diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1bdf88c5..cb7551bb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,12 +8,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
## Unreleased
### Added
-- `leave_one_out_mask` function (`rectools.models.nn.transformers.utils.leave_one_out_mask`) for applying leave-one-out validation during transformer models training ([#292](https://github.com/MobileTeleSystems/RecTools/pull/292))
-
+- HSTU Model from "Actions Speak Louder then Words..." implemented in the class `HSTUModel` ([#290](https://github.com/MobileTeleSystems/RecTools/pull/290))
+- `leave_one_out_mask` function (`rectools.models.nn.transformers.utils.leave_one_out_mask`) for applying leave-one-out validation during transformer models training.([#292](https://github.com/MobileTeleSystems/RecTools/pull/292))
+- `logits_t` argument to `TransformerLightningModuleBase`. It is used to scale logits when computing the loss. ([#290](https://github.com/MobileTeleSystems/RecTools/pull/290))
+- `use_scale_factor` argument to `LearnableInversePositionalEncoding`. It scales embeddings by the square root of their dimension — following the original approach from the "Attention Is All You Need" ([#290](https://github.com/MobileTeleSystems/RecTools/pull/290))
+- Optional `context` argument to `recommend` method of models and `get_context` function to `rectools.dataset.context.py` ([#290](https://github.com/MobileTeleSystems/RecTools/pull/290))
### Fixed
+- [Breaking] Corrected computation of `cosine` distance in `DistanceSimilarityModule`([#290](https://github.com/MobileTeleSystems/RecTools/pull/290))
- Installation issue with `cupy` extra on macOS ([#293](https://github.com/MobileTeleSystems/RecTools/pull/293))
- `torch.dtype object has no attribute 'kind'` error in `TorchRanker` ([#293](https://github.com/MobileTeleSystems/RecTools/pull/293))
-
+### Removed
+- [Breaking] `Dropout` module from `IdEmbeddingsItemNet`. This changes model behaviour during training, so model results starting from this release might slightly differ from previous RecTools versions even when the random seed is fixed.([#290](https://github.com/MobileTeleSystems/RecTools/pull/290))
## [0.15.0] - 17.07.2025
@@ -24,7 +29,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Fixed
- [Breaking] Now `LastNSplitter` guarantees taking the last ordered interaction in dataframe in case of identical timestamps ([#288](https://github.com/MobileTeleSystems/RecTools/pull/288))
-
## [0.14.0] - 16.05.2025
### Added
@@ -33,7 +37,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- `map_location` and `model_params_update` arguments for the function `load_from_checkpoint` for Transformer-based models. Use `map_location` to explicitly specify the computing new device and `model_params_update` to update original model parameters (e.g. remove training-specific parameters that are not needed anymore) ([#281](https://github.com/MobileTeleSystems/RecTools/pull/281))
- `get_val_mask_func_kwargs` and `get_trainer_func_kwargs` arguments for Transformer-based models to allow keyword arguments in custom functions used for model training. ([#280](https://github.com/MobileTeleSystems/RecTools/pull/280))
-
## [0.13.0] - 10.04.2025
### Added
@@ -53,7 +56,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Changed
- Interactions extra columns are not dropped in `Dataset.filter_interactions` method [#267](https://github.com/MobileTeleSystems/RecTools/pull/267)
-
## [0.11.0] - 17.02.2025
### Added
@@ -68,14 +70,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- `ImplicitRanker` `rank` method compatible with `Ranker` protocol. `use_gpu` and `num_threads` params moved from `rank` method to `__init__`. [#251](https://github.com/MobileTeleSystems/RecTools/pull/251)
-
## [0.10.0] - 16.01.2025
### Added
- `ImplicitBPRWrapperModel` model with algorithm description in extended baselines tutorial ([#232](https://github.com/MobileTeleSystems/RecTools/pull/232), [#239](https://github.com/MobileTeleSystems/RecTools/pull/239))
- All vector models and `EASEModel` support for enabling ranking on GPU and selecting number of threads for CPU ranking. Added `recommend_n_threads` and `recommend_use_gpu_ranking` parameters to `EASEModel`, `ImplicitALSWrapperModel`, `ImplicitBPRWrapperModel`, `PureSVDModel` and `DSSMModel`. Added `recommend_use_gpu_ranking` to `LightFMWrapperModel`. GPU and CPU ranking may provide different ordering of items with identical scores in recommendation table, so this could change ordering items in recommendations since GPU ranking is now used as a default one. ([#218](https://github.com/MobileTeleSystems/RecTools/pull/218))
-
## [0.9.0] - 11.12.2024
### Added
@@ -115,7 +115,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Removed
- [Breaking] `assume_external_ids` parameter in `recommend` and `recommend_to_items` model methods ([#177](https://github.com/MobileTeleSystems/RecTools/pull/177))
-
## [0.7.0] - 29.07.2024
### Added
diff --git a/README.md b/README.md
index 740630fe..b383177b 100644
--- a/README.md
+++ b/README.md
@@ -24,17 +24,16 @@
RecTools is an easy-to-use Python library which makes the process of building recommender systems easier and
faster than ever before.
-## ✨ Highlights: Transformer models released! ✨
+## ✨ Highlights: HSTU model released! ✨
-**BERT4Rec and SASRec are now available in RecTools:**
+**HSTU arhictecture from ["Actions speak louder then words..."](https://arxiv.org/abs/2402.17152) is now available in RecTools as `HSTUModel`:**
- Fully compatible with our `fit` / `recommend` paradigm and require NO special data processing
-- Explicitly described in our [Transformers Theory & Practice Tutorial](examples/tutorials/transformers_tutorial.ipynb): loss options, item embedding options, category features utilization and more!
+- Supports context-aware recommendations in case Relative Time Bias is enabled
+- Supports all loss options, item embedding options, category features utilization and other common modular functionality of RecTools transformer models
+- In [HSTU tutorial](examples/tutorials/transformers_HSTU_tutorial.ipynb) we show that original metrics reported for HSTU on public Movielens datasets may actually be **underestimated**
- Configurable, customizable, callback-friendly, checkpoints-included, logs-out-of-the-box, custom-validation-ready, multi-gpu-compatible! See [Transformers Advanced Training User Guide](examples/tutorials/transformers_advanced_training_guide.ipynb) and [Transformers Customization Guide](examples/tutorials/transformers_customization_guide.ipynb)
-- Public benchmarks which compare RecTools models to other open-source implementations following BERT4Rec replicability paper show that RecTools implementations achieve highest scores on multiple datasets: [Performance on public transformers benchmarks](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results)
-
-
-
+Plase note that we always compare the quality of our implementations to academic papers results. [Public benchmarks for transformer models SASRec and BERT4Rec](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) show that RecTools implementations achieve highest scores on multiple datasets compared to other published results.
## Get started
@@ -48,11 +47,10 @@ unzip ml-1m.zip
```python
import pandas as pd
-from implicit.nearest_neighbours import TFIDFRecommender
from rectools import Columns
from rectools.dataset import Dataset
-from rectools.models import ImplicitItemKNNWrapperModel
+from rectools.models import SASRecModel
# Read the data
ratings = pd.read_csv(
@@ -67,7 +65,7 @@ ratings = pd.read_csv(
dataset = Dataset.construct(ratings)
# Fit model
-model = ImplicitItemKNNWrapperModel(TFIDFRecommender(K=10))
+model = SASRecModel(n_factors=64, epochs=100, loss="sampled_softmax")
model.fit(dataset)
# Make recommendations
@@ -105,22 +103,22 @@ pip install rectools[all]
## Recommender Models
The table below lists recommender models that are available in RecTools.
-See [recommender baselines extended tutorial](https://github.com/MobileTeleSystems/RecTools/blob/main/examples/tutorials/baselines_extended_tutorial.ipynb) for deep dive into theory & practice of our supported models.
-
-| Model | Type | Description (🎏 for user/item features, 🔆 for warm inference, ❄️ for cold inference support) | Tutorials & Benchmarks |
-|----|----|---------|--------|
-| SASRec | Neural Network | `rectools.models.SASRecModel` - Transformer-based sequential model with unidirectional attention mechanism and "Shifted Sequence" training objective 🎏| 📕 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb) 📗 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) 📘 [Customization guide](examples/tutorials/transformers_customization_guide.ipynb) 🚀 [Top performance on public benchmarks](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) |
-| BERT4Rec | Neural Network | `rectools.models.BERT4RecModel` - Transformer-based sequential model with bidirectional attention mechanism and "MLM" (masked item) training objective 🎏| 📕 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb) 📗 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) 📘 [Customization guide](examples/tutorials/transformers_customization_guide.ipynb) 🚀 [Top performance on public benchmarks](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) |
-| [implicit](https://github.com/benfred/implicit) ALS Wrapper | Matrix Factorization | `rectools.models.ImplicitALSWrapperModel` - Alternating Least Squares Matrix Factorizattion algorithm for implicit feedback. 🎏| 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Implicit-ALS) 🚀 [50% boost to metrics with user & item features](examples/5_benchmark_iALS_with_features.ipynb) |
-| [implicit](https://github.com/benfred/implicit) BPR-MF Wrapper | Matrix Factorization | `rectools.models.ImplicitBPRWrapperModel` - Bayesian Personalized Ranking Matrix Factorization algorithm. | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Bayesian-Personalized-Ranking-Matrix-Factorization-(BPR-MF)) |
+
+| Model | Type | Description (🎏 for user/item features, 🔆 for warm inference, ❄️ for cold inference support) | Tutorials & Benchmarks |
+|---------------------|----|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|
+| HSTU | Neural Network | `rectools.models.HSTUModel` - Sequential model with unidirectional pointwise aggregated attention mechanism, incorporating relative attention bias from positional and temporal information, introduced in ["Actions speak louder then words..."](https://arxiv.org/pdf/2402.17152), combined with "Shifted Sequence" training objective as in original public benchmarks 🎏 | 📓 [HSTU Theory & Practice](examples/tutorials/transformers_HSTU_tutorial.ipynb) 📕 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb) 📗 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) 🚀 [Top performance on public datasets](examples/tutorials/transformers_HSTU_tutorial.ipynb)
+| SASRec | Neural Network | `rectools.models.SASRecModel` - Transformer-based sequential model with unidirectional attention mechanism and "Shifted Sequence" training objective 🎏 | 📕 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb) 📗 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) 📘 [Customization guide](examples/tutorials/transformers_customization_guide.ipynb) 🚀 [Top performance on public benchmarks](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) |
+| BERT4Rec | Neural Network | `rectools.models.BERT4RecModel` - Transformer-based sequential model with bidirectional attention mechanism and "MLM" (masked item) training objective 🎏 | 📕 [Transformers Theory & Practice](examples/tutorials/transformers_tutorial.ipynb) 📗 [Advanced training guide](examples/tutorials/transformers_advanced_training_guide.ipynb) 📘 [Customization guide](examples/tutorials/transformers_customization_guide.ipynb) 🚀 [Top performance on public benchmarks](https://github.com/blondered/bert4rec_repro?tab=readme-ov-file#rectools-transformers-benchmark-results) |
+| [implicit](https://github.com/benfred/implicit) ALS Wrapper | Matrix Factorization | `rectools.models.ImplicitALSWrapperModel` - Alternating Least Squares Matrix Factorizattion algorithm for implicit feedback. 🎏 | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Implicit-ALS) 🚀 [50% boost to metrics with user & item features](examples/5_benchmark_iALS_with_features.ipynb) |
+| [implicit](https://github.com/benfred/implicit) BPR-MF Wrapper | Matrix Factorization | `rectools.models.ImplicitBPRWrapperModel` - Bayesian Personalized Ranking Matrix Factorization algorithm. | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Bayesian-Personalized-Ranking-Matrix-Factorization-(BPR-MF)) |
| [implicit](https://github.com/benfred/implicit) ItemKNN Wrapper | Nearest Neighbours | `rectools.models.ImplicitItemKNNWrapperModel` - Algorithm that calculates item-item similarity matrix using distances between item vectors in user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#ItemKNN) |
-| [LightFM](https://github.com/lyst/lightfm) Wrapper | Matrix Factorization | `rectools.models.LightFMWrapperModel` - Hybrid matrix factorization algorithm which utilises user and item features and supports a variety of losses. 🎏 🔆 ❄️| 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#LightFM) 🚀 [10-25 times faster inference with RecTools](examples/6_benchmark_lightfm_inference.ipynb)|
-| EASE | Linear Autoencoder | `rectools.models.EASEModel` - Embarassingly Shallow Autoencoders implementation that explicitly calculates dense item-item similarity matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#EASE) |
-| PureSVD | Matrix Factorization | `rectools.models.PureSVDModel` - Truncated Singular Value Decomposition of user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#PureSVD) |
-| DSSM | Neural Network | `rectools.models.DSSMModel` - Two-tower Neural model that learns user and item embeddings utilising their explicit features and learning on triplet loss. 🎏 🔆 | - |
-| Popular | Heuristic | `rectools.models.PopularModel` - Classic baseline which computes popularity of items and also accepts params like time window and type of popularity computation. ❄️| - |
-| Popular in Category | Heuristic | `rectools.models.PopularInCategoryModel` - Model that computes poularity within category and applies mixing strategy to increase Diversity. ❄️| - |
-| Random | Heuristic | `rectools.models.RandomModel` - Simple random algorithm useful to benchmark Novelty, Coverage, etc. ❄️| - |
+| [LightFM](https://github.com/lyst/lightfm) Wrapper | Matrix Factorization | `rectools.models.LightFMWrapperModel` - Hybrid matrix factorization algorithm which utilises user and item features and supports a variety of losses. 🎏 🔆 ❄️ | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#LightFM) 🚀 [10-25 times faster inference with RecTools](examples/6_benchmark_lightfm_inference.ipynb)|
+| EASE | Linear Autoencoder | `rectools.models.EASEModel` - Embarassingly Shallow Autoencoders implementation that explicitly calculates dense item-item similarity matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#EASE) |
+| PureSVD | Matrix Factorization | `rectools.models.PureSVDModel` - Truncated Singular Value Decomposition of user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#PureSVD) |
+| DSSM | Neural Network | `rectools.models.DSSMModel` - Two-tower Neural model that learns user and item embeddings utilising their explicit features and learning on triplet loss. 🎏 🔆 | - |
+| Popular | Heuristic | `rectools.models.PopularModel` - Classic baseline which computes popularity of items and also accepts params like time window and type of popularity computation. ❄️ | - |
+| Popular in Category | Heuristic | `rectools.models.PopularInCategoryModel` - Model that computes poularity within category and applies mixing strategy to increase Diversity. ❄️ | - |
+| Random | Heuristic | `rectools.models.RandomModel` - Simple random algorithm useful to benchmark Novelty, Coverage, etc. ❄️ | - |
- All of the models follow the same interface. **No exceptions**
- No need for manual creation of sparse matrixes, torch dataloaders or mapping ids. Preparing data for models is as simple as `dataset = Dataset.construct(interactions_df)`
@@ -215,6 +213,7 @@ make clean
- [Grigoriy Gusarov](https://github.com/Gooogr)
- [Aki Ariga](https://github.com/chezou)
- [Nikolay Undalov](https://github.com/nsundalov)
+- [Aleksey Kuzin](https://github.com/teodor-r)
Previous contributors: [Ildar Safilo](https://github.com/irsafilo) [ex-Maintainer], [Daniil Potapov](https://github.com/sharthZ23) [ex-Maintainer], [Alexander Butenko](https://github.com/iomallach), [Igor Belkov](https://github.com/OzmundSedler), [Artem Senin](https://github.com/artemseninhse), [Mikhail Khasykov](https://github.com/mkhasykov), [Julia Karamnova](https://github.com/JuliaKup), [Maxim Lukin](https://github.com/groundmax), [Yuri Ulianov](https://github.com/yukeeul), [Egor Kratkov](https://github.com/jegorus), [Azat Sibagatulin](https://github.com/azatnv), [Vadim Vetrov](https://github.com/Waujito)
diff --git a/examples/tutorials/transformers_HSTU_tutorial.ipynb b/examples/tutorials/transformers_HSTU_tutorial.ipynb
new file mode 100644
index 00000000..4e52099a
--- /dev/null
+++ b/examples/tutorials/transformers_HSTU_tutorial.ipynb
@@ -0,0 +1,1303 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Transformers HSTU tutorial\n",
+ "This tutorial tells about *Hierarchical Sequential Transduction Unit* *(HSTU)* - sequentual transduction architecture proposed in paper [Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers\n",
+ "for Generative Recommendations](https://arxiv.org/abs/2402.17152).\n",
+ "\n",
+ "RecTools implementation of HSTU is fully compatible with our `fit` / `recommend` paradigm, requires NO special data processing and is widely customizable.\n",
+ "\n",
+ "The important part of current tutorial is the fact that **HSTU metrics on public datasets from the original paper may be underestimated**. We show the actual metrics in the common academic leave-one-out setup below, using the stable sorting algorithm for interactions.\n",
+ "\n",
+ "### Table of Contents\n",
+ "\n",
+ "* HSTU architecture\n",
+ "* Rectools implementation of HSTU model\n",
+ "* Results on Movielens datasets\n",
+ "* Ablation study for Relative Attention Bias\n",
+ "* Context-aware recommendations for HSTU model\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## HSTU architecture"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "HSTU is a sequentual transduction architecture challenging recommendation systems problems. We make short overview."
