diff --git a/8_rag/1_naive_rag.md b/8_rag/1_naive_rag.md new file mode 100644 index 00000000..ff278e25 --- /dev/null +++ b/8_rag/1_naive_rag.md @@ -0,0 +1,119 @@ +# **Basic RAG (Retrieval-Augmented Generation)** + +## **Overview** + +Retrieval-Augmented Generation (RAG) is a powerful framework for combining **retrieval** (fetching relevant context) with **generation** (producing coherent and contextually accurate responses) to create intelligent, factual, and context-aware systems. + +Most RAG pipeline leverages **retrieval** to access external knowledge and **generation** to produce fluent, natural responses, making it an essential architecture for modern AI systems. + +This guide focuses on implementing a basic RAG pipeline using **Haystack**, a relatively lightweight yet feature-rich llm orchestration framework that simplifies the process of building and customizing such systems. It would be straighforward to implement the pipeline with other framework like LangChain and Llama-Index which all possess identical core functionalities required for RAG. + +## **Core Concepts in RAG** + +### **Indexing Pipeline** + +The **Indexing Pipeline** prepares your knowledge base by preprocessing raw documents, splitting them into manageable chunks, and embedding them into vectors. These vectors are stored in an efficient database to support fast retrieval. + +#### **Steps in the Indexing Pipeline** + +1. **Document Collection**: + - Source raw documents from relevant repositories, such as Wikipedia, internal databases, or research articles. + - Examples: `.txt`, `.pdf`, `.docx`, JSON, or other formats. + +2. **Document Cleaning**: + - Use tools like Haystack's `DocumentCleaner` to remove noise, boilerplate text, and irrelevant sections. + - Focus on retaining meaningful content. + +3. **Document Splitting**: + - Split large documents into smaller, coherent chunks (e.g., paragraphs or sentences). + - Use Haystack's `DocumentSplitter` to define chunk size and overlap for better retrieval performance. + +4. **Embedding Generation**: + - Convert text chunks into dense vector representations using pre-trained models (e.g., `SentenceTransformersDocumentEmbedder`). + - Embeddings capture semantic meaning, enabling similarity-based search. + +5. **Document Indexing**: + - Store embeddings and metadata in a vector database or document store, such as `InMemoryDocumentStore` or `FAISS`. + +**Indexing Workflow** + +```plaintext +[Raw Documents] + | + v +[Document Cleaner] -- Removes noise + | + v +[Document Splitter] -- Splits text into chunks + | + v +[Document Embedder] -- Converts chunks into vector embeddings + | + v +[Document Store] -- Stores embeddings for fast retrieval +``` + + +### **Retrieve + Generate Pipeline** + +The **Retrieve + Generate Pipeline** processes user queries by retrieving relevant knowledge and generating context-aware responses using retrieved content. + +#### **Steps in the Retrieve + Generate Pipeline** + +1. **Query Embedding**: + - Convert the user query into a dense vector representation using a model like `SentenceTransformersTextEmbedder`. + +2. **Document Retrieval**: + - Perform similarity search in the document store to retrieve the top-k most relevant chunks based on query embedding. + +3. **Prompt Construction**: + - Combine the user query and retrieved documents into a structured prompt for the generative model. + - Ensure clarity and relevance by organizing context logically. + +4. **Response Generation**: + - Use a text generation model (e.g., GPT-3, SmolLM2) to generate a coherent and factual response based on the constructed prompt. + +**Retrieve + Generate Workflow** + +```plaintext +[User Query] + | + v +[Query Embedder] -- Converts query into vector + | + v +[Document Retriever] -- Finds top-k relevant documents + | + v +[Prompt Builder] -- Combines query + retrieved documents into a prompt + | + v +[Text Generator] -- Produces contextually grounded response +``` + + +## **Evaluation** + +To ensure the RAG system performs well, evaluate both retrieval and generation components using appropriate metrics: + +- **BLEU** (Bilingual Evaluation Understudy) focuses on **precision** and evaluates how much of the generated text matches reference text n-grams. +- **ROUGE** (Recall-Oriented Understudy for Gisting Evaluation) focuses on **recall** and evaluates how much of the reference text's n-grams are captured by the generated text, making it ideal for summarization and text generation tasks. +- **MRR** (Mean Reciprocal Rank) evaluates the effectiveness of an information retrieval system and tasks like question answering by considering the rank of the first relevant result. + +Feedback may be used to iteratively improve embeddings, retrieval thresholds, or prompt formatting. + +## **Example: Basic RAG System** + +1. **Setup Knowledge Base**: + - Collect documents and preprocess them using the **Indexing Pipeline**. + +2. **Integrate Query Handling**: + - Implement the **Retrieve + Generate Pipeline** to handle user inputs. + +3. **Evaluate and Adjust**: + - Evaluate the pipeline and monitor retrieval and generation quality. Incorporate feedback for adjustment. + +⏩ Try the [Basic RAG Tutorial](./notebooks/naive_rag_haystack_example.ipynb) to implement a Naive RAG pipeline. + +## **Resources** + diff --git a/8_rag/2_advanced_rag.md b/8_rag/2_advanced_rag.md new file mode 100644 index 00000000..7841c893 --- /dev/null +++ b/8_rag/2_advanced_rag.md @@ -0,0 +1,54 @@ +# Advanced RAG Strategies + +Advanced Retrieval-Augmented Generation (RAG) techniques address the challenges faced by naive RAG. These strategies enhance the process of document retrieval and improve the quality of answers generated by large language models (LLMs). There are multiple possible optimizations in each step of the pipeline for an advanced RAG, but particularly retrieval stage is the focus. + +The strategies are thus divided into pre-retrieval, retrieval, and post-retrieval to address the following challenges. + +- **How to achieve accurate semantic representations of documents and queries?** +- **What methods align the semantic spaces of queries and documents (chunks)?** +- **How to align the retriever’s output with the preferences of the LLM?** + + +## Pre-Retrieval Strategies + +Efficient data indexing is essential for improving the retrieval performance in a RAG system. Key pre-retrieval strategies include: + +- **Improve Data Quality**: Remove irrelevant information, resolve ambiguity in entities and terms, confirm factual accuracy, maintain context, and update outdated information. +- **Optimize Index Structure**: Adjust chunk sizes to capture relevant context or incorporate graph structures to represent relationships between entities. +- **Add Metadata**: Enhance data filtering by adding relevant metadata such as dates, chapters, subsections, and purposes to document chunks. +- **Chunk Optimization**: Fine-tune chunk size to balance between too large or too small chunks to improve the embedding process. + +### Key Pre-Retrieval Techniques: + +- **Sliding Window**: Chunking method with overlap between text blocks. +- **Auto-Merging Retrieval**: Starts with small text blocks and later provides larger, related text blocks for the LLM. +- **Abstract Embedding**: Focuses on Top-K retrieval based on document abstracts for a comprehensive document context. +- **Metadata Filtering**: Leverages document metadata for enhanced filtering. +- **Graph Indexing**: Converts entities and relationships into nodes and connections to improve relevance. + +## Retrieval Strategies + +During the retrieval phase, the goal is to identify the most relevant document chunks to the query. This requires optimizing the embedding models used to represent both the query and chunks. + +- **Domain Knowledge Fine-Tuning**: Fine-tune embedding models using domain-specific datasets to capture the unique aspects of the RAG system. +- **Similarity Metrics**: Select an appropriate metric to measure the similarity between the query and chunk embeddings. Common metrics include: + - Cosine Similarity + - Euclidean Distance (L2) + - Dot Product + - L2 Squared Distance + - Manhattan Distance + +Several vector databases support multiple similarity metrics, allowing further customization/optimization. + +## Post-Retrieval Strategies + +After retrieving context data (chunks) from a vector database, the next step is to process this information and pass it to the LLM. However, some retrieved chunks may be irrelevant, noisy, or repeated, impacting the LLM’s ability to generate accurate answers. + +### Strategies to Address Post-Retrieval Issues: + +- **Reranking**: Prioritize the most relevant chunks by reranking the retrieved results. This ensures LLMs are given the top-K most pertinent context, reducing performance issues caused by excessive context. Available reranking techniques are offered by libraries like LlamaIndex, LangChain, and HayStack. +- **Prompt Compression**: Filter out irrelevant context and shorten the prompt before inputting it to the LLM. Techniques such as mutual information or perplexity estimation, along with summarization, help in reducing context length and noise. + +⏩ Try the [Improved RAG Tutorial](./notebooks/improved_rag_haystack_example.ipynb) to implement improved RAG pipelines. + +## **Resources** \ No newline at end of file diff --git a/8_rag/3_modular_agenic_rag.md b/8_rag/3_modular_agenic_rag.md new file mode 100644 index 00000000..14561c23 --- /dev/null +++ b/8_rag/3_modular_agenic_rag.md @@ -0,0 +1,79 @@ +# Modular (Agentic) RAG + +## Why Modular RAG is Needed + +**Vanilla RAG** faces two main challenges: + +1. **Single retrieval step**: If the retrieved documents are irrelevant, the generated answer will be poor. +2. **Semantic mismatch**: The user’s query might differ in form from the document’s content, making semantic similarity-based retrieval suboptimal. + +**Modular RAG** addresses these limitations by introducing an agent that can: + +- **Formulate the query** to optimize document retrieval. +- **Critique and re-retrieve** if necessary, improving retrieval accuracy and ensuring better answers. + +## Key Components of Modular RAG +### 1. **Module Components** + +In a modular RAG system, module, tool, and agent are often used interchangeably, though they represent different levels of abstraction within the system. These components represent different parts of the system that work together to enhance functionality. + +- **Search Module**: Expands retrieval by integrating data from various external sources like search engines, tabular data, and knowledge graphs, enhancing the relevance of context during retrieval. +- **Memory Module**: Stores past interactions (queries and answers) for ongoing context awareness, supporting dynamic tasks and conversations. +- **Custom Function Tool Module**: Executes advanced workflows, such as database queries or system commands, allowing the agent to interact with external systems. +- **Code Module (Agent)**: Specializes in coding tasks like analysis, generation, refactoring, and testing, enabling the agent to handle software development tasks. + +### 2. **Other Components** + +- **Fusion**: Performs parallel retrieval on original and expanded queries, intelligently reranking and merging results for optimal context. +- **Routing**: Directs the next action based on the query, such as summarization or searching specific databases, ensuring appropriate responses. +- **Orchestration Agent**: Coordinates the flow of information between modules, optimizing the efficiency and effectiveness of the overall RAG system. + +## AI Agent + +AI agents are modular systems where the output of LLMs controls the workflow, enabling interaction with external tools, programs, or systems. They provide the necessary "agency" for LLMs to autonomously navigate tasks and processes. The agent's role is to translate LLM outputs into executable actions, bridging the gap between the language model and the real world. + +AI agents bring an additional layer of intelligent orchestration, improving how different modules work together dynamically, rather than relying on static, predefined processes. Indeeds, agency in AI agents exists on a spectrum, with the LLM's control over the workflow increasing at each level: + +| Agency Level | Description | Example Pattern | +| --- | --- | --- | +| ☆☆☆ | LLM output has no impact on program flow | Simple Processor (`process_llm_output(llm_response)`) | +| ★☆☆ | LLM output triggers an if/else switch | Router (`if llm_decision(): path_a() else: path_b()`) | +| ★★☆ | LLM output determines function execution | Tool Caller (`run_function(llm_chosen_tool, llm_chosen_args)`) | +| ★★★ | LLM output controls iteration | Multi-step Agent (`while llm_should_continue(memory): execute_next_step()`) | +| ★★★ | One agent starts another agentic workflow | Multi-Agent (`if llm_trigger(): execute_agent()`) | + + +![Agent](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/open-source-llms-as-agents/ReAct.png) + + +**When to Use Agents** + +Agents are useful when flexibility is needed in the workflow. If tasks are too complex for predefined steps or criteria, an agent can adapt and determine the necessary actions. For simple tasks with a predictable workflow, agents may be unnecessary. + + +### `smolagent` Library + +The **smolagent** library provides a simple yet powerful framework to build AI agents. While you can manually write code for simple agents for chaining or routing, more complex behaviors such as tool calling and multi-step agent workflows require predefined abstractions to work effectively. Here's why **smolagent** is helpful: + +1. **Tool Calling**: When an agent needs to call a tool (e.g., fetching weather data), the output format from the LLM should be predefined, such as: + `Thought: I should call tool 'get_weather'. Action: get_weather(Paris).` + This ensures the LLM’s output can be parsed and executed by a system function. + +2. **Multi-Step Agents**: If the agent’s output controls a loop (e.g., iterating over a series of tasks), a different prompt may be needed for each iteration based on memory. This requires integrating memory into the system. + +Given these needs, **smolagent** provides essential building blocks that enable seamless orchestration: + +- An LLM engine that powers the system +- A list of available tools the agent can use +- A parser that extracts tool calls from LLM output +- A memory system that stores relevant information +- A system prompt synced with the parser + +Additionally, since agents are powered by LLMs, error logging, and retry mechanisms are essential for ensuring robustness and reliability. **smolagent** handles these elements, making it easier to build complex workflows that are reliable, flexible, and adaptive. + + +### Resources + +https://huggingface.co/docs/smolagents/index +https://huggingface.co/docs/smolagents/examples/rag +https://huggingface.co/docs/smolagents/conceptual_guides/intro_agents \ No newline at end of file diff --git a/8_rag/README.md b/8_rag/README.md new file mode 100644 index 00000000..43c64843 --- /dev/null +++ b/8_rag/README.md @@ -0,0 +1,138 @@ +# Retrieval-Augmented Generation (RAG) Module + +## 1. Overview of RAG + +Large Language Models (LLMs) have revolutionized natural language processing and generation. However, their reliance on static training data presents significant challenges. Retrieval-Augmented Generation (RAG) addresses these limitations by combining dynamic retrieval with generative capabilities. + +### Why RAG is Needed + +- **Reducing Hallucination** +RAG grounds responses in external knowledge, ensuring accuracy by dynamically fetching relevant context instead of relying solely on static, memorized information. + +- **Real-Time Knowledge** +Unlike static models, RAG can integrate live data, making it ideal for fast-changing domains like finance, news, and research. + +- **Automation and Scalability** +RAG simplifies complex workflows by dynamically accessing and integrating diverse information sources, enabling scalable applications such as customer support, enterprise search, and workflow automation. + +- **Domain-Specific Adaptability** +With tailored retrievers and modular design, RAG excels in specialized applications such as legal, healthcare, or education, offering flexibility and precision. + +- **Cost Efficiency** +By offloading retrieval tasks to external systems, RAG reduces computational costs and eliminates the need for frequent model fine-tuning. + +## 2. Stages of RAG + +Although there are many RAG variants, the main workflow can often be divided into three key stages: + +| **Stage** | **Sub-Components** | +| ------------------------- | ------------------------------- | +| **Index** | Ingest, Chunk, Embed, Store | +| **Retrieve + Generate** | Retrieve, Generate, Orchestrate | +| **(Optional) Evaluation**| Evaluate responses | + +### 2.1 Index Stage +The **Index** stage prepares the knowledge base for efficient retrieval by processing and organizing documents. This stage often involves: +- **Ingesting Documents**: Extracting text from raw documents (e.g., PDFs, HTML). +- **Chunking**: Splitting documents into smaller, manageable pieces. +- **Embedding**: Converting chunks into vector representations using embedding models. +- **Storing**: Indexing the embeddings into a vector database for efficient search. + +- **Tools/Frameworks**: + - **Ingesting**: OCR tools, document processors. + - **Chunking**: **LangChain Text Splitters**, **HayStack Preprocessors**. + - **Embedding**: **OpenAI Embeddings**, **Hugging Face Transformers**. + - **Vector Database**: **Qdrant**, **ElasticSearch**, **Pinecone**. + +### 2.2 Retrieve + Generate Stage +The **Retrieve + Generate** stage retrieves relevant context and generates responses based on it: + +#### **Retrieve** +Fetches the most relevant chunks of information using: +- **Retrieval Methods**: Dense vector search, hybrid retrieval (vector + keyword). +- **Advanced Techniques**: Re-ranking models, custom tools (web search integration, agents, etc.). + +#### **Generate** +Uses retrieved chunks as input to generate responses: +- **Generating Methods**: OpenAI, Anthropic, etc. (vision language models for image+text tasks). + +#### **(Optional) Orchestrate** +Manages interactions between retrieval and generation for complex workflows such as in advanced retrival techniques. For example: + +- **Prompt Augmentation**: Prepares the final input for the generator by formatting and enriching retrieved chunks (e.g., adding context, query reformulation, or applying templates). +- **Dynamic Query Refinement**: Iteratively adjusts the query or retrieval parameters based on feedback or partial results to improve the quality of retrieved information. +- **Tool Invocation**: Dynamically calls external tools or APIs (e.g., search engines, databases, or calculators) as part of the response generation process. + +### 2.3. (Optional) Evaluation + +Evaluation measures retrieval quality and response generation accuracy. +- **Metrics**: BLEU, ROUGE, MRR. +- **Frameworks**: Frameworks like RAGAs for evaluation. + + +## 3. The Architecture of RAG Systems + +The architecture of a Retrieval-Augmented Generation (RAG) system determines its capabilities, scalability, and the enhancements it offers beyond the basic Naive RAG approach. Over the years, research and innovation in the RAG space have introduced diverse architectures that optimize various stages of the RAG pipeline, in particular to adressing the following questions. + +- **What to retrieve?** Selecting the most relevant chunks or embeddings from the knowledge base. +- **When to retrieve?** Deciding at what point in the workflow retrieval is required, particularly in iterative or dynamic processes. +- **How to use the retrieved information?** Determining how retrieved content is incorporated into downstream tasks, such as response generation or query refinement. + +Broadly, they can be categorized into: +- **Naive RAG**: Most basic pipeline, directly passing retrieved chunks to the generator. +- **Advanced RAG**: Pipelines which incorperated different techniques to improve the quality of responses with Pre-retrieval and Post-retrieval processes. +- **Modular RAG**: Pipelines that further enhance functionalities by integrating modules that interatively refine result or dynamically adapt based on task-specific requirements. + +Following are common RAG architectures: + +### 3.1 Naive RAG +A simple architecture where the query is sent to a retriever, and the retrieved chunks are passed directly to the generator for response generation. +- **Features**: Most rudimentary pipeline with no intermediate steps. +- **Effect**: Easy to implement with low complexity, suitable for general-purpose question answering and quick prototyping due to the straightforward design. + +### 3.2 Retrieve-and-Rerank RAG +This architecture incorporates a reranker to prioritize retrieved results based on relevance. +- **Features**: Addition of a reranking model that scores and reorders retrieved documents before passing them to the generator. +- **Effect**: Improves relevance and precision by filtering out less relevant information, making it more effective for tasks requiring high retrieval accuracy, such as customer support and legal document search. + +### 3.3 Multimodal RAG +Combines text and visual inputs for tasks that require reasoning across multiple data modalities. +- **Features**: Includes a multimodal retriever, an image encoder, and a text generator capable of handling both text and visual data. +- **Effect**: Enables tasks such as visual question answering and image captioning by integrating diverse data types. + +### 3.4 Graph RAG +Uses graph databases or graph neural networks (GNNs) to model relationships between entities and retrieve structured knowledge. +- **Features**: Incorporates a graph retriever and node representation model to leverage entity relationships within a graph structure. +- **Effect**: Provides enhanced reasoning capabilities over structured data such as knowledge graphs, making it highly effective for tasks like scientific research, complex entity reasoning, and technical documentation. + +### 3.5 Hybrid RAG +Integrates multiple retrieval mechanisms to combine the strengths of different search methods. +- **Features**: Utilizes both dense vector search and keyword-based retrieval methods in a multi-retriever setup. +- **Effect**: Balances precision and recall by integrating complementary retrieval approaches, which can be useful for handling diverse or multilingual datasets in domain-specific retrieval tasks. + +### 3.6 Agentic RAG (Router) +Routes queries to specialized retrievers or tools based on their type using an agent-based approach. +- **Features**: Includes a router agent that dynamically assigns queries to the most appropriate retriever or processing tool. +- **Effect**: Scales effectively across diverse query types. This allows for adaptability in systems requiring varied query handling, such as customer support across multiple domains. + +### 3.7 Agentic RAG (Multi-Agent RAG) +Expands the router architecture by involving multiple agents that collaborate to solve tasks dynamically. +- **Features**: Comprises multiple agents, each capable of interacting with retrievers, external tools (e.g., Slack, Gmail), and generators. +- **Effect**: Enables complex workflows and dynamic task-solving by leveraging agent collaboration. This supports integration with external systems, making it suitable for enterprise-level workflow automation and advanced search systems. + + +## Exercise Notebooks + +| Title | Description | Exercise | Link | Colab | +|-------------------|-------------|----------|------|-------| +| RAG Basics | Learn how to construct a RAG pipeline | 🐢 Use a simple retriever and generator
🐕 Try vector database integration
🦁 Explore hybrid retrieval | [Notebook](./notebooks/rag_basics.ipynb) | Open In Colab | +| RAG Evaluation | Learn how to evaluate retrieval and generation | 🐢 Evaluate a simple RAG system
🐕 Use RAGAs for advanced metrics
🦁 Analyze retrieval and generation alignment | [Notebook](./notebooks/rag_evaluation.ipynb) | Open In Colab | +| Agentic RAG | Learn how to set up and use multi-agent RAG for task automation | 🐢 Create a database query react-agent
🐕 Build an agent for solving math/coding problems
🦁 Integrate multiple tools for workflow automation | [Notebook](./notebooks/agent_rag.ipynb) | Open In Colab | + + +## Resources + +- [Haystack Documentation](https://docs.haystack.deepset.ai/docs/intro) +- [RAGAs Toolkit](https://github.com/ragas-toolkit) +- [Youtube: How RAG Turns AI Chatbots Into Something Practical](https://youtu.be/5Y3a61o0jFQ?si=epzQv1UIJe53OoLB) +- https://www.promptingguide.ai/research/rag diff --git a/8_rag/notebooks/1_naive_rag_haystack_example.ipynb b/8_rag/notebooks/1_naive_rag_haystack_example.ipynb new file mode 100644 index 00000000..71abe0c3 --- /dev/null +++ b/8_rag/notebooks/1_naive_rag_haystack_example.ipynb @@ -0,0 +1,14716 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "5hxScxn4C2C7" + }, + "source": [ + "# Implement a basic RAG pipeline with Haystack 2.0\n", + "\n", + "This notebook demonstrates how to implement a basic Retrieval Augmented Generation (RAG) pipeline using the `Haystack` orchestration framework and HuggingFace e.g `SmolLM` LLM Models. You can select your difficulty by trying out different models, knowledge base, tasks, or reimplementing in a different frameworks or even without one.\n", + "\n", + "
\n", + "

Exercise:

\n", + "

Implement a RAG pipeline and tailor it to your needs.

\n", + "

Difficulty Levels

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🐢 Follow step-by-step instructions. Try different embedding and generative model variants.

\n", + "

🐕 Try different documents and evaluation questions.

\n", + "

🦁 Reimplement part of or the whole pipeline with different modules or frameworks.

\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4spPB1okEaLy" + }, + "source": [ + "### Dependencies \n", + "- **`haystack-ai`**: Core framework for building RAG pipelines. \n", + "- **`wikipedia`**: Downloads data from Wikipedia (optional; any document source can be used, such as PDFs, web pages, or local text files). \n", + "- **`sentence_transformers`**: Generates embeddings for document chunks. \n", + "- **`transformers`**: Utilizes open-source LLMs for generation. \n", + "- **`accelerate`** and **`bitsandbytes`**: Enable efficient, low-memory model inference using quantized versions. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "R_Abekd7m0Ps" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: wikipedia in /home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages (1.4.0)\n", + "Requirement already satisfied: haystack-ai in 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You should set the environment variable TRANSFORMERS_CACHE to a writable directory.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cc170ac2bad5466386ba5baf58cdaecd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "indexing = Pipeline()\n", + "indexing.add_component(\"cleaner\", DocumentCleaner())\n", + "indexing.add_component(\"splitter\", DocumentSplitter(split_by='sentence', split_length=2))\n", + "indexing.add_component(\"embedder\", SentenceTransformersDocumentEmbedder(model=model_emb,\n", + " device=ComponentDevice.from_str(device),\n", + " meta_fields_to_embed=[\"title\"]))\n", + "indexing.add_component(\"writer\", DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE))\n", + "\n", + "indexing.connect(\"cleaner\", \"splitter\")\n", + "indexing.connect(\"splitter\", \"embedder\")\n", + "indexing.connect(\"embedder\", \"writer\")\n", + "\n", + "indexing.run({\"cleaner\":{\"documents\": raw_docs}})\n", + "indexing.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inspecting the Document Store:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of documents in store: 1306\n", + "Metadata of document: {'title': 'FIFA World Cup', 'url': 'https://en.wikipedia.org/wiki/FIFA_World_Cup', 'source_id': '019091237080c1d52ababf9a11051c2c90933e79c19917e7af322bc15750c48c', 'page_number': 1, 'split_id': 0, 'split_idx_start': 0}\n" + ] + } + ], + "source": [ + "num_documents = len(document_store.filter_documents())\n", + "print(f\"Number of documents in store: {num_documents}\")\n", + "\n", + "document_meta = document_store.filter_documents()[0].meta\n", + "print(\"Metadata of document:\", document_meta)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The RAG Pipeline (Generate + Retrieve Stage)\n", + "\n", + "In Haystack 2.0, we use the `HuggingFaceLocalGenerator` for managing Open Source LLMs. \n", + "\n", + "Our RAG Pipeline retrieves relevant documents based on the user's query and uses them to generate a grounded response with the LLM.\n", + "\n", + "It consists of the following components:\n", + "\n", + "- **`SentenceTransformersTextEmbedder`**: Encodes the user's query into a vector that captures its meaning.\n", + "- **`InMemoryEmbeddingRetriever`**: Finds the top 5 documents most similar to the query vector.\n", + "- **`PromptBuilder`**: Generates a prompt by rendering a template string using the Jinja2 engine.\n", + "- **`HuggingFaceLocalGenerator`**: Integrate Open Source LLMs from HuggingFace.\n", + "\n", + "We start with `SmolLM2-1.7B-Instruct`, and later test smaller variants. The model is loaded with **4-bit quantization** using `huggingface_pipeline_kwargs` in the Generator." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "generator = HuggingFaceLocalGenerator(\n", + " \"HuggingFaceTB/SmolLM2-135M-Instruct\",\n", + " huggingface_pipeline_kwargs={\n", + " \"device_map\": \"auto\",\n", + " # \"model_kwargs\": {\n", + " # \"load_in_4bit\": True,\n", + " # \"bnb_4bit_use_double_quant\": True,\n", + " # \"bnb_4bit_quant_type\": \"nf4\",\n", + " # \"bnb_4bit_compute_dtype\": torch.bfloat16\n", + " # }\n", + " },\n", + " generation_kwargs={\"max_new_tokens\": 1000}\n", + ")\n", + "generator.warm_up()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "prompt_template = \"\"\"<|system|>Using the information contained in the context, give a conscise answer to the question.\n", + "Report the source URL.\n", + "<|user|>\n", + "Context:\n", + " {% for doc in documents %}\n", + " {{ doc.content }} URL:{{ doc.meta['url'] }}\n", + " {% endfor %};\n", + " Question: {{query}}\n", + " \n", + "<|assistant|>\n", + "\"\"\"\n", + "prompt_builder = PromptBuilder(template=prompt_template)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rag = Pipeline()\n", + "rag.add_component(\"text_embedder\", SentenceTransformersTextEmbedder(model=model_emb,\n", + " device=ComponentDevice.from_str(device)))\n", + "rag.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store, top_k=5))\n", + "rag.add_component(\"prompt_builder\", prompt_builder)\n", + "rag.add_component(\"llm\", generator)\n", + "\n", + "rag.connect(\"text_embedder\", \"retriever\")\n", + "rag.connect(\"retriever.documents\", \"prompt_builder.documents\")\n", + "rag.connect(\"prompt_builder.prompt\", \"llm.prompt\")\n", + "\n", + "rag.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def get_answer(query):\n", + "\n", + " results = rag.run({\n", + " \"text_embedder\": {\"text\": query},\n", + " \"prompt_builder\": {\"query\": query}\n", + " }\n", + " )\n", + "\n", + " answer = results[\"llm\"][\"replies\"][0]\n", + " return answer\n", + "\n", + "# print(get_answer(\"What is RAG?\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "questions = \"\"\"\n", + "What was the result of the 2022 FIFA World Cup?\n", + "Who won the UEFA Champions League in 2024?\n", + "Which country won the 2024 Copa América?\n", + "Who won the 2024 Copa Libertadores?\n", + "Who is the current champion of Major League Soccer (MLS) in 2024?\n", + "What is the top team in the Premier League this 2024/2025 season?\n", + "Who won La Liga in 2024?\n", + "Which Bundesliga team has the most titles in the last decade (2015–2024)?\n", + "Which Serie A team won the 2023/2024 league championship?\n", + "Who is the top scorer of Ligue 1 this 2024/2025 season?\n", + "Who won the UEFA Euro Golden Boot in 2024?\n", + "Who won the most recent Ballon d'Or in 2024?\n", + "\"\"\".split('\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Who won La Liga in 2024?\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "45185866b02649669692dc2f52de6585", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Batches: 0%| | 0/1 [00:00\n", + "Context:\n", + " \n", + " The 2024–25 season was won by Real Madrid with three games to spare, while Barcelona won the 2023–24 edition.\n", + "In 2023, Real Madrid were the most successful club in the La Liga, winning the 2023–24 edition.\n", + "<|user|>\n", + "Question: Who won La Liga in 2024?\n", + "<|assistant|>\n", + "<|user|>\n", + "<|assistant|>\n", + "<|user|>\n", + "<|assistant|>\n", + "<|user|>\n", + "<|assistant|>\n", + "<|user|>\n", + "<|assistant|>\n", + "<|user|>\n", + "<|assistant|>\n", + "<|user|>\n", + "<|assistant|>\n", + "<|user|>\n", + "<|assistant|>\n", + "<|user|>\n", + 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You can select your difficulty by trying out different models, knowledge base, tasks, or reimplementing in a different frameworks or even without one.\n", + "\n", + "
\n", + "

Exercise:

\n", + "

Implement a RAG pipeline and tailor it to your needs.

\n", + "

Difficulty Levels

\n", + "

🐢 Follow step-by-step instructions. Try different embedding and generative model variants.

\n", + "

🐕 Try different documents and evaluation questions.

\n", + "

🦁 Reimplement part of or the whole pipeline with different modules or frameworks.

\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4spPB1okEaLy" + }, + "source": [ + "### Dependencies \n", + "- **`haystack-ai`**: Core framework for building RAG pipelines. \n", + "- **`wikipedia`**: Downloads data from Wikipedia (optional; any document source can be used, such as PDFs, web pages, or local text files). \n", + "- **`sentence_transformers`**: Generates embeddings for document chunks. \n", + "- **`transformers`**: Utilizes open-source LLMs for generation. \n", + "- **`accelerate`** and **`bitsandbytes`**: Enable efficient, low-memory model inference using quantized versions. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "R_Abekd7m0Ps" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: wikipedia in /home/duydl/Miniconda3/envs/py310/lib/python3.10/site-packages (1.4.0)\n", + "Requirement already satisfied: haystack-ai in 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may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.)\n", + " return torch._C._cuda_getDeviceCount() > 0\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1c3a9fbb4c9746f3858f90667726b395", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "indexing = Pipeline()\n", + "indexing.add_component(\"cleaner\", DocumentCleaner())\n", + "indexing.add_component(\"splitter\", DocumentSplitter(split_by='sentence', split_length=2))\n", + "indexing.add_component(\"embedder\", SentenceTransformersDocumentEmbedder(model=model_emb,\n", + " device=ComponentDevice.from_str(device),\n", + " meta_fields_to_embed=[\"title\"]))\n", + "indexing.add_component(\"writer\", DocumentWriter(document_store=document_store, policy=DuplicatePolicy.OVERWRITE))\n", + "\n", + "indexing.connect(\"cleaner\", \"splitter\")\n", + "indexing.connect(\"splitter\", \"embedder\")\n", + "indexing.connect(\"embedder\", \"writer\")\n", + "\n", + "indexing.run({\"cleaner\":{\"documents\": raw_docs}})\n", + "indexing.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inspecting the Document Store:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of documents in store: 1306\n", + "Metadata of document: {'title': 'FIFA World Cup', 'url': 'https://en.wikipedia.org/wiki/FIFA_World_Cup', 'source_id': '019091237080c1d52ababf9a11051c2c90933e79c19917e7af322bc15750c48c', 'page_number': 1, 'split_id': 0, 'split_idx_start': 0}\n" + ] + } + ], + "source": [ + "num_documents = len(document_store.filter_documents())\n", + "print(f\"Number of documents in store: {num_documents}\")\n", + "\n", + "document_meta = document_store.filter_documents()[0].meta\n", + "print(\"Metadata of document:\", document_meta)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The RAG Pipeline (Generate + Retrieve Stage)\n", + "\n", + "In Haystack 2.0, we use the `HuggingFaceLocalGenerator` for managing Open Source LLMs. \n", + "\n", + "Our RAG Pipeline retrieves relevant documents based on the user's query and uses them to generate a grounded response with the LLM.\n", + "\n", + "It consists of the following components:\n", + "\n", + "- **`SentenceTransformersTextEmbedder`**: Encodes the user's query into a vector that captures its meaning.\n", + "- **`InMemoryEmbeddingRetriever`**: Finds the top 5 documents most similar to the query vector.\n", + "- **`PromptBuilder`**: Generates a prompt by rendering a template string using the Jinja2 engine.\n", + "- **`HuggingFaceLocalGenerator`**: Integrate Open Source LLMs from HuggingFace.\n", + "\n", + "We start with `SmolLM2-1.7B-Instruct`, and later test smaller variants. The model is loaded with **4-bit quantization** using `huggingface_pipeline_kwargs` in the Generator." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "generator = HuggingFaceLocalGenerator(\n", + " \"HuggingFaceTB/SmolLM2-135M-Instruct\",\n", + " huggingface_pipeline_kwargs={\n", + " \"device_map\": \"auto\",\n", + " # \"model_kwargs\": {\n", + " # \"load_in_4bit\": True,\n", + " # \"bnb_4bit_use_double_quant\": True,\n", + " # \"bnb_4bit_quant_type\": \"nf4\",\n", + " # \"bnb_4bit_compute_dtype\": torch.bfloat16\n", + " # }\n", + " },\n", + " generation_kwargs={\"max_new_tokens\": 1000}\n", + ")\n", + "generator.warm_up()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "prompt_template = \"\"\"<|system|>Using the information contained in the context, give a conscise answer to the question.\n", + "If the answer is contained in the context, report the source URL.\n", + "If the answer cannot be deduced from the context, do not give an answer.\n", + "<|user|>\n", + "Context:\n", + " {% for doc in documents %}\n", + " {{ doc.content }} URL:{{ doc.meta['url'] }}\n", + " {% endfor %};\n", + " Question: {{query}}\n", + " \n", + "<|assistant|>\n", + "\"\"\"\n", + "prompt_builder = PromptBuilder(template=prompt_template)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ftra2aNmy5Ssv1W7evIlRo0bB0dERzZo1w7Jly2q9v127dsXEiRPRvHlz2Nra1umlOfsfDqd64OTkhPbt2wMA3NzcMHjwYLz99tsAACJCSEgIFi1ahIKCAnTo0AE5OTlYtGgRPvnkEwDAyJEj0b9/fyQlJWHFihWQSqUYP348cnNz8c0338DLywtvv/029PX1AQCDBg3C4MGD4eTkpHCNT58+Rb9+/bBjxw6YmpqiXbt2SExMREhICLZt2ya37bp165CTk4OAgAA8efIEP/30E+bMmQM7Ozv4+/sjNjYW7733HtLS0uTed+PGDbi4uMDS0hKXLl1CQEAALl26pPBxAIAjR45g1KhRiIqKgqOjI8zNzXH37l25fpKTk9GvXz/8/vvvMDIygru7OxISEmq9vwkJCThz5gzeeecd9OnTB2+88YbCx5XVHodTPejcuTPGjRsHAOjTpw82btyIb7/9FgDw22+/4ejRo2jdujWioqJw9OhRREVFwc3NDQcOHEBMTAxQNqhsbm6OVatW4dNPP8WVK1cQEBCADz74AADw7bffwtzcHACwevVqbNy4EZ07d1a4xuXLlyM5ORnBwcG4cuUKwsPDcebMGWhra+Prr79GaWmpsG3z5s3x+++/IywsDDNnzhT2a//+/di6dSsCAwORl5cnBE+5xYsX48SJE7h+/TpmzJiBkpISzJ07V+HjIJVKMWvWLMhkMnz33XeIiIjAxYsXMXas/KTUX3/9NZ49e4bhw4fjxo0bOHHiBFavXl3r/dXU1MSRI0ewevVqbN++HRJJlRNlMyWpdlJNVrd+++03AICRkREWLVokLC8/C7p27RpatWqFpk2bYsmSJRg/fjz2798PBwcHfP/990qrIzw8HCgbG5s3b56w3NjYGFlZWUhOThaWWVlZQU9PDwDg6OgIALCxsRHWe3h4AAAeP34s10f5PgHAtGnTsGLFCly/fh3Pnj1T6DiUlJTg0aNHcHFxQXBwsLCNkZGR8P9EhFOnTgEA5syZA21t7Qrb1HR/vby84OXlpeCRZMrC4aRi5Zc+f/75J/78888K68tDAAACAwMxZ84cpKenY+zYsTA1NVVaHeVBsn///leu19fXR0FBgUJtlZ9ZEFGl2+jq6sLU1BRZWVnIy8tT6DiU1+js7Fxpu7m5uXj+/Dm0tLTg4OBQ6XY12d+Xg43VDw6neiaTyeS+NjExAcou28ov0SqzePFipKenAwC+//57DBo0CN7e3tX2oQgTExMUFhbi8uXLcHd3f+U21U0fXRN5eXl4+vQptLS0YGVlpdBxiIyMBF4I9FcxMTGBgYEB8vPzkZGRAWtr60q3q8/9ZTXHY071xNjYGACEDyLKZDJIpVJ07doVALB+/XpkZmYK2798JysiIgKrV6+GlZUV5s6di4KCAnzwwQdyH1F4uY/i4mKF6ysf5F22bJnwvpKSEqV+qjs/Px8oO6NaunQpiAjdu3eHnp6eQsfB3d0d+vr6SEpKwp49eyq0W65ly5YAgG+++Ua40/fyWV997C97PRxO9aRdu3bQ0tLCmTNn0K1bN7Rt2xYpKSkYOXIkPD09kZSUhPbt26Nv375o3749+vfvjxs3bgBlZxkTJ05EaWkpli5din//+98ICgrCrVu3MHXqVKEPPz8/AMDw4cPRp08fTJ8+XeH6Zs6cCUNDQxw4cABt2rRBv3794OPjgzFjxijtE+ezZs1Cnz594OPjgzVr1kBfXx9fffUVUHZHsrrjYGJiIty5++STT+Dr64tu3bpVuLv2+eefAwC2bduGli1bwt/fH5MmTar3/WWvh8Opnjg7O2PVqlVwcHDArVu3IJPJoKenBwMDAxw9ehTvv/8+DAwMEBUVhfz8fAwZMkQYU5o9ezb++ecfvPPOOwgKCgIArFq1Cs7Ozjh48CA2btwIAJg7dy769u0LqVSKmzdvwsrKSuH6vLy8EB4ejr59+yI/Px9RUVEwMjLCsGHDanWZ+DIfHx8EBATgzp07yMvLQ+/evREeHo7WrVsDgELHAQC++OILzJw5E/b29nj06BF0dHTQqVMnub7efPNNbNy4Ed7e3nj27Bmys7PRp0+fet1f9vqqvCe6fv36+T4+Pl+1aNGi/ipijKm9iIiI5/fu3Zs6YcKEnyvbhgfE1djz58+Fz1dVJTg4GO+880691MSYojic1JhUKsWZM2eq3c7f379e6mGsJjic1Fj554gYa4h4QJwxJkocTowxUeJwYoyJEocTY0yUOJwYY6LE4cQYEyUOJ8aYKHE4McZEicOJMSZKHE6MMVHicGqkZDIZZs+eDXd3d4SEhKi6HLUVHR0NKysrpUx0+scff1T5iOKAgACsXbv2tfsRC1GHU/kTE8sf8O/p6SnMzfY6tm3bhsWLFyulxtoIDQ2FhYXFK18+Pj71UsPPP/+MEydO4M8//1TqRAl1rX379pUeu/Hjx9eqzb///ltuwgRU8T26detWjdpOSEiAk5MTdHV1a1Xby21VNdFCYmKiWk3EIOo//N21axfWrVuHn3/+GV5eXkhKSoKdnd1rt7t69WpMnjxZKTXWxueffy48mfFf//oXWrVqJTzR8sUZSlB2hqOhofx/Q3bt2oUPPvig0mdsV6W0tBSamppKr0mR9vft2weZTIaioiL06tULS5YsQY8ePQAAFhYWtepv9+7d0NKS/1WIjY3FmDFjKvycuLm51ajt+Ph4NG/evFZ1vSwhIeGVz4wHgPT0dGRmZla6vjJ1/b18HaI+c4qOjkbbtm3h7+8Pe3t79O7dW1j36NEjYRZWR0dHjB07Fjk5OcjMzETLli2xdetWdOnSBXZ2dggICMDz588BAF26dMHdu3cxZ84cODk5ISMj47XaQ9ksvr6+vrCxsUH37t2FWUf27NmDrl27wtbWFm3bthVmk7W3t4eHhwfc3d1x7949dOnSBR4eHvDw8MAHH3yAKVOmYPDgwXB2dkZqaioAYMOGDejQoQNsbW3h4+MjzHu3aNEifPzxx5g8eTKcnZ3h7u6OAwcOAAAePHiAESNGwMnJCa1atcIvv/wCmUyGzp0748aNG9iwYQMCAgKAshBcsWIFWrduDQcHBwwYMAA3b94Eyh4TbGlpiSVLlqBXr17o2LEjiAh+fn6YN28eunXrJkyouXfvXvTq1Qt2dnYIDAyUO06VHY8TJ07AyckJy5cvh5+fH0JDQ1FSUoKZM2fCzc0NzZo1w9dffw2UhYOHhwdKSkpARPD39xeO3alTp17Z/sqVK+Hm5ob8/HwQEUaOHIk33ngDeXl5+Oyzz7Bjxw4cO3YMjo6OOHLkCEpLS5GYmIi+ffsKbZe/UPY4ZEX3OyEhAYWFhejSpQtcXFwQGhoq9xjgK1euICgoCPb29nBzc8OCBQuEdadOnULv3r1hb2+PIUOG4OrVq8KZUU5ODqZPny78HC1cuBBmZmawtbWtst1XfS8bpPXr18+/dOkSZWVlqeS1Y8cO0tTUpMWLF8stv337Njk6OlJAQABFR0dTYmIi2draUlhYGKWkpBAAGj58OMXFxdHZs2dJIpHQ5s2bKSsriw4cOEBGRkaUmZmplPamT59O1tbWtHPnTkpOTqY///yTsrKyaMGCBWRsbEzbtm2je/fu0bx588jJyUluP65cuUIA6MyZM5SVlUWZmZlkYGBAfn5+dPv2bUpNTaWsrCyaPXs2OTo60unTpykjI4OGDRtGw4YNo6ysLPr000/JysqKdu7cSffu3aPBgweTl5cXZWVlUUBAAL311lsUHx9Pv/76K12/fp2ysrLo7NmzBIASExOFWiZMmECtWrWiv//+m1JSUqhfv37Uo0cPysrKohMnThAACgkJofT0dKEuKysr6tKlC12/fp3++usv0tXVpa5du1JsbCz99ttvBIB27NhR7fH46quvSENDg3788UfKysqi1NRUWrJkCdnZ2dHly5cpIiKCwsPD5Y7dqlWryNjYmJ48eVJt+/fv36emTZvSt99+SwsXLiRTU1O6evWqsE5LS4tOnToltP3nn38SANLX1ydDQ0MyNDSU678m+21ra0v9+vWjxMRECg8PJzMzM5o1axZlZWXR8ePHSVdXl7788ku6c+eO8N7r16/T3r17SUdHh+bPn0+xsbG0dOlSAkCHDh2ijIwM6ty5M7Vu3ZpOnDhB0dHR5O3tTZ07d6623cq+l/X9On78eN769es/rCp/RH1ZN2DAAKxduxafffYZzp8/jw0bNsDAwABhYWF4/vw5wsLCYGhoiD/++APZ2dnw9PTE3bt3IZFIsGzZMhgbG8PGxgba2trCpdHVq1fRrl07uUul2rb3+PFjfP/999i6dSv69esHlD1DKTs7G4sWLcKMGTMwcOBA5OTkIDY2tsIpd0xMDLS0tITl//zzD/Lz8/Hdd98Jlyj379/H0qVLsXv3brRr1w4om2576NChAIC7d+9ixIgR6N+/P1D2rO6kpCSg7JQ9LS0NpaWlwuwmABAXFwcLCws0adIEAHDz5k389NNPuHDhgnB28M4772D+/PlA2b/+VlZWWLRoEbS0tKClpYWCggJkZWVh7ty5cHR0RHFxMUpLS/HZZ5/Bzs4OTZo0gYaGBvT19as9HvHx8ejfvz+GDx8OADA0NERpaSlyc3Px9OlTdOzYscLlVExMDHx8fCCRSKpt38DAANOmTcPy5cuRl5eHrVu3wtXVFSg7O9fU1BRmbEHZJZ2FhQVOnDghLCufPLQm+/306VM8evQIW7duRZMmTdCkSRMEBQXh/PnzmDFjBubOnYvu3btj+vTpKCkpQWRkJMzNzdG0aVMMHToUISEhCA0NBQC8/fbbmDlzJry9vXH48GHcuHEDERERwjBHu3bthHGtqto9d+5che+lWIn6sg4Ahg0bhnPnziEuLk6YmfXSpUuQSCTw9PSEo6MjQkNDsXLlSrRv3x5xcXFwcnISpklKTU1FcXGx8EsXGRkJX19fuT5q295ff/0FTU1N9O3bV669yMhI5OfnY926dWjWrBm8vb0hk8nwww8/yG0XGxsLd3d34YcqISEBFhYWcr8ov/76K5o2bSpc0hYVFSEmJgbt27cHyoLmxUH0O3fuCPu6cuVKuLq6ws/PT67vuLg4uaA8ffo0LC0t5dp58uSJMEFCQkIC3njjDWH23PJlMpkM5c+XT0pKglQqFdoon8TB29u72uORkJAgjBuV++ijjzBhwgQMHjwYH330UYUZUWJiYtCqVSuFj3f37t2RkZGBvn37yk12EBkZiVatWkFHR0eubU9PT7i6ugqv8n2vyX7Hx8dDU1NT7rgSEaRSKYqKinD16lVcv34dzs7OcHZ2xpEjR3DgwAE8fvwYt2/fRmBgoNz3rPwflLNnz6Jjx45y469xcXHw8vKqsl0dHZ1Xfi/FSryx+QJXV1eMGzcO+/btE5aFhIRg6tSpkMlkMDQ0FJbHxcXhxQkZYmNjoaurKwxKXrt2DaNGjarQR23ai4iIgJ6e3isHrCUSCW7cuIH8/HyYmJi8cpsXf8FQdgbx8mQSKSkpaNasmfD16dOnUVJSgtatWyM3NxepqakV6hs0aBBQNm345s2bsX37dkyZMgWjR4+GpaVlhbs+OTk5aNq0qVy/v/76K9566y2hri5dusitj4+Ph5OTkzAZZlxcHJo2bSoMsMfFxcHS0hI2NjaIj4+v9HhIpVLcunWrwn5ramriyy+/xNChQ9G9e3cMHDhQ+GUlIiQkJGDMmDEKHe/nz5/jX//6F5ydnXHy5EmkpaUJ06dHRkaiTZs2cn3HxcXB09Ozwverpvt9+PBheHh4CDc5cnJycPz4cblJQzds2IC2bdtCT09PCMjyqbDMzMyE7X7//XfhH5SnT5/KrXv48CFiYmLk/sF5VbuVfS/FSrRnTo8fP8aWLVtw9epVnD59Gvv27RMuazp06IADBw7g1q1bKCwsxLlz54T3xcfHy/1LFRsbCw8PD2hpaUEqleLp06eIj4/Ho0ePkJ2d/VrttW/fHtnZ2VixYgXykKrXAAAgAElEQVQyMjJw7tw5pKamolWrVtDV1cXKlSshk8mQmJiIu3fvVtjH2NhYuXBKSEio8Etqb2+P1NRUFBYWIj09HfPnz4e2tjZ0dXURHx8PDQ0N4RdJKpUiKSkJLVq0QFJSEvbt24cnT57g0aNHsLS0FKZYKv9Xtlzr1q1x69YtXL16FYWFhVi+fDnu378v3FF8VV0vH5eXz+Be/Lqq43Hnzh0UFRXJ/WIVFRUhLCwMjx49wsOHDyGTyeQC+u7du8jNzRWOXXXHOzQ0VJgz0MTERO5jJBkZGUhOTkZaWprwMZXY2FiYmZnh5s2bwqt8XU32u/x43r9/H0lJSRgzZgxMTU3xySefQFdXF61atcK6deuQk5ODjIwMXLlyBQDg4uICAwMD7Ny5EwUFBTh06BB27NghfM9atGiBixcv4vbt20hPT8fHH38MmUwGLy+vKtut7HspVqINp5SUFGzYsAGBgYH49NNP4efnJ/xQzZgxA61atUJAQAD8/Pxw8uRJ4X0vn+m8+LWWlhY++ugjrF69Gl26dMGdO3deqz0fHx8sW7YMW7ZsQevWrTF37lxoaWnB2toaa9euxf79++Hj44OQkJAKs++Wh8aLl3CvulX8/vvvo3nz5vD19UVgYCB69OiBoqIi3Lt3D3FxcXBzcxMuC2/evIni4mK0aNEC0dHR+Prrr9GmTRucOXNGuF2ekZGBjIwMuXDq378/Jk6ciNGjR8Pb2xvR0dE4duwYmjRpgszMTGRkZFSo6+Vf0pd/6F9cX9XxSEhIgI2NjdzHAG7duoUdO3bA19cXn3/+OdasWSPMb4ey8HhxrK6q9sPCwnD27Fls27YN5ubmmDx5Mnbu3CmMy4WEhODKlSvw9fXFr7/+isePHyM9PR2rVq1C586dhdeKFStqtN8ymQwREREYPnw4evfujf79+8PJyQlHjx4VhgjCwsLw5MkTdOrUCW+//Tb++ecfoGzy0DVr1uCXX36Bj48PTp48CScnJ2F/J0+ejM6dO6N3794IDAyEq6srrK2thcvwytqt7HspVjxvXSNz9OhRfPjhh7h586ZwacJYfVNk3jrRnjkx5YuNjcWsWbPw73//m4OJiV6DGBBnyiGVSrFp0yb4+fmpuhTGqsXh1Ii0bdtW1SUwpjC+rGOMiRKHE2NMlDicGGOixOHEGBMlDifGmChxODHGRInDiTEmShxOjDFR4nBijIkShxNjTJQ4nBhjosThxBgTJQ4nxpgocTgxxkSJw4kxJkocTowxUeJwYoyJUrVPwrx//35xTk5OcXXbMUZEGhKJRKbqOpj4PXnypNrsqXIDiURy+OnTp/88ffpUqYUx9VNaWqr/4MGD75ycnCaruhbWMEgkkj9UXQNrBMaPH286fvz4Z6qug6kPHnNijIkShxNjTJQ4nBhjosThxBgTJQ4nxpgocTgxxkSJw4kxJkocTowxUeJwYkrx/PlzIqJCVdfB1Ee1f9/CmCIMDQ0lAPRUXQdTH3zmxBgTJQ4nxpgocTgxpUhLS5MRUbSq62Dqg8ecmFLY2NhoAGir6jqY+uAzJ8aYKHE4McZEicOJMSZKHE5MKcoGxC+rug6mPnhAnClF2YB4J1XXwdQHnzkxxkSJw4kxJkocTowxUeJwYoyJkkTVBbwOIvoEQJiq62BMpPpJJJLfVV1EbfGZE2NMlDicGGOixOHEGBMlDifGmChxOKkxMzMzTJ8+vU7adnBwwMSJE2u0TXBwMPz8/OqkHqZ+OJxYvTE2NoaJiYmqy2ANBP9tHas3q1evfu02iAh3795F8+bNlVITEy8+cxKJdevWwd3dHfr6+vD29saCBQtQWPjfmZaio6NhYmKCY8eOoU2bNtDR0YGbmxs2b96MRYsWwcnJCSYmJggKCkJGRoZcuzdu3EC3bt1gYGAAT09P/PTTTzXqGwBKS0sxb9482NrawsTEBIGBgcjPz5dro7ptXFxcIJFI0K1bN2FZUFAQPv/8c8yZMwdNmzaFubk5xowZg5ycHGGbiIgIvPHGGzA0NISbmxsGDx4Mf39/FBUV1ej4RkVF4ezZszV6D2O1RkSfkBqYP38+GRsb0+zZs2nXrl00b948MjY2pvfee4+IiKKioggAOTk50dGjR+n06dPUsmVLAkDdunWjP/74g3bv3k1GRkY0duxYoV1TU1Nq0qQJLV26lMLCwqhTp04EgFasWKFw30REEyZMIAAUEhJCW7ZsoZEjRxIAmjBhgsLb/P7779SuXTvq2rWr8J7AwEDS1NSk0aNHU0REBG3ZsoV0dHRoxowZRESUkpJCRkZG1KNHDzp8+DCNHz+eANCiRYtqfIwvXLhA+vr61LNnTzp37lyN399Ava3q39FGSx3C6cGDB6StrU0HDhyQW/7jjz8SAMrKyhLCad++fcL6zZs3EwCKjY0VloWEhJCNjY3wtampKX322WfC11KplDp16kTGxsaUl5enUN+RkZEEgGbPni23jY2NjRA8imxDRPT2229XCCdvb2+SyWTCsoCAAGrVqhUREX3zzTckkUgoLS2NiIhkMhl5enpSt27danCE/yc9PZ1mz55N5ubm1KtXLzp//nyt2mlAGnQ48WWdip08eRIlJSUYM2YM9PT0hNfkyZMBAPfv3xe21dfXF/5fT++/81fq6uoKyxwcHJCZmVlpX5qamvj444+Rm5uLq1evKtT3wYMHAQDTpk2r0FY5RbapjIGBASSS//0VlbOzMx4+fAgASE1NhbGxMZo2bQoAkEgkaN68OZ4+fVplm//884/wev78ubDc2toaCxcuREpKCvz9/fHmm29iw4YN1dbIVIMHxFUsLS0NAHD06FE4ODhUWO/m5obY2FiF2pJIJCCiKrext7cHAGRnZyvUd0pKCkxNTWFhYVFpm4psoygdHR1IpVKh/5ycHMTExKBVq1YoLi5GVFQUevfuXWUbzZo1E/5/9+7dGDlypPB1Tk4O1q5dizVr1qBXr17o2rXra9fM6gaHk4qZm5sL/+/l5VXn/aWnpwMAmjZtKoRTVX1bW1sjJycHRUVFcmdpNd2mNsaNG4eVK1di4MCBeP/993H+/HlIpVJ89dVXVb7v0KFDwv+Xf67q2bNnWL58OcLCwtC6dWvs3bsXPXv2VFqtTPn4sk7FevfuDQ0NDfzwww9yy1+8HFGm/fv3w8LCAm3btlWob19fXxARdu3aVWmbimxTG1ZWVvj+++9haGiIuLg49O3bF1FRUXB3d6/yfUFBQcKr/Ezx1KlTOH36NPbt24fz589zMDUAfOakYm5ubggNDcWqVasQEBCAoKAgPHr0CGvWrEF4eDjatWv3Wu1v3boVNjY2MDIyQnh4OH799VesWbMGurq6CvU9YsQILFiwABMnTkRsbCzatWuHv/76SxgXAqDQNrVx5coVBAcH44cffoCuri40NDRw9+5d2NjYKDSe9aIBAwbg3Xfffa16WP3icBKBFStWwMHBAWvWrMHx48dha2uLIUOGCP/q15a+vj5mzJiBLVu24ObNm3B1dcXGjRsREhKicN+ampo4fvw4Jk2ahHXr1sHU1BTvvvsurKyshDYU2aY2nJ2d4erqipCQEMhkMmF5u3btcOnSJRgYGCjcVk22ZeLAD5tjolZaWiqcJZWWluLQoUMYNmwYTp8+Xe3AOGvYD5vjMycmWklJSejRowcGDhyINm3aoLCwEAcOHICBgQE8PDxUXR6rYxxOTLRMTU0xevRo/Prrr9i+fTvMzc3RrVs3rFu37pUffWDqhS/rGFNfDfqyjj9KwBgTJQ4nxpgocTgxxkSJw4kxJkocTowxUeJwYoyJEocTY0yUOJwYY6LE4cTqzapVqyCRSJCXl6f0tseOHVvt87Be3iY2NhYWFhY4fPiw0uthr4/DiTVa2traMDMzg46OjqpLYa/Af1unRohI7nncrGqenp64e/fua7dz584duLq68rFXMj5zqgPJycl49913YWZmhqZNm2L69Ono1asXfvzxRwDAnDlzhAkKyl29ehUSiQTHjx8Xlp07dw6dO3eGvr4+nJ2dERISgkePHgnrW7ZsiZEjR2LhwoVo0qQJjI2N8Z///Ad6enrIysqSa/+9996r0USU//zzD4YMGQJjY2M0adIE/fr1w9WrV4X1QUFB+OKLLxAaGgpTU1OYmJhg8ODBOH/+PN566y0YGBjA1dUVO3bsqND2rFmzYG9vDxMTE7z77rtISUmpUd8AsHfvXnh7e8PAwAB+fn6Ij4+v0E9V22zZsgUSiQQSiQSnTp0Cyi47O3fujH379sHNzQ2Ghobo3r07EhMThfcVFxfj888/h4ODA0xNTREQEIAePXpg/fr1Ch9b1giIcWqotLQ0srW1JTMzM1q4cCFt27aNAgICCACtW7eOiIhmz55Nurq6cu+7cuUKAaDffvuNiIhOnTpF2traFBISQjt27KDvv/+eXF1dycvLi54/f05ERD4+PmRmZkaDBg2iv/76i06ePEkPHz4kTU1NCgsLE9ouKioiU1PTClM3VebRo0dka2tLPXr0oI0bN9KmTZuoV69epKenJ0xFFRgYSABo8uTJFBkZSQsWLCAApK2tTWvWrKGIiAgaMmQIaWpqUmJiIhERrVy5kgDQ4MGDacuWLTR9+nTS1dUlZ2dnevr0qcJ979q1iwDQm2++ST///DPNnj2bNDU1ydPTU9iH6ra5d+8eLVmyhADQyZMn5erz8/OjM2fO0IkTJ8jFxYU6duwotBsaGko6Ojq0bNky2r17N9nZ2ZGpqSkVFhbW4qelzjXoqaEaNDGG07Rp0wgAXb58WViWkZFR43Bq0aIFTZo0SW6bxMREAkAHDx4kKgsnKysrysvLk9tuwIABcr9Qv/76KwGguLg4hfbhk08+obZt21JJSYmwrLi4mJycnCg0NJSoLJxatGgh9z5nZ2d69913ha9TUlIIAP34449EL/zy5+bmVqjtP//5j0J9FxQUkLW1NfXo0YOkUqmwzYgRI4TgUWQbIqK//vrrleFUPk8eEdGKFSsIAD158oSkUinp6enRJ5/878du586dBIBOnTql0LGtZw06nHjMSclOnz4NX19fdOzYsdZt3Lt3D/Hx8bh169Yr51V7cS67Tp06wdDQUG59cHAwhg0bhsTERHh5eWH//v1o06YNWrRooVD/4eHhSE1NhZGRkdzy4uLiSufRQ9lcei/Powegyrn0Bg4cCBcXF5w9exbz5s2rtu9Lly4hIyMD69evl3uOuJbW/36UFdmmKi8eT2dnZwDAw4cPUVJSgsLCQri5uQnryydbqG4uPVZzHE5KlpWVhfbt279WG+VTNs2fPx9DhgypsN7Ozk74/5d/iQEgICAAlpaW2Lp1K/7zn//gyJEjmDVrVo36HzhwIBYvXlxhnZmZmcLtlA8QKzKXXnZ2tkJ9h4eHAy/NTfey8jGsqrZRVPmdPKlUCisrK5iZmeHSpUv497//DQC4fPkyJBIJWrdu/dp9MXkcTkpmZ2dX7awj1d3VKZ/LLj8/v1Zz2eno6GDMmDHYvn07unTpguzsbIwaNUrh95ubmyMzM7Ne5tFD2Vx65YP11fVtbW0tvKcyimxTG5qamvj8888xa9YsjB49Gg4ODggLC8OUKVP4scF1gO/WKVmHDh1w9epVREVFVbpNkyZNUFRUJHdH7Z9//hH+393dHc7Ozti0aZPcHHJSqRTFxcUK1REcHIwHDx7gs88+Q7du3eDo6KjwPvTp0wd//vknIiMj5ZbXxVx60dHRuH37Nvz9/RXqu23bttDU1MTOnTsrbVORbWpr0qRJ6Nu3L9LT0/H06VPs3LkTK1euVHo/jM+clG7GjBnYuHEj+vTpg2nTpsHe3l7u4wEo+wXU0NDAlClTMHXqVMTGxuLzzz8X1kskEqxcuRJDhw5F586d8fHHH0MqlWLbtm0YO3Yspk6dWm0dbdu2hbe3NxISEvDZZ5/VaB/mz5+PY8eOoW/fvvjss8/QpEkTHD9+HFKpVCmfph4zZgyGDh2K5ORk/PDDD2jWrBkmTJigUN+Ojo4IDg7Gzz//jIKCAvTv3x+PHj3C0aNHYWNjAwAKbVNbI0eOhKWlJQICAgAAGhoaSE1NrVH4s0ZAjHfriIguXLhAnTp1Il1dXbKxsaGQkBC5u3VERNu2baPmzZuTvr4+9e3bl7Zs2SJ3t46I6NixY+Tr60s6OjpkZWVFQ4YMoWvXrgnrfXx8aMSIEZXWERwcTNra2pSZmVnjfUhISKB33nmHDAwMyMjIiHr06EH79+8X1gcGBpKvr6/cezw9PWnMmDFyywDQggULiMruho0YMYLGjBlDFhYWZGZmRiNGjKDU1NQa9V1QUECTJk0iCwsLMjU1pYEDB1LPnj3l7sQpsk1ld+tedTcxKiqKqOzunbGxMQEQXlpaWrRt27YaH+N60KDv1jXoj7Q2lAkOMjMzYW1tjXXr1mHixIn11u+QIUNQXFyMo0eP1luf6u7FefRQdpduwIAB0NbWxoULF1Ra2ys06AkO+LJODe3cuRM7d+7E77//jtOnTwvLs7OzhVvjlVm2bBk++uijeqiyYRo/fjyio6MREBAAa2trJCYm4vr163zM6gCHkxrauHEjiouLcfz4cfTq1UtYbmxsjOjo6Crfa2lpWQ8VNlz9+/dHSkoKVq5cieLiYri6uuKrr74SPlrAlIcv6xhTXw36so4/SsAYEyUOJ8aYKHE4McZEicOJMSZKHE6MMVHicGKMiRKHE2NMlDicGGOixOHEGBMlDifGmChxOLEakclk0NfXF6ZVevz4sbB82rRpsLa2xogRIwAAoaGhGDBgQJ3VYmVlJdSRkJBQZ/0w1eBwUmPNmzfHkiVLKizPyMiAo6Mj9u7dW+X7Hz16hKCgIKSmpgrLbt++jcLCQhw/fhxpaWlo2rQpACAsLAxHjx5FXFwcfv75ZwDAtWvX4Ovrq5R9mTRpEg4ePCi3LDY2Ft999x10dHSEiQaY+uBwUlO5ublITk5G27ZtK6wzMTHBwIEDq33u9ZkzZxARESH3lMfY2FhIJBJ07dpVCCYA2Lx5MyZMmCBM7imTyXD9+nWlhNODBw8QFhaGli1byi23sbHBgwcP4OnpqfDMKozVC7E+CVMMLly4QADo8ePHcsvv378vPMExOzubiIjWrVtHzs7OZGBgQG+99RaVlJTQzp07SUtLi7S1tcnQ0JCmTJlCRERff/01ubi4CO2VlpaSl5cXASAXFxfq1asXERHFx8cTAEpJSRG2PXToELVu3Zr09PTI09OTjhw5Iqy7e/cuBQYGkpGRERkbG1PHjh0pISGBUlJSSFdXlzQ0NMjQ0JDat28vtz9vv/02jRo1qo6OYoPXoJ+E2aBxOFVu9erVZGtr+8p1GzZsEAImJiaGAFB4eDg9fvyYDh8+LGzXpUsXWrp0qdx7hw8fTu+8847cssjIyAqTUe7YsYOsra2Frw8cOEDm5uZ08uRJKiwspG+++YaMjY3p+fPn9PjxY7K3t6cZM2ZQXl6eMHnorVu3iIho1qxZNGDAgFfui729PS1cuLBWx6gRaNDhxJd1aio6Ohrt2rV75brY2Fi0atUKKJvRBQDu3r0La2trBAYGAgBKSkoQFRWFTp06VXjvy5dXN27cgKWlpdxlXmRkpHBJV1paiqlTp2L+/Pno06cPdHV1MWrUKOHSc968eXB2dsbSpUthaGiI5ORkWFhYCJNX/v333xXqQNmTPR88eFChHqYeOJzUVHR09CvHmwAgJiZGCKe2bdti9+7d+Oabb9ChQwfcu3dPeH9JSYncmFFJSQlu3boFHx8fufZu3LhRISCioqKEyUVjYmJw//599OnTR1ifkZEBlN1x27t3Lz788ENh3d9//w0/Pz+g7C7g1atXXxlOcXFxAMDhpKY4nNSQVCpFXFxcpWdOL4YTyqY7SkpKQn5+PpYtWwaUzWTr6ekpNzV3UlISSkpKKoRTTExMhWXXr18X+iifzdfW1lZY/3//93/o0KEDdHR08OzZM2FSTQA4cuQIOnToAACIj49Hbm7uK2dRjouLg4GBgVJm9mXiw+GkhhISElBUVARDQ0MkJiYKr+zsbKSlpSEjI0MIjnXr1iExMRFpaWnIz88XLqXS09ORkZGB5ORk3L17Fyi7pNPQ0IC3t7dcfzdu3JALp5KSEmRnZ6O0tBQA4O3tDV1dXWzfvh0lJSU4duwY1q1bh8WLF8PU1BTGxsZISkoCAKxcuRJRUVEwNjYW6kDZZeKtW7fkpjaPjY2Ft7c3NDT4x5iJDA+Iv9r27dvl5lUrf/3+++904sQJ0tHRoZKSEnr+/Dn16tWLDAwMyNbWlmbMmEFSqZSIiGJjY8nJyYm0tbVp2LBhREQ0Z84ccnNzk+vr8ePHBIDOnTsnt3zWrFlkZmZG9+/fJyKi3bt3k6urKxkYGFCnTp3oxIkTwra7d+8mBwcHcnd3pz59+lDXrl3p/fffJyKi/Px86tq1K2lpaZGtrS3JZDLhff7+/jRu3Lg6PJINXoMeEG/QOJzqV1BQEAUGBsotO3jwIOno6NCzZ8/qvR4bG5sKdxOZnAYdTnw+zBQWGxsLJycnpKWlobS0FNevX8eUKVPw5ZdfwtTUtN7qyMzMxM2bN5GWlsaD4WqMp4ZiCiksLIShoSFkMhlQNtPtnTt3UFJSgs6dO9dbHUQEU1NT5ObmAgBSUlLkPsHO5DToqaH4M/9MIXp6esIAdzll/d1cTUgkEuTk5NR7v6z+8WUdY0yUOJwYY6LU0C/r8gA8VXURDCAiSUFBgamBgcEzVdfCBAWqLoAxlRs/frzp+PHjOZiY0vBlHWNMlDicmFKkpaXJiOiyqutg6qOhjzkxkbCxsdEAUPHRAYzVEp85McZEicOJMSZKHE6MMVHicGJKUTYgfkXVdTD1wQPiTCnKBsT9VF0HUx985sQYEyUOJ8aYKHE4MaXgMSembDzmxJSCx5yYsvGZE2NMlDicGGOixOHEGBMlDifGmCg16NlXmGoFBwcf0dLSGoSyiQdQNjtKuQ0bNvDPF6s1PnNitaalpTWPiFLKgwllIVX2uqnS4liDx+HEam3Dhg3REonk/CtWlcpksgMqKImpEQ4n9lpKS0u/I6KUF5cR0a3s7OxVqquKqQMOJ/ZaNm3adB3AxfKxJiKCRCI5uH///gxV18YaNg4n9tpKS0uXSSSStLIvkwGsUXFJTA1wOLHXtmnTputEdIH+6+BPP/30SNU1sYbvtW/1bt68WU8mk23S0NDQUU5JrCGSSqUmubm5XY2Njc9oaWkVqboepjpSqfTZRx999OHrtvPaf/ibm5uro6enN6xdu3b8R8QMAAaqugCmOqWlpYiKiioCoPpwAgANDQ2Zi4uLMppijDVgJSUliIqKUkpbPObEGBMlDifGmChxODHGRInDiTEmShxOjDFR4nBijIkShxNjTJQ4nBhjosThxBgTJQ4nxpgocTgxxkSJw4kxJkocTowxUeJwYoyJEocTY0yUOJwYY6LE4cQYEyUOJ8aYKHE4MUFxcTF++eUXFBYWKvyepUuXonnz5oiMjKzT2ljjw+HEBD179kRwcDCKihSfPCUyMhJPnz5FYmJindbGGh+eMYUJcnNza/ye1atXIyIiAgMGDKiTmljjxWdOamLdunWwsLDA+++/j4CAADg4OMDDwwM5OTkAgIyMDISGhsLd3R22trZ48803cfjwYeH9vr6+ePjwIQCgWbNmsLCwwL59+wAALi4usLCwwKJFi9CqVSs0adIEy5cvx+DBg+Ht7Y1x48bh+PHjQltV9ZWXlwdnZ2dYWloiJSVFeE9KSgosLS3h6ekpnLnduHED7777LhwdHeHs7Ixhw4bhxo0bCu8za9g4nNTM0aNHkZmZicGDB+P999+HiYkJnj59in79+mHHjh0wNTVFu3btkJiYiJCQEGzbtg0A8Pbbb0NfXx8AMGjQIAwePBhOTk5yba9cuRJvvPEGunXrhlGjRqFTp06wtbWV26a6voyMjDBy5EgQEfbu3Su8b9u2bSAifPDBB9DV1cWVK1fQv39/nDlzBp6ennB1dcXp06cxYMAAxMbGVrvPrOHjcFIzzs7OOH36NH744QfMmTMHALB8+XIkJycjODgYV65cQXh4OM6cOQNtbW18/fXXKC0txbfffgtzc3Og7FJt48aN6Ny5s1zbS5Yswfr163Hw4EHY29vjiy++gJ+fn9w2ivT1r3/9CwCwZ88e4L8zxGLXrl3Q1tZGcHAwAGD69OkoKCjAzz//jFOnTuHs2bNYuXIl8vPzsXjx4mr3mTV8POakZvr16yecAZULDw8Hyi6p5s2bJyw3NjZGVlYWkpOT4ebmVm3bgwcPrnYbRfry8PBAz549cf78eVy+fBmZmZlIS0vD0KFDYWNjg/v37yMmJgba2tqIiooSJmksv4t47dq1aveZNXwcTmrGyMiowrLHjx8DAPbv3//K9yj6i/2qtmvb14cffojz589j9+7dwljXhAkTAABpaWlA2eyxYWFhFdrQ09OrcV2s4eFwagRMTExQWFiIy5cvw93dvdrtZTJZnffVr18/ODg44ODBg8jPz4evry86dOggtAEANjY2iI+Pr3UtrGHjMadG4I033gAALFu2DMXFxUDZWcnLl0fGxsYAgNu3bwNlH8qsq740NTURHByMvLw8yGQyjB8/Xljn5uaGpk2bIi0tDT///LOwPCMjA3fu3KlxTaxh4nBqBGbOnAlDQ0McOHAAbdq0Qb9+/eDj44MxY8bIfRq8fHB7+PDh6NOnD6ZPn15nfQHAe++9B11dXTRt2hRBQUHCcg0NDcydO1dor2PHjujTpw/at2+Pr7766jWOBGtIOJwaAS8vL4SHh6Nv377Iz89HVFQUjIyMMGzYMLlLuLlz56Jv376QSqW4efMmrKys6qwvALCyskJQUBA++OADaGtry60bPXo0tmzZgvbt2yM1NRXx8fFwdUVTibQAACAASURBVHWFv7//axwJ1pBIXreB1atXmxgYGGQMGTJERzklMcYaqpKSEvzyyy9F48eP11Ng8yrxmRNjTJQ4nBhjosThxBgTJQ4nxpgocTgxxkSJw4kxJkocTowxUeJwYoyJEocTY0yUOJwYY6LE4cQYEyUOJ8aYKHE4McZEicOJVSo6OhpWVlY1mmSzMlKpFDY2NnJTO70oICAAa9eufe1+mPpoVOHk5uaGLVu2KLx9v379cPPmTeFrIkLv3r1FP7vtvn37YGFhUeFVk30HgISEBDg5OUFXV/e1a7p9+zakUmmlj+5NTEyEl5cXUDbLS69evV67z5EjRwr73rx5c4wcOVJ4ymd10tLSMHbsWDx48KDabU+fPo2WLVsiOTn5tWtm/9NoniH+4MEDZGVloWXLlgptf+fOHURERCAxMREeHh4AgMOHDyM6OrrCEx3rm0wmg4ZG5f+uxMTEwMvLq0IY2dvb16if+Ph4NG/evNZ1vighIQHNmjV75WQK6enpyMzMhLe3NwCgTZs2KC0trVU/BQUFQh+xsbGYMGECPvjgA6SmpmLGjBmYMmUKjh07Vm07Fy9exLVr16o8ZqWlpdDU1ISzszP69u0LCwuLWtVcU+X9qrtGc+YUExMDDQ0NtGjRAiib5mjJkiUICgqCnZ0dOnbsKPcw/d27dwMAkpKSgLKHaH3zzTfAS8/WvnLlCoKCgmBvbw83NzcsWLAAALB161YEBgZiwoQJcHd3h6urK5YuXYqZM2eiWbNm8PDwwIEDB4R2jh07hu7du8POzg6dOnWSm0HX398fU6ZMweDBg+Hs7Iz169fD1tZW7smSS5YsQcuWLSGVShEXF4cePXrAw8ND7mVgYAA/Pz/MmzcP3bp1g52dHfz9/bF371706tULdnZ2CAwMxPPnz4GyQCksLESXLl3g4uKC0NBQuWCubN9RdqY0cuRIODo6omPHjjh06JBwZpSTk4Pp06fDw8MD7u7uWLhwIczMzGBra4vQ0FCMHj0aBQUFAICTJ0+iZ8+eWLNmDXx8fODo6Igvv/xS6CciIgL9+vWDk5MTOnXqBH9/f1y7dg1ZWVl4+PAh3nzzTXh6eqJPnz546623IJVKhffu2bMHXbt2ha2tLdq2bSvMSnzgwAF8+umnePLkiVx/8+bNw6BBg/Dxxx/D29sbe/bswb59+9CxY0dER0fD1NQUAPDo0SNMnDgRzZs3h6OjI8aOHYucnBx8//336N69u9zP5ciRIzFmzJgqj2deXh4sLS2FM8qOHTsq/HPfkDWqcHJzc4OBgQEAIDs7G8ePH8fChQsRHR0NmUwmnGnIZDLs2bMHjo6OwiXc1q1bhV/M8v9GREQgICAA3bp1Q2xsLHbu3ImVK1ciNTUVUqkUly9fRo8ePXDt2jUEBgbiu+++g5+fH65fvw4/Pz/88MMPAIAjR45g8uTJWLBgAZKTkzFixAhMmDAB+fn5kMlkSEpKQmJiIjZu3Ij4+Hh0794dRUVFSE1NBcrCcvPmzfjwww+hpaWF2NhYbNmyBY6OjnB0dMTKlSsBABKJBNnZ2YiMjMTu3btx9uxZxMfHY8eOHdi1axcOHTqEixcv4vz580BZOBkbG+OXX37Bnj17cPToUaHmqvb9wYMH6N+/P0xNTfH/7N15XFRl///xF5uAuwiyCQgiOyoiKLmkmPueZlZm5B25/dRvqXi75G2KqeWWpmbd5ZLpXZlL3i6p5EqmgikwC6sigQqDyIAIDAy/P75wvgyLW6CjXs/Hwz/mnDPnus6Zmfdc5wxenxMnTrB+/XpOnDiBp6cnpaWljB07lqioKHbu3ElERATR0dFScK1duxZ7e3vpS0Sr1SKXyykrK+PcuXMsWLCAL7/8kuzsbNLT03nttdcYM2YMycnJDB06lJs3b9KpUydiY2MBaN++PaWlpURERLB3714mTJgAwIYNG5gzZw5z584lISGBkJAQFi1aBMDo0aPx8/NjwYIFpKWl8cknn0jnQ6lUMnXqVGQyGaNGjZLar+jv7du3GTBgAIWFhZw4cYLo6Gj+/PNPDh48SN++fZHL5WRlZQFw6tQpzpw5w7Jly+57PpVKJWVlZWRlZXH8+HFOnz5dz58W/fDCXNbFxcXpXNKlpKSwefNmaZmTk5N0qXTy5Enu3LnDihUr2LRpE/n5+axcuZIlS5YwefJkaeT00Ucf0aNHD2bNmoVGoyE6OpoWLVpgbW1Neno6QUFB0rdiq1at6NSpE6+99hoAPj4+ZGdnU1payrx585gzZ450n2XUqFGEh4dz/fp1GjRoQEFBAatWrZIuG9q1a4eRkRFJSUk4OTmxZ88e8vLyGD9+PDdu3EClUvHTTz/h7OwM5XN1U37Jc/v2bT766CMcHBwoLi6mtLSUmTNnYmdnR6tWrTA0NMTc3JycnBxu3LjBtm3baNWqFa1atWLEiBGcOnWK2bNn3/fY58yZg729PV9++SUGBga4urrSsmVLPD092bdvHzExMVy4cAE7OzsA/Pz8pPtaeXl5pKenSx/25ORkfH19mTZtmrQt5UUQjh8/jo2NjRQ4lV9DmUwGQKdOndBoNNjb27Nw4UJef/11cnNzWbZsGbNnz2bIkCGo1Wri4uKky0qNRkNsbGy1YgoKhYKZM2dK75mK+nlyuZyxY8dCeejdvXuXDRs20KhRIyIjI8nNzcXd3R0vLy/s7Ow4ffo0I0eO5KOPPuLDDz/E0dGR0NDQWs/nyZMnsbS0ZNmyZRgbG2Ns/GJ8bF+okZOvry8AaWlpqNVqvL29pfVJSUnSvaWdO3cyfPhwunbtSnJyMuvXr8fBwYHRo0djYmJCYWEhRUVFREVFceXKFZycnHBycuKXX35h9+7dNGjQAJlMJn3AKP+wVA7H+Ph4PD09kcvlZGRk8PLLL0vrVCoVAC1btkShUGBhYaHzXFNTU5ydnUlMTARg8+bNjBkzBgsLC+Li4jA2NqZHjx64uLjg4uIi1YFTKBRotVqpX/Hx8ZSUlEjnITExEa1WK/XLyMhI5xyVlZVRUlLywGM/ceIEQ4cOxcDgf6eoV6vVpKWl4eHhwYkTJwgMDJSCqeLcVIycKtp1d3eXHld9naysrGjRogWNGzcmLy+PoqIisrKy+OKLLxg8eLD0evfo0YNLly7Rv39/AgMDefvttwGIjo6moKCATZs24ezsjKenJ1qtVhoVxsbGotFo6NChg9Rubm5utdeJ8l8hExMTpWA7e/YsBgYGuLu74+DgwPTp01mzZg2dOnUCoG/fvpw6dYqdO3dSWFjItGnTHng+FQoFL730UrUiEM+7FyKC8/LySE1NlT7gMpmMpk2b0rp1a6j04fHy8kKtVnPo0CF+/vlnnJ2dMTY2Zu3atRw4cAADAwNMTEx07jl9/fXXdOzYETMzMxo0+L8aD3K5XKfckUwm45VXXtF5PHHiRNRqNZQXkKxw4MAB/Pz8sLKyQi6X64RcBQ8PD5KSkvjjjz+4cuUKmzZtgvIRorOzs05fKvfJ0dFRCiuZTIa1tTVWVlbS45YtW2JjY8O+fftwc3OTbi6r1WqOHDlCSEjIA489JyeHFi1aSI+PHTuGsbExrq6u5OTk0Lx5c2ldRkYGsbGx0odbJpPh4uIijUpkMpk02qx4XPm+oVwup1+/fqSkpNCvXz/pEiwuLo7evXtjY2PDO++8w7hx4/j444+xtbWF8kvcmJgYCgoKaNq0qc4PDNHR0bi6utKoUSOdc2diYlKtbHtSUhJFRUU6r9GECRP4n//5H7Rarc4+KA+n2bNnExERwYYNG2jQoIH0pxr3ey8FBQVVez2fdy/EyCkuLo6ysjJp5FR1VBMXFweAp6cnP//8Mw4ODgQFBWFoaIiHhwdDhgyRbkJWjJxMTU3x9fVl06ZNqNVqsrKyuHjxIpR/y6anp0vf+Hl5eVy/fl16XFBQwNWrV/H29sbNzQ1TU1N++OEHNBoNR48eZcuWLSxcuBDKRzs1hVP79u05c+YMq1evplevXtLIIzY2FltbWxISEqR/FYUoq45CZDJZrY+joqIoLCzkr7/+Ij4+nrfeeotmzZoxZcqU+x47gJeXF3v27CE3N5eYmBj+9a9/4eLigomJCV5eXpw5c4akpCQyMzOZPHkyWq1WZ+RUcbylpaXEx8fr9DEuLk5ab2hoSI8ePYiPj2fu3Ll88sknNGrUiOLiYhISEqTXOzg4GCsrK7799lsAfH19MTU1Zc2aNWi1WpRKJSkpKVIbKpWK7OxsUlNTuXbtmvQ6tGvXrtroRS6X07JlS6ytrQHo3Lkzu3fvJjExkcLCQk6ePKmzfc+ePcnOzqZr167SZfyDzmdt74Hn3QsTTtbW1rRq1QpqCSd7e3uaNWvGrl27GDdunLSuaiHHyt90GzZsIDs7my5dutC/f3/pjSyXyzE2NpYuTSpuqleMDpRKJVqtFm9vb6ysrNiwYQObN2/G2dmZlStXsnXrVunyQaFQSM+rbPTo0WRnZ3P8+HEmTZokLZfJZJw+fZquXbtK/yqOp2o4VX3TV6zXarVcuHCBMWPGEBwczMCBA3F0dOS///2vVBW4tmMHWLVqFQUFBfj4+DB//nx8fHykY5g2bRpdu3YlODiY4cOH4+LigpWVlXRfTC6XS9smJSVRWFhY7fK48vlwc3PjrbfeYseOHfj6+nLhwgXi4+PRaDRSOBkZGTF27Fi2bt1KYWEhVlZWbNy4kZ9++glvb28mTJigMxoeOXIkZmZmBAYG8vHHH1frV2VVR7azZ8/G19eXYcOGERAQwLFjx3S2b9SoEf379yc8PFxneW3nU6VSkZWVVWPbzztRt+4ZtmDBAo4cOcLFixel+zsvsoCAACZNmsQ//vGPp92VF1Zd1q17Ie45PW8uXrzIqVOn+Oqrr9iwYcMLGUyFhYUsWbJE+ju1Y8eOkZmZSXBw8NPumlBHRDg9gxYuXIhKpWLJkiU6N4tfJPn5+dy8eZM33ngDrVZLYGAgv/zyi/TnE8KzT4TTM+jw4cNPuwtPnaWlJd98883T7oZQj16IG+KCIDx7RDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJgqCXRDgJdeqf//wnY8aMedrdEJ4DIpz0UExMDGPHjqVNmzY4OzszYsQIrly58tDP/+OPP3j33Xd1lkVERODj48PVq1frocf/58qVK3Ts2LHW9StWrKBXr1712ocKYWFhHDhw4Im0JdQ9EU565syZMwwYMAALCwt++ukndu7ciZGREaNHj+b27dsPtY9du3ZhbGyss8zJyYl+/fphYWFRTz0HrVaLTCajQ4cOtW7ToUMH+vXrV299qJCRkcG///1vPD09670toX6IcNIj+fn5TJw4kWHDhrFx40YCAgIICgriyy+/JDs7m7Nnz5KQkICnpyefffYZfn5+tGnThgkTJpCfnw/AzJkz2bFjBwcPHsTBwYFffvmFH3/8kcDAQC5fvkyzZs0AUCqVjBo1itatW+Pp6cnKlSulfowcOZIVK1YwYsQI7OzsCAwMRC6XS+u//vprOnfujK2tLd7e3nzyyScAJCYmkp+fX+vIafr06bz55pvcu3cPAJVKhY+PD9u2bSMoKAg7OzuGDRvG3bt3AejRowfz5s0jODgYBwcHBgwYQHx8vHScr732mrTv69evY2FhQWxsLOnp6fj7+2NoaEjv3r3p3bs3AL///ju9e/fG3t6e7t27k5iYCMB3332Hs7MzsbGxdfyKCn+HCCc98vPPP5Odnc2CBQt0lltZWdGgQQNUKhWlpaXcunWLhg0bcvLkSX744QciIiLYvHkzAEuWLMHIyIj//ve/pKWlMWzYMF577TXGjBmDl5cXANeuXWPQoEG88sorJCYm8tVXX7F8+XLOnTsHQG5uLkeOHCE8PJzLly+j1WrZunUrAKtWreKLL77g66+/5q+//qJ79+6kpqZC+eWopaUl9vb2NR7f2rVrsbe3l/phZmZGRkYG586dY8+ePRw+fJjIyEiOHz8OwI0bN1CpVOzYsYPTp09TVFTE3LlzAZDL5fj4+Ej7lslkmJiY4O7ujr29PVOmTKFPnz6kpaVx4sQJysrKCAkJoX///sTGxjJr1ixcXFwAMDc3p1mzZjRo0KCOX1Hh7xDhpEfOnDlDx44dad26tc7yGzduUFxcjLW1Nenp6bRq1YqpU6fSrFkzunTpwksvvURCQgIAly9fxsjISOeDa2BggFwul0IhPDyc7t27M3nyZMzNzenRowd2dnbIZDIAUlJS+Oc//4mPjw+tWrXCyckJQ0ND/vrrLz799FPWrFmDn58fRkZGXL16lU6dOklt3++SLi8vj/T0dKkfKSkpGBgY8Nlnn2Fra0v79u0xMTHB0NCQgoICcnJymDVrFnZ2djg7OzN69GjpOBUKRbVwateunRQwUVFR+Pv7S+vLysooLS0lNTUVMzMzRowYgZGREQCjR4/m8uXLuLu7/+3XUKg7Ipz0iEqlqhZMAMeOHcPY2JjAwEAUCgXe3t4663NycmjZsiUA0dHR+Pr66owCSkpKSExMlO6/HD9+nJdffllaX1ZWxu3bt7G0tCQtLQ21Wq3TRlJSEm5ubhw4cABra2uCg4MBKCoqIjY2Vgqn2NjY+4aTXC7HyMhICgGZTIajoyNNmjQBIC0tjeLiYtzc3FAqlZiamtK2bVud47SwsOCvv/6q1keZTCaFlVar5fLlyzrhZGhoyJ49e4iPj8ff358TJ048xCsiPE0inPSItbU1ycnJOsvUajWrV69m9OjRWFlZIZfLdW7ypqenExMTQ58+faA8nKoGRFJSEkVFRXh5eaHVarl79y7W1tbS+oiICEpLS+nZsycymYymTZtKIalWq0lLS8PLy4vr16/j7Oys8zyNRkP79u0BiIuLk0ZFNZHJZLi4uGBmZiY9rrx9XFycFEhyuRx3d3dpdKPVajly5AivvPIKSqUSExMT2rVrB+XhGhUVJYVTfHw8eXl51c5Dhw4dpGCeP3/+Q78uwtMhwkmPjBkzhpiYGJYuXUpMTAwHDhxg8ODBmJubEx4eDuWjj5KSEnJzc4mKiuLtt9/m5ZdflsIpKyuLq1evcvPmTTIyMqTntGzZEmtrawwNDfH29mbv3r3cu3cPpVLJ3Llz+eCDD7CwsKgxMAA8PT2xt7cnLS2NwsJCMjMzWbRoESYmJpiamqLRaFCr1ZSWltZ6fJUvLSseVx79xMXF4ebmhrGxMQqFAhMTE1QqFUlJSUyZMgW1Ws20adO4d+8eWq2WzMxMioqKmD9/Punp6dK+srKyoPzPGpKTkykrK5PuZ2VnZ3Pnzh3pflNeXh4vv/wyK1asqNPXUvj7RDjpkT59+rB27Vr27dtH//79WbRoEX369OHXX3/FwsKC0tJSEhIS+PPPP/H29mb8+PF069aNLVu2SPuYMGECFy9exN/fX/obn6qhsH79elJTU3F1deXtt98mNDSUsLAwqGU0Y29vT7NmzRg/fjxt27bF39+f4cOH07NnT4qKikhNTcXExIQZM2YQFhYmhWJVVUd9Vduq/Fgul1NUVESXLl3o06cPRUVFHDp0iBYtWhAcHExAQIC0rkJFOFWse+ONNxg6dCgAp0+fZty4cQQEBGBmZsaqVaugfESWnZ2NWq3+26+fULcM/u4O1q1b17Rhw4ZZr776qvipo54lJCQQFBTEX3/9hbm5+dPuTr3y8PBgw4YNOuEj6D+NRsP+/fuL3n//fbO/uy8xcnqGyOVyHBwcnvtgUqlUZGZm4ubm9rS7IjxFIpyeIQqFQroJ/DyTy+WYm5vX+Mul8OIwfohtBD1R8QeIz7uePXuSnp7+tLshPGVi5CQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4fSEZGVl8eabb+Lk5MTXX39d4zb//Oc/GTNmzBPvW1VKpfJpd6FOHDhwAAcHB7RaLQAXLlzAwsICCwsLunbt+kT68DTafF6IcHpCZs2ahZmZGUqlkrfeeguVSoWPjw979+6Vtrly5QodO3aUHt+8eZNx48aRnp7+xPq5fv16jh8/Lj3Oysrigw8+wNPTk9atW9OzZ0+dPj9ITcdQ07HXh5iYGHx9fTE0/N+3uUKhoFGjRigUCo4cOQLAt99+i7OzM61bt6Zr167MmzePW7duPVZft2/fzvLly3WW1dTmozz/RSbC6T6WLl2Kh4cHHTp0YOPGjY+9nzt37nDo0CGmT5+Oubk5DRs2pEmTJvTv35+2bdsCoNVqkclkdOjQQXremTNnuHTpEvb29o/Vbmlp6SNtf/LkSQ4fPsyUKVMAuHHjBn379kUmk/HFF1/wyy+/0LNnT9577z3Onj37UPus6RiqHnt9qRr2CoUCd3d3rK2tad68OQDnzp3D39+fQ4cOMWvWLI4ePUpwcDC3b99+5L6uW7cOW1tbnWU1tfkoz6/sUV/PZ50Ip1rs37+fVatWkZmZSVpaGgsWLHjoD2Rlf/75J926daOsrIyJEyeyaNEiMjIysLW1ZcuWLbRp0waAxMRE8vPzpQ/T7t27mTp1KtnZ2Tg4ODBv3jwAiouLCQ8Px9fXF1tbW4KDg5HL5QAcPXoUR0dHVq5cSUBAANOnT0ej0RAWFoarqyvOzs4sXry4xn6WlpYyZ84cpk6dKo00Zs6ciampKfv27aNPnz506tSJ8PBw3NzcOHDgAAA9evRg3rx5BAcH4+DgwIABA4iPj6/1GGo69jt37jB9+nTatm2Ls7MzoaGh5OfnA7Bs2TImT57MtGnTcHJyol27duzevVvq9+nTp+nbty92dna4uroyduxYNBoNlI+cKoe9UqnEw8ND57jj4uLo3r077du3Z/To0ezfv5/bt2+zc+fOGvuan59PWFgY7u7u2NnZMXPmTACCgoJISUlhwYIFODo6kpWVVWOb6enpvP766zg6OuLr68v+/ftrfX5+fj4tW7ZkxYoV9OrVi8DAwEd+/z3LRDjV4vz589WWxcbGPvJ+/Pz8mDp1Kq6urpw/f55FixZhZ2fH559/jqOjI02bNoXyD5KlpaU0whg9ejR+fn4sWLCAtLQ0PvnkEwBCQkI4dOgQ27dvJyEhARsbG2lUp1AoKCgowNHRkYsXL/Lpp5+ydetWDh06xOHDhzl27Bj9+vWrsZ+HDh0iNzeXAQMGQHlYHjlyhLCwMBo2bKizrZ2dnfThu3HjBiqVih07dnD69GmKioqYO3durcdQ9diLi4sZNWoURUVFREVFcebMGS5cuMCmTZsAKCgoICIigkGDBhEXF0fPnj1ZvXo1AGfPnmXcuHFMmjSJ69ev89FHHxEXF4eJiQkZGRlkZmbqhJNCodAJinv37pGUlISvr6+0zN7eHldXV1JTU6v1VavV8uabbxIdHc0PP/yAUqmURplLly6lcePGpKamcv36daysrGpsc/78+VB+L+rLL7+kU6dOtT5fqVRSVlZGVlYWx48f5/Tp04/8/nuWiXCqReXLgQqenp6PtS+ZTFbtG1uhUODl5SU9vnz5ss4HSaPREBsbi7+/v7Tst99+48iRI6xbtw4/Pz9u377N1atXpX7J5XIGDhwo3VRv1KgRpaWl5OXlkZOTg6ura603ZX/66Se6du2KkZERAJGRkRgaGjJo0KBq216/fh0bGxsKCgrIyclh1qxZ2NnZ4ezszOjRo0lISKj1GKoe+86dO0lPT2f9+vW0aNGC1q1b061bN2k0mJKSwuuvv87AgQNp0qQJ3t7eGBoaUlZWxqxZswgNDWXUqFEYGxtz7do16cN+5coVGjVqhJubGwDZ2dlkZWXpvIYymYzS0lJ8fHx0+peTkyN9aVTu68GDB/nzzz/ZuXMnHTt2pGnTptLlXlRUFH5+ftKos7Y2S0tLuXnzJqWlpXTr1g0HB4dan69QKLC0tGTZsmUYGxvTqFGjGl+755UIp1oMHTqUsWPH0qpVKxwcHAgNDaVXr16PtS+ZTFYt2ORyuU44xcbG6oRTbGwsGo1GZ1lkZCSNGzdm1KhRtGnThuDgYAYOHMjkyZOh/M3cs2dPnXZCQ0OZOHEiI0eOJDQ0lMLCwhr7ePbsWfz8/KTHWVlZWFpaYm5urrNdYmIiV69e5aWXXkKpVGJqaqpzPyYnJwcLC4taj6HqsR8/fpygoCAaNGggrVepVFhaWkrnztvbW1qXnJyMm5sbcXFxJCQkMH78eGldVFSUFE4xMTFSkFWcG0DnSyIuLg4rKyusra2lZTKZjIyMDDp37lytr6dOnaJLly4621eIjo6uMYSrtrlmzRpcXFwICAhg/fr1D3z+Sy+9hImJSbX2XgQinGphbm7Oxo0bUSqVXLlyhRUrVjzWfkpLS0lISMDd3V1nedVwiouL03kcHR2Nq6trtW9LX19fkpOTiYmJITk5mYULF2JoaEhJSQmJiYk6+wAwMjJi3rx5/Pbbb+zfv59ff/21Wh+zs7O5c+cOjo6O0jIbGxtpeWXh4eE4OTnRv39/5HI57u7u0mhLq9Vy5MgRXnnllfseQ+VjV6vV2NjYSOtu375NZGQkffv2JS8vj7S0tBrPU2pqKsbGxtLIIzs7mwsXLkgjXplMq3tVVgAAIABJREFUpvM8hUJBkyZNdG7MV/yaV+HevXvMnDkTNzc3+vbtW62v+fn51cK6wqVLl2jfvr3OspratLS0ZMuWLaxYsYJ//etfZGdn1/p8uVz+2KP154EIp3qWlJREYWGhzrfnrVu3UKlU0pteo9GgVqt1fo1RqVRkZ2eTmprKtWvXAOjcuTPR0dEcPXoUrVbLyZMnpZFQcnIyRUVFOm/moqIiNmzYwI0bN8jIyECr1eLs7FytjyUlJQDSpQzAgAEDaNiwIVOmTJHuBY0fP56TJ0/y1VdfYWJigkKhwMTEBJVKRVJSElOmTEGtVjNt2rRaj6Hqsfv6+vLbb79x8+ZNsrOzmT59Op07d6Zv377I5XIMDQ2lYC8pKSE+Ph4vLy/s7e0pKSkhJSUFjUbDrFmz0Gg0NG7cGMrDqvL5rPjVrLK4uDgsLS2RyWT89NNP9O3bl/T0dL777juMjY2r9bVTp05ERERw/Phxbt68Kd2YLykpIScnB7lczo0bN8jNza2xzfj4eH788Ueys7O5ceMGLVu2pFmzZvd9fuWAzcjIwN3dnR07djzku+/ZJsKpnsnlckxMTHQufeRyOQ0aNMDV1RUAExMTZsyYQVhYGBkZGQCMHDkSMzMzAgMD+fjjjwEYOHAgU6dO5cMPP8THx4dly5ZhamoK5W9kGxsb6ZKK8kuwHTt24O/vz5w5c/jiiy+qfTtT/m3eoEEDnRvfVlZW/Pjjj2RmZjJ8+HBCQ0MxNzcnIiJCuvyQy+UUFRXRpUsX+vTpQ1FREYcOHaJFixa1HkPVY581axZubm506dKF7t2707p1a77//nsMDAyQyWS4urpKx5iQkEBxcTFeXl7SDw2DBw+mR48eNGzYECsrK+mXwvfff5+IiAh+/vlnKP/VrHJwa7Va5HI5P/74I3379mXt2rUMGjSIM2fO0K5duxr7+u677zJ27FgmTZpEYGAg+/btA8DY2JjQ0FDWrVtHUFAQycnJNbZ5+fJlFi9eTIcOHfjtt9/YtWsXxsbGNT5fpVJVu1/VsmVLbG1tSU1NfYx34rPH4O/uYN26dU0bNmyY9eqrrzZ4iM1fOHPnziUqKopjx4497a7c1+jRowkJCWHIkCEP/RwPDw82bNhAnz596rVvdcHFxYXZs2dL9+eexTY1Gg0jR45kzZo1UoDqG41Gw/79+4vef/99s7+7LzFyqkd79uxh586d0k/r+mzq1Kn88ccfD729SqUiMzNT+jVMn2VkZHDnzh2sra1RqVTPbJsrV65k3rx5ehtMdc34aXfgedakSRNOnz6Nk5PT0+7KA/Xu3fuRtpfL5Zibm9O6det661Ndqfi/gu+99x6+vr6cOnXqmWzzWfiSq0sinOpRxS8+z4pHCaiePXs+0f/z93dU/u8oz3ObzxtxWScIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4SQIgl4S4fSEbdmyhUOHDtXJvr7++mupJtqECROgfI7ximXffvstgDSXkiA8S0Q4PUF5eXmsWLHisQol1OSNN96Q5nGqmMxuzZo10myXFcuio6NZu3ZtnbQpCE+KCKcn6PPPPyczM5O4uLg62V/jxo1p2LAhN2/elCYla9GihTT1a8WyV155hc8//1wqmSQIzwIRTk9Ieno6mzZtYuTIkTojp4qKstu2bSMoKAg7OzuGDRvG3bt3mTFjBqNGjdLZz9ChQ3nvvfekxxVTc1SeMVGpVGJjYyPNSNmsWTM0Gg3R0dFP4EgFoW6IcHpClixZgp+fn1RjrWKeaDMzMzIyMjh37hx79uzh8OHDREZGcvz4cTw9PUlMTJT2ERcXR2RkJBMnTpSWKZVKzMzMdOYGr1orjfJCCy/K9K7C80HM5/QEXL58md27d3PkyBFpPuq4uDi6detGSkoKBgYGfPbZZzRp0gQbGxtMTEwwNDTEw8OD9PR0CgoKaNiwIZs3b8bf35+AgABp30qlknbt2kkVUCqWde/eXXqsVqspKiqiuLj4CR+5IDw+MXJ6AhYuXMjgwYPp3LkzzZs3x8LCQrq0k8lkODo60qRJEwDS0tIoLi7Gzc0NDw8PysrKSElJQaVS8fPPPzNp0iSdfdc0cX9iYqLOyKni1zpbW9sndMSC8PeJkVM9O3ToEJGRkTRq1Ig2bdoAcPfuXemmeNX6anFxcVKhSmNjY5o3b05SUhJHjhyhRYsWDBs2TGf/SqWSHj16SI+vXr1arRTV+fPnMTIykgpOCsKzQIyc6lFJSQmLFi3iH//4B9evX+fatWtcu3aNMWPGSCMnuVyuU9E2Li4ONzc3jI3/93ujQ4cO/PLLL2zZsoUJEyboVH+9c+cON27c0Bk5KRQKDAwMdMLp3Llz9OrVS6dslCDoOxFO9ejbb7/l1q1bhIWF6Sx3cXEhPj4ejUZTbeRU9XFISAgHDx4kJyeHkJAQnf3U9ktd69atpcvEe/fuERERwfTp0+vtOAWhPojLunr0/vvv8/7771dbPnPmTGbOnAnlVWAr27Ztm87j4cOH8/nnn+Pj40PLli111imVSho3biyV5K5YVnnUtHPnTvr06aNz6ScIzwIRTnpKq9USERHBnj17UCgUfPPNN9K6wsJCcnNzuXz5Mu7u7hgYGKBWq7l37x5yuZx+/foBkJqayi+//MJ33333FI9EEB6PuKzTU/fu3WPChAkkJSWxa9cunb9j+ve//42npyfbt2+XLunef/99PD09dX69i4uLY9u2bTRt2vSpHYcgPC5RjlwQhDojypELgvDcE+EkCIJeEuEkCIJeEuEkCIJeEuEkCIJeEuEkCIJeEuEkCIJeEuEkCIJeEuEkCIJeEuEkCIJeEuH0nPn999+xt7enrKzsoZ+Tm5vLzZs367VfT0r37t1Zt26d9HjUqFFSHb+9e/fWWTuurq7Sfitmlrh69Wqd7V8Q4fTEhIWFceDAgXpvJz4+nrZt22Jg8HD/bfLGjRuMHz8eM7P/+69Q+/bto2/fvtjb2+Pl5cXkyZPJzMx86D5s376d5cuX6yxbsWIFvXr1eoQjeXSFhYUkJCTQoUMHaZlCoSAsLAyFQsGQIUMAGDZsGPPmzXuofd68eZNx48aRnp6uszwyMpLw8HAaNGhA27ZtAZg6dSpqtbpOj+lFJsLpPpYuXYqHhwcdOnRg48aNj72fjIwMaSaB+hYfHy8VUSgrK+PWrVu1bltWVkZoaCgzZsygefPmUF6Uc9KkSQwYMIDDhw+zcuVKzp8/r1OO6kHWrVtXbb7yDh06SFO51BeZTEZJSYkUTnfu3OHmzZv4+/tjbW0tzSIaHBxM586dH2qfZ86c4dKlS9jb2+sst7a2JiMjA1dXV2nW0o4dO7J9+/Y6P64XlQinWuzfv59Vq1aRmZlJWloaCxYs4OzZs4+8n/T0dPz9/TE0NKR379707t0byudrWr16Ne3bt6d169YMGjSIhIQEAH744Qf69OnDzJkzadeuHZ6enqxYseKh2quoxgLw8ccf1zjZXYXdu3eTl5dHcHAwAJcuXWLp0qV89tlnzJw5k/bt2zNo0CAWLlzI2bNnuX379gP7FhQUREpKCgsWLMDR0ZGsrCymT5/Om2++yb1796TtDh48SI8ePbCzs6NLly4cOXIEHlDHj/L51+fNm4e7uzv29vYEBARII9IrV67Qpk0bKWgVCgVUqnwM4O/vz+LFi2nUqBEAy5YtY/LkyUybNg0nJyfatWvH7t27pfMzdepUsrOzcXBwqDbaqlpcwsbGhoiIiId6nYQHE+FUi/Pnz1db9jhlxO3t7ZkyZQp9+vQhLS2NEydOADB//nz279/P7t27USqVNG/eXJrOV61Wc/XqVfr160d0dDRz5sxhxYoVNfapqoqR04oVK9izZw8bNmyoddvPP/+ccePGSY83bdqEq6srb731ls52dnZ2AGRlZT2wb0uXLqVx48akpqZy/fp1rKysWLt2rXSJCPDLL78wbdo0lixZwtWrV3n99deZOHEiBQUF963jV1ZWxjvvvINSqeTkyZMkJyej0Wi4c+cOlIdT1Uu6xo0b07p1a2nZjz/+CCD1paCggIiICAYNGkRcXBw9e/Zk9erVAIwePRo/Pz8WLFhAWloan3zyic55qTrrqKgNWLdEONWiY8eO1ZY97mVZVFQU/v7+0uOEhAS++uorNm7ciJubG40bN2bw4MHIZDIoH2317t2b/v3707RpU0JCQmjSpIk0sqrNnTt3uHXrFrt372bnzp0cOHBA54NZmUwmQy6X69S3O3v2LIMHD8bQUPdtcf36dSi/lHlQ36KiovDz89PZR15eHunp6Xh5eVFaWsq8efOYM2cOvXr1wtTUlFGjRpGXl8f169d16vjZ2trSvn17qY7f3r17uXDhAt988w22traUlpaSnp4uVZW5cuWKzutWUVy08v03pVJJkyZNpKmNU1JSeP311xk4cCBNmjTB29tb6rtGoyE2NlbntaugVqvJyMjQeU+oVCpRG7AOiXCqxdChQxk7diytWrXCwcGB0NDQx7qhq9VquXz5ss4bPCIigpYtW+pUXcnOzsbS0hLKP1SV1xUXF3P37t0HVk+pKHhw7Ngx+vfvj5OTU63bnjlzhkaNGuHm5iYty8rKqjHMjh49ire3N82bN39g36Kjo6t9mOVyOUZGRri7uyOXy8nIyODll1+W1qtUKgBatmx53zp+e/bsYciQIVKZ9T///BMzMzM8PDwoLi5GqVTSvn17nfNRtfKxXC7XCRSZTKZzPMnJydI5iY2NRaPR6IzGqp7ryvu6du2aqA1Yh0Q41cLc3JyNGzeiVCq5cuXKQ9/zqSo+Pp68vDydN7harcba2lpnuwMHDtC3b18o/wBVrsBy+PBhzMzM6Nat2wPbat26Nd9//z1btmwhMjKy1m1TUlKwt7fXqRRsY2NDUlKSznYxMTHs3btXKub5oL5dunRJJyAoDwAXFxfMzMykX7NsbGx0jt3Pzw8rK6v71vFLTU2Vav9VtO3j44ORkRGJiYkUFxfrPLe2cKrYJi8vj7S0tGrtVTyOjo7G1dVVuj9VmVKppGHDhjpfABcuXCAwMLDWcy48GhFO9SwrKwvKLzmSk5MpKyujffv2JCYmEhUVRWFhIStXruSvv/7i//2//4darZZKkOfk5HDgwAFmz57N3LlzpRu9tVEqlXTs2JGBAwcyceJEpkyZUutP2yUlJdXmFn/ttdfYvn07u3btIjY2lm+//ZZXX32V4cOH89Zbbz2wbyUlJeTk5CCXy7lx4wa5ublQJRDc3NwwNTXlhx9+QKPRcPToUbZs2cLChQulbWur42dvby+F58WLF9myZQuNGzeG8pEn5fd9AG7dukV2djbu7u46x1i5L3K5HENDQ2mbkpIS4uPjpfUqlYrs7GxSU1OlqskVFAoFbm5u0iVgfHw82dnZjBw58r6vkfDwRDjVs4CAALp06cIbb7zB0KFDARg4cCCTJk3izTffxNPTk8uXL3Pw4EFatWqFXC6ncePGrFu3Di8vL8LDw5k/fz5Tp059YFvx8fHSZcZHH31EkyZNav17HltbWxo2bKizbM6cOYwfP57FixczYMAAvvvuO+bNm8dXX30F5R/m+/XN2NiY0NBQ1q1bR1BQEMnJydLzKvplZWXFhg0b2Lx5M87OzqxcuZKtW7dKl3n3q+P30UcfIZfL6dixI2FhYfTv31+619W5c2f69u3LmDFjoJbLrsLCQlJSUqT9yWQyXF1dMTU1hfJ7gZVHXyNHjsTMzIzAwEA+/vhjnXNV9Ze6AwcO8NJLLz30nygIDyYKHOiZLVu2sGvXLo4ePVqv7Vy8eJEPP/yQM2fO6F3f6sKXX37J8uXLq4146oqnpydTpkxh2rRpFBUVERQUxLZt2/D19a2X9p4VdVngQNSt0zNyuVznJnVlGzZs4Pvvv69xXUhIyH3/pqmqgIAAWrRoQU5OjnSD+e/0Td8oFApcXFy4desWTZs2xdzcvE72m52dTU5ODrdu3ZJGTkuWLGHmzJkvfDDVNRFOeqZyUcyqpk6d+lCXdw9r48aNqFSqRwqn+v4r77qiVCq5fPkynp6e7Nixg0GDBv3tfZaVldGpUyfy8vKgfPSUm5tL165dpf8aI9QdEU565uDBg0+srdr+Bqo2T7Jvf9evv/5a5/s0MDCo8Y8sRTDVD3FDXBAEvSTCSRAEvSTCSRAEvSTCSRAEvSTCSRAEvSTCSRAEvSTCSRAEvSTCSRAEvSTCSRAEvSTCSRAEvSTC6Rnzz3/+U5oWRBCeZyKcnjFV58kWhOeVCKf7qKu6dXVFq9Uik8lqnNNaX1TMRCkIf5cIp1rUVd06jUZDWFgYrq6uODs7s3jxYgDy8/MJCwvD3d0dOzs7Zs6cKT3n66+/pnPnztja2uLt7S2VJEpMTCQ/P19n5HTx4kVGjBiBvb09rq6uLFmypM77dL8ae0ePHsXR0ZGVK1cSEBDA9OnTobyS8KRJk2jbti0ODg6MGzdOVMMVHomYMqUWtdWtq1xK6WFs3bqVQ4cOcfjwYQwMDFCpVGi1Wt58803u3r3LDz/8gIuLizTX+KpVq9i+fTtbt26lffv2TJkyRZqmIyYmBktLS6n67IULFxg+fDgzZ85ky5YtJCQkMHDgQEJCQqTSR3XRp/nz5/P777+ze/du7OzseP/99wkLC2Pfvn0oFAoKCgpwdHTk4sWL3L17l9u3bzNgwAD8/Pw4ceIEZmZm9O7dm4MHD/LGG2880vkTXlwinGpRV3XrSktLycvLIycnh8DAQFxdXTlw4AB//vknUVFRUhWWpk2b8tdff/Hpp5+ya9cu/Pz8ALh69SqjRo0C4PLlyzqXdB999BE9evRg1qxZaDQaoqOjadGiRbXKLn+nTxU19k6fPi3Ngjl48GAWLVoE5RPQDRw4ULpJ36hRI1avXs3du3fZsGEDjRo1IjIyktzc3GrFBgThfkQ41WLo0KGcPHmS3377DVNTUwYMGPBYdetCQ0O5ffs2I0eOZNCgQaxfv55Tp07RpUuXGstDWVtbS+XBi4qKiI2NZenSpVA+cqsoPVRUVERUVBSWlpY4OTmh0Who3749u3fvpkGD+0/n/ih9epgae2+//bbOc86ePYuBgQHu7u4YGBjQqlUr1qxZIxW/FISHIcKpFhV16/4uIyMj5s2bx6hRo+jRowdDhgwhPz+/xjmtr1+/jrOzs/Q4IiJCCh3KyySFhIToPOfrr7+mY8eOmJmZPTCUHqdP96uxV1JSQmJiok61lAoTJkzgf/7nf9BqtTXWfROEBxE3xOtRUVERGzZs4MaNG2RkZKDVanF2dqZTp05ERERw/Phxbt68ye7duwGwt7cnLS2NwsJCMjMzWbRoESYmJpiamqLRaFCr1dKvYaampvj6+rJp0ybUajVZWVlcvHixzvt0vxp7ycnJFBUVVbvc7dy5M7t37yYxMZHCwkJOnjxZL+dXeL6JcKpHiYmJ7NixA39/f+bMmcMXX3xB+/bteffddxk7diyTJk0iMDCQffv2ATB+/Hjatm2Lv78/w4cPp2fPnhQVFZGamoqJiQkzZswgLCyMjIwMKK/Gkp2dTZcuXejfv/9DlUF61D7dr8aeQqHAxsamWpn02bNn4+vry7BhwwgICODYsWP1cn6F55uoWycIQp0RdeuE+6rL+naC8LSIcHoO1XV9O0F4GsQ9J0EQ9JIIJ0EQ9JIIJ0EQ9JIIJ0EQ9JIIJ0EQ9JIIJ0EQ9JIIJ0EQ9JIIJ0EQ9JIIJ0EQ9JIIJ0EQ9JIIJ0EQ9JIIpydIq9WSlJT0tLtRJ7p37866deukx6tXr8bCwgILCwupMIKTk1O9zOVUtW1XV1ep7fj4eCif3lh4tolwekJKSkqYMGECOTk50rJ9+/bRt29f7O3t8fLyYvLkyWRmZj70Prdv387y5ct1lq1YseKxphN+FIWFhSQkJOjMZ65QKOjVqxcKhYLFixeTlpZGXl4e7dq1q/e2IyMjCQ8Pp0GDBrRt2xaAL774osYiFcKzQ4TTE/Lpp5/Stm1bAgICAFizZg2TJk1iwIABHD58mJUrV3L+/Hnee++9h97nunXrsLW11VnWoUMH+vXrV+f9r0wmk1FSUlItnHx9fbG2tqZRo0bI5XLMzc2xs7Or97atra3JyMjA1dUVY+P/nWgjODiYNWvW1GnbwpMlwukJSE1N5csvv2TSpEkAXLp0iaVLl/LZZ58xc+ZM2rdvz6BBg1i4cCFnz57l9u3b/PDDD/Tp04eZM2fSrl07PD09WbFihbTPoKAgUlJSWLBgAY6OjmRlZTF9+nTefPNN7t27J22nVCoZNWoUrVu3xtPTk5UrVwKgUqnw8fFh27ZtBAUFYWdnx7Bhw7h79y4Ad+/eZd68ebi7u2Nvb09AQAAHDhyA8qrDbdq0oXnz5lA+KkxKSsLDw0NqVy6X07JlS0aPHo2joyMjRowgLS3tgf160LqqbVd+TuXpgm1sbDhz5gwajaZOXkPhyRPh9ARs2rSJ4OBgrKyspMeurq689dZbOttVjDKysrJQq9VcvXqVfv36ER0dzZw5c1ixYoV0qbJ06VIaN25Mamoq169fx8rKirVr10qXiADXrl1j0KBBvPLKKyQmJvLVV1+xfPlyzp07h5mZGRkZGZw7d449e/Zw+PBhIiMjOX78OGVlZbzzzjsolUpOnjxJcnIyGo2GO3fuQHlAVB65JCcnU1xcrBNOCoUCIyMjli9fzsmTJ8nPz2fatGkP7Nf91tXUdgWlUqnTvlar5d69e490mSzoFxFO9Uyr1bJnzx66desmLTt79iyDBw/G0FD39F+/fh3KL1PS09Pp3bs3/fv3p2nTpoSEhNCkSROp0m5UVBR+fn46+8jLyyM9PV0Kp/DwcLp3787kyZMxNzenR48e2NnZIZPJSElJwcDAgM8++wxbW1vat2+PiYkJhoaG7N27lwsXLvDNN99ga2tLaWkp6enpUmmnK1eu6NT1UygUUimoCnK5nJCQENq1a4eLiwtTpkzh999/R6PR3Ldf91tXU9uUV4jJyMjQGTlVFAQtKiqqk9dRePJEONUzuVyOSqWSimRS/sFp3bp1tW2PHj2Kt7c3zZs3R6FQ6NSKKy4u5u7du1IxgejoaPz9/au1ZWRkJIXE8ePHefnll6X1ZWVl3L59G0tLS2QyGY6OjjRp0gSAtLQ0iouLcXNzY8+ePQwZMoQWLVoA8Oeff2JmZoaHhwfFxcUolUqpXBVAfHw8Tk5ONGzYEMrnkU5KStLZpqysDK1Wi1arvW+/7reuprYpHzVRpejptWvXMDQ0fGCBUUF/iXCqZ8nJyQA4OjpKy2xsbKr9SUFMTAx79+6V7kvJ5XKdenCHDx/GzMxMGoFdunSp2odUJpPh4uKCmZkZWq2Wu3fv6nw4IyIiKC0tpWfPnshkMp39x8XFYWpqStu2bUlNTaVNmzY6bfv4+GBkZERiYiLFxcU6z1UoFDqXVPHx8RQXF+tcfv3444+89NJLmJiY1Nqv7t2737fPNbVNeTg1bNgQJycnadmFCxfw9vYWNfOeYSKc6llFnblmzZpJy1577TW2b9/Orl27iI2N5dtvv+XVV19l+PDhvPXWW6jVatLT0ykoKCAnJ4cDBw4we/Zs5s6dS/PmzSkpKSEnJwe5XM6NGzfIzc2FKoFmaGiIt7c3e/fu5d69eyiVSubOncsHH3yAhYUFcrlcZ2QWFxeHm5sbxsbG2NvbS+F58eJFtmzZQuPGjaG82m/l46KGcIqKisLIyIiEhARu3rzJ3Llz+f3331m6dOl9+2VpaXnfPtfUdkX7bm5u0iVuWVkZf/zxB6+++mqdv57CkyPCqZ7Z2tpiZGSEqamptGzOnDmMHz+exYsXM2DAAL777jvmzZvHV199BeUh07hxY9atW4eXlxfh4eHMnz9fKlpgbGxMaGgo69atIygoSBqdyeVynUub9evXk5qaiqurK2+//TahoaGEhYVB+Sir8gik8uOPPvoIuVxOx44dCQsLo3///tK9rs6dO9O3b1/GjBkD5fd0UlJSdNr97bffmDZtGjNmzMDf35/4+HgOHjyIr6/vA/t1v3VV265Q9Ze68+fPU1RUxDvvvFNHr6LwNIi6dfWsuLiYdu3aceXKlWo/f9dmy5Yt7Nq1i6NHj9Z7//6uuLg4evbsyalTp6TwedI8PT2ZMmWK9Gvgu+++S48ePZgwYcJT6c+LrC7r1omRUz1r0KABoaGh0k/hD0Mul+Pm5lav/aorFb/UNWvWDLVa/UTbzs7OJikpiVu3bkkjp0OHDtG4cWMRTM8BEU5PwOzZs3XuOT2IXC6v8//2UV+USiVlZWV07NhR549E61tZWRmdOnUiMDAQKv1Sl5uby+eff/7E+iHUH3FZJwhCnRGXdYIgPPdEOAmCoJdEOAmCoJdEOAmCoJdEOAmCoJdEOAmCoJdEOAmCoJdEOAmCoJdEOAmCoJdEOAmCoJdEOAn17o033mDp0qV1sq9hw4axcePGGtdFRkbqTDgnPNtEOOmpmzdvMm7cONLT0592V/42hUKBq6trneyraiGDqu1UrKuoLrN37946aVd48kQ43cfSpUvx8PCgQ4cOtX5bPw6tVvvAbc6cOcOlS5ewt7d/rDaqzhb5tOTl5ZGWliYVu/w7MjMzUalUOhPLVaZQKKR1TZo0oX///o/VbmlpKcXFxX+7v8LfI8KpFvv372fVqlVkZmaSlpbGggULOHv27GPtq0+fPsyYMYORI0fi5OQk1W/7z3/+Q7du3bC1taVjx47s27cPgN27dzN16lSys7NxcHBg3rx5AHTt2lVnOpCdO3fi7OwMQH5+Pi1btpQq/lZMJTJy5EhWrFjBiBEjsLOzIzAwELlc/lD9rq1/27ZtY/jw4UycOFGqrvLpp58SFhaGs7Mzbm5u7N69GypNqfLvf/8bZ2dngoKJcfZRAAAgAElEQVSCOH36tNRGcXEx4eHh+Pr6YmtrS3BwsNQ/tVrNrFmzcHNzo127doSHh9O8eXOpkOjx48cJDg7G3t6eV199laioKDw8PMjIyMDW1pYtW7ZIc6Hf7zykp6cTEhJC27ZtcXd3Z/jw4WzevPmxXmuh7ohwqkVNpaxjY2MfeT9arZb4+HiUSiXffPMNcrkcJycnNmzYwJw5c5g7dy4JCQmEhISwaNEiAEaPHo2fnx8LFiwgLS2NTz75hKKiIpKTk/Hx8ZH2LZPJpHnAK0IgKyuL48ePSwGQm5vLkSNHCA8P5/Lly2i1WrZu3frAft+vfyUlJZw/f56ePXty6dIlhg8fzqpVqwgICODKlSsEBASwfv16KB/NmJqaSuERGBjIu+++K01MFxISwqFDh9i+fTsJCQnY2NiwceNGSktLGTt2LFFRUezcuZOIiAiio6Oly7Zjx44xbtw4RowYwcWLFxk0aBCxsbF4enpiZ2fH559/jqOjI02bNr3vedBqtYwbN46GDRsSExPDxo0b+f333wkODn7k11qoW8ZPuwP6qmptNKqUHnpY165do6CggFWrVkllnXJzc1m2bBmzZ89myJAhqNVq4uLipP1rNBpiY2P517/+Je0nPj6e0tJSnaIEcrlcCiuFQoGlpSXLli3D2NhYKsudkpLC5s2bpe2cnJyq1cur6kH9S09PJygoSCoK2qpVKzp16sRrr70GgI+Pj1SMQC6X0717d6lE+rx58/juu++4cuUKGo2GI0eOcPToUfz8/EhNTeXq1at069aNffv2ERMTw4ULF6Rio35+ftJc7PPnz2fChAlMnz4dgP79+xMWFib1UaFQ6MyRXtt5SElJ4cqVK/znP/+hUaNGuLi4QHmBCOHpEq9ALYYOHcrYsWNp1aoVDg4OhIaG0qtXr0fej0KhwMLCQmfEEx0dTUFBAZs2bcLZ2RlPT0+0Wq002oiNjUWj0eiUVpLL5VhaWmJjY6OzrCKsFAqFVHqpQlpaGmq1WifQkpKSHjgF8IP6V1NxhMrHFx8fL4WEXC7XOY6ysjIoH31FRkbSuHFjRo0aRZs2bQgODmbgwIFMnjyZEydOEBgYKAVTRTseHh6kpaWRlJTE8OHDddZZWFjQqlUrqd2KPt7vPJibm2NkZIRKpaKkpITFixfTrl27Z2aa5OeZGDnVwtzcvE5ugletP1fBwMCAmJgYCgoKaNq0qc43dXR0NK6urjo116pWGElJSSErK0sKBblcTlBQkE4bMpmMpk2bSgU81Wo1aWlpNfbnUfonl8sZMWKETjuvvPKKzuOJEydSVlZGXFwc7733nrTup59+okmTJnTu3JmzZ8/i6+vL/v37pXYq5OTk6BSEyMjIkC7bcnJyAHTW//rrrzrnRy6XM378+AeeB3t7ezZt2sQHH3xASkoKTk5O7NixAyMjoweeI6F+iZFTPat6eQHg6+uLqakpa9asQavVolQqSUlJkdarVCqys7NJTU3l2rVrABQWFnL79m00Gg2ZmZnMmDEDIyMj6R5MTe3UVDiTh7g8vV//cnNzSU9Pl0YheXl5XL9+XXpcUFDA1atX8fb2JikpiTt37pCamkpubi7fffcdy5YtY/HixVJARUdHc/ToUbRaLSdPnqSwsBAALy8vzpw5Q1JSEpmZmUyePBmtVouHhwdt2rShYcOGfP/999y7d4+9e/eyY8cO6VzcunULlUolHfuDzsPLL7/M9evX6dOnD19++eUzM3/7806EUz2r/PN2BSsrKzZu3MhPP/2Et7c3EyZM0PnpeuTIkZiZmREYGMjHH38MwLhx48jPz8fDw4OQkBBatWpF27ZtMTc3R6VSkZWVVa2dmj6U9vb2Dyy2cL/+yeVyjI2NpZLnVUuBK5VKtFot3t7e/PbbbwwfPpxTp07h6enJ5s2b2bhxo1RPbuDAgUydOpUPP/wQHx8fli1bJt1TmjZtGl27diU4OJjhw4fj4uKClZUVlpaWNG3alC+++IL9+/fj7e3NsWPHcHR01LmUbNCggfS3VQ86D82bN+edd94hLS2NoKAg6fJVeLpEgQNBqGTGjBmo1Wq2bNnytLvyTKrLAgfintML6N69e/Tp06fW9WfOnHlh7rmsXr0aLy8vfH19USqVRERESH8yITxdIpxeQObm5vz+++9PuxtPXVlZGfn5+cyaNYs7d+7g4eHB0qVLdX4FFJ4eEU7CC8vAwICFCxeycOHCp90VoQbihrggCHpJhJMgCHpJhJMgCHpJhJMgCHpJhJMgCHpJhJMgCHpJhJMgCHpJhJMgCHpJhJMgCHpJhJMgCHpJhNMTpNVqSUpKetrdqBPdu3dn3bp10uPVq1djYWGBhYUFM2fOhPKpcE+ePFnvbbu6ukptx8fHA3D16tU6b1d4skQ4PSElJSVMmDBBmsURYN++ffTt2xd7e3u8vLyYPHkymZmZD73P7du3s3z5cp1lFdVX6lNhYSEJCQk60+8qFAp69eqFQqFg8eLFpKWlkZeXV+cTt9XUdmRkJOHh4TRo0EAqBfXF/2fvvuNruv8/gL9ubvaSvfeSJUIkKSIIX1RrFm0Re5Ua/RotSodoq6q0qmaN1qjGl/ZbRBFVs0TIvJlIRES2JCIy7n3//vgm5+eKWZd78H4+Hh4P94zPeZ975OVzRs7nu+/uOUgFe35wOD0jX375Jdzd3REcHAwAWL58OSZNmoRevXohJiYGX331Fc6cOaP0StuH+fbbb4Vhkpq0bt1aGEzgaUlNTUVDQ0OzcGrVqhWsra1hYGAAmUwGPT09pXeAP61tW1tb49q1a/Dw8BAGdoiIiMDy5ctVum32bHE4PQO5ublYs2YNJk2aBAA4f/48Fi9ejKVLl2LmzJkICAhA7969sXDhQpw4cQJlZWXYuXMnunXrhpkzZ8LT0xM+Pj5YsmSJ0Gb79u1x6dIlfPjhh3ByckJxcTGmTZuGoUOHoqamRlguPT0db7zxBhwcHODj44OvvvoKuGNE3C1btqB9+/aws7ND3759UV1dDQCorq7GvHnz0LJlS9jb2yM4OBi///47ACAxMREuLi7CO7wbGhqQnZ2tNBKvTCaDubk5Bg0aBCcnJ/Tv318Yr+9BdT1s3t3bvnOdO98EamNjg+PHj6O+vl4lx5A9exxOz8Dq1asREREBS0tL4bOHh4cwtFKTpl5GcXExKisrcfnyZfTo0QPx8fF4//33sWTJEuFUZfHixTA0NERubi6uXLkCS0tLrFixQjhFROOwVL1790b37t2RlZWFdevW4YsvvsDp06ehq6uLa9eu4fTp09i9ezdiYmJw8uRJHD58GESEkSNHIj09HUePHsXFixdRX1+PGzduAI0BcWfP5eLFi6irq1MKp7S0NEilUnzxxRc4evQobt68ialTpz60rgfNu9e2m9w9TLlCoUBNTc1jnSYzceFwesoUCgV2796Njh07CtNOnDiB1157rdnYaFeuXAEaT1Py8/PRtWtX9OzZE8bGxhg1ahSMjIyQmZkJADh37hzatGmj1EZVVRXy8/OFcIqKikJYWBjeeecd6OnpoVOnTrCzs0NqaiouXboEiUSCpUuXwtbWFgEBAdDS0oKGhgb27NmDs2fP4ocffoCtrS3kcjny8/PRtm1boDEg7hzXLy0tDRKJRHivOBp7TqNGjRJGBJ48eTJOnTqF+vr6B9b1oHn32jYaR1O5du2aUs+puLgYAFBbW6uS48iePQ6np0wmk6GkpARt2rQRphUXFwvDFN3p4MGD8PPzg4mJCdLS0pTGWaurq0N1dbUwMGd8fDyCgoKabUsqlQohcfjwYXTu3FmYT0QoKyuDhYUFUlNT4eTkBCMjI6BxbLe6ujp4eXlh9+7deP3112FqagoAuHDhAnR1deHt7Y26ujqkp6cjICBAaDcjIwPOzs7Q19cHGt8jnZ2drbQMEUGhUEChUDywrgfNu9e2cY9BFtDYO9PQ0IC1tfUjHScmPhxOT9nFixcBAE5OTsI0GxubZo8UJCUlYc+ePcJ1qbvHu4uJiYGurq7QAzt//nyzH9LU1FS4ublBV1cXCoUC1dXVSj+csbGxkMvlCA8Pv+eIJDo6OnB3d0dubi5cXFyUtu3v7w+pVIqsrCzU1dUprZuWlqZ0SpWRkYG6ujql069ffvlFGPTzfnWFhYU9sOZ7bRuN4aSvrw9nZ2dh2tmzZ+Hn56c09h97vnA4PWVyuRwAlIZjGjx4MH788Ufs2LEDycnJ2LhxIwYOHIh+/fph2LBhqKysRH5+Pm7duoXy8nL8/vvvmD17NubOnQsTExM0NDSgvLwcMpkMBQUFqKioAO4KNA0NDfj5+WHPnj2oqalBeno65s6di/feew9mZmZKowWjMZy8vLygqakJe3t7ITzj4uKwadMmGBoaAoAwzHjTfuEe4XTu3DlIpVJkZmbi+vXrmDt3Lk6dOoXFixc/sC4LC4sH1nyvbTdt38vLSzjFJSL8/fffGDhwoMqPJ3t2OJyeMltbW0ilUmE8NgB4//33MWLECHz66afo1asXfvrpJ8ybNw/r1q0DGkPG0NAQ3377LXx9fREVFYX58+djypQpAABNTU2MHz8e3377Ldq3by/0zmQymdKpzcqVK5GbmwsPDw9ERkZi/PjxmDNnDnCfIcWbPi9YsAAymQyBgYGYM2cOevbsKVzrateuHf71r39hyJAhQOM1nUuXLilt98iRI5g6dSqmT5+OoKAgZGRkYN++fWjVqtVD63rQvLu33eTuO3VnzpxBbW2tMD4eez7xuHVPWV1dHTw9PZGYmNjs9vf9bNq0CTt27MDBgwefen1PKiUlBeHh4fjrr7+E8HnWfHx8MHnyZOFu4OjRo9GpUyeMGTNGLfW8zFQ5bh33nJ4ybW1tjB8/XrgV/ihkMhm8vLyeal2q0nSnrkWLFqisrHym2y4tLUV2djYKCwuFntP+/fthaGjIwfQC4HB6BmbPnv3QIcDvJJPJVP5rH09Leno6iAiBgYFKD4k+bUSEtm3bIiQkBLjjTl1FRQW++eabZ1YHe3r4tI4xpjJ8WscYe+FxODHGRInDiTEmShxOjDFR4nBijIkShxNjTJQ4nBhjosThxBgTJQ4nxpgocTgxxkSJw4kxJkocTowxUeJwYoyJEocTY0yUOJwYY6LE4cQYEyUOJ8aYKHE4McZEicOJMSZKHE6MMVHicGKMiRKHE2NMlDicGGOixOHEGBMlDifGmChxODHGRInDiTEmShxOjDFR4nBijIkShxNjTJQ4nBhjosThxBgTJQ4nxpgoaaqiEYVCIf37779vqqIt9vwiIg2JRKJQdx1MfYhIIpFIpKpo64nDSUdHp0Yul4/Lzc1VRT3sOSWXy/Xy8/OXOTk5TVV3LUy9NDQ06tRdA2OCMWPGGE2YMCFJ3XUwxhhjjD0XJBMmTHBSdxHsxcF365hKTJgwwRgAn9YxleFwYirR0NCgAHBF3XUwxhhjjD0X+JoTUyk+rWMqwdecmKpxODGV4GtOjDHGGGOPavDgwdJx48a9ou462IuDT+uYSpiamhpqaGgcUHcd7MXB4cRUQlNTUw5Apu46GGOMMcbEb/DgwdKJEye2U3cd7MXBp3VMJUxNTQ2J6LC662AvDg4nphI1NTUNRBSn7joYY4yxp0ol7/pl7OOPP9bw8PDwPn/+fIm6a2EvBj6tYypx7do1Iw0NjdPqroO9ODicmKrUE9Fv6i6CMcYYY0z8Bg8erD1x4sTR6q6DvTj4tI6phKmpqR4RLVd3HezFIVF3AU+CiCYDWKXuOhgTqV4SieQPdRfxT3HPiTEmShxOjDFR4nBijIkShxNjTJReynDasGEDJBIJrl+/ru5Snpnc3Fzk5OSou4wHGj58OLy9vdVdxjN19uxZ3L59W91liNJLGU4vm4sXL8LNzQ3nzp1TdynsDps3b0ZoaChu3ryp7lJEicPpJVBfXw+FQqHuMthduMf0AiOiyfQILly4QJ06dSJdXV1ydXWlXr16EQAqKCgQlvnxxx/Jx8eHtLW1ydnZmaKiokgulwvzb926RXPnziVXV1fS1tYmT09PWrRoETU0NNChQ4cIAJ0+fVppuwYGBvT+++8TEdHy5cspLCyM1q1bR/b29qSjo0MhISF06NAhGjVqFJmYmJCVlRXNnDmTGhoahDaqq6tpxowZZGVlRUZGRtSuXTv6+eefhfnLly+n0NBQ2rlzJ7m7u5O+vj6FhYVRWloaERFdvnyZACj9GTlyJBERZWRkUEREBBkYGJCDgwNNnDhRaZ8fRWVlJW3cuJHq6+sfaz0iop9//pm8vb1JT0+P2rVrR23atKGWLVsK8+vq6mju3LlkZ2dH2tra1Lp1a9qzZ49SG7m5uTR8+HCytLQUvtOdO3cSEdH8+fNJR0dHafm4uDgCQDExMURE1K9fP3r//fdp6tSpZGxsTEZGRtS/f386evQode/enfT09MjV1ZV++uknpXYuX75MAwYMIENDQ7K0tKSePXtSXFycML9fv340Z84cmj9/PllZWZGJiQkNHTqUKioqiIho06ZNzY7Lpk2biIho37595O/vT3p6euTr60srV6587O+2UU91/4y+tB4lnNLT08nY2Jjc3Nxo+fLl9N1335GVlZVSOG3evJkA0LBhw+g///kPzZ49myQSCUVFRRERUUNDA0VERJCWlhbNmjWLfvrpJ5o7dy4NHz6ciOiRwwkAde3alU6dOkU///wzGRkZEQCaOHEinTt3jj755BMCQBs2bCAiIrlcTt26dRNq37ZtG40bN44A0A8//KDUbnBwMB05coQOHjxILi4uFBISQkREt2/fpm3bthEAWrRoER0/fpwyMzOJiCgsLIwsLS1p3bp1tGzZMnr99dcf+19/ZWUl2drakru7O23evFkpWB9k+/btwvexYcMGmj9/PkmlUqVwGjVqFGlqatJHH31E0dHR1K9fP5JIJHTs2DEiIrp27RrZ2tqSlZUVffnll7R582YaO3Ysff3110SPEU4AaOrUqRQfH0+LFi0iAKSlpUXfffcdnT17lgYOHEhSqZTS09OJiKigoIBsbW0pPDycfvjhB9q4cSN16dKFdHV1KSUlRWhXKpXS0KFD6ezZs7R582bS1tam2bNnExFRYWEhzZo1iwDQ3r176fjx41RYWEhVVVVCWG/dupXef/99mjt37mMfl0YcTuryKOHUp08fMjU1paKiImHa6tWrhXBSKBRkb29PnTp1Ulpv7NixZGRkRFVVVfTzzz8rBcLdHiec7qxj5MiRZGlpSQqFQpjm5uZGb731FhER/fLLL6StrU35+flK7b711lsUEBCg1O7169eF+V9//TUBoNLSUiIiSktLIwAUHR2t1I6DgwP16NHjYV/hQ92+fZvWrl1Lnp6e5OHhQVu2bHlgSNXU1JClpSWFh4crLffmm28K4ZSenk4AaMGCBcJ8hUJBHh4e1LVrVyIimjRpEmlqagqhcbdHDSdfX1+lZZydnWnQoEHC5ytXrhAAWrNmDRERTZ48mQIDA5V6i3V1deTk5ETTpk0T2vXx8VE6tn379qVWrVoJn5v+HRYXFwvTsrOzCQB99tln9/3+HsNzHU4v9DWnmpoaHDhwAJGRkbC0tBSma2pqCn/PyspCfn4+Bg4cqLRuz549UVVVhczMTMTExEBPTw8jR4584pr09PSEv+vq6kJbWxsSyf//FpGDgwNKSv73vrb9+/ejvr4ebm5u0NXVFf5ER0fj6tWrSu0aGBgIf3d2dgb+946lB9YSGRmJgwcPYurUqSgqKnpo7devX0dOTg5ycnJQWloqTNfR0cGECROQnp6OqKgovPfeexgxYsR92zlx4gSKi4sxY8YMSKX//77DO4/LX3/9BQBKx0UikaBHjx7Chf2YmBhERESgZcuWD639Qe48Jmg8Ljo6OsJnBwcHAFA6LsnJyTA0NBSOiZGREfLy8pSOi76+vtKxdXZ2fugxcXNzQ4cOHbB48WKsXLkStbW1T7Rvz7MXOpwKCgpQX18PV1fX+y5TUVEBALCyslKabmZmBgDIz89HYWEh7OzslH6QnhaJRAIiAhrDwMbGBgkJCUp/UlJSEBd3/9d1a2trAwAaGhoeuK3Fixdj+fLl2LlzJ9zc3LBq1YN/TfGtt96Cq6srXF1dMX/+fKV5crkcO3fuxOLFi2FhYYG33nrrvu1cuXIFAP7xcamqqkJVVRUKCwvh6Oj4wJpVoSlg7jwur7/+erPjIpPJHvgdamtrP/SYSCQSxMTEYOTIkZg1axa8vLxw7NgxFe/R80HzEZZ5bjX1lh7UK2j6X/HOngAAFBYWAv/7bXuYmJgIn+/lzv8dVcnU1BTFxcVwcXGBrq7uE7fX9MPVRCKRYMaMGRg7diwmTpyId999F4GBgejYseM914+KihJ6D03BolAosGHDBnz55ZcgIixYsACRkZEPDPLHPS52dnbC9MLCQmhra0NfXx8mJiYPfFbtaR6XkpISlT2TdfdxMTY2xqpVqzBr1iz069cP/fr1Q15eHgwNDVWyvefFC91zMjIygqenJ6Kjo1FXV3fPZWxtbeHi4oL9+/crTd+1axcMDAzQpk0bRERE4ObNm/j555+Vlmn6X7Dpf/c7u+zXr19/4i559+7d0dDQgNWrVytNr66ufqx2mk757j6laLqVbWRkhE8//RQAcP78+fu2ExYWhv79+6N///5o3bo1AKCsrAyLFy/GnDlzkJ6ejlGjRj20hxkYGAipVIpt27bdd5mQkBBoaGgoHZfa2lrs3bsX7du3h1QqRUREBGJjY5s9XHrncamtrUVZWZkwTxUPonbv3h2nTp1CfHy80nRVHxdXV1dMmzYNN27cwOXLl5+47ufNC91zAoCPPvoIw4cPR4cOHTB69GhIpVKsWLFCaZlPPvkEI0eOxLhx49CzZ0/ExsZiz549+Pjjj2FgYIDIyEisWrUKI0eOxNmzZ9G6dWskJyfj8OHDOH/+PLy9veHs7IyoqChYW1ujqqoK8+bNe+Jni4YPH461a9di9uzZuHz5Mtq2bYvExETs2bMHaWlpza6V3I+DgwPc3NywbNkyGBgYoKysDNOmTcPgwYNhbGyMHj16CCEQFBT0WDWampoiKytLOJV8FI6Ojhg9ejQ2bNiAmpoavPrqqygoKMDevXthY2MDAHB3d8fIkSPx0UcfoaGhAe7u7li3bh0KCwuxdetWAMDChQvx+++/o3379pg2bRpsbGxw8OBBGBoaYv369ejevTs0NDQwffp0zJgxAykpKXj//fcfa//u5eOPP8a+ffvQo0cPzJw5E1ZWVjhw4AAaGhrw66+/PnI7HTp0gKamJqZPn44xY8agpqYGo0ePRsuWLfHmm2/Cz88P33//PUxMTODu7v7EdbNn6FGfc/ruu+/IxcVFuEU7cuTIZs85rV69mjw9PUlLS4tcXFxoyZIlSndaSktLady4cWRhYUH6+vrk5+dHixYtotu3bxM13gUKCQkhPT09CggIoF9//fWed+uqqqqENidOnEj29vZKtXbu3Jm6desmfK6oqKBJkyaRhYUF6ejokL+/P33++edUV1d333Z///13AkAXLlwQpp09e5b8/PzIwMCAvL29KScnhz799FPy8PAgXV1d8vDwoHXr1j3evaAnUFNTQ++++y6ZmZlRixYt6PXXX6fOnTsrPUpQU1NDM2bMIGtra9LW1qY2bdrQ3r17ldpJSkqiXr16kYGBAZmamlL79u1p+/btwvwff/yR3N3dSU9Pj3r06CE8NnLn3bqgoCClNlu2bEnDhg1Tmtb0KEaTtLQ0eu2110hfX58MDQ0pPDxc6W7ovdqdOXMmtWjRQmnaxo0bycbGhkxMTCgiIoLKyspo9OjRZGtrS/r6+hQaGkrHjx//h9/y8323jl82x9iLi182xxhjqsbhxBgTJQ4nxpgocTgxxkSJw4kxJkocTowxUeJwYoyJEocTY0yUOJwYY6LE4cQYEyUOJyZ42LuGXvTtM3HhcGIAgNjYWCxdulTl7T7OuGxpaWn497//rfIa2POJw4mhqKgII0aMQGRkpErbfdxx2Vq1aoXExETs2LFDpXWw5xOHE8Nnn32Gtm3bCm+fVJV/Mi7b6NGjMW/ePD7FYxxOL7v6+nr8+OOP6NWrl9L0s2fPIjw8HHp6erCwsMCYMWNQXl4uzO/fvz88PDwwZMgQmJiYwNzcHJGRkcKrdzdv3ox33nkHaHwtr0QiwebNmx9az6uvvoqcnBwcPnxY5fvKni8cTi+5I0eOoLy8HP7+/sI0mUyGrl27ora2Fps2bcLChQuxZ88eDB48WGnd/Px8hIaG4tChQ1i8eDH279+Pnj17oqGhAb1798asWbMAAHv37sXx48fRu3fvh9Zjbm4OKysrREdHP4W9Zc+TF/41vezBmoZgunMklKioKEilUvzxxx8wMTEBGkc9iYyMxLFjxxAeHg4A8PPzw8yZMwEAwcHBaNGiBYYOHYqYmBj06dNHeLVsaGgoLCwsHrkmNzc3HD16VKX7yZ4/3HN6yeXl5QGNgxw0OXr0KCIiIoRgQuM4fgCEMePupenU8MyZM09Uk6GhIa5evdpsVBL2cuFwesk1DYl15yCSFRUVDxzH735atGgBDQ0NVFVVPVFNurq6qKure+S7fOzFxOH0kmsKnTvvrDk4ODxwHL/7uXbtGhQKRbOBLh+3B1RTUwMtLS2l3hx7+XA4veSsra0BALdu3RKmtW/fHkePHkVNTY0wbdeuXUDj2HX388MPPwCNQx7hAeOyAUBycjLWrl2L+vr6ZvNqamqa9dwYe6486tBQ7P62bdtGAOjMmTPCtMzMTNLR0aHw8HDavn07ff7556Srq0tdu3YVhsvq168f6ejo0AcffEAbN26ksWPHkkQiod69ewvtZGdnk6amJnXu3Jm2bNlCa9asEeb5+voSAPrpp5+a1eTs7Ez9+/d/6vv+Eniuh4Z6rnE4PdnjD9gAACAASURBVLni4mLS0NCgrVu3Kk0/evQovfLKK6Sjo0Pm5uY0fvx4qqioEOb369ePHB0dqWvXrmRoaEhWVlY0adIkpWXoHuOyNZkyZQpZWFhQYmKi0vK3b98mDQ0NWrt27VPb55fIcx1OPG4dQ58+fWBvb481a9Y88jr9+/fH1atXH3j37p84cuQI+vfvj0uXLj3W4wfsnnjcOvZ8++KLL7Bv3z7U1taquxT88ssvmDVrFgcT43Bi//8w5XfffafWOnJycpCTk4M5c+aotQ4mDnxaxwQXL14UnupWh+vXr8PY2Bj6+vpqq+EF81yf1vGvrzCBOoMJAGxsbNS6fSYufFrHGBMlDifGmChxODHGRInDiTEmShxOjDFR4nBijIkShxNjTJQ4nBhjosThxBgTJQ6nl9CpU6cgkUggkUjg4+Oj7nLUYtCgQcJ3sHr1anWXw+6Bw0nE8vLyIJFI7jlgwB9//AFHR0dcvHjxgW2cOHECQ4YMUZqWkpICAwMDFBQU4NSpUwCAyMhIGBgYwNTUFG3btsWKFSv+0aCYYrR+/Xp89NFHStPWrVuH06dPA42/+MzEh8NJxBISEqChoYFWrVo1m+fq6orXXnsN5ubmD2xjy5Yt0NRU/hXKlJQU+Pr6wsbGRngneGxsLGbMmIFDhw5h2LBhWLhwIfr376/iPVKPL7/8Evb29krTzMzMUFxcDABKY/YxphIv+pswP/30U/Lx8Wk2/aeffiIAFBQUREREdXV1NGXKFDI3NycTExP64IMPiIho0qRJJJFISFdXlwwMDGjXrl1ERBQREUGjRo0S2issLCQAdPr0aWHazp07CQDFxcUREVFKSgr961//In19fbKxsaFFixYJy1ZVVdGUKVPIysqKdHV1adKkSURE5O3tTV988YWw3KZNm8jExISIiNauXUtdu3alYcOGkYWFBZmamtInn3xCU6ZMIRMTE7K0tKRt27YJ654+fZoiIiJIT0+PzM3Nae7cuUREVFRURPb29rR27Vry9fUlXV1d6tKlC928eZOIiHx8fAgAGRgYkKGhIRUWFgptfv7552Rra/vEx0nEnus3YT7XXvRwGjhwIL399tvNpisUCho+fLgQMCtXriQHBwdKT0+njIwMOn78OBERVVdXk6amJp09e1ZpfSsrK1q6dKnw+cCBA6ShoSH8QBMRlZWVEQD65Zdf6OLFi2RqakrLly+nW7du0ZEjR0hDQ4OOHTtGcrmcunTpQu3ataNz585RRUUFZWZm0u3bt0kqlVJMTIzQ5nvvvUfh4eFERLRq1SrS1tamjRs3UmVlJU2YMIG0tLRo69atdOPGDerbty8FBgYSEdHJkydJR0eHFi1aRKWlpXTixAkCQDk5OVRVVUUAaPjw4ZSfn0/x8fEkkUgoOjqaiIj++OMPMjQ0JLlc3ux7HDZsGHXv3v2Jj5OIPdfhxK9MEbGEhARMmjSp2XSJRILk5GSMGDECACCXy1FRUYHS0lJ06NABXl5eQOMAmFKpFK1btxbWLSkpQVFRkdKpTEJCAjw9PYXRUgCgrKwMaByLbv78+ejSpQtmzJgBAOjatSscHByQlJSE4uJixMXFITs7W3jlibGxMS5cuAC5XK607aSkJOFzXl4eOnXqhNGjRwONo8CEhIRg2LBhAIDWrVujpKQEADBr1ixERETgww8/RH19Pc6ePQszMzPY2toiNTUVEokE33//PYyMjGBrawstLS1oaPzvisXff/+N4OBg4fOdUlNT0aVLlyc4Quxp4mtOIlVVVYXLly8jMDCw2byGhgakp6cL16LeffddTJ8+Hd27d8fQoUOFC9lnzpxBmzZtoK2tLaybkpIC3HUROCEhQSlEAGDfvn2QSqVo164dYmJi0L17d2EeEaGkpARWVlY4fPgwOnbs2OxdTMnJybC0tIStra3StKbtJCUlISAgQJh3Z3ABgEwmg7+/P2pra/H3338jPj4exsbGMDY2xq5du/DHH39AW1sbSUlJcHFxEca4y83NRV1dnXAX8syZMwgNDW32HSoUCqSnp/P1JhHjcBKphIQEENE9wykjIwO1tbVCOEmlUixatAjx8fGIjo7G3r17gcYfzLZt2yqtm5KSAmNjY6WBLy9cuKC0nUuXLuHTTz/F6NGjYWJigps3byqFzIEDByCXyxEREYGqqqp7vrkyNTVV6Qc/OzsbRUVFQgDdGVS4T1jd+XnHjh24evUqKioqcPLkSbRr105Y7s4bBomJidDR0YGnpycA4OzZs82+AzS+9fP27dt8p07EOJxEKiEhARYWFigtLUV6errwp76+HsnJybCwsICNjQ1qa2uxbNkyXLt2DVevXoVCoRDeaFlUVITs7GwUFBQIw4g33alrcuvWLWRlZcHQ0BDnzp3D119/jdDQULRq1QrffPMNNDQ0EBAQgJ07d6KmpgYymQzTp0/HvHnzYG5ujpCQEBw4cAAxMTEoKCjA9u3bgcaBMUtLS1FfX4/CwkKMGzcOUqkUfn5+uHHjBvLy8oTwqaqqQk5OjvD51q1buHjxIgICAqCjo4PAwEAsX74cN27cQFFREf7++2+h/rtDLDExET4+PtDU1ERDQwPKysqQlJSEa9eu4caNG8JyKSkpkEgkHE7s6XiRL4iPHTuWACj90dDQoFu3btG8efOoa9euRESUkJAg3KXy8vKiLVu2CG1s376djIyMSE9Pj7755hsiIgoLC6Nx48YJy5w+fVpo38jIiDp27Ehr166lhoYGYZkLFy5QcHAw6enpkaenJ3377bfCvLq6Opo4cSKZm5uToaEh9evXT6jL1dWVzMzMKCwsjN58803y9vYmIqJjx46RpqYm3b59m4iITp06RRKJhKqqqoiI6OzZswSAbty4QUREiYmJFBoaSnp6emRvb680xp6VlRXt3LlT+Dxw4ECKjIwUPk+bNo20tbWpRYsWSjcGFi1aRM7Ozio5ViL2XF8Q5wEOXjJmZmZYuHChcHH7ZfX222+jsrIS+/btU3cpT9NzPcABn9a9RPLz81FeXg4bGxvhAcSXTWVlJa5fv47k5GS+GC5yHE4vkdTUVKCx19CjRw91l6MWQ4cOFR5B4HASNz6tY+zFxad1jDGmahxOjDFRet5/feUmgHJ1F8H+p6amxlhPT69S3XUwQY26C2BM7SZMmNBiwoQJNx5hUcYYe7YGDx4sVXcN7MXB15yYqkiMjIys1V0Ee3FwODGVmDBhgrGmpqZM3XWwFweHE1OJ6upqarxBwRhjjDHGHkYyadIkK3UXwV4cfFrHVGLChAnGCoUiU911sBcHhxNTierqaiKiF2OgO8YYY4yxp42vOTGV4tM6phJ9+/Y1lMvl29RdB3txcDgxlbCwsIBEIuEnxBljjDHGHoVkwoQJTuougr04+LSOqcSECROMASSpuw724uBwYirR0NCgAHBF3XWwF8dzPcABU6/Ro0f/V1NTsw8ASCT/+6dERML89evX878v9o9xz4n9Y5qamguJ6EpTMKExpBr/8K+ysCfC4cT+sfXr1ydIJJK/7jFLrlAodqmhJPYC4XBiT0Quly8jIqVrTUSUVVFRsUJ9VbEXAYcTeyIbN25MBHC86VoTEUEikeyOjo5+Occ7ZyrD4cSemFwuXyqRSK43frwM4Ds1l8ReABxO7Ilt3LgxkYiO0f/sXrduXYG6a2LPvye+1btp0yZdhUKxUUNDQ1s1JbHnUUNDg3FVVVVHIyOjI5qamrXqroepT0NDw43x48ePe9J2nnjE36qqKm1dXd3Bbdq0ed5HD2aq8bq6C2DqI5fLceHChVoA6g8nANDQ0FC4uLiooinG2HOsvr4eFy5cUElbfM2JMSZKHE6MMVHicGKMiRKHE2NMlDicGGOixOHEGBMlDifGmChxODHGRInDiTEmShxOjDFR4nBijIkShxNjTJQ4nBhjosThxBgTJQ4nxpgocTgxxkSJw4kxJkr8al321FVWViI2NhYSiQT9+/dXeftXr17Ftm3bYG5ujnHj7v922Pz8fMTGxsLHxwfBwcEqr4OpFvec2CM7c+YMsrOzH3u9w4cPY+zYsThy5IhK6hg4cCBCQkJQWVkJADh16hSWLFkCmUz2wPVWrVqFGTNmID09XSV1sKeLw4k9klmzZuHVV19FRkaGWuuQy+WIj4/HxYsXUVZWptZa2NPFp3XskVRVVam7BACAVCrF/v37UV5eDh5U48XGPadnYPfu3TAzM0Pnzp3RsWNH2NraIjAwEIsWLUJdXZ2wnIuLC8zMzPD555+jVatWsLKywldffQU0XreZNWsWvL29YWtriw4dOmDr1q3CusnJyTAzM8P8+fPRp08fODo6IiAgAB988AG+//57dOzYEQ4ODujcuTP++usvYb1///vfMDMzw8CBA+Hv7w87Ozt06tQJO3bsEJaZNm0aoqOjAQCRkZEwMzPDtGnTHvt7iIuLQ3BwMGxtbdGxY0f89NNPSvOb9r+hoUGYtmDBApiZmWHTpk0AAEtLS3Tq1Al9+/ZFRUXFA7d36NAhdOvWDba2tvD398fBgwebLZOUlIRBgwbB0dERzs7OGDx4MJKSkoT5q1evhpmZGUaMGIG+ffvCwcEBXl5ewikle3o4nJ6h1NRUeHl5oVevXigvL8fy5cvv+UO+fPlydOjQAWFhYXj77bdRX1+PN954Axs3boS2tjZeeeUV5ObmYtq0aVizZo3SuqtXr0ZlZSX69u2L0tJSrFu3Dh9++CHs7OzQrVs3pKSkIDIyEtevX1daLyEhAeHh4ejYsSPS0tIwZcoUITyCgoLg6OgIAHjllVcwYMAABAUFPfb+Z2RkQF9fH25ubkhPT8f06dOxbNmyx2qjd+/e0NZ++Pit//3vf/H222/jwoULcHR0hKmpKS5duqS0TFxcHF599VUcOXIELVu2hJubG2JjY9G7d2+kpKQoLbt3716UlJRgwIABGDFiBIyNjR+rbvb4OJyeoTfffBObNm3Cpk2b8Ndff0FXVxfR0dHIz89XWm7JkiVYu3Ytdu/eDXt7e+zatQvx8fEICAjAmTNnsGfPHuzZs0dYtrb2/wfYdXd3xx9//IFVq1Zhzpw5AIDu3bsjOjoaW7ZsQb9+/XDz5k2cOHFCaZtRUVH4/vvvER0dLQTeihUrAAAjR47EK6+8AgCYMmUKfvjhB4wcOfKx9/+NN97AX3/9hZMnT2LXrl2QSCT46quvUFJS8shtbNmyBYaGhg9cpqGhAXPnzoVCocCyZctw9uxZHD9+HMOHD1dabtasWaipqcGGDRtw+PBh/Pnnn1i+fDlu3bqFL774QmlZZ2dnxMbGYuXKlfjwww8fc8/ZP8Hh9AxJpVLh7y4uLnjllVdARIiPj1dabsCAAUqfjx49CgAYPnw49PT0AAAhISHw9PRERUUFUlNThWUtLCygq6sLAEJvx8bGRpjv5eUFACgsLFTahqbm/19+fOONN6CtrY3Lly+r9KJzU+0A0LVrV4SGhqK2thZnzpxR2TbQeKpWUFAAFxcXjB49Wph+Z6hdvXoVycnJ0NLSwoULF7BgwQIsWLBA6DGdP39eqc1evXop1c+ePr4grkZmZmYA0Ozayd09g9LSUgCAtbW10nRzc3NkZWWhvLwcVlZWj7RNiUQCACCiBy5jamqKwsJCVFRUCHWqWlPNqr7Y3hS8zs7O912m6bS2vr4eq1ataja/KeCbPKy3xlSPw0mNrl69CgBo0aLFA5czNzcHgGanP00/YE3zVeX27dvCtu6uTaFQqGw7Tftva2sL/G9Ye5Vsoyn07r6udqema0Y2NjYPfT6KqQef1j1Dd14bOnLkCOLi4iCVShESEvLA9cLCwgAAO3bsENo4ePAgcnJyYG5uDj8/P5XVRkRYtmwZ5HI5WrZsKfSajIyMAEB4CPPOu4yP6tatW8LfY2JicP78ebRo0UJ4WtvS0hJovDiPxh5j0ynto2iqydPTE3p6esjIyMDPP/98z+17eHjA2toa169fx4YNG4TpxcXFuHjx4mPvG1M97jk9Q9HR0ZDJZKivr0dWVhaICBMmTFC6JnQvgwcPxpo1a3Du3DmEhobC0dERcXFxAID58+dDS0vriWubPXs2tmzZguvXr+PatWuQSCRKF35DQkKwceNGfP7559i3bx9qampw8uTJx9rG7t27kZmZiZqaGiEAPvroI+jr6wON16GysrIwaNAg+Pr6Ii0t7ZFO+QwMDAAAJ0+eRHV1NYyNjTF58mQsW7YMkydPxtKlS6Gnp6fUQ9LQ0MCCBQvw7rvvYs6cOVi3bh2MjY2RkZGBzp07Kz2mwdSDe07PkKOjI8rLy5GTkwNPT09ERUUhKirqoevp6uri119/xdChQ3Hz5k3ExcXB3d0d33//PUaNGqWS2tzd3ZGdnY3y8nKEhIRg586deO2114T5gwYNwsSJE2FsbIzU1FSYmpo+VvsGBgaYMmUKysrKcPXqVQQEBOCHH35Qqn/u3LkYPHgwtLS0kJmZid69ez/S7+J17NgR7u7uACA8LvDBBx9gzpw5sLe3R0FBAbS1tREaGqq03tChQ7F582a0bdsWeXl5kMlkcHNzQ7du3R5r39jTIXnSBr799ltjfX394oEDBz784ZOX1O7duzFu3DgMHz4c3377rbrLUfLvf/8bmzdvxpo1azBkyBB1l8Oec/X19fjtt99qJ0yYoPsIiz8Qn9axf2z27Nm4fPnyA5cJCQkRnrdi7HFwOLF/7MyZM82epL7b3bfkGXtUfFrHGFMZVZ7W8QVxxpgocTgxxkSJw4kxJkocTowxUeJwYoyJEocTY0yUOJwYY6LE4cQYEyUOJ8aYKHE4McZEicOJiV5YWNgD3+awZMkSDBs27JnWxJ4+DqfnQHx8PNzc3GBpaQl3d3e8+uqrOHDggLrLAhpfhTt8+HClEWTKy8vh7e0NGxsbODs7o2vXrsK4dw9TUlICf39/YXSZ27dvIzMzE61bt77vOunp6fD29lbB3jAx4XB6Dpw7dw4GBgaIjY3Fvn37EBgYiBEjRtzzdSVyufyZ1nb8+HGcP38e9vb2wrSkpCQUFRXh0KFD+Ouvv9CnTx9MmjQJ5eXlD23PyMgIPXv2FF4el5qaioaGhgeGU1paGnx8fB679mf9XbHHw+H0HEhKSkKHDh0QEBAAb29vTJ06FQ0NDUhPT8fBgwfh5OSEr776CsHBwcIgnenp6XjjjTfg4OAAHx8fYeRgIkJwcDAWLlyIsLAwYbDNnTt3okuXLrCzs0O/fv1QXV0NAOjUqRPmzZuHiIgIODo6olevXsjIyAAA7Nq1C1OmTEFpaSkcHR0xb948oHH0YQ8PD7Rq1QqmpqaoqKiAq6srDAwMsGfPHjg4OCgNYtC6dWusXr0a165dg62tLTZt2iQMNZ6YmAgXFxeYmJgI9a9atQpt27aFk5MTZs2ahUuXLgk9p4KCAkyaNAnu7u5wdHTE8OHDhdF5Fy5ciD59+uCdd96Bj4+P0vvFmfhwOD0HEhISEBQUBCJCXl4ePv30UxgZGSE4OBhpaWm4desWnJycEBcXhy+//BI5OTno3bs3unfvjqysLKxbtw5ffPEFTp8+DYlEgoqKCsTHx2PHjh34888/IZPJsHXrVmzfvh179uzB8ePHhSHLCwoKUFJSgq1bt+LYsWOora3F3LlzgcZX97Zp0wYffvgh8vLy8NlnnwGN4ZSdnQ0LCwu4urrit99+w3/+8x9oa2tDJpPB19dXGGmlsrISeXl5wlDo33zzDZycnITRURITE5V6TYsWLcKKFSvwySefIC4uDtXV1SAieHp6oqysDL169cLt27fx559/Ij4+HhcuXMC+ffuAxh5Weno6pkyZgtTUVLzxxhvP/FiyR8fhJHI1NTXIysrCBx98ACsrKwQGBqKwsBC//vorLCwsIJPJ8Oqrrwqv2DUwMEBUVBTCwsLwzjvvQE9PD506dYKdnR1SU1NRU1ODsrIyLFiwAI6OjnB1dYVcLsfMmTNhZ2eHoKAgaGhoQE9PD7du3UJ5eTlmzZoFOzs7uLq6YtCgQcjMzAQa392TnJzcbGjypKQkREVF4fr16zh06BAACIMlyGQypdFimgYdaJqWlpYGX19fYX5iYiICAwMBAPn5+Vi5ciVWrFiBPn36wNraGuHh4XB3d4eOjg5WrVqF6upqrFq1Ck5OTsjKykJFRQVatmwptD1z5kz4+/tDQ0ODX4QncvwmTJFLSUmBQqFAZmYmbt26BWtra+jo6Ajz09LSEBkZqbTO4cOHsWDBAuEzEaGsrAwWFhZIS0uDQqEQAiAjIwMNDQ1COGRlZUGhUMDHxwfp6enQ0dERrv+g8WJ303BRycnJqK+vV+rZ1NTUIDs7G35+ftDU1ERQUBDGjh0r3G2TyWSIiIgQlk9NTYWdnZ3QpkwmQ7t27YDGoZ7S09MREBAAADh27Bg0NTXx6quvKq3fdEp34sQJSCQStGzZEhKJBFZWVli+fDnatm2LiooKXLt2DZ07d1bJcWFPH/ecRC4pKQnOzs6wsLCAk5OTUjA1NDQgKytLqaehUChQXV2tNDpwbGws5HI5wsPDIZPJlE6bUlNTYW1tLYwZl5qaCnNzc2GwyZYtWwrDqCsUChw4cADdu3cHGu8ienh4CEMzoTFc5HK50gXqgoICmJqaorq6GleuXFGad/bsWfj7+yut37Q/WVlZqKurEz6Xl5fD2NhYafDN2NhYpfbGjBmDrKwspKenIz4+HoMHDxba1dLSgoeHhwqOCnsWOJxELjExUTgtudvFixdRW1ur9MOpoaEBPz8/7NmzBzU1NUhPT8fcuXPx3nvvwczMrNlpVWpq6n0/p6WlQUtLCyUlJcjOzsbkyZNRWVmJqVOnAo23/UtLS5Gbm4ucnBygsTdlZGQEiUSCjIwMLFmyBOvXr8e4ceNQV1cHIhKGC4+OjsaePXuEcCosLERJSYkQRk3DsDfdVfP19UVxcTH27duH6upqfPTRR8jIyBC+n3bt2mHXrl3IysrC7du3lQbkTEtLg6enp0rG+GPPBoeTyD0onNLS0mBjYyOcEjVZuXIlcnNz4eHhgcjISIwfP14YAeXucLr7Gs+d82UyGWpraxEaGopu3bqhtrYW+/fvF8asGzBgAHR1dRESEoJPPvkEaOzpVVVVwdfXF6+99hpOnz6NtWvXYsKECTA1NcW4ceMwdepUBAUFITU1FVpaWkrb09bWFno37dq1w7/+9S/helqXLl0wY8YMTJs2DaGhoUKPrimcZ8+ejVatWqFv374IDg4Wrnc1tf1PHjdg6sMDHLD78vb2xqpVq3iQSfbIeIAD9tSVlJSgqKgIXl5e6i6FvaQ4nNg9yWQy6OnpwcHBQd2lsJcUP0rA7ik8PFzp9+UYe9a458QYEyUOJ8aYKHE4McZEicOJMSZKHE6MMVHicGKMiRKHE2NMlDicGGOixOHEGBMlDifGmChxODHGRInDiTEmShxOjDFR4nBijIkShxNjTJQ4nBhjosThxBgTJQ4nxpgocTgxxkSJw4kxJkocTowxUeJwYoyJEocTY0yUOJwYY6LE4cQYEyUOJ8aYKHE4McZEicOJMSZKHE6MMVHicGKMiRKHE2NMlDicGGOixOHEGBMlDifGmChxODGV+uCDDzBkyBB1l8FeABxOIpSUlIS33noLLi4ucHV1Rf/+/ZGYmPjI6//9998YPXq00rTY2Fj4+/vj8uXLT6Hi/5eYmIjAwMD7zl+yZAm6dOnyVGtoMmfOHPz+++/PZFtM9TicROb48ePo1asXzMzMEB0dje3bt0MqlWLQoEEoKyt7pDZ27NgBTU1NpWnOzs7o0aMHzMzMnlLlgEKhQGpqKlq3bn3fZVq3bo0ePXo8tRqaXLt2DRs2bICPj89T3xZ7OjicROTmzZuYOHEi+vbti++//x7BwcFo37491qxZg9LSUpw4cQKZmZnw8fHB0qVL0aZNG7i4uGDMmDG4efMmAGDmzJnYunUr9u3bB0dHR/z3v//FL7/8gpCQECQkJKBFixYAgPT0dLzxxhtwcHCAj48PvvrqK6GOAQMGYMmSJejfvz/s7OwQEhICmUwmzF+/fj3atWsHW1tb+Pn54bPPPgMAZGVl4ebNm/ftOU2bNg1Dhw5FTU0NAKCkpAT+/v7YsmUL2rdvDzs7O/Tt2xfV1dUAgE6dOmHevHmIiIiAo6MjevXqhYyMDGE/Bw8eLLR95coVmJmZITk5Gfn5+QgKCoKGhga6du2Krl27AgBOnTqFrl27wt7eHmFhYcjKygIA/PTTT3B1dUVycrKKjyh7EhxOIvKf//wHpaWl+PDDD5WmW1paQltbGyUlJZDL5SgsLIS+vj6OHj2KnTt3IjY2FmvXrgUALFq0CFKpFHv37kVeXh769u2LwYMHY8iQIfD19QUA5OTkoHfv3ujevTuysrKwbt06fPHFFzh9+jQAoKKiAgcOHEBUVBQSEhKgUCiwefNmAMCyZcvw3XffYf369bh69SrCwsKQm5sLNJ6OWlhYwN7e/p77t2LFCtjb2wt16Orq4tq1azh9+jR2796NmJgYnDx5EocPHwYAFBQUoKSkBFu3bsWxY8dQW1uLuXPnAgBkMhn8/f2FtlNTU6GlpYWWLVvC3t4ekydPRrdu3ZCXl4c///wTRIRRo0ahZ8+eSE5OxqxZs+Dm5gYA0NPTQ4sWLaCtra3iI8qeBIeTiBw/fhyBgYFwcHBQml5QUIC6ujpYW1sjPz8fVlZWmDJlClq0aIHQ0FB06NABmZmZAICEhARIpVKlH1yJRAKZTCaEQlRUFMLCwvDOO+9AT08PnTp1gp2dHVJTUwEAly5dwgcffAB/f39YWVnB2dkZGhoauHr1Kr788kssX74cbdq0gVQqxeXLl9G2bVth2w86pauqqkJ+fr5Qx6VLlyCRSLB06VLY2toiICAAWlpa0NDQwK1bt1BeXo5Zs2bBzs4Orq6uGDRokLCfaWlpdilB/QAAIABJREFUzcLJ09NTCJhz584hKChImE9EkMvlyM3Nha6uLvr37w+pVAoAGDRoEBISEtCyZcsnPoZMdTicRKSkpKRZMAHAoUOHoKmpiZCQEKSlpcHPz09pfnl5OczNzQEA8fHxaNWqlVIvoKGhAVlZWcL1l8OHD6Nz587CfCJCWVkZLCwskJeXh8rKSqVtZGdnw8vLC7///jusra0REREBAKitrUVycrIQTsnJyQ8MJ5lMBqlUKoRAamoqnJycYGRkBADIy8tDXV0dvLy8kJ6eDh0dHbi7uyvtp5mZGa5evdqsxtTUVCGsFAoFEhISlMJJQ0MDu3fvRkZGBoKCgvDnn38+whFh6sThJCLW1ta4ePGi0rTKykp8/fXXGDRoECwtLSGTyZQu8ubn5yMpKQndunUDGsPp7oDIzs5GbW0tfH19oVAoUF1dDWtra2F+bGws5HI5wsPDkZqaCmNjYyEkKysrkZeXB19fX1y5cgWurq5K69XX1yMgIAAAkJKSIvSK7iU1NRVubm7Q1dUVPt+5fEpKihBIMpkMLVu2FHo3CoUCBw4cQPfu3ZGeng4tLS14enoCjeF67tw5IZwyMjJQVVXV7Hto3bq1EMzz589/5OPC1IPDSUSGDBmCpKQkLF68GElJSfj999/x2muvQU9PD1FRUUBj76OhoQEVFRU4d+4cIiMj0blzZyGciouLcfnyZVy/fh3Xrl0T1jE3N4e1tTU0NDTg5+eHPXv2oKamBunp6Zg7dy7ee+89mJmZ3TMwAMDHxwf29vbIy8vD7du3UVRUhI8//hhaWlrQ0dFBfX09KisrIZfL77t/d55aNn2+s/eTkpICLy8vaGpqIi0tDVpaWigpKUF2djYmT56MyspKTJ06FTU1NVAoFCgqKkJtbS3mz5+P/Px8oa3i4mKg8bGGixcvgoiE61mlpaW4ceOGcL2pqqoKnTt3xpIlS1R6LNmT43ASkW7dumHFihX49ddf0bNnT3z88cfo1q0b/vjjD5iZmUEulyMzMxMXLlyAn58fRowYgY4dO2LTpk1CG2PGjEFcXByCgoKEZ3zuDoWVK1ciNzcXHh4eiIyMxPjx4zFnzhzgPr0Ze3t7tGjRAiNGjIC7uzuCgoLQr18/hIeHo7a2Frm5udDS0sL06dMxZ84cIRTvdnev7+5t3flZJpOhtrYWoaGh6NatG2pra7F//36YmpoiIiICwcHBwrwmTeHUNO/tt99Gnz59AADHjh3D8OHDERwcDF1dXSxbtgxo7JGVlpaisrLyiY8fUy3Jkzbw7bffGuvr6xcPHDiQb3U8ZZmZmWjfvj2uXr0KPT09dZfzVHl7e2PVqlVK4cPEr76+Hr/99lvthAkTdJ+0Le45PUdkMhkcHR1f+GAqKSlBUVERvLy81F0KUyMOp+dIWlqacBH4RSaTyaCnp3fPO5fs5aH5CMswkWh6APFFFx4ejvz8fHWXwdSMe06MMVHicGKMiRKHE2NMlDicGGOixOHEGBMlDifGmChxODHGRInDiTEmShxOjDFR4nBijIkShxNjTJQ4nBhjosThxBgTJQ4nxpgocTgxxkSJw4kxJkocTowxUeJwYoyJEocTY0yUOJwYY6LE4cQYEyUOJ8aYKHE4McZEicOJMSZKHE6MMVHicGKMiRKHE4NCoUB2dnaz6Zs2bcL+/ftVso2ioiJUV1erpC32cuBwesk1NDRgzJgxKC8vV5peVVWFJUuWIDk5WWXbGTVqlEraYi8HDqcHWLx4Mby9vdG6dWt8//336i7noRQKxWOv8+WXX8Ld3R3BwcFK07/55hsUFRUhJSVFJbXZ2dmhsLAQFy5cUEl77MXH4XQfv/32G5YtW4aioiLk5eXhww8/xIkTJ/5RW3l5eRg9ejRcXFzg5eWFd999F+3atQMAvPLKK/jmm2+EZbdv3w5XV1fhc1xcHPr37w97e3t4eHhg0aJFwrxu3bph+vTpGDBgAJydnbF27VrY2toqhdSSJUvg7++PhoaGZnXl5uZizZo1mDRpktL0/Px8rF69GgMGDFDqOZWUlMDf3x9btmxB+/btYWdnh759+6K6uhrR0dEP3baNjQ1iY2P/0XfIXj4cTvdx5syZZtP+ySlOUVERevXqBX19fRw5cgR//fUXLly4gLCwMNTW1uLixYvw9/cXlk9NTYWfnx8A4OzZs+jbty/CwsKQkpKCbdu2Yfny5cjLy4NCoUBGRgbS09Pxww8/QCaToVOnTqitrUVeXh4AoK6uDps2bcK4ceOgqanZrLbVq1cjIiIClpaWStMXLVqENm3aYNKkSbhy5QoqKioAALq6urh27RpOnz6N3bt3IyYmBidPnsThw4fh4+Pz0G0rFApcuXLlsb9D9nLicLqPwMDAZtN8fHweu52lS5fCzs4O3333Hdzc3GBra4v6+noEBgYiIyMDcrlcCCMAkMlkQlgtWLAAnTp1wqxZs2BoaIj4+HiYmprC2toaOTk5uHXrFpYtWwYzMzMYGBjA09MTUqlUuLi9e/duVFVVYcSIEc3qUigU2L17Nzp27Kg0PSEhAbt27cLHH38MT09PABBO7S5dugSJRIKlS5fC1tYWAQEB0NLSgoaGxiNtu7i4GLW1tY/9HbKXE4fTffTp0wdvvfUWrKys4OjoiPHjx6Nz586P3c4ff/yBAQMGQCKRAABu3bqFS5cuwcfHBzKZDBYWFrCxsRGWl8lk8PPzQ21tLc6dO4fExEQ4OzvD2dkZ//3vf7Fr1y5oa2sjLS0NZmZmSr0uHR0duLq6IisrCwCwdu1aDBkyBGZmZs3qkslkKCkpQZs2bZSmL1y4EK+99hratWsHExMTmJmZCT3G1NRUODk5wcjICGg8Xa2rq4OXl9dDt01EuHLlCmxtbR/7O2Qvp+Z9fQYA0NPTU8lF8Bs3bsDQ0FD4LJPJoFAo4OPjg5iYGKXe2KVLl1BcXKwUOOvXr0dgYCB0dXWhra2t1I6vr2+z7Xl7eyM7Oxt///03EhMTsXr16nvWdfHiRQCAk5OTMG3//v04efIkDAwM4OLiAgCorq4Wek6pqalK20xJSYGOjg7c3d0fuu309HRUVFQgJCTksb9D9nLintNTFhISgk2bNiErKwslJSXYvXs37OzsYGxsjNu3b6OsrAz19fUoKirC9OnTIZVK4e3tDR0dHbRq1QqrV69GZWUliouLERcXJ7SblpZ2z3AKCAjA8ePH8fXXX6NLly7w9va+Z11yuRwA0KJFC6DxVv/HH3+MsWPH4sqVK8jJyUFOTg6GDBki9JyaenVNUlJS4OXlJVxTetC2T58+DRMTE3Tt2lVl3y17sXE4PWUrVqyAiYkJunTpgl69eiE2NlYIleHDh+PmzZvw9vbGqFGjYGVlBXd3d+jp6QEAVq1ahdLSUoSGhqJnz57IyckR2k1LS7vnNbBBgwahtLQUhw8fbnYX7k62/8fenYfHdPf/H39mI0IsISKJJLISEVukGnvtrdrpZinuovjS1tab0mqlLVq1V1st6nZ34y6llruotaWWVraZbCQRiZAESYisc35/3Mn5mWyoiRy8H9flusyZmXPe58yc13zOmcl5OzpiYWFB9erVAVi/fj2XL19m9uzZRo/z8PAgKiqK/Pz8UiOnkrcrWvbOnTuZMGGCum5C3InZ/c5g5cqVtW1sbFKHDBlS7S4e/tgbOHAgrVu35t133620ZcybN4+9e/dy6tQp9VxXSXl5eXh7exMSEkLdunUrddl6vZ4RI0Zw9OhRatasabJlCe3Jz8/np59+yp0wYYL1/c5Lzjk9YNHR0bz44ouVMu9Tp05x+PBhvvjiC9asWVNuMAFUq1aN8ePHc/z4cZ5++ulKW3ZOTg4LFizg66+/lmAS90TC6QHKzMzk8uXLf+snCXfj7bffJi0tjYULFzJ8+PA7Pn7WrFmcOXOmUpcdERHBu+++W+65LyHKI+H0ANWuXZurV69W2vz37NlzT4+vXr06HTp0qNRlBwQEmGT+4vEjJ8SFEJok4SSE0CQJJyGEJkk4CSE0ScJJCKFJEk5CCE2ScBJCaJKEkxBCkySchBCaJOEkhNAkCafHQKdOnVi5cuUDX25kZOQDX2Zl2LlzJy4uLmrzhpMnT2JnZ4ednR1PPvmkSZZRfGVTOzs7mjZtqk6Pi4szyfwfRhJOD8js2bPZuXPnA19uTk4O0dHRtGrV6q4ef+LECcaOHXvfy121ahX79+9Xb6empvLGG2/g6+tL48aN6dKlC9u2bbvr+ZVVV3E3mHuZz98RGhqKv78/5ub/2130ej01a9ZEr9ezd+/eCp+7adMmFi1adMdleHp6otfrGTBggNEfSU+ZMoXMzEwTrMXDR8KpAqbqW5ecnMyXX35ZaVcjqEhERAQFBQV3HU7ffvttmZ1a7sWhQ4fYs2cPkydPBuDSpUv06tWLiIgIVq9ezY4dO+jSpQuvvPLKXbfbKqsuW1tb+vTpo14muLKEhIQYNbzQ6/U0bdoUBweHO14La+XKlRVeN734iqTVqlXDwcGBhIQEo/dJ69at2bRpk0nW42Ej4VQOU/WtS0pKIiAgAHNzc5566imjy9R+9913dOzYEUdHR1q3bs327dsB+Prrrxk4cCATJ07E29sbDw8PlixZwuzZs3F3d8fHx4etW7cC8P3339OjRw9mzJiBt7c3vr6+LF68WF1GSEgITZo0MdqJ1q1bR7t27XB0dMTPz48PPvgAgBkzZrB582Z27dqFi4sLO3bsgKJwefXVV/H09MTFxYWRI0eW+2leWFjIm2++yZQpU9SRxowZM6hevTrbt2+nR48etG3bluDgYHx8fNi5cyfR0dH4+vry0Ucf0aZNG5o0acK4ceO4ceNGuXUlJyfj6OjIhg0b1OudX79+nWnTpuHp6Ym7uzvjx49X5zF48GAWL17MoEGDcHJy4oknnkCn06l1HzlyhF69euHk5ISXlxcvvPAC+fn5UDRyuj3cIyMjjUY3SUlJPP/887i6uuLv789PP/0EQFBQEOfPn2fevHm4urqSmprKjRs3qF+/PosXL6Zbt25G11Q3GAzqtij2OPf6k3Aqh6n61jk7OzN58mR69OhBYmIiBw8ehKJL8L755pvMmTOH6OhoxowZw4IFC6Doet5//PEHXbp04c8//2TgwIEsXbqUwMBAQkJCCAwMZNWqVVB0jai4uDh69+7NmTNnePPNN1m8eLFaf0hIiNGOtXTpUlavXs26deu4ePEinTp1IiEhAYr61VlYWPDzzz+TmJjIgAEDuHr1Kn379iUnJ4eDBw9y5swZ/vrrL3bt2lXm+u7evZuMjAz69u0LQExMDHv37mX27NnY2NgYPdbJyYnU1FQKCwu5fPkyNjY2HDp0iO+//54DBw7w+eefl1uXk5MTK1aswNXVldq1a5OXl8fQoUPVrjVHjx7l5MmTapOFjIwM9u7dS3BwMGfPnsVgMLBx40YAjh07xsiRI9U+ffPnzyc8PBwrKyuSk5O5cuWK0TbU6/VG4fTWW29B0bmozz77jLZt20LRyLtWrVokJCRw4cIF7O3tiYyMRFEUUlNT2b9/P0eOHFHnEx8fz61bt4zmXVhYqL4+jxsJp3KYqm8dwOnTp42ua5SRkcGHH37I9OnTefbZZ1EUhfDwcHX+SUlJBAUFMWLECGxtbWnYsCFt27Zl+PDh1K5dmxYtWqjX4k5KSuKpp56iT58+1K5dmzFjxmBra0t0dDSUOCS5ePEiS5YsYdmyZbRp0wYLCwvi4uLUnens2bNYWFgYdX9Zs2YNN2/eZM2aNbi6uhITE0NGRobRSdvbbdmyhSeffBILCwsAfvvtN8zNzXnmmWdKPfbChQs0atSIpKQkGjZsyJQpU6hTpw7t27enQ4cO6jqUVRclmjx88803JCUlsWrVKurVq0fjxo3p2LGjOjo6f/48//znP2nRogUNGzbEzc0Nc3NzFEVh5syZjB8/nqFDh2JpaUl8fLy6TUJCQqhZsyY+Pj4ApKenk5qaavReKCwsJCUlhcLCQjp27IiLi4v6urdp00YdQRbX3KBBAz788EMsLS2Nrg5a/AXC7fNOS0sjLy+vgnfXo0vCqRym6ltnMBg4e/asUTidOXOG7Oxs1q5di7u7O76+vhgMBnU0VFYjgdt3zKioKPUNrNfrjTqi5OXlcfPmTezs7MjLyyMyMpKWLVtC0bdODg4OdO/eHYDc3FzCwsLUHfHMmTP4+/sbtaA6duwYZmZmNG3aFBcXF6ZNm8ayZcvU55R07Ngxo154qampNGjQoFRjg5iYGOLi4ujQoUOpdQC4du0a9evXL7cuSrTH2r9/P0FBQUaPSUtLo0GDBiQmJpKZmWm0jNjYWHx8fAgPDyc6Otqo+efp06fV9QsNDcXPz8/oZDhFbbCKLVu2DA8PD6MRbXHdJS+2p9fr6dChA1ZWVqW2XWRkpNqZp1h8fPxj2+tPwqkcxX3rIiMjCQkJYfHixRVek7s8UVFRZGVllTohbWZmRmhoKH/99ReJiYmsX7+eBg0aQImuv5RoUV7ydsn+dXv27MHa2pqOHTsSExNDXl6eev+FCxdwd3dXH3vgwAHy8/PV8Dpz5kyZJ87HjRtHTEwMkZGRnDlzptxLAKenp3P9+nWjXniNGjVSp98uODgYNzc3+vTpg06nMxotJCUlERoaSo8ePSqs6/Z1z8zMNGpOevXqVX777Tf1RHzt2rVp3Lix+tjExESaN29OQkIClpaW6mgnPT2dkydPqqPNkh8Uer0eW1tbnJ2d1WkNGjRgw4YNLF68mHfeeYf09HQA/vzzT3Xb3l5zeSPwkueyKDpUfFx7/Uk4VbLU1FQoOjw4d+4ciqLg7+9P9erVWbZsGQaDgcjISM6fPw9Fh3xJSUlq+GRlZXHhwgX1dnZ2NnFxcfj5+ZGZmUlSUhLZ2dlcu3aNnTt3MmvWLObMmUPdunXVnaT4GyFnZ2cSExPJycnhypUrLFiwACsrK7U9VGpqKnFxcaSkpJCcnAxAu3bt2Lp1KzExMeTk5HDo0KFy17WgoACKLkdcrG/fvtjY2DB58mT1XNDo0aM5dOgQX3zxBVZWVuh0OgoKCsjIyOD06dOMGjWKrl27quFUVl2XL18mLS1NDQ5/f39+/fVXUlJSSE9PZ9q0abRr104Np5LNQCk6fHJ2dqagoIDz58+Tn5/PzJkzyc/PVxuhpqenq9uP276pKxYVFcUPP/xAeno6ly5don79+tSpU4eCggKuXbuGTqfj0qVLZGRkqM8vq98gZbT7ioqKIj09ncGDB9/hXfZoknCqZIGBgbRv354XX3yR/v37A2Bvb8+nn37Kli1b8PPzY9y4cep5BZ1Oh6WlpboDlDwPERkZicFgwM/PD51OR61atVi5ciXNmzcnODiYt956iylTpkBRsPTq1YvnnnsOgNGjR+Pp6UlAQAADBw6kS5cu5Obmqidcx40bx6lTpwgICFB/kzVr1iz8/f0ZMGAAgYGB7Nu3r9x1bdCgAdWqVTM68W1vb88PP/zAlStXGDhwIOPHj6dGjRocOHCAgIAACgsLiY6O5q+//sLPz4/Ro0fTsWNHNmzYoM6jrLp0Oh3VqlXDy8sLgJkzZ+Lj40P79u3p1KkTjRs35t///jdmZmZlhpOzszN16tShTZs2TJkyhX79+tG5c2dsbGywt7cnKioKgAkTJnDgwAH+85//qNv/9gA5e/Ys7733Hq1ateLXX39Vf/JgaWnJ+PHjWblyJUFBQZw7d460tLRS56uKFRYWEhsba3Tfzp076dChA+3atbvLd9ujRfrWPcQ2bNjAt99+yy+//FLVpaiGDRvGmDFjePbZZ+/q8dHR0QQFBXHx4sWHouGmh4cHs2bNYtKkSSadb0xMDO3bt2f//v20bduW3NxcgoKC+Prrr/H39zfpsiqTKfvWycjpIabT6dRvkbRiypQpnDhx4q4fr9PpcHFxeSiCKTk5mevXr+Pg4EBaWppJ5mkwGLh8+bLahLR4xLxw4UJmzJjxUAWTqUlrqIeYTqejd+/eVV2Gkdt/ZHo39Ho93t7elVaPKRUfYr/yyiv4+/tz+PDh+55nSEiIem6tSZMm1KxZk4yMDJ588sm7Hn0+quSwTghhMnJYJ4R45Ek4CSE0ScJJCKFJEk5CCE2ScBJCaJKEkxBCkySchBCaJOEkhNAkCSchhCZJOAkhNEnC6TFQVX3rqpKbm1u5154q2YdOaJOE0wPyuPWtM7V76VGXmJhIVlZWuX9QXLIPndAmeXUqIH3rql7xVSjvpUedTqejRo0aODk5lXl/yT50QpsknMohfev+Xt+6YitWrKBz585G01544QVGjBgBFfSJK6uvW1k96m7evMncuXNp2rQpzs7OBAYGGl0ls379+gwbNgxXV1cGDRpEYmKiWkfJPnQVrd+GDRto1aoVjRs3ZujQoeqliEXlk3Aqh/St+3t964r16tULnU6nXkP98OHDHD16lA8//LDCPnFl9XUr2aNOURRefvllIiMjOXToEOfOnSM/P19toqDX67GwsGDRokUcOnSIGzduMHXqVCgaxd7eh66i9dPr9cyYMYOlS5fy119/8Y9//ENTo8pHnYRTOaRv3f/ca9+6Ys2bN8fJyYkjR45gMBiYP38+06dPx8XFpcI+ceX1dbu9McC2bds4efIkX331FY6OjhQWFpKUlKTOQ6fTMWbMGHXUOXnyZH7//Xfy8/NL9aGraP2KR0nx8fE0aNCgzN57ovJIOJVD+tb9z732rbtdr169OHz4MN988w05OTlMnTr1jn3iyuvrdnsbqB9//JFnn32WevXqAfDXX39hbW1Ns2bNyM/PJzY21qglk6IoGAwGDAZDqT50Fa2fv78/X375JZ988gndu3c3OjQUlU/GqOUo7lt3v+7Uty47O5vatWsbfXOk0+kYNGiQejsiIoKePXsa3Z44caL62Nu/XausvnWvv/46BoPBqEPtnfTq1YtZs2Zx4MAB1qxZQ7Vq1crtEzdjxgx1fYKCgkrNS6fTqYGWkJCgdrIpXucWLVpgYWGBXq8nLy/PaD1++OEHOnToQPXq1UsF/53Wb8iQIfTq1YtevXqxatUqlixZctfrL+6PjJwq2ePUt66kLl26kJ6ezpNPPkm3bt3UGirqE1dWX7eSPeqcnZ2JjY0F4NSpU2zYsEF9/unTp7GwsCA6OpqUlBTmzJnD77//zvvvvw9l9KGraP3Wr19PdHQ0V65cITs72yjYReWTcKpkj1PfupJq1qxJnz59CA4OVqdV1CeuvL5uJXvUzZ8/H51OR+vWrZk9ezZ9+vRRz7H9+uuvTJ06lddee42AgACioqLYtWuX2sWkZB+68tYvOzub7du30717d/r378/gwYOZMGHCPb/+4u+TBgcPMS32rROPN1M2OJBzTg+xquxb9/vvvzNz5swy72vVqhVr16594DWJR4uE00OsKvvWdejQgd9//71Kli0eDxJOD7E7/RBSiIeZnBAXQmiShJMQQpMknIQQmiThJITQJAknIYQmSTgJITRJwkkIoUkSTkIITZJwEkJokoSTEEKTJJweIIPBoF6H6GFXshfeJ598gp2dHXZ2duqF4yrb0KFD1WXeTcsoU8nNzTW6kuf98vLyUtcjKiqq3GlxcXEmW+bDQMLpASkoKGDcuHFcu3ZNnbZ9+3Z69eqFs7MzzZs3Z9KkSVy5cuWu57lp0yYWLVpkNK24a0llKqsXnl6vp1u3buj1et577z0URSEgIABHR0d8fX0ZMGBAqb8FvJday+r7p9frmT17Nnq9nmeffbbc56akpDBy5EiSkpLueV3L8vnnn/PLL7+Qn59vkvn99ttvBAcHU61aNbX1VVnTVq9eXWbjjUeVhNMDsmTJEjw9PQkMDARg2bJlvPrqq/Tt25c9e/bw8ccf88cff/DKK6/c9TxXrlyJo6Oj0bRWrVpV+pUKyuqFp9fr8ff3x8HBgZo1a3Lu3Dni4uL48ssv2bBhA76+vowaNYrly5ffc61l9f27fv06KSkpBAQE4ODgUOqa47c7evQof/75J87OzuU+5varY1YkPT2dTz75hLy8PHVEc78cHBxITk7Gy8tL7e5S1rTu3buzbNkykyzzYSDh9AAkJCTw2Wef8eqrrwLw559/8v777/PRRx8xY8YMWrZsyTPPPMPbb7/NsWPHuHr16h370QUFBXH+/HnmzZuHq6srqampTJs2jZdeeolbt26pj4uMjGTo0KE0btwYX19fPv74Y7itg+7XX39NUFAQTk5ODBgwgJs3b8Id+sKV7IVXUFBAbGwszZo1U5cbHh6OjY0NTz/9NE8++SSLFy9m3LhxLF++nMLCwjJrPXjwID179lRHW1FRUeX2/dPr9QBGy/z999956qmncHZ2plOnTsTExLB161amTJlCeno6Li4uzJ07F4BffvkFV1dXPv74YwIDA5k2bdpdvZaLFy/Gy8sLNzc3o1Zh+/bto2vXrqxevRo/Pz+jZbVr145PPvlEfWxWVhZubm5s3LjR6HUqeQXQktMaNWrE0aNHTTZi0zoJpwdg7dq1dO/eHXt7e/W2l5eX2mCyWHGH2tTU1Dv2o3v//fepVasWCQkJXLhwAXt7e5YvX64eIlLU0uiZZ56hZ8+exMTE8MUXX7Bo0SKOHz+OtbU1ycnJHD9+nB9//JE9e/bw22+/sX///jv2hSvZC+/cuXPk5eUZBUVoaCjNmzc3atzQrVs3MjMzuXbtWqlaf/31V0aMGMGQIUMIDQ3l559/xtXVtdy+f3q9nlq1atG4cWMo6rAyZswY+vTpQ1hYGDNnzsTDw4Nhw4bRpk0b5s2bR2JiotpAVK/Xk52djaurK6dOnbqrxgUxMTFs3LiRd955B09PT8LDw9X7DAYDOp0ORVE4fvw48+bN47PPPiM9PR1fX1+jc43//ve/sbCwUC+fTFEQ3b79yppmMBi4devWPR36P8wknCqZwWDgxx/TNpTKAAAgAElEQVR/pGPHjuq0Y8eO0a9fP6Mdl6LuKBQN6e/Uj+706dO0adPGaB5ZWVkkJSWpO3xwcDCdOnVi0qRJ1KhRg86dO+Pk5ERERATnz5/HzMyMjz76CEdHR1q2bImVlRXm5uZ37AtXsp23Xq9X2ysVCwsLM2pnBXDt2jXMzMywtbUtVevbb7/NuHHjmDx5Mvb29nh6eqq9+Ur2/SteZrNmzTAz+9+VphVFobCwkISEBKytrRk0aBAWFhbk5+cTFhZW6vk6nY6nn35aDYi76Srzzjvv0LVrVzp37oyXl5fRyOncuXP4+/szdepUateuTZs2bQAwNzenWbNmajgZDAbWrVvH6NGjsbGxgaLGqMnJyUajpLKmFTfLyM3NvWOtjwIJp0qm0+lIS0tT36wUvcmKP/Fv98svv+Dn50fdunUr7EdHURunsnY4CwsLNST2799v1GtPURSuXr1KgwYNiIiIwNXVFVtbWwASExPJy8vDx8enwr5wJXvhUdT+ys3NTd3ZKDqsKxlO+/btUzvP3F5ramoqOp2OgQMHltomZfX9o4xRhbm5OT/++CNRUVEEBASoI6ywsDDy8/NLtbzS6/V06dKl1PLKc+zYMX755RfeeecdgFIjJ51OZ/R6xcbGYm9vT7169WjWrBkxMTFQ9BpfuHCB8ePHG60LJZq2ljUtPj4ec3NzHBwc7rruh5mEUyU7d+4cAK6uruq0Ro0alfpJQWhoKNu2bVPPS93eRJIS/egoOm91e0BQdKLaw8MDa2trDAYDN2/eNHojHzhwgMLCQrp06VKqf1t4eDjVq1fH09OThIQEmjRpYrTs4r5wJXvhcdsoptiVK1e4fPmy2vGEoqt27tq1S+0Mc3utN27cgKJegSWV1/evrMOgVq1aqYH81ltvQVGIe3l5GY2MCgoKiImJKdWCqjyKojB//nwsLCx49tlnadKkCe+99x7Xr19XG21GREQYBcnt27dZs2ZkZGSQmprK559/zrPPPmt0cj4yMhIbGxvc3NwqnHby5En8/PzuqXfgw0zCqZIVfwtUp04dddrw4cPZtGkT3377LWFhYaxfv54hQ4YwcOBARowYccd+dAUFBVy7dg2dTselS5fIyMiAEoFmbm6On58f27Zt49atW0RGRjJnzhzeeOMN7OzsSn3Sh4eH4+Pjg6WlZYV94Ur2wqOMcLr9cOf48eO89dZbjBs3jilTpjB8+PBStbq4uGBvb8+SJUtITk4mLCyMkydPQjl9/y5fvkx6errRYWTxObP09HSuX7+Oh4cHFJ34T09PJyEhgfj4eCj6wMjNzTUKkwULFtC+ffsyTzZ///33REVF8eeffxIfH098fDy//fabuq6FhYVERUWV2p7F6+fj40ONGjVYvnw5hw8fVj+Abt9+Pj4+RofoJacpisKJEycYMmRI2W+0R5CEUyVzdHTEwsJCbVwJ8OabbzJ69Gjee+89+vbty7/+9S/mzp3LF198AUU7bkX96CwtLRk/fjwrV64kKChIHZ3pdDqjHW7VqlUkJCTg5eXFqFGjGD9+PLNnz4ZyWp4X366oL1zJXni5ubmcP3/eaLnF4fT000/z8ssvExcXx3fffcd7772nPub2Wi0tLdmwYQPx8fEEBAQwcuRIsrKyoJy+f2Ud8hw5coSRI0cSGBiItbU1S5cuBWDw4MFYW1vzxBNP8O6770LRjt+oUSP1EBmgdevWnD9/vtRPCm7dukVwcDCTJk0yGu00btyY6tWrExYWRmxsLDk5OaW2Z3F9VlZWjBkzhrVr19K6dWvat29vtIy7+abujz/+IDc3l5dffpnHhfStq2R5eXl4e3sTEhKifvV+Jw9TP7rw8HC6dOnC4cOHjQ7jKtNnn33GokWL1JGQKezatYv//ve/Rr96N6X09HRatGjBihUrjL6loyhkJ0+ezNSpU8udNnbsWDp37sy4ceMqpT5TMWXfOhk5VbJq1aoxfvx4jh8/ftfPqcp+dPeq+Ju6OnXqkJmZ+cCW6eHhweXLl41+J/V3xcbGcvToUT766COT1He7q1evsn37dl588UWaNGnC4MGD1fvS09OJjY3l8uXL6iiprGm7d++mVq1amg8mU5NwegBmzZpldM7pTnQ6Hd7e3pVak6lERkaiKAqtW7c2+pFoZS/z7Nmz+Pr6qt/K3Q8vLy8WLVpkdOhtKqdPn2b69OnUr1+f77//Xv0lu6IotG3blieeeAKKRkplTQPIyMhgxYoVJq9N6+SwTghhMnJYJ4R45Ek4CSE0ScJJCKFJEk5CCE2ScBJCaJKEkxBCkySchBCaJOEkhNAkCSchhCZJOAkhNEnCSQihSRJOQghNknASQmiShJMQQpMknIQQmiThJITQJAknIYQmSTgJITRJwkkIoUkSTkIITZJwEkJokoSTEEKTJJyEEJok4SSE0CQJJyGEJkk4CSE0ScJJCKFJEk5CCE2ScBJCaJKEkxBCkySchBCaJOEkhNAkCSchhCZZmmImBoPB4sSJEzdMMS/x8FIUxdzMzMxQ1XWIqqMoipmZmZmFKeZ13+FUvXr1W4WFha8kJCSYoh7xkCosLKyRlJS01NXVdWpV1yKqlrm5eV5V1yCEasKECXUmTJhwvarrEI8OOeckhNAkCSchhCZJOAkhNEnCSQihSRJOQghNknASQmiShJMwiZs3byqKouRUdR3i0WGSX4gLUbNmTTPAuqrrEI8OGTkJITRJwkmYREFBgQE4XtV1iEeHhJMwCUtLS3MgqKrrEI8OCSchhCZJOAkhNEnCSZhESkqKnHMSJiXhJEyiUaNGcs5JmJSEkxBCkySchBCaJOEkhNAkCSdhEikpKQaDwRBT1XWIR4f8bZ0wiaIT4t5VXYd4dMjISQihSRJOQghNksM6E1IUpdHjetmQyMhI282bN5spitKkqmupQglmZmZKVRfxqDCr6gIeJYqiHAM6VnUdosrYm5mZpVV1EY8KOawTQmiShJMQQpMknIQQmiThJITQJAknoUpISCA+Pr6qy3hoyPaqXBJOAoBz587h4eHB6dOnq7qUh4Jsr8on4aQRilK1P4/Jz8/HYDBUaQ33QraXEPdAUZRjAwcOVDw9PZXhw4crderUUezs7JSRI0cqly9fVopNmTJFcXBwUHbs2KF4e3srZmZmyv79+xVFUZRNmzYpvr6+SrVq1RQ3NzclODhYKSwsVJ9bp04dZdOmTcrTTz+tVK9eXXFwcFCmT5+ubNu2TfH391esra2Vdu3aKadPn1afc6ea4uLiFMDo38svv6zcqx07digxMTH39Jxly5YpgDJt2jTF0dFRqVGjhvLUU08Z1V/R9vrjjz+Uzp07K9bW1kr9+vWVsWPHKlevXr2v7XWnmirYXg2q+j0oRJmKw8na2lr5+OOPlZMnTypr165V7OzslNatWyv5+fnqzmZtba34+/sr+/btU7Zt26YYDAZl48aNCqCMGDFC+c9//qPMmjVLMTMzU4KDg412Nmtra+XTTz9VTp48qQwbNkwBFCcnJ2Xnzp3KwYMHFV9fX8Xd3V1d3p1qysnJUf79738rgLJw4ULl6NGjSnR09D2H05IlSxQrKyvl5ZdfVmJjY+8pnCZOnKicOHFC+c9//qO0aNFCsbW1VeLi4ircXhEREYqNjY3yxBNPKN9++62yYsUKpW7dukqPHj3ua3vdqaYKtpeEk9Cm4nAKCAgw2gG/+eYbBVB27Nih7myAcuLECfUxBoNBcXZ2Vjp37mz03H/84x+Kra2tkpWVpe5skydPVu8v/hRfvXq1Om3Tpk0KoERGRipKUTjdqSa9Xq8AypYtW+4qVMoTHh6ujBo1SqlRo4YyduxY5dy5cxU+vjgIitdPURQlISFBsbKyUqZPn64o5WwvRVGUF198UbG1tVWuXbumTvvXv/6lAMrhw4cV5W9ur7upqZztJeFkQnLO6QHo27cvAH/88Yc6rWbNmrRv3169HRMTQ1JSEkOGDDF6bp8+fcjKyiI6OlqdVqNGDfX/1tb/+1O+6tWrq9MaN24MQFpa+X9JUVZNd+v69evEx8cTHx9PUlKS0X1+fn5s2rSJyMhIsrOzadq0KTqd7p7m7+rqiq+vb4XbC+DQoUN0796dunXrqtP69OkDYHSi2hTbq6yaROWScHoA6tSpg7m5OVlZWeq0WrVqGT0mIyMDgIYNGxpNt7OzAygVAhUxM/vfn0xWdNK4rJru1vLly3F3d8fd3Z0ePXqUuj8+Pp5Fixaxa9cuXn75ZZydne95GfXq1atwe1G0zR7U9iqrJlG55KoED0BycjIGgwEXF5dyH1P86Z2enm40/fLly1C0YzyImu7mW7AXXniB1q1bA2Bra6tOj46OZuHChWzZsoXnnnuOs2fP4unp+bfqu3jxIs2aNavwMY0bN35g26u8mqr6W8NHmYTTA/DVV18B0KFDh3If4+joSJMmTdi9ezdTp05Vp2/dupWaNWvSpk2bSq2pZs2aUBRad9KsWbMyg2PNmjUUFBQQEhJC06ZN/3Zthw8f5ty5c4wbN67CxwUFBbFz505u3bqlHrpt3boVgE6dOv3t5d9NTfeyvcTfI+FUCcLDw5kzZw4+Pj789ttvrF+/nmeeeabCcAJ49913efnll3nllVfo06cPBw4cYNu2bSxYsEDdGSqrpsaNG+Ph4cHSpUupWbMmV69eZdq0aeo5mrvx4YcfYmNj87fqmzhxIr169eLcuXOsWLGCRo0aGYV0Wd566y2+++47+vbty6uvvkpCQgLvvvsuTz31FF27dv1bddxtTWVtr9GjR8sliExIwqkSNGzYkD/++IPVq1djY2PDxIkTWbx48R2fN3r0aLKzs/nkk0/YtGkTzs7OLF68mFmzZlV6TWZmZnz33XeMHTuW119/HRcXF55//nnc3Nzuehl/N5go+lHjrFmzyMnJoWvXrixdutTokLEs3t7e/Pe//+Wf//wnY8eOpVatWowaNYqPP/5YPY90Pyqqqazt5eXlJfuT0KbyfkpQ1bRYU7GyvravavdRk/yUwITk2zohhCZJOAkhNElO4JmQXEP8sSfXEDchGTkJITRJwkkIoUkSTkIITZJwEkJokoSTEEKTJJyEEJok4SSE0CQJJyGEJkk4CSE0ScJJCKFJEk5CCE2ScBIm83//93/8+OOPVV2GeERIOAmTSEpKYs2aNbRo0aLcxxQWFj7QmsTDTcJJ3JOLFy/Sr18/bG1tcXV1ZevWrSQmJuLp6Ym5uTlt27YlICAAgBs3bmBubs6CBQto27btfV1XXDx+5LKi4p68/vrrUNRpJTo6miZNmuDi4sL06dMJCQlh165d6mMjIiJQFIUrV65w8uRJcnNzq7By8bCRcBL3pLCwkOTkZAoKCoyaCJw4cYJu3boZPTYsLAx7e3tWrFiBpaUllpbydhN3Tw7rxD354osv8Pb2xsfHh48++ggAg8HA6dOnS3XkDQ8Pp0uXLlhZWVVRteJhJuEk7om9vT0//PADq1atYvbs2aSlpaHT6cjKyqJt27ZGjw0LC8Pf37/KahUPNwkncdf0ej2bN28mLS2N5ORkGjRoQN26dbly5QoAZ86cISYmRu2CGx4eLuEkhBYoinKscpoVacOmTZsUZ2dnxcbGRunQoYNy4sQJRVEUJTs7W+nYsaNiaWmpODo6KgaDQbly5YoCKFFRUVVd9oMkraFMSBocmJA0OHjsSYMDE5LDOiGEJkk4CSE0ScJJCKFJEk5CCE2ScBJCaJL8PYFpHQZsq7qIqmAwGMyvXbvmXL9+/cSqrqUK5Vd1AUKIEiZMmFBnwoQJ16u6DvHokMM6IYQmSTgJkygoKDAAl6q6DvHokHASJmFpaWkOOFZ1HeLRIeEkhNAkCSchhCZJOAmTSElJMSiKcqKq6xCPDvmdkzCJRo0amQNPVnUd4tEhIychhCZJOAkhNEnCSZhESkqKwWAwRFV1HeLRIeechEkUnXOSrpnCZGTkJITQJAknYRJFPyU4VdV1iEeHHNYJkyg6rAus6jrEo0NGTkIITZJwEkJokvStE3/b2LFjd1haWvYHMDP731upuNsvwLp16+T9Jf42GTmJv83S0vJtRVEuFAcTRSFV9C+6SosTDz0JJ/G3rVu37qyZmdnhMu4qNBgMW6ugJPEIkXAS96WwsHCpoigXbp+mKEpMRkbG8qqrSjwKJJzEfVm/fn0IcLT4XJOiKJiZmf24ZcuW1KquTTzcJJzEfSssLPzIzMwspehmHLC6iksSjwAJJ3Hf1q9fH6IoyhHlf3784osvpNGBuG/3/VXvhg0brA0Gw3pzc/NqpilJPIwKCgpqZ2VldbS1tf3V0tIyt6rrEVWnoKDg+vjx41+53/nc95+vZGVlVbO2th7epk0b+VMYAfBsVRcgqk5hYSF//fVXLlD14QRgbm5uaNKkiSlmJYR4iOXn5/PXX3+ZZF5yzkkIoUkSTkIITZJwEkJokoSTEEKTJJyEEJok4SSE0CQJJyGEJkk4CSE0ScJJCKFJEk5CCE2ScBJCaJKEkxBCkySchBCaJOEkhNAkCSchhCZJOAkhNEnCSQihSRJO4o62b9/OlClTuHbtGgA5OTl89tlnzJkzp6pLE48wCSdxRwsXLuTbb78lLy8PgPT0dObOncuvv/5a1aWJR5iEkxBCkySchBCaJOEkTGLkyJG0bNmSRYsW0aJFC9zc3Ojduzffffcdo0aNwsfHBx8fH6ZPn87NmzerulzxEJBwEiZz8eJFVqxYQefOnfH09OT06dNMnjyZkydP8vTTT2NjY8PGjRtZtGhRVZcqHgISTsKklixZwtq1a9m9ezd16tQB4JtvvmHFihV8//33AOzdu7eKqxQPAwknYVIuLi4AWFtb07BhQwAaNWoEgI+PDwCXL1+uwgrFw0LCSTwwZmZmACiKUtWliIeAhJMQQpMknMRdK/4RZrHc3Nwqq0U8+iScxB3VrFkTgH379gFQq1YtABITE4mMjKzS2sSjS8JJ3NELL7yAra0tOp0OgDp16jBgwADq1avHmTNnqro88Ygyu98ZrFy5sraNjU3qkCFDqpmmJCHEwyo/P5+ffvopd8KECdb3Oy8ZOQkhNEnCSQihSRJOQghNknASQmiShJMQQpMknIQQmiThJITQJAknIYQmSTgJITRJwkkIoUkSTkIITZJwEkJokoSTEEKTJJyEZl27dg07Ozu5LMtjSsIJWLZsGYGBgUbT8vPzcXJyYsuWLZW23MWLF9OtW7f7nk+vXr2ws7Mr9S82NtYkdd7u+++/x8XFBYPBYPJ5lxQSEoKlpSV+fn7qtLy8PD7//HN69+6Nm5sbLi4uDB06tNJr+TtOnDjB2LFjq7qMh5ZlVRegBWFhYaXCKSwsjJycHPz9/e95frdu3aJGjRp3vK9Vq1YUFhb+rZqL52MwGNDr9SxcuJBevXoZPcbDw+NvzbsiYWFh+Pr6Ym5e+Z9roaGhNGvWDGvr/10aKDMzk2HDhpGamsobb7xB69atycrKIioqqtJrKclgMNxxG3z77bdYWv79XaywsBALC4u//fyHnYycgPDwcJ544gmjaadPn8ba2hpvb2+2bdtG48aNjUYLrVq1Yu3atQCcPHmSvn374urqSvv27enRowd//vknAJs3b6Z9+/a4uLjQr18/unbtSnZ2NtOmTeOll17i1q1bUHQJ3K5du7J69Wr8/PxwcXFh7ty56vLKm09MTAzZ2dkMHDhQ7apb/M/c3JwjR47Qq1cvnJyc8PLy4oUXXiA/Px+AgwcP0rNnTxwdHfH19VV38lOnTjFo0CCcnZ3x8vJi4cKFRtuqZcuWZW7HXbt20blzZ5ycnGjfvr1Rf7rz588zYsQIXF1dcXNzU3vYJSQkMHLkSHV6r169iI6OBuDs2bO0bt1ancfrr79OVlYWBw8eZPTo0bRs2ZKOHTsybtw49TF5eXkEBwfj7++Po6Mj3bt3V6/g+eGHHzJp0iSmTp2Km5sb3t7ebN26VX1uRevdo0cPXnvtNQYPHoybmxuJiYkArFu3jnbt2uHo6Iifnx8ffPABADNmzGDz5s3s2rULFxcXduzYAUBkZCRDhw6lcePG+Pr68vHHH6vLePvtt+nfvz+TJk3C19eX7777roJ37aPvsQ+n7Oxszp8/z9y5c3FxcVH/zZ8/H19fXywsLNDpdDRv3lz9pMzMzCQxMZEWLVqQlJTE8OHDee655zh37hz9+/cnJSWFtm3bsnv3bt566y1WrVpFTEwMFhYWNGvWDBsbG5YvX46zszPNmzeHok9inU6HoigcP36cefPm8dlnn5Genl7hfMLCwgAICgpSaz9x4gQAx44dY+TIkbz66qtcuHCB+fPnEx4ejpWVFb/++isjRoxgyJAhhIaG8vPPP+Pq6srJkycZMGAAnTp1Ijw8nH//+98sW7ZM3RnDw8PLHE3u2LGDqVOnsnDhQuLi4nj++eeZOHEi2dnZpKSk8Mwzz1C3bl0OHz7MmTNn6Ny5M6mpqfTr1w9PT0/0ej379+/nzJkz6mgjNDSUVq1aARAbG8v27dtZsmQJdevWLff1HDNmDLt372bTpk1ER0fTqFEjPv30U/W1PnDgAM888wzh4eF06dKFTz75BIo+YMpbb4PBQFRUFJGRkXz11VfodDrc3NxYunQpq1evZt26dVy8eJFOnTqRkJAAwMKFC7GwsODnn38mMTGRAQMGEB8fzzPPPEPPnj2JiYnhiy++YNGiRRw/fhwAvV5PZGQkU6ZMISIiQrOHqw/KY39YFxERgaIo/Prrr1Sr9v+vNPziiy/SokULAHQ6ndF5j+JPYj8/P3bu3EmjRo3UT283Nzc1xLZt28bQoUPVUZmTkxM5OTkAZGVlkZSUpIbTuXPn8Pf3Z+rUqQC0adMGAHNz8wrnExYWRqdOnVi+fLlan4uLC4qiMHPmTMaPH6++yePj42nbti0UfUqPGzeOyZMnA2Bvbw/A/Pnz6dy5MzNnziQ/P58zZ85Qr149HBwcSEpK4urVq6XCqbCwkLlz5/Lmm2+q59CGDh1KcHAwFy5cYOPGjTg5ObF69Wq1dx3A9OnTady4Me+++y4AFy5coF69enh4eJCZmUlcXJwaTkePHsXW1paOHTuqz3/33Xf58ssvMTMzIzQ0lD///JO9e/fyyy+/0KZNGxISEoiLi1Ofc/78eZ5//nmefvpp9fUrHi1WtN7x8fFkZ2ezdOlS7OzsoKj1+pIlS/j222/V1youLk7d1mfPnsXCwkJ9DwEEBwfTqVMnJk2aBKCOMiMiIggKCkKv1zNjxgz1OcWHs4+rxz6cwsLCcHNzo1mzZuo0g8FAYmIi48ePh6Iw6t69u3p/REQETk5O2NnZUatWLbKyssjNzSUzM5PVq1fTr18/KOpSkpaWBsCZM2f46aefWLlypTpPCwsLmjZtqt6+PQBjY2Oxt7enXr16Fc4nLCyMli1bljq/FBYWRnR0tHr4RNGhao8ePUhNTUWn07Fs2TKj5+Tm5nL69GkaNGiAm5sb+fn5tGzZkq1bt1KtWjXCwsKwsLBQA7WYTqcjOTmZrl27qtOK661fvz6HDx/mueeeMwomRVHYtm0bwcHBRvUV7+hhYWGYm5ur2yQtLQ1bW1uj8zyTJk2ievXqbN68mbp16/Lbb79Rq1Ythg4dipmZGRYWFrz88stqGERERNC/f3/1+efOncPHx+eO663X67GzszMKmp07d+Lg4KC+L3JzcwkLC+P9999XXyd/f3+jD7z9+/czf/58o21w9epVGjRoQEZGRqlt+LiTcAoLMwomij4Bi0+G37x5kwsXLuDr66vef/LkSfWNOnjwYHQ6Hb179+b8+fP07t1bPe8wb9485syZw5NPPsmlS5eYMWMGw4cPh6IdxcPDQ/10jIiIUO8rvl0cAhXNJzw8nCFDhpRar4SEBCwtLdX24Onp6Zw8eZIZM2Zw48YNgHJP2q9bt47WrVtjbW1ttHOFhYXh7e1d6hM9MzMTbms7TtHO26ZNG+zt7cnKyiq1rIyMDDIyMnB3d1en7dmzRz2pHx4ejoeHh/q8Ro0akZKSwqVLl3B0dASgYcOGXLlyxSg0/P39+emnn8jOzqZ27drq9KysLBITE42CNTw83Cisylvv4sP62124cMGo9gMHDqihRlE4FY/6KPrAu3nzJg4ODkbPKSwspEuXLuh0OqysrPDy8irzNXkcPfbnnMLDw0uFU2RkJGZmZjRv3py8vDwUReHy5csAbNmyhW3btqk7hLm5OZ07dyYqKoo5c+bwwQcfqH3e6tevj5eXF0lJSXz11VdMnDhRXcbtb/jCwkKioqKMRk7h4eHq/eXNJykpibS0tFL1Azg7O1NQUMD58+fJz89XD1dq1aqFi4sL9vb2LFmyhOTkZMLCwjh58iTVq1fH39+ftWvXkpmZSWpqKqdOnTKqycXFhejoaPVfXFwcPj4+VK9ene+//578/Hx++eUXNmzYwNtvvw1AQEAA69evR6/Xk5CQwJ49e6hduza2trbqzx0+/fRTQkND1Z54V69eVbcNRSeka9asyUsvvcSePXsICwvju+++Y8+ePepr0a5dO86cOcMvv/yCwWDg0KFD6uGvTqfD3NxcHakWFBQQFRVF8+bN77jeer2+VDg5OzuTmJhITk4OV65cYcGCBVhZWVG9enUAUlNTiYuLIyUlheTkZHUUuG3bNm7dukVkZCRz5szhjTfewM7ODr1ej7e3N1ZWVvfw7n20PdbhVPw1fPEbtlhkZCTu7u7Y2tpSr149XnnlFaZOnUpAQAARERFYWVkZBYmPjw8jRoxg8+bN+Pv7c/LkSfW+bt260adPH1577TUCAwPJysqCop2leDQWGxtLTk6O0Q4QERFhNForaz7h4eEApeqn6JzVlClT6NevH507d8bGxgZ7e3uioqKwtLRkw4YNxMfHExAQwMiRI9W61qxZQ3p6Ou3bt6dPnz7Ex8er8wwPD2ffvt8XHPsAACAASURBVH08+eST6r9XX30Ve3t71qxZw+eff467uzsff/wxGzduVA9RPvjgA1xdXenduzc9e/YkMjISc3Nzli1bxpIlSwgMDGT//v20b99ePQc0dOhQrKysePPNN6HoPNuWLVuoWbMmkydPpn///qxdu5bx48czbdo0AJ5++mmmTJnC9OnTadGiBR9++KEaFhEREXh5eam3o6OjycvLU7d5Reut1+uNXguA0aNH4+npSUBAAAMHDqRLly7k5uaqJ8THjRvHqVOnCAgIYOfOnQCsWrWKhIQEvLy8GDVqFOPHj2f27Nml3g/if6RvnYkFBgby6quv8o9//MNoenJyMi1atOD3338vc6Rzt0w1HyEqgyn71j3255zuR05ODgsXLmTQoEE4OTmxb98+rly5Qvfu3YmMjGT79u0MGjQIKysrPvvsM5o2bYq3t/c9LcNU8xHiYfNYH9bdrxs3bpCSksKLL75I586d2bt3Lzt27MDd3Z3c3Fz1B5D9+/enoKCAn3766Z5/8Wuq+QjxsJGR031o0KABX331VZn3tWrVit27d9/3Mkw1HyEeNjJyEkJokoSTEEKTJJyEEJok4SSE0CQJJyGEJkk4CSE0ScJJCKFJEk5CCE2ScBJCaJKEkxBCkySchBCaJOEkhNAkCSchhCZJOAkhNEnCSQihSRJOQghNknASQmiShJMQQpMknIQQmiThJITQJAknIYQmSTgJITRJwkkIoUkSTkIITZJwEkJokoSTEEKTJJyEEJok4SSE0CQJJyGEJkk4CSE0ScJJCKFJEk5CCE2ScBJCaJKEkxBCkySchEn985//5LnnnqvqMsQjQMJJg0JDQ3nhhRdo0qQJ7u7uDBo0iJCQkLt+/okTJxg7dqzRtAMHDtCiRQvi4uIqoeL/LyQkhNatW5d7/+LFi+nWrVul1lBs9uzZ7Ny584EsS5iehJPGHD16lL59+2JnZ8eWLVv45ptvsLCwYNiwYVy9evWu5vHtt99iaWlpNM3NzY3evXtjZ2dXSZWDwWAgIiKCVq1alfuYVq1a0bt370qroVhycjJffvklvr6+lb4sUTkknDTkxo0bTJw4kQEDBvDpp58SGBhIUFAQn332Genp6Rw7dozo6Gh8fX356KOPaNOmDU2aNGHcuHHcuHEDgBkzZrB582Z27dqFi4sLO3bs4IcffuCJJ57g7Nmz1KlTB4DIyEiGDh1K48aN8fX15eOPP1brGDx4MIsXL2bQoEE4OTnxxBNPoNPp1PvXrVtHu3btcHR0xM/Pjw8++ACAmJgYbty4Ue7Iadq0abz00kvcunULgLS0NFq0aMHXX39NUFAQTk5ODBgwgJs3bwLQuXNn5s6dS/fu3XFxcaFv375ERUWp6zl8+HB13hcuXMDOzo6wsDCSkpIICAjA3Nycp556iqeeegqA33//naeeegpnZ2c6depETEwMAP/6179wd3cnLCzMxK+ouB8SThryn//8h/T0dObNm2c03d7enmrVqpGWlkZhYSGXL1/GxsaGQ4cO8f3333PgwAE+//xzABYuXIiFhQU///wziYmJDBgwgOHDh/Pcc8/RvHlzAOLj43nmmWfo2bMnMTExfPHFFyxatIjjx48DkJGRwd69ewkODubs2bMYDAY2btwIwNKlS1m9ejXr1q3j4sWLdOrUiYSEBCg6HG3QoAHOzs5lrt/y5ctxdnZW67C2tiY5OZnjx4/z448/smfPHn777Tf2798PwKVLl0hLS2Pz5s0cOXKE3Nxc5syZA4BOp6NFixbqvCMiIrCysqJp06Y4OzszefJkevToQWJiIgcPHkRRFMaMGUOfPn0ICwtj5syZeHh4AFCjRg3q1KlDtWrVTPyKivsh4aQhR48epXXr1jRu3Nho+qVLl8jLy8PBwYGkpCQaNmzIlClTqFOnDu3bt6dDhw5ER0cDcPbsWSwsLIx2XDMzM3Q6nRoKwcHBdOrUiUmTJlGjRg06d+6Mk5MTERERAJw/f55//vOftGjRgoYNG+Lm5oa5uTkXL15kyZIlLFu2jDZt2mBhYUFcXBxt27ZVl13RIV1WVhZJSUlqHefPn8fMzIyPPvoIR0dHWrZsiZWVFebm5mRnZ3Pt2jVmzpyJk5MT7u7uDBs2TF1PvV5fKpy8vb3VgDl9+jQBAQHq/YqiUFhYSEJCAtbW1gwaNAgLCwsAhg0bxtmzZ2natOl9v4bCdCScNCQtLa1UMAHs27cPS0tLnnjiCfR6PX5+fkb3X7t2jfr16wNw5swZ/P39jUYBBQUFxMTEqOdf9u/fT9euXdX7FUXh6tWrNGjQgMTERDIzM42WERsbi4+PDzt37sTBwYHu3bsDkJubS1hYmBpOYWFhFYaTTqfDwsJCDYGIiAhcXV2xtbUFIDExkby8PHx8fIiMjKR69ep4enoaraednR0XL14sVWNERIQaVgaDgbNnzxqFk7m5OT/++CNRUVEEBARw8ODBu3hFRFWScNIQBwcHzp07ZzQtMzOTTz75hGHDhmFvb49OpzM6yZuUlERoaCg9evSAonAqGRCxsbHk5ubSvHlzDAYDN2/exMHBQb3/wIEDFBYW0qVLFyIiIqhdu7YakpmZmSQmJtK8eXMuXLiAu7u70fPy8/Np2bIlAOHh4eqoqCwRERF4eHhgbW2t3r798eHh4Wog6XQ6mjZtqo5uDAYDe/fupWfPnkRGRmJlZYW3tzcUhevp06fVcIqKiiIrK6vUdmjVqpUazG+99dZdvy6iakg4achzzz1HaGgo77//PqGhoezcuZN+/fpRo0YNgoODoWj0UVBQQEZGBqdPn2bUqFF07dpVDafU1FTi4uJISUkhOTlZfU79+vVxcHDA3NwcPz8/tm3bxq1bt4iMjGTOnDm88cYb2NnZlRkYAL6+vjg7O5OYmEhOTg5XrlxhwYIFWFlZUb16dfLz88nMzKSwsLDc9bv90LL49u2jn/DwcHx8fLC0tESv12NlZUVaWhqxsbFMnjyZzMxMpk6dyq1btzAYDFy5coXc3FzeeustkpKS1HmlpqZC0c8azp07h6Io6vms9PR0rl+/rp5vysrKomvXrixevNikr6W4fxJOGtKjRw+WL1/O9u3b6dOnDwsWLKBHjx7897//xc7OjsLCQqKjo/nrr7/w8/Nj9OjRdOzYkQ0bNqjzGDduHKdOnSIgIED9jU/JUFi1ahUJCQl4eXkxatQoxo8fz+zZs6Gc0YyzszN16tRh9OjReHp6EhAQwMCBA+nSpQu5ubkkJCRgZWXFa6+9xuzZs9VQLKnkqK/ksm6/rdPpyM3NpX379vTo0YPc3Fx2795NvXr16N69O4GBgep9xYrDqfi+F198kf79+wNw5MgRRo4cSWBgINbW1ixduhSKRmTp6elkZmbe9+snTMvsfmewcuXK2jY2NqlDhgyRrzoqWXR0NEFBQVy8eJEaNWpUdTmVqlmzZqxZs8YofIT25efn89NPP+VOmDDB+n7nJSOnh4hOp8PFxeWRD6a0tDSuXLmCj49PVZciqpCE00NEr9erJ4EfZTqdjho1apT5zaV4fFjexWOERhT/APFR16VLF5KSkqq6DFHFZOQkhNAkCSchhCZJOAkhNEnCSQihSRJOQghNknASQmiShJMQQpMknIQQmiThJITQJAknIYQmSTgJITRJwkkIoUkSTkIITZJwEkJokoSTEEKTJJyEEJok4SSE0CQJJyGEJkk4CSE0ScJJCKFJEk5CCE2ScBJCaJKEkxBCkySchBCaJOEkhNAkCSchhCZJOAnNu3btGnZ2dpw5c0ad1qlTJ1auXKnednNz49ChQyZb5u+//46zszOKophsnuLeSDhVoU2bNrFo0aKqLkO1ePFiunXrVtVllBISEoKlpSV+fn4A5OTkEB0dTatWrQBITEwkKysLb29vky0zKioKT09PzMzMTDZPcW8knCrw/vvv06xZM1q1asWnn35q8vmvXLkSR0fHCh9jMBhMvtzytGrVit69e/+t5966dcvk9RQLDQ2lWbNmWFtbAxAREUFBQYEaTjqdjho1auDk5GSyZUZFReHl5QWAoihcvnzZZPMWd0fCqRw//fQTS5cu5cqVKyQmJjJv3jyOHTt2z/PJz89n9uzZeHl54e7uznvvvQdAUFAQ58+fZ968ebi6upKamgpAjx49eO211xg8eDBubm4kJiYC8N1339GxY0ccHR1p3bo127dvV5dx5MgRevXqhZOTE15eXrzwwgvk5+fz9ddfM3DgQCZOnIi3tzceHh4sWbKE2bNn4+7ujo+PD1u3bgVg2rRpvPTSS2rI7Nu3j65du7J69Wr8/PxwcXFh7ty56jI3b95M+/btcXFxoV+/fnTt2pXs7Ow7bo+DBw/Ss2dPHB0d8fX1JSoqCoCEhARGjhyJq6srbm5u9OrVi+joaADOnj1L69at1XmEhITQpEkT6tatC0XhVL9+fYYNG4arqyuDBg1St1tF26eieiIjI9WR2LvvvsuECRPu+bUX90fCqRx//PFHqWlhYWH3PJ+NGzeye/du9uzZw759+9SRyfvvv0+tWrVISEjgwoUL2NvbYzAYiIqKIjIykq+++gqdToebmxtr1qzhzTffZM6cOURHRzNmzBgWLFgAwLFjxxg5ciSvvvoqFy5cYP78+YSHh2NlZUVBQQF//PEHXbp04c8//2TgwIEsXbqUwMBAQkJCCAwMZNWqVQAsX778/7V3p2FNXesewP9JQKagiCAiU5VBkEEUqdU6oNUiotJK7WnrRGlxbLXnOtWxrdVWFGsdUCsVbbHHY+uBUir2OFXrjKAikASZoQQnRECQMet+uGRfAmFQAwnw/p7HD1nJXntlJ/mz9k5cLywsLDBw4ECgbsYmEonAGMOVK1ewdu1a7Nu3D4WFhYiNjcWaNWuwa9cupKWlQSAQwNHREfr6+s0ei7Nnz2LGjBmYNm0abt++jd9//50LZl9fX9ja2kIsFuP06dNISEiAlpYWUDdzks+SUBdO9W+LxWIIBAJs3rwZ586dw5MnT/Dxxx+3eHyaGg/qzZyCg4MRGRmJ0NDQZ37tyYvRUvcANFX9v9RyTk5Oz9xPbW0tSktLUVRUhJdffpk7VYiPj8fgwYPB5///34fs7GyUl5dj27ZtMDY2BgAUFxfj66+/xvLlyzF58mSUlJQgOTkZTk5OYIxh2bJlCAoKgr+/P9fHkCFDAAD5+fkYPnw4ZsyYAQDo3bs3hgwZgunTpwMAXFxcUFhYCAAoLS1Ffn4+F04ZGRlwdXXlPuSDBw8GAPD5fERFRcHf3x8vv/wyAKBv376oqKho8VisX78egYGBWLhwIQDA1NQUALBmzRpYWlriiy++AADk5uaiZ8+e6N+/P0pKSpCVldUonN58803utkgkQkBAADfTWbhwIebPn4/q6upmj09T43n8+DHu3buHY8eOQSKRICYmBpaWls/0upMXRzOnJkyZMgXvvPMOevfuDSsrKwQFBWHMmDHP3E9QUBDmzZuHN998E0FBQdyHOCEhAR4eHgqPFYvFMDY2houLC9eWkJCA8vJy7N27F/369YOTkxNkMhl27dqF5ORk3LlzB7Nnz+YeHx8fz334UlJSuLCR367fd2pqKhe4IpEIAoEAAwYM4G7LL0ADQHp6OkxNTdGzZ08IhUI8fPiQG190dDR8fX2bPQ4PHjyASCSCn5+fQjtjDFFRUZg1a5bCc5CHYVJSEvh8PjeWqqoqSCQSuLm5AXWnzenp6dxteZ8ymQxJSUlNHp+mxoO6UzrUndp6e3vDxsam2edG2gaFUxP09PSwZ88eSCQSJCYmIjg4+Lm+uREIBFi9ejXOnj2L6Oho/Pe//wUA3LhxQ+EDhbpAqB8mcjweD7dv38bNmzeRl5eH8PBwmJiYICcnB1paWrCysgIAFBYWIi4ujpv1iUQihTBKSUlRCJz6t1NSUtC/f3+Fi871Z4r1g27t2rXQ1dXFK6+8gmnTpmHp0qXcbKwpT5484Y5rfcXFxSguLka/fv24thMnTnDhlJycjP79+3PbpaWloaqqihtLamoqqqqqFGZWP//8M0aMGAGpVNrk8WlqPPI+LS0t8dNPP+HgwYO4dOlSs8+NtA0KpzZUWVmJ0NBQFBQUQCqVQiaToV+/fqipqUFRURFEIhEKCgpQXFwM1M2cGoaTq6srdHR0sH37dshkMkgkEmRmZgIALCwsUFNTg8zMTO4Uprq6GkKhEMXFxcjPz+fCp7S0FLm5udzt8vJyZGVlcbfrB2NtbS1SU1MVgiw5OZm7v1evXrCzs0N+fj4OHDiAefPmtXgsrKysYGpqii1btkAqlSIpKQlxcXHo3r07DA0NkZ6eDgDYs2cPbt++DaFQCAB49OgRNybUBUz92/Hx8RAIBLhz5w7u3r2LVatW4fLly9i0aVOzx6ep8aBu5uTu7g4fHx/MmzcPCxcuRElJyXO/D8jzoXBqQ2lpaTh8+DA8PDywcuVK7N69G25ubtDS0kJQUBB27tyJ4cOHIyMjA6gLp4bXtUxNTbFnzx788ssvcHZ2RmBgIKqqqoC660CLFi2Cr68vRo0aBX19fZiamiI1NRUikQhaWlrcaZr8VEXev0QigUwmUwgn+X3p6emoqKhodEpYf2xeXl7w9vbGkiVL4OnpidLS0maPhZaWFg4ePIjs7Gx4eHhg5syZKC0tBZ/Px/bt27FlyxZ4enri9OnTGDZsGPetmb+/P7S1tbFy5UoAwNChQzFhwgS8/fbbQN1F9o8//hhLliyBh4cHUlNTcfz4cbi6ujZ7fJoaDxqc7q5btw6GhoYK31SS9vHCvzDbuXNnd319/QfTpk3rppohkY5EKpXCxcUFly9fhqOjo7qHQ9Ssuroa0dHRlXPnztV90b7o2zryTCQSCX799Ve88cYb0NbWxr59+zBgwADY29vjgw8+gFgsVrrdjh074Onp2e7jJR0XhRN5JpWVlfjrr78QGhoKQ0NDeHt7Izo6GgKBAAcOHFD38EgnQuFEnsmgQYMQGxur7mGQLoAuiBNCNBKFEyFEI1E4EUI0EoUTIUQjUTgRQjQShRMhRCNROBFCNBKFEyFEI1E4EUI0EoUTIUQjUTi1I5lMxq1b1NE1rBv3zTffwNjYGMbGxli6dOkL9//pp59yy6IAQExMDKysrFpVjSY0NBQ+Pj6t7rs5cXFx3PN65ZVXAAABAQFcW3h4eJNtWVlZrdoHUY7CqZ3U1NQgMDAQRUVFXNuvv/6KCRMmcIUFFixYgPv377e6T2V179qj9lzDunGoW4vKy8sLYrGYqzADAH/88QemTJkCGxsbWFhYYMmSJa3aR2JiosI67rdv34arq6vCmutNEYvFzdawa9h3S30ZGBhALBbjjz/+AABs376dW9FUvkyMsrbdu3crLZRBWofCqZ1s2bIFtra23LIh27dvx/z58zFx4kScOHECISEhuHbtGj788MNW96ms7t2L1J5rrYZ141D3IXZ1dYWZmRkMDAyAuoous2fPxtChQ/Hbb7/h999/x7vvvtti/zKZDCkpKY2KGrQ2UEQiEWxtbVvdd3PEYjEGDBgAMzMzrhRVz549uRU55YvSKWsbN24ctm/f3qr9kMYonNpBTk4O9u3bh/nz5wN164dv2rQJW7duxdKlS+Hm5oZJkyZh/fr1uHjxIh49eoSjR4/itddew9KlS2Fvbw8nJycEBwdzfSqre9ew9hzq1l/y9/eHpaUlnJycEBISAgB4+PAhXFxc8MMPP2D48OHo27cvpk6dirKyMgBAWVkZVq9ejQEDBsDCwgKenp6IiYkBlNSNq6mpQXp6usJic1evXsWXX36Jb7/9Fp999hkGDRqEwYMHc6dGABAWFoahQ4fC3Nwczs7O+Oqrr4C6FUSfPHnSaOZUP1CaqnMnL68VHx8PZ2dnDBw4EHv37uW2U9b39evX8cYbb8DCwgJ2dnb48ssvFY6fskX0JBIJ+vTpg549ezbZ1qdPH1y4cIGrkUeeDYVTO9i7dy/GjRvHlR7au3cv7OzsuJJNcvKKtQ8ePOBKIr3++utISEjAypUrERwczJ0mKKt717D2XHZ2NiZNmoTx48cjLS0N+/fvx+bNm3HlyhXo6upCKpXiypUriIyMxIkTJ3Dp0iWcPn0ajDHMmTMHEokE586dQ0ZGBqqrq/H48WNASd24jIwMVFVVKXyId+7ciREjRuC9995Teky2bduG3bt3IywsDH///TdGjhyJnJwcoC6ITExMYGFhAdSttnn//n1un83VucvKysLTp0/h5uaGS5cuYdWqVVizZg2uXLmitO+4uDhMnToVI0eORHJyMn766Sds376dK8opFouVhpOy9oZtMpkMT58+faZTdfL/KJzamEwmQ2RkJF599VWu7eLFi/D19W10/SQ3NxcAYGZmhvz8fIwdOxbe3t7o3r07AgICYGhoyFXBVVb3rmHtuY0bN2LkyJFYsGAB9PT0MGrUKPTt2xcpKSnIzMwEj8fD1q1bYW5uDjc3N2hra3N16eLi4nDgwAGYm5ujtrYW+fn5XMmphqdYYrEYPB6PW6+cMYYLFy4o1Jar7++//8aWLVuwfft2DB48GAKBAFlZWVz/t27danRKZ2BgAAcHBwDA119/zdW5MzAwUKhzJxKJYGBggKVLl8LIyAizZs2Cvb09zp8/r7TvdevWYdSoUVi2bBmEQiESEhLQs2dPmJmZobCwEA8ePFBar1AikTRqb9gmr+JcWVnZ5PuDNI3CqY2JRCI8fPiQK3WEujetsiKNJ0+ehLOzM4yMjCAWixWqn1RVVaGsrIwrtqms7l3D2nOnT59WqLXHGMOjR49gYmKClJQUWFtbw9DQEACQl5eHqqoqODg4IDIyEpMnT+ZOT27evAldXV04Ojo2qhuHuoIANjY2XMXfJ0+eoKysjJspNhQTEwMzMzOMGzcOqPvwJiUlceGUlJSkECC3b9+Gs7Mz+Hx+i3Xu5PX26oc2Yww1NTWN+q6srER8fDwSExNhY2MDGxsb/Pbbbzh27Bi6devGLTnccIYkk8mQlpbWaJbUsC07Oxt8Ph9mZmZKjwNpHoVTG5NXVpGXuUbdtYiGPym4ffs2oqKiuOtSDWvYnThxArq6utwMTFndu/q152QyGcrKyhQ+GGfOnEFtbS1Gjx7dqOBmcnIydHR0YGtri5ycHLz00ksK+3ZxcYFAIGhUNw5KTmcMDQ1hYGDAVVBpKDc3V6FO3ZkzZ1BdXc09n/plqNCgZl5r6tzVD7b4+HhkZGRg7NixSvtG3bWvpKQkZGdn448//uD6EovFMDQ05E4B5bKyslBRUaHwnJW1xcXFwdnZmfuCgDwbCqc2Jq+v1qNHD65t+vTp+PHHH3HkyBEkJSUhPDwc06ZNg5+fH2bMmIGSkhLk5+ejvLwcRUVFiImJwfLly7Fq1SoYGRk1WfeufqDJq+RGRUXh6dOnkEgkWLVqFf75z3/C2Ni4UUXf5ORkODg4QEtLCxYWFlx4Xr9+HQcPHuTqyDWsG4cmrr+8+eab2L17NyIiIpCYmIhffvkFa9asAerq7eXl5aGiogL379/H559/Dm1tbejo6KC6uholJSUK/RcWFnK3W6pzl5CQgLt376K4uBgXLlzA+++/D39/f7z66quN+tbR0YGrqyv27t2LkpISPHjwANevX1d4XvJZaH3y09j6z7lhG2MMV69exbRp057xHUPkKJzamLm5OQQCAXR0dLi2lStXYvbs2diwYQMmTpyIiIgIrF69Gvv37wfqQkYoFGLnzp0YOHAgNm7ciDVr1mDRokVAXQ04ZXXv6teeA4Bdu3YhJycHdnZ2mDVrFoKCgrBixQqgiVLl8tvr1q2DSCSCu7s7VqxYAW9vb+5aV8O6cZWVlcjMzGx0/WXTpk2YPn06Nm/eDF9fX+zYsQPDhg0DAMyePRu2trbw8PCAn58fRo8ejcrKSuTk5EBbWxtLlizBihUrIJVKAQBz587FmTNn8J///KfZOncpKSno3r07zM3N4erqioULF2LmzJnYs2cPACjtOzQ0FIWFhRg2bBi8vb2RnZ3NPQdl15Xk7ZaWltwpsbK2a9euobKyEnPmzHmu9w2hunVtrqqqCvb29khMTOS+em/JwYMHceTIEZw8ebLNx/eikpOTMXr0aJw/fx6urq7qHo5K9e/fH8uXL8eCBQsU2j/88EOUlpbi6NGjTba9//77GDVqFAIDA9t93Oqkyrp1NHNqY926dUNQUBD3VXZriEQi7pspTSc/nenRo0enKtktlUrx+PFjmJmZ4eHDhwCAkpIS3Lt3T2GGqqwtNjYWQqGwywWTqlE4tYPly5crXHNqiUgkava/X2gSiUQCxhjc3d0VfiTa0cnLt3/44Yfw9/cH6k4vnZycFE73lLUVFxdjx44dahx950CndYQQlaHTOkJIp0fhRAjRSBROhBCNROFECNFIFE6EEI1E4UQI0UgUToQQjUThRAjRSBROhBCNROFECNFIFE5E7S5fvgwLCwswxtQ9FKJBKJwIAODu3buYOXMm8vPzFdqnTp2K1atXt+m+U1NTYWtrCx7vhf+rJ+lEKJyasWnTJjg6OmLQoEHcgmWqUL90U3uov6pkUy5cuIAbN240WpJ23LhxGDp0aBuO7v/Cyc7ODqhbQfLevXtttq+mjkV7vyakZRROTYiOjsa2bdtw//595OXlYe3atbh48eJz9XX48GEMGzYMVlZW8PX1xZgxY1BeXg40UzPt1KlTGDNmDHbv3g1nZ2dYWVkpzGAKCgowf/582NrawsrKCjNnzuTWU1q/fj2mTJmCBQsWwMnJCf/+978BAH/99RcmTJiAvn37ws7ODu+88w6qq6tx7NgxLFq0CIWFhQr78fDwwIYNG7g1sGUyGb755hu4ubnB0tISkyZN4lbI/Prrr7FgwQJ8/PHHsLGxgb29PY4dO9aq4yORSLglYr744gvMnTsXqampMDY25iqYPHnyTbHmDQAAIABJREFUBNOnT+e2ycrKgrm5Obf/pmrgnTx5EtbW1ggJCYGnpycWL17c7GuSn5+Pf/zjH7C2toarqyuio6Of6zUnL47CqQnKykgnJSU9cz+xsbFYs2YNdu3ahbS0NAgEAjg6OkJfX7/ZmmkymQwikQiMMVy5cgVr167Fvn37UFhYiEePHmHixImoqKjAn3/+iYSEBNy8eRPHjx8H6haAk0gkWLRoEVJSUuDv74+LFy9i5syZmD9/PnJzc7Fu3TokJydDW1sbb731FgYPHoy1a9ciLy+P+2D//PPPAMAt37tmzRpER0fj2LFjkEgkMDIy4pb9LS8vx5kzZzBp0iRudcxvvvmmVcdIPnMKDg5GZGQkQkNDYW9vDz09Pa4CSkhICM6cOcOddoaEhMDf3x8ODg7N1sATi8UoLy+HtbU1rl+/ji1btjT7msjXOY+Li8O+ffu4ijCk/VE4NUFZ6Wtl60m3JCoqCv7+/nj55Zehq6uLvn37cmWLmquZlpGRAVdXV3z88cfo3r07VxGEz+cjNDQUZWVlCA0NhbW1NdLS0lBcXMwtxi8Wi7F06VK4uLiAz+dDR0cHy5YtQ1BQEPz9/aGlpYXs7Gzug1ddXY2kpKRGpaYkEgkMDQ1hZWWFO3fuYP/+/dizZw8cHBwgFArh6+uLlJQUAEBmZib+8Y9/wMfHB4aGho3KMzXl8ePHuHfvHo4dO4Z//etfiImJgaWlJfh8PpycnCASiZCeno6wsDD06tULZ86cQVZWFqKjo7FixYoWa+CJRCL4+Phwa54bGBg0+5rU1tbi7t27qK2txauvvgorK6tnfs2JalA4NWHKlCl455130Lt3b1hZWSEoKEihBlxrCYVCbpnXhIQEREdHw9fXt8WaaQ2ro6Snp8PU1BQ9e/bExYsXuSKWVlZWWLx4MbZv344hQ4aguLgYUqlUYazJycm4c+cOZs+ezbXFx8cr1Imrrq5WKKmEBgUTzpw5g169eimMqbCwECYmJkBdgYT692VkZLRqqWH5ipOnTp2Ct7c3bGxsuPtcXFwgEonw6aefIiAgAG+99RZOnz6Nbdu24d1334W1tXWLNfDEYjFGjx7dqtcEALZv347+/fvD09MTu3btanH8pO1oqXsAmkpPT08lF8HXrl2LVatW4ZVXXkFBQQGWLl2K6dOnc1Vgw8LC4O7uDl1dXXTr9v+LiaakpChcY2lYLSUwMBCffPIJZDKZQl00kUgEbW1t7gIzAOTk5EBLS4ubBRQWFiIuLg5Lly4F6j6gdnZ2jeqr1S81VVJS0qg4ZExMDCZMmIDS0lLk5eU1qoM3ZcqUFo9PamoqLC0tERwcjNmzZ8PPz4+rzefi4oINGzagR48eOHToEK5du4Y5c+ZAIBDg6tWrQAs18GpqapCWltaoTl1TrwkAmJiY4ODBg4iIiMCSJUvw3nvvoVevXi0+D6J6NHNqY7169YKdnR3y8/Nx4MABzJs3D2ihZlptbS1SU1Mb1ZWTf8iGDh2KY8eOIS0tDRUVFTh37hz3OLFYDHt7e2hra3NtFhYWqKmpQWZmJqqrq7Fs2TJUV1dztd4ePnyIwsJC5OTkKJRGqh9Obm5uSEtLQ3x8PCoqKhASEoK///4bH330EUQiEfh8PndaWVNTg9TU1EahoIxEIoG7uzt8fHwwb948LFy4kLuw7+rqiidPniA4OBhCoRAjR44Ej8fDrFmzYG5uzj23pmrgZWRkoLKystHpeFOvSWpqKn7++WcUFhaioKAAvXr1eqa134lqUTi1Ay8vL3h7e2PJkiXw9PREaWkp0EzNtPT0dFRUVDSqKyf/kC1fvhyurq6YOnUqPD09cerUKe5xDWvXAcDgwYOxaNEi+Pr6YtSoUdDX14epqSlXkffNN9+Erq4uXn75ZXzxxRcAgIqKCmRmZnJj8PHxwfz58/Hee+/ByckJt27dwvHjx9G7d2+kpKTAzs6Oq813586dRlWBm5KamsqNd926dTA0NOS+LRw4cCAmT56MSZMmAXWB7uPjg08++YTbvrkaeGKxGH369OFKuLf0mty6dQsbNmzAoEGDcPbsWRw5cgRaWnRyoS5U4KAdSaVSuLi44PLly40q5BL1oNdEtVRZ4ID+LLQhiUSCX3/9FW+88Qa0tbWxb98+DBgwoMOUfVKF0NBQ/PTTT0rvCwgIwNy5c9t1PPSadBwUTm2osrISf/31F0JDQ2FoaAhvb29ER0dDIBCoe2jtZtGiRVwZdU1Ar0nHQeHUhgYNGoTY2Fh1D4PUQ69Jx0EXxAkhGonCiRCikSicCCEaicKJEKKRKJwIIRqJwokQopEonAghGonCiRCikSicCCEaicKJEKKRKJzakUwmQ3p6urqHoRIjR47Ezp07udvffPMNjI2NYWxszC1i19b8/f25fUZFRbXLPlH3//Pqryr6ouzs7LjnIV/GRllbVlaWyvbZEVA4tZOamhoEBgaiqKiIa/v1118xYcIEWFhYYODAgViwYAHu37/f6j5//PFHbN68WaEtODgYXl5eKh17QxUVFbhz547Csr5isRheXl4Qi8XYsGEDGGPw8PCAubk5nJycMHXqVK4Aw/OMdcWKFYiJiVFoE4vFWLFiBcRiMSZPntzktk3V5Hte3333HU6ePInq6mqV9Hfp0iVs3LgR3bp1g62tbZNtu3fvVlp4o7OicGonW7Zsga2tLTw9PYG6tarnz5+PiRMn4sSJEwgJCcG1a9fw4YcftrrPnTt3citCyg0aNAivv/66ysdfX0pKCmpqahqFk6urK8zMzGBgYICMjAxkZWXh+++/x8GDB+Hk5IRZs2bh22+/feaxSqVSfP/99wqL6D1+/Bh3796Fh4cHzMzMFFb+bKipmnz1taa2H+qWOP7mm29QVVXFzWhelJmZGaRSKezs7LjF7ZS1jRs3Dtu3b1fJPjsCCqd2kJOTg3379mH+/PkAgBs3bmDTpk3YunUrli5dCjc3N0yaNAnr16/HxYsX8ejRIxw9ehSvvfYali5dCnt7ezg5OSE4OJjrc/jw4cjMzMTatWthbW2NBw8eYPHixXjvvfcUCkRKJBL4+/vD0tISTk5OCAkJAeqW5nVxccEPP/yA4cOHo2/fvpg6dSrKysoAAGVlZVi9ejUGDBgACwsLeHp6cjOXxMREvPTSSzAyMgLqZoXp6ekKi7UlJydDX18fPj4+eOWVVxAcHIzAwEB8++23qK2tVTrWP//8E+PHj+dmW6mpqcjPz4eHhwf4fD7Gjh2LsWPHAnVhCEBhn5cvX8bYsWNhYWGBkSNHIi0trcmafE3Vs2tJcHAw7OzsYGNjo1AqrLk6g0OHDlUok1VaWgobGxscOnRI4XVquIJpw7Y+ffrgwoULKpuxaToKp3awd+9ejBs3DqamptxtOzs7zJgxQ+Fxffv2BQA8ePAAJSUlyMrKwuuvv46EhASsXLkSwcHB3LR+06ZNEAqFyMnJQW5uLkxNTfHtt99yp4gAkJ2djUmTJmH8+PFIS0vD/v37sXnzZly5cgW6urqQSqW4cuUKIiMjceLECVy6dAmnT58GYwxz5syBRCLBuXPnkJGRgerqajx+/BioC6f6s6aMjAxUVVUpBMXt27cxcOBAhfJQXl5eKCkpQVFRUaOxnj17FjNmzMC0adNw+/Zt/P7777C2toaFhQUWLlyI1157DXl5efjzzz+BunASCoWwtLQE6ioFBwQEwNvbG0lJSVi2bBn69+/fZE0+ZfXsWpKWloZDhw7hs88+g62tLZKTk7n7mqsz6OTkpHCt8aeffoJAIODKVaEuiBquxNmwTSaT4enTp8906t+RUTi1MZlMhsjISK6iCABcvHgRvr6+jeq65ebmAnVT+vz8fIwdOxbe3t7o3r07AgICYGhoyFW4jY+Px+DBgxX6KC0tRX5+PveB37hxI0aOHIkFCxZAT08Po0aNQt++fZGSkoLMzEzweDxs3boV5ubmcHNzg7a2Nvh8PqKiohAXF4cDBw7A3NwctbW1yM/P58otJSYmKtT1E4vFXKkquaSkJLi4uCg8v6KiIvB4PBgaGjYa6/r16xEYGIiFCxfC1NQUtra20NPT455rw5p6YrEYjo6O4PH+b6Vpxhhqa2uRk5MDXV1dvPHGGxAIBE3W5FNWz64ln332GcaMGYNRo0bBzs5OYebUXJ1BR0dHLpxkMhnCwsIwe/Zs6OvrA3WVbaRSqcIsSVmbvPqxvHJPZ0fh1MZEIhEePnzIvVlR9yaT/8Wv7+TJk3B2doaRkRHEYrFC9ZWqqiqUlZVxi/UnJCQo/cAJBAIuJE6fPq1Qv44xhkePHsHExAQpKSmwtraGoaEhACAvLw9VVVVwcHBAZGQkJk+ejJ49ewIAbt68CV1dXTg6OqKqqgoSiQRubm5cv6mpqbCxseE+bKg7rWsYTqdOnYKrqyt0dHQUxvrgwQOIRCL4+fk1OiYymQy3bt1SWvCz/qyCz+cjMjISqamp8PDw4GZYTdXkU1bPrjkXL17EyZMn8dlnnwFAo5lTc3UGHR0dkZaWBtS9xrm5uQgKClJ4LmhQtFVZW3Z2Nvh8fqMSXZ0VhVMby8jIAABYW1tzbX369Gn0k4Lbt28jKiqKuy5VvywTAJw4cQK6urrcDOzGjRsKAYG6C9X9+/eHrq4uZDIZysrKFN7IZ86cQW1tLUaPHt2oDl5ycjJ0dHRga2uLnJwcvPTSSwr7dnFxgUAgQFpaWqPKKvJZjNz9+/dx7949uLq6cm3Hjx/H8ePHuSV764/1yZMnQF2twIZSU1NRWlraKFyUnQYNGjSIC2R5WXFlNfmaqmfXFMYY1q1bB4FAgMmTJ+Oll17Chg0b8PjxY+Tl5XHPp36Q1D++jo6OKC4uxoMHD/Ddd99h8uTJChfnJRIJ9PX1FQqKKmuLi4uDs7Nzq2Z5nQGFUxuTfwtUv/7Z9OnT8eOPP+LIkSNISkpCeHg4pk2bBj8/P8yYMQMlJSXIz89HeXk5ioqKEBMTg+XLl2PVqlUwMjJCTU0NioqKIBKJUFBQgOLiYqBBoPH5fDg7OyMqKgpPnz6FRCLBqlWr8M9//hPGxsaN/tInJyfDwcEBWlpasLCw4MLz+vXrOHjwIFfjrrCwUOF5QUk41T/duXLlCtasWYPAwEAsWrSIK15Zf6xWVlYwNTXFli1bIJVKkZSUhLi4OKDeqUxiYiIyMjLAGMO9e/dQWFiocBopv2ZWWFiIx48fo3///kATNfmU1bP7/PPPMWzYMKUXm48ePYrU1FTcuHED2dnZyM7OxqVLl7jn2lKdQQcHB+jp6eHbb7/F+fPnuT9A9Y+fg4ODwil6wzbGGK5evYpp06Ypf6N1QhRObczc3BwCgYCr6QYAK1euxOzZs7FhwwZMnDgRERERWL16Nfbv3w/UfXCFQiF27tyJgQMHYuPGjVizZg0369DS0kJQUBB27tyJ4cOHc7OzhjXrdu3ahZycHNjZ2WHWrFkICgrCihUrACUVhOvfXrduHUQiEdzd3bFixQp4e3tz17qGDh2KCRMmcNdqKisrkZmZqbBfeTj5+Phgzpw5yMrKwr///W9s2LCBe0z9sWppaeHgwYPIzs6Gh4cHZs6cydX28/T0xLBhw/Duu+9yFYSVnfL89ddfmDlzJjw9PaGrq4tt27YBTdTkU1bPzt3dHZmZmY1+UvD06VNs3LgRCxYsUJjtWFpaQkdHB0lJSS3WGdTW1kZAQAD27t0Ld3d3DBs2TGEfrfmm7tq1a6isrMScOXPQVVDdujZWVVUFe3t7JCYmcl+9t+TgwYM4cuQITp482ebje1HJyckYPXo0zp8/r3Aa15b27duHzZs3K1QnflHHjx/Hf//7X4VfvatSYWEhXFxcsGPHDoVv6VAXsgsXLsTHH3/cZNv777+PUaNGITAwsE3GpyqqrFtHM6c21q1bNwQFBeHKlSut3kYkEsHBwaFNx6Uq8m/qevTowZURb4999u/fH/fu3VP4ndTzSk9Px4ULF7B161aVjK++R48e4ddff8W7776Ll156CW+++SZ3X2FhIdLT03Hv3j1ulqSsLTY2FkKhUOODSdUonNrB8uXLFa45tUQkEnWYIo8SiQSMMbi7uyv8SLSt93nr1i04OTlx38q9CDs7O2zevFnh1FtV4uPj8T//8z/o1asXjh49yv2SnTGGIUOG4OWXXwbqZkrK2gCguLgYO3bsUPnYNB2d1hFCVIZO6wghnR6FEyFEI1E4EUI0EoUTIUQjUTgRQjQShRMhRCNROBFCNBKFEyFEI1E4EUI0EoUTIUQjUTgRQjQShRMhRCNROBFCNBKFEyFEI1E4EUI0EoUTIUQjUTgRQjQShRMhRCNROBFCNBKFEyFEI1E4EUI0EoUTIUQjUTgRQjQShRMhRCNROBFCNBKFEyFEI1E4EUI0EoUTIUQjUTgRQjQShRMhRCNROBFCNBKFEyFEI1E4EUI0kpYqOpHJZIKrV68+UUVfpONijPF5PJ5M3eMg6sMY4/F4PIEq+nrhcNLR0XlaW1v7YU5OjirGQzqo2tpavfz8/G3W1tYfq3ssRL34fH6VusdACGfu3Lk95s6d+1jd4yCdB11zIoRoJAonQohGonAihGgkCidCiEaicCKEaCQKJ0KIRqJwIipRVlbGGGMV6h4H6TxU8gtxQgwMDHgAdNU9DtJ50MyJEKKRKJyIStTU1MgAXFH3OEjnQeFEVEJLS4sPYLi6x0E6DwonQohGonAihGgkCieiEnfv3qVrTkSlKJyISvTp04euORGVonAihGgkCidCiEaicCKEaCQKJ6ISdT/CTFP3OEjnQeFEVMlB3QMgnQeFEyFEI1E4EZXQ1dVljDGeusdBOg8KJ6ISpaWljMfjUTgRlaE3UxfCGDsC4B11j+MFWPF4vL/VPQjSPmjmRAjRSBROhBCNROFECNFIFE6EEI1E4URU6qOPPkKfPn0U2sLDw9G7d2/k5uaqbVyk46FwIm1OT08PPXr0gEAgUPdQSAdC4UTAGGvT/t99912kpaXBwsKiTfdDOhcKpy5GftoVExMDBwcH8Pl8nD17FgCQnZ2NadOmwdDQEL1798bEiRMRHx/PbfvGG2/Azs4Ob7/9NoyMjNCrVy/MmjUL9+/fb3J/AQEB4PF44PF4qKmp4dpb2ldsbCxcXV2hr68PZ2dn7N69u82OCdFMFE5dUHFxMdasWYM9e/YgMjIS48aNw927dzFixAgUFhZix44dCA4ORmVlJUaNGoWUlBRu2/z8fAwbNgynTp3Cpk2bEBsbC29vb4XgqW/x4sWYNWuWQltL+3ry5Aneeust6OrqIiwsDFOmTIFUKm3z40IIURPG2JFFixYxAOzq1ausvoULFzJ3d3dWXV3NtVVVVTFra2u2ePFixhhjfn5+zMPDQ2G7f/3rXwwA++233xhjjC1atIiZmZkpPObrr79mALi+W9pXeno6A8C++uor1oCluo8haT9UjrwLMjAwwLBhwxTaYmNjkZeXB6FQqNBeVVWFv/9u+n+MTJw4EQBw7do1TJkypVX7b2lf/fv3x4gRI7Bp0yYIhULMnTsXOjo6z/AMSWdA4dQFNQwF1J1qTZ48GZs3b250n5GRUZN99ejRA3w+H6Wlpa3ef0v74vF4OHHiBFatWoVly5YhJCQEERERGD16dKv3QTo+CicCAOjZsycePnwIR0fHZ9pOKpVCJpPByspKpfvq3r07QkNDsWzZMvj5+cHPzw8ikYiukXYh9GITAMD48eNx+fJlJCQkKLSXlZU1u92BAwcAACNGjAAA6OjooLS0FNXV1S+0r4qKCgBAv379sHjxYjx+/BhXr17VfY6nRgjRdPIL4g0vWDPGWEZGBjM2NmbGxsZs06ZNLCwsjPn7+zM/Pz/uMX5+fkxHR4d9+umnLDw8nH3wwQeMx+OxSZMmcY+JiIhgAJifnx/LyMhQekG8pX1VVlYya2trtnz5cnbo0CE2ePBgZmRkxKRSqbW6jyEhpA00F06MMSYWi5mvry/T19dnQqGQjR49mv3yyy8K4WRlZcXGjh3LhEIh6927N5s/fz4rLi7mHlNbW8uWLVvGzM3NWXR0tNJwamlfjx49Yu+//z4zNzdn+vr6bNiwYezChQv0bR0hnRVj7IjSVGolZT8laGcUTl0IXXMihGgkCidCiEaiNcS7EFpDnHQkNHMihGgkCidCiEaicCKEaCQKJ0KIRqJwIoRoJAonQohGonAihGgkCidCiEaicCKEaCQKJ0KIRqJwIirz1VdfcWWgFixYoJI+d+/ezfXp7+//bMt0kg6NwqkL+uCDD+Dj46P0vrFjx+KTTz5psY+PPvoIkZGRCm3JyckYP348CgoKEBIS8szbKxMQEICCggKYm5ujd+/eT1vcgHQaFE5d0K1bt+Du7q70Pm9vb7zyyivNbp+fn4/Q0FC4uLgotCcnJ2Pw4MHo06cPDAwMnnn7+mpra4G6YgwGBgYoKCiAlZVV82sGE0I6JsbYkerqaqajo8OOHj3aaCU3W1tbBoDFxMQwxhjLy8tjkyZNYkKhkFlZWbFffvmF5ebmMh0dHcbn85mBgQEbMmQIY4yx6upq1q1bN3bo0CGuv/Pnz7MhQ4YwPT095urqyiQSSZPb//7770woFLIvv/yS2dvbszlz5nD9XLlyhQFgf/zxh526jyEhpA0wxo7cvn2bAWCpqamNwunOnTsMAMvOzmaMMebv788mTZrEpFIpO3fuHNe+atUqhXXDGWNMJBIxAOz69euMMcZkMhkzNTVl69evZw8fPmRHjx5lNTU1TW6/efNmxufzWUREBGOMsSdPnnD3hYWFMV1dXVZeXk4rYXYhVBqqi7l16xaEQiHs7BpPQlJSUtC9e3fY2NgAdadWUqkUNTU1GDNmDPe4q1evwsvLS2Hb5ORk8Hg8DBw4EPi/IERtbS2ysrKgp6eHt99+u9ntk5KSMHXqVMycOROoK/xZf1yOjo7Q09NT2XEgmo+uOXUxt27dgpubG/j8xi99UlKSwnWg/fv3w97eHg4ODti6dSsAQCaTIT4+vlHF4JSUFPTr1w/6+voAAD6fj1OnTkEkEsHW1hanTp1qdvvk5GS89tprSseckpLS7PUpQkgHxxg7Mm7cOLZo0SKl1QPeeustNm/evEbtYWFhDAB78OABS0pKYgDY/fv3FR7j7+/PpkyZ0mjb2tpaNnPmTObs7MwYY0q3l18HO3funNJx9e3bl3399deMChx0LTRz6mISExNhamoKiUTC/SsoKADqZk6urq4AALFYjMOHD+Phw4eQSqUwMTGBkZER7t+/DwBISEhAWloaGGNA3czH2dmZ289vv/2G8+fP4+HDhygqKuJOI5Vtn5aWhsrKSqWzo6KiIkilUpo5EdKZ5ebm/gcAa/hv1apV7OnTp0wgELDz588zxhj78ccfmYWFBdPX12cjRoxgV69eZYwxVl5ezl599VWmpaXFzM3NmUwmYxUVFUwgELDDhw9zs53FixczIyMj1qNHD+bv788KCgqa3P7o0aPM3Nxc6azpwoULDADLysqimRMhndWL1q1ryq1btxgAdvPmTZX3vW/fPiYUCplMJmMUTl0LndaRFyb/ps7IyAjFxcUq6bOiogJ3795FQkICBg4cCB6PCgV1NRRO5IWlpKSAMYZ+/frh888/V0mfu3fvhrm5OcLCwuh6UxdFf466EKpbRzoSmjkRQjQShRMhRCPRf1/pWh4BKGqrzp8+fdpdR0enjM/n17bRLtqqX0JIZxYUFHRz3rx5g9Q9DtI50GkdURkejyerqamh9xRRCXojEZVhjKVoaWnJ1D0O0jlQOBFVGiCTybqpexCkc6BwIirD4/FqGWMCdY+DdA4UTkSVKJyIylA4EZVhjP3N5/OZusdBOgcKJ6IyPB7PjDFG15yISlA4EVWS0XuKqAq9kYjKMMZkPB6P3lNEJeiNRFSGx+OlM8Zq1D0O0jlQOBGVYYzZ8Hg8qt9EVILCiagSndYRlaE3ElGlAh6PRysHEJWgcCKqZMIY01X3IEjnQOFEVIl+IU5UhsKJqEzdKR2FE1EJKnBAXsgHH3wgAVDL4/F4fD7fgjFWBqASAJ8xVvH9998PUPcYScdEy/SSFyIQCPgAuADi8Xjd8X8/KwBj7N9qHRzp0Oi0jryQmpqaCMZYtZK7/ubz+bvUMCTSSVA4kRdSXV39DY/HS63fxhgDgBthYWGX1TYw0uFROJEXEhERUcYYOyiTyerPnvKrqqq2qXFYpBOgcCIvLC0tbSePx0vH/8+abv7www9/qXtcpGOjcCIv7Ny5czU8Hm8fY6ycMfZQJpN9q+4xkY6PwomoxKBBg8J1dHTu8vn8xAMHDpxR93hIx0fhRFRCT08vysfHR2/48OGZ6h4LIYQAAMLDw08lJiYWPXr0iF2+fPlueHj4d+oeEyGki6sfTPJ/FFBEFej/QZHnFh4efsrDw2OolZWVUf12ExMTIQDLkSNHOkVHR/+mvhGSjozCiTyXpoJJri6gbEaOHDmAAoo8Dwon8sxaCiY5ExMTPQoo8rwonMgzCQ8PP+vh4eHRUjDJUUCR50XhRFotPDz87NChQz2trKy6P8t2FFDkeVA4kVaRB5OlpaXwebangCLPisKJtOhFg0mOAoo8Cwon0ixVBZMcBRRpLQon0iRVB5McBRRpDQonolRbBZMcBRRpCYUTaaStg0lOHlBeXl4Do6Kifm3LfZGOh8KJKGivYJIzMTHRY4z19/LycqSAIvVROBFOeweTnImJiQ4FFGmIwokAAA4dOnTew8NjaHsHkxwFFGmIwong0KFD54cMGeJpaWlpoM5xUECR+iicurh6waSn7rGAAorUQ+HUhWlaMMlRQBFQOHVdmhpMchRQhMKpC9L0YJKjgOraKJy6mI4STHIUUF0XlYbqQtoymGJjY9GrVy9UVFSoumsMGDCgu52d3ZsRERGHVd450Vg0c+oi2jKYQkJC0LvxfXl7AAAC7klEQVR3b6Snp6Nfv364desWnJycVLoPExMTHQB2Y8aMGRgVFRWp0s6JRqJw6gLaMphkMhn27NmDI0eOoKysDBcuXEC3bt0wfvx4Ve8KvXr10qaA6joonDq5iIiIi4MHDx7aVteYeDweXFxccOHCBQwYMACmpqbYvHkzunXr1ha7o4DqQuiaUycWERFxcdCgQS+39cXv9PR0WFlZITQ0FLm5uZBKpW25Ozg4OOjb29vTNahOjqfuAZC2US+YtNtzv4wx8Hjt87a6c+dOeVpaWtSsWbNmtssOSbuicOpkDhw4YNitW7fDnp6eU3v37q3u4bS5nJycJ0lJSYdra2v/+f7776v+q0KiNnRa18l88MEHpbW1tT+KRKJ76h5Le0hOTq6sqKj4joKp86GZUyd16NAh/+7du4d6eXmZqXssbeX48eOFT58+HT937txb6h4LUT0Kp06sMwcUBVPnR+HUyXXGgKJg6hoonLqAzhRQFExdB4VTF9EZAoqCqWuhcOpCOnJAUTB1PRROXUxHDCgKpq6JwqkL6kgBRcHUdVE4dVEdIaAomLo2CqcuTJMDKjY29nF5eflYCqaui8Kpi9PEgIqNjX1cVVX1WmBg4A11j4WoD4UT0aiAomAichROBNCQgKJgIvVROBGOOgOKgok0ROFEFKgjoCiYiDIUTqSR9gwoCibSFAonolR7BBQFE2kOhRNpUl1Afefl5dVL1X1TMJGW0DK9pEkBAQH/KSkpWXju3LlCVfZLwURag2ZOpEWHDh16u3v37ntUMYOiYCKtReFEWkUVAUXBRJ4FhRNptRcJKAom8qwonMgzeZ6AomAiz4PCiTyzZwkoCibyvCicyHNpTUBRMJEXQeFEnltzAUXBRF4UhRN5IcoCioKJEKIRDh069HZkZOTDR48escOHDxeFh4cPUfeYSMdHMyeiEj/99NP42traH58+fTpq3rx5GeoeDyGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ0tb+F4S9Dt+ks04/AAAAAElFTkSuQmCC", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rag = Pipeline()\n", + "rag.add_component(\"text_embedder\", SentenceTransformersTextEmbedder(model=model_emb,\n", + " device=ComponentDevice.from_str(device)))\n", + "rag.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store, top_k=5))\n", + "rag.add_component(\"prompt_builder\", prompt_builder)\n", + "rag.add_component(\"llm\", generator)\n", + "\n", + "rag.connect(\"text_embedder\", \"retriever\")\n", + "rag.connect(\"retriever.documents\", \"prompt_builder.documents\")\n", + "rag.connect(\"prompt_builder.prompt\", \"llm.prompt\")\n", + "\n", + "rag.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f9c9e3f23390410bbdb0730209f54943", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Batches: 0%| | 0/1 [00:00Using the information contained in the context, give a comprehensive answer to the question.\n", + "If the answer is contained in the context, also report the source URL.\n", + "If the answer cannot be deduced from the context, do not give an answer.\n", + "<|user|>\n", + "Context:\n", + " \n", + " 8 in) deep.\n", + "Its main body is solid sterling silver and silver gilt, whilst its plinth is made of malachite, a semi-precious stone. URL:https://en.wikipedia.org/wiki/Premier_League\n", + " \n", + " The Premier League players decided to take the knee at selected \"significant moments\". They assured to \"remain resolutely committed to eradicate racial prejudice\". URL:https://en.wikipedia.org/wiki/Premier_League\n", + " \n", + " In the cases of Bayer Leverkusen and Wolfsburg, the clubs were founded by major corporations (respectively Bayer AG and Volkswagen) as sports clubs for their employees, while Hoffenheim has long received its primary support from SAP co-founder Dietmar Hopp, who played in the club's youth system.\n", + "After 2000 the German Football Association and the Bundesliga required every club to run a youth academy with the aim of developing local talent for the club and the national team. URL:https://en.wikipedia.org/wiki/Bundesliga\n", + " \n", + " These stars are a permanent part of their crest. However, Fürth has to leave the stars out of their jersey. URL:https://en.wikipedia.org/wiki/Bundesliga\n", + " \n", + " However, a\n" + ] + } + ], + "source": [ + "def get_answer(query):\n", + "\n", + " results = rag.run({\n", + " \"text_embedder\": {\"text\": query},\n", + " \"prompt_builder\": {\"query\": query}\n", + " }\n", + " )\n", + "\n", + " answer = results[\"llm\"][\"replies\"][0]\n", + " return answer\n", + "\n", + "print(get_answer(\"What is RAG?\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "questions = \"\"\"\n", + "What was the result of the latest FIFA World Cup?\n", + "Who won the last UEFA Champions League?\n", + "Which country won the most recent Copa América?\n", + "What is the top scorer in the CONCACAF Gold Cup this year?\n", + "Which teams participated in the latest Africa Cup of Nations?\n", + "Who won the last Copa Libertadores?\n", + "What were the standout performances in UEFA Euro 2020?\n", + "Who is the current champion of Major League Soccer (MLS)?\n", + "What is the top team in the Premier League this season?\n", + "Who won La Liga this year?\n", + "Which Bundesliga team has the most titles in the last decade?\n", + "Which Serie A team won the last league championship?\n", + "Who is the top scorer of Ligue 1 this season?\n", + "Who won the UEFA Euro Golden Boot in the last competition?\n", + "Who won the most recent Ballon d'Or?\n", + "\"\"\".split('\\n')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Which Bundesliga team has the most titles in the last decade?\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "24842a83c24b4154bbe5b6f7969ae586", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Batches: 0%| | 0/1 [00:00\n", + "Context:\n", + " \n", + " The Bundesliga is one of the most prestigious leagues in Germany, ranked 16th in the world in 2024. The league is divided into 12 teams, each with 12 members. The league is divided into 12 Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundesliga Bundes\n" + ] + } + ], + "source": [ + "q = random.choice(questions)\n", + "print(q)\n", + "print(get_answer(q))" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "py310", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + 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"1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/8_rag/notebooks/3_agenic_rag_example.ipynb b/8_rag/notebooks/3_agenic_rag_example.ipynb new file mode 100644 index 00000000..745ee6e2 --- /dev/null +++ b/8_rag/notebooks/3_agenic_rag_example.ipynb @@ -0,0 +1,449 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Implement Modular and AI Agents RAGs with `smolagent`\n", + "\n", + "This notebook demonstrates how to implement a basic Retrieval Augmented Generation (RAG) pipeline using the `smolagent` library combined with HuggingFace's models (e.g., `SmolLM`). \n", + "\n", + "
\n", + "

