From 5af152939d15d8c6ed6c832fa490971053836e92 Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Fri, 2 Aug 2024 17:11:38 +0200 Subject: [PATCH 01/32] docs: add_hybrid_lora_fine_tuning --- use_case_examples/lora_finetune/.gitignore | 2 + .../data_finetune/what_is_fhe.txt | 47 ++ .../lora_finetune/gpt2_fine_tune_hybrid.ipynb | 717 ++++++++++++++++++ .../lora_finetune/lora_fhe_module.py | 47 ++ .../lora_finetune/requirements.txt | 7 + 5 files changed, 820 insertions(+) create mode 100644 use_case_examples/lora_finetune/.gitignore create mode 100644 use_case_examples/lora_finetune/data_finetune/what_is_fhe.txt create mode 100644 use_case_examples/lora_finetune/gpt2_fine_tune_hybrid.ipynb create mode 100644 use_case_examples/lora_finetune/lora_fhe_module.py create mode 100644 use_case_examples/lora_finetune/requirements.txt diff --git a/use_case_examples/lora_finetune/.gitignore b/use_case_examples/lora_finetune/.gitignore new file mode 100644 index 000000000..2644f82cb --- /dev/null +++ b/use_case_examples/lora_finetune/.gitignore @@ -0,0 +1,2 @@ +cache_dataset +checkpoints diff --git a/use_case_examples/lora_finetune/data_finetune/what_is_fhe.txt b/use_case_examples/lora_finetune/data_finetune/what_is_fhe.txt new file mode 100644 index 000000000..c30719d71 --- /dev/null +++ b/use_case_examples/lora_finetune/data_finetune/what_is_fhe.txt @@ -0,0 +1,47 @@ +Title: An In-depth Look at Fully Homomorphic Encryption (FHE) and Its Applications + +Introduction + +Fully Homomorphic Encryption (FHE) is a groundbreaking cryptographic technique that allows computations to be performed directly on encrypted data without the need for decryption. This revolutionary technology has the potential to significantly enhance privacy and security in various fields, including cloud computing, healthcare, finance, and more. This document aims to provide a comprehensive understanding of FHE, its working principles, use cases, and its potential impact on the future of data privacy and security. + +Understanding Fully Homomorphic Encryption + +To understand FHE, it is essential first to grasp the concept of homomorphic encryption. Homomorphic encryption is a method that allows computations on ciphertexts, generating an encrypted result that, when decrypted, matches the result of operations performed on the plaintext. There are different types of homomorphic encryption schemes, such as partially homomorphic encryption (PHE) and somewhat homomorphic encryption (SHE). However, FHE is the most advanced and versatile form as it supports an arbitrary number of operations on encrypted data without any degradation of the ciphertext. + +The concept of FHE was first introduced by Rivest, Adleman, and Dertouzos in 1978. However, it was not until 2009 that Craig Gentry, a computer scientist, proposed a practical and viable FHE scheme. Gentry's breakthrough involved creating a bootstrapping technique that allows the evaluation of arbitrary circuits on encrypted data. + +Working Principles of FHE + +FHE involves three primary steps: encryption, evaluation, and decryption. + +1. Encryption: In FHE, the data owner encrypts the data using a public key generated by the FHE scheme. The encrypted data is then sent to the data processor or cloud server for computation. + +2. Evaluation: The data processor performs computations directly on the encrypted data without decrypting it. The FHE scheme ensures that the result remains encrypted even after the computation. + +3. Decryption: The encrypted result is sent back to the data owner, who can then decrypt it using the secret key. The decrypted output matches the result that would have been obtained if the operations had been performed on the plaintext. + +Use Cases and Applications of FHE + +FHE has immense potential in various fields where data privacy and security are paramount. Some of the most promising use cases include: + +1. Cloud Computing: FHE can enable secure outsourcing of computations to cloud service providers. By using FHE, organizations can store and process sensitive data in the cloud without revealing the actual data to the service provider. + +2. Healthcare: FHE can facilitate the secure sharing and analysis of electronic health records (EHRs) without compromising patient privacy. It can enable medical researchers to perform statistical analysis and develop new treatments based on encrypted patient data. + +3. Finance: Financial institutions can leverage FHE to securely process sensitive financial transactions and perform risk analysis without revealing the underlying data. + +4. Artificial Intelligence and Machine Learning: FHE can enable privacy-preserving machine learning by allowing computations on encrypted data sets. This can help protect intellectual property and maintain data privacy while still benefiting from collaborative learning models. + +Challenges and Future Directions + +Despite the significant potential of FHE, there are several challenges that need to be addressed before it can be widely adopted. Some of these challenges include: + +1. Performance: FHE schemes are currently computationally intensive and require significant processing power and storage. Researchers are working on optimizing FHE algorithms and developing hardware accelerators to improve performance. + +2. Implementation Complexity: Implementing FHE schemes requires advanced cryptographic expertise, which can be a barrier for many organizations. Developing user-friendly FHE libraries and tools can help address this challenge. + +3. Standardization: There is a need for standardization of FHE schemes to ensure interoperability and facilitate widespread adoption. + +Conclusion + +Fully Homomorphic Encryption is a transformative technology that holds the promise of revolutionizing data privacy and security. By enabling computations on encrypted data, FHE can help protect sensitive information in various domains, including cloud computing, healthcare, finance, and artificial intelligence. While there are challenges to be addressed, ongoing research and development efforts are paving the way for FHE to become a practical and widely adopted solution for enhancing privacy and security in the digital age. \ No newline at end of file diff --git a/use_case_examples/lora_finetune/gpt2_fine_tune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_fine_tune_hybrid.ipynb new file mode 100644 index 000000000..2d5ff130b --- /dev/null +++ b/use_case_examples/lora_finetune/gpt2_fine_tune_hybrid.ipynb @@ -0,0 +1,717 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, DataCollatorForLanguageModeling, Conv1D, TextDataset, Trainer\n", + "from peft import get_peft_model, LoraConfig, TaskType\n", + "from tqdm import tqdm\n", + "import torch\n", + "import math\n", + "from pathlib import Path\n", + "import shutil\n", + "\n", + "from concrete.ml.torch.hybrid_model import HybridFHEModel\n", + "from lora_fhe_module import CustomConv1D\n", + "\n", + "SEED = 0\n", + "torch.manual_seed(SEED)\n", + "torch.use_deterministic_algorithms(True)\n", + "\n", + "model_name = \"gpt2\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModelForCausalLM.from_pretrained(model_name)\n", + "\n", + "if tokenizer.pad_token is None:\n", + " tokenizer.pad_token = tokenizer.eos_token\n", + "\n", + "# FREEZE WEIGHTS\n", + "for param in model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_text(prompt, model, tokenizer, max_length=30, fhe=\"disable\"):\n", + " # Encode the input prompt\n", + " inputs = tokenizer.encode_plus(prompt, return_tensors=\"pt\")\n", + "\n", + " attention_mask = inputs['attention_mask']\n", + " \n", + " # Generate text\n", + " output = model.generate(\n", + " input_ids=inputs['input_ids'],\n", + " attention_mask=attention_mask,\n", + " max_length=max_length,\n", + " num_return_sequences=1,\n", + " no_repeat_ngram_size=2,\n", + " top_k=50,\n", + " top_p=0.95,\n", + " temperature=0.7,\n", + " do_sample=True,\n", + " pad_token_id=tokenizer.eos_token_id,\n", + " )\n", + "\n", + " # Decode the generated text\n", + " generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n", + " return generated_text" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE? FH: A basic program that is used to calculate the height of an object, and then sets the minimum height to be\n" + ] + } + ], + "source": [ + "# Example usage\n", + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Who's Barack Obama?\n", + "\n", + "I think he's very much in a position to be president. I mean, he was nominated by his party.\n" + ] + } + ], + "source": [ + "# Example usage\n", + "prompt = \"Who's Barack Obama ?\"\n", + "generated_text = generate_text(prompt, model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "peft_config = LoraConfig(\n", + " task_type=TaskType.CAUSAL_LM,\n", + " r=4,\n", + " lora_alpha=32,\n", + " lora_dropout=0.05,\n", + " fan_in_fan_out=True,\n", + ")\n", + "\n", + "peft_model = get_peft_model(model, peft_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def replace_conv1d(module, module_index_to_skip=0):\n", + " for name, child in module.named_children():\n", + " if isinstance(child, Conv1D):\n", + "\n", + " # Skip the module if the index has not been reached, and decrement the index\n", + " if module_index_to_skip >= 0:\n", + " module_index_to_skip -= 1\n", + " else:\n", + " custom_linear = CustomConv1D(child.weight, bias=child.bias)\n", + " setattr(module, name, custom_linear)\n", + " else:\n", + " module_index_to_skip = replace_conv1d(child, module_index_to_skip=module_index_to_skip)\n", + " \n", + " return module_index_to_skip\n", + "\n", + "# Gradients of the first base layer that is used for fine-tuning are not needed. We\n", + "# therefore need to exclude the backward module from the remote_names since calibration \n", + "# won't get through it (which raises an issue with hybrid models)\n", + "replace_conv1d(peft_model, module_index_to_skip=0);" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class LoraTraining(torch.nn.Module):\n", + " def __init__(self, inference_model, gradient_accumulation_steps) -> None:\n", + " super().__init__()\n", + " \n", + " self.inference_model = inference_model\n", + " \n", + " self.optimizer = None\n", + " self.lr_scheduler = None\n", + " \n", + " self.gradient_accumulation_steps = gradient_accumulation_steps\n", + " self.max_grad_norm = None\n", + " \n", + " self.calibrate = False\n", + " self.run_optimizer = False\n", + " \n", + " def update_training_parameters(self, optimizer, lr_scheduler, training_args):\n", + " assert self.gradient_accumulation_steps == training_args.gradient_accumulation_steps\n", + "\n", + " self.optimizer = optimizer\n", + " self.lr_scheduler = lr_scheduler\n", + " self.max_grad_norm = training_args.max_grad_norm\n", + " \n", + " def forward(self, inputs):\n", + " # FIXME: handle multi-inputs in hybrid model\n", + " x, y = inputs\n", + " \n", + " # some parts on server side \n", + " outputs = self.inference_model(input_ids=x, labels=y)\n", + " \n", + " loss = outputs.loss\n", + " loss = loss / self.gradient_accumulation_steps\n", + " \n", + " # Update gradients\n", + " loss.backward()\n", + " \n", + " grad_norm = None\n", + " if not self.calibrate and self.run_optimizer:\n", + " assert self.optimizer is not None\n", + " assert self.lr_scheduler is not None\n", + " assert self.max_grad_norm is not None\n", + " \n", + " grad_norm = torch.nn.utils.clip_grad_norm_(self.inference_model.parameters(), max_norm=self.max_grad_norm, norm_type=2)\n", + " \n", + " self.optimizer.step()\n", + " self.lr_scheduler.step()\n", + " \n", + " self.inference_model.zero_grad()\n", + " \n", + " # Clean gradients after calibration\n", + " elif self.calibrate:\n", + " self.inference_model.zero_grad()\n", + " \n", + " return (loss, grad_norm)\n", + " \n", + " def toggle_calibrate(self, enable: bool = True):\n", + " self.calibrate = enable\n", + " \n", + " def toggle_run_optimizer(self, enable: bool = True):\n", + " self.run_optimizer = enable" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "GRADIENT_ACCUMULATION_STEPS = 2\n", + "\n", + "lora_training = LoraTraining(peft_model, GRADIENT_ACCUMULATION_STEPS)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "BLOCK_SIZE = 128\n", + "\n", + "def load_dataset(file_path, tokenizer):\n", + " dataset = TextDataset(\n", + " tokenizer=tokenizer,\n", + " file_path=file_path,\n", + " block_size=BLOCK_SIZE,\n", + " cache_dir=\"cache_dataset\",\n", + " )\n", + " return dataset\n", + "\n", + "train_dataset = load_dataset(\"data_finetune/what_is_fhe.txt\", tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer.parallelism = False\n", + "\n", + "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n", + "\n", + "EPOCHS = 100\n", + "PER_DEVICE_TRAIN_BATCH_SIZE = 4\n", + "\n", + "training_args = TrainingArguments(\n", + " output_dir=\"./checkpoints\",\n", + " num_train_epochs=EPOCHS,\n", + " per_device_train_batch_size=8,\n", + " gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n", + " save_total_limit=1,\n", + " use_cpu=True,\n", + " learning_rate=5e-4,\n", + " logging_strategy=\"epoch\",\n", + " optim=\"adamw_torch\",\n", + " seed=SEED,\n", + " data_seed=SEED,\n", + " weight_decay=0.0,\n", + " warmup_steps=0,\n", + " max_grad_norm=1.0,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "trainer = Trainer(\n", + " model=peft_model,\n", + " args=training_args,\n", + " data_collator=data_collator,\n", + " train_dataset=train_dataset,\n", + ")\n", + "\n", + "train_dataloader = trainer.get_train_dataloader()\n", + "\n", + "len_dataloader = len(train_dataloader)\n", + "num_update_steps_per_epoch = len_dataloader // training_args.gradient_accumulation_steps\n", + "num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)\n", + "max_steps = math.ceil(training_args.num_train_epochs * num_update_steps_per_epoch)\n", + "\n", + "trainer.create_optimizer_and_scheduler(num_training_steps=max_steps)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "lora_training.update_training_parameters(trainer.optimizer, trainer.lr_scheduler, training_args)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def get_remote_names(model):\n", + " remote_names = []\n", + " for name, module in model.named_modules():\n", + " # Some gradients are not needed for fine-tuning, so need to exclude the backward module \n", + " # from the remote_names since calibration won't get through it (which raises an issue with \n", + " # hybrid models). We however still need to include the associated module's forward pass in \n", + " # the hybrid model\n", + " if isinstance(module, Conv1D):\n", + " remote_names.append(name)\n", + " \n", + " elif isinstance(module, CustomConv1D):\n", + " remote_names.append(name + \".forward_module\")\n", + " remote_names.append(name + \".backward_module\")\n", + " \n", + " return remote_names\n", + "\n", + "remote_names = get_remote_names(lora_training)\n", + "\n", + "hybrid_model = HybridFHEModel(lora_training, module_names=remote_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "input_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (tokenizer.vocab_size - 1)\n", + "label_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (tokenizer.vocab_size - 1)\n", + "\n", + "inputset = (input_tensor, label_tensor)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "hybrid_model.model.toggle_calibrate(enable=True)\n", + "\n", + "hybrid_model.compile_model(inputset, n_bits=8, rounding_threshold_bits={\"n_bits\": 6, \"method\": \"approximate\"}, p_error=1e-5)\n", + "\n", + "hybrid_model.model.toggle_calibrate(enable=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\"):\n", + " device = \"cpu\"\n", + " hybrid_model.model.to(device)\n", + " \n", + " # Training loop\n", + " hybrid_model.model.inference_model.train()\n", + " \n", + " total_epochs = int(training_args.num_train_epochs)\n", + " epoch_pbar = tqdm(total=total_epochs, desc=\"Training Progress\", position=0)\n", + "\n", + " total_batched_samples = 0\n", + " for epoch in range(total_epochs):\n", + " total_loss = 0\n", + " grad_norms = []\n", + " \n", + " steps_in_epoch = len(train_dataloader)\n", + " for step, batch in enumerate(train_dataloader):\n", + " total_batched_samples+=1\n", + " \n", + " batch = {k: v.to(device) for k, v in batch.items()}\n", + " \n", + " # Gradient accumulation\n", + " is_last_batch_step = steps_in_epoch <= training_args.gradient_accumulation_steps and (step + 1) == steps_in_epoch\n", + " accumulate_gradients = (total_batched_samples % training_args.gradient_accumulation_steps == 0)\n", + " \n", + " run_optimizer = is_last_batch_step or accumulate_gradients\n", + " \n", + " hybrid_model.model.toggle_run_optimizer(enable=run_optimizer)\n", + " \n", + " loss, grad_norm = hybrid_model((batch['input_ids'], batch['labels']), fhe=fhe)\n", + " \n", + " total_loss += loss.item()\n", + " \n", + " if grad_norm is not None:\n", + " grad_norms.append(grad_norm)\n", + "\n", + " # Get current learning rate\n", + " current_lr = hybrid_model.model.lr_scheduler.get_last_lr()[0]\n", + " \n", + " # Get last grad norm\n", + " current_grad_norm = grad_norms[-1]\n", + "\n", + " # Log epoch results\n", + " print(\n", + " f\"Epoch {epoch + 1}/{training_args.num_train_epochs}, \"\n", + " f\"Loss: {total_loss:.4f}, grad norm: {current_grad_norm}, lr: {current_lr}\"\n", + " )\n", + "\n", + " epoch_pbar.update(1)\n", + " \n", + " # Save model checkpoint\n", + " if training_args.output_dir is not None:\n", + " save_path = f\"{training_args.output_dir}/checkpoint-{epoch + 1}\"\n", + " hybrid_model.model.inference_model.save_pretrained(save_path)\n", + " \n", + " epoch_pbar.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", + "Training Progress: 50%|█████ | 1/2 [02:29<02:29, 149.35s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/2, Loss: 1.8521, grad norm: 0.29645875096321106, lr: 0.0001\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 100%|██████████| 2/2 [04:58<00:00, 149.01s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/2, Loss: 1.8171, grad norm: 0.21446043252944946, lr: 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 100%|██████████| 2/2 [04:58<00:00, 149.21s/it]\n" + ] + } + ], + "source": [ + "torch.manual_seed(SEED)\n", + "\n", + "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fine_tuned_model = hybrid_model.model.inference_model\n", + "\n", + "hybrid_model.set_fhe_mode(\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE?\n", + "\n", + "He is\n", + "HE is not\n", + ".\n", + " (This is a not. HE\n", + "I\n", + "It is NOT.\n" + ] + } + ], + "source": [ + "fine_tuned_model.disable_adapter_layers()\n", + "\n", + "# Example usage\n", + "prompt = \"What is FHE ?\" \n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)\n", + "\n", + "fine_tuned_model.enable_adapter_layers()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Who's Barack Obama? I have an idea for the Obama, I think that is I can't even imagine. But let me just say, it\n" + ] + } + ], + "source": [ + "# Example usage\n", + "prompt = \"Who's Barack Obama ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE? I don't know. I'm just a big big fat fat.\n", + "\n", + "I have no idea, but I do not\n" + ] + } + ], + "source": [ + "# Example usage\n", + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE? F1 was FH is always F I think Fhehe F he F He He is, He, is he,\n" + ] + } + ], + "source": [ + "fine_tuned_model.disable_adapter_layers()\n", + "\n", + "# Example usage\n", + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE?\n", + "\n", + "You're probably thinking of a few different ways\n", + "As far as I'm thinking about it's not really that different\n" + ] + } + ], + "source": [ + "fine_tuned_model.enable_adapter_layers()\n", + "\n", + "# Example usage\n", + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def print_weights_and_size(model, print_detail=False):\n", + " total_weights = 0\n", + " for name, param in model.named_parameters():\n", + " total_weights += param.numel()\n", + " if print_detail:\n", + " print(name, param.numel())\n", + " \n", + " print(f\"Total number of weights: {total_weights}\")\n", + " \n", + " return total_weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of weights: 124587264\n" + ] + } + ], + "source": [ + "total_weights_size = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path = Path(\"gpt2_lora_finetuned_hybrid_deployment\")\n", + "\n", + "if (path.is_dir() and any(path.iterdir())):\n", + " shutil.rmtree(path)\n", + "\n", + "hybrid_model.save_and_clear_private_info(path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of weights: 39569664\n" + ] + } + ], + "source": [ + "total_weights_size_private = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weights removed: 68.24 %\n" + ] + } + ], + "source": [ + "print(f\"Weights removed: {(total_weights_size - total_weights_size_private) / total_weights_size * 100:.2f} %\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/use_case_examples/lora_finetune/lora_fhe_module.py b/use_case_examples/lora_finetune/lora_fhe_module.py new file mode 100644 index 000000000..e0cb283c8 --- /dev/null +++ b/use_case_examples/lora_finetune/lora_fhe_module.py @@ -0,0 +1,47 @@ +import torch +from torch import nn + + +class ForwardModule(nn.Module): + def __init__(self, weight, bias=None): + super(ForwardModule, self).__init__() + self.weight = weight # Assume weight is passed as a pre-initialized tensor + self.bias = bias + + def forward(self, input): + output = input @ self.weight + if self.bias is not None: + return output + self.bias + +class BackwardModule(nn.Module): + def __init__(self, weight): + super(BackwardModule, self).__init__() + self.weight = weight # This is the same weight used in ForwardModule + + def forward(self, grad_output): + return grad_output @ self.weight.t() + + +class ForwardBackwardModule(torch.autograd.Function): + @staticmethod + def forward(ctx, input, forward_module, backward_module): + ctx.backward_module = backward_module + output = forward_module.forward(input) + return output + + @staticmethod + def backward(ctx, grad_output): + backward_module = ctx.backward_module + grad_input = backward_module.forward(grad_output) + + # grad_weight and grad_bias are not needed when computing the backward for lora + return grad_input, None, None + +class CustomConv1D(nn.Module): + def __init__(self, weight, bias=None): + super().__init__() + self.forward_module = ForwardModule(weight, bias=bias) + self.backward_module = BackwardModule(weight) + + def forward(self, input): + return ForwardBackwardModule.apply(input, self.forward_module, self.backward_module) diff --git a/use_case_examples/lora_finetune/requirements.txt b/use_case_examples/lora_finetune/requirements.txt new file mode 100644 index 000000000..a222b3679 --- /dev/null +++ b/use_case_examples/lora_finetune/requirements.txt @@ -0,0 +1,7 @@ +# FIXME: Only works with source +# concrete-ml==1.6.1 +transformers==4.42.3 +peft==0.11.1 +datasets==2.20.0 +Jinja2==3.1.4 +ipykernel From 0a0c4553b8782de0e13147eeb3a8185ade97a16a Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Mon, 5 Aug 2024 11:08:24 +0200 Subject: [PATCH 02/32] chore: add makefile to use case --- .github/workflows/run_one_use_cases_example.yaml | 1 + use_case_examples/lora_finetune/Makefile | 10 ++++++++++ .../{lora_fhe_module.py => custom_module.py} | 0 ...ne_tune_hybrid.ipynb => gpt2_finetune_hybrid.ipynb} | 2 +- use_case_examples/lora_finetune/requirements.txt | 4 ++-- 5 files changed, 14 insertions(+), 3 deletions(-) create mode 100644 use_case_examples/lora_finetune/Makefile rename use_case_examples/lora_finetune/{lora_fhe_module.py => custom_module.py} (100%) rename use_case_examples/lora_finetune/{gpt2_fine_tune_hybrid.ipynb => gpt2_finetune_hybrid.ipynb} (99%) diff --git a/.github/workflows/run_one_use_cases_example.yaml b/.github/workflows/run_one_use_cases_example.yaml index 8c6db8728..2fecdc56c 100644 --- a/.github/workflows/run_one_use_cases_example.yaml +++ b/.github/workflows/run_one_use_cases_example.yaml @@ -19,6 +19,7 @@ on: - federated_learning - hybrid_model - llm + - lora_finetune - resnet - sentiment_analysis_with_transformer - titanic diff --git a/use_case_examples/lora_finetune/Makefile b/use_case_examples/lora_finetune/Makefile new file mode 100644 index 000000000..13bd27aa3 --- /dev/null +++ b/use_case_examples/lora_finetune/Makefile @@ -0,0 +1,10 @@ +# Useful for jupyter notebooks +export LC_ALL=en_US.UTF-8 +export LANG=en_US.UTF-8 + +TIME_NB="${USE_CASE_DIR}/time_notebook_execution.sh" + +run_example: one + +one: + @$(TIME_NB) gpt2_finetune_hybrid.ipynb diff --git a/use_case_examples/lora_finetune/lora_fhe_module.py b/use_case_examples/lora_finetune/custom_module.py similarity index 100% rename from use_case_examples/lora_finetune/lora_fhe_module.py rename to use_case_examples/lora_finetune/custom_module.py diff --git a/use_case_examples/lora_finetune/gpt2_fine_tune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb similarity index 99% rename from use_case_examples/lora_finetune/gpt2_fine_tune_hybrid.ipynb rename to use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 2d5ff130b..ee83145cb 100644 --- a/use_case_examples/lora_finetune/gpt2_fine_tune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -15,7 +15,7 @@ "import shutil\n", "\n", "from concrete.ml.torch.hybrid_model import HybridFHEModel\n", - "from lora_fhe_module import CustomConv1D\n", + "from custom_module import CustomConv1D\n", "\n", "SEED = 0\n", "torch.manual_seed(SEED)\n", diff --git a/use_case_examples/lora_finetune/requirements.txt b/use_case_examples/lora_finetune/requirements.txt index a222b3679..4890e2d42 100644 --- a/use_case_examples/lora_finetune/requirements.txt +++ b/use_case_examples/lora_finetune/requirements.txt @@ -1,7 +1,7 @@ # FIXME: Only works with source -# concrete-ml==1.6.1 +concrete-ml==1.6.1 transformers==4.42.3 peft==0.11.1 datasets==2.20.0 Jinja2==3.1.4 -ipykernel +jupyter From def6ef2d9445cf58e3680ac9773eec0183bd788a Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Mon, 5 Aug 2024 12:29:52 +0200 Subject: [PATCH 03/32] chore: fix pcc --- .../lora_finetune/custom_module.py | 8 +- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 168 ++++++++++-------- .../lora_finetune/requirements.txt | 2 +- 3 files changed, 97 insertions(+), 81 deletions(-) diff --git a/use_case_examples/lora_finetune/custom_module.py b/use_case_examples/lora_finetune/custom_module.py index e0cb283c8..d3531fbf8 100644 --- a/use_case_examples/lora_finetune/custom_module.py +++ b/use_case_examples/lora_finetune/custom_module.py @@ -12,7 +12,8 @@ def forward(self, input): output = input @ self.weight if self.bias is not None: return output + self.bias - + + class BackwardModule(nn.Module): def __init__(self, weight): super(BackwardModule, self).__init__() @@ -33,9 +34,10 @@ def forward(ctx, input, forward_module, backward_module): def backward(ctx, grad_output): backward_module = ctx.backward_module grad_input = backward_module.forward(grad_output) - + # grad_weight and grad_bias are not needed when computing the backward for lora - return grad_input, None, None + return grad_input, None, None + class CustomConv1D(nn.Module): def __init__(self, weight, bias=None): diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index ee83145cb..1fea22af9 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -6,16 +6,25 @@ "metadata": {}, "outputs": [], "source": [ - "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, DataCollatorForLanguageModeling, Conv1D, TextDataset, Trainer\n", - "from peft import get_peft_model, LoraConfig, TaskType\n", - "from tqdm import tqdm\n", - "import torch\n", "import math\n", - "from pathlib import Path\n", "import shutil\n", + "from pathlib import Path\n", "\n", - "from concrete.ml.torch.hybrid_model import HybridFHEModel\n", + "import torch\n", "from custom_module import CustomConv1D\n", + "from peft import LoraConfig, TaskType, get_peft_model\n", + "from tqdm import tqdm\n", + "from transformers import (\n", + " AutoModelForCausalLM,\n", + " AutoTokenizer,\n", + " Conv1D,\n", + " DataCollatorForLanguageModeling,\n", + " TextDataset,\n", + " Trainer,\n", + " TrainingArguments,\n", + ")\n", + "\n", + "from concrete.ml.torch.hybrid_model import HybridFHEModel\n", "\n", "SEED = 0\n", "torch.manual_seed(SEED)\n", @@ -43,11 +52,11 @@ " # Encode the input prompt\n", " inputs = tokenizer.encode_plus(prompt, return_tensors=\"pt\")\n", "\n", - " attention_mask = inputs['attention_mask']\n", - " \n", + " attention_mask = inputs[\"attention_mask\"]\n", + "\n", " # Generate text\n", " output = model.generate(\n", - " input_ids=inputs['input_ids'],\n", + " input_ids=inputs[\"input_ids\"],\n", " attention_mask=attention_mask,\n", " max_length=max_length,\n", " num_return_sequences=1,\n", @@ -141,11 +150,12 @@ " setattr(module, name, custom_linear)\n", " else:\n", " module_index_to_skip = replace_conv1d(child, module_index_to_skip=module_index_to_skip)\n", - " \n", + "\n", " return module_index_to_skip\n", "\n", + "\n", "# Gradients of the first base layer that is used for fine-tuning are not needed. We\n", - "# therefore need to exclude the backward module from the remote_names since calibration \n", + "# therefore need to exclude the backward module from the remote_names since calibration\n", "# won't get through it (which raises an issue with hybrid models)\n", "replace_conv1d(peft_model, module_index_to_skip=0);" ] @@ -159,60 +169,62 @@ "class LoraTraining(torch.nn.Module):\n", " def __init__(self, inference_model, gradient_accumulation_steps) -> None:\n", " super().__init__()\n", - " \n", + "\n", " self.inference_model = inference_model\n", - " \n", + "\n", " self.optimizer = None\n", " self.lr_scheduler = None\n", - " \n", + "\n", " self.gradient_accumulation_steps = gradient_accumulation_steps\n", " self.max_grad_norm = None\n", - " \n", + "\n", " self.calibrate = False\n", " self.run_optimizer = False\n", - " \n", + "\n", " def update_training_parameters(self, optimizer, lr_scheduler, training_args):\n", " assert self.gradient_accumulation_steps == training_args.gradient_accumulation_steps\n", "\n", " self.optimizer = optimizer\n", " self.lr_scheduler = lr_scheduler\n", " self.max_grad_norm = training_args.max_grad_norm\n", - " \n", + "\n", " def forward(self, inputs):\n", " # FIXME: handle multi-inputs in hybrid model\n", " x, y = inputs\n", - " \n", - " # some parts on server side \n", + "\n", + " # some parts on server side\n", " outputs = self.inference_model(input_ids=x, labels=y)\n", - " \n", + "\n", " loss = outputs.loss\n", " loss = loss / self.gradient_accumulation_steps\n", - " \n", + "\n", " # Update gradients\n", " loss.backward()\n", - " \n", + "\n", " grad_norm = None\n", " if not self.calibrate and self.run_optimizer:\n", " assert self.optimizer is not None\n", " assert self.lr_scheduler is not None\n", " assert self.max_grad_norm is not None\n", - " \n", - " grad_norm = torch.nn.utils.clip_grad_norm_(self.inference_model.parameters(), max_norm=self.max_grad_norm, norm_type=2)\n", - " \n", + "\n", + " grad_norm = torch.nn.utils.clip_grad_norm_(\n", + " self.inference_model.parameters(), max_norm=self.max_grad_norm, norm_type=2\n", + " )\n", + "\n", " self.optimizer.step()\n", " self.lr_scheduler.step()\n", - " \n", + "\n", " self.inference_model.zero_grad()\n", - " \n", + "\n", " # Clean gradients after calibration\n", " elif self.calibrate:\n", " self.inference_model.zero_grad()\n", - " \n", + "\n", " return (loss, grad_norm)\n", - " \n", + "\n", " def toggle_calibrate(self, enable: bool = True):\n", " self.calibrate = enable\n", - " \n", + "\n", " def toggle_run_optimizer(self, enable: bool = True):\n", " self.run_optimizer = enable" ] @@ -236,6 +248,7 @@ "source": [ "BLOCK_SIZE = 128\n", "\n", + "\n", "def load_dataset(file_path, tokenizer):\n", " dataset = TextDataset(\n", " tokenizer=tokenizer,\n", @@ -245,6 +258,7 @@ " )\n", " return dataset\n", "\n", + "\n", "train_dataset = load_dataset(\"data_finetune/what_is_fhe.txt\", tokenizer)" ] }, @@ -276,7 +290,7 @@ " weight_decay=0.0,\n", " warmup_steps=0,\n", " max_grad_norm=1.0,\n", - ")\n" + ")" ] }, { @@ -320,19 +334,20 @@ "def get_remote_names(model):\n", " remote_names = []\n", " for name, module in model.named_modules():\n", - " # Some gradients are not needed for fine-tuning, so need to exclude the backward module \n", - " # from the remote_names since calibration won't get through it (which raises an issue with \n", - " # hybrid models). We however still need to include the associated module's forward pass in \n", + " # Some gradients are not needed for fine-tuning, so need to exclude the backward module\n", + " # from the remote_names since calibration won't get through it (which raises an issue with\n", + " # hybrid models). We however still need to include the associated module's forward pass in\n", " # the hybrid model\n", " if isinstance(module, Conv1D):\n", " remote_names.append(name)\n", - " \n", + "\n", " elif isinstance(module, CustomConv1D):\n", " remote_names.append(name + \".forward_module\")\n", " remote_names.append(name + \".backward_module\")\n", - " \n", + "\n", " return remote_names\n", "\n", + "\n", "remote_names = get_remote_names(lora_training)\n", "\n", "hybrid_model = HybridFHEModel(lora_training, module_names=remote_names)" @@ -344,8 +359,12 @@ "metadata": {}, "outputs": [], "source": [ - "input_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (tokenizer.vocab_size - 1)\n", - "label_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (tokenizer.vocab_size - 1)\n", + "input_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (\n", + " tokenizer.vocab_size - 1\n", + ")\n", + "label_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (\n", + " tokenizer.vocab_size - 1\n", + ")\n", "\n", "inputset = (input_tensor, label_tensor)" ] @@ -358,7 +377,9 @@ "source": [ "hybrid_model.model.toggle_calibrate(enable=True)\n", "\n", - "hybrid_model.compile_model(inputset, n_bits=8, rounding_threshold_bits={\"n_bits\": 6, \"method\": \"approximate\"}, p_error=1e-5)\n", + "hybrid_model.compile_model(\n", + " inputset, n_bits=8, rounding_threshold_bits={\"n_bits\": 6, \"method\": \"approximate\"}, p_error=1e-5\n", + ")\n", "\n", "hybrid_model.model.toggle_calibrate(enable=False)" ] @@ -372,10 +393,10 @@ "def train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\"):\n", " device = \"cpu\"\n", " hybrid_model.model.to(device)\n", - " \n", + "\n", " # Training loop\n", " hybrid_model.model.inference_model.train()\n", - " \n", + "\n", " total_epochs = int(training_args.num_train_epochs)\n", " epoch_pbar = tqdm(total=total_epochs, desc=\"Training Progress\", position=0)\n", "\n", @@ -383,31 +404,36 @@ " for epoch in range(total_epochs):\n", " total_loss = 0\n", " grad_norms = []\n", - " \n", + "\n", " steps_in_epoch = len(train_dataloader)\n", " for step, batch in enumerate(train_dataloader):\n", - " total_batched_samples+=1\n", - " \n", + " total_batched_samples += 1\n", + "\n", " batch = {k: v.to(device) for k, v in batch.items()}\n", - " \n", + "\n", " # Gradient accumulation\n", - " is_last_batch_step = steps_in_epoch <= training_args.gradient_accumulation_steps and (step + 1) == steps_in_epoch\n", - " accumulate_gradients = (total_batched_samples % training_args.gradient_accumulation_steps == 0)\n", - " \n", + " is_last_batch_step = (\n", + " steps_in_epoch <= training_args.gradient_accumulation_steps\n", + " and (step + 1) == steps_in_epoch\n", + " )\n", + " accumulate_gradients = (\n", + " total_batched_samples % training_args.gradient_accumulation_steps == 0\n", + " )\n", + "\n", " run_optimizer = is_last_batch_step or accumulate_gradients\n", - " \n", + "\n", " hybrid_model.model.toggle_run_optimizer(enable=run_optimizer)\n", - " \n", - " loss, grad_norm = hybrid_model((batch['input_ids'], batch['labels']), fhe=fhe)\n", - " \n", + "\n", + " loss, grad_norm = hybrid_model((batch[\"input_ids\"], batch[\"labels\"]), fhe=fhe)\n", + "\n", " total_loss += loss.item()\n", - " \n", + "\n", " if grad_norm is not None:\n", " grad_norms.append(grad_norm)\n", "\n", " # Get current learning rate\n", " current_lr = hybrid_model.model.lr_scheduler.get_last_lr()[0]\n", - " \n", + "\n", " # Get last grad norm\n", " current_grad_norm = grad_norms[-1]\n", "\n", @@ -418,12 +444,12 @@ " )\n", "\n", " epoch_pbar.update(1)\n", - " \n", + "\n", " # Save model checkpoint\n", " if training_args.output_dir is not None:\n", " save_path = f\"{training_args.output_dir}/checkpoint-{epoch + 1}\"\n", " hybrid_model.model.inference_model.save_pretrained(save_path)\n", - " \n", + "\n", " epoch_pbar.close()" ] }, @@ -513,7 +539,7 @@ "fine_tuned_model.disable_adapter_layers()\n", "\n", "# Example usage\n", - "prompt = \"What is FHE ?\" \n", + "prompt = \"What is FHE ?\"\n", "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", "print(generated_text)\n", "\n", @@ -621,9 +647,9 @@ " total_weights += param.numel()\n", " if print_detail:\n", " print(name, param.numel())\n", - " \n", + "\n", " print(f\"Total number of weights: {total_weights}\")\n", - " \n", + "\n", " return total_weights" ] }, @@ -652,7 +678,7 @@ "source": [ "path = Path(\"gpt2_lora_finetuned_hybrid_deployment\")\n", "\n", - "if (path.is_dir() and any(path.iterdir())):\n", + "if path.is_dir() and any(path.iterdir()):\n", " shutil.rmtree(path)\n", "\n", "hybrid_model.save_and_clear_private_info(path)" @@ -689,27 +715,15 @@ } ], "source": [ - "print(f\"Weights removed: {(total_weights_size - total_weights_size_private) / total_weights_size * 100:.2f} %\")" + "print(\n", + " f\"Weights removed: {(total_weights_size - total_weights_size_private) / total_weights_size * 100:.2f} %\"\n", + ")" ] } ], "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": "3.10.11" + "execution": { + "timeout": 10800 } }, "nbformat": 4, diff --git a/use_case_examples/lora_finetune/requirements.txt b/use_case_examples/lora_finetune/requirements.txt index 4890e2d42..27e353aed 100644 --- a/use_case_examples/lora_finetune/requirements.txt +++ b/use_case_examples/lora_finetune/requirements.txt @@ -1,6 +1,6 @@ # FIXME: Only works with source concrete-ml==1.6.1 -transformers==4.42.3 +transformers==4.41.2 peft==0.11.1 datasets==2.20.0 Jinja2==3.1.4 From 8f7ccaabf7efb5499f73430bf2c08ab178f4d475 Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Mon, 5 Aug 2024 12:59:03 +0200 Subject: [PATCH 04/32] chore: add push_changes target to use case action --- .../workflows/run_one_use_cases_example.yaml | 21 ++++++++++++++++++- 1 file changed, 20 insertions(+), 1 deletion(-) diff --git a/.github/workflows/run_one_use_cases_example.yaml b/.github/workflows/run_one_use_cases_example.yaml index 2fecdc56c..0bd7ce296 100644 --- a/.github/workflows/run_one_use_cases_example.yaml +++ b/.github/workflows/run_one_use_cases_example.yaml @@ -24,6 +24,11 @@ on: - sentiment_analysis_with_transformer - titanic # --- refresh_use_cases_list.py: refresh list of use cases currently available [END] --- + push_changes: + description: 'Push the refreshed notebook(s)' + required: false + type: boolean + default: false concurrency: group: ${{ github.ref }} @@ -103,6 +108,20 @@ jobs: USE_CASE=${{ github.event.inputs.use_case }} make run_one_use_case_example USE_CASE=$USE_CASE + # Pull the latest changes if there are some + - name: Pull latest changes + if: ${{ github.event.inputs.push_changes == 'true' }} + run: | + git pull -X theirs + + # If the target branch is another branch, the current branch is automatically merged into it + - name: Push changes into the current branch + if: ${{ github.event.inputs.push_changes == 'true' && github.ref_name != 'main' && !(startsWith(github.ref_name , 'release/')) }} + uses: stefanzweifel/git-auto-commit-action@8621497c8c39c72f3e2a999a26b4ca1b5058a842 #v5.0.1 + with: + commit_message: "chore: refresh ${{ github.event.inputs.notebook }} notebook" + add_options: '-u' + stop-runner-linux: name: Stop EC2 runner needs: [run-use-case-examples, start-runner-linux] @@ -163,4 +182,4 @@ jobs: - run-use-case-examples: ${{ needs.run-use-case-examples.result || 'Did not run.' }}\n\n\ - stop-runner-linux: ${{ needs.stop-runner-linux.result || 'Did not run.'}}" SLACK_USERNAME: ${{ secrets.BOT_USERNAME }} - SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }} \ No newline at end of file + SLACK_WEBHOOK: ${{ secrets.SLACK_WEBHOOK }} From fa97cbdbb311ab244309ca2934c0bb55bb9d8669 Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Mon, 5 Aug 2024 13:02:04 +0200 Subject: [PATCH 05/32] chore: fix pcc --- use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 1fea22af9..7456bb56b 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -716,7 +716,8 @@ ], "source": [ "print(\n", - " f\"Weights removed: {(total_weights_size - total_weights_size_private) / total_weights_size * 100:.2f} %\"\n", + " \"Weights removed: \"\n", + " f\"{(total_weights_size - total_weights_size_private) / total_weights_size * 100:.2f} %\"\n", ")" ] } From ad4a63683a3829379774fa760f5b7c85d9719c4a Mon Sep 17 00:00:00 2001 From: RomanBredehoft Date: Mon, 5 Aug 2024 11:23:35 +0000 Subject: [PATCH 06/32] chore: refresh notebook --- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 4753 ++++++++++++++++- 1 file changed, 4534 insertions(+), 219 deletions(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 7456bb56b..1137be1ec 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -3,8 +3,114 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, - "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-05T11:10:01.577144Z", + "iopub.status.busy": "2024-08-05T11:10:01.576927Z", + "iopub.status.idle": "2024-08-05T11:10:09.037036Z", + "shell.execute_reply": "2024-08-05T11:10:09.035732Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b8ba25d862bd4e329deeb899e6c2ba52", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/26.0 [00:00 Date: Mon, 5 Aug 2024 15:16:26 +0200 Subject: [PATCH 07/32] chore: clean notebook --- .../workflows/run_one_use_cases_example.yaml | 2 +- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 539 +++++------------- 2 files changed, 154 insertions(+), 387 deletions(-) diff --git a/.github/workflows/run_one_use_cases_example.yaml b/.github/workflows/run_one_use_cases_example.yaml index 0bd7ce296..1f6a9148b 100644 --- a/.github/workflows/run_one_use_cases_example.yaml +++ b/.github/workflows/run_one_use_cases_example.yaml @@ -119,7 +119,7 @@ jobs: if: ${{ github.event.inputs.push_changes == 'true' && github.ref_name != 'main' && !(startsWith(github.ref_name , 'release/')) }} uses: stefanzweifel/git-auto-commit-action@8621497c8c39c72f3e2a999a26b4ca1b5058a842 #v5.0.1 with: - commit_message: "chore: refresh ${{ github.event.inputs.notebook }} notebook" + commit_message: "chore: refresh notebook(s) for use case ${{ github.event.inputs.use_case }}" add_options: '-u' stop-runner-linux: diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 1137be1ec..7f8b94398 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -13,102 +13,15 @@ }, "outputs": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b8ba25d862bd4e329deeb899e6c2ba52", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/26.0 [00:00 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpeft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LoraConfig, TaskType, get_peft_model\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 10\u001b[0m AutoModelForCausalLM,\n\u001b[1;32m 11\u001b[0m AutoTokenizer,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 16\u001b[0m TrainingArguments,\n\u001b[1;32m 17\u001b[0m )\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'peft'" + ] } ], "source": [ @@ -150,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.039858Z", @@ -188,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.046390Z", @@ -215,36 +128,7 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T11:10:09.356618Z", - "iopub.status.busy": "2024-08-05T11:10:09.356388Z", - "iopub.status.idle": "2024-08-05T11:10:09.657407Z", - "shell.execute_reply": "2024-08-05T11:10:09.656856Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Who's Barack Obama?\n", - "\n", - "I think he's very much in a position to be president. I mean, he was nominated by his party.\n" - ] - } - ], - "source": [ - "# Example usage\n", - "prompt = \"Who's Barack Obama ?\"\n", - "generated_text = generate_text(prompt, model, tokenizer)\n", - "print(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.659868Z", @@ -268,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.677476Z", @@ -303,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.685395Z", @@ -379,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.694260Z", @@ -397,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.698827Z", @@ -426,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.708968Z", @@ -464,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.716021Z", @@ -494,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.729999Z", @@ -510,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.735130Z", @@ -545,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.787004Z", @@ -568,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:10:09.791802Z", @@ -590,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:22:04.902103Z", @@ -666,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-05T11:22:04.909756Z", @@ -680,7 +564,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "\r", + "\r\n", "Training Progress: 0%| | 0/100 [00:00 Date: Mon, 5 Aug 2024 13:36:01 +0000 Subject: [PATCH 08/32] chore: refresh notebook(s) for use case lora_finetune --- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 2445 +++++++++-------- 1 file changed, 1266 insertions(+), 1179 deletions(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 7f8b94398..3ad0e8ccd 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -5,23 +5,110 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-08-05T11:10:01.577144Z", - "iopub.status.busy": "2024-08-05T11:10:01.576927Z", - "iopub.status.idle": "2024-08-05T11:10:09.037036Z", - "shell.execute_reply": "2024-08-05T11:10:09.035732Z" + "iopub.execute_input": "2024-08-05T13:21:52.624672Z", + "iopub.status.busy": "2024-08-05T13:21:52.624461Z", + "iopub.status.idle": "2024-08-05T13:21:59.638259Z", + "shell.execute_reply": "2024-08-05T13:21:59.637813Z" } }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'peft'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 7\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcustom_module\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CustomConv1D\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpeft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LoraConfig, TaskType, get_peft_model\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtqdm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m tqdm\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 10\u001b[0m AutoModelForCausalLM,\n\u001b[1;32m 11\u001b[0m AutoTokenizer,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 16\u001b[0m TrainingArguments,\n\u001b[1;32m 17\u001b[0m )\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'peft'" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f9bf5b7510d24f98bc21d17138eaafea", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/26.0 [00:00 Date: Mon, 5 Aug 2024 15:39:09 +0200 Subject: [PATCH 09/32] chore: improve refresh notebook and use case --- .github/workflows/refresh-one-notebook.yaml | 9 ++++++--- .../workflows/run_one_use_cases_example.yaml | 17 +++++++++++++++++ 2 files changed, 23 insertions(+), 3 deletions(-) diff --git a/.github/workflows/refresh-one-notebook.yaml b/.github/workflows/refresh-one-notebook.yaml index d17f3aa38..f3f72a828 100644 --- a/.github/workflows/refresh-one-notebook.yaml +++ b/.github/workflows/refresh-one-notebook.yaml @@ -195,11 +195,11 @@ jobs: with: token: ${{ secrets.BOT_TOKEN }} commit-message: "chore: refresh ${{ github.event.inputs.notebook }} notebook" - branch: "refresh-${{ github.event.inputs.notebook }}-notebook-for-${{ github.ref_name }}" + branch: "refresh-${{ github.event.inputs.notebook }}-notebook-for-branch-${{ github.ref_name }}" base: "${{ github.ref_name }}" - title: "Refresh ${{ github.event.inputs.notebook }} notebook for ${{ github.ref_name }}" + title: "Refresh ${{ github.event.inputs.notebook }} notebook for branch ${{ github.ref_name }}" body: "Automatic PR with notebook refresh of ${{ github.event.inputs.notebook }} \ - for ${{ github.ref_name }}." + for branch ${{ github.ref_name }}." add-paths: | docs/**/*.ipynb use_case_examples/**/*.ipynb @@ -211,6 +211,9 @@ jobs: with: commit_message: "chore: refresh ${{ github.event.inputs.notebook }} notebook" add_options: '-u' + file_pattern: | + docs/**/*.ipynb + use_case_examples/**/*.ipynb stop-runner-linux: diff --git a/.github/workflows/run_one_use_cases_example.yaml b/.github/workflows/run_one_use_cases_example.yaml index 1f6a9148b..d3941490e 100644 --- a/.github/workflows/run_one_use_cases_example.yaml +++ b/.github/workflows/run_one_use_cases_example.yaml @@ -114,6 +114,22 @@ jobs: run: | git pull -X theirs + # If the target branch is main or a release branch, a Pull Request is opened for everyone to + # review. + - name: Open PR + if: ${{ github.event.inputs.push_changes == 'true' && (github.ref_name == 'main' || startsWith(github.ref_name , 'release/')) }} + uses: peter-evans/create-pull-request@c5a7806660adbe173f04e3e038b0ccdcd758773c + with: + token: ${{ secrets.BOT_TOKEN }} + commit-message: "chore: refresh notebook(s) for use case ${{ github.event.inputs.use_case }}" + branch: "refresh-notebook(s)-for-use-case-${{ github.event.inputs.use_case }}-for-branch-${{ github.ref_name }}" + base: "${{ github.ref_name }}" + title: "Refresh notebook(s) for use case ${{ github.event.inputs.use_case }} for branch ${{ github.ref_name }}" + body: "Automatic PR with notebook(s) refresh of use case ${{ github.event.inputs.use_case }} \ + for branch ${{ github.ref_name }}." + add-paths: | + use_case_examples/**/*.ipynb + # If the target branch is another branch, the current branch is automatically merged into it - name: Push changes into the current branch if: ${{ github.event.inputs.push_changes == 'true' && github.ref_name != 'main' && !(startsWith(github.ref_name , 'release/')) }} @@ -121,6 +137,7 @@ jobs: with: commit_message: "chore: refresh notebook(s) for use case ${{ github.event.inputs.use_case }}" add_options: '-u' + file_pattern: 'use_case_examples/**/*.ipynb' stop-runner-linux: name: Stop EC2 runner From 665062258134c0f9d10b301d74a565cf67401e9d Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Mon, 5 Aug 2024 15:47:34 +0200 Subject: [PATCH 10/32] chore: fix pcc --- .github/workflows/refresh-one-notebook.yaml | 2 + .../lora_finetune/gpt2_finetune_hybrid.ipynb | 2752 +---------------- 2 files changed, 25 insertions(+), 2729 deletions(-) diff --git a/.github/workflows/refresh-one-notebook.yaml b/.github/workflows/refresh-one-notebook.yaml index f3f72a828..04469bca1 100644 --- a/.github/workflows/refresh-one-notebook.yaml +++ b/.github/workflows/refresh-one-notebook.yaml @@ -21,6 +21,7 @@ on: - FullyConnectedNeuralNetwork \n - FullyConnectedNeuralNetworkOnMNIST \n - GLMComparison \n + - gpt2_finetune_hybrid \n - HealthCarePrediction \n - ImportingFromScikitLearn \n - KaggleTitanic \n @@ -67,6 +68,7 @@ env: FullyConnectedNeuralNetwork: "docs/advanced_examples/FullyConnectedNeuralNetwork.ipynb" FullyConnectedNeuralNetworkOnMNIST: "docs/advanced_examples/FullyConnectedNeuralNetworkOnMNIST.ipynb" GLMComparison: "docs/advanced_examples/GLMComparison.ipynb" + gpt2_finetune_hybrid: "use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb" HealthCarePrediction: "use_case_examples/disease_prediction/HealthCarePrediction.ipynb" ImportingFromScikitLearn: "docs/advanced_examples/ImportingFromScikitLearn.ipynb" KaggleTitanic: "use_case_examples/titanic/KaggleTitanic.ipynb" diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 3ad0e8ccd..478ab51e6 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -3,14 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:52.624672Z", - "iopub.status.busy": "2024-08-05T13:21:52.624461Z", - "iopub.status.idle": "2024-08-05T13:21:59.638259Z", - "shell.execute_reply": "2024-08-05T13:21:59.637813Z" - } - }, + "metadata": {}, "outputs": [ { "data": { @@ -151,14 +144,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:59.640452Z", - "iopub.status.busy": "2024-08-05T13:21:59.640054Z", - "iopub.status.idle": "2024-08-05T13:21:59.643554Z", - "shell.execute_reply": "2024-08-05T13:21:59.643194Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def generate_text(prompt, model, tokenizer, max_length=30, fhe=\"disable\"):\n", @@ -189,14 +175,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:59.645335Z", - "iopub.status.busy": "2024-08-05T13:21:59.645066Z", - "iopub.status.idle": "2024-08-05T13:21:59.947893Z", - "shell.execute_reply": "2024-08-05T13:21:59.947376Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -216,14 +195,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:59.950238Z", - "iopub.status.busy": "2024-08-05T13:21:59.949987Z", - "iopub.status.idle": "2024-08-05T13:21:59.961972Z", - "shell.execute_reply": "2024-08-05T13:21:59.961566Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "peft_config = LoraConfig(\n", @@ -240,14 +212,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:59.963991Z", - "iopub.status.busy": "2024-08-05T13:21:59.963755Z", - "iopub.status.idle": "2024-08-05T13:21:59.968986Z", - "shell.execute_reply": "2024-08-05T13:21:59.968587Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def replace_conv1d(module, module_index_to_skip=0):\n", @@ -275,14 +240,7 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:59.970605Z", - "iopub.status.busy": "2024-08-05T13:21:59.970387Z", - "iopub.status.idle": "2024-08-05T13:21:59.975592Z", - "shell.execute_reply": "2024-08-05T13:21:59.975223Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "class LoraTraining(torch.nn.Module):\n", @@ -351,14 +309,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:59.977264Z", - "iopub.status.busy": "2024-08-05T13:21:59.976981Z", - "iopub.status.idle": "2024-08-05T13:21:59.979035Z", - "shell.execute_reply": "2024-08-05T13:21:59.978689Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "GRADIENT_ACCUMULATION_STEPS = 2\n", @@ -369,14 +320,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:59.980605Z", - "iopub.status.busy": "2024-08-05T13:21:59.980388Z", - "iopub.status.idle": "2024-08-05T13:21:59.987192Z", - "shell.execute_reply": "2024-08-05T13:21:59.986443Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "BLOCK_SIZE = 128\n", @@ -398,14 +342,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:59.988826Z", - "iopub.status.busy": "2024-08-05T13:21:59.988601Z", - "iopub.status.idle": "2024-08-05T13:21:59.992945Z", - "shell.execute_reply": "2024-08-05T13:21:59.992125Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "tokenizer.parallelism = False\n", @@ -436,14 +373,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:21:59.994543Z", - "iopub.status.busy": "2024-08-05T13:21:59.994329Z", - "iopub.status.idle": "2024-08-05T13:22:00.005905Z", - "shell.execute_reply": "2024-08-05T13:22:00.005146Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "trainer = Trainer(\n", @@ -466,14 +396,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:22:00.007744Z", - "iopub.status.busy": "2024-08-05T13:22:00.007551Z", - "iopub.status.idle": "2024-08-05T13:22:00.009725Z", - "shell.execute_reply": "2024-08-05T13:22:00.009378Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "lora_training.update_training_parameters(trainer.optimizer, trainer.lr_scheduler, training_args)" @@ -482,14 +405,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:22:00.012723Z", - "iopub.status.busy": "2024-08-05T13:22:00.012493Z", - "iopub.status.idle": "2024-08-05T13:22:00.061891Z", - "shell.execute_reply": "2024-08-05T13:22:00.061158Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def get_remote_names(model):\n", @@ -517,14 +433,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:22:00.063737Z", - "iopub.status.busy": "2024-08-05T13:22:00.063540Z", - "iopub.status.idle": "2024-08-05T13:22:00.066187Z", - "shell.execute_reply": "2024-08-05T13:22:00.065837Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "input_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (\n", @@ -540,14 +449,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:22:00.067796Z", - "iopub.status.busy": "2024-08-05T13:22:00.067577Z", - "iopub.status.idle": "2024-08-05T13:34:29.031521Z", - "shell.execute_reply": "2024-08-05T13:34:29.030428Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "hybrid_model.model.toggle_calibrate(enable=True)\n", @@ -562,14 +464,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:34:29.035674Z", - "iopub.status.busy": "2024-08-05T13:34:29.035491Z", - "iopub.status.idle": "2024-08-05T13:34:29.042957Z", - "shell.execute_reply": "2024-08-05T13:34:29.042120Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\"):\n", @@ -638,14 +533,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:34:29.045168Z", - "iopub.status.busy": "2024-08-05T13:34:29.044888Z", - "iopub.status.idle": "2024-08-05T13:35:08.049708Z", - "shell.execute_reply": "2024-08-05T13:35:08.048733Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -2180,14 +2068,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:35:08.051919Z", - "iopub.status.busy": "2024-08-05T13:35:08.051573Z", - "iopub.status.idle": "2024-08-05T13:35:08.059179Z", - "shell.execute_reply": "2024-08-05T13:35:08.058289Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "fine_tuned_model = hybrid_model.model.inference_model\n", @@ -2198,14 +2079,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:35:08.060970Z", - "iopub.status.busy": "2024-08-05T13:35:08.060719Z", - "iopub.status.idle": "2024-08-05T13:35:09.331028Z", - "shell.execute_reply": "2024-08-05T13:35:09.330346Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2227,14 +2101,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:35:09.333492Z", - "iopub.status.busy": "2024-08-05T13:35:09.333205Z", - "iopub.status.idle": "2024-08-05T13:35:09.338211Z", - "shell.execute_reply": "2024-08-05T13:35:09.336955Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "def print_weights_and_size(model, print_detail=False):\n", @@ -2252,14 +2119,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:35:09.340682Z", - "iopub.status.busy": "2024-08-05T13:35:09.340434Z", - "iopub.status.idle": "2024-08-05T13:35:09.344933Z", - "shell.execute_reply": "2024-08-05T13:35:09.343947Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2276,14 +2136,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:35:09.347557Z", - "iopub.status.busy": "2024-08-05T13:35:09.347175Z", - "iopub.status.idle": "2024-08-05T13:35:40.471315Z", - "shell.execute_reply": "2024-08-05T13:35:40.470636Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "path = Path(\"gpt2_lora_finetuned_hybrid_deployment\")\n", @@ -2297,14 +2150,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:35:40.474347Z", - "iopub.status.busy": "2024-08-05T13:35:40.474162Z", - "iopub.status.idle": "2024-08-05T13:35:40.478838Z", - "shell.execute_reply": "2024-08-05T13:35:40.478413Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2321,14 +2167,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-05T13:35:40.480637Z", - "iopub.status.busy": "2024-08-05T13:35:40.480483Z", - "iopub.status.idle": "2024-08-05T13:35:40.483164Z", - "shell.execute_reply": "2024-08-05T13:35:40.482799Z" - } - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2349,2551 +2188,6 @@ "metadata": { 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add disable adapters and print lora weights --- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 335 +++++++----------- 1 file changed, 128 insertions(+), 207 deletions(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 478ab51e6..3ad79a27a 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -4,106 +4,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f9bf5b7510d24f98bc21d17138eaafea", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/26.0 [00:00 Date: Mon, 5 Aug 2024 16:49:23 +0200 Subject: [PATCH 12/32] chore: add simulation execution --- .../workflows/run_one_use_cases_example.yaml | 2 +- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 1572 +---------------- 2 files changed, 47 insertions(+), 1527 deletions(-) diff --git a/.github/workflows/run_one_use_cases_example.yaml b/.github/workflows/run_one_use_cases_example.yaml index d3941490e..2072fd695 100644 --- a/.github/workflows/run_one_use_cases_example.yaml +++ b/.github/workflows/run_one_use_cases_example.yaml @@ -25,7 +25,7 @@ on: - titanic # --- refresh_use_cases_list.py: refresh list of use cases currently available [END] --- push_changes: - description: 'Push the refreshed notebook(s)' + description: 'Push refreshed notebook(s)' required: false type: boolean default: false diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 3ad79a27a..0ba0e8bc4 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -209,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -220,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -250,7 +250,7 @@ "\n", "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n", "\n", - "EPOCHS = 100\n", + "EPOCHS = 2\n", "PER_DEVICE_TRAIN_BATCH_SIZE = 4\n", "\n", "training_args = TrainingArguments(\n", @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -296,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -305,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -349,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -364,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -433,1537 +433,57 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\r\n", - "Training Progress: 0%| | 0/100 [00:00 3\u001b[0m \u001b[43mtrain_custom_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhybrid_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msimulate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[34], line 35\u001b[0m, in 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\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mremote_modules\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 418\u001b[0m module\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;241m=\u001b[39m HybridFHEMode(fhe)\n\u001b[0;32m--> 419\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1190\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1191\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1193\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1195\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", + "Cell \u001b[0;32mIn[25], line 34\u001b[0m, in \u001b[0;36mLoraTraining.forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 31\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# Update gradients\u001b[39;00m\n\u001b[0;32m---> 34\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 36\u001b[0m grad_norm \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcalibrate \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_optimizer:\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/_tensor.py:488\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 479\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 480\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 481\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 486\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 487\u001b[0m )\n\u001b[0;32m--> 488\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 489\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 490\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/autograd/__init__.py:197\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 192\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 194\u001b[0m \u001b[38;5;66;03m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 197\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/autograd/function.py:267\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImplementing both \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackward\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvjp\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for a custom \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFunction is not allowed. You should only implement one \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof them.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 266\u001b[0m user_fn \u001b[38;5;241m=\u001b[39m vjp_fn \u001b[38;5;28;01mif\u001b[39;00m vjp_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m Function\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;28;01melse\u001b[39;00m backward_fn\n\u001b[0;32m--> 267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43muser_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/use_case_examples/lora_finetune/custom_module.py:36\u001b[0m, in \u001b[0;36mForwardBackwardModule.backward\u001b[0;34m(ctx, grad_output)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbackward\u001b[39m(ctx, grad_output):\n\u001b[1;32m 35\u001b[0m backward_module \u001b[38;5;241m=\u001b[39m ctx\u001b[38;5;241m.\u001b[39mbackward_module\n\u001b[0;32m---> 36\u001b[0m grad_input \u001b[38;5;241m=\u001b[39m \u001b[43mbackward_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrad_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# grad_weight and grad_bias are not needed when computing the backward for lora\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m grad_input, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/ml/torch/hybrid_model.py:253\u001b[0m, in \u001b[0;36mRemoteModule.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\n\u001b[1;32m 245\u001b[0m HybridFHEMode\u001b[38;5;241m.\u001b[39mDISABLE,\n\u001b[1;32m 246\u001b[0m HybridFHEMode\u001b[38;5;241m.\u001b[39mCALIBRATE,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 249\u001b[0m }:\n\u001b[1;32m 250\u001b[0m \u001b[38;5;66;03m# Using quantized module\u001b[39;00m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_q_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 252\u001b[0m y \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor(\n\u001b[0;32m--> 253\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprivate_q_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfhe_local_mode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 254\u001b[0m )\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;241m==\u001b[39m HybridFHEMode\u001b[38;5;241m.\u001b[39mDISABLE:\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# Calling torch\u001b[39;00m\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/ml/quantization/quantized_module.py:443\u001b[0m, in \u001b[0;36mQuantizedModule.forward\u001b[0;34m(self, fhe, debug, *x)\u001b[0m\n\u001b[1;32m 440\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdequantize_output(\u001b[38;5;241m*\u001b[39mto_tuple(q_y_pred))\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y_pred, debug_value_tracker\n\u001b[0;32m--> 443\u001b[0m q_y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantized_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfhe\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 445\u001b[0m \u001b[38;5;66;03m# De-quantize the output predicted values\u001b[39;00m\n\u001b[1;32m 446\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdequantize_output(\u001b[38;5;241m*\u001b[39mto_tuple(q_y_pred))\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/ml/quantization/quantized_module.py:486\u001b[0m, in \u001b[0;36mQuantizedModule.quantized_forward\u001b[0;34m(self, fhe, *q_x)\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clear_forward(\u001b[38;5;241m*\u001b[39mq_x)\n\u001b[1;32m 485\u001b[0m simulate \u001b[38;5;241m=\u001b[39m fhe \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimulate\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 486\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fhe_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msimulate\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/ml/quantization/quantized_module.py:651\u001b[0m, in \u001b[0;36mQuantizedModule._fhe_forward\u001b[0;34m(self, simulate, *q_x)\u001b[0m\n\u001b[1;32m 648\u001b[0m predict_method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_circuit\u001b[38;5;241m.\u001b[39mencrypt_run_decrypt\n\u001b[1;32m 650\u001b[0m \u001b[38;5;66;03m# Execute the forward pass in FHE or with simulation\u001b[39;00m\n\u001b[0;32m--> 651\u001b[0m q_result \u001b[38;5;241m=\u001b[39m to_tuple(\u001b[43mpredict_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_input\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 653\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(q_result) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(q_result_by_output), (\n\u001b[1;32m 654\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of outputs does not match the number of output quantizers.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 655\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(q_result)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m!=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_quantizers)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 656\u001b[0m )\n\u001b[1;32m 657\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m elt_index, elt \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(q_result):\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/fhe/compilation/circuit.py:168\u001b[0m, in \u001b[0;36mCircuit.simulate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 165\u001b[0m ordered_validated_args \u001b[38;5;241m=\u001b[39m validate_input_args(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mclient_specs, \u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m 167\u001b[0m exporter \u001b[38;5;241m=\u001b[39m SimulatedValueExporter\u001b[38;5;241m.\u001b[39mnew(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mclient_specs\u001b[38;5;241m.\u001b[39mclient_parameters)\n\u001b[0;32m--> 168\u001b[0m exported \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 169\u001b[0m (\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m Value(\n\u001b[1;32m 173\u001b[0m exporter\u001b[38;5;241m.\u001b[39mexport_tensor(position, arg\u001b[38;5;241m.\u001b[39mflatten()\u001b[38;5;241m.\u001b[39mtolist(), \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mshape))\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, 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\u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/fhe/compilation/circuit.py:173\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 165\u001b[0m ordered_validated_args \u001b[38;5;241m=\u001b[39m validate_input_args(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mclient_specs, \u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m 167\u001b[0m exporter \u001b[38;5;241m=\u001b[39m SimulatedValueExporter\u001b[38;5;241m.\u001b[39mnew(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mclient_specs\u001b[38;5;241m.\u001b[39mclient_parameters)\n\u001b[1;32m 168\u001b[0m exported \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 169\u001b[0m (\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 172\u001b[0m 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\u001b[38;5;28;01melse\u001b[39;00m exporter\u001b[38;5;241m.\u001b[39mexport_scalar(position, \u001b[38;5;28mint\u001b[39m(arg))\n\u001b[1;32m 176\u001b[0m )\n\u001b[1;32m 177\u001b[0m )\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m position, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(ordered_validated_args)\n\u001b[1;32m 179\u001b[0m ]\n\u001b[1;32m 181\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;241m*\u001b[39mexported)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results, \u001b[38;5;28mtuple\u001b[39m):\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "torch.manual_seed(SEED)\n", "\n", - "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\")" + "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"simulate\")" ] }, { From e675d47e740a91034c7bc8ec9a07a6a4307c5e12 Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Mon, 5 Aug 2024 17:33:42 +0200 Subject: [PATCH 13/32] chore: clean notebook --- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 184 +++--------------- .../lora_finetune/lora_module.py | 65 +++++++ .../{custom_module.py => remote_module.py} | 0 .../lora_finetune/requirements.txt | 1 - 4 files changed, 97 insertions(+), 153 deletions(-) create mode 100644 use_case_examples/lora_finetune/lora_module.py rename use_case_examples/lora_finetune/{custom_module.py => remote_module.py} (100%) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 0ba0e8bc4..506b3febf 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -11,8 +11,9 @@ "from pathlib import Path\n", "\n", "import torch\n", - "from custom_module import CustomConv1D\n", + "from lora_module import LoraTraining\n", "from peft import LoraConfig, TaskType, get_peft_model\n", + "from remote_module import CustomConv1D\n", "from tqdm import tqdm\n", "from transformers import (\n", " AutoModelForCausalLM,\n", @@ -37,7 +38,7 @@ "if tokenizer.pad_token is None:\n", " tokenizer.pad_token = tokenizer.eos_token\n", "\n", - "# FREEZE WEIGHTS\n", + "# Freeze weights\n", "for param in model.parameters():\n", " param.requires_grad = False" ] @@ -138,75 +139,6 @@ "replace_conv1d(peft_model, module_index_to_skip=0);" ] }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "class LoraTraining(torch.nn.Module):\n", - " def __init__(self, inference_model, gradient_accumulation_steps) -> None:\n", - " super().__init__()\n", - "\n", - " self.inference_model = inference_model\n", - "\n", - " self.optimizer = None\n", - " self.lr_scheduler = None\n", - "\n", - " self.gradient_accumulation_steps = gradient_accumulation_steps\n", - " self.max_grad_norm = None\n", - "\n", - " self.calibrate = False\n", - " self.run_optimizer = False\n", - "\n", - " def update_training_parameters(self, optimizer, lr_scheduler, training_args):\n", - " assert self.gradient_accumulation_steps == training_args.gradient_accumulation_steps\n", - "\n", - " self.optimizer = optimizer\n", - " self.lr_scheduler = lr_scheduler\n", - " self.max_grad_norm = training_args.max_grad_norm\n", - "\n", - " def forward(self, inputs):\n", - " # FIXME: handle multi-inputs in hybrid model\n", - " x, y = inputs\n", - "\n", - " # some parts on server side\n", - " outputs = self.inference_model(input_ids=x, labels=y)\n", - "\n", - " loss = outputs.loss\n", - " loss = loss / self.gradient_accumulation_steps\n", - "\n", - " # Update gradients\n", - " loss.backward()\n", - "\n", - " grad_norm = None\n", - " if not self.calibrate and self.run_optimizer:\n", - " assert self.optimizer is not None\n", - " assert self.lr_scheduler is not None\n", - " assert self.max_grad_norm is not None\n", - "\n", - " grad_norm = torch.nn.utils.clip_grad_norm_(\n", - " self.inference_model.parameters(), max_norm=self.max_grad_norm, norm_type=2\n", - " )\n", - "\n", - " self.optimizer.step()\n", - " self.lr_scheduler.step()\n", - "\n", - " self.inference_model.zero_grad()\n", - "\n", - " # Clean gradients after calibration\n", - " elif self.calibrate:\n", - " self.inference_model.zero_grad()\n", - "\n", - " return (loss, grad_norm)\n", - "\n", - " def toggle_calibrate(self, enable: bool = True):\n", - " self.calibrate = enable\n", - "\n", - " def toggle_run_optimizer(self, enable: bool = True):\n", - " self.run_optimizer = enable" - ] - }, { "cell_type": "code", "execution_count": 26, @@ -226,18 +158,12 @@ "source": [ "BLOCK_SIZE = 128\n", "\n", - "\n", - "def load_dataset(file_path, tokenizer):\n", - " dataset = TextDataset(\n", - " tokenizer=tokenizer,\n", - " file_path=file_path,\n", - " block_size=BLOCK_SIZE,\n", - " cache_dir=\"cache_dataset\",\n", - " )\n", - " return dataset\n", - "\n", - "\n", - "train_dataset = load_dataset(\"data_finetune/what_is_fhe.txt\", tokenizer)" + "train_dataset = TextDataset(\n", + " tokenizer=tokenizer,\n", + " file_path=\"data_finetune/what_is_fhe.txt\",\n", + " block_size=BLOCK_SIZE,\n", + " cache_dir=\"cache_dataset\",\n", + ")" ] }, { @@ -246,11 +172,9 @@ "metadata": {}, "outputs": [], "source": [ - "tokenizer.parallelism = False\n", - "\n", "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n", "\n", - "EPOCHS = 2\n", + "EPOCHS = 100\n", "PER_DEVICE_TRAIN_BATCH_SIZE = 4\n", "\n", "training_args = TrainingArguments(\n", @@ -291,21 +215,14 @@ "num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)\n", "max_steps = math.ceil(training_args.num_train_epochs * num_update_steps_per_epoch)\n", "\n", - "trainer.create_optimizer_and_scheduler(num_training_steps=max_steps)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ + "trainer.create_optimizer_and_scheduler(num_training_steps=max_steps)\n", + "\n", "lora_training.update_training_parameters(trainer.optimizer, trainer.lr_scheduler, training_args)" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -326,8 +243,15 @@ " return remote_names\n", "\n", "\n", - "remote_names = get_remote_names(lora_training)\n", - "\n", + "remote_names = get_remote_names(lora_training)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ "hybrid_model = HybridFHEModel(lora_training, module_names=remote_names)" ] }, @@ -433,56 +357,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "Training Progress: 50%|█████ | 1/2 [02:53<02:53, 173.99s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/2, Loss: 1.8678, grad norm: 0.20942462980747223, lr: 0.00025\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[35], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m torch\u001b[38;5;241m.\u001b[39mmanual_seed(SEED)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mtrain_custom_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhybrid_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msimulate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[34], line 35\u001b[0m, in \u001b[0;36mtrain_custom_model\u001b[0;34m(hybrid_model, train_dataloader, training_args, fhe)\u001b[0m\n\u001b[1;32m 31\u001b[0m run_optimizer \u001b[38;5;241m=\u001b[39m is_last_batch_step \u001b[38;5;129;01mor\u001b[39;00m accumulate_gradients\n\u001b[1;32m 33\u001b[0m hybrid_model\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mtoggle_run_optimizer(enable\u001b[38;5;241m=\u001b[39mrun_optimizer)\n\u001b[0;32m---> 35\u001b[0m loss, grad_norm \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabels\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfhe\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 37\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grad_norm \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/ml/torch/hybrid_model.py:419\u001b[0m, in \u001b[0;36mHybridFHEModel.__call__\u001b[0;34m(self, x, fhe)\u001b[0m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mremote_modules\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m 418\u001b[0m module\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;241m=\u001b[39m HybridFHEMode(fhe)\n\u001b[0;32m--> 419\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1194\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1190\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1191\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1193\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1195\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1196\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n", - "Cell \u001b[0;32mIn[25], line 34\u001b[0m, in \u001b[0;36mLoraTraining.forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 31\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# Update gradients\u001b[39;00m\n\u001b[0;32m---> 34\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 36\u001b[0m grad_norm \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcalibrate \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_optimizer:\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/_tensor.py:488\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 478\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 479\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 480\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 481\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 486\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 487\u001b[0m )\n\u001b[0;32m--> 488\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 489\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 490\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/autograd/__init__.py:197\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 192\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 194\u001b[0m \u001b[38;5;66;03m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[1;32m 195\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 197\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 199\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/autograd/function.py:267\u001b[0m, in \u001b[0;36mBackwardCFunction.apply\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImplementing both \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbackward\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvjp\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m for a custom \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFunction is not allowed. You should only implement one \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mof them.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 266\u001b[0m user_fn \u001b[38;5;241m=\u001b[39m vjp_fn \u001b[38;5;28;01mif\u001b[39;00m vjp_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m Function\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;28;01melse\u001b[39;00m backward_fn\n\u001b[0;32m--> 267\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43muser_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/use_case_examples/lora_finetune/custom_module.py:36\u001b[0m, in \u001b[0;36mForwardBackwardModule.backward\u001b[0;34m(ctx, grad_output)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbackward\u001b[39m(ctx, grad_output):\n\u001b[1;32m 35\u001b[0m backward_module \u001b[38;5;241m=\u001b[39m ctx\u001b[38;5;241m.\u001b[39mbackward_module\n\u001b[0;32m---> 36\u001b[0m grad_input \u001b[38;5;241m=\u001b[39m \u001b[43mbackward_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrad_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# grad_weight and grad_bias are not needed when computing the backward for lora\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m grad_input, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/ml/torch/hybrid_model.py:253\u001b[0m, in \u001b[0;36mRemoteModule.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\n\u001b[1;32m 245\u001b[0m HybridFHEMode\u001b[38;5;241m.\u001b[39mDISABLE,\n\u001b[1;32m 246\u001b[0m HybridFHEMode\u001b[38;5;241m.\u001b[39mCALIBRATE,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 249\u001b[0m }:\n\u001b[1;32m 250\u001b[0m \u001b[38;5;66;03m# Using quantized module\u001b[39;00m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_q_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 252\u001b[0m y \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor(\n\u001b[0;32m--> 253\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprivate_q_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfhe_local_mode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 254\u001b[0m )\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;241m==\u001b[39m HybridFHEMode\u001b[38;5;241m.\u001b[39mDISABLE:\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# Calling torch\u001b[39;00m\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/ml/quantization/quantized_module.py:443\u001b[0m, in \u001b[0;36mQuantizedModule.forward\u001b[0;34m(self, fhe, debug, *x)\u001b[0m\n\u001b[1;32m 440\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdequantize_output(\u001b[38;5;241m*\u001b[39mto_tuple(q_y_pred))\n\u001b[1;32m 441\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y_pred, debug_value_tracker\n\u001b[0;32m--> 443\u001b[0m q_y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantized_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfhe\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 445\u001b[0m \u001b[38;5;66;03m# De-quantize the output predicted values\u001b[39;00m\n\u001b[1;32m 446\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdequantize_output(\u001b[38;5;241m*\u001b[39mto_tuple(q_y_pred))\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/ml/quantization/quantized_module.py:486\u001b[0m, in \u001b[0;36mQuantizedModule.quantized_forward\u001b[0;34m(self, fhe, *q_x)\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clear_forward(\u001b[38;5;241m*\u001b[39mq_x)\n\u001b[1;32m 485\u001b[0m simulate \u001b[38;5;241m=\u001b[39m fhe \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimulate\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 486\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fhe_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msimulate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msimulate\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/ml/quantization/quantized_module.py:651\u001b[0m, in \u001b[0;36mQuantizedModule._fhe_forward\u001b[0;34m(self, simulate, *q_x)\u001b[0m\n\u001b[1;32m 648\u001b[0m predict_method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_circuit\u001b[38;5;241m.\u001b[39mencrypt_run_decrypt\n\u001b[1;32m 650\u001b[0m \u001b[38;5;66;03m# Execute the forward pass in FHE or with simulation\u001b[39;00m\n\u001b[0;32m--> 651\u001b[0m q_result \u001b[38;5;241m=\u001b[39m to_tuple(\u001b[43mpredict_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_input\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 653\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(q_result) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlen\u001b[39m(q_result_by_output), (\n\u001b[1;32m 654\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of outputs does not match the number of output quantizers.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 655\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(q_result)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m!=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_quantizers)\u001b[38;5;132;01m=}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 656\u001b[0m )\n\u001b[1;32m 657\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m elt_index, elt \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(q_result):\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/fhe/compilation/circuit.py:168\u001b[0m, in \u001b[0;36mCircuit.simulate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 165\u001b[0m ordered_validated_args \u001b[38;5;241m=\u001b[39m validate_input_args(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mclient_specs, \u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m 167\u001b[0m exporter \u001b[38;5;241m=\u001b[39m SimulatedValueExporter\u001b[38;5;241m.\u001b[39mnew(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mclient_specs\u001b[38;5;241m.\u001b[39mclient_parameters)\n\u001b[0;32m--> 168\u001b[0m exported \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 169\u001b[0m (\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m Value(\n\u001b[1;32m 173\u001b[0m exporter\u001b[38;5;241m.\u001b[39mexport_tensor(position, arg\u001b[38;5;241m.\u001b[39mflatten()\u001b[38;5;241m.\u001b[39mtolist(), \u001b[38;5;28mlist\u001b[39m(arg\u001b[38;5;241m.\u001b[39mshape))\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;129;01mand\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m ()\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m exporter\u001b[38;5;241m.\u001b[39mexport_scalar(position, \u001b[38;5;28mint\u001b[39m(arg))\n\u001b[1;32m 176\u001b[0m )\n\u001b[1;32m 177\u001b[0m )\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m position, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(ordered_validated_args)\n\u001b[1;32m 179\u001b[0m ]\n\u001b[1;32m 181\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;241m*\u001b[39mexported)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results, \u001b[38;5;28mtuple\u001b[39m):\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/concrete/fhe/compilation/circuit.py:173\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 165\u001b[0m ordered_validated_args \u001b[38;5;241m=\u001b[39m validate_input_args(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mclient_specs, \u001b[38;5;241m*\u001b[39margs)\n\u001b[1;32m 167\u001b[0m exporter \u001b[38;5;241m=\u001b[39m SimulatedValueExporter\u001b[38;5;241m.\u001b[39mnew(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mclient_specs\u001b[38;5;241m.\u001b[39mclient_parameters)\n\u001b[1;32m 168\u001b[0m exported \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 169\u001b[0m (\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m arg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m Value(\n\u001b[0;32m--> 173\u001b[0m \u001b[43mexporter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mposition\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflatten\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtolist\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;129;01mand\u001b[39;00m arg\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m ()\n\u001b[1;32m 175\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m exporter\u001b[38;5;241m.\u001b[39mexport_scalar(position, \u001b[38;5;28mint\u001b[39m(arg))\n\u001b[1;32m 176\u001b[0m )\n\u001b[1;32m 177\u001b[0m )\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m position, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(ordered_validated_args)\n\u001b[1;32m 179\u001b[0m ]\n\u001b[1;32m 181\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msimulator\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;241m*\u001b[39mexported)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(results, \u001b[38;5;28mtuple\u001b[39m):\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ - "torch.manual_seed(SEED)\n", - "\n", "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"simulate\")" ] }, @@ -494,7 +372,7 @@ "source": [ "fine_tuned_model = hybrid_model.model.inference_model\n", "\n", - "hybrid_model.set_fhe_mode(\"disable\")" + "hybrid_model.set_fhe_mode(\"simulate\")" ] }, { @@ -525,11 +403,13 @@ "metadata": {}, "outputs": [], "source": [ - "with peft_model.disable_adapter_layers():\n", - " # Example usage\n", - " prompt = \"What is FHE ?\"\n", - " generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", - " print(generated_text)" + "peft_model.disable_adapter_layers()\n", + "# Example usage\n", + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)\n", + "\n", + "peft_model.enable_adapter_layers()" ] }, { diff --git a/use_case_examples/lora_finetune/lora_module.py b/use_case_examples/lora_finetune/lora_module.py new file mode 100644 index 000000000..6fde7d047 --- /dev/null +++ b/use_case_examples/lora_finetune/lora_module.py @@ -0,0 +1,65 @@ +import torch + + +class LoraTraining(torch.nn.Module): + def __init__(self, inference_model, gradient_accumulation_steps) -> None: + super().__init__() + + self.inference_model = inference_model + + self.optimizer = None + self.lr_scheduler = None + + self.gradient_accumulation_steps = gradient_accumulation_steps + self.max_grad_norm = None + + self.calibrate = False + self.run_optimizer = False + + def update_training_parameters(self, optimizer, lr_scheduler, training_args): + assert self.gradient_accumulation_steps == training_args.gradient_accumulation_steps + + self.optimizer = optimizer + self.lr_scheduler = lr_scheduler + self.max_grad_norm = training_args.max_grad_norm + + def forward(self, inputs): + # Remove this once hybrid model supports multiple inputs + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4568 + x, y = inputs + + # some parts on server side + outputs = self.inference_model(input_ids=x, labels=y) + + loss = outputs.loss + loss = loss / self.gradient_accumulation_steps + + # Update gradients + loss.backward() + + grad_norm = None + if not self.calibrate and self.run_optimizer: + assert self.optimizer is not None + assert self.lr_scheduler is not None + assert self.max_grad_norm is not None + + grad_norm = torch.nn.utils.clip_grad_norm_( + self.inference_model.parameters(), max_norm=self.max_grad_norm, norm_type=2 + ) + + self.optimizer.step() + self.lr_scheduler.step() + + self.inference_model.zero_grad() + + # Clean gradients after calibration + elif self.calibrate: + self.inference_model.zero_grad() + + return (loss, grad_norm) + + def toggle_calibrate(self, enable: bool = True): + self.calibrate = enable + + def toggle_run_optimizer(self, enable: bool = True): + self.run_optimizer = enable diff --git a/use_case_examples/lora_finetune/custom_module.py b/use_case_examples/lora_finetune/remote_module.py similarity index 100% rename from use_case_examples/lora_finetune/custom_module.py rename to use_case_examples/lora_finetune/remote_module.py diff --git a/use_case_examples/lora_finetune/requirements.txt b/use_case_examples/lora_finetune/requirements.txt index 27e353aed..ef6bef917 100644 --- a/use_case_examples/lora_finetune/requirements.txt +++ b/use_case_examples/lora_finetune/requirements.txt @@ -1,4 +1,3 @@ -# FIXME: Only works with source concrete-ml==1.6.1 transformers==4.41.2 peft==0.11.1 From 72a4c7b856d947d0f0f259c86f0fb4094266c118 Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Tue, 6 Aug 2024 10:53:45 +0200 Subject: [PATCH 14/32] chore: add loss plot --- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 181 ++++++++++++------ .../lora_finetune/requirements.txt | 1 + 2 files changed, 128 insertions(+), 54 deletions(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 506b3febf..8a0ab408d 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -10,6 +10,7 @@ "import shutil\n", "from pathlib import Path\n", "\n", + "import matplotlib.pyplot as plt\n", "import torch\n", "from lora_module import LoraTraining\n", "from peft import LoraConfig, TaskType, get_peft_model\n", @@ -45,21 +46,19 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "def generate_text(prompt, model, tokenizer, max_length=30, fhe=\"disable\"):\n", + "def generate_text(prompt, model, tokenizer, max_new_tokens=30):\n", " # Encode the input prompt\n", " inputs = tokenizer.encode_plus(prompt, return_tensors=\"pt\")\n", "\n", - " attention_mask = inputs[\"attention_mask\"]\n", - "\n", " # Generate text\n", " output = model.generate(\n", " input_ids=inputs[\"input_ids\"],\n", - " attention_mask=attention_mask,\n", - " max_length=max_length,\n", + " attention_mask=inputs[\"attention_mask\"],\n", + " max_new_tokens=max_new_tokens,\n", " num_return_sequences=1,\n", " no_repeat_ngram_size=2,\n", " top_k=50,\n", @@ -76,14 +75,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "What is FHE? FH: A basic program that is used to calculate the height of an object, and then sets the minimum height to be\n" + "What is FHE? FH: A basic program that is used to calculate the height of an object, and then sets the minimum height to be the object's height.