+ ]
+ },
+ {
+ "attachments": {
+ "495d54e1-0c6c-4d9e-85eb-39affd6c920b.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the simplest case, \"Sequentialized Unified Features\" input refers to a sequence of user interactions, and the training objective of the architecture may follow the standard \"Shifted Sequence\" approach (paper reports its main results on public datasets using this setup). Meanwhile, other approaches (e.g. Generative Recommender) are also explicitly discussed and provided in the original repository. \n",
+ "\n",
+ "According to paper, HSTU consists of a stack of identical layers connected by residual connections. Each layer contains three sub-layers: Pointwise Projection\n",
+ "(Equation 1), Spatial Aggregation (Equation 2), and Pointwise Transformation (Equation 3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\\begin{equation}\n",
+ "U(X), V(X), Q(X), K(X) = \\text{Split}(\\phi_1(f_1(X))) \\tag{1}\n",
+ "\\end{equation}\n",
+ "\n",
+ "\\begin{equation}\n",
+ "A(X)V(X) = \\frac{\\phi_2 \\left( Q(X)K(X)^T + \\text{rab}^{p,t} \\right) V(X)}{N} \\tag{2}\n",
+ "\\end{equation}\n",
+ "\n",
+ "\\begin{equation}\n",
+ "Y(X) = f_2 \\left( \\text{Norm}(A(X)V(X)) \\odot U(X) \\right) \\tag{3}\n",
+ "\\end{equation}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "where $ f_{i} $ is a linear mapping, $\\phi$ is the SiLU function, and $\\text{rab}^{p,t} $ is the sum of two attention matrices: relative positional attention and relative time attention. The first important change is the use of elementwise SiLU instead of Softmax. This approach allows for faster learning in a setting where the set of items is constantly changing. Moreover, the denominator of the softmax normalizes attention, capturing all the previous context of user interactions, and this is not always justified. SiLU changes the absolute value of token-to-token attention. The second change is the weighting of the classic self-attention output by the $U(X)$ matrix, which solves the DLRM problem of feature interactions.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There is an added $\\text{rab}^{p,t} $ (Relative Attention Bias) component added to attention during computation. While position bias utilizes difference in items positions, temporal bias is more complicated. To predict the next item, model uses the actual time of its appearance. This is a simulation of inference with time aware setup, where in addition to the user's history, we would also provide the user query time when preparing recommendations. In this case, the predicted token will be conditioned on that time. In this way, the consistency of the model is maintained between training and testing (or real-world inference). \n",
+ "Temporal bias utilizes timestamps differences: all possible cross-item differences $a_{i,j}$ are quantized into buckets with logarithmic asymptotics. Each bucket value corresponds to the learnt parameter $w_{i}$ which then forms the matrix $\\text{rab}^{t}$. Quantification of the timestamp difference actually breaks down the user's previous history into time microsessions relative to the query generation moment."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\\begin{equation}\n",
+ " a_{i,j} = t_{i+1} - t_j\n",
+ "\\end{equation}\n",
+ "\n",
+ "\\begin{equation}\n",
+ "\\text{bucket}(a_{i,j}) =\n",
+ "\\left\\lfloor \\frac{\\log(\\max(1, |a_{i,j}|))}{0.301} \\right\\rfloor\n",
+ "\\end{equation}\n",
+ "\n",
+ "\n",
+ "Timestamps differences are formed the following way:"
+ ]
+ },
+ {
+ "attachments": {
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+ "image/jpeg": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RecTools implementation of HSTU model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**In order to fully reproduce the architecture** that authors of \"Actions...\" used for public benchmarks in table 4 of the paper:\n",
+ "1) We use \"Shifted Sequence\" training objective\n",
+ "2) We don't incorporate actions interleaving\n",
+ "3) Therefore, current RecTools implementation for HSTU is a traditional sequential recommender (like SASRec) which is enhanced with an updated sequential transduction architecture (HSTU).\n",
+ "4) The important point is that unlike simpler architectures, HSTU is capable of context-aware recommendations (where context is formed by the desired timestamp of recommendations for each user).\n",
+ "\n",
+ "What's changed (we reassured that each modification still provided the same quality as the original code):\n",
+ "1) Jagged tensors logic was removed\n",
+ "2) Parameters initialization logic was replaced by xavier distribution for consistency with other RecTools models\n",
+ "3) Left padding istead of right for consistency with other RecTools models\n",
+ "4) No Q,K caching option"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-07-25T14:28:54.541177Z",
+ "start_time": "2025-07-25T14:28:54.536864Z"
+ },
+ "trusted": true
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import warnings\n",
+ "import json\n",
+ "\n",
+ "import torch\n",
+ "import pandas as pd\n",
+ "from lightning_fabric import seed_everything\n",
+ "from pytorch_lightning import Trainer\n",
+ "from pytorch_lightning.loggers import CSVLogger\n",
+ "from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint\n",
+ "from rectools.dataset import Dataset\n",
+ "from rectools.models import HSTUModel\n",
+ "from rectools import Columns\n",
+ "from rectools.model_selection.splitter import Splitter\n",
+ "from rectools.model_selection import LastNSplitter\n",
+ "from rectools.metrics import (\n",
+ " CoveredUsers,\n",
+ " Serendipity,\n",
+ " NDCG,\n",
+ " AvgRecPopularity,\n",
+ " CatalogCoverage,\n",
+ " Recall,\n",
+ " SufficientReco,\n",
+ ")\n",
+ "from rectools.models import SASRecModel\n",
+ "from rectools.model_selection import cross_validate\n",
+ "from rectools.models.nn.item_net import IdEmbeddingsItemNet\n",
+ "from rectools.models.nn.transformers.utils import leave_one_out_mask\n",
+ "\n",
+ "from utils import RecallCallback, BestModelLoadCallback, get_results\n",
+ "\n",
+ "# Enable deterministic behaviour with CUDA >= 10.2\n",
+ "os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\n",
+ "\n",
+ "warnings.simplefilter(\"ignore\", UserWarning)\n",
+ "warnings.simplefilter(\"ignore\", FutureWarning)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "RANDOM_STATE=42\n",
+ "torch.use_deterministic_algorithms(True)\n",
+ "seed_everything(RANDOM_STATE, workers=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Results on Movielens datasets"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the original repository user interactions were sorted by timestamps without a `stable` sorting option, which means that interactions with the same timestamp could be reordered from their original order. Unfortunately, both ML-1M and ML-20M datasets used in the paper have considerable amount of interactions timestamps collisions. This resulted in multiple consequences:\n",
+ "1. The leave-one-out test that was used to report metrics could greatly differ from other papers (we found up to 25% difference in targets) => metrics cannot be directly comparable\n",
+ "2. The order of interactions for each user used for model training could greatly differ from order used in other papers => metrics cannot be directly comparable (again)\n",
+ "3. The sorting without `stable` option may easily provide different results on different machines => both the original test and training interactions sequences used by authors of \"Actions...\" are unavailable and cannot be reproduced even with the original repository code.\n",
+ "\n",
+ "See the reference code:\n",
+ "https://github.com/meta-recsys/generative-recommenders/blob/88512dbd71b053226bc4ef8ec1630e3db53e55e5/generative_recommenders/research/data/preprocessor.py#L267\n",
+ "\n",
+ "**The interesting part about the whole story is that: HSTU metrics recalculated on datasets with stable sorting (which we hope is used more often) are actually higher then those reported by the authors.**\n",
+ "\n",
+ "These findings emphasize that direct comparison of metrics reported by different academic papers may be highly misleading."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Results on MovieLens-1M\n",
+ "| Method | HR@10 | NDCG@10 | HR@50 | NDCG@50 | HR@200 | NDCG@200 |\n",
+ "|---------------------|--------|---------|--------|---------|--------|----------|\n",
+ "| HSTU paper | 0.3097 | 0.1720 | 0.5754 | 0.2307 | 0.7716 | 0.2606 |\n",
+ "| HSTU RecTools (stable sort) | 0.3226 | 0.1880 | 0.5894 | 0.2471 | 0.7856 | 0.2769 |\n",
+ "| HSTU-large paper | 0.3294 | 0.1893 | 0.5935 | 0.2481 | 0.7839 | 0.2771 |\n",
+ "| HSTU-large RecTools (stable sort) | 0.3642 | 0.2164 | 0.6194 | 0.2736 | 0.8031 | 0.3015 |\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "#### Results on MovieLens-20M\n",
+ "| Method | HR@10 | NDCG@10 | HR@50 | NDCG@50 | HR@200 | NDCG@200 |\n",
+ "|---------------------|--------|---------|--------|---------|--------|----------|\n",
+ "| HSTU paper | 0.3273 | 0.1895 | 0.5889 | 0.2473 | 0.7952 | 0.2787 |\n",
+ "| HSTU RecTools (stable sort) | 0.3441 | 0.2066 | 0.6002 | 0.2632 | 0.8008 | 0.2938 |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Code for reproduction:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-07-25T14:28:56.939027Z",
