cover_mean_check = True\n",
+    "kl_div_check = True\n",
+    "student_probs = student_probs_2\n",
+    "exclude_sep = False\n",
+    "layer_num = 4\n",
+    "if cover_mean_check:\n",
+    "    print(\"COVER MEAN CHECK\")\n",
+    "    top_k = 5\n",
+    "\n",
+    "    for i in range(layer_num):\n",
+    "        teacher = teacher_probs[i][0]\n",
+    "        student = student_probs[i][0]\n",
+    "\n",
+    "        head_sum = 0\n",
+    "        for h in range(head_num):\n",
+    "            coverage_head_sum = 0\n",
+    "            for row in range(seq_length-1):\n",
+    "                if exclude_sep:\n",
+    "                    tc_argsort = teacher[h][:seq_length-1,:seq_length-1].sort(descending=True)[1][row][:top_k] # top-k\n",
+    "                    st_argsort = student[h][:seq_length-1,:seq_length-1].sort(descending=True)[1][row]\n",
+    "                tc_argsort = teacher[h].sort(descending=True)[1][row][:top_k] # top-k\n",
+    "                st_argsort = student[h].sort(descending=True)[1][row]\n",
+    "\n",
+    "                max_idx = 0\n",
+    "                for idx in tc_argsort:\n",
+    "                    tmp = torch.where(st_argsort == idx)\n",
+    "                    max_idx = max(tmp[0].item(), max_idx)\n",
+    "\n",
+    "                coverage_ratio = max_idx / student.shape[1]\n",
+    "                coverage_head_sum += coverage_ratio\n",
+    "\n",
+    "                # print(f\"H{h} : {coverage_head_sum/seq_length}\")\n",
+    "\n",
+    "            head_sum += coverage_head_sum / seq_length\n",
+    "        print((head_sum / head_num).item())\n",
+    "\n",
+    "if kl_div_check:\n",
+    "    print(\"KL DIV CHECK\")\n",
+    "    for i in range(layer_num):\n",
+    "        if exclude_sep:\n",
+    "            if len(sep_index) == 2:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000; teacher_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000; student_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "            else:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                \n",
+    "            teacher = torch.nn.Softmax(dim=-1)(teacher_atts[i])\n",
+    "            student = torch.nn.Softmax(dim=-1)(student_atts[i])\n",
+    "            \n",
+    "            student = torch.clamp_min(student, 1e-8)\n",
+    "            teacher = torch.clamp_min(teacher, 1e-8)\n",
+    "        else:    \n",
+    "            teacher = teacher_probs[i]\n",
+    "            student = student_probs[i]\n",
+    "        \n",
+    "        neg_cross_entropy = teacher * torch.log(student) \n",
+    "        neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "        neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "        # p(t) log p(t) = negative entropy\n",
+    "        neg_entropy = teacher * torch.log(teacher) \n",
+    "        neg_entropy = torch.sum(neg_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "        neg_entropy = torch.sum(neg_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "        kld_loss = neg_entropy - neg_cross_entropy\n",
+    "\n",
+    "        kld_loss_sum = torch.sum(kld_loss)\n",
+    "        print(kld_loss_sum.item())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1276,
+   "id": "81c66000",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "device(type='cuda', index=0)"
+      ]
+     },
+     "execution_count": 1276,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e75aa306",
+   "metadata": {},
+   "source": [
+    "# Per Layer Comp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "1241c853",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "06/21 03:10:07 AM Loading model models/BERT_large/sst-2/pytorch_model.bin\n",
+      "06/21 03:10:07 AM loading model...\n",
+      "06/21 03:10:07 AM done!\n",
+      "06/21 03:10:07 AM loading configuration file output/BERT_large/sst-2/exploration/1SB_O/config.json\n",
+      "06/21 03:10:12 AM Loading model models/BERT_large/sst-2/pytorch_model.bin\n",
+      "06/21 03:10:13 AM loading model...\n",
+      "06/21 03:10:13 AM done!\n",
+      "\n",
+      "06/21 03:10:14 AM loading configuration file output/BERT_large/sst-2/exploration/1SB_O/config.json\n",
+      "06/21 03:10:19 AM Loading model models/BERT_large/sst-2/pytorch_model.bin\n",
+      "06/21 03:10:19 AM loading model...\n",
+      "06/21 03:10:19 AM done!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "cos_func = torch.nn.CosineSimilarity(dim=-1)\n",
+    "mag_dict = dict()\n",
+    "seed=42\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "mse_func = MSELoss()\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model_dir = \"models\"\n",
+    "output_dir = \"output\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    model_dir = os.path.join(model_dir, \"BERT_large\")\n",
+    "    output_dir = os.path.join(output_dir, \"BERT_large\")\n",
+    "\n",
+    "first = True\n",
+    "for st_model_name in [\"1SB_O\", \"1SB_O\"]:\n",
+    "    if first:\n",
+    "        teacher_attnmap = True\n",
+    "        num = \"TI\"\n",
+    "    else:\n",
+    "        teacher_attnmap = False\n",
+    "        num = \"q\"\n",
+    "    \n",
+    "    student_model_dir = os.path.join(output_dir, task_name, \"exploration\", st_model_name)   \n",
+    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "    if first:\n",
+    "        # Teacher Model Build\n",
+    "        teacher_model = BertForSequenceClassification.from_pretrained(teacher_model_dir, num_labels=num_labels)\n",
+    "        teacher_model.to(device)\n",
+    "        teacher_model.eval()\n",
+    "        model = teacher_model\n",
+    "\n",
+    "\n",
+    "    # Student Model Build\n",
+    "    student_config = BertConfig.from_pretrained(student_model_dir,\n",
+    "                                                    quantize_act=True,\n",
+    "                                                    quantize_weight=True,\n",
+    "                                                    weight_bits = 2, # Always Ternary when \"quantize_weight = True\"\n",
+    "                                                    input_bits = 8,\n",
+    "                                                    clip_val = 2.5,\n",
+    "                                                    quantize = True,\n",
+    "                                                    ffn_q_1 = True,\n",
+    "                                                    ffn_q_2 = True,\n",
+    "                                                    qkv_q = True,\n",
+    "                                                    emb_q = True,\n",
+    "                                                    cls_q = True,\n",
+    "                                                    clipping = False,\n",
+    "                                                    layer_num = -1,\n",
+    "                                                    mean_scale = 0.7,\n",
+    "                                                    quantizer = \"ternary\",\n",
+    "                                                    act_quantizer = \"ternary\",\n",
+    "                                                    init_scaling = 1,\n",
+    "                                                    clip_ratio = 1,\n",
+    "                                                    gradient_scaling = False,\n",
+    "                                                    clip_method = \"minmax\",\n",
+    "                                                    teacher_attnmap = teacher_attnmap, # CHANGE\n",
+    "                                                    parks = False,\n",
+    "                                                    stop_grad = False,\n",
+    "                                                    qk_FP = False,\n",
+    "                                                    map=False,\n",
+    "                                                    act_method = \"clipping\"\n",
+    "                                                    )\n",
+    "\n",
+    "    student_model = QuantBertForSequenceClassification.from_pretrained(teacher_model_dir, config = student_config, num_labels=num_labels)\n",
+    "    student_model.to(device)\n",
+    "    model = student_model\n",
+    "    print()    \n",
+    "    \n",
+    "    mag_dict[f\"{num}_ffn_mse\"] = []; mag_dict[f\"{num}_attn_cos\"] = []; mag_dict[f\"{num}_attn_mse\"] = []; mag_dict[f\"{num}_ffn_cos\"] = []\n",
+    "        \n",
+    "    student_model.eval()\n",
+    "    teacher_model.eval()\n",
+    "    student_model.to(device)\n",
+    "    teacher_model.to(device)\n",
+    "    \n",
+    "    teacher_outputs = teacher_model(input_ids_sliced.to(device))\n",
+    "    teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_zip = teacher_outputs\n",
+    "    \n",
+    "    student_logits, student_atts, student_reps, student_probs, student_zip  = model(input_ids_sliced.to(device), teacher_outputs=teacher_outputs)\n",
+    "    for l in range(layer_num):\n",
+    "        tc_attn_context, tc_attn_output, tc_value_vector, tc_sa_output = teacher_zip[l]\n",
+    "        st_attn_context, st_attn_output, st_value_vector, st_sa_output = student_zip[l]        \n",
+    "        st_ffn_output = student_reps[1+1]\n",
+    "        tc_ffn_output = teacher_reps[l+1]\n",
+    "        \n",
+    "        # for token in range(len(tokens)):\n",
+    "        tc_attn_output = tc_attn_context\n",
+    "        st_attn_output = st_attn_context\n",
+    "        \n",
+    "        mse_attn_diff = mse_func(st_attn_output[0,:,:], tc_attn_output[0,:,:]).item()\n",
+    "        cos_attn_diff = torch.mean((1-cos_func(st_attn_output[0,:,:], tc_attn_output[0,:,:]))).item()\n",
+    "        mag_dict[f\"{num}_attn_mse\"].append(mse_attn_diff)\n",
+    "        mag_dict[f\"{num}_attn_cos\"].append(cos_attn_diff)\n",
+    "\n",
+    "        mse_ffn_diff = mse_func(student_reps[l+1][0,:,:], teacher_reps[l+1][0,:,:]).item()\n",
+    "        cos_ffn_diff = torch.mean((1-cos_func(student_reps[l+1][0,:,:], teacher_reps[l+1][0,:,:]))).item()\n",
+    "        mag_dict[f\"{num}_ffn_mse\"].append(mse_ffn_diff)\n",
+    "        mag_dict[f\"{num}_ffn_cos\"].append(cos_ffn_diff)\n",
+    "#             mag_dict[f\"{model_num}_mean_{l}\"].append(st_output[0,token,:].mean().item())\n",
+    "#             mag_dict[f\"{model_num}_std_{l}\"].append(st_output[0,token,:].std().item())\n",
+    "    first = False\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f10e548b",
+   "metadata": {},
+   "source": [
+    "# Attention Output Comp Min-Max\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "ff15898f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'output/sst-2/exploration/1SB_M'"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "student_model_dir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "6ac37737",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "06/15 04:22:44 AM Loading model models/rte/pytorch_model.bin\n",
+      "06/15 04:22:44 AM loading model...\n",
+      "06/15 04:22:44 AM done!\n",
+      "06/15 04:22:44 AM loading configuration file output/rte/exploration/1SB_O/config.json\n",
+      "06/15 04:22:46 AM Loading model output/rte/exploration/1SB_O/pytorch_model.bin\n",
+      "06/15 04:22:46 AM loading model...\n",
+      "06/15 04:22:46 AM done!\n"
+     ]
+    }
+   ],
+   "source": [
+    "if teacher_model is None:\n",
+    "    teacher_model_dir = os.path.join(model_dir,task_name)\n",
+    "    teacher_model = BertForSequenceClassification.from_pretrained(teacher_model_dir, num_labels=num_labels)\n",
+    "    teacher_model.to(device)\n",
+    "    teacher_model.eval()\n",
+    "\n",
+    "st_model_name = \"1SB_O\"\n",
+    "student_model_dir = os.path.join(output_dir, task_name, \"exploration\", st_model_name)   \n",
+    "student_config = BertConfig.from_pretrained(student_model_dir)             \n",
+    "student_model = QuantBertForSequenceClassification.from_pretrained(student_model_dir, config = student_config, num_labels=num_labels)\n",
+    "student_model.to(device)\n",
+    "model = student_model\n",
+    "\n",
+    "teacher_outputs = teacher_model(input_ids_sliced.to(device))\n",
+    "student_outputs  = model(input_ids_sliced.to(device), teacher_outputs=None)\n",
+    "\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_zip = teacher_outputs\n",
+    "student_logits, student_atts, student_reps, student_probs, student_zip = student_outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "13f0b734",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([ 0, 21,  4, 19,  9], device='cuda:0')\n",
+      "tensor([ 0, 34, 27, 20, 11], device='cuda:0')\n",
+      "tensor([ 0, 35, 28, 27, 34], device='cuda:0')\n",
+      "tensor([28, 35,  0, 11, 26], device='cuda:0')\n",
+      "tensor([28, 35,  0, 10, 30], device='cuda:0')\n",
+      "tensor([28, 35, 13,  5,  1], device='cuda:0')\n",
+      "tensor([28, 35,  5, 21, 13], device='cuda:0')\n",
+      "tensor([35, 28, 21, 12, 13], device='cuda:0')\n",
+      "tensor([28, 35,  9, 34, 27], device='cuda:0')\n",
+      "tensor([28, 35, 21, 34, 27], device='cuda:0')\n",
+      "tensor([27, 34, 35, 28, 31], device='cuda:0')\n",
+      "tensor([27, 34, 35, 28, 33], device='cuda:0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "for l in range(layer_num):\n",
+    "    token_avg_tc = teacher_probs[l][0,3,:,:].mean(dim=0)\n",
+    "    print(torch.sort(token_avg_tc, descending=True)[1][:5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "3645f7b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "token_avg_tc = teacher_probs[-2][0,3,:,:].mean(dim=0)\n",
+    "outlier_index = torch.sort(token_avg_tc, descending=True)[1][:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "d1447f6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cls_index = 0\n",
+    "\n",
+    "teacher_list = dict()\n",
+    "student_list = dict()\n",
+    "\n",
+    "teacher_list[\"cls\"] = []\n",
+    "student_list[\"cls\"] = []\n",
+    "\n",
+    "for i, outlier in enumerate(outlier_index):\n",
+    "    teacher_list[f\"ol_{i}\"] = []\n",
+    "    student_list[f\"ol_{i}\"] = []\n",
+    "\n",
+    "for i, punc in enumerate(punc_index_1):\n",
+    "    teacher_list[f\"comma_{i}\"] = []\n",
+    "    student_list[f\"comma_{i}\"] = []\n",
+    "    \n",
+    "for i, punc in enumerate(punc_index_2):\n",
+    "    teacher_list[f\"period_{i}\"] = []\n",
+    "    student_list[f\"period_{i}\"] = []\n",
+    "\n",
+    "for i , sep in enumerate(sep_index):\n",
+    "    teacher_list[f\"sep_{i}\"] = []\n",
+    "    student_list[f\"sep_{i}\"] = []\n",
+    "\n",
+    "# Order\n",
+    "seq_length = len(tokens) - 1\n",
+    "# layer_num = 12\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        token_avg_tc = teacher_probs[l][0,h,:,:].mean(dim=0).clone().detach()\n",
+    "        token_order_tc = torch.sort(token_avg_tc, stable=True)[1].clone().detach()\n",
+    "        \n",
+    "        ratio = torch.where(token_order_tc == cls_index)[0] / seq_length\n",
+    "        teacher_list[f\"cls\"].append(ratio.item())\n",
+    "        \n",
+    "        for i, ol in enumerate(outlier_index):\n",
+    "            ratio = torch.where(token_order_tc == ol)[0] / seq_length\n",
+    "            teacher_list[f\"ol_{i}\"].append(ratio.item())\n",
+    "        \n",
+    "        for i, sep in enumerate(sep_index):\n",
+    "            ratio = torch.where(token_order_tc == sep)[0] / seq_length\n",
+    "            teacher_list[f\"sep_{i}\"].append(ratio.item())\n",
+    "        \n",
+    "        for i, punc in enumerate(punc_index_1):\n",
+    "            ratio = torch.where(token_order_tc == punc)[0] / seq_length\n",
+    "            teacher_list[f\"comma_{i}\"].append(ratio.item())\n",
+    "        \n",
+    "        for i, punc in enumerate(punc_index_2):\n",
+    "            ratio = torch.where(token_order_tc == punc)[0] / seq_length\n",
+    "            teacher_list[f\"period_{i}\"].append(ratio.item())\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        token_avg_st = student_probs[l][0,h,:,:].mean(dim=0).clone().detach()\n",
+    "        token_order_st = torch.sort(token_avg_st, stable=True)[1].clone().detach()\n",
+    "        \n",
+    "        ratio = torch.where(token_order_st == cls_index)[0] / seq_length\n",
+    "        student_list[f\"cls\"].append(ratio.item())\n",
+    "        \n",
+    "        for i, ol in enumerate(outlier_index):\n",
+    "            ratio = torch.where(token_order_st == ol)[0] / seq_length\n",
+    "            student_list[f\"ol_{i}\"].append(ratio.item())\n",
+    "        \n",
+    "        for i, sep in enumerate(sep_index):\n",
+    "            ratio = torch.where(token_order_st == sep)[0] / seq_length\n",
+    "            student_list[f\"sep_{i}\"].append(ratio.item())\n",
+    "        \n",
+    "        for i, punc in enumerate(punc_index_1):\n",
+    "            ratio = torch.where(token_order_st == punc)[0] / seq_length\n",
+    "            student_list[f\"comma_{i}\"].append(ratio.item())\n",
+    "        \n",
+    "        for i, punc in enumerate(punc_index_2):\n",
+    "            ratio = torch.where(token_order_st == punc)[0] / seq_length\n",
+    "            student_list[f\"period_{i}\"].append(ratio.item())           \n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "afb6e60a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 980x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 980x560 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for dict_list in [teacher_list, student_list]:\n",
+    "    fig, [ax1,ax2] = plt.subplots(2, 1, figsize=(14, 8), dpi=70)\n",
+    "    \n",
+    "    # fig, ax1 = plt.subplots(1, 1, figsize=(14, 5), dpi=70)\n",
+    "    color_list = [\"b\", \"c\", \"tab:blue\", \"tab:orange\", \"crimson\", \"red\"]\n",
+    "    punc_name = [\"comma\", \"comma\", \"comma\", \"period\", \"period\"]\n",
+    "    # ax.plot(list(range(layer_num*head_num)), dict_list[\"sep_0\"], label=\"sep_0\", color='gray')\n",
+    "    # ax.plot(list(range(layer_num*head_num)), dict_list[\"sep_1\"], label=\"sep_1\", color='gray')\n",
+    "    # ax.plot(list(range(layer_num*head_num)), dict_list[\"cls\"], label=\"cls\", color='tab:brown')\n",
+    "    \n",
+    "    for i, ol in enumerate(outlier_index):\n",
+    "        ax1.plot(list(range(layer_num*head_num)), dict_list[f\"ol_{i}\"], label=f\"index-{ol}-{tokens[ol]}\", linewidth=2.5)\n",
+    "    for i, punc in enumerate(punc_index_1):\n",
+    "        ax2.plot(list(range(layer_num*head_num)), dict_list[f\"comma_{i}\"], label=f\"comma{i}-{punc}\", linewidth=2.5)\n",
+    "    for i, punc in enumerate(punc_index_2):\n",
+    "        ax2.plot(list(range(layer_num*head_num)), dict_list[f\"period_{i}\"], label=f\"period{i}-{punc}\", linewidth=2.5)\n",
+    "    \n",
+    "    ax1.legend(fontsize=15, loc=3)\n",
+    "    ax2.legend(fontsize=15, loc=3)\n",
+    "    ax1.set_xlim([108,144])\n",
+    "    ax2.set_xlim([108,144])\n",
+    "    \n",
+    "    for l in range(layer_num):\n",
+    "        ax1.axvline(x=l*12, color=\"lightgray\")\n",
+    "        ax2.axvline(x=l*12, color=\"lightgray\")\n",
+    "\n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "id": "bd76f529",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "teacher_list = None\n",
+    "student_list = None\n",
+    "student_model = None\n",
+    "student_outputs = None\n",
+    "teacher_outputs = None\n",
+    "with torch.no_grad():\n",
+    "    torch.cuda.empty_cache()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5152fbe1",
+   "metadata": {},
+   "source": [
+    "# Norm Based Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "e926f233",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "norm_func = torch.linalg.norm\n",
+    "layer_num = 6\n",
+    "add = 18\n",
+    "table_tc_prob = [[0] * head_num for i in range(layer_num)]\n",
+    "table_st_prob = [[0] * head_num for i in range(layer_num)]\n",
+    "table_diff_prob = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "table_tc_fx = [[0] * head_num for i in range(layer_num)]\n",
+    "table_st_fx = [[0] * head_num for i in range(layer_num)]\n",
+    "table_diff_fx = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "table_tc_afx = [[0] * head_num for i in range(layer_num)]\n",
+    "table_st_afx = [[0] * head_num for i in range(layer_num)]\n",
+    "table_diff_afx = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "token_index = 54# punc_index[0] # outlier_index[0]\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    \n",
+    "    tc_prob = teacher_probs[l+add]\n",
+    "    st_prob = student_probs[l+add]\n",
+    "    \n",
+    "    tc_context, tc_output, tc_value, tc_output_hs, tc_norm = teacher_zips[l+add]\n",
+    "    st_context, st_output, st_value, st_output_hs, st_norm = student_zips[l+add]\n",
+    "    \n",
+    "    tc_transformed_norm, tc_weighted_norm, tc_summed_weighted_norm = tc_norm\n",
+    "    st_transformed_norm, st_weighted_norm, st_summed_weighted_norm = st_norm\n",
+    "    \n",
+    "    \n",
+    "    for h in range(head_num):\n",
+    "        table_tc_prob[l][h] = tc_prob[:,h,:,token_index].mean().item()\n",
+    "        table_st_prob[l][h] = st_prob[:,h,:,token_index].mean().item()\n",
+    "        table_diff_prob[l][h] = (tc_prob[:,h,token_index].mean() - st_prob[:,h,token_index].mean()).abs().item()\n",
+    "        \n",
+    "        table_tc_fx[l][h] = norm_func(tc_transformed_norm[:,h,token_index], dim=-1).item()\n",
+    "        table_st_fx[l][h] = norm_func(st_transformed_norm[:,h,token_index], dim=-1).item()\n",
+    "        table_diff_fx[l][h] = (norm_func(tc_transformed_norm[:,h,token_index], dim=-1) - norm_func(st_transformed_norm[:,h,token_index], dim=-1)).abs().item()\n",
+    "        \n",
+    "        table_tc_afx[l][h] = norm_func(tc_weighted_norm[:,h,token_index], dim=-1).item()\n",
+    "        table_st_afx[l][h] = norm_func(st_weighted_norm[:,h,token_index], dim=-1).item()\n",
+    "        table_diff_afx[l][h] = (norm_func(tc_weighted_norm[:,h,token_index,:], dim=-1) - norm_func(st_weighted_norm[:,h,token_index,:], dim=-1)).abs().item()\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c970a765",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plt' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fig, [ax1,ax2,ax3] \u001b[38;5;241m=\u001b[39m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m3\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m32\u001b[39m,\u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m      3\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset_xlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhead\u001b[39m\u001b[38;5;124m\"\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m); ax1\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayer\u001b[39m\u001b[38;5;124m\"\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m)\n\u001b[1;32m      4\u001b[0m heatmap\u001b[38;5;241m=\u001b[39max1\u001b[38;5;241m.\u001b[39mpcolor(table_tc_prob, cmap\u001b[38;5;241m=\u001b[39mplt\u001b[38;5;241m.\u001b[39mcm\u001b[38;5;241m.\u001b[39mBlues)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "fig, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(32,8))\n",
+    "\n",
+    "ax1.set_xlabel(\"head\", fontsize=20); ax1.set_ylabel(\"layer\", fontsize=20)\n",
+    "heatmap=ax1.pcolor(table_tc_prob, cmap=plt.cm.Blues)\n",
+    "cb = fig.colorbar(heatmap, ax=ax1)\n",
+    "cb.ax.tick_params(labelsize=20)\n",
+    "ax1.set_title(\"TC PROB\", fontsize=25)\n",
+    "\n",
+    "ax2.set_xlabel(\"head\", fontsize=20); ax2.set_ylabel(\"layer\", fontsize=20)\n",
+    "heatmap=ax2.pcolor(table_tc_fx, cmap=plt.cm.Blues)\n",
+    "cb = fig.colorbar(heatmap, ax=ax2)\n",
+    "cb.ax.tick_params(labelsize=20)\n",
+    "ax2.set_title(\"TC || f(x) ||\", fontsize=25)\n",
+    "\n",
+    "ax3.set_xlabel(\"head\", fontsize=20); ax3.set_ylabel(\"layer\", fontsize=20)\n",
+    "heatmap=ax3.pcolor(table_tc_afx, cmap=plt.cm.Blues)\n",
+    "cb = fig.colorbar(heatmap, ax=ax3)\n",
+    "cb.ax.tick_params(labelsize=20)\n",
+    "ax3.set_title(\"TC || af(x) ||\", fontsize=25)\n",
+    "        \n",
+    "fig, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(32,8))\n",
+    "\n",
+    "ax1.set_xlabel(\"head\", fontsize=20); ax1.set_ylabel(\"layer\", fontsize=20)\n",
+    "heatmap=ax1.pcolor(table_st_prob, cmap=plt.cm.Blues)\n",
+    "cb = fig.colorbar(heatmap, ax=ax1)\n",
+    "cb.ax.tick_params(labelsize=20)\n",
+    "ax1.set_title(\"ST PROB\", fontsize=25)\n",
+    "\n",
+    "ax2.set_xlabel(\"head\", fontsize=20); ax2.set_ylabel(\"layer\", fontsize=20)\n",
+    "heatmap=ax2.pcolor(table_st_fx, cmap=plt.cm.Blues)\n",
+    "cb = fig.colorbar(heatmap, ax=ax2)\n",
+    "cb.ax.tick_params(labelsize=20)\n",
+    "ax2.set_title(\"ST || f(x) ||\", fontsize=25)\n",
+    "\n",
+    "ax3.set_xlabel(\"head\", fontsize=20); ax3.set_ylabel(\"layer\", fontsize=20)\n",
+    "heatmap=ax3.pcolor(table_st_afx, cmap=plt.cm.Blues)\n",
+    "cb = fig.colorbar(heatmap, ax=ax3)\n",
+    "cb.ax.tick_params(labelsize=20)\n",
+    "ax3.set_title(\"ST || af(x) ||\", fontsize=25)\n",
+    "\n",
+    "\n",
+    "fig, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(32,8))\n",
+    "\n",
+    "ax1.set_xlabel(\"head\", fontsize=20); ax1.set_ylabel(\"layer\", fontsize=20)\n",
+    "heatmap=ax1.pcolor(table_diff_prob, cmap=plt.cm.Blues)\n",
+    "cb = fig.colorbar(heatmap, ax=ax1)\n",
+    "cb.ax.tick_params(labelsize=20)\n",
+    "ax1.set_title(\"DIFF PROB\", fontsize=25)\n",
+    "\n",
+    "ax2.set_xlabel(\"head\", fontsize=20); ax2.set_ylabel(\"layer\", fontsize=20)\n",
+    "heatmap=ax2.pcolor(table_diff_fx, cmap=plt.cm.Blues)\n",
+    "cb = fig.colorbar(heatmap, ax=ax2)\n",
+    "cb.ax.tick_params(labelsize=20)\n",
+    "ax2.set_title(\"DIFF || f(x) ||\", fontsize=25)\n",
+    "\n",
+    "ax3.set_xlabel(\"head\", fontsize=20); ax3.set_ylabel(\"layer\", fontsize=20)\n",
+    "heatmap=ax3.pcolor(table_diff_afx, cmap=plt.cm.Blues)\n",
+    "cb = fig.colorbar(heatmap, ax=ax3)\n",
+    "cb.ax.tick_params(labelsize=20)\n",
+    "ax3.set_title(\"DIFF || af(x) ||\", fontsize=25)\n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 415,
+   "id": "35336d97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Avg Attention\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        token_avg_tc = teacher_probs[l][0,h,:,:].mean(dim=0)\n",
+    "        token_avg_st = student_probs[l][0,h,:,:].mean(dim=0)\n",
+    "        \n",
+    "        # Logging\n",
+    "        for i, sep in enumerate(sep_index):\n",
+    "            teacher_list[f\"sep_{i}\"].append(token_avg_tc[sep].item())\n",
+    "            student_list[f\"sep_{i}\"].append(token_avg_st[sep].item())\n",
+    "        \n",
+    "        for i, punc in enumerate(punc_index):\n",
+    "            teacher_list[f\"punc_{i}\"].append(token_avg_tc[punc].item())\n",
+    "            student_list[f\"punc_{i}\"].append(token_avg_st[punc].item())\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "6261fa56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 16, 68, 68])"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tc_weighted_norm.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a47e4e94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is for Output Save Code\n",
+    "seed=42\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "mse_func = MSELoss()\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model_dir = \"models\"\n",
+    "output_dir = \"output\"\n",
+    "\n",
+    "tensor_dir = f\"tensor_files/{task_name}\"\n",
+    "if not os.path.exists(tensor_dir):\n",
+    "    os.mkdir(tensor_dir)\n",
+    "    \n",
+    "if bert_size == \"large\":\n",
+    "    model_dir = os.path.join(model_dir, \"BERT_large\")\n",
+    "    output_dir = os.path.join(output_dir, \"BERT_large\")\n",
+    "\n",
+    "# Teacher Model Build\n",
+    "teacher_model_dir = os.path.join(model_dir,task_name)\n",
+    "teacher_model = BertForSequenceClassification.from_pretrained(teacher_model_dir, num_labels=num_labels)\n",
+    "teacher_model.to(device)\n",
+    "teacher_model.eval()\n",
+    "\n",
+    "teacher_outputs = teacher_model(input_ids_sliced.to(device))\n",
+    "torch.save(teacher_outputs, os.path.join(tensor_dir, f\"teacher_outputs.pt\"))\n",
+    "\n",
+    "# Student Model Build\n",
+    "name_1 = \"1SB_O\"\n",
+    "name_2 = \"1SB_M\"\n",
+    "model_list = [name_1, name_2]\n",
+    "\n",
+    "for st_model_name in model_list:\n",
+    "    \n",
+    "    student_model_dir = os.path.join(output_dir, task_name, \"exploration\", st_model_name)   \n",
+    "    student_config = BertConfig.from_pretrained(student_model_dir)             \n",
+    "    student_model = QuantBertForSequenceClassification.from_pretrained(student_model_dir, config = student_config, num_labels=num_labels)\n",
+    "    student_model.to(device)\n",
+    "    student_model.eval()\n",
+    "\n",
+    "    student_outputs  = student_model(input_ids_sliced.to(device), teacher_outputs=None)\n",
+    "    torch.save(student_outputs, os.path.join(tensor_dir, f\"{st_model_name}_student_outputs.pt\"))\n",
+    "    \n",
+    "    \n",
+    "file_name = \"1SB_M\"\n",
+    "\n",
+    "# Load\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_zip = torch.load(f\"tensor_files/{task_name}/teacher_outputs.pt\")\n",
+    "student_logits, student_atts, student_reps, student_probs, student_zip = torch.load(f\"tensor_files/{task_name}/{file_name}_student_outputs.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07df970e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "norm_func = torch.linalg.norm\n",
+    "layer_num = 6\n",
+    "add = 6\n",
+    "table_tc_prob = [[0] * head_num for i in range(layer_num)]\n",
+    "table_st_prob = [[0] * head_num for i in range(layer_num)]\n",
+    "table_diff_prob = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "table_tc_value = [[0] * head_num for i in range(layer_num)]\n",
+    "table_st_value = [[0] * head_num for i in range(layer_num)]\n",
+    "table_diff_value = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "table_tc_context = [[0] * head_num for i in range(layer_num)]\n",
+    "table_st_context = [[0] * head_num for i in range(layer_num)]\n",
+    "table_diff_context = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "table_tc_output = [[0] for i in range(layer_num)]\n",
+    "table_st_output = [[0]  for i in range(layer_num)]\n",
+    "table_diff_output = [[0]  for i in range(layer_num)]\n",
+    "\n",
+    "token_index = 0 # outlier_index[0]\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    \n",
+    "    tc_prob = teacher_probs[l+add]\n",
+    "    st_prob = student_probs[l+add]\n",
+    "    \n",
+    "    tc_context, tc_output, tc_value, tc_output_hs, tc_norm = teacher_zips[l+add]\n",
+    "    st_context, st_output, st_value, st_output_hs, st_norm = student_zips[l+add]\n",
+    "    \n",
+    "    tc_tranformed_norm, tc_weighted_norm, tc_summed_weighted_norm = tc_norm\n",
+    "    st_tranformed_norm, st_weighted_norm, st_summed_weighted_norm = st_norm\n",
+    "    \n",
+    "    \n",
+    "    for h in range(head_num):\n",
+    "        table_tc_prob[l][h] = tc_prob[:,h,:,token_index].mean().item()\n",
+    "        table_st_prob[l][h] = st_prob[:,h,:,token_index].mean().item()\n",
+    "        table_diff_prob[l][h] = (tc_prob[:,h,:,token_index].mean() - st_prob[:,h,:,token_index].mean()).abs().item()\n",
+    "        \n",
+    "        table_tc_value[l][h] = norm_func(tc_value[:,h,token_index,:], dim=-1).item()\n",
+    "        table_st_value[l][h] = norm_func(st_value[:,h,token_index,:], dim=-1).item()\n",
+    "        table_diff_value[l][h] = (norm_func(tc_value[:,h,token_index,:], dim=-1) - norm_func(st_value[:,h,token_index,:], dim=-1)).abs().item()\n",
+    "        \n",
+    "        table_tc_context[l][h] = norm_func(tc_context[:,h,token_index,:], dim=-1).item()\n",
+    "        table_st_context[l][h] = norm_func(st_context[:,h,token_index,:], dim=-1).item()\n",
+    "        table_diff_context[l][h] = (norm_func(tc_context[:,h,token_index,:], dim=-1) - norm_func(st_context[:,h,token_index,:], dim=-1)).abs().item()\n",
+    "        \n",
+    "    table_tc_output[l] = norm_func(tc_output[:,token_index,:], dim=-1).item()\n",
+    "    table_st_output[l] = norm_func(st_output[:,token_index,:], dim=-1).item()\n",
+    "    table_diff_output[l] = (norm_func(tc_output[:,token_index,:], dim=-1) - norm_func(st_output[:,token_index,:], dim=-1)).abs().item()\n",
+    "    \n",
+    "        \n",
+    "\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4c8e862",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kl_loss = torch.nn.KLDivLoss(reduction=\"batchmean\")\n",
+    "mse_func = MSELoss()\n",
+    "\n",
+    "map_diff_map = []\n",
+    "map_diff_output = []\n",
+    "\n",
+    "value_diff_map = []\n",
+    "value_diff_output = []\n",
+    "\n",
+    "context_diff_map = []\n",
+    "context_diff_output = []\n",
+    "\n",
+    "output_diff_map = []\n",
+    "output_diff_output = []\n",
+    "\n",
+    "ffn_diff_map = []\n",
+    "ffn_diff_output = []\n",
+    "\n",
+    "teacher_model_dir = os.path.join(model_dir,task_name)\n",
+    "teacher_model = BertForSequenceClassification.from_pretrained(teacher_model_dir, num_labels=num_labels)\n",
+    "teacher_model.to(device)\n",
+    "teacher_model.eval()\n",
+    "\n",
+    "teacher_outputs = teacher_model(input_ids_sliced.to(device))\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_zip = teacher_outputs\n",
+    "\n",
+    "for st_model_name in [\"1SB_O\", \"1SB_M_O\"]:\n",
+    "    student_model_dir = os.path.join(output_dir, task_name, \"exploration\", st_model_name)   \n",
+    "    student_config = BertConfig.from_pretrained(student_model_dir)             \n",
+    "    student_model = QuantBertForSequenceClassification.from_pretrained(student_model_dir, config = student_config, num_labels=num_labels)\n",
+    "    student_model.to(device)\n",
+    "    \n",
+    "    student_outputs  = student_model(input_ids_sliced.to(device), teacher_outputs=None)\n",
+    "    student_logits, student_atts, student_reps, student_probs, student_zip = student_outputs\n",
+    "    \n",
+    "    for l in range(layer_num):\n",
+    "        tc_prob = teacher_probs[l]\n",
+    "        st_prob = student_probs[l]\n",
+    "        \n",
+    "        tc_ffn = teacher_reps[l]\n",
+    "        st_ffn = student_reps[l]\n",
+    "        \n",
+    "        tc_context, tc_output, tc_value, tc_output_hs, tc_norm = teacher_zip[l]\n",
+    "        st_context, st_output, st_value, st_output_hs, st_norm = student_zip[l]\n",
+    "\n",
+    "        tc_transformed_norm, tc_weighted_norm, tc_summed_weighted_norm = tc_norm\n",
+    "        st_transformed_norm, st_weighted_norm, st_summed_weighted_norm = st_norm\n",
+    "\n",
+    "        ffn_diff = mse_func(tc_ffn[0,:,:], st_ffn[0,:,:])\n",
+    "        output_diff = mse_func(tc_value[0,:,:], st_value[0,:,:])\n",
+    "        \n",
+    "        if \"1SB_O\" == st_model_name:\n",
+    "            ffn_diff_output.append(ffn_diff.item())\n",
+    "            output_diff_output.append(output_diff.item())\n",
+    "        else:\n",
+    "            ffn_diff_map.append(ffn_diff.item())\n",
+    "            output_diff_map.append(output_diff.item())\n",
+    "            \n",
+    "        for h in range(head_num):\n",
+    "            map_diff = kl_loss(st_prob[0,h,:,:].log(), tc_prob[0,h,:,:])\n",
+    "            value_diff = mse_func(tc_value[0,h,:,:], st_value[0,h,:,:])\n",
+    "            context_diff = mse_func(tc_context[0,h,:,:], st_context[0,h,:,:])\n",
+    "            \n",
+    "            if \"1SB_O\" == st_model_name:\n",
+    "                map_diff_output.append(map_diff.item())\n",
+    "                value_diff_output.append(value_diff.item())\n",
+    "                context_diff_output.append(context_diff.item())\n",
+    "            else:\n",
+    "                map_diff_map.append(map_diff.item())\n",
+    "                value_diff_map.append(value_diff.item())\n",
+    "                context_diff_map.append(context_diff.item())\n",
+    "            \n",
+    "fig, [ax1,ax2,ax3] = plt.subplots(3, 1, figsize=(14, 12), dpi=70)\n",
+    "x_len = layer_num*head_num\n",
+    "ax1.plot(list(range(x_len)),map_diff_map, linewidth=2.5 )\n",
+    "ax1.plot(list(range(x_len)),map_diff_output, linewidth=2.5 )\n",
+    "\n",
+    "ax2.plot(list(range(x_len)),value_diff_map, linewidth=2.5 )\n",
+    "ax2.plot(list(range(x_len)),value_diff_output, linewidth=2.5 )\n",
+    "\n",
+    "ax3.plot(list(range(x_len)),context_diff_map, linewidth=2.5 )\n",
+    "ax3.plot(list(range(x_len)),context_diff_output, linewidth=2.5 )\n",
+    "\n",
+    "fig, [ax1,ax2] = plt.subplots(2, 1, figsize=(14, 8), dpi=70)\n",
+    "x_len = layer_num\n",
+    "ax1.plot(list(range(x_len)),ffn_diff_map, linewidth=2.5 )\n",
+    "ax1.plot(list(range(x_len)),ffn_diff_output, linewidth=2.5 )\n",
+    "\n",
+    "ax2.plot(list(range(x_len)),output_diff_map, linewidth=2.5 )\n",
+    "ax2.plot(list(range(x_len)),output_diff_output, linewidth=2.5 )\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ce5207d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10, 5), dpi=70)\n",
+    "i = 0\n",
+    "fs = 15\n",
+    "# for a in [prob_other_diff, prob_cls_diff, prob_sep_diff, prob_punc_diff]:\n",
+    "# label = [\"Output\", \"Map\"]\n",
+    "for a in [diff_dict[\"0_prob_\"]]:\n",
+    "    plt.plot(list(range(layer_num)), a, label=label[i], linewidth=2.5)\n",
+    "    i += 1\n",
+    "    \n",
+    "plt.xlabel(\"Layer\", fontsize=fs)\n",
+    "plt.ylabel(\"Avg. $α$\", fontsize=fs)\n",
+    "plt.title(\"Average attention weight\", fontsize=fs)\n",
+    "plt.legend()\n",
+    "plt.show() \n",
+    "\n",
+    "plt.figure(figsize=(10, 5), dpi=70)\n",
+    "i = 0\n",
+    "# for a in [tr_other_diff, tr_cls_diff, tr_sep_diff, tr_punc_diff]:\n",
+    "for a in [tr_punc_diff]:\n",
+    "    plt.plot(list(range(layer_num)), a, label=label[i], linewidth=2.5)\n",
+    "    i += 1\n",
+    "    \n",
+    "plt.xlabel(\"Layer\", fontsize=fs)\n",
+    "plt.ylabel(\"Avg. ||f(x)||\", fontsize=fs)\n",
+    "plt.title(\"Average Norm || f(x)||\", fontsize=fs)\n",
+    "plt.legend()\n",
+    "plt.show() \n",
+    "\n",
+    "plt.figure(figsize=(10, 5), dpi=70)\n",
+    "i = 0\n",
+    "# for a in [wt_other_diff, wt_cls_diff, wt_sep_diff, wt_punc_diff]:\n",
+    "for a in [wt_punc_diff]:\n",
+    "    plt.plot(list(range(layer_num)), a, label=label[i], linewidth=2.5)\n",
+    "    i += 1\n",
+    "    \n",
+    "plt.xlabel(\"Layer\", fontsize=fs)\n",
+    "plt.ylabel(\"Avg. || $α$ f(x) ||\", fontsize=fs)\n",
+    "plt.title(\"Average Norm || $α$ f(x) ||\", fontsize=fs)\n",
+    "plt.legend()\n",
+    "plt.show() \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f1880ad",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51233774",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59d72d08",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/BERTviz-base.ipynb b/notebooks/BERTviz-base.ipynb
new file mode 100644
index 0000000..5b62e5b
--- /dev/null
+++ b/notebooks/BERTviz-base.ipynb
@@ -0,0 +1,1283 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "9f99d9c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import absolute_import, division, print_function\n",
+    "\n",
+    "import pprint\n",
+    "import argparse\n",
+    "import logging\n",
+    "import os\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"3\" # Set GPU Index to use\n",
+    "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\n",
+    "import random\n",
+    "import sys\n",
+    "import pickle\n",
+    "import copy\n",
+    "import collections\n",
+    "import math\n",
+    "\n",
+    "import numpy as np\n",
+    "import numpy\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler,TensorDataset\n",
+    "# from torch.utils.tensorboard import SummaryWriter\n",
+    "\n",
+    "from torch.nn import CrosnsEntropyLoss, MSELoss\n",
+    "from tqdm import tqdm\n",
+    "from transformer import BertForSequenceClassification,WEIGHTS_NAME, CONFIG_NAME\n",
+    "from transformer.modeling_quant import BertForSequenceClassification as QuantBertForSequenceClassification\n",
+    "from transformer import BertTokenizer\n",
+    "from transformer import BertAdam\n",
+    "from transformer import BertConfig\n",
+    "from transformer import QuantizeLinear, QuantizeAct, BertSelfAttention, FP_BertSelfAttention, ClipLinear\n",
+    "from utils_glue import *\n",
+    "from bertviz import model_view\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "class AverageMeter(object):\n",
+    "    \"\"\"Computes and stores the average and current value\"\"\"\n",
+    "    def __init__(self):\n",
+    "        self.reset()\n",
+    "\n",
+    "    def reset(self):\n",
+    "        self.val = 0\n",
+    "        self.avg = 0 \n",
+    "        self.sum = 0\n",
+    "        self.count = 0\n",
+    "\n",
+    "    def update(self, val, n=1):\n",
+    "        self.val = val\n",
+    "        self.sum += val * n\n",
+    "        self.count += n\n",
+    "        self.avg = self.sum / self.count\n",
+    "\n",
+    "def cv_initialize(model, loader, ratio, device):\n",
+    "    \n",
+    "    def initialize_hook(module, input, output):\n",
+    "        if isinstance(module, (QuantizeLinear, QuantizeAct, ClipLinear)):\n",
+    "            \"\"\"KDLSQ-BERT ACT Quant init Method\n",
+    "            Ref: https://arxiv.org/abs/2101.05938\n",
+    "            \"\"\"\n",
+    "            if not isinstance(input, torch.Tensor):\n",
+    "                input = input[0]\n",
+    "        \n",
+    "            n = torch.numel(input)\n",
+    "            input_sorted, index = torch.sort(input.reshape(-1), descending=False)\n",
+    "            \n",
+    "            index_min = torch.round(ratio * n / 2)\n",
+    "            index_max = n - index_min\n",
+    "            \n",
+    "            s_init = (input_sorted[int(index_min)].to(device), input_sorted[int(index_max)].to(device))\n",
+    "            \n",
+    "            # MATPLOT\n",
+    "            \n",
+    "            fig, [ax1, ax2, ax3] = plt.subplots(1,3, figsize=(16, 4))            \n",
+    "            \n",
+    "            sns.histplot(data=input.reshape(-1).detach().cpu().numpy(), kde = True, bins=100, ax=ax1)\n",
+    "            sns.rugplot(data=input.reshape(-1).detach().cpu().numpy(), ax=ax1)\n",
+    "            sns.histplot(data=module.weight.reshape(-1).detach().cpu().numpy(), kde = True, bins=100, ax=ax2)\n",
+    "            sns.rugplot(data=module.weight.reshape(-1).detach().cpu().numpy(), ax=ax2)\n",
+    "            sns.histplot(data=output.reshape(-1).detach().cpu().numpy(), kde = True, bins=100, ax=ax3)\n",
+    "            sns.rugplot(data=output.reshape(-1).detach().cpu().numpy(), ax=ax3)\n",
+    "            # fig, [ax1, ax2] = plt.subplots(1,2, figsize=(12, 4))            \n",
+    "            \n",
+    "            # sns.distplot(input.reshape(-1).detach().cpu().numpy() , hist = True, rug = True, kde = True, bins=100, norm_hist=False, kde_kws=dict(linewidth=0.5), rug_kws=dict(linewidth=0.5), ax=ax1)\n",
+    "            # sns.distplot(output.reshape(-1).detach().cpu().numpy() , hist = True, rug = True, kde = True, bins=100, norm_hist=False, kde_kws=dict(linewidth=0.5), rug_kws=dict(linewidth=0.5), ax=ax2)\n",
+    "            # # plt.axvline(x=s_init[0].detach().cpu().numpy(), color='r', linestyle='--')\n",
+    "            # # plt.axvline(x=s_init[1].detach().cpu().numpy(), color='r', linestyle='--')\n",
+    "\n",
+    "            ax1.set_xlabel(\"Input Activation\")\n",
+    "            # ax2.set_xlabel(\"Output Activation\")\n",
+    "            ax2.set_xlabel(\"Module Weight\")\n",
+    "            ax3.set_xlabel(\"Output Activation\")\n",
+    "            \n",
+    "            ax1.set_ylabel(\"Density\")\n",
+    "            ax2.set_ylabel(\"Density\")\n",
+    "            ax3.set_ylabel(\"Density\")\n",
+    "\n",
+    "            ax1.set_title(f\"{module.name} Input ACT histogram\")\n",
+    "            # ax2.set_title(f\"{module.name} Output ACT histogram\")\n",
+    "            ax2.set_title(f\"{module.name} Weight histogram\")\n",
+    "            ax3.set_title(f\"{module.name} Output ACT histogram\")\n",
+    "            # plt.savefig(f\"plt_storage/hook_inputs/sst-2-fp/{module.name}.png\")\n",
+    "            plt.show()\n",
+    "            plt.close(fig)\n",
+    "            # module.clip_initialize(s_init)\n",
+    "            # logger.info(f\"{module} : min {s_init[0].item()} max {s_init[1].item()}\") \n",
+    "\n",
+    "    \n",
+    "    hooks = []\n",
+    "\n",
+    "    for name, module in model.named_modules():\n",
+    "        hook = module.register_forward_hook(initialize_hook)\n",
+    "        hooks.append(hook)\n",
+    "    \n",
+    "    model.train()\n",
+    "    model.to(device)\n",
+    "    \n",
+    "    for step, batch in enumerate(loader):\n",
+    "        batch = tuple(t.to(\"cuda\") for t in batch)\n",
+    "        input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch        \n",
+    "        with torch.no_grad():\n",
+    "            student_logits, student_atts, student_reps, student_probs, student_values = model(input_ids, segment_ids, input_mask, teacher_probs=None)\n",
+    "        break\n",
+    "    \n",
+    "    for hook in hooks:\n",
+    "        hook.remove()\n",
+    "\n",
+    "def str2bool(v):\n",
+    "    if isinstance(v, bool):\n",
+    "        return v\n",
+    "    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n",
+    "        return True\n",
+    "    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n",
+    "        return False\n",
+    "    else:\n",
+    "        raise argparse.ArgumentTypeError('Boolean value expected.')\n",
+    "\n",
+    "def load_vocab(vocab_file):\n",
+    "    \"\"\"Loads a vocabulary file into a dictionary.\"\"\"\n",
+    "    vocab = collections.OrderedDict()\n",
+    "    index = 0\n",
+    "    with open(vocab_file, \"r\", encoding=\"utf-8\") as reader:\n",
+    "        while True:\n",
+    "            token = reader.readline()\n",
+    "            if not token:\n",
+    "                break\n",
+    "            token = token.strip()\n",
+    "            #vocab[token] = index\n",
+    "            vocab[index] = token\n",
+    "            index += 1\n",
+    "    return vocab\n",
+    "\n",
+    "def attention_pattern(model, loader, device):\n",
+    "    \n",
+    "    def initialize_hook(module, input, output):\n",
+    "        if isinstance(module, BertSelfAttention):\n",
+    "            \n",
+    "            attn_mask = input[1]\n",
+    "            attention_output = output[-2][\"attn\"]\n",
+    "            \n",
+    "            seq_length = (attn_mask == 0).sum()\n",
+    "            \n",
+    "            print(attention_output[0,:,:seq_length,seq_length-1].mean().item())\n",
+    "            \n",
+    "\n",
+    "    hooks = []\n",
+    "\n",
+    "    for name, module in model.named_modules():\n",
+    "        hook = module.register_forward_hook(initialize_hook)\n",
+    "        hooks.append(hook)\n",
+    "    \n",
+    "    model.eval()\n",
+    "    model.to(device)\n",
+    "    \n",
+    "    for step, batch in enumerate(loader):\n",
+    "        batch = tuple(t.to(\"cuda\") for t in batch)\n",
+    "        input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch        \n",
+    "        with torch.no_grad():\n",
+    "            student_logits, student_atts, student_reps, student_probs, student_values = model(input_ids, segment_ids, input_mask)\n",
+    "        break\n",
+    "    \n",
+    "    for hook in hooks:\n",
+    "        hook.remove()\n",
+    "        \n",
+    "def get_tensor_data(output_mode, features):\n",
+    "    if output_mode == \"classification\":\n",
+    "        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)\n",
+    "    elif output_mode == \"regression\":\n",
+    "        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)\n",
+    "\n",
+    "\n",
+    "    all_seq_lengths = torch.tensor([f.seq_length for f in features], dtype=torch.long)\n",
+    "    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n",
+    "    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)\n",
+    "    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)\n",
+    "    tensor_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,all_label_ids, all_seq_lengths)\n",
+    "    return tensor_data, all_label_ids\n",
+    "\n",
+    "def do_logging(run, student_model, teacher_model, test_dataloader, device, global_step, args, vocab):\n",
+    "    \n",
+    "    if args.bert == \"large\":\n",
+    "        layer_num = 24\n",
+    "        head_num = 16\n",
+    "    else:\n",
+    "        layer_num = 12\n",
+    "        head_num = 12\n",
+    "        \n",
+    "    nb_steps = 0\n",
+    "    \n",
+    "    kl_div_sum = [0 for i in range(layer_num)]\n",
+    "    st_sep_avg_sum = [0 for i in range(layer_num)]; st_cls_avg_sum = [0 for i in range(layer_num)]; tc_sep_avg_sum = [0 for i in range(layer_num)]; tc_cls_avg_sum = [0 for i in range(layer_num)]\n",
+    "    cover_sum = [0 for i in range(layer_num)]\n",
+    "    cover_teacher_sum = [0 for i in range(layer_num)]\n",
+    "    \n",
+    "    batch_num = 0\n",
+    "    \n",
+    "    for batch_ in tqdm(test_dataloader, desc=\"Logging Test\", mininterval=0.01, ascii=True, leave=False):\n",
+    "        batch_ = tuple(t.to(device) for t in batch_)\n",
+    "        \n",
+    "        if batch_num >= 1: # Visualize Attention Map only First Batch \n",
+    "            args.log_map = False\n",
+    "        \n",
+    "        with torch.no_grad():\n",
+    "            input_ids, input_mask, segment_ids, label_id, seq_length = batch_\n",
+    "\n",
+    "            teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids, segment_ids, input_mask)\n",
+    "            student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids, segment_ids, input_mask, teacher_probs=teacher_probs)\n",
+    "            \n",
+    "            # Layer\n",
+    "            for i, (student_prob, teacher_prob) in enumerate(zip(student_probs, teacher_probs)): \n",
+    "\n",
+    "                # Head\n",
+    "                for head in range(head_num):\n",
+    "                    \n",
+    "                    if args.log_map:\n",
+    "                        \n",
+    "                        word_list = []\n",
+    "                        \n",
+    "                        for word in range(seq_length):\n",
+    "                            word_list.append(vocab[input_ids[0][word].item()])\n",
+    "                        \n",
+    "                        student_prob_map = student_prob[0][head][:seq_length,:seq_length].clone().detach().cpu().numpy()\n",
+    "                        teacher_prob_map = teacher_prob[0][head][:seq_length,:seq_length].clone().detach().cpu().numpy()\n",
+    "                        \n",
+    "                        fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(16,8))\n",
+    "                        ax1.set_title(f\"{i}th Layer {head}th Head Teacher\")\n",
+    "                        heatmap = ax1.pcolor(teacher_prob_map, cmap=plt.cm.Blues)\n",
+    "    \n",
+    "                        ax1.set_xticks(numpy.arange(teacher_prob_map.shape[1]) + 0.5, minor=False)\n",
+    "                        ax1.set_yticks(numpy.arange(teacher_prob_map.shape[0]) + 0.5, minor=False)\n",
+    "                        \n",
+    "                        ax1.set_xlim(0, int(teacher_prob_map.shape[1]))\n",
+    "                        ax1.set_ylim(0, int(teacher_prob_map.shape[0]))\n",
+    "\n",
+    "                        ax1.invert_yaxis()\n",
+    "                        ax1.xaxis.tick_top()\n",
+    "\n",
+    "                        ax1.set_xticklabels(word_list, minor=False)\n",
+    "                        ax1.set_yticklabels(word_list, minor=False)\n",
+    "\n",
+    "                        plt.xticks(rotation=45)\n",
+    "                        \n",
+    "                        ax2.set_title(f\"{i}th Layer {head}th Head Student\")\n",
+    "                        heatmap = ax2.pcolor(student_prob_map, cmap=plt.cm.Blues)\n",
+    "\n",
+    "                        ax2.set_xticks(numpy.arange(student_prob_map.shape[1]) + 0.5, minor=False)\n",
+    "                        ax2.set_yticks(numpy.arange(student_prob_map.shape[0]) + 0.5, minor=False)\n",
+    "\n",
+    "                        ax2.set_xlim(0, int(student_prob_map.shape[1]))\n",
+    "                        ax2.set_ylim(0, int(student_prob_map.shape[0]))\n",
+    "\n",
+    "                        ax2.invert_yaxis()\n",
+    "                        ax2.xaxis.tick_top()\n",
+    "\n",
+    "                        ax2.set_xticklabels(word_list, minor=False)\n",
+    "                        ax2.set_yticklabels(word_list, minor=False)\n",
+    "\n",
+    "                        plt.xticks(rotation=45)\n",
+    "                        \n",
+    "                        plt_folder_name = os.path.join(\"plt_storage\" + \"/\" + args.exp_name)\n",
+    "                        if not os.path.exists(plt_folder_name):\n",
+    "                            os.mkdir(plt_folder_name)          \n",
+    "                        plt_folder_name = os.path.join(plt_folder_name, f\"step_{global_step}\")\n",
+    "                        if not os.path.exists(plt_folder_name):\n",
+    "                            os.mkdir(plt_folder_name)                        \n",
+    "                        plt.savefig(plt_folder_name + \"/\" + f\"L{i}_H{head}.png\")\n",
+    "                        plt.close()\n",
+    "                        \n",
+    "\n",
+    "                    if args.log_metric:\n",
+    "                        \n",
+    "                        student_prob = student_prob\n",
+    "                        teacher_prob = teacher_prob\n",
+    "\n",
+    "                        # Attention Map\n",
+    "                        student_attn_map = student_prob[0][head][:seq_length,:seq_length].clone().detach()\n",
+    "                        teacher_attn_map = teacher_prob[0][head][:seq_length,:seq_length].clone().detach()\n",
+    "\n",
+    "                        # KL Divergence\n",
+    "                        kl_div = F.kl_div(student_attn_map.log(), teacher_attn_map, reduction='batchmean')\n",
+    "                        kl_div_sum[i] += kl_div\n",
+    "\n",
+    "                        # Special Token Prob Mean\n",
+    "                        st_sep_avg = student_attn_map[:,-1].mean()\n",
+    "                        st_cls_avg = student_attn_map[:,0].mean()\n",
+    "                        st_sep_avg_sum[i] += st_sep_avg\n",
+    "                        st_cls_avg_sum[i] += st_cls_avg\n",
+    "                        \n",
+    "                        # Ground Truth\n",
+    "                        tc_sep_avg = teacher_attn_map[:,-1].mean()\n",
+    "                        tc_cls_avg = teacher_attn_map[:,0].mean()\n",
+    "                        tc_sep_avg_sum[i] += tc_sep_avg\n",
+    "                        tc_cls_avg_sum[i] += tc_cls_avg\n",
+    "\n",
+    "                        # Coverage Test\n",
+    "                        coverage_head_sum = 0\n",
+    "                        coverage_teacher_head_sum = 0\n",
+    "                        for k in range(student_attn_map.shape[0]):\n",
+    "                            st_argsort = student_attn_map[k].sort(descending=True)[1]\n",
+    "                            tc_argsort = teacher_attn_map[k].sort(descending=True)[1][:args.tc_top_k] # Top-5\n",
+    "                            \n",
+    "                            max_idx = 0\n",
+    "                            for idx in tc_argsort: # Teacher Top-5                             \n",
+    "                                tmp = torch.where(st_argsort == idx)\n",
+    "                                max_idx = max(tmp[0].item(), max_idx)\n",
+    "                            \n",
+    "                            coverage_ratio = max_idx / student_attn_map.shape[0]\n",
+    "                            coverage_teacher_ratio = (args.tc_top_k - 1) / student_attn_map.shape[0]\n",
+    "                            coverage_head_sum += coverage_ratio\n",
+    "                            coverage_teacher_head_sum += coverage_teacher_ratio\n",
+    "                        \n",
+    "                        coverage_head = coverage_head_sum / student_attn_map.shape[0]\n",
+    "                        coverage_teacher_head = coverage_teacher_head_sum / student_attn_map.shape[0]\n",
+    "                        \n",
+    "                        cover_sum[i] += coverage_head\n",
+    "                        cover_teacher_sum[i] += coverage_teacher_head\n",
+    "                        \n",
+    "                        nb_steps += 1\n",
+    "        \n",
+    "        batch_num = batch_num + 1\n",
+    "    \n",
+    "    if args.log_metric:\n",
+    "        nb_steps = nb_steps / 12\n",
+    "        \n",
+    "        for l in range(12):\n",
+    "            run[f\"attn/L{l}_KLdiv_mean\"].log(value=kl_div_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_st_SepProb_mean\"].log(value=st_sep_avg_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_st_ClsProb_mean\"].log(value=st_cls_avg_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_tc_SepProb_mean\"].log(value=tc_sep_avg_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_tc_ClsProb_mean\"].log(value=tc_cls_avg_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_st_cover_mean\"].log(value=cover_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_tc_cover_mean\"].log(value=cover_teacher_sum[l] / nb_steps, step=global_step)\n",
+    "\n",
+    "    args.log_map = True                    \n",
+    "\n",
+    "\n",
+    "def do_eval(model, task_name, eval_dataloader,\n",
+    "            device, output_mode, eval_labels, num_labels, teacher_model=None):\n",
+    "    eval_loss = 0\n",
+    "    nb_eval_steps = 0\n",
+    "    preds = []\n",
+    "\n",
+    "    for batch_ in tqdm(eval_dataloader, desc=\"Inference\"):\n",
+    "        batch_ = tuple(t.to(device) for t in batch_)\n",
+    "        \n",
+    "        with torch.no_grad():\n",
+    "            input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch_\n",
+    "\n",
+    "            # teacher attnmap test\n",
+    "            if teacher_model is not None:\n",
+    "                logits, teacher_atts, _, teacher_probs, _ = teacher_model(input_ids, segment_ids, input_mask)\n",
+    "                # teacher_probs = 0\n",
+    "                logits, _, _, _, _ = model(input_ids, segment_ids, input_mask, teacher_probs=teacher_probs)\n",
+    "            else:\n",
+    "                logits, _, _, _, _ = model(input_ids, segment_ids, input_mask)\n",
+    "        \n",
+    "        # create eval loss and other metric required by the task\n",
+    "        if output_mode == \"classification\":\n",
+    "            loss_fct = CrossEntropyLoss()\n",
+    "            tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))\n",
+    "        elif output_mode == \"regression\":\n",
+    "            loss_fct = MSELoss()\n",
+    "            tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))\n",
+    "\n",
+    "        eval_loss += tmp_eval_loss.mean().item()\n",
+    "        nb_eval_steps += 1\n",
+    "        if len(preds) == 0:\n",
+    "            preds.append(logits.detach().cpu().numpy())\n",
+    "        else:\n",
+    "            preds[0] = np.append(\n",
+    "                preds[0], logits.detach().cpu().numpy(), axis=0)\n",
+    "\n",
+    "    eval_loss = eval_loss / nb_eval_steps\n",
+    "\n",
+    "    preds = preds[0]\n",
+    "    if output_mode == \"classification\":\n",
+    "        preds = np.argmax(preds, axis=1)\n",
+    "    elif output_mode == \"regression\":\n",
+    "        preds = np.squeeze(preds)\n",
+    "    result = compute_metrics(task_name, preds, eval_labels.numpy())\n",
+    "    result['eval_loss'] = eval_loss\n",
+    "    return result\n",
+    "\n",
+    "def soft_cross_entropy(predicts, targets):\n",
+    "    student_likelihood = torch.nn.functional.log_softmax(predicts, dim=-1)\n",
+    "    targets_prob = torch.nn.functional.softmax(targets, dim=-1)\n",
+    "    return torch.sum((- targets_prob * student_likelihood), dim=-1).mean()\n",
+    "\n",
+    "processors = {\n",
+    "    \"cola\": ColaProcessor,\n",
+    "    \"mnli\": MnliProcessor,\n",
+    "    \"mnli-mm\": MnliMismatchedProcessor,\n",
+    "    \"mrpc\": MrpcProcessor,\n",
+    "    \"sst-2\": Sst2Processor,\n",
+    "    \"sts-b\": StsbProcessor,\n",
+    "    \"qqp\": QqpProcessor,\n",
+    "    \"qnli\": QnliProcessor,\n",
+    "    \"rte\": RteProcessor   \n",
+    "}\n",
+    "\n",
+    "output_modes = {\n",
+    "        \"cola\": \"classification\",\n",
+    "        \"mnli\": \"classification\",\n",
+    "        \"mrpc\": \"classification\",\n",
+    "        \"sst-2\": \"classification\",\n",
+    "        \"sts-b\": \"regression\",\n",
+    "        \"qqp\": \"classification\",\n",
+    "        \"qnli\": \"classification\",\n",
+    "        \"rte\": \"classification\"\n",
+    "}\n",
+    "\n",
+    "default_params = {\n",
+    "        \"cola\": {\"max_seq_length\": 64,\"batch_size\":1,\"eval_step\": 50}, # No Aug : 50 Aug : 400\n",
+    "        \"mnli\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":8000},\n",
+    "        \"mrpc\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"sst-2\": {\"max_seq_length\": 64,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"sts-b\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"qqp\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":1000},\n",
+    "        \"qnli\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":1000},\n",
+    "        \"rte\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\": 20}\n",
+    "    }\n",
+    "\n",
+    "from bertviz import head_view, model_view\n",
+    "# from bertviz.transformers_neuron_view import BertModel, BertTokenizer\n",
+    "from bertviz.neuron_view import show\n",
+    "import bertviz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aab9bf6b",
+   "metadata": {},
+   "source": [
+    "# GLUE Task Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "75f78270",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_name = \"sts-b\"\n",
+    "bert_size = \"large\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07b94ac6",
+   "metadata": {},
+   "source": [
+    "## Model Dir, Device"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "7fec849f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model_dir = \"models\"\n",
+    "output_dir = \"output\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    model_dir = os.path.join(model_dir, \"BERT_large\")\n",
+    "    output_dir = os.path.join(output_dir, \"BERT_large\")\n",
+    "\n",
+    "student_model_dir = os.path.join(model_dir,task_name)\n",
+    "student_model_dir = os.path.join(output_dir, task_name, \"quant\", \"ternary_save\")\n",
+    "# student_model_dir = os.path.join(output_dir, task_name, \"quant\", \"step_2_da_10\") # DA-A4W2 51.2\n",
+    "teacher_model_dir = os.path.join(model_dir,task_name)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43a99315",
+   "metadata": {},
+   "source": [
+    "## Dataset "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "3fb545e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "04/01 06:45:11 PM Writing example 0 of 1500\n",
+      "04/01 06:45:11 PM *** Example ***\n",
+      "04/01 06:45:11 PM guid: dev-0\n",
+      "04/01 06:45:11 PM tokens: [CLS] a man with a hard hat is dancing . [SEP] a man wearing a hard hat is dancing . [SEP]\n",
+      "04/01 06:45:11 PM input_ids: 101 1037 2158 2007 1037 2524 6045 2003 5613 1012 102 1037 2158 4147 1037 2524 6045 2003 5613 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      "04/01 06:45:11 PM input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      "04/01 06:45:11 PM segment_ids: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      "04/01 06:45:11 PM label: 5.000\n",
+      "04/01 06:45:11 PM label_id: 5.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_2198397/3831030937.py:189: DeprecationWarning: an integer is required (got type float).  Implicit conversion to integers using __int__ is deprecated, and may be removed in a future version of Python.\n",
+      "  all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Processor & Task Info\n",
+    "processor = processors[task_name]()\n",
+    "output_mode = output_modes[task_name]\n",
+    "label_list = processor.get_labels()\n",
+    "num_labels = len(label_list)\n",
+    "\n",
+    "if task_name in default_params:\n",
+    "    batch_size = default_params[task_name][\"batch_size\"]\n",
+    "    max_seq_length = default_params[task_name][\"max_seq_length\"]\n",
+    "    eval_step = default_params[task_name][\"eval_step\"]\n",
+    "    \n",
+    "# Tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained(teacher_model_dir, do_lower_case=True)\n",
+    "\n",
+    "\n",
+    "# Load Dataset\n",
+    "data_dir = os.path.join(\"data\",task_name)\n",
+    "processed_data_dir = os.path.join(data_dir,'preprocessed')\n",
+    "\n",
+    "eval_examples = processor.get_dev_examples(data_dir)\n",
+    "eval_features = convert_examples_to_features(eval_examples, label_list, max_seq_length, tokenizer, output_mode)\n",
+    "# dev_file = train_file = os.path.join(processed_data_dir,'dev.pkl') \n",
+    "# eval_features = pickle.load(open(dev_file,'rb'))\n",
+    "\n",
+    "eval_data, eval_labels = get_tensor_data(\"classification\", eval_features)\n",
+    "eval_sampler = SequentialSampler(eval_data)\n",
+    "eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=32)\n",
+    "eval_data, eval_labels = get_tensor_data(output_mode, eval_features)\n",
+    "\n",
+    "eval_examples = processor.get_dev_examples(data_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f3b63b5",
+   "metadata": {},
+   "source": [
+    "# Model Build"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "0c3a8929",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "04/01 06:44:58 PM Loading model models/BERT_large/sts-b/pytorch_model.bin\n",
+      "04/01 06:44:59 PM loading model...\n",
+      "04/01 06:44:59 PM done!\n",
+      "04/01 06:44:59 PM loading configuration file output/BERT_large/sts-b/quant/ternary_save/config.json\n",
+      "04/01 06:45:05 PM Loading model output/BERT_large/sts-b/quant/ternary_save/pytorch_model.bin\n",
+      "04/01 06:45:06 PM loading model...\n",
+      "04/01 06:45:06 PM done!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "build_tc = 1\n",
+    "build_st = 1\n",
+    "\n",
+    "if build_tc:\n",
+    "    # Teacher Model Build\n",
+    "    teacher_model = BertForSequenceClassification.from_pretrained(teacher_model_dir, num_labels=num_labels)\n",
+    "    teacher_model.to(device)\n",
+    "    teacher_model.eval()\n",
+    "    model = teacher_model\n",
+    "\n",
+    "if build_st:\n",
+    "    # Student Model Build\n",
+    "    student_config = BertConfig.from_pretrained(student_model_dir,\n",
+    "                                                    quantize_act=True,\n",
+    "                                                    quantize_weight=True,\n",
+    "                                                    weight_bits = 2, # Always Ternary when \"quantize_weight = True\"\n",
+    "                                                    input_bits = 8,\n",
+    "                                                    clip_val = 2.5,\n",
+    "                                                    quantize = True,\n",
+    "                                                    ffn_q_1 = True,\n",
+    "                                                    ffn_q_2 = True,\n",
+    "                                                    qkv_q = True,\n",
+    "                                                    emb_q = True,\n",
+    "                                                    cls_q = True,\n",
+    "                                                    clipping = False,\n",
+    "                                                    layer_num = -1,\n",
+    "                                                    mean_scale = 0.7,\n",
+    "                                                    quantizer = \"ternary\",\n",
+    "                                                    act_quantizer = \"ternary\",\n",
+    "                                                    init_scaling = 1,\n",
+    "                                                    clip_ratio = 1,\n",
+    "                                                    gradient_scaling = False,\n",
+    "                                                    clip_method = \"minmax\",\n",
+    "                                                    teacher_attnmap = False,\n",
+    "                                                    parks = False,\n",
+    "                                                    stop_grad = False,\n",
+    "                                                    qk_FP = False,\n",
+    "                                                    map=False,\n",
+    "                                                    act_method = \"clipping\"\n",
+    "                                                    )\n",
+    "\n",
+    "    student_model = QuantBertForSequenceClassification.from_pretrained(student_model_dir, config = student_config, num_labels=num_labels)\n",
+    "    student_model.to(device)\n",
+    "    model = student_model\n",
+    "    print()\n",
+    "\n",
+    "    # Quantization Option ACT/WEIGHT\n",
+    "    for name, module in student_model.named_modules():\n",
+    "                if isinstance(module, (QuantizeLinear, QuantizeAct, ClipLinear)):    \n",
+    "                    module.act_flag = True\n",
+    "                    module.weight_flag = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc2361c6",
+   "metadata": {},
+   "source": [
+    "## Activation Quantization Clip Value Initialization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a32aa6cf",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# for name, module in student_model.named_modules():\n",
+    "#             if isinstance(module, (QuantizeLinear, QuantizeAct, ClipLinear)):    \n",
+    "#                 module.act_flag = False\n",
+    "#                 module.weight_flag = False\n",
+    "                \n",
+    "# cv_initialize(student_model, eval_dataloader, torch.Tensor([0.005]), device)\n",
+    "\n",
+    "# # for name, module in student_model.named_modules():\n",
+    "# #             if isinstance(module, (QuantizeLinear, QuantizeAct, ClipLinear)):    \n",
+    "# #                 module.act_flag = True\n",
+    "# #                 module.weight_flag = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34a0dfd7",
+   "metadata": {},
+   "source": [
+    "## Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "0c515c68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Student Model Inferece\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Inference: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47/47 [00:27<00:00,  1.69it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Student Result : {'pearson': 0.8887880506300738, 'spearmanr': 0.8850681732784779, 'corr': 0.8869281119542758, 'eval_loss': 0.6736781127909397}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "eval_st = 1\n",
+    "eval_tc = 0\n",
+    "\n",
+    "if eval_st:\n",
+    "    print(\"Student Model Inferece\")\n",
+    "    student_model.eval()\n",
+    "    student_result = do_eval(student_model, task_name, eval_dataloader, device, output_mode, eval_labels, num_labels, teacher_model=teacher_model)\n",
+    "    print(f\"Student Result : {student_result}\")\n",
+    "\n",
+    "if eval_tc:\n",
+    "    print(\"Teacher Model Inferece\")\n",
+    "    teacher_result = do_eval(teacher_model, task_name, eval_dataloader, device, output_mode, eval_labels, num_labels)\n",
+    "    print(f\"Teacher Result : {teacher_result}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "265abf87",
+   "metadata": {},
+   "source": [
+    "## BERTViz Model View"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0eedf469",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input_ids : tensor([[ 101, 1037, 2402, 2775, 2003, 5559, 1037, 3586, 1012,  102, 1037, 2775,\n",
+      "         2003, 5559, 1037, 3586, 1012,  102]], device='cuda:0')\n",
+      "tokens : ['[CLS]', 'a', 'young', 'child', 'is', 'riding', 'a', 'horse', '.', '[SEP]', 'a', 'child', 'is', 'riding', 'a', 'horse', '.', '[SEP]']\n",
+      "A : a young child is riding a horse . \n",
+      "B : a child is riding a horse . \n",
+      "tensor([ 9, 17], device='cuda:0')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sampling Sentence \n",
+    "i = 0 \n",
+    "num = 2\n",
+    "for step, batch in enumerate(eval_dataloader):\n",
+    "    model.train()\n",
+    "            \n",
+    "    batch = tuple(t.to(device) for t in batch)\n",
+    "    input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch\n",
+    "    i = i + 1\n",
+    "    if i == num:\n",
+    "        break\n",
+    "\n",
+    "seq_length = seq_lengths.item()\n",
+    "input_ids_sliced = input_ids[:,:seq_length]\n",
+    "input_id = []\n",
+    "for i in input_ids_sliced[0]:\n",
+    "    input_id.append(i.item())\n",
+    "tokens = tokenizer.convert_ids_to_tokens(input_id)\n",
+    "\n",
+    "\n",
+    "\n",
+    "sample_sentence_a = str()\n",
+    "sample_sentence_b = str()\n",
+    "index = 0\n",
+    "\n",
+    "for i, word in enumerate(tokens[1:-1]):\n",