\n" ] } ], @@ -96,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -141,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -152,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -248,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -257,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -273,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -288,9 +287,19 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", + "\u001b[1;31mClick here for more info." + ] + } + ], "source": [ "def train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\"):\n", " device = \"cpu\"\n", @@ -303,6 +312,8 @@ " epoch_pbar = tqdm(total=total_epochs, desc=\"Training Progress\", position=0)\n", "\n", " total_batched_samples = 0\n", + " epoch_losses = [] # List to store the loss for each epoch\n", + "\n", " for epoch in range(total_epochs):\n", " total_loss = 0\n", " grad_norms = []\n", @@ -316,7 +327,7 @@ " # Gradient accumulation\n", " is_last_batch_step = (\n", " steps_in_epoch <= training_args.gradient_accumulation_steps\n", - " and (step + 1) == steps_in_epoch\n", + " and (step + 1) == steps_in_epoch # noqa: W503\n", " )\n", " accumulate_gradients = (\n", " total_batched_samples % training_args.gradient_accumulation_steps == 0\n", @@ -339,6 +350,9 @@ " # Get last grad norm\n", " current_grad_norm = grad_norms[-1]\n", "\n", + " # Store the total loss for this epoch\n", + " epoch_losses.append(total_loss)\n", + "\n", " # Log epoch results\n", " print(\n", " f\"Epoch {epoch + 1}/{training_args.num_train_epochs}, \"\n", @@ -352,46 +366,64 @@ " save_path = f\"{training_args.output_dir}/checkpoint-{epoch + 1}\"\n", " hybrid_model.model.inference_model.save_pretrained(save_path)\n", "\n", - " epoch_pbar.close()" + " epoch_pbar.close()\n", + "\n", + " # Plot the loss evolution\n", + " plt.figure(figsize=(10, 6))\n", + " plt.plot(range(1, total_epochs + 1), epoch_losses, marker=\"o\")\n", + " plt.title(\"Loss Evolution During Training\")\n", + " plt.xlabel(\"Epoch\")\n", + " plt.ylabel(\"Total Loss\")\n", + " plt.grid(True)\n", + " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", + "\u001b[1;31mClick here for more info." + ] + } + ], "source": [ - "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"simulate\")" + "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\")" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fine_tuned_model = hybrid_model.model.inference_model\n", "\n", - "hybrid_model.set_fhe_mode(\"simulate\")" + "# In simulation, we can only generate a single token at a time because of fixed size circuits\n", + "# and how `generate` works (only the last token from the previous generation is kept)\n", + "hybrid_model.set_fhe_mode(\"disable\")" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "What is FHE?\n", - "\n", - "FHE is a cryptographic technique that enables computations on arbitrary data structures. It consists in generating computable FAs\n" + "What is FHE?I\n" ] } ], "source": [ - "# Example usage\n", "prompt = \"What is FHE ?\"\n", "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", "print(generated_text)" @@ -401,10 +433,21 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", + "\n", + "\u001b[1;31mClick here for more info." + ] + } + ], "source": [ "peft_model.disable_adapter_layers()\n", - "# Example usage\n", + "\n", "prompt = \"What is FHE ?\"\n", "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", "print(generated_text)\n", @@ -414,9 +457,20 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", + "\n", + "\u001b[1;31mClick here for more info." + ] + } + ], "source": [ "def print_weights_and_size(model, print_detail=False):\n", " total_weights = 0\n", @@ -438,15 +492,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of weights: 124587264\n", - "Total number of LoRA weights: 147456\n" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", + "\n", + "\u001b[1;31mClick here for more info." ] } ], @@ -456,9 +512,20 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", + "\n", + "\u001b[1;31mClick here for more info." + ] + } + ], "source": [ "path = Path(\"gpt2_lora_finetuned_hybrid_deployment\")\n", "\n", @@ -470,14 +537,17 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of weights: 39569664\n" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", + "\n", + "\u001b[1;31mClick here for more info." ] } ], @@ -487,14 +557,17 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Weights removed: 68.24 %\n" + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", + "\n", + "\u001b[1;31mClick here for more info." ] } ], diff --git a/use_case_examples/lora_finetune/requirements.txt b/use_case_examples/lora_finetune/requirements.txt index ef6bef917..877d6e48f 100644 --- a/use_case_examples/lora_finetune/requirements.txt +++ b/use_case_examples/lora_finetune/requirements.txt @@ -3,4 +3,5 @@ transformers==4.41.2 peft==0.11.1 datasets==2.20.0 Jinja2==3.1.4 +matplotlib==3.7.5 jupyter From b0eff663d2eadaa4e125ca4067be93a0ffdb6501 Mon Sep 17 00:00:00 2001 From: RomanBredehoft Date: Tue, 6 Aug 2024 09:22:51 +0000 Subject: [PATCH 15/32] chore: refresh notebook(s) for use case lora_finetune --- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 4728 ++++++++++++++++- 1 file changed, 4507 insertions(+), 221 deletions(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 8a0ab408d..621c1c89f 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -2,9 +2,115 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:08:46.790302Z", + "iopub.status.busy": "2024-08-06T09:08:46.790089Z", + "iopub.status.idle": "2024-08-06T09:08:53.779323Z", + "shell.execute_reply": "2024-08-06T09:08:53.778880Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "07f1ece4243243ad996c4e81d10f8278", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/26.0 [00:00here for more info." - ] + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:21:24.492190Z", + "iopub.status.busy": "2024-08-06T09:21:24.492002Z", + "iopub.status.idle": "2024-08-06T09:21:24.499066Z", + "shell.execute_reply": "2024-08-06T09:21:24.498661Z" } - ], + }, + "outputs": [], "source": [ "def train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\"):\n", " device = \"cpu\"\n", @@ -380,208 +567,4307 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:21:24.501232Z", + "iopub.status.busy": "2024-08-06T09:21:24.501072Z", + "iopub.status.idle": "2024-08-06T09:21:59.695649Z", + "shell.execute_reply": "2024-08-06T09:21:59.695143Z" + } + }, "outputs": [ { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", - "\u001b[1;31mClick here for more info." + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 0%| | 0/100 [00:00here for more info." + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 2%|▏ | 2/100 [00:01<01:16, 1.28it/s]" ] - } - ], - "source": [ - "peft_model.disable_adapter_layers()\n", - "\n", - "prompt = \"What is FHE ?\"\n", - "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", - "print(generated_text)\n", - "\n", - "peft_model.enable_adapter_layers()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + }, { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", - "\n", - "\u001b[1;31mClick here for more info." + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/100, Loss: 1.4975, grad norm: 0.49957117438316345, lr: 0.00049\n" ] - } - ], - "source": [ - "def print_weights_and_size(model, print_detail=False):\n", - " total_weights = 0\n", - " total_lora_weights = 0\n", - " for name, param in model.named_parameters():\n", - " total_weights += param.numel()\n", - "\n", - " if \"lora\" in name:\n", - " total_lora_weights += param.numel()\n", - "\n", - " if print_detail:\n", - " print(name, param.numel())\n", - "\n", - " print(f\"Total number of weights: {total_weights}\")\n", - " print(f\"Total number of LoRA weights: {total_lora_weights}\")\n", - "\n", - " return total_weights" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + }, { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", - "\n", - "\u001b[1;31mClick here for more info." + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 3%|▎ | 3/100 [00:02<00:58, 1.66it/s]" ] - } - ], - "source": [ - "total_weights_size = print_weights_and_size(hybrid_model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + }, { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", - "\n", - "\u001b[1;31mClick here for more info." + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 3/100, Loss: 1.4868, grad norm: 0.4209097921848297, lr: 0.00048499999999999997\n" ] - } - ], - "source": [ - "path = Path(\"gpt2_lora_finetuned_hybrid_deployment\")\n", - "\n", - "if path.is_dir() and any(path.iterdir()):\n", - " shutil.rmtree(path)\n", - "\n", - "hybrid_model.save_and_clear_private_info(path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + }, { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", - "\n", - "\u001b[1;31mClick here for more info." + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 4%|▍ | 4/100 [00:02<00:50, 1.91it/s]" ] - } - ], - "source": [ - "total_weights_size_private = print_weights_and_size(hybrid_model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ + }, { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe file '.venv_lora/lib/python3.10/site-packages/typing_extensions.py' seems to be overriding built in modules and interfering with the startup of the kernel. Consider renaming the file and starting the kernel again.\n", - "\n", - "\u001b[1;31mClick here for more info." + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4/100, Loss: 1.4791, grad norm: 0.494621604681015, lr: 0.00048\n" ] - } - ], - "source": [ - "print(\n", - " \"Total weights removed: \"\n", - " f\"{(total_weights_size - total_weights_size_private) / total_weights_size * 100:.2f} %\"\n", - ")" - ] - } - ], - "metadata": { - "execution": { - "timeout": 10800 + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 5%|▌ | 5/100 [00:02<00:44, 2.13it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 5/100, Loss: 1.4361, grad norm: 0.4343641698360443, lr: 0.000475\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 6%|▌ | 6/100 [00:03<00:40, 2.29it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 6/100, Loss: 1.4400, grad norm: 0.489236056804657, lr: 0.00047\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 7%|▋ | 7/100 [00:03<00:37, 2.45it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 7/100, Loss: 1.4170, grad norm: 0.5628056526184082, lr: 0.000465\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 8%|▊ | 8/100 [00:03<00:35, 2.56it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 8/100, Loss: 1.3922, grad norm: 0.4798496663570404, lr: 0.00046\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 9%|▉ | 9/100 [00:04<00:33, 2.71it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 9/100, Loss: 1.3769, grad norm: 0.5302374958992004, lr: 0.000455\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 10%|█ | 10/100 [00:04<00:32, 2.81it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 10/100, Loss: 1.3705, grad norm: 0.4688620865345001, lr: 0.00045000000000000004\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 11%|█ | 11/100 [00:04<00:30, 2.88it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 11/100, Loss: 1.3480, grad norm: 0.4672289490699768, lr: 0.00044500000000000003\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 12%|█▏ | 12/100 [00:05<00:29, 2.94it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 12/100, Loss: 1.3207, grad norm: 0.588039219379425, lr: 0.00044\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 13%|█▎ | 13/100 [00:05<00:29, 2.97it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 13/100, Loss: 1.2922, grad norm: 0.5210426449775696, lr: 0.000435\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 14%|█▍ | 14/100 [00:05<00:28, 2.97it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 14/100, Loss: 1.2897, grad norm: 0.47131291031837463, lr: 0.00043\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 15%|█▌ | 15/100 [00:06<00:28, 2.99it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 15/100, Loss: 1.2512, grad norm: 0.660230815410614, lr: 0.000425\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 16%|█▌ | 16/100 [00:06<00:28, 2.95it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 16/100, Loss: 1.2728, grad norm: 0.5080279111862183, lr: 0.00042\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 17%|█▋ | 17/100 [00:07<00:30, 2.71it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 17/100, Loss: 1.2300, grad norm: 0.6563513875007629, lr: 0.000415\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 18%|█▊ | 18/100 [00:07<00:30, 2.67it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 18/100, Loss: 1.2194, grad norm: 0.624626100063324, lr: 0.00041\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 19%|█▉ | 19/100 [00:07<00:29, 2.72it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 19/100, Loss: 1.2062, grad norm: 1.0465713739395142, lr: 0.00040500000000000003\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 20%|██ | 20/100 [00:08<00:28, 2.82it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 20/100, Loss: 1.1679, grad norm: 0.6334244608879089, lr: 0.0004\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 21%|██ | 21/100 [00:08<00:27, 2.89it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 21/100, Loss: 1.1653, grad norm: 0.7065843939781189, lr: 0.000395\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 22%|██▏ | 22/100 [00:08<00:26, 2.94it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 22/100, Loss: 1.1242, grad norm: 0.5430387854576111, lr: 0.00039000000000000005\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 23%|██▎ | 23/100 [00:09<00:25, 2.97it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 23/100, Loss: 1.1281, grad norm: 0.8001676201820374, lr: 0.00038500000000000003\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 24%|██▍ | 24/100 [00:09<00:25, 2.99it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 24/100, Loss: 1.1169, grad norm: 0.5735489726066589, lr: 0.00038\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 25%|██▌ | 25/100 [00:09<00:24, 3.02it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 25/100, Loss: 1.1163, grad norm: 0.644894540309906, lr: 0.000375\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 26%|██▌ | 26/100 [00:10<00:24, 3.03it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 26/100, Loss: 1.0545, grad norm: 0.5457342863082886, lr: 0.00037\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 27%|██▋ | 27/100 [00:10<00:24, 3.03it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 27/100, Loss: 1.0625, grad norm: 0.5880767703056335, lr: 0.000365\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 28%|██▊ | 28/100 [00:10<00:23, 3.02it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 28/100, Loss: 1.0756, grad norm: 0.7266576290130615, lr: 0.00035999999999999997\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 29%|██▉ | 29/100 [00:11<00:27, 2.59it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 29/100, Loss: 1.0279, grad norm: 0.761441707611084, lr: 0.000355\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 30%|███ | 30/100 [00:11<00:25, 2.72it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 30/100, Loss: 1.0476, grad norm: 0.5998173356056213, lr: 0.00035\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 31%|███ | 31/100 [00:11<00:24, 2.81it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 31/100, Loss: 0.9871, grad norm: 0.6272050142288208, lr: 0.000345\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 32%|███▏ | 32/100 [00:12<00:23, 2.87it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 32/100, Loss: 0.9927, grad norm: 0.696142315864563, lr: 0.00034\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 33%|███▎ | 33/100 [00:12<00:22, 2.91it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 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"\r", + "Training Progress: 100%|██████████| 100/100 [00:34<00:00, 2.80it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 100/100, Loss: 0.5508, grad norm: 1.031651496887207, lr: 0.0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "Training Progress: 100%|██████████| 100/100 [00:35<00:00, 2.85it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:21:59.698838Z", + "iopub.status.busy": "2024-08-06T09:21:59.698515Z", + "iopub.status.idle": "2024-08-06T09:21:59.703832Z", + "shell.execute_reply": "2024-08-06T09:21:59.703467Z" + } + }, + "outputs": [], + "source": [ + "fine_tuned_model = hybrid_model.model.inference_model\n", + "\n", + "# In simulation, we can only generate a single token at a time because of fixed size circuits\n", + "# and how `generate` works (only the last token from the previous generation is kept)\n", + "hybrid_model.set_fhe_mode(\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:21:59.705538Z", + "iopub.status.busy": "2024-08-06T09:21:59.705264Z", + "iopub.status.idle": "2024-08-06T09:22:01.089261Z", + "shell.execute_reply": "2024-08-06T09:22:01.088697Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE?\n", + "\n", + "The FSH paradigm is the concept that allows the computation of arbitrary values to an external processor. This technique is useful for many different purposes,\n" + ] + } + ], + "source": [ + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:22:01.091195Z", + "iopub.status.busy": "2024-08-06T09:22:01.090992Z", + "iopub.status.idle": "2024-08-06T09:22:01.643046Z", + "shell.execute_reply": "2024-08-06T09:22:01.642482Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE?\n", + "\n", + "FHE is a single-layer, multi-direction, and multi. It is designed to be an efficient, single directional, inter-\n" + ] + } + ], + "source": [ + "peft_model.disable_adapter_layers()\n", + "\n", + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)\n", + "\n", + "peft_model.enable_adapter_layers()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:22:01.645392Z", + "iopub.status.busy": "2024-08-06T09:22:01.645151Z", + "iopub.status.idle": "2024-08-06T09:22:01.649247Z", + "shell.execute_reply": "2024-08-06T09:22:01.648023Z" + } + }, + "outputs": [], + "source": [ + "def print_weights_and_size(model, print_detail=False):\n", + " total_weights = 0\n", + " total_lora_weights = 0\n", + " for name, param in model.named_parameters():\n", + " total_weights += param.numel()\n", + "\n", + " if \"lora\" in name:\n", + " total_lora_weights += param.numel()\n", + "\n", + " if print_detail:\n", + " print(name, param.numel())\n", + "\n", + " print(f\"Total number of weights: {total_weights}\")\n", + " print(f\"Total number of LoRA weights: {total_lora_weights}\")\n", + "\n", + " return total_weights" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:22:01.651565Z", + "iopub.status.busy": "2024-08-06T09:22:01.651265Z", + "iopub.status.idle": "2024-08-06T09:22:01.655080Z", + "shell.execute_reply": "2024-08-06T09:22:01.654255Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of weights: 124587264\n", + "Total number of LoRA weights: 147456\n" + ] + } + ], + "source": [ + "total_weights_size = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:22:01.657485Z", + "iopub.status.busy": "2024-08-06T09:22:01.657184Z", + "iopub.status.idle": "2024-08-06T09:22:32.577447Z", + "shell.execute_reply": "2024-08-06T09:22:32.576542Z" + } + }, + "outputs": [], + "source": [ + "path = Path(\"gpt2_lora_finetuned_hybrid_deployment\")\n", + "\n", + "if path.is_dir() and any(path.iterdir()):\n", + " shutil.rmtree(path)\n", + "\n", + "hybrid_model.save_and_clear_private_info(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2024-08-06T09:22:32.580118Z", + "iopub.status.busy": "2024-08-06T09:22:32.579874Z", + "iopub.status.idle": "2024-08-06T09:22:32.584968Z", + "shell.execute_reply": "2024-08-06T09:22:32.583755Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of weights: 39569664\n", + "Total number of LoRA weights: 147456\n" + ] + } + ], + "source": [ + "total_weights_size_private = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": 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0.00/26.0 [00:00 6\u001b[0m \u001b[43mtrain_custom_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhybrid_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdisable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[24], line 37\u001b[0m, in \u001b[0;36mtrain_custom_model\u001b[0;34m(hybrid_model, train_dataloader, training_args, fhe)\u001b[0m\n\u001b[1;32m 33\u001b[0m run_optimizer \u001b[38;5;241m=\u001b[39m is_last_batch_step \u001b[38;5;129;01mor\u001b[39;00m accumulate_gradients\n\u001b[1;32m 35\u001b[0m hybrid_model\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mtoggle_run_optimizer(enable\u001b[38;5;241m=\u001b[39mrun_optimizer)\n\u001b[0;32m---> 37\u001b[0m loss, grad_norm \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabels\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfhe\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 39\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grad_norm \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/torch/hybrid_model.py:413\u001b[0m, in \u001b[0;36mHybridFHEModel.__call__\u001b[0;34m(self, x, fhe)\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call method to run the model locally with a fhe mode.\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \n\u001b[1;32m 405\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;124;03m (torch.Tensor): The output tensor.\u001b[39;00m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_fhe_mode(fhe)\n\u001b[0;32m--> 413\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/use_case_examples/lora_finetune/lora_module.py:32\u001b[0m, in \u001b[0;36mLoraTraining.forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 29\u001b[0m x, y \u001b[38;5;241m=\u001b[39m inputs\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# some parts on server side\u001b[39;00m\n\u001b[0;32m---> 32\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minference_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m loss \u001b[38;5;241m=\u001b[39m outputs\u001b[38;5;241m.\u001b[39mloss\n\u001b[1;32m 35\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m 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hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/peft/peft_model.py:1430\u001b[0m, in \u001b[0;36mPeftModelForCausalLM.forward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, task_ids, **kwargs)\u001b[0m\n\u001b[1;32m 1428\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m 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\u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1433\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1434\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1435\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1436\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1437\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1438\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1439\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1441\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m _get_batch_size(input_ids, inputs_embeds)\n\u001b[1;32m 1442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attention_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1443\u001b[0m \u001b[38;5;66;03m# concat prompt attention mask\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/peft/tuners/tuners_utils.py:179\u001b[0m, in \u001b[0;36mBaseTuner.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any):\n\u001b[0;32m--> 179\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:1302\u001b[0m, in \u001b[0;36mGPT2LMHeadModel.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1295\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\u001b[39;00m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;124;03m Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set\u001b[39;00m\n\u001b[1;32m 1297\u001b[0m \u001b[38;5;124;03m `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`\u001b[39;00m\n\u001b[1;32m 1298\u001b[0m \u001b[38;5;124;03m are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`\u001b[39;00m\n\u001b[1;32m 1299\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1300\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1302\u001b[0m transformer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1303\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1304\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1305\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1306\u001b[0m \u001b[43m \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1307\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1308\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1309\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1310\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1316\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1317\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m transformer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;66;03m# Set device for model parallelism\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:1116\u001b[0m, in \u001b[0;36mGPT2Model.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1104\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m 1105\u001b[0m block\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m 1106\u001b[0m hidden_states,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1113\u001b[0m output_attentions,\n\u001b[1;32m 1114\u001b[0m )\n\u001b[1;32m 1115\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1116\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mblock\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1117\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1118\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_past\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer_past\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1119\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1120\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1121\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1122\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1123\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1124\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1125\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1127\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:651\u001b[0m, in \u001b[0;36mGPT2Block.forward\u001b[0;34m(self, hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m 649\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n\u001b[1;32m 650\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mln_2(hidden_states)\n\u001b[0;32m--> 651\u001b[0m feed_forward_hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmlp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 652\u001b[0m \u001b[38;5;66;03m# residual connection\u001b[39;00m\n\u001b[1;32m 653\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m feed_forward_hidden_states\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:571\u001b[0m, in \u001b[0;36mGPT2MLP.forward\u001b[0;34m(self, hidden_states)\u001b[0m\n\u001b[1;32m 570\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, hidden_states: Optional[Tuple[torch\u001b[38;5;241m.\u001b[39mFloatTensor]]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mFloatTensor:\n\u001b[0;32m--> 571\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mc_fc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 572\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mact(hidden_states)\n\u001b[1;32m 573\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mc_proj(hidden_states)\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/use_case_examples/lora_finetune/remote_module.py:49\u001b[0m, in \u001b[0;36mCustomConv1D.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mForwardBackwardModule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward_module\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/autograd/function.py:598\u001b[0m, in \u001b[0;36mFunction.apply\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_are_functorch_transforms_active():\n\u001b[1;32m 596\u001b[0m \u001b[38;5;66;03m# See NOTE: [functorch vjp and autograd interaction]\u001b[39;00m\n\u001b[1;32m 597\u001b[0m args \u001b[38;5;241m=\u001b[39m _functorch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39munwrap_dead_wrappers(args)\n\u001b[0;32m--> 598\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 600\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_setup_ctx_defined:\n\u001b[1;32m 601\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 602\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn order to use an autograd.Function with functorch transforms \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 603\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(vmap, grad, jvp, jacrev, ...), it must override the setup_context \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 604\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstaticmethod. For more details, please see \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 605\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://pytorch.org/docs/master/notes/extending.func.html\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 606\u001b[0m )\n", + "File \u001b[0;32m~/Documents/concrete-ml/use_case_examples/lora_finetune/remote_module.py:30\u001b[0m, in \u001b[0;36mForwardBackwardModule.forward\u001b[0;34m(ctx, input, forward_module, backward_module)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(ctx, \u001b[38;5;28minput\u001b[39m, forward_module, backward_module):\n\u001b[1;32m 29\u001b[0m ctx\u001b[38;5;241m.\u001b[39mbackward_module \u001b[38;5;241m=\u001b[39m backward_module\n\u001b[0;32m---> 30\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mforward_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output\n", + "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/torch/hybrid_model.py:254\u001b[0m, in \u001b[0;36mRemoteModule.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\n\u001b[1;32m 246\u001b[0m HybridFHEMode\u001b[38;5;241m.\u001b[39mCALIBRATE,\n\u001b[1;32m 247\u001b[0m HybridFHEMode\u001b[38;5;241m.\u001b[39mREMOTE,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 250\u001b[0m }:\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# Using quantized module\u001b[39;00m\n\u001b[1;32m 252\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_q_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 253\u001b[0m y \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor(\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprivate_q_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfhe_local_mode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m )\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;241m==\u001b[39m HybridFHEMode\u001b[38;5;241m.\u001b[39mCALIBRATE:\n\u001b[1;32m 258\u001b[0m \u001b[38;5;66;03m# Calling torch + gathering calibration data\u001b[39;00m\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/quantized_module.py:479\u001b[0m, in \u001b[0;36mQuantizedModule.forward\u001b[0;34m(self, fhe, debug, *x)\u001b[0m\n\u001b[1;32m 476\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdequantize_output(\u001b[38;5;241m*\u001b[39mto_tuple(q_y_pred))\n\u001b[1;32m 477\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y_pred, debug_value_tracker\n\u001b[0;32m--> 479\u001b[0m q_y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantized_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfhe\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 481\u001b[0m \u001b[38;5;66;03m# De-quantize the output predicted values\u001b[39;00m\n\u001b[1;32m 482\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdequantize_output(\u001b[38;5;241m*\u001b[39mto_tuple(q_y_pred))\n", + "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/quantized_module.py:520\u001b[0m, in \u001b[0;36mQuantizedModule.quantized_forward\u001b[0;34m(self, fhe, *q_x)\u001b[0m\n\u001b[1;32m 512\u001b[0m assert_true(\n\u001b[1;32m 513\u001b[0m n_values \u001b[38;5;241m==\u001b[39m n_inputs,\n\u001b[1;32m 514\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_values\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m inputs, expected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_inputs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Either the quantized module has not been \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 515\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproperly initialized or the input data has been changed since its initialization.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 516\u001b[0m \u001b[38;5;167;01mValueError\u001b[39;00m,\n\u001b[1;32m 517\u001b[0m )\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fhe \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdisable\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_clear_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_x\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 521\u001b[0m simulate \u001b[38;5;241m=\u001b[39m fhe \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimulate\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 522\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fhe_forward(\u001b[38;5;241m*\u001b[39mq_x, simulate\u001b[38;5;241m=\u001b[39msimulate)\n", + "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/quantized_module.py:563\u001b[0m, in \u001b[0;36mQuantizedModule._clear_forward\u001b[0;34m(self, *q_x)\u001b[0m\n\u001b[1;32m 561\u001b[0m error_tracker: List[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 562\u001b[0m layer\u001b[38;5;241m.\u001b[39merror_tracker \u001b[38;5;241m=\u001b[39m error_tracker\n\u001b[0;32m--> 563\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mlayer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 564\u001b[0m layer\u001b[38;5;241m.\u001b[39merror_tracker \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 566\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(error_tracker) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 567\u001b[0m \u001b[38;5;66;03m# The error message contains the ONNX tensor name that\u001b[39;00m\n\u001b[1;32m 568\u001b[0m \u001b[38;5;66;03m# triggered this error\u001b[39;00m\n", + "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/base_quantized_op.py:391\u001b[0m, in \u001b[0;36mQuantizedOp.__call__\u001b[0;34m(self, *q_inputs)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mq_inputs: ONNXOpInputOutputType) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ONNXOpInputOutputType:\n\u001b[1;32m 380\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Process the forward pass of the quantized op according to the implementation.\u001b[39;00m\n\u001b[1;32m 381\u001b[0m \n\u001b[1;32m 382\u001b[0m \u001b[38;5;124;03m The calibrate method needs to be called with sample data before using this function.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 388\u001b[0m \u001b[38;5;124;03m ONNXOpInputOutputType: Quantized output.\u001b[39;00m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mq_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/quantized_ops.py:375\u001b[0m, in \u001b[0;36mQuantizedGemm.q_impl\u001b[0;34m(self, calibrate_rounding, *q_inputs, **attrs)\u001b[0m\n\u001b[1;32m 370\u001b[0m input2_q_values_copy \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 371\u001b[0m copy_function(input2_q_values) \u001b[38;5;28;01mif\u001b[39;00m is_encrypted_gemm \u001b[38;5;28;01melse\u001b[39;00m input2_q_values\n\u001b[1;32m 372\u001b[0m )\n\u001b[1;32m 374\u001b[0m \u001b[38;5;66;03m# Core matmul operation in full integers with a shape change (INTEGERS)\u001b[39;00m\n\u001b[0;32m--> 375\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tag(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mop_instance_name \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.matmul\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 376\u001b[0m \u001b[38;5;66;03m# We implement our own encrypted matmul to be able to round before PBS\u001b[39;00m\n\u001b[1;32m 377\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_encrypted_gemm:\n\u001b[1;32m 378\u001b[0m matmul \u001b[38;5;241m=\u001b[39m matmul(input1_q_values_copy, input2_q_values_copy)\n", + "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/contextlib.py:139\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, typ, value, traceback):\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] - }, + } + ], + "source": [ + "# Avoid the following error from HuggingFace when training :\n", + "# \"The current process just got forked, after parallelism has already been used. Disabling\n", + "# parallelism to avoid deadlocks...\"\n", + "tokenizer.parallelism = False\n", + "\n", + "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fine_tuned_model = hybrid_model.model.inference_model\n", + "\n", + "# In simulation, we can only generate a single token at a time because of fixed size circuits\n", + "# and how `generate` works (only the last token from the previous generation is kept)\n", + "hybrid_model.set_fhe_mode(\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 5/100, Loss: 1.4361, grad norm: 0.4343641698360443, lr: 0.000475\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 6%|▌ | 6/100 [00:03<00:40, 2.29it/s]" + "What is FHE?\n", + "\n", + "The FSH paradigm is the concept that allows the computation of arbitrary values to an external processor. This technique is useful for many different purposes,\n" ] - }, + } + ], + "source": [ + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 6/100, Loss: 1.4400, grad norm: 0.489236056804657, lr: 0.00047\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 7%|▋ | 7/100 [00:03<00:37, 2.45it/s]" + "What is FHE?\n", + "\n", + "FHE is a single-layer, multi-direction, and multi. It is designed to be an efficient, single directional, inter-\n" ] - }, + } + ], + "source": [ + "peft_model.disable_adapter_layers()\n", + "\n", + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)\n", + "\n", + "peft_model.enable_adapter_layers()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def print_weights_and_size(model, print_detail=False):\n", + " total_weights = 0\n", + " total_lora_weights = 0\n", + " for name, param in model.named_parameters():\n", + " total_weights += param.numel()\n", + "\n", + " if \"lora\" in name:\n", + " total_lora_weights += param.numel()\n", + "\n", + " if print_detail:\n", + " print(name, param.numel())\n", + "\n", + " print(f\"Total number of weights: {total_weights}\")\n", + " print(f\"Total number of LoRA weights: {total_lora_weights}\")\n", + "\n", + " return total_weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 7/100, Loss: 1.4170, grad norm: 0.5628056526184082, lr: 0.000465\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 8%|▊ | 8/100 [00:03<00:35, 2.56it/s]" + "Total number of weights: 124587264\n", + "Total number of LoRA weights: 147456\n" ] - }, + } + ], + "source": [ + "total_weights_size = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "path = Path(\"deployment/gpt2_lora_finetuned\")\n", + "path.mkdir(parents=True, exist_ok=True)\n", + "\n", + "if path.is_dir() and any(path.iterdir()):\n", + " shutil.rmtree(path)\n", + "\n", + "hybrid_model.save_and_clear_private_info(path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 8/100, Loss: 1.3922, grad norm: 0.4798496663570404, lr: 0.00046\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 9%|▉ | 9/100 [00:04<00:33, 2.71it/s]" + "Total number of weights: 39569664\n", + "Total number of LoRA weights: 147456\n" ] - }, + } + ], + "source": [ + "total_weights_size_private = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 9/100, Loss: 1.3769, grad norm: 0.5302374958992004, lr: 0.000455\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 10%|█ | 10/100 [00:04<00:32, 2.81it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 10/100, Loss: 1.3705, grad norm: 0.4688620865345001, lr: 0.00045000000000000004\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 11%|█ | 11/100 [00:04<00:30, 2.88it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 11/100, Loss: 1.3480, grad norm: 0.4672289490699768, lr: 0.00044500000000000003\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 12%|█▏ | 12/100 [00:05<00:29, 2.94it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 12/100, Loss: 1.3207, grad norm: 0.588039219379425, lr: 0.00044\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 13%|█▎ | 13/100 [00:05<00:29, 2.97it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 13/100, Loss: 1.2922, grad norm: 0.5210426449775696, lr: 0.000435\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 14%|█▍ | 14/100 [00:05<00:28, 2.97it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 14/100, Loss: 1.2897, grad norm: 0.47131291031837463, lr: 0.00043\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 15%|█▌ | 15/100 [00:06<00:28, 2.99it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 15/100, Loss: 1.2512, grad norm: 0.660230815410614, lr: 0.000425\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 16%|█▌ | 16/100 [00:06<00:28, 2.95it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 16/100, Loss: 1.2728, grad norm: 0.5080279111862183, lr: 0.00042\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 17%|█▋ | 17/100 [00:07<00:30, 2.71it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 17/100, Loss: 1.2300, grad norm: 0.6563513875007629, lr: 0.000415\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 18%|█▊ | 18/100 [00:07<00:30, 2.67it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 18/100, Loss: 1.2194, grad norm: 0.624626100063324, lr: 0.00041\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 19%|█▉ | 19/100 [00:07<00:29, 2.72it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 19/100, Loss: 1.2062, grad norm: 1.0465713739395142, lr: 0.00040500000000000003\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 20%|██ | 20/100 [00:08<00:28, 2.82it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 20/100, Loss: 1.1679, grad norm: 0.6334244608879089, lr: 0.0004\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 21%|██ | 21/100 [00:08<00:27, 2.89it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 21/100, Loss: 1.1653, grad norm: 0.7065843939781189, lr: 0.000395\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 22%|██▏ | 22/100 [00:08<00:26, 2.94it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 22/100, Loss: 1.1242, grad norm: 0.5430387854576111, lr: 0.00039000000000000005\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 23%|██▎ | 23/100 [00:09<00:25, 2.97it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 23/100, Loss: 1.1281, grad norm: 0.8001676201820374, lr: 0.00038500000000000003\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 24%|██▍ | 24/100 [00:09<00:25, 2.99it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 24/100, Loss: 1.1169, grad norm: 0.5735489726066589, lr: 0.00038\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 25%|██▌ | 25/100 [00:09<00:24, 3.02it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 25/100, Loss: 1.1163, grad norm: 0.644894540309906, lr: 0.000375\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 26%|██▌ | 26/100 [00:10<00:24, 3.03it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 26/100, Loss: 1.0545, grad norm: 0.5457342863082886, lr: 0.00037\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 27%|██▋ | 27/100 [00:10<00:24, 3.03it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 27/100, Loss: 1.0625, grad norm: 0.5880767703056335, lr: 0.000365\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 28%|██▊ | 28/100 [00:10<00:23, 3.02it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 28/100, Loss: 1.0756, grad norm: 0.7266576290130615, lr: 0.00035999999999999997\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 29%|██▉ | 29/100 [00:11<00:27, 2.59it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 29/100, Loss: 1.0279, grad norm: 0.761441707611084, lr: 0.000355\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 30%|███ | 30/100 [00:11<00:25, 2.72it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 30/100, Loss: 1.0476, grad norm: 0.5998173356056213, lr: 0.00035\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 31%|███ | 31/100 [00:11<00:24, 2.81it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 31/100, Loss: 0.9871, grad norm: 0.6272050142288208, lr: 0.000345\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 32%|███▏ | 32/100 [00:12<00:23, 2.87it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 32/100, Loss: 0.9927, grad norm: 0.696142315864563, lr: 0.00034\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 33%|███▎ | 33/100 [00:12<00:22, 2.91it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 33/100, Loss: 0.9811, grad norm: 0.677040696144104, lr: 0.000335\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 34%|███▍ | 34/100 [00:12<00:22, 2.96it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 34/100, Loss: 0.9478, grad norm: 0.5834782123565674, lr: 0.00033\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 35%|███▌ | 35/100 [00:13<00:21, 2.98it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 35/100, Loss: 0.9502, grad norm: 0.7100946307182312, lr: 0.00032500000000000004\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 36%|███▌ | 36/100 [00:13<00:21, 2.99it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 36/100, Loss: 0.9314, grad norm: 0.6227987408638, lr: 0.00032\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "Training Progress: 37%|███▋ | 37/100 [00:13<00:20, 3.00it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 37/100, Loss: 0.9117, grad norm: 0.6908254623413086, lr: 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"output_type": "stream", - "text": [ - "\r", - "Training Progress: 100%|██████████| 100/100 [00:35<00:00, 2.85it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-06T09:21:59.698838Z", - "iopub.status.busy": "2024-08-06T09:21:59.698515Z", - "iopub.status.idle": "2024-08-06T09:21:59.703832Z", - "shell.execute_reply": "2024-08-06T09:21:59.703467Z" - } - }, - "outputs": [], - "source": [ - "fine_tuned_model = hybrid_model.model.inference_model\n", - "\n", - "# In simulation, we can only generate a single token at a time because of fixed size circuits\n", - "# and how `generate` works (only the last token from the previous generation is kept)\n", - "hybrid_model.set_fhe_mode(\"disable\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-06T09:21:59.705538Z", - "iopub.status.busy": "2024-08-06T09:21:59.705264Z", - "iopub.status.idle": "2024-08-06T09:22:01.089261Z", - "shell.execute_reply": "2024-08-06T09:22:01.088697Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What is FHE?\n", - "\n", - "The FSH paradigm is the concept that allows the computation of arbitrary values to an external processor. This technique is useful for many different purposes,\n" - ] - } - ], - "source": [ - "prompt = \"What is FHE ?\"\n", - "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", - "print(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-06T09:22:01.091195Z", - "iopub.status.busy": "2024-08-06T09:22:01.090992Z", - "iopub.status.idle": "2024-08-06T09:22:01.643046Z", - "shell.execute_reply": "2024-08-06T09:22:01.642482Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What is FHE?\n", - "\n", - "FHE is a single-layer, multi-direction, and multi. It is designed to be an efficient, single directional, inter-\n" - ] - } - ], - "source": [ - "peft_model.disable_adapter_layers()\n", - "\n", - "prompt = \"What is FHE ?\"\n", - "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", - "print(generated_text)\n", - "\n", - "peft_model.enable_adapter_layers()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-06T09:22:01.645392Z", - "iopub.status.busy": "2024-08-06T09:22:01.645151Z", - "iopub.status.idle": "2024-08-06T09:22:01.649247Z", - "shell.execute_reply": "2024-08-06T09:22:01.648023Z" - } - }, - "outputs": [], - "source": [ - "def print_weights_and_size(model, print_detail=False):\n", - " total_weights = 0\n", - " total_lora_weights = 0\n", - " for name, param in model.named_parameters():\n", - " total_weights += param.numel()\n", - "\n", - " if \"lora\" in name:\n", - " total_lora_weights += param.numel()\n", - "\n", - " if print_detail:\n", - " print(name, param.numel())\n", - "\n", - " print(f\"Total number of weights: {total_weights}\")\n", - " print(f\"Total number of LoRA weights: {total_lora_weights}\")\n", - "\n", - " return total_weights" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-06T09:22:01.651565Z", - "iopub.status.busy": "2024-08-06T09:22:01.651265Z", - "iopub.status.idle": "2024-08-06T09:22:01.655080Z", - "shell.execute_reply": "2024-08-06T09:22:01.654255Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of weights: 124587264\n", - "Total number of LoRA weights: 147456\n" - ] - } - ], - "source": [ - "total_weights_size = print_weights_and_size(hybrid_model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-06T09:22:01.657485Z", - "iopub.status.busy": "2024-08-06T09:22:01.657184Z", - "iopub.status.idle": "2024-08-06T09:22:32.577447Z", - "shell.execute_reply": "2024-08-06T09:22:32.576542Z" - } - }, - "outputs": [], - "source": [ - "path = Path(\"gpt2_lora_finetuned_hybrid_deployment\")\n", - "\n", - "if path.is_dir() and any(path.iterdir()):\n", - " shutil.rmtree(path)\n", - "\n", - "hybrid_model.save_and_clear_private_info(path)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-06T09:22:32.580118Z", - "iopub.status.busy": "2024-08-06T09:22:32.579874Z", - "iopub.status.idle": "2024-08-06T09:22:32.584968Z", - "shell.execute_reply": "2024-08-06T09:22:32.583755Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of weights: 39569664\n", - "Total number of LoRA weights: 147456\n" - ] - } - ], - "source": [ - "total_weights_size_private = print_weights_and_size(hybrid_model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "execution": { - "iopub.execute_input": "2024-08-06T09:22:32.586811Z", - 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if pip install -r requirements.txt; then + if pip install -r requirements.txt --extra-index-url https://pypi.zama.ai/cpu; then echo "Requirements installed successfully." else echo "Failed to install requirements." diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 1dec89ff0..1a2310767 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 103, "metadata": {}, "outputs": [ { @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ @@ -175,13 +175,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n", "\n", - "EPOCHS = 100\n", + "EPOCHS = 2\n", "PER_DEVICE_TRAIN_BATCH_SIZE = 4\n", "\n", "training_args = TrainingArguments(\n", @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 110, "metadata": {}, "outputs": [], "source": [ @@ -240,7 +240,9 @@ " # from the remote_names since calibration won't get through it (which raises an issue with\n", " # hybrid models). We however still need to include the associated module's forward pass in\n", " # the hybrid model\n", - " if isinstance(module, (Conv1D, Embedding)):\n", + " # Also include the embedding and language model head as they represent a lot of the model's\n", + " # parameters\n", + " if isinstance(module, (Conv1D, Embedding)) or \"lm_head\" in name:\n", " remote_names.append(name)\n", "\n", " elif isinstance(module, CustomConv1D):\n", @@ -255,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 113, "metadata": {}, "outputs": [], "source": [ @@ -264,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -280,9 +282,24 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 0%| | 0/100 [3:52:58 6\u001b[0m \u001b[43mtrain_custom_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhybrid_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraining_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdisable\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[24], line 37\u001b[0m, in \u001b[0;36mtrain_custom_model\u001b[0;34m(hybrid_model, train_dataloader, training_args, fhe)\u001b[0m\n\u001b[1;32m 33\u001b[0m run_optimizer \u001b[38;5;241m=\u001b[39m is_last_batch_step \u001b[38;5;129;01mor\u001b[39;00m accumulate_gradients\n\u001b[1;32m 35\u001b[0m hybrid_model\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mtoggle_run_optimizer(enable\u001b[38;5;241m=\u001b[39mrun_optimizer)\n\u001b[0;32m---> 37\u001b[0m loss, grad_norm \u001b[38;5;241m=\u001b[39m \u001b[43mhybrid_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabels\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfhe\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 39\u001b[0m total_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grad_norm \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/torch/hybrid_model.py:413\u001b[0m, in \u001b[0;36mHybridFHEModel.__call__\u001b[0;34m(self, x, fhe)\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Call method to run the model locally with a fhe mode.\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \n\u001b[1;32m 405\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;124;03m (torch.Tensor): The output tensor.\u001b[39;00m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 412\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_fhe_mode(fhe)\n\u001b[0;32m--> 413\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/use_case_examples/lora_finetune/lora_module.py:32\u001b[0m, in \u001b[0;36mLoraTraining.forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 29\u001b[0m x, y \u001b[38;5;241m=\u001b[39m inputs\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# some parts on server side\u001b[39;00m\n\u001b[0;32m---> 32\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minference_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m loss \u001b[38;5;241m=\u001b[39m outputs\u001b[38;5;241m.\u001b[39mloss\n\u001b[1;32m 35\u001b[0m loss \u001b[38;5;241m=\u001b[39m loss \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/peft/peft_model.py:1430\u001b[0m, in \u001b[0;36mPeftModelForCausalLM.forward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, task_ids, **kwargs)\u001b[0m\n\u001b[1;32m 1428\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_peft_forward_hooks(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1429\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mspecial_peft_forward_args}\n\u001b[0;32m-> 1430\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbase_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1431\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1432\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1433\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1434\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1435\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1436\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1437\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1438\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1439\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1441\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m _get_batch_size(input_ids, inputs_embeds)\n\u001b[1;32m 1442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attention_mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1443\u001b[0m \u001b[38;5;66;03m# concat prompt attention mask\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/peft/tuners/tuners_utils.py:179\u001b[0m, in \u001b[0;36mBaseTuner.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any):\n\u001b[0;32m--> 179\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:1302\u001b[0m, in \u001b[0;36mGPT2LMHeadModel.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1295\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\u001b[39;00m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;124;03m Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set\u001b[39;00m\n\u001b[1;32m 1297\u001b[0m \u001b[38;5;124;03m `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`\u001b[39;00m\n\u001b[1;32m 1298\u001b[0m \u001b[38;5;124;03m are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`\u001b[39;00m\n\u001b[1;32m 1299\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1300\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1302\u001b[0m transformer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1303\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1304\u001b[0m \u001b[43m \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1305\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1306\u001b[0m \u001b[43m \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1307\u001b[0m \u001b[43m \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1308\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1309\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1310\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1316\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1317\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m transformer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1319\u001b[0m \u001b[38;5;66;03m# Set device for model parallelism\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:1116\u001b[0m, in \u001b[0;36mGPT2Model.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1104\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m 1105\u001b[0m block\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m 1106\u001b[0m hidden_states,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1113\u001b[0m output_attentions,\n\u001b[1;32m 1114\u001b[0m )\n\u001b[1;32m 1115\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1116\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mblock\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1117\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1118\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_past\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlayer_past\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1119\u001b[0m \u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1120\u001b[0m \u001b[43m \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1121\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1122\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1123\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1124\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1125\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1127\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:651\u001b[0m, in \u001b[0;36mGPT2Block.forward\u001b[0;34m(self, hidden_states, layer_past, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions)\u001b[0m\n\u001b[1;32m 649\u001b[0m residual \u001b[38;5;241m=\u001b[39m hidden_states\n\u001b[1;32m 650\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mln_2(hidden_states)\n\u001b[0;32m--> 651\u001b[0m feed_forward_hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmlp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 652\u001b[0m \u001b[38;5;66;03m# residual connection\u001b[39;00m\n\u001b[1;32m 653\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m residual \u001b[38;5;241m+\u001b[39m feed_forward_hidden_states\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:571\u001b[0m, in \u001b[0;36mGPT2MLP.forward\u001b[0;34m(self, hidden_states)\u001b[0m\n\u001b[1;32m 570\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, hidden_states: Optional[Tuple[torch\u001b[38;5;241m.\u001b[39mFloatTensor]]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mFloatTensor:\n\u001b[0;32m--> 571\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mc_fc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 572\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mact(hidden_states)\n\u001b[1;32m 573\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mc_proj(hidden_states)\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/use_case_examples/lora_finetune/remote_module.py:49\u001b[0m, in \u001b[0;36mCustomConv1D.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mForwardBackwardModule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward_module\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward_module\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/.venv/lib/python3.10/site-packages/torch/autograd/function.py:598\u001b[0m, in \u001b[0;36mFunction.apply\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_are_functorch_transforms_active():\n\u001b[1;32m 596\u001b[0m \u001b[38;5;66;03m# See NOTE: [functorch vjp and autograd interaction]\u001b[39;00m\n\u001b[1;32m 597\u001b[0m args \u001b[38;5;241m=\u001b[39m _functorch\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39munwrap_dead_wrappers(args)\n\u001b[0;32m--> 598\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 600\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_setup_ctx_defined:\n\u001b[1;32m 601\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 602\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIn order to use an autograd.Function with functorch transforms \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 603\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(vmap, grad, jvp, jacrev, ...), it must override the setup_context \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 604\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstaticmethod. For more details, please see \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 605\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://pytorch.org/docs/master/notes/extending.func.html\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 606\u001b[0m )\n", - "File \u001b[0;32m~/Documents/concrete-ml/use_case_examples/lora_finetune/remote_module.py:30\u001b[0m, in \u001b[0;36mForwardBackwardModule.forward\u001b[0;34m(ctx, input, forward_module, backward_module)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(ctx, \u001b[38;5;28minput\u001b[39m, forward_module, backward_module):\n\u001b[1;32m 29\u001b[0m ctx\u001b[38;5;241m.\u001b[39mbackward_module \u001b[38;5;241m=\u001b[39m backward_module\n\u001b[0;32m---> 30\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mforward_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output\n", - "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/torch/hybrid_model.py:254\u001b[0m, in \u001b[0;36mRemoteModule.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m {\n\u001b[1;32m 246\u001b[0m HybridFHEMode\u001b[38;5;241m.\u001b[39mCALIBRATE,\n\u001b[1;32m 247\u001b[0m HybridFHEMode\u001b[38;5;241m.\u001b[39mREMOTE,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 250\u001b[0m }:\n\u001b[1;32m 251\u001b[0m \u001b[38;5;66;03m# Using quantized module\u001b[39;00m\n\u001b[1;32m 252\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_q_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 253\u001b[0m y \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor(\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprivate_q_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdetach\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfhe_local_mode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m )\n\u001b[1;32m 257\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfhe_local_mode \u001b[38;5;241m==\u001b[39m HybridFHEMode\u001b[38;5;241m.\u001b[39mCALIBRATE:\n\u001b[1;32m 258\u001b[0m \u001b[38;5;66;03m# Calling torch + gathering calibration data\u001b[39;00m\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprivate_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/quantized_module.py:479\u001b[0m, in \u001b[0;36mQuantizedModule.forward\u001b[0;34m(self, fhe, debug, *x)\u001b[0m\n\u001b[1;32m 476\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdequantize_output(\u001b[38;5;241m*\u001b[39mto_tuple(q_y_pred))\n\u001b[1;32m 477\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y_pred, debug_value_tracker\n\u001b[0;32m--> 479\u001b[0m q_y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantized_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfhe\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfhe\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 481\u001b[0m \u001b[38;5;66;03m# De-quantize the output predicted values\u001b[39;00m\n\u001b[1;32m 482\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdequantize_output(\u001b[38;5;241m*\u001b[39mto_tuple(q_y_pred))\n", - "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/quantized_module.py:520\u001b[0m, in \u001b[0;36mQuantizedModule.quantized_forward\u001b[0;34m(self, fhe, *q_x)\u001b[0m\n\u001b[1;32m 512\u001b[0m assert_true(\n\u001b[1;32m 513\u001b[0m n_values \u001b[38;5;241m==\u001b[39m n_inputs,\n\u001b[1;32m 514\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_values\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m inputs, expected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_inputs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Either the quantized module has not been \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 515\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproperly initialized or the input data has been changed since its initialization.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 516\u001b[0m \u001b[38;5;167;01mValueError\u001b[39;00m,\n\u001b[1;32m 517\u001b[0m )\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fhe \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdisable\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_clear_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_x\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 521\u001b[0m simulate \u001b[38;5;241m=\u001b[39m fhe \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimulate\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 522\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fhe_forward(\u001b[38;5;241m*\u001b[39mq_x, simulate\u001b[38;5;241m=\u001b[39msimulate)\n", - "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/quantized_module.py:563\u001b[0m, in \u001b[0;36mQuantizedModule._clear_forward\u001b[0;34m(self, *q_x)\u001b[0m\n\u001b[1;32m 561\u001b[0m error_tracker: List[\u001b[38;5;28mint\u001b[39m] \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 562\u001b[0m layer\u001b[38;5;241m.\u001b[39merror_tracker \u001b[38;5;241m=\u001b[39m error_tracker\n\u001b[0;32m--> 563\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mlayer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 564\u001b[0m layer\u001b[38;5;241m.\u001b[39merror_tracker \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 566\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(error_tracker) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 567\u001b[0m \u001b[38;5;66;03m# The error message contains the ONNX tensor name that\u001b[39;00m\n\u001b[1;32m 568\u001b[0m \u001b[38;5;66;03m# triggered this error\u001b[39;00m\n", - "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/base_quantized_op.py:391\u001b[0m, in \u001b[0;36mQuantizedOp.__call__\u001b[0;34m(self, *q_inputs)\u001b[0m\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mq_inputs: ONNXOpInputOutputType) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ONNXOpInputOutputType:\n\u001b[1;32m 380\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Process the forward pass of the quantized op according to the implementation.\u001b[39;00m\n\u001b[1;32m 381\u001b[0m \n\u001b[1;32m 382\u001b[0m \u001b[38;5;124;03m The calibrate method needs to be called with sample data before using this function.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 388\u001b[0m \u001b[38;5;124;03m ONNXOpInputOutputType: Quantized output.\u001b[39;00m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mq_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mq_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Documents/concrete-ml/src/concrete/ml/quantization/quantized_ops.py:375\u001b[0m, in \u001b[0;36mQuantizedGemm.q_impl\u001b[0;34m(self, calibrate_rounding, *q_inputs, **attrs)\u001b[0m\n\u001b[1;32m 370\u001b[0m input2_q_values_copy \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 371\u001b[0m copy_function(input2_q_values) \u001b[38;5;28;01mif\u001b[39;00m is_encrypted_gemm \u001b[38;5;28;01melse\u001b[39;00m input2_q_values\n\u001b[1;32m 372\u001b[0m )\n\u001b[1;32m 374\u001b[0m \u001b[38;5;66;03m# Core matmul operation in full integers with a shape change (INTEGERS)\u001b[39;00m\n\u001b[0;32m--> 375\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tag(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mop_instance_name \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.matmul\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 376\u001b[0m \u001b[38;5;66;03m# We implement our own encrypted matmul to be able to round before PBS\u001b[39;00m\n\u001b[1;32m 377\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_encrypted_gemm:\n\u001b[1;32m 378\u001b[0m matmul \u001b[38;5;241m=\u001b[39m matmul(input1_q_values_copy, input2_q_values_copy)\n", - "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/contextlib.py:139\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[0;34m(self, typ, value, traceback)\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, typ, value, traceback):\n\u001b[1;32m 140\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m typ \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 100%|██████████| 2/2 [10:17<00:00, 308.74s/it]\n" ] + }, + { + "data": { + "image/png": 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dS38M/ZEjRwDrrsKKiNyNCpiIyGMyZcqUu27v3Lkz1apVY8OGDQwaNIiRI0eSlpZG3bp1mTVrVvp3gAH07t2bpUuXsnLlSpKSkihTpgwjRoxg4MCBAHh6evL666+zZs0aZs6cia2tLZUrV2b+/Pm89NJLmcp5r+9OWrduXYYC1rp1a/Lly0dsbGyGpx/eVqxYMf766y/+85//8PXXX5OYmIiPjw+//fbbA682ValShRkzZjBkyBD69+9P1apVmTlzJnPmzMnwpcQAP/zwAyEhIfTr149bt27xySef3LWA3R5btmxZpk+fzuLFiylevDiDBg264zvNssLtJ0iazWZcXFzw9vamU6dO9OjR465X/2bPnk3Pnj35/PPPcXV1pVu3bjRu3Dj9+90et/feew+LxcLYsWMZMGAAvr6+LF26lN69e+Po6PjYjjt48GDi4+OZM2cO8+bN4+mnn+aPP/7ggw8+eGzHtNaMGTMoXrw4P//8M4sXLyYwMJB58+ZRqVKlx/qzEZG8wWTRXaciIiLCP08oLFKkCC+++CLff/+90XGylYiICGrWrMmsWbNo37690XFEJAfTPWAiIiJ5UGJi4h33Ys2YMYPr168TEBBgTKhs4ubNm3dsmzBhAmazmUaNGhmQSERyEy1BFBERyYO2bNlCv379eOWVVyhcuDC7du1i6tSpVK9enVdeecXoeIYaPXo0O3fupHHjxtja2vLnn3/y559/0qNHjwxPKBUReRhagigiIpIHnTp1it69e7Nt27b0B4G0bNmSzz//nKJFixodz1CrVq1i2LBhHDx4kLi4OEqXLk2HDh348MMPsbXVf7sWkUejAiYiIiIiIvKE6B4wERERERGRJ0QFTERERERE5AnRQuaHlJaWxoULFyhQoIC+lFFEREREJA+zWCzExsbi4eGB2Xz/a1wqYA/pwoULehKSiIiIiIikO3v2LKVKlbrvGEML2Pr16/niiy/YuXMnUVFRLF68mDZt2tz3M7Nnz2b06NEcPXqUggULEhQUxBdffEHhwoXvGDt37lxef/11XnjhBZYsWZK+PS4ujg8++IAlS5Zw7do1vL296d27N7169cp09gIFCgD//JBdXFwy/bnHITk5mZUrV9K8eXPs7OwMzSI5g+aMWEtzRqylOSPW0pwRa2WnORMTE4Onp2d6R7gfQwtYfHw8vr6+dO3alRdffPGB4zdt2kTHjh0ZP348wcHBnD9/nl69etG9e3cWLVqUYeypU6cYMGAAfn5+d+ynf//+rF27llmzZuHl5cXKlSt555138PDwoHXr1pnKfnvZoYuLS7YoYE5OTri4uBg++SRn0JwRa2nOiLU0Z8RamjNirew4ZzJza5KhD+EICgpixIgRtG3bNlPjN2/ejJeXF71798bb25uGDRvSs2dPtm3blmFcamoq7du3Z9iwYZQtW/aO/fz111906tSJgIAAvLy86NGjB76+vnfsR0REREREJCvlqHvA6tWrx+DBg1m2bBlBQUFcvnyZhQsX0rJlywzjhg8fTtGiRenWrRsbNmy4Yz/169dn6dKldO3aFQ8PD8LCwjhy5Ajjx4+/57GTkpJISkpKfx0TEwP807yTk5Oz6Awfzu3jG51Dcg7NGbGW5oxYS3NGrKU5I9bKTnPGmgw5qoA1aNCA2bNn065dOxITE0lJSSE4OJhJkyalj9m4cSNTp04lIiLinvv5+uuv6dGjB6VKlcLW1haz2cz3339Po0aN7vmZkSNHMmzYsDu2r1y5Eicnp0c6r6yyatUqoyNIDqM5I9bSnBFrac6ItTRnxFrZYc4kJCRkemyOKmAHDx6kT58+DBkyhBYtWhAVFcXAgQPp1asXU6dOJTY2lg4dOvD999/j7u5+z/18/fXXbNmyhaVLl1KmTBnWr1/Pu+++i4eHB4GBgXf9zKBBg+jfv3/669s32jVv3jxb3AO2atUqmjVrlm3Wv0r2pjkj1tKcEWtpzoi1NGfEWtlpztxeHZcZOaqAjRw5kgYNGjBw4EAAfHx8cHZ2xs/PjxEjRnDp0iVOnTpFcHBw+mfS0tIAsLW1JTIyEg8PDwYPHszixYtp1apV+n4iIiIYM2bMPQuYg4MDDg4Od2y3s7Mz/Bd+W3bKIjmD5oxYS3NGrKU5I9bSnBFrZYc5Y83xc1QBS0hIwNY2Y2QbGxvgny8/q1y5Mvv27cvw/kcffURsbCxffvklnp6eJCYmkpycfMcXpNnY2KSXNRERERERkcfB0AIWFxfHsWPH0l+fPHmSiIgI3NzcKF26NIMGDeL8+fPMmDEDgODgYLp3786UKVPSlyD27duXOnXq4OHhAUD16tUzHMPV1TXDdnt7e/z9/Rk4cCD58uWjTJkyhIeHM2PGDMaNG/cEzlpERERERPIqQwvYjh07aNy4cfrr2/dYderUienTpxMVFcWZM2fS3+/cuTOxsbFMnDiR0NBQXF1dadKkCaNGjbLquHPnzmXQoEG0b9+e69evU6ZMGT777DOrvohZRERERETEWoYWsICAACwWyz3fnz59+h3bQkJCCAkJyfQx7raP4sWLM23atEzvQ0REREREJCsY+kXMIiIiIiIieYkKmIiIiIiIyBOiAiYiIiIiIvKEqICJiIiIiIg8ISpgOVxqmoWtJ6+z86qJrSevk5p274eaiIiIiIiIsXLUFzFLRsv3RzHst4NERScCNsw4uoMSBR35JLgqz1UvYXQ8ERERERH5F10By6GW74/i7Vm7/n/5+j8XoxN5e9Yulu+PMiiZiIiIiIjciwpYDpSaZmHYbwe522LD29uG/XZQyxFFRERERLIZFbAcaNvJ63dc+fpfFiAqOpFtJ68/uVAiIiIiIvJAKmA50OXYe5evhxknIiIiIiJPhgpYDlS0gGOmxh2OisVi0TJEEREREZHsQgUsB6rj7UaJgo6YHjBuSvhxXvlmMwcuRD+RXCIiIiIicn8qYDmQjdnEJ8FVAe4oYab//6dNjZI42duw4/TfBH+9kSG/7ic6IflJRxURERERkf+hApZDPVe9BFPefJriBTMuRyxe0JEpbz7NhNdqsCbUn+d9SpBmgRmbT9N4bBjztp8hTU9HFBERERExhL6IOQd7rnoJmlUtzuZjl1m5YSvN/epSr3xRbMz/XBcrUTAfE994mjfqXOWTpQc4ejmO//yyjznbzvLpC9XwKeVq7AmIiIiIiOQxugKWw9mYTdT1duMZdwt1vd3Sy9f/ql/enWV9/PioVRXyO9iy5+wNXpi0iUGL9nE9/pYBqUVERERE8iYVsDzCzsbMW35lWRvqT9uaJbFY4OdtZ2gyNoxZW07rS5tFRERERJ4AFbA8pqiLI+Pb1WB+z3pULl6AGwnJfLRkPy9M2sjO038bHU9EREREJFdTAcuj6ni78XtIQ4YGV6WAoy37z8fw0pS/GLhgD1fjkoyOJyIiIiKSK6mA5WG2NmY6N/Bm3YAAXnmmFAALdp6j8Zgwpm86SUpqmsEJRURERERyFxUwwT2/A1+84suid+pTvaQLsYkpDP3tIM9/vZFtJ68bHU9EREREJNdQAZN0T5cuxK/vNmREm+oUzGfH4YuxvPrtZvrNi+ByTKLR8UREREREcjwVMMnAxmzizWfLsG5AAK/XKY3JBIt3n6fJ2HB+2HCCZC1LFBERERF5aCpgclduzvaMfPEplrzTAF9PV+KSUhjxxyFafrmBv45fNTqeiIiIiEiOpAIm9+Xr6crit+sz6qWncHO25+jlON74fivvzdlFVPRNo+OJiIiIiOQoKmDyQGaziXa1S7M21J+O9cpgNsHve6NoOjacKWHHuZWiZYkiIiIiIpmhAiaZ5upkz/AXqvNbSEOeKVOIhFupjFp+mOcmrGf9kStGxxMRERERyfZUwMRq1TwKsrBXPca+4ot7fgdOXI2n44/b6DVzJ+f+TjA6noiIiIhItqUCJg/FZDLx0jOlWDvAn64NvLExm1h+4CKB48L5es1REpNTjY4oIiIiIpLtqIDJI3FxtGNIcFX+6N2QOt5uJCanMXbVEVpMWM/aw5eMjiciIiIikq2ogEmWqFzchXk9nuXL12pQzMWB09cS6Dp9B2/9tJ0z17QsUUREREQEVMAkC5lMJl6oUZI1oQH0bFQWW7OJ1YcuEzg+nHGrjmhZooiIiIjkeSpgkuXyO9gyqGUVlvf1o0H5wtxKSeOrNUcJHBfOigMXsVgsRkcUERERETGECpg8NuWLFmBWt7pMbv80HgUdOff3TXrO3Ennads5eTXe6HgiIiIiIk+cCpg8ViaTiZZPlWB1qD/vNi6HvY2Z8CNXaDF+PaOXHybhVorREUVEREREnhgVMHkinOxtGdiiMiv6NcK/YhFupaYxOew4gWPDWbYvSssSRURERCRPUAGTJ8rb3ZnpXWrzXYdnKFUoHxeiE3ln9i46TN3GscuxRscTEREREXmsVMDkiTOZTDSvVpzV/f3p07QC9rZmNh67ynMTNvDfZYeIS9KyRBERERHJnVTAxDCOdjb0a1aR1f38CaxSlJQ0C9+tP0HTsWH8GnFeyxJFREREJNdRARPDlS7sxA+davNj51qUKezEpZgk+syN4LXvthB5UcsSRURERCT3UAGTbKNJ5WKs6NuIAc0r4mhnZuvJ67T8agPDfjtATGKy0fFERERERB6ZCphkK452NrzXpAKr+/sTVL04qWkWpm06RZMxYSzceY60NC1LFBEREZGcSwVMsqVShZyY8uYzzOhah7JFnLkad4sBC/bwyrebOXAh2uh4IiIiIiIPRQVMsrVGFYuwvE8jPgiqjJO9DTtP/03w1xsZ8ut+ohO0LFFEREREchYVMMn27G3N9PIvx5pQf573KUGaBWZsPk3jsWHM235GyxJFREREJMdQAZMco0TBfEx842nmdK9LhaL5uR5/i//8so+2U/5i77kbRscTEREREXkgFTDJceqXc2dZHz8+alWF/A627Dl7gxcmbWLQor1cj79ldDwRERERkXtSAZMcyc7GzFt+ZVkb6k/bmiWxWODnbWdpMjaMWVtOk6pliSIiIiKSDamASY5W1MWR8e1qML9nPSoXL8CNhGQ+WrKfFyZtZOfpv42OJyIiIiKSgaEFbP369QQHB+Ph4YHJZGLJkiUP/Mzs2bPx9fXFycmJEiVK0LVrV65du3bXsXPnzsVkMtGmTZs73jt06BCtW7emYMGCODs7U7t2bc6cOfOIZyRGqePtxu8hDRnWuhoFHG3Zfz6Gl6b8xcAFe7gal2R0PBERERERwOACFh8fj6+vL5MmTcrU+E2bNtGxY0e6devGgQMHWLBgAdu2baN79+53jD116hQDBgzAz8/vjveOHz9Ow4YNqVy5MmFhYezdu5ePP/4YR0fHRz4nMY6tjZlO9b1YNyCAV2uVAmDBznM0HhPG9E0nSUlNMzihiIiIiOR1tkYePCgoiKCgoEyP37x5M15eXvTu3RsAb29vevbsyahRozKMS01NpX379gwbNowNGzZw48aNDO9/+OGHtGzZktGjR6dvK1eu3MOfiGQr7vkdGP2yL6/VKc2QX/ez/3wMQ387yNztZxn+QnXqeLsZHVFERERE8ihDC5i16tWrx+DBg1m2bBlBQUFcvnyZhQsX0rJlywzjhg8fTtGiRenWrRsbNmzI8F5aWhp//PEH77//Pi1atGD37t14e3szaNCguy5VvC0pKYmkpP9byhYTEwNAcnIyycnGfiHw7eMbnSO7eapEfhb2qMu8HecYt/oohy/G8uq3m3nBtwTvt6hI0QIORkc0jOaMWEtzRqylOSPW0pwRa2WnOWNNBpPFYskWj4szmUwsXrz4viUIYMGCBXTt2pXExERSUlIIDg7ml19+wc7ODoCNGzfy2muvERERgbu7O507d+bGjRvp95ddvHiREiVK4OTkxIgRI2jcuDHLly9n8ODBrFu3Dn9//7sed+jQoQwbNuyO7XPmzMHJyemRzl0ev7hk+OOMmc2XTVgw4WBjIahUGo2KW7DRo2hERERE5BEkJCTwxhtvEB0djYuLy33H5qgCdvDgQQIDA+nXrx8tWrQgKiqKgQMHUrt2baZOnUpsbCw+Pj5Mnjw5fWnjvwvYhQsXKFmyJK+//jpz5sxJ33fr1q1xdnbm559/vuux73YFzNPTk6tXrz7wh/y4JScns2rVKpo1a5ZeROXu9p2PZujvh9h77p8rmOWLOPPJ81V4tmzeWpaoOSPW0pwRa2nOiLU0Z8Ra2WnOxMTE4O7unqkClqOWII4cOZIGDRowcOBAAHx8fHB2dsbPz48RI0Zw6dIlTp06RXBwcPpn0tL+efCCra0tkZGReHp6YmtrS9WqVTPsu0qVKmzcuPGex3ZwcMDB4c4la3Z2dob/wm/LTlmyq6e93FnyTkMW7DzLqOWRHLsST4dpO2jlU4KPWlWhRMF8Rkd8ojRnxFqaM2ItzRmxluaMWCs7zBlrjp+jClhCQgK2thkj29jYAGCxWKhcuTL79u3L8P5HH31EbGwsX375JZ6entjb21O7dm0iIyMzjDty5AhlypR5vCcg2YLZbKJd7dI8V60EY1dFMmvLaf7YG8W6w5cJaVKBbg29sbfVukQRERERyXqGFrC4uDiOHTuW/vrkyZNERETg5uZG6dKlGTRoEOfPn2fGjBkABAcH0717d6ZMmZK+BLFv377UqVMHDw8PAKpXr57hGK6urndsHzhwIO3ataNRo0bp94D99ttvhIWFPd4TlmyloJMdw1+oTrvannzy6wF2nP6bUcsPs2DHWYa2rkajikWMjigiIiIiuYyh/5l/x44d1KxZk5o1awLQv39/atasyZAhQwCIiorK8OXInTt3Zty4cUycOJHq1avzyiuvUKlSJRYtWmTVcdu2bcs333zD6NGjeeqpp/jhhx/45ZdfaNiwYdadnOQY1TwKsqBXPca+4ot7fgdOXI2n44/b6DVzJ+f+TjA6noiIiIjkIoZeAQsICOB+zwCZPn36HdtCQkIICQnJ9DHutg+Arl270rVr10zvR3I3k8nES8+Uolm1YkxYdZSfNp9i+YGLhB25zLsB5eneqCyOdjZGxxQRERGRHE43uoj8DxdHO4YEV+WP3g2p6+1GYnIaY1cdocWE9aw9fMnoeCIiIiKSw6mAidxF5eIuzO3xLF++VoNiLg6cvpZA1+k7eOun7Zy5pmWJIiIiIvJwVMBE7sFkMvFCjZKsCQ2gZ6Oy2JpNrD50mcDx4YxbdYTE5FSjI4qIiIhIDqMCJvIA+R1sGdSyCsv7NqJheXdupaTx1ZqjBI4LZ8WBi/e9j1FERERE5H+pgIlkUvmi+ZnZrQ6T2z+NR0FHzv19k54zd9J52nZOXIkzOp6IiIiI5AAqYCJWMJlMtHyqBKtD/Xm3cTnsbcyEH7nCcxM2MHr5YRJupRgdUURERESyMRUwkYfgZG/LwBaVWdGvEQGVinArNY3JYcdpOjacP/ZGaVmiiIiIiNyVCpjII/B2d2Za59p81+EZShXKR1R0Iu/O2cWbU7dy7HKs0fFEREREJJtRARN5RCaTiebVirO6vz99mlbA3tbMpmPXeG7CBv677BBxSVqWKCIiIiL/UAETySKOdjb0a1aR1f38CaxSjJQ0C9+tP0GTMWH8GnFeyxJFRERERAVMJKuVLuzED51qMa1zbcoUduJybBJ95kbQ7rstHL4YY3Q8ERERETGQCpjIY9K4clFW9G3EgOYVcbQzs+3kdVp9tZFhvx0gJjHZ6HgiIiIiYgAVMJHHyNHOhveaVGB1f3+CqhcnNc3CtE2naDImjIU7z5GWpmWJIiIiInmJCpjIE1CqkBNT3nyGGV3rULaIM1fjbjFgwR5e+XYz+89HGx1PRERERJ4QFTCRJ6hRxSIs79OID4Iq42Rvw87Tf9N64kY+XrKf6AQtSxQRERHJ7VTARJ4we1szvfzLsSbUn+d9SpBmgZlbTtN4bBhzt53RskQRERGRXEwFTMQgJQrmY+IbTzOne10qFM3P9fhbfLBoH22n/MXeczeMjiciIiIij4EKmIjB6pdzZ1kfPz5qVYX8DrbsOXuDFyZtYtCivVyPv2V0PBERERHJQipgItmAnY2Zt/zKsjbUnxdrlsRigZ+3naXxmDBmbjlNqpYlioiIiOQKKmAi2UhRF0fGtavBgl71qFy8ANE3k/l4yX5emLSRnaf/NjqeiIiIiDwiFTCRbKi2lxu/hzRkWOtqFHC0Zf/5GF6a8hcDFuzhalyS0fFERERE5CGpgIlkU7Y2ZjrV92LdgABerVUKgIU7z9F4TBjTNp0kJTXN4IQiIiIiYi0VMJFszj2/A6Nf9mXRO/WpXtKF2MQUhv12kOe/3sjWE9eMjiciIiIiVlABE8khni5diF/fbchnbavj6mTH4YuxtPtuC33n7uZyTKLR8UREREQkE1TARHIQG7OJ9nXLsC40gNfrlMZkgiURF2gyNpwfNpwgWcsSRURERLI1FTCRHKiQsz0jX3yKX99tQA1PV+KSUhjxxyFafrmBv45fNTqeiIiIiNyDCphIDuZTypVFb9dn9Es+uDnbc/RyHG98v5V35+wiKvqm0fFERERE5F9UwERyOLPZxKu1PVkXGkCnemUwm+CPvVE0GRPO5LBj3ErRskQRERGR7EIFTCSXKOhkx7AXqvNbSENqlSnEzeRURi+P5LkJ61l/5IrR8UREREQEFTCRXKeaR0EW9KrHuFd9cc/vwImr8XT8cRs9Z+7g3N8JRscTERERydNUwERyIZPJxItPl2LtAH+6NvDGxmxixYFLBI4L5+s1R0lKTjU6ooiIiEiepAImkou5ONoxJLgqy3r7UdfbjcTkNMauOkLLiX9x4G+T0fFERERE8hwVMJE8oFLxAszt8SxfvV6TYi4OnLl+k+8O29Bj1i7OXNOyRBEREZEnRQVMJI8wmUy09vVgTWgAbzX0wmyysC7yKoHjwxm36gg3b2lZooiIiMjjpgImksfkd7DlPy0q8oFvKvXLuXErJY2v1hwlcFw4Kw5cxGKxGB1RREREJNdSARPJo4rlg+mdnmFK+6fxKOjI+Rs36TlzJ52nbefElTij44mIiIjkSipgInmYyWQi6KkSrA71573G5bG3MRN+5AotJqxn1PLDJNxKMTqiiIiISK6iAiYiONnbMqBFJVb0a0RApSIkp1qYEnacpmPD+WNvlJYlioiIiGQRFTARSeft7sy0zrX5rsMzlCqUj6joRN6ds4s3p27l2OVYo+OJiIiI5HgqYCKSgclkonm14qzu70+fphWwtzWz6dg1npuwgf8uO0RckpYlioiIiDwsFTARuStHOxv6NavI6n7+BFYpRkqahe/Wn6DJmDB+jTivZYkiIiIiD0EFTETuq3RhJ37oVItpnWtTprATl2OT6DM3gnbfbeHwxRij44mIiIjkKCpgIpIpjSsXZUXfRgxoXhFHOzPbTl6n1VcbGfbbAaJvJhsdT0RERCRHUAETkUxztLPhvSYVWBMaQFD14qSmWZi26RRNx4axcOc50tK0LFFERETkflTARMRqJV3zMeXNZ5jZrQ5lizhzNe4WAxbs4ZVvN7P/fLTR8URERESyLRUwEXlofhWKsLxPIz4IqoyTvQ07T/9N64kb+XjJfm4k3DI6noiIiEi2owImIo/E3tZML/9yrA0NINjXgzQLzNxymiZjw5m77YyWJYqIiIj8DxUwEckSxQs68vXrNZnTvS4ViubnevwtPli0j7aTN7Hn7A2j44mIiIhkCypgIpKl6pdzZ1kfPz5qVYX8DrbsORdNm8mbGLRoL9fjtSxRRERE8jYVMBHJcnY2Zt7yK8vaAf68WLMkFgv8vO0sjceEMXPLaVK1LFFERETyKEML2Pr16wkODsbDwwOTycSSJUse+JnZs2fj6+uLk5MTJUqUoGvXrly7du2uY+fOnYvJZKJNmzb33F+vXr0wmUxMmDDh4U5CRO6paAFHxrWrwYJe9ahcvADRN5P5eMl+Wk/cyM7TfxsdT0REROSJM7SAxcfH4+vry6RJkzI1ftOmTXTs2JFu3bpx4MABFixYwLZt2+jevfsdY0+dOsWAAQPw8/O75/4WL17Mli1b8PDweOhzEJEHq+3lxu8hDRnWuhoFHG05cCGGl6b8xYAFe7gSm2R0PBEREZEnxtbIgwcFBREUFJTp8Zs3b8bLy4vevXsD4O3tTc+ePRk1alSGcampqbRv355hw4axYcMGbty4cce+zp8/T0hICCtWrKBVq1YPPHZSUhJJSf/3D8WYmBgAkpOTSU5OzvQ5PA63j290Dsk5jJozb9QuSYsq7oxZdYyFu86zcOc5Vhy4SJ8m5WhfxxNbG62Kzq7094xYS3NGrKU5I9bKTnPGmgyGFjBr1atXj8GDB7Ns2TKCgoK4fPkyCxcupGXLlhnGDR8+nKJFi9KtWzc2bNhwx37S0tLo0KEDAwcOpFq1apk69siRIxk2bNgd21euXImTk9PDnVAWW7VqldERJIcxas74OYBndVhw0oZz8SmMWBbJ1LDDvOydSnkXQyJJJunvGbGW5oxYS3NGrJUd5kxCQkKmx+aoAtagQQNmz55Nu3btSExMJCUlheDg4AxLGDdu3MjUqVOJiIi4535GjRqFra1t+pW0zBg0aBD9+/dPfx0TE4OnpyfNmzfHxcXYfzEmJyezatUqmjVrhp2dnaFZJGfILnOmZ5qF+TvPMW7VMaISkvn6gC2tfUrwfosKFHNxNCyX3Cm7zBnJOTRnxFqaM2Kt7DRnbq+Oy4wcVcAOHjxInz59GDJkCC1atCAqKoqBAwfSq1cvpk6dSmxsLB06dOD777/H3d39rvvYuXMnX375Jbt27cJkMmX62A4ODjg4ONyx3c7OzvBf+G3ZKYvkDEbPGTugY/2yBPuW4ouVkfy87QxL90ax5vBl+gZWpHMDL+y0LDFbMXrOSM6jOSPW0pwRa2WHOWPN8XNUARs5ciQNGjRg4MCBAPj4+ODs7Iyfnx8jRozg0qVLnDp1iuDg4PTPpKWlAWBra0tkZCQbNmzg8uXLlC5dOn1MamoqoaGhTJgwgVOnTj3RcxIRKORsz3/bPsVrtT0Z8usBIs7e4LNlh5i34yzDW1ejfvm7/wcVERERkZwmRxWwhIQEbG0zRraxsQHAYrFQuXJl9u3bl+H9jz76iNjYWL788ks8PT3p0KEDgYGBGca0aNGCDh060KVLl8d7AiJyXz6lXFn0dn0W7jzH58sPc+xyHG/8sJVWPiX4qFUVShTMZ3REERERkUdiaAGLi4vj2LFj6a9PnjxJREQEbm5ulC5dmkGDBnH+/HlmzJgBQHBwMN27d2fKlCnpSxD79u1LnTp10h8lX7169QzHcHV1zbC9cOHCFC5cOMMYOzs7ihcvTqVKlR7XqYpIJpnNJl6t7UmLasUZtyqSmVtO88feKNYeukxI0/J0a+iNg62N0TFFREREHoqhN1fs2LGDmjVrUrNmTQD69+9PzZo1GTJkCABRUVGcOXMmfXznzp0ZN24cEydOpHr16rzyyitUqlSJRYsWGZJfRB6fgk52DHuhOr+FNKRWmULcTE5l9PJIgiZsIPzIFaPjiYiIiDwUQ6+ABQQEYLFY7vn+9OnT79gWEhJCSEhIpo9xt338m+77Esm+qnkUZEGveizefZ7/LjvMiavxdPpxGy2qFeOjVlXxdMseXwMhIiIikhl6vJiIZHsmk4kXny7F2gH+dGvojY3ZxIoDlwgcF85Xa46SmJxqdEQRERGRTFEBE5Ecw8XRjo+fr8qy3n7U9XYjKSWNcauO0Hz8etYcumR0PBEREZEHUgETkRynUvECzO3xLF+9XpNiLg6cuZ5At5920G36dk5fizc6noiIiMg9qYCJSI5kMplo7evBmtAAevqXxdZsYs3hyzQbv55xKyO5eUvLEkVERCT7UQETkRwtv4Mtg4KqsLxvIxqWd+dWShpfrT1G4LhwVhy4eN8H/YiIiIg8aSpgIpIrlC+an5nd6jCl/dN4FHTk/I2b9Jy5k07TtnPiSpzR8UREREQAFTARyUVMJhNBT5Vgdag/7zUuj72NmfVHrtBiwnpGLT9Mwq0UoyOKiIhIHqcCJiK5jpO9LQNaVGJFv0YEVCpCcqqFKWHHaTo2nN/3XtCyRBERETGMCpiI5Fre7s5M61yb7zvWolShfERFJ/LenN28OXUrRy/FGh1PRERE8iAVMBHJ1UwmE82qFmN1f3/6NK2Ava2ZTceuEfTlBj774yBxSVqWKCIiIk+OCpiI5AmOdjb0a1aR1f38CaxSjJQ0C99vOEmTMWH8GnFeyxJFRETkiVABE5E8pXRhJ37oVItpnWvjVdiJy7FJ9JkbQbvvtnD4YozR8URERCSXUwETkTypceWiLO/biIEtKuFoZ2bbyeu0+mojQ5ceIPpmstHxREREJJdSARORPMvRzoZ3G5dnTWgAQdWLk5pmYfpfp2g6NoyFO8+RlqZliSIiIpK1VMBEJM8r6ZqPKW8+w8xudShbxJmrcbcYsGAPL3/zF/vPRxsdT0RERHIRFTARkf/Pr0IRlvdpxKCgyjjZ27DrzA1aT9zIR0v2cSPhltHxREREJBdQARMR+R/2tmZ6+pdjbWgAwb4epFlg1pYzNB4Txs/bzmhZooiIiDwSFTARkbsoXtCRr1+vyc/dn6Visfz8nZDMoEX7aDt5E3vO3jA6noiIiORQKmAiIvdRr1xh/ujtx0etqpDfwZY956JpM3kTH/yyl+vxWpYoIiIi1lEBExF5ADsbM2/5lWXtAH9erFkSiwXmbj9L4zFhzNxymlQtSxQREZFMUgETEcmkogUcGdeuBgt61aNKCReibybz8ZL9tJ64kZ2n/zY6noiIiOQAKmAiIlaq7eXGb+81YPgL1XBxtOXAhRhemvIXofP3cCU2yeh4IiIiko2pgImIPARbGzMd63mxdkAAr9YqBcAvu87RZGwY0zadJCU1zeCEIiIikh2pgImIPAL3/A6MftmXxe/U56mSBYlNTGHYbwd5/uuNbD1xzeh4IiIiks2ogImIZIGapQux5N0GfNa2Oq5Odhy+GEu777bQd+5uLsUkGh1PREREsgkVMBGRLGJjNtG+bhnWhQbwRt3SmEywJOICTcaE8f36EyRrWaKIiEiepwImIpLFCjnb89+2T/Hruw2o4elK/K1UPlt2iKAvN/DXsatGxxMREREDqYCJiDwmPqVcWfR2fUa/5IObsz3HLsfxxg9beXfOLi7cuGl0PBERETGACpiIyGNkNpt4tbYn60ID6FSvDGYT/LE3iqZjw5kcdoyklFSjI4qIiMgTpAImIvIEFHSyY9gL1fk9xI/aXoW4mZzK6OWRBE3YQPiRK0bHExERkSdEBUxE5Amq6uHC/J71GPeqL+75HThxNZ5OP26jx4wdnL2eYHQ8ERERecxUwEREnjCTycSLT5di3QB/ujX0xsZsYuXBSwSOC+erNUdJTNayRBERkdxKBUxExCAFHO34+PmqLOvtR11vN5JS0hi36gjNx69nzaFLRscTERGRx0AFTETEYJWKF2Buj2f56vWaFHNx4Mz1BLr9tINu07dz+lq80fFEREQkC6mAiYhkAyaTida+HqwNDaCnf1lszSbWHL5Ms/HrGbcykpu3tCxRREQkN1ABExHJRpwdbBkUVIXlfRvRsLw7t1LS+GrtMQLHhbN8/0UsFovREUVEROQRqICJiGRD5YvmZ2a3Okxp/zQeBR05f+MmvWbtpNO07Zy4Emd0PBEREXlIKmAiItmUyWQi6KkSrA71573G5bG3MbP+yBVaTFjPqOWHSbiVYnREERERsZIKmIhINudkb8uAFpVY0a8RAZWKkJxqYUrYcZqODef3vRe0LFFERCQHUQETEckhvN2dmda5Nt93rIWnWz6iohN5b85u2v+wlaOXYo2OJyIiIpmgAiYikoOYTCaaVS3Gqn7+9A2sgIOtmb+OXyPoyw189sdBYhOTjY4oIiIi96ECJiKSAzna2dA3sCKr+/sTWKUYKWkWvt9wkqZjw1my+7yWJYqIiGRTKmAiIjmYp5sTP3SqxbTOtfEq7MTl2CT6zoug3XdbOHwxxuh4IiIi8i8qYCIiuUDjykVZ0a8RA1tUwtHOzLaT12n11UaGLj1A9E0tSxQREckuVMBERHIJB1sb3m1cnjWhAbR8qjipaRam/3WKpmPDWLDjLGlpWpYoIiJiNBUwEZFcpqRrPia3f4aZ3epQtogzV+NuMXDhXl7+5i/2n482Op6IiEiepgImIpJL+VUowvI+jRgUVBknext2nblB8MSNfLRkHzcSbhkdT0REJE9SARMRycXsbc309C/H2tAAWvt6YLHArC1naDwmjJ+3ndGyRBERkSdMBUxEJA8oXtCRr16vyc/dn6Visfz8nZDMoEX7aDt5ExFnbxgdT0REJM8wtICtX7+e4OBgPDw8MJlMLFmy5IGfmT17Nr6+vjg5OVGiRAm6du3KtWvX7jp27ty5mEwm2rRpk74tOTmZ//znPzz11FM4Ozvj4eFBx44duXDhQhadlYhI9lWvXGH+6O3Hx89XpYCDLXvORdN28iY++GUv1+O1LFFERORxM7SAxcfH4+vry6RJkzI1ftOmTXTs2JFu3bpx4MABFixYwLZt2+jevfsdY0+dOsWAAQPw8/PLsD0hIYFdu3bx8ccfs2vXLhYtWkRkZCStW7fOknMSEcnu7GzMdGvozZoB/rxYsyQWC8zdfpbGY8KYufkUqVqWKCIi8tjYGnnwoKAggoKCMj1+8+bNeHl50bt3bwC8vb3p2bMno0aNyjAuNTWV9u3bM2zYMDZs2MCNGzfS3ytYsCCrVq3KMH7ixInUqVOHM2fOULp06Yc/IRGRHKRoAUfGtavB63VLM+TXAxyKiuHjXw8wd/tZhr9QnWfKFDI6ooiISK5jaAGzVr169Rg8eDDLli0jKCiIy5cvs3DhQlq2bJlh3PDhwylatCjdunVjw4YND9xvdHQ0JpMJV1fXe45JSkoiKSkp/XVMTAzwz5LG5GRjv+T09vGNziE5h+aM/K8aJQuwqGcd5u44x/jVxzhwIYaXpvxF25oevN+8Au75HTRnxGqaM2ItzRmxVnaaM9ZkMFkslmyx1sRkMrF48eIM92vdzYIFC+jatSuJiYmkpKQQHBzML7/8gp2dHQAbN27ktddeIyIiAnd3dzp37syNGzfueX9ZYmIiDRo0oHLlysyePfuexx06dCjDhg27Y/ucOXNwcnLK9HmKiGRnscnw+xkzWy7/s0Ld0cZCkGcafsUt2JgMDiciIpJNJSQk8MYbbxAdHY2Li8t9x+aoAnbw4EECAwPp168fLVq0ICoqioEDB1K7dm2mTp1KbGwsPj4+TJ48OX1p4/0KWHJyMi+99BLnzp0jLCzsvj+su10B8/T05OrVqw/8IT9uycnJrFq1imbNmqUXUZH70ZyRB4k4e4Nhvx9m/4V/rvZXLOpMiyLRvP2S5oxkjv6eEWtpzoi1stOciYmJwd3dPVMFLEctQRw5ciQNGjRg4MCBAPj4+ODs7Iyfnx8jRozg0qVLnDp1iuDg4PTPpKWlAWBra0tkZCTlypUD/vmFvfrqq5w+fZq1a9c+8Afl4OCAg4PDHdvt7OwM/4Xflp2ySM6gOSP3UrtsEX59z52528/wxYpIjlyO58hlW07ZHOKj56tRzMXR6IiSQ+jvGbGW5oxYKzvMGWuOn6MKWEJCAra2GSPb2NgAYLFYqFy5Mvv27cvw/kcffURsbCxffvklnp6ewP+Vr6NHj7Ju3ToKFy78ZE5ARCQHsTGbaF+3DC2rl2DU8kPM236W3/ZeZO3hK/QJrECXBt7Y2ejrJEVERKxhaAGLi4vj2LFj6a9PnjxJREQEbm5ulC5dmkGDBnH+/HlmzJgBQHBwMN27d2fKlCnpSxD79u1LnTp18PDwAKB69eoZjnH7wRq3tycnJ/Pyyy+za9cufv/9d1JTU7l48SIAbm5u2NvbP+7TFhHJUQo52/Np66qUSjzFqr8Ls+dcNP9ddpj5O84xrHU1GpR3NzqiiIhIjmFoAduxYweNGzdOf92/f38AOnXqxPTp04mKiuLMmTPp73fu3JnY2FgmTpxIaGgorq6uNGnS5I7H0N/P+fPnWbp0KQA1atTI8N66desICAh4+BMSEcnFPPPD/Jfr8Ou+S4z68zDHLsfR/oettHqqBB+2qoKHaz6jI4qIiGR7hhawgIAA7vcMkOnTp9+xLSQkhJCQkEwf49/78PLyuu8xRUTk3sxmE6/W8qRF1eKMWxXJzC2n+WNfFGsPXyakaXm6NfTGwdbG6JgiIiLZlhbvi4iI1Qo62THsher8HuJHba9C3ExOZfTySJ6bsIHwI1eMjiciIpJtqYCJiMhDq+rhwvye9RjfzpciBRw4eTWeTj9uo8eMHZy9nmB0PBERkWxHBUxERB6JyWSibc1SrA31562G3tiYTaw8eInAceF8ufooicmpRkcUERHJNlTAREQkSxRwtOOj56vyZx8/ni3rRlJKGuNXH6H5+PWsOXTJ6HgiIiLZggqYiIhkqYrFCvBz92f56vWaFHNx4Mz1BLr9tIOu07dz+lq80fFEREQMpQImIiJZzmQy0drXg7WhAfT0L4udjYm1hy/TbPx6xq2M5OYtLUsUEZG8yeoCdvbsWc6dO5f+etu2bfTt25fvvvsuS4OJiEjO5+xgy6CgKvzZpxF+Fdy5lZLGV2uPETgunOX7L+prQUREJM+xuoC98cYbrFu3DoCLFy/SrFkztm3bxocffsjw4cOzPKCIiOR85YvmZ0bXOnzz5tOUdM3H+Rs36TVrJx1/3MaJK3FGxxMREXlirC5g+/fvp06dOgDMnz+f6tWr89dffzF79uy7fnGyiIgI/LMs8bnqJVjd35/3GpfH3sbMhqNXaTFhPaOWHyY+KcXoiCIiIo+d1QUsOTkZBwcHAFavXk3r1q0BqFy5MlFRUVmbTkREcp189jYMaFGJlf0a0bhSEZJTLUwJO07guHB+33tByxJFRCRXs7qAVatWjW+++YYNGzawatUqnnvuOQAuXLhA4cKFszygiIjkTl7uzvzYuTY/dKyFp1s+oqITeW/Obtr/sJWjl2KNjiciIvJYWF3ARo0axbfffktAQACvv/46vr6+ACxdujR9aaKIiEhmmEwmAqsWY1U/f/oGVsDB1sxfx68R9OUGRvx+kNjEZKMjioiIZClbaz8QEBDA1atXiYmJoVChQunbe/TogZOTU5aGExGRvMHRzoa+gRV56elSDP/9IKsOXuKHjSdZuucCg1tW4YUaHphMJqNjioiIPDKrr4DdvHmTpKSk9PJ1+vRpJkyYQGRkJEWLFs3ygCIiknd4ujnxfcdaTOtSG6/CTlyOTaLvvAjafbuFQ1ExRscTERF5ZFYXsBdeeIEZM2YAcOPGDerWrcvYsWNp06YNU6ZMyfKAIiKS9zSuVJQV/RoxsEUlHO3MbDt1nee/3sjQpQeIvqlliSIiknNZXcB27dqFn58fAAsXLqRYsWKcPn2aGTNm8NVXX2V5QBERyZscbG14t3F51oQG0PKp4qSmWZj+1ymajg1jwY6zpKXpaYkiIpLzWF3AEhISKFCgAAArV67kxRdfxGw28+yzz3L69OksDygiInlbSdd8TG7/DDO71aFcEWeuxt1i4MK9vPzNX+w/H210PBEREatYXcDKly/PkiVLOHv2LCtWrKB58+YAXL58GRcXlywPKCIiAuBXoQh/9mnEoKDKONnbsOvMDYInbuSjJfu4kXDL6HgiIiKZYnUBGzJkCAMGDMDLy4s6depQr1494J+rYTVr1szygCIiIrfZ25rp6V+OtaEBtPb1wGKBWVvO0HhMGD9vO6NliSIiku1ZXcBefvllzpw5w44dO1ixYkX69qZNmzJ+/PgsDSciInI3xQs68tXrNfm5+7NULJafvxOSGbRoH20nbyLi7A2j44mIiNyT1QUMoHjx4tSsWZMLFy5w7tw5AOrUqUPlypWzNJyIiMj91CtXmD96+/Hx81Up4GDLnnPRtJ28iQ9+2cu1uCSj44mIiNzB6gKWlpbG8OHDKViwIGXKlKFMmTK4urry6aefkpaW9jgyioiI3JOdjZluDb1ZM8CfF58uicUCc7efpcnYcGZuPkWqliWKiEg2YnUB+/DDD5k4cSKff/45u3fvZvfu3fz3v//l66+/5uOPP34cGUVERB6oaAFHxr1ag4W96lGlhAvRN5P5+NcDBH+9kZ2nrxsdT0REBABbaz/w008/8cMPP9C6dev0bT4+PpQsWZJ33nmHzz77LEsDioiIWKOWlxu/vdeAOdvOMGZFJAejYnhpymZeeroUHwRVpkgBB6MjiohIHmb1FbDr16/f9V6vypUrc/26/gujiIgYz9bGTMd6XqwbEEC7Wp4A/LLrHE3GhPHjxpOkpGrJvIiIGMPqAubr68vEiRPv2D5x4kR8fX2zJJSIiEhWKJzfgVEv+7D4nfo8VbIgsUkpDP/9IK2+2sjWE9eMjiciInmQ1UsQR48eTatWrVi9enX6d4Bt3ryZs2fPsmzZsiwPKCIi8qhqli7EkncbMG/7WUavOEzkpVjafbeFF2p4MLhlFYq5OBodUURE8girr4D5+/tz5MgR2rZty40bN7hx4wYvvvgikZGR+Pn5PY6MIiIij8zGbOKNuqVZFxpA+7qlMZng14gLNBkTxnfrj5OsZYkiIvIEWH0FDMDDw+OOh22cO3eOHj168N1332VJMBERkcehkLM9n7V9itdql+bjX/cTcfYG/112mPk7zjGsdTUalHc3OqKIiORiD/VFzHdz7do1pk6dmlW7ExEReayeKlWQRW/XZ/TLPhR2tufY5Tja/7CVd2fv4sKNm0bHExGRXCrLCpiIiEhOYzabeLWWJ2tDA+hc3wuzCf7YF0XTseFMWneMpJRUoyOKiEguowImIiJ5XkEnO4a2rsbvIX7U9irEzeRUvlgRyXMTNhAWednoeCIikouogImIiPx/VT1cmN+zHuPb+VKkgAMnr8bTedp2eszYwdnrCUbHExGRXCDTD+F48cUX7/v+jRs3HjWLiIiI4UwmE21rliKwSjG+XH2UaX+dYuXBS4QfucI7AeXp6V8WRzsbo2OKiEgOlekCVrBgwQe+37Fjx0cOJCIikh0UcLTjo+er8mptT4b8up8tJ64zfvURftl1jiHPVyWwajGjI4qISA6U6QI2bdq0x5lDREQkW6pYrAA/d3+W3/dG8dkfhzhzPYG3ZuygSeWifBJclTKFnY2OKCIiOYjuARMREXkAk8lEsK8Ha0L96elfFjsbE2sPX6bZuPWMXRnJzVt6WqKIiGSOCpiIiEgmOTvYMiioCn/2aYRfBXdupabx9dpjBI4LZ/n+KCwWi9ERRUQkm1MBExERsVL5ovmZ0bUO37z5NCVd83H+xk16zdpFxx+3cfxKnNHxREQkG1MBExEReQgmk4nnqpdgdX9/QpqUx97GzIajV3luwno+//Mw8UkpRkcUEZFsSAVMRETkEeSztyG0eSVW9mtE40pFSE618E34cQLHhfP73gtaligiIhlk6imIS5cuzfQOW7du/dBhREREciovd2d+7FybNYcuM+z3A5y9fpP35uxmTrkzDGtdjQrFChgdUUREsoFMFbA2bdpkamcmk4nUVD0JSkRE8iaTyURg1WI0rODOt+EnmBx2jL+OXyPoyw10ru9Fn8AKFHC0MzqmiIgYKFNLENPS0jL1R+VLREQEHO1s6BNYgdX9/WlWtRgpaRZ+2HiSJmPDWbz7nJYliojkYboHTERE5DHxdHPi+461mNalNl6FnbgSm0S/eXto9+0WDkXFGB1PREQMkKkliP8WHx9PeHg4Z86c4datWxne6927d5YEExERyS0aVypK/X6F+WHDSb5ee5Rtp67T6qsNdKznRb9mFSmYT8sSRUTyCqsL2O7du2nZsiUJCQnEx8fj5ubG1atXcXJyomjRoipgIiIid+Fga8O7jcvTpmZJPvvjIMv2XWT6X6f4bc8F/hNUmZefLoXZbDI6poiIPGZWL0Hs168fwcHB/P333+TLl48tW7Zw+vRpnnnmGcaMGfM4MoqIiOQaJV3zMbn9M8zqVpdyRZy5Fn+L9xfu5aVv/mL/+Wij44mIyGNmdQGLiIggNDQUs9mMjY0NSUlJeHp6Mnr0aAYPHvw4MoqIiOQ6DSu482efRgxuWRlnext2n7lB8MSNfLh4HzcSbj14ByIikiNZXcDs7Owwm//5WNGiRTlz5gwABQsW5OzZs1bta/369QQHB+Ph4YHJZGLJkiUP/Mzs2bPx9fXFycmJEiVK0LVrV65du3bXsXPnzsVkMt3xGH2LxcKQIUMoUaIE+fLlIzAwkKNHj1qVXURE5FHZ25rp0agca0IDaO3rgcUCs7eeofGYMH7edobUND0tUUQkt7G6gNWsWZPt27cD4O/vz5AhQ5g9ezZ9+/alevXqVu0rPj4eX19fJk2alKnxmzZtomPHjnTr1o0DBw6wYMECtm3bRvfu3e8Ye+rUKQYMGICfn98d740ePZqvvvqKb775hq1bt+Ls7EyLFi1ITEy0Kr+IiEhWKF7Qka9er8nP3Z+lYrH8/J2QzKBF+2g7eRMRZ28YHU9ERLKQ1QXsv//9LyVKlADgs88+o1ChQrz99ttcuXKFb7/91qp9BQUFMWLECNq2bZup8Zs3b8bLy4vevXvj7e1Nw4YN6dmzJ9u2bcswLjU1lfbt2zNs2DDKli2b4T2LxcKECRP46KOPeOGFF/Dx8WHGjBlcuHAhU1fgREREHpd65QrzR28/Pn6+KgUcbNl7Lpq2kzfxwS97uRaXZHQ8ERHJAlY/BbFWrVrp/3fRokVZvnx5lga6n3r16jF48GCWLVtGUFAQly9fZuHChbRs2TLDuOHDh1O0aFG6devGhg0bMrx38uRJLl68SGBgYPq2ggULUrduXTZv3sxrr71212MnJSWRlPR//+MXE/PP97ckJyeTnJycVaf4UG4f3+gcknNozoi1NGeerI51SxFUtQhfrDzC4ogo5m4/y7J9UfQLLM/rtT2xyQFPS9ScEWtpzoi1stOcsSaD1QWsSZMmLFq0CFdX1wzbY2JiaNOmDWvXrrV2l5nWoEEDZs+eTbt27UhMTCQlJYXg4OAMSxg3btzI1KlTiYiIuOs+Ll68CECxYsUybC9WrFj6e3czcuRIhg0bdsf2lStX4uTk9BBnk/VWrVpldATJYTRnxFqaM09WQD4oXQ0WnrThfEIKw34/zA9rD/FK2VS8CxidLnM0Z8RamjNirewwZxISEjI91uoCFhYWdseXLwMkJibecbUpqx08eJA+ffowZMgQWrRoQVRUFAMHDqRXr15MnTqV2NhYOnTowPfff4+7u3uWHnvQoEH0798//XVMTAyenp40b94cFxeXLD2WtZKTk1m1ahXNmjXDzk5f5ikPpjkj1tKcMdbbaRZ+3n6W8auPcT4hhQn7bWlbowTvt6iIe34Ho+PdleaMWEtzRqyVnebM7dVxmZHpArZ37970//vgwYMZrhalpqayfPlySpYsmekDP4yRI0fSoEEDBg4cCICPjw/Ozs74+fkxYsQILl26xKlTpwgODk7/TFpaGgC2trZERkZSvHhxAC5dupR+L9vt1zVq1LjnsR0cHHBwuPN/5Ozs7Az/hd+WnbJIzqA5I9bSnDGGHdClYTla1yjF6OWRzNtxlsURUaw+dIV+zSrSsV4ZbG2svq37idCcEWtpzoi1ssOcseb4mS5gNWrUwGQyYTKZaNKkyR3v58uXj6+//jrTB34YCQkJ2NpmjGxjYwP883CNypUrs2/fvgzvf/TRR8TGxvLll1/i6emJnZ0dxYsXZ82aNemFKyYmhq1bt/L2228/1vwiIiKPonB+B0a97MPrdUsz5Nf97D0XzfDfDzJv+1mGvVCNZ8sWNjqiiIg8QKYL2MmTJ7FYLJQtW5Zt27ZRpEiR9Pfs7e0pWrRoehnKrLi4OI4dO5bhGBEREbi5uVG6dGkGDRrE+fPnmTFjBgDBwcF0796dKVOmpC9B7Nu3L3Xq1MHDwwPgjkfh375X7X+39+3blxEjRlChQgW8vb35+OOP8fDwuOP7wkRERLKjGp6uLH6nAfO2n2X0isNEXorlte+20NrXgw9bVaGYi6PREUVE5B4yXcDKlCkD/N+SvqywY8cOGjdunP769j1WnTp1Yvr06URFRaV/0TNA586diY2NZeLEiYSGhuLq6kqTJk0YNWqUVcd9//33iY+Pp0ePHty4cYOGDRuyfPlyHB31P1giIpIz2JhNvFG3NEHVizNmZSRztp1h6Z4LrDl0iT6BFehc3xt72+y5LFFEJC+z+iEcAMePH2fChAkcOnQIgKpVq9KnTx/KlStn1X4CAgKwWCz3fH/69Ol3bAsJCSEkJCTTx7jbPkwmE8OHD2f48OGZ3o+IiEh2VMjZns/aPsVrtUvz8a/7iTh7g/8uO8y87WcZ/kJ1GpTP2odSiYjIo7H6P42tWLGCqlWrsm3bNnx8fPDx8WHr1q1Uq1YtWzwCUkREJC96qlRBFr1dn9Ev+1DY2Z7jV+Jp/8NW3p29iws3bhodT0RE/j+rr4B98MEH9OvXj88///yO7f/5z39o1qxZloUTERGRzDObTbxay5MW1YozftURZmw+xR/7olh7+DLvNSnPW37eONhad7+2iIhkLauvgB06dIhu3brdsb1r164cPHgwS0KJiIjIwyuYz46hravxe4gftb0KcTM5lS9WRPLchA2ERV42Op6ISJ5mdQErUqQIERERd2yPiIigaNGiWZFJREREskBVDxfm96zH+Ha+FCngwMmr8XSetp3uM3Zw9nqC0fFERPKkTC9BHD58OAMGDKB79+706NGDEydOUL9+fQA2bdrEqFGj0p9iKCIiItmDyWSibc1SBFYpxperjzLtr1OsOniJ9Ueu8E5AeXr6l8XRTssSRUSelEwXsGHDhtGrVy8+/vhjChQowNixYxk0aBAAHh4eDB06lN69ez+2oCIiIvLwCjja8dHzVXm1tief/HqAzSeuMX71EX7ZdY4hz1clsGoxoyOKiOQJmV6CePtx8SaTiX79+nHu3Dmio6OJjo7m3Llz9OnTB5PJ9NiCioiIyKOrWKwAc7rX5evXa1LcxZEz1xN4a8YOuk7fzqmr8UbHExHJ9ay6B+zfBatAgQIUKFAgSwOJiIjI42UymQj29WBNqD+9/MthZ2Ni7eHLNB+/nrErI7l5K9XoiCIiuZZVj6GvWLHiA69yXb9+/ZECiYiIyJPh7GDLB0GVeaVWKYYuPcCGo1f5eu0xFu06z8fPV6FFteJa3SIiksWsKmDDhg2jYMGCjyuLiIiIGKBckfzM6FqHFQcu8unvhzh/4ya9Zu3Cr4I7Q1tXo1yR/EZHFBHJNawqYK+99poeNS8iIpILmUwmnqteAv+KRZkcdoxvw0+w4ehVnpuwnm4NyxLSpDzODlb9s0FERO4i0/eAaQmCiIhI7pfP3obQ5pVY2a8RjSsVITnVwjfhx2k6Npzf9lxIfyiXiIg8HKufgigiIiK5n5e7M9O61OGHjrXwdMvHxZhEQn7ezRvfb+XIpVij44mI5FiZLmBpaWlafigiIpLHBFYtxqp+/vQLrIiDrZnNJ67R8ssNjPj9ILGJyUbHExHJcax6DL2IiIjkPY52NvQJrMDq/v40r1qMlDQLP2w8SZOx4SzefU6rZERErKACJiIiIpni6ebEdx1rMb1LbbwKO3ElNol+8/bw6rebOXghxuh4IiI5ggqYiIiIWCWgUlFW9GvEwBaVyGdnw/ZTf/P81xsYuvQA0Te1LFFE5H5UwERERMRqDrY2vNu4PKtD/Wn5VHHSLDD9r1M0GRPG/B1nSUvTskQRkbtRARMREZGHVtI1H5PbP8OsbnUpV8SZa/G3eH/hXtr9sI2zcUanExHJflTARERE5JE1rODOn30aMbhlZZztbYg4G83YfTYMWXqQGwm3jI4nIpJtqICJiIhIlrC3NdOjUTnWhAYQ7FMcCyZ+3n6OxmPCmLP1DKlaligiogImIiIiWat4QUfGveJDSNUUKhbNz98JyQxevI+2kzcRcfaG0fFERAylAiYiIiKPRfmCsOSdZxnyfFUKONiy91w0bSZt4j8L93ItLsnoeCIihlABExERkcfGzsZM14berBngz0tPlwJg3o6zNB4TxozNp7QsUUTyHBUwEREReeyKFnBk7Ku+LOxVj6olXIhJTGHIrwcI/nojO05dNzqeiMgTowImIiIiT0wtLzd+C2nIpy9Uw8XRloNRMbz8zWb6z4/gcmyi0fFERB47FTARERF5omzMJjrU82LdgADa1fIEYNGu8zQdE87UjSdJSU0zOKGIyOOjAiYiIiKGKJzfgVEv+7Dk3Qb4lCpIbFIKn/5+kFZfbWTLiWtGxxMReSxUwERERMRQNTxdWfxOA0a++BSFnOyIvBTLa99toffPu7kUo2WJIpK7qICJiIiI4WzMJl6vU5q1oQG0r1sakwmW7rlAkzFhfBt+nFspWpYoIrmDCpiIiIhkG4Wc7fms7VP89l5DapZ2Jf5WKiP/PEzQl+vZePSq0fFERB6ZCpiIiIhkO9VLFuSXXvUZ/bIPhZ3tOX4lnjenbuWd2Tu5cOOm0fFERB6aCpiIiIhkS2aziVdrebJ2QACd63thNsGyfRdpOjacSeuOkZSSanREERGrqYCJiIhItlYwnx1DW1fjj95+1PYqxM3kVL5YEclzEzYQFnnZ6HgiIlZRARMREZEcoUoJF+b3rMeEdjUoUsCBk1fj6TxtO91n7ODs9QSj44mIZIoKmIiIiOQYJpOJNjVLsjbUn7caemNjNrHq4CUCx4UzYfUREpO1LFFEsjcVMBEREclxCjja8dHzVfmzjx/1yhYmKSWNCauP0mx8OKsPXjI6nojIPamAiYiISI5VsVgB5nSvy8Q3alLcxZGz12/y1owddJ2+nVNX442OJyJyBxUwERERydFMJhPP+3iwJtSfXv7lsLMxsfbwZZqPX8+YFZHcvKVliSKSfaiAiYiISK7g7GDLB0GVWd63EX4V3LmVmsbEdccIHBfO8v1RWCwWoyOKiKiAiYiISO5Srkh+ZnStwzdvPk1J13ycv3GTXrN20fHHbRy/Emd0PBHJ41TAREREJNcxmUw8V70Eq/v7E9KkPPY2ZjYcvcpzE9bz+Z+HiU9KMTqiiORRKmAiIiKSa+WztyG0eSVW9mtEk8pFSU618E34cZqODee3PRe0LFFEnjgVMBEREcn1vNyd+bFzbX7oWAtPt3xcjEkk5OfdvPH9Vo5cijU6nojkISpgIiIikmcEVi3Gqn7+9AusiIOtmc0nrhH05QY+/f0gsYnJRscTkTxABUxERETyFEc7G/oEVmB1f3+aVy1GapqFqRtP0mRsOIt3n9OyRBF5rFTAREREJE/ydHPiu461mN6lNt7uzlyJTaLfvD28+u1mDl6IMTqeiORSKmAiIiKSpwVUKsryvn4MbFGJfHY2bD/1N89/vYGhSw8QfVPLEkUka6mAiYiISJ7nYGvDu43LszrUn1ZPlSDNAtP/OkWTMWHM33GWtDQtSxSRrGFoAVu/fj3BwcF4eHhgMplYsmTJAz8ze/ZsfH19cXJyokSJEnTt2pVr166lv79o0SJq1aqFq6srzs7O1KhRg5kzZ2bYR1xcHO+99x6lSpUiX758VK1alW+++SarT09ERERymJKu+ZjU/mlmdatLuSLOXIu/xfsL9/LSN3+x71y00fFEJBcwtIDFx8fj6+vLpEmTMjV+06ZNdOzYkW7dunHgwAEWLFjAtm3b6N69e/oYNzc3PvzwQzZv3szevXvp0qULXbp0YcWKFelj+vfvz/Lly5k1axaHDh2ib9++vPfeeyxdujTLz1FERERynoYV3PmzTyMGt6yMs70Nu8/coPWkjXy4eB9/x98yOp6I5GCGFrCgoCBGjBhB27ZtMzV+8+bNeHl50bt3b7y9vWnYsCE9e/Zk27Zt6WMCAgJo27YtVapUoVy5cvTp0wcfHx82btyYPuavv/6iU6dOBAQE4OXlRY8ePfD19c2wHxEREcnb7G3N9GhUjrUDAnihhgcWC8zeeobGY8OYs/UMqVqWKCIPwdboANaoV68egwcPZtmyZQQFBXH58mUWLlxIy5Yt7zreYrGwdu1aIiMjGTVqVPr2+vXrs3TpUrp27YqHhwdhYWEcOXKE8ePH3/PYSUlJJCUlpb+Oifnn6UjJyckkJxt7g+7t4xudQ3IOzRmxluaMWCs3zRm3fDaMeak6rzztwfDfD3PkchyDF+/j522nGdKqMjU8XY2OmCvkpjkjT0Z2mjPWZDBZssmXXZhMJhYvXkybNm3uO27BggV07dqVxMREUlJSCA4O5pdffsHOzi59THR0NCVLliQpKQkbGxsmT55M165d099PSkqiR48ezJgxA1tbW8xmM99//z0dO3a853GHDh3KsGHD7tg+Z84cnJycrD9hERERyXFS02DDJRN/njWTmGoC4NmiaQSXTiO/3QM+LCK5VkJCAm+88QbR0dG4uLjcd2yOKmAHDx4kMDCQfv360aJFC6Kiohg4cCC1a9dm6tSp6ePS0tI4ceIEcXFxrFmzhk8//ZQlS5YQEBAAwJgxY/j+++8ZM2YMZcqUYf369QwaNIjFixcTGBh412Pf7QqYp6cnV69efeAP+XFLTk5m1apVNGvWLEMRFbkXzRmxluaMWCu3z5krsUl8seooi3dfAMDF0ZZ+geV5vbYnNmaTwelyptw+ZyTrZac5ExMTg7u7e6YKWI5agjhy5EgaNGjAwIEDAfDx8cHZ2Rk/Pz9GjBhBiRIlADCbzZQvXx6AGjVqcOjQIUaOHElAQAA3b95k8ODBLF68mFatWqXvJyIigjFjxtyzgDk4OODg4HDHdjs7O8N/4bdlpyySM2jOiLU0Z8RauXXOeLjZMb5dTdrXLcOQXw9wMCqGYb8fZsHOCwx/oRq1vNyMjphj5dY5I49Pdpgz1hw/R30PWEJCAmZzxsg2NjbAP/d73UtaWlr61avb92zdbT9paWlZnFhERERys1pebvwW0pBPX6iGi6MtB6NiePmbzfSfH8Hl2ESj44lINmToFbC4uDiOHTuW/vrkyZNERETg5uZG6dKlGTRoEOfPn2fGjBkABAcH0717d6ZMmZK+BLFv377UqVMHDw8P4J+rZLVq1aJcuXIkJSWxbNkyZs6cyZQpUwBwcXHB39+fgQMHki9fPsqUKUN4eDgzZsxg3LhxT/6HICIiIjmajdlEh3petHyqBF+siGTejrMs2nWeVQcu0bdZRTrWK4OdTY76b94i8hgZWsB27NhB48aN01/3798fgE6dOjF9+nSioqI4c+ZM+vudO3cmNjaWiRMnEhoaiqurK02aNMnwhMP4+Hjeeecdzp07R758+ahcuTKzZs2iXbt26WPmzp3LoEGDaN++PdevX6dMmTJ89tln9OrV6wmctYiIiORGhfM78PlLPrxWpzRDft3P3nPRfPr7QeZvP8uwF6rxbNnCRkcUkWzA0AIWEBBw36WD06dPv2NbSEgIISEh9/zMiBEjGDFixH2PW7x4caZNm5bpnCIiIiKZVcPTlcXvNGD+jrOMXn6YyEuxvPbdFlr7ejC4ZRWKF3Q0OqKIGEjXw0VERESymI3ZxOt1SrNuQABvPlsakwmW7rlA07FhfBt+nFspuu9cJK9SARMRERF5TFyd7BnR5il+e68hNUu7En8rlZF/Hiboy/VsPHrV6HgiYgAVMBEREZHHrHrJgvzSqz5fvOxDYWd7jl+J582pW3ln9k7O37hpdDwReYJUwERERESeALPZxCu1PFk7IIDO9b0wm2DZvosEjg1n0rpjJKWkGh1RRJ4AFTARERGRJ6hgPjuGtq7GH739qOPlxs3kVL5YEUmL8etZF3nZ6Hgi8pipgImIiIgYoEoJF+b1fJYJ7WpQpIADp64l0GXadrrP2MHZ6wlGxxORx0QFTERERMQgJpOJNjVLsjbUn+5+3tiaTaw6eInAceFMWH2ExGQtSxTJbVTARERERAxWwNGOD1tV5c8+ftQrW5iklDQmrD5Ks/HhrDp46b7fmyoiOYsKmIiIiEg2UaFYAeZ0r8vEN2pS3MWRs9dv0n3GDrpO386pq/FGxxORLKACJiIiIpKNmEwmnvfxYE2oP28HlMPOxsS6yCs0H7+eMSsiuXlLyxJFcjIVMBEREZFsyNnBlv88V5nlfRvhV8GdW6lpTFx3jMBx4fy5L0rLEkVyKBUwERERkWysXJH8zOhah2/efIaSrvk4f+Mmb8/eRccft3H8SpzR8UTESipgIiIiItmcyWTiuerFWd3fn5Am5bG3MbPh6FWem7CekX8eIj4pxeiIIpJJKmAiIiIiOUQ+extCm1diZb9GNKlclORUC9+Gn6Dp2HCW7rmgZYkiOYAKmIiIiEgO4+XuzI+dazO1Uy1KuzlxMSaR3j/v5o3vt3LkUqzR8UTkPlTARERERHKoplWKsbJfI/o3q4iDrZnNJ64R9OUGPv39ILGJyUbHE5G7UAETERERycEc7Wzo3bQCq/v707xqMVLTLEzdeJLGY8JZtOucliWKZDMqYCIiIiK5gKebE991rMX0LrXxdnfmalwS/efv4dVvN3PwQozR8UTk/1MBExEREclFAioVZXlfPwa2qEQ+Oxu2n/qb57/ewCe/7if6ppYlihhNBUxEREQkl3GwteHdxuVZE+pPq6dKkGaBnzafpsmYMOZvP0tampYlihhFBUxEREQkl/Jwzcek9k8z+626lC+an2vxt3j/l7289M1f7DsXbXQ8kTxJBUxEREQkl2tQ3p1lvf0Y3LIyzvY27D5zg9aTNjJ48T7+jr9ldDyRPEUFTERERCQPsLc106NROdYOCOCFGh5YLDBn6xkajw1j9tbTpGpZosgToQImIiIikocUc3Hky9dqMq/Hs1QuXoAbCcl8uHg/bSZtYveZv42OJ5LrqYCJiIiI5EF1yxbm95CGDHm+KgUcbNl3Ppq2k//i/YV7uBaXZHQ8kVxLBUxEREQkj7K1MdO1oTdrBwTw0tOlAJi/4xyNx4QxY/MpUlLTDE4okvuogImIiIjkcUUKODD2VV9+ebseVUu4EJOYwpBfDxA8cRM7Tl03Op5IrqICJiIiIiIAPFPGjd9CGvLpC9VwcbTlUFQML3+zmf7zI7gcm2h0PJFcQQVMRERERNLZmE10qOfFugEBvFbbE5MJFu06T9Mx4UzdeJJkLUsUeSQqYCIiIiJyh8L5Hfj8JR8Wv9MAn1IFiU1K4dPfD/L8VxvZfPya0fFEciwVMBERERG5pxqerix5pwEjX3yKQk52RF6K5fXvtxDy824uRmtZooi1VMBERERE5L7MZhOv1ynNugEBvPlsaUwm+G3PBZqODePb8OPcStGyRJHMUgETERERkUxxdbJnRJun+O29htQs7Ur8rVRG/nmYoC/Xs/HoVaPjieQIKmAiIiIiYpXqJQvyS6/6fPGyD4Wd7Tl+JZ43p27l7Vk7OX/jptHxRLI1FTARERERsZrZbOKVWp6sHRBA5/pemE3w5/6LBI4NZ9K6YySlpBodUSRbUgETERERkYdWMJ8dQ1tX44/eftTxcuNmcipfrIikxfj1rIu8bHQ8kWxHBUxEREREHlmVEi7M6/ksE9rVoEgBB05dS6DLtO10n7GDs9cTjI4nkm2ogImIiIhIljCZTLSpWZK1of509/PG1mxi1cFLBI4LZ8LqIyQma1miiAqYiIiIiGSpAo52fNiqKn/28aN+ucIkpaQxYfVRmo0PZ9XBS1gsFqMjihhGBUxEREREHosKxQow+626THyjJsVdHDl7/SbdZ+ygy/TtnLoab3Q8EUOogImIiIjIY2MymXjex4M1of68HVAOOxsTYZFXaD5+PWNWRHLzlpYlSt6iAiYiIiIij52zgy3/ea4yy/s2wq+CO7dS05i47hiB48L5c1+UliVKnmFrdAARERERyTvKFcnPjK51WHHgEp/+fpDzN27y9uxdNChXGP8CRqcTefx0BUxEREREniiTycRz1Yuzur8/vZuUx97WzKbj1xi1x4bRK44Ql5RidESRx0YFTEREREQMkc/ehv7NK7GqXyMCKrqTajHx/cZTNB0bxtI9F7QsUXIlFTARERERMVSZws583+FpuldOxbNQPi7FJNH75928/v0WIi/GGh1PJEupgImIiIhItlC9kIU/Q+rTv1lFHGzNbDlxnZZfbeDT3w8Sk5hsdDyRLKECJiIiIiLZhoOdDb2bVmB1f3+aVy1GapqFqRtP0mRMOIt2ndOyRMnxVMBEREREJNvxdHPiu461mN6lNt7uzlyNS6L//D288s1mDl6IMTqeyEMztICtX7+e4OBgPDw8MJlMLFmy5IGfmT17Nr6+vjg5OVGiRAm6du3KtWvX0t9ftGgRtWrVwtXVFWdnZ2rUqMHMmTPv2M+hQ4do3bo1BQsWxNnZmdq1a3PmzJmsPD0REREReUQBlYqyvK8f7z9XiXx2Nuw4/TfPf72BT37dT/RNLUuUnMfQAhYfH4+vry+TJk3K1PhNmzbRsWNHunXrxoEDB1iwYAHbtm2je/fu6WPc3Nz48MMP2bx5M3v37qVLly506dKFFStWpI85fvw4DRs2pHLlyoSFhbF3714+/vhjHB0ds/wcRUREROTRONja8E5AedaE+tPKpwRpFvhp82majAlj/vazpKVpWaLkHIZ+EXNQUBBBQUGZHr9582a8vLzo3bs3AN7e3vTs2ZNRo0aljwkICMjwmT59+vDTTz+xceNGWrRoAcCHH35Iy5YtGT16dPq4cuXKPcKZiIiIiMjj5uGaj0lvPM0bda7yydIDHLscx/u/7GXOtjN8+kJ1nipV0OiIIg9kaAGzVr169Rg8eDDLli0jKCiIy5cvs3DhQlq2bHnX8RaLhbVr1xIZGZle0tLS0vjjjz94//33adGiBbt378bb25tBgwbRpk2bex47KSmJpKSk9NcxMf+sPU5OTiY52djL37ePb3QOyTk0Z8RamjNiLc0ZsZY1c6ZOmYL8+vazzNx6hq/XHifi7A1aT9pIu1ql6B9YnkJO9o87rmQD2envGWsymCzZ5FEyJpOJxYsX37cEASxYsICuXbuSmJhISkoKwcHB/PLLL9jZ2aWPiY6OpmTJkiQlJWFjY8PkyZPp2rUrABcvXqREiRI4OTkxYsQIGjduzPLlyxk8eDDr1q3D39//rscdOnQow4YNu2P7nDlzcHJyevgTFxEREZGHFn0Lfj1tZufVf+6scbK18HzpNOoVtWA2GRxO8oyEhATeeOMNoqOjcXFxue/YHFXADh48SGBgIP369aNFixZERUUxcOBAateuzdSpU9PHpaWlceLECeLi4lizZg2ffvopS5YsISAggAsXLlCyZElef/115syZk/6Z1q1b4+zszM8//3zXY9/tCpinpydXr1594A/5cUtOTmbVqlU0a9YsQxEVuRfNGbGW5oxYS3NGrPWoc2bbqesM//0wkZfiAKju4cKQ5ytT09M1i5NKdpGd/p6JiYnB3d09UwUsRy1BHDlyJA0aNGDgwIEA+Pj44OzsjJ+fHyNGjKBEiRIAmM1mypcvD0CNGjU4dOgQI0eOJCAgAHd3d2xtbalatWqGfVepUoWNGzfe89gODg44ODjcsd3Ozs7wX/ht2SmL5AyaM2ItzRmxluaMWOth50yDCsX4o3cRZm45zbiVR9h/IYZXv9vGq7VK8Z/nKlM4/53/jpPcITv8PWPN8XPU94AlJCRgNmeMbGNjA3DfL+VLS0tLv3plb29P7dq1iYyMzDDmyJEjlClTJosTi4iIiMiTYmtjpksDb9YOCOClp0sBMH/HORqPCeOnv06RkppmcEIRg6+AxcXFcezYsfTXJ0+eJCIiAjc3N0qXLs2gQYM4f/48M2bMACA4OJju3bszZcqU9CWIffv2pU6dOnh4eAD/XCWrVasW5cqVIykpiWXLljFz5kymTJmSfpyBAwfSrl07GjVqlH4P2G+//UZYWNgTPX8RERERyXpFCjgw9lVf3qjryZBfD3DgQgyfLD3A3O1n+fSFatTycjM6ouRhhhawHTt20Lhx4/TX/fv3B6BTp05Mnz6dqKioDF+O3LlzZ2JjY5k4cSKhoaG4urrSpEmTDI+hj4+P55133uHcuXPky5ePypUrM2vWLNq1a5c+pm3btnzzzTeMHDmS3r17U6lSJX755RcaNmz4BM5aRERERJ6EZ8q4sfS9hszZepovVkRyKCqGl7/ZzIs1S/JBy8oULaDvgJUnz9ACFhAQcN+lg9OnT79jW0hICCEhIff8zIgRIxgxYsQDj921a9f0JyOKiIiISO5kYzbRoZ4XLZ8qwZiVkczdfpZFu8+z8uAl+gZWoFN9L+xsctRdOZLDabaJiIiISK5XOL8DI1/0YfE7DfAtVZC4pBRG/HGIVl9tYPPxa0bHkzxEBUxERERE8owanq4sfqcBn7/4FIWc7DhyKY7Xv99CyM+7uRidaHQ8yQNUwEREREQkTzGbTbxWpzTrBgTw5rOlMZngtz0XaDI2jG/Cj3MrRU9LlMdHBUxERERE8iRXJ3tGtHmK395ryNOlXUm4lcrnfx7muS/Xs+HoFaPjSS6lAiYiIiIieVr1kgVZ2Ks+X7zsg3t+e05ciafD1G28PWsn52/cNDqe5DIqYCIiIiKS55nNJl6p5cma0AA61/fCbII/91+k6dgwJq49SlJKqtERJZdQARMRERER+f8K5rNjaOtq/NHbjzpebiQmpzFm5RFajF/PusjLRseTXEAFTERERETkX6qUcGFez2eZ0K4GRQo4cOpaAl2mbeetn3Zw9nqC0fEkB1MBExERERG5C5PJRJuaJVkb6k93P29szSZWH7pE4Lhwxq86QmKyliWK9VTARERERETuo4CjHR+2qsqfffyoX64wSSlpfLnmKM3Gh7Pq4CUsFovRESUHUQETEREREcmECsUKMPutukx8oybFXRw5e/0m3WfsoMv07Zy8Gm90PMkhVMBERERERDLJZDLxvI8Ha0L9eTugHHY2JsIir9Bi/Hq+WHGYhFspRkeUbE4FTERERETESs4Otvznucos79sIvwru3EpNY9K64wSODefPfVFalij3pAImIiIiIvKQyhXJz4yudfjmzWco6ZqPC9GJvD17Fx2mbuPY5Tij40k2pAImIiIiIvIITCYTz1Uvzur+/vRuUh57WzMbj13luQnrGbnsEHFJWpYo/0cFTEREREQkC+Szt6F/80qs6teIppWLkpJm4dv1J2g6Noyley5oWaIAKmAiIiIiIlmqTGFnpnauzdROtSjt5sSlmCR6/7yb17/fQuTFWKPjicFUwEREREREHoOmVYqxsl8j+jeriIOtmS0nrtPyqw18+vtBYhKTjY4nBlEBExERERF5TBztbOjdtAKr+/vToloxUtMsTN14kiZjwlm065yWJeZBKmAiIiIiIo+Zp5sT33aoxU9d6+Dt7szVuCT6z9/DK99s5sCFaKPjyROkAiYiIiIi8oT4VyzC8r5+vP9cJfLZ2bDj9N8Ef72RIb/uJzpByxLzAhUwEREREZEnyMHWhncCyrMm1J9WPiVIs8CMzadpMjaM+dvPkpamZYm5mQqYiIiIiIgBPFzzMemNp5n9Vl3KF83PtfhbvP/LXl6c8hd7z90wOp48JipgIiIiIiIGalDenT/7+PFhyyo429sQcfYGL0zaxODF+/g7/pbR8SSLqYCJiIiIiBjMzsZM90ZlWTsggDY1PLBYYM7WMzQeG8bsradJ1bLEXEMFTEREREQkmyjm4siE12oyr8ezVC5egBsJyXy4eD9tJm1i15m/jY4nWUAFTEREREQkm6lbtjC/hzTkk+CqFHCwZd/5aF6c/BfvL9zD1bgko+PJI1ABExERERHJhmxtzHRp4M3aAQG8/EwpAObvOEeTMWH89NcpUlLTDE4oD0MFTEREREQkGytSwIExr/jyy9v1qObhQkxiCp8sPUDwxE1sP3Xd6HhiJRUwEREREZEc4Jkybix9ryGftqlOwXx2HIqK4ZVvNtNvXgSXYxKNjieZpAImIiIiIpJD2JhNdHi2DOsGBPB6HU9MJli8+zxNxobzw4YTJGtZYranAiYiIiIiksO4Odsz8kUfFr/TAN9SBYlLSmHEH4do9dUGNh+/ZnQ8uQ8VMBERERGRHKqGpyuL32nA5y8+RSEnO45ciuP177cQ8vNuLkZrWWJ2pAImIiIiIpKDmc0mXqtTmnUDAujwbBnMJvhtzwWajA3jm/Dj3ErRssTsRAVMRERERCQXcHWy59M21Vn6XkOeLu1Kwq1UPv/zMM99uZ4NR68YHU/+PxUwEREREZFcpHrJgizsVZ8xr/jint+eE1fi6TB1G2/P2sn5GzeNjpfnqYCJiIiIiOQyZrOJl58pxZrQALo08MLGbOLP/RdpOjaMiWuPkpSSanTEPEsFTEREREQklyqYz45Pgqvxe0hD6ni5kZicxpiVR2gxfj3rDl82Ol6epAImIiIiIpLLVSnhwryez/LlazUoWsCBU9cS6DJ9O2/9tIMz1xKMjpenqICJiIiIiOQBJpOJF2qUZE2oP939vLE1m1h96BKB48MZv+oIiclalvgkqICJiIiIiOQhBRzt+LBVVf7s40f9coW5lZLGl2uOEjgunJUHLmKxWIyOmKupgImIiIiI5EEVihVg9lt1mfTG05Qo6Mi5v2/SY+ZOukzfzsmr8UbHy7VUwERERERE8iiTyUQrnxKs7u/P2wHlsLMxERZ5hRbj1/PFisMk3EoxOmKuowImIiIiIpLHOTvY8p/nKrOibyMaVSzCrdQ0Jq07TuDYcJbti9KyxCykAiYiIiIiIgCULZKfn7rU5tsOz1DSNR8XohN5Z/YuOkzdxrHLsUbHyxVUwEREREREJJ3JZKJFteKs7u9P76YVsLc1s/HYVZ6bsIGRyw4Rl6RliY9CBUxERERERO6Qz96G/s0qsqpfI5pWLkpKmoVv15+g6dgwfo04r2WJD0kFTERERERE7qlMYWemdq7N1E61KO3mxKWYJPrMjeD177cQeVHLEq2lAiYiIiIiIg/UtEoxVvZrRP9mFXGwNbPlxHVafrWB4b8dJCYx2eh4OYahBWz9+vUEBwfj4eGByWRiyZIlD/zM7Nmz8fX1xcnJiRIlStC1a1euXbuW/v6iRYuoVasWrq6uODs7U6NGDWbOnHnP/fXq1QuTycSECROy4IxERERERHIvRzsbejetwOr+/rSoVozUNAs/bjpJkzHh/LLznJYlZoKhBSw+Ph5fX18mTZqUqfGbNm2iY8eOdOvWjQMHDrBgwQK2bdtG9+7d08e4ubnx4YcfsnnzZvbu3UuXLl3o0qULK1asuGN/ixcvZsuWLXh4eGTZOYmIiIiI5Haebk5826EWP3Wtg7e7M1fjkghdsIdXvtnMgQvRRsfL1myNPHhQUBBBQUGZHr9582a8vLzo3bs3AN7e3vTs2ZNRo0aljwkICMjwmT59+vDTTz+xceNGWrRokb79/PnzhISEsGLFClq1avVoJyIiIiIikgf5VyzC8r5+TN14kq/XHGPH6b8J/nojbz5bhtBmlSjoZGd0xGzH0AJmrXr16jF48GCWLVtGUFAQly9fZuHChbRs2fKu4y0WC2vXriUyMjJDSUtLS6NDhw4MHDiQatWqZerYSUlJJCUlpb+OiYkBIDk5meRkY9e83j6+0Tkk59CcEWtpzoi1NGfEWpozOZcZ6N6gDM9XL8bnyyNZtv8SMzaf5rc9FxjYvAIv1SyJ2WzK8uNmpzljTQaTJZss1DSZTCxevJg2bdrcd9yCBQvo2rUriYmJpKSkEBwczC+//IKd3f+16+joaEqWLElSUhI2NjZMnjyZrl27pr8/cuRI1q1bx4oVKzCZTHh5edG3b1/69u17z+MOHTqUYcOG3bF9zpw5ODk5WX2+IiIiIiK5UWS0iV9Omrl085/SVSa/hZe9Uymd3+Bgj1FCQgJvvPEG0dHRuLi43HdsjipgBw8eJDAwkH79+tGiRQuioqIYOHAgtWvXZurUqenj0tLSOHHiBHFxcaxZs4ZPP/2UJUuWEBAQwM6dO2nVqhW7du1Kv/crMwXsblfAPD09uXr16gN/yI9bcnIyq1atolmzZhmKqMi9aM6ItTRnxFqaM2ItzZncJTk1jRlbzvD12uPE30rFZIJXnylFaLPyFHKyz5pjZKM5ExMTg7u7e6YKWI5agjhy5EgaNGjAwIEDAfDx8cHZ2Rk/Pz9GjBhBiRIlADCbzZQvXx6AGjVqcOjQIUaOHElAQAAbNmzg8uXLlC5dOn2/qamphIaGMmHCBE6dOnXXYzs4OODg4HDHdjs7O8N/4bdlpyySM2jOiLU0Z8RamjNiLc2Z3MHODnoFVODFpz3577JDLIm4wLwd51hx8BIDmlfi9TqlscmiZYnZYc5Yc/wc9T1gCQkJmM0ZI9vY2ADc95GXaWlp6VevOnTowN69e4mIiEj/4+HhwcCBA+/6pEQREREREXk4RV0cmfBaTeb1eJbKxQtwIyGZj5bsp82kTew687fR8Qxh6BWwuLg4jh07lv765MmTRERE4ObmRunSpRk0aBDnz59nxowZAAQHB9O9e3emTJmSvgSxb9++1KlTJ3054ciRI6lVqxblypUjKSmJZcuWMXPmTKZMmQJA4cKFKVy4cIYcdnZ2FC9enEqVKj2hMxcRERERyTvqli3M7yENmbnlNONWHmHf+WhenPwXrzxTiv8EVcY9/50rzXIrQwvYjh07aNy4cfrr/v37A9CpUyemT59OVFQUZ86cSX+/c+fOxMbGMnHiREJDQ3F1daVJkyYZnnAYHx/PO++8w7lz58iXLx+VK1dm1qxZtGvX7smdmIiIiIiIZGBrY6ZLA2+e9/Fg1PLDLNx5jgU7z7HiwEVCm1eifd3S2NrkqAV6D8XQAhYQEHDfpYPTp0+/Y1tISAghISH3/MyIESMYMWKEVTnudd+XiIiIiIhkrSIFHBjzii+v1/FkyK8HOHAhhk+WHuDnbWf4tE11anu5GR3xscr9FVNERERERLKdZ8q4sfS9hnzapjoF89lx+GIsr3yzmX7zIrgck2h0vMdGBUxERERERAxhYzbR4dkyrBsQwOt1PDGZYPHu8zQZG84PG06QnJpmdMQspwImIiIiIiKGcnO2Z+SLPix5pwG+pQoSl5TCiD8O0eqrDWw+fs3oeFlKBUxERERERLIFX09XFr/TgM9ffIpCTnYcuRTH699vIeTn3URF30wfl5pmYevJ6+y8amLryeukpt37uRLZTY76Iub/1969x0ZR93sc/8xS2G5rW6nYdikoEKBSkBLCradqw01uaVKDgUofKff00HIgBkRRKQ3kcDSI8VqjgaqxtKHEYh+oBQQFrfAgPBRBK17AE7DcDfTC5aCd84eH5uwDFLaxM7Pt+5Vsws7OwmeSb8p8+pvZBQAAANC6uVyG0obcp7H9YvTy1h9U8I//1t8PVmt71Wn9x8heir3bo/8sq9LJi1cktdMHP+6TNyJYOSnxGtvPa3f822IFDAAAAIDj3B3SQctT+6k0+yENvO9uXfqfP/Rfn3z/f6thvh/SceriFf37h/9U+eGTNqW9cxQwAAAAAI7VLzZCGzL/TS893l8u4+b7XL8AMffv3zn+ckQKGAAAAABHc7kMde0Yoqa6lSnp5MUr2nvsN8tyNQcFDAAAAIDjnam9s+8Gu9P97EIBAwAAAOB4UWHBf+l+dqGAAQAAAHC8Id0j5Y0I1i1uA5MhyRsRrCHdI62M5TcKGAAAAADHa+cylJMSL0k3lLDrz3NS4tXuVp/U4RAUMAAAAAABYWw/r/L+NlAxEb6XGcZEBCvvbwMD4nvA+CJmAAAAAAFjbD+vRsfHaPdPZ7T1i3/o0YeHKrFnlONXvq6jgAEAAAAIKO1choZ2j9T5KlNDu0cGTPmSuAQRAAAAACxDAQMAAAAAi1DAAAAAAMAiFDAAAAAAsAgFDAAAAAAsQgEDAAAAAItQwAAAAADAIhQwAAAAALAIBQwAAAAALEIBAwAAAACLUMAAAAAAwCIUMAAAAACwCAUMAAAAACwSZHeAQGWapiSppqbG5iTStWvXdOnSJdXU1Kh9+/Z2x0EAYGbgL2YG/mJm4C9mBv5y0sxc7wTXO0JTKGDNVFtbK0nq2rWrzUkAAAAAOEFtba0iIiKa3Mcw76Sm4QYNDQ2qrq5WWFiYDMOwNUtNTY26du2q48ePKzw83NYsCAzMDPzFzMBfzAz8xczAX06aGdM0VVtbq86dO8vlavouL1bAmsnlcqlLly52x/ARHh5u+/AhsDAz8BczA38xM/AXMwN/OWVmbrfydR0fwgEAAAAAFqGAAQAAAIBFKGCtgNvtVk5Ojtxut91RECCYGfiLmYG/mBn4i5mBvwJ1ZvgQDgAAAACwCCtgAAAAAGARChgAAAAAWIQCBgAAAAAWoYABAAAAgEUoYAFg165dSklJUefOnWUYhjZu3Hjb93z++ecaOHCg3G63evbsqffee6/Fc8I5/J2Zjz76SKNHj9a9996r8PBwJSYmasuWLdaEhSM05+fMdRUVFQoKCtKAAQNaLB+cpTnzcvXqVT333HO6//775Xa71a1bN61du7blw8IRmjMzBQUFSkhIUEhIiLxer2bMmKHz58+3fFg4wsqVKzV48GCFhYUpKipKqampOnLkyG3fV1xcrAceeEDBwcF68MEHVVZWZkFa/1DAAkB9fb0SEhL05ptv3tH+x44d04QJEzR8+HBVVlZqwYIFmjVrFifUbYi/M7Nr1y6NHj1aZWVl2r9/v4YPH66UlBQdOHCghZPCKfydmesuXLigqVOnauTIkS2UDE7UnHmZNGmStm/frjVr1ujIkSMqLCxUXFxcC6aEk/g7MxUVFZo6dapmzpypb7/9VsXFxdq7d69mz57dwknhFDt37lRWVpb27Nmjbdu26dq1a3r00UdVX19/y/d89dVXeuKJJzRz5kwdOHBAqampSk1N1eHDhy1Mfnt8DH2AMQxDJSUlSk1NveU+ixcv1ubNm32GLS0tTRcuXFB5ebkFKeEkdzIzN9O3b19NnjxZS5cubZlgcCx/ZiYtLU29evVSu3bttHHjRlVWVrZ4PjjLncxLeXm50tLSdPToUUVGRloXDo50JzOzatUq5eXl6eeff27c9vrrr+vFF1/UiRMnLEgJpzl79qyioqK0c+dOPfLIIzfdZ/Lkyaqvr9emTZsatw0bNkwDBgzQ22+/bVXU22IFrBXavXu3Ro0a5bNtzJgx2r17t02JEGgaGhpUW1vLiRKalJ+fr6NHjyonJ8fuKHC40tJSDRo0SC+99JJiY2PVu3dvLVy4UJcvX7Y7GhwqMTFRx48fV1lZmUzT1OnTp7VhwwaNHz/e7miwycWLFyWpyXOTQDkHDrI7AP56p06dUnR0tM+26Oho1dTU6PLly/J4PDYlQ6BYtWqV6urqNGnSJLujwKF+/PFHPfPMM/riiy8UFMR/JWja0aNH9eWXXyo4OFglJSU6d+6c5s6dq/Pnzys/P9/ueHCgpKQkFRQUaPLkybpy5Yp+//13paSk+H2ZNFqHhoYGLViwQElJSerXr98t97vVOfCpU6daOqJfWAED4GPdunXKzc3V+vXrFRUVZXccONAff/yhKVOmKDc3V71797Y7DgJAQ0ODDMNQQUGBhgwZovHjx2v16tV6//33WQXDTX333XeaP3++li5dqv3796u8vFy//PKLMjMz7Y4GG2RlZenw4cMqKiqyO8pfgl9btkIxMTE6ffq0z7bTp08rPDyc1S80qaioSLNmzVJxcfENS/jAdbW1tdq3b58OHDig7OxsSX+eYJumqaCgIG3dulUjRoywOSWcxOv1KjY2VhEREY3b+vTpI9M0deLECfXq1cvGdHCilStXKikpSYsWLZIk9e/fX6GhoXr44Ye1YsUKeb1emxPCKtnZ2dq0aZN27dqlLl26NLnvrc6BY2JiWjKi31gBa4USExO1fft2n23btm1TYmKiTYkQCAoLCzV9+nQVFhZqwoQJdseBg4WHh+vQoUOqrKxsfGRmZiouLk6VlZUaOnSo3RHhMElJSaqurlZdXV3jth9++EEul+u2J1Romy5duiSXy/c0tV27dpIkPj+ubTBNU9nZ2SopKdGOHTvUvXv3274nUM6BWQELAHV1dfrpp58anx87dkyVlZWKjIzUfffdp2effVa//vqrPvjgA0lSZmam3njjDT399NOaMWOGduzYofXr12vz5s12HQIs5u/MrFu3ThkZGXr11Vc1dOjQxmulPR6Pz2+s0Xr5MzMul+uGa/CjoqIUHBzc5LX5aD38/RkzZcoULV++XNOnT1dubq7OnTunRYsWacaMGVyZ0Ub4OzMpKSmaPXu28vLyNGbMGJ08eVILFizQkCFD1LlzZ7sOAxbKysrSunXr9PHHHyssLKzx3CQiIqLx58bUqVMVGxurlStXSpLmz5+v5ORkvfzyy5owYYKKioq0b98+vfPOO7Ydx02ZcLzPPvvMlHTDIyMjwzRN08zIyDCTk5NveM+AAQPMDh06mD169DDz8/Mtzw37+DszycnJTe6P1q85P2f+v5ycHDMhIcGSrLBfc+alqqrKHDVqlOnxeMwuXbqYTz31lHnp0iXrw8MWzZmZ1157zYyPjzc9Ho/p9XrN9PR088SJE9aHhy1uNi+SfM5pk5OTbzhXWb9+vdm7d2+zQ4cOZt++fc3NmzdbG/wO8D1gAAAAAGAR7gEDAAAAAItQwAAAAADAIhQwAAAAALAIBQwAAAAALEIBAwAAAACLUMAAAAAAwCIUMAAAAACwCAUMAAAAACxCAQMAwAaGYWjjxo12xwAAWIwCBgBoc6ZNmybDMG54jB071u5oAIBWLsjuAAAA2GHs2LHKz8/32eZ2u21KAwBoK1gBAwC0SW63WzExMT6Pjh07Svrz8sC8vDyNGzdOHo9HPXr00IYNG3zef+jQIY0YMUIej0f33HOP5syZo7q6Op991q5dq759+8rtdsvr9So7O9vn9XPnzumxxx5TSEiIevXqpdLS0pY9aACA7ShgAADcxAsvvKCJEyfq4MGDSk9PV1pamqqqqiRJ9fX1GjNmjDp27Kivv/5axcXF+vTTT30KVl5enrKysjRnzhwdOnRIpaWl6tmzp8+/kZubq0mTJumbb77R+PHjlZ6ert9++83S4wQAWMswTdO0OwQAAFaaNm2aPvzwQwUHB/tsX7JkiZYsWSLDMJSZmam8vLzG14YNG6aBAwfqrbfe0rvvvqvFixfr+PHjCg0NlSSVlZUpJSVF1dXVio6OVmxsrKZPn64VK1bcNINhGHr++ee1fPlySX+WurvuukuffPIJ96IBQCvGPWAAgDZp+PDhPgVLkiIjIxv/nJiY6PNaYmKiKisrJUlVVVVKSEhoLF+SlJSUpIaGBh05ckSGYai6ulojR45sMkP//v0b/xwaGqrw8HCdOXOmuYcEAAgAFDAAQJsUGhp6wyWBfxWPx3NH+7Vv397nuWEYamhoaIlIAACH4B4wAABuYs+ePTc879OnjySpT58+OnjwoOrr6xtfr6iokMvlUlxcnMLCwtStWzdt377d0swAAOdjBQwA0CZdvXpVp06d8tkWFBSkTp06SZKKi4s1aNAgPfTQQyooKNDevXu1Zs0aSVJ6erpycnKUkZGhZcuW6ezZs5o3b56efPJJRUdHS5KWLVumzMxMRUVFady4caqtrVVFRYXmzZtn7YECAByFAgYAaJPKy8vl9Xp9tsXFxen777+X9OcnFBYVFWnu3Lnyer0qLCxUfHy8JCkkJERbtmzR/PnzNXjwYIWEhGjixIlavXp149+VkZGhK1eu6JVXXtHChQvVqVMnPf7449YdIADAkfgURAAA/oVhGCopKVFqaqrdUQAArQz3gAEAAACARShgAAAAAGAR7gEDAOBfcHU+AKClsAIGAAAAABahgAEAAACARShgAAAAAGARChgAAAAAWIQCBgAAAAAWoYABAAAAgEUoYAAAAABgEQoYAAAAAFjkfwHS/NGoaKB29wAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -447,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -460,16 +471,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "What is FHE?\n", - "\n", - "The FSH paradigm is the concept that allows the computation of arbitrary values to an external processor. This technique is useful for many different purposes,\n" + "What is FHE?? I am a doctor and have a Doctor? My doctor is a psychiatrist and I have done a research into my daughter who I read the paper I\n" ] } ], @@ -481,16 +490,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "What is FHE?\n", - "\n", - "FHE is a single-layer, multi-direction, and multi. It is designed to be an efficient, single directional, inter-\n" + "What is FHE?, and I think it is a C-, C, F- and G, I believe it's is not a, O'B, B\n" ] } ], @@ -506,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -530,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -548,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -563,14 +570,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Total number of weights: 39569664\n", + "Total number of weights: 38783232\n", "Total number of LoRA weights: 147456\n" ] } @@ -581,14 +588,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Total weights removed: 68.24 %\n" + "Total weights removed: 68.87 %\n" ] } ], From 445ee0e2d69991d05b821e2c2536437e1f69d4b0 Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Tue, 6 Aug 2024 19:18:42 +0200 Subject: [PATCH 19/32] chore: fix remote embedding and lm_head --- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 119 +++++------------- .../lora_finetune/lora_module.py | 3 + 2 files changed, 33 insertions(+), 89 deletions(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 1a2310767..3ee688f37 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 100, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -175,13 +175,13 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n", "\n", - "EPOCHS = 2\n", + "EPOCHS = 100\n", "PER_DEVICE_TRAIN_BATCH_SIZE = 4\n", "\n", "training_args = TrainingArguments(\n", @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -241,7 +241,9 @@ " # hybrid models). We however still need to include the associated module's forward pass in\n", " # the hybrid model\n", " # Also include the embedding and language model head as they represent a lot of the model's\n", - " # parameters\n", + " # parameters. Side note: \"lm_head\" does not appear in model.parameters() because the weights\n", + " # are directly tied to the embedding ones, but we still need to remove both modules in\n", + " # order to get rid of the weights\n", " if isinstance(module, (Conv1D, Embedding)) or \"lm_head\" in name:\n", " remote_names.append(name)\n", "\n", @@ -257,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -266,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -282,24 +284,9 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 0%| | 0/100 [3:52:58" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Avoid the following error from HuggingFace when training :\n", "# \"The current process just got forked, after parallelism has already been used. Disabling\n", @@ -458,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -471,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -490,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -513,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -537,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -555,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -570,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -588,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [ { diff --git a/use_case_examples/lora_finetune/lora_module.py b/use_case_examples/lora_finetune/lora_module.py index 6fde7d047..41211adba 100644 --- a/use_case_examples/lora_finetune/lora_module.py +++ b/use_case_examples/lora_finetune/lora_module.py @@ -35,6 +35,9 @@ def forward(self, inputs): loss = loss / self.gradient_accumulation_steps # Update gradients + # We need to set requires grad to the loss manually because the inference model's last + # step is the "lm_head" layer, which is detached from the graph by the hybrid model + loss.requires_grad_(True) loss.backward() grad_norm = None From 9053e1bf70962b4c898b08e9ba62b7557a0f3cf9 Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Wed, 7 Aug 2024 10:34:36 +0200 Subject: [PATCH 20/32] chore: temporarily remove embedding/lm_head from remote --- use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 3ee688f37..a51c6ce6c 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -244,7 +244,8 @@ " # parameters. Side note: \"lm_head\" does not appear in model.parameters() because the weights\n", " # are directly tied to the embedding ones, but we still need to remove both modules in\n", " # order to get rid of the weights\n", - " if isinstance(module, (Conv1D, Embedding)) or \"lm_head\" in name:\n", + " if isinstance(module, (Conv1D)):\n", + " # if isinstance(module, (Conv1D, Embedding)) or \"lm_head\" in name:\n", " remote_names.append(name)\n", "\n", " elif isinstance(module, CustomConv1D):\n", From 039f08babb85ebff3dabd2e148a2ca0a88e5c7bf Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Thu, 8 Aug 2024 19:15:02 +0200 Subject: [PATCH 21/32] chore: add 16b training, without embedding layers --- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 1757 +++++++++++++++-- .../lora_finetune/requirements.txt | 4 +- 2 files changed, 1596 insertions(+), 165 deletions(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index a51c6ce6c..8ee6072c4 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 19, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -159,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -229,23 +229,26 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "def get_remote_names(model):\n", + "def get_remote_names(model, include_embedding_layers=False):\n", " remote_names = []\n", " for name, module in model.named_modules():\n", " # Some gradients are not needed for fine-tuning, so need to exclude the backward module\n", " # from the remote_names since calibration won't get through it (which raises an issue with\n", " # hybrid models). We however still need to include the associated module's forward pass in\n", " # the hybrid model\n", - " # Also include the embedding and language model head as they represent a lot of the model's\n", - " # parameters. Side note: \"lm_head\" does not appear in model.parameters() because the weights\n", - " # are directly tied to the embedding ones, but we still need to remove both modules in\n", - " # order to get rid of the weights\n", - " if isinstance(module, (Conv1D)):\n", - " # if isinstance(module, (Conv1D, Embedding)) or \"lm_head\" in name:\n", + " # We can also include the embedding and language model head as they represent a lot of the\n", + " # model's parameters. Side note: \"lm_head\" does not appear in model.parameters() because\n", + " # the weights are directly tied to the embedding ones, but we still need to remove both\n", + " # modules in order to get rid of the weights\n", + " if (\n", + " isinstance(module, Conv1D)\n", + " or include_embedding_layers\n", + " and (isinstance(module, Embedding) or \"lm_head\" in name)\n", + " ):\n", " remote_names.append(name)\n", "\n", " elif isinstance(module, CustomConv1D):\n", @@ -255,12 +258,14 @@ " return remote_names\n", "\n", "\n", - "remote_names = get_remote_names(lora_training)" + "# Do not include embedding layers as the model does not converge when quantizing them, even with\n", + "# 16 bits\n", + "remote_names = get_remote_names(lora_training, include_embedding_layers=False)" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -285,25 +290,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "hybrid_model.model.toggle_calibrate(enable=True)\n", "\n", - "hybrid_model.compile_model(\n", - " inputset,\n", - " n_bits=8,\n", - " rounding_threshold_bits={\"n_bits\": 6, \"method\": \"approximate\"},\n", - " p_error=1e-5,\n", - ")\n", + "hybrid_model.compile_model(inputset, n_bits=16)\n", "\n", "hybrid_model.model.toggle_calibrate(enable=False)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -386,166 +386,1599 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Avoid the following error from HuggingFace when training :\n", - "# \"The current process just got forked, after parallelism has already been used. Disabling\n", - "# parallelism to avoid deadlocks...\"\n", - "tokenizer.parallelism = False\n", - "\n", - "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fine_tuned_model = hybrid_model.model.inference_model\n", - "\n", - "# In simulation, we can only generate a single token at a time because of fixed size circuits\n", - "# and how `generate` works (only the last token from the previous generation is kept)\n", - "hybrid_model.set_fhe_mode(\"disable\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 0%| | 0/100 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Avoid the following error from HuggingFace when training :\n", + "# \"The current process just got forked, after parallelism has already been used. Disabling\n", + "# parallelism to avoid deadlocks...\"\n", + "tokenizer.parallelism = False\n", + "\n", + "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"simulate\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "fine_tuned_model = hybrid_model.model.inference_model\n", + "\n", + "# In simulation, we can only generate a single token at a time because of fixed size circuits\n", + "# and how `generate` works (only the last token from the previous generation is kept)\n", + "hybrid_model.set_fhe_mode(\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE?\n", + "\n", + "FHE? FHS is a groundbreaking concept that allows the use of cloud computing to help protect data and privacy while still allowing data to be\n" + ] + } + ], + "source": [ + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE?\n", + "\n", + "FHE is a new form of the term \"fission energy\". It is the energy of fusion of a process which is in a state\n" + ] + } + ], + "source": [ + "peft_model.disable_adapter_layers()\n", + "\n", + "prompt = \"What is FHE ?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)\n", + "\n", + "peft_model.enable_adapter_layers()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def print_weights_and_size(model, print_detail=False):\n", + " total_weights = 0\n", + " total_lora_weights = 0\n", + " for name, param in model.named_parameters():\n", + " total_weights += param.numel()\n", + "\n", + " if \"lora\" in name:\n", + " total_lora_weights += param.numel()\n", + "\n", + " if print_detail:\n", + " print(name, param.numel())\n", + "\n", + " print(f\"Total number of weights: {total_weights}\")\n", + " print(f\"Total number of LoRA weights: {total_lora_weights}\")\n", + "\n", + " return total_weights" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of weights: 124587264\n", + "Total number of LoRA weights: 147456\n" + ] + } + ], + "source": [ + "total_weights_size = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "path = Path(\"deployment/gpt2_lora_finetuned\")\n", + "path.mkdir(parents=True, exist_ok=True)\n", + "\n", + "if path.is_dir() and any(path.iterdir()):\n", + " shutil.rmtree(path)\n", + "\n", + "hybrid_model.save_and_clear_private_info(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of weights: 39569664\n", + "Total number of LoRA weights: 147456\n" + ] + } + ], + "source": [ + "total_weights_size_private = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total weights removed: 68.24 %\n" + ] + } + ], + "source": [ + "print(\n", + " \"Total weights removed: \"\n", + " f\"{(total_weights_size - total_weights_size_private) / total_weights_size * 100:.2f} %\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Around 95% of the remaining weights are from the embedding layers (wpe and wte) as well as the final lm_head layer." ] } ], diff --git a/use_case_examples/lora_finetune/requirements.txt b/use_case_examples/lora_finetune/requirements.txt index 3acc5b89a..936361389 100644 --- a/use_case_examples/lora_finetune/requirements.txt +++ b/use_case_examples/lora_finetune/requirements.txt @@ -1,10 +1,8 @@ # Use the latest public version of Concrete ML once the embedding layer feature # is released -# concrete-ml==1.6.1 +# concrete-ml==1.7.0 -e ../../. transformers==4.41.2 peft==0.11.1 -datasets==2.20.0 Jinja2==3.1.4 matplotlib==3.7.5 -jupyter From b5c659d830954c8957f152c8a5698a238fa76460 Mon Sep 17 00:00:00 2001 From: Roman Bredehoft Date: Fri, 16 Aug 2024 15:51:01 +0200 Subject: [PATCH 22/32] chore: seed text generation --- .../lora_finetune/gpt2_finetune_hybrid.ipynb | 226 +++++++++--------- 1 file changed, 116 insertions(+), 110 deletions(-) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb index 8ee6072c4..d103253ee 100644 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb @@ -246,8 +246,8 @@ " # modules in order to get rid of the weights\n", " if (\n", " isinstance(module, Conv1D)\n", - " or include_embedding_layers\n", - " and (isinstance(module, Embedding) or \"lm_head\" in name)\n", + " or include_embedding_layers # noqa: W503\n", + " and (isinstance(module, Embedding) or \"lm_head\" in name) # noqa: W503\n", " ):\n", " remote_names.append(name)\n", "\n", @@ -397,7 +397,7 @@ "To disable this warning, you can either:\n", "\t- Avoid using `tokenizers` before the fork if possible\n", "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "Training Progress: 1%| | 1/100 [04:21<7:11:59, 261.81s/it]" + "Training Progress: 1%| | 1/100 [04:24<7:17:11, 264.97s/it]" ] }, { @@ -411,7 +411,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 2%|▏ | 2/100 [05:37<4:08:32, 152.17s/it]" + "Training Progress: 2%|▏ | 2/100 [05:41<4:12:04, 154.33s/it]" ] }, { @@ -425,7 +425,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 3%|▎ | 3/100 [06:53<3:10:17, 117.70s/it]" + "Training Progress: 3%|▎ | 3/100 [06:58<3:12:09, 118.86s/it]" ] }, { @@ -439,7 +439,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 4%|▍ | 4/100 [08:11<2:42:54, 101.82s/it]" + "Training Progress: 4%|▍ | 4/100 [08:14<2:43:19, 102.08s/it]" ] }, { @@ -453,7 +453,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 5%|▌ | 5/100 [09:29<2:27:34, 93.21s/it] " + "Training Progress: 5%|▌ | 5/100 [09:32<2:27:46, 93.33s/it] " ] }, { @@ -467,7 +467,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 6%|▌ | 6/100 [10:46<2:17:27, 87.73s/it]" + "Training Progress: 6%|▌ | 6/100 [10:49<2:17:14, 87.60s/it]" ] }, { @@ -481,7 +481,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 7%|▋ | 7/100 [12:02<2:10:11, 84.00s/it]" + "Training Progress: 7%|▋ | 7/100 [12:05<2:09:55, 83.83s/it]" ] }, { @@ -495,7 +495,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 8%|▊ | 8/100 [13:19<2:05:08, 81.62s/it]" + "Training Progress: 8%|▊ | 8/100 [13:21<2:05:04, 81.57s/it]" ] }, { @@ -509,7 +509,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 9%|▉ | 9/100 [14:36<2:01:38, 80.21s/it]" + "Training Progress: 9%|▉ | 9/100 [14:38<2:01:25, 80.06s/it]" ] }, { @@ -523,7 +523,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 10%|█ | 10/100 [15:53<1:58:45, 79.18s/it]" + "Training Progress: 10%|█ | 10/100 [15:55<1:58:29, 79.00s/it]" ] }, { @@ -537,7 +537,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 11%|█ | 11/100 [17:10<1:56:41, 78.67s/it]" + "Training Progress: 11%|█ | 11/100 [17:12<1:56:09, 78.30s/it]" ] }, { @@ -551,7 +551,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 12%|█▏ | 12/100 [18:27<1:54:34, 78.12s/it]" + "Training Progress: 12%|█▏ | 12/100 [18:29<1:54:17, 77.93s/it]" ] }, { @@ -565,7 +565,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 13%|█▎ | 13/100 [19:46<1:53:25, 78.23s/it]" + "Training Progress: 13%|█▎ | 13/100 [19:45<1:52:30, 77.59s/it]" ] }, { @@ -579,7 +579,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 14%|█▍ | 14/100 [21:04<1:52:18, 78.35s/it]" + "Training Progress: 14%|█▍ | 14/100 [21:01<1:50:33, 77.13s/it]" ] }, { @@ -593,7 +593,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 15%|█▌ | 15/100 [22:25<1:52:00, 79.07s/it]" + "Training Progress: 15%|█▌ | 15/100 [22:19<1:49:13, 77.10s/it]" ] }, { @@ -607,7 +607,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 16%|█▌ | 16/100 [23:43<1:50:10, 78.70s/it]" + "Training Progress: 16%|█▌ | 16/100 [23:35<1:47:48, 77.00s/it]" ] }, { @@ -621,7 +621,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 17%|█▋ | 17/100 [25:01<1:48:45, 78.62s/it]" + "Training Progress: 17%|█▋ | 17/100 [24:52<1:46:34, 77.04s/it]" ] }, { @@ -635,7 +635,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 18%|█▊ | 18/100 [26:20<1:47:31, 78.68s/it]" + "Training Progress: 18%|█▊ | 18/100 [26:09<1:45:12, 76.98s/it]" ] }, { @@ -649,7 +649,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 19%|█▉ | 19/100 [27:40<1:46:32, 78.93s/it]" + "Training Progress: 19%|█▉ | 19/100 [27:26<1:43:45, 76.85s/it]" ] }, { @@ -663,7 +663,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 20%|██ | 20/100 [28:58<1:44:54, 78.69s/it]" + "Training Progress: 20%|██ | 20/100 [28:43<1:42:29, 76.87s/it]" ] }, { @@ -677,7 +677,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 21%|██ | 21/100 [30:17<1:43:43, 78.78s/it]" + "Training Progress: 21%|██ | 21/100 [30:00<1:41:12, 76.86s/it]" ] }, { @@ -691,7 +691,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 22%|██▏ | 22/100 [31:34<1:41:51, 78.35s/it]" + "Training Progress: 22%|██▏ | 22/100 [31:17<1:40:02, 76.96s/it]" ] }, { @@ -705,7 +705,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 23%|██▎ | 23/100 [32:52<1:40:17, 78.15s/it]" + "Training Progress: 23%|██▎ | 23/100 [32:34<1:38:48, 77.00s/it]" ] }, { @@ -719,7 +719,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 24%|██▍ | 24/100 [34:10<1:38:58, 78.13s/it]" + "Training Progress: 24%|██▍ | 24/100 [33:51<1:37:29, 76.97s/it]" ] }, { @@ -733,7 +733,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 25%|██▌ | 25/100 [35:27<1:37:18, 77.85s/it]" + "Training Progress: 25%|██▌ | 25/100 [35:11<1:37:22, 77.90s/it]" ] }, { @@ -747,7 +747,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 26%|██▌ | 26/100 [36:46<1:36:37, 78.35s/it]" + "Training Progress: 26%|██▌ | 26/100 [36:27<1:35:37, 77.53s/it]" ] }, { @@ -761,7 +761,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 27%|██▋ | 27/100 [38:05<1:35:29, 78.49s/it]" + "Training Progress: 27%|██▋ | 27/100 [37:45<1:34:08, 77.38s/it]" ] }, { @@ -775,7 +775,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 28%|██▊ | 28/100 [39:27<1:35:25, 79.52s/it]" + "Training Progress: 28%|██▊ | 28/100 [39:01<1:32:39, 77.22s/it]" ] }, { @@ -789,7 +789,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 29%|██▉ | 29/100 [40:50<1:35:06, 80.38s/it]" + "Training Progress: 29%|██▉ | 29/100 [40:19<1:31:27, 77.29s/it]" ] }, { @@ -803,7 +803,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 30%|███ | 30/100 [42:11<1:34:06, 80.67s/it]" + "Training Progress: 30%|███ | 30/100 [41:36<1:30:12, 77.32s/it]" ] }, { @@ -817,7 +817,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 31%|███ | 31/100 [43:33<1:33:06, 80.97s/it]" + "Training Progress: 31%|███ | 31/100 [42:57<1:30:04, 78.32s/it]" ] }, { @@ -831,7 +831,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 32%|███▏ | 32/100 [44:55<1:32:06, 81.27s/it]" + "Training Progress: 32%|███▏ | 32/100 [44:11<1:27:26, 77.15s/it]" ] }, { @@ -845,7 +845,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 33%|███▎ | 33/100 [46:14<1:30:10, 80.76s/it]" + "Training Progress: 33%|███▎ | 33/100 [45:26<1:25:17, 76.38s/it]" ] }, { @@ -859,7 +859,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 34%|███▍ | 34/100 [47:33<1:28:04, 80.07s/it]" + "Training Progress: 34%|███▍ | 34/100 [46:41<1:23:31, 75.93s/it]" ] }, { @@ -873,7 +873,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 35%|███▌ | 35/100 [48:53<1:26:41, 80.02s/it]" + "Training Progress: 35%|███▌ | 35/100 [47:55<1:21:47, 75.50s/it]" ] }, { @@ -887,7 +887,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 36%|███▌ | 36/100 [50:13<1:25:34, 80.23s/it]" + "Training Progress: 36%|███▌ | 36/100 [49:10<1:20:18, 75.28s/it]" ] }, { @@ -901,7 +901,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 37%|███▋ | 37/100 [51:34<1:24:24, 80.38s/it]" + "Training Progress: 37%|███▋ | 37/100 [50:25<1:18:48, 75.05s/it]" ] }, { @@ -915,7 +915,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 38%|███▊ | 38/100 [52:53<1:22:45, 80.09s/it]" + "Training Progress: 38%|███▊ | 38/100 [51:39<1:17:26, 74.94s/it]" ] }, { @@ -929,7 +929,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 39%|███▉ | 39/100 [54:15<1:21:53, 80.55s/it]" + "Training Progress: 39%|███▉ | 39/100 [52:54<1:16:08, 74.90s/it]" ] }, { @@ -943,7 +943,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 40%|████ | 40/100 [55:36<1:20:35, 80.59s/it]" + "Training Progress: 40%|████ | 40/100 [54:10<1:15:08, 75.15s/it]" ] }, { @@ -957,7 +957,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 41%|████ | 41/100 [56:55<1:18:58, 80.32s/it]" + "Training Progress: 41%|████ | 41/100 [55:25<1:13:49, 75.08s/it]" ] }, { @@ -971,7 +971,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 42%|████▏ | 42/100 [58:18<1:18:15, 80.96s/it]" + "Training Progress: 42%|████▏ | 42/100 [56:40<1:12:39, 75.16s/it]" ] }, { @@ -985,7 +985,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 43%|████▎ | 43/100 [59:40<1:17:19, 81.40s/it]" + "Training Progress: 43%|████▎ | 43/100 [57:55<1:11:20, 75.10s/it]" ] }, { @@ -999,7 +999,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 44%|████▍ | 44/100 [1:01:00<1:15:33, 80.95s/it]" + "Training Progress: 44%|████▍ | 44/100 [59:12<1:10:35, 75.63s/it]" ] }, { @@ -1013,7 +1013,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 45%|████▌ | 45/100 [1:02:22<1:14:28, 81.24s/it]" + "Training Progress: 45%|████▌ | 45/100 [1:00:27<1:09:15, 75.56s/it]" ] }, { @@ -1027,7 +1027,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 46%|████▌ | 46/100 [1:03:42<1:12:51, 80.96s/it]" + "Training Progress: 46%|████▌ | 46/100 [1:01:42<1:07:49, 75.36s/it]" ] }, { @@ -1041,7 +1041,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 47%|████▋ | 47/100 [1:05:04<1:11:47, 81.28s/it]" + "Training Progress: 47%|████▋ | 47/100 [1:02:58<1:06:35, 75.39s/it]" ] }, { @@ -1055,7 +1055,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 48%|████▊ | 48/100 [1:06:24<1:10:04, 80.86s/it]" + "Training Progress: 48%|████▊ | 48/100 [1:04:13<1:05:26, 75.52s/it]" ] }, { @@ -1069,7 +1069,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 49%|████▉ | 49/100 [1:07:46<1:08:56, 81.11s/it]" + "Training Progress: 49%|████▉ | 49/100 [1:05:29<1:04:07, 75.44s/it]" ] }, { @@ -1083,7 +1083,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 50%|█████ | 50/100 [1:09:09<1:08:08, 81.78s/it]" + "Training Progress: 50%|█████ | 50/100 [1:06:46<1:03:14, 75.90s/it]" ] }, { @@ -1097,7 +1097,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 51%|█████ | 51/100 [1:10:32<1:06:58, 82.01s/it]" + "Training Progress: 51%|█████ | 51/100 [1:08:01<1:01:50, 75.73s/it]" ] }, { @@ -1111,7 +1111,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 52%|█████▏ | 52/100 [1:11:54<1:05:33, 81.96s/it]" + "Training Progress: 52%|█████▏ | 52/100 [1:09:16<1:00:27, 75.58s/it]" ] }, { @@ -1125,7 +1125,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 53%|█████▎ | 53/100 [1:13:15<1:04:07, 81.85s/it]" + "Training Progress: 53%|█████▎ | 53/100 [1:10:31<59:06, 75.45s/it] " ] }, { @@ -1139,7 +1139,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 54%|█████▍ | 54/100 [1:14:38<1:02:49, 81.95s/it]" + "Training Progress: 54%|█████▍ | 54/100 [1:11:46<57:44, 75.33s/it]" ] }, { @@ -1153,7 +1153,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 55%|█████▌ | 55/100 [1:15:59<1:01:19, 81.76s/it]" + "Training Progress: 55%|█████▌ | 55/100 [1:13:01<56:25, 75.24s/it]" ] }, { @@ -1167,7 +1167,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 56%|█████▌ | 56/100 [1:17:22<1:00:18, 82.24s/it]" + "Training Progress: 56%|█████▌ | 56/100 [1:14:17<55:11, 75.25s/it]" ] }, { @@ -1181,7 +1181,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 57%|█████▋ | 57/100 [1:18:42<58:30, 81.65s/it] " + "Training Progress: 57%|█████▋ | 57/100 [1:15:32<53:57, 75.28s/it]" ] }, { @@ -1195,7 +1195,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 58%|█████▊ | 58/100 [1:20:05<57:19, 81.89s/it]" + "Training Progress: 58%|█████▊ | 58/100 [1:16:47<52:38, 75.21s/it]" ] }, { @@ -1209,7 +1209,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 59%|█████▉ | 59/100 [1:21:26<55:53, 81.78s/it]" + "Training Progress: 59%|█████▉ | 59/100 [1:18:26<56:11, 82.23s/it]" ] }, { @@ -1223,7 +1223,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 60%|██████ | 60/100 [1:22:50<54:55, 82.39s/it]" + "Training Progress: 60%|██████ | 60/100 [2:35:53<16:07:46, 1451.67s/it]" ] }, { @@ -1237,7 +1237,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 61%|██████ | 61/100 [1:24:14<53:54, 82.95s/it]" + "Training Progress: 61%|██████ | 61/100 [2:37:09<11:15:17, 1038.91s/it]" ] }, { @@ -1251,7 +1251,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 62%|██████▏ | 62/100 [1:25:50<54:52, 86.65s/it]" + "Training Progress: 62%|██████▏ | 62/100 [2:38:25<7:55:01, 750.04s/it] " ] }, { @@ -1265,7 +1265,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 63%|██████▎ | 63/100 [1:27:22<54:24, 88.23s/it]" + "Training Progress: 63%|██████▎ | 63/100 [2:39:41<5:37:49, 547.83s/it]" ] }, { @@ -1279,7 +1279,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 64%|██████▍ | 64/100 [1:28:42<51:26, 85.74s/it]" + "Training Progress: 64%|██████▍ | 64/100 [2:40:56<4:03:43, 406.21s/it]" ] }, { @@ -1293,7 +1293,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 65%|██████▌ | 65/100 [1:30:01<48:52, 83.78s/it]" + "Training Progress: 65%|██████▌ | 65/100 [2:42:12<2:59:02, 306.92s/it]" ] }, { @@ -1307,7 +1307,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 66%|██████▌ | 66/100 [1:31:20<46:39, 82.33s/it]" + "Training Progress: 66%|██████▌ | 66/100 [2:43:27<2:14:35, 237.53s/it]" ] }, { @@ -1321,7 +1321,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 67%|██████▋ | 67/100 [1:32:39<44:45, 81.38s/it]" + "Training Progress: 67%|██████▋ | 67/100 [2:44:43<1:43:58, 189.05s/it]" ] }, { @@ -1335,7 +1335,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 68%|██████▊ | 68/100 [1:33:58<43:00, 80.64s/it]" + "Training Progress: 68%|██████▊ | 68/100 [2:46:00<1:22:49, 155.31s/it]" ] }, { @@ -1349,7 +1349,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 69%|██████▉ | 69/100 [1:35:17<41:27, 80.23s/it]" + "Training Progress: 69%|██████▉ | 69/100 [2:47:16<1:08:00, 131.64s/it]" ] }, { @@ -1363,7 +1363,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 70%|███████ | 70/100 [1:36:36<39:55, 79.86s/it]" + "Training Progress: 70%|███████ | 70/100 [2:48:32<57:29, 114.97s/it] " ] }, { @@ -1377,7 +1377,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 71%|███████ | 71/100 [1:37:55<38:26, 79.53s/it]" + "Training Progress: 71%|███████ | 71/100 [2:49:48<49:55, 103.30s/it]" ] }, { @@ -1391,7 +1391,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 72%|███████▏ | 72/100 [1:39:15<37:11, 79.71s/it]" + "Training Progress: 72%|███████▏ | 72/100 [2:51:05<44:25, 95.20s/it] " ] }, { @@ -1405,7 +1405,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 73%|███████▎ | 73/100 [1:40:35<35:54, 79.79s/it]" + "Training Progress: 73%|███████▎ | 73/100 [2:52:20<40:13, 89.38s/it]" ] }, { @@ -1419,7 +1419,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 74%|███████▍ | 74/100 [1:41:56<34:43, 80.13s/it]" + "Training Progress: 74%|███████▍ | 74/100 [2:53:36<36:56, 85.24s/it]" ] }, { @@ -1433,7 +1433,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 75%|███████▌ | 75/100 [1:43:17<33:27, 80.28s/it]" + "Training Progress: 75%|███████▌ | 75/100 [2:54:51<34:17, 82.29s/it]" ] }, { @@ -1447,7 +1447,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 76%|███████▌ | 76/100 [1:44:37<32:04, 80.19s/it]" + "Training Progress: 76%|███████▌ | 76/100 [2:56:07<32:05, 80.22s/it]" ] }, { @@ -1461,7 +1461,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 77%|███████▋ | 77/100 [1:45:56<30:37, 79.87s/it]" + "Training Progress: 77%|███████▋ | 77/100 [2:57:22<30:10, 78.74s/it]" ] }, { @@ -1475,7 +1475,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 78%|███████▊ | 78/100 [1:47:15<29:11, 79.62s/it]" + "Training Progress: 78%|███████▊ | 78/100 [2:58:38<28:32, 77.83s/it]" ] }, { @@ -1489,7 +1489,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 79%|███████▉ | 79/100 [1:48:34<27:51, 79.60s/it]" + "Training Progress: 79%|███████▉ | 79/100 [2:59:53<26:58, 77.09s/it]" ] }, { @@ -1503,7 +1503,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 80%|████████ | 80/100 [1:49:54<26:35, 79.76s/it]" + "Training Progress: 80%|████████ | 80/100 [3:01:09<25:33, 76.66s/it]" ] }, { @@ -1517,7 +1517,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 81%|████████ | 81/100 [1:51:14<25:16, 79.84s/it]" + "Training Progress: 81%|████████ | 81/100 [3:02:25<24:15, 76.59s/it]" ] }, { @@ -1531,7 +1531,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 82%|████████▏ | 82/100 [1:52:35<24:03, 80.21s/it]" + "Training Progress: 82%|████████▏ | 82/100 [3:03:41<22:54, 76.38s/it]" ] }, { @@ -1545,7 +1545,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 83%|████████▎ | 83/100 [1:53:55<22:41, 80.08s/it]" + "Training Progress: 83%|████████▎ | 83/100 [3:04:56<21:33, 76.09s/it]" ] }, { @@ -1559,7 +1559,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 84%|████████▍ | 84/100 [1:55:15<21:21, 80.10s/it]" + "Training Progress: 84%|████████▍ | 84/100 [3:06:12<20:16, 76.00s/it]" ] }, { @@ -1573,7 +1573,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 85%|████████▌ | 85/100 [1:56:34<19:56, 79.77s/it]" + "Training Progress: 85%|████████▌ | 85/100 [3:07:28<18:58, 75.88s/it]" ] }, { @@ -1587,7 +1587,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 86%|████████▌ | 86/100 [1:57:54<18:34, 79.60s/it]" + "Training Progress: 86%|████████▌ | 86/100 [3:08:43<17:40, 75.73s/it]" ] }, { @@ -1601,7 +1601,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 87%|████████▋ | 87/100 [1:59:13<17:14, 79.55s/it]" + "Training Progress: 87%|████████▋ | 87/100 [3:09:59<16:24, 75.74s/it]" ] }, { @@ -1615,7 +1615,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 88%|████████▊ | 88/100 [2:00:33<15:56, 79.73s/it]" + "Training Progress: 88%|████████▊ | 88/100 [3:11:15<15:10, 75.89s/it]" ] }, { @@ -1629,7 +1629,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 89%|████████▉ | 89/100 [2:01:52<14:34, 79.53s/it]" + "Training Progress: 89%|████████▉ | 89/100 [3:12:31<13:55, 75.97s/it]" ] }, { @@ -1643,7 +1643,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 90%|█████████ | 90/100 [2:03:12<13:15, 79.51s/it]" + "Training Progress: 90%|█████████ | 90/100 [3:13:47<12:39, 75.96s/it]" ] }, { @@ -1657,7 +1657,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 91%|█████████ | 91/100 [2:04:32<11:57, 79.78s/it]" + "Training Progress: 91%|█████████ | 91/100 [3:15:03<11:23, 75.96s/it]" ] }, { @@ -1671,7 +1671,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 92%|█████████▏| 92/100 [2:05:52<10:39, 79.96s/it]" + "Training Progress: 92%|█████████▏| 92/100 [3:16:19<10:07, 75.89s/it]" ] }, { @@ -1685,7 +1685,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 93%|█████████▎| 93/100 [2:07:13<09:21, 80.23s/it]" + "Training Progress: 93%|█████████▎| 93/100 [3:17:35<08:50, 75.83s/it]" ] }, { @@ -1699,7 +1699,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 94%|█████████▍| 94/100 [2:08:44<08:20, 83.37s/it]" + "Training Progress: 94%|█████████▍| 94/100 [3:18:51<07:35, 75.92s/it]" ] }, { @@ -1713,7 +1713,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 95%|█████████▌| 95/100 [2:10:20<07:15, 87.17s/it]" + "Training Progress: 95%|█████████▌| 95/100 [3:20:07<06:19, 75.97s/it]" ] }, { @@ -1727,7 +1727,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 96%|█████████▌| 96/100 [2:11:46<05:46, 86.74s/it]" + "Training Progress: 96%|█████████▌| 96/100 [3:21:23<05:04, 76.04s/it]" ] }, { @@ -1741,7 +1741,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 97%|█████████▋| 97/100 [2:13:05<04:13, 84.58s/it]" + "Training Progress: 97%|█████████▋| 97/100 [3:22:40<03:48, 76.19s/it]" ] }, { @@ -1755,7 +1755,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 98%|█████████▊| 98/100 [2:14:25<02:46, 83.18s/it]" + "Training Progress: 98%|█████████▊| 98/100 [3:23:56<02:32, 76.20s/it]" ] }, { @@ -1769,7 +1769,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 99%|█████████▉| 99/100 [2:15:45<01:22, 82.11s/it]" + "Training Progress: 99%|█████████▉| 99/100 [3:25:13<01:16, 76.43s/it]" ] }, { @@ -1783,7 +1783,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 100%|██████████| 100/100 [2:17:06<00:00, 81.94s/it]" + "Training Progress: 100%|██████████| 100/100 [3:26:29<00:00, 123.90s/it]" ] }, { @@ -1797,7 +1797,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 100%|██████████| 100/100 [2:17:07<00:00, 82.28s/it]\n" + "\n" ] }, { @@ -1835,7 +1835,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1844,11 +1844,14 @@ "text": [ "What is FHE?\n", "\n", - "FHE? FHS is a groundbreaking concept that allows the use of cloud computing to help protect data and privacy while still allowing data to be\n" + "FHE is a breakthrough in computer security. It allows computations on encrypted data and store it in a database, which can be used to\n" ] } ], "source": [ + "# Seed for best reproducibility\n", + "torch.manual_seed(SEED)\n", + "\n", "prompt = \"What is FHE ?\"\n", "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", "print(generated_text)" @@ -1856,7 +1859,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1865,11 +1868,14 @@ "text": [ "What is FHE?\n", "\n", - "FHE is a new form of the term \"fission energy\". It is the energy of fusion of a process which is in a state\n" + "We're talking about the FH and its use of the H, the use the D, and the C, which are often used interchange\n" ] } ], "source": [ + "# Seed for best reproducibility\n", + "torch.manual_seed(SEED)\n", + "\n", "peft_model.disable_adapter_layers()\n", "\n", "prompt = \"What is FHE ?\"\n", @@ -1923,7 +1929,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1938,7 +1944,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1956,7 +1962,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [ { From 2f20b0f34ddf247fa3ff4946b60b6afdb4f99d82 Mon Sep 17 00:00:00 2001 From: jfrery Date: Mon, 23 Sep 2024 12:45:56 +0200 Subject: [PATCH 23/32] chore: rename use case and notebook + add readme + refacto + fix --- .github/workflows/refresh-one-notebook.yaml | 4 +- .../workflows/run_one_use_cases_example.yaml | 2 +- deps_licenses/licenses_linux_user.txt.md5 | 2 +- poetry.lock | 64 +- pyproject.toml | 1 + src/concrete/ml/torch/lora.py | 261 +++ tests/torch/test_lora.py | 313 +++ .../lora_finetune/gpt2_finetune_hybrid.ipynb | 1998 ----------------- .../lora_finetune/lora_module.py | 68 - .../lora_finetune/remote_module.py | 49 - .../lora_finetune/requirements.txt | 8 - .../.gitignore | 0 .../lora_finetuning/GPT2FineTuneHybrid.ipynb | 483 ++++ .../Makefile | 2 +- use_case_examples/lora_finetuning/README.md | 66 + .../data_finetune/what_is_fhe.txt | 0 .../lora_finetuning/requirements.txt | 4 + .../lora_finetuning/utils_lora.py | 79 + 18 files changed, 1274 insertions(+), 2130 deletions(-) create mode 100644 src/concrete/ml/torch/lora.py create mode 100644 tests/torch/test_lora.py delete mode 100644 use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb delete mode 100644 use_case_examples/lora_finetune/lora_module.py delete mode 100644 use_case_examples/lora_finetune/remote_module.py delete mode 100644 use_case_examples/lora_finetune/requirements.txt rename use_case_examples/{lora_finetune => lora_finetuning}/.gitignore (100%) create mode 100644 use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb rename use_case_examples/{lora_finetune => lora_finetuning}/Makefile (79%) create mode 100644 use_case_examples/lora_finetuning/README.md rename use_case_examples/{lora_finetune => lora_finetuning}/data_finetune/what_is_fhe.txt (100%) create mode 100644 use_case_examples/lora_finetuning/requirements.txt create mode 100644 use_case_examples/lora_finetuning/utils_lora.py diff --git a/.github/workflows/refresh-one-notebook.yaml b/.github/workflows/refresh-one-notebook.yaml index 04469bca1..8b9887101 100644 --- a/.github/workflows/refresh-one-notebook.yaml +++ b/.github/workflows/refresh-one-notebook.yaml @@ -21,7 +21,7 @@ on: - FullyConnectedNeuralNetwork \n - FullyConnectedNeuralNetworkOnMNIST \n - GLMComparison \n - - gpt2_finetune_hybrid \n + - GPT2FineTuneHybrid \n - HealthCarePrediction \n - ImportingFromScikitLearn \n - KaggleTitanic \n @@ -68,7 +68,7 @@ env: FullyConnectedNeuralNetwork: "docs/advanced_examples/FullyConnectedNeuralNetwork.ipynb" FullyConnectedNeuralNetworkOnMNIST: "docs/advanced_examples/FullyConnectedNeuralNetworkOnMNIST.ipynb" GLMComparison: "docs/advanced_examples/GLMComparison.ipynb" - gpt2_finetune_hybrid: "use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb" + GPT2FineTuneHybrid: "use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb" HealthCarePrediction: "use_case_examples/disease_prediction/HealthCarePrediction.ipynb" ImportingFromScikitLearn: "docs/advanced_examples/ImportingFromScikitLearn.ipynb" KaggleTitanic: "use_case_examples/titanic/KaggleTitanic.ipynb" diff --git a/.github/workflows/run_one_use_cases_example.yaml b/.github/workflows/run_one_use_cases_example.yaml index 2072fd695..37ab49b81 100644 --- a/.github/workflows/run_one_use_cases_example.yaml +++ b/.github/workflows/run_one_use_cases_example.yaml @@ -19,7 +19,7 @@ on: - federated_learning - hybrid_model - llm - - lora_finetune + - lora_finetuning - resnet - sentiment_analysis_with_transformer - titanic diff --git a/deps_licenses/licenses_linux_user.txt.md5 b/deps_licenses/licenses_linux_user.txt.md5 index 4344e4be6..9519c1c94 100644 --- a/deps_licenses/licenses_linux_user.txt.md5 +++ b/deps_licenses/licenses_linux_user.txt.md5 @@ -1 +1 @@ -31249a607336424af0d790feb9db6252 +af423f91bb5313f1f1670ea72892d364 diff --git a/poetry.lock b/poetry.lock index e6aeb0f7f..f3b716bb4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,36 @@ # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +[[package]] +name = "accelerate" +version = "0.34.2" +description = "Accelerate" +optional = false +python-versions = ">=3.8.0" +files = [ + {file = "accelerate-0.34.2-py3-none-any.whl", hash = "sha256:d69159e2c4e4a473d14443b27d2d732929254e826b3ab4813b3785b5ac616c7c"}, + {file = "accelerate-0.34.2.tar.gz", hash = "sha256:98c1ebe1f5a45c0a3af02dc60b5bb8b7d58d60c3326a326a06ce6d956b18ca5b"}, +] + +[package.dependencies] +huggingface-hub = ">=0.21.0" +numpy = ">=1.17,<3.0.0" +packaging = ">=20.0" +psutil = "*" +pyyaml = "*" +safetensors = ">=0.4.3" +torch = ">=1.10.0" + +[package.extras] +deepspeed = ["deepspeed"] +dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.2.1,<0.3.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.2.1,<0.3.0)"] +rich = ["rich"] +sagemaker = ["sagemaker"] +test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"] +test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"] +testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] + [[package]] name = "aiohttp" version = "3.9.5" @@ -3098,7 +3129,6 @@ files = [ {file = "msgpack-1.0.8-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5fbb160554e319f7b22ecf530a80a3ff496d38e8e07ae763b9e82fadfe96f273"}, {file = "msgpack-1.0.8-cp39-cp39-win32.whl", hash = "sha256:f9af38a89b6a5c04b7d18c492c8ccf2aee7048aff1ce8437c4683bb5a1df893d"}, {file = "msgpack-1.0.8-cp39-cp39-win_amd64.whl", hash = "sha256:ed59dd52075f8fc91da6053b12e8c89e37aa043f8986efd89e61fae69dc1b011"}, - {file = "msgpack-1.0.8-py3-none-any.whl", hash = "sha256:24f727df1e20b9876fa6e95f840a2a2651e34c0ad147676356f4bf5fbb0206ca"}, {file = "msgpack-1.0.8.tar.gz", hash = "sha256:95c02b0e27e706e48d0e5426d1710ca78e0f0628d6e89d5b5a5b91a5f12274f3"}, ] @@ -3987,6 +4017,35 @@ files = [ mako = "*" markdown = ">=3.0" +[[package]] +name = "peft" +version = "0.12.0" +description = "Parameter-Efficient Fine-Tuning (PEFT)" +optional = false +python-versions = ">=3.8.0" +files = [ + {file = "peft-0.12.0-py3-none-any.whl", hash = "sha256:a47915efb08af50e9fda267b7bf1b5b6eff33ccbb08791bdb544dccb8788f674"}, + {file = "peft-0.12.0.tar.gz", hash = "sha256:253205bd478e985ccdc7f04804aab9c95f479130c517bf6e474b8d509db5f4a4"}, +] + +[package.dependencies] +accelerate = ">=0.21.0" +huggingface-hub = ">=0.17.0" +numpy = ">=1.17" +packaging = ">=20.0" +psutil = "*" +pyyaml = "*" +safetensors = "*" +torch = ">=1.13.0" +tqdm = "*" +transformers = "*" + +[package.extras] +dev = ["black", "hf-doc-builder", "ruff (>=0.4.8,<0.5.0)"] +docs-specific = ["black", "hf-doc-builder"] +quality = ["black", "hf-doc-builder", "ruff (>=0.4.8,<0.5.0)"] +test = ["black", "datasets", "diffusers (<0.21.0)", "hf-doc-builder", "parameterized", "pytest", "pytest-cov", "pytest-xdist", "ruff (>=0.4.8,<0.5.0)", "scipy"] + [[package]] name = "pexpect" version = "4.9.0" @@ -4822,6 +4881,7 @@ files = [ {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"}, {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, + {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"}, {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"}, {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"}, {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, @@ -7059,4 +7119,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<3.12" -content-hash = "edea984f8f20343e5c2f234524fb89463546c1bd559086c6fe5298354daa233d" +content-hash = "cc719ba69679a3c476c57e0df57ed81c3a20ada7855c3ca905a5b7cec9817ff6" diff --git a/pyproject.toml b/pyproject.toml index 4ffdfbfa0..410c3d951 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -124,6 +124,7 @@ torchvision = [ {version = "0.17.2", markers = "platform_system=='Darwin' and platform_machine!='arm64'" }, {version = "0.18.1", markers = "platform_system!='Darwin' or platform_machine=='arm64'" } ] +peft = "^0.12.0" [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/src/concrete/ml/torch/lora.py b/src/concrete/ml/torch/lora.py new file mode 100644 index 000000000..03fb99b89 --- /dev/null +++ b/src/concrete/ml/torch/lora.py @@ -0,0 +1,261 @@ +"""This module contains classes for LoRA (Low-Rank Adaptation) training and custom layers.""" + +import torch + +# pylint: disable=abstract-method +# pylint: disable=arguments-differ + + +class LoraTraining(torch.nn.Module): + """LoraTraining module for fine-tuning with LoRA.""" + + SUPPORTED_MODELS = ["gpt2"] + + def __init__(self, inference_model, gradient_accumulation_steps) -> None: + super().__init__() + + self.inference_model = inference_model + + # Validate the base model type + self._validate_model_type() + + self.optimizer = None + self.lr_scheduler = None + + self.gradient_accumulation_steps = gradient_accumulation_steps + self.max_grad_norm = None + + self.calibrate = False + self.run_optimizer = False + + def _validate_model_type(self): + """Validate the model type. + + Raises: + ValueError: If the model type is not supported. + """ + try: + # Access the base model from PeftModelForCausalLM + base_model = self.inference_model.base_model.model + + # Retrieve the model type from the configuration + model_type = getattr(base_model.config, "model_type", None) + + if model_type not in self.SUPPORTED_MODELS: + raise ValueError( + f"Unsupported model type: '{model_type}'. " + f"Supported models are: {self.SUPPORTED_MODELS}" + ) + + except AttributeError as e: + raise ValueError( + "Unable to determine the base model type. " + "Ensure that the inference_model has a " + "'base_model.model.config.model_type' attribute." + ) from e + + def update_training_parameters(self, optimizer, lr_scheduler, training_args): + """Update training parameters for the LoRA module. + + Args: + optimizer: The optimizer to use for training. + lr_scheduler: The learning rate scheduler to use for training. + training_args: The training arguments containing gradient + accumulation steps and max grad norm. + """ + assert self.gradient_accumulation_steps == training_args.gradient_accumulation_steps + + self.optimizer = optimizer + self.lr_scheduler = lr_scheduler + self.max_grad_norm = training_args.max_grad_norm + + def forward(self, inputs): + """Forward pass of the LoRA training module. + + Args: + inputs: A tuple containing input tensors and labels. + + Returns: + A tuple containing the loss and gradient norm. + + Raises: + ValueError: If the model does not return a loss. + """ + # Remove this once hybrid model supports multiple inputs + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4568 + x, y = inputs + + # Correctly pass labels as a keyword argument + outputs = self.inference_model(x, labels=y) + + # Use getattr to safely access the loss attribute + loss = getattr(outputs, "loss", None) + if loss is None: + raise ValueError( + "The model did not return a loss. Ensure that 'labels' are correctly provided." + ) + + loss = loss / self.gradient_accumulation_steps + + # Update gradients + # We need to set requires grad to the loss manually because the inference model's last + # step is the "lm_head" layer, which is detached from the graph by the hybrid model + loss.requires_grad_(True) + loss.backward() + + grad_norm = None + if not self.calibrate and self.run_optimizer: + assert self.optimizer is not None + assert self.lr_scheduler is not None + assert self.max_grad_norm is not None + + grad_norm = torch.nn.utils.clip_grad_norm_( + self.inference_model.parameters(), max_norm=self.max_grad_norm, norm_type=2 + ) + + self.optimizer.step() + self.lr_scheduler.step() + + self.inference_model.zero_grad() + + # Clean gradients after calibration + elif self.calibrate: + self.inference_model.zero_grad() + + return (loss, grad_norm) + + def toggle_calibrate(self, enable: bool = True): + """Toggle calibration mode. + + Args: + enable (bool): Whether to enable calibration mode. + """ + self.calibrate = enable + + def toggle_run_optimizer(self, enable: bool = True): + """Toggle optimizer execution. + + Args: + enable (bool): Whether to enable optimizer execution. + """ + self.run_optimizer = enable + + +class ForwardModule(torch.nn.Module): + """Forward module for custom convolution.""" + + def __init__(self, weight, bias=None): + super().__init__() + self.weight = weight # Assume weight is passed as a pre-initialized tensor + self.bias = bias + + def forward(self, input_tensor): + """Forward pass of the forward module. + + Args: + input_tensor: The input tensor. + + Returns: + The output tensor after applying the forward pass. + """ + output = input_tensor @ self.weight + if self.bias is not None: + output = output + self.bias + return output + + +class BackwardModule(torch.nn.Module): + """Backward module for custom convolution.""" + + def __init__(self, weight): + super().__init__() + self.weight = weight # This is the same weight used in ForwardModule + + def forward(self, grad_output): + """Forward pass of the backward module. + + Args: + grad_output: The gradient output tensor. + + Returns: + The gradient input tensor after applying the backward pass. + """ + return grad_output @ self.weight.t() + + +class ForwardBackwardModule(torch.autograd.Function): + """Custom autograd function for forward and backward passes.""" + + @staticmethod + def forward(ctx, input_tensor, forward_module, backward_module): + """Forward pass of the custom autograd function. + + Args: + ctx: The context object. + input_tensor: The input tensor. + forward_module: The forward module. + backward_module: The backward module. + + Returns: + The output tensor after applying the forward pass. + """ + ctx.backward_module = backward_module + output = forward_module.forward(input_tensor) + return output + + @staticmethod + def backward(ctx, grad_output): + """Backward pass of the custom autograd function. + + Args: + ctx: The context object. + grad_output: The gradient output tensor. + + Returns: + The gradient input tensor after applying the backward pass. + """ + backward_module = ctx.backward_module + grad_input = backward_module.forward(grad_output) + + # grad_weight and grad_bias are not needed when computing the backward for lora + return grad_input, None, None + + +class CustomConv1D(torch.nn.Module): + """Custom 1D convolution module.""" + + def __init__(self, weight, bias=None): + super().__init__() + self.forward_module = ForwardModule(weight, bias=bias) + self.backward_module = BackwardModule(weight) + + def forward(self, input_tensor): + """Forward pass of the custom 1D convolution. + + Args: + input_tensor: The input tensor. + + Returns: + The output tensor after applying the custom 1D convolution. + """ + return ForwardBackwardModule.apply(input_tensor, self.forward_module, self.backward_module) + + +class CustomLinear(torch.nn.Module): + """Custom linear module.""" + + def __init__(self, weight, bias=None): + super().__init__() + self.forward_module = ForwardModule(weight, bias=bias) + self.backward_module = BackwardModule(weight) + + def forward(self, input_tensor): + """Forward pass of the custom linear module. + + Args: + input_tensor: The input tensor. + + Returns: + The output tensor after applying the custom linear module. + """ + return ForwardBackwardModule.apply(input_tensor, self.forward_module, self.backward_module) diff --git a/tests/torch/test_lora.py b/tests/torch/test_lora.py new file mode 100644 index 000000000..85c394c10 --- /dev/null +++ b/tests/torch/test_lora.py @@ -0,0 +1,313 @@ +"""Tests for the LoraTraining class and related modules in lora.py.""" + +from collections import namedtuple + +import pytest +import torch +from torch.optim import SGD +from torch.optim.lr_scheduler import StepLR + +from concrete.ml.torch.lora import ( + BackwardModule, + CustomConv1D, + CustomLinear, + ForwardBackwardModule, + ForwardModule, + LoraTraining, +) + + +class DummyConfig: + """A dummy configuration class to mimic model config.""" + + def __init__(self, model_type): + self.model_type = model_type + + +class DummyBaseModel: + """A dummy base model class to mimic base_model.model.""" + + def __init__(self, model_type): + self.model = DummyModel(model_type) + + +class DummyModel(torch.nn.Module): + """A dummy model class to mimic the actual model.""" + + def __init__(self, model_type): + super().__init__() + self.config = DummyConfig(model_type) + + @staticmethod + def forward(x): + """Dummy forward method.""" + return x + + +class DummyInferenceModel(torch.nn.Module): + """A dummy inference model.""" + + def __init__(self, model_type): + super().__init__() + self.base_model = DummyBaseModel(model_type) + self.linear = torch.nn.Linear(2, 2) + + def forward(self, x, labels=None): + """A simple forward method that returns a loss.""" + logits = self.linear(x) + loss = ((logits - labels) ** 2).mean() if labels is not None else logits.mean() + Output = namedtuple("Output", ["loss"]) + return Output(loss=loss) + + +def test_lora_training_init_supported_model(): + """Test that LoraTraining initializes correctly with a supported model type.""" + inference_model = DummyInferenceModel("gpt2") + lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + assert lora_training.inference_model is inference_model + assert lora_training.gradient_accumulation_steps == 2 + + +def test_lora_training_init_unsupported_model(): + """Test that LoraTraining raises ValueError with an unsupported model type.""" + inference_model = DummyInferenceModel("bert") + with pytest.raises(ValueError) as exc_info: + LoraTraining(inference_model, gradient_accumulation_steps=2) + assert "Unsupported model type" in str(exc_info.value) + + +def test_lora_training_forward(): + """Test the forward method of LoraTraining.""" + inference_model = DummyInferenceModel("gpt2") + lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + + x = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + y = torch.tensor([[0.5, 1.5], [2.5, 3.5]]) + + loss, grad_norm = lora_training((x, y)) + expected_loss = ((inference_model.linear(x) - y) ** 2).mean().item() / 2 + assert loss.item() == expected_loss + assert grad_norm is None # Since run_optimizer is False by default + + +def test_lora_training_forward_no_loss(): + """Test that LoraTraining raises ValueError when model does not return a loss.""" + + class NoLossInferenceModel(DummyInferenceModel): + """An inference model that does not return a loss.""" + + def forward(self, x, labels=None): + Output = namedtuple("Output", ["something_else"]) + return Output(something_else=torch.tensor(1.0)) + + inference_model = NoLossInferenceModel("gpt2") + lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + + x = torch.tensor([[1.0, 2.0]]) + y = torch.tensor([[0.5, 1.5]]) + + with pytest.raises(ValueError) as exc_info: + lora_training((x, y)) + assert "The model did not return a loss" in str(exc_info.value) + + +def test_lora_training_toggle_calibrate(): + """Test the toggle_calibrate method of LoraTraining.""" + inference_model = DummyInferenceModel("gpt2") + lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + + assert not lora_training.calibrate + lora_training.toggle_calibrate(True) + assert lora_training.calibrate + lora_training.toggle_calibrate(False) + assert not lora_training.calibrate + + +def test_lora_training_toggle_run_optimizer(): + """Test the toggle_run_optimizer method of LoraTraining.""" + inference_model = DummyInferenceModel("gpt2") + lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + + assert not lora_training.run_optimizer + lora_training.toggle_run_optimizer(True) + assert lora_training.run_optimizer + lora_training.toggle_run_optimizer(False) + assert not lora_training.run_optimizer + + +def test_lora_training_update_training_parameters(): + """Test the update_training_parameters method of LoraTraining.""" + inference_model = DummyInferenceModel("gpt2") + lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + + optimizer = SGD(inference_model.parameters(), lr=0.01) + lr_scheduler = StepLR(optimizer, step_size=1) + TrainingArgs = namedtuple("TrainingArgs", ["gradient_accumulation_steps", "max_grad_norm"]) + training_args = TrainingArgs(2, 1.0) + + lora_training.update_training_parameters(optimizer, lr_scheduler, training_args) + + assert lora_training.optimizer is optimizer + assert lora_training.lr_scheduler is lr_scheduler + assert lora_training.max_grad_norm == training_args.max_grad_norm + + +def test_lora_training_forward_with_optimizer(): + """Test the forward method with optimizer enabled.""" + + inference_model = DummyInferenceModel("gpt2") + lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + + optimizer = SGD(inference_model.parameters(), lr=0.01) + lr_scheduler = StepLR(optimizer, step_size=1) + TrainingArgs = namedtuple("TrainingArgs", ["gradient_accumulation_steps", "max_grad_norm"]) + training_args = TrainingArgs(2, 1.0) + + lora_training.update_training_parameters(optimizer, lr_scheduler, training_args) + lora_training.toggle_run_optimizer(True) + + x = torch.tensor([[1.0, 2.0]]) + y = torch.tensor([[0.5, 1.5]]) + + # Compute expected_loss before the forward pass + with torch.no_grad(): + expected_loss = ((inference_model.linear(x) - y) ** 2).mean().item() / 2 + + # Save the initial parameters + initial_params = [param.clone() for param in inference_model.parameters()] + + # Perform the forward pass + loss, grad_norm = lora_training((x, y)) + + # Assert that the loss is close to the expected loss + assert abs(loss.item() - expected_loss) < 1e-6 + assert grad_norm is not None + + # Check that parameters have been updated by the optimizer + for initial_param, param in zip(initial_params, inference_model.parameters()): + assert not torch.equal(initial_param, param) + + +def test_forward_module(): + """Test the ForwardModule.""" + weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + bias = torch.tensor([0.5, -0.5]) + module = ForwardModule(weight, bias) + + input_tensor = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) + output = module(input_tensor) + + expected_output = input_tensor @ weight + bias + assert output is not None and torch.allclose(output, expected_output) + + +def test_backward_module(): + """Test the BackwardModule.""" + weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + module = BackwardModule(weight) + + grad_output = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) + grad_input = module(grad_output) + + expected_grad_input = grad_output @ weight.t() + assert grad_input is not None and torch.allclose(grad_input, expected_grad_input) + + +def test_custom_conv1d(): + """Test the CustomConv1D module.""" + weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True) + bias = torch.tensor([0.5, -0.5], requires_grad=True) + module = CustomConv1D(weight, bias) + + input_tensor = torch.tensor([[1.0, 0.0]], requires_grad=True) + output = module(input_tensor) + + expected_output = input_tensor @ weight + bias + assert output is not None and torch.allclose(output, expected_output) + + # Test backward pass + output.sum().backward() + expected_grad_input = torch.ones_like(output) @ weight.t() + assert input_tensor.grad is not None and torch.allclose(input_tensor.grad, expected_grad_input) + + +def test_custom_linear(): + """Test the CustomLinear module.""" + weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True) + bias = torch.tensor([0.5, -0.5], requires_grad=True) + module = CustomLinear(weight, bias) + + input_tensor = torch.tensor([[1.0, 0.0]], requires_grad=True) + output = module(input_tensor) + + expected_output = input_tensor @ weight + bias + assert output is not None and torch.allclose(output, expected_output) + + # Test backward pass + output.sum().backward() + expected_grad_input = torch.ones_like(output) @ weight.t() + assert input_tensor.grad is not None and torch.allclose(input_tensor.grad, expected_grad_input) + + +def test_forward_backward_module(): + """Test the ForwardBackwardModule.""" + weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True) + bias = torch.tensor([0.5, -0.5]) + forward_module = ForwardModule(weight, bias) + backward_module = BackwardModule(weight) + + input_tensor = torch.tensor([[1.0, 0.0]], requires_grad=True) + output = ForwardBackwardModule.apply(input_tensor, forward_module, backward_module) + + expected_output = input_tensor @ weight + bias + assert output is not None and torch.allclose(output, expected_output) + + # Test backward pass + output.sum().backward() + expected_grad_input = torch.ones_like(output) @ weight.t() + assert input_tensor.grad is not None and torch.allclose(input_tensor.grad, expected_grad_input) + + +def test_lora_training_invalid_inference_model(): + """Test that LoraTraining raises ValueError when inference_model lacks required attributes.""" + + # Create an inference model that lacks base_model + class InvalidInferenceModel(torch.nn.Module): + """An invalid inference model without base_model attribute.""" + + @staticmethod + def forward(x): + """Dummy forward method.""" + return x + + inference_model = InvalidInferenceModel() + with pytest.raises(ValueError) as exc_info: + LoraTraining(inference_model, gradient_accumulation_steps=2) + assert "Unable to determine the base model type." in str(exc_info.value) + + +def test_lora_training_forward_calibrate(): + """Test the forward method when calibration is enabled.""" + inference_model = DummyInferenceModel("gpt2") + lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + + # Enable calibration + lora_training.toggle_calibrate(True) + + x = torch.tensor([[1.0, 2.0]]) + y = torch.tensor([[0.5, 1.5]]) + + # Perform the forward pass + loss, grad_norm = lora_training((x, y)) + + # Since calibrate is True, grad_norm should be None + assert grad_norm is None + + # Ensure that loss is computed correctly + expected_loss = ((inference_model.linear(x) - y) ** 2).mean().item() / 2 + assert abs(loss.item() - expected_loss) < 1e-6 + + # Ensure that gradients have been cleared (zeroed) + for param in inference_model.parameters(): + if param.grad is not None: + assert torch.all(param.grad == 0) diff --git a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb b/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb deleted file mode 100644 index d103253ee..000000000 --- a/use_case_examples/lora_finetune/gpt2_finetune_hybrid.ipynb +++ /dev/null @@ -1,1998 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import shutil\n", - "from pathlib import Path\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import torch\n", - "from lora_module import LoraTraining\n", - "from peft import LoraConfig, TaskType, get_peft_model\n", - "from remote_module import CustomConv1D\n", - "from torch.nn import Embedding\n", - "from tqdm import tqdm\n", - "from transformers import (\n", - " AutoModelForCausalLM,\n", - " AutoTokenizer,\n", - " Conv1D,\n", - " DataCollatorForLanguageModeling,\n", - " TextDataset,\n", - " Trainer,\n", - " TrainingArguments,\n", - ")\n", - "\n", - "from concrete.ml.torch.hybrid_model import HybridFHEModel\n", - "\n", - "SEED = 0\n", - "torch.manual_seed(SEED)\n", - "torch.use_deterministic_algorithms(True)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "model_name = \"gpt2\"\n", - "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", - "model = AutoModelForCausalLM.from_pretrained(model_name)\n", - "\n", - "if tokenizer.pad_token is None:\n", - " tokenizer.pad_token = tokenizer.eos_token\n", - "\n", - "# Freeze weights\n", - "for param in model.parameters():\n", - " param.requires_grad = False" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_text(prompt, model, tokenizer, max_new_tokens=30):\n", - " # Encode the input prompt\n", - " inputs = tokenizer.encode_plus(prompt, return_tensors=\"pt\")\n", - "\n", - " # Generate text\n", - " output = model.generate(\n", - " input_ids=inputs[\"input_ids\"],\n", - " attention_mask=inputs[\"attention_mask\"],\n", - " max_new_tokens=max_new_tokens,\n", - " num_return_sequences=1,\n", - " no_repeat_ngram_size=2,\n", - " top_k=50,\n", - " top_p=0.95,\n", - " temperature=0.7,\n", - " do_sample=True,\n", - " pad_token_id=tokenizer.eos_token_id,\n", - " )\n", - "\n", - " # Decode the generated text\n", - " generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n", - " return generated_text" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What is FHE? FH: A basic program that is used to calculate the height of an object, and then sets the minimum height to be the object's height.\n" - ] - } - ], - "source": [ - "# Example usage\n", - "prompt = \"What is FHE ?\"\n", - "generated_text = generate_text(prompt, model, tokenizer)\n", - "print(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "peft_config = LoraConfig(\n", - " task_type=TaskType.CAUSAL_LM,\n", - " r=4,\n", - " lora_alpha=32,\n", - " lora_dropout=0.05,\n", - " fan_in_fan_out=True,\n", - ")\n", - "\n", - "peft_model = get_peft_model(model, peft_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def replace_conv1d(module, module_index_to_skip=0):\n", - " for name, child in module.named_children():\n", - " if isinstance(child, Conv1D):\n", - "\n", - " # Skip the module if the index has not been reached, and decrement the index\n", - " if module_index_to_skip >= 0:\n", - " module_index_to_skip -= 1\n", - " else:\n", - " custom_linear = CustomConv1D(child.weight, bias=child.bias)\n", - " setattr(module, name, custom_linear)\n", - " else:\n", - " module_index_to_skip = replace_conv1d(child, module_index_to_skip=module_index_to_skip)\n", - "\n", - " return module_index_to_skip\n", - "\n", - "\n", - "# Gradients of the first base layer that is used for fine-tuning are not needed. We\n", - "# therefore need to exclude the backward module from the remote_names since calibration\n", - "# won't get through it (which raises an issue with hybrid models)\n", - "replace_conv1d(peft_model, module_index_to_skip=0);" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "GRADIENT_ACCUMULATION_STEPS = 2\n", - "\n", - "lora_training = LoraTraining(peft_model, GRADIENT_ACCUMULATION_STEPS)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "BLOCK_SIZE = 128\n", - "\n", - "train_dataset = TextDataset(\n", - " tokenizer=tokenizer,\n", - " file_path=\"data_finetune/what_is_fhe.txt\",\n", - " block_size=BLOCK_SIZE,\n", - " cache_dir=\"cache_dataset\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n", - "\n", - "EPOCHS = 100\n", - "PER_DEVICE_TRAIN_BATCH_SIZE = 4\n", - "\n", - "training_args = TrainingArguments(\n", - " output_dir=\"./checkpoints\",\n", - " num_train_epochs=EPOCHS,\n", - " per_device_train_batch_size=8,\n", - " gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n", - " save_total_limit=1,\n", - " use_cpu=True,\n", - " learning_rate=5e-4,\n", - " logging_strategy=\"epoch\",\n", - " optim=\"adamw_torch\",\n", - " seed=SEED,\n", - " data_seed=SEED,\n", - " weight_decay=0.0,\n", - " warmup_steps=0,\n", - " max_grad_norm=1.0,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "trainer = Trainer(\n", - " model=peft_model,\n", - " args=training_args,\n", - " data_collator=data_collator,\n", - " train_dataset=train_dataset,\n", - ")\n", - "\n", - "train_dataloader = trainer.get_train_dataloader()\n", - "\n", - "len_dataloader = len(train_dataloader)\n", - "num_update_steps_per_epoch = len_dataloader // training_args.gradient_accumulation_steps\n", - "num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)\n", - "max_steps = math.ceil(training_args.num_train_epochs * num_update_steps_per_epoch)\n", - "\n", - "trainer.create_optimizer_and_scheduler(num_training_steps=max_steps)\n", - "\n", - "lora_training.update_training_parameters(trainer.optimizer, trainer.lr_scheduler, training_args)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "def get_remote_names(model, include_embedding_layers=False):\n", - " remote_names = []\n", - " for name, module in model.named_modules():\n", - " # Some gradients are not needed for fine-tuning, so need to exclude the backward module\n", - " # from the remote_names since calibration won't get through it (which raises an issue with\n", - " # hybrid models). We however still need to include the associated module's forward pass in\n", - " # the hybrid model\n", - " # We can also include the embedding and language model head as they represent a lot of the\n", - " # model's parameters. Side note: \"lm_head\" does not appear in model.parameters() because\n", - " # the weights are directly tied to the embedding ones, but we still need to remove both\n", - " # modules in order to get rid of the weights\n", - " if (\n", - " isinstance(module, Conv1D)\n", - " or include_embedding_layers # noqa: W503\n", - " and (isinstance(module, Embedding) or \"lm_head\" in name) # noqa: W503\n", - " ):\n", - " remote_names.append(name)\n", - "\n", - " elif isinstance(module, CustomConv1D):\n", - " remote_names.append(name + \".forward_module\")\n", - " remote_names.append(name + \".backward_module\")\n", - "\n", - " return remote_names\n", - "\n", - "\n", - "# Do not include embedding layers as the model does not converge when quantizing them, even with\n", - "# 16 bits\n", - "remote_names = get_remote_names(lora_training, include_embedding_layers=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "hybrid_model = HybridFHEModel(lora_training, module_names=remote_names)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "input_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (\n", - " tokenizer.vocab_size - 1\n", - ")\n", - "label_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (\n", - " tokenizer.vocab_size - 1\n", - ")\n", - "\n", - "inputset = (input_tensor, label_tensor)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "hybrid_model.model.toggle_calibrate(enable=True)\n", - "\n", - "hybrid_model.compile_model(inputset, n_bits=16)\n", - "\n", - "hybrid_model.model.toggle_calibrate(enable=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "def train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"disable\"):\n", - " device = \"cpu\"\n", - " hybrid_model.model.to(device)\n", - "\n", - " # Training loop\n", - " hybrid_model.model.inference_model.train()\n", - "\n", - " total_epochs = int(training_args.num_train_epochs)\n", - " epoch_pbar = tqdm(total=total_epochs, desc=\"Training Progress\", position=0)\n", - "\n", - " total_batched_samples = 0\n", - " epoch_losses = [] # List to store the loss for each epoch\n", - "\n", - " for epoch in range(total_epochs):\n", - " total_loss = 0\n", - " grad_norms = []\n", - "\n", - " steps_in_epoch = len(train_dataloader)\n", - " for step, batch in enumerate(train_dataloader):\n", - " total_batched_samples += 1\n", - "\n", - " batch = {k: v.to(device) for k, v in batch.items()}\n", - "\n", - " # Gradient accumulation\n", - " is_last_batch_step = (\n", - " steps_in_epoch <= training_args.gradient_accumulation_steps\n", - " and (step + 1) == steps_in_epoch # noqa: W503\n", - " )\n", - " accumulate_gradients = (\n", - " total_batched_samples % training_args.gradient_accumulation_steps == 0\n", - " )\n", - "\n", - " run_optimizer = is_last_batch_step or accumulate_gradients\n", - "\n", - " hybrid_model.model.toggle_run_optimizer(enable=run_optimizer)\n", - "\n", - " loss, grad_norm = hybrid_model((batch[\"input_ids\"], batch[\"labels\"]), fhe=fhe)\n", - "\n", - " total_loss += loss.item()\n", - "\n", - " if grad_norm is not None:\n", - " grad_norms.append(grad_norm)\n", - "\n", - " # Get current learning rate\n", - " current_lr = hybrid_model.model.lr_scheduler.get_last_lr()[0]\n", - "\n", - " # Get last grad norm\n", - " current_grad_norm = grad_norms[-1]\n", - "\n", - " # Store the total loss for this epoch\n", - " epoch_losses.append(total_loss)\n", - "\n", - " # Log epoch results\n", - " print(\n", - " f\"Epoch {epoch + 1}/{training_args.num_train_epochs}, \"\n", - " f\"Loss: {total_loss:.4f}, grad norm: {current_grad_norm}, lr: {current_lr}\"\n", - " )\n", - "\n", - " epoch_pbar.update(1)\n", - "\n", - " # Save model checkpoint\n", - " if training_args.output_dir is not None:\n", - " save_path = f\"{training_args.output_dir}/checkpoint-{epoch + 1}\"\n", - " hybrid_model.model.inference_model.save_pretrained(save_path)\n", - "\n", - " epoch_pbar.close()\n", - "\n", - " # Plot the loss evolution\n", - " plt.figure(figsize=(10, 6))\n", - " plt.plot(range(1, total_epochs + 1), epoch_losses, marker=\"o\")\n", - " plt.title(\"Loss Evolution During Training\")\n", - " plt.xlabel(\"Epoch\")\n", - " plt.ylabel(\"Total Loss\")\n", - " plt.grid(True)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 0%| | 0/100 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Avoid the following error from HuggingFace when training :\n", - "# \"The current process just got forked, after parallelism has already been used. Disabling\n", - "# parallelism to avoid deadlocks...\"\n", - "tokenizer.parallelism = False\n", - "\n", - "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"simulate\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "fine_tuned_model = hybrid_model.model.inference_model\n", - "\n", - "# In simulation, we can only generate a single token at a time because of fixed size circuits\n", - "# and how `generate` works (only the last token from the previous generation is kept)\n", - "hybrid_model.set_fhe_mode(\"disable\")" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What is FHE?\n", - "\n", - "FHE is a breakthrough in computer security. It allows computations on encrypted data and store it in a database, which can be used to\n" - ] - } - ], - "source": [ - "# Seed for best reproducibility\n", - "torch.manual_seed(SEED)\n", - "\n", - "prompt = \"What is FHE ?\"\n", - "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", - "print(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What is FHE?\n", - "\n", - "We're talking about the FH and its use of the H, the use the D, and the C, which are often used interchange\n" - ] - } - ], - "source": [ - "# Seed for best reproducibility\n", - "torch.manual_seed(SEED)\n", - "\n", - "peft_model.disable_adapter_layers()\n", - "\n", - "prompt = \"What is FHE ?\"\n", - "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", - "print(generated_text)\n", - "\n", - "peft_model.enable_adapter_layers()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "def print_weights_and_size(model, print_detail=False):\n", - " total_weights = 0\n", - " total_lora_weights = 0\n", - " for name, param in model.named_parameters():\n", - " total_weights += param.numel()\n", - "\n", - " if \"lora\" in name:\n", - " total_lora_weights += param.numel()\n", - "\n", - " if print_detail:\n", - " print(name, param.numel())\n", - "\n", - " print(f\"Total number of weights: {total_weights}\")\n", - " print(f\"Total number of LoRA weights: {total_lora_weights}\")\n", - "\n", - " return total_weights" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of weights: 124587264\n", - "Total number of LoRA weights: 147456\n" - ] - } - ], - "source": [ - "total_weights_size = print_weights_and_size(hybrid_model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "path = Path(\"deployment/gpt2_lora_finetuned\")\n", - "path.mkdir(parents=True, exist_ok=True)\n", - "\n", - "if path.is_dir() and any(path.iterdir()):\n", - " shutil.rmtree(path)\n", - "\n", - "hybrid_model.save_and_clear_private_info(path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of weights: 39569664\n", - "Total number of LoRA weights: 147456\n" - ] - } - ], - "source": [ - "total_weights_size_private = print_weights_and_size(hybrid_model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total weights removed: 68.24 %\n" - ] - } - ], - "source": [ - "print(\n", - " \"Total weights removed: \"\n", - " f\"{(total_weights_size - total_weights_size_private) / total_weights_size * 100:.2f} %\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Around 95% of the remaining weights are from the embedding layers (wpe and wte) as well as the final lm_head layer." - ] - } - ], - "metadata": { - "execution": { - "timeout": 10800 - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/use_case_examples/lora_finetune/lora_module.py b/use_case_examples/lora_finetune/lora_module.py deleted file mode 100644 index 41211adba..000000000 --- a/use_case_examples/lora_finetune/lora_module.py +++ /dev/null @@ -1,68 +0,0 @@ -import torch - - -class LoraTraining(torch.nn.Module): - def __init__(self, inference_model, gradient_accumulation_steps) -> None: - super().__init__() - - self.inference_model = inference_model - - self.optimizer = None - self.lr_scheduler = None - - self.gradient_accumulation_steps = gradient_accumulation_steps - self.max_grad_norm = None - - self.calibrate = False - self.run_optimizer = False - - def update_training_parameters(self, optimizer, lr_scheduler, training_args): - assert self.gradient_accumulation_steps == training_args.gradient_accumulation_steps - - self.optimizer = optimizer - self.lr_scheduler = lr_scheduler - self.max_grad_norm = training_args.max_grad_norm - - def forward(self, inputs): - # Remove this once hybrid model supports multiple inputs - # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4568 - x, y = inputs - - # some parts on server side - outputs = self.inference_model(input_ids=x, labels=y) - - loss = outputs.loss - loss = loss / self.gradient_accumulation_steps - - # Update gradients - # We need to set requires grad to the loss manually because the inference model's last - # step is the "lm_head" layer, which is detached from the graph by the hybrid model - loss.requires_grad_(True) - loss.backward() - - grad_norm = None - if not self.calibrate and self.run_optimizer: - assert self.optimizer is not None - assert self.lr_scheduler is not None - assert self.max_grad_norm is not None - - grad_norm = torch.nn.utils.clip_grad_norm_( - self.inference_model.parameters(), max_norm=self.max_grad_norm, norm_type=2 - ) - - self.optimizer.step() - self.lr_scheduler.step() - - self.inference_model.zero_grad() - - # Clean gradients after calibration - elif self.calibrate: - self.inference_model.zero_grad() - - return (loss, grad_norm) - - def toggle_calibrate(self, enable: bool = True): - self.calibrate = enable - - def toggle_run_optimizer(self, enable: bool = True): - self.run_optimizer = enable diff --git a/use_case_examples/lora_finetune/remote_module.py b/use_case_examples/lora_finetune/remote_module.py deleted file mode 100644 index d3531fbf8..000000000 --- a/use_case_examples/lora_finetune/remote_module.py +++ /dev/null @@ -1,49 +0,0 @@ -import torch -from torch import nn - - -class ForwardModule(nn.Module): - def __init__(self, weight, bias=None): - super(ForwardModule, self).__init__() - self.weight = weight # Assume weight is passed as a pre-initialized tensor - self.bias = bias - - def forward(self, input): - output = input @ self.weight - if self.bias is not None: - return output + self.bias - - -class BackwardModule(nn.Module): - def __init__(self, weight): - super(BackwardModule, self).__init__() - self.weight = weight # This is the same weight used in ForwardModule - - def forward(self, grad_output): - return grad_output @ self.weight.t() - - -class ForwardBackwardModule(torch.autograd.Function): - @staticmethod - def forward(ctx, input, forward_module, backward_module): - ctx.backward_module = backward_module - output = forward_module.forward(input) - return output - - @staticmethod - def backward(ctx, grad_output): - backward_module = ctx.backward_module - grad_input = backward_module.forward(grad_output) - - # grad_weight and grad_bias are not needed when computing the backward for lora - return grad_input, None, None - - -class CustomConv1D(nn.Module): - def __init__(self, weight, bias=None): - super().