+ "start_time": "2025-07-25T14:28:56.934930Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def get_movielens_df(dataset_name: str = \"ml-1m\") -> pd.DataFrame:\n",
+ " if dataset_name == \"ml-1m\":\n",
+ " ratings = pd.read_csv(\n",
+ " \"ml-1m/ratings.dat\",\n",
+ " sep=\"::\",\n",
+ " names=[\"userId\", \"movieId\", \"rating\", \"timestamp\"],\n",
+ " engine=\"python\",\n",
+ " )\n",
+ " elif dataset_name == \"ml-20m\":\n",
+ " ratings = pd.read_csv(\"ml-20m/ratings.csv\")\n",
+ " ratings = ratings[ratings[\"rating\"] >= 0]\n",
+ " ratings.rename(\n",
+ " columns={\n",
+ " \"userId\": Columns.User,\n",
+ " \"movieId\": Columns.Item,\n",
+ " \"timestamp\": Columns.Datetime,\n",
+ " },\n",
+ " inplace=True,\n",
+ " )\n",
+ " ratings[Columns.Datetime] = pd.to_datetime(ratings[Columns.Datetime], unit=\"s\")\n",
+ " ratings[Columns.Weight] = 1\n",
+ " return ratings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-07-25T14:28:57.317489Z",
+ "start_time": "2025-07-25T14:28:57.313127Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Prepare trainer function for models\n",
+ "\n",
+ "# We use callbacks for calculating recall on validation fold, making model checkpoint based on best recall, early stopping and best model load\n",
+ "# We train for maximum 100 epochs\n",
+ "# This is the most common academic training setup for sequential models\n",
+ "\n",
+ "RECALL_K = 10\n",
+ "PATIENCE = 5\n",
+ "DIVERGENCE_TRESHOLD = 0.01\n",
+ "EPOCHS = 100\n",
+ "recall_callback = RecallCallback(k=RECALL_K, progress_bar=True)\n",
+ "# Checkpoints based on best recall\n",
+ "max_recall_ckpt = ModelCheckpoint(\n",
+ " monitor=f\"recall@{RECALL_K}\", # or just pass \"val_loss\" here,\n",
+ " mode=\"max\",\n",
+ " filename=\"best_recall\",\n",
+ ")\n",
+ "early_stopping_recall = EarlyStopping(\n",
+ " monitor=f\"recall@{RECALL_K}\",\n",
+ " mode=\"max\",\n",
+ " patience=PATIENCE,\n",
+ " divergence_threshold=DIVERGENCE_TRESHOLD,\n",
+ ")\n",
+ "best_model_load = BestModelLoadCallback(\"best_recall\")\n",
+ "callbacks = [recall_callback, max_recall_ckpt, best_model_load]\n",
+ "\n",
+ "# Function to get custom trainer\n",
+ "def get_trainer() -> Trainer:\n",
+ " return Trainer(\n",
+ " accelerator=\"gpu\",\n",
+ " devices=1,\n",
+ " min_epochs=10,\n",
+ " max_epochs=EPOCHS,\n",
+ " deterministic=True,\n",
+ " enable_model_summary=False,\n",
+ " enable_progress_bar=True,\n",
+ " callbacks=callbacks,\n",
+ " logger = CSVLogger(\"test_logs\"), # We use CSV logging for this guide but there are many other options\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-07-25T14:28:57.576665Z",
+ "start_time": "2025-07-25T14:28:57.574215Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# This splitter will cut off the last interaction for the test\n",
+ "loo_splitter = LastNSplitter(n=1, n_splits=1, filter_cold_users = False, filter_cold_items = False)\n",
+ "\n",
+ "# `leave_one_out_mask` passed to the model in the configs below will cut off next to last interaction for validation during training\n",
+ "\n",
+ "# Both test splitter and validation mask use stable sorting algorithms,\n",
+ "# As well as RecTools data preparators that generate model training sequences during `fit`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-07-25T14:28:57.900664Z",
+ "start_time": "2025-07-25T14:28:57.894450Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Prepare test metrics\n",
+ "\n",
+ "metrics_add = {}\n",
+ "metrics_recall ={}\n",
+ "metrics_ndcg = {}\n",
+ "k_base = 10\n",
+ "K = [10, 50,100,200]\n",
+ "K_RECS= max(K)\n",
+ "for k in K:\n",
+ " metrics_recall.update({\n",
+ " f\"recall@{k}\": Recall(k=k),\n",
+ " })\n",
+ " metrics_ndcg.update({\n",
+ " f\"ndcg@{k}\": NDCG(k=k, divide_by_achievable=True),\n",
+ " })\n",
+ "metrics_add = {\n",
+ " f\"arp@{k_base}\": AvgRecPopularity(k=k_base, normalize=True),\n",
+ " f\"coverage@{k_base}\": CatalogCoverage(k=k_base, normalize=True),\n",
+ " f\"covered_users@{k_base}\": CoveredUsers(k=k_base),\n",
+ " f\"sufficient_reco@{k_base}\": SufficientReco(k=k_base),\n",
+ " f\"serendipity@{k_base}\": Serendipity(k=k_base),\n",
+ "}\n",
+ "metrics = metrics_recall | metrics_ndcg | metrics_add\n",
+ "metrics_to_show = ['recall@10', 'ndcg@10', 'recall@50', 'ndcg@50', 'recall@200', 'ndcg@200', 'coverage@10',\n",
+ " 'serendipity@10']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-07-25T14:28:58.300971Z",
+ "start_time": "2025-07-25T14:28:58.297457Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def evaluate(models: dict, splitter:Splitter,dataset: Dataset, path_to_save_res:str) -> None:\n",
+ " cv_results = cross_validate(\n",
+ " dataset=dataset,\n",
+ " splitter=splitter,\n",
+ " models=models,\n",
+ " metrics=metrics,\n",
+ " k=K_RECS,\n",
+ " filter_viewed=True,\n",
+ " )\n",
+ " cv_results[\"models_log_dir\"] = {}\n",
+ " for model_name, model in models.items():\n",
+ " cv_results[\"models_log_dir\"].update({model_name:model.fit_trainer.log_dir})\n",
+ " with open(path_to_save_res, 'w', encoding='utf-8') as f:\n",
+ " json.dump(cv_results, f, ensure_ascii=False, indent=4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### MovieLens-1M"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "!wget -q https://files.grouplens.org/datasets/movielens/ml-1m.zip -O ml-1m.zip\n",
+ "!unzip -o ml-1m.zip\n",
+ "!rm ml-1m.zip"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-07-25T14:29:01.642342Z",
+ "start_time": "2025-07-25T14:29:01.638310Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "config = {\n",
+ " \"session_max_len\": 200,\n",
+ " \"lightning_module_kwargs\": {\"logits_t\": 0.05}, # logits scale factor same as in the original repository\n",
+ " \"item_net_block_types\": (IdEmbeddingsItemNet,),\n",
+ " \"get_val_mask_func\": leave_one_out_mask, # validation mask\n",
+ " \"get_trainer_func\": get_trainer,\n",
+ " \"verbose\": 1,\n",
+ " \"loss\": 'sampled_softmax',\n",
+ " \"n_negatives\": 128,\n",
+ " \"use_pos_emb\": True,\n",
+ " \"dropout_rate\": 0.2,\n",
+ " \"n_factors\": 50, # embedding dim\n",
+ " \"n_heads\": 1,\n",
+ " \"n_blocks\": 2,\n",
+ " \"lr\": 0.001,\n",
+ " \"batch_size\": 128,\n",
+ "}\n",
+ "config_large = config.copy()\n",
+ "config_large[\"n_blocks\"] = 8\n",
+ "config_large[\"n_heads\"] = 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-07-25T14:29:05.813236Z",
+ "start_time": "2025-07-25T14:29:01.676257Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "dataset_name = \"ml-1m\"\n",
+ "pivot_name = f\"pivot_results_{dataset_name}.json\"\n",
+ "ml_df = get_movielens_df(dataset_name)\n",
+ "dataset = Dataset.construct(ml_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hstu = HSTUModel(\n",
+ " relative_time_attention=True,\n",
+ " relative_pos_attention=True,\n",
+ " **config\n",
+ ")\n",
+ "hstu_large = HSTUModel(\n",
+ " relative_time_attention=True,\n",
+ " relative_pos_attention=True,\n",
+ " **config_large\n",
+ ")\n",
+ "models = {\n",
+ " \"hstu\": hstu,\n",
+ " \"hstu_large\": hstu_large,\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "evaluate(models,loo_splitter,dataset,pivot_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-07-22T18:59:56.958732Z",
+ "start_time": "2025-07-22T18:59:56.812389Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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MgHKkdVVVQe3QAHO4unx/IiMjGTNmTLU1gW+//TbJycmcffbZNdquzb+pH3/8kZEjRxISEkJkZCSxsbHcd999ADW+7oe3FxERAUCbNm2OePxY/3a3bduG1WqlW7duRz1HRBoflaURkUajX79+dOnShXfffZf77ruPd999F8MwqlUJfPTRR3nggQf485//zIwZM4iOjsZqtXL77bcfd/3Cybj11lt5/fXXuf322znjjDOIiIjAYrEwbty4en3dQ1UFzMMZhnHM55155pls27aNTz/9lK+++opXX32Vp59+mpdeeqnaSNXR2j/R161PR+vToTweD3FxcUdcgwPUCGcn6/CAUPVzceeddzJq1KgjPqdDhw61bv+tt95i4sSJXHLJJdx1113ExcVhs9mYOXMm27Ztq1Nf8/LyvM+Hg0VEUlJSvOe0a9eOmJgY7/3du3djtVqJjIys02sBJCYmsm/fvhrHq44lJSXVuc2jGT9+PB988AE//fQTPXv25LPPPuOmm26qEejh+D/b27Zt45xzzqFLly784x//oE2bNgQEBDB//nyefvrpGv/2m9K/IRGpHwpXItKoXHPNNTzwwAOsXr2ad955h44dOzJgwADv4x9++CFnnXUW//73v6s9r6CgoNoHwdpISUnB4/Gwbdu2aqMumzZtqnHuhx9+yIQJE3jqqae8xyoqKigoKKh2Xl2m66SkpLB69Wo8Hk+1D34bN270Pu4r0dHRTJo0iUmTJlFSUsKZZ57JtGnTqoWrk5GSknLEr9vh76W279kX057at2/PwoULGTJkyDFHRqpec8uWLaSlpXmP5+TkHHdU8Fiq2vL392fkyJHH7evatWuPec6HH35IWloaH3/8cbWvT9UoWF2Eh4dXm/qWkJBAXl4eBQUF3vBUUFBQbZTqlVdeYfDgwQQHB9f59fr06cOSJUtqfN+XLVtGcHAwnTp1Oupz6/r9GT16NLGxsbz99tsMGjSIsrIy/vSnP9W5zwCff/45DoeDzz77rNqo1MlUaKyt9u3b4/F4WL9+PX369Kn31xMR39C0QBFpVKpGqaZOncrKlStr7G1ls9lq/LX3gw8+8K5dqYvzzz8fgH/+85/Vjs+ePbvGuUd63WeffbZG6e+QkBCAGqHrSC644AIyMzOrVWBzuVw8++yzhIaGMnz48Nq8jePKy8urdj80NJQOHToct/x1XVxwwQX88ssvLF261HustLSUl19+mdTUVO/Uptq+56oP8LX5Oh7NlVdeidvtZsaMGTUec7lc3rZHjhyJv78/zz77bLXv8ZF+DuoiLi6OESNG8K9//euIozY5OTne25dffjmrVq064kbaVX2qGv04tI/Lli2r9jWvrbS0NFwulzfQDRgwgISEBMaPH8+6detYv34948ePx+PxkJGRwf/93/8xe/ZsZs6cWefXAhg7dixZWVl8/PHH3mO5ubl88MEHjBkz5ojrsarU9fvj5+fH1VdfzX//+1/mzJlDz5496dWr1wn1+0hf88LCQl5//fUTaq8uLrnkEqxWKw899FCNETKNeIk0Xhq5EpFGpV27dgwePJhPP/0UoEa4uuiii3jooYeYNGkSgwcPZs2aNbz99tvV/qJdW3369OHqq6/mhRdeoLCwkMGDB7No0SK2bt1a49yLLrqIN998k4iICLp168bSpUtZuHAhrVq1qtGmzWZj1qxZFBYWYrfbvXvkHO66667jX//6FxMnTmTFihWkpqby4Ycf8uOPPzJ79mzCwsLq/J6OpFu3bowYMYJ+/foRHR3Nr7/+yocffsgtt9zik/YB7rnnHt59913OP/98brvtNqKjo3njjTfYsWMHH330kXe0orbvOSgoiG7duvH+++/TqVMnoqOj6dGjR63WJVUZPnw4119/PTNnzmTlypWcd955+Pv7s2XLFj744AOeeeYZxo4d690ja+bMmVx00UVccMEF/P777/zvf/+r82jo4Z5//nmGDh1Kz549+dvf/kZaWhpZWVksXbqUjIwM7/5sd911Fx9++CFXXHEFf/7zn+nXrx/79+/ns88+46WXXqJ3795cdNFFfPzxx1x66aVceOGF7Nixg5deeolu3bpRUlJSp35V7RX16quvMnv2bIKCgnjttde48sorvV/jq6++miFDhvDAAw/QtWtX5s+fX61gCZgjO1XvobKyktWrV/Pwww8D5ubgVaFm7NixnH766UyaNIn169cTExPDCy+8gNvtrlH6/HAn8v0ZP348//znP/n222+ZNWtWnb42hzrvvPMICAhgzJgxXH/99ZSUlPDKK68QFxd3xMDsSx06dOD+++9nxowZDBs2jMsuuwy73c7y5ctJSko64aArIvWsIUoUiogcy/PPP28AxsCBA2s8VlFRYdxxxx1GYmKiERQUZAwZMsRYunRpjTLntSnFbhiGUV5ebtx2221Gq1atjJCQEGPMmDHG7t27a5Syzs/PNyZNmmTExMQYoaGhxqhRo4yNGzcaKSkpNcpPv/LKK0ZaWpphs9mqlVs+vI+GYRhZWVnedgMCAoyePXtW6/Oh7+WJJ56o8fU4vJ9H8vDDDxsDBw40IiMjjaCgIKNLly7GI488YjidTu85EyZMMEJCQmo8d/jw4Ub37t1rHE9JSalR3n3btm3G2LFjjcjISCMwMNAYOHCgMW/evBrPrc17NgzD+Omnn4x+/foZAQEB1d7n0fp6pO+vYRjGyy+/bPTr188ICgoywsLCjJ49exp33323sXfvXu85brfbmD59uvfnasSIEcbatWuP+P093LG+P1Vfl/HjxxsJCQmGv7+/kZycbFx00UXGhx9+WO28vLw845ZbbjGSk5ONgIAAo3Xr1saECROM3NxcwzDM8tuPPvqokZKSYtjtdqNv377GvHnzjAkTJhgpKSnV2jr85+JIpdi//fZbIyAgwFi2bJn3WFFRkbFkyRJj8+bNhmEYxqpVq4xt27Yd9b1XlcU/0uXw7+n+/fuNv/zlL0arVq2M4OBgY/jw4cby5cuP2vahTuT70717d8NqtRoZGRk1Hqv6WcnJyal2/Ehfp88++8zo1auXERgYaKSmphqzZs0yXnvttRrnHenfhGGY34ubb7652rEj/cwc7ef3tddeM/r27WvY7XYjKirKGD58uPH1118f8T2LSMOzGIbGlkVERFqim2++mQ8//JBPPvmEwYMHH/GcJUuW0L59e58WnTgV+vbtS3R0NIsWLWrorohIC6I1VyIiIi3UM888w5gxYxg2bBjXXnstn3/+OVu3bmXHjh3MmzePcePGcdZZZx1xLVhj9uuvv7Jy5UrGjx/f0F0RkRZGI1ciIiIt3Keffsqjjz7K8uXLvcUSLBYLw4YNY+rUqd5NvBu7tWvXsmLFCp566ilyc3PZvn27d6NiEZFTQeFKREREALOC4fbt2/F4PHTo0MHne4HVt2nTpvHQQw/RuXNnXnrpJZ9V3BQRqS2FKxERERERER/QmisREREREREf0D5Xh/F4POTm5gLmPiAWi6WBeyQiIiIiIg3BMAzKysoAiImJ8e7beDQKV4fJzc0lPj6+obshIiIiIiKNSFZWFnFxccc8R9MCRUREREREfEAjV4cJDg723s7KyiIkJKQBeyMiIiIiIg2ltLTUO6vt0JxwNApXhzl0jVVISIjClYiIiIiI1KoWg6YFioiIiIiI+IDClYiIiIiIiA8oXImIiIiIiPiA1lydIMMwcLlcuN3uhu6K1AN/f39sNltDd0NEREREmhCFqxPgdDrZt2+fd0MxaX4sFgutW7cmNDS0obsiIiIiIk2EwlUdeTweduzYgc1mIykpiYCAgFpVDpGmwzAMcnJyyMjIoGPHjhrBEhEREZFaUbiqI6fTicfjoU2bNrWqdS9NU2xsLDt37qSyslLhSkRERERqRQUtTpDVqi9dc6bRSBERERGpKyUEERERERERH1C4EhERERER8QGFqxZkxIgR3H777Q3dDRERERGRZknhSmolNTWV2bNnN3Q3REREREQaLYUrERERERERH1C48gHDMChzuk75xTCMOvfV4/Fw9913Ex0dTUJCAtOmTfO+h2nTptG2bVvsdjtJSUncdtttgDmdcNeuXUyePBmLxeKtpDdt2jT69OlTrf3Zs2eTmpp6Ml9OEREREZEmSftc+UB5pZtuU7885a+7/qFRBAfU7Vv4xhtvMGXKFJYtW8bSpUuZOHEiQ4YMobCwkKeffpr33nuP7t27k5mZyapVqwD4+OOP6d27N9dddx1/+9vf6uOtiIiIiIg0eQpXLUyvXr148MEHAejYsSPPPfccixYtIi4ujoSEBEaOHIm/vz9t27Zl4MCBAERHR2Oz2QgLCyMhIaEhuy8iIiIi0mgpXPlAkL+N9Q+NapDXratevXpVu5+YmEh2djY333wzs2fPJi0tjdGjR3PBBRcwZswY/Pz0IyIiIiIip5BhwJavwGKDjiMbujd1ok/OPmCxWOo8Pa+h+Pv7V7tvsVjweDy0adOGTZs2sXDhQr7++mtuuukmnnjiCb777rsaz6litVprrPuqrKyst76LiIiISDPm8cDGz+H7JyFzNcR0gvZngbXuAwoNpWkkAjklgoKCGDNmDGPGjOHmm2+mS5curFmzhtNOO42AgADcbne182NjY8nMzMQwDG+Ri5UrVzZAz0VERESaMbfLDBu7foJ9K6FVR+h2McR2gQOfwZo0twvWfgRLnoLcTeYx/xDoNBpcDggIbtj+1YHClQAwZ84c3G43gwYNIjg4mLfeeougoCBSUlIAc5+r77//nnHjxmG324mJiWHEiBHk5OTw+OOPM3bsWBYsWMD//vc/wsPDG/jdiIiIiDRhLgfs+Q12/WgGqt3LwFlS/ZzFjx4MWd3+AAm9ml7Qcjlg1bvww9OQv9M8Zo+AQdfD6TdCcHSDdu9EKFwJAJGRkTz22GNMmTIFt9tNz549+fzzz2nVqhUADz30ENdffz3t27fH4XBgGAZdu3blhRde4NFHH2XGjBlcfvnl3Hnnnbz88ssN/G5EREREDlGYASXZR388uBVEpdR/PwwDcrfA5gWQvcEMTM7Sg5fKA9dl+8Fz2FILewS0PR2ST4O9v8O2byBviznas+QpiEwxg1b7cyC+B4TG1v/7OZbKcijOhJIsKM2FslwozYHSvIO3szdCSaZ5fnArOONmGPBXCIxo2L6fBItxIpslNWOlpaWEhoYCUFJSQkhISLXHKyoq2LFjB+3atSMwMLAhuiingL7PIiIiTZzbZYaY5a/A9sXHPz+qHXQYCR3OgdRhYA/1TT9cTnMEavOXZn/yd9TueSGxkDIYUoaY13Hdqq89qigy29zwKWxZCK7yw54fB/HdIK77getuENsZAqp/tq3BMKBwN+xZARm/miNo+1aZo2JB0RAcdeA6+uC1x2UGqeJ95nXRXqgoqN37DE2AIbdBv4nH71sDOF42OJzC1WEUrgT0fRYREWmySnPhtzfg19fNkACABcKTzOsaDHN0xeM6eMjqb44SdTgH2g42g0VlGVRWmCGm8sDFVWGGkSMx3GZA2foNOIsPHrcFmOEtZbA5QhMQaoaKgJCDtwMjIKJ17af5OUth60LY8LkZiPJ3mu/rSPxDIKQVBMeYAS4kxrz4BULmGvP5pccY5asLv0AIjTODXtXrHPq6oXHm19e/8X7Wqmu40rRAEREREWnaDMMMBctfgXWfgNtpHg+KhtP+BP3/DFGpR39+RRHsXAJbF5khpWCXeX/nEt/0LyQOOo0yCzSkjfDdqFiVgBBz3VW3P5j3naXmlLvsdZC1HrLWQvZ6KMszpx4WlEJB+tHbs/pBfHdI7gfJ/c2piLYAc7pi+f6a1xYbhCdCWCKEJRy8DoxseuvATpLClYiIiIg0PVXT7TbNh03/O2SUCkg6DQb+DbpfVrtRkcBw6HKheTEM2L/dDFrbFsG+1eAXAH5B4H/IxS/QvLZYj95uVKoZqhL7gvUY5/laQAi07mdeqhgGOIoOrH/KO7D+qWotVC44iiGuqxmoEnub7+1wrdqfuvfQRClciYiIiMjJMwzI22oGnn2rzQ/nh67LqboObmWO5JxI2CgvMEeWNn5hXjuKDj7mFwTdL4WBfzUDwomyWMwQ0ao9DLruxNtpbCwWc7phYIRCUj1SuBIRERGRuvO4IWudWSo8/SfzujSnds+NaAO9x0Hvq4//Qd9RYoapNR/A9m+rr40KiYPOo6HzBeZ0uyONtoicQgpXIiIiIk1VaZ45dS1zjVmdrbzAvK4oPHjb5TSLJ3T7gzntLSSm7q9jGGYFuL2/w97fzOuMFeAorH6ezQ6tB5jT0TxuKM+vuT6nPN+cwvf9E+alzSDo80dz1KmqBLfLab6vNR/AxvnVK+HFdjHDVOcLzBGqUzndTuQ4FK5EREREmgqPB/athC1fw5avzGp0R6sKd6htB9YPzbsdUodC14uh6xiz6MChXA6zcl5VOe2cjWaQ2vPbkSvIBYSaVfVSBptV35JPAz/7sftSWW6ORK1619yrafcy8/K/v5vhzx4O6+eaIaxKdHvoeQX0HAsxHY//fkUaiEqxH0al2AX0fRYRkUakNNfcp2nrQvNy+NS7+J6QOsQckQqMNC9BkQfW10Sam9Fu+h9s+Mzcr8jLAm0GmmGmeJ95Kcs7ej8sNnOvpKQ+kNTXDFLxPcF2En+rL9oHa/4LK98xg9yhQuOhx+VmqErq2+KqzknjoFLsIiIiIk1ZZTmkL4Vt35qhKnN19ccDwqD9COh4nrnpbXjS8duM7w5n3mnuf7T+MzNoZSw3R4wOZws4WE47qp0ZopL6QnwPCAj2wRs8RHgiDPl/MPg2c4RszQfm3lFdL4Z2Z1bfNFekCVC4akFGjBhBnz59mD17dkN3BYCJEydSUFDA3LlzG7orIiIiDccwzH2Iti4yCzak/2wGjEPF94D2Z0HHUeYaJb+AE3utqFQYcpt5KdxjjoRZrIfsUZQIQVGnfpTIYjFDXPJpp/Z1RXxM4UpqJTU1ldtvv53bb7+9obsiIiLNnct5YA+ebCjJNgshhMZBdJpZZe5kpqHVhbsSKsugssK8dlUcvB8Uaa4Dqs0eSkdStt9cb7R1kXldkln98bBESDvLDFRpI8z372sRydBvgu/bFWnBFK6kSausrMTf37+huyEiInVRUWRu0pq/w7zevx0K0s0gVZJtVpQ7GqsfRLY1g1Z0mjltLbKtGRTCkyE45sSqx5Vkw+5fIOMX2L3cLBpRWXbs51isEJkCsZ3NIgsxnSGmE4TFg7MMnKXgLDlwXQqVpeZo0fZvzQIRhxai8A82C020P9sMVbGdtcZIpAlSuPIFwzj+L+D64B9c51+8Ho+Hu+++m1dffZWAgABuuOEGpk2bhmEYTJ8+nddee42srCxatWrF2LFj+ec//8mIESPYtWsXkydPZvLkyQAYhsG0adOYO3cuK1eu9LY/e/ZsZs+ezc6dO+v8dhYsWMDDDz/M2rVrsdlsnHHGGTzzzDO0b2/uf7Fz507atWvHe++9xwsvvMCyZct46aWXuPbaa5kyZQr/+c9/sNls/PWvfyUzM5PCwkLvlEOPx8OsWbN4+eWXyczMpFOnTjzwwAOMHTu2zv0UEWn0qjZaDYoyK7kFHHsBts8U7TNLbB9eervqumifGaTKco/fltXP3MMoNNZ8H8WZsH8HuB0HA9mR2ALMUZ/wZDNwBceY1ev87OZjfnazXLif3VzbtOdXM1QV7DpGZyzm/7n+gea1nx1Kcswy5Pk7zMvmBXX/esV1hw5nQ/tzoO0ZJz4KJiKNhsKVL1SWwaO1WEzqa/ftrfN/mG+88QZTpkxh2bJlLF26lIkTJzJkyBAKCwt5+umnee+99+jevTuZmZmsWmVWFPr444/p3bs31113HX/729/q450AZjWWKVOm0KtXL0pKSpg6dSqXXnopK1euxHrIXyHvuecennrqKfr27UtgYCCzZs3i7bff5vXXX6dr164888wzzJ07l7POOsv7nJkzZ/LWW2/x0ksv0bFjR77//nuuvfZaYmNjGT58eL29JxGRU8YwIONXWDEH1n50cF8gq5+5F1DqUPPSZpDvwpZhQPZ62DAPNn5u7rVUWyGxB0eeotMgKsUsohASZ1aJC4qqOQLl8ZgV7arCVdXIV2GGOSJUkgVupxmUjhmWjsQCcV3NPZraDILW/c2peP7BZig7/I+ZhmGOduVugtzNkLPZvM7dbIbJgJADl9BDboeY1ftSzjBHqGpTiEJEmhSFqxamV69ePPjggwB07NiR5557jkWLFhEXF0dCQgIjR47E39+ftm3bMnDgQACio6Ox2WyEhYWRkJBwrOZPyuWXX17t/muvvUZsbCzr16+nR48e3uO33347l112mff+s88+y7333sull14KwHPPPcf8+fO9jzscDh599FEWLlzIGWecAUBaWho//PAD//rXvxSuRKRpKy+A1f81Q1X2uoPHYzqZIzOFuw/uI7TkqYNhK7YL+AWahRFsh43s+AdBUDQER1e/9gswA07GcjNMbZhnBpwqFitEtD7yc4OjzdAUnQbR7cAeVvf3arWao1ERydBuWM3H3ZVm+Craawauor3miJnLaY54uSoOue00+5vYG9oMML8mVRvY1obFYk7/C4s3q9qJiKBw5Rv+weYoUkO8bh316tWr2v3ExESys7O5+eabmT17NmlpaYwePZoLLriAMWPG4Od36n5EtmzZwtSpU1m2bBm5ubl4PB4A0tPTq4Wr/v37e28XFhaSlZXlDYIANpuNfv36eZ+/detWysrKOPfcc6u9ntPppG/fvvX5lkREfM/jNkdH9q2C7d/Buk8OjlL5BUL3y6DfRHP/IosF8nfBzh8OXJZUD1t1FRBq7nXkKDx4zGY3R2G6XgSdRpt7LTUUm7+5/iqybcP1QURaNIUrX7BYTt189pN0ePEHi8WCx+OhTZs2bNq0iYULF/L1119z00038cQTT/Ddd98dtWCE1Wrl8D2oKysrT7hvY8aMISUlhVdeeYWkpCQ8Hg89evTA6XRWO+94m7cdrqSkBIAvvviC5OTkao/Z7cfZRV5EpLY8nhMrpHAszjLI22oGqapL5pqDYapKXDczUPW60pxOd6ioFPPS9xrzflXYKtoDLsfBURy3w7zvcpjT3cvzzQ1ly/ZDRQEYHrM4A5ibznYaBV0uMvdZsof69n2LiDRRClfiFRQUxJgxYxgzZgw333wzXbp0Yc2aNZx22mkEBATgdrurnR8bG0tmZiaGYWA5MBf90OIWdZGXl8emTZt45ZVXGDbMnOrxww8/HPd5ERERxMfHs3z5cs4805yW4Xa7+e233+jTpw8A3bp1w263k56erimAIuJ7Jdmw4F5YP9dcLxTbyZySV3WJ7WxOh7NYzHU6bmf1KnKOEnOtUOFucypbQbp5u2D30Qs/+IdAYi9I7AM9LjPXCdW2wFFV2KoLj8cMWOX5Zp9ju5z4PksiIs2YwpUAMGfOHNxuN4MGDSI4OJi33nqLoKAgUlLM/4BTU1P5/vvvGTduHHa7nZiYGEaMGEFOTg6PP/44Y8eOZcGCBfzvf/8jPDy8zq8fFRVFq1atePnll0lMTCQ9PZ177rmnVs+99dZbmTlzJh06dKBLly48++yz5OfnewNfWFgYd955J5MnT8bj8TB06FAKCwv58ccfCQ8PZ8IE7fEhIifA44Hf34SvH4CKA9Pkiveal+2Lq58bEGau73GWgOGu0dQxBUaY64ISe5thKrG3uW7JavPFu6gdq9VcMxUcfepeU0SkCVK4EgAiIyN57LHHmDJlCm63m549e/L555/TqlUrAB566CGuv/562rdvj8PhwDAMunbtygsvvMCjjz7KjBkzuPzyy7nzzjt5+eWX6/z6VquV9957j9tuu40ePXrQuXNnbxn44/n73/9OZmYm48ePx2azcd111zFq1ChstoMfPGbMmEFsbCwzZ85k+/btREZGctppp3HffffVua8iIuRshnm3w64fzfuJvWH0LDPw5ByoHpe7xawkl78TnMU12/ALPFhBLriVuTluZNsD120OXgdGar8jEZEmwmIcvmimhSstLSU01Jw7XlJSUmN9T0VFBTt27KBdu3YEBmo/isbI4/HQtWtXrrzySmbMmHFCbej7LCJH5HLAD0+bVffcTrOw0Fn3w6AbwHaUv1e6HOY6J4v1QJgKNqf1He18ERFpNI6XDQ7n45W3J+b5558nNTWVwMBABg0axC+//HLUcz/++GP69+9PZGQkISEh9OnThzfffLPaORMnTsRisVS7jB49ur7fhjSQXbt28corr7B582bWrFnDjTfeyI4dO/jjH//Y0F0TkaausgLytsG2b81S5y8NhcUzzWDV4Vy46WcYfMuxg5Kf/cA6rA4QnmhO81OwEhFplhr8t/v777/PlClTeOmllxg0aBCzZ89m1KhRbNq0ibi4uBrnR0dHc//999OlSxcCAgKYN28ekyZNIi4ujlGjRnnPGz16NK+//rr3vqrCnXpVKf9I/ve//3kLV5wsq9XKnDlzuPPOOzEMgx49erBw4UK6du3qk/ZFpIVwlsHyV2Dv72YxicLdZqGJw4XEwvmzzJLnmq4nIiKHaPBpgYMGDWLAgAE899xzAN6y4LfeemutCxqcdtppXHjhhd4pYBMnTqSgoIC5c+fWuT+aFug7W7duPepjycnJBAUFncLe1I2+zyItiGHAhs/gy/vNQHU4/+CD658SesGQ22qWOxcRkWaprtMCG3Tkyul0smLFCu69917vMavVysiRI1m6dOlxn28YBt988w2bNm1i1qxZ1R5bvHgxcXFxREVFcfbZZ/Pwww97izMczuFw4HA4APMLKL7RoUOHhu6CiMix5WyG/90N278170e0hYF/hah2B4pKtDUr5GmESkREaqFBw1Vubi5ut5v4+Phqx+Pj49m4ceNRn1dYWEhycjIOhwObzcYLL7zAueee63189OjRXHbZZbRr145t27Zx3333cf7557N06dJqFeSqzJw5k+nTp9ep76oD0rzp+yvSzDmK4bvH4ecXwOMCmx2G3g5DbjcLToiIiJyABl9zdSLCwsJYuXIlJSUlLFq0iClTppCWluYt2z1u3DjvuT179qRXr160b9+exYsXc84559Ro795772XKlCmAOXKVlJR01Nf29/cHoKysrFFPa5OT43Q6AY4YxkWkgZXthx3fmRvu2uzmZrZV136B5m2b/9FHm/J3wbePQPE+836n82H0TIhud+reg4iINEsNGq5iYmKw2WxkZVVfMJyVlUVCQsJRn2e1Wr1Tzvr06cOGDRuYOXPmUfdESktLIyYmhq1btx4xXNntdm/Bi+N9mLbZbERGRpKdnQ1AcHCwd7NaaR48Hg85OTkEBwfj59ck//4g0ry4HJD+szl1b9u3sG8V4IPR5ah2ZmGKTqOOf66IiEgtNOgnx4CAAPr168eiRYu45JJLAPOD7aJFi7jllltq3Y7H4/GumTqSjIwM8vLySExMPNkuA3iDX1XAkubHarXStm1bBWeRhmAY5ga8W76Cbd/Arp/AVV79nLhuEN8d3JVmWXSX48B1xYHblUdv3+oH3S+BM24BfxWsERER32nwP8tPmTKFCRMm0L9/fwYOHMjs2bMpLS1l0qRJAIwfP57k5GRmzpwJmOuj+vfvT/v27XE4HMyfP58333yTF198ETCreEyfPp3LL7+chIQEtm3bxt13302HDh2qlWo/GRaLhcTEROLi4qisPMZ/4NJkBQQEYLU2im3gRFoGZxnsXGIGqi1fQUF69cdD4yHtLGh/FqSNgLCjz24QERFpKA0erq666ipycnKYOnUqmZmZ9OnThwULFniLXKSnp1f7kFtaWspNN91ERkYGQUFBdOnShbfeeourrroKMKftrV69mjfeeIOCggKSkpI477zzmDFjhs/3urLZbFqTIyJyokpzYe1HsPlL2PkDuA+ZgWCzQ+pQ6HCOGariuqpin4iINHoNvs9VY1PXWvYiIlIHhgHpS2H5v829pdzOg49FtIWO50LH86DdMAjQ718REWlYTWqfKxERaSEqCmHV+/Dra5Cz4eDxpL7Q43IzUMV00uiUiIg0aQpXIiLie24X5O+A7A2wdSGs+RAqD2zS7h8MPcdC/z+b4UpERKSZULgSEZETZxhQsAsy15ojUtkbIWcj5G6uPuUPILaLGah6XQVBkQ3SXRERkfqkcCUiIrVXng97VsCe3yDjV/N2We6Rz/UPNqf6JfSE3ldDymBN+xMRkWZN4UpERI7O5TD3mtrwubmR7/5tNc+x+pvV/OK6QVwXc4QqtgtEpoC2NBARkRZE4UpERKpzOWDbt7B+Lmz8AhxF1R+PToPkfpDcH1r3N0em/Hy71YWIiEhTpHAlIiJQWQE7voN1cw8EqsKDj4UlQrc/QIeRZqgKjm6wboqIiDRmClciIi1VcSZs+crcxHfbtwer+QGEJkD3S6DbJdBmkKb3iYiI1ILClYhIc1FZDqveg4J0CIwwK/IFRkBg5MHb5QUHAtUC2Pt79eeHJUHXMWaoanO6ApWIiEgdKVyJiDR1zlL49XX48Rkoza7bc5P7QafR0GkUJPRSNT8REZGToHAlItJUOUpg+avw07MHy6FHtDHDkrPEHKWqKISKgoO3LVZIG26e0/E8CItvwDcgIiLSvChciYg0NRVFsPwV+Ok5KN9vHotMgTPvhF7jwC+gYfsnIiLSQilciYg0di6nuT5q14+QvhR2LQVnsflYdBqceRf0vAJs/g3bTxERER8xDINSp5tQe9OKK02rtyIiLYHHbQapnT+a1xm/gqu8+jkxncxQ1f0ysOlXuYiINF0lDhebMovZlFnMxswiNh643TkhjP9ef0ZDd69O9D+yiEhjkrUOPrsV9qyofjy4FaQMhpQh0PYMs/iEqvmJiEgTk1viYM2eQlbvLmTt3kI2Zhaxe3/5Ec/dml2CYRhYmlCxJYUrEZHGoLICvn8CfpwNHhfYw80KflWBKqaTKvmJiDQThmHgdHuw+9kauiv1qtzpZsWufFZlFLAmo5A1ewrZU3DkIBUfbqdzQjhdEsLokhBG54Qw2seGNqlgBQpXIiINb9dP8NltkLfFvN/lIrjgSQhPbNh+iYiIT+0rLOeDXzP476+7ycgvJzjARnRIAK1C7bQKCThwO4DYUDvdEsPp0TqC8MCGXU9rGAb7S52EB/njbzv+jAnDMPh1Vz4f/prBF2v2UeJwVXvcYoG0mBB6tY6kR3IE3RLNQBUV0jyKMSlciYg0lIpCWDgNfn3NvB8ab4aqbhc3aLdERMR3Kt0eFm3I5v3l6Xy3OQePcfCxMqebMmc5GflHHs0BM4j0bB1Bz+QIereJpHtSOMEBdfsIn1VUwcINWWzLLiXUbiM00I+wQH9C7X6EBvoRHuhHgM3G3sJydu8vMy/5B27nl1FR6SHQ30qv1pH0S4nitLZRnNY2klahdu9r7N5fxse/7eHj3zPYlVfmPZ4YEchpKVH0bh1Bz+RIeiSHE9bAgbE+WQzDMI5/WstRWlpKaGgoACUlJYSEhDRwj0SkScrdCvPvgLxtEBByyCXUvPYPhi1fQfE+8/zTJsC5D0FQZIN2W0RETo5hGOSXVbJ7fxnz1+7joxUZ5JY4vY+fnhbNuAFtObNTLMUVleSVOskrcbK/1EFuiZP9pU72FpSzZk/hEUOX1QKd4sM47ZCQ0y4mpNr0OcMw2JhZzML1WSzckMWqjMJ6ea8prYI5rW0U+wrL+Xn7fu/xkAAbF/RM5PJ+rRmYGo3V2rSm9h2qrtlA4eowClciclIMA1a+A/PvgsrS458f3R7GPAPthtV/30RE5KQZhkFOiYNt2aVszy1hb0E5+woq2FdYwb7CcvYVVuBweao9JzbMzth+rbmyfxvaxdT+s+X+UierD6xXWr2nkNUZBWQVOWqcFxXsT98DQSu3xMnCDVnVgpnFAn3bmKNODpeHkgoXRRUuShyVlDhclFS4KK90kxAeSOvoYNpEBdM2Opg20UG0iQomISKQ3fvL+C09n992FfBbej5bskuq9cFigcHtWzG2X2tGdU+o8+haY6VwdZIUrkTkhFUUwrzJsPYj837qMDjrPrNAhbP0wKXk4O2gKOh7LfgHNWy/RUSaIMMwyCpysDmrmM1ZxezILaWi0oPL48HlNrzXlR4Dt8eD22Pg8YDHMHAbBh4DPB4Dj2HgZ7MSFexPVHAAkQeuo4L9iQwOwN9mZWdeKVuzS9iWU8K27BKKKlzH7V9MaAB92kRyZf82nNUlrlbrlWojq6iC39PzWbErn9/SC1izpxDnYWEOwO5nZVjHGEZ2jefsrnHEhQX65PWrFJZXsnJ3ASvTCwj0tzKmdxJJkc3v/zOFq5OkcCUiJ2T3cvjoz1CQDhYbnH0/DLkdrM27EpSINF4ut4cShwuXxyDmkLUxTdXO3FK+2ZjtDVNbsksorkXIqQ8WC7SJCqZ9bAhtos2RnaSIIBIjAkmMCCI+wn7KKgE6XR7W7S3kt/QCVu4uINjfxjld4xjaMabZjB41pLpmA33FRUROhscNPzwN3z4KhhsiU+Dyf0ObAQ3dMxFpJgzD4Jcd+9mSXUKZ00Wpw21eO92UOQ5cO10UV5jTu4oPmeZVpXtSOFf0a80f+iQ3qapsbo/Btxuz+c/Pu/h+c06Nx21WC6mtgukYF0aHuFBCA/3ws1rMi82Kv82Cn9WKn82CzWrBaqm6gNViHrNYzIBSUFZJfpmT/LJKCsqc3tuOSjdtW4XQITaU9nEhdIgLJbVVCIH+jeOPZwF+Vvq2jaJv26iG7oqgkasaNHIlIrVSWQ7bvoGlL8CuH8xjPcbCRf+AwIiG7ZuINBsrduXz+IKNLNux//gn10KAzcq53eK5on9rhnWMxdZICw3sL3Xy/vLdvL1sl3ftkMUCQzvE0KdNJB3jw+gUH0q7mJBmv1eUNCxNCzxJClciclSOEtj6Naz/zKz05zywmNc/BC58EnpfrY1+RcQnNmcV88SXm/h6fRZgjk6c2TGG8EB/gu02ggP8CA6wERLgR7DdvA4L9CPUbpbYrrodGuhHSYWLz1bt5b+/7mbd3iLvaySEB3LZacmkxYbi9niodBu43B5cHsN7Oz48kBGdY4kL9+16nSNxewxW7MrnveXpzFu9z7uOKCLIn6sGtOGaQW1JaaXPZXJqKVydJIUrEanG5YD1n5qXrQvBVXHwsfDW0HUMDPwbtGrfcH0UkWZj9/4ynl64mU9+34NhmGW3x/Zrzf8b2YlkHxQLWLe3kA9+zWDuyj0UlFXW+nm9Wkdwdpc4zukST/ekcJ+V1i5zuliyJZev12fxzcZs9pceLFneIzmc8WekcnHvpEYzBU9aHoWrk6RwJSJehRnw3jWwb+XBY1Gp0O0P0PUPkHyaRqpExCeyiyt44dttvL1sF5Vu86PZ+T0SuOO8TnSIC/P56zlcbhauz+aLNXspdbgPrFE6sE7pwHolm8XCxqxiVu0uqPbcuDA7Z3eJ47SUKMocrkPWKJlrlgrKzPLekcH+xIcFEh9uJz4i8MDtQKJDAlidUcDX67P4YWtutbLl4YF+nNstgWtOb0vfNpHV9m4SaQgKVydJ4UpEANi1FP77JyjNgaBoGPAX6HoxJPRUoBIRn8krcfDy99t5Y+lOKirNkDG0Qwx3jepM7zaRDdu5A7KLK1i8MYdFG7NYsiWXMqf7+E+qg9ZRQZzbLZ5zu8UzIDXaZyXLRXxB4eokKVyJCL++BvPvBk8lxPeEcW9DVEpD90pE6olhGBQ7XOQUO7yX/DInkcEBJEcG0ToqiNhQu8+mwgEUlDl5Zcl25vy4k9IDYaVPm0juPK8zQzvG+Ox1fM3hcrNs+34Wbchia04JEUHmXlAH94gKIDrEn5AAP/LLKskqqjhwcZBdbN7OLnbQNjqYc7vGc273eDrHh2mEShotlWIXETlRLif8725Y8bp5v/tl8IfnIEB/ZBFpiioq3eQUO8iuCk0l5nVuiaNakMopcRxxE9ZDBdisJEYGkhwZRHJkEJ0Twjg9rRVdE8PrVHGvqKKSfy/ZwWs/7KDYYe7R1CM5nCnnduKsznGNPmTY/Wyc2SmWMzvFNnRXRBolhSsREYCSbPjveEhfCljgnKkwdLKmAIo0UoZhkJFfzqbMYvYWlpNZWH10JKvIQWF57Qs2AITZ/YgNsxMTZicq2J/80kr2FJSTWVSB0+1hV14Zu/LKqj0nPNCPge1acXpaNGe0b0XXBLPYg2EY5BQ72J5byo4Dl+05pSzfud/bry4JYUw+txPndYtv9KFKRGpH4UpEWjaPB3Z8B5/eDEV7wB4Bl78Knc5r6J6JyAGFZZVszCxiU1YxG/YVsymziM1ZJZQcGPk5Frufldgwu3kJNa9jDrmOCz94/GgV6VxuD5lFFezJL2dPQTkZ+eWs3F3ALzv2U1ThYuGGLBZuMEumRwT5kxQZRHpeqXe63+E6xIVy+8iOXNAj0adTDUWk4SlciUjLYxiw93dY+xGs+8QMVQAxnWDcuxDToWH7J9ICGYbB3sIKtmWXsDW7hG055mVrdim5JY4jPsffZqF9bCiprUKID7cTF25Wo0sID/TeDw/0O+lRIT+bldZRwbSOCq523OX2sG5vET9vz2Pp9jyW7zBHpapGpqwWaBMdTLuYENrFhJAWE0KHuDAGtotutJv3isjJUUGLw6ighUgzlrXeDFRrP4L8HQeP28Ohx2Vw7kMQGNFw/RNpAIZhkF3sYP2+ItbvLWLDviL2FpTjZ7XiZ7Ngs1rwt1m9pbqtFgsVlR7KnC5KnW7KHC7KnG5KneZ1SICt2uhQbKg5zS421I6fzUJeiZO8Ugf7S50HbjvZX+okq6jimFXokiOD6JIQRucDl66J4bSLCWlUleVcbg9r9hSSV+IkNSaEttHBBPg1nv6JSN2poIWIyOEyfoUvpsC+VQeP+QdDp9HQ43LoMBL8AxuufyKnQKnDxb7CcvYVVrCvoIKtOSVsOBCo8g7ZuPVkOV0e8ssq2ZxVUufn+lktpMaE0CE2lPZxIbSPDaVDXChpsaGE2hv/RxY/m5W+baMauhsi0oAa/28qEZET5SyDbx6Gn18ADLAFQMfzzFGqTqNVBVCapcKySn7alsvS7Xnsyisjs7CCvYXlFFccfX2S1QJpsaF0SwynW1I4qa2CMQyo9Bi43B5cHgOX28Dl8eD2GAT52wgKsBES4Eew/cB1gI1AfxtlTveBCnwV5BY7vRX6cooduD0G0aEBxIQEEB1ip1VoAK1CAmh1YJSrdVRQoxqJEhGpK4UrEWmednwPn90K+TvN+73GwahHIKTx7h8jciKcLg8rduXzw9YcftiSy5o9hXiOMuE/LNCPxIhAEiOCaBsdTLekcLolhtM5IeyoxRxOROeEMJ+1JSLSlChciUjzUlEIXz0Av71h3g9Photmq/qfNAvFFZVszyn1FnxYt7eIX3bsp7yy+lqlDnGhDO0QQ9fEMBIjgkiMCCQhIpCwQP8G6rmISMugcCUizcem/8G8yVC8z7zf/y8wchoEhjdot0SOxjAMtuWUkllYQXmlm/JKNxWHXMqdHvaXOtiaU8K27FIyiyqO2E5MaABDOsQwtEMMQzvGkBgRdIrfiYiIgMKViDQHWevh66mw9WvzfnQaXPwcpA5p2H6JHEFheSU/bc3lu805fL85h72FRw5MRxMbZqd9rFnsoWNcKAPbtaJLQpj2SxIRaQQUrkSk6SraC98+AivfAcMDVj84/SY46z7w11/upWEZhkFRhYuCMifZxQ6Wbsvj+805/L67APchi6IC/KykxYQQ6G8jyN9GoL/Ve9vubyMiyN8MU3GhtI8JJSJYU/tERBorhSsRaXoqiuDHZ2Dp8+AqN491+wOc8yC0at+wfZNmyTAMtueWsjO3lBKHi6IKFyUVLkoclZRUuCh2uCgqN4NUfpmTgrJKCsorq4WoQ3WIC+XMjrEM7xzLoHbRPi0mISIiDUfhSkSaDnclrJgDix+DslzzWJtBcN7D0GZgg3ZNmheHy83aPUWs2LWf5TvzWbErn/0nuBdUcICNqOAAeiZHMLxzLGd2iiU5UiOrIiLNkcKViDQNpXnw/jWQvtS836qDWayiy0Vg0VoTqbsyp4usIgdZRRXeS2ahg7V7ClmVUYDD5al2vt3PSsf4UCKC/Amz+xMa6Eeo3Y8w77U/kcHmJTokgKjgACKC/DUqJSLSgihciUjjl7sF3r4C8neAPRzOmQr9JoJNa0+aGrfHIKfYwb7CcvYVmoGmvNKN0+U5eHF7cFR6qHR7CLH7ER9uJy48kPjwQBLCA4kPtxMR5I+lDqHa6fLw6879LNqYzY9bc9mTX06x4+ib6gJEhwTQPyWK/qlR9E+NpkdSBAF+2uBWRESOTuFKRBq3HUvg/WuhogAi28IfP4C4Lg3dK6mFwvJKvli9j5+25bKvsIJ9BeVkFTuOug6pLgL8rLSODKJDXCid4sPoGB9Kx7gw0mJDvCNFeSUOFm/K4ZuN2Xy/OeeIYSo4wEZCeCBx4XbiDwS4DrGh9E+Nol1MSJ0CnIiIiMKViDReK9+Fz24FTyUk94er34XQuIbulRyDy+1hydZcPlqRwVfrs3AeNrUOwGa1EB9mJ+HAxrbBAX7Y/awEHLjYbVbs/jb8bRaKK1wHpuwdnL6XX1aJ0+Vhe24p23NL+Wp9lrdtqwVSW4UQGujHmj2FGIfkuJjQAM7qHMfZXeLolBBGfHggoXb9NygiIr6j/1VEpPHxeGDxo/D9E+b9bpfApS+pvHojtjmrmI9WZPDJ73vILnZ4j3eKD+Xi3kmkxYaSGBFIYkQQsWF2bCexJ1NFpZucYge78srYnFXMluxitmSVsDmrmKIKF9tzS73ndksM55yuZqDq3TpSe0GJiEi9UrgSkcalsgLm3gjrPjbvD7sDzvo/sGqtS2NgGAbZxQ7W7y1i/b4Dl71F7Dgk0EQF+/OHPsmM7dea7knhPp9aF+hvo010MG2igxnaMaZG3zZnFbO/1MnAdtEkRiiQi4jIqaNwJSKNR0URvHOlWRHQ6gdjnoG+1zZ0r1qMcqeb/WVO8kvNfZrM/Zqc5JdVsr/UydbsEtbvKzpiSXI/q4Wzu8Rxeb/WnNU5rkEKP1gsFu+6KRERkYbQKMLV888/zxNPPEFmZia9e/fm2WefZeDAI+9Z8/HHH/Poo4+ydetWKisr6dixI3fccQd/+tOfvOcYhsGDDz7IK6+8QkFBAUOGDOHFF1+kY8eOp+otiUhdVRTBW5dDxi9gj4Bxb0G7Mxu6V82SYRhk5Jezfl8RGw6MPG3ILGL3/vJaPd9qgfaxoXRLCqdrYjjdEsPp1TqCyOCAeu65iIhI49bg4er9999nypQpvPTSSwwaNIjZs2czatQoNm3aRFxczYXr0dHR3H///XTp0oWAgADmzZvHpEmTiIuLY9SoUQA8/vjj/POf/+SNN96gXbt2PPDAA4waNYr169cTGKi/aIo0OhWFB4LVcgiMhPFzIalvQ/eqWSlzunj3l918uS6TDfuKKK44chlyf5uFyOAAooL9iQo292qKCvEnMjiAlOhguiWF0yk+THs3iYiIHIHFMIyTr4l7EgYNGsSAAQN47rnnAPB4PLRp04Zbb72Ve+65p1ZtnHbaaVx44YXMmDEDwzBISkrijjvu4M477wSgsLCQ+Ph45syZw7hx447ZVmlpKaGhoQCUlJQQEhJyEu9ORI6rohDevAz2/HogWH0KSX0aulfNRnFFJf9Zuot//7Cj2nQ+f5uFjnFh5shTUjhdE8PokhBOVHDd9o8SERFpzuqaDRp05MrpdLJixQruvfde7zGr1crIkSNZunTpcZ9vGAbffPMNmzZtYtasWQDs2LGDzMxMRo4c6T0vIiKCQYMGsXTp0iOGK4fDgcNhVrcqLS2t8biI1JPyAnjrMtizAoKizGCV2Luhe9Us5Jc6ef3HHcz5aSdFB0ap2kYH85eh7RjYLpr2saHaEFdERMTHGjRc5ebm4na7iY+Pr3Y8Pj6ejRs3HvV5hYWFJCcn43A4sNlsvPDCC5x77rkAZGZmets4vM2qxw43c+ZMpk+ffjJvRUTqqrwA3rwU9v52IFh9Bom9GrpXTZrL7SEjv5x3f0nnzZ93UeZ0A9AhLpSbz2rPmF5J+NkUqEREROpLg6+5OhFhYWGsXLmSkpISFi1axJQpU0hLS2PEiBEn1N69997LlClTAHPkKikpyYe9FZEayvMPBKvfISgaJnwGCT0buldNgtPlYVNmMdtzS8jIL2f3/jLS95exO7+MvQUVuD0HZ3p3Swzn1rM7MKp7gvZ3EhEROQUaNFzFxMRgs9nIysqqdjwrK4uEhISjPs9qtdKhQwcA+vTpw4YNG5g5cyYjRozwPi8rK4vExMRqbfbp0+eI7dntdux2OwA2mxZpi9Srfavg01sgc7WCVS1kF