+    "    if word == \"[SEP]\":\n",
+    "        break\n",
+    "    sample_sentence_a += word\n",
+    "    sample_sentence_a += \" \"\n",
+    "index = i\n",
+    "\n",
+    "for i, word in enumerate(tokens[index+2:-1]):\n",
+    "    if word == \"[SEP]\":\n",
+    "        break\n",
+    "    sample_sentence_b += word\n",
+    "    sample_sentence_b += \" \"\n",
+    "\n",
+    "sep_index = torch.where(input_ids[0] == 102)[0]\n",
+    "\n",
+    "if len(sample_sentence_b) > 1:\n",
+    "    sample_sentence_b_start = segment_ids[0].tolist().index(1)\n",
+    "\n",
+    "print(f\"input_ids : {input_ids_sliced}\")\n",
+    "print(f\"tokens : {tokens}\")\n",
+    "print(f\"A : {sample_sentence_a}\")\n",
+    "print(f\"B : {sample_sentence_b}\")\n",
+    "print(sep_index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "780d1da2",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "from bertviz.transformers_neuron_view import BertModel, BertTokenizer\n",
+    "from bertviz.neuron_view import show\n",
+    "\n",
+    "bertviz_neuron_tc = 0\n",
+    "bertviz_neuron_st = 0\n",
+    "bertviz_model_tc = 1\n",
+    "bertviz_model_st = 1\n",
+    "\n",
+    "# Quantization Setting\n",
+    "if bertviz_neuron_st or bertviz_model_st:\n",
+    "    for name, module in student_model.named_modules():\n",
+    "            if isinstance(module, (QuantizeLinear, ClipLinear)):    \n",
+    "                module.act_flag = False\n",
+    "                module.weight_flag = False\n",
+    "            if isinstance(module, QuantizeAct):    \n",
+    "                module.act_flag = False\n",
+    "                module.weight_flag = False\n",
+    "\n",
+    "if bertviz_neuron_tc or bertviz_neuron_st:\n",
+    "    if bertviz_neuron_st:\n",
+    "        for name, module in student_model.named_modules():\n",
+    "                if isinstance(module, BertSelfAttention):    \n",
+    "                    module.output_bertviz = True\n",
+    "    if bertviz_neuron_tc:\n",
+    "        for name, module in teacher_model.named_modules():\n",
+    "                if isinstance(module, FP_BertSelfAttention):    \n",
+    "                    module.output_bertviz = True\n",
+    "\n",
+    "    model_type = 'bert'\n",
+    "    model_version = 'bert-base-uncased'\n",
+    "    \n",
+    "    tokenizer = BertTokenizer.from_pretrained(model_version, do_lower_case=True)\n",
+    "    if bertviz_neuron_tc:\n",
+    "        if len(sample_sentence_b) > 1:\n",
+    "            show(teacher_model.cpu(), model_type, tokenizer, sample_sentence_a, sample_sentence_b, display_mode=\"light\")\n",
+    "        else:\n",
+    "            show(teacher_model.cpu(), model_type, tokenizer, sample_sentence_a,display_mode=\"light\")\n",
+    "    if bertviz_neuron_st:\n",
+    "        if len(sample_sentence_b) > 1:\n",
+    "            show(student_model.cpu(), model_type, tokenizer, sample_sentence_a, sample_sentence_b, display_mode=\"light\")\n",
+    "        else:\n",
+    "            show(student_model.cpu(), model_type, tokenizer, sample_sentence_a,display_mode=\"light\")\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    layer_num = 24\n",
+    "    head_num = 16\n",
+    "else:\n",
+    "    layer_num = 12\n",
+    "    head_num = 12\n",
+    "    \n",
+    "all_layers = list(range(layer_num))\n",
+    "layers_to_show = all_layers[18:]\n",
+    "\n",
+    "if bertviz_model_tc or bertviz_model_st:\n",
+    "    \n",
+    "    if bertviz_model_tc:\n",
+    "        print(\"teacher_map\")\n",
+    "        for name, module in teacher_model.named_modules():\n",
+    "                    if isinstance(module, FP_BertSelfAttention):    \n",
+    "                        module.output_bertviz = False\n",
+    "        teacher_model.eval()\n",
+    "        teacher_model.to(device)\n",
+    "        teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "        model_view(teacher_probs, tokens, include_layers=layers_to_show,  display_mode=\"light\")\n",
+    "        \n",
+    "    if bertviz_model_st:\n",
+    "        print(\"student_map\")\n",
+    "        for name, module in student_model.named_modules():\n",
+    "                    if isinstance(module, BertSelfAttention):    \n",
+    "                        module.output_bertviz = False\n",
+    "        student_model.eval()\n",
+    "        student_model.to(device)\n",
+    "        student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_probs=teacher_probs)\n",
+    "        model_view(student_probs, tokens, sample_sentence_b_start,include_layers=layers_to_show, display_mode=\"light\")# , include_layers=[0, 1])\n",
+    "        \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a0039fe",
+   "metadata": {},
+   "source": [
+    "## Forward Check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "64cf6575",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KL DIV CHECK\n",
+      "2.9676687717437744\n",
+      "5.659456253051758\n",
+      "3.098494052886963\n",
+      "2.3057239055633545\n",
+      "2.587374210357666\n",
+      "1.6189770698547363\n",
+      "1.2007269859313965\n",
+      "1.662299633026123\n",
+      "2.1250391006469727\n",
+      "2.056593418121338\n",
+      "3.305050849914551\n",
+      "4.6170477867126465\n",
+      "4.530083656311035\n",
+      "4.024961948394775\n",
+      "4.323973178863525\n",
+      "3.5094804763793945\n",
+      "3.292818784713745\n",
+      "2.7897772789001465\n",
+      "4.441211700439453\n",
+      "8.378562927246094\n",
+      "20.496295928955078\n",
+      "37.17641830444336\n",
+      "40.135982513427734\n",
+      "76.01957702636719\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torch.nn import MSELoss\n",
+    "mse_func = MSELoss()\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    layer_num = 24\n",
+    "    head_num = 16\n",
+    "else:\n",
+    "    layer_num = 12\n",
+    "    head_num = 12\n",
+    "\n",
+    "\n",
+    "attention_pattern_check = 0\n",
+    "cover_mean_check = 0\n",
+    "kl_div_check = 1\n",
+    "mse_check = 0\n",
+    "attnmap_mse_check = 0\n",
+    "\n",
+    "exclude_sep = 0\n",
+    "\n",
+    "for name, module in student_model.named_modules():\n",
+    "            if isinstance(module, BertSelfAttention):    \n",
+    "                module.output_bertviz = False\n",
+    "for name, module in teacher_model.named_modules():\n",
+    "            if isinstance(module, FP_BertSelfAttention):    \n",
+    "                module.output_bertviz = False\n",
+    "                \n",
+    "for name, module in student_model.named_modules():\n",
+    "            if isinstance(module, (QuantizeLinear, ClipLinear, QuantizeAct)):    \n",
+    "                module.act_flag = True\n",
+    "                module.weight_flag = True\n",
+    "\n",
+    "seed=42\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "\n",
+    "student_model.eval()\n",
+    "teacher_model.eval()\n",
+    "student_model.to(device)\n",
+    "teacher_model.to(device)\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_probs=teacher_probs)\n",
+    "\n",
+    "probs = teacher_probs\n",
+    "if attention_pattern_check:\n",
+    "    print(\"Attention mean CHECK\")\n",
+    "    for i in range(layer_num):\n",
+    "        if len(sep_index) == 2:\n",
+    "            print((probs[i][0,:,:,sep_index[0]].mean() + probs[i][0,:,:,sep_index[1]].mean()).item())\n",
+    "        else:\n",
+    "            print(probs[i][0,:,:,sep_index[0]].mean().item())\n",
+    "    \n",
+    "if cover_mean_check:\n",
+    "    print(\"COVER MEAN CHECK\")\n",
+    "    top_k = 5\n",
+    "\n",
+    "    for i in range(layer_num):\n",
+    "        teacher = teacher_probs[i][0]\n",
+    "        student = student_probs[i][0]\n",
+    "\n",
+    "        head_sum = 0\n",
+    "        for h in range(head_num):\n",
+    "            coverage_head_sum = 0\n",
+    "            for row in range(seq_length-1):\n",
+    "                if exclude_sep:\n",
+    "                    tc_argsort = teacher[h][:seq_length-1,:seq_length-1].sort(descending=True)[1][row][:top_k] # top-k\n",
+    "                    st_argsort = student[h][:seq_length-1,:seq_length-1].sort(descending=True)[1][row]\n",
+    "                tc_argsort = teacher[h].sort(descending=True)[1][row][:top_k] # top-k\n",
+    "                st_argsort = student[h].sort(descending=True)[1][row]\n",
+    "\n",
+    "                max_idx = 0\n",
+    "                for idx in tc_argsort:\n",
+    "                    tmp = torch.where(st_argsort == idx)\n",
+    "                    max_idx = max(tmp[0].item(), max_idx)\n",
+    "\n",
+    "                coverage_ratio = max_idx / student.shape[1]\n",
+    "                coverage_head_sum += coverage_ratio\n",
+    "\n",
+    "                # print(f\"H{h} : {coverage_head_sum/seq_length}\")\n",
+    "\n",
+    "            head_sum += coverage_head_sum / seq_length\n",
+    "        print(head_sum / head_num)\n",
+    "\n",
+    "if kl_div_check:\n",
+    "    print(\"KL DIV CHECK\")\n",
+    "    for i in range(layer_num):\n",
+    "        if exclude_sep:\n",
+    "            if len(sep_index) == 2:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000; teacher_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000; student_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "            else:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                \n",
+    "            teacher = torch.nn.Softmax(dim=-1)(teacher_atts[i])\n",
+    "            student = torch.nn.Softmax(dim=-1)(student_atts[i])\n",
+    "            \n",
+    "            student = torch.clamp_min(student, 1e-8)\n",
+    "            teacher = torch.clamp_min(teacher, 1e-8)\n",
+    "        else:    \n",
+    "            teacher = teacher_probs[i]\n",
+    "            student = student_probs[i]\n",
+    "        \n",
+    "        neg_cross_entropy = teacher * torch.log(student) \n",
+    "        neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "        neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "        # p(t) log p(t) = negative entropy\n",
+    "        neg_entropy = teacher * torch.log(teacher) \n",
+    "        neg_entropy = torch.sum(neg_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "        neg_entropy = torch.sum(neg_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "        kld_loss = neg_entropy - neg_cross_entropy\n",
+    "\n",
+    "        kld_loss_sum = torch.sum(kld_loss)\n",
+    "        print(kld_loss_sum.item())\n",
+    "\n",
+    "if mse_check:\n",
+    "    for i in range(layer_num):\n",
+    "        print(mse_func(teacher_atts[i], student_atts[i]).item())\n",
+    "        \n",
+    "if attnmap_mse_check:\n",
+    "    for i in range(layer_num):\n",
+    "        if exclude_sep:\n",
+    "            if len(sep_index) == 2:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000; teacher_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000; student_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "            else:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                \n",
+    "            teacher = torch.nn.Softmax(dim=-1)(teacher_atts[i])\n",
+    "            student = torch.nn.Softmax(dim=-1)(student_atts[i])\n",
+    "            print(mse_func(teacher, student).item())\n",
+    "        else:    \n",
+    "            print(mse_func(teacher_probs[i], student_probs[i]).item())\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "e1cfe75a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.155358076095581\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "i = 9\n",
+    "teacher = teacher_probs[1][:,i,:,:]\n",
+    "student = student_probs[1][:,i,:,:]\n",
+    "\n",
+    "\n",
+    "\n",
+    "neg_cross_entropy = teacher * torch.log(student) \n",
+    "neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "# p(t) log p(t) = negative entropy\n",
+    "neg_entropy = teacher * torch.log(teacher) \n",
+    "neg_entropy = torch.sum(neg_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "neg_entropy = torch.sum(neg_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "kld_loss = neg_entropy - neg_cross_entropy\n",
+    "\n",
+    "kld_loss_sum = torch.sum(kld_loss)\n",
+    "print(kld_loss_sum.item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "81a859c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4594290256500244\n",
+      "0.5127482414245605\n",
+      "0.5085060596466064\n",
+      "0.44210290908813477\n",
+      "0.3044252395629883\n",
+      "0.4317352771759033\n",
+      "0.43944454193115234\n",
+      "0.34624576568603516\n",
+      "0.45526742935180664\n",
+      "0.4619622230529785\n",
+      "0.2999594211578369\n",
+      "0.5414872169494629\n",
+      "0.4033381938934326\n",
+      "0.3882777690887451\n",
+      "0.3862929344177246\n",
+      "0.29909467697143555\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "head = 7\n",
+    "for head in range(16):\n",
+    "    teacher = teacher_probs[23][:,head,:,:]\n",
+    "    student = student_probs[23][:,head,:,:]\n",
+    "    neg_cross_entropy = teacher * torch.log(student) \n",
+    "    neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "    neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "    # p(t) log p(t) = negative entropy\n",
+    "    neg_entropy = teacher * torch.log(teacher) \n",
+    "    neg_entropy = torch.sum(neg_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "    neg_entropy = torch.sum(neg_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "    kld_loss = neg_entropy - neg_cross_entropy\n",
+    "\n",
+    "    kld_loss_sum = torch.sum(kld_loss)\n",
+    "    print(kld_loss_sum.item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "dab8d045",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[[2.1772e-02, 1.2781e-02, 4.9408e-03,  ..., 2.0158e-02,\n",
+       "          6.9626e-02, 1.4999e-02],\n",
+       "         [1.0919e-03, 2.1085e-03, 1.0577e-03,  ..., 1.6647e-04,\n",
+       "          4.3606e-04, 1.8829e-01],\n",
+       "         [2.0435e-04, 2.2089e-04, 1.0611e-04,  ..., 4.0684e-05,\n",
+       "          4.6589e-05, 1.9174e-01],\n",
+       "         ...,\n",
+       "         [1.2242e-03, 9.6587e-05, 5.1341e-05,  ..., 2.4791e-03,\n",
+       "          8.4426e-04, 2.3197e-01],\n",
+       "         [3.1989e-05, 1.6445e-05, 4.2517e-06,  ..., 5.4571e-05,\n",
+       "          1.5931e-05, 1.9737e-01],\n",
+       "         [2.7168e-02, 1.6892e-02, 1.5799e-02,  ..., 1.5508e-02,\n",
+       "          2.3137e-02, 4.5687e-02]]], device='cuda:0', grad_fn=<SliceBackward0>)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "student_probs[23][:,7,:,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "857ecb5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.8724, 0.4891, 0.6094, 0.6159, 0.4543, 0.4242, 0.5964, 0.1814, 0.7470,\n",
+       "         0.3960, 0.4766, 0.3705, 0.6179, 0.2615, 0.6612, 0.4897, 0.6067, 0.3899,\n",
+       "         0.4389, 0.7189, 0.3123, 0.2026, 0.3890, 0.2045, 0.5183, 0.4821, 0.4648,\n",
+       "         0.2569, 0.2587, 0.4544, 0.2224, 0.6254, 0.7420, 0.1829, 0.5220, 0.2288,\n",
+       "         0.6594, 0.6613, 0.4734, 0.7735, 0.7583, 0.2122, 0.6728, 0.2626, 0.7567,\n",
+       "         0.2797, 0.7548, 0.4164, 0.7275]], device='cuda:0',\n",
+       "       grad_fn=<SelectBackward0>)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "teacher_probs[1][:,9,:,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2440e7dd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/BERTviz.ipynb b/notebooks/BERTviz.ipynb
new file mode 100644
index 0000000..5ba373c
--- /dev/null
+++ b/notebooks/BERTviz.ipynb
@@ -0,0 +1,2724 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9f99d9c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import absolute_import, division, print_function\n",
+    "\n",
+    "import pprint\n",
+    "import argparse\n",
+    "import logging\n",
+    "import os\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\" # Set GPU Index to use\n",
+    "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\n",
+    "import random\n",
+    "import sys\n",
+    "import pickle\n",
+    "import copy\n",
+    "import collections\n",
+    "import math\n",
+    "\n",
+    "import numpy as np\n",
+    "import numpy\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler,TensorDataset\n",
+    "# from torch.utils.tensorboard import SummaryWriter\n",
+    "\n",
+    "from torch.nn import CrossEntropyLoss, MSELoss\n",
+    "from tqdm import tqdm\n",
+    "from transformer import BertForSequenceClassification,WEIGHTS_NAME, CONFIG_NAME\n",
+    "from transformer.modeling_quant import BertForSequenceClassification as QuantBertForSequenceClassification\n",
+    "from transformer import BertTokenizer\n",
+    "from transformer import BertAdam\n",
+    "from transformer import BertConfig\n",
+    "from transformer import QuantizeLinear, QuantizeAct, BertSelfAttention, FP_BertSelfAttention, ClipLinear\n",
+    "from utils_glue import *\n",
+    "from bertviz import model_view\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "class AverageMeter(object):\n",
+    "    \"\"\"Computes and stores the average and current value\"\"\"\n",
+    "    def __init__(self):\n",
+    "        self.reset()\n",
+    "\n",
+    "    def reset(self):\n",
+    "        self.val = 0\n",
+    "        self.avg = 0 \n",
+    "        self.sum = 0\n",
+    "        self.count = 0\n",
+    "\n",
+    "    def update(self, val, n=1):\n",
+    "        self.val = val\n",
+    "        self.sum += val * n\n",
+    "        self.count += n\n",
+    "        self.avg = self.sum / self.count\n",
+    "\n",
+    "def cv_initialize(model, loader, ratio, device):\n",
+    "    \n",
+    "    def initialize_hook(module, input, output):\n",
+    "        if isinstance(module, (QuantizeLinear, QuantizeAct, ClipLinear)):\n",
+    "            \"\"\"KDLSQ-BERT ACT Quant init Method\n",
+    "            Ref: https://arxiv.org/abs/2101.05938\n",
+    "            \"\"\"\n",
+    "            if not isinstance(input, torch.Tensor):\n",
+    "                input = input[0]\n",
+    "        \n",
+    "            n = torch.numel(input)\n",
+    "            input_sorted, index = torch.sort(input.reshape(-1), descending=False)\n",
+    "            \n",
+    "            index_min = torch.round(ratio * n / 2)\n",
+    "            index_max = n - index_min\n",
+    "            \n",
+    "            s_init = (input_sorted[int(index_min)].to(device), input_sorted[int(index_max)].to(device))\n",
+    "            \n",
+    "            # MATPLOT\n",
+    "            \n",
+    "            fig, [ax1, ax2, ax3] = plt.subplots(1,3, figsize=(16, 4))            \n",
+    "            \n",
+    "            sns.histplot(data=input.reshape(-1).detach().cpu().numpy(), kde = True, bins=100, ax=ax1)\n",
+    "            sns.rugplot(data=input.reshape(-1).detach().cpu().numpy(), ax=ax1)\n",
+    "            sns.histplot(data=module.weight.reshape(-1).detach().cpu().numpy(), kde = True, bins=100, ax=ax2)\n",
+    "            sns.rugplot(data=module.weight.reshape(-1).detach().cpu().numpy(), ax=ax2)\n",
+    "            sns.histplot(data=output.reshape(-1).detach().cpu().numpy(), kde = True, bins=100, ax=ax3)\n",
+    "            sns.rugplot(data=output.reshape(-1).detach().cpu().numpy(), ax=ax3)\n",
+    "            # fig, [ax1, ax2] = plt.subplots(1,2, figsize=(12, 4))            \n",
+    "            \n",
+    "            # sns.distplot(input.reshape(-1).detach().cpu().numpy() , hist = True, rug = True, kde = True, bins=100, norm_hist=False, kde_kws=dict(linewidth=0.5), rug_kws=dict(linewidth=0.5), ax=ax1)\n",
+    "            # sns.distplot(output.reshape(-1).detach().cpu().numpy() , hist = True, rug = True, kde = True, bins=100, norm_hist=False, kde_kws=dict(linewidth=0.5), rug_kws=dict(linewidth=0.5), ax=ax2)\n",
+    "            # # plt.axvline(x=s_init[0].detach().cpu().numpy(), color='r', linestyle='--')\n",
+    "            # # plt.axvline(x=s_init[1].detach().cpu().numpy(), color='r', linestyle='--')\n",
+    "\n",
+    "            ax1.set_xlabel(\"Input Activation\")\n",
+    "            # ax2.set_xlabel(\"Output Activation\")\n",
+    "            ax2.set_xlabel(\"Module Weight\")\n",
+    "            ax3.set_xlabel(\"Output Activation\")\n",
+    "            \n",
+    "            ax1.set_ylabel(\"Density\")\n",
+    "            ax2.set_ylabel(\"Density\")\n",
+    "            ax3.set_ylabel(\"Density\")\n",
+    "\n",
+    "            ax1.set_title(f\"{module.name} Input ACT histogram\")\n",
+    "            # ax2.set_title(f\"{module.name} Output ACT histogram\")\n",
+    "            ax2.set_title(f\"{module.name} Weight histogram\")\n",
+    "            ax3.set_title(f\"{module.name} Output ACT histogram\")\n",
+    "            # plt.savefig(f\"plt_storage/hook_inputs/sst-2-fp/{module.name}.png\")\n",
+    "            plt.show()\n",
+    "            plt.close(fig)\n",
+    "            # module.clip_initialize(s_init)\n",
+    "            # logger.info(f\"{module} : min {s_init[0].item()} max {s_init[1].item()}\") \n",
+    "\n",
+    "    \n",
+    "    hooks = []\n",
+    "\n",
+    "    for name, module in model.named_modules():\n",
+    "        hook = module.register_forward_hook(initialize_hook)\n",
+    "        hooks.append(hook)\n",
+    "    \n",
+    "    model.train()\n",
+    "    model.to(device)\n",
+    "    \n",
+    "    for step, batch in enumerate(loader):\n",
+    "        batch = tuple(t.to(\"cuda\") for t in batch)\n",
+    "        input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch        \n",
+    "        with torch.no_grad():\n",
+    "            student_logits, student_atts, student_reps, student_probs, student_values = model(input_ids, segment_ids, input_mask, teacher_probs=None)\n",
+    "        break\n",
+    "    \n",
+    "    for hook in hooks:\n",
+    "        hook.remove()\n",
+    "\n",
+    "def str2bool(v):\n",
+    "    if isinstance(v, bool):\n",
+    "        return v\n",
+    "    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n",
+    "        return True\n",
+    "    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n",
+    "        return False\n",
+    "    else:\n",
+    "        raise argparse.ArgumentTypeError('Boolean value expected.')\n",
+    "\n",
+    "def load_vocab(vocab_file):\n",
+    "    \"\"\"Loads a vocabulary file into a dictionary.\"\"\"\n",
+    "    vocab = collections.OrderedDict()\n",
+    "    index = 0\n",
+    "    with open(vocab_file, \"r\", encoding=\"utf-8\") as reader:\n",
+    "        while True:\n",
+    "            token = reader.readline()\n",
+    "            if not token:\n",
+    "                break\n",
+    "            token = token.strip()\n",
+    "            #vocab[token] = index\n",
+    "            vocab[index] = token\n",
+    "            index += 1\n",
+    "    return vocab\n",
+    "\n",
+    "def attention_pattern(model, loader, device):\n",
+    "    \n",
+    "    def initialize_hook(module, input, output):\n",
+    "        if isinstance(module, BertSelfAttention):\n",
+    "            \n",
+    "            attn_mask = input[1]\n",
+    "            attention_output = output[-2]\n",
+    "            \n",
+    "            seq_length = (attn_mask == 0).sum()\n",
+    "            \n",
+    "            print(attention_output[0,:,:seq_length,seq_length-1].mean().item())\n",
+    "            \n",
+    "\n",
+    "    hooks = []\n",
+    "\n",
+    "    for name, module in model.named_modules():\n",
+    "        hook = module.register_forward_hook(initialize_hook)\n",
+    "        hooks.append(hook)\n",
+    "    \n",
+    "    model.eval()\n",
+    "    model.to(device)\n",
+    "    \n",
+    "    for step, batch in enumerate(loader):\n",
+    "        batch = tuple(t.to(\"cuda\") for t in batch)\n",
+    "        input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch        \n",
+    "        with torch.no_grad():\n",
+    "            student_logits, student_atts, student_reps, student_probs, student_values = model(input_ids, segment_ids, input_mask)\n",
+    "        break\n",
+    "    \n",
+    "    for hook in hooks:\n",
+    "        hook.remove()\n",
+    "        \n",
+    "def get_tensor_data(output_mode, features):\n",
+    "    if output_mode == \"classification\":\n",
+    "        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)\n",
+    "    elif output_mode == \"regression\":\n",
+    "        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)\n",
+    "\n",
+    "\n",
+    "    all_seq_lengths = torch.tensor([f.seq_length for f in features], dtype=torch.long)\n",
+    "    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n",
+    "    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)\n",
+    "    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)\n",
+    "    tensor_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,all_label_ids, all_seq_lengths)\n",
+    "    return tensor_data, all_label_ids\n",
+    "\n",
+    "def do_logging(run, student_model, teacher_model, test_dataloader, device, global_step, args, vocab):\n",
+    "    \n",
+    "    if args.bert == \"large\":\n",
+    "        layer_num = 24\n",
+    "        head_num = 16\n",
+    "    else:\n",
+    "        layer_num = 12\n",
+    "        head_num = 12\n",
+    "        \n",
+    "    nb_steps = 0\n",
+    "    \n",
+    "    kl_div_sum = [0 for i in range(layer_num)]\n",
+    "    st_sep_avg_sum = [0 for i in range(layer_num)]; st_cls_avg_sum = [0 for i in range(layer_num)]; tc_sep_avg_sum = [0 for i in range(layer_num)]; tc_cls_avg_sum = [0 for i in range(layer_num)]\n",
+    "    cover_sum = [0 for i in range(layer_num)]\n",
+    "    cover_teacher_sum = [0 for i in range(layer_num)]\n",
+    "    \n",
+    "    batch_num = 0\n",
+    "    \n",
+    "    for batch_ in tqdm(test_dataloader, desc=\"Logging Test\", mininterval=0.01, ascii=True, leave=False):\n",
+    "        batch_ = tuple(t.to(device) for t in batch_)\n",
+    "        \n",
+    "        if batch_num >= 1: # Visualize Attention Map only First Batch \n",
+    "            args.log_map = False\n",
+    "        \n",
+    "        with torch.no_grad():\n",
+    "            input_ids, input_mask, segment_ids, label_id, seq_length = batch_\n",
+    "\n",
+    "            teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids, segment_ids, input_mask)\n",
+    "            student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids, segment_ids, input_mask, teacher_probs=teacher_probs)\n",
+    "            \n",
+    "            # Layer\n",
+    "            for i, (student_prob, teacher_prob) in enumerate(zip(student_probs, teacher_probs)): \n",
+    "\n",
+    "                # Head\n",
+    "                for head in range(head_num):\n",
+    "                    \n",
+    "                    if args.log_map:\n",
+    "                        \n",
+    "                        word_list = []\n",
+    "                        \n",
+    "                        for word in range(seq_length):\n",
+    "                            word_list.append(vocab[input_ids[0][word].item()])\n",
+    "                        \n",
+    "                        student_prob_map = student_prob[0][head][:seq_length,:seq_length].clone().detach().cpu().numpy()\n",
+    "                        teacher_prob_map = teacher_prob[0][head][:seq_length,:seq_length].clone().detach().cpu().numpy()\n",
+    "                        \n",
+    "                        fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(16,8))\n",
+    "                        ax1.set_title(f\"{i}th Layer {head}th Head Teacher\")\n",
+    "                        heatmap = ax1.pcolor(teacher_prob_map, cmap=plt.cm.Blues)\n",
+    "    \n",
+    "                        ax1.set_xticks(numpy.arange(teacher_prob_map.shape[1]) + 0.5, minor=False)\n",
+    "                        ax1.set_yticks(numpy.arange(teacher_prob_map.shape[0]) + 0.5, minor=False)\n",
+    "                        \n",
+    "                        ax1.set_xlim(0, int(teacher_prob_map.shape[1]))\n",
+    "                        ax1.set_ylim(0, int(teacher_prob_map.shape[0]))\n",
+    "\n",
+    "                        ax1.invert_yaxis()\n",
+    "                        ax1.xaxis.tick_top()\n",
+    "\n",
+    "                        ax1.set_xticklabels(word_list, minor=False)\n",
+    "                        ax1.set_yticklabels(word_list, minor=False)\n",
+    "\n",
+    "                        plt.xticks(rotation=45)\n",
+    "                        \n",
+    "                        ax2.set_title(f\"{i}th Layer {head}th Head Student\")\n",
+    "                        heatmap = ax2.pcolor(student_prob_map, cmap=plt.cm.Blues)\n",
+    "\n",
+    "                        ax2.set_xticks(numpy.arange(student_prob_map.shape[1]) + 0.5, minor=False)\n",
+    "                        ax2.set_yticks(numpy.arange(student_prob_map.shape[0]) + 0.5, minor=False)\n",
+    "\n",
+    "                        ax2.set_xlim(0, int(student_prob_map.shape[1]))\n",
+    "                        ax2.set_ylim(0, int(student_prob_map.shape[0]))\n",
+    "\n",
+    "                        ax2.invert_yaxis()\n",
+    "                        ax2.xaxis.tick_top()\n",
+    "\n",
+    "                        ax2.set_xticklabels(word_list, minor=False)\n",
+    "                        ax2.set_yticklabels(word_list, minor=False)\n",
+    "\n",
+    "                        plt.xticks(rotation=45)\n",
+    "                        \n",
+    "                        plt_folder_name = os.path.join(\"plt_storage\" + \"/\" + args.exp_name)\n",
+    "                        if not os.path.exists(plt_folder_name):\n",
+    "                            os.mkdir(plt_folder_name)          \n",
+    "                        plt_folder_name = os.path.join(plt_folder_name, f\"step_{global_step}\")\n",
+    "                        if not os.path.exists(plt_folder_name):\n",
+    "                            os.mkdir(plt_folder_name)                        \n",
+    "                        plt.savefig(plt_folder_name + \"/\" + f\"L{i}_H{head}.png\")\n",
+    "                        plt.close()\n",
+    "                        \n",
+    "\n",
+    "                    if args.log_metric:\n",
+    "                        \n",
+    "                        student_prob = student_prob\n",
+    "                        teacher_prob = teacher_prob\n",
+    "\n",
+    "                        # Attention Map\n",
+    "                        student_attn_map = student_prob[0][head][:seq_length,:seq_length].clone().detach()\n",
+    "                        teacher_attn_map = teacher_prob[0][head][:seq_length,:seq_length].clone().detach()\n",
+    "\n",
+    "                        # KL Divergence\n",
+    "                        kl_div = F.kl_div(student_attn_map.log(), teacher_attn_map, reduction='batchmean')\n",
+    "                        kl_div_sum[i] += kl_div\n",
+    "\n",
+    "                        # Special Token Prob Mean\n",
+    "                        st_sep_avg = student_attn_map[:,-1].mean()\n",
+    "                        st_cls_avg = student_attn_map[:,0].mean()\n",
+    "                        st_sep_avg_sum[i] += st_sep_avg\n",
+    "                        st_cls_avg_sum[i] += st_cls_avg\n",
+    "                        \n",
+    "                        # Ground Truth\n",
+    "                        tc_sep_avg = teacher_attn_map[:,-1].mean()\n",
+    "                        tc_cls_avg = teacher_attn_map[:,0].mean()\n",
+    "                        tc_sep_avg_sum[i] += tc_sep_avg\n",
+    "                        tc_cls_avg_sum[i] += tc_cls_avg\n",
+    "\n",
+    "                        # Coverage Test\n",
+    "                        coverage_head_sum = 0\n",
+    "                        coverage_teacher_head_sum = 0\n",
+    "                        for k in range(student_attn_map.shape[0]):\n",
+    "                            st_argsort = student_attn_map[k].sort(descending=True)[1]\n",
+    "                            tc_argsort = teacher_attn_map[k].sort(descending=True)[1][:args.tc_top_k] # Top-5\n",
+    "                            \n",
+    "                            max_idx = 0\n",
+    "                            for idx in tc_argsort: # Teacher Top-5                             \n",
+    "                                tmp = torch.where(st_argsort == idx)\n",
+    "                                max_idx = max(tmp[0].item(), max_idx)\n",
+    "                            \n",
+    "                            coverage_ratio = max_idx / student_attn_map.shape[0]\n",
+    "                            coverage_teacher_ratio = (args.tc_top_k - 1) / student_attn_map.shape[0]\n",
+    "                            coverage_head_sum += coverage_ratio\n",
+    "                            coverage_teacher_head_sum += coverage_teacher_ratio\n",
+    "                        \n",
+    "                        coverage_head = coverage_head_sum / student_attn_map.shape[0]\n",
+    "                        coverage_teacher_head = coverage_teacher_head_sum / student_attn_map.shape[0]\n",
+    "                        \n",
+    "                        cover_sum[i] += coverage_head\n",
+    "                        cover_teacher_sum[i] += coverage_teacher_head\n",
+    "                        \n",
+    "                        nb_steps += 1\n",
+    "        \n",
+    "        batch_num = batch_num + 1\n",
+    "    \n",
+    "    if args.log_metric:\n",
+    "        nb_steps = nb_steps / 12\n",
+    "        \n",
+    "        for l in range(12):\n",
+    "            run[f\"attn/L{l}_KLdiv_mean\"].log(value=kl_div_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_st_SepProb_mean\"].log(value=st_sep_avg_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_st_ClsProb_mean\"].log(value=st_cls_avg_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_tc_SepProb_mean\"].log(value=tc_sep_avg_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_tc_ClsProb_mean\"].log(value=tc_cls_avg_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_st_cover_mean\"].log(value=cover_sum[l] / nb_steps, step=global_step)\n",
+    "            run[f\"attn/L{l}_tc_cover_mean\"].log(value=cover_teacher_sum[l] / nb_steps, step=global_step)\n",
+    "\n",
+    "    args.log_map = True                    \n",
+    "\n",
+    "\n",
+    "def do_eval(model, task_name, eval_dataloader,\n",
+    "            device, output_mode, eval_labels, num_labels, teacher_model=None):\n",
+    "    eval_loss = 0\n",
+    "    nb_eval_steps = 0\n",
+    "    preds = []\n",
+    "\n",
+    "    for batch_ in tqdm(eval_dataloader, desc=\"Inference\"):\n",
+    "        batch_ = tuple(t.to(device) for t in batch_)\n",
+    "        \n",
+    "        with torch.no_grad():\n",
+    "            input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch_\n",
+    "\n",
+    "            # teacher attnmap test\n",
+    "            if teacher_model is not None:\n",
+    "                logits, teacher_atts, _, teacher_probs, _ = teacher_model(input_ids, segment_ids, input_mask)\n",
+    "                # teacher_probs = 0\n",
+    "                logits, _, _, _, _ = model(input_ids, segment_ids, input_mask, teacher_outputs=None)\n",
+    "            else:\n",
+    "                logits, _, _, _, _ = model(input_ids, segment_ids, input_mask)\n",
+    "        \n",
+    "        # create eval loss and other metric required by the task\n",
+    "        if output_mode == \"classification\":\n",
+    "            loss_fct = CrossEntropyLoss()\n",
+    "            tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))\n",
+    "        elif output_mode == \"regression\":\n",
+    "            loss_fct = MSELoss()\n",
+    "            tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))\n",
+    "\n",
+    "        eval_loss += tmp_eval_loss.mean().item()\n",
+    "        nb_eval_steps += 1\n",
+    "        if len(preds) == 0:\n",
+    "            preds.append(logits.detach().cpu().numpy())\n",
+    "        else:\n",
+    "            preds[0] = np.append(\n",
+    "                preds[0], logits.detach().cpu().numpy(), axis=0)\n",
+    "\n",
+    "    eval_loss = eval_loss / nb_eval_steps\n",
+    "\n",
+    "    preds = preds[0]\n",
+    "    if output_mode == \"classification\":\n",
+    "        preds = np.argmax(preds, axis=1)\n",
+    "    elif output_mode == \"regression\":\n",
+    "        preds = np.squeeze(preds)\n",
+    "    result = compute_metrics(task_name, preds, eval_labels.numpy())\n",
+    "    result['eval_loss'] = eval_loss\n",
+    "    return result\n",
+    "\n",
+    "def soft_cross_entropy(predicts, targets):\n",
+    "    student_likelihood = torch.nn.functional.log_softmax(predicts, dim=-1)\n",
+    "    targets_prob = torch.nn.functional.softmax(targets, dim=-1)\n",
+    "    return torch.sum((- targets_prob * student_likelihood), dim=-1).mean()\n",
+    "\n",
+    "processors = {\n",
+    "    \"cola\": ColaProcessor,\n",
+    "    \"mnli\": MnliProcessor,\n",
+    "    \"mnli-mm\": MnliMismatchedProcessor,\n",
+    "    \"mrpc\": MrpcProcessor,\n",
+    "    \"sst-2\": Sst2Processor,\n",
+    "    \"sts-b\": StsbProcessor,\n",
+    "    \"qqp\": QqpProcessor,\n",
+    "    \"qnli\": QnliProcessor,\n",
+    "    \"rte\": RteProcessor   \n",
+    "}\n",
+    "\n",
+    "output_modes = {\n",
+    "        \"cola\": \"classification\",\n",
+    "        \"mnli\": \"classification\",\n",
+    "        \"mrpc\": \"classification\",\n",
+    "        \"sst-2\": \"classification\",\n",
+    "        \"sts-b\": \"regression\",\n",
+    "        \"qqp\": \"classification\",\n",
+    "        \"qnli\": \"classification\",\n",
+    "        \"rte\": \"classification\"\n",
+    "}\n",
+    "\n",
+    "default_params = {\n",
+    "        \"cola\": {\"max_seq_length\": 64,\"batch_size\":1,\"eval_step\": 50}, # No Aug : 50 Aug : 400\n",
+    "        \"mnli\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":8000},\n",
+    "        \"mrpc\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"sst-2\": {\"max_seq_length\": 64,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"sts-b\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"qqp\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":1000},\n",
+    "        \"qnli\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":1000},\n",
+    "        \"rte\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\": 20}\n",
+    "    }\n",
+    "\n",
+    "from bertviz import head_view, model_view\n",
+    "# from bertviz.transformers_neuron_view import BertModel, BertTokenizer\n",
+    "from bertviz.neuron_view import show\n",
+    "import bertviz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aab9bf6b",
+   "metadata": {},
+   "source": [
+    "# GLUE Task Selection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 560,
+   "id": "75f78270",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_name = \"rte\"\n",
+    "bert_size = \"base\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    layer_num = 24\n",
+    "    head_num = 16\n",
+    "else:\n",
+    "    layer_num = 12\n",
+    "    head_num = 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07b94ac6",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "## Model Dir, Device & Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 561,
+   "id": "7fec849f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model_dir = \"models\"\n",
+    "output_dir = \"output\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    model_dir = os.path.join(model_dir, \"BERT_large\")\n",
+    "    output_dir = os.path.join(output_dir, \"BERT_large\")\n",
+    "\n",
+    "student_model_dir = os.path.join(model_dir,task_name)\n",
+    "# student_model_dir = os.path.join(output_dir, task_name, \"quant\", \"ternary_save\") # DA-A4W2 51.2\n",
+    "student_model_dir_1 = os.path.join(output_dir, task_name, \"exploration\", \"1SB_S\")\n",
+    "student_model_dir_2 = os.path.join(output_dir, task_name, \"exploration\", \"1SB_S_M\")\n",
+    "student_model_dir_3 = os.path.join(output_dir, task_name, \"exploration\", \"step_2_S_M\")\n",
+    "teacher_model_dir = os.path.join(model_dir,task_name)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 562,
+   "id": "3fb545e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "08/02 01:12:08 PM Writing example 0 of 277\n",
+      "08/02 01:12:08 PM *** Example ***\n",
+      "08/02 01:12:08 PM guid: dev-0\n",
+      "08/02 01:12:08 PM tokens: [CLS] dana reeve , the widow of the actor christopher reeve , has died of lung cancer at age 44 , according to the christopher reeve foundation . [SEP] christopher reeve had an accident . [SEP]\n",
+      "08/02 01:12:08 PM input_ids: 101 11271 20726 1010 1996 7794 1997 1996 3364 5696 20726 1010 2038 2351 1997 11192 4456 2012 2287 4008 1010 2429 2000 1996 5696 20726 3192 1012 102 5696 20726 2018 2019 4926 1012 102 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      "08/02 01:12:08 PM input_mask: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      "08/02 01:12:09 PM segment_ids: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      "08/02 01:12:09 PM label: not_entailment\n",
+      "08/02 01:12:09 PM label_id: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Processor & Task Info\n",
+    "processor = processors[task_name]()\n",
+    "output_mode = output_modes[task_name]\n",
+    "label_list = processor.get_labels()\n",
+    "num_labels = len(label_list)\n",
+    "\n",
+    "if task_name in default_params:\n",
+    "    batch_size = default_params[task_name][\"batch_size\"]\n",
+    "    max_seq_length = default_params[task_name][\"max_seq_length\"]\n",
+    "    eval_step = default_params[task_name][\"eval_step\"]\n",
+    "    \n",
+    "# Tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained(teacher_model_dir, do_lower_case=True)\n",
+    "\n",
+    "\n",
+    "# Load Dataset\n",
+    "data_dir = os.path.join(\"data\",task_name)\n",
+    "processed_data_dir = os.path.join(data_dir,'preprocessed')\n",
+    "\n",
+    "eval_examples = processor.get_dev_examples(data_dir)\n",
+    "eval_features = convert_examples_to_features(eval_examples, label_list, max_seq_length, tokenizer, output_mode)\n",
+    "# dev_file = train_file = os.path.join(processed_data_dir,'dev.pkl') \n",
+    "# eval_features = pickle.load(open(dev_file,'rb'))\n",
+    "\n",
+    "eval_data, eval_labels = get_tensor_data(\"classification\", eval_features)\n",
+    "eval_sampler = SequentialSampler(eval_data)\n",
+    "eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=1)\n",
+    "eval_data, eval_labels = get_tensor_data(output_mode, eval_features)\n",
+    "\n",
+    "eval_examples = processor.get_dev_examples(data_dir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f3b63b5",
+   "metadata": {},
+   "source": [
+    "# Model Build"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 563,
+   "id": "0c3a8929",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "08/02 01:12:11 PM Loading model models/rte/pytorch_model.bin\n",
+      "08/02 01:12:11 PM loading model...\n",
+      "08/02 01:12:11 PM done!\n",
+      "08/02 01:12:11 PM loading configuration file output/rte/exploration/1SB_S/config.json\n",
+      "08/02 01:12:12 PM Loading model output/rte/exploration/1SB_S/pytorch_model.bin\n",
+      "08/02 01:12:13 PM loading model...\n",
+      "08/02 01:12:13 PM done!\n",
+      "08/02 01:12:13 PM loading configuration file output/rte/exploration/1SB_S_M/config.json\n",
+      "08/02 01:12:14 PM Loading model output/rte/exploration/1SB_S_M/pytorch_model.bin\n",
+      "08/02 01:12:15 PM loading model...\n",
+      "08/02 01:12:15 PM done!\n",
+      "08/02 01:12:15 PM loading configuration file output/rte/exploration/step_2_S_M/config.json\n",
+      "08/02 01:12:16 PM Loading model output/rte/exploration/step_2_S_M/pytorch_model.bin\n",
+      "08/02 01:12:17 PM loading model...\n",
+      "08/02 01:12:17 PM done!\n",
+      "08/02 01:12:17 PM loading configuration file output/rte/exploration/1SB_S/config.json\n",
+      "08/02 01:12:18 PM Loading model models/rte/pytorch_model.bin\n",
+      "08/02 01:12:19 PM loading model...\n",
+      "08/02 01:12:19 PM done!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = \"cpu\"# torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "\n",
+    "# Teacher Model Build\n",
+    "teacher_model = BertForSequenceClassification.from_pretrained(teacher_model_dir, num_labels=num_labels)\n",
+    "teacher_model.to(device)\n",
+    "teacher_model.eval()\n",
+    "\n",
+    "# Student Model Build\n",
+    "student_config = BertConfig.from_pretrained(student_model_dir_1)\n",
+    "student_model_1 = QuantBertForSequenceClassification.from_pretrained(student_model_dir_1, config = student_config, num_labels=num_labels)\n",
+    "student_model_1.to(device)\n",
+    "\n",
+    "student_config = BertConfig.from_pretrained(student_model_dir_2)\n",
+    "student_model_2 = QuantBertForSequenceClassification.from_pretrained(student_model_dir_2, config = student_config, num_labels=num_labels)\n",
+    "student_model_2.to(device)\n",
+    "\n",
+    "student_config = BertConfig.from_pretrained(student_model_dir_3)\n",
+    "student_model_3 = QuantBertForSequenceClassification.from_pretrained(student_model_dir_3, config = student_config, num_labels=num_labels)\n",
+    "student_model_3.to(device)\n",
+    "\n",
+    "# Q Model Build\n",
+    "student_config = BertConfig.from_pretrained(student_model_dir_1)\n",
+    "q_model = QuantBertForSequenceClassification.from_pretrained(teacher_model_dir, config = student_config, num_labels=num_labels)\n",
+    "q_model.to(device)\n",
+    "\n",
+    "print()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 564,
+   "id": "a6bddc04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch = next(iter(eval_dataloader))\n",
+    "input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch\n",
+    "seq_length = seq_lengths.item()\n",
+    "\n",
+    "input_ids_sliced = input_ids[:,:seq_length]\n",
+    "seq_length = len(input_ids_sliced[0])\n",
+    "\n",
+    "input_id = []\n",
+    "for i in input_ids_sliced[0]:\n",
+    "    input_id.append(i.item())\n",
+    "tokens = tokenizer.convert_ids_to_tokens(input_id)\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    _, _, _, teacher_probs, teacher_values = teacher_model(input_ids_sliced)\n",
+    "    _, _, _, student_probs_1, student_values = student_model_1(input_ids_sliced,teacher_outputs=None)\n",
+    "    _, _, _, student_probs_2, student_values = student_model_2(input_ids_sliced,teacher_outputs=None)\n",
+    "    _, _, _, student_probs_3, student_values = student_model_3(input_ids_sliced,teacher_outputs=None)\n",
+    "    q_logits, q_atts, q_reps, q_probs, q_values = q_model(input_ids_sliced, teacher_outputs=None)\n",
+    "    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 565,
+   "id": "d3c080d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 565,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(input_ids_sliced[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 566,
+   "id": "9728c493",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 566,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(tokens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 567,
+   "id": "fbd0c63d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([12, 12])"
+      ]
+     },
+     "execution_count": 567,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prob_1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3dbc93cf",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "fs = 20\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        fig, [ax1, ax2, ax3, ax4] = plt.subplots(1, 4, figsize=(22,5), dpi=150)\n",
+    "        \n",
+    "        plt.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9, wspace=0.12, hspace=0.2)\n",
+    "        tc_prob = teacher_probs[l][0,h,:,:]\n",
+    "        q_prob = q_probs[l][0,h,:,:]\n",
+    "        prob_1 = student_probs_1[l][0,h,:,:]\n",
+    "        prob_3 = student_probs_3[l][0,h,:,:]\n",
+    "        \n",
+    "        heatmap = ax1.pcolor(tc_prob, cmap=plt.cm.Oranges)\n",
+    "        ax1.set_xticklabels(tokens, minor=False)\n",
+    "        ax1.set_yticklabels(tokens, minor=False)\n",
+    "        ax1.set_xticks(numpy.arange(len(tokens)+0.5), minor=False)\n",
+    "        ax1.set_yticks(numpy.arange(len(tokens)+0.5), minor=False)\n",
+    "        ax1.set_title(f\"Teacher SA\", fontsize=fs)\n",
+    "        ax1.tick_params(axis='x', labelsize=fs)\n",
+    "        ax1.tick_params(axis='y', labelsize=fs)\n",
+    "        ax1.get_yaxis().set_visible(False)\n",
+    "        ax1.get_xaxis().set_visible(False)\n",
+    "\n",
+    "        heatmap = ax2.pcolor(q_prob, cmap=plt.cm.binary)\n",
+    "        ax2.set_xticklabels(tokens, minor=False)\n",
+    "        ax2.set_yticklabels(tokens, minor=False)\n",
+    "        ax2.set_xticks(numpy.arange(len(tokens)+0.5), minor=False)\n",
+    "        ax2.set_yticks(numpy.arange(len(tokens)+0.5), minor=False)\n",
+    "        ax2.set_title(\"After Quantization(w/o QAT)\", fontsize=fs)\n",
+    "        ax2.tick_params(axis='x', labelsize=fs)\n",
+    "        ax2.tick_params(axis='y', labelsize=fs)\n",
+    "        ax2.get_xaxis().set_visible(False)\n",
+    "        ax2.get_yaxis().set_visible(False)\n",
+    "        \n",
+    "        heatmap = ax3.pcolor(prob_1, cmap=plt.cm.Blues)\n",
+    "        ax3.set_xticklabels(tokens, minor=False)\n",
+    "        ax3.set_yticklabels(tokens, minor=False)\n",
+    "        ax3.set_xticks(numpy.arange(len(tokens)+0.5), minor=False)\n",
+    "        ax3.set_yticks(numpy.arange(len(tokens)+0.5), minor=False)\n",
+    "        ax3.set_title(\"Ternary QAT\", fontsize=fs)\n",
+    "        ax3.tick_params(axis='x', labelsize=fs)\n",
+    "        ax3.tick_params(axis='y', labelsize=fs)\n",
+    "        ax3.get_xaxis().set_visible(False)\n",
+    "        ax3.get_yaxis().set_visible(False)\n",
+    "        \n",
+    "        heatmap = ax4.pcolor(prob_3, cmap=plt.cm.Blues)\n",
+    "        ax4.set_xticklabels(tokens, minor=False)\n",
+    "        ax4.set_yticklabels(tokens, minor=False)\n",
+    "        ax4.set_xticks(numpy.arange(len(tokens)+0.5), minor=False)\n",
+    "        ax4.set_yticks(numpy.arange(len(tokens)+0.5), minor=False)\n",
+    "        ax4.set_title(\"SARQ QAT\", fontsize=fs)\n",
+    "        ax4.tick_params(axis='x', labelsize=fs)\n",
+    "        ax4.tick_params(axis='y', labelsize=fs)\n",
+    "        ax4.get_xaxis().set_visible(False)\n",
+    "        ax4.get_yaxis().set_visible(False)\n",
+    "\n",
+    "        # plt.tight_layout()\n",
+    "        plt.show()\n",
+    "        plt.close(fig)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 468,
+   "id": "e043f448",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 12, 12, 12])"
+      ]
+     },
+     "execution_count": 468,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "student_probs_1[0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 491,
+   "id": "3d0c6066",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ranking_loss_func(student_probs, teacher_probs):\n",
+    "    Loss_ranking = 0\n",
+    "\n",
+    "    loss_ranking_list = []\n",
+    "\n",
+    "    for l in tqdm(range(layer_num)):\n",
+    "        for h in range(head_num):\n",
+    "            student_prob_plt = student_probs[l][0,h,:,:]\n",
+    "            teacher_prob_plt = teacher_probs[l][0,h,:,:]\n",
+    "            Loss_ranking = 0\n",
+    "            for h in range(seq_length):\n",
+    "                for idx in range(0, seq_length-1):\n",
+    "                    for jdx in range(1, seq_length):\n",
+    "                        p = (student_prob_plt[h][idx] - student_prob_plt[h][jdx])*(torch.sgn(teacher_prob_plt[h][idx] - teacher_prob_plt[h][jdx]))\n",
+    "                        # print(max(0,  - p.item()))\n",
+    "                        Loss_ranking += max(0,  - p.item())\n",
+    "            loss_ranking_list.append(Loss_ranking)\n",
+    "    return loss_ranking_list\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33efbeb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 460,
+   "id": "5716ce81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = dict()\n",
+    "a[\"Ternary\"] = st_1\n",
+    "a[\"1SB\"] = st_2\n",
+    "a[\"2SB\"] = st_3\n",
+    "torch.save(a, \"sst-2_ranking_loss.pth\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 492,
+   "id": "5395c9d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:02<00:00,  4.22it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:02<00:00,  4.18it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 12/12 [00:02<00:00,  4.24it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "st_1 = ranking_loss_func(student_probs_1, teacher_probs)\n",
+    "st_2 = ranking_loss_func(student_probs_2, teacher_probs)\n",
+    "st_3 = ranking_loss_func(student_probs_3, teacher_probs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 493,
+   "id": "7279f340",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Ranking Loss(CoLA)')"
+      ]
+     },
+     "execution_count": 493,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(10, 5), dpi=120)\n",
+    "fs=20\n",
+    "lw=3\n",
+    "ax.plot(list(range(layer_num*head_num)), st_1, label=\"MSE(Ternary)\", color=\"tab:blue\", linewidth=lw, alpha=1)\n",
+    "ax.plot(list(range(layer_num*head_num)), st_2, label=\"KL-Div(SARQ-1step)\", color=\"orange\", linewidth=lw, alpha=0.8)\n",
+    "ax.plot(list(range(layer_num*head_num)), st_3, label=\"SARQ\", color=\"tab:red\", linewidth=lw, alpha=0.8)\n",
+    "\n",
+    "ax.legend(fontsize=fs, loc=1)\n",
+    "ax.set_xlabel(\"Head Number\", fontsize=fs)\n",
+    "ax.set_ylabel(f\"Ranking Loss(CoLA)\", fontsize=fs)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "id": "0c6c75fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "magic_number = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "id": "b1fbcb83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "ranking_dict = dict()\n",
+    "tc_ratio_dict = dict()\n",
+    "st_ratio_dict_1 = dict()\n",
+    "st_ratio_dict_2 = dict()\n",
+    "q_ratio_dict = dict()\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        ranking_dict[f\"{l}_{h}\"] = []\n",
+    "        tc_ratio_dict[f\"{l}_{h}\"] = []\n",
+    "        st_ratio_dict_1[f\"{l}_{h}\"] = []\n",
+    "        st_ratio_dict_2[f\"{l}_{h}\"] = []\n",
+    "        q_ratio_dict[f\"{l}_{h}\"] = []\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        token_avg_tc = teacher_probs[l][0,h,:,:].mean(dim=0).clone().detach()\n",
+    "        token_order_tc = torch.sort(token_avg_tc, stable=True, descending=True)[1].clone().detach()    \n",
+    "        ranking_dict[f\"{l}_{h}\"].append(token_order_tc[:magic_number])\n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "id": "0ef6b786",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        token_avg_tc = teacher_probs[l][0,h,:,:].mean(dim=0).clone().detach()\n",
+    "        token_order_tc = torch.sort(token_avg_tc, stable=True)[1].clone().detach()    \n",
+    "        for token in ranking_dict[f\"{l}_{h}\"][0]:\n",
+    "            ratio = torch.where(token_order_tc == token)[0] / seq_length\n",
+    "            tc_ratio_dict[f\"{l}_{h}\"].append(ratio)\n",
+    "\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        token_avg_st = student_probs_1[l][0,h,:,:].mean(dim=0).clone().detach()\n",
+    "        token_order_st = torch.sort(token_avg_st, stable=True)[1].clone().detach()    \n",
+    "        for token in ranking_dict[f\"{l}_{h}\"][0]:\n",
+    "            ratio = torch.where(token_order_st == token)[0] / seq_length\n",
+    "            st_ratio_dict_1[f\"{l}_{h}\"].append(ratio)\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        token_avg_st = student_probs_2[l][0,h,:,:].mean(dim=0).clone().detach()\n",
+    "        token_order_st = torch.sort(token_avg_st, stable=True)[1].clone().detach()    \n",
+    "        for token in ranking_dict[f\"{l}_{h}\"][0]:\n",
+    "            ratio = torch.where(token_order_st == token)[0] / seq_length\n",
+    "            st_ratio_dict_2[f\"{l}_{h}\"].append(ratio)\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        token_avg_st = q_probs[l][0,h,:,:].mean(dim=0).clone().detach()\n",
+    "        token_order_st = torch.sort(token_avg_st, stable=True)[1].clone().detach()    \n",
+    "        for token in ranking_dict[f\"{l}_{h}\"][0]:\n",
+    "            ratio = torch.where(token_order_st == token)[0] / seq_length\n",
+    "            q_ratio_dict[f\"{l}_{h}\"].append(ratio)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "id": "17ec8118",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc_ranking = dict()\n",
+    "st_ranking_1 = dict()\n",
+    "st_ranking_2 = dict()\n",
+    "q_ranking = dict()\n",
+    "for i in range(magic_number):\n",
+    "    tc_ranking[f\"{i}\"] = []\n",
+    "    st_ranking_1[f\"{i}\"] = []\n",
+    "    st_ranking_2[f\"{i}\"] = []\n",
+    "    q_ranking[f\"{i}\"] = []\n",
+    "    \n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):\n",
+    "        for i in range(magic_number):\n",
+    "            tc_ranking[f\"{i}\"].append(tc_ratio_dict[f\"{l}_{h}\"][i])\n",
+    "            st_ranking_1[f\"{i}\"].append(st_ratio_dict_1[f\"{l}_{h}\"][i])\n",
+    "            st_ranking_2[f\"{i}\"].append(st_ratio_dict_2[f\"{l}_{h}\"][i])\n",
+    "            q_ranking[f\"{i}\"].append(q_ratio_dict[f\"{l}_{h}\"][i])\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "id": "ea585c88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hhBBCiGklQYgQQgghhBBiWkkQIoQQQgghhJhWEoQIIYQQQgghppUEIUIIIYQQQohpdURBiFLqCqXULqXUbqXUNSXuf4dSaqtSartS6tqC2//Xfd5WpdQ3XsgNF0IIIYQQQrw8HTYIUUr5ge8CFwAnANcqpRoL7m8CrgfOBlYBFyillrl3/xZY7j7vDKXUBS/s5gshhBBCCCFebo4kE3IKsE1r3aW1jgJ3AK8puH8hsENrPaK1toGHgTcAaK3v1EYG2Ai0vaBbL4QQQgghhHjZOZIgZBbQVfB7F8XBxB7gWKVUm1IqBFw67n6UUtXA5cCD419cKXWNUmqdUmpdf3//s9x8IYQQQgghxMvN8x6YrrUeBj4G3AzcC2wB7Nz9SikF/Dfwn1rrjhLP/6XW+jSt9WktLS3Pd3OEEEIIIYQQL3FHEoR0U5zZaHNv82itb9Zan6y1PgvoAXYX3P1vwIjW+t+f78YKIYQQQgghXv6OJAh5kny5VRWm3OquwgcopVrc/88A3gLc4P7+Qcyg9A+9kBsthBBCCCGEePk6bBCitc4CnwQewAwu/3et9ZBS6nal1Cz3YT9WSm0H7gE+5ZZoAfwImA88pZTaqJT6+xf6DxBCCCGEEEK8vPiP5EFa61uAW8bddlnBz1dP8rwjen0hhBBCCCHEq4esmC6EEEIIIYSYVpKpGCeRTdA51nm0N0MIIYQQQojnbHb1bMr95Ud7MyYlQcg4nWOdfOGxLxztzRBCCCGEEOI5+9qZX2NJ/ZKjvRmTknIsIYQQQgghxLSSTMg4s6tn87Uzv3a0N0MIIYQQQojnbHb17KO9CVOSIGSccn/5Szp1JYQQQgghxMudlGMJIYQQQgghppUEIUIIIYQQQohpJUGIEEIIIYQQYlpJECKEEEIIIYSYVkprfbS3waOU6gUOHO3tcDUDA0d7I17FZP8fffIZHH3yGRxdsv+PPvkMji7Z/0ffK+EzmK+1njH+xpdUEPJSopRap7U+7Whvx6uV7P+jTz6Do08+g6NL9v/RJ5/B0SX7/+h7JX8GUo4lhBBCCCGEmFYShEzul0d7A17lZP8fffIZHH3yGRxdsv+PPvkMji7Z/0ffK/YzkHIsIYQQQgghxLSSTIgQQgghhBBiWkkQIoQQQgghhJhWEoQIIYQQQgghppUEIeMopa5QSu1SSu1WSl1ztLfn1UApNUcp9aBSartSarNS6mr39kVKqfVKqT1KqZ8qpdTR3tZXMqVUhVLqoFLqO+7vpyiltrn7/0tHe/te6ZRSC5RSD7jfgy1KqUr5DKaXUuoT7v7erpT6oTLkM3gRKaX+opQaUUrdWHBbyX0u14QX3vj9714H7lBK7XQ/g38seGyTe47arZT6s1IqdPS2/JWj1HfAvd1SSj0x7rvxivoOSBBSQCnlB74LXACcAFyrlGo8ulv1qpAFPq61XgG8Bvi+UqoS+DfgK1rrxUATcPlR3MZXg38B1hX8/mPgbcAy4DKl1KqjslWvHv8NfMn9HpwLpJDPYNoopZqBjwJrgFXu/09DPoMX2w+Ad427bbJ9LteEF16p/f9NrfVy4FTgI0qpxe7tnwVu0lovAfYB0lH7wij1GQD8AxMX8H5FfQckCCl2CrBNa92ltY4Cd2AaxeJFpLXu0VpvdH/uBQaBBuAM4Db3Yb8HrjwqG/gqoJRaAizHHPMopWYBfq31Zq21DfwBuOIobuIrmlJqJZDRWj8CoLUeBlqQz2C6+YEQEHD/Wchn8KLSWj8IjOV+n+zc4/b4yjXhBTZ+/2ut41rrh9yfo8AuYKZ79+uA37k/y/5/gYz/DACUUg3AW4GfF9z2ivsOSBBSbBbQVfB7F9B2lLblVUkptQbwAQlgWOfnkJbP4sX1HeBzBb/Ld2F6LQGiSqm/KaU2KKU+j3wG00prPYD5HhwCuoF7Mdko+Qym12THfSNyTZhWSqk5wHHABvemWq112P1Z9v+L61+B6wG74LZX3HdAghDxkuFG/r8F3n+0t+XVRCn1eqBda91+tLflVcwPnA18GDgduBg4/6hu0auMUqoek+WYj7mwnwFUHM1tEuJoUUqVAX8ErtVax4729ryaKKVOAOrdDMkrmv9ob8BLTDfFUWUb8ORR2pZXFfeEdzOmFvVxN+3YoJRSbtTfhvl8xAvvNOCt7oQAVZgylAgTvwuy/188XcB6rXUHgFLqdkwDWD6D6XMRsMcthUMpdRtwHvIZTLdS1+FuYAi5JkwL9/r7W+B2rXXhYOmwUiqXDZH9/+I5DThbKXUAUx5arZT6OfABXmHfAcmEFHsSOFYp1aaUqgIuBe46ytv0iuee8P4buF9r/TsA9wu2jvygq3cAfzsqG/gKp7X+nNZ6jtZ6PvAp4Bda6+sAWyl1nFLKh6lNlf3/4nkKaFFK1SulLOAc4GnkM5hOHcAZSqmQu7/PAzYhn8G00lp3U2KfyzVhWn0DiGutvzbu9luBd7o//x2y/18UWuv/1Fq3udfktwJ3aK3f/0r8DkgmpIDWOquU+iTwACZA+5bWeugob9arwZnAW4DNSqmr3NveCXwG+INS6gfAfeQHY4np8VHgBkxPzO+01luO8va8Yrnnns8DDwMKuFtrfatSahD5DKaF1nqdm4F6BnAw55xbgD7kM3jRKKXuBVYDlUqpTuBqJj/3yDXhBVZi/78Ns5+3K6U2ug/7jNb6LkxwcqNS6uPAVuCL07/FrzylvgNa67WTPPwV9R1Q+fEtQgghhBBCCPHik3IsIYQQQgghxLSSIEQIIYQQQggxrSQIEUIIIYQQQkwrCUKEEEIIIYQQ00qCECGEEEIIIcS0kiBECCGEEEIIMa0kCBFCCCGEEEJMKwlChBBCCCGEENNKghAhhBBCCCHEtJIgRAghhBBCCDGtJAgRQgghhBBCTCsJQoQQQgghhBDTSoIQIYQQQgghxLSSIEQIIYQQQggxrSQIEUIIIYQQQkwr/3N9olLqL8B5wH1a6zeVuP8U4NdAGfBbrfV1h3vN1tZWvWDBgue6SUIIIYQQQoiXkCeeeKJPaz1j/O3POQgBfgD8Cnj3JPf/GHgbsA14TCn1F631lqlecMGCBaxbt+55bJIQQgghhBDipUIpdaDU7c+5HEtr/SAwNsmbzQL8WuvNWmsb+ANwxXN9LyGEEEIIIcQrx4s1JmQW0FXwexfQVuqBSqlrlFLrlFLr+vv7X6TNEUIIIYQQQrxUHPWB6VrrX2qtT9Nan9bS0nK0N0cIIYQQQgjxInuxgpBuijMfbe5tQgghhBBCiFe55zMwfVJa626llK2UOg4zMP2twPtejPeaDmPpMe48cCcntpzIorpFAHQMx7lrWy+prFP02LqKAJevmkldRRCA9b3r6Yn1cPnCy7GUheNobt/aQ2XQz/nLn3/mZ3t3hIfaB3C0Lrq9ra6c162ehWWp0k/MpmDLn6B1Jcw64Vm958aOUdr7xnjd6lmEAr7SD9IadvwNKhph/pkApOwUt+69lWMaj2FF44pJX79jOM4juwe5YvVMakKByTdkz32QjsIxrwNV+u+MZ+LcdeAuVjatZGn90slfqj/KvTv6sJ3i/dhcXcaVx82iPDjJ3+k4sPVGqJ0D806ffFvHemH33bD0Uqhqdt9zjKcPjnDZqplUu3/n5oHNdMe6ec2812Ap00dw7/Y+dvVNHH512sIG1sxrmPQtu0cT3L6lZ8IxWhPy88Y1s6kIlv76a625a1svQb/FBctbzY3ZNGy9CZqWwuw1AIzG09y+pZfTFjawsLlq0u3Y0RNhW3eE162eRdBfut9Da83dB++mOlDNGW1nTPpaR2pTxyiP7R1k3NfiiCgFZy1u4rjZdZM+ZrLj5bmylOLsJU0c21YLwGBikAc6HuDstrOZUWkmFGnvG+P+nf0T3rOluowrpjpGS4imo9x54E5WNa1iWcMywBwvt23uIW07h3m2ec83njgby1JorXmk6xH8yj/lZxdNR7nn4D2cNOMk5lTPmfzF+7ZB53pY9SYIVk76sIc6HqLMX8ZpM0+b9DHhVJhb991KPBMvur0iUMFr57+WxvJGALZ1h9nWFeHSVTO87+J4WmvuO3QfVcGqKd+zlI6xDtb1rOPiuRdTF6o78ic6Dmz7M1S1wIJzntV7lnRoHQztgVVvBn/w+b/eOPsGoqw/MMIVq2dOen4BYPe9oG1Y8hpQimTG5rbNPbTVl3PawsZJnzbZdbe2PMAVx+Wvu89VOJ7hti09nLqwgUXuOa0n2sPdB+8mbaeLHlsVrOL1i15PRaACgMf3DtIXSXL5qsnPcxk7w637bmVJ/RKObToWgIGxFHdu7eHMxU1Tnke3doXZ3hPhquPbJn39UpIZmz9v6GIknj7sY6vK/Lz++FnPbj86jrk2VDXDwvMmf1y0H7b+2VyzD6eyGY5/O/imuP6PE8/EuX3/7axuXs2S+iXmxkgP7LodFl8I9fOP+LVeKtJZh79t6mZRSxXHz6k72pvzong+U/TeC6wGKpVSncDVwBeBa7TW3cBHgRuAEPC7w82M9VJ2675buXnPzdx/6H5+dMGP2NI1xtdv20EiY5d8/IO7Bvjq61ZSU2HzvQ3fI+tkaSxv5MTmU/nOXbt4Yv8wAPMaK6Y86RyO1ppv3rmTkVjpk8vM2hCnTnZC3303PPkLCNXAu26ZtBE/3s3PdPFfj+4HTGP2tcfOLP3Avm3wyL+bn1/3Q5i5mtv33c7/tf8ftcFafnbxz1Al3jOZsfnCzVsZjqUZiqb4xwuXlH796ADcf70JdpqXQ/Oykg97oOMB/rDrD7R2tPLDC35Y8jGOo/nG7TvoH0uVvH/t3iG+fOWK0ifmg4/B2h+Dv8zsx0Co9PY+9V9mn+99AN74X2BZfO/e3RwaiqNQvPnkOUTTUb791LdJO2l8ysfF8y6mYzjOD+7bXfIl79vRx2/ee8qkjaafPLiHTR3hkvelsg7XnL2w5H0bDo3w4wf2AnDsrFpaakKw/lew6Qaw/PDm30DtbP7yTBd/3tDFA7v6+dnfrSkZ8CYzNl/92zZiKZtEOss7T59f8j3bR9r51dZfYWFxTOMx1IfqSz7uSCTSNv86xffzSNy/o5+fvXMNjVVlE+5LZmyuu3X7pN+75+qBnf38/F1rqKsI8qddf+LBzgdpH27nX077F2KpLNf9bTvhRKbkc9fuHeJLkx2jJdxx4A5ubL+Rm9pv4v3HvZ9zZ5/HN+7YyYHB2BFvb0NlkAuPaWVt91p+vPHHKBQL6xZ6QdN4t++/nZt238Tt+2/nB+f/wGu8TXDfdSZoVxac+M6SD9k5vJOfbPoJAF8/6+te59B4v9v+Ox7peqTkfdF0lA+s/oA5j96xk9F4hgd29fPV1600x/w4mwY28YstvwDgS6d/iZWNK0tvfwk/3fRT9ozuYSw9xnuPfe8RP4+eZ+Dx/wDLB++82ZyvnwutYcNvzfcYTOfQskuf22tN4dt37aJzJMHegSifu+yY0g8a3GPO3QC+IOGZZ3Hdrdtpdztarj5pNu88bd6E68PGjtHDXHf7+errj6Wtrvw5b/9fnunkpg1dPNw+wE/faTpbfrv9t2zo31Dy8aOpUT60+kMMRlP82x07cTQ8uX+Yf7l8BVVlE5tXD3Y+yB92/YEKfwX/dcl/cWAwzpdv2cZoPMN9O/v56d+tKdmxNxhN8eVbtpHOOmRsh3ecOu+I/6a/bermhicPHfHjM/bk14aS9twLa39kjtFZJ05+jK7/tQkIjpQ/BKvfcsQPv3Xfrdy0+yYe6nyI/7jgP8yNj/8QDj4OW26E13zN60B7uXhs7yD//fgBKoI+bnjfaZN3Kr+MPZ/ZsS7SWjdrrSu01rO11mu11pe5AQha63Va65Va60Va66+8YFt8FOwPm0b3cHKY/97wMF++ZRuJjI1lKWbXl3v/ZtWZC1dvOMm1N27isYO7yDpZAJ7p28oXb97qBSAA6w+MPK/tGoimvIZQY1XQ2w6fe6Bu6SrdAAVg5KD5fzIC6cM3PBxH88tH9nkBCEBfpHSjHYBoX/7ntT8Bx/FO5OF0mEg6UvJpf3mmi2H3b3rywDB6sq7s+BBeN3dsYNLNGEmafdwf78fRpXt4N3eFvQCktabM24+tNaYBuqc/yqf+tImu0cTEJ4c7zP+zKUiVnCzOyO2P4X2w63YcR9M1Yl5vyP1794zuIe2Ynx849IB5eEFDN7dtM2vNcZaxNZs7S3/G8XSWLV1mH9dVBLy/qTpkLowP7hogO0mP993b85/dYDRtepO23mhucLLwxM8A04MH5njf3lP681y7b4hYyjQa7t3RjzNJ5qAz2mleHofBxGDJxxypR/cMeg2VWXWhou/okfxTygRpv19X+sL95w1d3vfuubz++H+5RlMiY/O/bmNhOGnOE7tGdmE7Nn9a3+EFIIXvmTtGd/dHufbGzXSXOkZLOBQx7+Pg8NPNP+VrD/+a/YOmh7LwO1DqX64X9qkDI2TsDP+7838B0Gh6Y72TvmdX1MxVEklH+Ovev5Z+UCpqAhCA/h2TvtbWwa3ezw92PFjyMfFMnHU9Zsr32mAtbVVttFW1Ue43+zt3zEUSWUbjZt92jiT41I2b2Tcwsbd2fd967+ffbvvtpOeT8RLZBPtG95nXH+s8oud4Ij3m/44N8ef4vXBs0yGUC0AAhvY+t9eawnAsTad7Tnt87xBbJ7v+dD/j/Zh87Cd87sb1XgAC8Kf1nXzv3t1F56cHd/XzlcNcd/siKT594yZ29U5xHj6MnnASgK7RhPddyn1mVYEq7xiqK6sDTDbuQPgATx8cIXdq29oV4TM3bWYwOvH6uHvEdCjFs3Ee3LOXz960xTv2hqJpbn6ma8JzAH679iBpN/tz3xTn0VKeOmDOJeUB35Tf6zL3e90XSR7xawP5wMKxp7wWE3H/tkAF1M2d/F8u+7nrdp5NKnvb0DbAXOvjmbi5Jne639lMHO74NOy+59n9bUdZv/tZxNP28+pUeyl7UcqxXmlyF8+RWIZfb7iNeucqygM+Pn/5MRNSZLdu7ubnD+8jksjy3QeeJtBoU+a3uHHzU1RFTAmSZSkcR7Ph0AhvPnmKsoTD2D+QDx6ue92xzG00PYtfuWUbTx8cYedUJ+NYwUxkyTCUTZ6RSWcdvndvO4/uLr4IRlPZyV8/W3ACHthJdNdt7BnZ493UG+ultqy26ClD0RQ3PZ2/SI/GM+wfjJXOFhWWV2Qmb3jlGvUazVh6bMJ7Aty/wzS6q8r8/OQda7xGltaa3607yJ/Wd3oXuC9dsZJlM6rzT44V7JNxJR/F21uwjU/9krFZZ3tlNbmLS/tIu/eQveG9dIx1kLbz5Si5Xj7H0bzrV08STmR4+uAIZy5umvB2mzrC3oXqi1esYGmr2eb1B4b5qtuj/vTBkQmZsrFkhicLAuV4OgtP/gzsgh74/Q9D90bi6XwG5r4d/V4pUaH7d+SPs+FYmo2do5w4d2KWYygxlN+G9HNvRADcv9N8nrPry/nJO04smXGbyg/u3c29O/q4b2cfV66eWXT8DUZT3LTBHKPHz6njutevfNavX8p37trFQ+0D3LW1lytWzfKO25SdYkPPbv66yeyfk+c38KUr86WMjqP57doD3LShi95wkk/fuJkvXZn/vCdTGCw4Gm498GfKgqtZVfkG/uPta7yOjFL+88G93L6lh40dI9y67zYGEvmGRy7oLyWcyjdKb9t3GxfNvYjmiubiB0UKhg4Olc4AQr4xB/B49+O8a8W7CIwr3Vjbs5aMY47bz5zyGS9b8tttv+W2/fnt7g4Xnz9GYmk+e9OWovO71ppN/Zu8xxyIHOCRzkc4d865k25jzp6RPTiY7/hAfIpGWinJgoZ8snSgP6VM0mSWDj5WfHvkhR+iuXNcR8SvHt3Pd65ePbH3ts8EkMmsQ3fHfhaX3UNH2QVcftxMdvWOsac/ygM7+xmNp/ncpcdwx9Yefv3YAYBJr7u3be7hZw/vJZLI8vm/bOEzr13OKQsmL1WdTGHJ0oZDI7TWBL1OkasWX8WVi64ETCfBxx/4OCk7xe+2/w7f8BuB/LX90FCcT/1pE1993UrmNebP4Qci5u8YS2b5t3ufIJidj89SzKgJ0TWa4ManO7l4RWtRBnZP/xgP7MyfRwfGUmzpCrP6CMpzxpIZLyh705rZU7Y3vn77DtbuHSKSmOK6PuENeouCyqLjdbzcfYvOh3M/PfnjdvwNHv4OjByAwfZJqxwKZZwMe0fzgXVntJOl0VHIldBZPtOBdv/XzDV79VuPuPrjaBpL5j+LRMamskR27eXuqM+O9VLTP5bkqQPD3r9H93ZzcLSPgbEUQ7EUSd9uaiptvvnGVRNr9DIJrpgV57OXLCPgUyT0AD3hBB3DcSJ2Dw5pXn/8LN5zbBm1zig7eiLEpmrIH8Y+t3Qi4FO01edT0POaIauG2TsQ9Rq4E0QHSGUdbK0hOTrpe0RTWb58y1YvAFk+o5q2eh8Z1c9YsnRpCADZ4t6ULev/E0fnI/m+eN/4Z/D7dYdIZR38ZJltd4LWbDg0ybYVNuqnCEJSdsoERNohkpp4EY+nszy21zTwzl7aVFRrO5YZ4/yVPj5w7kKUwrvAFTbSi3onp9iOogAlMULm6d95v6Zts1/aR9pNb1LWnDgf7nyYTMHnl+upsizFiXPrAHOh1FqjtWZ/eL9Xt7zh0AiWtlnu62JxY7605IS59dRVmMbafQUXtpyH2wfJ2pqsGsVWY+jeraaEDGDpJVDmNm7X/YRYIkXa6sUhw6N7Bkiki3tqBsZSbOocLbrtvh0TP3eAoWQ+CIlmJq8ZdrTDvvA+r3E5Xk84wdauCGU6yZVzkhz2MjPaAQfXFv17z5xe6n0JtIb/enQ/tmOzb9S85+/cHskQST68eBh1aN2E50/4l+vNnozW/P2yNNUqiaPhV4/tJ1nw/fn1U4+TtTUhknxg4VDRa1sd63jPMfD+c8wxGk5k+Pyft3Dn1t6i89j6A8NE3O+r1m7GIpviwtnnojON2I4mHthIxcw7yOqCMrN0DA49UfSe55TtZkVmGzNjT/LHjb+GVMz8s7OMpkYn/TNHkqOkMg5am0bDDTtvmPigSCeONpkoHe0v2ahxtMPu0XwQEs1Eebr/6QmPe6jjIQBmV81mYW2+vCQX+IwkTSanN5ykTCeZZXfxD2eaRmEiY/OVW7bxwC7zHemN9dKfMD/7lCmXuWHXDUWf02QKt3UoOYTtuN+TdMxkRqeSipC2HbKO9jKtWmsOhA+QyB4m65UYhVs/kQ9AZh1Pau5ZpGwHIvnOnqHEEP3x5z89/o7eMfw6Q5t77t7dH+Wh9gGi6WhxBqhvG7G0TddIAtvRXJy8mw+e2sgHz13E19+wyju3PXNolPf/br0XgNRVBPjmG1ZwfKjPnCcLXH7cTD576XICPkU66/Cvt23nzq3jsnJjfRAfZioj8fx55emDIwwnh70AsqUiP4azIdTA6xa9DoAtg1t5vOspAC49dgbvPM2USg1F03z6xs3ct6OPpw4Ms3ZfP7sGDzIcS9MXSZJhmPKgj69euYJ/OdVHkPSEDKzW2qs+qPelmGeZIHay8+h4mztNZ9Rsu4Mzgu1TnqeWJrcyP7uPaHKKCgfM98Y7XtrvAsgfo25bInc9imXyHaU6GSaZdcgGpy4pDM86i5R2G9u77jD7MjE0ZYb8UORQ0TWha6wLOp40vwQq4PU/gQo3KH3ip/DY9yfug8E9Ra85EB949p0Gz0YybK4/UygKQtKSCXlV2HholP+4P38wpq0eBsvzJ/uA3+H/nTJJ7/ydn4PuZzhj8UVc/7oP8eG7/g/ANPSBS0/UXFO/leSD32HJWJbrq7/Mpo5RzijRk30k9rtByPzGSq/nMmNnuHvg3+mv6Kc5/l72DkQ5ZubEL/3oYBcDw3GCfot5U/Re/OLhfWx1y3pOWdDAtZcs4403fpSBikMcjL0HmKTud9wgvo2pQUikTS0yTCjb2DcQ5T63B/tz5Tczc/Bx/uh/HU8frONNa2ZPfP3CC/BUmZDBdtOjEihnNDXKHIp7gh7dPegFahcd05p/STvDvzzyL/Qn+vnCqV/gs5cu5zt37SKddfj67Tv4+TvXmLrxWL7xzFSNgnGNldCOm6hz5jFq1ZPOOqZh1fcMjOwH7UDdPB7pfIQZsy/wnhPw5QOkE+bV88CuAYaiaQ4Nx2mPPs4vt/ySlooWPnfy59hwcIT/l7yJS9QTWE8NwukfAcBnKc5f1sJfnuniyf3DhBMZassLMxp9xPwbCJfdjdIBana4vXhl1eY1mpaaGvWBXWR8v2WwfDeh7FKs1BtZu28wP5AdM8Yhl00/ZUEDT+4fZu3eIaKp7IR66SPNhNy671b+Z8f/cHbb2Xz0hI9OuP9+N7D6aOw/OHP7CFivhzM/Ada4/hatYdMfzAVpnFrgX61m/in7ETZ29vGxe//AQHoPjcE2Du6+GEtX8eXg75n52P4Jzy1JWXDmx2DlVRPvszPw0Ldo3H031wdn8/HkB3n64AhNfhOIJTM2mwd2UM9SvlL2B1ofLZ0duHL122i85P/xnXt2k8o6/PiBPRMeUx708S+XHcOcZod0fBDGelicfJA7h99PMHAT/vJuOhJb+d8d/8vfH/v3ZsDprZ+AgV1Fr3OMhvfFo9xUbXMoovFbijIUKaUIRycPuPYN99EdiVNTVkFLrcVj3Y9x6YJL84NIAcJddIcTJNI21SE/TQO78c85qeh1uqJdRY0bMAFH4WDxnmgPu0bMdp8759yibFVzeT77MpgYpDuc5sPRHzPP6WBh5ADzr7iGr9/RTiJj87172lncXMXW8EbvOe899r38YssvGEmOcOu+W3nT0jdN+jcD7BrO7z9b24ykRmgqb4Jb/skMEr/032Bu6YHuoyNDDAzH8SnFnESYALCuZx3f3/B9ltQt4WtnfW3yN370u9C/3fy86AKy53yGG37+bc4ciTNLH6LScYhm4/zzg/9M2k7zD6v+gYvmXTTl3zLl39k9yodiP2aFdYhba97KbdlT+M+1D/Gb/beTsONcd8Z1LAvWExnqoX8syeOBMzgj8zjzazSr0ncBx1Ae9PHFK1bwH/fv4f6d/V6pUltdOddd0kbLY58xZXqtx8Jrvw6hfPb1jEVNfO2qINffup1oKsuPH9jDcCzN206ZgxraC7d8FJQP3nbDpOMWRgsyIVs6w3QX9Ic0lRdfp69YeAX3HbqP7sgAA9a9NDOfk+bVc9L8Bhoqg/zH/buJp22+f6/5zmasPgbK8y8YDI3xzStWsXD/DfDM7/l8aBVfSb61KAO7bt8wW7siKO1wvf+/KI/s43vWu3ls72o+mM5OPfgfE0itym7mmsSvaVs3+SQPAOdF06yOp3nAdxVwcsnHrOtZx4+e+RFZJ8t7Vr6H17bfSSrr0DESx1KKObFRlJPlF5t/wYOdD7KgdgHfOOsbKK0ZGhpgJJpkz74kl04yh0vGdvjkzXt5TXghV1TuoGLPvQyufgufefwLZJ0s3z7320XBYE7hdwygO9YNHU+YX9pOhJblJhC541rT8N92s/k33iX/CvPP4sGOB/nZpp8R8AX47nnfnfDZP292Fm78B1O+9sZfQlPpsa+F1Sav1HIsyYQcRtbKN45qg/XMri/nmaHHJz7QzkLvZvPznntZufHrLJ6ZIOCzUEBrTYi22G3wyL9T5lNUqDRz7A42HHru40JydcsLmvInl754H1kVBTRpXwc7e0uk8B2b+KiJ8NNZh9TY0MTHuLZ1m+evmVfP5y87hqBfEXNMKr8/NbGh48k1ugMV6Jmr2WRlTS+U24NVGITkenu0hlDAYnV5HxVBH7PtDrb3REr3ABRlQiYvg0rlalQzCSJjE+tt73PLheY0lLOkJR9Ybujf4PV8bhvaxhmLmrju9WY2E9spyNDEC/bdlJkQ975FF4CysLMpLk/eCpjPoGPr/5Ec3gfa4VwnAJk4I6kRdoe3ey9RVpClOXFOvZdN3nBohO1D5nH98X4+8/AX6IztZ3lmh7lI7bmvqLb2wmNavL/j4fZ8T8/BwRhPj9xGuOwuQFOuRxkZc0vETnyXueCvuApqTVBYnlqL0pqUbx+aLPcWlF5prb2g8ti2Wt52ign+Mrbm0d0Te5dyYyBg6iAkV4aTG6dVyHE09+/ox9I2S6weApaC7bfAPV8sLg90HDNgsUQAktPGAOdYDzJU/j+s79mM1rC5bw8DZb9lhXqU5fowPdiFtAOPfs9MBFFY45yOw52fNRMWAAucTo4NmGN032AYtBmTk7a6mGcNsMxpL/XqxqYbOKPj51x35TIv0zVeIm3z5Vu2cfv2Hd4MNRXd+zkx8QzNqbdwxuzjAdjQ5w7C3XX7hAAEwFIwElKsK3dwHM3ZToA52gLtMDp6oOR7J7NJwknzPQ3ET6HCb0pHf7v9t0XjvrKjXSTdi+1YMsvN9z404ftfWIp17mxTDrWxfyOjBRndhzpNFsTC4uy2s4ueX1gCNpAYYHiwnzn2IQI+hdp1O8dv/zZfv9KUbmkNO3vH2Ni/EYB51fO4cO6FrGpaBcAte28pOnbHc7TDntHi82R/vN9kNYbc2/u3l3imER4ZAG2+q2Nh8z47hs1Ymd2ju6fOhvSZGnkWnAMXfJH1nTH2puoASKZSEBvgYOQgSTuJg8MvtvyCP+784+Tj8KaQzjrUdj3Agux+ygM+/l/VNhK+Hexzfkd3ZBQwmd6HHnmAvkgSreHJqguoWflaqsv8sP2v3jhFv8/i4xct4c0nz8FScGxbDd++pImW+z+ZHyfUtxX++pEJWcYVs2r41puOo7nalDPd8OQh/uO+3Thrf2TOwekoDJceD5NI2yQz+cxzKuuwoeuA9/v40sGQP8Tblr+NWNomaw2TCm70SlIvWtHKl65cQWVZfpB5xspnL4J+i9OXWiwMjJjOEGB1djMz1IiXgc3YDv/9uDnPnRQ8yBzdRU15gFPST5DOOjy2Z/LrNphz8IZDIxyT2UFF0HfYrHCuI3NRbGPJY+CO/Xfw/ae/T8bJoNH8euNP+J/YPqLpLNo9RodHevjWU9/iwc4HAXOebh9ph3SUdMY0qNf3OYTjpTPZTx0Ypi+S5MnAqSTTNqTGeGjLfxPNREnaSZ7oeaLk8wqzjQCdw+0w6maU5pxi/l8zE17/Y5h1/KT7QD/+I/6y60/856b/xMEhZaeKxoK9YJKj+fEzuXZjCYXVJnHJhLw6nFUwTSbA3/bv566DFYT85bz9mDfzux2/Y/fobrqj3cyqmpV/YrQ4RZzsWk+0IsW8utlo5UPF+tjVcRCoRAEVQR9NzgAbDo2itX7WdeXRVNYbGL6gOR+ERNIRLKUI+i2ymRF29ozBuBl4negAyXT+4I6ODjFxDiDTqBuKmfdYMasGn6WIZxJeje9YdooAyi0pwl9Gx3FvZOSBh0FDWXyYVFVzURDy5P5hb4D1G0+cTVl7BqfMR1k6heNoNnWOTpy2MVOQWZiiJCJt5xuf4cEdsPC13u/dowlvQPWFy1uLPoPCwa65MUHHttXSWlNGXyTFzt4Ir13ZemTlWFrn72teDmVV2E/dyJr0eh4OnsuKwXW0998EfnPif3M2xAYUY8DWkbXAeQAECzIhtRUBFjVXsac/ytMHR6Ap37Dvj40yEvodfYFB1gSrTaA0vA8aTcNqXmMlS1qq2N0f5b4dfVy5ehZZJ8vXHv8+0aAJsC00tXqUEeWHmjYTfAD4/HDah+Guz5MgSbUeI+qrJWP1saXTT18kSWtNiJ29Y3SPJt1928Ki5irmNlZwaCjOfTv6i2ZV01oXZUKmKsfKBSjjp8sE2NptJhio1AlqCrI7HHgUbv1n03PqL4cHvgb7TCOVurlw/r8Uj4l64Ot09W9me/lt2DTj2BaJWDOJzH6UCtNV+Wf2ZoMsq5oDl33LZDomkwzDfdfDWA8883tTj3zOteb2Oz8Dg/mLp6Xg75t28sme2cQzSXrCGdMgt8K8pWUz/mFlapuv+ikE3Zmlsil46FswsBN238Ox8WH+621fZTBdfGo/OBTne/eY3v1fP7GJ2lACv4byuM1l6naaV7+WxQvO4edbdtKf6GdkrJv6p35pntywAC6+vqiG+pcPfIfuoR0ofFx5wZf5v/s/CspmNFG6fCGcCntT/+pMI5fMvZK/7Psj7SPtrOtZx+mzTNdocuhgUZyW7W/ns3/ezFeuXEl9pZn5K5fhaCpv4qrFV/FQ50M4ODzS9QhXLroSRzs83PkwAKtbVk+Yaa0wEzIQH8AeNMFzIBfkH1rLosQXaPS9kSG7kt5IlO3D273XU0rxd8f8HZ995LOk7BR/3PVHPrT6QyX/7u5o94TjeTAxCLpgFrMpMtF2PH9fxv25sESkO9pdemYwx8mXHrWuBMvivh19DPpMj27GLcka0sUZpT/v+TPDqWHet+p9+K0jbx7s7x3gtXHTqRIK+HgotYN0dR86oxmJ21SHAty2bS8n7zzELCDjr+JTb76IltDp8McnTeb8iZ/Ca78BgFKKd542jzee2Eb56G7UnR+DhHu9aT3WBCGjHfDXD8Ol3yrqSZ7TUMG333QcX/3bdvYPxujefD+92bXMqAlhKSYtySo1he2mHtOQDflCVAcmjrM6q+0sVOZ/gE6ylWuxuQYw1+M18xr4zXtPYShqXvfGPTt5sLMClCLgU0Qyg2a8nTt5jd9SvK95N9f3nMLmzjDfvGOndw59Z9NOrCHTEXUse7C0zX07+rh4ReuEbcrpGE4wFE3TZndREfKbxviZH5v08f33/x5r659oszuIJxJUVphzjKMdbth5A7fsvQWAmmANIX+I/r7N3OJLcTAU4MJ4DQHC/GffXXRVFM9O9mDHgyybd5m3jMAYVTy0e4DXrZ7FeLlOwXb/UiK6jnpiPHToPqgy1/9NA5u8cTmFxmdCugYLJrWYfUr+51AtXPF9t71WUArftQHnke/wq/g+7tn0C6jInzM29W/itfPz7YYXRGGbJTp5yVdhJiSefu6l+y9lkgkZpyLoZ1ZdufcvavcT8FssqJvD2bPPxnJ3Wa6nzRMp6GVvW0O3ckwDYfQQc2LDkAyzW9k4DYsgVENF0E+TM8jAWMqbUeTZKJxKc2FTvgGVG/xZHvBhqxG290Qm9GocPHSgaK2B2CSZkEgyQ9Y2j2t2B8rFsybtChC3p6ivzX3J/CE22mEI1aKAM2MxyKa8MSFZ2/Fqfhurglx1Qhtkk4T8Pqqs/PiGCYoGpk+eCSkMQiLjesBypTuWgvOW5Rsm4VTY6/mEfBACeIPSd/aMQSpSPGB7siDETpvecIBAOaz5e5LK7M8PxH7KGQP/R7uVBctPi7+CJizOtkwgvHdsMw4JfJaaMMAzVzu9rTtCnzvRwKLaRSTT4CfBb+ptHvK725erj3Vd4GZD9g7E2Nk3xL89+S02Da8FYEb5HGqJ4dc2I2g49QPFawrMOwM983jClqZGR6grU2R8Zh/lBlDm9m0oYHHm4iaUUlzkvufO3rGimcZimRhJO39SnioTkptVLVXwuebkLmANgVR+AF+dW36X6zm97Z/zAUjrsaZnrGW5ye64/3asuJQv+2NErCwNKkJ1+mz0wFuoTV1KrYqRURmuD8R5ctn5JogpeO6Ef60r4aqfmDI2gPY7zSwtf/1IPgBZdpnJkAFLxp5iQUMATYaYe9EJWAp/wnw2zD0dmpfmX79xEVzxPZhzqrm/62mCt3+cWYFY0Xns9EWNfOONq6irCKBUP04mjT/jUKEVtXqMd5avLVpHp/3pn+Ubfad9xOxH9z03pYfop4us8lOWOZMDmYXU+c24o9FE6XPCYGLENHwBS1eysvZcWsrN8fC/O/6XjPs9skfyYwcqgj5m2d3sG4hx7Y2b6Bwx3/NcJmRp/VJmVc3ytvuhzofQWrNtcJs3xuic2RPX1qgIVFAVMOfMgcQAgRFzXgj4fGbQLKAGdvHx2A9otAfZNbzTmyjg+ObjAZhfO5/z5pxn3tedIamUwqxN7trRH+83QWlOYrTkcwEoGMyfdYOQwvEbheemCc/LnXPKGwjHMzx5YIRBKxeEaAh3ecG/3/Izv2Y+YBqN337q24cfc1Igsf4GapwwjtL8OZTmN744TcEslq5EOVV0DMfZ2tvDfPsAQb/F/BUnM7+5GqpnwKqrzYscfBw6i