__init__() - self.forward_module = ForwardModule(weight, bias=bias) - self.backward_module = BackwardModule(weight) - - def forward(self, input): - return ForwardBackwardModule.apply(input, self.forward_module, self.backward_module) diff --git a/use_case_examples/lora_finetune/requirements.txt b/use_case_examples/lora_finetune/requirements.txt deleted file mode 100644 index 936361389..000000000 --- a/use_case_examples/lora_finetune/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ -# Use the latest public version of Concrete ML once the embedding layer feature -# is released -# concrete-ml==1.7.0 --e ../../. -transformers==4.41.2 -peft==0.11.1 -Jinja2==3.1.4 -matplotlib==3.7.5 diff --git a/use_case_examples/lora_finetune/.gitignore b/use_case_examples/lora_finetuning/.gitignore similarity index 100% rename from use_case_examples/lora_finetune/.gitignore rename to use_case_examples/lora_finetuning/.gitignore diff --git a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb new file mode 100644 index 000000000..79a8787cf --- /dev/null +++ b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb @@ -0,0 +1,483 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dfccd8e6", + "metadata": {}, + "source": [ + "# Fine-Tuning GPT-2 on Encrypted Data with LoRA and Concrete-ML\n", + "\n", + "In this notebook, we peform fine-tuning of a GPT-2 model using LoRA and Concrete-ML. This allows us to fine-tune a model in a privacy-preserving manner.\n", + "\n", + "LoRA weight can be used " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eca73e44", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "import math\n", + "import shutil\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import torch\n", + "from peft import LoraConfig, TaskType, get_peft_model\n", + "from tqdm import tqdm\n", + "from transformers import (\n", + " AutoModelForCausalLM,\n", + " AutoTokenizer,\n", + " DataCollatorForLanguageModeling,\n", + " TextDataset,\n", + " Trainer,\n", + " TrainingArguments,\n", + ")\n", + "from utils_lora import generate_text, get_remote_names, print_weights_and_size, replace_conv1d\n", + "\n", + "from concrete.ml.torch.hybrid_model import HybridFHEModel\n", + "from concrete.ml.torch.lora import LoraTraining\n", + "\n", + "# Set random seed for reproducibility\n", + "SEED = 0\n", + "torch.manual_seed(SEED)\n", + "torch.use_deterministic_algorithms(True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b965a1a", + "metadata": {}, + "outputs": [], + "source": [ + "# Load pre-trained GPT-2 model and tokenizer\n", + "model_name = \"gpt2\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModelForCausalLM.from_pretrained(model_name)\n", + "\n", + "# Ensure tokenizer has a pad token\n", + "if tokenizer.pad_token is None:\n", + " tokenizer.pad_token = tokenizer.eos_token\n", + "\n", + "# Freeze model weights\n", + "for param in model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2337a6b4", + "metadata": {}, + "outputs": [], + "source": [ + "# Example usage of the pre-trained model\n", + "prompt = \"What is FHE?\"\n", + "generated_text = generate_text(prompt, model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20564b59", + "metadata": {}, + "outputs": [], + "source": [ + "# Configure LoRA\n", + "peft_config = LoraConfig(\n", + " task_type=TaskType.CAUSAL_LM,\n", + " r=4,\n", + " lora_alpha=32,\n", + " lora_dropout=0.05,\n", + " fan_in_fan_out=True,\n", + ")\n", + "\n", + "# Apply LoRA to the model\n", + "peft_model = get_peft_model(model, peft_config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0472e5b", + "metadata": {}, + "outputs": [], + "source": [ + "# Replace Conv1D layers with CustomConv1D for FHE compatibility\n", + "# Skip the first Conv1D layer (module_index_to_skip=0)\n", + "replace_conv1d(peft_model, module_index_to_skip=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ac49f9d", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up LoRA training\n", + "GRADIENT_ACCUMULATION_STEPS = 2\n", + "lora_training = LoraTraining(peft_model, GRADIENT_ACCUMULATION_STEPS)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d10d71e8", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare dataset for fine-tuning\n", + "BLOCK_SIZE = 128\n", + "\n", + "train_dataset = TextDataset(\n", + " tokenizer=tokenizer,\n", + " file_path=\"data_finetune/what_is_fhe.txt\",\n", + " block_size=BLOCK_SIZE,\n", + " cache_dir=\"cache_dataset\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a01acd1", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up data collator for language modeling\n", + "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n", + "\n", + "# Define training arguments\n", + "EPOCHS = 100\n", + "PER_DEVICE_TRAIN_BATCH_SIZE = 4\n", + "\n", + "training_args = TrainingArguments(\n", + " output_dir=\"./checkpoints\",\n", + " num_train_epochs=EPOCHS,\n", + " per_device_train_batch_size=8,\n", + " gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n", + " save_total_limit=1,\n", + " use_cpu=True,\n", + " learning_rate=5e-4,\n", + " logging_strategy=\"epoch\",\n", + " optim=\"adamw_torch\",\n", + " seed=SEED,\n", + " data_seed=SEED,\n", + " weight_decay=0.0,\n", + " warmup_steps=0,\n", + " max_grad_norm=1.0,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c8864b2", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize Trainer\n", + "trainer = Trainer(\n", + " model=peft_model,\n", + " args=training_args,\n", + " data_collator=data_collator,\n", + " train_dataset=train_dataset,\n", + ")\n", + "\n", + "# Prepare for training\n", + "train_dataloader = trainer.get_train_dataloader()\n", + "\n", + "len_dataloader = len(train_dataloader)\n", + "num_update_steps_per_epoch = len_dataloader // training_args.gradient_accumulation_steps\n", + "num_update_steps_per_epoch = max(num_update_steps_per_epoch, 1)\n", + "max_steps = math.ceil(training_args.num_train_epochs * num_update_steps_per_epoch)\n", + "\n", + "trainer.create_optimizer_and_scheduler(num_training_steps=max_steps)\n", + "\n", + "lora_training.update_training_parameters(trainer.optimizer, trainer.lr_scheduler, training_args)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae2094a4", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the names of the remote modules (layers to be converted to FHE)\n", + "remote_names = get_remote_names(lora_training, include_embedding_layers=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a21298ee", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the HybridFHEModel with the specified remote modules\n", + "hybrid_model = HybridFHEModel(lora_training, module_names=remote_names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56ec41b8", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare input data for calibration\n", + "input_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (\n", + " tokenizer.vocab_size - 1\n", + ")\n", + "label_tensor = torch.randint(0, 2, (PER_DEVICE_TRAIN_BATCH_SIZE, BLOCK_SIZE)) * (\n", + " tokenizer.vocab_size - 1\n", + ")\n", + "\n", + "inputset = (input_tensor, label_tensor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20dfe2d8", + "metadata": {}, + "outputs": [], + "source": [ + "# Calibrate and compile the model\n", + "hybrid_model.model.toggle_calibrate(enable=True)\n", + "hybrid_model.compile_model(inputset, n_bits=16)\n", + "hybrid_model.model.toggle_calibrate(enable=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18e450e6", + "metadata": {}, + "outputs": [], + "source": [ + "def train_custom_model(\n", + " hybrid_model, train_dataloader, training_args, fhe=\"disable\"\n", + "): # pylint: disable=too-many-locals\n", + " device = \"cpu\"\n", + " hybrid_model.model.to(device)\n", + "\n", + " # Training loop\n", + " hybrid_model.model.inference_model.train()\n", + "\n", + " total_epochs = int(training_args.num_train_epochs)\n", + " epoch_pbar = tqdm(total=total_epochs, desc=\"Training Progress\", position=0)\n", + "\n", + " total_batched_samples = 0\n", + " epoch_losses = [] # List to store the loss for each epoch\n", + "\n", + " for epoch in range(total_epochs):\n", + " total_loss = 0\n", + " grad_norms = []\n", + "\n", + " steps_in_epoch = len(train_dataloader)\n", + " for step, batch in enumerate(train_dataloader):\n", + " total_batched_samples += 1\n", + "\n", + " batch = {k: v.to(device) for k, v in batch.items()}\n", + "\n", + " # Gradient accumulation\n", + " is_within_accumulation_steps = (\n", + " steps_in_epoch <= training_args.gradient_accumulation_steps\n", + " )\n", + " is_last_step_in_epoch = (step + 1) == steps_in_epoch\n", + "\n", + " is_last_batch_step = is_within_accumulation_steps and is_last_step_in_epoch\n", + "\n", + " mod_total_batched_samples = (\n", + " total_batched_samples % training_args.gradient_accumulation_steps\n", + " )\n", + " accumulate_gradients = mod_total_batched_samples == 0\n", + "\n", + " run_optimizer = is_last_batch_step or accumulate_gradients\n", + "\n", + " hybrid_model.model.toggle_run_optimizer(enable=run_optimizer)\n", + "\n", + " loss, grad_norm = hybrid_model((batch[\"input_ids\"], batch[\"labels\"]), fhe=fhe)\n", + "\n", + " total_loss += loss.item()\n", + "\n", + " if grad_norm is not None:\n", + " grad_norms.append(grad_norm)\n", + "\n", + " # Get current learning rate\n", + " current_lr = hybrid_model.model.lr_scheduler.get_last_lr()[0]\n", + "\n", + " # Get last grad norm\n", + " current_grad_norm = grad_norms[-1] if grad_norms else None\n", + "\n", + " # Store the total loss for this epoch\n", + " epoch_losses.append(total_loss)\n", + "\n", + " # Log epoch results\n", + " print(\n", + " f\"Epoch {epoch + 1}/{training_args.num_train_epochs}, \"\n", + " f\"Loss: {total_loss:.4f}, grad norm: {current_grad_norm}, lr: {current_lr}\"\n", + " )\n", + "\n", + " epoch_pbar.update(1)\n", + "\n", + " # Save model checkpoint\n", + " if training_args.output_dir is not None:\n", + " save_path = f\"{training_args.output_dir}/checkpoint-{epoch + 1}\"\n", + " hybrid_model.model.inference_model.save_pretrained(save_path)\n", + "\n", + " epoch_pbar.close()\n", + "\n", + " # Plot the loss evolution\n", + " plt.figure(figsize=(10, 6))\n", + " plt.plot(range(1, total_epochs + 1), epoch_losses, marker=\"o\")\n", + " plt.title(\"Loss Evolution During Training\")\n", + " plt.xlabel(\"Epoch\")\n", + " plt.ylabel(\"Total Loss\")\n", + " plt.grid(True)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ca82a81", + "metadata": {}, + "outputs": [], + "source": [ + "# Avoid parallelism error from HuggingFace during training\n", + "tokenizer.parallelism = False\n", + "\n", + "# Train the model using FHE simulation\n", + "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"simulate\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd666f38", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the fine-tuned model\n", + "fine_tuned_model = hybrid_model.model.inference_model\n", + "\n", + "# Set FHE mode to disable for text generation\n", + "hybrid_model.set_fhe_mode(\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc5d9b27", + "metadata": {}, + "outputs": [], + "source": [ + "# Inference using the fine-tuned model with LoRA weights\n", + "# Seed for reproducibility\n", + "torch.manual_seed(SEED)\n", + "\n", + "prompt = \"What is FHE?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21e2a1d1", + "metadata": {}, + "outputs": [], + "source": [ + "# Original inference without LoRA weights\n", + "# Seed for reproducibility\n", + "torch.manual_seed(SEED)\n", + "\n", + "peft_model.disable_adapter_layers()\n", + "\n", + "prompt = \"What is FHE?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)\n", + "\n", + "peft_model.enable_adapter_layers()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c97425ee", + "metadata": {}, + "outputs": [], + "source": [ + "# Print weights and model size\n", + "total_weights_size = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31367ff5", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the model\n", + "path = Path(\"deployment/gpt2_lora_finetuned\")\n", + "path.mkdir(parents=True, exist_ok=True)\n", + "\n", + "if path.is_dir() and any(path.iterdir()):\n", + " shutil.rmtree(path)\n", + "\n", + "hybrid_model.save_and_clear_private_info(path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1dda636", + "metadata": {}, + "outputs": [], + "source": [ + "# Print weights and size after saving\n", + "total_weights_size_private = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "506ad2f5", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate and print the percentage of weights removed\n", + "percentage_removed = (total_weights_size - total_weights_size_private) / total_weights_size * 100\n", + "print(f\"Total weights removed: {percentage_removed:.2f} %\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "465cb18b", + "metadata": {}, + "outputs": [], + "source": [ + "# Note: Around 95% of the remaining weights are from the embedding layers (wpe and wte)\n", + "# as well as the final lm_head layer." + ] + } + ], + "metadata": { + "execution": { + "timeout": 10800 + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/use_case_examples/lora_finetune/Makefile b/use_case_examples/lora_finetuning/Makefile similarity index 79% rename from use_case_examples/lora_finetune/Makefile rename to use_case_examples/lora_finetuning/Makefile index 13bd27aa3..8942d2e22 100644 --- a/use_case_examples/lora_finetune/Makefile +++ b/use_case_examples/lora_finetuning/Makefile @@ -7,4 +7,4 @@ TIME_NB="${USE_CASE_DIR}/time_notebook_execution.sh" run_example: one one: - @$(TIME_NB) gpt2_finetune_hybrid.ipynb + @$(TIME_NB) GPT2FineTuneHybrid.ipynb diff --git a/use_case_examples/lora_finetuning/README.md b/use_case_examples/lora_finetuning/README.md new file mode 100644 index 000000000..45663d484 --- /dev/null +++ b/use_case_examples/lora_finetuning/README.md @@ -0,0 +1,66 @@ +# Privacy Preserving GPT2 LoRA + +This project demonstrates how to fine-tune GPT-2 using Low-Rank Adaptation (LoRA) weights with Fully Homomorphic Encryption (FHE). The goal is to train a specialized model in a privacy-preserving manner, with minimal memory requirements. + +## Overview + +Fine-tuning large language models typically requires access to sensitive data, which can raise privacy concerns. By leveraging FHE, we can perform computations on encrypted data, ensuring that the data remains private throughout the training process. In this approach, the LoRA weights are only known to the user who owns the data and the memory hungry foundation model remains on the server. + +## Key Features + +- **LoRA Fine-Tuning**: Fine-tune GPT-2 by adapting low-rank weights. +- **Fully Homomorphic Encryption**: Perform training and inference on encrypted data. +- **Hybrid Model**: Combine traditional and encrypted computations for optimal performance. +- **Low Memory Requirements**: Minimal client-side memory needed for LoRA weights. + +## Setup + +### Installation + +Install the required packages: + +```sh +pip install -r requirements.txt +``` + +## Usage + +### Prepare the Dataset + +Replace the data-set in the `data_finetune` directory to the one you want to use for fine-tuning. + +### Run the Fine-Tuning Script + +Execute the Jupyter notebook `GPT2FineTuneHybrid.ipynb` to start the fine-tuning process. The notebook is structured into several steps: + +## Deployment/Production Scenario + +In a deployment or production scenario, the model can be fine-tuned as follows: + +1. **Server Setup**: The server hosts a foundation model with generic weights. +1. **Client Setup**: The user (client) has a set of LoRA weights and the sensitive data required for fine-tuning. +1. **Fine-Tuning Process**: + - The client requests inference and backward passes from the server, which uses the generic weights/parameters. + - Any computation that requires the LoRA weights is executed on the client's end. +1. **Storage**: The LoRA weights are stored on the client's end for later inference, ensuring full privacy of both the specialized model and the sensitive data. + +## Results + +The fine-tuned model can generate specialized text based on the provided data-set while ensuring data privacy through FHE. + +After fine-tuning, the model's weights are distributed between the client and server as follows: + +- Total weights removed from the server: 68.24% +- LoRA weights kept on the client: 147,456 (approximately 0.12% of the original model's weights) + +Note that the embedding are not considered for now but contain a significant amount of weights (around 30%) for GPT2. They will be considered in a future version of Concrete-ML. + +## Conclusion + +This project showcases the potential of combining LoRA and FHE to fine-tune language models in a privacy-preserving manner. By following the steps outlined in the notebook, you can adapt this approach to your own data-sets and use cases. + +## References + +- [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685) +- [PEFT](https://github.com/huggingface/peft) +- [Concrete ML](https://github.com/zama-ai/concrete-ml) diff --git a/use_case_examples/lora_finetune/data_finetune/what_is_fhe.txt b/use_case_examples/lora_finetuning/data_finetune/what_is_fhe.txt similarity index 100% rename from use_case_examples/lora_finetune/data_finetune/what_is_fhe.txt rename to use_case_examples/lora_finetuning/data_finetune/what_is_fhe.txt diff --git a/use_case_examples/lora_finetuning/requirements.txt b/use_case_examples/lora_finetuning/requirements.txt new file mode 100644 index 000000000..9c97da539 --- /dev/null +++ b/use_case_examples/lora_finetuning/requirements.txt @@ -0,0 +1,4 @@ +transformers==4.41.2 +peft==0.11.1 +Jinja2==3.1.4 +matplotlib==3.7.5 diff --git a/use_case_examples/lora_finetuning/utils_lora.py b/use_case_examples/lora_finetuning/utils_lora.py new file mode 100644 index 000000000..dafbdea85 --- /dev/null +++ b/use_case_examples/lora_finetuning/utils_lora.py @@ -0,0 +1,79 @@ +# Utility functions for LoRA finetuning notebook + +from torch.nn import Embedding +from transformers import Conv1D + +from concrete.ml.torch.lora import CustomConv1D + + +def generate_text(prompt, model, tokenizer, max_new_tokens=30): + # Encode the input prompt + inputs = tokenizer.encode_plus(prompt, return_tensors="pt") + + # Generate text + output = model.generate( + input_ids=inputs["input_ids"], + attention_mask=inputs["attention_mask"], + max_new_tokens=max_new_tokens, + num_return_sequences=1, + no_repeat_ngram_size=2, + top_k=50, + top_p=0.95, + temperature=0.7, + do_sample=True, + pad_token_id=tokenizer.eos_token_id, + ) + + # Decode the generated text + generated_text = tokenizer.decode(output[0], skip_special_tokens=True) + return generated_text + + +def replace_conv1d(module, module_index_to_skip=0): + for name, child in module.named_children(): + if isinstance(child, Conv1D): + # Skip the module if the index has not been reached, and decrement the index + if module_index_to_skip >= 0: + module_index_to_skip -= 1 + else: + custom_linear = CustomConv1D(child.weight, bias=child.bias) + setattr(module, name, custom_linear) + else: + module_index_to_skip = replace_conv1d(child, module_index_to_skip=module_index_to_skip) + + return module_index_to_skip + + +def get_remote_names(model, include_embedding_layers=False): + remote_names = [] + for name, module in model.named_modules(): + # Exclude the backward module from the remote names + # This is done on the client side + if ( + isinstance(module, Conv1D) + or include_embedding_layers + and (isinstance(module, Embedding) or "lm_head" in name) + ): + remote_names.append(name) + elif isinstance(module, CustomConv1D): + remote_names.append(name + ".forward_module") + remote_names.append(name + ".backward_module") + return remote_names + + +def print_weights_and_size(model, print_detail=False): + total_weights = 0 + total_lora_weights = 0 + for name, param in model.named_parameters(): + total_weights += param.numel() + + if "lora" in name: + total_lora_weights += param.numel() + + if print_detail: + print(name, param.numel()) + + print(f"Total number of weights: {total_weights}") + print(f"Total number of LoRA weights: {total_lora_weights}") + + return total_weights From 4d51e35140237d6148b897d03ca446bf7eab8bfa Mon Sep 17 00:00:00 2001 From: jfrery Date: Mon, 23 Sep 2024 10:53:17 +0000 Subject: [PATCH 24/32] chore: update licenses --- deps_licenses/licenses_mac_intel_user.txt.md5 | 2 +- deps_licenses/licenses_mac_silicon_user.txt.md5 | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/deps_licenses/licenses_mac_intel_user.txt.md5 b/deps_licenses/licenses_mac_intel_user.txt.md5 index 4344e4be6..9519c1c94 100644 --- a/deps_licenses/licenses_mac_intel_user.txt.md5 +++ b/deps_licenses/licenses_mac_intel_user.txt.md5 @@ -1 +1 @@ -31249a607336424af0d790feb9db6252 +af423f91bb5313f1f1670ea72892d364 diff --git a/deps_licenses/licenses_mac_silicon_user.txt.md5 b/deps_licenses/licenses_mac_silicon_user.txt.md5 index 4344e4be6..9519c1c94 100644 --- a/deps_licenses/licenses_mac_silicon_user.txt.md5 +++ b/deps_licenses/licenses_mac_silicon_user.txt.md5 @@ -1 +1 @@ -31249a607336424af0d790feb9db6252 +af423f91bb5313f1f1670ea72892d364 From 9024a71d69e7f0a99620749867a4ffb925ef520c Mon Sep 17 00:00:00 2001 From: jfrery Date: Mon, 23 Sep 2024 13:36:44 +0200 Subject: [PATCH 25/32] chore: fix forbidden words --- use_case_examples/lora_finetuning/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/use_case_examples/lora_finetuning/README.md b/use_case_examples/lora_finetuning/README.md index 45663d484..bb7e037c0 100644 --- a/use_case_examples/lora_finetuning/README.md +++ b/use_case_examples/lora_finetuning/README.md @@ -53,7 +53,7 @@ After fine-tuning, the model's weights are distributed between the client and se - Total weights removed from the server: 68.24% - LoRA weights kept on the client: 147,456 (approximately 0.12% of the original model's weights) -Note that the embedding are not considered for now but contain a significant amount of weights (around 30%) for GPT2. They will be considered in a future version of Concrete-ML. +Note that the embedding are not considered for now but contain a significant amount of weights (around 30%) for GPT2. They will be considered in a future version of Concrete ML. ## Conclusion From 9d4f95686b4a00641ef86f7dcdedd3584841cd9b Mon Sep 17 00:00:00 2001 From: jfrery Date: Mon, 23 Sep 2024 15:34:03 +0200 Subject: [PATCH 26/32] chore: fix codeblock --- use_case_examples/lora_finetuning/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/use_case_examples/lora_finetuning/README.md b/use_case_examples/lora_finetuning/README.md index bb7e037c0..df2b09cb6 100644 --- a/use_case_examples/lora_finetuning/README.md +++ b/use_case_examples/lora_finetuning/README.md @@ -19,6 +19,8 @@ Fine-tuning large language models typically requires access to sensitive data, w Install the required packages: + + ```sh pip install -r requirements.txt ``` From 2e7f15d3896813d7049ddb222882cb50453b73b8 Mon Sep 17 00:00:00 2001 From: jfrery Date: Mon, 23 Sep 2024 16:53:21 +0200 Subject: [PATCH 27/32] chore: update notebook executed --- .../lora_finetuning/GPT2FineTuneHybrid.ipynb | 1548 ++++++++++++++++- use_case_examples/lora_finetuning/README.md | 1 - 2 files changed, 1517 insertions(+), 32 deletions(-) diff --git a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb index 79a8787cf..891e9b11f 100644 --- a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb +++ b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "eca73e44", "metadata": {}, "outputs": [], @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "8b965a1a", "metadata": {}, "outputs": [], @@ -70,10 +70,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "2337a6b4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE?\n", + "\n", + "FHE is a new form of electromagnetic radiation that is produced by the electromagnetic fields generated by microwaves. It is an electromagnetic wave that travels\n" + ] + } + ], "source": [ "# Example usage of the pre-trained model\n", "prompt = \"What is FHE?\"\n", @@ -83,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "20564b59", "metadata": {}, "outputs": [], @@ -103,10 +113,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "f0472e5b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "-1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Replace Conv1D layers with CustomConv1D for FHE compatibility\n", "# Skip the first Conv1D layer (module_index_to_skip=0)\n", @@ -115,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "5ac49f9d", "metadata": {}, "outputs": [], @@ -127,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "d10d71e8", "metadata": {}, "outputs": [], @@ -145,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "8a01acd1", "metadata": {}, "outputs": [], @@ -177,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "3c8864b2", "metadata": {}, "outputs": [], @@ -205,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "ae2094a4", "metadata": {}, "outputs": [], @@ -216,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "a21298ee", "metadata": {}, "outputs": [], @@ -227,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "56ec41b8", "metadata": {}, "outputs": [], @@ -245,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "20dfe2d8", "metadata": {}, "outputs": [], @@ -258,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "18e450e6", "metadata": {}, "outputs": [], @@ -348,10 +369,1428 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "0ca82a81", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 1%| | 1/100 [04:13<6:58:00, 253.34s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100, Loss: 1.5293, grad norm: 0.43492022156715393, lr: 0.000495\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 2%|▏ | 2/100 [06:27<4:59:25, 183.33s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/100, Loss: 1.5049, grad norm: 0.352533221244812, lr: 0.00049\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Progress: 3%|▎ | 3/100 [08:25<4:07:52, 153.32s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Avoid parallelism error from HuggingFace during training\n", "tokenizer.parallelism = False\n", @@ -362,7 +1801,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "bd666f38", "metadata": {}, "outputs": [], @@ -376,10 +1815,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "bc5d9b27", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE?\n", + "\n", + "Fully Fully Scaled FFI™ Cryptographic Provider (FHE) is the foundation for the modern healthcare system. Flete-\n" + ] + } + ], "source": [ "# Inference using the fine-tuned model with LoRA weights\n", "# Seed for reproducibility\n", @@ -392,10 +1841,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "21e2a1d1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What is FHE?\n", + "\n", + "FHE is a word that has been popularized by the Internet meme-culture meme of the 1990s.\n", + "There are two things that\n" + ] + } + ], "source": [ "# Original inference without LoRA weights\n", "# Seed for reproducibility\n", @@ -412,10 +1872,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "c97425ee", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of weights: 124587264\n", + "Total number of LoRA weights: 147456\n" + ] + } + ], "source": [ "# Print weights and model size\n", "total_weights_size = print_weights_and_size(hybrid_model.model)" @@ -423,7 +1892,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "31367ff5", "metadata": {}, "outputs": [], @@ -440,10 +1909,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "a1dda636", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of weights: 39569664\n", + "Total number of LoRA weights: 147456\n" + ] + } + ], "source": [ "# Print weights and size after saving\n", "total_weights_size_private = print_weights_and_size(hybrid_model.model)" @@ -451,10 +1929,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "506ad2f5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total weights removed: 68.24 %\n" + ] + } + ], "source": [ "# Calculate and print the percentage of weights removed\n", "percentage_removed = (total_weights_size - total_weights_size_private) / total_weights_size * 100\n", @@ -463,7 +1949,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "465cb18b", "metadata": {}, "outputs": [], diff --git a/use_case_examples/lora_finetuning/README.md b/use_case_examples/lora_finetuning/README.md index df2b09cb6..cf16d2176 100644 --- a/use_case_examples/lora_finetuning/README.md +++ b/use_case_examples/lora_finetuning/README.md @@ -9,7 +9,6 @@ Fine-tuning large language models typically requires access to sensitive data, w ## Key Features - **LoRA Fine-Tuning**: Fine-tune GPT-2 by adapting low-rank weights. -- **Fully Homomorphic Encryption**: Perform training and inference on encrypted data. - **Hybrid Model**: Combine traditional and encrypted computations for optimal performance. - **Low Memory Requirements**: Minimal client-side memory needed for LoRA weights. From 8e8409a51fc1db922f34fac54a61244eba17dd0d Mon Sep 17 00:00:00 2001 From: jfrery Date: Tue, 24 Sep 2024 19:46:41 +0200 Subject: [PATCH 28/32] chore: lora more generic for the MLP --- src/concrete/ml/torch/lora.py | 291 +++++++++++++++++++++------------- 1 file changed, 179 insertions(+), 112 deletions(-) diff --git a/src/concrete/ml/torch/lora.py b/src/concrete/ml/torch/lora.py index 03fb99b89..2786d2944 100644 --- a/src/concrete/ml/torch/lora.py +++ b/src/concrete/ml/torch/lora.py @@ -1,6 +1,7 @@ """This module contains classes for LoRA (Low-Rank Adaptation) training and custom layers.""" import torch +from transformers import Conv1D as TransformerConv1D # pylint: disable=abstract-method # pylint: disable=arguments-differ @@ -9,65 +10,94 @@ class LoraTraining(torch.nn.Module): """LoraTraining module for fine-tuning with LoRA.""" - SUPPORTED_MODELS = ["gpt2"] - - def __init__(self, inference_model, gradient_accumulation_steps) -> None: + def __init__(self, inference_model) -> None: super().__init__() self.inference_model = inference_model - # Validate the base model type - self._validate_model_type() + self.replace_layers_with_custom(self.inference_model) self.optimizer = None self.lr_scheduler = None - - self.gradient_accumulation_steps = gradient_accumulation_steps + self.loss_fn = None + self.gradient_accumulation_steps = 1 self.max_grad_norm = None self.calibrate = False self.run_optimizer = False - def _validate_model_type(self): - """Validate the model type. - - Raises: - ValueError: If the model type is not supported. + def replace_layers_with_custom(model:torch.nn.Module, skip_first: bool = True): """ - try: - # Access the base model from PeftModelForCausalLM - base_model = self.inference_model.base_model.model - - # Retrieve the model type from the configuration - model_type = getattr(base_model.config, "model_type", None) - - if model_type not in self.SUPPORTED_MODELS: - raise ValueError( - f"Unsupported model type: '{model_type}'. " - f"Supported models are: {self.SUPPORTED_MODELS}" - ) - - except AttributeError as e: - raise ValueError( - "Unable to determine the base model type. " - "Ensure that the inference_model has a " - "'base_model.model.config.model_type' attribute." - ) from e + Replace torch.nn.Linear and TransformerConv1D layers in the model with CustomLinear layers, + optionally skipping the first eligible layer encountered. - def update_training_parameters(self, optimizer, lr_scheduler, training_args): + Args: + model (torch.nn.Module): The model whose layers are to be replaced. + skip_first (bool): Whether to skip replacing the first eligible layer. + """ + skipped = False # Flag to track if the first layer has been skipped + + def _replace(module: torch.nn.Module): + nonlocal skipped + for name, child in list(module.named_children()): + # Skip modules containing "lora" in their name + if "lora" in name: + continue + + if isinstance(child, (torch.nn.Linear, TransformerConv1D)): + if skip_first and not skipped: + skipped = True + continue # Skip the first eligible layer + + # Determine if weights need to be transposed + weight_transposed = isinstance(child, TransformerConv1D) + + # Create the CustomLinear layer + custom_layer = CustomLinear( + weight=child.weight, + bias=child.bias, + weight_transposed=weight_transposed + ) + + # Replace the original layer with the custom layer + setattr(module, name, custom_layer) + else: + # Recursively apply to child modules + _replace(child) + + _replace(model) + + def update_training_parameters( + self, optimizer=None, lr_scheduler=None, loss_fn=None, training_args=None + ): """Update training parameters for the LoRA module. Args: - optimizer: The optimizer to use for training. - lr_scheduler: The learning rate scheduler to use for training. - training_args: The training arguments containing gradient - accumulation steps and max grad norm. + optimizer (optional): The optimizer to use for training. + lr_scheduler (optional): The learning rate scheduler to use for training. + loss_fn (callable, optional): Loss function to compute the loss. + training_args (dict or namespace, optional): Training arguments containing + 'gradient_accumulation_steps' and 'max_grad_norm'. """ - assert self.gradient_accumulation_steps == training_args.gradient_accumulation_steps - self.optimizer = optimizer self.lr_scheduler = lr_scheduler - self.max_grad_norm = training_args.max_grad_norm + self.loss_fn = loss_fn + + if training_args is not None: + # Check if training_args is a dict or an object with attributes + if isinstance(training_args, dict): + self.gradient_accumulation_steps = training_args.get( + "gradient_accumulation_steps", 1 + ) + self.max_grad_norm = training_args.get("max_grad_norm", None) + else: + self.gradient_accumulation_steps = getattr( + training_args, "gradient_accumulation_steps", 1 + ) + self.max_grad_norm = getattr(training_args, "max_grad_norm", None) + else: + self.gradient_accumulation_steps = 1 + self.max_grad_norm = None def forward(self, inputs): """Forward pass of the LoRA training module. @@ -77,44 +107,47 @@ def forward(self, inputs): Returns: A tuple containing the loss and gradient norm. - - Raises: - ValueError: If the model does not return a loss. """ # Remove this once hybrid model supports multiple inputs # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4568 x, y = inputs - # Correctly pass labels as a keyword argument - outputs = self.inference_model(x, labels=y) + # Forward pass + if self.loss_fn is None: - # Use getattr to safely access the loss attribute - loss = getattr(outputs, "loss", None) - if loss is None: - raise ValueError( - "The model did not return a loss. Ensure that 'labels' are correctly provided." - ) + # Assume model computes loss internally + outputs = self.inference_model(x, labels=y) + + # Use getattr to safely access the loss attribute + loss = getattr(outputs, "loss", None) + if loss is None: + raise ValueError( + "The model did not return a loss. Ensure that 'labels' are correctly provided." + ) + else: + outputs = self.inference_model(x) + loss = self.loss_fn(outputs, y) loss = loss / self.gradient_accumulation_steps # Update gradients # We need to set requires grad to the loss manually because the inference model's last - # step is the "lm_head" layer, which is detached from the graph by the hybrid model + # step is the "lm_head" layer, which might be detached from the graph by the hybrid model loss.requires_grad_(True) loss.backward() grad_norm = None if not self.calibrate and self.run_optimizer: - assert self.optimizer is not None - assert self.lr_scheduler is not None - assert self.max_grad_norm is not None + if self.max_grad_norm is not None: + grad_norm = torch.nn.utils.clip_grad_norm_( + self.inference_model.parameters(), max_norm=self.max_grad_norm, norm_type=2 + ) - grad_norm = torch.nn.utils.clip_grad_norm_( - self.inference_model.parameters(), max_norm=self.max_grad_norm, norm_type=2 - ) + if self.optimizer is not None: + self.optimizer.step() - self.optimizer.step() - self.lr_scheduler.step() + if self.lr_scheduler is not None: + self.lr_scheduler.step() self.inference_model.zero_grad() @@ -141,38 +174,45 @@ def toggle_run_optimizer(self, enable: bool = True): self.run_optimizer = enable -class ForwardModule(torch.nn.Module): - """Forward module for custom convolution.""" +class ForwardModuleLinear(torch.nn.Module): + """Forward module for linear layers.""" - def __init__(self, weight, bias=None): + def __init__(self, weight, bias=None, weight_transposed=False): super().__init__() - self.weight = weight # Assume weight is passed as a pre-initialized tensor + self.weight = weight self.bias = bias + self.weight_transposed = weight_transposed # If True, weight is (in_features, out_features) def forward(self, input_tensor): - """Forward pass of the forward module. + """Forward pass for linear layers. Args: input_tensor: The input tensor. Returns: - The output tensor after applying the forward pass. + The output tensor after applying the linear transformation. """ - output = input_tensor @ self.weight + if self.weight_transposed: + # Weight is (in_features, out_features) + output = input_tensor @ self.weight + else: + # Weight is (out_features, in_features) + output = input_tensor @ self.weight.t() if self.bias is not None: - output = output + self.bias + output += self.bias return output -class BackwardModule(torch.nn.Module): - """Backward module for custom convolution.""" +class BackwardModuleLinear(torch.nn.Module): + """Backward module for linear layers.""" - def __init__(self, weight): + def __init__(self, weight, weight_transposed=False): super().__init__() - self.weight = weight # This is the same weight used in ForwardModule + self.weight = weight + self.weight_transposed = weight_transposed def forward(self, grad_output): - """Forward pass of the backward module. + """Backward pass for linear layers. Args: grad_output: The gradient output tensor. @@ -180,7 +220,33 @@ def forward(self, grad_output): Returns: The gradient input tensor after applying the backward pass. """ - return grad_output @ self.weight.t() + if self.weight_transposed: + grad_input = grad_output @ self.weight.t() + else: + grad_input = grad_output @ self.weight + return grad_input + + +class CustomLinear(torch.nn.Module): + """Custom linear module.""" + + def __init__(self, weight, bias=None, weight_transposed=False): + super().__init__() + self.forward_module = ForwardModuleLinear(weight, bias, weight_transposed) + self.backward_module = BackwardModuleLinear(weight, weight_transposed) + + def forward(self, input_tensor): + """Forward pass of the custom linear module. + + Args: + input_tensor: The input tensor. + + Returns: + The output tensor after applying the custom linear module. + """ + return ForwardBackwardModule.apply( + input_tensor, self.forward_module, self.backward_module + ) class ForwardBackwardModule(torch.autograd.Function): @@ -217,45 +283,46 @@ def backward(ctx, grad_output): backward_module = ctx.backward_module grad_input = backward_module.forward(grad_output) - # grad_weight and grad_bias are not needed when computing the backward for lora + # grad_weight and grad_bias are not needed when computing the backward for LoRA return grad_input, None, None -class CustomConv1D(torch.nn.Module): - """Custom 1D convolution module.""" - - def __init__(self, weight, bias=None): - super().__init__() - self.forward_module = ForwardModule(weight, bias=bias) - self.backward_module = BackwardModule(weight) - - def forward(self, input_tensor): - """Forward pass of the custom 1D convolution. - - Args: - input_tensor: The input tensor. - - Returns: - The output tensor after applying the custom 1D convolution. - """ - return ForwardBackwardModule.apply(input_tensor, self.forward_module, self.backward_module) - - -class CustomLinear(torch.nn.Module): - """Custom linear module.""" - - def __init__(self, weight, bias=None): - super().__init__() - self.forward_module = ForwardModule(weight, bias=bias) - self.backward_module = BackwardModule(weight) - - def forward(self, input_tensor): - """Forward pass of the custom linear module. - - Args: - input_tensor: The input tensor. - - Returns: - The output tensor after applying the custom linear module. - """ - return ForwardBackwardModule.apply(input_tensor, self.forward_module, self.backward_module) +def get_remote_names(model, include_embedding_layers=False): + """Get names of modules to be executed remotely. + + Args: + model (torch.nn.Module): The model to inspect. + include_embedding_layers (bool): Whether to include embedding layers. + + Returns: + List[str]: List of module names to be executed remotely. + """ + remote_names = [] + for name, module in model.named_modules(): + + # Skip if the name contains 'lora' since they will be done on client side + if "lora" in name: + continue + + # Check for Linear or Conv1d modules + if isinstance(module, (torch.nn.Linear, TransformerConv1D)): + # Skip lm_head if include_embedding_layers is False + if "lm_head" in name and not include_embedding_layers: + continue + remote_names.append(name) + + # Check for CustomLinear modules + if isinstance(module, (CustomLinear)): + # Skip lm_head if include_embedding_layers is False + if "lm_head" in name and not include_embedding_layers: + continue + remote_names.append(name + ".forward_module") + remote_names.append(name + ".backward_module") + + # Include Embedding layers and lm_head if requested + elif include_embedding_layers and ( + isinstance(module, torch.nn.Embedding) or "lm_head" in name + ): + remote_names.append(name) + + return remote_names From 7dca5887750e39bdf0604d15dcac7843f4701d89 Mon Sep 17 00:00:00 2001 From: jfrery Date: Tue, 24 Sep 2024 19:46:59 +0200 Subject: [PATCH 29/32] chore: add LoraMLP notebook --- docs/advanced_examples/LoraMLP.ipynb | 536 ++++++++++++++++++ .../lora_finetuning/utils_lora.py | 37 +- 2 files changed, 537 insertions(+), 36 deletions(-) create mode 100644 docs/advanced_examples/LoraMLP.ipynb diff --git a/docs/advanced_examples/LoraMLP.ipynb b/docs/advanced_examples/LoraMLP.ipynb new file mode 100644 index 000000000..4e792f0dd --- /dev/null +++ b/docs/advanced_examples/LoraMLP.ipynb @@ -0,0 +1,536 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Low-Rank Approximation Fine-Tuning\n", + "\n", + "This notebook demonstrates encrypted fine-tuning of a small MLP model with LoRA. A model trained on an initial dataset is adapted to a second dataset using LoRA fine-tuning.\n", + "\n", + "The fine-tuning dataset and the LoRA weights that are trained are protected using encryption. Thus, the training can be outsourced to a remote server without leaking any sensitive data.\n", + "\n", + "The hybrid model approach is applied to fine-tuning: only the linear layers of the original model are outsourced to the server. The forward and backward passes on these original weights are performed with encrypted activations and gradients. The LoRA weights are kept by the client, and the client performs the forward and backward passes on the LoRA weights." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import shutil\n", + "import time\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from peft import LoraConfig, get_peft_model\n", + "from sklearn.datasets import make_circles, make_moons\n", + "from torch.utils.data import DataLoader, TensorDataset\n", + "from tqdm import tqdm\n", + "\n", + "from concrete.ml.torch.hybrid_model import HybridFHEModel\n", + "from concrete.ml.torch.lora import LoraTraining, get_remote_names\n", + "\n", + "# Set random seed for reproducibility\n", + "SEED = 42\n", + "np.random.seed(SEED)\n", + "torch.manual_seed(SEED)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preparation\n", + "\n", + "Two datasets are generated: one for the original training, and a second one on which LORA fine-tuning is performed." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Task 1: Two interleaving half circles (make_moons)\n", + "X_task1, y_task1 = make_moons(n_samples=500, noise=0.1)\n", + "# Task 2: Two concentric circles\n", + "X_task2, y_task2 = make_circles(n_samples=500, noise=0.2, factor=0.5)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "def plot_datasets_and_boundaries(X_task1, y_task1, X_task2, y_task2, model=None, titles=None):\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + " if titles is None:\n", + " titles = [\"Task 1 Dataset\", \"Task 2 Dataset\"]\n", + "\n", + " for ax, X, y, title in zip([ax1, ax2], [X_task1, X_task2], [y_task1, y_task2], titles):\n", + " ax.scatter(X[:, 0], X[:, 1], c=y, cmap=\"viridis\", edgecolor=\"k\")\n", + " ax.set_title(title)\n", + "\n", + " if model is not None:\n", + " x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n", + " y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n", + " h = 0.1 # step size in the mesh\n", + " xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", + " grid = torch.FloatTensor(np.c_[xx.ravel(), yy.ravel()])\n", + "\n", + " with torch.no_grad():\n", + " Z = model(grid)\n", + " probabilities = torch.softmax(Z, dim=1)\n", + " Z = probabilities[:, 1].numpy().reshape(xx.shape)\n", + "\n", + " ax.contourf(xx, yy, Z, cmap=\"viridis\", alpha=0.3)\n", + "\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "# Plot datasets\n", + "plot_datasets_and_boundaries(X_task1, y_task1, X_task2, y_task2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create the original MLP and test it" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training on Task 1 without LoRA:\n", + "Epoch [20/20], Loss: 0.0036\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Convert datasets to PyTorch tensors\n", + "X_task1 = torch.FloatTensor(X_task1)\n", + "y_task1 = torch.LongTensor(y_task1)\n", + "X_task2 = torch.FloatTensor(X_task2)\n", + "y_task2 = torch.LongTensor(y_task2)\n", + "\n", + "# Create DataLoaders\n", + "batch_size = 32\n", + "train_loader_task1 = DataLoader(\n", + " TensorDataset(X_task1, y_task1), batch_size=batch_size, shuffle=True\n", + ")\n", + "train_loader_task2 = DataLoader(\n", + " TensorDataset(X_task2, y_task2), batch_size=batch_size, shuffle=True\n", + ")\n", + "\n", + "# Define an MLP model without LoRA layers\n", + "\n", + "\n", + "class SimpleMLP(nn.Module):\n", + " \"\"\"Simple MLP model without LoRA layers.\"\"\"\n", + "\n", + " def __init__(self, input_size=2, hidden_size=128, num_classes=2):\n", + " super().__init__()\n", + " self.fc1 = nn.Linear(input_size, hidden_size)\n", + " self.relu = nn.ReLU()\n", + " self.fc2 = nn.Linear(hidden_size, num_classes)\n", + "\n", + " def forward(self, x):\n", + " \"\"\"Forward pass of the MLP.\"\"\"\n", + " out = self.fc1(x)\n", + " out = self.relu(out)\n", + " out = self.fc2(out)\n", + " return out\n", + "\n", + "\n", + "# Instantiate the model\n", + "model = SimpleMLP()\n", + "\n", + "# Training loop for Task 1\n", + "\n", + "\n", + "def train_model(model, train_loader, num_epochs=100):\n", + " \"\"\"Train the model.\n", + "\n", + " Args:\n", + " model (nn.Module): The model to train.\n", + " train_loader (DataLoader): DataLoader for training data.\n", + " num_epochs (int): Number of epochs to train.\n", + " \"\"\"\n", + " device = torch.device(\"cpu\")\n", + " model.to(device)\n", + " model.train()\n", + "\n", + " criterion = nn.CrossEntropyLoss()\n", + " optimizer = optim.Adam(model.parameters(), lr=0.01)\n", + "\n", + " for epoch in range(num_epochs):\n", + " total_loss = 0\n", + " for x_batch, y_batch in train_loader:\n", + " x_batch = x_batch.to(device)\n", + " y_batch = y_batch.to(device)\n", + "\n", + " optimizer.zero_grad()\n", + " outputs = model(x_batch)\n", + " loss = criterion(outputs, y_batch)\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " total_loss += loss.item()\n", + "\n", + " # Print loss every 20 epochs\n", + " if (epoch + 1) % 20 == 0:\n", + " print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss/len(train_loader):.4f}\")\n", + "\n", + "\n", + "# Train the model on Task 1\n", + "print(\"Training on Task 1 without LoRA:\")\n", + "train_model(model, train_loader_task1, num_epochs=20)\n", + "\n", + "# Plot datasets with decision boundaries\n", + "plot_datasets_and_boundaries(\n", + " X_task1.numpy(),\n", + " y_task1.numpy(),\n", + " X_task2.numpy(),\n", + " y_task2.numpy(),\n", + " model=model,\n", + " titles=[\"Task 1 after Training\", \"Task 2 after Training\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Apply LoRA to the model using peft" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Apply LoRA to the model using peft\n", + "lora_config = LoraConfig(\n", + " r=1, lora_alpha=1, lora_dropout=0.01, target_modules=[\"fc1\", \"fc2\"], bias=\"none\"\n", + ")\n", + "\n", + "peft_model = get_peft_model(model, lora_config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup FHE fine-tuning with LoraTraining and HybridFHEModel" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up LoRA training\n", + "lora_training = LoraTraining(peft_model)\n", + "\n", + "# Set up optimizer and scheduler\n", + "optimizer = optim.Adam(filter(lambda p: p.requires_grad, peft_model.parameters()), lr=0.01)\n", + "\n", + "# Update training parameters, including loss function\n", + "lora_training.update_training_parameters(\n", + " optimizer=optimizer,\n", + " loss_fn=nn.CrossEntropyLoss(),\n", + " training_args={\"gradient_accumulation_steps\": 1},\n", + ")\n", + "\n", + "# Create the HybridFHEModel\n", + "remote_names = get_remote_names(lora_training)\n", + "hybrid_model = HybridFHEModel(lora_training, module_names=remote_names)\n", + "\n", + "# Prepare input data for calibration\n", + "batch_size_per_task = batch_size // 2\n", + "inputset = (\n", + " torch.cat([X_task1[:batch_size_per_task], X_task2[:batch_size_per_task]]),\n", + " torch.cat([y_task1[:batch_size_per_task], y_task2[:batch_size_per_task]]),\n", + ")\n", + "\n", + "# Calibrate and compile the model\n", + "hybrid_model.model.toggle_calibrate(enable=True)\n", + "hybrid_model.compile_model(inputset, n_bits=8)\n", + "hybrid_model.model.toggle_calibrate(enable=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fine-tuning on Task 2 with LoRA:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training: 100%|██████████| 10/10 [04:32<00:00, 27.26s/epoch, Avg Loss=0.2978, Time=27.49s, FHE Mode=execute]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training completed. Final Avg Loss: 0.2978, FHE Mode: execute\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Fine-tune the model on Task 2 using LoRA\n", + "\n", + "\n", + "def train_hybrid_model(hybrid_model, train_loader, num_epochs=50, fhe=\"disable\"):\n", + " \"\"\"Train the model using the hybrid FHE model with gradient accumulation.\n", + "\n", + " Args:\n", + " hybrid_model (HybridFHEModel): The compiled hybrid model.\n", + " train_loader (DataLoader): DataLoader for training data.\n", + " num_epochs (int): Number of epochs to train.\n", + " fhe (str): FHE mode ('disable', 'simulate', or 'execute').\n", + " \"\"\"\n", + " device = torch.device(\"cpu\")\n", + " hybrid_model.model.to(device)\n", + " hybrid_model.model.inference_model.train()\n", + " hybrid_model.model.toggle_run_optimizer(enable=True)\n", + "\n", + " # Create the main epoch progress bar\n", + " epoch_pbar = tqdm(range(num_epochs), desc=\"Training\", unit=\"epoch\")\n", + "\n", + " for epoch in epoch_pbar:\n", + " total_loss = 0\n", + " start_time = time.time()\n", + "\n", + " # Create a progress bar for batches within each epoch\n", + " batch_pbar = tqdm(\n", + " train_loader, desc=f\"Epoch {epoch+1}/{num_epochs}\", leave=False, unit=\"batch\"\n", + " )\n", + "\n", + " for x_batch, y_batch in batch_pbar:\n", + " x_batch = x_batch.to(device)\n", + " y_batch = y_batch.to(device)\n", + "\n", + " loss, _ = hybrid_model((x_batch, y_batch), fhe=fhe)\n", + " total_loss += loss.item()\n", + "\n", + " # Update batch progress bar\n", + " batch_pbar.set_postfix({\"Loss\": f\"{loss.item():.4f}\"})\n", + "\n", + " # Calculate average loss and epoch time\n", + " avg_loss = total_loss / len(train_loader)\n", + " epoch_time = time.time() - start_time\n", + "\n", + " # Update epoch progress bar\n", + " epoch_pbar.set_postfix(\n", + " {\"Avg Loss\": f\"{avg_loss:.4f}\", \"Time\": f\"{epoch_time:.2f}s\", \"FHE Mode\": fhe}\n", + " )\n", + "\n", + " print(f\"Training completed. Final Avg Loss: {avg_loss:.4f}, FHE Mode: {fhe}\")\n", + "\n", + "\n", + "print(\"Fine-tuning on Task 2 with LoRA:\")\n", + "train_hybrid_model(hybrid_model, train_loader_task2, num_epochs=10, fhe=\"execute\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize fine-tuned model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Enable LoRA adapters (already enabled by default)\n", + "hybrid_model.model.inference_model.enable_adapter_layers()\n", + "\n", + "# Plot datasets with decision boundaries after fine-tuning\n", + "plot_datasets_and_boundaries(\n", + " X_task1.numpy(),\n", + " y_task1.numpy(),\n", + " X_task2.numpy(),\n", + " y_task2.numpy(),\n", + " model=hybrid_model.model.inference_model,\n", + " titles=[\"Task 1 after Fine-tuning\", \"Task 2 after Fine-tuning\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Disable LoRA adapters to see the original model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ffj999/58ssvyc3NJSgoiJNOOonPPvtsj03y/vGPf1BWVsadd95JYGAgr7zyym6PnTFjBsOGDWuw7cknnwTqS7z3lNS44YYb2Lp1K++99x5TpkwhNTWVTz/9lG+++YZZs2bt9Xs48cQTeeaZZxg5ciSTJ0/GNE0yMjLw8/Pj0UcfpUOHDgwfPtwdX3x8POeeey4XXnjhXq/dGgzDYNy4cQwfPpwxY8bw/fff4+vrS7t27bj33nv3qcx91KhR3H333dx///3U1NQwZMgQ0tLS6Ny5M2PGjOGpp57igQceIDU1lU8++YTPP/98n77rQ61nz55MmjSJhx56iCeffJL4+Hiefvpp/vrrL1avXt3i8YiIiIgc6w6XMcaqVauoqakhLy+vwX13+Oijj/aY1NAYoyGNMTTGEJHmZ7PU4UdEROSYdfHFF7Ny5UrS09NbOxQRERERETkKaIwhIs1NPTVERESOEVVVVQ1+Tk9PZ+LEifTt27d1AhIRERERkSOaxhgi0hpUqSEiInKMiImJ4YYbbqBdu3Zs3LiRESNG4HQ6WbJkCSkpKa0dnoiIiIiIHGE0xhCR1qCeGiIiIseIAQMG8MUXX7B161YcDgennnoqzz//vAYbIiIiIiJyQDTGEJHWoEoNERERERERERERERE5IqinhoiIiIiIiIiIiIiIHBGU1BARERERERERERERkSPCUddTwzRNsrOzCQgIwGaztXY4IiIiIiJHLcuyKCsrIzY2FsM4eudLaYwhIiIiItL89nV8cdQlNbKzs4mPj2/tMEREREREjhmbN28mLi6utcNoNhpjiIiIiIi0nL2NL466pEZAQAAAP3z2M36+fq0cjcjBWZ2xDYAOXRP2+9y1yzfRKSnyUIckIiIi4lZRWcFF/3eO+xn8aKUxhoiIiIhI89vX8cVRl9TYUQ7u5+uHn59/K0cjcnB6pvmzan0um9ML6NQ9cZ/PW700k55p7ZovMBEREZGdHO1LMmmMISIiIiLScvY2vjjqkhoiR5vU5CgAVi3NBNhrcmP10kz3OSIiIiIiIiIiIiJHk6O3m5/IUWZHomL19uRGU5TQEBERERERERERkaOZkhoiR5A9JTb2lOwQERERERERERERORooqSFyhGkqsbHjv1WlISIiIiIiIiIiIkcz9dQQOQKlJkexan1ug8SGEhoiIiIiIiIiIiJytFOlhsgRKjU5yp3IUEJDREREREREREREjgVKaogc4ZTQEBERERERERERkWOFkhoirWTV+lxWrc9t7TBEREREREREREREjhhKaoi0gp2TGUpsiIiIiIiIiIiIiOwbJTVEWtiOJEan7ol06p7YusGIiIiIiIiIiIiIHEGU1BBpBbsmM1StISIiIiIiIiIiIrJ3SmqItKBV63MbJTR2/KzEhoiIiIiIiIiIiMieKakh0kKaSmjsoGWoRERERERERERERPZOSQ2RFrAvVRiduieqWkNERERERERERERkD5TUEGlmOzcG35/jRURERERERERERKQhJTVEWsC+JjS0DJWIiIiIiIiIiIjI7impIdICVi/NPKTHiYiIiIiIiIiIiByLlNQQaWapyVHA3hMWO/bvOF5EREREREREREREGvJo7QBEjgU7EhWrticudl1mSgkNERERERERERERkb1TpYZIC2qqakMJDREREREREREREZF9o0oNkRaWmhzFqvW5DRIbSmiIiIiIiIiIiIiI7J0qNURawc5JDCU0RERERERERERERPaNkhoirSQ1OUoJDRERERERkRa2an1ua4cgIiIiB6FZkxqzZ89m0KBBxMbGYrPZGDdu3B6PnzVrFjabrdGvrVu3NmeYIiIiIiIiInIMWLU+l07dE5XYEBEROYI1a1KjoqKCbt268c477+zXeWvWrCEnJ8f9KzIyspkiFBEREREREZFjwa6JDCU2REREjkzN2ih84MCBDBw4cL/Pi4yMJDg4+NAHJCIiIiIiIiLHtNVLMwH1NxQRETlSNWtS40B1794dp9NJWloaQ4cO5bTTTmvtkEQa2DGjRw/BIiIiIiIiRwaN30RERI4Oh1Wj8JiYGEaOHMnYsWMZO3Ys8fHx9O3bl8WLF+/2HKfTSWlpaYNfIs1p5xJllSuLiIiIiIiIiIiItJzDqlKjY8eOdOzY0f1zr169WL9+PcOHD+eTTz5p8pwXXniBYcOGtVSIcozbkcTo1D0RqC9bXrU+VzN+RERERERERERERFrAYVWp0ZSTTjqJdevW7Xb/Y489RklJifvX5s2bWzA6OZbsmtDY9b9FREREREREREREpHkdVpUaTVm6dCkxMTG73e9wOHA4HC0YkRyLmkpo7Lpf1RoiIiIiIiIiIiIizatZkxrl5eUNqiwyMjJYunQpoaGhJCQk8Nhjj5GVlcWYMWMAeOONN0hKSqJLly5UV1czatQoZsyYwdSpU5szTJE92ltCo1P3RC1DJSIiIiIiIiIiItICmjWpsXDhQs4880z3zw888AAA119/PaNHjyYnJ4dNmza599fU1PDggw+SlZWFr68vXbt2Zdq0aQ2uIdKS9pbQ2EGJDREREREREREREZHm16xJjb59+2JZ1m73jx49usHPjzzyCI888khzhiSy3/a1b8aOxIaIiIiIiIiIiIiINI/DvlG4SGvb10TF6qWZqtIQERERERERERERaUaHfaNwkda0I0mxantiY3dVG0poiIiIiIiIiIiIiDQ/VWqI7IMdCYumqja05JSIiIiIiIiIiIhIy1BSQ2QfNZXY2PHfqtIQERERERE59Fatz2XV+tzWDkNEREQOI0pqiOyHnRMbSmiIiIiIiIg0n52TGUpsiIiIyA5Kaojsp52TGEpoiIiIiIiINA+Nt0RERKQpahQucgD0cC0iIiIiItL8NPYSERGRXalSQ0REREREREREREREjghKaoiIiIiIiIiIiIiIyBFBSQ0RERERERERERERETkiKKkhIiIiIiIiIiIiIiJHBCU1RERERERERERERETkiKCkhoiIiIiIiIiIiIiIHBGU1BARERERERERERERkSOCkhpyzFm1PpdV63NbOwwRERERERERERER2U9KasgxRckMERERERERERERkSOXkhpyzNiR0OjUPZFO3ROV4BARERERERERERE5wiipIceUTt0TG/ysxIaIiIiIiIiIiIjIkUNJDTlmpCZHsXppJoD7/6cmR7VeQCIiIiIiIiIiIiKyX5TUkGPKzokNJTREREREjjyzZ89m0KBBxMbGYrPZGDdu3B6PnzVrFjabrdGvrVu3tkzAIiIiIiJySHm0dgAiLU3JDBEREZEjV0VFBd26dePGG29k8ODB+3zemjVrCAwMdP8cGRnZHOGJiMjuWLDqr7/4c/lyLMuic+fOdO3aFZtha+3IRETkCKOkhoiIiIiIHDEGDhzIwIED9/u8yMhIgoODD31AIiKyV3l5+Tz/3HOs37AeL8OBDXCaTtrExvH4E48TF9emtUMUEZEjiJafEhERERGRo1737t2JiYnhnHPOYe7cuXs81ul0Ulpa2uCXiIgcmOqqah7/z3/IysymHal0NnvSyexJe9Io2lrMfx57jNIS/T0rIiL7TkkNERERERE5asXExDBy5EjGjh3L2LFjiY+Pp2/fvixevHi357zwwgsEBQW5f8XHx7dgxCIiR5dZs2aRm5tLotmZQEKwbf8/f4JIMjtTVlrO5MmTWztMERE5giipISIiIiIiR62OHTty22230bNnT3r16sWHH35Ir169GD58+G7PeeyxxygpKXH/2rx5cwtGLCJydJn9y2wCbaF449NonydeBFmh/DLrl1aITEREjlTqqSEiIiIiIseUk046iTlz5ux2v8PhwOFwtGBEIiJHr7Lycjwsz93u98RBeUVFC0YkIiJHOlVqiIiIiIjIMWXp0qXExMS0dhgiIseEuLg2VBvlWFhN7q80ymjTJraFoxIRkSOZKjVEREREROSIUV5ezrp169w/Z2RksHTpUkJDQ0lISOCxxx4jKyuLMWPGAPDGG2+QlJREly5dqK6uZtSoUcyYMYOpU6e21kcQETmm9B/Qn7nz5lJEHqFENthXShFlZjEDBt7cStGJiMiRSEkNERERERE5YixcuJAzzzzT/fMDDzwAwPXXX8/o0aPJyclh06ZN7v01NTU8+OCDZGVl4evrS9euXZk2bVqDa4iISPPp3q07Z555JrNmzqKCUoKJwAYUU0ihbSsn9DiB0087vbXDFBGRI4jNsqym6/+OUKWlpQQFBTHt+3n4+fm3djgiIiIiIketiopyzr6kFyUlJQQGBrZ2OM1GYwwRkYNjmiY/jPuBH8b9QGFxIQAB/oGcf8F5XHHFFXh4aM5tUwryC1i4cCE1NTUkJiZy3HHHga21oxIRaT77Or7QvxoiIiIiIiIiItJsDMPgksGXcNFFF5GdnY1pmcTGxiqZsRs1zhpGjBjBzJkzsSwwbAYuq47YmFgeeugh2qe0b+0QRURalRqFi4iIiIiIiIhIszPsBnHxcSQkJCihsTsWvPLqq8ya+QsxViJpnESadRLtSaMkt5zHH3+c7Kzs1o5SRKRVKakhIiIiIiIiIiJyGEhPT+e33xYQZyUTQQx27Niw4U8Q7cxUzBr45ttvWjtMEZFWpaSGiIiIiIiIiIjIYWDWrFl4Gz4EE9Zonx07IWYEs3+ZjWmarRCdiMjhQUkNERERERERERGRw0BpaSmeOLDtpiO4Ax9q62qpcda0cGQiIocPLWAoIs2mtraORQsXkl+QT3BQMCeeeCIOb0drhyUiIiIiIiJyWIqMjKSaSkxMjCbmIldShr+fPw6HxtYicuxSUkNEmsUvv8zmf+/9j7LyUgwMTEx8vH34x7XXMmjQBa0dnoiIiIiIiMhhp9/ZZ/PNt9+QRzZRxDXY56SaYiOfC849H5vRdCWHiMixQEkNETnk5s2bx2uvvUoIEXTieLzxxUk1edVZvP/+/zBsNs6/4PzWDlNERERERETksBIbG8Oll17K2LFjcVJNGFHY8aCMIvKNHELDQrh08KWtHaaISKtSUkNEDinLtPjow48IIowEUtzrgDrwJo5kLCw+/fQzzjnnHLwcXq0crYiIiIiIiMjh5frrric0JJRvv/2W9OLlABiGQa9Te3HLLbcQGBTYyhGKiLQuJTVE5JBam76W3G25tCetycZmkbThr8rFLF68mFNOPaUVIhQRERERERE5jNlg0IWDOP/881m3bh01NTXExccRHBzc2pGJiBwWlNQQkUOqtLQUAAc+Te73whuAkpKSFotJRERERERE5Ehj2A06dOzQ2mGIiBx2jNYOQESOLhEREQBUUt7k/ioqGhwnIiIiIiIiIiIisq+U1BCRQyqxbSLtEpPJs2VhYjbYZ2GRyxZCQ8Lo3r176wQoIiIiIiKHNwtyc3PJzMyksqKytaMRERGRw4yWnxKRQ8sGt91xK4//5wnWmyuJsGLxwQ8nVeSRTQWlPHrHYxh25VRbQ0VFBU6nk6DAIOwe9tYOR0RERESkgd9++43PPv2czI0ZAHjYPTmj9+nccP0NhISGtHJ0IiIicjhQUkNEDrnOnTvz/PPP8f6oUaSnr3ZvT4hL4IGb7qVnz56tGN2xaenSpXz95desWLUCAH+/AAYM6M/lV1yOj0/T/U9ERERERFrS1Kk/8/bbbxFgCyaRTnjiRbmrhHm/zOPP5St4/fXXCA4Jbu0w5ShVUVHBsqXLcNbUkNi2LUntklo7JBER2Q0lNUSkWXTq3InXXnuVLZu3kF+QT1BQMEmJiWBr7ciOPTNnzOSNN97AzwgkgRQ88KS0ophx3//A4sVLeOHF55XYEBEREZFWVVFRwf/e+x9hRBFnJWPbPnDwI4BgM5z1RSv47LPPuOtfd7VypHK0MV0mH4/5mAk/TaCmtsa9PaV9Cvfddx/xCfGtGJ2IiDRF67+ISLOKi4+je/fuJCUlKqHRCspKy3j77bcJIZJkswuhRBJICHEkkWx2YdPGTYwdO7a1wxQRERGRY9wvs36hrraWaBLcCY0dHHgTakYxc+YsnE5nK0UoR6u3336bH77/gZDaKFI5ga6cSiKdydqQw6OPPkpubm5rhygiIrtQUkNE5Cg2Y+ZMXC4XsU0MDn3wI9gMZ9LEyZimuZsriIiIiIg0v+ycHHzsvnji1eR+PwKoqXVSXFTcsoHJUW1j5kamTZ9GLEnEkIAXDgwMggmlnZlKTWUt336rSWAiIocbJTVERI5iWzZvxtfmj8duBof+BFFWXkpZaVkLRyYiIiIi8jd/Pz9qrBpMmp5sU0v9skA+vlo2VQ6dmTNn4jAchBLVaJ8HngSbEcycMRPTpUlgIiKHE/XUEBE5iixauIjx48ezZs1a7HaDoOAganBiYTWq1ACooxYAh8PR0qGKiIiIiLidfvrpfP7F5xSRR9guL5hNLAqMXLp26UpgYGArRShHo6LiIrzwwdjNWsk++JJb66Squgo/P78Wjk5ERHZHSQ0RkaOBBR9//DFjvxuLvxFIkBmGiUl+ZQE1ppNsMmlD0i6nWBQZeRzf7Xi8fbzd28vLK1i3Lh3LgpSUFPz99fAuIiIiIs0rLj6O3mf0Zu6cuViWRSiRGBhUU8VWNlJFOVdffXVrhylHmdDQUJxUYWJiNLGYSRUVOLwc+PioQkhE5HCipIaIyFFg4cKFjP1uLG1IIsKMdW+PNhPYzDryyMYbH0KJwoaNWmrIJpMqs5zLL78cAKfTyQcffMD06TOora0v7/f08OSsfv24+aabcHirmkNEREREms+9996L3W5n1qxZbDU24WHzoNpVhb+fP4/d9xhd0rq0dohylOnX72zGjh1LAVuJILbBvlpqKDby6Hd2PwxDq7eLiBxOlNQQETkKjB//I/5GYIOEBoANG21oRzEFbGY9ucYWPPGiyqzA08uTh+59iLS0NFx1LoYNHcbqv1YTbsYSQjhgo7guj+k/T2fL5s088+wzeHj8/c9GVWUV6zesByC5XbLWNxYRERGRg+Lp5cn9D9zP1Vdfzfz586mqqqJNXBy9Tj0VTy/P1g5PjkJxcW04b+B5TJo0iRqchBGNBx6UUkyesQVvfx8uu/Sy1g5TRER2oaSGiMhRYM2aNQSbEU3us2MnkGCKqV+KyjvAwUVnX8SVV1yJr58vAHPnzWXFyhW0Jw1/gtznRhGPnxnIylUrmDtnLn369qHGWcPHH3/M1ClTcdY6AXB4Oejfvz/XXXcdXo6mm5KLiIiIiOyL6JhoLhl8SWuHIceI2267jeDgYL7//nvyqrPd27umduWuu+8iPCK8FaMTEZGmKKkhIkc3q35ZJcNux9Ozhf/Ks2D16tXMmjWLouJiwsPC6Hd2P9q1a3fIb2W3G5iYu93vwsSfICJpQ275Fib8NJHevfuQnFwfy9QpUwkwgvE3gxqd608QgbYQpk6dyhlnnMHTTz/NyhWrCLdiCKb+Ab+4Jp8JP01k48aNDBs2DMOu8mwRERERETn82QwbV119FZdccgkrVq6gxllD27ZtiW0Tu/eTRUSkVSipISJHJdM0mTxpMj+O/5GsnCwAjks7jsGXDqZnz57Nfv/a2jpefeUV5i+Yj4/hg6fpjdOo4seffuScs8/hrn/ddUjXZe3Rsyd/zFlItBmPDVvDWKihnGJiSCSQEPytQNa5VvDx6I95+plhAORty8Pb9N3t9b0tX7blbmPu3Lks/3M5yaQRsFNFhw8J+FuBLFu+jHnz5nH6Gacfss8mIiIiIiLS3BzejhYZK4qIyMHTVFoROeqYpskrL7/Ce++9R0VODQmkEEcyGas2MWzYMH788aeDvoez2skvs37h++++Z+bMWVRVVTXY//77/+O3336nLR3pYB5PO1LpaPYgjmSmTZvGl19+edAx7Oyiiy7EaVaxmfWYuNzba6khkzXY8SCUSAAM7ISbMSxdtoTCwkIAgoODqbE5d3v9Gls1wSEhTJkyhQAjuEFCY4cAggkwgpkyZcoh/WwiIiIiIiIiIiI7qFJDRI46M2fOZO68uSTSiWDC3NvDzCiyyWTU++9zwgk9iYmJOaDrT540mY8+/IgqZxUehicusw4vLy+uve5aLrzwQkqKS5g2dRpRVvz2htv1DGyEE00N1Yz/YTyXXnopDofjoD8vQEpKCvfefx9vvvkGZRThZwZiYlJGMQYG7UjFY6e/8r2pr8ooKiwiNDSUfmf3450171BFJT40rNioopISq5Brzr6K774di4/pt9s4vE1fcnNzD8lnEhERERERERER2ZUqNUTkqDPhpwkE2UIbJDQAbNiIoS0ehidTJh9YNcH0adN5d8S7+DoD6UxP0syT6ExPAmpCGTVqFBMnTmTp0qXUmXWEba+M2FUokVRWVbL6r9UHFMPunHlmX/733v+46JIL8YtxUEYRUbShMz3wI6DBsdVUAvUVGgB9+vYhPj6eDGMVhWzD3P5/hWwj01hFfFw8Z/btS1BQEDVU7zaGGpwEBTau4hARERERERERETkUlNQQkaPOxo0b8beafrFuYOBrBpCRkbHf13XVufj4448JIYI4knHgDYAXDuJoRyhRfPbp51RVV2+/V9PFcPbt22tqa/Y7hr2JjIrkuuuv45VXX8HDwwMTEw88GxxjYlJgbKVrWlfCwusTPw6Hg+eff57juqexiXSWM5/lzGcT6aR1S+O555/D4e3gzH5nUkqROymysyoqKaWQs/qddcg/l4iIiIiIiIiICGj5KRE5Cnl6elFXV7fb/aatDof3/i/7tHLVSopLiulAt0bNuC0sgolgQ/kKamrqe1OUUUQQoY2uU0oRNmxERUUxceJEfv/td2pqakhun8yA/gNoE9dmv2PblX+AP1dffTVjPhmDCxcRxOCFNxWUkstmqqmk2lnN7bfdTlBwMP3OOos+ffswZOgQsrNzWLlyBQBdUrsQ2ybWfd2zzjyLH8f/RMbWv4g22xK0vRqmhAK2GhuJjW7DWWcpqSEiIiIiIi3Igi1ZWZSWlhIREUFERPjezxERkSOWkhoictQ5tdcpzJ05jygzDmOXgrRqqiizSjj55JP3+7rlZeUAeG2v0AAwschjC/nkUkt9MmPc9+OIiooiN28zfmZAg0qJGpxsM7YQ3yaehx96mOrqavwJwo4na/9KZ/wP47n5llsYNOiCA/noDVx22WV4Obz44vMvWV25xL3d4eXAVeMiZ/02HKYv2Vtzeeevdxg/fjzPPfccsbExxMY23W/E28ebF154nldffZXlfy53f78mJl27dOWhhx46oISRiIiIiIgcfmqcNWzYsAHTNElKSsLH16e1Q2pk8aLFjB79MZkb/67G79a1GzfdfBOJiYmtF5iIiDQbJTVE5Khz0UUX8cus2WxiLW1ohydeAFRRwSYjnYjQCM44/Yz9vm5kZH2PjErKCCQEE4tM/qKMYkKJJJBQLEyKCvLIJx+Hl4O1dcsINsPxxpdKKigkF0yLTZs3YcPAwqKCMqJJIM5sxxbW8f77/8NVV8dFF12EzbDtJao9sMGFF17IgP4DWLZ8GRXlFfz2228smP8byXQhwAyuP86qXzoqM2sVbwx/gyHDhuzxssEhwTz73LNs2riJlStXAtAlrQsJCQkHHquIiIiIyH6yTItNmzZRVV1NbGwMgYGBrR3SUcNV5+LzL75g4k8TqKiqAMDh6eDsc87mhhtuOGwmMs2fN5+XXnwJP1sgSXTGgTeVlJO+Yj2PPPwIL738MklJia0dpoiIHGI2y7Ks1g7iUCotLSUoKIhp38/Dz8+/tcMRkVay8I+FvPzyy9Q4a/AzAjAxqTDLiIqMYtjTT++2EmGPLLj77rsp2FxEO6sLReSxmXW0I5VAQhocmstmcthEn959WPjHQiqqKvDx9qGquopgIogmDm986ys3yCafbAw8MPl72ayoiChuvf1WTjzxxIP9OgCorKjkuuuuI7Q2mmjiG+0vZBubSGfEuyMOyRJYIiJy9KuoKOfsS3pRUlJyVL9M1BhD5PAzc8ZMvvjiC7bmbgXAbtjp1asXN950E2FhjZeAlX1nmRYvvvgSvy1YQDgxBBOODYMSCsg3ckjpkMKzzz2Lp2frzpOtq6vjnzf8E1upB23p2GCJYBcu1hsraNc5kedfeH6P19myJYvcrVvx8/enQ4cUDEPtZ0VEWsu+ji9UqSEiR6UTTjyB0aNHM2PmDNauTcfTw4OeJ/Tk5JNOxu5hP7CL2uCOO+/kicefYL21gjqzlgBCGiU0ACKIo9DYhr+/P198+QW1dXX868678Kr2oS0p7gfu+ibjSYBFAbkkk4Yv/lRTQW5eFs898xxPPPkEJ5x4wkF8G/XWrV9HTW0NwTS9vmww4WxiHStWrFBSQ0REREQOW99//z0fffQRwYTTji544kW5Wczv8/7gr7/+4vXXXyc4JLi1wzxiLV6ymPkL5pFIxwZjBx98CTCDWL16BTNnzODc/ue2YpSwcOEiSkpL6MjxjXoe2rETYcayYuUKcrfmEhUd1ej8DRs2MHLESFavWe3eFh4WwXXXXUvfM/s2c/QiInIwlH4WkaOWr58vF1xwAQ88cD9333M3vXr1OvCExnapqZ154cXnSegQjxMnAQQ3eZyBDR/Tn02bNoENMjMzyMnNIZzYRg/cABHEYmHiohY7dvwIJIlO+BPEqFGj4FDU1O3DNWz7dpiIiIiISKsoLipmzMdjiKQNiXQkkGB88CWCWJLNNEqLyvjyyy8P+X1N06S2tm7vBx4Fpk79GT8jgCDCGu3zI5BAWyhTpkxphcga2rYtF7vNjg++Te73JQCA3G25jfZlZGTy70f+zeb0LBLpSConksJx1BVYvD78dSZPmtyssYuIyMFRpYaIyH7q2LEjL738Iv93zf9RV16z2+Nctjp8fOob6ZWWlgLg2KnJ+M68cGDDRt1Oy0/ZsBFBG9bnrGDt2rVERkXy669zKCkpJjwsnNPPOAN/f799jju5fTKenl4U1+Y3ufxUCQVYWKR16bLP1xQRERERaUkzZswAy0YkcY32eeEgxIxg+vTp3HTzzYdkeaTVq9cw9tux/LHwD0zTRXRUNOdfcAHnn38eHh5H5yuV3JxcvE3fJidjAfhYfmzL3dbCUTUW4B+Ay3JRS427j+LOnFTVHxfQePmS0R99hL3Ok3ZmF+zUT3zzwgtfArBj58MPP6Rv3754+zQ9fhMRkdalSg1pMavWN54dIXIk6927NyVGPi5cjfZVUUmZVcxpp58OQGTEjibj5U1eq4oKLCy8aNhwz4f6pMj333/PP2/4Jx+M+oDxY39k5IiR3HD99fzww/h9jtfPz49+/c4i35ZNOSUN9lVTyVZjE927dicuvvEAUURERERkf5imSWVFJabLPKTXzc3NxdvwxWM3czT9CMRZ43RPKjoYc+bM4dF//5s/F64g2kwggRScuS4++uBDnh72DHV1R2flRmBQIDU2527311B9WPRROunkk/Dy9CKP7Eb7LCzyyaFNbBxJiYkN9hUUFLJk6RLCzRh3QmMHGzaiiMfpdDJv3rzmDF9ERA6CkhrSInYkNJTYkKPJhRddiM3TRqZtNdVUAvUPz+WUsNFYTUx0LKefdhoA8QnxpLRPIc/IxqThwM7CYiub8cSB/y7LWVVtn100d95cws1YUq0T6OTqQWd6ElgbzgcfjGLq1J/3OeabbryJjp07so4VbLCtIptMMlnNWpYRHh3OAw8+cBDfiIiIiIgc64oKi3j/f+9z9VVXc9XVV3HFFVfy1ltvszVn6yG5vn9AALWWE3M3i6Y6qcawGfj5Nr0k0b4qKy3jjeFvEGSF0d7sSiSxhBJJWzqQRCrLly/jxx9/PKh7HK7OPLMvZVZxkxOyanBSYivgzLPObPG4duXn58fll1/ONrLIZiO11FfRV1PFJtIppYjrb7ieXQtOCgsKAPCh6ap3Lxx4GV7k5+c3a/wiInLglNSQFtOpeyKgxIZsZ8GmTZtYteovCvILWjuaAxITE8Owp4dhBFisZglr7ctYYyxhHSuIjovi2eeexdPL0338rbfdSq29mvXGSorJx0k1pRSxjpWUUkgbEjF2euK2sNjGFgwMwoklhgT3jDRPvIgjiRAi+PzTz/Z5BpzD28Ezzz7DA/c/QEJqGwivJaJ9KLfefivDh6uhooiIiIgcuLxtedx/3/1MmTiVwOow2tKR0NooZk+fzf33P0BmZuZB36NP797UmE6KyWu0z8Sk0NjGiSeedNDLBs2YOZO6urpGz+gAAQQRbIUz4acJR2VDutNPP53EtklkGqspIg8TCwuLEgrJMFYREhrKgAEDWjtMAK688kquueYaijxyWcVCVhp/sJrF1PhW8eADD3LKKSc3OicoKAj4e3mqXdVRS61Z6z5OREQOP0fnApByWFm1Pted0OjUPZHVSzNbNR5pfQsW/MYnY8awectmoL7Et0ePHtx0883ExbVp5ej2T+fOnfnwo4+YN28e6WvXYrfb6dGjB127dsVmNBz8dOzYkeeff55Roz5g9Zq/3NvDw8KpKIBi8vHEgTe+VFNBLlsopwQLi0him7x/ODGkFy9n9erVpHZJ3aeYPTw86HtmX/qe2fcAP7WIiIiISGPvjniXytIqUsyuDXochJsxZFSvYvjrw3nzzTcbzZzfHwltE+h9em/mzJ2Ly3IRSiR27FRSTo5tI3WGk6uuuvKgP0vGhg342QLwsBr3agAIIISNeWuoqq5y99E7Wnh6efLsc8/w6quvsXTpEgzWYbPZcFkuOiR34JFHHsE/wL+1w6xng6uuuooLzr+ABQsWUFpaSmRkJCeffHKDCWY7i4yKpFPHzmxem0WgFdYoaZVHNnYPO7169WqJTyC72LBhA7/M+oXSslKiIqPo168fEZERrR2WiBxmlNSQZtVUVUan7omsWppJanJUK0QkrW3mjJkMf2M4gYTQjlS8cFBBGauWrubhhx7m1VdfoU0rJzZM0yQ/Lx+bzUZ4eHij5MSuPD09OPmkkygtKeHnn6cxY/oMgkOCSUlJIT4hnsiISE448QQcDgcdOnbg5VdeIjsrm/z8fIKCgmjbti2//vorH374IemFy93XjYqMoveJ5/PThJ+abHwHuHtwVFRWHLovQERERERkP+Vty2PxosXEkdzo2dUDD6LMeDZkrmLt2rV06NjhoO517/334eXwYsb0GeTYMrHbPKg1awgNDuPfDw4huX3yQV0fwNPLq8neeTu4qMOGDQ+Ppl+cH+kCAwO5/777+Pjjj5kzZw41tTU4vBx06NgRu92+9wu0MP8Af84+5+x9Pv7666/jiSeeIJO/iLLi8cWfWmrII5s8srnmimsICAxoxohlV7U1tQx/fThz5s3BYXjjhYMqKvnyiy+54qoruObqaw4qISoiRxclNaRZpSZHNUpsqFLj2OWsdjJy5EhCiSSe9ti2P5F440uQGcb66j/58KMPefLJJ1slPtM0+eGHHxj/w3gKCuuXxIqMiOSiiy7iggsu2G1yo7SklP889h+2bNlCIGGAB5vLNrNx00ZsGFiY+Pn4ce3113LeeecBENsmltg2sZSWlvLt2LHM+XUOXp5epHZOJTU1lZ49e5KamsrKlSv5acJPVFKGH42b8VVQ3wAxJqbpSo59VVBQyNQpU/hr9WrshsHxPY7nrLP64e/f9DqzIiIiIiI727hxIxYWAYQ0uT9ge++4zI0bDzqp4enpwT333sM111zDb7/9RlV1NW0TEujZsyeG/dCssn3ySScxZcpkKijDj4Yvty0siow8unftjqfn0flaZVvuNh55+BHKSssJMSPwxhdnTRVTJ07l19m/8tLLLxEbG9PaYR6wLmldeOqpp3j77bdJz19O/dtyC2+HN9ddcR2XXXZZa4d4zBn53nvMnz+fBFIINiMwsGHiYhvZfPnll4QEhzDwvIGtHaaIHCaOzn995bCSmhzFqqWZDZaeUpXGsWne/PlUV1eTSKo7obGDBx6EmTEs+mMRxUXFLd7bwTItXn/tdX799VdCiKQd9Us5FeflM2rUKDIyMrjnnnuanBny9jtvk5udSwrdqKaCjawlhAiiiMcbH5xUs61qCyNHjsRu2Ok/oD8AGzM38vjjT1BRXk6gFYodTzbmbWbVX6soKyujS5cudOnSheioaLZu20yS1Rljp1ZILlxsM7Lo3CH1oJbtmjtnLq+99hpYNvzMQExMlixewheff8mQoUPo1KnjAV/7SFNRUcGyZcuocdaQmJRIYmJia4ckIiIickTwctRXZ7iohSaqjF3UAeDwaroC+UCER4Rz/gXnH7Lr7axHzx4kxCewOSudeDPFndioo44cMqk0y7j0skub5d6Hg7ffepuq0ipSzG547bKU2IbyVbz5xpu89PKLrRjhwTu+x/GMGjWK5cuXk7N1K/7+fpzQ84SD7sci+68gv4Dp06YRY7UllEj3dgM70cRTQzVfffU1/Qf0xzDUHlhElNSQFrIjsbHjv+XYtC03Fy+7A4er6YdEX/wxMcnLz2vxpMZvv//O7F9nk0hHggl3bw8kBH+CmDZ9Gmf07s3xx3dvcF5eXj6/L/idWJLwxocMVhFEGAmkuBM3DryJpz0WMGbMJ5zVrx+GzcbTTz+Nq8Kkk9XDXaJvmRaFbGPK1CkkJyczYOAA7rn3Hp56cgjrrRWEmdE48KGKCgqMreBlcvsdtx/w587IyOSVV18hyAwjjmTs1JeS11LDpuq1DBsylPfef4/AwMZVIkcT02UyZswYfvrpJ2pqa9zbO6Z05N777iUuPq4VoxMRERE5/HXu1Bk/X38KKrcRR1Kj/QXk4mH35Pgex7dCdPvPMAyGDRvGkKeGkL5lOX5GAIZV37sDm8U9/7qHrl27tnaYzSInJ4ely5eSQEqDhAaAJ15EmXH8tXoVGzM30jaxbStFeWjYDBvdunejG91aO5S9Ml0mf/75JyWlJYSHR9C5U6e9LpV8pPhj4UIsC0Jp+n1RGFGkF/3JhvUbaJ/SvoWjE5HDkZIa0mKUzJCAgABqXTXUUYdHE3/91FANQGBAy79AnzxpEv5GEMFmeKN9IUSQZ+QwedLkRkmNNWtWY2ISTBgVlFKDk7Z0bFSJAhBBLGvKl7Bs6VJqa2vJy8+jI90brDlsw0YYUZRRwrhx4xgwYABpaWm8+OILfPLJJyxbvgwAA4MTep7AdddfR0JCwt83scCyrH1+uB0//ge8cBBPSoMGeZ540dbswKrqRUyfNp1LBl+yT9c7Ur399tvMmD6DCNoQRhQeeFBKMZvXb+Hf/36U4cNfJzIqcu8XEhERETlGeXp5cumlg/nkk0/wwkE40RgYmFgUk0eubTPnDTjviJosExYexn/f+i9/LPyDBQsWUOOsoW1iIueec06LT8JqSZkZmUD9BK+m7NiekZFxxCc1DhkLqqurMQzDXbV0KM2aOYuPPvqIouIi97boqGhuv/12evTsccjv19KcTieGzcCwmq7CsOPpPk5EBJTUEDkiVFRU8OOPPzJ1ylQKCwvx9/enX79+XHjRRYSFhbZ2ePusV69evP/+++SbOUQT32CfhUWBbSspySlERbd8Amzz5s34mk03grNRvyzT2rVrG+3bUfpqYVG3o6SepitRHPgAUFJawob1G/C1++PjarpnRTChZOasobikmODgYDp07MAzzz5DUWERJaUlhIaEEhj094Bw1aq/+P6771i0aBEul4ukxCQGXTiIs846a48JjkV/LCLQDGuQ0NjBAy8CrBAWLlx4VCc1NmZuZNr0acSRTDjR7u3BhOFvBpJeuYxvx37LnXfe2YpRioiIiBz+Lr30UoqKivjxpx/JN7Jx4EMN1TjNavqc0Ycbb7qxtUPcb4bd4OSTT+bkk09u7VBajKdn/QtkF3V40LgRet32Bupeh3ApsSOVZVpMmTqV8T/8wJasLQB06tiJwYMHc8qppxySe8yYPoM33nyDYMLpQLftlfvl5OZu4emnn2bI0KGNJt8dadq2TcBl1e22l2QZxRiGQZuDWHZZRI4uWohO5DBXUlzCQw88xNdffI1VYCfWSsJR5s9P4ydw3733krUlq7VD3GfBIcFcdNFF5LKZrWymjloAqqhkI2uosMq49rprWyU2X19fatn9rI9anBQVFeGsbnhMamoqdsNOEfl44QCoL0lvQtX27REREVgHGGdIaAiJiYkNEhrTp0/nsUcfZcXClUS64mhDOwo2lvDf//6X4a8PxzJ3fzeX6WrQp2NXBgZ1tXUHGO2RYcbMGTgMR5Olzh54EmxGMnPGTEzTbIXoRERERI4cNsPGLbfewoh3R3DhJYPo2ft4Bg4awJtvvMmDDz2Ih4fmVR4JuqR1wdvhTQHbmtxfSC6enl507Xb4L9nUnCzT4o0332DEu+9SnlVNAinE056s9K08/8LzfP31Nwd9j9raOj744ENCiKAtHfDFHzt2/AkiiVT8COSDUaM44AHmYaJb125ER0WTY2zCtT1ptoOTavKNbE495VSCg4NbJ0AROewoqSFymHvvf++Rn5tPe6sr8bQnnGjakERHsxu15S5effXV1g5xv1x//fVceuml5NtzWMVCVhq/s4YlmAF1/PvRf9O9e/dWievUU0+lmAJqqWm0rwYnpRRiWi7mzJ3TYF9wcDB9z+zLNmMLddThgx+5bMHc5anSwiKXLUSER3Bc2nGkdelCpaucKiqajKeEQmKiYwkOCt5j3Pl5+bz11tuEEkl7syuRtCGcaJKsTiTQgVmzZzFz5swG52zL3cZvv/3GokWLSE5uT5lR3OS1TUzKjRI6HuWNwouKivHCp8lqFQAffHHWOKmuqm7hyERERESOTG3i2nDd9dfxwAP3c+NNN5LUrnGPDTl8+fj4MOjCC8knm3y2usc2FvX9/7axhYEDBuDv33TVebOxYMWKFUydMpU5c+ZQVVnVsvffxbx585g5cyYJpJBIR0KJJIwoks0uRJPAp59+Qsb2pbwO1JLFiykrLyWKuEZLHBvYiLBi2bR5ExsyNhzUfVqbzbDxwIMPUutRTbqxjFyyKKaAbDayzviTgJAAbr7lltYOU0QOI5omIXIYKy4qZt68ecSYiXhvX7poBw+8iDYTWL9hFenp6aSkpDR7PAUFhUyZMpk/l/+JZVl07dqV/v37ExYets/XsBk2rrv+Oi6+5GIWzF9ARUUFUdFRnHTSSfs9c2vzps1MmDCBhQsXYZomnTt3YtCgQXTq3Gl/PxrH9zieL7/6ig2sIo5k/AjAwqKSMjaxDg+88DCMJitjbrv1NnK35rJi5Qp8bL5UWmWs40+iiMMbX5xUkWfLpoJS7rvjCWyGjZNPPpnwsAi2FK0nyeyEx45G4VgUkUcRedxx8R3s5j2729SpUzEwiCWp0UNuCOEU2/L46aefOKvfWRTkF/DOO++waNEirO0DE09PT2rNWraQQQjh+OCHgYGFRTYZuKw6BgwcuN/f55EkLDQUJ1WYWE0mNqqowNvhjbdP08uKiYiIiIgcbf7vmmsoKixk2vRp5BlZeFne1NicOM0qep/Rh3/+858tGs+qlat4883/krM1273N4eVg8ODBXHXVVa3SMHvChAkEGMGEmBGN9kUSR5GxjcmTJnHHnXcc8D0Ki4qwYXMvZbwrb+oTS4UFhbRr1+6A73M46NSpI6+9/hrffP0Nc+fOpc5Vh4+3DwPO7s/lV1yuKg0RaaBZkxqzZ8/mlVdeYdGiReTk5PD9999z8cUX7/GcWbNm8cADD7By5Uri4+N54oknuOGGG5ozTJHDVkZmJqZp7rZBWwDB2DBYv259syc1/vjjD1568SVMl4W/GQTA2jVjGTt2LP/+97856eST9ut6gYGBnNv/3D0eY7pMSkpK8HJ44efXcBbQvLnzeOWVV/DAgwAzDAMPFs5bzK9zfuWG629g8KWD9yueyIhIwKKWWtJZjhcOLOqXnfLGh3Z0Zj0r8fFt/DDp7ePNs88+y2+//ca0adPYuHETxcVFZNT+5T4moU0CD918P8f3OB4Au4edp4Y8yeP/eYK/KpYQZIVgx5NKo4wKs4xzzjmXAQMG7DXu9evX42sGYMfe8LvDRSXleFrebNiwgdKSUh555BFKC8uJI5lAQqilhs2166ildvssrGwM7Pjij8uoo9qs5K67/kVsbMx+fZf7y+l0UlVZhX+Af6ssSXDWWWcx9ruxFLCVCBp+1lpqKDLyOKff2e7+KSIiIiIiRzvDbnDPvfcwaNAgps+YQWFBAcEhIZx15pm0T2nforGsS1/Hk08+ibfLl/ak4UcgtdSQX5PDl19+SU1NDdffcH2zxmCZFkuXLWPDhvV4enpyQs8TyMzMJMBsuselgQ1fM4ANGw6ugiI0JAQLCydVeOPbaH/19sr/BQsW0DYxkYiI8IO6X2tLSEjgwYce5N777qWqsgo/Pz8Mu8ZhItJYs749qqiooFu3btx4440MHrz3F4wZGRmcf/753H777Xz22WdMnz6dm2++mZiYGPr379+coYocljy3v+A1d1lTcgcTEwuz2V8E527N5cUXXsSvLpB4Utwv0F2mi83mOl588SXeefdtYmIOzctvp9PJ2G/HMnHiJErLSgBI7ZzKFVdcQY+ePcjLy+fVV18l0AwlnhT37HrLtNjKJkZ/PJoOHTuQlpa213sVFxczbdp0Nm3aRFBgEDWldcSR5O6L4U8QAQRTyDbqzFp6ndqryesYdoNTe51KTW0tbwwfjg0Df4IxcVFFBQWFhY0SM4mJiYwY8S5Tpk7h19lzqKqqJC0xlYEDB9KjR4+9VmkAeHh6Ytlc7jVUTUy2sml7mXj9nxubZWPI0CEUFRTTweqGFw5MTDJZg5NqokkgiFBMTIrIJ59swkMjGPrwU3Tu3HnvQRygTRs38eVXXzJv3jxM08TL04vje/Tg4osuoktal2a7767iE+IZOGAgkydPphYnoUThgQdlFLPN2IKvvw+XXXZZi8UjIiIiInK4SGqXxM3tbmrVGD799FM8TQdJVqq7H6AXDmJJxI6d78d9z6ALBxEa2nSC4WCtS1/Hyy+/zNbcrXgaXpiWi1GjRuHl6dXk8sU7mLhweDsO6t7H9+hBgH8gueVbSCClQXW+iUUuWdjxYMa0GUybPo27776bfv36HdQ9DwceHh4EBAY02JaXl8+sWTMpKCggODiYM/ueSVR0476IInJsaNY3oQMHDmTgfixbMnLkSJKSknjttdcA6Ny5M3PmzGH48OFKasgxqUOHDvj5+FNYtY02NF6HtpBtGDaD448/fp+vaZomzmon3t7e+1yiO2HCBDBtJJCCsVNFgB07CaSw2lrMhAkTufnmg3/YdTqdPPH4E6xLX0+IFUESsbioY/OaLIYOG8q/7voXudu2gWUjjuQGywXZsBFNAuVGMT+O/3GvSY2pU39m5IgRWCb42vypo5YqKtnCBpLpgg9+mNuXg8qxZXL6aafTJq7Nbq+3ft16hr/+OsFWOG1o507+1FLDxqo1DB06lP+9/36DtWcDgwK5/PLLufzyyw/o+zruuDTmz59HOn9iw0YtTmpwEkkbggkHbJRQQMb6DOx4upe5KmAblZTRnuPw4++HRT8C8MWPTfnpuOqaTqYdCqtXr+HJJ57AqLMTakZRTjHVtVX89tsCfvttAYkJSdx62y2kHbf3xNShcNvttxEUHMQP435gW/XfS4x1Te3Kv+7+134tsSYiIiIiIodGaUkpS5Ys2T72azxjP5xYtlnZ/Dr7Vy66+KJDfv+cnBwef/xx7DWepNAVX9MfC4ti8smqzaCQbcRsT67szEk1ZRTT69QrDur+np4e3HTTjbzx5htYWEQSiwNfqignly1UUkoSnfGzAsm2MnjrzbeIjY1t1slpLc6CMWPG8N3Y7zAMOw6bD06rii8+/4LzL7iAW26+uVWWHxOR1nVY9dSYP38+Z599doNt/fv357777tvtOU6nE6fT6f65tLS0ucITaXFeDi8uuvhCvvjiC7zxJZRIbNiwsCiliFxjE31699mnF67bcrfx7dhvmTljJs4aJz4OH/qd3Y9LL7uMsLA9z2hZtGgxAWZIg4TGDgYGAWYwixcthkOQ1Phh3A+sS19HO6tLg5ftIWYEW1jP2++8jWEzCLRCGz04Qn1iI8AMZcWKlXu8z5LFS3j77bcII5oY2uJheWBhUU4pG1nDWpbhY/ej1qqh1qzhtFNP475779tz7D/8gMPmTZzVvkGyxRMv2lodWVWxkJEjRvDQww/t8/fhrHayadMmDMOgbWLbBlU5a9as4bNPPsOGDQM7dgwqKQPAgS8+29dX9cGXAIJI50+KySOUSArJJZDQBt/xDiFEkGfkMHXq1GZJKlimxeuvvYZnnTeRZhsyWY03viTSEW/8qKaSvE3ZPPnkkwwZOqRFmscbhsE111zDpYMvZcWKFThrnCS2TSS2TWyz31tEREREDh/pa9OZMGECa1avwe7hwYknncB5A88jIrJx3wRpfiUlJVhYjXpM7mDHjpfhoLi4uFnu/91332PWWLQ3U93jTxs2QonEA082sIr1rCCRTnhRX5VRRSWbjXSCAoM588wzDzqGs/qdhWEYjP5oNGuLl7u3O/Ahic7u5arjSKbKqGDc9z8cVUmN7777jm/HfksMCYSbsdixY+Iin1wm/PQTfr6+/N8//q+1wxSRFnZYJTW2bt1KVFTD0rGoqChKS0upqqrCx6fxP2IvvPACw4YNa6kQRVrcFVdewbZt2/5u0Gb6UGfUUGmW0/247tx55517vcbmTZt59NHHqKl0EmxG4o0v1c4Kpk76mTlz5vLKqy83+t/ezkyXq8lZMTvYsB+aWf0WTJw4iUArlCoqqKAUb3y39w6xEUMiheRhWhbu9ZaavIyJbZeJGrU1tfz+++9sy9tGYGAgUyZPwd8IIs5s5y7htWEjgCAS6MAGVtLjlO7Ex8dz+umnE58Qv9fwFy1aRKAZ2mSzaU+8CCCY2b/Opm1i271WZtQ4a/j000+ZMnkKVc4qAAL8A7nwwkFcfvnlVFZWMnTIMGxOD1I5Ac/tFRguXGSxgc2k440PvvgD4EcgAQRTQC6hRFJDNcE0nQyzYcPb9CEnJ2evn/lA/Pnnn2zN3Up70sgiAx/8SaaL+8+YNz4EEkqGtYp333mX9957r8Vm3ji8HfQ8oWeL3EtEREREDi/fjf2O0R+Pxsfwxc8MxqSaH7LG8+P4n3jyySfo1r1ba4d4xKquqqamtoYA/4D9erYPCQnBsBlUWhX4Edhofx21OM0qwsOboZeEBb/MmkWwGdHkhLoAgvExfKmxVfOXaxF+RgAWFhVmGeEhEQwbNrTJnowHou+ZfenduzePP/4461dtIJ72+BLQYDkqGzaCzDAWLVp4SO55OKitqeWbb74lnBii+HtMbmAnklhc1DJu3DgGXzq4yXeGInL0OqySGgfiscce44EHHnD/XFpaSnz83l8+ihwpDKO+QdvA8wYy7edpbMvbRnBQ/YyPrl277lPfhTfeeANXpUmK2Q0PPLdvDSfcjGFD2Urefuttnnn2md2e3zm1M3Ny52KZVoOHJqC+usEo4rS0pntN7I1pmixbuozNWzYDUFhUANgoJh8bBiYuvHAQTwoBBOGLPyYuSimijjo8dvlrbEcpcGJsW+rq6vDw8OCXX2bz3nvvUV5ehofhSZ1ZC9BoTdIdAgjCx/AhLDyMq6+5ep8/i8tlNlnNsoOBHS8cfPbJZ/Tt03e3s73q6uoYNmwYq1b+RbgVTTxhWFgUlefzxedfsHnzZtq3b09VVSWdrZ7uhAbUz1SKoz0VlJJHNm3p4N7niz+FbAPAAw9qqN5trHVGDUFBQfv82ffHxk2bMGx2bJZBFRW0I7VR0szARpQVx7rcFSxZsoTc3FyWLF2Cq85Fx44dOffccwkOCW6W+ERERETk2LNs6TJGfzyaKOKINhPc4wSX6WKjtZZnn3uODz4YRWBg4xfrsnvLly3n66+/Zvmf9RUGwUHBnHfeeVwy+BIcjr33m/AP8Ofkk09h6e9LCTUjGyUXtpGFYdjp3bv3IY/dNE2qndVE0HScNmx4mA6OOzGNE048kTWrV2PY7RzfvTunnHrKIe99adgN/Pz88MK7yQQP1I856+rq6ucAHgUrMq1YuZKKynLiSGlyfyhR5NZsYemSpZza69QWjk5EWtNhldSIjo4mNze3wbbc3FwCAwN3m3F1OBz79A+hyJEuJSWFlJSm/yHfkw0bNpC+Lp1EOu+U0KjniRcRZhuWLV9GTk7Obht9XzDoAqZNn0Y2mcSS6H7At7DIYSPVZhXnn3f+fse2YsUKhr8+nLz8POw2D1xWnbsiI4wo7NippJwcNpLBKpJJo45afPHHSRUbWUsiHd0PtiYW2WTgpJo1a9bw8EMPExkVxfz58wgmgk6k4G36UEUFa1iKsZu/Am3YMGye1Dh33/StKR06dGDdn+uJMuMa7TNxUUYxIURSbOQxbdq03SZMZs2cxYoVK0imC/78nVjwIwA//Jn962y2ZGXhbwU3SGjsYGAjmAgK2Npgu5NqXNRRTD5BhJNPDtEkNLpGBWWUmSX07dt3r5+5uLiYP/74g+qqauIT4unWtdteZ155ezuwLBMn9RUovk0sgbXz9pdefIlqZzX+tiBslo3Fi5bw5Zdf8dDDD9Kr14El00REREREdvbDDz/gZwQ0SGjA9j6CVntW1Szi559/5tJLL23FKI8sM2fM5I033sDXCCCOZDzwpKykmK++/IrFi5fw7LPP4OVoPJ7Z1bXX/oNly5ay3rmCCLMN/gRSg5MCtlLINm74xw2NmkofCobdIDQkjMqiMsJovLKBiYnTqKRNmzYMGNCfAQOavxdsSkoKixctxmW6mqweKbMVkZycfFQkNACqq+rHjJ67vMvYYcdYtrp69xP2ROTotPv1ZFrBqaeeyvTp0xts+/nnnzn1VGVbRQ5UZkYmAIEEN7l/x/qbGzdu2u012rVrx6233koe2aw1lpHDJnLYxFpjOdvI4pabb6F9Svv9imv9+g0MGTKU6oJaUuhKe+s4ANrQjkhi3Q9ovviTRGe88N6esKgihEgS6UQFJazkdzaylk2sYxV/kE8OcSQTTATrN6xnwfwFBBBMW1Lc67B644sHXpRT3GRstdRQ5SqnbWLb/fpMgy68gHKzvkJiZxYWW8jAxEUEMfjgR87Wrbu5CkyePJlAW0iDhMYOwYTja/hTkJ+/lyXBbJi4yGQ16fzJBv6ihALCI8LJZA3b2IKFyTr+pJQiLCxMTIrIY6Oxhnbtkjnl1FN2e31XnYv3Rr7HP2/4J2+/9TYffvAhTz31FLfeciurVq7a4/d04gknYhgG5dT3QKrZntzYVRXlgA2jxpPO9CTZ6kI7Uuls9STAFcwrL79CZmbmHu8lIiIiIrIvVqxYQaAZ2mQltwee+FuBrPhzRStEdmQqLS3lrbfeJoRI2ptphBNNMGHEk0w7qwvpa9cybty4fbpWXHwcL730EokdE9jIGlbyB+ksxwyq4c4772LwpYOb7XMMGNifEqOAKiob7csjhxrTyTnnnNNs99/VueeeCzbIYgPmLksyF5JHqVXEoEGDWiye5rZjJZZySprcv2N7XHzjiYUicnRr1qRGeXk5S5cuZenSpQBkZGSwdOlSNm2qf3n62GOPcd1117mPv/3229mwYQOPPPIIq1ev5t133+Xrr7/m/vvvb84wRY5qO2a+uKhrcn8d9UsxObz2PEPmggsu4KWXXqLnqcdT5V9ClX8JPU7pxosvvsSgC/f/oemLzz/Hw+VFktUZPwIooQA7HoQS2ehYA4NwYqigDF/8CSCIQEJIpgsmJhWUUUUFwYTTkeNx4aKYPMKJwcIkgthGa42GEUUhuVRsb6y9Q321RyaeDk/O7Lt/Td1OPOFELr7oYrLIYC3LyGULOWxiNUsoJJd42uOFgzqc+Pv57fY6uVtz8bH8m9xX3+/CD5vNRoVRiknjXiYuXGwjq37mEE68cOCkCguLpMQkhg9/g/vuvY8bb7qJqPgoNrCKFbbfWWGrTxAd1y2NZ55+eo/l0u+88w6TJk4i0oyjCyeRZp1MCl2pzHfy1FNPsWHDht2eGxwSTP/+/SkmDzuebCMHq4keKVlkYAMSrY7upntQv3RWPCl44sX4H8bv9j4iInJ0mj17NoMGDSI2NhabzbZPL8VmzZpFjx49cDgctG/fntGjRzd7nCJyZLGs3ffsq3eUTH1vIdOnT8d0ubDjwUbWsJG1FFOAiYUfAQRb4UycMHFPrRIbaJvYlhdfepER747gySef5IXnX2D06NHNXh1x0YUXERsbywZjJblspooKyilhE+nkkMngSwa36Av1kNAQ7rv/Popt+aQby9jKZvLIZoNtFZtYS79+/fap4v5IERcfR2rnVHKNLOp2eafhwkWusZmkxCRS2u//qhYicmRr1uWnFi5cyJln/v1ScEfvi+uvv57Ro0eTk5PjTnAAJCUlMWHCBO6//37efPNN4uLiGDVqFP37N38Jn8jRqnv34/H09KKgNpdoGvebKSAXXx9fUruk7vVanTt3pnPnzgcdU1VlFQsXLiTGSnRXG9RRhydeu60+2PFSO45kd4LCub0nRAe6uXtrmJjkkUU40QQRRj45OGi8fF0UbSinhHX8SQiRBBBEHbUUG3lUUckj9z+y/03dbHDjjTeSnp7OX6tWU80WDAz8CSaBFPwIoJhCqswqzuh9xm4v4+8fQFWZs8E2E4tCcikgl2oqsJd54DJdbCGD+J2+E4B11M8gSyaNgO3VHhYWJRTwxx9/EBcfx/U3XA/ARRdeyOrVq1mzZg2G3U73bt322hQ9a0sW06ZPI45kwol2b/cjgCSrM+mu5Xz15Vc89p/HdnuNm2+5mcrKKmb9MpNi8jAwiCIOB944qSaPLCopJ4CQ3S6xFWSG8fvvvzfYXuOsobKqEn9//0O+hq2IiBweKioq6NatGzfeeCODB+99dm5GRgbnn38+t99+O5999hnTp0/n5ptvJiYmRuMMEXFLS0tj9dK1RJptGlVr1FFLua2ELmlprRTdkWfevHmYmBSwFT8CqMNJEavxxpd2pBJAMJnFa6isrMTXz3efr9smrg1t4to0Y+QN+fj68OJLL/LR6NHMmjWLnNr6d1hhoWHcdtltnH/+/i/DfLB69+5NTHQM48aN448//qCuro7kdsncMuhG+vTpc9Tl3/71r3/xyCOPkF61nFAzEh/8qKaKQmMrNk+49757j7rPLCJ716xvfPr27bvH2Q5NzZDq27cvS5YsacaoRI4t/v5+nHfeQH784Uc88SKESAxsmFgUsJV8crj64qub7E2TnZ3D1pwc/Pz9SElJwTCaTjhsy93GhAkTmDNnLjVOJ4lJiZx//vmccsopTT5clFdUYFomDrzd27xwUEN1k82/ASopx8BwLyEFuJcv2nnQUUEZddQSRrS7aXclZQ3uBfUN1NqQyFqWU+tfxcbyXAybwQk9T+Cyyy+nU6eOe/hW98BWX3X24IMP4ajzIdZKxAc/TEwK2Ua2kUn3446nc6fdJ4f6ntmHL7/4ihorAS+8MDHJ4C/KKCaIMEKIoNZ0UmTbRqGVS5VRRqAZhoFBia2AKqucOJLdCY0d31Ew4VRRwcQJE7nyyivx9vEGG3Tq3IlOnTrx559/8vnnn7Nh/QYcDgen9jqVgQMHNmrI/cvsX/A0vAg1m66qCTWj+O2336iqqtptPyQPDw8eePB+Lrv8Uj768COWLV1OoZmLgYGJub2puneT68T+fS87dXX1lSqbNm3iqy+/Yt68ebhMFw4vB2eddRZXXHklYWGhe/odExGRI8zAgQMZOHDgPh8/cuRIkpKSeO2114D6SRpz5sxh+PDhSmqIiNuFF13EosVPsZXNRBPvHmOYuNhsW4enlwfntuAyQ0eyJYuXsGbNGsKJIYa27mf6CsrIZA0Z/EUokdiw4enZdK+Ew4l/gD933/0vbrzxn2RtycLT05O2iW13Oz5uCSkdUnj4kYdb7f4tKS4+jtdff50vvvyCX2fPoc5Vi92wc9ppp3HVVVdp6SmRY5SmsYocA264/gZKS0uZOXMm24zNeJoOqqmijlr69O7DlVde2eD4jZkbeW/ke6xY9feasRHhEfzjH//gzLMaLsm0auUqhg4diqvWJMgMw4cgNqzI5Pnlz9PvrH7cc889jRpHBwUG4unpRWVtubunRyjh5LCRXDbThqQGx9dSs73iwpsqKrEwKSSPQnIBKCaPsO0VAzuWY/LAE0+88CeIbWQRSGiDF+QWFtvIIigwiA8/+ghXXR0enh4NZvebLpNZs2YxduxY8vLy8HZ4c1a/s7j44osbvejfITMzk+nTp9OpU0fWrl3LGudSHIY3LuqoM+s45aRT6pfU28NMkoHnDWTSxElklK4ixmxLBaVUUEp70hr02Yi2Elhn+xPTo45yjyJM00VERASbNpcTQkST1w4mglznFtauXUvXbl13fBmMGjWK8T+Ox9fwx9cMpJoKvtnyDePH/8jTzwxr0KS+tLQUL5v3bqtqHHhjWiaVFZW7TWrskJCQwJChQ/jqy6/4/PMvCCcWb3wJIpStbKKIvO1Jjsb3KjeKSUlpz9o1a3n88ccx6uxEmQl44U1VTTkzps7gtwW/8cqrrxAR2fT3ISIiR7/58+dz9tlnN9jWv39/7rvvvt2e43Q6cTr/rposLS1trvBE5DBx/PHdue7a6xjzyRhKjQL8zGAsXJQaRdjsNh7/z+MEBgW2dphHhG+++QY/WyBtrKQGE9D8CCCRDqTzJ3lkY2ExceJELrr4ooO6X1FhET9Pm0ZW1hZ8vH3odVovuh7X9ZDP3vfz86NDxw6H9qKyT6Jjorn//vu58847KSstIyAgAId344mZInLsUFJD5Bhg97Bz++23k52VzZq1a6ijDjseeNg8mD17NvHx8Vxx5RUAbN60mUceeQRbjZ22dMCPQGpxkpefw/A3hlNVVcV5558H1A/4n3vuOTxrvEmxOv2dNDDjKWQbM2bMoGOnTo3WOfVyeNG3Tx9mz5hNmBmFJ1544EUMbckmg1qchBGDJ56UUUqBkY3dblDjqiHdXA5AgH8gN1x6A6tXr2bxH0vwNQPwwQ9v6kuXyygmlEjakEQ6f5LOciJpgy8BOKkinxwqKOWROx/B09MDT8+Gfx3W1dXxyMOPsH79ene/h2pnNd9//z0TJkzg1VdfbdBI3DRN3nnnXX7+eSoOw4HNNKihBrDhsrno1LkT11xzDWn7ULIeGBjICy+9wAvPv8iGjau29wCJadQ43I6deKs9a2uW8dDDD3HyySczdcpU3n7nHWy7STjsSA64XH/34pg1axbjfxxPG9oRbka7Bx51Zi2Z1at5etgzfPDBKHd/lsiISJxW5R6rarw8vQgICNjrZ/07hl8IIYxY/v5Ow4kmj2yyyaQNDQdE+WylzCzh/PPP5/XXX8ezzkGSmer+MxhMKOFmNOtLVzDyvZE8+eST+xyLiIgcXbZu3UpUVFSDbVFRUZSWlu62qvCFF15g2LBhLRWiiBwmLrv8Mo7rehwTfprAmtVrsHt40eekCxh43kDqauuYN28eDoeDtLS0JivdBZzVTlasXNFoidwdfAlwLzkbTDgffPgBbdq04YQTT2jyeqUlpUz9+WcWL1qEq65+XDVg4ABiYmIAmDhhIv97/38YloGPzZ86apg4aSKdO6Xy5JNP4B/QdK9COTI5HA4cEfrfnogoqSFyzBj++nDWr9tAEp0JJAQbNkzLRS5ZfPrZp4SFhdHv7H6M/ng01NhINtPcL4i9cOBLAB548NGHH9G3b198/Xz5dfavlJeX04kejZYJCiWSUooY/8MPDOjfv9EsmWuuuYY//ljI+rIVhJux+BOEL37bm4YXUkwBAIbN4OSTTubmm28mKCiILVlbsBt24uPjMewG5WXl/Oc/j7N24zICbaE4LG888GQrmwggGB/8SOE4sslkE+nu+ycmJPLAP++lZ8+eTX5fb7/1NuvWryOAYKJJwBd/aqkhj2zyarJ57LHH+Oyzz7AZNlx1Loa/8Qa/zp5NGDFUmxVUUEogofVJIZeTv1as5oXnX+SFF54noW3CXn+/YmJi+O9/32T2r7N59dVXCaLpJZR88cfb7sPaNWs5+eSTt88csiilgGDCcVJNIbmUUQJY2DAwDIN27dq5rzHu+x8IsoUSYcU0uLYHnsSb7fmrdDFz5szhrH5nAXDmmWcy5pMx5JFFzE5JCKivqikycunTt687CbIviouKCCS8wTYHPsSRzBbWU07x9qXTDEqNIsrMYi44/wJ8/XzJzsmmPWmN/gx64kW4GcuiPxZRkF9AWHjYPscjIiLHtscee8zdDxDqKzXi4/fcc0pEjg4dO3akY8e/l6LNzs7hzTfeZMXKv6vYfbx9uOSSS7jyyisbVaXvyjItbDbbMbPmf21dfTNn+25eN9mwYeCBH0G0pQMuo5bvvvuO7sd3Z/68+cyfP5/KykoSEhJIbt+eEe++S3W1kwArGBsG69In8MMPP3D33XfjH+DPyPdGEk4sMSRgt+xYWJRRzPq163nxhRd59rlnj5nvXkTkWKKkhsgxYMuWLBb8toAEUhq8HDewE0MCTqr46quv6NmzJ4v+WEQsSY1eENuwEUU8hbXbmDN3Lueeew5//fUXfkYADtN711sCEEQoG7PW1s+C3KXpdlh4GK+++grvv/8+f/z+ByYmAOFhEfzzqutJSUmhqrqa2JiYBks97fwyHurXN3311VeYNWsW06ZNo6iwiKSgRLKysljnXE7w9kZi/gRRY1SBB9z/wP30OrXXbh9u6+rq+OWX2fjiTxKpGNsP9MJBG5IwMMit2MLceXOpra3jow8+pLi0GIACcrBhNF4qingyKlfx/PPPM2LEiL0OfrZ/6XTsUD+gsrZ/P7uysDAtE7u9/vcrMTGRzp1TyVyTict0kcUGbBjuRFYJhVgmLF++nDN6n4HT6WRD5noSSGny+g588DcCWbFihTupERIawj/+7x+M+WQMNTgJJxoPvCijmHwjG58AH66+6qq9f76dhIaFUr6lstH2cKJx4MNG1pBr24xhGKSkdODCC2/jtNNOY8KECRgY+NH0UgABBGNikpWVpaSGiMgxKjo6mtzc3AbbcnNzCQwM3O0yiQ6HQ7OwRYS8vHz+/ci/qSmvoS0dCCCYOuooqN7KF198QUlJCbfdflv9wRYsW7aM6dOnk5+fT21dLWWlZWTnZGPYDLp168Ylgy+he/furfqZmpu/nx/hYeGUFhQTvMukJaifBFVNBbHbK7GDzHBWrFzB3XfdTVZOFv5GEHbTgxXLVlJjOnHgTSo98aC+94ZpmmSRwVv/fYvYuDYE2IJpYyW6q0Js2AgkhFizHctXLCc9PZ2UDn+PdZxOJ8uXLaeyqoo2sbG0b99eSQ8RkSOQkhoix4A/fv8dD8ODYLPxQyXUV1VsyF3FX3/9hYmJD35NHueJF152B/l5eQAYhuFemqkpO/bZbE0/JUZGRfL4E49TUFBITnY23t7etEtut98N17wcXpzb/1zO7X+ue1t+Xj7ffPMNM2bMILfGiYfdkzN6n86VV1xJbJvYPV5v5YqVuMw6ImjjTmjsLIJYcsni66++JnNjJsFE0IFueOFgFYsIJ7rRUlEeeBJrJpGe8yfLli/b58FMZGQk0VHRFOXmufuP7KyMYmpMJz12qjh58IEHePjhh9lcvI4gwkggxZ2kMjHZwnpee/012ia2JSoyqtE198Vll19GYGAgX375JekFfwL1S1v17NmT2269jfCIpv+s7c4555zD6NGjqbYq3UuI7WBhUkctQ54cQs8TGlbWeHt7Y2LiwtXkUlh11ALgpRdTIiLHrFNPPZWJEyc22Pbzzz9z6qmntlJEInKk+Pabb6guryLF7IoH9VXIHnjShiS8cDBh4gQuuOACIqOiePmll/jt99/wNfzxMn2oppxqqvDGlxArkrXL1/PU0qe45eZbGHThoFb+ZM3IBudfcD6fjPmEECuCgJ3GRSbW9klXNkKJ3H64AdgoyC2kI93xMevHoqZpkssWctlMGSWEbE+QGBjE0Y5Ko5QtWzbTlg5NLnMVRAgOw8GC336rT2pY8O3YsXz7zTdUVv09mSqxbRL33HM37VPaN+OXIiIih5qSGiKtoKCgkBUrVmCaJh07diQ2NmbvJx0EZ40TO/bdNnbeURq8Y0aik2r8aNwPoY46akynu0Fet27dmDJ1ClVU4rPLi2iAYls+7dul4O3TdCXHDmFhoYSFNb280oEKjwjnjjvv4NZbb6WiogIfX99GfTN2p6KiAgBvmp696YEnBgabNm4ihAgSSMGGjSoqMHERRNMVAb4E4GU4WLN6zT4nNWyGjcGXXsq7776DD/6EE+NOtFRSTpaxgY7JHem0U4l8ZFQkp512GpMnTKEtKRg7Vd3UDwLaU0EpP/30E3feeSftEpMp2FhIqBXZ6P5Oqik3S+mS1qXRvnP7n8vZ55zN+vXrqa6qJrZNG8LCQqmtrasvszdslJWWMWnSJGbOmElpWRlRUZH079+fs/r1a/D70X9Af37++Wc2ZK8i0owjiFAsLIrIY5sti+O7H0+PHj0axdDzhJ7YDTsF5laiiGu0v4BcQoJD6JDSdCWKiIgcecrLy1m3bp3754yMDJYuXUpoaCgJCQk89thjZGVlMWbMGABuv/123n77bR555BFuvPFGZsyYwddff82ECRNa6yOIyGGssqKSmTNn8tdffzF37lzCzVh3QmNnYUSTZ2QzY8YMqqur+eOPhSTSiSAzFBs29zJImazGSRXtzTSyyWTUqFEcf/zxxMU3fnY9Wlx00UUsW7KM5cuXE0jo9gqXWgrZRg3VtKWje9JVIbmARRszucHkOgODGBKopIxtZBFMWMNqDDOMaqr2uMyVh82TmhonAJ999hlfff0V4cSSQCe88KKcUnI3b+Gxxx5r1DNRREQOb0pqiLSgqqoqRrwzgtm/zsa0/l5O6Pjux3PfffcREtp4Jv6hkJSUhNN0Ukk5vjRulFZGMV6eXnTu3Jm01DQyVm8i2AxvVKWQTw42m43TTz8dgFNOPYXwsAi2FKXT1uyEF/VJEQuLbWRRahVx+6W3NMtn2ld2D7s7CQNQXVXNkiVLqKioIDY2ltTU1Eblxh06dADqkwZNVa3U4MSkvtF2JHE7zQyq///mbpaK2rFvx1JR+2pA//7kZGfz/bjvKTC24mP6UWfUUm6WkBCbwH8e/0+jz7By5SoCCWmQ0NjBwEaAGcqSxUsAuPiSi3h9+OvkkUM4OzUKp47NxjqC/IM54/QzmoytfjmoFNauWcvzzz3HhvUb3H+227dvT962PMrKygmywvAjmPyKIt59911mTJ/BsKeHuRNePj4+vPDCC7z77rssmL+ALawHwNPDk3PPPoebbrqpySW7goOD6T+gP5MnTsYDz+19N2y4cJFHNoXkctsVt2HY96/6R0REDl8LFy7kzDPPdP+8o/fF9ddfz+jRo8nJyWHTpk3u/UlJSUyYMIH777+fN998k7i4OEaNGkX//v1bPHYRObwtXbqUF55/AWe1Ex+bPy7L1eTkLah/6e7Ah9zcXBYsWECk1YbgnSY37VgGKZq25JBJDAnE0JYSo4BJkyZxy62tO05qTh4eHjw19CkmTZzETz/+xObcdYCNYEJJIMU9ga6YfCooxWHzxt9qejnZUKLYyBpc1DZILu2YsFdGcZMV7U6qqXRVkNg2kcLCQr759huiSSCav/sjBRKCnxnIuro/+eyzz+rHVXJUKMgvYO68uZSXlxMTHUOvXr1weKt6X+RooqSGSAsxXSbDhg5j7Zp0YqxEQojY3uOggFXL/+LRRx9j+PDX8fVr+qH5YJx4womEhoSRU7KRJLNTgxfd1VRSYGzlzL598fH14drrr+Xxxx4nw7aKaKu+QXYdteSRQx5ZXDb4MoKDg4H6h9Whw4bwxONPsLpkMQGEYMeDSqOUarOKK6+40p0AaXUWfPPtN3z79bdUOavcm2OiY7n7nn+Rlpbm3hYeEU6bNnFsy8oimPAG/UUsLHLZjA0DC7NBNYcDHzxxUERegzLrHUooos6s3f91dG3wzxv/Se8+fZg6ZQpbtmzB18+P3mecwam9TsXDo/Ff5ZZpsqfFYW3UNy0E6Nu3L+vWrWP8j+MpMrbhZwbioo5SoxCHtzdPDRm2x6bf06dN583//re+MTcxsP3Pdfq6dOx40JHu7oQXFlRQRnr6X3z88cd/r0EMBAYF8uhjj1KQX0B6ejp2u53OnTvjH9A4Ebezm2++maqqKmbOnEmusRkvm4NqqwrTdHHVlVdx/vnn7/F8ERE5svTt2xfL2v3yl6NHj27ynCVLljRjVCJypMvOyubZZ57Fp86fdqRhtzxZwQKqqWryeBOLGqqoc9VRU1tDCBFNHhdKBNlkUE4pIYTXv0RPX9fksUcTDw8PBl04iEEXDmLhHwt5/vkXqLIqKDULqaCUcqOEUrOINjFtKN5Wgs3V9NhlR/LC3GXZ4zKjiMjwSAry8wgxIxpM3jOxyCETHx8fTj/jdCZNnITNMoig8TLEduyEmtH8/vvvlJeV73XsIYc302Xyv/f/x+SJk7HZbHgaXjhd1YwcOZI777yTPn37tHaIInKIKKkh0kIW/LaAVX+tatRAOpRI/MwA1uQuZcrUKVxyySWH/N6G3eDhhx9iyJChpLOMYDMSLxxUUEaxkU90TBQ33HADAJ07d2bI0CG8+eZ/SS9Y7n557/B0cNWlV3HVLg2gExISGDFyBDNmzGTe3LlUVzvp2a4rAwcOPKzWJf3888/58qsviSCWRFK3f/5ScnO38NSTQ3jppRcbNJB75OGHefCBB0k3lxNNPL4EUIuTPLIppoBwYsgnh2qq3LO3DGxEEEs2GfjiTxhR7qqHSsrJMTJI7dTlgL+X5OR23HHnHY22V1VVUZBfgK+fL6Gh9ct4dUnrwrQt0zBNq1HFjYVFuVHMKcedXL/BVp8YOOnkk5g0cRIb1mfg5/BjwGln079//z1WEG3N2cpb/32LUCKIo737XtHEk8sWtrIJJ9V/JzUAPwIIM6OZ9vM0rrvuuiabyO9PU28PDw/uv/9+Lh18Kb/88gslJSVERERwVr9+ROxnb4+jQV1dHRkbMqitqyMhIQF//6Z75IiIiIjI38b/OB6by0Zbq6P7RXowERSwlXBiGvVvKyQXp+kkLS2NefPm7Xap3x3bre3V3C5ceDo8m/GTHH5OOPEE3nzzDcaPH8+8efOora0lMTGRWy64ibq6Wt58802cVOOg8bLFpRTiiRee26s0LCzyyKHcLOVf/7yTr7/+hg0bVxJkhuNPEHXUUGTk4aSK/zz0HxwOB0XFRTgMb+yupivmffDBtExKSkoOKKmxLXcbkyZPYtnSZViWRdeu9ePh6Jjo/b7WzpxOJ7Nnz2bJ4iW4TJOOHTpw9jlnExjYdFWLwPvvv8/kiZOJJoEwKxq7y46TanKrN/H66/WTSE888cTWDlNEDgElNURayIzpM/A3gvA3G8/gd+BDkBXG9J+nN0tSA+pfcr/++mt88803zJkzB5fpwsPwIDIqkgEDBzSY7d+tezdGffA+y5YtZ2tODr5+fpx4wgm7rSLx8/Nj0KALGDToggOOzzRNlixZQsaGDLwcXpx04kkH/RC4Q0lxCd988w1RxBNDgnu7P0H4WgGst/5kzJgxPPPsM+59Se2SePX1V3n034+R6Vzj3u7AmwQ6EEw4xRSQS8PmdBHEUE0lW1jPNrbgRyB1thrKrBIS2iTw6KP/PiSfCaCwsJBPPvmE2b/MprauviF2p46dueb/rua8885j0qRJZLGBONq547OwyCaTKrOSCy7Y6ffLBl27dqVr1677FcOkSZOwYSOO5AbJExs2ooijhHzyyWlUuRJMOLm1m8nIzCQ1tbN7e9aWLCZMmMCihYsxTRedUzszaNCgBgmn3Ulom8C11127X/EfTSzTYux33zHu+3GUlpUA9ct39enThxtvuknJDREREZE9WDDvNwLNsAbJiSjiKKWQdfxJNAkEEkwddRSwlTyyOeecczn55FMY9b9RlFBIOI3HL8UUAvX99WqooZxiTj7p0hb7XIeL+IR47vrXXdz1r7sabK9x1vDhBx+SVbGBRKtjg1UFyiimkG3YbR5stTZhw6DMKKLCLOPyyy6n12m96NGjB+PGjWPixIlsLNmKYTM4+aSTueyyy9xjiNCQUJxmNXXUNUpOAVRRiWEzCNq+IsH+WDB/AS+//Ao2y0aAWX/+T5kTGD9+PA8++CCnn3FgKxdkbMhgyFNDKCktwd8WCJaN3xb8xheff8HDjzzMSSefdEDXPZoVFBQyadIkokggkjbu7Q68iSeFWlstn37yKSeecOKeFjUQkSOEkhoiLaSosAgvc/cNs73xoai4qFljSGibQLdu3fj111/xMDzxMwMpy6nkg1EfMPbb73jm2adJSKh/6W8YBscf3x2O737I7m+6TGbOmsmkiZPIysrG18eH3n1706lTJ/733v/Iy8/D0/DCtFyMGjWK0087nXvuuWevjcb3Zs6cOViW1WS5sYFBmBnDsuXLKCosalCV0K5dO47vcTzLFiwn2krAjge++LsTBLEksom1WFhE0gZvfKmknFqc2DBI7Z6Ks6oa/0B/evfuQ69evfa5WfneFBYW8tCDD1FWVEa4GYsfAdRSw5b0bIY+NZSHHn6Iu+++m7f++xYVRgmBZihgo8wopMqs5NZbbyXlEDTPXvHnCgIJbXJmWv06wmEUsLXRvh0z1Qzj7/MWzF/Ayy+9jB07AWYoNuz89uvvzPplFjfdeBMXXXzRQcd7NBsxcgRTJk8hjChSaIuBndK6Qn6ZOZv09HRefuVlfHx89n4hERERkb2orqomc+NG7IZBYlLSIXvGbU01tU78d+mn58CbZNLYwjoyWf33dk8Hl1x4Cdf+41oMu8GJJ5/E0j+W4G8GNVie1kk1W9lIAMHYsLHRWIO/XwBn9TurxT7X4c7L4cWjjz7KsGFPs8a1lGAzHA+8qKCEEgrpkNKB2LhYlixagst00aljRwZdcAE9evYAwNvHm6uurl9RoKqqCk8vz0bL8/bp04ePRn9EnpXdYJIb1FfOFBpbOfmkU/Z7EtDWnK28/PLL+LuCSaC9OyFjmiZbWMerr71KYmLifjeFr6yo5Mknn8JVYdGJHjis+vFwnVXLltoNvPjCi7zx5hsktE3Yy5WOLfPnzwNoMrlow0aYFU1G5mpytuYQExPT0uGJyCF25D95iBwhwiPCyc7IZXc9pKuoJCx035fcORCrVq7irbfeIpQo2liJ7ocuJ9VsKl3DU08M4X/vv7fH/gkHqq6ujmefeZbFSxYTZAsl0AqjtrKGH74fz3fmd3jb/EihK35mACYmReSxYP5vVFa+yNChQxvNpKitrcNuGPvUALq4uBgvw4GHq+m/8hzbBx7FJSWEhIbgqnMxf8F8fpn1C1s2b6HSKseOh7uh3Q6BBGPHgxrvKtKrl7u3x0TH8tDt93N8j+P370vaD5988gllRWUkm8c1WNop2AxnE+m89dZbjBkzhqR2Sfz4448sXVJfCn3ScScyaNAgOnTscEjiMDzs7gRF06ydGqn/rYg8/Hz9aZeUBEB+Xj4vv/wK/mbI9gHB9jJ90yKHTXzw4QekpKSQ2iX1kMR9KNXW1DJnzhxWrFgBNhtpaWmcftppeHq13LIC69etZ/LkycSR3OAh3gdfAs1Q1m1ezoQJE7nssmNvVqCIiIgcOs5qJ2PGjGHq1Kk4a5wABPgHMmjQBVxxxRX79Gx+uGqX1I6MVRvBjG+w3QdfkjmOv4yFpKZ1ZuB5A+nerXuDKva77rqTR//9KOlblxFkheGNH9VUUkweYMPT5mK1tZgg/yCGPT0MPz9V0O7suK7H8cYbwxk3bhxz58zl/9k774CmzvWPf87JJkDCliGCiKJ17713tXbaeW93f9293VPttnvcLttq7brdrXUPnHXUURUnKArK3jMhhCTn/P6IoEhYCo72fP6pzcl5z3sSkrzP+32e51tpryQiIoLrJ09n3PhxHj0E6yBQp61tNWY/M9dOv5bvf/geFw4CCEWDFitl7na5UiU9e/Zo9ryXLVuGIItEElsryUtEJIJYrJSxZOkS7r777maNu2btWizl5cTRB+0p5uhqNEQSyyESWLRoEfc/cH+z5/x3xmqxohG19bYZq46brRbruZxWw8hQVVWFVqtVqkcUFJqJImooKJwjxo4dy59b/6SMYnyp7VFgo4IyirhmwpVNHk9ySSQmJmK1WgkLC2tS9sdvv/2Gl2gkQmpfa6NZh55IqRNJJbvYuHEjY8aOafqNnaCkuIT9B/YjSRKdOnUiJCSk1vFffv6FhIQE2tMFX/nk/beRIjnKAVyyA8MJczcRkQBCUElqdu3eRWJSIp07d0aSJFatXMXixYtJz0hHQKBHj55cddWV9GhgEerl5YXdVYmDqpperKdiw4qAgL+fH5ZyCzNmzuTo0SP4iCbUkhYRFSkcJIIYTAQgABbKyBGP4eXlxXvvvUtubi4lpSUEBgQSFxfXqgsSW4WNPzb8QaAUVkvQAHcGShsiSarcxabNmxgzZgwPPfRQq82lb58+fH/oe4+l3DIyReSjPyXjTUamlEIKyWH61Ok1AtqKlStAhrbE1AoIBARCicQilrB48eILTtRITk7mxRdeorSsBKPo7m0bH7+K+V/MZ9asmcR0iDkn84iPj0cvGgiQQuocM+CFSQ5g5YoViqihoKCgoKCgcMY4HE5mzZrF4UOHCZBCMRPgTkayFPDD9z+Qnp7O448/ftFuzE2eMpnX9r9GEXn4E1zzuIxMLmk4pCpuv+N2oqKi6pxrNpt5+523Wb5sOfGr4iksysLH15cekT1Qq9XodXp69urJ8OHD0el0dc5XgIi2Edz/wP2ttlF//fXXU1BYyOr4eApOqSTXYcCAkU/nfEpgUGCz/BZ2707AR/LzWLUuIuAj+bN7V0Kz57p92zZ8MNcSNE6OK2KSAti+bTs80Oyh/9aEhoVid1Vix1aTuHgqVsoRBZHg4GAPZ59bSopL+PW3X1kdvwZrhQWDzsCoMaO46qqr/5G+kAoKZ4IiaigonCP69O1Dr1692Juwj2A5DDNBiIiUUEi+mEnbiEjGjR3bpLFWrVzF//73v1rtquI6xXHPPfcQ3T7a80ky7Nq1myAp3GPmvA49RsGXnTt3NkvUqLRV8smcT9iwYQOS5M7YFxDo268vDz7wICazCZfTxZIlS/GXQ+oIOmrUtCWGw+yhnGJM+NccM+GPQTSwYcMGOnXqxFtvvMXmLZsxEUAksbhwkbzvKDP2zODee+9j4sQJHueYm5uLDOSRSTi1Xx8XLvLJpHv37pjMJl54/gXSU9NrqkYAnFRxlIMc5xAiKkRRxCk5CG8TwdNPP0VQcBBBwUHY7XZyc3LJysoiNDQUQWydiC6/IB+H04E3ng3idOjRqwxkZWa1yvVPZcKECfz0888ccxwiik41woaERAYpOLDjwM5R4QAaWYddtGKVLAwdMrSW6fy+vfvxlkyoqJtVIyDgI/mxb+++Vr+f5lBcVMzMGbMQKkU60xud5F44V2IjvfwIM2bM4ONPPsZ8Br15m0tubi46ycvjZxvAgA/Z+amtPg8FBQUFBQWFc4fL6eLPP/8kPj6ewoJC/AP8GTN2LEOGDG5aZnszWbd2LYmJiXSgK8ZT1qFGfDDiw8ZNGxk/fnyDyUatQZW9isKiQvQ6fa1Wss1l8KDBjBs7jvjV8ZQKRZhkfyQkSsUCyqVSbrn5Fo+CRjVGo5Grr7maq6+5+oznoFAbu91OSUkJRi/jGRl4n4osy+zfuw9vTAQRhoSrRtCQkUklkc/mfEbfPn1r4rjCgkLS0tPR6XR07Bhb53MlS1K96283ArLUUFV7barsVeTm5mK1ViB4iIuqUaHC4XA0edx/CoMGDsLb6E22Na2W7yWAEweFYjYDBw7E13R+jdbz8/J5/PEnKC8tx08KIoAw7PYK1qxYw8Y/NvH6G68TERHe+EAKCv9wFFFDQeEcIYoizz7zLF988QXx8fFkO9PcjwsigwcP5p6772mSd8TC3xcy74t5+BNELN3RosNKGWnJGTz15FO8+fabNb4YpyNJLo+bxtUIsojD6WzyPUkuiRdeeIFDSYcJldrhRyAgUkohe3fu5amnnuadd9+muLiEsvJS2uO5msQLb7ToqKC8lqghIKBGh6Xcwto1a9m8ZTPt6ISZk226AqU2ZJLKxx9/RFFhIddeey0q9cl7lFwS69evxxtf8snChZNA2qBFh4VyckmnCjvhEeFkZGSyc9dOIomt1WpKjZZO9OQYh7Fpyrhm+jV07tyZbt26gQA2m43//e874leuwma3AdAmpA1XX3MN48eNa/FsNaOXu/LBQZXH4xISDtmBwcuzsXtLYvYzM2vWTF54/gUOOnfgix8CIqUUIeEiwD+Aa665hu3bd1BaWkpoaCzjxo+nV8+etV4XodHXSEY47UmyJLN371527dqF0+kkNjaWIeew7dOKFSuwV9qJk3rXqlLRYyBaiiOpYherVq5i+rXTW30uPr6+OEU7suS53VcVNry9zy4QVFBQUFBQULhwqLRV8sILL3Dg4AF8RDM6yUBx1jES9rzFksWdeOGFF2q1R2oJVq5YiUnwxyjX3RA0E0CB6M2qVavOmahhtVr5/rvviV8VX7MGj42J5brrr6Nf/6Zn29cgwAMPPECXLl1YuHARx44nA9A1ritXXPVgszL4Fc6O4qJivvvuO9atW0+Vw93mrGePnlx/w/V07tz5jMZMOpREdm42HeiKN6ZaxwQEQoggOX8fBw4eoE1IGz79bA47tv+FJLtFCT+ziSuvuoZpl11WE8d07daVNVlrkCQZ8bQ1uIyMRSxmSLfBjc6t0lbJd999x6pVq6iwVQDuVlMSkscqEItYcs4qwi8mNFoN9913H2++8SYpwgEC5DZoTuwzFIo5aI1abr3l1vM9TT766CMqSiuIlXrUqsYJksJIqTjIu++8w9vvvH0eZ6igcHGgiBoKCucQrU7L3ffczU033URiUiKSJBEbG4u/v3/jJwMWi5Wvv/mGQMKIOKXiwEwgPpIfRxz7+Pqrr3luxnN1Txbcxtf5KUUEynWNs5w4qRDKm2UevWXLFg4cPIAeL/LJooh8/AnCnxCMkg+Hs/YQHx/PgP4DTpwhexxHRkby4Msg4aKSCkLatGHJkqX4Cv6Y5dq+IwICYbSjiDx++PEHko8k89yzz9UIG1arlQpbBVF0wkwguaRTRF7N+UZ8MYhG7HY7CQm7EREx47ncM5hQDjvy6dGzB506dQLc2UPPPfscqSnHCJBCiMDPbTaXm8+HH35Abm4u//rXTU1+TZtCQGAAsbEdyTmSi0kOqLOJXUQeTsnB0CFDWvS69dG9e3fmzZvH77//zsaNG6mstNPWP4KpU6cydtxYRFFk8qWTGxyjR48eHD70Cy4PwpuMTJlYTN8evWseK8gv4MUXX+LY8VT0ogFRULF4yWLmzp3LM888c07aVG3ZvAVfya9O2y1wByG+sh9bNm85J6LGiBHDWb9+HRZK8cFc65gTB6ViIZNHT2r1eSgoKCgoKCicGz797FMOJR12b9BKJzZoJXd7laNHEvn440947PFHW/SaObk5GGXPlRACAjrJSHZ2dqPjSJLEwYMHKSkuwd/fn86dOze7wrnCWsGTTzxJVmY2AVIIYZhx4iAnJZeXXn6pwSruBhFgzNgxjBk7BofDiSgItRKmFFqfwsIiHn/sMcqLy/GXQvDCBwd2kvcd5Zmnn+HZZ5+lb7++zR+3oBCgpuXx6RhOtMxNO57Gu+++jSyVcffNvvTqpqOsTGLVBivz5s2lpLiYm2+5GYDJl05mxfIVZJJCBCdbPMvIZHIMu1TJlClTG5yX3W7n2eeeI/VoKv5SCGG0pxIbGRwlm+OEEVUr3iskl3KplClTpjT7NfgnMGToELy8vPj2f/8jOfkQ4E4wHTxoMDffcnOdNtnnmtzcXHbt3kUksXXai6nRECy1JflIIqkpqfV34VBQUAAUUUNB4bzg7eN9Rpk+mzZuxOVwEkLdUkQVKgKkNvy14y/KSss8llROnTqVd997t06fWAmZTFIQRIHx48c1aS6Vtko+/vhjADTo8MKIhTIySSWb44ioUaNh6ZKlTJ0ylZDgEIrz8uu0nwIopxQnjjqbsXlk4ZQcjBs7lt9+/ZU2cjuPcxFR4Y0JJ1Xs3rmbZcuWMfUy9+JRbzCgElVUSXaCCcefEKyU4cKFDj06DBxiF74+vkiShCAICLLnoKq6BNjlOinALFu6jKNHj9JB7orXKdUdvviRixc///wTo0aObJLnSXO44YbrefGFF0knmTa0Q4sOCYki8sgSjjFy+CjahNYVr1oLs5+ZW269hVtuveWMzh8/YQK//vor6VLyCaM992stIZPFMWySlamXXQa4+znPeG4GhblFxNAVb8kXAYFKbGRaU5g1axbv//e/hIWFttTteaTSXomqgZ9REQ2VlZWtOodqevfqzSVdLuFQ0mHCpCjMBCIgYKGUHPE4Oi8dl514/RQUFBQUFBQubkpLSlm/bj0hUts6GedGfAiR2rJp00Zuve1WAgKaljzVFHy8fai02Os97hDs+Po2vOb9c8ufzJ07l/yC/JrHQoJCuPOuO+k/oH+T5/LzLz+TlZlNjNQVAycrUsxyABmk8NmnnzJ40KCzajOj0SjbJeeDL+fPp7zYQozUrZZ/oJ8UTJpwiHfffY+vvv6y2S3WTCb3Z6WSilpV+dVU4q722Z2QQKWtjPde8ifQ3x2TBAeq6NDeTEiQmq9//pUJEybQJrQNkZGR3PfAfXz0wUdYxVJ8JffnrVwsplKycffddxMT077BeS1btoyjR44QI3etmZc3phPCSAoWSvEjGBGRMqGIMrmYiRMnMnDgwGbd/z+JXr170at3L/Jy87BarQQGBuLjW/c9Px8cO3YMoM7eRzXV+yUpqSmKqKGg0Ah169gUFBQuWAoKC9CpdB7NrsGdXSIhUVRc5PH4yJEjGTNmDGkkkyIeJJ8sckgnWUygVCzkkUcfbnL//3nz5lFhtRFLd2LoggoNVsrQYSCIMAJOiCbZOdmsWbOGK6+8kmLyyScb+ZSKDRsVpHMEERErZdiwUk4pxzlMDmlce+21tAltg0ajwUX9rbFcONGiw0QAS5YsrXlco1EzePBgisU8JFxuwzZMmPHHgBelFGCXKhk+YgSdOnXCJbsop8TjNUopRKvREtXupLiyfNlyzHJgLUGjmiDC0Ik6Vq1a1aTXtDn06dOHRx55hApdOYns5JAqgYPiX2RwlOHDh/NAKxnstRYBAf48/fTTVGjKSRR3kcYR0jnKIXEXhWRzz933EBfnro7ZsnkzmdmZtJM64YOpJnOpuu0TTli0cGGrz7l9+xgqxLJaf8/VyMhUiGW0byCIKSsrY//+/SQlHcLZjLZvnhBEgZkzZ9Kvf1/SSGY/29gvbOcoBwgI82f27FcJVAznFBQUFBQU/hYkJiXhklyYCfJ43I9AJFni4IEDLXrdUaNHUSYUemyBWoGFcrmEUaNG1nv+li1beO2116gqkIilO90YSCzdsOU7eOWVV9zGx01AckmsXLEKsxRUS9AAd8VIKJFIksyatWuacXcKFwKWcgubNm0iQAqtJWiA23i7jRxJuaWMbVu3NXvsS7peQoB/APlk1lm/y8jkkYnZZGbf3gTGj9DVCBqnMmWcEW+jmjVrTv5tjRs3jrfffYchowYjBVTh8rczYPgA3nr7LSZNbrxSujqePF1oCSKU9nTBjo1sjpNJCiEdgnj0kUe59557W7zF8d+R4JBgottHXzCCBoBW497LqW9vw4XbK0Wn1Xk8rqCgcBIl9UBB4Vwgg7XCilqtRqc78x8nk8mMXarCidNjyxv7iewSk6+pzjFwb3w+9OBD9OrVi8WLFpOSkoJaraZf//5ccfnlTe7LaSm3sHbtWkKIwIgPVsrJIpUQImhDZM0mcxsiyeAoH374IR988AFTpkxlyZLFFIk5GCRvHEIVFrmU4OAQYmM7sG3bthqvkaDAIO6dfh8TJ7jLxvsPGMCOTTsIliLq9CutpAIrZUTibp2VlpOMw+Gsya6aPn0627ZtJ5Uk2kiReOFdU9WQI6QxaMAgdwaNDO2jYshOO45BMtYSjyoop0DMZtyYsSf7E8uQm5dLOJ43rkVEdJKRrKzWMeweMXIE/Qf0Z9OmTWRlZeHl5cWQIUNbvUKhtejTtw8ff/wxy5cvZ+fOXUguF/26jmTy5Mm1TBk3b9mMj2DCIBvrjCGiwiQFsXHjJu6+5+5mXT81JZXExEQEQaB79+6EN2LONnnyJP7cuoVCcgmkdlVMATlUSBYmT67bdqusrIy5n89l48aNuCQXAL4+Jq648nKuvOLKMzaYN3gZePqZp8nOzmbXzpM+I126dFGCHgUFBQUFhb8TsntDtv6f95MtcFqSSZMmsXzZclLKDhIqtcMHMzJQRiHZ4jHaRUQxuJ72p5IkMffzufjiTxSdauIFI75E40MqSXz++ef069ev0bWQxWLBYi0n0EP1OrhbuBhFbzIzMs/qfhXOPbm5ubgkF954rrDR44VOpScjI6PZY4uiyG233cabb72JQDLBRKDHQCU28sighALuvfE+Pv74I2KiPLdZ0+kE2oZpyM/Pr/V4TEx7HnrooWbPCdnd1i0Cz3G4L354YyauXweeffbZM44TWgq73c6GDX+wa9dOXC53K+3x48c1OTFSAbpc0gWD3kBRZR5hRNU5XkQuGrWGHj3OjTeRgsLFjCJqKCi0ElX2Kjb8sYHV8as5duwYtkq34NC9W3euueaaMzLQGzZsKF/Mm0e+nEUotc3AJSQKxRx6dO2Bn78f5WXlHE4+DEDH2I4nsxMEGD58OMOHDz/jezt69CgOp6PGe6KAbHQYagka7ksJhNMei1DC8hUruOuuOxkxYjgrVqwgIz0Do9GbYcOHMmzoMLQ6LRaLleysLDRaDe0i29VatF1xxRVs3rSJNA4TQXvUuM2gbVRwjCR06DETSCE5iIKISnWyEK1dVDtefOlF3n7rLZIL9qIRNTWbySNGjOC+++6reW2eeOoJnn7qaQ6VJWCS/NGix4aVMoroENOhdnslAfR6PY7KBsrwseNlrLv53lIYDAbGjWtay7ALGbvdTqWtksDAQG6+5eaaPrWesFXYUMn1/3xp0FBsb3rbp/z8At584w2SDiXVGPFJSPTp1YeHH30YX1/PQVWPHj2YcukUlixdQrlQgkl2l5uXCkWUyoVcdtk0unbtWuucCmsFTz35FHnZ+YRIkfhidnuwlOfx9Vdfk5+fz913N0+MOZ3Q0FAunXLpWY2hoKCgoKCgcOHSsVNHREGkRC4kiLrJLKUUICAQ1ymuRa/ra/Jl9uuzeX3266QcO4haVCPLMi7ZRY+uPXjsscfqbdm0f99+CgoLiKV7HT84t0lzOMl5+0hMSqJLl4aNoHU6HQICjhMZzacjI+OQqzB4Gc7sRhXOG3q9HnB7wnlCwoVTcp7xezts+DAkSWLe3HkcKttd87jJ18TDtz/C8GHD+Pzzz8jKcXk83+WCnDwncZd4TiJsNgIYdAYc9vrjSZfowMtoPO+CxrFjx5g5YxalpSV4CyaQBf7a/hc/fP8Djz76CEOGnhs/x4sdnU7HtMun8eMPP6JFjz8hiAjIyBSTT66QweSJl15Q1SUKChcqiqihoNAK7Nu7j9dee41ySzkCAn4EEUIkTpwcPXCMmftmcv8D9zd7M9psNnP11Vfz408/IuEiEHdZrpUycoUMqsRKrr32Wj784EPWrl2H0+VeDKpVGkaNHsWdd9yB3qA/6/sThNrZX1bKanr4n46IiFEys3/ffgA6depUY7J9Ot7eRmI7ejYqj4lpz5NPPskbb7zJAdcOvPBBQsKGBR162tMFAYESsYA+ffogirW763Xp0pnP537O7t27STuehk6no1+/fgQF1y7bDwsL5b//fZ9ly5axds1aSsrzCQkJ4bqJVzN27Fi0utqtv/r378/mP7YQTEQdg2sLpdiwEhhQ29xc4STJycn8+MOP/PXXX0iyhNFgZPyE8VxzzTV4+3g28WsbGUnS/iQkSa5TtQNgFcqICG+4yqIai8XK0089RVlhOVHE4Ys/IFNCAfv27GfGczN56+23PAfoAtx111106NCB339fyLHjyQBEt2vPbVfczKiRI+ukUC5avIic7Bw6SN3Qn9IuwYgPerxYtmwZEyZMJDo6qknzV1BQUFBQUPjn4e/vz9ChQ/lz81aMkg9epxgf26ggV8ygf78BBIcENzDKmREaGsp7779H0qFDJCUmIqpEevboSWS7yAbPKypyt8etNmOWkSmliCLycFBVU4XuzoBvRNTQ6+jdpw9Ju5MIkELqrAdLKaJSsjF06NAzvEuFsyU3J5c9e/ciSRKd4+JoF+XZG/F0wsPDaRvRlsKMHHww14kvC8lDkl0MGjjojOc2YuQIhg4dSkJCAoVFRfj7+dGzV88aj45hQ4eycv1mLh3nhZehdky5fksFxaUORo4cWXtQGfbt28e6desoLS0lIDCAcWPHERsb22jF9PARw1m/+g+CJc/xpFUqZ9iw8/u3bLPZmPncTBwWF3H0RiefEJ9kJ5muFN58603ahIY26h+i4Oa6666juKiYlatWki9mopX1OAQ7lZKN4UOHc9ttt57vKSooXBQoooaCQguTkZ7BCy+8iOgQERBozyX4nGLgFyCFkEEKH3/0MX379MXP33Npa33ceOONaLRafvn5Z/LtJ9sahQaH8sR9j/Ltt99y+FAyQVJYTSVFiauAdWvWkZGewSuvvtxsU7XTiY2NRafRUezIJ/REdYbUQHm7jFSrcuJMGThoIPPnf8H9999PeZkFI75E0QlfApBwkc4RbJKVq6662uP5oijSp08f+vTp0+B1TGYT199wPdffcH2jczIajbhwksJBwok+0dpKpoxCMkhBhZrExMQzut+/Ey6ni+3bt3Ps2DG0Oi0D+g8gLy+Pl196GS16QuUoNGipsJWzeNEStm/bzhtvvuExQ2XihAksWbKYPDJoQ9tax8oooVQu4sbJ1zVpXqtWrqSgoJBOck90VAt+Av4Eo5e8OHxsD5s3bWLkqJGeBxBg9JjRjB4zGpvNhoDQoHC4csVKTFJALUGjmgDaUCBmEx8fz1133dmk+SsoKCgoKCj8M7n7nnvISM8k+dheTII/OtmAHRtlFNM2om3r+qsJEBfXqcbvrCn4+bljnkqs6DGSSiLllOCFW5SppAKAn378kX59+55s91oP1157LU/tfpI0DhFKFDr0SMiUUkimmELPrj3p1LHp81NoGSwWK++//z7bt207kQAnADKXdOnKo48+0rjHmwA33HADr7/xOpmk0Ia2qNEiIVFMPtnCcUaPGl0nMa25qNQq+vT1HBNec810tm3byozXivn3dG+6d9FhrZBYtb6C//1mYfjw4bUMnKvsVbz++mx2/LWT8FAdYW0Edv0lsWLFCkaPGsWDDz6E2EAsfMUVV7BhwwaOOZIIk6JO+GTKlFFElphKbPvYRuPX1mb9uvWUlpW6BQ1Oxjpq1LQllkqsLFy4kEceefg8zvLiQRRF7rv/PqZMmcKaNWsoKCzAz+zH6NGjm9wSXEFBQRE1FBRanAW//47gEpGRMRNYS9CAavO6dpTKBcSvXs306dc07wICTJ9+DZdNncquXbuwVlgJDQ2l6yVd2bBhAwcTDxJLN4yn9CENIQJvyZfEpH1s2tjABm0TMXgZGD9hPMuWLsMo++CNiVIKCCOqTqaUCxflYglje5/dNasxmU288847zJw5i8ysDCTRSSG5VMjlCKLIww893GjJekuSlZWNFz44sHOYPWhOLLpdOPHBjBFfjiQfOWfzOZ9ILony8nL0ej06/UnvmP379vPmm29SXFKMTqXHJbv46quvUKs0GCRvoulS83djJgB/KYSU3AN88+033HvvvXWuE9kukhuuv4Hvvv8Om2DFTw5EREUphRQL+fTq2YuxY8c2ac5r16zFLPvXWpxX44U3PoKZtevWNekzYzA0UgYvQ2FRIW3p4PGwiIBOMpCXm9uEmSsonBsOHlX+HhUaxmaznu8pKCj8I/H2NvLGm6+zbt06Vq1aRWFhEYH+flw37hrGjB5Tay12IdCtWzf8/QLIK85EhQYrZbSnC76cTPCyUs6xrCQ++OADnnzqyQbHi4vrxNNPP82777xLkm0XBpURp+ygSrLTp2cfnnjiCcVT7BzjcrqYNXMmx1KOE0EMfgQCImUUcjTpKE899RTvvf8+3t4Nt+YdMnQI95Tdw9y5cyly5WEQvaiSq3BIVYwYNoJ7q1sHtxLhEeG8/Mps3n33LWa+kYFKFHBJMmqVinHjJnDnaclHc+bMYe++BJ55yJ/+vfQIAkgSrN1cwUdfrCcoKJgbb7qx3uuFhYfx4osv8trs1zlUkoBeZcAlu3BIVXTv0p0nn3qyTheCc82OHTvwxuQxZhIR8JUC2LFtx3mY2cVNu6h23Hb7bed7GgoKFy2KqKGgcCoy7Ny1kyWLl3D48GHUKjV9+/Xlsssua3LJ7JZNm/GV/Mknk5DTssirUaPGgDdpx4+f8VT1Bj2Dhwyu9Vj8qnh8BT+Mcl0PACO++Ap+rFq16qxFDYBbbrmFjMwMdu/ejZfgjUOuIp1k2hKDeKJs1oWLdJIRVQITJ00662tWExQcxEcffcj2HTvYtm0rjioH0e2jGTtmLCZzC/U3bSIajRoRkTj6UEYxNizuhR3+GDCSQzoqtarxgS5irFYrP//0MytXrsJaYUFAoHefPlw7fTp6g55Zzz+P3ulFJ3picBmRkCihgAzXUSSkOvGmHgP+Ugjr1qzjtltv81j5cN311xEaGsqvv/zKsbRDAPiZ/bhhyo1ceeUVTX7Ny8rK0NdjRAiglXWUlpQ2+bVoEAGMXt7YKzz7fcjIOMWqej08FBTONdWCRlzPqPM7EYULGqu1/HxPQUHhH4tWp2XCxAlMmDjhfE+lUUSVyB133s4bb7xxwkOjbS1BA9ztOEOktvy55U/y8wsIaiSrf8CAAXz11Vds2rSJtHR3e9lBAwfVyqK/mJAlmV27dhG/Op7C/EL8AvwYM2YM/fr1O++b2k3hzz//JPlIMrF0x8jJamszgRgkbw4XJLBy5QquuuqqRseaNHkSw4YNY/2G9eRk52A0Ghk2bBgRbSNa8xZqiIlpz4cffMzBgwc5fvw4Wp2WPn361DHELi4qZt26tdxyrTcDep+MWUQRxg7zIiPLyZIli7j66qsbFBrjOscx74u57NixgyNHj6BRa+jbt+8Fk7XvcDhrYnxPqFDjdHr2QVFQUFBoLkmpeU16niJqKChUI8P8L+ezYMECjKIvPpIfEi7+WLuJtWvX8sQTTzBocOO9OyvtlZjRIiDgxFnv8yTBiVarrff4mZCfn49Orj9bXCcbTvSpPXs0Wg2zZs1i+7btrFq1itRjqRQVFlAulOAjm5EBi1iCqBJ5+pmnGw1KmouoEhk4cAADBw5o0XGbS58+fdj5106cVGHGHzP+NcdkZErFQvr17XseZ9i6WCxWnnriSbKysvGTggiiLU6qSNp9iKd2P0VcXBxql5pouXONCbeIiD/BqFCTSiIVlNeqLALwwUyOI428/DwiIz33aB4xcgQjRoygpLQEl9OFn79fswO+oOAg8sqL8NQ9TUamUrTRsU3L9YYdNXoU8ctWESyF1/SOrqacEiokS4uIjgoKZ4siaCgoKCgotDRDhw4l5WgKv/z6C354bh/kRxAZHGX//v2MGjWy0TF1eh1jxo5p0XmeDxxVDl599VV27tqJUfRBJxnIPJrN1q1b6XpJV2bOnFmT6HNg/wEWLVpIwp4EZEmmc5fOTJkylX79+p3Xe1i7di0+ohmjVLd9rA49JjmANfFrmiRqAHj7eDNlypSWnmbTEaDLJV3ockmXep+SkJCAS5IYPcxzu7QxQw0sWJZPUlISPXr2aPByKrWKgYMGMnDQwLOadmsQG9uBg/sPIkkuj+JGuVhM+wtEgFFQULh4aW6XAEXUUFA4wbbt21iwYAHhRBMkhdU83kZqRzrJvPnmW8ybN7dRD4zw8HDK0svxxZ8icgkktE5LJivlWCULAwe27ILFbDaTlZfncYMWoEqwE+p3dv1HT0UUxVoLr9zcXFYsX+E2BRcEJvYaw4QJEwgI/PsaZY8aNYrvvvue49bDtJM6osWdgePCRSap2GUb06ZNO8+zbD2+++47srNyiJG6YjjFJ8JfCiGNwyQeTCSEtjWCxqn44ocWHcUU1BE1HLgzfXS6RlonCNTJmGoOEyZO5MMPPyCLY0jIqBDxxR8jPpRRjFUqY/z48YA7e67KUYVOq6tpZ2Cz2Vi3dh0bN27Eaq0gMDAArU5LRnomAN17dGfy5MlERLiNy6+4/HLWr19PasVBQqRIfDAhI1FEPjliGj269qB7t+5nfD8KCi2BImgoKCgoKLQWXbp0gV+pYwBdTfWjsiSdu0ldAMybN4+E3Qm0pws+0gmDbMmd9JKUeIg5c+bwn4f/w9IlS/n0s0+JitBx9aU6VCr4868kXnopgenTp3PTTTedt3soKSlBI9W/dteip7Ss5NxN6BzgcLqTGPU6z4lVhhNG405n/cmOFwMTJk7gt99+I4vjhBNd6/NbRB7lUglTp9x1HmeooKBwsXKqkFEdfza1ElwRNRQUTrBo4SJ8RFMtQQPcPSLDaU+itJOVq1Zy3XUNGxBPvnQyn835jFCiKKOINJIJx22CLCNTQTlpYjKR4ZH07dcXZEhOTia/IB+Tr4nOXTrXyTa3V9rZsWMHpaWlBAYF0qdPH49m32PGjuGjQx9hw4qB2r1KbVgpk4v419jGza+bit1uR5IkDHoDCBASEsLNt9zcYuNfDBi8DLzw4gvMmjmLpPJdGPFFRIVVLENG4j8P/YfYjrHne5qtQpW9ijXxq/GXgmsJGuD+3ITSjhIKcFDl8XwBAQ06XKdVNMnIFAu5tGvbjpDgkFabP7hFE1EQyZMz0WHAhZNcMtChx0EV/fv1p23btsyZM4e1a9ZSaa/EaDAyZtxYhg0byuuvvUFRYSE++KFGQ/bx/dipRIcBI76sylzFsqVL+c9//sPIUSMJCg5i9quv8uYbb5KScQCVoEKSZUBmyOAhPPjAg0r/Z4XziiJoKCgoKCi0FsmHk1m9ZjUCIofZgy9+BBKKF941zymhEIC4uLjzNc1WpbSklLVr15KRmYlBr2fwkCG0jYggPj6eIDm8TksuH8yESBGs37CesWPH8tlnn3HZBG9uu94X4cSa8YrJsGCZhS9//Inu3brTvcf5SZAJDgkmOzUXWZI9ilaVWAkKarkEuwuB2A5ur7ydeytrtZ+qZkdCJaIoEhV9cbZEqyYkJIR777uPjz78EJtYjq8UiIhIuVBMmVzM+HHjGTp06PmeZi0cVQ62bNlCekY6ep2eQYMGER4RTlpaGgsXLmLHtu04HA5iYmK4dMqlDBo0SInDFBTOIZ7EjOaiiBoKCic4dOgQgVK4x2Nq1BhlX5ISkxodZ/z48WzdspW9+/biLZsoo5BSCjBgxIUTO5VERUQx64XnObD/AJ988gkZmRk15wcGBHLrrbcybPgwkGHxkiV8+8032CptiIhISPj6mLjr/+5k+PDhta49cuRIlixeQmpGIiFS2xPmbO7gIEdMIzKiHSNGjjiLV8nN1j+38ttvv5F0yP16hIdFcNllU5k4cSKCKGCvtJOVnY1GrSY8PBxB/HuvDmJi2vP555+xbv06dysql5NOHTsxfvx4Alu47daFREFhITa7jTDMHo/r0KNFRwUWj8dduLBhQYMGGbmmZVsO6ZTKRdx93Z2turA8sP8A777zDiY5gFDaoUXnbhl2QowMCArkpn/dxMMPP0JVhR2zFEQwXlTarCxfspxlS5aiRkMnetWY5snIFJFHOkcIJJQIqT0ZpPDee+/RPqY9kZGRtItqx4cffcjBgwc5evQoGo2G3r17ExwS3Ho3q6DQBBRBQ0FBQUGhtVi2bBlz5szBIHoRRCgAJRRQRB5t6UAAIVRiI1dMp3eP3oSFhzUy4sXHqpWrmPPpHERBIqqtluJSiUWLFxEdFYXD6WiwJVemlMpPP/+M2aTm5uknBY1qLp/kzbrNdpYuXXLeRI1x48axZcsWyijGdEpLXoAKLJRRzPUTpp+XubUW0e2jievUka9+Ok7HGA1+ppOtmbLznPy0sIKBAwcQEODfwCgXB+PHjyMiPJwFCxawc+cuJMlFTIcO3Dn1NkaMGHFBCQI7tu/g3XffxWK1YFAZcMhOvv7mazp37kLy4WRUsgqTFIABNakHjzN7/2wmTJjIfffee0Hdh4LC343T20udbdypiBoKCicQRRGZ+sucZWRUqsbNh9VqNTNmzWTBb7+xZMkSpFL3mKI3dOzQmcunTaNXr14cOHCAmTNn4iX70J5L8MIbOzbyCzN58603cTqdWCusfP75ZwTShii6oEOPjQpyy9N566230Kg1tXw+tFotN996M/Pnzyct7QjpHAHcGfF9+/TloYcearydTyP8/PPPfPPNN/iIZtrSAQGRsqwi5syZw759+zD7+bE6Pp5Ku9sMOSgwiKuuvorJkyb/rRcIBi8DkydPZvLkyed7KucM/QmzOyeeTeEkZFy4qKIKGxV1qjnyyDhhGl6ITdyNVtBSIVlBkLnz9rsYMnRIq87/xx9/wiB4EynH1mSTCQiYCQAEjuUn8uYbb+GskIiVeqBGc+LMIAKlMJLZi4iqRtCoPj+AECyUUkAWgbQhghisQilLlyzlnnvvqX5ioz16FRTOJYqgoaCgoKDQWhw9cpRP53xKIGGES1E1665Q2pFBCukcoYRCrJTSJqQN//nPf87vhFuBv3b8xYcffciEUUb+dZUPPj4isuzO5H/v03REAUTZcwsj4UQb19ycTHp31+ChYB9BgP69NKzbcqg1b6NBevfqTb9+/dj5106C5DD8CEJApIRCCsQsoqPbM2ZMw/4nzemn3iWmdSu6m8p/Hn6UZ55+kvufLmT0ED3hoWpS0qrYsMVOQEAwd//fPed7ii1GrfhF5oKM7xMTE3nl1VfwlfzoTEd0LgMSEgXkkJh4EBMBtKNjTXvkECmCIvJYuXIFl3TpwshRI8nKzGL79u1UVVURHR1NaFgYq1auZO+efSBAj57dmTRxEm1C25zfm1VQuEhoiaoMTyiihsJFT3FRMRs3baSkuISAgACGDx+Oj29dc7LG6NWrF3u27yNYCq9TLuugCiul9OzZs0ljaTRqpl87nauvuZriomLUajUmk+mUJrHw2WefY5C9iZa71HhuqPHBi04IJDN37lxcLhcBJzZFqzHgRTs6IiPx5fwva8okMzMyeeONN0g9loooqGqqOmLax/Dggw8S3f7sS16PHzvON998QwhtaSO1rXmd/AmiiDw2bd6MSlARKLehLf64cFFckMecOXPIy83jlltvOes5KFw4+Pv70yEmltyUXMxyQJ3PTSmFuHCiQUcyewkmHB/MuHBSRC4lFPLvf/2bHj17sGnjJqxWK6FhoYwZPQazn7lF5ngk+Qjbd2ynyl5FdPv2DB40CI1Wg81mI2HPbiKI8Vgeb8IPnagnPSONKDqfImi40aIllEjSSD7Rbqp2ubkfQRSTTxV2dOjxkfxI2J3QIvekoNDSKIKGgoKCgkJrsmTpEnSivpagAe5kkHDaU0oheLu49drbGD9uHAYvw3mcbevw008/0LWTnntuNtVUWbiFCD333y7z+ofF5JNNGO3qnFt6oiWX0eiN3V5/n3G73W02fb4QRIGnn36ab775huXLlpNrd3cjUKs0jBg5nDvvuBOtTlvnvDPd7DqYcOyM5tnSYkhYWCjvvPseixYuYu261ZSWlBIQ6McVV0xj6tSpePt4Nz7Ixcg5FDQcDifJhw/jcDiIjIxs0Of0hx9+wICRSDrV7LO4BQwZAZG2xNTxe/QnmFKhkN9/X8ifW7fy559bUAtqVIIKu2RHQEAlqPGV3dddcnwpixcu5rHHH2PwkMGtdt8KChc7rSVmVKOIGgoXLzJ88+23/PrrLwiygFbUYXdVMm/eF9x0041ceeWVzfqhnXb55WzdupVMUgkjquaHzkkVx8XDeBm8GD1mdLOmKIqiR5Ps48ePc+x4KtF0rmMiLiAQQgRJlt0AtKNznfMFBAIJ42jufpKTkwkMDOTpp56myuJ0m8vJZmRkSigg7dgx5nwyh9mvz67j1dFclq9Yjk7UE3KKoFGNEycCECN3wYuTopIvZgwY+W3Bb4waNYp2UXUX6goXL9dddy0vv/IyGaQQSiTqE62kSikig6P44k8ksRxiN3lCBjlyGgBhoWFcfekdyDJs37adtm3bMmToEAyGlgliLeUWXnvtNfbu24tW1KES1NhcVj7z9uXxJx4jKioKADV1Aytwf8bUaLBTiW897bWqH7dhrSNqeBqxoUowBYXzhSJoKCgoKCi0Nvv37cdb8vOYSCIiYCIAo1nLtGmXnYfZtT4lJSUkHTrMI3eb67SNAhjYx4CPdxkFlmz8CUJ/SnWznUpyxXR6de/FJZdcws8/f0e5RcLHu3Zc53DIbNxmZ9CQ4acPf05Rq9XceuutXHfddSQnJyNJ7iQ7T0mHZ7vZdaZrF09iyNkKHf7+/txy6y3uJL4LtILhYkSWZH797TcW/LaAcksZAKIgMnDQQP7vrv+rI25YrVZ273Ynrp2+z1JBOUZ86iSrVeMt+5GSepS042lEEotZDkSURSqwkEkqNtlCEOEY8EKSJNI5wptvvslH0R8TFhbaOi+AgsJFSGsLGaeiiBoKFy0//fQTP//8E22IJJBQ1C41ThzkuTL58qsvMRgMTJo8qcnjdenSmXvuvZc5n3xCmVCEUfJFRqJMKMag9+L552dhNBobH+hUZDhw4ABr166lqLgIf/8AxowZQ6XNBlDHzLsaPV4ICMjIaPHcLkqHe/O3tLSUP//8E4vFSiepJ5oTm7QCAv4Eo5F0JB7az187/qL/gP7Nm/9ppBxNwUvyqbNAACgkBxOBtQSNagIJo1DMYeWqVdx1151nNQeFC4v+A/pz77338fHHH1FEHga8cODAgR0fzLSjIypUGPElvHMId/3f/6FRq1m2bBlfzPvCbRYuarG7Kvnss8+46667GDtu7FnNSZZkXnzxJVKSU4giDpPkj4BAJTayrcd46cWXeP311/E2+mCxlmKmbo9bJ1XYJCsALpyIHsQP5wmD89MzfcDtY6NBhxYdEjLlYjHDuw87q/tSUGhpFEFDQUFBQeFcIAoiLuR6j8vICGeZfHUhY7fbATD5eK6iEEXw9VEDWg5b9mAiAD1e2LFRIhQSEhTMQ/95CFEUWbDgV17/oITH7jVhPuHfYLVKfDi/lHKrzJRLp5yr22oQg8FA9+6evT3O5YaXJzxds0WFDkXQaDE+nzuXpUuWEEAbQolGhZoyuZidW3fxRPITvP3u2/j6+tY831bh3mfxvIfi3l+pDzsVAERIMSfaEbvxwpsYunCIBPLIqGld1ZYOJLGLZUuXcsedd7TMDSsoXMScj+92RdRQuCiptFXyyy+/EEQYbWhb87gaDWFE4cTB999/z/jx45tVgjtx4gS6de3K8hUrOJSUhEajoV+/yxgzdkytH8um4HA4eeONN9i2bSsG0YhW0lMlJhIfv4qe3Xu674MKjz+4dmw1P7iVVHgUP2wnzJeDg4P58MOPMEuBNYLGqfhgwij6sn7D+rMWNfR6A64TG7mnU0UlgXg29BMR0EtGsrOzzur654rm9HJtKhdKz9czoaS4hKzsbAx6PVFRUXWM3ydOnMCKZcvJOpaNASNGVJgJxAtvBAQkJGxiOTExg4mOjmLu3HksW7qMECIJpA0ql4oq7OTY0/nvB//FaDTW8oppLnv37iXpUCLtuaRWlYUeA+3kTiRLe/j999+ZOHECvy9YSIAUUsvvQ0YmizREtQpRVlHoyq31PVNNEXkICDUCY83rRRHF5NGGSGQgixQckp0pUy6MIFNBARRBQ0FBQUHh3NGrTy/WrFiDJEXXSY6SkCgXixnRu/EWLrIks3fvXjZv2UylrZLw8HDGjht3wZsw+/sH4O3tRcIBOz271o398gpcZGXb+b//uxWHw0H8qngKCwswm/2YOv4mJk6cWJNcN2Pm87z00gvc/nA+3btoUalh74EqZFnFk08+SUTbiHN9e03mfIsZDdHqQodCs0lLS2PJksWEE03QKfsMQYTiK/mRXLiX3xf8zr9v/nfNMZPZjEFnwGovw5faVRw+mEnniMfWwdVdLrTo6pjcA4io8CeEHNJpi4yIgIiIj2Rmt9JiWOEfzvn8bldEDYWLkl27d1FpryQaz2V+gYRyuHQPiYmJdO3WtVljh0eEc8cdt5/1HOfNncuObduJohMmye03IEsyJRSyd99efLx9yLdm4SOba5Viy8jkkonR4I1Gqya3NJ12dKr1HAmJPCGTDu07ENkuEoulnCDC652LRtJSWlJ61vc0aNBA9u7Z43EhoEKNA3u95zrFKryNF34/0dbY6EtKOOZRKLnQF8X5efnMnTuPbdu2Isnu1kkhwSHccMMNjBo9qtZzL5t2Ge+9/x5tiKpV+eD+e07HLtmZOHEiJSUlLF2yhBAiCDnlb1aLjrbE4MTBt99+W+MVcyZs2rwZg2jERzLVOSYiYpaC2LJlC19/8w2bN28hOWcvgbTBGxNOHBSSSwXlPHjvg6SkpLB0yVK06DAThIiAhEwRueSThVajJdm1B18pAA1aLJRipQwdehxUcUjcjUOyc+/99xEdHXVmN6Sg0MIogoaCgoKCwrnk0smXsmLFCjJIJoIONVWuEi7SOIIsSEya1HCFvaXcwosvvkjSoSQMohE1Gjaxie9/+J4777iTS6dcei5u5YzQaNSMGzuBFSsWMXKwgai2J9vfOJ3wxXdleHkZGDNmDDq9jmmXT6t3rC5dOvP55/NYs2YNe/ckIEkS11zThfHjxzfqSVdlr2LNmjWsXLmK/Pw8fH1MjB47ikkTJ7Wq78OFLGY0RGNCR2vEchaLlfz8fIxeXgSHBLf4+BcTa1avQSfqCJDq7vno0GOWgli5chX//ve/a+JGjUbNmHFjWblsJf5SSK09CzOBZJLKMZKIpnNNcqmERDbHceLARzAjyJ6DUC06ZKQTLYXdibMCArKstBhW+OdyvuNKRdRQuCipsLpLA+trzaQ9UbFgrbCeszlVs2PHDn756RcSDyUCkEcmEhJ+BCEg4EcgdtlGgS0bl+wilURCaIsBI3YqySODYvK599b7MJtMzJ49mxQOEkQYOgzYsJIvZGEXbdx1110ABAUGUZFt8TgfGRm7WEGbNm3O+t5GjhrJjz/8yPGyQ7SVYmuy26uwIyJSRC4hRKCidnWMhVKsUjnDhl3Y7Xda6wvZ03gXutBRWFDIY489jq3MRqgcjTe+7g3/vBzefe9dysrKagVcI0eNZNu2bWzbuo1SAjDhj4REsZBPuVzCv/99MxFtI1ixYiWyJBPgQZB0e8W0ISXjIGnpaURGRp7R3G0VFajReOzbDKBBi9Pl5OiRI+Tn5yMiUkAOeWQC7kwctUZNVHQ0I0eOpLS0jD82biBXzEAr66gSbNglOxPGT+C6669n1cqV/LFhIxU2K2GBbTAa25OdnQ1IDO8+lClTphDdPvqM7kVBoaU53wtPBQUFBYV/Hm0j2/LYo4/x9ttvU85OfCS3/1+5WAICPPHkk7QJbSBWkeHVV18lJTmV9lyCj2RCQMCFi2zS+PSzTwkIDGTgwAHn7J6ay3XXXceevQk8+VIaY4fr6Rqno7jExcr1laRnOnjyqafR6T3Htqfj7W1k2rTLmuVBYquw8dxzz3HkyBFM+OONHzZLJd99+x3Ll63g9ddfIyg46Exvrw4Xq5DRGKfeS0sKHAX5BXz51Vds3rQJl+QCoENMLDfedAN9+vQ5q7EvVvLz89HKBo+trwG8MFJgycLhcKDRnhQKr7vuWv7asYOj+fvxl0LwwYQDB4VCLpLsAp1Ekn0X3pgRUVEhluGQqujdqzd79+xDkl2I1O32YcWCGs0poqz7O6xft1F1nqug8E/gQogrFVFD4aIkLMxdfmilDG/qZmNbcJtIhYV6bofUWvzyy698/fVXeAu+hJ8wpyqhkDSSqaCccNqfEDaCyHGlcd2117F27VqS8/fWjOHrY+KBWx5g3LhxAMyYMYMvvviClMyDNc+J7dCRO++4g7jOcQBMmDiBL7/8EptsrdOqqph8bFLFWfsUgLs36suvvsKsmTM5VLAbo+iDgIBVKkev0yPIAqnOg4RKURjxQUKmjEKyxGPEto+lb7++Zz2H1uB8LLrru46nMudqzqXg8d3331NRZqWD1L1WWzMfTGSi5asvv2LUqFH4mtxt2URR5Mknn2Tp0qUsWrSIY3mHAOgU24krr7ynpp2U1WpBJapRS55/fjQnhMpq4fJMCA8PZwt/4sJVR2AD9/eGn9mPOXM+RS950Z4ugICTKsQTxcQprgN8/OFHvP3u2zz2+KNcccXlrFu3juLiYgIC3N441ab3199wPdffcP0Zz1dB4VxxISw8FRQUFBT+mQwdNpSYDjGsWL6CvXv2IogCo3sMZ+KkiYSENLzGPXz4MPsP7CeazrVai6pQEU4UdqGCn3/66YIWNQxeBma/+hq//Porq1YuZ0l8EQICffv14d77p9fEda3FvHnzSE05RizdankgVsmRpBYf5M033+SNN9846+ucj7jKZrOxe/dubJU2IiIi6BjbEcGTI3sLU31/pyarnUm8lp9fwGOPPkZFWQUhUiRGfHBgJzcllxdfeJFHHnmEESNHtOTULwp8fX1xCnZkWfaYrGbHhk6nQ6PR1DnvzTff5OtvvmH9+vXkONIAiG7Xngeuv4fu3buzdt1atm3dTpWjitgOg5g4aRIajZq77ryLXDIJpXZyXSUVFJFLEKE13qfuFsNVTJ48+eQTZTiYmMjq+HhycnLwNfkycsRI+vfvj6j6+/oGKfzzaM24cm9mDrYmJqgroobCRUmXLl0IDw0nNycdL9mnllGvCxf5YiZxsXG0jazbB7+1SE09xtdff0UIbWkjt6354Q0ghEJySecIPvhhwr9mozU+fjUjR44gun00TocTk8lEj549UKtPfjT79utL3759SU1NpaS0lKDAwDq9UidNmsS6detJSTtAoBRWkyVfRD6FZDNm9Bg6x3VukfuMiAjns88+48+tf5KwOwGX5KJTpzhGjhhBRkYGr732Gsn5e9GJOlxIOCUHPbv25PEnHke8wAwAL8QMovrmcXplR2sKHHa7nQ3r1+MvtfHo0xJCW4rkXNatW1erWkNUiUy9bCpTp07FYrGgUqswGGr7TYSGhuKQqrBRUcvHohorZYiIjQa3DTF23Dh+/PFHckknlHa1FsEVWCgRChndbzTx8auIoWvN98eplV9BUjjJR5M4fuw47aLaEdMhhpgOMWc8JwWF840iaCgoKCgonG9CQ0O59bZbm33etm3b0Ik6fCW/OscEBPzlYJKPHKakuKTRFkznE4OXgX/96yZuvPEGLBYLOp0Ona5p1Rlng8ViZd26dQRJYbUEDXCvf0OkSJIOJZGaknrG1cUtFVftzcxp8Hj38JMVPZIs8f33P7DgtwVUVZ1sgxwZGclD/3mI2A6xDY5lt9v5Y+MfHNh/AEEQ6NatG0OHDkWrrRv/NMTZihtff/UVFWUVdJC6nRJ7+WCSA0gjmY8//piBAwc2uZLn78LIkSNZtnwZpRRiJrDWMSdOSsR8Ro8a7bFlscls4oEH7ueOO24nPy8fvV5PcHBwzXOnTJni0e/whhtu4H/f/Q87NvwJRo2GcorJIwuQceEkk1TKxWKqJDv3PXBfTaKbJEn89/3/snbdWgyiFzrJiFNMY8uWLXTq2IlZzz+Pt3ddr1QFhYuJ1hYzAOI6hWK1lDfpHEXUULg4EeCBhx5kxowZHHHtI0Bqgx4DFVRQJGaDVuaee+9ttcvbK+1s+OMP9u7dC7JM586dSUlJQScaCJHa1skkcAsbORSQgwl/yigGwFUEi35fjKgWmDFjBt17dK/3fhtaYOr0Ol6d/Qrz5s5jw4YNZDuPA+Bt9OGGaTdwzfRrztifwBMqtYqhQ4cydOjQWo93iO3AZ59/xq6duzhy5AhqtZo+fftecF4CF6KY0Rinz/P0io6WFDnKSsuoclThhefeumrU6EUvcnPrMVQXqLcvb//+/fH1MZFbnkYknWqVEztxUChm07dPX/z86watTSUoKJCbb7mZ+fPnYxds+MlBqE4sSIvEPKKi2hHboQPx8asw4utxDO8Tj2dnZ9csVBUULlbOZvHZ2OaCgkJTM6kUFBQUmkqFtYK169aye/duJEnCarGiEupvLao6sa1RVVV1Lqd5xoiiiK+v5zWoJ+yVdvbu24u90k5ku8hmt2hNTUnB4XRgIsDjcV/8ERFJTEw8GXPKsG/fPlasWE5a2jH0ej0DBw1h/Ljx+PieFEZaIq46da0R18mzZ2bNcw9l1/x71apVbN+2HXNgZ3zxR40aG1ZKpAJmvf8xt912G8GntdSqFkWSkpJ4+ZUXKSuzEBulQwLWrFnDV1/NZ8aMWXTo0KHZ9+FJ3ICG4zSr1crmTZsJltrWSSYTEGhDJEmVu9i8eTOjx4xu9pwuZuLi4hjQfwB/7diJQ67CnxBERMopIVdMR63XcOVVVzY4hsFgILJd0z8v1153LQEBAfz444+k5Lk7ZWg0Wvr27otOq+XgwURkXAzpOZipU6fSvn37mnN//uln1q1bRySx+Enu1uNI7qS9lCOHeO/dd3luxnNn9mIoKFwAtJagcaqY0VwUUUPhoqVLl868/tprfPvtt+zevRsZGRGR/v36869//atVqjScTicLFizg++9/wOl0oEGLBi2bNm4CQcAsB9bb89EHPwrJxU4lOaThgx9RdMIluTjuOMQrL7/CvC/mnbFJm9Fo5MGHHuS2227j+PHjqNRqYmJi0GjO7cdcFEV3dckF3mrqYhEz6uPU+bd0FYfRaERAoKoe43cJiSrZjo+Pj8fjDaFWq3nwwQfcfZHZT4DcBi16KiinUMxB46Xh9jtuP6v5A1xxxRUEBQXx0w8/cSzN3QrLy+DF1AlTuO7669iTsAcAB/Y6pvdAzb17GetWkygoXEy0hKBxJgtMhX8OTc2kUlBQUGgKhw4d4oXnX6DCWoERt3eGhRIkJOxUely3lVOC0csb/wD/8zDj1kOWZH788UcWLlyAtcJW83iXLnHcf9+Ddar366O6Wl5Gru9K7lhapar+Xz777DOWLF1CZLiO7l3UlJRJfPfdNyxcuICXXnwFq8v9PrRUwkRT1xrVz8vLzWNN/G+E0o5AvIEqoAojAnr8OSLuZcvmZTz11FO1r30om/KycubM+YzgjiHcMSEaP5P79QmQsnj30zJmPT+Djz76BLPJ3Ox7g4bjNKgdq+Xn5+OUnBjxHFfp0KNX6cnKzjqjuVzIVNoqiY+PJz5+NcXFxfj7+zFu/DjGjR3nrkoR4PHHH2fOnDmsXbuWTDkVEREJicjwSB57/LGzqu6vj7HjxjJmzBjSM9JxVDkIDQ1tNCZ0OJwsXLiIAELxp7bBuxFfQqV2bN+xnazMLMLCz22LdAWFlqCl99GaI2Y3hCJqKFzUdIjtwPMvPE9pSSmlZaX4mf1qZY60JCXFJTzzzDNkZGbghQ/emLBSTgUWAmhDqVyIC2e957twISNxiAQ0aGiLu5WNChWRcgcO2neyZs2aWu18zgRvH2+CgoJYumwp77z9Nk6Hkw4dOzDl0in1V4L8A2jOl3BrZCafWird0rR0FYeX0Ys+ffpwcHcS/lJwrfZuAEXk4ZCqGD58+JlMl/4D+vPSSy/xzTffknQoEXAHW4MGDuLmm29u2CiyGQwdOpShQ4ZSUFBAlcNBUGBgjYlcr169MOgN5FdmE0HdKqgCsjH5mrikyyUtMpf6kCWZAwcPkJOdg7e3N7169apVWl5lr2Lbtm3k5+fj6+vLwEGDlLJlhSajCBoKCgoKChcypSWlVNor8ffzR6PVUFZWxvOzXkCoVNGZPjWZ61XYSWIXmaQQRedaSWQVWCgW85k26bJaLXz/Dnw+dy5Llyxm2kRvJowKxuQrsueAne8XpPLU00/w9lvvEtKm8XV+hw4dMOgNFFfmY6BuBXIJBcjI9DgRK8avjmfJ0iXcfbOJiaOMVNtT7EgM4ocFVl5/73Pe++x11Kqmvd5nI2LUx4Y/NqAW1QRKdeMGFSr8pRC2/rkVW4UNg9fJdrhxnUL59ps1WC3Hef/FQIxelQAkpQRSKIZx9c2hfPBFKd+tWF2rK4Gvs5KlS5eSeDAJtUpFn759mDhpIkGBDZurn74GO13kKCt1AO5EKzwIGxIuHJIDo/Hvtf4vLSnlmWeeITMjE1/8MeBLcVk5cz+by8oVK3n11Vfx8fVBq9Py4EMPcuNNN7F71y6qHFW0j25PXFxcnU4UsiSzf//+mripR8+eZ5zgKYhCsyqiUlKOYrGWE+ohrgQwE0gGR9m9e7ciaihcdLSUoHH6b0FLxJl/r199hX8sJrMJk7muYXiLIcOrr75KXlY+HelR05ZHQqaAbLJIxZcAyijCQVWd0lEJF8XkISERTBhBhKHmpKGVGi1GTOzfv/+sRY29e/fy/KwXkF0SZgJRoSNh6x62bt3KFVdcwa233NqiragudJr7BdxaG3mnlkrXR0sJH56yg5orbFx/w/U8mfAUqUIioXI7vPDGiZMicskhjTGjxxAeEX7Gc+zWvRtvvPk6BfkFlFssBAYEtI4gKUBgUGCdh3V6HddMn87XX3+FCrHmM+mkilwyKSKPe264B5W6rtF4S7F/334++O8HZOee/Nsw6A1ce+21XHnllaxbv57P5nyK1WZFI2pxSg4+mTOHa6dPZ/r06f+oz7FC81EEDQUFBQWFC5Xdu3bz/fc/1CS3GHQGxo4bi4+PDzabjS5yb9SnxFNadETSiWMkcpjd+NMGDRoslFIiFBAd3Z5rp197vm6nVcjKymbJksXcdp0v0yadrOQf3M9At846HnqukB9//JEHH3qw0bF0eh2TL53Mgl8X4IU3JvxrWnlZKSdHTKNfn36EhoaCDIsW/s6A3gYmjXZvpCemueMIbyPce6+d/zyXydatWxk6ZGi912yNzatTKSkpQSvoEPG8VtfjhSRLlJWX1RI1ALZu28KQ/lqMXicTt+LaF9T8u2tsMUkHt3DH7dcA8O23i1mxcgVqQYNeMoFTZumff/H7woU899yz9OrZq8nz9iRydOo6mKLsYkyFdQ2xC8nDJbsafK0vRj744APysvLoSA/0p/gs2qggNeMgH3/8MU8+9WTN4wEB/owdN7be8Xb+tZNPPvmEvPy8mseMXt5MnTqFq6++Gq2ucZ8UWZLZvXs3q1evJjc3F7PZzKhRoxg0aFCjMaHLJQHU+/coICAIIi7J1eg8FBQuJFpC0GipqgxPKKKGgkITOHT4EEmHkmhPl1o+AyICwYRRQTk2LAgIpJBIFJ1qSqMdVJEhHMUluwgmnFAP2TFu6isHbjpWq5XnZ72A3uVFNJ1rDMlDaUcB2SxYsICOsR0ZMnRIo2NlpGewYsUKUlJS3D1UBw5kxIgRF41B2Zn0d23NjbymjFmf8HE2Ykf1vVdXbzRV3IiNjeX5F2bxztvvcrh4D2pRgyS5QIQJ4ydw5113nvGcTiUwKNCj6HAuuPqqq3BUVfHzTz+TL2ehE3XYXXZUahW33HgLkyZNqvdcySWxafMmVq5YSVZWFj7ePowcNZLx48c3qYVcUtIhZs6chUEy0oGueOGLAzv5ldl8+dWXHD16lI2bNuJPEJHEoZP0OKgi35HFt//7FlEUufqaq1vw1VD4O6IIGgoKCgoKFxrr1q3nvXffxSj6EkksarRY7aWsWLYSUS3gK5trCRrVmPHHjyAs6hJypDQkyYWf2Y/rJl/H5ZdfftHEKE1l/bp1eBvVTBpTN0Pfx1tk0mgDPy3awD333tukbPQbb7yRzEy3GGEUfdBJXjhEO+VSCR2iO/Dwfx4GoLy8nONp6Vwzxa9GzADoeEnpiX9piAjTsX/f/lob7WcrYiQUNJ4AdirOkABc7QKxyoY6VeUAVirRi+047qwk+8TYPQPdc6qy2/E21j2nGh9vkbQsdyvaxMREfvrpc4IIJZS2NVVCTt8Q8oMMvDnvax6435dBnWKaNf9q4npGce2t03j31U/ICVChRU9lYRYiIjIyBUI2Y8eOIyi44YqQi4nc3Fx27NhBBDG1BA0AA14ESRH8ueVPCgsKCQj07ANzKgkJCbz00kt4yyZi6YYBH+zYyK/I5Icff+C3337j0ksv5YYbb0Cn8/w94XQ6ef31N9i27eTnI0fMZ8dfO4jtEMsLL77YYLV8u3aRaDRayhyFGKjbqqqcElyyk44dOzV6PwoKFwot5cvYmnGlImooKDSBXTt3oRV1+Ehmj8f9CSaFAkwEUC4UkyTvwij6IiBglcrQaXXEhLcn91g+slQ3A8NBFVbK6Nqt21nN8+uvvsbpctKOjjWCBrgzA4IIo4RCvv/++0ZFjQW/LWD+l/PRiToMkg8uwcnOnTv54YcfePnlly/okskLTcxoDp6un3Qou2Z+ZytuNLdqo3v37nzxxTx27dpFekY6Br2B/gP64+9/8fQrzs/L59DhQ4iiyCVdLqld0SW4K1ImT57Mpk2bKC4pJsA/gGHDhnkUJuyVdjZv3kxmVhYb//iDnNwctOjQosdZVM7XX3/D4sWLee211xptBfD1V1+hk/VEy51rAjEdeiKIRkRg06ZNmPCnLbE13xcatIQRhYy7v/Kll15aJ/NMQQHc34OKoKGgoKCgcKFhtVr56MOP8COYtlJMzRrHFzN+UhCHq/bg5WGDuhodBpz6Sr755huqqqrQ6/UtU7kqQ0pqCoUFhfiafOkY2xFBPL8lsUVFRYQGq9FqPc8jMkJNlcOB1WrBbDY3Op5arebpp54mISGB+Ph4cnNyMZlN7kz0wYNqWnfJyHgHtCOv3JvA0FPFjJOIAsiyfNabVqcLGd0imz5GmHEEy374mmLJSRuhdpsgp+ykQHWQAUP60SfWnVC4Ly275npBvbrzV0EqPUp8EAToacqvOVeWZXbvcxIb5zaAXrRwEV6iN2FSdK0YXl1WSRC+JAo72Z2wG+MpG97Njdl69+7NkLF9WLFiGb7qYEzReioqJQqzj9EmOITbbru1WeNd6Bw+dBgZuV7jej/8yeQohw8fZlDgoIYHk2Hu53Mx4lurNZ0BLyKJBQRKHPks+n0Rhw8f5sWXXvIoAn77zbfs2L6dKOIwSf6nmHyXcywlifffe59nn3um3mkYjUZGjRzJ2jXr8JH8aiXCOqgiR0wjOjKauE6KqKFwcXAmgkZrV+h5QhE1FBSagEtyIQpiHTGimupNyUoq6N6jByHBwRw5egStRsvgwVcxbtw4EpMSeeGFF8glgxAiasZy4SJNSEan0zFmzJizmue2bdsw4lOn/VU1fgSRln7ULazUs1Dfvm0787+cTwgRhEht3fcmg51KjhcfYtasWcyZM6dV2/KcKWfzxXuhbuKdOq9TKznOROA4E2FDVF3Yxu/1UVpSygcffMCOHTtqTBFVoorRo0dz1//dVStLx2Q2cemUSxscb9269cz55BNslTZEVEi4EBEREKmgHBmZADkUa0kJs1+dzXvvv1dvkF2QX8D+A/uJpKPHzDJvzOSRSSBhHr9zgggjvyqL7Tt2MGLEmfmaKPx9Od2Msqlc6N+FCgoKCgoXP+vXrcfpcBBKZJ01jh4vdHhRRjESUp01koxMuVhCXMeOiCoRvaGuYfiZsH/ffj7//FNSjx2veSwstA233HIbAwcNbJFrnAl+fn5syXNSVSV7FDbSMpxoNRqMxsYrhKsRRIFevXvRq7fndkkHj+aCDIEB/hzKPM7lV5jrPCc900GeHMKQmI5A89YNZyNinE5AQADXTL+GH374EYfsIJA2aNBhoYQ8MRO1XuSGG27weC3nmOHMnLWGzCRfJo7xJiHrZBXE9p2VFHipmTRyGNu2bmPb1m0IkopjJOFPCL741fztqlHjI5s4lrKnplXVqQlp0LSYbeeunSxbvoyRgw38+xoBfz9I3OvHgUNe/PrbIT768AOeePKpRsdpCk6n0+2Fch40O8kl8ddff7Fly5YTj3juVCGd+K8g1i9wVnPs+DHS0tNoT5daXjvVhBBBEbm0IZKDBw+yZvVqJk6aWOs5lbZKli9bTpAchvk0ocWID22kSLZv30ZOdk6D3o+33XYbR44c5UjqPkwEYMCbKiopEQvw9jbyxBNPKO2DFS4Kmpsg19JVGQkF2VRarU16riJqKCg0gdjYWCpdNiqw1FLdqymlCBEVdmzs37efBJcDtajBKTlIS0vH29ubMWPHcNONN/Ht/76lVCzAKJmRcFImFqNSi8x8bialJSU1P/KXdOnSbM+CiooKVPUIGkDN5m5D/Pbbb/iIZtpItYMNHXraSh04lJfAtu3bGDx4cLPm1tpcSIJGwmklzi1F9TzPpnrjTISNiw2bzcZTTz1NQU4+EcRgIgAZiWKpgHVr1pGbk8uLL7+I2ISFMriFvnfffQd/gjDiRwFZhNKOQEJRocKFizwyySWdACmElGMpJCYl0rlzZ4/jlZa6M970NFxlocVzebQWHSIi5eVlTZq/wj+HMy0RVgQNBQUFBYVzQWZmJgaVNxqX53glgBAyOEoWxwknqlYsUkA2VqmMKVOntNh89u/fz8yZM+gYo2bmIwG0j1KTkeVkwbISXp39Kk888UQts+hzyahRo/jxpx9Zsc7KZRNqx58Wq8SKdTaGDR9+xkbIp1Kr0r1XFBMuH8Unn3zCmo06Rg8z1LwL5RaJd3424eOrY9plIxs1ZvfUUupshIzTueGGGzAajfz0488csuypebxLpy7cd9+9hIV57i7Qs1dPRo4cwSdfbWD7bjtDB1YgSTKbttrZvb+SaZddxublC0nJL0XfLhotehxUkomFImRCiMAr1VYzniyfjLFPXUs1NWb78Yfv6dJRx0N3mmtM2Tt3L6Vzd5DlTiyOzyQjPYOIthFn9DpV2ipZtGgRy5cvp7CoEK1Gy7Bhw7jyyitpG9n2jMZsLomJibzx+hsUFhWiEdwxTjH5BFH3PSomH7Wopks9sdSplJSUAO4qLk/o0CMiokKNSfBn+fLldUSNI0eOYLPbiMRziy8/gkjnKHv37m1Q1PAyevH666+xYuVKVq5YQX5eFt4+3lwx+nKmTJmCn79fo/ejoHA+OZ97aqf+XnSLDMVaXt6k8xRRQ0GhCfTr24/AgEAyCo8SQ9darZ0slFJIDtKJnAKNS0dHeqCVdNipJLcinff/+z4arYbp106nZ8+eLF22lENJh9BqvBg7cCSDBw/m66++ZtfuXZyU72V69+rNww8/3GQTdC8vI0X2Qqqwe9wQLSEfg85Qb5WG3W7nYOJB2tLBY4a4ASNG0YddO3ddUKJGS375NrefqyeqF+sJaZ7HOlux42zFjYtd2HA6nWzdupXDhw4jiiK9evWiR48eNR+dVStXkZ2dRUe5Zy3hIJgw9LIXe/fvZceOHQwYMKDxi8nucmQfzIQRw0F2EEQYIZwMKlSoCCUSJw5KKUQtaDhw4GC9ooa/v7ukuZIKjyKp6kRmYgWWGm+eU7FhRUIiJOTie+8UWg9F0FBQUFBQuNDR6/U4ZTsyddvxAjWPFZCFTSzDVwpAQKRcLKJcKuXyaZfTp3eflpmMDHM//4yOMWpeejKA6v15P5OKrnE6Xv+wmLlzP2PQwMZNgluDsPAwLp18KfO/X0ZxicSEUUbMviK799v57jcr9irtWZuj19e2d8LECRw5coQP5sazdLWNHpeoyawKJTHZgcOex4svvlSvoHE21Rj7y9ObdwNAh9F9eHJ4D46npeGoqiIgMJDAwEDKThuvq8/JzfslS5awfv0GvAwiyalV7Nzr9s/QalTcfPO/SU9PJ+lgEu2kjvgI5hNn+VAul5BKEuoQLUJ0GyQkLMfL6Np1vMe5NaXivqi4iKSkwzxyz0lB41Quv8JG/AYVy1ZvZejwYc2O3SqsFTzz9LMcP34MsxxIJLFUOexsXr+FTZs28cILL9Dlki7NGrO5ZGRkMnPGTLROAx3pgUUuJYtj5HAcA0a8ObnXUU4puaQxZsxYfE2+jY5d3Ra5kgqPcVMlNiQkNGgxyD7k5OTUeY4kVZt811dGISAg4HLVb/Kdk53D2nVrKSwswmw288yzzxLRzARVBYXzyfkSNE4XM5qLImooKDQBUSXywIMPMmvWLBLZiR9BaNFhoYxSClGhJpIY8snCgLFGUNChpy0dkHDx1ZdfMWzoMDp26kjHTh1rxrbb7Tzy8KPkZeUSSSxm3KbJJRRyYM8Bnnn6GaZMncKqVatIT09Ho9EyZMhgLr/88jrZGmPHjeHnn37mOIeJJg41GsBdoZFLBlbKGT14dL33Wf2DLjTQy1ZAxCXV/4N+rmnul29DX7zVX6gtlUHkaZxT+7meTnPFjrMRN87UQPx8c/jQYV555VWKS4rQijokWeK3Bb8R2TaSWbNmERQcRPyqeExygMdKCF/MeIu+rF69pkmiRlZWFsfSjhFNZyyUIOEiEM/vUyChFJKDCrXHoKQaP38/evXqRWJCEmY5AJHagXIxhQgI5JGOL361RFQZmRzS8TP7tVxQr/C3QRE0FBQUFBQuZIYMGcIvv/5CKYU1MU81MjJFYh7du3Tn+huu5/ffF5KQsBtJkoiLi2PqZZcxcMCAFmvfknrsGCmpx5jxyElBoxpBgGunefOfGfns3r37vLVhvfOuO/Hx8WHhot/5bdkpAkSnjjzx9IMNZo03RGMehKIgcv/99zN48GB+27CJP4/lotFomTCuMxMnTsTsZ6557tlUY+wsSCYxMQlLeTlGbx+uGTYMo7F+M+aG6B1efxXDntycGoEjJSWF//2xhNE3RjFyiIEe3qXY7RKFRS7e+qSMRQt/p6zMQogUeYqg4cZHMNNGjiAndx8BaCgIqUTTPoKIgY3/fdQncATgjqv9zZ7jb41GQPQqROXrBE6+d6fHblX2KrZs2cLxtOPodXoGDRpEZLtIvv3f/0hLS6OD3A0DJ1/bYCmMVEcSr7/+Bl98Ma9Vhbtff/0FnCLRUhwgclw8yJjBBrJyJRKT9+N9wpjbLlqxSBYEBK64/PImjR0ZGUn76BjyjmXiI/vVEibceyDpqNHgix9ZHMPoVffvq3379qhVGkpcRYRQV4goowgZiTgPCWuyJDN//nx+X/g7GlGLDgNVVPLzzz8xYcJE7rnn7iZ3B1BQOF+ca0GjJVsRKqKGgkITuaSLu0+jDgPFFCDhRIeBcKIJoA0gkMVxfE77WFWbdCcX7OPQoUPEdY6rdXzd2nVkZKTTkZ4Y8Kp53J8gDJKRQ5m7+WTOJ5iEAILkcJwOBxvWbGTd+vXMnDGDHj171JwzZcoUlixeQoXNwkH+wkQAKlSUUUwVdnRaHbfdflu992jQGwgPDac8uxh/D+WXVdipkMrpGNvRw9nnngtZ0KiP+savT+xoitBxNr4bF1PVRl5uHjNmzECqBBVqqiR7Tb/l9PR0/vPww8z/4guKS4rxpv7yXq1koKigsEnXtJ7o5ahFhw33v+vzrNGcEBFdspPu3bo3OO7Nt9zCo488SrJrHyG0xRsf7NgpILumFLuQHA6zx13ijjeVVFIoZGOVy3j6vqcRVcoCWcHNmfhoNHUh2hLVawp/b5ra81ZBQUEhpkMMfXr3YU/CXmQJTAQgIlCFnWyOY5MsXHvdtVzS9RIu6XpJq86lsKAAgPaRnrdEotpqEICCwqatGVsDURS54cYbuPLKK9mzdw92u53IyEiioqLOaLzGxIxTEQQBTZtwrr322gZjJzizaoxt27axbu06KlJT0YhaHJKDJXPncsONN3LFFVe0qPVAj5CTMdGCTz4hUpXLI5f7s7fIyAHbiZjBDFfe489H88vQtTHin+S5FZGZILJII1ncg1wg8+RTTzGwa2ytCv3G4rdTE9OyqyR8otrzR7KarnF1263kF7jIza8iPDy85j07PXbbsWMH7733LuXlFkKCdFisLv733f/o168ve/fsI0AKqSVoAIioCJOjOFSSwPbt2xk0uBFD7jNFhk1/bMJPCkFEhZVy7JKDCaPNdIjSsHWXjbV/2CgsLibQX2T4IDPvflbK/gMHmtxu684772DGczNIkQ8QTDheeGOnknwyKaGQSGKRcFEiFjBt1GV1zvf28WbkyBGsX7cBH8lUq5LeTiW54nE6d+pCdHRUnXN/+fUXfl/4O6FEESSFIiIiIVFILvErV+Hj7c2/b/73Gb98CgqtTUvuqTXG2VZleEIRNRQUmohWp2Xg4IHs3rqHLlKfOuZ1xeTjpKpO1hGA9kQpZFlZ3R74a9euxST4Y5C96hwz4IUvflRhJ1o+KYaESG05Lh/itdmv8eVXX6LTuytDzGYzs2fPZtas5ykpLaaUIgAkXJhNZl566SV8fRso4xRg6mVT+fTTTzHhX+teJCQyhVR0Oh0jR42sf4xzxMUoaDREfdeuXiA3tYrjTKo3LhZhY/GSxVRVOnDhxI8gQmiLHgN2Kskjk8LyHD76+CP8/Pwot9jqHceGFYtVJDcnl5A2Dd9vcHAwoiBikcvxOhEMWCjF14NoYsHtlREVGU1sx9gGx42OjuLGm27gq6++5hhJNY9r0NGWDgQQggwUkkMayTXH27eL4bFbH6FXr54Njq/wz6E1M2suhO9GhQufpva8VVBQUAB44sknePONN/lr519oRR0aQUOFy4pOp+OxBx+jW/du52Qe1e19M7Kd+PvVzVLPynUiA2ZT09oAtyZ6g77RCmNJkjh8OBmr1UJoaBhhYbV/u5uzXmjM9LW564PT20pl705gydx5BBJKFH3RSFocchV5VZl8OX8+Wo2GKVNazjulGlmWSUjYw23XeyMIAj0C8mo/IQB+rMqnQBuKFOeFU6gd76uTTiZUde/VgzvuuIOIcHdmf/VrcWqiWlPFjX59OrFjdxJ/xAUyPLag5rgkwVc/l6M36Bk2bNjJ806J3TIzM/nk3Vfp3V3LbdcHExqixumETdsrmPPVbhwOl8e4BdytpXUqA6mpqa0majhdTuyOk62x5RMtu40GAZUKhvQzMKTfyep6WYYPvyjDUVXV5Gtc0vUSXnzpRT755BNSMg7WPK5FRySx6NCTKiZi8DIwZcpUj2PccccdpB47xpGj+/DFDwNG7FRSKhTi5+fPY48+WuecKnsVv/3yG4GE1arwEBEJIhQnVSxetJirr74aL2PdvR4FhfPNuRI0WkPMqEYRNRQUmsG1069lx46/SJUTCZXb4YU3Ei6KyCeLVIyYPPbIr8ACQHBIcJ1jJSUlaGTPhsDgNr2yU1nrMRGRMDmaJNsuNvzxB+PHj6s5Ft0+mvnzv2Dr1q3s378fQRDo2q0rA/oPaFJZ6cRJE9m/fz+bNm/CV8jDWzbhwkmJWIgkOnnu6RkYDA0bHLc2fzdBoyFO9+c4U3Hj7yBsbNywEXBn9UUSW9N32d3mLQYZmY0bNnHTv27k66++ppIK9NReQJZRgg0Ljlw7DzzwALNmzWowE9BkNjFgwAAStidglrpiwJsc0jDiW6stlBMn2aSh1WiZ9fzMJt3PtGnT+H3BQlxlEoGEokaDF741GYvlYiEjhg3nX//6F/kFBZh8fQlvRm9WS7kFSZLcQmZLprspXDAogoaCgoKCwsWGwWBg5qyZpKSk8OeWP7FVVtI2IoLhI4af0xgjtkMs4eGhLFhWTLfOujqtQ39basHX15s+fS78dp9r1qzh++/+R17+yQ3xrl278H933YPVddJnoLH1QlPFDGh8fXC6kFFdLeF0Onnr2//hTzARQvua4xpByyXd21Igq9mQsJBeo6IbNSJviBBdpzqPybKMJEloNPVXO5t8BLKS07ChI0CoHbtXxhkpkyvwFkIZf8fVNYLGqXgSN6DhGO7GG29k777H+XhuJnsHRxEZrkFbls6C5RXk5juZNHESyLXPqX4vf/zxB9p1iGbaNB9CQ9zrQrUaRg72QqMReOPDYiqwYKRuYqOEjEt2otFq6p3b2aJWq/H3C8BSXIY/wejxQkRg195KIsLq7pvsT7Jjr5JoE9qGpKRDaDQaoqOiGq1Q79qtKx999BG7du1i3rx5pGekIwkSOUIaVZKd0OAwnnnmaQIC/D2e72X04vXXXmP1mtWsXLGS/Px8TL4mpoy9kYkTJ+LtU3euBw4exGqzEoHnLhYBtCHXkUFCQgKDh1w4fqQKCtD6gkZrChmnoogaCgrNILp9NC88/zxvvfkWh0v2oBV1uGQnkiyh1WpRVYlIyLV6OUpI5AmZtG8X47FUODgomNT8NE4kLdShAqtH028derxEH5KTk2uJGgAqtYohQ4cwZOiQZt+jKIo8/vjj9O/fn6VLl3H8+DG0Wi0jB49g2mWXNbkMtLVoqS/fhjbszsSkrjFONac7E7pFhjYr86eauE6hfxthw1phxYWTYMI9mksGE0aRlEtwSDBh4WGkZh8kWIrARIC7TzP55JKGD2bayXEcr0rilVdeYf78+TXVTp649dZbeXT/Yxyt2I+P5HeiLVQCgYShx4CNCgrIQtDA7NmzCQgMaNL9qNVqHn74P7z80svkkYm/FIKEhJVyisU8TP6+3Hrrrfj5+xEUHERJSQnffvsta9espaysnMDAACZMmMDESRNrbQJs/GMjv/76GympRwEICQph6rSpTJkyRenp+jdEETQUFBQUFC5G2rdvT/v27Rt/YmshwK233M4rr7zC7P8Wc+00b9q305CZ7eS3ZRbWbKzg3nvva9UN35ZgyZIlfPbZZwwbaOCR/wskKEBFYnIVPy86yguz/8stt97G0HGNez40JQkM6l8b1Cdi1Hne/v2UlpXSkZ4AmLs7ax03J+s4xEFcxwvo2acvILNt2zZWrlxBRkYaer2BIUOGMXHiREwmMyCzZ+9e9u3diyzLdO7cmd69eyOKVvaXZNS5/qBJnUgqKqKXS0uIqnY1QGmZi+RUJ20j2pKblYZR8kEvnFJFcDyfAtVB+vTvja+vb71G5Ke/To3FcCaziTdef4vvvvuOtWvXoNUFA2ZQm1EL2Sxfvpw1a9Zy8y3/ZuoplQaVlZXsPLiF//uXLyqVD4lpIXSOPNlibFAfA2bfUgrKcggirM51SynEKTno169fnWMtyaRJE/nh+x+xyWEY8MJMID8tKqJvTz1hbU5uSVqsEl98b8HH28jsV2bjlNx/G/5+AVxzzdVceumlDSdqCdC7T2969+nN0SNH2b17Ny6Xi05xnejRvQeC2HCWl1anZfLkyUyePLlJ92WvdCeeVrcgPp1qf1O73d6k8RQUzhUtWbl3Oi0hZuwvT6fS0rT2toqooaDQTLp268oX87/gr7/+Ii0tDZ1OR/8B/UlPS+fll18mlYMEyqEnNjut5ItZVImV3H3P/3kcb9yE8by9/y0slOJN7fJmC6VYKaVdPeq/JLhQq1re1EsQBUaOGnlBtJk6lTP58m1udUb14rS+hfiZcKo53ak0V+g4k7JmOPkaVPttNCRuXMgG4v7+AWTnZKFD7/G49oQxuL3SzuzZs/nwww/Ztn0b6bg39wVE/AgigmhEVETIMSRZd/HHxj8YN26cxzEB2oS24Z133ubLL7/kz61/IkkSdirJJAVwC4GDBw/mxhtubFYlBbgX3q+9/ho//PADO3fuREZGp9UxdswYrrv+OsxmMwDZ2dk89eRTlJdZMEsBBBJGZbaFr776irVr1zL7tdfw9jby3Xff8cMPP2AS/ImkIyICZflFfDH3C5IOJvHYE48pwsbfhINHcxVBQ0FBQUFB4SzoP6A/Tz71JJ9//imPzMqvedzX15t7772PiRMnnMfZNY7VauWrr75k8hgj//fvk3HksAEGfALa8cV35WzYvrxBUeNsW02dGuM0JX6yWCxEdvclBA0qwYkmuXZHAtWJZD6LxYIkuXjvvfdYv349nWL0DOmrpqjEyi+//MDSpYt56KFH+Prr+aSmHsffrEEUBX7++WdCQ0N49tkZdI1sV+f6l/UewTvvvMOeTrH06HIyqcnpgq/n56JSqXn6mad5+aWXOZybgEn2Ry97USlUUCIUEREawX333Yevry8Oh4O8vDyOlJezHxkQPMZ3TYnhfE2+3H3P3YSGhjLvi3kEEUqwbxyq0DicspPirAN8/tnn6LQ6xo8fD7hFDWQI8FfR8RJ3G9zEA+7YrXNkLqIIAf4ajpbZyCaNECIQEZGRKaeULDGV3j16n7FHS1O5bNplbN68hZT0A/hLIZgJItNWxn+ey2f0MC86tNeQk+tk9R92yi0ukCWCpAh88cOFg6LiPD797FMKCgu4+eabm3TNmA4xxHSIadX7ioyMBKCcEvw8+JGWU1LreQoK55vWrM5oKTGjmq7BTduHEmRZlht/2sVDWVkZJpOJ1Qu2YDTWLRFT+GdjsVhJTDyIyyUR26FDkzOqm8qehD188cV8Uo+l1DzWOa4Lt91+K5061S2BBXA5XcyYMYPEg0kEyWE1PhYlFJBHBiIqutC3jodHKUWkksjtt9/B1Kl//wzsi1XQqI89uTl1HmuuyLGvGYZ01SQ1Qdioee4FJmx8/933fP/D97Sni8fesFbKSWYvL7/8Mt27d6fCWsF1119HIKF444s3vqhPM/k+Iu5j0OgBPPDgA02aQ1lZGYUFhXh7e+Nl9KK8rByTyYTB6+zbJdgqbFRUVODr61snK/DRRx4lIyWL9lKXWkblNipIFQ8yZMRgpk2bxn/+8x9CiSSE2n9LJRRyjCQee+wxhg8fftZzVTi/tNaC9GISNIqKizhy5AiCIBAXF4ePt8/5ntI/Fmt5OdN69qK0tLRh366LHCXGUFD4+yK5JBISEigoKMBkNtG7V+8LvkIDYOWKlXwy52PmvRuMv/lkoltiWggdLyllxVorn35dztdff43JgzdIozGTDEeOHmXf5jWkHD2CRqOhb78BTJ0ylSIfF9C8uCnXfoisrCzmzZtHaLIZX6Huer5cLuEoB3jjzTc5lHSI+fPn8fD/mRk+6ORau6TUxczXi8nKdWHyEYmOFElOcWKvkggJUmOpAKfLwH//+yFm8+nXkPnoo49YuXIlnTvq6dtDS4VNYlOWPxU2mauvvobY2Fh8XJGsWrWS+PjVFBcV4R/gz7hx4xg/fjyiKPL9Dz8Qv2oF5eXubOKIiDAGXn45PXr0oKtPw5vY9cVwjioHN998CzqrF23pUPO4ZHK3083J3AUmJ198+QVqlRqXy8W//nUjY4cJ3HLdyd/fwwfc77XNJjP7zb1c0rUHCbsTUIsa9LIRl+CgQrLQpXMXZsycgdFY20S8NaiwVvD1N1+zZvUa7FXuygWj0RtRkCi3VGD0MhAaGk5qyjFi5W51WgjnkkE2x/nk40+anUTWmjzz9DMcTUwlRrqkpjID3K2JU8UDhLQL4b333z2PM1RQcHMuBI2WEDOqf1Os5RamdOzXaHyhVGoo/CNwOJx8MW8eq+LjcTjcZaYiIv0H9uf+++7H19QyQXiPnj14//33SE9Pp7S0lIDAAEJDG/5gq9QqZj0/i/lfzGf16tXkONIA0Gq09Ozak127d5FLOiG0RUTETiXpHKkxJZ43by6LFi7k+uuvZ+y4sS1yHxca50rQOBdiRjWerrUnt/4SZk+cqSHdxdqOavr06Sz8fSG5lel4Y67V5k1GJod0ggKD6NbVbTBZLfR54V3L9P5UZKRmCYK+vr61flRbMggweBk8iiNHko+QfCSZaDrXEjQADHgRKIWyccNGBAR0ooEgqW6LODMB+Ihmli5ZpogaFzn/dEHDYrEwZ84nbNq8GZfL3bdRq1UzZsxYbr/9DnRabSMjKCgoKCj8o5FBkmuv/0SVSO8+vc/jpM6MgoIC/M0aj4IGQPsoLbIsU1RUVEvUaFJ1hgx/rlrC4sWLiYnSMXaYhgqbk40bV7Lx+B6uuWY6Vw5uWqvjXPuhmn+P79KPJVXfkCdm4C2ZEE8x45ZkiTwxg/DQCDrGxvL2W28wfJChlqABYDap6NNDS9oyC2UWiZQ0kTHDDfj6iOzeZ+dYuh212sny5cu5/vobTpuNwH333Ufv3r1ZtmwJv69IQaNR069fZ6ZOnUpkZDv2l2RQrkpj0KTODJrUuZY/h8PhYMaMZzl6NJmJo/T06xWArVJm7cZifvnwY1TXXgtjxwD1x3P1xXB79uzBYi0n4hRBA0AsrUAyeeEX1oVsjnFg/wF69OiBSqVi3LgJrFy5iHEjvAgPdW/vdbykFBn44AMNBnMUjzz8CDabjVWrVpGdnY2X0ciwYUOb1JKppfAyenH33Xdzy823kJ2Tg1ajISwsDEEUkFzuz+O///Vv/OTAOoIGQBBhFIo5xMfHc8utt5yTOTeF+++/n8cff4Lkir34ScHoMWKngiIxD1EL7aIiuf3W27Hb7URFR3HppZcyaNAgxe9Q4ZxyIQoaTW1Z2BiKqKHwt0eWZGa/+iq7d+0mSA7HjyBEREopYtf23TyZ9hTvvPN2kzOtK6wVFBYW4uPjg9nPXPcJArSNbEvb0zKlJZfE9h072L1rF06Xi04dOzJ8+HD0Bj06nY6777mbf/37XxxJPgJAh9gOGI1Gfv31V7766iuKxXx0kgErZajQEEEM3phwUkVhQS7//eC/lJWVceVVV57tS3ZB0dqCxrmszmiMU+fQHIHj9IXx31XYUKlVPPPcM8ycMZMj8l6CicCAETs28sikQijn4Xueq1mc6w16YjvEknu0AH85uM54NqxYJQs9e/Y8x3fSPJKTkxEQPVangNs4PUs6xpHkI3hJ3rXEnlPxlkykpaW15lQVzhH/VEHDbrfz3IxnyctN47brvRnUR49Lgj/+tPHT4nhycrKZNesFVH/zykUFBQWFMyXteBoLfl/Als1bsFdVEREezqWXXsr48eNRqVu+pe2FxP79+1mw4Dd27dyFS5Jo3z6aSy+dwtgxY8/Zxm5L42syUVruxGKV8DaKJKbVXqtnZjtPPO9kQk5ja4PqNUFJ+hEWL17M3Tf7MnG0EUEQOGDzo99lEr8us/LD++8xvnsPvL3rr1w7Vczoaj6ZdHPvfffy3LPPcZT9BEhtMGCkkgoKxGzsYiXP3P8URUVF5Obmc8cNno2dEw9XoVJBt85annrAH63W/R5ePsmbrTsref2DIlas8CRqAAgMGjSYQYM8mzefOtf9JRm17mPbykMcPnyYV572Jy72ZCJF/156fl6k5tsff2T48OEUaTU1cWZTxQ2LpRwArYdWu2JpBRqc4AuJeYX0OPH4Nddcw19/beeJl3KYMs5Ajy46SspcrFxnY88BO9dedjOZhZV0iWnDv2/+t8d5nEv0Bj3R0VG1HhNVIg6Hk5KyEiKJ9XieiIhONpCbm+vx+PkiLDyM9957lx9+/JEN6zdQ5bCjVmno3r0bBw4cYPOGLfhKAXhhIPXAcWbvm83oUaN56KGHLtrvHYWLiwstIa65LQsbQxE1FP72JCQk8NfOv4imMyZOLooCaYO35Mvh7D2sWLGCK668osFx8nLz+Oabb9i8aXONaVXXS7py4403cknXSxo8Nzs7m+dnPk92bjZeKm9EWWR1fDzzv5jPU08/RY+e7mWJ0Wis+Xc1V111FQMGDGTlyhVs/GMTYrGajnQ/JWPbgDcmNGj55ptvGD1mdE0f/oudf5KgcTrVc6r242hO5UZCWuNVGxersNG9e3denT2buZ/P5cjRpJrHoyKjuPX2R+nVq2et51955ZW8/sbr5JBOMBE1G/5V2EkXjxDkH8TAgQNbfqIyWKxWBAGMXsYGs3EkSSI/Px8BgaCgoDoLXJVKhYyMjIRA3Q0HCXf5v06vo4Tyeq/jwIFOW78husKFT/V3YnP4uwgaAKvXrOZYaipvvxhEdOTJEv+rL/MhJlrD82/uYceOHQwcMOA8zlJBQUHhwiQhIYGXXnwJlaTGJAWiRktZeglz5sxh27ZtPDfjOdTq5m0POKoc/PHHH8SvXk1xYREBAQGMHTeWYcOHo9FcOFsN8fHxfPjBh0RFarl5ujd6vciOPTl8+MGH7Nu7l4cffuTMNxjdVgrnhWFDhzL/i3ksjbfSvbc7u7+6SsPphCWrbHTv3pUAf3fL5ebES0989DY9LtEzaYxbtDhgcyfX9GlTQMxVLm7f5GDNmjVMmzatzlj1iRnVxMV1ZvZrr/HVl1+yd9/ek8/t0pWbb76ZTp3iKCwsAECWPN97fqELSYJ7bzHXCBrVDOyjZ3A/A9t3lXO2b9DpAsfOo6sZPNGPKodMuUXCx/tkIsUVk7xZHO+uiLj99tuB2t6KDYkb+9KyKTd5o41uizW1HB/qtguroJyysnR6dIuqeS+7h7dh9muv893/vmPhijX8+LsFgJgO0Tz99HXuqgAuTL/EU9Go1ei0OuxVlR6Py8g4hSp8fC+8dqNBwUE88MD93HPP3VgtVlRqNf93111oHQai5DhU1fGb1JZi8lm3bh1xnTtf8J49Chc/F5Kg0dJiRjUXzkpDQaGViF8dj1H0wVeqm+WsxwuzHMCqlfENihq5Obk89tjjVFrsBEttMeKNnUpSE9N49rlnefbZZ+nXr5/Hc+12O889+xyWogo60gMvl3thWIWdjMqjvPTSS7z3/vtENNAbMiIinJtuuolly5YTRJs6LWgAgomgUM5h/br1XH7F5Y28KhcP/0RB41ROihsNL4ZPpXph3FhLqotV2OjSpTPvvPs2GekZFBYWYjabadeuncd4ZcjQIVyfdj3f//A9xWI+RskHF07KKcHka+aFF15o2cxEGVatWsXvv/9ORmYGAO0i23H5FZczZvSYWnOUXBILfl/A4oWLKSopAiA4KJjLr7icSydfWhNc9+zZEwEoIp9A6r5PxeRjNBgZNXoUnx/5HDuVdczUXbgoEwuZOExZPF/sNHdR2hAXk6ABsGbNKvr3NtQSNKrp1U1Px/Y61qyOV0QNBQUFhdOwV9p5ffbrGFw+RMmdarz6gmhDGSUkJCSwcOFCrrrqqiaPabFYmTFjBilHj+IjmNHJBtLyMnnv4HssX7acF196sUU8x86W/PwCPv7oI8aP8uKem00IJ9ZiE0Z5sWmbjTc/3kCvXr0ZNXpUk8csKy1j0eJFxK+Kp6SkBJOviTHjxjBt2rRzmlxm9jNzxRVXsnjddvKtFUy/1o6MyOGjVfzvVwvHM5y88sq/mlydUb0ekGSJpKTD/N+/3RUe1YJGj4A893VNKi7ppCUxMbGWqNGYmHEqsbGxvPzKKxQU5FNcXIzZ7EdQ0EmzZX9/f0JDQ9i0vZwBfepWLkgSREVqCAzwvI4f0FvPpm02Kmw2vAx12xk1hiS5yMrKRpJchIaGIYoCuxevJXPrAWRkjqWFoVY5ifFxcOsNvhi9RNQagbgYNZknYgBoeqJat8hQuka2YfHiRRQIdowpcq3qawmJPCGDqLZRxMTEgECtOO7uu+/mlltuoaCgAJ1eR1BgbePquJ5RJO0+xpJVf7JnxxrSjqei0+kZOHAIEydNxN/fc0XMOUOA4SNG8MeaPwiWwk8KAScooxibVMGI4SPO0wQbR61WYzKbWB2/GovFQhy969yHH0GUUsSihQuZOGGC0oZKodW4UASN1hIzqlFEDYW/PYX5hWglPUI9vxg6jBQVN5z9Om/ePOwWO7FS1xqjYSO+mKUgjguH+O/7H/Dll/M9bo7+8ccfFBQUEEcvdJxc2GvRESXHcci1m8WLFnHPvfc0OIey0jIcjioMeC7xVaNGL3qRl5/f4DgXCweP5v7jBY1T6RHSpkmZPtU0tSXVxSpsAES0jSCibcMBE8D1N1zPgIEDWLZ0GceOHUOn0zF48DWMHDWSnTt38sGHH3L82HF0Wi2DhgzisqmXERYe1vwJyfDRxx+zcuUKzATSjo4AlKYX/j975xkeVbW24Xv29JmUSe8hlFBDKKF3pCMqduxyFD9U7A0sVCmKgooFOyiiotjovYfea0ghBEghvU6m7f39GJIQZhISCAR07nOd6xwyM7tM2Xut9bzv8/Dxxx+TlJjEqFGjQGa3xZs5cybbY7fjhR+NaAlAXmYmX3/1NcmnkhkzZgzIwD/An27durFzxy7Uora8cktCIpcsskjj3tvupX+//vyx+E+Sc08QJjZGh72SyUQpZ2WJCEoZt912W+3Py8UNQW26NGoyKL3ZBA2A7OxsOrauWoSMCJeTdPbfcQ904cKFi7pk0+bNlBhLaE7zckGjDA8MGCQ/li5Zyl133lV9x4J04X9l8Pmnn5FyKoVIWqOT3MsfL6aQxMTjfDF3Li+//NK1OaFasHrVKlQqGf8b4VEuaJTRo7OWtZtLWbZsSY1FjeysbF5//XVyc/IwiL6E0IjSAiN///kPG9Zv5P3338M/wNH29FoR03UAJTYtq7f+zartZmQykCQICPRl/ITnsXjYBYnq5krgOB4QZAIZWl+OGrXlYsbFWK0V+XW1ETMuxdfXD99LFuABZDKBO+64k7lz5xLdUkX/Xrryzy8r24axVEJrkhxeV4bJbH9MLtSueEmSRP7++2/++edvsrKyAXD3cMPT3UBqahp+BOOFP7J4GYWRIgkFWXy82Mwr96lRq2Vk50gEhztm7tWkUE2GjGceuY9pc74muVE27pIXbqdMlFJMlpCGWSjlmWefKV8IL/tMD8XZP8fokEBCQ52//5IksW77Mrav30+gr5KoaG+k0jP8/ddvLF32D5MmTSEy0rn10/Xi7rvvZuuWLSSZjxEsNkCHOxIiuWSSJqTQtnU7WrWq3iGjrjCWGImPj0eURBo3alyrDpHjJ06gF9xRi45iHIAn3pw+d5LikuLrEtLu4r9FbcUMqHtB41oLGRfjEjVc/Ovx9vEmRTgHVbSumijB4Oncqx4gLzePXTt3EUzDckGjDAEZgVI4cQX77ZYXXR1tbHZs34GbzBO15FipJCDgKfqybdu2y4oaer0eQSZgkkxOHxcRMUsmPNxvvJbM2lJbi5VrKWhcPEi/Gi4OmLtSrtSS6loIGzcbjRo1YsxzY8r/LYoisz6czeYtm3AXDHiKvtiMVtatXMfaNesYP+EdoqOja7WPvfv2smrVSsJogg8Voo+X5EcmaSxZuoSuXbsS1TqK7du3sy12GxE0x4BP+XM98EKPJ6vXrKZX717lx/D8888zOW8yR48dQS94oBRVmAQjRrGYXr1688ADDyDIBd6d+i6TJk7iZPohdHI3ZMgothXirvdg4lsTCQisfzHKxZVTm8FpTYLdaiJoXBriVp8YohsQbyvgqNG5uJ8il6NvEXJDHfN/gdKi4vo+BBcu/vNIosShQ4dIS0/HTa8npkMMWm3F3CMxIQGd3B21zfkimwdeJOecIL8g32mnwaFDh/jzz784eOAAoiTRsGFDkhITCaZReRFFGXrcCRBD2bx5E/8bOdJ5BuF1JCkpkagWSjQa52JNh7Yqvv/5VI23N2fOHApyiogUoyt1xvqLIZzKP8rHH3/M1GlTr/q4a8KxxAwQZIx6/hEeevJudu/ZTUlxCUHBQURHR3Mk1S5G1GauBHZBo+XgzhyNO8sT/Qq4tJz8fKaNY/EmRt8VVj5Xqq2YURWiZVf5/x/c35ekpIF8+u1qlqwyEtVCQU6eyO79JrRaHefSikhKttAoonIHpyTB+q1GWrZsgVpdG+tViU8//ZS1a9fQr6eOXl19UMhl/LOqiB17z9rH7bKKcbsmAfSSxFkpi7VHIchfICHZxH0P9qhyD5crVGsd1Zp3Xnya+fPmkZxzjsyGYD51hqgWUYwcOZLIpo7Cw8VzOcDpfG7N2jWsWrmKZ/7nyYDecuKPugPNePz+NN6dncfUd6fw9dffolQ5dsNeL4KDg5g6dSoz359JfMZhFIISUbQhIdG9a3eef/75a97ZYDFbmP/DD6xasRKTxb7uopAr6du3D0+OerLSdbUqBEFAklUtuEkX1GHBlQHnoo6pb0HjeooZZbhEDRf/em7pdwvbYrdRQK5D0K6JUvJl2Tw40FmAmJ309HRERPR4OH1ciw6loOJc6jmnj5eWliKXqv6pKVBiNpsvex46vY6YDjEc3XscH9Hfocoqh/NYRDO9et+4LZk1obY5GnUtaFwqYtTFAP3SgDm4OpGjtpZUNRU2wF7pUxNh49iB5BuiW+NKWblyJVu2bCaCZhhE3/K/B4phJEtxTJ86ne/nfY9G63zy74wVy1egF9zxFh2r83wJJFc4z/IVy4lqHcWKFStwFwwYRB+H53rjT7aQzoIfF9CyZUsUSgXt28cwdepU9u7by8YNG8nLy8M/wJ8B/QfQsmXL8gF+cHAQX8z9nD2797B//35EUaRZs2b06NEDldrRts7FzUFtuzRqMiitjaBxo3S1DWjdjvnzv2dUf19CgirfV4+fNJOwPYs33rj3hjne/wrFuqL6PgQXLv7THDxwkDlz5nA+8zz2AYGEWqXm3vvu47577wWZ3RZFxIqE5LR7vSyfS6FwXNBcunQpX331FXrBHX8xDBkyUhPTkJDwwnEcA2DAl3PiKU6cOOG06Ot6olCqKCioeoGxpESqcf5HRnoG+/bvI5xIB6tPFSr8xVAOHznM2TNna9RNfDVcOmfS6XSVrHmupJsdKu79nbt0Zd6M91jg58YDd7mjUNi/N3kFNj74Io/mnf2Jat26TuZKFwsZ4bomlR575v9kDBkUwL69e8jOysQ/QMVrL0cR3bo133//LSs25HL/cDd8vOwdGVarxJYdRnQ6E/ffF11p24KyU7XHceTIEdasWcNzTxro36vCsmr5OhlaQYen6GjRpJO5oYo/S2xOBrbQIDoPiiQ82vm6QRmXFqpB5flcyxYteO+990jPyCAvL5cskwxPgweRl8lLBJwWqkmSxJJ//qZTey0De9vPq2mrfE4e9eRsThDPjxJ55o3zbN22jb59+1R77NeaJpFNmPvlXA4eOsippFMoVUo6xHQgMOjaj+1EUWTatGkc2H8AXykYL3yRIZBny2LD+o2knE5h2vRplxV+2rSJZtWqlRgpQYuj9VmeLIvIRpE1EkhcuKgpN4qgUVfzsPOm+Bo9zyVquPjXExMTQ3TraI4dOYa/FIY3fsgQyCebDOEsvr6+DBkytMrX6/T2G5EFk9ObkhUrNtFapVdnREQEJ4+dRBQr+2KWUSTLo0F4gxqdy4MPPMjr+1/nlHScQCkcHW7YsJFNOhmcoX+//gQH3zxWIpdSV4JGdQP0mggadVVpVMal27tU5LhSgaM2llQ1ETbAfkO70YSN1HOp7Ny5E5PJRHiDcDp36nx1ORgSLPl7CZ74YsC30kMCckKkRpww7mPTpk0MqkWAW/KpZPSih9PFAhkydKIHyaeSAThz5iw60Xm1uf25bsTFxXE64QyiZGPRokU0jWxabX5P+TkIAp06d6JT5+onbi5uLmpjx1cdN6ugATBgwEBWr17J2zPO8/A9erp11GKzSmzeYWTB78W0aNGMLl3qd/HMhQsXLq4nx48fZ9LESWglNyKJRocbFsxkmlNZsOBHrFYLDz74IDExMSxZuoRiCnC7JIBYQiJXlknTJk1xc6tshXL2zFm+/upr/AgmWIwoH+MoUHKauulmvtZ07NCBOXNiSU23EhxYefnDZoMN20x07FSzLKbEpEQAh0K5MjywL3onJiZeU1HjcnOmqhaqLrcwdfG9v01AILKRI/n+++9Zv81E+9ZKiksk9hw00ai9LyNGPEh7v4ZXfA5lYoPNaiW/oIAwXePyQPOLCdc1IbxlE3q37O/w2PMPv8GE8W8z8bVTtG6hxt1NxpETVvILrIwc+Tj92lbM8VNKEioJHGVcLHSsWrWK0GA1/XpWntdnZIpoRU9kl/qXXUAruZGelUPLQHfGjnqZM1JR+Vyvunne5brwAwMCCAywz7VqkpcIzrs2SowlnD59hrsGGyo9t0zYKDCH0KSZB0ePHKl3UQNAJsho27Ytbdu2va773b17N3v37aURLSv9xgMIxU305GT8YTZs3MjAgQOq3U6XLl3w9fHjbG48DcTmqLB3C0lInOccBVIuo+8edU3PxQWUGkvJys5Gq9Xi41PPmTHXmPoUNOp6zlhbpxSXqOHiX48gCLzzzjvM/XIuGzZsIFWsaC9u27odL770gsMA/mLCw8IJCQ4lOzUddwwOC5bZpINAlQspgwcPZsnSJWSQQiDhlV6fRw4FUi5PDBtZo3Np3KQxkyZPYtaHs4nPPoRcpkCUbAiCwNAhQ3niiSdqtJ0bkdoKGs642Bf2YupT0HDGxfu42i6O2lhS1VbYAOftyxdzLfM1TKUmZn80m9jYWBSCArlMgclWisHDwKuvvUp0m9rZQ5VhNBo5l3auPPPiUtRo0AvuxMXF1UrU0Gg0FFFa5eNWLGg09oUEnVZLSW7VHVpmzGjQ08zWBgmJQvI4nZjEO++8w8cff1y34eYubmhqOT4H/AABAABJREFU2qVRmxyNy3EjChoAOp2Wd6dOZ84nH/HJ1/v45Os8wH6f79GjO888Mwa53PXbcOHCxX+HH+b/gBodDaWW5cVTKtSE0BABgd8X/c6wW4fRrl07GoQ34OzZRBqIzdBin/uIiKRzhkIpj+fuecZh+8tXrEAlqAi6SNAAu8UUQB7Z+OJ4r8gjC0EQaN68+bU47VrRs1dPfv7lJ6Z/ks8bYzwJDbYvgRQWiXz1YwEZmRZefX14jbalvNDJYsOGAsdq7bKOF6Xy2ln4VDdnutLuDHB+77/zzjtp27YtK1euJDHhJP7NNDzcsykjegzGw6P6bgRnFOSsY9/+/SQnJ2Gz2chPNHHu3BmMRrvNT2RkY+697366dK5ZgUJwUDCffvoFW7ZsYfv2WIpMJnr3iWDwkCGEhlSe013aAQKOQoen20laRModslcMHjLSBWNFpswlmCjFx9eX6dPes28HA1Axz7vc3K4mhWo1zUsEx66NJl72z8rZ4TdtlX/hMYFSsfaB6v8mVq9ejZvggYfohYhEAbkUkIOIiBYdbniyetWqy4oaCoWCSZMm8tZbb3Mifx/ueCFHQYlQQKloZMT9I+jRo2qLMhdXR15eHgt+XMDGjRsxW+zz7aaRzXjgwRHExMTU89HVPf8GQcOZW0qRULNOcJeo4eI/gVqj5oUXXuCxRx/j8OHD2Gw2mjZtWrMwYBk8/PBDvPf+e5wliUDCUKJCxEY2GaSTQpdOXfDydl6xExYexqOPPsYPP8ynRCjCS7R3ihSQQz7ZdOvajd69e9X4XKKiovjm2685eOAgZ86eQaPR0qlTR6f+tzcLVyJo1LTy6GoEjYKCAlauXMGmTespLCwkICCQAQMG0bfvLXU2WamrLo6aWlLVRti4XM7GNQ0Ol+D9999n/779hNEEL9EPAQEjJaQVJjNp0iRmfjCTRo0a1XrTZf6lYlVBO4CEWOvF0W49uvH7osVYRSuKS26vFswUynK4vbu9YqxX714s+nURVtHskNVjopRCcgnGXv0mQ4YHXijEZpw8c5AdO3bQvUf3Wh2bi5ubmg5S6yIY/EYVNMrwMhgYP34iqampxMXFIQgCUVFR+Pg4t0C5UkpLS1m9ejVr164iMzMLg8GTvn37M2TIENz/BdlVLly4uPnJyszi6LGjNKCp025wP4LJFFPZum0rQ4cOZcKECbzz9jvEpR3ATfBAEBUYhSKsooWRI0c6tYmKjzuJTvRw2L4KNQZ8SeM0OtzQUdF9WkwhGcJZevXsVe95GgBqtZpJk95l0sTxPDvuPM0aq9FoZBw/aQbkvPrqazSJdFzwdkarVq1QKVXkWM4TRLjD4zlkoFQoad26dR2fhZ1rIWhc7r7fsGFDnn766SsuACsTDZKSkvj9t0WcP55NhzZKBEHGsXOlmEwS997uRsNwJas2nmPa1GmMHj2aoUOrdlK4GLVaTf/+/enf37GT43JcKnSImUq8gmxIVM6w7NNdy+4DuRRTiF5WeQxglkzkC9k8MHiEw/bL3qsjeVfftVHGxXM5qFnXRkJuAaFhIfz8ZzobY42oVdC5vZYenTSoVDLOplqJT82ka49h17Rg7UYnMyMTtajDjIlTHMdIMVr0CMgpIBsJibNnLTXaVlh4GHPnfsH6DevZHrudUmMpMY3aMHTIEBo3aXyNz+S/S35ePq+9+hq5WXl4i4G44YEFC2kJGUyeNJkXX3yRvrf0re/DrDNqK2jcSHZTdWX77hI1XPynMHgZ6NmrZ61f171Hdx5OfYSfFiwgmwxUqLBiQUREgZKdO3eye/fuKm1h7rnnbkJDQvjjjz84EXcCgKCAIO65/UluvXVotSFRZcF/sbGxGEtLCQ0NZUD//rRr34527dvV+lxuVG40QSM9PY033xxHQWEePTupCfCTE590ls8++5QNG9YxceJk1Oqa5y3UlKq6OGojbtSk0udGFzbi4+PZvWe3PfPiIosoLToipObEi4dYtGgRY8eOrfW21Ro1zZo2IzUhA2/R36H7ykgxxWIR7drV7vc1ZPAQlvyzlOTS44SKTdCgvbC9Es4Kieh0+vLKnsFDBrNkyVJOlZwgWGyIHnckJIop4AwJKFHhTeVsDh1uuAkebNu2zSVq/EeobZdGVfxbBI2LCQ4OJji4BoUJV0BRURFvv/0mKSkpdO2gplcnBanpBfz228+sXrOS6dPew8/P75rs24ULFy5qSn5BAQBqnPuyK1CilKvIz7NXYvv6+TLnszns2L7jwrzCSIPwBgwaPIigIOf3B6VSWWURSCiNOcF+TnIQD5k3akmDSWakQMqlaeOmjB49ug7Osm4IDQ3h88/nsm3bNvbs3YvVamHEA5EM6N8fT4Pn5TdwAZ1ex6233srff/2NGu0Fz30ZEpLd2lh2jiEDB+PuUffi95UIGnVRZVs2F8nJycHfoiLTknnZe6Bo2UViQiJ79u4iLS2VnPhiCgoKadNCyYyP/NDr7HNfk0nki/n5/LGsiFmT/Jj0mhdfL8jnm2++plu3rhgMzosGrxU9e/Ti00/n8GBPf8JCKpbKunTQcetABZmZR/EhAD0enNzvTQE5ZMhT8DIYGDx4SJXbvTJxo+q5XIi7mg0bNpB4Pp+NWi13de5OeLijyAb278Q///xDlihHpQ9FrUqnoFDkk6/zWPS3nJdHG/jyhyJ8fLy44/6BKJVKjh1IBvjPiRsGLwPZKcmc4jg2rEQSXd6VZsVKKsnkGDM4cvgIUa2jLrs9nV7HsGHDGDZs2LU+dBcXWLhwIblZeTQWoyrlHhkkH86QwOeff06XLl3Q6m7uPJObOT/jYjGjLlxSZJIkVZ2adRNSUFCAp6cna/+MRa937lfu4ubFYraQdCoJURSJaBBxTS9GRqORjRs2sn3HDkylJnJys8k9n4ePFIQNC3KUGPBBidruKeth5ft536NQVKEVSpCQkEBmZibePt40a9qsPNzXYrGScvo0kiQRHh5eHuhbWFDI5MmTiTsZh1bQo0CJkSJERJ566qkaV7DcyBxLzLjhBA2QeOWVlyguPMOUN7zw9amo2D8Rb2bizFz69R/KU089ddnjrguO5J0t//+1saY6mGF/z6qq9DmccvkKH7C3LkP1VlQn6njw++2337JyyWqai+2cZlScJ5V02Wl+++23y4a1OWPH9h1Mmz6NAMIIIKy8AtFEKaeFONx99Mz9cm6tbZ4S4hOYPHkKefm5aGVuIIkYKUGvc2P8hHdo0aJF+XNPJ5/m3XffJeN8Blq5FgkotRlRoqIJrR0CKAFOcYJG7cKZNGlSrc/Zxc1HTa6Plxug/hsFjWvNrFmz2LtnK++O9SIivOL6kpll463pufgFNGHatBn1eIQ3FsWFRQxr2pH8/PwrsiO5WXDNMVzUF0lJSSxbtpyE+ARUSiWdunRi0MBBiKLIY489RiiN8cFx/GXGzAn28uxzzzJgQPV2KVXx559/Mn/efFpIMSgv6SwVEYkT9hHZMhKbVSQnOxsfP18GDOhPr169axy+fbNhs9r4cNaHbN26Fa2gQylqsAgmjGIxXbt05dXXXqvzc69rQaPsng+XnyslJyez6ac/iIurCG1t27YNjz8+kkaNKlecl3VlrFq1iiXfr8LgISfAX05RsY2sHJF5nwSg01Yu5rPZJJ569TztotSMecJAUbHI/148z4gRj3D33XdXeWzXApPJxIsvPY+5NIunH3OnfWs1yOBEvIXP5xWQliFisVgJivIGZEiInE0Lom3bNvj6+tGtW1dCQ6u2AobK8zp/dTN27tjBkqX/EHciDkEu0K5tW+64405atmzpdC63+I/F/PjjjygUMkICFeTLgjEarbRuHcVbj49yWIvYvXs3U6ZM4c4hbsS0b4RSAa39z3M21cqU2TlkZdvQ6fVMmTy1Ugd8Xc/tbgY2bNjI7NmzACoJGmVISMTLDtGiXTMmTJxQH4d4w2C1WsnPL0Cn1d4wAoHJZOLhBx/GyxJAII6/QwtmjrOX0U+PZvCQwfVwhHXDzShoXElXRlFBEV3C+1x2fvHvHGm4+Nch2kR+/fVXlixZQlGx3VtNpVTRv39/Hn/8cTTauq2YP3vmLG+/9Ta5ebm4Y0COgkLysGIBJIKJqPT8QMKJK9jP7l276dqtq8P2dmzfwfffzyMtPbX8b5GRTXnyySfYv38/S5cso6i4EACdVs+QoYN58IEHmTptGqcSkmlMK9xET2TIsGEjjRTmzp2Ln68fHTtVHxp8I3O1XvHXRtCAkydPEh+fyPhXfCoJGgDNI1XcPljHP6tW88gjD6OtIiC+Lqmo7qld58bFXRs3W8dGcVExKlROBQ2w2x6IkkhpaekViRpdunbhkUce4ccffyRPyEQnumPDSiF5eHl6M3HSxCvKrWgS2YQxY55lxnvvUWotRoEKDTqKS4qZPGkKEyZOoHlz+2fXIKIBX371JXv37uXY0WMggy2bt2DOtDkVNEQkSoUiB49gF/9Oair4gkvQqEvy8/PZunULj92nryRoAPj5ynnsfj3vf3qM06dP06BBg3o6ShcAn332GTNnziQ9PZ02bdowZ84cOnXq5PS58+bNY+TIyvllarWa0tKqc5BcuLgRWLx4MfPnz0cjaNGJHojYWJiwkD8W/8mkSRNpHxPDsf3HMYi+yKkYt9gDac+gVCnp0f3Kvdv79+vPb4t+47QxjnCxaXngrRULZ0nCJhN5evTThIVXv5D7b0KukPP6a69z++13sG7dOnKys/Hy8uKWfv1o2aIFVQxdr5jLCRp1bTdVRoYpjoSEBOZP/YimjRS8McaL4CAFp05b+XP5CcaOfYPp02fQMDyn/DXhuiYsXryYZT+uAkCng/wCG+ezbPTorHEQNADkchk9OmmI3W2/HrvpBSLCVJw7d67a47sWqNVq3p0ylRkzpjH5wwQ83BUIgoy8fAuhocHMnv0mSqWSg4cOkhCfwImsA4QFpXFgXw6FRTYWLPiRHj2688ILL6FWq53uo2xedzjvLAuWzGHnzp24m8w8fLcWq1Vi0/YDjBu3i6effprBg+0Lr2VdG2k7TzBv3nzuutWNe4bp0evlWC2lbIgtYf6Ko7y3YD5vPV45Y/PPPxfTsqmGx+53RybL4kSSL4fP+4MCXn9W4pUJWfxv5JMOlr4Xz+2gGnFDgrz8PGxWG17eXtU6UNzo9OjRg2+++QZzocVB0AC7JbCX5M++ffuwWq1VF7P+iyksKOTXRYtYu2YNJcYSZMho3749999/P81b1G+GUm5OLiaLCT3OF8CVqNAqdJxLvf7XlrriZhM06rorwxn/vV+hi5sPCWbNms3WLVvwJpAgGiEgkG/JYfWqNSQmJjJt2rQrWth0htVqZcKECZQWmGlO+/LFRRGJDFJI4zRqdBjwLn+NFh1KQUVqWqrD9jZv3swHH3yAB940xt4GV0IRqYmpjBs7DkmS8CGIoAv++fnGbP784y8OHzrMyfiTNKIl7hfCxgDkyAkhApOshEWLFt20okZNL8jXW9AAOH78OGq1nHatnQ9Gu3XQ8OtfhZw+fZrmzVs4fc614FJxoz6EjeqoS2EjMCgII8XYsFWapJdRTAF6nRt6N/0V7+Pee++lc6fOrFixgqRTp9Co1XTt1o0+vXvXWCgtKixiz549GI1GQkJD8PH24b3330dv8yCMxuUhkmZMnC6J482x45g2fVr5oE8QBDp27FhuXefn68dXX35FEfm4UdkOIZOzmEQTgwbVPLzcxc1JXdlOweUFjTJcgoadpKQkrFYbnWOcV511bm+/NsTFxblEjXrk119/5eWXX2bu3Ll07tyZjz76iEGDBhEXF4e/v7/T13h4eBAXVzG5kl2aBOvCxQ3G3r17mT9/PgGEEiCGl3eVWiULyaVxTJo0mYkTJ/D2kbdJtBzBXwxBhwcWSskkjTyyGD1y9FVV0bp7uDN58mQmTJjI8aK9uMk8QZJRLCtAqVAw7o2x/1pBw2q1smPHDvbvP4B4IY+xT98+aLVakEHz5s3KC1WuFfUhaJTNk1p6BPHR3HeIbqnknZe9KYuaiwhT0rWjhnFTc9i8/kMaPj6yPJsiJyeHhQsX0DBcyfNPGogIVyJJEs+/lYlCXvU1VyGXIV7wD5FEiZw8kWb1VP3t4+PL9Onv89tvv7Fh/TqKiwsJDvZmwICB+Pj4oNfrycrKYu4XX9C1g4bHH3LHZHCzCxKxIl/9uIOPP57F66+Pq3Y/5sR0Ni74kzufiqRDW28C5PZA4ztvlfjmpwK+mDuX6OhogoODaRMQyIH0VBbvXk2Pzloeu79i0VahlDGgt56Skjx+3bifLUnxuHu40dY3CLPJzJEjx3h2pKH8nte8URYAJ5J8KdGFEB5SyPHjx+nXr5/DMZZ976qa323evJk/Fv9O0qnkC++dF0OGDOOuu+68KRf8lUoFbaKj2b/tUJXPUaBAQsJq+e+JGgUFBbz+2utkZmThJfoTSARmTJw4cJKxB8by5rg36dTZeXHJ9UCnsxebWi7JxClDRMIsmtFdh6LUa8GV5mfA9Q8Ev9IMpivhv/UrdHFTcujwITZv2UQDmuJFhX+nFj3uooGTJw+zdu1ahgyt2seyNuzYsYPMrEya0a5StbSAjEDCKaKATM5VEjWsWLGJVocLpMVi5cu5X+GFH+FElledq1DjIXoRzyEkREIvCBoAetxxkwycjD+KQqbEXTI4HGNZlUDcyTgK8gvw8Ly57B7qW9C43MVVEAREEUQbOCs2sVornlcf1MaXFSp7s16tsHEoLq1aG6oyYeNq6devHwt/+okMzhBEg0odGyaM5AqZ3DZo2FV/BuENwvm/0f9X4+dbzBYkSUKhVDB//nyWLlmKxWpBQEBEtF8DbNBAaopAxbGpUNOQFhwT9/DW228x68NZNIhwXBAdMHAg27Zt49jRY3hJAXjghYiNPDLJI5v777uf8AbOPXNd/Du42utjGQey0mokaBwpPOMSNC6i7JpiNjt3Z7WUXf/lte/kclF3zJo1i1GjRpV3X8ydO5dly5bx3XffVZm1JJPJCAx0fddd3Dz89edfuAmeBIhh5Ys0KtQoUBIuRnK8aC+JSUnMeG8Gc+d+yfETx8pf6+vjy4sPv8gt/W656uNoEtmEb779mk0bN3Hg4AFsNpHmzZsxoP+Ay85BRJvIrl272Lt3L1arlcjISPr06YNOf2MvKp09e47JkyaQnnGehuEaVErYsGE9P/w4nzfeGEvbtm2v+THUp6ARZQhl//79nD+fxWtP+3HpLU+jljHqYSWLl51FyFHDhY9z2fJlCILIpNf8cHe3309lMhltWqrZtL0Ei0VEqaw8dpckiR37jDRvYi8E2n2wlMwsM92vosPoarBYzEx5dwoH9h+kTSsNkR0VZGQV8OOC+SxfvoypU6fz668LadJQxavPGBAEGVDCGXT06yVHEDz4+KtYRow4TXh45bG+0VjCtm3byMjIIDZ2Gw3D1Tzeo5gjRWoybHZ7twC5mcfv92DLDhMrVqzgiSfsnRceJaVkHDzCsJdbcdSopJU2t9K2B/TWMn/RGfLPJuDesi0HstJopjMA9s/rUpo3sndtKAJDsZZNbqvAWeHaL7/8wsKFC4mJ1vLqM15o1DJ27TPxy88/cfz4Ud5+650r6nqvb5o2bUps7HaskqW8OO1iCsklwC8AjabuszVvdBYsWEBmRhZNxKhKWU7eYgApxDF79kfMnz+v3Er9euPh6UFUyyiST6TgJfo5OD7kkYlFNNOjR/1cW66Gax0IDlcvaFyPrgxnuEQNFzc8q1evQSe4YRB9HR7T444n3qxcubLORI39+/ajF9zRio6DbRkyvPHnDAmIiOWLltlkgABdu1a2ntq7Zw+FRQU0xzEXQEAggFCSiaMUY3moMIAHBtzwxCgVV2m/o7jw8zWbzVd1vteb+hI0aqMWt2nTBovFxva9Rnp20SJdovYnppTQs5uWhmHZ5d6xl0NQ1n3VQpQhtFaWVPaujasTNoBqbajA/tkeO5B8Vd0aPj7ePPb4Y3z//feYKMWHAOQoKCKPbCED/wA/7r3n3ivefm2JjY3lzz/+JO6k/b12d/OgqKgQf0LxIxA5SkooJNF4DG8CKgkaZShQ4oE3BZZcPvvsM96f+b7Dc5RKBRMnTmTRokUsX76CpCJ791dIcCiP3/sit9xy9YsTLm586sp26nJc7Kntwk7Tpk3RajVsii3h4XsdF+s2xRqRCQJtoqPr4ehcgH3cs3fvXsaNq6iCFQSB/v37s3379ipfV1RURIMGDRBFkfbt2zNt2jRatWp1PQ7ZhYvaI8HhQ4dxw8Bx9pWLGmo0+BKML4G4yTw5dPAggwcP4r33Z3D27Dky0tPR6/U0bda0TotvtFotg4cMrpUPeUZ6BhMnTeDcuVTCQ9RoNTI2bFzP/B/m8dqrr9OhY4c6O766pNRYyoQJb6NVFfHxu35EhNkXNrOybXz2fQFT353CRx99QkhoSI23KdpEtm3bxurVK0lPT0Xv5k6vnn0YOGgQbpd0HV/crXm9BA1ni1EZGekIgozIRpUXdsvmRTFBZj47kktGRkZ5jsT22K307qotFzTKGNJPz7K1xXz5YwHPPO55QQiwCxq//FnE2VQbox81sG2Xkc++L6BNm9a0qCcrmwULFnD82GGmjPUhumVF1/75TCtvz8jjvRnTiE9I4oWnDOXnARCmLAGgVxcd+w4r2Lp1Kw8+WCFqrFmzmm+++RqTyYS3QUlegQVRlPhmQQH/ewAEORwp8ibDpiJAZaZdlJKTJ0+Uv77MLrGdXyY5BHPU6FVJ2CgoklAoZOTm5jLkwrwurjiPkOBAdh3Ip1dXx84XP68M0jJURLdpQmpaKkv+WcL2HdswlZoIDw9n8OCh9OrdG7kgVBI23JQmFi5cyIN3unP/8Aqbpo5tNXTvpGHSB/tZu3YtgwbffN3lt/S7hR9//JFU6ynCLipKBSgkjzxZNo8Pe7zObeZudEylJjas24CPGFhJ0AB78W8QERwv2cu22Fj69u1TH4cIwIgHRzD+7fGkEE8g4ajRICKSRxapslN069rtpuouvFnspq5nZ8aluEQNFzc8GWnpaERdlYv7Wtw4n3G+zvYnimKV+wLKH5OQELGRTQYZpDB0yFAMXoZKz83MzEQuk6ORnFcjabEHTVowVRI1wC7YFFOAiVKn/voF5OGmd8fLy6s2p3dDcKULdtdD0BAtuwgNgvvuasjug2do0lCkY6hdOJIkiW27Slm2MI97772fhh7VD7YzszJZv249mZmZuEesJ6p1a/z8/Co952rFjtrmbVytsFGTfA2oG2HjzjvvxMvgxa+//EpSmr36UKlU0ad3bx57/DHc3K9PUOsvP//Cwp8X4i4YCKMJMmTkFmXZW4+xIEeJDBl6PJCjQO5E0ChDQI4SJSfiTpCSkkJ4uGPXhVKl5KGHH+L+EfeTmZmJQqHEz9f3Pzd4/i9S0xyNqhY0LqYuczQuDXe7WkwmE0ePHiUnJweNRkOLFi3w8fGp031cMQLc/mhvdu3aSegJe45S2U/v9Fkr647KGHx/J0SPHDJMOdVu6r+CGzVf2KsLsrKysNlsBARUvr8EBARw4sQJp69p1qwZ3333HdHR0eTn5/PBBx/QrVs3jh49Smio87GByWTCZKooaigoKKi7k3BxdUiwa9culi1bRlLSKZRKJV27dmHYbcMICqqZ5d7FGEuMrFixgjWr15CTk4Onpyf9B/Rn6NCh122scSk2mw0RkUJy8cIfAz5ISOSRxTmSMFKMVbKQlJTE1199TdNmzejWrRuhtVhov5ZYzBbGT3gbmZjHhxP9aNLQvjCek2tj7vwCps+YxocfziIiIqJ+D9QJGzduJDsrh7kz/Qnwq6g09/WRM+55A//3WhZLlixh9NOja7Q9i8XKjOlT2b1nL1HNNPToqCAzu4SfFv7AsuVLmDZ1BgGB9uvZ5RawqrKdrAtB49J5kpubG6IokZVtw8/X/j6UCRphyhKOZdouPK9iUbu0tBQvT8fq/JAgBWOeMDDnmzz2HzLRu5sWhVzG1p0lnEu34eEu5/3P8ykosBIT047XXnuD+hj4mkylrFq1ktsG6CoJGgD+fgpGPaLn3VlJAHgbnHchRGhL0OtleHsml/8tNnYbc+bMoX8vHQ/c6Y+vj5ySEpEV60tY8HsBCgU8PsKDKLeccmGj1AQKhZJtsbGsWrmclJTTyGRw6JiJWwec52C2P0eNXiQkm9nyazLxSRYAFv36K6dPn2bkyJEEBgTS/vZhrF6zml37iujUvmJNwWKR+GJ+ARYTtGwRwmvTZ6JUivToaMHgoeDgsdPMnj2bHTu38/prbyCXy8u/l5+89xUBoY24e5hjLlXbKDUxbTWsXLX8phQ1PDw8eOHFF5j14SxMMiMG0a88XzWfbKKj2zDstmH1fZjXDgmOHTtGWnoabm5utG3bFrVaTWZmJiaLCfdL7JHLUKNBK9dxJiXlOh9wZaKjo3n19VeZ88kcTpTuQyPXYZHMWEULPbr14IUXX6jX46sNLkGjZrhEDRc3PB6eHqQK6SA6f9xEKe7ujkFOV0rTpk1Zv349JkqRISC/8J8y8slBhowE4SAWrIiijYGDB/Lkk086OXZPbJINM6byYL3Kx24EcNraaMaETCYjTUomnGblProAJRSSJ2QyfPAdN1VbZ10s2F1rQQPsIXfP3P8S48e/zbtvJhPdUkOAn0B8ko3kMyZ69OjOiBH3V7MliZ9//oVff/0FtVogNEjJ+R1WFn+1lL59+/Dcc8+Xe3CmlDjv9Kit2FGbvI3rJWwAV52v0advH/r06UNqaiomk4mAgIDralmQEJ/Awp8XEkQ4AWLF++WNP9lkcIYEPPDC84IdnQ43CsglkHAHcVREopA83PDARClnz551KmqUoVAormhxxsXNSV3laNTEdqq2gkZdDlLXr1/Hl19+gclkJtBfRV6+jeVGK3369Oa5555HqayflvWLaT78IWbGn2PB7B00CFXTsIGctHSRuMRSWrZszqsPjLpp/XivBUUFRfV9CJela9eulbppu3XrRosWLfjyyy+ZMmWK09dMnz6dSZMmXa9DdFFTJPj8i89ZuXIlboIHetETGzZWLV/NqlWrGT/+HaLb1LyTqiC/gLFjx5KamoZB8saLAErPG/l54S+sWb2GGe/NwMf3+ouu23fYu44a0AwDFfv3xBsNWtI4A0iI6TbWr9jAkqVL+OZrA2++NY4WLa5f3ltVbN22jbS0DD6d5k9YSMWSg7eXnNeeNfDsuGz+/uvvG3KBaefO7US3UlcSNMpQqWT07qZm0/ZtNRY1fvnlF/Yf2M+EV3xoH10xH8zMsvHO+7nMeG8as2d9zLEk5wtYiYmJ7N23l/NWGcHBwdw6pHulx69U0LicVUiHDh3QajX8s6qYJx7yqCRoSJLEktVFBAb60bRp0/LXhIaEcuj4SR508j7c0l3Lgt8LyMkT2bBdDhI0bdqBbj3DsFptqNVqunTp4hBYfT05fTqFkpJSundydIgAiInWoFHLsYpw/KSZtlGO8/ucXBvHt2bSs2VE+dxy4cIFdGirZcwTBsrinHQ6gbuHuWGzwa9/F3LnUD2eHnKi3HLs4eqSGt9AE+/NmEHLZhr691Cyabuc35YU0iVGQxuf82zZYeTXZe6ENwpg7K3F+PvKOZFg5u8Ve3jttSO8//4HPHbXrZw+nczMf+JovSGLdlFKCosl1m81kVcg8fprrzN79oc0CLFw751NUSk9aO1/nrtuhZ37Snlvzg6WLl3KHXfcUX6OWcUphIXIiU8NoEW44/i1TQslP/5+tg4+kfqhV69e+Pn5sfj3xezZswdREgkMCGTkrSO5ddit/9osjSNHjjDnk09JS6/IidVpdYwYMYLu3e3XHQvOXUJEJCySBY22frJwLqZHjx507NCRrVu3cvbcWbRa3Q0l+teE+hQ0rqTwrb4EDXCJGi5uAvr06cPuPbspphA9lcULC2YKhGzu63dfne0vJiYGuVzBCdt+pAtKigdeBBCGGRMF5NC7d298fX1xd3enR48e+Pn7Od1W506d0Kg1nDelVsrNAHunx3nOoUaLhsqLIyZKyZdlc8stt7BhwwYSOIRB9EOJkiLyyZNl0ahRI+6/v7qF9RuL2izYVdVSXdWFt64FDQAPD09mfjCLbVu3sWnTRk6n5hPaIJCRowbSrm1bqqseWr58OT///DMj7nTnziF6NBoBq1Viw7YS5s7fhFarY/To0ZX2dykXix21ETguztuob2GjzoLDZRAcEnzlr78Kli1fhgo1/jh+h3wIIIt0skgrFzV8CCSJo2SRhh8VxywhkUEKFkx44k0umWivZFG0zObf1bXxr6K2A9ersZ2qT0Fj9+5dfPTRR/TrqeOhu/3x8ZZjNkts2Gbkm5+2IggCL774cp3t70pRKJSMG/cmBw8eZM2aNZzPOo93gBdv3n8LHTt1RC7cPMUE/0Z8fX2Ry+VkZFQeV2RkZNQ4M0OpVNKuXTsSEhKqfM64ceN4+eWK72NBQQFhYTePZcG/lQ0bNrBy5UrCaIKPWDG2EMVwkqU4pk6dyrx582ocjv35559zPi2TplJ0pfG4SQolKfsYH330EVPedS58XUtWLl+Jm8wTg1RZUBERySETFSoa0BSd5I7MJqOUEs4VnmLC+AnMmTOnvPK/vtixYwctm6orCRplKJUy+nZXs2R1LC9w44kaJrMJb7eqB1rubgKmGtr/WswWVq5YxtB+ukqCBoCfr5z/e8SdD74XWbVxL2HhoZXGAfn5+cyc+R6HDh3Bs2EEcoWM9LRklv4TwOtvjKNRo0YOi1Mmk4ktW7dw6NBhjL4KwhuEM6KvYwB0TeZIGo2We++7n2OH5rN2i43hMTYMnnLOZ1pZ9E8RsbtLeeXVZ8sDqAEGDxnKjBmH2bbLSPdOlX+DKzeUkJMr8uyzzzJo0I1ZwV92KmIVxZSSZB/TRzZpyvJ1CfTtoSXQv+I7LokSC34vRKlU0rt3b3Q6HXuTY/H3Ocut/XyQOflaDemn4+c/C9ix18Sgvjpycm28/2k+1jMi5yWRl94MoU9z+wRgUF8db0zJ4oW3MxncV8eytSV0jC5lwD1Nkck8KQVubZhLj85aXpuYw7x58xg3dhzjXxrDz0vXsGfvbn74bT8qtZLOnXty5/A7SUhIIL+gkGff8SfQ356zcfi8P639z9O5vYaeXbQsXfYPt99+e/lnrdHoyC+JA9w4nmIXNiSp4j3MzRdv+syJFi1a8PY7byPaRGw2G0qVYxHqv4kTJ+IY/84EtKKexkShxwMLpWQa0/ju++8wmUw0imhM5ukMPCUfp3kVVtFC1y5d6ukMKqPWqOnX3/HadzNwrfMz6qI7A24MQQNcooaLukKCgsICZMjsXRN1uODWtVtXGjVqzOnkOALFBhiwX0QLySNNOI2HhwdDhw6tk33l5+Xz9ttvgwj+BKPHAzMmskgjnkMA9OnVh5deegmZ4OQkJTh8+DB79u7BarHSpEkT7rv/fn74YT4g4UcwKtSUUkIGZygiH6VMRb6Uiyd2Gyn7eSXj4+3D/554giFDhvD774vZuXMHoiTibfBmxNARDB8+HLXGsTrkRqamORqXUtVCXXUBt1cjaJShVCjp08feKVBTRNHGb78t4pYeOh64s0KEUyhkDOitp7BI5KfFqxgxYgQGg6HK7ZQdS0pJAqJl1xV1blwuSPymEjbqiTI/66os6TzwIpv0i/5tQI8H5zhFHtkY8L1gF5FJCUUEE0Eh+bjp3YmqqZe7BFu2bOGff/4hPj4eQRBo06YNd955Z62qUWvL+YzzxMbGYjQaCQ4OpmvXrvUW/PZf4Gq72G6GNuJffllIdEsNzz1ZUamoUskY1FeHKEp8+cNGRox4gMDAG6FDSUabNm1p06ZtfR+Ii0tQqVTExMSwbt06hg8fDtitQ9etW8eYMWNqtA2bzcbhw4erHT+q1WrU6ptrnPVf4O+//sFT5oOPVHlMISAnVGrCidK9bNiwgaG3Xn5ukJ2VzY7tOwimoUOBkRoNAWIoBw8d5NzZc7XKT6gLzpw9i15yzPXJIwsTRprTrtIxa9ARITUnzrKfJUuW8OQoxw7y64nZbMJdfzlhwHIdj6jmRDRoxLat8Vit4Kwg+8BhCw0jGjo+4IQzZ89QWFRcZeW/2iMcjSqfPFMaA9pWhNdabVYmTHyH7KyzPPRUSyIbK2nZOJP4JD++/CGPd95+i6cmT8TT4FF+34+Pj2fKu5PIyyugSe8wZFmwd8UK1i5YwFtvvVPewVObe/xdt4fj792XP79ewt8/WNHr5BQVW9Fo1HTt2pVTSacoKS6hV69e6PV6unbtRq/ePfng863s2m+iW0c1og02bTeyfU8pAwcOvGEFDYAGDSJwd9exZaeRJo0cx7y79pViMok89NDDfP75p7w2OYth/bVEtVCTk2dj5TojR+NMvPDCC+h09t+ntsRu1xMaYkPChOwS9wZ3NwG1SsY/q4qJ3V3K4eNmdHodWq2c9v553NvOnrUBEOBjZuYEX75eUMDvS4uQJHj4HneCfDMByi2pWnnkMnyolm8W7CC/IB9PD08evn0QD98+iMMp9vFi2fxu+fLlNAzTlIszZQHiZcJGlxgNm2IzyS/Ix+BpAOzdjrNm7UDlkUVJicTkT3UknrZSkJlM4wglqekiPXsPqONPp34Q5AKCvO7yiW5Ufpg/H7WkoaHUojwbUo2WUBohIGfRokWMGTOG2R/N5ixJBBGOAiUiEvlkkyok07VT15sqr+JGw2U3dWW4RA0XV4UkSqxatYq///qbc2nnAAgLDWP4ncMZ0H9AnYgbCoWCKZMn8+GHH7Jv/z7OIiCTCdgkK40aNOaNsa/j4ek46HdGUVExmzdtIjU1FZ1eR48ePSrZv/zwww/kZOYSKUVXyrHwJoBTHMeqLmXMc2OcCho5OTlMmTyFxKRENHItcuQssS3BTe/GkMFD2LhxI1mlaQgIiIgYPA2MeXgMa9eu5UTccRSCXf23ihYaRzRm7LixuLnpiWwaybg3x2Kz2jBbzGg12puuSrsmXRpXGgzujLoQNK6U+PgEsrNzGdTX+eRlYB89PywqZM+ePfTv3/+y2wvXNblKYaN6O6oyYQNwEDfK3vMDKZcXNqqjTNi4GSkqKkbpJNOmDBtWxIu88axYsApm/H38OZ+ZSTGFyJDhhgcRNMdIMdmk87/7/lezih8J5n45l+XLl+Mh8yJIikCySZw4EM/b+97mqaeeYtiwuvV1tVqtfPH5F6xduxZBJkchKDHZjLjp3Xjuuefo2q3r5TfiosbUxpbvctzIA9XMzPPExyfy+hgvp5WK/XromPdrEbGxsdx11911um8X/z5efvllHnvsMTp06ECnTp346KOPKC4uZuTIkQA8+uijhISEMH36dAAmT55Mly5daNKkCXl5ecycOZPTp087tQ51ceNitVpJSk4kDOdjNhUq9IIHx44fr5GokZCYgIhY3m15KQZ8SCGek/Hx113U0Gm1GPMcuwHyyMINTwcRBkCOHE/Rh82bN9e7qBHRIILVqw9hNkuoVI4X/f2HzUQ0uDEXvwYPGczSZUv59e9CHrzLvdI9a9suI4eOG3n11ZoV1JVnMTqp/D+eEoAEFNrOV+p2ANi5YydJicmMfjmKkCA5zRtlATKaNlYy6XUvnvlUy65dO3n5yUcByM3LY8LEdwgJsPHk2GZ4GRS08TnP+Ux/Pvoqn0mTJvLpp59hc8+u8f29bI704MDHub37PWyP3U5ubg6HDh3i8OEjHNi/i+QkBeezzHz33bf873/2YryXX3qFyMimLF3yDxu32Rfbw8KCGTPmTgYOHFijfdcXKpWKIUOG8ccfv9GyqYrOMRXdJmfOWvj6pyKioloSHR3Ne+/NZMGCH1m8bCML/ygEoFmzSMaPf4CYmA7lrwsIDCTjaB5ZJ0T8uvs5CBunz1owlkqoteGodD488URboqOjGTNmDF072K9NF2dtBHiZeeM5Lx562owgQFBAxZJeG5+KrA11pDs2sYDMzEw8PSpyEC4tXBMEAbNF4mLs3zc4nORPcrEZyK3Updq9e3d+XfQz78zIoLBYJDxEQe+uoaiULYg/kUSx0UJ+fh6SKDkvBHVxQ5GVmcWRo0cIp2m5oHEx/gSRZU3FZDbzzDPP8tWXX5ErZqIT9FgkMyaxlI4xHXnppZfq4ej/HbgEDUdE694aPc8lari4ciT49LPPWLNmNQZ8iaAZEpB/Nos5c+Zw6lQyT40aVScL8O4e7kycNJEzKWc4eOggNptI8+bNaNa0WY23v3bNWubOnYvVYkUr12GWzPzyyy/06NaDF196EZvNxsaNG/EVgxyCuQVkhBDBCdN+du7cSc9ePSs9brPaGP/2eDJSz9OYVrjZPJEhw4SRcyWnWLduHTNmzCA9I52CggL8/fxp164dcoWcgYMGkhCfwOHDh5EkiZatWtG8meN5yRVytIr69yi8Uqq7QF+poFGdP2x9CBpgD5gD8PRwXtGh18mQK2SVwkcvx/URNtI5Uui8awOotmMDuC7B4fWBTCajgBwsmFFSuWLLho1cMpFht4wrpoBM4RwqnYrHHnuMhT8tJDUtFZAwUkSK7CTIYMS9I8qriy/Hzl07Wb58OaE0xleqeH/9xCBSSebrr76mbZu2hIbV3cDis08/Y8OGDQTTEB/JH8Emx4SRtOIU3nvvPaZMmULr6NZ1tr//MjW15SvjSm2nboSBqtFoz5Dy8XJu3aRSy3DTyykpKanzfbv493H//feTmZnJ+PHjSU9Pp23btqxcubI8PDwlJQVBqLgP5+bmMmrUKNLT0/Hy8iImJobY2FhatmxZX6fg4gooXyBGqvI5EmKlz746yhbpxCqC+8r+Lq+HKt2evXvy+6LFWEVLpew9GzaUTnL6ylCgoqg093ocYrUMGjSIP//8kwWLCxk5wqOSMLBrfyl7DxoZ89yNGbYbHh7OY489xvz58zkWZ6F3Nw0qFezYa2LHHiN9evemZ4+el98QEBoWhsHTgy07jbRoWjGOPJ5iv1YVWjMwGm0O46rNWzbTKKbxRYJGBW56gdbNFRzaugYuiBqrV6/CbC7lnjHN0GkF2vicB8DfT85bLxl48qUs1u75nefufOSyx+xsfuSmd2PAgAF89dWXHD16hCcedGdAHx0atUBuno1f/irkiy++wN3djR49enLH7Xdw+223k5ObgyAT8PIycLNU5T3wwAOcOXOaaR/vJLJhCZGN5WScF9l3uJSwsJALIebg6enJs8+O4YknniQrOwutVouptJTz5zNJTEqkcaNGgAwfbx/ato3mx9+P0OO0BY/GXrRqZsPLIEe0qVnwexFeXp588MEHyOX25bns7Gygsg3WxcJG2RWwxChSXCKi11Vco8o++7hET3TNg9Dr9Q7neLGw0a59O5YuW8rJJAtNG1UuuGreKIuf/9TSMKZTpQxTpVLJC8+/xBtvvM6AXlqeGWlAJjNx8qgnMW1a0rf7KWZ/uYPNW7bQu3evq/1I/jMYS4zEbo8lNycXb29vunbrivY6ZFTk5+cDoMH5vhSoUMpV5OXmMuKBEXTr1pUNGzaSeu7chULhnjRuXH9ZODc710rQuBntpsruP7XBJWq4uGL27t3LmjWrCScSb/zL/+6FL1mks3TpErp17UpU66g622dYeNgVtbTt2rmLT+Z8gjcBBBGO0qZCvGAJs337DgT5J9xz7z1YrBbcMDjdhgYdGrmWM2ccOwR27NhBytkUmtIGHW7lf1ejJUJqzknbAVasWMFzzz/ndNtNIpvQJLLuFtZvJGq6aPdvEDQAQkJDEQQZh46ZKlXOlHHspAWrRao2INoZ9SlsXM6KqqbB4TejsOHv78fp5BKSOE4DmpYP9kyUcoYERGxISBzHXknQtnU7mjVryswPZqIT3AjA/l7mk41RKua+e+/jwYecxSc6Z9mSZbgJnviKld9XGTKCaEC+kM2KFSsY9dSoOjnf1NQ01q1fZxdRqNinGi3hNCVJdoSFCxcyPXp6nezvv0xtBrBXYztVnU3fxVzrgaqvry9KpZKjcWaaRzpaOqRlWMnOMRMScvOE+LmoX8aMGVOl3dTGjRsr/Xv27NnMnj37OhyVi2uJXCGnVctWpJw4h48Y4GANacJIkVhAmxpaM7Zo2QKVUkWuJZNAHOcXOWQiCALRra+d1WNVDB0ylGVLl3Oq5DghYkN0uCMhIUdBEXmISAhOFolLZAV1WuhwpQQGBfLEk0/y9ddfcTLRSt/uGrQaGbsOlLJtVyldunSh3y03rt/53XffTWhoKH//9SeffX8MgPCwUJ5++nYGDRpU4+pzpVLB0FuH8esvvxDVXIXBPwKZDJq2yic13cr77xfSrFkkTSMrwrYPnUsnV6bAxyON5o28HLZ5IN+PJt6n2F5sLP/bzp3baTMkrJKgUYZeJ9D/Ti/i1sXCZUSN6uZHubm5rFixgofvceO2QRXzXS+DnNGPeZKdK/Lzzz/Ro0cPQIZMZl/Qv9mQy+WMHfsmu3fvZs2aVRxLSMPd3ZNnn+1D7959KtkSWq1WVCoVpcZSPp3zCceOnSh/LDQ0mEcffYycnByOHDmCaLOx95CJgm2p/GqS6DzYH4sV9hyw8eabr5QLGqJo4+jRI2g1KmbNzcXLIKdbRw1D++mJ8skBYGW8B96N9Zw9UsSqDcXcdWvl3FFRlDi07hTBwa3J1ltxNkIsGzceFCWCO7Zl9txjvPOygeBA+3HYbBKLlxZz4FAqjz1+q8M8b/ee3Wg0cp58yLNctGzaKp+TRz0JCG1I63ZFLF+2xCVq1AQJ/v7nHxb8+CNmsxmloMIimvniiy949NFHue32265q88XFxWzetJn0jHTc3dzp2atneREIgJe3/TpTSkmltawyLJix2Mx4+9h/zx4eHtxxx+1XdUwu7LjyMyoLGRffewqsxTV6vUvUcHHFLF++HL3ggZfoGJLtQwA5QgbLVyyvU1GjKgoLClm7di27du3CbDYTGRnJkCFDaBDRAICFC3/GXWYgTGpcPgESkOGNP5IksnnL5vJ2WAvO/V1FRKyS1WnoVWxsLHrBA53oeBMQEPAUfdm6dWuVosblyMvLw2g04u3tfVP6O9ekS+NirkTQKKM+BQ0AH28fOnbswG9LDtCxrQbvi6qSS00iPywqJCQ0kNZX8Lv4NwgbwE2Vr9F/wAC++fprLJg4wT60uCEgo5hC5CiQkLj77ruJbh1NcEgwJSVGXnjhefwIJliMKL/eBBBKOiksWrSIjh070qxZ1SHuF5OQmICbaG89t2KlhCJkyNDhhhw5etGD+Pj4Ojvfbdu2ohCUeIv+Do8JyPAWAzh67Ch5uXkYvAx1tt//KtfadupGETQAdDo9vXr1ZMnqLfTuqsXXp+LaKNrgx9+K0Ot1dOvW/ZodgwsXLm5+7rrrLqa8O4V0zhBAWPnCvgUzKUICBnevGlfR6/V6Bg4axPJly9FIOjzxRoYMCYlC8jgvO0PfPn0r3e+Ki4tJTEgEGTRp3ASd3tEGqi4weBmYOu1d3p3yLiczD6GVa5GAUpt9ITuTcwRQ+ZpdQC75Ug6P33r5avzrwW23DSM4KIg//1zM5/OOABASHMgTTzzCrUOH1rlPfWJiEju2b6fUVEpYWDi9evZEo73ysOLOnTvTuXNnzCYzongh+PgKmg3uu/c+Uk4n89nP5wjyL6Rx81x+WyOy50ApgUEBvP7G2HL7qbJ7fnCQG3t32xBFCeEiAeVAvh9RwZksW2whKKhiLG0J1KLRFDsIGgAZNhWBplSSLdVXfF9ufrRz5w5AYlAfx8p/mUzG0H46Jn2QSkpKCuHhDap/U25wZDIZnTp1olMnx/mWJEmsWbuG5cuWkJR0GplMhlwAXx85rz3rRdNGSlIzbPyzModp0+xFQEP76xgx3B1PDzkmk8iaTUa+XXiexp3CmP3BnTRoYP+MbaKND2a+z7ZtsbRqrqZNSzdy8kRWri9h9cYSJr7qg4e7wD/fnMKvYVM6juzIpr17gEIG9dWj1wmkZVhZ+EchR06YeGe83Satum78NhHB3H//A/z0048888YRoltqMHjIOHLCSnaehQdGPMDdd/Uvn+cBRIcEkpiYSOsWCtTqyj+Kpq3sVf+R+9Ss31ZzZ4L/MstXLOfbb7/Bl2D8CUElqjBj4rz5HF9/8zVKlYrBg68si2blipV88803WC1W1HItFsnMjz/+yMCBAxk9ejRyhRxvb2/atmnHycPxGEQfBCp3VWdwDqVSSY/urjF6XXGz2E3BtZknViVkXAkuUcPFFZOcnIxedHcaoCtDhk70IPlU8jU/joT4BCZMmEBxcQkekgEBOSlJ61m+YjlP/O8JunXrRtKpRCJo5vRYvfAnTUjhZHw84WHh5JzJwBMvh+fmkolVtNClSxeHbRiNRuRi1T8nJapa2Q2VsX//AX75+ReOn7BXCKlVavr168eDDz5Y4xyR+uRyXRrOLsxXKmhkmOLqXdAo46n/G80br7/Ki+9kM+QWLY0iFKRl2Fi5vpS8AhmTJ7/ClbZg3wzCRnXcbMHh/fv3Y9nSpWRnZGMQA7FhA0CNjiIhj5DgYEaMGFEuNn726WeoBQ1BFwkaYL8mBhJOgZDD0iVLayxqKOQKrFg5QwK5ZJZbYQjI8SYAK1ZUyroL7y4uKkYpUzn1UwVQXbC8KC4udokaV0FtczSuxHaqutyhi7me3qiPPvoYhw8f4pWJOQzrr6VFUyVZOSLL15UQn2Th9ddfR61WcezYMeLj45HLBdq3b09wsKt7w4ULF3Y6durIo488yg8//kCekIle9ETESgG5uOndmDRpIip1ze+LI0eOJCMjnd27d6MT3FCLWsxCKcViIdGtohk9ejQAplIT3333HWvXrcNisWddqJVqBgwayOOPPVarfdaUiIgIvvr6K/bs3sPx48eRyWREt4nm8OEj/PbbIkpkRXhJvsgQyCeHPDLp2LEjffv0rfNjuVJiOsQQ0yEGs8mMzWazW6nUsQuRscTI+++9z979e1EJapQyJSW2Yr79+hteeOkFunXrdlXbv9rPVpALDLvrcZolJnDo5A6OxoPezZ3/G92Hvn372vMSqXzPV8oHsXLlSlasK+HWAXYRoUzQOBFvYudeI088OQSw3+8DAoNJ3rUW8S6fSiJIhk1FK30OXx+2EBHhvKCqpotLxcUlaDQCer3zMaKvt30htKTE6PTxG5GMjHQ2bdpEQUEBfn5+9OnTF09PzyqfL0kSH300i40bN9OxnZqhT3piNsG6LSUkJlvILxDx91Pg76cguqWSh5/JIKq5mqce8SwXrtRqgWED9ZjNIj/9kUrrZ2IoIBvRsou/l5xh+/btjHvBmy4xFYLcI/e4M3FmNm9Nz8JsAR8fb6Y8OoaAwABmA5v27mXhH+dx08vJy7eg1+t45ZVX6BATU76NqjIUAXq1bYGb22iOHj3K6didZBeV0rlrBIMHDyaiYQRg/15mZWVx5OhZiotLUCqUlBRU/d6qPLKQyz1vmjlffWGxWPlpwUK8CSCUhuV/V6EmlEaI2PhpwQIG9O+PXOHcwrUqtm7ZyudffI4PgQQSdsGtxEY251mzeg0KhYL/G/1/AIwc+Tivv/46idZj+Ish6HHHjIlMUsklkycfe/Kaifj/Na613RRUX/AGNRMzoO7miZfaStXlWpxL1HBxxWg1WooorfJxGxa0GkPtNyzBkSNHWLt2LVlZWXh5edG3b1/atWvn0Opbaixl4sSJSCUyWkjtyz3vRVEinRS+/e5b1Bc6K6rynxUQUMoUlJYaefDBB5nx3gzOcYpAwlCgvGBTlUWaLJmuXbpxLvUc8fHxBAcHExkZCTIICwvj0P7DiKLodDGwiPxaW2ps2rSZWR9+iF7mQThNUaKiyJzP2lXr2LdvHx988MFNIWxUdbGu7sJ8JYJGTbgeggaAn68fH3wwm0WLFvH3qnUYjYUoFHK6devGfffdX2vrqUspO/6UEvv51EbcqCthoyqaNwviUFzaZW2obhZhQ6vVMn36dD784EMOHTmEgICE3bO7bXQ7Xnn55UrdU8ePHcdNNDi1hJAhw000cOL4CYfHqqJjp46sW7sOkBFAGAZ8kJDIJYtMziEh0aHjnXVwpnaCgoIw2YyYMaPCcRJfTCFKhRJvH+ehqi4uT13naDgbtN4IGRrO8PLyZubMD1mwYAGLlm7EbLLPhqOiWjJ58oN4e3vzwgvPcerUadRqOTabxFdfiXTu3IkXX3wJvd6xG9KFCxf/Pe659x5iYmJYvnw5SYlJqNQq7u4ynH79+uHmXrvrhFKp4O233ubgoYOsXbOWzKwsfLy9ueWWW2gf0x5BELBarUycOJG4E3H4isEY8AUkci1ZrFi2gjMpKUyaNKnOOw8ABEGgU+dOdOpcMdZr26YtYaGhLF68mOQU+3Xc28uHR25/hOF3DL8mx3G1XAvRBwAJpk+fwZHDR4igGR6iDwIyzJhINZ3m/ffeZ9q0abRsVT/5OeWLV+0b0rx9Q25jgMNznM2JGjdpzLBhw/hqwVLikyw06BZBpM9ZflhkYtlaI82aN2fQoEHl9/v7evXmjWXL+XtFMXfeav8NZNjs7/mKdcUknzHxv1GO4ea1mRuFhIZQXGzj9BkLDcKUDo8fjTMjCDICA2/ssT3YLZ6+/PIrVq5ciVYj4OujJP28mfnz5/Pww49w1113OX3dli1b2LBhE68840WvLhWdL0P76/ju5wK+XpBPu9ZqggMVnD5ro7hEYmh/vUMQPMCgvnoWLC5i585dDBo0iNPFJzlz6g9GPqiqJGgAuLkJjHnCwAtvZ9K/f3+eHDUKnda+wPzqiCfZ3r07x48fp/icksDAQLp164ZKVfk3d7kMxfZNwlGqlLRt1xagUgHbkSNH+GnBjxw9dhwAT0M4/n5+pJwwkZFpI8Cv8mK7zQbrthgJbWr/XpX9Dm70eV99cOjQQQqLCmhGY6eP+xFMXMEBDh85TNu2bWu+YQl+WvATnvgQSqOL3Erk+BGEiMiKlSu477778PL2omGjhkybPo3PP/+CxMTj5ZsxeBoY8/AYBg4aeDWn6eICN0t+Blz9PPFaChkXc11Ejc8++4yZM2eSnp5OmzZtmDNnjtNWPoB58+YxcuTISn9Tq9WUlla9eO6ifujeozu/LfrdIcAO7C3g+bIc7uhxa622abVa+WDmB8Ruj0Ur6FGLWhKFJDZt3kTbNm156623UGsqFhA3bdpEYWEhzWlfKcRXQEYQ4ZQIBWzdsgWlUkWRJR897g77NFGK0WYkNCSUbt27MXr0aL75+htyxPNoBT1WyYRJNBEeFs6hAwfZvj22/LUR4RGMeW6MPQzvrz/J4AxBVG63LSKfAnK4f2jNPe+NRiOfffopXvgSJkWW34Tc8cRb9Ccx0+5rP/rp0TXe5vWmJgt3znI0qroQX07QuNzF9noJGmV4e3szevRonnzySYqKitDpdA6Dy6vlSrs26kLYOJBy9cHhN4uw4eXtxbvT3iXldApHjx5FJpMRFRXl1LNaLpcjVRE4ChcCTOU1r7Lx8/NDRCSS6ErXryDC0eNOEsfw8q69wJCTk8Oxo8cQJZFmzZqV+6r26tWLb775hnRzCmE0rtRtYsZMjpBOr969rkto3b+R2uZoXI7qBq43mqBRhpeXN8899zyjRo0iJycHnU6HweBFTk42L774PJ5uJia97kN0SzVWq8TWXaV8+9M+Jk+exPTpMxCE2lWpuXDh4t9Jw0YNeXbMs3WyLZkgo23btlUuGG3ZspWjx47ShCjcqKjiDiIcveTBwUMH2b59O9171Mya40zKGTZu3EheXh4+Pj70vaUvQUHV20lUPmDo07cPffr0IS8/D5tNxMvLUOOA9H8TJ0+e5MDB/UTQHAMV+Q0q1IQTSaLMxK+LfmXSpEnX/diqu+fbbDZKSkqIzy1AIZc7Xax6atRTBAcH8+eubWxbXEBJRjZ6vYbBg4fx0EMPcbLUvv02AYEQEMi9997LvF9/Y/8RC90GG5BhImHLWfYfNnL77bc7fL+rmhvZszOWExu7FZOplNDQBgwZMpQOMR3w9jEwf1Ehb77ghUJRMUbML7Dx53IjnTp1xGBwzAG50fj++3msWrWSJx50Z2AfHWq1QGGRyOKlRcybNw83dzcGDnBcxF2+fCnRLTWVBA2w21U9co87G7aWsGpDMSMf8MRotMd5+3g5H7fo9QIatUBJSYn930Zv4mJPM/itcCRMyC4pyIwIVxIcqEan06PT6rBYzGzdupXY2G2Ultot11r16Yi/v3+Vc87LCRtl48qLO/P37t3Lu1Om0DhCySujvQgKUJBwKp/lGwQ0IeFMnZ3KuBe8yrMki0tEvvqxgIwsGxYxmcjocOSCwIkDydV8Iv9digqLAFBXUYCrQlPpeTUlJSWFc2nnaERLp24lPgSQIaawY8cOhgy1d31FRkYye/YsTp1KJiM9Hb1eT8uWLWvdIeLCOf+V/Iy6tJaqCddc1Pj11195+eWXmTt3Lp07d+ajjz5i0KBBxMXF4e/v6NkN9uCZuLiKymtnyraL+mfIkCEsWbKEU8YThIqN0WKvFjBSzFkhEXc3NwYMcKxGqY4ff/iRHdt3EEEzPEUfu6+taPe1PXL4CHPnzuWFF18of/7Bgwdxk3mglhz9UmXI8BR9OHL0CH379GXrxq0YRF/UVDxXRCKN0+h1buWtyUOHDqVH9x5s2LCBtLQ0dDodxcXFrFi5Al+CCKMZKlQUUUDGmbO89eZbvD/z/fJW+FJZCQbJDzly8skhV5ZJVFQUgy7xQTSZTOzds7e83bVdu3bllVWbN23GbDLTiAYONyE1GrxEf9atW8f//ve/a1f5VAdcrkvjYqqyUanOE/5GFTQuRqFQYDAYrtn260vYAOokOPxmETYAwhuEE96g+i6bmA4x/HXmb2yiDfklfqQiIgVCLoM71twTdc+ePXjg7VSQ9cALveDB5s2bahzCZzQa+eKzL9i8ZTOiZBdfZMjo0LEDzz/3PJ4GT0Y/PZqPP/4Yi8yEjxSIEhXFFJAtpKP3dOPhh28Mr+6blRvBdqq+BI2L0Wi0lWyllixZgsVSwpSxPnh62H87KpWMW3po8fcReGv6Cfbs2UOnTp3r65BduHDxH2XNqtV4yLxwkxxtaTww4C4YWL169WVFDdEm8tnnn7NmzWpUgho1Gkox8suvvzD8juGMHDmyxgHUAMiodoxZVFTMoUMHsVisNGrYkLBw5576NzOxsbGoBQ2eomOBh4AML9GP/fv3YywxotVd/4KMS+/5ubm5/Pbbb6xdu5bS0lJ0oQ1p17Yh7vp7CQm95J4sg9AuMXxxz1BSU89htdoIDgpCrVY7XaB65JFHaNSoEVsO/8Pvn+8GIDKyMa+8cueFcWLFd6uquVFiUiITxr+NxVxKt05qvDwEDh0/xrvv7qNfv1t49pnnmD59Gm9MzuHWgVoC/BTEJ5pZssaITdTxxBM1L+KrLwoKCli2fCkjhlcOPHd3E3h8hAfZuTYW/foL/fv1dxAKT506xYg7qlh8VglEt1RzKsWezxkYIEcQ4MgJE2EhjstuSactFJfYyt0cyvalLSwEfByEDUkCq1VCLhfIzDzP+PFvc+5cGlHNNRg8ZcRuPc7SpUsZ9OQD9O5FtfM7qN6Oqqwzf19GKp9++glto9S89aIBudz+HYpspKRTOyMvTVKRrwrh6TdO0yJSiUYtcDTOhCjBg3e5s3BxHvv37aNDhw40bxvBsQPJl53zlRSXUFhUiKeHZ7WZOLk5uWzYsIGsrCw8PD3o3bt37cThG4SAQPvnUUwR7jjeY4opuPC82s2VS4x2G7iq3EoUKJALCkqMJQ6PNWwYQcMLtmMu6ob/gqBxvcWMMq65qDFr1ixGjRpV3n0xd+5cli1bxnfffcfYsWOdvkYmkxEYWLOWGBf1h8HLwJR332XypMnE5e1HL3dDAkpsRfh6+TFhwnjcPRwX4qrCWGJk+fLl+FHW1m1HhgwPvAgQw9iwcQOPPvooXt72ChCbKFK9MauAKIocO3octV5NYvERvER/9HhgxUy2kIFRKmLsC2MriQMenh7cMfwOAPLz8nn88ccJILRSF4YHXrhJHsRZDvLBBx8wZ84cAoMC+X3RYpKS7RcDg4eBEcPu5+6770GhuPBzk2DJ0qX8tGABJcaS8kBCL4MXo0ePpmu3rpw7dw6NXIfK5vwm5IYnGeYz5OTkEBh04/1WquvSuJIcDWfcDILG9aI+hI26yNeAm0/YuBxDhg7ln3+WkCLFEyY1QXHhNmvDxhkSEGUiQ4c6tv9XRWZmFlocAxnL0Ig6zmdk1mhbNquNiRMmEX8yniApAi/sPtx5ZHFo7yHGjRvHrFmz6NevH+5u7vz000JOJdutshSCgu49uvP444/j47KeuiLqOkfjSm2nbgRBwxmbNq2nbzdNuaBxMVEt1DSOULNx4yaXqOHChYvrTsb5DDRS1V7ialHH+QzHgOZLmf/DfNatWUsIjfER/REQEBHJJI2//v4LDw8P7rn3nqs+XqvVyrzv57Fi5cry/A+AVi1b8cILL9yQc4crxWg0opCpnFYiA+Wd/KWlpddV1HB2z8/MzOS1116jMK8IL9EPv+BwjHlpbN+8gx3bdzB12lSaRNrnK1aLlZ+3rOfEri3kF+Ti6+NH/wEDCQ8Pr/Ze36SDL8P7j8cyxr6wrlRWdlOobl5ktVqZOnUKAb5WJrzih7t7xYL+xtgSPvpyPU2aRPLu1Kn8svAnPv7qMAAKpZwe3XvwyCOP4OfnvGj1RmLPnj1YLTaG3OJ8fD20n57N27NISEigadOmlR5TKpUUlVTdkV1UIqJU2r+L3gY5AX5yfl9aRNcOGgyeFeMbq1Vi/q8FaDQqZDJ7Voe3txdhYcFs3p7LGx1KOGPRVRI2TiSYOZ9lpnXr1kyZMgmrOZs50/wID1Ve2Cb8sayIn775GR9vb4iqWtiAmnVt/LFuM4Uebjx8j6Jc0CijoFAkP+8MT/+vJXkFzUk7lozVKnHvbe70763D4CmwbK2RhIQEOnToAFCtsHHqVDILf1rI7t27ECWxfO7x0EMPVb5mSbBo0SJ+/nkhCgUE+qvIyraycOFCBg0azNOjR9+QFnxV0bxZM0KCQzmfdga95F7JylxE5LzsHOEh4UQ2iazVdgMDAxBkAkVSQXnx8cUYKcYimglx5dZdc/5Lgka9FBFfy42bzWb27t3LuHHjyv8mCAL9+/dn+/btVb6uqKiIBg0aIIoi7du3Z9q0abRq1epaHqqLK6Rx40Z8+903bI/dztGjR0EGraNa06VLl1q3qR0/fhyT2YQ3zgdDXvhzTjzFoUOH6N2nNwAtmjdn544dWDGjcOL/nk8WGnSUZJgooIAWLVqSlJBIhsX+445uGc2IB0cQFeU8OA1g85YtSBL44XjBF5DjTzBnziYyZcoU3hn/Dj269yAvPw+rxYq3t7fDTfWfJf/wzTff4EsgDWiBCjWllJCRd4YZM2bw9jtvo9VqsUpmRCSn3vwW7KHjN7IFjLOLdl0Gg4NL0LiY6yFsXEpNhI3L5WtAhbDxb8DPz5e33nqTadOmcdyyF/cLVZ1FQj4yQcbYN94gOCS4xtvz9PQkP6/qdmOzrBSDoWZi0PYd2zl+4piDfYYPAehFd06eO8iatWu57bZhdv/uTp3IyMigpKQEP39/3NyqFldcVE9d5WiUcaW2U/UpaJSUFLNx40YSEhJQKBR06NCBDh06lNtJFRQWEehf9T0t0F+gsDD/eh2uCxcuXJRj8DSQmZVT5eMWWSkBXtVft4uLi1m6dBl+hOBHxXVaQCCAECyYWbz4D+644w6UKse8ghojwexZs9m2dRv+hOKNP3IUFJJL0olTvP76G3z08Wy8r8C68kYkJDQEo1iEFUc7ZLDbAOt1bnh4XL8cwqru+V9++SXF+SU0EaORQuzvvztyfMQgTlmOMevDWXz+xecUFRUx7vOPSNy/izYtNTSNEEhOyWDatL1ED+vOvffeR0xI5fv4pff3S8WMi6lqXrRjxw6yMnOY8FJlQQOgTzcdew6YWLLkb+bO/ZLJU6aSl5dHUXERPt7eaLU3T4Cw0WhEoZTh7u5cCPMyCOXPu5SOHTuzMXYLI4ZLley3ANLPWzl8zMz/PepJVraVv1cWk5ZhQ6fT8PKEHIYN0BDZSEV6ppWlq4tJOWvF31fJlCnv0rFjB8aOG8cdw+/i0zmfsnS1ilv7S8gEGWcskJNn45OvSwgPD0UmE0hOTmHam77lggaAQgH33eHGiQQLO35fTquoqGrnd3B5YUNenIdCKaPAEAxULqIqq9cM8s0gIiyEtq2a0dq/Qty12cBsEisKOy/i0mK2uLg43n7rbQSrgiApAjVajGIxO7buYs+evdx5151oNRpCQ0NJS0tjwU8LuHeYG3fe6oZeJ2A2S6zeVMK3C1eh02oZ+b+RDvu8YZHBs2OeYfw7E0iUjuIrBqFBRyklZAlpmAUjzzw7rvo6XicYDAY6d+7M/l0HMIjelazaRSTSZSkY3A107Nixjk/IxcVcS0GjuvkgVO94cik3q6AB11jUyMrKwmazlXt1lxEQEMCJE86DUps1a8Z3331HdHQ0+fn5fPDBB3Tr1o2jR48SemlLJnYLH5PJVP7vgoKCuj0JF5dFoVDQs1dPevbqeVXbsYk2AGQ4F0PKVGurzVr+t/79+/PTwp84Y06igdS0krKdRTpF5NOAZhjw4RzJxJ88yZdffokoiWi12hoNcPNyc1ELahQ25z8XzQXle+++vWzcsJFb+t1SZSu4qdTETz/+hC9BhNKo/O9a9DSgGUkcY97383j99df5+ZefySPTQeSRkMiRnadl85Z4GhxbFOubyy3e1VUw+PUQNHJzc9m5ayfGEiOhoSHExMTc0H7u117YqDo4vCphAy6frwHVV+7cbLRr346vv/6a1WvWcPDAASRJonXrgQwcNKjWXQ79+t3C9999j4nSStZ5ACUUUSjlcUu//9VoW+vWrcddMOAmOl43NOjwwJs1q9dw223D7H+U1b7V2YUjtc3RuFwIXFUD2MsNXGty3bxW7N69iw8+mInZbKZJhApjqcTKlSsJDw9l/PiJ+PsHEODvx8mkPKevF22QcMpGdNt/T3WxCxcubh5u6XcLXyd+TSkl5eP+MowUUyDlcku/h6rdxv79+7FYzPjg/DrmQwBxJakcPXa0dmGwlxAfH8+WrVsIJ7LSHMKAL3rRg5OFB/n7r79vrkW/aujbpy/z580nzXqa0EuywIyUkCtkcvug266bJ3xV9/zs7Gx2795NsNSwXNAQ8u22L3LkBIjhJKYe4ejRoyzcvI7MhH18MNGXyEYVC5FLjuv5bckp9i5bTsxTT5X/vbbd61Ue+7GjhIWoKy2UX0z3jhq27EgnLy8fg8FQ/t+bjZCQEKwWibgEC80jHYsiD58wIZPJnBYhDR8+nM2bNzLzszyeftyjvPvizDkrM+bYhc+vFxTwxTwJrVbDY489Rs+ePfnppwUs+H0TNps9Z8PfV85dt7px721uHDpu4v1P9zF/3jyefPJJUlJS+HrBPyxdY6RVcwW5uTmkSjpatvDgvgfGs2zZMgL8VLRs5tyCuk93DR9+fooGgjspYuFVCRt6vZ7CcymEuZk4kG+/drX1tIsbIUEKvD0F1m0pYfRj5zly0pd9Vn/aB9uFjR17jRhLRWJiYipt06FLX4KPZn2E0qqhkdgC4cJakAcGfER/4ksOsWDBj6hVAiaziFIp0C5KzcP3VqzjqFQyhg3QU1ws8vvSpdxzzz21cgypb6Kiopg2bSrz5s3j2PFj5X9v1bwVj498nGbNqv78quOJJ57g1WOvklB0GG8xED1umDGRLWRQSjFvv/i2Ky/jGnItBI0bqTsD6l/QgOsUFF4bunbtSteuXcv/3a1bN1q0aMGXX37JlClTHJ4/ffr0egn+clH3NGrUGEEmkC/lVKpgKiMf+0AhMrKi9c7N3Y2xY8cybeo04sR9uIs+yJFTSC4lFOFLMAbs2RyBhJIjZrBt2zaG3zm8xsfl5e2FSSytsgLIiH1A6o6BZcuWcUu/W6rc1u7duzGajETQ0uExGTL8CCbp7DFEUaRrl67s3rUbRDDgh4AMMybSOU2JVMiIB0bU+ByuN9V1aVyMM1/4mggal+NqL642m41vvvmGlStXABJqlUCJ0YavrxfPP//SVU0265IzZ85w/MRx5IJA69bR+Pv7X5WwAXAkr/qBb1XB4YdTnFtN1TRfo4x/iw2VwcvAfffdy3333XtV2xkwYADLly0nKfMYQWI4HvgAEnlkkS6k0DC8ET169KjRtnKys1GJVXvTqtGSl5t7Vcfrwjl1EQx+pTka9W03lZSUyPTp04lpo2L0o354XwjMPJlo4cMvzjNhwjvMmfMZAwYM5ocf5nHXrRYahle+167bWkJGppmBA2uX0+WIhCRJyGQ3jy2BCxcu6p9+/fqxdMlSTmUcJ1AMxxNfyu7FGUIKYSFh9O7Vu9ptlBXhOZtLACgvTMsvLta7EtatX4dG0OIl+jnZhwp30YsVK1bSvHlzWrVqhYfn9etguBa4e7jz9NNP88mcTzDLTHhJ/ihQUkQ+OUIGwcFB3HuVY7GaUt0CVsqZFCRJQhtsf6xM0CjDDQ8EBNadPMqBLWt59n+elQSNo0YvGkXA7W3O8Mfy1Tz88MPodLo6tuOVIVbtrETZQzd73mmbNm0ICvLnx98KmPCqAZWqYkyQX2Bj8RIjMTHt8PN1/A01aNCAsWPfZObM93jipfM0b6zGZJaIP2XC38+Hp566B1GU8PDwoGPHjuWuCsOH38mmTRsBMBhkaNQyFi8tYuWGYl7+Py/uGabnj+WreOCBB3nyiSfp2aMHK1euJOXMaTQaHcN79KBp1wYoVWew2UTUKhlVfQxqlf0Bq9VKlHfN5ndVCRudOnVCqVSwfG0xD9yVyZFUPw7k+9HWMxOZDDRqGZu2G9m83YhEOl7e4bRurqdP82zm/lBE27bRNGzY0GF/FwsblGZzLu0cjYkqFzTKUKAkgDBSiOejKT4UFsMPv+Zz5ISZhCQLTRpVvp4OvkXHwj8L2bN3L3379nHYr9VqZe3adaxcvoK0tDS0Oh29+/Ti9ttux8fXp8r353rQvEVzZrw3g4yMDHJz8/D28sI/4Ors3PwD/Plw1of8uGABW7dsJc1mt6WLbhXNQw8/RIsWLeri0F1Ug0vQuPZcU1HD19cXuVxORkblyu2MjIwaZ2YolUratWtHQkKC08fHjRvHyy+/XP7vgoICwsL+fSFo/wV8fLzp0qULe3fuxV30REOFDYUZE+eFFKJaRBEeXjmoNyYmhk8++YTff/+d9evXI6BAhxuNaIk7hvKKHQVKtIKedCc2OtXRq2cvvv3mWzKl1EqZGgAiNjJJxQNv3PDg7Jmz1W4rPz8fAcGh4roM9YVzzsvP5+VXXmbWrNls3x5LunAahaCi1FqCSq3ipTEv3zAL6xdTVZdGTW2naipoVHfhrYuL6xdffMG6dWt4+B43BvXRo9cLJCWbmb+oiMlTJjFj+nsOHqvXk+ycbD6aPYuDBw+X/00mk9GtWxfGjHmecL393FNK7O9F7bs2nA98q6vkaR0exIGU/1Zw+PVAp9cx/b3pzPpgFoeOHLqQwQMg0bF9R1588UWUysq38sKCQtZv2EBKSgoatZqu3boS1SoKX19fMlNOVsxKL8GE8V9jR3GjUFc5GmXUNkejvgUNgL/++hNfb4HXnzGguGju2bSxknHPe/LC22ls376dIUOGsHnzRt6ansKdg3V0ilFTaoKNW0tYuaGEAQMGXHGl2tGjR/jrrz/Zu3cvNptIkyaNGDp0GLfccotL4HDhwsVl0Wg1TJ8xnVkfzuLAoQPIiC+/F8e0ieHFl16slM3njAbh9jlEEXl44OXweAF5AISFhTs8Vhtyc3JRiRqHjAkrZs6QRD7ZYILpM6ajEBT0vaUvT/3fU6jVznP8bgb6D+iPl5cXv/76Kyfi7G4QWo2WoQOHMmLECPT6a2+febmKXLVajSY4HBtWVPlmh8dt2FA0DMGak4YkQc8uFfPgo0b796WNz3l8u2j5+c9CTp48SVAL+3Nqeo+/3NwoOro1S5cuJTHZTOMIx+/zlu2lhIeH4HmTC2EymYznn3+JiRPH88rEHIb20xIcqCAx2cyytaVYbVpGjfq/Kl/fsWNHvvvue9atX0/8yZMIcjm339Webt27o1Q4ipaiKDJj+lTcdDJeGu1Fuyg1MkFGRqaVrxcUMP2TXF57xoDJVEhcXBzt27enWbPmNGvW3GFbKSUJdOkgZ+lSE+fSrIQEOS7n7dpXiq+vT6Uumurmd2U4CxD3cHfnjjuGs2jx7yjkMob2t3EqP5AtZ33YtOgkqedtaNDiSzAq1JTkFHHoiI0DR9wJ8vXg1dder3J/ZfO+A8dSALuw5ww37OeRmi7Soa2aCa/68PKELH75p5C3X6w8b/FwlyMIMkqdWIeZTWYmTZrEkSNH8JR5Y5ACsJjMLP17GWtWr2Hq1Kk0bOQowFxvAgICHJxurgY/fz9efvklnh49mpycHNzc3G5It49/GzWdA4JL0LharqmooVKpiImJYd26dQwfPhywX9TXrVvHmDFjarQNm83G4cOHqwxWVavVN/UgzEVlnn76acYmjyU+/SCeki9adJRiJF/IxuDlyUsvveT0dSGhITz11FOsX7+eIMLxddLpISJhkUzo9W61OiYPTw+8vL3JyDqLFSu+BKFCTRH5ZHAGC2YiaEYumZf9LqrUKkREznEKdwyVRBewt7AD+Pn5oVarGTduLCkpKcRui8VoNBISEkLPXj1vqiyNm03QSEtLY/Xq1Tz1iCe3DqiYBDWKUPHOy168MjGbn39eyIQJE694H1dDcXExb705DrMpm1ef9aJLezU2ETbFGvnht11MnDieGTPeRy6XX6Udlf39vnTweznvVVdweN3j7e3Nu9PeJSUlhePHjiMTBFq3jiIoyPF93rBhI5/OmYPNZkMnuGHDypKlS2jerAWDBw9i957dFJKPO56ISBSTjw0bIFFADiMGXX1AqQs7Nc3RqOlA9kpzNK5W0LicXcXlMBtjeegeBXKl6cIiYAUNwuGOIUoyUpejFDRMnXwPa9as4ciRw5xIstuJ6vU6XnmxD927dUa07K71/g8cOMCypUvx81Pw6rNqlAoZ8Uln2bb5YwrzNnD7bbdTZbmjixohWh0XD1y4+Ldh8DIw+d3JnEk5w9Fjx5BhtwwJCa1ZyGqTJk1oFNGIjJQz6ER3FBdNw62YyRTOEd0qmuDg6sXty+Ht7Y1ZKEUSpfI5hg0bCRzFioUwmuCJNxISuWImG9ZvICM9g8nvTkYQHEVeURTZv38/J+NOIpfLadeuHZFNaxdYe9VIcOTIEVauWsW5s+dwd3ejd+/e9OzZs1xMiukQQ0yHGPLy8jCVmvD29r66bJJaUBOLEZObJzq9jqy0k4ThOE/JamjFlJVKRIOBAJRFMl4saADlYc0lqlSgcY3u8TW9j3fq1ImgIH8+/iqPCa964eNtr5qXJImV60vYvsfImOfupNbm/jcgrVq1YsZ77/Pzwp/48oe9SJKEUqmgZ8+ePPjgQ/j7V18h7+bmzh2331Gjfe3fv4+09PNMe8uXVhdZRgX4KXhjjIHn38pk7Wb7fdRms1W7rXBdE4Lah7Omx2rWb83j3tt90Kgv/M5t8MufBazfWoKvr47333+ffv360aFDB2Qyodr53cVcOtd7+OGHsVpt/PzX3/z6TzGe7jmUKAOREYA7BTSkRbkFuAdeWArMpBmycY9oiId79QJY87YRZBScxs2nARZ0qLIdxxPWC1mimgvLLCqVjFsH6Phyfj7FxSJ6fcV161icGVGUnBY5//zLzxw7epzGtMJNqljUDxTDOGU8zrRp0/jqq6+QCTf/99sZWp2WEJ0rFPxa47Kcuv5cc/upl19+mccee4wOHTrQqVMnPvroI4qLixk50u7j+eijjxISEsL06dMBmDx5Ml26dKFJkybk5eUxc+ZMTp8+zZNPPnmtD9XFDYCnwZMPZ33I8uXLWb1qNZm55/Dw8OSeAXcz7NZh1bZIa3Va2sfEcHz/CbzFAIeA7TwyMYsmevWsefaHKIqkp2eQlZWJHg9yyCCbik4PLW40oRVqNOTKshjU07k1hsViZe7cuaxbuxYZMrLJIJNUVKgJJxI3PBERyZSl0rRJM0IvmiCFh4c7dKfciFS3gFeTHA24OkGjjKu9uG7ZshmdVs6AXo7CkUIh49b+Wj7/fj+FhYW4u19/r85Vq1ZxPjODT6f5ERRgv4QrgUF99YSHKhk7JZ7t22Pp0cP+Pa+psGEymdi6dQvJycmo1Wo6d+6CyU/r1If1egWH3wjCRkF+AXEn7d+/Zk2b1YlNg2gT2bV7N5s3b6KwoIig4EAGDhhIk8jqv7uXuxYcOniIj2bPxgt/gmmAwqZEQqKQPJLiE1nJKlpHRXPs6FH0kiclFGLFUv56tUpNkyY3xuDkZqemA9raDGSdURPbqavh6getEmcPZOLV2oOwKtaWZFkFFBWY7fvQQeR9rSi+tZiUlBQUCjkNGzZyGjJZEzIzz/Pd+z8ysI+W0Y96IhMkQIIOApu3w4dfbKJ1UAy9e/e57Hn8GxZxrhUF1uL6PgQXLq4bYeFhhIVfgSuADF58+UXGvTGOeNMhvEU/1OgwUkyecB61XsOzY5696uPr168fy5YvI/eiXL5sMjBhpBltK2WC+BOCRtRz6Mgh9uzeQ6fOlceJp5JOMeO9aaSlZWDwUGK1wYKfFtCqZQveeGMcBi/DVR/v5ZBEiTlz5rB23Vp0ghsaUc95WRYHDx7k999+Z+r0qZW6TK93xkNN7/dyQc6wW3vyzTffoEKNH8EIyBGRyGpoIy1zPwMHDaR9THv49ltid5fi2y6kXMwoI3Z3CY3aexEUFFSrBama3McFQc7bb0/gnfFv8dSr5+nUTo3BQ+DQcStnU83ceuutDBxwtTaQNw6NGzXm7bfHU1RcRHFREZ6eBjSaqm1ar5TY2FjCQ1W0bOo4EFIqBQb00rFgcSGCICPyMnMB+2tUPDl8DF/8Npsv5mfi5aHBTS/w98piCgptRDZUER5WQnLKXqZMiaV9TDveHPcWKpW6yhxFURTZt28fSUlJKJRKOsTEkKtWlQsb/xs5kuHD72Dbtm0UFBRQUlLC2p0H8G7YGuFU5RZwJSp0eTJOnoxnW1wC3ZtVf04xMTF8LH1CgSwHbx9/hOzCSo9nkYHBQ07zyIri0aAABRJQVCJR1ohlMkn8+HsRISFBREVFVdqGxWxh5YpVeEv+uFG5S0GBgmAxgvjzh9i3f59DBogLFzXFJWjUD9dc1Lj//vvJzMxk/PjxpKen07ZtW1auXFneUpWSklKpKiQ3N5dRo0aRnp6Ol5cXMTExxMbG0rKlYwaBi38nOr2Oe+69h3vurX3F8AMjRjB2/1iSZScIkhqgRYeIjRwySZMl06NrDxpENKh2GxaLlRUrlrNs6XLS0lNRCPafSQiNSOY4IhJ+BOKOFzrcMFFKIsewYeHQwUO8+PxLtG4TxZAhQ8urrT6e/RFbt20jSGqANwHIkVNMIamcJoljBNKAfFk2ZrmRJ0fdvAJeVV0aF1NdjoYzauMVWxcX18LCIry9FKjUzi1JAv0VSJJEUVFRvYgaGzaspXtHTbmgcTEtIlW0bKZhw4b15aIGXF7Y2L17F7Nnf0hxcQnBgWqKS0QWLVpE27ZtuO3px8igKmGj9sHh10TYkODkyZOs37Ce3Nw8fLy96devH42bNK7+ddVgNBr56quv2LRhE1bRCoBCUNC7b29GPTmKpFNJZGZm4eHhTps2bWq86FpUWMSEiROJjz+JXvBAKSqJOxLHypUrGTpkKP/3f/93xRVCi35dhE5wJ0ysCMqUIcMDL0LFxpyIO8akSZP50VhCQmIC3gTgRxBKVBSRz3nrOcaPH88HH35YSVh1UTuuhaBRH7ZTdTNoldGgQRj7j2QypJ+j/UepSeT4SQtDb60s1un1+jrx+V25ahVajcDIBzwcfle9umpZu8XI8uVLnYoaFouZ5StWsGrlcs6dS0enU9Ote0/uvPNOQkPqz87LhQsX9YvRaCQ9LR2lSkVIcHCt7tkRERF8OHsWv/22iE0bN2O1WVCr1PS9pS/33nsffn6+V318TSKb0KdPXzZv3GS3lSSAHDLwxMch5BzsYbxuggdr162tJGpkZ2Xzzvi38PO28P54P5o2UiJJsPtAKXPnJzB+wtvMmvXRFYvONeWvv/5i3bp1hBOJl+hnH99I9kzD5IwTTJ86nZkfzKwX3bk29/vmzYJo3vR28nLzWLx4MVlCOhq02Br6UpCWSK9ePRk16imUCgXt2kazcG8Ok1ulcvFyTXySme1JCqKiWtMpuGY2uLXttgwLC+PTOZ+xdu06tm/fSmqmkUZNInhmzOALC8V190bn5ORQairF18cXlap6+7ZriZveDbdaOjnUBqOxFC9PocosEoNBjs0GPXt2x2BwtKZzRqtWrXjTezJLYxdz/MQxlqw2opBLTBnrQ3TLisX/vQdNzPj0EN999x2jRz8N4CBsnDx5kpkz3yMjIxNPDyUms8i877+nY8cODHz0UY5gn+95e3lz27DbAPjr778wZ6ah8WuBsSFoT1UubnDDg7P5+8nJybms/bCb3o3hd9zB74t/R1Aq8PAxoMguxoqVTM6RQwZP3eHJxZeahCQLggw2bC2hWRMV59KtLF9XSlaOxJTJLzl8TdPS0yguKSKICKfHoMMNtaAhLi7OJWq4uCJcgkb9cV2CwseMGVOl3dTGjRsr/Xv27NnMnj37OhyVi38jTZs15Z3x7zBr1mziCvajlmuwilZEyUbv3n147jK2ZxaLlUkTJ3Lk8BE88SGMJhjFErJIxYyJxkRxiuOkkUIOmQgIFyyjZAiSQNHZUqCUFSkrWbJkCa+8/Arh4eFs3rqZcCLLK6YA9LjTmJbEsZ9UThHZKJJRT42iefMr8wyvT5x1adSV7RTUPPyuLggI8Cc900x+gQ1PD7nD4/FJZlQqBV5eNRt01jX5+fmExFR96Q4JFEhOdQx7rkrYOHkyjunTpxETreaJhwII8JMj2mDnvlI++/4of875hgdef67K/dU2OLyMmgSHlwkb1WG1Wpk9azZbtm5BI2hRihrMQilLly2lb9++vPD8Cwjy2nnmW61WJoyfQEJ8Iv5iGAbswXF5YjYb129iy+YtmC0Vfsge7p48+tijNQox/vCDDzmdeJpIWqMX7V0fkiiRTQYrVqwgMDCQ4XcOr9XxAhhLjBw6cogwmjj4aAO4Y0AjaImNjeXUqVMEEFopI8iAL+6iFwmmQ/y0YAFvjH2j1sfgooKaDmgvl6EB1Q9kr5XtVF0OWocMuZW5c+ey92ApMW0qKiAlSeKn3wsxlooMGjT4qvfjjKTERKKaK9FUIVJ3bKNi/qIkh7+bTCYmTHiHkyfj6NZRy239PcjJFVm3dSNbt2xmwoSJtGzZ6pocswsXLm5MioqK+eGH+axftwGzxW6HEhQQxH3330e//v1qvJ3g4CBeeOEFnnnmWYwlJej0ujoXBl584QV8vL1ZunQpGeazyJDhRdWCiUrUkp2ZXelvS5YsQbSVMuk1X9zd7NdQmQw6t9fg6y3n5QkpxMbG0qtXrzo99osRbSJ//fUXXvhXmkMBaNERLEYQF3+ckydP0rTZ9c26q42gIUlw8MBBNm3aREFBAX379kWpVJKtVRLkpqRPn+do1LBR+WuGPv0gP334Ic+/lUXn9mpCAhUkpVjJlJSEBAfzwj2P1egYr/Re7ubmzvDhw8vtwwGMxhKWL1/OkSNHkclkREVF0adPnyuyRN69exeLFv1CXJw9M1Wn03DLLf158KEHr6m4UF+Ehoby997tlJaKaDSO45FDx0yo1XKefvrpWm03OCiYp+5+jmMZ+ykp+YQBvTSVBA2AmDZq7humZ9GSNTz88MO4udkL8sqEjWOpsUwe/zENQiRee9qXyEYqrBaJbbuNfPXjAUrmFnP/Cy+Uz9fL5nwajRabZEOWkYcUYMDYUF9J2LBc6ASPbOSL2Xr5ed9DDz9MyYXvmJcqCIWfErNooij7LA/e6c7Qiwpj8vJtLFtrJCAwiMXLsjBbChEEgc6dO/H62Adp2DDCYftywT6nFx2MUCsQEcufV9ecTj7NP0uWsHvXbmw2K5GRkQwbNowOHTq4GoH/BbgEjfrluogaLlxcT9q1b8f3875j165dnEk5g0arpUuXzpcNXMrIyOCrr77i8OHDBNEAP4LLFweLKSCTszSmNc1oSyF5FJCHFQtGivHEmwZEInDhhimKnCWRD2d9SL9+/VEJagyin8M+BQR8CSJNdppp06eh1qgdnnOjU91FvK5sp6qjri+wvXv34fvvv2fRP4U8+ZBnpaqa3DwbS9eU0rNn72vSnlwTfH19STrtPOxekiSSTtsICHbuA+tM2Pj9998IDlDw+hhDeQWMIIeuHTUoVTKmfHgM2dk8MkKrtqG6VsHhYP9eHTuQXGW3xvfffc+2bdsIpyleoq89SFuUyCWTTRs24e3tzWOP1WzyV8aWzVs4EXeCSKLRU9GNo0WHKNnQWtxoQDO0Fzq1zhee5dNP5yDabAweUvXibEpKCnv376UBTdFfFIYnQ4YvgZRQxJ9//sntt99eayHGZLYvsMhx7vEjQ4ZCpuR0cjJI4IdjJ4YcOd5iANt3bKe4uPi6BGv+26htMHh11JftVF1fUwcOHMiePbuZ+tFeenTW0LGdhpISkQ1bTRyPL+X//u//LutdfaUolEqMBVU/XmK0e2hfyi+//EJiYjxTx/nQPLKievSuYXrenZXH++/P4Jtv/p+98wyL6lqj8Du9AMPQRRApYkHF3nvvGqNJ1FRTjOkxxSTGEpOo6dEUY6rpmmLsvZfYewMLioggHQaYYdo598cIigxVsOTOe5/7I1POOTMO++y91/etNb/GK5RduHBxe2DMN/L6a6+RfOkyPkItPNBjx0ZmSipzPptDeno69426r1LHVCjkKGoocFkqk/LwIw9z7733cvzEcb6aOw+TE6/6QixSE96+xQNyt+/YSrcOqiJB41oiQhVE1Vezfdu2GhU1Ei8lkpWdRQRNnD6vwwuFVMnhI0dumqhx7brHarNy9uxZBEGgTkgdNOriG/xHL13GYrHw95/fcvjIUerUVlHLX8KRQ3ZyvWrRvVVTHnnkYaSSq9/x8dyLdIqIpMVHn7Jhw3o2b97AqXM51G8bxKDmLRnVuR8KRcW7GqrjXn7ixAlmzHgHo9FEVH0ViPD11zv47befmTx5Gg0blgy0Lo21a9fy5ZdfEh2l5pVnvNB7SDly0syqjas5duwI7733wX9u/tmnbx/+/PMPFizO5ZFRumJry1NnLWzfXcC9944uEhwqS/qpXJKPZdD0yXBEzEgovp/QraOGXxcZiImJoU2bq4VtTfTBvL9yJeEtNEx+SIVG47guuUJCt45aPHUypn0Qg5icQvMWLYqt+dq0aYNUIiFTTMU/VY7V372YsJHBZfz9/AkLC0cilZS77pNJpYx/cjzD7xrO1q1byc7JJjkuizOIJCSlcvKUBS+9lGMxFhatNILEjXffnYFO54Ehx4C7uzsabekCW2DtQHx9/MjOSEN3JXj8WgxkYRUsJWyrqoPdu3bz/vvvo0CJTvBGiozTR87y9qG3GTp0GI8/9phL2LhDqayYAS5BoyZwrcZc/CeRy+V07NgROpb/WnOBmc8//5zt27cDjs2/JOLJIIW61EeLO7UJ5RwnOM9JahGCO3qUqLlALFJk1KV+UUAWOMSKYOqRTw6xMTEoJaoSGR+FKFEjiAJGo/GOFDXg1tlO1cQA6+Hhwdixj/LNN9+QniEwsLcb3noZx2PN/LPKBBItY+4fU23nqyy9e/fjm2++Ji7eQkRo8UXN/iMFxMWbuf+hvqW+v1DYALBarezdu49HR7vjbG+uVbSKAD8VO3f+S8d7B1UqX6OQGw0OL8SZDVVebh5r1qzBXwzGm6uioQQJ3vhjxsSK5Su49957K1VJtn79enQSL9zEq4sLEZEkLqDFg3Ciiv6eNWipS30kSPnpx5/o0bMHKpXzv+NDBw8hk8jxFJ1XS3rjx9ns41y4cIGw8DCnrykNnU6Hh7uOvLxs9HiXeN6KBaM9D6VKiUqmQW5zfvvX4IYgCGRnZf/nFpU1TXUHg8PNt52qiTFVKpXxxhtvsmLlClatWs7WnQ5/8OjoxkybNrJG2/zbtGnDl1/s4XKqjVr+xX/zdrvI5n/NtG3bodjjVpuV9evX0K+HupigAaBWSXniQQ+en5TG7t276dy5c41duwsXLm4flixdQtKlZCKEJmiusXDS4UUyGn7//Xe6d+9OQK1bmwN2PRqthjZt2pA0LIn5P8ynAGMJCyoD2eQJBnpf122Sn5ePv2/pm+e+3hIy8/Jq5LoLEQSHV39payhwzPkEoexw5eqi8D4fGR3Cn3/+yfLlS8nJcfj/azQq+vTpx4MPPohKpSq6169b8wenTp1gykvetIpWIZFIOJjlx8WY83z76yr8/f0YcfcIwHF/L7y3a7Uahg4dytChQ6t0b6+uLvb09HTefns6kWHw4jj/ovDw9Awbn84zMH36NL788qtiuSalYTAY+Oabr+nXQ8tTj1wtHGsapaJbBy0T307ir7/+4pFHHqmWa79d8PP147HHHufbb7/lfIKd3l01uGklHDxWwPqtBUTWr8/dd99d5eMX/p2EKI2k4V5C2JBfqXey24Xr3ilycNkGuowIwqBUocFS7NlmjZXUCVKyfft2WrRoUbyYzbsOffv1Zd3adUgFGT4pArYAHXlhagznY8kmnRdGv1Bkz1fRgraAgADuvffeov/+48elbN+xjUmzjgIglUhp06YVjz8xrsiuz8+/ZOHo9UilUobfPZxvv/0GDR74ElBUvGoin4s41sbvvvs2Q4fexX2j7itmkV9VsrMcGcEegjchRF4dy4Q6pJHMsmVLaRwVRYeOHco+0C3g/LnzrFy5ktjYUygUctq0bUO/fv3x8Sn/b/3/gVstaFREzIDbc21Y3bhEDRf/34gwa9Ysjh45ShDheOOHBCn5GEginjhOUJ9meOBJGI2IJ5YzHC16u0wiQy/6FhM0CpEiwUPwJis7iwKhADt2ZJRsaTSSi1qlxkN38/MZbpSaCge/mcHgzhg8eDAeHu4sWPA7U9933HykUgnt2rXlsceewM+3/MnTtWRlZbFs2TK2bttEriGfgABf+vQdQP9+/VCpKtfx0bt3bzZuXM+U9+O5d4gbHdqosdlg2y4Ti1bm06ZN63I3CR3Cxl4sligEQcDby3mrrUQC3nopJpOpqE3ZGeXla5RGZfM1rufo0aNYbVa8cb6J4E0AKZZEThw/Qes2rcs8x7Wkp6WjEouLIAWYMJFHGI2cLq4DCCbGdIB9+/aVutFptdmQSCRISul8Luz0stltFb7WovdKpQwcOIBFfy/CW/BHy9X2fRGRZOKRK+SE1AnhxLGTpY5HBZiQIsXT07PEcy5Kp7IT29vRdqomJ60ymYxhQ4cxbOhQ8vONyOXyUsW/6qRb164sXPAbs+Zk89pzemrXckx78/IFvv45h9R0G68OHVbsPWlpaRgM+bRp7sPpOAvrthhJSbPhppXSpb2Gdi3VBPgpiYs76xI1XLj4f0CENavW4Cn4FBM0CgkgiExpCus3OOxdbkf69uvL2rVrOZ98En+hDp74ICKQRRqpkkSaNm5KmzZtir0nsHYgJ09d5q4BJY8nCHAqzk5085rNFwqqHYSb1p1sY0axDtdC8jFgEcw0anjjGUzlUXifb9CsLp/O/pStW7bQv6eWbh19UMgk7D5gYunaFcTFneGe8c8jl8vw0kvZvv1fnnrEk9ZX7BcP5/gRHZxGdLA7CYk2li5dzLBhw4g1Jld7sUJ13M9XrVqFVGLljRf80Gqurnd9feS88aKexyaksXbtWkaPHl3usTZv3gQIPDBCVyJfok6QnN7d1Kxfv4aHHnoQaQ3ZAIHDZvbAgQNkZKSj1+tp3bpNjWd6DBkyBD8/XxYt+ouPv3JsoOv1HgwbNpJ77rnnhs5f2KW0c38BPTtLuWjVFhM2du8vQCqVEhkZWex9giBgMhVQX5UBBJNiVxIguypsSCQS/Hyk5OdftZW6Vth44olxmM1mNm3aTKosAWWGGnx9IELOI50fKWHLV5lO/ULue2QY0dHRXL58GX9PGbVr18bH16cqXxNDBg8m6dIlVq5aSYY0CY3gjgUz+RgIClDwzFgf9hw288cfC8nMzOSZZ5+p0nmuZd26dYh2kWDCS6wf/QjEIM1k+bLlt52osXTJUr7/4XvUUg1ugicCRv6OX8SSxUuYOm1qpTtaDDkG8vLy8PLyKrOj5k7hVgkalenOgOrJWITbW9AAl6jh4v+cEydOcPDQQUJpVKy62R1PImhCLAdJJZE61MMDPUqpihbtWtCmdWs8dB788tMv5CeayziDBLVKg9FoJFVMLOZhD2DBTJY0jd69et1xNhalDebV0aUBNzdHwxndunWna9duXLgQj9FkIrBWYJVyNBIvJfLmpDewmPPo3klFLT81p89l8dOPP7Bt62beeWcmWm3JRXJpKJVK3nlnBt9++w2/LNrK/IUObxW1WsmAgYN5+OGHSywULBYLmZmZqNWqYgF0GsUJvLw8OR5joVPbkhOMvDyBuAQLbTpctSpy1q1RSGVtqAqpqg2V1erwa3W2OX/t49fmX1QELy8vktJSudZ21XqleslZyCaACjVSiYysrOxSj1s/MhKbYCUfA+6UFA1yyESlUBEcXLWJx8iRIzl48CDnzp1AL/jhjic2rGSQQgH5aBVurFi5AoB0kgig+L+VgJ0MaQqtW7XG3eO/52lc09zJtlM3rwpHclM7gFQqNaNHP8B3337NUxNTaRChRK2GE6cs2O3g7q7lyJEjhIeHF1XkKa7ci5esyuPAUTMBfjIiIxSkpQt88EUW4aEKTAUCCoVzqzcXLlz8tzBbzGQbsgkh0unzUmSoRS2Xk8sf328VGo2GWbNm8cUXXzjsea9UJculcnr07MG4ceNKVCX36zuAefO+4uRpC1H1i2+4rt2ST0qahX59S+8Mrg4USoWjYGPRP+hEbzyumTtZsZAkjScoIIjo6OgavY5r1zxHjh5ly+YtvPiknu4dr86dI8IUtGymZvr8fI4ePcKY0YMcgoCUotcdzvGjSe20ovf06qplzeZ0Np7Zz8CWJQuSbqRDo7ru5/v27aZjG1UxQaMQdzcp7Vsp2bdvd4VEjcRLl6gbrESnc14B36ShkmVrsjAYctHr9Td66U7ZunUL33//LdnZucjlEmw2EXd3LQ8++DADBjhR8KqR9u070L59BwyGHCwWK15eXshkNy7e1A6sTatWLfjlr+M0rKekTi0jABetcDFRyoIlRjp27ICPT3ExQCqVERDgS+wZI326ZXI8z7uYsGG1isTF2+jZq/garVDYOGVKZsKLE7hn5D1s3bYVgyGXgAB/ajeIxs3d+VyvKsJGo5ZhNCKM2MPxVRY0AJDAk+OfpEePHnz++eckJl6kaUMFPbvq6dhag0IhoXEjFYEBMub9tJZBgwcRGhpa9fMBsbGxaEUd8lK2XT0EL2JP3ZhtbHVz9OhRvv/he/wJppYQUiTG2AQbFyyneOftd/j+++8rtFaMjYnlt99+48jRIwDIZQo6d+nMgw88UKEOm9uR/xdB407o0CjkztpFdeGiHKwWK4cPH8aQayDAP4DGjRsXtT06Y+u2rWikWjyFkpvVMmR4E0AaSQQTQR4GCgQTAwcMoFnzZgAcOnSIjUkbEQSxhPouIpInzaJT844EBQXz888/YcGMNwHIUZBHNunSZHReHoy6b1T1fhE1THmD+Y12aVRU0KjpQVYikRAaWp4NkEhefj4yqRSN5voNb5GPPnwfDzcT777jWyx4/Fy8hcnvXWD+/B945pmyA+yvR6vV8sILLzJ27FjOno1DKpXSoEH9EufPy8vl999/Z+PGjZhMBQA0alSfe+8dTatWrUgwnuWRB5sx9+t/6dNdS3jdq5t0ogi//ZOLYHd0hwAV6NZwnq8BNWNDVWjRlEsWeicBmAYcgelhYZWzcurVpzdfnvoCE/locEzKFTgW9CaMqCjZXVOACUG041NG+310dDRBgUEkp1wgTGiE/Jr8CyN5ZEgv07dPnyqFLgKo1CrGPzWeKZOnkGG6TDrFv1e10Y1ahJJJCskkYMOGL4EoUJFHDqnSiwhyK/fff3+Vzv//yu1gO1VIVSaud9KktbL8/PPP/P3339QNVuLvp+JSso24BDuCACMGu5GbJ/LLLz+TkHCBCRMmABL8/Pzw8vLkwNEcxj/sSb8eWqRX5hGn4yy8+2kmefkCrVpVvPvLhQsXdy5KhRK5TIHV7ryISUTEJrHc9paNnnpP3pz8JmmpaZw6dQqpTErjqMZ46osXWRhyDJw6fQq9lxcNGzXgrQ9PM7CXlnYt1ZitIlv+NbH5XyMDBw68KTkWo0aN4syZMxw+chhPiTca0VFdbZBk4ObuzqQ3J5W51qsuCtc869etJbi2km4dS87VbJ7B1AvL5+D+DYwZPQibzYZcLuGkyR9JAcUEDQCtWoK2YSB1nXRt3w6CBoDVakGrKf37dXeTYLGUVeB3Fa1GQ2a23bFudvJvlpklIJFIaiy3cMeOHXz88Sd0aa/hvmH+1AmSk5xiY9GKPL766iskUgn9+5WejVdd6HTV3w39/PMvMmnSazz3ZgodW6sJCZITfzGLcyYt3t7BPPXU007f16fPAP7883eG9LXSJCQTgON5jrXMjrUZ5Bhs9OnTp8T7iq37gutw/5jia4djCcnlrv0qI2xA+TmLFaV+/fpkZmUwuK+WR0eX7ADr09WNP5ca2bBhI48//tgNnUsmkyFyve3XVUQEZNVgc1WdLFm8BDepB4FCSJFFF4AcOSFiJDEFB9iwYQN3Db+rzOMcOnSYd6a/jQoNdaiHCjX59jx2bdvFoYOH+OjjD8vNvL3duBMEjerozrjT1oYuUcPFbYfJaGLLli0kJCSgVqtp1749DRs0KDdAadWqVfz6y2/k5ecWPRbgH8DTTz9Ni5YtnL4n15CLXFQVG7CvRYUaATv55JIoPUt4SESxaqBBAwexZvUaLhFHEBFFwoaIyCXiMQsFDB48hIiIcPSeno7w0bTjgMMupmPHjjz26GPovfSV+IZuD5wN5jfapXErgsGriiAIrFmzmhUrlpGY6PiMDRrU4667RtCpUycAYmJiOXfuAtMnehcTNADCQ5XcNUDL3ys288gjY6u0GNbpPGnZsqXT5/Lycnn99YlkZl5mcB8NTRv5kJ1jZ+3mBN5++22ef/55evXqRYcOZs6ePc2kmen076GmeRMVhjyB9VtMHD1ZwPjx44t1d0Dp3Rql5WsU2lCVNbmtjA1VobAREhJC46jGnIs9j5ugKxIeACxYSJUm0rxpCwIDy7f6uZbu3bqxcsVKzifEECAEo8cPJSqUqEglER1exURMEZFUEnHTupewbyiGBF574zUmTXqTU8bDeAq+qFBhJI8cSQYRYRGVDjVHhFOnT7Fu7TouXrxIXFwcUpuMejRFhRojeZzjJCFE4o0jjNkdHSo0pJJIGklFh6pTuw7Pv/B8pfM8/p+pTtupinioljaZrWqXRk2Op4IgcODAAWJiYpBIJTRv1pymTZtws9IQ9+zZw99//82jo3UM7e9W1MFmsQh8/FU2y9cZ+fZjf6LqK/n06y307NmLZs2aIQgidruNXl00DOhVfFyuH6Fk/MOevP95Vo1bVbhw4eL2QCKV0LlLZ3Zv242fULvIKrKQXLIxCUa6dOlyi66wcvj5+zmtkDWZTHzzzTds3bwVm+CwwZTLFATXqcv67SksXp0OQIC/L0888QBDBg++KderUCqYNm0aW7ZuYc2qNSQnJ+Pm5s7IHiMZOGBgta+hTCYTW7ds5fjx44iiSK26jWka3bTo+ZSUZOpHyErcyY6lOuZYURFpLFvv2MSMCI9ADAriwkUrg9tnlzjXmvMeCElxhISEFHv8dhE0AMLDIzl4bB9jRbFEJ7goiBw4YqNBlPMupuvp1KkzixcvYc+BAjq0KS4K2e0i67YU0Lp1yxoRNURR5Kef5tOupZqXn9IXfZbAADnPPqZHEER++/UXevXqhUJ+53Vienl58fHHs1m7dg2bNm3g8IksfHz8GNirL4271kPlVnLzHmDIkMHs3LmDSTMvMqSfhtbN1NiMyayI0XE8VsI999xTagd5WQVtFVn73Yiw0aCuL6dOnaKgoIDg4GD8A/wrfAyrzUZubh5hIXqnz8vlUCdIRkZ6eoWPWRrNmzdn3959WLCgpPi8UUTEIM2geQvn+1S3imNHj+Et1HK6P6ZAiRueHD16tExRQ7ALzP50NlrRg1DxqnWzO554C37E5R3nu2+/583Jk2rqY1Q7LkHj9sUlari4rfh3x7/MnjMbq9mKRuaGTbSy6J9FRDeJ5vVJb+BeSivj8uUr+Pbbb/AmgIbUQ4kGE7lcTktk+tvTefvtt522JvsH+GOW7EcQBae5GEbykCLlLMcICQph6ltTilUD1Qmpw3PPP8fncz4nX2rAQ/BCgoRcaRYFgonx48cTEREOQO8+venVqxfx8fGYCgoIql27RHXUncDJuJQyB/Oqdmnc6hyNyiCKIp9++jHbt++gY1sN9w31wmYT2bozkffff58xY0YzatRoTp85jUolpVmUc+/4di3V/LYol4SEBBo1ql4/4IV//EFGxmU+nOpDUODVob5rew2f/5DDvHlziYqK4szhBMLDIhiicWPlmmQWr8oAoEGDSCZPvo+2bdsVO25ht0ZVbKjKyteAittQXZuv8cILLzBx4kRO5x5BL/iiwQ0T+WRL0/Hw9OC55yrXBQOgVCl59913+PyLz9mzew8XiSt6zoqF85IT+InBaHHHjIk0ksgmnecfex6FsuyFUGhoKJ/NmcPy5cvZvGkz2flpBAQEcPeAR+nfv1+lcgZEQWTuV1+xdu0aNFINKsEdBWqM5JLIOSKIIpM01Gjwui5IvRZ18KM2ZzmG3EPGU0+Np3Onzjdrv/k/QWUmtxWxnYLyJ7RlUdkJbE1OWuPjzzNr1gySk1Px9VEi2EX++vMvwsPrMmnSFPz9K774vIqIKFJiU6U0li9fSqP6aoYNKN4er1RKefYxPY++mML6rUZGDHbnn5VG1q5dQ7NmzUhMvIjBkE+Pzs4tDtq1VKPRyDh48GClu8BcuHBxZzJy5Ah2/buT82IsgWJdtLgjIJBNOknSeKKjoivtMX47YbXamDZ1GmfPxOEvBKPHFxGRbHsGiQmJREbW48mnnkSpVFG7dmC1BOhWBplcRq9evejVq1f5L74BYmJiePfdd8jPy6dhpApUwRxZc5GVm/5i8uSpREVF4e6hIzWt+D25UNBoGJ7Ohi02PNwdG8jWAG/8/HzZuPIk3ZvocXe/KohtveTBv0tj6dq1Cx4eJXMVbwdBA2DgwEFMmvQvS9fkc9d199NFK/NITrEw4eVBFTpW/fqRtGrVnM++O4bVDh1bq5HLJaSk2Zi/IJcLiVaefPre8g9UBWJjY0lJSWPCE75O5xHDB7izcXsahw8dok2btjVyDdWB2VyAKOJU+NFqtQwffjfDh5cMHU8w7kWqKPm5NBotM2fO4ueff+afVRtYuNhRHOrlZWfAgyNo1brsrtSbLWyIosiZi8eY8+kGkhNOFj3eqmULnnzyKWoFln8chVyOm1ZDYpLzDENBgKTLAm3r6it0TWXRo0cPfv/9dxKMp6kr1C8qwBMQSOIC+UIed901rJyj3FxEKLXglyvPiZQSEHmF/fv3k5WdSQOal3AzUaDEV6jNvr17ycrMwsu78vbeN5v/B0HjThQzCnGJGi7KJTHxEpcuJaLRaIiKiqqx7IeTJ07y4Ycf4in6EEgoSrvSoWCTRczJWGbOmMHMmTNLbLoVmAr45eef8SWQYMKLHndDR5jYiHOcYP4P8/l09qclztmndx8WL15MOsn4E1TsuQJMZJJKnZA63P/AA7Rt0waprOQkvlevXoSFhbF8+XKOHD6KCLRr2o4hQwaXCOSSSCV3dBV0WTYr1ZGlURHbqdthoP333x1s3bqdic96Fcui6NlZy59Lc/nt9wW0a9cemVSGXRCx2kSUypKTA7PFMSGQy6s3DM9ms7Fxwzr699AUEzTA8Rt8cKQHW/5N5emnxyORgM5DgSrYHatNzn333cegQYNKdGdcS1VtqMrK16jMxPba9uNagbWYPWc2SxYvYf36DaQZk3B392BIn8HcddddVa7g89B5MGnSJFJTUjl50jFpjoqKIiUlha+++opzl04Uvdbby4eXHnmJ7j26V+jYvn6+jH10LGMfHVulaytk6dKlrFu7lmAi8BECiiag+eRynhgucBo7NrTonE5OZcjwxIeU3It88MEH7Oy0k+effx61pmba/v+LVEbQqMik1hnlTWgrYtl3PTU5cc3KymLKlMn4eFn46C1fIsOViKLI8VgLX/6QzJSpk5gz+4sKV2LGx5/nn8X/sGvnLsxmC3Xq1GbAgEH07z+gzPlIbGwsD4xwnoHj4S6laSMlsWetSCQSGkbKOZvgGNPsdkeFrcrJmA0glYJCLsFud74YduHCxX+PkJAQpr/zNh+8/wGns46gkqqxY8cmWGnXqh0vvfxSlYoCDAYDhw4ewmyxEBYWSmS9yFtSXLB92zZiT8USSTRuXN1gDyAId8GDmFPHuHgxkW7dut78i7tJZGZm8vb0twirI/LiOH/8fGXEJHjgXSuT2d/Ymf72W3z5xVy6dunGp58e5NwFK+F1FcUEjaxsO9t3mxk+vIfjvi6Bl8eNZfLkN3j69Qx6d1VTy0/G4TwvDm+IISAgkMcee7zYdVT2nl7TG1FNmjRh5MiRzF/wN/sPW+jUVoUowo49Zk6cKmDUqFE0bNiwgkeTMHHi63z88Ud8PHc/33rIcXeTkZxiRuum5bXXX6/2Iq9Ccgw5ANSu5XzNVfvKeiknx1Aj578xRLZu3crSpYs5e/Y8AOHhdRk6dDg9e/agIoNGiLZeqcKGxWIhMTEBi9mCViNDJpOQlZXNvsWrCalbF4kfpRayQflrP6Ba1n8Avy9YwB8LF9K7m4Z7h0QTHZHC0Rgzfy49wWuvvcpHH3+Kn19JS+JiSKBHj15s2LaWof3sJdwUtu02kpZhoWevnuVeT3lo3bS89dZbTJ06jRjTATxEL6RIyZcasAlWnhr/FI2bNL7h81QnUVGNOHvsHP5CUInnbFjJl+TQuHHZIv6lpEvIpQo0gvOCZHd0CAgkX75824saLkHj9sclargolYSEBOZ+OZeTMVdVcA93HaNG3ceQIUOqfdL9x59/oJG4UUeMLFJ0JUjwxBuJIOH4ieOcjIkhKqr4ZGfPnj0UmAsIp+TAK0WCr1ibuHMxHD1ylOhmxbs1gusEM3z4cBYvXkwBJnzwR4qcXLJIlyYTGFCLWe+9V2qHSCHh4eG88MILN/gN3N5UZEC/0S6NsqjpYPDKsHr1Kpo2UjsN1x4x2J01mwtYs2YNQ4cOxfaNyI69BfTsXHJzbfO/RvR6D8LCwks8dyPk5GSTn19A4wbO8x289DICA6SolFLeesUHnU5KZradf/ZK+Wr+H/j5+dO3AuGPlbWhKqS68zW8vb159LFHefSxR7Hb7MiqUSTyD/Av1tLsH+DP3LlzOXPmDKmpqeh0Oho3buxU8KxJBLvA4sWL8cIfX4r/PbnhQTDhxHMKLR5YKd3r2IIZJWr8CWL3rj0Yje/x1ltvuTo2yqGiORqF3E62U9U9cTWbzezevYu0tHR0Og8uXbqExWJk+it+RWGgEomEpo1UTH1ZxjOvp7Fl65YK+VYfOHCAmTPfxcdLxsjBKjx1Go6ezOL7779j7769TJkytVSbCIlEil0ovZLMbofCfM60DAE3N8dGXu3atdFq1ew9WED9iJIWU7FnrBhybdSvX/oC34ULF/89oqIa8cMP37Nv/z7Onz+PUqGkbdu2BNep/CaCzWbjh+9/YM2atdjs1qLHw8MieOmlCYTUDSnj3dXPuvXr0Um8cBNLdgy4ocNDqmf9unX/aVFjzeo1CHYLk17ww81NSkyCw+vd10fGpBf0PDYhjTVr13DfvfexeMki3v44iYH3RVI/wmE5dfi4me9+y8fNXUdIR8fGceF9/dNP57BkyVLWbdmIPcgDfUEmI0bcx9ChQ9Fqr64PKntPr8r93GKxsH37dg4cOIDNZqNeZD369ulTZjHTQw89RL169Vi+fAlf/+y4xsaNGzHpzbto3659pa5Zo9EyefJU4uPPs2vXbgoKCggJCaFz586V6lauLH6+jo3uuHgrLaNLrhPi4h1/h35+t1948fz581m8eAmtotW8ME4PwL97U5g9ezZnz55h3LhxVFXYsFqtTJs2heysJF5/3pu2LdRIpRBz2sI3v2Tw4zsfMXv2ZyRd+W2WJm6Ul61YXmEbwNEra8DSxI309HT+/OMP7rvLndHDPYA8Tp+oTc/OKbSKVvHilEwWLljAc88/V+53Mfzuu9mxYxuT38vmgZFutIpWk2+0s36riQVL8ujcuVOJ4tSqElk/km+++ZqNGzeyd+9erFYbDRp2ZED//gQFl9y/utUMGzaM6Uemk0Ii/gQVFcYJCFwkDrlcTp8reZulodFosQt2bNichqRbcITRa7VVy5G8Wdzugsb/q93U9bhEDRdOSUpK5rWJryMWQCgNcEeHFSvpecl8+9235OfnM2p09YVbmwvMHDp0iOBrcimuxQM9aqmGnTt3lhA1srOzkUsVKAXnEyE1jsFy8pTJtGvbjueeew6d51VfybGPjMXXx5e///6bM9nHAJBL5XTu2oXHH3usXEHj/4nSBvSjly6XGLirs0vjdhtsL1yIZ2hf5xtpMpmE6Cg58RfOERQURLt2bfjh94ME+Mlp3MCxQSYKIht2GFmzycSY++8vqja+fPkya9as4dy5sygUSlq3bkOPHj0q7S2rVjt+85nZdqfP2+0ihjyBPl01RZuO3noZ/XpoMRWILFjwK71690ImLV0cKKtbo5CqtCJD5Wyorg0OB6pV0CgViWOCGlm/eia7VSHhYgJZ2VnUw3mljA4fpMhQoiSbDEwY0VBcWLNiIZs0/AnGhwBkgpyDhw4SExtTY5Vy/wVutu1UeRPaW1nRuXHjBr777luMRhMe7nLy8u1IpdCz89Wx5Vpq15LToomKf3dsL1fUKCgo4KOPP6R5EwWvPatHoXAcr18PN46dNPPWx0dZvHgx997j3KoiOjqabbuOMXxgSR/wzGw7x2LMjB2lIzHJxqFjBTzzTDcAVCoVvfv0ZcXalbRtqS4mbOTmCXzzay5BwbVo1qxZpb4rFy5c3PlIZVLatWtHu3btyn9xGcz+dA47dmwnQKyDDwFIkZNHNpcvJPD666/z6exPb2qAakZaOmrReWcbgErQkpZ24/7ytzN79+6iQxtlMUGjfmNHdb9WK6VDGxV79+7mgfsf4O2332XmvG/5Z8UFzObTyKRSjCYb4WGhPDjhSdzctcXu6/5+/ox74gk6jupPtH+AU/ujymxOVfVenph4kWnTppKWlkGjSDUqFfz1517+WLiQF1+cUGYuTMeOHenYsSOC4OhmvFELstDQMEJDb557QUREBKGhIfy1PIXoKBVy+dV/A0EQ+WNpHv7+vjRt2rSMo9x8Tpw4weLFS3j8fh1D+l21/+rZWcvqjfnM+2klbdu2o3nz5hU63vXCxs6dOzl//gKfTPcjIuzq2jaqgZLpE/U8NTGDlStX8uCDD5VrP1xeUVtZwgaU37WxafMmVCpJMRu0+o1ziDkRQKOQFAb01PD3ii2MHz++XDtgPz9fZs56nzlzPmHmnDNFjysUcvr27c/jjz9exrsrj4fOg7uG31VuuPbtQKvWrRg9ajQLFi4gR5qBu+CJgIBBmokoFZg0aVK5Furt27Vj3ryvyBAuE0DxMU1EJJPLBNaqTWjd0Br8JDfGrRI0CqlpQeN221+7EVyihgun/P7bb9gL7NQToovUVTlK6lAPOUoW/rGQfv36Va1dTIQzZ8+QnZ2Nj48P4WHhmM3mK+copeISCXKJggKTqcRzPj4+2AQrZgpQUXLzt4B8AAIJ4dD+w7zxxiQ+/vijqxYrEhgydAiDBg3i3LlzWKxWgoOD0OmcB2r9P1JWjkZZm3YV6dKoKLfTgKtSqTDkll79bjCIqFSO39cLL07gnbffYtKM00SGqQgIkHD2nMDlVAu9e/dm5IiRAKxZs5p58+ah1UqJbqTAaIKvvz7AH38u4O3p7xASUrfC1+fm5kbz5tGs3XSKXl20SKXFF0+79hdgMIh0alv876WOwkjbFmqOx2YRczKmQv7Q5XVrVDZfo7I2VM6Ejf8HCi1yJE6ygByPgxQpKrSoMXKOk9QhAg/0AORjIJFzSJHjg+O788QbjVTLtq3bXKJGKVRF0ChvYlvVHI2bUdFZFjt27GDOnM/o2VnLqOH+BPjJyTHYGf9qKj5epYuLPt5Szl/KL/f427ZtxWQ0Me4BvyJBo5CmUSp6dNSwZvVKRo4Y6XRzZdiwu5g8eT8/LjTw4D26og2M3FyBj77MQquR4K2X8dZHWdSuXYtu3boVvffBBx7gzJlTvPbOadq3UtMoUkFqup3NOwuQSjW8++4bFc72cOHChYtriTsbx7btWwkhEm+udoLq8EIreHDGdIS//17EM888fdOuSe/lRUp6GqXZpFslBQR4l70Bc6djtVpx01y9dxUKGoW4a6VYLY65f0JeAQ888CBqpZnDhw8jCAKNGjXC7KcHifP1T3UVKVT1Xm42m5k6bQpuqjzmvu9fZE+bny/wzS8GPv74I2rVCiAysn6Zx7nZeSrVh4Rx455k6rSpTJ6Vyd2DtYTVUXAxycaS1UaOxZiZNOmV2+7zrV69iuDaSgb3LVlk2b+nltUbTaxZs6rCokYhgtUhbGzfvo3GDdTFBI1CPHUyunVQsW3bFh588KEK5So61n/OuzXgxoSN9PR0AgMUaNTF51+FwkZ4qBmzxYAhNxcfH+duBdcSHBzEhx9+zLlz5zh/7jwKpYLmzZu79oCA0WNGEx0dzcqVK4mNPYVCLqd/u/4MGjSQwMDy7wV6Lz0DBwxk1cpVSJFeEe9lWLGQQiLZZPD4A6/cts4At1LQqMi9wiVoFMclargogclk4t9//yVACHHaLuZHbdLFZLZs3cLw4cMrdex9+/bx/Xffk5ScVPRY3ZC6PPb4Y3i468jLM6CnZDinDStGIY86dUreINu0bYObxo0UUyJ1iCjmHS8gkMol3NARQB10gg9nEo+wYeMGBg8eXOw4UpmUepH/jT/s6qQiORrOujSuH6xL26Arzzv2dsnRuJb27Tuxdetq7h8poFYVn/ymptk4dNzMk092BMDdzZ1Zs95n3779bN26BYPBQNNmAbzUpy8NGzYAJBw7doy5c79iUB8tj9yrQ3nlmKlpNmbOyeGtt6Yyb963KJUlrVBK4957RzF58pt8PC+bR+71wM9Xjs0m8u9eE1/Oz6FNcxURoSWP19DTIRzKJYeglC6AQsrr1iirYqci+RoV4f9R2MhIz2DLls1IkWIgs5j/dSH5GLBhBUSUaMgli3OcRIoMKVJsWFGjpR6Ni0LrJEiQoyQ3L+8mf6I7i8pMcKuao1FIdXVpVPfkVRRFfv31Z9q2VPP8E55FG/yeOhnhdRUcjzWDk9+lKIqcOGUjsoHzxe61xMWdo26wCn8/51PVNs1VrN+ahcFgQK/Xl3g+OjqaceOe4Ntvv2PrLgutmikwGQX2HHKEbCqVMj74MosGDerx2muTioRoAJVKzbvvzGDduvWsW7eaQ8dTcHPT0qdPX4YMGYKvbzl+zS5cuHBRCpu3bEElVaMXStrcyJGjF/zYsnkzTz01/qZtsPbq3YuvTs912tVpJA+DmEWvXg/clGu5VYSF1+PgsT2cvBDA9Zq1CBw4aiWsXmSJtU9oWChQ9mbV9ZtUZrOZXbt2kXz5Mu5ubjRsX4uOIVHlXuON3Mu3b99ORnom737gT2DA1fuqm5uU55/w5Mx5G0uXLuWVV16t9LHvFJo0aco7b7/LDz98y4xPzxU9HhpahylTHqVVq1aVOl5GRjopKSm4ublTt24INbFDGx8fR/MmcqeFFBKJhJbRCvYcPufknaXj6NY4i2DdS16eAX+f0scZP18Z+flXC1FupbDhqfMkNd2G1SqiUJQUNn75xR+ZNAt3t8q5bISHhxMeXr1W0P8FGjdpfEN5H48//jhWm411a9eSIk1EKVFRYDciV8gZ/+h4una9Pe0MXYLGnYdL1HBRAkOOAbtgLzGpLUSOHLVUTUZ6RqWOu3vXbmbNmoUHeiJoghoNJvJJu3iJ6W9Np3Pnzvy7Yyc+gj8art6MRESSuIBMJqNnz5KBTSqVirGPPcoXX3yOgB0/aqNCjZE8UrhIAUYicAzIGrTo8GLZ0mV4e3sTUiekSl64/y+UNahXZPC+nsp2adxOORrXMmTIEDZsWMfM2dk886iOgCubbucuWPn06xy8fbzo3r170eulUlmZdgVLlvxDeKiKJx7wLDZp9feTM/FZT56amMqOHTuc/v5Lo0mTJrz66kQ+/3wOO/emEVhLSW6uHUOuDakUBvZ2PuE7edrC5eNZeD3pXVTFU+Z59MEcz676xLasfI2jp5IrFBpXKGz8PxAXd47Jb07GXGBGhYY0ktDhXUzYsGHlEucBCSkkElQ7mAeGjaJuaF2mTpmKzKKkLvVxx/M6EdhOAUZq1fr/EIcqS2VyNMqznaqIl2pZ42VlujRqYvIaFxdHUtJlnn7Yp8RCu293LZ/My2b/4QJaNy/eDbZui5FLyRaefb78PA25XI7RJCCKJe2jAPJNjpLi5cuXER8fj0KhoGXLlnTr1q1IoBg8eAhNm0azevVqzpyJRS5X0KNHHTw8dGi1Gpo1a0aDBg5x+XoUCiWDBg1i0KBBFf1aXLhw4aJccrKzUaJ2arcLDttcs8WM2WxGo7k5fuM9enRn5YoVnE88SS0hBM8rBWbZpJMivUhoSFixbrb/IgMHDuLdExfYfaCAhx8u3o29bE0el5ItDHykO+C8mAsqJmhs37GDr+Z+QV6eEW+9En24lvX74GhENE+MG1eq9euN3sv37dtH4/rqYoJGITKZhB4dVSxadXuuu6qTxo0b8/HHs0lIuEBGRgZ6vRdhYaFURpC4dOkS33//LQcOHEIUHXORkJAgxox5kI4dO1br9SqVKgy5peeDGXIFlMqSFtwFBQVs2bqF/fv2YbVaCA+vR//+/Qi48lssFDY6tFGxao0dQQBnGmrsGRuBgcXnoxWxIK4JYaNb92788ccfbNhmZECv4utYs1lk57FLtOrYj7hL2TVS6JacnExebh5+/n5Oi2lcFEcqk/LMM08zcuQItm/bTm5uLgEBAXTt1u22tXevrKBR0f2w6hA0rl37uQSN4rhEDRcl8PDwQCqRUiCaiqxKrsWOHbNortRgLggC8+Z9jQ5vQmlQtJGmQIm7qOc8Jzl7No6QkBDiEk6gF/zQoceGlUxpKvmCgeefeR4PXcnKT4C+ffugVCj46aefOJN5tOhxDW5E0Bg3dIiIpHIJA1lkpwi89957ADRqGMWzzz5DnZA62Gw2LGYLGo0GifQ27Ye7yVQ2GLy6ujRu50G3Vq1aTJkyjffem8GTr6QSXleN1SaSkGgmMNCfd96eXomFqMjBg4d45D53pxt3tWvJaRip4uDBA5USNQA6depEq1at2LZ9G4kXE1Gr1XTo0IEPPpzFouXZNG2kLGbrUlAg8OeyfBo0qEeruh2KqnjKEzagdBuqQmoyXwMcv9OTh+P/090agl1gxrvvIimQ0lBoiRQJcZzkLMeKhA0LZrKl6Wi0Gj6c9gHBwcG4ad2K1mpDhw5l8T9LUAiqYoIGQCpJ2AQrfXr3uQWf7vamum2noPxwuNK4Gb7b5ZGblwtAgG/JzZcu7TX8u7eAGbMz6dNNS8c2amx22LbLxNadJvr370/jxuVXpLZp04bly5dzItZCk0bFF+yiKPLPinykUgnLly+mSUMFhlyRL7/cxe8LfmP6W+9Qt67Dsq9u3bqMHz++Gj61CxcuXNw4vr6+mDEhICB1YiFpIh+tRotapXby7ppBpVIxY+YM5syZw/59+0nA4TEvQULbNu144fnny/WoL4/z586zbPkyDh04jCDYadQ4iiFDBlfI6vRmEBXViM6dOrN40z+cSVLRqZ0KCfDvXgvHYgroec9o6oaE3JCgceDAAT768EM6tVXzwEh/pL5azGaRi3su8vNfawAYP/6pEsepjnu5zWZFU3psClqtFKvVVuXj32mEhNStlLVvIUlJl3jttVdw11p5eqyOqPoqUtNtrFifwXvvvcfzzz9P73JClCtD+/Yd+euvBeQY7Hjqis+5cvMEdu6zMOyuTsUeT0hIYNq0KWRlZdO0kQqNGtasOc7ixf/w1FNP069fP8Dxe2rRogVHjh5hwzYjfbsX/4HEnLaw77CJ8eMHlLiu8oraCiktOByuChtABQPEg+nTtw/f/rYBQ55A/x5u6HRSjseY+fXvfLJypEyaPAJTllitHfwHDhzg999+5czZOACkEilt2rZm7COPUjuodrWc479MQEAAI+8Zeasvo1xutaBRFq5A8LJxiRouSqB109K2bVuO7DuGj+CPlOI30HQuIwh2ul1TiV4eR44cJTMrg/pEl9hIkyLBT6zNuaSTzJw5kwP7D7BmzVrSjQ6LqsYNG3PvvffSomWLMs/RvUd3unbrymsTX+PCmYuEiJFocCs6XzIJpJKIL4H4UAsFCvLI4cLpBF555RUaNGjI0aNHEEQBvU5P/4H9GT58+E2rkrrdqEqORll2KlXJ0ridB90mTZrw/fc/sn37dmJjY5HJZIx5oBnt2rVDJqt4WLUoOgLqlMrSRTSVUoLN5jz0uzzUajV9+/Qt9tgzTz/HtGnTmPhOJkP7aqlTW875i1aWrjGRnilh5kyHj3NhFU95VNSGqqr5GpXhv2xDtXfvXtIz0mlA8yJrwAgak0EKmaSSSxZI4K5hdzFk6FCnfrLD776bnf/u5FzKCXyEWujwcojHpJJFGqNGjSLgBjs1BEHgxPETpGdkoPf0JDo6+uYEuNcQtyJHozrDwWtiHC0MsD11zlrCHkoqlTByiDt7DhawbbedtZszAQgM9OfJJx9i0KCBVKQislmzZkREhPHpN4m88Zwn9cIdNmkFZoG587NJTLbSraOGJx/yRKtxbAympNl477Mcpr01hXlffYNaffM2BV24cOGiIvTq1YtF/ywig8v4UXxDzIKFLGkaA/oMuOnFVTqdjilTppByOYWTJ0+CRELjqCj8A/zLf3M5bN26jU8/+QSlRIVO8EaChCN7jrJr104eevCh22LD62RcCuMnPEJ0+/osW7aEr3+OBREaRTXg/ucG0iiq0Q0JGgC///4rjRuoePkpPWmiQ6xv5ZNFq4GO4OOf/lzDPffcg4/PVYvD6rLhjYiIYOnSQxQUCKjVJcW0vYfMRETcvODussjLy8VsNqPXe1VqTXUz+OmnH9GqrXww1RsPd8f3GFxbToumKj7/LofvvvuGzp07V9v8o1+//ixbtpR3PslmwpOeRVkoySk2Zn+Tg0yuon//q92vFouFt6ZPxcMtn1mT/IrcBMxmgR8XGpg7dy5BQUFFYmLHBt050uIIew8d4nSchW4dNSiVUvYcMLFivYmoqEb06tWr1OurSHB4ecJGeQVu13ZtPP3U02g0Gv5esYoF/+QhkTjW0nXqBPHOuy86ClrqUm3WxDt27ODDDz6kSUMVbzzvjb+vjFNxFpauPsyrE1/hww8+cgkb/wFuB0GjOtd+1/NfFjQAJGJhz9x/BIPBgKenJxsW78TNzf1WX84dy4X4C7zyyisobCr8hTq444kNC+kkk0YSQ4cN5bHHHqvw8Tas38Bnn39GNB2dtlvbsHKcvUya9Cbt27fDbrOTnZODSqnE3aNy/447/93Je++/RwRN8MATADMFxHCAQEIIoPhN1YaNWA4BAgHUQYGSPAxkS9MICanLrPdmotH+fwkbVbWdKqtL4/qBurwujZsx6GZkZmDIMeDt7Y2np2eNn680Xn75RXSaJKa+UnITOjdXYOyLqYwZ8yAjRoyotnOeOnWKX375iaNHjwMOX9ZWrVrw0EMPExpafFGTYDxbbrdGoahRVsXOkZTLpU5qj5XRghxbQRsqoMiG6r8obPw4/0dWL1tDA7tzgddwJTfj22++LVOYMOQYmP/jfLZt3YbVZgXA18ePLl0706FDR+rVi0Aur1rNw949e/n6669JS08rekyv0zP2sUfp0aN7lY55qylL4L2eo5cu35CfakVbj29ll0Yhkya9Rl5OHO9P8S62QWK3i8yck8nJUxaMJpEJE14kKioKf/+A67rRREwmEwqFstTfW0ZmBtPfmkp8/EUiQlXodRJiz1oxmuzU8lfw5Xu+yGTF5xQpaTbGv5pWrBrRRc1iMOQTEjicnJyc/3TApmuN4aK6+Hre16xatQofAvEhADlyDGSRJr2E1lPLp59+gpe3162+zGoh5XIKT45/Er3gSzD1itaBIiKXuUgKF5k5c+Yt7dhwtu6xCwIAJ5JTHc/doKBxOSWFcU88wWvPetGxrYYUu5Im7plFzxuNAg89l8YjjzzKkCFDgepdD6Wnp/PEE4/Tq4uapx/xLCaabdtt4uO5WUyY8CI9ejjrChc5dOgQK1eu4GzcGRRyBa1atWXIkCEEBQVVy/UBHDx4kL///oPjx2MA0Onc6du3P/fcc89tUWRoMOTw0EMPMe5BXQn7I4C0dBvjXknjueeeL1MIAEeuyr59e8nOzsbb24c2bVqjUDjPToyLO8s7775NZkY24aEqpBIJZ88XoNfrmDJlarFw940bNzBnzmd89YE/tWsVn1uJgsiEqZn41WrK5MlTrz4uivy1+Tf27t3Fpq2OXD03Ny19+/ZjzJj7UalK2lsVUtH1H1DqGhBg/+l4zBYzHetEIFc4nxPGFnVs1CI3N5f9B/ZTYCogJCSEqKioEo4HN7omtFqsjH30YZo2sPPqM17FsnZy8wRefTuTkLrNeHPylCod/04gLTWNXbt3YTKaCAoOol279ihK+fe5U7ndBY3y8mfL404WNCq6vvhv/SJdVBt1Q+syY4ajDflc4omix1VKFffdfR+jR42u1PE89YXigslpVkcBRgD0VzaWZXKZ0yrjitC+fXuaNG5CbMwp/IUgvPAjkxSkSPGlpJIuR44fgVzmIj4EIEWGHl98hADiEk6wcOFCxj46tkrXcidyI4JGaVSmS+Nm5GjExsby668/cfSo47ctlUpo07YNDz34cFEYvSAIHDlymOTky7i7u9OmTWs0ZfVt3wCDBg1l9uzZbPnXSPdOV89hs4l89VMOUqmMPn2qr5UZoEGDBrz77kwyMtLJzsnB28sbL6/SF9Dl2VBVxF8Vym5Dri4bqv9qcLhUKkW88r/rO97AsUFQ+Lqy0HnqeOGFF3jsscc5c+YMK1Ys5+CBgyxevJjFixfjqfPk7rvv5q677qpUpei+ffuYOWMmHuiJJBoNbpgxkWK4xKeffoIoCPTsVTkLtVtNZXM0alLQKORWd2kU8sQT45k48RVempbGiMEe1AtVkHTZxtI1+ZyKs/DGC178sdjItm1bi22SmM1mli5dwpo1q0hPzyoaf0eOGEmDBg2LncPH24dPP/2MPXv2sGvXLgrMBQwcVIcVK5bTo5OyhKABEOAnp0lDJfv373OJGi5cuLgtGTduHN4+3vyzaDGnrnSmS5DQskVLnn76mf+MoAGwevVqZMgJJqJYYZsECbWoQ640i+XLVtxyG6rr1z0yqbTUdU9lBY0Cs5kN69cDjuDl6wUNcNg/ebhdDWWu7vWQr68vzz77HJ999hmn4+x076RCrZKw75CZA0cL6NGjG92793DyTpH5839k8eLFhNdV0aezElOBiW3/rmfDhnW88cYkWrVqfcPXt2HDBj777DMa1Vfzwjg9Og8pR46bWbFiMUeOHGLGjFm3vPsyPT0DQRCJDHNuxebnK0fvqSAlpfS5oyiKzJ37JZs2bcRqtSNXSLBZRTw8tDz66BNOxZCIiHp8+8137Pj3X44fO4YowqChjenSpQtKZXEh5MCBgzSKVJcQNAAkUgndO6n45a9DgEhh16xEIuHeng/Qtl1bRo9KJ9/ciKCg4DLFjEIqHhzuvGMjJjaGP/5YyMGDh9D412GeSkm38IaMGjWqaP+okOtzNno4/b1e8/obXBPu2bMHgyGP+0f6c71DtIe7lOEDtMz76QBZmVn/qTEbwGq18dXcuWzauAmJRIJcqsBsL8DDXceLE16gTZs2t/oSqwWXoPHfwCVquCiV+g3q8+WXXxJ76hSXEhPRaDS0aNmiSpUSzZu3wMNdR1reJepQr9iGnCPrIokAv4ArgZ03hlQmZdq0aXzzzTds3rSZJCEecATvyXDewqrBDREBGzaUV16jwQ1vwZ91a9fxwIMP/udU6bKobI5GITeapXEzBt4jR44w/e23CA2SM+FJPbVryTl3wcrSNYeZOPEY7733Aenp6cyd+wVpaRnIZBLsdhGNRsWIESO59957qUyYXEXo0aMHx44d5dOvN7F+q5E2LdQYjSKb/jWRlS0yceJEdLqa6STx8fEt1ubujIraUEHV25DLaj92FhZXFv/V4PDmzZvz96K/ycNQ1IV2LVmkExgQiK9v2f+ehUglEr795htSk9MIEOqgwws7djINqcz/cT6pqak8Of7Jil2cCN9/9z1ueBJKw6IxXoMbdYlEAvzww3y6duta5S6Qm01VbKfKo6o5GlC1cPCaJCwsjGbNWnDi+D4++za76PEGEUreetWHZo1VpKULfPurwz9dKpVhNhcwZcpkzp07S/eOappGeWEwCKzbcpjXX9/PxImv0aFDh2LnkclkdOzYsVj45tKlS9CoSx+HNWoJNtv/jze4Cxcu7iwkUgn33HMPdw27i5iYGCwWCyEhIdVi9XS7ceLESdwFT6f5IRIkeAhenDhx/BZcmYPSujErKmgkJSezYsUK9u7diRjsSahGQ8GAQbRt1w4JkJKSwpQpb5KSkkrd5jpOZSlo4yTKITnFRlaOhVq1atXYeqhXr14E1g5kyeJ/+HXRAew2gXr1wnjhhSH07NnTabbfzp27WLx4MY/fr2NwX7ei1zx4j8CHc7N5//33+O67729onWIw5PDVV3Pp003LM2OvdpG0bqameycNb8yI559/FjFmzP1VPkd14HHFPeJymp164Y7HCgoE9h42k5sr4OEuwZBnRVdKBqjdbufll1/i3Lnz9OuhZcRgdwL85FxKtvHH0lzmzJmDXCGnW9duJd6rUCjp0b1HuRv5drudsrQIlVKC3S4gipTYqA91q49UIsWPDCADKD9TEaoubOzbv4+ZM2cSEiRn/MOe+Pvks+u0mh3HTrJ/9gd8+OKrJTJcS+ZslL0uvBFhI/lyMh7uCoKcCEQADeopEESB1LTU/5yo8cXnn7N16zYCCcVH9Edql1GAkcv5CcyYMYOZM2cRFdWoRs5tyDGwbt06duz4lwKTibqhoQwY2J/mzZpX6xZMTeQlQvUIGq4MjcpxZ+wsuLh1SKBhwwY0bHhjYoNCIeeRsQ/z+eefA+BPECo0FGAkhUQMZPL0Y69Xm3+sSq3iueef46GHHiImJobt27eza+dux8aKE2GjABMSJEU+9YW4oyfVdInMjIwb9pkHyMvNI+HiRRRyOeHh4bedz3xZVcllbdpVpkujvI25mhx4RVHkyy8/o3F9BVNf9kIud/ze6kco6dpBw2vvZPLJJx+RkHCRZo2VvP6MLxFhCjKy7KxYZ+TXX3/DZrMzZsyYar0um81KWprDrif2rIWTpy1IALsA3j5eJaqXbwUOYaNi3RoVmdQ6w5WvUTbR0dGEhoSSlBhHXaERahwCs4hIOpfJJo0xI56p8Di6bPkyLidfpp7QFPU1HXRueKBGy8pVK+nbrx9hYaHlHuvMmTMkJScRQZMSXSQSJPgTzKncQxw6dOiOqu6pzhyN0qhopQ5UX5eGyWRi+/ZtXLyYiEajpmPHToSGhlb42IWoNWrC6qiYMF5HeqaAp05KYMDV+6hGI0EURex2h6ixaNEizp8/y4w3vKkfcbW6cEAvLR9/lc3s2Z/SvHnzcosnIupFsO/wBYb2L/mcySRwNMbK0KGRlf48Lly4cHEzUSgVRDeLvtWXUaMUdpmWjohUUnaHaU1R2rqnooLGkaNHeOedt1GrRFoMCqaONImDx6zMmHGYfv368dRTTzFjxjtIxGxmfRLC5n9N/LvoHHdH+8I161FBgN8W5aHVaunQ1rEjXVProahGUUQ1igLEKxvbZc8ZV6xYSpMGaob0K265p1RKee5RPY9OSGXDho3cfffdVb6mjRs3AQIP36srMYeNCFXSq7OKtWvXMGrU6HK7kWsSPz9/GjWqz4p1F2jfSsXK9UYWLsnFaBKRy8FmA5mUUm2k/vjjDy7En6dXVy1Pj9UXPR4U6Ci0s1hEfvn5R7p07lLlzxkZGcnChXvIzROKMj+uZe8hM/XqhZf67174u0swni23S/9aCuenx7Md81Vn68DCee6RlIvYbTbmzPmUlk0VvP6cF7Ira/KWzfJJ6W7jzS/gs3/+ZOqj45yerzIFb4Vz+ZOH4yu1LnR3c8dkspf6Xaam24ted8sR4eTJk5w46XChaNy4CVGNGlVJBLiUeInNWzYTTAS+XP1u1WgJERsQJznGgt9/551336muqy8iPj6eNydNxpifj070RoaCo6lH2bV7JwP6D+Cpp566YWGjprozoGJd+VD9a77r+X8SNMAlari4ifTp0wdRhB9//JHYvENFj+t1eiY+ObFYFWZ14an3pH2H9oSFhbFjxw7SScH/OgsqO3bSScIT3xKChx1Hpef1rZ2VJdeQyw8//MDWrduw2R0e9nqdnrtHOCxeqrnw/4ao6S4NcD5I34zK4iNHjnD5chovjfMtEjQK0Wqk3DPUjU/nXSAyXMXkCV5F1ia+3nIeGaVDLodFi/5iyJAheHiUrMIxGo1s3ryZo0ePIogCjRo2onfv3uV6jP/ww3xOnjyGUiGhW0cN0Y1VV6qX80m4lMW0aVP47LMvqu+LqEEqLmw4t6FqGhLI4VLyNRo2CORoBfM1/pM2VBKYPGUyb775JqdSD+Eu0aMQFZikuZgEE4MHD6F/Jex21q5Zi6fgU0zQKMSHWqRLk9iwfj1PjHui3GNlZWUBFAkt11P4eFZWdoWv71ZSmRwNqJigUVbFTnXaTpU3lm7fvp0vvvgcs9lMYC0lubl2Fi78g/bt2/LSS69Uyt6hXkQ9ft65HYVCQsPIkvfJvQfNhIQEoVAoEQQ7a9aspndXdTFBA0Amk/DoGA92vZTGixNewGIuwMPDg65de9CvX78S4+3gQUP58MMP2bTDSM/OV3+/giDy/e8GLBaRvv36VvhzuHDhwsX/Exazhe3bt3Pk6FEQRaKioujWvVuN5Ba0aNmcM6f/xCbYShSPiYjkSDPo0Kp9tZ+3LKpqtQtX7+VGk5H3Zs0iqr6Mu54MQ6GQ0szHg1HDYcM2I59/txaNVkt8fAKT3wnGWy9lfE8jE3fAy9PSuWuAOw0jlaSl21m5wcjxWDPvTr8LhUJ+kzaiJCUq9UsiEht7mkfuK5kfAaDTSWnSUElMbMwNXUli4kXCQpR4eDjfyI+OUrFqYxb5+flO1183k/vvf4ipU6cwYWo6CYk2BvXRcld/d/z95FxItLLgn1zmzp2Ll5cXbdteFQRsNhvLVyzDLsDw/iW/T4lEwl0D3HntnXRiYmJo3Lhxpa/NYMjBYrFgt4vM+ymbCU96FVvvbtlp5ODRAiZMGFLusQq79CsjbEDF14G/b9qINdCDR+6zFQkahQT4yenfOp2lO0+wJzGedsGhTo9TlU7+yggbHTp24Ntvv2HNJiP3DC0uXIgirFhvJDw8rFpzZapCSkoKs2bM4lz8ORRSx/zaKvxCeGg4b7z5BgEBlVsH79ixA7lUgbdQsnNQigQfoRZHjh7BYDBUa46a3Wbn7elvIxhFGootUeD4LKIgkkkKq9esJjw8nH79q24teyfYTUHVBY3/NzGjEJeo4eKm0rdvH3r06MHBgwfIzsrG19eX5s2bV6pjQRREjh49yubNm8nOdhyjd58+NGzQoFRxIKBWAIMHD2bFihXYsOJDwJVA8BySuYANK7UoPniIiGRKUqgXHnlDLYXGfCOvv/46KUmp+AlB6PBCwE6mIYUf5v9Aaloa4yqwaVjT3EiXRnWEg0PND8DJyclIpRLqR5TiheojRRBgWH+tU6/2oX3d+WdlPv/u/Jf+/YqXCJ86Fcs770wnL89Ik4YOr/ffftvHwoW/89prr5fqN2symVi7dg1SiYQZk3xKVi/PzWLXgQROnYq95R0bFenWgNsvX+O/hH+AP1988QVbt21jx/btGI1G6oQ0pV+//pXqqBMFkYzMDOpQ+t+cVJCxaeNm1qxdi0qppFPnzgwbNozg4JKTd29vRwaSCWPRJPRaCnOTki5dwmq13dZ2fpXN0SiL6sjRqIyfanmT2aNHj/Lxxx/RuZ2ah+/zw9dbjs0m8u9eE1/9eICPP/mINydNrtC5AHr37sVvv/3K1z8beGm8vtjiefcBE3sOFjBunGPxnJNjIDvbQPMmzu+nvt5yggKlmExp9O6iJSU9nYULf2X16pXMmDGLWrWufk9dunTm8JHDzPlmPVt2FtCuhQpTgcDWXWYSk6w8//wL+Pn6VfhzuHDhwsX/C3Fn43jrrekYDDm4ST0ACVu3buWnn35m8uQ3adyk8pupZdGvbz8W/b2IBOsZ6or1i6yABQQSOYdFMDN06LBqPWdZFN7jfYLcWLBgAWlpaej1err36E72lQ3B0rIDr72Xb9m8BZPJSP9HGl0RNFKLnuvdVcuu/WY2b95Ek87e1AmWOzI03GV8MNWXn/4w8MOCHOyOQm/Cwury0XsdiahX77bbjJJIJNiF0p+322+800alUpNjEBBF0WkHQbZBQCKR3HCRYXUQHR3Nyy+/wieffMTgvlqeeEBf9FzdYAWvPevF9I+z+Pnn+bRt24bCzYmkpCTy8xxz4cBS7IxqX+l0NRgMlb6urdu28tmcOUgkAkG15OzcW0Ds2VR6d9Gi1UjYf8TC0ZMF9OrVix49yrawKuRq14ZjblmZro3yhA0up6DzkJHt5U0QWSWejo5S8ctf58nOyeGwuvQ1YU0KG3q9nsGDh/D7P0sBx7rc3U1KcqqN3xflcvSEmclTHrilxanGfCNvvD6J/Kx8wmmMh+Cwgcslm+SEeCa9MYnPP/8crVvFc0Hz8vNQSVROLQMBFKiKzn29qHEp8RLbd2wnLy+fWrUC6N6tO+4eFetk2bNnD+kZ6TSgebG1pAQJPtQilxyWLFnqyMurwne+ZuNedu/Zw/G4/QiCSFTjRgwbNoxWLVs5fb1L0LhzuH13FVz8Z1Eo5LRr165K7zWbzcycMZNDhw+hlbqjEFTESGNYt34d3bt158UXX0Qqcz4AP/7447i5u7Fk8RJSzVc3XDUqDRKzFAtmVGiQIMGGjctcJFfM5rn7nqnStRayfPlyki4lEyk6t3hZsWI5ffv2qZL1R3VT2S6N6rSduhm4ubkhCCKZWQI+3iWFtIuXHJ0519qnXItOJ8XDXU5Odk6xx3Nycnhr+jTqBom8/JRjoxDAYBD47PtsZs2ayWeffU7t2iU3g+PiziIKdnr31DqtXn7sfk92HSjg22+/48MPPyy3TfxmUNGqndshXwP+ezZUKrWKvn370LdvnyofQyKV4KZxw2wqKPGcgEg8sZjIR2fyxh8/bFYrW9ZvYfOmzUydNoXo6OJ2GfXq1SM4KJi0pEu4i57FwkBFRFK4iBQp/yz+h00bNzFl6hQi699+9kA14a96s3I0CilrMvvnnwuIDFcy4Uk90isWD3K5hG4dtUgkEj7+ai/x8ecJDQ2r0Lnc3T14+eWX+eCDD3j2jQx6dFLh4S7lwFEzB44U0KlTR/r3dwjACqVDTM7Ld25DIooiJhN0aqtmzAhHJeaD99iY8l42H3wwi08+mc3VVYyE5559lujoaFauWM73v59FLpfTqlUrnn1+OA0b3nrLPhcuXLi43TDkGJg6dSqCUUJDWqISHJ15FsxcNJ1l+vTpfPHFF9Wa7eHl7cXkyZN59913OWndj4eoR4IEA1nYsaH31JGakkJERHi1nbM0TsalkJ2VzY9/zCMpOQmlwrGRnJYh8Pfff9Om/yBeealkoZmz9U5MbAyRPerg4V5c0CikbW89qevykSam0NT9asZTgJ+Mic96YTB4snW3ke9+NTB9yjB0np634WaUhGbNotm26wTD+ruVWIOkZ9o4Hmth3Lgbs1Dr2LEjK1as4OBRM62aFe8WtdtF1m4uoHXrlhUKrr5Z2O0id/UvuVkrkUq4a4CWaR9c4ty584SHO37X1353Z89bnXa3no23AODvX7mijJiYGD795BO6dtDw+BgdHh5SzsVb+HVRLn+vyEUUJNRvUJ9XXhlC165dqOyOcFW6NsoTNtQaDXlxCdisjTmBF401xYWNrGyH4te4jj+BtRxd/ECpnfxQuZyNk1eK3spbHz4y9hEAFi5ZzsIlebi7ycg2WHF31/LSyy/fckvd9Rs2kJmRQQNaoOLq344OL1SChlPph9iwcQNDhw6t8DEDawVishuxYnFaqGbEgEKhxMvrapGS1Wrji88/Z/OWzSikSpQSJSa7kR/n/8hjjz3GgIEDyj3v8ePH0crc0didd4bp8SE+6ZSjQ8Szch0iP/6+nHXr12GzG9Hb/ZEg4cyROKYfms7o0aMZPXp0sde7BI07i1tnSujCRRWYN3cex44eI4xGRArRhNGQ+kILQohk29Zt/L7g91LfK5FKGDNmDD/9/BOT3pjEhBcn8M7b7xBQqxZ2bJzjJDEc4DRHOck+MrnMk+OepH37qgkwhaxZswa9WJrFSyAqqZr169bf0DlulKp2aYBz26nSBu3SBmrBuvemDMKtW7dGrVayckN+iedEUWTvIccGb9wFi9P3p6XbyDFY8fMrPtlct24dVouZN57TFwka4BBBJj6tR60UmDv3K6fBtXl5edgFaN7E+UTdx1tGcG05p0+fZvbsTxDFsnyJa56K/jsV/luXtSlb1s29rMlBRTwti15b6KNaier7/xd69OxBjjQNG8V/l2kkkUsW4UQRTiP8qE0gdWkgtEBt0/LerPcwm83FDyaBJ8aNIx8D5yUnySUHGzbyyeU8sWTj6AppSAtseSJTp0wlIz3jJn7ailPdtlPOqIkJbnm2U7m5uRw9eoL+PTVFgsa1dGqrxlMn599/d1bofIW0b9+BDz/8kPoN27N4tYXvfsslOy+QZ599jldemVjkC+3u5k6TJo1Yv7XA6Th26JiZ9Ew77VpdtT/x9ZbzxAPunD17npiY2OveIaFb12588MFHLF68hL/++pvXX3/DJWi4cOHitiMrM4tNGzexds1azpw5Q5kREzXIunXrMOYbCRMaFNsAU6IiVGyI3Wpn1apV1X7eZs2b8eyzzyKINqyaTLS1sunXU8XLT+mJDLMw671Z7KzkvacyWK02du6L5eiRo8z8dCpJSUmMHu7Oz1/UYva7fvz0eQB3P9SQ3TtX8/333xd7b2mbVgX+Kmx2nAoaKXYlgh1MZ5JITjETn2At8RqdTsqFizZ6dvMgKqDFbbsZNWzYcOLizfz0Ry4229Ufbo7BzkdfGnB311a48r80mjRpTJMmjZj9jYF9h02IguM86Zk2PpmXzYVECyNG3nND56hODAYDcoUEP9+yOy5yDFeL4GrXro2XlyduWgkLl+RitxcfBGw2kT+W5hFYO4CIiIhKXc8/ixcREqTkhSc8iyy8wkOVTH3Zh/en+GIXRO666266du1KVdsKCn+flbGLbqIPpok+mBTzqRLrwfbt2lFQYCcjNoFmPqmcMHlxwuTYJBdFkTWbjYSH1y3q0i38+ytrbl0kbpSzdwHF14eCIHBg/wGWLF7CmtVryMzMLHqdVCrl0cce5Yf583n88XEMGnwvL7/8Cj/O/5lu3bpW+LuoKbZt2YoO72LjeSEq1OjwZuvmrZU6Ztdu3ZAr5FzmYok8JAsWMqUpdO/WDZX66t7FvHnz2Lp1K8FE0EhoRaS9GY1ohc7qzVfzvuLfHf+We96a2uPYvPMY69avQ2WRU8/elABJMP6SIOqJTalFCAsWLOD4ieOA47dz9NJlGjYIvCmCRuHfRuHfSmURrHuL9tFu13vIzcDVqeHijiEzM5PNWzYTKNbFE++ixyVI8MafAowsX7ace0beU2yQvR6NRkP7Du1BhNffeIPki45wW4AcMrBhQ4KEfAx4enre0DVftXhxPjmRIkElaEhNLTkhvtlUpUujojkalbFPqUm0Wi133z2CBQsWoFRIGNzXDXc3KZnZdv5alsu+Q2ZCQ+uydHUyndto0Giu6r6i6JhsOkJ1i+e/7D+wl7YtlOh0JXVipUpK144a1mw8yltvTWHq1OnF2qcjIx3V6rl5znu7RVEkL09AIoHNm7fSpEk0ffpUvUK/uqhItc7tlq/hojh33XUXW7Zs4bzpJLWEurijQ0AgjUt44YeO4jZBUqQEieHE5h9kx/Yd9Ordq9jzLVo0Z9q0aXzz9TfEJR8velyFmlAaoMcXgFChIbHmg6xavZoHH3yg5j9oBams7dTtlKNRSFkT2oICEwDeeuf1LDKZBE+dHKPRWKlzAkRE1OPll18p93UjRtzL9OnTmfdTDg/eo8PdTYooihyLsTD7m2waRSqJql/cHrBFExUajYyYmBgaNWpU6Wtz4cKFi1uFxWzh66+/ZuOmjQiCgGNDUSQiPIKXX36Z4DoVH+Ozs7NZv349Bw8cxGaz0ahRI/oPGEDt2hUv9Ni9ezceohdyJ9W3MmToBB927dxdVJ1cbYjwx58LiG6s5q1XvLk2A7lLOy2zPsviu++/pX2H9tUaBF1gKuDbn//h8OFDpBgSAJBKoXFDJfcNu5rNEJPpz+hh6ShlHvz+zypGjRqFzlNX6r38eO5FwsPC2L9iJZeS/QkKvLqlkmJXIgKxmy/SuHEjUlJSmTvfwNSXvXC/Jmz49Lk8cvIK6NNi0C0Nvy6PZs2a8fjjj/Pdd9+xdaeZFtEKTCaBfYctqNVqpk2bjkZTcWsb50iYNGky7703k3c/OYGPtxIPdykJiWZUKhUTJ068Em5+e+Dn54fNKpKQaCUkuKSl8dl4a9HrCpHJZAwaPITff/uVIyfMTH0/g+ED3QmuLSf+opW/l+cRF2/j3XdfoDLCgyAI7Nu7n7Gj3J0WrNQLUxJeV8XevXvo0KFD5T/sNVRnzkbt2rXp1q0r3/32Lwo5dGmfwomcAPZlenJw+QUOHjXzxuujkVzzXZTVyV9IZQPE1y3dyheffUbi+WNo1DIsFpF5X8+jT5++PPnkOORyx9+2Xq9n4KCBFf7MN4vc3Fyn3RSFKFCSl5dXqWO6u7vx+BNPMHful1ixXGPbbiBDmoybpztj7r+/6PVpaelsXL+BQOoWCxZXoCSIcKxY+P333+nUqVOZP+3GTRqzctVKTBjROCkGziaDoMCgSuV4nIxL4cD+/RTYcwilZYluM3+CyJFlsHLFSgS9Y53q6s6483CJGi7uGI4cPoIgCnjjvE3QC39SCy4RGxtLs+bNyj3eqdOnOHnyBEGEo0GLHAUeOEQMEZF4SSx//vEnnTt3BoljYpyalopapcbf379C841CixeLyez0eRERq9R6S0PPygrELa3SoTptp25GQPi13HffKKxWK38tXszfK/Lx1MnJzLKiVCoYN24cTZs2ZeLEV3n93UxGDnWjYYSC1HSBZWvz2H2ggGeeeaZEkK7NakGjLn1BotVIcXeTcupULL/++iuPPvpo0XM+Pr74+nmzdnMevbpoS9xsDx0zk5kt0KuLhs07TPzyy8+3XNQonNRWhNslXwP+ezZUN4p/gD8zZ83kww8+JC7xODKJDEEUEBHRXSMcX4sKNVqZO2fOnCkhagC0aNmCr+Z9xXPPPU9aQga1qYsbumKLEjlyPAVvdmzfcduIGlWxnSqNm52jARUbR/V6PVqtmuOxFpo3KVnRlZll51KymUFDak6AbtWqFc8++wzz5s1j87+pRIQqycoWSE6xUDdYzhsveJUYAwXREch4O1jvuXDhwkWFEeH9D97n4P5D1BLr4o0/MmQOr/P4BF577XXmzJmNr59vuYc6cfwEb09/G4vFcsW+Scq5s6tYtmwZzz33nNP7sTPMZguyMpb/cuRYrKYKf8SKcvr0aRITk3jyfh+u37+XSODeIe68PD2No0eP0rx582o5p7nAzJR3PiEtLYVO3XJp2dSbvHyBtVuMHD1hYdmaPIb2d+dY6lWrrb7dtPz8l4H9+/fj3cwhopd2L7+vew82/v4bH32Vw+QX9fh4yxwdGgIc33CBE6cKmDx5BN7e3kybNoVxr6bTrb0KL70Mi83M+QQzoe6RDB8+vFo+b00ydOhQmjVrxurVqzh75jRyhYIxY9rSp08fdLrSCwCNRiObNm3kwIED2AU7kZH16d+/v9PMK3d3D959dyanTp1i9+7dmM1mBg6pS7euNRNgfyO0bNkSvV7HgsV5THxGj+QaMcFiFli0PJ9GjeoTHFR8PjVyxAjiz59nx45/ibtg5Z1PrnYEqFRyXnnlVZo0aVKpa7ELdgRBwM2t9HWou5sEi6Vkt1BVqE5h47nnnsdmszL7m13MX5iHlz6LVJk/SLx46qmHShQRQvUKG3FxcXz9yxwaBQdw9zPR9GqbTr5RYMNWIz//vQ6LpYAJE16q8GesLCdPnGTlyhXExZ1BrlDQulVbBg4cWCn7v9pBtTmTHgel5N6YpPk0CKr8pnf//v3QeXjw+2+/cz4xBgCZVEanzp0Z+8gj+PhcXSfu3r0bJBK8xZJr7MI8jHOJJ0m8dMlpLmMh7du3x8fbh8TsOMKEhshxCIYiIpmkkk06Y4Y/U+YeXF5ePuvWrmXTxk2YccPTU09a3kXc7Xqn6wiJRIK7Xc+ZjBwG4BI07lRcooaLOwbrFeseSSmuaYXBc84sfq7nzJkzfPTRRwBc4hxJnEeHN4GEoEaLBAleoh/xCae4cOECK1auZPOmzVisDnEiNCSUUWNGOb3ZXk/3Ht3ZsGYDfkIQ8uv+5HLJxiTk0a1bt3KPUxNUpDK5tMG9IrZTFR2wb+aALJFIePDBhxgyZCg7duzAYMjB19eXzp27oNU6qgLee+99vv32az768mTR+wID/Xn5lWfo1rXkv1W9eg3ZszsRm00sFpQLhbZWJhrUUxJcW86a9Wu4//4xqFRXNxWfGv8077zzLnPn5/DQvTo83K9WL386L4vIcAXPPa4nwFfG74tzWLBgAe7u7jRv3pw6dZwLATeDykxoK5Kv4Yzqytco7NZwCRvFCQ0N5csvv+TEiRPEnYvDbrczf/58BOxOXy8iImBHJiuZSVOExDGJ1eCGO84Xu3IUmAuqf+OkKtzpORoVndgqFEp69uzN6k2r6dFJW6yyVBREfv4rF4VCUeP3o759+9G2bTs2btxAQsJF6oQpMWzfRpNGEjx1JX9Xew8VUFBgJ7pZ2Z7dVpuVzZs2sX79WlJSUtDpPOjatQf9+w+oVFWXCxcuXFQHJ0+eZN++fcU6FcHhda4V3DltPMKSJUt4/InHyzyOIcfA22+/jdyiIkJsWrTRIwgClzjP5599TkhISIVyqurVi2Bn4i5EQSxWbACO+3ueNIeoiOq38MvIcNhNhtUpWdUOEBqiKPa66uCnhStISb3Mm6/ZiQi7eg/o2FbDjwsN/PhHLj6Roeg8oGF4OgDu7hKkUjhnNeKN83v5teudadOmM3XqZMa9kkb3IT6o1VbObkskI9PKgw8+SNu2DgvjOXM+Z+XKVezcuY36Ebn4+ARwf79edOnS9bbu0riWunXrMn78UxV+/dmzZ5k+fRp5eXk0a6xCo4SVy4/zz6JFPP/CC/To7syySkKDBg1p0OD2tpGUy+WMGzeeDz/8kOkfZ3HXAC21A+ScjbeyaHk+F5NFZswYB4DZXMD27Tu4cOECKpWK4cOH0717D9auXUNCQjxKpZJ27ToycuTIonVoZVDIFQQHB3LwWA69upR8f16+wKk4C/e1rlhWWkWoLmFDqVTy2mtvEB8fz/bt2zEajfQODMS/aVM0Wk2pBW9FVlQ3mLOx8I8FBPhKmTLJzvnTMmISAmgUksKwAe5oNFK+nL+FkSPuoU5I9a+1f//9dxYuXEhQoIo2zRQYjQJr1y5j1aqVTJ5SMruwNPr378/BQzPJIbOYkwlANpnkCTn0u5JtV1k6dupIx44dSUpKwmQyERAQ4DT022QyoZDIkYnO14aF9yyTqexOcLlcztSpU5k8eQqx+QfRiV7IUGCU5pIv5NKvbz/69+tX6vvT0tJ54/XXKRDccEOPYCkg2ZBAjphZ7B5cgtq+SKXiHSFouMQM57hEDRd3DJH1HH+8BrLQO6kiziETqURKWHjZN+0Tx08wdepU5DYldaiHGg1GjKSTxBmOUY8maHBDeuXP49133yUzPQsfoRYeeGLFSubFFN577z3GPzm+3FbE4cOHs2XLVs4XxFBLCMEdHSICWaSTLL1AdFR0hW9c1Ul5G3mV6dIoa6PudrCdcoZer2fw4MFOnwsLC2PmzPdISrpESkoK7u7u1KsXWWql8IABA1i9ejW//5PLPUPcOXrSjMksUqe2nBOxFuITbIwdpcNNK2XRinQuXkykXr2rN6M2bdry9DNPM++rr9i0w0hEXQXZBoGUNDuR4QqmvORNcoqdnQccmR//LFqIIILVKtKqVXMmTHjlpm/aVaVb40ZsqI4lOO8Oqoqw4eI6JI6W38ZNGgOwbes20s6n4y2WrBQykofJbqRlq5ZlHjI0rC57Evc53TgByJcaiAytnGdwTVKdORrlCRo323bqWkaPHs2RIweZ+E4KA3pqaNpIRVa2nTWbC4g9Y+bFCS9WaVFdWfR6PSNGjCz6bx9vbxYuXEhUfRWd2qqLxtoLiVa+/TWPJk2iiAgv/fdiNhfw1ltTOXkyllbN1LTqqeByajZ//bWAtWvXMGPGrCJfZhcuXLi4GWzevBmNVIun4FPiOTkK9IIvGzduKlfUWL9hPWazpZigAQ5LyGDCMUoNLF26lFdeLd8CcODAgWzctJFUEgmg+HwrgxTyhVwGDRxUwU9YcfReegASkmw0blDSKiXhkqOK3EvvVeK5ynIyLgVE2HloPa3amokIK15cIQFG3eXB5hhfDp8wM27M1Zy9E7EW5HXr4OfnV6qgcS2hoaHMnTuPjQcWcebMKQpiMmjTthcDBgwgLOxq8Lmvrx8PP/wwD45xdH/4Upv169fx8ssvYjAY8Pf3p3fvfnTr1q3I7uZOxmg0Mn36NGr5Wnhtuj8+3o7NzoICgW9+NTBn9myCg4KIjKx/i6+06nTu3BmFQsEvv/zItA8uFT3eqFF9ZswYR/369dm7dy+fzv4EY76R2oEq8vLs/Pnnn7Ro0YyJE1/Hzc15IHJlGThwMN999x0HO6tpGX21aE4UROYvNGAXJPSuYDdXRbkRYQPgeLZjYzdA1YDQ0FBCQ0NLvPZIyuUyO/lvpGvDZDKxd88+Hn/AA4VCQv3GOZw+4VkkbPTopOHnv/LZtm0b9z9wv9NjV5V9e/excOFCHrpHx92D3CncXnjsfpH3Psti5owZfPf9D7i7l//7aNeuHe3atWffnr14E4DXlc37LNLJJIUO7TvQrm3F/31KIHF0g5RFcFAQZsFcqm1UHgZkUhkBAeUXFYaFhzF37pesXbuWHdt3YCow0bhuIwYOHEjLli3L7NL46KOPsImeBFEHuVVS9NqLnCWLdOyiHZnkqvBiru2OKIqk5R2kX7e+ZV6XS9C4vbnz75ou/m8ICw+jQWQDEuIScBPci/kHFmAiXXqJDu074O3t3DYFABE+++xz1HYtYUQhvdL14YYOb/w4w1ESOUckTcklC4VCQUZ6JvWEpqi52vrqKXpziXi+++47OnXqhKe+9NbbQouXD97/gLik48ilcgTBYfHSoX0HXnjhhWJtqzeDilYmX795V9aAXlnbKbj51lOVpXbtIGrXLr1NspDQ0FDGjh3L/PnzWbo6D9s1Re5SKfTsrKF5EzUxpy1XHitZldW/X3+SLiWxZMkSdB5Swuoq6NBaQ7MoJYY8gcnvZaBWwbRXvGnRRIVdgH/3mvj+9+O89dYUPvjg45u+EHJMaCs2ma2IsAGl21CVl68Re6p0S7TruZ27NeLOxrF02TIOHTiIIAo0bNiQIUOHVJsdw/WYTCays7PxcPcoqr4ZOXIk73/wPskkEEAdpFdmhSaMJErPUqd2HVq2KFvUGDBwAFu2biGdZPwoPhnOJI08wXBbeNNWNkejLMqy5SvkVthOXYuHhwfvvfchCxb8zor1G/lrmcNnt0mTRrw1fRQtmreo1PGqi3vvvZeLFy/y4Zc7+Hu5ivoRMlLSBI6cKKBOnWBeffW1Mt//008/ERd3hlmTfWkUeXVucP9IG5NnZfPRRx/w0UcfU9WATBcuXLioLDmGHBSCyqmwD6BCQ6rxEoIglFmtf+jgITxEfTFBoxAJEnSCD4cOHarQNUXWj2T0qNEsWLiAPKkBT8EHCRIMkkxyxEwGDx7i2DiqZho2aEhgYAD/rDQQVV/JtTVCogiLVubh7aWnWbPy7YPLovCeHhYVSHp6Fk0a6ku8ptBuqpZ/LoacRLiSH1ZQIPLjHwbc3HwY1KXkvLa0Dat8xSXat2/P4/1HlnjPtRTer91Mel5680VSU1No31pNy8YyzpyLZ86cOcyb9xVSqeM7qVs3lBEjRtC+fXvutHvXpk2byMvLKyZoAKjVUp591JOY0zaWLlvKKy+/eguv8sZxbCi35dy58+QYcvDz8yuynDp16hSzZs2kTXMVj43xx99PjiCI7D5QwJfzjzNr1gzefXcG586dZ9euXRQUFBASEkKXLl1K2ByXx4ABAzl06CDvfnqQjm3UtIxWYTSKbNxeQPxFCy+88AL6ahAMr6eqwgZUpuCtZoSN/Px8RFGklt/VtfP1woavt4zc3NxKfa6KsHTZYhpFqhkxuHjXg0YtYcKTnjw2IY2NGzcybNjQco8lkUp4/bXX+PPPP1mxYiVn8hxrEQ93HaOGjOKee+6p8X2mtu3aovPw5HJeAnXFBkXrRnAEi2dIk+nUqVOFCzA99Z7ce9+93HvfvRW+hvPn47mYmk8AwQ5B4xoCCCGLNM4TQ6jYALlEgbm2O4IokGg4jCgXShXzq0vMAJegUZO4RA0XdxQvvfISr018nVO5h9ELfqhQYyKfHEkGAQH+jB8/HnDYS23bto283DwCAgLo1bs3fn6+HD9xnOTLSdSjSZGgUYgMGbWoQzynHOq2JBWpKMFb8C8maIBjEVGLOmQJKWzatInhd5fthxoWFspXX83l+PHjxMXFoVAoaNWqFQG1bt3GalmCRlkhuNVpOwX/ncE5MzMTiQSG9HOjf083vDylHI0x89vfuew5WEBiko2tu4x4eXkSEhLi9BiDBw9m6dIltGmhpl+Pq9UZqzYYMRoFPnrLH2+9Y3Egl0K3jloCA+S8Ov08u3fvonPnLjfls15PRSez5eVrVGTyWtqktaLB4bezDdXmzVuY8+lsVFI1OsEbCVJOHoxl3/59jB41mtFjRgOOyqukpCSsNiuBtQJRqVWkpKRw4MABrFYb9SIiaNy4cZlr4JSUFH779Td2bN+BTbAhQULLli0Zc/8YOnXuxIPJD/LLL7+QLU1FI7hjl9jIFXOo5VeLadOmlTtBbtSoEcPvGs7iJYvJlxjwFB0bJzlkkk06vXr2ot0VW4ZbRXXaTlVXjkZVqOwY6uHhwbhxT/LII2PJyspErdbg6Vm6MH8zkEplvPrqq/Ts2Yv169dxJiEJD3cdzz7bna5du6JSqUp9r8lkYuPGDdw1QFtM0ADw9Zbz+APuvPPxWc6cOXNHV4W6cOHizsLX1w+z9AiCIBbb5CnERD56nb5c+yHBLpQqjABIkSDYSzFUd8LoMaMJDQ1l8eIlxJ5yeKVHhEXw2LBH6N69e7XvnwuCwIEDB/Dx9mP/kRSefMXK/SPc6dRWy8UkK4tW5rFjj4kJE55CJi/D2rIcrr2n2wUBmUxKZnZxK81CQaNBWBoZaamY8qXsPVhAUoqNlevzydHX5qX77yvxfZe2aVXR9c61G1KT3n0dqyWDL2b5ERggJz9f4M1ZDvur2gECQbXlnImzcurUaWbOnEX37t2YMOGlOypX6uDBA0RHqYoJGoVIpRK6dVCxfP2BW3BlNYGE8PDwEo/+/fef1Kkt59Vn9EXWxFKphI5tNCgU8O4nx5k48VViY0+j08nxcJexfLmFH374jueee6FCFteFyGQyJk2azKpVq1i1ejnbd19GKpXQqlUrHn/y7krndFSGyggbCQkXSEhIQKVSER0dfUuFDZ1Oh0qt5PQ5C62aXZ1jFgobB0/7k3T5Mt16VDzfokKIcOL4ScaOKmnjBKD3lNG4oZITJ45XSNQAkMlljB4zmpH33MOlS451dlBQMArFzdnqlcvlvPjiC8yYMYM4yTG8hQCUqMgnlyxpCm6e7owdO7ZGr2HH3pMO62OLqsQ9TClREixGcJGznJQcQB/YAMRs0nPPIFNIef311512o9wO3RngEjQqgkvUcHFHERgYyJw5s1m2fBkbN2wkMzcFHy9vRvW7j8FDBiOXy5nx7gz27N2DWqpBgQoT+SxcsJAx948p2rhxw7lSXPj4Rc4QFhZG3Lm4Ul8rR45G6kZSUlLFLl4CTZo2oUnTmptYVITKVCZfy3/Fdqp0RPLy85HL5JWukElNTWXZsmU8fJ+O4QOvTlLaNNfQuIGKFyen8dm3WZw5b+P++0eU2lHh7+9P06ZNWbTiJJ3aanC/Evq2dZeRLu01RYLGtdSPUBLVQM2WLZtviahRGRuqQmoiX6OQOzVf43LyZebMmY0eP+oIEUUL6gAhmFQSWbBwAY2iGpGamsbff/3F5SvfkUqlxtvbm+TkJKRIkUik2EUbwUHBTHxtotN27uTkZF55+VUsRit+QjBa3DFjIubwKV4/+jpvvfUW99xzDx3ad2DN2rXEx8ej0Wjo2LEjnTt1QqF07ol9PWPHjiU0NJR//vmHCwmnAQisVZsn73qSAf0H3NLCw8oIGoXUZI5GITXZpXE9SqWSgApYYdUEFouFDRvWs279GlJT0vDQudOta08GDRpEq1atKnWs+Ph4TCYzHVo5v1e3bKpCpZJyMibGJWq4cOHiptGrVy9WrFhOJin4UnysNVNAtjSdu/uXHxLdqHEUsbGnEAQ7UorPA0VEDNIsGkU1qtS1dejYgQ4dO2Cz2RBFamzzKz8/n+lvvU3sqRjcpB544kNGei6ffJ3NJ19nA+DtrWfChKfo0aN7lc9TeE/Xekv57LPP2LNnF6IosnazkcF93FAoJEWCRsPwdI6csJCcZkcqsTNjTiYSQBURwrChw2jbpk2xY1+7aSUCR48c4fjxE8h986hbN4QhLToXe70jQ2E7iYmJqNUaenT1wM/PjxBtPeLjz3P8+Elee86LwADHdz7722xS0uzMeMOHJo0cm6uiILJxh5Evf8hhy5at1K/foFTL3NsRu92GqqTLWBEqlQS73Xl2238Bi8XCvn37efx+XYmsRYDW0Wp8vGWcjTvDq8940aG1GplMQmqajR//yOWDD95n5syZREU1rvA5ZTIZQ4YMYciQIVgsFuRyGVJp1UXCylCesJGYeJHPP/+MmJjYose0WjXDhg1n1KhRnMhJKndtCHAkxfG3WF05G92792DN5o307e6Gj9dVcbl+4xy+mqdApQuhZ8+e5X8BlUQikSCW9QKRMoXs0lAo5E7XfTeD1m1aM3PWTBb8toDDRw9fuR4l3bt1Y8z99xcLFq9uTsalIJFKybGkIBLm9LvT4tif6TB4KNnZ2Ugw0LfxaPr06VNkj3gtLkHjzsIlari44/Dy9uLhhx/m4YcfLvHcRx99zIF9BwilQVFLtR07qVzi199+pV8/R1CSFQtKSlZ+WnAEgffp15cHH3iAhx56CItodnodIiJW0YLWreb9x6uLimzkVaZLA6pmOwW3j/WU3W5n+fLlrFq1nMuX0wCHFcvw4SNpc93CpjS2bNmMWi1lQK+SvwWtRsrA3m78uNBAl65dGDFiRJnHeuqpp5n46su88lYGdw/S0rCekhyDQC3/0iemAX4SLmcYKnStNUVluzVuxIbqv5ivsXrNamTICSa82GRMggR/gjFIs5j31TySkpPQ40s4UciQYzBnkpKchBIVkUQjFxXkYeBy8gUmvTGJ2bNn4x9QvMrom6+/wWa0ESlc9ef2wBNvwZ/zYgyzZ8/m2+++JbhOMI8//ljVP5QEevTsQY8ePcjLy0MQBXQeutvGRaGigkZFbKduNEejqrZTd+Ik12QyMXXqZM6cOUv7Vmq6tJZzOTWPpUv+YsOGtcyY8R6BgWULSNdSWOVsszlfIgqC4/+yOySM1YULF/8NIiLC6de3H+vWraMAEz74I0NODlmkSy/h4+vNsKHDyj1O/379+GfRIhI5RzD1iro+RERSuUS+kMuQIUOqdI01bVv68UcfE3cmjgia4CE4CssERDJI5hLn6d+/P0+Oe/KGOzQaNg/lwMEDTJowA71OQv/uSsxWLSvW5TNzThbdhtTHxwvqh6ax56CZz7/LRqGQIZXI8PDQEdWvJ926dqVlveKd1Nfew5OTk5k1awbx8QnoPRXUjvJg3Q8LWVd3AW+88Sa1awexc+dOPv98DkajkVr+KiLCbHwdYydc14AJE17i5MmTyOUS2rZwFE8lXbax92ABzz+hLxI0wGEp07urG+cuWNmwzcTy5UsZPHgQt80EqhwiIxuwfPlxCgoE1OqS9949By1ERpYfbH+nYrGYEQQRL73zeYdEKkGvkxJUS0bndlfdIPz95Lz8lJ5Xpmfy119/MW1axUWNa1Eqy1CUaojShI2UlMu89vpEvDysvPasF82aqDDkCqzdbGThwgXk5uYybtyTQPGcDWdUd9dG8559ObB/H6+9k8HwgVqaRanIMthZs9HEv3tNDO8/mqRMM9Xq3CWBJk0as2PPaYb0hesbsDKz7RyPtfDoY02r8aQ3h0aNGvH2u2+Ta8jFaDSi1+tRqUvvtK4OCve2uvVry8Il88kmA2/8Srwuu7YED2U9nh5/P2pN2cWrLkHjzsO1wnPxnyElJYXt27ZRS6yLHt+ijUEZMgIJwQs/Duw/gEKuIB3nm6IZXMZL78VT48ej89TRqlUrsqSpCE709BwyMAsFdLlFlj+VpSqVyYU427RzVn1c2cH7Vg/SgmBn1qwZ/PTTfBqG5/PKM148+5gngvU877zzDitWLK/QcbKysgnwU6BWOR9SQ4LkiCI8/NDD5doMBAUF8f6HHxEcEs3c+Qaem5SG2SxyKs7q9PWiKHLmnJ2AgIpvAlY3lf13LO/3UThBKK3CvWlIYKn5BUVVOOVsRIPjb6GqnUvVTczJGNwEzxK2eOAQNjSCO0nJSQQSQigN0OGFGx4EUpd6NMWGlUxSkSDBA0/ChCisJitLliwB0WGPlpqSyuXkyxw8eBBfoXYJf24pUmqJIaSlp3Hk8JHq+3AScPdwd3ip3gbr8cINkIpQUdupsrhdbKduF36Y/wMXE87x4VQfXnvOi7sHefD0WD1zP/BFpcjno4/eg7Jr2IoRHh6GTufG1t0mp8/v3F+A1SoQHX1jXu0uXLhwUVmeevopRo8ZjUlr4BSHOcl+kiXnadm2JR9++CEeOo9yj+Hn78dLL71EtjSd09JDJBFPMgmclR4jmQuMum8ULVremjyksriYcJH9B/YTKNTFg6sWh1Ik+FEbbwLYu3vvDdkqFd7PjUYjH3zwPs0by/nqfR/uH6nj0dE6przkTVxeLeb9mMOnX8Uw9oV0Zs7JJCwiih/n/8xff//N8x/OoHfvXmUKGvn5+UyePAmr+TIz3vDh/dlBfDvBxsxJvtitqUye/CZ79+3lgw/ep0UTmP+Znq8+dGfCOD2jO4kcOriPjz/+CJAgXnN7O3zcjEwGXdo532Tr0UmL2SySnJxCdnZ2lb+nm02//v2wWuGbXwwIQvH7+eqN+cScLmDw4KoJcXcCWq0ber0HJ045L4zMzRO4cMlK8yYlN3xlMgn9e6g5ePAQJpOxpi+1Wimcl14tXBSZN28eVouRkCApCZdsGI0igQFyHhnl+BtdsWIFyckO14vC9WFZ8+Ly1ohwTddGGXP0hg0C8fDwYOyrk6jfsA3f/5bLs2+kMWVWJmcT3Hn++ecZ+7TD9re614rDhg3nVFwBfyzNLTYe5BsFPp2Xg1qjpmfPHtV6zpuJh86DgFoBN03QaNg8lKCgIFq3bs1l6QWMYl7Ra0RRJEfMxCDJpFuXZmUKGofTk4v2vMoqViu0FHYJGrcPrk4NF/8Z9u7dh0QixVssqc4CKFCSlpFUVNkkRYYftZEhw4aVFBLJJJWnxzyDVObYWLzvvvt4/eDrXJCcIlCsixoNAgLZpJMkjad1i9bUi7xzBpvyNvKcdWmUZTvlbDC/k2yn1q/fwP79B5g8wYtWza7e5Hp31fLjQgPfffcdbdq0Kdeixdvbm5RUa6kVSRcSbcgVsgoHZAUHBTNlyjQyMjNIS03j4MGD/PnnH5yOs1A/onj1zdZdJhKTLDz9bN8KHbsmqWxQXFVtqAopK1/jTggONxgM7Nq5i9y8PPLy8hAp3RPbSC4y5PhRMrheizt6/MjgMgE4/v7kyPEU/Fi/fj1HjxwlITEBADeNGyIi7jjPUNDijgQp58+fvy03SW6UqixMbkaOxs20nbqVGI1GtmzexMjBWuqFFx/LvPUyHhvjztsfn+fEiZOOXJgKoFAoGThwMH/99ScNIpR0aqsu2iQ7F2/h+9/yaNmiWalZRi5cuHBRU0ilUkaNGsXdw+/m1KlTWK1W6oaGVtqKo0vXLgTXqcPyZcs4sP8Adrudxo0aMWTIEJo1vz0F24MHDyKTyNGLvk6f98aPs9nHuXDhAmHhYZU+/rX38y1btmAuKOCpRwJQKK6KJPKAOrw4Dn5eEEtSqpKBAwbRrn27oi6B0u7j169z1q9fT1ZWFvM+8EPw0tDEPROAxg2VTH9Vz5OvpjL3yy8IC5Hx0lMapFKoozCCAkI7aZFI4JN5e+nRoxd2u8jOfQV07aDBbheRSSUonFgUAaiU1z5+G1SFVBA/Xz+ef/4FZs/+lJOnbXTrqEKtkrDnoOWKoDH4SgD6fxOpVEqfvv1Zvuwf+nS1EhpytYhIFEV+W2RAsDvWm87w0ksRRZGCAjMazdXXmM1m8vLzcHdzLzNr7FZS2LFRkL+dme+v5fDhI/h6y8jMtnPgmJk/lubywEgdIwa707+nG38sNbJp82buH3M/cCsCxGHgA2N5ctx4kpKSUGvUhIWFIZU41vM1YVncqnUrHnjgQX799Rc27TDTupkCo0lg934LSBVMnTIFNze38g/0f4yzYt0XJ7zIlMlTOXP+KB4SPQpBhVlqxF7Liwb1a/HAgw+UerzbJRAcXIJGVXCJGi7+M5jNZmQSGVKxZAtzJqmkcgk9vvgTRDbppHCRFBKRo8COFZlcxtgHxtK/f7+i99VvUJ83J7/JJ598SmzeQTQyDVbRhk2w0qFtByZMmHAzP2KVqUhlclmV7RW1naro4C1Y994WA/Xq1Sto00JdTNAAh9flmLs92LC9gAULFiKTSUlKSkSrdadz58507twZheLqhlz3Ht357bdfWb4un3uGFq+6y8sXWLnBROdOnVGpKpfV4ePtg4+3DxER4Rw5cpCp78cxbICWDq3UWKwiW3cZWbXBRPfu3Wo0BK4iVDZbo+KT1rJtqO7EfA1REPn1119ZvHgJgt2OTCrHKliQICGdyyW8twVECjDihs5pJweAGx5kkoKIWNSlpkKN2WImJzGPUBogRUaGKQXIx4oZFSV/j3ZsiAhcSLhQ5mdISkomJsYRyta4SWMCAm6PbJKyqGy3WllWfIXc7ByNQm6H8bMqxF+Ix2y20q6V3unzLZuqkCvg559/4v33P6jwcUeNGkViYiIffvkvfy1XERkmIyVN4OjJAsLD6/LSy69U0ydw4cKFi8qjVClpGn1jdiJhYaE8/8Lz1XRFNY/VZkMqkSIRnW/GF+aD2Gy2Sh/7+vv5mTNnqBemKuaLX5ih0bR+OoP7avlkXjYjRo5Ao3HY/VRU0ADYsWMr7VqqELw0XI+PtwwfvZSU9Cyee9z9qqBxDZ3bafjmlxz++PMPWrRoxve/nyAkSE69cCUWq8ih42ZaRpeck+05WIBMBoGBtdHrnRej3K50796doKAgli1bysoN+7HZ7ERGRjJp0pArgsadI9JUhZEjRnLgwD5en3GR/j00NL9iu7Rui4ljMQVIpZJScxNOxFrw8NAWFcMlJyezcOECtu/Yjs1qR66Q0blTZ0aNHk3twJIBx7eaEG093v5oOipZPIP6yrmYKCOqgZJnH9OzabuRn/804K2X0aOzhsAAOZkZmcXe30QfzLHsRLYeWcrBgwc4sjUetVpF+/adGDRoEH5+fhXO2aioFdVFo5noUoppCseZk1dsi6tjvXjvvffQvFkzVq1aybHTp1EolAwd1pb+/fvj4+tzw8f/L1Paek7noeOjjz5k586dbNy0keysHDzDohk4oBMtW7ZEInX+9+YSNO58XKKGi/8MoXXrYhUs5JOLG1c3lgVEkriAHj/qEokECVrc8aM2WaSTQTJqD3e+mjsXnWfJSvpWrVvx44/z2blrFxcvJqBSqejQvgPBde6MjoTKVCZXpkvjWqpqn3KriY+/SN8uzlv/lUoJbhrYtGkTAX5KGkbK+B975xkYRdVG4Wdneza9N0IogdBC753QmyBFsYCKvSuCSBVBURHLhwXFioIoTem99y6E0AkkgZDes33m+7EkELPpCaDu80t3dmdnl82de+9533NS0yQ++eQIy5cvZcY7s/DytE06fLx9GDr0fn5ZtpyMLCt9uzvj4S5wMtrIrytz0RuUPDhqVIWvMyMzk/Dwhly/foOlq7JZsjIbAFdXZx58cBQjR47kXlkclKdbI1/YKI3S8jXsTVYrkq9xp4SNX375hWXLluFLMD4EoBCVGNBzg1jiuYSAvMAPVMRKPJcRETFjKiRa3I4JY5EA0fzujlrcChBVoyWLNFK4YbdbI4UbgAyV0r4fb2ZGJp9+8ilHjx8teEyGjLbt2vLySy/j7OJcka/kjnGv5WiUl39ylwbcyrUwm+zbS1msEohw7tw5srKyytzdJghyJkyYQK/eJ9i8aRNXrifg6urG6693p2OnjigVZQu3d+DAwb8Ho9HIrl27OHL4CGazmbp169K7d2+8fex3DjioWurWrYNZNJFHNjqKjuVZpKNSqggOrth6Kv9+bjabSU5JJinFwobtebRvqSHWYLv/htdOAcB40wVIfjM4uTyCBtgCz5t2sK0X8rs08tlzUE9iii302tVZKCJogM1SyN1N4PKlywy+zxZU++rUqzRtpMHdVcaCXzKZPVmJu9utedzlq2ZWrs3BaoUhQ++nOub5FouFffv2sX37NjIz0/Hy9qFnZC9at25dql1uWQgLC2Pcf7SoQKvV8t6777NkyRI2bdnIynWpAISH12PcuIHMm/c/fl6WxfOPuxWyYIuLN7Nph4G+/e5DLpcTGxvLW29NQK0y8/BQJ0JqKImNN7N+237eGHeY9957/66FQxfHli1bOHbsKHK5jDr9ffFoJLFsVQ4r1uYw7jkP2rbUsHxNDh1bq0lItNCiTeHONUkS2fbTMrZu3UpY11D6DVeTdimHTRtXsWHDet5+ewbh4eFA6V0b5RE2SlszVvV6sV79etSrX6/S5/mvUJbiNKVSSdeuXenatWupFsJw7wkaDjGjYjhEDQf/Glq0bIG3lzc30mKpJYUXbPDlkIEFE34EF9oMVKLCl0Cc0HExO4qEGwl2RQ0ApUpJ165d7sjnqErKWplsbwOvpEG+MrZT99KmnFqtIivbvuXPhm15JCZbefpRN/pFOiHcVPevxJqZ+UkS789+lzlz5pK/yBgzZjTOzjpWrFjGmk1JBedp0LA+Eya+WOFKmqNHj/L++++hkIu0aaECNBw+YSI3V+Shhx6hf//+FTpvdVBcSFxpVKbF+J8WHJ6VlcXKFSvxJZgAbtnhaNBSk3pYMBPHBTJIQUBONulYsaBAiYE8csjEBfdC57RiJY2kQllCBvJIJ7mIXZUcORISGaRwDRV+BKNAiYiVVBJJJA45Aq5uRQUPg97AW2+9RXJCMiGE4Y43IJFOCscOHWfK5KnM+ehDlCr7G8jpaemsX7+eXTt3kZeXR3CNYPr160fHTh2rZPFcEvdqjsZ/JRw8n9q1a6NQCuzYpy9iPwWw95ABixVA4uTJk3Tq1KngWG5uLqmpKeicnQsE5cLIaN6sOc2b/fts0xw4cFA+4mLjmDplKukZ6TjL3JBJAieO/8XvS3/npZdeIjIy8m5f4r+ephFNCfAP5HrSVWqJDVDctu2gJ5cUIYGekZFonYp2P5TE7ffzAwcO8MUX88jMzEajgW8WZrLg50w69Xfj+dE52DIsJLbvNdC4cUNUalW5BQ2A8HZBxF2/WkTQAFi3JZdG9VW4u1uIjTeDnfzr9AwrCYlWmjVWsXnTRr7+egFHjx5h+/Zt6FxSSElO5pk3kujRWUuAr4Lzl03sO2RAAvr06U3vXr3K9R2VhdzcXN5+eyrnzl2kcX0NtYMFYmKv8+67h2jZsgWTJk0q1JXuoPw4OTnxxBNP8OjoR0lLTUOtVuPu7g6AyWRm3rx5XL8h0rubBnc3OX+dNrJhmx4f30BGjBgBwBdf/A8PNwuzJ3vhrLPNlVs11dCnm45Js9P44ov/MWfOx3frIxYhNTWFr776goiGKt54zhMXF4E4sxODeot89o2BOV+k8/Sjbhw8msFvf+aQk2uhR/fC+REbN25k27ZtvPaMB906mojK8YT2WkaPMDDrkwzefW8m3337Q0Eg+j9Z2HBQNirSbQ+VEzTKKmZA+RxL/s6/YX13t3GIGg7+NQiCwBvjxzNt6jQuWE/iLvqiRk0qtkFQg/1JswabV2V6esadutQ7SlkHf3uDvr0Jvz3bqbJyrw3a7du3Z+vuvdw/QIdSeWtTVRIllq/JoXM7DQN6Ffa0DA1R8sLjLsz46CJnzpylQYP8KngZw4YNZ9CgwZw6dQqDwUCNGsGEhNSs8PWlpqXy/vuziWgo543nvAryOiwWie8WZfL1118TFhZW4A18L1B9NlQlT1ZPxJacr1EWYQOqP19j/779iKKID0WvVXYzPDOHTIzqPKxGK174IUdOArE44cxVzhNEbdzwQkBGHjlc4zIWzFixcIZjWLFgxYoM8KGwmKZAiTNuGDGQyg1SuYESNWZMiFhxwZ1sMujQoUOR69u6dSvXr10njKZoueXx64UfWlHH+St/sXv3bnpE9ijy2itXrjB50hT0eXpcRU/UuHD1TDxzTs9h1+7dTHzzTeSKotaBVcG/IUcjn3tl7KwoSqUKuSBn7ZZc6tZW0rW9tqBC8dxFE98tyqRlhJqjJ42YLWYAkpOTWLhwIXv37sViUzxo2DCchx562BH+7cCBgyIYjUamTp2GMctEOC1QSzZbH6to5ToxzPtsHoGBgbfN3xxUBzJBxsSJbzJp0mTOG07gLnqjRIOebDJkqYSGhPLYmMfKdc7b7+cn/jrB7NmzadNczSPDvakRpCQnR2ThdnfWbTyPk8qZR4a78OvKbKLPG5g6dViFBI1E4zlatGzJjzN3sifCg05tb60nJQnOXjQx9iE3FEojG5cm0LOBD4H+t7ZYJFHi56VZKBQyHn/QlVempHD23FkiI3sSGdkTgPT0dFasWMH2bZvJzctGqVRQs1YtxoweQ/PmzamOLo3PP/8f167F8OE0b+rXvSVeHP3LwPvzTvDjjz/x1FNPVfn7/hdRKpRFbFp79eqFh4cHS5f9xsfzbfNCnU5DZM/+PDhqFDqdjtjYq5w5c56JL3kUCBr56HQCDw3V8d5nF4mJiaFWrfLn0lQHGzZsRCGXePMlT5y0tmuuocwjDidef07Lk6+a+CvK1jq1bE0OAwYMIKBQ4Z/E6tV/0r6Vlm4dbX9rjZ3TOJXjyclEBUEBAqfPZfPdd9/x1FNPoVDY/tbutLABVWtH5aB47mVBw2E3dW/gEDUc/Kto2LABc+d+xNJly9i7Zw9W0YpGrQGjrSrIiaLWKHpyAcod2nevU9aNvPKGg9ujPAP5vTRoDxkylN27d/HhFxk8M9oVb0/bkLj/qIHkVCvPPWbfv7Z5YzXubkqOHz9eZFGsUqlo2bJllVzfxg0bEWRWXn/Ws1AAuUIh4+lH3TgeZWH1mlW8/tq4Knm/qsImbJTfhupuB4ffiW6N7Jwc5IIChWi/m0F1M+fCarXiSzC+BGLFSiLXUKBCjoKrnENAjoAcC7YsDgmJbNJxxwcFCrLJQE8uN7hKELULdan5EsxlTuOOFxp0WLEgR4EcBYlCHA3qNWTNmtVEnYwCmYxmzZsxcMAAtm7diisehQSNfJxwxlXmwdatW4uIGqIo8u7MdxHzJMLF5ii4+dlFyCSNwwcPsWLlioKqtOqgKnI0yjoBLgvlnfz+m1qSvX28uX4tgU/mZ7B0VTZ1a6lITLJy5oKJenWUtG+j4ehJI3Xr1CUpKYkJE8YhyPJ4dISO8LoqklKsrN0Sw7Rp05g4cSLt2rW/2x/JgQMH9xB7du8hPT3NJmjclh0lR04wddALuaxcsZIGkx2iRnVTq3YtPvvsU/7880927NhJSm4Cvj6+jO73KAMGDECtKXvY8d83thYv+oXwMBVvvuRBfrNnTJ4/nduCp4srvyzLYtteAzk5VsY+MRZlLdt9t7yCBsDQVl253OUwc7/aw1+njXRso0Ums1lPAXh6mmnWSMvxbQIT3klmQE9nGjdQkZ4psn5rLtHnTLz8lDv+frZ1hjHfD+smHh4ejB07lrFjx5b5+6gMSUlJ7Nt3gOcecy0kaAC0bKrh/gE6Vq7byMMPP4yTk/0w66rk0qWLrFmzhqiok8hk0KhRUwYNGkTt2rWr/b0riiiKpKamgEyGt5d3IQupstKqVStatWpFRkYGBoMBLy/PQt0xsbG232ZEQ/t/J00b2x6PjYu9Z0SN48eP0LaFunwadAgAAQAASURBVEDQyKeGMg+UMGqYktPnjAgCDB06nNGjHy30vNzcXOLirjFyoEfBYympVr7/9ByXr5rxaxFMRGc169ev5+DB/UycOOmuWFGBo2vjTuAQNByUBYeo4eCeJDk5hc2bNxEXG4dGq6FDhw60bNmyTBYlNUNr8sYb43j11Vcw6A2oNRqeHDuWpIx4alK/0OaeiESS7BpBAcGE1b13qt2riqqynSpucP8nhoPfTmhoKJMmTWbOnA956vVk6oSqyTOIXLtuAkCltD9BlQkyFAoZVtFardd36tQJWjZTFpkY2i4CwmoJ7N+3j/HXr+Hi4kqXLt3uKf/4iuRrVFdweHj9AE6eSyjTBDX6xJVqm5z6+/thFk0Y0NvtHssjGxkylAol1pvhmXLkhBDGVc6hwQlfgjFjxIgeEQvIQIcroVJ4gb2DhEQaicRxCS3OeHHr8zjjggIl2UIGWWI6WrkOi2TCKBoJCQrhzNloYs7H4CzaFhQ7Nu9g86ZNOOmc0P3N+up2VJKG9PT0Io8fPXKUxORE6hFxS9C4iRueeODD6lWrGXb/MAR51dpQladLo7QcDaiaCXBFBI1/E7179eGnn37ExUVGUICSxCQrLi4C41/woF5tJZNnp1KjRiA1atTgww8/QC7k8dF0zwKv8fAw6NRWwwfzMvj883nUqlWbbdu2cerUCSRJomGjJvTt2xcfb5+7/EkdOHBwNzh69CjOMteCDo3bkSHDTfTmyJGjIHGvxJH9q/H18+Wpp5/iqacrXvX/942txKREzp49z/gX3AsEjfxg8PDaKYT4O/Hrimxq1WrI888/T5La9g9dEUEj/579+uvjCK1Vm7VrVrFphy0fwdPTg+BgT06dTWREW5HZk3xYvDKLPzfksOQPW3aUIMCAnk5EdnZi/xGbCFIzJARJstksxsXFodVqaN26Na6udyYM/NSpU0iSRNf29l0MurbXsmRlNufOn6t2S8cNGzfw1Zdf4eutpH0rJZIE+47sZvv2bbz44kv07NmzWt+/vIiiyKpVq1izZhVJSbbMloAAXwYPHkr//v0rJG7kW1L9HY3GNoZlZInodEXnx+kZNgtlraZ8Fm7VidVqRaUq/jtQpmdjNKp4YkxzBg8dU+S47OYftPXmEttkkpj2YSpms8Sst7xoHC5yOteT5PucWbwgmbffnsann83D/2YnzO0B4g5h459LecUMcAga/2UcooaDe47Vq9fw3bffIsjkaCUdoszC1q1bqRVamxkz3sbdw71M5zEajWzfvp2Tp07h5eXFxYyLWInGnxqo0aInjyRZPLlk8cbTr/2rFjZl8Y8vaeAvS45GRWyn7kVatmzFDz/8yI4dO7l48QIKhZIxjzXjiy8+58AxA03sVMdcvGwiJdVE/Xr2N9+rCkkCwc7k2GqV+GxBBrsOGAgKkBPsd53EpHg+/vgYf/yxnBkzZuFmJxPhTlKRfI2yCBtQseDwfO62DVXbNm1xdnbhRk4cNQkrJLJasJAiJNCiWQvc3d3Zu2MffmINBGS444WSxiQSTxK2cHWtWkvLiJYcPnyYGtQt5FctQ4YX/mSSRhLxeOKLDBkG8ojjElYsjHt9HDk5OcTHxaPRaKhZsyYff/wx3gQSJIYWXJsohhLPJdJykoDiLaKMgp4aPkUrxc6cOYNWrsXJ6mL3dW54cTkzmqSkJPwDSv+3KSvlmRCXJUfjbgaD/5smvb1792HNmtVkZKRx8rSR7p20hNZQcvaCic+/z8BqFfhkxmSysjLZv38/Yx9yKRSeCiAIMkaPdOH5N5N47vlnUcihZYQSmQBrV19g5coVvDFuvF0bNQcOHPy7MVssIBUvkAvIEau5KMVB1WHvXp6TnQOAn49t3nO7oAHg5CTg6qKkUaNGVSJoAAiCnOHDhjN06FBuJNg2xvwDAoiLWcmHEz7lG7kTjz/gyrNj3Hn8QVcuXrGw4OcM0jNFRo90JTtHZPGKXMLD65GXl8ezzz1FwvUkFEoZVouEXCGnX9/+PPHEE8jl1WPHmY8k3RJc7JHvBiqJUrVeR0xMDF99+RX9e2p58mG3gvzCMQ9IfL0wk88/n0e9emGVsvKtSiRJ4uNP5rJn9x66ddDS8RFPREli94Fsvvnma2JiYnjxxRco76aCKIokJychSeDr64NwM9C+SZPGaDQqPpiXRnCgAn9fBT27OBXYm23YlouTk4aIiIiq/qgVpl69Bhw8cA2LRUKhKPw9SJLEvsN6snOt9Hw90u4a0UnrRN26tdl1IIHunbTsOWTgWoKF/73nQ81gW2FUY+c0ovDkudf9mTwhntWrVhWxSqtMIRyUvcsfCgsbcPftqCwWCwcPHORq7FU0ag3t2rUjMKhi2Z75iFYRSZKqzSr4du6GoFHe/AxwCBr3Eg5Rw8E9xaGDh1iw4Bt8CMRfCkGOHCTIJYvY2AvMfGcmcz+eW+pcITr6DDNnvINer0eHGzJAQCCHLC5wquB5Qf5BvPHMazRv8e8JFi1LZXJxA7+9jbuSPOLLMpj/EwZurdaJfv36Af0KHrt06TIrViylbQtNobbfnFyR+Quz8fPzoVWrVtV6XQ0aNGLD+gsYjCIa9a2Vx7LVOew+oOf1Z93pcpsn/aUrJt6Ze425c+fwzjuzqvXaykJlhI3iuBPB4dVpQ6VUKXn+ueeYM2cOl2VmvKUAVGjII5sUIQFBDWPHjsVitbBzx05iOUcQtVGiQocrAYRiFkxoXFXMnz+fb7/7Dp3gilosWpEK4I43sVzgNIeRo8CIHgVK1DINP/34EwsWLCjojvjss89QC5pCggaAgIxgapMtSydHyiSXbHQUFiiyySRbzKB3795FrkEmCIiSrXtEVsLgLROqXlkuz4TYEQx+Z9DpdLz/wYd88P5szp+/yKbteVisIBfA28eXqVOnUqNGDS5cuIDVKtIgzH7nWVCAAmdnGW6uMuZM80bnZPsdGwwi877P5KOP5vC/efMIDqrYosOBAwf/TMLq1uXIoSNYJattHfE3cmTpNquWf1Ex07+V4ja3vL29EQQZ5y+Z0OuCbM+5KWgAJKVYSc80Y/azWQtXVtC4HbkgJ+jmfUU0HyIkJITnX3ie+V99xe4DRpo2UpKnlzgRZUStkvHgEBf+3JDLhu0GLFYVDz1yP9OnT6duqMCrU72pX1dJTo7Ehu25/LpyDQajgZdefKm8X1W5CA+3FQ4dOGqki51ujX2HDSiUcurWrVOt17FmzWq8PJWMfeiWoAEgl9tsdg8fN7Nu3Tqeffa5ar2OsnLw4AF27dzNhBc96Njm1vfWprmWiEYqPv92E126dC5z3pckSaxa9SerV9/q+vD29mDgwPvo3ac3H7w/G4PBhN4oJztH5GR0LsvX5DBsoDMuzjL+3JDLqFGjCjo67gX69+/Phg0b+Om3LJ54yLVgjSpJEr/9kcONJCvTp0+gnmeTYteI9913P3PnfsTKdUpOnzXSOFxdIGjk09g5jagcT/rc78n6ZdsIDg7G3d2dVq1aoVTanltH58zvBzfgkS0nJCSEevXqFaxDyipsnLwpbJSlYwO4610bx4+f4OO5H5OZlYFGrsUiWfjxpx/p2KEjr776arks/8DWbb9ixQqioqKQkKgVWovB9w0mskdktdxDHYKGg4rgEDUc3FMsXboUF5k7gVLhTTUdrgSJdbhw6TRRp6No3LhxsefISM9gxttvozCqaUBDlNi8KS2YiecSOUImY58cS1hYGPXq1ftXLmrKciOoTI5GWSuO/8kD98gHRnL+/FmmffAXLZtqaFRfSWq6lR37jMhkat6ZOalMdmiVoV+/fvz55x988X0mrzzljkIhw2wWWbsll/49neja4ZbPrSRJ5OZJhNYQOHHiJN988w0jRozAw8OjhHeofioubNz94PDqmpR26twJjUbDwp8WEhN7BrB1VrRo1oKxY8cSXMM2UZr41kTmzJnDGdNRdDJXRJlIrpiFt4cP78x8B62T9ubwVXolnTs+yAAnauCGFwYpj/Mpf3Ho8GHatWsLQNTJKFxED7vCg4CAi+RBriqDGMsZfMQg3PECIINkkmTXadKoCe3bF803aBoRwdKlv5NDFi4U7SDKIBk/Hz98fXzL9gWWgfLaTt1rweD/5LGzLPh4+/DRRx9z6dJFzpyx/Q1ERDQlJCSk4Dk6nW18S06zUju06Dny9CIGg8TwgdoCQQNAoxF49Sk3njyTyto1a3nmmWeq9bM4cODg3qJXr14sWbKEa9bLBFMX4bZ7WjrJZEppPDG4qO2Jg3uH4ja2Ll++zKpVqzh27DCCIGPFulyeCpNo0Si14DmSJPHrymx09WvToEGDKhU07BHiVJeQPnVpGtGUDRvWc/HiBURRol49kZiYS/ywJAu1WkmXLl0ZOXIk3333LT5e8PYb7qhuFiy5uMgYMdgFnZPA1ws3M2zYMAIDKldZXRLBwTVo1iyCH387Q91aykLB5hdjTPy+OpeuXbpWux1W9Jko2rVUIpcXnXcqFDLatlQSHX3KzivvDhs3biA8TFNI0MinZ2cnVm3Qs2HDhjKKGhL/+99nbN++ne4dtXQa7YlMBnsPGVi48CfWrFlFXm4mU8d50jJCjUwmw2QU+WNDLouWZwMw+L7BPPDAg1X8KStHzZo1eeaZZ5g/fz7Ho8x0bqtGkMGewyauxBp59NFHC7Ini1sjdu3ahdjYq/y4ZClqtUCb5vY34hs7p3FRloNXHWfW7fuVq39loNM5M2bMaBISEli7dg0mkxnVzbmlv0nFyy+/Rp2bWS1VbUUFJdtRHT58mFV/riI6OhqZTEbjxo25b8gQmjdvVup5y8LFCxeZ+c5MnERn6tMcrdUJEZEMUjh44CAfzvmQqVOnlvl8K1eu5IcffsBZcCOQWggIpF1N57PPPiM6+gwvvfhile2j3b5uqw5Bo7Ld9uAQNO5lHKKGg3uGnOwczp0/R8jfLFnyccENjVzLoYOHShQ1NmzcgNlopo7UBOG2n7gCJSHU4yzHuH79OgMGDqiaC5cg6nQU27fvIDMjA28fb3pG9qRu2J0fsMrTpXE75cnRyKesA/o/deBWKpRMm/Y2W7dtZdPG9Sxbex0nrZZevfowcNDAO+LX7uvry7hxbzB37kdEnUmhQxsVObkimVkiXdrdEjSyc0Rmf5bG6XMmAvzk1K+rYtOmtWzYsJ4nn3yK/v37V/u1lkT+pLW83M3g8Or2SG3V2hYOGH/tGjnZ2fj4+uLl5VnoOW3atuGHH35g67ZtXLhwAYVcTouWLWjfvgPpaWns2rULlVpNrpiNgTw0dgK800lGizPBFLaFcsIZrdyJ6NOnC0QNZKXLI35+ftSrV48d23eQIF4BbH8rvXr2ZOzYsXYzMSIiIqgZUpPr8ZcJFRsUBLfm536kkcwzQ5+p8k6N8thO2aMqgsErMwH+p46d5aFOnbrUqWP/cwYGBlK7dk3WbUmkTXNNEZ/qTTvyEEXo3K7o5oJSKdCxjZITJ45Wy3U7cODg3sXD04NXX3uVj+d+jF6Wi7vohYCCbFk6WVI6Pbr3oFu3bnf7Mh0UQ3GCxp49e/ho7kd4ecjp2VmFyaRlS7QXX30XzfBBLjSopyQpxcrazXlE5fgweHBfWtQNKXSOqhQ0/m6tGxAQwOOPP1HoMavVSm5uLk5OTigUCvT6PA4dOsxTj7gWCBq307OLll+W57Br504efHCU3fe9du0ax44dw2IxU7duGE2aNKYiO4uvvvo6kydP5KVJibRvpSE4QMGlq2aOnDBSt24dnnrq6XKfs7zIALEEiytbrsK9U30YHx9L59b2t89kMhmNwxVEX4wt07mOHTvO1q3beO0Zd7p1vDV/b95EQ/MINR/OS2NgbydaNb3VhaFSC4y8z4XYaxbOXXZi7BNjK5ThUd3079+f0FqhrF61itWbTyBJ0LBhE8Y+dR9NmxYWfPLnurF5twsbMh59dDTt2rXj448/4dTZBKxWsOfK9tdpI9IFC7XqticowsTZk9f48ssvEQQZIwY707e7J26uZlad8GD7PgOTJ0/ko48+ITjI1uF1p4SNxYsXs2TJEpwFN3zEYEDi3F8XmX58Go+NeYz7h91fti+3BH777XdUkppQKRwB2/giINgsiEWBw4cPc/HCxTLtUcXGxvLDDz/gRzD+YkjB3pyX5EcaSWzevIlWLVvSvkPRYrbyUpHujNu51wUNh5hR/ThEDQf3DFevXgVAXszPUoYMOQpMZlOJ59m7Zy+CJCeaI4iIqFDjhT8+BCAgx1X05NDBQ5UKrMvHZDTxwQcfcPjIYbSCDqWoxiScYt26dURGRvLSSy9VezX/3ynphlDZHA0oe9DtvRgMXl7kcjm9e/Wmd6+iljp3io4dOxISEsKaNWs4+tdRjAYjoEdx889EkiQ+/DyNuGtm3h7vSbPGtmqe3FyRRSuymD9/Pt7e3rRpU7YuierCJmz8s4LDq9OGCgAZBAcHlfgUZxdn7rtvcMH/Z2Zk8v77szly+AjSTQlChoyrXKA2DQo60yQkUkggmwxCCCtyXttrpUJCQrNmzdixZReiGFqoshVARCRbSKdLi/48MfYJHnvsMS5cuAAyqF+vPs4uziV+zilTpzB50mTOJh/DFQ8UKMkTctCLufTp05cBA6pIZKbsXRoljYdVESRX0QnwvZxBVB1YrVYOHjzAqVNRSJJEo0aNaN++PQqFglGjHuHdd9/lfwsyeXiYM95eCgwGkU078vjptyyCA+R4e9qfMygVMqxWSwWv5yCXLl9CqVDQqlVr6tb9Z9/LHDj4r9GlSxcC/ANY+cdKDh86jMVioXadOjw58HG6du1aLXaHDipPcZtbqampfPzxXDq1VvPK0+7I5bYcjdotRX5bcYMvf8woeG5w2xaM6NeN4b27FTrH7fdtq9VKZmYmKpUKZ2fnCt+vS1vnyOVyXF1dC/4/JzcXUZQI8LPvS69SCfh4KsnMzCpyLDc3l08/+4SDBw6hVAooFDL0eishIUGMHz+RmjXLlzvh6enJ3LmfsnnzJrZt28Lp85l4eQXy7LN96NEjEpVKVa7zlQWzxczmTZvZuHEd16/b5ll7s0TGPlQ0f8FkEjlwzES37veORbRW60RaRl6xx9MzRLTasoV2b9y4nto11XTtUPT5HVtrqVsrh2sJ9rN/enZxYveBVOLj46lRo+ga6F6gYYOGNGzQsMzPt7dODAurx4Q3J/DKyy/zx7ochg0qvNY4EWXk+CkjwVJdlBcMEKYhPCKQfSeTeOoRV/r31BU8d2jLDIICvFmgl/j9tyW8/vq4gmM13XX8unYz3509gxQbT40aofTt288mwNz8Wd4ubEDZ7aiiT1wh9mosS5YsIYCa+Im3xhgfMZAbxPLjTz/StGlT6lTC7s1oNHL48CECpJoFgsbtuOPFDUHD7j27yyRqbFi/AbWgxu82QSMfT3xJF5JYu3ZtpUWNyggale20B4eg8W/BIWo4uCeIj7/GzJkzERDIJgM3PIs8x4iBPGsOtWsXP+BHnYoiLjYOOUp8CUaFmhyySCSOTNKoQyPkyDFbShZGysrX33zDsaPHCSUcN9ETGTIkUSKNJLZv3Y6Ptw8PPfxQlbxXaZS2kVfeHA17lNd2ykHVUKNGDZ57zuYnazAYGDNmNAeOGqkTquL8JTMno01MesWD5k1uVfPodAJPPeLG1XiRpUt/u+uiRj7/xODwu+mNejt6vZ6JE98i5UYywdTBDS9AIpFrpHCdMxzFFS8UKMgVstCLuajR4kHRrqI8ctBb9UQ0uRUuOGDgADZv3kw8lwimdsGkWEQkjotIiAVdP65urrRs1bLM1+7n58fnn3/Ojh072LVrF7m5eTQKCadv3z40btS4ytuXyzo5Lqm6524KGv+Vye/Vq1eZNWsGiYkp1AhSI5PBunXr8PLyYMqUqbRt25ZXXnmFb775mh37kvH2UpKZZcVkEqkRXIPs7AT7YZSixMFjZsIbli/3KPpMNB/N+YCUlHR8vFUYjSKLF/9KREQjJkyYWO1WHA7KxxdffMGcOXO4ceMGTZs2Zd68eSXe65YuXcrUqVO5cuUKYWFhfPDBB3e9k9FB9RFWL4wJEybc7ctwUEZKun9v3LQRuULi2cdcCwQNgI4t0+jY0oc33k5FFAIZ8PSTeHt706Sm/XVNfXcPFi1axLp168nOtgkHHQc0olOnTgxp1a3M11rRdY6riytqtZKLMeZCc/Z8cnNFEpJMdPctPG8TRZGZM2dw9eoFXnnanc5tNSgUMk6fNfHd4hQmT36LTz79rNxd5E5OTtx33xDuu29IhT5PeTCbTcyY8TZRUadp20JDZActV+IsbN2dx/++zeDFJ9xQqWzzTpNR5H/fZZKnl+6pMbpjxy4sW/Yrjz1oxc21sDCVnGLh8Akjjz/etUznunYtjohwRbGdFhEN1ew/ord7zFlne01phZ7/NPKFDaBgrVgrtBYjRoxk4dLfOXvJTPcOGlRqgUNH9WzdrcdV5oGHZBsPlBcMpNXNpl5rd3p1LSwWWcwSKWeuIoruHE46zyOPPETHTp1p1aoV8/73Gbl5OUS0q4WutY64g0eZOm0f/fv159lnny0kbADl7tpYsXwFXt718U3xKnRMhgx/QsgUUlm7di0vv/Jyhb87g96AKIkosZ+vIkOGUqYiNye3TOe7ePEiTqJrkSK3fJxFNy5dulzh662o3RSU3GUPDkHjv4hD1HBwT/DDD98jGcGbAFJIwANvdNyqbBGRSJBdQavR0rVLF7vnsFgsfPjBh+hwoRYNEG6GA3riizf+XCSKG8SSTQaeGndiYq5Qq1Zoha85Iz2DrVu34C/VLPCWB9tNwws/DOSxatUqhg0fhlpdvlCmilLaTaE8ORoVtZ1yDODVi0ajoWfPXqzasI4WEWoOHzfg4S7QunnRSYxMJqN3Vw0fz79AZmYmbm53d0OuMsHhxQkbZQ0OtydslKWduNq7NcrBpk2bSbh+nXo0LWQ1FUQoOly4wlm0fgoUSgUNa7XEw9ODP//8kzQS8cSvoNLGiIF44TKBfoG0aNGi4DyhoaG88uqrfPbpp+TIMnAW3QHIFtKREHljwhv4B5Q+ASwOjVZD33596duvb4XPURbKajtVUnVPcRPhfMoyEXYIGiWTnZ3NtGlT8HA18NksH0JDbMGOcfFm/vddFtOnT2XevC+JjIykQ4cO7NmzhxuJN3DW6ejYsRMGg56XXnqZ73/N5KmH3QoqryVJ4udl2SQkmhg3flCp12G2mFm/bj2rV/9BcnIK9euomPqq7XpEUeLwcQNf/niOGTPeZs6cuXe8+9KBfX777Tdef/115s+fT9u2bfn000/p06cP586dw9e3aD7Pvn37GDVqFLNnz2bgwIEsXryYIUOGcOzYsRItTR04cFBJJDh27BibNm/kRsJ1nJ1d6Ny5K926dSsIrS2tIOH8uXNENFTipBUKBI3bg8E7tVXz2zHw9vEutlAr3N2DyZOncOniJTxEH7wJwjNCIuVaGt+/MxftCwb69Cl9flKZe7VaraJTpy6s27KLXl2dcHcrvDG+fG0OVquM7t27F3r8zz//IDr6LDMnehHR8NaasnEDNe9MUPL8xBRW/bmKsWPHlvua7hS//fY7Z89GM2uiF43Cb3WBNKin5IvvMzlywkC7VlokUeLQCRMGI7wxbnyVZ4tcvnyZXbt2kZ2dha+vHz0ie5RZDOrbty9r165m+ofpvPiEK3Vrq5AkibMXzXz+XRbuHh706NGjTOfSanWkpacXezwlzYJgv6GHo38ZUatVBAaUPFf9J2Jvrfjoo48QHBzEypXL+eBzm72XXABvMRg/QhBuE4ZMFxJx76AmTVDjh030MZslZn6cyqkzJtq2MOLdqibZuRr2r9rMpk3rqRGo4pN3fPBwt4lIp7qGc/yUkT+/XUft2rXp3aewY0N5uzYuXotCZlQhebkiAUJqdsExGTKcRTfOnztfqe/NxcUFnVZHnj4LdzvFwRYs6MU8/Mv4m1GpVYjY7xQCsGJFpVQWe7wkKtudAcUXpVWVoOHIz/hn4RA1HNx1MtIzOHr4KIHUwhNfcsnmIlG4440L7pgxkcoNLDIzU96YUjAB/jsHDhwgIyuD+jQvEDTyccIZb/xJIQERkaQbFl555WW6d+/Oyy+9jFxRzKyhBI4fP44oinhiP9zWEz+S9dc5d/YcEU0j7D6nqoi+lFiq7VRxgkZ5bKdKwzGA3xlGjx7Nmego3poVg6+3HK1GhlCMlYKry82qJ5PxTl5isVQkX6OqhA17lFXYiD5x5a53a2zZvBk3vOxmZ7jjhbPgSnBI8K0QOAkMBiMbN24gVUjESXTGgpks0nF382DatGlFLDi6d+9GWFgY69at49TJU8iQ0aV5e/r3718pQeNOUF7bKXvYE3pvpzzB4BXh7o6dEqdORbF3717y8nIJDAyiV6+eeHl5V8u7bd68mZycbD6e4YOn+617cI1gJdNe9+Cpccls2LCBUaNGodVq6dWrF2ArYNDr9Xh5efHc88/x1ZdfceykhU5tVQgyGfuOGIm7ZuKJJ56gXr16JV6DrXJ0OqdPR+PrLeDmKvD2eE80mptexIKMti21uLrImTjrEkeOHLlnut7+63z88cc89dRTPP744wDMnz+ftWvX8v333zNx4sQiz//ss8/o27cv48ePB2DmzJls3ryZzz//nPnz59/Ra3fg4L+CaBX5+OO57Nq9m1ohasLrKkhKSeLLL6P4c9VKZs58j8QMM1DyBpcgCJgN2BU0AK7JA5ALlhJDwX///XcuXbxIbbEROpkL7hE2e0KPcyoEsgsEUnd3j+I/TwXXOTExMaxYsZwDB/ZjNJpRKQVefCuFh4c507yJmvQMkfXbctm5T8/o0aMLXcPRo0f48cefqBmsoEmDopZQLi4C3Tuq2blz2z0ralgsFjZuXE+fbtpCggZAzy46XF0E3v0kneiLzjjrdPTq1Yz+/fvhV4YCkrJiNpv45JNP2LNnLx7uSny95OzZY+bXXxfz4IOjePDBByitbdjNzY2ZM9/l3XdnMu7tJPx8VIgSJKeYqFEjkFnTpqLT6Uo8Rz6dOnXmp59+IDnFgo934S25tHQr+44Y8XQTMBjEgjkJQGy8mVWb8ujWvSdabdH1wL+BosKGjO7de9C9e3fS0tK4ePEis2bNQodbIUEDQImaG0fjqfu4HxfNXvjJTSxdnUP0eTPvvOlFkwZqIJu/Un3x8Qrl108v8urTbnjcNg9tEpSMTObD5V51WLXqD3r37l3kp1Gerg2lQokRK5LZjEypRPRyKTgmpGZjxYqiggJBwXnkAr1692LN6rV4iX6oudWpIiGRSBySTKRnZGSZzte2bTuiTn6LCSMqCu+9iYhkCilEdijbufKpqu4Mh6Dh4O84RA0Hd53klGRERJxwRkCgDo1IIYFUEkknGdnNxrfevXvTqnXxVhKXL11GK3dCa7V/g3fFgySu4YUfQVId0kli5/aduLi48uST5Z8Emi22yfDfBZR85DcfN5vN5T53eSir7ZQ97E3+SxroyzK4/5cHcKPRyLZt29i2bTNpaal4enrRo0cvevToUaXdOmq1milTp/P444+j14tk50okJVvw9Sk6pB87ZcTVVYeHR9GqjbtFefM14JawURylBYc3CQngRGzFg8Ph7ttQpael44R7scdVopa0lLRbD8jgheefp0vnzqzfsJ7Yq7E4ObkzovP9REZG4qSzP1YGBwfxdBVkDt1Jylr1UxU5GiVRGdupuzl25ubm8t57szh16jT+viq8PAQOHjCzZMmvPP7EE9w3+L4qf8/9+/fQtoW6kKCRj4uLQIfWKvbv38OoUbaw1ISEBH7//Xd2796JyWRBq1XTvXsPJk+ezK7dO9my+6+bmRyteP7FQTRq1KjUa1i+fAVnzpxh5ptevPtZKv0jdYU2D/JpUE9F7Zpqdu/e7RA17gFMJhNHjx7lrbfeKnhMEAR69uzJ/v377b5m//79vP7664Ue69OnD3/88Ud1XqoDB/9pli5dyp69e3jjeQ86tdGSv/8Yd82Ftz9K5sNPv2HMY4+Xeu9u3qIFv6xLIStHok1EaqFjxzN8OHU2m4Y1Cq9fbt/AkoB1a9fhLnoXEjSUFwwgk+Ev1SRdSmHLli0MHz6iyPtXZpPqyJEjvDf7XXw85YwYqMHV1YmT0Sb2Hdbzzc+ZiKLteT4+XrzwwhP06dOn4LUWi4X//e8zXF1kBPgVb1Xk76sgOzvb7rF7gaTkJDIzs2nTwsvu8TbNtfj55NKhfWfGjBlTLdcwf/58Dh3az6tPu9OlvRa5XIbBILJyXS6LFy/G3cOdvmXo1KlZsybz53/NkSNHOX36NDKZjCZNmtCiRYtyhXb37NmLVav+ZPpHGbzwuCsN69k2tc9dNPPFD1nonJzJyjHywlup9O6qwcdbzrmLJrbvNRIQEFRt39O9wu0B4kCBuOHp6UWbNp4EBwWTnHANZ7GwsOGBDzeMsazZlMvwQTL+yvLkZKKcXl2dbgoaNpp6JXEwJpOQDkHk+rgDhbtmGgcmc6a+GysumsjKysLVzRV75K8nTt5cR9oTN1q3bc261ettmYV/2xayeOkQZS6EN21f6XXmiBEjOHjwEJeSTuMl+uGMOxbMpJFEJqmMffxJ3D3cy3SuyB49+P2337mac44aYhiamyKJGRPxssuIgsjAQaV3Q0PlxAwovTsDqjYLERx2U/80HKKGg7uOq4vtJmHCUCBs+BKEL0GISIhYiOYIoTVDyc7KJv5aPEqFktq1ayPIb20+KJVKrJIVCalIoBHY2uQAfAlGuGkRZcbE+nXrePCBB0oOurVDnTq2bI9s0u1mgGSRjoBAaK1a5TpvRSjuBlEVORpQ9i6Nf+sgLopWjEYjGo222AlrTk42U6ZO5krMVVo30xBRX05s/DW+/no+GzetZ9bMd3F2drH72org6elJ+/btuHj+CFbRwreLspjwokchb/lLV0xs2m5gwMD7UCjuveG+PDZUkC9sVCw4PJ+KBoffCzZUnp6eZOTkFHvcJOjx9g4t/KAMmkQ0oUlEk+q9uLvIvyVH427y0UcfEhNzlmnjPGkRoUYmk5GnF1myMpvvvv0Oby9vOnbsWKXvadDrcQspfgPA3U1O2l+piKKV2Ng4Jk2aiEZtZuQgLcGBCmJiLWzcsZkDB/bxwQdz8PMbX673F0WRDRvWEtlZQ8P6SvLyJLw8iu/a9PKUkZdXfECogztHSkoKVqsVP7/Ci38/Pz/Onj1r9zU3btyw+/wbN4oXw41GI0bjrS7HrKyi4b0OHDiwj8ViYe3a1fTt7kTntoX97WsEKXhmtAuf/XwNua7k4q+T127g1zACac0Slv+ZRFhNNzxu2jYdSfVh88484s9e5sX3nyl4zd/v2Qa9nrT0NGpSv7CgcROFTIGTzJnY2KJrocpsVOn1eubOnUOLJkrevG2O3qe7jpPRTsyYm0ZkZCR9+vSlbt26RewNDx8+THp6Jp3aajh70YzVKiGXF71vnrtkxs+vdAslURQ5e+4sOdk5+Pv7ERJSvnDxiqKQ29YgBqNo97gkShhNUrWtVVJTU9i6dStjH3Kle6dbBT0ajcCo+11ISLKwbOnv9O7Vu0wWk4Igp02bNpUqctDpdMyc9S7vvTuLSe/G4+mhRBBkpKSaCAoOYPbsKSCTsXzZcpav3YXRaMbTy52hQwczZMgQnJz+nV0af8e+dbGMZ597lunTp3NZOI2vGIQGJwzkkSwkgAg/L80iJc1K62ZGcnPVRPRxB6RC55YkkCfFA+6c1nvQSFtY2Ah0SgBcOJWejM6cW2w+I5RsSTVwwEDWr1vPVek8NaS6KGQ2AcssmYkzR2NS6Rn2yECS47KLFKuWR+RwcXVhzpwP+WnhQnbs2EGC2WbVVSO4Bk8/+ASdu3Qu87mcdE7MmjWT6dOmczbjGM6CKzJJIFfKQqVSMXniZIKDg4q8rrhi2+oSM6BkQcPRnfHf4N7b5XLwn8PP34+wumHcuHQDN8mrkCAhICOJG8gEGadOneLbBd9iEW2TUU93T4aNGM6ggQNBBm3atOHXJb+SSVqhjIt80khCg1OhFjov/LhhieX48ePlGugB6tSpTVjdMOIvx+EkOqPkVjutEQPJwjXatmmLl1f1VciXxW6lsjkaZRnk74VNueogISGBZcuWsmvXToxGM87OTkRG9mLYsGG4u7sXeu6XX35BSnI8H7/jTa2QWy2kMbFmpn0Qz5dffsGECUUtMSrD6DFjmDD+JEgWDh038NKkJPr10OHhIedUtJFte/SE1qzFyJEPVOn7VgUVydfI524Gh9/Nbo1evXvx3bffoScP7d8sqLLIIEfMIrJXz7tybXeL8ggaFc3RKE+g3D8xR+Py5cscPXqcCS960LLprWweJ63A46NcibtuZenSJXTs2IEqS3QHQmrW4mR0MpIkFRGLJUni+EkDOTkWPv3sU+Jir+LrZWXWRC90OtuGQ/vW0C/SiYmz0vnqqy95++13yvX+6RnppKVl0qqZBzKZjEB/OdHnTfSLLGodYTaLnL9koVv3qvX2dnBvM3v2bGbMmHG3L8OBg38ksbGxZGRm0aW9fQtDnWdNzPIL/HXyJGFhYXafk7+p1SwilOnTZjDjnek8+Voy9bvVQqGEc0ej0OtFXnzxJRo2aADYv2crlUpkgoBnY9um+u2CBtjuORbMaLWF8+kqe4/euWsner2BZx71KVR0BLYg6G4dtBw/cYwXX3zJbtHUtWvXcHVRMLS/M+Omp7Bxex79exa+R8XEmtlzSM/DDxXtMLmd7du38csvP5OcfKvTpX79ujz99HPFfv9Vha+vD0HB/mzfk0mb5toix4+dMpKRaaZly5bV8v6HDx8GILJz0fcGm8i0c18KMTGXqVPnzs3HAgMCmTfvC078dYKoU6eQJGjUqFGhro9XXnmFV155GbPFglJROYuifyq3rxnB1rUREdGUd955hwULvuXylehbzw0K4fUnXuHq1SusWLGM9Vttv3eTuT6JVhV+8lvh6o3qq1i3JRe3vGtkOgUVETb2HjLiqXSibcPaRMXeKNhDKW4N+XdLKrCJG/7+/kyePJn3Zs/mjOloQW5sjiwLjVrN9GnT8fLywsur8P7V2RNXyi1yuLq58tJLL/Lk2LHcSExErVbbslcqMH2vGVqTbxZ8w969ezl+4gSi1Ur98HB69OiOTqerUgHjdspiNZWPQ9BwAA5Rw8E9wqOjH2X6tOlc4Sz+1ESLExbMJJNAInFoVVoOHziCrxiMCx6IWEjNSGLBgm9IS01lzGNjqFO3DhFNIjh7+hxyUYEzrsiQIWIlkWtkkkoIYYVEEzm2yYHJZCru0krk9ddfZ+LEtzif8xfuojcatOjJJUNIxdvHi+eee65Kvh97lLaZVxU5Gvn8F22nYmJimDR5IhqVhWEDNAT4OxNz1czmrWvZv38vH3zwYYHXfEpKCvv27efpR10LCRoAtUKUPHS/jm9+3k9KSgre3uX3p4+Li2Pbtq2kpaXj6elBjx6R1KhRg8CAQEaOHMW3335Lu5YaLBaJH5ZkIYrgrJNhtdo2DrVa+5P4u83dzNeoSHB4frfG3RI2evXqxcaNG4m5Fo2vGIw7XohIpJNMshBPRMMI2rRpfcev625RXkGjOErL0YCyTYgrOhm+22PnwYMHcXFW0K6lpsgxmUxG765a3p93lZSU1AqNX8XRr18/Jk3aw4ZteUWEhO179VyOtTC4r45VG3YCMG2cZ4GgkY+Hu5wHhjjx2TcnSEy8US7/7fzNgbw8W+Ve7246fl6axaDeOurVKez5/ce6XDKzLPT5W1ikg7uDt7c3crmcxMTCC+rExET8/e3/Bvz9/cv1fIC33nqrkGVVVlYWNWoU3wno4L9DdlY2N27cQK1RE1IjpCr13n8N0s2iaHuZb2di/ZABggwk0X71/t+rdMPqhTH/q29YtHMrly5dwJqWQq/eg+nXry8B/gElbmApFApat2pFuukqnue08DcBIYdM9NZc2rdvX/BYVWxUXbp0idAQNd5e9rdcWjVVs2VnGtnZWbi6uhU5rtVqyTNYCfRX0D/Sia8XZhITa6ZHJye0GhmHjhtYsTaHwIBABgwYUOx1bNi4gS+/+JJObTWMf84bX2855y6ZWPpnHJMmvcX7779fzZv5Mu4fOoJ58+axYq2CwX2cC0SeizEmPv8+mwYN6hEeXnzBUmUwGo2oVAIajf0/VLeb+YO3d+bdKWQyGc2bNad5s+aFHtfr80hPT8fZ2QVXV9dqFzT0ej3XE66jVCgIDq5Rpo6VO8ktO6pbBXFNmkTwv/99xpUrV0hNTcPDw53atWsDMlq2bMmgQYOJjj7N5/M+4/yOWMLr1ikkbLRrocHdTeDTr9OZMUHgijmA03oPGmrS2Lo7j72H9Dz//GPIkBXsnZRWHAf28zaCgoIYdv/9nI4+TXZWNp5enjRr1ozIHpE4O9t3C/n7+ubvIkdJa1Gtk5ZatUKLPV5WVGoV3Xt0x69mw4LHrt7IAXLsXmNlqYruDLgzgoZDzLh3cIgaDu4JaoXWQqVWkW3MIJM0BOSIWJEhoEKN3qCnDo1x4daET4crarQsX7Gcnj17EhQcxJtvvsk7M97h3IUonARn5KKSXLIQsRJASJFQ7+yb/omhoaEVuu6g4CA+/exTVv35J1u2bCU55zoe7h4M7zOMQYMG4eJadXZDt1MWQePvFDf4V4Xt1L8PiU8++Qg/b5FZb96qDO7STsuAnk68OSud+V/PZ/KkKQCcO3cOUZTo2Nq+eNChtYb5P2Vy7ty5cm0KiqLIV/O/YuOGjbi5KggKUHDsqIXly1fQp09vnnvuOTZv3kirZhomvmyrNrZYJExmCa1GxuqNufywZBuPPPJwtYX9VpbK5GvcjeDwu2lDpdFqeG/2e3z5xZcc2H+AeC4BIMgE/Pz88fH14eCBg7Rt27aQNd+/kYoIGhXN0aiuYPB7xbLPZDLipBXsWloAuDgLBc+rSho3bszAgQOY/9Najp8y0rm9FkEGew/p2XfYQK+uTjz2gAs79hrIyrbSrLH9XKL8x2NjY8slari6ulK/fl227omnS3st/Xs4ceCIganvp9Iv0onWzWybSdt2267ngQceIDjYsaF9L6BSqWjZsiVbt25lyJAhgO1+uXXrVl588UW7r2nfvj1bt27l1VdfLXhs8+bNhTYx/45ara7SPCwH/3zS09L57vvv2btnD1bRZmsb4B/IQw+Nomu3rnf56u4tagQH4+zsxP7DeurXubUheybWthFnkCWRm2e1m39U3H37ojGbtu3a8OTIwjlPZdnA6jeqPfOn7CRWlkmgFIpKpkaSJLLJIF5+ibDa9WjatClQdZtVSoUSvV6025EIkKe3KT/KYkKC27Vry4IF37B5Rx5PP+qGn4+CPzfmsGmHzQpRLget1okPPvgIjaZoYQKAwWDgxx++p1dXJ154wq3gOtq11NK8sZoJM9P46acfeeedWZX6rKXRq1dPEhIS+Om3ZazeaCA8TE5qusS5i0ZCQ0OYOHEy1aUOhtSsicFg5dxFM+FhRcPWT0QZkcsFAgOL2uncaRITb/DLL7+wd+9eLBbbGNOyZXMeeuhhwsLqVfn75eXlsXDhQrZt24LBYNvs9/PzZujQ4fTv34+7pdiaTCaSkhJRKpU3rSNt12EvRDw0tBahoUXtvpVKJU2bNuOhhx/lk08+IWxbLPW6hpCITdi4Gm9GQiA+QeKpcSm0b5WFu5vAt0l+3EhyonfvtvTtWzhn5fbiOCi+awNs41fU6XhmfbWAkyf/wnz9GgpBidFqIDExkcjI4gUNu+f725on+g6vR6tawLidqurOAIeg8V/EIWo4uCfYuGkTFpOVcFqSRzYmDMhR4IYnMgROc5hsMgqJGgA+BJAqJLBlyxbGPDYGF1cXPpzzIcePH2fPnj3k6fO4dPESGSlZeIqF1WwLFpKEOGrXrEOdunUqfO1eXp48/sTjPP7E4zarxjt07y9vjgYUL2g4ujQKc+bMWa5ciWPGhKKVwd5eCoYPdOKbnw+TmpqCl5d3QcGXKEl2zkZBCGA58uMAWLx4MZs3beLZMW706uqEQmETLTbvzOObnzeDTMbVq3GMHu5ZsEhRKGQF1U+9ujqxcGkO+/cfYODAgeV78ztMxfI17l5w+N3q1nB1dWXiWxNJTUllzZo1rFq1CqvFSk5iHgcSD7F121YC/AN5550Z+PnfvVDzO0F5JteVydEoiX9yjkY+oaGhLF9u4lqChaCAotPCY6eM6HQavL1L9+suHzKeeupp1qxZy6UrZg4es9mB1AhU8MxoN/p2d0ImyPDxEsjKtpKVLeJhJ1Q8O9s2wFZk8/n++0cwe/ZsFv6ezYNDnHl7gidLVmazcXseK9flAhAcHMArr4wgMjKyEp/VQVXz+uuvM2bMGFq1akWbNm349NNPyc3N5fHHHwdg9OjRBAUFMXv2bMBm4dG1a1fmzp3LgAEDWLJkCUeOHOGbb765mx/DwR0iIyOD01GnEUWRevXrFclXKdM50jN44403yErLxk8MwQU3zJhIu5HI3I/nkpGRwX1D7iv9ROXkRsINVq1ezZ5duzEYDQQFBdGvf38ie/RArig+B+huo1Kr6NOnH6tXraRZEzXNGqkLBA2vgDSmfpBN7dqhNLhpGwUVK0Ioa+ZV38btcJ/4Jh/P/ZizpmNo5TqskgWDVU94WDhTpkxBshxBourWNa3btGbNmjVEnzPTKLzwZrokSWzbZaBRo3C0Wvv5CF5e3vTq3ZuFSzehUsnoH+nEwN46os4aWbc1j4NHDbzwwkvodEVtE/PZf2A/er2BkYN9iwgrarXAkL5OfPrNSZJTkvGp8vv87cgYPXo0Xbp0YePGjVy7Ho+Xr44J93eiXbt21Zr916xpMwICfPnp9yzefsMdtfrW2i45xcLytXm0b9++iLXwneZ6wnUmvjkehdzAoyN01A1VkpBkYc3m00ycOJHp098mIiKiyt7PYDAwZcpbJFyPZUg/J1o1dSVPL7Ftdx7z588nOTmJMWMeq7L3Kwt6vZ5FixexdcsmcnNt88KQkCCGDR9J927dbf9vN0S8eLp37871hAR+WbIEjy3nCO0QTE6uyL4NqdSsWYN33nmDQ4cOsW/fbs5dNhAS4kWPUd2pU7eO3ZzW8nRtbN28nH17dlDTpTnOAfWQyQS4lkJC3hU+/PBDtNO0dm3XTCYTV2Ov3vz8IahVRee41Sky3EnK250BEObnwcFDBzHoDQQHBxdk3ToEjf8mDlHDwT3Bwf0HcJE8UKFCZScPwwNvskglkMKBZgICaklbyFJAJsho0bIFLVq2AGz2AuPfmMCF7JN4iD5o0WFAT7qQhEKj4PXXX6u6D3IHBI3oS4ml3sTKkqORT3GD/n85HPzKlSsIgoymDe1vkrWMUCOKErFxcXh5edOwYUPkcoFd+/UM7lu04mL3AT1yuUDDhg3tnM0+er2e1atXMbS/rpA1i0Iho1+kjpRUK6s3bwcoNtxWqxVw0srJ09/b4bYVzdeozuBwKD5f427bUAFkZmXyxx9/4CJ6EEQtlKJtsZxHDlcSz/LiC7ZqZZkgo0lEE4bcN4QmTZpw8uRJ1q5dx6VLF1Gr1LTr0J7+/frh5V103L1XKcsYmE915mhUVtC4V8bODh068O23C/huURZvveKOUnlrsR8Ta2b9Vj09e/VHpSpa3VhRjEYj2dnZODs74+7uSvtWFkYOdkVCwtVFKNh4kSSJPL0MuVzGph15PDCkaPfjxh25uLrqCm2MlZX27dvzxBNP8MMPP7Bxh57wOkrSsyTy9BJBwQG8+srr1K9fD4e3zL3HAw88QHJyMtOmTePGjRs0a9aMDRs2FGxWx8bGFrLP6NChA4sXL2bKlClMmjSJsLAw/vjjDxo3bny3PoKDO4DRYOTrr79m+/btBZ0VMmS0atWKl15+qVybmEt+W0JmWhZ1xSYF+XxadLjgjpIr/Pjjj3Tt1rVKN0bPnj3H9KnTsJpF3EQvnPAgJSaDLz7/nP379jF5yuRq3QyuLA899DAxly8z/cPjNGkWRlCAnlwxiX1HjLi5ujPtzYkF4311Chr5tGvXnp8W/sTOnTu5cuUKarWatm3b0rBhQ0SzLXehKu/NzZo2o3adUD7++hpvveRG3dq2+6jBKLJoeTZR5wxMnTq8xHM88/QziFYr83/awsLfc/BwV5CYbEIuV/D888/RsWPHEl+fmpKKi7MCXx/7v5PaNW1dImmpqUVEjfj4OA4fPozZbKZWrdq0bNmy0rZEoaGhPPPMM6U/sQqRyWS89tobTJs2hVempNG3uwY/XzkXLpvZvNOA1smdsU8+eUevyR4LvvkGrdrIh1O9cHW1fc+NG9iyV96Zm8G8eZ/y9dffVpk11Nq1a7h69SofTvMq+B0ANG2kplaIgh+WrKB79x6EhIRUyfuVhtFoYMqUScTHX6F/pJaWEd7k6q1s2ZXKJx9/QnJSUqGcyLKvH2U8/NDDdO7UiQ0bNnLtWhwuNbx4dcZgujQZhEKhoFatWjzwQOEMypK6/uHWeHQi1n7Xxo2EG2zdtpVg6uCWpQYMiG5OEORNgOSN6fo+3n//fb777jtcXW05G2aLmSW/LmHt2nXk5dmKa5ycdPTv349RD436V+WqlFXMgFv3gEY1/Fi2dBmzli8nT68vOF6ndm0GvvAw/v7+DkHjP8i9Owty8J/CaDKioPhqIzlKJIpWwUtIWGQmXFyKt3ny8/Pjk08/Yfny5WzdvIVEYzwqpZru3bsxfPjwf1Q1c2nB4OXJ0SjJVuW/HA4ONmsLUZTI1Us464puZmXl3KwMvrnJ5+7uQZcuXfh15W7q1lLRsP6tzb/ocyZ+XZlL165dcXf3KPF9Y2OvcunyZZQKBVarFb3eQO9uvnaf27u7E8vW5CAIMk6fNRXJ8gCIjTeTmWUmOOjut1OXxt0MDrfHvZ6vsXLFSlSoCaEewm0brpmkYZKMaM3OuOMNSEQfPcvhw5Np3LgxUVFR6ARndKIbuZhYuXwla1at5u133i52U/ha/DXWrFnDgQMHsVgs1K1bh4EDB9oqi+7wXm9pY+DtVDRH498eDP53lEoVr497g1kzZ/LKlDR6d9Xg6Snn9Fkj2/caCQ4O4eGHH66S90pISODXJYvZs2cPFrMVhUKOv38A2/YmcP9ACc+/dWIcPGYgIdFEp04d+e3Pfbi7CUR2tnWtmYwi67blsXZzHqNHj0apLJ/oIkmSze88NJR3353F8RMniIuNI8RLw4MPd6BN69YIwr1bBe0AXnzxxWLtpnbs2FHksREjRjBiRMlhug7+PYiiyMx3ZhIdHY2fGIIH3sgQyCSVk8dOMvHNiXzyySdonUrPHTObLWzdshUP0adA0MhHhgw/apAmJbF923aG3j+0Sq7fYrHw3rvvoTCrqSuGI89fJ0mBZJHO8ePHWbF8BSMfGFkl71cdKJUKpk2bzrLV2zl+/CiHzl7C2dmbUQ92p3ef3ri62DbyqlvQuP1erdU60bdvv0LPy783u5q8+HPLn2SkZ+Dl5UWXLp3tZl2UFZlMxtQp05gxYzrj3o6jTqgaN1cZZy+YMRhFnn76KVq3LnnOq1AoeOmllxk+fAS79+wmJzsHf38/unTtirOudOsaNzc3cnKtpGdY7XY7xidYbj7PveCxvLw8Pvl0LgcPHEarkaNWC2RkmvH19Wb8+AnUrx9evi/iHiA8PJw5c+aydOnv/LxsHxaLFScnDZGR/RgxYsRd79JITknm2LHjvDDWtUDQyEepFHh0hDPjZ6Rw4sQJWrRoUSXvuXHjerq00xQSNPIZ2FvHyvV6Nm3exJNj74zgs2rVaq5ejeH9KZ7UCb01p2vTXMvi5dksWrSYLl26FsrCshciXhwhITV5+umnCz0WlXEJrBRrZwzwV6JtrClJ3LBnSbV3714UghJP8dZaXsi0FRqKbk54BzYmmevM+Hw+cydNwCqKzH5vNseOHsNT8iMIW/dBel4yK5avIOZKDFOmTEV+j+WdlJeKWE2B7Xv+4ccfWLFiJT4EUpMGKFGRQybpGgMLf/qJ9197vYSz/XsK0hwUxiFqOLgnqFO3DgeuHUASpSJtfhISWaSipuiiI5M09GIeXbuW7GPr5eXJ008/xZNPjkWv16PVaP+xnvPlydHIpyI5GmURNP6tA3vLli1QKORs2ZnHkP5FFwybd+bh7u5CvXq3JkDPPvsciYk3eOvdszSuryEkWCA2XiTqnIGGDcN55plni32/Gzdu8L//fUJU1JmCxxQ3LQW0xYTa5fvch4WFsXJ9DB3aaAptCFosEguXZuPu7kqbNm2Lfe/Y2KtcuHARuVxO06ZN8fAoWXipTu5WcHhFbajuZr7GgQMH8BD9Cgka2WSSSBwB1MSPW3+/vmIQ17lCVFQUvgQTIIYUjLNW0coV01lmzZzF999/j1pTeLPm6NGjvPfuewiigIvohQYtZ09c4OixGQweNJgnn3zyjgkb5cnRyKcitlPw7w4Gt0fzZs35cM4cli9bxsKl+7FaRTw93Rg6dDBDhw5Fqy1906804uLieOutCaiUJh6+34nQGkrirllYtSkRo0Fk0rvpjBmpo3VzDXqDxNbdeSxekUPr1i15/fVxfPGFmi9/2MbiFXn4+8q5lmAhO8fCfffdx7Bhwwq916VLF1mzZi3nzp1GEOQ0b96K/v37ExBg+zfftWsXixYtJCEhqeA1ERGNeOqpZ6lZs3BHqAMHDv6ZHD58mJNRJ6lNI1xxL3jcCz90oivnb5xg48aNDBk6pNRzZWVlYTQZ0eFq97gCBSpJTcKNkq0ry8P+ffvJyEynPs1vCRo3ccUDD8mHNWvWMHz48Ht6TXP2SjKNmzRm+KP2bVCLEzRK8k2vym7K/DXNrhUHWL58GXI5eHooSEkz8/0P3zHqwYcYMWI4FZ3seHl588kn/+PgwYMcOLAfo9HEwEE16N27N76+9guX7BEQEMDIEeUXsNp3aM8338znj/U5PD6qsEBjsUj8uT6PBg3r37ZRLPHuuzO5fPksrz3jTqe2WhQKGRdjTHy3OIdp06Yx9+OPCQ6q2BzoblKzZk3eeGM8ZrMJvV6PTueMXH5vFC8kXE9AkiQa17fvEhBWW4lKJXD9+rUyiRoGg4EdO3cQHR2NDBkREU3o3LnLbV23EjduJHN/P/uinUIhI6y2nMQbxe8vVDWbNq2nS3tNIUEjn2EDdazdmsfmzZt49NHRhY7ZCxEvK6WtI6HwWhLsixv2LKlycnNQylQIFB2fhcw8BPLIIo64WNh64hQpqalEXU+iplQfV9mt9bgTzrhI7hw9cpT9+/fRqWOnMn++e42KdGfkf7fJycn8sfIPAgjBT2YbfyzhapzQ4nYlkwvyUyz+9VfenDDB7vkcgsa/F4eo4eCeYED/AWzfvp0k4vGj8I0ihRsY0IMMcqRMdLgiIZJGMjeEq7Ro2qLMtj6CIJToO3ovU5YK5aqwnYL/Zo7G7bi7exDZsye/LN+Mu5tAl/ZaBEGGySSybmseG7bl8fjjj6FQKBBFK3l6PU5aLe++O5t9+/axdetmoi+l4unpxfjxvejQoUOxk+a0tDQmvjUBjTKPCS950DpCTV7Bhl42sz5J48Np3kV8cI9H2UJ7H374ET777BPGTUtjQC8N9euqSEqxsm6znpg4M5MmjbdrTZCUlMSnn35MVFR0wWNyuUC3bt149tnn7lo46t0IDoeK2VDlcze6NcxmM3IKV1alkoAGJ3wp3JkjQ0YAoaSTjBVLIeFYjpxgqQ5nc46xc+dOevfpXXAsOyub999/H53VlRCpXsGkXBIlUrjBqtWr2LtvHx07dqR///4EBpY+Qa0s5bGdKo6ydGmUxD89GLw46tSuw4QJb2K1WjGZTGi1GqpSsfryy3m4u5qZPdkL55tZRS0ioFc3J958J42UdBnvz0sveL5CIad7j0ieefoZFAoFr7zyKkOHDmX79h1kZKTTtIUPPXr0KBAq8lm9ejULFizA10dFm+YKzGbYvm0t69atZeLEiaSlpfPFF1/QrqWGFx/zwtdbztmLZpauvsDEieP54IOP7pjVggMHDqqPrVu3ohNccRXdixzToMVN8mLz5s1lEjWcnLTIkGHEYPe4iIQJI3l5ervHK8L5C+dxkjujtdrPW3DDi8uZ0aSmpeHj411l71sdlDcH8E4LGgfXHuf3339n1FAXBvXWodMJZGeLrFyfw88//4yTkxMDBgwo8VwlIZfL6dChAx06dCjX60TRytmzZ8nNzSUgMLBCQoKzzpkHHhjFwoULMVskBvV2xt9HztlLZn5dmcPFK2ZmzhxT8PyTJ09y6tRppr/hSYuIW+HjdWupmD7OnRfeSmXF8hW8/PLL5b6WewWlUlXu7s7qJr94JC1dJMDOkiI7W8JsFovNX8lHFK2sWbOWRYt+xmg0EVZHjSTC9u3b+fnnn5g6dTp16tQFZOictSSnWO2eR5IkklMkaoeVPci6MlitVhITU3hgsLvd42q1QN1QJdeuXS/2HJWzMy5d2IDyWVJJgb4YrHmYMBbp8APIJQsBgZysa2Smx/LXsSOoBSfUQTUwAurrOQXPdZV54CxzZdOGTf9IUaMqgsB37NyBIJPjLQUCNkEDQBObCzIFXlY/9u/bR16eHqe/dWA6BI1/Nw5Rw8E9Qb369XjwwQdZsmQJOUIWbqIXMiBTlkaWlE6XLl05f+4cFxOjUAhKRNGKhESnDp1sk6r/iN31nerSKIl/s+3U7Tz99NPk5ebwydd7Wbg0lwAfObHXLWRlWxgyZAidOnXiyy+/ZMeObRgMJtRqFd26dWPEiJF07ty5zO/z559/YDJm8/F074K2cJUahg10wddHwUdfpHP4uAEJiI23oFbJaFhPxeLluTRqFE6zZs346KO5/PLLIn77cycmUzYAzZo35d3nRtGwQVHBLysrk7feehO5LJsJL3rQprkagxG278lj0YqdpKalMuPtd4oIKXeS6goOtzcRLYsNVXHcLRuqkJCaZMRm4CPdWtTnkoMnPnZD7QRkuOJJHjlFjqnR4Cy4EhUVRe8+vRFFkbNnzrJp0ybMRjNh1ClUZSRDhg8BZJBCZmoWG9ZsYN2atYyfMJ72HdpXy+ctj+1UPuWxsbid6szR+Ccgl8urpDPjduLj4zh9+iwTXvIoEDTycdIKjHnQmZlz0xj3xjjMJnNB+O3ePbt55dUX0Tnp6NSpKz179WTMmDH23gKA6DPRLFiwgKH9dYwe6Yog2P4WnnxIZO78TD744H0EQaBXVydeeMKtYIzz9VHQqpmaN2ak8dNPPzJ16rQq/fwOHDi486SlpqEWNcUe16AlLS2tTOfSarUolUpSzAl44Vek8jbjZtGAPq/qMszkghwR23rH3n1dxLYZKb+HuzSKy8CqiN0UVI+g4ScPZvnySQzuo+PBobfsjF1cBEaPdCUrW+T335fQp0+fO5pfsmnzJpb8uoiUlFtif8OG4Tz77HOEhtYq17mGDx+GXC7n999/Ze3mWx2K/v4+TJv+YqFsoV27dhMUqKJ5k6KbsBqNQO+uGpav3cXLL7/Ef2YBfgeoU6cOfn7erNuaWyRUHmD9tlwUCjmtW7cu9hx79uzm22+/ISMjkwZhKl57xgcfb9tvNiHRwsfzM5k+fRpffjkfV1dXunbpxpbdWxg6QETnVHgcOXXGxJU4I4+N7VKhz5OamsKOHTtJS0vDw8ODrt26lhhEL5cLqNVKUtOKF1nSMiR8AkoWdapT2ICydW2AbfwyGU1sCKtFgsVASIyq0DhuwkQyCbjjTaYsDavVSsL1BMiQIWC7jxgDbwlK6us5aEQdCQlV1w1Y3fx9f6oi3RkACTcSOHXyFCdOnEAl0yCXCYUFjZuocUIURbKzswqJGg5B49+PQ9RwcM/w0EMPUbtWbVau/IMzZ23V42F16/H0fWPp0rkLkiRx8uRJYmJiUKlUtGzVsiAQ8t/OnejScNhOFUapUDJ+/Jvcf/9Ftu/YQVZmJg0ifIiMjASZjHHjXgdJz5C+GkJq6IiLt7Bx+3YOHNjP7NnvExxcfDj17WzbtpXIThq7PredWmv41tVWvWy1gquzDL0RzGYJtVrB+DefA2yt7a+88grPPPMMaelpOOt0JXoAr169hpycDD6f7YW3p+02oFTC4L7OBAYomDn3L06cOE7z5lXj2Vpeqjc4vOQK/uJsqE6eSyi2W+NuCBsDBw7giy++IJM03PAEbMKFFbHY14iIheyqbkdCApmM3bt288MPP5KSmgyAM24osB9K544317hME7Ed8Vxizpw5fDX/qyofl8trO1VSODgUL2iUlDMEjklx2ZBITk5GFEV8fHwKMini4myCY0S4/Q6wiAa2BbxotdKjRw8++WQuO3fupm6ompaNFaSmZbLw5x9YtfoPZs16j8CAQLvnWbN6NcGBKsY84FpIlFWpBV552o3HX07CYLDw4BD3IqKtk1bgvj5avvrxKBkZ6aVmIDlw4ODexsvLi2uXblDcbdGAHi9PzzKfz2q1ImImhjMEEooWHVaspJFEAldQosZkMlfR1UOLFi1YsXIFOWThQtE5XQYphASH4OlR9s9QXoxGIzk5ubg4O6NSl6+yvbS1S0UEjaoKgL39vnzgwH7y8gwM6GXfCqp/Tx2bdyYTHX2aiIimJZ63qvhz1Z989+13dO2gZeAL3nh7yTlzwcTvf15m4sQ3+fDD8nYUyhg6dCj9+/fj2LHjZOdk4+/nT5MmTYrcC3NycvDxFIotbPLxlmM0mrBaxXvGuumfSlTUKVavXsWZM9EIgoCPjz97Dqbg4SYwYrAzbq5yDEaRjdvyWPJHDvfdd19BoPTf2bNnNx9+OIdAfzlajYwpr3vipL0lVAT4KZjymjtPvp7M5s2bGDZsOEOGDGXnzu3MmJPOk4+4EFZbidUKew/pWfBLNg0bhtO8efNyfiqJX35ZxLJlS1GpBHy9lSSnmPnll5+57777eOyxx4v5bcno1KkzW3bt4b6+OtTqoiJLbLyRsU+XXjh4J4QNoFRxo2VYTR4d0o8flq0ippYFV9zRxuSRSxbJXAdkOONGmpREeHg4u3fvJikxteD1+fkbYBM4RDxx8XcuEAtKchGobkoqqM2nLCIGFM3NyCcnJ4dPP/2Ug4duFYY5hwdgQolAYUEDwEAucrm80N9IRdZu/611278Dh6jh4J6iXft2tGvfDqvlZvWR4tZkSSaT0bRZU5o2uzMTynuN6uzSKM+A/18b4OvUqXuzTfcWkye/hU5rYPZkT9xcb/5GW8OAnjreei+NeZ//jw/en1PquSVJIjMzmxpB9gWImDgL2bkSDeqpGDvKldqhKoxGkR379Hy3OJtff13EpElTCp6v0WiK3ey7nR07ttK1vbpA0LidlhFqQmuo2bZt+10TNaD6gsNtwkbx3RoVtaGq7nyNs2fO8seff3D86HFEUaRe/XqEhYVx4cJZ3PDGFXcUqMggmUBqFqkitWAhi1R8Kfo3bsRArpiNUqlkzkdzcMeLMCJIJB4rlmKvKb96VECgBnU4Ix1lw/oNjHms+Er6inIv2E6BQ9AoHomNGzfxxx/LuXbz38DT041+/QYybNgwNFpbtXRGphUXl6JVxRmZtl1HjUbLn3/+we7de3jjBQ86t71VaZWSamHanAzee3cW8+Z9YXdRHBV1kj5d1XaPOWkFWjZVcfi4EW8v+9PfurWUSJJESkqqQ9Rw4OAfTmRkJPsP7CeLdFwp/PdsII9MUrm/9+NlPp+Xlxf6ZBMG9JzjBAqUiFgREfHEF72QU6U2UBEREYTWrMW1uEvUFMPRYqtQFpFI5hoZpPLYsEerpVg+Pv4aS379lb1792IVrSjkSrp06cyoUaPw8y+9cKGkYoTiCg/K0qFREhURNAByc20bh96e9jfovT1sj+dVYRdOSeTk5vDLzwsZ1FvHk4/cWh90bK2lRWM1495O4+dfFjL5tvl/WVGrNbRvX3JHbUCAP5tPWTCZRFSqovfrsxdMePt4OASNSrJs2TIWLlxIzWAVfbupsVqt7D4Yg0wmY/02Peu35+HrpSI9w4LRJNKvX3/GjHnM7rlE0cr3339Lh9YaYq+Z6dRWW0jQyMfNVU6b5ioOHNjPsGHD8ff355133mXOnA8YPyMJV1cFZpOE3mCldesWvP76+HJ37a9cuZLff/+dh+53YVAfHU5aAYNBZN2WXBYu/QOtVsuDD46y+9qhQ+9nz549zPokgycfcaFmsBKrVeLgMQNf/ZhNgwb1aNa0WZmu41bORukB4rdzu7AB9gPE8ymLuNG3Tx8OHTrIkcNHMfgFQy0AGc4xbvgQSJxwkdo1a1O/fn26de/Gtxe+xYgBNYW7DM2ZaSRxlkEjxha4CJRFWKhOyipaFEdxYgaA2WJh2rRpXLl8lRDq4hwejBUzcVwmK/Y8/rLC37NVspAmT6RDhw4F3eYOQeO/g0PUcHBPcruY8V+nuNZtKNmLtrhq5OKqnMq6CPivEx8fx6lTp3njBY9bgsZNXFwEHr5fx/vzznHlyhVCQ0NLPJdMJkOn03Alzn5l39JV2fh4yZn+uieqmxUrarVAn+46tFqBuV8e4uLFi9StW74bb0ZGJjUC7bfvymQyggJkZGSUzZKhOqmIsFHWKpuqtqEC2+I9+sSVKu/W2LRpM198/jlaQYeb6IOAwJXoWLLFTCIiIkhOTCY28QIAAgKxnCeYuihu3uLNmIiVXUCURCTEQlYWFszECRdxdnJh/759eOBDCGHIkOGKB/Fcsju5lpDIIBln3JAhQ4YcF9GDv/76q0o/e1XZTuVTWdupivBfmBh/+913rPpzFZ3aahkzwgOlQsbBYwZ++20x58+fZfz4N3FxcWL99lyeftS9yOvXb8tDq9UQERHBgm/nE9lZW0jQAPD2UvDC465Mejeev/76i2bNmlXoWq1Wiexs0a64cj3RVlDh5ma/EtKBAwf/HFq1bkWzps04dTIKXykID2z3zwxSSRbiCfAPpHfv3qWf6CY1Q2tyJPko9WiKET1G8hCQF1g7polJ9OzZs+o+gAymTZvKlClTOJdwHBeZO3JJgV7IwSgaGDFiJD169Ki697tJzOUYJk6ciGQCP7EGGpzQW3PZu3M/hw8f5oMPPyQ4OKjY15cmaJTEnbKcuv2+HBRk+yynz5lo2qhoN+Hpc7YMu8DA0ouGqoI9e/ZgsVgYPqhoB45WKzC4j5avFx4mMzMTN7fiu7IrSq9evVi+fAV/rs9lxH0uhY5djTezY5+RYcOGVPn7/pc4ffo0Cxcu5MEhLjw41LlAOHhomMT8nzLZvFPP8OEjMJtNuLq60qVLF3x8ig+VP3nyFCkp6bz1kjcfzEvHRVe8JZ2zTuB6sqng/8PCwpg//xuOHz/OpUsXUSqVtGzZqkLZYmazieXLl9I/0okHhtz67Wg0AvcPdCErR2TlypUMGTIUjaaoNWBISAhvvz2DOXPe5+VJyfh4q9AbrOTkWGnevCnjx08o+K4yMjKIiorCarVgMBhJTU1BoVDQsmXLQsWIFV1LAmVaT0Lp4sb48eOZNnU6Z8+dxUXmhotvbay1LMQKmWi1NRn82GiQ2YT4P//4k5iUaALE0AIxPpsMrgsx+Hj7FNxjKiso3E1KEjPy2bd3LxcuXqRueDs0Mtt6wCnWjLtk5QbXsEhmvPFHgYocMkkW4hFUMh566CFHZ/1/EIeo4aBKMOgN7Ni5k7NnziAIAk2bNqVDx44olY6fWGWoStup4tq2y5Oj4Rjk4erVWACaN7Zvo5LvQXs19mqposaOHTvIzTWwdZeMIX2dC3xPAUwmkQNHDTz2gGuBoHE7ndpo+OFXJbt37yq3qOHp5cHl2KLZCmDrHrkSLxLeoHjf0ztJ/mS0PJQ1OLw4KhMaDlUbHH79egJffvEFnvgRLNYuECN8xSCSuc7JkyeZNnUajRs3Rq5QcOLEcT54/wPOWI6ik1yRkMglE41GQ7e23dmxYztZQho60Q0rFrJk6Wg0GoYNG8ZPP/1EOPUK3sMDH24QSyznCSUcJTctgpC4wVXyyKE2hTNbJFGqks8NVWs7VZkujcpMjv8LY+aFC+dZ9ecqnnrEjYG9dQWPt4jQ0LaFhhkfHWXfvn0MGzaSH3/8EQ9XOYP66NBoBIxGkfXb8li5LocHHniQrKwsUpLT6djGvp1Kw3pKPN2VREVF2RU1GjeOYM/hw4y637lIdWGeXuTIX0YkZKzZnMuo+wtv1litEqs35tGwYXiJmwcOHDj4ZyAIApOnTObbBd+ydctWEsSrtscRaN26DS+9+CLav4WJFsfZs+c4fPgwCpRc4jT+1MATv5v2U4kkcY3WrVrTsGHRHLPK4O3jzbx589izdy979+whLy+PkJAQ+vTpQ63a5ctVKBMSfPrppwgmBbXFRsixFe+44oGX6MelvNN88fnnzH5/tt2XV6QQC4ovxqpKQSOfv9+Xw8PrExISzOIVyTSoqyw059brRX77M5f69cMICalZpvNXlpTkFDzclbi72S/wq11TiShKpKenVYuoERgYxAMPPMAvv/3G1WsWenbRonMSOHbSyKpNeQQGBnPffYOr/H3/iYiiyNGjRzl06CBGk4laobWIjIws1iIqnzVrVlMjSFVI0AAQBBlPPuzK/qMmDAY9Tz75VJmuIz3DlrsSEqQgNETJsVNGHhkhFZkHSaLE8VMWGkfUKfS4IAi0bNmSli1blun9iiMqKoqsrFz6RdpfQ/btoWPluiROnDhOu3b2O4YaNWrEt9/+wMGDB7kccxmVUkXr1q2pXbs2YLPE++abr9m+fTsWy638DZVShlwOv/yyiIiIRkyYMLHAirkq7Kig5K4NKF7c0Gq0vPveLLZu3crGDRtJTDyPs7MLg7t3p1+/fri7uXPiZlHdo5PeYNnS5Vy7fo24q7b1r0U0E1Y7jLfemljme9a9SFnEjHw2nj2AV8M6aCRtIZupAGoiR8EN4kjh1vnq1anPiy+9gMLbtr9RlWs2s9nEjh072bJ1K+mpaXh7e9GzVy86d+mMUmHfotnBncWx4+yg0kRFRfHurPfIy8tFJ7ggAVu2buGHH37k7RnTS93YrWrycvO4GnsVQRCoXas2SlX5Bht9np4tW7eya8dOcnJyCAwOpG+fvrRq1QqZcOcD0arCdqo0HLZTZUettokWWdlikcDb/McB1KqSvYclSWLx4l9o1VRD3HUzb72bygNDnWnTTENunsS6LTmIIvj62F/UCIIMH085OTn2xYmS6BnZh99/X8SwARaCAgrfBvYcMnDtuokXXqzCasNKYpuMVr0vanV1a1RlvsaG9euRyxQESbWKBIX6EEiGkMqaNWto1boVAK1bt+bb775l8+bNRJ+Ottn2NW1KZGQkzi7O9OvXl3Vr13Hp0iVUKif6t+9Nnz592LN3D4JMjka6NVmWI6cWDYjhDGc4igseyJGTRQYWTARSq6CKSEQkW8iga7OOlf7Mt1OVtlMV6dL4LwSDV5b16zfg56Oif8+i3V8tIjQ0b6Jh48Z1fPDBHHJycli8YjnL1+bh56skKdmM3iAycOAgRo0aRcIN279VOd0OChg0aDBvvrmXH5dkMeaBW0HhJqPIp99kYDZLiCIs+SMbs0ViYG8dnu5yLsaYWLw8h4sxZmbOHF3h78KBAwf3Fmq1mhdefIFHRz9KdPQZRKuVsHr1ym0TtXrVKrSCjlpiQ65xmXguEc8lAATkyJDZ1jvVsExQqpR0796N7t27Vf3J/8bFixeJuRJDbRoWCBr5KFDiKwZzOvo08fHXCnVrlFaEUJZg8OKoKkGj+PuyjBdffJmpU6cwbkYaA3tpCQlSciXOzKqNejKyBGbPfrHU8+eTkZHOzp07b9oYutO1a1e8vcv+e3N3dyczy0JOrv11RnyCzRa0pOy8yvLwww/h7e3NihVL2f2hLVhco1HRvXtvHh39KFptyWHNpZGXl8eevXtIS7V9Rx07dsLFxaX0FxZDbOxVVq9ezdFjh7FarISF1WPAwEE0b1bePIiyk5qWyjsz3iYm5io1glS4OAvs3bOLRYt+4aWXX6Zrl67FvvbcuTN0b6+ya+2kUgm0ilBy9kx0ma8lP1fnaryFfj2cmPFRGhu25dEvUlfoeSvW5ZCYbGJ8v/5lPnd50OsNAHgUI8h5utt+z3qDocTzKBQKOnbsSMeOhdcUoijy7nuzOHsmikdH6OjWQYtSaesM/nlpFhq1jGeHuPD94rPMmDGdOXPmFmS7VcaOCqiUuNHYpQYqpYp+ffvRr28/u6+5tUYJoGPjSWw8cIyrV21CfM2QmgQGBeBdQtD6vUZx43ppe1T5YpDxSizyK7loZF6FjstkMvwIxipZydGm8eJLLxIcHExoaGi1rNlycrKZMnkqMTExuAruqEQtscnX+fT0p6xft453Zr5T6fHQQeVxiBoOKkViYiIz3p6ByqwlnBaoRVsroYE84jIvMnXKNL6a/xXOzrpSzlR59Ho9P/zwA9u2bsNktrVVOutcGDx4ECMfGIkgFN+KmU9SYhKTJk0iJTkFVzxQoCY60Vad1aljJ9544w0EeennqQpK6tIoyXbKHiV1aZSlVdshaNyicePGODlp2LQjj8ceLFqJs3F7LiqVgvXr1/H99wvQaLR06NCZvn374u7uXvC8S5cuceNGEi897oW/n5yvfszki+8y+VzKBECjBkGAcxdNtG9VtCojTy9y9ZqZ1h3KHxI2YMAAduzYyqT3knhwiI42zTUYjBLb9+Sxcn0unTp1pHHjRuU+b3VT0Qobe+R3axQnbJyIrVhoOFStsHHu7Dl0oluRjIx8XER3zp8/X+gxd3d3RowYASOKPr9BgwY0aNCgyOPu7u6IkrWI1ZQOF8JpznWukEYSMmSo0VKbBjjhDNg6N+K5iIi12Ml6eSmP7VRJmyX5VDQcHBzty6Vx7XocDevLCwSEv9OkgZKV6+MBGaNHj6Z///5s376d1NRUOnp40K1bt4JweX8/P7y9Pdhz0ECzxkWtCc6cN5OWYaZRMeNTgwYNePrpp/jmmwXsPWSiTQslZjPsO6LHZIK3XvEgwEfB9I/SWLkuh+VrclAoZVjMEt4+nkyZ8iaNGzeusu/GgQMH9waurq60a9e2wq8/dfIUrqIHKlTUIhwTRvTkIkOGDlfiuERU1OlynVMURc6dO0debh4BAQEEBt0Ze6OSiIuzbSg52wkmBwoCy+NiYwtEjbJ2VVZ1jkZZ1jD5lHZfDg8P5/0PPmDxol/46sdjSJKEIMho06Y1kx4ZXUYrHonffvud335bglwu4eOlIiXNzM8/LywlILkwnTp14vvvv2P1plxGDS280W82i6zemEez5k3xLEfIffmR0adPH3r37k18fDwms4nAgIAq2bxbvXo1Cxf+hNlsxt1NSVa2mQULvmHkyAd54IGRlFcZ3L9/P3M++hA3F4Eu7VSo1QKHjp1i+rQjDB8+nNGjq75QQRRFZrw9nZzsBD6Y6k14mK2QLStL5PslWXzy8cf4eHvTsKH9uYogCJgtxXc2my1Suay4mzRpgo+PF0tX5TDxZXcG9nZi/k+ZHDpuoEMbDaIVdu3TE3XOxIMPPkC9evXK94HLSHCw7e/x1FkjHVoXXbuePGPbnwkOKt+8Op+jR49y4vhfTH/DkxYRt+aIPTo50SBMxUuTkklLszL5VQ8mvHOZQ4cOFekIqUzXBthsqNceWYQkSQQHB9MstFuxr7klbhQfJl4cfdq1gHaFsy1P2Cm6K85ZoLooTYTOpzwFtreP9fnfma+vL4mxp0G0/xqjoCcwMJBOnToBlS9CK+7e8PnnnxN/NZ4wInCSnG3DkwS5ZHHp4lnmz/+a1157rVzv6aDqcYgaDirFmjVrkCwQKoUXqujR4ERNMZyzWUfZunVrtbepmowmpk6ZyuVLMXiLAbjhhYiV9NwUlvy6hGvXrjFu3LiS50kSvPfebLJTc6hP81sbeyJkkMK+vftYVms5I0fa2SmsJkpaIBS3gVfecPCS+C9VG5dGQkICly5dRKFQ0Ldvf/74YyU+XnJ6d9OiVApYLBIbd+SyYl0uogi5Wefo2EpBWkYuy5cvYd261bzzzrsFnUt5ebZWSm8vOd6eCqa+7kVisoXLV80olTIa11cx6tlENm3X07eHDn/fwsP1stU5mEwikZGR5f4sTk5OvPfeB8yf/yXf/HyA+T/ZhBStVsPgwUN59NFHqZZyw0pQudbhO29DVVXB4QqlAqm4GR0gIaKQV/5W3rp1a3RaHUn6eGpQeGInR4EJI1qc0eJEGklcFc7hInoCEtlCGhYsjHtjHP4B5RfZ/k55bKdKEzQqaztV3slxPv8VQQPASetEWkbxi/PUdCsWi4UTJ07QrFkzvL29baKbHQRBzoABg/n554U0j1DT8bZFcUqahS9/zCIkJKjEoMiBAwfRsGEjvvzyCzZuv0CAr5zITk70i9QR4Gf7W/lslg9PvZ5ERLPWNGvaDH9/P5o3b15Q0efAgQMHJaFCjYq/2ZBKZbdf3LJ5C4sWLSI1LbXgscYNG/Psc88SUrP8XvZVgUFvICMzA7DlbRX5fIAZWwac5mYQa1nu15UJBi/JNre89+fS7st1atdh6tTpZGVlkpmZhbu7e7m6B9asWcOiRYsYMdiZof2c0ekE9HqRdVtz+XnpH2g0WkaNsh+QfDvu7u7cf/8wlvz+OwaDyICeOny85ERfMLN4eQ5x10Vmv/Roma+rMshkMmrUKPtGbGls2rSRBQsWMLC3E8MGeuDpLicrS+TPDTksWrQIpVLJ/fffX+bzpaenM3fuHNq1UPHaM+4oFLa1y4NDJFZtyOX7X5fRoEE4rVuXfd1QFo4ePcKVK7F8OM2b+nVvdea7ugq8/KQbV2KtrFixvFhRo2mzFuw5tINHR0gF15xPbp7I4RNmhgxpYfe19hAEgbFjn+T99z/gvc8yGDZAR0igklWbcvj8W9sar0aNICZOfJQOHTpU4BOXjZCQEBo0rM+SlTE0bahGpyts5fbr8lxq1wklLKxic+Rt27ZSJ1RdSNDIJ8BPQcc2Grbt0XP/QBdqh6rYvXuPXZur29eUULaujaysLD6eO5djx48hyGxzRXVdP4KDNnD/sPup51v8OSojbtzO38fKU7EJZRYZqoryuoGUhD0xI59evXtx4OABMknDTVZYwM2VsskijYd6j+TEiRNcz4tGq9UyqHmncr1/aYJGckoyB/YfIFCqjZPMudAxncwVXzGIXbt28fjjjxcqXHVw53GIGg4qxb69+3EVPYu0KAOoUOGKBwf27692UWPLli1cvHCROjRGx60JqA4XnHBm566d9OnTh8ZNiq/AjD5zhssxl6hNwyKhuO54k00mq1etYtj991d7kHlZPGn/TlWHg//Xqo2LIzUtlc/nzePo0WMFj8nlAqIo8c3PmSxZmUVggJLrCWayciRkMhj3vAdd2t3ajHv8QStvf5TBu+/O5Ouvv0EQ5AQE2P6tos+bCgQLPx8Ffj62/75w2YQoSmic3HlzZhr39dXSrLGGrGwrG7fr2XdYz5gxo/HyLNyWWVbc3Nx48823SE1L5fKlyygUcsLDG6DV3rtenRXJ18jnTttQQdUEh7dq1YrTp3/CLJkKMi3yEZHIFFLp2LryCxS1Ws2jYx5l/vz5SNisrdRo0ZPDDeLIJYtaNMAFd3KETPyCfTEbzcgEGb1a9KL/gAElBoeWl7LaTkHpYXmVsZ0qL/9FIbhjx87Mm3ecuGsWagQVnlbm5ors2KtHrRSYNm0aDz/8MA888ECJ5xs6dCgxMZf5cN5u6tXR06i+nJQ0kQNHjbi5uTFr1pRSq11r166NXC6jTXMNb75UtJrVxVmgU1sN52LimDxpcvk/tAMHDv5TNIlowpF9x/AXQ4pYQVqxkiNk0KRp9zKda/Wq1Sz4dgEe+BBGBEpU5JLN5bNXmDBhAh999BHBNSomqFeEvNw8fv75Z7Zs2YLRZAvFTuUGARTNkEjlBjonZxo3alRmQaMk7oSgkZG6BSlZyUXtRerUqVPq/cPV1a3c1k4Wi4Xff/+NXl2deGT4rS5urVZg2EAXsnMk/vhjBUOGDCnTPPuRRx5GpVKyfPly/lifVPB4YKA/M2a8VG2V9tWJ1Wpl8a+L6NZBy1OPuBc87uoq8OhIVwwmkaXLfmPAgAEFVr+lsXHjRgRB4vnH3QqJAzKZjPv6ObP3sInVa1ZXuahx4OBBQoLVhQSNfARBRmRnNd//ehSr1YpcXnTPYNDAQWzbupV532by3OOuaG7muOTkisz9KgOQ06dvn3JdU4cOHZk4cSI//PAdE2clFzweEODLmDFPVKuYcTsvPP8iEye+yWvT0hjQU0PNGgrirllYu8VARpbAe7NfoaKFc+npadQIKt4xo2aQgsMnbNZWft5CQQGhPW7ZUZVeMGc2m5kyZQrX464TQj3cJS9ARtaFdOIvR/FTdjZPPa1Cq9WWaE31d3HjdioidFSlwHAnKUnMyKdVq1a0at2aY0eP4S0G4IkPMmRkkEqycJ3AgEB2n1pN1t4s9BcSAIll6gWMfGAkw4cPp7TfWHH7XMnJSWzfsYO01DSysjIRJQl37O+1uOPNdesVzp07R9u2Fe8EdVB5HKKGg0phMpnQUHwVi4ACg8FY7dexYcNGXPEsJGjk44E3KcJ1Nm7cWKKoEXXqFCpBhYvobve4B95czIri2rVr1VpFVdW2U/Yo64bdf13QyM7OZtJbb2I2pfPK0+60aa4hN1fktanJ1A9T89hIV3Ye0JOWYaVhPSU79umJaKAuJGgAuLnKeekJF16blsyhw4dp17YdPj6+tGjRjGWro2ndTIOL861JmsUi8cuyHHx9vfnww49YuPAnFq/YxU+/ZQMQFOTPq68+Q48ePSr9Gb08vQqEkZiYGK5di0er1dKkSQSqUnJB7gZVna9R0W6NsthQ5VMZG6qePXvy++9LiTWcJ0SsVyBsWLESzyVMGBk8+L4KnTsfi8XCgf0HiLkcQ72wely4cIE0bo1DajSEEl6Qn6FGS+3adXj99apvty2v7VRJgkZFuzQqWgWaz39t3OzcuTPLl//OzI/TeHGsC00a2HyiL181M//HTGQCzJnuxY59ehYtWkTjxo1p1Kh4eztBEBg37g26dOnKxo0bOPRXHE5OTjzySFd69eqFs3PZKmcNBj1ugcUvft1cBIzG6p+fOHDg4J/PoMGD2b1nN9e5QiChBcKGiEgcF0EGffv0LfU8OTm5/Pjjj3gTSDC3gr5VqHEVPbhoPMnChQuZNHlS1Vy4BFeuXiE1JRU3Nzfq1q1baK/HoDfw1luTiIuNw0v0xxUPkkkgkXjkKPHGHwEBK1ZSSCCFBMaMGMOFuDSgbIJGeYLB86kKQeP69evs2PIhly5dJiHKdr3+/j6MGvUw3btXfv58O9HR0WRkZNGkgTuff5fBlTgzaqWMNi00RHZxon+kEyvXJXH8+DE6dChL9piMkSMfYNCgwRw7dozcvFwCAwJo1KhxmSys7kXOnDlDWmoGg3rbzxcZ2MuZNZts31FxIdJ/5+zZM0Q0VKJzsn+vb99KxZI/zlT4movDZDThqiv+38HF2Vb8ZrFY7IoaoaGhjBs3jo8//pjDJ5Jp2VSJxQJHT5oQBCWTJ0+tUNFahw4daN++PdHR0aSnp+Hp5UWD8AZ39DcTElKTjz6ay+JfF7Nw6T4sFityuUD79u158MFRZbRys4+npxdXY2OQpKIh6ABX4i14ucuxWCTOnDfRqUvpm/5lcQLYs2c3V67EUI+mhSr23fFEa3Xi3PETxO0/Sb0ebcuUu2FvfKsqoeNepixiRj4ymYy3Jk7k559/ZsP6DSQZbXbSCoWCZs2akWy5iCXHieAL3jgRihkTycbrLFy4EIvFyoMPPljsue0LGhI//vgTf6xciSCToxa06K15Ff+wDu4oDlHDQaWoVasWF09dtut3JyGRJ2TRsnZEtV9HUlISbtifJMmQoRa13LhR8maZJEmUpXJAKkd7eUWpCtupfCrTpfFfZ82aNaSnpzLvXS98b3ZQRJ01kquXGDPSlZohSkaH2ILoU9IsrFibS8e2RVtiAWqHqgjwU3E66jTt2rYD4Mmnnmbim+MZNz2NwX201K2l5HqihTWb9FyNtzB12kt4enry6quv8eRTT5FwPQG1Wk2NGjWqdIJ6+fJlvvjif1y4cLngMRcXJ4YPH8nQoUO516yooOL5GsULG8V3a1SFDVVFhQ0XVxdmzHibt99+mzO5R9Fhy9fIFTJBJmP8uPHUqhVa7vPmExsby9vTZ5CSmoxOcAFkSEi44YUbnihR4YxboQ0cA3n4+lZ9WF1FbKeKozLh4FAxQeO/mj+kVqt5Z+a7vPvuTKa+fwV3VwG1WkZishUfLzkz3rCNnyMGO7Nzv5G1a9eUKGqAbTHTpk0b2rSpeHVlSEgtTkYnFrv4/SvaQo0a/7xqVwcOHNx5wsPr8/TTT/PNN9+QLaTjLHogYSVbyECUibw5cQLeZQgf37N7N1aLFT+KdjbKkeMpBnDo4CGysrJwdS2a21YeoqKi+PbbBVy+HFPwWI3gQB57bCyt27QGYPWa1cRevUpdqQlabPmHTjijQMF1YrhBLFrBCSMGRNHKsPuH0aCpzeKjMoJGsddcSs5VWe/NiYk3WPzzq6hVFoa1s9J4jC+paVbWbsnmk08+JScnl0GDBpXpXGVBr9cD8PH8DHy95TRtpCYrR2Th0ixWrMth+jjPQs8rK1qttkhY8j+VnNwcAHy87bsd+N58PDe3+Or6vyMIAhZT8cctZqlMmZrlJTQ0lP37d5OdLeLiUvT8x04ZCQjwRa0uvjisU6fO1KtXn/Xr13P27GlkMoFhw5rRp08fPDw8KnxtMpms1DlWdRMYGMQb48ajf15PVlYWOmcd0aej2bR5E5Io0aBBA9q1a4dCUfpWpMlkwmQ2oXPSERkZyYwZezn6l5FWzQqvt68lWNh72MAjw1xYuzmXjCyR3r3L1u1Smh3V9u07cBE8bJkKf0Mt0+AuebJt6zbuv39YweNRGbcKSEsLFod/l9BRUuFYaWLG7SiVSp544glGjRrFhQsXyFVew98/gN+WLEGdoCHonDfCzfm9CjVB1EKQBJb+/jsDBvTHxaX4e+jf12tLly5jxYoVBFATHykAQZJjkPI4y3EySMWbotedQQpyuZz69Uv/93VQvThEDQeVYuDAAcz6axZpJOGJb6FjicRjEPX079+/2q/DxdkZk95Q7HGzYMLNreTFQaPGjVgkLiKHTFxwL3I8g1RcnF0JqkKLlb9Tli6Nv1PVXRoO26lbbNu2ma7t1QWCBsD1GxZ0TjJCb4oZ+eRv+krFxB9IkoQoUmhzLTgomA/nzOWXn3/m+1/3Y7XaXty0aRPefe6RQoHOzjpnwsLCquqjFRAbG8ukSRPx95WY/JoHjcPVpKWLrN2Sww8//Ehenp6HH34YgEuXLrJmzVrOn49GEOQ0b96K/v374+9f+RyF8lC5fA37weFQcRuq6hY26tWvx4JvF7Bt6zaOHz+OVbQSXj+c3n364OVV8aDI3NxcpkyegjnbQn2aoxVtAZCxXCSDFAIJLWLFl8x1zKKJnpE9K/y+JVEVORr5VCQcvKI5Gv91IdjH24dPP/mM5557BqU8heaNNdSvq6RNcw1yuW3Mk8lktG2hYtfBolWTqakpHD58GKPRSEjNmjRr2qzSwm2/fv2YuHMX67bkMaCX7v/snXV4U2cbh+8TT92dAoUipWhxiru7TBjzMfvY2MaADR8wdGMKTJgyNny4FKe4S4HiFKi7JU3O+f4ILZSmbWrAttzXtesaJ8k5b9LkvO/7/J7n+eV7bOe+TCKvZjPhmYpfn1ixYuXfQa9evahdO4gN69dz7ux5ZHIlrRp3o2fPHnktRYsjLj4etVyN0mg+2GmDLSIiSYlJZRI1zp45y8SJEwgMUDDhXVeqVlYQdcfA6o2JfPLJJ4wdO5YWLVuwaeNmHCW3PEEDTOtZPwJwxYvLwmk8/N1o1aoV7du3Jy7NtE4tq6BRUmPwks7Nv/32Gwp5DuOHyXFwML03b08FdWqp+P73FH7+eQnt27ezuPKvOGJiTPu3ZwfZM7CXHTKZaf5KTDIybX4iU+aaKkV8S2mQ/G/A28v0N4+I1NM8pGALrguRJr8WLy/LW+rUb9CAn386TlKyEWen/GKJJErsOaSnQYOGZRi1eTp16sTSpb+zZFkqb7/kiCC7v145c17H/sPZPPfcUxSXFObh4cGIESPKfXylRZIkMjIzUClV5VKtr9VqSUxM5P33RnPnTjQ+XmpkMpNZvJubMx99NIFq1czHG86fP8eKFcs5duwEkiTh4upEl87daNiwPrO/OsPgPra0b6VFqRQ4eCybpSvTcHGUcemKnvAj2TRp0jjPx9ISimpHlZKcgkpUFfrnVEoaUlNS8x3LvV/lJtSBZeLGgzwpQkdxHoQPUxLhoijyYlYy8KypJtipDTEx0UTuOkwVauYJGg/ihg9xxjuEh4fT1Uz1pLkENJ1Ox6qVK3HDG0/h/j1aI9jgJLlxl+vYSHb5qnQypFRiZbdp06aN1U/jCcAqalgpE02bNqVL5y5s3baVVCERB8kFCYlkWTxpYjJPP/001apXq/BxtO/YnuV/rsBD8isQhMsgjXQxhXbt2xV5juA6wVTxr8KdqOtUFWvnM8hLIZFEIYahvYZalFVQFsxtFIoL4pVnlQZYBY1cEhOTqeyXPxhmo5WRrZNIzxCxe8AAzcVZho+XnL2HsmhmZrEeeTWHmDg99erlr1zy8fZhzJgPSU9PIzExEXt7hzJl6JSUpUt/w9FeZMY4F7Ra0/ux0cp47TknHOzlLF+xnB49erBv3z6+++47PNxVNG2oQK+HsO3r2LhxPR9+OI4mTZo8sjFD6f01CjMOL0sbqpIIG7ExsVy4eAEBgaA6dSwWJWxtbendpze9+5RfZuGOsB2kpqRSi0b57nc++JNOMpGcxpNKOOCMgRwSiCGRGAIDA1mxYgU+Pj506NihXBZzlradskTQKGvbqdJivW8K2NnZ4emSwvPDzAfj9HoJxQOeVDk5er799lt27NiBIIBSKSM724i3jwej332fmjVrlXo0QUF16Nu3L4t/XcvJczpaN9Mik8H+w9kcOJpFp06dHvl9y4oVK/9sqlUL4H+j/lfq1zs6OqAT9RgwoDCzDddhyuQvU5WGBN99t4ga1RRM+9CV3G2Lq7OcekFqZi5I4rvvF9GkaRPiE+KohPm5S4sNtpIDnl6eDBk6pEQeGhVhDG4pmZkZZKfvo0sdPQ4O+TOrBUFgSB97Nu+IZffuPfTs2bNE5y6M/fv3EFxbxeA++UUSF2c5773uzJtjY3Fzc6FWrf9uRm/lypWpWbM6f629RYNgdZ6PBEBOjsjSVen4+nlRp06Qxefs2LEjfy77g7lfp/Dh2044OMjyzvfTn6ncuq3njbf7lfdbwdHRkbfefpsFn3/OtVtGOrXWYGcrcPyMnn2HsgiuW49evXqV+3UrCp1Ox+rVq9m8ZSOJCckIgkBISEMGDRpcqNm5JWRmZjJhwnhsNBnMm+xG9QCTUHIjKoevf0hl4sSP+eLLrwu02tq3bx9z586hir+K155zwMFBxulzOlav/ovKlQNo264Ty9aG8dsKU1vm3Ni2JEFCopFatWrx8ccTSjVmc4lzXl6exN46h2Q0X/mbLcvEw9N8BXt5iBsPYqnQUd6Ul1BRHA/f7x+OWaWmmsQjNea9iZSCEoVMSUpKSoHHCquoP3v2DBmZmVSi4J7DjwAuc5ZLnMIBZ9SSFp0si1QpiZrVazBy5GsWvzcrFYdV1LBSNgR46623qFWrFmvXruXGzUgAalavSf8BIyvclEoSJbZt387ePfswSgYiOIYWGzzxxwFnkkkgWnaDalWr06K4/pwCjP9oPOPGjeNC0nEcJVeUqMiSpZMmptC0cVOGDBlSYe+luKBeSUq4y1KlYQ3M3cfFxYkbUfn7KTZrpGHxryls2ZnBwF73Ny+CINCrsy2Lf02lfp0MOrWxyVv4xCca+PKHVHx9vWjUqJHZa9nZ2ZdbxpilZGRkcPDgIV5+xiFP0HiQPl1sWb0xgz///JONGzfSv4cdzw2xz8tAe/kZkTlfJzNz5gz8/f3IyMjA3d2djh270LZt2woXAKHkbahyKU0bqsKwxDg8NTWV5X8t59KlS6QlXAdAJsgIDQ3ljTfewMbWpsTvoawcCD+APc75BA0ABSqqU48rnOE2V7l977hSoQQD3Lxyk7tXY8gSTeaiL738Upk2byVpOwXFV2jAo287ZcVEvfoNWbf2KhmZYoEe1zk5IvsO62nRsm3esfnz53HkyCFeetqBjq21aDQCFy7n8POyVCZOnMCcOfPK1H/5pZdepGrVqqxdu5p5394AoFIlH15//fl7GVxPXms9K1as/HtpHdqaH3/8kXjpLl7kX2uIiCTIoqkfXB9nl9Int1y9dpVr128w6b37gkYuggBD+trx3uQ4zpw+g1atRV+It5CEhEGWg62tbZkFjVwehTF4RuoejKKRwKrmM80dHeS4u6qIjY01+3hJ0el0RERcYuQI8+bifj4KKldS4OEVwH99zhk58g3Gjx/HB1MS6dNNS9VKSqLuGPh7SxY3bxuZMuVtSvIZ2dnaMWHCJKZOm8xLo2NpXF+FWi1w/HQOqWlGXn99JEG1LRdJSkL7du1xd3Nn1aoVfPebqZrA09Od4cOH0rt370eyByoPdLpsJk78mCtXLtO+lYb6wc6kpops3X2O8eNP8P777xMa2rpU5w4LCyM5OZlPZ7vj7nb/86jsp2TCe8688l4cmzdtzusIAKa96YIFnxPaTMu7rznm7TlbNdHSqY2ej2ZepVGjxvz80y9s27aNQ4cOcuXKFYxGI5Wr+NOv7wDatWtLWX5r96s2TOv7Ll27cODgAVJIwon8yWgZUhqpUiLPdXu6yHOaEzcepLyFjn8SxQkZD+JyTwDLIiNfhWEueklHjlGPm1v+dpBF7dWy7nV7MZdooBCUVJeCOcth7H1sMBqM+Lv70rnzCEJbtzbtja08dv4Zd1srTzYCdOrciU6dO6HL1iGTyVCqHsEPXIIvvviCHTt24Ci44E+gqWSbOK5jyoSWkAhpEMLo0aORK8z38HwQL28vvvzqK7Zt3cruXbtJT0+nmk9VunbrSouWLSqkL+eDFFalUZKMp6I2B1D4RGENzBWkQ4fOrF79FwN7GfC814LK2UlO9462/L4iDaVCoEt7GzRqGYnJRm5EmUqnv/ohhb+3ZFG3toKkZInDJ7JxdHTi+Ree4ZdffkGv11GlalXatG6DRmPeg6MgElG3b5OdlY2Xl2e5CCApqSmIooS/n/mpwNZWhpuLkpMnT+Lvp2LEUPt8GSqZWRJ3YnIQRSPuzjE0DFJw5fo1FixYwPbtW5g0aWoJ3l/JedRtqE7eLNxboyjj8OzsbMaPG8/d29FUUtTC3zUQISGFJCmeA/sPEBMdw8xZMx/5BigrOxt5IcsAFSqccCfTLoUPx45h2R/LiDh/gSrUxFF0RUDAgIFo6RaLFy/G2cmZVqEl7/tcUh+N4gSNR12lYW3Xdx+dLpuTJ46jzxGZ9WUiH7zhktdrOjNL5KsfUkjLEOnVy5QZe+XKZfbvP8C7I51o1/K+qFc7UMXkMU78b3wiy5f/xXvvvV+GUQl06NCBDh3ak56ejiSBvb0d//XAkhUrVh4Pzi7ODOg/gJUrVyJixA0flCjJJI1o4RY6IZtnhz9b4vPevHmTyEuRyORyRNEIQNXK5uf3alVMe7S4+HjatG3Dru278RB9kZN/n5RGCpliOtVqNy12ji5ufn4UxuBgmpNttFqizyZxp7kjdWoVFDays0WSUgxl9iwRRSMbN25iw/q/AVj4cwrhR7Lo192OkPr5174qlbzM1ysriYmJnD9/HpCoVat2gaDfo6Batep8+ulsfvnlZ77+4USeT2W9esHMePO5UlVn1q5dm0ULF7Nt2zaOHTuCIc1Am7a16N6jO34V3O4rODiY4OBgDAYDBoMBjUbNP219sXLlKq5evcz0cS7UqHb/99K1vQ2fLUphwYIFNGzYCFvbggHk4ggP30tIfXU+QSMXezsZoU3V7N+/J5+osWvXLgyGHF58yjlP0MglMEBFh1ANW7ZsYtiwYQwYMIABAwaUeFyWkrvPbFjXyNNDa7Fs+SUyRS+ccQcEUkggXnaXWjVq0a5de4vOae5+VhFCx5NMSUSMh3F1daVBg4ZcOh2Jo+iKXLg/b0mSRAy3UKvV+RKri9urVapk2vOnk4oTrgUez8BUHfLBBx8QEBBg8VitPDqsooaVckWtURf/pHIiPDycsB1h+FMDF+l+yZ8rnkRzkxii+Oijj2jWrFmJzmtnZ0v/Af3pP6B/eQ+5UAqr0ijODLckbaeK6kdrDcyZp1evXuzaFcb4GUk8PcCWZo00ZGeLONjJkBD48Y9Ufl+VgZOjnLj4HBQKJSNHvkZ2to6wsO2EH0nBzt6eIUPacezYUebNnYeLsxI7WzkbN+pY8uMPjB79Hk2aFB2Q37t3L8uW/c6tW3cAUCjlhLYK5fnnX8DFpfSeCg4ODshkAjejDNStXfC3m5EpEp+Yg0wWT99u6gIlt58vTiYrS+LLGR74+dyfTiIi9UyZE8kPP3zPm2++VerxWULZhI3C21CZEzag8DZUuZhrQ7Vjxw5uRd2ihlQfTY4NglIOri64JyixEe24GHma/fvDadu2jcXjt5T09AySk5Kwt7fH0Sl/FmFAQFX2Xt+HJEp5njAPkiFLpXpAAC7OLpw9dxZ/AnHi/iZYgQJv/NGRyR9L/zCZWZZgL1eexuBQ9tYWJa3SsN4387N06R/cvHmdl59x4NcVabzwTgwN66qRy+D4aR05BokxY8bi52f6Xe3ZswcXJyWtmxUsIdeoZXTroOb3lfv536hR5ZAJJTzySjgrVqxYMcfw4cNRqVSsXLGS2JzbeUlYnm6ejB31QYlMR+Pi4lnw+XxOnzmbdyy3z/idu0acHQsmdEXdMQDg5OhI/wED2LN7D9dyIvARK2ODPSISKcRzR3ad2sGhdBvQrsgxWCJoFEZhHlelFTQAarrVo2HD+mzYGkG7FlqUyvwJaZt3ZqLXi7RpU7rscwBRFJkzZzYHDhykZRMNQ3o7ka2T2Lk/i6nzEnl1uGOel1NsnIHLV3V06Va7mLNWDJmZmXz77dfs3bsPUTSJCDKZQMuWLXjjjTcf+dxYtWpVJk2aTGJiIklJiTg6OpVZYHFwcGTgwEEMHDionEZZMhQKxT+mMuNBRFFky5ZNdAjV5BM0wPQdefEpe/YfjmXnzp2lqsjOys7Cz6PwhFBnJznZ2fk9Ua/fuE6VSuoCHim5NAhWsyksibS0NBwdzVdHlSe5a/xBAwfg5rabo0ePEn7IVMOuUqno0qkzL7zwAkpl6deplggd/3SBoyxCxsO88OILjPngA67mnMNd9MUGe3LIJo67JBPPGy+9gUajzZewW9RezdQarya3Im9hJzqgEO7/LY2SgVjZbapVrWYVNJ5g/nl3Xyv/SdLTM9DpdDg5OuZVXGzYsAF7mRMuYv4ehgICnviTLIvjxIkTJRY1HheFBfZK2naqtP1orYG5gtjb2zNj5iy+/upLvvjueN5xlUpB9+496NWrF4cOHSItLRVPT08CAwP5/PPPuHHjFi5OSgRB4NatNFavXoFCbmTCey6E1FUjyARi4wx8vzSNmTNnMmvW7EJNwNevX8/ixYtp0lDDC0NccHKUcSZCz9pN4Xz44XnmzJlXak8DO1s7mjdvxvptx+kQqi3QgmrdlgwMBrC1LbhQu3XbwMmzOkaPdMonaIAp03pgLxv++nsHI55/HjtbuwKvL09K668BRbWhKhhEz21DVVJ/jW3btuGAMxrBlI0u5eQgKJWIrvbYJoC9zImwbdvLVdS4e/cuv/7yK+EHwhFFk7Fng/oNePbZZ6lRswYA3bp3Y9v2bcRyG0/yLy4TiSNdTKFHzx4cPHgQhUyJk3h/05lNFjHcIpkEJETSopKZNWsWz7/wPJ6elpuhl5cxeGkFjVxKu7i23jdN6PV6tm3bQo+OWnp2tiO0mZawPZmcOq9HQqJdKy1bdmbm2/Snpqbh4S7PMxJ/GC8PBQaDkeysbJT21vJuK1as/DsQZALDnhpGnz59OHbsGBmZmfh4e1O3bt18hsPFkZqayvhxH4KUwgdvONOskQa9XmLHvkx++jONVRvTCarpktdv/uKVHNZvS+fwMR0yQcbhw4fx8vJi2ifT+PTTWVxKOI1apsaIiEHMoUFIB/oP6I9MKDwoWVzCQWEVGuU9Lz+cZPDMM8MZN+5Dps1PZvhgOwIDVKSmimzakcGyNel0794Dd3cPi8//MDt37iA8/ADj/uecz0eva3sbflyayve/p9ConhpnRxlf/pCKnZ3NvZY4+YmJiebGjZuoVCqCgoLKxZz5QQwGA5MnT+TWrSu8/IwDoc00yASBfYezWLrqMB9PuMPsWXPL/bqW4OLiUqbErMdNdHQ0O3bsICEhHkdHJ9q1a1emdpmPg7S0NJKSUmgQbL7dnbOTnKr+am7cvFGq8/v5+XM64g6SZN6L4sz5HCpVyh8oVqvVpKaLhb4mNc20p1GZ6QoiikYSEhJQKJQ4OztRnlUzVexr8nyvmrRu3ZqnoqORJAkP7+7Y2JS8gsUSHr7/nU3OH8t50kQOS2JNZREyHqRqlap8+uksFi1axIULEXnH3dzceXf4aNq3b1/ixLO3336LD8d8yGXdGVyMHmiwJZtMkuQxCCqBUWXw0rJS8VhFDStPNCeOn+Cvv/7i3PlzANjZ2tOtW1cGDxnM1avXcBDNL4ZkCNiIDly9cvVRDrdUlFeVRnE+GkVVaVgDc4Xj6uLKxImTiY6O5vLlSBQKBcHBwXmZTbllrxkZGfzvf2+hUqQx4yM3gmqYFlsXInP48vsk0jMlagao8jasHu4KxrzpxDsTEli5cgVjx44rcO3U1BR+XPIDvbrY8vIzDnmLu2pVVLRqomH05ESWLfuDkSNfL/X7e/rpZ/ngg5N8NDOJpwbYULeWmoQkkY1h6azfmsmQIUO4fTuK/YePMqyfXd4Yzl/SIQjQson59lKhzbT8tiKNy5GXadCgQanHZykmYaN0bajMCRtQeBsqS/w1HhQ2khKTUEvafGvrB4UNdYKWxMREi8ddHLejbvPBBx9gyDLiLVbGBjt0ZBN55jJjx45j8pRJ1KtXj8DAQIYNHcayP5eRIaTiJLkhIJBCIikk0LFDR5o3b85vv/+GQlAgwxTYyCKDy5xFjhwv/NGgJZsMjh48xunTZ5g9exa+fr5FjvH8lZhHImjkUljgpKgKtqL4d983JXQ6PSqVyuyG0hzR0dGkp2fSpKFJ+HJ0kDOglz0DHkjsO3rKQGRkJE2bmn6jnp4eHAjPIVsn5jMMzSXyag62tlpsH4PfjBUrVqxUNDa2NrQuQ7XAhvUbSElJ5KsZbri7mRK+lEqB3l3tSMsQ+XNtOrO/TmJoXztOn9fxw9JU1KhxxBsRid1he9getp33Rr/H9z98x/Fjx7l85TJKpZImTZqQnqMuk4dGaQWNks7L5oJXNWrUYOLEyXz55QLenxyPWi1DrxdRKOT07duXESOet/j85ti4cT0h9TX5BA0w+es9O8iesL2ZzP4qkbgECYNRwYQJH6NW318vx8TEsHDhNxw7diLvmL29DX37DWDwoMEWz73FsXffXi5cuMTsiW7UrH5fuOje0Zaa1VW8N+kGO3fupGvXruVyvYpB4vLlKyQlJeLs7EL16tUoa7A6x5CDTJAhlxffmtrceJYs+YnVq1djayvHz0vJ3TgDK1asoHPnTrz55pvIZKU576MnV8zKFQoeRpIkUtNF1OqSd+KIi4/jQkQEsbE5bArLpEen/MH/fYeyiIjMZvz4HvmON2/WnL/X/s2JMzoa1cu/x5REie27s6lfvy5a7f21YY4hhxXLV7B580aSkkwG0VWq+DNw4CDatm1X4rEXRTXH2lRzNFVd3cw8h2jqPF0qf8eS8OA98XFUcTxK0cISqlWrxuzZs4mKukVMTAy2dnbUCAwE47FSVdL7+1dm3mfzWfbHH+zbuw+D0YhcLqdVaCueGvYUvr5F72utPF6sooaVJ5awsDC+WPAFtjIH/AlEgZLUjGTWrF7LiZMnUSoUGDAU+nojRlTqR595UhpKWqVhqY9GaduqWCmIl5cXXl6FZ5Rt376NpKREFj5khla7hoppY9147YNYtu3OYMAD5uIKhUDnNhp++vMQxnuT54Ps3LkTAYlhfe0LbHDc3RR0b6/l7607eOmll1AqS/dd9/f3Z8aMGXz99Zd8Mv9a3nE7OxtGjBjBwIEDOB8RwdgPw/n5z7Q8o3BBAEkCyfw6mHvFAeW2MbOU8vLXKKoNlSX+Gg8KG66ursQmJxR4Xq6wIbg54+pWfq37Fi1ahDFLorpYL8/0zBYHnEQ3rgkRLPh8Ad99/x0ymYynn3kaf39/Vq1azeUrkQD4+frxdN836NqlCwim70i2MYtsMlGj5SaXUaGhOsF5PbgdccFV9OJK5jm++uorZn46s9DxWSpo5FIWY/CK9NH4t5GZmcnq1avZunUzSUkpqFQKWrUKZdCgwXn9ZgtDoTR9z3Q68zcEUZTuBZTu3xs7duzIsmXLWLMxg2H987e/iI0zsG13Nh079fjHBAesWLFi5VGya1cYrZup8wSNB3mqvwM79+s4ftpA+JE4ADzwxZvKee0mRbEKUVxm/vz5BAYG0rhJYxo3aQwUnnD1MEUJGuZ4FIJGLvXr12fx4u85ceIEd+7cwcZGS5MmTcvF2+La9Ru8MNR8FbJaLSO4tpqTZ0V69OhBjx4981WwJiYmMnbsGBSyDEa96kSDYDXp6SJbd2Xy+2+/kZyUxKuvvlbmMQLsCNtOvSBNPkEjl4DKSkLqq9mxY9sTK2qcOHGcH374nps376/T/f39ePHFl2nUqFEJzyaxc9cu1q9bS2SkKekxOLg2ffr2p3mz5hafZdWq1axevZoRQ+zp2dkWtVqGwSCxbXcm3/0Whq2tHS+++GIJx1Y6RNHI9rAwtmzewM2bUajValq0aEnfvn3zWn0WhVarpX79umzbfYnObWwKVIqdOKMjNk5fos8nd1xTJk9EkFJo3VzDol9SOH1eR+sWWuQy2H84i32HdLRt25pmD507OLgOtYNq8vniK4weCfXrmNofp6aK/Lw8lUtX9UydOiTv+QaDgWnTpnLu7Bk6tdHQpIEL2XqJnftimTdvPnfv3mXYsKdKNH5LuW8ofjnvXlTR4gYUX8XxqK77JODnVynvu17WtsA+3j6MHv0eb775Jmlp6djb2+UTo608uVhFDStPJKmpqXz91Tc440ElsVreAtwBZ1xEd65eO0fVgKrcTL+Jt+iP7CFzOz060kmmRfPBj2P4FlPSKo2i+tKW1Bgc/u3Zxo+Wffv20rSheTM0Vxc5LRqr2Xc4O5+oAeDkKEMURXJycgqIGnfvRuPrrcoz232YmtWVZK1NIzU1FVfX0vejrVatOvPnf86Vq1e5HXUbrVZLvXr18jJzgmoH8corr/Ddd9+x77CeZo0UJCebgpf7jmTTIbRgJvW+Q1mo1apC22pVBKX114Ci21CVxl/jQWGjc5fOfPPtN2RK6dgI+TfBafoEslQZ1GtcPhvK2JhYTp46eU8Izv9dlCHDS/InMv40p06dpmHDBgCEtg4ltHUo2VnZSJKEVpu/qqRFi5bY2zlwN+MmHpIvWaQTQFABU1EFSjxEP86dP0dU1G38zFRrWBooAcuNwYsTNKw+GsWTnp7GuHFjiYm5Q4dWamoGOhEXb2Tb7nDeOxDO5ClTCaodVOjrvb288fbxIGxfWoHMOoAjJ7JJSzcQEhKSd8zd3YOhQ4fxxx9/cDfGQJf2tjjYyTh5NptVG7OwsXVm8KAnew63YsWKlcdFcnIKlXzMJ0QIAtSsriAprSqiBFcjruEtVc7nnyVDwI9qpJHMps2beOGFF/Kdo7gqjaIEjfS0dOSZyRyLv0vt2rWw0dpYJGhYiqVzsUwmIyQkJN/cUx6oVUrSMgrJ6gHS0yVCQkJ44YWCwe0VK5aTo09l7nQ3XO75Brg4yXn5WUc83eV8//sGevTsWS4m10lJCdSrVXhigL+vgn1Hyq9SuDw5duwY06ZNJbiWmpfGuFDZT8mNKAOrNsQzdeoUPv54Ao0bN7bwbBLffruQTZs2EVJfw1svOWI0wt4D15gxfQbPPPMMQ4cOLfYsOTl6Vq1aQY+ONgWS1Lp3tCUlVWTlhvUMGTqkRK13RdHI4SNHuBx5GYVCQUhISLH7J6PRyKefzuDw4SM0bqChzUAtKakiO8N3smvXLiZMmEC9evWLvfagQUOYOHEiX/6YwvNDHHBwkCFJEqfO6VnwXSq1g2oSHFzH4vcCcPjwYW7evM28yW5Uq6qkfp1M/t6SwewvkwBQqwX8/Px49933zCS/CXw0fgLTp09l0uxLeHmocHSQcfWGHpDxv//9j/r177+vrVu3cvrUaaaOcaFu0P37YasmWv5ck8bSpX/QsGHDUpnQW8rjEjdyeRLFhkeJpd4ZlqJWa6xixj8Mq6hh5YlkR9gORFHEh8oFDGxtsMNJdOfu3WhEucR16RJ+UgAqTBNZFplEySJxsHekffv2j2P4JaIkVRpgvu1UaX00rJQfmZnp1Khc+MbB1VlB5NWcAsfPROhxc3dBoym4MbWzsyMx2YDBIKFQFKx4iI03IggCNjbl0Z5FoFpANaoFVDP7aO/evQmqE8T6des5FXEeuVyBt7een5clUKWSkoDK93ubnjqnY+WGDDp37l5OY7Oc0ggbRbWhKq2/BtwXNtxr1aFqlQCu3YjAQ/TDCTckJJKJI1Z2mxrVAqlZqxbnr8QQVM1yPwpz3LlrMpO3xXwmog12yJBx5/btPFEjF43W/AJOqVQw+r13mT5tOjekdADsMG/OZ3/v+K2bNwuIGhVlDG6OivDR+LcKGgA//fQziQl3mDvJlUq+95eGvbvaMm1uMvPmzuG7774vtGpCEAT69x/EN19/Q/UqSnp3sc27Z128rOebn9KoW7cO1atXByT27w9n48b1XL16BVsbDQePi+wKjwdALpfRsmVLXnrp5UdiAmnFihUr/0RcXF24ejPF7GOSBNdvilSr4cmpk6dwlFwL7KfAlOxgJzpy9gGj8eKSD4qan8NvXmXzps2c2L0V472SXbVKRaunutOhQwcaFRKoL0miwZMwFzdr1oKd+/YzuLddgfX5rdsGzl3U8e67BT0ddTodm7dspl9XbZ6g8SDdOtjw198Z7AjbwXPPPVfmcbq4uHEzqnDR4kaUARdX1zJfp7yRJInvvltEvSA1k953RnavgsDZSU69IBVT5yXx3fcLCQn5zqKK8KNHj7Fp0ybeeMGRru3vt0Hq1sGWP9ek8fvvv9O4SeNC90C5REREkJqaTtf27mYf79rehj9Wp3Hi+Alat7astdylS5eYNWsmcXEJuLqo0OtFli5dSnBwEB9+OK7QddDff//N0aNH+Xi0C43r31+/D+0rMn1BMrNmzeTHH38qNjhbv359Ro0axddff8WeA7EEVFaRkioSHaundu0afDT+Y0ra7uvw4SNUqaSmeoCpQqhzW1s6tbEhJVVElGDfwSyWLIsCJLOvd3BwYNasOZw5c5bw8HCys7Np1aYyHTp0KFBptWXLRpo21OQTNHIZ0NOWdVvT+eCDMYSENGTYsKepWbPiWjU9bnHjv8iTMB9YefxYRQ0rTyS3oqKwEWxRSObNQe1wJD7jLmPHjmXB5wu4kH0cW5k9IhIZYiquTq5MmToFmxL24tbr9Fy+fBmj0UiVKlWwd7Av/kWlpDyqNErrowHWKo3yxse3EucunSrU2OzsBR0+XvlvuZFX9ew+kM2QoQMwt2Bs06Y1f/31F7sPZNGxdf7vssEgsXlHFo0bh+TrK1qRVAuoxqhRo/L+nZqaysSJHzF64g0aBGvw9ZZx5bpIxKVsGjSox/PPv1DE2SqOsggb5jAJG4X7a1gibDzz9jvsXr2c/fv3c0c0tfmSy+W0a9eOV197Fa3G1Jf5/MnrAKUWN2zvGdYZ0KOm4EbGSA4iYonFppCQEGbNnsW3337L5SuXMZCTJyQ/SA4m4U6jyX/t0ggaT5KPxr950ZyVlcnu3TsZ2NMmn6ABoFHLePEZO0ZPjOfo0WN5fhjm6Na1KzHR0fy0bBV/b86iRjU5CYkSkdd0VK9elQ8/HAtIfPnVV2zbuo3gmhoG9VKSmSmxM1yHwSDn6aefoVOnTjg5OVXsm7ZixYqVfzidOnVh6dJfGdzbgJ9P/nt3+NEsbt3R8dobHTl98jRSIcFDMIUVH163FjZXm5ufc/Q57N+/jy3nTnPj+nUybt/EW6qMAy5ISKQG6Dl85AjZN27S8KOPClzrnyBoGAwGDh48yLFjR8kxGHB1cSUhycjsr5MZOcIhT6C4el3PnG9S8fb2oFWr0ALn+eGHHzDkGKla2fz+VqmU4eMl58yZ0+Uy7o4dOzFv3mkiIvXUDszfgurKdT3HTmXz5ludy+Va5UnEhQju3InmrRfc8gSNXGQygSF97Bg3PZbz589Tp07xVQSbNm0gMECTT9DIZXAfO7bt0bFx40befuvtIs+j0+kAcHAwX0HvcK+yPvd5xREdHc3EiR9T2RfGv+1GQBUVoihx5EQ23/x0iSlTJjJ37vwCCSWSJLFx4zpaN9eABF98n0xWloivt4LObW144wUHXns/lj179tK5c/F/3w4dOtC4cWN27NjBzZs3qa5W07JlS+rWDaY0/iU5OTnY2eR/nSAIODma3oe9nQxRlDAYjKhUhSUECtStW5e6desWea2oqDt0DjVv1q1UyqhTU82dGAOJcecYN34sEydMqnCvx9KKG5IkcvLkKXbu3EFiYhLu7m507NjpXqVMyf8ON2/eYOvWrdy5cwc7OztCW7emcUjIv6Kta3lXZ1j5Z/NIRI2vv/6aOXPmEB0dTf369fnyyy+L3BgvX76cCRMmcP36dQIDA5k1axY9evQo9PlW/n1o1GoM5CAhmc0sMpCDgEBISAg//fwTu3bu4sLFC8hkMho0aEjLli1RKi3/eotGkT/++IP169aTkZUBgEKupF27trz08kvY2pqfLEtLcQE+S6s0wOqj8aTQrWs3pkw5zJ4DWbRtmT9gvP9IFpFXc6jsp2TX/kzs7GScOJPNtt06AgKq07dPX7Pn9PevTJs2oSz8ORy9XqJ9qBaNWsaNWzn89GcaUdFG3n5n2KN4e2ZxcHBgzpx57Nmzh507wzh9IQl3d0/Gju1C8+bNH+uiqbStqAozDYeijcMtETY++OADXnzpRS5dvIQgCNSqXQsnR6f8z21QhQsnr5e6aqNa9Wq4uboTnxBttlojjmiUCpMJaEkJrBHIJ598wojnRpCQE403lQs8J4EYbG3s8m0yH7WgUVj1GpSugu3fLGgA3Ll7F50uh0b1zGcDVquiwslRybVr14pcu4HAiBHP07ZtO7Zu3cLdu3fx8rNl0LBQmjVrikwmZ+eunWzbuo1Rrzrla1s3rL/E3G+S+fPPZU9sb28rVqxYeZLo0b0HO3eG8dHMGIb0taFZIw16vcTO/Vms2phBixbNqVe3HvUb1uPA7oN4iwWr340YSZcl06WhqbLdkhaRD87PMdExTJr0MfEaFU6OClKirlGThmgEU6KGoZYaZ0B7PYvD1w9z6tSpfAHFf4KgcefuHaZMnsjdu7EEVFGjVQuE79chk8k5cSaHl9+NpUaAmqxsieu3dPj6ejF50tQ8E+ZcMjIy2LkzDIUSou6Y94Q0GiWiYwwkp17i+vXrVKlSpUxjb9UqlI2bNjBl7mWG9bWhTUsbZALsP5zN0tXpVK8eQLtyNlIuDxLiTT50Af7m9/IBlU3H4xPiLTrf1atX6BRqXkiSyQQa1FFw/drlYs9TqZI/ACfP6sy23j151iRm+Pv7WzSuNWvWoFIamfi+KzZaWd54moVocXKUM2bqNQ4fPkzz5i3yvS4tLZWYmHjkgpxd+7Pw91Pg4iRn4/YMVqxLZ8RQB6r6a4iMjLRI1ADTnq5fv34WPbc4AgKqEh6+l7Q00WwL5WOnTb+Th38jpUGrVZOYZCz08aQUET9vBe+97sy0+Ul88cVnfP/9j2b3qFevXmXDxg1cvHARpVJJkyaN6dqtG64upatmevBedTPzfhDe3L5Up9MxY/p0Tpw8gY3cDpVRQ6T8MmFhYbRs2ZL3338fhcL8d7ggEj///AsrV65AJdegMdpglOWwa9cuAgMDmTx5Mvb2ZfcWepSY8zP8t+7LrJScChc1/vzzT0aPHs3ChQtp1qwZn3/+OV27duXixYt4eHgUeH54eDhPPfUUM2fOpFevXixdupR+/fpx/PhxgoODK3q4Vp4QWrZsybr160gjGQec8z0mIpEki6VRg0Z5Pf+79+hO9x7dS3cxCebPn8++vftwxQs/AhGQkWJMZPfOPVy9co1Zsz9FbaY9UGkoKsBXVJWGpW2noPjNgbVKo/wJCQmhXfu2fL54D6fO62ndTIMgwL5D2YTtzaJOnSCMRgOfLboEgKOjPX36DmDI4MF532NzjBr1DkqlkkW/7OLHP9LQauSkpObg7OzIhI/HEhhY41G9RbMolSo6duxEx46dHus4zFFSYcOSNlRlETZOX7xLPV8vWrRoYfY5ec8tg7Ahk8l45tmnWbBgAXKUeOGLAhVGjMQTTSxRDOg7ADt7y3v9PoiNrQ19+vVlxfLlKFDiihcyZIgYieMu8dzhuYHPoVKbNiqPQ9AojNIIvv92QQNMrUEA0gvpD56TI5KtE1GpLNtMValSpVCT0/Xr/iaknqZAMEChEHjjeUdefDeW7du3079//xK8AytWrFj576G10TJjxqcsXLiQ738PZ/GvplZUWo2a3r36Mvy54SBAn9592L1zN1FcxZcAZLlG4Ri5RSQyuUC3rt3yzltUlcaD87NoFJk6dTJGLxte7WPLit8u4iS4oeG+oAGguZmBGhds5XZs27YtT9QozZz8qOdivV7PpIkTUMpT+GK6O5UrmebBlFQjC39O5fAJPX379icpKQmFUsFTzzamadOmBTzyAM6fP49Ol0Nocw1bdmXQq4stdrb5A74792eSnCri5CBnw4b1vPnmW2Ua/9WrV3F0cESnE1myLI1f/krDKJoC56Ghobz++hsWB5bT0tKIjY1Bq9Xi4+NDaTLHLSW35VLUXQOBAQXHF3XXJAo9nBhUGCqVivTM9EIfT88QUSqL3+d7eXnRsGF9/lobQaO66rzKA4CMTJHfVmQQEFCZGjUs8xPct283nduo8wSNB6lZXUVgVTV79+4rIGrI5HJkMsjIlJg+zpU6tVQIgkC2TuSvten8tCwVZyc5CsXjSS7r2LEjv//+O9//nsqoVx3zVdscP51N+JFsXnjhmXK5VsuWrdmxL4zBfUU06vyf49Xrei5e1vP+m84oFALPDTFVHh87dowmTfLvC9f+vZYfvv8BtVyDndEJkSxWXFvJ6tVrmDRpIsHBRVeMFIe56o1cZMqmLFq0kNOnzxBAEPZGJwRBQDJKJJPAwQOH+PXXX8169Jhj8+YtrFy5Am+q4G70QSYIIEE6Kdy4EsnsWbOZ9sknZXo/FYU58QL+3fswK2WnwkWN+fPn88orr+SZjy1cuJANGzbw448/Mnbs2ALPX7BgAd26deODDz4AYNq0aWzbto2vvvqKhQsXVvRwrVQgWZlZ3I2+i0qlxs/Xt8i1UJ06dagTVIfIC5fxFQNwwBkBAT167nKdLCmDwUOGlMu4zp47y569e6hMDZy53yNTgy8OohOR10+zdetWevfpXeZrWRLgezigV9K2U9YKjceFwDuj3qVqlQDWrV9L2B5TD1s3N2eGDx9M//79kclkpKamkpOjx8nJ2eym52GUShWjRr3LU089w8GDB8nOzsLPrxLNmjWz6PUPExkZydZtW4mJvoudnQNt2rahaZMm/4pSVHPkChuWUnwbqvIRNoqjLMJGx44dycjI4OeffibREI1ariVH1CFKIr1792b48OElOt/DPPPM02RkpLNx40ZiZbdRCxqypSyMooH+/fszaNAg4PEJGuXpowH//oW0r68f3j4ebN+TRsO6BVuW7T2UTXa2kaZNC/YHLwmSJHH58lVGjjCfHebgICOohoqLF58kPyiJ06fPcOzYMQwGA9UDqxPaqhVKZdmzC61YsVIx6HQ6zp45i06nw9/fH79K/951sYODA2PGjCExMZGrV64il8upVbsWWq027znVqlfj7f+9zZdffkm6kISt6ARIpMqSkMkFxo4di5u7G+evxBTbdupBjhw9wq2o27z2XDDt6yayOMmIk2QLQn5BA0ytZ1RGLXFxcUDJBY3HlYy1d+9eYmLi+GaWB77e98Mmjg5y3nvdiTfHJpCUlMi77442+/qEhHiOHD2KXqcnMzMTgL5d7Th1NoGPZsYzfJADDeuqSUsX2bYrk6Wr06gdqKJaVQUnzpWtBdX+/fuZM2c2Pl5Khg+yw9ZWxokzOg4d11G/fj1Gjx5t0do/Pj6en35eQvj+cAwGU0Z8lSr+PPXUM8Um6ZSW4OA6uLm7sGpDOmPecs7XskySJFZtyMDN3flee6Tiadq0BTt3bOC5IQUD30nJRo6c0jH8Wcvey+uvv8nYsR8w6uNEurXXUMVfwe27Bjbv1JGZLWfG9NFYKvhkZGTh5lJ4q2tXF4HMzIwCx29H3UYU4e2XHQmufV+M0ahlDB9sz7WbOZw8q6Nhw0YWjaO8cXBw5H//G8X8+fO4dtNAx9Ya7OxkHD+tI/xINiEhjejZs2e5XKtfv77s3r2TT+abWsH5+SjyjM6//CGZyn4KWoSY1rbVqqiwtZETFRWVT9Q4c+YMP3z/Ax744GWsjEwwfUeMooEbOReZNnUa3//wA/b2lrYll7hw4SJ79+4lIyMDb29vOnXqiKurm9n7WETcTlISd9KxvhtOACTnPXbppBvZUgYbN25k6LBh2BTTclqSRFauWIGz4I4n+b0N7QRHfMSqnDp9imvXrlK1aoCF76f8KUy8gH//vstK+VOhooZer+fYsWOMGzcu75hMJqNTp04cOHDA7GsOHDjA6NH5FwZdu3ZlzZo1FTlUKxVIWmoaP/38M7t27SInRw+At5cPw4YNpX2HQoy8Bfjoo4+YMWMGZ8+dRS3ToBCUZBkzUKqVfDDqA4KCapfL+LZt24aNzA4n0a3AY1psccSVLZu3lIuoAcX3qTVHSdpOFYe1SqPikMlk9O/fn759+xITE4OEhJenZ75Nw8MGZ5bi4eFBnz59Sj02SZL49ttv2bx5Mx7uKqpXkRFzW2LG9H3UrBnIpMlTsLN9MHtf4sTJk2zdspm7d+9ga2tH69ZtaNeufQG/hCcdk7BRUn8N822oCjMOB8uEDTD91ita2OjTpw8d2ndgz549xMXF4eDoQOvQ1ri5F7zPlRSZTMbIkSPp06cvu3btJCkpGVdXF9q3b4+np2mcJRE0cilK0MiltIJGaX00/gv3SkEQGDRwKF9++SWVfBT072GLWi1DkiSOnspm8a9pVK8ewN27d/H09EShKN3SURBM2aF6feG93fV6cCiFWFsRJCQmMGP6NCIjr+LmqkKjFli3bh0//vA9Y8aMLbbPsxUrVh4tkijx519/smb1GjKzMvOOB9UO4u2338bXz7eIV/+zcXFxwcXFpdDHO3bqSM1atdi4cSNnz5xFLpPRoVEbunfvjruHe5FtpwpLOjh27Bg+IQF0aWRK4nF0kKHLziwgaOSil2Xj7OxcKkHjcXHkyGGCa2ryCRq5KBQC7VupWbvlYIHHcnL0fPvtt+zYsQMApVJApxMRBLh2M4fp49348rtkps1PfOB8IIrw+guObN6RUaY6iNTUVD7/fD6hzbS886ojcrnpbF3b23L0VDbTPzvNxo2b6NWrV5HnSUhMYMyH7yMZ0xgxxJY6tVQkJBnZFBbLzJkzeeutN+nSpfxbRspkckaMeIF5c+cx99tkhvS2w99Xwc07BlasS2f/4Szee+91ixOyevXqyZYtm5i5IJm3X3bAzcX097wbY2DeNynY2tjRqZNlFedeXl7MnfsZy5cvZ83mMLKz01AqFbRp04bBgwfj42P5fcbX14vzl5Lo0algm2uDQeLiFSOhrQue7/DhQzg5Kmhcr+B+TBAEOrWx4fhpHVWrVrV4LOVNmzZt8PDwYNWqFfz051FEUcTPz5uXX+5F9+49SpWYZw4fH18mTZrMp5/O5M2xsXi6y8nJkUhMFgkMUDLufy4oFKbvf0aGqfL4QdEXTG3AbOX2eBur5BPQ5IICf7EG5/VHLa4izs7O5tOZn3L8xHE0ci1KVGRJGfzxxx8899xzDBgwoMBrYiOSuXsmHlcCyHzoh1+jQRJV0XALHbevr6Va9ftm9ub2tXfu3CEmNoYAgsxqaw64oJApOXLkaDmIGhJRUVGcPXsOgMDAQKpVM43Pkvv2f2GPZeXRUKGiRnx8PEajMS/IkYunpycXLlww+5ro6Gizz4+ONh9E0ul0+cyYUlNTyzhqK+VJenoGY8Z8SFx0HK6iF/Y4YcRAYnQsn33+GQkJCQwaPMjsa+3s7ZgxYwYXLl7k4IEDeRlX7dq1Q2ujNfsaS0hOTmb7tu1cvXoVhVJJZGQkalFr1rsDTMJGXFzxPWaLo6gMqFxKW6Vh9dF4tFy9epUTJ45jNBoJrFGDBvUb5FsEyWQyvL2LD9A+StasWc2WLZsZOcKRru1t8kqBz13U8+kX1/hs/lwmTJgMgCiKfL7gM3bt3E2VSmpq15ATGx/DwoXnWbNmNZ98MgM3NzdEUSQ2NhZBAHd3D2Qy88Z5TwIlFTag5P4aULywkeuvURJhA0wG4iUVNuzs7ejRs+L8qHx8vHn66acLHC+poPFwSwtzmGvBl4slgkZJ+a8IGrl07tyJhIR4/vhjGX9vzaRaZSWx8UaiY3OQyeDy5atMnToVtVpB9+69ePFFy0rg8yPQoEEDdoefo3dX2wJmsXdjDEREZjOqx+PJLHwQg8HA5EkTyUiPZtpYV+rWNrV2uBNtYOEvqUydOoXPPvsMP7+C9wArVqw8Hr7//nvWr1+PG974UwsFStJJ5trFG4wZ8yGffTYfD8+CrY+LQhIlIi5cICE+HkcnR4KDg5/otU5R+Pn58uqrrxQ4XpoqcoAEjRK17jZg6jffobWWZavjcZdysLulz/fcNCmFDCmN0F6mzPqS7lce13ys0+koylbR3k6GPienwPF58+Zx9OghXnragY6ttWg0Aucv5TBzQSK/rUhj1gQ35k5x5/JVPdejctDpJFauT6eSrxJfLwUHjubQomXp58KwsDBE0cgrz7jmCRq5NK6voWUTDRs2/l2sqPHbr79hzElj3mQXXF1MQehqVaBJAw3fLEnhu+++o1Wr0HL3ngRo26YtkiixZMkP/O9gXN5xJycH3h09krYl8ALx9PRiwoRJzJg5nVdGx1GzuhqjES5dycbFxZEpU6aUIAsf3NzceP3113n11VfJyEhHa2OD0mK/g/t07dqTH3/8nguRemo9ZOT+95Z0kpJz6Nq1S4HX5eQYsLWRI8jMxy5s75l0G43mvVseFbVq1WL8+I+RJAmj0VjqpJjiCAqqww8/LmH/vv0sWrwQR1sdn4x1Ibi2Ot9ac+vuTECgWbPm+V5/+vRpXIxeBdalAApBiZ3oyOnTpy0SNT777DNOnzpNFWrhaHRBEASMkoFYovjpp59wdnGmfbv8Sb2GHNPfSUZBoSfzHORIEtEkYZvtXGQbKwAFsTRuCD5kUlgKoq1MwN3lRqkFY1ESOXTwIHv37EOfY4rDCggcPiDh4+ND//79cXZ2+U/to6w8Xh6JUXhFMnPmTKZMmfK4h2GlEFatXElMdAzVxbp5PVYBHHDmLhp+++032rZrh3th2cMC1KpVk1q1zAcWS8ruXbtZsOALJFHCBjtEQSRDTEVL4f3ldWSVaKFjDkuM9wqjuCqNkprsWSeY0pOSksKcObM4ffosNlo5CoVAapoBXz8vPhwzvsyGfhWF0Whk7drVdG5rQ/eO+TcedWqqeOkZOz5beJxbt25RqVIlVq9exZ7de3jvdWdaN9fkLfJu3zUwZW4iM2dOp1Wr1qzf8DfxcaYsMw8PN3r37kOfPn3NLgqfFCraXwPKX9gAU7Dh/MnrAKUyEH9UVJSgURzFCRql8dH4byEwbNhTtG/fnm3bthMREUFM3BkCqyp56VkHqldRcjfGyOqN6axZs4aoqCgmTpxY4qv079+fjz8+zo9LUxk+2B6VyhQcjIs3MPurFFxdnWndunV5v7kSc/DgQW7cuMX8qW5Uq3I/0ODjpeCjUU68MTaBNWvW8tZbZet3bsWKlfLhzp27rFu/Dh+q4oFP3nEn3LATHYnMPM2ff/3F229b/ps9dvQYixYtIvqB6kwXZ1defOkF2rRpU67jfxxYMl8X5fXn4+PN4e06EhKNuLrI6dHRhp03HYiK2I+nVBUHXJAQSSKOGNlNQnvUpXr16iWejx/nvqVq1QA2bTqFTieiVpsxPD6lo+pDa//IyEjCww/w3uvOtGlxf+9bp6aKOZPc+N/4OP73URytm2mo6q/k5m0Dew5m4ewoY+QIR77+MYXUNGOZ2vNcvXqVGgEqHBzMC3CN66vZdygavV5fqKdGdnY2e/fuZmhfbZ6gkYsgCDw1wJ6wvbHs2bOH7t1L7msZFx/Hjes3UCqV1KpVy6y/YLt27QgNDeXEiRMkJSXi7OxCw4YNSxUcr1u3Lj/+sIRdu3YREXEeQZDRvVc9WrduXeAzMBqNhIeHExa2jcTEBJydXejQsROtWrbKd225XI6Dg2OJx5JLt25d2b9/LxNnR9Kjo4YmDbVkZYvs3JfFvkNZDBw4EH//ygVeFxAQwJo1Ou5EG/DxKvhZHDulw8HBDlfXslZnS5yPiCBs+3YSEhJwcnKmQ4f21KtXj5J4qgiCUGGCRi5KhZJ27dohCDBv3nwOHtdR2U+Fg4OAXi8StjeL31ak0bVrN5yd8/u1IhVeQfzAk4p9RtTtKA4cOIA/1XES7puLywUF3lRBRzZ/LfuT9u3a8eDnV6OGyR8zlQScHmiFnksKicgEgWrV7ldWFHZf9PSuRMqVbKSsWLyFKgUez5TSuEUMI/rULdW9VRSNzJz5KYcPHcYRVxxxR8RIIrFkkEbC+QyuHv6aL774HIrulGXFSrlRoakmbm5uyOVyYmLyB3RjYmLw8jIfgPDy8irR88eNG0dKSkref7duFW4SauXRIokSW7ZsxUl0zydo5OKBHzJBTtj27Y9kPBciLvDZ/M9wMDpTWwqhmlSHQLEuPlQli3TSSSnwmhz0pMgS6dCpQ6mva+mmwdIqDXPBO0sFDSulx2AwMGnyBG7euMDYt5357RsPfvnKnU8/dkMtT2LChI9ISIh/3MM0y/Ub10lMTKF9K/Ori9CmWtRqGcfvVZ+sW/83ndtqadNCm0+g8PVWMHKEHZGRV/jll59oEJTNpPddmPS+C3VrZrJkyRIWLPgcSxZ+j4PcxZulv4Wifle5v8PC/G0KqyrIJff3XlTbuQKvya3aKINIWpFUpKBRVJVGRQga/1Xx19PTi2effYaUlESCaqiYPcmN2oFqlEoZ/n5KRr3qTOe2Nhw/fpSDB823ES2KevXqM3Lka6zbmslL78Yz5+tEJs9J5NX340hO0zBp0hSLTUsrkvDw/dSsps4naOSiVsvoEKohPHzfYxiZFStWzLFr506UMhVuFJwPFChxFj3YvWs3BoNlmcvHjx1n2rRpZMboqE4wdWlOIPUQk2Du3Lns3rW7vN/CI6UkgkZh+5Mh3Tug1mhY+HMKOTkiN+RuPDfUnsCaAje4yBkOcJZD3JFdp8uwpjz11DDqufhbPMYnYd/StWtXsrJEflmehvRQ4PPA0SyOn9HRo0f+aofdu3fh5qoitFnBPGlvTwX9ethiNAqcjFCxZFkqu8Kz8PVWUCtQyYfTEtl9IJt33nmHSpVKXwmoVCrJzCp8LZ6ZJSEIQr4WQFG3ozhx8gRXrl4BJBITE9DrDdSsbn5OdnGS4+WhKrSbRmEkJMTzySdTefmll5k6dSoTJkzghRdGsGzZH4iiWOD5CoWCJk2a0KVLV5o0aVKm4LhWq6V79+6MHv0e7777Lh07diyw5sjOzmbChPHMmTMHfcYFgqonYMi+yLy58/joo7FkPdDarqwolSqmTJlGz5592bZHYvz0eKbNS+TqLUfeeutNRox4zuzrWrVqiYODHYt/TUWvz/+ZXbqiZ8uubLp06Vamz8poNDJ37lzGfjiWs6f3oJZFEHlxHx9/PIGpU6eg1+uLP8ljoG3bdrz66its2anjhXdjeGtcAs//L46FP6fQvn1HXnnl1QKvCapThzRZUoHfOIBByiFDlkqdOsX7txw5fBiFTGFWmABwkTy4fecOt2/fyXe8UqVKBNepQ4z8FnpJl+8xnZRFnPw2TZs1s0ikUqvVdO7SmQRZDJlSWr7HjJKBO7IbuLm50bhx42LPZY7tYWEcOnSIKtSkilATZ8EdV8GL6tTFHR9yRD3pqWn8/fe6Up3fipXSUKGSqUqlIiQkhLCwMPr16weY2pqEhYUVmt3WokULwsLCeOedd/KObdu2rVAjKrVabVbZt/L4ycrKIi09FVfMB6PkyNFiy93o4rNwy4NVq1ahkdlQSayer9WUG94kEctVIvClKs64ISAjlSSiZTdxcLCnZ4/SZcuUpq88mA/kFdV2ylL+q4G68uDgwQNcvXKdeZPdqB5wfwFcu4aKqWOceW1MHOvXr2fEiOcf3yALwXjP2E9TyK1SLge5TMBoMHL7dhSJCcm0ae5q9rkNg9XYaAUa1Vfz9ktOeccb1dNQL0jFZ4t20rp1G0JCQsr7bZQLucbhllZsQOFtqCyp2Dh5s2jj8NJUbJTWZ+NRUBJBozgsNQYvin+7MbjRaOTAgQOEhW0nMTEOZ2dX2nfoWCCbsCRcv36DmzdvM+E9l7w2dQ8ysJcd23ZnsmzZHzRvXnKT0B49etKgYUM2b9rM1auRKFVqXn21Ke3bt0NbjAHioyIrKwsnx8KzEJ0dZWRlFTTvtGLFyuMhITERDVpkheTrabBBn6MjIz0DR6diMqsl+O6777HFgarUztsz2GKPDTURiOT7778nNDQUueLJ8AAqCeUhaOTOy2M++JDpM6bzv0UK6gVlUlUVk1fREBAQQJ8+fahU1x4HB4d/ZIKBp6cnr702km+//ZZLV4y0b6VGoxE4ckLHwWPZtGrVkvbt87eSSU1Lw9NNZnb+BPD3UyKKIt98vYjklOR7PienuHFHok3b+vTo0QNf37L5vzRu3Jjt27dz+Zqe6lXzB+0lUWL73ixq1KjOihUriI+PIyIigps376+p/Px8GDhwMABxCUaz19DrRZKSDdjaWd56KiUlhbFjx2A0pPDGCw6E1NOQkSmybU8my5YtIyExkTffeLMU77j8WLRoEVeuXGLGeDfq1Lr/2UVE6pk27wrffvsNo0e/X27XU6vVPP/88zzzzNNER8egUCjw8jLfCikXpVLF+++P4ZNPpvL2+EQ6tVHj4iznbISevYeyqV69OkOHDinROG7evMHp02cAidpBQezds5fw8H2MHulEm+ZaBJmAJEkcPpHNvG9PsmjxIt5+6+0yvvuKoVev3rRt247du3cRHRODg709oa1b4+PtY/b5ffv2ZfKJycQShYfkl/fZi5JIlHAZhUJO586di72uTqdDLlMgk8zPQ3JMLcrMCULvvPsuYz8cy6WkkziKbmjQkkUGKbIEPD09eeONNyx9+zz7zLNEnL/A5StncZJcsZHsyUFHsjwemUrG2LETSt1GccO6DTjgjKOQP04gCALekj+JxKAU1YRtD+O558yLclaslDcV3n5q9OjRjBgxgsaNG9O0aVM+//xzMjIyeOGFFwB47rnn8PX1ZebMmQCMGjWKtm3bMm/ePHr27MmyZcs4evQoixcvruihWiln1Go1cpkcnagz+7iERA46bG0Lb/1Ubkhw5MgRPMVKBbwzZAhUI5gLHOcWl4niCgICIiKBVQP5YMwHxW+AiqC4IF9h2crl3XbKStnYs2cPtQM1+QSNXOztZbRvpWbPnl1PpKiRmJSITAaHT+gIMJN5fPaCnswsI4GBgXkVuIVtxhBAJgN/n4K9Y9u21LJ2cxZbtmx6YkUNuC9sWEJRbaigeGEDKNI4PK9i4+Ldf6ywUZoKDSjaGNxSQaO8fTT+SWRnZzNt2hTOnDlHUE0NtQNk3Lwdx7y5J9lYex2TJ0+1WCTIzMxk3769xMfHk5pqyuzy9zG/RPT2VKBQUKbKWB9vn1L6cjwaKlXyZ+eO0+TkiCiVBTd+ZyL0VKr07zUdtmLln4azsxM6shERzQobOjJRKpTYWND7P/JyJLfvRBFAnQJ7BgEBD/y4mHqCEydO0LhJ6bJdHxflKWgAhISE8Nbs8Rw8eIgDG8LYrc+hcuVKvPVWTzp27Ei8wbTW+icKGrl0794db29v1qxZxeJfTyFJEpUr+zFyZG+6dOlaIDjo4e7B0SM56HUiKjMtq65c02NnZ4NWq8XGxoaXX3q53MfcvHkz/Px8mPN1PGP/50hVf9OaPSNT5NufUrh+U48oRnLn9lVESSQjQ8LTXc7/XnbCKMKaTYksWLCAypUrsXFbLG1baAt4c4TtzSIzy0hoaKjF41q9ejXpaUks+MQVdzfTGsPVRc5LTzvi46lg4c9b6Nmj52Nr55uSksLu3bsYPtg2n6ABUDtQxTMD7fj+9708//yLuLi4lOu1lUpViapzGjRowNy581m1eiV//R2OXp+Dl5c7zzwzhJ49e1qc9JucnMz8eXM4eeoMCqXpjpeTI6FQCPTvbkvblvfXkYIg0KyRlqcHGPl1+Q6GPzscJyenIs8vSRJHjx5l67YtRN+5jZ29Pa1bt6V9+w4FDLvLE3t7e3r16m3Rcxs1asTTTz/N0qVLSZbHY290QkQkVZ6IJJMYP24cjo7mY0FHjhxm3bq/iYi4gCSJ6A0GsshAKxSca9JIRqVS4eVVcO/m4eHB5ws+Y/369WzfFkZsahQuzi481fUpevboWSLfGo1Gw4wZ09m0aRObNm7kTuw1tBotHdt1pG/fvmXy/bx56yZemK+4kwlybCUH9OhITbP6HFt5dFS4qDF06FDi4uKYOHEi0dHRNGjQgM2bN+eZgd+8eTPfYqBly5YsXbqUjz/+mPHjxxMYGMiaNWsIDi6+5MvKk4VcIadly5YcDT+Gu+hdYJORQiLZYhYtmjfn8KHDZGRk4O3jQ62aNUvSptEiREnEKBqRF/KVV6BAK9gS2CCA5i1aIBqN1KhRg+qBpV9QW2IMbg5LzcGhZJuEJ2Vz8E8lPT2NwqxfANxdFWRklF9Jcnmy7u+1ONjLWbs5ncYN1PmytlJTRRb/moKXpzt16waTYzDg6GhP+JEsgmoWFEDOXdCTniERVKPgY4IgUKemglMXoir0/ZQXJfXXKIyihI3i/DVyqVXTm9MXTb99Sw3Ec4WNXB6VwPFw+6uS3OcsETRyKaug8U8OoljC4sWLuRx5genjXAmufX/zmptN+M03X/Peex8Ue56NGzewZMkScnJycHFWkpRsMj29disHD/eCc+btuwYMBlCr5aSkpJCWnoaLsws2NkULKKIomlr0CQJurm5PtPdO165dWbNmDSvWZfDUgPyeWmfOmzJ0X3219P3OrVixUr60b9eev/76iwRicH+oQtyAgURZHG3atkGpLH7rm5SYBIC2kIbguS11E5OSyjjqR0tJEhAsETTANB97eHgy4ZVX4V5bFwk4fuwYX/32CXGxcahiM2gVGkq3bl1xcnqol/0DPMnzcIMGDWjQoAGiaMRoNKJUFt4msVOnTvz111+s3ZLB4D7554/YOANbd2fTuXPvCp0DZTI5kydPZdLkCbzz8V0Cq6qxtRWIiNRjMIi4uyl543kH6tcxvY8zEXoW/pzM54uT+fwTdyaOdmbON8mcPJ9AdlYOn36ZxHODHajkqyA7W2T7niyW/JlKx44dCs1+L4jE9u1b6dhGkydoPEjntjb8tTaT7WHbK0TosYSIiPMYDEbaNDcfbG/dTMviX1M4f/4coaGP3/+rSpUqjH73PUa/OxqjUczXTswSdDodEyd+REpyNO+/6UyLEA2CAH+uSePPtel0aG3+HtihlQ1L/kjl1OlTtG3TttDzi6KphdW+ffupXkVN3VpyYuMTWLz4Imv/XsMnn8zA3c18q6ZHzbBhw6hXry7r12/g4oWLKBVyujXrRs8ePQsVAX797VeW/7WcmtU1PD1AQ06OxLJV6dwWrxEgBSET7se+sqUsEuXRtGtXeFWyg4MjTz/9DE8//UyBx3Q6HQcPHSQmOhp7ewdatmxZqNACpuTifv363euWI1FewTWVUlmkAb0RAyJGXF3Md3ywYqUieCRG4W+99Vah7aZ27dpV4NjgwYMZPHhwBY/KyqNg8JDBHDp0iGtSBD5SFbTYIiKSTDx3hGv4evkyY/oMMh5o41DJrxJvvf0WtWvXLrdxyGQy/P38SY1KxpWCgT8DBrJIJygoiG7dupb5epb2vC9rlYYl/NOyj59UvH18OX3iMpIoIZipYrgQmYOX9+PPmn+Y9PQ0zpw5x8gRjuzYl8mH0+Jp0lCDUgFx8SLXbuaQkyPRoWN9QECpUNKtWw9WrvqLRvXUNKp3vydwYpKRb5akYG8nEFzb/IYuIUlEq7U8m+RxUdI2VCZhw3y1BpSfsFGSdlQPBiYeFjjMURLRo7hzlUawtVTQOBl/95EKGrk8iYGUwkhJSWHX7p0MH2ibT9CAB7MJ9/H8Cy8WubHYuXMHCxcuontHG4b0ccbFWU56hsir78ewcl0GIfU0KBT373eSJPHX2jQUCtBq1QwfPhwAhUJOy1YtGf7scDwf+NtcvnyZDRvWc/LkcdLT09DpTG0svH086NunPz169KDcsxjKAV9fX54d/iy//fobl67m0KG1Fq1G4PDxbHbsy6Ze/Xp06dLlcQ/TihUr9/D186V7t+5s3rwZA3pc8ESJklRSiJXdQqGRM3TIUIvO5exiCrxnk4mSgmudbLIAcHnYbPYJxlJBoyiPv8Lm5QfnYwn4+quvuBB9EI1Mi/JiNinoWPbHH/z111+0bNmCbt26Exxchwfv/U+yoPEgMpkcmazowLGXlxeDBw/it+UruBtjpHM7GxzsZJw4o2Pl+kzs7V0ZNGhgmceSk6Nn//5wrly5cs93ojFBQUHkfq4eHh58+cXXHDx4kEOHDpGToyc4WMfJkyeYOsYZL4/7YaB6QWqmfODKyA/j2L47k3497Hiqnx37D8cxaNAgtm3bwlvjYnFyUpKZacRgkOjYqROvj3zd8vEaDKSmplPV38ns4wqFgL+fnIT4hLJ8LGXifrW6+cdz//SXL1+hWbNmRYpbjxahxIIGwO49u7lx4xYLPnGncqX7FfjVqpr+X6Mxvz7T3NsaGnKK9ihavnwFBw6EM+ZtZ1o1uS8U3Yk2MHlOErNnzWTOnHk8KevAoKA6BAXVsei5p0+fYvlfy3l+mAP9e9zvOlKzupJJs5O4IB7HTfJChYZ0UkmWxeHp5cmIESNKPK7du3exaNFC0tMzcXRQkpFp4LvvFtOnT19GjBhhgUBafp9v8xYtCN99AA/RL59oA5AlZZBBGjJBRtdyiKdZsWIpj0TUsPLfpUqVKkyePJk5c+ZyMfkkapkGIwYMooFKvpW4FXULN7ypRE1UqMkglZjbUXz80cfMmjWrTJUSD9OzV08WLlxICok4cr9kVELiDtdBRrkGKSzZODxMeZuD5/KkbxL+CXTp3IVtW7exeWcm3TvmD9pfiNRz6Hg2r77a/TGNrnB0OlP7Nw83OdM+dGXuN0kcPpaNKIFGLZCtk1AoICbmfhB76NChXL16mSlzj1EvSEOtQCWx8QYOHNWhUKrQ6YwkJYu4OOdfQMcnGDh8IpsRIx5/9pIllKQNVS6FtaGC8hc2wLKqDbBMZDh/8rpF5yrJOS3hwXudpcbgRVGYoJFLSQWNf6LwG3EhAkOOkdaFZBO2aX4vm/DceVq3Nv97FEWRpUt/o1VTLa8955i3KbKzlfHua05M/yyJCZ8mMKSvHdWrqrgTbWDNpnTCj2QD4OygY/ggR7w9lVy+qmfdtoO8//5JZs2eg4+3DytXruTnn3/GzVVO0wZqsrJUHDiWjQC4O6ewcOEirl2/zptvvMGTsqF9kCGDh+Dt5c3KVcuZ+/V1AFxcnRg8uB8DBw0skwGnFStWyp/XRr6Gnb09f69dS4z+fnVlYNVARr0zCi9vy+bTwOqB+Pr4EXvnNnY45mtBJSERSxSODo40bNiw3N9DRVAWQSMXc4KGuf3Jls2b2bp1KyH1auF8UUUiMcQLd5DJoUolOH92P3v27CE4OIjx4z8mKSmJc6d/JSkpAVmiltDQUJo0aVLqfu9PCsOHD8fZ2YWVK5cTtjceALlcRosWLXjllVdxcCh9W2OAM2fOMGfOpyQnp+HrrSYrS2TlypXUqlWD8eM/zmsJpFAoCA0NzWsRNWbMezRpoM4naOTi7qageYiavQez6NfDjkp+StRqGc7Ozvy45CcOHTzE7du3UavV9/y89vH888Oxt7enTZv29OjRo8jMcaVCgY2Nhtt3zAfCRVHiToxIY/+yfTZloUbNGshkAvsPZ9Ozc8EErf2HsxAw+XRu376Vp59+hh6l9N18EtizeycNgjX5BA2AapWVyGRw5EQ2XdsX/ByOnDTtLQOLiNMYDAY2bPibru20+QQNAB8vBa89Z8fUeZe5ePEiNWvWKod382jZsGE9VSqp6dc9/+dTL0jDnImujJuewF3DTSRJws7Onr5d+zJw0EDsSth2/fDhw8ybN582LbQ8PcADb08FaekiG7dnsGzNagRBKJVQUlr69+/H3r17uSFexE8KQCmYEqsypTRucAkZAl7eXvTs+c/9XVj552HdkVmpcILrBvPjjz9w5MgRrt+4jlqlJqhOEOM+HIcnfnhTOe+59jhhKzlwWTzDL7/8wtRpU8ttHF27duX4iRMcPnQIR1yxxwkjRlJk8WSJGYx6exROzk5lvo6lVRpgPsBXnDl4SRBzDlsFjXKiZs2adOvWjYU/bybyWg7tW2pRqQQOHc9mw/YsateuSefOnR73MAvg6OiEvb0Np8/riLpj4MhJHX262dK/ux0uznJi4gz8uSaNsL1n2L17F23btkOhUPDRRx+zb99+tmzZxI7wO9jZOjFkSDtatWrJ+I/GMWlOEm+96ECNaqaF8IXIHL76MRUnZ2c6drr/OcTHx3P5ciRyuZzaQUElXsxVNCZho2RtqB6FsAGUStwoivISKUpCSdpNWeKjUZSgURYfjX/cffJeOmGh2YT3jqemFt7TNjIykpiYeN55uWArqCYNtAzrl8Nfa9OZPCcx77hCKSATIDhIzaT3XPKqOOrUVNG+lQ1jpiXy/XeL6d27Dz///DND+9ozrL9dnkfPqxkiM79I5OoNPS8/68D3v22hTes21K1bt7SfRIXSunVrWrcOJTk5BYPBgIuLc7FZulasWHk8yGQyhg9/lkEDB3Lq1Cl0Oh3+/v5UDahashMJ8PIrLzFtyjSuEYEnfmiwRUcmsdwhmXhGvzT6H2ESXhJBwxyFVU7uuX6S1NQ0nLJ1+FeujICpSmPN6jXUru+HS6SaVJK4xRW6tbfl2UH22NvJkCQ4cVrH/EWXeOedUfj7xmFro8DVkEZ8osT03bsJDKzGpElTcHBwKOO7z8+dO7dNptznToMEwcH16NGjBz4+FeGPJNCrVy969OjOlStX0On0+Pn54eTkyL59+9i4cT1Xr15DqVTQuHEz+vbta7GPxK1bt5g6dTK1AuXMGOeBr7cCSZQ4cVbHl99fY/KUicyf95nZuSojI50alQv/3rq7KLhyzdSCMj7BgE4n4uDogFKhJDQ0FJ1Ox5QpEzl3LoLGDTQ07awkJj6V1av/YsvWTcyYPrOIz1OgffuObN+7hb7d7HBwyL+A2Xc4m9g4PR06dMh3XK/Xc+DgAWJjYrG3t6Nly5ZlFoUKw9XFldDQUP5YHU7tQGU+H8Ibt3L4bXkqTUM0DB/kwNrN6SxcuIgcg4G+ffqW4aoSR48eY/PmjdyKuolWo6Vly1C6du1WpEhUHqSmphJUveBC0s1VQZOGGpauSqNekBpvz/shw4REI78uzyA4OAh//8oFXptLVFQUyclptG5uvndzo7pqbG3lnD595okTNUTRyKlTp0hOTsbV1ZXg4LoFhNbIyEu0b6E0WyVRPUBFm5ZarkV58MknM7GxsSlluzmJ3377hQbBGkaPdMo7h72djKH97JEkWP73Gvr371/u98vCqFKlKuPHj2f2rNmc1x1DI9kiYkBHNgICjZuEMGrUO8W2pLVipTyxihpWHglyhZzmLZrTvEVzADZu3IgoirhTsA+nDBluojcnT50kKTEprwy8rMjkMsaNHcuWLVtYv349t6IuI5PJaRwSwoCBAwkKKr92V6Wt0iiu7RSYAndWc/DHw+uvv46vny9r164mbI+pPNrWVkPXrr149tlnn6Ay5PsoFAo6derK5k1rEQSJHp1seOnp+4tkT3cFb7/sRFa2aeHUunUbZDIZMpmcNm3a0KZNmwLn/OSTGUyfPpUxU6Nxc1UhSZCQqMff35dPJk3AztaOlJQUvvnmKw4dOowomgKwarWSjh0789JLLz5xn1VJ/TUqWtiAguJGeQgbj4qSVGeA5cbghfFf8dHIpUaNmshkAuFHzGcT7jtsao+ydetmevY03+IpI8PU9tHd1bwyMrSfPX+uNbVlTElNRgC8vHw4evQoIwbb52tLBeDgIGNgLy1f/3iC7GwdgQEanhpgl28jZ2srY/Trzrz8bgySBJV8VWzevOmJFTVMCMUaYVqxYuXJQWujzdtvlJaQkBA+nvAxixYtIjL2TN5xF2dX3nvxfdq2Lbg2emRIcO3aNVJTU3F3d8fH17ynQVlMwcF85eTFSxf5fsuf3Lx1G/3NmwBUrVqZESNeoFr16ijc07GTTPNwnOwWQdXVjBzhSO40IAjQqL6aZwYqOHo6jvatbBjUTEShMFXQn7+o59MvbzB79qd88skMyz4PC9i3by/z58/HxkagWUPT+nPXrk1s2LCB0aPfK7SisazIZHICA2sApvaNCxZ8QVhYGPWCNAzpoyIjU2T3gb3s3rOLsR+Oo2nT4tehq1atwsFe4ONRTnlG5IJMoFE9DR++LePDadc5fOQIzZsV/A14e/sScSmxwPFcLkTq86o4Vm/KQKvV0LTJ/TH9+usvREZeYuZHbtR+wFvv6YFGJnyaxOzZn/L5519QWPXlgIED2L9/Dx99msjwwbaE1NOQkSGxfW8GS1dl0KpVC2rUCMx7/oNtdxwcFGRkGFn83WL69e3P8OHDK8SXZOTrrzNxQhTvTb5OSH01VfwU3IgycPRUNpX9FLz9ohP29jLeeskJlQqW/v47XTp3KZXptSRJfPHFAsLCdhBYVU3zBkqSU9NZvnwZ69f/zdSp0yvUNN3Dw5PIq7FmH3t9hCOvfxjL/8bH0a6Vlqr+Sm7dyWHXfh02do6MGvVuMWc37f2EIoquBEFAuve8J4Xdu3ex5KcfSUxIzjvm7u7Kyy+/SosWLfKOKRRysnX6Qs+TrZNQqVQlMvh+mFu3orh+/SYTRruY/a736GjLn2vTOXjwAF26PLp2T40bN+ann39i586dXLx4gYyMDKpVq07Xrl1wdS3CgNSKlQrCKmpYeSwkJyWjkqtRGJVmH1ffM+FLTk4uN1EDTMJG9x7d6d6jO0aDEZlMZtYfobRYUqVhbgNREnNwS/gnB+ueZARBoG+fvvTu1YuoqCiMRhEfHx/UanXxL36MDB06lPDwfcTExNG3W8FKCUEQ6NPNjrHT4rl48WKxfjZ+vn588/Uijh8/zrlz5xAECK5blwb1GyAIAllZmYwf/yFpqbG89pwDzUM06PUSu8KzWL5uM3FxsXz88YQnxii4dP4aj0bYgPv3ipIYiT9OyrM6A6w+GuZwcXGhVcuW/L4ynNo1VARUvj+X3ojK4feVadQKVHEh8gYRERfM/qZzjQ8jInPMmnZGXs1BFGHosKE0bGBqs7Ju3TrOnDlO9QDzomRwLTWSJHHh4gWeG2Rr9jfu4iQnuLaKsxF66tRUcOl62SoSrVixYqUiaNykMSEhIZw/f574hAScnZwIDg5GJn98rZGOHD7CkiVLiLp9v71WrZq1eOWVVwh8IBhcXoLGg3PzhYsX+eTnz3F3lfFOnzQCKntwO9rAmo0xTJk6lbc+umduG5lKDg6kSWl07eDMw9OAUdRx5WYOAdoshrXS8mAAPKimiteft+fTL85y5cplqlUr+xwddTuKefPmEdpMzVsvOqJSmf5+r+pFvl6Syvz586gaUBU/3wfXEBKJiUkYDAZcXV1L5VfwMDt27CAsLIx3X3OiXav7WczD+knM+zaZOXNm8+OPS7C3ty/iLBAevp++3dR5gsaD1ApUEVBFTfj+/WZFja5duzFt2lH2H8kq0BLo0LEsIiL1vPG8I9/9lsz6rZm88MILecH67Oxstm3bRp+u2nyCBpjm9VeetWPS7Bucj4ggqHaQ2bG7u7kzY8anfP75Z0z/7H77V4VSTudOXXn5lZfJ/T4cOnSIefPm07allqf6ewCwdnM6h45ls2b1Co4fP8Y777xb7kF/O1s7Pv10Djt27mDzpg2cOH0DTw85rw53pH2oFs0Dn/uAnnZs3B7LwUMHad+ufYmvtWHDBnbs2FHgO/HCMCOT5yYzffpUFi78rly+f+bo3Lkr06cf5fCJLJo2zP99uH3XQFaWRIsWLTgVEcmOfUk4OTnQs3dP+vTuU2wVia+fHw4Oduw/lEXtwIJrxlPn9aSnG6hjoYfFo2D37l3Mmzef0GZaBo5yo5Kvgms3DSxfl86nn37KuPHj8n5XjRo1Ze/+bTw3RMy7p+SSli5y5ISOwYOblGk86enpAHi6mf/7OzjI0GplpKdnmH28IrGxsaFnz57WNlNWngisooaVx4KLiwt6o44c9IWY8GUiIOBcgSZ8FVU2bkmLl9KYg0PJ2qv8U4N1/wRkMnmRJbdPGjY2NvTt25/FixcXujDycjcdT0tLs+icgiAQEhJCSEhIgcc2bdpMdHQ0Cz5xw9f7/jQzpK89lSspmPH5UU6eOpkXKH0SeBzCBsDJm6bAgaXiRnm3pCpPyrs6A4oXNHIpjY/GP/0e2bdff/aH7+e9SXE0bqChsp+CW7dNvjb+fgrGj3LmrXEJnDp1qlBRo169OixfF0lIfTW2Nvc3ZQaDxO8r0/H0dKN+vfp5x7VaLXq9SGqqWKB1BEB8oskIHIkCgawHERCQgKRksVTZjVasWLHyKBBkAnWCn4yg2/59+5k9ezb2OBFAEGq0ZJHOrcjbjBs3jpkzZxJYI7CAoJGZmcmhQ4dITk7GxdWF5s2aczE+yfQcCwSNO3fvsn79erZu2YRfCy/mvJaFSmW6b3t7KmgUrGbGF8ns3LkDlwwZyUI8NpIpgcbBLv88IaEjOdVIxL54JrzngjmaNtRgZyfnyJGj5SJqbNywAXs7GW+/5IhSeX88KpXp2Mmzejas38Brr70GwK5du1i1ajnX7wnuzs6OdOvWg0GDB6FUmE/Gs4QNG/6mcQNNvuA1mAyyXx/hyIvvxrJ9+3b69+9fxFkksrKycXEqPJnKxUkgKyvL7GONGzehdetQ5n69n9PtdIQ20yITBPYdzmTzjkxkMvjmpxRsbDS88MIL9O/fL++1t27dJCsrmxaNzWdi1w9So9XKuRBxoVBRA8DPrxJz587nypXLXL16DZVKSYMGDR8Kkkv8/vsvNKyr4d3XnDh8PJs53ySjUUPzEC0KBRw4coNRo0bxxhtv0LVr+Wapq1QqunXthr+/P2M/HMv4Ua74+RQMm7m5KLDRyklKTCrxNSRJYt26NbRuri3wnXB0kPP2Sw68OyGOw4cP56sQKE+aNm1Ks2ZNmPXVUfp0yaF1M01eFfCazZnUq1eHMWPGlEpUUSqUdO/ek5Wr/qJBXTWN62vyHotPMLDolzQCqlWhTp3CvyuPEqPRyJKffiS0uZb3X7/f6qlGNRXj/+fMJ58nsWTJ9zRr2gxBEOjVuxfbtm1l3rcpvP2yI3a2pntLUrKRud+koFCoy+zV6uHpgSAIRFzWU8mv4L3n1m0DGRlGvLw8y3QdK1b+6VhFDSuPhdDQUL777jtiDbfxJX+vWxEjCbJoGjVoVC4eF08SJTEHL4zignf/hmCdlfLH398UVI+8lkONagWFxEtXTSW05bEwCgvbSqummnyCRi5NG2qo4q8mbPv2J0rUgEcvbED+qg0oXtyoKL+NslLe1RkPUlE+Gv90tFotogh9u9lyITKHnTeycHaU8cqzjnRorUWtEpDJBCSp8NL+V18dyYdjx/D+5ET6dNVSrYqSO9EG1m3J4nqUgYkT/5fXRzgmJpqICxHIZPDCqGh8vRV0bW9L57ZaVCoZkiSxcXsG3t4euLt7EH7kMn26FbxmSqqRsxf19Otmy+qNGbz00mNs42LFihUr/wCMBiMLFy7EEVcqUyPPvFyNBgfRhSuc47vvvuf5kaMBk6AhSRJr1q7h99+WotfrUMgUGEQDDlWr07lzZ54a1qPAdR6en0+eOsm0aZ8gGSSUNdx5pmUKKpUm32tkcoFeQ135asoFevfuzbp167DFAZVMwcmzOhrVMwXgJUzmwi46U/axVmO+4kUuF9Co5RgM5g2lS8qZMydpHqLKJ2jkolAItGis4syZEwD88ccf/PHHHzRpqGFob2c0aoEjJ7JZseJPIiLOMXHiZBSKkodPRNHI5cvXeOMF89ntDg4yageqiIy8VMyZBHz9vDh7IZWuZgoD9DqRi5dz6NLV/F5REATee+89qlSpyoYNf7N5h6mVrpOTPa1bt6VGjRo4OTnRpEkTNJqH/s731gI5OeZHZhRNZt+WmrxXq1a9UNHq5s1bXL9+i+fecyEu3sicb5Jp0kDNu6/eb7n18jMSPyxN4ZtvviEgIIDAwECz5yoLri4m4e36rRyzokZsnIHMLCOurgUFutTUFJKTUzh27Cg7d4YRFxeHg4MD7dp1pGfPHhgMRu7ejeX5IeYTOAMqK/HxUnHmzGmSk5PZsnUjd+9EY2trQ+vWbenVuzfubu5len+CIPDhh+P4/fff2bxlI6s2mEzttVo1Xbv24LnnRpSpSmTo0KFcvXqFafOOElxTQ81ABXEJRg4c1eHk5MzkD8dTWKuyR83p06dITEhm4DvuBaqMBZnAgJ52jJ8ey4ULpupnP18/xo4dy+zZs3jxnVjq1lZhNMKZCB0ajZYJEyfi5FS25FxXF1dCQhqyeuNZWjbR5gknYPqtLV2VhpOTPU0saFv3X+LGjRts3ryZy5evoFaraNGiBR06tEertfp8/FuxihpWHgt29nY8++yzLPlpCUaMuOONEjUZpBIru41RkcNzzz33uIdZIs5fiSl1mXd5VWn8W4J1Vsqf4OC6eHq6sXRVOhNGOyOX31+w6XUiy9dmUqNG9XKpQElISKRTK/PtaQRBoLKfjPiEuDJfpyLIFTYspbyEDaDM4kZhlFX0KO78D46nKB4UcC0RNCw1Bv+v+Gg8jJe3F46O9hhFkdmTCmZOnr+oJzklh0qVKrFhwwaSk5Nxc3MjNDQ0r8evv39lZs+ay6+//sziX4/k+d/Uq1eH6dOfy6vwiIy8xMSJH6NUGOjT1Q43FznnLur4/vcUwo9kMeoVJ9ZsTif8SDbvvjsSW1s7PvnkE1atT6N/z/u+Gtk6kQWLk1HIYP9hHa6urgVMQa1YsWLFSn6OHTtGSmoKNWmYJ2jkIkOGu+hNVHwmifEJtOxkqqBdv349S35cghveuOODSlKT4aMmWRfPH0sX4uNlS9t2bfPO87CgkZGZwcwZM9EYbHCtUZ0YbhMYUDBbOMaowsvTVJ3n5+dHjx492LhxIwpJyaYdGaZ+/JVFACopM8lylaNUChw9mU2dmgXXilev64lP0FO9erVy+ewkSaKoTsOCYHrOrVu3+OOPP3h6gD1D+91vAdWonoaWTXVMnHWGrVu30qNHQTGoOARBhkwmQ68vPMlAnwMyCwLI3bv1YsmSH+jewYaghz6/v9alk54hFtlfXyaTM3jwYAYMGMDt27cBCR8f32LFmsqVq+DkZM/u8ExqmWkndOh4NjqdSIMG9c28umSkp5uqxj3cFGzemYlKCaNeccrXckuhEHj1WUeOnzawfv063n13dJmv+zCenl7UqVOL1Ruu0qyROp8wJkkSy9elY2OjoXnz+62+Tp8+xbJlf3D27HkAZDLwdJfTu4sNiUnprF79F9u2b+Hpp569d57Cr280ihw8cICNGzfStKGGtk3VxCfo2b5tHdu3b2XatBkEBASU6T0qFApGjBjBsGHDuHr1KhISVatULZcqWoVCwUcffUx4eDhbt2xi35G72NrZ8uyz7encuTN2dkW3WnuUJCUlA+Dva/534H9P1Lp+/Xre2rhJk6Z8990PbNu2jYiI8yiUAiOer0/Hjh2xsy3Y7rk0vPjSy3w45n3en5xI/x5aalRTER1rYP3WLM5f0jFmzJgyVZD921i5cgU///wLarkGG6M9RsHA2TNn+PPPv/jkk2n4+/s/7iFaqQCsooaVx0b//v1Ra9Qs/f0PLqadzDseULkab771BlUDqhb+4n8oDwf+CqvSKCyQV1Tw7t8UrLNS/shkMl5//S2mTZvKRzMS6dvNBj9vBVdv5rBmUya3oyWmTx9ZLtdydnbi5p1Us49JksStOyI+fq7lcq2KwCRsWFatAeUjbEDZxI3CsET0sARLRIuiKGl1hqWeQv9VQQNMpf3duvVg5aq/aFxfTcO697Mqk1OMLPw5FQcHe+bPnweCiJODkqTkHL7/fjHPDn+Ovn36AlCpUiXGj/+YlJQUEhMTcXCwz2f0J4pGZs36lEo+MOl9N2y0pk19ry62RFzSM2FWAq99EItCoeDVV1+hfXuTSDF06FB+/vNPtu/T0bSBkswsib0Hs8jWSYgiKNUeTP5oIjY21swpK1asWCmK2LhYZIIcrWT+fql19QWuoHU13Z91eh2//74UFzzxFaqi87FDB6hSMnHHgUzc+Pnnn2ndpjUymczsHL1z506ys7OpUbMxBnLQ37zJnRg3XJzvB95jjKbgtntmHJIEjo6OjBw5khYtW7L+73XIOMafa+Op55ZNcJCa08kiW3dlYTDAhu2ZtGisyVc9nJUlsvi3dNzcXSwyzbaEoKB6HDwYxkvPSCgU+dUNg0Hi4LEcmjSrx5Ytm3FyVDKwV8FgZN3aapo10rBly8ZSihoCDRvWZ9f+8/TqUtBv6k60gQuR2XTv1ajYc3Xv3o2DB8OZNOcCndtqaFxfQ1a2RNieLI6dzua5557L88wqCrlcXqIAn0KhoFevPixdupSa1VW0banNex9XrutZ/GsaDerXpUqVsu/fPTw8EQSBi1d0nLuop3EDDRozlT2CTKBVUxW7D50q8zUL4/nnX+Kjj8Yx4dMkhvS1JTBARUysgbVbMthzIIvXX38dtdq0/tq7dy/z5s0lMEDFqFedcHWWc+6Cno1h6ew9mMmnH7kzrJ/ERzMT+e67hWjUAvsOZtGicUEB4fI1PTFxBhTyJKaPc80nYA3rb8/k2UnMmjWDb79dbHF1TFGo1eoCrUp1umzSMzJwsLdHqTSfrFYcMpmM0NBQQkNDyzzGisQltyrnZo5Z37jrUaYSpW+//Zb9+/fz6quv4O9fGScnJwYPHlxh4/Lz9WP27HksWfIDC38+lpd8VKNGdSZNepaGDYu/Z/xXOHz4MD///Aue+OFprITsnku9XtJxI/UCkydNZtF3i6wi0L8Qq6hh5fEhQI8ePejSpQvnzp4jIyMDb2/vf6SYUZxBeFFBxQc3EGUxB4d/T7DOSsXQqFEjpk6dxi+/LOHTL+9XI7g4O/LsswOpXr18vj8dOnTmr79+Z0gfA14e+aeZE2d0XL2uY/iIjuVyrYrE0jZUUH7CBpRO3CgMS8SICxfvllm0KIySVmeAZT4aMbqL/ylj8MIYOnQoV65EMnnOceoHawgKVBKXaGTfIR2CoCArK42hfe3p3dUWezsZSclGVqxP54fvf0Cr1dKl8/1+v46OjmaNHw8fOUJsbDxj37ovaORSu4aK7h1t2LY7h4ULv8vng/XMM89Qv359Nm7cwIFjFzGIRnz9fAgICKBly5Y0aNDQrJG4FStWrFjJj6ODI6JkRI8OFfn9FERXe3LIIFUfi4OjAwAnTpwgMzODStRA52MK0stSMgGTp5E7PkQmnOZCxAX0nqb79sNz9IWICzjXDkAhKbC9kY1GrmH1xnTq1HTOd+8Otktk4cp0bG1tCAlpDAjUr1efurV16PVtObzxJFu2bGZjWBIymUDjxiG8/Fo/fv/9F8ZNj6RlEy1BNZQkJBoJ26sjWy9nypSxyGTl433Yq1dPtm7dwqJfUhg5wjGvUtlolFj8awrJKQZstDaEhW0FycCiX1Lo1sGGalXyBzbr1lZxZNntUo+jX78BTJhwgh9+T+W5IfZ55sKxcQZmf5WMm7sLrVoVH/hVKlVMnjyV5cuXs2XLRjZsSwQgIKAyH3wwhNatW5d6jMUxaNBgbt++zWeLdrFifSY1AuTExEmcvZBNQEBl3nt/TLlcx83NjUaNGrJq41lsNFLxDYqKqHYoKzVr1mTqtE9YtPBbpsy98cAYnRk16hU6duwEQFZWFl999SWhzbS8+5ojsnvlQfXrqGkfqmXMlDiWrk5l5AgnunXQsOSPNLp3tGFTWCYNdmfQqY1N3u8qMcnIl98no5BDz87aAhU5drYyXn3Ong+mxHLixPF7v7vy48aNG/z55zIOHDiA0SiiVitp07YdTw17Cjc3854q/3Tq1auLm7sLf61LZ9zbzggPlHeJosTyv9PRytS4iZWIPHuFDz4Yw5w5cx5J5r+vry8ffzyRpKQk4uLisLe3t0i4/K+xatUq7GWOeIn++eYolaDGTwzkYsIJDoQfoE0ba9vbfxtWUcPKY0ehUFC/HEpVHzeFtZ4qrO2UuSoNMB/IK671lNVHw4ql1K0bTL16Dbh48TJ2dnIC/OWkZ2Tzww8/sm/fHiZOnIK9fdnKgbv36M72sK18NDORZwba0iJEgz5HYtf+LJauzqBBw/pPfGZJSf01oHyFDSgobpRW2CiOihY0LBUzwHJBo6T8W1vzKRQKPv54Anv27GXr1k1s2X0HW1t7evduyfr16+jbzZanB97/PTs7yXnlWUfS0kWW/fE7nTp2LDZwFBkZiZurqkCAJ5dmDTX8vTmDjIz0fKIGQHBwMMHBwWV/o1asWLHyH6ZJkyZo1BpidXfwe8CLUHS1R0Lijj4SGxsbqlY1PZaWZmrfI/iYeu7nChq5qO8JI2eS4qjp6Wx2ntZ5qiFeQnMzAwQBL2Nljp68yPxvkxnWzx6Fpw0pqSKLV6ewKSyDl19+GbU6v+BS3akO1Z+uw1NPPU1GRjoqlRqVyjSX1Ko1g02bNrJ5y0b2HYpBa6OhdWhH+vXvh4+3Tzl9cqY2i2+//TZffvklx8/k0CLEdP0Dx/QkJhnQaNSsW7eGkHpq7O21HD+jY+uuTJ4ZaM+Qvvfnz8QkIzZaTWGXKZb69eszcuRrLFq0mN0HdNStrSAjE06f1+Hk7MjUKVPyPpviUKlUPPPMMwwbNoyEhASUSiXOzk5UhD/B9evXWbt2DYcOHUSv11O1ShUGDhxAXFwcUTF3sbd34L332tGyVasCGdAGg4GDBw8SGRmJXC6nUaNGBAfXsWiczZo3Z/HCE6jVcOeuAZ1ORK3On1ghiRLhR/TUqVOxe4qg2kEsWPAFV65cISYmBnt7B+rUCcq3ftq7dw86nY4RQ9zzBI1cvD0VdO9ox99b0nlhmAOO9qbXNQ9Rsyksk69+SGHdlgzqBqlJTjFy6LgOG62AwQjNQ8xXZwUGKHF2UnLx4qVyFTUuXrzIhAkf4ewIzw+1w8dLwZXrOWzasYtjRw8za9YcPItYo/9TkcnkvPzSK3z66SymL0hiQA87/P0UXLuZw/K/0zl9TkdVauMguOAkunJZd4Yff/yRyZMnF3neqNtRHDxwkOzsbCr5V6JlixalrnpxdnYusM62YiLHkMP58+fxo5rZhCmtYIOtzJ4TJ05YRY1/IVZRw4qVMlJclQYULmiUpEqjsKzkf2uwzkrFsHXrVpYvX8GIIfb06WaXV4p/7qKeT7+8zpw5s5g69ZMyXcPO1o4ZMz7lyy8+Z8HiUyy4d1yhkNOuXXtee+21cimVrmieBGEDHjDsvFm2qo1HRWnEjAex+miUDJlMTrt27WjXrl3esfDw/WRl6ejV2bwpaa/OtuwOjyci4gJ16tTJOy6KIklJiSgUShwdHQABpUKBXi/eMwAtuFHIvtcjXGEt57ZixYqVCkGj1dCjRw9WrV6FnmyMGMgkHTv8ydAnYiAHMmH/vv20bt0aDw9PAHRkoU0pmMaeQTqqqpVwcnI0O1efTbtFlapVObphIzpsUQtanAQ3/CWRg4evcT0zG4VcR9qF29jYaHjppZfo06dP3usfTrYSBKFA/3yVSkXfvv3o27cfplT7iqvc69ixE9WqVWPd+vUcO2tqVVS/QTOOHTuKi5OOCe965LXVMhpNWdm/r0zD309B8xAt2TqRHft0tGrVqUzj6NGjJw0bNmLz5s1cuXIZlUbFyJFNadu2Xak8DORyOR4eHmUaU1EcOXKYmZ/OxNVJTq9OauxsbThxJoqVKy/TunUrZs2aS06Ont27dzNj+nTS0tKpVKkSw4YNJTk5mZmfziAxIRlvTxU6ncSKFSuoUaM648Z/hKtL4W1oIyIiWLxoETWqq8jJMXL5moEvvk/mnVed8nwtRFFiybJU7sboee+DPoWeq/wQijQ3v337Nt6eKtxczYfX6tZW8edaicRkEZXK9F2PjjV5zXzwpjP7D2dx+pwOjUbg2YH21K6hYszUePQ5otnzSRLkGEQUivKpaLp3VhYsmE+VSgJTxjijuSciNW6goWt7Gz6clsTixYuYMGFSOV7zyaFly1aMGzeOJUt+YNz02LzjWpkmT9AAkAsK3EQfTpw4QUJigtnvsk6XzeefL2D//v0oZAqUMiVZhiwW29nzzrvv0KRJk0f2vv4LiEbT70RWxDwik2QYReOjGpKVR4hV1LBipRworkrDHJaYg4NlWcn/1mCdlfJFkiRWrlpOaHMtA3rl31zWqani9RH2zPryNFevXi2z8ZyriyuTJ0/jzt07RF66hFwuJzi4Lk5OTmU676PmSRE2wHTPKI+WVBVJaQUNSyo0crEKGsWTnpEBgJur+c2u+73jmZmm5xkMBlatXsWmjRtISEgCIKBaFQb0H0SjRiEsXfoHR09l07RhwaDLzn1Z+Pp6IZfL+Omnn9i/fw/Z2dn4+fnRrVsPWrdu848QMa1YsWLlScZgMCAgI5VEbLDD37UxEqAnHQM5qAQNGzZsoHXr1tStG4xH7brcvXWWKgTlC/SIiCQEiHh6eNOxacFK+dz5eFi79mz/bSk3UyOpItZCKahwETyoEuxEHHeJv3CB/v3789RTT6HRlNVUuOJbEVapUpW333o77987d+0kLGwHM8Z55PMJkcsFhvaz40yEjr83Z+DrpWTRL6lk6WT07Vv24Lm3tzcvvPBCmc9T0aRnpDNnzmwa11PxwZtOeUlQvbvCgSNZzP46HA+PX9i6dQsZGRmI92Lvly5dYufOMARBRo0AJVPec8ffT4kkSZw6p+PLH24waeIEPvt8QaG97f/443eq+CmY9qELCoXAlp0ZLPolhbMRsbRookEhFzhwTE9CooGRI1+jRo0aj+pjKRStVktKmgGDoaB3C0BCsimYqtUIpKaJyGQQcUkPgFwOH77tku/5oijh4iRj574sGgQXrBA6cjKb9HRjuVa+nz17lqiou8wY75YnaOTi5ChnUG8tX/94nLj4ONzd3MvtuqVFp8vm7t27KBQKfH39yqWlaYsWLWjevDnffPsNYVvDqCrWxka0L3BuG+yQJIn4uDhcXVy5cuUy4eHhZGWZ1r9Hjxzl5IkT+FMdJ9EdmSQjmyyiM68zY8YMPv10JjVr1irzeK2YUKvV+Pv7kxqVhIvkWeBxvaQjg1RqWT/zfyVWUcOKlQqiJG2nylKl8V8L1lkpPXfv3uHunVhefdrF7OPNGmmwtZVz+PAhbty4zvHjJzAajdSoUYOOHTuWqi2Vj7dPubYReBzkChslwVJhA+BUjOn3X5KqjSdN3CirmAHFCxqlaTsF/z1BA8DnXq/diMgc6tQsWOYeEWnaSHt5eWM0Gpk+/RNOnT5Bh1ZamjZyJlsHO/dFM3fuXJ5++mnq1KnFN0uu4OwoJ/CegaLBILF+awb7DmUxeHBPRo36H5Kko20LNS6Ocs5cuM68efPZv38fH344Drm8PLMJrVixYuW/xenTZ5AQ8aYK7q6mwIyUk4MD9YjmJrHSbS5fvgLA2Tux9OzZg+8Wz+WqdA53yRs1WrLIJF52B1HmxhvDByE8JCY8nGAwZcpkJnw8kfPpx3DAGb+6ttyWZZJ68TrPjRjBoIGDCowzd2+SlZVFXFwcWq0Gd3d3HoVwURJOnzpFYFU1vt4FwyGCINCmhZZvlqTw1rhYnJ0dmTRpLD4+vo9hpI+HnTt2YjDkMHKEc4EgfYsmWhrVy2LNmtXI5eDtKWf4YAca11eTmSURtieT31eloVJJVPI1fb6CINAgWMNH78h5d0JUob3tk5OTOXnyNKNevS+kdG1vS3AtNRvD0jlxVkdMnEiVytX4aMKbVAuoVvEfhgW0bNmSpUv/YO/BLNqH5m8ZJYkSW3ZkElRDxe1oI7+tTMfPrxK7wm/h5irnr7XpNKyrzickiCJotTJ2hWdRo5rJvyy3WvbyNT0Lf0qnTp1aBAYGltt7uHnzFnK5QFBN82JT/SA1kiQRFRX1WEWNrKxMfv31N8LCtpOVlQ2At7cHAwYMpmvXLpT1XiMIAtWrVWebtBU1WrNiiQ7TdZVKFVMmT+bY8eOo5WoUgopMQzoSEpWpgbNw/3PSCFr8xZpckZ1h2bI/mTTp31nx8rjo06c3X3/1DUnE5fvcRUkkSnYFtVpD+/btHtfwrFQgVlHDipUycP5KTKFVGmBZ26lcLMlQtmKlLBgMBsC0SDaHXC6gVspYs2Y1mZnZ1KyuQa2CX37dz++//8YHH4yhaVPLqhX+bZiEDcurNcAyYQPyV21AycQNuN+WyhzlKXgU5gNkbkyWUJLqjNK2nXqcgsbdu3e5dOkiMpmc4ODgR9oHt06dYHx9vVi6MpnJH9xv1wCQlSXy19oMateuQaVKldiyZQsnTpxg0vsuNAi+3w+9dTMtf65JY+nSpcycOZPvvl/E+5OvU7OaGlcXgQuXjSQm5dC/fz927gzD10tk0nvu2NqarjW4Lxw5mcXMBUdYs2Y1Ax8IfuXk6ImLi0OpUuHu5saTFuyyYsWKlSeN5OQk1GhxczWtJ6ScHMAUgPOS/EkkFkkS85KqevYIpZKfAz8t+ZnLVy7knadaaEteGdafmjXyr0vMzclVq1Zl4aJvCQsL42LMYQyGHBpp/en+5miqVqnKw4g5h8nMzOCrH79i585d5OSYBPSAqgEMe3oYzZs1L8dPpGwYRRG5mYz6XHID6u+88w6t27QutKqgPBBF8V7A+vHMhUajkT17drNly2aio+9iZ2eLTKYisKoaZyfzCQlKhakCXK0SmDHeDSdH0/MclTCglz0e7grmfJ3Ehcgcate4n1wRUFlJ7Rpq9u/fZ1bUSM9IB8DTLX+YytdbwSvPOgEw8oN46tWr98QIGmDybmnZsgULfzmMIEBoMy0KhUBCopFflqcSEanHx0vJ+Onx1K5dg4kTp7B69WpWrFhOYpKR9yfFMaCXPQH+Sm7dNfD35kyiY0WaNW/G4l8PsWZzFjWryYhPgIjIbKpWrcyHH44v8Tijom6xevVqwg/sJztLh5+fL92796BLl65oNBqMRun/7J13dBRl24evme3plTQgIfQWeg819KI0FUHFinw2bAjSuw1BsGHBV1BQqSK9hd57752E9J5sts18fywJxGwaCQg61zmeIzs7zzwz2Zl57vt3F9LTZdzc8v8WU9Ls6TiGUmdm3TvZ2dmMHv0h0VHX6dXZicb1XMgySmzekc7XX39NXHwczz7zbKmP06JFC7777jvirbcIIG8zcFmWSRRvUaVSFX755ReOHT1BCNVxs3khCiI3uUQKCbiTvyyVKIh4SuU4fPgwRmMWBoPjnikKJadTp86cPn2GyMhIkoRYXCR3rFhJUyUiq2TGjhqrXO9/KYqooaBwHyhJ2anCsjRiTeeUXhoKZGVlcenSJQQBKleuck91dwH8/PxxdtZz8Eg2Navmj96+eMVMUooFP1+Z6ePL5UavpabZ+PbnND7+5CNmzviC4ODgUp3Po0xJylBByYQNoMTiBhQsJtydzfF3ChM7ChMv7rVPxt2URMy4m3spO3U/kGWZU6dOERcXh6urC/Xr18/T9C85OZkvv5zFwYOHcz9TqUTatm3L0KH/h15/741Gi4sgCLz++puMHz+eDyYn0auzE+UD7A0PV6w3kpQs8vF7rwOwdt0qmtTX5xE0cujX04U1m43s2bObz6fPZN++vezYsZOsrAxatCxPly6diY6OZvnyPxn3zh1BI4cm9Q10CDexes0q+vTpi8mUzcKFv7Fp8wYyM4wAhIRUpH//J5XGfQoKCgqFYLVY8fWuiYCQK2jkIAgCbrIXZp29IXhOUFVYWBgzZn5OVFQUKckpxGlF2tSvmW/swt7LLi4uNO9ag+bUKPI9bMzKYsaHX5OUkIS3zR9X3LFgIeFaDNOmTuP111+nS5cu93T+ZU3NGjXZvm0bCUlWfLzyu0T2HMwmtHIIHTp0uC/Ht1jMrF69hrXrVnErOg6dTkvLli3p168fFSs+uHW2xWph6pTJHD58lPp19HRuqyE+IZktu7KpEFhwhuXlaxY0aoGObZxyBY27adVEz8/eKrbsysojagB4eQhkGrPy7QPg5emFWqPiwmUztWvkt1WSU2zEJVjw88tfYuaf5p133mHmzBnM/G4vcxdm4O6mJuqWCVEUCAwMIDg4hOeeb0ezZk0RRRXPPPMMPXr0YNmyZezdu5tZ38fnjlWrVg2mTh1MrVq1uXDhAhs2rOfWrSjcvV0Y3rsNLVq0QK0umSvv5MkTTJw4EVcXmR4RejzdtZw4k8D333/P3r17GDbsbdQaFeu3ZvLEY/mz89dtycLL24Nq1couO6SkrF69imvXrvHpOG9Cg+8IjQ3q6gmpoOaXRYtp37495YOKbzM4ws3NjX79+rHoj0Ugy/gQgEbQYpQziRVukCmn0bHTAObM+Y4QquMh+OTuKwMatIiC4yBCLfaMF6MxW3GylyGCIPD228No2rQJq1et4crVK2jVGjq26kivXj3/U5l2/zUUUUNB4R4pqEF4ScpO5XCvWRr/xbIq/yVMJhM///wzmzdvJDvbHu1mMOjo1KkzgwcPzuNILQ46nY6Ijp1ZvX41LRrrqRJ6Z3+jUeKHX9NRiTDxAy8C/O68HtzdVLz/mgdDP0hkxYoVvPXWW2Vzgo8Y99JfA4ovbEDpxI2/U5gAUVh2R1kIF44oSampuylM3HXE/eyjcezYMb799muio+8I125uLjz99CB69OhOVpaRUaNHYsxMYNgQD5o30mO1wNbdWSxctp2EhDgmTZr6QHpM1KlTl2nTPmLBgl+Y9f1xAERRoHHjRoz8cHCuOHnzRhQdmjs2qtRqgaqhak6dOsnx48cIq1ePli1b5flOZOQWAvy0BFdwHMXavLGejduSiIqK4osvPufmzWt0jzDQsK43GVkSm7bHMX36dBISEujbt28ZXgEFBQWFfw8efpWR0qR8gkYOEjY8K1fLZ38ABAUFEa8TKSxf8O73sgxcvHCBlJQUtOWy8PP3p24R72HJsp/tO3aQGJ9EFakOOuFOAI675MVNLvP999/TqlXLfE3D/wnatWvHL7/MY/aPaYwa5pGn9M+m7ZkcOJLNsGGP35djm0wmJkwYy7lz5whvaqBfN3eSkiU279jN7t27GDt2HGFh+fud3A9+/+03Tpw4zqQR3tSrfSe4oXrlTL75OZWr1y2EVMz7fpdlmbR0CZNZJijQsTtJEAUqBKlJSc3b6NpqlTlz3kaLVo7Xtk5OToS3Cmflxt20Dzfg7nZHMJFlmT9WpKNWqx/KQAidTs/IkaO4du0au3btIsuYRa+gINq2bVug89rT05OXXnqJl156icTEBBITk3B3d88j2lStWrXUZaYsFjOffPIxNaqKjB7mge727717R2dOnDExcfpJNm7cRLeu3flt+SpcnEU6tjGg0YhkGSVWrMtk8/YsXn31WUTxnysnun79Wto01+cRNHJ4vKszf603snHDBl544cVSH2vgwIEIAixduow4SxQqQcQq23B3dWfkGyO5cOECOpUOd1vejAwdepKJxSpbUAv555lBGgaDATc3t1LPUeHvCLRs2SqfraLw70YRNRQUSkFBpaeKW3bqZPqNAp1791o/XuHfgc1mY/LkCZw/f5Y+3Zxo1dQDZJkd+7JZvnY1N25cZ/z4CSVeWD4z6BnOnT3NiKmXaNlYj5+visQkG4dPWMjIkgitpM0jaOSgVgu0a6Fl4459ZXSGjyYPQtiAshU3HHG/hAtH3KuYAQ9XH42TJ08yceIEalfX8MYLPlQLVRMTJ7FiXQbfffcdVpsVm9VGXGwss6f6EOh/+z4ywGNdXQiuoGHcJ6c4cOAAzZo1K/P5OaJ69epMmjSFpKQkUlNT8fLywt3dPc939Hp9HqeDzSYjCpBtkvlxYRqHjmUjSVcYP34COp2GiIiOvPjiS2i1dlFUFEWsNruzwVHdYYtFBmDTpk1cv36Vj0d7ERpyR1Bt3sjA/EVpzJ8/j/DwcMqVK3c/LoWCgoLCI8vpS7FUr1mdLbvW4Sl7IAp513422YopyIWmtWo53L+w8rd/t0X2HzjATz/OJfpWNBXD7E63ciYNrwx5pVBHu81qY9Nv2/CSyuURNMAeQRsgVyDZFsfWrVvp2bNX8U78PmIwGPjww9FMnjyRV95LoE1zLS7OKg4ft3D+UjZdunQmIuL+ZGksWryIixfPM+VD7zyZ0326OTN1VgqfffYpP/30vxIHL5UUi8XMunVr6NHRkEfQAIho48Qff6Xz8ZdJjH3XOzd722yS+GVpOsZsGbVG4Op1q8OxJUnm6nULzRvnzU5dsS6DpBRLoRk7zzzzLO+/f4ThE5Nqdnt7AAEAAElEQVTp091AnRo6EpOtrN6Uxf7D2QwdOhRnZ+dSnv39Izg4+J6y2r29ffD29in6i8DNqJtcvHARtVpNWFhd3NzcC/3+rl27SU1NZ+jocrmCRg51a+qIaK1n3brVfP/9XLKzjcyZt4kFSzPx9VETHWPBbJZ4+umn6dGje4nPq6yQZZmYmHj6dXd8rhqNSJVKam7dKrhiRkkQBIGBAwfx2OOPs2/vPjIy0vHz96dJ4yaoVCqOHz+BWtDkW/t6UY4YrhHDdYLk0DzbTbKRZDGebp26lDjTRkFBwTHKnaSgcA8UlaXhiJKUncpBKT3132XHjh0cP36KaaN88qRfDyyvoVZ1DeM/PcbevXtLHImg1+uZMmUa0z//nL0HD2C22J2ZarWIi4sLXu7mAvd1dhYxmwve/l/hQQkbcP/FjftJacQMePj6aMyb9z+qVFIz7r07TTMrBIm88ZIHWi0sXPAr3t7etGqivyNo3EW92jqqhuqIjNz8QEQNWZbJyMhAq9Xi5eWFl5fX3Vu5ceMmRqORxo2bErlzO+V8VWzcmsWFKxZEEZydBKw2eLa/K62aGZAk2LHXyJJV64mOjmLChImIoop69euxbNkyzpy3UMtBU/Lte7KpUCGQHTu20raFPo+gkcOTj7uwdrORTZs2MnDgoPt2TRQUFBQeNXJsjv7P9mTr3nVck89TQa6SGwFskc1EB2Wg0ah5ol+nfPsXJWjczd69e/noo49wxYNmYXXRoMVyIZ54MZpx48YzcdJE6jkQNiTLfjIzMzFmZxOA4+hjtaDFIDoTFRVdsgtwH6lbty6zZ3/FqtWr2Ld3NxaLhZCQGowZ05OmTZtQFj0uZFlGkiRUKrsQZbPZ2LB+HZ3aGvKVgtXqRIY868ZrI+LYuWsX7du1L/XxCyMqKor09CyaN87vSFerBSaP8Oat0Qm8PjKO2tV1uDgLnDxnIStLok+f3ixf/iebt2fxeBdnyvnmXfds3plFUopEaqrEkRPZZJtktuzKZt8hI08++SQhISEFzqtcuXJ89tl05s79ke9/OYAk2YMjgsr78/77zzyUWRoPivj4OGbPnsWxYydyP1NrVHSM6MQrr7xcoBB28eIFygfqHK5PAZo00LMuMondu3fTo0dP+vTty/Zt20hNTSO8jS/t27crtuByvxAEAScnPQmJksPtsiyTkChTpXrZCl4uzi5ERETk+7xChQoYbZlYZBMa4Y4oqBY0BMqVuMllTBjxlgPQoCGdFJJUsfj4evHUUwPKdI4KCv9lFFFD4eFChlOnTrF9+3bSM9Lx9/OnY6dOBAY+uKji4lLSLA1HlCZLQyk99e9m06b11Kutd1hPtn4dPbWq69m4ceM9pVcuXPgb+/buo0O4Ex3bOGEwCBw6ls3yNRkcOiGTmmrD3UF93EPHzISGht7T+fzbeJDCBjxa4kZpxQx4+ASN6Ogozp27wMg37wgad9O3uwtrNsWRmJhI+xYFR1ZWDBKJik+8L3PMwWTKZtmy5axfv4akpFQAGjasT//+T1CnTl127tzJ778v4Pr1KIDbzUllvvlfKo3CdLz2gjsnz5jYsS+bT8f5UK3ynfN58nFXqlfRMu6T47miav169QkJqcDsH2MZ955HrsEsSTJrNmWx+4CRoUMHM2fOHGpV83A4Z71OpEolzUPl7FJQUFD4p8kRNHJsjtGjRzPto484Yz6E823xIENIxU1bheHvDcHTK2+BqeIIGjnvaUmSmDNnDm54Ur9uVQQBNBeyMQieuEgeXBFP88N33/PV11/hyNlfyc2+prHgOPhFkiUsshkn54erhry/vz8vv/QyL7/0cpmOe+HCeZYtW8a+ffuwWm2ULx9At249adSoIamp6TSu5+Vwv6AANUEBOi5funzfRQ3hds1/yeZ4e4CfGp1WpF79ZkiShNlsokuXSnTp0hV/f38EQWTFn8sYMTmBp/u60ri+nqwsmc07svhzbQaVKlXi1IVEdh1IAqBixfIMG9avWBkwfn7+jBo1hqSkJGJiY3AyOBESEsw/1Uz9YSAtLZUPPxyJIKfx3v950rSBjmyTTOSOLH77cwNJSQmMGTMWR9dIrdaQbZIdZtWevWDmpwX29eKMGTMA+99q8OAXaNKkyX0/r5IQHt6GTTu20Ke7MwZD3oyTE2fMXLtp4oWXWz+QubRt25affvqJW+ZrVJCr5rmuLrgjIpBOGunYr61Wq6V9+3Y8++yzuLraS/ClpaWxc+cOEpOS8PTwpE2b1oVm3ZjNZlQqVa5IqqCgoIgaCg8R2cZspk37iKPHjmAQnVDLWvYJ+1mydAlPPPEkzz7zzEOxjjl9KdahoHE8KsZhHVsoeXNwULI0/uvExcXSqnHBj+jKwSqOnskrmCUmJbLyr5Vs3baZ9LRMypXzoVOnrnTv3j23OfH169dZvnw5LzztRu9uLrn7hgZraNZQz7vjE5g6K5lPxnrnWZxt3Z3FiTPZfPBBjzI+00eXshA2gFKJGw+TsFEWYgY8fIIGQEpKCgAVghz3jfDxVuNkUKHV6bl6w7FDR5Zlrt6Q8A/yvV/TxGTKZuzYMVy5cpEO4Xrq1fIkNV1i49YzjBkzhoiICDZu3ETj+nqef8ILb08Vp86ZWbIyHYtV5rUX3PHxVrNlp5GGdXV5BI0c6tXWUbPaHVFVEARGjx7HuHGjeX1kPGG1dHh7Cpw8ayM23szjjz9O165d+XHuDySlOPacyLJMYrJEQAWDw+0KCgoK/wasViv79+3n2rVr6HQ6mjVrRlB5x81L/y5oADRs2JCf5s5l8+bNnDp1CkEQKFezLv37dSywHE9hgkZd33LcvHkTSZKIjY0lKSmJFmFhuYJGDqIg4CsFcvnGaS5evEiVKnfq++fYJc7OztSvX58Lxy/hKfnma5KbQiJmm4nW4eHFuFKPNrt37+azzz4lwE/NM/2dcXUROHoyhZ9++pG9e+0lwrKMssN9ZUkmyyih0Theb5Ql5cuXx8vLnW17sqhVPX8ZncPHTWRm2ejbty/VqlXLt//zzz9P+fLl+fnnn/j6p1S47bzV67X06/8EgwYOskfPJySgVqvw9vampMZ8/kzT/y5//bWS9PRkvprmjY+33UbU66FvT1cC/NV8PPsgJ06cpG7duvn2bdSoEcuWLePEGTNhte5kFZy9YGbsJ4lUDFQz8i03KgdriIqx8ufaBKZMmcKIESNo2bLlAzvHoujbtw87dmxn4ucpvDzIhSqVtFitMnsOZfPdvDRq1qpOgwYNHshcnJyceOPNN5j5+QzMogkvqRxqtGSQQgKxaNDhhR9JYiwaJw0ffzyNihVzypLJLFmylIULFyLZJHQqPSYpm5/mzuXJp55kwIAB5NwrkiSxfv16Vq5cyc2bNxEEgbCwMPr260uD+g/mXBUUHmYUUUPhoWHWrFmcPHGSStTETfJEQECSJeKJYvHiRfh4e9Ote7d/dI4lLTt1r83Bi3LmKVka/37c3T2IjokqcHt0rA13d4/cf9+8eYNRoz7EYsmkfSs9gX7OnL+Uxq8L5rF9x1amTvkIZ2dnNm7ciIe7mp6d8hu/FctraNfSQOSOLIZPSKJdKz0aDew/YuLg0WwiIiII/w8YoyWhNMIGUAbihmOB9H6LHQUJs6URM+DhFDQAvLzsTQCv3rBQ3kFTzNh4K5lZNpo0rc/Ondu5cdNChfJ5HRKHjpu4dNXEoOc65n5mNBrZtm0rZ86cRRRF6tevR8tWrdCo782ZsXTpMq5cucjUD72oGnpHkOjczonp36SwefMmurR34v+ed891XoRU1NCyiZ53x8ezYGk6w4Z4kphso3b1ggWG0GCRUxficv/t5+fH7NlfsW3bNnbt2klUfBZ1wiryfteuVK9enZiYGMoHVWDpquuoROgQ7oyb2x2H17FTJqJumRn62oOJrlNQUFB40Bw/dpzpn00nJS0FnUqPTbby87yfadGiJe+8/TZ6w53eAwUFUAG4ubnRp08f+vTpk2t/OBI0jibcKlDQkGWJGwcOMWvZchISEwDQafVUDHNDjS6PoJGDAXsgTEJCYh5RA+7YJQOeHsCo46O4LpwnQA5GJxiQZIkU4okWr9C8aXNCQioV42o9umRmZvLFFzNo0VjHu0M9UKns79qObZzZujuLWd+fQq0RmPFtMov/SqdzO2c6tbU3ZAY4dMJEcorlgUTIZ2ZmEhRUkU3bTrB+SxaeHiLtWznxWBdnMjJlvp2XTs2a1ahWzXGTapvNRsWKFWnRoiXbt2/DaDRhMOjp2LETvXr2RBRFTKZssrOz0WjUyDI4aL2lUEwit2ykXUtdrqBxN80b6SkfqCUycrNDUaNu3TpUrVqZWT9cZ9QwdyrfLgX646+pBAepmTbaG63W/hss56umfm0dH32ZzPc/zKFZs2YPTWZAYGAQkyZN5rPPPuG98Ql4uGswmSWMRhuNGjXgvfeHO+zvdr9o26Ytnh4eLFiwkDNnzgCgQoU3fvhRHrWgwVsqxwXjCRYvXsJ7770HwKpVq5k/fz7lCKQc5VHbNFhlC/FEs3Dhb+h0evr06YMkSUz/7DN27d6NO95UpAo2WeLSiSuMPzaeoUOH0r37P9fnREHhYUARNRQeCm7dusWu3buoQBXcuRONISLiRwVMGFm8eAldunZBFMVCRrp/OIqYupuyzNJQUGjbtgNz5/5A1C1rbnO+HK7dtHD4eDavv56Tvi0zffqnuLlkM2WkD+5u9oVnj07Qp7szoz++wdy5c3nrrbeIjY2lcrDaYQkdgOpVNGzcBq6eNZm78BiSJFOlSiWGDXuMDh068FCkSz1k3KuwAWUnbvydgsQOKJ7gUVTPn9KKF454WAUNsJenqF27BstXX6FpA12u4Qf2LIPFf2Xg7KznxRdf5OrVy4z++BZP93GmeSM9FovMtj1GFq/MpFGjBjRq1AiAEydO8NFHU8nKMlI1VIfVKrN582bmzf+Z8eMmlrjJpD2Say0dwvV5BA2wl5gKDVGz9yA83cc1n8Hn6aGiVydnFi7PYMizEu5uIlExjpt/Aty8JeHunrfMiU6np3PnLnTufKf5pyTZ+Prrr1i/fgMGgwp3V4FfFqfzy9J0Bj/hRo9Ozuw7nM03/0ujVq0ahIWFleicFRQUFB4FLl26zMSJEzHYXKhOAww2JyQkUkjgwL4DfPLpJ4wfNx6EwgWNu8kRNBzZHwUFVeW82/csX8HatWvxohyh1EZExFAjnTRSuH7hIFWoi/i394SJLAA8PO6URvl79nitmrUYNWoUM2fO5EzmYQwqJyySGatkpVWLVrz99ttFntejzpYtkVitFl4e5JkraABcvGzmh19TcXYS6BDuhI+XPVPyh19T2X3AyLh3vbh0zcqXP9rfhzVr1riv80xMTODDD0eSmppIlw5OhFTQcCPKyrrITFZvysRkkilfPpAPRnzI39f+JpOJxYsXs27tatLSMwHwui2IaNQCm7esYffundSv34Ddu3djNNpFsqAgf/r1e5KOHSPyjalQNCnJaVQMcnG4TRAEygeIJCcnF7C3wOjRY5kwYSzvjrtB9co6tDqZC1csjBrmmWddCyCIAgN6u/LO2HiOHDlC48aNy/hs7p3q1avz/fc/cOjQIS5duoRGo6Fx4yb31Jy9LAgLq0dUVDRnz5ylCmEYcAJkkognWY7HhhVsAjt27ODVV4eg0+v5/bff8MaPQOGOyKsWNAQQjE22suiPRfTo0YMdO7azc9cuQqiBh+Cde9v4SP5EcYXvv/+ORo0a4efn94+cu4LCw4Aiaig8FBw8cBBRUOEpOy7L4YkflxJPcu3adSpVCnmwk7uLgspOOaKkWRrFceg9KAeewj9PREQEa9asYuwn8bz4tAvNG+mRZZk9B0389FsGFStWoG3bdgCcOXOWy5evMWG4V66gkUNIRQ19ujnxx4qtvPjiC7i6unDmuuSwpipAbLwNJyc9EyZMQpZlZFlCFB1H59y6dYsNGzYQHR2Nk5MT4eHhNGzY8IFGyDwslEbYgNKLG3+nYLEjpkjBoqgxypK/C7nFFTRyHCkP8nn4/PMvMXr0h4z9JJknH3OmWqiWmHgrK9ZlsmOvkddefw0PD0+mTPmIb7/9mu9/2cuceTl1dNV06NCZsLC6TJkyiZtRN4iPi8fPV8XHo31yy1rdjLby+bepjBs3hm++mVNgORFHZGSkk5ycSr3ang63p6dL+Hir8PRwfD9Xq6zFYpFJSpFo38qJuQtTuRFlpULQnaViSqqNvYeMHDuZzbBhRdf6njv3JzZt2sgrz7jTqa0BnU4kNc3G4r8ymLswjYXLMjBmSzRoUI/hwz/4Tz47FBQU/v0sWrQItaQlRK6BiN15KCLiRTlESeTQoUNs3nGEgKDAYgkaORQmaBQUVKVLTmHt2rWUpzI+gv097xFmBZxwOQ8XuUkSsfhwZw0gyzJxQjR+5fyoXj3v2uTv7+GmTZsyb948du/ezY2bNzDoDbRo0YKgIMdltv5tXLp0mcrBujzvWkmS+fTrZIL8NIwf7oWzk/038FhXOHPeXv7n5ffiSU2zUaVKJT78cDQldfonJiWSmZGJr68PBkPRfUu++eZrrJZkZk/xztPku28PF0ZOSUCt82PmzFnodLo8+1ksZiZMGMvFi+fp1FZPozAvMo0ym7ZlsWZTFi8McOOzcd68PTae3bu20LOzM43q+WA0Smzansrs2bOJi4tj4MCBJTo/BfDy8uDqDaPDbbIkcz1KonZd70L292LmzNns3buXXbt2ERsbA1ykcojj7OBKFdWIokB8fHxZTL9MEUUVTZo0pUmTkttc94PY2Fj0agPONhcssplLnCKbLNzwxIAzRjIwSUamTJlKv379SEtPpzqVHY7lQyBnsw5z7NhRVq1ajbvoiYec9+8qCAKBcjApJLB+/Xqee+65B3GaCgoPJYqoofBQYLaYUQkqBNnxAk59+6dqMTuuVX6/KSpqqiyyNKBkEcoK/24MBgNTp37EjBnT+ezrE6hUArJsN4waNqjHO+++n2toXLhwAa1WpH5tncOxmjXQ88vidK5du07r1m3YuHETR06YaBim51aslQtXLKhUULmihs3bTbRtay+PIwgCguDYAfrHH3+wYMECXF3UVKmkJuqGPcq8Ro1qjBs3HhcX1/tzYR5icgz761n3JmxA2Ysbf+dBCBXF4W4xo6TPvX9C0AB7ZNjkyVP57rtvmPT5tdzPfXw8GTZsCBEREYC9NMiIER+SmJjAhQsXUKlUVK1ajW+//ZpPP/2M6lX0NKmrIjZex4Gj2UyblcTkEfb6yOUD1Yx5x4Mh78cRGRlJr169ij0/rdZ+/6elSw63uziLpKRJmE0SWl3+jMfYBNvt7wlEtDawLjKTcZ8k8NyTblQO0fDV3FQuXDEj3R7+22++5siRI7z55lv5nB5g70Oydu0aBvZ1oWfnO+KMu5uKlwa5kZhs4+Q5FVOnTaZKFUWsVyg+SUlJvPnmm6xcuRJRFOnXrx+zZs3CxcVxBCtAu3bt2LZtW57PXn31VebMmXO/p6vwH8ditrBv71785eBcQeNu3PFGLxo4efoU7XsUr3Z9QX38CmsMDvY1wKzf/0CvcsLbZo/stQsa9h4aGsEdV9mTGK7jIruhw0A2WcQKN0mTk3j9xQ/vNJcupMefVqulXbt2xTqXfxsajYZMY96GzAeOZhMbb+ODNzxzBY0calbT0j3CiXWRJj788EOaNWtWoqoER48e5bffF3Dm9Lnbx1fTpk0bnnnmGby9fRzuExsby8GDh3n9Jbc8ggaAl6eKZ590Y/rX0cTGxlKxYsU821etWsW5c+eY+qE3NareyQoNb6pn/qJ0fv4jjZQ0GzZJ5uOxPoSG3PlOwzA9i1aoWfD777Rr15bAwP+G0FUWZGZmUr58MFt3HaFfTxf8/vZ323kgm+gYM2+9HVHoOCqVilatWtGqVSsuXLjAe++9x61Ym8OSVrHxNiRJxt3drURzlSSJQ4cOcerUKQBq165No0aN/rFqGw8CV1cXLJIZSbZxnfNYsVKd+hiEO+vfNDmZs2fPsm7dOgA05O9bB6DFvqbOzMzi2rWr+EkVHWqcoqDCWXLlypXLZX9CCgqPEIqoofBQUCmkEhbJTBYZOJPfGZpGMhq1hsB/IMqnoD4aUHiWRmEGxd8pTtkpJUvjv4enpyeTJ0/l+vVrnDp1GkGAOnXqUL583vJBarUam03GYpHR6vKverLNcu73atWqRVhYbT79+ix+vmlcvX6nxIwogqhS0bNnz0LntWVLJAsWLGBAH1f6dXdGqxORZZmTZ8x8+s1lPv30EyZNmlIGV+DRxJ61YTf2y0rcKEth45+iNEJGDv+UoJFDzZo1+eKL2Vy6dInY2Djc3FypVatWnmwmk8nE1q1b2bJlM6mpyXh7++Lk5MLBg/sZ9bYnzRre6VVxK9bKuE8S+fTrFD4Z640gCHh7qWgYpmPv3t0lEjX0ej31G9Rj47azdG7rhCDmfRZ4eYiYTDKbdhjp3jFvBojVKrNqQyZ1qmtzs71ef9GDSZ8n8sX3KQgCyDJUraShUzsn3FxFjp40sWnHTi5dusgXX8zOJ2zsP7AfSZLo0j5/tokgCHTv6MzuA4n55qmgUBSDBg3i1q1bbNy4EYvFwgsvvMCQIUNYuHBhofu98sorTJo0KfffTk5FRzMrKJSW7OxsJFnKdVT9HQEBV68QTMb8fSwcUVSGeEF9NHLsj6ibURhszgiCkEfQyMEFNzJJ5SxHcj/zcPfkgyEf0KKFXXT5p9/FDzNNmzZl3bp1nLtoyXX6X7xixdtLRZVKjp2YzRoaWLEukwoVypfI8btz506mT/+M6lW0vPeaJ75eKs5cMLFy/Q6OHT/KZ59OdyhsXLlyGVmWaVpf72BUaFrf/lu9dPlSPlFj3fo1hDc15BE0wP5ef6q3C+u3ZLJpexZtmxvyCBo59O7mzMoNRjZs2Mjzzz9f7HP9L3Px4kUmThxPenoGOh2MmJzAoH6uNG2gJ9skE7nTyJJVGbRq1YJatWoVe9yqVasQVN6f5WtTqFNDm289tnxNBs4uBho1Kn7pqZs3bzBlymSio2Mo52v/+y9btozAQH/GjBmbz4b9txAe3pr5838hhuukk0oI1fIIGgBugie+UgDHjx8HIJM03MmfWZOBPcs7MDAQjUaLzVpwOVhJtOUGNSko/FdRRA2Fh4L6DepTzrcctxKvUUmqiYo7DqJsskgUY2jTtg0uLsUvxVEWFNVHAwrO0vg7BWVp3EsdeYX/FhUrBlOxYsF1Qhs0aIDNJrNtn5FObfLfI5E7s/DwcCW0ciiCIPDWW+/w1puvk5pmYdgQD5o11GMyy2zdmcVvyzP4+eefGDNmXAGlYGSWLFlEs0Z6nu5zR4AUBIG6tXT832BXPvnyOJcuX6JyqOO02v8CpS1HlcMdcaPsszYeBPdaXsoRD48TRaBy5SpUrpx/HmlpqYwZM5rr12/QqJ6OqnVVXLqWyoH92fTo5JxH0AAI8FMz5Dk3psxI5sJlC9Uq2w1AVxeR1FhTiWfWv98TjB07ltk/pvL8AFfc3VTIsszRkybmLcrEw8OduQvTMFtkOrdzwskgcu2mhfmL0rl63YIgCoz9OAmzBc5eyMbHx5PAIG8uXbpIh3ADb77kkWv0tmhsIKK1Ex9OjWHp0iUMHDgoz1yMRiNarYiri2MHjdft0hzGLMelFBQUHHHmzBnWrVvHgQMHcmt8f/nll3Tv3p3p06cTGBhY4L5OTk74+z8c2WoK/x2cXZxxdnIhMysNDwcOLBs2zJioWKPomugF9dEorqAB4OLqSpQYA/Z4l3xNwS2Y8fL2Yuj/DSU5ORkfHx/q16+PSqV+iN7DDy8NGzYkJKQi07+9xcg33KkSqkUlgsUsIUkyogMhP9tkT4FUqYrvmjGZTHz9zZe0bKrnvaEeuePWrKalbQsn3p+YxLz583j3nffy7atW28sNZRllHAXhZxntPw6NOm9ZIkmycSs6jn7d3fPvBOh1IlVDNRw9aaZmtQKi0LUiVSqpuXWr4DLNjzopKcmcPn0GWZapXr06Pj6OM2aKg9GYxcSJ4/H3MfPFpHIAfPNzKl//lMpXst35rdNp6Nnj8dsliEoSKCIwePCLTJs6jc++SeGp3i4El9cQG29l+ZoM1kVm8corrzjMxnVEeno6Y8aOxtWQxWfjfXLXtOcvmflqbhJjxo7my9lf4+r678vm9/Pzo3OXzmxYtwEBATcHz3oAD3yINUURGBhIXEwUrpIH4l1VESRZIk68ScXyFalWrSrNmjVl3479lLOVRxTyrqezZSPpcirNmjW7r+dWllitVuLj41Fr1Pj6+KD01lEoCxRRQ+GhQBRFPvjgA8aMGcN5yzE8JV+06MgigxQxAT9/P1584cV/ZG4FCRpllaUBRTv6lCwNhcIICAigZcsW/O+3/fj5qKlbU4sgCEiSzKbtWayLNDJo0DO5xklkZCQ2yconY31y05ednaBvT1eCAtVM++IQx44do379+vmOFRsby40b0TzX33Hd/mYN9bi4qDiw/8B/WtSAshM24NETN8oiK+NuHhVHyqxZs0hJjuaLyT4EV7Dfb5eumnl3XDbhzQwO92kUpsdgEDh51kS1ylpsNpljpyw0bBR6+xsyZ86cZfv27aRnpOPv50+nTh3xc/A+CQsL45133uGrr75k+744QivqSE2TiI03U7NWdUaOGMXvv//OvD/W8cvidAx6FekZVjw93OjeI4Jz584Tk5CKm5sb//d/nWjcpDEvv/QyKjU8/5R7vii+apW1RLQ2sGb1Sp5+eiCCIHDq1ClWr17FqZPHsVpsfPJlEoP6uVE+MO+S8+RZE6IoEBBYvMAABQWAPXv24OHhkadpaceOHRFFkX379tGnT58C912wYAG//vor/v7+9OrVi7FjxyrZGgr3HVEU6dKlM3+tWIm35I+eO+8CGZlkbzMyEhEdCy8bUxaCBkDr1uEcPHgAQ1gmmgtynm0W2UKKmEDv9o/TtGleR9mj8h7+pxFFkXHjJjBhwjjem3CT0BAdKlEmLUPm0PFsmtTPvxbYuttIQGC5Eomuu/fsJivTyLP9y+UTSry9VDzWxcCvS3fy6pCh+fpz1a5dC71ey+YdWTzTP7+qEbkzC41GRVhYWL5z0+k0pCQ7LnMJkJxiQ62GhCTH35FlmYQkiaq+D9ez12azsW/fXs6dO4coijRo0OD2+Rff6Zqdnc13381h27ZtWK32kp6iKNC8eXNef/2Ne3LmR0ZuIT09gxkTffHytDu/x7zjRVy8vXzw3IXpVK3WiBdfvDc/SfNmzfngg+F8//13vDUqHrVGwGqRcXbW8/LLL9OrV+HZ+3ezYcMG0tPS+HzcnbmCfa04YbgnQ96PZ+PGjfTt2/ee5vqw839DhxJ18yanTp4u5Fv231Pfvn344YcfuWA5jrcUgAEnsjGSKN7Cqrbw5ptvAAK9e/dhx46dXBcuECRXQiPYhSKjnMF11QV8vXwJDw+//ydXSiwWM7//8Qfr1qwjPSMdgKCgIPr3709ERAcUcUOhNCiihsJDQ7Xq1Zg5cyZLly5l+/btmC1m3N3c6du1D3369ClRw9SyoLCyUzkUZFj8ncKyNJQMDYWy4K23hjFlykTGfnyG0GAd/uUELl21OzM7depI//79c78bGbmRNs11+eqxAjRtoKdieS2RkZsdihoWiwUAZ4PjXhsqlYBBr8JitZTNiT3ilKWwAXaB4H712ygtZS1k5PCoOFKib0Vz4MBBhg3xyBU0wF62CaCgHtjC7f9yomaXrMwgIdFC9+7dMZmy+eSTjzl48DB+vlp8vUUOH7KwePFinnrqyduNNvMO3K5dOxo3bkRk5BauXb+GTqujZcsWGAwGDh06RO3atenevTunTp0kK8uIXq9jxYrlrFq1mtBgHV7uAucvxTNv3k0kSUKWZSpX1ODm5jjjol4dHeu3JJOVlcXq1av49dcFlA/U0iFci8nszPY9WQwbG8fw1zxp3sjuzElJtbF0lZEmTRrj7VVwU0sFhb8TExNDuXLl8nymVqvx8vIiJsZxsAnAwIEDCQ4OJjAwkOPHjzNixAjOnTvHsmXLCtzHZDJhMt3JmEpLSyv9CSj8J+nfvz/79u7jcuwpvCQ/XPHAhpUkYrHiQr9BPQp9FpaVoAEQHh7O9mN/EZd0E5tswA17kEoWGUSLV9A76enRI68j81F5Dz8s+Pj4MHv2l+zfv589e/ZiMplIyzzPNz+lMPptFVVC7Y5Jm01m9cZMtu8x8tprzxeQIe2Y6KhovL20+Jdz7M6pXV2L1ZJGfHx8PhvaYHCie/eeLFuxnPIBatq2MCCIArIks/tQNr8tz6Rjxy64uf1d8BBo0aIlG7fv4fFuzmi1edcFZ86buXbTRoUgFeu3ZNKnmzN6fd7vnDhj5vpNMy+90rrY53q/uXDhPB99NJWEhGQC/LRYrLB06TJCQ4MZPXosvr7lihzDZrMxadJ4Ll06z+AnnWnd3IBKFNh1wMhvyw8wZswoPv30M3Q6xyW/CuLw4UOE1dLh45X371zOV005XzVRt6wsX3u0RGP+nfDw1jRv3oJDhw6RkJCAu7sbTZo0KfFcd+/eTvNGujyCRg5eniqaN9Kxa9e2f62oIYoqhgwZwltvDSONZIeZeSkkotPpaN26DdWqVWP+/F84dOhQbg+eBvUb8Oxzz+YGBoaGhjJy5EimT5/OGfMhnAVXJEEi05aOn48fEydNRKt1nBX1sGCxWpgwfiKnT5/GUyqHLxWRsJESHc+sWbOIiYlh0KBBRQ+koFAAiqih8FARVD6It4a9xZtvvonFYrE/pP8B4baoslMFZWlA4c357qY4fTSg8EZ8Cgo5ODk5MWXKRxw+fJit27aSlpZK/YZ+dOrUmWrVqnL3jZScnErFIMcLVUEQqBgkkpSU5HB7uXJ+ODvrOXQ8m9o18i+irt2wEJ9gpnJoqIO9/5vcD2ED8jYTL4yyED6K+7wqa5H2UXKknDltj8wKb5L33qoYpMbVRWDnPmNuKv7dHD5hIssok5ElM3paEifPZvPMM4MIDQ1l+vRPOXXyGCPf8qR5Qz2CKGAySfy5NpOFv/+Bl7c3Xbt0zTemi4srjz32GAA3btxg1qyZnD9/MXe7RqOiS5euDBw4kLeGvYmTLoPZU31zxZjEJBvf/JzK3J9+BCAlTcrT9PRu0tIkBAFOnz7Nr78uYGBfV5583CX3u88/5cqMOSl89nUy77wKcfFWVm3KRpKdePnlV+7lUiv8Cxk5ciSffPJJod85c+bMPY8/ZMiQ3P+vW7cuAQEBREREcOnSJSpXdpxV+NFHHzFx4sR7PqaCQg4uri588tknzJs3n61btxJjuQ5AcNWGtG7dmj69C46GLktBAyBJuswzzz7D0llzOccZtCotAiImWzb+vv6MGj0Kb+87zrhH6T38MCGKKpo3b0Hz5i0Ae0mi8ePH8d6Ea9SoqsPHS+DsRRsJiRZ69+5N16753+WF4eTkREamDZNJQqfLH3QQn2TPFHB2dpwR8eyzz5KYGM/M73awcFkWFcuL3IyWuBVrpkWLZrz8yssO9+vbtx/v797FR7NTGPKsGwF+amRJ5shJE198n4JWI3AjyoZao2Li5ym88owrocEarFaZvYeymTMvjZq1qlO/foMSne/9Ij4+jnHjxlAhEMa9Y18HybLMqbNmZv94i3HjxjBr1ldFOo737NnDyZNnmDbKJ4991C3CmVrVtLw99jqbN2+me/ceJZqfJEkUdmidVsBmKzhzprio1epSlzHKMhrx8ii4J4yXh8jlm//ukqMhIZWoVas2l85dwmBzQifcycxKl1NIEKPp3rk7BoOBkJBKjBs3jtTUVFJSUnB3d8fDwyPfmM2aNeN///uJyMgtXLhwAbVaTePGjWnevDkqleMgw4eJDes3cOrUSULl2rgId0rXeeBNLDf4448/aN06vNBS2woKhaGIGgoPJYIooNX9M6pzcQWN0mZpQPEdgIohoVAcRFGkcePGecpyOMLLy4NrURkOt8myzPUoidCqjiMGtVotHTt2Yc36VbRsos/T9NBkkvhxQTpe3h40a9b83k/kX0hZCxtQ/OdHTsmqB3GssuTf4kjRakW6tndm+doM6tXW0ajeHdEjNt7Kt/NSEUVYuiqDWrVqMGpUH5o3b0FMTAw7duzi/553o0XjO0aRTifyVG9Xbt6ysnTJIjp36lxgY9H4+Dg+/HAEHm5mRr/jScO6ejKzJDZszeL3FWs4f/4cSYnJTP3UN0+0p7eXihFveDD0g0RMZh2x8VkcP22mXu28dZUlSWZdZCZ169Zl3bq1hIbo8ggaABqNyJsvezD4zVg++zoZjUZN69atGTRoULGiHxX+G7z33ntFNo0NDQ3F39+fuLi4PJ9brVaSkpJKVLolx3lz8eLFAkWNDz/8kHfffTf332lpaVSo8O9scqpw/3Fzc+PNN9/g5ZdeIiY2Fp1OR0qWSI0GIQXuU9aCRk6AQvPyNWj+2aecP3+eI0eOYLPZqFGjBg0aNEC4q257cd7DN6NusnHDRmJiYnBxcaZ16zbUq1evRFkH/wU8PDz5/POZ7N27l507d5CemUGTpuXp3KXzPZVrbdmyJT///DObdxjp3jFvJoYsyazdZKR69SoFvmdVKhXvvfc+PXs+xubNm0lMTKRuPU+GRXSgVs2aFBRVGBISwtix4/nss48ZOjyO8oE6srJsJKVYcXd3pW375nTv3h2L2cKnn33MO2Pj8fLUYjLZyMyy0ahhfd4b/sFD8/tYuXIVomBh3Hu+ODvZf/uCIFCnpo6x76p4c1QMO3ZsJyKiY6HjREZuolZ1vcOAr+AKGpo20LN584YSixpVq1ZjxZ/HyDJKOBnyr/X2HjbfDl4rLTJnz57j4MGDWK1WqlSpTPPmLVCri+8uLB9UgZNnjzsMgpFlmRNnrZQP+ve/Q4d/8D6jRn7IudgjuOGNVtaRLWaRJicTVrsugwcPzvN9d3d33N0d96nJ4e5gpUeNtWvW4IZ3HkEjB1+CSFLFsm7d+jzBJwoKJUERNRQUHFBYY3AouDm4kqWh8DCSnp5OdHQ0Op2O4OBgIiI6sXjxb/TvaSXAL+9rYO+hbK7fNDNkaMG1nQcOHMiZM6cYOeUyrZvpqVVDQ1KSxMbtJtIz4K23XmHJkiWkpqbg7e1D+/bt8fLyuuf5W61Wdu/ezb59+zCbTYSEVKJzl874+vje85j/BPdD2CgOj2KJu0dR0KhZqxYAOw9k0yE8b2TkgD6u7DlkZNLnSdSspqNGFQ1x8Vb2HTHh4+PLV1+Nx8/fP09TzgMHDqBWC7Rr6bgXR+e2Tmzfk8D169cICank8DtLlixFFLKZ9qEPrq52Y9jdTcUTj7ni7aVi1vcXqVbZcfkKrVakXUsd67ZYEUWY/k0S7w71pH4dHYIgkJxi46ffUrkeZWPa6wOZMmUyvbtoHToqnAwijcJ0pGQGM2HCJAwGx+ek8N/F19cXX9+in+ktWrQgJSWFQ4cO0ahRI8DeJ0qSpBJFmR49ehSw96QqCJ1OV+wGqQoKxcXgZKBSpRB7EFUx/LplJWjkcGdNIFCtWnWqVXOczVn0e1jml19+YfHiJWhVOvSSE1bRzMaNm6hTuzZjHuKeNVFRUWzbtpXU1DR8fOzr1NI0cy4uarWa8PDwMqmB7+fnR0REBHN/24wgQES4Aa1OJD7Byq9LMzh51sS4cQOLGEWgevXqVK9+5zeQmJjA4sVLiIuPw83NjbZt2lKxYkWMxixu3LiJWq2mbt06/PTTPHbt2smly5fRqDU0bdqUmjVrcPeP+scf5rL/wAEuX7qMRqOhSZPGDtcrsbExrF+/gStXLqPV6mjatAnh4a0fyPN3167ttG2pzxU07qZCkJqwWnp27dpZpKiRlJxIteCCsxSCy6u5sDO5xPPr0rULS5Yu5rt5abz1ijsq1Z3ru3ZzJqfPZTNyZK8Sj3s3KSkpTJs2jbNnz6JT6RAFFUZrFu7u7owcOZLatWsXa5yuXbsxceIBtu020q5V3nt/2x4jl6+aeHZwt1LN9VHA28ubL2Z9webNm4ncvIW0tDQq+pWnS5cXCA9v/UhkV5Ql0bdu4SdXcPi+EwURvc2FqKioBz8xhX8NiqihoHAXRfXRKKw5uCMKMy6ULA2F+01KSjI//e8ndu7cidViT0MPCChHr16P4+1djlHTEhnY14lmDQyYzBJbdhlZ9FcmzZo1ydcc8G4MBgNTp05j5cpVrF+/hsidCajVKvz9A3ByVvHpp5/hZFDh66MhJs7CL7/M56mnBvD00wMoaT25uLg4xk8YQ9TNGKpX1uHiIrDyr0MsXryIoUOH0rXro7U4zrmfr2c9WGHjUeJRFDQAAgMCadKkMfP+OEblYE2evhrnLlmIT5QwGPScu5jNxStmZBmsVhm/cn64ubnnETQAzGYTWq2AVuv4nnFxEW9/z3H/GlmW2bo1kl6dDbmCxt20bWFg/qJ0MjJtBZ6Tq4uILMPrr7/JnDlfM+GzJHy8VLi6CFy7aUUURd555x1q1aqNIIAkFzgUNsnuJFYEDYXSULNmTbp27corr7zCnDlzsFgsvPHGGwwYMIDAwEDA7rCMiIhg/vz5NG3alEuXLrFw4UK6d++Ot7c3x48f55133qFNmzaFvusUFO43hQVRHY+KKVEQVVGCRkkDqop6B69du47Fi5cQQDC+tkBEQUS2yaSTwtkz55nx+QzGjB1TrGM+KCTJxrfffsv69RtwcVFRzkfD1hgLCxb8ypNPOu5T9TDz2muvIcsS383fwrw/MnBzUxGfYEGn0/HOO+/QqFHhmdt5kfnjj0X89ttCdFqRoAANcYlWFi9aTFBQIAkJCZhMZgB8fDzp3bsfjz3Wi/btOxQ4oiiqaN6sOc0Lyd5eu3Yt3303B4NBpE51DelJMGvWHn7/fSETJ04mMDCoBOdQcozGbLw9CnYye7oLxKdmFTmOl6c316MK9iNcu2nFy6vkwpm3lzfvvP0un38+nbMXrbRpocOgg31HLJy9kE3Pnj1p2bJFicfNQZJsjB83nugb0YRSE1ebJ4IgYCSLW+lXmDBhPDO/+ILyQUX7LRo1akRERAe++D6SIyfMhDe3i1I795nYtjuLiIgOucEI6enpbN26laioKAwGA+Hhrahc+dFa9xeGweBEz5696NmzdILTvwG9Xo8lw1zgdptoKbBMnoJCcVBEDQWF29xr2akcCsrS+DtKlobCgyAtLZURIz/AmJnIs/2dqV9bR1qGxPot6Xz//Q/069eP69ev8fVPh/hKTgVAq1XTuXM3XnzpRYoy6nQ6Pf3790cQ4Ndff0WlkhHlOOJi7Q7WFo20vPaiByazzIq1mfz222+4urrSs2fBdaP/jiTZmDhxHDZzErOm+BJS0e70zc6WmL84jW+++RY/f38aPCR1eUuCPWsj7z2uiByPrqCRw7BhwxgzZjRvj71BwzAdQf4qrly3cfx0NlqNCh9viVee9SaslhabDXYfzObHX88wceI4Pv308zzRWyEhlcjMtHHhssVhL44jx7PRaFQEBjp+95jNJoxGE+UDHIsIKpVAkL+ac5fMWCwSGk1+4ePwcTMhISF06tSJtm3bsmTJYo4dOwaCwMCBDeneowcuzi4AhIXVZ8e+g/nKTwGkp0scPWlmwID6xb2UCgoFsmDBAt544w0iIiIQRZF+/foxe/bs3O0Wi4Vz586RlWV3RGm1WjZt2sQXX3xBZmYmFSpUoF+/fowZ83A5XBX+OxQniMqRvXE04VaBgkZh5NgeRQVUFfcdLEkSS5YswVPwxY87YwqCgBueBEoh7D+wn5s3b1C+/MNTbubnn+exceNGXn3OnY5tDGi1IkajxIp1mfz2+x+4ubk9Uk5ItVrNsGFv89RTA9i1axdZWZkEBAQSHh6OXl+yJs/r1q1jwYIFPPm4K327O2MwiKRnSLw7Lp6kxFv06+FCkwbuGLNlIndm8eOPP3L58iVef/11NJp7Kxl99OhRvv32W3p2duK5J9xye4NE3bIybVYKEyeO5+uv55SoBFJJKV++PCfOXqevA/NEkmROnbfRoGHFIsfp0KEjn356mFNnzflKUF27YWH/kWyGDOl8T3Ns3bo1gUGB/LViBeu22MtDVa1alVGje9G8WTNKI8Tt37+fK1evUJW6OAt3GsMbBCdCpBqcsx7lz+V/8sYbbxRjNIG33hpGpUqhrFy5gq274wHw8/PhpZcG0qtXL0Bg06ZNzJnzLZJkpUKgjuRUG0uXLqVJk8YMH/5BiX+7D5rs7Gy2bdvG/v37sVgsVK4cSpcuXUtUAvPvGI1GEpMScXZyxtPTswxn+8/Tpk1rNq+PxM9WHpWQ917OktPJkNNo1ar02WsK/10UUUNB4S7upeyUkqWh8DCyePES0lITmDHRO09pmbBaOgLKqVi6fBk//jgXm83GpUsXUanU1K5dCxcX12IfY8OG9cybN59+PV3o18MFZ2cRs1li8w4jP/yaisFJ5JVn3BnYz5XEFBuLF/9Bt27dCky7jYmJITJyM/Hx8bi5uePl7cWNG9F8PsEnV9AA0Ovt4168LLF82dJHUtSAvPd3Tlmqu/mviRyPuqAB4Obmzmeffc7WrVvZsmUzB08k4+NTjmbNDBw/foipozxxd7P//tVqaNPcgL+viuETL7N3715atWqVO1aDBg3w8/Plp9/SmfC+B3r9HdEhOsbKn+uMtGnTFhcXVyxWC0ePHCElJRUfH2/q1auHTqfD2cXAletW2rbMP1erVebaTTMWi8xvyzN49gnXPGLEzn1Gjp/O5v337Za+Vqtl4MBBDBw4yOG593rsMUaO2M1PC9MY/JQbarV9rMwsienfpqBSaenUqVOpr7GCgpeXFwsXLixwe0hICLJ8J22oQoUKbNu27UFMTeE/hM1qz3JTqe+tlEhRQVR/pyhBo6g+GgXbHjLp6emI0mEMTk7FegffvHmD+Ph4KlPboT/VAx+ixMscOHDwoRE10tPTWbNmFQN6u+TpQWEwiAzo40pCko3FixfRrVv3UpWHyczMJCUlBTc3N1xdi7+mLg3+/v7069fvnveXJBuLFv9Bu1YGBvW7M+cNWzNJTpX4bLwPle5ah9euriWkvJofF2xhz57ddO3ajaefHlhiZ/Ty5UupGqrj5UHuedYfQQFqPnjdnbdGx7F3794yKddVEF27dueLL77g8PFsGoblnf+qDZnEJ5iL1cS9RYsW1KlTk8kzz/N0b2fatDCgEgV2HTCycHkmwcEViYgouLRvUVQOrcw779zp8WQ0Gjl06BDr128gICCAunXr3lOfkp27duEsuuIsu+XbJgoqPGw+7Nyxs5iihl3YfOyxx+jVqxcJiQkA+Hj75M7t4MGDzJ49m05tnXj2CS/c3VTYbDJ7Dmbz1U9H+HzGdEaPKjrgwGQycenyJSSbRKVKlXB2di5yH5A5fvwEZ8+dRSWK1K9fv8TZITdu3GDsmDEkp6TgijuCrOLU8dMsX778nioIJCcn88svv7Bt23YsFns2Q82aNRk4cCD16tUr0VgPK48/3pvIzVu4Kp8lUKqEQXBGlmXSSSZKdYXgoGCaN1d6cSrcO4qooaCAPWLqXlPAIX+WRkERU0qWhsKDQJJsbNq8gc5tDQ5r5ffr6cKqTUY2b97MU089hZ+fXzFHti8Gt2zZQkpKMmfOnKZNCwPPPXlnIazVinSLcCYzS+K3PzN48jEX3N1UdOvgxKZtCZw5c4Y6derkG3fevPksW7YMJyeRCoEaDsfbSE6x4Ows5hE0chAEgfbhOubMO47FYr7nKLGHhb87Ef4ucvybBI7Cnm+PsqCRg06no0uXLnTo0J7U1DScnZ146603aNdSlyto3E21ylpqVNWzbdvWPKKGKIq8//5wxo0by5ujk+jcRkc5XxXnL5vZvMOEt7cfL7zwAhs3bmT+/J9JTU3P3dfHx5OXXx5CREQnNm1eTa/Oznh75T32hq1ZpGfYHb9LV2Vw+ryVdi11aLUC+w6Z2HfYSLt2bWjdunWxzrtWzVoMHTqU7777jp37zDSqp8Fskdl/2AyCmtGjxxbZCFFBQUHhoUaGXbt28eeff3LuvH1NX71adXr37m1/fhfDp1hYlkZRjcH/TlGCRg6OBQ2ZyMhIli1bRjnv6/aP4nU89vhj9OjRo1AHqcViz8pV4dj5LyCgElRYrI7LI/4THDp0CLPZStcOjsucdO3gxMZtCZw7d5ZatYrXQ+BuoqOj+PXXX9mzZw82m4QgCDRu3IiBgwbdUyPwB8nFi5dIiE+i69C85ZE2bssivJk+j6CRQ7cIZ5atzsDDQ2Ld2pWcPn2SKVOmodMVT9iw2WwcPXqcIc+6OfytBVfQEBqi49Chg/dV1GjXri27du1k6heHiGhtoHkjPWaLzLbd2ew+YKRPnz4EB1dk69atHDt2DJtkw93Nnfj4OGJio3EyONGqVRs6dGjPuHET+e67OcxfvI2ffksDQBQFWrRozmuvvVHsa1M4MosWL2bpkiUYjdm5nwYEluP1194qcUlFY5YRlaQu8NmlQUu2yQjIlCQjRBAEh70PFy36nTo19Lz+4h0hS6USCG9mwGaTmTFnP9euXSM4OBiQOXfuPJs3byYpKQlPL0/atW3H4cOHWL1qDVlGezamRqOlQ0R7XnzhxQJLnN68eYNPPvmYa9du4Oqixmaz25516tRk+PCRxcqOsFjMjBs3DlOqhRpyA3SCAQR75lo0V/nmm28JDAwkLKx4YkRycjLvvfce6cnp+Nj8ccYNC2ZunItm/LhxvD/8fcLDi7cGf5gJCAhg0uRJTJv2EedSjmJQOSHJNkw2E9VCqzFm7Oj/XJ8RhbJFETUU/vPcax8NKNjIgIINDCVLQ+F+k5mZRWaGkeqVHS/QDAaRikEaYmIK/m3/HbPZzMcfT+PgwcOUD9Ti7gpZWWa6dXBcH7ZLe2d+XZLO/qPZdGrjjNfterWZmZkcPHiQhMQEPDw8aNSwIWvWrmXp0qU8+4QrvTo7o9OJWK0y2/YY+eanFOYuSOXVwR75juFksC+GrVYbmvz21iNNfpHj0RM4ChIv/u3PtqSkJH77/Te2bd1CdrYZURQQRQGVqmBj1s9XIDEtLd/n1avX4PPPZ7B06VIWrdyO2WzF3d2VHj360LdvH/bs2cuXX35Ju1YG+vXwpUKgmsvXLfzxZwaffPIpb731JpHoGD4pnqf7utIoTE9GhsTG7VmsXJ9Jtwgnrl6XMFn9UOtc+PbnUwBUrFie//u/XnTp0rVEkX/du3enTp3arFmzhvPnz6JSqXi8d2O6dO2Ct5d3yS+mgoKCwkPE/PnzWbJ0Ca6CB+WxO6pvXYjjk08/oV+/fgwePLhY4zgKpCpK0CgogOpe+2j873//Y/ny5XiI3tTxLk/aSTOpQiI/fP8D58+f45133i3w+R8YGIhWqyXNnIwT+bMRskjHbDNTOTS0wOM/aLJN2QgCuLk4bubs4W7//G5HcXG5fv06I0d+gLPByotPuxJSQU1UjJVVG48zcsRxJk2aTM2aNUs1//tJdrb9nD3c8l6b2Hgbj3d1HDSkVgtUCdVis8m8/rw7I6deZuXKVfTv379Yx5QkG7Iso9MVvMZw0glYrNZinkXRXL16hb/+WsmxY4eRJIkaNWrRo0dPPvxwFMuWL2PNmlWs35IEQIUKgbz55stUrVqFV4cOISE+idAQLUnJNlJSbQT6qwirpSMpReKHH87y559LmTx5GsOGvc3gwYM5ffoMsixTvXr1Mm1Cv2DBQv744w96d3OmZ6dy+HirOHvBwoKlqUyYOJ6Ppn1E9eo1ij1eUFAQxw4fR7JJiEL+eyNTSCPAP4Cy6DWTnJzM2bPnee81T4fPllZNDfzwawa7d+/GarUwc+YXXL9+HbWowSA5YxFNrF+3HgEBXwKoQFUERFItiWzeEMnVy1eY9tG0fIFuKSkpjBkzChcnI1M/9KZ2DS2yDAeOZDNn/kXGjRvDjBkziwyQ27lzJ4mJidSgoV3QuI0oiATJlTCKGSxfvrzYosb8+fNJT06nsq0uWkGX+7mH5MN14Txfffk1TZo0RafTFTLKo0GNGjX46ae57Nu3jwsXLqBWq2nUqBE1a9bgUepjpPBwoogaCgrcW9mpHEqSpVEcQUPJ0lAoLXqDHpVKJDbBcQNgSZKJT7RRs07x0+LnzJnDiRPHGPW2J00b6DlxxszYjxPx9HBsHLo4C2g0AqbbtuHx0yYAvv56Nikp6QgCyDK4ublgtVrp3M6J/r3uzEetFoho7URqqo0Fy9IZ0Mc1X5T7wWNmAgP9MRge7tqrZcGdBuN3MjgeVnHj7mfYv13A+DuJiQl88MFwLOZUenczUC3UhbgEGys3ZLJ2UxZNGxioVzuvcSJLMhcu26hZO++7JC4ujpiYWzg5OfPWW8N48823MJvN6PU6QMBitTB//s+0D3di2Ct3It4qh2j58C1PJs9IZsmSRVSuXJWrV07w9dxU5Nv9c1xcBJ7u48qTj7mwckMm8xbfZNnS5VisFiSbVCoDqmLFYIYO/b973l9BQUHhYeTUyVMsWbqEQCpRTg7M/dxH9ieOaJYuXUrjRo2pXafgKP+CAqnup6DhyPY4e/YMy5cvJ4hKtKpnIOsUeAj2slFuxLN16zZatGhJixaOGxAbDE506NCBzRsicZe8MQh3Sr/YZCu3xOv4evvSoEHDAuf3oKlYsSKyDCfPmqlbK/877vhpe+mX8uWLF3x2N3PmfIOXh5WPR3vh7GxfF9epqaN9SwPjPkvhyy9n8c0335LjvEtOTmbjxg1cuHgRzW3nXuvWbdBq/5ms46DyQYiiwLFTJgL87riHXJxF4gqwJWRZJi7eSkgFDVVCtbRprmf9+jXFFjU0Gi3BweU5cCSRiNb5s2dS02ycvWRicOuq93ZSf2Pb9m3MnDEDL08NrZtpUath3+GDjBq1m+eee44nn3iS/v36kZCQiEol4u3tTUZmJq+/9n94umUz8eNyHDtl4vtfUnn9RXc6tXXKXXfFxFmZOD2FqVMnM3v2V3h4eNKypYO6n6UkNTWVpcuWMKC3K0/3vWMz1aymZcJwL96fEM/UqZP55tvvcvudFUWXLl34888/ieMm/uTtHZIpp5EqJNKv+0tlMn9jthEATzfHtqNaLeDqomLbtq389ttvODsJBPqpiYm3YhTT8JT8ycZIMNXwEO4IRXqccJE8OH/hBJs3b85XAmrNmjUYszKYOdEHD3e7LSkI0KyRAT9fNcPG3GDnrl20b9e+0PkfOnwYF9ENvZw/G0QQBDwkH44cOYosy0UGBBmNWWzfvh0fW2AeQSNnLH85mLPGw+zatYsOHToUOtajglqtplWrVnmy0hUUygLHTxQFhf8IpSk75ai2bUFGRknLTv3XHIEKZYtGraFp06as2WQkM1PKt33nvmySki20bdumWOOlpCSzZUskz/RzpllDA4IgEOivQhDuiBV/5+xFC2azTPlAFWlpEvP+sJfGqV3Nyqwpviz/OYBvPilHk/oSWVnZuLs6fh11aueM1Qr7j+SNnDt4LJud+4z06GFvOvdfoaJTldzng2TZ/1CIoDnzyPkvZ47/xefY3LlzkWxpfD7Ri6f7uNKonp5uEc7MmuJD7RpaZs5JxmaT8+wTuctIdIyZTp3tDSRv3rzB+PFjefnllxkzZizvvvsuQ4e+wo6dO27Xq7b/3o8eOUJqajr9ujvnM54EUaBPD2eiomKwWCx2o+0VD14d7MbED7z430w/nurtiiAKSLK9VAjYnx3/hogwBQUFhbJmzZo1GERnfMlvF/gSgEF0ZvWa1UWOU5DdUZC9UdaCBsCaNWsxqJxo2SB/UIin4IuL6M6a1WsKHB9g8ODBBFUI4KJ4ghvyRRLlWG7J1zmvOoZNa2LkyBGI4sPjaqhVsyYVKwbxy+IMsrPzro1T02wsWpFFo0YNSlCS1U5UVBQnT57mycedcwWNHLQ6kUH9nLl5M5pTp04DsH37dl566UV+W/g7Z/ad5+juk8yaNZshQ17lxo3CG77fL7y9vGnatAlLVmWRlHxHxGjdXM/mHVlkOLAlTpwxc/WGlTYt7A7eWtU1xMYmYLM5FkEc0aNHL/Ydtpd5uhurVWbOvDRUKg0RZeDQjY2N5YuZM2nb0sD30715foAbz/R3Y/ZUbwb0cWX+/PmcPHkCUVRRrlw5vL19AIHNmzeTnp7GmHc8CPJXsXJDBm1aGOjcLu+6y7+cmjdecuPatRscO3as1PMtiJ07dwISPTvn7x+hVgv07uZCSko6Y8eMLvbfISgoiIEDBxLDDa5yhhQ5kXQ5hZvyZS6Lp6lRsybduhXdU6Q4+Hj7YDDoOX7G7HB7XLyVW7Em4uJjeGeoB/O/8ufbz8ox94tytGmpI55otOhwJ3/mr7Pgirvgxbp16/Nt27FjK62b63IFjbsJqaghrJae7cXouyXZbIiFuE9FRCRJQpbz3y9/JyEhEYvFggv5e5kA6AQ9erWBqKibRY71KJKWlsrFixe5desW9tJmCgr3jpKpoaBQAGVVdqroBn12FEFDoSw4ceIEixf/wdGjxwEY/FYs4U31DHnOHVGELTuN/O/3dFq0aFbs5mhHjx7DZpPocFcklY+Xmsb19SxdlUGzhvo8C0WzSWL+ojS8PEUuXLEw+8c00jIlmjTQM/x1j1xDIChAzZsvuSNLMuu3ZvJUbxc0mryLRRdnAVGEXxZnkJIi4eIicuSkmQNHsmnSpDE9enQv7SV7JHGUuVEY95rVUZyxlWeWndTUVPbs2cNLA13x8cq7vNJoRJ4f4Ma74xKY8V0y/bq7YLLAtt1ZrN9iJCIiglo1axIdHcWIEcNxc7UybIgHNatqSUiysWpDGp9P/5yszCy6dbNHoCWnpABQPtDxUq7C7c9v3rxOWpqZ85fsRmS1yhpeGOBOrepaZFlm514TYWF179NVUVBQUPh3cOniJZwl91wR+G4EBFwkdy5duFTicUvaGBxKJ2gAXLt6FSebKwICWafyb3eW3Lh2/XohswZnZ2c++fRTVq78i7Vr13Ej8SI6nY527drSp29fAgMCC93/wSMwbNg7jBkzmrfHJtGtg56gADWXr1lYuzkbWXDi1VdLnmUYHR0N2JtnO6LO7c+joqPQajXMmDEDD9mHQDkEtaABGUwYuZ5ynrFjx/Ldd9/9I8EFrwx5lREfvM874xLp2t5A5UpqnA0imVkSYz5K5JVn3alVTYPVKrNrfzY/LEildnUtDeva55qYJKHT2WvB2my2YtXI79y5CydOHOeTL3fRMMxIozAd6ZkSW3aaSEy2MWLESFxcSt9sfd26tRgMAv832A2V6s79KwgCA3q7sPuAiVWrVlKnTt610L69e2gUpsPbS3Xb4W7jpUGOs8NrVdPg463l+PHj1K9fv9RzdkRqagrurhpcCyihFhRgX/ddunSFffv20rJl8SLiBwwYgJ+/H0sWLeHqzbMAuLi40rdbH5588qky61uo1Wrp0CGCNZvX0a6lIXe+ADabzP9+t2fyv/qMG+1a3rE5vTxUvPWyB7dibVy+JCHIjoPZ9JIzCXHx+T7PyMignHfBv0dfH5Hrt1KKnH/VqlXZvXsvVtliv3f/RpqYTKWKlRDFon/7Ob0/LDgWeCRZwiKZcXJy3APoUSU2Nob//fQ/9u7bhyTZxZ/QSqE88+wzNG7c+B+encKjiiJqKPxnKSpLAxxHTRWVCu4IpY+GwoNg+/btzJjxOZVDtLz+ojueHiLHT5tZvyWLHfvtmQ42K7Rr14bXX3+z2ONab9ez1f+t7u0rz7gxcnIC74yNp3uEM5UraYiJs5faiYmzIknwx59GwsLCOHToMH26u+SPKBcE+nZ3IXKnkd+WpzOwrxtq9Z3vHD9tRpIgpFJtFq86i9lsITi4PEOH9qRz587FWjj+mynuM+Punhz36xj/dWJibmGzSdSp6dj4qxyixckgsveQhZ17EwDw8HDjmWeepW/fvoDA/PnzcDZY+XSsFy63Iz4D/NT4+ai4FWvlh+/n8MOP3+PvV45atezG95XrFkJD8h9z/ZYsBAEqBlro+4oXFQLVXLluYenqTMZ+msiE97w4dtrEhSsmJr7Q+297y6SnZyAI4OLiQkmzobKzs4mMjGTbtkjS0lLxLedP506dadmy5X/+nlVQUHg00Wp1ZJNZ4HYbVpy0jpvUQuF2R2Flbu+mMFsDih9IZTAYqNAgscDtVszoi+FYNxgMPPnkUzz55FNIku12Zkbpsmejb0WzaeMmYmJu4eziQpvWbahbt06pxwWoWrUan332OX/88TvzFu3GZrM74tu0acuAAQPw9S1X4jFznJNJSbbc/nF3k5hij5h3MjixfPlydIKe8lLlPP0LdIKBilI1ziYdYceOHXTs2PEez/De8fXx5cMPR/P1N9+wZOUVrDYZURSoVas28fGxjJqagEEvIElgMss0bajn7Vc8EEQBk0li9aYsnJ1d6dOnDwBVq1amZ6/HaN+uHQX97URR5L33htOoURPWrFnFz39cRatV07RpS3r37kOlSpXK5NxOnzlFw7padLr8YoAgCLRsrGP9ttP5tpnNJvy87PvkxJKrRMfnIgj2IKziROnfKz4+PiSnWEhKcfxbu3zNgiBAlRAdW7ZuKbaoAdC+XXvat2tHfEICVosVX19f1OqydxUOGjSQEyeOMnxiDJ3bGahTU0tiko31W7K5ct2MVqPKE0SXgyAKdO/ozOcXkrFgQiPkfz6ZycbVLb8I5u8fwNlLjp+dsixz+pyJtPQYMjIzCi3bFRHRkQW/LuSmfJmKctU893CyHE+qnMTzjz9TnMuAj48PVatUIeZyDO6Sdz77OIk4rJK1RH/Dh53Y2Bjee2845kwzAVIwzrhhxkTCtRgmT57Mu+++Q9u27f7paSo8giiihsJ/ktI0B4fip4KXpOyU4jhUKA1ZWVl89dVs2jQ3MGyIO+LtRXeT+gY6tnZixOREqlWvy5tvvkm5ciUz2qpUsTfDPHA0mxaN7xjsfr5qPh3nw/RvU1i4PB1JAlEUaNq0CW+93QdfX1/c3Nw4cvQohw4dpnyAY2dm0O2I8qWrMtm2J5sRb3hSrbKWLKPEL4syCA6uwOTJk7EbRTIgkJqaSvStW3h5ev3roljuB8rz5f6j09mj91JSJYId+JOMRgmzRea5556nVq1aqFQiwcEhuUZjWloae/fu45Vn3HIFDbAbqeM/sYsgPTo54eer5uzFVCIjN6LVqli4PIPRwzwR7jK0zWaJZaszaFBXx9h3vXKfB+V81TSqp2f0R4lMnpGEySzz/PPP06B+g9t7yqxfv4G//lrOjRv26NOQkAo89lgfOnaMoDhOpaSkJMaMGUV09C2a1NdTPUTk4uVzfPrpMRo0qMeYMWPLLOpPQUFB4UHRvGUzltxcilWyov6bCW3FSpqYTOdWJSuVU1iWRkEUlKVRXEEDoFV4K44enEvKSVO+eu5W2UKqmETv1o8XOc7dlF6wllm4cCG///4HGlGLAScsgpn169YTVrcuo8eMxmAo/XqvYsWKDB/+ASZTNhmZmbi5upbqnVSzZk28vD1YszmTt0Lzj7NmUxZ6vZZGjRoya9YXeNn8HTZk1gkGXAQ3Dh488I+IGrt372L659PRqqFpQx0mk8yJs2YuXbrIyA9HAfDVl1+SnpbEkGfd6BbhhCgKXL9pYeoXyaSm2ahZzciTvdxRqWDPwShmzpjJubNnGTp0KIUJGx06dLivfQNUopiv9OfdWK0ygoNSaSEhlTl86AY2m4yvtwofbxW7D2TTMCx/tsbFy2bi4s3UqlWr0LnIsszx48c5ceIEMjJ1atemfv0GRfZgAGjVKpwffviBJSvTGfKsR55tRqPEinUZNK6vx8VZICYptcjx8iPg6+Nb5LdSUpLZsGEDhw8fwmazUq1aTbp170b5oKKfPS4urnz88af88cciNmzewPI19sbsDRvWJ7yiKxfP788T3HY3OX0cM0nHg7zPLbNsIlVMpEfEwHz7de7cla+++pKTZ0zUqZl3vy27jNyKtaESTSxetIgXXnixwLm7ubnx/vD3+OSTTznPUdxtPqhQkSGmkiYn0759ezp0iCjyGuQwcNAgJk2axA3hIv5yRbSCDkmWSCaOW+JV2rVpR0BA8QTvR4Gff56HOdNMFVvd3EwXJ1xwl7y4IVzg22/m0Lx5C6UMrkKJUUQNhf8spcnScERpy04pKJSGrVu3YrFYGDzAM9eBmUNwBQ2d2xmI3HUBLy+vEo8dElKJmrWqM3/RFWpU0eJ5V3SQ1QZx8TJNmjRhyJBXcXFxyWd0+vrYm7ldvmahQV0HkUVXLQD83/PuRO40Mu6TRLq2d2LnATNZRhVTprxDjjF05sxZfvttQW55LbVGRXircJ555tkSizUKCmVJcHAwgYF+rItMy9cMHGDT9iwkCVq3Dr9drzkvSUlJSJJMlUp3UtplSWb6N0n4+qiZNMI7V+zo0Qke7+rEmI+TOXAkm8kzk+nb3ZnygfZSGvP+SCfbJDOgt2u+54FaLfDU4y5MnJ7Ee++9e1dUlMxXX33Nhg0baNHYwFOPeSLLsHNvArNnz+by5UsMGTKEooSNGTOmY8yK58tpvnlKYx09aWLqFyeYP/8XXnqpbJpOKigoKDwounbtyoo/V3DNfI7yUmV02B2bJrK5KVxEo1XTpYvj2vOFBVOVtJdGYRQ3M7xje08unHHmquoM5W1VcBLs0cnZchY3xcvo9Bq6d3+w5T03bNjA77//gT8VKScFIQoisiSTTjKnT51lxoyZjB49usyOp9Ppc4MRSoNKpeKpJ5/m22+/xd1NRd8eLri6iBiNEms2Z7JsdQZPPTUAg8EJq9WGSMHijyCJmM2WUs+ppFy/fp3p06fTsomO119wQ6+3rzUyMiU+/zaFj6ZN5ds53/HFF7P47NOP+f6XE/z+ZyYGvUhsvL18zoA+rgzofScju3M7Z9ZvyeSb/62lceMm/2hpmbCwBixdeo7MTClf3xNJktmxz0y9sPyNvbt3786GDRtYsjKDp3q70j3CiQVL02lcX0fzRneCvFLTbHz9czr+/uVo1Kjg87x16xbTpk3h2rUbeHloQIDFixZTvnwgo0aPKVIUcHZ25rnnBvPDDz+Qli7Rs5ML5XxUnLlgZtFf6SSnSIx4w4Xp36ZRqXLJesMUl1OnTjF58kRsNjNN6mvRagS2b7vK6tWr+L//e40uXboUOYaLiysvvfQSgwcPJjUtFYPegJOTE5s2bWLnzp3EJ1jx9cnvpjx51mwX0qSLSLKEBz4ICKSRRIzqOp4ennTtmv8Z3L59e5YtX8qE6bfo2cmZFo31WK2wdXcWm7Zl4UU5VJKaDes38OyzzxWaodK8eQtmzJjBihUr2Ld3HxarhdDQUF7p+SJtWrcpljiVQ6NGjXjnnXf4+uuvOWM+hF5lwCJZsEoW2rZuy5tvvlHssR520tLS2LNnDwFScL7SXYIg4CdX5KzxMLv37C6yYbuCwt9RRA2F/xzFydIoadmpgqKmlD4aCg+KGzduUCFI5zAdGSCsto4V65JITU1x6FAtireHvcuHo0bw+ocJtG+lp3yAvZTNtj0mvL196du3P1u2bCEjIxN/f3/atm2Ls7O9kV3lypUJCanAkr/iqVtTlycCR5Zk/vgrA19vFZ3bOdGmhYGX341lxfos2rdvT//+TxAUFATA4cOHmTJlMsHl1bz1igf+vmouXDazcuNu3n//CJ99Nh2/QupMKyjcTwRB4KkBA5k5YyY/LRR54jFXXF1ErFaZyJ1ZzFuUQceOnQq8/3JS5qNjrVSrbI/2PHbaRNQtGx+P8cyTvQFQpZKWxzo78ee6bKLj3Bj9UVzuNm9vLyApj0ByN5VD7J/fHQ116NBhNmzYwBsvu9OpzZ0mlG2aG1i7Wcuceatp0aIldesW3H/j2rVrHD9+kg/e8MzX66N+HR2Pd3Vi1Yb1DBo06HbTcwUFBYVHAy8vLyZOmsTkSZM4m3EYF9ENGciU0nBxdmHi2Im3n72OKSqYKod7zdIorqAB9mf/u8+PZOLViZyPPYaTyhkBgUxrBh5unowdOwkfn5KvFe8VWZZZ9MdiPAUf/KmQ+7kgCLjhRaAUwr59+7gZdbNY0eAPmm7dupKZmcnC3xawckMWvt4aEpOtWCwSvXv35umnnwYgtFIl4i4n4ivn7zdik61kiem52dEPklWrVuHmKjLsFfc8a3QXZ5Hhr3vw4rB41q9bx8CBg5g0eSoXL15kyZIlnDx5EjDj6SHy5GP5S8x2ae/Mxq3ZrFmz6h8VNbp27cLy5UuZ/m0K77/mgbOTfT1lNkt8/0sa8YkWHnvssXz7hYaGMmjQIBYsWMDx0xbCm+kIDdbw0axkalTNpF4tHYkpNnbuM6HXOzFp0pjbJdjyk5WVxZgxo9Cq05k2yoda1e3rsLMXLHzzcwJjx4xm9uyvcHUtvIdIr169OHf+HLt2bmfH3uzcz2tV0zJttCc3oq1cv2lmyNDO93q5CiQtLY0pUyZRJQQ+eKNcbm8Pi0Xip4VpfPPNNwQHB1OjRo1ijadWq/H2utP0Ozw8nB9//J55i9J5d6hHnqCcW7FWVq7PAAlUGhXXLRe4KVwEBCRZonrl6gz/YLjD66dWq6lTpy6R0QmsWmdk+ZoMAHSiBj85mHIEkU4y8VnRpKal5pmTIypVqsTbb79drHMsinbt2tGsWTN27NzBrehoDE5OhLdqRWBgUJmM/7AQnxCPJEk44/j3rRP06FQ6YmMKr5aioOAIRdRQ+E+RI2gUZFjca9mpv1MS40IRNBTKAr1eR1qaDUmS80VmA6Sk2mv66u7RkRgQEMDMGbP466+/2LJlEykpafj4eNG7dy+uXbvGiBEjMBhEPNzUxCVY+N//fuKVIUPo3KkzIPDyy68yYcJ4xn2SRL+ezlSqqOHmLSvLVmdw9KSJEW/YM0ycDAIR4U7sPqhl2LC3c48vSTa+/HIWYbU0jH7bM9foql1DS/twA8MnJvPj3B8ZPWrMPZ2fgkJZ0L5de9JSU5k3bx5rI40E+mtJSraSlm6lXfu2vDr01QL39fbypm7d2qxaf5HwpgbUaoELly24uAjUqOpYnGjaQMcfK9J5//0PkCSZlJQUfHy8SUxMYsqUKUTH2KgQlH+pFx1rfx64ubvnfrZu3RpCQ3R0dFDLuGsHJ1ZvMrJ27ZpCRY2TJ0+i1gg0a+j4OdO6mYHFf8Vz+cplatUsvESDgoKCwsNGjRrVmTt3Ltu2bbvt0IXaderQrm1b9AbHz72CemkUZnOUJEujpIJGTkBV+aDyfPfdHPbv38/x48eRJJmatWrSonkLTp06yaxZX5CRkYGfvz+dO3WiYsXgYh+jpNy4cYO4+DhCqe0wGdADH6LEyxzYf4DyfR4+UQME+vfvT+fOndi2bTsJCfF4eHjQpk2bPIEMPR/rxcwZM0kmHk/hTpkfWZaJ5ioSMp07Fx3pXtYcOXKQ8KZah2V/nAwiTRtqOXLkEAMHDgLg2LFj7N69G3fRC4NKT6N6Yp4G3HfTqJ6WDTsu3df5F4WHhyejRo1h6tTJvPh2PI3ra9CoBQ4ds5CRaePNN9+icmXH9vhTTz1FcHBF/vxzOXPm2Zto+/n5kmXSsXl3Jk4GV/r0aUu3bl3x8PAscA4bN20kOTmJbz/xpZzvnXVZzWpaJg735NXh8WzYsJ5+/foXeT5vvvEmt6JvcuP6VVo00RHe1IC3l4ptu7P4a30W4eGtCAsreK12r2zatAmz2cT7r/nmaVau0YgMedadY2es/PXXimKLGn9Hr9fzxhtvMX36Z8TFJ9E1woCXh8ipcxZWbcjAbBXo3rMbTz75JFlZWRw7dhybzUbNmjUK/Pvl4OLsgiiIVLM1xIQRAdBJTrml4CyyPUPKoC+4L9L9wmAw3LaX/73k9CoxY8LJgbBhk61YJAvOhfQ0UVAoCEXUUPjPUVZlp5Q+GgoPEy1atGTp0mXsP5KdmxKdZZS4eMWCJMms22wkLKx2oQ3QisLT05PBgwczePDg3M/ef/89Ll26wP89706HcANarUhyio2Fy9L56suvcHN1pXnzFoSFhTFx4iTmzv2eSZ9fy92/QqCaUW970rTBnUWkt6eKLKMxz7EPHjxEYmIyY972zWd02dP9DXw3/wDJycl4ehZsVCgo3G8ef7w3bdu2Y8uWLcTGxuLq6kqbNm2oUKFCkfsOeuZZxowezZSZyTzT3wWNWsBqAZsNHGXDZ5vsNaI1Gi0hISG5n1cMtuDm5sKf6zJ48yWPPPvIssyfazLw8/OhRvU7huf161dp3kDjMHVeEATq1VZz4uzVYl0DBQUFhX8reoOeLl270KVr0c7nwpqDQ36bo6RZGsW1Of5Ojv0hiiqaN29B8+YtAHs0+YTx4zl56hROKmfUNi1HVcf4a8Vf9OnTmxdeeIGyaNj9dyxWu0Px771KchAFEbWgxmIxl/mxyxI3N3d69epV4PZ2bdtx9MhRtmzZQooQj6vkiQ0bqapEsqVM3npr2APNkMlBkiQ0moL/rlqNgCTZgyFiY2OYP38+fpQnQA7mEsfJyCi4ZFZGlg2t5p/PzKxXrx5z5nzP+vXrOXrsMJLNRvsOtenWrWuRUfE594jFakGySfdU83/Xzh00baDPI2jk4OWponkjHTt2biuWqKHT6Zg8eRpzf5rLtq1b2LLTbjM5O+np27ffbfGp7O/TY8eOUL+OFne3/FUBBFGgTTMdqzYdKdUxwsPDcXNzY9Gi35n1vV04Nhj0tG/fjYEDB+Lm5gbYhaqSZDO0atWKpUuXkk4yHkLee0yWZZLEWOrVqaf0abxP+Pn5FdoYPZEYZGRatspfBk5BoSgUUUPhP8O9NgcvqOwUFJwGXpKIKQWFsqBatarUrx/Gl3NPYbPBmfMmNm03Ysy2Oz1FERo1MpCdnV1mZV8WLV7E+fMXGPKsO1073ClX4+mh4rUX3IlLkPj994U0b94cEKhbty5ffDGbjz/+hJMn9jH2HU+qVs7vRD1x1kxQUN7U/KioKJwMKkKDHUes16mhQ5JkbsXcUkQNhX8cDw8P+vTpU+L9atWsxdixY/nyq9m8Nz4BtUbAapHZfTCbNs3zR49F7jLi4+tJxYp5BRONWsMzzzzLN998i0qEfj1d8PNVE3XLyh8r0tl7KJvhw9/MUyZBp9OTmpZR4NxS0uQiM73q1KmD1SKz70g2rZrkn+/O/dkYDHpCK4UWdSkUFBQUHmkKEzTKIkujJI3BcygqoGrmjBmcPXOeUGrhavNAEAQkm0QCt1i+/E98fcvRs2fPYh+vuAQGBKDVakkzJ+NE/uCbTDkdk81EaOiDL81UlgiCwNtvv029evVYuXIlV65cRhRVNG7cmN59ev9jGYzVq9dk3+EDPPuEnG9NbrXKHDhqIby1fW4bNm5ELWjwk+2/OxfJi4NHr5OcYsvTcw8g2ySxfa+Zdu2K3zy5tGRkpLN582bOnDmLKAqE1atHu7bt0Ov1eHl58fTTT+eWAyspGrXmnr1nWVkZVKnouDQVgI+XigtXs4o9npOTE4/1eoxb0VGcPHkagMysbPbt20vVatVo3qz5vU20ELKyjHgVUh1LrbYLZKUlLCyMsLAw0tLSMBqNeHl5otFoSzVmlSpVaNigIcePnUCWZNzxRhREzLKJW8JVjHIGTw14stRzVyiYZ559hgkTJnJNOE+AXBGdYMAmW0kghljhOt26dS+y9JeCgiMUUUPhP0FRZadyKG2zvuKmgCtZGgplj8CIER/y8UfTmP7NCUQR+vVwpXVzA6IIu/cbWbr6CBMmjGPylKn2hXkpsNlsLF2yBI1GIKJNfuelIAh072hg2hfXuHUrhoCAnHtI4Iknn2D37t2cuWCmWpW8i9Tjp00cOpbN62/kbVBpcDKQbZLIyJTy9RYASEiyR5A5GZQIG4VHm8DAQBo1asLWLVswmcxoNCq+m5+Gt6eK2tXt94vNJrMuMovN27N4+eWBiGL+qLmuXbshSRK//Dqf9Vvi0GgELBYZNzcX3nnnbVq3bp3n+y1ahLN8+SJefFrC1TXvPZacYmPfIRMDB4YXOvfg4GDCwmrz08LzhJTXEBRwZ5l57JSJP9dm0q1bL6WfhoKCwr+aojI0oHRZGvcqaBTGzaib7Nu/n4pUxU24ExwiCiLlCCJbzmLp0qV07969wL4B94rB4ET79u2I3LgVd8kbg3BnLWeTbcSI1/Dx8qFhw4Zletx/AkEQ6NChAx06dADknE8dfjchIYF169Zx5PBhbDaJqtWqUKNGDfz9A6hatSpabekcvTn07NWLkSN28fvyDAb0udMbQ5Zk5i5IJS3dmts4PjoqGoPshCjY1x3e+JEoRzF5RjLDX/cgwM/+3k9MsvHl3FTMZpGePQvOXilLjhw5zLRpH2Mxm3ER3JCR2bVrN7/+8ivjx4+natWqD2QejggMLM/pc4nIcn7hCODUOSuBgUVn9OZw/fo1RowYjo+XxNtDPKheRUtMnJWVGxKYNnUa7777Lu3atSvDM5CJjY3l6lUTRqOEwZD3GSDLMjv3ZVOzZtmVvXJzc8vNzCgLRoz8gM8+nc7BQwfRqrRoBC1Ztkx0Wh3vDxtOnTplX7LrXoiNjWXz5k1E37qFi7MzrVu3oXbtWtyP7JsHSYMGDRk+fLi9MXrmYXQqPRbJnn3XvXt3Xn755X94hgqPKoqoofCvpziCRlFZGndT2rJTCgr3C2dnZ7p178Gx4ycY+64XDcPuOA6feNyVOrV0jJx8lu3bthMRUbqoqdOnT2M0ZuPhLqLXOTZuvT3tBk+WMW/kUeXQyvTt25efflvG2YsW2rXSo9MK7DuUzcbt2TRoUJ+IDnnn17xZM77/7jvWb8mkX8+8YUKyLLNmcyZB5f0JCbl/NZ8VFO43ly9fZsyYUWjUFh7rosOvnIHTZ81s35vNqKkJhIZo8fMRuXBFIiHRTM+ePXjssYKdBd279yAioiMHDhwgJSUFb29vGjdp7FDU7NatK6tX/8XEz5MZ9op7bi+OazcsfPFDGs7OLnTq1KnIc3j33eGMGTOKNz68RZP6egL8RC5etnHyXDYNGtTjueeevfcLpKCgoPAQUxqbA0rWS+NessILC6g6dPAgKlGFh+S4/JEX5biYeJLr168RElKpxMcuisGDB3Pm9BkuRR3HXfLFGVfMZJOsige1zLgRo0stppw5c4aVK//i+PFjgEzNWrV5rNfjhfaKur8U7KQ8cuQwU6dOQ7JKuEqeCAhsvhLJ+vUbAHukfq9evXj66QEOAxtKQq2atXjuueeYP38+ew6aaNlEh02CHXtNxMRZeP311yhf3u5wNxgMWEULss3unFcLGkKk2ly7fpqhw+OoWkmLSg3nL1kwGPSMGTMWf3/HlQ3Kkuhb0UyZMg0nmwtV5bqosQs+JjmbGxkXGT9+PHPmzCnUSW6xmNmydSubN20gMTEBDw9POnToSEREBDpd6YIxunTpxsSJ+9m2x0i7lnkDsHYfMHLuYjajRhW/n8pPP83F21Pik7FeON0WGAL91TSoq2PGnBR++GEOLVu2LDPh69y5c6SkpKJWw9f/S+HtIXf6G8qyzLLVGVy5bqHvE+3K5Hj3A4PBiXHjx3H58mX27NmDMdtIxQoVaN26DQbDg++l4YhFi/5gwYKFqAQ1TjhjEcysWbOWsLAwRo8eheERD94LDw+nadOm7N27l1u3buHs7EzLli3x8vL6p6em8AijiBoK/wmKY1wUFDFVnLJTJYmYKipSSkGhNGzevJHqVfR5BI0calbV0jBMz8aN60stamRm2svUpKRK3Iy2Uj4w/+vk5FkzoggH9u/H388fZ+c7Jaqef34wgUGB/Ll8GdO+iAbA09Od/v0fp/8T/VGp8hpoHh6edO3ajQVLV6PVCHRq54ReJ5KSauOPP9PZdyib996/PzVkFe4fsixz4MABNm5cT1xcLG5u7rRr157WrduUmSH2qCDLMtOnf4q/j41JI7xxvp2R1KmNM727WRgxJYlMowcmKZCmzQLo1KlTkY0RwV57OTy88AwLsN9jEydNYcrkSbzxYRzB5XXIwPWbJsqV82HSpHHFipjz8vJixowviIyMZNu2SG6eSKOcrx8ffNCZli1bltr5oqCgoPAwUtyscCibLI2SUBzbw2q1IqJCKGAdJWJ/dlssBfdPKIzTp0/x558rOHL4MJIsU71aNXr26kXLli0AARcXVz797DP+/PNP1q1bx/WUGLQaLa3btKZf/36UDypdad81a9YwZ84cygdq6R6hQxQEdh84xujR+xk8eDD9+vUr1fhlSVJSEtOmTsNgdaGiVBWVYF9jS7JEFFdIJAZdljOLFy3i1q1o3n//fUq7/u3fvz/Vq1dn1aq/WBN5CkEQCKvXjHfff4zq1avnfq9Vq1Zs2rSJTNJwwR0AJ8GF6lIjkojn8tWr+JTz5pVX+tC+ffsH1qNg1cpViJJAsFQ9t/kzgE7QEyJV43TWYTZt2kTfvn0d7p+VlcW4cWO4cOEijerpqdNMzY3oaL7//nvWrl3D1KnTcHNzv+f5NWrUkPbt2zHr+20cP2UmvJkeUYSd+7LZvMNIeHgrmhWzZFRiYgKHDx/lrVc8cgWNHARB4Ok+rmzfE8e+ffvyZeXeK5cvX0GlEnj3VQ9mfJfM6fNxtG6mR6sV2X/YyNUbVgDc3T3K5Hj3k9DQUEJDH74yqJs3b+bXXxfgRwX85CBEQYUsyaSRxOmTZ5jx+QxGjxnzT0+z2ETfiiYxIRF3d/fbZXLtzyitVkubNm3+2ckp/KtQRA2FfzXFSf+GkpWdKos+GkrpKYX7RXx8HLWqFBzJVjlYzda9hfeXKQ4BAfaeF84GgfmL0hjxpicq1R2DKjHJxp9rM/D1VrFo8e9s2bKZqVM/vqsBokDnTp3p3KkT8QkJ2Kw2fH1984kZd/Pyyy8RFxfLjwsO8MvidDzcRXvZqdvZ+0ePHKFN6zYO07ovXrxIZGRkbrR6RESH+xJp+Ogik51tQqPRFPo3KEusViuffPIR+/YdoGqonpqhItGxccyefYIVK/5k8uSpuLvfuwH5qHHixAlu3oxm2iifXEEjh+AKGp7s5cTC5YnMnDkTF5dCihqXgsqhlfnhh7ns3r2bU6dOIQgwYGBdWrRoUaLfhV6vp3v37rnlKhQUFBT+zZRE0CiIfzJLAyC0cmUskpks0nEmv4CdRhIajZagexAX1q9fzzfffINBdMbLFoCAwLUzN/n41Mc89vhjvPzSS4CAk5MTAwcOZODAgVisFjRqNWURrHLt2jW+++47enV25qWBbgiifcwnH5dZuCyDefPmUbt2bWrUqFHqY5UFGzasx2a15RE0wF4KrLwcSgYpAFSQq7J9+w66du1GnTp1Sn3cunXrFpm10rBhQ6pWrca1y+cJsoXihj3COoUEEoVbIMIHH4x44KWe9u7dh6vNK4+gkYNa0OImebB3794CRY3vv/+OqKgrTB/vQ5XQO0E1129aGPvJLWbPnsWYMeNKMUOBYcPeJiSkEitXrWDzjiQAvL09efbZ/vTp08eh/ZJDZmYmkZGRHD9+jLT0dAACyjlelwX6q3FxURMXV3p7LweNRoPNJlO/ro4Zk3xZvTGTXfuzsUkyVUO1dO3gzJx5qWi1pStv/G/BaDSyZcsWDh06iMVipWrVKnTp0pVy5coVsIfMoj/+wEPwIYCKuZ8KgoA73tgkG/v27+fmzRu5WVMPK+fPn+fHH3/k7NmzuZ+FhITwwosv0KB+g39wZgr/VhRRQ+FfS1GNwaHwslMlbdRXHJQsDYX7jbu7B9Ex8QVuj4qx4e7uW+rjBAcHU716FVKTr3LwWDYjJiXQraMz5bxVnL1gZvXmTNQqgWmjvbHZYMzHKXz++ad89NGnfxtJwNenePMxmcycPn2KWtV11K+jxWiU8fNV0bq5gUPHspn53RZq1qxJ58530retViuzZs1k27Yd+HhrCfIXOX3SxooVK+jUuROvv/Z6mdeGfpQwm82sXPkXa9euJi4uEVEUaNK0Cf379ad69ftr3C9Y8CuHDh9kzLueNKl/J+37ynULEz6LZuaM6UyYOPm+zuFh4vLlyxj0KmpVv2MQms0SaRkSzgaRRvX1zFuUzo0bN6lZs+Z9m4daraZNmzZKFJWCgoJCCSiOoFFY6SlHFDdLQ5YlBAfOXCi+7VG/Xn38/fyJjr9KqFQrjzPdKGeSIMbQtk2bEkfeR9+K5ttvv8Fb9iPIFprruC0nBxFPNH+t+It6YfVo0qRJnv1K2/vtblavXo2nh5rnB9wRNMDuMBzYx4Ude02sWr2y2KKGzWYjOTkZrVZTquj9gjh69Bgukmeev0EOgiDgIXuTRDwVqUqc6iabNm0qE1GjOAiCwPjx4/j44485efIkalEDghWrTbYHGdlg/PixPP30IHr16klBopQk2RAEsVBHfkmwWizoKLhElIgKi9lxllFKSgrbt29n8JPOeQQNgIrlNTz7hDNfzT1EbGwMfgUENxYHURTp06cPjz/+OLGxscjI+Pv5FZnBevr0KSZPmozRaLydHSMgIDDukyRGve2ZLzs/Nc1GVpYN1zLsRxEQEIAoCkTuyKJXFxdee8Ejz/bv5qXg7u5K1WrVyuyYjypXr15h7JhxpKfbs5kEWeTk8VMsXbqMN998g4iIjvn2iYqK5lZMDKHUcnjLeOBDtHiF/fsPPNSixrlzZxk1ajRam54QqmPAmWyySLh+i4njJzB6zGiaNGn6T09T4V+GImoo/Ct50CngSpaGwsNCu3YdmDXrBJevmgkNybswj46xsu9wNi++mH8xdS8M/b/XGfXhSDw9IDbByuwfUgAQRfD1VvHa8+74eNlfMy8OcObjL89y5coVKlUqKkNC5vjxE+zYuYPMjAz8/e2ldo4dO0pWlpH3hvri45339dW2pRO7DphYteqvPKLG//73P3bt2smwIR60a2lAFAWsVplN27P4bv4mvL28GDhwUJlcj9Jy48YNDh8+hNVqpUqVqoSFhZWZsecIs9nMhAljOXfuHG1a6KnX14PUNIlN248ycuRBhg//gJYtW96XY5tM2axbt4bHuzjnETQAKlXU8NJAVz7/9hg3btygQoWHd/F+r6SlpbJ582auXbuOTqejeYvmqNUqLDYJs1kmM0vi9z/T2bbbSLZJRhShRlX7/azRKFFwCgoKCvdKYmISqakpeHp44unlWfQORVDcrPAcHNkd95KlkZAQz/Lly4mM3EJmZgYeHp506dKZxx9/PDebL0fQKI7tIQgCI0Z+wOhRYzhvPoqHzQctejJJJ5l4ZAn27N6Dt7c3Awc+XexSguvWrkONhkAq5VvT+AqBpAoJrFq1Kp+oUZacP3+GJvU1uT0A7kYQBZo30rDn8JkixzGZslm0eDHr1qwjPcMeLV+1ajWefPIJmjVrVmbzlSSpiG+IyNj/ZnqbE7ExZReRXxzc3NyYNm0a69atZc6cOdStoWVgX1eqVdYQG29jxboMfvjhB6xWK3369MndT5ZlIiM389eKlVy5egVRFAgLq0ffvn2pX79+qeZUpWoVzhw+hyxVyPc7k2SJTFUazao3crjvhQvnsVpttGzqWBQJb2rgyx9TOX3mTKlEjRxEUSQgIH+ViCtXrrB+/TquX7+Bs7MTrVqFU7NmTSZOmIjGrKemXAuNoAPAioXr1gtMm5XMl9N8cxu0A6zckIlaraJF8xalnmvOvCZNmoSIyLxF6Xh5qmjZWI9w26ZaF5nF2sgsnnvuuTIVIx9FsrOzGTd2PNYMiRpyI7SCDgS7iBfFFWbP/pLAwKB8wUlms71htqoA96woiKhENWaz6b6fQ2n4bs73aG16Kkt1crOmdBhwk7y4Kpzhm2++Ze7cxv/pgEKFskcRNRT+tRRlYJS0UV9py04pWRoKD4I2bVqzctUKJky/yfMDXAhvqkcUBfYcyubn3zPwD/CnY8fS9dPIoXJoZcaOHc/UKZOw2mx0budEcAU1xmyZyB1ZTJ2VzLh3vahbS0fThnrUaoEzZ04XKmoYjVlMnTqF48dPEuivxddH5NhRK0uWLCEkJISqlXT5BI0cWjTS8cX3NzCZstHp9GRkpLN+/VqeetyFDuF3IgvVaoGuHZyJibOxcuUK+vXrV+oGgKUhIzODmTM+58CBQ+h0KjQagYwMK0Hl/flg+IfFEIHujWXLlnL+/Dkmj/SiZtU7AliPTs7MmJPCF1/MoH79+qWohyxz7tw5Dhw4iMViIbRyKC1btkSj1nDp8mUyM7Np3dxxlk7LJnpm/SBw/Pjxf52oERkZyddffwVIVAnRkpousXbtWipVCsZqkVm9KZPVm7KwWmR6d3ehWqjdSbBqYwaiCFlZmf/0KSgoKCg8clw4f4F58+Zx/MRxwB7r3KBBAwYPHkyl0Ht7z/4/e+cZGEXVheFnZns22fRCIAVC7713CCBVRGn2AmJBVLAAIgJiQVCwIuonoDQFRXrvvfceekvv2T7z/VhIjNmEBEKf5xfszM69M9lk77nnvOctjCr8Oreq0vg3Fy9e4L333seaZcXHGYAfIZhTMpn3519sWL+Bz8d9jsl4EihaMVVUVFkmTprI3LlzWbVqFZIkoUaNPyH4EkCKOZG5f/7JlStXeOedwvk4HD9+HA/J5LYtEICn5MuJEycKPcebQRRFbHY53+M2u4xKVfC2iNVq5YMPRhBz8hQ+UhCBlMKJk9iYOMaOHUv//v3p3Llzscy3SpUqnDqxAElyIgq5k0eyLJNKAp64Elc20Yq3z91p1bl69UoqRGn5cLBfdsKoRLCaAc/6oFYJzJ49kw4d2l8zN5b5+utJrF69Bm/RjzCikCSJkwdi+HDfhwwY8DIdO3a66bl06tSJXbt2kcBVAsmJ42VZJpYLWJ0WHnmkQz7vds1dcro/6pSun3W7Co1kpk2bzrx589Cp9BicnjhFO9u378DkZcJmsxMlVUct5CQM1IKGSLkCRxw7mT0/nbde9iU5xcnCFZnMW5RB79698PIqjlalMl999RWiVU0FqSqXOMW4b5MJ8FMRGqLm7HkbaRkynTp14rHH7h1fmsJgt9sQRVWxtt1dt34dqWmpVJRruxIa1xAFFaXkKMxiBv/8Mz9PUiMkJAStVku6LRkjeX9uZjkDq8NC5G2KCYuDc+fOcSrmFKWpmOfvvSAIBMlhnEw8wP4D+5U2VArFipLUUHjgKErF1K1US8Vajxe5p62i0lC43Wg0WkaP+phJkyYyacouvv7JtZCQJJkaNarx1luDrwUXxcO6detQqRx8MTKQkiVyvlK6tTfy8VdJfPljMj9NCEaWZWTZ1WO0IL766ktiTh3lw8F+1K6uQxAEbFaJ+csymTHvLKXD8zePtjtcAev16o99+/Zjszlo28L9/Ua38ODvJXEcPnyE2rVrF/XWiwVJkhgz+iMuXojh7QE+NKlvQKWCYyft/Px7Mh98MJSvvvq6gB6sNz/usmVLaNVEnyuhAaBSCbzY18SWnXGsW7f2poLMtLRUPv10LIcPH8PHW4NBL/L331Z+8ZnCO++8j0rl+hnlV6gjCIDgCkZvDpndu3ezfPkyrly+hNHTi2bNWtC6dWsMBsON336bOHDgAJMmTaJNcwPP9TTh5SUiyzKHjtmY8MNlTCZPfr/mF/Pl6ED8/XICrbbNDYz+MplJkyby00+/KFVOCgoKCoXkyOEjjBgxAq2kI5xy6DFgJotj+0/w7rvv8smnn9y0B0BxqzTyIyfukBn/xQScmRLlpBpo/rXRGSiFEhN/iJXLPqNHjx43FXeEhIQQEREBQHlq4CF4Zh8zYsJD9mTjxo088kjhfBw0GjUy+SsPJJxobpBQuFVq1a7L4oV/YTZLGP5jrGyzSWzeYaNpM/dV/NdZuHAhJ0+eIkqqjFHIaevjIwVwiTP8/PPPNGrUEH//gAKuUjgeeeQR5s+fz0UhhlJy2ewNQlmWucp5LJgpRRQZciqZcjotW7W85TGLyuUrlzl+/BTvD/R1q4Dp3tGTRStj2bp1G61bt2bjxk2sXr2GcMrjJwdm58MCpBJc4gw//jiFmrVqEXrNs6+o1KlTm65du7BgwULShSS8JD9AJlVMJENK4/nnn8vXS69ixYpoNCo2bTfzWOe8G8obt5kRRYEqVavc1NxuxOrVq5k3bx4liCDQGer6ectgJpPT6UcQENxW8IuCCh85kPWbY9m2Oxar1YlWq6FPnz707t27WOZ28uRJzp49SxkqoRG0REiVCCSd5KR4zifZAQlIpkuXzgiCgCzLHDhwgG3btmGxWAgPD6dNmzaYirEV1q0gSRLLly9n4cKFXLx4EUEQqFGjBj169KBGjRq3fP09u/fgKZjctkITBAFvZwC7d+3Oc8xgMNC6dWtWr1iDtxSAQciJXSXZyRXxHH7evtS/jYq2WyUuLg4ADzzdHr/+elxs3B2bk8LDgRIRKzyUHLh0tVjaThUWRaWhcCcxmUyMGPEhkyf/wKuvvsaAV17h22+/ZcyYsfj5+RXbOGZzFuvXr6VrO0OuhAaAVivyQh9vkpIldu6zuMzknDK//fYbX301wW1y4+LFC2zbtoP+T3tRp4Y+Wz6u1Yn07OZFhbJazpy3cf5i3p64siyzYauVypUrotG4NuntdpeU19PDfWWVp9H1uu3aeXeD3bt3c/ToCd4f6EOLxh6o1QKCIFCpvJZR7/kiCjYWLPin2MdNT08nKSmVWtV0bo/7+aooE6HjzNmzRb62JEmMGjWSSxdP8cHbvkydFMDkL/z57rMgIkraGT16FGq1Br1ey9adFrfX2LnPgsMuU7ly5ZsY38n48eMZNWo0cVf3U61iMp76s/z00xTefHMg8fF3bzE9d96flCuj5fXnvfHyci3BBEGgWiUdbw8wkZaWgVOCHp29ciU0wPU79VxPL+LjE9m7d8/dmL6CgoLC/YcM33zzLXqnB1FSNfwIwgMv/AmmrFQVtUPH9999X+TL3qpKI7+4oyB1OLg2GU+fiSFECs+V0ADQCXpaVffl2LHjeFn9Cz2//7J82TK8Zb9cCY3r+BCAh8rIipUrCnWtunXrkS6mYJfzrrUkWSJNlUi9Brd3o+6RDo/glFR88V0KGZk5CZYss8RXP6aSkSXTqVNBKguZJYuX4CP550pogOs7vAThiAisXLmqWOYbFBTE4MFvkyIkclzcy0X5NJflsxxjD7FcJIRwrFg4Jx6nYoWKd2WjMyUlBYBSoe7bDfn7qfAwqLLPW7R4MSbRBz8ht0JXEARCiUQtaFi+bFmBY16+cpl58+YxY8YM1q9fl92y59qVeOmll3j33XcJLRfCFfEMV8TzlK1emo8++oju3d0bhAN4eXnRqlUb5izI4uDR3O19TsTY+G1uBo0bNyq0B2DRkJk7dx4+QgDBQqlcFe4GwUg45XDgIJM0t+8WUeHl5cWTTz7LG28MYurU6fTp06fY2teeP+8q7vTEB3D9vIyCiVJCFJFCRcJxJU7PX7hAWloqQ4YMYcSIEaxZvpYd63Yxbdp0nnvuOdasWVMs87kVJEnii3FfMHnyZDIuWQinLKFyJCcPxDBixAiWLS/481cYHA4HgpT/FquIiDMfSdAzzzxNSGgwMeJBLsoxJMvxxMoXOanaj0WdxTvvvlvotn93A5PJlRC04j62s117/V5JcCk8OChKDYUHisKoNIradio/itp2SlFpKNxpQkNLEhpa8rZd/8qVq1itdmpUdS97jwzX4OsjsveglW27LdSooqVeTT0z/9pEbGwsY8d+mkvyu3PnTvR6FU3y6Wnbs5uRsV/Z+OK7ZD4c7EdggOsrzOGQ+XNhBgePWhg2PCdoKV26DAC7D1hpVDdvdf7uA67AJTIi8qbuvzjYsGEDZSJ0VK2UN7ngaRRp3VTPmvXreOmlfsU6rlbrCkL/Hdz/G1mWSc+Q0GryV8bkx+7duzh58jSfDg+gcoWc95cKVTP8TR9eG5rE0qVLads2mr+XLqN6ZV2u82LjHfxvZiZVqlSkTJkyRR5/3ry/2LRpE++85kuT+jnJsSuxDkaOS+Gzzz5hwoSvKEzbjOLEarWwb+9+Xn3eO5dR6XWqVdISFKgiLt5J9Urun3vZMlo8DCrOn79AnTp1b/eUFRQUFO57jh07xqXLF4miKuJ/6vlEVARJJYk5fZQzZ85SunRkka5dFHPw/xZTQd64Iz/+XUwVExODgIDXtU3Gf1O+ZjJ2PDl88Chnz53F1/fmPEPi4+PxlYPdfk0KgoCuCD4Obdu24Y8//uBc1gnCpXLZLVkcsp1Lwmns2OnSpetNzRNc3607d+4iLS2VwMBAateuk6edTEBAAMOHf8Cnn47lhTfjqFVViyjC3oN2nJLAe+++T8mS+a+XbTY7CYkJhONezaMS1HjgxcWL+ceNRaVp02aEh4ezcNEidu7YSUpammvDFIFYLoAADRs0ZNCgQXdlo9Pfz5U0O3PeTljJvFtKsfEOMrOc+AdcOy/mNH6S+8+UKIgYJRMxMTFux7LZbHz77desW7cBg16F0agiIdGGyfQjAwe++S8/E4GmTZvStGlTZFl2qX4Ludbr1+8lrl69xAefHqZyBT2lw0UuXJI4cMRC+fJlee21gYW6TlFJTEzk0qVLRFLR7VQ98UaNhlSSrpmE5yDLMhmqFGpWrU63bo/elvnp9a6YzIEdLXljFTuuQjOdTsfoUWM4f/o8UVTB0+GNIAg4ZDuXpbNMmjSJgAB/qle/dTXEzbJmzRq2bNlCBBXwEfz/oxY6zeQfJlOndm0CA29eHV++fHn27dmLU3KgEvL+XqSLyZSNcv93xNPTiy/Gf8Hff//NsqXLOJd2FbVaTdOmTXj88ccJD4+46XndCcqXr0BwUBAJ8VcwyqY8ibV4LuPh4XHXuiMoPLgoSQ2Fh5I7qdIAJaGh8GCi07kWt+kZ7jfG7XaJrCyZ5WuzKBOhYcgrfphMImVLa3n/42OsWrUKrVaD1WajdGQkNpsdrVZAo3Ff4eLtpUKSICXNSf8hcURFaAgKVHHkhI3kFInWrVvTsEHD7PMjIyOpVKk8v889S+XyWrxNOUFfYpKTGfPS8fPzITY2lpCQYO70JjdARkY6gQH5jxscqCI9Pb3YxzUYPKhevQqrNsTQtplHnk32/YdtxMbbaNiwYT5XyJ9NmzZTOjx3ouI6Op1I22Y6/l66kd9+m8HZs2cYOvYItavrKVtaw5VYB1t3WwgMCOTtwe8UeWyn08nixQto19JA0wa5E1muPs+ejBp/mqNHj+XpZ3u7sdlcgZ+Xp/vPtyAIeHuKxMU7SUmTcOckkpklYbVJGAx3zwNGQUFB4X7iyhXXGt9dn3LX66Zr510udFKjKCoNKHzcUZhiKrVajYyMhISKnHVN+ZrJAKQdclWv34phr8nLhNXivtoWXD4OPj4+hbqWp6cXo0aNYtRHH3EsY7drY1YWyBLTQBR4Z8g7REZG3sQsZRYsWMiMGTMwm82IgoAky/h4e/PygAE0adIk19k1a9bkxx+nsGLFCg4cOABIdHu0Gu3bt7thyyi1Wo0oqrBL7pW9sizjEO3o9cXb3jI8PILXXn0NXnX9PybmFMePn0ClUlGzZo1iMa2+WYKDg6latTJ/Lz1Fw9o6tLqctY0sy/y5IAOjp4GGDRpmtxJNFK8Qx0VUgoin059AQtELrmcmCQ60Wvfq4UmTvmLHjq28+rw3rRob0OpELl91MHVOOp999iljx46lcuXcraGKqlTQ6fSMGvUxW7duZfXqVRw+GYePty9vvd2Wpk2b3jYDbIfDVbX/34TrdQRBQJAFMkm/lqjJua84LpHlzGTrtm2MGTOaHo8/TuVKRVc4F0TNmjXRarUk2q5Sgryb6olcxdPTC2SZEydPUIYqeAk+2cfVgoYwuSxWwcIff/x5V5MaixYuxCT64iPnVrEJgkCoHEkKiSxfvoKnnnrqpsdo164df/wxh0vy6Vyt4wCS5FjS5BRe7pp/oZqHhwdPPvkkTz7ZF6vVikajLVS72ZSUZDZu3ERqaioBAf40bdYMT6P7NlC3C0EQePa55xg3bhwiJwmSw9ALBmyylTgukcBVXur7Uvb+gYJCcaEkNRQeGAoTYNwNlYaS0FB4UBFFEZUIK9ZlUadG3k3WjdstWG0y7Vt78FJfE1qta1FWqbyWClFafvjhOyQJRNHl+REYGEBamoNTZ2yULZ13Q3zXPgsqFdjtIElw8oydmHN2ggNVlAjWkJmZd/P/jTfeYujQ9xg4LJG2zfWEhao5d9HOinVZyDL4eGcyYsQI2rZty8CBA4tNrl1YQkJKsH3bQZxOGZUq79jHTtkpUaJ4/TSu0+Pxnoz8cCQ/TEvlmZ4mPI0uf4fDx2xMnJJGhQrlqFbtxv2y/4vZbMbPJ//n6OcrYrHY0Gq1jB79MevXr2fVquWs2RKLyeTHM0+3ITo6GqPRWOSxL126SFJSKs0auG+7UauqDi9PNQcPHsiT1DCbszh06BB2u4MyZcoQElK8mwWenp74+/uw75CVxvXybnykpDo5c8GOyUtg6ZpMqlXOu+hfvcH1ua1fv0GeYwoKCgoKebn+XWLHio68f3uvt8Qo6gZQYVUa7hQakL9Ko6DWUwC1atVGFFUkSbEEktt/IOswJBKH0ehJhQrlbzi//GjVpjVz/5hLsBSWy+wWuObjkEarVi0Lfb1y5crx088/sWbNWvbu3YvT6aRixYq0b98OH5+bU5MsWLCQn3/+mQBCiKQyOvSYySQ27SLjxo1j+PDh1K9fP9d7fHx86dmzFz179irSWKIo0qhRQ/Zs25vjefAvMkgly5lJkyaNb+peCktUVFmiou6duPL5519g2LChDP8smZ7dPChfRsvVOCcLlmewabuF1157DY1Gw5dfjsditVC1opY61T1Iy5BZuymekxmxREiun126kEr9BvXzjHH+/Dk2btzMG/18aNMsx2cgNETNe6/78O6oJGbPmc3oUWNu+X5UKlW20uNOERgYgI+3N2mpSZjI+7tgljOxY8OOjVOqg3g7/QCBFBLIIgMTvnjJPhzZc5zdu4by1ttv0aJFy2Kbn9FopGvXrsybNw+1rMGfEERBRJKdxHOFBK7y3OPPsWvXbgxqD7wcedX7giDgJwVy4MABLBZLtvrjTnP23HlKSOH5qIVUeEienDlz+pbG8PPz46233mbChAmYxUy8nf6IqMgQU0iTU2jfoT3NmhXm8yWg0934OcmyzO+//8a8eX+53iNqsTot/PzTLzz73LN06dLllu6nqDRt2hS73cZPP/3MsYw9qAQVTtmJXqfnhSdfoGvXOzsfhYcDJamh8EBRmACjKCZ9t6LSUHw0FB50Ll26hFOCbbstTP8jjSe6eGIwiMiSzLY9Fqb8loooQlSEJjuhce6Cnd/mpnPitI3rHtCutlQ65i5MRqWCX2amMXKwH3p9TtB47qKNuYszAOgcbaRlEw+0Gti5z8qcf9IRcBJz+lSeOZYsWZIvv5zIvHnz+GfZEhwOGaOHQJvmHnTv6Imvt8jazWa+/mkVERHht02+nR/t2rVj8eLFLFuTRafo3Jv4p8/Z2bTDzNNP9bwtY9eqWYuBAwfyww/fs25LHGUjtaSkS1y6bKNcuSiGDx9B/uoVmUuXLmM2mwkJCXZVaV2jZMmSrFi+C5tVylW5d51Dx2yEhgYjCAJqtZo2bdrQpk2bYrmn658pd+2dXAdAEHPOA5e6Y/r06SxduhiLJacKs06dWrz22kACAm7d9BNcQV2HDp34889ZtGpqy2XQLkkyv8xMQ6US6N3dxJTpqUydncbjXTzxNIo4HDJrNmUx7c90otu1K1ZvHAUFBYUHmRo1a2A0GIk3X6EUeVsaJnAFb5M3VaoUzgS4sCqN/AqpiqrS+G/s4e/vT6uWLVm/bj0aSYc3flSolULGIYkk4kgUrtL30b7Z/mLXsVgsrFmzhrVr15GWlkpISAjt27enYcOGeSqBO3XqxIoVKziTeoQQZzgm/JCRSCaBq+J5KpSrkCdhcCMMBg86depEp06dcr1us9nYtGkT27dvx2KxEBkZSfsO7Qs0jLZaLcyYMYMAQiglROWMIRiJkMtzRjjK1KlTqV+/HsWlwn388R5s37aN8+IJSkiR6AS9q1UnyVxUnaZc6bLUrFmrWMa6XyhXrjwffzyWKVMm8/GXOZvBQUEBvPX2K7Rq2YqlS5eyceMm3n3dlyb1c5KKfbt78smkZA4eOYYWPT4mH1q62YzftGkTXp5qWjTKm5BUqQQ6tNXz7c/7SU9Px8vLvRrrVklMTGDFipWcPh2DRqOhbt16NG3aFK226C1a/4soqujYqSOzZ8/BW/LPpXJwyg4ui2fwNfnyxqA3WLRoIfv27scpOTFgpDQV8RZcRTwBzhJcEE4xadLX1KpVC5PJfWvgm+Gpp54iMzOTpUuXEideQifqsUhmnLKDHo89xmOPdefbb79DhTrf4jAVLqWLzWa7a0kNjUaN0+rezwJAEqV81UJFoVmzZpQoUYL5/8xn145dOJwOypYtS+fO/WnatAnF2RlgzpzZzP1zHsGUIoASqGUNdtlGrP0iP/30E3q9nujo6GIbrzC0atWapk2bsnPnTuITEvDx9qZ+/QYYDMWrZFNQuI6S1FB4aCiKOXhBJn2FUWkoPhoKDwPXF6Wdoj34e0kGS1ZlEhmmIT7JSUKik+qVtRw4YsNwLTkRc9bG8E8T8fcVeeU5b0qHa7hw2cGC5RlMnZNOUIBIaIiWmLN2Xh8aT3QLDwIDVBw7aWP1xiwcDnjnNd9cbYU6RaupXlnHmyPiMAoOt/MMCAigSZPGLF68mA8H+1G7ui7Xort1Uw8OHbWxcOECunTpWiiZb3FRunRpOnfuzJTfFhFzzk6bph7o9bBzr5UFy7OIjIikY8eO2edfvnKZf+b/w6ZN68nMNBMaGkL7Dh15pMMjNxVcRUdHU69eXVatWs35C+cppdPR/+VG1KxZK9/AZNOmTcyePZPz5y8CoNaoaNqkKc899zx+fn60a9eOv/76iz8XZfJkj9wB5rGTNjbvsPD0072LPNfCULJkSby9vdiyM4sqbtpfHTpqIy3NQZUq1+X5MpMmfcXGjRvp0dmTNs188NCL7DpgYca8g7z99ptERZXlypWL6PUGGjVqSvv27QvdduO/dO/enX37djPisxO0aKynZlUdaWkSK9dncu6ig7cG+NK8oQGbTeb3uWksWplJiWANKakSaekOWrVqwcv9X775B6SgoKDwkKHT6ejZuye//vorKlQEEooaDXZsxHGJJOJ4pe8rqNSF9yUorH9fcak0/ht7DHjlFVJSU9i9ezeN6+g5dMCCVWXG6rTQvl07evbMXQyRmJTIsKHDiL16FRO+qGUdp2JPs3fvZ9SpU5dhw4fmaq/j7e3NZ599xhfjxnHy1DFEQUTGVQ3QoH6DYvNxuHr1KiM++IDYuDi8RG9EScWh/YeZP38+L7z4At26dnP7vp07d2I2m4kkb6sdQRAIkEM5ffEwp0+fuSlvLndERZVl+AfD+WLceI6Z9+ChMuKQHVidFiqWq8gHHwy/42rfe4EKFSoyYcJEzp49w9XYWExeXlSsWOnaWlpm8eJ/aFhHnyuhAaDVibz+ojcvvR2HYIAxH49xu9mdmZmFr48Ktdr9sw30c30OzWbzbUlqrFmzhm+//QaNGipX0JCeAhMnbmbWrN/56KMxBXqxFJYejz/OkSNH2b9vP96CHx6SF3ZspKoSUGlEPhw+hgoVKlCnTm369euPPU4inNxKrOwWSlIiq1at5rHH8jdGLyqiKPLKK6/Q7dFurFu7luSUFPz9/GndujVBQS41eWRkJKucq7DLNjRC3vV3Oin4+Pjg6ZlbEXflyhWWLVvGyRMnUKnV1KtXj9ZtWt+W1kkNGjZg56ZdBDlL5lFbWeQsMmT3aqGboWzZsgwZPKRYrpUfZnMW8+bOI5AShAjh2a9rBC2lKINTtjNjxgzatGlzR2NbAI1GS+PGTW58ooJCMaAkNRQeCG5kEF5cbaeKgpLQUHjQqVSpEr6+3lhtdn6aEMTqjWauxjkoW1pDs4YGdu23cOyknTrVXVUvP05Lo0SQmk+H+2erMMpHaWnWQM/IcUnEnLNTo4qOAc/68PfSDOYtysBqk/H3U+FlFDHoBbcm4mEl1TSpb2DfITnPsevs338AH29NnoTGdZo11LN6YwJXY68WWB1YnGRmZrJ161b8/Pxo2bIF+w4eYPWGBAD0ei2tW7fjmWeezZYfHz9+jJEjP0SvddK+hY4AfxNHTqQyber/2LplM6NGjS6UVBnA4XCwa9dOYmNj8fT0osMjHQoVQCxZspjJk3+kbk09zz7hh6+3yKGjNuYv3cK77x7miy8mUKJECZ5++mmmT5/O6XN22jQz4GEQ2b3fwvJ1FsqXr5CnUrO4UKvVPPJIJ+bOnUOtqnrq1sx5HglJDn6Ynk5kZDhVq7raap04cZJ16zYwqL8PrZvmtDVo1cTA3oMWNmxNIzH+EPVraEhJS2fu3FksWrSA0aM/pnTp0kWYmcyBAwdYs2YtgiDi7ePH5h1prFqfBUC9mjr6Pe2TnYjp3tGTiFJqRo1PIiCoKo2alKN58+aEhblz2lBQUFBQKIjuj3bHbrMze/Yc4p2X0ap02JxWVBo1zz/1PI90fKRQ1ylKq1t3CY2CPPyKgk6nY+TIkZyNmce+VUdI9UglMCCQtm3but3E/2LceJLikikv13J5GAiADGkksXfvHmbOmMmzzz6b6z0hISFM+PJLTp06xYkTxxFVKmrWqFlsrRklycnIkSNJS8ygIrXQyx4ggCRJXOUcv/z8C6ElQqlXr16e9548eQoQuMJZ1LIGP4LwEHI2tHW4vvtT01KLZa7XqVOnLtOmT2Xjxk2cOXMGrVZD/foNqFSpInfDl+1eIjKyNJGRuddFFouV8+cv06Ojj9v3BPirKVtGR8nweoSHh7s9JzQ0lKVLbaSkOvHxzptIO3rCjsGgu+lik4I4cuQwkyZNom1zAy/2NWEwuGKXS1ccfPp1Ch99NILvv5+cRxVVVDRqDSNHjmT1mtUsWbyES5cuYTAY6NC8PV26dMn2TrFabcTGxroM69183NSCBk88OXPmzC3NJz9CS4TSt++Tbo+1atWKaVOnctl+lnC5XK5YK1NOI0WMp1fHnrk211esWM7333+PCg1GyYQkODl44CBzZs9h1OhRlC1bvHsp3bt3Z/OmzZwXTlJKLo36WvLFLGdwXnWSIL8gmja5fzbid+3ajdVmIwD3cWsAJTiZdJDjx4/fcR9BBYU7iZLUUHhouBVzcHDJvwur0lASGgoPAyqViiee6MWUKVPw81HxaAcjRqOI1SqxfG0WcxdkoNYIbNhmJjxUzfEYG8MG+eZqKwWg1Yr0ecyLEZ8lsmuflX5PefPGSz4MfNEbh0NGoxF5/+MEAvzEfKvgoiI0bNudv6mlLMuI/2pJ9F+zvWw/Czn/xEjxITN37jzmzJmF3e7A6KEiI9OBh4ee3r17Ua9ePUqVKoXBkLPJLklOPh/3GRGlYORg/+zA6pE20Dnagw/HnWTWrNk899xzNxx969at/PDDd6SkpKHXq7BanWgna3jsscfp3bt3vs84LS2NX375hY5tPej/tHf2eVGRWprU1/P2yCRmzprJa6++xuOPP05gUCDz5v7J59+cB8Bk8qRLl+706tXrliT7KSkpLF++nC1bNmKxmAkLi6BDh47UrVsHEOjZsydnzsQw5sudVKmgp2I5NfGJTrbusuLt48OHI4dzPRpcvXo1wYFaWjbOXUG4Yl0WG7ZaeP0lb5eR+rV7faGPkzETUhjz8Sim/PgzavWNl1F2u43PPvuMnTt34eOjIiPdiSSDyUvAZgOn0+UPk57uzP5cnj5rY/K0DMLCQvnwwxHFUhGroKCg8NAiQM9ePenwSAc2b9pMckoy/n7+NGnaFE/Povk33Uyr23/jTqWRn0K8oLa3kn0nTSu2pmnF1gXO5fTp0xw5cphIKmabMl/HJPgRIIWwdMlSevfu7dbAtWzZssW+uQiwY8cOrly5QnlqoBdy1juiIFJCjsQsZvLXvL9yJTWcTicTJ05k/fr1qNHixEEm6SRwFV85kDBcxrxmMgEIDAws9nnrdHratm1b7NctHDKnT5/h6tUreHp6UqVKVVSqe3d9cH0D22bLf21tt4NGk78Jd4uWLZg69X/MmJfOq8975zbKjnewZI2Zli2jC72ulCQnO3bu5HRMDGq1mjp16xJVJsrtuX///RcRpbS89rx3rramJUu4/DxeHxrH5s1baNmyZaHGLgiVSkW76Ha0i26X7zkuw3oBp+RemQ7gFJxotbfH1LwgjEYjg94cxPjxEzglWPCRAtCgJZ0UUsQEypUrR/fuOeqRI0eP8N133+MvBxNK6WzlhF22ci7rBB+NHMlPP/9crC2LospE8d577zJh/ASO2HdjFLyQBIlMZzrBAcGMHj36lhNUd5LMrExAQIP7OWtw/T3Pysq8g7NSULjzKEkNhfueG1VN3UmVhuKjofCw0blzJzIyMpgzZzb/LMsiOFBDQqIds0WibXQ0WZkZ/Dh9G7Iso1bBj7+lsmmHmU5tPan4L0+ByuVd/46Nd7Jmk5k21zaSNRpXECHJMhcu5b+Iv3TVga9v/v1jK1euzB9//MEnk5I5dNRKllkmNERF+5ZGHmnjwZadZvz8vLOroW4nf/89n+nTp9O9o5FuHfzw9VERF+/gjwUZzJ49h9DQkpQrl1tWvmPHDhLikxj+RmB2QuM6Fcpq6dBKz4oVy3jyybx9tP/Nnj17+Pzzz6hfS8+TPQIJL6UhOcXJohWZzJo1C1mW8q3CWr9+PTJOej/qlSfxEeCvpmMbA38vXUu/l/qh1Wpp0bwFLZo3JzExEbvdQUBAQKGSAAVx5swZPvzwAyyWTBrX0+FjUnHwyEFGj96VbfauVqsZOnQ4W7duZfmyJWzaeQWjp5Enn2xFu3bRufw/kpISCSsp5kl4LVyeSeN6eqKb597s8japeKOfiYHD4tm2bVuhzCSn/PQTBw7soXM7DxatyKJjWw86RRuZOjudnXstaDUCVqvMp18nYzAI+PtquHjZRqlSoXz00WgloaGgoKBQTJhMpkKrMv5LYVUa+SU0blal4a6gqijxxqFDh1CJKkySe0NuHwKJM1/m7NmzVKhQ4abmeDPs3rMHo8oTDymvSlQQBHykQA4fOYzVas1OtkyfPo2NGzYQTll8CUIQBGRZJpk4LhCDBg0hcgTx4iXKR5WnVMkbF6PdLxw/fpzvv/ueM2dzqvB9vL3p+9STdGjf4S7OLH+0Wi1VqlRk/eazRLfwyLN2PHveztnzVnr1qZ3vNTyNnvTr/zLffvMtsQkSj7Qy4Ouj4uBRK4tWmjEa/ejbt2+h5nP8+HHGjfuM+PhE/Hy1WG1Ofv99BjVrVOOdd9//T/sqmd279/D040a3Pm1hJdVUiNKxa/euYklqFAaVSkWtWnU4tu84Ac4SeZ5nppxGpjODBg0aFup6NpuN5OQk9HoD3t637sHRtGkz/Pz8+PPPeezZsxtZlvHz9aVPp9506/ZorqTpP3//g0E0UtJZJtd9aAQd4VJ5jmXsZt26dTzyyM39vc6Phg0b8b9ff2XNmtWcPHUKjVpN3br1aNiw4T2dIHSHq7OATBbpGDHlOZ5JGgAlSuSfZFdQeBBQkhoKDwQ3qpoqDpXGjVB8NBQeTgR69+5N+/btWbduLbGxsaSkpGKz2UhLdcn+ZVnG20uFt7eA3Saza5+VDVstvNjXRNcOrmA2PtFl3KZSwTc/p7D/sJVmDfUIgsCm7WZOnLIjy7DvkJWaVXNXEiYkOli/1cpjj7nvvQyg1+tQqQROxNjo0s4Tfz+RIyds/DYvnZUbMrl8VaJv3+63fUFrtVr4449ZdG7nwXO9cwKIoEA1r73gTWaWxHffTWLmzOmElChJ+3YdaNSoESdPnSLAX0uZCPfVV/VrGZi/NIHY2FhKlcqvRZHMb79No0oFHe8P9MkO0nx9VDzd04Qgwl9/zaNLl65u+xJfuXKF0GAd3ib3z6hCWQ0WSzqpqSkEBgZde1XA3794jLadTicffzyaAF8bI4cEZs9DluVss/eyZaPo2LEToijSpEkTmtxARu7j68v+PRKyJGc/j7R0iQuXHfR5zH1v5vBSGsJL6Th06NANkxqpqamsXr2Kvt2NLF+bRZP6el560sTwT5K4eMXOWwN8aFrfgEoFR47bmfJ7KldiJV555RXatWt33wVYCgoKCg8yN9vq9jr5qTRuhsLGG9e9MApz5p3EYXcgkH+fd/HaMafTtT7MzMxk8eIlBMql8BOCs88TBAE/grHKFuK5RKaQhk1lpV//l27vDdxBYmJOMXz4cDQOHWWohAde2LESn3qF77/7HqvVmq//yN3m0Ud7MHbsWGbMS6f3o17Z3hhX4xxMmJxKSEgQDRsWvAnfLrod3iZv5syZyWffuJI6Wq2a5s1b8swzzxRqQ/7ylcuMHDmCiFIwbGAAZSK1OJ0yO/ZY+GHaUUaNGsm4ceOz1SWSJONwODEY8m8rZjAIOOz2wj6KYuHxx3swfM8wLginCJUjUQuuuCBTTueC6hRhJcKoW7dugddIT09n1qyZrFq5GovVpXKvVKkSffr0oWbNmrc0v8qVqzByZBXsdhs2mx0Pj7zJLIA9e3fj5yYxA6AVdHjizZ49u4s9qQHg5eVFt26PFvt17zTVqlUjOCiIqwnnKS1VzuUT4pQdxIuXqFyxMqGht+77oqBwL6MkNRQeaAqqmCqqSqMwraeUhIbCw4qvry8NGzZi5MgRXL0aR6VyeoxGOHjEgihCaroTb5Oa8FIaYs7ZyTI7+WVmGuWjtFQsp2XxykxUKvDx8eWRjp1YvWoF67fEAWAyGZFliAxX89k3STz9uImWTQzotALb91r4dWYaRqMpl6H2v3E6nXzxxTgqltXx4WCf7PZX7VsZ6dTWxvBPEgkOCaZHj8dv+3PavXsPmZkWurYPynNMEAS6dvBky84EypfOJCHpOJ9/vp969eoSFRWFzSYhSbnbaF3HbJEAUKvzl5xfunSZmJgzDHvT123VWZd2RuYtymTrtq1u5e+enp4kpzhwOGS3ho1xCU4EQcDg4ZHnWHGwbds24uMT+WBQYK7EiiAItG7qwe79FubO/ZO0tDRAoGrVKte8M/IPSFu3as3yZcvZuMNC84Yuibtw7fyCOpEVtkvZwYMHcdidlA7XEBvvZFB/X3btt3L0pI2xQ/2pWiknQVelopaP3/fn1fcSuHT5kpLQUFBQULhHKKwq/GZVGrfS9vZGVK1aFafkJI1kfPDPczyVBAwGA5GRkbc8VlEoV64ca9eswSZb0Qp5216lCUmUCA7Bw8P13Xzw4AFsNhv+BOc5F8CPYGK5iKmEibffepMKFSre1vnfSab+Og21Q0uUVDV741KNhnDKIcoqfp/+G9Fto/G4TeuvW6FBgwY8++yzTJs2jZXrrVSvrCItHQ4cseLv78fo0aMKtd5p0KABDRrUJy4uHrPZTFBQYK42rTdi/t/z0WuduVq4qlQCjeoZMJlUDBt7it27d1GvnssoWhRFypSJYOe+WNq3ytuiLiNT4sgJG7163dnYv0qVKrz51lt8/fXXpEqJGPHCKTjJdKYTViKMUaNHFWgKnZ6ezrvvvEPc1QT8pCBK4o0dGxeOX2bkyJG8/fZbtGjR8pbnqdFoC1SOO51SduLSLbKAw+G85Xnc70iSk127dnP6dAwajYa6desREREBuOKfNwYNYuTIkcRwCH8pGB0GzGSSKF4Fncwrr7xyl+9AQeH2oyQ1FO5rCgoy8quY2pdwxW2lFNyaSkNJaCg8zDgcDj4a9SEqMYVvPw0irKTr62Xct0ns3m/l3dd9s026JUlm5fosfpiayne/ptCojoGFKzIJCyvFqFGjCQgI4InHnyAhMQFkmY/HjKZSWYm3B3jz/dRUfp6RypTfcswf1Wro1Kk5JpP7Sq3t27eTmJjMh28F5vHzKB+lpVO0kZUb0pAk523fSM7ISAcgKMD9OMGBrtebNdRTv5aB3fstfPL1bry9fUhLd7Bzr4UGdfL2l12zyUzJUiGEhLgP9sEVyACEBrn/6vc2qfDwUJGelub2eLNmzZg9ezZrt2TlacvkcMgsXW2mdu1ahTIc/y9paamsW7eeuLhYvLxMNG/ePI9c+vDhw5QM1REZnjdxcyXWwcnTdhISkli08E8QYOZMB2XKRPD++8PzNTatVKkiTZo0YtJP27ga66BNMw88DAIB/io2bDPTtEHeZ33ugp0Ll6z0faraDe/L4XC1TLPbXVmQQD+RZWsyKBOhyZXQuI6Xp0irJjrWr1/LSy8+OFWmCgoKCvc7RVWF/5eiqDQK9tIoWswRVSaKihUrcvbkOQxOD3T/8tVIk1NIEK/StUNXdDqXuXZGRjoLFy5ixYoVJCcnYzKZaNOmDd26dcXHx30Lq5uhVauWTJs6lUu200RIFXJVGafKiaQKiTzR9SWuFybY7a7vUxXu10/XX3/6qaceqIRGYlIi+w/sdyUwhLybwMGUJMl+lc2bNxMdHX0XZnhjevToQb169Vi2bBnnzp1Bq9Mx4JWGtGzREr1eX4QrCQQF5S0KKgwbN62nYytdnhauAFUqaCkToWPjxo3ZSQ2Ajp268N2337F5p5km9XJ+b5xOmZ9+S0OWRaKj77y/SsuWLalVqxarVq3kzNmzaDVaGjRoQL16dW/YsnTWrJnEXU0gSqqay2PHVwrkgnCKb7/9jnr16t/2BFmF8hW4cOwigXJek2uHbCdLTKNixQfn97goXLp0iQULFrBh/XrMWWYkZLSiFgmZadOmU6d2bQa/MwRPoyfVqlXj008/5bfffuPAgQMAiIJAvXr1eeaZZwgLy0+9r6Dw4KAkNRTuewoKMgrbdupWVBqKj4aCAmzbtpUrl2P5emxgdkIjLt7Blp0WBjzrTZ0aOUGLKAq0b2Xk/CU7S1dnMXdRBl26dKFfv5zgVRAEAgMCkWWZM2fP06GlN3q9yNsDfHmmpxcHjthwOmXKl9Hy6+x04uLi8p1bzOkYAvy1bjfDAerV1PP3khu1bioernt2nDxtp3xU3gqm46dsrvMCXc+wTg09Hdt4sG7LVqpWrcT3U09iMqmodM2PxOGQ+WdZBpt3mBk4MOf5uSMw0NUG6uRZO2Gl3CcGMjIcBAW5T4yEhYXRokUzfpy2GbsNWjczoNeJnL9oZ+qcdC5cdvLqwN6FfxjX+Oef+UybPg0BmRLBWhKT7MyYMYPodtG8+sqr2YkmQRDcdsfIzJT48PNEVCoY874/1Sq5ns2BI1a+/d8l3nvvHcaN+yIfvxSBwYPf4X//+4U/Fy5nxrz07CMJiU6Wrcmkfasc6XxKqpOJP6URFBRww3YJAGXLuTaeYhNc1WZHTtpIz5Czk1fuCA5Uk5GRccNrKygoKCjcfm7Fuw/yL6a6Hnu4K6iCwinEC8u7777LsKHDOB63FxN+aGUdFjGTNDmVWjVq8dTTTwGQnJzMe+++R0J8At5SAKFEYkkx88/fC1i7Zg2fjxtHcHD+xRNFwWDw4N333mPs2LGcEPbh7fRHjYYMMZU0OZkGDRrkUuCWLh0JQBrJ+JLXADyNlGvnlS6W+d0rJCUmAWDA/SazRtChUelITEy8k9MqMuHh4fTv3/8ujS6TlWnB3y+v78B1/P0EMjOzcr0W3TaaAwf288W3m1hd3Uy9mjoysyTWbLZy5aqdwYOHFGuiryh4e3sXWWFut9tYtXI1flJQroQGuNbYIXI4x2y7Wb9+/W1p+/RvunTtzGdHPieBKwQIOX8fJVniknAGRIF27fI3TX9QOXDgAKNHjwYHOCQ7BjwpSRk8ZE8kWSKVRA7sO8ToUaP5/PPPEQSBChUq8PHHH5OcnExqaip+fr75FvopKDyIKEkNhQeSopiDX+dmVBqKj4bCw4LdYcdqtWL0MLrtf7pr126iInVEhOVslu85aEUQoGWTvNXuAK2auIyTR4z4gHr16rk9RxBArVaRZZayXwvwU9O6ac7XV1aWjJdf/m2XNGo1Vmv+rZuyLK4NZ40m/2sUF9WqVSM4OIAZ8zIY8bZvrjZOVqvE3AUZVCirJeJfSYem9Q0sWJZA7959mTbtV94fc5qoSB0BfgLHY5ykpNp5oucTN6wW8/cPoHbtmvy95AiN6uhzVavJssysv9Px8vKgYcMG+V7jjUFvolZrmPLbWn6dnY6HQUVKqh1fX2+GD3+vyNWRq1ev5pdf/kfXDkZ6dvHCy0vEZpVYtdHMzzNWodPqsoPgqlWrsnDhQk6ftVEmMichtGpDFkkpTn74PIiga8mg7bvNzJ6fQVy8A0hlwMsv06RZU5579nkCAnJ7fKjVavr3f5m+ffty4MAB7HY7ZcpEsXjxIn6YupTFq8zUqKwhJU1i+x4rHh5GRo/+sFCqnlIlS1GjRjUWrThO5fJa5i7IoHxZDfsPu5JyKlXez+PxGBvBIXk3bBQUFBQU7g43q9K4UTHVzSrEi0pAQACTvp7I6tWrWbtmLWmpaUSWiKB9+/Y0adI4u7r7+++/IzkhhXJS9VyKjmCpJKfTjvDlV1/x+WefFdu86tSpw8SJXzF//j9s3bIFm91OWFgYz3Z+itatW+dqo1OqVBhVq1Yl5uhpPJ0mNP9qWWWXrcSrLlK9cjVKlnyw+sf7+ro2zc2YMZBXCWuXbdicVvz87s7m+v2BQGhoMMdOpvFIm7xHHQ6Zk6cdNG+Z+/dYEAQGvz2EWjVrsXjxQqb8dhaNRk29evV56+3ulC9f/g7Nv3hITk7BYrUQivsNb62gw6AycunSxVsey2q1sGnTZi5duoTR6EHjxk1yKbAbN25M586dWLRoMalCIp6SDxJOUlWJ2GQr7wx+J/uz/7BgtVr49NNP0TuMqCUN6aRQhiqoBNffZ1EQ8SUQlaTh2LHD7Nu/j1o1a2W/39fX96F7ZgoKoCQ1FO5jjsTEug0y8utrW1BgkV+VFNy4UkpJaCg8yMTEnOLPP/9k27ZtSJKMj48X0e060OOxHrmkyQ6HHY//mOk5nSCqBHQa9+oBw7VWUAVJz5OTUwCZVRuyePQRzzxJifMX7RyPsdK5u/ukCEDduvWYOXMWO/ZaaOimddPqDRZKlQottupDq9XCmjVrWbd+DelpqQQHlyA6uh0NGzZEFFW8+urrjB49muGfJtGtgwclS6g5c87O30szuBLrZOzQ3D2vr/s3eHp68cUXE9ixcyebNm4kKyuL5i1CadeuPeHh4YWa2wsvvMi7777Du2OSeKyjB+WjtMTGO1i4Ios9Byy8/fbbBfbA1ag1DBr0Jn369GXbtm2YzWbCwkpRv34D1OqiLSlkWWbOnJk0qW/gxb45AZZWJ9KxrRGzRWLmX0vp2bMnPj4+NGhQn5CQICb9nMLIIb74+bgW+Rt3mGlQS5+d0Fi5IZNvf06lZlUdw970xd9XxZHjNuYv28a77x7iiy8muDUv9/T0onHjHGPxAQMG0LhxY5YuXcL+Y+fQ6XT07t2Mdu2ii1QB9cagNxn6/nvEnEsCWSbTLJGULLF8bRYd2+Zu43X6rI3NOy08/VSvIj1LBQUFBYXipzAqjRslNPLz8CsooZFf7HEr7W4NBg86d+5C585d3B6PT4hn546dhMplciU0wKUGCHaGcfTIEc6dO5fd0704CA+P4I033uCNN9644bmDBr3Bu++8x4m0A/hKAejxwEwWKWICXiYjAwtxjfuNgIAAqlatypkj5/CR/PO0oIrnErIss3nzFpo2bXZP+mrcC7Rr9wi//T6Vzu08KFcm9zr3n2UZpKQ6aN+ufZ73CYJAmzZtadOmLS65cP6K6Hud6/GWA6vb45IsYZdtRfIqccemTRv57rtvycoyExSgJS3dybRp02nVqgWvvz7wWpwh0L9/f2rUqMmihYs4cfIkGrWKJvUb061bVyIjHyzFVWHYuHETWZlZVKQCx9lPIKHZCY1/44U3HiojGzdszJXUUFB4WFGSGgoPJIU1B78VlLZTCg86e/fu4eOPxxAcqOL53l74+6k4csLKogV/sXPHdj799HOMRtembFRUFL9v3UxamoTJ5Aq4oiI1OOwy+w5bqVUtb+Ji1z4LarWKiIj8N+SXL1+OIMhciZX4+ucUXnrSG0+j6/rnLtr5ZGISnp4GGjdunO81ypYtS/XqVfn+12OYPFVUruAKZux2iflLMtm6y8ygQf0ojkAlOTmZDz4YyqVLV6hTQ0e5qipOnk7hs8/2UqdObYYNH06tWrUZM2YM06dP4/NvTmS/t0yEmk+H+xMVmTvY2rTdjMnkSVh4GKKoomGDhjRscOPWR+4ID4/g88+/4JdffmLilAP/er0Uw4Y/k891Zfbu28eypUs4d+4MOp2eRo2a0KFDh1uS3Z8+HcPVq/G8/nxe41JwGbn/PjeDHTt30C66HaKo4oMPRjBixAf0HxxPgzo6fEwiFy46qHytHVeWWeLn39Jo28KD11/wzlYVlS2tpWkDA4NHJvH7778zaNCbhZihQPXqNahevcZN3yNAYEAgX375FYsWLWLFimWkpLg8S36cnsrpc3ZaNfXAoIMde60sWJ5F6cjS+Zre3ypms5n1G9YTExODRq2mbt161KpVy636SkFBQUEhf5XGjdpOQdHU4dcpzrZTReHM6TNIsow37r/XTfgBcOrUqWJNahSF4OAQvpr4FfPnz2flypXEZV7GaDTSKboj3bt3f2CrlJ999lmGDh1KjHSYEDkMIyZsWInnMolcxYcADu4/yJjRY/jk00/+850uExsbS2JSEieOnyAhIR6j0ZOmTZs+VP32O3bsyJYtmxjx2Wk6tjVQr6aeLLPEmk1mNm0306BBfY4ePUJGRgaVKlXkvzGB1Wphy5YtxMbGYTK5imB8fHxuYiZ3LzFiMpmoWqUKZ49ewFcKyrP2SyURm9NGkyb5x1Q3Yt++fYwfP54m9fU884RLQW2zSqzdYubnGRsBeOutwdfOFq4ZwOevEH+YOHHiBB5qT7QOPRJOtOT13gNXok0tacnMzLzDM1RQuDdRkhoK9yX5VU4V1Ry8IJVGrPV4gZVSoKg0FB5c7HYbX345geqVNAwd5ING40okNKlvoH1LO+9/fIkZM2ZktwZq06YtM2fOYMrvqbz1sg8qlUCFshrKRGj436w0xkZos5MdABcvO5i7KJOmzZoWWPW+Z89OGtbRUa+mnm9+SWXLDgsVy2vIypI5edqOp6eA3qBHoy64ddS7777P6NEfMXTsKUqH6wjwFzgR4yA1zUHv3r1p08aNHr0AUlJSWLlyJceOHUVUqahZoyatW7diwoRxZGXG5/IWAdhzwMInk/bx+2+/8/zzz1O1ajXGjRvPlStXSElJ4ccpP5CRdjlbvXKd7bvNLFmdxRNP9LrhPRaWiIgIRo/+mLi4OOLiYvH09CIyMgL3QZbMDz9MZunSpUSG66hfQ01qejp//fUHixYtYNSoMURF3dzfQbPZApCtuPgvnkYRrVbEbDZnvxYeHsG3337PqlUr2bx5I+cvm/Ewajh60nXOxm1mbHaZvt298gRrfr4qurTTM2v+Bvr3fxmDwX1btNuByeRN375P0rfvkwCkpqby11/zWL9uLSvXJwCg12tp3bodzz77bLZha3Gye/cuxo//ArPZQmS4DrNZYtGixZQpE8GIESPdqlcUFBQUFPKnIJVGccYdd4LraksnEu5WGxKuVp1qzd3dPvDz8+OFF17ghReex+FwolaruJ+r5wtDhQoV6NChA4sXLSaGw9mvq1FTktIEUIJ0KYXDRw6zf/9+atasCcDWrVuZNXMmZ8+dA0BAQCfqceJk1qxZNGvajDffGlSgQvdBQafTMWbMWGbMmMGyVcuZt8i19jJ5eaASVWzfvoPt23cCMqVKleKtt96iXLlygKtV6pQpU7CYLWjVOuxOGz/99DOPPtqNZ5559oaFIYlJifwz/x/WrF5NekYGfn5+tG/fji5dumYXh90pevfpzYcjPuS8cJIScgRaQYckS6SQyGXxNPXr1L8llcSs2TOoUFbL4AE+CNfU9VqdSPtWrvv8/tf19Ordh9ASeQ3CH3ZUKhUSTgRBQCvryCQNf/J2EZBkCYsqk5CQ/JPjCgoPE0pSQ+G+Jb/KqeIyB78RSkJD4UFmy9atpKam8+KTQdkJjeuEl9LQsa2BRStXXduA1WEymXjrrbf54osvOH0ukVZNdHgaRTwMIkdOWBnwbizRLTwICVJz6qydjVstBIeE0u+lgk0DnU4nOp1Ii8Ye1KiiY+X6LE6fs+PtJdC1vSdnztnYsDPn/IzMDLIys/Dx8UGrzQnSTCYT48aNZ/fu3WzctBFzVhat24QS3a4dpUoWbRNhx44dfPHF58iyk2oVtdic8NNPO/j99+lkZpp5d6BvroQGQO3qerq192DJ8qX07dsne9O6RIkSlChRguHDRzDig+G89n4s9WrqCApQcTzGyYkYC40aNaBXr+JvRxQUFERQUFCB56xYuZKlS5fy6vPetGuZY5j9Qm+J0V8m8/HHY5jy0883lXApEVoCURQ4dMxKyRJ5lyMnT9uwWJx5fj5eXl507/4Y3bs/BsD27dsZO3YsW3eauRLrJDhQhb9f7kRJapqTTdstnLvowGZzcP78uSL7fxQn3t7ePP/8Czz77LOcPXsOh8NBWFipW5b858eZM2cY+8lYalfT8PLTgQT4q5FlmaMn7Hz542VGjvyQSZO+KZRPiIKCgsLDQH5tbqFglcbNxB2F8dG4ldZThaFSpUrodXqSrXGUIK8SI4lYVCoVNa6pFy9fuUxqSir+/v43XEsUlgsXLrB16xbMZgthYWE0adIEnc59tTIIRW57eT+zb99uBFHGVwrEEx9UqPDCN7sdlZfsg0H0YMOGDdSsWZNly5by/fc/YBBcm8l+BFGCCDSy9tomdjxbtmxBVIkMHjy4oKEfGPR6PS+++CJPPfUUsbFXmT17Dps3bSaIUgQQ4jKqJ5Wrly8wfNhwJnw5gYsXLzJp0iT8CCKSyuicehyynQTnVf6a9zeiKPL008/kO+alS5d47733MWeY8ZECMBGEOTGDP2b/wbp16/n888/x9r5zps7Vq9dg8JAhfPP1Nxyz7cagMl7zZbHRoG59Bg8ZctPXTkpK4uiR4wx+xTc7ofFvWjUx8OvsDDZv2swTTzxxK7fxQFK7di0WL16cncyI5QKBcgkMQm4vnXguYXPaHkojdQUFdzw8KwGFB57iMgeHwqk0FBQeZM6dPUdwoNbtZjNArap6/lyQQHxCfPamc+PGTfjii2Dm//M3fy7cjt1up3TpSJ59tgWJSUmsW7+WtLQ0AgP9eaLXY3Tu1PmGvX/LlavI1i0XcDhkfLxVPNHVK/uYLMv8uTCT8uWqc/z4MWbPns2ePXuRZRm9XkvLlq3p06dPdjsCURSpV69evqbkheHChQt89vmn1Kuh5bUX/PHydAWTh49b+XBcEioVNKjlvsq+aUMDcxfFc/r0GSpVqpTrWGBAIF99NYm1a9ewfsM6rhxLJzg4lGG929GgfoO71B5IZuGCf2hQx5BdYXUdk0lkUD9vXh8ax5YtW2jRvEWRr+7v50+9+vWYt2gfDWrr8fHO2VC32yV++zODoKAAatUquF9s/fr1adq0MV98v5WoSDUpqRI2q4RWJ7o+IwsymLMgAwAfk4hKBUOHDaV3r9707NmTu1nhKYoqypQpc9vH+fvvvwj0U/He6znm9IIgULmClqFvePP2hxfZvn1bLk8RBQUFBYX8cafSuBkfjevcTZUGgMFgoFPnjvz919/oZA98CUAQBGRZJpUkYsWLtG7TmgsXLjB61GhOxZzKfm/VqlV58cUXblq5abVa+Oqrr9iyZSsaUYtaVGNxmPlpyhQGvTmIhg0bFddt3pc4HA4uXnR9tox44yfkTSIJgoBG0pGRkUF6ejpTpvyEH8GY5Qy88CaMstlrSVEQ8SMYSZJZv349Tz755ENV9a3T6VCr1WzatImSlCZQyFENeOGDh+TFKccBZs2axZkzZzAJvoTJOc9PLWgIIQxZlpk//x8efbQ7Xl5ebkaSGT9+PI5MBxWkGqiFnGKrQKkkp2MPM2XKFN55553bfcu5aNasGXXr1mXDhg1cvHgRg0FP48ZNiIyMvKXrZmW52iH9t7DoOlqtiLdJRVZW1i2N86BSp05dSoaGcjE2hjBnWVJI4iSHCJRLYMIXJ06ShDhSSKBXr16ULFnybk9ZQeGeQElqKNx3FFQ5VRzm4IWpllJUGgoPOjqdjkyzE4dDzt4E/TdpGa42BDptbsl62bJlGTL4+uI8d9/Yl158qcjz6NjxEZYuXcrU2Wm8+KQp1+b+vEUZnL9oo3XbCgwdOpSwUBWvPGdyqRxO2Vm6ZhV79uzi83Ff4O/n3rehqCxcuBCTp8jgV7yzFSxx8Q4+/jIJh11GrYL88g/XX5aR3R43GAx07NiJjh07Fctcb5WMzEzOnbvA4x3d96gOK6kmMkzHoUOHbiqpAfDSi/14770hvPVhEp3a6ilbWsvVOAdLVpm5HCsxcuSbiKJY4DUEQWDIkHeYO3cuCxYswGxJZ/UmM4+0MbJweSYz5qXTo7Mn3R/xxMtLJD1dYv7SDH7/fQY6vZ5uXbvd1NzvH2S2bt3K4511bn+XoyK1lCutY+tWJamhoKCgAAUbhN/IS6OoCY273Xbq3zz11NPExyewYcMG4lUX0Tr12FQWzM4s6tSqS7169RgxYgQeeBJJRXQYMJPJ6SNnee+99/n000+zW/YUhfFfjGf3rj2EUw4fKQBRFrFi4Yr5HJ999jljx46lSpUqt+GO7w9EUUQURQw6yDTn35ImS8igRIkQ1q1bhyRJ+BPCSfYTSUW3xTF+BHFVPMuWLVt47LHH7sSt3DOsW7cejUqLvzPv76VKUOHrDGLLls1IkkwZKrt9foGUIM5xkW3bthEdHZ3n+MmTp4iJiaEMlXIlNAD0goFAZyibN2+mX7+Xbsmj7mYwGAy0b5/XGP1W8PcPQKtVc/SElSoV8rY0i09wEBdvJzQ0FEmSsNls6HS6e87XLSMjnczMLHx9fXOp/m83oijy0aiP+GD4B5yIO4CnYEIja4jjErFcBCAkKITXe75OOzefNwWFhxUlqaHwQFAUlUZh5N+KSkPhYadBg/rMmDGDLbssNG+Y23tAlmVWrDNTpkwEgYGBBVzl1hep4eERvPzyy0yePJn9R+w0a6BDpYKtO22cPGOlV69e/PPP39SorGHYmzlV6LWq6Wnb3MA7o5OZ+uuvDB5883Lqf7Nr93aaN9Tmask14690rFaZmlW17DtkY8ceC43q5fVr2LzTgsGgp0zp21+ZXxwI135+suw+CQMgyTKS04nT6byp1kXBwcF8Mf5LZs6YyZx/1mOzpSMIAnXr1uGNt/oUenNEFFX07NmLHj0eZ9y4z/l55nasVpm5i9Lp0NqDZ3qass/18hJ5uqeJjCyJmTN+w9fHl0aNGmb3lDabzcyePYuVK1dgsZoREQgpUZKnn37mvjQzlGWwWm14m/L3EDGZBKxW6x2clYKCgsK9jbsCquvxRn4qjZtRhheG29166joqlYohQwbTpUtnVq1aRUJCAj4+vrRu3ZoqVSrz0kv98JS9iZQrZrc9MuCBj+RHjOMwkydPZsKECUUa8/Tp02zfsYMIyuMr5KwpdYKecLkcMcIh5syZzejRY4r1Xu8nRFGkbt3anDi+l1RrPGYpNLut1HXiuYxDthMdHc3ixUswiEZEyXVM49YlxaXYUIvaXN5lDwupqaloBV325/i/6DEgSa71rxb3Cmy1oEElqPI1bI6JOYUgCHjJ7hMWJvy4JJ3hzNmz1Kp5/5vcGwwGmjVrwcIV62nZ2ECAf85WoyzJ/D4vA61Ww6FDh/hx8o/Y7DY8PT2Jjo6mR4/HCvRYvBMcOXqEWTNnsX//fsBVuNeqdWv69u1zx5JOwcEhfPf992zatInNmzdjtVooVaoUNWrUJCw8jJKhJe+5JJCCwt1GSWoo3FcUVDlVWJUG3Jr8W1FpKDwMREaWpl69ukyeuhedFurX1COIAplZErPnp7PngIX33utNcbbuSUlJwW634evrl6tPcseOHYmMjGDBgn+Yv3w/siRTqVIVPnq2KxaLheTkVJ59JzBPFXqAv5pHOxiY/udm+vd/OR9peNFw2B3o/2XmbbNJbNxmRpbh9Rd8mDglhZ9nphERpiE0JOceDhyx8s+yTDp06IJefyMTaJmDBw+xZs1qkpOT8fPzp02bNlSpUpk72SrJaPQgMjKcTTviaNE4d5swm03itz/TuHTZxvmLq1i9ejV169ahR48n8rTWuhGBAYEMGjSIAQNeJiUlBaOnJ55Gz/+cJXP+/AUyMtIJCgomIMC9qbVKpeKdd95l8uTJTJ2zElmW6dzWvQlj52hPlq2JY/z48ZhMRp577kXq1avHW28NIjExCV9vkejmHmg1Ilt3XWHs2LF07db1phRHdxNBEAgPL8n+w0l52ogBWKwSx07a6dQ5/C7MTkFBQeHeoqBYAwpuO/VfbqWQ6u4hUKFCxTy+U/v27SMhIYFyVMuzESwKKgKlkpw8eYzz588THl7475PNWzajVenwduZV1IqCiJ8UzL59+8nIzHCzNniwSU1NJSYmBlEU6dixMzt37sboIRBjPkiAFIoJP5w4SCKOZOLp3r07pUqFYTQasctWNOgQEcggDSOmPNe3ymYsDjOlwu61zyDY7TY2b97Cjh3bsVptlC5dmvbt2xEYWDz+Lf4B/lglM07ZgUrIuyVmJhO1Wo3D4SCLNPTkLQwxy5k4ZAchIXmVMwBqtQZZlpFwonKz7SbhAEDzAHnDPPPMMxw8uJ8ho5Lp0k5P5Qo6EpOcLF1t5tAxCzqtli0btuLnDEaHgayMDBb9s5jNmzYz7otx+Pn53ZV579y5g7FjP8GAkTDKokFHpi2NNSvXsHv3bsaPH5/dzvh2o9Vqad26Na1bt74j4yko3O88OH9BFR4a8ms95Y6iqjQKCiwUlYbCw8aQIUP49JOxfDLxIEGBWvx9RE6ft+FwQL9+/WjSpHha1WzdupW5c//g5MkYAEwmT9q160CvXj2zDbUrV65C5cp5Ww/88ecfeJs0RJRyX4VWrZIWhyONq1evFktSo1y58uzce4jej8oIgkBGpozTCcGBKgID1Lw9wJcRnyfw+tA46tXUUyJYzckYG4eO26hcuSLPPPN0gde3222MG/c527fvpGSolvBQkSOHJFatWkXjxo0YMuSdO2iMKdCt26NMmvQ1S1dn0qG1yyjcZpP48PMkTp620bKJgZpV9aSlS6zccJChQ3czZMg7NG3atMij6XR6gt0knHfs2MHvv0/j7Nmcv9+1a9fkhRdecrtxolaref3114mMjGTKlCkE+LtXkAT6uzZlXuhr4sx5B19//TWRkREkJSXRrIGeQf1zlD99HvPk7yUZTJuzgNq1alO7du0i39/d5JFHOvPTT1PYf9hKjSo5pquyLDNzXjpmi6QYDiooKDz0XE9oFKTS+C/3s49GUYiNdT0bD9yvpYx4Zp9XlKRGVmYWGkGbb8W8BpeK0mK2PDRJjYzMDKZMmcLGDRtxOq+1e9XpqF27Fvv27UNGIk68yFXJ9RlTq1Q81efJaz5h0LRpU/744w/SSMaHQOK5jI8cgE7IKaqRZIkrwjk8jZ40bnRveZZcvXqVkSM/4MqVOCqV0+PpCYsX7WHu3D8ZMGAAHTo8cstjtG7Vmpm/zyCey4SQ+/PqkO0kqeJo2bIliYmJHNt/HJPkh1rIiTVkWSZWuIC3lzf16tV3O0atWjURRZEkKY5AQvMcTyQOo9FI+fIVbvl+7hV8fX0ZN248v/32G7PnuxTYABUqlCPAPxlzspUyUuXsRJIvgQRIJTiddJiffvqJ9957747P2W63MfGrSZhkH8LlCtl/i0z44OcM4nTSIaZNm8abb755x+emoKBwY5SkhsJ9Q35eGgcuXXVbNZUf7oKLwsq/FZWGwsOEweDB6DEfc+ToUTZt3ITZnEWdhqG0bdO22CppFi5cyE8//USNqnoGv+qLp4fA3oNWFi78m0OHDjBmzFh0Ol2+7zfoDWSZnVgsUi4FxXWSU6Vr93IjdUTh6NixM6NG7Wbxyiw6tzNi9BAQBMjIlHA4ZPz9VEz4KJA1m8ys32rm/CUzmmsb4++88252i6P8+PnnX9i7dzfvv+FLwzr6bJPOzTssfDVlG1OnTb1ppYAsy6Snp6PVaguhFnHRoEEDSpUqyeRpl1iwLIOa1XQcOW7l4mUnHw8NoFL5nPvp2NaDr35MZdKkidSsVbNYNh/Wb1jPhPETqFlVz4eD/QgOVHM8xsa8xUd47/13GPf5eMLCwty+t2JFV5XpkRM26tTIe7+Hj9sAqFJeS9f2RjIzJXbsPYdeJ/Dq8z65lD+CIPBYJy82brUwb96f911So3379uzcuZ3RE/bTorGBejV0ZJol1m60cui4hX79+hEUVDzVjwoKCgr3IwV59l0nv3jjZnw0Csudaj11I7y8XN/pNqzo3LTjseFqYWgyFa2ApGTJUMzOTByyLY/vAEAmqRj0erx97m5rmjuF1Wph+NDhXDx/iWApDG/8kZFJtsazd+9+qteoTtWqVTkdE4Pdbqd69ep07Ngx1/oyMjKSxo0bsWPbDoKkMDJI5ST78ZdLYMSEHStJYixmMnl/0Ps3XJveSZxOJx999CEiKXz7SSBh14qWLFaJ6X+k8f33PxASUoKaNWve0jgBAQE80bMnc+bMwS7b8CcENRoySCVedQmdUUefPr2xWq28M+QdYiyH8JdC8MALGxYSxatkks77A9/Pt/2qv38ALVq0YOP6DWglHSb8stf1ScSRKFylT7c+d9S34U7g5+fHoEGD6N+/H/HxCRiNHly9GsvQoUOJokoeZYxO0BPgLMHWrVtJSUm+4/4iW7duIz0jnYrUzpNc1Ql6/JwhbNiwgX79+mE0uld/Kygo3D2UpIbCfcGNpOD/xV1f2/xUGoWRfysqDYWHF4HKlSpTuVLlYr9yQkICv/zyM906GHm+T44JeO3qepo1NDDsk1MsXLiQxx9/PN9rNGzYgJ9//pnVG810is690JRlmaVrsggPL0mpUsVTDVmnTm0effRRfvp9Plt2WmlYR4tKBZlZMpt2mGnZ2AODQaRTtJFO0UZkSWboJ4kYDFr8/d23TLpOWloaq1atoE93I43q5sjcBUGgaQMDFy87+GvJMvr07lOkRbXVauXvv/9i2fKlJCWmAFCzZnUef/wJqlevke/7LBYLH3wwjKTEK5QKVXE11smSVVmoVNC6qUeuhAaAKAq80MeLLTvjWLtmLV26dCn0HN1hs9mY8uMPNG9k4O2XfTh/2cH6rWYyMp20aqJj1QYLkyf/wLvvvoe3d97NjrJloygTFcmsv69QtaIWnS4nULFaXW3UykRoiCqtQRAEKpfXsHOfhVrVdBgM7itGmzbU88eCk7d0X3cDtVrNBx98yD//zGfJksWs3pAIQJUqFRkx4vF8qwwVFBQUHgZulNAoSKVxsz4a95NKA6BOnToYDAbizZcoRVSuY7IsE89lAgMDi1x13rJlS379dSpX7OcpJUfl6hdvlc0kifG0b9sOjdq9IvdBY+XKlZw7d5ZycnUMQk5xSAnC8ZCM7Nu7j0e7PUrPJ3oWeJ233x7M119PYsOGjahE16Z7rJQTC1evWp0+ffrccwbs27dv4/Llq0z8OCehAaDXifR7ypsTMU7mz//rlpMaAE8+2RcvLy/++ONPTqTtz369RtUavPLqK9mtrr6YMJ5f//cru3ftQrrmM1c+qjzPPDukwHU0wKuvvkpycjL79u3DoDKicWqxqSxYnGbatmmbra55EDEYPLJVW1u3bkUUVXhK7pOTJny5JJ3hwoWLdzypcfHiRfRqPXqne+85T7y54jhHfHwcRmPpOzo3BQWFG6MkNRTueW5GCp4f+QUYhQks7oUqKQWFB4lVq1ai04n06e6Vx/SsfJSW5o30LFu2mMcf70F+XhKBgUG0bt2KX2evQ6uFVk08UKsF0tIkZv+Txs69Ft59t2++7y86Ai+88DyVKlVi0aIFTPvzGCAQFCAyeWoqoiDQpL4elUogKcXJ73+mceyEjaiyUTe88qFDB7HbnVSrpGP52kwkCcpHaYiKdCUPWjc1MOvvdA4fPkz9+oXbhLZarXw48gNiTp2kdVM9Nav6kpYusWrDcUaM+JA33xxEq1bue7YuXbqEc+fOo9NClhke7+KFySQyZXoqtaq7V8/4+qgoE6Hj7Nkzbo87nU4kWSrU5sS2bdtIT8/iiS6BTJiczMZtFkxeIn6+IpeuOJEkmauxh3j66aepWLE8ZcqUJTU1FVmWqVy5Mq3btOa1V19n+PBhDBmVROdoAxGlNJy7aGfRikziEpyMed8/+7Pn7+cK+h2O/I3RHQ7uW4M+tVpNjx6P89hjPUhPT0Ot1uDh4XHjNyooKCg8wBS2cKqwqvDi9NG4l4qqdDo9vXv35tdff0WQRYIIRSPosMoWYrlACokMfnZwkb8jPT29GDBgAN988w120YqfFIQaLemkkqyKJSDInz59+tymu7r3WLF8BSb8cyU0rmPCD6PoxcqVK2+oGNVqtQwZ8g59n3ySbVu3YbGYCQ4OoULFCnibvIulJevtYOfOXZSJ0FE6PO86URAEWjfVM+W3/TidznwVEoVHoGvXrnTs2JFjx45hsVgICyuVpxVqqZKlGDFiBCkpKSQkJODl5UVwsHsfjf+i0+kYPXoUBw4cYO3adaSmphIUFEibNm0pV67cLc7//kGr1SJLUr7+Ig7s186788lLg0GPQ3IgyZLbNnh2XMpuvd590kNBQeHucluTGklJSQwcOJCFCxciiiI9evRg0qRJeHrm35KiZcuWrF+/PtdrL7/8MpMnT76dU1W4xymoesqdQXhRVBo3CizupYBCQeFB4uLFi5Qrrcm3Kr5aRS2rNyRis9kLlGa/+tprOJ0Ovv1lA9PmZOLnq+byVRsg8vLLL9O0abNinrlAo0aNaHStB/GcOXOY++dMKlfQMuGHZH6eIeJjErl4xYFaJSADXbt2u+FVMzIzEQR4d3SCaxQBJAkqltPyZn8fjNeek8NhL/RM58+fT8ypk4wd6kf5qJxn2K6FB9/+msq3335LnTp1MZnyGkiuWLkMvR5Cg9SMes8fo4eI1SoxZXoqGRmS2/FkWSY9Q0KrzZ302L59O/Pnz+Pw4WMAlC1bmi5dutGqVSvcJZySkpJYsWI5RoPAX0sy2L7HyqD+PjRvaECtFkhPdyWtFq3IompFLcdjTnLixAkql9chigJTp25l5szfGTZsOJ9//gW//z6dH6buQZZlBAHq1NDx1gBfykTkBE9arWseew9ZSU1z4m3KHSzLkszazVlUr16ncA//HkUQBEymh6ONh4KCgkJBFFQ4dZ3iVGlA0RUa91JRVffujyJJTmbNmk2C/QpqQYNdtuFh8GDgSwNp0bzFTV03OjoaHx8fZs+axclTJwDQabW0adOGTp06cvLkSdRqNRUqVCiwJemDQEJiIl6yn9taHEEQ0EkG4uLiCn290BKhPPbYY8U4w9uLw+HAoM8/MabXu9o3FU9Sw4VaraZq1ao3PM/HxwcfH5+bGEGgevUaN1R1PMjUqVsXQRQK9Bfx8fambNk7n+hp1KgxU6dOI4k4Asj9t1uWZZLEWCLDI/M1hFdQULi73NakxpNPPsmVK1dYuXIldrud559/nv79+zNz5swC39evXz9Gjx6d/X+lkvDhpaDqqVtVaRSln+29FFAoKDwo6PUGklOlaxvNeQOY5FQnguDa4A4JyX+DQKPW8PbbQ+jZsxcbNmwkMzOTdiEhtGzZ8o5Uoj3yyCP89defXIl10P9pb5JTnGSZJcpFadm+24zDwQ09SCTJyfJlS9DrBJ7vY6JFYwNajcDOfRamzk7jg88S6dbeiCAIREUV7u+RLMssX76EVk30uRIaAIIo8OwTJtZviWPNmjXUqlWLtWvXkpycjJ+fH23atCYuNh67Xeb5vt4YPVwJFZ1OpGZVHas2ZNGuhQeCmPvnduiYjSuxNho2bJj92h9/zOH332dQtYKeV57zRqWCrbuu8NVXEzl+/DgDBgzg39F7TMwpPvzwAywWM06nzLrNZl5+xpvWTXPWAl5eIi896c35iw6OHLdRoayWt1/xIcDPtaxJTnEy6adUPv54DN988x0jRowkJSWZIUMGE+ibzrBBfqhUOWM6HDKLV5rx8DBgNpv54rtk3hvoh5en677tdon/zUrjapyTQW91L9TzV1BQUFC497mRjwYUXqVREIUppPo394qXRm4EevR4nEce6cjWrVtISUklIDCAhg0a3nKyoV69etSrV4/ExATMFgsatYapU6fyxhuDkCRXIYWHwYOu3brQu3cfRNF9Qcz9jrfJG3OGJd/jNtGK7x1uz3MnKVuuLJu3bHBbXAKwa5+VsLDQB86H4kHH38+f1q1as3btWtSSBh8CEAQBSZaI5zJJxPLiEy8WW6KqKISEhLi8TzZsRJQEfAhEFETsso2rXCBNSub1vrljFQUFhXuH25bUOHr0KMuWLWPnzp3UrVsXgG+++YaOHTsyfvx4QkPzZmiv4+HhUeAGlsLDQWGqp9ypNP6LO5VGYeXfikpDQeH20aRJY1asWMGhYzaqVcodDNvtEivWZaHVCsyZM5tBg9684fVKlQqjb9++t2m2+ZORkY7ZbMPLqGLKb6nodQKSDDabTPXKWtIyYO68P6levXq+19i5cxcnT57m46H+uZ5Fg9oGykZqee39OGb+nUHdunUKLXnPzMokISGZGlXcB78mk0iZCA3Lli3jf//7Hz7eGkJDVOze7WDevHmoNSp0OpfXxL95rJMnI8cl8t2vqTzby4SXp4gsyxw5YefLyWmUK1cm+15jTsfw++8z6PuYF70ezUkwRbcwsnxtJt//upQ6depSr149wFWh9/HHYwgNcvLmy4G89n4cGrVA66Z5Jd+CIODro0IUBYa96YenMWeDw9dHxfsDfXjx7XiWLl3Kc889h4+PL6+9PpDRo0bx4WfJdO/kQXhJNecu2flrURbHY+wMH/4Bs2fP5PDxUzw/KJb6NXVodQI791rIzJLR6/W8//5QvLw8aNWqDd27d7+hV4qCgoKCwr1HYdpOFaTScMeh9AtuVRo3k9C4l/Hw8KBNm7a35dr+/gFkZGYwZPAQEmMTKSFFYMIPCSdJ5jj+mPMH8fHxDBo0iAdxk7FN2zbM+H0GVikMnZDbkD1DTiVDTqV1G/dtQx8E2rRuze+//caP09N4e4APanXOz3j7HjNbd1no3//WPNsU7g6vvDqAjMwMtm3bRqzqPFpZj0XMwua00b17d7p17XrX5jZw4OtITicbNm7kiuo8WkGL2ZmFSq3itf6v0bBho7s2NwUFhYK5bUmNrVu34uPjk53QAGjbti2iKLJ9+3a6d8+/0nHGjBn8/vvvhISE0KVLF0aMGJGvWsNqtWK1WrP/n5aWVnw3oXDXKUz11H9xJwV3F2AUNri496qkFBQeDGrWrEVwUACffZPIm/19qFtdjyAKXIl18POMVBKSJNq1NLBi3QYGDBiATqe/8UWLgcSkRA4cOIDklKhQsQKlShb8t2Ljxk0YPVR892kgMecdHD9pQxShemUdkeEaVm7I5Nuf95OWlua2zRPAunVrKVdalye5Ay6vh+aNDKzdbOW1114v9H1oNVoEQSAtPf9WURevODCbL/PKc960be7yI3EllMxM+S0VWQSrTUavywkqa1TR8fqL3vwwNZW1m7MoH6UnLV3i4mUbZcuWZsSIkdnKmyVLlhAYoOWJrnnbTrZvZWTlOgtLly7OTmps3bqVxMRkRg0OJDRETaVyWs5fsucy+f43Z87baVRPnyuhcR29XqRJPR07dmzlueeeA6BWzVqMGjWK//36C2O+PJt9blRUaUaPfolq1apRu3ZtNmzYwNy5c9l3JA6VSsDu0KDV2mndVKR0uDeXrjhZvW4pGzas55NPP7vhZ+Tf7Nq1i8WLF3L8+HFUKpFaterQtWs3ypZVvmsUFBQU7gSFKZy6Tn4qjfxaT/2XoijD/83DHH8sWriQuKuxlJNqoBNyihpKUhq97MHq1Wvo2LEj5cqVL8ZRZQ4cOMCSxUs4c+Yser2eJk0b0759B7y971zLxg6PdGD5smWcSTxMsDMcb/yRkUkmnljxAhXLVaRhwwZ3bD53Gk9PLwYPHsy4ceN4fWgirZvq8PIU2X3Axq59Zho1akiHDh3u9jQVbgKNRsuwYcM4efIk69atIzUtleCgINq0bUtoifwLnu/U3Ia88w69evdm48aNZGVlEhJSgpYtW+DpeW/6zygoKLi4bUmNq1evEhQUlHswtRo/Pz+uXs2/bVDfvn2JiIggNDSUAwcO8N5773H8+HH++usvt+d/+umnjBo1qljnrnD3ORITW+T+tkVRaRQmoXGvV0kpKNzvCIJAjZq12bhxJR9/mYyvj4jRQ+DSFSeeRoFhg3wRBFi8MovU1DSCgm5vUsNsNvPDD9+xYcNGJCnHLLpmzeoMGvRmvhX5GRkZ+Pio0epEKpXTUqlcbkl8cIDrqzYrKyvfpEZqagolQvKvOAwNUSPLVi5evICfny+FqU50Op1o1CLL1mbSvpUH4n9aRe3Ya8FsdvJEFy86tDZmv67RiHSKNnLmvI2V682s22zOdRygbXMjew9Z2XsAAkMaEFZaz0v9G1GrVq1crcTOnomhZhV1nrGvU7u6llWbTmf//8iRI4SX0hFWyqUO6dTWyLjvkjl/0U54qbzmgVlZEh6G/J+F0UPEbs/tQVK9eg0mfjWJc+fOk5SchL+fH+Hh4Vx/pqIo0rJlS1q2bAnAp5+O5fixPXz2QSDBgTnLpu4djXzwWTKTJn7JF198mWsMh8NBWloaBoMBg+H6hozMtGnTmDfvL8pH6Xm0gwa7HTZs28qQDRt48823ssdUUFBQULi93CihUVwqDSiaj4YSf8CK5SvxlgJyJTSu40cQ8apLrFy5qhiTGjKTJ//IkiVL8FB54uH0wkIGs87PZsH8BYwZ+zGlS5cuprEKxtPoyWeff8aXE77i4KGD2a+LgkCjRo0ZOHAgonjnW/TcSRo2bMQX48cz/++/mbd4Gzabg9KlI3jt9U60bdP2gW09VhDnz5/n/Pnz6HQ6qlevdscKvW4H5cqVu2dN0sPC7o7qX0FB4eYpclLj/fff5/PPPy/wnKNHj970hPr375/972rVqlGiRAnatGlDTEwMUVFRec4fOnQob7/9dvb/09LSCAsLu+nxFe4+hZGDg/vKqcKqNArLw1wlpaBwJwgMDERA5OP3fdh7yIrdLvNYJw1N6+vR6UQWr8xEFAU8PfNW+hcnkuRkzJhRxMQc56UnTbRsbECjhm17rEz74yjDhr3PhC8n4mnMO4+QkBBi42ykpDrx8c4baB4/aUOn0+Dr65Pv+IGBwZw4fipff5FTZ2yIgsQHH4ygTFQkHwz/kICAgtserVu3DofTyfmLMHFKCi/0MeHjrUKWZfYftvHVjylIEoSXUrFuSxY+JhXVKmnJyJRIS5No18LIqg1m/jczDR9vkQa1XEoau11i0cosNm2z0K9fP7p0yb8NgFbrul5+pGc60WpzNi0EAZz/Or1BHT0+3iJTZ6cx7E2/XG0I4uIdpGfI7Nxnpf/Tcp7EiSzL7Npvo2xZd22/BCIiIoiIiCjwGSYmJrB9+w5efsaUK6EB4G1S8fQTRsZ+dYqY0zFElYkiLS2V2bPnsGbNKrKyLIiiQN26dejVqzepqWnMm/cXL/Y10bVDzueod3dPvv1fKpMmTaRy5cp5CkIUFBQUFIqPGxVO/ZtbVWkUlesJjYc9/khKTiQU90kEQRDQOvXEx8cX23irVq1myZIllKIM/s6Q7HWYQ7JxJusYo0aN4ueff0atvq12pNn4+wcw9pOxnD9/nmPHj6ESVVSvXo3AwIdnfRBVJorBg4dc+5/Mg9RqLDMzkxMnjiPLULZs2XwLngAuXrrI15O+5tixY9mvGQwGund/lF69eruNGRQUFBQeJor8zTx48ODsNg75UaZMGUJCQoiLi8v1usPhuKHh639p0MAlrzx16pTbpIZOp7tlYzKFew9FpaGg8HDQokULZsyYwbmLDp7pmXtRb7VKLFltpkGD+vm2ICwuduzcyaFDR/j4fX+qVXZ9p1yJdXD6nJ0AP4ETMXFM/Goigwe/jcGQey4tWrZg6tT/MevvdAY8650rwEhKdrJ4tYVmzVoUWFUVHd2WtWvXsnGbheaNXJv8CYkOlqzOYsM2M8nJToICVTRvaGD1pkuMHPkBEyd9g0adV71wnb1791K1op7olga+/SWFzTvMlA7TkJYhERvvxGAQUInwxXcp2e/RaQVsdhlZhuuFcEHBoXw66RLBgRpCgkXOnneSmuage/fudOnSOd/xL1++hEqlYfseC5OmJBPdwkil8prs52OxSGzabqNV65w+tdWqVWfRosWcPmenTIQGtVpgUD8fxk5KZvBH8TzS2khQgIpjJ+0sXZOB0UMgIdHJH/9k0OtRz1zPfsGyTM5ftPHyK53yneONOHv2LJIkU7eG+3VGnequn2lMTAz+fv689/47ZKQl0rG1nkrl/YhPdLJ09QHee38vkRERlI/S50poAIiiQP+nTWzdaWX58mU8/fQzNz1fBQUFBYX8KWzh1M2oNNxRVC8NUBIa4GpBZEkzuz0myzJ2lQ0fH59iGk1m/t9/4yP6EyDnTlapBS2lpCiOJ+1j69atNGvWrJjGLBzh4eHXlKR3jr179/DPPws4dOgQAFWrVqVbt67UqlX7js4jNw/Gxr3NZuN///uFlStXY7fbAFcnkxYtWtC/f7888UVsbCzvvfMeTrNEJBXwxBsnDhLNV5k1czZpaWn07//y3bgVBQUFhXuGIic1AgMDCQwMvOF5jRo1IiUlhd27d1OnTh0A1qxZgyRJ2YmKwrBv3z4ASpS4PRUxCvcWha2eUlQaCgoPBiEhIXTo0IFfZi4nM0uiQ2sPTF4iR07Y+X1uBvGJ8M57t18GvG7tWsqV0WUnNJatyeTH6akYPQSqV9ZRvbKOXbt2MGDAy4waNZrIyJwKQk+jJy+++BLff/8D8YkSHdt44Oer4tBRK/OXmVFpvHjyqScLHL9q1ao0b96MiVM2cfqcndLhaiZPS0UAmjc24GNSceCIldnzM6hVTcfeg5fZsnkzLVq0zPeakiShVkPzhgZqV9OxbrOZsxfs6HQuI+3f/0ynVlUdPR/1wmGXGfNlEoEBIl3bexJeUsPZC3b+XpJBQkICAwcO5OTJk6Snp9O6TRDR0W0pVSo/VaTMb7/9xp9/zsXLU0Xl8loOHLWxZpOZOtV1vDvQl4xMia9/TsNmF+ncOSfp0KBBfUJCgpg0JYUPB/vi76eidnU9Y9/357tfU/hhairgSrhIEoSGaNDpHMz6O53dB6y0aGRAEGDLDiuHjlvo0aMH1apVK9RnwB0ajStplGWW3R43W+Ts86ZNm4o5M5EJo/xyqTqiW3jwycRk9h8+Q89H3SuO9DqRmlU1HD9+c73XFRQUFBQKx+1SadxKzAFKQdW/adOmDYv+WUywVAqNkLulZxpJmJ2ZGAx6Dh06SNWqVbmVTe+MzEzOX7hABOXdXsYgGDGqvDh06NAdT2rcaebOncv06dMxil74S67P+fF9Jxm55yOefvppnnjiibs8w/sXSXIyZvQYDh06RKAUii+BgECKI4H1azdw8cIFPvn0EzSanM/7nDlzsJntlHNWRy241qNqNIRSGrWsZdGixXTu0uWu+1EoKCgo3E1um4ayUqVKdOjQgX79+jF58mTsdjuvv/46vXv3JjTU9Yf30qVLtGnThunTp1O/fn1iYmKYOXMmHTt2xN/fnwMHDvDWW2/RvHlzqld31z5C4UGisNVT/0VRaSgo3N8MGPAyBoOeuYsWMfOvdFQqAadTpmTJEEaPfuuO9DFOTkmiVKhLmnDoqJUfpqbSsa0Hz/cyob1mUp2Q6OCTSal8NGokP06ekkt50aHDI5hMJmbNmsmYL11/f9RqFY0bN+a5557H38//BjMQePvttwkJKcHixQswmzOIKq1h1BB/jNdMsHt392LrTjPjvksmOFDN5s1bCkxqVKpUkVmzdpGeIeHlKdK5ncsXQ5ZlXh8aR/XKWkYM9kMQYNDweEpHaBjzrl/2/VYqr6VlYwNDxyaxcuVyxo0bX6hnuWTJEv78cy5PP+FF1/ZGtFoRWZLZtsfCV5NTeHlwHGkZMgYPAyNGjCD4XxtBoqhixIiRjPhwOP2HxFGvpg4fb5EDRxxcuuKgQcMGtItuR1RUFDt37uTCxQsY9Aa8vb3ZsWMbv8w8BMhUrlyJoUO70ahRo/wnWggqVKiIp6cHqzZk8ULfvEahqzdkoVarKF+uPN9+8w19uxvytKlSqwWe6+3FwGEWzp6357nGdWx2UKkevj7RCgoKCneCwhZOFadKo6goBVUuunXrxto1azmd4TLLNuGLjEQScVzhLABLFi1h0aLFhIaGMnjw2zfvryG7L1p42Dhx4gTTp08nmDBCpLBs5WuQVJKrXOC3336jevXqVKhQ4S7P9P5k27bt7D+wnzJUwST4ZL8eTCk8JW9OnDjIunXriY6OBsDusLNh/Qb8nCHZCY1/E0AJ4sXLrFu7lr59Cy6cUlBQUHiQua2NIWfMmMHrr79OmzZtEEWRHj168PXXX2cft9vtHD9+nKysLMDVe3vVqlVMnDiRzMxMwsLC6NGjBx988MHtnKbCPcD1hEZhTPsUlYaCwoOFKKp4/vkXePzxJ9i1exfmLDNhYWFUq3Zr1XdFwd8vgDPnzyLLMvOXZVI6XE3/p7wR/uXTEOCv5t3XvRnwThwbNmzMDjyu07hxExo3bszFS5cwZ5kJDg4usE/ufxFFFU899RShoaFMnDiRQS/6ZCc0rtOonoHmjSzs2GPBbM4s8Hp169ZjxowZDB2bQPdHjDRtYECnEzl52s7Fy05efsYHURQ4etLGuYsORr/nn53QuI7BINKrm5HPvjnB+fPnCA8v2INCkiTmzvuTVk09eLyLV/brgijQqK6BtCclfpiaSp8+fenWrdu/TLRzCAsL49tvv2f1qlVs2bKJS3EZOGUHak0i27dtZ/u27fj5+9C9ew9eevFFrn9GOnfuDLhaZxVXj2GdTkeXLt2YM2c2JUuoadvcA5VKQJZktu62MOOvDFq1boPVasVud1C9svs2VeGlNHibVBw4akeS8vp/pKQ62XfIyrPP1imWeSsoKCgo5FDUtlPFpdIobOspyb5DiT3+hZ+fH5+N+4wJX4zn5KljuL7nXckHA56UpiIatGSSxtWr5xk2bDhffvnlTflqenp6ElYqjNRLideq53NjlrPIdKRTpUqVW7yre5vFixdjUBkIcYblWkMJgkCIHEaaKpF//pnPq6++es3n7vasz+0OO7t27iIhIQGTt4n69eq7XSveb6xcuRIv0RuT7JPnmFHwwiT4smL58uzYIiszC5vdhh737XdFQUQnGEhMSrqd01ZQUFC457mtSQ0/Pz9mzpyZ7/HIyEjkf1VHhIWFsX79+ts5JYV7mMIkNP6LotJQUHhw8PLyolXLVndl7LZto/noo83s2GNh7yErT/fwypXQuE5IkJrK5fXs2bMnT1LDhUCpkoXvny3LMvv272PD+g1kZGYQEhxCSkoKJYLVhJVy75fRuK6edZvNBAUFuz0uSRK//vorixYtRBBkUlPhm19S+XF6GrWr6zh7wQlARCnXEuDyVQcAVSpo3V6vSgXXRv3ly5dvmNQ4e/YsCfFJRPd3b2LesrGBKb+n4enpWWCQ6mn0pFu3R4mObse77w4hKzOJJx/zoE4NPVlmmdUbsvjl519IiE/gxRdf/Nc7BaxWC6tWrWTV6hUkJyXj6+tL69bRREdH31Rg3KtXLxIS4vn+11X8scBMREmRy7ESV2JtNGhQj5f7v0xCgsuwNCXNvSm6zSphscrYbDLf/ZpKv6dM6K8lkFJSnXz+bSoGg4E2bdoUeX4KCgoKCvlT2MKp67hLaNxOlYYSe7gntEQoE778kpiYU2zfvp3Zs+cQSiRBQsnsczzxprRUmVOO/fzxxx8MHjz4JkYSeLT7o3zzzTckcBV/gnOMwmU7F8VT+Hn70rhx40JdTZZl9u3by/nz59HpdNSrX78Qat27z/FjxzE6fdwWhQiCgNHpzeZNW9i0aTMlQ0Pp1v1R2rdrX6xG1es3rGfK5CmkZ6SjElU4JSd6nZ6+T/bl0Ue74S6RIklOduzYwZo1a0lOSiIgMJC2bdtSu3bte8pEOz4uHp1kyDcXpJc8iE9IzP6/h9EDjUaD1Z4F5P38SLKEFTN+vn63acYKCgoK9we3NamhoFDcKCoNBQWF20GtWrWoX78u477fjdMho9XmHwhpteB0Om95zKysLMaOHcPBg4cJK6klyF9k/brdpKQ6CApU5fs+x7WhW7VynwCaNm0qCxcu4MkenjzS2ojRKBIb72DGvHTWbzFTslQocJmzFxzUqKLCw+C614QkJyFBeZcFCcmuAQtj1n7d+NDT6P75abUCWrWYfd6NWLDgH2JjrzDhI3/CSubMrVI5LeEl1fwy8x+io9tmJ1vS0tL44INhXLhwgQa19TSsoeb8pTh+/fUXVqxYxieffIrJlLeNVEGIosjAgW/QqVNnVq1aRUJCAtVreTOoVSsqV6oECISGhhIeXoplaxOoU0OXJ5Beu8WMzSbz3HPPMX36NLbutFKzqgabHfYetKLXGxg58iM8Pb3cT0JBQUFB4aa5lbZT17ldKg1QYo+CiIoqy6ZNm9CpdAQ48/4MVIIKH2cQmzdv4o1Bb6BRuy8IKYjo6LbExJxiyZKlJKti8XCacGAnTUzCw8ODD0eORK2+8bbJ0aNHmTB+AnHxcahFFU5ZYvLkH2nTtg0DBgy4qbndKdQaDRbcm7MDSDjRoKUEEaReSeKH737gxPETvPHGQIpDtbF161YmjJ+ArxBARcqilz2wYSXOeon//e9/CKJAt67dcr3HbDYzetRoDh85jKdoQivpuXjqCps3b6Zu3boMHfp+Lo+Ku4mPrw8ply5cFxzlwSpY8PPJWZ9q1BqaN2/OpnWb8XfTgiqRq9idNlq1annb5qygoKBwP6A0b1a4L7hRoHGd/CqmCoMi/VZQeHgRBIH33h/KIx06o1IJbNvtPrBLz5A4fNxG+fI32bv5X0yc+CWnY47x0Tt+fPOJPx8O8eOXrwJp09yDuHgnJ2Lcb/yv22LG18d0zRwzN6mpqSxcuJA+3T15vItXdvuq4EA1b73sQ82qejRqFeHhocxblIkkydSqpsPDILB4lft2VotXZeLjY6Jy5YJbL0iSxPr16xFF2HvA6vacY6fsZJmdRBbSJ2XFimW0bKzLldC4Tse2Rny8NaxcuSr7tR9++I7kpMt8NSaA9wb60utRL955zZdJHweSlnqVb7/9plDjuqNMmTL079+fYcOG8dqrr1G5UmVyAnmB3r37sHOvhZ9npJGe4VJsOJ0y6zZn8cvMdFq0aEb37t358ccpPNKxO+mWKOxU4JlnnufHH3+iQoWKNz03BYWHibFjx9K4cWM8PDzw8fEp1HtkWebDDz+kRIkSGAwG2rZty8mTJ2/vRBXuOsXRdkpRadx90tLS0KJHFNxvXegw4HA4MV9raV10BAYMGMDo0aOpVq8q6mAJ7wgjvfv05rvvv6NMmTI3vMK5c+cYMWIE5kQr5ahGFakBVeX6lJAiWL1qDRO/nHiTc7sz1KtXlzQxGafsyHPMKTtJJREfAvAVAomkAmGUZdWqVezcufOWx5Zlmam//oq34Eu4XB694Cqi0Qo6SgllCCCEmTNmYrVacr3vu++/4/ixE0RRhbJyNcKFcpSVqlGaiuzZs5epU6fd8tyKizZt25AmJZMlp+c5ZpHNpJFI27Ztc73eu3cvNAY1p8XDpMiJOGUnVtnMZfkMl4WzdOzYkdDQknmup6CgoPAwoSQ1FO46hTXu+2+gsS/hSqFUGoVpPaUEFQoKChq1hn79+jFgwKvsP2xj9cbcwbHDITN5Wiog4nQ6mTLlR2bOnMnFi0VPpl68dJFt23bw0lNe1Kqmz67sV6sFXn/BG0+jwJeTk4mNzwkuZUlm4fIMduyx8Myzz+GuMm7rtq3IssQjbYx5jgmCQJd2Hpw9e4GuXbtz8KiVUeOTOXXaTud2RhYsy2T23+lkZro25NPSJKb/kcaq9Vn06tXnhlWKv/76K0uWLCGilJq/lmRkt7W6jtks8eusDEqEBlOzRs0bPiNJkkhISKZcGfeVjWq1QJkIFbGxrk2rhIQEtm7dRp/uRiL+07orrKSaJ3sY2b59J3FxcTcc+2Zo2rQZ/fv3Y9kaCy+8GcdbHybxwpsJfPVjCnXqNOD1198AICgoiGeeeYaPP/6U0aPG8Oijj+LlpSg0FBQKi81m44knnuCVV14p9HvGjRvH119/zeTJk9m+fTtGo5H27dtjsVhu/GaF+5pbaTt1nduh0rgeeygFVTcmMDAQs5yFJLtXyZrJQK/TYzR63sIoAjVr1mTYsGH8+NMUvv7ma3r16lXoxOmcObNROdWUliphFEwIgoBKUBMohFJKKs3GTRs5e/bMLczv9tKx4yOoNALnxBM4ZHv26w7ZzjmOISPjT87n3U8IwlM0sWTJklse+9SpU1y5epUAOdRty6hASmI2m9m1a3f2a4mJCWzauIlgqRRe/zLeFgQBb8GfICmU5cuXk5lZsP/cnaJp06aUiYrirHiMRDkWSXYiyRJJchxnVEcoUaJEnhakwcEhfPbZZ5SKKslZjnGQbRxlD2m6JHr16kn//v3v0t0oKCgo3Dso7acU7nkKq9K4WZSgQkFB4d+0a9eOEydO8vVPK1izwULdWlqyzDLrt1hJSLSDIPDHH7MIDdGSlOxg9uzZtGzVgoEDC9/2YPeuXeh0Is0a6PMcE0WBfk958/VPKQx4J57a1XX4eoscPOrgapyN7t0fzdd7ITMjA4NBhZen+5qFwABXW6uIiAhGjBjBzz9PYfinOX9jZ89PZ+6iDPz9NCQk2QGRp59+mk6dOhZ4P8nJySxatJAne3gS3cKDYZ8k8PbIeFo3NVC+jJbYeCfL1mZhsWoYM+adQvU5FkUBo1FPbLz7TQxZlomNl6hczZUQOHHiOJIk07he3mcK0Liege/+l8qJE8cJCgq64fg3Q+fOXWjWrDlr1qzh6tWrGI1GmjVrRulCKlMUFBRuzKhRowCYOnVqoc6XZZmJEyfywQcf0K2bq33J9OnTCQ4OZv78+fTu3ft2TVXhPuDApav5JjSKqtIoLErsUTRat27DrFmzieMyIeQ2A7fJVpJVcbSLjkalyr915+3EZrOxZcs2QqQwRCHvHHwIJFZ1gfXr1xMZeW+uBwIDgxgxYgQfjxnLEfsuPCVvQCadVAREIqmETsi9vjJKJs6ePXfLY6elpQEuxY07tLjael4/D+DgwUNIkuTW3B3Al0Cu2i5w7Ngx6tSpc8tzvFU0ag0fjxnNpK+/Zsf2HVyQT2Ufq1W9Fm+99aZb37fw8HDGTxjP2bNns31aqlev/kCYpyu4Z9++ffzzzz8cPnQYQYCq1arx6KOPUq1atbs9NQWFexIlqaFwX+BOpfFfDqVfuGkvDSWoUFBQyEHg9ddfo3btWixevIg/FsSgVqsJDy9HbPxhOrfzoHc3L7y8ROx2ibWbzUz5bSMazf/bu/PwJsvsb+DfJ0mbdEvT0h0KtCwFpdiibIWBOmwVWXVEcRlxw0H0J6OCgCyCC24DLq8zzqgso46OG6iIMICUfVFohVJadkp32tI23dI2ud8/KoXSJE3SpEna7+e6es00eZ70ztMQc3ruc44nnnryKYt+Ql1dPTw8ZFAojP9x39tLgt4AeHl5IvV4LZSeHujWrQfmPPvg722PjAsLC0dFRT0u5tQbbdmUcaoWMpmE4OAQxMT0wc0334wTJzJQXFwEjUaD8IgI7N2zF5cvX0anTp0wYsQfLJpBsXfvHshkwG2jfODjLcNri4Lx3U8V2LqrCj9urYKHh4S6OoGXX16GXr0sbd0lYeTIW7Ft1zZMvc3Q2ErrisNHdcjJq8Ws2SMBNMy+AABT4070+oZGxpKJ9hX24u/vj6lTpzr0ZxCR5c6dO4f8/PwmrT38/f0xePBg7N+/n0mNdsqS1lOWbJyypkrDktZTTGhYLyQkBNOmTcN///tf1IoadEIoFPCAFqUokufC198Xd901zWnrq66uhsGghyeMb6qQSTJ4QIVybfPWQ66kf/+b8PHqj7Ft2zakpR1DevoJqCq90AP9ms10AIA61MFLqWz1z72y0aQKFfBE88erRiWEEAgJuZrAMBgaqoplMJ7Ikn5vSGIwtH4Gnr34+vrhhYUvoKCgAGlpxyAE0LdvX3Tu3HILqe7du6N79+4m7hU4cSIDKSlHoNfr0at3bwwaOBAymXOSfGS7r776Cp988gl8ZH4IMIQCEEg/koFffnkBjzzyMCZPnuLsJRK5HCY1yKlaCjjMBRumgoxrtRRcsO0UERknISFhGBIShv3+vcATT/wFgweo8Oh96sZKAw8PGcYm+kCnE1j9+TZMv2c6goKCWnz0Hj2iUVFRj5Nn6hDTs+kQwxMna/Hm+6Xw85Uw6g8KaPyVOJZeiyPHTmD9t98gZn6Myd2IgwYPgr+/Hz77Rot5T2ogk11NmmgrDFi/qRq33HIzAgMDG59n3759mzzGpEmTLLtE19BqK+Dno4CPd0MQ6ecrw/13qXHfn/xQWytwIbsec5cVwce3eVssc6ZOnYpdu3ZiyeuX8dB0X9zYxxO1tQK7DlTjw0/LIZMBp06dRP/+/XHDDTdA4SHHrv3VmDK+eQuKnfuqoVDIceONppNCRNT+5Oc3fJYMDQ1tcntoaGjjfcbodDrodFfnA127S5jcgy3tba+wtUrDXOspJjRsd99998Lf3x9ffvklTpUeAwDIJAmDBg7GzJkzERAQ4LS1+fr6QqVUoVpXAQ06NbtfL+pRIyoR6qAqUXvy8/PD1KlTMXXqVKxfvx7r1q2D+D2BcK16UYdyWTFGDW/9Jo7IyEj06tULuWdyoDYENJmdIoRAoZSNAP8AxMfHN94eExMDAChDsdFqjTIUQyaToWfPXq1en72FhoY2+++RrUpKSvDqK6/i5KmTUMqVkCQZauq/RlCnICxYuAC9erne8yfjMjIy8MknnyAMkQg1RDbGmiH6LsjDBXz88Wr0i41Fj+geTl4pkWvhTA1yupYCDkurNEwxFVwwsCAiS2VlXUR2dh6S/uhttHXS6BHekMmBAwcOWPR4cXHxCI8IxUefaVFZdTVYrKsz4NV3S9AzygMfrQzFQ9P9MXW8L5Y8F4jFzwTi119/xYYNG0w+rofCA7NmzcbBIzVYtOIy9v1SjTPna/HT9ko892IJKqs98PDDj1j9/FsSEhKCy2W1KLzUdI6GJElQKmU4c76uoULEgoTPtUJDw/DKKyuQd0nCCyuKcc/MfNz7RD7eX12GAbFKTBjrg3Xr/o3k5GSo1f64NfFWfLGhEkfTmw4qTzuhw3/WV2LkyJHQaJz3hw8iMm7+/PmQJMnsV0ZGRpuuacWKFfD392/8ioyMbPkkchv2rtKwFOMOW0mYMGEC1q5dgzfffBMvv/wyVq9Zg4ULF1q0mcSR5HI5Ro8ZhcvyQtQKXbP7C5EDA/T44x+Ntw51VWPGjIbaT43zskxUi4rG22tENc7LMqFUeWL8ePPtSS31+OOPo06uwxlZGkpFEXSiGuWiBOdk6ShHCWY98ZcmlQedO3fGTf1vQr48CzpR3eSxqkUlLslyMGzYMKcmuxytrr4OixctxoWzWYjGDeijvxl99APQGzeh5nItFi9a7LA5cmR/G3/cCC+5N0IR2STWlCQJ4egGldwLm3780YkrJHJNrNQgl2VtlYYtAQYDCyKyxJVhsgEa4xUSXl4yeKvkFg+dlSQJc597HouXvIAn5xdj1AglQoLk2Lm/GuXlBjz5ggYqVdN9BwNilejb2wNffPEZNmz4BiqVCkOGDMOECROa7PhKSEjA0hdfxH8++wSvv9fQs1cmkzB48CA8OOMhRIRH2HIJzBo2bBg+/PCf+Hx9Bf7vMf8mH8a1FQZs+KkagwcPsqiV1fXkcjkqK2tw5wRfqP1k8FBIGNBfifDQho8wBYUGfP31f5GYOBIzZ85EQUE+Fr+WhpieKnTrIkNWjgEZp2rQr98NePzxv9jtOROR/Tz77LOYMWOG2WOio6NteuywsIbPhwUFBQgPv/r5saCgAHFxcSbPW7BgAZ555pnG78vLy5nYcBOWtJ4CzFdpGIs1zG2iKtBltlilwbij9WQyeeMufVcybdo0HNh/EGdK0xCkD4cfNKhHHYqlAlzGJdw3/T6nJ1+s5evrh5dfeQkvvrgMmUW/wUfuCwgJlXotNH4aLF6yHJ0Cm1em2KJ37954dcWr+Oijj5oksLt26Yq/PvR/uPnmW5qdM+eZOZj//HycvPQb/A2doIQXaqRKlEkl6NKlC/7yl/b9mW/f3r24mH0RMbgJXtLVCmVvyRdRhr7I1KXghx9+wCOP2H8zE9lf5olM+Oo1RjfPSZIEX70/Mk603OKQqKNhUoNcWmuqNMy1nmJgQUTWCAsLg1wuQ9oJHaK6Nu8rfC6rDuXaenSJNP0Hjev17NkTK//2NjZs2IBN23egsrICXl4qdA73ROfwpv95NhgEVn5wGWknanFDjCcG9JNQqq3Cz9s3YuvWLXjxxeXo06dP4/HxcfGIj4tHQUE+tNoKBAcHw9/f+oSCpVQqFR577HG88847KCs3YMJYbwR3UuDEKR2+/bEaVTUeeHDGQzY99qFDh+DlJce9d/gZnUEyNtELL63MQV5ePsLDw7F8+Us4dOgQtm/fhnM5RQgI6IT580djyJDB7C9M5KKCg4MRHGx84GtrRUVFISwsDNu3b29MYpSXl+PgwYOYNWuWyfOUSiWUdugXT85hrhLc3MYpU22nrrBlExXb3bZ/Gk0A3vzbm/jow4+wf/9+5BjOAQCCAoMw+57ZGDdurJNXaJuuXbvhww8/xIEDB3D06FEIIXDDDTdg2PBh8FA0/zzcGr1798Ybb7yB3NwcFBUVwd9fg27dugIwPn+uU2AnrHp7FbZs3oxtW7ehtPQSOgUF4c6xUzB27FioVMZnnLQXe/bsgZ/MH16iectVuaSAvyEIu3buYlLDTcgVcujRvNXbFQYYoFDwz7dE1+O/CnKa9DMFJgMOe1VpGNsxxcCCyH0YDIbG0umQkJDGYdCtodfrsX//fuzcuQPl5WUIDgnF2DFj0b9/f5gKnNRqNYYNG4b1Px3A0IEqBAVe/c9nfb3Auv9qEdhJg4G3DLRqLeHh4Zg1K6d3fAAAQUFJREFUaxZmzZoFIQTWrFmD/Xs3QQjRZKfOj1srsfdQDeY9FYBhA70ab7//TgNeXlmKV199CR9/vAYeHk3nc4SGhsFObXtbNGrUKHh7e+Ozzz7BsreyATTsLLrllgF4+OFHba4Qqa3VwUslNzlU3c9X9vtxtQAadnEOGTIUQ4YMtennEZFry8rKQklJCbKysqDX65GamgqgIVHs69vwx50+ffpgxYoVmDp1KiRJwpw5c/Dyyy+jV69eiIqKwuLFixEREYEpU6Y474mQU5mq0gCMxxotVWm0hJup2r9OgZ3w/PPPo7S0FLm5OVAqlYiKirbLZ1dnksvlGDZsGIYNG9bywXYQEdEZEREtD88GAF8fX9x5559w551/cvCq2oLAhQtZ0Gq1CA0LRXCQ+UR/ZUUl5AYPU6ELPOAJbXWJA9ZpH3l5edi8eTNOnz4NDw8PDBo0ELfeeiu8vLydvTSnGDhwIH764ScYDFFN5soADXN5ymWXMXpQonMWR+TCmNQgl2VJlYatGFgQuTaDwYDvvtuAHzZ+j6JLDR/Ig4M7YcKESZg8ebLNAWJFZQVeXLoEJ0+eRt/eKoSHSDh95gIW7dyN4cOH4dlnnzM5hPuhhx7CvHnpeHZpCcaP8kLvHh4ouKTHT9urkZ2vx+LFC0yeawlJkhAfH4cNGzYg41Qd+vZuSFAIIfDjtkoMH+zVJKEBNLS9euJhNZ54vhB79u7FrYm32vzz7WHo0KEYOnQIsrIuorKyEiGhIa1uTdC9e3eUXK7Fhew6dOvSfFfgkWM6qFSedhu6SESubcmSJVi3bl3j91eGx+7YsQOJiYkAgMzMTJSVlTUeM2/ePFRWVmLmzJkoLS3F8OHDsXnz5na/k7cjaqn11NGcfKuHg19hrkqjpRl+1HFoNBpoNBpnL4PcyIGDB/Dvdf9GdnZ2421xcTfh0UcfQ9euXY2e0zmyC05nnIHQC6Mti6pkWkREmE7eOtPmzT/hgw8+gBwe8DGoYZD0SE1Jwef/+QIvvbwc3btHOXuJbe7222/Hpk2bkCUy0UX0hEJqiHnqRR2yZKcg95CQlJTk5FUSuR4mNcjlWFOlkaa9aDTAsGTHFBG5JiEEVq76G/bs3oNRf/DCsEGBEALYc6ga69atxfnz5/DXv/4VJrcmmfHO26uQn3cery8OQp9eV5MGew7VYNUH+/D555/j/vvvN3pup05BePPNv+HzLz7Ht5t2oKZG21iJ8OTT09G7d+/WPG0ADQPEu3ePxNv/KsDiZzToEqFAudaAvAI9/jzN+B/fOocr0D1SiYwTGU5PajSQTAZgthg0aDACA/3x8WdaLJqjgafyakLrYnYdNm6twq23tv82A0TUYO3atVi7dq3ZY4QQTb6XJAnLly/H8uXLHbgychW2VIJfSWhYW6VhCW6mIiJTkpOTsXLlSqilAETjBnhChSqUI/PoacybOw9vvvWm0XlOSePGYcvmLShCHoLRtBq6QpShDCW4d/zdbfU0LJaWdgx///s/EIQwROBqVUKt0OFCRSaWLHkRH374r3bd/rGisgJb//c/7Ph5B8q1FQgLC8O4pLGYP/95vPH6GzhRfxg+BnXDsbJyeHp4YPHixW43l4eoLTCpQU7R0i4qe1RpmBvWR0Su65dffsGunbsxd3YAhg++WpkwoL8KN93giZUfJGPEiBFGhwaak5ubg4MHf8HTMzWNCQ2g4Y9dfxjshdNna7Fp00bcddddJj9IBwYGYvYTs/HYo4+htLQUPj4+8PHxse2JGiFJEhYtWoolSxbhyQX5iO2rwpWHr6sTJs+rqxcu2d4gPz8f33//Pfbt3w1djQ5dukTitttuR2JiosXrVSgUeOaZuXjppWX4v0UlGJuoRFAnBU6c1OHnPTqEhUXggT8/4OBnQkRE7YG1baeuMFWl0dKAcCIiU3Q6Hf75wQcIlIIRKXo1Vlyo4AV/Qyec1h3D6tUfY+nSF5ud26NHT0yZMgUbNmxAFbQIEMGQIEMZSnBZVojYfrEYPWp0Gz+jlq1fvx4+cj901kc3qTDxlJToauiFjNIU7N69G6NHu97a7aGwsBDz589HScll+ItAeAovZJfmYlX6KvTv3x/v//3v2LlzJ9LS0iBJEmJjYzFmzGio1Y6bjUjkzpjUIKcxtovK2ioNa7EEnMj1bdnyE3r3UDVJaFwxYqgXNmyuxuYtm61Oavz221HI5RKGDzK+o39kgjc2/HQJZ86ewQ19bzD7WJ6enggJCbHq51sqJCQE7777/7B7927s27cX2uoqaDQXsXNfDUYmNO8ze/psLXLyavFg3E0OWY+t0k+kY9mLL0LpWY/EBCU0/p44mn4Rb7/9Ng4c2Ifnn7e8XVf//v3x5ptv4etvvsZn3+xDfb0eGo0akyZNwB133AFv747Zf5eIiK4yt2nK1uHgpqrCyXp1dbVISUlFaVkpgjoFIS7uJshktrftJHJ3Bw4eQGVVFbqiT7MWUnJJgSBDBI4cSUFJSQkCAwObnf/www8honMEvv36W5wtOAEA8PP1w53j78Tdd09zwcHSAikpKQjRRxptmaWUvOArqXH48GG3TGoYDAYcPnwYhw4dhK62FlHdozBq1Cio1erfjxB4/bXXUHm5CjGGOCglVUPjAQFoUYrjacexceNGPPzww7jrrruc+VSI3IarvcsRWVWlYUvrKZaAE7m2nJyLGBxnPMiVJAn9YhRISbchqWkwQJLB5MBpj99vN+j1Vj+2vXl6emLUqFEYNWoUAGDnzmT87W8r8e1GLaaM94VM1rDW/MJ6rPpXOTp3DsOggdYNKXekurpavLbiFfToBix+JggqVUNVxtTxwK+pNXj1nV/w/fffYerUOyx+zO7do/Dcs3Ohn6NHbW0tVCqV0YCIiIg6LlOtpwDjVRrm2k61pKWYg5uprtqyZQvWrfs3Kiq0jbcFBgRg5uOPIyEhwYkrI3KewoJCeMo9oTQ038gFAN7whRACly5dMprUACQkjUvCuLHjUFhYAL1ej5CQUBdMZjQQ4vd4DKartSUhg94FYjFrFZcU48UlL+JC1gV4y30gFwrs2rkbn376KZ5++mmMGDECJ0+ewqnTpxGFPg0JjWv4SRoEGcKwZfNm3HffvVAq2VaXyBKu+W5H7ZqpXVT2rNJgGTiR+/Ly8kbxZa3J+4tK9PD2tr7lU0xMDOrrBH79rQaD4psHDwcOV8PTU4HuUa43nG7kyJG4eDEb6778Ept+1iG2jxxl5QIpaToEBXXC8uVLXWq34969+1BaqsWrC0IaExpX3BKnQmKCF378cSOmTJlqdWJCLpfDy8t48EdERHQ9czEGYDqhYUmVRksxBzdTAZu3bMbf3/87AhGCPugJJbxQjUoUlmbj9ddfxwsvvIBBgwY5e5lEbc5P7Yd6Qx3qRV3jYOhr6VDTeJw5kiQh1A0qyiRJQo8ePVF4pgjBovn7br2oQ6WsHDExrZ9T2JYMBgOWLlmKgpxC9EJs4zyMelGHXHEeK1euRFBwEE6dPAm5TA61wViCCvBHEApqcnD+/AXExMS05VMgcluu14CbOgRTu6haW6VBRO5v+PCR2P+rDsUlzXfpFBXX41CKDsOGjbD6cXv27ImYmF5Y/Z9KFJXUN7nv9LlarN9UhcTEP8LXx9fmtVtDr9fjUtEllJeXAQCysy/ik0/+jXfffReffvopcnNzrjlawv33349Vq1YibsBI5BZ1gV7WG48//he89977iIjo3CZrttTJk5no2kWJzuHG904MHqBEYWERSktL23ZhRETULlk7rw9oue2UOZylYZm6ulqsW/tvBCIEXaVeUEnekCQJ3pIvuokY+EGD1atXAzA9N4yovUoYOhQymQyX0Py9SAiBYlkeevTogYjwCCNnu6eJEyeizHAZJaLpe7ZBGJAtnYVMLsPo0WOctDrb/Prrr8jKykJXfS/4SOrG2xWSByJFT3hJ3vjm62+A3zdyCRPvd1duZyU6keVYqUEuwV5VGubKwA11h7hbisgNjB07Fhs3fo8X37qMJx5So0/Php1LJ07W4f015dAEBGD0GNv6rD733FwsWDgfTzxfhD8MUSEiVI5TZ+tx8EgNevbsgUceedieT8WompoafPXVl9iyZTPKyysAAAEB/rh8uQxqtQLhIQocOFCHL7/8EhMmTMBjjz3W+OG2R4+eeOrJpxy+xtaSyeWorRUQQhj9YK6ra/hfuZx7K4iIyD7MtZ4yxViVxpVYw9xw8Jaw9VSDw0eOoLKyApFovvNakiQEiwicyT2OU6dOo1evXk5YIZHzqNX+mHrnHfj6y68BIRCMcCgkT1SLShRI2agU5fjzn//a7Lzs7Iv47rvvsH/fftTW1qF79+64feLtGPGHES7/B/HExJFIS0vD//73P1yWLsHXoIEBepTJi1GHWsx7bh78/d1rKPbBgwfgI/dtrNC4liRJ0OiDcfjwr5g+fTr0Bj3KUQINgpodW4oi+Pj4oHv37m2waqL2gUkNchn2qtLgriki9+bn54eXXn4Fr77yMua/lIugTp4QAiguqUXXrp3x8pJFNldThIaG4e1V7+Knn35CcvJ2HDxSjuCQEMycmYTRo8fA09PTzs+mKZ2uBosXL8SFC+cwZoQK8bGB0FYa8L8dVbh8GZg2yRsTx/qiVmfATz9XYc0XG6HR+GPatLsdui57i4+Lx/fffY+M03Xo26v5NU3eW43oHt2vGZxHRERkf0dz8q2q0rA0oWFJvMHNVEBZaRkACUoY7w+vQkM7SVZu0vWqq6ug1+vh6+uLhmnK7dP9990PmSTDt99+i8L6HMglOepFPdR+/nh+9nzExw9ocnxKyhG8/PIrkBnk0Og7wQcK5J4qwN/e+ht+OfQLnn32WRdPbEh48snZGDAgHht/2IgzZ85AofDAsMEJmDx5Erp3d702wC3R1dZCLky3AZZDAYNBoGvXrrjxxhtxOuMMlHoveElX2ymXimIUS/n40+1/cng8StSeMKlBbaql0vDr2atKg4jcS5fOXfD++/9AamoKjh1LgyQB/WJjEXdTXKs/qKvVatx99924++62TxRs2PAdzp07ixULA9Ez+uoH1pFDvfDhp2VY83k5hg3yQqBGjsm3+aKoRI8NG9Zj8uQpUCqVbb5eWw0YMADdukXinX8VYNFfNegS0fBxo75eYP2mChz+rQbPPncn2nOQSkREbcPa+OIKU7M07JHQoAZBQZ0ACNSgEl5oviGlGpUAgODg5ruWyT2UlJRg+/btyM/Pg6+vL0aMGIEePWxP6O3btxfffLMep06dBACEhYZh0uSJGD/+dshk7a/CV5Ik3HfffZg8eRIOHjwIrbYCYWGhuOWWgc0GfldVVeG1Fa/BW++HboYYyKTfr4do2OW/e9du9Ot3I5KSbnPCM7GGhISEYUhIGObshdhFVPfu2Lt7r8nZKBVSKUKDQ+Dp6YF58+Zh4cIXcDLnN/hJGngalKiWV6FSX44hg4dg+vTpTngGRO6LSQ1qc9eXhrc0vO96rNIg6hgkSUJ8/IBmO5Tcl8CWLZtw6zBVk4QG8HtAc6ca23ZVY/uuKtw1qWEg4LhbffD9lkKkpx93q+sgSRIWLVqCJUsX4ckFBYjtq4LGH0jL0KPkch3uuedujBwx0tnLJCKidsJY6ylTMUZLVRrmWBJrsOXtVXFx8dBoNCgoy0E30bvJxhSDMOCSLBdRXaPYbsVNrV+/HuvWrYMMElSSL+qgw/r1GzBo0CDMnTvX6g05X371JT795FOoZQHoip6QQYaywsv46MOPcDwtHXPnzW1HiQ2BS0VFMOgNCA4Ohq+vH/74x1EoLS2FTCaDQtF8539y8g7odDpEiX5XExq/00hBKEUxvv/+ByQlJYEbh9rO6NFj8Nln/0Gu4TwiRc8m73NaUYpSWTFmTJgBQEJAQADefnsVdu3ahR07dqC8rBw9Irpi7NhxuOWWW1y8yobI9TCpQS7BmtZTxrRUpcHggoicrbq6BkVFl9GvT4DR+328ZejZ3QMXc68OMff3awhYdDpdm6zRnkJDQ/Heu+9j9+5d2LdvH0q0VRgytBuSkpLcsrSciIjcj7HWU4DtVRpkHblcjpkzZ+KNN96AkPQIFp2hhBeqUYFCWS6qpQo8NnMe+AdY95OcnIw1a9YgBBEIRSTkkgJCCJSiCEd+PYx3330Xc+fOtfjxsrIu4NNPPkUoIhEuuja+JDQIRqnohL379mLwrsFITEx0zBNqMwKbN2/G+m/XIy+/IfEaGBCAqOgonD9/AcXFxQCAbl274Y4/3YFbExNx5WKcPHkKPjI1PA3Gk0VqEYCs7FPQ6XRQKo23fCP78/f3x1NPPYm3334HOqkaGkMQFPCAVipFqawI/WNjMWHChMbjlUolxowZgzFj3GsgOpErYlKD2owjW08BrNIgItfm4eEBmUxCaZne6P1CCFwu0yOy89X/NKekNSQzunbt2iZrtDdPT0+MGjUao0bZNtidiIjIHFtbT13Pkha3llZpuDqDwYBTp042tLkJD0OXzo6NoYYPHw6FQo7VH6/B6YK0xtu7R3bDzMfnoV+/WIf+fHIEgS+++AIaqRMicHWjiiRJCEAwDAY9du/ejQceeABhYaa7LFxRWnoZa9asgUKmQIAhuNn9GqkT1FIAfvxxk9snNf71r39h48YfoZGCEIU+kCBD2eUiHD6cAk94ojv6QMCA0uwirFq5CtnZ2Xjg/gcAAHK5DAYYTD72lfvaTzWL+7j11j8iODgY33zzDY4cSYEQAiFBIXhwwp8xceKkZq3EiMg++C+L2pSx0nBrGNs9xZ1TROQOFAoFhgwZjK27jmD8aB8oFE13Jf52vBa5+Xr85cGGoZll5Xp8saES/fv3Q0REZ2csmYiIrpNxrhBeXpXOXgZdw1TrKVMDwq2p0rBljoYrV4cnJyfjk08+waVLlxpv6xPTB4/Pehw9ons47OcOGTIUgwcPwcmTJ1FWVoagoCBER0eBFRruKTs7G7m5uYjGDUZ/hQEIQa7sPA4ePIDJk6eYfJyamhp88MEHSE5OhsHQ8Af5DByBvwhEJHo2mU/gY1AjK+uCvZ9Kmzpx4gQ2bvwRnRGFYEQ0Xjs1AqAWgTiHDAgYECAFI0AEowDZ+OrLrzAsYRiio6MRHx+PrVu3oRqVTYZMAw2bo8pkRejX90Z4eHDQtDP06xeLfv1iUV9fj/r6eqhUSvA9jsixmNSgNmFqF5WxgMPa1lMAqzSIyD3ceeef8Pzzv+CN90vx6L1+CAlWQBgEfj1ag7f/VYqQIDkqqwz4fL0WW3bUQMALi2c/6exlExHR73r37wofHz9nL4PsyFSVRnsbDL55y2b8/f2/Q4Mg9EIsPKBEFbS4eCoH85+fjzfffMOh7SElSUJMTIzDHp/azpW2qAo0H4oMADJJBoWkMNs+1WDQY9mLy5CZkYlwQzcEIAiAhDKUIA8XcBpp6CX6Qy41zJbQow5KT/f+Y/3mzZvhJfdGkL55YtVf6gRf4Y8i5CMADdUqwYjAZXkBftr8E2Y/MRtDhgxFSHAILpacQjd9DJRSw0YogzCgAFnQGsow9Y7/a9PnRM0pFApWZhC1Ef5LozZjTZWGsdZTtlZpcJ4GEbmKXr16Y8GCBVi58m+Y+VwhIjurUFGhR0lpHQIDNbhcpsXr712GSuWJxMRRuGvaXQgOal6GT0RERMaZGxBu7SwNaxIartx6qqamBqs/Wo1AhCISPRqH0XpCCbUhAKfrjmH16jVYvny5k1dK7iAsLBwKhQLa+lJ4w7fZ/dWiAjq9zmz71AMHDuB4+nH0wI3wkzSNt3dCKLyFH04iFSUoQDAiYBB6lMmLMWrYKEc8nTZzMesivPS+JodB+0KNIlx9/5JJMnjp/XDhfEOFikKhwLLly7Bo0SJklKTAD/6QCQWq5Vro9Do8/PDDGDhwYJs8F2c4c/YM0o+nQ5KA2Nj+6Natm7OXREROxqQGOZy9et2a0l52TxFRxzBw4CCsXbsOu3btxvnz5+Hp6YkhQwYjJiYG9fV6VFVVwcfHB3K53NlLRWnpZWzc+CN27vwZ5eVahIQEY8yYJIwbNw5KpfEhhURERM5makD49Vqq0rCWq26k2r9/H3Q6HaIR2ewPqjJJjiBDOFJTU1FcXIROnYKctEpyFz4+Phg5ciR2J++GRt+psWIAaKgayJNlQaMOwMCBg0w+xrZt2+Er84ef0DS7z0vyhr/ohBIUQi0CkSM7CyEXmDRpoiOejl3k5ubg8OEjqK+vQ48ePdC/f39c33rI28cbhVKxyceoQx3kaPr5Xy+rh7eXd+P3nTt3xgcf/AM7d+7EgQMHoavRISp6KJKSktClS6Rdn5OruFR0CW++8SYyMjIgkzVcH4NBj/6xsXhu7nPQaAKcvEIichYmNahNmOp1ez1rWk9xlgYRuSulUoUxY8Y0u12hUECtVjthRc3l5uZg4QsLUF2lxcihSoSHKnHqbDHWrPkYO3f+jJdeehXe3t4tP1AL9Ho9JAmNQQoREZG9WVOl0d7aTgHApaIieMo94WkwviHBCw39+YuLS5jUIIvMmDED6ceP43RhGgIMQfCGGnXQoURegHpZHZY+t8TsBp2S4hIoDSqTIwdU8EI5SpCBI/D28sbShUtdcsZcdXUVVq5chYMHD0Imk0MGGeoNdYiIiMDzzz+PqKirLd2G/2E4jh37B3SiukkiCADqRR0u4xKCcfX9qEZUQytKMWz4sCbHKpUqjB07DmPHjnPsk3MBVVVVWDh/AcqKteiOPlAbGhIY5ShBZvopLFz4AlatWsXNVkQdFJMa5FAtVWkY20VlaespoH0FG0RErkPgrbfegLeyCiuXBiFAczUoPXu+Foteu4DVq1fjySdtnfchsGvXbvzwwwZkZp4GANx4Yx9MnjwVQ4YMtcP6iYioIzI1INwYYzGGrQkNV29366/2R52hFvWiFgqp+VwCHaobjvP3b+ulkZvy9/fHm2+9ha+++grbtm5FYVUuZDIZhgwejLumTWtx8HynoEAUXigChPH7a1AFXz8fPDhjBkaM+AOUSpUDnkXrCCGwfNlLyMw4ia7oCY0hGBIkVKIcefkXsHDhQrzzzjsICQkBACSOTMTXX36N8yUZ6KyPhg/UkCQJ1aICWTgNCUAnhEMIgSpokS0/g+DAYIwYMcK5T9SJtm79Hy5dKkKMiGuSCNIgCEq9F05m/4adu3Zi7JixTlwlETmLzNkLoPbrSkLDmlkalrK0SsPVAwwiIleUmXkSp0+fwyP3+jZJaABAdHdPTB3vjZ07d6CissKGRxf48MOP8NZbb8HLIxuzH/bHrBn+kPTn8eqrK/D555/b50kQERHBukpwoH1umkpISIBcocAl5Da7zyAMKJLlo09MH4SGhjphdeSu1Go1HnnkEXz2n8/w6aef4ssvv8T8+QtaTGgAwKhRo1FhKIdWlDa7r1pUolwqwT3Tp2PMmDEumdAAgJSUFBxPP46uhl4IlEIhk2SQJAm+kj+iDH1RX1OPDRs2NB6vUqnwyquvILhzEE4jDZmKI8iUpyATv0Enq4YeelxQZOCkIhWncAyBYQF45dWXO3QVwo6fd0CNwGaVLQDgJflALdMg+ecdTlgZEbkCVmqQQ1mT0DAWcJjqcwu0z4CDiMgVnDx5Ep6eMsT3Mx5EDRmgwqdfa5F14QJuuOFGqx47JTUVP/zwAx7/sz/Gj/ZpvD3pjz746jstPv38c/Tv3x8Xsy9i29YtKCwshFqtRmLiKCQljYOvr1+rnhsREbVPpgaEA8Yrwe3FHTZR+fn5Ydq0afjss8+gFwaEoDM84IkqVKBAdhFVqMCfH/yzs5dJbkomk1vdPnXIkMGI7ReLE+knEGLojAAEQ4IMpShCoSwbXbpEYvTo0Q5asX3s2rUL3nIf+Ok1ze5TSB7w13fCjh07MHPmzMbbQ0ND8d577+Ho0aNITU2FwWBA3759EB8fjwMHDiAzMxMyuRzxcXEYMOBmk0PFO4qyci2UwnSbMg+DEqVl5W27KCJyuOzqsxYdx6QGOURLbadMlYYb63VrrCycCQ0iIseRy2UwGATq6gU8PZtHEbo68ftx1s/B2LTpR0R1VeK2Uc3ncdw50Rdbd+nw+usrUF6uxS1xStzSzwN5hZfxxRefYsuWn/DqqysQHBxi/ZMiIqJ27/r4wlyVhqnWU9Yw1B2y+hxnufvuaVAo5Pjyv18ivSYPDX8lFAgKDMLSp5agX79+zl4idSBnz55DcEgwsrKykKfNQq64AACQSRKGDBmC2bOfhErlmhUaV2i1Wij0niYTD57wwqWqPDT02Lp6jCRJuOmmm3DTTTc1OX7kyESMHJnouAW7ofCwUGRdzjXdpkxWhajw3m27KCJyqKyq0xYfy6QGOYwj2k4REZHjxcXFob5eYM+hGvxxePPkQ/LeaqjVvoiOjrb6sc+fO4PhAz2MBoAymYT4WAW279bi9cWd0LvH1b7f906tx6LXSvHW397E66+9afXPJSKijsnUgHBjbNk45epVGldJuPPOP+H22yfg119/RUVlBcJCw9C/f3/IZOxKTW1F4JNPPsFXX30NlVwFb70avlBDizKo/fyw8IWFVlcBO0toaCiOyo/BoDdAJjX/N1QFLYKCgmCyzIBaNGbcWKw6vgoVKIOv1HTmT7koQYUo7xAD04k6gmuTGTLFzRadw08vZHctVWkYY2nrKWuqNNyhFJyIyBVFRHTG4CGDsPo/WqRn1jbeLgwCP++pwo/bqjBhwkR4eDQfNtoSDw9PVFQaTN5fUWmARi1rktAAgJBgBR6+1wcn0jNx9qxl5ahERNQxmGs9dT17tZ5y11hDpVJh+PDhSBqXhLi4OCY0qE0lJ+/EV199jXB0Q4x+ALpKvdBD6oe+GIC6Kj3effc9GAymPye6krFjx0Cnr0Exmr//VItKlMmKkZSU5ISVtR8j/jACsf1icV52AnniAqpFBapEBXLFOZyXZWLgwFswcOBAZy+TiFrpSkJD5jEIMo9BFp/HTzBkV5YMBzcVdFjSeoqIiNrG00/PQecuPbDglSLMXVaMv/3jMp6YX4x3/lWKkSNHYtq0u2163EGDhmLPIR2qq5sHrGXlehw4XIOhtxhvNzAwTgWFQsKJEyds+tlERNR+tWXrKXdqO0XkOgS+/eYb+MsCESp1aVK1q5S80EXfE7m5uTh8+LAT12i57t2jMHHiROTgHLLEKVSKclSLChSIizgrS0dkZBdMmDDB2ct0a3K5HEtfXIrxE8ajTFmETPyGk/gNFV6lmDp1KhYsWMjELJEby6o63SShAQBppdlIL8u16Hy2nyK7s6TtlLF5GvbEQIOIqHV8fXzx2muv49ChQ0hOTsZlbTn63hiKp54egxtvvAG2ltLffvt4/PTTJrz6TimenqlGUGDDR5H8wnqs/EcZDAZgULzxpIZeL2AwgMELERE1smZAuDnWtp5yxyoNImcqL9fi/IUL6IbeRj9G+kh+8JJ7IzU11W123z/22KMICQ3Bt19/i1OlxwA0VCUn3joSDz30ELy8vJy8Qvfn6emJRx99DPff/wDOnTsHSZIQFRUFpVLp7KURUStcn8wAGhIaABCi7GXRYzCpQU7niNZTAAMNIqLWksnkGDJkKIYMGWq3xwwODsHixUvw6qsv47FnLiGmpxIGA3DyTA38/f3g6SmQkqZD7A3NA5W9h2pgMAj079/fbushIiL3Z+mGqTTtxVZXgnPzFJFtrrSVksw0DJFBBr1e31ZLsgMJkydNxsQJE3Du/HnU19WhS5dI+Pj4OHth7Y5KpULfvn2dvQwisgNj1RkAEKqMAQBU1lZY9DhMapDdWDJL42hOvtGgw56tpxhoEBG5tn79+uHjj9dgx44dSD+RDgkSxt7WHyNGjMAXX3yB7777Fr2iPTDkZlVja4LTZ2ux+osKDBx4Mzp37uzkZ0BERK7MXOup1rgSZ3DzFJH1/P39ERIcgvKiEmjQqdn9NaIalfUV6Nu3jxNW1zoymRw9ons4exlERC7NXHXGlYTGbwX5qKmotOjxmNQgu7Kk9VRbYKBBROTavLy8MH78eIwfP77J7ffddx/y8nLw2rsHEN1NiR7d5cgrMCAtowY9e0ZhzpxnnLRiIiIgLbcQXt6WBVrkXJa2nmI1OFHbkCQJEydNwJrVa6AWgdBIVxMbelGPHNkZ+Pv6I2HYMCeukoiIHMFUdQbQNKEBAH19LdvEyKQGtRlj/W5Ti/KaBRzGysItDTYMdYcYaBARuTGFQoHnn1+AlJQUbN36P5zPzYdarcGzz92KhIQEeCg8nL1EIurAevcKhY+vn7OXQWaYqtJobespVoMTtd7EiZOQcSITe/fthVrSwMegRh3qUCYrhsJThmWLlvOzHhFRO2JpdcYV/fwiUanVWvTYTGqQXVjSegpw7IBwBhpERO2DJEkYMGAABgwY4OylEBGRG7JmQLgl2HaKyD5kMhnmzpuLoXuG4sdNm3AxKwsqpQq3/2E8JkyYgJCQEGcvkYiI7MSa6ox+fpGN96VnW/Y3ZiY1yG7MtZ4yVaVxPWMDwq3BQIOIiIiIiK5lKsawpvUU4wwi+5DJZBgxYgRGjBjh7KUQEZED2FKdAQDHshr+ThzbKdSin8OkBrUZWweEF+gyW3xsVmkQEREREXVs5gaE29p6inEGERERkWVsrc64ktCICwpHZQXbT1Ebaan1lKVVGuaY20HFcnAiIiIiIgLs33oKYJxBREREZE5rqzPigqz//MakBtmFudZTgGVVGq1pPcVAg4iIiIiIiIiIiKjt2KM6wxZMapBDHc3Jt2o4uLWtp1gOTkRERERERERERNR2nFGdcS0mNahV0s8UtFilcT17tp4CWKVBRERERERERERE1BZMVWdcSWYAzaszriQzgNYnNAAmNciBzFVp2KP1lKHuEBMaRERERERERERERA7m7OqMazGpQQ5hbDg4YL5K4/rWU+aw7RQRERERERERERGR47lCdca1mNQgm7XUesrSKg1TCnSZZltPsUqDiIiIiIiIiIiIyDGsqc6w5yDwljCpQXZnqkrDlDTtRVZpEBEREREREREREbkIV6vOuBaTGmST9DMFZu83VqWRWpRncZVGS1ilQUREREREREQdi8DJk6eQkpICg8GAPn1iEBcXD0mSnL0wImpHXLU641pMapDNjLWesqVKw5gCXabR2zkcnIiIiIiIiIg6mpKSEqxY8RoyMzPgIfOETJJBp69BeFgYFr7wArp16+bsJRJRO+DK1RnXYlKD7M7aKg1TrafMzdMgIiIiIiJyJG6oIiJXUVdXi0WLFuFSXhGi0Rd+hgAAQBW0yL10Hi8sWIh3/997CAwMdPJKichdXUlmAKYTGs6uzriWrE1/GtE1rK3SICIiIiIiMia1KM/o7eZiDm6iIiJ3sWfPHmRnZ6O7PgZqKRCSJEGSJPhIakTp+6KqqgY/bvrR2cskIjd1bXWGzGMQ0kqzW0xoHMvKw7GsPMQFhbd5QgNgUoPagKkAA2CVBhERERER2Ye1leFERO5i167dUMs08JJ8m92nkDzgb+iEnTt2OmFlROTOsqpOm203FaqMwW8F+SbbTTkjmXEF20+R1UwNCT+ak2+09RTQPMAwtWPKHJZ/ExEREREREVFHU6HVQmHwBEzMA/eEEuVVRW27KCJqF8wNA7/CFdpNXY+VGmQTY0PCrWVsxxTLwImIiIiIiIiIrurcpQuq5RUQQhi9v0qmRUS4c//ASETtw/UJDWOcndAAmNQgBzPWesqWKg0iIiIiIiIioo5o3LhxqNZXoQTNO2doRSnKDZcx7rYkJ6yMiMg5mNQguziak2/yPmO9bU1VaRARERERERER0VV9+/bBuKRxyMZZZIlT0IpSVIpy5IhzOC/LQGz/WPzx1j86e5lERG2GMzXIbkzN07hWS1UaplpPGeoO2bQmIiIiIiIiIiL3JuGJWU+gS5cu2LB+A84UHwcA+Pj4YuptUzF9+j1QKPgnPiLqOPiORw6TWpRn1yoNDgknIiIiIiIioo5IkiRMnjQZEydMRG5uDgwGA8LDw+Hh4enspRERtTkmNcgq6WcKbB4SbmuVBhERERERERERATKZDF26RDp7GURETsWZGtRqxuZpGBsQDnCWBhERERERERERERHZjkkNsgtj8zSMtZ4yhVUaRERERERERERERNQSJjWoTaRpLxqt0rCEoe4Q52kQEREREREREREREZMaZH+mWk8ZU6DLZJUGEREREREREREREVmESQ2yWPqZgma3Hc3Jb7H1VEsDwomIiIiIiIiIiIiILMGkBlmlT1x3m87jgHAiIiIiIiIiIiIiai0mNciuUovy7Dog3FB3qLVLIiIiIiIiIiIiIqJ2gkkNstnRnPwWjzHVesqaKg0OCSciIiIiAHjllVeQkJAAb29vaDQai86ZMWMGJElq8pWUlOTYhRIRERERkcMonL0Acm/G5mlcz1jrKaDlKg0iIiIiomvV1tbirrvuwtChQ/Hxxx9bfF5SUhLWrFnT+L1SqXTE8oiIiIiIqA0wqUF2k1qUZ9FxnKVBRERERLZYtmwZAGDt2rVWnadUKhEWZnyjDRERERERuRe2nyKLpJ8psGhIuKXzNCyp0jDUHWLrKSIiIiJqteTkZISEhCAmJgazZs1CcXGxs5dEREREREQ2YqUGOUya9mKz1lOs0iAiIiKitpSUlIQ77rgDUVFROHPmDBYuXIjbbrsN+/fvh1wuN3qOTqeDTqdr/L68vLytlktERERERC1gpQa1KP1MQbPbjubkWzRPwxhLqzSIiIiIqP2bP39+s0He139lZGTY/Pj33HMPJk2ahNjYWEyZMgUbN27EL7/8guTkZJPnrFixAv7+/o1fkZGRNv98IiIiIiKyL1ZqkFlXEhottZ6yZJ6GtVUabD1FRERE1P49++yzmDFjhtljoqOj7fbzoqOjERQUhNOnT2PUqFFGj1mwYAGeeeaZxu/Ly8uZ2CAiIiIichFMalCLLJmlAVg2T8OSKg0iIiIi6jiCg4MRHBzcZj8vOzsbxcXFCA83/dlVqVRCqVS22ZqIiIiIiMhybD9FJhlrO2WpNO1Fm89l6ykiIiIiMiYrKwupqanIysqCXq9HamoqUlNTUVFR0XhMnz59sH79egBARUUF5s6diwMHDuD8+fPYvn07Jk+ejJ49e2LcuHHOehpERERERNQKDktqvPLKK0hISIC3tzc0Go1F5wghsGTJEoSHh8PLywujR4/GqVOnHLVEMsNc26mjOfkWPcb1Q8KtwdZTRERERHS9JUuWID4+HkuXLkVFRQXi4+MRHx+PX3/9tfGYzMxMlJWVAQDkcjmOHj2KSZMmoXfv3njkkUdw8803Y/fu3azEICIiIiJyUw5rP1VbW4u77roLQ4cOxccff2zROW+88QbeffddrFu3DlFRUVi8eDHGjRuH9PR0qFQqRy2VTDDXdsraIeEFuky2niIiIiKiVlm7di3Wrl1r9hghROP/9/LywpYtWxy8KiIiIiIiaksOS2osW7YMAFoMOq4QQuDtt9/GokWLMHnyZADAv//9b4SGhmLDhg245557HLVUuo61badSi/IsmqdhCbaeIiIiIiIiIiIiIiJTXGamxrlz55Cfn4/Ro0c33ubv74/Bgwdj//79TlxZx2Ku7ZSlWjNPA2DrKSIiIiIiIiIiIiIyzmGVGtbKz2+Y0xAaGtrk9tDQ0Mb7jNHpdNDpdI3fl5eXO2aBHYi5hIYj52mwSoOIiIiIiIiIiIiIzLGqUmP+/PmQJMnsV0ZGhqPWatSKFSvg7+/f+BUZGdmmP789sbTtlC3zNCzFKg0iIiIiIiIiIiIiMsWqSo1nn30WM2bMMHtMdHS0TQsJC2vY2V9QUIDw8Kt/NC8oKEBcXJzJ8xYsWIBnnnmm8fvy8nImNmxga9up1KI8i47jkHAiIiIiIiIiIiIiai2rkhrBwcEIDg52yEKioqIQFhaG7du3NyYxysvLcfDgQcyaNcvkeUqlEkql0iFr6mhsnaNhjyHhbD1FRERERERERERERC1x2KDwrKwspKamIisrC3q9HqmpqUhNTUVFRUXjMX369MH69esBAJIkYc6cOXj55Zfx/fff49ixY/jzn/+MiIgITJkyxVHLJFjedsoSadqLNs3TANh6ioiIiIiIiIiIiIjMc9ig8CVLlmDdunWN38fHxwMAduzYgcTERABAZmYmysrKGo+ZN28eKisrMXPmTJSWlmL48OHYvHkzVCqVo5bZ4VnTdupoTr5D52kQEREREREREREREZnjsKTG2rVrsXbtWrPHCCGafC9JEpYvX47ly5c7allkhK1tp+w1T4Otp4iIiIiIiIiIiIjIEg5rP0Wuzx5tp+wxTwNg6ykiIiIiIiIiIiIiahmTGh2cpVUaR3PyWzzGlnkahrpDTGgQERERERERERERkUWY1CCLXTtPw9LWU+aw7RQRERERERERERERWYNJjQ7KmtZTpqo0Wmo9VaDLbHGeBqs0iIiIiIiIiIiIiMhSTGp0YNYMCL+2SsOYNO1Fq342qzSIiIiIiIiIiIiIyFpManRAra3SMNV6ytp5GqzSICIiIiIiIiIiIiJrMKnRQVlSpXEloWGsSqOl1lPmcDg4EREREREREREREdmCSQ0yq6W2U9Zi2ykiIiIiIiIiIiIishWTGh2Mpa2nTA0HN9Z6ytg8DXNDwlmlQURERERERERERES2YFKjA7J0QLipKg1jracsmafBKg0iIiIiIiIiIiIiag0mNTqQ1lZp2AOrNIiIiIiIiIiIiIjIVkxqdDAtVWmYGw5urPUUEREREREREREREVFbYVKDmjE3HPz61lNp2ovNWk8V6DIdsi4iIiIiIiIiIiIi6tiY1OggLGk9Zc+2U6aGhBMRERERERERERER2YpJjQ7EkgHhpqo0Wtt6ikPCiYiIiIiIiIiIiKi1mNToACyt0jDXdgow3nrKGhwSTkREREREREREREStoXD2Ahwl41whvLwqnb0Ml2GuSqM1baeun6dBREREREREREREROQo7Tap0bt/V/j4+Dl7GW7DXJVGalFesyoNUzgknIiIiIiIiIiIiIgche2nOjhbqzTMtZ7ikHAiIiIiIiIiIiIicgQmNcjmKg22niIiIiIiIiIiIiKitsSkBtkNW08RERERERERERERkSMxqdGBtdR6ylSVhrWtpwx1h9DVu6f1CyQiIiIiIiIiIiIiugaTGh2cudZT5rD1FBERERERERERERG1NSY1OihbqzSIiIiIiIiIiIiIiJyFSY0OzJYqjTTtRaNVGpynQURERERERERERESOxqQGNZNalGfTecbmaRARERERERERERER2QuTGh1QS62nAFg9IJyIiIiIiIiIiIiIyNGY1OigTLWeaqlKg62niIiIiIiIiIiIiMhZmNToYGyt0miJqdZThrpD6Ord0+rHIyIiIiIiIiIiIiK6HpMaHZAtVRqmBoQTEREREREREREREbUVJjWoCVuqNIiIiIiIiIiIiIiI2gKTGh3I0Zx8m6s0TCnQZZpsPUVEREREREREREREZE9MalAjc1UabD1FRERERERERERERM7GpEYHYW5AuK1VGkREREREREREREREbUnh7AXYmxACAFBZWeHklbiW6qpK9O4VisoKbbP7aiorcUOXUFRqjdxXUYl+IaGo1Da/noW6UwCACpnpa22or0Z5fWUrVk5ERERErkqrrQJw9TN4e3Xl+VUxxnBZNZWVJuOZSu/mv7cqXZXZOAZgLENERETtW0V1NWSKq5+HqrRVqKxt+vmopqISlbj6GaumshKVquafuezlyuftluKLdpfU0P7+QXbq5BFOXgkRERERUceg1Wrh7+/v7GU4zJUYY9roPzh5JURERERE7V9L8YUk2tm2KoPBgNzcXPj5+UGSJGcvB+Xl5YiMjMTFixehVqudvZx2g9fV/nhNHYPX1f54Te2P19QxeF3tj9fU/lp7TYUQ0Gq1iIiIgEzWfjvbulqM4Qj89+Wa+HtxTfy9uCb+XlwTfy+uh78T18TfSwNL44t2V6khk8nQpUsXZy+jGbVa3aFfkI7C62p/vKaOwetqf7ym9sdr6hi8rvbHa2p/rbmm7blC4wpXjTEcgf++XBN/L66JvxfXxN+La+LvxfXwd+Ka+HuxLL5ov9upiIiIiIiIiIiIiIioXWFSg4iIiIiIiIiIiIiI3AKTGg6mVCqxdOlSKJVKZy+lXeF1tT9eU8fgdbU/XlP74zV1DF5X++M1tT9eU7qCrwXXxN+La+LvxTXx9+Ka+HtxPfyduCb+XqzT7gaFExERERERERERERFR+8RKDSIiIiIiIiIiIiIicgtMahARERERERERERERkVtgUoOIiIiIiIiIiIiIiNwCkxpEREREREREREREROQWmNRwgFdeeQUJCQnw9vaGRqOx6JwZM2ZAkqQmX0lJSY5dqBux5ZoKIbBkyRKEh4fDy8sLo0ePxqlTpxy7UDdTUlKC++67D2q1GhqNBo888ggqKirMnpOYmNjstfqXv/yljVbset5//310794dKpUKgwcPxqFDh8we/9VXX6FPnz5QqVSIjY3Fpk2b2mil7sWa67p27dpmr0mVStWGq3V9u3btwsSJExEREQFJkrBhw4YWz0lOTsaAAQOgVCrRs2dPrF271uHrdCfWXtPk5ORmr1NJkpCfn982C3YDK1aswMCBA+Hn54eQkBBMmTIFmZmZLZ7H91XTbLmmfE/tOBizuCbGPa6JcZNrYOzlmhi7uR7Gf66JMaR9ManhALW1tbjrrrswa9Ysq85LSkpCXl5e49fnn3/uoBW6H1uu6RtvvIF3330XH3zwAQ4ePAgfHx+MGzcONTU1Dlype7nvvvtw/PhxbN26FRs3bsSuXbswc+bMFs977LHHmrxW33jjjTZYrev573//i2eeeQZLly7FkSNHcNNNN2HcuHEoLCw0evy+ffswffp0PPLII0hJScGUKVMwZcoUpKWltfHKXZu11xUA1Gp1k9fkhQsX2nDFrq+yshI33XQT3n//fYuOP3fuHG6//XbceuutSE1NxZw5c/Doo49iy5YtDl6p+7D2ml6RmZnZ5LUaEhLioBW6n507d2L27Nk4cOAAtm7dirq6OowdOxaVlZUmz+H7qnm2XFOA76kdBWMW18S4xzUxbnI+xl6uibGba2L855oYQ9qZIIdZs2aN8Pf3t+jYBx98UEyePNmh62kPLL2mBoNBhIWFiTfffLPxttLSUqFUKsXnn3/uwBW6j/T0dAFA/PLLL423/fTTT0KSJJGTk2PyvJEjR4qnn366DVbo+gYNGiRmz57d+L1erxcRERFixYoVRo+fNm2auP3225vcNnjwYPH44487dJ3uxtrras17LQkBQKxfv97sMfPmzRM33nhjk9vuvvtuMW7cOAeuzH1Zck137NghAIjLly+3yZrag8LCQgFA7Ny50+QxfF+1jiXXlO+pHQ9jFtfEuMd1MG5yDYy9XBNjN9fH+M81MYZsPVZquJDk5GSEhIQgJiYGs2bNQnFxsbOX5LbOnTuH/Px8jB49uvE2f39/DB48GPv373fiylzH/v37odFocMsttzTeNnr0aMhkMhw8eNDsuZ999hmCgoLQr18/LFiwAFVVVY5ersupra3F4cOHm7zGZDIZRo8ebfI1tn///ibHA8C4ceP4mryGLdcVACoqKtCtWzdERkZi8uTJOH78eFsst93ia9Vx4uLiEB4ejjFjxmDv3r3OXo5LKysrAwAEBgaaPIavVetYck0BvqeSeYxZXAvjHsdj3OR8jL1cE2O39oP/XlwbY0jjFM5eADVISkrCHXfcgaioKJw5cwYLFy7Ebbfdhv3790Mulzt7eW7nSn+50NDQJreHhoay99zv8vPzm5WsKRQKBAYGmr1G9957L7p164aIiAgcPXoUzz//PDIzM/Htt986eskupaioCHq93uhrLCMjw+g5+fn5fE22wJbrGhMTg9WrV6N///4oKyvDW2+9hYSEBBw/fhxdunRpi2W3O6Zeq+Xl5aiuroaXl5eTVua+wsPD8cEHH+CWW26BTqfDRx99hMTERBw8eBADBgxw9vJcjsFgwJw5czBs2DD069fP5HF8X7WcpdeU76lkDmMW18O4x/EYNzkfYy/XxNit/WD855oYQ5rHpIaF5s+fj9dff93sMSdOnECfPn1sevx77rmn8f/Hxsaif//+6NGjB5KTkzFq1CibHtPVOfqadlSWXldbXds7NjY2FuHh4Rg1ahTOnDmDHj162Py4RLYaOnQohg4d2vh9QkIC+vbti3/+85946aWXnLgyoqtiYmIQExPT+H1CQgLOnDmDVatW4ZNPPnHiylzT7NmzkZaWhj179jh7Ke2GpdeU76nujTGLa2Lc45oYNxG1PX7OILIcY0jzmNSw0LPPPosZM2aYPSY6OtpuPy86OhpBQUE4ffp0uw0QHHlNw8LCAAAFBQUIDw9vvL2goABxcXE2Paa7sPS6hoWFNRveVV9fj5KSksbrZ4nBgwcDAE6fPt2hPpwHBQVBLpejoKCgye0FBQUmr19YWJhVx3dEtlzX63l4eCA+Ph6nT592xBI7BFOvVbVazV06djRo0CD+0d6IJ598snEIa0s79vi+ahlrrun1+J7qXhizuCbGPa6JcZP7YOzlmhi7tR+M/9wHY8irmNSwUHBwMIKDg9vs52VnZ6O4uLjJB9P2xpHXNCoqCmFhYdi+fXvjh/ny8nIcPHgQs2bNcsjPdBWWXtehQ4eitLQUhw8fxs033wwA+Pnnn2EwGBo/cFsiNTUVANr1a9UYT09P3Hzzzdi+fTumTJkCoKG1x/bt2/Hkk08aPWfo0KHYvn075syZ03jb1q1bm+xU6ehsua7X0+v1OHbsGMaPH+/AlbZvQ4cOxaZNm5rcxteq/aWmpna4905zhBB46qmnsH79eiQnJyMqKqrFc/i+ap4t1/R6fE91L4xZXBPjHtfEuMl9MPZyTYzd2g/Gf+6DMeQ1nD2pvD26cOGCSElJEcuWLRO+vr4iJSVFpKSkCK1W23hMTEyM+Pbbb4UQQmi1WvHcc8+J/fv3i3Pnzolt27aJAQMGiF69eomamhpnPQ2XYu01FUKI1157TWg0GvHdd9+Jo0ePismTJ4uoqChRXV3tjKfgkpKSkkR8fLw4ePCg2LNnj+jVq5eYPn164/3Z2dkiJiZGHDx4UAghxOnTp8Xy5cvFr7/+Ks6dOye+++47ER0dLUaMGOGsp+BUX3zxhVAqlWLt2rUiPT1dzJw5U2g0GpGfny+EEOKBBx4Q8+fPbzx+7969QqFQiLfeekucOHFCLF26VHh4eIhjx4456ym4JGuv67Jly8SWLVvEmTNnxOHDh8U999wjVCqVOH78uLOegsvRarWN75sAxMqVK0VKSoq4cOGCEEKI+fPniwceeKDx+LNnzwpvb28xd+5cceLECfH+++8LuVwuNm/e7Kyn4HKsvaarVq0SGzZsEKdOnRLHjh0TTz/9tJDJZGLbtm3OegouZ9asWcLf318kJyeLvLy8xq+qqqrGY/i+ah1brinfUzsOxiyuiXGPa2Lc5HyMvVwTYzfXxPjPNTGGtC8mNRzgwQcfFACafe3YsaPxGABizZo1QgghqqqqxNixY0VwcLDw8PAQ3bp1E4899ljjfwTI+msqhBAGg0EsXrxYhIaGCqVSKUaNGiUyMzPbfvEurLi4WEyfPl34+voKtVotHnrooSYB07lz55pc56ysLDFixAgRGBgolEql6Nmzp5g7d64oKytz0jNwvvfee0907dpVeHp6ikGDBokDBw403jdy5Ejx4IMPNjn+yy+/FL179xaenp7ixhtvFD/++GMbr9g9WHNd58yZ03hsaGioGD9+vDhy5IgTVu26duzYYfQ99Mp1fPDBB8XIkSObnRMXFyc8PT1FdHR0k/dXsv6avv7666JHjx5CpVKJwMBAkZiYKH7++WfnLN5FGbue1/+3ne+r1rHlmvI9teNgzOKaGPe4JsZNroGxl2ti7OZ6GP+5JsaQ9iUJIYQ9Kj6IiIiIiIiIiIiIiIgcSebsBRAREREREREREREREVmCSQ0iIiIiIiIiIiIiInILTGoQEREREREREREREZFbYFKDiIiIiIiIiIiIiIjcApMaRERERERERERERETkFpjUICIiIiIiIiIiIiIit8CkBhERERERERERERERuQUmNYiIiIiIiIiIiIiIyC0wqUFERERERERERERERG6BSQ0iIiIiIiIiIiIiInILTGoQEREREREREREREZFbYFKDiIiIiIiIiIiIiIjcwv8HdfaE9S58QgQAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Disable LoRA adapters\n", + "hybrid_model.model.inference_model.disable_adapter_layers()\n", + "\n", + "# Plot datasets with decision boundaries after fine-tuning\n", + "plot_datasets_and_boundaries(\n", + " X_task1.numpy(),\n", + " y_task1.numpy(),\n", + " X_task2.numpy(),\n", + " y_task2.numpy(),\n", + " model=hybrid_model.model.inference_model,\n", + " titles=[\"Task 1 after Fine-tuning\", \"Task 2 after Fine-tuning\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Print statistics and save the model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "trainable params: 260 || all params: 902 || trainable%: 28.8248\n", + "trainable params: 260 || all params: 260 || trainable%: 100.0000\n" + ] + } + ], + "source": [ + "# Enable LoRA adapters (already enabled by default)\n", + "hybrid_model.model.inference_model.enable_adapter_layers()\n", + "\n", + "# Print trainable (lora) parameters\n", + "hybrid_model.model.inference_model.print_trainable_parameters()\n", + "\n", + "# Save the model and remove all layers that will be done on the server\n", + "path = Path(\"lora_mlp\")\n", + "\n", + "if path.is_dir() and any(path.iterdir()):\n", + " shutil.rmtree(path)\n", + "\n", + "hybrid_model.save_and_clear_private_info(path)\n", + "\n", + "# At this point, the hybrid_model only contains the trainable parameters of the LoRA layers.\n", + "hybrid_model.model.inference_model.print_trainable_parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "This notebook demonstrates how Low-Rank Adaptation (LoRA) facilitates parameter-efficient fine-tuning of models on private data. By leveraging FHE, the training process ensures that sensitive data and private model updates remains secure.\n", + " \n", + "**Key Takeaways:**\n", + " \n", + "- **Efficiency with LoRA:** While this example utilizes an MLP model with a relatively high proportion of LoRA weights due to its simplicity, the approach scales effectively to larger models like large language models (LLMs). In such cases, LoRA typically accounts for **less than one percent** of the total model parameters, ensuring minimal memory and computational overhead on the client side.\n", + "- **Scalability and Practicality:** The hybrid model approach demonstrated here is particularly beneficial for scenarios where client devices have limited resources. Memory heavy computations are offloaded to a secure server and the client handles only the lightweight LoRA adjustments locally." + ] + } + ], + "metadata": { + "execution": { + "timeout": 10800 + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/use_case_examples/lora_finetuning/utils_lora.py b/use_case_examples/lora_finetuning/utils_lora.py index dafbdea85..d54dd4d9a 100644 --- a/use_case_examples/lora_finetuning/utils_lora.py +++ b/use_case_examples/lora_finetuning/utils_lora.py @@ -3,9 +3,6 @@ from torch.nn import Embedding from transformers import Conv1D -from concrete.ml.torch.lora import CustomConv1D - - def generate_text(prompt, model, tokenizer, max_new_tokens=30): # Encode the input prompt inputs = tokenizer.encode_plus(prompt, return_tensors="pt") @@ -19,7 +16,7 @@ def generate_text(prompt, model, tokenizer, max_new_tokens=30): no_repeat_ngram_size=2, top_k=50, top_p=0.95, - temperature=0.7, + temperature=0.1, do_sample=True, pad_token_id=tokenizer.eos_token_id, ) @@ -29,38 +26,6 @@ def generate_text(prompt, model, tokenizer, max_new_tokens=30): return generated_text -def replace_conv1d(module, module_index_to_skip=0): - for name, child in module.named_children(): - if isinstance(child, Conv1D): - # Skip the module if the index has not been reached, and decrement the index - if module_index_to_skip >= 0: - module_index_to_skip -= 1 - else: - custom_linear = CustomConv1D(child.weight, bias=child.bias) - setattr(module, name, custom_linear) - else: - module_index_to_skip = replace_conv1d(child, module_index_to_skip=module_index_to_skip) - - return module_index_to_skip - - -def get_remote_names(model, include_embedding_layers=False): - remote_names = [] - for name, module in model.named_modules(): - # Exclude the backward module from the remote names - # This is done on the client side - if ( - isinstance(module, Conv1D) - or include_embedding_layers - and (isinstance(module, Embedding) or "lm_head" in name) - ): - remote_names.append(name) - elif isinstance(module, CustomConv1D): - remote_names.append(name + ".forward_module") - remote_names.append(name + ".backward_module") - return remote_names - - def print_weights_and_size(model, print_detail=False): total_weights = 0 total_lora_weights = 0 From f736d8423ad8675e4882846083977d4e58f80f0d Mon Sep 17 00:00:00 2001 From: jfrery Date: Wed, 25 Sep 2024 12:27:35 +0200 Subject: [PATCH 30/32] chore: pcc + test --- .github/workflows/refresh-one-notebook.yaml | 2 + docs/advanced_examples/LoraMLP.ipynb | 8 +- src/concrete/ml/torch/hybrid_model.py | 15 +- src/concrete/ml/torch/lora.py | 64 +- tests/torch/test_lora.py | 440 +++--- .../lora_finetuning/GPT2FineTuneHybrid.ipynb | 1255 ++++------------- .../lora_finetuning/utils_lora.py | 1 + 7 files changed, 569 insertions(+), 1216 deletions(-) diff --git a/.github/workflows/refresh-one-notebook.yaml b/.github/workflows/refresh-one-notebook.yaml index 8b9887101..bf0732344 100644 --- a/.github/workflows/refresh-one-notebook.yaml +++ b/.github/workflows/refresh-one-notebook.yaml @@ -30,6 +30,7 @@ on: - LinearSVR \n - LogisticRegression \n - LogisticRegressionTraining \n + - LoraMLP \n - PerrorImpactOnFMNIST \n - PoissonRegression \n - QGPT2Evaluate \n @@ -77,6 +78,7 @@ env: LinearSVR: "docs/advanced_examples/LinearSVR.ipynb" LogisticRegression: "docs/advanced_examples/LogisticRegression.ipynb" LogisticRegressionTraining: "docs/advanced_examples/LogisticRegressionTraining.ipynb" + LoraMLP: "docs/advanced_examples/LoraMLP.ipynb" PerrorImpactOnFMNIST: "use_case_examples/cifar/cifar_brevitas_finetuning/PerrorImpactOnFMNIST.ipynb" PoissonRegression: "docs/advanced_examples/PoissonRegression.ipynb" QGPT2Evaluate: "use_case_examples/llm/QGPT2Evaluate.ipynb" diff --git a/docs/advanced_examples/LoraMLP.ipynb b/docs/advanced_examples/LoraMLP.ipynb index 4e792f0dd..ede4d5f3c 100644 --- a/docs/advanced_examples/LoraMLP.ipynb +++ b/docs/advanced_examples/LoraMLP.ipynb @@ -37,10 +37,10 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", "from peft import LoraConfig, get_peft_model\n", "from sklearn.datasets import make_circles, make_moons\n", + "from torch import nn\n", + "from torch.optim import optim\n", "from torch.utils.data import DataLoader, TensorDataset\n", "from tqdm import tqdm\n", "\n", @@ -84,11 +84,9 @@ "# Task 2: Two concentric circles\n", "X_task2, y_task2 = make_circles(n_samples=500, noise=0.2, factor=0.5)\n", "\n", - "import matplotlib.pyplot as plt\n", - "\n", "\n", "def plot_datasets_and_boundaries(X_task1, y_task1, X_task2, y_task2, model=None, titles=None):\n", - " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n", + " _, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n", "\n", " if titles is None:\n", " titles = [\"Task 1 Dataset\", \"Task 2 Dataset\"]\n", diff --git a/src/concrete/ml/torch/hybrid_model.py b/src/concrete/ml/torch/hybrid_model.py index ea1dfafcb..fcbc6287b 100644 --- a/src/concrete/ml/torch/hybrid_model.py +++ b/src/concrete/ml/torch/hybrid_model.py @@ -565,13 +565,22 @@ def save_and_clear_private_info(self, path: Path, via_mlir=True): """ path = Path(path) path.mkdir(parents=True, exist_ok=True) - for name in self.module_names: - module = self._get_module_by_name(self.model, name) - # Remove private information + + # Save the complete model (including private info) for the developer + complete_model_path = path / "complete_model.pth" + torch.save(self.model.state_dict(), complete_model_path.resolve()) + + def clear_private_info(module): for attr in ["private_module", "calibration_data", "private_q_module"]: if hasattr(module, attr): setattr(module, attr, None) + for child in module.children(): + clear_private_info(child) + + # Clear private info for the entire model + clear_private_info(self.model) + # Save the model with a specific filename model_path = path / "model.pth" diff --git a/src/concrete/ml/torch/lora.py b/src/concrete/ml/torch/lora.py index 2786d2944..8c717470b 100644 --- a/src/concrete/ml/torch/lora.py +++ b/src/concrete/ml/torch/lora.py @@ -1,5 +1,7 @@ """This module contains classes for LoRA (Low-Rank Adaptation) training and custom layers.""" +from typing import List + import torch from transformers import Conv1D as TransformerConv1D @@ -26,16 +28,19 @@ def __init__(self, inference_model) -> None: self.calibrate = False self.run_optimizer = False - def replace_layers_with_custom(model:torch.nn.Module, skip_first: bool = True): - """ - Replace torch.nn.Linear and TransformerConv1D layers in the model with CustomLinear layers, - optionally skipping the first eligible layer encountered. + @staticmethod + def replace_layers_with_custom(model: torch.nn.Module, skip_first: bool = True): + """Replace linear layers with custom ones. + + This method replaces eligible linear layers in the model with custom layers + that are compatible with the LoRA training procedure. Args: - model (torch.nn.Module): The model whose layers are to be replaced. - skip_first (bool): Whether to skip replacing the first eligible layer. + model (torch.nn.Module): The model to replace layers in. + skip_first (bool): Whether to skip the first eligible layer. """ - skipped = False # Flag to track if the first layer has been skipped + # Flag to track if the first layer has been skipped + skipped = False def _replace(module: torch.nn.Module): nonlocal skipped @@ -47,16 +52,16 @@ def _replace(module: torch.nn.Module): if isinstance(child, (torch.nn.Linear, TransformerConv1D)): if skip_first and not skipped: skipped = True - continue # Skip the first eligible layer + + # Skip the first eligible layer + continue # Determine if weights need to be transposed weight_transposed = isinstance(child, TransformerConv1D) # Create the CustomLinear layer custom_layer = CustomLinear( - weight=child.weight, - bias=child.bias, - weight_transposed=weight_transposed + weight=child.weight, bias=child.bias, weight_transposed=weight_transposed ) # Replace the original layer with the custom layer @@ -107,6 +112,9 @@ def forward(self, inputs): Returns: A tuple containing the loss and gradient norm. + + Raises: + ValueError: If the model does not return a loss when `self.loss_fn` is None. """ # Remove this once hybrid model supports multiple inputs # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4568 @@ -244,9 +252,7 @@ def forward(self, input_tensor): Returns: The output tensor after applying the custom linear module. """ - return ForwardBackwardModule.apply( - input_tensor, self.forward_module, self.backward_module - ) + return ForwardBackwardModule.apply(input_tensor, self.forward_module, self.backward_module) class ForwardBackwardModule(torch.autograd.Function): @@ -287,7 +293,7 @@ def backward(ctx, grad_output): return grad_input, None, None -def get_remote_names(model, include_embedding_layers=False): +def get_remote_names(model: torch.nn.Module, include_embedding_layers: bool = False) -> List[str]: """Get names of modules to be executed remotely. Args: @@ -299,30 +305,22 @@ def get_remote_names(model, include_embedding_layers=False): """ remote_names = [] for name, module in model.named_modules(): - # Skip if the name contains 'lora' since they will be done on client side if "lora" in name: continue - # Check for Linear or Conv1d modules + # Skip 'lm_head' if embedding layers are not included + is_lm_head = "lm_head" in name + if is_lm_head and not include_embedding_layers: + continue + + # Handle different module types if isinstance(module, (torch.nn.Linear, TransformerConv1D)): - # Skip lm_head if include_embedding_layers is False - if "lm_head" in name and not include_embedding_layers: - continue remote_names.append(name) - - # Check for CustomLinear modules - if isinstance(module, (CustomLinear)): - # Skip lm_head if include_embedding_layers is False - if "lm_head" in name and not include_embedding_layers: - continue - remote_names.append(name + ".forward_module") - remote_names.append(name + ".backward_module") - - # Include Embedding layers and lm_head if requested - elif include_embedding_layers and ( - isinstance(module, torch.nn.Embedding) or "lm_head" in name - ): + elif isinstance(module, CustomLinear): + remote_names.append(f"{name}.forward_module") + remote_names.append(f"{name}.backward_module") + elif include_embedding_layers and (isinstance(module, torch.nn.Embedding) or is_lm_head): remote_names.append(name) return remote_names diff --git a/tests/torch/test_lora.py b/tests/torch/test_lora.py index 85c394c10..df0f7c457 100644 --- a/tests/torch/test_lora.py +++ b/tests/torch/test_lora.py @@ -1,19 +1,24 @@ +# pylint: disable=redefined-outer-name + """Tests for the LoraTraining class and related modules in lora.py.""" from collections import namedtuple +from types import SimpleNamespace import pytest import torch +from torch import nn from torch.optim import SGD from torch.optim.lr_scheduler import StepLR +from transformers import Conv1D as TransformerConv1D from concrete.ml.torch.lora import ( - BackwardModule, - CustomConv1D, + BackwardModuleLinear, CustomLinear, ForwardBackwardModule, - ForwardModule, + ForwardModuleLinear, LoraTraining, + get_remote_names, ) @@ -45,49 +50,128 @@ def forward(x): class DummyInferenceModel(torch.nn.Module): - """A dummy inference model.""" + """A dummy inference model with various layers.""" - def __init__(self, model_type): + def __init__(self): super().__init__() - self.base_model = DummyBaseModel(model_type) - self.linear = torch.nn.Linear(2, 2) + self.base_model = DummyBaseModel("gpt2") + self.linear1 = torch.nn.Linear(2, 2) + self.conv1d = TransformerConv1D(2, 2) + self.linear2 = torch.nn.Linear(2, 2) + self.lora_layer = torch.nn.Linear(2, 2) # Layer with 'lora' in name + self.lora_layer_name = "lora_layer" def forward(self, x, labels=None): - """A simple forward method that returns a loss.""" - logits = self.linear(x) - loss = ((logits - labels) ** 2).mean() if labels is not None else logits.mean() - Output = namedtuple("Output", ["loss"]) - return Output(loss=loss) + """A simple forward method that returns logits or loss.""" + x = self.linear1(x) + x = self.conv1d(x) + x = self.linear2(x) + x = self.lora_layer(x) + logits = x + if labels is not None: + loss = ((logits - labels) ** 2).mean() + Output = namedtuple("Output", ["loss"]) + return Output(loss=loss) + return logits + + +@pytest.fixture +def base_inference_model(): + """Fixture for creating a DummyInferenceModel instance.""" + return DummyInferenceModel() + + +@pytest.fixture +def base_lora_training(base_inference_model): + """Fixture for creating a LoraTraining instance.""" + return LoraTraining(base_inference_model) + + +@pytest.mark.parametrize("skip_first", [True, False]) +def test_lora_training_replace_layers(base_lora_training, skip_first): + """Test that LoraTraining replaces layers correctly.""" + original_linear1 = base_lora_training.inference_model.linear1 + original_lora_layer = base_lora_training.inference_model.lora_layer + + # Replace layers with custom layers + base_lora_training.replace_layers_with_custom( + base_lora_training.inference_model, skip_first=skip_first + ) + + inference_model = base_lora_training.inference_model + + if skip_first: + # First eligible layer should be skipped + assert inference_model.linear1 is original_linear1 + else: + assert isinstance(inference_model.linear1, CustomLinear) + + # Check that other eligible layers are replaced + assert isinstance(inference_model.conv1d, CustomLinear) + assert isinstance(inference_model.linear2, CustomLinear) + + # 'lora' layers should not be replaced + assert inference_model.lora_layer is original_lora_layer + + +@pytest.mark.parametrize( + "training_args", + [ + {"gradient_accumulation_steps": 2, "max_grad_norm": 1.0}, # dict + SimpleNamespace(gradient_accumulation_steps=2, max_grad_norm=1.0), # namespace + None, # None + ], +) +def test_update_training_parameters(base_lora_training, training_args): + """Test update_training_parameters with different types of training_args.""" + inference_model = base_lora_training.inference_model + optimizer = SGD(inference_model.parameters(), lr=0.01) + lr_scheduler = StepLR(optimizer, step_size=1) + loss_fn = nn.MSELoss() + base_lora_training.update_training_parameters(optimizer, lr_scheduler, loss_fn, training_args) -def test_lora_training_init_supported_model(): - """Test that LoraTraining initializes correctly with a supported model type.""" - inference_model = DummyInferenceModel("gpt2") - lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) - assert lora_training.inference_model is inference_model - assert lora_training.gradient_accumulation_steps == 2 + assert base_lora_training.optimizer is optimizer + assert base_lora_training.lr_scheduler is lr_scheduler + assert base_lora_training.loss_fn is loss_fn + if training_args is None: + assert base_lora_training.gradient_accumulation_steps == 1 # Default + assert base_lora_training.max_grad_norm is None # Default + else: + assert base_lora_training.gradient_accumulation_steps == 2 + assert base_lora_training.max_grad_norm == 1.0 -def test_lora_training_init_unsupported_model(): - """Test that LoraTraining raises ValueError with an unsupported model type.""" - inference_model = DummyInferenceModel("bert") - with pytest.raises(ValueError) as exc_info: - LoraTraining(inference_model, gradient_accumulation_steps=2) - assert "Unsupported model type" in str(exc_info.value) +def test_lora_training_forward_loss_fn_none(base_lora_training): + """Test the forward method when loss_fn is None.""" + x = torch.tensor([[1.0, 2.0]]) + y = torch.tensor([[0.5, 1.5]]) + + loss, _ = base_lora_training((x, y)) -def test_lora_training_forward(): - """Test the forward method of LoraTraining.""" - inference_model = DummyInferenceModel("gpt2") - lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + expected_loss = ( + base_lora_training.inference_model(x, labels=y).loss + / base_lora_training.gradient_accumulation_steps + ).item() - x = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) - y = torch.tensor([[0.5, 1.5], [2.5, 3.5]]) + assert abs(loss.item() - expected_loss) < 1e-6 + + +def test_lora_training_forward_with_loss_fn(base_lora_training): + """Test the forward method when loss_fn is provided.""" + loss_fn = nn.MSELoss() + base_lora_training.update_training_parameters(loss_fn=loss_fn) - loss, grad_norm = lora_training((x, y)) - expected_loss = ((inference_model.linear(x) - y) ** 2).mean().item() / 2 - assert loss.item() == expected_loss - assert grad_norm is None # Since run_optimizer is False by default + x = torch.tensor([[1.0, 2.0]]) + y = torch.tensor([[0.5, 1.5]]) + + outputs = base_lora_training.inference_model(x) + expected_loss = loss_fn(outputs, y) / base_lora_training.gradient_accumulation_steps + + loss, _ = base_lora_training((x, y)) + + assert abs(loss.item() - expected_loss.item()) < 1e-6 def test_lora_training_forward_no_loss(): @@ -97,11 +181,12 @@ class NoLossInferenceModel(DummyInferenceModel): """An inference model that does not return a loss.""" def forward(self, x, labels=None): + """Forward method that does not return loss.""" Output = namedtuple("Output", ["something_else"]) return Output(something_else=torch.tensor(1.0)) - inference_model = NoLossInferenceModel("gpt2") - lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) + no_loss_inference_model = NoLossInferenceModel() + lora_training = LoraTraining(no_loss_inference_model) x = torch.tensor([[1.0, 2.0]]) y = torch.tensor([[0.5, 1.5]]) @@ -111,203 +196,202 @@ def forward(self, x, labels=None): assert "The model did not return a loss" in str(exc_info.value) -def test_lora_training_toggle_calibrate(): - """Test the toggle_calibrate method of LoraTraining.""" - inference_model = DummyInferenceModel("gpt2") - lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) - - assert not lora_training.calibrate - lora_training.toggle_calibrate(True) - assert lora_training.calibrate - lora_training.toggle_calibrate(False) - assert not lora_training.calibrate +@pytest.mark.parametrize("enable", [True, False]) +def test_lora_training_toggle_calibrate(base_lora_training, enable): + """Test the toggle_calibrate method.""" + base_lora_training.toggle_calibrate(enable) + assert base_lora_training.calibrate == enable -def test_lora_training_toggle_run_optimizer(): - """Test the toggle_run_optimizer method of LoraTraining.""" - inference_model = DummyInferenceModel("gpt2") - lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) +@pytest.mark.parametrize("enable", [True, False]) +def test_lora_training_toggle_run_optimizer(base_lora_training, enable): + """Test the toggle_run_optimizer method.""" + base_lora_training.toggle_run_optimizer(enable) + assert base_lora_training.run_optimizer == enable - assert not lora_training.run_optimizer - lora_training.toggle_run_optimizer(True) - assert lora_training.run_optimizer - lora_training.toggle_run_optimizer(False) - assert not lora_training.run_optimizer - - -def test_lora_training_update_training_parameters(): - """Test the update_training_parameters method of LoraTraining.""" - inference_model = DummyInferenceModel("gpt2") - lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) +def test_lora_training_forward_with_optimizer(base_lora_training): + """Test the forward method when run_optimizer is True.""" + inference_model = base_lora_training.inference_model optimizer = SGD(inference_model.parameters(), lr=0.01) lr_scheduler = StepLR(optimizer, step_size=1) - TrainingArgs = namedtuple("TrainingArgs", ["gradient_accumulation_steps", "max_grad_norm"]) - training_args = TrainingArgs(2, 1.0) + loss_fn = nn.MSELoss() + base_lora_training.update_training_parameters( + optimizer, + lr_scheduler, + loss_fn, + SimpleNamespace(gradient_accumulation_steps=1, max_grad_norm=1.0), + ) + base_lora_training.replace_layers_with_custom( + base_lora_training.inference_model, skip_first=False + ) + base_lora_training.toggle_run_optimizer(True) - lora_training.update_training_parameters(optimizer, lr_scheduler, training_args) + x = torch.tensor([[1.0, 2.0]]) + y = torch.tensor([[0.5, 1.5]]) - assert lora_training.optimizer is optimizer - assert lora_training.lr_scheduler is lr_scheduler - assert lora_training.max_grad_norm == training_args.max_grad_norm + # Save initial parameters + initial_params = {name: param.clone() for name, param in inference_model.named_parameters()} + # Perform forward pass + _, _ = base_lora_training((x, y)) -def test_lora_training_forward_with_optimizer(): - """Test the forward method with optimizer enabled.""" + # Ensure that only parameters with "lora" in their name have been updated + for name, param in inference_model.named_parameters(): + if "lora" in name: + assert not torch.equal( + initial_params[name], param + ), f"Lora parameter {name} was not updated" + else: + assert torch.equal( + initial_params[name], param + ), f"Non-lora parameter {name} was unexpectedly updated" - inference_model = DummyInferenceModel("gpt2") - lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) - optimizer = SGD(inference_model.parameters(), lr=0.01) - lr_scheduler = StepLR(optimizer, step_size=1) - TrainingArgs = namedtuple("TrainingArgs", ["gradient_accumulation_steps", "max_grad_norm"]) - training_args = TrainingArgs(2, 1.0) - - lora_training.update_training_parameters(optimizer, lr_scheduler, training_args) - lora_training.toggle_run_optimizer(True) +def test_lora_training_forward_calibrate(base_lora_training): + """Test the forward method when calibration is enabled.""" + inference_model = base_lora_training.inference_model + base_lora_training.toggle_calibrate(True) x = torch.tensor([[1.0, 2.0]]) y = torch.tensor([[0.5, 1.5]]) - # Compute expected_loss before the forward pass - with torch.no_grad(): - expected_loss = ((inference_model.linear(x) - y) ** 2).mean().item() / 2 - - # Save the initial parameters - initial_params = [param.clone() for param in inference_model.parameters()] + _, _ = base_lora_training((x, y)) - # Perform the forward pass - loss, grad_norm = lora_training((x, y)) - - # Assert that the loss is close to the expected loss - assert abs(loss.item() - expected_loss) < 1e-6 - assert grad_norm is not None - - # Check that parameters have been updated by the optimizer - for initial_param, param in zip(initial_params, inference_model.parameters()): - assert not torch.equal(initial_param, param) + # Ensure that gradients are zeroed + for param in inference_model.parameters(): + if param.grad is not None: + assert torch.all(param.grad == 0) -def test_forward_module(): - """Test the ForwardModule.""" +@pytest.mark.parametrize("weight_transposed", [False, True]) +def test_forward_module_linear(weight_transposed): + """Test ForwardModuleLinear.""" weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) bias = torch.tensor([0.5, -0.5]) - module = ForwardModule(weight, bias) + module = ForwardModuleLinear(weight, bias, weight_transposed=weight_transposed) input_tensor = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) output = module(input_tensor) - expected_output = input_tensor @ weight + bias - assert output is not None and torch.allclose(output, expected_output) + if weight_transposed: + expected_output = input_tensor @ weight + bias + else: + expected_output = input_tensor @ weight.t() + bias + assert torch.allclose(output, expected_output) -def test_backward_module(): - """Test the BackwardModule.""" + +@pytest.mark.parametrize("weight_transposed", [False, True]) +def test_backward_module_linear(weight_transposed): + """Test BackwardModuleLinear.""" weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) - module = BackwardModule(weight) + module = BackwardModuleLinear(weight, weight_transposed=weight_transposed) grad_output = torch.tensor([[1.0, 0.0], [0.0, 1.0]]) grad_input = module(grad_output) - expected_grad_input = grad_output @ weight.t() - assert grad_input is not None and torch.allclose(grad_input, expected_grad_input) + if weight_transposed: + expected_grad_input = grad_output @ weight.t() + else: + expected_grad_input = grad_output @ weight - -def test_custom_conv1d(): - """Test the CustomConv1D module.""" - weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True) - bias = torch.tensor([0.5, -0.5], requires_grad=True) - module = CustomConv1D(weight, bias) - - input_tensor = torch.tensor([[1.0, 0.0]], requires_grad=True) - output = module(input_tensor) - - expected_output = input_tensor @ weight + bias - assert output is not None and torch.allclose(output, expected_output) - - # Test backward pass - output.sum().backward() - expected_grad_input = torch.ones_like(output) @ weight.t() - assert input_tensor.grad is not None and torch.allclose(input_tensor.grad, expected_grad_input) + assert torch.allclose(grad_input, expected_grad_input) -def test_custom_linear(): +@pytest.mark.parametrize("weight_transposed", [False, True]) +def test_custom_linear(weight_transposed): """Test the CustomLinear module.""" weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True) bias = torch.tensor([0.5, -0.5], requires_grad=True) - module = CustomLinear(weight, bias) + module = CustomLinear(weight, bias, weight_transposed=weight_transposed) input_tensor = torch.tensor([[1.0, 0.0]], requires_grad=True) output = module(input_tensor) - expected_output = input_tensor @ weight + bias - assert output is not None and torch.allclose(output, expected_output) + if weight_transposed: + expected_output = input_tensor @ weight + bias + else: + expected_output = input_tensor @ weight.t() + bias - # Test backward pass + assert torch.allclose(output, expected_output) + + # Test backward output.sum().backward() - expected_grad_input = torch.ones_like(output) @ weight.t() + if weight_transposed: + expected_grad_input = torch.ones_like(output) @ weight.t() + else: + expected_grad_input = torch.ones_like(output) @ weight + assert input_tensor.grad is not None and torch.allclose(input_tensor.grad, expected_grad_input) -def test_forward_backward_module(): +@pytest.mark.parametrize("weight_transposed", [False, True]) +def test_forward_backward_module(weight_transposed): """Test the ForwardBackwardModule.""" - weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]], requires_grad=True) + weight = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) bias = torch.tensor([0.5, -0.5]) - forward_module = ForwardModule(weight, bias) - backward_module = BackwardModule(weight) + forward_module = ForwardModuleLinear(weight, bias, weight_transposed=weight_transposed) + backward_module = BackwardModuleLinear(weight, weight_transposed=weight_transposed) input_tensor = torch.tensor([[1.0, 0.0]], requires_grad=True) output = ForwardBackwardModule.apply(input_tensor, forward_module, backward_module) - expected_output = input_tensor @ weight + bias - assert output is not None and torch.allclose(output, expected_output) - - # Test backward pass - output.sum().backward() - expected_grad_input = torch.ones_like(output) @ weight.t() - assert input_tensor.grad is not None and torch.allclose(input_tensor.grad, expected_grad_input) - + if weight_transposed: + expected_output = input_tensor @ weight + bias + expected_grad_input = torch.ones_like(output) @ weight.t() + else: + expected_output = input_tensor @ weight.t() + bias + expected_grad_input = torch.ones_like(output) @ weight -def test_lora_training_invalid_inference_model(): - """Test that LoraTraining raises ValueError when inference_model lacks required attributes.""" + assert torch.allclose(output, expected_output) - # Create an inference model that lacks base_model - class InvalidInferenceModel(torch.nn.Module): - """An invalid inference model without base_model attribute.""" - - @staticmethod - def forward(x): - """Dummy forward method.""" - return x - - inference_model = InvalidInferenceModel() - with pytest.raises(ValueError) as exc_info: - LoraTraining(inference_model, gradient_accumulation_steps=2) - assert "Unable to determine the base model type." in str(exc_info.value) - - -def test_lora_training_forward_calibrate(): - """Test the forward method when calibration is enabled.""" - inference_model = DummyInferenceModel("gpt2") - lora_training = LoraTraining(inference_model, gradient_accumulation_steps=2) - - # Enable calibration - lora_training.toggle_calibrate(True) - - x = torch.tensor([[1.0, 2.0]]) - y = torch.tensor([[0.5, 1.5]]) - - # Perform the forward pass - loss, grad_norm = lora_training((x, y)) + # Test backward + output.sum().backward() - # Since calibrate is True, grad_norm should be None - assert grad_norm is None + assert input_tensor.grad is not None and torch.allclose(input_tensor.grad, expected_grad_input) - # Ensure that loss is computed correctly - expected_loss = ((inference_model.linear(x) - y) ** 2).mean().item() / 2 - assert abs(loss.item() - expected_loss) < 1e-6 - # Ensure that gradients have been cleared (zeroed) - for param in inference_model.parameters(): - if param.grad is not None: - assert torch.all(param.grad == 0) +def test_get_remote_names(): + """Test get_remote_names function.""" + + class TestModel(torch.nn.Module): + """Test model for get_remote_names test.""" + + def __init__(self): + super().__init__() + self.linear = torch.nn.Linear(10, 10) + self.conv1d = TransformerConv1D(10, 10) + self.embedding = torch.nn.Embedding(10, 10) + self.lm_head = torch.nn.Linear(10, 10) + self.lora_layer = torch.nn.Linear(10, 10) + self.lora_layer_name = "lora_layer" + + def forward(self, x): + """Forward method.""" + return self.lm_head(self.linear(x)) + + model = TestModel() + + lora_training = LoraTraining(model) + remote_names = get_remote_names(lora_training) + expected_names = [ + "inference_model.linear", + "inference_model.conv1d.forward_module", + "inference_model.conv1d.backward_module", + ] + + assert set(remote_names) == set(expected_names) + + # Test with include_embedding_layers=True + remote_names_with_embeddings = get_remote_names(lora_training, include_embedding_layers=True) + expected_names_with_embeddings = [ + "inference_model.linear", + "inference_model.conv1d.forward_module", + "inference_model.conv1d.backward_module", + # FIXME: https://github.com/zama-ai/concrete-ml-internal/issues/4609 + "inference_model.embedding", + "inference_model.lm_head.forward_module", + "inference_model.lm_head.backward_module", + ] + assert set(remote_names_with_embeddings) == set(expected_names_with_embeddings) diff --git a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb index 891e9b11f..90df84e33 100644 --- a/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb +++ b/use_case_examples/lora_finetuning/GPT2FineTuneHybrid.ipynb @@ -36,10 +36,10 @@ " Trainer,\n", " TrainingArguments,\n", ")\n", - "from utils_lora import generate_text, get_remote_names, print_weights_and_size, replace_conv1d\n", + "from utils_lora import generate_text, print_weights_and_size\n", "\n", "from concrete.ml.torch.hybrid_model import HybridFHEModel\n", - "from concrete.ml.torch.lora import LoraTraining\n", + "from concrete.ml.torch.lora import LoraTraining, get_remote_names\n", "\n", "# Set random seed for reproducibility\n", "SEED = 0\n", @@ -80,12 +80,13 @@ "text": [ "What is FHE?\n", "\n", - "FHE is a new form of electromagnetic radiation that is produced by the electromagnetic fields generated by microwaves. It is an electromagnetic wave that travels\n" + "FHE is a new type of energy storage that is designed to be used in a variety of applications. It is used to store energy in\n" ] } ], "source": [ "# Example usage of the pre-trained model\n", + "torch.manual_seed(SEED)\n", "prompt = \"What is FHE?\"\n", "generated_text = generate_text(prompt, model, tokenizer)\n", "print(generated_text)" @@ -101,7 +102,7 @@ "# Configure LoRA\n", "peft_config = LoraConfig(\n", " task_type=TaskType.CAUSAL_LM,\n", - " r=4,\n", + " r=8,\n", " lora_alpha=32,\n", " lora_dropout=0.05,\n", " fan_in_fan_out=True,\n", @@ -114,41 +115,17 @@ { "cell_type": "code", "execution_count": 5, - "id": "f0472e5b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-1" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Replace Conv1D layers with CustomConv1D for FHE compatibility\n", - "# Skip the first Conv1D layer (module_index_to_skip=0)\n", - "replace_conv1d(peft_model, module_index_to_skip=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, "id": "5ac49f9d", "metadata": {}, "outputs": [], "source": [ "# Set up LoRA training\n", - "GRADIENT_ACCUMULATION_STEPS = 2\n", - "lora_training = LoraTraining(peft_model, GRADIENT_ACCUMULATION_STEPS)" + "lora_training = LoraTraining(peft_model)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "d10d71e8", "metadata": {}, "outputs": [], @@ -166,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "8a01acd1", "metadata": {}, "outputs": [], @@ -175,17 +152,17 @@ "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)\n", "\n", "# Define training arguments\n", - "EPOCHS = 100\n", + "EPOCHS = 50\n", "PER_DEVICE_TRAIN_BATCH_SIZE = 4\n", "\n", "training_args = TrainingArguments(\n", " output_dir=\"./checkpoints\",\n", " num_train_epochs=EPOCHS,\n", " per_device_train_batch_size=8,\n", - " gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n", + " gradient_accumulation_steps=2,\n", " save_total_limit=1,\n", " use_cpu=True,\n", - " learning_rate=5e-4,\n", + " learning_rate=5e-3,\n", " logging_strategy=\"epoch\",\n", " optim=\"adamw_torch\",\n", " seed=SEED,\n", @@ -198,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "3c8864b2", "metadata": {}, "outputs": [], @@ -221,12 +198,14 @@ "\n", "trainer.create_optimizer_and_scheduler(num_training_steps=max_steps)\n", "\n", - "lora_training.update_training_parameters(trainer.optimizer, trainer.lr_scheduler, training_args)" + "lora_training.update_training_parameters(\n", + " trainer.optimizer, trainer.lr_scheduler, None, training_args\n", + ")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "ae2094a4", "metadata": {}, "outputs": [], @@ -237,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "a21298ee", "metadata": {}, "outputs": [], @@ -248,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "56ec41b8", "metadata": {}, "outputs": [], @@ -266,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "20dfe2d8", "metadata": {}, "outputs": [], @@ -279,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "18e450e6", "metadata": {}, "outputs": [], @@ -292,7 +271,7 @@ "\n", " # Training loop\n", " hybrid_model.model.inference_model.train()\n", - "\n", + " hybrid_model.model.run_optimizer = True\n", " total_epochs = int(training_args.num_train_epochs)\n", " epoch_pbar = tqdm(total=total_epochs, desc=\"Training Progress\", position=0)\n", "\n", @@ -303,29 +282,11 @@ " total_loss = 0\n", " grad_norms = []\n", "\n", - " steps_in_epoch = len(train_dataloader)\n", - " for step, batch in enumerate(train_dataloader):\n", + " for _, batch in enumerate(train_dataloader):\n", " total_batched_samples += 1\n", "\n", " batch = {k: v.to(device) for k, v in batch.items()}\n", "\n", - " # Gradient accumulation\n", - " is_within_accumulation_steps = (\n", - " steps_in_epoch <= training_args.gradient_accumulation_steps\n", - " )\n", - " is_last_step_in_epoch = (step + 1) == steps_in_epoch\n", - "\n", - " is_last_batch_step = is_within_accumulation_steps and is_last_step_in_epoch\n", - "\n", - " mod_total_batched_samples = (\n", - " total_batched_samples % training_args.gradient_accumulation_steps\n", - " )\n", - " accumulate_gradients = mod_total_batched_samples == 0\n", - "\n", - " run_optimizer = is_last_batch_step or accumulate_gradients\n", - "\n", - " hybrid_model.model.toggle_run_optimizer(enable=run_optimizer)\n", - "\n", " loss, grad_norm = hybrid_model((batch[\"input_ids\"], batch[\"labels\"]), fhe=fhe)\n", "\n", " total_loss += loss.item()\n", @@ -369,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "0ca82a81", "metadata": {}, "outputs": [ @@ -377,1567 +338,867 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 1%| | 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Loss: 0.7439, grad norm: 0.8428460359573364, lr: 0.00024\n" + "Epoch 48/50, Loss: 0.0976, grad norm: 0.3275894224643707, lr: 0.0002\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 53%|█████▎ | 53/100 [1:49:13<1:31:53, 117.31s/it]" + "Training Progress: 98%|█████████▊| 49/50 [1:34:16<01:54, 114.07s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 53/100, Loss: 0.7578, grad norm: 0.8999559283256531, lr: 0.000235\n" + "Epoch 49/50, Loss: 0.0897, grad norm: 0.3097344934940338, lr: 0.0001\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training Progress: 54%|█████▍ | 54/100 [1:51:12<1:30:18, 117.78s/it]" + "Training Progress: 100%|██████████| 50/50 [1:36:13<00:00, 114.71s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 54/100, Loss: 0.7499, grad norm: 0.96401447057724, lr: 0.00023\n" + "Epoch 50/50, Loss: 0.0938, grad norm: 0.35130730271339417, lr: 0.0\n" ] }, { "name": "stderr", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Avoid parallelism error from HuggingFace during training\n", + "tokenizer.parallelism = False\n", + "\n", + "# Train the model using FHE simulation\n", + "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"simulate\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bd666f38", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the fine-tuned model\n", + "fine_tuned_model = hybrid_model.model.inference_model\n", + "\n", + "# Set FHE mode to disable for text generation\n", + "hybrid_model.set_fhe_mode(\"disable\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3e91ad0b", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 56/100, Loss: 0.7436, grad norm: 1.019648790359497, lr: 0.00022\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 57%|█████▋ | 57/100 [1:57:06<1:24:31, 117.94s/it]" + "What is FHE??\n", + "\n", + "Fully Homomorphic Encryption (FHE) is a groundbreaking cryptographic technique that allows computations to be performed directly on encrypted data without\n" ] - }, + } + ], + "source": [ + "# Inference using the fine-tuned model with LoRA weights\n", + "# Seed for reproducibility\n", + "torch.manual_seed(SEED)\n", + "\n", + "prompt = \"What is FHE?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "21e2a1d1", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 57/100, Loss: 0.7072, grad norm: 1.0293934345245361, lr: 0.000215\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 58%|█████▊ | 58/100 [1:59:05<1:22:55, 118.45s/it]" + "What is FHE?\n", + "\n", + "FHE is a term that refers to the ability to generate a number of numbers from a given number.\n", + ". The number is the number\n" ] - }, + } + ], + "source": [ + "# Original inference without LoRA weights\n", + "# Seed for reproducibility\n", + "torch.manual_seed(SEED)\n", + "\n", + "peft_model.disable_adapter_layers()\n", + "\n", + "prompt = \"What is FHE?\"\n", + "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", + "print(generated_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c97425ee", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 58/100, Loss: 0.7108, grad norm: 0.9379361867904663, lr: 0.00021\n" + "Total number of weights: 124734720\n", + "Total number of LoRA weights: 294912\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 59%|█████▉ | 59/100 [2:01:03<1:20:48, 118.25s/it]" - ] - }, + } + ], + "source": [ + "peft_model.enable_adapter_layers()\n", + "\n", + "# Print weights and model size\n", + "total_weights_size = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "31367ff5", + "metadata": {}, + "outputs": [], + "source": [ + "# Save the model\n", + "path = Path(\"deployment/gpt2_lora_finetuned\")\n", + "path.mkdir(parents=True, exist_ok=True)\n", + "\n", + "if path.is_dir() and any(path.iterdir()):\n", + " shutil.rmtree(path)\n", + "\n", + "hybrid_model.save_and_clear_private_info(path)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a1dda636", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 59/100, Loss: 0.7145, grad norm: 0.8663557767868042, lr: 0.000205\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 60%|██████ | 60/100 [2:03:05<1:19:28, 119.21s/it]" + "Total number of weights: 39717120\n", + "Total number of LoRA weights: 294912\n" ] - }, + } + ], + "source": [ + "# Print weights and size after saving\n", + "total_weights_size_private = print_weights_and_size(hybrid_model.model)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "506ad2f5", + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 60/100, Loss: 0.6868, grad norm: 0.974902868270874, lr: 0.0002\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 61%|██████ | 61/100 [2:05:06<1:17:54, 119.86s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 61/100, Loss: 0.7064, grad norm: 1.1623306274414062, lr: 0.00019500000000000002\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 62%|██████▏ | 62/100 [2:07:21<1:18:45, 124.35s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 62/100, Loss: 0.6720, grad norm: 1.0193554162979126, lr: 0.00019\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - 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"stdout", - "output_type": "stream", - "text": [ - "Epoch 66/100, Loss: 0.6632, grad norm: 1.0385353565216064, lr: 0.00017\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 67%|██████▋ | 67/100 [2:19:04<1:14:14, 134.99s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 67/100, Loss: 0.6333, grad norm: 1.0786832571029663, lr: 0.000165\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 68%|██████▊ | 68/100 [2:21:02<1:09:16, 129.90s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 68/100, Loss: 0.6248, grad norm: 1.1498541831970215, lr: 0.00016\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 69%|██████▉ | 69/100 [2:23:04<1:05:52, 127.48s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 69/100, Loss: 0.6158, grad norm: 1.0729987621307373, lr: 0.000155\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 70%|███████ | 70/100 [2:25:02<1:02:13, 124.46s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 70/100, Loss: 0.6147, grad norm: 1.0749378204345703, lr: 0.00015\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 71%|███████ | 71/100 [2:27:17<1:01:42, 127.67s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 71/100, Loss: 0.6362, grad norm: 1.116703987121582, lr: 0.000145\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 72%|███████▏ | 72/100 [2:29:43<1:02:08, 133.15s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 72/100, Loss: 0.6331, grad norm: 1.1760586500167847, lr: 0.00014000000000000001\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training 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"stream", - "text": [ - "Epoch 96/100, Loss: 0.5596, grad norm: 1.1739269495010376, lr: 2e-05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 97%|█████████▋| 97/100 [3:35:05<08:09, 163.19s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 97/100, Loss: 0.5330, grad norm: 1.0165910720825195, lr: 1.5e-05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 98%|█████████▊| 98/100 [3:37:51<05:27, 163.96s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 98/100, Loss: 0.5294, grad norm: 1.0572295188903809, lr: 1e-05\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 99%|█████████▉| 99/100 [3:40:33<02:43, 163.37s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 99/100, Loss: 0.5995, grad norm: 1.4120018482208252, lr: 5e-06\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 100%|██████████| 100/100 [3:43:19<00:00, 164.30s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 100/100, Loss: 0.5611, grad norm: 1.0591293573379517, lr: 0.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Progress: 100%|██████████| 100/100 [3:43:20<00:00, 134.00s/it]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Avoid parallelism error from HuggingFace during training\n", - "tokenizer.parallelism = False\n", - "\n", - "# Train the model using FHE simulation\n", - "train_custom_model(hybrid_model, train_dataloader, training_args, fhe=\"simulate\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "bd666f38", - "metadata": {}, - "outputs": [], - "source": [ - "# Get the fine-tuned model\n", - "fine_tuned_model = hybrid_model.model.inference_model\n", - "\n", - "# Set FHE mode to disable for text generation\n", - "hybrid_model.set_fhe_mode(\"disable\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "bc5d9b27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What is FHE?\n", - "\n", - "Fully Fully Scaled FFI™ Cryptographic Provider (FHE) is the foundation for the modern healthcare system. Flete-\n" - ] - } - ], - "source": [ - "# Inference using the fine-tuned model with LoRA weights\n", - "# Seed for reproducibility\n", - "torch.manual_seed(SEED)\n", - "\n", - "prompt = \"What is FHE?\"\n", - "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", - "print(generated_text)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "21e2a1d1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "What is FHE?\n", - "\n", - "FHE is a word that has been popularized by the Internet meme-culture meme of the 1990s.\n", - "There are two things that\n" - ] - } - ], - "source": [ - "# Original inference without LoRA weights\n", - "# Seed for reproducibility\n", - "torch.manual_seed(SEED)\n", - "\n", - "peft_model.disable_adapter_layers()\n", - "\n", - "prompt = \"What is FHE?\"\n", - "generated_text = generate_text(prompt, fine_tuned_model, tokenizer)\n", - "print(generated_text)\n", - "\n", - "peft_model.enable_adapter_layers()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "c97425ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of weights: 124587264\n", - "Total number of LoRA weights: 147456\n" - ] - } - ], - "source": [ - "# Print weights and model size\n", - "total_weights_size = print_weights_and_size(hybrid_model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "31367ff5", - "metadata": {}, - "outputs": [], - "source": [ - "# Save the model\n", - "path = Path(\"deployment/gpt2_lora_finetuned\")\n", - "path.mkdir(parents=True, exist_ok=True)\n", - "\n", - "if path.is_dir() and any(path.iterdir()):\n", - " shutil.rmtree(path)\n", - "\n", - "hybrid_model.save_and_clear_private_info(path)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "a1dda636", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of weights: 39569664\n", - "Total number of LoRA weights: 147456\n" - ] - } - ], - "source": [ - "# Print weights and size after saving\n", - "total_weights_size_private = print_weights_and_size(hybrid_model.model)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "506ad2f5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total weights removed: 68.24 %\n" + "Total weights removed: 68.16 %\n" ] } ], @@ -1949,7 +1210,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "465cb18b", "metadata": {}, "outputs": [], diff --git a/use_case_examples/lora_finetuning/utils_lora.py b/use_case_examples/lora_finetuning/utils_lora.py index d54dd4d9a..36135a489 100644 --- a/use_case_examples/lora_finetuning/utils_lora.py +++ b/use_case_examples/lora_finetuning/utils_lora.py @@ -3,6 +3,7 @@ from torch.nn import Embedding from transformers import Conv1D + def generate_text(prompt, model, tokenizer, max_new_tokens=30): # Encode the input prompt inputs = tokenizer.encode_plus(prompt, return_tensors="pt") From bbc3266c1da86e68069e771abedcc0e51c41dd3a Mon Sep 17 00:00:00 2001 From: jfrery Date: Thu, 26 Sep 2024 08:53:40 +0200 Subject: [PATCH 31/32] chore: add docstring loratraining --- src/concrete/ml/torch/lora.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/src/concrete/ml/torch/lora.py b/src/concrete/ml/torch/lora.py index 8c717470b..96c44854e 100644 --- a/src/concrete/ml/torch/lora.py +++ b/src/concrete/ml/torch/lora.py @@ -10,7 +10,20 @@ class LoraTraining(torch.nn.Module): - """LoraTraining module for fine-tuning with LoRA.""" + """LoraTraining module for fine-tuning with LoRA in a hybrid model setting. + + This class is designed to enable Low-Rank Adaptation (LoRA) fine-tuning + in a hybrid model context. It allows selective execution of forward and + backward passes in FHE. + + The class replaces standard linear layers with custom layers that are + compatible with LoRA and FHE operations. It provides mechanisms to + toggle between calibration and optimization modes. + + Args: + inference_model (torch.nn.Module): The base model to be fine-tuned. + + """ def __init__(self, inference_model) -> None: super().__init__() From 0379e15242e653db34fe75c8279c6f32aafb546f Mon Sep 17 00:00:00 2001 From: jfrery Date: Thu, 26 Sep 2024 09:18:17 +0200 Subject: [PATCH 32/32] chore: make transformer lib optional --- src/concrete/ml/torch/lora.py | 20 ++++++++--- tests/torch/test_lora.py | 66 +++++++++++++++++++++++++++++++++++ 2 files changed, 82 insertions(+), 4 deletions(-) diff --git a/src/concrete/ml/torch/lora.py b/src/concrete/ml/torch/lora.py index 96c44854e..0a05e577b 100644 --- a/src/concrete/ml/torch/lora.py +++ b/src/concrete/ml/torch/lora.py @@ -3,7 +3,17 @@ from typing import List import torch -from transformers import Conv1D as TransformerConv1D + +try: + from transformers import Conv1D as TransformerConv1D +except ImportError: + TransformerConv1D = None + +# Create a tuple of linear layer classes to check against +LINEAR_LAYERS: tuple = (torch.nn.Linear,) +if TransformerConv1D is not None: + LINEAR_LAYERS = LINEAR_LAYERS + (TransformerConv1D,) + # pylint: disable=abstract-method # pylint: disable=arguments-differ @@ -62,7 +72,7 @@ def _replace(module: torch.nn.Module): if "lora" in name: continue - if isinstance(child, (torch.nn.Linear, TransformerConv1D)): + if isinstance(child, LINEAR_LAYERS): if skip_first and not skipped: skipped = True @@ -70,7 +80,9 @@ def _replace(module: torch.nn.Module): continue # Determine if weights need to be transposed - weight_transposed = isinstance(child, TransformerConv1D) + weight_transposed = TransformerConv1D is not None and isinstance( + child, TransformerConv1D + ) # Create the CustomLinear layer custom_layer = CustomLinear( @@ -328,7 +340,7 @@ def get_remote_names(model: torch.nn.Module, include_embedding_layers: bool = Fa continue # Handle different module types - if isinstance(module, (torch.nn.Linear, TransformerConv1D)): + if isinstance(module, LINEAR_LAYERS): remote_names.append(name) elif isinstance(module, CustomLinear): remote_names.append(f"{name}.forward_module") diff --git a/tests/torch/test_lora.py b/tests/torch/test_lora.py index df0f7c457..b1d30bfb0 100644 --- a/tests/torch/test_lora.py +++ b/tests/torch/test_lora.py @@ -2,8 +2,10 @@ """Tests for the LoraTraining class and related modules in lora.py.""" +import sys from collections import namedtuple from types import SimpleNamespace +from unittest import mock import pytest import torch @@ -395,3 +397,67 @@ def forward(self, x): "inference_model.lm_head.backward_module", ] assert set(remote_names_with_embeddings) == set(expected_names_with_embeddings) + + +def test_lora_without_transformers(): + """ + Test the lora.py module when the transformers library is not installed. + """ + + # Save the original transformers module if it's already imported + transformers_original = sys.modules.get("transformers", None) + + # Mock the transformers import to simulate it being unavailable + with mock.patch.dict("sys.modules", {"transformers": None}): + # Reload the lora module to apply the mocked transformers import + if "concrete.ml.torch.lora" in sys.modules: + del sys.modules["concrete.ml.torch.lora"] + import concrete.ml.torch.lora as lora # pylint: disable=R0402,C0415 + + # Ensure that TransformerConv1D is None + assert lora.TransformerConv1D is None + + # Create a simple model without any Conv1D layers + model = torch.nn.Sequential( + torch.nn.Linear(10, 20), + torch.nn.ReLU(), + torch.nn.Linear(20, 5), + ) + + # Initialize LoraTraining with the model + lora_training = lora.LoraTraining(model) + + # Check that layers have been replaced with CustomLinear + replaced_layers = [] + for name, module in lora_training.inference_model.named_modules(): + if isinstance(module, lora.CustomLinear): + replaced_layers.append(name) + + # Assert that CustomLinear layers have been added + assert len(replaced_layers) > 0, "No layers were replaced with CustomLinear." + + # Prepare input data + x = torch.randn(3, 10) # Batch size 3, input size 10 + y = torch.randint(0, 5, (3,)) # Batch size 3, number of classes 5 + + # Define a simple loss function + loss_fn = torch.nn.CrossEntropyLoss() + + # Update training parameters + lora_training.update_training_parameters(loss_fn=loss_fn) + + # Perform a forward pass + loss, grad_norm = lora_training((x, y)) + + # Check that loss is computed and gradients are updated + assert loss.requires_grad, "Loss does not require gradients." + assert loss.item() > 0, "Loss should be greater than zero." + + # Since optimizer is not set, grad_norm should be None + assert grad_norm is None, "Gradient norm should be None when optimizer is not set." + + # Restore the original transformers module after the test + if transformers_original is not None: + sys.modules["transformers"] = transformers_original + elif "transformers" in sys.modules: + del sys.modules["transformers"]