1fw264Cfk/P57f0fFZnFOJweY56foDNSp82kdwwIo2zOsdp/ZSIiMgp1KDhKiAggH79+rFo0SIuueQSwCxosWjRIm655ZZat+PxeLxrptq1a0dCQgKLFi3yhqmioiKWLVvGjTfe6Ou3ICK1tX8HfPsIrPnAvB/cypwKmNCjYft1iu0tKGf+mn3MX7OPrdklBAXYCAnwI9huIzjAj5AAG8F2PwzDYHVGIRn5NcujRwb70ykujNbRQbSJCqbtgQ1120YHExdm1yiViIhIA2nwaYFTpkxhwoQJ9O/fn4EDBzJ79mxKS0uZNGkSAOPHjyc5OZmZM2cC5vqo/v370759exwOB/Pnz+fNN9/kxRdfBMxNJG+//XYefvhhOnbs6C3FnpSU5A1wInIKlebC90/A8n+Dp9I81vMKOPsBiEpp2L6dIvsKy5m/JpP5a/axYld+tcfMYhOOoz7XYoHO8WH0bRvFaW0jOS0lirSYEI1IiYiINEINHq6uuuoqcnJymDp1KpmZmfTp04cFCxZ4C1Kkp6djtR5cgF1aWspNN91ERkYGQUFBdOnShbfeeourrrrKe87dd99NaWkp1113HQUFBQwdOpQFCxZojyuRU8lRAkufh5/+Cc4S81j7s2HktGZdEdDjMdhTUM7mrGI2Zhbz7cZsfj0kUFks0D8ligt7JnJG+xgq3R7KnG5KnS7KHFXXLlwegy4J4fRuE0FYoH8DviMRERGprQbf56qx0T5XIifBMMyy6us+gdXvQ2mOeTyxjxmq2p/VkL07YRWVbsqcbsor3VRUuil3unG43JQ7PZQ6XezILWVLVglbsovZml3irdJ3qAGpUVzQM5HzeySSEKE/9IiIiDQFTWqfKxFpBgwD9vwG6z+BdZ9CYfrBx6LawTkPQLdLwdp4S4AbhsH+Uic788rYlVfKzrwy0g9c78orJb+ssk7tBdispMWG0Ck+jL5tIxWoREREWgiFKxE5MXnbYMUcWD8XCg4JVP4h0Hk0dLsEOo0Gv4AG6mDtrNiVz0Ofr2NVRuFxzw3wsxLoZyUowEagv40gf/O6TXQwneJC6RgfSsf4MFKig7WflIiISAukcCUidVO2H757HJa/erBAhX8IdBoF3S+FDiMhILhh+1gLuSUOZv1vIx+syADMtVBJEUGktAo+cAkhtVUwbaNDaB0dREiAHzZV4RMREZFjULgSkdpxOeCXV+D7x6HiwChP+7Oh30TocG6TCFQALreHN3/exT++3kxxhQuAK/q15u7RXYgNszdw70RERKQpU7gSkWMzDHPq38JpkL/TPBbXHUY9bIarJmTZ9jwe/GwdGzOLAeiRHM5Df+jBaW2jGrhnIiIi0hwoXInI0e1ZAQvuhd3LzPuh8XD2/0Gfa8Bqa9i+1ZLD5eaHLbl89FsG89dkAuYmvHeN6sy4AW011U9ERER8RuFKRI5szYfwyQ3muir/YBh8Gwy+FeyhDd2z43K6PPywNYd5q/fx9fos7/Q/iwWuHtiWu87rTFRI4y60ISIiIk2PwpWI1PTTs/DV/5m3u1wEFzwB4UkN26fjcLk9LNmSy7zV+/hqfaY3UAHEhdm5oGciV/RvTfekiAbspYiIiDRnClcicpDHY4aqn5837w+6EUY92qj3qNpTUM77v6Tz/q+7ySpyeI9XBaoLeibSPyUKq6b/iYiISD1TuBIRk8sBn1wP6z4x7587w5wGaDm1ocQwDFweA/9j7BPlcnv4ZmM27/6SzuLNORiGeTw6JIAxvRK5sFeSApWIiIiccgpXIgLlBfD+tbBzCVj94ZIXodcVJ9zcrrxSft6eR0JEEN2TwokJPXaJ85xiB0u25PD95hyWbMklr9RJZLA/saF2YsMOXELtxITZKa6o5MMVGdVGqQa3b8XVA9tyXvd47H5No9CGiIiIND8KVyItXdFeeGssZK+DgDAY9xakjahzM5mFFcxbvZfPV+9j1e6Cao8lRgTSPSmCHsnh9EiKoGtSOLv3l/H95hy+25zDur1FNdorKKukoKySLdklR3y9ViEBjO3fmnED2tIuJqTO/RURERHxNYUrkZZsy9fw+f+Doj1mmfVrPoTEXrV+el6Jg/lrM/l81V6W79zvnZ5ntcBpbaPYX+ZkR24p+wor2FdYwcINWUdtq0dyOGd2jGV4p1jax4WSV+Ikt8RBTvGBS4mD3GIHDreH83skcG43jVKJiIhI46JwJdISFaSb+1dtnGfeb9URrv0IolJq9fT9pU5mzt/Ax7/vwe0xvMcHpkYzpncio3skEhtmTgUscbjYsK+ItXsKWbuniHV7C9mSXUJUsD/DOsZyZqcYhnaI9Z5fJSbUTmfCfPN+RURERE4BhSuRlsTlMMusf/8kuMrB6gen3wjD/w724wcZwzD4YEUGM+dvIL+sEoDerSMY0zuJC3omkhQZVOM5oXY/BqRGMyA12nus0u3BZrGo4ISIiIg0KwpXIi3Ftm9h/p2Qt9W8nzIULnwS4rrW6ulbs0u4/5M1LNuxH4AuCWE8cmlP+qVE1bkrx6oEKCIiItJUKVyJNHeZa8yRqvVzzfshcTDqEeh5Ra3KrFdUunn+26289N02Kt0GQf42Jp/bkUlD2ikkiYiIiBxC4UqkOSrbD2s+hN/fhMzV5jGLFQZeD2fdC4ERtWrm+805TP10LTvzygA4p0sc0//QndZRwfXVcxEREZEmS+FKpLnwuGH7t/D7W7DxC3A7zeO2AOh8Ppx5FyT0rFVTm7OKeXT+BhZvygEgITyQaRd3Y1T3BCyneFNhERERkaZC4Uqkqassh2X/gl9eNkuqV0noCX3/ZE7/C44++vMPkVPs4OmFm3nvl3Q8BvhZLYw/I5XJ53YkLNC/nt6AiIiISPOgcCXSVHk8sPp9+OZhKMowjwVFQc8roe81kNi71k1VVLr59w87eOHbrZQ63QCM7p7A38/vog16RURERGpJ4UqkKdr2DXw1FbLWmPfDW+MZcS/WXleAn/3YzwXKnW6yiirIKqpgc3YJL3y7lX2FFYBZWv3+C7sxsF3tRrtERERExKRwJdKUZK6Br6ea4QrAHkF+v1u5I/10vvlvEfaPvyEs0I+wQH9C7X7mJdCPQH8b+0sdZBU5yCqqoLjCVaPp5Mgg7h7dmTG9krT/lIiIiMgJULgSaQqK9sE3M2DlO4ABVn88A/7K2/YreeTbLCoqiwBwuDw4SpzkljiP22SQv42EiEDiwuyc3SWOCYNTCfS31fMbEREREWm+FK5EGrPKCvj5efj+KagsNY91v4wdvacw+esiVu7eB8DpadFMv7gHIXYbxRUuShwuSipcFFVUUuJwUe50Ex0SQHx44IGLnVC7nyr/iYiIiPiQwpVIY2QYsOFz+Or/oGCXeaz1ACrPfZQXt0bx7BtbqHQbhNn9uO/Crowb0EZBSURERKSBKVyJNDaZa2DBvbBziXk/LBHOfYhVkSP5+8dr2Zi5GTA39H340h4kRgQ1YGdFREREpIrClUhj4SwzR6pWvA6GB/wCYfCtbGj/Z2Z/v5cv1y0FIDokgAfHdOPi3kkarRIRERFpRBSuRBoDZym8c9XB0apul7Cl9908+Us5X371OwAWC1zaJ5n7L+xKq9Djl1sXERERkVNL4UqkoTmK4e0rIH0pBISRfu5LPLoxkQWv7wTMUDWmVxK3ndOBDnFhDdtXERERETkqhSuRhlRRCG9dDhnLcfmH8VTsTF78yAJkYrHARb2SuO3sDnSMV6gSERERaewUrkQaStl+XP+5FL/MlRQRyjUlf2dNcTQWC1zYM5HbzulIJ4UqERERkSZD4UrkFDMMg+Xrt5D42dW0cWwlzwjjT8572eXfnj/2TWbi4FSFKhEREZEmSOFKpJ45XG42Z5awdm8ha/cUsn7LNmaW/B9trLvJMcKZHjWTPw0+k4t7JxFi1z9JERERkaZKn+REfCwjv4xvNmazdk8ha/cUsTmrGJfHACCWfN4JeJSO1j0U+bUi/9IPeK57vwbusYiIiIj4gsKViI8Ullfy/LdbmfPjTpxuT7XHIoP9GRrvYnr+47Sq2IMnLInwifMIb9W+gXorIiIiIr6mcCVykirdHt76eRfPLNpCQVklAP1TohjcvhXdkyPokRxBkq0QyxtjoGIXhLfGOnEeRLdr4J6LiIiIiC8pXImcIMMw+Gp9Fo/9byM7cksB6BgXyn0XdmVEp1gsFot5YnEWvDEGcjdDeGtQsBIRERFplhSuRE7AmoxCZnyxnl927AcgJjSAyed24qr+bfCzWQ+eWJJ9SLBKhomfK1iJiIiINFMKVyJ1YBgGry7ZwWMLNuL2GNj9rPxtWBo3jGhP6OGV/kqyYc5FkLvpQLCaB9FpDdNxEREREal3ClcitVTicHH3h6uYvyYTMDf6vf/CriRFBh3h5MOC1YTPFaxEREREmjmFK5Fa2JpdzPVvrmBbTin+NgtTL+rGtaenHFxXdSjvVMBNEJZkBitVBRQRERFp9hSuRI5j/pp93PXBKkqdbhLCA3nh2tM4rW3UkU+uLIe3LoecjWawmjhPwUpERESkhVC4EjkKl9vDrAUbeWXJDgDOSGvFs3/sS0yo/chPMAyYNwUyV0NwjIKViIiISAujcCVyBPsKy7n9vZUsO1AN8Prhadx1XufqlQAPt+J1WPUOWKww9jUFKxEREZEWRuFK5BCGYfDBigxmzFtPcYWLkAAbT17Rm/N7Jh77iRkr4H9/N2+fMxXShtd/Z0VERESkUVG4Ejkgs7CCez9ezbebcgDo0yaSp67sTfvY0GM/sTQX/jse3E7ochEMub3+OysiIiIijY7ClbR4hmHw0W97mP75OoorXAT4WZlybif+NiwNm/UI1QAP5XHDR3+Bogxo1QEueQGOVEFQRERERJo9hStp0bKKKrjv4zUs2pgNQO82kTw5thcd48Nq18C3j8D2xeAfDFe9BYER9ddZEREREWnUFK6kxVq0IYvJ76+kqMJFgM3K7ed25LphaccuWnGojV/AkqfM2xc/C3Fd66+zIiIiItLo1fJTZP16/vnnSU1NJTAwkEGDBvHLL78c9dxXXnmFYcOGERUVRVRUFCNHjqxx/sSJE7FYLNUuo0ePru+3IU3Ipyv3cN2bKyiqcNGrdQTzbhvKTSM61D5Y5W2DT24wbw+6EXqOrb/OioiIiEiT0ODh6v3332fKlCk8+OCD/Pbbb/Tu3ZtRo0aRnZ19xPMXL17M1VdfzbfffsvSpUtp06YN5513Hnv27Kl23ujRo9m3b5/38u67756KtyNNwLu/pHP7+ytxewwuOy2Zj28cTKfaTgMEKM+H968FRxG0OR3Om1F/nRURERGRJsNiGIbRkB0YNGgQAwYM4LnnngPA4/HQpk0bbr31Vu65557jPt/tdhMVFcVzzz3H+PHjAXPkqqCggLlz59a5P6WlpYSGmtXhSkpKCAkJqXMb0ni9umQ7D3+xAYA/nZ7C9Iu7Yz1e0YpDlRfAm5fA3t8hJA6u/x7Cj1OmXURERESapLpmgwYduXI6naxYsYKRI0d6j1mtVkaOHMnSpUtr1UZZWRmVlZVER0dXO7548WLi4uLo3LkzN954I3l5eUdtw+FwUFRU5L1I82MYBrMXbvYGqxuGt+ehP9QxWFUUwluXmcEqKBrGz1WwEhERERGvBg1Xubm5uN1u4uPjqx2Pj48nMzOzVm38/e9/JykpqVpAGz16NP/5z39YtGgRs2bN4rvvvuP888/H7XYfsY2ZM2cSERFBREQESUlJJ/6GpFEyDINHvtjA7IVbALhrVGfuOb8LlrqUTHcUw1tjYc8KCIqCCZ9BfPd66rGIiIiINEVNulrgY489xnvvvcfixYsJDAz0Hh83bpz3ds+ePenVqxft27dn8eLFnHPOOTXauffee5kyZQpgDv0pYDUfbo/B/81dw7u/7AbgwTHdmDSkXd0aqQpWGb9AYCSM/xQSevq+syIiIiLSpDXoyFVMTAw2m42srKxqx7OyskhISDjmc5988kkee+wxvvrqK3r16nXMc9PS0oiJiWHr1q1HfNxutxMeHu69SPPgdHmY/P5K3v1lN1YLPH55r7oHK2cpvH0l7P7Z3MNq/FxI7F0v/RURERGRpq1Bw1VAQAD9+vVj0aJF3mMej4dFixZxxhlnHPV5jz/+ODNmzGDBggX079//uK+TkZFBXl4eiYlaH9NSFFdUMmnOL3y2ai9+Vgv/vLovVw5oU7dGnGXwzlWQ/hPYw+FPn0BS3/rpsIiIiIg0eQ0+LXDKlClMmDCB/v37M3DgQGbPnk1paSmTJk0CYPz48SQnJzNz5kwAZs2axdSpU3nnnXdITU31rs0KDQ0lNDSUkpISpk+fzuWXX05CQgLbtm3j7rvvpkOHDowaNarB3qecOllFFUx8fTkb9hUREmDjxWv7cWan2Lo1UlkO714FO5dAQJgZrJL71U+HRURERKRZaPBwddVVV5GTk8PUqVPJzMykT58+LFiwwFvkIj09Hav14ADbiy++iNPpZOzY6pu2Pvjgg0ybNg2bzcbq1at54403KCgoICkpifPOO48ZM2Zgt9tP6XuTU29rdgkTXvuFPQXlxITamTNpAD2SI+re0Py7YMf3EBAK134ErY8/QioiIiIiLVuD73PV2Gifq6Zrxa79/OWNXykoq6RdTAhvTBpI21bBdW/o97fh05vAYoVrP4b2Z/m+syIiIiLS6NU1GzT4yJWIL3y5LpPb3v0dh8tDnzaRvDZxANEhAXVvKHMtfHGHeXvEfQpWIiIiIlJrClfS5L29bBcPzF2Lx4BzusTx7B/7EhxwAj/aFUXw3/HgKocO58KwO3zfWRERERFpthSupEmbt3ov93+yFoBxA9rw8CU98LOdQBFMw4DPboX92yC8NVz2MlgbtJimiIiIiDQxClfSZG3YV8RdH6wGYNKQVKZe1A2LxXJijf3yMqyfC1Z/uGIOBEf7rJ8iIiIi0jLoT/PSJOWXOrnuzV8pr3QzrGMM/3fhSQSrjF/hy/vN2+c9DG0G+K6jIiIiItJiKFxJk+Nye7jtvd/Zvb+cttHBPHt1X2zWEwxWZfvhvxPAUwndLoFB1/u0ryIiIiLScihcSZPz+JebWLIllyB/Gy+P70dk8AlUBQTweODj66AoA6Lbw8XPwomOfomIiIhIi6dwJU3Kpyv38PL32wF48oredEkIP/HGfn4etn4NfoFw5X8g8CTaEhEREZEWT+FKmoy1ewr5+0dmAYubRrTnwl6JJ95Y2X747nHz9ujHIKGHD3ooIiIiIi2ZwpU0CftLnVz/5goqKj2M6BzLHed1PrkGlzwFjiKI7wmnTfBNJ0VERESkRVO4kkbP5fZw89u/saegnNRWwTxz1UkUsAAo2A2/vGLeHvmg9rMSEREREZ/Qp0pp9GYv3MLS7XmEBNh4eXx/IoL9T67BxY+B2wEpQ6HDSN90UkRERERaPIUradR+2bGf5xdvBWDW2F50ig87uQazN8Kqd8zbI6epOqCIiIiI+IzClTRaRRWVTH5/JYYBY/u15qJeSSff6KKHwPBAl4u0WbCIiIiI+JTClTRaU+euZU+BuVHwtIu7n3yD6ctg0xdgscI5U0++PRERERGRQyhcSaP06co9zF25F5vVwtNX9SHU7ndyDRoGLJxm3u5zDcSeZLVBEREREZHDKFxJo5ORX8b/fbIWgFvO6kC/lKiTb3TLV5D+k7lh8Ih7T749EREREZHDKFxJo+L2GEx5fxXFDhd920Zy69kdTr5RjxsWTjdvD7wOIpJPvk0RERERkcMoXEmj8tJ32/hl535CAmzMvqoPfjYf/Iiu+QCy10FgBAydfPLtiYiIiIgcwUl/cnU4HDgcDl/0RVq41RkFPP31ZgCmXdydlFYhJ9+oywHfPGLeHnI7BEeffJsiIiIiIkdwQuHq66+/5oILLiAqKorg4GCCg4OJioriggsuYOHChb7uo7QAZU4Xt7+3EpfH4IKeCYzt19o3Df/6GhSmQ1giDLrBN22KiIiIiBxBncPVG2+8wQUXXEBERARPP/008+bNY968eTz99NNERkZywQUX8Oabb9ZHX6UZe+qrzWzPLSUhPJBHL+2JxReb+7ocsOQf5u3hf4eA4JNvU0RERETkKOpc3/qRRx5h9uzZ3HzzzTUemzhxIkOHDuWhhx7iT3/6k086KM1fQZmTd5alA/DoZT2IDA7wTcPrP4PSbHPUqu+1vmlTREREROQo6jxylZ6ezsiRI4/6+DnnnENGRsZJdUpalnd+Sae80k2XhDDO6hznu4Z/edm87v9nsPn7rl0RERERkSOoc7jq3r07//73v4/6+GuvvUa3bt1OqlPScjhdHt74aScAfx2W5pvpgAB7V0LGL2D1h9Mm+KZNEREREZFjqPO0wKeeeoqLLrqIBQsWMHLkSOLj4wHIyspi0aJFbN++nS+++MLnHZXmaf6afWQVOYgNszOmd6LvGl7+innd7Q8QFu+7dkVEREREjqLO4WrEiBGsXbuWF198kZ9//pnMzEwAEhISOP/887nhhhtITU31dT+lGTIMg1d/2A7AhDNSsPvZfNNw2X5Y86F5e+B1vmlTREREROQ46hyuAFJTU5k1a5av+yItzLId+1m7p4hAfyt/HJTiu4Z/fwtcFZDQE9oM9F27IiIiIiLHcNKbCIucqFeX7ADg8tNaEx3iowqBHjcsf9W8PfA68NUaLhERERGR4/B5uFq1ahU2m4+md0mztT2nhEUbswD489B2vmt460Io2AWBkdBjrO/aFRERERE5jnoZuTIMoz6alWbk9R93Yhhwdpc42seG+q7hqvLrfa/VpsEiIiIickrVec3VZZdddszHCwsLfVdOW5qlgjInH64w90L7qy9HrfK2mSNXWGDAX3zXroiIiIhILdQ5XH3++eece+653hLsh3O73SfdKWneqjYN7poYzhntW/mu4eUH9l/reC5Ep/muXRERERGRWqhzuOratSuXX345f/nLkUcGVq5cybx58066Y9I8Vds0eGg7341yOkvNKoGg8usiIiIi0iDqvOaqX79+/Pbbb0d93G6307Zt25PqlDRfX6zZS1aRg7gwO2N6J/mu4TUfgKMQotpB+3N8166IiIiISC3VeeTqpZdeOubUv65du7Jjx46T6pQ0T4ZheMuvTxicSoCfj+qpGAb88op5e8BfwaodBkRERETk1KtzuLLb7fXRD2kBft6+n3V7D2waPNCHo5vpP0PWWvALgr7X+K5dEREREZE6qHO4OlRpaSk//vgj+fn5dOjQgX79+vmqX9IM/fuH7QCM7deaKF9tGgwHy6/3ugKConzXroiIiIhIHZxwuPrHP/7BI488Qo8ePUhISGDlypW0bt2aTz75hPDwcF/2UZqB7KIKvtmYDcCkIT4sv16SDRs+M28P+Jvv2hURERERqaMTWpxy//33884777B06VK+++473n//fTZu3Ejv3r2ZPHkyAHv37vVpR6Vp+3z1PjwGnNY20rebBq/9CDwuSO4Pib18166IiIiISB3VOVz9/PPPvPrqqyxYsICcnBy+//57vv/+e5YsWcJZZ53F+++/j8vlYtSoUXz33Xf10Wdpgj5buQeAP/RJ9m3Daz4wr3td6dt2RURERETq6ISqBd5yyy3ExMTwxz/+kcWLFxMQEEBAQACFhYX069ePgoICJk+ezIwZMxg+fHh99FuakB25pazKKMRmtXBhr0TfNZy3DfasAIsNul/mu3ZFRERERE5AnUeufvrpJ845x9xHaNCgQUyYMIH8/Hz279/PM888Q1JSEjExMVx22WUsWbIEh8Ph805L0/LpgVGroR1iiAn1YbXJqlGr9mdBaKzv2hUREREROQF1Dlf79+8nMjISgNdff51bb70Vf39/AG666Sbmz59Pbm4ukZGRWCwWcnJyfNphaVoMw+Czleb6uz/08eGmwYZxMFz1vMJ37YqIiIiInKA6h6u4uDjS09MBiIqK4qeffvI+tnz5cgDCwsIoKirC6XQSHR3to65KU7R2TxHbc0sJ9LdyXvcE3zW893fI22rubdXlQt+1KyIiIiJyguq85mrEiBF8+umnjB49moceeog//vGPzJ07l5CQEL766isefPBB7HY7c+fOpU+fPgQHB9dHv6WJmHtgSuDIrvGE2k9qW7XqqkatOp8P9jDftSsiIiIicoLq/Gn3lltu4fTTT+fOO+/k0ksvZe3atXz11Vc4nU7uu+8++vXrR3l5OTNmzODuu++ujz5LE+H2GHy+qmpKoA+rBHrcZgl2UJVAEREREWk06hyuunXrxn333cf555/P3Llz6datGzfeeKP38YKCAsaNG0eHDh0YP368TzsrTcuy7XlkFzuICPJneCcfFpzY8T2UZEFQFLQ/x3ftioiIiIichBOap3XPPfcQFhbGmWeeydlnn83gwYMJCgpi9erVfPDBB1x55ZU8/fTTvu6rNDFVUwIv6JlIgN8J7Vd9ZGs+NK+7XQJ+Ab5rV0RERETkJJzwIpibb76ZcePG8cknn7BmzRpcLhcdOnRg6dKltG/f3pd9lCaootLN/9ZmAj6uElhZARs+M29rSqCIiIiINCInNZzQqlUr/vrXv/LMM8/w/PPPM3ny5BMKVs8//zypqakEBgYyaNAgfvnll6Oe+8orrzBs2DCioqKIiopi5MiRNc43DIOpU6eSmJhIUFAQI0eOZMuWLXXul5y4xZtyKK5wkRgRyMBUH1aM3LwAHEUQ3hranO67dkVERERETlKdRq5Wr15d63N79epVq/Pef/99pkyZwksvvcSgQYOYPXs2o0aNYtOmTcTFxdU4f/HixVx99dUMHjyYwMBAZs2axXnnnce6detITjaLJjz++OP885//5I033qBdu3Y88MADjBo1ivXr1xMYGFjr9yAnrmrj4It7J2G1WnzXsHdvq7Fg9eFUQxERERGRk2QxDMOo7clWqxWLxcLxnmKxWHC73bVqc9CgQQwYMIDnnnsOAI/HQ5s2bbj11lu55557jvt8t9tNVFQUzz33HOPHj8cwDJKSkrjjjju48847ASgsLCQ+Pp45c+Ywbty4Y7ZXWlpKaGgoACUlJYSEhNTqfchBRRWV9H94IU6Xhy9uG0r3pAjfNFyeD092ArcTbvwJ4rv7pl0RERERkSOoazao08jVjh07TrxnR+B0OlmxYgX33nuv95jVamXkyJEsXbq0Vm2UlZVRWVnp3ax4x44dZGZmMnLkSO85ERERDBo0iKVLlx4xXDkcDhwOB2B+AeXkfLk2E6fLQ4e4ULolhvuu4fWfmcEqrpuClYiIiIg0OnUKVykpKT598dzcXNxuN/Hx8dWOx8fHs3Hjxlq18fe//52kpCRvmMrMzPS2cXibVY8dbubMmUyfPr2u3Zej+HSlubfVJX2SsFjqY0rgFb5rU0RERETER+oUrj777LNan3vxxRfXuTN19dhjj/Hee++xePHik1pLde+99zJlyhTAHLlKSvJhdbsWJruogp+25QJwcW8fbhxctBd2/mDe7jnWd+2KiIiIiPhIncLVJZdcUqvzarvmKiYmBpvNRlZWVrXjWVlZJCQkHPO5Tz75JI899hgLFy6sVjyj6nlZWVkkJiZWa7NPnz5HbMtut2O32wGw2WzH7bcc3bzV+/AY0LdtJG1bBfuu4TUfAga0PQMi2/quXRERERERH6lTuTWPx1OrS22LWQQEBNCvXz8WLVpU7TUWLVrEGWeccdTnPf7448yYMYMFCxbQv3//ao+1a9eOhISEam0WFRWxbNmyY7YpvlFVJfCSPj4ctQJNCRQRERGRRu+ENxH2lSlTpjBhwgT69+/PwIEDmT17NqWlpUyaNAmA8ePHk5yczMyZMwGYNWsWU6dO5Z133iE1NdW7jio0NJTQ0FAsFgu33347Dz/8MB07dvSWYk9KSqr1yJucmB25pazKKMRmtXBBz8TjP6G2cjZB5mqw+kG3S3zXroiIiIiID51UuCotLeW7774jPT0dp9NZ7bHbbrutVm1cddVV5OTkMHXqVDIzM+nTpw8LFizwFqRIT0/Hesh+Ri+++CJOp5OxY6uvu3nwwQeZNm0aAHfffTelpaVcd911FBQUMHToUBYsWKA9rupZ1ajVkA4xxIbZfdfwmg/N6/bnQEgr37UrIiIiIuJDddrn6lC///47F1xwAWVlZZSWlhIdHU1ubi7BwcHExcWxfft2X/f1lNA+VyfGMAzOeeo7tueW8o8re3PZaa191TA81x/ytsJlr0CvK33TroiIiIjIcdQ1G9RpzdWhJk+ezJgxY8jPzycoKIiff/6ZXbt20a9fP5588skTbVaaqDV7CtmeW0qgv5Xzuh+7GEmdZK42g5VfIHQ+33ftioiIiIj42AmHq5UrV3LHHXdgtVqx2Ww4HA7atGnD448/zn333efLPkoTULW31ciu8YTafbiUb+3H5nXH88Ae5rt2RURERER87ITDlb+/v3ctVFxcHOnp6QBERESwe/du3/ROmgS3x+DzVVUbB/uwSqBhwLoD4arHZb5rV0RERESkHpzwEEPfvn1Zvnw5HTt2ZPjw4UydOpXc3FzefPNNevTo4cs+SiO3dFse2cUOIoP9ObNTrO8a3rMCCtLBPwQ6jvJduyIiIiIi9eCER64effRR7ya9jzzyCFFRUdx4443k5OTwr3/9y2cdlMavqkrgBT0TCfA74R+pmqqmBHY+HwJ8uCGxiIiIiEg9OOGRq0M3742Li2PBggU+6ZA0LRWVbhasNfca8+mUQI8H1n1i3u5xue/aFRERERGpJyc8zLBjxw62bNlS4/iWLVvYuXPnyfRJmpBvNmZT7HCRFBFI/5Qo3zW8+2co3gv2COhwju/aFRERERGpJyccriZOnMhPP/1U4/iyZcuYOHHiyfRJmpCqKYEX90nGarX4ruG1H5nXXS4EPx9uSCwiIiIiUk9OOFz9/vvvDBkypMbx008/nZUrV55Mn6SJKCyr5NuNOQD8oU+S7xp2u2D9p+ZtTQkUERERkSbihMOVxWKhuLi4xvHCwkLcbvdJdUqahgXr9uF0e+gcH0bXxHDfNbxzCZTmQFA0pA33XbsiIiIiIvXohMPVmWeeycyZM6sFKbfbzcyZMxk6dKhPOieN29zfzb2t/tDXh6NWcHBvq24Xg83ft22LiIiIiNSTE64WOGvWLM4880w6d+7MsGHDAFiyZAlFRUV88803PuugNE6ZhRX8vCMPgIt7+zBcuZyw4XPzdndtHCwiIiIiTccJj1x169aN1atXc+WVV5KdnU1xcTHjx49n48aN2kS4Bfh81V4MAwakRtE6yod7UG1fDOX5EBIHqRoBFREREZGm44RHrgCSkpJ49NFHfdUXaULmHlIl0KeqpgR2vwSsNt+2LSIiIiJSj0545ArMaYDXXnstgwcPZs8e88P2m2++yQ8//OCTzknjtDW7mHV7i/CzWriwZ6LvGq6sgA3zzNuaEigiIiIiTcwJh6uPPvqIUaNGERQUxG+//YbD4QDMaoEazWrePl1pFrIY3imW6JAA3zW8dSE4iyE8GdoM8l27IiIiIiKnwAmHq4cffpiXXnqJV155BX//gxXdhgwZwm+//eaTzknjYxiGN1xd7Mu9reCQKYGXgvWkBlVFRERERE65E/4Eu2nTJs4888waxyMiIigoKDiZPkkj9vvuAtL3lxEcYOPcbvG+a9hZCpv+Z97uoSmBIiIiItL0nHC4SkhIYOvWrTWO//DDD6SlpZ1Up6Tx+t+afQCc2y2e4ICTqodS3eYvobIMolIh6TTftSsiIiIicoqccLj629/+xv/7f/+PZcuWYbFY2Lt3L2+//TZ33HEHN954oy/7KI3INxuzAXw7agWwfq553f1SsFh827aIiIiIyClwwkMP99xzDx6Ph3POOYeysjLOPPNM7HY7d911F3/961992UdpJNLzytiWU4rNamFYx1jfNex2wbbF5u3OF/quXRERERGRU+iER64sFgv3338/+/fvZ+3atfz888/k5OQQERFBu3btfNlHaSS+3WSOWvVPiSIiyP84Z9fB3t/AUQiBkZCsKYEiIiIi0jTVOVw5HA7uvfde+vfvz5AhQ5g/fz7dunVj3bp1dO7cmWeeeYbJkyfXR1+lgVVNCTyrS5xvG966yLxOG6GNg0VERESkyarztMCpU6fyr3/9i5EjR/LTTz9xxRVXMGnSJH7++WeeeuoprrjiCmw2fUBubsqdbpZuzwPgbF+Hq20HwlWHc3zbroiIiIjIKVTncPXBBx/wn//8h4svvpi1a9fSq1cvXC4Xq1atwqJCBM3WT9tycbo8JEcG0TEu1HcNl+fDnhXm7fZn+65dEREREZFTrM7TAjMyMujXrx8APXr0wG63M3nyZAWrZu7glMBY336vty8GwwMxnSGite/aFRERERE5xeocrtxuNwEBAd77fn5+hIb6cCRDGh3DMFi8KQeojymB35jXmhIoIiIiIk1cnacFGobBxIkTsdvtAFRUVHDDDTcQEhJS7byPP/7YNz2UBrc5q4Q9BeXY/ayckRbju4YNA7YeCFftFa5EREREpGmrc7iaMGFCtfvXXnutzzojjVPVlMAz2rciKMCHxUpyN0NRBtjskDLYd+2KiIiIiDSAOoer119/vT76IY1Y1f5WPp8SWFWCPWUwBAT7tm0RERERkVPshDcRlpahsKySFbvyATirs0qwi4iIiIgcjcKVHNOSrTm4PQYd4kJpE+3D0aXKCtj5o3lbJdhFREREpBlQuJJjqlpv5fMpgelLwVUOYYkQ1823bYuIiIiINACFKzkqj8fguwMl2Ed0jvVt41VTAtufDdojTURERESaAYUrOarVewrJK3USZvdjQGq0bxv3lmDXlEARERERaR4UruSoqqYEDusUg7/Nhz8qRfsgex1ggbSzfNeuiIiIiEgDUriSo/r2QLga4fMqgQdGrZL6QEgr37YtIiIiItJAFK7kiLKLK1izpxCoj/VWVVMCVYJdRERERJoPhSs5osUHCln0ah1BXFig7xr2eGD7t+Zt7W8lIiIiIs2IwpUc0eJN9TQlcN9KKMuDgDBoPcC3bYuIiIiINCCFK6mh0u1hyeZcoB72t6oqwZ42HGz+vm1bRERERKQBKVxJDct37qfY4aJVSAC9kiN827hKsIuIiIhIM6VwJTVUrbca3jkWq9WHG/xWFEHGL+ZthSsRERERaWYUrqSGH7aYUwJ9vt5q5xLwuCA6DaLb+bZtEREREZEGpnAl1RRVVLIxswiAQe2ifdv41gPrrVSCXURERESaIYUrqeb39AI8BrSNDiY+3Icl2OHg/lYqwS4iIiIizZDClVTz6879APRPjfJtw0V7IX8HWKyQMsS3bYuIiIiINAIKV1LN8gPhakCqj6cEpv9sXsf3gMBw37YtIiIiItIIKFyJV6Xbw8rdBQAM8PXI1e5l5nXb033broiIiIhII6FwJV7r9hZRUekhMtiftJhQ3zaevtS8VrgSERERkWaqwcPV888/T2pqKoGBgQwaNIhffvnlqOeuW7eOyy+/nNTUVCwWC7Nnz65xzrRp07BYLNUuXbp0qcd30Hx411ulRPl2fytHMWSuMW+3UbgSERERkeapQcPV+++/z5QpU3jwwQf57bff6N27N6NGjSI7O/uI55eVlZGWlsZjjz1GQkLCUdvt3r07+/bt815++OGH+noLzcpybzELH6+3yvgVDA9EtIWIZN+2LSIiIiLSSDRouPrHP/7B3/72NyZNmkS3bt146aWXCA4O5rXXXjvi+QMGDOCJJ55g3Lhx2O32o7br5+dHQkKC9xITE1Nfb6HZMAyDX3fmA/W53mqQb9sVEREREWlEGixcOZ1OVqxYwciRIw92xmpl5MiRLF269KTa3rJlC0lJSaSlpXHNNdeQnp5+zPMdDgdFRUXeS0u0I7eUvFInAX5WeiRH+LZxrbcSERERkRagwcJVbm4ubreb+Pj4asfj4+PJzMw84XYHDRrEnDlzWLBgAS+++CI7duxg2LBhFBcXH/U5M2fOJCIigoiICJKSkk74tZuyqlGrPq0jsfvZfNew22VOCwSttxIRERGRZq3BC1r42vnnn88VV1xBr169GDVqFPPnz6egoID//ve/R33OvffeS2FhIYWFhezdu/cU9rbxWF5fmwdnrQVnCdgjIK6rb9sWEREREWlE/BrqhWNiYrDZbGRlZVU7npWVdcxiFXUVGRlJp06d2Lp161HPsdvt3jVcNpsPR22akF93Va238nExi6r1Vm0GgLVlfm1FREREpGVosJGrgIAA+vXrx6JFi7zHPB4PixYt4owzzvDZ65SUlLBt2zYSExN91mZzk1PsYEduKQCntfXxyJXWW4mIiIhIC9FgI1cAU6ZMYcKECfTv35+BAwcye/ZsSktLmTRpEgDjx48nOTmZmTNnAmYRjPXr13tv79mzh5UrVxIaGkqHDh0AuPPOOxkzZgwpKSns3buXBx98EJvNxtVXX90wb7IJWHFg1KpzfBgRwf6+a9gwIP1n87bWW4mIiIhIM9eg4eqqq64iJyeHqVOnkpmZSZ8+fViwYIG3yEV6ejpW68HBtb1799K3b1/v/SeffJInn3yS4cOHs3jxYgAyMjK4+uqrycvLIzY2lqFDh/Lzzz8TGxt7St9bU/Jrfa23KkiH4n1g9YPkfr5tW0RERESkkWnQcAVwyy23cMsttxzxsarAVCU1NRXDMI7Z3nvvveerrrUYy+t7vVVibwgI9m3bIiIiIiKNTLOrFih1U+Z0sW5PIVAPI1fe9Va+W0MnIiIiItJYKVy1cCt3F+DyGCRGBJIcGeTbxtOrKgUO8m27IiIiIiKNkMJVC1e1eXD/1GgsFovvGi4vgGyz+IgqBYqIiIhIS6Bw1cJVbR48wNdTAjOWAwZEp0FonG/bFhERERFphBSuWjCX28NvB4pZ9EvReisRERERkZOhcNWCbcwsptTpJtTuR5eEcN82rvVWIiIiItLCKFy1YFX7W52WEoXN6sP1Vi4n7PnVvK2RKxERERFpIRSuWrBfq/a38vWUwMzV4KqAoGiI6ejbtkVEREREGimFqxbKMAxvMYv+vt482Lve6nTwZQVCEREREZFGTOGqhcrILyeryIGf1UKfNpG+bTz9Z/Na661EREREpAVRuGqhft1ljlr1SI4gKMDmu4YN42C40norEREREWlBFK5aqOVVmwf7er3V/u1Qlgs2OyT18W3bIiIiIiKNmMJVC/Vrfa+3Sj4N/Oy+bVtEREREpBFTuGqBShwuNmeVAPWxebDWW4mIiIhIy6Rw1QJt3FcEQHy4ndgwH48uab2ViIiIiLRQClct0IYD4apbYrhvGy7Ng7wt5u02A33btoiIiIhII6dw1QKtPxCuuvo6XGUsN69jOkOwj9dyiYiIiIg0cgpXLdD6fcVAPYarNgN8266IiIiISBOgcNXCuD0GmzIPTAtMqqdw1VrhSkRERERaHoWrFmZHbikVlR4C/a2ktgrxXcMeN+xZYd5WuBIRERGRFkjhqoWpKmbROSEcm9Xiu4ZzNoKzBAJCIbaL79oVEREREWkiFK5amHqrFFg1JTD5NLDafNu2iIiIiEgToHDVwqz3hqsw3zas9VYiIiIi0sIpXLUwG+qtDPuv5rXClYiIiIi0UApXLUheiYOsIgcAXXwZrsoLzDVXAMn9fdeuiIiIiEgTonDVgmw4sL9VSqtgQu1+vmt472/mdVQqhMb6rl0RERERkSZE4aoF8U4JTNCUQBERERERX1O4akG8lQK1ebCIiIiIiM8pXLUg6+ujmIVhHBKutN5KRERERFouhasWwuFyszW7BICuvizDvn87lOeDzQ7xPX3XroiIiIhIE6Nw1UJszS7B5TEID/QjOTLIdw1XjVol9QG/AN+1KyIiIiLSxChctRDr9x6cEmixWHzXsNZbiYiIiIgAClctRlUZdt9vHqz1ViIiIiIioHDVYtRLpUBnGWSuNW9r5EpEREREWjiFqxbAMAxvpcBuvhy52rcSDDeEJUJ4su/aFRERERFpghSuWoB9hRUUlldis1roEBfqu4YPnRLoy3VcIiIiIiJNkMJVC1A1JbBDbCiB/jbfNaxiFiIiIiIiXgpXLcDBSoE+3N/KMGC3wpWIiIiISBWFqxZgQ+bBMuw+U7QHSjLBYoPEPr5rV0RERESkiVK4agGqyrD7tFJg1ZTAhB4QEOy7dkVEREREmiiFq2au1OFiZ14p4OORq4xfzWtNCRQRERERARSumr2NmcUYBsSG2YkJtfuuYRWzEBERERGpRuGqmdtQH/tbuZywd6V5W+FKRERERARQuGr2qsKVT6cEZq0BtwOCoiA6zXftioiIiIg0YQpXzdz6ffVQhv3Q9VbaPFhEREREBFC4atY8HoNNmWalwO71USlQUwJFRERERLwUrpqxXfvLKHO6sftZSW0V4ruGveGqv+/aFBERERFp4hSumrH1e80pgZ0TwvCz+ehbXZID+TsBCyT3802bIiIiIiLNgMJVM1YvlQL3HFhvFdsZAiN8166IiIiISBOncNWM1UulwD0rzOtkTQkUERERETmUwlUztr4+wlXV/lZJfXzXpoiIiIhIM9Dg4er