8f2VPQ+jbr1E+ZYVArO+Ee46semIwRMQHHLP014XmNVGd/4f6s4vq2S1yVvJpbK0jWaMOMWjiAIyQW8e4ZM1r+pvKlkR2EkkUVFzDEWDKb5y+6/FN1f5vd538HhdCcBv0XAZ16nb3Qfzn4TLOc6MtaknqTBzWTmxh6uaPIzP2x64xXQWGazwN7Ptu4IPeHJP6OnDw1jaZuZTjcVQR+0rJjyXJWZZUqwfNom0W0a8Rknw4+e+ZEXgMyomMH1Z17P1075FxYlzTn74WCAX9Vn+FFDloNps83nzD6Hs9rOAswEEalYvxeExFRlyTEto/E06w+ac45WFtuqTmeXsulLDHkdvDuGd0wYhzWUGPK+77OrTJZ+INZDBm3OWzXjlhFQyhx3BX97aulr+PeaCu7xpSE+xJyKGd72bxva5p2fXjCFmfnoxH2RUzgmJCnlWK9OuZN8W1UbtWW1nNBq0goPdz5cPEVjuGDg3SX/StcSczHDyXB+1PSERYPl9Fz4Oaif72ZCTC/6cynJ2lcQhBSWY4VTYQh3UZ/qQqsBRuOZCetf7D9woOj31Fjp9x8oeF4uvZ3IJKhIDzHT7kE7/ZOn7nNfMn+ZmVWmoolFBFniWBAbIJqJEk1HuXt7n9cgfdfp88wc6XYaBcyoMK+94eDIhPdx0nG+5o/xmUCU1BRTDKcKeszD4YP55zvaayifMLe+aDaSR7oeMYMRx3pheB+9kUPeVMu58TVdownGRsYNfJwsI1MYnATKoaKBp+ovB6BCx9Ba0x4MQt1cllabQY3nZs2FKqszJPw7Si5OdeI801uTsTP0RM3J//SZZ1EVfTMVjgZL8XN/grcGw7x163/w1lvf6v37zYFP0FP5Tborv8lHHriGdV0bsB1Nmb2Az676OG1Z09jvKGuauEiaUsROfD9jPlBaU2mPUltrjuV7d/STzNhemdcZi5q8RfTOW9pCLo66v+AiNJwcJpVxODQUp2s0QbjEBAI5kwUhibTN43vNNpw9N5Q/sZ3zabPoHZie0153uaL5Z8EV351QG74vvI9/3fNHYqEqLODjaYeFyaUoFC3pRXwppQmiyARCfLfnAe46cNek2+qpaIArfwCz3TrrrqfzjdAT32Vmill+BQAqHeUjC3rQKn/xaQnZ7E2739Gll0x8/WAFXPJ1E8yAKfO55R8nBMWLmqv4ztWrqQ+a926wFaNWHfWVASp23kibCpjpa+0s7fvcqSznnApz8vXhWmt+v/33BP0WZVYVVZnTefrgCHVlZtDn6CRTb+8b6qdMp5hld3Ne8mkGxrK8/Zi3A9Cf6OfOA3dCJoF2yxtHrTpm1pWzonwYv864s9NtZkNnjzknOw5L2x+EW/6Jc91yq46xDvZH9nsdRJWBSk5uLV3bnqvx7hzrpTFlBodma+bABV+C1W8DoNkZ5GOx73MoYsrTQr4QSxvygdpblr2FkC+ERvP7Hb8v+T65rE1jqJGZVaYxNJAYKG54TJEJsQqm/XYSYbTWRQN0J53yt7ChXdHIQ7sHcBxNUpUTsypJ2w460uW9VmOoka+c8RWvzGzjwEbec+d7is4Z77jtHfxh5x8mvldskMY9NwHwfw0t3Bo018U5yuL1bZ/Ab7dga40mwlKrm9n15VTMOS7//BPeAeV15ufb/hl+fl7+3x2fNsexLwAXfin/XV79Frjwi2bdokzc1Pq33120WZVlfr68eK8pecI04rpGE9ix0p1uowUlQue7nVJDSTNRR6lxCADPdIxS5swjlF1KRdDHHfvvKDnIX2vNwchBb1+jITPWYzp5yqpNcAX4oj18+JjiTrWPzOtA5a4tSlEdCnBMdicam88+eB2ffPCTREssAPj0wRGanX7Gymw+URblN4mDEx5TyDdjJbhFW5nuzWit+d7T3+OxbjOxwaLaRVx35nXMqJxBbfdmvpguY7XjJ0IFBwJphn2AY/OGxW/gw6s/zIVzLwTMFNVP9a33MpxRq4p9AzH2DUT5U/uf+OA9H2Rj/0az6HJB5+g6/0k8aGUAjXKvCVkn65Ue5xTOKnn+3PNBa5x0zHQEF2ZBJjGWHuNrT/wr60Mm8DzGVnw1MJvzZp8HmGvNzuGdJZ+7a3gX/3jfP/LXPZNMOT4JnUnyfX/cXJt77yz6nn3h0S/QF+tDa10UhExVjvVQx0N8+N4Pe1OSv5xIEDKFrJP1yobaqtqAfA3ycHKYLYMF6y/mpjysbIJAOV0zV0LVDEIoTnMCUFYDtbNpj3ZCzWz8lmKee3J8+uCzD0Jy0/POrA0VrZQciXRBOorPyRBUvWicCVP19nYXr3+QjZd+/8FovgHfWGW+oPGOtQQzYfw6S5ARRhKTlM64jcSEL2C+wD4/q1tPYoa2TC08ZlzIo3vMRXBeYwXnLW0xZQRuANMSMhezvkiK7nBxA78j0ccWK8sBZbMzPvlg2MIZM8KxfjN2B7OGSi7IuqBg5XqttSnFcmwCSZPpsJNh7zhYXjA9b2fHuHUkJsuEjA9CgEdC5zFsmYbbzuBcemtngi/AErc3cn4szLzqeWgNcf+WovEgOctn1FAR9GGriJeqHYuVY2Vm84bwXJrdr7cGdCaB1jba/c+ycOuVNWPJDJFkhvLsscxXb+V0aw/17sVge+XiklMZ9pe3ErZMuj6QiRIMdaHR9EWS/PqxA94Js3Cwf31l0Auc7tuZn+t+3cEDdI0mSNsOibRNV7j08ehox7vYpu10UWD62J5Br5779LaCrE2oBk7/KJxWUC6z8ipTXuSfWIT4YMeDZJwMgepZXOvUcKkK8sEyE2j8XeBBzrEUX8pUUF03H43mV1t/xQ07bzh8HX2wAl77zXwQoSw4+5Nw8j+Y/TvrBLMqNjBv5BEq3QGn5QEf5U6MXSpr/pa5k4zo9PlNMJNb3C/SPSH7BWZs2ryGMZSCxizc1vJ+6ipDkE1hPfVfLKlbArEB2nXKbGPhfgMORg5yaMwc96c0XoZFkGcOjVDjrjyesJMls1T7R/qp1DF82uY1icfpHklw2szTvCzGTbtvIjzY7pVs7alagwU0Vfj5xJogljK9gl+47V4zo2B6jKWRAejZxOmhGQQs03t85/47vbrxM2adMWHa3pxco7Ij3Gtm4gOspiVmAoPTPginfxS/z6LKGSMcN0HIyqaV3vsA1IfquWKRCR63DG4pmZUozNrkysAG4gP5tVBg8tkJHQdfQSmXTo55tfE5k2ZCEgXfoYr6op7nQasZx9Gkhzu88SyN5Y2U+8v5zCmfKRpDowv+y+osd+y/Y8Kxrp/6Jdl0AlA8WVcJwUoWaB/XpYJ88MwTKLPMObO1YpS2ujJ8SpkMYU6wEk76h4IX1Pl/AMEquOw7XrbQs/giUw4ZrDQ95Q/8K2y8If+8ZAT/hv+mtSYEzcvo9c0knXUYHii9lk1hJuRCd4INW4WJp7MTp5J2PeNeu+cFXkOZ30dWZ0s2BAcSA8Sz5rp36sxTTRY9m6RPObDmPWbhSHfxzpNTT3jjE89c3MTcQXexzbo50HYSAUtxenAPSd8edo5soXOs01sjIyeZsdnWHWGW3c2OCuhXDnePbJvyPFVVXUOPzwTKqn87T/Y+ydN9JsN0QssJfOn0L+WnuG+/k3IUH/YtwJc9GcedMe6tdgNvXf5WlFIsb1juZTsf7FtflAkBuGnzM9zUfhMjqRF+tvln3L29OKDemaxkrbsQ79lpvHFkGwc2Fj0uF4RUBio5ZcYpplRbO3QpJ58hnkR/vJ8vPfYl8xqBck6vWcy/ZCuo3HE7y61yynzmGrFpYFPJ5/9h1x/oT/Tzlz1/eVZjqUYSA6y1Muba7GSLvme7R3fzxce+yI7BPUUVK1PNjnX3wbsZSg7x880/f95T3E83CUKm0BvrxXanlc0FISe2nujVhuamgATy5Vg15nHd0W4or6Vtzlk0vfbfaGheCUqZg73G1ELOtkZQ2mFrV/hZp9r2DU4clA4QcdOSSoFfpbHVGDsK5m+PJDMkR4sb7TpeuieucLGlxsoysLMkNt/gDXBTaA6NTpJKdHtutpMiq00DeXXjSlq1BWjQ0B3tYbe7SNXJ8xtMuVFB5qI+kO8FGB+oxQtmx0lNMSYkVRCERHTWGwyauyhXBH1F4032R/bTMdYB2SSX2G5j1sl6F/v5jZVeQNDbM64BMFkQUljW4C6U1h/X/LjqH/l9xTv5Zf0VptYfWNborgGRTXJu60lorcn4utH+ib13Pktx/Jw6stao1+jvGjIn7eOyI3wzW8X7AjN5VzbEu7JlvKvlDN614l3evzcufjs1qQupTFxAMHwVdakrOH/pLPzd66l1FFnlZ0CXPkX0jA0QVbmATKOzvQQD5jO5fYs5vlprylg5qzjTkAtKhqJpNnWO8sCufn7+2GbvAmVee7Tke0YzUTTmcQ6Ol50CvEHws+pCzKsuyFCW1Zgvw+q3wht+ahozZ3584mxZrtwF7bgZazhxpempP8nayTeW7+VS3xMoYMmiS7j+vO96F9ib99zMTzb9pGh7SvL54bzPmWDk//0cVrwuf59leQFKqms9rVV+WmtCHN86G1Jj7LccMosuMD3Ck1EK1rw3v9hXiQHPWmsGkv0E/RZzKxv50Ftej7XcZOXYfTdLkwlIRdhn2WSWX2rGgxTINQAsLC5bbDJko/EMlt+dPcbOFK0HktMRHsSvM4Q01OgE4cEuU/e/wgRNiWyCm9r/5JV/DjTnZ4s6pzHM5y47hoBPEdOH6AknceIx5rnHZmU2YxofmJKsXOlUriezlFwmpDfaR4tteq7LZ+azHKx4PX5LMWJpwto01HOlWIVeM+81+RLdwusBZkX23IKIS+qXeA1ZE4QUnIMny4SkoziFU9GmxiZMGTppEFKQCTkQL2Of22l19pImryQrNdzplWM1hsw5MGAF+MjxH+Hak64tOlfkApOknSwe4zK4h+yOO8jamseDJ5ENAIEKzreD1MZHaEp38cbjlzGjJkRDedTsKWVB87hZFY+5Ei7+qukwKPx35sfgTb+afDBx2xq48oferIs88VMz6YTjwDO/g9QYCmi6+BNEVI27W0tP9ZpbgLSqzM/cxgpaa4LYVoR42i4aR5TjFExQcvrcxd7YnML1ZHJyWRCA05pP8AYl91bWm/F2gXKvFNDa/xBfuWwh//yapXzi1ErocV9v6aVeo3qB6iHrW0/W0SQy9oRMyJauMFlbM9vuIuNXoHxkLasogB2vJhTggG++2YbBbfzPjv8BzBiqT675JCG35NKsDWIC887Gc6hNX8aZYyv52JCf81P5DitLWZwzx5wjto4dYBhNVvlJUYZGc9uhP3rn/Z6xQbZFzPcn16YZdnaQCJpg7IJYjFWVbSX3b+6cvaRuCU3lTZS57YEuS8HM1ZP+vfvD+/niY180M2kBly24jH+66AcEfGWgHQJP/YqVjSZYHh/4gAlgclmZRDZRtF7S4YQL1lO6JOPjXce8k3eteBeXL7gchSKcDvO1J64j6cuXkE/VRswtPxDLxLip/aYj3o6XAglCplB4gs8FIQErwBltZwDwZO+TZmVOyJdjuUFI7uIzu+kYmHeGl8ZvH2mHWvOYqoAZ/JuxNdu6p1jkZ5ys7XBo2LzvwuZxQUjBPP1llo2tRopWkN14aJQ6xzzG79amTtYTlwtC6ioCphxoxy3Eo31ez7hC0zFWulcpNzB9kzbbWRmoZHH1HOpRBFGAw/aBDq/32sswFAQUQZ1ivntC2jAuCEkUBCFTzdWfLmgYhpWGvq1u6Y5bM760uajUKdeQCNhZrrKDZuYGO2OCSszMLUtazYlxeGBcBmaysSnjMiG2ownH04xYDWwInsSoNq8T8oWY25TvITyragG5r+iYKj2Dxonz6rFVmIztkLEddncpfDrLAt8AdVhctPzNXB5o4nKnjMvTmssXXu79+/BJVzM7eCZV2VMI2UtQKC5Y1gid66nTiqQKkXBKl9f0xYZJEySjAihApSIsml3cA3P+8pYJ41hOnt/gTc/74wf28t2728kQNpMpuAPvRxKlvwvj13nJ9bj3RZLeNNIXHtOKKly5Plgw4LzlGFNaNMkkEIlsgoNuyd6y+mVwwt9BqBYLOHbfr/ChwReEU97PzKqZfO2sr3nrTzzc+TD/9uS/Hb6OXimYd3rpKRmXuEGIdrDSEapDfk7wVQCaDJr9s1ZN/dpggpkWN5Dt3Trh7kg6QtL97ixuXmR6iU96r9cLu7TdrAmTVRb7cyWlBXJjpRbWLeT0BbO98rqRlDuQ08kyEptYjtIbHcJPllx8qN05+ZfWL+XMWWcCcE/fk3ToLI6y8LWugHL3NYf2cNrCRv71Dasg2AtaM3MsRjhmehJJRSaM/Wirais9YNuVy4SE431ksVEKKmcuzz/AH0QFK9lVplHaxnE0q5snNmiKSnS7ikt0C2fFWla/zHvPoeQQdmRcEFKqFzUV8YIyAJUem9DQ6Yv1lV4zJzdrn+Xj3n3mmPT7FG8+aY4XhDijnQwl3XKs8nxHjFKKk2acVHSuOH9u/ljwAiGtYd2PSWaypFUZt1acSXnAgmAlLbmmRceTLGpsoSrkZyw5QhYNDQvzEyvk3xQWngfHXV3879j/B9WTD74GoGmxGXdVb0pZ2fpnM+vc1j+b3xecQ+2Ck4i6GRlnknKs3Boh9ZUm0D9mtgI08bRNY2jiFK37BqOEE+Y5J86r946PncM7J5wHcosUBqwAizs2UOWuDdU7/zTTOQGw1B38nIlT07OO85e1ULbvXnf/WLDkNV5pZLbMAr8p9YsksxMmP8hdL+fSTdKPl/UtVbaVUxXyc8BvOh0esgfoc0v93rb8bcUZxd13e8fr08GTUShq7DnMzip8qeJjOVc5op0sT5XbxFQVq+fWkfLtYUzvJ57OEvKFGEtmiAYeR/livOFE0z5K+LeQCVTRgp/l2sfquFtBEe/1KhMydsYbj7W0fimWspiZMW2PzupGCOTXyCq0eWAzX3n8K966Ru9c8U7evfLdWLVtcKxZeJKDj7HabwLXjrGOoslTYOK44MnGhZVSGIRcbPu5fMZpXL7wct618l3804n/hN/yE88kGQ7dSNxvKm6mKseKFJTB3n3wbnqmmCr9pUaCkCl4DU/LX1QTmuthyzgZ1navNb0uuZ6t2raSZVy5soPOaCcxt9cmFPAx2zI9VhsOjha9d8dwnB/dv5stnRMbZJ0jCe/itKBgUDoUr0ocxCZrjbBvIOpF0U8fHKHOGcVnKW+Ocb97AB+KHOIXm3/hzTSRK1dqqiozU0o+/WsS6IJMiENPdJJFrtzAYKM7tmBV0yp8gQoslMmGaM3OgfxCPctnuD3m2RS7VJZf+BL0Z8Y4cY5J/27pChctvJhLbQOkp2j4FQYhEaWhbxuP7hkk5b5WbrpaMJ/nY12m/vVkq4pqLGZqHzgZL6iE/LiQxGgfRU2HSceEFNweKGc0nqYgy0ocsx8W1S3C5waxALWpKM2BZQCMsiXfwMkkzVod7Xdz4lwThAAkUwF6RjUtTh/VAfcNmpfBbHedhY4niy4Qfp/FuUvzPXzzmypZqA9Baox6NwhJOuGSaeaB+BAok1ovA8gkaKksnra2sMwtJ+i3OMd9z76I2S/BsjhtdeXUugMyk3aartGJwc/4NHMuCMkNSFfKfc9cEOIvKx5Qfxj7Rvd5C5MtqV9iZsw66e+LH7TqajOoEdMA/dLpX/IaH5sHN/PVtV/Nd0w8W3VzYMaxpJT2VsdeOdLl/S279eEXxnO0w9/KFLdYKfTgrvwsda7esS6v3HFGbnxDRYOpyweWaHcqz4pG2hPFWc54Ju71Oh7fcjzVoYC3MntHND+jYHh04vTJI7FBfNqh2jFnj+Bo/lh52/K3EbACOHaav1ZmGVaNzKirygdqbvZy2YwqZjaPUmFlWJh2GI6lzTkqNcZxzccVzYJ17uxzSw4kzsn1bOtsilGfWYPHalpc9BirvJb2MgcfDvVlrbRWlm4M5xpaI8mRohLd3L4KWAHm1c7zsi+2thmJFTQSHNubMvn2fbfzh51/wNEOqehIUYbQSo9NGG/g4ExYcwkAd4IAp7yeh3abxtMpCxqY11jBaMB8L+PpOFH3epHLhEymaEaxXCB08HHo2kAyY3N/2QWkQ8rMMGb5aKp392Xnk/kSnmySMLq4FGsKPdEefrnll2wZ2HL4B1fPgNf9CGaYadTpeMLMfmT54dQPYFmKbMiUv6rDDEyvqzDnjLnN5prpaE0sXjHh8blrtqVg9Zw6Vo+NQLiL7OhBtt3+cbjz896/g5t+D+Eu5o4N4dt4AzOwIFBBb1nB685Y5XVi0n6naVe4iwEy+ySoaoa6eVDVyuNWhmqfOR9EkxmGxnXcbDg0AlqzzN9LDJ0PQqaYedBnKfrKFxFTmlvKMpBJsLhuMWfMOiP/IK3z29R2IlsjJrOfK7HSTtYswulqqWjhmIZj0I7N+pBDVFVw2sJ6EuWmsyOdruTTJ3+WsWQWrTLUtKxnQWMlWRUm5TtIVivOazgWhWJ1707v+pXLTOwL7/MqLZbWL4X4MG1J8/5dZcWzdOU80fME33zymyTtJH7l52MnfIzK9Cn8+rH9pp3kdj4BHL/nMXIX+cKSLEc7PNzxcNHr5gLNIxFJhbEdTdbWVGuVn64XU0b6+VM/T9AKAQ6jZbcS9T816TohGTtTlOGytT3pGLWXIglCppBreM6qnIXPyo+7WFC7gDnVZtadx7sfh1h/fpakmraSZVyFM8/sdkwjRQEn1ZufC8uNtnaFufbGTdy1rY9v3bWzqC4Q8osUwsRMSGEQ4ieLrYZxNOzpj3rzhtfpUSrL/Ci3l8CXjbO5dwNffOyL3HvoXu8Azk0t2FgZNNOLJiPE0d5MHgpNb4leTwCyKXqx6XNMg+f45uO9XokZ7poCB8OmgTWrLuQ1QMkm+bk/yb2+NLeqBGtmmyArnXWKskWJgoZeapIUs9aaVMEq7Uk0yd4t3kQAM2tDLGvNj/F4pu8ZxjKmAXuu2+vQpi2wM2YFVlfuOZXZUS+YAabIhBTcHihnOJZvGGpsksoEu0vrl5oxRbljbayXGYETzW5RY+wddVOze++DLTfCg9+g2RejssK8/siY2b9tdhcVudmhGpfkB+eN9RRPoEDxmI0Ll7egOs3Kv3UESFJmBsaXCPKG3JrzhKpmhTaf3XB4PTNqzTasnFXDzNrSF4GLCgK/2fXlnLwoQFnA8sZBAKw9MLHMJDJu0HNumt71B02j4rjZtSZgzl0Ey6p5NnKNRp/y5XvRj3ldvoe1vM5rrOeU+8v59Mmf9hqi+8P7uefgPc/qfYssvZQ0mDFVyQj1w4eYrX1QVsuu0SnWCXE90vkIv4/s4H/8SXY6CRgqnj+/b2AruavqjNaCnv1VV0NVKxUoZvsqoby+aMAnmFlicue1XOB1wlzT8N85mg8ARiPFqwA7jiaZMgFNnTaBQWOqw5sDv7mimcsWXIaTTbE9qHkmVMHM2hA0ug3ZoT2gNYcih9AqzYxyhyWO+Y6EExlGRwdN6UebyYZYWN7MNpPJlUapbJIRnyZbVp9fUTmnop72oMbSDnMqlpd4FWOyEt3c/ltUt4iAFfDK93Ac+scP4E+M0h3t5jfbf8Nf9vyFLYNbiIaLG8u+bJyBmNmPhdPnlizJchvaw06VN+D6omPMNOTKXednQNvedaswE1JKY6ixeHYvgJ23ATDoVPNg2fm0NBRMZDLHbbj2bqHOCpn3cbImG9167JTvBWbfffGxL3LPwXv49bZfH/bxgBkzdfl3iyfSWPkGb10j5X6+VjpiGvjj5NauqHevRbXVCa/D7WD/xOmKc9eRZTOqqUr2sfjp/6EyFYN0lM39z5iV6t1/B8c6IB1l/tgQZJO0ah9UttAbLwgglcqPGeveAHvuyXduemPJFMw5hYd8GUI6mats5q+b9nnlZD3hBN2jSWp0hAZfnGhhEDJFJgQgUzmL26r95jqfTfKuFe8qDub7tnrXEGfJazkwZM61MWWu045mQnnheXPOA8dm0Ad7An52R9dSVm6uszp8Bt39DQQz5piIWBtJ0EvCvw3QZG2Hs1eYiSKakjFmW+b6snnAVAbkvmMWljlnd64350ugR9nYTnHD3dEOv9r6K2xtU+4v53Onfo5F1SfyvXvb+fOGLn6/7qDpfFrzHgBmjHTQ6s6kV1iStXN4p7eOWO578WyCkKHEKFlH4zgaK2XDuM7clY0recfCT+FzzHllLPggsdTEKaSh+Lo4q9KU+q/vWz9hnNBLlQQhU8jNPJILJHKUUl4N8s7hnSSGC3qAa9pKlnHNr53vDWpsj3Z4s4GsqDRfxq7RBH2RJI/uHuSLf91KLGW+PKPxDM+Mmz1rr7tIYXXIbwKEAuGCng4LB8syEfb2ngj7B2OMxRJUO2NUBH1k3FKS9WU2X3/iG140nVv0K1eONS8Yhq2mzjDRtAgC5oRmaT2hRtmTTbLRynoBy3HNx4G/MAjR3pd4WS4LAkQSw3Qq929XmuVNAUIB8xqFgVq8IOuQLjEQFszEAjqXPfCZ/RSJ9WG5vQ4za0PFa4O4CyzVB2s5bsxcxNvcgKl77JCXiVjuZkJqnXFjeTKTZUKKy7EKg5CM1Y/GLPa0tH6pCUAq3cZKtJcqTANYUTATTm6RM+1A19NUV5kgxLJN782KYL9ZqC9UY4Ka3GJNAJ3Fg5XnN1XygXMXcuXqmVy2aiZ0mCAkWDUfrSwcrRksXJDRNZIy+0frOpbWmcbiwfAB/vGCuZy7tJmPnL94wnNylrRW8+4z5vPaY2fwb286jrg9CsDcmjZvVfinD3VPeN5kmZDcxXd+oxuQ5zIhweIs4eHkLmjza+Z7AxKxfHDRV8yg2Iu+mh9vUcBv+fnQ6g+xqNYELs/0P/Os3rfIwvNI5Uofon2UoViq/RCqoX2kfcrBj8lskj/s+oM37minZUNf8biQXnf+/BCKmhkFQYi/DC6+Dhadz9LFl3nj1wrfL9cTWBWoYrH7ma9xJxoYoRl3UiTCY8Wf3UA0BZiGSYMbYLfZnfQUTDZx1eKrqMqa79L9VRFaa8vyQUg6BmO93oJkVibGSaF8mUXMXQzvDUvewEVzL+JDx3/osI3qXM++5aQZ8UHSPRcW6qwIkFJgYdPkZiRLCVgBzmwzJWVP9j5JLBPD0U7RoHQoWOzOyTCoxjWCk+F8JwMmKx2PFAchjoYBNzu/qHaRdz3pKrEIay4Tsj9mjuO6igAnugFjqHEuAANobwze4TIhPsvn7VMvExI+hAM8o5eSVmXUVprzXE2whtA8sz+wM9SHu71z4CjOYTMh63vXc/3a670OofGdD1Pyl5nv6cn/AMsvN6WGrkC1m4my7ZIlyMNuJqTezYSEM4OEgj6UDrCto7gBGEtlvbGWa+bVQ8cT+FCs0n4IVLCxotJMNjHrBGIzV9EfDEGggnn1i6DtRGYsuhgCZd4sSJ5csKG1WeAUzDlnfn6ygAON8ziobCwcGsrMcTQYj3DtjZvoGk14GZpZdhcVQT9Rpb1r71SZELP7IjxaodDAaTrIsoZxx/2uO92dWUFP48leObWXCdF6wr49deapBN2g76mKOGsHbqUm5Cdgz6Ass4KfP7yXmvQ5BKwA5QGLG3f/L6mgyX41BxfTsuhicx0Djk+az2HboJk2N3fOnlM9h4pABXQ8Ya7ZVoCs5ZuQOTwQOeCVYF2z6hqObTqW+3b0ewUCt27uMdMezzrBe87x1fMB2Dq41QtqcqVYIV/I++4Xjvs5nKFkGDRUOm48XKKipEy1UpM2kzFolZ30e1B4+9uPebvXIfLbbb8tnsH1JUqCkEk42vHKsdqq2ybcn+sJzOos23qfyt9RM6tkGVfACnj142ZwunnNuf5R76k/vG8337prJ1lbEwpYZm5vKFqJGvKZkIXNlUWN6IydIe72WudurSwzNb+7esfYcGiUWh1GKU1F0IduXMwDlTY31NqkMvmGfCwbw3E0g27j7pT+P5meLMtHYs4pKGWBMpmQ4VTpQX5kU2aVdKWYWz3XXMDc3pgZ2sK2bZL2GA4pVszM91gXrnwaRxN0khw3uw6AZ3KrlAOJgsAjVWLhOoBUegwvl+o2HiPKoT5q3qNwLEg4FeaZPtN4PLtuGZb7tFyvSiod82pCa8sDzKoLUaMj44KQSTIh4wamFwchpgGhtTYlQOCV+zDWi3ZCWDqEpZQ3gC63sjAAHU/iC5iLtU+bIOTYMvd4aVxies4qm7yFCkvNmHTFcbN4/zmLCGbHYMA0UgMNx3v395aooc4FqkFVzfJFppbZdjIExh7lU5csY07DxPKFQm9aM5uPnL+Y6jK/N03ovJp53jG/tbePrF18Ap0sCIm40xjWuDOpkBs78iwyIVpr74JWmLUETA37RV+ecqVdpZQ3NqB9pP25l2SVVZGe5QYH2qFMw9LGlWD5GEmOePPhl3LrvlvNbEeWBf4y2lUW+opLWXrdMqhWFURVjSuXa1kOF32FpXPMRbXw/bTW3oDQ45qPw3I7F5a0VFFV5ielQlQ4prd4NFZcxnVoKIajTOOn2Q1CGp0h+gby544K5ecqd5cN+tLsiT6ZD0IAhnabMlHHpjWT9oIZyK+fUe4v533Hva/k2iDjVQQqKPdV4Ndphn26+L1cW60UKAhqTciZO+Xr5TJhuRLdjrEOr1MndzzVBGsIWkGwMwyUCEIKGzGd0U4S0eLOJ9vRDLilV62Vrd5iuZNlQrJas3vMfCfOW9birYjd0NBIQpUzwJFnQiA/mH8gPmBaTpEeUhmbfmVuD4bcz7i82Yy/cr9/tX07vHPgaFmlNzFLKfcevJd/X//v3uQCUHph0ilZVn7q64KxJ8HqgoUax5VkOY72Mka5IGQwPkhl0I9P17J3MFa0yvemjlGvpPbEufXgZpCPr2iDujn01bTQe8Fn4crvc/DMD5tSy7o5zD//K3DF95ix2AQbSTtZ3LisngFtJvvtdV4tuqBoJr+HsiOAwg+cUVNPfUUQRyXoi6S49k+buGubOUaOCw0QsBRRhdcJFysYS1lKL/eQVEF8WvP2sURxB1omCXvvNz8vPI/9I/ljOBs0155SmZByfzknOWb795SNkbRjBP0WK6uuRGGRzDj4dA2nt1wCCrYObQXfKADNvhPM57nkNWb/jnSBY5O0k+wc3pk/ZzcsNcdk55PM0pZ3vR//3cidwywsjm8+3p2qP3++sh3Nfz92oKizaXXFLG/f7RndQyKbYF23mQHtjFlneIHaYGLwsJmmnLB7LatylFm7pkRFSTRl1tnJGTuCIGRGxQzetMxMY30gcoBHOh85ou05miQImcRgYtA7EbaVt8KhdUUnrsV1i6kMmANk86Cb9iqvg7Iqr4xrZuXMojKu3MVo98hunGpzYFem+phdb3ouN3eG0dr0Wn39Dau8Upkn9g95pQtaa2+mk/HjQSLpiLdA1Ry38Yw1gkazszfC0weHqXNGKfP7UApu9g9xe5V5fKUq8xbfimfiDMfNauXzs/uYNWymvGTlG4gHgkXlWKOTBCGZbJJtlg1K5Qd0uj20rdoinTUNR9saKcqE7A7n68njSkMm7vXgHRqO0z9mLuzxghKslJMpmV5PFw5k9odA+QijaYqZTEKZP//ZPNr1qDce4NxAvixjVm52KCdTdEJb3lpFjRPxeoLMH32YKXotH/gCDBUEIWmfOVZaK2ZSHXQbzdXu4kpjvWRsjd9pQqmCTMhofmrgTMcTZDEzwPicWtCaNscNVgobVrmSrO5nJowT8HSu92puQzPzU8H2lQhCIhnTQApZtSxcfhWWO0Xj7j23TXzd6AAcXFvyM4pmot7A2nn4qfKZoC6RjbKrrzjoGN8TlLJTJDO2N1aoptwtmchdCJ5FENIT6/F6CScEIUcoN3uSre3nlQpP5RohQBDF0qVXeL+3D5cuyRpODnsLigHgL6dd2ejerUXjgPrcsQgzypsnHaBftGih+349sR4vc1k4QNuyFCe4i2b6bNPQGB0XKO0ZGsRHGhQ0+/xex0msp+BvGevm1KSiNatwrAB/3vN/JCqb8o2voT0ms5COsVT78FmKqGXOf/YkExkcTpUux9KaEZ+mbMbETMembBgFLMgoIvHJx5dAcYnuQ50PFZWy5fanUspkQ5wM/cWjySAZLirn6I52kx4fhGjHy0I0lzd7WfbC8Wqe+BDRZJawO4PdhQXjs2bWlTNoNTOiNDqbJmgFvcUbp5LrUPPWOXGyJDIOg1YTlsIbm9Zc0WzOde5xXN65gaDbyRWumVHyuNNa83+7/o9fbPkFDg5VgSpvLELKTj2r6U8nU1FnMlG21mRjxdetaDrrlT3nBqb3J/qpCPrwObVoDTc8dYi7tvVy17Ze7thqGvo15X4WNQShy8wWddycfMYiV76TG7CsUMytMcFsa0W+DHbCmJ7cAPUSv2ecDI/0PQmBECc5AWbEIzRWBVnU4kcpM411rpNyVfkAGk3UH/T2+VTTt24b2saws4OUKuPchI9WrWCgYCXyA4/kO9qWXsJ+d4bOUMCisdHsW0frkotvnpXKzWrow1KKk1tP5qoVxWt4fOikt1BfZq71fp9C6QBl2aXe+wEstxVBt9z2nkP3eFmNpfVLYbAdkhFmaAvLzYJPCELcbO7i+sVUBavY3hOhL2KOzTkNpn3y+N4htg/kA84VZc1e+eOmgU081fuU18Fw3pzzmFczz3vskWZDwlnzN1Q5JvApHBOSM5bMYOl8ED2WKR2EFH6mNWU1XDT3Iq8s64ZdN0w5cc9LgQQhk8hlM3Ac2tb/Fu74DNzzRe9+n+XLL+w05jaca0ztaeECh4VyF6OknaQz5B5c4S7WuBdxMCVC33rTcSxprfYGTWdtzSO7zUlzMJommjIN+IXjpucNp8NeELLUrZn2W2kcFSWSyLK1K0KdM0JF0MfjVoaHE6Yx3mzDu6su5ZgGM22iRtMVNheUi1P34LeUadCd+G4SGTMfvMIMTE/YsZJjBnbZY6TQgFUQhJgGhQlCTGPTFwgzr6DXfFfBhTiOhkzCK/kA2NRhtiteMCNMGoqzDbnbC3tklAWBEBGlaU2asofCTMjj3Y8DJricnVtBvrKJWfhNVsnOFp3Qjmt0UJgZqTK5C/jhFisMVIBS3lgbgLRljrP5NQUBQ242mLFe0hkbv9OIUu5xlY4VnbAGk8OobIryoB+/rqNZjVKF+36FQUhuwblsKr9g33i5LElFI9Utq8ANwPpLBCFjGbNvK/w1lIdqmVttjv1dg9uKBibS/Qz86d1mtprtN094HW/GkUyS+RtuoDzWiQ8HRyUmzIhWKgiJJPLHQT4T8uzLsYoajQ3PLQhZVLfIa8zlZpF6LlL188yAWqCsrJqZSy7zXnf8OI2cP+76Iyk7hULx+kWvh0CIqNL0xPvzqX6t6U2ZfTrD/bxKmVk5c8L7Ff4942eJyn0/tV2B1jA6borevUMD+HUWBTQ0LCTozsqXHSgYrxLuwrHhijEL5Q8ymhrl+8/8kET9fHN3/3ZTP5+OsdTxY1U20RkwmWUn+SzKdQpUp03DaMQHtbOLp4yNpCPsy0RQCo5N2QyMTT3rmVLKy4a0j7TzcKcZtNpS0ZIfmI2bJbDdcqyKRm/KZZ0YKQpCuqJdZGLms3LcTp8omqR7LikMQnqiPcVlF9k0pMaIJrNEVC2LW6q8WQYBZtaWM+hrIuwDnU3TWN445SB+b9sLphjW7ni+ZMZm0NfM/KZKRlJD3t8MeNPJqrFu6twgZKSynlK2Dm7lpt03eX/bdWde502PChx++usjUFPvnlc1jI0U9zyPxvLnkbqCTEjQb1EdMFmi2zb38KP79/Cj+/ewsWMUgBPm1GP1bfXO/Y3zz/OC0Vyve65hOqNyhlkM1P05Z0IQsuAcb7Y6amcXla9t7N9oGp3BSs6zA1TGBsGxqa7M8tlLl3ursQPMp4cE4BRkUabKhPxl91/wKYVNLefF3A6dvoIZ9trdUqyaWTDjOPa6naHzGyvxhapwlGXWfho/5XQ2xbJ0lnoHHCz8lo93HPMOzlna7GXnlrRUsbSlgbcsewsAfsuiPLuc0dzm1s+HlhUEUKxMmc8qtx4QuG0rN0sTUD5m1JlZvgqv2fFM3Jt0J9dhlJvUpDzg47rXH+vN3PhfT+QnnSl3MiyvX+7t/wc7HgRM1mFp/VLmVs91W0RHPi5kzA3mqnNBSHRiEBJNZouCkHi2dABZOGtkVaAKv+Xn71b8HWCy2bfuu/WItulokSBkEp3RTrCzWOFDzOp3L/y9W/MNHPIHcl8mQi821MwqLuOaJAgBaPe5h3g2yaWLgtSU+1k1u5Zvvek4b0DvwuYqb87se911LfYN5NN9E6fnzWdClrqZkABZsirfmKtzRqko87HR56ACZdQ6io8O+SmLZ7zMDkB3eBSAGXYvfp8Fiy+EUI2ZlUpZ+DGZENvRDMYnZkP2OeZiaSkrX1fq1qU2oci6GYSGurg3javt2OyNFZw03CBkRm2I8oD5ewbGUuA4JAqCkJTSJbMQ6cL6V2WBv5yw0rSkDuLXGS8I0Vp7WYbVzathyK3NblpGsKqVZm1NzIRU5wOJkYB7cTvcFL3u35+bhUVbY9iWOWHPqSyYTjSXCckm8WUiJhOCYiA+QKZw/BEwoDRkYtRXBKgNNvCWRWlv2lQKZ/tpXeUtlEjHE0zgOPnb55xCVXkAnza9qAPjxoQ42iGeNdtd5a8DYOkss67Dbp1C7zUzn7DnPrj92nxQsuOWCVOReqs/J0eZ4ShCQKVfmyCkoPwOJk7Rm7bT3hSZADXluSDk2WdCco3t+lD9YevjJ2Mpy4x9wvSYPdfe25SThspmUBZlJ74HKxDySvVKBSEHwge8AdHnzjmX18x/jZd1bLdsryERHT1I1P3ezGgoMUWwSynlnaty75frQZxXM69oFirID05H1+BoXTQuDeDQyAB+siilqGtYSrLcfF8CIwXHcqSbjO2wPO1jToXpQd84sJHrnD7COOwe3mkqKzNxc26bfQp2LnOYem5BSFXCNByHfIrGmQuK7msfbgfLh0KxJAVjkcO/x9ltZ3uDVCcr7WuuMEFIv3LM99ydhWck1lfUoxnLxLw1n4bcKXWHlAZ3YoCWihbv+pJ20kWrqOdq8tO2Q8Sq5rjZ+SAIYGZdiEGriVGfRttpGkPjBuRPIjeOJmkniY7sQ+MGIVYTS1rLTScYBaVdBatV5yYkGC2bOKYK8tMZ+y0/1595PW1VbQR9+fGOpRbAfLYaGhqx3YxtbFwQMlywUGF9RRDbsb1SxNPnzccqEaOVB3xmHF1unJ0/BDNWee2CbUNm3ML+iOmkLOwxrwnWEPKZ68GEICRQDmvebUqCTnlfUeYo9z2vq5zBcdpPtfudiGVinLGoia9dtYqmqiDHtgSpSfeZQem+fBCSG2dTSm+sF5+lKMuuZEi5nRS5MWXRfugyCxey9BKwLG9Q+sLmKkJlfmKqypzexwch7viHS8Z8QJA3LnkLM6tmUlse4I1rZlMR9PGO00yG6Nw557KmdQ3VwRoqM6cyOFawKK2bDTk+HvVm+AOoDlbTmnVg21/MDfPOoM3d14XX7C2DW7xqh+NbjieRtnl0j2n8n72kiaaqMt56igkg2wfihLO5zHqc1S2m42VfeJ+X5T5vznkopQj5Q15QeaRBSMQ2bYIqR7lByMT11qKpLBbl5IrrJwtCcueNqkCVV3lzYsuJrGpahd/ye2trvVRJEDKJrv6tMHqQ5kyGAAVnoP4d3o+5Bgd2xgzCrm1jKDHknTBnVxX3NtaF6rwZUtqz+Qt1mzXM7957Kl9/wyqvFyYnlw3Z3RelYzjupVoDPkVbXfHsQ4XlWLlMiOVkKC/Pv1erFSHk97Hb7wOlWGQHqNCKVHTEDOxydY+NorRDrR41mZAq8yVLZBOgFDNtC4U2gyXHL9JjZzmAaezMCjXkLyZej4wikDE/V1Xme2YOjh00DTBXXOE17KtD5oQwlsxANkmi4IuVgpJBSKqwh1RZECgnjIOls8y2O71FB2OZmJdebQ41wXAuCFkMVa3Mzs2QVXBCmxmIYrkXhy7tXnQnHZjuBiduEJArxyqvyu+3tsIgpCqfqq9IDeLXjVjKTMfZ3bsx/7ja2aa2PB2jPOjjt+++mEta3ROVLwi1BXXs/iDMcst8SgUhw/vyqyzPPpmKoB+fdtdDKZjTHEwwYLvHWXXANHCWzjkTfEHCStO386+w+U9w33X5WeMAhveblHmBoeSQOWZTYzRgUa0V5X5wVJI9/VFGCxoH4y+gSTtJJJl//drycZmQ5xCELK1fekS9wpPJNUAGEgP0FE7D+iyk7BSEarCaluFfdTXgrluCucgV1shrrfnd9t+h0ZT5ynjLsrfQGGqkoWoGKB/tyoY+c9Hs61nvPW9Gy9RrjuQazwciB4imo96iXKXWymioDLKgqRJb1+NoGHXSaPe7oLWmf6wPn7ZRQF39Am8QeGXsgPcaTriTjO0wYtVz+dxrvMXx9ukkXwjEeCzVB5k4IcdmjrZgzinoMlPGaaWKg54jVR8z54cRfwCb4lLB9pF2UD7KgBlZSI4NHTaorAvVcXzL8UW3TQhCyk051pBycKpaIVQHwMESJVUDaXN+MOt6KIYsjXYHxjZXNBddX4rKTuLDaExpzJiqpmHc5CVNlWWM+JsZtQDt0OgvHRiMV7hq+MDIbjK2Q5gqUirEjIb8MenNAlbV7C12WYcFKCK5NTHG/60FZWa5ILesoPH8QgQhjdUhxtzytGSkuONspCgICTCSGvFmgnvtMcv40wfP4E8fPL3o3w3vP40Vs2ry59NZJ4A/6H1HUnaKbUPbvIkD5tfM995DKeU1XItmyMpZ/Vb4+9vN2imucCrMhn5T9nXO/EvwlTdQqRWkY6TsFBk7w4pZNfzXu0/mG+dVYqHdQekFmZD05JmQsfQYlqWwdAX7rHnmG9G3NT8tb+74X3IJ4XjGy+gvaKqkPOAjqqpMOVaJIMTRmjVJixPG3sFVi/OLtL7ztHn88QOns2aeO3OZsvj0yZ/mI8d8i4BuJpGx8+tjuIu1rnb8Re+xtH4pav0vzSQLyoJT3u+N4+0c6/S+t7kZtaqD1SyoXcDafYNeOfWFbun7ZatmMqsu5D5Xm32QiXvn9VyDXqE4e3a+9C732ebWmZqK1poxd9bQylwmJD5kpuouEElmUVheNiTplD7P5SoEvHJuzPH1D8f+A98773tcvfTqw27T0SRBSCndG+naeTM4WTPTwpr3eAO7CtOTjeWNzC1vBe2YQdg1s4suBrmBg4W8Hs1kQU9MuGvCom5gGvwnzA+irTg2MW7duofdA6Z3Zl5jpclQFAi7CwX5gFYsc4JyMjTU5k88SyrjRHDodUdez3F7u+34CJUFF6P+6BjVeoyg5a4LUtXsbRPKos1RWJgp5nK14h47xUF34OW88oK59d1MQDrrUJk1DXJfIH8yaR9p94IogAyajNugzAchWcgmzXgRV4rDZ0L8vgAEQoSVGTw3397vjQkp3P4Wx8n3sjQuhuqZtLlrhRTOQmMlhr1Zu/Ym3Z7gbKLkuAevTMsN8oZjKXd3mEaq0mXUBwv2U3U+VV+ZHnDHhLgBz/BOc0dZNSy52AQhmSRVvnITROayOA0L8otg5eRKskYOTJyNI3chVRbMPonKoB9rkiBkODXsDcqsdWt4l9Yv83p12we3wtofmQeU18MV3yOTm/Fp1x3Fr5UchlSUKkdThqIKRbnPZEIAnnHLHqB0JiSSyJdp1JQHzP5/lmNC4pm4lwl7ruNBcryOCZ57SVYuyAj6gt7nnjtv2Npm88Bmwqkw4VSYtT1rzUBO4HWLXkdDqAGllHl8oNzNhJggpHcgN1OWorV1YjBRKLcfbG1z277bvPFx4xvZOWvm1ZOhEUebhRXjowcAM7ufSpvMsE9BTf1ibz2OxnQX8aT5biSHDqG1aXC31VXxkeM/YsrK/CH6lcPjVgYSwyx2fPiUD9rWoELm8/VP0bsLlP5OAjPcMkPbF5ww4L99pB0sP4scHz4UgcyYVwY7ldy4upySQYidxQaGK2qx3UDqQNz0hPpUfpzaoG3OjTFVRVyVM+wzQYiFRUOogRmVM7zMizdeDCA+hO1otIYxVTOhY8uyFKq2jbDPPKZRH1kzoGitkPBBku54EID66vz5tzBYyZVk1WoF/jJGJ/mscrMYFb5HYRByuMHpJRdsHKeuPEDUMvs7O1YchOQ6OyxlyjoLZ1VqLjcL2oYCvqJ/PkuZMpphtxzbnYVwecNyb9tv23ebt47F/Nr5Re+ZC0L6YhN7wUt5rOsxLzA6Z+55MPtkqlBepjk3ps2ylDeDYuEaIYWPGS9tp0naSXzKBCH7fQtM4zgZNlPy5kqxZp0ANTPZP5RvUyxqLgxCmDjzWGLUi1+iqtLr/JtKU1V+m3OzdBKqgflnMQOLllTcC4qWWpUm6w6w4vVQP8/LEibtJMPJYbTW3hid45rMxBq5CX9m1oY4xp0cJ+Cz+PszTeAccYLmuEjHmFM9pygDfGzTsd5EDZDPcnVFuw57LCayCTLu51jtDkzXeuJkCbnzTS4IyepY0VppOblMSGEQAjCzambR+nYvVRKEjLf3AfTtn6TLSQGK2YsuMQuWNbkXk77iAaerK83Bvs2yyVS3ekGIQpUMQnL15j3JQfaVudNMRooHT2mtubH9Rq65+xquffSjRGp/TF/lD/n1vs9z28CXifs3eWVahUwQYlOjLSyUWRDJsakoyx/cs4Mxdisb3Okd5/pNL75OhIsyIQOxMHXOKH7LPUTcaWPjGVOOVastqm0zyG98OVY6NUa3O83u/ILaV3wBUBbJjE21O1tGSue3rX24fUK5TtwdeFbt1vuPJTOQiRdlQtJQshQqXVA611xuylsioSq01szPHvDKsQqnGW6OFzR0GxdDdau3VshYZiy/Dkt8kFDAR1KVszcWMD0mWpv1HcbzxoSESGcdr+Gc8ZnGWdBpo2gdIrcUB6A6PYhPVxOwTEOiM7cQXN08mHOaKetA0+SWO3jT95aY7SfXIABg+1+L78sFIS3HQKiWijKflwkJp4qDkJHkiKn9BW8gYUtFC7XVswFlet8BamcTuezbXHfoVv6+MsNOlYU99xYNjB9KDEEyTKN7KqrSCr/SBINmPz5TMC6k1OxYuUyIpaC6zJ8PQOCIg5Ddo7u9Hq7nG4TUh+q9C1Lh4lbPRq7Xt7ARtqhukdfg/Pb6b/P+e97P++95Pz/Y8AMAGkINXLEwP4B9Wf0yCIToVDbxoXazbs+IOTYC/hD1hQ3FEhbWLfTe7/b9twNmOsrJ9s+Jc+tJ0gLaBPmjI+Y47RiJU6bMxb4GC6t2NmWtJqvj11kGOs02ZYbN2iKDVhOz6szU2W8/5u38/ap/yOeh3UHptKwwmaJyt8zITheVZxQ58Bj86hJY95/FtydGackFtf5Q0Tkg62TNdLmWz1uPpEpHGYxO3RAGUwaRG08T8oWYW108q1azv9IrqfrD3jC37IyRzDoccMdTzK6eTYNbHjWEOZZ95bUkVAUjPtCOTUN5A37LT8AX8BZQLMqEJEa8QdZjVrW37kUhq76VpDI9u4325CsxFypaKyTW45Vi1VUEvLJSoKhxlivJqkNBoJyRcR0aOblyssJG05GWY/1q6694753v5W97/zZltsqyFBn3fOWMKzEdcceE1FYEsSxVVN7WPNV3pbNgZkw3CAn4At54ls2Dm727xx8LXhASP7IgJDct7KLaRWbcyZxTqdLKLMqYTRUHGINmvNVYeW1+3SkmD0Jyvek+NxNy0D/fO8ez+Y/59aWWXQrky8ItBXMbKygP+oipyikzIQB2sKZkh+t4zaWCEICll6JQHJ/RXvC19IB77QpWmTI2iqtQuqPddEW7vM/0+Jbj6YskvYWgLzympSj7feqCBo5tqyFJOSOxDKlEFFU4wQ5MmIFvQa0JXLI6W3rK7AKFFSsBXYbWpjNgfElWbjKicp/bUaxiJEqsmp777GqCNRPuezmQIGS8SDcRO03UUlDbxqyFF5nbcyux9m0v6llb7dbEp9DstGPexaC5ornoJOo9vmm1dyL/qj/KZpUtCkJsx+Znm3/Gn9r/5A3Gq3Zn/bEdTcaxSfh3lA5C0iYTUoMZSN7q9nD5VB+WMjWsTSrMLssGy0/IF2Kmmzq3kqNU+PNByFB8jLpcKRaAO51nrhyrHEWjXToT0hE+4BU3zC8sSVMK/CESGZsaO0jQZxHJjHqzN+we2Q3aoUbnTwhxt6GQm/loLJmFTNKMF3GllC45KDxVsKp6k9vDFi6rRGtYYO/3BsjmSgEsLBrG3L8lWGlK0KpmmBmytAOOnb/Yx4YIBXyErRpiTjDfQ1FqhqyCgemF5UVpTAAWsFuKezgsn1eSVZ0dQmF5mZKuXOq+bi40LWXAb/ZLSzphxkJESsyMlVMzK78K9TO/h7U/cTMH8XyGb7bJlgR8FkFlTmpjmdGii/tIMr+Sc67BpJRiadNKKKsyve+tK+l/zVf50pYfm9rosmoesjKmVOrQWu+1BiOHIBOn0f3Mq1GgbeoqzbG/4dCoWewumyyathMglU15Y0KqQn5zcSsIPI90YHquFMtv+VlQs+Awjz683MVq29C2Zz+9KObvguIgpNxfzsqmyddXePvytxPy59fOWFK/BPzlaGC3TsPATnrdY7c11OBNsTuZcn+5N5NPrlRxZdNKb22K8ZbPrCbrNx0yjqMZdWcE6hhOEMA0POuVBdUzqJuTHwQe7dwBdgY9Zo7rQV8zMwoWuXzt4tfxibJ5XknsCY7fy+j5y83xaWvtrTA/wb4HTJCy6Q9FHUipvnZq3I9G+cuKzmEHIwfNsaZ8LMd8vyp1tLgxNImAL8DF8y4GYE3rmqLZEQFaChaefXI0xaiuIJbKctDNECyoWeD14g4ps99D1fXEVQUjPg2OXZQtKDlNb2KYrKPJqCApFfKmnC3azpoAWimTCZlsVr9xvLVCNAwkhkhnHQatZha3VHnn0OpgtTf4GjCrgFc2UactCFaRtJMTZutxCmb9KgxgjrQca233WtJOmt/v+D2/3T712gi63D1fJYqDoVFvjRB3Ziw3E1LmK/PWXSgp13lT0+Ytigh4YwhyqoPV3rkyZ0aFCUKimehhp3Y9ED7gjTc4Z47bAJ59ElW572Ns3PSwbkY8Vl3cE364IMRyMyHDqoFU0O353+kObA6Ue+uV5MrCZ9dXUOY3maGo5WZCxs+OlRz1mkx28Mg6hpqq88fswFjBZz/7JKho5HQnAMkwNXaWRYMHzH0n/J3JvFNchdIV7SrqEDqu6Tge2Gk+X6Xg/OXF+0gpxdtOmUtKlZmSxogJVnIlog2hBm+duJzC8T6HGxdiOovNDkljOoHtEtP0RnNTz7vBhaPiJVdNnywT8nIhQch4x7+drmMuhdo5EKzMrxHSssL8PxMHt5cPYLltykhQPjaF93gXg/HjQXJmVs3kH0/4R/zKT9Ly8c1AjIfdcopkNsm313+bBzrMwN551fP4pxP+ic+c8glmOf+PMns+YKZCLBWE5Aam12gFjYvMooBAON3Hd9+8mu9fvYJAOmwyIb4Ai+sWU+auIlvuxMhkA94sDyOJMeqdEfw+Zb6pFU042nEbJBaVKOpt0NqZsGDhgYJpdue5M4V4/GUkMzZ1tp+QO9i8P97PaHLUNAS0ZrXOlxHFc/WObiYkksuEFHSmmHKsEpmQgpNyc5UZ7D3qBobVToTqjOkZyW1/fagef27gd+MiMz959QxvrRDsTH7WtPggoYBFRNWSVmX59UKmCkL8IW88iHZngAKwdJWZu75QdSsaqLNNj11jaCZoTZc7+JP6eWBZDJSZhnbT2EA+C5Lb/lIuvi5/sdz8R3jga9CxLl+PWrCwYaXf9DRnHbtoPMZIygQhSgepK88HrksblkL1DDrq29h+xgf4wvpv5cdFBCppd9cAof0u7zlDQyYAaMQHoVqqtTLZu5AJLsKJDPsGoyWnliycHWvCeBA44kxIbhrahbULCfhKN7KfjVzJUsbJeGMpng0vE1JQRgHwiTWf4J/X/DMfO+FjRf++fPqXi+qTwTRm/cEqQNFuZaHjSXrdrOKMEhnaUsYvVJariy4l4LNY3jbfzJCjNWG3N7BjJI5iFKWgPlAFvgCNLbNJWua4SffvhrFesu5sefFQqzdDTc6pLSfw/XQV38hUmYUb3YxesKIOMBdwPdng9MLG5tqfeJnWsa6dlGtFSCt8weJMSG6RQSyL5Zb5DCp1jMGxwwchAG9e9mauO+M63nfc+ybcV5OMEsA0/kd8EFVVjNk2ve5EHvNqTCmJYzsMuNNVV9Y2ErMqGHYzIYU987mApSvale8oiA9jO5qIO/6hrkQmpLwiSQY/WkNt7DDlbAWaypvAyTKg06RthwGriba68nwmo7y4QYc/CFf9lNqz/tlbsyM8bva0cCrsdbgV/m2FnXhTBfOFAcrt+2/nhxt+SKZwLFqB3Krp/nHZ3fELFRaOUZl0jJhj5wdr50pdXeO/K/Nr5k94nVwWC0oMTh8nlwXxW37OnOUuBFleR9XyK83P6SjRnmfc7XK8cY3RyuLAZ7JgJ3d+NZmQclCKSI2b9cwdVwvP8z7D/DIBph1SHjCZEK01enw5VnIUR2viqoJQcGJAXEpF0E+5e70oykBavv/P3n+HSZKd553o74RLn1m+q910j+keh3HAGGDgAQokDAFSJFd0giQS4upypUeGorCS9kpaebPile6VdlciV6tdLi9BiZKWkARSEkiABEVYkgMzGAzG9cx097SrLpOVPiPO/nHOiTiRprzLnnifp5+uyozMjIqMOHG+85oPzr2P+6TH3246/J16pOZflePwhu+LN8t7+bigvbh+MZbGnq2epZarxVKsB0/VWKgkizcGi9U8baEe77fVMXvD3Bv4e2//e/ytt/2t1IIPwFRuiprulbJZTK+RzQN0mEPi0JdRKiGr24/o6IXJae0bi0STZndYEvq6ZkKEEB8SQjwrhHhOCPGxEc//kBDi60KIbwghPiGEyI16nyMFIbh05jHw1UkWFxPH3pBsY62o+WuvcX+k+j88df2pmIobTMay8eTJJ/mLT/xFCn6REPinrRf5V8/+K/765/963G35gbkH+GtP/jXeevKtvOP02/ju8+8iCFW1HYo1zswWht7XRPTWEDB1G4tCXfDtXoO5WsQJr04fyQuaCTk/fZ5cRVXiZbnOcqOnVrEk1LvrTEXLSo5VmAHXSxqwCUERwXQoQEZcHci4frmu+lhMS4fawOpPh4B+KJkJ3bgIudK4YqX+RDwc2UWIGgCqcRHSp9ep0xsypg8zId24IBAsaOp7VUgi/doZ3S/E3HAWiguwpCcghkmoHFcF12BCVuMGrhBQmqUjcskKxcgixBjTi3F374hWzDK5sjSs9awcR0qYiRRbMp8/DmGX10RIiITps/TCHsuGCWk3kkZSADNjipDqCfjIP1GyK1Ba2s/8HfVzrgLzySq1KUIiKVMyiqWm8oS4skIxSL6r89PnQThEfoG/8aW/G6flqMfhYq6oNMqvfF6ZZ6OIm7qom62dhemzigmJ+jhuJ06k+b2XV0Z2i+1GSTpWHM+7TTlWJKO4QeZupVgG56fPx8k3O5FkjZJjAZT8Ek8cf4InTz6Z+nff7H1D7+G7PndM36WbFobw7Ke4qn1ai1Njzo0BnJtKJ2iNMqXbePPZU4R6YntlRV0nF5ebRKKBEDClpTCO67BcUGOZe/OFOBkLwJ0+PfzGs3cxh8Md0lW68DlVHOUrU4C6n7cbK6N3yl6VvfoNxYwA7atqvClGeXzXTYVrPLusYjxPlk9SLczgOoJy1NgSEwJJImCKEdAQ61dYkKoj9ZrboyFKvCIipQmXkrO1s5wsnyQM+9QdaAlJbXqWpigoJkSmmRBzf2r0GpZUdIl+JKk7VTxXDBV1AEHQoK9ZnuLKmIazI2DSva6bZERnnuO1QswczBXnhl9Unmdq8eH4V9PbwWDQf2GwFSYkklH8nGHpPv/a5/lbX/xbI+NofX2/c3uNlCx0eaBRoSlKN5RiXXsmWfQ4lV4ZXywtpnqB2KZ0g1SvkFHmdI1+1Oe3L/02gE6OSsa18sM/GsutGt/4ZVWArL4ayxMb+fTEtBf1RhZ0xm9n5FgAS+WBBD3dr6TTD7m4rO5p6SJELYhFnfV0IElLNXZsOKW4sNgKjCRr6LrTKVl3RoK5hpZzP/HfqoLXgpmDvbT6Es/cVIFCDy88zNOX17i6puYL773nGKNQLfh09HQ1ssz8Z2tnhxgtUOyJYY5fshZhR0Hdy/T9Piqx5lSHeoXY/rNZLTuNRGOoCIlkFBeWrzsmRAjhAT8DvAd4BPhpIcSs9bwA/iHwLimlmcH/wV3s64HBFBLTuenEJ1GaTaJTbV/I2iWV1uAGvFp/NV4x3qgIAVVV/4/nfkRNbmXEv3n2X/HCqlq9eOuJt/Lxxz+e8mi8995jqhkd4HkRfYYHWEPz1aQDfinJIg97aqWlcZ2XRUQXqYqQmfMUq+qCKsl1rq+1Kfkl+pEkpKM8Ia5IS7EAhGJCZkKBQLLaWUv1CrmwrvTdZ6UTm9Hjw9VXp9xU6MQT2CvNK/FEMI/D+VQRov5OY0xvdUPqAyst45iQjr4JCceJYyND4bHkqoGztqpM3sbTMueVEnPYrB6AtT9D+UL6iQFUa4pLU8foYDEhI/qVJMb0QtyFPhKNONPdkUU6/QGatXwMiWQ6WgYpOVZQRUgfuEYEU2fU6qPu7DovncT0XT0BuQ2kSNoszhnVDAxzUzr1mGJ/NGr+lNrXKF2EXNN/uyvLlHLJTeWO2h14Qn1PERGe4/GnH/nTfPS+j6oNclWeE31FRT//adYufp6+/uzZ255MmBAZ0olanD+m/rbffXk5xYQE2h/TCRM51k6ZkEv1S/G5u1dFiO/4vGFODXm7KUJGyTm3g/PT58HP85wT0mgusarDHBbnh4uWUbCZkOOl46mV21F49OwcnlQ37tfWlL751Zstek4LgaBWSiZ0bS17K66/CGsX4yKkODuCQZ61JkPWOVooJ0bR5tporwEDshu++M/V5FOzhq6s4bnOSCbk3PQ5yNfwHKHGxy14QjZF/Qrz0iHCpeessy7KvOzoaz8KFRNSUUUIwDVPMjMzz4qToyMYkmPZ95l4gaR1kzCKWBNVpovB6JV8b52+8MhJyK0OaNg76/DZvwdP/eLQy1S6V5drIkQiueHMcXwqYZKGmBCNqdxU/PNgETLOf7EVY7ptAv7he36YR489CsAzN5/hr/zXvzK08p+vqvcPI0nXMqcbOZZhjeIipLBBEWKkWI6nDNsDsEMcbLmOwXR+Oh7LNmJCnrr2VLwI865T70o9Vygt4Ohjtl6/DM/9pxQjXs8VGcQoVtk8JgQUdEDNlfxAbPyiCt14ZakZB5PcrtsEGE8ImK7p1qJRexUpJeuiHEftbwXzlTFFyMztMH9P8vuxN6RSxAzMtfH8yvPxefLQ/EOp3iBvuXN0HHvOc+g5ahFBdsYnitkwvpCX117e0JtkmJBAghQFVpypoZheI8UCOFZWczQp+qx10vOcRq8Rxw7b/YgmCbthQh4HnpZSXpJSrgO/CrxvYBsBFIUQLlACdpZZecAwHWiHCgnTNMjo56WE1Us8HPlx0ymDUab0QZxZfJi/2SvpCa4aBD9854f5k4/8ySHt9R1zJc7UVFGR95zU6pHaFRkbnmpSQFBk0Qx8YVeZ39av822hT27X59zUOco1dRE6MmJ5RcX09qMISZuaXFWr9SUrGQsUEyKUHMtBEkkZ30giGfGyXt0+K92h1YmVrjrlqm6fk9r3cLVxNW4idM6rqMQPjaaOMjZFCMBKPT2x6AhGp2Pp/c0JL6YqpZR8y1cTnZmr/xXZrsd6cFuvHTMhrgelefUdRT3dP6YXT25q8yfoCsXuhHJ0SlfChOS5qQfUYqETR/yqImSYCYkkBLJDSTZYLJ2IV+4uecozcr11Xa2CeXnmSM6hkX6QQfgFeN/fhHsTMzO3vTm1yZRe8QklqSLkRuum3u8KJYsJCdyAO/Uqe8Er8Jce/0s8efJJztbOqvPZC3huSk9kn/1Vlp79j+pn4TB75h2Qr6nvXkvD7jmpzp1nr9ZZteQ2hmbv9Duxyb86qgjZgifErHrD3hUhkExALq1fGpIrboZxTMh2cX76PHgFWkh+19GTNddnsbY138t8YT5e9XtkYXiSNYhj1TwF3cV8ub1Mo9Nndb1OR/QUE1I6Hm8r9TnqduvIy1+lG0pWnSnmp6eG33juXBzUwOnkHC1VkxXJ1vrq4KvUqrApQgyTXX8NvvqL+GuKrQ3cYwiRsKE32zfjn89Pn4fCFJ7raGP67mNiWVdFSChc+mKVhlPmoqtiERaCGiW/xKnyKSJdhFz1YGZ2jiWTKCTDlHnbvs/EndObyphedyojpVgAXblKKHyqEdCqpyeNX/45ePZT8MX/FdbSt2zTbLElJUtOkZZTZK7sxqznSCaEjYsQcy/zHC+13VaM6ba/pBJU+KlHfyr25Fxcv8gvPftLqe1L08mxW72pJv79MAkLmSr6qR4hGzIhprnr8QdjmZKNNy68Mf75rqnhMdkRTlzYb1SEmMaXtaA2xEYKIShVT4EbqDjeL/1sMj8JSjTMPcaa6o1iiEyRU/bLVPPquF/yTidNE+/+rrj4f/FG8nrTMNl4QkDdY1MJWdqY3hDlWP2wFczqaOkb9REFqDbIA/CWn0z1UjEYnL/l3Tznps7xlZfV/evJu2bH7o8QIvlOx/X/GoApNJv95nDbAgvGu1uKoCd8VsTUEBNiR8+fqCTj3I3mSuq97ILydceEACcAewnlEhB/61KVgn8S+AZwGahLKT87+CZCiI8JIb4ghPjCtWvXBp8+FMQdzytjipDVi4rmby1Dr8kiDosDKyanNuhIHKN6kjkc/nqvzPfOPsKffuRP8yP3/shI06gQgp/6jseYLgbMlnPp5lSoAqEfqlXmKgK8AtWps+QRCROyflWZhhGcrJymHJQJyjNx19L66hJFr0g/lESioz0hTsyENI3RWziUcGImJIxkPNG61rxGW98wbpPuEBNyo60+61gRFnXvkUvrl3hxVXkxzjsl8hCXIU09qTeeEICVljXhcFy6SKJRfUJ0EeILN14liCT8Tk6t6PjdNRq/9y/j4mq+04rfE92pWX24NqeHPW62b9Iy5m8gqC4ktK1kTBGSRPTeND1C8l0Q6u90ZIleOLByUlmMV1NmopscKx3D1UXGpWJN+UHM5DYoKSbEYCtFiPk73/7n4Z0fVzHUd31HehdyRYT0lRzL0lCbyF5XligNSD1+/IEf54O3f5C/8da/ERupfcfnDt0b4lkth+Dmiyy9/Dn1c67KbFk1bivLpAipaXN6GEmuruvCh4TVspmQqilSzcqn66eiKcfBrHrb/Qn2AvZkYbtsiFn13ZMiRPem+VxchOQ2ZTQMhBD8qUf+FB+8/YN877nv3dJrprXxt95v8vK1ZSrycvxe09VEahUcU+xGP5R0X/odpJTccGY5PjWsz6Y4A+/+S0PnaKVSibuJd9ZHMCHdehL5fe93J+Eiv/sv6ffVuVUonQVU0dQLe4kfBH38NBNSluss7UURopmQHh6hs0ZdlLjsqXSc23TBVw2q5LQv7ronqdZmWQ/0iCgj5iw5SNEvxoWizYT0I0ldMyGjcLO9hHR8pkKhWCgzpi2/nE7OG0iDXCguQNhFAhf8mgqD8JICZhwT4rt+3Ax30BNi7mXzhfnUvc+wBACdaPSxt4uTvJfHEQ4//oYf58kTiuX99MufTsUXV6wipH5TjZ8rVsPT6WKQ6hEylglpLcMNvYAxIMUyeGj+IX7k3h/hjz/wxzlePj5yG2NOH5eQtdZd43evKt/J20+9fSjoAKAclKG0oBoTNpfg6f9bPTF7J+u64LAN/6PM6ba52dxrVzooD+Gjfwwe+uF4W2NKny4Fcfyziui1mBBbBtlWcqx1Ud6WHGvOYkKGmIV7vxse+xi896+kOsrbGCxCHpx/kHpbsqKld/ef2Jg5EFplILZZhMDGvpC1zhqCiEok6BJYTEgy/7XlWKdqCVtzc8D0b8uUX5eekI0ghPCBnwAeQBUsQgjxo4PbSSl/Tkr5ZinlmxcWDj/TuNVvqd4FjGAzbF/ItW8mAzfwkNUfYDo3neo+PhaFafALlBH8YPEMT558csPN711Y5Fi1iOeKISZEDeyaltNMiKidUglZUZ8r9YvQuKb04a7POR0VbG6yAK3VG7EcS9KiKtc0EzIsxyo7DtMhumFhkpB1Ye1CfOO/PXJSE8FOP+RGR51y8wUZy8WevflsTJeeFzkchCqeSAqfWiGZ7K6aC1E48QppbwTF3DUryo6fMCFILnhzPO2/ASEE17/5b2P96rzRl06dSTM4xpyu9/Gy6dUB+OV5OugiJBrBhIT9hKHw8rEBMpdThYkQAofCCE/IYuwHnJY3yXsBi3qTSzk1sTSrLaXCLCW7oeZWixC1A3DPB9SNZuAGV875uFI1oDLXRD/qs6YnEU5UoThwUzlTPcNH7/+oipC0YFiG52WHSLOGS1Lf/PNV1aW8ME0FR0WYSsgFyQrY1fUVtU9BOfZbtPsdGt0xTEhQHrk6NgjDhOwlCwJqsnZcr/xvtwjZKyZkOj/NQvkEOB7fcNRx8vxCOkJ1E9w3ex8fvf+jW77BLdZUgVN34Atf/RZVaSLLoWYV9tXjdxJq6V6rpa7xG848J6xkrBTO/QF9jia3rGrBp6knPr3GCCbElmIVpuEtf1L9LKO46K9OKQ+URLLUXooZ2aJXVJOY/BSeKyhFDa7XR0yGtov6FeZx6OECEatOxGu+VLHhvjrGQgimpToO1zxwCjXW9ektJMw46fPCTLYur19WSXe9VmxMHxXPC4rxcbyAqVDQDaMkofGL/yzVq8nuiwXGE9JFSnjFL7FYzXGzvbU4W8NyjGNCBs9L13HxHHWOjJNj2UWIYU6EEPzwPT+M7/hERPzCM78QbzM9mxTgjRX1uXZi4UwpSN1bx/ZZuPi7iWH79OgiRAjBh+/8MN9x5jtGPg9JTO84JuR3Lv1O3GfknafeOXKbsl+GXIlGVf9t5vubvSsuOOyFh1HmdFOEVINqnES51u4rw/2b/mjqfviSNqXfYYXjKDmW9oSMYULWne3JsUyvkE4/Gu7R47jwxj8Md7137OsHF5Efmn+Il24kf/sd8xvP0Zycfj7sDjUSHIUTpROxgmWjhCwjxypH0BMBK860KkJaN9V8AZ0CqnFmKrkuVgYK+Nc7E3IZi/nQP1+2fn8Y6EspX5FShsC/BTaeZR8BxOlHjEi4mr0zWdm/+nQqWvehE2+Jf97MDxJDCBXtB6mCZhwc4cQrM4NMiDGlA9TQXozaqTgh6+rKSyzVL3FDRLEpHdA3WbVNq35TybHCCFfU8QcaFcbGdARlwEcQRAFhlPgqLqxeACnJIyh1Iv71U9f4xJde4RNfeoV/8dsX6Eh1kc7kZGzMi6xuxedQg11RR7Y2dTyozYSst9aS42dWQkelY2mqPnA8qjkjx1IpE/8+/2GEcLguO9DQK3Fr+kYwOImvLMa9QgAuLSeaW686T1cHAERyhDfF9ohYTIjv64aFoojAGfaElOaRehI9G90k58LJrnrNJT2Ox7GWtTMJbQ5JDO8uUQxcHFkhipIiZKWzEsfzKk/IsOl1FMz51oq6XDylpD1LQoIbUC0dU6lUxhMCIENcP5lgXNc0dCWoxJONRq8dzwOGipAt+EHWumtxetdeFyGQJOR8/cbX4/SfrWCvPCEA56bPg5ePr7CF0uKm8by7wdn5MyBgzZE8+8K3KXJFMX4CatPJdXV8usoVR03ATEdk4y/YKkqBR0voRl6tUUXISvJzYVqFMdz1XiKgH0XUnSon5xPd+/Xm9dibdm76nDpO+Rqeo+RYvVCqidlO0VmHTp15KejpxL1l7yp9QAJn3eQanuqr7/6qL8ALaPl6bI/AH1joMPebi+sX1UQG6EeSa57D1fArKb8eKLnMUmsJx/OpRI5mQi6pifXL/1VtZM6RASZkJjeNE/aQSC67eY7XCinpyUYFrmGjB4uQVDDIAEwhPk6OZRcnZnECVDH0wTs+CMDvXfs9vn7962ofqlW6uojrrKnPNaZ0UHIs+9469u+5qKVYpTmYuWP0NluAKULWumvW/TXBZy9+FlB+O2N8HkRZy07rC/emF15mz8VFyKLVr2sjOVY1qFo9uYbP9SiSMRNiT+JNOhaYImTVvADaa0gJDVHakScEBmJ6t4hqUE1NzB+af4gXdAHlOILT08MSOhteXh3XMJJxT5KN4Dpu3Avmgo4oH4U1ff6XIkEXn2XDhEgJeh613lHnpCPgeGUmlm6vDPRgsRv4vh6LkC8BbxBCnBRClIH3A//Jev4S8KAQwmgc3gs8yxGHnbc+VEw4LixoQ9TVbyQNfPwi9514Il61GZJxbYSqZlsGzYFjYAbFkUyIDuOuSqEMy9WTcRFyZf0yz+nUqnQRkjAh3fUVSp5iQlwaQ40KU8Z04YCAYlQgkjK+kSgaMuJkKLi62uHnv3yVX/jiK/zCF1/hU19/jZ4I1ITEC1MDI6iir6RXAoqGCdE3n6pVhNTNgGAxIZ0NmJDACfAdJQeIpGr6c809Ruf8h7hOBJ01nF6LGVOEDE7iy4tMIShKCVHIRa0nB8hV5wiFRyg8RUMP9iuxU7v8Akva3Oq46oYTmJXcwYhex6WbV8XfdHSTQucaJ/UmFyO1IhubQYvH4NSb9A5VYg/PblHKeZoJgRW9sqV6hKjnVTrW1m4q9iT/2/PKk7AkIsWCaHkV+SmVjgUQhThOcuyW9CSzGlTjiUmjmzwfnx9xEbL5yr2J5h3cv72C8YW0+i2eWXpmy68zEy57UrVTnJ9JJFnAlv0gO8Xs1G04AhoOVHvXyYnr6ht1fKasVfKFSo7Luq+IacC1nj+mGk5uEY4j6OnGgNGoPiGDTAjA4z8RR9Neck9w13TCdl9uXI5loaZDPfkanivIyTae7O3OF6KNp3ORQ1cXIU3veSLhICWckckYN91Vz9/0HHphj4anxo2ZUKR9TyT3qeX2Ms21y0TAs17IV8q/wZeX/zW//O1fTm3f7Ddph21818GRNfqhpH/zZfjC/xz/zTz0Q+rnpedT7K7XqTOjgry47rks1vLxvWioR8gAYibEWiWPZJSSYw3CFOLbYUIMPnLnR+LY1J//5s8TyQghBF0duNFfV5+7YhUh08Ug1SNkJAP44m/CCypljVOPb4lxHYeNErJeWXslTlp65+nRLAgQN8dsuC7c/cHkidk7Y9ZjNj+LK9Q5NUqOZYoQJccyPbmGY46v1TtxEuTZ2XQREgqPtiiohSEzWdaSyEgb0/PbkGMZTwgQR9tvF2Yh+WT5JPPFeS7oAuq2mWLcrHgc/ILF7GxRknW2dhbYWI612lkBJJUIuiJgRUzRNzdVLckyxvRy3qPgF3CFLgx76XHOfG++4+/J/eIwsOMiRErZB34K+AzwFPAPpZRLQohPCSFOSCkvA38X+B0hxNeBGvDP9mCf9xXdsEtNGwRtk1wMI8m69i1Y0ZPR6gkKfpE/eNcf5GT5JO8+/e6tf6Dp2bB2cePtNMxq0aDxSZnStcQAoZiQ8gKLqJN3vbvG77fUIFf0i0mBFZRwPbVN1Fqh4BXphRJBUyVjwZAnxHVcAsAVgmKUJ7I8IUqOJTnRF4TCQwqHgu/G//ByTBUDvKgzVIScnz4fT+JLekW8pW8+ed+JvSsNQyfbRciodKxYW68Gs2pQRUpJJNRA1H/4j3Bd/+3Ta1fxjdJisMdGZRGB4GSkErIumd4X+Rol3SejQzB6sLJ+74ggXvUVrjHNq9WLITkW0NFFyEx0k8L6ReVLAdpCpAy088V5ePAH1bn0yB/e1U3RhmJC0nKsm+2bcSddR5ZTEb0bYSo/FevFv+1IOPcHWCpOQ35KSbEA8jXyoIJDo5CubMYrZ2YFyC5CmlaBN5SOtVE6mIbpZlz0iiPTa3aL+2fvjxuA/tal39ry68yEay+YkPNTyteAl4N8jcWpna/abgVT5WMIx0MCRa7hsYwjBJ4bpCSqnuuwXj4LkDBrtdPjezKMQagnYLRH9LpIFSFT6v/KIq/c+xNcc4/xmeA93DE3G+/Xl698OWas7p6+O36dWaQpyuaWe4WMhG7IWIwkoZZw9v1XCHHIRzDfT1aeZ7pqPIiEw+XGZVq6IJ8OBQz0RLEXvS6tPM/n6PJz0yE9EeE6gi9f+XJq+5uaLfFdh1Dq1MDnP5OkKj36Y4nESEZwPZGfsnaJeenEfU6O1/Lje4QMYJQca7WzGktxR0m5cs7GTEjKEzIwCSv6Rf6bu/8bAF6uv8xnX/0sAH29H1InIZrYdN8VFAN34x4h3/i38Om/qiQ6Xh7u35pXahzse+DVRtoXEvcGEVZvkBEwRch6dx0e+3E48TDc9R10p87Ex7YSVJLtNvCEVHPVeEFnFBNyaSUpSG+bSZiEfKDuTUnX9BX1hGYjlTF950zITq+799/+fhaLi3zfOdVDZLC/yUbIFbbHhEDiC7neuj6ScQqjkPVuHSnRxnRLjgVxw0LDuJp47UBL3Qaj6u3icbtj51HB1pedRkBK+UngkwOPfcD6+Z8C/3Q3n3HQ+I4z38F3nPkOWv3W6C/VmKD67SQdo6ZuAt93/vv4vvPfN/yajWDkWO019S+/8QquLceKZBRLK0w8bx6hmvcEJXBcFovzSgbQ7/AFqS6Kc+VTiSRDCDVJqTcphHUccoRRhBRtJb8WDhTVjcrQxUW/jEDgCEE5DLgpG1xvXWetu6YmqzLiZN+hi08p5/KJn0ikanz+9+FrT0G/w0JhAWVtVxegKkL+i/oMvXlTd8kWQlDJe6w0eylGxhQh3f5wEdLVA7CZzNVyNSSvEgm1rV+e5tqx83D1d1mw5VBDciyl7T8pXZ6LulwyBWBpLjbadUWOKGoO9yuxVhFX+z6gPifUEcs5nSgyqghp6yJkNloi37iomyYKcH1eXns5TqyaL8wr0+0P/sLQe+wGpcDDjVSRdLO9QiSjuFEhQMmrxoXhVnBu+hzXWtf49spz8J5/xNKv/0loXU+YkMIUAkFZClZkn3q3zkzpJJdWWtS7dQqeGmxNE79WP7kxGR1zbEzfghzLNLB6YP6BkYbP3cJ3fZ488SSffuXTfPG1L/Jjb/ixDVeKDfbKEwJwW/U2cn6RjvZjHCsvbvyCXWIqN4Xj+YT9PnmuscaqSsYacZOMpu+EpeT33OyIHiGbQGoJghjRRyaeCOUqqfTCb1bfxs9VTiKESvSaK8zR6DX4xg3lfxCIJM0oX4vlqiohaxcxvXW1eNEPATkDYpm8D1Hf4XgvRFh679l2H8qAcLlUv0RTNCkA0yFDRYgtG/7FV/4L3/BbRH2IcPBcwdXmVa40rsQTXpP8FLiCHvPAS/R6HXKBp5qg3vvdSnrquEoLf/XpJIJ29RIzeqiqOz1OTBX48kUtCx2TjGVgipDVjopsFUKM7RFiYMburaRjjSra33363fzahV/j1fqr/NKzv8RbTrxF9b1aS7qmL2tPyExJxRmP7BEidfLUU3qMzdfg/X8P5nfHoM4WZvEcj37UT/lC+lGfz11UwR2DvUEGUdIG6vXeugpx+O5/DEC9lVxc5aBMOSiz2l0d8oQM9poINRPS6PYJI5ka419bTe5ni7Wk6AtcB0eguqZH9YQJaa8SgZZjbc8TkvddyjmP9U5/xwzkE8ef4InjqrlpuxdyWe//nZv4QQByBXXMIymJus0trdjb/WBeXnt5qIdTvVtHan9JORJ0XZ+6qNDXvYOEblhoPDBGGpd3yjTCmzQGmBDbyzOp2D9x8IRj7GRhwTqpzCp3dQtJWONQs6Rbm/lCpGRer/b0ol5KD2jieatGU6+9K8eMjrTXpG0m+wNxgW5RSRVKskGn62s5Vp/Q1QWInqCZyX/BL4IQuomXYgDWumuJvEVKTnQFPRHE5rIYxqjeb+O7fjIBRUtH9E2lYFa7ZT+RmekLclQR0hkV0RuvKKtjUQtqmglR31vOc7lRnAbXZ87IgErz6gZjY6BXyNXemmqWWJwj8NSNviNyo9OxLD32SjcZgLvSFCFqMByK6AVaebWyOB0tE9Q1E+L6IARfvf7VuHjbMEZyFyjmXFxpVoMiVjurLLeXCSU4skAp2B79e16HIbzWeI3VzmrMrthMCEAZB6JQFSFlNbFY7yXpLWZybk9AhuRYm8TzXmlciRNpNuoEvlsYGUUn7PDF17646faRjOLVy70oQjzHi5PJIEnj2S/UcjUc10cICLhB6DQQQsRxzzb8hUT2uO5UmJ/dQTpZXk0U3FGdoA0TMnA9v7aqzpvZUkDgJT47cz2drpxOejTlp3BQTdxK0S5jejUT0vRqOFL9rYXAJcLleE/Q0w0Xw0gy3WnhSxCuy7PLzxLqaPXpkHScLmoCYla5n15/BYmkGLlMd34AV0tqn7r+VLy9YS4816GDkqP1+nol9s0/qcZ7P5/0Z0n1xbrIdCiIhKDtNFis5hJPxxaZkL7sx6vE43qEGGwmx7IfH3W9uI7Lj96r8nBWOit88vlP4pTVeONrRsZ4QkzS0/U11exvXjpw43n17zN/OylAqifgI/80afi6CzjCiY/bi6svcmH1AhdWL/DZVz8bxx6/49Q7NnyPiq+ugWa/SWgZqO2V+LJfjhm/QSbE7jVRDRImRMp0vwqAK/ramSkFqXhbIQR57QuJpEz8WO2VOMxBNSvc3pRzVo//O/GEDOLCUiP2EG6FCcmX1HGVElqNEYscI2D7dkb5QtR3qnaiEoEb5JHCYVXUdEyvKsqNFM4wIXlX7Uujn2Z87VSzSUVWhGwXhalEQmVgFxLbhV3AWEb3kfjiP2P+038TmurmahKpwG5UqCfTenVkeup2At2F2uD8XJLkBeCXpwC90qevNVeGdF1iKRYMFCFeHkdANfQwdoYvX1W0v4NkoSfp4Y8oQvTEVa9im0lR2S+rNCH9uJkENJHxRN6sdnciNRA6womlR50RTQI7+m/O6c+s5qpE2pguBHgOXGvfgNI8CybidpSpe6BXSBT2uSKimCEqBZ4uQkbJsZL9Wuolg3YnUjeCgqflWIOeEKARqJXFQHbwr3+dPII5veJhVvFhk4Zau0A55+HIRBe73F5WnpBI4sjyyE7MG8H2XXzlylfiGMy4EPVy4BeUOT0KWe+uM1sKkES0Q3Vcq0E1npi0+x0kkpznJDfELRrT7UnZZp3Ad4NzU+c4UVITPSMH2Qj2iu9eFCGQPu62Bn0/MJ1TRb0jBAV5jXUdblEbsUo+NzvLTUcVJzecOU7UtlfUAjiaOfYGbs5AMhEqpIubV26qc+n4lFpoGjREp/xB+Sn1/o6gLLfeNX0ktCdk2Z3Fjaq4QpDzHCLhcKIvaNfVyvVqq0c5ajIfCnBcnrr2FI6jJv5T/WFPiBAiSXKM+iyEDj+8skA+PMcZfX/56rUkoW1Jr5AX/QKU1aSpF0Zw6lE4/UTyxsf0gtvVbyRJUGuXmeoL+nhI0cUPmrG8ajMmxG6mZiK/x/UIMdjMmN4Ok4WI3JhI7ocXHo4XGv7Di/+BbkntRzFcod3tx+lY00Wf6FufYunyl2H5AvNf+7fwb35c/XvuP6s3m79HFSBT22ftxsEwVF+68iU+/rmP8/HPfZyf/frPAmrhzG56OAq2zLHRTwoPu9go++XEOzIgExqMea1aSZRrA76QyyvqeJ8YESBRCFzWRVk3K7SYEGN32GafEEgSsm7s0BNiw6R6wdaKkEIpYRdajRHjy6jXeIV4TjMqIWutsxYHCJUjQbWs+yo5U6rP2IAnxETPFz21XSscXYRkTMjrDYsPpH+v7qIIKc6CoZE3K0IufE6tzuiJrkmkgqQIqZJmQhwT06shgLuOJ02UAPJ6Zags13ltWY0YHiEdV6RMzrEcy9NFiCOoRW4szzF55sfdIo42Xdm6TkCtsIGOvYt4YF4dy8cXH8eRxHG2RT1gNpEqdpJktbutJVpVN485hTuDhvAooqujDQN9LGo5xYSEokHgOjT7TVVY5SrMz+rAgbNvZyQqi5yUrpIpRH0uiUglo6AG3w45NYgMGdOtIqRjJHBdQqn/Tne8J2TdT27qTl2xZCcLurmVZWLcNyYk8CwmRPlCbrZvEkm5LVO6wZnKmXhS8fnXPh8/HjMhkJjTZUi9V2emFBDRoq/ZMJsJCaMICBM/SBRZcqyNB2UzKTtdOZ1i4/YaQoiYDXnm5jNDuu9BbLayuxM8cfwJBIKF4sL4yNE9Qskv4bk5HEew7kY0XLVOMD2ieevxWoGnfeWxe9a7m+Pj4nk3gKu9Hm7YjuMtYxgmxCpCnnltja9fVBOkexbVtTeYgJQuQtSE1XMdSrstQjQTssQ0rlSphL7rEOJwoifoN9T+rjS7lGSTY31wHI8rzSs4QhDhMNWHcIQJ3zTGO+8U+en1HI6coeC7vPGYklE9vfQ0PZ3uZzOQ4dzdrDtlGjKnWBBbMmf8j+21JIRl9RJTfUkfD98VvLCahDtslQmBpFfIuB4hBuYa2MyY7gp3qMGvjQ/f+WH1PlGXhp5ke7LPzdWVWI41VQy4+fyvYbiE+cHp0ekn4EP/HyV52kM8OP/g2Ofed/Z9ceDNOJQt1rdheRds2ZWRY8Fwx/TBmFc7iXLQF2LkWIvV4WtVJWSV080KWypNsS88ugTbkmOB1TV9D5gQ02RxvpJL/Y3jUCwlC1nt5taKEEh8IXZ/GgPTqFAC5QhqugiJfSGNtByrrIuQkqfuZ2bx0sBONZtU7MoT8rrFwn3w7K8mv++mCHEcRe8uX9g4IavfVatQCDwZ0ifNhBg51pTU7IBhHHRM76tCDa2n8SkONCtzi1Ox3OCVJbWdS0jXYTQT4hXAy+MKwVQoYqOyGcxucwr0woie6zNXHtDp2s0L+20+cudHeHzxcbVCa03gi7kqNKApiJkQk9rR1RP4mltkJVQDy1AR0m/T1bRnoKV11UAxIVJ08LwoZe6ff+ufg/x87P8YQmWRhdcEftilJ0MuiTAuQkqBS1cESjW2ARNyvaW15cUekb7XF3VvgNFFiJocC5E0bzxZO8NX11+Ityn5pa31pNkBSjnXYkKUnEGlY0ncaOvxvAau43Ln1J18c+mbPH0jkXikioB8jUpTJHKsqYBINAkj1dCtmqsiO+p7DSOJpJcUIb1GsmK7gTG9F/Z4ekl9/n5KsQzefvLtfOJbn0Ai+a1Lv8UPnP+BsdtulPazU9w5dSf/5L3/hJJf2hfviw0hBLX8NEvrV/ArAV23B0hq1eGV4+O1PH8z/738dvB2rjvz/Og24nkNAr2qHUWSfmsVr2ydS3ERMgWoaNqf+5xKGyrlXL7nETVub8iEuB4EZTynQ1mu89yo7s1bhfaEvBZN4UU1pCu06d1joQ9RSyUcLq83qcoWC32B0N+X4wjAoRZKeo0VBr/F77nre3ji+BMs/qe/yo1wmbpTYbrk8/D8w/yHF/8DnbDDt25+iwfmH4g9IXOFOaozs/ydyl/meNnlHw8GcthN4K4+rVb/1y5R60Ff+HiuwzeXvhlvshkjazcDNezJuB4hBpt5QrYaZ23LVZxK8vPyjassN1RxNl3wuH5Bp9jlqiy89eNgBbgwe9eehX7Y+K6z38V9s/cNMRR5L5/yGIyDPf7Xe3WOo+5hNhNS8kvjmZCBmFfTvwfSCVlRJLmypu6zo6K0C77LumOM6WqyTXtVybqE6tu0nWaFAPOGCdENC3djvt6OKR1UM9QmApB0WiPknmNgWMlL65eG9tksFgugGMFUrQqv9VkR06mGhYkxXd3bKoEpQhopL7AdKDCpyJiQncB03gUlISnuciV1KwlZq6+CjHAQcXfsuGM2A0yIV0iaelkxvQDn/anhgVTHUJZlg5V1ByElLiFthzieF5IipOgXwcvhCEFRhogonbd9mjxSMkaOZf3eb8dSAtdxB4oQNbnoIelpatesXnTQ+mi/lBjTo04yAQXoNTG3rZwuQgwTAuB6rdTxmy8tqmJw3CBXPoaD4IROsLkkIigaJsSWY41hQoTguv6xUkgmMqUN5Fjr3gwSB4FIipCZu1PbbKfx3HZRDDwcAoTMxQlZK50VosgkY21/QmtSh4wGWSDSncrzNcpaPqjkWDkioQ5cGMk0EyIlUvSsHiHWjWIDT8i3bn4rnrzspxTLYLYwywNzivH7rVd/i0gOf9cG+yHHAnWebMUUvxeY0ufkDUfSFBIcLxXPa3CsmgfH4bq7QCHwkmJyG8iVpuKf19eW008OyLE+99wNvn1V3bR/8LHbYmbVnjxX/MpQah8F3bBQrrPU6MSLLttCtxHLqF7tVXGlNrwLqDkz+Aj6YQjddeor6u841hc4rpoQOkJQiAI8BP3WMBNixlGntUwYSeqiwnQx4J6Ze+LzyDTNNOzDTH6GE1N5WqLIhWZuOCa8fCy5t139hmJEOnVKPUmIR+A6cTEPm8uxKkEFR085TBESJ2uNYei22idks3jSvLX45ZeTIuTmtdfiyNmTXON63zCpZebOvE0lTZ14WMl09yl9SAjBmeoZ7pu9L/XvjtodW+rpYzwhkC4wTBHiCY+8m4+LkEEmZEiOZbEEdl+cG+sd+rrJ56imovnAeEJQgQbd9bhRoWlkuF0mxHhCdtujJ4okF5aG+5tshGrBpy20B7e5NU8IJJHZ7bAdF/wGJsW0GIGLYKqqvrsVR3tCWsv0u+04ttwsvFb0YmUkZfz9dcNuLEfMPCGvN0ydjT0XVE+kuvjuCIZJ2YgJWUlyp+f0TdCs5PejvhpwjCfE6gtAZZFFa93s/ChNeGEKz3HIyTZB5OAQIkBNIMqWHEsnUBkmxHEEPj0I01X4Yl99Xk9s4AmB2P8x6veSZWRt6htWwoRovaRfQuhj35Ey/X69VsKE6JWialDFTB8ctxUfPweHmcImFHuckKU+76LlCSlqOdaGzQq9Assttd/5fLKflQ2YkHYIK85U6t53ciFN3W8mgdgNSrrIcHXDwiuNK6z31rUca/tMCFj9FzRquVpaRmEaFsqQ9d660mnrIIEwkqmIXsOEGN0slvzg3y99lb/x+b8xshOxmYzl3Bz3zNyz7b9hJ3jX6XcBir185ub4niG27GSvmJCDxpRO4HpFs6+4/ki9f+A58UrnYi2/o1XOQiUpYBt2EdLvJudDfopuP+L/+J0L8Wd94IGE8bTljOemzw3vh2lYGK3TD2VaJ1+/Av/3fwe/9/Mb76iWYkXAq70arqzh69ShOV8dr14/gvYqzTU1cVkIBY6rr0EBhUiNneGoxoyg5Iitm/QjyZpTZaoY4Ls+988qRuOp60/FjQpBFcdGAhdJ+PH/4yv80f/9S/G///k3X0AaNkQ3540AGUmErOK7Ttzss+Jv3CMElIfPrNiahCw7DncUzDVgpGSDMOEUmxXs9vOiUIjH1BvXXosfX2w/z3WhxuFcrjYxEheTjgXpAsPIsUp+CSFEvF07bKeap5rX+I5Pzs2paHZ9btpMyOXVZIFtLBMiyrE8m/aqKkIi1SPEbLMd2POHzaSQr95s8tP/+qv8+68OB/xcWmnF99jbZ7dWhBivJ0CvvbWIXhiIzF5Pz+nUeR9RDtXxzecLlHMeK6ZhIdBcThQuRo41lU+aLRvmarB4nFRkRchO4Dhw/GH182Cc605gmJDW8lDySYzlpAiZ1ytWZiU/HnhkpLql20WI63NGT1Qd4N7KiGZlmgkBKMkQTxchDWSKCTGeEFWE5HAdyMkOTlRLkRDHuuq9ugTMDXpC7BvVoITKWu0qWkVBU69oVvI+SElXT26KfinRDAvSBUCvScd49H2bCVGPOW47XoWbzk9vqCdWH66KN9Or4zUREWltcCzHknI4Hcv87ufjRoVBYEXLBuOLkE4/4qYznRQhpXlOzaTPt/3yg4DSwQeeoxsWyriRW7hDTwgMFyEpPwhAYUozISGhDCkVwrgI6UVRypgeRoNMiLp2JJJfuvxZvrH0DX72az8bs18GxpR+/+z9qlP7AeCxxcfiSdpvvvqbY7fbLybkIDFVnAfhck1P6HCDlCnZhpFGbHV1chCFcvK+rbpVhNidhYszfPKrl7mmdeV/7MmzqUZlJb/EjF70eMOcxXIb5KfwXUFJJ9qlJkPPfkqxBL/7vyu/2DjoIqQfRtx0ZnBkiSntN7mtrK7pbqiKkPb6CgCL0sHRfgDHEeQj3ZF6VE8UUOd/FBJGkjVRZbqozu2HFhTb92r9VV6tv0pXe+pm87PcPleKx5flRpel9eTfr379CjdK+npdfgmuP0s/jJASpJzDd5NibavjkDkPltvLm/YIga0zIZtdKzYT0nF9fB27vLyUeLRm15/nJhJcn5nSsYnpu1DyLGO6xYSYn40XZBxjYvsKhBAIIeKFnbVWck5fseJ5j48IkUg8IfqB1gq0V4ikSsYCttWsEEjNHzbzhfz8F17mW1fq/G+//dKQod50eQe4Y37zHlKgrrm+HrP72yhCTBAJwKV6ughZ664hZURJD41ersBU0U/kWEBrJSmiqnERMgWYgJiV+L0MMibk9Yi3/Rl48/8LnvgTu3+vGauB2M0XRm9jGiMCC5EEvYokpYxNfnFEr5+WR52bupOf7Bf4qV6RhdptDCE/FXdHr8gmgRQgBA0hR3pCYmO6EPiyhyur8QU0lZui0FWrLD3hp7qeAgNyrPFMSNGi9ps6qrCa9/Do03GUP6DgF5ObFGlTeNRtqBhdIKcH4VpQi1dppNuM9chbuoFavUIAusANbWEs5jw6Iq88IWOM6dIvcNM0xfLVNr7jxxPTUXKsXii56czimJvh9FkqQSW16rFfyVgGScNC4lVPlY5VobTFRoU2qkFVpaBpDJnCDROioyY9rxMXIY70CdxggAnpD8mxukBPf8/fWPoGv3ft9+K3X2ot8Wr9VSCZnB0EAjeIG4598bUvJjHTA7glipDcVKovB66fltxZ+Il33MEfe+tZPvqWszv6rHItOX/M5B1INSqsU+JffVl95/cdr/KWO4fls3/+0T/PR+/7KO87877hDzFMiFTn13XbF7J8Qf0fhXGhMRLr6rleKFl2phEIfvKBP8cfvf+P8vhxFVwQRpJO/SaddbXvOcdhUV8rjhAEkWnMOGahqnUTCfSjKJZjQdr39Buv/Eb881xhjmPVPP/9++/hex45Gf/7yMMn4sLkq129qislPP9peqGkJ3wiORdP5GHr49B0Tp0HK52VlKdx3Os3M6YbOcq4ZCyDwAliUWtb9ujpCXmvnoS7lFe+RVtI8Apxk9FJgOu48f7aPpC6jjU3nhHbO5LabkTMa9I1PWFMTDJWreCPbFKbpGOlmRApd8OEJPOH6xswIautHl96SQUuhJHkt76dbub84vX1eB8XBhdGN0Ckj2vU2XoRkvfy8fk8kgmJIioRhMIlFwRMFX2Wnem4a3p3NSmMjSdkRhchADf0ouygl2dSkRUhO0V5AR76wdicvCvM3JHoTW88P3obW46l5Sq9qMdqZzWpiGVEjQE5FkD1JO+MAh6VvtL5DiJfi1e1StE6hQgtxxKqsRNK8mVW0Aq+ZkKEIKCLG03FA8/Z6ll6HTXBEn5+OJIvJcca7KlheUJsBsZ0yy74BLJNOzZ1l9NFiMVC9KwYyyA1CKtTXorGplKAFHSvkFPGX+O4XNIJVcXApSOsjukpb4r2Mzj5uBeI4+nBPFcjp4/PqD4h3b5aNY3X46ZV6kbc7Z79ZUIgaVgYF29SpZy7skQxtzOTs238HWJCTDoWEmREJ2ri+zoBR7ewDNxABQwAkegO9QhpIlMSyV945hdi+cHXrn8tfvwgTOk2TN5/O2zzpde+NHKb/TCmHzRquZoydBtswIQsVPP8wTeeYmZwsWKLqFSnMLEN3eZK8oRVhPy7Z1ux7v9jb7995Ar3nVN38sE7PjiaGcvXcMcxIRZDvWGfJ6tHSF+oz3js5H28//b3s7CQrJwu37xOT/8driM4pZN2XEfgSXUMRbeeHmMMmio0QkqoO1Wm9TFdLC3G0cyfu/S5eHMjQX3yzjl+/G23x/8+9vY7uEuvFn9ueTopKK98nV4YseTM4cipdBGyxXHIblhopzvu1pi+WcEuhEixKqEuhsqRuncWowZB4xJtAD+/aVFz1JDqmq5hkrLMc+b/we1GJSxV9OS33kmKEJOMNYoFAdVcsGGM6aCKkJZmQkQJ1xGpc2YryHluXBAtbdAo9Le+fT3pOg78xjPXUs8bJuSOuVIsNdsS9CKhHNWHaAOYe/RgEaIieiWlSNAjIOc5VAs+DVGip+cWfbsI0X/7TCH5bm40VbFlS+8yOVaG3SEoJr6QpRFFSBTByqvxrwvSiRv4XWtd25QJSfU1KY/wEBSmdUoLlGWDkpQIAc0gMbjbK7d2RG8gu7iyFps1z1TPEGpzdpAbsZq0RSakUJjFTC6aetWmkvcIaCpdMlAMKnHyVQdSRUjXukADzYQIIfBQBYl0mkln3K0UIa4PpTmOS22tdLw4gq8YeIknJArTsgwtEWuTTLKkUANiLagRuKbj+yg5VqiKEDNpmhpRhOwzE1LKqZheM8CrG4zAkeUdMSEwUISMY0IgTsjytXxNREVo3iT39K8Q6XNM0k8MzboIaTlJE0tQN4JPv/JpIJFiHSseGzYg7zPOT5+PWaDPXvzsyG3sFV9bQjJJmMpNgSVvLFhhAnuNwPfouGqc6dvmUS1Z6IQRn3pOnSvvvnuec8d2sGKYr+EANaHS1+IiJAqT6FrYOGJdFyErrjrfqwUvTgo6PjNNXycS1VeuE2rjueN6nNRFiBDEDQ5l2B+WfQI0l+LrdE1UYjkWJAEM9gr40AKAhTeeUZ/1tSst+jOJhLIXRtxw5pjOzaW8atuVY9lMiCe8sUxZzIRE3ZGBDltNx7Lfq91vxzG7VamO9T3OKzjoxSyvsKnR/ajBSK5GMSHmOTvKd+tMyLAnZFwRUvBd2uTpa7UA61eg3ybSTMh2WRCDOSshaxx+/Rk1cTf1xXPX1nl5KWEvTDzvVpOxYmgfjRx1vW0AOyHLxmpXeUIqEXSFT85zmS4GSOGwLHTS37rNhHj6/wBHqrnOTe0JM8WjQGRMSIY9gGmSN6oIqV+O+2cAzEkHdKO3G80byckopVpFHmRCUkXICCYkV1VJLaiu6eVIe0L8ZLBpWn6LolcEXzUrDOjiRbrRDooJMRPEXH6EUXEgojcFmwnJT8fsUFNPLit5Hxc1eEopKQbVuBFhR6SZkI6ll8xZ8XWuVBOWtlyKjfZbZhMqi/gI1XfF8eIBRjEhOaRUxtMUw6P/ppZVhPRRA2I1V4216WEkh1J3FBMym9zsNRNyupLEne47E5JL5FigihBHFhE4O/KEQJKQBSNWQI0nBCAKWeuu4WnmKArz8Hv/J7mv/hJCD9SSXtJcS69WNYOk+DVyt19+9pdZ667x9RtfB9i0Adh+QAjBO08p6c03l745FJUJtwYTouRYyb6PSsbaS/S0Jj6yDduaCVlphTTJ47uCP7xDyZeJ+A0cKNBKJkNrl1NNYDcuQpSU8QbqvY5VknFwvpqn4ei+SGs34wIqylU5ZUUbFz11HCMphxoWAtC6qSWKDk1RijuAw3AKXNkvb1jkvkkXIf1QcilI5MK9UHLdmed4Of2dbnUxxDAh9W49lnfOFebGpkAFTvI3GP+IjY5euNpK0WD+3nbYxi2rcacSqeN4j6MW+VqOA15u4hYA4m7oNhPSG8+EjPKE2Gylkbiu6TAVKWXcLd00+RxEIVDtAdYoKSG0lpBHUtJwytuO5zWIe4WMKUJeutHgBR2/+4ceuw1XVyK/rtmQ5UaXlaY6d7bqBzEQOXVcncHAmU1wqqzmXGvdtbjIa/fbdMKOMqZrJiTwnHgR7YacUi9eT6Rkccd038WRal9u6vHBvG/ZL28pRe2oYnL3/FaDMbgvXxg2ONqUPzCNwNOTQpsJqSBwGMGEnHoM7ngn3PcRmD47/Nmuh5uvgFANCytRDyEEDUuaYDMhSo6VV/Sq7OLKGR6afidPLD7BY4uPIXURUijunAnJ56oIoQatpu4CW8l5+CSDZyFXIdCazS6kjOnduGmTiOVYoGREAPUwYZa2zCbo1J8TA0VIQRvTgeGYXl0YNaPkWHalxYRYBtlBX0gvjHjRvYMXak/C3e+HYyrm9W0n38bD8w/zA+d/YN96hBiYhoWGYo8icCO16rKTdCyAU5VTfOD2D/CmY2/ijcfSjTNNRK8AlZDVXcdx1fHs9/Ow9Dw5BKLfVYotYfUJ0RrZllU8/+A9PwioVcGf+crPxDffB+fGNwjbT5hGVpDW9BqYtB8HB0/s7PgeNqZyU6pjfVCC0lwc2btf6OvJVWR7JbRuekWWkMLhodNTw41TtwrTsNARlKN1bhhPyEp6XB6bbhhF8YTsolRjzXw12RfXEURaTtFcXSLQK9giV+Wxxcd49NijfPD2D5IPFIsWjitCmioZq+5UkMJJMSH3z92fOp82YkEAzh9Lgie+2k8WsQwTclst3U9pqyl9pgiRSF5cUUEXGy2k2IX4KEnWVuVYkBQqnbBDUNFFiGZC7ggvANDOqX4Wk8aEmJVwm+EwBYkpPop+MfbF2FKeUV23DRNiDN43G92Yrd+ICQFYF7oI0fMWI8faLRNyfYwx3bAgriP40EPHefx2xXJ95tlrhJGMWRDYPhPimCKkv70iZFRCViKbl5Qj5ZnNeQ5TpgihRiTB1QqNYuDGBVXBd3H0AuqKlqePYrAmEVkRclQwq5mQqD9UdNimdAAHwZxe/bvevK66cAJVs5A+yIS4PvyBvw5v/3Njs86dwhSuUFn4U9r70bB03c3+ABOi+4QEsodA8PjM9/HnHv1zuLgIzdoUCiMueHuFaZDitJgQxy9SMEWILigcR1DwtdEbKORq5PTf2h4wpsdyLCEIrMJH6Au5ZxUz22FCQJvTHY/L65eRUqooP9RnRNFATK/+G9dDNdC4jqDVTxoM2UXIoC+kG0ZI4fDbp38C3vXfx9K4clDmLz7xF/n+89+/tf3eBUqBi6MjeoE4nhfYMRMihOCP3P9H+AuP/YXhyUN+CgdBSZvT13vrSG1M73YDWLtEAEgZ4hBpJiRtTG9aRcijxx7l0WOPAsTRuJ7wuH/OasR2gLCLxuaIG5uRYwVuMDHpPIOo5WrguIqBLc6OjOfdS0g9eRL2xFybtG+E6no/Pb0Lo7E2hRpfSLwiOzhOj+vztH41XmB5sa8mSDYTAiB0odNcu0FRqvNCFGrk3Bw//dhP89H7P4qjYzqjSEJnlSFoJqQuKghBqu9KwSuk4qg3iyR3HcHDt6m/+zPLanyUJEXI7dPH4p4fsHmPEINaPlltNwERGy0C2ePDKHP6VtOxIDGvt/ttCjX1mZVoHVf2OdG7AEBHy28mlQkxiyy9qBeb9k00ryOcoe06YScu5OzJrPHZ1dt9pJS8ZsXznhjDhBj/Z0OU1Tm6qr5fZUwvDftDtwjTK2Sp0R1SC/TDiN/UJvQnbp+hmvd57z2qIF5p9vj9V5ZjP4jjCG6b2d444OkixO23hlIWN4ItmTYJWbZsvhxBVwTk/IQJWRU1Qinx2sorZQpB0D279NxlVRczt0KjQsiKkKMDO+p36bn0c2bFzerMPq9Xaq63rscrqjVzgQ4WIVtBXhkNp6IValEXIaBlGbhsOZbpEyKAvNAFS0fJw9aaTdDa3XJpxAXv+olefwMmBDegpKn4VAGkixAkFPNT5PzRTEinq38WTjojXjdWNH+ZQAz7EsZBJ2Sdkg64Huu9dda6axQDl67OE48kaZmZ/rmui5CZkpcyAuYso96gL6TTU7/bhcpBo5gbYEKkjLuo75QJ2RBBGYQTx/TWu3X6ulkhXZeoeRNHM4EefYToUTbeFGNM95LV06JX5Efv/VFckdwA75m558Aa9w2iaLGUG8mxJm0SZCPv5VMryftehOTU5MnpppmQMJKs6Fjb09ucfKSgJ86+46gixEyGBpmQtddir14KRpYCvNBTk/WFanrS7JaU/Em012IDvF+cSm0jTBEi2ZQJqeb9WGJrYKfBbcaEALzpNrVPz6/naefn6UfK9H7Dnef0dCX2cWylR4iBfS5InV44rlEhbM6EbDUdC9Jxv6UpVYQIIu7qP0de3UFo+2qbSWNCBo3p9v3ajuaNZVuaMbEZkVGekDCStHphqgjZlAlxdEyvVnREUnVMLwQ7u4+ZXkJDPXqA33tlJZZavUcXH286Mx1P7D/9zLU4Gev0dGHb91K/oI5rINt0dLjFVlAJKtQCNW5cXFeLE6YISeRYyhNS04zliqNier3uGp7sxc2ZQR1bo+Iw8wfzf8Wv0A8jXry+vrNGqoeMrAg5KijOxJ19h3whZsVt5vbYKDWvJ+jXm9fjk7tmboCDcqytIF/DcwQnw0tqFRrdJ0QjZUz3izGjkRMhQkasd9RAsLSaDGrl0gj9pRAJGzLOE+LlwHEoajlY0/rsnGsMyVCIixChPSEWE2JoaeGktfVRQe+G+hu31CPEQPcKOanlWAAX6xdjTwjAUMNC/fNaX21fLYZxt/BabmM5lvk9cHe2grQXKAUuAjcu3qJINS+EnTMhG8Jxkq7pss+N1g2EULrkWtQn1B17AwSe7BEEUZJ2YozpFoOX9/IcLx/nO89+Z/zYQUbzDsKO/hxVhNhMyCTDnmyOS8baKxiGwOtbx7O1TC+MqDvqXD01vYuiU3tCPFdQlnWiSHKz2U3GZfNdRX3FegxCFyu9CK47aqJ0rJqeyOXKuueQXKeoi5BceSq1jVvUxtVIjo7p1cb0NVFlqjg8ptlpcOPSqGwYczrAq/7t9MKIUHisiCmO1/Jx8bAdX9qognSjfbEXkEYVIdthQkyhZJgQE33+YO9rseylrceOSVsEMKbzRq9BJKNUcWGzr4PFyriYV3sCXG/342Sscs5LPWfDeD5U1/Rk7hBJaO5CjmXLKH/ruRup54wUa6roxz4mz3V4193qnPziS0t88zUtuduuKR0ICuqYuDJUC6zbgDGnX15XqXmDcqyuUOlYxru1rIuQSMJCdC32g4A6toYJafTSRUg1V+Wf/daL/OlPPMUnvpzIzCcFWRFyVCAEzN6pfr5hMSHSWnGbOqO01sAC6gS93tJyLCl3yYTUKOU8puQy056WQRHFiSQpT4hbiAsJxwGfHus6T/ymVYRUymMuerNqNcSEtFPPF/VqVNO6+fiu+VlSyE+rm49wdESvxYQYqZdwUubGqG+KEPX7RqtwQ5i/F0rznMjNxn//xfWLlHLeQBFie0LUz2uaCSlY3dKrQVqONciEmN9z/uEyIQBEJZBKjx4zITtMx9oU+ZpKeYtCLq1finvYzIYd+rrQ9iPFhOR865jpG2tLF695Nx8b9r7v3PdxvHScsl/myRNP7s9+bwGbybHild0J7RFiMGXl2u83E+IWNFPRX08kE61luv0o7k+wKybEL4LrE7gOpUgVCN+8tJrIZE++Kdl2VEyvLlZaufk4nndQjlXS/U5KskFJy7FMYWJQyBfpC2+8J0TLsVS39OGJ4unKac5Pn8d3fB6c39wTNVfOcdusOm6/Ld5IN4Sv+Q8ihcPxWoHHFh8DiP/fCopecWjRZ6MxeCM5lpRyW54QmwkRxZm4Qe8D/a/jOQ5h9WTSW2pCI3ojIlr9VmqBwy4uBlO0xnXdtqVAa61e3CNkHAsCCRMyWIQ0RREpnB0XIXfOl+Pz+Wd/60X+/198BSkVK/JF3RvknefnU8yfYUX6oYyjfbdrSgfIF60Gj/Ux/XnG4FRFeamMJ8QsFntSkpPQRY0phrV51T1NTzqEkeQ727+W+g58V+DqZM92X0noYiYkqPDNy+rnb18d08j0CGMynY+3KmbPwcWvwM0XVfEhBDSXwJisp88ouQq6VwhK+3mjdSPplg47ZkKqeY9SrsRFtwf0kY5Hu9+m6BfjCZPv+CpLX0/CXaHM6etajrVSX9f5L1Atj7noYyZk0BPSTT1f0DeNZphM6l1HMyFSUMzVVIEhHDqE6Yje/mg5VhQWwSNeBdtWxG1QhB/6RYoyYuazf4ab7ZtcWr/EWxZdOjr9KopI9iOK4r+xrpkQz++gexxSy9XoRBsUIZoJ2W62+l6ipFe3nKhMJBvaE1JRRrnt5K1vB/ka5boqQq43r8erlPNhk76eJPhajuV7FkWuV/WargP9tPSpHJT5B+/4ByDYOvO1D8i5OVzhEsowJZkw2M7K7lGGzX7YBcl+wNcMQSFq0ez0KOV8aK/QDVXTvqmin1pV3DaEgHyNILzBgm40+szzL/AO8/2deRJe+bz6ee0i8Kb061cuALAcnMBY0QZN8pXpOW4COdkhkD3VCLY0ldqmlPNoihJ5uTZchIQ9aK+pRoVehZniMJMmhOB/fPJ/jMf0reBNt03zylKT/7hyhvAN/wu/8vQKU0WfQuDywTs+yLtPv3vL72X2YSo3Ffdogo2ZEJsRHCxCulE3lnRtyZhu0rH6bSjM4DmCLlCO6rhOgc7C3dD6JqAX2iYIgz1AtsyEjOm6bfuJ1iwm5PjU+CIkHxvTkzRFgLrYWbd0g0Lg8re/9wH+6ief5nq9wy9+6RWW1jvcNluMI6nfe2869fOO+TK3z5VSndK3Hc+LKkKMAKzZ2N4E3/hCrreu0+6342NdjhwEgsjN4TiCkjagr1HjwuJ3Mf38v+OB/tfo954DlI9LCEHRrbCCKr5X2itxH5hqUI0bIa9bfV0mBRkTcpRgfCGdekLr2+ZHmwkJB7R/pkcI7IwJ0ZIDVwhKCECAcOMVlVS3dIjZCkcIcnRjOdZqXV2oriPwgzH7YYzDmzEhmj5vRsnNRzjqNZ50cBw3ZkK6yFRR0zXvZcmx+mEEA56QbffZcH3wcvEAc3n9MkXfpSPU35SSY4WduKnYqi5CXC+ZeFaDatwnRO3z0fOEGN+HKytEUklBXFnecaPCLaEwRVk35IyI4iJksb8e33R8CZ60ihApY2N6S7Mfgzp13/UPtQABfTPxDa0+3hMy6UXIQcqxgpJ5f8na6opatAl7iglxyrtjQQzyUwjgzoq6yb/28rOJWHXh3kRKO5iQJWU8hl9z1USpkveG4kpr0/MxOyuI1Cr9gOG0nPNoiqJa6BhMVtNpYMqYXk3F89pwhLOtosFIsjr9iN94sQFCpFbDt/NeBvb5sFGPENhYjrXdOOu4T0jYhlwV15Jteo6gNZf4MidVjgWK5bDHFrtAGWRCTLEy2GvCXoWvt3u8FjMh4+cWiRyrnDJx1/UK/k6ZEFBM5j/4/gc5qwuJ//zNq/yL334JgDvnSyMLjPfem2bYbp/ffhFSKCfHpLm+PSbENqdfblxOUkxN3L0+H4UQMdPz1bnvpq6b8j56+RdTHrOS9vZEUnK5cTmWdefdUlx8rLezIiTDbmDkWJD4Qmzz49RtcREy1x+I8U0VITthQqbiH0tSqI7HgqEiJJ7Y6ULH0TG9xpheX1eDm+eKdByvjbGekE7q+bgIsSOLhdrG191Fc14OhKAjSPcJMQWJVYR0wyjWVRpPyE77bJgB5uL6RTzXwfECJI4qQszf1UuSvJb7apARbvI3D6ZjdcO08a2nmZDcoTIh6mbk6IaFkVSsyL5JsUCxcjiqGRxqIdp1BCfCVfrGExKBTx/X1ces14oDEUygwmGZzzdDSSfbjZJj3SpFyHQumVjutxzL9k406jfjHiHdMGJdVHaXjGWgzemnC2pBpNS8qBYNhIDa6SQ0ZLBXSGs5Zi0u6XjeQT8IgFecimWHoM538unirZhzaYgikZRE7YF0LJ0GFmpj+nRpb4rt+45Xyekxqq4nOBtNRLcC+3zYqEcIbMyEdOxI9230CemEHXAcwkJyjrqOoDOT9EOZtOvPZjvWe+txkSEQaUbYT7wjML7XhO37eHW5RUubsk9swITYxnSbCVmTuy9CAGbLOf7uH3yAB09pb5T+jEEWxOBd5xditn6uHMSJX9tBsZQsBLQaOy9CLtUvJfIpXVdI6xwzMb3XuwGfCr4LgOnmBXj+0/E2JU/ti9+rc/HX/gLozumEyfXY6GZFSIbdYOq2xORofCGmCCnNKzmQLkKmu+10HwEpqbILJsS64RURccdjM1Ey0pGCeW9dYLg6ptdU4vV1tZ3nOOk4XhubeUJcw4ToyZpMLiyJYUJc2r0wxYRE3WEmxHVcPG0i7/SiuFmhURLttOO4GWCW28s0e02KOY+uCNTAaJgQXYREUtKWegB01HNlv4zv+GMjeqWUiTH9UNOx1I3DjxaIpMQXFRwK+2NKN8hrJiRKijLfcTkZLilPiF/EkxJHRvhanmfLU5p6jdo2gR8lDMZk2thOB+ijjNuqtwGqF45JidkvFCqJd6K5tgztFSKp9OB1UeH0zB4Uo3p8nA/aOAKOhVfVDb+8qJjdmp5w2B3UIRWv/mKok7FG9SvJT+G7ibxRFSFpJqSU82gJ0x1+oAhp6kaFEm1M35vzJ/AcHjiV/v42mohuBXYRstki0EZMSNuS6W4rHUvfd0yvENcRuLkibasB41FdwBgHOwGr0W3EcquSX0oVF/bYYxvYB2NeXUfEY/yzV5LJ92J1/HHJj/CESGBVr+zvtFmhjVLO4699+H7ecX5Of6bDO86PPodqRZ+33aW8Vg+dntrR5/n5cizd7rbWN9k6jZn8TFwcX1q/lDAheiEtVYTo6/XySov/6j/JdXdBjQFf+uexr7TqV6hFqxS7N7jYXVVFiIR+P3kfE6k8Scg8IUcJjgszd8D1byVMiJFj6W7ZxhPi9BrMFk5ytallWzKiJo0nZOdyLNBMiJ64m4mSKUYSOVZiTA/oclUXIY3GNpiQoT4hhgnRRUhgFSHaI9PXUYqe9Ki3+2qypgfZbr+BuT129Q3Llt90wwiBj5BBLH3YMRMy0IzIJGRFUTMxppsiJCI2rsfd0rUJMGclX9lyrF4ojZLrcOVYmvHIh+f5g2fP8LULHs/h7E88r0GcjhXG33vBLTITXaQfuUQn3oj3nDIA+7rjcaoI0YxIYSfXwQHArEyO8oTEEb0TFhE6iEcWHuHPP/rnWSwt4jr7m+5Wrk5hpknt9RVo9eICft0pc2oPmZCgu8a5YxUWnr9KsxsyM6WKrZgJqeuYXsNqWEz2t9qqWFoYwYSQr2nvlyq8PWe0HOuyLkLC1qAc62YsVVxzqkzvURECKvL0KxeW498Xd8uEWKz7ZotAG3pCrN+306ywL/v0oh5z84sUVp4n8BzEwn20o+2931GCzYTUe/WYCbGlWJD4PiSSZq+ZMjcPopL3aXZDnruaTL43KkBdRxB4DutROY6KlTqeF9hxn5BB+K7DT/2Bu3nbXfMsVHMp/8og/tR7zvHmO2Z55Lbxkr8NERRVM9FQ0m1tzxMihOBk+SQvrL7A5XVLjmUUD9Yirel1dXG5RSRcPpn/CA86Pw+N6/C1X4KHf4QPLf8SrzhrSEdw0QnV/THs0uvlAfUdhZGk04/27FgfBDIm5KjB6FKXXlD/28lYEDMhdNbTqSIyohYzITszphsU0XIskolSzISYFaIBY3q3H9HphzRaW2FCNvOEqOeNBrKPpKf7fvRiJsSj3u6pG4suQjp2x3Rj8LWKEOOxcGQJIcT2eoQM4FQ56SJ8af2SalgogrQnREvCQinpki5CjC7aLjB6ls+nZ8X1BocoxzJMiMDhTPEh6KvBfF+ZkMJUIi3UBcWUTrwKI0lr8U0E+lC5pgixjJgtrZU9qkzIRp6QWyWi1xEOjy0+xunK6X3/rGJlOl5U6DZW42QsgLqobLtB2UiYRZr2Km+8bZpj0VXavZBuVf99hgnpd1SYiIHpGl2Y4WpbjanHqqOYkFoqgGKUHKsUeDS0yTcajOjVPUJ6wqdDbqQxfad448AE7sQGCUlbgc2EbJZO6AgnXkjayBOyHWM6KDZEFGcp5zw1vh67XxnWR2w7CfBdPz4GjV4jKUKCdBEyKNsa1S3doKp9Ic2umjQXfHfDCb/ZRjEh6vdIShq6CNmtHMuG4wjecucsd26SeJX3Xd5+bn7nwRR+KV5P6LeHx+vNYBYrX62/mhxrXYRIb1iOZWRvT3v30114WD351V+EX/s49639DuUI+sLjotDzg36Ldid97k+aOX1XsxshxIeEEM8KIZ4TQnxsxPOzQohfEUJ8SwjxTSHEnaPeJ4MFY06vv6aaXxnd3/RZ9X9OX3T9NnO5RIbgI2IWYKfNCg2KEMuxhozp/iATIgh0fsSl5RaeXk3yXQHemBvhpp4QzYTkLIpZp6n0pPosV/qsDTIhZvIvZcyEpCMeQ/3aIg7b7BEygGpQjVeZLq1fohC4dMhpOZZmePT/YSRjJqQbrcevBwbkWIn8yGZFDlWOZd04Gt0+DT3A7S8TMqWYEFC9F4Bp/R33Q8lK7X48qfcr0t+5zYRE6hw5qpIK4wm5lY3pBwmRq8SSiV5zRRUhYURH5AhyeaZHxNVuG6Yg6DV5fCGiHK0jJbzU18lO1WRRIuULWb4AQLOUPL9QGTHBdVycQjIRdB1nrDEdQA4VIUtxt3SEYGqPPCGgOmQvWoXH4h4WIVvpV2JH69qwf98Kc2hfU+2wnYQJABx7Q6oIOapjx0YwBUa9W4/lWINMyGCK1kZMSHWg4Fis5WMv5TjkfZdIuHQc3VdKd0uHvZFjHThcH6FVIeFOihATYGOM5FJSidQxFFYRMlTcCUH7TX9Cec56Lbj4FRxHIGWVJW+RtmaXg36X+oCgpPF6KUKEEB7wM8B7gEeAnxZCDC4r/2Pgl6SU9wCPAld2+nmvG8yeS35+4TeSnw3tb03MF+xIPTeH2I0nxC+o5CfAQZA3q7X9McZ0Kx3Ll6rwuLDUINA/e47YgidkXLNCbUy3Vmea7RUAOvr9XRlQb/e0J0Q3mopTqXp0pGn0Z914NBPiyipCCBZLixsdkQ0hhIibEV1avxT3ClHG9IEiRCZFSCtMa3BdR8TmObvwsBsXHmYR4rlObExtdsJ4VexAPCEQ+0JmhVpaC6XgUlijp29soWlQ10kkAy1dqO4kuecgYCYLo4zptwoTcqBwfSI9LvVba9Ba0T1CKpyeKW46cdoSrEWaO/ovxIltv1/XxUn1RLKtXYRoT8hKLnl+JBMCBFYkr5OvJJIujWLOjYsQ+p00k7z8ku4RUsN1BOU9Do4wjeCmiv7YZnVbhc0+b2UMToJFBozpO0zHil9b1oZm4cDCvWmPyQQuAhhfSLPXjBc4bOYDhlO04oZ3GzAhBhvF8xqYQqPl6L4lkpi920sm5MAgBJFm1MPO9jwhkDanAyAjSpomEtb8aFRfn8Lxu+H8++PfL86+lRfdhwkR8Ryv2uvEfVAM6hOWkLWbkepx4Gkp5SUAIcSvAu8DflH/XgMelVL+KICUcnvtJl+vmLlDTaqlhBd+PXk89oQkhceclWVeMw35XD8uJrYFIdSNtqEYh1JQpR21EznWGE+I64i48HjpRjNmRTzXSUz2gxgrx0ozIYWcXYQoTXJbm9QdGVBv91nQxnSweoP0mnSFaTqVXOhmYl/uvYU3LSzy39z93ZsclI1xsnySby9/m0v1S5z1Xboip7SwA0xIFEm6+vtp9lURYsdU5lyHVhSmihAjHYPDLUJANSzs9LtpJmTf07GMHEsVIXP6e192pnh5uUeHCrBKGOljrZmQSAhaerJyVOVYcRGykSdkwuQgh42+V8btNpVMqeXG3dL3JBkLUtIo5+rXKQYu9Xaf375R5A9JichX1QJRp57E9HYb8Xh6w00SfEYyIUChOsts+WXCSFKuzgw9bzMhccNCLwf1K3D5KfpRxLe8e5kq+nvew+cH3nSKtVaPt921OXOxGW6v3s5H7vwIEsldU3dtuv1YJqS/czlWu9+GO98D156BY/dBvhozIQIxkYsAJe2hTHlCgvFMyFp3LWZMxnlCbJzYgheooBvrNtwysIKUcs89IQcN6ReBFaLu9pkQW7at3kxSDgUgEf7GRUgp58Gb/4RSA8zcwTebT8A3/plK7fUK0KlT6bWpr62kXjdpTMhuZhInADuP8BJgl323AzeEEL8A3Ad8FvhpKWXqCGkZ18cAbr/99l3szi2CoKhMjqsXE19IvppQxzYT4iQncc0kZe3GjJuvJUVIfoql5pW4+BiSY7k+CAdHRATaLH7hRoM52QUBrp8wFEPwx8mxBpgQKz++qSMp27KnWpiQY63Vi9OxwLop9VqYtQGbCTGTfD86xk888MGRUZnbgVnluNa8xj2VyJJjGWO6OnZhJOk4OQo5SStUx9FeeQo8h1YvTPlAukfEEwJQzrksN2C11Yt9K/ttTPcR5BB0NBMyF6ljuuTM8eKNBm3U8euZ1UvdN6ETFOMGZkdVUmEM8+2wTT/qx+ltkYzoaSnZJE6CDhNhUMZtXkO060RNSS+MWPfKnJreo3PACu7gta9SDDxe6xZ4ed3j0kpLmd9rp9Skdk0nZFnJWBcZ3yMkRn4q8XIM+EEAcp5D28hcTK+Q0hw8958BNc58OXiMuT30gxjMlnP8he+6Z0/eSwjBD9/7w1vefitMyFbSsWzJVifsKGnzuz4+9H45N7dhbPBRhWFCGr1GzIQMyrFsZuRK40o8Vo4uQgaYkC3I8OKu6bo3SCShoVmRiZRjAejiTlrpm1vFQnEBz/Hoa1mxYkLUj45VhAzKsfK+oxYfvRq85y+rx77yKq7UyWOGCZECd+VbQOJ0eF15QjaBh2JL/gGqhew88McGN5JS/pyU8s1SyjcvLGxsUnvdYHZgdciY0iFVhMw7yc2mir7AdyNBMTc+N6CkdbvNXpNe2IsvonhiJ5TcyrGYkAtLDXx6uEKkLrAhbJEJKVo34mZHraq0ZF+1UpQ56u1+qgjpRj3VObjXpKMH1yBlRkwm9rk9YBdOVdQqR0RETyzriF7bmK4myD08IuFSyCU3UZsJMUyHvX/dPd7X3aCoWY8b68n3ta/NCr0A/GIqpndOr+xdd+d56cY6TVEDob5z2W2CXtFrWit/R7UIMZ4QSEuyUpMqZ/LkIIcKzZqKbp12fSlO5dmTRoWQkmOx/BLFwOWqo+5Xv/uyTo4yCVmGCbEazb6ovSMbLnzYn5EflscIIeLxP5IS2muKMX/21wB4OX8Py87MnsXzHhVs5glxcNJx9Zu8D5DyfxiYhbZJZSFNgbHWWRtbhHiOFxdjrzVeix8fJccaYkKmNh9PTVd04wOJLCZkIuVYgAjUGOL1m6rh8TbgOi7HS8etRyRFbf10ArsISV+zo4z0hcDFkSUkxtQuqCCYrj+X2u71VIRcJs18nNSPGVwCXpJSPiWljIBfAR7exee9fjBYhEzbRUgyqExHMh58d9UjxMBM+ssLcTxuo9dITZRSEzsvp9KxtARrpdkjkF2V8rIRPW5WrcJuqiMo5iYTMyGJdrjZrdMO20Rme5mn3u6ljOlxw8JeC3O7CqzjkZ7Y735AtPWebXk99oTIXiILA+igBphcLrnx2b0TTH+AzhH0hACU9I3ler1jPbbP6d75GhUrprfWWUcIuOHM8dpqm6aYRpHa0Ft5JZZjNYPk+z7qnhBIS7K2GzmaIYHQk3OvV6e7rtKpVLf0PSpEbZO4lHiOoFdRPr3ff2VFPW4SstYuq+LAJBvmKrzcVN/nyB4hBjb7MYIJUY+r/YjlWFe+HntQvpJ7HGBvjPhHCOZa6EZpJsR4OHJebku+n5QcKxwuQsxjkxqPbaRX11vXY4ZjUI4FCetxeT2Zro0uQtJj/FYCCUyhsaaZkL7w6Ykg9dykwc2p+0hetnc0wbfnCWU3Z5aLN2RCRvmuioEbN1uWEvDzVKTgZOfF1HavpyLkS8AbhBAnhRBl4P3AfzJPSilfA64JIYzG6l3AM7v4vNcP5s6lf586m/xs0aZOt8G7b3s3Zb/M496UenA3E6+7vkNRj3e/P9bTN3qNeIUIBnT2Xh4BlJzkpPfpaVP6RkWINZiZFakoVCwGxK/N52vxCdrsrtHqrgNSq7wKrMVMiJ7Eo7uV91r0hGFCkv2106f2YmI/V5gjMF6P6BodkUNKkLEnRP1tLVOEBMkkPuUJ0QVRSo51RNKxQHlCIF2E7KsxHaAwRcUwIWGPmlT+oxvOPFJCnUQz31m5EBvTW35y3h1ZJsQqQuyErO0abTMkcArqesr364S6kV/TrXJsjP9i23C9FAsNUD2uJBBfu7iixhaTkNVrqk7pmgmRU7fxyrIqNjeUh9mSr9zwpBDA1X9nFOki5NuKBcEv8kX5BgCmSrfWuTNOjmV+32rRkDKmD7Lw1mNbkXYdRRjWw0g67cdsmPHnSiPJCRpsVgjpdKzAc7YU+2wKja/5D0OuwuXF98TPHTajv1N4eXXd52mz1tpdEVJ1C3F8sWstmAWek7qnlvPDi3x5PylCIgl4BUoRnAkvIGQyX1ifMGP6js8K7e34KeAzwFPAP5RSLgkhPiWEMFEgfxb4N0KIrwNV4Gd3ub+vD8wMJBmbZCxQN0Ozut+p87EHPsbPvu9nOacnurtiQs6+Ff7If4BHfjRlnt2ICQEou8mg58ueblS4BTkWJEWIfVPQz4ugRFGnJDW76zRbpmGWQFJkrZ32hHRBTQB6zZgJyVkTPjOxdxwRp9vsBo5wOF5WVGs9vE5H9wKJuumO6S2pvhvPS4q5QU+IvX+DPx+2J8QwIXYfk331hEDChER9iHpUEHiOw3VHyVqaYipe/eysvhozIZNQhNgMjc2EbLfvQYYEno63nY2W4qz9fHVubw3aA+zEydvvBdR18Y1La8MJWZoJqedPxMl8t89t0NdgC0yIny8hcdQkpHENXvgMAP2z72S5q67TW5UJGSfH2mrBnuoTEg4XIcavd1THjc2wUcFhwzAhNhs0MqLXmggv1vJbupaM7+NFcRo++km+cfaPqsd9d8/DEg4Kfl4d15zssNbubbL1MGxzes3Nxx3NvSB9ntnm9MqI+2sx8HBkInPDL1CMBDnZYTG6Es9pJs2YvqvZjZTyk1LK81LKu6SU/1w/9gEp5WX981eklG+UUj4gpfzDUsrhKz/DMEpz6ZuQLceCuGu6mXg5wkl8CLvtEq1jIe0Y0ZbV2TwlcTFNBS0mJJDdjRsVWq8DkuLD1uialSjHpSjUoNbsrdPSMb0CCClSbytTr6MlaW2kYh96rcQT4ttMiJoI7OWKjBlgVrtX4hjeqNdRMjP9nTQidZMUrjqOrnBTN4eRnpBwb1mb3aA4QnpV2m8mxMT06q6wVSnwHMGSLkKkyBHp7727djFuVti0kuGOajqWvV8mAhvSq7MZE7I9+DreVhDR1kVIZWrjbtzbhu3ZAM7cdW+88vv7ryyni5DlC0qWBVxxkmSs2+eHJ4Uj33+EJwSglPNpOEXFhDz7a/EYs3ZbsuK8l40KjwLMtTAuHWurTIjd+NBm93f6fkcNG0mvbAwWJjk3N3LRw5YEbbVBpUnAavVCcJw40j3nTyYLAhAU1PHKy/aOihAT5Q9QdpRaAsDLpY+pLcka6QnxXdyYCdFFiJ4mnO2/xEnNsq53wqHXHmVM7plxK0OIxBfi5aE0YNg3soCulVttCoU90sGbYsPOHIfRTEhhUI7lbkeOpfd7BBOi3jvp3N7srKgHBUSySL3dQwhBoD+rK4iZEDNUBNbA3N2PIkSb05e71+gINYhEUv9durBaj9Tjpgip5qopDbNhOo6qHKs0woRePDAmRMmxqgh6+Vn6+hgL6SN1wdGuX07kWNbkfRI8ISk5VmQ1X5tQc+xhIV+ein82N/na7B4HndgLQ34Rv7LAA6fUY7/78rJKMDTn3KtfBC2ReLGvvG153+H4hsZ06/3HyLFKOY+WKCpPSF0bi6snuFG+O97mVjOmG8nruHSs7RTs5roaxYTYHpNJxFaZkMHtRhUqkPaEHN9CPC8kcqxuPyKMZMxK7rt8dx+RK2o5lmzvqAfHifKJuIdbRSSFhhek70/2dTtKjlUYlGM5Lq6rEkRvjy5wcsoUIdsvlA4TWRFyVHHqMfX/8YeGmlbFRYjVJTopQvaGSjYJPhERNzs348cHPSEARWdAjrWpJ8R6biQTktyoi8L012jR6iitt2JCSjQ6IWEk414gHWRiTBcSSAoU2J8i5HTlNAARfZYd9beohKwW9JpEJHKsUKgJ56AJMDdCjtU5QnKsUUxIcb9NhoUpbo9ciEJO4eEj6FkrSgIfaSYn9dcSOZbr6efFkV3RzLk53Jjhy4zpewG7CDFYmD82vOGuPmSAnRaCR25Tn3txucVKq5eY0y9+Jd70my3lXzo7W9pYklI7pYoYexFqAKWcR0PomE6D89/JcjOZHE3vYbf0o4DN5FjbKdjNmDBSjmUa8rqTKcfaSsEBw4zJKFM6KFbDTGzvPzF6m0HY5vN2L6StmZBJ7REC4BcqCKHkWPXW9if4gRtwblr5fG/zrECaXPo8s5mQUcb0QuAi8BBS9yMDWnlluT4vX44lXI0JY0L2eTkzw47xwA/A/N0wd374uZFFiJFj7S0TArDUWop/TjEhOt2hIKwihJ5qVLhdY3qKCUleW3QDCKHZb9Oy/t4QNZCqrunq/brobuW9pvrZcVOTOZM4tZfMwrkpNbg4QrDkq14VccPCXlv1CNEyrT6qCLGTsez9GeUJ8V2xNx2fd4FBJuRA9L35Go9Kj7/eK7Lo5IGuikDVxIGQuilnCJ3WMkjNmOn9KniFQz9u4yCEoOgVqffqKSbEjg3N5FjbQ3GguZ/E4fixPS5CbOO4jk2/73gyOXv2Sp0nqifhxnPJopAb8PXVPNDnjvkN/CCgkg+//39Tr506PXKTsu6aHtnzjHPfyfIrSQE7Vbi1zp1xxnTDXGznWjH3g1ERvRNvTB8oLgpeAdcZnvxvlQkB+Lvf9wCvrba5Z3H8NqnPtBiPVi+8JZgQ4RdxhCCUkmazvvkLRuAvPPYXeLX+Kse//WVuAKHwyPnp6fdmcixzDFWvEDXfu+nexR18iQV5gxlXPVZ/PXlCMuwjXA9OvjEVyRtjQznWHjEh1qrKjdYN9bFuLj2o6WIiR1KEBHQVE7KViF7YnAnR79MM2zS7apLvCEFPRwDW2/3Y99EG6LUIuw36AMJJ3aA6ekDci3heg6n8FAuFBRwBK94KoKlSw4REkq4ODehKXYTk0kWI747yhOx9wbRTDMbxjpJn7TnyUwgEd0uPWlt97+50YvAT+GAmJyJZFW6KpAg5yojljv2MCdkL+IVqqjBuOCVOTG/gv9gJbM+G9umdmS2R13r3Z15bU2yGhW7lFDcaalJw+9wW9qd6AmbvHPu0kmMVVBQ4wImHoXqc5aYag/O+M7lN4cZgHBNirpftXCuGNdkoondSr72h4sIfXTgMbjeOCQElEbr3eHXLCzo249HqJkXIXt5zDxxBMTZ9txvrm2w8GpWgwn2z9xF11TnWxSc3wA7ZxvTqGDkWgCOLRFLi4PCqUIyp5wiOt1VU7/oOfCuHicOf4WTYPgaM6coEvT+eEIDrTdVFfcjoq4uJnMWElBzVTHDLTIjZ73FMiN62GXZoavNxHugKNclca/cSOZZQxvRu1yyXO6mmb2aSv9fypvMz53GEYM1VjFHcsLDXIpQJE9KN1AA2OOjHTMgIT0hwBAbvwSSsffeDwMh0oMJskhInpIfjqSKkjVWE6P+Pqh/EwBT54yJ6J3UidGjIVXGtiVI/qO19AW+fk5oJcR3BuWNqsvfs1XrSsFBjOUgald2xkSl9iygFSo4FulfI+ferz2mqCfmt5geB5FroRT0iK4rUsBnbKkI2kGOZ9zvqCxjjkHNzeE4yNpeC0efbIGOyEROyXQzKsVpd9X1NMhOCX4qLkM4gE7J6Cf7z/wAv/uaW3irUXdd7IhiShaeYkBFFiOMIcp6DI4tIKSkFJZ7rH6Mrcriu4Fj7BfXeoUypKo46siJkEhHLsXRVbrMIe+wJAbjRVkzI0OCsf89ZoWclV1OBG+l07W7qmzIhugiJurT0hK0oBV3tFam3+6oIEU4c0dvV3bURzkA2/P6wC+emz4GAjrtOQ0hVhPTb0G8TaTmWRNLsqwFskAkZ5QmJixD38CVFgzeQfU/GgpFFSGnBKkLwcTwfENgijZZQx+2oTyTsCGwDMzFyhZuaTGTYAvLVVOy2KE7v/WdUFpOfZ+6If7xXS1W+fXWdfvl46iWXUAldjoDb9qB7eynnseJMARC6Bbj9HQC8eF2NjXPlW68ISbHZVvGw3T4hkEitBuVYkYzi957UBQAhRIrlGOUHgWHvyEZMyHYxKMcySXWT7AkhKMULHJ3WABPyjX8DL30Ovvi/bumtop5hQoaLkEUrtGKuPPocLAQurpwikjBfmGepGfKKexueI5ipfzvebpIaFmZ3ukmEKUJ6TQj7CZsA+yLHWtHRuMNFiLpQfJkwIXFSlrfBzXA7nhC9H62oR9MUITj0UKsG9bhhoRMb0zsWE+Jbka37VYScnz4ff96FQHIqAroN6DUJI0lX5JB0QE+QB4uQjTwhR4HGHmJC9rtbOowuQuZuw3dv0gslAg/XccD16PQTJqSFXnk7ovG8BnYzUIOdpP1k0PByyiOkJz1+aR+KkMUH4bGPQXEWqkmxcY/2hXT7Ea/0p7nDesmLPRUpfXK6sCcTsVLO5Yv+m6lE67z/iQ9wOihycbnJs1fUAsfjt89s8g6Th7Svrxvfh3aUjjWGCemG3bjL+CQn05X9Mis6RXJcETIo09ovJsSWY002E1KIFzj67QEmZOUV9f/6NRXLt4lsLS5CxLAc666FMj/2trPkPJcTU6PncQXfpdx6nNuKVf7Que/i//3UEhe8s7zVeZWg/gKODImEy3q7z8yENC3NmJBJhN25t1tPTOmwZ3Isu+Awg/OQxEUP1r5M1qILYgtMiOPFDQaTImQME6Ina30ZsqrlWCXHQ7jqAl5r9dRNyBQh/RbdniXHsm9g+5COBXCmcoacm0M4Dq/4mgnp1CEKlRyLHK7fjsencZ4QW47VO0KekCEm5CA8IbkK2P6j4gwiKDFTUt+nKxzyXg7cgI417jcjdf4ddTmW2b9RRcikrsQeNkJrMlWo7XGPEFAphW/8w3DPB1IP322Zdr+x4qcWUb7enAK26AfZAso5j5ZT5N8XPszS9MMA/Ma3rqndE/Cu83scS3wEYBcZtm9qJ+lY44zpdlEyqelYkJZajeobAsMyrT1lQvw0E9KK+4RMcBESJKl2YbuZfm7tkvo/6qeDgsbAeEJGybGEEHzvI6f4wAPHR70UMExIhfP5DzDtK2XABfd2PEfgyS4nQrU/k8SEHP4MJ8P2YQ8unfV9YUJcxx2iucd5Qlx6CBmBlORMEbLRREpYHdUHmRAh1Iqm+Uzzt0rJkjamFxw/Nm7ZXdM7Aui16JreIwPGdDPJ32t2wXVc7py6E6GZkEhKaKlYY5WOFZDPJTe9jSJ6TTfV/fKv7ASDzMeBMCFCpNkQ3QhuVq/uVAu++t4dP25MiRC0dBEyMXKsEcb0rAjZGaRVhFSm96EIGYNq3o+jTL91JfGFRMLhG3U1ft2xUaf0bcBmJZudPlEk4yLkjWemmZ6Q1c/tICWp1cWClHJHRfu4PiF288JJTceCtIJhVGQvjDCmj+lJsxPkg+R+1bbTsSa5CPGLsRwr7K7H92jCHtSvJNs1l0a8OA3ZHy/H2grMgmCrF3JjXd0vLrhncV2BI+Bs+DIAjW5WhGTYT9hMSGd/mBAYHsSGJna64HGFUKlY9FUyFmxsTIfEFzLoCfHyKUqzaAZMGXFDez2KbkA1PyjHEiqWt9ei2xtThPTVgLgf7MLd03fjOIJXfOhJCU1ThEBH5MgFyU1vnCcElKkMkiLE9w7fE+I6Ik4AggPyhMBAEaJSh2bLVhFimBBThARlWqHO+p+UImSEJyQrQnYIq8v49F43KtwE92pJ1rNX6nGvkGZuga6WjW7YKX0bsJPq1jt9vnpxhSU9GfmOe/c4kviIYFCOBcqkbhj67Vwv45gQ+/dJlmPZUqtxcqzADeLO8bC3cqzAdTBTgLV2n1D3s5h4T4j+o3JRh4Zmd6hfiRuSAvHC40aQPcOE+DtaDLU70t9sqGuh6ZRwpk7jOIIz4QUA1nfQVPGwkBUhkwg7trdT3xcmBIaLkGE5lvaEuA5vPVvmvvkgWSXfbCAfYkJMEZK+oRRMwSUjGjpCseAEcTfXusWEmIjejsWEjDKm77UcC5Q53RGCjhC86kRxERJpOZbnj2dCfIvt6OhCab9Ym53CZj8OJB0L0pGoemL3nfcvcmIqz4cfOqG+W9dPjOlBOZ7UH3lPiImVDtv0NXuTeUJ2h4W5OQLPYarok6/MHehnG0nWtXqH1ds/CLVTPH/8Q/Hzd+yRHCvvJ5O8RrfPrz+jWJByzuOxs7eeHwRGG9PtiN2ddEwfjOi1mZGj2uR0K7Dv0eOKEEhLtfZSjiWEiCfKy41EOjfRnhDHReh5SV62qJsI3LXL6e2ay5u+lWFCeiLA30HojJG7NbthfHx9V+BNncQVgkqkJGGZHCvD/sKmT7vrh8eE6AFdAH/2XWf4W999Lr5BbsqEmOd7A3KsgVWtoj1ASk3tunmrCEmM6TETYm4wQhA4dp+Q/fNZmCJEInjJjVJyrK7I4XjqO8q7+aGbZjCCCentk4l+p7CbJ41qpLQvSDEhqgh56PQU/+wPP8p33r+ojqMbqGhmIArK8eTiqDMhdpFkJFkZE7I7lCoznJkpMl/OpRsLHgDspoVPi3Pwg7/AF3JPAjBdCvYsOlcIEUuyrq11+PyLSgLyzrvnj8xYsdcYVYTY3pDtFA1m28G4X1uONdFMiMVqjPOEQFKgODhjZVs7hUnIutm0+h75k31uipwar3Oyw1pLT/CNH8RgC0yImeeETm5HzXRNMdfuhSzpImSmlEPkawigItR5nBUhGfYXwcEwIYPMx7giBNCRtJ3Rz43CFpmQor0aHuv981QsOVZsTBdAd52uWdUa4wnZj5t1NahS8+eROLzgR5YcS9IVAY6rvqNBKRakfR/GPB/LsY6AJwTSK1kHtqplTyQH+i8AQ0xIO5ecr0fdmG7f+A17k3lCdglbplrYh3SsDXBquhBfF998TXnXXtKxuXvFghiYIuSzz16Px4v33nPrGdINRsmxbPnUTjqmD77HrcKEbMUTAkkRUg7KOGJv7zGFkUzIZAexujl1LPO0LSZkoAhpbl6ECH3ORTtku/MWE3Kzoc7ZuXIQj32mCGlkRUiGfYUXJJN1uwixDd97gEFJy7AxfaDfR6oI2eQii4uQTvr/gf0v5aaSX6wixDam513VJ6SHJGrdJN4LqwiRUu5bOpbB8eIdRAgueIkxPdLNCvtCTUxGFiHecBGy3/u6XdiG2MHI3n3DCGO6DeMF6mh/UdPqP3PUmRB7gmASsjImZJewixB78eIA4DiCe7Qk69krdaJI8tIN9b3uVTKWgWEizWrnbTNF7lrYG+P7UcQoJiRVNGwnHcta5LIlWbeiJ2Rcx3RIipC99IMYmCLkZiOJ7s9POBPiagl8XrapG7/F6vaZEBGOVnxsFaaYa3VtJiSI1TFl3a63nnlCMuw7zA3XlmN5hU1zqreDrcqxgLg538jnRmGLTEguX0tO0kjLsbxSzISstftKcqVXczrtVSXLgpQnxI6/3a+J/anSHUgcbrqSm6GyS4dSUnfqvNZ+BoAz1TNDr0sVIaH6G3vh0SpCDoUJWXxQ/T9/T8p0bGC+205J6f9bs0mHhqPuCbGbgRomJCtCdonFB9T/s3ftKSO8Vdy9qM7R56+vc3G5FScD7XURMhiR/Z57FnYk7ZgUjGJC7CJkO9eLfQ/rWItmJtBiu+931HDX9F24wqUW1DhWGh9UcPfM3QDcO3Pvnu9DXt8fli05VtGfbCbEz5dBaDlWzIRcTG+0FSbEpLvtMIGtoNPH2pYxXRUhaj5YoI0jw4mSY032mfF6RlCGxg2dTa1vQHt8492qMR1QTIYTJr9vRjea127ChAi/SFEK1kXSkK7ol+gXVBESRRIpvbj46iJjeY7n+DHVbDcC3C+z99nKOX4LARKeFSGPRerna7kvUHXAd3y+567vGXqdXWgYGdZ+Ssd2ghQTclDU+qlH4Yd+UTWHGwGzQtqZvxve+5M0oxZc/Qxw9OVY9v4ZT4iZYGXG9B3ixMPwQ5+A4syeLsZsFfceVxOBfij59DNX48fv2KNkLAP7+nMEvPsWlmIBqSSnUZ6QnaRjQZoJMQWJ7/h4zuROixaKC/yT9/4Tcm5uw3HkQ3d8iDcdexOLpcU93wfDhNj33ElnQoTump6XbW60+xBFsPZaeqMtMCHOLpkQc2wjyUARohZAXCEoyFYmx8pwADBMiB3Ru8dFyLY9IXb2+naZEPPawRWCoEiR9ISiEJRiYzpAP3QTJgRio/KoZCzYv4n96copPF3Xf1uoDP9nA2i5L+EKwYfu+BBzheHUnsBNiqLYE9I7wp6Qg2hWaFA9MTbkIGZCoi5M3ZYylx51OZZ9bQ3KsSZZDnLoqB7fPBRjZY690wABAABJREFUn3D3YiWuff7LN1URkvMcTtT2eHHIWhB45LbpiemMvFM4wokDRkalY22rT4jl97DZlPjam2A/iMFMfmZTs7kQghPlE3vuB4F0w8L4sUlOxwIIiiqqXmpPyPrVWB4e+xU3Y0KiCBFqFmWHY7ztrTHtSmZKQawUcBxBUTapZ0VIhn2HSciyPSF7vPprS0ZgMyZkm3Isf1CONZoJwctTGChCikElVYSE/aQDe0ckTEhgvVenv/9yrFLOZ6avisNvi5BeFPHJagRCNYT6yF0fGfm6kZ4Qw4QckSLEXn09MCZkEyRSO52XbqXEHfUiJO/mcfTwO1iEZEzIZKIYeNw2o8ZII4c4O5d0W94r2EXIrdobZBDmWu9FahK3YybE9oRY9yuzgJEtAOweowqOie4TAuAXcYQgh/aE2PG8RgbaXokl4yMRxh2tkvnPNjHq2M6WcvGitCOgKJsZE5LhABDoAuEoMSG7Mqa3048bBCWKcoAJ8SupyMv1NnER0oW4eV1grWp1D4AJKQYu0/0pAF5yQv6L2+M1PV/4nju/f+zEOO0JiYgiGTd5OirRhsdq6lhW8t7Ila7DQMyE6Mm73X38qMuxhBBDDQszT8jk497jae/SXvtBAI5V1flRLXg8fvut2RtkELH00jAh/f1hQrJrb/cYLDg8VxwZRn/H8A0T0mGt1Uv7QRbfoP6XElor49+j3ybS9IXwdyfHsjFTtuRYjqAwYUXI0VjSzLB9pIzp+8SEDHpCBs2+rq8m/zJSxYRMfBuby7GMJ2SQCRm4ON1gSI5VzFWpVfMEnkO3H3F1LbTkWAkTkrOKsoMoQkqBR7U/AznoAb8UdIkiBy+a5ztv/46xrxtkQmzW5qgM3k/eOct/+847uHO+vOcruzvFYBFiVjMdnFR/mKOKol+k3qvT6DcIozBuWpgxIZOLexYr/No3rsS/73U8Lyj2I4wk9x2vHhnP2H5j8Fo3/zs42/JwjIvoNT9nTMjuMThRzh+Rhru7QlDCESJJxzLJWIVpqJ1KtmvdhNJoDyP9bjxFcnbI1I9iQmaKAYSGCRGUoibtXkQ/jPCOyPxhI2RFyKQi5QkxRcgeMyEDRcfQAG0igXtNVUzIJJWKzW4MY9Oxhj+j6AYgk7i/Qq6K4wjOzpb49tU6l272MaReF0lXe0ICb0wRsk8XZjHnUgq150NKeoBEMNV9D5WcP/Z1g31CDqJg2i581+FDDw7H5B4m7NVRKWVchBT94kSkBdlMSDey5CVOtho7qbhnkAnZY1M6qJXmjzw83DfnVoa51gfTsQI32Na1Pjaid0KanE4CTIKTwUR3SzcIirgO+LJLvdVJeoTUTkLBYiM38oWkmJAdyrEGCrxC4KrCJFLzQdcRFLQiYL3T37MmqfuJozHDybB9xExIA7p19fMeMyG2pCXv5keb2OzO56Hl69jsxmCKjbCndJTjmBCgZE3KHCCnqUeTOnNpuRczIW2BZkJEqgjp9BOtZm6f5ETFwEVS4lhfIAEJ3NYtMR/cveGN0ndFfLi6YZSOE56AlYzDgr2q2Yt6sazpqMfzGpgipNFrpOJCM0nI5OJELR/71YSAs7N7X4S8HjEox9ppiIPv+HjCS70HJExIdu3tHkNMyBGR7+4KWo4F0G2tw6qWY1VPqTQ+g+bS+Pfod+J1WjfYqTE9fSxnTSiF40JQVkyIVB7DSYnp3dUMRwjxISHEs0KI54QQHxuzjSOE+KIQ4pd381kZBmA35mrcUP/vY0TvWI29zWjEhcQWqu9xPUZGFCFF68ZQlAIRqH0xUoeb9YiIJKK3gwQhUqte3QMwpuc9l66T42xP7YuQ8ObmYspEPwpCJJrZTn+gCDkinpCjiMEmZoYJmZTVTFMsNfvN1IQok2NNLoQQsS/kRK1wa0zAjgAGQyh24+EwhUsmx9ofDJ7zE5+MBakixOs36K9oJqR6QrVLcLXSYaOYXosJcXbIhAwe21QyXq6C4yhjOkxOEbJjOZYQwgN+Bng3sAr8rhDi30kpB0vBHwcuALfAmXiEEFgdcg9AjjV2ddnu92GW87cykNvFRreRpEqMKkK8vKE3VFKWnmQaqYPApxtK8qiI3i6obumWL+AgInodRyC9Iu9ad6jnJY+0HQirmxYhoCRZRorV6SWszVHxhBxFpCKYrSLkqJvSDcx+NnqNVNpPNhGabPzAo6dYb/f58MNHS744yRjHhOykCAncAHoDRYiWY90KEb2HjWEm5Ba4hwXl+O+aD69RbzSZLvpKjiWEkmStX91EjpWkY+2UCcl5Do5QfULAYkIAchVcIeIiZFLM6bs5Ox4HnpZSXpJSrgO/CrzP3kAIMQP8IPDPd/E5GUbBZkIM9njy5TpuPCiPXV02hU+KCdnCjcGeaLVXRz+uUbQ+u4CIP/PsbEnXPS7dSBVAHeMJEU56pfwAPCEATlBgLhT82fUcb2m7dEVANT/eDxLvky6MemFEL5RDj2cYxmARYtKxJoUJScmxMibklsE9i1X+3vc/yFvvGu4JlGFnGDKm93dehJjxYZQcK1sA2D0GmY/iEYl03xWCIoHrkPddToUXWWv3VEFR1aZ0I8nahAmRmgnxgp3do4QQKTZkepAJsYqQevvWL0JOAJes3y8Bg265vwX8DWBseLIQ4mNCiC8IIb5w7dq1XezO6wwji5C9n3yZ1drxciwr5coUIVu5MYwtQoZfW7D6lRSliIutvO9yolZAIOiFaqLfEUaO5Qw0K0xOwf0sQlwtFYskhJGkI3JUtlGEdAaN6RkTMhZDRUhvMouQZi8tx8p06RkypDHOmL6TosFcX6mO6Vmj0D3DcDrWLXAP03OrasHjVHgxSbGsarbTmNM3YELCbsvyhOz8HmUXeSk5Vr4aNyuEyZFj7dvZIYR4BJiWUn52o+2klD8npXyzlPLNCwsL+7U7tx4OgAmBZKI0dmKX8oSM93UMv87apr0y/H4W7AKoAKlGP8ac3u2prukdVDzuIBNiJva+K/Y1YtbNmSJExkVItbAFOZYeqLv9iG4YDj2eYRjpInNy5VjtsJ3qcZIVIRkypDEuoncnrGH8Xv0RTEgmx9o1hj0htwAToudB5ZzH6UiZ0pd6SafymAnZwJje67aSt9tFEWKb02dL1r0iV8EBKrx+5FiXSTMfJ/VjBm8G3i6EuAB8Ani/ECKTZe0VbE+IwT4wIY8sPALAQ/MPjd5gFBOyKznWCE+I9bcWLU8IqI7EAJ2eA0JYxvSBIkSbvXP7nFnu59X+RJEkkpIOW2NCTAqW8oQcvYjeo4hBY/qkybFsn9VKZyX+OStCMmRIY1+M6ZoJ6UU9+rKfei7DzjEox7o1PCHqvu4KwRl/BYAXOlP0TIhMLMdaHvsW/U7CvPn5nZ9ndpE3W7blWKogKgtV7Lwe5FhfAt4ghDgphCgD7wf+k3lSSvm/SClPSinPonwhvyql/Ild7W2GBF4uSWQw2IcV4B+590f4F9/5L3jPbe8Zsx/GE9JJR/RuBn/rnpBSkLA+BeGBm6ys3Gly+KVPJAUdoCMYlmPpif1+p00ZJqQfSaREy7G2zoT0hiJ6szyHcbAnDN2wmzAhExbRC7DcTm5eWRGSIUMasRwr2oMiRLMdg/6Snb5fhjQG5Vi3RJ8Q615jFhVfY5YvX9DyKyPH6jZUu4IRCDUT0hceOX/zhclxKI6TY+kipCRbCBnR6Ix1QRwp7HhGJqXsAz8FfAZ4CviHUsolIcSnhBBZLMh+Q4hhNmQfmBAY7pyeQtwnpGV5QrYZ0buZMT1IGoAVB9779jl1DIT0CBF0hKS7AROy3x6LIKeOVV+by7si2FYRMuQJyZiQsbC/XzvmtrBP18Few76ultoJjZ8Z0zNkSGMvjekmut1IsGxvyKSwqEcZg3KsWyKm2nHiRd5i4OK5ghvOPL/+jPYx271CxrAhfV2EdAl21SbALvKmi2ljOoDnSHJ0aHQngwnZlVhPSvlJ4JMDj31gxHafBT67m8/KMAK5SvqEP4zJV+wJ6STMzHYjelsrox/XKOZqyc8DN53pos9U0edG5BNKQYOICIYjejUTst+T+nwhTyhckGoVokNuS+lY4/qEZEXIeNgTEFvONClMSEqOpX1RrnDxnFtAQ50hwx7CFOb9qE8YhXvChJjiwzCo9nMZdg7XEQSeEy+mDTIjE4ugBL0mAsWGXJdz/P6Fm6w0u0yluqYvQfX40MtDzZD0REB5F4oM47Gp5L30/ED7U0xC1utBjpXhsJGrpn8/DENuyhPSTT+24eu2zoQEQZlZqU7V426alRFCcPtcCSFVEbIupHkidYMyZu/9ntQXA5cOlgxMbK0IydlyLD14O46IGyRlGIbvJMd1tZOcQ5Oymmkb6I0cK5ODZMgwjJxjjeVRd0/SsQybkpJjbeXelWFT2IXHLcGEQGqRt5r3WHLmiCT85revQ3E22W5MTG/YVUVIV/i78qaemtJ90uYGFCqaCXGFoBg1JsaYni25TTJyByPH2hC+5QlxTTrWFm4Mrg+Oq5oUbmJMF0GJv9wvclFEPJY7NvT87XMlxFWfXgRrjilCHHzLMxN7QvbZmF4MVG8QE5PX3Uk6li5Cclk874ZwtO+nE3ZSnopJYUJsOdbNtrpxZUVIhgzDGEzC20061mCfkJQcy52MBYyjjrzvstrqAbeIJwRiczooWXdt8Xa4Ab/+zDU+cv+9yXZj5FiRYUIIdrUY+uGHT7BQzfGGk7X0E3pR2nRNX56QIiSb5UwyBmN6D5MJCbtJ5/atriaZYsUuQkZNwvwCJ6XLE5GPEwz/jXfMlxH4RAjWSIqQNBNiipD9ZkI8OiLNhJRz2/GEhIl/JZNibQozCbHlWJPChOTdPI4eguu9OpD5QTJkGAX7uqj36kg9zu9EPmXeqx22iWSU6pyeMSF7Azsh69ZhQqy5R1DirfffAcBLNxq8uNJPipQxvUJkzzAhu/OE5H2Xd929wFx54FzNJXKskmyynsmxMuw7bGO6G6RSow4MNuuhm8VtvQjR25kiRDjDiV+QZnhGsD13aDmWxKEpo/i9bE9IzC7s88S+FLh0LTmWCAp4W2A0AneYCcmKkM1hCs2UJ2RC+oQIIYb2NWNCMmQYhn1d1Lv1+OedFO2DqXo2E5JF9O4NCpbnYTCyd2JhL4BWT/C28/P4rpJL/+dvXoXCtHpuTK+QhAnx92ceYuRYjqAgm7R6IWEk9/5z9hjZLGeSYTMh/iENnqMKju0yIVE/+V2M8EDYE7URRcjJqQK+4yMRxJbuMc0K93tiXwjcFBOSy2+QLGYh8YRI1YmVrFv6VmAmJ7Yca1KYEBgumLIiJEOGYaSYEKsI2Y0xHZQky2ZCMmP63uDW9IRY9/LqSSp5nzffobwg//Frr3Ghpe8743qFpJiQfTgmXgBePjamw2R0Tc9mOZMM25i+UYzufmLUhG+rq0mD240rXuzCY8R7O45grlQmQiBN4T/YJ+SgmJBcWo6VK4xoKjkCozwhGROyOcx3bK9mToonBIbjr7MiJEOGYYxjQnYU0Wu9pt1vx2OHg5MKu8iwc+Qt9qN4qxQhNhNSOwXAj7z5DFNFdc58+SpcX+8gxzAhsp+kYxkGZc+Rq+A6gpJsAFkRkmG/YRmlDo8JGfG5W70xDBUhY/6GFBMyeoJ5rFJG4iDlaE9Ip38w6VgFXxnTDYrFrRWHSURv5gnZDgYnIZ7jpQIJjjpKXvr8yDwhGTIMw77O17prIx/fKmzJVTtsx0xIzsshRjHxGbaNW5MJseVYJwGlwviffuAhTkzlqTsVVpo9Xnj51VSvrxg6CCFy9/E8y1dxBBSk8udOQkJWNsuZZKTkWIfFhOxGjpXb+HeDTTwhAMerFeQAE2JP6A5K4lTKeXFEb08ElAtbm1SagiOS0OoeTMF0K2Bw0j5JUiwYZkIyTXqGDMMYK8fagZHclly1++1dxf1mGA1ThAix/+qDA4O96FtN+nEfq+b5+9//EKWpBQB66zf4q7/yjSEWQuhiV+4n252r4GRMSIYDw5HwhIz43K3eGAYLirFMyOZFyKmpKhHJRH5cEZLb51WZouUJ6YjclrqlQzo62KxeZJ6QzTGo4Z60ImSwu3vGhGTIMIxxTMhOPBx24dIJO3GzwswPsncwsbx5z8W5VXpd+cNyrPjXgs/3vvUBSjkPT/Z54dIV/q8vvJzaRuhid3+LkKrqE2I8IROQkJXNciYZqSLkkHTwB8GEOC6c+wOQr8HpJ0ZucnqqSkfkCIVL38mD66Ujeg+ICcl5Dk8HD9AWBb7iP0plC40KIc16mE6nt8wK0j5icNI+SX4QGJZjZZ6QDBmGkSpCOkkRspOiPSXTtYzpGROyd3j89lnKOY9337Nw2Luydzj9uErAOvNkujmhRlCZ53gtTyFwqUR1vvXaWup5oZtiyv2Mgc5VcZykCJkEOVbWrHCSkSpCDmkF+CA8IQDv+R8gilQnnhGo5Ao4ns/l/gmqgccCSUftKJJxVF3O39+JvRCCK8V7+MvO30YKh/92i0yIbVRrdDUTkhUhm2Jw0j5pTEhmTM+QYXN4jodAIJG7ZkLsYqPVbyVyrIwJ2TPcvVjhFz72xK3DgoCSYP3ovx07B6E4g0BJ0SpyjcurbaSUsf/Dibpqu/2WYwElVBFSn4AiJJvlTDLsPiG3MhNiMO7iR03ecp4DQkmvAieIL/6OZRI7CIlTMXCRQn1OdYtMSG4EE5LJsTbHEBMyIT1CDAaLkEyOlSHDMIQQcYG+lxG93bCbyLEyJmRPcUsVIAYbzEFMnxDfdahGdVrdkLWWLgKiCBGqDvL7Kp3Pq8TUMi2QciKYkGyWM8nwC0qqBIdXhIxiYLYc0btFT8gWELhJF9JuP8JP+UHC+Of99oRAujlTtbDVIiR5jWFt/IwJ2RSDk5BJk2NlfUIyZNgaTOqdYUIcHDxn+2IO3/Fx9NQnY0Iy7BnyUyAEviuoSnWOXl5VBS79NnHbwH2VYyl1TCBCArqZJyTDPkMImD2nfp6543D2wfFUp3MbO2ZCdr4KnHNzsXxJAkTJzal7wExIKUg+e6vGdH/EfmWekM0x6XKswaIpY0IyZBgNc633IrWiHLjBjqJOhRDxdWZ7QnaStJUhQwzXg3wN33WoRKoIubKq+1f120Q6ulPsp3Re944zDQsnIR0r84RMOj7w92H5Ahx74HA+XwjFYPSayWM7bla485WonJtLTeSjKGEWUnKsA5jY20zIVouQUfuVeUI2x60mx8o52UQoQ4ZRGFxw2I18quAVaIftVBFScCdrASPDEURhBre1woxYB2wmpBO3D3AOgAlxnckpQrJZzqQjX4PjD22sVdxvbNfbMfZ1u5NjeZa5OwwtJiRMipCDYBeKthxrB+lY8WOZJ2RTTDoTMlSEZKuxGTKMxOCCw25YQzNu2B3TM09Ihl1Dm9MXfLUo+9rKMBPi7KcnJGZCyIqQDK8j2PSi4yU+lc0wxITsfAKW9/I4QuBoer4fWkxI72CLkFJOFUC+K7b8eaMKjowJ2RyTXoRknpAMGbaGISZkFx4OU+ynOqZn116G3aIwA8Ccq5oFjmRCDsCYbpiQzJie4fUBu3jYzmrSHjMhkETddnvJqZ1mQvbfmD5TUvsyX8ltWbM8quDIPCGbY9KN6VlEb4YMW0PgpJmP3VwrpoBp9Vp0dXTqpC1gZDiC0P1DplGekFFMiJvbT0+IkmNlnpAMry/YxcN2zOV7yIR4wsPBwXcdOv2IjlWEdHpJOtZBsAsfeMNxmt2QJ26f2fJrRhUcGROyOQYlGZM2kci7eRwcIkwzzcyYniHDKAwWHbsqQvS9Z7W7uifvlyEDAEV1zy9FDYSIWO/0qbd7VCwmxN1PJsTLg+PhOj0K/SbNbkgUySMdl5zNcjLsHjtmQvbOmG4STzx9sbW7VhHSP1g5Vq3o8+Nvu503nKxt+TWj5FijErMypDGo4540Y7oQIrXPWUxohgyjMVig78Y/Za6z1U5ShGSekAy7hpZj+S6UpTKnX1ltI/stpA7p9YJ9XCgTQjUsFIJS1EDKpPnxUUU2y8mwe9irz9tZyR1cEdilKTfv5eOJe7sn4mje7gGnY+0EjiNwB1YrMmP65hhKx5owORakC6eMCcmQYTT2kgkxBYxdhEwai5rhCKKYNCysSNVU8/Jqm7CXMCHefsqxAPJVHEcZ0wEanXCTFxwusllOht3jCDAhkE7IEtLn+rpqQmV7Qo5qEQLD+3YQjRUnHZNuTIe0LySThGTIMBqD18aepGPpZKxR758hw7ahmRDPEcwI0yukRa/TijfZ9yIkV8HVnhCA9U5vfz9vl9jVjEwI8SEhxLNCiOeEEB8beK4ohPhVIcS3hBBPCyH+1O52NcORRcoTso2BfKfRvmNg9woReFxbUzcYw4Q4gliudRQxKBXLmJDNMWRMnzA5FkDJy4qQDBk2g+mYbrAb6eKoxYpMjpVh19DGdAHclleBB5dX2vStIsTfT08IQK4WG9MB1m9VJkQI4QE/A7wHeAT4aSHE7MBmf1dKeQ/wBPDfCSHu2vGeZji6OCJMSM7NxUWGkB5X1xQT0umrizDnuTvqsHtQGCw6Au/o7utRwV42MDss2ExIJsfKkGE09pIJGfXazI+VYdfIVVSbAuBkTvcKWW3R76oipCcCcsE+KxxyFRzHKkLat64n5HHgaSnlJSnlOvCrwPvMk1LKppTyN/XP68CzwPHd7GyGIwq7T8i2mJCBQX+XE7Ccm8NxVK8Qgc/1epoJOcpSLBjev8DN5FibwZ6Y+I6P72ytOeRRQkFfP57w8JwssDBDhlHYyz4ho147iQsYGY4YhIgTso75qvB4bbVN2FVzkR7+/ofjxHIs1avkVpZjnQAuWb9fAk6O2lAIcRp4EPi9Ec99TAjxBSHEF65du7aL3clwaEgxIbsoQvaACQHVKyTNhExmEZLzj/b+HgXYRcckmtIhkWNlLEiGDOOxp+lYI+41WRGSYU+gfSHzLAOw0uzRbqmCoCv8/e9Vlq8iBJSkKoJuWTnWViGEyAG/BPy0lLo0syCl/Dkp5ZullG9eWFjY793JsB/YqSfEHeiuvgfGdFDJFAKfq9oTYoqQo978b1COlUX0bg4hRFx8TqIfBODumbsBuHPqzkPekwwZji72NB1rxGszP1aGPcGcch0cW/5dFsKrAKzWVVxvTwQHwoQIIC+6eLLHevvWZUIuk2Y+TurHYgglwP8/gU9JKX95F5+V4SjDLh62O5B7O5RyjYCh2D1HybGu1SebCTnq+3tUYCYPk5iMBfDE8Sf4R+/6R3z88Y8f9q5kyHBkMcSE7EHHdPu9HJGNtxn2AA//KLgBgQvf3f4kAPV1VYR0CfZf4ZCrArBYy/O3P3g7H3zwxP5+3i6xm6PxJeANQoiTQogy8H7gPw1s83eAppTyb+7iczIcdaSYkG2yGTs1tY+AuUl5roOQHjcbXbr9KPGEHHFmYdgTcrT396ggZkImVI4FcLx8fCL9LBkyHBT2o2N6/HtmSs+wV6gehwe+H88RvKH/Dc71v8164wDlWLoIKfou905L5itHm+Hb8SxHStkHfgr4DPAU8A+llEtCiE8JIU4IIU4BHwceF0I8pf99557sdYajhZ16QiApPBxXybN2gZQnBDWhu77eiYuQo+6xsIsQIdTfkWFzmOJzUpmQDBkybI79lGPtxl+SIcMQHv4RRL6G7zp8pPV/021pORYHI8eK0anv72ftAXY165NSfhL45MBjH7B+zWZRrwfs1BNib78HpkDbE9KV6tS+ttZORfQeZeQs5sN3nSMdJ3yUMJuf5dL6JeaL84e9KxkyZNgn7Kkca+B+U3CzBYwMe4hcGR79Mfz/+Hc40blEGF0BoHsQnpB8Nfl5AoqQo700nGEysJsixMT77sFKlLmxGE8IwNW1zkRG9GZSrK3jx97wY/zIvT/C99z1PYe9KxkyZNgn7CV7kTEhGfYd93437dIpAFypFkL7ToC33/f2FBOytr+ftQfIZjoZdg97AN+2MX3vmRDHEZR0V9Lr9XZiTD/iE3u7CDnq0rGjhOPl43z4zg9Ty9UOe1cyZMiwT9hLOdagdDPzhGTYczgul+/9Y6mHIucAYtj9EpiQhYwJyfC6wK7kWPmdvW4Eck7yHvMV1XvBZkKO+sTeH5BjZciQIUMGhcHght0UIYPSrsxPlmE/ULjjLXzbuzv+PTqIGGjHUXIwgPbq/n/eLpHNdDLsHkHJ+rm8s9f6pY232wLsPhGLFaWLvGp5Qo46E2J7Vo66dCxDhgwZDhJ72TE9k2NlOAgsThX5lcL3IPVUOzyoBEedkDUJTMju4ogyZAAVSXf/90LjOpx4ZHuvve97YP0qPPiHdr0bD84/yBsX3sjJ8knaS7P8Hpe5Vu/QjyakWaEtxzriBVOGDBkyHCT2Uo7lCIecm6MTql5SmRwrw35goZLjqneCXy58Pw/3nuKb1bcdzAdnRUiG1x3e9md29rrFN8CH/397sgsFrxA3fPuVpy4BcLPRxdNRt0c9HSvzhGTIkCHDaHiOh4NDRISDg+fsbvqSd/NJEbIHnsQMGQbhuw7zlTyfl2/l87m3ci6/TaXITmHM6ZkxPUOGw8GxanJT6YcSOPoT+8DqC5J5QjJkyJAhgRAi9nIEbrDrCHObScmYkAz7hRNTybl1YHOQ/OQwIdlMJ8MtiYURXUInyhNyxPc1Q4YMGQ4apnDYjRTLwGY/Mk9Ihv3C8VoSenBgagzDhLQzJiRDhkPBQnV4Zeuom71TfUKO+L5myJAhw0EjLkL2Ik0xY0IyHABSTMhB3dcnyBOSzXQy3JIo5zxKufSqw1Gf2NsSrKO+rxkyZMhw0DByrL1mQjJPSIb9wmL1MIoQzYR01yEKD+Yzd4hsppPhlsVCJX1jmSRjelaEZMiQIUMaeynHypiQDAeBE1OWHMs/KDlWNfn5iLMh2Uwnwy2LY9X0jeqoT+ztVZLME5IhQ4YMaRgmZC+KhowJyXAQOFbNYzIUDowJyWdFSIYMh45j1UEm5Gif7qmI3iO+rxkyZMhw0DhWOgbAYmlx1+9lFzIZE5JhvxB4DmdnVTPmxdoBnWdGjgVHPqY36xOS4ZbFfGWwK+7RntgHmSckQ4YMGcbiR+/9Ue6fvZ83Lrxx1+9ly7GydKwM+4m/9IF7+daVNZ68c+5gPjBVhKwfzGfuEFkRkuGWxSATctQn9pknJEOGDBnGoxJUeMepd+zJe9kSrIJX2GDLDBl2h8Va/uBYEJgoJiSb6WS4ZTHYK+SoT+zTnpCjbaLPkCFDhklGyhOSybEy3EpIGdOzIiRDhkPBYK+Q3BGf2Od9F0cb2Mr5jKTMkCFDhv1CxVerxZ7jZXKsDLcWHBcC5UM56g0Ls5lOhlsWpldIo6NysnP+0a65877LH3/HHbxwrcETt88c9u5kyJAhwy2LJ44/wTM3n+H89Hl8xz/s3cmQYW9x7A3Qb0Np/rD3ZENkRUiGWxoLlTwvdRrAZMTefujBE4e9CxkyZMhwy6PoF/nJh3/ysHcjQ4b9wQf+/mHvwZZw9GdlGTLsAqZXiOcKHKN1ypAhQ4YMGTJkyHCoyIqQDLc0TELWUY/nzZAhQ4YMGTJkeD0hm5lluKXx1rvmKAQu77p74bB3JUOGDBkyZMiQIYPGrooQIcSHhBDPCiGeE0J8bMTzjwshnhZCPC+E+Cu7+awMGXaCe49X+cQffzN/4p13HvauZMiQIUOGDBkyZNDYcREihPCAnwHeAzwC/LQQYnZgs38K/BBwN/ABIcQDO/28DBl2iswLkiFDhgwZMmTIcLSwGybkceBpKeUlKeU68KvA+8yTQogTgCel/JqUMgQ+AXxoV3ubIUOGDBkyZMiQIUOGicduipATwCXr90vAyW08D4AQ4mNCiC8IIb5w7dq1XexOhgwZMmTIkCFDhgwZJgGHbkyXUv6clPLNUso3Lyxk5uEMGTJkyJAhQ4YMGW517KYIuUya2TipH9vq8xkyZMiQIUOGDBkyZHgdQkgpd/ZCZUx/BngXsAr8LvCklHLJ2uYrwI8BTwP/FfjjUsqvb/CeV4ALO9qhvcc8cP2wd+J1jOz4Hz6y7+DwkX0Hh4vs+B8+su/gcJEd/8PHrfAdnJVSLg4+6O303aSUfSHETwGfQTEqf19KuSSE+BTwMSnlZeBPAr8I5IGf36gA0e85tIOHBSHEF6SUbz7s/Xi9Ijv+h4/sOzh8ZN/B4SI7/oeP7Ds4XGTH//BxK38HOy5CAKSUnwQ+OfDYB6yfvwDcv5vPyJAhQ4YMGTJkyJAhw62FQzemH2H83GHvwOsc2fE/fGTfweEj+w4OF9nxP3xk38HhIjv+h49b9jvYsSckQ4YMGTJkyJAhQ4YMGXaCjAnJkCFDhgwZMmTIkCHDgSIrQjJkyJAhQ4YMGTJkyHCgyIqQDBkyZMiQIUOGDBkyHCiyImQAQogPCSGeFUI8J4T42GHvz+sBQojTQojPCiG+KYT4mhDiB/TjdwohviKEeF4I8b8KIcRh7+utDCFEUQjxshDif9K/Py6EeFof/79y2Pt3q0MIcbsQ4jP6Ovi6EKKUfQcHCyHEn9XH+5tCiP+vUMi+g32EEOLfCSGWhRC/bD028phn94S9x+Dx1/eBXxVCfEt/B3/K2nZOj1HPCSH+rRAif3h7futg1DWgH3eEEF8cuDZuqWsgK0Is6AaMPwO8B3gE+GkhxOzh7tXrAn3gz0gp7wPeB/wjIUQJ+HvAX5NS3gXMAR88xH18PeAvA1+wfv+nwA8BdwMfEEI8cCh79frBvwT+ir4O3gl0yL6DA4MQYh7V2+pNwAP6/zeTfQf7jX8MfHTgsXHHPLsn7D1GHf+/K6W8B3gC+O+EEHfpx/974N9IKc8BLwLZQu3eYNR3APDjDDfwvqWugawISeNx4Gkp5SUp5Trwq6hJcYZ9hJTyNSnlU/rnK8ANYAZ4EviPerP/C/juQ9nB1wGEEOeAe1DnPEKIE4AnpfyalDIEPgF86BB38ZaGEOJ+oCel/ByAlPImsED2HRw0PFRzXV//c8i+g32FlPKzQN38Pm7s0Su+2T1hj/H/sHfe8XEU9/9+Zq9KOvUuuci9GxdcQ6+hBgLp5ZuENNIboSchQCABkgAhJCGV5JeQBHDovZrihjG4d7nIlmT1dv3m98fe7u2d7lSscpI9Dy+/ON3t7c5tmZn3fFri+ZdSdkkpX4u+7gC2A+XRjy8G/h59rc7/IJF4DQCEEAXAx4E/WN475p4BJULiqQBqLH/XAJVpastxiRBiIWADvECTjOWQVtdiaLkTuNbyt3oWhpcpQIcQ4gkhxHohxHWoazCsSCmPoD8H+4FDwIvo1ih1DYaXVPd9IWpMGFaEEGOBucD66Fu5UsrW6Gt1/oeWW4GbgbDlvWPuGVAiRDFiiCr/B4Evp7stxxNCiA8BO6SUO9LdluMYO3Ay8DVgGXA2cHpaW3ScIYTIR7dyVKEP7MuBzHS2SaFIF0IIF/Bv4CopZWe623M8IYSYD+RHLSTHNPZ0N2CEcYh4VVkJrElTW44roh3e/9B9Ud+Kmh0LhBAiqvor0a+PYvBZCnw8mhDAg+6G0kb3Z0Gd/6GjBlgnpTwAIIR4Gn0CrK7B8HEWsCvqCocQ4ingNNQ1GG6SjcOHgEbUmDAsRMffB4GnpZTWYOlWIYRhDVHnf+hYCpwshKhGdw/NFkL8AfgKx9gzoCwh8awBZgshKoUQHuA84Lk0t+mYJ9rh/RV4WUr5d4DoA7aKWNDVp4An0tLAYxwp5bVSyrFSyirgB8ADUsqfAmEhxFwhhA3dN1Wd/6FjLVAihMgXQmjAKcA7qGswnBwAlgsh3NHzfRrwHuoaDCtSykMkOedqTBhWbgO6pJS3JLz/JPCZ6OtPo87/kCClvF9KWRkdkz8OPCOl/PKx+AwoS4gFKWVICPF94BV0gfYLKWVjmpt1PPAB4GPA+0KIS6LvfQa4GnhICHE38BKxYCzF8PAN4F/oKzF/l1JuTHN7jlmifc91wOuAAJ6XUj4phGhAXYNhQUq5KmqBeheIoPc5jwN1qGswZAghXgROALKEEAeBj5C671FjwiCT5Px/Av08bxFCbIhudrWU8jl0cfKwEOI7wCbgxuFv8bFHsmdASvl2is2PqWdAxOJbFAqFQqFQKBQKhWLoUe5YCoVCoVAoFAqFYlhRIkShUCgUCoVCoVAMK0qEKBQKhUKhUCgUimFFiRCFQqFQKBQKhUIxrCgRolAoFAqFQqFQKIYVJUIUCoVCoVAoFArFsKJEiEKhUCgUCoVCoRhWlAhRKBQKhUKhUCgUw4oSIQqFQqFQKBQKhWJYUSJEoVAoFAqFQqFQDCtKhCgUCoVCoVAoFIphRYkQhUKhUCgUCoVCMawoEaJQKBQKhUKhUCiGFSVCFAqFQqFQKBQKxbBy1CJECLFCCNEshHg4xeeLhRCbhRC7hBA/OvomKhQKhUKhUCgUimMJIaU8ui8KcRqQDfyflPLyJJ+vBa4ANgNvAl+SUm7saZ+lpaVywoQJR9UehUKhUCgUCoVCMbJYvXp1nZSyLPF9+9HuUEr5alSIdEMIUQHYpZTvR/9+CLgQ6FGETJgwgVWrVh1tkxQKhUKhUCgUCsUIQghRnez9oYoJqQBqLH/XAJVDdCyFQqFQKBQKhUIxikh7YLoQ4otCiFVCiFX19fXpbo5CoVAoFAqFQqEYYoZKhBwi3vJRGX2vG1LKP0opl0opl5aUlAxRcxQKhUKhUCgUCsVIYUhEiJTyEBAWQswVQtiAjwNPDMWxFAqFQqFQKBQKhYVIJN0t6JWjDkwXQrwInABkCSEOAh8BbgS+GBUh3wD+BbiBv/eWGWu0Udvq45lNh/GHer/IY/IzOHdWGQ6bpt8U258GRwZMPjPld7yBME9vPMyiqgLGFWam3G57bTuv7zhCuA9ZzuZU5rJ8UiFCCAj5YdOjUDIDKuYB0NwZ4JlNtbT5gr3ua6RQVZjFB2d3S7hg4g2EeWxDDS3eof1Ni6ryWTi+IOXn9W0+nt54GF8f7pe+IIAlEwuZNzZPf8PXBlsfh7FLoGgKoN+jz22uxRsMx323IMvJebPLyHY79Df2r9L/JVIyE6aeo+8+GObZTbXUtvniNnHYNM6aUcL4wiz9jZYD+v0d9Mbvy1MCMz8ETn271Xsaae4KcPbMMmya0J+LHc+A0wMTTwUgGAny7N5nmZI/hekF0/X9dDbC1sf03ztaKJoKUz8ImkYwHOGFLXWUZLs4sSp6v/jbYctj0NnQ+74y8vXzmJEHwIYDLRxo6uKcWaW47DaQEna9BHWbhu73AOSNhekXgd2JlJJXttejCcGpU4tj/cuWx6Gtpvd9ObNgxsWQXQrAzrp2XttxhFCk9z5tVkUOJ00u0o8Z9MLWJ6AtqdG9dzylMPNi8x5dtaeRlsR7dPtT0Li7933Z3TDtg5BfBcCBpi5W7WnkzBmlFGQ59W0Ovw97X4NI/PNJ4WSYdj5oGqFwhBe31pGf6WTJxEL9c38HbHsKymZD6SwAGjr8vLKtnuWTi6jMy9C3a9oD25+FcCB+/zmVMOMicLiRUrJyZwOBUIQzppegaQLCQdj2JGSXw7ilALT7gjyzqZamzvh9ZThsnDurjLJct/5Gwy7Y+Zy+j9GAEDBuOYxdBEB7oJ0X9r3AwtKFjM8Zr2/TckC/rxLPY1ax/iy6PACs2dtEY4efs2eWYjfG+h3P6mP9xNNACHzBMM9trmVSsYfZlbn6frqa9O3GLYMCPTvoweYu3t7dyBnTSyj0uPTtajdB/RaYfiE49TnB27sbee9gy1H9dJdd46wZpYwtiM4vmvbC/rf1vipT75s21bTy1u4G+vAojgjsmuC0acVMLslOuc2eIx2s39/CObNKyTHGwANroLkaZl4Cdv35fKPmDQBOqjxJ3yYUgG1PQN54GHMiAK3eIC9uqWP+uDwmFuv3AW2HYOuTEOw6uh9RNqfHuSG+Vv1+HLcMCicB+v3y+o4Gzp1VGrtf9rwK7/8XPngbuHOOri3DwFGn6B0Kli5dKkdLdqwb/reR9w609nn75ZMK+eG507C9fQ9sXqG/edkfzQljIr94dhsrdzYwpdTDLz86L+k2Uko+/9e1NHYEkn6ejE8vHcfHFo3TJwgr7wJHJnxmBU0BjWseeZ/Drb7edzLC+Pllc5lZkfwh+++6Azz49r4hb4PDJnjwiiV4XMl1/W1Pb+Wt3Y2DftxvnzmFs2aWwpoH4N1/gN0F59/BAdcUrn10I60pxNfkEg+3XDKbrJ2Pw5t3pz7ARXcTKJnLT5/cnPJ+z3DY+Okls5juOAJPfFufVCejdBacfyf/29zMn97YC8CpU4v53lmT0VbeqYsXgEVfhAWf4aFtD7Fi1wpcNhf3nnEvuaEgPP7No59kppOZFxNe/l3ueH4Hb+7SxcZXTp3IhdNy4Knvw5Ftfd9XfhVcdDcvVfv59Ys7AVg4Pp/rzpuO850H4L1/DcEPSELVScizfsIDb+7nifcOA3DZgkr+b0kF4vkb4cDqvu8rqxguvpd3ml3c8tRWQuG+j0ufWDyOTy4sg2evgZp3+vsr4imdDeffwaObmvjLm9UAnD6tmO+cORlt5R2w/Zm+78vpgQt/xc5IOdf/bxPeQJiyXDe3f3gOhfVvw4s3gUyxKDH9AiInfZ+7XtzJ6zv0++WLJ0/gQzPz9fulfgtoNjjnVurz53PNoxs50u7H47Jz24fnUBXaC0//oPtigMGYE+Hc2/j7usP8Z+0BAM6fU85XTx6PeOkm2Pu6vt1J36Fj8kXcsGIju490Jt1VboaD2z48h7G+HfDMD3UBOpqwOeDj/wRPidnnFLgLuOf0e3Bodnjki9C4K/l3S2bABXfx2JYW/rhS79NOnlLED86eivbmXfpkFODEz+M/4TPc9MQWNh5sRRPwg3OncXKlTe/TWg/qY/GFv2KPGMN1KzbS6Q9TmuPi9svmUtSwFp6/Ub9fymbD+XfyyPuN/PWt6gH99CyXjZ9dOoeJ8iA8+V194pw7Bi66h5WHItz53PZRI0AMxuRncP+nF6b8/Fv/epe9DZ2ML8zktg/PITvQAP/+lL4YMH45nP1TNjRu5rY1twFw3ZLrOKFgpn7+97+t7+TUq2mrOptrH93I/sYunHaNmy6exezMFnjiW+BtGdiP+MRDkFOe/DNjrHdkwPl3ss8xgWse2UiHP8QJY3O55ZI5sO9teP56/TdVLoALfzWw9gwCQojVUsql3d5XIqT/tHYF+eyfVxORkJfpIMNhS7mtNximpSsIUvLd3Fc43fcSwvhw/qdh8Ze6faemxcuV/3gHKUHTBI98dZm+spJAS1eAz/xpDdB7O9p9ITr8IQCuOGkCl3T+1xRDHaf8mKvX57G/SVfupTluNJFyVyOG2jYfUsLnP1DFhxeMSbrNzU9uYc3eJlx2LbYCOYiEIpIj7fqg+7NL5zBnTG7S7T775zU0dwbwuOxku4/aAGnS0hXEGwyjCfj+OdM4ZefP4eBaAPyam5+Er2BTsAIhoCzHnbS9l2W9x2cD/9GvtTNLX2U36KiHcIDImEXcKr/Amr1NABR6nDgt92J9u59wRDLe1sAvXH8iM9QGQoOciti+wgF9f8B+91S+3fpJQiK6AiUl1+Q8x3L/Sqy3XOfiL/H1uhfxhvRJ1KXjzuHjW1+Flv36BjkV+nFGOoEO8LYggddcp3NX25n66ivglH7uyf0nlb7oBMdTqk+IUhEJQ7s+2a93jeNbbZ+hU8SspFdmr+Q8/7P6eXRlgzv5vThggl7o0gX1RvdCrm+9BBm9FpoMc3vuo8zwbdC3zSrSrQKpkNK0lrQ5ivh21xdokLk4bIIiY0UvBUafZpMh7sj9L1N8UetPVrEuxvuD5R7d557Gd1o/EXePXpv7HMt80Xs0I9+0lqSk7RDICF6bh2sCX2RPuNj86LSMXXw7/DfsRPRzk1UU+57lfnnTfQo/bz3HvF8cMsCvc/7JOP9Oc/OgsPMLPs8qf5X53gzHYW62/QlXxAuaHbItluKQHzqPALDDPZerWi8nIvRxQ8gIN+U+wTzfGvNZjEj4q/NjrOicC0Bxtgu7ZXAw+uA5jkP8xPYnnBGffg97Sns+PyOFthr9HpxzOSz/Jr959zesrFkJwJfnfpkzZQY8e62+rfW+Cgehow6Ag+4pfKv1kwRFdHyRkqtyXuBk/2ux8wj8z3kRf+mIzcGy6eRez18pDMQWVXy2LK4LXsHOUOz8neTezfflg9hlyHyv2j2d77Z+nJBwkOWyxVb0+0Fdm4+IhCn2On7m+BPucExkNjor+GbH52gna8jGzsGmMxCizRtC0wQrrlyuW/USkFJy2f1vEYwuckwp8XB7yQs4dzwZ22jiafw4U7KteTsAM/Kn85MuCXteMzcJS3jA8Sme6pppvlepNXGn+094Qi36M5tdYT67fcJyT3HRr6FifvLtXvwJ7H4FAL+WwY3hK9gajAmW35wSYfzam/U+zZmlC5DiaX1vxxCRSoQMfDZ0HLJ6b6O5OnDrJXN6dJcKhCL89MnNlO18iLH7n+VIhoPibJfeOe19PakIeXjdQQxtGIlIDrf6YiZTCweaYqtc118wg+llqU1uDR1+rnlkI3VtPv70xl7m5e2kCghLyRsvrmA/HwOiq4pLxvV2CkYEX/9/69nf1MWeFCt0ALuPdABw+vQSvn765EFvgy8Y5qO/fxspYU9DR1IR0tIVoDnqxvD5D1RxzqzU7mN9ZX9jF9c8+j7tvhB3vbCDOc6d5APBiORwYxMfj9zLbz3f4PJzTuPsmbEBTUrJb1/dTd36p/lAy4McdmqUl1eifeg3kGvJJbHuL8h3/krdlpUcti8A+zjdmvfB6bprSpTVexr53ZNv8rnme6mllcr8TFxnXwdTzo7tKxKB139B23tP4K9fzxccXv5XciUFniymVP+TktaXaMh0UDRhLsLXBu2Hef6d3+DNzdXdjiJhnnvvT1zcZScTASd+HhZ+bsDncFjwdyCf/C5H9m6ksv4JPugOcGDix6lraecj9X+kq20H7TlusuddCid/v+dBS0pYdT8d6/4fbUe2cIXtD/y/gm8wprSQgl0rmNLyGHVuO6XjpiEuvjteVA4moQA8dx1NO97EXf8aH3P6WDXmC4QjktMO/QF763qaPS7yZ5wGZ/8UbL0MM+//F+/KuzlyZB9f0n7D73O/w1UfWhpzV0lBU2eAax9+l7MP/xla36Ml20XerLPgzB/rVoL+EInAa7fT9v5TBOrf4fMOL4+XXEmeJ5Op1f+P4taX9Xt00nzE+XeZ7jAp2f0ygedv4nBDHZ/jbn7j+RaTp87gyNa3OL/1DxyyRagoKcR+8d3xE4RAJ/Kp79Gw5z1K65/mQpefXVWfpKmjk0vr/oS/dRttOW5ypp5M6OB6DjU0c2n4PuqyvkrF9CXs3rGRTzXcyyGtizEFHhxn/xSqPmD5nWF48ce0bHkZUf8Wn3Z4eaniyzjtdk488Bc8rW/RlOWkcPIiIi37OXT4ECcF/kJ15mcoXXA+Xzttku76FuX5zbU88sJrfLz1Xg5pfioLs3Gc+zMYu7h/5z9dvHgT7H5Zd2+Z9ym6QjEXmsd2PcZprUFsoAuQj//TdNUhEoGVd9L23mN4j7zLFbYuHi39OoU5Hibs/RflB17Qx/qJc8DfQd2BPczx/4sPZATxTrmI6kN1fL75tzS3HiQjN4PMaacT2P06hxvr+Yy8h/s832Ly1FnUblvFxXX3U2OLUFlcgL1yHm073iBYv47PObw8WXolt14+n5LsHoR+Cl7fcYQHn3mdzzTdy2HRQWV+Fs5Jp9C1/RWaj+zgy9pv+Uv+t7jxsiVMLvEMzvkeQl7ZVs8vX9hBJCJp94XIzewuzLoCYVOAANTV1nC4egVjcx1oDjeEfGzZ+yLbsu3g0cfprfteYVtHmOnYoXIB4YbdHKqt5YzgA+zL+jyeaaexc+8evtByL3U048jLwHXG1TDjwv79gPY6+OdH9dc9uRtHPwtGJIcaG/hU5F7uy/4mDY4KKv27ES88ANmavsBx3s9HhADpiVGwlDjyMNxqKvMyGFuQ0eO2TrvGj8a+zyXhFwDYESjk9dwPIUFf1W2ujtu+vt3Hy9vjUxUfaE7uW3jQ8v7Y/J4HxSKPi1svnU2RR+9EDx3YTYs3yKEWH5Wt72KTIT68oJJPLB7b435GEhOL9dXIPQ0dST9v7QqarmqTintZuTxK3A6b6YO9uz55OwwhBMT8RgfIuMJMbr5kNplOG46wl8a6g7T5Qrzum0QgDJmyi1ucf+Hs8nh3DCEEV44/xNfkvwDJkZCbezOuJOiJN/1GZn2Ymk7o8IU40/8iC8fn84Nzp8UJEIAlJRHuzPgLebKFcETyu8glHCo+Kb6xmsbKis/zvG8GALPC2/h10ePcMv4dzou8CsAWfwkPFX8LLvwVvsxCntL80FFHaVhC60G6Ql08ZwvAvE/Cgv8blHM4HEhnFn/P/wY7Aro//8XhF/npuHe5u+BRpkd2goSnvbN5u+L/el81E4J3Kz7OCt+JSAkTI9Xcnf8fflK1hY+F9ZW86kAef8z5BtKdN3Q/yu7kibKvscan9xUnRdbyi4rXuKPsBRaHNwDwlq+KZ8q/0rsAAXaVncdfA2cRkZKSSD2/9jzI7MLem1GQYeeXpc9yYkQPN3zNN5kXy7/cfwECoGm8Xv4FXvDpsUezw1v5dfET3Dp2LedF9BXQzf5SHir8Ru8CBKgrXs59kcsIRyQe2c4v3H/lmsk1XK3pK9odYTu/dnwJb278woh0ZPLPgm+wxa9bTs6PvMot49/h10WPMTOir8w+55vJc2O/xa9tn6MrrOGQAW7Q/so1U2q4zfkXMmUnwbDgXj5OQ9GJCb/TxvPlX2albyIAiyLv8cvS5/h55UpOjeheCO/4K3mk5Jvcm/E1GoNuBBGulA9xZVVtnAABOKfCxy2OP5MhvQTCcLf4FM0F8/p37tPJ/E/r/w/5YdMjpuUVoK5lL6saNuh/zP1YTIAAaBpvVXyOZ72zQMKMyA5+Xfg/bhn7DheGXwZgW6CYfxR8i99nf4ODQb3f/2zkf9w4YTt35z3E2EgNUsK//ct4bfw3+Y38KKEweCId3O76C9dMqeFa7W/YZYjOsI277Vfw7Jhv8qJPn1TOjWzhV8VPUJLVfysIwCllQW53/QVPpJ1QGO6TH+W18d/kIf8ypISxkRruzv0Xk/NGxzQx32KtaepK7qJujWmaUJTF6f6XCQT8HGr1EbjwHqiYz/9sfvC14ulqwtPVBP42Vtj8ULkA/1m3crf7KzSHnAgifFv+k+umHOCXmX+jQDYRiUj+EL6A/SVn9P8HWOM2fD24+vtaCUYkm9qzCIYFmbKTnzn+zJVj9vGVzt/j93bhkzY492d6fMkIR1lCEmj1Bnn8vUMEQhGuOEkPEusKdvHawdeYXTSbQlc57x1sITfSwmddqxFr1vW8Q18rzm1PUZ7nZktHNvc7vkakTmNW+GGKshywd6UZvAiwYn0NkYgE4aPLvhFnaBIHm5P79R5o7qIiXMNysZGsDZvjP8wdC9POi5vYlOa4ueXSOVz78AYKWps40q4HRLrx8tmqZi5dfmq3QSYp9duiAXIXmObpd+reoTPYySljTtG3kRJ2Pt9NZA2IrGI9oDLqsiIyduO1HaSmeSa+YBh3gjva7oYOJEE6HevZ1rWDxq0xFw23zc2pY0+lKCPqCtFyAHa90D2gMqcCppxrBuCuqV2DRLK0PGZVnFicxcFmb0qf6d1HOsmNtDA/tIFxrilAaiGyuWEzhzsPc8a4M9B6cTeaVOzhJxfP4vePPI2Uunn9qayTyM48gW9r/6HA5tX9x6ecE/tSOIi25X+UZjvxCzd32r7KodpMjjyxmckWgXSw2UtJaDGn8zJLxCYuXObWEytY6WqCJ79DYaQJR7ab3wfP43W5mPf+t4lTpsRcTIJhyZMbD0PGZ8jUwpyZtQvX4VVweBUVeW7e78jjd44r6djQgs2dS9b8S2h/5x6IhPhyXQ0rbCE2afBUTi4fmPlJVq6voX2UJE840uHn9R3tZGd9nauDv2Wipw3b+j/jBFx5GTzXNZV/uj6J9vwOLjjcHufqkkhYwtMbDxN0XYaLIBdlvE9G82ZYvZny3Ay2d7j5rePrNO8KcOSZbVTk9n91tC90+EM8t7kOV9aX+V7gdyzKqsex43EAnPkZvN1Zzp+dVxB4/QB7moJkOVOLAgm8sKWOdvtZiAw/n3a9jsd3EJ78Xu+r6c37yNr3Jo48Nyu7xvFX5+cJv1rNzgY/GY7+TZwC4QhPbaxFZHyGr9hCnJG5B9eht+DQW1TkuXkveo92bmjmsHd7r+4pb+xqoC6yAH9GJ1c6niCXVnj5ZkoyQEYy+QWfY2drKYf/t4k5lbHJR2NngFe3t+LJ+ho/DP6WiZ5WbBsexIV+bl/snMw/XJ8m8soeYBz1mVfwA/Egxe4IvPRTijSwZ7u4J3w5a4Oz2b5iI8smxhSdNxjhmU2HsWVewbcCD3BS5kEc1S8C+qLa2s4SHnB+Bd/aOiCTrZ6vcV3495R5IoiXfqIH7lpdBnc+T7Hdh83j4teRj/Gufxp7/reRxUbShRGOEDYuKFxIUeM7sHkF3gkzYh92NbLCFmCBrYDHOubie3Ov+VEwLHlq42FkxqfI1EKcnbkTV+0aqF1DRZ6bjR253O/4Gh0b9cnkmqyvc234fso8AbS37yEPcOa5edS7gEedF8PzO4AT8GZ+jK/bVpAn2uGln1LiBsIZ3MH/sb29gldfrcaW8VmutP2F0zP34Kx5U3cXiwYo94vdr1Ak2rFnu7gvdAmrwifw8vM7wHkxDhngsox3yGzfqY8h0eQ1PTLmRKjUYzF8IR8ra1YyJW8KVblV0fPZpMdUBZIv1vVK8TSYcCoIQTgS5tUDr1KaVcrsotkAFDpCnORfyS77ZJq7ZjGB7guPLV1BguIIfls1X15yEfmH1tIFrJKz2LImzMcWX8F7L6yGYIgPtrchgUdssMHlYMfiK/jXC3tZdySX3Vlf5Vr5AKVZIF75GQWAI8fNnwNn8aI4ibWPbeL0acX0wxkLpOT8rgiZ9gieVHGVQLCrlUMtXt6xLeJQZiXfsj1Mnq2L02t+RzU+QsLGo4VfJst3kJbNm7hw4oUUZvRhVSdNKBGSwD9W7ePZTbXYNMHFJ1RQnO3imb3P8J8d/2GMZwwfqriGUFhyufe/LDiyB5r7NtjZskupuvyX5D9/hH2NXbzTNYZF4gAF1SthwWcA3W3nuc21AJRWbmFz28t0BbZxsCm5Oe1Ak5cvdP6RMfZW2JDEIuMphTHxAVqVeRn87OwSAv+IYFglczIcXJK7u28CxNcGT30PAp36YDTjIp7e8zR/2/I3fV/OHOaVzIO6zfDKz/p0bvqF0wNTz+Hx3Y/z6pEHaXZ70bxZVDfO7eaOtudIJx2O1bQ7V/JWnQeRUAvz+X3P85NlP6G8qzUWlJeM/avgrJt4dPdj/GfHfwC47eTbmJirryZOLPLw+o4GDjZ34Q+F9SxFFupq9vHtjl9RqrXhXO2Dc25JepjXD77Obzf8FonEaXPGBF0PzCjP4TsLXYRe0nVfva2UBUvOoiBvCrz2Cz3j0oZ/dvuecGRS8ck7qFgnOFTdzHsHWrsFnue4TuMc+SYVuQ60jQ/BGdfHPvS16oNT60F925O/ykz7Wbz+2m6OtPt5ZH33rEiZLidTP3IXrvW3wkFdvNtyxzDx8jvJfeYwHS1eHly1G2flKzhyxzC5pZZZ0oYMu9iUmUWLO5evrXgQf+sJvZ6XkUZ+URkTzv09tue/B+36M+6auJxpc6/G9cR2vMEwj23oW8C9w25jxiW3kLH9bj0DCqBlFVB1+a8peqmZ5voO3h6CJAiJeLJzmHzh/Theu8YM3HWUzmDyybeS+fhuAl1Bnt1U26d9aQJmXvRdPI0VsPFhfX+pgoETcFbMZerSm8h8chdt3hBPbzx81L8py+1i6kd/hWvdLWaQuy1vLJMuv5PcZ2robPHxyvYjfd7fjLM+S66YAKvuB0Bodko+fDPjdhWxc2s9O+ra2VHXfcKRV1jCxHN/h+3FH5iJGFzjFzNtwXW4Ht+BN6AvIE1ceCbFY6ciLEHueWdfxfSuE1m7ah+HWnxJn8WszEymfOweHG//SO+rAXvRRCZ/5BdkP7kPXzRurHzSHMoX3oN49od6/7jxv0l/Z8F5NzClZRbvrj3AgSYvB5r6kBVthPCuXMCdjnXYA510Ne8Bh5McbLQFuzgg4J7gZN54J/k1z3A6mXb5Hbg2/NxMxGDLqWDiZb+k4PlaOqJxlsVjJjPulN+hPfMd050mc9YFzJjwJWxPbScc9e+eftonyHNOgLfuBUAIjZJLb2FCdTnbo8+S2+1iykd/iXPdLVCzXj9uf5JAJJB35veYHljKqmiQu82mMfMjN5K59wE9a1f9Fv1fb2z4J5z8ffzTzuX2NbeztWkrDs3BNYuvYba7VA/YHmhSkRM+QWTxl7hvw328eehNbMLGr077FaWObMrevIEPe9dyxFZCc2fysbOpK0Cz+3+EtAYe3PAmP/aAFrbzovMcaqqbOGx7CXLG4G6t4byAHYnkKZcNX04lv1r3CAer9axVY6YtpPyEWYhnrzYTMWQv/RwzPRfz4ks7ae4M8GiS56435nQ5yJGtlLY0ksq5vrFJz2bXZc9k2imXk5c5Ed74FXZNkJPh4m7xSd6vKyF3+4MEpZfazlquXnx1v9syXCgRksAl8yt5fnMt4YhkxbsH+fIpkzjcqQ9qBzsO8vLOPWgyzAy5W59sOjL1LAU9kTcOTv4+2XljueWSUq55ZCMb/XOZ2LEbbf8m8tprIbuMxzYcMv0VK4u62Nml0RVqTmkJaWyooyDShMPu0ANRbdHVOW+TPiM9srWbCAEYY2vBl5dBQ7ufQM44SmxHEPvegMj3QOtFVG1eoQsQgPZaXtr/kilAAN4+/LYuQqydTUb+wIOIjd/UVsOz1c/y/7b+P1x2fZ9e+1b2HDkjiQjpwGvfitOu4bI5yXLEVkaa/c20+Fu4eeW13FRXS3HQq7fR6kcfDuiZnqrf4Mmnv8J/6MBY2th4ZKMpQiZF/WUjEvY1djG11JIesLOBk7ffSlakBZfLoQ8akUi387z68Gru33A/UnfU4+1Db/dJhABU2RroysvgcJfgQ8vncvmJY0GM13/Pu//oniEnqwiWfQNH+WyuPS/C717bzbp9zd32O6tiEuXuy9C2Pw67XtRjMXIq9BShT/9QTwEKukvDgs9wAWDT4NH1Nd1SERdlOfnyqROZXJaji7A3fg1dDXDKVeRml3HLpYVc88j77OnawOGWekqyXVy67GrEtpeYXTKTqsABVu7bRCiwkhJmk5/p7ptoHgFMKs7iW2dMISfLCRf8Us9Kl10GH/g20+wufnLxLB5YuYfGzuQuBFbyMhx84aQJzBmbB5U36vdr0x446btkFIznpg9V8JuXd7G1NvVK2mBQmZfB10+fREl+JlxwJ7x2h+4GdcpVVLhzuPWSTH776i4O9SHbXpbTxicWj+OUqcUgv6EHU25/pnvq2mSUnwAnf5+xLg83f8jN/a/upq796LIzFXmcfPXUSUwqzYZzb4U3fqWv3p5yFbnZpdx6aQH3vrSTvY29p9502jQumV/BhXMrgI/rCzY7nocFn0WrWs63xkly3A5e33mkW/ahiUVZfOOMyeR6XNH75U7ILIKTvstkh5ubLp7Fn9/Yy9wxuXxqyXiENgnOEvokcMaFMOMiPoruCvzk+4cIJGQaK8txceVpkxlTlKX7jL9+p97XnXIVxZkF3HJJNr99dRcl2W6+euokHHYNzrsd3r6vexppR4b+/E87j09Liduu8eymWoKjJKVSc2eA3aKKnfapzIjsxNt2EAqqODUoeFNqNCB5QrSRh369rAliirKcfOmUiUwpz4Hin+pZBjvq4JSryMkp5+ZLirjnpZ1kOm1844zJuJx2uOBX8Oav9axaS77KQs3G9Rdo/Gv1fk6dVsyH5lUCl+sJBbY/A/M/jZhwMleOl2Q5bWw+1MaXT5nIxNJsOCd6jx5tRji7G+Z+BGZdymWA3SZ4bfsRPr54HAurCmDcD3UXod2vpM7iZhDogJCf4Bt3cef+p9ka0seTYCTIHat/xnVtAaa1RYVcZgH0z0agj2HBLuR7/+SB5g28Gdb3H5ZhHt/5KF86sA17w1aEgOJwPVtaG4HuyREOttYT0hrQiLCvbS8/t8PVM0/F3TGNYEM16+vXMr4wi3PmfgFPrW75OqtsPE/sf4n19evIFycwo7hKd022aXDubbD2j3rc1bxPcbYQCOC/7xzAG+xfOn4pJZ3tWeREWmlpTiFCQgHCfr3/KSws4qMnjgUxTr9ftj6B8wOXs+m1TDpsb9PR2kJJjouLJ13cr3YMNyo7VhKM9LhOu8af/u9E7nv/Dt5veB8poePw2VR1ZnJT5B6KPS7d784a+NcHGjr83PbvV/jqIX1luWvhlUw84/+44q/r8AbDLJlQgD//X7x9YBPNXUEmBq7lP19ZHjfp8gbCXHvf3/l6x70UZ7vI+/gf9NR9AP/5P90Naso58avXBkZ6XoBTr4bXfq6//tBvevYhDHTpgVNRU+HK8fO5L1RrTpwBsh3Z/P7s32PbvMJczeFLr/Qubnrjnx+H9sO8MuFEfhc4aL5d3dBJJJzJZ6tu5ptnxluM/u/BZ9kUupdst50bPvBNzhp/lvnZc9XP8ecNv4PWA5SGI/wklE3BWT81a1QA0QDca3n+0Fv8ye7Vsw15ykDA/JL5XLP4GgDafEE+9YC+EvX10yfxwdnR+IquJkKPfYu9u7YCemaZvAxHt9TM79a/y51r7yRkyX7i0Bw8cM4DZNh7EbgAz9+gu/WVzIBLf9e389lX2g7DQ5/UB6EZF8Gyr+upP2ujmYjmXA7LvtG/LCApONTayUdXfBVvpAlHpJhbP3A7Z84ooysQ4uuPPMz73r8D8MHKT3PLOR8fNSJEoVCMTIzU6bPlTm5x/oXPutoIuXP5ZIcfJ/Bru4MGmcN4+Wn+9X8f7WblVkRp2U/osW/yy1AN72hhyClnTuVytjVsJti8h4xQgB8Fs5i48Euw8Chi+rzNyMe/xd/at/OMLQBZxdg9JYTCQexth7mnAwrRqG7sIhiOsHnej7jkog91282Pn3+EJw/+mVzZTrGmW6TmjDud08ZfzTUv/wqvfRPlOR7+fuHvyHfrC5LNvmY+/eRXqG3rJCM0m7vO+mGsZs8gEolIXvzFJ5gQ3El47DKmf+433TfqbGDv3R8kFJbsmP1dLrj00902+eULW/jX/p8gRSdnTVrA7afePOhtPRpSZccaHRFHw8xHTtQDLgOhCI+/d4jWgO6m0hUI0SH3MTG8hyyjHsRRBP4UeVz88COnccStF0Oqfe95bn9mm1lU7qOLxtLia8Fp1wBJV7CLhoRaIAebuygJ6+ncnHZNt7YYGK+NdKaJGFYKdw5MOj2WdtDIDZ+KbU+aAmS1FuS3TeuRSHKcOXx6hv4wtAfb2da0DfzR7A5Oz8AFCIDLw5tagN836G48+a58Pjn9k7gcNiKik/frt8dt7g2E2d+lB6y67TZOLI0P0Dy3YC6fbuuASIg6EeGW8jG0Vs6LP6bdyWuzPsifMvTJbqG3nSURfRDa3rSdSHR1KMftMAP+zbgQXxs8fRXBxmoAXnOdZlpuOPyeeYhNDZu4a91dhGQIt83N52d/HtBXkN6tf7dv56Y5Wgclb3zftu8POeWxmJIdz+p1AAwBMuOiQRMgAHs63qUkz4dNE3gCy7nnpV28vK2Om5/cwpGGCuyRYnIzHLQ73jbPvUKhUBwtHzlRT+2+icnsso0lBOBrJRPBklAGrdHUyjnFG5QA6YFI7hjumzyfd2wSkJzY2sA1hYv5bhfYQn68SG7Nz+bAlNOPav/Snce/pp/CM1Fnj0kdTfy06CRE+2FCgXaetPlhwilo0bmGoyV5QdF9HTsRUlIW7uCkiAMcmWz0HuLF+t8TceuLhbJzJjnOPPM7Oc48ZKeeijfi3sr4klCyXQ8YTRNoGXpGwEBnS9JtutqazBpK+QXFSbcpr9hFRHQigUx/tzn/iEO5YyVhQlEWiycUsGZvE0++f5icKl2EdPrD+G0HmI5Nr8lRMLHXSpTbm7bT4G1geUW8JaM0x41j2QW0vHY/E0N7+Ov+g6Blc8LYXKaUeGjxt5j1GCKiiwPNXRRnxwKrDzZ7KY3oIsSeVRDXju3uTFq0IIta9qFJ2X2SaFQxzqnUzeljF+sr6XtXwtKvxW3f6G3kzUNvEgh2wYYHwObDDzxt8xOJZOJxeLhh6Q1UZFXw8I6H8YV9rK5dzSwjsMoVc00KRUK8tP8l2gP9dxXxR1p40u5FShvZzmyuX3o9RRlF3L/+H3T6Q+xq30AodD726tcgq5i9sgqvTRcmU/KmkmfNFtRxBJ78Lhd5A/htLv6bm0eNCHHLqltYUr7E3Kwr2MUze5+B3EpyW2u5sVNyoL6a1Vl2umxO9r/9a6pcegDmZRxmr6+T4h0uyBkL+96Cxl34QxFecZ3JE+6L+FzREWjdB7Xvw5zL2d60nTvW3kEwEsRlc3HtkmuZmj+Vx3Y9RpOvibW1a1lesbznExMOQVsNXiSviy7adzzc73Obimn505hTPEfPSGVUQT78vv7hlHPoWvJVXt/3HB1HG2iYwMqDK3HYNU4oq6J9/2w6/RF+9YJeE0GgcUrZ+eyXj1DXVceqw6v4QGVqC+T+tv3sbtnNKWNOwdaPbEnBcJC3D7/NxNyJjMlOXnsGoMHbwFuH3iKQWEU5CeNzxrOobFGf2wDQFmjj9YOv4wvFuzN5nB5OHXNqjxay9468RzgSZkHpgpTb+EI+3qh5gxZ/S7/aZVCUUcSpY/qYyCKKN+Tl1QOv0hmMT+CQac/k5DEnk+1MXeV4R/MOmnxNLC5b3GvChuEkGA7y1qG3mJA7gXE5qVObN3gb2FC/gWUVy+LcQoeb6tZqDnUcYlnFspTXTkrJW4feosBdwIzCGUm3OVaYXJLNgnF5rN/fwoO+peDR42MygHX25bhDuYRdr9Mpqvn7lr/HPXfZzmzOGHsGjh5q+2yo34DT5mRm4cyU23hDXl4/+DoLShZQnJl8YglwoP0Ae1v3sqx8WY/H7AsdgQ7ePvw280vmx5KzJKG6tZrqtmo+UPkBHFryY0ZkhN+/93veat4KuWM4obWe7wQc2F++lYXAN7VM7sl205GRw82rbuGm5TdRnpCNsTdW7FrBYwdehNwxjG+t5TqvHc+7D7HY7mO1Bi9lurn05O/RvnUzzuA+MtuSi5DDvt1kyU6mhyRfkxkEJy5hded+tjZtIS/TQV2bJNJ+Im/uatDdQ4GVO48g20+EzHXkZTr446YHmF4wvV/tT0VFVgVLK5aafZrTkwdtEPEmz45V11CPX0jedUum5HSfvociId6ofQ6Py46/q4gNu/No6QqQlzly67woEZKCj5w4hjV7m+gKBGltaSA3006HP0REHGGy3ad7NJbP7XEf/rCfn63+Gb6wD7fdzcLS+PiMgllnkvXeX6lp8TI7tIlVzmV89MSxeENeApFA1BICEeHlQFMXC8bF4hUONHdREqlD0wSOwgnm+68ffJ376l8HexfnhkN8vqMekZ3gG2lYQoyCclWn6AKk/bAeDGpxFfrde7/j/Yb39Qqg4UawofsfRsAt9Wqi43P0FfgFpQt469BbrK1dy+dEhW5ms4iQFbtW8PDRTpKDuuDKknDDkhsYm61bq2YWzOW1jlV0atup27aayjd/Cpqd7TNuIGjTY3lOG58wkV95l1n07bJF38fvDPL47sfZ376f/e3drUfZzlxu/OBNlL96F5mNO0wrz7bN/6EqogvDZZ0BpvkCCD/IdR7T43Vj3hk80XYu5XkZ2Cvm6iLk8PsgJX/a9Cd8YR8OzcFVJ15ldmyLyhbxXPVzrK9bTzAc7HnAaTsIkTDP2gI81LgeOnYc1elNhobGb878DYX542HCKbFiTRNOgdOu4YkdD/PorkcH7XgGn5j5YcYtmMN1KzaZAbinTC3iu2ct4/uvvUltVy0v7n+xRxHyi7W/4Ij3CBEinDnuzD4dNxgOcse6O3jvyHs4NSfXLrk26eThQPsBbnr7pn6J6dtPvp0JuRN63zDK37f8ndcPJrdMtgfa+cjUjyT9rK6zjttX306ECB+f9nEunXJpt218IR+3rblNt1gOgLAM9/ncAjy681Ee3/140s+e3/c8Ny2/iVxX99ogKw+u5L4N9yGRnD3+bK6YfcWIcMULRoL88p1fsr5+PfmufH571m9TCqTfvPsbtjZtpaajhv+blZ4U0/6wn5tX3UxHsINmfzMXTLwg6XYra/TzLRB8a/63WF7Zy0LIKOcjJ45l/f4W3mUcLhw4CWKLaPzdt5QsLYPMgnfRRIQn9zzZ7btdwa6kzxjoC5C3rbkNDY2fnfyzlM//nzf9mdcPvs6awjXcuOzGlO28Y+0d1HXV8daht/j+id9PKQp6oz3Qzk1v38SB9gNU5VRx+8m3J32eOgId3PT2TXSFulhXu47vLPwOdi1+yiil5C+b/sKrB18FYEbpfL6/+CIcz14H0QWUZVMvIThhMfe9fz+tgVZ+uuqn3LT8JkoyS/rU3qf2PMW/t/8bgMrcKq5ffiueZ6+DriYuDbtY7Xbizy7jmX3PMzF7As62feR27Uv6u9tCtRTIdiaHbdjKpvPNU24l8M4vebf+XbJddiK2GdhlLv9Zd4CTJuvi7L/vHMQu8yi2nUC2axcbGzaysWFjn893b2xs2MiX534ZIQSZ2focTwTaiURkt4KLjQ0NvO6J8HpmhNyDf6Zy0lTG5sTKKrxR8wYN3gYKspy0ty4jGJY88d4hPrOsatDaO9iMnCWlEcb0shzmjslF4qe5y09XIEREShwEaXBGV/LKehYhDd4GfGH9QXzvyHvdN8ivwlU0noq8DD5g28IHZ5cxpzLXXJ0UAuyaICy6ugWnH2jqoixch9OmIfJ1EfD2obe5f8P9YNMnxs/ZAvxz05+Ji/uRsrsIGbc0llvf4pIVkRG2N28HCQ5vMxkIMuyZZLjzGSttXKsVMykvlhpwUam+2tvka2JPVzRDTVSEmFYFwK7ZybBn9O+fzcV4aeNaW1ks5R9w+vhlAIS1FjYfWBtteIj9O/8KgMMmOHmsxSTZsAv2v62/nvMRxAkf5ZPTP8nlUy8nx5nT7bhVOVVct/Q6xhbNhPPvIHfsMiqFE4TGVoemB9E6s3BkePALNz7c+IVbt0zN+xQPOy8FIfT6IOXRrE7eZkLN+zjQdgCASydfqlscoiwu01OT+sK+3ju7qMvdfhEGmxMNrf/nNuGf26ando0Q4VBH9F5Z/GUomABTz4UzfwSazRRsg3FM49/8kvmcPOZkJpdkc9PFs5hYnMV5c8r47llTsdtspihoM9z9kiCl5IhXD4Lc2ri15/MXJRQJcff6u83nNBAJ8PM1P2dXc3yGptrOWm5ddaspQHr7PQb9mfBLKXmvXm+H9VnRot11Ypus7G7ZTQTdVe2h7Q/x1J6n4j4PhoPcue5Osz1um7vf18gu9InI/3b9j3Bfgsejv+nNmje7/SbjXjvceZibV93cTditObzGzBgH8MK+F/j7lr+T7ljGcCTMb979Devr1wN6oov9bcndX70hL9ubdKvsQIXfQHiv/j06grrV8ondTxBMTEeO3uc/ulNfWJBI7n33XtbWrh3Wdg43sytzmVmeQ1gLckTmgc3Je+7TadHyyXRk8vWFXyDXmZv0GXj78Nsp9/vmIf1+jxDhf7v+l3Sbus463jj4BgCHOlNnjgqGg9R16Qtx79a/y73r7+3zs2elK9jFz1b/jAPt+thT3Vad0u332epnzeKNa+vW8tsNv41zg5VS8s9t/+T5fc8DMCVvClcvvhpX5UI9kUHhJJjzETj5B5wy7jS+OOeLgD4/uPntm2n09p7B78V9L/LglgcBKMks4fol15NbPENP2FAykwmTzmX+RL0UwbPVz9IedUnO8dfqMawWtjdtR4ZDOGSIaREbTDsfh83J9xZ+j+UVyxmfO54vzP0EoCeYWVvdxJrqJvZHE1FcMfcTjMkeM2hjnSEiXz7wMn/b/DeklGTn6p4VGeFOjrR3T+rR3NzABncEBPgIcPOqmzncoc+1IjLCY7seA2BC3lhOHrMUj8tOTsbArGZDjbKE9MBHThzLOzW7CUtJfTTjipsAe52Sk8L0KkKsrg7bGlMMPhNOxd3yD05z7ue05WUgBM3+WKYip91GRHTFFSYEqG1oJi/SjNPlgPzxvFP3Dve+ey8RIngy8ihotrFfhHn84Cu4dk7h8qmX61/0tcRS0eZEK2S7c6Bivp42de/rsOgKQJ8Y+MN+8LfxTb+dJZEMOOXHegzCO38FEe8bOb90PnbNTigSYnVXDZPBFCEv7HvBdMO4cemN/Tdnrv69nv3FHn/M08YtwSY0wjLC2qatGBUxDnjfx+YqpchVFb/i8q4e3IzdbRaqEkLwkakfSbm6bJJZAOf/ghnvP0DN/hfZ5spDnvU7hBD42n1c+1c9XuV7p0zl9OklBEIRqtfpg9TE4qyYCAEaDrxlThYNq47BjIIZeBweOoIdrK5d3aNbjREP0qgBNicLSxfyg0U/6Pl39EJ7oJ0vPq8PGI2+6ECROwY+8te47Zp8TQAsrVjKtxd8e0DHTMaM8hzu/vj8uPec0QxwPblBBSOxyVV1W3Wvx4nICL/d8FvW1umTran5U9nTsgdf2MfP1vyMHy39EVW5VRzpOsLNq242n88rT7iS08ae1uO+v/Xyt6jrqqO6tfd2GNR21ppxaF+d+1VOHnMyAA+8/wAv7n8xJgyTUNMZnxbywS0P4rK5OGv8WYQiIX61/lemsP1AxQf4xvxv9Nu96a2at7j73bup76rnrUNvme3riT2te8x76WsnfC3OivXQtodYsWsFB9oP8LPVP+PGpTeS6chkQ/0G7l5/t96nOTwUugvZ176Pp/Y+hdvu5qPTPtqvdg8WERnhd+//jlWH45OobGvaFrdAYrCzeaf5rO9v308oEuq2ojwcrKldY75u9jfz6sFXOXv82XHbrDq8yswGaRM2wjLMr9f/mqtOvErPeniM8tFFY3j3yY14cXLEUcH/mubjAM6bXc4FkyZwwaT48/Rs9bP8ZdNf2Ne2j7rOOkqz4r0NIjISd75XH15NTUcNlZ7KuO0e3/24eW+0B9qRUia1SrQH48X56trV3P/e/Xxt3tf6/Px6Q15uW3Mbe1r1rIbG9V2xawXzS+bHJ74Jec1FQ2O7Nw+9idPm5Mtzv4wmNB7e+bBp2RyfM55rl1wbW3ipmA+X/znu+GePPxt/2M/ft/ydem89t66+lR8v+3FS6yfoXh1/3PhHAArcBdy49MZYvYuCCXCpnvb60qbtvPvWRrpCXax3tzMWXSD563fhGhObo21u3Ioj7MMloUpq5njstDnN8SsQivD0hnU0dgT497oD5ncLPU4umTuTj9ju6tO57gtdwS5uWXULu1t380z1MzhtTs7IL6QdEEQ4dKSB0tz4ucGetj20afqcBaHFWZZ2t+w2hewlky9hZt4Uspx2Mnqo0zQSUJaQHjhhTC5jo+6S4YhEk1lkaQG2i5BuRfCk9t8EaPG1mK/3t+9P7jtfFR3AIyG9HgXQaqmW6bQJIsIbZwkJhiNEopNPp01jo4jwy3d+SViGybBncP3SH3GDawJjpA1CAf674788sfsJ/cvW1LmGJQR0FxvQs2q1RFdJWqv1amJdjVRFbPpEdMJpMRerQIeebjZKhj2DuUX6Q78m0KCvXrqyCYQD5orsjIIZR+dP6YrGvPjbdWtOlGxXNmVuverwFr9+TlqIsNseJlu2M7vA4gLXvA/2Rl2KZlwEGXn9bwcwrUDPwtXib6G2U8/dXuxx4YkmKzAqpO9v6tILTxKt2O4p0VOzAnWHY2kVEwcwmxYLpH+n9p2eV7yilpBmuwsEFGQMvEiYx+ExV2mafd1T9xoYIqTQPXyFkAwR4g+nTsNq/aymvaZHwRKREX7//u/NVcs5RXP40dIf8Z2F30FDozPYya2rb2VTwyZuXnUzDV49RekXZn+hVwECmJPSvW17e97QwtammPXG6pNvTGCOeI+k/P017boIKckoocCt3wt/3PhHXj3wKr959ze8U6ffd4tKF/VrAmNlacVSyjL1+/h/u/7XpyQBxoTMrtmZXxIvLD827WOcP+F8QBcrt6+5nXfq3uHOdXeaCRuuW3IdNyy7gTEePU7nkZ2PpFxdHkqklKb7DMDMwpmUZ+n+7dbrZsVq/QhFQtR0DH8NjWAkaF57g8d3PR7Xt0gpzXNaklHCj5f9GLfNTSgS4s51d7KlsQ+1IkYpC8blU56vPwut3iBCunDYBJfMr0y6vWH1h3hxZ7CrZVdc3ymR5iq1QaO30XRjAv0apXqurRZCo79dWbOSP238U5+sgoFwgDvW3sGOZt1d96xxZ/GJ6fqq/47mHWxpir+2L+570bSaXbUo5ir8yoFX+Nvmv/H47sdN1+oxnjFcv+T6PsU6XTjxQj427WMA1HTUcOuqW5POi1YdXmWmrM915vKjpT9K6b41rWAaMwr0fnJtYBuBqNW061D8b9pYvwUXfsYHBTZXTtJELk67xqXRa76zroOddXrbPrxgTPdivQMk05HJdUuuY1y2Hkv22O7HWBncaYbk1tV3r02zw68LSLuwm8mADMuScT1KMkpYXrGcIo9rxAsQUCKkO0GfXixr/d8RQrB8Ssylwh2aSgYB9mhhfCWpA80MrJYQidRdmxIpnqZPTgGq9YEt3hKiERFdtHQFzSrRta0+iqOZsfY6I9yx91FCEX2wvnbxtUzMm0hu/kRuCGZSKvU7+h9b/8Fz1c8liBBLBzv+pFhA+toH4L1/U731UeisJzMcpAQB8z6lZ7oyBAFAgvuE4UpUG/FxUETAlcPLB142V3YvmXxJr+ctKa5oNe9IqFvdi1lRoXEk0sohwqyWQSTgkR2cVmrJXvbev3QBY3PA3I8dXTvA7PAgNsEQQjCpRO+E9zToFp89R2Kd6ySjGnnUelbXEOsg4zrX5mrYv9oMZG4PtrOtuQcXjpZ9RJA0RTtIY+I5EIQQ5n5MS0gCoUiIVr9+TY1UhsOBaQmJpBYWVtERIWK6HiQipeRvm//GqwdeBfTretWiq3DYHCwqW8TX538dgaAt0MbNq2423SE+NeNTnFt1bp/aOyFH9wM/2H4wqftLMox7qjijOC5otDJbf14lMqU1xJjgTi2Yyo1LbyTXmYtEcv9795uuI/OK5/HtBd8+6tV4TWh8aLKe/vJgx8Fuk9tkGC49swtnk+nIjPtMCMFnZ36Ws8bpKbS3N2/nF2t/QTASNGNzJuVNIseZw41LbzQn/f/a9i9ztTYV/rCf1w68xhO7n+j132sHXutR8Esp+cfWf/DCvhcA3WL2w0U/ZFbhLEB3/Us2IUwUJz1ZxbY3be9TW5/a85S5ANIXtjRuMV1rDPFc7603xTfobj772vSFnIsnX8y0gmlcvfhqnJqTYCTIz9f83JzEpqLB28DrB1+Ps0YONfVd9aw8uHJAxxRCsHyyx/xbky7OmllKQVbygN7CjEIm5+mLX8lEiHG/OzQHH6jQrX4rD66kvitWMffJPU8SisRb9tsCyd1Mre6n317wbabl6wthL+6PuSulwohd2tyoB92fXHkyV8y5grPGn4XHof/mFTtXxLYPB834l6n5U5lXPI+rF13NpFzd9dqo0wVQmlnKDUtvSGnNSMalky/lQ5P0/mNf+z5uXX1r3L39n+3/4Z7195gW0BuX3thrILsRl+OXPl7O0vuXYH3sXvWFfOxp3YNT+pkYEIRK5qTM2nnurDJyMmJ9Y26Gg3Nmdq85Mhh4nLHEPgAP17/FG1l6H9LQEC9C/MEw1ejP/HQ8XDTpIj4zUy90Xe+t52CHXrrg4skXp8XSerQoEZLI2/fq9S3e/Tt0NVGWL80A8fJwBTZChIFduT1bQYBumWeS+gMLEbOG7F8NIX/c9xw2XYSAXiFd/3+XmRnrwcwwfiJ6cPOiq8xVevLGkY/GjcEsc+Xkz5v+zKtGsKvdBZmWFeysQijRB1P2vAarfsvefa+Bt5mqiA3hKYulanXFOmsjZa/BwrKFuu96JMIaLUjImWVaYSbmTuSE4qOsdh0nfOJXTk4ao2e0EjLCahHkbYd+vYpDkg+0Rf1d22thp+67ytQP9mrF6onizNjk0HpNJxbp52XPkQ6klKYYyc9yxrJTRJMZ1PmaIBwyfY0BvTDaiivhmR9yQiBs+suvOdx9kAN0K1TLftqRhKLB64MhQqz7MawdibT4W0w//cE6Zl9wReOdeprQJ1o+9rYmt0JsadrCs9XPAjA5bzI/XPRDc/8AJ1WexJfmfinuO5dPvbxfxZ+MYNSwDHOgI7kYSsSIY0m0GFpdOZKtpkdkxHSlGeMZQ4WnghuW3mBONEBfuf/+id8fcHadk8ecbF73FTtX9Lgae7D9oNnexeWLk24jhOCKOVdwcmXMtcuu2eNWYQHy3HncsPQGijP05/evm//Ky/tfTrpPb8jLLatu4bfv/ZZ/bP1Hr/9++95vUwbOgz5JNyZnE3IncM3ia8iwZ5jtaw20dhMGwXCQnc07495L5SLY5Gvip6t+2qe2PrjlQe5Ye0fKtiZi9CGZ9ky+OPuLppAzLFlSSlbs0iei+a58ThtzGhC7X+yaHV/Yx+1rbk8pog60H+Calddw34b7+OfWf/a5bQPlzrV38psNv+HJ3d0Dx/vDuCKHmZHSJpxctiB1djyILbjtaN4Rb/WQ0jzfc4vn8rFpH0NDI0LEHAvbAm28tP8lAPNeBlImu7C+X5JZwjWLrzFFwdN7n+4xTuzNmjfNuI8lZUu48oQr0YQex2dYIDc2bDT38erBV805yIenfFgPmI6u2o/PjlkPCt2F3Lj0xn4vQgkh+MT0T3Be1XmAbv203tuP7Hwk5tWx5Pq4wOtUzC2ay6TcSdg0wStZEELq8Z9RdjbvJBAK4JRBJgQEtorU8xC3w8aHToj1tRfPq8DtGDqLQq4rlxuW3kBJRgkIG49nh6mxS1pb4kXIO4d20qHpc8CFLn3h8sKJF/LRqTG3VOuzO1pQIiSRWR/W/x/yw8aHaQ+2Uexx4bJl8pVxHjOIZqu991OX6MqSMijRcIUK+eDg2rjvOe0aEaIiJBoXcrDZS2m4joiQHI7mLr9symXMLpod22e0Vkixr50fLfguea48AH5f+zpvaUHILu+eunf+p3VXK6EhhaBaiwCCKi1DL1Jni/56qyBIECE5zhxm5E8BJKu1EG94D5kuLJdMvuTos9pYhY8vfrXohIpKnOFKNMK8LgK8ZxN4hZu5fhueXU/oFd7fe0ivwCw0OOETR9cGC8bEw7rKObFYt4R0+sPUt/vZXa+LpYlFFjO1YQkREQh6412x3v+PGa/jOLLN9MFeU7sm+SSv8wgEvTQJCVELwXCJEGtQ4XCKEKem/86QDKV0A0q0kqSa9L1/RE837LK5uHbxtd1W6AHOHHcmX5rzJYozivnYtI9x+ZTL+9Vea0acvsSFNHobqffqq6VWixvog74hTA23Kyv1XfXmanCFR19ZG5czjuuWXMcYzxgWly3mh4t+aFqTBoJDc5hibHfr7h4TKBirwhpat3o9VjShceUJV3L2+LMpySzh+wu/z9zi7nF3RRlF3LD0BnPy84f3/8AbNW/EbZPofqL18p+I5rN7as9TeEPxllbQJ5ZGwHa+K5/rllxnup9YXeYS+/g9rXvMa2Jcu1SieFPDJnNlvC9tPdhxsE+r/xEZMa/BwtKFOGwOcyW6pqOGdbXr2NK0xTxXF0y8IE6kziuZx3cWxNwTb1l9SzfrYmLChhf3vWhaSoeSjkAH+9p1603iPdBffGEvxdkunJqTTy+ZSGmOu8ftDRECsK5unfn6QPsBart0MbqodBGlWaVmhrFXDrxCi6+FZ/c+a7peGW5RkFqEtAVjY57H6SHTkclVi64y30uW1dHAsG7lOnP55oJvxqUsP7fqXPO+NBJNPL4rFucxr3he3HGvX3o9swtnMzF3IjcuvbHHlMI9IYTgs7M+y8WTLsZlc3W7x8uzyk2vjr7u79Ipl2LTBM02wfoMib21Wk9hD7o3QdCLDRgbFGSMm9/j/i48oZxZFTnMqsjhwrn9Syd8NBRmFHLjshtx2TMQwEueMO0tzXHj/mv730YjjACW5lSZ7394yof52LSPkefK47MzPzvgBabhZvTYbIaLwkkw/gOw703YvILWeReQ4bSxeFw5Z9rqea3Oxna7YLu3rtddJVpCdrfsxh/2x622AvrENCNPT4O7dyUtGTGXAJsmsDl84NctIKCLkSWROnwOzSw0mBhXQH5sxaIs4OeGpTdw01s30R7ez2/sXpxuN92mA+OXwef01aRGbwPtL30dgAnzvg5jTolt14MlBGBxwSw28zj7RJiHGtaBpq/i9rdWQhxWc29CR12Rm0E209FYx34tgi8CbVoOC8MtiEAnrPszbItmCZp8JuQm9/PtDzMKZvBGzRvUddXR5GuiwF0Qc7kCdtd3UN2oW0ImFVtESN44yMijLtQBwS5KM6PXzdcGWyw+w201LJl6GqsOr9KzjbXuictEBpjxIE1EzPtgsOIzTBHiTS5CrC6DwylCrB2sP+xPWi+jr5YQY8I4JW8KHqcn6TYAZ40/i7PGn3U0zSXXlUu+K59mf3OfguSNLEoA0wvjLSFCCCo8Fexp3ZPUEnKw/aD52hAhAJPyJnHXaYMXUGlwxrgzeHTno7QF2lixa0VSwQAxd5VpBdN6dduwaTYzi05PlGWVcePSG/nJWz+hLdDGfe/eh1Nzsrh8McFwkLvW3WW6n5wy5hRz9TcVmxs289NVP6U92M5L+1/iwokXxn2+pXELO1t0i8ZFky4ixxlbiCnKKKIoo4gGbwNbm7Zy+rhYQTbjHtPQOHnMybyw7wWq26qJyEi39hjXPt+Vz/1n3Z9yweatQ29x9/q7Ad1NxwzWTcHO5p2mO6zRB5885mQe3vkwDd4GVuxaYQoqj8OT9F5fVLaIb87/Jve8ew/tgXZuXXUrP1n+E8qyyrolbAB9IeDpvU/HTbCHAmOCDbooO9xxuN81KAy6Ql1kOG3MrSzko4t6X30v95QzNnssB9oPsPrwajPIf3XtakC/5gvLdFfhSyZfwhs1bxCMBPnvjv+arpHzS+bHZUZM5Y5liJNMe6YZr5fnysNtc+ML+1K6zUJsIak4s7hbWl+P08M5Vefw+O7HWVu3loe2P2QugiRbNMx15faYRrg/aELjUzM+xadmfGpQ9rewdCFjs8dQe6SBV7LCnOMN6O7NRZPZ2rgVEfQyNigQmht3Wc9xqZlOO7df1nPiocGmJLOEs6rO4fG6zWx0SeZ2HKLVGzS9KN49sg6NCJOCgpLcmAu3EIIPT/kwH57y4WFt72ChLCHJiGZNIthFazSAONeVC7XvM13awZHBjpad3fw5EzFEiOHzH5bh5GZTTdOFD8C+N2lJsKA4HfqkyghOr2lsozhyBK9DmCvghqXDxBp01bKPsdljuX7p9WSGdXeyX3l3mSvBybCu2hp+7SZxlpDuneainNhkuTnqh3zJ5EsGVmSsB0uIpgmm5cxDI4KUEIgIwrKIgtxo577xYTAmpvMGp8Mz3d6IZT6rzMswq6K/sasBX1BfqbeKE4RAls6mXkQgZLGEbHoklrUMoK2GeSXzTN/O1YdXd29Eiz4AN2sxS8hgxWcYwqIt0Jb0PjfEiUDEF4IcYgxLCKTOkJVoCdnftr+b1SQYDrKrRX8WEyf7g40ZnJ5CDFkxLGs5zhzTT9iK4ZKVTIQYmVE0NMqyyo62uX3GZXOZ7hxbGrfECSiDI11HzGw81pXjwaDSU8n1S67H4/AQIcLd6+/mnbp3uOfde9hwZAMAS8uX8tW5X+2175lZOJOp+VMBeHL3k93c/QxXpWxHdtLaKMksoxATIVW5Vcws0OMIvSFvXGyAgfHd6QXTe7QYW/v6vhSbNPoOp+Y03WHtmp2LJl0E6NYaw5J1/oTzUxbCXF65nK/M/QqgL0LcvOpmdjbv5JZVt8QlbDDqYT1X/Vy3wpSDTWLCh2TxGX3FsIBl2rtbRFNh3NNbGreYAdaG1Wlm4UxTrI7NHmsGs7+4/0XzvFw6+VI8Do9p3eotJsRa0NMauzeQBCIXTrzQFCeGO2J5VjlLy0d+tW0rmtC4ZPIlhG0uGmywxhaCxp0EI7pLpBb2MjEgqMuYFPPqGGFcOPliHFFL1X73DnPOV9tZS533IBoR5gdsaO6+x+CMdJQISUbpTKjUO9LWhm0gI+RqLmjex4yIDRwZ+MP+XicVRnasE0tPNPOKp8qgYrpk+dtpadNXNI26ADa7ni/6YLOebcnbeBBNRvDaYyIk35Uw+czIA+NGjWbSmpBRyjV+J24EIc3OHWvvSFlHwejcHZojblUViCtAmMwSUojGZBk1+Wo2M1vDgOghJgRgVuk4KoOSiJSE0XCHp+Kf9cn4japO0lP7DQJjPGPMAcEIHNc0QVXU9ertPbGVqYnF8avsrcVT8SEh5KfUnq3nM9/0SPwB2g6R6chkTpEupJK6ZDVXA9DoygYhyLRn4rb37ELQV4wsWxKZdKJjrHrmOHOOumjW0WB1JUrlipI4gQxEAt0m7TstiwiJbk+DTVXUdL6vbV+vmaR6m4gaIqS2s7ZbELXxG8uyyobtmpxbda45aUuWrco6KRyQJTQFVblVXLv4Wj2Lkwzxi7W/MI+5oGQB35j/jTj3k1QIIbh0sh7c2uxv5rWDr5mf7WzeGZukTzw/6TNm3EP1XfWmq2JERkwRMr1gelz63kSrWKu/1bx+vYlia1/f0+QTdDcyY1I8r2ReXNvPGHsGuc7YZMZtc/eacOH0cafz+dmfB/Qg9BvevMF0PTISNhjn0Rvy6slQhpBEF8eBiJCu6CJQKhGWDEOEhGWY9fXrqe2sNa0zi8rj7/fEooYzCmYwrWAamtBMS2xvMSFWEQK9u81C7B5JtUCV68rljHFnxL33oUkfGtiiYZpYXrGcTHsxYaHxrDuEPLKDva17CYT92MJ+JgQEjdnTet9RmihwF3CaPQ+Ag85aNtfpbo9ra9cSDEfQiLAgbNfLKhwjjL67bLiYr6+Yt4X94G0lx693UNOkHRE1XfdUeCoYCZq5vUszS03fxpTfqVgAjkyCSNqjWXgMs7LQgkhC1Lf7OdTqJT+gr3h22olZQpKtRhsuWdEVc9oPM03a+WEwE4c9g0AkwK/X/zrpSrfRuY/LHtd9ELc5wBHtqJOIEPztLA5HJ0HCNjjZGhwZejwHdLOEAEzJgzk+ARIiaLhD0yie/gEotgzo8z8zsDZYEEIwPT+6+tnYPS4kFNYFQ6bTRmlOvPtdXW5slbq0qxW2Ph47jxNP0//f1QRBrznIHe483D0bjuGO5daPOZhuUdaJTrKiUsZ7g5ESuD9YXRlTWUKSpblMnKwYq/Y2YWNK/pTBa2ASjLgQf9hvBo4noyPQYfrap0pjbYiQkAx1W0034kQSaxEMJZmOTHPiur5+Pa/sfyXuc2MCPDF34lH7j/fG5PzJXLP4mjgr2ZyiOXxv4ff6Jcbml8xnfI7eZz626zFT5BlWkJ4m6dbrZdxb+9v2mxmpZhTMoCyrzPS/T3U/Ama/kgprX9+bJaS6rdp0r0kUgU6bkwsnxdzOzqk6p0e3RIMPVn2wmwuNNWHDlPwp5uLJ03uexhfqXnRtsDAWAo1Fvl0tu/pUBC8ZpiUkSWxYKsbnjNcDitGD/62FHa1pfEF3izRS2ANx7jOGxaQ3EWJ1A4SYsEglQiIyYoqQnsaHiyZehE3o43xRRlGfav+MRGyajRmeMwji5KAWYX3dOn3OFfQipKQqKGjPn5XuZvbIJRnj0ARohHn5oJ79b/XhNYRCIcYHBCWaLX5RdpSjREgqKhZA6SxahQRvE7mdeqaCTKeHcdFJS0qrBsQF5eW588xBakfzjuRuXHYnjF9GGxL8HSBjReyc0QxZUsLqPU2URtPzdtg1sDnNqsfdMFyyopYQ2vRJyixp5yszPwvog1gya4ixUmcNrI3DGKxSiJDTIg7GSBuzimYNTrYGIWLqP4klZGJOhCVejZIw5IYqybVVUZ6bAcu/CRn5MPNiKBlctxtjxfJA+wHTFG9kyDLbVZzVbUW7zuE0BVVZYzW8/2/9g7LZMO382IZth+JW6Y0UfCZRcdnk0CfmgylCrH7myQY4473hjAeB+JiQlO5YlveNyUniyrNR82BC7oTuMVqDTJUliLCn4HTrAkUq64yRphfi7wcppbmSbt1mODh/4vmmaP3D+3/grZq3AL0PNH7TYLtiJTKjcAY/XPRDCt2FLCpdxA9O/EG/AzSFEGYKcSN97f62/WYK4nOrzk1ZC6HSU2muUhvjgjW1trHiPS5HTxiSaEU3vpPlyDK3SYW10nxvIsSYFNuEjQUl3Yuenj3+bKbkTWF89vhucTA9cfGki/nUjE+R7czmI1M/0i1hg2ENMWJshoJAOGCmqrbG4RhFR/vL0bhjCSFMcbfhyAZW1qwE9Gx7yWJ1PjXjUxS4C/hAxQdMoQYxcdGWxL0ZYm5aiSKkt9i99kA7IRmK2zYZxZnFXDblMrIcWXxu1udGVYrXROYWLsMmPUhgRdsOtjZshaCX8pDAgZ1Q0dC63w6U0swSFgVsaDLCtra32du6ly2N2xEywmy/0LO1KkvIcYAQBOd+DC8SIiFyD23S3y+dbWZD2d60PaV7RZwIceWZkwp/2J96IlJ1Mi1CPx4hr1nERq8VoneQb+1u1NPzCmjPygFB6mBPQ4R01OrZvowaIUJj6YQPxlLAJpiw2wPtpo9vsgrAQOwhSCFCctG4K5zHj5bfNHjZGgzhYynmaDAmI0ieFFzV4GCC9xwmFmWjaUKf2H/2f3Dy9wenDRaMa2qtARMXhE5CPEiUWu8RcGTgRpCz41nd6gEw/7PxBSTbDlGUWWS65cVZQnyteiIDiNUIGUSrRJ4rz/RTHkkixLranaqwlzUmxLh/rc9cOBI2MwHNLOy93s9AKcksMSc2PQWnGxN2t81trsgnUppZaq5YWl3MWvwt5qr7cFpCQJ8YXb/0erKd2USI8JsNv2Fd7TrW1a4z0zgPhStWInOK53Dfmffxg0U/OGq3xKXlS830tY/tesy0gjg1JxdMvCDl94QQZn9gXEczVsxTafbRxqJO4n1gfGda/rQ+ucEYcSG9iRAjHmRW4aykVo4Mewa3nHQLvzj1F/2q9QC6EHng7Ae4fOrl3RZaZhbOZEqevlj3xJ4n+lwjpz/sb9tvVhtfXLbYfGZSpjTvhaNxxwJYUq6niA9GgqYrVirRXZVbxf1n3c+3Fnwr7pwZAjaxMrqBaQlJWAE3+vz2YHvSc2wNWO9tfLhs6mX8+dw/D8uzOpQUZWVRHNAF3k78vFe7BoJdTAgIDtjGkevp3dqXVlzZnOd3oBHBFwpw17q7CIR0V6zZPk0XIcoScnzQWjbTzDqUa/jjl59gWjU6gh1JU2VCvK9uniuPaQXTzEldSgvK2CU026KuT/4Oc0XMrgkzLmRHXTsl4XocmkabS5/wpgxGjqbpRUq9CnrUEoKnFIczkwWl+srY2tq1cWLKOmGzruLG0YslBIim+z3KlLzJMGJcklhCHMEOM8d7l8gy3aKGkqqcKlPIGZOI8YVZaJafnKwddV114MigVGoIo+J80RQYu1ivqG5MQtoO4dAcZk0So1geELNuAU3GSpdr8ASBXbObk5JEv3MpZVqqpUP/Y0KMYOPqtmozpmZf2z58Yf15Mop+DSVCCHOC1FMcmdEvTCuYljKOwa7ZzaBzqwixvh5uEQK61daomhyWYX61/lc8secJsz1jsnuuuTBYHHUK8Cia0Mz0tQc7DvLWId2qc+a4M3udpBvjgmEZNa6n1apl9Kct/hYzZtAb8pp9bio3vEQMlyxjH8k41HHItJalqs8yUFKdbyNlKuj9hzXGZrCwCrmq3CqWlOliYGvj1pQB3j1xNO5YoLufJSaG6a/lrydLiJQyZUyItf9NulhksZAM5vgwksnPckJoOZkRfeoTDnYhgz4m+AV7bRPJz3T2vpN04s6hSrMz028nFJHUdR0hGI7gCXsoiQi9cruyhBwftAbaIGpSzYlWHqd8btygkkpQWFeo8lx5cWb2lCLEmUlLYTQvdqCDsZ7owC2gIDvqwiUlJZE6nHaNZrtuYUg5OFrS9NKyL2YJia62Gx1ls7/ZzBQEsaB0DS21a4BpCUnS2RvvubK7fzYQTEtI8mO6ogWFOkVWt2DwocCmxeIJDJc2p11jTEFsEEt0zwKo66wDeyZl0vL4zf+0LthsDvBE0+9FRaORQauu0yJCovEgPiRd0dXAwY7PSBX02BHsMAXAcFZLh/5lx7JrdnPluSPYYVr3rM9fXyd9A8VcAW+tTlrzxRfymQKlt0B5M0OWZQHE6prVLZHEMGEU8HPb3IQiITP+ZahdsQabk8acFDe5swt7XOxEKox7SSJ57eBr5hhgzaSXLDh9e9N2c0W/zyIkOum1psVNxLBwC0SP9VmGigUlC8zido/tfqzHavTJkFKytXGr6eqaiPG8FLgLyHHmmEIrQoR3at/pd3sNS2J/LSGa0OKsB2Ozx/Y7TbBpCUkSE+INeU2XqmxH8sB0SC5C4lKpD3P8XrrIz3TQpJWzrCs6VvhaQUaoCgp22yfpImUk48rBadc4u0MfJ4KhCIFQhPJgKQ5b1C9CWUKOD1r9rfpE2uYkV0YzURVNI8+dZ5rsUwWaGw+/2+Y2XQOMAaYnN66Wgir9e+EgRd5Yh5SbpXfgebIFl/TjsGm0avrl65YZyyCrBAy3hCQiZF7JPDNw0xpQZ6zKVXgqUvvLGysyPVpCBvlB6SEmBF+rmR63S2R2c4saKowJ4+6W3aYQmRTNkOWwCcbkdx/QdEuIm1IRdVPLGwtVljosOdGV7Oj1MmqJxFlCjPS87myIrpoPtmtUqqBH69/D7o5lsYQkpuI1MNy0nJozvlhgdNJnPLNjs8f2KRB3MDDa0R5sT5rTf1fLLsJSf8atk9ZkWNP0GoLG8I0vcBf0exI1mEzNn8rVi6+OCwgfbe4dDs3BxZMvNv8+eczJpjWyJ6yWUaOyOsSLyrGesaY7nTGJNu5Hh+boc3E2Q4T0VBDQiGWZmj912BcLICHGpqvebE9feW7fc/zk7Z9w57o7k36eGLc4xjPGHJf7myVLSnnU7lgQf48fjeg2REhHsKPb3MBq1Um0hFivazIRYgTpexyeIY99GynkZzqRQmO8fxxuiV6PKyLIjmjstU8gP3OEF/Nz5+K0aUwMhnCHqgiEIwTCEcYG8nQrSHSbYwUlQnqg1d8KAvCUkOvIglmX6gHkxDq+VNlujMHB2knEuXElyfMP0OIpBAT5UmDf96YZCJnl1idcRlC6ww7N0dWRbjVCDDQt5pLVtAfaozEF0Uluhj3DLDC2+vBqc0JjdO4p40Gg15gQYNgtIVkuOyF7JtMq8qgqHB4RsrxiOQ7NQYQIt6+5nV3NuzhjRiluh8Y5s8qw2+IfMW/Iqw8qQlA6Zik4MmH5t/RrZWDEhRgiJGoJafA2xFYTo+5YTdmxjEODLQiM1eDESbPVPWu4RUhfsmMZ7zttTiqyKswJ8d7WvfrqahI3maGmt+B0o012zW760qfCsHT4wj5z4mGIkHS4YiUys3AmPzjxB3gcHk4oPoGJuX2bWI8kzhh7BpNyJ1HgLuhzETCbZjPd/6zuitasYA6bw3RNMyzOZtHM/Cl9zuZltYQks6xB7J4YLmtfMpZWLDUnzkaa475ixLNsbdrK4Y74cTYcCZvxF8azZQ0Sf7/hfdO9qi/4w37TGtWfwHSDWYWzmFk4k6KMom7pbvuCcY4ksps1pCcRkufKM2MGk1pCeknPeyxiWDoatPFc7nfiQHBWwMEhWzlekUnBSLeEuHOwaQJNE5T6l6DJTByBSZSFNN3l3OkxFx6PBZQI6QHj4be7csn83DOw7GvmZ8bqdG1nbdJBwHj4rQIhzo0rRX2OlrAPHBnkSQ2qV5rmV5crKkIiupAI2TSC0YlrShECMZesmvVgrLBYgp+NVZu6rjoOtB/AF/KZg1fKeBDoe0zIYNKjJaQNhyaYPr6Sn182Vw9KHwbKPeX84MQfYBd2fGEft625jfzcVh768jK+euqkbttbXapK538ePv+0HgtixbCEdNRCJGzea2EZNl2KDHesxqzY4DLYgsBaCMt6j8cFO6bTEtKbCNGc2DSbGY9R3VZNTUeNOcgPpwip8FjEUFv3uBAjiHly3uReEzmM8cTiK4xn1XDHsn6WTuaVzOOBcx7guiXXDThOIx04bU5+dvLPuP+s+81is33BSFqS6m+IFX+tbq2OK5rZn/vR6PNDkVDSgoDekJeOoN5PDlVq5L6gCa1bwH5fMArMGSRaNg51HjJdQq3WTiMuJBQJ8W7du30+nlWwZDj6bwmxa3Z+vOzH3HfmfX2ymiVizXqVKEKsfye6XmtCM+ODklpCon31cPfT6STLacNhE9TYKjnJb+PvgWxO8dvZY5+EJiDHPcItIa5cBHpW1IJwBjPFD8juugyP7DrmMmOBEiE9Ylgzclw5CC3+VBkTw65Ql9nZWzH8ga0CId+dT1mmHlTaoxuXK5s8BDTuJjtquteigemGJaQzp8gM+u5xlcOwhAQsA1VObLV0YelCcyVlde1q9rXtMzPapEzPC7EHIeSHUMJkcMgsIRYXsEiCO1s0Y5Zw5w77pGdeyTy+veDbaGh0BDu4ZdUt1HYdSrqtUdgLohaOZG01RGIkDB115r0GUZesoE8XKEBTNDmBXdi7rZINFGPgCkaCcfe4Mdi5be5+B3EOFOtKccrsWBZLCMTE9N7WvXHPXW9uT4OJXbOb2e4SLSGhSIidLfqEqy+r1lZ/85qOGrqCXeaix3Cn5+2J0VjsbKAkXr9kwsKwMNd11bGpcZM5me6PxcLa5yeLCzEXK+CoJsWDSWLAfl/Y07InLvFEogixJniwLpZNzJto9lv9ccmyipCjsYQMFKsISQyqtwarJ+vjeypYeDxaQoQQFGQ5OWgbSygiEQhCEcke2yTyMp3DtkB51ETnVk6bRpbsYlutPp/Kkp3HXGYsGKAIEUJcKITYLoTYKYT4YpLPPyGE2CiE2CSEeEgIMaqcElsD+sQ2WeC34SIDCQHDUQwRkvjwG7UltjZtTWpBafW3gtNDXjQQPqdLb4MUXWgCSiJ12DRBh6XgXY+WECNNrxWLJSTbmW2mKV17eG18xpG+WEKguzVkqC0h0N0aYnTUaVolWFy+mK/P/zoCQVugjZtX3Zz0vjDeswlb6sxSFpFI26H4e62rDloP6Gk/gKaoe2CBu2DQJ32pgh6NjCvpWF0TQphCpLfAdEOEGGK6yddkxj6VZJQkzeM/lJjpghPSs+5t3WsKqr6shmfYM8x752DHwTjXznQFpSt0JudNNmvTQHKha+1Xn937LKAnAelP0Uxrn58sLsRasC/dIsSayryv1pDE7RKLEBpC3uPwxP0+a5D4u/Xv9jk1sBEPAkcXEzJQrOKimyUkmrbXJmxJBZLRF/RkCRnuLIbpJj/TSZ1WSjCiL+KGIxH22CeO/HgQMEWGw66RKTuNoZ7MSBcOm1CWEAMhhB34JXAGMB+4SghRaPlcAHcBp0kpZ0ff7ptz7QjB6NxznUlESOLqtAUppSlCEgWMUQ23ydfEEe+Rbt9r9jeDzU5ejl6oMLtpL0TCdATbKc/NoDRSr2fGyoztt2cRkpDdKiMfnPEd2aJyvdPe177P9MMtzijuOWjXGhhlzZAVicQEwlDFhCQeE4YuGL4fnFR5El+a+yVAX4G6e/3d3bYx7pXijOKUaVjja4XUkGHPMFfK6jrroLna/LhZ64M17ChJFfSYrhohBkZcSG8pehMtIaAXFIPkbjJDjdGOBm+DOdGQUvJc9XOAPhE1Ygp6wxqcbhUhI8Ud63jFaXMyKU93w8x2Zie9HtYaMMb9OCF3Qr8mv3GWEF93S4h1bEm3CBmfM94M2DfqKfWGIUKsv9NahNCMW8yp6mb5NlyyfGEf7ze836fjxVlChtm6C/H1P1K5Y2U7s5Na+VMlEPGGvObvOp7csUCPCwkLO3V23WrcZCumTcsd+ZmxIGYJsQuyZMyDJd/mxSaEsoRYWAxsllLWSCk7gGeAcxK2EUCmEMIGZAHJo7hHKGaV0iQXPd+db67IJoqQzmCnWRU9MXPV1ILYJCOxZoD1e3njPqAf298JrQdo9zVz9qQMsmU72W47LW59gq+hJW2fSe6YWN0JiF9lj7KoNJbZY3PjZqAXKwiktoRYO9Chyo4FelV5K770WkIMzhx3JpdP1asH727d3S2g0rCEWK0b3XBmQkae/jpZhqw6/RrhzqExrK/gDcUgE1c13ZJv3pj0pCvloxEzkcoSYlgVjOdzXM440+XQIB3BunHpWaOpeh/c8qBZZXlB6YI+T4CM4Oaa9pgI8Tg83SoqK4afM8adgYbGGWPPSDppzHRkmm65Bv29H7Od2eY9naxgoeGO5XF40potDeID9lPFQlqJyIgpQpaVLzNF29rDugiRUvaYPGV6wXQzlrKvLllGel5IjyXEZXOZ6cdTBaaneraNfrrZ1xyXWSudCUTSjVELZK1zMQiNtRn6fKpgpNcIAT1ZjWbDadPIlLH7siDqkp/uOc5gMxARUgFYUzzVAOYMV+q+Rt8ANgGHgHYp5auJOxFCfFEIsUoIsaq+vn4AzRl8erKEaEIzAxYT3W6sPrqJlpDyrHJzJTdRhFi/lzfxDJj/abKlgJCf9iNb+XBlGxOLPeS6HbQ49H3kuHJ6dsOxOSDXIjxyurtrFGYUdsvI02NmLEgQBJZO05q5ajgtIUYV9RGwSnDWuLPMwpSJg6AhWK2WtKSYaXqT1Aqpja7ulc2lyT90Pr8Z9gxzBbPJHxMh6Q52NAbrVCl6jfeN58xpc3ZzUxrOoHSD8TnjzYljdVs1/97+b57e+zQAk3In8fV5X+/zvgxLSGug1az+XuGpGJVB4Mcap409jb+d9zc+OeOTKbdJ7F/7a5nThGaOLclEyJEu3RIy3C6HqTB+357WPfhCvh633d+23xQF0wumm8lTtjRuoS3QxhHvETMYP9limU2zsbBsIQDv1L7Tp/okVnesdMSEQMwlK1VMSKqYP2OhMyzDcQImnanU043hdvWCdhLy88/wrO10APJGgyUkau1w2DRyiFlCckX0Hh0Bc5zBZMgiB4UQDuDLwBx0wSKEEJ9O3E5K+Ucp5VIp5dKSkr5nIRlqIjJiPvypigEmrd9AfBXbxMmhJrS4bD1WrL69ea48WPRFcsafpLcn5KXz5Z+YF6wlWlm9R1csc2eWuJAkIgS6V9Q9ekuIxUIx6DEhVhcwyzHDQTAGkRGQPzvfnW/6d1tFSDASNP2ae7SEQPc0vca91nEI2bQbgHDZHFqj4muoJhvGfg1LSCAcMIPU0xXsaLhZ9TUwHeKTLOQ6c82q48OJy+Yyg8of3/04K3atAGB89niuW3Jdv9xArKJqe5Pu4jIS0vMqdKz3XjIS+9ejSZLQkwgxFgqKM9KXGcuKYekJy3BcYdxkbGuOxYNML5huulcZRQjjgtJTLJYZwqU92N6nOBSrJcSo6zXcGB4NiSIkVbV0A6tF2ho3YxUhx1NgOsTS9PpDEdqCNjr8uofJqLCEALj1DFmlLt21WJNhMkXA/OxYYiAi5BAWy0f0tTUl0DwgJKXcL6UMA48CywdwvGGlM9hp5g1PJUKMiYw14xF0r5aeiDEAJWbJsZpP8935IATZcz8O0RR87cYEPyOPlrA35f67kV8Ve53EHQviXbKgl8xYoIsQY9U13ZYQ6/FHiKnSGAStAZUNXQ3mPdW7JcQiQqQ07zWfr4W2qMm9pWCCub8C19CsdCVmXrEObOkKdjQsIamCThNjQiB+sjK9cHraLAbGs29MNCqyKrh+6fX9LppoFRxGNjslQkYP1v51jGfMUbnRGRNL66KXgWEJSXc8iMGUvCnYNT1g36iJkwojXXVFVgW5rlzGZMeKEK6tW2uOm07NmfKen1s017Tirq5d3Wv7jNgJt82dtqxuhgtZqpiQVPeIte+39s/GuGPX7Medm2a+RWzsaeiwvD8KAtPBnMeUOPWFtkwjPa/ls2OFgTxta4DZQohKIYQHOA94zvJ5DTBXCGFI8DOBvkWljQCsVolUD7AxkWz2Ncf5pxsiRENLunphTIia/c1xx0n2vWxXDnhKwZVDm4imScgbF6tDEhUoPWINTk9hCSn3lJspRLOd2b2bbzXNUivEIgjiYkIGWYTYnbEK8NaYEF/sHJJCMA431qq5RkCl1WKW6BPeDUMsBr3gbY6JlmAXtSICjgyaMvPMzYcqPqMnEZI2d6youOg1O5ZmsYTkxCZ96XDFMtthmXyWZJRww9IbUi5y9ESOMwePI164KBEyerDeB0cbn5TKEhKKhMzxYaSIEIfNwaRcPWDfEBnJiCsmGnXhiitCeOR98/NxOeNSCgaHzcGC0gUArK1d260KeSKGCElHULpBKkuI8XdfLCHW/tlw7y5wFRx3bpoFWTGxseeIJbh7NLhjgelyVWDTXRezZIdeqBBGzBxnsDhqESKlDAHfB14BNgB3SSkbhRBPCyEqpJSHgNuBt4QQG4Fc4PeD0OZhwUjPCz24YyWmTo1iZsZy5ybtJK0TIqtpOdn3cpw5enh/djntY3Q/VyaeHksB7OqDmbXyRF0QZJdDUeo0kB+s+iCgVwHvU6dl+CYOlyXEus84S8gQH/MoKM0qNd3u1hzWXbKsFrOSrF5cD+MyZB2KiZCQlzoRgdJZNAVazE2GShCMaBGSIibEcNOyWkKm5E+hJKOEDHuGOaFJB4vKFuGyuSjNLOXGZTcetRudEKKb6FAiZPSQ68plduFsNDROqjzpqPZh9P2JIqTZ12xaSNNZqDARQ2ztbNlpJmBJpK6rzvw9VnFmLOoEI0FThPRmrTes+02+Jna37O5xWyMmJF3xIBBb7LRaQkKRkOkqlioBjcvmMhckkqZST1MCkXSSZ7GE7D4SW7Ac8dXSDaLXuszlx24TzC4Em1HfZITMcQYLe++bpEZK+TjweMJ751te3wfcN5BjpIv+WEJADxgem62n1TULBKUQCGOzx2ITNsIyzN7WvcwrmQckL3BoHltA2+xL4MxbCdrddFT/W9+2L5aQrEL41MOg2fRA9RScOf5MlpQvIcuR1fs+IYUgiHagjowej3XUuLKh80iCJcRy/BHkL7m4bDH72vaxtXErbYE2M4FBvivfDJpOSUKtkNzSWbg1J76gjzrhhLK58e57fRGjR4Hh8tER7CAQDpiDnIZ2VCv4g4Fx7lJZQkx3LIslxGlz8svTfklYhtPm8w26C+cfzv4Dds1uuqccLZWeSjPlqUNzjKgJp6J3rl96Pb6Q76hX342+vyPYQTAcNLPGxaXndY8MSwjAzMKZPLb7MfxhP3tb9yati2LNnmUVIZPyJlHgLoibZPcWtzi/dD52zU4oEmJN7Zoe67CMCEtIdKy3Fie0CpIcR2o3nHx3Ph3BjhGVSj2d5GU4EEIvp2W1hOSNGncsfWzNpov/98UluA++hXgh/rNjheOvpG0fsZpEU61AFGcUm1mQkllCUsVrOGwOM8WmNTjd8O21Tigz7Blm8av2QDu4suOsNH2KCQFwuPskCjxOT99Nt6YIsQiCoSpU2O2YKSwhI8hfMjGg0syM1VtQOuj1XBzRVJFtNQghKNFcgNQtIeVzzeDTHGeOOQEZbBILFlrdANPlO93fYoXm92yOtAoQA7fdPWABAvHV0Ss8FcdlhfLRjCa0AU16reOE1RpirZY+koTp1Pyp5niZKljcCEovdBfGBdVbixAa9GYJybBnMLdoLqBbo5MVBzYwrA3pTGdsuFsFIgHTmmsVIancsSB51fTjWYTYbRo5bn2cONQaFZhOGy57itpcIw1jHuNrI9NhQwuMvLjXwUKNWikwsg55HB5z0pOIw+Yw3Sn6I0Ig5pJlDU43fDitK8xCCLPzMTqkuOxbQ7QC3id6soQMuQhJ4gKm2fQc2yOEMdljzNiPNbVrYjVCegtKBz3oP1sPxjQzZIV0F4Y6AZTMNAeZocx8kihC0p2eF3rOjiWlTOqOdSxiLYSnXLGOP6zjizW9uxGQ7NAcIyogOdORybgcPe4wVXC6YQmZXtA9eYQ1zk5DM2MYe8L4Tm1XLQfbD6bczrSEpNEdyyoyDGuIdTG0JxGSWLAwHAmb3hzHowiBWPyHoT1HjSsWxFzdIyE9LnSEznEGAyVCUtBbgSADM3VqZ/9EiBGcXttVa/qjGp1GorAwzbTRNsXVE+mrJWQoSCYIhlyEGHEoFuFjrREyggLwhBBm6uP3G97ve40Qg4Q0vWVe3eJU53KD3WUOOEOZpcq67yZfk+lnnK7MWNBzTIjV1/xYFyHWNL1KhBx/WPt+q/uw4Y5VlFE04gKSjaQQ25u2dwsWb/Y1m31ksropMwpmmLEPldmVfbL+LixbaNbm6alwoTEGjwRLCMQWHPsqQoz+2BgTWvwtZlzQ8Zae1yAxE1beaEnPCwl12Npi850RNscZDJQISYFZqLAXv/fEWiHBcNAspNRTvIbVn7W6rZpgOGjWX8hN8PlLtITE1RPpS0zIUNGTa9RQFdRJ6gJmVEsfeb6SxkpcKBIiGNFjFfrkjgWWgoUHIRyitF2fXLTaHXhD3ljl8iFc6cpx5WATugm7ydc0Ikz8RkxIshS9VuuINSbkWKQoo8gsgDi/ZH66m6MYZqx9vzU+zHDHGimZsawY4qIj2EFNe03cZ1brSLKMYTbNxpnjzgRgSfmSPh0vx5ljHrOnVL0jyR0LYuIjLiakhwVRoz/2hrx4Q17TYg3pXTBKJ/kJosOaMWvEY50/+dpilpAROMcZKAN3TD5GMeIuerWERCeUR7qOEJGRPlspjMxJoLtkWf1fEy0hiZVUjWNk2DN6D3AeSkyrRAdEInra3nTEhBiWkBHoKzkpbxL57vy4SUK/LSHeFjj8nu6O5QDsmdR11pluF0MpCDShkefKo9HXSKO30bTypTPjihkTksQSYn1vqOJkRgqa0Lj1pFvpCnalLUmAIn24bC4y7Bl4Q96kMSEjUoRY0mNvadrC2Jyx5t9GnEi2IzvO1dDKJ6Z/gnOrzu1Xn7e4bDGbGzezr20fdZ11SReBRlJgOsTGeuP/bpu7x/4sVexe4mfHE4mWkERRMqKJK8zcBv6RO8cZKMoSkoL+WkJCMkSDt6HXQoUGmY5MM16guq26R/GSmD/ciAlJqysWxASBjMQqlpsiZIgtISE/hALDc8wBoAktzpcZjsISArDjWUpl9HF1ZLC3da854R7qQcbYf3VbNWEZBtIbi9RTnRDre8e6JQR0QaYEyPFLYppeKeWIK1RoJdeVaxYeTAxON+qHTCuYltKNTAhBYUZhv9zMTiw70XydzCUrIiP4Qno9hnRbQozAfcMCYhYq7GVsixMh3qa4APW0ekukkcSaIKNKhMRZQlpjlpAROMcZKEqEpMAQIb09/HG1Qjrr4oPGe/HFNOJCqlur41ysEr9nWkL88ZaQESNCICYETEHQvwrQAzqmNSZkBGIVIRn2DLMybq9Ya4XsfZ0iBDa7GzQtznVhqH1+DavHnpY95ntHW99iMDArpkeC3fzKrS5aabUSKhTDQGLBwvZgu7k4YbWujyQMV6utTVvZ07qHPa172N60nf3t+4Hk8SADoSijiMl5kwG9cGEivpAPiR69nM7AdE1oZsxLNxHSi0eGdQxo9jebIiTXmZsysc6xTmIg+qgKTHcnipCRPccZCModKwmBcABfWF8ZyXX2vMporXxd11UXNynqTSRU5VSx6vAqDnYcpL6rPuX3DBHiC/sIhoOxAPZ0B5wlCoJQARgr0UNmCUkI2MoqHNExIRALqOwIdlCaWdr3VTxPKQhNtzSFfNgQFLkLqSM+n/5QCwLDp9jq6jQSsmOBLkSsYsPaxmM9MF2hMMYAY/GroSuWnrcoc+RZQkDvD1858ArNvmauXXlt0s8Hm8Vli9nVsosdzTto9bfGWQ+NeBBIrzsW6GN9e7C93yIkx5lj1kRp9DaarrppnyOkkUTLx6iplg56OQVHpu5hYg1MH6FznIGgLCFJsFolenN1yHRkmivb1mqvmfbMXidBRp7zsAyzqWFTyu9ZixS1B9tjtRpGmiXEmiVruCwhUlqCtkbmKoFNs3HGuDMAmFs8tx9ftEN2Wdxbpbl6LFG9NyZah1oQJBvIRooISXTJigtMVyJEcYxjjAGGddxaI2QkFSq0Mq9kHm5b8no9pZmlcfGSg8WcojkASCQH2g/EfWbEg0B6LSHQ3fXa8H7oKTMW6G5qBa5YrRAzc2IaLdbpprs71iizCFlqhYz0Oc5AUJaQJFjT4vXF37o0q5T2lnbqOuvwOPXJd19WIKzFlgwRkkxYWF3C2vxtpkhKvwhJkUYOYKj81BNFSNCr59JObM8I4xPTP8EZY8+gLKus942t5FSYKXoBSgunw+E3zb9dNteQD5yJgsPj8KR1gt+TCImLCVEiRHGMY4wzrf5WIjJipucViLQmj+iJXFcu95xxD4c7D8e9LxCMyxk3KIU8EynJLDFfGzEzBkZ6XkhvTAhgLmiaKXqDfRMhoLvN1nvr4wLTj9egdICC0WwJAX0+014LHXWjYo5ztCgRkoQ4S0gv7ligr97satlFbVcthRF95aEv4iXXlUu+K59mf7PpRpIsiMzaAR3qPERI6jdk2k2tI8ES4otdq5G8SqAJjXJPef+/aI0LyR1Dad6EOBFS4C4Y8loAiQNZugc2a8B5oggx0iAnbqdQHIsYC1FhGaYj2GFaQvLceSM6FiDXlTusCRWyHFm4bW58YR8Nvoa4z6yWkAxHekWI1RIipeyzOxbEV00fCanU002G04bboeELRrBpgmzXKJvuGvOZ1oPd3zuGUO5YSbCKkN4C0yG+YKHhjtXX7EFGcLpBMuuGVYQcaIuZktOeFcfuAmO1uZsIGaKHxemJFevxtydYX469B5QcS6rKsrndLCnDMciMOBFitYRElCVEcfxiHS9afC1mLMBIDUpPF0IIM1uY1WUNEmJC0uyOZU3H7w15zeKrfbKERPvlmo4as19MZxbDkYBRoDA/0zHiCnf2ijGfsXhCHItzHCVCkmDUCLFr9j51SkaGLF/YR02HXoCprwLBWrQQehchRgYRGAEdjBAxi8dwWUI0TRciEHUBsxzzGFwliLOElM3pVmMkHSIk3RY45Y6lUOjEiRB/S1y1dEU8pgjpSrCEBC2WkHS7Y0XH+s5AZ/xiaD8sIda4uOM5JgRiLlmjzhULYkHo1jHuGJzjKBGSBDM9rzOnT+rZOjE0OoC+TtSscSGpvufQHGbnaBUhabeEgKVgoUWECA0cWUN4TKNgYYI71kg4H4NNyUzd2uTIgLGL43ybYXgEgdPmNFNHwgiwhGipLSH+yPFTMV2hsLrvtvhbRnShwnTTmyVEQ0t7Wm9DbESIUNtVa77fF0tIsrEg3QtG6WZmhX4+Z5aPwsl7soLPx+AcZ5Q5yQ0PRmB6Xyf5yYrP9TVovC+WENA7J2/Ia6bytQlbnzqmIcdltUpEXaOcWbrFYsiOmQMcGlUxIUdNViF8/J/R10W40e8Rw+3PSJ871BS4C+gIdpiv00lcSt4UlhCbsGHTbMPaLoViuPE4PNiFnZAMUddVZ45dSoR0xxQhvgYiMoIm9DHKWi093S47VovHwfaDSd9PRbKxYLjGh5HKZ5aO55SpxYwvSK+b3VGRLB3vMTjHUZaQJJjV0vsQlA66W1TiqmtfXaVKMkviXL5SfS9RcOS6cs1ONK0YQs1qCRnqXNZxLmBR4ePI1HNrH4t4ivV/UayWt+ESBNbjpNvE77Bc51QiZCQH5SoUg4UmNDNucVfzLvN9FRPSHUOEhCKhOFcnQ4Sk2xUL4sd5w7U78f1UJFo93Db3iPhN6UTTBBOKstC0URYPAt3jP47ROc4ImMWOPIzc3H21hAghurnJJMtyleq71rzoPVlCrKQ9HsQgWUyIc4jiQcxjWlzAjuH82amwWt7SIULSfe9ZYz2s/s8QEyHpdqtQKIYL43nc1RITIcoS0p3izJgws7pkGe5Y6Q5Kh+QiREPrUxHFRBGS785Pu2VHMQASF3OP0TmOEiFJMALT+xNzkeiS1Z8aHta4kFTiJVGEjIh4EEgeEzLUD0syF7BjMGtEKqyWkOHy+bXWHEh3/QGr1TEYDsZ9ZqToVUHpiuMFY8ww3CVBiZBkWM+JEcAPscD0dKfnhfhx/lCHnhUp25ndJ68Hh+aI89443l2xRj2J86hjdI6jREgCERkxLSF98cM0sE4M7cIeF8jbG/NK5pn7yEoR0J1ojh0xAWfKEjLszCycCeguF8N1H0zNnwpAeVa5WVArXfQlRa8SIYrjhUTLZJYjq08r58cb+a58tOiUZ6RaQlw2l+lKaojK/sR+WseDETNHUBwdiaLjGJ3jqMD0BDqCHUSIAP2zNljrN+S6cvtlBj2h+ARuO+k2ijKLUq54JNYrSXu1dAOjXcEu8Lbor4fcEpIkO9YxukqQjJmFM/n5yT8n350/bHFBc4vmctvJt1GcUZx2E78mNOyanVAk1C0mxHDPUjEhiuOFxHGqyK2sIMmwaTYKMwo54j0SL0KiFdNHQvyEEIIcZw6Nvkbzvb7UKjMocBdQ3VZtvlaMYpQl5Pikv7m5DQbqIjMxb2KPx0tcfR45IsTSro46/f/DZQmRkdgxj9FVglRU5VYNq0ueEIKJuRNHRkY2Yi5Z3SwhERUToji+SBxvijKVCElFsjS91uxYI4HEPrY/fa41aUi63WYVA8SRpZc7MDhG5zhKhCRgpDiEgVlCBpvEjmjkiBDLgyF1C9KwZccC8DZ3b4fimMdwt0q0hBgxIsodS3G8kDgWqHiQ1CQrWDiSsmNB97G+P4uhcVkMVUzI6EbT4hd5j9E5jhIhCbRa6k70R0wUZRSZ/qZDkT2omwjpY/atISdZZfThsoRYGWrhoxhRmJaQFCl6lQhRHC8oEdJ3rLVCDEZSTAh0Fx39ESHWuYdyxzoGsFo/jtE5jhIhCVgtIf0xg9o1O5PzJwMwMXfioLcrURCNSEuIwXDFhPTWDsUxSypLiOGOpaqlK44XlAjpO8a56Qx20hXsIhwJm3FkIyE7FnQXHf2Zh0zO0+cgGfYMyrPKB7VdijRgnfcdo3McFZiewPyS+eQ4c+gMdfY7uPWqRVdxoO0AMwpnDHq7umXHGjF1QpJ0kMoSohhijJiPxJgQY0KhLCGK44VEEaIKFabGKtAavY1x8TQjxhLiOnpLyNicsdx20m1kOjJHTIyLYgBY5zXH6BxHiZAESrNKu9X86Cs5zhxmFc0a5BbpZNozsQkbYRkmy5EVVzU6rSQTIcNVJ2Q4j6kYURgio1udEBUTojjOcNgceBweM6WrsoSkxlqw8Ij3CC57LIHFSI0J6W8ykIl5g++JoUgTce5Yx+YcR7ljjRKEEGZnNGJcsQA0GzgTapsMtdnQ7oZEEZZMDCmOWQwrZWLFdJWiV3E8YowJds0+cgrZjkCswdpHvEfMoHQYQZaQAcSEKI4xrHOpY9Qda0AiRAhxoRBiuxBipxDii0k+LxRCPCaE2CaE2CKEmDSQ4x3vGCJkxLhiGSQKgKEWBEJ0d/k6Rh9QRXIMS0eiCFEpehXHI4YIKXKnrjWlALfdbY6jDd4Gs0YIjJyYkIFaQhTHEHHZsY7N++CoeyshhB34JXAGMB+4SgiRmBPubuDfUsrpwIlA7dEeTwHjc8bH/X/EYH04bE6wD8ME0GqaFNrQx6EoRhSmO1ZEuWMpFBNyJwAwKU+t8/WGtVbISLSEDCRFr+IYozD6PHtKjtk5zkBiQhYDm6WUNQBCiGeAc4B/Rf/OBU6UUn4aQErZlWpHir5xxewrWFq+lDlFc9LdlHjSodadCcfU1Orf8USy7FhSSvNv5Y6lOJ64fOrlTCuYxszCmeluyoinOKOYva17dUtIyGIJGSExIVbR4ba5R078p2L4GbsUzr8Tcsccs3OcgYiQCqDG8ncNUGn5ewLQIIT4f8BM4FXgKillyLqTqBvXFwEmTJgwgOYc+2Q6MllUtijdzehOOkTIcRCwpUhNsjohIRkigl4wU1lCFMcTbrt7ZI4NI5CRbgnxOGIr3soKcpyjaTD22H6uh1Ja2dGtJXcAC4Fi4POJG0kp/yilXCqlXFpSUjKEzVEMGWmxhFhMkyoQ87jDtIRYUvRaM2WpOiEKhSIZhghp9jXTHmgH9ID+kWJxsGk2U4ioeBDFsc5ARMgh4i0fldH3DGqAvVLKDVLKCPAYMG8Ax1OMVNKRweE4qCSqSE0yS4j1tQpMVygUyTBEiERyoP0AMHKsIAaG+FAiRHGsMxARsgaYLYSoFEJ4gPOA54wPpZSHgXohhOFjdRqwdQDHU4xU0m4JUR318UaymBCrVUS5YykUimRYizkaImSkxIMYjMsep/8/Z1yaW6JQDC1HHRMipQwJIb4PvIIuZn4hpWwUQjwNfFFKeQj4LvCIEMIBbAAeGIQ2K0YaKiZEMcxY3bGklAgh4tL1KhGiUCiSUZgRS+J5qEN33hhpIuSKOVewuHwxC0sXprspCsWQMqCK6VLKx4HHE9473/J6HbBgIMdQjALSnh1LiZDjDavICEVCOGyO+JgQJUIUCkUScpw5ODUngUiAsAwDetKXkUSuK5eTKk9KdzMUiiHn2Mz5pRhe0m4JUTEhxxvWwHPDAhLnjqUC0xUKRRKEEBRnFse9N9IsIQrF8YISIYqBk47AdGtMiHLHOu6wWjoM8WGNDxkpmW4UCsXIo9AdX1d5pAWmKxTHC0qEKAaOp1SvlA6QN3Z4jplTAZpNf507TMdUjBjiREi4uwhR2bEUCkUqEi0hI80dS6E4XhhQTIhCAeiWiAvuBF8rFE8bnmNmFsAFd0GgCwonDc8xFSMGq7tVMhGi3LEUCkUqjDS9BsoSolCkByVCFIND+QnDf8yK+cN/TMWIIJklRGXHUigUfSFRhKiYEIUiPSh3LIVCMepIGhMSUTEhCoWid5QIUShGBkqEKBSKUUdPMSEaGnahjLwKhSI53dyxVEyIQpEWlAhRKBSjjmQxIcGIXifEaXMihEhLuxQKxcinwF2AZpn+KEuIQpEelAhRKBSjjp5S9CpXLIVC0RN2zU6+O9/8WwWmKxTpQYkQhUIx6ugpMF1lxlIoFL1hdclSlhCFIj0oEaJQKEYdce5YkXh3LFUjRKFQ9IZVhKiYEIUiPSgRolAoRh02zYZN6MUqEwPTlTuWQqHojTgRotyxFIq0oESIQqEYlTg0XWwkihBlCVEoFL0xNnssoAsQ5Y6lUKQHlcdSoVCMSlw2F76wLyZCom5ZKiZEoVD0xrKKZbQGWpmQMwGbZkt3cxSK4xIlQhQKxajEsHgYsSCGGFHV0hUKRW/YNTsXTrww3c1QKI5rlDuWQqEYlRixHyomRKFQKBSK0YcSIQqFYlRiWDyM1LwqRa9CoVAoFKMHJUIUCsWoxBAbiRXTVWC6QqFQKBQjHyVCFArFqMSwhCRWTFcxIQqFQqFQjHyUCFEoFKMSU4QkxoRoKiZEoVAoFIqRjhIhCoViVJLojmXGhChLiEKhUCgUIx4lQhQKxagk0R3LiAlRIkShUCgUipGPEiEKhWJUYnXHCkfChGU47n2FQqFQKBQjFyVCFArFqMSI/QiEA6Y1BMClqexYCoVCoVCMdJQIUSgUoxIjFW8gEjDjQkBZQhQKhUKhGA0oEaJQKEYlhggJhoNKhCgUCoVCMcpQIkShUIxKHLbk7lgqRa9CoVAoFCOfAYkQIcSFQojtQoidQogvpthGE0KsFkI8PJBjKRQKhRUjRW+ECN6gN/a+soQoFAqFQjHisR/tF4UQduCXwOlAK/COEGKFlLIxYdMrgGrAdrTHUigUikSsYqMj2GG+Nty0FAqFQqFQjFwGYglZDGyWUtZIKTuAZ4BzrBsIIQqAjwN/GMBxFAqFohuGJQSgLdCW9H2FQqFQKBQjk6O2hAAVQI3l7xqgMmGbW4GbB3AMhUKhSIrVEtIZ7DRfG7EiCoVCoVAoRi5DFpguhJgP5EspX+1luy8KIVYJIVbV19cPVXMUCsUxRip3LBUTolAoFArFyGcgIuQQ8ZaPyuh7BkuBk4UQ1cBDwHlCiG5uWVLKP0opl0opl5aUlAygOQqF4ngiToQELCJEuWMpFAqFQjHiGYgIWQPMFkJUCiE8wHnAc8aHUsr7pZSVUsoq9LiQZ6SUXx5QaxUKhSKKVWyowHSFQqFQKEYXRy1CpJQh4PvAK8AG4C4pZaMQ4mkhRMUgtU+hUCiSYo39MCwhAoFdG0iom0KhUCgUiuFgQKO1lPJx4PGE985Pst2rwKsDOZZCoVBYsVo8DEuIQ3MghEhXkxQKhUKhUPQRVTFdoVCMSqzuWEZ2LOWKpVAoFArF6ECJEIVCMSpJFpiu0vMqFAqFQjE6UCJEoVCMSpLVCVGWEIVCoVAoRgdKhCgUilGJXbOjRbuwCBFApedVKBQKhWK0oESIQqEYtSQWJlTuWAqFQqFQjA6UCFEoFKOWRBGiqqUrFAqFQjE6UCJEoVCMWrqJEOWOpVAoFArFqECJEIVCMWpJFB0qMF2hUCgUitGBEiEKhWLUkhgDomJCFAqFQqEYHSgRolAoRi2Jlg/ljqVQKBQKxehAiRCFQjFqSRQdKjBdoVAoFIrRgRIhCoVi1KKyYykUCoVCMTpRIkShUIxaEmNAlAhRKBQKhWJ0oESIQqEYtajsWAqFQqFQjE6UCFEoFKMWFZiuUCgUCsXoRIkQhUIxakl0v1IpehUKhUKhGB0oEaJQKEYtqmK6QqFQKBSjEyVCFArFqEWl6FUoFAqFYnSiRIhCoRi1JIoOFZiuUCgUCsXoQIkQhUIxakmMAVExIQqFQqFQjA6UCFEoFKMWl6ayYykUCoVCMRpRIkShUIxaVMV0hUKhUChGJ0qEKBSKUYvKjqVQKBQKxehEiRCFQjFqcWjxMSDKEqJQKBQKxehAiRCFQjFqUe5YCoVCoVCMTpQIUSgUo5bElLxKhCgUCoVCMTpQIkShUIxaEkVHonuWQqFQKBSKkYkSIQqFYtRiDUR3aA40obo0hUKhUChGAwMasYUQFwohtgshdgohvpjwWaYQ4hkhxDYhxGYhxDcH1lSFQqGIx2oJUdXSFQqFQqEYPdiP9otCCDvwS+B0oBV4RwixQkrZaNnsdinla0IID7BOCPGMlHLXwJqsUCgUOlYRoqqlKxQKhUIxehiIJWQxsFlKWSOl7ACeAc4xPpRSdkkpX4u+7gC2A+UDaaxCoVBYsbpjqRohCoVCoVCMHgYiQiqAGsvfNUBlsg2FEGOBucD6JJ99UQixSgixqr6+fgDNUSgUxxt2zY5AACozlkKhUCgUo4khj+IUQriAfwNXSSk7Ez+XUv5RSrlUSrm0pKRkqJujUCiOIYQQpvhQlhCFQqFQKEYPAxEhh4i3fFRG3zMRQgjgQeBpKeXDAziWQqFQJMUQH8oSolAoFArF6GEgImQNMFsIURkNPD8PeC5hm9uALinlLQM4jkKhUKTECEhXIkShUCgUitHDUYsQKWUI+D7wCrABuEtK2SiEeFoIUSGEGANcDSwWQmyI/jt3UFqtUCgUUYzUvModS6FQKBSK0cNRp+gFkFI+Djye8N75lj/FQPavUCgUvWHGhChLiEKhUCgUowZVXlihUIxqlpUvw6k5WVS2KN1NUSgUCoVC0UcGZAlRKBSKdHPplEu5aNJF2DXVnSkUCoVCMVpQlhCFQjHqUQJEoVAoFIrRhRIhCoVCoVAoFAqFYlhRIkShUCgUCoVCoVAMK0qEKBQKhUKhUCgUimFFiRCFQqFQKBQKhUIxrAgpZbrbYCKEqAWq092OKMXAkXQ34jhGnf/0o65B+lHXIL2o859+1DVIL+r8p59j4RpUSSnLEt8cUSJkJCGEWCWlXJrudhyvqPOfftQ1SD/qGqQXdf7Tj7oG6UWd//RzLF8D5Y6lUCgUCoVCoVAohhUlQlLzx3Q34DhHnf/0o65B+lHXIL2o859+1DVIL+r8p59j9hoodyyFQqFQKBQKhUIxrChLiEKhUCgUCoVCoRhWlAhRKBQKhUKhUCgUw4oSIQqFQqFQKBQKhWJYUSIkASHEhUKI7UKInUKIL6a7PccDQoixQohXhRBbhBDvCyE+En1/khBinRBilxDid0IIke62HssIITKFEPuEEHdG/14shNgcPf8/Snf7jnWEEBOEEK9En4ONQogsdQ2GFyHEd6Pne4sQ4h6ho67BECKEWCGEaBZCPGx5L+k5V2PC4JN4/qPjwDNCiG3Ra/BNy7ZF0T5qpxDiUSGEO30tP3ZI9gxE39eEEKsTno1j6hlQIsSCEMIO/BI4A5gPXCWEKExvq44LQsB3pJQzgXOAXwshsoCfAz+RUk4GioAL0tjG44HrgVWWv+8DPgFMA84XQsxJS6uOH/4K/Cj6HJwK+FHXYNgQQhQD3wAWAnOi/1+KugZDzd3AZxPeS3XO1Zgw+CQ7/7dLKacDS4CvCyEmR9+/BnhESjkF2AOohdrBIdk1ALiC7gW8j6lnQImQeBYDm6WUNVLKDuAZ9EmxYgiRUh6WUm6Ivq4FGoACYDnwVHSzfwAXpaWBxwFCiCnAdPR7HiFEBWCXUr4vpQwDDwEXprGJxzRCiFlAUEq5EkBK2QSUoK7BcGMH3IAj+k9DXYMhRUr5KtBu/J2q74mu+KoxYZBJPP9Syi4p5WvR1x3AdqA8+vHFwN+jr9X5HyQSrwH/n733Dpclvcp7f19Vdd45nBznTM4jjRISksZIgMgYgxHhgq8FGLB9fTHhCi4gY5l0gce+WIZrCYwECFmAEZIQyqOsiZp80syZk3cOvTt3V/juH1+oqu7e+/Q+50yQ1O/zzDP79O7dXV1d9X3rXe+71gKEEFPADwL/PfHY19w9MCQhaewBLib+fRHY+wIdy9clhBAvBVygCazJuIf08Lt4bvF7wFsT/x7eC88vrgNqQogPCSG+IoT4ZYbfwfMKKeUy6j44B8wBn0SpUcPv4PnFZtf9NMM94XmFEGI/cDvwFf3QuJRyQ/88PP/PLf4T8B+BMPHY19w9MCQhQ7xooJn/e4CffKGP5esJQojvBk5KKU++0MfydQwP+EbgZ4BXAW8E7nlBj+jrDEKISZTKcQi1sX8DUHwhj2mIIV4oCCFywP8EfkFKWX+hj+frCUKIu4BJrZB8TcN7oQ/gRYY50qxyL/DAC3QsX1fQC94HUF7UL2nZcUoIITTr34v6foa4+ngl8IO6IcAIyoZSofdeGJ7/5w4XgYeklOcBhBAfQQXAw+/g+cMbgGe0FQ4hxD8Ar2f4HTzf6LcPzwGrDPeE5wV6/30P8BEpZbJYekMIYdSQ4fl/7vBK4BuFEGdQ9tBRIcR/B36Kr7F7YKiEpPEAcKsQYq8QYgR4E/CxF/iYvuahF7w/Az4tpfxzAH2D3UdcdPXDwIdekAP8GoeU8q1Syv1SykPAzwPvlFL+BhAKIW4XQrgob+rw/D93eBDYIYSYFEI4wGuBhxl+B88nzgPfIITI6/P9euAxht/B8wop5Rx9zvlwT3he8VtAQ0r59q7HPwz8qP75Rxie/+cEUso/klLu1XvyDwL/KKX8ya/Fe2CohCQgpQyEEP8euBdF0H5XSrn6Ah/W1wNeDfxz4HEhxPfox34U+CXgfUKI/wJ8irgYa4jnB/8a+CtUJubPpZRPvMDH8zULvfb8MvA5QAAfl1J+WAixwvA7eF4gpbxPK1CPABFqzfkgsMjwO3jOIIT4JHAHUBJCXAC+n83XnuGecJXR5/y/GXWejwohHtVP+yUp5cdQ5ORvhBD/DngS+NXn/4i/9tDvHpBSfnmTp39N3QMirm8ZYoghhhhiiCGGGGKIIYZ47jG0Yw0xxBBDDDHEEEMMMcQQzyuGJGSIIYYYYoghhhhiiCGGeF4xJCFDDDHEEEMMMcQQQwwxxPOKIQkZYoghhhhiiCGGGGKIIZ5XDEnIEEMMMcQQQwwxxBBDDPG8YkhChhhiiCGGGGKIIYYYYojnFUMSMsQQQwwxxBBDDDHEEEM8rxiSkCGGGGKIIYYYYoghhhjiecWQhAwxxBBDDDHEEEMMMcQQzyuGJGSIIYYYYoghhhhiiCGGeF4xJCFDDDHEEEMMMcQQQwwxxPOKIQkZYoghhhhiiCGGGGKIIZ5XDEnIEEMMMcQQQwwxxBBDDPG8YkhChhhiiCGGGGKIIYYYYojnFUMSMsQQQwwxxBBDDDHEEEM8r/Au9w+FEH8HvB74lJTyn/X5/cuB/wHkgPdIKX/jUq+5c+dOefjw4cs9pCGGGGKIIYYYYoghhhjiRYT7779/UUq5q/vxyyYhwH8B/hT4sU1+/w7gzcBTwBeFEH8npXxiqxc8fPgw99133xUc0hBDDDHEEEMMMcQQQwzxYoEQ4ky/xy/bjiWl/AxQ3eTN9gCelPJxKWUIvA/4jst9ryGGGGKIIYYYYoghhhjiawfPVU3IHuBi4t8Xgb39niiEeIsQ4j4hxH1LS0vP0eEMMcQQQwwxxBBDDDHEEC8WvOCF6VLKd0kpXymlfOWOHTte6MMZYoghhhhiiCGGGGKIIZ5jPFckZI608rFXPzbEEEMMMcQQQwwxxBBDfJ3jSgrTN4WUck4IEQohbkcVpv8g8BPPxXtddSw8CWe+AJ0avPbnN33aXLnJp44t0g6i1ONjhQzfefseClkXgMeWH2OhvsAbD74RRyjO9/mnl/Ech1cdmb7iw31mqcpnT64gpUw9vmeiwJtu3YUQov8fBh049kGYvRF23bqt9zw2X+GLz6z0PH7J9+yDcqPDR55YoNEJLvncqVKW77xjDxlXc+fTnwe/Add/y8DvtynWTsO5++DGb4f8GABPXtzg2ZU6b7p1V/yeQ2wbTy9WOTpf4Vtu2UU+477Qh3NVcH6twQOn13jjLTsZy2cG/rsoknzq+BKzoznu3D+xrfc8t9rgobNrvPHmnYxu4z1fCARRwCfOfoKlRtpi6zker937WvaP7X+BjuzFjYcWHqLaqfL6/a/f1jo6KI7NVzizUudbbtmF41z91z9fOc8jy4/wun2vYzw3ftVff1A8tvwY8/V5vvngN9t9d4jnBlJKPnXuU8wUZrhzx50v9OEM8VWGK2nR+0ngDqAkhLgAfD/wq8BbpJRzwL8G/grIA39+qc5YLxqsnYLH/kr9/A3/Brxcz1MeO1/mNz9yjEYn3PRlfuDu/TSDJr/34O/RiTpM56e5e9fdPLtc43c/egKAt33Xzbz04NQVHe7vfPQECxutvr+bGcnx8sObvP4zn4Qv/SEUJuFH/w4G3PD8MOI3PnSUWrs/aXCE4Ftv7enCtine9+B5/uHx+YGfX8i4vOm23VBfhU/8GsgIpo7AzLUDv0Zf3PufYOVpRWpe9i9p+SG/8aGjNP2QiUKG114/e2Wv/3WM3/noCRYrLTKuw7fdtvuFPpyrgj/89NMcm69Safn8i1cP3lb8Q4/P8a7PnybnObz7f385pdzgS/AffOIEp5brtPyIH3rFgcs57OcNDy0+xJ899Wd9f/fY8mP87mt/9/k9oK8ClFtlfv+h3yci4sDYAY5MHLmqr19t+fza3z9Jy4/wXIc33rzzqr4+wB899kec2jjFJ858gl96+S+xb3TfVX+PS2G1ucrvPvC7BDJgJj/D3bvuft6P4esJR9eO8s4n3knGyfDHb/hjRrIjL/QhDfFVhCvpjvUGKeWslLIopdwnpfyylPLbNAFBSnmflPIWKeURKeXbrtoRP9fIJ7I3rUrPrz91bJFf++BTNDohjoAdozn7X8ZVgfyzy3VALYadqAPAqfIpAJ5ZqtnXeufnThOEEZeLMJKWgIzkPHscJsP1+IXy5n9c0X0DmuvQqQ/8nkfnKpaATJWy9j0LOsP9F/edpb4JQemHi+tNALKekzqX3f+Zc/vYhQ31h/VlRUAAaosDv19fdBqw+ox+LZW5fXqxRtNXJHN+o3llr/91DCklS1V1ja7W2i/w0Vw9nF1tAHB+bfBro9zo8N77zwHQDiIWKv2TB/0QRpLT+j2Xqy/+87jcWLY/zxZmmS3MMpoZBeBs5SzNYHhPdWOpuUSEWtO6FaSrgc+eXKblq9d/9Pz6VX99gOWm+t6Xmkv82pd+jadWn3pO3mcrfO7C5wik2oMu1C487+//9YbV5ioAfuSz2LjCvXiIrzs8J3asr2qkSMgGjKgMuJSS9z5wjvc9cB6AQtblrW+6kbsOTNqn/+5Hj/P5p1eYK6sNttwu299drKmg3/wO4GK5yT88Mc9339m3cdglUW359uefet01vP4GVdj/tg8+xcNn1zk630uiLNqJ7sqtMuQGy148dFZtXjnP4Z3/291kPcVjHz1f5lc/8CQbTZ/3PXief/mawbLDG031GV59ZJqf++YbNn3ef/3003zsqUWOzVeQUiKCRAAXDB7M9cXaKTB2Nl8FescS584c4xDbR8uP7Kntti5+taLRCawKulofnBD85f3nUurpSrXNkdnB7rvFSosoUifSkOMXMwzJGMmM8F+/6b8CcHT1KP/hy/8BUETkxqkbX7DjezGi2onX5EbQuOqv/4mjcYB4bL5vd/0rRpJc1v06v3nfb/JTd/wUr9332ufk/boRyYhPn/+0/bcJkId47tBK7L8rzZWrruAN8bWNIQnpRm4s/rmlsu5+GPGHn3qae0+oLM/MSJZf/85bODRTSv3pnokCAAsbLaSUrLfibJPJyMxvtDgQnCUQHnPuXt57/zlef/0Oxovb93gng+PxQvz3L52osXrqNKeWr6Hlh319+LJVodEJCSPJU0+dojah/t5zBS89OBn73Ns1WHwK9twFXpaHz64BcMf+CUtAAO7cP8Err5nivmfX+OBjc3zLLTvZ1zmjyM3E5taRiiZSY4WtP/9Nu8f42FOLrNU7LFXb7EyRkCvMDK88Hf+sVaGj2yEhS8eUrW10cBvaix5Lx8HNwPSVbSj1RK3PdklIFEkevVBmtdZJPZ71HF56cJKRbViZtvGmMPcITB2GYn8r40o1Pp61eqfvc7rx7HKNjz21kHpseRvKUFKNa12ChJytnEVKyaHxQwO/vnndh86s95Cc8UKGlx6cxN1GDYEJRvNe3j52aCw+njMbZ74qScjT609T8ApXxWa0Umtzcb3JbXvHcRxBrROr5A3/6pKQU8s1q9CDUtOWq21mR3vtxpcLP/TxI7VWvnL3K3l06VFaYYt3PPoOlhpLfN913/ec1LkkcWztmFKRQh+igJVmb+3i84kzG2fIuln2jOx54Q7CrGmTh6C0eR1qteVzfKHKnfsntlUD2Q7jdWyr890OQo7OVbhlz3gqdngu0Q7bHFs9xo1TN6bWoiFePBiSkG7kJ+KfW2UAPvDIRUtAjsyW+NXvuJnpkd7Fe68mIU0/pNzwU0rIfG0eP+xw+Oz7+We1D+JmsvzH/C+y0pnlL+4/y8/es/2ahr4kxG9xz7Ff58baMn8w+vM8s3Qrt+7tLRCcW1ykoVWZv7/vGE8liMrt+8b5T997m/rH538PTt0LL/0xFq//IWs/ufvgZM9r/u+vOcxDZ9cJQsn/+uTn+bdrvwnCgdf8n3Dzd/U8X0ppP8P4ACTE4OhchZ2ZhJ3jSq0dSRLiN4kiObgS8swn4VP/EUqz8Ob3gfs1cEutn4EP/DQ4HvzQ/9w0GB8EjXYc0La3mcH/0qlVfuejx/v+7huOTPPWb7vpso9rU5z6NHz6P8L0tfDP/qTvU5ZrMQEuN3z8MNpy05ZS8s7PP4uUkM84CCFodsJt2aouluP33KqJw1prjV/+/C8TyYg//KY/ZKYwM/B7/MkXTvPRJxf6/u4nX3sN33nH4IGUISEFr2AfK2aK7CzuZLGxyOnK6YFf68WC85Xz/N9f/L9xcPgXt/4LvvnQN1/R6/3K3z3BXLnFW990I99w7QyVTrzmXG27mlFBHAFaUOPYfIXZ0atX65ZUb16262V877Xfy+88+Dustdb465N/zU3TN3HL9C1X7f364d5z9ypVu3wWopDV4guXGJqvzfPWz78V13H5zdf8JgfGXqA6rtOfgU/+B0VCfuDdmz7tt//xOI9f2ODNLz+wrZqzJAnZSnl6/4Pnef9DF7hj/zj/8btvfc4JKcB7j72Xj575KG88+EbecttbnvP3G2L7GLaN6Ea+VwkxnaCOzJb4rX96e18CArB7ImbaF8vNFAmJZMj8p36Nl61+CIDxLPzo5JMAfOypBZ5drrFd9CUhtQWKsokQsCe8yNG5/pastbU4YzEi09L84xc2OL+mNxQToK+e4uGzsbLz0j4kZPd4ge/R1rKVc8epd0JVt/H534f7/lhlZBJo+iFBKNPHvwl2j+eZ0GrR0flK2oJ1xUrIyfhnv865tUbKNlNpbhL0dRrw5f+mfq4vQ3sL+9tXE84/qL63sKO6hl0BrkQJOb6w+fk8dRn3y0BY1df72rOxRa8L3eRh/RJqyBefWeXJi+qzfP9L99tkxXZISNLG2fQ3P4/nKucIZEBExHxt8IYPACcXN7foPDm3sa3X6kdCAKvOnN746iMhz248C0BExJ88+Sf8xdG/IJKXZzFsdkLmNLF8dkUpFCk71lVUQjpBxGdOqBqT11w3w1hBJUq2tOteBpLEqeAVODR+iLe/+u0IVLB5duPsVX2/bjT8BvfN3wdhBzcydsn+pPr5wLMbzxIR4Uc+737q3T0dLJ83rKp6VNbPqK6YfXB+rcHjut7y6aXtWfVSSkhrcyXklFbiHju/wRefeX5scmcqZwC4UB3WBr1YMSQh3XAzkNU2q9YGa/WOvXnuuXGHbb3bD8aOBcp2Ze1YUQgbFzh7+rNEUgKCjOfwGvkwBVciJTpTur1FKklCrJ2pXcURkPNcSrLed6PpBBHtWhmAiWKGX3zdLt7/U6/iXT92ty1q/+Qx7R9urNnXfeiM+jwHporsGOsvbf7A3fuZKGbIyxYrtTb2Ez32V/Cp/5AiDOVGfztZPwghuFmrIcfmK+AnMoX+FWQNg45anA06jZQKAlsoIY/+JTQSi+mV1qa8WLDwePxz/coKZBtXQEIWdeH29TtHef9PvYr3/9Sr+IGXqdauyWvnqsI0o5DRptdVN3lY3YKEtIOQP/2iCrh3juX4nrv2WgvMdkjIfJKEXEIJMdhuXcGKtod995177Pl+ne4Kd2EbBfgQ+8TzbnqdODymasUuVC/gh19dtVbJcwvwoWc/xH9++D/TCQez5KVeqxH/TbWlvs8kCbmaSsiXn12lrhXJb755FzfuSqyjVxHdJARgujBtbTD1YPAGKJeDL859UdnBwg6vD7MA1PzGC9YEIXm9PLn6JA8tPvSCHIdJpqqfy32fkqwXWqlt73oeVAlJ7gV/+sXTtIPnvratohODrfBrZG/+GsSQhHQhkhHns3keFT60K6ns/92HtraljOUz1qc+V26y0d5Q3tTyOfAbnCPkaOYW/rL4I2Rdh2x7nX91jcocPHmxwpdPbS87YILjYtaN7SA6iMpnHEaiOscXKrag1eCpuQ3ykdoQSjmPbGeDQtZl51ielx9SCsenjy8RtJu2UDtsVXhMd9u6+1CvCmJQyLr8+DccIkebThBRboawQ9tmnv0MfPjnVEcuNiFRoDLQ9d6MirFknVtr0GolAqwrCf7Xz0CUCOr8/iSkhyBW5uDx96cf2+ZxRJEcqKag2vK31XHsiiAlLDxBKKWybVyi89hqczWdDZbSdhgDbPADbHvTWayozW33eJ5C1qWQdZkdyerXimhu0SL7spHcsDfpGrfctUlv9R1+4JGLlmz8768+TNZzmNVK6so2akKSdqytCtOTgc92gq+WH1rFb/d4wZ5vU/d2odzE30Ynv0bQIIwkuW4SMq5ISChDztfO6+tlud9LXDWs1tqXTPCEUZiq4esHc24nc5NcP3k9APcv3M9vfPk31Fq/DSTVM1MXV/MTNSFXsTD9E0eVGrBzLMdtu0vcMa2+xzMr9at6DzWDpvo+w4CiV7SPlzLqGrqUurPR3rgiYnrvuXsBOOiO8LJI22JluO3i9NVau2fPvBx0k9b3HH3PC0O8kwp9Y63n10EYce+JJZCS8ajMcnV7+9jgJCS+1parbf7uKxe39T6XA2NxbF+pW2KI5wxDEtKFvzn5N/x8NMcfZJpEzTIP6ULsXeN59oxfurDJWLLmyk3WW6uwcV7ZWoDjowf50+K/5JHMXXglRWhey4PsHFNByXazA33rKfSCU8i4lGSNejvk/Hp68f/K6SVyso0jhCpaTwReb7xZeWjLDZ/Hno4tE7WNdTo6k93PipXEPTfs4IBu+jPXELS+9ffhsO6Osvik8qcClU0K6/n878FffB8c/4fU6xoSIiUsrpbjX1wJCVl9Ov1vv8FRbT0xbYHDSCprWRL3/ZH9XuPj2N5C93sfP8GP/ekDfQc/GlRbPm9590P8xHseGrgI+opQmSOor3FmpcGZ1Tp+ZXMS8qlzn+JnPvUz/PFjf6weaFXgw/8O/vL74bH3AV1KyBY2on4wSoi5PwAmiln7c7n5HJyPFAnpb0voVjA2IxO1dsBfP6RsALftG7fDSWdG1WdYq3cIBwh2/DBKBQZbBY7JQHo7JCSp5iSLlfdPqox2FEnmy4PfZyv1GqdX6nzuRCVFAAwJAW3JevBd8Jf/DB79q4Ffezv4+FML/Pj/eJB33PvMls/7rQd+i5/+5E/z6NKjmz7HBJU7Szv51Vf+Kq/Y/QoAni4/zX/+yn/e1nEl72WzDj4XdqzFSovHzqtr+g037sD50L/ljV/5GQ4EZ4gknNjCgrddNPyGSs6snaJw7kH7uCEkWxGrc5Vz/PQnf5pf/sIvX5bF7WzlLKc2lO3onsw0Mya0icJtFaffe3yJH/8fD/JfPvX0pZ98CZjrJeOovW2pscQ/nP6Hrf7kuUGyE2azl2g/eGadcsPn+5vv59crv87tlc9ti5wmlcByu2ybE3Sj+zX/+uELz2m78SAKLLEfKiEvXgxJSBcOjR0Cx6WN5EJ9gUfOlQFViD1IIZXxe89ttChXLiolBKA0w9HsNJFwKRVyeDe+CQDv/H385N0qqF+stPn7R+YGPta+JMQqIS5F2dtuFuCp0yowKmZddQEkJNqXHpxksqSCpAePnbKPN7V9q5BxU0Xi/eA4gpfvU4FMQ+aYrwNv+A9w/beqJ8w9AmGwaXcvzusNbO7R1OteM1uyxGBpPT5m/CtYYFbSm00QRpQr6nwl2y+nLFkXH4bTn1M/731J4ji2J/s/cVEFB09e3DyLenZV1adUWwGfPLq1KnFVsPgkLT8kklKRr9XNr8cH5h8A4OHFh5EbF+Hvfyb+zs5+EehWQgYPLqot32bOkta/iUQXuefEkpW0K2yihHSTjs3I4ZmVuv3MP/TyA3b9mB1RnyeSg7X4Xay0SHIVP5Sbzhe6XCUkGQzMjMRE78B0nNHuTmZs+Xp1FfiU6yI12HQ8N85kTt1XZzbOxNfLxYcHfu3t4Ct6/TY1Of3ghz5PrDyBRPLEyuYzdQ3Bm8pPkXWz/LuX/Dtet+91gGo/vJ3geb2RVELU+UkWpl8tJcTYaoWAb7phCpaOkvME10WqvmWzmsHLQXPjHOgOX4WFx+zjgyghR9eOEsqQc9Vz1P3t27Y+c/4zAHiOx2sCmJY6tJEhq63BlRCzJj+xxZo8KMy9eOfsnbYT3N89/Xc9CslzjuS8sz4kxFixbghUE5Aj4altkYNki16J3FRRNOv5K6+ZwhHKFv7uL50Z+H22i2S3ucuxTA7x/GBIQrpwaPwQCFX3cayyYNn7VhakJHaPKxJysVyhrjMw08KDwhQr7UUkoaoduUGREKKQl/kP2g5W73/o/MA2DZNBG+ujhHiOYMZTQUhyo5krN6mU1SJYzOn6lmbZ/t51BN90o5o3cvbCRQId/bSaDTzpc9eBwdr3jbvq2NoipzZcx4GDr4qf0Fi1gb3rCIqm1iaKoKEzV11BVMZ1uGGXGni2Wk4srFeihBgSItRnavohOanO/yuvidsZWtUmCuFLau4B+XF4+U8ljmN7WR2zKHe2CM6TytjHjy5eFZvAllh4wn7nAKLen/hIKW1xca2xwsYHfgrK5+MnrJ0GKbtqQgbPrhkrFsDOJAkpJJSQ54SEJIKPdm/xexTJnvuzu4WwgRkkCrB/Kg7mjRIC6Xa/m2GujwKxmSUrGXBdLglJKiE7R/OW+J9bGzwwbofqvYXMpIgoxGrImcqZ+B5vXXnQ1w9mQGZri2vPDNiDrVuMJu1YAI5wuHXmVvv77agXSTuWmfd0tZWQKJI2cXHX/glm82qdcYADI+oePzp/9c5789x99udCQkE19SFbfaZmIoHT2uZ67oc+n7ugkkIv2/kyRisLFBEUEKpD1jbsWGZNvhr2V0taC1P82C0/hkDQClu87/j7rvi1t4X25iRktdZWbfelZE9WfT952dpW+/DuAL/fPSQTe8HNe8b45luU4+KzJ5evem2SQZLUD5WQFy+GJKQLs4VZRvSieVIXHWc9p2+b237Yo+1YzbCKbKuMzm2jh0AIOkFAINaVrWvyIOxUG5g48RF+4jWHcITKFr9HZwcu5WM2Hu60EhJvKruyaiE5mhhM9fDZdUpaISlmtW+2q1jtDTfvBGAkqlJpBXTCCD+MKMjmJa1YBgWhFqamKMQbbjHRLrS+bEnIRDETq0ytsgr0oa+yYIrTK9VqXPR+uX7PKIonpc9ch0TS0iSklHO5dW+s+Fgl5PiHVeckgJe9BUqJz7SNzTOMpCUfWykEbT9S/jMpWay0eOoqZi77YuGJlPffbaz07RK13l5no7OhpP7yOS6011W6de9L9YFXobme8gFvRwlZSkwT37WJErJxte1YUWgzuUD6Z41Ky7cd3Qw2UzMWtYWqkHEZy8etm2cT3fUG2eyTM0IMNiMhySzkdgJZQ0KKnmTEk0rBDX0cGXBgUp3/JAnZam2SUlqfuEMuNVQV4nkhZytniTpNJBC1yvh6nfHD6KqRbaNSbWUFTE4n3yxgDaPQ1n1MFeLawGTtQ7Km45LHlSDQlaZPGIWpzO3VKKZ+9ELZFhm/8eZdqfV0/4g6HycXagNZAi+JKKQ5/xUAMggylTm7bhglZKvC9OS5SwaMgzRreWjxIfv39+x+la17mJHOtu1YJlBu+uEVXYORjGISkpvimvFreP3+1wPw2Quf5VT5VOr5z2nnrC2UkE8fXyKSUKDJbFGFgznZ2p4S0hXg9zvf7SCyam4h4/EjrzhISSdB3/m5Z5+T5FqShARRQBgNngQb4vnDkIR0QQjBoYJSAs6FamG7be84OW/zrlhJGDvWqLyA1BmCW3e/AqTydgfOStxF68ZvV/8vn+Oa6KzNDtx7Ypm/P/Yl3vLxt/C/nv5fm75XMoi3SPg/p1y16SxWWnYzfvjsOkXZIOc5ZMzwsWY5FWjunShw694xRqJqyhZTlPWBSUg2Um2C2+Riu0op0ZO+sRIrOfnE8ScL0vvYrIwVzA3bcVB7uUrIxnn7t39byPATmSqPEZCXLW7aPZbKum80fbWJP/gu9cD0EbjxO8BLtGveBhlKBpGdLQp+/U6TX6j9Lv++9ntkZdsWmXbj4bNr/Oif3M/7Hjg38DH0oFWB9TMEkWTOVa2WCZppT7HGmY0zyq5UmQMkFzwH3vgb8PKfiJ+0fiZVS7OdmpAFTUIckbYH5TMu+YxattavthLSrqQJVx8Sktyc90+p+3gzJWRRKyE7xnIpK+dkMWsH/60MsNlfLPcGpK1O77n0Q/+yZ00sV9t8Y/uz/Hb55xF/8kZ41xvsfz83/wscCp7l3FqDSEb89gO/zc9+6mdZbvQvKFcbvgrmhMym7FgQt+lth22eqqxyarnGsdMX+af/7Uv2vx9+1/1XbD+MImlrXbZS4ZIkZLMWo+V2mQh1zqdyMQkZzY7an7djI0oqIX4oWWvW7OvD1bFjmfM3mvd4+eGpFAnZXVDv1fRDzqxeha5V5+6jqZWcokTdOzq5VczompAtSHHy3Bkl5MmLG/zIn9zPe758Zsu3Nlas6fw0t2Um7OPTUmy7MN3sdVJuv5tfEtVOlU7kc261wQce3iCMJG++8c22W9x7jr7HPvehM2v80Dvv5/0Pnt/s5S4fQSe9PzZjK5iU0tr17t4RkfccHEeQl+0rUkL6ne9kMqqYdRkvZnjzy9UskqeXanz26avfnKLS1TI/WUA/xIsHQxLSB4dKe5ASLrgBruwMbMUC2K0Jxt7omM1uXHfttyHwkJAmIde8HjL65xMfSWUH/uThf6DWqfHxMx/v+z5RJG1XlX6F6QAjooXQPuVj8xVafsjjF8oUZT1WQUAVWHepDm+4aSejskoniKx/+dpxNp2R0g3hN3AdQVvkYttMcuhdfcV6oVPHX08sRn2CKGPHytKJJ0dfLglJFKV/MlilKiT3ZwJytLlp9xj5jGOtKBtNX1mMTFbp7n+pLGbJOQjbOI7k1Outhvjllp9kdzjH3vAC1wcn+cIzKz1WgWYn5P/91DOUGz4ffGzwmqIeLD4FQBBKHsncBeiYPNHtyuBM5Ux8LoTLhZvepJoPTByMn7R+hkY7bccaNONn7FgzIzm8LvvfuCaHl5xkv120ulSmPnasJAm5YacixKv1Tt/PZT7Dzq521o4jLLEaZLM3M0KS7cEbfq9dJDmXCLYXyLbWL/KdrQ+SdXo/x4hocpf/CBfXmzy+/CSPLD3CamuVx1ce7/NK6n1NYlPQS0KSxeknWhWkhKxs48n4+6y1A/7Lp57mz+87e9lZ4krLt1l+P5SbZluTdqxyq39hbVJhmszH+4EJsGF7SkiyJgRgoZrOUPuRf0WdlKJI2s6Or71+Vk2oTiRJZnPxd3JV7DAnPkJDa9MFPReEDdX9yBamb0FCkr8zweIXnlmh0gz48GPzW14Dxhb6it2vwKnEHZdmpAMyYqXRu35thmTxdH2LVtiXwnprnWYnpBNGzK26nFmtM54b57uOqKG9x9eO2yTBZ08uU2sH/P1jF6++ItI9uyphvX5qrmKtnvccUPGA5whysj1QcsRgECUkacs1Mc6337abab0OPn7+6tsxK/6QhHw1YEhC+uDQ2AEiKWkKyLHA3QcHnxg9kvMYK3jMylMqgPNyTE9fz1hGWZwUCdFBSbYI19yjfj51L+Oeb7MDS81Fqq2A9fZ634xmtRXYpG1KSUgEUlkXprNqszs2X+GJixv4oaQkG3YhiP8uvQi8+toZJoXKThn7yV07B1ODAOg08ByHlsjHPfHdDBT0Bl5foawfT5GQRlIJ6f3co/kMB6aKZGUnVhMud06IrgeJciNsyIBIQl1I8rLFzbvHEELYeptK009nxse1UuB64Ojzsg0S0hzQpuTUYuXjcPAsfij53Ml01uhvvnLBqk3VVtBjfxkYi2p4ZlN6PJlRVsFIyr5tek9vnI67g2WLXJDxz4woJbFbCYkkqXqTLQ+lYlSE3o50RvnrDuSuGN11CX0K0w1pEAJu2KVawHWCqLd7GrEdK9ndy2A7s0JMV6ojsyX7WL/uNd0Fr9tRQu64+D48GeC5LrzqX8M3/pz6b3Q3WdehJOuEkeTDz8RJkc2CymbQ1POQwJE5aq10MDdbmGUko87dRUd9h4Wsy79+1Sw/8/oj/NTrrrHn5/0PnucPPnFyy7qpzdA9v2Wz+yyphGxWWJs8t1P5hBKSSSghmzQy6Ifua3epXu55zpWoIcmBq7cbK3Hi+8pFTavaX3FxemMNzn2ZhpCQH0+QENUAxRamB41Ng+x+SohRypt+uPUsHh1cjmZHLfEBbIes1cbSwMF9kng02pdv31lrrdl905WjdhbMdZPX2ecs6EGK5nNWmkFf1fOK0ENC4uvYFKQXsi53TKnPmnEd8lxZTUi/RgDJ9aqQ0YTHdditO45e9p61BYZKyFcHhiSkDw5NXGszedMjC+waoDVvEgdGHUpyDgmU8pNk3AxFoaxIflIJgdiS5Tfg2c/w7bftZu9EnsBZY6XeJpKw2Kc4eNMZG4kbTwC369rqo3MVO2xwwmmq1rxJdHlF8xmX68fSC8NNU5fuDmbhNxNKSGKRMjUUiZqQTZWQTcjFzXvGyMo2LT9UubfLrQnRJKQ6eZAQJU/XHCjS5rqdKkia0Me20fTTtqSEDQM9jGtbJCRpx9oiwMo01EYlBNzmKbk+OVhqsdLi776SngY7v3GZytDCE0TAKXGAVUd9T3KTWSFnkiTEzXKhljiGSZ3pXj+TyoDB4BaHJR3A7+pHQsx3crXtWD0kZHMlZLKYZXY0Pra1LktWJ4gsMexWQkApPMnX2wydILIBwZHZEfv4QCRkQHIu5x7hupoapDa3701w+/fDzd+t/ps4QNZzKEV1Ipo8uBAPXNvMftQKWjZBImSWapcSIoTg4NhBkJILrvoc+YzLNx3O86bbdvMdt+/h977/Dku6PnNimV//4JPbDlS6bXKtTRTHbltZv0zuWrs/CTEBNgyuhPhhZOv5DBZ198EkrqQuJKlu2G6GyfWpU7ePX/Hk9JMfUzUhAIUpCkKv5xtqvTJKSCjDTQPBfjUhlcT3fXa1PyGTUtogOOtmLfEBbccC/LCdsiluhcZVUkLWWms24eLIUUs0dpV22efM1+eBuDsaXN1uZUCvuqv3+UYnsK3hX3f9LFmtoiolpLU9JSS4tBJS77JjGZi5at1q6dVA93e+3YYHQzw/GJKQPpgdvQZXLyCTY9sbdARwh3eWmhMhpWRiZA8AItBswFsn5yWC+Z23wISaAs2Jf8BzHX7oVbNI0SaMJOv1jl2sOPNF+OC/gfnHUwt0MoiX7Qrvdpv8jlenieTmKfU5Tq3Uuf+0+ixHxqTKVTkJS1afSaoHCvFC5DqCfcXBFwrfr/Fnoz73F+dYrSVufl0XIjclIYnzvUkQddPuUXJ0CEKpFvrL2aylhJWTAGxM7APhEElJ3ZEcGMPWAI0nSUgy05mNgw9bF7INMjRowXZGZ2kdIbjBncOTPk8v1Tizoo7lT794Gj+UJLtHX1Y2LfRh6RhhJDnlHmQ5/w/85bhLIGWaGKJaHy7VF0BGHJEuuFmqnWo8sG1SW7LKZ1OfE7YmXAZSyoSVqUtFWDrO9178f7jdf+x5U0JOlU/xG1/+DR5ZesQSgpmRXKpWZaWrOH251raBeD8SMqgSsrARB/QpEtInoL4sJSSKCL7w/xJJyVPZHP99pMqTK0/Gv8+Pk3EdRqnT9I7S9OPj3SxTr+xY6qAFvUoIaEuWjLjgSSQSV4jU+Z8qZfmtf3q7qmVAtdj9hb9+vG+R/mZY6/pOBlFCoL+nfU1nkEczo2TceL3KuBmyjroOtqwJefaz8MF/C0vH+9oIVxI2GYOG36Dlh7z9w0d51+ef3fy1+8CQkF3jedtyPbWedurcvEfbCWsdS/q3gycubPDWv32M9Uf+HoBmcRK8LIWcTtBoa1TSsrbZOUqqaiZY9DbO87O1P+SftD7JhbX+fxdEga2lybm5mISM7VF2LBi4Q1YUyRRRNQmUryx+hbd96W0cXzt+ydcwMCREyCwOWbtfzxRm8PS+a5KLyXlZx+Z76+8MPnTqQ7z9vrf3XK9bolsJaW1AFPK5kyv2fnjjzTutQuK5goz0Was2BioWT5JAU+/SvyYkXgOSJGRUuziqfdaIK8V2lJD33n+Ot33wqedEkRliawxJSB+c3HDZ7auoTmYG76xhcJP/FFVHEuIwPqbsVUFH2ZA8V7LYSGSWhYAbtBqy8CQ019k51bI1G7V2YGVbHnwnzD8OT/x1/xkbQYcLQZ2PuB2+4gR8yfG5blwtJFEkbWbwYEn/7fi++Dj6bIIjUYWcpy6RYtbF2WR4Wz884W/wcDbkRH6e863EDICiImNRbRVfy9VjhQQZStWEtFQHqy7cvHucrLb/NDvh5SkhtUWrbKyPzAKCUApqDhxKjEHpS0IcL12QfjlKSIqEbC7755tqwxECxnNwSMZqyBMXNvjSM2rB/47bd9uC7e0MlbNYOQlhhyCMeDw7QtM7xgOFkONu2KOEnKmcsSrIa8KMstlBrIZMHtIfsgxd1pZB2vSWG74lKz0B/NG/Z1f9OG9qfeTqt+jt3rC1EvKuJ97FU6tP8adP/qnt2jU7mmOqFJOQ7qz7YqK711ZKSK0dbJqhB5hLBN2XIiHdNqKB7DzHP0y4rDrE/cXYHi52TvOfv/Kf42AxP4YAprwWjcyjKRK5mR2rFcRzTZw+hemgOmRFUURTwLqjkhzdiZBC1uVXvu0mvuP23YAi1z//148NXMOwMoAS0gyaVP30utZXCdEEL6mCGIxk1feypRLyyF/A/GPwyJ+nitIN1pu9n6kRNHjozDr3n17j7x+dG7h1O8TqRmqmU3J98mMSAlsHv5vhfQ+eo3bucepLiiA1R1WWv2hqZjQhSJKQrYirgVFCblr/FEeCZ/iO1ofY85XfV0XWXUgGllknCxW9Bu28JZ4VMmCHrHYQpfpSmNbS7zv+Po6tHeMjz37kkq9hoEhIhCsVITPKlyMcdhaVNdskF5MB+GYtk1ebq7z32Ht5YuUJvjT3pYGPo0cJkRJaG3Y21Z6JPNftGAHdCdRz1DlzglYq0bkZkiRw94i6TxtBo2dtSO53xVy834/qroGDvNd20a2EbEZC1usd/uqBczx8dp37nn2eZ7gMMSQh/fDgvM/eQICA1XAbWQcAKdlXfYyqCy1y5Fzlx63V1P+zrsOFato+w767458Xn2KhvkBBB5RBGDFfm1eLyZqeYN5Y609C2lVOiPhmPylC9hU6thOPwe68/tuxPXE9Q3cWOAwQ7Sq7xwtMj2RV5rZPl6S+CDrUZIAAJIIV915W9fAyo4SEtSXbiWg80YWqO+veL7DfOZaj6KjP0PI1CelDVrZEYkhhuTiuNiAEbQG7SvE5HEuREB1kZEukpIfLUEJShelbqAP5liIAjhC4QvCGKXV+7j2xxH/X2dHRvMebX37AzqiZuxwlZEFlv/0ILmRUEBHi8YQb9hSmm3oQAXxDlAFXfX/2utYkRAKjjXSh/CAdshYSAfyObiWkU8N1BDPRCs22f1m1Apui+x5o1zizcYZnN9R5XmosMVdTgcPMSJaRnKcKfunNum/WYtggOYtjKzXEfJeOgN0T8cyOQWtCtvTCt6vw4LsIooh5dw8XvUk8R1DtVPmbk3+jnpPX61emiu8s0U50ctssoEy+r5C5vjMXDo0rEgJwMSNxHPrOCnEcwU+97ghv+cbDCKGCuV/5uyeslWQrdA+R7DcrpF9WuZ+n3RC8ZFG6gbFkbUlCzNq5+CRrfchEuTtYRJG8ZPvn8oDK31q9Y5XEm3cnbKNdSsie8bzdO7ZbnC6l5NRyjVd07lPF/5kCzYK6VgqmhfHGRZCSkherxv2IaySjnpoQKSXj7Xn72K7lL8JH/n3PNdKJ4nOSjcI4mbbjJqYQSvGXg5GQbvtVoxNQ9+ucq6qOg91kdSust9YJQxmTkESQbSxZ8/V5OkGUSijMlVt9baafu/A5G+xva5hjvz27uW4TA7OjunOfJiFmfckxWJveZGC/p7TH/tythqS6Y2V6lZBaO7jqRfmDkpBTy/F9O+g9NsTVw5CE9MED56rsCLI4QlD2EzaTQVA+R6mzQsWRtEQBohJRJFmvFhDSI+MKLtYupv9m6how2aKFJ5mvz9uOQBI4W5mDpaPx85sxCSlkXBsI0d7ghBMvpCecgKxfTWVQr90xQi7Ui1huzAYZPXYs/e+MK5gqZpVdYlAS4tdpouL0CIdI1HnfcR3UaBIS+S0KykXcVZjeFQD0ISFCCMY9tajZBXy7RWemM5aXo+xl1JRwfTuM5OL3TBamS9MtKTeaeinbIWsbBfID2bE6DTKBek9Dee7Oq0C/2gqsJetHX3mQ0XzGFvldFglZVJOiy7m9tDSRDYTLE5leEqKUEJ890mEiO8Z0URWiWxKiO2RJYDaYT/3tIDUhW6oIQRvXEbgyYFKuX90OWX1qQkz7T1CceamjLBlm857Wakh31t0MKhzNe6muVgaDzgox9T2zo3kyrmMV0n5Z/W4SEsqwb6cni4ffDa0NglDygcL3EgnfrjsfO/MxtU7p9eHhXAshJUHgcPPULcBghemCbMrzbrB3ZC+u9uzPZbQdq1uJSuC779zLW990E1nPwQ8lv/2Px/lfX7mwZeDSTUL6EeAkCTEqR1871lZKiC6y37Iw3QSOzTLN1bP2YWOVqui1NUlyGkEj9Rn6ncd+SBKKm3cn5lsl16fQR4Q+N+pug9utRViqtglade70H1EF2NfcQ0OvwYWibsXuN6C5nlJC+lkEW0ELGU99oq3br08FKuHiiyydMELOPw4f+JlU8XmyKDrXTNy/k4fIZEeY0LNCBpma3k3s6+2Qp9eftse2nRodY8dypLo2kpar3SWlGCzUF/oqAMcW0t9FJCM+ff7T9t/bGmTZbwhoc92uH7ZLZsPYsdT9n5ftbZOQfaOxs6L7fJtzm8+oNsAGpiYkCCWtbbRwHwSD1oQ8uxzft8+FLWyIrTEkIV2YKzeZK7eYCEo4ApChbQE4EM7fj+cqW09L5Ak6BZZrbcJQ4MkpMq7D+WpXP3DHhR03qZ8XnmChvoCXuFEvVudg4Yn4+c04+EoVpbcqHE8oIfMiYqO+yE2JbNhLD05Ca4Mmknc3TvOACZC67VjJQnU3VloGgt+kjUQgkFox+OS5jypFRxemh5FkLFIL5LiZc+K3et+j34IbhZQyamPohBGhlJsSgGqnyrufejePL3e1EzVKyNQRyp2KJiECIaAj40XJECQ/lARNfWzZmNQFUcBfBkt8zulcfSWkGremLGdV9mxH/Wl2jMTf+aGZEt+i58uYhgdzG5fIgHdDSquEXMxfSyTUYh3isehIFusLKaXpzMYZCDsciFxOtscpOZqEGDtWbgRKs0SRZFeUnmsyiB1rSWdxDQFOYrmzwV/lOpz3JDPh8tXNXLUq+JFkudam3gnx21U+f/Hz9tdBJGl7asiYUTJMi8nugHdRb+D9VBCAmYQSslURqKnv2TuRh/nH+a7G3zIWbfTU2kAcKJugGLYInNbPwlNqBtH58bt52rsezw2swBfKkPc89R7Ij9NB8mA2xCEiF9xA3pkANs/IKiUEQCDw+iohjnDY4am14KIntR1r62TPq45M89v/9DbbHe1/fPEMf/KFzdfmbvtSP+JmSIhAcOPUjervtrJjFXpJyEBKSCdex4S+1/IZh536OjDT0mcL8SylZtBMWbcqAxJuQyhKOZd9k1u0EO/UrF3r7Gq9p4nEVji1VOMO/1Fysk0kJe1r32SvtcLIzviJG+dTxft1v67Wm8f+Jzz6XpCy57y1ghaVWp3JSO1B/5h/E5/NfKNSXDYuwAd+GlaUhTAZBOeSe9b4fsiNMY0YWAnpvqcanYATayfif29n+GdjlUhK3KhXCTEkpNKpsFjtvea7Valjq8dSZHlbDQvMflqYiB9rrNnPWjCqhE7+uY5ygAw6NT2lhIzESkj3+TYqUyE5GgBSQ1yr7auXUIpklBr+2X2sSSSVkBcdCfFbcN8fq5qybeIPPn6C3/3o8Z5umi82DElIF0xv9WI0QUYIiEKV+R0U5+6jLiQdJ0soXFrtvA0kvGiajOukOwkZ7LpN/X/lBPO1i2RcB5P/Xm9t0Jh/NH6u36Su7U1JFaFcm2NJ6GBRqK/2ZPUst+yJs2F3H5qEdpWPuh0+Un+WPwwW8JG9SkgjkVU1tSN95ib0RadBS+ggWHoI6dIJA/782J+nSMiEJiF2IWr02Sj6DCzET3T3MkOlNiEAnz73aT5y+iP80WN/lP6FISEz16qe7n6IxMERgkpC+Uqe347xbSeK0r948Yt8sLPIH3tN6tuYE9BIBERRJAn6DSysLlhv/TPj3wCAaFf5zkPxQvkT33jYZpYMCam3w4GzpoAqINUb+CnnMJFQ12uIiwQeER27SbXDNnO1OQg7zLYkj26UeHZeBVIXqwmFb/IgkZTsDNP1JIMoIQuJuguny0r4oeZFPpnx+eBYyGy0TPkqKiHV8goX1huUGz4LlRYP+us2QLp+8nqCMKLjnCeiY5UMUxey2rVhm0GFs33a8wKUsq4NALba7I2qtXuiAF/8L7yyfi+vb9/bUxMipbSBcjIY2DRwevrjakK8m+Hz0/8MAM/TgYJW9h5dfpRHmgs86Pg0BThEFP07aHfUPbGVHSuSEkfmEIhNiz13OCrrP5eROOLSJATgup2j/N7332EHRf79o3ObNmLortPpd+2ZYGkyP2m9+j1Z3KBp6xSSgwoNrBKymU0m6EAU34/5VTWPZ7KYtXaUeqDW84nchC3wbfiN1EDO7SohN+4aS98/3YmaRHF6JOHk4uDr16nlGjf7Sp1fdmdZKhyy1qjCaHz9sXExNVW+ETRg6Rjc99/g/v8Plo/3XKOtsEVj5TxoBWLJ2cnfFb6PCzf9SyWvtzbgvncAXTUhZs9ys1Ccgfy4qgsZsDC9245V74QcX4+L0QdtmdwJO1bZMnasZHCb7JB1NqHqGHLdrUrde/7e1L+3R0L0PTW2z8YEtMoxCcm66vrUx+sQzwrZrhIylh2z90I3CbEDj7u6co4mxgtcTQJQ7VRT6lr3sSaRJiEvssL0U5+Gx/4K7v1NCLd3fu4/vcbnn17h3NqVDz59LjEkIV3YMZrjpQcnKZYm2I+7PSWk04D5x1gXksBVC2+tkbOBhBfNkPEc5mpzRLJrQ9x1OwAy7LCwcQbXEeRCNYsiCCMWlo+mnt6pqaAxGSSfWI/rHBzt0z9en+flh6f41lt38QMv288NMzkIWnxF+CBcOsLhlAi3VkImVHE9g9rSfNWZSwhwZJGS/zKCSPLw4sM8qlvOhpFkXJZxHGEl2dS0dPtafW6goGW9q6DnmGwitZqAYq21FsuxjbW49mTmes5vrBBE0iohlYT3N3l+/T4k5OjqURCCEFjcRuF+q7trVF8SEishZyZfZR9+08wqb7hpJ2/5xsPcvm/CPr470Up6W5ashbgb0lF5wJKQQKgN41EnsMXpZytnlTc59Nnnw7KzA7+tgsmNzkYsgU8eJpKwO+qyYw0guRs7Vj8VoRw1EQI2XMlMtHLVitMfPrvG0dPnCUKJxCGKJJ+MaiAlM4UZfvimHyaIJFKEdNyzsRJSUv/vnmOwuEWLYVCWwkt1yGr5oQ2k90wUoLmOIwQ7oqVe64hft9arJAnZNGAx1//EQU63VZLC0XVW33roW5nMqe/03Rc+xSccHyGgGBXIRvtptFz7nv3Q8JtIVHteiAt8uzGDCoA3HKg5ciASAsqi98vfdpP999mV3uNoB2FPQXw/EmKahOwo7mCmoBIkdb+eOm+pGSGXo4R0naexDZVdnyplbVOOZqCeM5odjSeMB414xhKDKSEtP7RB1c3JonToo4TUuWZWK/4odWNQnFquU5Lq+YvOLhbq8d8WCzOxZXXjAhk3Q8bRZMuvQzKhtvpMz3XUDtp01s7Zfy87Sh16fPKNcOcPqwfnH4NOPW3Hqun9Y3yvGiSbH7dT01dal1ZCuu+pWrvDM+vPxL/3B1OY11vrqfa8kP7ukiTkfCVeH1+hO8E9s1yztW51v8598/elj3M7JMTUGhUmYut1Y42mHnZayrqp2SGgitNztHospv2QDOzzbp7pvGo800tC1PsVs90kJKGEXEUS0q8lcz8SUmsHtn7K/PtFBTN8M2jBNuKLThBZ4mfI7YsVV0RChBDfIYQ4IYR4Wgjxlj6/f7MQ4gkhxJNCiPcJIQYbt/0C4hXXTPO277qF19xyhMORq5SQjTOD/fHFhyEKKBMR6o1ko5ax3Yoms7txhJqG21MQueMmEA4bSFqtMkLAlHc9ADJosSDTN1CkW9kmg+TjFUWWijjcklcWmRPtFVxH8LP3XMuPvvIgol2lQsTTTqhsYI7LcSfsVUIMCREOjOnBfNtRQvSPnlNgxH81WaEyJO955m8I3IwiIdEGY3lPFcbBJiSkz4LrN3GEsNaRIIo2JSHJLJvNcK7GGwvT13FuQwVkUii/aiWxKSbPb9g0hemx3eXE+gmbYVoOtqGEDNK6tjqPlFBxxukUd4HuPpNbPcr/8Ybr+O4796aevjcxf2Y7rUyt1a80w6nGCBFGCfEAyZNOSEcvhmc2zqisrozYGzgsOzN0WhMmaZkoTldKyEhUpRjF57MTXtqOZTaFfoMKWzrQbgiUEnIV7FgffXKe3/jQUfJhDSFgzZlk3ZE84ajP+fr9r+f6yetxpToeP3PKXhfGjrXR9K2a1eyEthtOv85YBoaEbNb1aCEx72XvRB6CNo4jmI5We5SQZGesfSOxN3vTgMUkHfLjrNTaSEIcR29a+Ql+6KYfAmC+vcYxXWd2k3MIgUO1qa531QWr97qtaeuRQJ2bph/2VfqmQn0fCTgjwt5OPltg93jBZvnn+szF6bbHQX8roJkRMluYtSQE0kHUWiJIM+QsCdMda1PVqWsNG2nNU4pqTBSzdtBsK0qQkMSE8aQda5Ag7eRi1aqnye5X/Y6DTp2c57JvUr3fsyvbU0Lyek9qixyL1fi7K2QK8Z7RNSuk7tdTSQ/Wz/SQkGbQJFxXfydxaOTUXnZ+rQGHXqOeFIVw4aG0EmJq14xynx9XbXqjkHKrTBBtff661+TFxvlU4Xsgg61rrDSSM0LiwvT4vafyU5aUmUYXoGIPUEm1p5dUwPmluS/Z9zwwqpKB2xpiaeqscmPxoODmeqJGw+2pw/RcoexYAyghyTqLrJtlurAZCdFKSNeQ5JEUCbl6KkQ/EtKvJuT0cvrae9HZsZKOlEHjL9Jz5Ca7LM0vNlw2CRFCeMAfAP8EuAv4BSHEdOL3Avh94PVSylv1w//0Co71eYUoTHBIL2ALjYXeDaaxBo+/XxV3mv8e/58ArOcKOBkVEK5VM1xYV397YCwODnrqQrJFmD7CvIjsZrGveD1Ceoigybzo2kAbKuhItrc9qW1e1ztFbiqqgPV0UE1PNG1t8LgTqJhRuCBcTohgcyWkMKkWMFAL2iC1Bn5sx8p6RRyyXJf/FgAu1i7yiXxWKyEbmw8qNOhHLoI2grid4FZKSHKDs5K8sWIJB6auYUkTOsdxEUA1EbilZ7DoTEROBR0b7Q3VZlGzoaVg8K4l3UFkX5tSZZ4IyaozpZoP7NS3UbI+KIGJYsZafC5up02vLkr3Z2+m4UdIXROCHEMi8JEc1TU1pzdOQ+AzKx0KkcpSuuGMLUS2TRcmD9k+8zsTdSGXUkIiXZMB/VWElg4kOg5MRYtXXJj+Z188zTvuPQVRyIhosXeiQLu4i4cKEaEEISNev+/1OMJh0lPTjsPsaaguwPF/YDanPo+UWOtMcuZCv2npBlspIdVOlX849XFCoTae3eMFCFoqORGt0ezK2CUtRHtHYnK6acCiVYcoP8lavYMkLkovuAVes/c1XDtxrVojUMbQb8ip112v6fblyL4kx5AQR8afvV+GcdzPYEynH3U7/G3jDH978m/525N/yyfPfnLL4WKuI9ilz61R/YIo4HMXPseZjTM9Viygp/BVSmmTQTuKO2wABeni9PV2TPC26o7VClv9g9RO+jsII8mh8AxTpQxjhQySiI4mISOZEauE1Pw61abPHZ1HOBQ8O1AbU2PFch1hB65a9KkJAexQyFNLg61f6/UO5YZPXqrXa4oCy/U4S1vMFGMi0DUrpOk37HqjXqyXhLTClv27NWeKG/ZMAHButQEzN8T1DefvT5OQqg7oxxIkBLWHS2RP44ZudNfELLZ7Z7MMokKst9Yt6XZ1YXoniGxNkiMcq4YsNdXaWMi43Lpn3KpSpmXyveeUFevg6EFuLuxU9ZxbNUDohtmz8jEJCRtrtj1+MeulA12UHcvUhJyrnONzFz63KYFLxhZ5L2+J/GaF6cWumpCkEtJvntDlIjkjxBC+fkpI0ooFz02r4CtCkiBu0bijG8nkXCrGehHiSpSQlwNPSSkvSilrwD8C39z1HAEUhRAuUALm+WpBfpzDUtmxgN66kC//V/jyO+ChP43/08FhefIgGddBSI8wzNme7Ycn9uIJddP1tOkF2HkrCyKyw/f2ju7BlRO4QUuRk1FV0CYBp5W2Y7XDNqf1jX+jN8YNI3o+iQw4VT4Vv0e7yiOmg5ZWQk6KkChopusvUiRES+tROFgHKL9BS6fGc9ojOhLexpHxIwB8UNQJpVJCUlKhqQlJdFPp+376/HjakhVEsn/tCGkSYrMzRgmZPMRKu02to14vowcUVhIbTTHr2hbH0iz+2o51cv2kfpa6jZbCwdWH7iLZvsG5VkLWnGk1r2WXJiEbF3o2DlAWn90TKnCfH9SO1a6pImVgY0wV5kaiqe2AR/Ckul6/snYMiGeEHJYukVR2LIc8o7qtb7JDlsnI7goTJOQSNSErtbYlLz3teYlJCECBVcr1y58q/dTcBn/7FRXsHByJ2D+Zp5BxcSf38mBBDRu9dewws7rbTyG6FgDpVLlw79vgs7/L4bkP2tcz7VSTCsZWSogZdrhcbffYPN53/H38/Zk/p5L9JI4j2DniQRTgCEFGdhB6wrFBUglJkpDNlRA9OdkdIZIgRcc2w8h7eRzh8OO3/Lgi2MLhjsjjWh0wrFWFVb76Zf+7lRDYxObQbrMrUO/5iBPw/nCV9598P+8/+X7e+cQ7eduX39Yz+yQJ05LaqH4PzD/AOx59B2+/7+0sVnsDte57rupXba3HjuIOayWBNAkxP3uOx1i2S10g3QigrxqSWIMkSrm9JnhW14R4SNpIJJFUvnpTk1Nu1jkcPsuPNf6Mn67/N1q1S9vVTPB6ZHbEDlyNj6NXCQE4skMd/9xGs2/r524YxSSn9e6WyLNSTyghXiEmIaZNryZqddNu3mD9LPWu5E07bOPp+rJydieHZtTfnltrqEYn+16unnj+fnwTBEchubZ+nW4lRO/hlypO71ZCVv0zvc8ZoDh9tbWqlRCBI2PrbjLLborTV3QLdtNF77D+rMfmK5ytnOXUhtq779n/eoonPgbVBZrJ2rutIGV8rnNjUFR2rzCxdxSzXUqI45FxHXKyzXqjzW/e/1u849F3cP/8/X3fwtw/kFZC1pprKZW0pxBeI+fF3T2fKzuWIUbJYzV4touEVFtXv1XwFaF5eUpIslbya9mOtQdI3g0XAbv7SfVN/mvgSWAOqEopP3MF7/f8Ij/OQekiZARS9lqy9LRtHE8Fpea/sT2Ud95IxnVwZAmBsBm4fZMjNgPS06YXYNdtimxEISPCY+/YFF40iRe1WSBS80S8PG26Qq4AAQAASURBVJGUlKJ0Yfoz5WcIpbqJr8/PcO34NerLjUJOJCa9Rs11HhPqeRO5CXBcakIyJ6K0Jctc/EkSAvGsjK3QaWhDj6CgA/b1Zsir975a/SwkfhRpO1YfJcTUoED/mhBNOEzQFEYDKiEmO1OZs+/zpdOxIpXN6MFJUZwxEUIwXsjgyAQB0/YL2znFKCHRpeVrg+6sW49VREqoLiAlrFoScnv8+8Un6QfbIWtQErIRf/7lrNq8I9Ek6zo4coQDgfruH6mfJ4gC1TM/7HBIOjSdIk1HEcapnLquLQnJj9HMTACwK1qwQy8v1R0r6c/tUUKkpKWvcSEETSKiSrr71nbwjPbACwFv/9Z9ZLUSsDJZYN1VX8HLinHtgWwdBASe6/CIDg7G2nHh/ZrOvi8mlI0do5e2Y/mhtPYtg7n6HH4Y4TvL7BrL4eosnsmUFlrpgn+T5c062ZStaHMlpAxARQdJER1L6nOuOq7rJq/jB2/4QfY6Bf55mGdHRt1jUZjDjzafWVDX96ypCYH+AUbQafK6usOYFBQQFKSk4OZtYfbpjdP8yhd+hXOVcz1/C7H90Kh+pr6j6ld5alXV0DkiboPbTYCNFQsUCSlmitY2lAxYjRIylZuKraMJJLs/Vfv5thPBf1DcgZRwKFQkZCyfsTVYUSRTdqyNdo3docrbZaSPW9s6hxdFMjGkcLT3CX1qQgCumVHrmZRwZvXSWXajmBRkm6zn0CLPaiNRE+IllJCgBfUVS6wala59r75Mo6smoRW0yNbVGl3L7WS/tovV2oFSPg+8Uv/tCu2yvjZCP6a8KRIiQO/hlypOT5IQiWQjVMmZ/aP74+cMYIVaa60RRhJXlnBFHGL1mxVS7qhr0HS5NN3Kjs1XrAriOR6vmbqZQktdW81OdbBAOWjbobLkRq0SIusxsS8kSUi2BPlxrYS0kTJgWf/ODkzuQkoJcWMlJJBBSo3YrCYEnpuBhYaE5N28TRKkHCEap7Qdy6yrYXT1WwVfEVJKyOA1IclayYmvVTvWpSCEyAA/CdyGIixCCPEjfZ73FiHEfUKI+5aWtjkY8LlEfpwCgl3akpVSQqSEqg4CXvpj8C8+Ev/35r9i3fPIeI6VYg32jOftgrZZh6wF3d1qFx4zoznGwxyODBU52XU7FCYJI8moTJOQk2snIQpxgWuLu8kXZzgk1Q1/YjmWv58pn6KqrVLfe90/VSQK1JDDpCXL/NxNQgbxbfsN2kKCcBjJqg2k3OjYzVU6Hk3Zz46lb7iRHXYAXn87ltq0XWPH2qImJJlls4FFVZOQ0d08cO6cfi3BTl24V4k6KdvZeCFDTrYV2QF7Po4bcmdqQuTg9QndC12PQtCugN8gkpI1Z0oF8ZOH46L4hU1IiJ0V0hpso9qIr8N5OY0kIhItsp6DQ54dgfJjL/lVHlh4QMnyUYdDkcuKE7cTHc9oEpK4rjfyKiexlyU7K+NSSkh6UGFXAB/6VmETQNOROINmBfvAdFXaMZpjTMSB4vGc2qQLEqabE/bxcs0jE+4m4wi+EqisdJ74eFe0f98MKpwqZeMZPn0wk5oVkr5+634dP4wIRVVbsQwJUbtloZW2LibnWLiOa4lEc5OaKvN661Lfk8K39kYTMAJ873Xfyx+M3Mo10mXKNamFnK1h6heU1fV7Ju1Y/dr0Bu0mL2k5/M5Gnj/rjKn/vvH3+LNv/TO+77rvA1Ti4Ne+9Gs8kVjDDIzqt17v0OyEqWM5vqaeP1nK2sGv3UqIIS2gSAgQe9oThcymJqSfFQvSSkjfYv2Efaa+46UAHAjPM5VXdtpIqOMODQnRSnClXWdClu3fOpfI5J9da1glo6coHXqVEH2sh2djEtVtT+mHU8s1POlTdEM8R9ASecqt+DOmlBCAygVL1Bq13mC21kVMWn6DbFvtBc3ibg5Ox8r42dUG7HuZXXPby0qhdUIfa+yxJGSMUYR6fICp6c1EYigUG3R0ou+uHXfZxwdRQpQdS+JEo+xNtEjuV5zeCutENG2HSENCKq0WnzjzGQBetvNljNZWKepumUHoX7K+BUgHrflx0E0VZHPd7m+FTKIwvTgF2SKeqwrTI9HGN3VumyiqxuIkEGScTEpNTJ5vWxPSl4SoGOBqd8cCGMuN2bWw297Z8kNrlb9+ZxzjvGg6ZEVROia7DDuW5wrVfOBFjCshIXMklA/9c3I88p1AIKU8J6UMgf8FfEP3i0gp3yWlfKWU8pU7duy4gsO5ytAB6SFtyUopIY21OMOgLVJJlFtlHAFFL52N2jNRYO+oOmUXqxd7izpHdrCQUcH3Ll+1Ad2vb5wqktr0NVCcUptVlxJyfO04yJBD0iVXmIT8ODdE6uI7uX7CvtejuuWgh8PrDr6BWZ25OCnCdHea5MKUS2xog3Ro6KjuWAiHsZxahDeaPnlXL8iOR12qzzCRS2QWjRJSmoGMDkC3UEJMh6wwkkSd/otkqjC9uar82ZpIRaO7eHJBZRdLWY+DBXX9VUiTmrGCR4F4ABvZEp2wY7um5TVhWiJEDlB4Db3SvwnOy60yZytnQXdNie1Yrur4slMNijsz90Df4jujhDT9sG+9RDNocqp8KiYohoR4eS52Skg6OEKRMkcWGA10FjDy+cAzH1A/hz6HpctSgoSUhPq53C7b/uzrWXVv7I4WYyXkElkm0xmrkHFTPeQBCJq0E5dLw4Fc/fIdnrb97XjBXvs1JE92zoOAlzQdllbKgAqim52QfHgEz5GcED4NJK5ft3K3adNr7Fib1YOcq5xjrbXWNTU9TWDrfl0NaBNtdo459no0JGSk05+EmEDZZp77ZW4TG9tqYO7JtB0rBb0WjtDAcQSOzNMJ1PXTLygzAUvSjlXtQ0KkIStJdaG1gRCCH7jhB/hXt/8rXOHSDJr81gO/ZTPDBnu6GjEkj+VMXZH0qVLWtvPuJsAmSHKFa4cQWk97Imu+1aBCoHcORjf8Bh0kJ0XA4sQd6j1lwM7O2ZQSEnYpIbVOnbEo4W1vrm6ZWEgNKewuSodNlZCRnGdtg4PUhZxarpOTbXIZF9dxaIs8G60uJWQsERpsXIxJiDmv++62v250ZdnbnYq1ZPqlPbZwHuD8ekPVN+y8WX0E7UjIykjNR88UoKgD4fw4DkK16ZWXHliYXJM77gW73t+54874OQMqIUEU4cpRDk1vbccKIwicNRuIm++t5T7Nmu7GeM+Be2D9NDmpuUMUbHkcc7U5df0m9/PcqK2lkaFPQaprTtmxzF4/DZmSrQmRtG2B/WYkxKgLOVcNb02qsOZ8SykTJMTreQ2jhFzNzlRGhRnLjlNvCaTsrQk5u9qwluE79k/Yx/utVS8IWmWl4hlsQwkxe/94IdNXvX0x4UpIyAPArUKIvUKIEeBNwMcSv78I3C6EMOmjbwJO8NWCvFoMTIes89Xz+KEO6qqJwKcfCdF+7amuzNmu8bztXNOJOizW05YKKSULGbUY7aqvMzua5dqOei0pXBYcaZWQkYQSEsmIp8tPQxQq4pEbVSRE+/lrnaq1fz1SVYHzTU6BQqbI9dPKbnLcCWI7VpKBX5YS0lT5YeEwni/pzwZBqD6bdDwaSEAy49bj9zQ1IaXZuC6kX62H3kxtrYaEeqM3g+dHfmrhWWmupL67uXCCDf1dFbMu+3Qxf4X08MPxQoaCbMZKSHaEU+VTBNoa9MrxG9T7ISk30t/pZuipCQlC/NDnrV94K7/4uV/k2PwDSCBCKyE6m8vOWzkqAn6p8hi/8rn/q4fIJgOzfvMTfueB3+GXv/DL8RA+Q0LG97Jc6xCJJp4rEIAjC9TlrKqNikLObpwBCeNBwCSCRRFvOAUnTiCYa23ZU61ix+UGY07bfs6tYFSEHWO5nsUz0kMwQVmo6g6MtBbi72WbMF3r9kwoEuIj+VOviS8EjhC8vOmwvKI2UtPBKhceISMiIuBxJ4BO3XYfMR2ZjB2rXz3IsdVj/OLnfpFf+twvMZaPl9/uWSGVds1+rtGRtr3mzSkZD1ZtlhJ6A2VDQvoGD4ngZNFXzxsvSjOWyNqhLDQJcdsV9k7kcWTOtpTuFwxZEpKwY/UrOpU6cSDchBqaOLZ7DtzDW1/+VgpegVCG/PHjf5yyZiW7wc2VW6lj2fCXCcQaMyM58ro2ot11z5mi9OnCNI7OrJsgqp8da1MlJNEtr2+b3k6DP/Ka/Gqmwd81LyL1tjtROZEmITKthNT9JhNROX6fcH1Lq4ghIbvG8/0tGN2EMaHQmOL0S3XIUi1NW+Rki5znWCWkousxPOHhOZ7aP/UeysYFRUyi0Fr1OPhqGxTXuxqStDo1a/cLR/dSyLqWsNuZB/uVJatduajqQcwMhbG98U2ir9uZAWeFpEiIcx4JjGbGODx2OH7OAEqI6Y7VTUL62bHCKCJw1m2DmZmRHDtGczS8J2j5EdP5aW6buY2lcyeobLTpBBFB4G96HPO1eX7uMz/HL37uF2km7IZJJSSSWCdFyo5VmIJMAdcRFGgTiXbc8W8TEmLqLIzaMJmfVGSQ+B7yQ2nXslKujxKiW/QPOoxzEJgE3fkVyReeLrNcbfeQkGQ9yJ1JEvJi6ZDV1bVsOyRkXSshL/bOWHAFJERKGQD/HrgXeBT4fSnlqhDiI0KIPVLKOeC3gS8JIZ4AxoH/7yoc8/OD/ASglZAoJCJSfnjYkoRIKS0JmS3FmbPpEZWROzJxxD725GraUrPWWqPtqZt5d2ODWa/FLb4KagM3z3x9QZEQKRnVPdrHChkuVi+qDFwUcqP0lHKRH7NKCFHIybWTlFtlntU2gzsz6thumLkNECyIiA3zudobMQMvTG2/JsSvq+5YjsNEIV6EO746nlC4tB21KE2jSU2rrArfQQ2aMtnYfoufDsg816El9IC+em8Gr3uhXmmuIBN92b+yXiAS6u92jEwwbeYECEmQIFsThSz5LhJyYl3xaQeHb5i+xT53aUB7UHdNSCeIeGr1KRtMPrXypKJp0qEsJmy9ArtuVa1jkSxVLvR40PeMpwOzbhhb4TNlXZxvSMiYISEtMo6DEEoJWWGCOyOdvQp9iAIORQIQzMtYenfDmJCYzm9Lnhr+5grB7khdx5eyY5makH4BfKdTTYyfEjSEZCZaviz5vBNENvDfM5GnVl/iP3kNvugEIBxuknn2BIJ6dYN6O7AkJBPtZFIHiY8IRUJMm96VWgcppR1U2K/F8EfPfBSJpNKpUAvKVkVJTk0PooBqO752C/lmjx2ru02vKeA2diKTTe9PQsr2x/m2Wm9G8vGZ3UwJobXB/sli2o7VJxgytgeHzbtjRZFE6AAmSGRPu1uF3zZ7G2971dvsv5NNNmZHcraOZa6cVkLCKKLtPstUKWsJfPe1Z0iIGVIIWDvJWksV1kYyoqyP6UqUkGdECMLhZPVZ5tw9au7K6lFG8l6iJkR9b4ZAtsMWowk71lhU2dI3f8zWg/RRQaA3oZNYy4/MKiJ1drWRIrfdMC1NC2gS4ioSUvcbIBX5tcmDca2iGjuW36Rhhtjuuh0mDgJQ724+oG2XkXBwx9X+emBKXc/nDQk5oOYmdYig0yAXaCVxPKHA6D18GjGQHcusyUJAx1Xr+DVj15P38jawvpQSIqVkWSsLjhxhx1jOWlGTdV+TuUnyrqrvDMV6qjbyul05Ou5Zmr6qo/zyqTUef+JRMrqjlQyDTUnB6Y3TSNQU+otJ90aiRW8kYzt3IdOlhGRLCGDc85Fie0oIqPoVQ9bN+U7udd0T0yGuh7maSsiGnmlWb2YQMku15dsGNAbP6vlCs6O51IytF40dq7v5zGXUhLzYO2PBFdaESCk/KKW8Xkp5rZTyv+vHvk0TEKSU75BS3iSlvE1K+WYp5Tb6hr7A0BakQ4nuGtaSZYJ1N2s7Thg0g6Zl3LtH4yDNZKh3FHfY7jWPLD2S+tuFxoKSk4Hd0mVs6UGuC5bISPCdnPp9YUIpIVGVnOeQz7hqqqtUysINkauzUONM4bBDH/+J9RM8uvyo/Sx3FdTGe8PUjapLFnBiQwemSR9iYUIdk37OIEqIbNdVxlo4TBXiLGGrrW6IEJeW2aekznwmWX9pOqGEbOJpB9xskY5QAWCj0a9ANv2YH/lUy2fsv7+44BKJGoWMy3RhkrFEprPajLNIRgkJLAkp2aL0Q+OH2F+KN77lAexBYSRti0SDdhDx6NKj9t8XaheQUrLuTBAJN1ZCdtzMBbORB80eEjJW8KzvtntWiJSStg5my+2yumaMH3t8P8vVNpFo4LkCR4CQecrOJHeGeuOIAl2UriapL4nYjtXuZFSjA2IlZE6obJ/jCHbqAtu+81AS2GrIXzu5CLsZmg7MhsupqdKDYmGjZct+8vkav3r279U8DMflJTtewr/TKk5eNjm+ULVtdAUOL9X9+h91fKJO3dZ2rNXbVNuBJQfdn6HSqfDQwkP23zW/ZievJ5UQUw9ikMk2LBk3BZRT0Zr1//uRz0ZH3UdmjsWWSkji/r7QNCQkfr9kTYg6CTqobW1wYLqIwCEIXJB97rHQtwqhkFlLsrqVkFonIKNrqKJSfB31G1h4YOxA3yDQcYQ9x3MbzdTvglDSck8xnbJj9VdCZgvx+xsS50c+1U6VcrushnNCyu+ehCOc2ELVRwmR7RrrQoIQrLQWOO0dxnUEYukpXAEZT9tLRQkhhH2tIJTk5IZN7E9E5U0DpNVa2xL4vvUgURRbiA0SSsg1WgkJI7nlhGWjlORpkfNU58CWyBPRIYhk+tqxs0IuqNkhWskMskWYugamlMJQ1wGjVeDCDk0pWXWmGS2o63O/JiH22KaPQHGaNkCnRlava5b4gE2ezWzTjjVaDAkcFUDvH7kWRziWmF+KhFT9Ku1AfUeuHGVmJGcJRpJACiGYLewkksqONZYIFkfGFpAiwA8j5hd289sfOcZssEBOqgtByIjmJoODkzWQq0mbW76LhGg7dzHjJKzX8b475vpEtO0+takSohMOWTfOuHdbGpMKU7+aEDOs+LmoCQmDAoIMEpjbSMcuZjjnkdnScza5/YpwBUqI6Y71Na2EfM3D9SA7wjgOU476Ik/rYYDGr8/orlj61dhILA4HxuLNLWkdMIVuT648GVu8gIXaArg5EA67pIN4/H+SdV1mQkGbHPO1eSiompCibDCVV+99Yu0ERCE7pMMEjiJQmSI4HtdrJefE2glFeqKIHdJhj65/ODB2gILuo33CFBUnu5UUptRnNGrIAEpIx6+pbVs4TBfjLGGzoy63ENd6+0eN3SApyZdmLRnbyo7lZQu0NQlpNS9NQgBWdB2Hn5/m2HKL0KlTzHpM5CYYTcwJqCSk7LGCR0GqabmRhChTsO15b5i8gYnCNBkdJC1v0kUkiW4VBFRw/ujyo/bfF5rLujOWWtCzrl68MwUu5M256SUhQohE16D0xhFEgQ2oKu2KCvj0whaN7WW13iaihecIq4Ssi0muiRxGpYDIt+15pZQsu/H1XW0HtumCUULWwjw1ZxRHwA7dptdmo6Oo57vtBJG1NPVrz9tM1sB4OeqOCsbLtW0M79KY0wSt41zkL0/9LnPa9vgtmVl+4WW/wPToFEJAQTY5Ol+xJGQk53G36WwjJGeCKlNF3bq23rEqCOiakDAAnaX9woUv2AAd1EbZb1ZIkoQIIBLVWAnRLGRCrtPqqLUjueZM5ifxw4icrr/qa9sw7bcdlwsNdV0VNAlxcGxffQujhAQtDowZdTVHEMmeoKwRNCy5c8naTbDbZ11tBWQ1CXHyY3HDhT4kxBFOaop4EslucOazhpFEAh33HONFYeuRklamSEYs60TDjuIOpJQ0O2HPwMJki+DN7FgQF6fX+8xwqHcq+EgQLhV/jVPuIdUEoLUBG+fxsvq7RX0W81nDKATdqQ5Uwmaj2T9AMq15YRMS0i+ITMwvMUoIwLPLddphW71/F0zgtq8Y4QjVobBFHolq3FHIJEiILhCXGxfJkLPH0Ji5XtW3GSUkaIGMrNIkww4tVPtvE5wbJaTSDNho+GpPOvBKOkJCp0bOFGona1HcDGRLqiYkCqn79S3nfJhgOVdYxPSg3lO4BiA1QHIrrDVVPQiAG40yVcpaq1W33Wgyp9bPQKyn5mU0HJUMdGSezzzpMSqrjIqmLUwHaHRZuQ1qif15xexhbha8XFwTImFEVhEC8mE14UCYVvPKgBGno5WQwQrTjRIC9ExNTykhmV4Sksuq31db/lVpjxvJyO6Lvp9DSHUNLVRr9vWDMLKd4K6ZHSHrOXaduJrzSq4IPSRk8ML0jYaPL5bxstVUjPlixJCEbAVTF+KqDdIUIlslpE89SHKw1TVTsU8+KfcZEtIO2xxdO2ofX2gsgFBTc0sIWD+D5wgmQ5c2GdUmT9eEAOzKqoDnxNoJkCE36G5Y5MbUIp0f44bIswMXH1l6BGTIXZGH0IGFIxyu061UTzT1wtaMP4OdsmpIyAA3QssE/8JhLFe0N3ezpe1YEso661UKyuq5yWnpSTtW38J0nRXOFhRpA9rNfl16egOC1YoKkJfFJFJCJGqUci6T+UnGinFQXUkQsTGthKhjl1zsVGzG8/qp63EyBWZ1lmpxgJqQ7kGFAEuNBTX4UGO+U8GXEWuO2piNEuKHPguGkPhNan3mKMSzQtJBftITu9HeSHXGquV2EoQSKZp4roMjwCFPUxTAK3CH9FRAHfocjhzC7Li1woHaYI3CZzpkNdohC84uHCGY8dVnawehGhb5vjfDX3wfmCnHKDXA7EF9BxUmMkEik6fhSAQRzdXtd8iaKzcJRZXV/F/RimqIKOR/C/L8i5HrcYSDkxshn3EpyCbHEiRkdjTH7e6YXTgfFT47dADf8iPOrMbX4c6SA+//UXjvDyCbG9x7Pl1YXfNrfaemVztVW3ORcR21pnQVprsypL2hrrWk133Em+An3/MQn3hqnUj2HyZoAv0wN0a1rScnZ/X/vXxvIaMhIcDBknqeIEc7iHoHzQUtW9Cbcwt2InJ3d6xqyyerJ25n8sWU5asfNgsCzbo6vxHXhOwuquBWioD18Nm+Ssh6a912GNpR3MFv/+Nxfuhd97GyEQdTK82V1IC7rUhIPGCwN0lj7LkIhyCMOJmdsvVsLDyB62oSEhVSn5UwoCWwHdbGoo1N7VjGijWS89iX6MhkkST8fRJKk6VYtXp0/jQ/+fGf5Je/8Ms9NWempelB/RJKCckRCaWE2GMHGN+HBC6ubPCuf3wM2VHH0Ji9Vr/pIfVvISHoxMMiA5+OUINQTXC+fyr+TOfXTV3IK+gAyIisNNJ6oisXJNr0XnpWiAmWZVb11xHSYyKj1rTNSHA31tuqMxaAI0eZHsn2VUIAxjxNQpw1WxchpeRs7SkcIciFhxE43DGyzv6pImOajEqg2W+wb9fxrRjlxyiZbgZyo7a7Zj7jIpJ7fUIJKYk2EW37WTYbHGpJiBffN90DC7dSQv7syT/jPWf+L5rucSLZ27DlclD36zbZ1mnnbIOMpt+ybdkvrDetynONns0y8hy0Cr4iXKYSEkaSSstnLf9+Prjwdt57/L3PwcFdPQxJyFbQG+MBfRHbILGqs91j/TtjGVw3u9P6xW/bG2/kN0zdYKXnpAVnXveBN50zQNU9ONE0fihZaCwg8xOWhOzINFhvrbPUXFL1IMa7bzb0/Dg3JgYutsM2RIqEJAOLG7UqctrfUB7PRpKETKj/26npl74RWp2YhOS9vO3TX2mqVn5hJFl3CoontfUmbxbVbEllY6wS0q8mRAdsXt5OpvfbvZtDv6zVqs4gnW1rEuY1yHoOE7kJxhK2kGoiuB9PkBDfyXOy/LT93Q2TN4CXV7Y3YPkSvmOAVqfXknS69lT8DwlB2GERZUkALJFbqC8Q6WwVMqTyzMd7Xstkh+c3mqnMUnJY00ZnI7ZiAUuuugYi0VTdUdwCAheEICjM8o1hBiKfXbjswMEf2ZN6z0rLZ9+oCgDWW+vU/Tr1TsCCq0jIVEdt7Hs2HoUP/ht1D3VqMP+YfY3kkL9+9RTtRNDkeHkaevUK1vvPkdgK8xst2u5ZPC9ECPg/cgf49iiHMNd7VpGQvGxycqFqWwfPjOQY8Vvs04R/QYTM5OJN6+icCgYdATOdi2omTXOdZ898Oq4p06j5tYSVq2OLQM+ur9tsXC7jqmy8rQmJ/z7cUOtFMvFRrmVZqXVod1zafrRlTUjbjTPm2UxMQnqQWCt26sSHI3OEUdQTlDWDpiWSBa9gg6tuG1GtFZBTISSZXDGxvvRPctggsOueNqpfueFT0+vO/uLNCN0e+GLzaF8lZDlht5wtzvLAmTWCUHJyLkoV1iYtPJvVhIBKHEH/xEfZKFWOQxBJ1t2QjrF2LTyJ0K2PpSYh1tIUBTSFVCREq3K1Wn8l2rQavWa2ZNWyFJLnzXSP6lJtjBrylaX7aYUtzlTOpAhuO4hbmu4fUV+yp7tjSdEmjKK0HWt8H0Ekafoh17eesYSmockHk4fw9ahGwrbKoMsIooC2kKw6MzaAN0oIJCxZ++62inou8Z4p5MeZ1lPTgU2L040SBtASKlGVifbQ1pftlvbGBJQSos7NeHaCnBd3+eueBVRyVbAuRRtXz+C5ULvASmuFUs4lFx7htn3j/NuXZMk4IqGESBqN/vtMai6WIb/J7paFSVWYHlV7BxUWpywJKdBGijaRlJsnM9hECdFkstwu44d+iliUcumakIcXH8YRkrb7LHB16kJMUXoUSZBFhG7QI4XPx55Sa2ayAYMZ1jn2HLQKviJcJgmpNH1da1TDdYS1Sb9YMSQhW0FvvrOJwVz1dgVqOts9sqvnT0xAIBDMFqf4L//8Lv7bD7+E6xJ9qDNOhttn1eC5ZF2IITm7Jq+1j3mOoI1azOt+nWoma0nItNuwBdJpJWTUHv8+6VCU8aaUiSJull6q2PyGklq4g8hXBcuGSOXH4loQ0wFmgBuhGcQkpOAVmNKWjI1mSN7LE0aSspPHEQJhFlOjhJS0HcKQkL6F6U37HCernhf0ISFJf6wpHl1prSKBo7Uikoh8TgVCE7kJRouxclVJTKQeL2TI63Kmjlu053xHYYdacL0cs5qELF2iAwv0V0LO1hUJGc2MglSF5+edkHWthJhs6PnqeUXU9PmpPvvpeL6KhilOb/mxvQnSSkjdr+Ob+phMkaWO+ptINMm4Tmr2QSc/w50yw+8Ub+Q/iR04CNrFLhLSDFKTui9UL9DshCw6O3EcGAnWuaf9Kb5z4R1pYpnIfC8lZoT0VUKSAZ6Xo6VXL1k+3/PcS+FiuUkk6mRcgYPDK0yMbALu3CiFjEtBtmgHEScW1HU/O5qD5rq9p1rAdCbetI7Ob9jnuYnA797FBwA1TNDYnZJ2LCnj7lofeOwUEmXFmipmVDZen7OUSqGHbq4l7ZOh+t6EzBHKXrsUYGtC6m78HXue+gw9nbEgFcRkg6qtFwqjXlLQDOJW1nkvb4OOertrWnnCjpXbhhLSHeTv1iREElFta7UyyJEPlY3mmcqTZHXxelIJMfUgAJPZWZvx3WhEdtNeba5aO9ZIZiTle+85vi2UkHVfr5nCIYwkgVinOqG6ErLwOFLEx21eK4wkjgxpOyrQd/X37lf6z9IyM2qSs2dSCOJ7366xPSRErZEXG6eMGynVBjzZ0nR3SXep87K4mZzKmnfXhIzvs7bCl7ZNkkXQMMm7wiSNvL4Gg44ieaGvrHRaCTGFtcWsZxN6loRkS3SKSp3KIVQAXehSq3Jjqanpm5GQdhARSZCEVEKl5GbDfdRNa9lB7VhtNahQyCwzI2qPNZay7gx73ontv41IHZdJSu4YzfP/fMd38fbvvpVCTa1vYXYCD7VWNLsGPBqkSIj57pKNZQqTSF2Y3peE6ARXFh+pRw4H4fZISHeb3maChBS6lJBm2MQRAinUubkaHbJMe151DxURZK3y+Jmn52n5oW1FPV7IMK2TpEYJefGQkMsrTC83fSKaICLlpNlCwX0xYEhCtoLefHcE8UW5tHI87hzVTwnRwetYdgzXcRkvZmxRXRKm9/h8fZ752jyRjKyVZ/fsrXYYk+cKKlyrMhKRZD5qE+pNfsap2wLpkvDYqwNhK7/qPunXC71AyIhbIkct2Pk4sLh27GBiuvqJ+OIvJDJ/+cGVkLYJMrUSMlFSi/BavaPabUaSiqMXBqOAGDJS7CIh/WpCzGNenoyeQxJ0ep9n/LEZJ8Ou4i7VpjFq0wpC5qIJIlGjmFXnbDI3iZcpUNK3RNJnn1RC2qJgz/n1U9fb4zBKyEp7va+XOoluEhLRYaGlfMD3HLiHjP5+L4oooYSoxdsOAyypxgLVsA0PvjP1eun5CfF5aScDEaCyfkZ/wL0s6yBGihauI1JtR9t59Z0catYY0UM6G8X0tV9t+ewtxVnIc5XztIOIBXe3IpsCvrP5QXXv5EbjYZSJoNOoDaN5r2ezgi4S4jhUdGtXxwyf3AbmLAlxGM2O4pgMvAmGsyXyGcd+7yb4mhnJQquMCdUbSCYzfuJ1zYyQvM3qt5F8YU0FYa/Y/Qob5NY79dSskKVqm4fPrnN8Ud0TE8UsGc9RJMTWhLi0zf2sFVmT+BjPjtPoaCsIOdVpewslpEqChLhqjespSgfbZQhAtDYoZj31+lJuQkLUz8VMYdNpyJWWrwrTBWTzpUuTENO2NkgHznu09VDSsQFvp5MhFx7BEYLV1jJN1NqSUkK0Xz7rZMmIuG5ttd5JDSy81IwQg9HMFkqIPmaJQyQlgbOGP6s76m1cQKLWqTBQn8WskS4hLaEsTyaICmv9bThrNUNCNiFKyevArLFBS1ksNY7MjiCJaHDBnstkzZmpBwHYkde2vGyRyVJWKyHSfk8AZEs0HHVedoUbSiHzctTR658Q1E0NR9hW5z1Ug2I7Apbd2VStxIHu4nSgrVWdLKjOWH2shAUEJb2mbmbHMtl631kEoc5JNtoXT/rObNFtLgFTE+LKERvcbpZhz0TxNVX11fdqkpLXThzhJfv3KlVLr9PV0mEyOtHY7GPDhfT1t2LulYSSqZSQ2I5l93o3E9eSgm5Oos5zEEo6UafvvmZISDJ5MZ2orVxtrVLfoiakFaj9RqLWh6sxo8MQ51BqEiIzTGgi2Oi0+PKpVauEXDNbsomd0Xx/1fYFQzPx3YC6N7r28H4oNzq26+dQCflqh755dyQC3KW1xKiTfjUhenG41Bd/5+yd9udHlx9lrbWGH6mLf9fYAZhWaojnuiw7asMKIslCp0Jbt96dEHU7tfv67BQOQl2wxlKhSdQNkf6ao0hZsRK/A8iXZvVQxoiTq8fimpBkVmlQJSTo0NSfw5AQo4Ss19XU9DCSVJ2M2lgbq3rQhxlUqC1RloT0yTyZjcDLKz858cyBJEwWuJQpqexM6LNCRKMdquDeadiWgRM60BrTWepkBrCU9SjoydhLIqNqd9BWLFBKiL6VIhkN0IUl2bLQpeOetf70u3fezR5dg3RBxDUhRgmxJCSTg/w4NSSc+EdYOm5f0wRmkC5OT9qxADZ0fQzj+2zNQybbQYj07INWTgctGxdUm16gkd+Zeq1IqsLa8ay6Z05vqNdecHZqEqIW+lV3Gr7nj2BEq0466GwGTT5w4Q9Yy/0NO0f7Z3PbqQDPoeaq7y5X7yUhf3nsL/m5z/yctTgm0fJDVmsdQtEg4zqMZ8fi69qSkBFcIZjw0h2FlBJSJo9RQiRFWnZwpsGO0bz13N/v+DS1p/qfHPgn9txW/Woqc71YafEnX3iWSBNBY2Mst8tEiTooo445ugmCye5O5idtJlHIrAp4o6C3MFErIRt6Wnoh4xJpa1TS222RSFjQ2qCUcxFStRftVlpaQctaAIteMaGE9ClMp4MrhFIzLQnZxI61SSZ6ppQj4woi0bZ1NM22Sy64xg5fXGgfA1TjAzMEb6mpO2MVZ1PkZLXWTnX3Mev5pUiIVUL6NO4o6+8u0tdM6Kwjdt2mH5PIUL1Hp5NFSmmVEBdVE+Jm87YWSPYhIe0g5EDtMX6l8hvcWPly/wNMqo+lmcTj8fm8ZnaEQKwgRZuWbiCRXAdNS9OZkSyFyKjRJaaKWSLRUYXpXSR21VP3ecG4QjOF1HdYH9XrQNixJEQCTcdlXUymuhb1tOkF2vrazEnRa8UCe11N6694pbUZCVHXZ8e5gKPnBGXDPVbBG1gJaSlbnytHmS6pe8kUpneCKDUfyvfzCJnFEYKV1iINv2H387t23hW/qCYhrZH9eFJ1empuYltMHl85bKuGCH3sWCPddizTgEZfxxnHwUGTkC2K002L3qRKmOwit9pctQQv4woybhxyBlGAH/mKhGgl5GqoEJaERODKIg4ZRnIeGddBCp+PH12wtU2mHgReZHYsKePvJtnxbYAkcLnhEzlqHRqSkK92mAWsXcfRp2pp42z8+y0GFU4ksof9MF2Y5uCYKqB8ZOmRVLC0a2QX7H+5+sfsTXSYRsgsfig5W5mjJlQQsxic5tkN5aW8OWNsJGM9w5pu1wufi+QlkV7Uk4FFfiKerr52jMgqIQkSMqgS4jds+12EQ97N2w45641YCak7niIhflPZArrtWGYz61cQZ5SQTJ6sJiFu1O7pOmWyQqVMSW1wkc+KkLSDiDVnmt1TofXYm9amY7rbVjUR8DqOYFwP2ztK/B43TBkSEishyMhmWTdDciOaLGZouc8SSUkpU+LaiWvZqzPdc46kIvQmq0nIxeQcktIsFVOk/qX/F2PGH81nbNvDuQQJ6XS16NwwCsLY3piEeOr/Y9lYwm9meluT1nK9136lFbB7RD0+p7P0dWeUYGQ3joAz3mH+6+jPwcT+nsz348uPs9w+S8t7mtJI/2usaTZYoTzyHdfDR1JspTuShVHIh099mIu1i3xp7ks9r2NqTyLqZF2H8WTglCAhAONuB5Eozp0dzUFrg7y2YzUFiE6dqVI6eN81noO22gjudXyQETuLO7lp6iZrdat1akwUMjbL/T8fPM/5tSaRaDFdytlrM5Sh3ViFl6fsqXsko+ubktn6Ssu0x81ZW1SPJUuf89WwaD+TKTzta8fSXYbM3xazHo7MEfVp0Zu0Y5UyBeuH90OZskPVWh08Gajg2stdUgmxE7e7gkDHEeweLyBp2+x9o+3iUmIio4LSi81j9vmGqBg71o7ijtS6sVbv2CBqtbnaM4l+M9juWEG9p8PPepQmIYFYo7DrOvDyNAAvUvecjAq0/IiMk0FKF1eGNB2JM30ER9/nok8twFq9w2s6n2c6WuXg0if6H2CShBQT93PCkrVzLIfIqTXBtNJOKsJxS9ORmLxki0yUMna6dqowHVhAd/dLkpDE9Vg3633oM+UWrR1r1RmhlI9tNBC36S03fKusdVwP3KxSQmZv7P3cZmBhuLUdy1iGAmcVx4G8mMShsG0lRDU8kDhylKmRtBICaUWw1g7woilcR9X6PbHyBKFpoT+rSUhz3d4T/tgBOwA01SkwgdT9KEPWkOm9vjillZCaml5uAl0zakDf544Az1Vrging7k5iJR9L1pKNZces5XS1GduxuutBWolmG1YJuQoqhLFjuWQQqDkhriMYy3tIfJ68WLHHZOpB4LmZ3H7Z6NRj1cPUUMFgJKTZIRSahAhxyVj0hcaQhGwFvYB5QYdpzSaXqzoTnS2lvZYahoSYoHYrmC5ZR1ePcrYak5tdxV1w5w/D634J942/jkDgyUmCKOJcZZ6qGCFE8pH2E4C66d+Q0QpC8pj0xXdtp8MvvPTn+b+v/2F2mK88lR2Z4PrkdHXTJSulhCQ6qkRbzHrwG7SMoVjXhJiMrh9KPKG86nXhxBtM5WJ8c9maENMz3rfZdwtDTLwCBT0MMSM7rNbSQXaShBglpCxUzndDjONl4g3FKiE6CKt02T5GHfWeJ7UiUvAKtiUtboYdaDIgZcpv3g/JQr2xvEfbPYWUcMfsHbiOy/5InZeLrovUM0FynoMf+akOWjgu1RnlfWfxKXjmk/ZXcXF6vHGkNpEopBzqzz++386pcDQJGU9cH/Vsbxa4ktvR81i15VslpJwIXpZf/R84ftO/4b+Vfpa1qKiCNBN06g1jsbFoC7PHi/2vr7YJAExW2M3QcKDYWbVtcEFlwkx3lGTRtoEhZpGjakLGE37mpB0LoJBxyBK/9mxJ2bFMqNVGQqfeY4PZMaaUkAUijjoByIh79t+DEMIWMdf8Go4j7N+a72qiFNrg3WDN1ChlCtQyOrDT92kyW282cYeszfqnAqegbQPIpUBdI7OjOXtt9LVjQapwvJRztd1L6pa8cdDdCEzdgKCYyacCj2SGsVmPM3V4CSUkaPW1YFoS0qfGZc9EnkjEMw1qugvf/qJSkOebp4jURAmbADCJAkVC4vux5UeMZtS6V26XbdB6KSXEkJAgCnomM29o8h9Jo4RUGS24ML6PKhGu1N+ZLNgg1ZE5XEI6DmTGdtLJqmPy+qisq7UOs6H6PMVok0x9v5oQSJEQIQSFEXVNtTRhNOS3u6Wp/btMkcmChxR+b2E6cC5Qx+0gyEeAV0gFyo3EnK3xVhUv9EHCqjtiFQSDVHG67kLXDjswvo/sdd8Ct3xv7+c2icRQgpSb2rFM7YcUHaWCONr+162EXKI71nJzlUhK3GiUGWPHSswASd4DlWaAJydxhMN8fd5ascayY1wzodd1Y5kFoomDCPJqPs8mrfJTJCQKWRFRrxISSTKyw4jjp2eEQDyfC8jqvcAOLOzTJMZYfJNKiBDCqokrzRV7f3V3xjJrjhDgOFdfCTE2S0d4OEIwms8gnHQscU2iNfVIoonG1WgVfEVI1vxMHox/HqA7abnhE4k6riPwHC9V3/lixJCEbIWEl3I2p35eNgHm6O5e/ylxd6zx3HjP77phSIgf+Xz63KfV32XHVdYlW4Qbv4381F5Gch5eNEUQSi7W5qg5o9xXjFgK1QX5gzf+IEWzQCT9n+ZnKbl74jpuziUyYLn+SghRwMl2HyXEkBspt54V0kmTkJybY6oUL8KqoFXSdIQttmQ5thLFNSGJjFr34hfESkhBzyHJ4rNa35yEKKnfJwLOe+NEwkU66vc5N2czwGOeISHp9xwRarE9rYv1bpi8AUfX7SAEI15BWXRkdEkS0kzI09JbI3Q2kEh7PezVQVjHcQlFWR2j57JQX7CZMhMUVUvTsQ3h/j+2vf/3aktW0o6VqgkJO1TM95SwYwmhA+F8TGZrma4ArDhFQ/badirNgDF9XVUSGZvM9EHKe+8hEBk1nCuSPZnvueqirXUa24SExG0i9Xl3szQciKIQmej0leoW1TWBG2DOKCHGjiUSwU4XCVF1IeqcOgKmsqpNsbFjNTUJmSqlSciusTy0q3zG1bMwZMRr970WiANqY92Z7bKf3bwvByJta1gzmU8vR03PF8i215B+O62ENJNKiPqTFAlJDCpcaKtrZGYkGysh/bpjJc+LVkKMHctYKgxaobJjCZkln/Xsxg5pS1a7FXuWU0oI9N1oTXDb8Bs9AcKeiQJStPGNhaiuvpsbJpTlSRLSds+olw4igiiw5GK2MJtSJgE8Oa7/TtpA6VIkpGSUIroCwaDDulZPC8KsaZK2XIfxvVSExIkSJMTa6RQJaTsgRmbx9bWQTbQMNlitNpiK1OPZzUhIqjtWkoSk1/LAU/dR20/bsZItTY/MlhJKyAilgnpuEMrU9RNFkmc6E/bfHllwvdT1WE+sM6XqEnmthKw7xZQVC0jVVpouXZ2wA26G3J671HXUDUNCdIestdZaT9thSLTnxVftcXViouHr707bg9th21pnu+GHPhvaTujKUbsmJOtakoXXlZaPG01aJcTMibpj9o54b0mQEDl5CCnVcdT72MKklPG1JyNAsiqitBKi7VgAk049MS1dX9+JOS+uo9YukxzqR8C6J6YbJElI06hJXdPSk9eBo2vSrsaMDnPNujpVNJotglB1Ltfviq/PQsZld6IBirnerlar4CtCsmFASgm59Jy2csO3nbHGcmPxtfQixYv76F5oJDbGHbrwcMkEOKO9nbH8yKeqO6EM0pHguonrbEBipkzvKvW+7uxoDjdSSshifYElp8jHRiIEEYfGDnHP/nvijTulhCQ29tZG2m+dTysh02a6euhzXGf7U9Pgk6+7lSTo12lqbpFxs7iOm5raGYYekZS0dcElkKpnsDUhyWCo25JllZA8xZJi+VnZYbWWzkAa60bRKzKTn1HD9oDznvosoS4InchN2JqFMU99H5Uu6blEkw6SOV3wZa1YGsLLs1M6gEy1/+wHE/TkMy4Vqdr9RloJAdinBy9GIkPgrCirrivsNQJw05TqrlPz6/Cqn1UP1lfgMdUT3HQNmi83bUY8pYSEHcpCF/KW9lBu+EgiImGUkDh7UnW7ArDxfT2BG6gNdiyrriszwRvU5mPsZKAHFhoSrEnI2Y1Y4SnmNyMhaSVEuFkaQqpuMStx+9sk8dhMCZFIXK8JAsZlYhk0x6U/f8Z12JFTG+NkKYunN7iC6Y6lh6VNd3Ul2jmWJ2pV+KzOvN2RnbQFm6aI2XRSmk387euunyWbURu7VdqANdNhycvT1CqUjCSN8hk6kXq+smMZJSS2Y6VIiD43EphrqftydjRnn9MdTFgkSEgp62o7lrZ7JQKihq+UEEfmyGecVACWzHKauT6OQAU+qbqTXhJi1smIqMcWouxYHUIpCSNJo6Xe87rJI4xmRxFC0PLUcNG2H7HaXLVKWbcSAiCiXoV7UCUEujpk+Q3KmuzvcnfrzyxYbS/C+H6qaBIiwZFF+/3JKKNJiIDiDFJnqvP+eg8Ja6ycRxCpdSLszVYD8ZrpuHHbdUgpIWutNQKh7sdISoJQWmvLycV4zT+yYyQedJgt2vtVAkTxWr9ca7NA3PY8pLd4v+YIENo4s3ycXBgCkrJTSNmYQGWqTWB/bk2R0X41CSkYO5aeFeJHfs+AV4gTQ1L4OAKyhoR0KSGwuSVrvb1uVQNHjtg1IW3HSgwrbQV4Us2MaYUtq2i+ZMdLEi+qHRKlWQrFUSIdWDfDdo8joRW27HVtWxIL2dMdy9y341TTNSEQ134CwulSQro+dxAFdvhq9/m3zR2aK1Zl6umMlXg94cQDC68U5vt1dM3bWD4mVi85GH++7nbWm61V28VTcxu8/cNHU/fMtrEpCbn0a240fSJNQgZx5LzQGJKQrZAkIY4Kipf9GhLZtx4k6Z8dpBjIdVwbeBokZ4QYzI7m8OSUbZX3uZFFmgIcGfDjt/y4Yrpm4+6nhIAiKYaomAmqBrlxEIJrpQtBm7M6OO2rhMAlSEhTWVSAglYzklniurZK+CLE8fTjSSWkNIASkuiOVSgUEUKTkGqahPRTQgAuOLqjlvZNJgnjqMlSJ7uBSEletpjLSHy9/18/eX36mLy8atM7iBLix/L0WqgaHRTZo9SzKGRnfQ0PCIWL76yQdR2EEFzQVsCMk+HIxBF1nH6NcN/LYe9L1Ys/oxQ1Y8fyQ2kVolRNSOizISRkR1gLTZvTNroJF2O5MVts3ZJeOnAZ22eLeQtZ19YuVFq+VUKaQROpu+AUc67t7gXQ9sN0IbKULCSGFuaz/TcA24lFKxeOl6Wu3zxFQhLtlZP3pMH8RhNJy37W8UQL61gJUde7AG6dVU/cM1GwTRuMEhIAfruSsmNlXMFkMcPF1gpr+l56rRcHsaYwve7XiWTEvkl1rec8hx9/9SF73U7lp2zwvWYCNy9HW8/1iaRkbf0Z+7rpwvScbeWdqqPQSkgoJWWpXntmJGfP7aZ2rKQSkvMQxEpLsmOVKUwXZClkXEZycQCW9Fp3UkpIvjdh0oVk16V+s0IireA1OyGOnhoxM5Lnrh134Qhoek9SyX6apu+n7s9+JCT0e+0Ll1RCMv2VEL+9QV1bKsfkLkDgOULZKsf2UhUSZIRDpJUQrWT5ICS0HdSaqNfFsbCcKqQHCNZVEwjXEYiwk7ImxgdimnkU0mtrgoScWDthkwVeNEs7ULVID55Z452fV7WHk6Ws6vpkFJRMkWIu/l6DIP6+58pNVpwZQn2/NpkAukhr0AAvS0kCZ79EHiW2V5x8jx0LYkvW6ZU6QRTYoHtz8qzWoykZzwpZ66MmWTuWVkKMMm46OyVJyGbF6WuteEaIK0ftmrCpEtL08aLJ2BEAODi2fT8QKyFThylmPULUddYkSg8VptuKpc7LCt12rClLQib95fi6sHas+P43CakgjED21oQk95PuWjKj4q6312M7Vp/OWPZzaxJSuRpKiIlz9NydsVz83R3ekbVtn2/YlU42pEnI5ZOhv37oAvefXuO9929/fpWFmdUmHBjbF7tuBiAh640OoajjOS/+ehAYkpCtkbRjiSxISTvylY2ljxKSzMAO2pHAWHAM+ikhMyM5PK2EhJHkbHYdBLwqcLnJdGiySkjSZpXuatOXqAA4DuTGGJMCwraymMDmJGRLO1bd2rHynslEZGygWtUT5qToxLYAs9AKJ87IJBbDFAmJooQdq4Dw8niOUiDK9XQdR5KEjGdG8XQ7yjldNNeR6nMkrXOmIFtGUZzRDFp4ImLRg1BnzQ+MHUh/bi/HDqntWM1L2bHUBpHxAtYD9dlHuE79sraEJyW7pUvoKCXEdsbSJGTPyJ7UMdeCOuhuOybTvWc83hRMDUS3ElJBKiuWrqVRgwrVe41kRixxaAchjCS6YY3vs0XGhYxrPc/VVsBYRl1zUSSJdIvHYsa1E9/V60XxNRh2kJ0Gq61YPRJOnyAKaOrjn9YBh+s4LOtOYv56fxKy3u7NHF8stwhF3c6PmDAZRTcbX3cJe8133TjGd92xh7e85rANkPPqQNVxtSspor1zTE0dbyQI0K5E3GhqQiTKPvHtt+/m+16yl7d91y3MjOTsdTeSHbGZrHUT6Ht5OkW1RkRSslY+bV9X1YQYO1Z2EyVEHVMQStvgIlWYfkk7VoVi1sWReauwJYMy06LXkTlynmt770PaatFuGSVEaCVkInGM5Z63L3nx99FtC9k9oYblAbR9EKj3nBnJ8QPX/wDTeZWNr2Xu593H/4iL9VhR3FHc0aPqtVo5W1hrMGhhOqSDwXUzUwqIwjG8aALXESzUF2B8LzWkUjAIEeSsEuJqomFIiKu7yY3JDSrNdLJFltW6YO7dvutzYs1MZruTzz25fpKs6+BSIBvupx1EPL2yzNs/fJSWH+G5gp9+3RGlGhvyki2Ry8Tnr+PH3/dcuUVH5PiLwo/wyfwbWWSfqmdIkNa6Xwc3RwkB9WVL7mtOtkcJAbheB44nFqrUEh0RNychEwBq0J8mIf2mfxvLkHB8NRhS1yRaJSRzaSVkrblm581kxKg9fs91bD1Eqiak5eNGU6ls/HWT16U6E9q9ceIgpZyLr9WkJqTrBkjfh57eg1e67Fh+dswOE51qJ+YrddWEBEjbqliikhbdNSHJ/aT7/NtCfr9JXU987K4JSZ5HKXwk8qrWhES65m2iEH93ofT51e+4me97yV6+/+79qb8bzfVXrLb9/ppomhqqy4JRQvLj4HqJ7qSD1oTUcMVQCfnqRyKg34FjM+mLItqyMxYMZscCepSQLe1YobIbhLhkJPxIkFMbtpQJgpEkIV3ZRcOi+xTUU5hQG0DQ2YSEdBGazeA3MEtLXmcHHUcwbtv0qt9Foo0wqofx6BanFSGCLjtWYvFLFn16OcgUbCvOSiW+QZP+2FKmhNMqM6U/1pLOKnSken7yRjV2ImQUZ1TaNTxHsOhJQikYzYzFzzPIFLQSIllvrfe2RU3A+Izb7mnQmbx8qJQNdMeqfdIhxCVwVmx20rTn3T+63wayoOVn81136hAG1o4FKvMP3UpIRykh43tZ0Ta2SDTxdGA+kh2xxKHtR3FLXdB2LH3cCctNpRkrIZFUJCTnOXiu02vHSlyb5cp5Oom6gpD+JKSlj3/czePp7mqLeuBepAMxiAu1QVkGkkFhsxOyXu/YehCAMTMrIZ/oLJewo01nfH7itdeoIkYdIBeksLNOWu2qbccJekYI0EjYcgp+fN0ms+a1To1SzuPHX32YW/eqc5K8bk0Gfi2M1T8vX6IhSkQS1irx5x7PTlq1wWGTmhB9/EEEDV2jMF50rK2ib3cs6FMTEtuxUudXd8cSZChkHYoZ155Sc2xBGNmW2lYJSa5J21RCpktZHF170+7EgcRUKctscZZffOmvkwnVcM0nVh/iz4/+uXpNr0gpU+pRQlYbnVQ9jie81P3WD93fqcF6QnVpBSO40SSeq5WQ8f1UtEoyIh0EwgY/bkfdD20HKM6QGVNEypERtXK6uNrVHfPMOtg9hBBIKCE5FdQYJbxLCUHAbPYQrixSbvg8u7qsWrrmPP7jd9/Kq47o82K+g0yRTGJOTqsTkxCz7jyWvYuP5L8DKBAl6xYwJCRLSauReSmIEDRF2spncPNu9T34oeTppfg+39SOpdejvMQOLOxuHABxDYBj5+Wo82OUkKRCuFlx+lp7TbezFcwUJrusPumBhapdb4RDgWKCYKeSks1yrHZMHlLt3FGfp4HsmaidtAHu0Q1CVoRUTgfzkmTp6O6L4424GY4lIY6jOpghcU0cQP+Bhcn9pJuEmPMVEVHXIw6Km3THAlBLcUCtfWV2LCll3KJXz92ZzMdrRztsc8OuUX781YdT9WpASnm7kg5ZxumwWuv0tCYfGLZrmf5ezPp4CSVESkm5aWpCnKES8lWPBAOdjbA1Bcsi2nJQIQxWmG6ed2T8iP33ZnYshyLIHO0gIhQOb2i6zOCooMJvgimWS27mmSI4+sZqbcSbe74rgAbIaxIiw7jFbnJY4aBKiN9UPnmEJSGgJj8DrNfMi0vCsa7Wr8muLZspIcmfvQJ4ORs4V2vxcSX9saVMCarzTGsVY92JkEiaobqhk6rVuPlZRnGP/I7yVy56kkg4zOZ7vyOlhDiWUG1VF9LSG15NPIMQAkcWcUOtNOjWtnulQygyBGKVjKvazpo2zvtG9tm6AtAkJEkS2xVGcp5dVP/xyQX+66ef5t4TF1iqtFmtdZCBzwZRqijdcVu2Tmc0M2qJQzuIoNRNQtRnyHmuzfhVWnFNSCQhomk3npQdKwhT1+DSxllrbxZAJ+qfaWzrTa/gZBnJjOA6ghVXt2hOFKZ3W7CSdSEmMIpEnYz+fOOB3viSpD1x7aaud21nyrsZe281OxU7zRlgx1hOPx4HW8WERSaZNTc1ZPatwo4t9B7JjDBVMCQkno1TyLisOcpWsa5nhWScDFGY6FCDS6SbTfQrTG86JaRWckbycbBxSSUkaDHqBjjkkeh29sm5D/pnIXPkPRfHEZR0QaoZRFZrB7bjmCUhXW2Au5G0w3S3BRZCMKKLo00m3hHYWrTZ4iQzrR8iH9xgi+nV4yqw727tvVrrpAauTeYnL1ncWfAKto17MhjcSHRjqrXGdA2Ao5SQwiRVPevGXHmVpk/LD8nrAbktIaE0S248ViKb5VhdASg01brgmlk1/dZns24aMmfOtb5Gm0GT0xtKVbtm/DocWSSSEik67Bjz+L0fuMOSZMIg7raVLdnORgCNdnyekk0xANvWOZlRr/t1ZcfSCkgOCFDdtpJdpQxu3DVmSe3RhVgJ2FQJ0Xt4LqGE9FMyDAkRel5FyWTyuyamwxZ2rOaantJdYmYkbWs0a7HJkhsyIhDMFuK1NUVCygmSMHmIUtazJMRH4tfS10HyuA5oRXpVRKm9u9kJqWoFdKTWh4QAZAo0AUfE64IfRj3nLUkiuucLFaSA+ir4Tbsm9CghifolRwgifGtHvFzU/bpt3tLxzZyWrFU2+7UZNkh38rt8MpQcRnx+fetuapuih4QMNiKh1g4IojZSdIY1IV8z0JvvpN8hoyOlJSFhpFexMMFO3s1v7q3uAzOYyMFhZ2lnz+9nRrKqTW80TTuIcOUY36yLL2mspyW6ZDAqRNp738+ylficZqCUjyTIFsFLZJe8RB3JJgPFgNiOpQcVGpg2vcnCxXCk6wYZhIQkOzxl8uAVbOBcq8ebbzJQKWVKUJlnRm90VSdE0gJds5AkIaNGwZJRHMx26jEJwWEy2/vd4+V1+2N1EreqC2l0QkIarIZHcQTkwmvomDWvopUQt0SIQIoA4VVVC1udrd43uq+PEpImIQD7JtTG+exynY89tcjxpXUqLZ9KvYkfBGwgiUb32Pa8o8V4AxjJdtmxjPInHBjba1t45jNO2o6VTSshxgecTSghnS4lZKl2wWbVHSE2tTu0dHCecxUJEQJqGW3Naq5YL3x3MXrSJmkmmkdCzQgBGDO2jiQJcb34Gkxmlo0Skh2zdqyW32CymLWWwz3jBega5FdItJ1NfnfdAXWyaLaYKTKVUyRkXRef4+UoZl1WnSmkhDUd5CorVlezAH2vpTK3+vhrjgpEJooZO6gQtqoJia+vUdFE6O5o3QML62aoosyqicxgLVnGjlVtBeSk7homRNyOe4tZISklpE8mupjTQaQ+roliPGMil3ERZJhsfw8vmXqD/RujOje7lJD1esd294HBVG0hhO2QlbJjJeZSbLRH8KIpPEew1lqjHXWoar/6pA6cKi2fcsNnVF/rTdeFTJ7iZJz46FTi4FNKyUhb/XtLJSTRUVD9X59Pfaynyqds0ubOnTfbot58xuVXv/MweyeS63Hi/GdHaEVN+971ZhxoGhvobm0NNZ0Rk99fw2+Am1N2KSAjBQEeEZ2+dqxSzuPgtDrPJxZjEpJ1NlFCAPJjNsEG/e1Ylog6xjqkjrkdRARhdMnrD+LCdDcatdPSDca6lJBkbcjuklLpJnOTdnaYesEz8c+TBylkXAIxitQsrFlP7zHJ6+6gTs40haAhE62xOyFVR93L2SCxjydr/jJFGqJLCYlkTwCfVJRyTpcd6+JXoLEC1XlLQrqnpSfJqBlYWO8E1uZ5OUiun+22ISEZq/C2t5g4nnEde4xXYgtLKqumlfT2X6SbhAw2LNq05wW1HgyaDH8hMSQhl4LefJ12hRldYLeUyaoWul0wwc6gViyDbzn0Ldw5eyf//MZ/3jcIMC08x/zXkgkOMdP8TgrGttRcT1+Y3SqH+XdrIyYh/ZSQwiQFYvm41V03AnFGZUslpKF6awmR+iwmKynUWClcIWgXut6jOAgJ6aOE6HPRatRtMW4PCanOMyMdJIK20yAUNZtRS35fY1a+lFRMFrNTp+PAhgMRDqNe74wMvJwtTIetSUjTD6llP09ESwUv/l20g1DVLmglZF9pt/XuBs4K56uxf3ffSB8SklJC1PXw5lcc4LodIxyYKnJgqshoXpJ1HTwCpIR2GPGVWsYqIcV8nJ0rZUppJeS6N8CBV8ErfgoyedvCM59xGdUZpErLt37mMJKEok4xpxb1rexYy7W5mIQ4W5AQPU8h7+bt529kdTAcRWreDL1teZOkxA5vdFXh3khmhEz3tHQDmy3uo4TkxlWnIaDp18l6Dv/i1Ye5+9Ak99ywA8IOTZ1xd4FMInBLer67O/Uks5mjmVF7bdZloBo+eHnyGZc1Z0bVhGj1NdkZy0DVhfRXQkw9yFghkwrKLmnHAkapq7oL6dhZIQZ13TVJkLN2PnN9GKtFtRWQleqas3NCku9xCRLSTdwAcrqZgdBF6ckgMK+vPYHDK2e+h391+7/itpnb+M5rvlOdkq6akJUuEnKponQDO4QyoYSUdRF0QQrajODKSUuOFuoL1DQpmNakrNIMWKt3GNeks+W6SCkpTe7ELM9BJVZZK7UGE3riuq0J6ReoJAvTIfaYa8JyYk01yPCExz+77W5u37OLiUKGvRMFpNsVSCVJTrZIw2/ozySo6rcJI8lCRX3H1+9U96rp2JZss6zsWB4jOojNRRAIpYT0s2MB3KQtWU8vl+PD2MyOBZAfVzVc0dZ2LElkJ3ePJPafhh8OZsdqrRGEEa4cSSmjgJ37YzL9yZqDbz74bdw6fSs/fuuP2y6NQExCSjOQG8VxBHm3QIijpqZvQUIO6H0W4aYGNDY6ob33neRQYzdB+LIlZfeSkX1OJC9hx+pWQkzr39AnCI0Skv4+k9+DKwSSDlLGFrjLga0HiSREcU2qOb6tlBCIi9MvVwmRUqrGKxrn1q6UhOi1Z0A7lmnPC3zVKCH97/IhYiQ2xh0RzAPLmf7Srwl2Bi1KNxjLjvHWV7x109+b6cm58DC58DChjOwEXZrrm9dudB2/VTD6KSGmJkSjmR+hp0dMblS1gd1SCWnQFBKEl1qYjBLiSFNULGjmSum/Ne15AdycUnKkTLfoTQ4y83IpO1ZGdlhvdJgZyaUW5GKmCNUFRUKcDJFsETplMnqBTX5fmdwYBQRNJFXTRaVTZd5Vm2aEoOj0V0IKCEalQ5Wt7Vhlf4565hHGhccNY3dzenEfESrblNEkZNfYAVhRXcN8Z4WLNbVJZJwMO4o7cIRDxsnEbaEne1uc3rl/gjv/+Z324d+6/2M8ulykVWlDHZDwn768SEYHeLmcTxNdQyOcdE1IYRLe9Nv2tdpWCYkL0yvNwA5HimQLRMNacVIkxI8gsTguNpYSSsjmdoe2DurzXt4GfJ2Mev0gkrBxATl5qEcJSdqz5rQdq5jvqPa8uXFY15nl7vsiO6Ku9yQJ0QFyoTABDWWDaelC2++5ay/fc9de9bz6iroPUAGoCH1lY3G9VJF1rYvQJ+1ZyZoQpGSNiN1ejoJwOa/tWGthE2S0CQnJEUWd9PnUBK2i7+6xvJeyRXQHExYJElKSdQSurTtJdztq6vfuVULM4LdqyyerJyQ7Zk4IpAYidiPjZMg6WTrdn8f8PhMP+QNSQaDnOjiOIIokLT/k2w/cwz0H7omPuUsJ2Wh0mMzFxGNQEmLqQpJrT1kHReNSsEoWL5q0qsFCfYGqXsdnwiZISbXlU250mIjUOiddj1bYopAr4jtFMmEDWY/Xlo2lswitYHjWjtVPCTF2LE0yu+xYJ9YVCTk8fpjxQoH/85vu5Oc/q85lT4e55PnPFGm25/AcB196rDfU97BUbdmM9nU7R/jsyWUcrYSYNsuFxODCYnEGOutkJfh4SDq2i1E3bto9xj8+sUCl1WQkiMh4zuZ2LID8OA6CrFTVZv2SHM1OiCSwnapGsgnS0Q4Zy+ft9ddvaB+o6eBBJMnJ/5+9/46XJbvu+9Dv3lXVufuEm+feOzlghoOcCYogSIhigGmREkUlkqIs0bIly0/2x3KipA9tKzgovycq+T3TkkxKphJlgaYkC6SYAJIg8iBPwL0zN57cuar2fn/sULuqq/v0PXcGA4B3fT4kzpzbp7u6wt5rrV9Y/ZJGDIqBhe4ZDZPcp04/wm958E8tvqGz590s0JFO0uEoi0ArJuOyNsgJ/jtxh9OO3yolt6e3uTwwIuxJmnEkbFHotvuQigUGCfFFCKDaKJUtPHcrhekTc99roMU+GZdWCtOlFOQiA22aFNUZMeuG14MoXVj0thN/fCVdZE30WzE3j2YnnlcyyxQhkHN1b4ll9qrI5kWxsaAJWS1M35/MPRIS3XPH+hqJsAixideNSNS+1HVg77QIOS4iKQo6E6CFRDlh9GQXwk1iVRGyko616WcfAEzrXuORkFUWvWPTsbXT0l1sOyQkKELGSQX1CelYUhZdu2VISNI2Ql1ZFCG71o42XDB7SQ+OXuKUFmjL45/Ll/wiXEKuGl3jEgYcOveR+YiXpAIBGkmiguMEPn51n4PULLBuIv2NcZmv60JrzZX8ZwBNUwh+p97iG2a/wDfMfoH8E//E84CTwUVawiQ/M24Vzljd+4hkVJq8bZCQ0EK5fqFynacHmy2SSKKEZCSUp6O4+RQumXJ0rHm+OBPEC9Nj6fnOR9MUpTSDxqCgYzkkJIDiZ1luUAR7zLemO14TspKOZekUrbjlqS9pYq5JbouQcTYuDc+DslDdISHNpjkXg8Yg0EpVkRAHgYdFyD4A7dZ24Y6VmgSyFLMjb/DgqCYueYtkVAwsTMtFSJjA9hq9oAhR7AqDhHQaRhOiNeygIE9LgwpdCBqLHUyLhBxaq89+KylRFJZPTC/OTUeZY5Z2YGFJmO7oWDRo2funFyBlYDUhDglJ2oUZwAokBAo0pK4TLa2jmkdCKnNbWiGqVwl3/7v1QGloiOL7ripCtNb8+vO73DicFkhIUFju27V5oGMQgkhvkFgdyLXRNQ7td99Qip4ecjjN2B3P2bK6KCETv5aN7SR3RkXyObpV8PoLOladJsQZG1Tc3+ZDlFZ8bs/MUXHzj0LjDa+Nc1FCQrpMsomxB9ZN9sbmOrwU6EEeO2vtriuzZUrmIZaG3NCQicRoQpYkok9dMMemRerXoZVIiBOn2+xwORKS+s5/v1k8B96md8X9p7XmtrVVlbrP9gISYimr4xk8/0vMdwpDiaUJt0NCgjkR3aRDjnmuxgHCAcWe1026nLKaojok5Ei6IsTeL53K/Z10TANFK6QURLqP0qsteheE6a5RqaHJvvmY6sT0ijBdW1ro3YjCnZlMrosipN+KPcJ7HBLiGiYndceqUjtfOIlDVmi97JEQ+zyuYqFQOGOBybHu0bG+FsJtjLNDzs7MDbWDqp26umsnjd8pHWudOFPZVLVDPyb7y4cQQnHzHl3zcHS9MN1C1jYmzUW6mZudsBISnI9M8iXL3aktK0z3dCwpmCQV6kennNz7DmlJmB4iIS1bhJjbuEHqnZ5KSEhskRAkygrU0uglIwpHlp1vkk5RhLjkdTbkJWFcTwQd5vPie/2bZ27w3/7TT/IvntlFgRGnA7crXSoXv3b91xjxPAC/fTTjjZ//+3zP5Kf4nslPEf/KXyvObf88TTvoa6JveWesi/2L/r1KRUij55PiZdfHbb5nlUm4ms0W3U6xIcvInFuXTHk6Vrp4r3thelII05WF0gfNogipRUJcImifrVuz/TXpWK4IafskSYsZO/KUKUIOr5YKDhehYcQ1Oy09is1nbDQGxflai45ldV+dM4UmROdQ7bDNh7hUpV0pQoC1ihCPhGgAbWaOxE1ajYgdeZoUbdyV8tTMCKkgIW6gYJ071p6lKvSa5QnWy+lYxZrRtkVIleOf5impKia2tyyS5gSfzinG0LHmCAEy6DgfV4S4c1aLlEk3md48m9udchLoUBmH4IXh6FjngunJIi++76oi5Gc/dZ0f/RfP8F/94497S/KSJsRe3y6Ojiq5YO2urw2vMbKFah/BGXWTw0nK3mjOdj4CASKK/fmdN8yaHwdzLuY7xppaI4maNferi6omJKBjXTm64u+BJ6zle7/RRzgNXbXpVEFCxpmhYwka7I3MPei0V2CHwglXtJrfjdNx2TxkcMkcliqE6b0ldKyz/Sbb3QaazF+745AQgJbds+uLkMwPKgQYBI5KVXF63f03TIdMrSYt0n1OLyAh5rs8Of0o6v/+b3jNh/80aE07iUp6OR/Tw4KSExQhvaSNEkatMV4yJ6STdGjPRvS0ABlxOzBHmMxzhqJahFSQkAodS6q+p9GVDjFI6KtFYGdc3KNNbZ7nbsWNKlx3IiE8Fe5uROGuYBbECMy+NGgn/vhWaUIgKBZPWoRUqZ3D+YLxxbERup5VkRA7V2tZ7I/n5GKIFIJBo79gNf6VGPeKkOPCJfGj25xOzSKTy2hh4NHudNcnQJd6l172w3C6EBfCFyG7RRJl6UmlcBt7eGMvoWOVNCFJTRHiEpGVwwrHzAQLSEgtHSuqPCDdShHixZPB4hdSsxKjCXGuMA0980hIqQiRDRje5JSWKOGQkGtIARutjbLzTdJhYM/DgUte50OuihwtJLE67bsko1nGj//K8+a1aUSuNGfUcmF6mqf8+Kf+dzQQqz6/fXhASAF2iTj98/DQu0m0de9RN3lpaATr4RRtl4gfzY9MN9kvVPVJnNt8T6czJGbY3/e9/RSvvbTB+Y0WG12zgLriprGie1y4Y8lSJ+/QitPdnBDX/Yql8Bu8TwRbm+RobqdD+93FUiQkzzNSm0Q043YxQVtMuBVt2yLkWok64pJHV4SM5xn7li6C5blvhILWKnTdLBI1wCz+FkmQ7U2f+EwFixSY2bBExyq9D8XU9GqC54oSgdFUDZoDHPC6ZzUh7SRiT25xNTHT4lEpl/uXFzZOQROlgs0+m/tj2LEDKvutuJRMLHXHCtyrWplFQip0rEk+8UmmoKBjDTzPupiK3GCOFAIRft4xRYhbT+o60amemPfT5npWOfnNFfeySxIubxdrnsy3eP2Z13Nf974FG3UXWmv+xccNJe/2cM7e0aI71r6jyNh1L4kEl6zJwxf2v4CyqMgAwRl1i1xpru3us6FmZhWSsT+/qdWpJNNiLdcHRit2GG8RdeyeMKtDQqqaEFewjPnMbjEs1iEhUkjfjFgHCYmlQOomw1nGPFMeCTnVM/dB1w649Jq9bFQuuC+9DfrnGXYfIydCiNwPO62GEIInLwwsErJ+EdK0VtzL3LE0c6/JCAfcueJ5FRKyP9239rwQ6d5SJOT+/EvkWtOY3qKtJ7UDGc0blp2xXPSbPYuEaCazg1JC6u67btKF2QGnkCAqRUiacyj7psBdSsdqMxGAViSigaRNrheF6UuRkGxOa1rQsRLMGrdqYrqUAo1zDrt7TUhT9HwRPWjFfl1bFwk5qVVwtQiBE1Cywvkv1SJEq8XhzUE4YfpXy6BCuFeEHB9uY9SKc7bLTZQsJJlO2Afw+HZlmvbLENUiJO7a7txkr9i0l9CsFqJuTkhrk1ZAx1pAKSCgpywvQlTojhV0Vd0wt1CYPtFZufscakKgEKeHhUe2iIRITFHT0GlRhAT8WDneAa3oIEisa4gWM6QUi9S5RpeBvc5HgUXvVaF8EXJgnU3+4a9d8UltSkKuNGftsKqj9Ghhs/uXz/1Lf988MH09bUw36u92f5j/fOMvc+13vR/+8Afg9/wk9M4QK7MAKVJPMbrYK5AQlyT4RNYXifV0rGk2BQ2t6RF9O+ciFyP+3He/lr/9/W8m1eZ4q3Ssuu7xNBCmhxvp4SS1dCxsEWL+TQhRvJ9DVloDdtAolaG0JlanzHTrmiRhNh/inMdaSaewKBY5N+UWSmvyw2slPciDgweBoggJKSI55v7YLBUhVU2ITdTc/Z5OCsSjvUnLJcXoxe7z7Mj8HvDpTLB5OHF6VRMymgezbYRECsmmtdt0SIhxyUn4jKWMyDzjsa3HvONOUfA3zJAxdz7tOqGB25krQirC9GVFCPhnNbG6FUevcUnZJJ14wbHUTe80EyIhWmsOpxlNPTNi5qSmCMlmZcTTRp3mAkwxMEknJJFAWjx3gY5lj6U6mNAct/ld6AC1N874b97+3/CXvukvlYfHBfGFm8OS+81zt7LS8SmtOLDnvmlpYtvdhrdhf3H4oqElioiBEJxWJlncv/kSbSVMkigLpEpZ2+DmvLjHpXXTG7bOL4jNS7GAhBSaELd3ne+cLw9utfvJYXU9qRYhqaNj2VlQ47lHHC9smHPabcYemQNTuJaKkMFF+D0/yefP/HsgzHpeh1i4eOq+AZqUea5QSh8rTAdoKWUGDlfeV2vNaJ5bJMTS41qBJsQiIb4IrkFCnDMWgFTdRXcsqwk5pXaMaYfSbOr9pZSzqjOWi36zTU5k+iEqLV0Ld1y9pAfTQ07VICHjec5I9JA+RadsxQ8lJKQZtZC6YZoZ1WGF9p5qyEa5kTfeQSK8xjS29KAFOlZQEBgkxKytd+NM5ZpQibAzygR0G3GBhKy4pyCY53JCq+AqHQtO4JAVNozdtQnXoBW6kGJGSE1u8xUad1WECCHeJ4T4rBDi80KIP1Tz76eEEP9cCPEZIcQzQohH6t7nKzrCqekuSZfLi5Be0islii9XnK5sqknfVsjjAAlZQrNa63dVd6y4ZlFfAwmZuUWxatHr3bEkQhv7zHE2LgqPpOMdx5RWXBteQ3s6VvAQl+aE2PkCQhJLQYMZt4dlTYhxxnrJ/0k3Lro+om6iaNL2SMihTRAnk30zG0YYPcjBJOXF/Qk//bHifeeiYZCQPHe5cuke2Z3u8k8//08N3zy/zBOzNlIYw5tr0QW0kMxyjBbGboQiP+WP08WlfoGylehYUPD2lxgHzPIZqIyWytlAQpT4BEMI4RNi976ruseuMGnGsrSRHk0zK0y3dKxmsfF4oXtAx7olFOicXEGiziKFKLnnuJgECEcr6frE0AwsNPdlfnidfU9REGwmJtkrihBrz8vcTwMehEvgAh3LaaDsPR126FubtGyRPEMvJn7zAAlxz1XwmjonpfC/w+F32/Y4Qk0IwOcaZi7yZdGgHbc9HevCwFmiNss0CleE6EKYHiIhsYhXw/dN5xR4QDuJfFLpkkk3Ld18doGE9AO63iTNGc4yEse/j2voWFC70S6jY83yGQpFEkmvOasmge5entZQC12SOWgnvqDesbTOkltRJf7VM2Xd10s7miw351tpxdH8CGUphA1ljmur0/DWwNotFFHCBpIzuVkvpgc3zHA9RAkJ0bYr2siH3qq8NTZr0LRz34LY3IfWft2cRgnP7DzDM9kRz4iMZ7IDj4Q4FASA4S0Gln60gIS48y8jiBoGCYkE0hZae+O5nxFycdPci/3m4oDLBfMQIfycl0iKWitdF09d6KPtMzzLNLFcgihAUYTYWSHV93WFjCa1Q/Ngs1U8f14TYs9HXZNkf7bvp6X3kg1/77twa+R2UIRsqP3aWShAUYS0t0rPxaDVRlua0aQysNDTseI2zI44rWWtJuRQDPxMYPMHi5qQoghpW5e95e5YCyiUNU5oa4HWmkg7mthyOhYCktg8m2uLwo9uLKB+7l4t9CCJdRWzFr35zAyd3r9SS2tyqO1JrYLrkJA7dshy17TRDezLQ/fL5bqQ/XGKkl9dRciJ3bGEEDHwl4D3AAfAh4UQ/1RrHaql/irwD7XW/4cQogMsX9G/UqNkTWmq+2kdEmLdRR7fevzYwVYniSoS0upb6tJ0/9j5Hwux5HUlTUidA5irxtOJd/qphnMKqiIhjVjSbUaMZjmSBpHMzCLUPQ07XyhRsX7sYz/Gv7v67/ghlfBtUO6Kug0kapSmq8fRmIZOuTEym7NL5pwzlv/qjXOAWSQlNSYCgSZkmI5QWvHS1HaSROSRkP/1F54zDhzCJFdzYbrOBi1TgOTm+CYPDB5gOB/y137jrzHNp2gNG/P3ckF92DgDJR32lCmE5tVkP9uCqOhYxSLmXOec/2dfhKRVJGSFJiSf00SwYZGQUC/h3scl+CV3rCCU0qS5QyWikpXm4SSlFdsp5mJKKykeeZcIzoMi5KZQoHKU1iTqLJE08wrmal7a3GZBUtpKuiRBEXJbmg0nz1L2j64ySxW3DiL+4ZU9xOYMembDdEiIjMdexLsRoH/LNSGuCCnOFe1NP4xzImqKkECY7ulYNTa9C0iITSTCgYbb1k1rDwWJsejVaF5IFC3gNblF7uzmfarXIIkMR1+pEAkxx59r7W06+82YA/tMrURBoKSP6zQjxLyFUtUixHxnQcNf77AQPZpmHE3T1UgImIKpV7bC9pz8Ch3G/XcSSahxx4KwAC4nCfNM+eSx04g41W16m9xVMU1zfv5zZh15+uIGn3zxAHSLo2nKVtdQCveme96yW6Wm2AqREB9Rgw2Rc8YiIYP8gKa2m2WU+Osn7fnIlUYPbyJ6Z2nNdpgBef8iNG1RVNVw5CloM6D1R770L7ly9afN+U3sPTu5BUJ6PQg7X4R//B8waAObZ2voWPaetYWDoWMVBeCtoxk3D8tISKcZIWmR20d/nI1pp0UB6u736dxcp0iIldSZh073iCNzLdM0Wvo6oFyE6HwhmXbday0KOla30aYZS2aZYmxd3drJcjrg/mzfUM205Gx3cc91a+S22jUNGg0b+oD2Et1LnSgdHJpgnpmJsEWIRUp8ESJi0MrQsaRkd7qL0gopJNM0Zyh7hR4EaulYYytMb0dtDqnRllEgGQuOelYP6RBgKcagF+lY1WKw2VCoKQvattq4/QX4J38Yeufgd/3vfqaZ14Qoc61cU8HtJbNsBv/6T8MLvwzv/i/hNd9Relt3nZxV8J26dIVIyH2bLV7an56gCLF0rLA4DHWrK5CQg8mcXJi5Zq+ENvmViLvJlt8GfEpr/aLWegj8DPCt7h+FEBvAW7TW/weA1nqstT6BVcCrHMHGKBCcjYybS1iETLIJzx88D5gi5JWIEAmJpKDRtzdonvoBd/U0q7oipOZ1MiJuDkhsyjuRNQt7ZSp3XUxdoiWkX7RdbAUOWZE0HW8uv938o/tf4DM7pjP3jLK3S7j4+cm/QfKStCwdy0wDh2JBNoMK7flpdGlGptgR9v8tFCFxg4F0nducYTrkRUvxEVIS69PcOJzya8+bheK73mBQr7lN+E4jEbrQhdwc3+RP//Kf5tO7nwbgrWe/iUSd51x+wzgQb172yEc1QUozSay2vJbiQu9CqePnipBROiJX+UpNiNLKULryOS0NG5gi5GBuXpupzG8yjuq0jI41Df67lUR0G7E/xsNpSkMUCbSU4VTdyvsFRYjQDSK14d9nQQQ5D4uQnk9aYim5HZn7LFeaz197lhf3J+RZB6l77E9Srh9OuTHc8UXIdj/z7ZANFRRY1WfFaULyudFTWD2Iee0mHZc4QS0SMgYQ0YI7FhTXbpkwvYSE2M9xSEi7EZGLXUYS0JonZsady9Gx+q0koL/AXM3NlHB7/LnSpTkh7rqvXYRMD8w112VNiCmyzb3fjNqmyIYSUjacZV6YvoiEhDbTi/ew5+RX7g2PejYjIpq85nx/YSaBu/eqSEjYuWwnkaeN3j6mCPmVL+74hOP3vf1+XndpA6lbhs+uzXXcn+2DViitmU7N+v3Yub5HQnxECRtCGjqW1gz0AQ1thcMiKoqsgUGNtYbZ4S04fLGgAG1eWk7HsudnB80VR+UKG2VK0Y7bfmguL33EHMdsDCpbFKbbWTDu80JhOsBnrx95ROw+S3HrNRMEEWjr7FShY7kCczo3/x5JsVJEHEnBmYFDt47pbfoiBFD5gk3ryF5HJeY+OW9GTZ80V5GQWjrWdI9MKSLd5VRv8Tnqt2JaekJHjwMk5GA5Hcs1zjbK+tJuI0bRMcJ0dEk/4BsYwto+a3P/ZDrzNKXRLGNC27tEmi+2RJgOdKOGpWNp5mpu9hkby5EQ04d2CLDAIGVVAf5CERKb914LCbnxSVPgH12Dax/zv3b3qnZFiD2/bm2bpWP40gfNi6/+6sLb9pplVP9OI7T7fsLOx7l6p1PTXRES0uRKSEh9k1Frze54hhKmyfbVgoTcTRFyH/Bi8N8vAiEP6SHgthDiHwghPiKE+MsWPfnqikpicqZh/jucAxFOm33N9mtekcMIkZCNdoIIF46Dq7XHWvu7pF2ehB5Ge9PSAGBaW4SE1XjNg5DNDU8VQCx6tzuuuqNjTbIJPP098P3/FN75R/3rXDdj1wkT6yamh8lL3CaWgoTUFyElfqxb0PsXaMpNAJ8g1Ym3Bi4Z04rD2SFXUrOAt2UTqbsexd3sJPzet91PrxmTWjpWgmDLCsY/cvMj/Mgv/ojhfgPf+sC38q0XfxcA59V1pBDozQf954aIg9aaeaaI1WnfnasaHoSuXsN0GMxZWLw2fsHPU5oIQ7WQsticqsMdKZCLNNclaDpM5JqxmcEQTk1PRCHqVHJcei0EdKzmgJtCobWmofoImj4JWOi6BYhBq1EUIZEU7Fm+78Ek5WNfes7oS3SPSxvmGRnOMv67n/k1vnjLfMeNbtFp23T2w1HD0PvCCHm482EZCWlt0LL/PqmjYzlhuowDOlbFNhqTwIUbuytKQh3Cli2y9oRCRQ06jYh5dNW4CAFPzDOYHfkO4qAd020YIbBDJqbZ1Dt7KQ0jy5nuNWN/byx1xgq+s3mzAzqNyNNrpvkUpRWTtKBjtYNzGbriGCQko8HcUF/CLmr4LNYVITXuU1DcK60k4s9995v5C7/jdYuHvgQJCTUinUbEaYugODrWsvhXz5g15cJGi6+7b8BvfeocQrdJc2UoZ/OhQUKUQVqkHWD3vtdd4HT7dKmZEMUtesLQSQf6kE21b4wJZAKiWMsagwIFnexdJ9294oXeyfblektp8OjxVZH74uM/e+x38z+mPfN/b/wT/I1v+RuFC5hdLweWvrR0TohFCifZBCGgZe/TT75YvP6iL0LMfuLQknE2XqRjAZOZOT4pVyMhAKf6kf+bBRQ5DO+OJUBlC+87sUWGsei1fxK3vLOfSyxXCtMtHUvWDCoEM6vmYrQPUC5C6oTpSsHQNjl750r/1GlG5LoDGoNW2IQ1V7n/Xl37DJ7S0g9UdZSsSZqDEMziIKldNicE6MUJggYaU/yG587pK1bSsTD6y6oeBCjNJwJI4sK44tgIncGumKJCa13MCcksDdAiG04TMg3QSXafW3jbENU/SRHi1hMp4JGz5nm8cTir1aItjeq0dKhoQuqLkGmqmOZHgP6qGVQIr6wwPcagJf8z8GbgDPBD1RcJIf6QEOKDQogP3ry5fMr0qxYV6tK5lumkh0hIOG32kc1XRvYyaMUk1iZno52UBxRaW8x6mtUSy966aDmHLOHtUMt/e0wRko6NKB0WNCFQ2GZKmkURAgZ2dGhAPvML3a6dkF07JyREQuzAwqaeMUlzJvO8ZFdYFCHnibXltdvPq3tQB5ZOhFYczg+5at2AzseboZyPH3jng7QbRpg9t8J0gLO2A/GJ25/gYH6AQPD9T30/f/DpP8gshVinnMlvGfvD7YeKUxpspHPLW4jVab8xhva8UC5CDueHK4XpXpBn6Vib1mnncHaI1rrU7axqQsLjgXLiVjggOUFfitRFFz8XRaJRqwmxxXtbdZB6VRFSHF+r0ffHKARkiWQmmsbhRhwhheC3PvEwf/bfe4efUfHc7k0Pi/c6RSd0YB3vaG1Alf9fKkJGBRJiZ5y07THU0bHU7NA8C1LSdpt0OAMkoFuFaEgdErJli4McONIZrThiLq+SiZgtLTiNJD94yQ8DHARIiLsnx9nYF1HzqIu2yWioCbkjJKQZI2mVhMaTrBCmd4ImQS8oQkazjKFFQgwdK2gmNNdDQuZqXpoFEyaFZ3sDP5G8dOhVU4TgeFy0GxHb1lrVWc3WxUv7Ez75onnG3vvUOYQQvPORU3Tt8R1OU4+EZHmO1KDp8EPveohWEiGFLNMqm5tIBJEwDlkbyjY97HV336+zeRptt+zZwQ0/I0Qj6Z66TO7O5QISYp6lq1bXJpG88cwbeFBH5v8am6WJ9BwZx6+BTdoXZu+49290UFr5ItY5Sj132/y7EHB+w3wHV4hqq40Zp+OyeYiQRiA+M9cuEqs1IQDbXfNarWO+eGvF/AR7XzWX0LFckWE0IQKBoCEbfsaRd8dyaE02XbDoN0iINtPSu/VNvkvxPmAGqyqt2dAH9XSf6X5hgFEpQkxzoUUuJBNdaELCZ6Bjn3mnCQG4bSnFbu2eNzbNi5O212L6aHQZ20eoLxNvOFClZB1XhHTMYC00M9oVPUiqUoPOBhFZJGSt5D90kLLIxiSb+PfMUnPfeSTEaUJmB4UW6+CqoZUHERYhJ3HImgTW9Q+cKtbwK3dCyaorQpK2LyiXFSH7looFXz2DCuHuipCXKCMfF+3vXLwIPKe1/qjWWgH/HHhD9U201n9Xa/0OrfU7zp49W/3nVz+iuJSMnLH2invTPdLc3KThtNmVLh13EUIIj4YM2vGiowXUC9OTDoTQax0Vy4Wz6Y1ipnUuEscWIRMj0gVDx4rKdKxNNyvEJpt1Ar/QiWVfzVFoaueEhEhI0iaWksRa/N0ezgKRXqcQpvcvEGuTSLl8s26YzyCpFCE2SbvYKhaFR8/2+JbXmPu130q8JgTgTFJch0Qm/Ik3/wne9/D7EEIwSXNOq9sIzDRaeWpJEWJ/TtQZj4SE9rxQLkLMwEL7uenE0IeCKIqQlKaGDUsJyXS2IBL1c0LCAYPpsiKkSGbBJGBOFAiQESIhLhEM6VgW9lctc1/YFWmRjhUUM40+jahBwzpbNZpzdqW9NtGIS1ttnjh7nrPdU5wftNjqNPwUWfP3VtQbtWi5961DERvFJlJCQlqbIIRxxxLS0rHKSdDUdY9FVKACwX28gGK5n+37hEXKKVls8jvZBCkFWXyVnIhHMjswcTccfhbTbUZWUGp+N06LImQS94PXFu5YSwcVugjcq/pxZosc+/7ZOLDoFSUkJNzY98ZzJmlOQ88sHSsofMK1dgUS4r9Pzc/ha8JYpglZRseapPlSf///59NGeyEFfg1oxhG/5RHzfA6nGbfGB1w72iHPc/oKTm1u8Y2PFbq3kJLVs/ORImmKkIE2a2C3ooEZtJscSvOMp0c3md02RciOPMWz0w/zgy/8FP9czkyxG9IMPRKiQAjOds7SaG8W/14tWlwRgvQNrhIly70+6TLNpj6pG1j6orvnTveanoLjihCVm/M7Skdl8xBMJ1fliT0Xi3SdamzYIkTohE9fW86Td/eVoWOpBYekcaAJkULQjJoIIRaREHs9NHrh2HYmeyitkbq74Mzm4rzcByC1a/umWuKONQyasRVdVLthDCFyIoNWWCSkhGRbZHULgbR7v3PIco2K1DI6avOIpO2RkH4UecMBpVmvCLGaEIeE5CKlm5QbA3XXNorMvbaWRW+IhBxchf0rJe3SfG6LEIvQu2NU6Rj/7iqDw5DMUx4ceRKHLEfRbCcR9weW31fWpWQpVRRYYREixEqmAywOKvzNQMf6VeBpIcRFIUQP+HbgZ90/aq2vATeFEC7L+ibg03fxea9ehA5Zg/sBsxDdntyunTb7SsUT581N+MiZ3vqCcyHKr637Oxenn6CpgbhVPzDuuCJkPjL+4lCLhDxuOZLbnR5C1MPa4UKSIzisFiFu8QppHHHTuGNpk3jvjOYFPzZqFRzLwQWEKg9qqhNvDVyCqBW3Rze5ZYubRzfuoxlLIin44W98uMR5n4umpyw90b1o32fAn3nnn+HtFwq9yyTNOZ9f98eQlIqQPPjZFSEXiUVELGMe23ysdJzephabvK64Pn7RVyktBJv9+/y/HcwPSkmGowI1omJ5qDs2CJCQdmBtqIpkNgtkYFU6VtposycUGhjkMYLGUiRkFiTqLXufu+Pc7ml25TZRImk2cxqxZLO1SRIldBtdTvUafMNrOsaNTEC/Y2lLzcHyaelQLkJmwwIJsQlcK26BiGrpWGNXTMuoKMZr3LGgKDxKE6RDTUjgWLWbjTiYHZBHe2REPJJZrcNu0f8ZtBM/l8EhFZNs4o9/IgukqxFLf65XzlqA0jnaiqYIXY+ECN2gFdAv3DMDcN1atzZs15mKbsw3UmqKkPCchPdHqQtcN9+IcFhhuYNdomM1Y0/HAjy1M4xcaf7Np02S+OYHtksJ57c/9SBgzPF+/YVr/PKzzxtthRJ849c9UHLaCsXp/dYWNPtIITiT32JT7QPQa1mkza5/g3bCgTTXQA1vo/bNjJDb0Wl+9eb/Q4rm/4nm1g0ruB/t379o6ViX+pcWqYYutIZDU4T0tfADbks2vZ6O1Sldh61W2cr4vs1i/e8FSIjW5pqVzEMwDQzXdY/WoGMpndKMJYJjihAw9EltkJBpVmiXIBCmWzqWew4cEjKuTEyHxb1r1ybFke6xucTx6izmNQ5VHugldKxh4LpWKUIMEtJA+SLEdM1LTSRLMY0RbFpb5xIdCxj2LWPjTE3O4iamA4MoLpAQVR5YuBwJsUWIo0AJzXZcocDV5BeuCFkLgagMauTKhwraoIbpzHy2a4C04pZ5MNMJpRI0tEKmjNoenWByuzu/7SRiq5N4U461bXpnh8VQ6SpNzmkUl+hx9+ygQjBaya+WIuTEGg2tdSaE+M+BD2CKmf9Ja70jhHg/8Ie01i8BfwL4x0KIBPgo8HdehmP+8kdrw1fMZzcfhRu/AMDNyU3mar4wbfaViv/o3Y/wza85y1MXBhBLk3SGyWYdEuJ+7yC+VUjI638P7b0Pw/jF44uQqlgRjqVjfcOjp9nuNviFm5/h51787ILvOFQ6bkKyKzSb2dRsjkIEwvSyJiQKi5DhpODHhohA/wLkXcxQPPOrOjpWo9EzLmha8Zlbn3DfiEc2LvNXf88byXPN/aeKDWnQjnk+oGN9y/bTXLj8Tu4f3O8HCroYz3POqesIASJuIfrnSaLnSHNd4jW7ZCnWG/yHT/0Ir7u4xal2eVFaQEJC+HV2CN3i9bN8ZriwWtEE2r3zcPhxwHirh934AgmpmXJe+dnRXPxAulnKPDNolxYzpmp5EXJL20VeazbyiD25go4VbLJNex/2kz67013e9GCLt7a/jvjKswhl/s5d183mJqN0xH3bOf/h738zk3nGT1/5Vf9vHNrnou7ZKd3vFSQEixxIySzPFoqQiUvshLQd1L1adywo7vlJNvHasrBI2QqkdHvZiM/tfQ4pBLkQ3McWMCM9vAE8DFg6ViPyglJ/Pu3xO1G6G8zlkonj6VjFOdoQYzus0Lz/KBt5TYjUDT8jBAyK22/F7I9Tb5Pc0HMiIRd1OK0NYyRxTBESJl0uMZLIpbqWwqK3jIS4zjCUkRAwzYxwgCHAR760552z3vtUOUF8zfktWnGDaTbnF754hYPsGtvAaSG5cLr87IZFyKAxgI3LRDdvcUbdZKAPEELQaw5Aj3zC22/GHMhN4AUY3UJMDe3lS9EG18dXQUTcEIoxms58VNy/6QSN5qpUQRESonzBvTs79PfpwNKXYBkS0ikl41udchHinLGgSO5CW2eni3HX9XCSImxKsg4da5bPaCUR02nMp68dmQJ4maVya0BraIqqXOdkKiOxw3Kd8FyL1M4zskVI4uxay0gI2MKwXRzHyO5LUveWTno/pZx+w+ot1JCkrl6xdCaEgE55gG+nabRYuYwYk9YWIR2nm2x0Od05w+5sryhC7Hd54YHvgQfeC2efXPh4lbT9Pj6QskTHqtOElNgfWvsipNM7BweHTCVsRxVkO7i2sYjJdIaQds7OLCdXupZW6SM0CQG48iGOzjzojxNlrpVDmppRE+weOEXTc7TqveeBd/u3iaTwLp4nmdzuzm+nESGE4PJWh89cP+LKugML66alu2huAFeWIiEHdlAhQDtpHo9sf4XEXWlCtNY/rbV+XGv9qNb6b9vffYctQNBa/7rW+k1a69dqrb9fa71a7feVGkEH8Ozp4qG9Nb5VO232lYp2I+INlzcLl4kQUoflBUYJCVmhCYliWpv3G4pJXRdKRsXmVTeLolKEVDskUgqevrjBdtt2+OroWKEdpBDsYhJnz5H1wvSyJiSSgqYddvTS4b7/p05o79u/QJoLpO4ghaCbdP1GVIpG1/ChtfKuVgAX+5e4uNkuFSDgkJCCjiXzOU+ffnqhAAGTBJ3Pr5lke+sBkDJwjQoS/SBZur//AJcHlxfeqxk1/VyHw/lh+fpXkjhjz2sW+SaCjUBfcjArkBCJ9B2/UBMSIiFhIucKlRAJGc1yIm08ocbpUfBa8z1dsXXTJjAaOJUbdx2HLi0WIWN/7NIeX9fei1M14uKlhzgQykDsWvsukCtG9mf7XNxs8+jZvu/qbjQ21kdC5gES4oSucQuEZCI0urIxjF3SJiLarqAIhxUGRYZLIMJCMEy4W0pZhy3B7uzAFiHG4KEvjWOSOixsqAft2CAhuunvyRAJOcS8t9ug75iOBWyIiRGmO82JRUKU1giaC3MSXBJ67cA4eTX03FzruiIE7oiO5e6VdtJemoSGSEipC14RpofIRp04/V/b2SCbnYS3PVimsgghONczx384H6LEEQg4F8cLvPuQjtVv9GHjEpEUPJg/j9SKWAo6tth1ib6Ugkli7ufG6BqRTSyf60nj9mZ1Ps+LvCxOz6bsok3nXEgzxypqFBzzsAixKAgUmhDzfeqQkF7pOT1VKULC4Y/dQJieW9voknkIFglBIrSZ7XDcYLm5mtNKIgQJB5OUlw5WFC2tDUMqst8nfG9Ht5IyRQg8cumOeTxbjYTsTfd8MR7p7oIzm4ut3CTnM1Hc8456VwqHhLS3FkxkjCtdw9CxnEUvlSLE6dyaA05ZCrGjYzlUp9lswaU3L+pBgKkQvvG2IUVAx6poQrKa5sXs0O/V7f5F4+QmYFOX18fwfTwlWhRJ//A4FMKxG1wB9NJHObTfMVfaU4JLdCy7/s6ELuaTVZAQKByyTiJMd+uJQ4IfsLnCC+siIe57AXQqDVKPhCzThBg6lhSCrdbmyhlHX0nxSgrTv3bCJe4ypr1x2dNgboxveFH6he6FWn3BKxpVPucy0XmYYK0SplMkIrVICBSJbnVCNMB8xNS7i7SXzkvxTiiBkNVFyYnFIiFAsfFldUiImc3at3Du9WHxHt1wM+6fZzLPidQAIWrseV0kHesMoxjaZLKNYLt3ofbl/VbMHOOOpaEolGpiPM85r26YImTzAaBGsF35OUQkwhBClAcWrrDxM4MKrWhOCwbB0MOD+UGJBuSumyuOoOyIVdKExG4gnUVCpimjeeYLvcO02GQdvcsVNDenO144eSpXRELStl3IhSLEOSBZuiAUdLThfAiDCxy4eyVP/bV1z2Q4D8X9vNE8pgiJW+VEzb3WFv9tqwnJgSx8HpRi4pIUKWm7giJI9jpJB2mXXzefpUSpCOky2ZRtbYZY7s52+czuZ5BC0FD3cRTZbpnrnuKQkNggFSUkxBz/gS1CXMfWudQc7461WRyfHiF1y7vmjNOxdckyxVGzYsfpipDrh1Or3bKdzuqMAbc+rRCmQzkJdPfKMj0IFAW11mWTBde5lMIN3iwMQKp0rINxygefM0nCNz1xljhafC4vb24jAMUUxZBYCk4JuUA7WyxCLhIJQU+ZeyGSgo5d38OCa940v4tmB96e96W2m81k7tXnRV5en7MpLwr7nYUwSIgQwRyc4LVHRRHSJ6BjhUWIt+gt07HOdMtNsAsbIR3LJHYOPQt1aJ6OZTn4gsZadKx5PqdtixCAT7+0gpLV2jB6R4vsTPMp3Pos/PM/yv1f+AcARNadySMhjeVISHj/HcwOfBEida9E6fGhNf3UJMnPxQUFt5/vL752iTMWWCQEowmZgFnns3kx0FLDR575EjeOZuhWn9PWgOT25DZZrvx8pzq3Kv/dguR1QxIgIeWp6a6QEzrmR/7ZJ/gbP/cF9LBYh9obhrqugJ4of8/w2vp9OChCVqIQ2azIBx62KEY+5/DGJ+0LIm8XXZoT4oqQ3jm4YB309l9YePtBsJfdaYwDTQjgkdSbR9PSvqm15u898/f48x/68+VZUaHgfgEJsc/XCk2Im5b+1eKMBfeKkPXCeXVvPwRScrZjYPhb41uFHuQVpmLVRhUJWab3WFcTQtEFWgqF+1kUdUjIxA9oa1a53uFn2EIn09mCQ0YZCZHsus3TIRpemF6eEwLQsXDu7VGgb5jZxaq1AUmbSZqTqDNIIZZPtm90PBLi7PwuKYlYgjQN2gmpSNDaDmFdVsABs/mMM+qmoYPZQVQLQ/wqPzdqkh0XpXkTK+a4uGnpAE0g6Z3zHciDaYGEhMlvGQkJi5DFAmkQTMW+eTgzRYgUJS55tdi6NbkFMqKrBJt6SrsRLR1IN7UbSFNEfkilH/iXDqF/gT3Xv1OpdwZxmp/9wF7X3WMbjYDOuExP1Qi6TxU6Vitq+cRvHNJV0pF/DhARbT/ks/hOUkiPdrhNKNyMQiSEbMaWFiAkN0Y3ePbgWVOE5JfYs5bTkXXrSyJBM5aWzx6htTRFwvzIf9c95aYJm83WPesLQ8eqEZyjHkO/0buk0k26l5TpWFAUPFmuaVhAPBJ1mhC7PtXwnsMipI6OtboICUwWgnvZJQ2txNAnhBCekrVTmRXywed2fFH3W59cTBABNlt9eq2YXB4SR0b3sqUlhNcT2G5tGyQOjFOWRUJcxFLQsfTLMNFX9ne51uS5JkVzK7aJi30unpN5Gd1IJ8aeFxAiKta9urkiwWDXGEHPJtf+OVaquI+TchFytld+hu6rRUJaKFWeE9K1wzidxbQkXpuOFUeCrl3/n1mlC2lt0Aw0LpPnfxF++o/D9U/y8NV/RluNkXb/cAi+O+ZpmqOULiGFYTK+N9vzYnypu15LUorJHg3M/fRsZGiTUgiS6c7ia30RsmjU00msMF1I5mgyOyvEOY1NU834YJ/DScqIji9CDuYHHE6L87myCMmnHlUbiGL+S2nwKUURcv0g52NXDviZT1zn9s1Cm9bZetAjKk1V1nCE19at1YqwCFmBQoR6kAfe5XOS/VufAqAle97F0gnNm3FQhJx+tBgCuX+l0GDYcOvi2pPbg3CFhju/rgjRGl7cL87d9dF1/q9n/y8+euujvP+59xdv4OhYMl7ck44tQuYoO6jwq8UZC+4VIevF078Dvv4/gff8twCc6Rgo77N7n/XzQh7ffmWGFK6MBSRkCR0rvJlXaUIooNVji5A6JCQde3esdtJb/HcbyzpKsFiE+MSyioSU6FjWBlJad6xx0UXtuK6dnT46TRX99N28eeu7+IGnfqD+AJMOAy1tEWIWlUs6Kos5gxi0EjuzQRr6ywokpHH0ElIrS8d60PyuZijgOkgI4ClfR/Mja+Nnu3CVInGWzcBqMJpCQnvL/+3BvNCEhDSh8HPLehWX0BSFyiAQY147mFokpHw9vSbEFjE3xjdARpxSgq4a0W3ES5G4qevWi2LzdMc6nJsiZN8WrG2ETyRcl810KxVpnvrkZxCOLVq2aLtrPt4t6FQVJATKFsLMht5dBiHpOIR0Xr7XS0UU+EQi/G4ApBPj+S8kn939LJnKTH2UX2YXU2RFs31inTJoJ8bZpxkjEL7zPAk27p284EtrrT2t4lg6VhT77nlHjSx1xg4szAwSotEIvUjH6gfd4SZzo4kS3BEdqxW1PHoUrhvewnaJKB3K93LYkXT0lHCa8ylr01ulY334BXMO79tsLVAyXXSTLmd6Tc5vjzjbN8nbhhYLtBcpJH/yrX+SH3r6h/iGi98Ag0veGQ4gigRtqwcIv6vumt/lSpMpzRebktweuiu4nxdqoQh50drznumcKWiyjUWELkRCAPp5BQkJXesa3VIyfr5fNLhkYM8LZU1Irs3gO/eeXhNik75EtECwFhICcGFgnpWV4vTmwNKxcpjsMfvFv+hNTpTWnFPXvTDaIYIOCdHaDGhdhsTtT/e9ziPSPe+qVYqj66boBl6IHwSEKTqtfqIUjo7VXSxC4kiSyBY5tkACGO/6NS2iTVebazIS3ZKO8KUApag+n2FMsolf1/oqpxXHCJ0s1YRkWfFeO0ER0u5dQNnnNVHlaxOu726N1icpQrqn4dJbAbi5/ywAnajIixyq0ZqOwNpMz7YeKIqQfF4MM7bhCpcT0bGCpgZQcsgKJ6eHNP4PXPlAYfns7XmLsQU+VuVelJGQrxZROtwrQtaLRhde+zsNEgIeCdmdFtDZq4OEBJBb0oE6fQOUE6x16Vj5km6+t9CsWfDnI6bC8I5XdVXDSepVK9aSC0uIhGQVJCRZLEJaVhOyNykSwo7bYG3BNklzIt3jTdvv9cXkQjg6VoiEaFnWCATRb8UgBHNhxekrOnjtsbFSlVK8rEjI4fzQVgWuW1K+PtN86pGQRnMDothTlQ5mB74LH4rdy93joECyRUQzlp53Gtqw3jicFnSsUhFSFFtaa26Pb4OwRYge0WnGS6die/5xYDcdJvE6abOfmKRvSxfnyy3GCsXR/MhPiAfYDH05liGEjod7eDV4rXnPsAiZhG5E86FHQqSMabpzWvlOpSIKGAXJ4AISgsnanXA9EpJE3ceOMMeSq7Llp0uEhBWnT6ZhEWKev34rZq7m/j2PpWOBP0/t3ByztLqQUWqF6crQaVqVwjkU6yZ2WrqA5UhINiuedRtCiNr7w/28qogq3cs11MIwcXTD5nYDJCTNFR/90j4Ab3mgxtbURi/pIaUgaY6J7D2wxSISAvDo1qN824PfZnRpFSSEpOs1IZnKvB28tImpKUIUH2m2iKUput/74G8DzFDCNLjeZFOuiNxQscKBp249C+/LAAkBGOQ5BEPgSq8NhOkCwbl+8R3P9JskwbrV8fdjy6NJbvZIKEyHAolYBwkBuLhp9rSrexOPpiyEo2OhYXiTqVZ+rVQa41jokBC7d3WDwnQ0y0lkQmwbF6UiZLZv7XnbdJJGvaD66Jr//S15hiPZN/89rhQheVpQcmroWGBm8Cgig3BahyxXhAjdpKPMsR3RLhUh146KImSZbsV/N7uudVRGp2kduXRRPCit/PXL8mC2xo4tYpM2jcamL5bkCiTEIYI5xfO20iEr1E20t+D+dwBwMx1CNqflBhKL4rlu7nzR/8ls41JRhMCiQ5a3m787dyyAU92Gb3CEs0I+s1cUIbvTXT5+6+Pl71alYgE0AiQktOB2X2M8Q4kR8T061td+uCLERS/pcV/vviWvfgUjLEJWCc7XFaZTICGZysoDqqp/v9QdCzMjZEVCUIK1Kx3vRTqWQ0KsQ5a36C1rQgCapGbDnA1xjeje1HYN2pum6+uEYyvQBRodY0+J9on7JSJT6NXEhkUB3NT0VUhId2SSWSUT49ZFzSRxykl/c0XXqprILhtYOM/noHIj7Lbd1LAIcbqEMPldSsfKyt0eoOR3fzBJfREynA99l8e9n9JmCvvN8U2QEdsZdPWIbiNagYRYypAoPsdpQnI7gGzPJrSbwXT30IJ5b7pX0hwNQjnSsiLEJWoHgZ98aNErnetSkJzNDr3FZTvpIJo1HWcWkRD3v7GIy6YO2dQWVsX12G7eh6TBTWWut9KaLb3nOdCOSmKQEJhYKpkGbtpBXuG0dP99jgsnys/N/eJsgMdZIUyXurFwzzpNAEDTDSqE5UgIFGiIUvCxn4T3/0k6u8/DwVXGn/hH8P4/CR/9Pwph+oo1J3ze9fVPwAf+HOw9X6JjuXB0rEdu/iz8yt8AlfPMS4c+wXjzg8s3+FLxaO/7TV1DO6tGswfBvao6Z2oR43hgnt00V2gNzzQFcSR5+vTTPLb9BAiJAr50VBTNej72SMilQAvmC6OSJsR2he29aWaF5MW6HL42EKa34haDVsPraUIqFkDD2kGHjmouQote915wfBHikJDLm8WetlQX4ix6bUx7p+G7/yZ0ti0ScgNhm1geCQnQu/E8QwjhG2hhEbw320MpvZyKBXB0HSkFuYg4EBsciA2LhNwqv25023J6qaVjgTlfLrmfiHIRgmrRsbboB6rthekAN4OCZyUdKw2KkCyzTnvNkjA9FPanafFekwOL4nROIUSL3K1ZeT0S0sxS2l/4t5CnpGrmm/9rIyHtbbj0VjSaW0LBfEjDosP9VuKNTpo7nzevlxGz9gYMLhasgUoRMvBIyMndsVzhIYTwaEho0/u53c+V/u4DVz5gfqgMKhynY378Uz/OL7/4y+XcrQYN2Z0M0SK7h4T8Zogz7XIH/fGtx5eKsF/R6AQduVU0q15wvBXLv2qEG3ntJhBW49VIx6YDLJZbZVY/Y3URItjDaULGpkvkYMsQCbE/JxJiMnJm5FoTi5jG2C5Y7W3SXHvubpWzXoqkw4az8LOOUpeSASW+RBBu0ZqTWDrW8s1zMDPJ7Kh9n3+/hi9CisLjTpEQV0QUwt56JKSp8ahQWIS4TSxEQspzQha7x2GRMqh440uMJkRTTGMPKTEH06E5ZhGxnRskJNSELBYh1nElQPtC/cowHbIfm01lMys2j9AsYn+2X9xfecrmR3/S/CyEgfXrwtOxAu52CQmxtIjAfSykY7WTblG82kLQH78tIN35cUVIN+mWnU2yGdtalOD5C23DK7+ubBdR6SVIiE0e7PdWWnOE+dxwUCGsiYTY+6uRWTc19/7pxBhN2M+s0j26QXLWYO6pKSuLkNmhocD96z8FH/wxuPIhOpN9mI8YHbwAVz4EH/pbjG1ytZKOFTtNgmLzQ/8LfO5n4SN/vxhEFyRlp3tNNtQ+v+3gH6E//g/h6q/x65aK1YwlT9+3XFdXLUIEsFEjTK+LaLPQqEX902X6j9O9dAfMRAutYSfS3IqNk9abzr6JBwcP+uTxuVFBqzqYHzK0CHVp4GmVjqUUHNkk0lq3Oocsj1CHlMJGt1QACiE42zfX8/7txWvRa8ZI3UKVaxB/zlzi6faHVe5YWusACen7InMpJat3Fl/WJ21m7/yjRuu59SBKa87n19GijMRUkRCgVrO2P91H6RVULDBIiBDsiW20kBzIDaMLrNKxSjNC6pGQbqONFgKFMPvtZNffHypL6Fg61l7eYtAYeAfFG+NANL5i/wuRkHY2o9Mws0LyQBPiCkCANCvW9fzI6lm6Z9CqgbLro87r3bFaR9dpXPsYjHfJdU6nYdaFlSiEK0LipnmuOtscnX7MOHPOR8jcPJ/hDJbmDetyGbfNWi0j2LTPQkWc7pCQ8Tz3NLt1QmvtG3ThenJ5y9wzbmDh4fyQl0am2HeU6F+//utmb3L7jG0w/71n/h7vf+79/M2P/02ycA2pNhkzxTA1TZtIiNr5Z1+pca8IOUFUkZDXbL/m1TmQdWlW518Pr/898PY/AoN6hyf/lkEiUuuQ5TvtR0XHxsXcWvSKaGVXNSxCFjQh9uHabG6CkIyENjqTdFIWfNcgIVIIEp2imJIrbZI51/lub5XsOFehC96iF/xwv1PJ8iKv14rNCBNh5zKky4uQraktQrpFMlBMEl90x5IC312sC7eIjdKREfk365GQWWaE6S2E77I4GPxgflA7qVtK4T87tAx2wvTwHHYbESELwWlCoCgsQ0rMiy7ZkUUR0k0KJGRBmK7MpteUhWVldeDfvv3AzSBRCmHpvemeEainU9j/EhsHtuv72u8tF/Rh1OmALBLSjJoFHUtQdKfmQ9yd2m70l85kKJkKUIite9XPzKZsI/1nAVzuPgrAbtpExy1ypdlSe74Y7IYcfKUZuyJEFXNC+q249Iyv5Stvi4QkdUVIy2tC/KyEGmF6iJQ19Mx3KEvNhOD9zZd7Dv7F/wue/0Xz31sP0OmehUaXUbdorIwt9WiVMN0lqQ/nXyQa2gT98KUFISkYJOS0um2AV6Xh8CU+/IKhSbw+tEividK104q+FsRJd5HfXRPt0w9yutdkq9Pg1NmLtUjIoBWzbwcWfqapyIiJpOANZ9/ARnODbWkS6OcnRTJ7deoSXVk246gWIZPdwgr93FPm86yjVIGEBGhe4I7ljvWH3/0w733yHN/1hkVmQM/ZRlcSO2/Ra+lYHXtPrNKEZCrzk9pbcZMnzptnaak4/cxraL/+95q1b+MyU+d6t/kASsF5dR2E1YTEi0jIJC0XSAtIiNZI3VuOMFg61q4068yBtEhI2NyAldPS/a+tvki5QanjncLeO7UIPrCTmcLQidPdrBAoa6Cq4ZCQGEjSqZl54RBVe73D5sV0XryXHN82n949A6rpkZAsq2mKaU0rnZk9yb5fr2X2lpUohCtC2lv+ubp1zo5OSCdEc/MM+DVnPibZfc5gR0m7KG63rEvZ7nOltw+pxXciTp+myqdEYRPG2fReP5gyz1QJBfn+p74fMCY9v3j1F0t0rGcPnvUIySyfMQkp7rMyErI/mX9VTkuHe0XIiaKKhLzS80GWRpg4raJZSQnv+CPwht9z7Fsej4TYTTZPF2lH6dhY9AqxsggpDX0KkqA0T/3GE3b1dlGmAAmT+9LEdPNZkRQ0mKPEFKU1nahhjhOgvVUSpK5GQtqGhgCgMi7p5c5Y7nO7jcKmdykSonK2UpMcTLsFLcIlNaF1qENCGoHuoi7CpGeUjgJhbz0S0giLEIsSzPKZP+/VBLhuhok7j62gqBBClNAQR8eCotMfJm/XR3azlRGbOUQ6Y5CkBR2rMsjSFSGtYDBWeKyH80MOLGq2NZ/5jm036fpO4P5sn4OXfh0OrhCrjA6Ywvwd/zFLo6oDEtKjgSVhejg1fTa0dCxhDBrCDlY4sLBCpQuRkFLUICEPDx4DQCFQ3dPkWrOp9vwG6oTAjss9tseWxh3fnew141K3+U7oWHHq6FiG438wO/D0zTqL3m4oTC/RsZZoQgB+/i/ALcudfvRb4Hv+Lt2HvwU2LjG+/FZPp3DOZOsgIe+Yf7CwBR/eWErH2rQc9lxpDneucWXX3I9veWB1h9E5PQGgldWDLD+uUmxcYquTcLrXQHZPl5s1boBgO+FQuCJEk4qYBzfu94nmQ7G5N5+bFQnn1fm++UGIMm24atEbitLPfh3gpqZnDNMhucrLk9iT7oIe5033b/Gfvvcxj4iE0W1Gx9Cx3DyO4+lY4X3bjJo8ecHsf1+4OSyhyD6EIHn7H0F0z4AI7H8tErKh9tG6oJaZ46hBQpJ6JCTXmkh3S/d5KY6uI4DDhskdSnSs8Hw4JCRKlpplDOy6lxOZ5se4QEKYF+91c27WPXdv7AZOXEsRGwphelsLRDah07SDT3U9EjJLi2e9rw5IcwXd0+R5hLK0sXk+Kn3PSTaBPKWFmf1ENjNFSdO8ZmXyHxYhNm5su6ae5oK9j/1+dPPTdkivoUUW1/4B87/7XyppLAZBEXK0zvR2d1hL8ovL2+bZUBqu7o357J4Z69CKWrzrvnfx6KZpKH3ghX+Dts+Xbm/z45/6cV9oA4xCJkaFiXIwTsntoMJI3kNCvuYjiRJ/kWMZ88jGI6/OgYSakGME5+tGmIisREJgkZI1H5lkTEbe6ve4zwgX85CK9cDgAZt0CaMLWUBCFoXpUphJzFpMUQq6oftRpQhZ5Q5C0rWaECDPuKSipaJ0F4N2fLww/fBFhHWoSgcP+F9XXaOgoGaF6EFdhPSp0sDC6pyQdAIqpwW+eK2ba9OvID6rZphUdTVhtzsKipACCQmLELPZChExSM1CuykmSzUhM1V2rqke6/XRdXLb3dxEwPC6ff+iK7T3wi9w8KmfAq0YECF+639nCvNVXepmBZVobXganRlWaLnZ6KLAmB+Z/5Z28GNYVARFiB+2mI1JP/GPGNvNs1d1lsumbCCQ9rNOt09zOqBVpq0zKKXZ1Hv+GrQSaQcaOrqU+dxZXDy/g1ZSOs93UoTI2aHh+Nv335kW9rV1wwrD7mLC3N8bC0hIuI456tobfz+850cgbhRJYD6BwQVSNOmampCWnvD69KMFHWi8w2xmktkqHWvLFiFZrrhxrdBXvPmYIiR8HtGq1hlraWwEKEX3TK0b06CVsC83SdF8oaFRIuEt59/kX/eQRf5eSA9N0QBctWYMZ6JO+Rx5i96xSRCDQYWcehRkbGip9n2G6bBCx+qspcfxX6kZI3TLD9D0v0+6aK1999t1+lchIWER0ogavghJc80Xb9W7B/3qc7uk1snJFzi2CMnQSF2eAB7qO5yLmqdj2ecpt3oZpUCqJTNClPKC/2nLFiFyw1ASs1l5rXbNme7Z5fRfqzHLiaww3bpjaZDBHnJjZhp1Tpy+HxSm1SZBGI6O1UHA3LgWCjtzyCMhwbWZzs2zHOuUnhqaZlr3NPNMAI6qnJVQtFk2gzylo4WZ/YSGfE7HFiFLDQagEO4H7qC3kibIiBh46MDoP7wj33Uj+m4iIW4VBVTokBXOxwn2sTtxyKoOPnVxOaAmXtmbeGesxxqbRL/6d3jPZA7DW3zp+m/wrDXi+ZXZzZKDFsDY7m+Z0vzyp58raUz2JylKmvs+iaLyOvQVHveKkBPG+Y4ZNvXwxsP1U7e/HJG0iy7bMfM/1o0wEandBMKbu+rln04sHUuuTGhiGdOwtJqw4x0WIQ8OHjQ/OIestIKElNyxzGIrhUNCjCakG97eFTrWSiSkYTQhAkDnXNRyqT2vi0ErMcL0FRa92e1nfTMo21gsQkIkZBYgIasiXGxKAwurE9PttWpq4YuQuonui0jIol7FIyFVyk3AwZW6g5OUODF4WFDdtHMttlpbRPacDMTYCz+rgyynqszXhiKJB7hydAUs4rGlZSmh2mxuQp6y/8IvGrRERmxefBs8/E0L338hqtc9eM5kcJ9PQzrWzLpjCUsvC5PQIIkbJPb8Tw4Y/fJf5+jqh8z3WkBCpkgE2zYBemLridL9exSb6xnSsYQQdBoxEtvBtG5307i4X/qtuPSM34k7FtmMjST3Fr2jdBTMSqiZExIkZw099/fGAhISxUUhLSN4938Jb/vDPiFz52aSTmDjsknCbFJx3JyQN81/g1hnxX2lNbFNzKp0rE29b76m0hzcNvfS/dsdzg5Wn6MSGqOVcTVbGwm5v/i5e6Z2Qny/FbMvN/liQ5MKgYhi3nj2jf51D7ascF1lnnd+NTP35aVqYuKeH5WZc2gLd6KGWSPaW8aq3E1Nnx0WRbQwCd069sgues0YQYJW5aK/m3SZporMDtLrN4+ZVUW5E9+MmrzmfN/TP5+pEad/8sUD/od/+Wmu7GSkmfL3/XzwgBlgKSC2a4xroDUi6RG7ZUjI4fwQjUZpTaR79cL08W1/Dudto/M4EJsFGhg6ZPkZIUucG4F+swlIMiRjNHp0m1E2Is9zXjv/vH/dS1NTODhx+sHcJO/tJCrokDXh6FgdBKRjQ8eySIi7JuH5n8zMs+mmv88zDZ3TBmXU5nmZCEqC8nE2hnxeICEA2ZR2wxa8d4iE3JzcgqTLKS35uuEHefP81wokxA4xbDX6BgVz99VmsQeH4vRwrbqjImRe3+Q802v69fCF3QOePXgWlOKJFz4MH/sJvv7ZD9GY7MF0nw/IOTM0f/+6oaCGa/JYCua54urehJ//+LP8iX/0UX75i+becfa8AFutzVdHo3zC+Oo50q+weN/D7+OBwQN8z2Pf8+oeyJt/EM4+BY9968vydsfSsUJaUg0SMhEYi97Q3WfF54Sd2NC56FL/kpkJIISZFZJNywhDmLzYxFVKQUPPUExQStMNG26d7dKQvZXuWEmHJoLvyps8riLerZLFjnglBu3kWDpWumO4p5mIkYOCFlGX6LsiZFXHCsqFxNH8qOgk5/NSMTRzhQB4OlYdb7TahXeFw7xmWGH12MIOkiCmaRfQOiTEzdc51T7rC7MBI594hYMstdZMdZmvDZDIxC/SV4dXvUX1JqLU2dpqbcF8yD45h2jYuJ+NcANaFdUipDIgtGWTkklIx5oPGdvnoB23y0loDRJCPudIaMZOE7KAhJjr+N2bT/HQxkO87+H3lTjde9Jsxptqn36QBHWbkbdEndjO8Tgqnt9e6+TuWADb0aREr3HUASNMr94bIR1rZhKwqFHf7X3TD8KZ18C3/0/wmu8o/ZNbN0bZCAYXzXnPnZZgeSKcRIJ3pB8EIA029rbVS7QDekojlpwT5nlJc0VmxbZvWeGK5aJ07XTOpr4DOtapR8w6fumtcOmttOKWH7rmkZB2wq8nb+VXOsZhqRm1eXyrmFH1kE1y0YrnDsx6czU3f3upUTn+qlbJFe798wYdbG8ZTYhFQg7mB8U93jA6lztGQhCgC0plLExDKux8D5rmfM2yWakREUYVCek0Yh44Zb5PnTj9Zz9lkVHdYJoqf99Poh5D2WMuINJWd2abWmbejnmeqkiI+957M5MQO3esWiQkmEORd8312ZcbRSEwqitC6kXpAN1mgtQNS8fSzKe7ZNkUcXiFR1OjOfxM8iS3xCn2xnO2W6ZJMVMzFPOVehCw95o0dCzSCd1mjCQxtE67fvnmhYbp3BYhyjwz8yyH7mnG8wyNLSgFpWng02wKam5mOvkiZEYzcZqQdYqQTf+rm+Ob0NnmjGiAyvh947/P6278U3Pv3ngGgIZFTvy9s3HZNDqgVISEa9WdOGSV6N7BORZCsNU1e9NzB8+ZfS2f8URm7u1Oa5N3yAHImF9M4KfO3c+OPb/f98T3+ff55I09ntvLSHNFR4+ZZ4q/8DOf4Z9/9EX27KBCAZxuH79OfSXFcmLgvVgZbzn/Ft5y/i2v9mHA63+3+b+XKVY5VwEri5A8HZFaJKR9jBtMJ+lwMD8ofUaIhGw0N9hsbbIrJHvCTukNEYY6JAToygwtZiit6YZe2q1NpmlxvKvpWGaj+b158BnH0LH6rZhUJIaSsqQIyW0RclOepdUsNuJaYXqNA1VdhElPCQkBowuxHTU3TK+FWHDHKr3fUiRktUUvlLm0YAqkGXu17lg7Nvnbap33v+syQseb/r/H2ZiNaINMZd7mt5rs9Bo9ppOpQUKEABkbS9Rg3sFGcwPmI/aEptnoQdyo/e61Ub3ulQW+nXTZp1KEzI68S9xCERIK0x2dTGUM0Rxh5tJ0q59p76f3bn0d733LDwHw+eDZu6k2OIdJ7ttR8Yx0GjFy3CDXxl1M0fKi9HYSkUTy5EgIcCqaGnqNhUAcEiJoLNAIQ668d8daNkvodd9r/q8mQiREDe4zxZ7KQKuVSIjYfZb78yso4Plzv43Te+9HAb1sDxqLyOgZaRKq4Syjp/aB1fNBqscHgFbmXlyXjiUEfPN/6/9TYgrDSTbx62S/GXMrPsMnWxfJ5T6Pt54gDmbnnOqcpa8FR7YIef2Z13NkO/wXm5XjD5/1+bAo3K11OO0trwkBi4S4+zcpJ+PrFiEAOm8CxYwQIYQXpQNstsw5VCgyldUyDUqaECvGf+q+Ac/dHvHp64dorb2WbjjL+KUvmPVGkDDLcv/343nGDXmOvj5C2vMUPgedRszhJGPkrFcrmrV9a33tkZAlgwp99C/AzRmHjo4F9UVIdzkSYpoLTTJhkJCRymDvBcgz2krwG40385NtQzPdHc2LYYBao8SQdrJ67SsjIRM6iTDNDK2Z2LXIzW1SWqO1Q6Bd4a6he4bR1TmKjhk8KTRM9v1neE2IFoVrWTYlScx5XlqE5FmhdwzW4luTWxA32Xzsfey/9AE29D6PfOmn4P1XfOOn1T0D6X6x5kWxKUT2ni8VId1GjBRmPRvO1kdCxvPlTAvToJvy0vhZSEDmKY9p+5rv+3u8Z/Qi/+5XfpQJ8NP22Xjt6dfy7svv5sef+XGOphn/6y9/hj9Ph00x5+svNfi1UcLBJOXv/sJzbHYSlBtU+FU0LR3uISH3ohLhAnynSMgsgOqPS2jqrFhdsioQ9Bt908ERkh1hHaeWumMVP280ckvHgo7jlDf7EDdKdKyVRUjcKDzEXaxBx5qJ5mqL3j1jBXgjOl/qlLjkfJ4r3/mb5evRsZpR0wuvDRJST5eb2eShoQtheituLVynBSTEaUJqCqQFTUjFpndgURnneOYSU41mb3bL/s19YDthXTWuLYIn+cSLGpvVIsQer3OWiqMGvSoSEndhPuZAaA5sglNHRauNBU3IZvk/7edPhS7RscbCaqOSCh0roB/6gk/nHAptChelajUh5ssFHfzg/nkpK675ZjAUrNuMPJcbnTMBjjDf33X7XDKVyIRIru6Qmi9cnLetaGw1Iea/HSIidYNWo3xvJJH0BW1Dz00XeA3b2mq4dUOhmPbPF5Pp83Q1Jeiz70cIyEXM585+G0RmWrw7X1VXo1OOjpVrmnrGZpTy5IXjedYLRciSQYXrRtWNSUpBo3FILs3xPTp4ben1otnjQR2Bynn+4HmDEFrDhkvtig11FQlxybJzUOxse3csgMM0oGM1yuv3OnQsx9HXquHnOFVnhABstALjkiVDc6t0LMDrQg4nGS/uF3/385+9ZRJjDBIyywokZDzPuR5dYC5AOge+AMV398V45kTzBR1La83+bB+tzdeRukevjo7l1qK4RaNnCsEJbWTDPs9uVsh8VKwhK5AQQ7NskllNyEhoUIZm+Ink7fz99veT2XlKt4dzr19VSpOL0coZIe67mSLERF+mCFtoTLMpuSqKuFxp/2+Pdl2BolCtLcZpTq5t08BaCbuY5lNPx/LzW7IZjdjcB5M0NwL3atiiD/BFiNLKI+vd/mP8ld6f4KXookFbX/ywf3nTntNZ2My04vSd3S/wub3PobVGSuEL5rA4Pi6WaUKgWG9vTM3QxPtlywzPbG9Ba4Mnt5/0FH8AieQHnvoBWlGLvVHKjcMpmZ4yjzpc2Gjz1gsJ/8v3vp6Ldh7PvhWmR1J+VTljwb0i5F5UIpKRT2prkZBGt7AKDXUHecrEbQzHaEKAWitWh4T0Gj2kkIbLKqSZFZKOV7hjFT/3oszTsXqZ7WLY7u1sXU0ILHYvjytC2gkpRpiu6zQhSiEPrwBwXZ4vLVJuHofW+M2ymEq++jiFEMWskPkRhB3+4PrMMpOkt6JG6buFyXgkooWOZjjlvHgvJ0xfbsMKsGmPpUrHUoyZ2nPUi08xFuYzO2pY6wg0zaZ+PkyrcnxVAd5m0jOUj6D7uDnaA4zV82FkZ4msu1Cv0IQAtBLjkDUFr/dQs0NvVd2JO+WCOUBCfLGhcm64eTg6LyeyKi8c3oL7PLx/X5gXx9hLi42+a/39ldagciZoDrW59s4L3yUTa1GxoFSEbYmxH1YIhflNnTA9/MyGdkjImp8ZREms3dkyxR5APl/ejc/m8Pl/hRSCT8ZPcyR60D2L0rClTRFSoqjMR7QpNxLeeUERr5jX4yKWcVHYOyTkBMWWi7q5FFnr0/7np0+/rvwHjR4P6QjQPH/wrEEILSJ8KUhyzGuD+2x6WDgzOSSktUmCoG3/voyEGDG5K2LvBAlBN1G2CimmpRcd5612cVzLdCEhEuKQkrBI/PS1okH2bz5d2BULnTDLlO/oT+Y5N+R5ZkIjLaLWjMMixByzQ0Lc9ch1zlzN/bR0oc1U8ZVISP88g45FwIUgcroPV4SU7HlX0LEaBglRtggxhbhg0jzDBxvfQhTcpzujmUd9lcYgIccUIZPUuGN1bHHQj+ZIi1cobQqImZr5/xZWfP5oz/zuUPS5Nc6ZzDOgjUYyhpImZJqODXKDoHHW2EGjFZ28EM+P6lCIcFCh1TbuTfc8dbcttzmQm/z13h9ndiFgqmxephW4QfrYepAMzY8cfYI/9Yt/ik/vmmfLFQ1Hd4CETObFaxeMOZoxGs1uahqRT+TSfz6YffybLn+Tf/17H3gv9w/u52NXD9m1dWmnlfP0w5fM/JrZIec3WvzP3/s6nr5oB9ZaJOSraVo63CtC7kVNLHMoAgxlwOkZ3BRSsPa8Lgs5Hgmp+wzXMXeJ8VZrq5iaXnXHCjf24OdONAOhjEWv28DsYuU6FcfN3jDvWeleHqMJ6bdi5qKB1qDmNRvn4YvozBRpN6LzJXvbkKbkkv35mkgIVAYWLnEvm9lz0axQfUJaUi/pLdgB19Kxaix6ocylBdhq1xchafSS1w9sJOcZSXNMrXxYi4TMrH0jQKtyXaqogS8ujq75v9ncfd78TkS+IFifjrVaE+JsekOL3tnsyHw7IU3SIgNdQKAJacdtIhGByrhhXVFQqlyEhAVtkLSHyc6zY/N6IaA5LQaSdZsxUjc9nWYiNLuqjIS4JG+Vm10pWhveTWyDkZ0T4uhYRbe5em9AIfhscHIkJDw346TtHWPI58vpWC/8EkwPEQI+2HiH0TT1rKOYpVqVOpfDm8QV4e6bT+esGwXCpdjS4lgq56qoCqF3p7vsiV8BoJE/wOWNCm2n0eVBbZ6zcTriI9d/A9Cc0pJ21WY8PK6954tBsK4IcQYWuQJtn2OPhHSZ5TMU9TTJunD6Cqlb3hHVXc/rh+Y+TCLBdqc4rtkSk4+5WkRCzvZbnOqZJN+J05+9NeQLN00W93X3DaxltWZ3YmfzzDKuR+fNeA2AbF56FtzAwqomBEyTZG+6R67MjBCBKA3l9OGQkMF9vPXBbbrNiG947DTSDUh1s0LWmBECZn6J0A1SYobNDUatPmxcYhL1kLrJ6V7DP2u7o3lQhGhyMVwbCWk7hFrMEVbH42x6CyRKImwaeX/TnNMDscGXdseM5zmCBlpEdrK7aZBorZnMDgFNSwtaD7/Hf3bTDvOFcmHqIyxCbEPEoSAATWF+NxMtpt/8P8DT32O0gk/++/4+KRUhmw+wi2aXDFRqROMU+saTuGNJsUijHrQTMrHLTJk94jVT+xw5hy7gm+//Zs52znKxd5HvfcLQUT93/QipjWrmO1+/zWDDUirt3t5vJfzodz3Ne16zjRZT+q2YjZfJpOjLFfeKkHuxEN7xZ5lF4nnbfbv+ieJ383HRO3Rc+BXhNCPh0CeXrLoixNGx9oRCpdMCCZGRFyEDRuBqE6M4NolrrjTd1C42FradzIsO/qrZG0ANEnKMRW/LCNMB8rSmeNt/wVOtrssKHStI2FyyP1si/q79bHu+hvNh2eK0hISYc9eqIAelIqQG7anOCdFaF6L5Y+hYp+xi6IrLODK2sXP5Ilqb5GEQ38fI6hSa+VGtLek0HeL4G80K7aN6zJuOcjIf+YGaW7fscKhG1zG//KDGY6N63at0rLgFQppN1lIpxk575OhYUNxPwb0hhKAXd0ArrosCCSkVVlk9+hfeF9dGmpHsGvrBMCxCDB3LCYvHaHZycxxuk3V0l7WREBn5c7DBgaFjqRAJEUQiqS3y+y8HEhIkgaN8wsQVhavoWJ99v3l9ss3n4idMod87h9LaW/GWkNHhTeLK8T85mLNu+EJJOTrWmpqQmqhawv7EZ37CDtUTbMy/mVPdiq6m0eMhZb+Lyvn4rY8BcEnLRSey8N7e+ULxc6AJARhoioGF86IIudNBl73KAE0oztU1S5+6sNmmHej97oSOBfCUpWQ5cfq/fsagIEkk+KF3PYS01KHdsUkGx2nOdXmembvc+cxb9EIxsLDqjgWmSXIwO/B6EKB+TojX2pznodNd/sEfegf/5be9ptB9OE1IaVr6iiKkYeatIAS3z72J4Xv+a2h0LDWqZYZd2mLs9nBOIhP6jT5Ka5QYrWQBaK0LTYh2RcisQEKsTa9rXkiKc3VaDhHC2A9/aWfMZJ6b9cENVbRUqkxl5K75gSC5/HaEtR+PJoWIv3ZGRw0S4pwWASK16X/ud5rwrv8U/uDPwuu+t74I2X6I227tzeZ+r3L36p0I01flF71mTBpdNdRVpXh8bOGNoAjZaG7w17/5r/MX3/0X/Z6+N06RtAwKK2cF3drR9jCNyt//rtM8cqZHvxXfQ0LuxVd/uA1lqUXi+afN/w5vFt2b1E5LhxO7Y1WLEIOECHJsl99z4ysbXpDQxMK8RmlNd2apA1aI7Tr4x8HRwGKX9hjf7UHbCNMBVDorDT8CYPc5lIZcRNyWp0vHEKIdzoXKISHH0bGgQEIO54cmUXWbqO2WaK2Z2U27WZknE9KSFrQIhJoQc+5mWTEVdqHbE7pjCdi2SMjR/MgLy5txxDy6itKaRzcfZZbCSJhEJEmPagdZToNiqtrNrR7zVjegMRxdg73n2RzaTmOQdJ2YjrUECfHC9GzunahKxbi7n4LNA6BnqY9hEVJGQsIipEjOpBQ+mciVZk9smcQ+SGQMEtLwnP4JmtuZOY4qErJ2EQJeU9TLD61g1fza0FIatJK4tsh3G3tLzE3P4G7pWNmYsb3HWnlWb0s5vAlXfw2Az298A1pIU0R3z6B0oaEJp2MzvEFsXbs0kmYiGVjEZJ3oJT0zeA2TZN0VHSsQgH9+7/P8u6v/jkgKOunrSdR5n2z6aPY4jzQGFFqhrKvcRS0XZ7LE7WJGTohq9y1ty66bxiErs3Qse/8GM0JgtT2yi64foFmgZ+5ef+nA3IcXN9slJGIZElJ1x3Lx1H1mfXtxf8Lt4Yyf+6wpyt/x8CkeO9ujYfel/YnZGybznKHocSSb5lRk89KzUCAhZWE6WCRktmdmhFjtw8IQwDwt6Fa2uPPWvN0qHcs+u43eyqaXs8w1xzAxM3MwdtKSFtvdBqd75nvujsx52mxuejpWZ9lARQzCpFCGSmo7Nu0SEkIJCXF6EIDmbIckkhzIDa7sjRnNM4RuGiQEDWPzrE3yiaeYtoREbFyiaZ8RMS3Wr1oUwk0UlxFHusU8U74IaUZNsrSYGeavhUVL3X1SymsGl7jtlo18VuQgjo51B0iIQ8vq8ot+K2Euzb63HbU47VLvoAhxEa6de+M5QreIpDDFodv/KsOI92f7vsF2TxNyL77q49gi5NzTxc8ODZmPzEIDayEhq4TpTtDsNCEAO+mwSMiqmyn4hEZI836mCLEbpk0cp34A4Bq3fZWOtQ4SIszCnysNeWXz3H8BpTW35FmUiErdqPB4CiTEHusqK2EbJU2IEAUly3Z15mqOtpScZoWGFGpC6pGQMh0rFKg3V8wJaSdRQQNAeeF4I1Gk8hpam3kXo3leKkLqBllOg8S9WSk6qse80Q8Gvh1dgysfKma+BNdwsO5wT1npZFeRkKgFMmICXlhaPAeBxsbdTxWUrGcL152AjlXSuSwpQqB8b+zLLTuBuegKuiFjwhYhYwE3U1uENCtFyDrOWC5sB7KnDguLXm2QkLoZIf67Ns137crMXI+65/i4jw6uxSgdMbY0yU6+pGP5uZ/1tLwvbn8jQAkJaesJTT2tICE3iKVgItrsyU2TzIQORseEKUKsHgSO1ZOtihAJ+d8+9b8B0Ixa9OffiBSw1akUIY0uEsH9Spri0zZDLutosXkjA9G8Nc2g0S0SHbtumlkhuVlfAk3I3SAhC0WIQ0I2WiVNxjI03iXBkYhK7mBOnA7wv/3S897d6Fu/7jxSCs50bcNmZtaW8Tw3iIJz5ctn9cJ0R8eqFMH70/3VSMjwRiGWcsWdC0fHmu4b1ydXjKxAQcC6N1lkYpxNGNlrolWCQLLVbbDdNffF7sicJ1OEaHK5GgkZB+YyrgjpMPVFSG7pWIVFb2zPi0SMbtOIZImOJWmgZVSaEzLNpn62T7u1DVFC0655+bg4X7UFgH2PSTTgB/9/v84f/4mPcN02Qs+0zzC0iNWgnSzMQnFrXAkJiRvstAtbe1eEOKR45bySSrg9su789lsx88gMPX1YBnuwm9q+JPZGc6RuEkthbMlrkBAoXNqAr6pp6XCvCLkXNeEe1lAMWYrN+4tE1xUh6YRZqAlZU5heNyekSscC2M1GRQJX9972d9ohIUoFdCyLhLhu1nGidDghHcssXLUOWYfXbBFyhkZcDMGC1UhIYw0xrEMDXBFX7ZZM06nXBTQrC1TIH61OS4fFOSHTQKC+qAkpumLdZlyiernFXcU30cI4uTyx/QTjWcZI9JBCIKYHtYMsp4F1c5VOVj3mrY37C+OEw2vwpQ8SIRgkPd8Rk8g7mygbXvsK37bthOlCewqYv6NlUIx7Olb5merhOu42dF6mFZU0IWV0sVOZFWLoWEER0owRukmiTYI+lhE3LBLiqHMumVgniSw+2CAh7WwfQYzQsaF6aG1F6fX3rOtSn27ZgutOPtN9dFxBQhrmPdp5BrPh4h9YFITzT5NZlMxoQs75RHhT7ZXXhNEtklgyapziMNo0gnqXIK4RT2w/AVrxpLIJ6cuAhLw0eokv7BvK1Lc/8NuJ6PLUfYPSOgJ4xPYhHVmNh/mOBgmpOQ53X4Z6ENeJdXQsh4SUNCGdEpX2jpAQ3cQZH3WTLkfT1Cec9222ywNzjxGmhygIwIOnuv5a/vznzDU722/yuovmuT0/MOdnNJ8abYItLvatbW2cp6WiJhSma60X7r+92R5KG02IeX1lbynZ895X/jdXhGhtdCEOCVkhSgczyV1osxZMsrFv8KjcnIvtCh0LbBGinEXviiIkC4oQh3jraSBMNwML3fnXtgg500whn9OIDRJydXfCeGaGmSIiUjSpdccy9ry2COmZwsw1x+b5lNPWHtuhOKWwRciO6pLmmhf3J3zy5pfMMXTOeDerqkkKFPNfZpUG4U7b7gXZzO+h/btBQmrOr4wmZNJ8/4edAKm9uWD5Xo298RxBs0BCXEMjLdAkKObVAPfoWPfiqz+ORUKEgHPWGtJOIzUdYBty/SIkVSlpnpKqtBjI1SwL0wH280mtVakPu8C4IkSo3Avr3IM+dZ2KtehYdyZM77ViUmEha6UXp6aPb6O04ctWN6qyJsTSnpwmZA0kxBVt42xsXEKaZSRkNrmNS0Za7VOlvw21EQuTuikKpOpxwaJFb7cR+anFnUZUSvQd13YuTTdIa3h863HG85yR6JiZdbND0HqhQJ0FjlKtynWoHvNma7vYxHe/CNc/bn7fLbqQ/Ub/zibKus8Uoh4JscJ0PTsyz4EvxqMiaXGFRfBdAKqlUFPj3emAlUhIuNnty03T+Rve9N3vbiNCENPSM7SGYe88OeZvXFfanePmspkddeEsnlOTLBiHLOuUoxdnhLj4rU+d429+/5t58rRNHE+AhITuU5N0wtged0cLOLhafnGewa3PmJ8vvMEf1zTNoVcMydzWe+V7eXgDCbz5qSd41+teQzOSd4SEfMdD38FfftN/wQ+7OUPrzgmpiWpyf6F7gf/4bb+Dv/X9b+ZHv+vpxT+wBfND1qbXFReXdFS/blabK2G3vrUJQjLQwiMhKi2QkLBJddxcKIBOEplRPoGjWifucO2guMcvbrZLqFw1Yaz+vkr7jaTg8fPlNeJbnjznu+L32SIk1XNuDafe9erQNjNaWVpCK53QXCmjhQsbBLcnt0lVSq4g0kaTtYCyB4MKF5CQTmCZPLq11rR0sJPcLeo+zWYM0yFKabQyv9vsJJyySMhknjOZ5wwaG2gwFr114nkbIRLi9s9ETUmk04SY1/giRJm16r7YJO+NWHIgNpmkOdcOp6YIkc4OfATp1CIhlo5lTW5ado+eA083zXm4frC8CDmguG+/uGM0N2c7Z73Vc9UkBYrmaqpSTw8GuJ3YeygPNCHewnyJVXBNOE1IXX5xe/68//mRib3fjxmYq7VmN0BCxul4qfGMa+B2k27tXJ2v5LhXhNyLhXAJSa07lovztgjZ+aIRK6YTrwmRouhmL4uSC1I+Kbr4wCAxD1orbtGxm8yumhdduBV0rAyzcEkUbdde9sJ0R8c6iSZkdRESSUFku7K50mXajdYwuo3SmgOxsWDfV4eEuKR/HSQkTPZH6WiBjjULuuPNTqUICdCKOnTAO1rlGqV0GQmpfA8hhO+wdxtxierlkJCpMIliNzpPJ+kwnmeMZBcphNmY0kmJBw8wtYmPBJIKHat6zFutrWKzf/bnvSh7c+sh/5o75sy6RK05WJjwbTQhEQpI50MzI6SOlljjjgWUB2oC3eqSvAIJCTe7facJ0cq77XSaMZFWDKyT0EG3oHk41Mpx7u8ICbHPUyM9ItKZp9dorZEsp2OBSTKlo3KcAAmBwNQiGzOJzffoIOCwUoTsfL44f+dfW+ibMgW9s14cfVYclDUs9nlpbJyns2kL2vH6RYgQgvuSHtI1Qe5mTkhlHfrBr/tBEplw32a73jkvNknfgw4J0YotLeku06YsFCEXip+lhNaGnRWSoXXOkUveTiBMl1JYPUOZjhXO9Liw0SKRCdI+B8v2IEfHqttnnrpQrGlCwHufLO77y1ub9ifN527se63HyK4rTURBTaPsQjeZ5zSjpp9i/9LQFBgOCek2awxPHBLS7C82ssKBhKNbAR1rNRIihPC6GaU1tye3jR5Em99tdxtsB4YFO6MZ3dhSY8WYFZKQAAmBjisG52N6tpBWWhuLXjesMDfP+pnIoJCNSLJv6UZKmfXAIdDOpncyOwA3GHJw2Zye9jYgmAnN45EpKm4c1TRBHRKSm/tWk7M72yHLNWfaZ7yjVtUkBcoFa9hg3XGdM604nJjnPERS1qVkTdLlSMi1iZkPInSDy4d2P67Rg4QxmuekuUboJpGU5tqE+11QhOxNzXn5atODwF0WIUKI9wkhPiuE+LwQ4g8teY0UQnxICPFTd/NZ9+LLF677ttQdCwpxulZw89OmCBHOwah9rPtUqQhJJ74DAWW+/rZNZHfQhTNG3YZnC5NMm2NuaIFwnGyHhNyJMD3sXsbNshvXsj9pBUVImDxO90FlKGU61tVFqqoJUUr7eSFV3UVdhLqIw/lhMSvE0rHmAZWkWZnEe7w7VvnYpsGslTptjVu8242odB2P5kdorRlhoPOBvB/AakIMHcsc88ECEuKKkCYCUUmkFtyxmptFIuUcdNqbbG0URcjaehAXbuGviNKhEKaD7fTNjwpNiIwKRNAlexVNSD8rb3A9TTnC11e+ezspsglPxwJP6+g1Iy7mL9LWxhB5v7FdfG6rgoQcYyRRClvISino6yOTVCrtkZBldKziO7ki5A4+M4hubM7lKB0xRhX89SoScv2Txc9nn/L36zTNodFlLs35PCMDkadS5WTQPS/jXV/QrhXzoNh8mZCQN5x5A288+8bVfyCMJfAlLYm1Bq2MMxYsKUIqz/zgQvm/21seCUEpDh3KVxGmr1vE9poxUrdK7ljX9q0uKZFsdxsIIXwjbNketIyOBQXtD+D1lzY5OyiaVpc2Bq405PO3dv0Qwom9Ri2A/aIICS13R3NjfuC+a1iERLq3KEqHxSn0YYQNoZ0vFPSaoFmwLFxx6ooQYxPskJBGybBgZzinLd050Si5hGYNFbMBe03TMZ1GgtCJt+j1wwpz851PCfMMJbFkGGgeSkiIMHv4JGgWtDbNPtCK2xA3maG5Xxmb3puHdUWIoTTdtEVILo4AzeE05WznrHfUGtQgIeEaFyJstwO75/HsgFSlHgmB9SlZrslZZ4H8paEpQtr5ORLX0DimCNmzeh5JyyMhesmw6P3ZPvCbrAgRQsTAXwK+GXgj8F8IIU7VvPQ/AJ4/6efciy9/ONhyKR0L4PQTRWJ+/eMwHxqL3jX0ILBodVhCQoIO+pZNkveEgsm++eUKJGSOeXBbSnjrUI+ELJlvURth93JNn/+mnca9oAkZmc601ppDubGyCJlnyutB4M6RkNLUdFvYTcdBERJSAIDT7dOe0nS5f3nxOwXHOsvy0ryQuoF0D5wy1/Xydodm1PQL/+H8kOuj62SmH0ZPmM3HaEI6nsYVFiGuKzez6EFLL9q6Vt2xBs3BYiJ16W1sBDSqtWeEuHCbxfbDC//UjJpBETKG6aEfoNeKOwXtawkdq1cRVHd1pQpZiYQU90apCLHi9E4j5uH8WZrK/H4nKu7jfmVY4Uk0IVJAXx1Zeg2BJuSY58slOifUSri1Y5SOGGcTiBJLx3qx/EJLxWPrQWgN/HG5e/goMUXZafaLv5nuF8lg72zA21dle9DjIg2u811Y9LpnMpEJP/B1P7DeHzX7JAieaGyC1jyh7fVYi45VU4QgjaZMK45cgR0I01tRa216Y7cZE+tttBZIJPf17gtE6UXzyu0hS+eEOLe/muL5iXN9b5LxvteVv0+n0aZh1/8v3NplbPeEPI5BRMZVbO/54PVFMupQE6/TcUWI0kjd9RTHUgSDChciigtNwI2gYD5GmA7QSwpkYme6Q6bMswcGCXF0LDDi9IYs9oiMGu2UjZLOx+0r6dhSOxsLc0JSW4RsY/YaGbfY2Cw0CZImwhUhtpE4PSooau1N0xxqRA2IW0yBc/MrANw6mvli1XxZkwNo4NrM3B+52AeMle6ZVoGE9FdoQqBY98bp2DQyXGmaTTmcHZaKmMM1bXp9flEpQrTWvDgyzbfTWbf4TscUIc5UwCAhAoViGjZDa+hYd7y3fQXECmDu2Hgb8Cmt9YsAQoifAb4V+An3AiHENvC7gT8H/Ed38Vn34ssYrsuysgiJG6YQufFJ83/bj9gp0XKtwWdVgd9hID4Oi5BtK7LaDYYd1SIhrgjRM0DS0sIUA3HLdyKnqfPxXscdK/iMNd1tWm07TbeKhNjOqtKafbHJ/cdoQsJE/040IWCLkFaAhGjNzA3DQtDqlJGQZtTkR9/5o+xMd3hi64mF916FhNQlm3/kmx7h6x85zZsf2PLHdmtyi8PZIZ/d+6wvNtraJFceCZHLkZCJLUZasJCIh0VIv9E3eopqInX/O9iKim7X2jNCXLzpB+DME3DxTQv/1E7anqI1FRqGN402Skg6YYKXFF3F0vHPK25ZeaXbvkITEiZIh2KAdIP7LJ2o24x5KHuWqxqUTDjMi45ev5mgtPKd5pO4Y0khGGhj05tbOlakG6vRuzwtEIUTWPRC2TFqnI0hatBmXkZCtC4SO0sdbQV201prDqNtNniOLb1f/N2qgXGj20VRclyESMhdFCEPbTzEn3nnn6Hf6HOxd/H4PwC/Xv3x3pM8093gLTs/bY+jDkGuHFtdEaKFL0IOhXFCo9FhMjT37jp6EBfdZkykB7yp88P88Nc/zOn2aV7aN9ftwkZxP/hG2DHuWHVISLsR8Re+53Xsjua8/vJm6d9acYtmLJllOc/t7nPOipJllKHjBo1ZWqJjheiGm+Dt7r+DuUn8lBZI3anXWgSDCutPyGlT3N76bPG7Y+hYYLVwU5OXZyqzSEgbKWDTUpEiKciV5vZwxvntoghJ9dGyt/WNH4EoBsPOx3SaMXLYRKlZCQnJMjMpZNM9Q93TXO51ecnqOYRuIiJHx7JIiBfrC1pWF9GKWgYJEUf0sl26eshI9rg9nHHOIVmzQ2s7rdmxQ1fvP5uyc2Towtf3mn6uR+jU6CJc41xuszPdsRb/TbPWpqYhOmgVReO6Rch4ifHN4fyQWT4jkoJzeW7yEjgeCRlbJES3/NyicRTjn7agCPHOoo07RPm/AuJu6Fj3AWHr6UWgukr+WeC/B+4Ax74Xr3a4hzXTGeky60sodCE3noHZkUnC1kRCqpOxwyIkpNhst0zCs4cqErg6GofdCCfWCairjIYhdJ9wOov16FhBAlmdNLwk2m3zN0aYHiIhrgiBQzlY+PwkEt6QZpYpf5ywnp3wUiREZYYmNym81Zs1CcPlwWXecPYNtRS6UhGSKl/IQX0xN2glfMNjxRwUtygezg/57O5nEQJjZ5mb30+sRW9UQ8dyXbmp67iyiIREshB/e1eQMJESEi69tQRTb1bE5cdGowOPvGfBGQvspHGHhKBheMP8b9Wm2t1P2axE6+nNy0VJt/q8uWJWSE9rcBEWgVrIIkG2dKxuInkof46mhky2GAaf1WvFJUrCSeaESCEYqAOkbqG1o2Mtd8cCyvSykxYhwRTxSWqREICDK8WLjq4VTQu7TrliX2kzU2FfmvtlMw8QjnBgXPfsIm9/3XDfU9zdnBCAp049VYtSLg17r21mKV/fuUgDsTjg1b+20mCpJsDtLTYIihCPhBTuWOs4Y7lw1tBxdpEntp9Aa81LB+Zc3bdZnCc/MPcYd6xlNMLL252FAgTM3uYaOzujETed7kCkEDVNo2PvOf/6sLCoIiEuzEQNuUjHSicFelaHhEBxf4X3yxqFbq9R0LEAT8dy1rRSCo+G7IzmRIGQe6aOR0LacRsROPp17WySEh1LQ5qZ79zP7SynzinuP1WcH6EbtjkiPB1rZud6tKIGMjbH2IyaELeYYYbaXsxNYXojpGTZc5nmmqEwe9zjFxVSCKRu8a8+ue9nFtW6Y9XQsXYmtkHnm0RTDqf7JU3JunQsZ9pSLUJuWSZCJASX0onJD5r9tZyxACKafn8ch254AYXd5U91lOqv9HjFhOlCiDcCW1rrnzvmdX9ICPFBIcQHb968ueql9+LLFGFCsmxiLVAUIekYbnzCd4DX4ZeH3TM3eRZMZzu0SNyyE7CHQjP3G2AdEmI5xLgiJDcLUqfgwTvO5np0rGCjWZOO1epYJERXkBDLAZ3RZCZaC4uUEMLTrmap8uJ0WK8IaUZN76hkkJDQQeOQmRWtIeM74/5TRmnmeV4qkNY5j05/cTh3SIigkV/2lLPRPGMsOogACakK02euCKmhY0GBhngoOixCzj4FrUGpCHk5u0VOmA4YJHB4w2y2oTMWlO+ngJLVdwM1bSHTy7PyoMvQEa5SJFbvI9m3CaTt5sfjG2zoI1rKFCFjex47jYhIilKCd0dISNKGpIMQsIGhY+XK0A7EMcL00nNxAncsKBzRhvOh6ZRHDUPHmh0VQ7ycdTgsICFgdCF7YhPADCJ059whIUKYBLHqYLRuOB//cCDglyvcejUPZystKRTCta29uahf6WyTIGgpZaamu3k2jZ4ZIMudUfkceje0dqaH08xPIy8VIXUzHYKYq+V0rFXRjJpFMSrmnr5jipCGaXQcXfO6pW4NHav6fYVy9ryVIqRkz1ujCYGyLsT99xr6w0HTNrxsEZIp5aelu3CzQvZGc/Ks4QcLTtUhy8KtuZ2kUxSo8xGdRlyiY83yGbnW/j27md1jume4vF3cQ5KG0ftFsckPJruM7X7YCtbHVtzympAkEly0upAbh8H1t820LFccWXpZLg7ot2IivcGHXyiaCbXC9KB56ZC021aI7nMKnXO4+3masSSx6IOz/V0VSmlPx6o2Gd0wRSkFD2UHhimx/dCx68LuyOlb+p4tNs5nxTNrkZBUpR4xvCPr+a+QuJsi5CXKyMdF+zsX7wB+ixDieeAngW8XQvzt6ptorf+u1vodWut3nD17PBfyXrzyES6yKylZ576u+Hn/CrO6DvCyz4jKRcgyOHG7U9wTu7j5AnWaENs9t64bPZ2bh912vZ3FIixyNmsj3IzX7C50O+Z1udLo0NXF2ns615A64VpohVuiY61TMFEZWBgKr2dHzGyBd6IipJS4FUhIEomFYVB14a7ni8MXeXH4IgJI8ou+azSe5WghUY5WNasRptvEtSmiBXcqMNaMYOxLAVN4OjTowXcBcK57zjvunO8u6UqeIFpxq0BCBHB0zdAOZBUJCe4n1/XMU3p2aJqbct/TlPUEKwZ0hpoQgGRgv5dLpK9/0hgcaUEqm77L6fjSpSLkTlGJzjYCOC2P7NR0g4TIFRa95vuESMgJNSE2edmZ2i6mbPjBap6S5YqQzrZPAMu0R8UOmwAkOjNaECiQkM5pw9mPG0VR72mNa4S7xms2MF7W8MnjkJW25lA+vrpE2a6fAw3kGYdoNJp/dvUDfOjahwA7VHbNcIJfR216qeKM5cKtU8vcsVYJ01eFo2MBaAqKphYpxA2aWhgqn0XVmrH0uaIfWFhBfoQy/92t0rFK1L4lFKuKUcg6onQIixDz3+G0dBen7NT028M500z5WSZjh1rUhKNjdeJOgA5M6DQi74LnihClNMLOxuqkRRFyf1CECGIaUQJuavpkj6lFNNrBvmq0dYJp0kIKwUOG5c/1JUjIkegjBQzzXQathEiVUeo6i96SO5ZN2l0R0m4VzcrD258uOT0eroGEhHt2tQlzc2KbQjLi4fS2aVIeY88LsG+RkFPt4hkdpaPAgt/kTMNgcKFzFv1qirspQn4VeFoIcVEI0QO+HfhZ949a6x/TWl/UWj+I0YX8jNb6h+/qaO/FlyXCruhKm972JmwWNIGJ0GvNCAFIosR378dpoQmpOhdtBxqGXXE8EjLWGVIINlRqukQdA3muWiTqD/DOhem9ThsQaA3pLKRjmYVuD7NQ1mkpwsnkd4qEQFCEpBUkZHrIzJ7bOEqI5HpFjYtQGB9qQtYtjlwRsjs1XSwhBA11ybuAue6RcsXn9MBv8r4IcboFubixAPyBr/sDfM+j38N3P/bd2A+B3/qj8OY/AE//DsDQ+v7YG/8YP/jUD9ZqX04aYREyxeiWDB0rKnPlw/vJFRmTfXouebbJVFeL8tC9FUlkeB83Ykm8YYsQNzX9+set61iLlNjT2pzoMuTb33kRYhLPLY58cnLcsEKgcMaCE7tjOaQstQ0H4qSYCVQtQs497TuO4XHNMsVNbZ5HISkSRj8wLkgGHRpyR3QsN0/j7qhYJwpnBTsbrqawQrnBUuvgZJKzAQLyOftC87fjKT/x+X8MGDee733ie9c+tJ5N1F0Rcu2g2F8u1tCxliIhK4Tpq6IVtYyBWCRN4WFDizlETfy77RpKlpTCP2cOsanSsVRmnu2FaemOBguLiIeLahGyhigdYNA0e40zX8lzjawgIad7jo41YzzPiLRzlVuBhKQhEuLMNIZ0mgYJyYM5IQYJiYl0RsO9Z/c0FzfbpSZ/J2mDjE1+cHiNSWrWt2ZAc3bXcRY3EcDDwvSzSw5Z1pgmVZqR6HKm3+T25BbNRPpGlD8/a9KxXBFytncfPZv3HO4+W3qPdZAQV6DCIhLi6Fj9aIPTas9cs2P0IFAI0091iv18nI2D59sUIaGpz28qJERrnQH/OfAB4KPAX9Ra7wgh3i+EWKLCuhdfDbE2EgLF0EKw7ljR2tSOsOPti5AqEtIrNsY9sQoJMb8b6YxICjbyuek4VJyxYE1h+gmQkEGnwdwOLJxMgm72+DYa2MF8t7oiyCX1RhNy50iIO2+LSMgBM5vU3hHlxn1+KXHLfRGy1jlksahsyCaJOscsyxkFC7f2YvqDhUGWbsNoyXqawuXBZb7vNd/n9UMAXHwzvOWHSsnXuy6+i+94+DuOtY++k2hFLYQt7Jw170RQowkJ6Vg2MZzs0cS6gzgkBFHi+nr6Uk0S2Q7oH/1WXCQwk33zd9c/gRSCI85Yf/9J8VrKDYaTICEAmwwRNL3ji9TH0bGWWw6vGwtDNWVMxyGrh1cNJcs5HJ1/nX9Z+CxN05ybehMwXG1ffHh73iCpcYniHQws9Nf4VUFCHB1rVBR969Cx6ooQNzVdmyLkQzLl30YZCLjUu8Sf/YY/e0d6FZeoT1NFlitetPa87SRis1M83+tqQu4UCXGUnGYiS0iI0ilEMS2XqJZses0x+4nY4XOtQWWd0ut8OD2IjJfrCqv6jzWLkG4z8cU/GCRE6Cbb3eIchnSs0SxHamOF7uxc68IhIe24XTRO0klJE+L26lyBoMFAHxbuht3TtJKIs/1iPekmHZCRQYh3Po+7ou1WoYlwBcI8SlBozqjbNPWU6weLSMiQDkpEnO5H/ru89fKDpe9RR8cK1zjnuubQ1FPtUwzs8RwdXrHvsf7U9DC/qDIdbozN2nJGWkcvrSGYW7UsnCbkdLcyB8y7X/4mL0IAtNY/rbV+XGv9qNb6b9vffYfW+qXK635Oa/077+az7sWXL0qakFVICBS6EPDuWOsmNKEVq5sTUi1CNrrn/E266zQhSyamKzRjFJHQbKi5FaabZOk4V6eFKLljrZdI9Fsxc2GHwE0CwfHoFhrYEybRrhPGOzrWPFPMgmOtHUhW99nL6Fij20zdhn0C+kuVwuIpbeucQ6CflBfFC50HEUhmmfIca/OGhaNXdZDlxHY9lxUhr2aYmQbWfc0idRM0yKomJDj3aVGECAR9LQ3tB+hoToSEDFpJmcqx+yzsPU8kBfvyPEprUjVDozwlJuwyr+NoVwrb2R3oA6RuktkixMwJWXFvlJCQu3PHKv3O0V0OrsLNZ4p/cPOMWDRZuJF1AWsKUUVCwnPZPQkS4myIT+6MdeJwTZN0vHrAK1SKkBqaol0/B1oW1sVC8trTr+W/f9d/z+n2mm5hNsJEfTTLuebseTdbpebAuu5Yd4qEJDIhljHNOCohIbmzdncF524gTm9UkJDg/nMzQqBAeXw4Y4T21nL+/0IRcrwzFhjql9ANNNgZPYaOtRXQsVwRojRcP5gQ6S5CsLoICc0GvDB9QqcRm8GDwJ6lLiqrCdlQB4U9uEUNnVW7FJhBhw4JUbkfatxuF00jr9ew4vQkggv5S9w4CrWV5nzuWWesXrfYY7/x4Ue8hkMK6NbtsbLhB01W6Vin2qcYdM25P5gfwui2p616d6xsDr/6d+BzP2soe0FM5svzC/cZF2wmkyvWQkL2rCbkdK9bYo0sFCHpb+Ii5F58bUYJCVk1sBBKm/z0DjQh4eeESEj1IZJRwibmod51SEjdhpq0cUtSQk5bCcOXtQPmwiJkbTqWo/8c42LhYtBKSO1CPZtaJCSbw/TQdJBcEXIMHWt2F3Ssw9mhSWhdgrf7nNHqAK0TdGWridvMIyFrIjQVJORi5xHArOEhzC3dIMAACQFDD5hZEWrrDhOOL1e0kw4gfIev1h2rRMdyRYjZVLsIj4T0kWUkZMVgv7DjNmjH5QTmi/8WMBvyrriIUpBrhWLqN9e7Q0JMEdJTR0jVKZAQOqttpcPO9suFhACdgZUnHrxYULHiJpx6zL8mvGdH84ypijiUA0NZG900z6pLHHt1RcgdICGOjnUXgwpPHGHX3R3zsjU5HMC5UWMBbJsDWwg/5fo98Tb/1dv+qwVa0joRztIYzjOvCQlF6XB3c0KOi1ZkdCHampholEFCgKZ7hr70K/DJfwIU4vShHYQX0iyNDsrcjwvCdKczWrV/dE6GhBihuPnubrDtIh2rODdX9iYlJERX5xHZ8JqQpFMU0OmYbkOYwYNAam3Ec6sJOaV3imGz9vgftEXIoJ2Y95IRbrWZCA0IWkER4lH6uMkcTRJJHsyfZ280L+jJkz00cMsWIe1W0ax5YPMC73zEUkTtwMtqCCEKxCWfo7Ri167Bp9unGWyY2VWHKLj+iUU61jP/DD7y9+EDfw5+8S+VXA4nS/ILpRW3JqZ5cRGDqIxpMTvGJn6eKYaWsrjdafg1z9CxnCbEaHt+UyMh9+JrM6pJ4MrYuOw3qjux6A0/ZzgfMrQ80Trnom1pFlavCVkyJ2Rkk+2YjLamhITcMR0rbsA7/yg8+i3w8LvX+j6DduKRkHRqz5t1AtG6EKbXISEucZtXNCHrIiFu9sXB/MBsME4Xsvc8Mz/J/s7t+8qakJypR0LWpGNVrueD/Uf9z47zChB1CjpWdZDlzCY/X6lFSCtugZRMrGh3bN2xjqdj7QO28LCOOF1Nyf+9QEIW7/lWFQkJE5gvfgAAHTU54HFyrckVZPKmp2PdlTDdPlcxORv5gNb8jXTTtxGrM6sL1BVzT9b+6Jpz0bYJBAdXiyLk7JNGXG4jLKjdNOJ9uWkSqOEN/6wC9ZqQdFye/7Eq3OteFSQkKNLcd1qGhGw+AK/7Pnjq34cLNdPY4wY0+3xL3uDtKuGHshb/YeeRkoPhnUSvhIRkvGTpWPdtlI9vFRKitT4xHQucQ5ZECXMPaDIPVLQe/EazdmoFv/RX4Zf/32x2bBPMdqZDJCRXIRJSOSchErL0YPq+AQGsLUzvNiOkNn/nnAaXuWMBXN0bE+kuUghSlRpaT02EFr3hvduTKUI7ZCWwBSbhcf0lgy+0Nz118dtfe4Gvf+QUf/BdD1kHwdjQsbC07SihHTQTfDEpJdNTD5NEkifTTwMUNsqTPXKlOcScb5EUzZqznbP84Dsf5G0PbfMHvv7BpefNfc40N0MJM20S/VOtUwz6l0BEZhbOjU96OtbhNDUb+Gf+r+KNnvlp+L//a/+ch0hIuL/vTffIlPmMS3btuxGd52i2emqFo2IBbHWS0mwk7/hphyC7IqQZNU/0PLzaca8IuRcLESYkxyIhQsD515KiTZ2/pkUvFAI/Z2EHi51zgFPSvJ93x1oyMX0kXBGS01KUNCF3TMcCePp74Fv+9NpzQvrNmLmw3aKZLULcjBClObCWoKs1IVV3rPUeUTf7YpabYVK+W7L3PK6X2Frze4QhpQicu04uTAeQSB4cFEVIuNDG1kCA6QHtQLsyykZM7SLe/ApdYJ1N70RoZmDuUinLtKG4jo5lkpRe7GaNCHrIShGyShMSIiGJuU9dl9bed4f9R5H6AkqZ5CGNbtS6Y91xR9nNCpGCDT2kP/1WNubfYoacrbo3XsY5IaXfbT5ofpgPiyGF554uvSYcorg/NgnlntgyhmvDm+UZISGqFIqHx2uiIemrWYQEzYZVA17BrN/v/I/ht/xntc5zALQ3OYPkP8s6fJtqIu5C5xJa3r64N/HNoSoS4oXK+QylVenfMpWhbUJ7IiQkbiGloNsy76tJvaahuXEZfvuPwcYl84tP/J9857UfI9FzdkbmWazSsZzr1ILrodOErCpCnBW0iztBQiwyYZpWAkFScccqfp6myiIh5r/3ZnvURckdK2icdOXc07GcI5ejYz2qLXUtMIE43WvyX3/Hk7znNWfNe8nIIiAWKY4aJY1iSTR+4XXEkeDh/Is09bSYFTLZI80VR3ZGiJJ2Snhjg2bU5OygxZ9631N80xPLz6G/r7JZYc+LRUKaG5C0OULD9U/6dXI0y8mvf6oYYumu15UPwU//JzC8tRQJCXOby9Yq/7o8z/AYnUmpCOk2SrORqk0RV4R8NaIgcK8IuRc1IYNC4lhhOsC5p32iexI6lhNuQT0SsmUFXXurkJAkQEJ0RlsL07HxRUg4ZO/OHKLWDSkF2p63zE3CtnQIpTUHa1j0zjPF3MLdUpjBTeuEH9SH5fw6JCQdew5us6bAWyeawbH5IuQEwvTLg8veWhKKziJAo7tpflBZ4XQEHEwPTFcSaN2pbuHLFM4ha0ohTl9AQqQs0RsAn6T0XEdQRmboXi0SsoYmRIiFJGa49SSSBJGb7lkqr/uBcV7wH7WQ4g63AtuNi+zAwjBWomTu+wi51jyEuqjSsRKZkIQca0eTCETpUC7od8dVJOQmDAPNR50wHdbXhbhr/Gpa9IJ/du7KpSugzZj3P3lh1QusUz93o7jPl9GxYNEhK/zvk3R+XfK7Yb+GFqm3G29FLVOA/Pa/4anGlw9/gz82/Ou8Yef9qI/8AzrP/jtT3E0PUUp516mlwvTj6LyO7hc11qb+dhsx0tOxFFK3EIiSuL8ZRyV0JrJ0LMDP5QojVal3nGsnZSSky9wXPSES0tSK85mVAFeKfhft2LpjeSREWySkuObhDI/puadIpCTSOY9lnzezQrS2RYj2RcjcTmmvOmOtCvc503xaWHzjipABJG2GQpPd/iybcVEozD/1L1BofjkRPPetfwYe/zbzDztfgH/2HyF3v1h832B/d1QstOaiLUJuROePFbuHLIHtbkHHGqWjhfXoXhFyL74mwy3UrjOyMs6/Nki+5NouTK6j5K02WVKE2NftCYVC19tNxi0/SCsio6Mg1dKjGCW49BUqQgCERWnyuU22XBGCYChMcnCcRa+bobEu2gDlKeD7s/0SejOzlrHNO5hsvOzYXDG31sBHzH3UsHS6J7aeKH2nsNvT6BWJTjsrfr8z3fEiwNYJLV1f6WhbJGOKLj0HCx17t+k6qo7tUjvxflcmSMSSImQNTQgs0Dlmp58yH61MZz+VN/1r3bN9km6yK0KkEPT1UemfVgvTnWD75EP8qsL0TtyBQUXPIASce6r0qxIdy957e3LLJGaTXeOsBSYZDJ4nuoG96mjNWSGejvUqFM51hc8JUSdgMTGu0eSsG+Esjc/dKDj9921UipBgD6k2wsIi5KRICEDPIyFzn5z792ttwHf+ZXjkm4kjweX8S3zn5F+gPvi36Hzyn5hi9OganfnEz8ooFSFKwdRNEa8UcdVwSWXv7NrPRNu6VYGhY0ndot2IFp697QANkarnwa696SIS4qhY4OaEFM9ZR0wRDglxtsBacym7jn+sApOa0rHGbe+OpdEGEakgIeHPs8F9yPaASAqeTJ8xSMh8BPmcTJlBhUkkOEjN3nomsPE/LkKEzSEhAsFWa8vkHo4irnPOTo1Vb1NPEc9+gN8QGX+1rfnvfuMvMfmG/xTe8gfNm45u8cjH/xfQGinKFGbXYG1qzZa9ttfleY6mRc5TF3tBg26r0yjTsSrIrC9CkntFyL34GorjLBJLcfYpppfebDa6RueONSFh1NGxtu2ml4GBSpdY9H5SmEJjoBQDBYf0Se2COc1OQMc6QUh7bMot6G5aerKJspO1V7ljhXSsddEGoDQRfH+6X3LImgrMoMITJvHlY7szYboQgu98+Ds51znHtz7wraXv5Lo9SSRI+sXC2gncocxm6QqfV4Haska0IoOETIQ29rwAMlq8v73bjOVjW+HquwaPcKF7ge9oWovUOovemmS2lUR829PnuX+7wzsetolySCMSAnWmXIRkcockNufTIyEnSVCbGyAjpISBLs8dWK0JcfSykyfFzahJJIrP6CQdkyiHyffWgws0SuNkZu6/fbvJmyIEU+jesK5a1WSwtVmYVKyDhOQpWOH0q64JcXHC6fTAYhFyF+hOI5LE1sXoudvmOe80oqKItrEKCXGidPN+J0BC7Huf3Yi4tNXm6x/bKDQh4X0ZN+Cb/xSHT/0+xqJDJmJSYjqy+MxBXiDsnfC+n+4XKFRY0NbFa77T2CPbmUbrRLcZFcL0zExL3+4snovTAT1L0vbPTZ1DVmhUUaVjtcUcWUFClNI8lL1knp+oAacfrz3Wgq4KKXY1jxql9bFEx1IpXH47SSR5KnuG6weT0qDCoehxbtAqZnzcARLiip2wCNlqbRHL2BQhSQsQHArN9tHnAHh9+lH0fMxzMofWBuNszCd3PgVv/kF45x8z7zt6iUfyL5riMFg7btv9/4xsElu0+Xp0/tgBiK5J0m1GNGJZFqZXmiL3kJB78TUZbjE+1qIXjJjs6/8obD1wImF6GHUP0nbwuz2hazdUHTX5iDQP9uszjURwJHp+KFY46TtaY9L3SUM2zLHp1G6cFgmZNoqN/Lg5IU6Yvq4eBAwv1tkP7s32CstbYG4tY0/U8Q6PLZiYvq4wHeB3v+Z389e++a9xeXC5thvdbcYl+ksy3vXC173ZXoGEvBoJ3RrhOn1TtBdf1tISXQe5goQ8OHiAv/Kev8LvHNghimsiIQB/9D2P8v/5fW8qnHB6QZds60FaXXMfJLkrTjSHlj7hGgwnKkKkhPY2UggGqlyErJ4T4uZWnBwhEEKU0JBO3DFFg+Pxw1JqiCuQ3L23L7YKZ58bnzL/W+XlC3FnNr1BR/lVp2O5uJtn52UsQoQQniLkXJ3McLvymlzSJa5AQk4qTAcQMuXHfv+b+e43Fdd7AcWXEvGWH+JHNv48f3LjL/Lx3/Z/0vmBn/bns2+/Q7sReUoXUFCx4Hgk5OKb4Pf+pNEgrhmtOPIaDQ1Immx1F+mN291i3RBIOrG5N+qKkJD1YNyxAuG4GhNVNCG50jyafcnsp2ee8Dbj1WgnbRAwlxFDtz5GSekaLxSdl99BEgk21D5q59mgCFEciQGn+ngzm7PtO6djzbIZOxM7I6Rlkvp+o2+e9aTFIZrevhHGv33+IXKludkqNHcfvflR84ZPfRc0eiitecf8VxbWPoeEnFXGCnwuWxyIDe98tSyccYbT+DhUfZSOTFHrqKz36Fj34ms13Ca/bGJtNcKNYt2ZA+1KItKJO94PO4ztwM5uF1WrCXlhessPM3yjRTKHsu8f9skdWsueNOKGPTZ3PmzSMk4CulHNMXi0IVUebbgTOlYkI09lM3SsAAkBkPGJhhWa4zi5MH3xvQI6ll1oO42ovLAOb/gEfm+65zuKza/QIsRpQibgxZeIaJGOFfju19I1Kv7vwJ0jByEScv61niLikBCAnbmhHblndl0N10J0TpkipIKErCyeV2hc7uijg3Prjz+kZFX0IC5c8eyF6TIoQlzxUDerwYlB1xGmhw5ar8Y9K+ViofBy0rHusrDqVqxsL2wuHluJjpUvL0Ka8mQWvVDc/+H716HFocB7ZzT3GgeAjczsLwtzKazzHbC2zuNOQkpBSxbPbdUZy0V47AC9ZBOop2OFTpiGjhW8fzr1+3WIhDycXTHPzxIqln8vABlZXacAmZSucVhMTvMpXH4rcWTO6fbOR3wRkuWaI9mj2yncve5IExK4YzkkxM268SyMuM2h0LR2P8O5/DoPZc+Sa82tjWKY52/c/A3jQhk34dFvQWl4XfoxtuIyzcppQs6kMwSw07gPhDiWjuU0a5v2mpboWEIU69Ho1tLxBl8tca8IuRe1cUdICMcv5HVR5XbX6UEAtoIi5Kas14R8ZO+zgLmhX6fspFPR9wOm3HyLV1IPAhA3zUItsjISMorNRtSMZbljZsOLv/MCCVnXnteF04XsTffKmhCh74qO5ShUJ7HorUajhISYhbjbiE3i5PQMw5s+sdyf7RfC9K/0IkSUNSGLSIg9/vmwnq7hrtnc0tGUKmg96167sIt/7rU+4ZO0iZR5jq6NzERg92yftDg1RQglYXpjyf3twyU6L2MR4n8OkZAlSZErgl1TYiR7iGoHt1vDMb+TWSHpq1yEwGKhcDfalGon/y6/UyhOh0VROlSEyhUkJKRj3Y0mxBUzx2lMQoH3znBGJCP/Hlt2js9SUTq8IkUIQDvYP6VulZyxXJyq/G6QmDVgLSQkvIdmR/RtE0UpjdbGAKaj5wYJWYI8muO011fGZtZX1ABRbkKW6FjZDFobTLcNMvzQ5BPMjm6jgUwphqJP0iwaHyfVhITT0iFI4pM2hyhEOua7Z/8cgEwJbjaKNWt3usuVI7OO8sR3oLUm0SmvnX3YvyZTmZlDojVnRuZ+OGzeBxw/hd01SRzFzp2ruZobDa2lZKWjmz73uleE3IuvqQgHCa4T4evu1B3LRZ0eBKDd7HNBm1v1Q5GqFe99bMdQKR5VERtWfTcUfT9gygnTX2kkJHFFSD5DKwVj6+UtzXer04NAxYHqBHQsKByyjDtWUbjNsEXIXdKxxrPcixJPjoQU38kNuOs4saqjEg1v+gLVICGOjnXnc06+HOGE6TNHxxKR2WSXFSHppBhkBotIyNRusCeZqXHmSZMwt7fg8tuKc4tBQ6QQPH9kLDXd5nUiOpY9bikFg0CYfmxx6ulYd1eEdOMiQfLNjAfeZdC0C6+vn/7N4jPVasSIKv2qDglxhcmdFiGvBh0LFilZd1P0VTUNdzmAsTpPoypKh/WRkLtxx/JIyBrzcpzAe8eit28aPEwEvG6aInW+gO74IkSI4zUhJ4wwiZc0a5GQamGyYbWDtUVIVZguI9i8bH7x/C/Qs/ey0gYN6VhDmUiK0tDiavjns9FjF+3XuZAxIYX0Bibu+uaX3g7Aw9lzDG8+T6Y0E9pkImEmrgGmqDjTXr8Icdd+mA79OXBISCITo71IDBIigKdyQ8n60sbr2Q0mkwN85OZHzA9nnuBWwzRA3jD6Jf/vtye3USiY7nN2ZpCbWxvmPB2LhIwcEmJND4L1zswKMcc8DGzF7wnT78XXVFQX6uMi3Bju1B3LxdJKPmnzbmUWqGdkzvXR9dI/D+dDPrv3ORCSN+nE0ysOZYGEfLnoWK4ISfSM6XDfd7IPIlMgLENiwuTIdUlOioSE7lgpmhzuUhNijuMgmHB+UiSkrrDyk4Zd8jcqkJBpNoG7mPj+5Yh2ZISXCjgQGqSst731dKwxjGs6pa4ISceQZycrQhod+N0/Ab/3H0F7s5QcJeockRRcObpCprK704SAKUIqmpBjXdNeJiQktOn1SMjZ18AP/DS8768sdRmqmj10GtEi8lFbhDg61o5BqFZFiY71KtlKv6JIyF3SsZrle6QOCVmlCXm5kBBX3Li9SyB8IlwNJ/DeGZrP/uOP/i7+1/mAx+eCgT6oQULsfJbWxvL5K3cZ3QCREsuQkF75/GytKEJqG4mPf7v532sf46LYRegEpTW50vS1ORfZ4HKp6VUNXyy1N9n7+j/iu/jVJo3XazjDjIe/3nw3FNGz/9boQaQprm/Pnwfg0c1HieT6e7r7jJCO5ooQsGwMGXFoLe4ju478eu8NfjaNtGmzL0KE4FO9dwFwfvY87BpXrVvjW8YufLTDWS3hzBO8dOqdACs1IUpp9sdlTUi43oUOWUfjQqN2Dwm5F19Tcad0LPe6SERrT9OtLkIbjSULWdzmG/PE3KxC8HNXfq70zx+//XHTcRCCN6jY00GGou+F6c5xqt14ZW/5ZstsDIlOGe1d878/4DgkpPi965LcKRLiHLL2p/t+TogvDe9KE2KOrVyEnKyYi6NFuo63mu0VdCyfWFoUBKD5FbrIOiQEMHSD6owQF16YPqqna4Tfb35U6EHgzpL2uOFFoq1E+gFliTpPJASZyrh6dLUoQu6SjtXUUxKbkBx7X6xw+7qTCLvAC5PpVyR9VQSv04gWi47eCjqWVuVrVxfhNOpXq3CuDib9StKEVJGQGk1IWFy83EiIS0RTlZKr3D8Hzai5IJB34QTerkMtemdpI1AaNtUBvUph5TUhrxAVC6DXKNOxtmqKkNMVTci2LShH6Yg0L3fjHR2rFbWKxP7xb/Nr2+vHv4KgQa41SmkGyuz5+dnlKAiUm427wbNRpQeHzlUAW5ef8gwCN6hwKPpocq5PngeM7fudRF3R6oTpUCTyh11z3SIpOJIDPhkXhcpbz78VgM/uftZPnv9I663kIja9j8+8H7CDCse3QeemCPn6/4R+23z+4WR5EXI4Tb34f6siTAfnkGWO52i663p094qQe/G1Fb4TfdzEdBuhyHXZQr7sM1wso2ORtDmF5PUqBiH5+as/X5qi65wqtkTCg9r0KYSAQ9HnqCJMPymNaN1otYsNenr7S/7nW3r5oEJzXHePhDg61lF6RGoXrZlboV4GTUjYvTkpEgKLxZXv1jtNyOyItnCJirnOMRB/JWtCbOK7i14+sNMlzumkQtewxXcruP9nw5MhIZUQQpTE6a4AfO7gOf9s360wHQF9i4YcayudvTxISJjY3MnxV4+vlUSLbliVWSvmA4sk5FiHrHAq/KumCanQse6m6Eva5b9/GelYvWbsJ1OHEcvYm5TMwmIcmKmXh44FJuF1z8EqVMXRsW4P7WfbJDBXmoE+oLMMCakOenwZo18tQjqL53HQSkpukKcCalh1arqjY5Wep+4puP8dADx5+EtI3UApUHlKV5n9ILqwXJRefb/d6W7t74GFAcmNJOb5jilw0lwZUbroI5s3UZjPfmL7ZShC2kUR4nSph03z/ERS8OvJW9mZFcf92x78bQAoFB+/9XEA9vI2n4hfa9bDz/8s5Cm3bj8DkwO6WtB99L1w/rX0rR7qaAUSEg4qdBS7BTqWvf8OUaDNe90rQu7F11R4JCSdGBeIYyLsJq0bVfegZcJ0t5G/RzUAye501z/8SitfhLw+6nubWikEQxlY9M6/PML0Vrv4TtmeEa7Nc8UvWQbZhRr+M5STo0OPhNzZsYYDCw9s8TEPipC7dccq/+7k57H6foUmpOhIt+3UeK8H0eKuE9dXKpwwHSwSUjcjBIoOcjYtXJZaZt6G+fcgcZxVkZCTD2p0dDepe3Qi8xnPHz7v0cuT0vRobyOg5JB1LBKSvvzuWAsuZCuiShczSEhQdDT79Ul2ZUDYygjpWK/WPftyumNBuaN/t3SsgCJYR8VysYwS7OhYd4K6l943OBeTbLLWvBxHxxrPc+MQ2DGJq9KaTbVf44615rT0u4hTrdNIbeZaJOpcLRIipSjRtM4E1LqqQ5ZDQhaepye+A4BufsCmmhh3rHRCYreW5sXXrzzOEhISFCHVdScUjbvY2X4jYOycDR2rT6tjdBACweNb9bNJlkX1MxOZlPIOX4Q0zMR4Fbf5lcY7OZibZ76X9Hjy1JOeteFyj0ma86HG200RMj2EF36Jm1/4V4DmjIjh7X8EwBfcR9N0aV4VDvGtCtMBRtmo0ISgDXWXe0XIvfgaC5dEKRSZWu3kACcTuVYTtaUPkU3A3qRiBrbz9W+/9G8B09E9mBt3njcFNriRFByJQVGEWNvbZXSolyvanWKD1vumCLk2Fkx0kzgS/K63XK79u0ZUHFdmvefvWBMSDixMjyDpmEGFCJDyRF1DqC847g4JKb+fT0qCZLDtqALOGQtxV4n4KxluGBfAjtD109Kh3BU/fNH+cZCklJCQo5cFCQE8VUQgONMy998X979oXFa4OyQEMA5Ztgg5tsh3SMgrIUxfI6pFUqcRl+lYdXoQKOhYsAYSYiknyWpq2CsaL6cmBMr36V0iISEd62INFcuFQ26raLwrQk66nlWREIe0rGrShIn8zmgOUYJub6G0ZkMdFro2F2OHhLxyRcig1eHM5D/gzPgP0xSn6VfRGBuhQ9a5XtH1P5gdlF5Xi4QA3P9OaG8hpeBCtovSGpFNaGjjLtc+ff/K44xlTGyRbTebo04zV3e9p+fehEaS5coPKlQNs3be37//jhoQsFiEnG6fLjE3HBvjUM3gd/5/+eCb/mduR2cYZuZ6numcQQrJG8+a4uijtz6K0orJPOdz8RPMHbXrl/86N4dmHtPZs0/7vc2hgFmuPUW8GqVp6Xb2y1JNiNCgMlpR68TPw6sd94qQe1Eb4YIcWvcti5PMHEhk4hcnOB4JSRD8lqZxvfnwjQ9zMDvw4rBIRLy2WSywQkpGosvQCdPnjo71yt7ynXYXC8Ygjl5iNM95cW4WkO9+40XOb9RvdHU0lpNqQqAYWDhDQ2TO8VcqEtKuakKAjuuYOySEr2AkJCqQkKl1x6pHQoIN88AVIQFdYwEJCYuQu0BCguTkXNskDM8dPFcc/0nPq02wjDjdOMcce896JOTukuKTIiEL917y/2/vzKPkuuo7//m9erV2V/UmdUtqtRZLsmRJXmRsyyuWwQs2JjABAgwOSQgkGQaynIQhYc7kcLJnTpYJk0xymORMBpgTwpBJAsEGkuCQMCB2j4n3TQbJi9bel+qquvPHW+rWq6Wr6lXXq2rdzzk66q719ntV993f/f6+v1/AmB5MzfKwk2WfxVoVsjwlJORiPRSdrI4FHVVCslqJ3q1tKCHeTnm7Cl7Q9O6nYzX4jukG73NuSlYpPYZSMKSmK30uSpWr361jEDKQsImpHHE1xkgmXt/PovlCtmjdtoPpWJ4yWhXUx2y49C4sga2lC1AqEisuk1BwKrkXWSPQFpGq9O5a86N3PvXCA2Mjozxr72a1qCiUSsxKlkWczb1LR1tTQaB6rtP9IFBeg8zn5yllJ4gPO72HlkoXQJUbI141fhXgGPyfmz7BSqGEEosz225zXmjhLKelBFaczTtuKb++9tmfrVMhy+sREo+VG3umYinfEL+wulD2hFCCUqFvVRAwQYihDvok0UyFLF8JaWGhq09O0NgT4nHbgLOIKqgCXz71ZT8I2T+6n4xWwnXVzqLE8kv0ep2+11sJsRIpv6JGfO4kZ+dXmJZhRgYSvPkVtVUQgEQs/EJfD0JmVmYgmXUWxW7KQlhPiE6YKmN1PSGJgXL5Rj8I8ZQQQu+erxdO8zLtb7LqeUK0xZuvhAyXb9M//yuzHVRCyhe+qcHy98ej7SDETkAyS0ycCkGwhhKi9z0Jq4TEO6OEpIPG9HpBCDTfK8QrdRqlhym5TkFIB9QdfcHeMB0rUMXKo5NKyHJxuamgZlOgYSFAPulsIAyVpisrfuXnwVNy1zEI0ctv10rF8tikdU3PpVL+d2daLxNOg3QsgP33YImQKikGS3NIMU9cwenM3qbGGpwPa805tdKxJnJJHo0foqQUhaLitA0lnHG2akrX38NDr4wF5WwMhWIuP0cu7SgRRWuGolJ+Y8QrNl/hBwXfeKncG2Rmxx3O34ByKiUObmY8u638+pr/ab5OrxCvMtZIJuEHliLin5elwpK/KeIpIYPBTYc+wgQhhprok0YzFbL8/PIWF7oVQUhdJaT8mKn0ZvYN7wPgc899jmemnwFw5FHtvVfdnM1FVwnx0rHWLCEaFjvtGwGX586RL5SYtXL86I07GwZAnVBCUnbKv8BeWLkAw1OsCOBOvGH7hFS8V5h0rGCZVP0C7u5KZ/JuSosXhPSDJ8TLT48lGhvToez30JWQmF1euHbUE1I+vruGLqm6v+3qWOCY061ymd6GwWlFUBVOCdky4CiiMYm11Cegyo+UiDmKRc5dKIzuqf9kv1dIg3Ss/CI8/38rHx8FejqWVjihbUZ2Of/ntjV8WDNsHUoh4tRk2LO5vqoSrJbk0XElpIkqcbrB+7xbpncl4Xx3c2q2UgnRq6cFyxt3EN1bM1qjR4jHzjFnTpnIJbEsKVdR1Mr0LheW+d6sU0hF38zyGdnJ0sgBkkrIqjlAkVDC+WxzgUA6kA5Ya370z3dBD0JSPGIfRrlL1e/Hi9juhl2rpnSo/szopnSAoWS5QudsfpZsyqZEnpIsUCwpvzHiQHzAf/9vvfwd/zlWbhtMHXVUkHgGkllfPYFKFXC2ThBy3k3HCgaW3maLV5GLgc3M4QQh/dojBJyiMwZDFRUTdRMVsryJo9X88nQ8DW6MU7dErz6B2Slu2/4Knpp+itNLp/2bj4wfgee/6/9eTI7AilOFwqusAevfJwQ76fcp8Rr7JYYmOHZpgx1WaishrXpCAEZSI7y48KKzy3XDe1lJp+Hs14BeSseq4wkBZ1f6/LOkl+chgZ+OlYSeDUKSsSQWFqWh7U7gkMiunY7lEdwpTQ46O+krc5UqSYeUkO25CdJ2uq3mojXxu6Z7QUiDz2yH0ssAprJT/MI1v0AqlqooyLAWwQDYV27u+g04/Rjsvb3+k70KWY2M6d/5eNkPcOVbmx5Xx9HTMzqhIF72Omce3nJF6JeayKX45XsPArB9pL5a5HsE6hjT295U0cv/FjQlpMFn0jN4n5lb4dyC8/ilhPPdHS7NYOsbTItl8/V6NSqEys2FRkrIbQfGEREunXB2y4eTw5yaP1URhBx/8bh/nb9h2w01X2du152kvv1tRDkdM2wsloeaU0KCamXtzvTVnpDxXJLTsQk+MvCTxFnltP19NsWE0dRoS5sP9d43GIToG6Gz+VlyqRGK4qi8xZJiIlNWTY+MH+Gx84/xzPTTFFkkRsZpAXDbL3H24Y/By/8CVHZ0H9SCkHpKyIUFTwmprHbmrZX89PiBzczNKSiumnQsw8ajG+lYUJ6cUrEU8Vh1iUGgcgEWT3PDthsqJpNN6U1sH9xecbEtuZP/wkrBqWbist59QrBTFSURAY4e3l/VGyNIskZw1I5/xdvFurByATKjrExe5VdfCtus0MMSJ1+1XWruRnu4PRrSS55psveN6SLiXEDtpGMulzopDbVy6auCEPciuDJX9k+I5XQCbxPdNDuUSrArt6vyLdutjgV+md6cX6K3QXCql67tQEB57ZZruXxz4/KgQYIBsK9Oju6GA/f4/VVq4qdjnat9/8wp+O4nnZ933ghT17U0to6ip2OFVJ2c10jCgdeWO2iH5Jpdo1yzq7FKsJYnxCvh2yr6BttKcaXpfjljgYaFC/awMw6VZ0C0MeppTuuphGibC7W6pXvEYxZ3HJxg55gz/1RcI1y83ltbB7bWTXNa3XWMmLtvrYB5Rsmkm/seBzc6GnlCdOVr00CSmCU8ET/Av8YvJx87hR2z2D+yv+lWADrBNLBgOlZFELIySy5tU7SmASqUEMA3p5eUYiXmeOzScRvSI7w8camvPnopXFCphNTrmu5VxwoGll4ana+EZMZ8JaRuFkkfYIIQQ030hUkzxnRvZ7XV/HJvMqrrB4EqJSQTz3D91uv9m46MH3EmJO29lbuLvLBS8HuEOONbfyVED0KyKZvJybUv3DU9IW2oNiMpZ1Hr5ft6F1gLq61yllCtyCTjsbYuAP7zGwYhzk5TemnaudL1QYleqL6o1vQp1FJCgosUb/GoG9PtVN0O4M2g56vn0nF2D+2uuL9tTwiUg5BmSvTqi8mI/D1BpWYgWNWoEV56VX7eaTgZ5Ph/c7wAlg3XvyfEKDuAno7Vo16qtajnCQmbjhVshNhMnxAoV8jyPCFzsfIGQrY4XX5gBErI6EDzAZl/jXCVkBfnX+Sx848BcNvUbXXn9sxAlpesXc4vCmas8YpFdSNaCUL0oNOyhM1Z5/YSS6jYOSxpLxVLfw+PesZ08JSQuK+ElJSqSK2ayk4xmhp1ghD7aefvcs/JGbeT+VBiqOI9k3bMv57O1VNCvG7pmdrpWF4VMwY2MWs8IYaNSstKiLeb1OKCxtuVaSit2qlyrry7c/yqHa/y7756/Ory41wsd3FXUjC9qHX6XmdjOnYK2yr3KhkbTDaVGx6PCUGxpFZgshbBXS7vgp2IJdoOHIK7x2FT2oLBVUU+tRuEZIoFUEXfE5K0YuV+Gj1IMxfZmjvSwUWKV4FpZbbsCQmpAI25xlQ7JuRS1UFIuHSsUSwLBkoLiCo1NqZXKCEd2J1vg+Bnt6XPcm6y/PPnfhGWtRKnJ78FJ77s/Hz4jR1TDNom0WElJAK8Rddsfraip4JXWrrdIMQSq2LB66V3rXXtGhv0lBDnezlrldOH03nNB+J5QpLZxspaSBzjsvPzRK756653jZhZnqGkSnzx+065ewuLW7ffWvd5mUSMZ2MH/d/PylZyNRpN1nxuQBleqzqWfr4ncu7tsZNlP0gbpnT9PTyC6VjxWNxXxGbzsyRtCxVzvudJK1eRrSEiXD1+NSWlWI49SUFm/MDQC0J0FcTDC9xqVcdayhf9IjrDmdpKiLcpvJoecQrPqBJZqzezBJohVBAiIveKyBMi8pSIvCtwX0ZEHhCRx0XkERF5X7ihGrpJ0LzXCKVU05J2kNfvfT137bqL+y67r/6DROC2Dzo51nuc4OPA6AHedfm7ePtlb/dlUV0xiWk7zGfnyvLuejcrxE4ynIkznImzbThF3JLKbst1cFJ6Kse2ZvfpGni7XLMrs5RUqalGXGtR1WU6ZJljXQmxJKCMeMZ0xNlVdi9Gaau3a6AHP/c1F/aWVV0xqUoJ0dKxdCUkBNftHuWNV0/yM6/eRzoRq0rHCqeEjGKJIJTIqrnGKYS60T6i3fmGKtxabDsCu252fn7xYfib9zgpWKUifOXDzu3pYbj6HZ0ZbBj0ICRsj5CImBx0gr65/FxF6pC+sdIufqpXcbnpDTQvmD+/uEqppDiPE4SIQHxZS9HzGxUOtz2+ZhgZSPCeY3t4y7VTXLm9+ffygpASJS4sX+CfT/4zAEcmjjT0V2USMc5YO7hgjTBtDfOStbNtJaTWsfZuC/Ym2+IGWHnrJPGYkIql2Jnb2dT71nsPcBoP1pqnvayMufwcIkIs4ZYfZ7jqsa/Z/RqUEpQUmEs86GdavLzoNFTU07c8vApZ8zW6pp/XGxXWScfylJC5ZFntzJZq9xzpB9o2pouIDfwecBswA3xLRP5aKaUnzP6WUupLIjIIfFNEHlBKPR1uyIZuYIlFKpaqkKvrsVpapeTm7re6q7plYAvvPPzOtR+4+xbnn8YdO++ofIy2Y2xnyzscZxfKX+x1N6aLEIun2TzoNQuxms4LTthWRepYGCWkoArMr86HTl2A6oVbaCVEe71Mwq5UaLx0LARKq+XqWL0ehNhNBCHgLAhXtfTGekpIfr5jSkjCtvjRm8rqx7bBbcStOKulVSwsEmGOrZuOBU6VoMbpWL2nhLQUhFgW3PGr8NU/hH/9K5g5CX/z7+CSY3DhhPOYa99dXR43CuwExBJOSeQeTmNshK7YnZg5wWjKmUfDGtPBNUHnnfx6r1z1WkGN12+jVFLMLK0yW0pREJukFCuLFXShW7rHaw5vbfk53kYVwJdOfslPy7pt6raGzxtI2AgJ5i3X4C6JCqN1I4LpqY1K9IITHHqqw7gXhMROMhCz2Du8l1ibqrgllj/3BVUQj1wix+nF034zRxWbgRLYNYKQqewUh4dv4aXZL7BkP8aJ+Se5MnOIM0trKyG10rEuLOhBSMCY7s6ZnhIynyjPodlCnn4lzJbmdcAjSqlTSql54AHgTu9OpdSiUupL7s/zwBNA698YQ2R4E8VaJXr1+0PtqoZFW9gkBsvqgyefQ7jSss2PQ7s4pkeaTiOqXuy3H4SA4wtpphvw2uMKKDShlZDy61XU1wdHCREhDsRKRRxjSLhdz27QdBCi+0JqpWt4QcjybHnR3uHvlG3Z7Mg6/UKSdjKUv4f0qF/9NVuaa6w0drA6VrvUbZTZLJYFN/003Pg+Zwt8eQYe/Vvnvk0CmkdSAAAgc0lEQVT7YP89HRppB/B8If2qhGQnfR+b3lyzk0qI3jU8HWt8nPR+G+cWVpjPF5mRYccDqJdtXpp2X3D9g5B20MvQ3v/s/c5tiaFyRkEdLEsqjpEou6LvRSOqlJAa1yM9CNHN6VtyKRQFVq2XiMekbT9I8H2CpnQP7/jM5h2fm+cJiZVqV++8IvcaLOX8Pf/rsf/JXH7ON4/rHhIPr7N9LWP6eS0IqZuOtbpISZWYi5UDwOzqxRmEbANOab+fAiZrPVBEpoArgG/XuO9dInJcRI6fPn26+smGyPAmjrWCEK++uP6cSNDMmOnhsgx6ppvpWFC5aGyhV0DQAJ6ItWFMT5YvfNMr0011A16LqnSs0J6QSiWkgpgNmTEEIa3wlZB0mApOXaDKmF6vUZ1eIStV46LmBSHFvJOSBeuSuuTtMg+E7H5NZsxvzplTM9VBpc6qFoREtDCualbY7mf58jfBnb9WGUzd+L7w/Tg6iff56gVlpg3iVpyprOOtOTF7wr+9E+qut2mgl6lda47UO4+fm8+zmC8wbQ05SqBeMW3JNaanm1PAu41+jZhbdeaYW6dubapwyYB2jbVUal2M6VCZAr51KMWq9RJKCsRjVuggxJub6wUhXrnb2ZVZ5vPzKHE+b6pQOwhRpRTZ/CuxRHh+7nk++cQn/ftqKSFeA8SaSoibjiVSXfHMU5MUTvr7rAA4c282v3bxoF5l3WdMEUkCfwm8XylVVVJEKfWnSqnrlVLXj4837qVg6C5+dZIGnpCSKvGxxz4GQDae5bLRy7oytppsvxZ23gRXvo30cLmet1dSEbqQjgWVC5OBtf0gHsFd2nY8IUPawnZ6ZXqd0rE65wmpuWgdcOaBTKlUro7V4yklTRnToVIJqZWml9TqvXu7q+vwt99zyT1cNnoZb9r3pnAvlBgglcqQTdlcNyHs3tQgqNHnkYiCympPSIhWWbtuhtd9GLZeCde80/m/l7j6HbDtKjjwuqhH0jaef+nEzAn/to6kY7nP1ZWQZkv0glMha2GlyKw15BQUqVBCupeO1Q4D8YGq8sbHpo419dzh+DiZ1atJFQ6QKE11NAgJlk722LN5kH1TswwmbTKJuN+suF3etO9NHBw7yJ0776x5v1chay4/x+ml0361y0K+djC/vFokUzhCWpzr1hee/4J/Xy1PiNe3qZYnxEvHGkrHq0r96xtbi4VF5lbnnU07IOdtWPUhYZoVvkCl8jEJfF1/gDg6/0eB+5VSnwrxXoYI8CblRkrIF7/3RZ6ffR6Atxx4S/0d4G6QyMBrfgOAgZJCxFnDnnXTsUTa81m0jL7L20IQUlUKt420p2w8iy02BVXgwvIFP4AMc8EOHrPwnpDy82suAgfH4fSjpIsFvD4hyTBdvbtAM8ZLoNKYXmuRopeqXnDzzNchdWlycJIP3fih8C8kgmRG2VJcYcsuu3Ep4VUtvSwixUD/7Il0ID1z/AD8wIdDjmqd2Ptq518fs3toNw9+/0FOL51mPj/PQHygI+lY3vdVD0LWmiNT8RgDyRgLK0XOza+wsFJgRlwlxPOErC6VP+c9GoSIOF3TPd/C/pH9fhGAtRhIxhm+cJf/ezbZZjrWGp4QPQixLGHn1hnOWCl25KZCrzFunbqVW6fqVwHTg5CXF172g4H8Su2GgIv5IoLFTvtu8pSXuRZWTbWlXB2rgFKqIh32vFvJM5iKBZW+moXVBeZX58GySRULxBcvVD2+XwgzA38dOCwik67x/G7g84HH/CawqJT6tRDvY4gIb+Kop4QsrC7wiSc+AcDO7E5evaN3LniWJX6qhWdMT9mxNZsGdgR90dhEZSyPau9F64t9EfFzWqdXpjuyaygiFQFSJ43pA7Vy8j1zeiFfTsfq8TKj+i5q3IrXb6RWEYTUUEL0qkbe7laPq0BkXIPnYp0mfh7ePBJh3wr9s5eyw/W7Maw/eiW3E7MnKqomdUIJ0YuuNJOy6lXIOreQZzFfZMYadoKQpQtQLJT9INCzQQhUegfXMqTr6PO1HZOmg/hmSvTqc6gehCilePLCk0D7pXlbwbt+lihxYvaEm24qrKwMUChWV6HyismMJy7lui3lBqWj6dGaKW6ej6ZUUhWFaACm/R4h1dcPPXV2cXWRufwcWDZZJeUNqz6k7SBEKVUAfh54EHgI+F2l1DkRuV9EtonIduADwHUi8pD7764GL2noMeo1i/L41JOfcr4IwI8c+hEs6aF8aMqyZ6nkpPS0k97UFp3yhLRpAPcuMLonJGw6k754C21M1z0hyTpKCJAurJT7hPR6EKId35qNCj30dKxaJTxTNZp29mineB8vrUxv0lYLr69GhOfSsoR4zAk8Mo38K4aeYGduJ+LmvT8381zF4jSUMb3GfLiWMR3KvULOL+SZd5WQmCe5L50v+0Ggp4MQr0JWKpbi+m3Xr/HoMvp8PZi0mw7im1FC9POpb3y+tPCSbxI/MHqg6bG2i96w8JnpZ7AsIVbKIVg1U6iW8k4gkU5Y3HfZfX7gUa/3WWXX9MrX84zpwW7pUBnILawuOMfEsskSKIzQZ4RaTSilPq2UulQptVcp9RH3tnuUUi8opU4qpUQpdUgpdZX7L6iUGHqYRsb0U/On+PwJ53Qe3XKUQ5sOdXVszTAQWOB2xZQOAU9I80GIvriPWVKVE9osXq33C8sXOuIJCY6tnU7ula+lp2PVUkLcIARxDNpAKso0vybQL7INVZtWlBCPHg/AmlJCioVyM79Ne9d/TA3wlLyuzQeGtknZKbYNbgMcJUQPQkIZ02ukdzYT1Phd03Vjujc1Lpwp+0Gg6dLsUXDz5M2k7TRv2PuGllRmXQlptlEh1PCE1Aj46nlC9Mpoe4b3NP2e7eIZ0wGenn7auRYrt2LWUnUQsuyqGZmEzcTABO84+A4G44PcvvP2mq8/mNSDkMoKWZ4xPWhKh2pPyHx+vqyELJ6DPu0VEsYTYtjgNPKEfPSRj1JUReJWnPsONmg0GCHBIKQrpnQIKCG1a5HXQl+ct6uCQLn6yfRKuURv+CCkPLawzQoTgT4hVXjGdCXlPiE9HoToi5qGOctreUISg/hmJo+eV0K0IESp2r6Q7321vECLuIxt0raYo43yvIZI2JXbxan5Uzw385yfXgod6BMSoBm12DOnn5lbYXm15BjTlft5XzgLK7PaCw63Pb715ujWo1y35bqW0xHT2nzdrCkdWveE6NkXz806QUjGzjCRmah6XqfRSxgvFZaIiWCXhoHaXc4XXSXEW1/ctesu7tx5Z91jqwdvs5oSUiiW/CCnphJiB4zpbjrWIO51culCS+uNXqG38mcMPUU6XtsT8p3T3+GhMw8BcO8l99YsQ9cLBCfJrvQIgcogpBVPSLwzKU/rooTEu+kJ0ZQQ//1DlpJdZ9LxJpWQxBpBiGVVqyH94gkp5iFfVQDR4YkHyo+dOtqdcdXB+/y21KjQEBleOekX51/003KgM31CdJqZI8cGncd4ufy+MR0qlRA7Vfld70Ha8UPp83UrQUgylsRC92NVH3/bsrHFeU092PSUkJ25nV3xcOnpWEBACakOQpZ8JUQvelF/nPpxm9eCkGnttUdqeEJsy/Y/o0urS24QEifnBcGL/ekLMUGIoS7eRL1cWEa5O7NKKT7+6McBJ6/09XtfH9n41mIgEVU6ljvBxtMVvUvWotJ30f5YPSVkubhMvuSZ8nvIE6KnY9XyhKRHIJYg00dBiL6oaZyOpf0d9XLGk4EqLD2vhGhpJ7MvVN+/cM5RQgD239108871wvv8hirPa+ganjm9RImnpp/yb09Y4atjediW3VSfjLHADnVRbJRXFn3hbNkX1cN+kDBUekKaT8cSEV8hFqRuOWRPofI2PpVSfnlmLxhdbxKxRMX4nCBkGKithHjpWM2uL/Qu8/rrndWaKtdKx4Kyyu57QmKuJwT61pxughBDXbyJukTJX8w+dv4xTs6fBOCHLv2hnq5aFOxB0bV0rC2XO/9vv6ZxydIAiQ4t9PXKJ/7rdTIdK+Rx3DSYYCKXxI4JB7bUKHsoAoPjHCjFEOCyko3V412fm/aETBwCy4ah7ZDdUvsxyYA5vdeVkLG95cDiOx+tvv/Jz/lpdVz6mu6Nqw6HJ51F46FtNYoAGHoOffH55Pkn/Z87UR3LY60eIR5jg9WLQ+Wp3Ytny0pID/tBwlDhCUm3FsR782IylqyrFHjnxVPwzy2f8xsqdisIgUo1RATS4gSVtTwhXjpWs0FIPGb5j9WVkOPPOJ46S2BqpLaKNmA7m1gz+RknZc2yGVTuWqFPzelmK8hQF333fLmwTDKW5MHvPwg4+Yk3T94c1dCaYjAqT8glt8J9f9Vyx1x9od/pIKTZi2w9OhUgAdgxiz/8t1ezUigxlK6zmzY4zsGZk/xxPuvs9PS4GlBRHauRJ2R4Cu77lKOI1FMEgh2uIyxp2xQDm+DwG+HhT8Jz/wInvwXbX+HcpxQ8cb/z89Yrnb8/Yn785t284cgkmwZ7+zNlcBhMDLI5vZkzS2d4/Pzj/u2drI7VbEDjlejVkcHNsPi8swj0vFw97AcJg64eBq+va+EFIekGG0rBIERvUrk7170gJJvIcnrptP/7UHITs6vVSkixpMgXXN9iC+md2ZTN0mrRr45VLCn+8XHn/a7dNcpQjXQsKF9bXl542blBhGw8AyuFvg1CjBJiqIu+cF0qLLG4usjxF44DcNPkTaEuAt2gqjpWN3PABza13JCtYqEfwr8yXOMCGPZcJTvYJ8R7jboBCPjm9BEsbKSyAWQP0rQSAk6qht3gfPSbEgJOd26v5PBX/yuU3Pr3Lz0MM45yyoHXRjK0ICJiApA+w0vJmsk331ywEcFNmWbTVYfS8apeU7FBVwlZ0JWQDZqOVeEJaT4dC7QgpEEpZL9/i5uO5ZnS41bcr5LWDXLaHBy34oy6G3uzgZK6ep+PVtK9vZQsrzrWN0+cZ9ptVHjHwfrme8+c/vLiy+WxprwS6Wv0aepRTBBiqIu+Y7FcWOYrL3zFT8tqpcFRVAR3asLu4K83nfKE6NU9PMJ7QjqXjtUUg4FiBz2uhLQUhKxFUAnphyAkmYVr3+X8fP45eOzTzs+PuypIPAO7XxnN2Ax9T61UnDAbK8HqWM0qxZYlVY3k4jl3rlo8Vw5CNqgnRE9xzrVgTActCGkwP3rXKc+YrpvSY130kunpWJvTmxlyPRpBY/qyFoS0UuhC75oO8PePOkHFcCbONbvqZ1B4Ssj0ynT5tTJuGwCjhBg2GvqOxVJhiS9+74uA0x39kqFLohpW00TWJ6RNdCUkTIneuBUnG6/0WnS2OlYXpo3BwG5Qjy/E41bcrxI3lQ2ZclSlhPR2AOaz/7WOPwTgG38Gcy/Bs//k/L731T2vZhl6l11Du6puCxOEBHfjW3mtMU1FE4F41g1CVpdgZc59g43pCZnIpUjYFiKwc1NrxUJ25HYAsD27ve5jgp3svXQsTwnrFvpG3ubMZr+sbrC5oNeoEFrbnPNUpPmVAhcW8nzjhFPQ4NUHxhv2BxuoUaBlcMC9VvapMd14Qgx10Xcsnpp+imdmngHg2NSxrpTKC0tQCen1vgCdrEA1nBr2DX0Q3hPS6XSsNekzJURE+NANH+LFhRc5OHYw3ItVVcfqk8W7ZcGN74PP/IyzGPu7nwOvvHePpGIZ+pPgItSW5qpZ1aNKCWlhk0OvkJVJxLCCcxVsWCUkm4rz+z90FSuFIpPDrc1Lb770zRwcO9iw67l3nVoprjCbn+XcspNi1E1TOlQ2LBzPjFNYdoKGoCdkKaQSMre8yhcfP03JtRLd3iAVC2qrSLnsVueHPg1CjBJiqIs+MX/uuc8BTinDW7bfEtWQWqI6Hat/gpBELGQQEjCn12rO1QqdMs03TVUQ0ttKCMBYeozDmw5jScjj028lenW2XQV73FRNr1zv6G7YXH/hYTCsxWhqlKFEeXc6rMctGHS0skkzqgUhAwnb8f8F2aBBCMCOsQz7JmpUNVyDRCzBkfEjDdOxvPO6XFiuNKV3OQjR07HGM+O+EhJMx1rMt+cJybprk9mlgp+KdXBrju11qmJ5BJWQVCxF3MsayM87apyHUjBzqukxRYUJQgx10Sdqb0fimolrKnYJeplgid7eV0K0hX7IlKeqICR0id4uKyED/ReEdIyqIKTP/vajPwX6InH/a1sqVW0wBBGRipSs0EFIIOhoZZNGT8caSF58Qch64q05VoornJg9AYCFxY7sjq6OI+gJ8coRL+aLFIol/z49HauV9UUuXU7HOjXtBA5rqSBQHYTkErnKhsieGlIqwpd/Hz71TjjzRNPjigIThBjqUmvh2g+GdI+gJyTVR8b0sErISKryIhg2CNmUdZ6fTdmhx9YUiUzlYrzfFuJh6GclBJz+J1e9zfk5loB9t0c7HsOGQE/JChuExK14ZQfvFpQQPR1rIBlzPFzB8ZggpC30Er2eKX0yO0k81lolrrBsGSj3cNqR21FRCWx+pewL0Y3prWzOVaWKx2PcvLdGMBvAq47lv05iEAY2l29YPAv5Rfj8B+HRv3XSYY//cdPjigLjCTHUxRKLVCzlm8TGUmNcsfmKiEfVPEnbwo4JhaKTcNm1PiFtUqmEhBtrUAkJe9G+4ZIx3v3KS9izeaCqROW6MThRNnpeTEFIUGnsx7/96h9xeiWMXmIWZIaOoKfkhN1UERGSdpKlwlLLr6c3LMwkbEflG9hUTj+07OqNBENTeOchX8z7QUi3U7EAtg1u471XvRdLLCYHJzmduuDfN7tUYNitltV2OlagvPEt+zY1paQElZBsIlupxJ1+HL7yh3DuafcPuQru+JWmxxUFJggxNCRtp/0g5NjUsfD57l1ERBhM2n797V5Px0p0UglJlhd+CSsR+rwlbIsfuLJ7ddoBxxfiTab9pgaEIaVVxxILurwL2BGsGBz+wahHYdhAdDIIASf1xwtCWjKmB9OxoDIISY+Y9MM28c7D4uoiC6sLQPcrY3no3tec1tNKN6d7SkjMkpYqWmYD5Y2bScWCamN6NpF1NnssG0oF+NqflO/cdyfc+h96/vrRPytKQyTok/OxqWPRDaRN9IoVqT4ypof2hGgNC8Oa0iNDN6f3oxrQLgmtT4idMgsagwHHIOylTXWiUa6egtWSElJhTHevKXpevlH+2sY7ryVKKJwMhiiUkCB60KCb0xfdIKTV8v/6602NpjmwpTnlzOsT4r9OPOtUJcyMVT7wFT8Kt32w5wMQMEGIYQ08g9bhscN+H4R+YjBZ/hKmEr39cU8nYn6qU6vdaIPoSkjY8ryRkZt0/o9nIHYRibbxtLOzBReXAmQwNMASiz3DewCq+iC1g77B1socmYrHGHJ3xkfctJyKvHwThLRNrfMQlRKik0tVKyFKKR57cRaoVjbWYigd9/eWbr9soumWBzXTscDx4YFz3bjtg3DNj/XN5tVFdGU3tMNbDryFvz/x97zx0jdGPZS2GNQqZPV6s8JUPMZPvfISnj27wNHd4Zpd6c2WOpG6EAmXvgbOPwvbro56JN1FxMkpX7pwcSlABsMavP2yt/PZZz/LvZfcG/q19AVvK+lYAO85tofjz50vp9HoefmZjdmosBsEFa6JzETV7n8UpOIxErZFvlBidskxph9/9jzfPTkDwLH9rW3QZlNx3nnTbk5NL/HaK7Y2/bygMd0PQq57Nzzy13Dw9bD1ypbGEjUmCDE05NDYIQ6NHYp6GG2jV8jqdWM6wN2XNz8hNSJtp0nGkqwUV/o3CEnl4NgvRj2KaEgOukFIn547g2Ed2DO8h5+++qc78lr6vNjqHHnj3k3cqFcz0tNhtFRYQ2sElRC9LHPU5FI2Z+fzzC6vki+U+LMvO8b5TYMJfvDqyZZf7w1HWn9OMpYkJjGKykkD84OQLZc7//qQ3s5PMRhC4gUhliXEu1FatkcQEb9CVt96Qi5mPCXLKCEGw7qgqx+hN2r0dCyjhLRN8Fq1Oxe9H8TDS5GeXS7wtw+d4uVZp2DPj920u2sbnCJSoYb0S8+2Rlw8qzLDRUnOzdXM9IEK0mlGU87FsFGHWkOPknKDkLg5dwbDelDhCQkb7OtFNNImCGmXoBLSC6Z0D69h4ffOLfC/v3kScLqc37Jv7f4enURPT9sIQYhJxzJsaI7tH+ebz1/glfs2r/3gDcbdu+9mfnWeO3feGfVQDK1y8PUw9yIc+jdRj8Rg2JBUeELCFu8YnID998DM92DH9SFHdvES9IT0VBDiKiHPnHFKB4vAu195SdOm8k5hghANEbkX+F0cReW3lVJ/Grj/OuB/AEngo0qp3u6aYthwTI1m+IO3Hol6GJFwdOtRjm49GvUwDO2w46jzz2AwrAsV6VhhU1ZF4NgHQo7IoAeDI8mRigIrUaP3CgG447IJ9o4P1nn0+jFglytkdaJKXNS0nY4lIjbwe8CrgCPA+0UkUKyYPwLeBuwH7hGR/nTOGAwGg8Fg2DC02yfEsH7owWAvqSBQWaY3nYjxwzfsjGQcXpneVCxFvA/6gKxFGE/IdcAjSqlTSql54AHAz/sQkW2ArZR6WClVBD4BhK+rZzAYDAaDwRACPfAI7QkxdIR0rOyB64X+IDqeJwTgbddNMZwJ3zCzHdKuT3AjpGJBuCBkG3BK+/0UMNnC/QCIyLtE5LiIHD99+nSI4RgMBoPBYDCszRWbryBjZzgyfsQoIT1CPBbnqs1XkYqluHHbjVEPp4Jrd40ykIxxeHKIe6/YFtk4rpm4hrgV54ZtN0Q2hk4iSqn2nijyJuCYUuq97u/vB5RS6nfc368BPqSUutf9/c3Ard7ja3H99der48ePtzUeg8FgMBgMhmZZLa1ii911c7GhPkopCqpA3Oq9VKNSyVkvW1a0n5fV4mrfpWKJyNeUUlVVG8IoIS9QqWxMurc1e7/BYDAYDAZDJMStuAlAegwR6ckABJzgI+oABOi7AKQRYYKQrwOHRWRSRAaBu4HPe3cqpV4AiiJyhYjEgLcCnwk1WoPBYDAYDAaDwdD3tB2EKKUKwM8DDwIPAb+rlDonIve7pnSA9wJ/ATwJfE4p9d2Q4zUYDAaDwWAwGAx9Tqg+IUqpTwOfDtx2j/bzceBQmPcwGAwGg8FgMBgMG4sw6VgGg8FgMBgMBoPB0DJtV8daD0TkJeBE1ONw2QyciXoQFzHm+EePOQfRY85BtJjjHz3mHESLOf7RsxHOwS6l1JbgjT0VhPQSInK8VjkxQ3cwxz96zDmIHnMOosUc/+gx5yBazPGPno18Dkw6lsFgMBgMBoPBYOgqJgipz59GPYCLHHP8o8ecg+gx5yBazPGPHnMOosUc/+jZsOfApGMZDAaDwWAwGAyGrmKUEIPBYDAYDAaDwdBVTBBiMBgMBoPBYDAYuooJQgwGg8FgMBgMBkNXMUFIABG5V0SeEJGnRORdUY/nYkBEpkTkn0TkURF5WETe7N6+R0S+KSJPi8ifiIhEPdaNjIhkROR5Efkd9/frROQR9/j/ctTj2+iIyG4RedD9HnxXRAbMOeguIvJz7vF+VEQ+LA7mHKwjIvLXInJBRD6l3VbzmJtrQucJHn/3OvCAiDzunoP3aY/d5M5RT4nI/xGRVHQj3zjU+g64t1si8rXAd2NDfQdMEKIhIjbwe8CrgCPA+0VkLNpRXRQUgJ9VSh0E7gT+i4gMAL8NfEgptRfYBLw2wjFeDPxH4Lj2+x8BbwP2A/eIyOWRjOri4c+BX3a/B7cCK5hz0DVEZDPwXuAVwOXu/9djzsF68wfAOwK31Tvm5prQeWod/99SSh0AjgL/XkT2urf/IvBXSql9wLOA2ajtDLXOAcCPU93Ae0N9B0wQUsl1wCNKqVNKqXngAZxFsWEdUUq9qJR6yP35JeAsMArcCHzWfdjHgddFMsCLABHZBxzA+cwjItsAWyn1sFKqCHwCuDfCIW5oROQQsKqU+hcApdR5YBxzDrqNDaSAuPvPwpyDdUUp9U/AnPd7vbnH3fE114QOEzz+SqlFpdSX3J/ngSeAre7dPwB8zP3ZHP8OETwHACIyCrwV+Ih224b7DpggpJJtwCnt91PAZERjuSgRkVcAMWAJOK/KNaTNuVhffgf4Je13813oLvuAeRH5jIh8W0Q+iDkHXUUpdQbne/A94AXgH3DUKHMOuku9z/0Y5prQVURkCrgC+LZ705BSasb92Rz/9eXXgV8FitptG+47YIIQQ8/gRv4fBX4i6rFcTIjI64EnlVJPRj2WixgbuAV4D3ADcAdwW6QjusgQkREclWMXzoX9RiAT5ZgMhqgQkSTwl8D7lVILUY/nYkJEjgAjrkKyobGjHkCP8QKVUeUk8PWIxnJR4U54f4OTi/oVV3YcFRFxo/5JnPNj6DzXA291CwIM4qShzFL9XTDHf/04BXxTKfV9ABG5H2cBbM5B97gdeNpNhUNEPgscw5yDblPrOvwCcA5zTegK7vX3o8D9SindLD0jIp4aYo7/+nE9cIuInMBJD82KyEeAn2SDfQeMElLJ14HDIjIpIoPA3cDnIx7Thsed8P4c+KJS6mMA7hfsOGXT1duBz0QywA2OUuqXlFJTSqldwC8A/10p9StAUUSuEJEYTm6qOf7rxzeAcREZERELeCXwLcw56CbfB24UkZR7vI8B/w9zDrqKUuoFahxzc03oKr8JLCqlfi1w+98BP+z+fB/m+K8LSqk/VkpNutfktwIPKKV+YiN+B4wSoqGUKojIzwMP4gRo/1kpdS7iYV0M3AS8BXhYRN7g3vbDwAeAT4jIHwD/SNmMZegO7wX+Amcn5mNKqe9GPJ4Nizv3fBD4Z0CALyil/k5EzmLOQVdQSh13FajvACWcOefTwMuYc7BuiMg/AFcCAyJyEngz9ecec03oMDWO/9twjvOjIvKQ+7APKKU+jxOcfEpEfhb4V+A/dX/EG49a3wGl1FfrPHxDfQek7G8xGAwGg8FgMBgMhvXHpGMZDAaDwWAwGAyGrmKCEIPBYDAYDAaDwdBVTBBiMBgMBoPBYDAYuooJQgwGg8FgMBgMBkNXMUGIwWAwGAwGg8Fg6ComCDEYDAaDwWAwGAxdxQQhBoPBYDAYDAaDoav8f+7cX9iGuUTtAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 980x980 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, [ax1, ax2, ax3, ax4] = plt.subplots(4, 1, figsize=(14, 14), dpi=70)\n",
+    "\n",
+    "# cl = plt.cm.plasma(np.linspace(0, 1, 3))\n",
+    "# cl = [\"plasma\", \"b\", \"dodgerblue\"]\n",
+    "for i in range(magic_number):\n",
+    "    ax1.plot(list(range(layer_num*head_num)), tc_ranking[f\"{i}\"], label=f\"ranking-{i}\", linewidth=2.5, alpha=0.8)\n",
+    "    ax2.plot(list(range(layer_num*head_num)), st_ranking_1[f\"{i}\"], label=f\"ranking-{i}\", linewidth=2.5, alpha=0.8)\n",
+    "    ax3.plot(list(range(layer_num*head_num)), st_ranking_2[f\"{i}\"], label=f\"ranking-{i}\", linewidth=2.5, alpha=0.8)\n",
+    "    ax4.plot(list(range(layer_num*head_num)), q_ranking[f\"{i}\"], label=f\"ranking-{i}\", linewidth=2.5, alpha=0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fda6327",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34a0dfd7",
+   "metadata": {},
+   "source": [
+    "## Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c515c68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eval_st = 1\n",
+    "eval_tc = 1\n",
+    "\n",
+    "eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=32)\n",
+    "\n",
+    "if eval_st:\n",
+    "    print(\"Student Model Inferece\")\n",
+    "    student_model.eval()\n",
+    "    student_result = do_eval(student_model, task_name, eval_dataloader, device, output_mode, eval_labels, num_labels, teacher_model=teacher_model)\n",
+    "    print(f\"Student Result : {student_result}\")\n",
+    "\n",
+    "if eval_tc:\n",
+    "    print(\"Teacher Model Inferece\")\n",
+    "    teacher_result = do_eval(teacher_model, task_name, eval_dataloader, device, output_mode, eval_labels, num_labels)\n",
+    "    print(f\"Teacher Result : {teacher_result}\")\n",
+    "    \n",
+    "eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "0eedf469",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input_ids : tensor([[ 101, 2198, 5720, 2000, 3021, 2055, 2370, 1012,  102]])\n",
+      "tokens : ['[CLS]', 'john', 'talked', 'to', 'bill', 'about', 'himself', '.', '[SEP]']\n",
+      "A : john talked to bill about himself . \n",
+      "B : \n",
+      "tensor([8])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sampling Sentence \n",
+    "i = 0 \n",
+    "# num = \n",
+    "num = 0\n",
+    "for step, batch in enumerate(eval_dataloader):\n",
+    "    model.train()\n",
+    "            \n",
+    "    batch = tuple(t.to(device) for t in batch)\n",
+    "    input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch\n",
+    "    i = i + 1\n",
+    "    if i == num:\n",
+    "        break\n",
+    "\n",
+    "seq_length = seq_lengths.item()\n",
+    "\n",
+    "input_ids_sliced = input_ids[:,:seq_length]\n",
+    "input_id = []\n",
+    "for i in input_ids_sliced[0]:\n",
+    "    input_id.append(i.item())\n",
+    "tokens = tokenizer.convert_ids_to_tokens(input_id)\n",
+    "\n",
+    "\n",
+    "\n",
+    "sample_sentence_a = str()\n",
+    "sample_sentence_b = str()\n",
+    "index = 0\n",
+    "\n",
+    "for i, word in enumerate(tokens[1:-1]):\n",
+    "    if word == \"[SEP]\":\n",
+    "        break\n",
+    "    sample_sentence_a += word\n",
+    "    sample_sentence_a += \" \"\n",
+    "index = i\n",
+    "\n",
+    "for i, word in enumerate(tokens[index+2:-1]):\n",
+    "    if word == \"[SEP]\":\n",
+    "        break\n",
+    "    sample_sentence_b += word\n",
+    "    sample_sentence_b += \" \"\n",
+    "\n",
+    "sep_index = torch.where(input_ids[0] == 102)[0]\n",
+    "\n",
+    "if len(sample_sentence_b) > 1:\n",
+    "    sample_sentence_b_start = segment_ids[0].tolist().index(1)\n",
+    "else:\n",
+    "    sample_sentence_b_start = None\n",
+    "\n",
+    "print(f\"input_ids : {input_ids_sliced}\")\n",
+    "print(f\"tokens : {tokens}\")\n",
+    "print(f\"A : {sample_sentence_a}\")\n",
+    "print(f\"B : {sample_sentence_b}\")\n",
+    "print(sep_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "265abf87",
+   "metadata": {},
+   "source": [
+    "## BERTViz Model View"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "780d1da2",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "from bertviz.transformers_neuron_view import BertModel, BertTokenizer\n",
+    "from bertviz.neuron_view import show\n",
+    "\n",
+    "bertviz_neuron_tc = 1\n",
+    "bertviz_neuron_st = 0\n",
+    "bertviz_model_tc = 0\n",
+    "bertviz_model_st = 0\n",
+    "\n",
+    "# Quantization Setting\n",
+    "if bertviz_neuron_st or bertviz_model_st:\n",
+    "    for name, module in student_model.named_modules():\n",
+    "            if isinstance(module, (QuantizeLinear, ClipLinear)):    \n",
+    "                module.act_flag = True\n",
+    "                module.weight_flag = True\n",
+    "            if isinstance(module, QuantizeAct):    \n",
+    "                module.act_flag = True\n",
+    "                module.weight_flag = True\n",
+    "\n",
+    "if bertviz_neuron_tc or bertviz_neuron_st:\n",
+    "    if bertviz_neuron_st:\n",
+    "        for name, module in student_model.named_modules():\n",
+    "                if isinstance(module, BertSelfAttention):    \n",
+    "                    module.output_bertviz = True\n",
+    "    if bertviz_neuron_tc:\n",
+    "        for name, module in teacher_model.named_modules():\n",
+    "                if isinstance(module, FP_BertSelfAttention):    \n",
+    "                    module.output_bertviz = True\n",
+    "\n",
+    "    model_type = 'bert'\n",
+    "    model_version = 'bert-base-uncased'\n",
+    "    \n",
+    "    tokenizer = BertTokenizer.from_pretrained(model_version, do_lower_case=True)\n",
+    "    if bertviz_neuron_tc:\n",
+    "        if len(sample_sentence_b) > 1:\n",
+    "            show(teacher_model.cpu(), model_type, tokenizer, sample_sentence_a, sample_sentence_b, display_mode=\"light\")\n",
+    "        else:\n",
+    "            show(teacher_model.cpu(), model_type, tokenizer, sample_sentence_a,display_mode=\"light\")\n",
+    "    if bertviz_neuron_st:\n",
+    "        if len(sample_sentence_b) > 1:\n",
+    "            show(student_model.cpu(), model_type, tokenizer, sample_sentence_a, sample_sentence_b, display_mode=\"light\")\n",
+    "        else:\n",
+    "            show(student_model.cpu(), model_type, tokenizer, sample_sentence_a,display_mode=\"light\")\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    layer_num = 24\n",
+    "    head_num = 16\n",
+    "else:\n",
+    "    layer_num = 12\n",
+    "    head_num = 12\n",
+    "    \n",
+    "all_layers = list(range(layer_num))\n",
+    "layers_to_show = all_layers[20:]\n",
+    "\n",
+    "if bertviz_model_tc or bertviz_model_st:\n",
+    "    \n",
+    "    if bertviz_model_tc:\n",
+    "        print(\"teacher_map\")\n",
+    "        for name, module in teacher_model.named_modules():\n",
+    "                    if isinstance(module, FP_BertSelfAttention):    \n",
+    "                        module.output_bertviz = False\n",
+    "        teacher_model.eval()\n",
+    "        teacher_model.to(device)\n",
+    "        teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "        model_view(teacher_probs, tokens, include_layers=layers_to_show,  display_mode=\"light\")\n",
+    "        \n",
+    "    if bertviz_model_st:\n",
+    "        print(\"student_map\")\n",
+    "        for name, module in student_model.named_modules():\n",
+    "                    if isinstance(module, BertSelfAttention):    \n",
+    "                        module.output_bertviz = False\n",
+    "        student_model.eval()\n",
+    "        student_model.to(device)\n",
+    "        student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_outputs=None)\n",
+    "        model_view(student_probs, tokens, sample_sentence_b_start,include_layers=layers_to_show, display_mode=\"light\")# , include_layers=[0, 1])\n",
+    "        \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64cf6575",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from torch.nn import MSELoss\n",
+    "mse_func = MSELoss()\n",
+    "loss_cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6)\n",
+    "norm_func = torch.linalg.norm\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    layer_num = 24\n",
+    "    head_num = 16\n",
+    "else:\n",
+    "    layer_num = 12\n",
+    "    head_num = 12\n",
+    "\n",
+    "attention_mean_check = 0\n",
+    "cover_mean_check = 0\n",
+    "kl_div_check = 1\n",
+    "mse_check = 0\n",
+    "attnmap_mse_check = 0\n",
+    "norm_check = 0\n",
+    "\n",
+    "exclude_sep = 0\n",
+    "\n",
+    "for name, module in student_model.named_modules():\n",
+    "            if isinstance(module, BertSelfAttention):    \n",
+    "                module.output_bertviz = False\n",
+    "for name, module in teacher_model.named_modules():\n",
+    "            if isinstance(module, FP_BertSelfAttention):    \n",
+    "                module.output_bertviz = False\n",
+    "                \n",
+    "for name, module in student_model.named_modules():\n",
+    "            if isinstance(module, (QuantizeLinear, ClipLinear, QuantizeAct)):    \n",
+    "                module.act_flag = True\n",
+    "                module.weight_flag = True\n",
+    "\n",
+    "seed=42\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "\n",
+    "student_model.eval()\n",
+    "teacher_model.eval()\n",
+    "student_model.to(device)\n",
+    "teacher_model.to(device)\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_probs=teacher_probs)\n",
+    "\n",
+    "\n",
+    "if cover_mean_check:\n",
+    "    print(\"COVER MEAN CHECK\")\n",
+    "    top_k = 5\n",
+    "\n",
+    "    for i in range(layer_num):\n",
+    "        teacher = teacher_probs[i][0]\n",
+    "        student = student_probs[i][0]\n",
+    "\n",
+    "        head_sum = 0\n",
+    "        for h in range(head_num):\n",
+    "            coverage_head_sum = 0\n",
+    "            for row in range(seq_length-1):\n",
+    "                if exclude_sep:\n",
+    "                    tc_argsort = teacher[h][:seq_length-1,:seq_length-1].sort(descending=True)[1][row][:top_k] # top-k\n",
+    "                    st_argsort = student[h][:seq_length-1,:seq_length-1].sort(descending=True)[1][row]\n",
+    "                tc_argsort = teacher[h].sort(descending=True)[1][row][:top_k] # top-k\n",
+    "                st_argsort = student[h].sort(descending=True)[1][row]\n",
+    "\n",
+    "                max_idx = 0\n",
+    "                for idx in tc_argsort:\n",
+    "                    tmp = torch.where(st_argsort == idx)\n",
+    "                    max_idx = max(tmp[0].item(), max_idx)\n",
+    "\n",
+    "                coverage_ratio = max_idx / student.shape[1]\n",
+    "                coverage_head_sum += coverage_ratio\n",
+    "\n",
+    "                # print(f\"H{h} : {coverage_head_sum/seq_length}\")\n",
+    "\n",
+    "            head_sum += coverage_head_sum / seq_length\n",
+    "        print(head_sum / head_num)\n",
+    "\n",
+    "if kl_div_check:\n",
+    "    print(\"KL DIV CHECK\")\n",
+    "    for i in range(layer_num):\n",
+    "        if exclude_sep:\n",
+    "            if len(sep_index) == 2:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000; teacher_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000; student_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "            else:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                \n",
+    "            teacher = torch.nn.Softmax(dim=-1)(teacher_atts[i])\n",
+    "            student = torch.nn.Softmax(dim=-1)(student_atts[i])\n",
+    "            \n",
+    "            student = torch.clamp_min(student, 1e-8)\n",
+    "            teacher = torch.clamp_min(teacher, 1e-8)\n",
+    "        else:    \n",
+    "            teacher = teacher_probs[i]\n",
+    "            student = student_probs[i]\n",
+    "        \n",
+    "        neg_cross_entropy = teacher * torch.log(student) \n",
+    "        neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "        neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "        # p(t) log p(t) = negative entropy\n",
+    "        neg_entropy = teacher * torch.log(teacher) \n",
+    "        neg_entropy = torch.sum(neg_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "        neg_entropy = torch.sum(neg_entropy, dim=-1) / seq_lengths.view(-1, 1)  # (b, h, s) -> (b, h)\n",
+    "\n",
+    "        kld_loss = neg_entropy - neg_cross_entropy\n",
+    "\n",
+    "        kld_loss_sum = torch.sum(kld_loss)\n",
+    "        print(kld_loss_sum.item())\n",
+    "\n",
+    "if mse_check:\n",
+    "    for i in range(layer_num):\n",
+    "        print(mse_func(teacher_atts[i], student_atts[i]).item())\n",
+    "        \n",
+    "if attnmap_mse_check:\n",
+    "    for i in range(layer_num):\n",
+    "        if exclude_sep:\n",
+    "            if len(sep_index) == 2:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000; teacher_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000; student_atts[i][:,:,:,sep_index[1]] = -100000\n",
+    "            else:\n",
+    "                teacher_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                student_atts[i][:,:,:,sep_index[0]] = -100000\n",
+    "                \n",
+    "            teacher = torch.nn.Softmax(dim=-1)(teacher_atts[i])\n",
+    "            student = torch.nn.Softmax(dim=-1)(student_atts[i])\n",
+    "            print(mse_func(teacher, student).item())\n",
+    "        else:    \n",
+    "            print(mse_func(teacher_probs[i], student_probs[i]).item())\n",
+    "\n",
+    "if norm_check:\n",
+    "    for i in range(layer_num):\n",
+    "        print(mse_func(torch.linalg.norm(teacher_values[i], dim=-1), torch.linalg.norm(student_values[i], dim=-1)).mean().item())\n",
+    "        # print(loss_cos(teacher_values[i], student_values[i]).mean().item())\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a0039fe",
+   "metadata": {},
+   "source": [
+    "## Norm-Weight Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d95ec106",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from torch.nn import MSELoss\n",
+    "mse_func = MSELoss()\n",
+    "loss_cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6)\n",
+    "norm_func = torch.linalg.norm\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    layer_num = 24\n",
+    "    head_num = 16\n",
+    "else:\n",
+    "    layer_num = 12\n",
+    "    head_num = 12\n",
+    "    \n",
+    "sep_w_list = [ AverageMeter() for i in range(layer_num) ]; cls_w_list = [ AverageMeter() for i in range(layer_num) ] ;punc_w_list = [ AverageMeter() for i in range(layer_num) ] ;other_w_list = [ AverageMeter() for i in range(layer_num) ]\n",
+    "sep_n_list = [ AverageMeter() for i in range(layer_num) ]; cls_n_list = [ AverageMeter() for i in range(layer_num) ] ;punc_n_list = [ AverageMeter() for i in range(layer_num) ] ;other_n_list = [ AverageMeter() for i in range(layer_num) ]\n",
+    "sep_v_list = [ AverageMeter() for i in range(layer_num) ]; cls_v_list = [ AverageMeter() for i in range(layer_num) ] ;punc_v_list = [ AverageMeter() for i in range(layer_num) ] ;other_v_list = [ AverageMeter() for i in range(layer_num) ]\n",
+    "\n",
+    "\n",
+    "\n",
+    "for batch in tqdm(eval_dataloader, desc=\"Inference\"):\n",
+    "    model.train()\n",
+    "            \n",
+    "    batch = tuple(t.to(device) for t in batch)\n",
+    "    input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch\n",
+    "    seq_length = seq_lengths.item()\n",
+    "    input_ids_sliced = input_ids[:,:seq_length]\n",
+    "    sep_index = torch.where(input_ids[0] == 102)[0]\n",
+    "    \n",
+    "    student_model.eval()\n",
+    "    teacher_model.eval()\n",
+    "    student_model.to(device)\n",
+    "    teacher_model.to(device)\n",
+    "    \n",
+    "    teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "    student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_probs=teacher_probs)\n",
+    "\n",
+    "    probs = student_probs\n",
+    "    values = student_values\n",
+    "    \n",
+    "#     probs = teacher_probs\n",
+    "#     values = teacher_values\n",
+    "    \n",
+    "   \n",
+    "    \n",
+    "\n",
+    "    for i in range(layer_num):\n",
+    "        # Attention Weight Analysis\n",
+    "        if len(sep_index) == 2:\n",
+    "            sep_w = (probs[i][0,:,:,sep_index[0]].mean() + probs[i][0,:,:,sep_index[1]].mean() / 2).item()\n",
+    "            punc_w = (probs[i][0,:,:,sep_index[0] - 1].mean() + probs[i][0,:,:,sep_index[1] - 1].mean() / 2).item()\n",
+    "            sep_w_list[i].update(sep_w)\n",
+    "            punc_w_list[i].update(punc_w)\n",
+    "        else:\n",
+    "            sep_w = probs[i][0,:,:,sep_index[0]].mean().item()\n",
+    "            punc_w = probs[i][0,:,:,sep_index[0]-1].mean().item()\n",
+    "            sep_w_list[i].update(sep_w)\n",
+    "            punc_w_list[i].update(punc_w)\n",
+    "        cls_w = probs[i][0,:,:,0].mean().item()\n",
+    "        cls_w_list[i].update(cls_w)\n",
+    "        other_w_list[i].update(1 - (sep_w + punc_w + cls_w))\n",
+    "\n",
+    "        # Attention Norm based Analysis (|| alpha f(x) ||)\n",
+    "        if len(sep_index) == 2:\n",
+    "            sep_n = (norm_func(values[i][0][0,:,sep_index[0],:], dim=-1).mean() + norm_func(values[i][0][0,:,sep_index[1],:], dim=-1).mean() / 2).item()\n",
+    "            punc_n = (norm_func(values[i][0][0,:,sep_index[0] - 1,:], dim=-1).mean() + norm_func(values[i][0][0,:,sep_index[1] - 1,:], dim=-1).mean() / 2).item()\n",
+    "            sep_n_list[i].update(sep_n)\n",
+    "            punc_n_list[i].update(punc_n)\n",
+    "        else:\n",
+    "            sep_n = norm_func(values[i][0][0,:,sep_index[0],:], dim=-1).mean().item()\n",
+    "            punc_n = norm_func(values[i][0][0,:,sep_index[0]-1,:], dim=-1).mean().item()\n",
+    "            sep_n_list[i].update(sep_n)\n",
+    "            punc_n_list[i].update(punc_n)\n",
+    "\n",
+    "        cls_n = norm_func(values[i][0][0,:,0,:], dim=-1).mean().item()\n",
+    "        cls_n_list[i].update(cls_n)\n",
+    "        \n",
+    "        values[i][0][0,:,sep_index[0],:] = 0\n",
+    "        if len(sep_index) == 2:\n",
+    "            values[i][0][0,:,sep_index[1],:] = 0\n",
+    "            \n",
+    "        values[i][0][0,:,sep_index[0] - 1,:] = 0\n",
+    "        if len(sep_index) == 2:\n",
+    "            values[i][0][0,:,sep_index[1] - 1,:] = 0\n",
+    "        \n",
+    "        values[i][0][0,:,0,:] = 0\n",
+    "\n",
+    "        shape = values[i][0][0,:,:,:].shape\n",
+    "        if len(sep_index) == 2:\n",
+    "            num = shape[0] * (shape[1] - 5)\n",
+    "        else:\n",
+    "            num = shape[0] * (shape[1] - 3)\n",
+    "\n",
+    "        other_n = (norm_func(values[i][1][0,:,:,:], dim=-1).sum() / num).item()\n",
+    "        other_n_list[i].update(other_n)\n",
+    "\n",
+    "        # Attention Norm based Analysis (|| f(x) ||)\n",
+    "        if len(sep_index) == 2:\n",
+    "            sep_v_list[i].update((norm_func(values[i][1][0,:,sep_index[0],:], dim=-1).mean() + norm_func(values[i][1][0,:,sep_index[1],:], dim=-1).mean() / 2).item())\n",
+    "            punc_v_list[i].update((norm_func(values[i][1][0,:,sep_index[0] - 1,:], dim=-1).mean() + norm_func(values[i][1][0,:,sep_index[1] - 1,:], dim=-1).mean() / 2).item())\n",
+    "        else:\n",
+    "            sep_v_list[i].update(norm_func(values[i][1][0,:,sep_index[0],:], dim=-1).mean().item())\n",
+    "            punc_v_list[i].update(norm_func(values[i][1][0,:,sep_index[0]-1,:], dim=-1).mean().item())\n",
+    "        cls_v_list[i].update(norm_func(values[i][1][0,:,0,:], dim=-1).mean().item())\n",
+    "\n",
+    "        values[i][1][0,:,sep_index[0],:] = 0\n",
+    "        if len(sep_index) == 2:\n",
+    "            values[i][1][0,:,sep_index[1],:] = 0\n",
+    "        \n",
+    "        values[i][1][0,:,sep_index[0] - 1,:] = 0\n",
+    "        if len(sep_index) == 2:\n",
+    "            values[i][1][0,:,sep_index[1] - 1,:] = 0\n",
+    "        values[i][1][0,:,0,:] = 0\n",
+    "\n",
+    "        shape = values[i][1][0,:,:,:].shape\n",
+    "        if len(sep_index) == 2:\n",
+    "            num = shape[0] * (shape[1] - 5)\n",
+    "        else:\n",
+    "            num = shape[0] * (shape[1] - 3)\n",
+    "\n",
+    "        other_v_list[i].update((norm_func(values[i][1][0,:,:,:], dim=-1).sum() / num).item())\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb671dd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "l_sep_w_list = list(); l_cls_w_list = list() ;l_punc_w_list = list() ;l_other_w_list = list()\n",
+    "l_sep_n_list = list(); l_cls_n_list = list() ;l_punc_n_list = list() ;l_other_n_list = list()\n",
+    "l_sep_v_list = list(); l_cls_v_list = list() ;l_punc_v_list = list() ;l_other_v_list = list()\n",
+    "\n",
+    "fig, [ax1, ax2, ax3] = plt.subplots(3,1, figsize=(12,16))            \n",
+    "x_axis = list(range(layer_num))\n",
+    "\n",
+    "for layer in range(layer_num):\n",
+    "    l_sep_w_list.append(sep_w_list[layer].avg); l_sep_n_list.append(sep_n_list[layer].avg); l_sep_v_list.append(sep_v_list[layer].avg)\n",
+    "    l_cls_w_list.append(cls_w_list[layer].avg); l_cls_n_list.append(cls_n_list[layer].avg); l_cls_v_list.append(cls_v_list[layer].avg)\n",
+    "    l_punc_w_list.append(punc_w_list[layer].avg); l_punc_n_list.append(punc_n_list[layer].avg); l_punc_v_list.append(punc_v_list[layer].avg)\n",
+    "    l_other_w_list.append(other_w_list[layer].avg); l_other_n_list.append(other_n_list[layer].avg); l_other_v_list.append(other_v_list[layer].avg) \n",
+    "\n",
+    "ax1.set_title(\"Attention Weight-based Analysis\")\n",
+    "ax1.plot(x_axis, l_sep_w_list, label=\"SEP\", linewidth=3)\n",
+    "ax1.plot(x_axis, l_cls_w_list, label=\"CLS\", linewidth=3)\n",
+    "ax1.plot(x_axis, l_punc_w_list, label=\". or ,\", linewidth=3)\n",
+    "ax1.plot(x_axis, l_other_w_list, label=\"Other\", linewidth=3)\n",
+    "ax1.legend()\n",
+    "\n",
+    "ax2.set_title(\"Attention Norm-based Analysis\")\n",
+    "ax2.plot(x_axis, l_sep_n_list, label=\"SEP\", linewidth=3)\n",
+    "ax2.plot(x_axis, l_cls_n_list, label=\"CLS\", linewidth=3)\n",
+    "ax2.plot(x_axis, l_punc_n_list, label=\". or ,\", linewidth=3)\n",
+    "ax2.plot(x_axis, l_other_n_list, label=\"Other\", linewidth=3)\n",
+    "ax2.legend()\n",
+    "\n",
+    "ax3.set_title(\"Attention Value-Norm-based Analysis\")\n",
+    "ax3.plot(x_axis, l_sep_v_list, label=\"SEP\", linewidth=3)\n",
+    "ax3.plot(x_axis, l_cls_v_list, label=\"CLS\", linewidth=3)\n",
+    "ax3.plot(x_axis, l_punc_v_list, label=\". or ,\", linewidth=3)\n",
+    "ax3.plot(x_axis, l_other_v_list, label=\"Other\", linewidth=3)\n",
+    "ax3.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "138ef071",
+   "metadata": {},
+   "source": [
+    "### Weight - Norm Based Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5ee6ace",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "seed=42\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "\n",
+    "student_model.eval()\n",
+    "teacher_model.eval()\n",
+    "student_model.to(device)\n",
+    "teacher_model.to(device)\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_probs=teacher_probs)\n",
+    "\n",
+    "\n",
+    "probs = student_probs\n",
+    "values = student_values\n",
+    "# probs = teacher_probs\n",
+    "# values = teacher_values\n",
+    "\n",
+    "table_sep = [[0] * head_num for i in range(layer_num)]\n",
+    "table_cls = [[0] * head_num for i in range(layer_num)]\n",
+    "table_punc = [[0] * head_num for i in range(layer_num)]\n",
+    "table_other = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):        \n",
+    "        prob_sep = ((probs[l][:,h,:,sep_index[0]].mean() + probs[l][:,h,:,sep_index[1]].mean())/2).item()\n",
+    "        table_sep[l][h] = prob_sep\n",
+    "        prob_cls = probs[l][:,h,:,0].mean().item()\n",
+    "        table_cls[l][h] = prob_cls\n",
+    "        prob_punc = ((probs[l][:,h,:,sep_index[0]-1].mean() + probs[l][:,h,:,sep_index[1]-1].mean())/2).item()\n",
+    "        table_punc[l][h] = prob_punc\n",
+    "#         prob_other = 1 - (prob_sep + prob_sep + prob_punc)\n",
+    "#         table_other[l][h] = prob_other\n",
+    "\n",
+    "fig, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(32,8))\n",
+    "ax1.set_xlabel(\"head\", fontsize=30); ax1.set_ylabel(\"layer\", fontsize=30)\n",
+    "# ax1.set_title(\"SEP Probability AVG\")\n",
+    "heatmap = ax1.pcolor(table_sep, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax1)\n",
+    "\n",
+    "ax2.set_xlabel(\"head\", fontsize=36); ax2.set_ylabel(\"layer\", fontsize=36)\n",
+    "# ax2.set_title(\"CLS Probability AVG\")\n",
+    "heatmap = ax2.pcolor(table_cls, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax2)\n",
+    "\n",
+    "ax3.set_xlabel(\"head\", fontsize=36); ax3.set_ylabel(\"layer\", fontsize=36)\n",
+    "# ax3.set_title(\"PUNC Probability AVG\")\n",
+    "heatmap = ax3.pcolor(table_punc, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax3)\n",
+    "plt.show()\n",
+    "# ax4.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "# ax4.set_title(\"Other Probability AVG\")\n",
+    "# heatmap = ax4.pcolor(table_other, cmap=plt.cm.Blues)\n",
+    "\n",
+    "\n",
+    "norm_type = 0 # 0 : f(x) 1 : p*f(x)\n",
+    "table_sep = [[0] * head_num for i in range(layer_num)]\n",
+    "table_cls = [[0] * head_num for i in range(layer_num)]\n",
+    "table_punc = [[0] * head_num for i in range(layer_num)]\n",
+    "# table_other = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):        \n",
+    "        norm_sep = (norm_func(values[l][norm_type][0,h,sep_index[0],:], dim=-1).mean() + norm_func(values[l][norm_type][0,h,sep_index[1],:], dim=-1).mean() / 2).item()\n",
+    "        table_sep[l][h] = norm_sep\n",
+    "        norm_cls = norm_func(values[l][norm_type][0,h,0,:], dim=-1).mean().item()\n",
+    "        table_cls[l][h] = norm_cls\n",
+    "        norm_punc = (norm_func(values[l][norm_type][0,h,sep_index[0] - 1,:], dim=-1).mean() + norm_func(values[l][norm_type][0,h,sep_index[1] - 1,:], dim=-1).mean() / 2).item()\n",
+    "        table_punc[l][h] = norm_punc\n",
+    "#         prob_other = 1 - (prob_sep + prob_sep + prob_punc)\n",
+    "#         table_other[l][h] = prob_other\n",
+    "\n",
+    "fig, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(32,8))\n",
+    "# fig, ax = plt.subplots(1, 1, figsize=(8,8))\n",
+    "ax1.set_xlabel(\"head\", fontsize = 28); ax.set_ylabel(\"layer\", fontsize = 28)\n",
+    "ax1.set_title(\"SEP ||f(x)|| Norm AVG\")\n",
+    "heatmap = ax1.pcolor(table_sep, cmap=plt.cm.Blues)\n",
+    "# fig.savefig(\"2SB.png\")\n",
+    "fig.colorbar(heatmap, ax=ax1)\n",
+    "\n",
+    "ax2.set_xlabel(\"head\", fontsize = 28); ax2.set_ylabel(\"layer\", fontsize = 28)\n",
+    "ax2.set_title(\"CLS ||f(x)|| Norm AVG\")\n",
+    "heatmap = ax2.pcolor(table_cls, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax2)\n",
+    "\n",
+    "ax3.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax3.set_title(\"PUNC ||f(x)|| Norm AVG\")\n",
+    "heatmap = ax3.pcolor(table_punc, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax3)\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "norm_type = 1 # 0 : f(x) 1 : p*f(x)\n",
+    "table_sep = [[0] * head_num for i in range(layer_num)]\n",
+    "table_cls = [[0] * head_num for i in range(layer_num)]\n",
+    "table_punc = [[0] * head_num for i in range(layer_num)]\n",
+    "# table_other = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num):        \n",
+    "        norm_sep = (norm_func(values[l][norm_type][0,h,sep_index[0],:], dim=-1).mean() + norm_func(values[l][norm_type][0,h,sep_index[1],:], dim=-1).mean() / 2).item()\n",
+    "        table_sep[l][h] = norm_sep\n",
+    "        norm_cls = norm_func(values[l][norm_type][0,h,0,:], dim=-1).mean().item()\n",
+    "        table_cls[l][h] = norm_cls\n",
+    "        norm_punc = (norm_func(values[l][norm_type][0,h,sep_index[0] - 1,:], dim=-1).mean() + norm_func(values[l][norm_type][0,h,sep_index[1] - 1,:], dim=-1).mean() / 2).item()\n",
+    "        table_punc[l][h] = norm_punc\n",
+    "#         prob_other = 1 - (prob_sep + prob_sep + prob_punc)\n",
+    "#         table_other[l][h] = prob_other\n",
+    "\n",
+    "fig, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(32,8))\n",
+    "ax1.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax1.set_title(\"SEP ||p*f(x)|| Norm AVG\")\n",
+    "heatmap = ax1.pcolor(table_sep, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax1)\n",
+    "\n",
+    "ax2.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax2.set_title(\"CLS ||p*f(x)|| Norm AVG\")\n",
+    "heatmap = ax2.pcolor(table_cls, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax2)\n",
+    "\n",
+    "ax3.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax3.set_title(\"PUNC ||p*f(x)|| Norm AVG\")\n",
+    "heatmap = ax3.pcolor(table_punc, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax3)\n",
+    "plt.show()\n",
+    "# ax4.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "# ax4.set_title(\"Other Probability AVG\")\n",
+    "# heatmap = ax4.pcolor(table_other, cmap=plt.cm.Blues)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4722d72",
+   "metadata": {},
+   "source": [
+    "### Norm Based Cosine Similarity Comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57688e44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed=42\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "\n",
+    "student_model.eval()\n",
+    "teacher_model.eval()\n",
+    "student_model.to(device)\n",
+    "teacher_model.to(device)\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_probs=teacher_probs)\n",
+    "\n",
+    "st_values = student_values\n",
+    "tc_values = teacher_values\n",
+    "\n",
+    "loss_cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6)\n",
+    "\n",
+    "norm_type = 0 # 0 : f(x) 1 : p*f(x)\n",
+    "\n",
+    "table_sep = [[0] * head_num for i in range(layer_num)]\n",
+    "table_cls = [[0] * head_num for i in range(layer_num)]\n",
+    "table_punc = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num): \n",
+    "        # cos_sep_1 = loss_cos(st_values[l][norm_type][0,h,sep_index[0],:], tc_values[l][norm_type][0,h,sep_index[0],:]) \n",
+    "        # cos_sep_2 = loss_cos(st_values[l][norm_type][0,h,sep_index[1],:], tc_values[l][norm_type][0,h,sep_index[1],:]) \n",
+    "        cos_sep_1 = mse_func(st_values[l][norm_type][0,h,sep_index[0],:], tc_values[l][norm_type][0,h,sep_index[0],:]) \n",
+    "        cos_sep_2 = mse_func(st_values[l][norm_type][0,h,sep_index[1],:], tc_values[l][norm_type][0,h,sep_index[1],:]) \n",
+    "        # table_sep[l][h] = (1-((cos_sep_1 + cos_sep_2) / 2)).item()\n",
+    "        table_sep[l][h] = ((cos_sep_1 + cos_sep_2) / 2).item()\n",
+    "        \n",
+    "        # cos_cls = loss_cos(st_values[l][norm_type][0,h,0,:], tc_values[l][norm_type][0,h,0,:])\n",
+    "        cos_cls = mse_func(st_values[l][norm_type][0,h,0,:], tc_values[l][norm_type][0,h,0,:])\n",
+    "        # table_cls[l][h] = (1-cos_cls).item()\n",
+    "        table_cls[l][h] = (cos_cls).item()\n",
+    "        \n",
+    "        # cos_punc_1 = loss_cos(st_values[l][norm_type][0,h,sep_index[0]-1,:], tc_values[l][norm_type][0,h,sep_index[0]-1,:]) \n",
+    "        # cos_punc_2 = loss_cos(st_values[l][norm_type][0,h,sep_index[1]-1,:], tc_values[l][norm_type][0,h,sep_index[1]-1,:]) \n",
+    "        cos_punc_1 = mse_func(st_values[l][norm_type][0,h,sep_index[0]-1,:], tc_values[l][norm_type][0,h,sep_index[0]-1,:]) \n",
+    "        cos_punc_2 = mse_func(st_values[l][norm_type][0,h,sep_index[1]-1,:], tc_values[l][norm_type][0,h,sep_index[1]-1,:]) \n",
+    "        # table_punc[l][h] = (1-((cos_sep_1 + cos_sep_2) / 2)).item()\n",
+    "        table_punc[l][h] = ((cos_sep_1 + cos_sep_2) / 2).item()\n",
+    "\n",
+    "fig, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(32,8))\n",
+    "ax1.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax1.set_title(\"SEP Cosine Similarity w/ Teacher\")\n",
+    "heatmap = ax1.pcolor(table_sep, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax1)\n",
+    "\n",
+    "ax2.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax2.set_title(\"CLS Cosine Similarity w/ Teacher\")\n",
+    "heatmap = ax2.pcolor(table_cls, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax2)\n",
+    "\n",
+    "ax3.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax3.set_title(\"PUNC Cosine Similarity w/ Teacher\")\n",
+    "heatmap = ax3.pcolor(table_punc, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax3)\n",
+    "# plt.show()\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f31801d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed=42\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "\n",
+    "student_model.eval()\n",
+    "teacher_model.eval()\n",
+    "student_model.to(device)\n",
+    "teacher_model.to(device)\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_probs=teacher_probs)\n",
+    "\n",
+    "st_values = student_values\n",
+    "tc_values = teacher_values\n",
+    "\n",
+    "loss_cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6)\n",
+    "\n",
+    "norm_type = 1 # 0 : f(x) 1 : p*f(x)\n",
+    "\n",
+    "table_sep = [[0] * head_num for i in range(layer_num)]\n",
+    "table_cls = [[0] * head_num for i in range(layer_num)]\n",
+    "table_punc = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "for l in range(layer_num):\n",
+    "    for h in range(head_num): \n",
+    "        cos_sep_1 = loss_cos(st_values[l][norm_type][0,h,sep_index[0],:], tc_values[l][norm_type][0,h,sep_index[0],:]) \n",
+    "        cos_sep_2 = loss_cos(st_values[l][norm_type][0,h,sep_index[1],:], tc_values[l][norm_type][0,h,sep_index[1],:])         \n",
+    "        table_sep[l][h] = (1-((cos_sep_1 + cos_sep_2) / 2)).item()\n",
+    "        \n",
+    "        cos_cls = loss_cos(st_values[l][norm_type][0,h,0,:], tc_values[l][norm_type][0,h,0,:])\n",
+    "        table_cls[l][h] = (1-cos_cls).item()\n",
+    "        \n",
+    "        cos_punc_1 = loss_cos(st_values[l][norm_type][0,h,sep_index[0]-1,:], tc_values[l][norm_type][0,h,sep_index[0]-1,:]) \n",
+    "        cos_punc_2 = loss_cos(st_values[l][norm_type][0,h,sep_index[1]-1,:], tc_values[l][norm_type][0,h,sep_index[1]-1,:])         \n",
+    "        table_punc[l][h] = (1-((cos_sep_1 + cos_sep_2) / 2)).item()\n",
+    "\n",
+    "fig, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(32,8))\n",
+    "ax1.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax1.set_title(\"SEP Cosine Similarity w/ Teacher\")\n",
+    "heatmap = ax1.pcolor(table_sep, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax1)\n",
+    "\n",
+    "ax2.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax2.set_title(\"CLS Cosine Similarity w/ Teacher\")\n",
+    "heatmap = ax2.pcolor(table_cls, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax2)\n",
+    "\n",
+    "ax3.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "ax3.set_title(\"PUNC Cosine Similarity w/ Teacher\")\n",
+    "heatmap = ax3.pcolor(table_punc, cmap=plt.cm.Blues)\n",
+    "fig.colorbar(heatmap, ax=ax3)\n",
+    "plt.show()\n",
+    "\n",
+    "# # ax4.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "# # ax4.set_title(\"Other Probability AVG\")\n",
+    "# # heatmap = ax4.pcolor(table_other, cmap=plt.cm.Blues)\n",
+    "\n",
+    "# norm_type = 1 # 0 : f(x) 1 : p*f(x)\n",
+    "# table_sep = [[0] * head_num for i in range(layer_num)]\n",
+    "# table_cls = [[0] * head_num for i in range(layer_num)]\n",
+    "# table_punc = [[0] * head_num for i in range(layer_num)]\n",
+    "# # table_other = [[0] * head_num for i in range(layer_num)]\n",
+    "\n",
+    "# for l in range(layer_num):\n",
+    "#     for h in range(head_num):        \n",
+    "#         norm_sep = (norm_func(values[l][norm_type][0,h,sep_index[0],:], dim=-1).mean() + norm_func(values[l][norm_type][0,h,sep_index[1],:], dim=-1).mean() / 2).item()\n",
+    "#         table_sep[l][h] = norm_sep\n",
+    "#         norm_cls = norm_func(values[l][norm_type][0,h,0,:], dim=-1).mean().item()\n",
+    "#         table_cls[l][h] = norm_cls\n",
+    "#         norm_punc = (norm_func(values[l][norm_type][0,h,sep_index[0] - 1,:], dim=-1).mean() + norm_func(values[l][norm_type][0,h,sep_index[1] - 1,:], dim=-1).mean() / 2).item()\n",
+    "#         table_punc[l][h] = norm_punc\n",
+    "# #         prob_other = 1 - (prob_sep + prob_sep + prob_punc)\n",
+    "# #         table_other[l][h] = prob_other\n",
+    "\n",
+    "# fig, [ax1,ax2,ax3] = plt.subplots(1, 3, figsize=(24,8))\n",
+    "# ax1.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "# ax1.set_title(\"SEP ||p*f(x)|| Norm AVG\")\n",
+    "# heatmap = ax1.pcolor(table_sep, cmap=plt.cm.Blues)\n",
+    "\n",
+    "# ax2.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "# ax2.set_title(\"CLS ||p*f(x)|| Norm AVG\")\n",
+    "# heatmap = ax2.pcolor(table_cls, cmap=plt.cm.Blues)\n",
+    "\n",
+    "# ax3.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "# ax3.set_title(\"PUNC ||p*f(x)|| Norm AVG\")\n",
+    "# heatmap = ax3.pcolor(table_punc, cmap=plt.cm.Blues)\n",
+    "# plt.show()\n",
+    "# # ax4.set_xlabel(\"head\"); ax1.set_ylabel(\"layer\")\n",
+    "# # ax4.set_title(\"Other Probability AVG\")\n",
+    "# # heatmap = ax4.pcolor(table_other, cmap=plt.cm.Blues)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcd90fce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed=42\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "\n",
+    "norm_type = 0 # 1 : f(x) 0 : p*f(x)\n",
+    "\n",
+    "student_model.eval()\n",
+    "teacher_model.eval()\n",
+    "student_model.to(device)\n",
+    "teacher_model.to(device)\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_probs=teacher_probs)\n",
+    "\n",
+    "\n",
+    "        \n",
+    "#         diff_sep_1 = mse_func(st_values[l][norm_type][0,h,sep_index[0],:], tc_values[l][norm_type][0,h,sep_index[0],:]) \n",
+    "#         diff_sep_2 = mse_func(st_values[l][norm_type][0,h,sep_index[1],:], tc_values[l][norm_type][0,h,sep_index[1],:]) \n",
+    "#         sep_list.append(((diff_sep_1 + diff_sep_2) / 2).item())\n",
+    "        \n",
+    "#         diff_cls = mse_func(st_values[l][norm_type][0,h,0,:], tc_values[l][norm_type][0,h,0,:])\n",
+    "#         cls_list.append((diff_cls).item())\n",
+    "        \n",
+    "#         diff_punc_1 = mse_func(st_values[l][norm_type][0,h,sep_index[0]-1,:], tc_values[l][norm_type][0,h,sep_index[0]-1,:])\n",
+    "#         diff_punc_2 = mse_func(st_values[l][norm_type][0,h,sep_index[1]-1,:], tc_values[l][norm_type][0,h,sep_index[1]-1,:]) \n",
+    "#         punc_list.append(((diff_punc_1 + diff_punc_2) / 2).item())\n",
+    "        \n",
+    "#         st_values[l][norm_type][0,h,sep_index[0],:] = 0\n",
+    "#         tc_values[l][norm_type][0,h,sep_index[0],:] = 0        \n",
+    "#         st_values[l][norm_type][0,h,sep_index[1],:] = 0\n",
+    "#         tc_values[l][norm_type][0,h,sep_index[1],:] = 0\n",
+    "        \n",
+    "#         st_values[l][norm_type][0,h,sep_index[0]-1,:] = 0\n",
+    "#         tc_values[l][norm_type][0,h,sep_index[0]-1,:] = 0        \n",
+    "#         st_values[l][norm_type][0,h,sep_index[1]-1,:] = 0\n",
+    "#         tc_values[l][norm_type][0,h,sep_index[1]-1,:] = 0\n",
+    "        \n",
+    "#         st_values[l][norm_type][0,h,0,:] = 0\n",
+    "#         tc_values[l][norm_type][0,h,0,:] = 0\n",
+    "        \n",
+    "        # diff_other = mse_func(st_values[l][norm_type][0,h,:,:], tc_values[l][norm_type][0,h,:,:])\n",
+    "        # other_list.append(diff_other.item())\n",
+    "\n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c00e0245",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ternary_prob_kl = kld_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afe5724e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sarq_c_prob_kl = kld_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80e66cb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sarq_prob_kl = kld_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0055a968",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eval_st = 1\n",
+    "eval_tc = 0\n",
+    "\n",
+    "eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=32)\n",
+    "\n",
+    "if eval_st:\n",
+    "    print(\"Student Model Inferece\")\n",
+    "    student_model.eval()\n",
+    "    student_result = do_eval(student_model, task_name, eval_dataloader, device, output_mode, eval_labels, num_labels, teacher_model=teacher_model)\n",
+    "    print(f\"Student Result : {student_result}\")\n",
+    "\n",
+    "if eval_tc:\n",
+    "    print(\"Teacher Model Inferece\")\n",
+    "    teacher_result = do_eval(teacher_model, task_name, eval_dataloader, device, output_mode, eval_labels, num_labels)\n",
+    "    print(f\"Teacher Result : {teacher_result}\")\n",
+    "    \n",
+    "eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=1)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e82f5612",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed=42\n",
+    "random.seed(seed)\n",
+    "np.random.seed(seed)\n",
+    "torch.manual_seed(seed)\n",
+    "mse_func = MSELoss()\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model_dir = \"models\"\n",
+    "output_dir = \"output\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    model_dir = os.path.join(model_dir, \"BERT_large\")\n",
+    "    output_dir = os.path.join(output_dir, \"BERT_large\")\n",
+    "\n",
+    "student_model_dir = os.path.join(model_dir,task_name)\n",
+    "\n",
+    "# st_model_name = \"ternary_save\"\n",
+    "# st_model_name = \"step_2_context\"\n",
+    "# st_model_name = \"step_2_output\"\n",
+    "# st_model_name = \"step_2\"\n",
+    "# st_model_name = \"sarq_step1\"\n",
+    "\n",
+    "\n",
+    "\n",
+    "build_tc = 1\n",
+    "build_st = 1\n",
+    "\n",
+    "if build_tc:\n",
+    "    # Teacher Model Build\n",
+    "    teacher_model_dir = os.path.join(model_dir,task_name)\n",
+    "    teacher_model = BertForSequenceClassification.from_pretrained(teacher_model_dir, num_labels=num_labels)\n",
+    "    teacher_model.to(device)\n",
+    "    teacher_model.eval()\n",
+    "    model = teacher_model\n",
+    "    \n",
+    "for st_model_name in [\"1SB_S\", \"1SB_S_M\", \"step_2_S_M\"]:\n",
+    "    student_model_dir = os.path.join(output_dir, task_name, \"exploration\", st_model_name)   \n",
+    "\n",
+    "    if build_st:\n",
+    "        # Student Model Build\n",
+    "        student_config = BertConfig.from_pretrained(student_model_dir\n",
+    "#                                                         quantize_act=True,\n",
+    "#                                                         quantize_weight=True,\n",
+    "#                                                         weight_bits = 2, # Always Ternary when \"quantize_weight = True\"\n",
+    "#                                                         input_bits = 8,\n",
+    "#                                                         clip_val = 2.5,\n",
+    "#                                                         quantize = True,\n",
+    "#                                                         ffn_q_1 = True,\n",
+    "#                                                         ffn_q_2 = True,\n",
+    "#                                                         qkv_q = True,\n",
+    "#                                                         emb_q = True,\n",
+    "#                                                         cls_q = True,\n",
+    "#                                                         clipping = False,\n",
+    "#                                                         layer_num = -1,\n",
+    "#                                                         mean_scale = 0.7,\n",
+    "#                                                         quantizer = \"ternary\",\n",
+    "#                                                         act_quantizer = \"ternary\",\n",
+    "#                                                         init_scaling = 1,\n",
+    "#                                                         clip_ratio = 1,\n",