Exercise:

\n", + "

Implement a Modular RAG pipeline tailored to your needs using `smolagent` and integrate AI agents for orchestration.

\n", + "

Difficulty Levels

\n", + "

🐢 Try different questions that could be handled with implemented modules/tools.

\n", + "

🐕 Experiment with new custom tools and knowledge bases.

\n", + "

🦁 Implement multi-step agent and multi-agent workflow for chatbot.

\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
╭──────────────────────────────────────────────────── New run ────────────────────────────────────────────────────╮\n",
+       "│                                                                                                                 │\n",
+       "│ Which country won the UEFA EURO 2024?                                                                           │\n",
+       "│                                                                                                                 │\n",
+       "╰─ TransformersModel - HuggingFaceTB/SmolLM2-1.7B-Instruct ───────────────────────────────────────────────────────╯\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[38;2;212;183;2m╭─\u001b[0m\u001b[38;2;212;183;2m───────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[1;38;2;212;183;2mNew run\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[38;2;212;183;2m───────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╮\u001b[0m\n", + "\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n", + "\u001b[38;2;212;183;2m│\u001b[0m \u001b[1mWhich country won the UEFA EURO 2024?\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n", + "\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n", + "\u001b[38;2;212;183;2m╰─\u001b[0m\u001b[38;2;212;183;2m TransformersModel - HuggingFaceTB/SmolLM2-1.7B-Instruct \u001b[0m\u001b[38;2;212;183;2m──────────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 0 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
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╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n",
+       "│ Calling tool: 'web_search' with arguments: {'query': 'UEFA EURO 2024 winner'}                                   │\n",
+       "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
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\n" + ], + "text/plain": [ + "╭─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n", + "│ Calling tool: 'web_search' with arguments: {'query': 'UEFA EURO 2024 winner'} │\n", + "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Error in tool call execution: Missing SerpAPI key. Make sure you have 'SERPAPI_API_KEY' in your env variables.\n",
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+       "\n",
+       "- web_search: Performs a google web search for your query then returns a string of the top search results.\n",
+       "    Takes inputs: {'query': {'type': 'string', 'description': 'The search query to perform.'}, 'filter_year': \n",
+       "{'type': 'integer', 'description': 'Optionally restrict results to a certain year', 'nullable': True}}\n",
+       "    Returns an output of type: string\n",
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\n" + ], + "text/plain": [ + "\u001b[1;31mError in tool call execution: Missing SerpAPI key. Make sure you have \u001b[0m\u001b[1;31m'SERPAPI_API_KEY'\u001b[0m\u001b[1;31m in your env variables.\u001b[0m\n", + "\u001b[1;31mYou should only use this tool with a correct input.\u001b[0m\n", + "\u001b[1;31mAs a reminder, this tool's description is the following:\u001b[0m\n", + "\n", + "\u001b[1;31m- web_search: Performs a google web search for your query then returns a string of the top search results.\u001b[0m\n", + "\u001b[1;31m Takes inputs: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'query'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'type'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'string'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'description'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'The search query to perform.'\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'filter_year'\u001b[0m\u001b[1;31m: \u001b[0m\n", + "\u001b[1;31m{\u001b[0m\u001b[1;31m'type'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'integer'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'description'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'Optionally restrict results to a certain year'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'nullable'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;3;31mTrue\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m}\u001b[0m\n", + "\u001b[1;31m Returns an output of type: string\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
[Step 0: Duration 17.65 seconds| Input tokens: 1,112 | Output tokens: 42]\n",
+       "
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 1 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
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Error in generating tool call with model:\n",
+       "The JSON blob you used is invalid due to the following error: Expecting property name enclosed in double quotes: \n",
+       "line 1 column 2 (char 1).\n",
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+       "EURO 2024 winner'}}}, decoding failed on that specific part of the blob:\n",
+       "''.\n",
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[Step 1: Duration 21.56 seconds| Input tokens: 2,468 | Output tokens: 91]\n",
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
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Error in generating tool call with model:\n",
+       "The JSON blob you used is invalid due to the following error: Expecting property name enclosed in double quotes: \n",
+       "line 1 column 2 (char 1).\n",
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[Step 2: Duration 20.45 seconds| Input tokens: 3,824 | Output tokens: 140]\n",
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Step 3 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
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Error in generating tool call with model:\n",
+       "The JSON blob you used is invalid due to the following error: Expecting property name enclosed in double quotes: \n",
+       "line 1 column 2 (char 1).\n",
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+       "''.\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;31mError in generating tool call with model:\u001b[0m\n", + "\u001b[1;31mThe JSON blob you used is invalid due to the following error: Expecting property name enclosed in double quotes: \u001b[0m\n", + "\u001b[1;31mline \u001b[0m\u001b[1;31m1\u001b[0m\u001b[1;31m column \u001b[0m\u001b[1;31m2\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31mchar \u001b[0m\u001b[1;31m1\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m.\u001b[0m\n", + "\u001b[1;31mJSON blob was: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'id'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'93024'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'type'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'function'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'function'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'name'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'web_search'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'arguments'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'query'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'UEFA \u001b[0m\n", + "\u001b[1;31mEURO 2024 winner'\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m, decoding failed on that specific part of the blob:\u001b[0m\n", + "\u001b[1;31m''\u001b[0m\u001b[1;31m.\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
[Step 3: Duration 21.44 seconds| Input tokens: 5,180 | Output tokens: 189]\n",
+       "
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+       "
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Error in generating tool call with model:\n",
+       "The JSON blob you used is invalid due to the following error: Expecting property name enclosed in double quotes: \n",
+       "line 1 column 2 (char 1).\n",
+       "JSON blob was: {'id': '93024', 'type': 'function', 'function': {'name': 'web_search', 'arguments': {'query': 'UEFA \n",
+       "EURO 2024 winner'}}}, decoding failed on that specific part of the blob:\n",
+       "''.\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;31mError in generating tool call with model:\u001b[0m\n", + "\u001b[1;31mThe JSON blob you used is invalid due to the following error: Expecting property name enclosed in double quotes: \u001b[0m\n", + "\u001b[1;31mline \u001b[0m\u001b[1;31m1\u001b[0m\u001b[1;31m column \u001b[0m\u001b[1;31m2\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31mchar \u001b[0m\u001b[1;31m1\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m.\u001b[0m\n", + "\u001b[1;31mJSON blob was: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'id'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'93024'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'type'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'function'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'function'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'name'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'web_search'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'arguments'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'query'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'UEFA \u001b[0m\n", + "\u001b[1;31mEURO 2024 winner'\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m, decoding failed on that specific part of the blob:\u001b[0m\n", + "\u001b[1;31m''\u001b[0m\u001b[1;31m.\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
[Step 4: Duration 21.56 seconds| Input tokens: 6,536 | Output tokens: 238]\n",
+       "
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+       "
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Error in generating tool call with model:\n",
+       "The JSON blob you used is invalid due to the following error: Expecting property name enclosed in double quotes: \n",
+       "line 1 column 2 (char 1).\n",
+       "JSON blob was: {'id': '93024', 'type': 'function', 'function': {'name': 'web_search', 'arguments': {'query': 'UEFA \n",
+       "EURO 2024 winner'}}}, decoding failed on that specific part of the blob:\n",
+       "''.\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;31mError in generating tool call with model:\u001b[0m\n", + "\u001b[1;31mThe JSON blob you used is invalid due to the following error: Expecting property name enclosed in double quotes: \u001b[0m\n", + "\u001b[1;31mline \u001b[0m\u001b[1;31m1\u001b[0m\u001b[1;31m column \u001b[0m\u001b[1;31m2\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31mchar \u001b[0m\u001b[1;31m1\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m.\u001b[0m\n", + "\u001b[1;31mJSON blob was: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'id'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'93024'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'type'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'function'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'function'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'name'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'web_search'\u001b[0m\u001b[1;31m, \u001b[0m\u001b[1;31m'arguments'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m{\u001b[0m\u001b[1;31m'query'\u001b[0m\u001b[1;31m: \u001b[0m\u001b[1;31m'UEFA \u001b[0m\n", + "\u001b[1;31mEURO 2024 winner'\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m}\u001b[0m\u001b[1;31m, decoding failed on that specific part of the blob:\u001b[0m\n", + "\u001b[1;31m''\u001b[0m\u001b[1;31m.\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
[Step 5: Duration 22.26 seconds| Input tokens: 7,892 | Output tokens: 287]\n",
+       "
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Reached max iterations.\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;31mReached max iterations.\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Final answer: assistant\n",
+       "The country that won the UEFA EURO 2024 is France.\n",
+       "
\n" + ], + "text/plain": [ + "Final answer: assistant\n", + "The country that won the UEFA EURO 2024 is France.\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
[Step 6: Duration 0.00 seconds| Input tokens: 8,234 | Output tokens: 310]\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2m[Step 6: Duration 0.00 seconds| Input tokens: 8,234 | Output tokens: 310]\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "assistant\n", + "The country that won the UEFA EURO 2024 is France.\n" + ] + } + ], + "source": [ + "from smolagents import ToolCallingAgent, CodeAgent, GoogleSearchTool, TransformersModel, HfApiModel\n", + "\n", + "agent = ToolCallingAgent(\n", + " tools=[GoogleSearchTool()],\n", + " # model=TransformersModel(model_id = \"HuggingFaceTB/SmolLM2-1.7B-Instruct\"),\n", + " model=HfApiModel(),\n", + " # max_steps=2, \n", + " verbose=True\n", + ")\n", + "\n", + "response = agent.run(\"Which country won the UEFA EURO 2024?\")\n", + "print(response)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py310", + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}