5558nNTWVwMBABg0axC+//HLUc9etW8fll19OamoqFouF2bNnn3SbzVVBmZN9hRUAdPFVGXbDgH0rzduJfXzTpoiIiIhIM9Gg4er9999nypQpPPjgg/z222/07t2bUaNGkZ2dfcTzy8rKSEv7/+3de3CU5f338c/mtDmZEzGbBASC8OMYjgGM+PuhJY+EOj6itBUn1UAdGRUsypQqVMQ+1OKRth4KLU+xdjyWPoUqP6U/GiUOGiCcgxzUKiKEJIRTYAMkZq/njzWrK6hBrnDvbt6vmZ29c98X33x35pohn7nv69oeevjhh5WdnW2lZqRq3czikowEpcTH2il6/IDkPSi5oqXsAXZqAgAAABHC0XC1YMEC3XbbbZo8ebL69eunRYsWKTExUUuWLDnr+OHDh+uxxx7TxIkT5Xa7rdSUpNOnT6uhoSHwigRV+49JkgbkWtx04sBW//vFvaXYBHt1AQAAgAjgWLhqamrSxo0bVVRU9EUzUVEqKipSRUXFBa05f/58paamKjU1Vbm5ud/pd4eaQLjqbDFcta634pFAAAAA4AyOhav6+nq1tLTI4/EEnfd4PKqpqbmgNWfNmqVjx47p2LFjqq6u/k6/O9S0hquBXdrhzlXOIHs1AQAAgAgR43QDocDtdgceM4yOjna4m/N37GSzPjnUKKmdHgskXAEAAABncOzOVWZmpqKjo1VbWxt0vra29ms3q3CiZjh67/O7Vl3SE5SeFGen6Ik66Xi1JJeUnW+nJgAAABBBHAtXcXFxGjZsmMrKygLnfD6fysrKVFhYGDI1w1G7PhKY2UtyJ9urCwAAAEQIRx8LnDFjhkpLS1VQUKARI0bot7/9rbxeryZPnixJuuWWW9S5c2fNnz9fkn/Dih07dgSO9+/fry1btig5OVk9e/ZsU82OYBubWQAAAAAXnKPh6sYbb9TBgwf1wAMPqKamRoMHD9bKlSsDG1Ls3btXUVFf3Fyrrq7WkCFDAj8//vjjevzxxzV69GitXr26TTU7gu2fh6t8m+Eq8OXBrLcCAAAAzsZljDFONxFKvF6vkpP9j72dOHFCSUlJDnd0bo41NmvQ//kfSdLmOf/L3pqr3wyQjn0qTfpvqfsVdmoCAAAAIexcs4GjXyIM+7ZX++9aXZJhcTML7yF/sJLYzAIAAAD4GoSrCFPVno8EZvSQ4i3WBQAAACII4SrCVLXHZhaB77cabK8mAAAAEGEIVxGmdTOLgZ3T7BXly4MBAACAb0W4iiDHGpv1yaFGSdKAzin2Crc+Fpg72F5NAAAAIMIQriLIlzezSEu0tJnFySPSkT3+4+yBdmoCAAAAEYhwFUGq2uWRwG3+97RuUmKGvboAAABAhCFcRZCqfe25mQXrrQAAAIBvQriKIO26DTvrrQAAAIBvRLiKEMcam7X3cHtsZsGdKwAAAKAtCFcRovWuVdeMRHubWZxqkA596D/mO64AAACAb0S4ihDt8khgTZX/PaWLlJRpry4AAAAQgQhXEaL1y4PZzAIAAABwBuEqQgS2Ye/CZhYAAACAEwhXEeBoY9MXm1nkcucKAAAAcALhKgJs398gyb+ZRWpirJ2iTV6p/n3/MZtZAAAAAN+KcBUB2mczi+2S8UnJ2dJFHnt1AQAAgAhFuIoAVfuPSpLy22O9FY8EAgAAAG1CuIoA7XLnqnW9FZtZAAAAAG1CuApzRxub9Onhk5Isb2ZRvcX/zp0rAAAAoE0IV2GudTOLbp0sbmbRfFI6uMt/zGYWAAAAQJsQrsLcts/XW1n98uDaHZJpkRIzpZRce3UBAACACEa4CnPb22W91Wb/e+5gyeWyVxcAAACIYISrMNe6mcXA9tjMgvVWAAAAQJsRrsLYEe8Xm1n0txmu9q7zv+cOsVcTAAAAiHCEqzC2vdp/16pbp0SlJljazKKhWqrfLckldb/CTk0AAACgAyBchbFt+/zhyupmFh+V+99zh0gJ6fbqAgAAABGOcBXG3vmwXpJU0M1iCProLf97jyvt1QQAAAA6AMJVmDpx+jNV7jksSbqyd5adosZIH632HxOuAAAAgHNCuApTFf8+pOYWo26dEpWXmWSn6MFd0olaKSZBumSknZoAAABAB0G4ClOrd9dJkkb/x8X2irbetepWKMXG26sLAAAAdACEqzBkjFH5+wclSVf2bodwxSOBAAAAwDkjXIWhfx/0at+Rk4qLjtJlPTrZKdrSLO1Z4z8mXAEAAADnjHAVhlrvWo3skaHEuBg7RfdtkJpOSAkZkiffTk0AAACgAyFchaF2XW/VY7QUxbQAAAAAzhV/RYeZk00tWvdx6xbs7RGurrJXEwAAAOhACFdhZu1Hh9T0mU+d0xJ06cXJdoqeapD2VfqPWW8FAAAAfCeEqzDTut5qdO+L5XK57BT95B3JtEjpeVJ6Nzs1AQAAgA6GcBVm2ne91ZX2agIAAAAdDOEqjOyp92rPoUbFRLk0qmemvcKEKwAAAOC8Ea7CSOsjgQXd05XstrQFe8MB6eAuSS4p77/s1AQAAAA6IMJVGGkNV1f2zrJX9ONy/3vuYCkxw15dAAAAoIMhXIWJU80tevff9ZIsr7f691v+dx4JBAAAAM4L4SpMVO45rFPNPnlS3OqTfZGdosaw3goAAACwhHAVJlbv/nwL9v+wuAX7wd3SiRopJl665DI7NQEAAIAOinAVJtplvVXrXauul0mx8fbqAgAAAB0Q4SoM7DvSqA/rTii63bZgv8peTQAAAKCDIlyFgda7VkMuSVNqQqydoi3N0p41/mPWWwEAAADnjXAVBlrXW13Z2+Iugfs3Sk3HpYR0KXugvboAAABAB0W4CnFNn/n07oetW7C3w3qrvNFSFNMAAAAAOF8h8Vf1M888o+7duys+Pl4jR47U+vXrv3H80qVL1adPH8XHxys/P1+vv/560PVJkybJ5XIFvYqLi9vzI7SbDZ8clrepRZnJceqfm2KvMFuwAwAAAFY5Hq5eeeUVzZgxQ3PnztWmTZs0aNAgjR07VnV1dWcd/+677+qmm27Srbfeqs2bN2v8+PEaP368tm/fHjSuuLhYBw4cCLxeeumlC/FxrGtdb/VfvS5WVJSlLdgbD0v7Kv3HhCsAAADACsfD1YIFC3Tbbbdp8uTJ6tevnxYtWqTExEQtWbLkrON/97vfqbi4WDNnzlTfvn01b948DR06VE8//XTQOLfbrezs7MArPT39Qnwc68pbv9/K5nqrHf+QfJ9JnnwpI89eXQAAAKADczRcNTU1aePGjSoqKgqci4qKUlFRkSoqKs76byoqKoLGS9LYsWPPGL969WplZWWpd+/euuOOO3To0KGv7eP06dNqaGgIvEKFMUYll3VTUd8s/Wcvi+Fq+//zv+dPsFcTAAAA6OAcDVf19fVqaWmRx+MJOu/xeFRTU3PWf1NTU/Ot44uLi/WXv/xFZWVleuSRR1ReXq5x48appaXlrDXnz5+v1NRUpaamKjc39zw/lT0ul0s3X9ZN/7d0uDKS4uwUbTjwxRbs/W+wUxMAAACAYpxuoD1MnDgxcJyfn6+BAwfq0ksv1erVqzVmzJgzxs+aNUszZsyQJHm93pAKWNbtWC7JSF1GSOndnO4GAAAAiBiO3rnKzMxUdHS0amtrg87X1tYqOzv7rP8mOzv7nMZLUo8ePZSZmakPP/zwrNfdbrdSUlICr4hW9Tf/+wAeCQQAAABscjRcxcXFadiwYSorKwuc8/l8KisrU2Fh4Vn/TWFhYdB4SVq1atXXjpekffv26dChQ8rJybHTeLg6skfav0FyRUn9r3e6GwAAACCiOL5b4IwZM7R48WI999xz2rlzp+644w55vV5NnjxZknTLLbdo1qxZgfHTp0/XypUr9cQTT2jXrl168MEHtWHDBk2bNk2SdOLECc2cOVNr167Vnj17VFZWpuuuu049e/bU2LFjHfmMIaN1I4vu/yld5PnmsQAAAADOieNrrm688UYdPHhQDzzwgGpqajR48GCtXLkysGnF3r17FRX1RQa8/PLL9eKLL+r+++/X7Nmz1atXLy1fvlwDBgyQJEVHR2vbtm167rnndPToUeXm5urqq6/WvHnz5Ha7HfmMIWP73/3vPBIIAAAAWOcyxhinmwglXq9XycnJkvx3wZKSkhzuyJK6XdLvR0pRsdLP3pcSM5zuCAAAAAhp55oNHH8sEBfI9s83sug5hmAFAAAAtAPCVUdgzBfrrQb8wNleAAAAgAhFuOoIqjdLhz+SYhKk3uOc7gYAAACISISrjqD1rlXvYsmd7GwvAAAAQIQiXEU6n096b5n/mEcCAQAAgHZDuIp0n66VGvZL7hSpZ5HT3QAAAAARi3AV6ao+3yWw77VSbLyzvQAAAAARjHAVyVqapR3L/cd8cTAAAADQrghXkezjcqnxkJSYKeWNdrobAAAAIKIRriJZ1ee7BPYfL0XHONoKAAAAEOkIV5Hq5BFp1wr/MY8EAgAAAO2O2xmR5vRxae0iqeIp6XSDlNJFuuQyp7sCAAAAIh7hKlI0eaX1i6V3fiedPOw/d3Ef6X8/JUVxgxIAAABob4SrcNd8StqwRFqzQPIe9J/r1FO6cpbU/3opKtrZ/gAAAIAOgnAVbppPSbXvSdWbpOrN0odl0oka/7X07tLoe6X8H7GBBQAAAHCB8Rd4qKupkvZv9Aep6s1S7Q7J1xw8JqWLNHqmNLhEio51pk8AAACggyNchbq/3iId/ij4XGInKXeolDtE6jxUuvR7Uozbmf4AAAAASCJchb680VLqJV8Eqdwh/p9dLqc7AwAAAPAlhKtQd+1vne4AAAAAQBuwRzcAAAAAWEC4AgAAAAALCFcAAAAAYAHhCgAAAAAsIFwBAAAAgAWEKwAAAACwgHAFAAAAABYQrgAAAADAAsIVAAAAAFhAuAIAAAAACwhXAAAAAGAB4QoAAAAALCBcAQAAAIAFhCsAAAAAsIBwBQAAAAAWEK4AAAAAwALCFQAAAABYEON0A6HGGBM49nq9DnYCAAAAwElfzgNfzglfh3D1FY2NjYFjj8fjYCcAAAAAQkVjY6OSk5O/cQyPBQIAAACABS7TlvtbHYjP51N9fb0kKTExUS6Xq91/Z0NDg3Jzc1VdXa2UlJR2/32IPMwh2MA8wvliDuF8MYdwvmzPIWNM4Mm2zMxMRUV9870pHgv8iqioKGVlZV3Q39nS0iJJSkpKUlJS0gX93YgMzCHYwDzC+WIO4Xwxh3C+2mMOfdujgF/GY4EAAAAAYAHhCgAAAAAsIFyFALfbrblz58rtdjvdCsIUcwg2MI9wvphDOF/MIZwvp+cQG1oAAAAAgAXcuQIAAAAACwhXAAAAAGAB4QoAAAAALCBcAQAAAIAFhCuHPfPMM+revbvi4+M1cuRIrV+/3umWEMLmz5+v4cOH66KLLlJWVpbGjx+v3bt3B405deqUpk6dqk6dOik5OVkTJkxQbW2tQx0jlD388MNyuVy6++67A+eYP2iL/fv368c//rE6deqkhIQE5efna8OGDYHrxhg98MADysnJUUJCgoqKivTBBx842DFCSUtLi+bMmaO8vDwlJCTo0ksv1bx58/TlPdaYQ/iqt99+W9dee61yc3Plcrm0fPnyoOttmTOHDx9WSUmJUlJSlJaWpltvvVUnTpyw2ifhykGvvPKKZsyYoblz52rTpk0aNGiQxo4dq7q6OqdbQ4gqLy/X1KlTtXbtWq1atUrNzc26+uqr5fV6A2Puuecevfbaa1q6dKnKy8tVXV2tG264wcGuEYoqKyv1hz/8QQMHDgw6z/zBtzly5IhGjRql2NhYvfHGG9qxY4eeeOIJpaenB8Y8+uijevLJJ7Vo0SKtW7dOSUlJGjt2rE6dOuVg5wgVjzzyiBYuXKinn35aO3fu1COPPKJHH31UTz31VGAMcwhf5fV6NWjQID3zzDNnvd6WOVNSUqL33ntPq1at0ooVK/T2229rypQpdhs1cMyIESPM1KlTAz+3tLSY3NxcM3/+fAe7Qjipq6szkkx5ebkxxpijR4+a2NhYs3Tp0sCYnTt3GkmmoqLCqTYRYo4fP2569eplVq1aZUaPHm2mT59ujGH+oG3uvfdec8UVV3ztdZ/PZ7Kzs81jjz0WOHf06FHjdrvNSy+9dCFaRIi75pprzE9+8pOgczfccIMpKSkxxjCH8O0kmWXLlgV+bsuc2bFjh5FkKisrA2PeeOMN43K5zP79+631xp0rhzQ1NWnjxo0qKioKnIuKilJRUZEqKioc7Azh5NixY5KkjIwMSdLGjRvV3NwcNK/69Omjrl27Mq8QMHXqVF1zzTVB80Ri/qBtXn31VRUUFOiHP/yhsrKyNGTIEC1evDhw/eOPP1ZNTU3QPEpNTdXIkSOZR5AkXX755SorK9P7778vSdq6davWrFmjcePGSWIO4dy1Zc5UVFQoLS1NBQUFgTFFRUWKiorSunXrrPUSY60Szkl9fb1aWlrk8XiCzns8Hu3atcuhrhBOfD6f7r77bo0aNUoDBgyQJNXU1CguLk5paWlBYz0ej2pqahzoEqHm5Zdf1qZNm1RZWXnGNeYP2uKjjz7SwoULNWPGDM2ePVuVlZX66U9/qri4OJWWlgbmytn+f2MeQZLuu+8+NTQ0qE+fPoqOjlZLS4seeughlZSUSBJzCOesLXOmpqZGWVlZQddjYmKUkZFhdV4RroAwNXXqVG3fvl1r1qxxuhWEiU8//VTTp0/XqlWrFB8f73Q7CFM+n08FBQX69a9/LUkaMmSItm/frkWLFqm0tNTh7hAO/vrXv+qFF17Qiy++qP79+2vLli26++67lZubyxxC2OOxQIdkZmYqOjr6jF24amtrlZ2d7VBXCBfTpk3TihUr9NZbb6lLly6B89nZ2WpqatLRo0eDxjOvIPkf+6urq9PQoUMVExOjmJgYlZeX68knn1RMTIw8Hg/zB98qJydH/fr1CzrXt29f7d27V5ICc4X/3/B1Zs6cqfvuu08TJ05Ufn6+br75Zt1zzz2aP3++JOYQzl1b5kx2dvYZm8Z99tlnOnz4sNV5RbhySFxcnIYNG6aysrLAOZ/Pp7KyMhUWFjrYGUKZMUbTpk3TsmXL9OabbyovLy/o+rBhwxQbGxs0r3bv3q29e/cyr6AxY8aoqqpKW7ZsCbwKCgpUUlISOGb+4NuMGjXqjK+AeP/999WtWzdJUl5enrKzs4PmUUNDg9atW8c8giSpsbFRUVHBf4JGR0fL5/NJYg7h3LVlzhQWFuro0aPauHFjYMybb74pn8+nkSNH2mvG2tYYOGcvv/yycbvd5s9//rPZsWOHmTJliklLSzM1NTVOt4YQdccdd5jU1FSzevVqc+DAgcCrsbExMOb22283Xbt2NW+++abZsGGDKSwsNIWFhQ52jVD25d0CjWH+4NutX7/exMTEmIceesh88MEH5oUXXjCJiYnm+eefD4x5+OGHTVpamvnHP/5htm3bZq677jqTl5dnTp486WDnCBWlpaWmc+fOZsWKFebjjz82f//7301mZqb5+c9/HhjDHMJXHT9+3GzevNls3rzZSDILFiwwmzdvNp988okxpm1zpri42AwZMsSsW7fOrFmzxvTq1cvcdNNNVvskXDnsqaeeMl27djVxcXFmxIgRZu3atU63hBAm6ayvZ599NjDm5MmT5s477zTp6ekmMTHRXH/99ebAgQPONY2Q9tVwxfxBW7z22mtmwIABxu12mz59+pg//vGPQdd9Pp+ZM2eO8Xg8xu12mzFjxpjdu3c71C1CTUNDg5k+fbrp2rWriY+PNz169DC/+MUvzOnTpwNjmEP4qrfeeuusfwOVlpYaY9o2Zw4dOmRuuukmk5ycbFJSUszkyZPN8ePHrfbpMuZLX4cNAAAAAPhOWHMFAAAAABYQrgAAAADAAsIVAAAAAFhAuAIAAAAACwhXAAAAAGAB4QoAAAAALCBcAQAAAIAFhCsAAAAAsIBwBQCAZS6XS8uXL3e6DQDABUa4AgBElEmTJsnlcp3xKi4udro1AECEi3G6AQAAbCsuLtazzz4bdM7tdjvUDQCgo+DOFQAg4rjdbmVnZwe90tPTJfkf2Vu4cKHGjRunhIQE9ejRQ3/729+C/n1VVZW+973vKSEhQZ06ddKUKVN04sSJoDFLlixR//795Xa7lZOTo2nTpgVdr6+v1/XXX6/ExET16tVLr776avt+aACA4whXAIAOZ86cOZowYYK2bt2qkpISTZw4UTt37pQkeb1ejR07Vunp6aqsrNTSpUv1r3/9Kyg8LVy4UFOnTtWUKVNUVVWlV199VT179gz6Hb/85S/1ox/9SNu2bdP3v/99lZSU6PDhwxf0cwIALiyXMcY43QQAALZMmjRJzz//vOLj44POz549W7Nnz5bL5dLtt9+uhQsXBq5ddtllGjp0qH7/+99r8eLFuvfee/Xpp58qKSlJkvT666/r2muvVXV1tTwejzp37qzJkyfrV7/61Vl7cLlcuv/++zVv3jxJ/sCWnJysN954g7VfABDBWHMFAIg4V111VVB4kqSMjIzAcWFhYdC1wsJCbdmyRZK0c+dODRo0KBCsJGnUqFHy+XzavXu3XC6XqqurNWbMmG/sYeDAgYHjpKQkpaSkqK6u7rt+JABAGCBcAQAiTlJS0hmP6dmSkJDQpnGxsbFBP7tcLvl8vvZoCQAQIlhzBQDocNauXXvGz3379pUk9e3bV1u3bpXX6w1cf+eddxQVFaXevXvroosuUvfu3VVWVnZBewYAhD7uXAEAIs7p06dVU1MTdC4mJkaZmZmSpKVLl6qgoEBXXHGFXnjhBa1fv15/+tOfJEklJSWaO3euSktL9eCDD+rgwYO66667dPPNN8vj8UiSHnzwQd1+++3KysrSuHHjdPz4cb3zzju66667LuwHBQCEFMIVACDirFy5Ujk5OUHnevfurV27dkny7+T38ssv684771ROTo5eeukl9evXT5KUmJiof/7zn5o+fbqGDx+uxMRETZgwQQsWLAjUKi0t1alTp/Sb3/xGP/vZz5SZmakf/OAHF+4DAgBCErsFAgA6FJfLpWXLlmn8+PFOtwIAiDCsuQIAAAAACwhXAAAAAGABa64AAB0KT8MDANoLd64AAAAAwALCFQAAAABYQLgCAAAAAAsIVwAAAABgAeEKAAAAACwgXAEAAACABYQrAAAAALCAcAUAAAAAFvx/SaO4vys2O3kAAAAASUVORK5CYII=",
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