+    "#                                                         gradient_scaling = False,\n",
+    "#                                                         clip_method = \"minmax\",\n",
+    "#                                                         teacher_attnmap = False,\n",
+    "#                                                         parks = False,\n",
+    "#                                                         stop_grad = False,\n",
+    "#                                                         qk_FP = True,\n",
+    "#                                                         map=False,\n",
+    "#                                                         act_method = \"clipping\"\n",
+    "                                                        )\n",
+    "\n",
+    "        student_model = QuantBertForSequenceClassification.from_pretrained(student_model_dir, config = student_config, num_labels=num_labels)\n",
+    "        student_model.to(device)\n",
+    "        model = student_model\n",
+    "        print()\n",
+    "\n",
+    "        # Quantization Option ACT/WEIGHT\n",
+    "        for name, module in student_model.named_modules():\n",
+    "            if isinstance(module, (QuantizeLinear, QuantizeAct, ClipLinear)):    \n",
+    "                module.act_flag = True\n",
+    "                module.weight_flag = True\n",
+    "\n",
+    "    student_model.eval()\n",
+    "    teacher_model.eval()\n",
+    "    student_model.to(device)\n",
+    "    teacher_model.to(device)\n",
+    "    teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_zip = teacher_model(input_ids_sliced.to(device))\n",
+    "    logits, loss, cls_loss, rep_loss, output_loss, attmap_loss, attscore_loss, coeff_list, student_zip = student_model(input_ids_sliced.to(device), teacher_outputs=None)\n",
+    "    \n",
+    "    \n",
+    "    kld_list = []\n",
+    "\n",
+    "    student_probs = student_zip[1]\n",
+    "    kl_loss = torch.nn.KLDivLoss(reduction=\"sum\")\n",
+    "\n",
+    "    for l in range(layer_num):\n",
+    "        for h in range(head_num): \n",
+    "            student = student_probs[l][0,h,:,:]\n",
+    "            teacher = teacher_probs[l][0,h,:,:]\n",
+    "            neg_cross_entropy = teacher * torch.log(student) \n",
+    "            neg_cross_entropy = torch.sum(neg_cross_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "\n",
+    "\n",
+    "            # p(t) log p(t) = negative entropy\n",
+    "            neg_entropy = teacher * torch.log(teacher) \n",
+    "            neg_entropy = torch.sum(neg_entropy, dim=-1)  # (b, h, s, s) -> (b, h, s)\n",
+    "\n",
+    "            kl_div = neg_entropy - neg_cross_entropy\n",
+    "            # print(kl_div.mean().item())\n",
+    "            kld_list.append(kl_div.mean().item())\n",
+    "     \n",
+    "    if st_model_name == \"1SB_S\":\n",
+    "        print(st_model_name)\n",
+    "        t_kld_list = kld_list\n",
+    "    elif st_model_name == \"1SB_S_M\":\n",
+    "        print(st_model_name)    \n",
+    "        s_kld_list = kld_list\n",
+    "    elif st_model_name == \"step_2_S_M\":\n",
+    "        print(st_model_name)\n",
+    "        sc_kld_list = kld_list \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    # (layer_context, attention_output, value_layer, self_output_hs)\n",
+    "    st_values = student_zip[0]\n",
+    "    tc_values = teacher_zip\n",
+    "\n",
+    "    norm_type = 2 \n",
+    "\n",
+    "    h_num = 1\n",
+    "    sep_list = []\n",
+    "    cls_list = []\n",
+    "    punc_list = []\n",
+    "    other_list = []\n",
+    "\n",
+    "    for l in range(layer_num):\n",
+    "        for h in range(head_num): \n",
+    "\n",
+    "            if len(sep_index) == 2:\n",
+    "                diff_sep_1 = mse_func(st_values[l][norm_type][0,h,sep_index[0],:], tc_values[l][norm_type][0,h,sep_index[0],:]) \n",
+    "                diff_sep_2 = mse_func(st_values[l][norm_type][0,h,sep_index[1],:], tc_values[l][norm_type][0,h,sep_index[1],:]) \n",
+    "                sep_list.append(((diff_sep_1 + diff_sep_2) / 2).item())\n",
+    "            else:\n",
+    "                diff_sep = mse_func(st_values[l][norm_type][0,h,sep_index[0],:], tc_values[l][norm_type][0,h,sep_index[0],:]) \n",
+    "                sep_list.append(diff_sep.item())\n",
+    "\n",
+    "            diff_cls = mse_func(st_values[l][norm_type][0,h,0,:], tc_values[l][norm_type][0,h,0,:])\n",
+    "            cls_list.append((diff_cls).item())\n",
+    "\n",
+    "            if len(sep_index) == 2:\n",
+    "                diff_punc_1 = mse_func(st_values[l][norm_type][0,h,sep_index[0]-1,:], tc_values[l][norm_type][0,h,sep_index[0]-1,:])\n",
+    "                diff_punc_2 = mse_func(st_values[l][norm_type][0,h,sep_index[1]-1,:], tc_values[l][norm_type][0,h,sep_index[1]-1,:]) \n",
+    "                punc_list.append(((diff_punc_1 + diff_punc_2) / 2).item())\n",
+    "            else:\n",
+    "                diff_punc = mse_func(st_values[l][norm_type][0,h,sep_index[0]-1,:], tc_values[l][norm_type][0,h,sep_index[0]-1,:])\n",
+    "                punc_list.append((diff_punc).item())\n",
+    "\n",
+    "            if len(sep_index) == 2:\n",
+    "                st_values[l][norm_type][0,h,sep_index[0],:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[0],:] = 0        \n",
+    "                st_values[l][norm_type][0,h,sep_index[1],:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[1],:] = 0\n",
+    "            else:\n",
+    "                st_values[l][norm_type][0,h,sep_index[0],:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[0],:] = 0        \n",
+    "\n",
+    "            if len(sep_index) == 2:        \n",
+    "                st_values[l][norm_type][0,h,sep_index[0]-1,:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[0]-1,:] = 0        \n",
+    "                st_values[l][norm_type][0,h,sep_index[1]-1,:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[1]-1,:] = 0\n",
+    "            else:\n",
+    "                st_values[l][norm_type][0,h,sep_index[0]-1,:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[0]-1,:] = 0\n",
+    "\n",
+    "            st_values[l][norm_type][0,h,0,:] = 0\n",
+    "            tc_values[l][norm_type][0,h,0,:] = 0\n",
+    "\n",
+    "            diff_other = mse_func(st_values[l][norm_type][0,h,:,:], tc_values[l][norm_type][0,h,:,:])\n",
+    "            other_list.append(diff_other.item())\n",
+    "\n",
+    "\n",
+    "    if st_model_name == \"1SB_S\":\n",
+    "        print(st_model_name)\n",
+    "        v_t_sep = sep_list \n",
+    "        v_t_cls = cls_list \n",
+    "        v_t_punc = punc_list \n",
+    "        v_t_other = other_list \n",
+    "    elif st_model_name == \"1SB_S_M\":\n",
+    "        print(st_model_name)    \n",
+    "        v_s_sep = sep_list \n",
+    "        v_s_cls = cls_list \n",
+    "        v_s_punc = punc_list \n",
+    "        v_s_other = other_list \n",
+    "    elif st_model_name == \"step_2_S_M\":\n",
+    "        print(st_model_name)\n",
+    "        v_sc_sep = sep_list \n",
+    "        v_sc_cls = cls_list \n",
+    "        v_sc_punc = punc_list \n",
+    "        v_sc_other = other_list         \n",
+    "    elif st_model_name == \"step_2_output\" or st_model_name == \"sarq_step1\": \n",
+    "        print(st_model_name)\n",
+    "        v_so_sep = sep_list \n",
+    "        v_so_cls = cls_list \n",
+    "        v_so_punc = punc_list \n",
+    "        v_so_other = other_list   \n",
+    "\n",
+    "    norm_type = 0 # 1 : Attention Output 0 : Layer Context\n",
+    "\n",
+    "    h_num = 1\n",
+    "    sep_list = []\n",
+    "    cls_list = []\n",
+    "    punc_list = []\n",
+    "    other_list = []\n",
+    "\n",
+    "    tokens\n",
+    "#     for l in range(layer_num):\n",
+    "#         if len(sep_index) == 2:\n",
+    "#             diff_sep_1 = mse_func(st_values[l][norm_type][0,sep_index[0],:], tc_values[l][norm_type][0,sep_index[0],:]) \n",
+    "#             diff_sep_2 = mse_func(st_values[l][norm_type][0,sep_index[1],:], tc_values[l][norm_type][0,sep_index[1],:]) \n",
+    "#             sep_list.append(((diff_sep_1 + diff_sep_2) / 2).item())\n",
+    "#         else:\n",
+    "#             diff_sep = mse_func(st_values[l][norm_type][0,sep_index[0],:], tc_values[l][norm_type][0,sep_index[0],:]) \n",
+    "#             sep_list.append(diff_sep.item())\n",
+    "\n",
+    "#         diff_cls = mse_func(st_values[l][norm_type][0,0,:], tc_values[l][norm_type][0,0,:])\n",
+    "#         cls_list.append((diff_cls).item())\n",
+    "\n",
+    "#         if len(sep_index) == 2:\n",
+    "#             diff_punc_1 = mse_func(st_values[l][norm_type][0,sep_index[0]-1,:], tc_values[l][norm_type][0,sep_index[0]-1,:])\n",
+    "#             diff_punc_2 = mse_func(st_values[l][norm_type][0,sep_index[1]-1,:], tc_values[l][norm_type][0,sep_index[1]-1,:]) \n",
+    "#             punc_list.append(((diff_punc_1 + diff_punc_2) / 2).item())\n",
+    "#         else:\n",
+    "#             diff_punc = mse_func(st_values[l][norm_type][0,sep_index[0]-1,:], tc_values[l][norm_type][0,sep_index[0]-1,:])\n",
+    "#             punc_list.append((diff_punc).item())\n",
+    "\n",
+    "#         if len(sep_index) == 2:\n",
+    "#             st_values[l][norm_type][0,sep_index[0],:] = 0\n",
+    "#             tc_values[l][norm_type][0,sep_index[0],:] = 0        \n",
+    "#             st_values[l][norm_type][0,sep_index[1],:] = 0\n",
+    "#             tc_values[l][norm_type][0,sep_index[1],:] = 0\n",
+    "#         else:\n",
+    "#             st_values[l][norm_type][0,sep_index[0],:] = 0\n",
+    "#             tc_values[l][norm_type][0,sep_index[0],:] = 0        \n",
+    "\n",
+    "#         if len(sep_index) == 2:        \n",
+    "#             st_values[l][norm_type][0,sep_index[0]-1,:] = 0\n",
+    "#             tc_values[l][norm_type][0,sep_index[0]-1,:] = 0        \n",
+    "#             st_values[l][norm_type][0,sep_index[1]-1,:] = 0\n",
+    "#             tc_values[l][norm_type][0,sep_index[1]-1,:] = 0\n",
+    "#         else:\n",
+    "#             st_values[l][norm_type][0,sep_index[0]-1,:] = 0\n",
+    "#             tc_values[l][norm_type][0,sep_index[0]-1,:] = 0\n",
+    "\n",
+    "#         st_values[l][norm_type][0,0,:] = 0\n",
+    "#         tc_values[l][norm_type][0,0,:] = 0\n",
+    "\n",
+    "#         diff_other = mse_func(st_values[l][norm_type][0,:,:], tc_values[l][norm_type][0,:,:])\n",
+    "#         other_list.append(diff_other.item())\n",
+    "\n",
+    "    for l in range(layer_num):\n",
+    "        for h in range(head_num): \n",
+    "\n",
+    "            if len(sep_index) == 2:\n",
+    "                diff_sep_1 = mse_func(st_values[l][norm_type][0,h,sep_index[0],:], tc_values[l][norm_type][0,h,sep_index[0],:]) \n",
+    "                diff_sep_2 = mse_func(st_values[l][norm_type][0,h,sep_index[1],:], tc_values[l][norm_type][0,h,sep_index[1],:]) \n",
+    "                sep_list.append(((diff_sep_1 + diff_sep_2) / 2).item())\n",
+    "            else:\n",
+    "                diff_sep = mse_func(st_values[l][norm_type][0,h,sep_index[0],:], tc_values[l][norm_type][0,h,sep_index[0],:]) \n",
+    "                sep_list.append(diff_sep.item())\n",
+    "\n",
+    "            diff_cls = mse_func(st_values[l][norm_type][0,h,0,:], tc_values[l][norm_type][0,h,0,:])\n",
+    "            cls_list.append((diff_cls).item())\n",
+    "\n",
+    "            if len(sep_index) == 2:\n",
+    "                diff_punc_1 = mse_func(st_values[l][norm_type][0,h,sep_index[0]-1,:], tc_values[l][norm_type][0,h,sep_index[0]-1,:])\n",
+    "                diff_punc_2 = mse_func(st_values[l][norm_type][0,h,sep_index[1]-1,:], tc_values[l][norm_type][0,h,sep_index[1]-1,:]) \n",
+    "                punc_list.append(((diff_punc_1 + diff_punc_2) / 2).item())\n",
+    "            else:\n",
+    "                diff_punc = mse_func(st_values[l][norm_type][0,h,sep_index[0]-1,:], tc_values[l][norm_type][0,h,sep_index[0]-1,:])\n",
+    "                punc_list.append((diff_punc).item())\n",
+    "\n",
+    "            if len(sep_index) == 2:\n",
+    "                st_values[l][norm_type][0,h,sep_index[0],:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[0],:] = 0        \n",
+    "                st_values[l][norm_type][0,h,sep_index[1],:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[1],:] = 0\n",
+    "            else:\n",
+    "                st_values[l][norm_type][0,h,sep_index[0],:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[0],:] = 0        \n",
+    "\n",
+    "            if len(sep_index) == 2:        \n",
+    "                st_values[l][norm_type][0,h,sep_index[0]-1,:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[0]-1,:] = 0        \n",
+    "                st_values[l][norm_type][0,h,sep_index[1]-1,:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[1]-1,:] = 0\n",
+    "            else:\n",
+    "                st_values[l][norm_type][0,h,sep_index[0]-1,:] = 0\n",
+    "                tc_values[l][norm_type][0,h,sep_index[0]-1,:] = 0\n",
+    "\n",
+    "            st_values[l][norm_type][0,h,0,:] = 0\n",
+    "            tc_values[l][norm_type][0,h,0,:] = 0\n",
+    "\n",
+    "            diff_other = mse_func(st_values[l][norm_type][0,h,:,:], tc_values[l][norm_type][0,h,:,:])\n",
+    "            other_list.append(diff_other.item())\n",
+    "\n",
+    "    if st_model_name == \"1SB_S\":\n",
+    "        print(st_model_name)\n",
+    "        vw_t_sep = sep_list \n",
+    "        vw_t_cls = cls_list \n",
+    "        vw_t_punc = punc_list \n",
+    "        vw_t_other = other_list \n",
+    "    elif st_model_name == \"1SB_S_M\":\n",
+    "        print(st_model_name)\n",
+    "        vw_s_sep = sep_list \n",
+    "        vw_s_cls = cls_list \n",
+    "        vw_s_punc = punc_list \n",
+    "        vw_s_other = other_list \n",
+    "    elif st_model_name == \"step_2_S_M\":\n",
+    "        print(st_model_name)\n",
+    "        vw_sc_sep = sep_list \n",
+    "        vw_sc_cls = cls_list \n",
+    "        vw_sc_punc = punc_list \n",
+    "        vw_sc_other = other_list   \n",
+    "    elif st_model_name == \"step_2_output\" or st_model_name == \"sarq_step1\": \n",
+    "        print(st_model_name)\n",
+    "        vw_so_sep = sep_list \n",
+    "        vw_so_cls = cls_list \n",
+    "        vw_so_punc = punc_list \n",
+    "        vw_so_other = other_list   \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a16b4e61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st_values[0][0].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cee053f1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b7999f0",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.ticker as mtick\n",
+    "fig, ax2 = plt.subplots(1,1, figsize=(8, 5.5), dpi=70)\n",
+    "plt.rcParams['axes.linewidth'] = 2.2\n",
+    "plt.rcParams['patch.linewidth'] = 2.2\n",
+    "\n",
+    "font_size = 23\n",
+    "line_w =1.5\n",
+    "\n",
+    "x_axis_num = layer_num * head_num\n",
+    "\n",
+    "# ax1.plot(list(range(x_axis_num)),v_t_cls, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax1.plot(list(range(x_axis_num)),v_sc_cls, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "# ax1.plot(list(range(x_axis_num)),v_s_cls, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# # ax1.plot(list(range(x_axis_num)),v_so_cls, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "# ax1.tick_params(axis=\"x\", labelsize=font_size)\n",
+    "# ax1.tick_params(axis=\"y\", labelsize=font_size)\n",
+    "# ax1.legend(fontsize=font_size, loc=1)\n",
+    "# ax1.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax1.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1f'))\n",
+    "# # ax1.set_title(\"CLS Value Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "\n",
+    "\n",
+    "ax2.plot(list(range(x_axis_num)),v_t_sep, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "ax2.plot(list(range(x_axis_num)),v_s_sep, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "ax2.plot(list(range(x_axis_num)),v_sc_sep, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# ax2.plot(list(range(x_axis_num)),v_so_sep, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "\n",
+    "ax2.legend(fontsize=font_size, loc=1)\n",
+    "ax2.set_xlabel(\"Head\", fontsize=font_size)\n",
+    "ax2.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1f'))\n",
+    "# ax2.set_title(\"SEP Value Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "ax2.tick_params(axis=\"x\", labelsize=font_size)\n",
+    "ax2.tick_params(axis=\"y\", labelsize=font_size)\n",
+    "\n",
+    "# ax3.plot(list(range(x_axis_num)),v_t_punc, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax3.plot(list(range(x_axis_num)),v_s_punc, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "# ax3.plot(list(range(x_axis_num)),v_sc_punc, label=\"SARQ_C\", color='b', linewidth=line_w)\n",
+    "# # ax3.plot(list(range(x_axis_num)),v_so_punc, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "\n",
+    "# ax3.legend(fontsize=font_size, loc=1)\n",
+    "# ax3.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax3.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1f'))\n",
+    "# # ax3.set_title(\"PUNC Value Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "\n",
+    "# ax4.plot(list(range(x_axis_num)),v_t_other, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax4.plot(list(range(x_axis_num)),v_s_other, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "# ax4.plot(list(range(x_axis_num)),v_sc_other, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# # ax4.plot(list(range(x_axis_num)),v_so_other, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "\n",
+    "# ax4.legend(fontsize=font_size, loc=1)\n",
+    "# ax4.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax4.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.1f'))\n",
+    "# # ax4.set_title(\"Other Value Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"=====================================================================================================================\")\n",
+    "\n",
+    "fig, ax2 = plt.subplots(1,1, figsize=(8, 5.5), dpi=70)\n",
+    "\n",
+    "x_axis_num = layer_num * head_num\n",
+    "\n",
+    "# ax1.plot(list(range(x_axis_num)),vw_t_cls, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax1.plot(list(range(x_axis_num)),vw_s_cls, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "# ax1.plot(list(range(x_axis_num)),vw_sc_cls, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# # ax1.plot(list(range(x_axis_num)),vw_so_cls, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "\n",
+    "# ax1.legend(fontsize=font_size, loc=1)\n",
+    "# ax1.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax1.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# # ax1.set_title(\"CLS LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "\n",
+    "\n",
+    "ax2.plot(list(range(x_axis_num)),vw_t_sep, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "ax2.plot(list(range(x_axis_num)),vw_s_sep, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "ax2.plot(list(range(x_axis_num)),vw_sc_sep, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# ax2.plot(list(range(x_axis_num)),vw_so_sep, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "\n",
+    "ax2.legend(fontsize=font_size, loc=1)\n",
+    "ax2.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "ax2.set_xlabel(\"Head\", fontsize=font_size)\n",
+    "ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# ax2.set_title(\"SEP LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "ax2.tick_params(axis=\"x\", labelsize=font_size)\n",
+    "ax2.tick_params(axis=\"y\", labelsize=font_size)\n",
+    "\n",
+    "# ax3.plot(list(range(x_axis_num)),vw_t_punc, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax3.plot(list(range(x_axis_num)),vw_s_punc, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "# ax3.plot(list(range(x_axis_num)),vw_sc_punc, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# # ax3.plot(list(range(x_axis_num)),vw_so_punc, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "\n",
+    "# ax3.legend(fontsize=font_size, loc=1)\n",
+    "# ax3.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax3.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# #x3.set_title(\"PUNC LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "\n",
+    "# ax4.plot(list(range(x_axis_num)),vw_t_other, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax4.plot(list(range(x_axis_num)),vw_s_other, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "# ax4.plot(list(range(x_axis_num)),vw_sc_other, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "#  #ax4.plot(list(range(x_axis_num)),vw_so_other, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "\n",
+    "# ax4.legend(fontsize=font_size, loc=1)\n",
+    "# ax4.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax4.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# # ax4.set_title(\"Other LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "plt.show()\n",
+    "\n",
+    "fig, ax2 = plt.subplots(1,1, figsize=(8, 5.5), dpi=70)\n",
+    "\n",
+    "x_axis_num = layer_num * head_num\n",
+    "\n",
+    "# ax1.plot(list(range(x_axis_num)),vw_t_cls, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax1.plot(list(range(x_axis_num)),vw_s_cls, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "# ax1.plot(list(range(x_axis_num)),vw_sc_cls, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# # ax1.plot(list(range(x_axis_num)),vw_so_cls, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "\n",
+    "# ax1.legend(fontsize=font_size, loc=1)\n",
+    "# ax1.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax1.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# # ax1.set_title(\"CLS LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "\n",
+    "\n",
+    "ax2.plot(list(range(x_axis_num)),t_kld_list, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "ax2.plot(list(range(x_axis_num)),s_kld_list, label=\"SARQ-1step\", color='c', linewidth=line_w)\n",
+    "ax2.plot(list(range(x_axis_num)),sc_kld_list, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# ax2.plot(list(range(x_axis_num)),vw_so_sep, label=\"SARQ_O\", color='tab:orange', linewidth=line_w)\n",
+    "\n",
+    "ax2.legend(fontsize=font_size, loc=1)\n",
+    "ax2.set_ylabel(\"KL_Divergence\", fontsize=font_size)\n",
+    "ax2.set_xlabel(\"Head\", fontsize=font_size)\n",
+    "ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# ax2.set_title(\"SEP LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "ax2.tick_params(axis=\"x\", labelsize=font_size)\n",
+    "ax2.tick_params(axis=\"y\", labelsize=font_size)\n",
+    "# fig, [ax1, ax2, ax3, ax4] = plt.subplots(4,1, figsize=(16, 24), dpi=70)\n",
+    "\n",
+    "# x_axis_num = layer_num * head_num\n",
+    "\n",
+    "# ax1.plot(list(range(x_axis_num)),vw_t_cls, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax1.plot(list(range(x_axis_num)),vw_s_cls, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# ax1.plot(list(range(x_axis_num)),vw_sc_cls, label=\"SARQ_C\", color='c', linewidth=line_w)\n",
+    "\n",
+    "# ax1.legend(fontsize=font_size, loc=1)\n",
+    "# ax1.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax1.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# ax1.set_title(\"CLS LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "\n",
+    "\n",
+    "# ax2.plot(list(range(x_axis_num)),vw_t_sep, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax2.plot(list(range(x_axis_num)),vw_s_sep, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# ax2.plot(list(range(x_axis_num)),vw_sc_sep, label=\"SARQ_C\", color='c', linewidth=line_w)\n",
+    "\n",
+    "# ax2.legend(fontsize=font_size, loc=1)\n",
+    "# ax2.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax2.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# ax2.set_title(\"SEP LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "\n",
+    "# ax3.plot(list(range(x_axis_num)),vw_t_punc, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax3.plot(list(range(x_axis_num)),vw_s_punc, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# ax3.plot(list(range(x_axis_num)),vw_sc_punc, label=\"SARQ_C\", color='c', linewidth=line_w)\n",
+    "\n",
+    "# ax3.legend(fontsize=font_size, loc=1)\n",
+    "# ax3.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax3.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# ax3.set_title(\"PUNC LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "\n",
+    "# ax4.plot(list(range(x_axis_num)),vw_t_other, label=\"Ternary\", color='r', linewidth=line_w)\n",
+    "# ax4.plot(list(range(x_axis_num)),vw_s_other, label=\"SARQ\", color='b', linewidth=line_w)\n",
+    "# ax4.plot(list(range(x_axis_num)),vw_sc_other, label=\"SARQ_C\", color='c', linewidth=line_w)\n",
+    "\n",
+    "# ax4.legend(fontsize=font_size, loc=1)\n",
+    "# ax4.set_ylabel(\"MSE\", fontsize=font_size)\n",
+    "# ax4.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.2f'))\n",
+    "# ax4.set_title(\"Other LN Output Vector \", fontsize=font_size, fontweight=\"light\")\n",
+    "# plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "170820ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, [ax1, ax2, ax3, ax4] = plt.subplots(4, 1, figsize=(12,16), dpi=80)\n",
+    "\n",
+    "x_axis_num = layer_num * head_num\n",
+    "\n",
+    "ax1.set_title(\"Period Token\")\n",
+    "ax1.plot(list(range(x_axis_num)),t_punc_list, label=\"Ternary_punc\", color='r', linewidth=1)\n",
+    "ax1.plot(list(range(x_axis_num)),SB_punc_list, label=\"SARQ_punc\", color='c', linewidth=1)\n",
+    "ax1.legend(fontsize=\"12\")\n",
+    "\n",
+    "ax2.set_title(\"CLS Token\")\n",
+    "ax2.plot(list(range(x_axis_num)),t_cls_list, label=\"Ternary_cls\", color='r', linewidth=1)\n",
+    "ax2.plot(list(range(x_axis_num)),SB_cls_list, label=\"SARQ_cls\", color='c', linewidth=1)\n",
+    "ax2.legend(fontsize=\"12\")\n",
+    "\n",
+    "ax3.set_title(\"SEP Token\")\n",
+    "ax3.plot(list(range(x_axis_num)),t_sep_list, label=\"Ternary_sep\", color='r', linewidth=1)\n",
+    "ax3.plot(list(range(x_axis_num)),SB_sep_list, label=\"SARQ_sep\", color='c', linewidth=1)\n",
+    "ax3.legend(fontsize=\"12\")\n",
+    "\n",
+    "ax4.set_title(\"Other Token\")\n",
+    "ax4.plot(list(range(x_axis_num)),t_other_list, label=\"Ternary_other\", color='r', linewidth=1)\n",
+    "ax4.plot(list(range(x_axis_num)),SB_other_list, label=\"SARQ_other\", color='c', linewidth=1)\n",
+    "ax4.legend(fontsize=\"12\")\n",
+    "\n",
+    "# fig, ax = plt.subplots(1, 1, figsize=(16,8))\n",
+    "# ax.plot(list(range(144)),t_kld_list, label=\"Ternary_other\", color='r', linewidth=1.5)\n",
+    "# ax.plot(list(range(144)),s_kld_list, label=\"SARQ_other\", color='c', linewidth=1.5)\n",
+    "# ax.legend(fontsize=\"20\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "288feea9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_cos = torch.nn.CosineSimilarity(dim=-1, eps=1e-6)\n",
+    "teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids_sliced.to(device))\n",
+    "student_logits, student_atts, student_reps, student_probs, student_values = student_model(input_ids_sliced.to(device), teacher_probs=teacher_probs)\n",
+    "\n",
+    "st_values = student_values\n",
+    "tc_values = teacher_values\n",
+    "\n",
+    "loss_cos(st_values[l][norm_type][0,2,sep_index[0],:], tc_values[l][norm_type][0,2,sep_index[0],:])\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "507b21a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = st_values[l][norm_type][0,2,sep_index[0],:]\n",
+    "b = tc_values[l][norm_type][0,2,sep_index[0],:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9aaef2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mse_func(st_values[0][0], tc_values[0][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f55a8270",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.matmul(a, b) / (norm_func(a) * norm_func(b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7c481d4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/QAT_analysis_plot.ipynb b/notebooks/QAT_analysis_plot.ipynb
new file mode 100644
index 0000000..c79fb4d
--- /dev/null
+++ b/notebooks/QAT_analysis_plot.ipynb
@@ -0,0 +1,590 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e258bcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from __future__ import absolute_import, division, print_function\n",
+    "\n",
+    "import pprint\n",
+    "import argparse\n",
+    "import logging\n",
+    "import os\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"3\" # Set GPU Index to use\n",
+    "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\n",
+    "import random\n",
+    "import sys\n",
+    "import pickle\n",
+    "import copy\n",
+    "import collections\n",
+    "import math\n",
+    "\n",
+    "import numpy as np\n",
+    "import numpy\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler,TensorDataset\n",
+    "# from torch.utils.tensorboard import SummaryWriter\n",
+    "\n",
+    "from torch.nn import CrossEntropyLoss, MSELoss\n",
+    "from tqdm import tqdm\n",
+    "from transformer import BertForSequenceClassification,WEIGHTS_NAME, CONFIG_NAME\n",
+    "from transformer.modeling_quant import BertForSequenceClassification as QuantBertForSequenceClassification\n",
+    "from transformer import BertTokenizer\n",
+    "from transformer import BertAdam\n",
+    "from transformer import BertConfig\n",
+    "from transformer import QuantizeLinear, QuantizeAct, BertSelfAttention, FP_BertSelfAttention, ClipLinear\n",
+    "from utils_glue import *\n",
+    "from bertviz import model_view\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import torch.nn.functional as F\n",
+    "        \n",
+    "def get_tensor_data(output_mode, features):\n",
+    "    if output_mode == \"classification\":\n",
+    "        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)\n",
+    "    elif output_mode == \"regression\":\n",
+    "        all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)\n",
+    "\n",
+    "\n",
+    "    all_seq_lengths = torch.tensor([f.seq_length for f in features], dtype=torch.long)\n",
+    "    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n",
+    "    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)\n",
+    "    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)\n",
+    "    tensor_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,all_label_ids, all_seq_lengths)\n",
+    "    return tensor_data, all_label_ids\n",
+    "\n",
+    "def do_eval(model, task_name, eval_dataloader,\n",
+    "            device, output_mode, eval_labels, num_labels, teacher_model=None):\n",
+    "    eval_loss = 0\n",
+    "    nb_eval_steps = 0\n",
+    "    preds = []\n",
+    "\n",
+    "    for batch_ in tqdm(eval_dataloader, desc=\"Inference\"):\n",
+    "        batch_ = tuple(t.to(device) for t in batch_)\n",
+    "        \n",
+    "        with torch.no_grad():\n",
+    "            input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch_\n",
+    "\n",
+    "            # teacher attnmap test\n",
+    "            if teacher_model is not None: \n",
+    "                teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_values = teacher_model(input_ids, segment_ids, input_mask)\n",
+    "                logits, loss, cls_loss, rep_loss, output_loss, attmap_loss, attscore_loss, coeff_list, _  = model(input_ids, segment_ids, input_mask, teacher_outputs=(teacher_probs, teacher_values, teacher_reps, teacher_logits, teacher_atts), output_mode=output_mode, seq_lengths=seq_lengths)\n",
+    "            else:\n",
+    "                outputs = model(input_ids, segment_ids, input_mask)\n",
+    "                logits = outputs[0]\n",
+    "                \n",
+    "        # create eval loss and other metric required by the task\n",
+    "        if output_mode == \"classification\":\n",
+    "            loss_fct = CrossEntropyLoss()\n",
+    "            tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))\n",
+    "        elif output_mode == \"regression\":\n",
+    "            loss_fct = MSELoss()\n",
+    "            tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))\n",
+    "\n",
+    "        eval_loss += tmp_eval_loss.mean().item()\n",
+    "        nb_eval_steps += 1\n",
+    "        if len(preds) == 0:\n",
+    "            preds.append(logits.detach().cpu().numpy())\n",
+    "        else:\n",
+    "            preds[0] = np.append(\n",
+    "                preds[0], logits.detach().cpu().numpy(), axis=0)\n",
+    "\n",
+    "    eval_loss = eval_loss / nb_eval_steps\n",
+    "\n",
+    "    preds = preds[0]\n",
+    "    if output_mode == \"classification\":\n",
+    "        preds = np.argmax(preds, axis=1)\n",
+    "    elif output_mode == \"regression\":\n",
+    "        preds = np.squeeze(preds)\n",
+    "    result = compute_metrics(task_name, preds, eval_labels.numpy())\n",
+    "    result['eval_loss'] = eval_loss\n",
+    "    return result\n",
+    "\n",
+    "def soft_cross_entropy(predicts, targets):\n",
+    "    student_likelihood = torch.nn.functional.log_softmax(predicts, dim=-1)\n",
+    "    targets_prob = torch.nn.functional.softmax(targets, dim=-1)\n",
+    "    return torch.sum((- targets_prob * student_likelihood), dim=-1).mean()\n",
+    "\n",
+    "processors = {\n",
+    "    \"cola\": ColaProcessor,\n",
+    "    \"mnli\": MnliProcessor,\n",
+    "    \"mnli-mm\": MnliMismatchedProcessor,\n",
+    "    \"mrpc\": MrpcProcessor,\n",
+    "    \"sst-2\": Sst2Processor,\n",
+    "    \"sts-b\": StsbProcessor,\n",
+    "    \"qqp\": QqpProcessor,\n",
+    "    \"qnli\": QnliProcessor,\n",
+    "    \"rte\": RteProcessor   \n",
+    "}\n",
+    "\n",
+    "output_modes = {\n",
+    "        \"cola\": \"classification\",\n",
+    "        \"mnli\": \"classification\",\n",
+    "        \"mrpc\": \"classification\",\n",
+    "        \"sst-2\": \"classification\",\n",
+    "        \"sts-b\": \"regression\",\n",
+    "        \"qqp\": \"classification\",\n",
+    "        \"qnli\": \"classification\",\n",
+    "        \"rte\": \"classification\"\n",
+    "}\n",
+    "\n",
+    "default_params = {\n",
+    "        \"cola\": {\"max_seq_length\": 64,\"batch_size\":1,\"eval_step\": 50}, # No Aug : 50 Aug : 400\n",
+    "        \"mnli\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":8000},\n",
+    "        \"mrpc\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"sst-2\": {\"max_seq_length\": 64,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"sts-b\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":100},\n",
+    "        \"qqp\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":1000},\n",
+    "        \"qnli\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\":1000},\n",
+    "        \"rte\": {\"max_seq_length\": 128,\"batch_size\":1,\"eval_step\": 20}\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "535b6819",
+   "metadata": {},
+   "source": [
+    "## Task"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63ce4c7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_name = \"cola\"\n",
+    "bert_size = \"base\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    layer_num = 24\n",
+    "    head_num = 16\n",
+    "else: \n",
+    "    layer_num = 12\n",
+    "    head_num = 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d8dfd91",
+   "metadata": {},
+   "source": [
+    "## Dataset Input Setting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10a16f27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model_dir = \"models\"\n",
+    "output_dir = \"output\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    model_dir = os.path.join(model_dir, \"BERT_large\")\n",
+    "    output_dir = os.path.join(output_dir, \"BERT_large\")\n",
+    "\n",
+    "teacher_model_dir = os.path.join(model_dir,task_name)\n",
+    "\n",
+    "# Processor & Task Info\n",
+    "processor = processors[task_name]()\n",
+    "output_mode = output_modes[task_name]\n",
+    "label_list = processor.get_labels()\n",
+    "num_labels = len(label_list)\n",
+    "\n",
+    "if task_name in default_params:\n",
+    "    batch_size = default_params[task_name][\"batch_size\"]\n",
+    "    max_seq_length = default_params[task_name][\"max_seq_length\"]\n",
+    "    eval_step = default_params[task_name][\"eval_step\"]\n",
+    "    \n",
+    "# Tokenizer\n",
+    "tokenizer = BertTokenizer.from_pretrained(teacher_model_dir, do_lower_case=True)\n",
+    "\n",
+    "\n",
+    "# Load Dataset\n",
+    "data_dir = os.path.join(\"data\",task_name)\n",
+    "processed_data_dir = os.path.join(data_dir,'preprocessed')\n",
+    "\n",
+    "eval_examples = processor.get_dev_examples(data_dir)\n",
+    "eval_features = convert_examples_to_features(eval_examples, label_list, max_seq_length, tokenizer, output_mode)\n",
+    "# dev_file = train_file = os.path.join(processed_data_dir,'dev.pkl') \n",
+    "# eval_features = pickle.load(open(dev_file,'rb'))\n",
+    "\n",
+    "eval_data, eval_labels = get_tensor_data(\"classification\", eval_features)\n",
+    "eval_sampler = SequentialSampler(eval_data)\n",
+    "eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=1)\n",
+    "eval_data, eval_labels = get_tensor_data(output_mode, eval_features)\n",
+    "\n",
+    "eval_examples = processor.get_dev_examples(data_dir)\n",
+    "\n",
+    "# Sampling Sentence \n",
+    "i = 0 \n",
+    "# num = 3\n",
+    "num = 43\n",
+    "\n",
+    "for step, batch in enumerate(eval_dataloader):\n",
+    "    # model.train()\n",
+    "            \n",
+    "    batch = tuple(t.to(device) for t in batch)\n",
+    "    input_ids, input_mask, segment_ids, label_ids, seq_lengths = batch\n",
+    "    i = i + 1\n",
+    "    if i == num:\n",
+    "        break\n",
+    "\n",
+    "seq_length = seq_lengths.item()\n",
+    "\n",
+    "input_ids_sliced = input_ids[:,:seq_length]\n",
+    "input_id = []\n",
+    "for i in input_ids_sliced[0]:\n",
+    "    input_id.append(i.item())\n",
+    "tokens = tokenizer.convert_ids_to_tokens(input_id)\n",
+    "\n",
+    "\n",
+    "\n",
+    "sample_sentence_a = str()\n",
+    "sample_sentence_b = str()\n",
+    "index = 0\n",
+    "\n",
+    "for i, word in enumerate(tokens[1:-1]):\n",
+    "    if word == \"[SEP]\":\n",
+    "        break\n",
+    "    sample_sentence_a += word\n",
+    "    sample_sentence_a += \" \"\n",
+    "index = i\n",
+    "\n",
+    "for i, word in enumerate(tokens[index+2:-1]):\n",
+    "    if word == \"[SEP]\":\n",
+    "        break\n",
+    "    sample_sentence_b += word\n",
+    "    sample_sentence_b += \" \"\n",
+    "\n",
+    "sep_index = torch.where(input_ids[0] == 102)[0]\n",
+    "\n",
+    "if len(sample_sentence_b) > 1:\n",
+    "    sample_sentence_b_start = segment_ids[0].tolist().index(1)\n",
+    "else:\n",
+    "    sample_sentence_b_start = None\n",
+    "\n",
+    "print(f\"input_ids : {input_ids_sliced}\")\n",
+    "print(f\"tokens : {tokens}\")\n",
+    "print(f\"A : {sample_sentence_a}\")\n",
+    "print(f\"B : {sample_sentence_b}\")\n",
+    "print(sep_index)\n",
+    "\n",
+    "for i, token in enumerate(tokens):\n",
+    "    tokens[i] = token\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "023c3648",
+   "metadata": {},
+   "source": [
+    "## Model Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "35d1b723",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "05/21 04:45:56 PM Loading model models/cola/pytorch_model.bin\n",
+      "05/21 04:45:56 PM loading model...\n",
+      "05/21 04:45:56 PM done!\n",
+      "05/21 04:45:56 PM Weights from pretrained model not used in BertForSequenceClassification: ['bert.embeddings.position_ids']\n",
+      "05/21 04:45:56 PM loading configuration file output/cola/exploration/1SB_S/config.json\n",
+      "05/21 04:45:58 PM Loading model models/cola/pytorch_model.bin\n",
+      "05/21 04:45:59 PM loading model...\n",
+      "05/21 04:45:59 PM done!\n",
+      "05/21 04:45:59 PM Weights from pretrained model not used in BertForSequenceClassification: ['bert.embeddings.position_ids']\n",
+      "\n",
+      "==> Load Model DONE\n",
+      "==> Test Inference\n"
+     ]
+    }
+   ],
+   "source": [
+    "st_model = \"qat\" # QAT, Q\n",
+    "\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "model_dir = \"models\"\n",
+    "output_dir = \"output\"\n",
+    "\n",
+    "if bert_size == \"large\":\n",
+    "    model_dir = os.path.join(model_dir, \"BERT_large\")\n",
+    "    output_dir = os.path.join(output_dir, \"BERT_large\")\n",
+    "\n",
+    "# Teacher Model Load\n",
+    "student_model_dir = os.path.join(model_dir,task_name)\n",
+    "teacher_model_dir = os.path.join(model_dir,task_name)\n",
+    "\n",
+    "if teacher_model is None:\n",
+    "    teacher_model = BertForSequenceClassification.from_pretrained(teacher_model_dir, num_labels=num_labels)\n",
+    "    teacher_model.to(device)\n",
+    "    teacher_model.eval()\n",
+    "    # Inference\n",
+    "    teacher_logits, teacher_atts, teacher_reps, teacher_probs, teacher_zip = teacher_model(input_ids_sliced.to(device)) #input_mask, segment_ids\n",
+    "\n",
+    "# QAT Result Model\n",
+    "if st_model == \"qat\":\n",
+    "    sarq_model_name = \"1SB_O\"\n",
+    "    sarq_model_dir = os.path.join(output_dir, task_name, \"exploration\", sarq_model_name)  \n",
+    "    quant_config = BertConfig.from_pretrained(sarq_model_dir)             \n",
+    "    sarq_model = QuantBertForSequenceClassification.from_pretrained(sarq_model_dir, config = quant_config, num_labels=num_labels)\n",
+    "\n",
+    "    sarq_model.to(device)\n",
+    "    sarq_model.eval()\n",
+    "    # Inference\n",
+    "    logits, qat_atts, qat_reps, qat_probs, qat_zip  = sarq_model(input_ids_sliced.to(device), teacher_outputs=None, output_mode=output_mode, seq_lengths=seq_lengths)\n",
+    "\n",
+    "\n",
+    "# Direct Quantization Model (Q)\n",
+    "if st_model == \"q\":\n",
+    "    sarq_model_name = \"1SB_O\"\n",
+    "    sarq_model_dir = os.path.join(output_dir, task_name, \"exploration\", sarq_model_name)  \n",
+    "    quant_config = BertConfig.from_pretrained(sarq_model_dir)             \n",
+    "    sarq_model = QuantBertForSequenceClassification.from_pretrained(teacher_model_dir, config = quant_config, num_labels=num_labels)\n",
+    "\n",
+    "    sarq_model.to(device)\n",
+    "    sarq_model.eval()\n",
+    "    # Inference\n",
+    "    logits, q_atts, q_reps, q_probs, q_zip  = sarq_model(input_ids_sliced.to(device), teacher_outputs=None, output_mode=output_mode, seq_lengths=seq_lengths)\n",
+    "\n",
+    "print()\n",
+    "print(\"==> Load Model DONE\")\n",
+    "print(\"==> Test Inference\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c3e638d",
+   "metadata": {},
+   "source": [
+    "## Attention Prob AVG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ad8246de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "q_avg_attention = []\n",
+    "qat_avg_attention = []\n",
+    "tc_avg_attention = []\n",
+    "\n",
+    "for l in range(12):\n",
+    "    tc_avg_attention.append(teacher_probs[l][0,:,:,sep_index[0]].mean().item())\n",
+    "    # print(teacher_probs[l][0,:,:,sep_index[0]].mean())\n",
+    "print()\n",
+    "for l in range(12):\n",
+    "    qat_avg_attention.append(student_zip[1][l][0,:,:,sep_index[0]].mean().item())\n",
+    "    # print(student_zip[1][l][0,:,:,sep_index[0]].mean())\n",
+    "print()\n",
+    "for l in range(12):\n",
+    "    q_avg_attention.append(q_zip[1][l][0,:,:,sep_index[0]].mean().item())\n",
+    "    # print(q_zip[1][l][0,:,:,sep_index[0]].mean())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8940138e",
+   "metadata": {},
+   "source": [
+    "## Attention Output MSE Loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f452e02f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mse_func = MSELoss()\n",
+    "\n",
+    "\n",
+    "q_attn_output_list = []\n",
+    "qat_attn_output_list = []\n",
+    "tc_attn_output = []\n",
+    "\n",
+    "for l in range(12):\n",
+    "    tc_attn_context, tc_attn_output, tc_value_vector, tc_sa_output = teacher_zip[l]\n",
+    "    st_attn_context, st_attn_output, st_value_vector, st_sa_output = student_zip[0][l]    \n",
+    "    q_attn_context, q_attn_output, q_value_vector, q_sa_output = q_zip[0][l]        \n",
+    "    \n",
+    "    # # print(mse_func(tc_attn_context,st_attn_context).item())\n",
+    "    qat_attn_output_list.append(mse_func(tc_attn_context,st_attn_context).item())\n",
+    "    q_attn_output_list.append(mse_func(tc_attn_context,q_attn_context).item())\n",
+    "    \n",
+    "    # print(mse_func(tc_attn_context,st_attn_context))\n",
+    "    # print(mse_func(tc_attn_context,q_attn_context))\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "211e2acf",
+   "metadata": {},
+   "source": [
+    "## Save Torch File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "195b5e9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(tc_avg_attention, \"tc_sep_prob_avg.pt\")\n",
+    "torch.save(q_avg_attention, \"q_sep_prob_avg.pt\")\n",
+    "torch.save(qat_avg_attention, \"qat_sep_prob_avg.pt\")\n",
+    "\n",
+    "torch.save(q_attn_output_list, \"q_attn_output_mse.pt\")\n",
+    "torch.save(qat_attn_output_list, \"qat_attn_output_mse.pt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "880adbac",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 560x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 560x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tc_avg_attention = torch.load(\"tc_sep_prob_avg.pt\")\n",
+    "q_avg_attention = torch.load(\"q_sep_prob_avg.pt\")\n",
+    "qat_avg_attention = torch.load(\"qat_sep_prob_avg.pt\")\n",
+    "\n",
+    "q_attn_output_list = torch.load(\"q_attn_output_mse.pt\")\n",
+    "qat_attn_output_list = torch.load(\"qat_attn_output_mse.pt\")\n",
+    "\n",
+    "\n",
+    "plt.rcParams['axes.linewidth'] = 1\n",
+    "plt.rcParams['patch.linewidth'] = 1\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 5), dpi=70)\n",
+    "lw = 3.5\n",
+    "fs = 20\n",
+    "tc_name = \"FP\"\n",
+    "qat_name = \"QAT\"\n",
+    "q_name = \"Q\"\n",
+    "\n",
+    "tc_c = \"r\"\n",
+    "qat_c = \"orange\"\n",
+    "q_c = \"dodgerblue\"\n",
+    "al=0.7\n",
+    "plt.xlabel(\"Layer\", fontsize=fs)\n",
+    "ax.plot(range(12), tc_avg_attention, linewidth=lw, color=tc_c, label=tc_name, alpha=al, marker=\"o\")\n",
+    "ax.plot(range(12), qat_avg_attention, linewidth=lw, color=qat_c, label=qat_name, alpha=al, marker=\"o\")\n",
+    "ax.plot(range(12), q_avg_attention, linewidth=lw, color=q_c, label=q_name, alpha=al, marker=\"o\")\n",
+    "ax.tick_params(axis=\"x\", labelsize=fs)\n",
+    "ax.tick_params(axis=\"y\", labelsize=fs)\n",
+    "ax.set_ylabel(\"Avg Attention (SEP)\", fontsize=fs)\n",
+    "ax.set_ylim(0, 0.8)\n",
+    "ax.legend(loc=2, ncol=1, fontsize=fs)\n",
+    "# ax2.plot(range(12), tc_attn_output, linewidth=lw, color=tc_c, label=tc_name, alpha=0.4)\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 5), dpi=70)\n",
+    "plt.xlabel(\"Layer\", fontsize=fs)\n",
+    "ax.plot(range(12), qat_attn_output_list, linewidth=lw, color=qat_c, label=\"QAT_MSE_loss\", alpha=al, marker=\"o\")\n",
+    "ax.plot(range(12), q_attn_output_list, linewidth=lw, color=q_c, label=\"Q_MSE_loss\", alpha=al, marker=\"o\")\n",
+    "ax.set_ylabel(\"Attention Output MSE\", fontsize=fs)\n",
+    "ax.legend(loc=2, ncol=1, fontsize=fs)\n",
+    "ax.tick_params(axis=\"x\", labelsize=fs)\n",
+    "ax.tick_params(axis=\"y\", labelsize=fs)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5df230f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!rm tc_sep_prob_avg.pt\n",
+    "!rm q_sep_prob_avg.pt\n",
+    "!rm qat_sep_prob_avg.pt\n",
+    "\n",
+    "!rm q_attn_output_mse.pt\n",
+    "!rm qat_attn_output_mse.pt"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Result_list.ipynb b/notebooks/Result_list.ipynb
new file mode 100644
index 0000000..e63c4fb
--- /dev/null
+++ b/notebooks/Result_list.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1236c376",
+   "metadata": {},
+   "source": [
+    "## A6000-2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0a482c6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "87.56231306081754\n",
+      "88.04265853498589\n",
+      "87.47464503042596\n",
+      "87.09374160623153\n",
+      "86.41176470588235\n",
+      "87.03814720563767\n",
+      "86.94946252612719\n",
+      "87.29294891198332\n",
+      "86.41176470588235\n",
+      "88.09242608960683\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "task = \"mrpc\"\n",
+    "\n",
+    "bert = \"BERT_large\"\n",
+    "size = \"base\"\n",
+    "\n",
+    "if size == \"base\":\n",
+    "    output_dir = os.path.join(\"output\", task, \"exploration\")\n",
+    "else:\n",
+    "    output_dir = os.path.join(\"output\", bert, task, \"exploration\")\n",
+    "    \n",
+    "seed_list = [1,2,3,4,5,6,7,8,9,10]\n",
+    "\n",
+    "for seed in seed_list:\n",
+    "\n",
+    "    folder_name = f\"sweep_{size}_G_AC_R_{seed}\"\n",
+    "    temp_dir = os.path.join(output_dir, folder_name, \"best_info.txt\")\n",
+    "\n",
+    "    f = open(temp_dir, 'r')\n",
+    "    print(f.readline())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "22354238",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "85.58823529411765\n",
+      "85.33263305322129\n",
+      "86.16504854368931\n",
+      "85.87909301651695\n"
+     ]
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'output/BERT_Tiny_6l/mrpc/exploration/1SB_tiny-6l_S_O_5/best_info.txt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [27]\u001b[0m, in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m folder_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1SB_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00msize\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_S_O_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mseed\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     13\u001b[0m temp_dir \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(output_dir, folder_name, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbest_info.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 15\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtemp_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28mprint\u001b[39m(f\u001b[38;5;241m.\u001b[39mreadline())\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'output/BERT_Tiny_6l/mrpc/exploration/1SB_tiny-6l_S_O_5/best_info.txt'"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "task = \"mrpc\"\n",
+    "bert = \"BERT_Tiny_6l\"\n",
+    "size=\"tiny-6l\"\n",
+    "\n",
+    "output_dir = os.path.join(\"output\", bert, task, \"exploration\")\n",
+    "\n",
+    "seed_list = [1,2,3,4,5,6,7,8,9,10]\n",
+    "\n",
+    "for seed in seed_list:\n",
+    "\n",
+    "    folder_name = f\"1SB_{size}_S_O_{seed}\"\n",
+    "    temp_dir = os.path.join(output_dir, folder_name, \"best_info.txt\")\n",
+    "\n",
+    "    f = open(temp_dir, 'r')\n",
+    "    print(f.readline())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aedf5ed4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/run_TI_step_1.sh b/run_TI_step_1.sh
new file mode 100644
index 0000000..566f357
--- /dev/null
+++ b/run_TI_step_1.sh
@@ -0,0 +1,59 @@
+# Quantization Range
+quantize=1
+
+# Quantization Range
+q_qkv=1
+q_ffn_1=1
+q_ffn_2=1
+q_emb=1
+q_cls=1
+layer_num=-1
+
+# KD & Ternary Option
+mean_scale=0.7
+bert=base
+
+#===========================================================#
+# Logging Option
+exp_name=TI_step1
+neptune=0
+save_quantized_model=1
+
+# Distill Option
+pred_distill=1
+rep_distill=1
+attn_distill=1
+output_distill=1
+
+# Teacher Intervention (TI)
+teacher_attnmap=0
+teacher_context=0
+teacher_output=1
+# TI-G options 
+teacher_gradual=0
+teacher_stochastic=0
+teacher_inverted=0
+
+# Training Type (downstream, qat_normal, qat_step1, qat_step2)
+training_type=qat_normal
+
+# DA Options
+aug_train=0
+aug_N=5
+
+learning_rate=2E-5
+# ===========================================================#
+
+CUDA_VISIBLE_DEVICES=$1 python /home/ms/workspace/git/Teacher-Intervention-KD-QAT/main.py --data_dir data --task_name $2 --bert ${bert} \
+--gpu 1 --quantize ${quantize} --qkv ${q_qkv} --ffn_1 ${q_ffn_1} --ffn_2 ${q_ffn_2} --emb ${q_emb} --cls ${q_cls} \
+--aug_train ${aug_train} \
+--output_distill ${output_distill} --pred_distill ${pred_distill} --rep_distill ${rep_distill} --attn_distill ${attn_distill} \
+--teacher_attnmap ${teacher_attnmap} --teacher_context ${teacher_context} --teacher_output ${teacher_output} --teacher_gradual ${teacher_gradual} --teacher_stochastic ${teacher_stochastic} --teacher_inverted ${teacher_inverted} \
+--training_type ${training_type} \
+--mean_scale ${mean_scale} \
+--exp_name ${exp_name} \
+--save_quantized_model ${save_quantized_model} \
+--neptune ${neptune} \
+--aug_N ${aug_N} \
+--num_train_epochs 3 --seed 1 \
+--learning_rate ${learning_rate} 
\ No newline at end of file
diff --git a/run_TI_step_2.sh b/run_TI_step_2.sh
new file mode 100644
index 0000000..cb72a2c
--- /dev/null
+++ b/run_TI_step_2.sh
@@ -0,0 +1,60 @@
+# Quantization Range
+quantize=1
+
+# Quantization Range
+q_qkv=1
+q_ffn_1=1
+q_ffn_2=1
+q_emb=1
+q_cls=1
+
+# KD & Ternary Option
+mean_scale=0.7
+bert=base
+
+#===========================================================#
+# Logging Option
+exp_name=TI_step2
+neptune=1
+save_quantized_model=1
+
+# Distill Option
+pred_distill=1
+rep_distill=1
+attn_distill=1
+output_distill=1
+
+# Teacher Intervention (TI)
+teacher_attnmap=0
+teacher_context=0
+teacher_output=0
+# TI-G options 
+teacher_gradual=0
+teacher_stochastic=0
+teacher_inverted=0
+# For step2
+step1_option=GRAD
+# Training Type (qat_step1, qat_step2)
+training_type=qat_step2
+
+# DA Options
+aug_train=0
+aug_N=5
+
+learning_rate=2E-5
+# ===========================================================#
+
+CUDA_VISIBLE_DEVICES=$1 python main.py --data_dir data --task_name $2 --bert ${bert} \
+--gpu 1 --quantize ${quantize} --qkv ${q_qkv} --ffn_1 ${q_ffn_1} --ffn_2 ${q_ffn_2} --emb ${q_emb} --cls ${q_cls} \
+--aug_train ${aug_train} \
+--output_distill ${output_distill} --pred_distill ${pred_distill} --rep_distill ${rep_distill} --attn_distill ${attn_distill} \
+--teacher_attnmap ${teacher_attnmap} --teacher_context ${teacher_context} --teacher_output ${teacher_output} --teacher_gradual ${teacher_gradual} --teacher_stochastic ${teacher_stochastic} --teacher_inverted ${teacher_inverted} \
+--step1_option $step1_option \
+--training_type ${training_type} \
+--mean_scale ${mean_scale} \
+--exp_name ${exp_name} \
+--save_quantized_model ${save_quantized_model} \
+--neptune ${neptune} \
+--aug_N ${aug_N} \
+--num_train_epochs 3 --seed 5 \
+--learning_rate ${learning_rate} 
\ No newline at end of file
diff --git a/transformer/__init__.py b/transformer/__init__.py
new file mode 100644
index 0000000..39ad678
--- /dev/null
+++ b/transformer/__init__.py
@@ -0,0 +1,8 @@
+from .tokenization import BertTokenizer, BasicTokenizer, WordpieceTokenizer
+from .modeling import BertForSequenceClassification,BertModel, CONFIG_NAME, WEIGHTS_NAME
+from .configuration import BertConfig
+from .optimization import BertAdam
+from .utils_quant import QuantizeLinear
+from .modeling_quant import BertSelfAttention, BertAttention
+from .modeling import BertSelfAttention as FP_BertSelfAttention
+from .modeling import BertAttention as FP_BertAttention
diff --git a/transformer/configuration.py b/transformer/configuration.py
new file mode 100644
index 0000000..b5ca148
--- /dev/null
+++ b/transformer/configuration.py
@@ -0,0 +1,148 @@
+""" BERT model configuration """
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import json
+import logging
+import sys
+import os
+import copy
+from io import open
+
+logger = logging.getLogger(__name__)
+
+#CONFIG_NAME = "config_bert_base.json"
+CONFIG_NAME = "config.json"
+class BertConfig(object):
+    r"""
+        :class:`~transformers.BertConfig` is the configuration class to store the configuration of a
+        `BertModel`.
+
+
+        Arguments:
+            vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
+            hidden_size: Size of the encoder layers and the pooler layer.
+            num_hidden_layers: Number of hidden layers in the Transformer encoder.
+            num_attention_heads: Number of attention heads for each attention layer in
+                the Transformer encoder.
+            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
+                layer in the Transformer encoder.
+            hidden_act: The non-linear activation function (function or string) in the
+                encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
+            hidden_dropout_prob: The dropout probabilitiy for all fully connected
+                layers in the embeddings, encoder, and pooler.
+            attention_probs_dropout_prob: The dropout ratio for the attention
+                probabilities.
+            max_position_embeddings: The maximum sequence length that this model might
+                ever be used with. Typically set this to something large just in case
+                (e.g., 512 or 1024 or 2048).
+            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
+                `BertModel`.
+            initializer_range: The sttdev of the truncated_normal_initializer for
+                initializing all weight matrices.
+            layer_norm_eps: The epsilon used by LayerNorm.
+    """
+
+    def __init__(self,
+                 vocab_size_or_config_json_file=30522,
+                 hidden_size=768,
+                 num_hidden_layers=12,
+                 num_attention_heads=12,
+                 intermediate_size=3072,
+                 hidden_act="gelu",
+                 hidden_dropout_prob=0.1,
+                 attention_probs_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=2,
+                 initializer_range=0.02,
+                 layer_norm_eps=1e-12,
+                 **kwargs):
+        super(BertConfig, self).__init__(**kwargs)
+        if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
+                        and isinstance(vocab_size_or_config_json_file, unicode)):
+            with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
+                json_config = json.loads(reader.read())
+            for key, value in json_config.items():
+                self.__dict__[key] = value
+        elif isinstance(vocab_size_or_config_json_file, int):
+            self.vocab_size = vocab_size_or_config_json_file
+            self.hidden_size = hidden_size
+            self.num_hidden_layers = num_hidden_layers
+            self.num_attention_heads = num_attention_heads
+            self.hidden_act = hidden_act
+            self.intermediate_size = intermediate_size
+            self.hidden_dropout_prob = hidden_dropout_prob
+            self.attention_probs_dropout_prob = attention_probs_dropout_prob
+            self.max_position_embeddings = max_position_embeddings
+            self.type_vocab_size = type_vocab_size
+            self.initializer_range = initializer_range
+            self.layer_norm_eps = layer_norm_eps
+        else:
+            raise ValueError("First argument must be either a vocabulary size (int)"
+                             " or the path to a pretrained model config file (str)")
+
+    def save_pretrained(self, save_directory):
+        """ Save a configuration object to the directory `save_directory`, so that it
+            can be re-loaded using the :func:`~transformers.PretrainedConfig.from_pretrained` class method.
+        """
+        assert os.path.isdir(save_directory), "Saving path should be a directory where the model and configuration can be saved"
+
+        # If we save using the predefined names, we can load using `from_pretrained`
+        output_config_file = os.path.join(save_directory, CONFIG_NAME)
+
+        self.to_json_file(output_config_file)
+        logger.info("Configuration saved in {}".format(output_config_file))
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        
+        config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
+        # logger.info("loading configuration file {}".format(config_file))
+        # Load config
+        config = cls.from_json_file(config_file)
+
+        # Update config with kwargs if needed
+        to_remove = []
+        for key, value in kwargs.items():
+            setattr(config, key, value)
+            to_remove.append(key)
+        for key in to_remove:
+            kwargs.pop(key, None)
+
+        #logger.info("Model config %s", str(config))
+        return config
+
+    @classmethod
+    def from_dict(cls, json_object):
+        """Constructs a `Config` from a Python dictionary of parameters."""
+        config = cls(vocab_size_or_config_json_file=-1)
+        for key, value in json_object.items():
+            setattr(config, key, value)
+        return config
+
+    @classmethod
+    def from_json_file(cls, json_file):
+        """Constructs a `BertConfig` from a json file of parameters."""
+        with open(json_file, "r", encoding='utf-8') as reader:
+            text = reader.read()
+        return cls.from_dict(json.loads(text))
+
+    def __eq__(self, other):
+        return self.__dict__ == other.__dict__
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
+
+    def to_json_file(self, json_file_path):
+        """ Save this instance to a json file."""
+        with open(json_file_path, "w", encoding='utf-8') as writer:
+            writer.write(self.to_json_string())
\ No newline at end of file
diff --git a/transformer/file_utils.py b/transformer/file_utils.py
new file mode 100644
index 0000000..8aa5c9e
--- /dev/null
+++ b/transformer/file_utils.py
@@ -0,0 +1,269 @@
+"""
+Utilities for working with the local dataset cache.
+This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
+Copyright by the AllenNLP authors.
+"""
+from __future__ import (absolute_import, division, print_function, unicode_literals)
+
+import json
+import logging
+import os
+import shutil
+import tempfile
+import fnmatch
+from functools import wraps
+from hashlib import sha256
+import sys
+from io import open
+
+import boto3
+import requests
+from botocore.exceptions import ClientError
+from tqdm import tqdm
+
+try:
+    from urllib.parse import urlparse
+except ImportError:
+    from urlparse import urlparse
+
+try:
+    from pathlib import Path
+    PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
+                                                   Path.home() / '.pytorch_pretrained_bert'))
+except (AttributeError, ImportError):
+    PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
+                                              os.path.join(os.path.expanduser("~"), '.pytorch_pretrained_bert'))
+
+CONFIG_NAME = "config.json"
+WEIGHTS_NAME = "pytorch_model.bin"
+
+logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
+
+
+def url_to_filename(url, etag=None):
+    """
+    Convert `url` into a hashed filename in a repeatable way.
+    If `etag` is specified, append its hash to the url's, delimited
+    by a period.
+    """
+    url_bytes = url.encode('utf-8')
+    url_hash = sha256(url_bytes)
+    filename = url_hash.hexdigest()
+
+    if etag:
+        etag_bytes = etag.encode('utf-8')
+        etag_hash = sha256(etag_bytes)
+        filename += '.' + etag_hash.hexdigest()
+
+    return filename
+
+
+def filename_to_url(filename, cache_dir=None):
+    """
+    Return the url and etag (which may be ``None``) stored for `filename`.
+    Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    cache_path = os.path.join(cache_dir, filename)
+    if not os.path.exists(cache_path):
+        raise EnvironmentError("file {} not found".format(cache_path))
+
+    meta_path = cache_path + '.json'
+    if not os.path.exists(meta_path):
+        raise EnvironmentError("file {} not found".format(meta_path))
+
+    with open(meta_path, encoding="utf-8") as meta_file:
+        metadata = json.load(meta_file)
+    url = metadata['url']
+    etag = metadata['etag']
+
+    return url, etag
+
+
+def cached_path(url_or_filename, cache_dir=None):
+    """
+    Given something that might be a URL (or might be a local path),
+    determine which. If it's a URL, download the file and cache it, and
+    return the path to the cached file. If it's already a local path,
+    make sure the file exists and then return the path.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
+        url_or_filename = str(url_or_filename)
+    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    parsed = urlparse(url_or_filename)
+
+    if parsed.scheme in ('http', 'https', 's3'):
+        # URL, so get it from the cache (downloading if necessary)
+        return get_from_cache(url_or_filename, cache_dir)
+    elif os.path.exists(url_or_filename):
+        # File, and it exists.
+        return url_or_filename
+    elif parsed.scheme == '':
+        # File, but it doesn't exist.
+        raise EnvironmentError("file {} not found".format(url_or_filename))
+    else:
+        # Something unknown
+        raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
+
+
+def split_s3_path(url):
+    """Split a full s3 path into the bucket name and path."""
+    parsed = urlparse(url)
+    if not parsed.netloc or not parsed.path:
+        raise ValueError("bad s3 path {}".format(url))
+    bucket_name = parsed.netloc
+    s3_path = parsed.path
+    # Remove '/' at beginning of path.
+    if s3_path.startswith("/"):
+        s3_path = s3_path[1:]
+    return bucket_name, s3_path
+
+
+def s3_request(func):
+    """
+    Wrapper function for s3 requests in order to create more helpful error
+    messages.
+    """
+
+    @wraps(func)
+    def wrapper(url, *args, **kwargs):
+        try:
+            return func(url, *args, **kwargs)
+        except ClientError as exc:
+            if int(exc.response["Error"]["Code"]) == 404:
+                raise EnvironmentError("file {} not found".format(url))
+            else:
+                raise
+
+    return wrapper
+
+
+@s3_request
+def s3_etag(url):
+    """Check ETag on S3 object."""
+    s3_resource = boto3.resource("s3")
+    bucket_name, s3_path = split_s3_path(url)
+    s3_object = s3_resource.Object(bucket_name, s3_path)
+    return s3_object.e_tag
+
+
+@s3_request
+def s3_get(url, temp_file):
+    """Pull a file directly from S3."""
+    s3_resource = boto3.resource("s3")
+    bucket_name, s3_path = split_s3_path(url)
+    s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
+
+
+def http_get(url, temp_file):
+    req = requests.get(url, stream=True)
+    content_length = req.headers.get('Content-Length')
+    total = int(content_length) if content_length is not None else None
+    progress = tqdm(unit="B", total=total)
+    for chunk in req.iter_content(chunk_size=1024):
+        if chunk: # filter out keep-alive new chunks
+            progress.update(len(chunk))
+            temp_file.write(chunk)
+    progress.close()
+
+
+def get_from_cache(url, cache_dir=None):
+    """
+    Given a URL, look for the corresponding dataset in the local cache.
+    If it's not there, download it. Then return the path to the cached file.
+    """
+    if cache_dir is None:
+        cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
+    if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
+        cache_dir = str(cache_dir)
+
+    if not os.path.exists(cache_dir):
+        os.makedirs(cache_dir)
+
+    # Get eTag to add to filename, if it exists.
+    if url.startswith("s3://"):
+        etag = s3_etag(url)
+    else:
+        try:
+            response = requests.head(url, allow_redirects=True)
+            if response.status_code != 200:
+                etag = None
+            else:
+                etag = response.headers.get("ETag")
+        except EnvironmentError:
+            etag = None
+
+    if sys.version_info[0] == 2 and etag is not None:
+        etag = etag.decode('utf-8')
+    filename = url_to_filename(url, etag)
+
+    # get cache path to put the file
+    cache_path = os.path.join(cache_dir, filename)
+
+    # If we don't have a connection (etag is None) and can't identify the file
+    # try to get the last downloaded one
+    if not os.path.exists(cache_path) and etag is None:
+        matching_files = fnmatch.filter(os.listdir(cache_dir), filename + '.*')
+        matching_files = list(filter(lambda s: not s.endswith('.json'), matching_files))
+        if matching_files:
+            cache_path = os.path.join(cache_dir, matching_files[-1])
+
+    if not os.path.exists(cache_path):
+        # Download to temporary file, then copy to cache dir once finished.
+        # Otherwise you get corrupt cache entries if the download gets interrupted.
+        with tempfile.NamedTemporaryFile() as temp_file:
+            logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
+
+            # GET file object
+            if url.startswith("s3://"):
+                s3_get(url, temp_file)
+            else:
+                http_get(url, temp_file)
+
+            # we are copying the file before closing it, so flush to avoid truncation
+            temp_file.flush()
+            # shutil.copyfileobj() starts at the current position, so go to the start
+            temp_file.seek(0)
+
+            logger.info("copying %s to cache at %s", temp_file.name, cache_path)
+            with open(cache_path, 'wb') as cache_file:
+                shutil.copyfileobj(temp_file, cache_file)
+
+            logger.info("creating metadata file for %s", cache_path)
+            meta = {'url': url, 'etag': etag}
+            meta_path = cache_path + '.json'
+            with open(meta_path, 'w') as meta_file:
+                output_string = json.dumps(meta)
+                if sys.version_info[0] == 2 and isinstance(output_string, str):
+                    output_string = unicode(output_string, 'utf-8')  # The beauty of python 2
+                meta_file.write(output_string)
+
+            logger.info("removing temp file %s", temp_file.name)
+
+    return cache_path
+
+
+def read_set_from_file(filename):
+    '''
+    Extract a de-duped collection (set) of text from a file.
+    Expected file format is one item per line.
+    '''
+    collection = set()
+    with open(filename, 'r', encoding='utf-8') as file_:
+        for line in file_:
+            collection.add(line.rstrip())
+    return collection
+
+
+def get_file_extension(path, dot=True, lower=True):
+    ext = os.path.splitext(path)[1]
+    ext = ext if dot else ext[1:]
+    return ext.lower() if lower else ext
diff --git a/transformer/modeling.py b/transformer/modeling.py
new file mode 100644
index 0000000..01c003c
--- /dev/null
+++ b/transformer/modeling.py
@@ -0,0 +1,396 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch BERT model."""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import math
+import os
+
+import torch
+from torch import nn
+from torch.autograd import Variable
+from .configuration import BertConfig
+from .utils_quant import QuantizeLinear, QuantizeEmbedding, SymQuantizer
+
+logger = logging.getLogger(__name__)
+
+#CONFIG_NAME = "config_bert_base.json"
+CONFIG_NAME = "config.json"
+WEIGHTS_NAME = "pytorch_model.bin"
+#WEIGHTS_NAME = "FFN_GT_KD_AUG.bin"
+
+def gelu(x):
+    """Implementation of the gelu activation function.
+        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
+        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+        Also see https://arxiv.org/abs/1606.08415
+    """
+    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+class BertEmbeddings(nn.Module):
+    def __init__(self, config):
+        super(BertEmbeddings, self).__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx = 0) 
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) 
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+        
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        
+    def forward(self, input_ids, token_type_ids):
+        
+        seq_length = input_ids.size(1)
+        position_ids = torch.arange(
+            seq_length, dtype=torch.long, device=input_ids.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+        words_embeddings = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+        
+        embeddings = words_embeddings + position_embeddings + token_type_embeddings
+        
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config, i):
+        super(BertSelfAttention, self).__init__()
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(
+            config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size)
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        
+        self.i = i
+        self.config = config
+        
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[
+            :-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(self, hidden_states, attention_mask=None):
+        mixed_query_layer = self.query(hidden_states)
+        mixed_key_layer = self.key(hidden_states)
+        mixed_value_layer = self.value(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)
+        
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            attention_scores = attention_scores + attention_mask
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+        attention_prob = attention_probs
+        attention_probs = self.dropout(attention_probs)
+
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer_ = context_layer
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        return context_layer, attention_scores, attention_prob, context_layer_ # , value_layer
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config, i):
+        super(BertSelfOutput, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(
+            config.hidden_size / config.num_attention_heads)
+
+    def forward(self, hidden_states, input_tensor):
+
+        hidden_states = self.dense(hidden_states)
+        self_output_hs = hidden_states
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states ,self_output_hs
+
+class BertAttention(nn.Module):
+    def __init__(self, config, i):
+        super(BertAttention, self).__init__()
+        self.self = BertSelfAttention(config, i)
+        self.output = BertSelfOutput(config, i)
+
+    def forward(self, input_tensor, attention_mask):
+        self_output, layer_att, layer_probs, layer_context = self.self(input_tensor, attention_mask)
+        attention_output, self_output_hs = self.output(self_output, input_tensor)
+        
+        return attention_output, layer_att, layer_probs, (layer_context, attention_output, self_output_hs)
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config, i):
+        super(BertIntermediate, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        
+        self.i = i
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = gelu(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config, i):
+        super(BertOutput, self).__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        self.i = i
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config, i):
+        super(BertLayer, self).__init__()
+        self.attention = BertAttention(config, i)
+        self.intermediate = BertIntermediate(config, i)
+        self.output = BertOutput(config, i)
+
+    def forward(self, hidden_states, attention_mask):
+        attention_output, layer_att, layer_probs, layer_value = self.attention(
+            hidden_states, attention_mask)
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+
+        return layer_output, layer_att, layer_probs, layer_value
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config):
+        super(BertEncoder, self).__init__()
+        self.layer = nn.ModuleList([BertLayer(config, i)
+                                    for i in range(config.num_hidden_layers)])
+
+    def forward(self, hidden_states, attention_mask):
+        
+        all_encoder_layers = [hidden_states]
+        all_encoder_atts = []
+        all_encoder_probs = []
+        all_encoder_values = []
+
+        for _, layer_module in enumerate(self.layer):
+            hidden_states, layer_att, layer_probs, layer_value = layer_module(
+                hidden_states, attention_mask)
+            all_encoder_layers.append(hidden_states)
+            all_encoder_atts.append(layer_att)
+            all_encoder_probs.append(layer_probs)
+            all_encoder_values.append(layer_value)
+
+        return all_encoder_layers, all_encoder_atts, all_encoder_probs, all_encoder_values
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super(BertPooler, self).__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class BertPreTrainedModel(nn.Module):
+    """ An abstract class to handle weights initialization and
+        a simple interface for dowloading and loading pretrained models.
+    """
+
+    def __init__(self, config, *inputs, **kwargs):
+        super(BertPreTrainedModel, self).__init__()
+        self.config = config
+
+    def init_bert_weights(self, module):
+        """ Initialize the weights.
+        """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(
+                mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+    @classmethod
+    # MSKIM
+    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
+        """
+        Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
+        Params:
+            pretrained_model_name_or_path:
+                - a path or url to a pretrained model archive containing:
+                    . `bert_config.json` a configuration file for the model
+                    . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
+            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
+            config: BertConfig instance
+            *inputs, **kwargs: additional input for the specific Bert class
+                (ex: num_labels for BertForSequenceClassification)
+        """
+        state_dict = kwargs.get('state_dict', None)
+        kwargs.pop('state_dict', None)
+        config = kwargs.get('config', None)
+        kwargs.pop('config', None)
+        
+        if config is None:
+            # Load config
+            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
+            config = BertConfig.from_json_file(config_file)
+
+        #logger.info("Model config {}".format(config))
+        # Instantiate model.
+
+        model = cls(config, *inputs, **kwargs)
+        if state_dict is None:
+            weights_path = os.path.join(
+                pretrained_model_name_or_path, WEIGHTS_NAME)
+            # logger.info("Loading model {}".format(weights_path))
+            state_dict = torch.load(weights_path, map_location='cpu')
+
+        # Load from a PyTorch state_dict
+        old_keys = []
+        new_keys = []
+        for key in state_dict.keys():
+            new_key = None
+            if 'gamma' in key:
+                new_key = key.replace('gamma', 'weight')
+            if 'beta' in key:
+                new_key = key.replace('beta', 'bias')
+            if new_key:
+                old_keys.append(key)
+                new_keys.append(new_key)
+        for old_key, new_key in zip(old_keys, new_keys):
+            state_dict[new_key] = state_dict.pop(old_key)
+
+        missing_keys = []
+        unexpected_keys = []
+        error_msgs = []
+        # copy state_dict so _load_from_state_dict can modify it
+        metadata = getattr(state_dict, '_metadata', None)
+        state_dict = state_dict.copy()
+        if metadata is not None:
+            state_dict._metadata = metadata
+
+        def load(module, prefix=''):
+            local_metadata = {} if metadata is None else metadata.get(
+                prefix[:-1], {})
+            module._load_from_state_dict(
+                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
+            for name, child in module._modules.items():
+                if child is not None:
+                    load(child, prefix + name + '.')
+
+        start_prefix = ''
+        if not hasattr(model, 'bert') and any(s.startswith('bert.') for s in state_dict.keys()):
+            start_prefix = 'bert.'
+
+        # logger.info('loading model...')
+        
+        load(model, prefix=start_prefix)
+
+        return model
+
+
+class BertModel(BertPreTrainedModel):
+    def __init__(self, config):
+        super(BertModel, self).__init__(config)
+        self.embeddings = BertEmbeddings(config)
+        self.encoder = BertEncoder(config)
+        self.pooler = BertPooler(config)
+        self.apply(self.init_bert_weights)
+
+    def forward(self, input_ids, token_type_ids=None, attention_mask=None):
+
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+        
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+        embedding_output = self.embeddings(input_ids, token_type_ids)
+        encoded_layers, attention_scores, attention_probs, attention_values = self.encoder(embedding_output,
+                                                  extended_attention_mask)
+
+        pooled_output = self.pooler(encoded_layers[-1])
+        return encoded_layers, attention_scores, attention_probs, attention_values, pooled_output
+
+class BertForSequenceClassification(BertPreTrainedModel):
+    def __init__(self, config, num_labels = 2):
+        super(BertForSequenceClassification, self).__init__(config)
+        
+        # MSKIM made exception for MNLI Classifier
+        if 'num_labels' in config.to_dict():
+            self.num_labels = config.num_labels
+        else:
+            self.num_labels = num_labels
+
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, self.num_labels)
+        self.apply(self.init_bert_weights)
+
+    def forward(self, input_ids, 
+                token_type_ids=None,
+                attention_mask=None, 
+                labels=None):
+        encoded_layers, attention_scores, attention_probs, attention_values, pooled_output = self.bert(input_ids, token_type_ids, attention_mask)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            return loss, attention_scores, encoded_layers
+        else:
+            return logits, attention_scores, encoded_layers, attention_probs, attention_values
diff --git a/transformer/modeling_quant.py b/transformer/modeling_quant.py
new file mode 100644
index 0000000..30b8eea
--- /dev/null
+++ b/transformer/modeling_quant.py
@@ -0,0 +1,482 @@
+# coding=utf-8
+# 2020.04.20 - Add&replace quantization modules
+#              Huawei Technologies Co., Ltd <zhangwei379@huawei.com>
+# Copyright (c) 2020, Huawei Technologies Co., Ltd.  All rights reserved.
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.w
+"""PyTorch BERT model."""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import logging
+import math
+import os
+
+import torch
+from torch import nn
+from torch.autograd import Variable
+from .configuration import BertConfig
+from .utils_quant import QuantizeLinear, QuantizeEmbedding, SymQuantizer, TwnQuantizer
+
+logger = logging.getLogger(__name__)
+
+CONFIG_NAME = "config.json"
+WEIGHTS_NAME = "pytorch_model.bin"
+#WEIGHTS_NAME = "FFN_GT_KD_AUG.bin"
+from torch.nn import CrossEntropyLoss, MSELoss
+
+def soft_cross_entropy(predicts, targets):
+    student_likelihood = torch.nn.functional.log_softmax(predicts, dim=-1)
+    targets_prob = torch.nn.functional.softmax(targets, dim=-1)
+    return torch.sum((- targets_prob * student_likelihood), dim=-1).mean()
+
+def gelu(x):
+    """Implementation of the gelu activation function.
+        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
+        0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+        Also see https://arxiv.org/abs/1606.08415
+    """
+    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+class BertEmbeddings(nn.Module):
+    def __init__(self, config):
+        super(BertEmbeddings, self).__init__()
+
+        if config.quantize and config.emb_q:
+            self.word_embeddings = QuantizeEmbedding(config.vocab_size, config.hidden_size, padding_idx = 0,config=config)
+        else:
+            self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+        
+        # position_embeddings and token_type_embeddings are kept in fp32 anyway
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, input_ids, token_type_ids):
+        seq_length = input_ids.size(1)
+        position_ids = torch.arange(
+            seq_length, dtype=torch.long, device=input_ids.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+        words_embeddings = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+        
+        embeddings = words_embeddings + position_embeddings + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config, i):
+        super(BertSelfAttention, self).__init__()
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads))
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(
+            config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.i = i
+        self.config = config
+        self.input_bits = 8
+
+        # ================================================================================  #
+        # Weight Quant Setting
+        # ================================================================================ #
+        
+        if self.config.quantize and config.qkv_q:              
+            self.query = QuantizeLinear(config.hidden_size, self.all_head_size,config=config, name=f"layer_{self.i}_{self.__class__.__name__}_query")
+            self.key = QuantizeLinear(config.hidden_size, self.all_head_size,config=config, name=f"layer_{self.i}_{self.__class__.__name__}_key")
+            self.value = QuantizeLinear(config.hidden_size, self.all_head_size,config=config, name=f"layer_{self.i}_{self.__class__.__name__}_value")
+        else:
+            self.query = nn.Linear(config.hidden_size, self.all_head_size)
+            self.key = nn.Linear(config.hidden_size, self.all_head_size)
+            self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        # ================================================================================  #
+        # ACT Quant Setting
+        # ================================================================================ #
+        
+        self.act_quantizer = SymQuantizer
+        self.register_buffer('clip_query', torch.Tensor([-config.clip_val, config.clip_val]))
+        self.register_buffer('clip_key', torch.Tensor([-config.clip_val, config.clip_val]))
+        self.register_buffer('clip_value', torch.Tensor([-config.clip_val, config.clip_val]))
+        self.register_buffer('clip_attn', torch.Tensor([-config.clip_val, config.clip_val]))
+        
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[
+            :-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(self, hidden_states, attention_mask, teacher_probs=None):
+        # Stop Grad (With TI, stopping gradient is required for internal distillation)
+        if self.config.teacher_attnmap:
+            hidden_states_ = hidden_states.clone().detach()
+            mixed_query_layer = self.query(hidden_states_)
+            mixed_key_layer = self.key(hidden_states_)
+            mixed_value_layer = self.value(hidden_states)
+        elif self.config.teacher_context or self.config.teacher_output:
+            hidden_states_ = hidden_states.clone().detach()
+            mixed_query_layer = self.query(hidden_states_)
+            mixed_key_layer = self.key(hidden_states_)
+            mixed_value_layer = self.value(hidden_states_)
+        else:
+            mixed_query_layer = self.query(hidden_states)
+            mixed_key_layer = self.key(hidden_states)
+            mixed_value_layer = self.value(hidden_states)
+        
+        # Batch Size : 16, Max_len_seq : 64
+        # q, k, v : 16, 64, 768
+        # transpose for scores : 16, 64, 768 -> 16, 64, 12, 64 -> 16, 12(head), 64, 64 
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_for_scores(mixed_key_layer)
+        value_layer = self.transpose_for_scores(mixed_value_layer)        
+        
+        query_layer = self.act_quantizer.apply(query_layer, self.clip_query, self.input_bits, True)
+        key_layer = self.act_quantizer.apply(key_layer, self.clip_key, self.input_bits, True)
+                
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        attention_scores = attention_scores + attention_mask
+        st_attention_probs = nn.Softmax(dim=-1)(attention_scores)
+    
+        if self.config.teacher_attnmap and teacher_probs is not None:
+            # Teacher Intervention Map (TI-M)
+            tc_attention_probs = teacher_probs[0][self.i] 
+            attention_prob = st_attention_probs # attention probs to return (for internal distillation)
+            attention_probs = self.dropout(tc_attention_probs) # replace student attention map to teacher attention map
+        else:
+            attention_prob = st_attention_probs # attention probs to return (for internal distillation)
+            attention_probs = self.dropout(st_attention_probs)
+            
+        # quantize both attention probs and value layer for dot product
+        attention_probs = self.act_quantizer.apply(attention_probs, self.clip_attn, self.input_bits, True)
+        value_layer = self.act_quantizer.apply(value_layer, self.clip_value, self.input_bits, True)
+         
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer_ = context_layer
+
+        if self.config.teacher_context and teacher_probs is not None:
+            # Teacher Intervention Context (TI-C) we insert TI-C stage for giving smoothing effect to Gradual Teacher Intervention 
+            context_layer = teacher_probs[1][self.i][0] # TI/CI - Layer Number - Context
+            
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+       
+        return context_layer, attention_scores, attention_prob, context_layer_ 
+
+class BertAttention(nn.Module):
+    def __init__(self, config, i):
+        super(BertAttention, self).__init__()
+        self.self = BertSelfAttention(config, i)
+        self.output = BertSelfOutput(config, i)
+        self.config = config
+        self.i = i
+
+    def forward(self, input_tensor, attention_mask, teacher_probs=None):
+        
+        self_output, layer_att, layer_probs, layer_context = self.self(input_tensor, attention_mask, teacher_probs=teacher_probs)
+        attention_output, self_output_hs = self.output(self_output, input_tensor, teacher_probs=teacher_probs)
+        
+        return attention_output, layer_att, layer_probs, (layer_context, attention_output, self_output_hs)
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config, i):
+        super(BertSelfOutput, self).__init__()
+        self.config = config
+        self.i = i
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(
+            config.hidden_size / config.num_attention_heads)
+
+        if not self.config.qkv_q:
+            self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        else:
+            self.dense = QuantizeLinear(config.hidden_size, config.hidden_size,config=config)
+            
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor, teacher_probs=None):
+        hidden_states = self.dense(hidden_states)
+        self_output_hs = hidden_states
+
+        if self.config.teacher_output:
+            hidden_states = teacher_probs[1][self.i][2] # SA-output
+
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states ,self_output_hs
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config, i):
+        super(BertIntermediate, self).__init__()
+        self.i = i
+       
+        if config.quantize and config.ffn_q_1:
+            self.dense = QuantizeLinear(config.hidden_size, config.intermediate_size,config=config, name=f"layer_{self.i}_{self.__class__.__name__}")
+        else:
+            self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+            
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = gelu(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config, i):
+        super(BertOutput, self).__init__()
+        self.i = i
+        
+        if config.quantize and config.ffn_q_2:
+            self.dense = QuantizeLinear(config.intermediate_size, config.hidden_size,config=config, name=f"layer_{self.i}_{self.__class__.__name__}")
+        else:
+            self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config, i):
+        super(BertLayer, self).__init__()
+        self.attention = BertAttention(config, i)
+        self.intermediate = BertIntermediate(config, i)
+        self.output = BertOutput(config, i)
+
+    def forward(self, hidden_states, attention_mask, teacher_probs=None):
+
+        attention_output, layer_att, layer_probs, layer_value = self.attention(
+            hidden_states, attention_mask, teacher_probs=teacher_probs)
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+
+        return layer_output, layer_att, layer_probs, layer_value
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config):
+        super(BertEncoder, self).__init__()
+        self.layer = nn.ModuleList([BertLayer(config, i)
+                                    for i in range(config.num_hidden_layers)])
+
+    def forward(self, hidden_states, attention_mask, teacher_probs=None):
+        all_encoder_layers = [hidden_states]
+        all_encoder_atts = []
+        all_encoder_probs = []
+        all_encoder_values = []
+
+        for _, layer_module in enumerate(self.layer):
+            hidden_states, layer_att, layer_probs, layer_value = layer_module(
+                hidden_states, attention_mask, teacher_probs=teacher_probs)
+            all_encoder_layers.append(hidden_states)
+            all_encoder_atts.append(layer_att)
+            all_encoder_probs.append(layer_probs)
+            all_encoder_values.append(layer_value)
+
+        return all_encoder_layers, all_encoder_atts, all_encoder_probs, all_encoder_values
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config, recurs=None):
+        super(BertPooler, self).__init__()
+
+        if config.quantize and config.cls_q:
+            self.dense = QuantizeLinear(config.hidden_size, config.hidden_size,config=config, name=f"{self.__class__.__name__}")
+        else:
+            self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+
+        self.activation = nn.Tanh()
+        self.config = config
+
+    def forward(self, hidden_states):
+        pooled_output = hidden_states[-1][:, 0]
+        pooled_output = self.dense(pooled_output)
+        pooled_output = self.activation(pooled_output)
+
+        return pooled_output
+
+
+class BertPreTrainedModel(nn.Module):
+    """ An abstract class to handle weights initialization and
+        a simple interface for dowloading and loading pretrained models.
+    """
+
+    def __init__(self, config, *inputs, **kwargs):
+        super(BertPreTrainedModel, self).__init__()
+        self.config = config
+
+    def init_bert_weights(self, module):
+        """ Initialize the weights.
+        """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(
+                mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
+        """
+        Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
+        Params:
+            pretrained_model_name_or_path:
+                - a path or url to a pretrained model archive containing:
+                    . `bert_config.json` a configuration file for the model
+                    . `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
+            state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
+            config: BertConfig instance
+            *inputs, **kwargs: additional input for the specific Bert class
+                (ex: num_labels for BertForSequenceClassification)
+        """
+        
+        state_dict = kwargs.get('state_dict', None)
+        kwargs.pop('state_dict', None)
+        config = kwargs.get('config', None)
+        kwargs.pop('config', None)
+        
+        if config is None:
+            # Load config
+            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
+            config = BertConfig.from_json_file(config_file)
+
+        #logger.info("Model config {}".format(config))
+        # Instantiate model.
+        model = cls(config, *inputs, **kwargs)
+        if state_dict is None:
+            weights_path = os.path.join(
+                pretrained_model_name_or_path, WEIGHTS_NAME)
+            # logger.info("Loading model {}".format(weights_path))
+            state_dict = torch.load(weights_path, map_location='cpu')
+
+        # Load from a PyTorch state_dict
+        old_keys = []
+        new_keys = []
+        for key in state_dict.keys():
+            new_key = None
+            if 'gamma' in key:
+                new_key = key.replace('gamma', 'weight')
+            if 'beta' in key:
+                new_key = key.replace('beta', 'bias')
+            if new_key:
+                old_keys.append(key)
+                new_keys.append(new_key)
+        for old_key, new_key in zip(old_keys, new_keys):
+            state_dict[new_key] = state_dict.pop(old_key)
+
+        missing_keys = []
+        unexpected_keys = []
+        error_msgs = []
+        # copy state_dict so _load_from_state_dict can modify it
+        metadata = getattr(state_dict, '_metadata', None)
+        state_dict = state_dict.copy()
+        if metadata is not None:
+            state_dict._metadata = metadata
+
+        def load(module, prefix=''):
+            local_metadata = {} if metadata is None else metadata.get(
+                prefix[:-1], {})
+            module._load_from_state_dict(
+                state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
+            for name, child in module._modules.items():
+                if child is not None:
+                    load(child, prefix + name + '.')
+
+        start_prefix = ''
+        if not hasattr(model, 'bert') and any(s.startswith('bert.') for s in state_dict.keys()):
+            start_prefix = 'bert.'
+
+        # logger.info('loading model...')
+        
+        load(model, prefix=start_prefix)
+        
+        return model
+
+
+class BertModel(BertPreTrainedModel):
+    def __init__(self, config):
+        super(BertModel, self).__init__(config)
+        self.embeddings = BertEmbeddings(config)
+        self.encoder = BertEncoder(config)
+        self.pooler = BertPooler(config)
+        self.apply(self.init_bert_weights)
+
+    def forward(self, input_ids, token_type_ids=None, attention_mask=None, teacher_probs=None):
+
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+        # extended_attention_mask = extended_attention_mask.to(
+        #     dtype=next(self.parameters()).dtype)  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+        embedding_output = self.embeddings(input_ids, token_type_ids)
+        encoded_layers, attention_scores, attention_probs, attention_values = self.encoder(embedding_output,
+                                                  extended_attention_mask, teacher_probs=teacher_probs)
+
+        pooled_output = self.pooler(encoded_layers)
+        return encoded_layers, attention_scores, attention_probs, attention_values, pooled_output
+
+class BertForSequenceClassification(BertPreTrainedModel):
+    def __init__(self, config, num_labels = 2):
+        super(BertForSequenceClassification, self).__init__(config)
+        self.num_labels = num_labels
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.apply(self.init_bert_weights)
+        self.config = config
+        
+    def forward(self, input_ids, 
+                token_type_ids=None,
+                attention_mask=None, 
+                labels=None,
+                teacher_outputs=None,
+                seq_lengths=None):
+        
+        encoded_layers, student_atts, attention_probs, attention_values, pooled_output = self.bert(input_ids, token_type_ids, attention_mask, teacher_probs=teacher_outputs)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        return logits, student_atts, encoded_layers, attention_probs, attention_values
+
diff --git a/transformer/optimization.py b/transformer/optimization.py
new file mode 100644
index 0000000..15945d1
--- /dev/null
+++ b/transformer/optimization.py
@@ -0,0 +1,300 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch optimization for BERT model."""
+
+import math
+import torch
+from torch.optim import Optimizer
+from torch.optim.optimizer import required
+from torch.nn.utils import clip_grad_norm_
+import logging
+import abc
+import sys
+
+logger = logging.getLogger(__name__)
+
+
+if sys.version_info >= (3, 4):
+    ABC = abc.ABC
+else:
+    ABC = abc.ABCMeta('ABC', (), {})
+
+
+class _LRSchedule(ABC):
+    """ Parent of all LRSchedules here. """
+    warn_t_total = False        # is set to True for schedules where progressing beyond t_total steps doesn't make sense
+    def __init__(self, warmup=0.002, t_total=-1, **kw):
+        """
+        :param warmup:  what fraction of t_total steps will be used for linear warmup
+        :param t_total: how many training steps (updates) are planned
+        :param kw:
+        """
+        super(_LRSchedule, self).__init__(**kw)
+        if t_total < 0:
+            logger.warning("t_total value of {} results in schedule not being applied".format(t_total))
+        if not 0.0 <= warmup < 1.0 and not warmup == -1:
+            raise ValueError("Invalid warmup: {} - should be in [0.0, 1.0[ or -1".format(warmup))
+        warmup = max(warmup, 0.)
+        self.warmup, self.t_total = float(warmup), float(t_total)
+        self.warned_for_t_total_at_progress = -1
+
+    def get_lr(self, step, nowarn=False):
+        """
+        :param step:    which of t_total steps we're on
+        :param nowarn:  set to True to suppress warning regarding training beyond specified 't_total' steps
+        :return:        learning rate multiplier for current update
+        """
+        if self.t_total < 0:
+            return 1.
+        progress = float(step) / self.t_total
+        ret = self.get_lr_(progress)
+        # warning for exceeding t_total (only active with warmup_linear
+        if not nowarn and self.warn_t_total and progress > 1. and progress > self.warned_for_t_total_at_progress:
+            # logger.warning(
+            #     "Training beyond specified 't_total'. Learning rate multiplier set to {}. Please set 't_total' of {} correctly."
+            #         .format(ret, self.__class__.__name__))
+            self.warned_for_t_total_at_progress = progress
+        # end warning
+        return ret
+
+    @abc.abstractmethod
+    def get_lr_(self, progress):
+        """
+        :param progress:    value between 0 and 1 (unless going beyond t_total steps) specifying training progress
+        :return:            learning rate multiplier for current update
+        """
+        return 1.
+
+
+class ConstantLR(_LRSchedule):
+    def get_lr_(self, progress):
+        return 1.
+
+
+class WarmupCosineSchedule(_LRSchedule):
+    """
+    Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+    Decreases learning rate from 1. to 0. over remaining `1 - warmup` steps following a cosine curve.
+    If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup.
+    """
+    warn_t_total = True
+    def __init__(self, warmup=0.002, t_total=-1, cycles=.5, **kw):
+        """
+        :param warmup:      see LRSchedule
+        :param t_total:     see LRSchedule
+        :param cycles:      number of cycles. Default: 0.5, corresponding to cosine decay from 1. at progress==warmup and 0 at progress==1.
+        :param kw:
+        """
+        super(WarmupCosineSchedule, self).__init__(warmup=warmup, t_total=t_total, **kw)
+        self.cycles = cycles
+
+    def get_lr_(self, progress):
+        if progress < self.warmup:
+            return progress / self.warmup
+        else:
+            progress = (progress - self.warmup) / (1 - self.warmup)   # progress after warmup
+            return 0.5 * (1. + math.cos(math.pi * self.cycles * 2 * progress))
+
+
+class WarmupCosineWithHardRestartsSchedule(WarmupCosineSchedule):
+    """
+    Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+    If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying
+    learning rate (with hard restarts).
+    """
+    def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
+        super(WarmupCosineWithHardRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
+        assert(cycles >= 1.)
+
+    def get_lr_(self, progress):
+        if progress < self.warmup:
+            return progress / self.warmup
+        else:
+            progress = (progress - self.warmup) / (1 - self.warmup)     # progress after warmup
+            ret = 0.5 * (1. + math.cos(math.pi * ((self.cycles * progress) % 1)))
+            return ret
+
+
+class WarmupCosineWithWarmupRestartsSchedule(WarmupCosineWithHardRestartsSchedule):
+    """
+    All training progress is divided in `cycles` (default=1.) parts of equal length.
+    Every part follows a schedule with the first `warmup` fraction of the training steps linearly increasing from 0. to 1.,
+    followed by a learning rate decreasing from 1. to 0. following a cosine curve.
+    """
+    def __init__(self, warmup=0.002, t_total=-1, cycles=1., **kw):
+        assert(warmup * cycles < 1.)
+        warmup = warmup * cycles if warmup >= 0 else warmup
+        super(WarmupCosineWithWarmupRestartsSchedule, self).__init__(warmup=warmup, t_total=t_total, cycles=cycles, **kw)
+
+    def get_lr_(self, progress):
+        progress = progress * self.cycles % 1.
+        if progress < self.warmup:
+            return progress / self.warmup
+        else:
+            progress = (progress - self.warmup) / (1 - self.warmup)     # progress after warmup
+            ret = 0.5 * (1. + math.cos(math.pi * progress))
+            return ret
+
+
+class WarmupConstantSchedule(_LRSchedule):
+    """
+    Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+    Keeps learning rate equal to 1. after warmup.
+    """
+    def get_lr_(self, progress):
+        if progress < self.warmup:
+            return progress / self.warmup
+        return 1.
+
+
+class WarmupLinearSchedule(_LRSchedule):
+    """
+    Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
+    Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
+    """
+    warn_t_total = True
+    def get_lr_(self, progress):
+        if progress < self.warmup:
+            return progress / self.warmup
+        return max((progress - 1.) / (self.warmup - 1.), 0.)
+
+
+SCHEDULES = {
+    None:       ConstantLR,
+    "none":     ConstantLR,
+    "warmup_cosine": WarmupCosineSchedule,
+    "warmup_constant": WarmupConstantSchedule,
+    "warmup_linear": WarmupLinearSchedule
+}
+
+
+class BertAdam(Optimizer):
+    """Implements BERT version of Adam algorithm with weight decay fix.
+    Params:
+        lr: learning rate
+        warmup: portion of t_total for the warmup, -1  means no warmup. Default: -1
+        t_total: total number of training steps for the learning
+            rate schedule, -1  means constant learning rate of 1. (no warmup regardless of warmup setting). Default: -1
+        schedule: schedule to use for the warmup (see above).
+            Can be `'warmup_linear'`, `'warmup_constant'`, `'warmup_cosine'`, `'none'`, `None` or a `_LRSchedule` object (see below).
+            If `None` or `'none'`, learning rate is always kept constant.
+            Default : `'warmup_linear'`
+        b1: Adams b1. Default: 0.9
+        b2: Adams b2. Default: 0.999
+        e: Adams epsilon. Default: 1e-6
+        weight_decay: Weight decay. Default: 0.01
+        max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0
+    """
+    def __init__(self, params, lr=required, warmup=-1, t_total=-1, schedule='warmup_linear',
+                 b1=0.9, b2=0.999, e=1e-6, weight_decay=0.01, max_grad_norm=1.0, **kwargs):
+        if lr is not required and lr < 0.0:
+            raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr))
+        if not isinstance(schedule, _LRSchedule) and schedule not in SCHEDULES:
+            raise ValueError("Invalid schedule parameter: {}".format(schedule))
+        if not 0.0 <= b1 < 1.0:
+            raise ValueError("Invalid b1 parameter: {} - should be in [0.0, 1.0[".format(b1))
+        if not 0.0 <= b2 < 1.0:
+            raise ValueError("Invalid b2 parameter: {} - should be in [0.0, 1.0[".format(b2))
+        if not e >= 0.0:
+            raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e))
+        # initialize schedule object
+        if not isinstance(schedule, _LRSchedule):
+            schedule_type = SCHEDULES[schedule]
+            schedule = schedule_type(warmup=warmup, t_total=t_total)
+        else:
+            if warmup != -1 or t_total != -1:
+                logger.warning("warmup and t_total on the optimizer are ineffective when _LRSchedule object is provided as schedule. "
+                               "Please specify custom warmup and t_total in _LRSchedule object.")
+        defaults = dict(lr=lr, schedule=schedule,
+                        b1=b1, b2=b2, e=e, weight_decay=weight_decay,
+                        max_grad_norm=max_grad_norm)
+        super(BertAdam, self).__init__(params, defaults)
+
+    def get_lr(self):
+        lr = []
+        for group in self.param_groups:
+            for p in group['params']:
+                state = self.state[p]
+                if len(state) == 0:
+                    return [0]
+                lr_scheduled = group['lr']
+                lr_scheduled *= group['schedule'].get_lr(state['step'])
+                lr.append(lr_scheduled)
+        return lr
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+        for group in self.param_groups:
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                if grad.is_sparse:
+                    raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state['step'] = 0
+                    # Exponential moving average of gradient values
+                    state['next_m'] = torch.zeros_like(p.data)
+                    # Exponential moving average of squared gradient values
+                    state['next_v'] = torch.zeros_like(p.data)
+
+                next_m, next_v = state['next_m'], state['next_v']
+                beta1, beta2 = group['b1'], group['b2']
+
+                # Add grad clipping
+                if group['max_grad_norm'] > 0:
+                    clip_grad_norm_(p, group['max_grad_norm'])
+
+                # Decay the first and second moment running average coefficient
+                # In-place operations to update the averages at the same time
+                next_m.mul_(beta1).add_(grad, alpha=1 - beta1)
+                next_v.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+                update = next_m / (next_v.sqrt() + group['e'])
+
+                # Just adding the square of the weights to the loss function is *not*
+                # the correct way of using L2 regularization/weight decay with Adam,
+                # since that will interact with the m and v parameters in strange ways.
+                #
+                # Instead we want to decay the weights in a manner that doesn't interact
+                # with the m/v parameters. This is equivalent to adding the square
+                # of the weights to the loss with plain (non-momentum) SGD.
+                if group['weight_decay'] > 0.0:
+                    update += group['weight_decay'] * p.data
+
+                lr_scheduled = group['lr']
+                lr_scheduled *= group['schedule'].get_lr(state['step'])
+
+                update_with_lr = lr_scheduled * update
+                p.data.add_(-update_with_lr)
+
+                state['step'] += 1
+                # step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
+                # No bias correction
+                # bias_correction1 = 1 - beta1 ** state['step']
+                # bias_correction2 = 1 - beta2 ** state['step']
+
+        return loss
diff --git a/transformer/tokenization.py b/transformer/tokenization.py
new file mode 100644
index 0000000..c61a4e9
--- /dev/null
+++ b/transformer/tokenization.py
@@ -0,0 +1,365 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes."""
+
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import collections
+import logging
+import os
+import unicodedata
+from io import open
+
+logger = logging.getLogger(__name__)
+VOCAB_NAME='vocab.txt'
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    index = 0
+    with open(vocab_file, "r", encoding="utf-8") as reader:
+        while True:
+            token = reader.readline()
+            if not token:
+                break
+            token = token.strip()
+            vocab[token] = index
+            index += 1
+    return vocab
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+class BertTokenizer(object):
+    """Runs end-to-end tokenization: punctuation splitting + wordpiece"""
+
+    def __init__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True, basic_only=False,
+                 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
+        """Constructs a BertTokenizer.
+
+        Args:
+          vocab_file: Path to a one-wordpiece-per-line vocabulary file
+          do_lower_case: Whether to lower case the input
+                         Only has an effect when do_wordpiece_only=False
+          do_basic_tokenize: Whether to do basic tokenization before wordpiece.
+          max_len: An artificial maximum length to truncate tokenized sequences to;
+                         Effective maximum length is always the minimum of this
+                         value (if specified) and the underlying BERT model's
+                         sequence length.
+          never_split: List of tokens which will never be split during tokenization.
+                         Only has an effect when do_wordpiece_only=False
+        """
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained "
+                "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file))
+        
+        self.vocab = load_vocab(vocab_file)
+        self.ids_to_tokens = collections.OrderedDict(
+            [(ids, tok) for tok, ids in self.vocab.items()])
+        self.do_basic_tokenize = do_basic_tokenize
+        if do_basic_tokenize:
+          self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case,
+                                                never_split=never_split)
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+        self.max_len = max_len if max_len is not None else int(1e12)
+        self.basic_only = basic_only
+
+    def tokenize(self, text):
+        split_tokens = []
+        if self.do_basic_tokenize:
+            for token in self.basic_tokenizer.tokenize(text):
+                if self.basic_only:
+                    split_tokens.append(token)
+                else:
+                    for sub_token in self.wordpiece_tokenizer.tokenize(token):
+                        split_tokens.append(sub_token)
+        else:
+            split_tokens = self.wordpiece_tokenizer.tokenize(text)
+        return split_tokens
+
+    def convert_tokens_to_ids(self, tokens):
+        """Converts a sequence of tokens into ids using the vocab."""
+        ids = []
+        for token in tokens:
+            ids.append(self.vocab.get(token, self.vocab['[UNK]']))
+        if len(ids) > self.max_len:
+            logger.warning(
+                "Token indices sequence length is longer than the specified maximum "
+                " sequence length for this BERT model ({} > {}). Running this"
+                " sequence through BERT will result in indexing errors".format(len(ids), self.max_len)
+            )
+        return ids
+
+    def convert_ids_to_tokens(self, ids):
+        """Converts a sequence of ids in wordpiece tokens using the vocab."""
+        tokens = []
+        for i in ids:
+            tokens.append(self.ids_to_tokens[i])
+        return tokens
+
+    def save_vocabulary(self, vocab_path):
+        """Save the tokenizer vocabulary to a directory or file."""
+        index = 0
+        if os.path.isdir(vocab_path):
+            vocab_file = os.path.join(vocab_path, VOCAB_NAME)
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning("Saving vocabulary to {}: vocabulary indices are not consecutive."
+                                   " Please check that the vocabulary is not corrupted!".format(vocab_file))
+                    index = token_index
+                writer.write(token + u'\n')
+                index += 1
+        return vocab_file
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
+        """
+        Instantiate a PreTrainedBertModel from a pre-trained model file.
+        Download and cache the pre-trained model file if needed.
+        """
+        resolved_vocab_file = os.path.join(pretrained_model_name_or_path, 'vocab.txt')
+
+        max_len = 512
+        kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len)
+        # Instantiate tokenizer.
+        tokenizer = cls(resolved_vocab_file, *inputs, **kwargs)
+
+        return tokenizer
+
+
+class BasicTokenizer(object):
+    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
+
+    def __init__(self,
+                 do_lower_case=True,
+                 never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")):
+        """Constructs a BasicTokenizer.
+
+        Args:
+          do_lower_case: Whether to lower case the input.
+        """
+        self.do_lower_case = do_lower_case
+        self.never_split = never_split
+
+    def tokenize(self, text):
+        """Tokenizes a piece of text."""
+        text = self._clean_text(text)
+        # This was added on November 1st, 2018 for the multilingual and Chinese
+        # models. This is also applied to the English models now, but it doesn't
+        # matter since the English models were not trained on any Chinese data
+        # and generally don't have any Chinese data in them (there are Chinese
+        # characters in the vocabulary because Wikipedia does have some Chinese
+        # words in the English Wikipedia.).
+        text = self._tokenize_chinese_chars(text)
+        orig_tokens = whitespace_tokenize(text)
+        split_tokens = []
+        for token in orig_tokens:
+            if self.do_lower_case and token not in self.never_split:
+                token = token.lower()
+                token = self._run_strip_accents(token)
+            split_tokens.extend(self._run_split_on_punc(token))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _run_split_on_punc(self, text):
+        """Splits punctuation on a piece of text."""
+        if text in self.never_split:
+            return [text]
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _tokenize_chinese_chars(self, text):
+        """Adds whitespace around any CJK character."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if self._is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def _is_chinese_char(self, cp):
+        """Checks whether CP is the codepoint of a CJK character."""
+        # This defines a "chinese character" as anything in the CJK Unicode block:
+        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+        #
+        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+        # despite its name. The modern Korean Hangul alphabet is a different block,
+        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+        # space-separated words, so they are not treated specially and handled
+        # like the all of the other languages.
+        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
+                (cp >= 0x3400 and cp <= 0x4DBF) or  #
+                (cp >= 0x20000 and cp <= 0x2A6DF) or  #
+                (cp >= 0x2A700 and cp <= 0x2B73F) or  #
+                (cp >= 0x2B740 and cp <= 0x2B81F) or  #
+                (cp >= 0x2B820 and cp <= 0x2CEAF) or
+                (cp >= 0xF900 and cp <= 0xFAFF) or  #
+                (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
+            return True
+
+        return False
+
+    def _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xfffd or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenization."""
+
+    def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """Tokenizes a piece of text into its word pieces.
+
+        This uses a greedy longest-match-first algorithm to perform tokenization
+        using the given vocabulary.
+
+        For example:
+          input = "unaffable"
+          output = ["un", "##aff", "##able"]
+
+        Args:
+          text: A single token or whitespace separated tokens. This should have
+            already been passed through `BasicTokenizer`.
+
+        Returns:
+          A list of wordpiece tokens.
+        """
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = "".join(chars[start:end])
+                    if start > 0:
+                        substr = "##" + substr
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr)
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens
+
+
+def _is_whitespace(char):
+    """Checks whether `chars` is a whitespace character."""
+    # \t, \n, and \r are technically contorl characters but we treat them
+    # as whitespace since they are generally considered as such.
+    if char == " " or char == "\t" or char == "\n" or char == "\r":
+        return True
+    cat = unicodedata.category(char)
+    if cat == "Zs":
+        return True
+    return False
+
+
+def _is_control(char):
+    """Checks whether `chars` is a control character."""
+    # These are technically control characters but we count them as whitespace
+    # characters.
+    if char == "\t" or char == "\n" or char == "\r":
+        return False
+    cat = unicodedata.category(char)
+    if cat.startswith("C"):
+        return True
+    return False
+
+
+def _is_punctuation(char):
+    """Checks whether `chars` is a punctuation character."""
+    cp = ord(char)
+    # We treat all non-letter/number ASCII as punctuation.
+    # Characters such as "^", "$", and "`" are not in the Unicode
+    # Punctuation class but we treat them as punctuation anyways, for
+    # consistency.
+    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
+            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
+        return True
+    cat = unicodedata.category(char)
+    if cat.startswith("P"):
+        return True
+    return False
diff --git a/transformer/utils_quant.py b/transformer/utils_quant.py
new file mode 100644
index 0000000..00f297d
--- /dev/null
+++ b/transformer/utils_quant.py
@@ -0,0 +1,221 @@
+import torch
+import torch.nn as nn
+import sys
+import logging
+
+from transformers import SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST
+
+log_format = '%(asctime)s %(message)s'
+logging.basicConfig(stream=sys.stdout, level=logging.INFO,
+                    format=log_format, datefmt='%m/%d %I:%M:%S %p')
+logger = logging.getLogger()
+
+
+class SymQuantizer(torch.autograd.Function):
+    """
+        uniform quantization
+    """
+    @staticmethod
+    def forward(ctx, input, clip_val=2.5, num_bits=2, layerwise=False):
+        """
+        :param ctx:
+        :param input: tensor to be quantized
+        :param clip_val: clip the tensor before quantization
+        :param quant_bits: number of bits
+        :return: quantized tensor
+        """
+        
+        ctx.save_for_backward(input, clip_val)
+        # input = torch.clamp(input, clip_val[0], clip_val[1])
+        input = torch.where(input < clip_val[1], input, clip_val[1])
+        input = torch.where(input > clip_val[0], input, clip_val[0])
+        # NOTE: dynamic scaling (max_input).
+        if layerwise:
+            max_input = torch.max(torch.abs(input)).expand_as(input)
+        else:
+            if input.ndimension() <= 3:
+                # weight & hidden layer
+                max_input = torch.max(torch.abs(input), dim=-1, keepdim=True)[0].expand_as(input).detach()
+            elif input.ndimension() == 4:
+                # TODO: attention score matrix, calculate alpha / beta per head
+                tmp = input.view(input.shape[0], input.shape[1], -1)
+                max_input = torch.max(torch.abs(tmp), dim=-1, keepdim=True)[0].unsqueeze(-1).expand_as(input).detach()
+            else:
+                raise ValueError
+        s = (2 ** (num_bits - 1) - 1) / max_input
+        output = torch.round(input * s).div(s)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        """
+        :param ctx: saved non-clipped full-precision tensor and clip_val
+        :param grad_output: gradient ert the quantized tensor
+        :return: estimated gradient wrt the full-precision tensor
+        """
+        input, clip_val = ctx.saved_tensors  # unclipped input
+        grad_input = grad_output.clone()
+        grad_input[input.ge(clip_val[1])] = 0
+        grad_input[input.le(clip_val[0])] = 0
+        return grad_input, None, None, None
+
+
+class AsymQuantizer(torch.autograd.Function):
+    """
+        min-max quantization
+    """
+    @staticmethod
+    def forward(ctx, input, clip_val, num_bits, layerwise):
+        """
+        :param ctx:
+        :param input: tensor to be quantized
+        :param clip_val: clip the tensor before quantization
+        :param quant_bits: number of bits
+        :return: quantized tensor
+        """
+        ctx.save_for_backward(input, clip_val)
+
+        input = torch.where(input < clip_val[1], input, clip_val[1])
+        input = torch.where(input > clip_val[0], input, clip_val[0])
+        # input = torch.clamp(input, clip_val[0], clip_val[1])
+        # NOTE: dynamic scaling gives better performance than static
+        if layerwise:
+            alpha = (input.max() - input.min()).detach()
+            beta = input.min().detach()
+        else:
+            if input.ndimension() <= 3:
+                # weight & hidden layer
+                alpha = (input.max(dim=-1, keepdim=True)[0] - input.min(dim=-1, keepdim=True)[0]).expand_as(input).detach()
+                beta = input.min(dim=-1, keepdim=True)[0].expand_as(input).detach()
+            elif input.ndimension() == 4:
+                # TODO: attention score matrix, calculate alpha / beta per head
+                tmp = input.view(input.shape[0], input.shape[1], -1)
+                alpha = (tmp.max(dim=-1, keepdim=True)[0].unsqueeze(-1) - \
+                            tmp.min(dim=-1, keepdim=True)[0].unsqueeze(-1)).expand_as(input).detach()
+                beta = tmp.min(dim=-1, keepdim=True)[0].unsqueeze(-1).expand_as(input).detach()
+            else:
+                raise ValueError
+        input_normalized = (input - beta) / (alpha + 1e-8)
+        s = (2**num_bits - 1)
+        quant_input = torch.round(input_normalized * s).div(s)
+        output = quant_input * (alpha + 1e-8) + beta
+
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        """
+        :param ctx: saved non-clipped full-precision tensor and clip_val
+        :param grad_output: gradient ert the quantized tensor
+        :return: estimated gradient wrt the full-precision tensor
+        """
+        input, clip_val = ctx.saved_tensors  # unclipped input
+        grad_input = grad_output.clone()
+        grad_input[input.ge(clip_val[1])] = 0
+        grad_input[input.le(clip_val[0])] = 0
+        return grad_input, None, None, None
+
+
+class TwnQuantizer(torch.autograd.Function):
+    """Ternary Weight Networks (TWN)
+    Ref: https://arxiv.org/abs/1605.04711
+    """
+    @staticmethod
+    def forward(ctx, input, clip_val, num_bits, layerwise):
+        """
+        :param input: tensor to be ternarized
+        :return: quantized tensor
+        """
+        mean_scale = 0.7
+
+        ctx.save_for_backward(input, clip_val)
+    
+        input = torch.where(input < clip_val[1], input, clip_val[1])
+        input = torch.where(input > clip_val[0], input, clip_val[0])
+
+        if layerwise:
+            m = input.norm(p=1).div(input.nelement())
+            thres = mean_scale * m  
+            pos = (input > thres).float()
+            neg = (input < -thres).float()
+            mask = (input.abs() > thres).float()
+            alpha = (mask * input).abs().sum() / mask.sum()
+            result = alpha * pos - alpha * neg
+        else: # row-wise only for embed / weight
+            n = input[0].nelement()
+            m = input.data.norm(p=1, dim=1).div(n)
+            thres = (mean_scale * m).view(-1, 1).expand_as(input)
+            pos = (input > thres).float()
+            neg = (input < -thres).float()
+            mask = (input.abs() > thres).float()
+            alpha = ((mask * input).abs().sum(dim=1) / mask.sum(dim=1)).view(-1, 1)
+            result = alpha * pos - alpha * neg
+        
+        return result
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        """
+        :param ctx: saved non-clipped full-precision tensor and clip_val
+        :param grad_output: gradient ert the quantized tensor
+        :return: estimated gradient wrt the full-precision tensor
+        """
+        input, clip_val = ctx.saved_tensors  # unclipped input
+        grad_input = grad_output.clone()
+        grad_input[input.ge(clip_val[1])] = 0
+        grad_input[input.le(clip_val[0])] = 0
+        return grad_input, None, None, None
+
+
+class QuantizeLinear(nn.Linear):
+
+    def __init__(self,  *kargs,bias=True, config = None, map=False, name=None):
+        super(QuantizeLinear, self).__init__(*kargs,bias=True)
+        self.weight_bits = 2
+        self.input_bits= 8
+        self.mean_scale = config.mean_scale
+
+        self.name = name
+        self.map = map
+        self.config = config
+        
+        self.weight_quantizer = TwnQuantizer
+        # Weight & Activation Quantization Setting
+        self.act_quantizer = SymQuantizer
+        self.register_buffer('act_clip_val', torch.tensor([-config.clip_val, config.clip_val]))
+        self.register_buffer('weight_clip_val', torch.tensor([-config.clip_val, config.clip_val]))\
+
+    def forward(self, input):
+        # quantize weight
+        weight = self.weight_quantizer.apply(self.weight, self.weight_clip_val, self.weight_bits, True)
+        q_input = self.act_quantizer.apply(input, self.act_clip_val, self.input_bits, True)
+        
+        # nn.Linear w/ Quantized input and output
+        out = nn.functional.linear(q_input, weight)
+        
+        if not self.bias is None:
+            out += self.bias.view(1, -1).expand_as(out)
+
+        return out
+
+class QuantizeEmbedding(nn.Embedding):
+
+    def __init__(self,  *kargs,padding_idx=None, config = None):
+        super(QuantizeEmbedding, self).__init__(*kargs, padding_idx = padding_idx)
+        self.weight_bits = 2
+        self.layerwise = False
+        self.mean_scale = config.mean_scale
+        self.config = config
+        
+        self.weight_quantizer = TwnQuantizer
+        self.register_buffer('weight_clip_val', torch.tensor([-config.clip_val, config.clip_val]))
+    
+    def forward(self, input):
+        
+        weight = self.weight_quantizer.apply(self.weight, self.weight_clip_val, self.weight_bits, self.layerwise)
+        
+        out = nn.functional.embedding(
+            input, weight, self.padding_idx, self.max_norm,
+            self.norm_type, self.scale_grad_by_freq, self.sparse)
+        return out
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..4ab7942
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,97 @@
+#*
+# @file Different utility functions
+# Copyright (c) Zhewei Yao, Amir Gholami
+# All rights reserved.
+# This file is part of PyHessian library.
+#
+# PyHessian is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# PyHessian is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with PyHessian.  If not, see <http://www.gnu.org/licenses/>.
+#*
+
+import torch
+import math
+from torch.autograd import Variable
+import numpy as np
+
+
+def group_product(xs, ys):
+    """
+    the inner product of two lists of variables xs,ys
+    :param xs:
+    :param ys:
+    :return:
+    """
+    return sum([torch.sum(x * y) for (x, y) in zip(xs, ys)])
+
+
+def group_add(params, update, alpha=1):
+    """
+    params = params + update*alpha
+    :param params: list of variable
+    :param update: list of data
+    :return:
+    """
+    for i, p in enumerate(params):
+        params[i].data.add_(update[i] * alpha)
+    return params
+
+
+def normalization(v):
+    """
+    normalization of a list of vectors
+    return: normalized vectors v
+    """
+    s = group_product(v, v)
+    s = s**0.5
+    s = s.cpu().item()
+    v = [vi / (s + 1e-6) for vi in v]
+    return v
+
+
+def get_params_grad(model):
+    """
+    get model parameters and corresponding gradients
+    """
+    params = []
+    grads = []
+    for param in model.parameters():
+        if not param.requires_grad:
+            continue
+        params.append(param)
+        grads.append(0. if param.grad is None else param.grad + 0.)
+    return params, grads
+
+
+def hessian_vector_product(gradsH, params, v):
+    """
+    compute the hessian vector product of Hv, where
+    gradsH is the gradient at the current point,
+    params is the corresponding variables,
+    v is the vector.
+    """
+    hv = torch.autograd.grad(gradsH,
+                             params,
+                             grad_outputs=v,
+                             only_inputs=True,
+                             retain_graph=True)
+    return hv
+
+
+def orthnormal(w, v_list):
+    """
+    make vector w orthogonal to each vector in v_list.
+    afterwards, normalize the output w
+    """
+    for v in v_list:
+        w = group_add(w, v, alpha=-group_product(w, v))
+    return normalization(w)
diff --git a/utils_glue.py b/utils_glue.py
new file mode 100644
index 0000000..a9cce10
--- /dev/null
+++ b/utils_glue.py
@@ -0,0 +1,617 @@
+import os
+import logging
+import sys
+import csv
+
+from scipy.stats import pearsonr, spearmanr
+from sklearn.metrics import matthews_corrcoef, f1_score
+
+logger = logging.getLogger()
+
+class InputExample(object):
+    """A single training/test example for simple sequence classification."""
+
+    def __init__(self, guid, text_a, text_b=None, label=None):
+        """Constructs a InputExample.
+
+        Args:
+            guid: Unique id for the example.
+            text_a: string. The untokenized text of the first sequence. For single
+            sequence tasks, only this sequence must be specified.
+            text_b: (Optional) string. The untokenized text of the second sequence.
+            Only must be specified for sequence pair tasks.
+            label: (Optional) string. The label of the example. This should be
+            specified for train and dev examples, but not for test examples.
+        """
+        self.guid = guid
+        self.text_a = text_a
+        self.text_b = text_b
+        self.label = label
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+
+    def __init__(self, input_ids, input_mask, segment_ids, label_id, seq_length=None):
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.seq_length = seq_length
+        self.label_id = label_id
+
+
+class DataProcessor(object):
+    """Base class for data converters for sequence classification data sets."""
+
+    def get_train_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the train set."""
+        raise NotImplementedError()
+
+    def get_dev_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the dev set."""
+        raise NotImplementedError()
+        
+    def get_test_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the test set."""
+        raise NotImplementedError()
+
+    def get_labels(self):
+        """Gets the list of labels for this data set."""
+        raise NotImplementedError()
+
+    @classmethod
+    def _read_tsv(cls, input_file, quotechar=None):
+        """Reads a tab separated value file."""
+        with open(input_file, "r", newline='',encoding="utf-8") as f:
+            
+            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
+            lines = []
+            
+            try:
+                for line in reader:
+                    if sys.version_info[0] == 2:
+                        
+                        line = list(unicode(cell, 'utf-8') for cell in line)
+                    lines.append(line)
+            except:
+                sys.exit('file %s, line %d' % (input_file, reader.line_num))
+
+            return lines
+
+
+class MrpcProcessor(DataProcessor):
+    """Processor for the MRPC data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+    
+    def get_test_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_aug_examples(self, data_dir, aug_N):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, f"train_aug_{aug_N}.tsv")), "aug")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, i)
+            text_a = line[3]
+            text_b = line[4]
+            if set_type == 'test':
+                label = None
+            else:
+                label = line[0]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class MnliProcessor(DataProcessor):
+    """Processor for the MultiNLI data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
+            "dev_matched")
+    
+    def get_test_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")
+
+    def get_aug_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train_aug.tsv")), "aug")
+
+    def get_labels(self):
+        """See base class."""
+        return ["contradiction", "entailment", "neutral"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, line[0])
+            text_a = line[8]
+            text_b = line[9]
+            if set_type == 'test':
+                label = None
+            else:
+                label = line[-1]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class MnliMismatchedProcessor(MnliProcessor):
+    """Processor for the MultiNLI Mismatched data set (GLUE version)."""
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")),
+            "dev_matched")
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test_mismatched.tsv")),
+            "test")
+
+
+class ColaProcessor(DataProcessor):
+    """Processor for the CoLA data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+    
+    def get_test_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_aug_examples(self, data_dir, aug_N):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, f"train_aug_{aug_N}.tsv")), "aug")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        if set_type == 'test':
+            for (i, line) in enumerate(lines):
+                if i == 0:
+                    continue
+                guid = "%s-%s" % (set_type, i)
+                text_a = line[1]
+                label = None
+                examples.append(
+                    InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
+        else:
+            for (i, line) in enumerate(lines):
+                guid = "%s-%s" % (set_type, i)
+                text_a = line[3]
+                label = line[1]
+                examples.append(
+                    InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
+        return examples
+
+
+class Sst2Processor(DataProcessor):
+    """Processor for the SST-2 data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+    
+    def get_test_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_aug_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train_aug.tsv")), "aug")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, i)
+            if set_type == 'test':
+                text_a = line[1]
+                label = None
+            else:
+                text_a = line[0]
+                label = line[1]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
+        return examples
+
+
+class StsbProcessor(DataProcessor):
+    """Processor for the STS-B data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+    
+    def get_test_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_aug_examples(self, data_dir, aug_N):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, f"train_aug_{aug_N}.tsv")), "aug")
+
+    def get_labels(self):
+        """See base class."""
+        return [None]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, line[0])
+            text_a = line[7]
+            text_b = line[8]
+            if set_type== 'test':
+                label = None
+            else:
+                label = line[-1]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class QqpProcessor(DataProcessor):
+    """Processor for the STS-B data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+    
+    def get_test_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_aug_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train_aug.tsv")), "aug")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, line[0])
+            try:
+                if set_type=='test':
+                    text_a = line[1]
+                    text_b = line[2]
+                    label = None
+                else:
+                    text_a = line[3]
+                    text_b = line[4]
+                    label = line[5]
+            except IndexError:
+                continue
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class QnliProcessor(DataProcessor):
+    """Processor for the STS-B data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")),
+            "dev_matched")
+    
+    def get_test_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_aug_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train_aug.tsv")), "aug")
+
+    def get_labels(self):
+        """See base class."""
+        return ["entailment", "not_entailment"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, line[0])
+            if set_type=='test':
+                text_a = line[1]
+                text_b = line[2]
+                label = None
+            else:
+                text_a = line[1]
+                text_b = line[2]
+                label = line[-1]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class RteProcessor(DataProcessor):
+    """Processor for the RTE data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+    
+    def get_test_examples(self, data_dir):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_aug_examples(self, data_dir, aug_N):
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, f"train_aug_{aug_N}.tsv")), "aug")
+
+    def get_labels(self):
+        """See base class."""
+        return ["entailment", "not_entailment"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, line[0])
+            if set_type=='test':
+                text_a = line[1]
+                text_b = line[2]
+                label = None
+            else:
+                text_a = line[1]
+                text_b = line[2]
+                label = line[-1]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class WnliProcessor(DataProcessor):
+    """Processor for the WNLI data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, line[0])
+            text_a = line[1]
+            text_b = line[2]
+            label = line[-1]
+            examples.append(
+                InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+def convert_examples_to_features(examples, label_list, max_seq_length,
+                                 tokenizer, output_mode):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    label_map = {label: i for i, label in enumerate(label_list)}
+
+    features = []
+    for (ex_index, example) in enumerate(examples):
+        if ex_index % 10000 == 0:
+            logger.info("Writing example %d of %d" % (ex_index, len(examples)))
+        tokens_a = tokenizer.tokenize(example.text_a)
+
+        tokens_b = None
+        if example.text_b:
+            tokens_b = tokenizer.tokenize(example.text_b)
+            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
+        else:
+            if len(tokens_a) > max_seq_length - 2:
+                tokens_a = tokens_a[:(max_seq_length - 2)]
+
+        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
+        segment_ids = [0] * len(tokens)
+
+        if tokens_b:
+            tokens += tokens_b + ["[SEP]"]
+            segment_ids += [1] * (len(tokens_b) + 1)
+
+        input_ids = tokenizer.convert_tokens_to_ids(tokens)
+        input_mask = [1] * len(input_ids)
+        seq_length = len(input_ids)
+
+        padding = [0] * (max_seq_length - len(input_ids))
+        input_ids += padding
+        input_mask += padding
+        segment_ids += padding
+
+        assert len(input_ids) == max_seq_length
+        assert len(input_mask) == max_seq_length
+        assert len(segment_ids) == max_seq_length
+        try:
+            if output_mode == "classification":
+                label_id = label_map[example.label]
+            elif output_mode == "regression":
+                label_id = float(example.label)
+            else:
+                raise KeyError(output_mode)
+        except:
+            label_id = 0
+
+        if ex_index < 1:
+            logger.info("*** Example ***")
+            logger.info("guid: %s" % (example.guid))
+            logger.info("tokens: %s" % " ".join(
+                [str(x) for x in tokens]))
+            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
+            logger.info(
+                "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+            logger.info("label: {}".format(example.label))
+            logger.info("label_id: {}".format(label_id))
+
+        features.append(
+            InputFeatures(input_ids=input_ids,
+                          input_mask=input_mask,
+                          segment_ids=segment_ids,
+                          label_id=label_id,
+                          seq_length=seq_length))
+    return features
+
+
+def _truncate_seq_pair(tokens_a, tokens_b, max_length):
+    """Truncates a sequence pair in place to the maximum length."""
+    while True:
+        total_length = len(tokens_a) + len(tokens_b)
+        if total_length <= max_length:
+            break
+        if len(tokens_a) > len(tokens_b):
+            tokens_a.pop()
+        else:
+            tokens_b.pop()
+
+
+def simple_accuracy(preds, labels):
+    return (preds == labels).mean()
+
+
+def acc_and_f1(preds, labels):
+    acc = simple_accuracy(preds, labels)
+    f1 = f1_score(y_true=labels, y_pred=preds)
+    return {
+        "acc": acc,
+        "f1": f1,
+        "acc_and_f1": (acc + f1) / 2,
+    }
+
+
+def pearson_and_spearman(preds, labels):
+    pearson_corr = pearsonr(preds, labels)[0]
+    spearman_corr = spearmanr(preds, labels)[0]
+    return {
+        "pearson": pearson_corr,
+        "spearmanr": spearman_corr,
+        "corr": (pearson_corr + spearman_corr) / 2,
+    }
+
+
+def compute_metrics(task_name, preds, labels):
+    assert len(preds) == len(labels)
+    if task_name == "cola":
+        return {"mcc": matthews_corrcoef(labels, preds)}
+    elif task_name == "sst-2":
+        return {"acc": simple_accuracy(preds, labels)}
+    elif task_name == "mrpc":
+        return acc_and_f1(preds, labels)
+    elif task_name == "sts-b":
+        return pearson_and_spearman(preds, labels)
+    elif task_name == "qqp":
+        return acc_and_f1(preds, labels)
+    elif task_name == "mnli":
+        return {"acc": simple_accuracy(preds, labels)}
+    elif task_name == "mnli-mm":
+        return {"acc": simple_accuracy(preds, labels)}
+    elif task_name == "qnli":
+        return {"acc": simple_accuracy(preds, labels)}
+    elif task_name == "rte":
+        return {"acc": simple_accuracy(preds, labels)}
+    elif task_name == "wnli":
+        return {"acc": simple_accuracy(preds, labels)}
+    else:
+        raise KeyError(task_name)